DIGITS-CNN/cars/architecture-investigations/fc/1-layer/1024/caffe_output.log

I0409 22:47:54.069082  4221 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-205237-935d/solver.prototxt
I0409 22:47:54.069238  4221 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0409 22:47:54.069244  4221 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0409 22:47:54.069301  4221 caffe.cpp:218] Using GPUs 3
I0409 22:47:54.081862  4221 caffe.cpp:223] GPU 3: GeForce GTX 1080 Ti
I0409 22:47:54.346282  4221 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99980193
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 3
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0409 22:47:54.347378  4221 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0409 22:47:54.348271  4221 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0409 22:47:54.348287  4221 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0409 22:47:54.348414  4221 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1024
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc6"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0409 22:47:54.348496  4221 layer_factory.hpp:77] Creating layer train-data
I0409 22:47:54.351049  4221 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0409 22:47:54.351462  4221 net.cpp:84] Creating Layer train-data
I0409 22:47:54.351475  4221 net.cpp:380] train-data -> data
I0409 22:47:54.351493  4221 net.cpp:380] train-data -> label
I0409 22:47:54.351505  4221 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 22:47:54.356187  4221 data_layer.cpp:45] output data size: 128,3,227,227
I0409 22:47:54.476730  4221 net.cpp:122] Setting up train-data
I0409 22:47:54.476753  4221 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0409 22:47:54.476758  4221 net.cpp:129] Top shape: 128 (128)
I0409 22:47:54.476763  4221 net.cpp:137] Memory required for data: 79149056
I0409 22:47:54.476773  4221 layer_factory.hpp:77] Creating layer conv1
I0409 22:47:54.476794  4221 net.cpp:84] Creating Layer conv1
I0409 22:47:54.476799  4221 net.cpp:406] conv1 <- data
I0409 22:47:54.476811  4221 net.cpp:380] conv1 -> conv1
I0409 22:47:55.046655  4221 net.cpp:122] Setting up conv1
I0409 22:47:55.046677  4221 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 22:47:55.046681  4221 net.cpp:137] Memory required for data: 227833856
I0409 22:47:55.046701  4221 layer_factory.hpp:77] Creating layer relu1
I0409 22:47:55.046712  4221 net.cpp:84] Creating Layer relu1
I0409 22:47:55.046716  4221 net.cpp:406] relu1 <- conv1
I0409 22:47:55.046722  4221 net.cpp:367] relu1 -> conv1 (in-place)
I0409 22:47:55.047016  4221 net.cpp:122] Setting up relu1
I0409 22:47:55.047024  4221 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 22:47:55.047029  4221 net.cpp:137] Memory required for data: 376518656
I0409 22:47:55.047031  4221 layer_factory.hpp:77] Creating layer norm1
I0409 22:47:55.047041  4221 net.cpp:84] Creating Layer norm1
I0409 22:47:55.047044  4221 net.cpp:406] norm1 <- conv1
I0409 22:47:55.047050  4221 net.cpp:380] norm1 -> norm1
I0409 22:47:55.047489  4221 net.cpp:122] Setting up norm1
I0409 22:47:55.047499  4221 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 22:47:55.047503  4221 net.cpp:137] Memory required for data: 525203456
I0409 22:47:55.047508  4221 layer_factory.hpp:77] Creating layer pool1
I0409 22:47:55.047515  4221 net.cpp:84] Creating Layer pool1
I0409 22:47:55.047518  4221 net.cpp:406] pool1 <- norm1
I0409 22:47:55.047523  4221 net.cpp:380] pool1 -> pool1
I0409 22:47:55.047581  4221 net.cpp:122] Setting up pool1
I0409 22:47:55.047587  4221 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0409 22:47:55.047591  4221 net.cpp:137] Memory required for data: 561035264
I0409 22:47:55.047595  4221 layer_factory.hpp:77] Creating layer conv2
I0409 22:47:55.047605  4221 net.cpp:84] Creating Layer conv2
I0409 22:47:55.047608  4221 net.cpp:406] conv2 <- pool1
I0409 22:47:55.047613  4221 net.cpp:380] conv2 -> conv2
I0409 22:47:55.054240  4221 net.cpp:122] Setting up conv2
I0409 22:47:55.054256  4221 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 22:47:55.054260  4221 net.cpp:137] Memory required for data: 656586752
I0409 22:47:55.054271  4221 layer_factory.hpp:77] Creating layer relu2
I0409 22:47:55.054280  4221 net.cpp:84] Creating Layer relu2
I0409 22:47:55.054283  4221 net.cpp:406] relu2 <- conv2
I0409 22:47:55.054289  4221 net.cpp:367] relu2 -> conv2 (in-place)
I0409 22:47:55.054711  4221 net.cpp:122] Setting up relu2
I0409 22:47:55.054721  4221 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 22:47:55.054725  4221 net.cpp:137] Memory required for data: 752138240
I0409 22:47:55.054728  4221 layer_factory.hpp:77] Creating layer norm2
I0409 22:47:55.054736  4221 net.cpp:84] Creating Layer norm2
I0409 22:47:55.054740  4221 net.cpp:406] norm2 <- conv2
I0409 22:47:55.054745  4221 net.cpp:380] norm2 -> norm2
I0409 22:47:55.055034  4221 net.cpp:122] Setting up norm2
I0409 22:47:55.055042  4221 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 22:47:55.055047  4221 net.cpp:137] Memory required for data: 847689728
I0409 22:47:55.055049  4221 layer_factory.hpp:77] Creating layer pool2
I0409 22:47:55.055058  4221 net.cpp:84] Creating Layer pool2
I0409 22:47:55.055061  4221 net.cpp:406] pool2 <- norm2
I0409 22:47:55.055066  4221 net.cpp:380] pool2 -> pool2
I0409 22:47:55.055094  4221 net.cpp:122] Setting up pool2
I0409 22:47:55.055099  4221 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 22:47:55.055102  4221 net.cpp:137] Memory required for data: 869840896
I0409 22:47:55.055105  4221 layer_factory.hpp:77] Creating layer conv3
I0409 22:47:55.055115  4221 net.cpp:84] Creating Layer conv3
I0409 22:47:55.055119  4221 net.cpp:406] conv3 <- pool2
I0409 22:47:55.055124  4221 net.cpp:380] conv3 -> conv3
I0409 22:47:55.064981  4221 net.cpp:122] Setting up conv3
I0409 22:47:55.064999  4221 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 22:47:55.065002  4221 net.cpp:137] Memory required for data: 903067648
I0409 22:47:55.065014  4221 layer_factory.hpp:77] Creating layer relu3
I0409 22:47:55.065022  4221 net.cpp:84] Creating Layer relu3
I0409 22:47:55.065026  4221 net.cpp:406] relu3 <- conv3
I0409 22:47:55.065032  4221 net.cpp:367] relu3 -> conv3 (in-place)
I0409 22:47:55.065454  4221 net.cpp:122] Setting up relu3
I0409 22:47:55.065464  4221 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 22:47:55.065467  4221 net.cpp:137] Memory required for data: 936294400
I0409 22:47:55.065471  4221 layer_factory.hpp:77] Creating layer conv4
I0409 22:47:55.065481  4221 net.cpp:84] Creating Layer conv4
I0409 22:47:55.065485  4221 net.cpp:406] conv4 <- conv3
I0409 22:47:55.065490  4221 net.cpp:380] conv4 -> conv4
I0409 22:47:55.075414  4221 net.cpp:122] Setting up conv4
I0409 22:47:55.075433  4221 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 22:47:55.075435  4221 net.cpp:137] Memory required for data: 969521152
I0409 22:47:55.075444  4221 layer_factory.hpp:77] Creating layer relu4
I0409 22:47:55.075451  4221 net.cpp:84] Creating Layer relu4
I0409 22:47:55.075456  4221 net.cpp:406] relu4 <- conv4
I0409 22:47:55.075461  4221 net.cpp:367] relu4 -> conv4 (in-place)
I0409 22:47:55.075737  4221 net.cpp:122] Setting up relu4
I0409 22:47:55.075745  4221 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 22:47:55.075749  4221 net.cpp:137] Memory required for data: 1002747904
I0409 22:47:55.075752  4221 layer_factory.hpp:77] Creating layer conv5
I0409 22:47:55.075762  4221 net.cpp:84] Creating Layer conv5
I0409 22:47:55.075765  4221 net.cpp:406] conv5 <- conv4
I0409 22:47:55.075790  4221 net.cpp:380] conv5 -> conv5
I0409 22:47:55.087088  4221 net.cpp:122] Setting up conv5
I0409 22:47:55.087107  4221 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 22:47:55.087110  4221 net.cpp:137] Memory required for data: 1024899072
I0409 22:47:55.087123  4221 layer_factory.hpp:77] Creating layer relu5
I0409 22:47:55.087134  4221 net.cpp:84] Creating Layer relu5
I0409 22:47:55.087138  4221 net.cpp:406] relu5 <- conv5
I0409 22:47:55.087144  4221 net.cpp:367] relu5 -> conv5 (in-place)
I0409 22:47:55.087630  4221 net.cpp:122] Setting up relu5
I0409 22:47:55.087641  4221 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 22:47:55.087644  4221 net.cpp:137] Memory required for data: 1047050240
I0409 22:47:55.087648  4221 layer_factory.hpp:77] Creating layer pool5
I0409 22:47:55.087656  4221 net.cpp:84] Creating Layer pool5
I0409 22:47:55.087659  4221 net.cpp:406] pool5 <- conv5
I0409 22:47:55.087664  4221 net.cpp:380] pool5 -> pool5
I0409 22:47:55.087703  4221 net.cpp:122] Setting up pool5
I0409 22:47:55.087709  4221 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0409 22:47:55.087713  4221 net.cpp:137] Memory required for data: 1051768832
I0409 22:47:55.087715  4221 layer_factory.hpp:77] Creating layer fc6
I0409 22:47:55.087726  4221 net.cpp:84] Creating Layer fc6
I0409 22:47:55.087729  4221 net.cpp:406] fc6 <- pool5
I0409 22:47:55.087734  4221 net.cpp:380] fc6 -> fc6
I0409 22:47:55.176427  4221 net.cpp:122] Setting up fc6
I0409 22:47:55.176447  4221 net.cpp:129] Top shape: 128 1024 (131072)
I0409 22:47:55.176451  4221 net.cpp:137] Memory required for data: 1052293120
I0409 22:47:55.176461  4221 layer_factory.hpp:77] Creating layer relu6
I0409 22:47:55.176470  4221 net.cpp:84] Creating Layer relu6
I0409 22:47:55.176476  4221 net.cpp:406] relu6 <- fc6
I0409 22:47:55.176482  4221 net.cpp:367] relu6 -> fc6 (in-place)
I0409 22:47:55.180379  4221 net.cpp:122] Setting up relu6
I0409 22:47:55.180389  4221 net.cpp:129] Top shape: 128 1024 (131072)
I0409 22:47:55.180393  4221 net.cpp:137] Memory required for data: 1052817408
I0409 22:47:55.180397  4221 layer_factory.hpp:77] Creating layer drop6
I0409 22:47:55.180404  4221 net.cpp:84] Creating Layer drop6
I0409 22:47:55.180408  4221 net.cpp:406] drop6 <- fc6
I0409 22:47:55.180415  4221 net.cpp:367] drop6 -> fc6 (in-place)
I0409 22:47:55.180444  4221 net.cpp:122] Setting up drop6
I0409 22:47:55.180450  4221 net.cpp:129] Top shape: 128 1024 (131072)
I0409 22:47:55.180454  4221 net.cpp:137] Memory required for data: 1053341696
I0409 22:47:55.180457  4221 layer_factory.hpp:77] Creating layer fc8
I0409 22:47:55.180464  4221 net.cpp:84] Creating Layer fc8
I0409 22:47:55.180469  4221 net.cpp:406] fc8 <- fc6
I0409 22:47:55.180474  4221 net.cpp:380] fc8 -> fc8
I0409 22:47:55.182288  4221 net.cpp:122] Setting up fc8
I0409 22:47:55.182296  4221 net.cpp:129] Top shape: 128 196 (25088)
I0409 22:47:55.182298  4221 net.cpp:137] Memory required for data: 1053442048
I0409 22:47:55.182305  4221 layer_factory.hpp:77] Creating layer loss
I0409 22:47:55.182313  4221 net.cpp:84] Creating Layer loss
I0409 22:47:55.182317  4221 net.cpp:406] loss <- fc8
I0409 22:47:55.182320  4221 net.cpp:406] loss <- label
I0409 22:47:55.182327  4221 net.cpp:380] loss -> loss
I0409 22:47:55.182334  4221 layer_factory.hpp:77] Creating layer loss
I0409 22:47:55.182917  4221 net.cpp:122] Setting up loss
I0409 22:47:55.182927  4221 net.cpp:129] Top shape: (1)
I0409 22:47:55.182930  4221 net.cpp:132]     with loss weight 1
I0409 22:47:55.182950  4221 net.cpp:137] Memory required for data: 1053442052
I0409 22:47:55.182953  4221 net.cpp:198] loss needs backward computation.
I0409 22:47:55.182960  4221 net.cpp:198] fc8 needs backward computation.
I0409 22:47:55.182965  4221 net.cpp:198] drop6 needs backward computation.
I0409 22:47:55.182967  4221 net.cpp:198] relu6 needs backward computation.
I0409 22:47:55.182971  4221 net.cpp:198] fc6 needs backward computation.
I0409 22:47:55.182974  4221 net.cpp:198] pool5 needs backward computation.
I0409 22:47:55.182978  4221 net.cpp:198] relu5 needs backward computation.
I0409 22:47:55.182999  4221 net.cpp:198] conv5 needs backward computation.
I0409 22:47:55.183003  4221 net.cpp:198] relu4 needs backward computation.
I0409 22:47:55.183007  4221 net.cpp:198] conv4 needs backward computation.
I0409 22:47:55.183010  4221 net.cpp:198] relu3 needs backward computation.
I0409 22:47:55.183014  4221 net.cpp:198] conv3 needs backward computation.
I0409 22:47:55.183018  4221 net.cpp:198] pool2 needs backward computation.
I0409 22:47:55.183022  4221 net.cpp:198] norm2 needs backward computation.
I0409 22:47:55.183025  4221 net.cpp:198] relu2 needs backward computation.
I0409 22:47:55.183028  4221 net.cpp:198] conv2 needs backward computation.
I0409 22:47:55.183033  4221 net.cpp:198] pool1 needs backward computation.
I0409 22:47:55.183037  4221 net.cpp:198] norm1 needs backward computation.
I0409 22:47:55.183040  4221 net.cpp:198] relu1 needs backward computation.
I0409 22:47:55.183044  4221 net.cpp:198] conv1 needs backward computation.
I0409 22:47:55.183048  4221 net.cpp:200] train-data does not need backward computation.
I0409 22:47:55.183053  4221 net.cpp:242] This network produces output loss
I0409 22:47:55.183065  4221 net.cpp:255] Network initialization done.
I0409 22:47:55.183568  4221 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0409 22:47:55.183596  4221 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0409 22:47:55.183724  4221 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1024
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc6"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0409 22:47:55.183813  4221 layer_factory.hpp:77] Creating layer val-data
I0409 22:47:55.185420  4221 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0409 22:47:55.185622  4221 net.cpp:84] Creating Layer val-data
I0409 22:47:55.185633  4221 net.cpp:380] val-data -> data
I0409 22:47:55.185642  4221 net.cpp:380] val-data -> label
I0409 22:47:55.185647  4221 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 22:47:55.189513  4221 data_layer.cpp:45] output data size: 32,3,227,227
I0409 22:47:55.229689  4221 net.cpp:122] Setting up val-data
I0409 22:47:55.229712  4221 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0409 22:47:55.229717  4221 net.cpp:129] Top shape: 32 (32)
I0409 22:47:55.229720  4221 net.cpp:137] Memory required for data: 19787264
I0409 22:47:55.229727  4221 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0409 22:47:55.229740  4221 net.cpp:84] Creating Layer label_val-data_1_split
I0409 22:47:55.229744  4221 net.cpp:406] label_val-data_1_split <- label
I0409 22:47:55.229753  4221 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0409 22:47:55.229761  4221 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0409 22:47:55.229861  4221 net.cpp:122] Setting up label_val-data_1_split
I0409 22:47:55.229867  4221 net.cpp:129] Top shape: 32 (32)
I0409 22:47:55.229871  4221 net.cpp:129] Top shape: 32 (32)
I0409 22:47:55.229874  4221 net.cpp:137] Memory required for data: 19787520
I0409 22:47:55.229877  4221 layer_factory.hpp:77] Creating layer conv1
I0409 22:47:55.229890  4221 net.cpp:84] Creating Layer conv1
I0409 22:47:55.229895  4221 net.cpp:406] conv1 <- data
I0409 22:47:55.229900  4221 net.cpp:380] conv1 -> conv1
I0409 22:47:55.233845  4221 net.cpp:122] Setting up conv1
I0409 22:47:55.233857  4221 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 22:47:55.233860  4221 net.cpp:137] Memory required for data: 56958720
I0409 22:47:55.233871  4221 layer_factory.hpp:77] Creating layer relu1
I0409 22:47:55.233877  4221 net.cpp:84] Creating Layer relu1
I0409 22:47:55.233898  4221 net.cpp:406] relu1 <- conv1
I0409 22:47:55.233904  4221 net.cpp:367] relu1 -> conv1 (in-place)
I0409 22:47:55.234375  4221 net.cpp:122] Setting up relu1
I0409 22:47:55.234385  4221 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 22:47:55.234387  4221 net.cpp:137] Memory required for data: 94129920
I0409 22:47:55.234391  4221 layer_factory.hpp:77] Creating layer norm1
I0409 22:47:55.234400  4221 net.cpp:84] Creating Layer norm1
I0409 22:47:55.234403  4221 net.cpp:406] norm1 <- conv1
I0409 22:47:55.234409  4221 net.cpp:380] norm1 -> norm1
I0409 22:47:55.234705  4221 net.cpp:122] Setting up norm1
I0409 22:47:55.234714  4221 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 22:47:55.234719  4221 net.cpp:137] Memory required for data: 131301120
I0409 22:47:55.234721  4221 layer_factory.hpp:77] Creating layer pool1
I0409 22:47:55.234728  4221 net.cpp:84] Creating Layer pool1
I0409 22:47:55.234731  4221 net.cpp:406] pool1 <- norm1
I0409 22:47:55.234736  4221 net.cpp:380] pool1 -> pool1
I0409 22:47:55.234762  4221 net.cpp:122] Setting up pool1
I0409 22:47:55.234767  4221 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0409 22:47:55.234771  4221 net.cpp:137] Memory required for data: 140259072
I0409 22:47:55.234774  4221 layer_factory.hpp:77] Creating layer conv2
I0409 22:47:55.234782  4221 net.cpp:84] Creating Layer conv2
I0409 22:47:55.234786  4221 net.cpp:406] conv2 <- pool1
I0409 22:47:55.234791  4221 net.cpp:380] conv2 -> conv2
I0409 22:47:55.243584  4221 net.cpp:122] Setting up conv2
I0409 22:47:55.243602  4221 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 22:47:55.243605  4221 net.cpp:137] Memory required for data: 164146944
I0409 22:47:55.243616  4221 layer_factory.hpp:77] Creating layer relu2
I0409 22:47:55.243624  4221 net.cpp:84] Creating Layer relu2
I0409 22:47:55.243628  4221 net.cpp:406] relu2 <- conv2
I0409 22:47:55.243634  4221 net.cpp:367] relu2 -> conv2 (in-place)
I0409 22:47:55.244136  4221 net.cpp:122] Setting up relu2
I0409 22:47:55.244148  4221 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 22:47:55.244151  4221 net.cpp:137] Memory required for data: 188034816
I0409 22:47:55.244155  4221 layer_factory.hpp:77] Creating layer norm2
I0409 22:47:55.244165  4221 net.cpp:84] Creating Layer norm2
I0409 22:47:55.244169  4221 net.cpp:406] norm2 <- conv2
I0409 22:47:55.244175  4221 net.cpp:380] norm2 -> norm2
I0409 22:47:55.244697  4221 net.cpp:122] Setting up norm2
I0409 22:47:55.244706  4221 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 22:47:55.244710  4221 net.cpp:137] Memory required for data: 211922688
I0409 22:47:55.244714  4221 layer_factory.hpp:77] Creating layer pool2
I0409 22:47:55.244722  4221 net.cpp:84] Creating Layer pool2
I0409 22:47:55.244725  4221 net.cpp:406] pool2 <- norm2
I0409 22:47:55.244730  4221 net.cpp:380] pool2 -> pool2
I0409 22:47:55.244761  4221 net.cpp:122] Setting up pool2
I0409 22:47:55.244767  4221 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 22:47:55.244771  4221 net.cpp:137] Memory required for data: 217460480
I0409 22:47:55.244773  4221 layer_factory.hpp:77] Creating layer conv3
I0409 22:47:55.244784  4221 net.cpp:84] Creating Layer conv3
I0409 22:47:55.244787  4221 net.cpp:406] conv3 <- pool2
I0409 22:47:55.244794  4221 net.cpp:380] conv3 -> conv3
I0409 22:47:55.261601  4221 net.cpp:122] Setting up conv3
I0409 22:47:55.261621  4221 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 22:47:55.261626  4221 net.cpp:137] Memory required for data: 225767168
I0409 22:47:55.261637  4221 layer_factory.hpp:77] Creating layer relu3
I0409 22:47:55.261646  4221 net.cpp:84] Creating Layer relu3
I0409 22:47:55.261651  4221 net.cpp:406] relu3 <- conv3
I0409 22:47:55.261658  4221 net.cpp:367] relu3 -> conv3 (in-place)
I0409 22:47:55.263229  4221 net.cpp:122] Setting up relu3
I0409 22:47:55.263242  4221 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 22:47:55.263247  4221 net.cpp:137] Memory required for data: 234073856
I0409 22:47:55.263250  4221 layer_factory.hpp:77] Creating layer conv4
I0409 22:47:55.263279  4221 net.cpp:84] Creating Layer conv4
I0409 22:47:55.263284  4221 net.cpp:406] conv4 <- conv3
I0409 22:47:55.263290  4221 net.cpp:380] conv4 -> conv4
I0409 22:47:55.274873  4221 net.cpp:122] Setting up conv4
I0409 22:47:55.274888  4221 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 22:47:55.274891  4221 net.cpp:137] Memory required for data: 242380544
I0409 22:47:55.274901  4221 layer_factory.hpp:77] Creating layer relu4
I0409 22:47:55.274909  4221 net.cpp:84] Creating Layer relu4
I0409 22:47:55.274914  4221 net.cpp:406] relu4 <- conv4
I0409 22:47:55.274921  4221 net.cpp:367] relu4 -> conv4 (in-place)
I0409 22:47:55.275416  4221 net.cpp:122] Setting up relu4
I0409 22:47:55.275425  4221 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 22:47:55.275430  4221 net.cpp:137] Memory required for data: 250687232
I0409 22:47:55.275434  4221 layer_factory.hpp:77] Creating layer conv5
I0409 22:47:55.275446  4221 net.cpp:84] Creating Layer conv5
I0409 22:47:55.275451  4221 net.cpp:406] conv5 <- conv4
I0409 22:47:55.275456  4221 net.cpp:380] conv5 -> conv5
I0409 22:47:55.283867  4221 net.cpp:122] Setting up conv5
I0409 22:47:55.283885  4221 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 22:47:55.283890  4221 net.cpp:137] Memory required for data: 256225024
I0409 22:47:55.283901  4221 layer_factory.hpp:77] Creating layer relu5
I0409 22:47:55.283910  4221 net.cpp:84] Creating Layer relu5
I0409 22:47:55.283913  4221 net.cpp:406] relu5 <- conv5
I0409 22:47:55.283921  4221 net.cpp:367] relu5 -> conv5 (in-place)
I0409 22:47:55.284420  4221 net.cpp:122] Setting up relu5
I0409 22:47:55.284430  4221 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 22:47:55.284433  4221 net.cpp:137] Memory required for data: 261762816
I0409 22:47:55.284437  4221 layer_factory.hpp:77] Creating layer pool5
I0409 22:47:55.284448  4221 net.cpp:84] Creating Layer pool5
I0409 22:47:55.284453  4221 net.cpp:406] pool5 <- conv5
I0409 22:47:55.284459  4221 net.cpp:380] pool5 -> pool5
I0409 22:47:55.284498  4221 net.cpp:122] Setting up pool5
I0409 22:47:55.284505  4221 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0409 22:47:55.284509  4221 net.cpp:137] Memory required for data: 262942464
I0409 22:47:55.284512  4221 layer_factory.hpp:77] Creating layer fc6
I0409 22:47:55.284520  4221 net.cpp:84] Creating Layer fc6
I0409 22:47:55.284523  4221 net.cpp:406] fc6 <- pool5
I0409 22:47:55.284529  4221 net.cpp:380] fc6 -> fc6
I0409 22:47:55.374289  4221 net.cpp:122] Setting up fc6
I0409 22:47:55.374308  4221 net.cpp:129] Top shape: 32 1024 (32768)
I0409 22:47:55.374312  4221 net.cpp:137] Memory required for data: 263073536
I0409 22:47:55.374321  4221 layer_factory.hpp:77] Creating layer relu6
I0409 22:47:55.374331  4221 net.cpp:84] Creating Layer relu6
I0409 22:47:55.374336  4221 net.cpp:406] relu6 <- fc6
I0409 22:47:55.374341  4221 net.cpp:367] relu6 -> fc6 (in-place)
I0409 22:47:55.374960  4221 net.cpp:122] Setting up relu6
I0409 22:47:55.374969  4221 net.cpp:129] Top shape: 32 1024 (32768)
I0409 22:47:55.374972  4221 net.cpp:137] Memory required for data: 263204608
I0409 22:47:55.374976  4221 layer_factory.hpp:77] Creating layer drop6
I0409 22:47:55.374984  4221 net.cpp:84] Creating Layer drop6
I0409 22:47:55.374989  4221 net.cpp:406] drop6 <- fc6
I0409 22:47:55.374994  4221 net.cpp:367] drop6 -> fc6 (in-place)
I0409 22:47:55.375018  4221 net.cpp:122] Setting up drop6
I0409 22:47:55.375023  4221 net.cpp:129] Top shape: 32 1024 (32768)
I0409 22:47:55.375026  4221 net.cpp:137] Memory required for data: 263335680
I0409 22:47:55.375030  4221 layer_factory.hpp:77] Creating layer fc8
I0409 22:47:55.375037  4221 net.cpp:84] Creating Layer fc8
I0409 22:47:55.375041  4221 net.cpp:406] fc8 <- fc6
I0409 22:47:55.375046  4221 net.cpp:380] fc8 -> fc8
I0409 22:47:55.376833  4221 net.cpp:122] Setting up fc8
I0409 22:47:55.376840  4221 net.cpp:129] Top shape: 32 196 (6272)
I0409 22:47:55.376843  4221 net.cpp:137] Memory required for data: 263360768
I0409 22:47:55.376850  4221 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0409 22:47:55.376855  4221 net.cpp:84] Creating Layer fc8_fc8_0_split
I0409 22:47:55.376876  4221 net.cpp:406] fc8_fc8_0_split <- fc8
I0409 22:47:55.376881  4221 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0409 22:47:55.376893  4221 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0409 22:47:55.376926  4221 net.cpp:122] Setting up fc8_fc8_0_split
I0409 22:47:55.376932  4221 net.cpp:129] Top shape: 32 196 (6272)
I0409 22:47:55.376935  4221 net.cpp:129] Top shape: 32 196 (6272)
I0409 22:47:55.376938  4221 net.cpp:137] Memory required for data: 263410944
I0409 22:47:55.376942  4221 layer_factory.hpp:77] Creating layer accuracy
I0409 22:47:55.376950  4221 net.cpp:84] Creating Layer accuracy
I0409 22:47:55.376953  4221 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0409 22:47:55.376957  4221 net.cpp:406] accuracy <- label_val-data_1_split_0
I0409 22:47:55.376963  4221 net.cpp:380] accuracy -> accuracy
I0409 22:47:55.376971  4221 net.cpp:122] Setting up accuracy
I0409 22:47:55.376974  4221 net.cpp:129] Top shape: (1)
I0409 22:47:55.376977  4221 net.cpp:137] Memory required for data: 263410948
I0409 22:47:55.376981  4221 layer_factory.hpp:77] Creating layer loss
I0409 22:47:55.376986  4221 net.cpp:84] Creating Layer loss
I0409 22:47:55.376989  4221 net.cpp:406] loss <- fc8_fc8_0_split_1
I0409 22:47:55.376993  4221 net.cpp:406] loss <- label_val-data_1_split_1
I0409 22:47:55.376998  4221 net.cpp:380] loss -> loss
I0409 22:47:55.377005  4221 layer_factory.hpp:77] Creating layer loss
I0409 22:47:55.377804  4221 net.cpp:122] Setting up loss
I0409 22:47:55.377815  4221 net.cpp:129] Top shape: (1)
I0409 22:47:55.377817  4221 net.cpp:132]     with loss weight 1
I0409 22:47:55.377828  4221 net.cpp:137] Memory required for data: 263410952
I0409 22:47:55.377831  4221 net.cpp:198] loss needs backward computation.
I0409 22:47:55.377836  4221 net.cpp:200] accuracy does not need backward computation.
I0409 22:47:55.377840  4221 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0409 22:47:55.377844  4221 net.cpp:198] fc8 needs backward computation.
I0409 22:47:55.377847  4221 net.cpp:198] drop6 needs backward computation.
I0409 22:47:55.377851  4221 net.cpp:198] relu6 needs backward computation.
I0409 22:47:55.377853  4221 net.cpp:198] fc6 needs backward computation.
I0409 22:47:55.377857  4221 net.cpp:198] pool5 needs backward computation.
I0409 22:47:55.377861  4221 net.cpp:198] relu5 needs backward computation.
I0409 22:47:55.377864  4221 net.cpp:198] conv5 needs backward computation.
I0409 22:47:55.377867  4221 net.cpp:198] relu4 needs backward computation.
I0409 22:47:55.377871  4221 net.cpp:198] conv4 needs backward computation.
I0409 22:47:55.377874  4221 net.cpp:198] relu3 needs backward computation.
I0409 22:47:55.377877  4221 net.cpp:198] conv3 needs backward computation.
I0409 22:47:55.377880  4221 net.cpp:198] pool2 needs backward computation.
I0409 22:47:55.377884  4221 net.cpp:198] norm2 needs backward computation.
I0409 22:47:55.377887  4221 net.cpp:198] relu2 needs backward computation.
I0409 22:47:55.377892  4221 net.cpp:198] conv2 needs backward computation.
I0409 22:47:55.377894  4221 net.cpp:198] pool1 needs backward computation.
I0409 22:47:55.377897  4221 net.cpp:198] norm1 needs backward computation.
I0409 22:47:55.377902  4221 net.cpp:198] relu1 needs backward computation.
I0409 22:47:55.377904  4221 net.cpp:198] conv1 needs backward computation.
I0409 22:47:55.377907  4221 net.cpp:200] label_val-data_1_split does not need backward computation.
I0409 22:47:55.377912  4221 net.cpp:200] val-data does not need backward computation.
I0409 22:47:55.377915  4221 net.cpp:242] This network produces output accuracy
I0409 22:47:55.377918  4221 net.cpp:242] This network produces output loss
I0409 22:47:55.377934  4221 net.cpp:255] Network initialization done.
I0409 22:47:55.378016  4221 solver.cpp:56] Solver scaffolding done.
I0409 22:47:55.378408  4221 caffe.cpp:248] Starting Optimization
I0409 22:47:55.378417  4221 solver.cpp:272] Solving
I0409 22:47:55.378420  4221 solver.cpp:273] Learning Rate Policy: exp
I0409 22:47:55.379362  4221 solver.cpp:330] Iteration 0, Testing net (#0)
I0409 22:47:55.379382  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:47:55.399120  4221 blocking_queue.cpp:49] Waiting for data
I0409 22:47:59.913327  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:47:59.957465  4221 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0409 22:47:59.957507  4221 solver.cpp:397]     Test net output #1: loss = 5.27887 (* 1 = 5.27887 loss)
I0409 22:48:00.067438  4221 solver.cpp:218] Iteration 0 (0 iter/s, 4.68878s/12 iters), loss = 5.28039
I0409 22:48:00.068953  4221 solver.cpp:237]     Train net output #0: loss = 5.28039 (* 1 = 5.28039 loss)
I0409 22:48:00.068972  4221 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0409 22:48:03.994359  4221 solver.cpp:218] Iteration 12 (3.05715 iter/s, 3.92522s/12 iters), loss = 5.27368
I0409 22:48:03.994411  4221 solver.cpp:237]     Train net output #0: loss = 5.27368 (* 1 = 5.27368 loss)
I0409 22:48:03.994423  4221 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0409 22:48:08.830438  4221 solver.cpp:218] Iteration 24 (2.48149 iter/s, 4.83581s/12 iters), loss = 5.27272
I0409 22:48:08.830497  4221 solver.cpp:237]     Train net output #0: loss = 5.27272 (* 1 = 5.27272 loss)
I0409 22:48:08.830509  4221 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0409 22:48:13.701555  4221 solver.cpp:218] Iteration 36 (2.46364 iter/s, 4.87085s/12 iters), loss = 5.28562
I0409 22:48:13.701607  4221 solver.cpp:237]     Train net output #0: loss = 5.28562 (* 1 = 5.28562 loss)
I0409 22:48:13.701617  4221 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0409 22:48:18.514457  4221 solver.cpp:218] Iteration 48 (2.49343 iter/s, 4.81264s/12 iters), loss = 5.2824
I0409 22:48:18.514506  4221 solver.cpp:237]     Train net output #0: loss = 5.2824 (* 1 = 5.2824 loss)
I0409 22:48:18.514519  4221 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0409 22:48:23.326609  4221 solver.cpp:218] Iteration 60 (2.49382 iter/s, 4.81189s/12 iters), loss = 5.2873
I0409 22:48:23.326668  4221 solver.cpp:237]     Train net output #0: loss = 5.2873 (* 1 = 5.2873 loss)
I0409 22:48:23.326680  4221 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0409 22:48:28.136777  4221 solver.cpp:218] Iteration 72 (2.49485 iter/s, 4.8099s/12 iters), loss = 5.27805
I0409 22:48:28.136895  4221 solver.cpp:237]     Train net output #0: loss = 5.27805 (* 1 = 5.27805 loss)
I0409 22:48:28.136907  4221 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0409 22:48:32.941704  4221 solver.cpp:218] Iteration 84 (2.49761 iter/s, 4.8046s/12 iters), loss = 5.28506
I0409 22:48:32.941767  4221 solver.cpp:237]     Train net output #0: loss = 5.28506 (* 1 = 5.28506 loss)
I0409 22:48:32.941778  4221 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0409 22:48:37.753037  4221 solver.cpp:218] Iteration 96 (2.49425 iter/s, 4.81106s/12 iters), loss = 5.29272
I0409 22:48:37.753085  4221 solver.cpp:237]     Train net output #0: loss = 5.29272 (* 1 = 5.29272 loss)
I0409 22:48:37.753096  4221 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0409 22:48:39.398953  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:48:39.704267  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0409 22:48:40.972460  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0409 22:48:42.088553  4221 solver.cpp:330] Iteration 102, Testing net (#0)
I0409 22:48:42.088585  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:48:46.458106  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:48:46.534135  4221 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 22:48:46.534186  4221 solver.cpp:397]     Test net output #1: loss = 5.27925 (* 1 = 5.27925 loss)
I0409 22:48:48.338774  4221 solver.cpp:218] Iteration 108 (1.13365 iter/s, 10.5852s/12 iters), loss = 5.28059
I0409 22:48:48.338830  4221 solver.cpp:237]     Train net output #0: loss = 5.28059 (* 1 = 5.28059 loss)
I0409 22:48:48.338841  4221 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0409 22:48:53.232730  4221 solver.cpp:218] Iteration 120 (2.45214 iter/s, 4.89369s/12 iters), loss = 5.27139
I0409 22:48:53.232767  4221 solver.cpp:237]     Train net output #0: loss = 5.27139 (* 1 = 5.27139 loss)
I0409 22:48:53.232777  4221 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0409 22:48:58.156252  4221 solver.cpp:218] Iteration 132 (2.43741 iter/s, 4.92327s/12 iters), loss = 5.22872
I0409 22:48:58.156379  4221 solver.cpp:237]     Train net output #0: loss = 5.22872 (* 1 = 5.22872 loss)
I0409 22:48:58.156394  4221 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0409 22:49:02.966440  4221 solver.cpp:218] Iteration 144 (2.49488 iter/s, 4.80985s/12 iters), loss = 5.27045
I0409 22:49:02.966496  4221 solver.cpp:237]     Train net output #0: loss = 5.27045 (* 1 = 5.27045 loss)
I0409 22:49:02.966507  4221 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0409 22:49:07.781404  4221 solver.cpp:218] Iteration 156 (2.49237 iter/s, 4.8147s/12 iters), loss = 5.21511
I0409 22:49:07.781453  4221 solver.cpp:237]     Train net output #0: loss = 5.21511 (* 1 = 5.21511 loss)
I0409 22:49:07.781466  4221 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0409 22:49:12.858525  4221 solver.cpp:218] Iteration 168 (2.36367 iter/s, 5.07685s/12 iters), loss = 5.19318
I0409 22:49:12.858578  4221 solver.cpp:237]     Train net output #0: loss = 5.19318 (* 1 = 5.19318 loss)
I0409 22:49:12.858590  4221 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0409 22:49:17.721390  4221 solver.cpp:218] Iteration 180 (2.46782 iter/s, 4.8626s/12 iters), loss = 5.15652
I0409 22:49:17.721442  4221 solver.cpp:237]     Train net output #0: loss = 5.15652 (* 1 = 5.15652 loss)
I0409 22:49:17.721453  4221 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0409 22:49:22.847829  4221 solver.cpp:218] Iteration 192 (2.34093 iter/s, 5.12616s/12 iters), loss = 5.23807
I0409 22:49:22.847884  4221 solver.cpp:237]     Train net output #0: loss = 5.23807 (* 1 = 5.23807 loss)
I0409 22:49:22.847898  4221 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0409 22:49:26.595958  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:49:27.254276  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0409 22:49:27.993767  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0409 22:49:28.489436  4221 solver.cpp:330] Iteration 204, Testing net (#0)
I0409 22:49:28.489513  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:49:32.958518  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:49:33.094182  4221 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0409 22:49:33.094216  4221 solver.cpp:397]     Test net output #1: loss = 5.1881 (* 1 = 5.1881 loss)
I0409 22:49:33.177574  4221 solver.cpp:218] Iteration 204 (1.16175 iter/s, 10.3293s/12 iters), loss = 5.12571
I0409 22:49:33.177616  4221 solver.cpp:237]     Train net output #0: loss = 5.12571 (* 1 = 5.12571 loss)
I0409 22:49:33.177624  4221 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0409 22:49:37.340734  4221 solver.cpp:218] Iteration 216 (2.88258 iter/s, 4.16293s/12 iters), loss = 5.18359
I0409 22:49:37.340782  4221 solver.cpp:237]     Train net output #0: loss = 5.18359 (* 1 = 5.18359 loss)
I0409 22:49:37.340795  4221 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0409 22:49:42.234980  4221 solver.cpp:218] Iteration 228 (2.45199 iter/s, 4.89398s/12 iters), loss = 5.19932
I0409 22:49:42.235041  4221 solver.cpp:237]     Train net output #0: loss = 5.19932 (* 1 = 5.19932 loss)
I0409 22:49:42.235054  4221 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0409 22:49:47.032968  4221 solver.cpp:218] Iteration 240 (2.50119 iter/s, 4.79772s/12 iters), loss = 5.20539
I0409 22:49:47.033017  4221 solver.cpp:237]     Train net output #0: loss = 5.20539 (* 1 = 5.20539 loss)
I0409 22:49:47.033028  4221 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0409 22:49:51.853164  4221 solver.cpp:218] Iteration 252 (2.48966 iter/s, 4.81994s/12 iters), loss = 5.0947
I0409 22:49:51.853214  4221 solver.cpp:237]     Train net output #0: loss = 5.0947 (* 1 = 5.0947 loss)
I0409 22:49:51.853225  4221 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0409 22:49:56.688650  4221 solver.cpp:218] Iteration 264 (2.48179 iter/s, 4.83523s/12 iters), loss = 5.19976
I0409 22:49:56.688700  4221 solver.cpp:237]     Train net output #0: loss = 5.19976 (* 1 = 5.19976 loss)
I0409 22:49:56.688711  4221 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0409 22:50:01.533262  4221 solver.cpp:218] Iteration 276 (2.47711 iter/s, 4.84435s/12 iters), loss = 5.17812
I0409 22:50:01.533381  4221 solver.cpp:237]     Train net output #0: loss = 5.17812 (* 1 = 5.17812 loss)
I0409 22:50:01.533391  4221 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0409 22:50:06.363451  4221 solver.cpp:218] Iteration 288 (2.48454 iter/s, 4.82986s/12 iters), loss = 5.04067
I0409 22:50:06.363498  4221 solver.cpp:237]     Train net output #0: loss = 5.04067 (* 1 = 5.04067 loss)
I0409 22:50:06.363509  4221 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0409 22:50:11.171166  4221 solver.cpp:218] Iteration 300 (2.49612 iter/s, 4.80746s/12 iters), loss = 5.1351
I0409 22:50:11.171206  4221 solver.cpp:237]     Train net output #0: loss = 5.1351 (* 1 = 5.1351 loss)
I0409 22:50:11.171216  4221 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0409 22:50:12.114235  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:50:13.130764  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0409 22:50:15.898541  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0409 22:50:17.295846  4221 solver.cpp:330] Iteration 306, Testing net (#0)
I0409 22:50:17.295876  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:50:21.702610  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:50:21.859093  4221 solver.cpp:397]     Test net output #0: accuracy = 0.0153186
I0409 22:50:21.859143  4221 solver.cpp:397]     Test net output #1: loss = 5.10796 (* 1 = 5.10796 loss)
I0409 22:50:23.764350  4221 solver.cpp:218] Iteration 312 (0.952939 iter/s, 12.5926s/12 iters), loss = 5.05809
I0409 22:50:23.764402  4221 solver.cpp:237]     Train net output #0: loss = 5.05809 (* 1 = 5.05809 loss)
I0409 22:50:23.764415  4221 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0409 22:50:28.679682  4221 solver.cpp:218] Iteration 324 (2.44147 iter/s, 4.91507s/12 iters), loss = 5.12274
I0409 22:50:28.679725  4221 solver.cpp:237]     Train net output #0: loss = 5.12274 (* 1 = 5.12274 loss)
I0409 22:50:28.679734  4221 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0409 22:50:33.548092  4221 solver.cpp:218] Iteration 336 (2.465 iter/s, 4.86815s/12 iters), loss = 5.07955
I0409 22:50:33.548171  4221 solver.cpp:237]     Train net output #0: loss = 5.07955 (* 1 = 5.07955 loss)
I0409 22:50:33.548182  4221 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0409 22:50:38.416586  4221 solver.cpp:218] Iteration 348 (2.46497 iter/s, 4.86821s/12 iters), loss = 5.03192
I0409 22:50:38.416635  4221 solver.cpp:237]     Train net output #0: loss = 5.03192 (* 1 = 5.03192 loss)
I0409 22:50:38.416646  4221 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0409 22:50:43.227648  4221 solver.cpp:218] Iteration 360 (2.49438 iter/s, 4.81081s/12 iters), loss = 5.06121
I0409 22:50:43.227692  4221 solver.cpp:237]     Train net output #0: loss = 5.06121 (* 1 = 5.06121 loss)
I0409 22:50:43.227701  4221 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0409 22:50:48.065443  4221 solver.cpp:218] Iteration 372 (2.4806 iter/s, 4.83754s/12 iters), loss = 4.98512
I0409 22:50:48.065490  4221 solver.cpp:237]     Train net output #0: loss = 4.98512 (* 1 = 4.98512 loss)
I0409 22:50:48.065497  4221 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0409 22:50:52.914916  4221 solver.cpp:218] Iteration 384 (2.47463 iter/s, 4.84921s/12 iters), loss = 5.10166
I0409 22:50:52.914968  4221 solver.cpp:237]     Train net output #0: loss = 5.10166 (* 1 = 5.10166 loss)
I0409 22:50:52.914978  4221 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0409 22:50:57.769318  4221 solver.cpp:218] Iteration 396 (2.47212 iter/s, 4.85414s/12 iters), loss = 5.02094
I0409 22:50:57.769369  4221 solver.cpp:237]     Train net output #0: loss = 5.02094 (* 1 = 5.02094 loss)
I0409 22:50:57.769380  4221 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0409 22:51:00.792229  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:51:02.162448  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0409 22:51:03.979949  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0409 22:51:06.312731  4221 solver.cpp:330] Iteration 408, Testing net (#0)
I0409 22:51:06.312749  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:51:10.634052  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:51:10.844585  4221 solver.cpp:397]     Test net output #0: accuracy = 0.0220588
I0409 22:51:10.844635  4221 solver.cpp:397]     Test net output #1: loss = 5.03741 (* 1 = 5.03741 loss)
I0409 22:51:10.926493  4221 solver.cpp:218] Iteration 408 (0.912092 iter/s, 13.1566s/12 iters), loss = 5.06879
I0409 22:51:10.926548  4221 solver.cpp:237]     Train net output #0: loss = 5.06879 (* 1 = 5.06879 loss)
I0409 22:51:10.926558  4221 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0409 22:51:15.141006  4221 solver.cpp:218] Iteration 420 (2.84747 iter/s, 4.21427s/12 iters), loss = 5.09318
I0409 22:51:15.141050  4221 solver.cpp:237]     Train net output #0: loss = 5.09318 (* 1 = 5.09318 loss)
I0409 22:51:15.141058  4221 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0409 22:51:19.965104  4221 solver.cpp:218] Iteration 432 (2.48764 iter/s, 4.82384s/12 iters), loss = 5.04506
I0409 22:51:19.965155  4221 solver.cpp:237]     Train net output #0: loss = 5.04506 (* 1 = 5.04506 loss)
I0409 22:51:19.965167  4221 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0409 22:51:24.791097  4221 solver.cpp:218] Iteration 444 (2.48667 iter/s, 4.82573s/12 iters), loss = 4.94556
I0409 22:51:24.791147  4221 solver.cpp:237]     Train net output #0: loss = 4.94556 (* 1 = 4.94556 loss)
I0409 22:51:24.791159  4221 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0409 22:51:29.634987  4221 solver.cpp:218] Iteration 456 (2.47748 iter/s, 4.84363s/12 iters), loss = 4.98926
I0409 22:51:29.635041  4221 solver.cpp:237]     Train net output #0: loss = 4.98926 (* 1 = 4.98926 loss)
I0409 22:51:29.635053  4221 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0409 22:51:34.459417  4221 solver.cpp:218] Iteration 468 (2.48748 iter/s, 4.82417s/12 iters), loss = 4.99261
I0409 22:51:34.459507  4221 solver.cpp:237]     Train net output #0: loss = 4.99261 (* 1 = 4.99261 loss)
I0409 22:51:34.459517  4221 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0409 22:51:39.313997  4221 solver.cpp:218] Iteration 480 (2.47204 iter/s, 4.85428s/12 iters), loss = 4.98419
I0409 22:51:39.314038  4221 solver.cpp:237]     Train net output #0: loss = 4.98419 (* 1 = 4.98419 loss)
I0409 22:51:39.314047  4221 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0409 22:51:44.147225  4221 solver.cpp:218] Iteration 492 (2.48294 iter/s, 4.83297s/12 iters), loss = 5.00092
I0409 22:51:44.147280  4221 solver.cpp:237]     Train net output #0: loss = 5.00092 (* 1 = 5.00092 loss)
I0409 22:51:44.147294  4221 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0409 22:51:48.986286  4221 solver.cpp:218] Iteration 504 (2.47995 iter/s, 4.8388s/12 iters), loss = 5.08446
I0409 22:51:48.986330  4221 solver.cpp:237]     Train net output #0: loss = 5.08446 (* 1 = 5.08446 loss)
I0409 22:51:48.986341  4221 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0409 22:51:49.273102  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:51:51.010774  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0409 22:51:51.676230  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0409 22:51:52.171375  4221 solver.cpp:330] Iteration 510, Testing net (#0)
I0409 22:51:52.171403  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:51:56.610823  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:51:56.847576  4221 solver.cpp:397]     Test net output #0: accuracy = 0.0245098
I0409 22:51:56.847617  4221 solver.cpp:397]     Test net output #1: loss = 4.99314 (* 1 = 4.99314 loss)
I0409 22:51:59.137660  4221 solver.cpp:218] Iteration 516 (1.18216 iter/s, 10.1509s/12 iters), loss = 4.93348
I0409 22:51:59.137713  4221 solver.cpp:237]     Train net output #0: loss = 4.93348 (* 1 = 4.93348 loss)
I0409 22:51:59.137723  4221 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0409 22:52:03.987942  4221 solver.cpp:218] Iteration 528 (2.47422 iter/s, 4.85001s/12 iters), loss = 4.97733
I0409 22:52:03.987994  4221 solver.cpp:237]     Train net output #0: loss = 4.97733 (* 1 = 4.97733 loss)
I0409 22:52:03.988005  4221 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0409 22:52:08.852067  4221 solver.cpp:218] Iteration 540 (2.46717 iter/s, 4.86386s/12 iters), loss = 4.9358
I0409 22:52:08.852193  4221 solver.cpp:237]     Train net output #0: loss = 4.9358 (* 1 = 4.9358 loss)
I0409 22:52:08.852205  4221 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0409 22:52:13.668972  4221 solver.cpp:218] Iteration 552 (2.4914 iter/s, 4.81657s/12 iters), loss = 5.01575
I0409 22:52:13.669021  4221 solver.cpp:237]     Train net output #0: loss = 5.01575 (* 1 = 5.01575 loss)
I0409 22:52:13.669030  4221 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0409 22:52:18.496017  4221 solver.cpp:218] Iteration 564 (2.48613 iter/s, 4.82678s/12 iters), loss = 4.89903
I0409 22:52:18.496078  4221 solver.cpp:237]     Train net output #0: loss = 4.89903 (* 1 = 4.89903 loss)
I0409 22:52:18.496091  4221 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0409 22:52:23.332466  4221 solver.cpp:218] Iteration 576 (2.4813 iter/s, 4.83617s/12 iters), loss = 4.95518
I0409 22:52:23.332526  4221 solver.cpp:237]     Train net output #0: loss = 4.95518 (* 1 = 4.95518 loss)
I0409 22:52:23.332540  4221 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0409 22:52:28.169344  4221 solver.cpp:218] Iteration 588 (2.48107 iter/s, 4.83661s/12 iters), loss = 4.75715
I0409 22:52:28.169390  4221 solver.cpp:237]     Train net output #0: loss = 4.75715 (* 1 = 4.75715 loss)
I0409 22:52:28.169401  4221 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0409 22:52:33.034236  4221 solver.cpp:218] Iteration 600 (2.46678 iter/s, 4.86464s/12 iters), loss = 4.92039
I0409 22:52:33.034269  4221 solver.cpp:237]     Train net output #0: loss = 4.92039 (* 1 = 4.92039 loss)
I0409 22:52:33.034278  4221 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0409 22:52:35.355270  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:52:37.422019  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0409 22:52:39.738430  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0409 22:52:41.540009  4221 solver.cpp:330] Iteration 612, Testing net (#0)
I0409 22:52:41.540031  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:52:45.889107  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:52:46.177991  4221 solver.cpp:397]     Test net output #0: accuracy = 0.03125
I0409 22:52:46.178041  4221 solver.cpp:397]     Test net output #1: loss = 4.92251 (* 1 = 4.92251 loss)
I0409 22:52:46.261435  4221 solver.cpp:218] Iteration 612 (0.907262 iter/s, 13.2266s/12 iters), loss = 4.83588
I0409 22:52:46.261487  4221 solver.cpp:237]     Train net output #0: loss = 4.83588 (* 1 = 4.83588 loss)
I0409 22:52:46.261497  4221 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0409 22:52:50.409279  4221 solver.cpp:218] Iteration 624 (2.89323 iter/s, 4.14761s/12 iters), loss = 4.77282
I0409 22:52:50.409332  4221 solver.cpp:237]     Train net output #0: loss = 4.77282 (* 1 = 4.77282 loss)
I0409 22:52:50.409344  4221 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0409 22:52:55.247087  4221 solver.cpp:218] Iteration 636 (2.4806 iter/s, 4.83755s/12 iters), loss = 4.75799
I0409 22:52:55.247138  4221 solver.cpp:237]     Train net output #0: loss = 4.75799 (* 1 = 4.75799 loss)
I0409 22:52:55.247150  4221 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0409 22:53:00.069984  4221 solver.cpp:218] Iteration 648 (2.48827 iter/s, 4.82263s/12 iters), loss = 4.96856
I0409 22:53:00.070036  4221 solver.cpp:237]     Train net output #0: loss = 4.96856 (* 1 = 4.96856 loss)
I0409 22:53:00.070048  4221 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0409 22:53:04.914465  4221 solver.cpp:218] Iteration 660 (2.47718 iter/s, 4.84422s/12 iters), loss = 4.84366
I0409 22:53:04.914510  4221 solver.cpp:237]     Train net output #0: loss = 4.84366 (* 1 = 4.84366 loss)
I0409 22:53:04.914517  4221 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0409 22:53:09.741343  4221 solver.cpp:218] Iteration 672 (2.48621 iter/s, 4.82662s/12 iters), loss = 4.66519
I0409 22:53:09.741479  4221 solver.cpp:237]     Train net output #0: loss = 4.66519 (* 1 = 4.66519 loss)
I0409 22:53:09.741492  4221 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0409 22:53:14.109205  4221 blocking_queue.cpp:49] Waiting for data
I0409 22:53:14.548789  4221 solver.cpp:218] Iteration 684 (2.49631 iter/s, 4.8071s/12 iters), loss = 4.67076
I0409 22:53:14.548846  4221 solver.cpp:237]     Train net output #0: loss = 4.67076 (* 1 = 4.67076 loss)
I0409 22:53:14.548858  4221 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0409 22:53:19.358399  4221 solver.cpp:218] Iteration 696 (2.49515 iter/s, 4.80934s/12 iters), loss = 4.82814
I0409 22:53:19.358464  4221 solver.cpp:237]     Train net output #0: loss = 4.82814 (* 1 = 4.82814 loss)
I0409 22:53:19.358476  4221 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0409 22:53:23.787799  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:53:24.155534  4221 solver.cpp:218] Iteration 708 (2.50164 iter/s, 4.79686s/12 iters), loss = 4.89735
I0409 22:53:24.155578  4221 solver.cpp:237]     Train net output #0: loss = 4.89735 (* 1 = 4.89735 loss)
I0409 22:53:24.155587  4221 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0409 22:53:26.139319  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0409 22:53:27.325037  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0409 22:53:28.343668  4221 solver.cpp:330] Iteration 714, Testing net (#0)
I0409 22:53:28.343691  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:53:32.510661  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:53:32.829365  4221 solver.cpp:397]     Test net output #0: accuracy = 0.0435049
I0409 22:53:32.829411  4221 solver.cpp:397]     Test net output #1: loss = 4.84813 (* 1 = 4.84813 loss)
I0409 22:53:34.779381  4221 solver.cpp:218] Iteration 720 (1.12959 iter/s, 10.6234s/12 iters), loss = 4.78946
I0409 22:53:34.779440  4221 solver.cpp:237]     Train net output #0: loss = 4.78946 (* 1 = 4.78946 loss)
I0409 22:53:34.779453  4221 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0409 22:53:39.687414  4221 solver.cpp:218] Iteration 732 (2.44511 iter/s, 4.90776s/12 iters), loss = 4.63193
I0409 22:53:39.687465  4221 solver.cpp:237]     Train net output #0: loss = 4.63193 (* 1 = 4.63193 loss)
I0409 22:53:39.687475  4221 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0409 22:53:44.539405  4221 solver.cpp:218] Iteration 744 (2.47335 iter/s, 4.85173s/12 iters), loss = 4.82361
I0409 22:53:44.539517  4221 solver.cpp:237]     Train net output #0: loss = 4.82361 (* 1 = 4.82361 loss)
I0409 22:53:44.539526  4221 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0409 22:53:49.455185  4221 solver.cpp:218] Iteration 756 (2.44128 iter/s, 4.91545s/12 iters), loss = 4.80122
I0409 22:53:49.455233  4221 solver.cpp:237]     Train net output #0: loss = 4.80122 (* 1 = 4.80122 loss)
I0409 22:53:49.455243  4221 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0409 22:53:54.364554  4221 solver.cpp:218] Iteration 768 (2.44444 iter/s, 4.9091s/12 iters), loss = 4.68829
I0409 22:53:54.364612  4221 solver.cpp:237]     Train net output #0: loss = 4.68829 (* 1 = 4.68829 loss)
I0409 22:53:54.364624  4221 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0409 22:53:59.255071  4221 solver.cpp:218] Iteration 780 (2.45387 iter/s, 4.89024s/12 iters), loss = 4.7231
I0409 22:53:59.255138  4221 solver.cpp:237]     Train net output #0: loss = 4.7231 (* 1 = 4.7231 loss)
I0409 22:53:59.255149  4221 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0409 22:54:04.030709  4221 solver.cpp:218] Iteration 792 (2.5129 iter/s, 4.77536s/12 iters), loss = 4.47977
I0409 22:54:04.030764  4221 solver.cpp:237]     Train net output #0: loss = 4.47977 (* 1 = 4.47977 loss)
I0409 22:54:04.030776  4221 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0409 22:54:08.824741  4221 solver.cpp:218] Iteration 804 (2.50325 iter/s, 4.79377s/12 iters), loss = 4.64771
I0409 22:54:08.824800  4221 solver.cpp:237]     Train net output #0: loss = 4.64771 (* 1 = 4.64771 loss)
I0409 22:54:08.824811  4221 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0409 22:54:10.501020  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:54:13.175520  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0409 22:54:14.706593  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0409 22:54:15.276017  4221 solver.cpp:330] Iteration 816, Testing net (#0)
I0409 22:54:15.276041  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:54:19.250862  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:54:19.624311  4221 solver.cpp:397]     Test net output #0: accuracy = 0.0484069
I0409 22:54:19.624351  4221 solver.cpp:397]     Test net output #1: loss = 4.69287 (* 1 = 4.69287 loss)
I0409 22:54:19.707929  4221 solver.cpp:218] Iteration 816 (1.10267 iter/s, 10.8827s/12 iters), loss = 4.66028
I0409 22:54:19.707983  4221 solver.cpp:237]     Train net output #0: loss = 4.66028 (* 1 = 4.66028 loss)
I0409 22:54:19.707993  4221 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0409 22:54:23.876225  4221 solver.cpp:218] Iteration 828 (2.87904 iter/s, 4.16806s/12 iters), loss = 4.94076
I0409 22:54:23.876272  4221 solver.cpp:237]     Train net output #0: loss = 4.94076 (* 1 = 4.94076 loss)
I0409 22:54:23.876281  4221 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0409 22:54:28.789570  4221 solver.cpp:218] Iteration 840 (2.44246 iter/s, 4.91308s/12 iters), loss = 4.26236
I0409 22:54:28.789619  4221 solver.cpp:237]     Train net output #0: loss = 4.26236 (* 1 = 4.26236 loss)
I0409 22:54:28.789628  4221 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0409 22:54:33.698683  4221 solver.cpp:218] Iteration 852 (2.44457 iter/s, 4.90885s/12 iters), loss = 4.58574
I0409 22:54:33.698751  4221 solver.cpp:237]     Train net output #0: loss = 4.58574 (* 1 = 4.58574 loss)
I0409 22:54:33.698763  4221 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0409 22:54:38.667444  4221 solver.cpp:218] Iteration 864 (2.41523 iter/s, 4.96847s/12 iters), loss = 4.62392
I0409 22:54:38.667520  4221 solver.cpp:237]     Train net output #0: loss = 4.62392 (* 1 = 4.62392 loss)
I0409 22:54:38.667537  4221 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0409 22:54:43.550221  4221 solver.cpp:218] Iteration 876 (2.45776 iter/s, 4.88249s/12 iters), loss = 4.4092
I0409 22:54:43.550282  4221 solver.cpp:237]     Train net output #0: loss = 4.4092 (* 1 = 4.4092 loss)
I0409 22:54:43.550294  4221 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0409 22:54:48.370714  4221 solver.cpp:218] Iteration 888 (2.48951 iter/s, 4.82022s/12 iters), loss = 4.488
I0409 22:54:48.370887  4221 solver.cpp:237]     Train net output #0: loss = 4.488 (* 1 = 4.488 loss)
I0409 22:54:48.370899  4221 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0409 22:54:53.173985  4221 solver.cpp:218] Iteration 900 (2.4985 iter/s, 4.80287s/12 iters), loss = 4.6424
I0409 22:54:53.174044  4221 solver.cpp:237]     Train net output #0: loss = 4.6424 (* 1 = 4.6424 loss)
I0409 22:54:53.174057  4221 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0409 22:54:56.885756  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:54:57.953279  4221 solver.cpp:218] Iteration 912 (2.51097 iter/s, 4.77902s/12 iters), loss = 4.37309
I0409 22:54:57.953341  4221 solver.cpp:237]     Train net output #0: loss = 4.37309 (* 1 = 4.37309 loss)
I0409 22:54:57.953351  4221 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0409 22:54:59.912039  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0409 22:55:02.091667  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0409 22:55:04.285298  4221 solver.cpp:330] Iteration 918, Testing net (#0)
I0409 22:55:04.285322  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:55:08.492120  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:55:08.902536  4221 solver.cpp:397]     Test net output #0: accuracy = 0.057598
I0409 22:55:08.902586  4221 solver.cpp:397]     Test net output #1: loss = 4.59414 (* 1 = 4.59414 loss)
I0409 22:55:10.773571  4221 solver.cpp:218] Iteration 924 (0.93606 iter/s, 12.8197s/12 iters), loss = 4.34955
I0409 22:55:10.773628  4221 solver.cpp:237]     Train net output #0: loss = 4.34955 (* 1 = 4.34955 loss)
I0409 22:55:10.773639  4221 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0409 22:55:15.875422  4221 solver.cpp:218] Iteration 936 (2.35222 iter/s, 5.10157s/12 iters), loss = 4.40821
I0409 22:55:15.875468  4221 solver.cpp:237]     Train net output #0: loss = 4.40821 (* 1 = 4.40821 loss)
I0409 22:55:15.875476  4221 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0409 22:55:20.815543  4221 solver.cpp:218] Iteration 948 (2.42922 iter/s, 4.93985s/12 iters), loss = 4.60082
I0409 22:55:20.815662  4221 solver.cpp:237]     Train net output #0: loss = 4.60082 (* 1 = 4.60082 loss)
I0409 22:55:20.815675  4221 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0409 22:55:25.852164  4221 solver.cpp:218] Iteration 960 (2.38271 iter/s, 5.03629s/12 iters), loss = 4.374
I0409 22:55:25.852202  4221 solver.cpp:237]     Train net output #0: loss = 4.374 (* 1 = 4.374 loss)
I0409 22:55:25.852210  4221 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0409 22:55:30.711621  4221 solver.cpp:218] Iteration 972 (2.46954 iter/s, 4.8592s/12 iters), loss = 4.31737
I0409 22:55:30.711679  4221 solver.cpp:237]     Train net output #0: loss = 4.31737 (* 1 = 4.31737 loss)
I0409 22:55:30.711691  4221 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0409 22:55:36.140674  4221 solver.cpp:218] Iteration 984 (2.21045 iter/s, 5.42877s/12 iters), loss = 4.24516
I0409 22:55:36.140710  4221 solver.cpp:237]     Train net output #0: loss = 4.24516 (* 1 = 4.24516 loss)
I0409 22:55:36.140718  4221 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0409 22:55:41.250609  4221 solver.cpp:218] Iteration 996 (2.34849 iter/s, 5.10967s/12 iters), loss = 4.30323
I0409 22:55:41.250659  4221 solver.cpp:237]     Train net output #0: loss = 4.30323 (* 1 = 4.30323 loss)
I0409 22:55:41.250669  4221 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0409 22:55:46.160804  4221 solver.cpp:218] Iteration 1008 (2.44403 iter/s, 4.90993s/12 iters), loss = 4.35442
I0409 22:55:46.160854  4221 solver.cpp:237]     Train net output #0: loss = 4.35442 (* 1 = 4.35442 loss)
I0409 22:55:46.160863  4221 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0409 22:55:47.163786  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:55:50.625869  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0409 22:55:51.338021  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0409 22:55:52.157647  4221 solver.cpp:330] Iteration 1020, Testing net (#0)
I0409 22:55:52.157675  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:55:56.231642  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:55:56.663017  4221 solver.cpp:397]     Test net output #0: accuracy = 0.0741422
I0409 22:55:56.663059  4221 solver.cpp:397]     Test net output #1: loss = 4.45789 (* 1 = 4.45789 loss)
I0409 22:55:56.746482  4221 solver.cpp:218] Iteration 1020 (1.13366 iter/s, 10.5852s/12 iters), loss = 4.16957
I0409 22:55:56.746537  4221 solver.cpp:237]     Train net output #0: loss = 4.16957 (* 1 = 4.16957 loss)
I0409 22:55:56.746548  4221 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0409 22:56:00.893090  4221 solver.cpp:218] Iteration 1032 (2.8941 iter/s, 4.14637s/12 iters), loss = 4.32383
I0409 22:56:00.893142  4221 solver.cpp:237]     Train net output #0: loss = 4.32383 (* 1 = 4.32383 loss)
I0409 22:56:00.893151  4221 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0409 22:56:05.917999  4221 solver.cpp:218] Iteration 1044 (2.38824 iter/s, 5.02463s/12 iters), loss = 4.38298
I0409 22:56:05.918052  4221 solver.cpp:237]     Train net output #0: loss = 4.38298 (* 1 = 4.38298 loss)
I0409 22:56:05.918062  4221 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0409 22:56:11.021534  4221 solver.cpp:218] Iteration 1056 (2.35144 iter/s, 5.10326s/12 iters), loss = 4.21331
I0409 22:56:11.021589  4221 solver.cpp:237]     Train net output #0: loss = 4.21331 (* 1 = 4.21331 loss)
I0409 22:56:11.021598  4221 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0409 22:56:15.867918  4221 solver.cpp:218] Iteration 1068 (2.47621 iter/s, 4.84611s/12 iters), loss = 4.23018
I0409 22:56:15.867975  4221 solver.cpp:237]     Train net output #0: loss = 4.23018 (* 1 = 4.23018 loss)
I0409 22:56:15.867987  4221 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0409 22:56:20.688589  4221 solver.cpp:218] Iteration 1080 (2.48942 iter/s, 4.8204s/12 iters), loss = 4.06049
I0409 22:56:20.688639  4221 solver.cpp:237]     Train net output #0: loss = 4.06049 (* 1 = 4.06049 loss)
I0409 22:56:20.688650  4221 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0409 22:56:25.468267  4221 solver.cpp:218] Iteration 1092 (2.51076 iter/s, 4.77942s/12 iters), loss = 4.25938
I0409 22:56:25.468384  4221 solver.cpp:237]     Train net output #0: loss = 4.25938 (* 1 = 4.25938 loss)
I0409 22:56:25.468394  4221 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0409 22:56:30.298019  4221 solver.cpp:218] Iteration 1104 (2.48477 iter/s, 4.82942s/12 iters), loss = 4.22342
I0409 22:56:30.298076  4221 solver.cpp:237]     Train net output #0: loss = 4.22342 (* 1 = 4.22342 loss)
I0409 22:56:30.298087  4221 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0409 22:56:33.313747  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:56:35.119938  4221 solver.cpp:218] Iteration 1116 (2.48877 iter/s, 4.82166s/12 iters), loss = 4.19847
I0409 22:56:35.119980  4221 solver.cpp:237]     Train net output #0: loss = 4.19847 (* 1 = 4.19847 loss)
I0409 22:56:35.119990  4221 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0409 22:56:37.078619  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0409 22:56:37.813922  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0409 22:56:38.367789  4221 solver.cpp:330] Iteration 1122, Testing net (#0)
I0409 22:56:38.367821  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:56:42.652226  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:56:43.222638  4221 solver.cpp:397]     Test net output #0: accuracy = 0.09375
I0409 22:56:43.222681  4221 solver.cpp:397]     Test net output #1: loss = 4.33436 (* 1 = 4.33436 loss)
I0409 22:56:44.950589  4221 solver.cpp:218] Iteration 1128 (1.22073 iter/s, 9.83019s/12 iters), loss = 4.15879
I0409 22:56:44.950644  4221 solver.cpp:237]     Train net output #0: loss = 4.15879 (* 1 = 4.15879 loss)
I0409 22:56:44.950654  4221 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0409 22:56:49.826668  4221 solver.cpp:218] Iteration 1140 (2.46113 iter/s, 4.87582s/12 iters), loss = 4.29467
I0409 22:56:49.826710  4221 solver.cpp:237]     Train net output #0: loss = 4.29467 (* 1 = 4.29467 loss)
I0409 22:56:49.826720  4221 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0409 22:56:54.660005  4221 solver.cpp:218] Iteration 1152 (2.48288 iter/s, 4.83309s/12 iters), loss = 3.96556
I0409 22:56:54.660043  4221 solver.cpp:237]     Train net output #0: loss = 3.96556 (* 1 = 3.96556 loss)
I0409 22:56:54.660051  4221 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0409 22:56:59.635874  4221 solver.cpp:218] Iteration 1164 (2.41176 iter/s, 4.97561s/12 iters), loss = 4.13805
I0409 22:56:59.636023  4221 solver.cpp:237]     Train net output #0: loss = 4.13805 (* 1 = 4.13805 loss)
I0409 22:56:59.636037  4221 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0409 22:57:04.660001  4221 solver.cpp:218] Iteration 1176 (2.38865 iter/s, 5.02377s/12 iters), loss = 4.31163
I0409 22:57:04.660059  4221 solver.cpp:237]     Train net output #0: loss = 4.31163 (* 1 = 4.31163 loss)
I0409 22:57:04.660071  4221 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0409 22:57:09.637526  4221 solver.cpp:218] Iteration 1188 (2.41097 iter/s, 4.97725s/12 iters), loss = 4.17388
I0409 22:57:09.637583  4221 solver.cpp:237]     Train net output #0: loss = 4.17388 (* 1 = 4.17388 loss)
I0409 22:57:09.637593  4221 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0409 22:57:14.748162  4221 solver.cpp:218] Iteration 1200 (2.34817 iter/s, 5.11036s/12 iters), loss = 4.14007
I0409 22:57:14.748211  4221 solver.cpp:237]     Train net output #0: loss = 4.14007 (* 1 = 4.14007 loss)
I0409 22:57:14.748220  4221 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0409 22:57:20.250819  4221 solver.cpp:218] Iteration 1212 (2.18088 iter/s, 5.50236s/12 iters), loss = 4.08061
I0409 22:57:20.250874  4221 solver.cpp:237]     Train net output #0: loss = 4.08061 (* 1 = 4.08061 loss)
I0409 22:57:20.250885  4221 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0409 22:57:20.529253  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:57:24.722190  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0409 22:57:25.987706  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0409 22:57:27.781911  4221 solver.cpp:330] Iteration 1224, Testing net (#0)
I0409 22:57:27.781937  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:57:31.647060  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:57:32.157392  4221 solver.cpp:397]     Test net output #0: accuracy = 0.112132
I0409 22:57:32.157438  4221 solver.cpp:397]     Test net output #1: loss = 4.26772 (* 1 = 4.26772 loss)
I0409 22:57:32.239333  4221 solver.cpp:218] Iteration 1224 (1.001 iter/s, 11.988s/12 iters), loss = 4.2109
I0409 22:57:32.239392  4221 solver.cpp:237]     Train net output #0: loss = 4.2109 (* 1 = 4.2109 loss)
I0409 22:57:32.239403  4221 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0409 22:57:36.387217  4221 solver.cpp:218] Iteration 1236 (2.89321 iter/s, 4.14764s/12 iters), loss = 4.15357
I0409 22:57:36.387274  4221 solver.cpp:237]     Train net output #0: loss = 4.15357 (* 1 = 4.15357 loss)
I0409 22:57:36.387285  4221 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0409 22:57:41.242990  4221 solver.cpp:218] Iteration 1248 (2.47142 iter/s, 4.85551s/12 iters), loss = 4.06025
I0409 22:57:41.243046  4221 solver.cpp:237]     Train net output #0: loss = 4.06025 (* 1 = 4.06025 loss)
I0409 22:57:41.243058  4221 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0409 22:57:46.080520  4221 solver.cpp:218] Iteration 1260 (2.48074 iter/s, 4.83727s/12 iters), loss = 4.11742
I0409 22:57:46.080565  4221 solver.cpp:237]     Train net output #0: loss = 4.11742 (* 1 = 4.11742 loss)
I0409 22:57:46.080574  4221 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0409 22:57:51.029592  4221 solver.cpp:218] Iteration 1272 (2.42482 iter/s, 4.94881s/12 iters), loss = 3.91593
I0409 22:57:51.029637  4221 solver.cpp:237]     Train net output #0: loss = 3.91593 (* 1 = 3.91593 loss)
I0409 22:57:51.029644  4221 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0409 22:57:55.870749  4221 solver.cpp:218] Iteration 1284 (2.47888 iter/s, 4.8409s/12 iters), loss = 4.09871
I0409 22:57:55.870796  4221 solver.cpp:237]     Train net output #0: loss = 4.09871 (* 1 = 4.09871 loss)
I0409 22:57:55.870808  4221 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0409 22:58:00.776712  4221 solver.cpp:218] Iteration 1296 (2.44613 iter/s, 4.9057s/12 iters), loss = 3.82859
I0409 22:58:00.776760  4221 solver.cpp:237]     Train net output #0: loss = 3.82859 (* 1 = 3.82859 loss)
I0409 22:58:00.776768  4221 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0409 22:58:05.679172  4221 solver.cpp:218] Iteration 1308 (2.44789 iter/s, 4.90219s/12 iters), loss = 4.02337
I0409 22:58:05.679308  4221 solver.cpp:237]     Train net output #0: loss = 4.02337 (* 1 = 4.02337 loss)
I0409 22:58:05.679317  4221 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0409 22:58:08.154258  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:58:10.586241  4221 solver.cpp:218] Iteration 1320 (2.44563 iter/s, 4.90672s/12 iters), loss = 3.83804
I0409 22:58:10.586288  4221 solver.cpp:237]     Train net output #0: loss = 3.83804 (* 1 = 3.83804 loss)
I0409 22:58:10.586297  4221 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0409 22:58:12.588752  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0409 22:58:13.274370  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0409 22:58:13.783205  4221 solver.cpp:330] Iteration 1326, Testing net (#0)
I0409 22:58:13.783224  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:58:17.661478  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:58:18.376577  4221 solver.cpp:397]     Test net output #0: accuracy = 0.125
I0409 22:58:18.376607  4221 solver.cpp:397]     Test net output #1: loss = 4.06494 (* 1 = 4.06494 loss)
I0409 22:58:20.211143  4221 solver.cpp:218] Iteration 1332 (1.24682 iter/s, 9.62445s/12 iters), loss = 3.71032
I0409 22:58:20.211187  4221 solver.cpp:237]     Train net output #0: loss = 3.71032 (* 1 = 3.71032 loss)
I0409 22:58:20.211195  4221 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0409 22:58:25.189129  4221 solver.cpp:218] Iteration 1344 (2.41074 iter/s, 4.97772s/12 iters), loss = 3.72013
I0409 22:58:25.189184  4221 solver.cpp:237]     Train net output #0: loss = 3.72013 (* 1 = 3.72013 loss)
I0409 22:58:25.189195  4221 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0409 22:58:30.094604  4221 solver.cpp:218] Iteration 1356 (2.44638 iter/s, 4.90521s/12 iters), loss = 3.87589
I0409 22:58:30.094664  4221 solver.cpp:237]     Train net output #0: loss = 3.87589 (* 1 = 3.87589 loss)
I0409 22:58:30.094676  4221 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0409 22:58:34.886184  4221 solver.cpp:218] Iteration 1368 (2.50454 iter/s, 4.79131s/12 iters), loss = 3.86457
I0409 22:58:34.886242  4221 solver.cpp:237]     Train net output #0: loss = 3.86457 (* 1 = 3.86457 loss)
I0409 22:58:34.886253  4221 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0409 22:58:34.886507  4221 blocking_queue.cpp:49] Waiting for data
I0409 22:58:39.841614  4221 solver.cpp:218] Iteration 1380 (2.42172 iter/s, 4.95516s/12 iters), loss = 3.85109
I0409 22:58:39.841730  4221 solver.cpp:237]     Train net output #0: loss = 3.85109 (* 1 = 3.85109 loss)
I0409 22:58:39.841742  4221 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0409 22:58:44.724138  4221 solver.cpp:218] Iteration 1392 (2.45791 iter/s, 4.88219s/12 iters), loss = 3.78409
I0409 22:58:44.724195  4221 solver.cpp:237]     Train net output #0: loss = 3.78409 (* 1 = 3.78409 loss)
I0409 22:58:44.724206  4221 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0409 22:58:49.811645  4221 solver.cpp:218] Iteration 1404 (2.35885 iter/s, 5.08723s/12 iters), loss = 3.99824
I0409 22:58:49.811702  4221 solver.cpp:237]     Train net output #0: loss = 3.99824 (* 1 = 3.99824 loss)
I0409 22:58:49.811712  4221 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0409 22:58:54.326213  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:58:54.667807  4221 solver.cpp:218] Iteration 1416 (2.47122 iter/s, 4.8559s/12 iters), loss = 3.59998
I0409 22:58:54.667856  4221 solver.cpp:237]     Train net output #0: loss = 3.59998 (* 1 = 3.59998 loss)
I0409 22:58:54.667865  4221 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0409 22:58:59.035248  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0409 22:59:00.481017  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0409 22:59:01.641072  4221 solver.cpp:330] Iteration 1428, Testing net (#0)
I0409 22:59:01.641098  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:59:05.538228  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:59:06.126914  4221 solver.cpp:397]     Test net output #0: accuracy = 0.139706
I0409 22:59:06.126943  4221 solver.cpp:397]     Test net output #1: loss = 3.97348 (* 1 = 3.97348 loss)
I0409 22:59:06.210090  4221 solver.cpp:218] Iteration 1428 (1.0397 iter/s, 11.5417s/12 iters), loss = 3.63388
I0409 22:59:06.210144  4221 solver.cpp:237]     Train net output #0: loss = 3.63388 (* 1 = 3.63388 loss)
I0409 22:59:06.210152  4221 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0409 22:59:11.103802  4221 solver.cpp:218] Iteration 1440 (2.45226 iter/s, 4.89345s/12 iters), loss = 3.79379
I0409 22:59:11.103924  4221 solver.cpp:237]     Train net output #0: loss = 3.79379 (* 1 = 3.79379 loss)
I0409 22:59:11.103933  4221 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0409 22:59:16.011729  4221 solver.cpp:218] Iteration 1452 (2.44519 iter/s, 4.90759s/12 iters), loss = 3.95379
I0409 22:59:16.011778  4221 solver.cpp:237]     Train net output #0: loss = 3.95379 (* 1 = 3.95379 loss)
I0409 22:59:16.011787  4221 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0409 22:59:20.957409  4221 solver.cpp:218] Iteration 1464 (2.42649 iter/s, 4.94542s/12 iters), loss = 3.64374
I0409 22:59:20.957450  4221 solver.cpp:237]     Train net output #0: loss = 3.64374 (* 1 = 3.64374 loss)
I0409 22:59:20.957458  4221 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0409 22:59:25.988780  4221 solver.cpp:218] Iteration 1476 (2.38516 iter/s, 5.03111s/12 iters), loss = 3.62728
I0409 22:59:25.988829  4221 solver.cpp:237]     Train net output #0: loss = 3.62728 (* 1 = 3.62728 loss)
I0409 22:59:25.988838  4221 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0409 22:59:30.868441  4221 solver.cpp:218] Iteration 1488 (2.45932 iter/s, 4.8794s/12 iters), loss = 3.46235
I0409 22:59:30.868494  4221 solver.cpp:237]     Train net output #0: loss = 3.46235 (* 1 = 3.46235 loss)
I0409 22:59:30.868503  4221 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0409 22:59:35.843410  4221 solver.cpp:218] Iteration 1500 (2.41221 iter/s, 4.97469s/12 iters), loss = 3.73683
I0409 22:59:35.843484  4221 solver.cpp:237]     Train net output #0: loss = 3.73683 (* 1 = 3.73683 loss)
I0409 22:59:35.843503  4221 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0409 22:59:40.773986  4221 solver.cpp:218] Iteration 1512 (2.43394 iter/s, 4.93027s/12 iters), loss = 3.49215
I0409 22:59:40.774041  4221 solver.cpp:237]     Train net output #0: loss = 3.49215 (* 1 = 3.49215 loss)
I0409 22:59:40.774053  4221 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0409 22:59:42.549768  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:59:45.682965  4221 solver.cpp:218] Iteration 1524 (2.44464 iter/s, 4.9087s/12 iters), loss = 3.73207
I0409 22:59:45.683017  4221 solver.cpp:237]     Train net output #0: loss = 3.73207 (* 1 = 3.73207 loss)
I0409 22:59:45.683027  4221 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0409 22:59:47.648345  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0409 22:59:54.016691  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0409 22:59:55.397037  4221 solver.cpp:330] Iteration 1530, Testing net (#0)
I0409 22:59:55.397068  4221 net.cpp:676] Ignoring source layer train-data
I0409 22:59:59.213768  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:59:59.849623  4221 solver.cpp:397]     Test net output #0: accuracy = 0.134191
I0409 22:59:59.849658  4221 solver.cpp:397]     Test net output #1: loss = 3.91781 (* 1 = 3.91781 loss)
I0409 23:00:01.778877  4221 solver.cpp:218] Iteration 1536 (0.745564 iter/s, 16.0952s/12 iters), loss = 3.60845
I0409 23:00:01.778926  4221 solver.cpp:237]     Train net output #0: loss = 3.60845 (* 1 = 3.60845 loss)
I0409 23:00:01.778935  4221 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0409 23:00:06.674006  4221 solver.cpp:218] Iteration 1548 (2.45155 iter/s, 4.89486s/12 iters), loss = 3.23984
I0409 23:00:06.674052  4221 solver.cpp:237]     Train net output #0: loss = 3.23984 (* 1 = 3.23984 loss)
I0409 23:00:06.674060  4221 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0409 23:00:11.505342  4221 solver.cpp:218] Iteration 1560 (2.48392 iter/s, 4.83108s/12 iters), loss = 3.57996
I0409 23:00:11.505388  4221 solver.cpp:237]     Train net output #0: loss = 3.57996 (* 1 = 3.57996 loss)
I0409 23:00:11.505396  4221 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0409 23:00:16.423166  4221 solver.cpp:218] Iteration 1572 (2.44024 iter/s, 4.91756s/12 iters), loss = 3.59601
I0409 23:00:16.425985  4221 solver.cpp:237]     Train net output #0: loss = 3.59601 (* 1 = 3.59601 loss)
I0409 23:00:16.426002  4221 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0409 23:00:21.318006  4221 solver.cpp:218] Iteration 1584 (2.45306 iter/s, 4.89184s/12 iters), loss = 3.45653
I0409 23:00:21.318068  4221 solver.cpp:237]     Train net output #0: loss = 3.45653 (* 1 = 3.45653 loss)
I0409 23:00:21.318079  4221 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0409 23:00:26.159416  4221 solver.cpp:218] Iteration 1596 (2.47876 iter/s, 4.84114s/12 iters), loss = 3.61984
I0409 23:00:26.159472  4221 solver.cpp:237]     Train net output #0: loss = 3.61984 (* 1 = 3.61984 loss)
I0409 23:00:26.159483  4221 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0409 23:00:30.967566  4221 solver.cpp:218] Iteration 1608 (2.4959 iter/s, 4.80788s/12 iters), loss = 3.48152
I0409 23:00:30.967619  4221 solver.cpp:237]     Train net output #0: loss = 3.48152 (* 1 = 3.48152 loss)
I0409 23:00:30.967631  4221 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0409 23:00:34.738517  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:00:35.781924  4221 solver.cpp:218] Iteration 1620 (2.49268 iter/s, 4.81409s/12 iters), loss = 3.34632
I0409 23:00:35.781997  4221 solver.cpp:237]     Train net output #0: loss = 3.34632 (* 1 = 3.34632 loss)
I0409 23:00:35.782006  4221 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0409 23:00:40.192273  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0409 23:00:41.123222  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0409 23:00:41.715690  4221 solver.cpp:330] Iteration 1632, Testing net (#0)
I0409 23:00:41.715714  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:00:45.560245  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:00:46.258227  4221 solver.cpp:397]     Test net output #0: accuracy = 0.16299
I0409 23:00:46.258275  4221 solver.cpp:397]     Test net output #1: loss = 3.80218 (* 1 = 3.80218 loss)
I0409 23:00:46.342689  4221 solver.cpp:218] Iteration 1632 (1.13634 iter/s, 10.5603s/12 iters), loss = 3.28325
I0409 23:00:46.342751  4221 solver.cpp:237]     Train net output #0: loss = 3.28325 (* 1 = 3.28325 loss)
I0409 23:00:46.342765  4221 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0409 23:00:50.344692  4221 solver.cpp:218] Iteration 1644 (2.99867 iter/s, 4.00177s/12 iters), loss = 3.53041
I0409 23:00:50.344832  4221 solver.cpp:237]     Train net output #0: loss = 3.53041 (* 1 = 3.53041 loss)
I0409 23:00:50.344844  4221 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0409 23:00:55.218924  4221 solver.cpp:218] Iteration 1656 (2.46211 iter/s, 4.87388s/12 iters), loss = 3.23318
I0409 23:00:55.218978  4221 solver.cpp:237]     Train net output #0: loss = 3.23318 (* 1 = 3.23318 loss)
I0409 23:00:55.218989  4221 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0409 23:01:00.153124  4221 solver.cpp:218] Iteration 1668 (2.43214 iter/s, 4.93393s/12 iters), loss = 3.08833
I0409 23:01:00.153172  4221 solver.cpp:237]     Train net output #0: loss = 3.08833 (* 1 = 3.08833 loss)
I0409 23:01:00.153180  4221 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0409 23:01:04.990857  4221 solver.cpp:218] Iteration 1680 (2.48063 iter/s, 4.83748s/12 iters), loss = 3.42887
I0409 23:01:04.990900  4221 solver.cpp:237]     Train net output #0: loss = 3.42887 (* 1 = 3.42887 loss)
I0409 23:01:04.990909  4221 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0409 23:01:09.908602  4221 solver.cpp:218] Iteration 1692 (2.44027 iter/s, 4.91748s/12 iters), loss = 3.13693
I0409 23:01:09.908658  4221 solver.cpp:237]     Train net output #0: loss = 3.13693 (* 1 = 3.13693 loss)
I0409 23:01:09.908669  4221 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0409 23:01:14.796869  4221 solver.cpp:218] Iteration 1704 (2.45499 iter/s, 4.888s/12 iters), loss = 2.88456
I0409 23:01:14.796926  4221 solver.cpp:237]     Train net output #0: loss = 2.88456 (* 1 = 2.88456 loss)
I0409 23:01:14.796937  4221 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0409 23:01:19.684229  4221 solver.cpp:218] Iteration 1716 (2.45586 iter/s, 4.88628s/12 iters), loss = 3.37315
I0409 23:01:19.684278  4221 solver.cpp:237]     Train net output #0: loss = 3.37315 (* 1 = 3.37315 loss)
I0409 23:01:19.684289  4221 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0409 23:01:20.843592  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:01:24.770344  4221 solver.cpp:218] Iteration 1728 (2.35949 iter/s, 5.08584s/12 iters), loss = 3.24472
I0409 23:01:24.770401  4221 solver.cpp:237]     Train net output #0: loss = 3.24472 (* 1 = 3.24472 loss)
I0409 23:01:24.770412  4221 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0409 23:01:26.728034  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0409 23:01:27.713567  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0409 23:01:28.390269  4221 solver.cpp:330] Iteration 1734, Testing net (#0)
I0409 23:01:28.390298  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:01:32.089346  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:01:32.793486  4221 solver.cpp:397]     Test net output #0: accuracy = 0.189338
I0409 23:01:32.793524  4221 solver.cpp:397]     Test net output #1: loss = 3.66122 (* 1 = 3.66122 loss)
I0409 23:01:34.497769  4221 solver.cpp:218] Iteration 1740 (1.23369 iter/s, 9.72695s/12 iters), loss = 3.20286
I0409 23:01:34.497836  4221 solver.cpp:237]     Train net output #0: loss = 3.20286 (* 1 = 3.20286 loss)
I0409 23:01:34.497848  4221 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0409 23:01:39.438226  4221 solver.cpp:218] Iteration 1752 (2.42906 iter/s, 4.94017s/12 iters), loss = 3.32501
I0409 23:01:39.438279  4221 solver.cpp:237]     Train net output #0: loss = 3.32501 (* 1 = 3.32501 loss)
I0409 23:01:39.438289  4221 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0409 23:01:44.356863  4221 solver.cpp:218] Iteration 1764 (2.43983 iter/s, 4.91837s/12 iters), loss = 3.01739
I0409 23:01:44.356920  4221 solver.cpp:237]     Train net output #0: loss = 3.01739 (* 1 = 3.01739 loss)
I0409 23:01:44.356930  4221 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0409 23:01:49.393169  4221 solver.cpp:218] Iteration 1776 (2.38283 iter/s, 5.03603s/12 iters), loss = 3.09368
I0409 23:01:49.393226  4221 solver.cpp:237]     Train net output #0: loss = 3.09368 (* 1 = 3.09368 loss)
I0409 23:01:49.393237  4221 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0409 23:01:54.266402  4221 solver.cpp:218] Iteration 1788 (2.46257 iter/s, 4.87296s/12 iters), loss = 3.48686
I0409 23:01:54.266527  4221 solver.cpp:237]     Train net output #0: loss = 3.48686 (* 1 = 3.48686 loss)
I0409 23:01:54.266536  4221 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0409 23:01:59.158047  4221 solver.cpp:218] Iteration 1800 (2.45333 iter/s, 4.8913s/12 iters), loss = 3.09484
I0409 23:01:59.158097  4221 solver.cpp:237]     Train net output #0: loss = 3.09484 (* 1 = 3.09484 loss)
I0409 23:01:59.158109  4221 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0409 23:02:04.166287  4221 solver.cpp:218] Iteration 1812 (2.39618 iter/s, 5.00797s/12 iters), loss = 3.02298
I0409 23:02:04.166337  4221 solver.cpp:237]     Train net output #0: loss = 3.02298 (* 1 = 3.02298 loss)
I0409 23:02:04.166350  4221 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0409 23:02:07.228256  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:02:08.947100  4221 solver.cpp:218] Iteration 1824 (2.51017 iter/s, 4.78055s/12 iters), loss = 3.35961
I0409 23:02:08.947158  4221 solver.cpp:237]     Train net output #0: loss = 3.35961 (* 1 = 3.35961 loss)
I0409 23:02:08.947171  4221 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0409 23:02:13.307348  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0409 23:02:14.947307  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0409 23:02:16.446231  4221 solver.cpp:330] Iteration 1836, Testing net (#0)
I0409 23:02:16.446256  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:02:20.192449  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:02:21.049209  4221 solver.cpp:397]     Test net output #0: accuracy = 0.206495
I0409 23:02:21.049260  4221 solver.cpp:397]     Test net output #1: loss = 3.57501 (* 1 = 3.57501 loss)
I0409 23:02:21.132441  4221 solver.cpp:218] Iteration 1836 (0.984836 iter/s, 12.1848s/12 iters), loss = 2.96911
I0409 23:02:21.132498  4221 solver.cpp:237]     Train net output #0: loss = 2.96911 (* 1 = 2.96911 loss)
I0409 23:02:21.132509  4221 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0409 23:02:25.225992  4221 solver.cpp:218] Iteration 1848 (2.93161 iter/s, 4.09331s/12 iters), loss = 2.85631
I0409 23:02:25.226073  4221 solver.cpp:237]     Train net output #0: loss = 2.85631 (* 1 = 2.85631 loss)
I0409 23:02:25.226086  4221 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0409 23:02:30.021986  4221 solver.cpp:218] Iteration 1860 (2.50224 iter/s, 4.79571s/12 iters), loss = 3.00068
I0409 23:02:30.022032  4221 solver.cpp:237]     Train net output #0: loss = 3.00068 (* 1 = 3.00068 loss)
I0409 23:02:30.022040  4221 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0409 23:02:34.927350  4221 solver.cpp:218] Iteration 1872 (2.44643 iter/s, 4.9051s/12 iters), loss = 3.04068
I0409 23:02:34.927400  4221 solver.cpp:237]     Train net output #0: loss = 3.04068 (* 1 = 3.04068 loss)
I0409 23:02:34.927412  4221 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0409 23:02:39.820534  4221 solver.cpp:218] Iteration 1884 (2.45252 iter/s, 4.89292s/12 iters), loss = 3.04749
I0409 23:02:39.820590  4221 solver.cpp:237]     Train net output #0: loss = 3.04749 (* 1 = 3.04749 loss)
I0409 23:02:39.820603  4221 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0409 23:02:44.673069  4221 solver.cpp:218] Iteration 1896 (2.47307 iter/s, 4.85226s/12 iters), loss = 3.04142
I0409 23:02:44.673120  4221 solver.cpp:237]     Train net output #0: loss = 3.04142 (* 1 = 3.04142 loss)
I0409 23:02:44.673130  4221 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0409 23:02:49.554177  4221 solver.cpp:218] Iteration 1908 (2.45859 iter/s, 4.88084s/12 iters), loss = 2.89947
I0409 23:02:49.554229  4221 solver.cpp:237]     Train net output #0: loss = 2.89947 (* 1 = 2.89947 loss)
I0409 23:02:49.554241  4221 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0409 23:02:54.460137  4221 solver.cpp:218] Iteration 1920 (2.44614 iter/s, 4.90569s/12 iters), loss = 2.98247
I0409 23:02:54.460188  4221 solver.cpp:237]     Train net output #0: loss = 2.98247 (* 1 = 2.98247 loss)
I0409 23:02:54.460199  4221 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0409 23:02:54.774709  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:02:59.304410  4221 solver.cpp:218] Iteration 1932 (2.47728 iter/s, 4.84402s/12 iters), loss = 2.90548
I0409 23:02:59.304535  4221 solver.cpp:237]     Train net output #0: loss = 2.90548 (* 1 = 2.90548 loss)
I0409 23:02:59.304546  4221 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0409 23:03:01.274400  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0409 23:03:02.003221  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0409 23:03:02.522507  4221 solver.cpp:330] Iteration 1938, Testing net (#0)
I0409 23:03:02.522534  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:03:06.184479  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:03:06.966831  4221 solver.cpp:397]     Test net output #0: accuracy = 0.214461
I0409 23:03:06.966881  4221 solver.cpp:397]     Test net output #1: loss = 3.55468 (* 1 = 3.55468 loss)
I0409 23:03:08.805305  4221 solver.cpp:218] Iteration 1944 (1.26311 iter/s, 9.50036s/12 iters), loss = 2.64986
I0409 23:03:08.805379  4221 solver.cpp:237]     Train net output #0: loss = 2.64986 (* 1 = 2.64986 loss)
I0409 23:03:08.805393  4221 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0409 23:03:13.689787  4221 solver.cpp:218] Iteration 1956 (2.4569 iter/s, 4.8842s/12 iters), loss = 2.68214
I0409 23:03:13.689836  4221 solver.cpp:237]     Train net output #0: loss = 2.68214 (* 1 = 2.68214 loss)
I0409 23:03:13.689846  4221 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0409 23:03:18.521872  4221 solver.cpp:218] Iteration 1968 (2.48353 iter/s, 4.83182s/12 iters), loss = 2.62908
I0409 23:03:18.521922  4221 solver.cpp:237]     Train net output #0: loss = 2.62908 (* 1 = 2.62908 loss)
I0409 23:03:18.521932  4221 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0409 23:03:23.448889  4221 solver.cpp:218] Iteration 1980 (2.43569 iter/s, 4.92674s/12 iters), loss = 2.93979
I0409 23:03:23.448956  4221 solver.cpp:237]     Train net output #0: loss = 2.93979 (* 1 = 2.93979 loss)
I0409 23:03:23.448968  4221 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0409 23:03:28.214217  4221 solver.cpp:218] Iteration 1992 (2.51833 iter/s, 4.76505s/12 iters), loss = 2.83412
I0409 23:03:28.214267  4221 solver.cpp:237]     Train net output #0: loss = 2.83412 (* 1 = 2.83412 loss)
I0409 23:03:28.214278  4221 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0409 23:03:32.983358  4221 solver.cpp:218] Iteration 2004 (2.51631 iter/s, 4.76888s/12 iters), loss = 2.69359
I0409 23:03:32.983458  4221 solver.cpp:237]     Train net output #0: loss = 2.69359 (* 1 = 2.69359 loss)
I0409 23:03:32.983469  4221 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0409 23:03:37.777755  4221 solver.cpp:218] Iteration 2016 (2.50308 iter/s, 4.79409s/12 iters), loss = 2.88323
I0409 23:03:37.777815  4221 solver.cpp:237]     Train net output #0: loss = 2.88323 (* 1 = 2.88323 loss)
I0409 23:03:37.777827  4221 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0409 23:03:40.283658  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:03:42.653092  4221 solver.cpp:218] Iteration 2028 (2.46151 iter/s, 4.87506s/12 iters), loss = 2.44453
I0409 23:03:42.653148  4221 solver.cpp:237]     Train net output #0: loss = 2.44453 (* 1 = 2.44453 loss)
I0409 23:03:42.653159  4221 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0409 23:03:47.150702  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0409 23:03:47.931728  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0409 23:03:48.432040  4221 solver.cpp:330] Iteration 2040, Testing net (#0)
I0409 23:03:48.432065  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:03:52.293066  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:03:53.119355  4221 solver.cpp:397]     Test net output #0: accuracy = 0.218137
I0409 23:03:53.119392  4221 solver.cpp:397]     Test net output #1: loss = 3.50765 (* 1 = 3.50765 loss)
I0409 23:03:53.202672  4221 solver.cpp:218] Iteration 2040 (1.13754 iter/s, 10.5491s/12 iters), loss = 2.74597
I0409 23:03:53.202733  4221 solver.cpp:237]     Train net output #0: loss = 2.74597 (* 1 = 2.74597 loss)
I0409 23:03:53.202745  4221 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0409 23:03:57.359491  4221 solver.cpp:218] Iteration 2052 (2.88699 iter/s, 4.15658s/12 iters), loss = 3.0551
I0409 23:03:57.359537  4221 solver.cpp:237]     Train net output #0: loss = 3.0551 (* 1 = 3.0551 loss)
I0409 23:03:57.359546  4221 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0409 23:03:57.731212  4221 blocking_queue.cpp:49] Waiting for data
I0409 23:04:02.164252  4221 solver.cpp:218] Iteration 2064 (2.49766 iter/s, 4.8045s/12 iters), loss = 2.89701
I0409 23:04:02.164314  4221 solver.cpp:237]     Train net output #0: loss = 2.89701 (* 1 = 2.89701 loss)
I0409 23:04:02.164330  4221 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0409 23:04:07.027915  4221 solver.cpp:218] Iteration 2076 (2.46741 iter/s, 4.86339s/12 iters), loss = 2.9996
I0409 23:04:07.028066  4221 solver.cpp:237]     Train net output #0: loss = 2.9996 (* 1 = 2.9996 loss)
I0409 23:04:07.028080  4221 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0409 23:04:11.908614  4221 solver.cpp:218] Iteration 2088 (2.45885 iter/s, 4.88034s/12 iters), loss = 2.64106
I0409 23:04:11.908672  4221 solver.cpp:237]     Train net output #0: loss = 2.64106 (* 1 = 2.64106 loss)
I0409 23:04:11.908685  4221 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0409 23:04:16.791747  4221 solver.cpp:218] Iteration 2100 (2.45757 iter/s, 4.88287s/12 iters), loss = 2.73515
I0409 23:04:16.791798  4221 solver.cpp:237]     Train net output #0: loss = 2.73515 (* 1 = 2.73515 loss)
I0409 23:04:16.791810  4221 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0409 23:04:21.708158  4221 solver.cpp:218] Iteration 2112 (2.44094 iter/s, 4.91614s/12 iters), loss = 2.70102
I0409 23:04:21.708217  4221 solver.cpp:237]     Train net output #0: loss = 2.70102 (* 1 = 2.70102 loss)
I0409 23:04:21.708235  4221 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0409 23:04:26.257009  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:04:26.566001  4221 solver.cpp:218] Iteration 2124 (2.47037 iter/s, 4.85758s/12 iters), loss = 2.5845
I0409 23:04:26.566046  4221 solver.cpp:237]     Train net output #0: loss = 2.5845 (* 1 = 2.5845 loss)
I0409 23:04:26.566057  4221 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0409 23:04:31.408893  4221 solver.cpp:218] Iteration 2136 (2.47799 iter/s, 4.84264s/12 iters), loss = 2.35223
I0409 23:04:31.408943  4221 solver.cpp:237]     Train net output #0: loss = 2.35223 (* 1 = 2.35223 loss)
I0409 23:04:31.408955  4221 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0409 23:04:33.392257  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0409 23:04:36.479161  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0409 23:04:40.077123  4221 solver.cpp:330] Iteration 2142, Testing net (#0)
I0409 23:04:40.077221  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:04:43.918247  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:04:44.780165  4221 solver.cpp:397]     Test net output #0: accuracy = 0.223652
I0409 23:04:44.780206  4221 solver.cpp:397]     Test net output #1: loss = 3.51202 (* 1 = 3.51202 loss)
I0409 23:04:46.507182  4221 solver.cpp:218] Iteration 2148 (0.794827 iter/s, 15.0976s/12 iters), loss = 2.40508
I0409 23:04:46.507225  4221 solver.cpp:237]     Train net output #0: loss = 2.40508 (* 1 = 2.40508 loss)
I0409 23:04:46.507232  4221 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0409 23:04:51.357843  4221 solver.cpp:218] Iteration 2160 (2.47402 iter/s, 4.85041s/12 iters), loss = 2.89141
I0409 23:04:51.357885  4221 solver.cpp:237]     Train net output #0: loss = 2.89141 (* 1 = 2.89141 loss)
I0409 23:04:51.357897  4221 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0409 23:04:56.181560  4221 solver.cpp:218] Iteration 2172 (2.48784 iter/s, 4.82346s/12 iters), loss = 2.34389
I0409 23:04:56.181607  4221 solver.cpp:237]     Train net output #0: loss = 2.34389 (* 1 = 2.34389 loss)
I0409 23:04:56.181617  4221 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0409 23:05:01.011524  4221 solver.cpp:218] Iteration 2184 (2.48462 iter/s, 4.8297s/12 iters), loss = 2.60752
I0409 23:05:01.011581  4221 solver.cpp:237]     Train net output #0: loss = 2.60752 (* 1 = 2.60752 loss)
I0409 23:05:01.011593  4221 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0409 23:05:05.934476  4221 solver.cpp:218] Iteration 2196 (2.4377 iter/s, 4.92268s/12 iters), loss = 2.42197
I0409 23:05:05.934535  4221 solver.cpp:237]     Train net output #0: loss = 2.42197 (* 1 = 2.42197 loss)
I0409 23:05:05.934548  4221 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0409 23:05:10.937059  4221 solver.cpp:218] Iteration 2208 (2.39889 iter/s, 5.00231s/12 iters), loss = 2.36415
I0409 23:05:10.937193  4221 solver.cpp:237]     Train net output #0: loss = 2.36415 (* 1 = 2.36415 loss)
I0409 23:05:10.937204  4221 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0409 23:05:15.780432  4221 solver.cpp:218] Iteration 2220 (2.47779 iter/s, 4.84303s/12 iters), loss = 2.43993
I0409 23:05:15.780478  4221 solver.cpp:237]     Train net output #0: loss = 2.43993 (* 1 = 2.43993 loss)
I0409 23:05:15.780486  4221 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0409 23:05:17.524461  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:05:20.611536  4221 solver.cpp:218] Iteration 2232 (2.48404 iter/s, 4.83085s/12 iters), loss = 2.48292
I0409 23:05:20.611590  4221 solver.cpp:237]     Train net output #0: loss = 2.48292 (* 1 = 2.48292 loss)
I0409 23:05:20.611603  4221 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0409 23:05:25.177143  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0409 23:05:25.919067  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0409 23:05:26.419538  4221 solver.cpp:330] Iteration 2244, Testing net (#0)
I0409 23:05:26.419565  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:05:29.946548  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:05:30.851783  4221 solver.cpp:397]     Test net output #0: accuracy = 0.250613
I0409 23:05:30.851830  4221 solver.cpp:397]     Test net output #1: loss = 3.30737 (* 1 = 3.30737 loss)
I0409 23:05:30.935035  4221 solver.cpp:218] Iteration 2244 (1.16245 iter/s, 10.323s/12 iters), loss = 2.43112
I0409 23:05:30.935086  4221 solver.cpp:237]     Train net output #0: loss = 2.43112 (* 1 = 2.43112 loss)
I0409 23:05:30.935096  4221 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0409 23:05:35.239392  4221 solver.cpp:218] Iteration 2256 (2.78803 iter/s, 4.30411s/12 iters), loss = 2.17861
I0409 23:05:35.239444  4221 solver.cpp:237]     Train net output #0: loss = 2.17861 (* 1 = 2.17861 loss)
I0409 23:05:35.239454  4221 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0409 23:05:40.026422  4221 solver.cpp:218] Iteration 2268 (2.50692 iter/s, 4.78676s/12 iters), loss = 2.80787
I0409 23:05:40.026479  4221 solver.cpp:237]     Train net output #0: loss = 2.80787 (* 1 = 2.80787 loss)
I0409 23:05:40.026492  4221 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0409 23:05:44.840906  4221 solver.cpp:218] Iteration 2280 (2.49262 iter/s, 4.81421s/12 iters), loss = 2.46849
I0409 23:05:44.841022  4221 solver.cpp:237]     Train net output #0: loss = 2.46849 (* 1 = 2.46849 loss)
I0409 23:05:44.841033  4221 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0409 23:05:49.675835  4221 solver.cpp:218] Iteration 2292 (2.48211 iter/s, 4.8346s/12 iters), loss = 2.53695
I0409 23:05:49.675889  4221 solver.cpp:237]     Train net output #0: loss = 2.53695 (* 1 = 2.53695 loss)
I0409 23:05:49.675899  4221 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0409 23:05:54.484436  4221 solver.cpp:218] Iteration 2304 (2.49567 iter/s, 4.80834s/12 iters), loss = 2.53656
I0409 23:05:54.484483  4221 solver.cpp:237]     Train net output #0: loss = 2.53656 (* 1 = 2.53656 loss)
I0409 23:05:54.484494  4221 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0409 23:05:59.310828  4221 solver.cpp:218] Iteration 2316 (2.48646 iter/s, 4.82614s/12 iters), loss = 2.36224
I0409 23:05:59.310871  4221 solver.cpp:237]     Train net output #0: loss = 2.36224 (* 1 = 2.36224 loss)
I0409 23:05:59.310879  4221 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0409 23:06:03.119390  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:06:04.126931  4221 solver.cpp:218] Iteration 2328 (2.49177 iter/s, 4.81585s/12 iters), loss = 1.99291
I0409 23:06:04.126986  4221 solver.cpp:237]     Train net output #0: loss = 1.99291 (* 1 = 1.99291 loss)
I0409 23:06:04.126996  4221 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0409 23:06:08.981230  4221 solver.cpp:218] Iteration 2340 (2.47217 iter/s, 4.85403s/12 iters), loss = 2.12249
I0409 23:06:08.981276  4221 solver.cpp:237]     Train net output #0: loss = 2.12249 (* 1 = 2.12249 loss)
I0409 23:06:08.981284  4221 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0409 23:06:10.945909  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0409 23:06:11.647964  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0409 23:06:12.142987  4221 solver.cpp:330] Iteration 2346, Testing net (#0)
I0409 23:06:12.143004  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:06:15.960016  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:06:16.929265  4221 solver.cpp:397]     Test net output #0: accuracy = 0.261029
I0409 23:06:16.929293  4221 solver.cpp:397]     Test net output #1: loss = 3.37592 (* 1 = 3.37592 loss)
I0409 23:06:18.696380  4221 solver.cpp:218] Iteration 2352 (1.23524 iter/s, 9.71469s/12 iters), loss = 2.46093
I0409 23:06:18.696431  4221 solver.cpp:237]     Train net output #0: loss = 2.46093 (* 1 = 2.46093 loss)
I0409 23:06:18.696444  4221 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0409 23:06:23.707955  4221 solver.cpp:218] Iteration 2364 (2.39459 iter/s, 5.0113s/12 iters), loss = 2.45224
I0409 23:06:23.708012  4221 solver.cpp:237]     Train net output #0: loss = 2.45224 (* 1 = 2.45224 loss)
I0409 23:06:23.708025  4221 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0409 23:06:28.584050  4221 solver.cpp:218] Iteration 2376 (2.46112 iter/s, 4.87583s/12 iters), loss = 2.00844
I0409 23:06:28.584095  4221 solver.cpp:237]     Train net output #0: loss = 2.00844 (* 1 = 2.00844 loss)
I0409 23:06:28.584105  4221 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0409 23:06:33.436560  4221 solver.cpp:218] Iteration 2388 (2.47308 iter/s, 4.85225s/12 iters), loss = 2.2414
I0409 23:06:33.436607  4221 solver.cpp:237]     Train net output #0: loss = 2.2414 (* 1 = 2.2414 loss)
I0409 23:06:33.436619  4221 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0409 23:06:38.292928  4221 solver.cpp:218] Iteration 2400 (2.47112 iter/s, 4.8561s/12 iters), loss = 2.19074
I0409 23:06:38.292992  4221 solver.cpp:237]     Train net output #0: loss = 2.19074 (* 1 = 2.19074 loss)
I0409 23:06:38.293004  4221 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0409 23:06:43.151742  4221 solver.cpp:218] Iteration 2412 (2.46988 iter/s, 4.85854s/12 iters), loss = 2.12521
I0409 23:06:43.151783  4221 solver.cpp:237]     Train net output #0: loss = 2.12521 (* 1 = 2.12521 loss)
I0409 23:06:43.151791  4221 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0409 23:06:48.011646  4221 solver.cpp:218] Iteration 2424 (2.46932 iter/s, 4.85964s/12 iters), loss = 2.14101
I0409 23:06:48.011801  4221 solver.cpp:237]     Train net output #0: loss = 2.14101 (* 1 = 2.14101 loss)
I0409 23:06:48.011816  4221 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0409 23:06:49.059224  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:06:52.832018  4221 solver.cpp:218] Iteration 2436 (2.48962 iter/s, 4.82001s/12 iters), loss = 2.26537
I0409 23:06:52.832068  4221 solver.cpp:237]     Train net output #0: loss = 2.26537 (* 1 = 2.26537 loss)
I0409 23:06:52.832080  4221 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0409 23:06:57.200848  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0409 23:06:58.482965  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0409 23:06:59.537976  4221 solver.cpp:330] Iteration 2448, Testing net (#0)
I0409 23:06:59.538007  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:07:03.007979  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:07:03.982746  4221 solver.cpp:397]     Test net output #0: accuracy = 0.269608
I0409 23:07:03.982790  4221 solver.cpp:397]     Test net output #1: loss = 3.302 (* 1 = 3.302 loss)
I0409 23:07:04.066547  4221 solver.cpp:218] Iteration 2448 (1.06819 iter/s, 11.234s/12 iters), loss = 2.0459
I0409 23:07:04.066604  4221 solver.cpp:237]     Train net output #0: loss = 2.0459 (* 1 = 2.0459 loss)
I0409 23:07:04.066617  4221 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0409 23:07:08.310890  4221 solver.cpp:218] Iteration 2460 (2.82746 iter/s, 4.2441s/12 iters), loss = 2.04063
I0409 23:07:08.310945  4221 solver.cpp:237]     Train net output #0: loss = 2.04063 (* 1 = 2.04063 loss)
I0409 23:07:08.310956  4221 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0409 23:07:13.161761  4221 solver.cpp:218] Iteration 2472 (2.47392 iter/s, 4.8506s/12 iters), loss = 2.36591
I0409 23:07:13.161818  4221 solver.cpp:237]     Train net output #0: loss = 2.36591 (* 1 = 2.36591 loss)
I0409 23:07:13.161829  4221 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0409 23:07:17.961061  4221 solver.cpp:218] Iteration 2484 (2.5005 iter/s, 4.79903s/12 iters), loss = 2.18935
I0409 23:07:17.961109  4221 solver.cpp:237]     Train net output #0: loss = 2.18935 (* 1 = 2.18935 loss)
I0409 23:07:17.961120  4221 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0409 23:07:22.760542  4221 solver.cpp:218] Iteration 2496 (2.50041 iter/s, 4.79922s/12 iters), loss = 2.20747
I0409 23:07:22.760648  4221 solver.cpp:237]     Train net output #0: loss = 2.20747 (* 1 = 2.20747 loss)
I0409 23:07:22.760659  4221 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0409 23:07:27.610536  4221 solver.cpp:218] Iteration 2508 (2.47439 iter/s, 4.84968s/12 iters), loss = 2.23414
I0409 23:07:27.610579  4221 solver.cpp:237]     Train net output #0: loss = 2.23414 (* 1 = 2.23414 loss)
I0409 23:07:27.610586  4221 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0409 23:07:32.453457  4221 solver.cpp:218] Iteration 2520 (2.47797 iter/s, 4.84266s/12 iters), loss = 2.22019
I0409 23:07:32.453505  4221 solver.cpp:237]     Train net output #0: loss = 2.22019 (* 1 = 2.22019 loss)
I0409 23:07:32.453513  4221 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0409 23:07:35.582684  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:07:37.301455  4221 solver.cpp:218] Iteration 2532 (2.47538 iter/s, 4.84774s/12 iters), loss = 2.09782
I0409 23:07:37.301506  4221 solver.cpp:237]     Train net output #0: loss = 2.09782 (* 1 = 2.09782 loss)
I0409 23:07:37.301518  4221 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0409 23:07:42.470921  4221 solver.cpp:218] Iteration 2544 (2.32145 iter/s, 5.16919s/12 iters), loss = 2.01636
I0409 23:07:42.470979  4221 solver.cpp:237]     Train net output #0: loss = 2.01636 (* 1 = 2.01636 loss)
I0409 23:07:42.470993  4221 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0409 23:07:44.426290  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0409 23:07:45.218546  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0409 23:07:45.713943  4221 solver.cpp:330] Iteration 2550, Testing net (#0)
I0409 23:07:45.713985  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:07:49.228451  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:07:50.248536  4221 solver.cpp:397]     Test net output #0: accuracy = 0.268995
I0409 23:07:50.248584  4221 solver.cpp:397]     Test net output #1: loss = 3.37247 (* 1 = 3.37247 loss)
I0409 23:07:52.050474  4221 solver.cpp:218] Iteration 2556 (1.25273 iter/s, 9.57909s/12 iters), loss = 2.16173
I0409 23:07:52.050527  4221 solver.cpp:237]     Train net output #0: loss = 2.16173 (* 1 = 2.16173 loss)
I0409 23:07:52.050537  4221 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0409 23:07:56.882403  4221 solver.cpp:218] Iteration 2568 (2.48362 iter/s, 4.83167s/12 iters), loss = 2.24688
I0409 23:07:56.882529  4221 solver.cpp:237]     Train net output #0: loss = 2.24688 (* 1 = 2.24688 loss)
I0409 23:07:56.882544  4221 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0409 23:08:01.752511  4221 solver.cpp:218] Iteration 2580 (2.46419 iter/s, 4.86976s/12 iters), loss = 2.13058
I0409 23:08:01.752564  4221 solver.cpp:237]     Train net output #0: loss = 2.13058 (* 1 = 2.13058 loss)
I0409 23:08:01.752578  4221 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0409 23:08:06.534072  4221 solver.cpp:218] Iteration 2592 (2.50978 iter/s, 4.7813s/12 iters), loss = 2.2466
I0409 23:08:06.534121  4221 solver.cpp:237]     Train net output #0: loss = 2.2466 (* 1 = 2.2466 loss)
I0409 23:08:06.534133  4221 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0409 23:08:11.530032  4221 solver.cpp:218] Iteration 2604 (2.40207 iter/s, 4.9957s/12 iters), loss = 2.35307
I0409 23:08:11.530072  4221 solver.cpp:237]     Train net output #0: loss = 2.35307 (* 1 = 2.35307 loss)
I0409 23:08:11.530081  4221 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0409 23:08:16.582602  4221 solver.cpp:218] Iteration 2616 (2.37515 iter/s, 5.05231s/12 iters), loss = 1.94119
I0409 23:08:16.582648  4221 solver.cpp:237]     Train net output #0: loss = 1.94119 (* 1 = 1.94119 loss)
I0409 23:08:16.582659  4221 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0409 23:08:21.402753  4221 solver.cpp:218] Iteration 2628 (2.48968 iter/s, 4.81989s/12 iters), loss = 1.9786
I0409 23:08:21.402797  4221 solver.cpp:237]     Train net output #0: loss = 1.9786 (* 1 = 1.9786 loss)
I0409 23:08:21.402807  4221 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0409 23:08:21.822044  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:08:26.234345  4221 solver.cpp:218] Iteration 2640 (2.48379 iter/s, 4.83133s/12 iters), loss = 2.30832
I0409 23:08:26.234395  4221 solver.cpp:237]     Train net output #0: loss = 2.30832 (* 1 = 2.30832 loss)
I0409 23:08:26.234402  4221 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0409 23:08:30.640142  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0409 23:08:31.343240  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0409 23:08:31.907001  4221 solver.cpp:330] Iteration 2652, Testing net (#0)
I0409 23:08:31.907027  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:08:35.374286  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:08:36.438161  4221 solver.cpp:397]     Test net output #0: accuracy = 0.276961
I0409 23:08:36.438202  4221 solver.cpp:397]     Test net output #1: loss = 3.32635 (* 1 = 3.32635 loss)
I0409 23:08:36.521405  4221 solver.cpp:218] Iteration 2652 (1.16657 iter/s, 10.2866s/12 iters), loss = 1.98099
I0409 23:08:36.521457  4221 solver.cpp:237]     Train net output #0: loss = 1.98099 (* 1 = 1.98099 loss)
I0409 23:08:36.521468  4221 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0409 23:08:40.651286  4221 solver.cpp:218] Iteration 2664 (2.90582 iter/s, 4.12964s/12 iters), loss = 2.01121
I0409 23:08:40.651335  4221 solver.cpp:237]     Train net output #0: loss = 2.01121 (* 1 = 2.01121 loss)
I0409 23:08:40.651346  4221 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0409 23:08:45.502904  4221 solver.cpp:218] Iteration 2676 (2.47354 iter/s, 4.85135s/12 iters), loss = 2.21237
I0409 23:08:45.502956  4221 solver.cpp:237]     Train net output #0: loss = 2.21237 (* 1 = 2.21237 loss)
I0409 23:08:45.502966  4221 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0409 23:08:50.443037  4221 solver.cpp:218] Iteration 2688 (2.42922 iter/s, 4.93986s/12 iters), loss = 2.00698
I0409 23:08:50.443092  4221 solver.cpp:237]     Train net output #0: loss = 2.00698 (* 1 = 2.00698 loss)
I0409 23:08:50.443104  4221 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0409 23:08:55.412714  4221 solver.cpp:218] Iteration 2700 (2.41478 iter/s, 4.96941s/12 iters), loss = 2.09146
I0409 23:08:55.412757  4221 solver.cpp:237]     Train net output #0: loss = 2.09146 (* 1 = 2.09146 loss)
I0409 23:08:55.412766  4221 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0409 23:09:00.298285  4221 solver.cpp:218] Iteration 2712 (2.45634 iter/s, 4.88531s/12 iters), loss = 1.70095
I0409 23:09:00.298337  4221 solver.cpp:237]     Train net output #0: loss = 1.70095 (* 1 = 1.70095 loss)
I0409 23:09:00.298349  4221 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0409 23:09:05.214813  4221 solver.cpp:218] Iteration 2724 (2.44088 iter/s, 4.91625s/12 iters), loss = 1.72599
I0409 23:09:05.214965  4221 solver.cpp:237]     Train net output #0: loss = 1.72599 (* 1 = 1.72599 loss)
I0409 23:09:05.214979  4221 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0409 23:09:07.737661  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:09:10.105505  4221 solver.cpp:218] Iteration 2736 (2.45382 iter/s, 4.89033s/12 iters), loss = 1.71215
I0409 23:09:10.105551  4221 solver.cpp:237]     Train net output #0: loss = 1.71215 (* 1 = 1.71215 loss)
I0409 23:09:10.105561  4221 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0409 23:09:15.019745  4221 solver.cpp:218] Iteration 2748 (2.44201 iter/s, 4.91398s/12 iters), loss = 2.38395
I0409 23:09:15.019795  4221 solver.cpp:237]     Train net output #0: loss = 2.38395 (* 1 = 2.38395 loss)
I0409 23:09:15.019805  4221 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0409 23:09:17.003062  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0409 23:09:17.669128  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0409 23:09:18.157436  4221 solver.cpp:330] Iteration 2754, Testing net (#0)
I0409 23:09:18.157465  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:09:20.858141  4221 blocking_queue.cpp:49] Waiting for data
I0409 23:09:21.364727  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:09:22.465420  4221 solver.cpp:397]     Test net output #0: accuracy = 0.276961
I0409 23:09:22.465466  4221 solver.cpp:397]     Test net output #1: loss = 3.29186 (* 1 = 3.29186 loss)
I0409 23:09:24.365828  4221 solver.cpp:218] Iteration 2760 (1.28402 iter/s, 9.34564s/12 iters), loss = 2.09203
I0409 23:09:24.365871  4221 solver.cpp:237]     Train net output #0: loss = 2.09203 (* 1 = 2.09203 loss)
I0409 23:09:24.365880  4221 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0409 23:09:29.244546  4221 solver.cpp:218] Iteration 2772 (2.45979 iter/s, 4.87846s/12 iters), loss = 2.2907
I0409 23:09:29.244596  4221 solver.cpp:237]     Train net output #0: loss = 2.2907 (* 1 = 2.2907 loss)
I0409 23:09:29.244609  4221 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0409 23:09:34.133077  4221 solver.cpp:218] Iteration 2784 (2.45486 iter/s, 4.88827s/12 iters), loss = 2.13466
I0409 23:09:34.133124  4221 solver.cpp:237]     Train net output #0: loss = 2.13466 (* 1 = 2.13466 loss)
I0409 23:09:34.133134  4221 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0409 23:09:38.959347  4221 solver.cpp:218] Iteration 2796 (2.48653 iter/s, 4.82601s/12 iters), loss = 1.9318
I0409 23:09:38.959421  4221 solver.cpp:237]     Train net output #0: loss = 1.9318 (* 1 = 1.9318 loss)
I0409 23:09:38.959430  4221 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0409 23:09:43.878307  4221 solver.cpp:218] Iteration 2808 (2.43968 iter/s, 4.91867s/12 iters), loss = 1.71923
I0409 23:09:43.878358  4221 solver.cpp:237]     Train net output #0: loss = 1.71923 (* 1 = 1.71923 loss)
I0409 23:09:43.878369  4221 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0409 23:09:48.773047  4221 solver.cpp:218] Iteration 2820 (2.45174 iter/s, 4.89448s/12 iters), loss = 1.67349
I0409 23:09:48.773098  4221 solver.cpp:237]     Train net output #0: loss = 1.67349 (* 1 = 1.67349 loss)
I0409 23:09:48.773109  4221 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0409 23:09:53.294226  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:09:53.574440  4221 solver.cpp:218] Iteration 2832 (2.49941 iter/s, 4.80113s/12 iters), loss = 1.4849
I0409 23:09:53.574499  4221 solver.cpp:237]     Train net output #0: loss = 1.4849 (* 1 = 1.4849 loss)
I0409 23:09:53.574512  4221 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0409 23:09:58.378664  4221 solver.cpp:218] Iteration 2844 (2.49795 iter/s, 4.80395s/12 iters), loss = 1.93694
I0409 23:09:58.378724  4221 solver.cpp:237]     Train net output #0: loss = 1.93694 (* 1 = 1.93694 loss)
I0409 23:09:58.378737  4221 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0409 23:10:02.724948  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0409 23:10:03.393484  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0409 23:10:03.884052  4221 solver.cpp:330] Iteration 2856, Testing net (#0)
I0409 23:10:03.884080  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:10:07.360874  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:10:08.494976  4221 solver.cpp:397]     Test net output #0: accuracy = 0.275123
I0409 23:10:08.495031  4221 solver.cpp:397]     Test net output #1: loss = 3.31038 (* 1 = 3.31038 loss)
I0409 23:10:08.578217  4221 solver.cpp:218] Iteration 2856 (1.17658 iter/s, 10.1991s/12 iters), loss = 1.84354
I0409 23:10:08.578272  4221 solver.cpp:237]     Train net output #0: loss = 1.84354 (* 1 = 1.84354 loss)
I0409 23:10:08.578284  4221 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0409 23:10:12.681093  4221 solver.cpp:218] Iteration 2868 (2.92494 iter/s, 4.10264s/12 iters), loss = 2.10282
I0409 23:10:12.681216  4221 solver.cpp:237]     Train net output #0: loss = 2.10282 (* 1 = 2.10282 loss)
I0409 23:10:12.681226  4221 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0409 23:10:17.779551  4221 solver.cpp:218] Iteration 2880 (2.35381 iter/s, 5.09812s/12 iters), loss = 1.4749
I0409 23:10:17.779580  4221 solver.cpp:237]     Train net output #0: loss = 1.4749 (* 1 = 1.4749 loss)
I0409 23:10:17.779590  4221 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0409 23:10:22.551591  4221 solver.cpp:218] Iteration 2892 (2.51477 iter/s, 4.7718s/12 iters), loss = 2.14451
I0409 23:10:22.551637  4221 solver.cpp:237]     Train net output #0: loss = 2.14451 (* 1 = 2.14451 loss)
I0409 23:10:22.551649  4221 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0409 23:10:27.500689  4221 solver.cpp:218] Iteration 2904 (2.42482 iter/s, 4.94883s/12 iters), loss = 1.41881
I0409 23:10:27.500744  4221 solver.cpp:237]     Train net output #0: loss = 1.41881 (* 1 = 1.41881 loss)
I0409 23:10:27.500756  4221 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0409 23:10:32.616322  4221 solver.cpp:218] Iteration 2916 (2.34588 iter/s, 5.11536s/12 iters), loss = 1.58917
I0409 23:10:32.616374  4221 solver.cpp:237]     Train net output #0: loss = 1.58917 (* 1 = 1.58917 loss)
I0409 23:10:32.616386  4221 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0409 23:10:37.611433  4221 solver.cpp:218] Iteration 2928 (2.40248 iter/s, 4.99484s/12 iters), loss = 1.62183
I0409 23:10:37.611495  4221 solver.cpp:237]     Train net output #0: loss = 1.62183 (* 1 = 1.62183 loss)
I0409 23:10:37.611510  4221 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0409 23:10:39.404417  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:10:42.485608  4221 solver.cpp:218] Iteration 2940 (2.46209 iter/s, 4.8739s/12 iters), loss = 1.89774
I0409 23:10:42.485657  4221 solver.cpp:237]     Train net output #0: loss = 1.89774 (* 1 = 1.89774 loss)
I0409 23:10:42.485667  4221 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0409 23:10:47.411288  4221 solver.cpp:218] Iteration 2952 (2.43635 iter/s, 4.92541s/12 iters), loss = 1.79051
I0409 23:10:47.411414  4221 solver.cpp:237]     Train net output #0: loss = 1.79051 (* 1 = 1.79051 loss)
I0409 23:10:47.411428  4221 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0409 23:10:49.407912  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0409 23:10:50.122573  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0409 23:10:50.616812  4221 solver.cpp:330] Iteration 2958, Testing net (#0)
I0409 23:10:50.616833  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:10:53.876490  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:10:55.057287  4221 solver.cpp:397]     Test net output #0: accuracy = 0.289828
I0409 23:10:55.057324  4221 solver.cpp:397]     Test net output #1: loss = 3.45094 (* 1 = 3.45094 loss)
I0409 23:10:56.814637  4221 solver.cpp:218] Iteration 2964 (1.27621 iter/s, 9.40283s/12 iters), loss = 1.46535
I0409 23:10:56.814693  4221 solver.cpp:237]     Train net output #0: loss = 1.46535 (* 1 = 1.46535 loss)
I0409 23:10:56.814705  4221 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0409 23:11:01.705849  4221 solver.cpp:218] Iteration 2976 (2.45352 iter/s, 4.89094s/12 iters), loss = 1.78577
I0409 23:11:01.705907  4221 solver.cpp:237]     Train net output #0: loss = 1.78577 (* 1 = 1.78577 loss)
I0409 23:11:01.705920  4221 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0409 23:11:06.579291  4221 solver.cpp:218] Iteration 2988 (2.46246 iter/s, 4.87317s/12 iters), loss = 1.63153
I0409 23:11:06.579344  4221 solver.cpp:237]     Train net output #0: loss = 1.63153 (* 1 = 1.63153 loss)
I0409 23:11:06.579356  4221 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0409 23:11:11.406796  4221 solver.cpp:218] Iteration 3000 (2.48589 iter/s, 4.82724s/12 iters), loss = 1.67912
I0409 23:11:11.406847  4221 solver.cpp:237]     Train net output #0: loss = 1.67912 (* 1 = 1.67912 loss)
I0409 23:11:11.406858  4221 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0409 23:11:16.267976  4221 solver.cpp:218] Iteration 3012 (2.46867 iter/s, 4.86092s/12 iters), loss = 1.45149
I0409 23:11:16.268028  4221 solver.cpp:237]     Train net output #0: loss = 1.45149 (* 1 = 1.45149 loss)
I0409 23:11:16.268039  4221 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0409 23:11:21.179301  4221 solver.cpp:218] Iteration 3024 (2.44346 iter/s, 4.91106s/12 iters), loss = 1.63855
I0409 23:11:21.179397  4221 solver.cpp:237]     Train net output #0: loss = 1.63855 (* 1 = 1.63855 loss)
I0409 23:11:21.179410  4221 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0409 23:11:25.152159  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:11:26.149963  4221 solver.cpp:218] Iteration 3036 (2.41432 iter/s, 4.97034s/12 iters), loss = 1.25533
I0409 23:11:26.150018  4221 solver.cpp:237]     Train net output #0: loss = 1.25533 (* 1 = 1.25533 loss)
I0409 23:11:26.150030  4221 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0409 23:11:31.070171  4221 solver.cpp:218] Iteration 3048 (2.43905 iter/s, 4.91994s/12 iters), loss = 1.92059
I0409 23:11:31.070225  4221 solver.cpp:237]     Train net output #0: loss = 1.92059 (* 1 = 1.92059 loss)
I0409 23:11:31.070237  4221 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0409 23:11:35.482775  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0409 23:11:36.754539  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0409 23:11:37.824357  4221 solver.cpp:330] Iteration 3060, Testing net (#0)
I0409 23:11:37.824386  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:11:41.035357  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:11:42.256968  4221 solver.cpp:397]     Test net output #0: accuracy = 0.279412
I0409 23:11:42.257017  4221 solver.cpp:397]     Test net output #1: loss = 3.29006 (* 1 = 3.29006 loss)
I0409 23:11:42.340235  4221 solver.cpp:218] Iteration 3060 (1.06482 iter/s, 11.2695s/12 iters), loss = 1.60915
I0409 23:11:42.340288  4221 solver.cpp:237]     Train net output #0: loss = 1.60915 (* 1 = 1.60915 loss)
I0409 23:11:42.340299  4221 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0409 23:11:46.475131  4221 solver.cpp:218] Iteration 3072 (2.90229 iter/s, 4.13466s/12 iters), loss = 1.62427
I0409 23:11:46.475178  4221 solver.cpp:237]     Train net output #0: loss = 1.62427 (* 1 = 1.62427 loss)
I0409 23:11:46.475186  4221 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0409 23:11:51.395234  4221 solver.cpp:218] Iteration 3084 (2.4391 iter/s, 4.91984s/12 iters), loss = 1.38564
I0409 23:11:51.395355  4221 solver.cpp:237]     Train net output #0: loss = 1.38564 (* 1 = 1.38564 loss)
I0409 23:11:51.395370  4221 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0409 23:11:56.283068  4221 solver.cpp:218] Iteration 3096 (2.45524 iter/s, 4.8875s/12 iters), loss = 1.61764
I0409 23:11:56.283128  4221 solver.cpp:237]     Train net output #0: loss = 1.61764 (* 1 = 1.61764 loss)
I0409 23:11:56.283140  4221 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0409 23:12:01.165993  4221 solver.cpp:218] Iteration 3108 (2.45768 iter/s, 4.88265s/12 iters), loss = 1.37679
I0409 23:12:01.166044  4221 solver.cpp:237]     Train net output #0: loss = 1.37679 (* 1 = 1.37679 loss)
I0409 23:12:01.166055  4221 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0409 23:12:06.035097  4221 solver.cpp:218] Iteration 3120 (2.46465 iter/s, 4.86884s/12 iters), loss = 1.29491
I0409 23:12:06.035145  4221 solver.cpp:237]     Train net output #0: loss = 1.29491 (* 1 = 1.29491 loss)
I0409 23:12:06.035152  4221 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0409 23:12:10.842269  4221 solver.cpp:218] Iteration 3132 (2.4964 iter/s, 4.80692s/12 iters), loss = 1.65298
I0409 23:12:10.842311  4221 solver.cpp:237]     Train net output #0: loss = 1.65298 (* 1 = 1.65298 loss)
I0409 23:12:10.842327  4221 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0409 23:12:11.911909  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:12:15.677443  4221 solver.cpp:218] Iteration 3144 (2.48194 iter/s, 4.83492s/12 iters), loss = 1.2053
I0409 23:12:15.677487  4221 solver.cpp:237]     Train net output #0: loss = 1.2053 (* 1 = 1.2053 loss)
I0409 23:12:15.677496  4221 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0409 23:12:20.468220  4221 solver.cpp:218] Iteration 3156 (2.50495 iter/s, 4.79052s/12 iters), loss = 1.43333
I0409 23:12:20.468276  4221 solver.cpp:237]     Train net output #0: loss = 1.43333 (* 1 = 1.43333 loss)
I0409 23:12:20.468287  4221 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0409 23:12:22.454236  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0409 23:12:23.801739  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0409 23:12:24.595197  4221 solver.cpp:330] Iteration 3162, Testing net (#0)
I0409 23:12:24.595227  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:12:27.949049  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:12:29.282124  4221 solver.cpp:397]     Test net output #0: accuracy = 0.309436
I0409 23:12:29.282168  4221 solver.cpp:397]     Test net output #1: loss = 3.32163 (* 1 = 3.32163 loss)
I0409 23:12:31.209327  4221 solver.cpp:218] Iteration 3168 (1.11726 iter/s, 10.7406s/12 iters), loss = 1.35752
I0409 23:12:31.209370  4221 solver.cpp:237]     Train net output #0: loss = 1.35752 (* 1 = 1.35752 loss)
I0409 23:12:31.209378  4221 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0409 23:12:36.015151  4221 solver.cpp:218] Iteration 3180 (2.4971 iter/s, 4.80556s/12 iters), loss = 1.54348
I0409 23:12:36.015203  4221 solver.cpp:237]     Train net output #0: loss = 1.54348 (* 1 = 1.54348 loss)
I0409 23:12:36.015215  4221 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0409 23:12:40.872479  4221 solver.cpp:218] Iteration 3192 (2.47063 iter/s, 4.85706s/12 iters), loss = 1.40338
I0409 23:12:40.872543  4221 solver.cpp:237]     Train net output #0: loss = 1.40338 (* 1 = 1.40338 loss)
I0409 23:12:40.872558  4221 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0409 23:12:45.679613  4221 solver.cpp:218] Iteration 3204 (2.49643 iter/s, 4.80686s/12 iters), loss = 1.4964
I0409 23:12:45.679663  4221 solver.cpp:237]     Train net output #0: loss = 1.4964 (* 1 = 1.4964 loss)
I0409 23:12:45.679675  4221 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0409 23:12:50.609246  4221 solver.cpp:218] Iteration 3216 (2.43439 iter/s, 4.92936s/12 iters), loss = 1.52899
I0409 23:12:50.609304  4221 solver.cpp:237]     Train net output #0: loss = 1.52899 (* 1 = 1.52899 loss)
I0409 23:12:50.609318  4221 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0409 23:12:55.483669  4221 solver.cpp:218] Iteration 3228 (2.46197 iter/s, 4.87415s/12 iters), loss = 1.42196
I0409 23:12:55.483793  4221 solver.cpp:237]     Train net output #0: loss = 1.42196 (* 1 = 1.42196 loss)
I0409 23:12:55.483803  4221 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0409 23:12:58.646945  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:13:00.374066  4221 solver.cpp:218] Iteration 3240 (2.45395 iter/s, 4.89007s/12 iters), loss = 1.48414
I0409 23:13:00.374104  4221 solver.cpp:237]     Train net output #0: loss = 1.48414 (* 1 = 1.48414 loss)
I0409 23:13:00.374112  4221 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0409 23:13:05.341627  4221 solver.cpp:218] Iteration 3252 (2.4158 iter/s, 4.9673s/12 iters), loss = 1.21814
I0409 23:13:05.341684  4221 solver.cpp:237]     Train net output #0: loss = 1.21814 (* 1 = 1.21814 loss)
I0409 23:13:05.341696  4221 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0409 23:13:09.778790  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0409 23:13:10.492182  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0409 23:13:10.991935  4221 solver.cpp:330] Iteration 3264, Testing net (#0)
I0409 23:13:10.991962  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:13:14.222894  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:13:15.522719  4221 solver.cpp:397]     Test net output #0: accuracy = 0.307598
I0409 23:13:15.522759  4221 solver.cpp:397]     Test net output #1: loss = 3.34859 (* 1 = 3.34859 loss)
I0409 23:13:15.605863  4221 solver.cpp:218] Iteration 3264 (1.16916 iter/s, 10.2638s/12 iters), loss = 1.33637
I0409 23:13:15.605912  4221 solver.cpp:237]     Train net output #0: loss = 1.33637 (* 1 = 1.33637 loss)
I0409 23:13:15.605923  4221 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0409 23:13:19.662003  4221 solver.cpp:218] Iteration 3276 (2.95865 iter/s, 4.0559s/12 iters), loss = 1.45009
I0409 23:13:19.662060  4221 solver.cpp:237]     Train net output #0: loss = 1.45009 (* 1 = 1.45009 loss)
I0409 23:13:19.662071  4221 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0409 23:13:24.525048  4221 solver.cpp:218] Iteration 3288 (2.46772 iter/s, 4.86278s/12 iters), loss = 1.30379
I0409 23:13:24.525094  4221 solver.cpp:237]     Train net output #0: loss = 1.30379 (* 1 = 1.30379 loss)
I0409 23:13:24.525104  4221 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0409 23:13:29.397294  4221 solver.cpp:218] Iteration 3300 (2.46306 iter/s, 4.87199s/12 iters), loss = 1.34009
I0409 23:13:29.397367  4221 solver.cpp:237]     Train net output #0: loss = 1.34009 (* 1 = 1.34009 loss)
I0409 23:13:29.397377  4221 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0409 23:13:34.316865  4221 solver.cpp:218] Iteration 3312 (2.43938 iter/s, 4.91928s/12 iters), loss = 1.52958
I0409 23:13:34.316921  4221 solver.cpp:237]     Train net output #0: loss = 1.52958 (* 1 = 1.52958 loss)
I0409 23:13:34.316934  4221 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0409 23:13:39.222867  4221 solver.cpp:218] Iteration 3324 (2.44612 iter/s, 4.90573s/12 iters), loss = 1.30851
I0409 23:13:39.222931  4221 solver.cpp:237]     Train net output #0: loss = 1.30851 (* 1 = 1.30851 loss)
I0409 23:13:39.222944  4221 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0409 23:13:43.995028  4221 solver.cpp:218] Iteration 3336 (2.51473 iter/s, 4.77189s/12 iters), loss = 1.32827
I0409 23:13:43.995091  4221 solver.cpp:237]     Train net output #0: loss = 1.32827 (* 1 = 1.32827 loss)
I0409 23:13:43.995103  4221 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0409 23:13:44.442965  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:13:48.777993  4221 solver.cpp:218] Iteration 3348 (2.50907 iter/s, 4.78265s/12 iters), loss = 1.25635
I0409 23:13:48.778038  4221 solver.cpp:237]     Train net output #0: loss = 1.25635 (* 1 = 1.25635 loss)
I0409 23:13:48.778046  4221 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0409 23:13:53.801877  4221 solver.cpp:218] Iteration 3360 (2.38872 iter/s, 5.02362s/12 iters), loss = 1.32086
I0409 23:13:53.801929  4221 solver.cpp:237]     Train net output #0: loss = 1.32086 (* 1 = 1.32086 loss)
I0409 23:13:53.801940  4221 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0409 23:13:55.759985  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0409 23:13:56.449887  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0409 23:13:56.941103  4221 solver.cpp:330] Iteration 3366, Testing net (#0)
I0409 23:13:56.941133  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:14:00.051872  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:14:01.386399  4221 solver.cpp:397]     Test net output #0: accuracy = 0.296569
I0409 23:14:01.386440  4221 solver.cpp:397]     Test net output #1: loss = 3.36251 (* 1 = 3.36251 loss)
I0409 23:14:03.223316  4221 solver.cpp:218] Iteration 3372 (1.27375 iter/s, 9.42099s/12 iters), loss = 1.26528
I0409 23:14:03.223371  4221 solver.cpp:237]     Train net output #0: loss = 1.26528 (* 1 = 1.26528 loss)
I0409 23:14:03.223382  4221 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0409 23:14:08.106837  4221 solver.cpp:218] Iteration 3384 (2.45738 iter/s, 4.88326s/12 iters), loss = 1.25061
I0409 23:14:08.106886  4221 solver.cpp:237]     Train net output #0: loss = 1.25061 (* 1 = 1.25061 loss)
I0409 23:14:08.106899  4221 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0409 23:14:13.038358  4221 solver.cpp:218] Iteration 3396 (2.43346 iter/s, 4.93126s/12 iters), loss = 1.22118
I0409 23:14:13.038414  4221 solver.cpp:237]     Train net output #0: loss = 1.22118 (* 1 = 1.22118 loss)
I0409 23:14:13.038424  4221 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0409 23:14:17.925722  4221 solver.cpp:218] Iteration 3408 (2.45545 iter/s, 4.88709s/12 iters), loss = 1.33619
I0409 23:14:17.925779  4221 solver.cpp:237]     Train net output #0: loss = 1.33619 (* 1 = 1.33619 loss)
I0409 23:14:17.925791  4221 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0409 23:14:22.835863  4221 solver.cpp:218] Iteration 3420 (2.44406 iter/s, 4.90987s/12 iters), loss = 1.40319
I0409 23:14:22.835916  4221 solver.cpp:237]     Train net output #0: loss = 1.40319 (* 1 = 1.40319 loss)
I0409 23:14:22.835928  4221 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0409 23:14:27.694635  4221 solver.cpp:218] Iteration 3432 (2.46989 iter/s, 4.85851s/12 iters), loss = 1.40962
I0409 23:14:27.694690  4221 solver.cpp:237]     Train net output #0: loss = 1.40962 (* 1 = 1.40962 loss)
I0409 23:14:27.694701  4221 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0409 23:14:30.242180  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:14:32.564316  4221 solver.cpp:218] Iteration 3444 (2.46436 iter/s, 4.86941s/12 iters), loss = 1.12618
I0409 23:14:32.564361  4221 solver.cpp:237]     Train net output #0: loss = 1.12618 (* 1 = 1.12618 loss)
I0409 23:14:32.564373  4221 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0409 23:14:37.439669  4221 solver.cpp:218] Iteration 3456 (2.46149 iter/s, 4.8751s/12 iters), loss = 1.30675
I0409 23:14:37.439713  4221 solver.cpp:237]     Train net output #0: loss = 1.30675 (* 1 = 1.30675 loss)
I0409 23:14:37.439724  4221 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0409 23:14:41.906412  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0409 23:14:42.608737  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0409 23:14:43.093484  4221 solver.cpp:330] Iteration 3468, Testing net (#0)
I0409 23:14:43.093506  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:14:43.229176  4221 blocking_queue.cpp:49] Waiting for data
I0409 23:14:46.339756  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:14:47.719772  4221 solver.cpp:397]     Test net output #0: accuracy = 0.315564
I0409 23:14:47.719821  4221 solver.cpp:397]     Test net output #1: loss = 3.45428 (* 1 = 3.45428 loss)
I0409 23:14:47.803205  4221 solver.cpp:218] Iteration 3468 (1.15796 iter/s, 10.3631s/12 iters), loss = 0.8945
I0409 23:14:47.803256  4221 solver.cpp:237]     Train net output #0: loss = 0.8945 (* 1 = 0.8945 loss)
I0409 23:14:47.803267  4221 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0409 23:14:52.000216  4221 solver.cpp:218] Iteration 3480 (2.85934 iter/s, 4.19677s/12 iters), loss = 1.52594
I0409 23:14:52.000265  4221 solver.cpp:237]     Train net output #0: loss = 1.52594 (* 1 = 1.52594 loss)
I0409 23:14:52.000277  4221 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0409 23:14:56.794953  4221 solver.cpp:218] Iteration 3492 (2.50288 iter/s, 4.79448s/12 iters), loss = 1.60377
I0409 23:14:56.795003  4221 solver.cpp:237]     Train net output #0: loss = 1.60377 (* 1 = 1.60377 loss)
I0409 23:14:56.795015  4221 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0409 23:15:01.765120  4221 solver.cpp:218] Iteration 3504 (2.41454 iter/s, 4.96989s/12 iters), loss = 1.51569
I0409 23:15:01.765255  4221 solver.cpp:237]     Train net output #0: loss = 1.51569 (* 1 = 1.51569 loss)
I0409 23:15:01.765270  4221 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0409 23:15:06.576894  4221 solver.cpp:218] Iteration 3516 (2.49406 iter/s, 4.81143s/12 iters), loss = 1.14858
I0409 23:15:06.576946  4221 solver.cpp:237]     Train net output #0: loss = 1.14858 (* 1 = 1.14858 loss)
I0409 23:15:06.576961  4221 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0409 23:15:11.534580  4221 solver.cpp:218] Iteration 3528 (2.42061 iter/s, 4.95742s/12 iters), loss = 1.26479
I0409 23:15:11.534637  4221 solver.cpp:237]     Train net output #0: loss = 1.26479 (* 1 = 1.26479 loss)
I0409 23:15:11.534651  4221 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0409 23:15:16.177668  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:15:16.442155  4221 solver.cpp:218] Iteration 3540 (2.44534 iter/s, 4.9073s/12 iters), loss = 1.11939
I0409 23:15:16.442207  4221 solver.cpp:237]     Train net output #0: loss = 1.11939 (* 1 = 1.11939 loss)
I0409 23:15:16.442219  4221 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0409 23:15:21.393115  4221 solver.cpp:218] Iteration 3552 (2.4239 iter/s, 4.95069s/12 iters), loss = 1.29188
I0409 23:15:21.393162  4221 solver.cpp:237]     Train net output #0: loss = 1.29188 (* 1 = 1.29188 loss)
I0409 23:15:21.393170  4221 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0409 23:15:26.181843  4221 solver.cpp:218] Iteration 3564 (2.50602 iter/s, 4.78847s/12 iters), loss = 1.15304
I0409 23:15:26.181900  4221 solver.cpp:237]     Train net output #0: loss = 1.15304 (* 1 = 1.15304 loss)
I0409 23:15:26.181913  4221 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0409 23:15:28.148808  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0409 23:15:28.850962  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0409 23:15:29.362701  4221 solver.cpp:330] Iteration 3570, Testing net (#0)
I0409 23:15:29.362731  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:15:32.301457  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:15:33.713325  4221 solver.cpp:397]     Test net output #0: accuracy = 0.313726
I0409 23:15:33.713376  4221 solver.cpp:397]     Test net output #1: loss = 3.35799 (* 1 = 3.35799 loss)
I0409 23:15:35.452772  4221 solver.cpp:218] Iteration 3576 (1.29443 iter/s, 9.27048s/12 iters), loss = 1.4353
I0409 23:15:35.452811  4221 solver.cpp:237]     Train net output #0: loss = 1.4353 (* 1 = 1.4353 loss)
I0409 23:15:35.452818  4221 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0409 23:15:40.377439  4221 solver.cpp:218] Iteration 3588 (2.43684 iter/s, 4.92441s/12 iters), loss = 1.03514
I0409 23:15:40.377490  4221 solver.cpp:237]     Train net output #0: loss = 1.03514 (* 1 = 1.03514 loss)
I0409 23:15:40.377501  4221 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0409 23:15:45.199770  4221 solver.cpp:218] Iteration 3600 (2.48856 iter/s, 4.82207s/12 iters), loss = 1.1912
I0409 23:15:45.199815  4221 solver.cpp:237]     Train net output #0: loss = 1.1912 (* 1 = 1.1912 loss)
I0409 23:15:45.199824  4221 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0409 23:15:50.025194  4221 solver.cpp:218] Iteration 3612 (2.48696 iter/s, 4.82517s/12 iters), loss = 1.21471
I0409 23:15:50.025243  4221 solver.cpp:237]     Train net output #0: loss = 1.21471 (* 1 = 1.21471 loss)
I0409 23:15:50.025252  4221 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0409 23:15:54.822758  4221 solver.cpp:218] Iteration 3624 (2.50141 iter/s, 4.7973s/12 iters), loss = 1.40644
I0409 23:15:54.822803  4221 solver.cpp:237]     Train net output #0: loss = 1.40644 (* 1 = 1.40644 loss)
I0409 23:15:54.822811  4221 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0409 23:15:59.676302  4221 solver.cpp:218] Iteration 3636 (2.47256 iter/s, 4.85327s/12 iters), loss = 1.14139
I0409 23:15:59.676378  4221 solver.cpp:237]     Train net output #0: loss = 1.14139 (* 1 = 1.14139 loss)
I0409 23:15:59.676395  4221 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0409 23:16:01.467571  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:16:04.453486  4221 solver.cpp:218] Iteration 3648 (2.51209 iter/s, 4.77691s/12 iters), loss = 1.01513
I0409 23:16:04.453589  4221 solver.cpp:237]     Train net output #0: loss = 1.01513 (* 1 = 1.01513 loss)
I0409 23:16:04.453605  4221 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0409 23:16:09.404412  4221 solver.cpp:218] Iteration 3660 (2.42395 iter/s, 4.95061s/12 iters), loss = 1.15631
I0409 23:16:09.404466  4221 solver.cpp:237]     Train net output #0: loss = 1.15631 (* 1 = 1.15631 loss)
I0409 23:16:09.404479  4221 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0409 23:16:13.769826  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0409 23:16:14.871091  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0409 23:16:16.142602  4221 solver.cpp:330] Iteration 3672, Testing net (#0)
I0409 23:16:16.142638  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:16:19.674230  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:16:21.150480  4221 solver.cpp:397]     Test net output #0: accuracy = 0.306373
I0409 23:16:21.150527  4221 solver.cpp:397]     Test net output #1: loss = 3.48413 (* 1 = 3.48413 loss)
I0409 23:16:21.233773  4221 solver.cpp:218] Iteration 3672 (1.01447 iter/s, 11.8288s/12 iters), loss = 1.01722
I0409 23:16:21.233821  4221 solver.cpp:237]     Train net output #0: loss = 1.01722 (* 1 = 1.01722 loss)
I0409 23:16:21.233831  4221 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0409 23:16:25.357784  4221 solver.cpp:218] Iteration 3684 (2.90995 iter/s, 4.12378s/12 iters), loss = 1.35962
I0409 23:16:25.357827  4221 solver.cpp:237]     Train net output #0: loss = 1.35962 (* 1 = 1.35962 loss)
I0409 23:16:25.357837  4221 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0409 23:16:30.288116  4221 solver.cpp:218] Iteration 3696 (2.43404 iter/s, 4.93007s/12 iters), loss = 1.05659
I0409 23:16:30.288169  4221 solver.cpp:237]     Train net output #0: loss = 1.05659 (* 1 = 1.05659 loss)
I0409 23:16:30.288179  4221 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0409 23:16:35.243929  4221 solver.cpp:218] Iteration 3708 (2.42153 iter/s, 4.95554s/12 iters), loss = 1.115
I0409 23:16:35.244285  4221 solver.cpp:237]     Train net output #0: loss = 1.115 (* 1 = 1.115 loss)
I0409 23:16:35.244298  4221 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0409 23:16:40.270032  4221 solver.cpp:218] Iteration 3720 (2.38781 iter/s, 5.02553s/12 iters), loss = 1.25646
I0409 23:16:40.270076  4221 solver.cpp:237]     Train net output #0: loss = 1.25646 (* 1 = 1.25646 loss)
I0409 23:16:40.270085  4221 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0409 23:16:45.605739  4221 solver.cpp:218] Iteration 3732 (2.24912 iter/s, 5.33543s/12 iters), loss = 1.28186
I0409 23:16:45.605794  4221 solver.cpp:237]     Train net output #0: loss = 1.28186 (* 1 = 1.28186 loss)
I0409 23:16:45.605805  4221 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0409 23:16:49.728176  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:16:50.704139  4221 solver.cpp:218] Iteration 3744 (2.35381 iter/s, 5.09812s/12 iters), loss = 0.816851
I0409 23:16:50.704197  4221 solver.cpp:237]     Train net output #0: loss = 0.816851 (* 1 = 0.816851 loss)
I0409 23:16:50.704208  4221 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0409 23:16:55.655580  4221 solver.cpp:218] Iteration 3756 (2.42367 iter/s, 4.95116s/12 iters), loss = 0.978124
I0409 23:16:55.655637  4221 solver.cpp:237]     Train net output #0: loss = 0.978124 (* 1 = 0.978124 loss)
I0409 23:16:55.655650  4221 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0409 23:17:00.911104  4221 solver.cpp:218] Iteration 3768 (2.28343 iter/s, 5.25524s/12 iters), loss = 0.899812
I0409 23:17:00.911149  4221 solver.cpp:237]     Train net output #0: loss = 0.899812 (* 1 = 0.899812 loss)
I0409 23:17:00.911157  4221 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0409 23:17:03.047052  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0409 23:17:03.902853  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0409 23:17:04.453230  4221 solver.cpp:330] Iteration 3774, Testing net (#0)
I0409 23:17:04.453260  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:17:07.463512  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:17:09.407240  4221 solver.cpp:397]     Test net output #0: accuracy = 0.29902
I0409 23:17:09.407280  4221 solver.cpp:397]     Test net output #1: loss = 3.56888 (* 1 = 3.56888 loss)
I0409 23:17:11.457082  4221 solver.cpp:218] Iteration 3780 (1.13793 iter/s, 10.5455s/12 iters), loss = 1.00701
I0409 23:17:11.457125  4221 solver.cpp:237]     Train net output #0: loss = 1.00701 (* 1 = 1.00701 loss)
I0409 23:17:11.457134  4221 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0409 23:17:16.688984  4221 solver.cpp:218] Iteration 3792 (2.29374 iter/s, 5.23163s/12 iters), loss = 1.00221
I0409 23:17:16.689038  4221 solver.cpp:237]     Train net output #0: loss = 1.00221 (* 1 = 1.00221 loss)
I0409 23:17:16.689049  4221 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0409 23:17:21.605410  4221 solver.cpp:218] Iteration 3804 (2.44093 iter/s, 4.91616s/12 iters), loss = 1.13428
I0409 23:17:21.605469  4221 solver.cpp:237]     Train net output #0: loss = 1.13428 (* 1 = 1.13428 loss)
I0409 23:17:21.605481  4221 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0409 23:17:26.516705  4221 solver.cpp:218] Iteration 3816 (2.44348 iter/s, 4.91103s/12 iters), loss = 0.783854
I0409 23:17:26.516746  4221 solver.cpp:237]     Train net output #0: loss = 0.783854 (* 1 = 0.783854 loss)
I0409 23:17:26.516754  4221 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0409 23:17:31.534390  4221 solver.cpp:218] Iteration 3828 (2.39167 iter/s, 5.01742s/12 iters), loss = 1.11264
I0409 23:17:31.534446  4221 solver.cpp:237]     Train net output #0: loss = 1.11264 (* 1 = 1.11264 loss)
I0409 23:17:31.534456  4221 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0409 23:17:36.424088  4221 solver.cpp:218] Iteration 3840 (2.45427 iter/s, 4.88943s/12 iters), loss = 1.0604
I0409 23:17:36.424135  4221 solver.cpp:237]     Train net output #0: loss = 1.0604 (* 1 = 1.0604 loss)
I0409 23:17:36.424145  4221 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0409 23:17:37.557211  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:17:41.351204  4221 solver.cpp:218] Iteration 3852 (2.43563 iter/s, 4.92685s/12 iters), loss = 1.03206
I0409 23:17:41.351248  4221 solver.cpp:237]     Train net output #0: loss = 1.03206 (* 1 = 1.03206 loss)
I0409 23:17:41.351256  4221 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0409 23:17:46.220636  4221 solver.cpp:218] Iteration 3864 (2.46448 iter/s, 4.86918s/12 iters), loss = 1.08033
I0409 23:17:46.220682  4221 solver.cpp:237]     Train net output #0: loss = 1.08033 (* 1 = 1.08033 loss)
I0409 23:17:46.220691  4221 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0409 23:17:50.645150  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0409 23:17:51.335295  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0409 23:17:51.828464  4221 solver.cpp:330] Iteration 3876, Testing net (#0)
I0409 23:17:51.828490  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:17:54.789288  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:17:56.365468  4221 solver.cpp:397]     Test net output #0: accuracy = 0.326593
I0409 23:17:56.365500  4221 solver.cpp:397]     Test net output #1: loss = 3.38648 (* 1 = 3.38648 loss)
I0409 23:17:56.448915  4221 solver.cpp:218] Iteration 3876 (1.17327 iter/s, 10.2278s/12 iters), loss = 0.796197
I0409 23:17:56.448963  4221 solver.cpp:237]     Train net output #0: loss = 0.796197 (* 1 = 0.796197 loss)
I0409 23:17:56.448972  4221 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0409 23:18:00.627496  4221 solver.cpp:218] Iteration 3888 (2.87195 iter/s, 4.17834s/12 iters), loss = 0.857789
I0409 23:18:00.627557  4221 solver.cpp:237]     Train net output #0: loss = 0.857789 (* 1 = 0.857789 loss)
I0409 23:18:00.627568  4221 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0409 23:18:05.541501  4221 solver.cpp:218] Iteration 3900 (2.44214 iter/s, 4.91373s/12 iters), loss = 0.928314
I0409 23:18:05.541560  4221 solver.cpp:237]     Train net output #0: loss = 0.928314 (* 1 = 0.928314 loss)
I0409 23:18:05.541571  4221 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0409 23:18:10.323570  4221 solver.cpp:218] Iteration 3912 (2.50952 iter/s, 4.7818s/12 iters), loss = 0.783376
I0409 23:18:10.323678  4221 solver.cpp:237]     Train net output #0: loss = 0.783376 (* 1 = 0.783376 loss)
I0409 23:18:10.323690  4221 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0409 23:18:15.258968  4221 solver.cpp:218] Iteration 3924 (2.43157 iter/s, 4.93508s/12 iters), loss = 0.815188
I0409 23:18:15.259013  4221 solver.cpp:237]     Train net output #0: loss = 0.815188 (* 1 = 0.815188 loss)
I0409 23:18:15.259022  4221 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0409 23:18:20.299573  4221 solver.cpp:218] Iteration 3936 (2.38079 iter/s, 5.04034s/12 iters), loss = 0.629147
I0409 23:18:20.299612  4221 solver.cpp:237]     Train net output #0: loss = 0.629147 (* 1 = 0.629147 loss)
I0409 23:18:20.299620  4221 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0409 23:18:23.523042  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:18:25.086717  4221 solver.cpp:218] Iteration 3948 (2.50684 iter/s, 4.78689s/12 iters), loss = 0.716686
I0409 23:18:25.086766  4221 solver.cpp:237]     Train net output #0: loss = 0.716686 (* 1 = 0.716686 loss)
I0409 23:18:25.086778  4221 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0409 23:18:29.929390  4221 solver.cpp:218] Iteration 3960 (2.47811 iter/s, 4.84241s/12 iters), loss = 0.854722
I0409 23:18:29.929450  4221 solver.cpp:237]     Train net output #0: loss = 0.854722 (* 1 = 0.854722 loss)
I0409 23:18:29.929461  4221 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0409 23:18:34.758891  4221 solver.cpp:218] Iteration 3972 (2.48487 iter/s, 4.82923s/12 iters), loss = 0.909898
I0409 23:18:34.758942  4221 solver.cpp:237]     Train net output #0: loss = 0.909898 (* 1 = 0.909898 loss)
I0409 23:18:34.758951  4221 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0409 23:18:36.710407  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0409 23:18:37.411151  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0409 23:18:37.913784  4221 solver.cpp:330] Iteration 3978, Testing net (#0)
I0409 23:18:37.913808  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:18:40.790786  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:18:42.427486  4221 solver.cpp:397]     Test net output #0: accuracy = 0.293505
I0409 23:18:42.427522  4221 solver.cpp:397]     Test net output #1: loss = 3.46502 (* 1 = 3.46502 loss)
I0409 23:18:44.343214  4221 solver.cpp:218] Iteration 3984 (1.25211 iter/s, 9.58386s/12 iters), loss = 0.881151
I0409 23:18:44.343289  4221 solver.cpp:237]     Train net output #0: loss = 0.881151 (* 1 = 0.881151 loss)
I0409 23:18:44.343304  4221 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0409 23:18:49.212872  4221 solver.cpp:218] Iteration 3996 (2.46438 iter/s, 4.86937s/12 iters), loss = 1.07481
I0409 23:18:49.212922  4221 solver.cpp:237]     Train net output #0: loss = 1.07481 (* 1 = 1.07481 loss)
I0409 23:18:49.212934  4221 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0409 23:18:54.063922  4221 solver.cpp:218] Iteration 4008 (2.47383 iter/s, 4.85078s/12 iters), loss = 1.04675
I0409 23:18:54.063980  4221 solver.cpp:237]     Train net output #0: loss = 1.04675 (* 1 = 1.04675 loss)
I0409 23:18:54.063990  4221 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0409 23:18:58.865020  4221 solver.cpp:218] Iteration 4020 (2.49957 iter/s, 4.80083s/12 iters), loss = 1.28259
I0409 23:18:58.865072  4221 solver.cpp:237]     Train net output #0: loss = 1.28259 (* 1 = 1.28259 loss)
I0409 23:18:58.865082  4221 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0409 23:19:03.757196  4221 solver.cpp:218] Iteration 4032 (2.45303 iter/s, 4.8919s/12 iters), loss = 1.08895
I0409 23:19:03.757256  4221 solver.cpp:237]     Train net output #0: loss = 1.08895 (* 1 = 1.08895 loss)
I0409 23:19:03.757268  4221 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0409 23:19:08.660498  4221 solver.cpp:218] Iteration 4044 (2.44747 iter/s, 4.90302s/12 iters), loss = 1.09347
I0409 23:19:08.660573  4221 solver.cpp:237]     Train net output #0: loss = 1.09347 (* 1 = 1.09347 loss)
I0409 23:19:08.660589  4221 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0409 23:19:09.210808  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:19:13.646075  4221 solver.cpp:218] Iteration 4056 (2.40708 iter/s, 4.98529s/12 iters), loss = 0.712282
I0409 23:19:13.646176  4221 solver.cpp:237]     Train net output #0: loss = 0.712282 (* 1 = 0.712282 loss)
I0409 23:19:13.646190  4221 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0409 23:19:18.507871  4221 solver.cpp:218] Iteration 4068 (2.46838 iter/s, 4.86149s/12 iters), loss = 0.750674
I0409 23:19:18.507917  4221 solver.cpp:237]     Train net output #0: loss = 0.750674 (* 1 = 0.750674 loss)
I0409 23:19:18.507925  4221 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0409 23:19:22.978427  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0409 23:19:25.518889  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0409 23:19:27.548887  4221 solver.cpp:330] Iteration 4080, Testing net (#0)
I0409 23:19:27.548921  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:19:30.414477  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:19:32.078531  4221 solver.cpp:397]     Test net output #0: accuracy = 0.33701
I0409 23:19:32.078581  4221 solver.cpp:397]     Test net output #1: loss = 3.41812 (* 1 = 3.41812 loss)
I0409 23:19:32.159873  4221 solver.cpp:218] Iteration 4080 (0.879032 iter/s, 13.6514s/12 iters), loss = 0.989332
I0409 23:19:32.159922  4221 solver.cpp:237]     Train net output #0: loss = 0.989332 (* 1 = 0.989332 loss)
I0409 23:19:32.159935  4221 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0409 23:19:36.275444  4221 solver.cpp:218] Iteration 4092 (2.91592 iter/s, 4.11534s/12 iters), loss = 0.898653
I0409 23:19:36.275502  4221 solver.cpp:237]     Train net output #0: loss = 0.898653 (* 1 = 0.898653 loss)
I0409 23:19:36.275514  4221 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0409 23:19:41.128767  4221 solver.cpp:218] Iteration 4104 (2.47267 iter/s, 4.85306s/12 iters), loss = 0.927848
I0409 23:19:41.128821  4221 solver.cpp:237]     Train net output #0: loss = 0.927848 (* 1 = 0.927848 loss)
I0409 23:19:41.128834  4221 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0409 23:19:45.961712  4221 solver.cpp:218] Iteration 4116 (2.4831 iter/s, 4.83267s/12 iters), loss = 0.906812
I0409 23:19:45.961866  4221 solver.cpp:237]     Train net output #0: loss = 0.906812 (* 1 = 0.906812 loss)
I0409 23:19:45.961876  4221 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0409 23:19:50.881386  4221 solver.cpp:218] Iteration 4128 (2.43937 iter/s, 4.91931s/12 iters), loss = 0.778899
I0409 23:19:50.881426  4221 solver.cpp:237]     Train net output #0: loss = 0.778899 (* 1 = 0.778899 loss)
I0409 23:19:50.881435  4221 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0409 23:19:56.013878  4221 solver.cpp:218] Iteration 4140 (2.33817 iter/s, 5.13222s/12 iters), loss = 0.714774
I0409 23:19:56.013944  4221 solver.cpp:237]     Train net output #0: loss = 0.714774 (* 1 = 0.714774 loss)
I0409 23:19:56.013974  4221 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0409 23:19:58.540141  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:20:00.804951  4221 solver.cpp:218] Iteration 4152 (2.5048 iter/s, 4.7908s/12 iters), loss = 0.691466
I0409 23:20:00.805017  4221 solver.cpp:237]     Train net output #0: loss = 0.691466 (* 1 = 0.691466 loss)
I0409 23:20:00.805028  4221 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0409 23:20:01.158149  4221 blocking_queue.cpp:49] Waiting for data
I0409 23:20:05.786095  4221 solver.cpp:218] Iteration 4164 (2.40922 iter/s, 4.98086s/12 iters), loss = 0.558491
I0409 23:20:05.786154  4221 solver.cpp:237]     Train net output #0: loss = 0.558491 (* 1 = 0.558491 loss)
I0409 23:20:05.786167  4221 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0409 23:20:10.622707  4221 solver.cpp:218] Iteration 4176 (2.48122 iter/s, 4.83633s/12 iters), loss = 0.981476
I0409 23:20:10.622761  4221 solver.cpp:237]     Train net output #0: loss = 0.981476 (* 1 = 0.981476 loss)
I0409 23:20:10.622771  4221 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0409 23:20:12.581066  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0409 23:20:14.469529  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0409 23:20:14.951153  4221 solver.cpp:330] Iteration 4182, Testing net (#0)
I0409 23:20:14.951180  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:20:17.819680  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:20:19.503291  4221 solver.cpp:397]     Test net output #0: accuracy = 0.336397
I0409 23:20:19.503342  4221 solver.cpp:397]     Test net output #1: loss = 3.40506 (* 1 = 3.40506 loss)
I0409 23:20:21.377859  4221 solver.cpp:218] Iteration 4188 (1.1158 iter/s, 10.7546s/12 iters), loss = 0.768805
I0409 23:20:21.377915  4221 solver.cpp:237]     Train net output #0: loss = 0.768805 (* 1 = 0.768805 loss)
I0409 23:20:21.377926  4221 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0409 23:20:26.337993  4221 solver.cpp:218] Iteration 4200 (2.41943 iter/s, 4.95984s/12 iters), loss = 0.629804
I0409 23:20:26.338044  4221 solver.cpp:237]     Train net output #0: loss = 0.629804 (* 1 = 0.629804 loss)
I0409 23:20:26.338054  4221 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0409 23:20:31.221566  4221 solver.cpp:218] Iteration 4212 (2.45735 iter/s, 4.88331s/12 iters), loss = 0.61607
I0409 23:20:31.221614  4221 solver.cpp:237]     Train net output #0: loss = 0.61607 (* 1 = 0.61607 loss)
I0409 23:20:31.221623  4221 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0409 23:20:36.215588  4221 solver.cpp:218] Iteration 4224 (2.40301 iter/s, 4.99374s/12 iters), loss = 0.828761
I0409 23:20:36.215653  4221 solver.cpp:237]     Train net output #0: loss = 0.828761 (* 1 = 0.828761 loss)
I0409 23:20:36.215667  4221 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0409 23:20:41.080302  4221 solver.cpp:218] Iteration 4236 (2.46689 iter/s, 4.86443s/12 iters), loss = 0.995863
I0409 23:20:41.080363  4221 solver.cpp:237]     Train net output #0: loss = 0.995863 (* 1 = 0.995863 loss)
I0409 23:20:41.080376  4221 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0409 23:20:45.698457  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:20:45.915709  4221 solver.cpp:218] Iteration 4248 (2.48183 iter/s, 4.83514s/12 iters), loss = 0.722569
I0409 23:20:45.915758  4221 solver.cpp:237]     Train net output #0: loss = 0.722569 (* 1 = 0.722569 loss)
I0409 23:20:45.915767  4221 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0409 23:20:50.974889  4221 solver.cpp:218] Iteration 4260 (2.37206 iter/s, 5.0589s/12 iters), loss = 0.757109
I0409 23:20:50.975031  4221 solver.cpp:237]     Train net output #0: loss = 0.757109 (* 1 = 0.757109 loss)
I0409 23:20:50.975042  4221 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0409 23:20:55.856675  4221 solver.cpp:218] Iteration 4272 (2.4583 iter/s, 4.88143s/12 iters), loss = 0.673616
I0409 23:20:55.856722  4221 solver.cpp:237]     Train net output #0: loss = 0.673616 (* 1 = 0.673616 loss)
I0409 23:20:55.856730  4221 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0409 23:21:00.209753  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0409 23:21:01.187553  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0409 23:21:01.677384  4221 solver.cpp:330] Iteration 4284, Testing net (#0)
I0409 23:21:01.677418  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:21:04.442154  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:21:06.248253  4221 solver.cpp:397]     Test net output #0: accuracy = 0.343137
I0409 23:21:06.248291  4221 solver.cpp:397]     Test net output #1: loss = 3.45565 (* 1 = 3.45565 loss)
I0409 23:21:06.331308  4221 solver.cpp:218] Iteration 4284 (1.14568 iter/s, 10.4741s/12 iters), loss = 0.853698
I0409 23:21:06.331368  4221 solver.cpp:237]     Train net output #0: loss = 0.853698 (* 1 = 0.853698 loss)
I0409 23:21:06.331379  4221 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0409 23:21:10.492673  4221 solver.cpp:218] Iteration 4296 (2.88384 iter/s, 4.16112s/12 iters), loss = 0.615812
I0409 23:21:10.492733  4221 solver.cpp:237]     Train net output #0: loss = 0.615812 (* 1 = 0.615812 loss)
I0409 23:21:10.492745  4221 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0409 23:21:15.473731  4221 solver.cpp:218] Iteration 4308 (2.40926 iter/s, 4.98078s/12 iters), loss = 0.914488
I0409 23:21:15.473780  4221 solver.cpp:237]     Train net output #0: loss = 0.914488 (* 1 = 0.914488 loss)
I0409 23:21:15.473789  4221 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0409 23:21:20.563357  4221 solver.cpp:218] Iteration 4320 (2.35786 iter/s, 5.08935s/12 iters), loss = 0.84824
I0409 23:21:20.563417  4221 solver.cpp:237]     Train net output #0: loss = 0.84824 (* 1 = 0.84824 loss)
I0409 23:21:20.563429  4221 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0409 23:21:25.525517  4221 solver.cpp:218] Iteration 4332 (2.41844 iter/s, 4.96188s/12 iters), loss = 0.59668
I0409 23:21:25.525672  4221 solver.cpp:237]     Train net output #0: loss = 0.59668 (* 1 = 0.59668 loss)
I0409 23:21:25.525684  4221 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0409 23:21:30.351503  4221 solver.cpp:218] Iteration 4344 (2.48673 iter/s, 4.82562s/12 iters), loss = 0.821203
I0409 23:21:30.351569  4221 solver.cpp:237]     Train net output #0: loss = 0.821203 (* 1 = 0.821203 loss)
I0409 23:21:30.351583  4221 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0409 23:21:32.182065  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:21:35.144568  4221 solver.cpp:218] Iteration 4356 (2.50376 iter/s, 4.79279s/12 iters), loss = 0.835945
I0409 23:21:35.144632  4221 solver.cpp:237]     Train net output #0: loss = 0.835945 (* 1 = 0.835945 loss)
I0409 23:21:35.144644  4221 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0409 23:21:39.941709  4221 solver.cpp:218] Iteration 4368 (2.50164 iter/s, 4.79686s/12 iters), loss = 0.81258
I0409 23:21:39.941771  4221 solver.cpp:237]     Train net output #0: loss = 0.81258 (* 1 = 0.81258 loss)
I0409 23:21:39.941783  4221 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0409 23:21:44.879176  4221 solver.cpp:218] Iteration 4380 (2.43053 iter/s, 4.93719s/12 iters), loss = 0.589477
I0409 23:21:44.879223  4221 solver.cpp:237]     Train net output #0: loss = 0.589477 (* 1 = 0.589477 loss)
I0409 23:21:44.879232  4221 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0409 23:21:46.839591  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0409 23:21:47.740705  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0409 23:21:48.227643  4221 solver.cpp:330] Iteration 4386, Testing net (#0)
I0409 23:21:48.227669  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:21:50.921452  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:21:52.679304  4221 solver.cpp:397]     Test net output #0: accuracy = 0.35049
I0409 23:21:52.679342  4221 solver.cpp:397]     Test net output #1: loss = 3.43285 (* 1 = 3.43285 loss)
I0409 23:21:55.024296  4221 solver.cpp:218] Iteration 4392 (1.18289 iter/s, 10.1446s/12 iters), loss = 0.620185
I0409 23:21:55.024355  4221 solver.cpp:237]     Train net output #0: loss = 0.620185 (* 1 = 0.620185 loss)
I0409 23:21:55.024367  4221 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0409 23:22:00.213850  4221 solver.cpp:218] Iteration 4404 (2.31246 iter/s, 5.18927s/12 iters), loss = 0.759502
I0409 23:22:00.213948  4221 solver.cpp:237]     Train net output #0: loss = 0.759502 (* 1 = 0.759502 loss)
I0409 23:22:00.213984  4221 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0409 23:22:05.082741  4221 solver.cpp:218] Iteration 4416 (2.46479 iter/s, 4.86858s/12 iters), loss = 0.698907
I0409 23:22:05.082803  4221 solver.cpp:237]     Train net output #0: loss = 0.698907 (* 1 = 0.698907 loss)
I0409 23:22:05.082815  4221 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0409 23:22:10.012223  4221 solver.cpp:218] Iteration 4428 (2.43447 iter/s, 4.9292s/12 iters), loss = 0.687902
I0409 23:22:10.012282  4221 solver.cpp:237]     Train net output #0: loss = 0.687902 (* 1 = 0.687902 loss)
I0409 23:22:10.012293  4221 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0409 23:22:15.666867  4221 solver.cpp:218] Iteration 4440 (2.12226 iter/s, 5.65434s/12 iters), loss = 0.506901
I0409 23:22:15.666914  4221 solver.cpp:237]     Train net output #0: loss = 0.506901 (* 1 = 0.506901 loss)
I0409 23:22:15.666924  4221 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0409 23:22:19.654296  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:22:20.602468  4221 solver.cpp:218] Iteration 4452 (2.43145 iter/s, 4.93533s/12 iters), loss = 0.670309
I0409 23:22:20.602517  4221 solver.cpp:237]     Train net output #0: loss = 0.670309 (* 1 = 0.670309 loss)
I0409 23:22:20.602526  4221 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0409 23:22:25.574250  4221 solver.cpp:218] Iteration 4464 (2.41375 iter/s, 4.97151s/12 iters), loss = 0.624443
I0409 23:22:25.574297  4221 solver.cpp:237]     Train net output #0: loss = 0.624443 (* 1 = 0.624443 loss)
I0409 23:22:25.574306  4221 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0409 23:22:30.549131  4221 solver.cpp:218] Iteration 4476 (2.41225 iter/s, 4.97462s/12 iters), loss = 0.573783
I0409 23:22:30.549232  4221 solver.cpp:237]     Train net output #0: loss = 0.573783 (* 1 = 0.573783 loss)
I0409 23:22:30.549242  4221 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0409 23:22:35.090117  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0409 23:22:35.741505  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0409 23:22:36.226748  4221 solver.cpp:330] Iteration 4488, Testing net (#0)
I0409 23:22:36.226781  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:22:38.912214  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:22:40.747992  4221 solver.cpp:397]     Test net output #0: accuracy = 0.360294
I0409 23:22:40.748032  4221 solver.cpp:397]     Test net output #1: loss = 3.30097 (* 1 = 3.30097 loss)
I0409 23:22:40.831276  4221 solver.cpp:218] Iteration 4488 (1.16713 iter/s, 10.2816s/12 iters), loss = 0.86071
I0409 23:22:40.831319  4221 solver.cpp:237]     Train net output #0: loss = 0.86071 (* 1 = 0.86071 loss)
I0409 23:22:40.831328  4221 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0409 23:22:44.975066  4221 solver.cpp:218] Iteration 4500 (2.89606 iter/s, 4.14356s/12 iters), loss = 0.780343
I0409 23:22:44.975121  4221 solver.cpp:237]     Train net output #0: loss = 0.780343 (* 1 = 0.780343 loss)
I0409 23:22:44.975132  4221 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0409 23:22:49.877141  4221 solver.cpp:218] Iteration 4512 (2.44808 iter/s, 4.9018s/12 iters), loss = 0.570364
I0409 23:22:49.877198  4221 solver.cpp:237]     Train net output #0: loss = 0.570364 (* 1 = 0.570364 loss)
I0409 23:22:49.877211  4221 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0409 23:22:54.731041  4221 solver.cpp:218] Iteration 4524 (2.47238 iter/s, 4.85363s/12 iters), loss = 0.530567
I0409 23:22:54.731093  4221 solver.cpp:237]     Train net output #0: loss = 0.530567 (* 1 = 0.530567 loss)
I0409 23:22:54.731103  4221 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0409 23:22:59.763264  4221 solver.cpp:218] Iteration 4536 (2.38476 iter/s, 5.03195s/12 iters), loss = 0.448092
I0409 23:22:59.763319  4221 solver.cpp:237]     Train net output #0: loss = 0.448092 (* 1 = 0.448092 loss)
I0409 23:22:59.763330  4221 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0409 23:23:04.837185  4221 solver.cpp:218] Iteration 4548 (2.36516 iter/s, 5.07365s/12 iters), loss = 0.427565
I0409 23:23:04.838558  4221 solver.cpp:237]     Train net output #0: loss = 0.427565 (* 1 = 0.427565 loss)
I0409 23:23:04.838572  4221 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0409 23:23:06.052544  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:23:09.675827  4221 solver.cpp:218] Iteration 4560 (2.48085 iter/s, 4.83706s/12 iters), loss = 0.521
I0409 23:23:09.675886  4221 solver.cpp:237]     Train net output #0: loss = 0.521 (* 1 = 0.521 loss)
I0409 23:23:09.675897  4221 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0409 23:23:14.782881  4221 solver.cpp:218] Iteration 4572 (2.34982 iter/s, 5.10678s/12 iters), loss = 0.645564
I0409 23:23:14.782922  4221 solver.cpp:237]     Train net output #0: loss = 0.645564 (* 1 = 0.645564 loss)
I0409 23:23:14.782929  4221 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0409 23:23:19.727870  4221 solver.cpp:218] Iteration 4584 (2.42683 iter/s, 4.94472s/12 iters), loss = 0.732224
I0409 23:23:19.727926  4221 solver.cpp:237]     Train net output #0: loss = 0.732224 (* 1 = 0.732224 loss)
I0409 23:23:19.727936  4221 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0409 23:23:21.667538  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0409 23:23:22.368150  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0409 23:23:22.849401  4221 solver.cpp:330] Iteration 4590, Testing net (#0)
I0409 23:23:22.849429  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:23:25.360913  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:23:27.179646  4221 solver.cpp:397]     Test net output #0: accuracy = 0.352941
I0409 23:23:27.179685  4221 solver.cpp:397]     Test net output #1: loss = 3.59511 (* 1 = 3.59511 loss)
I0409 23:23:29.125429  4221 solver.cpp:218] Iteration 4596 (1.27699 iter/s, 9.39711s/12 iters), loss = 0.731362
I0409 23:23:29.125473  4221 solver.cpp:237]     Train net output #0: loss = 0.731362 (* 1 = 0.731362 loss)
I0409 23:23:29.125481  4221 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0409 23:23:34.057653  4221 solver.cpp:218] Iteration 4608 (2.43311 iter/s, 4.93196s/12 iters), loss = 0.544891
I0409 23:23:34.057703  4221 solver.cpp:237]     Train net output #0: loss = 0.544891 (* 1 = 0.544891 loss)
I0409 23:23:34.057710  4221 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0409 23:23:38.952849  4221 solver.cpp:218] Iteration 4620 (2.45152 iter/s, 4.89493s/12 iters), loss = 0.706475
I0409 23:23:38.952986  4221 solver.cpp:237]     Train net output #0: loss = 0.706475 (* 1 = 0.706475 loss)
I0409 23:23:38.952998  4221 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0409 23:23:43.870784  4221 solver.cpp:218] Iteration 4632 (2.44022 iter/s, 4.91759s/12 iters), loss = 0.593291
I0409 23:23:43.870832  4221 solver.cpp:237]     Train net output #0: loss = 0.593291 (* 1 = 0.593291 loss)
I0409 23:23:43.870841  4221 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0409 23:23:48.792714  4221 solver.cpp:218] Iteration 4644 (2.4382 iter/s, 4.92166s/12 iters), loss = 0.65448
I0409 23:23:48.792763  4221 solver.cpp:237]     Train net output #0: loss = 0.65448 (* 1 = 0.65448 loss)
I0409 23:23:48.792773  4221 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0409 23:23:52.041788  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:23:53.580265  4221 solver.cpp:218] Iteration 4656 (2.50664 iter/s, 4.78728s/12 iters), loss = 0.813777
I0409 23:23:53.580328  4221 solver.cpp:237]     Train net output #0: loss = 0.813777 (* 1 = 0.813777 loss)
I0409 23:23:53.580340  4221 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0409 23:23:58.393311  4221 solver.cpp:218] Iteration 4668 (2.49337 iter/s, 4.81277s/12 iters), loss = 0.452823
I0409 23:23:58.393375  4221 solver.cpp:237]     Train net output #0: loss = 0.452823 (* 1 = 0.452823 loss)
I0409 23:23:58.393388  4221 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0409 23:24:03.158584  4221 solver.cpp:218] Iteration 4680 (2.51836 iter/s, 4.765s/12 iters), loss = 0.808291
I0409 23:24:03.158641  4221 solver.cpp:237]     Train net output #0: loss = 0.808291 (* 1 = 0.808291 loss)
I0409 23:24:03.158653  4221 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0409 23:24:07.672996  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0409 23:24:09.983656  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0409 23:24:10.772471  4221 solver.cpp:330] Iteration 4692, Testing net (#0)
I0409 23:24:10.772493  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:24:13.352787  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:24:15.231876  4221 solver.cpp:397]     Test net output #0: accuracy = 0.365196
I0409 23:24:15.231917  4221 solver.cpp:397]     Test net output #1: loss = 3.4974 (* 1 = 3.4974 loss)
I0409 23:24:15.315045  4221 solver.cpp:218] Iteration 4692 (0.987175 iter/s, 12.1559s/12 iters), loss = 0.664155
I0409 23:24:15.315094  4221 solver.cpp:237]     Train net output #0: loss = 0.664155 (* 1 = 0.664155 loss)
I0409 23:24:15.315104  4221 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0409 23:24:19.533450  4221 solver.cpp:218] Iteration 4704 (2.84484 iter/s, 4.21817s/12 iters), loss = 0.490401
I0409 23:24:19.533500  4221 solver.cpp:237]     Train net output #0: loss = 0.490401 (* 1 = 0.490401 loss)
I0409 23:24:19.533509  4221 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0409 23:24:24.402936  4221 solver.cpp:218] Iteration 4716 (2.46446 iter/s, 4.86922s/12 iters), loss = 0.770133
I0409 23:24:24.402993  4221 solver.cpp:237]     Train net output #0: loss = 0.770133 (* 1 = 0.770133 loss)
I0409 23:24:24.403004  4221 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0409 23:24:29.237784  4221 solver.cpp:218] Iteration 4728 (2.48212 iter/s, 4.83458s/12 iters), loss = 0.6628
I0409 23:24:29.237828  4221 solver.cpp:237]     Train net output #0: loss = 0.6628 (* 1 = 0.6628 loss)
I0409 23:24:29.237835  4221 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0409 23:24:34.019176  4221 solver.cpp:218] Iteration 4740 (2.50986 iter/s, 4.78114s/12 iters), loss = 0.593828
I0409 23:24:34.019227  4221 solver.cpp:237]     Train net output #0: loss = 0.593828 (* 1 = 0.593828 loss)
I0409 23:24:34.019237  4221 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0409 23:24:38.832620  4221 solver.cpp:218] Iteration 4752 (2.49315 iter/s, 4.81318s/12 iters), loss = 0.61338
I0409 23:24:38.832661  4221 solver.cpp:237]     Train net output #0: loss = 0.61338 (* 1 = 0.61338 loss)
I0409 23:24:38.832669  4221 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0409 23:24:39.337716  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:24:43.850343  4221 solver.cpp:218] Iteration 4764 (2.39165 iter/s, 5.01746s/12 iters), loss = 0.655319
I0409 23:24:43.850504  4221 solver.cpp:237]     Train net output #0: loss = 0.655319 (* 1 = 0.655319 loss)
I0409 23:24:43.850517  4221 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0409 23:24:48.653097  4221 solver.cpp:218] Iteration 4776 (2.49876 iter/s, 4.80238s/12 iters), loss = 0.604197
I0409 23:24:48.653156  4221 solver.cpp:237]     Train net output #0: loss = 0.604197 (* 1 = 0.604197 loss)
I0409 23:24:48.653168  4221 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0409 23:24:53.478011  4221 solver.cpp:218] Iteration 4788 (2.48723 iter/s, 4.82464s/12 iters), loss = 0.557006
I0409 23:24:53.478062  4221 solver.cpp:237]     Train net output #0: loss = 0.557006 (* 1 = 0.557006 loss)
I0409 23:24:53.478072  4221 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0409 23:24:55.431450  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0409 23:24:56.120111  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0409 23:24:56.849151  4221 solver.cpp:330] Iteration 4794, Testing net (#0)
I0409 23:24:56.849171  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:24:59.443711  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:25:01.354233  4221 solver.cpp:397]     Test net output #0: accuracy = 0.387255
I0409 23:25:01.354276  4221 solver.cpp:397]     Test net output #1: loss = 3.41566 (* 1 = 3.41566 loss)
I0409 23:25:03.088922  4221 solver.cpp:218] Iteration 4800 (1.24864 iter/s, 9.61045s/12 iters), loss = 0.539119
I0409 23:25:03.088974  4221 solver.cpp:237]     Train net output #0: loss = 0.539119 (* 1 = 0.539119 loss)
I0409 23:25:03.088982  4221 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0409 23:25:07.993470  4221 solver.cpp:218] Iteration 4812 (2.44684 iter/s, 4.90428s/12 iters), loss = 0.653143
I0409 23:25:07.993525  4221 solver.cpp:237]     Train net output #0: loss = 0.653143 (* 1 = 0.653143 loss)
I0409 23:25:07.993536  4221 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0409 23:25:12.842229  4221 solver.cpp:218] Iteration 4824 (2.47499 iter/s, 4.8485s/12 iters), loss = 0.663112
I0409 23:25:12.842267  4221 solver.cpp:237]     Train net output #0: loss = 0.663112 (* 1 = 0.663112 loss)
I0409 23:25:12.842276  4221 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0409 23:25:17.852418  4221 solver.cpp:218] Iteration 4836 (2.39524 iter/s, 5.00993s/12 iters), loss = 0.593891
I0409 23:25:17.852550  4221 solver.cpp:237]     Train net output #0: loss = 0.593891 (* 1 = 0.593891 loss)
I0409 23:25:17.852558  4221 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0409 23:25:18.686944  4221 blocking_queue.cpp:49] Waiting for data
I0409 23:25:22.862013  4221 solver.cpp:218] Iteration 4848 (2.39557 iter/s, 5.00925s/12 iters), loss = 0.683318
I0409 23:25:22.862061  4221 solver.cpp:237]     Train net output #0: loss = 0.683318 (* 1 = 0.683318 loss)
I0409 23:25:22.862071  4221 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0409 23:25:25.472913  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:25:27.716477  4221 solver.cpp:218] Iteration 4860 (2.47208 iter/s, 4.8542s/12 iters), loss = 0.497385
I0409 23:25:27.716536  4221 solver.cpp:237]     Train net output #0: loss = 0.497385 (* 1 = 0.497385 loss)
I0409 23:25:27.716547  4221 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0409 23:25:32.502257  4221 solver.cpp:218] Iteration 4872 (2.50757 iter/s, 4.78551s/12 iters), loss = 0.643173
I0409 23:25:32.502324  4221 solver.cpp:237]     Train net output #0: loss = 0.643173 (* 1 = 0.643173 loss)
I0409 23:25:32.502336  4221 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0409 23:25:37.272581  4221 solver.cpp:218] Iteration 4884 (2.5157 iter/s, 4.77005s/12 iters), loss = 0.458412
I0409 23:25:37.272642  4221 solver.cpp:237]     Train net output #0: loss = 0.458412 (* 1 = 0.458412 loss)
I0409 23:25:37.272655  4221 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0409 23:25:41.718730  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0409 23:25:42.540235  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0409 23:25:43.173173  4221 solver.cpp:330] Iteration 4896, Testing net (#0)
I0409 23:25:43.173202  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:25:45.605562  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:25:47.563360  4221 solver.cpp:397]     Test net output #0: accuracy = 0.378064
I0409 23:25:47.563410  4221 solver.cpp:397]     Test net output #1: loss = 3.42426 (* 1 = 3.42426 loss)
I0409 23:25:47.645035  4221 solver.cpp:218] Iteration 4896 (1.15697 iter/s, 10.372s/12 iters), loss = 0.542082
I0409 23:25:47.645095  4221 solver.cpp:237]     Train net output #0: loss = 0.542082 (* 1 = 0.542082 loss)
I0409 23:25:47.645107  4221 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0409 23:25:51.649413  4221 solver.cpp:218] Iteration 4908 (2.9969 iter/s, 4.00414s/12 iters), loss = 0.59926
I0409 23:25:51.649536  4221 solver.cpp:237]     Train net output #0: loss = 0.59926 (* 1 = 0.59926 loss)
I0409 23:25:51.649549  4221 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0409 23:25:56.499491  4221 solver.cpp:218] Iteration 4920 (2.47436 iter/s, 4.84974s/12 iters), loss = 0.542728
I0409 23:25:56.499550  4221 solver.cpp:237]     Train net output #0: loss = 0.542728 (* 1 = 0.542728 loss)
I0409 23:25:56.499562  4221 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0409 23:26:01.341331  4221 solver.cpp:218] Iteration 4932 (2.47854 iter/s, 4.84157s/12 iters), loss = 0.561028
I0409 23:26:01.341394  4221 solver.cpp:237]     Train net output #0: loss = 0.561028 (* 1 = 0.561028 loss)
I0409 23:26:01.341408  4221 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0409 23:26:06.210237  4221 solver.cpp:218] Iteration 4944 (2.46476 iter/s, 4.86863s/12 iters), loss = 0.545163
I0409 23:26:06.210278  4221 solver.cpp:237]     Train net output #0: loss = 0.545163 (* 1 = 0.545163 loss)
I0409 23:26:06.210286  4221 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0409 23:26:10.898403  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:26:11.084967  4221 solver.cpp:218] Iteration 4956 (2.46181 iter/s, 4.87447s/12 iters), loss = 0.497671
I0409 23:26:11.085024  4221 solver.cpp:237]     Train net output #0: loss = 0.497671 (* 1 = 0.497671 loss)
I0409 23:26:11.085036  4221 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0409 23:26:15.995543  4221 solver.cpp:218] Iteration 4968 (2.44385 iter/s, 4.91029s/12 iters), loss = 0.577721
I0409 23:26:15.995615  4221 solver.cpp:237]     Train net output #0: loss = 0.577721 (* 1 = 0.577721 loss)
I0409 23:26:15.995630  4221 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0409 23:26:20.879884  4221 solver.cpp:218] Iteration 4980 (2.45697 iter/s, 4.88406s/12 iters), loss = 0.593796
I0409 23:26:20.879928  4221 solver.cpp:237]     Train net output #0: loss = 0.593796 (* 1 = 0.593796 loss)
I0409 23:26:20.879936  4221 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0409 23:26:25.791893  4221 solver.cpp:218] Iteration 4992 (2.44312 iter/s, 4.91175s/12 iters), loss = 0.766923
I0409 23:26:25.792060  4221 solver.cpp:237]     Train net output #0: loss = 0.766923 (* 1 = 0.766923 loss)
I0409 23:26:25.792073  4221 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0409 23:26:27.761269  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0409 23:26:28.459877  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0409 23:26:28.952126  4221 solver.cpp:330] Iteration 4998, Testing net (#0)
I0409 23:26:28.952148  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:26:31.577860  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:26:33.564231  4221 solver.cpp:397]     Test net output #0: accuracy = 0.372549
I0409 23:26:33.564282  4221 solver.cpp:397]     Test net output #1: loss = 3.55572 (* 1 = 3.55572 loss)
I0409 23:26:35.350697  4221 solver.cpp:218] Iteration 5004 (1.25546 iter/s, 9.55824s/12 iters), loss = 0.359861
I0409 23:26:35.350744  4221 solver.cpp:237]     Train net output #0: loss = 0.359861 (* 1 = 0.359861 loss)
I0409 23:26:35.350752  4221 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0409 23:26:40.263375  4221 solver.cpp:218] Iteration 5016 (2.44279 iter/s, 4.91241s/12 iters), loss = 0.621054
I0409 23:26:40.263425  4221 solver.cpp:237]     Train net output #0: loss = 0.621054 (* 1 = 0.621054 loss)
I0409 23:26:40.263434  4221 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0409 23:26:45.191565  4221 solver.cpp:218] Iteration 5028 (2.4351 iter/s, 4.92792s/12 iters), loss = 0.431745
I0409 23:26:45.191610  4221 solver.cpp:237]     Train net output #0: loss = 0.431745 (* 1 = 0.431745 loss)
I0409 23:26:45.191619  4221 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0409 23:26:50.109467  4221 solver.cpp:218] Iteration 5040 (2.4402 iter/s, 4.91764s/12 iters), loss = 0.408379
I0409 23:26:50.109517  4221 solver.cpp:237]     Train net output #0: loss = 0.408379 (* 1 = 0.408379 loss)
I0409 23:26:50.109527  4221 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0409 23:26:55.115567  4221 solver.cpp:218] Iteration 5052 (2.3972 iter/s, 5.00583s/12 iters), loss = 0.366023
I0409 23:26:55.115620  4221 solver.cpp:237]     Train net output #0: loss = 0.366023 (* 1 = 0.366023 loss)
I0409 23:26:55.115630  4221 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0409 23:26:57.399236  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:27:00.568356  4221 solver.cpp:218] Iteration 5064 (2.20083 iter/s, 5.4525s/12 iters), loss = 0.560331
I0409 23:27:00.568413  4221 solver.cpp:237]     Train net output #0: loss = 0.560331 (* 1 = 0.560331 loss)
I0409 23:27:00.568425  4221 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0409 23:27:05.770856  4221 solver.cpp:218] Iteration 5076 (2.30671 iter/s, 5.20221s/12 iters), loss = 0.520092
I0409 23:27:05.770920  4221 solver.cpp:237]     Train net output #0: loss = 0.520092 (* 1 = 0.520092 loss)
I0409 23:27:05.770932  4221 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0409 23:27:10.618556  4221 solver.cpp:218] Iteration 5088 (2.47554 iter/s, 4.84743s/12 iters), loss = 0.470144
I0409 23:27:10.618611  4221 solver.cpp:237]     Train net output #0: loss = 0.470144 (* 1 = 0.470144 loss)
I0409 23:27:10.618621  4221 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0409 23:27:15.118647  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0409 23:27:15.815026  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0409 23:27:16.324880  4221 solver.cpp:330] Iteration 5100, Testing net (#0)
I0409 23:27:16.324908  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:27:18.800308  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:27:20.814832  4221 solver.cpp:397]     Test net output #0: accuracy = 0.370711
I0409 23:27:20.814882  4221 solver.cpp:397]     Test net output #1: loss = 3.61005 (* 1 = 3.61005 loss)
I0409 23:27:20.897815  4221 solver.cpp:218] Iteration 5100 (1.16745 iter/s, 10.2788s/12 iters), loss = 0.712708
I0409 23:27:20.897859  4221 solver.cpp:237]     Train net output #0: loss = 0.712708 (* 1 = 0.712708 loss)
I0409 23:27:20.897869  4221 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0409 23:27:25.038020  4221 solver.cpp:218] Iteration 5112 (2.89857 iter/s, 4.13998s/12 iters), loss = 0.57111
I0409 23:27:25.038069  4221 solver.cpp:237]     Train net output #0: loss = 0.57111 (* 1 = 0.57111 loss)
I0409 23:27:25.038076  4221 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0409 23:27:29.866515  4221 solver.cpp:218] Iteration 5124 (2.48538 iter/s, 4.82823s/12 iters), loss = 0.659307
I0409 23:27:29.866663  4221 solver.cpp:237]     Train net output #0: loss = 0.659307 (* 1 = 0.659307 loss)
I0409 23:27:29.866675  4221 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0409 23:27:35.142374  4221 solver.cpp:218] Iteration 5136 (2.27467 iter/s, 5.27548s/12 iters), loss = 0.469113
I0409 23:27:35.142434  4221 solver.cpp:237]     Train net output #0: loss = 0.469113 (* 1 = 0.469113 loss)
I0409 23:27:35.142452  4221 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0409 23:27:39.978962  4221 solver.cpp:218] Iteration 5148 (2.48123 iter/s, 4.83631s/12 iters), loss = 0.346556
I0409 23:27:39.979023  4221 solver.cpp:237]     Train net output #0: loss = 0.346556 (* 1 = 0.346556 loss)
I0409 23:27:39.979038  4221 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0409 23:27:43.907882  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:27:44.798153  4221 solver.cpp:218] Iteration 5160 (2.49018 iter/s, 4.81892s/12 iters), loss = 0.625366
I0409 23:27:44.798211  4221 solver.cpp:237]     Train net output #0: loss = 0.625366 (* 1 = 0.625366 loss)
I0409 23:27:44.798223  4221 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0409 23:27:49.639787  4221 solver.cpp:218] Iteration 5172 (2.47864 iter/s, 4.84136s/12 iters), loss = 0.395186
I0409 23:27:49.639845  4221 solver.cpp:237]     Train net output #0: loss = 0.395186 (* 1 = 0.395186 loss)
I0409 23:27:49.639858  4221 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0409 23:27:54.429239  4221 solver.cpp:218] Iteration 5184 (2.50565 iter/s, 4.78918s/12 iters), loss = 0.368724
I0409 23:27:54.429291  4221 solver.cpp:237]     Train net output #0: loss = 0.368724 (* 1 = 0.368724 loss)
I0409 23:27:54.429301  4221 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0409 23:27:59.254760  4221 solver.cpp:218] Iteration 5196 (2.48691 iter/s, 4.82526s/12 iters), loss = 0.645713
I0409 23:27:59.254808  4221 solver.cpp:237]     Train net output #0: loss = 0.645713 (* 1 = 0.645713 loss)
I0409 23:27:59.254817  4221 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0409 23:28:01.257632  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0409 23:28:01.954147  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0409 23:28:02.447686  4221 solver.cpp:330] Iteration 5202, Testing net (#0)
I0409 23:28:02.447705  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:28:05.043699  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:28:07.122689  4221 solver.cpp:397]     Test net output #0: accuracy = 0.381127
I0409 23:28:07.122727  4221 solver.cpp:397]     Test net output #1: loss = 3.53147 (* 1 = 3.53147 loss)
I0409 23:28:09.081487  4221 solver.cpp:218] Iteration 5208 (1.22122 iter/s, 9.82626s/12 iters), loss = 0.748553
I0409 23:28:09.081544  4221 solver.cpp:237]     Train net output #0: loss = 0.748553 (* 1 = 0.748553 loss)
I0409 23:28:09.081558  4221 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0409 23:28:13.979810  4221 solver.cpp:218] Iteration 5220 (2.44995 iter/s, 4.89805s/12 iters), loss = 0.472654
I0409 23:28:13.979856  4221 solver.cpp:237]     Train net output #0: loss = 0.472654 (* 1 = 0.472654 loss)
I0409 23:28:13.979864  4221 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0409 23:28:18.851915  4221 solver.cpp:218] Iteration 5232 (2.46313 iter/s, 4.87184s/12 iters), loss = 0.478913
I0409 23:28:18.851974  4221 solver.cpp:237]     Train net output #0: loss = 0.478913 (* 1 = 0.478913 loss)
I0409 23:28:18.851995  4221 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0409 23:28:23.777911  4221 solver.cpp:218] Iteration 5244 (2.43619 iter/s, 4.92573s/12 iters), loss = 0.451916
I0409 23:28:23.777971  4221 solver.cpp:237]     Train net output #0: loss = 0.451916 (* 1 = 0.451916 loss)
I0409 23:28:23.777979  4221 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0409 23:28:28.821647  4221 solver.cpp:218] Iteration 5256 (2.37932 iter/s, 5.04347s/12 iters), loss = 0.474637
I0409 23:28:28.821702  4221 solver.cpp:237]     Train net output #0: loss = 0.474637 (* 1 = 0.474637 loss)
I0409 23:28:28.821713  4221 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0409 23:28:30.086870  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:28:33.728010  4221 solver.cpp:218] Iteration 5268 (2.44594 iter/s, 4.90609s/12 iters), loss = 0.585804
I0409 23:28:33.728164  4221 solver.cpp:237]     Train net output #0: loss = 0.585804 (* 1 = 0.585804 loss)
I0409 23:28:33.728178  4221 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0409 23:28:38.613847  4221 solver.cpp:218] Iteration 5280 (2.45626 iter/s, 4.88547s/12 iters), loss = 0.459323
I0409 23:28:38.613903  4221 solver.cpp:237]     Train net output #0: loss = 0.459323 (* 1 = 0.459323 loss)
I0409 23:28:38.613914  4221 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0409 23:28:43.488843  4221 solver.cpp:218] Iteration 5292 (2.46168 iter/s, 4.87472s/12 iters), loss = 0.516604
I0409 23:28:43.488915  4221 solver.cpp:237]     Train net output #0: loss = 0.516604 (* 1 = 0.516604 loss)
I0409 23:28:43.488927  4221 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0409 23:28:47.928848  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0409 23:28:48.646134  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0409 23:28:49.395503  4221 solver.cpp:330] Iteration 5304, Testing net (#0)
I0409 23:28:49.395540  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:28:51.808326  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:28:54.069180  4221 solver.cpp:397]     Test net output #0: accuracy = 0.393382
I0409 23:28:54.069223  4221 solver.cpp:397]     Test net output #1: loss = 3.48645 (* 1 = 3.48645 loss)
I0409 23:28:54.152742  4221 solver.cpp:218] Iteration 5304 (1.12535 iter/s, 10.6634s/12 iters), loss = 0.413056
I0409 23:28:54.152791  4221 solver.cpp:237]     Train net output #0: loss = 0.413056 (* 1 = 0.413056 loss)
I0409 23:28:54.152801  4221 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0409 23:28:58.328595  4221 solver.cpp:218] Iteration 5316 (2.87383 iter/s, 4.17562s/12 iters), loss = 0.510089
I0409 23:28:58.328657  4221 solver.cpp:237]     Train net output #0: loss = 0.510089 (* 1 = 0.510089 loss)
I0409 23:28:58.328671  4221 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0409 23:29:03.139823  4221 solver.cpp:218] Iteration 5328 (2.4943 iter/s, 4.81096s/12 iters), loss = 0.284596
I0409 23:29:03.139865  4221 solver.cpp:237]     Train net output #0: loss = 0.284596 (* 1 = 0.284596 loss)
I0409 23:29:03.139874  4221 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0409 23:29:07.971210  4221 solver.cpp:218] Iteration 5340 (2.48389 iter/s, 4.83112s/12 iters), loss = 0.511792
I0409 23:29:07.971395  4221 solver.cpp:237]     Train net output #0: loss = 0.511792 (* 1 = 0.511792 loss)
I0409 23:29:07.971410  4221 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0409 23:29:12.784657  4221 solver.cpp:218] Iteration 5352 (2.49322 iter/s, 4.81306s/12 iters), loss = 0.65867
I0409 23:29:12.784696  4221 solver.cpp:237]     Train net output #0: loss = 0.65867 (* 1 = 0.65867 loss)
I0409 23:29:12.784704  4221 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0409 23:29:16.445575  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:29:17.950794  4221 solver.cpp:218] Iteration 5364 (2.32294 iter/s, 5.16587s/12 iters), loss = 0.677747
I0409 23:29:17.950851  4221 solver.cpp:237]     Train net output #0: loss = 0.677747 (* 1 = 0.677747 loss)
I0409 23:29:17.950866  4221 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0409 23:29:22.806195  4221 solver.cpp:218] Iteration 5376 (2.47161 iter/s, 4.85513s/12 iters), loss = 0.497443
I0409 23:29:22.806246  4221 solver.cpp:237]     Train net output #0: loss = 0.497443 (* 1 = 0.497443 loss)
I0409 23:29:22.806257  4221 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0409 23:29:27.614562  4221 solver.cpp:218] Iteration 5388 (2.49579 iter/s, 4.80811s/12 iters), loss = 0.310757
I0409 23:29:27.614616  4221 solver.cpp:237]     Train net output #0: loss = 0.310757 (* 1 = 0.310757 loss)
I0409 23:29:27.614629  4221 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0409 23:29:32.458146  4221 solver.cpp:218] Iteration 5400 (2.47764 iter/s, 4.84332s/12 iters), loss = 0.500814
I0409 23:29:32.458200  4221 solver.cpp:237]     Train net output #0: loss = 0.500814 (* 1 = 0.500814 loss)
I0409 23:29:32.458210  4221 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0409 23:29:34.459339  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0409 23:29:35.114312  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0409 23:29:35.911967  4221 solver.cpp:330] Iteration 5406, Testing net (#0)
I0409 23:29:35.911993  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:29:38.162187  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:29:40.303755  4221 solver.cpp:397]     Test net output #0: accuracy = 0.380515
I0409 23:29:40.303795  4221 solver.cpp:397]     Test net output #1: loss = 3.61086 (* 1 = 3.61086 loss)
I0409 23:29:42.145325  4221 solver.cpp:218] Iteration 5412 (1.23881 iter/s, 9.68672s/12 iters), loss = 0.447685
I0409 23:29:42.145372  4221 solver.cpp:237]     Train net output #0: loss = 0.447685 (* 1 = 0.447685 loss)
I0409 23:29:42.145383  4221 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0409 23:29:47.112828  4221 solver.cpp:218] Iteration 5424 (2.41583 iter/s, 4.96724s/12 iters), loss = 0.671363
I0409 23:29:47.112875  4221 solver.cpp:237]     Train net output #0: loss = 0.671363 (* 1 = 0.671363 loss)
I0409 23:29:47.112884  4221 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0409 23:29:52.153141  4221 solver.cpp:218] Iteration 5436 (2.38093 iter/s, 5.04005s/12 iters), loss = 0.420694
I0409 23:29:52.153189  4221 solver.cpp:237]     Train net output #0: loss = 0.420694 (* 1 = 0.420694 loss)
I0409 23:29:52.153201  4221 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0409 23:29:57.104034  4221 solver.cpp:218] Iteration 5448 (2.42393 iter/s, 4.95063s/12 iters), loss = 0.414837
I0409 23:29:57.104094  4221 solver.cpp:237]     Train net output #0: loss = 0.414837 (* 1 = 0.414837 loss)
I0409 23:29:57.104106  4221 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0409 23:30:01.970197  4221 solver.cpp:218] Iteration 5460 (2.46615 iter/s, 4.86589s/12 iters), loss = 0.303816
I0409 23:30:01.970250  4221 solver.cpp:237]     Train net output #0: loss = 0.303816 (* 1 = 0.303816 loss)
I0409 23:30:01.970263  4221 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0409 23:30:02.524825  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:30:06.854104  4221 solver.cpp:218] Iteration 5472 (2.45719 iter/s, 4.88364s/12 iters), loss = 0.285576
I0409 23:30:06.854158  4221 solver.cpp:237]     Train net output #0: loss = 0.285576 (* 1 = 0.285576 loss)
I0409 23:30:06.854168  4221 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0409 23:30:11.739562  4221 solver.cpp:218] Iteration 5484 (2.4564 iter/s, 4.8852s/12 iters), loss = 0.431286
I0409 23:30:11.739691  4221 solver.cpp:237]     Train net output #0: loss = 0.431286 (* 1 = 0.431286 loss)
I0409 23:30:11.739702  4221 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0409 23:30:16.622331  4221 solver.cpp:218] Iteration 5496 (2.45779 iter/s, 4.88243s/12 iters), loss = 0.406614
I0409 23:30:16.622375  4221 solver.cpp:237]     Train net output #0: loss = 0.406614 (* 1 = 0.406614 loss)
I0409 23:30:16.622385  4221 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0409 23:30:21.061981  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0409 23:30:21.735723  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0409 23:30:22.498241  4221 solver.cpp:330] Iteration 5508, Testing net (#0)
I0409 23:30:22.498276  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:30:24.689167  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:30:26.861002  4221 solver.cpp:397]     Test net output #0: accuracy = 0.375613
I0409 23:30:26.861052  4221 solver.cpp:397]     Test net output #1: loss = 3.64398 (* 1 = 3.64398 loss)
I0409 23:30:26.944479  4221 solver.cpp:218] Iteration 5508 (1.1626 iter/s, 10.3217s/12 iters), loss = 0.285909
I0409 23:30:26.944531  4221 solver.cpp:237]     Train net output #0: loss = 0.285909 (* 1 = 0.285909 loss)
I0409 23:30:26.944543  4221 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0409 23:30:31.020926  4221 solver.cpp:218] Iteration 5520 (2.94391 iter/s, 4.07621s/12 iters), loss = 0.291211
I0409 23:30:31.020985  4221 solver.cpp:237]     Train net output #0: loss = 0.291211 (* 1 = 0.291211 loss)
I0409 23:30:31.020996  4221 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0409 23:30:32.161286  4221 blocking_queue.cpp:49] Waiting for data
I0409 23:30:35.832799  4221 solver.cpp:218] Iteration 5532 (2.49397 iter/s, 4.8116s/12 iters), loss = 0.36908
I0409 23:30:35.832854  4221 solver.cpp:237]     Train net output #0: loss = 0.36908 (* 1 = 0.36908 loss)
I0409 23:30:35.832864  4221 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0409 23:30:40.713485  4221 solver.cpp:218] Iteration 5544 (2.45881 iter/s, 4.88042s/12 iters), loss = 0.602221
I0409 23:30:40.713536  4221 solver.cpp:237]     Train net output #0: loss = 0.602221 (* 1 = 0.602221 loss)
I0409 23:30:40.713549  4221 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0409 23:30:45.682528  4221 solver.cpp:218] Iteration 5556 (2.41508 iter/s, 4.96877s/12 iters), loss = 0.4968
I0409 23:30:45.682655  4221 solver.cpp:237]     Train net output #0: loss = 0.4968 (* 1 = 0.4968 loss)
I0409 23:30:45.682670  4221 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0409 23:30:48.239125  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:30:50.473804  4221 solver.cpp:218] Iteration 5568 (2.50473 iter/s, 4.79094s/12 iters), loss = 0.372444
I0409 23:30:50.473860  4221 solver.cpp:237]     Train net output #0: loss = 0.372444 (* 1 = 0.372444 loss)
I0409 23:30:50.473873  4221 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0409 23:30:55.333864  4221 solver.cpp:218] Iteration 5580 (2.46924 iter/s, 4.8598s/12 iters), loss = 0.643093
I0409 23:30:55.333906  4221 solver.cpp:237]     Train net output #0: loss = 0.643093 (* 1 = 0.643093 loss)
I0409 23:30:55.333914  4221 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0409 23:31:00.109287  4221 solver.cpp:218] Iteration 5592 (2.513 iter/s, 4.77517s/12 iters), loss = 0.352138
I0409 23:31:00.109344  4221 solver.cpp:237]     Train net output #0: loss = 0.352138 (* 1 = 0.352138 loss)
I0409 23:31:00.109356  4221 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0409 23:31:04.945371  4221 solver.cpp:218] Iteration 5604 (2.48148 iter/s, 4.83582s/12 iters), loss = 0.252884
I0409 23:31:04.945411  4221 solver.cpp:237]     Train net output #0: loss = 0.252884 (* 1 = 0.252884 loss)
I0409 23:31:04.945420  4221 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0409 23:31:06.922726  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0409 23:31:09.021607  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0409 23:31:12.522927  4221 solver.cpp:330] Iteration 5610, Testing net (#0)
I0409 23:31:12.522958  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:31:14.664773  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:31:16.872129  4221 solver.cpp:397]     Test net output #0: accuracy = 0.387255
I0409 23:31:16.872239  4221 solver.cpp:397]     Test net output #1: loss = 3.7244 (* 1 = 3.7244 loss)
I0409 23:31:18.718422  4221 solver.cpp:218] Iteration 5616 (0.871306 iter/s, 13.7724s/12 iters), loss = 0.66027
I0409 23:31:18.718480  4221 solver.cpp:237]     Train net output #0: loss = 0.66027 (* 1 = 0.66027 loss)
I0409 23:31:18.718492  4221 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0409 23:31:23.567684  4221 solver.cpp:218] Iteration 5628 (2.47474 iter/s, 4.84899s/12 iters), loss = 0.332484
I0409 23:31:23.567741  4221 solver.cpp:237]     Train net output #0: loss = 0.332484 (* 1 = 0.332484 loss)
I0409 23:31:23.567754  4221 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0409 23:31:28.463816  4221 solver.cpp:218] Iteration 5640 (2.45105 iter/s, 4.89587s/12 iters), loss = 0.505121
I0409 23:31:28.463866  4221 solver.cpp:237]     Train net output #0: loss = 0.505121 (* 1 = 0.505121 loss)
I0409 23:31:28.463877  4221 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0409 23:31:33.365720  4221 solver.cpp:218] Iteration 5652 (2.44816 iter/s, 4.90165s/12 iters), loss = 0.292218
I0409 23:31:33.365749  4221 solver.cpp:237]     Train net output #0: loss = 0.292218 (* 1 = 0.292218 loss)
I0409 23:31:33.365757  4221 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0409 23:31:38.008112  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:31:38.169167  4221 solver.cpp:218] Iteration 5664 (2.49833 iter/s, 4.80321s/12 iters), loss = 0.398947
I0409 23:31:38.169212  4221 solver.cpp:237]     Train net output #0: loss = 0.398947 (* 1 = 0.398947 loss)
I0409 23:31:38.169222  4221 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0409 23:31:43.005893  4221 solver.cpp:218] Iteration 5676 (2.48115 iter/s, 4.83647s/12 iters), loss = 0.318091
I0409 23:31:43.005939  4221 solver.cpp:237]     Train net output #0: loss = 0.318091 (* 1 = 0.318091 loss)
I0409 23:31:43.005947  4221 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0409 23:31:47.816422  4221 solver.cpp:218] Iteration 5688 (2.49466 iter/s, 4.81027s/12 iters), loss = 0.245903
I0409 23:31:47.816519  4221 solver.cpp:237]     Train net output #0: loss = 0.245903 (* 1 = 0.245903 loss)
I0409 23:31:47.816529  4221 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0409 23:31:52.624245  4221 solver.cpp:218] Iteration 5700 (2.49609 iter/s, 4.80752s/12 iters), loss = 0.388912
I0409 23:31:52.624300  4221 solver.cpp:237]     Train net output #0: loss = 0.388912 (* 1 = 0.388912 loss)
I0409 23:31:52.624310  4221 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0409 23:31:57.029404  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0409 23:31:57.731523  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0409 23:31:58.548178  4221 solver.cpp:330] Iteration 5712, Testing net (#0)
I0409 23:31:58.548202  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:32:00.760257  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:32:03.001807  4221 solver.cpp:397]     Test net output #0: accuracy = 0.39277
I0409 23:32:03.001852  4221 solver.cpp:397]     Test net output #1: loss = 3.62481 (* 1 = 3.62481 loss)
I0409 23:32:03.084893  4221 solver.cpp:218] Iteration 5712 (1.14721 iter/s, 10.4602s/12 iters), loss = 0.274273
I0409 23:32:03.084933  4221 solver.cpp:237]     Train net output #0: loss = 0.274273 (* 1 = 0.274273 loss)
I0409 23:32:03.084941  4221 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0409 23:32:07.150684  4221 solver.cpp:218] Iteration 5724 (2.95161 iter/s, 4.06557s/12 iters), loss = 0.362037
I0409 23:32:07.150733  4221 solver.cpp:237]     Train net output #0: loss = 0.362037 (* 1 = 0.362037 loss)
I0409 23:32:07.150744  4221 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0409 23:32:12.182215  4221 solver.cpp:218] Iteration 5736 (2.38509 iter/s, 5.03126s/12 iters), loss = 0.464331
I0409 23:32:12.182260  4221 solver.cpp:237]     Train net output #0: loss = 0.464331 (* 1 = 0.464331 loss)
I0409 23:32:12.182269  4221 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0409 23:32:16.966310  4221 solver.cpp:218] Iteration 5748 (2.50845 iter/s, 4.78383s/12 iters), loss = 0.2977
I0409 23:32:16.966357  4221 solver.cpp:237]     Train net output #0: loss = 0.2977 (* 1 = 0.2977 loss)
I0409 23:32:16.966367  4221 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0409 23:32:21.793808  4221 solver.cpp:218] Iteration 5760 (2.48589 iter/s, 4.82724s/12 iters), loss = 0.228483
I0409 23:32:21.793939  4221 solver.cpp:237]     Train net output #0: loss = 0.228483 (* 1 = 0.228483 loss)
I0409 23:32:21.793967  4221 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0409 23:32:23.701416  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:32:26.610076  4221 solver.cpp:218] Iteration 5772 (2.49173 iter/s, 4.81593s/12 iters), loss = 0.449365
I0409 23:32:26.610131  4221 solver.cpp:237]     Train net output #0: loss = 0.449365 (* 1 = 0.449365 loss)
I0409 23:32:26.610143  4221 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0409 23:32:31.476697  4221 solver.cpp:218] Iteration 5784 (2.46591 iter/s, 4.86636s/12 iters), loss = 0.335582
I0409 23:32:31.476747  4221 solver.cpp:237]     Train net output #0: loss = 0.335582 (* 1 = 0.335582 loss)
I0409 23:32:31.476759  4221 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0409 23:32:36.351364  4221 solver.cpp:218] Iteration 5796 (2.46184 iter/s, 4.87441s/12 iters), loss = 0.314929
I0409 23:32:36.351413  4221 solver.cpp:237]     Train net output #0: loss = 0.314929 (* 1 = 0.314929 loss)
I0409 23:32:36.351423  4221 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0409 23:32:41.190212  4221 solver.cpp:218] Iteration 5808 (2.48007 iter/s, 4.83858s/12 iters), loss = 0.418228
I0409 23:32:41.190272  4221 solver.cpp:237]     Train net output #0: loss = 0.418228 (* 1 = 0.418228 loss)
I0409 23:32:41.190284  4221 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0409 23:32:43.162320  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0409 23:32:43.885344  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0409 23:32:44.630865  4221 solver.cpp:330] Iteration 5814, Testing net (#0)
I0409 23:32:44.630897  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:32:46.827435  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:32:49.193409  4221 solver.cpp:397]     Test net output #0: accuracy = 0.396446
I0409 23:32:49.193439  4221 solver.cpp:397]     Test net output #1: loss = 3.69405 (* 1 = 3.69405 loss)
I0409 23:32:51.029417  4221 solver.cpp:218] Iteration 5820 (1.21967 iter/s, 9.83873s/12 iters), loss = 0.421708
I0409 23:32:51.029471  4221 solver.cpp:237]     Train net output #0: loss = 0.421708 (* 1 = 0.421708 loss)
I0409 23:32:51.029484  4221 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0409 23:32:55.948493  4221 solver.cpp:218] Iteration 5832 (2.43961 iter/s, 4.91881s/12 iters), loss = 0.532557
I0409 23:32:55.948563  4221 solver.cpp:237]     Train net output #0: loss = 0.532557 (* 1 = 0.532557 loss)
I0409 23:32:55.948572  4221 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0409 23:33:00.815075  4221 solver.cpp:218] Iteration 5844 (2.46594 iter/s, 4.8663s/12 iters), loss = 0.530252
I0409 23:33:00.815133  4221 solver.cpp:237]     Train net output #0: loss = 0.530252 (* 1 = 0.530252 loss)
I0409 23:33:00.815145  4221 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0409 23:33:05.642432  4221 solver.cpp:218] Iteration 5856 (2.48597 iter/s, 4.82709s/12 iters), loss = 0.340324
I0409 23:33:05.642482  4221 solver.cpp:237]     Train net output #0: loss = 0.340324 (* 1 = 0.340324 loss)
I0409 23:33:05.642493  4221 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0409 23:33:09.652380  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:10.430893  4221 solver.cpp:218] Iteration 5868 (2.50616 iter/s, 4.7882s/12 iters), loss = 0.351903
I0409 23:33:10.430945  4221 solver.cpp:237]     Train net output #0: loss = 0.351903 (* 1 = 0.351903 loss)
I0409 23:33:10.430958  4221 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0409 23:33:15.325392  4221 solver.cpp:218] Iteration 5880 (2.45187 iter/s, 4.89423s/12 iters), loss = 0.373365
I0409 23:33:15.325440  4221 solver.cpp:237]     Train net output #0: loss = 0.373365 (* 1 = 0.373365 loss)
I0409 23:33:15.325450  4221 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0409 23:33:20.101570  4221 solver.cpp:218] Iteration 5892 (2.5126 iter/s, 4.77592s/12 iters), loss = 0.366663
I0409 23:33:20.101629  4221 solver.cpp:237]     Train net output #0: loss = 0.366663 (* 1 = 0.366663 loss)
I0409 23:33:20.101640  4221 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0409 23:33:24.899307  4221 solver.cpp:218] Iteration 5904 (2.50132 iter/s, 4.79748s/12 iters), loss = 0.635323
I0409 23:33:24.899348  4221 solver.cpp:237]     Train net output #0: loss = 0.635323 (* 1 = 0.635323 loss)
I0409 23:33:24.899354  4221 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0409 23:33:29.282629  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0409 23:33:29.974767  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0409 23:33:30.472808  4221 solver.cpp:330] Iteration 5916, Testing net (#0)
I0409 23:33:30.472831  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:33:32.601187  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:35.045428  4221 solver.cpp:397]     Test net output #0: accuracy = 0.406863
I0409 23:33:35.045478  4221 solver.cpp:397]     Test net output #1: loss = 3.58028 (* 1 = 3.58028 loss)
I0409 23:33:35.128791  4221 solver.cpp:218] Iteration 5916 (1.17313 iter/s, 10.229s/12 iters), loss = 0.337232
I0409 23:33:35.128841  4221 solver.cpp:237]     Train net output #0: loss = 0.337232 (* 1 = 0.337232 loss)
I0409 23:33:35.128852  4221 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0409 23:33:39.308878  4221 solver.cpp:218] Iteration 5928 (2.87091 iter/s, 4.17986s/12 iters), loss = 0.271049
I0409 23:33:39.308919  4221 solver.cpp:237]     Train net output #0: loss = 0.271049 (* 1 = 0.271049 loss)
I0409 23:33:39.308929  4221 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0409 23:33:44.112438  4221 solver.cpp:218] Iteration 5940 (2.49828 iter/s, 4.80331s/12 iters), loss = 0.317867
I0409 23:33:44.112491  4221 solver.cpp:237]     Train net output #0: loss = 0.317867 (* 1 = 0.317867 loss)
I0409 23:33:44.112504  4221 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0409 23:33:48.910712  4221 solver.cpp:218] Iteration 5952 (2.50104 iter/s, 4.79801s/12 iters), loss = 0.325423
I0409 23:33:48.910773  4221 solver.cpp:237]     Train net output #0: loss = 0.325423 (* 1 = 0.325423 loss)
I0409 23:33:48.910786  4221 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0409 23:33:53.744352  4221 solver.cpp:218] Iteration 5964 (2.48274 iter/s, 4.83337s/12 iters), loss = 0.454795
I0409 23:33:53.744403  4221 solver.cpp:237]     Train net output #0: loss = 0.454795 (* 1 = 0.454795 loss)
I0409 23:33:53.744415  4221 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0409 23:33:55.017706  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:58.542318  4221 solver.cpp:218] Iteration 5976 (2.5012 iter/s, 4.79771s/12 iters), loss = 0.441786
I0409 23:33:58.542371  4221 solver.cpp:237]     Train net output #0: loss = 0.441786 (* 1 = 0.441786 loss)
I0409 23:33:58.542383  4221 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0409 23:34:03.343873  4221 solver.cpp:218] Iteration 5988 (2.49933 iter/s, 4.80129s/12 iters), loss = 0.341505
I0409 23:34:03.344012  4221 solver.cpp:237]     Train net output #0: loss = 0.341505 (* 1 = 0.341505 loss)
I0409 23:34:03.344022  4221 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0409 23:34:08.145918  4221 solver.cpp:218] Iteration 6000 (2.49912 iter/s, 4.8017s/12 iters), loss = 0.524947
I0409 23:34:08.145988  4221 solver.cpp:237]     Train net output #0: loss = 0.524947 (* 1 = 0.524947 loss)
I0409 23:34:08.145999  4221 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0409 23:34:12.959229  4221 solver.cpp:218] Iteration 6012 (2.49323 iter/s, 4.81304s/12 iters), loss = 0.478789
I0409 23:34:12.959281  4221 solver.cpp:237]     Train net output #0: loss = 0.478789 (* 1 = 0.478789 loss)
I0409 23:34:12.959292  4221 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0409 23:34:14.915972  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0409 23:34:15.620821  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0409 23:34:16.121428  4221 solver.cpp:330] Iteration 6018, Testing net (#0)
I0409 23:34:16.121455  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:34:18.124145  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:34:20.521178  4221 solver.cpp:397]     Test net output #0: accuracy = 0.404412
I0409 23:34:20.521219  4221 solver.cpp:397]     Test net output #1: loss = 3.5468 (* 1 = 3.5468 loss)
I0409 23:34:22.263511  4221 solver.cpp:218] Iteration 6024 (1.28979 iter/s, 9.30384s/12 iters), loss = 0.326363
I0409 23:34:22.263569  4221 solver.cpp:237]     Train net output #0: loss = 0.326363 (* 1 = 0.326363 loss)
I0409 23:34:22.263581  4221 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0409 23:34:27.029472  4221 solver.cpp:218] Iteration 6036 (2.518 iter/s, 4.7657s/12 iters), loss = 0.284308
I0409 23:34:27.029536  4221 solver.cpp:237]     Train net output #0: loss = 0.284308 (* 1 = 0.284308 loss)
I0409 23:34:27.029548  4221 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0409 23:34:31.813122  4221 solver.cpp:218] Iteration 6048 (2.50869 iter/s, 4.78338s/12 iters), loss = 0.326483
I0409 23:34:31.813186  4221 solver.cpp:237]     Train net output #0: loss = 0.326483 (* 1 = 0.326483 loss)
I0409 23:34:31.813199  4221 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0409 23:34:36.603488  4221 solver.cpp:218] Iteration 6060 (2.50517 iter/s, 4.79009s/12 iters), loss = 0.257412
I0409 23:34:36.603621  4221 solver.cpp:237]     Train net output #0: loss = 0.257412 (* 1 = 0.257412 loss)
I0409 23:34:36.603634  4221 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0409 23:34:39.900995  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:34:41.377293  4221 solver.cpp:218] Iteration 6072 (2.5139 iter/s, 4.77346s/12 iters), loss = 0.43396
I0409 23:34:41.377355  4221 solver.cpp:237]     Train net output #0: loss = 0.43396 (* 1 = 0.43396 loss)
I0409 23:34:41.377369  4221 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0409 23:34:46.161672  4221 solver.cpp:218] Iteration 6084 (2.50831 iter/s, 4.78411s/12 iters), loss = 0.333793
I0409 23:34:46.161733  4221 solver.cpp:237]     Train net output #0: loss = 0.333793 (* 1 = 0.333793 loss)
I0409 23:34:46.161746  4221 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0409 23:34:51.036945  4221 solver.cpp:218] Iteration 6096 (2.46154 iter/s, 4.875s/12 iters), loss = 0.326601
I0409 23:34:51.036993  4221 solver.cpp:237]     Train net output #0: loss = 0.326601 (* 1 = 0.326601 loss)
I0409 23:34:51.037003  4221 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0409 23:34:55.868232  4221 solver.cpp:218] Iteration 6108 (2.48394 iter/s, 4.83103s/12 iters), loss = 0.313291
I0409 23:34:55.868276  4221 solver.cpp:237]     Train net output #0: loss = 0.313291 (* 1 = 0.313291 loss)
I0409 23:34:55.868285  4221 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0409 23:35:00.242182  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0409 23:35:00.892774  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0409 23:35:01.383276  4221 solver.cpp:330] Iteration 6120, Testing net (#0)
I0409 23:35:01.383306  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:35:03.413693  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:35:05.812397  4221 solver.cpp:397]     Test net output #0: accuracy = 0.404412
I0409 23:35:05.812448  4221 solver.cpp:397]     Test net output #1: loss = 3.51903 (* 1 = 3.51903 loss)
I0409 23:35:05.895676  4221 solver.cpp:218] Iteration 6120 (1.19677 iter/s, 10.027s/12 iters), loss = 0.461135
I0409 23:35:05.895731  4221 solver.cpp:237]     Train net output #0: loss = 0.461135 (* 1 = 0.461135 loss)
I0409 23:35:05.895742  4221 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0409 23:35:10.140980  4221 solver.cpp:218] Iteration 6132 (2.82681 iter/s, 4.24506s/12 iters), loss = 0.430738
I0409 23:35:10.141124  4221 solver.cpp:237]     Train net output #0: loss = 0.430738 (* 1 = 0.430738 loss)
I0409 23:35:10.141137  4221 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0409 23:35:14.938141  4221 solver.cpp:218] Iteration 6144 (2.50166 iter/s, 4.79682s/12 iters), loss = 0.302292
I0409 23:35:14.938189  4221 solver.cpp:237]     Train net output #0: loss = 0.302292 (* 1 = 0.302292 loss)
I0409 23:35:14.938199  4221 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0409 23:35:19.868307  4221 solver.cpp:218] Iteration 6156 (2.43413 iter/s, 4.9299s/12 iters), loss = 0.313818
I0409 23:35:19.868352  4221 solver.cpp:237]     Train net output #0: loss = 0.313818 (* 1 = 0.313818 loss)
I0409 23:35:19.868361  4221 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0409 23:35:24.806658  4221 solver.cpp:218] Iteration 6168 (2.43009 iter/s, 4.93809s/12 iters), loss = 0.475929
I0409 23:35:24.806706  4221 solver.cpp:237]     Train net output #0: loss = 0.475929 (* 1 = 0.475929 loss)
I0409 23:35:24.806715  4221 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0409 23:35:25.400918  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:35:29.643183  4221 solver.cpp:218] Iteration 6180 (2.48126 iter/s, 4.83626s/12 iters), loss = 0.542522
I0409 23:35:29.643241  4221 solver.cpp:237]     Train net output #0: loss = 0.542522 (* 1 = 0.542522 loss)
I0409 23:35:29.643254  4221 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0409 23:35:34.484983  4221 solver.cpp:218] Iteration 6192 (2.47855 iter/s, 4.84154s/12 iters), loss = 0.294037
I0409 23:35:34.485033  4221 solver.cpp:237]     Train net output #0: loss = 0.294038 (* 1 = 0.294038 loss)
I0409 23:35:34.485044  4221 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0409 23:35:39.301398  4221 solver.cpp:218] Iteration 6204 (2.49162 iter/s, 4.81615s/12 iters), loss = 0.273314
I0409 23:35:39.301455  4221 solver.cpp:237]     Train net output #0: loss = 0.273314 (* 1 = 0.273314 loss)
I0409 23:35:39.301466  4221 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0409 23:35:44.132448  4221 solver.cpp:218] Iteration 6216 (2.48407 iter/s, 4.83078s/12 iters), loss = 0.311221
I0409 23:35:44.132568  4221 solver.cpp:237]     Train net output #0: loss = 0.311221 (* 1 = 0.311221 loss)
I0409 23:35:44.132583  4221 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0409 23:35:46.115972  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0409 23:35:46.780268  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0409 23:35:47.262732  4221 solver.cpp:330] Iteration 6222, Testing net (#0)
I0409 23:35:47.262756  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:35:49.196529  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:35:50.197048  4221 blocking_queue.cpp:49] Waiting for data
I0409 23:35:51.640022  4221 solver.cpp:397]     Test net output #0: accuracy = 0.417892
I0409 23:35:51.640069  4221 solver.cpp:397]     Test net output #1: loss = 3.60369 (* 1 = 3.60369 loss)
I0409 23:35:53.580173  4221 solver.cpp:218] Iteration 6228 (1.27022 iter/s, 9.44721s/12 iters), loss = 0.185326
I0409 23:35:53.580227  4221 solver.cpp:237]     Train net output #0: loss = 0.185326 (* 1 = 0.185326 loss)
I0409 23:35:53.580240  4221 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0409 23:35:58.391021  4221 solver.cpp:218] Iteration 6240 (2.4945 iter/s, 4.81058s/12 iters), loss = 0.49031
I0409 23:35:58.391083  4221 solver.cpp:237]     Train net output #0: loss = 0.49031 (* 1 = 0.49031 loss)
I0409 23:35:58.391094  4221 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0409 23:36:03.169062  4221 solver.cpp:218] Iteration 6252 (2.51163 iter/s, 4.77778s/12 iters), loss = 0.281358
I0409 23:36:03.169102  4221 solver.cpp:237]     Train net output #0: loss = 0.281358 (* 1 = 0.281358 loss)
I0409 23:36:03.169111  4221 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0409 23:36:07.968698  4221 solver.cpp:218] Iteration 6264 (2.50032 iter/s, 4.79938s/12 iters), loss = 0.229527
I0409 23:36:07.968750  4221 solver.cpp:237]     Train net output #0: loss = 0.229527 (* 1 = 0.229527 loss)
I0409 23:36:07.968760  4221 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0409 23:36:10.602983  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:36:12.956790  4221 solver.cpp:218] Iteration 6276 (2.40586 iter/s, 4.98782s/12 iters), loss = 0.265711
I0409 23:36:12.956837  4221 solver.cpp:237]     Train net output #0: loss = 0.265711 (* 1 = 0.265711 loss)
I0409 23:36:12.956848  4221 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0409 23:36:18.011083  4221 solver.cpp:218] Iteration 6288 (2.37434 iter/s, 5.05403s/12 iters), loss = 0.32247
I0409 23:36:18.011211  4221 solver.cpp:237]     Train net output #0: loss = 0.32247 (* 1 = 0.32247 loss)
I0409 23:36:18.011221  4221 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0409 23:36:22.834101  4221 solver.cpp:218] Iteration 6300 (2.48824 iter/s, 4.82269s/12 iters), loss = 0.411463
I0409 23:36:22.834146  4221 solver.cpp:237]     Train net output #0: loss = 0.411463 (* 1 = 0.411463 loss)
I0409 23:36:22.834156  4221 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0409 23:36:27.708127  4221 solver.cpp:218] Iteration 6312 (2.46216 iter/s, 4.87377s/12 iters), loss = 0.248066
I0409 23:36:27.708187  4221 solver.cpp:237]     Train net output #0: loss = 0.248066 (* 1 = 0.248066 loss)
I0409 23:36:27.708199  4221 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0409 23:36:32.097193  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0409 23:36:32.812721  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0409 23:36:33.314612  4221 solver.cpp:330] Iteration 6324, Testing net (#0)
I0409 23:36:33.314641  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:36:35.226754  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:36:37.709126  4221 solver.cpp:397]     Test net output #0: accuracy = 0.409314
I0409 23:36:37.709168  4221 solver.cpp:397]     Test net output #1: loss = 3.48419 (* 1 = 3.48419 loss)
I0409 23:36:37.792285  4221 solver.cpp:218] Iteration 6324 (1.19004 iter/s, 10.0837s/12 iters), loss = 0.224279
I0409 23:36:37.792335  4221 solver.cpp:237]     Train net output #0: loss = 0.224279 (* 1 = 0.224279 loss)
I0409 23:36:37.792346  4221 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0409 23:36:41.861750  4221 solver.cpp:218] Iteration 6336 (2.94896 iter/s, 4.06923s/12 iters), loss = 0.242696
I0409 23:36:41.861804  4221 solver.cpp:237]     Train net output #0: loss = 0.242696 (* 1 = 0.242696 loss)
I0409 23:36:41.861815  4221 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0409 23:36:46.673632  4221 solver.cpp:218] Iteration 6348 (2.49396 iter/s, 4.81162s/12 iters), loss = 0.304898
I0409 23:36:46.673684  4221 solver.cpp:237]     Train net output #0: loss = 0.304898 (* 1 = 0.304898 loss)
I0409 23:36:46.673696  4221 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0409 23:36:51.555002  4221 solver.cpp:218] Iteration 6360 (2.45846 iter/s, 4.8811s/12 iters), loss = 0.282758
I0409 23:36:51.555143  4221 solver.cpp:237]     Train net output #0: loss = 0.282758 (* 1 = 0.282758 loss)
I0409 23:36:51.555158  4221 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0409 23:36:56.239869  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:36:56.369742  4221 solver.cpp:218] Iteration 6372 (2.49253 iter/s, 4.81439s/12 iters), loss = 0.192877
I0409 23:36:56.369799  4221 solver.cpp:237]     Train net output #0: loss = 0.192877 (* 1 = 0.192877 loss)
I0409 23:36:56.369812  4221 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0409 23:37:01.223701  4221 solver.cpp:218] Iteration 6384 (2.47234 iter/s, 4.8537s/12 iters), loss = 0.49388
I0409 23:37:01.223742  4221 solver.cpp:237]     Train net output #0: loss = 0.49388 (* 1 = 0.49388 loss)
I0409 23:37:01.223750  4221 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0409 23:37:06.426589  4221 solver.cpp:218] Iteration 6396 (2.30653 iter/s, 5.20262s/12 iters), loss = 0.417026
I0409 23:37:06.426635  4221 solver.cpp:237]     Train net output #0: loss = 0.417026 (* 1 = 0.417026 loss)
I0409 23:37:06.426646  4221 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0409 23:37:11.299844  4221 solver.cpp:218] Iteration 6408 (2.46255 iter/s, 4.87299s/12 iters), loss = 0.266814
I0409 23:37:11.299901  4221 solver.cpp:237]     Train net output #0: loss = 0.266814 (* 1 = 0.266814 loss)
I0409 23:37:11.299913  4221 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0409 23:37:16.165364  4221 solver.cpp:218] Iteration 6420 (2.46647 iter/s, 4.86526s/12 iters), loss = 0.236475
I0409 23:37:16.165411  4221 solver.cpp:237]     Train net output #0: loss = 0.236475 (* 1 = 0.236475 loss)
I0409 23:37:16.165422  4221 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0409 23:37:18.162205  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0409 23:37:18.861544  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0409 23:37:19.363667  4221 solver.cpp:330] Iteration 6426, Testing net (#0)
I0409 23:37:19.363698  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:37:21.207577  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:37:23.728394  4221 solver.cpp:397]     Test net output #0: accuracy = 0.419118
I0409 23:37:23.728494  4221 solver.cpp:397]     Test net output #1: loss = 3.62556 (* 1 = 3.62556 loss)
I0409 23:37:25.652875  4221 solver.cpp:218] Iteration 6432 (1.26488 iter/s, 9.48707s/12 iters), loss = 0.270895
I0409 23:37:25.652935  4221 solver.cpp:237]     Train net output #0: loss = 0.270895 (* 1 = 0.270895 loss)
I0409 23:37:25.652948  4221 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0409 23:37:30.515007  4221 solver.cpp:218] Iteration 6444 (2.46819 iter/s, 4.86187s/12 iters), loss = 0.245724
I0409 23:37:30.515053  4221 solver.cpp:237]     Train net output #0: loss = 0.245724 (* 1 = 0.245724 loss)
I0409 23:37:30.515061  4221 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0409 23:37:35.357844  4221 solver.cpp:218] Iteration 6456 (2.47802 iter/s, 4.84258s/12 iters), loss = 0.138586
I0409 23:37:35.357892  4221 solver.cpp:237]     Train net output #0: loss = 0.138586 (* 1 = 0.138586 loss)
I0409 23:37:35.357901  4221 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0409 23:37:40.205785  4221 solver.cpp:218] Iteration 6468 (2.47541 iter/s, 4.84768s/12 iters), loss = 0.15915
I0409 23:37:40.205832  4221 solver.cpp:237]     Train net output #0: loss = 0.15915 (* 1 = 0.15915 loss)
I0409 23:37:40.205842  4221 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0409 23:37:42.119235  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:37:45.086304  4221 solver.cpp:218] Iteration 6480 (2.45888 iter/s, 4.88027s/12 iters), loss = 0.348911
I0409 23:37:45.086342  4221 solver.cpp:237]     Train net output #0: loss = 0.348911 (* 1 = 0.348911 loss)
I0409 23:37:45.086351  4221 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0409 23:37:50.060740  4221 solver.cpp:218] Iteration 6492 (2.41246 iter/s, 4.97418s/12 iters), loss = 0.261966
I0409 23:37:50.060787  4221 solver.cpp:237]     Train net output #0: loss = 0.261966 (* 1 = 0.261966 loss)
I0409 23:37:50.060796  4221 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0409 23:37:54.869204  4221 solver.cpp:218] Iteration 6504 (2.49573 iter/s, 4.80821s/12 iters), loss = 0.283436
I0409 23:37:54.869356  4221 solver.cpp:237]     Train net output #0: loss = 0.283436 (* 1 = 0.283436 loss)
I0409 23:37:54.869369  4221 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0409 23:37:59.667753  4221 solver.cpp:218] Iteration 6516 (2.50094 iter/s, 4.79819s/12 iters), loss = 0.238145
I0409 23:37:59.667809  4221 solver.cpp:237]     Train net output #0: loss = 0.238145 (* 1 = 0.238145 loss)
I0409 23:37:59.667820  4221 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0409 23:38:04.062559  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0409 23:38:05.943094  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0409 23:38:06.646884  4221 solver.cpp:330] Iteration 6528, Testing net (#0)
I0409 23:38:06.646906  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:38:08.681555  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:38:11.243002  4221 solver.cpp:397]     Test net output #0: accuracy = 0.420956
I0409 23:38:11.243050  4221 solver.cpp:397]     Test net output #1: loss = 3.69056 (* 1 = 3.69056 loss)
I0409 23:38:11.326478  4221 solver.cpp:218] Iteration 6528 (1.02932 iter/s, 11.6582s/12 iters), loss = 0.146658
I0409 23:38:11.326550  4221 solver.cpp:237]     Train net output #0: loss = 0.146658 (* 1 = 0.146658 loss)
I0409 23:38:11.326566  4221 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0409 23:38:15.510118  4221 solver.cpp:218] Iteration 6540 (2.86849 iter/s, 4.18339s/12 iters), loss = 0.290943
I0409 23:38:15.510180  4221 solver.cpp:237]     Train net output #0: loss = 0.290943 (* 1 = 0.290943 loss)
I0409 23:38:15.510193  4221 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0409 23:38:20.325500  4221 solver.cpp:218] Iteration 6552 (2.49215 iter/s, 4.81511s/12 iters), loss = 0.223807
I0409 23:38:20.325546  4221 solver.cpp:237]     Train net output #0: loss = 0.223807 (* 1 = 0.223807 loss)
I0409 23:38:20.325554  4221 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0409 23:38:25.207254  4221 solver.cpp:218] Iteration 6564 (2.45826 iter/s, 4.88149s/12 iters), loss = 0.193312
I0409 23:38:25.207350  4221 solver.cpp:237]     Train net output #0: loss = 0.193312 (* 1 = 0.193312 loss)
I0409 23:38:25.207360  4221 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0409 23:38:29.287674  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:38:30.039460  4221 solver.cpp:218] Iteration 6576 (2.48349 iter/s, 4.83191s/12 iters), loss = 0.228068
I0409 23:38:30.039502  4221 solver.cpp:237]     Train net output #0: loss = 0.228068 (* 1 = 0.228068 loss)
I0409 23:38:30.039511  4221 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0409 23:38:34.888108  4221 solver.cpp:218] Iteration 6588 (2.47505 iter/s, 4.84839s/12 iters), loss = 0.309051
I0409 23:38:34.888166  4221 solver.cpp:237]     Train net output #0: loss = 0.309051 (* 1 = 0.309051 loss)
I0409 23:38:34.888212  4221 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0409 23:38:39.670137  4221 solver.cpp:218] Iteration 6600 (2.50953 iter/s, 4.78176s/12 iters), loss = 0.272748
I0409 23:38:39.670193  4221 solver.cpp:237]     Train net output #0: loss = 0.272748 (* 1 = 0.272748 loss)
I0409 23:38:39.670204  4221 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0409 23:38:44.481144  4221 solver.cpp:218] Iteration 6612 (2.49442 iter/s, 4.81074s/12 iters), loss = 0.280012
I0409 23:38:44.481206  4221 solver.cpp:237]     Train net output #0: loss = 0.280012 (* 1 = 0.280012 loss)
I0409 23:38:44.481220  4221 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0409 23:38:49.290405  4221 solver.cpp:218] Iteration 6624 (2.49533 iter/s, 4.80899s/12 iters), loss = 0.226441
I0409 23:38:49.290458  4221 solver.cpp:237]     Train net output #0: loss = 0.226441 (* 1 = 0.226441 loss)
I0409 23:38:49.290468  4221 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0409 23:38:51.259810  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0409 23:38:51.912070  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0409 23:38:52.390146  4221 solver.cpp:330] Iteration 6630, Testing net (#0)
I0409 23:38:52.390170  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:38:54.217698  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:38:56.809201  4221 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0409 23:38:56.809361  4221 solver.cpp:397]     Test net output #1: loss = 3.61159 (* 1 = 3.61159 loss)
I0409 23:38:58.726001  4221 solver.cpp:218] Iteration 6636 (1.27185 iter/s, 9.43511s/12 iters), loss = 0.258938
I0409 23:38:58.726056  4221 solver.cpp:237]     Train net output #0: loss = 0.258938 (* 1 = 0.258938 loss)
I0409 23:38:58.726068  4221 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0409 23:39:03.589568  4221 solver.cpp:218] Iteration 6648 (2.46746 iter/s, 4.8633s/12 iters), loss = 0.16392
I0409 23:39:03.589617  4221 solver.cpp:237]     Train net output #0: loss = 0.16392 (* 1 = 0.16392 loss)
I0409 23:39:03.589625  4221 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0409 23:39:08.423852  4221 solver.cpp:218] Iteration 6660 (2.4824 iter/s, 4.83403s/12 iters), loss = 0.263547
I0409 23:39:08.423904  4221 solver.cpp:237]     Train net output #0: loss = 0.263547 (* 1 = 0.263547 loss)
I0409 23:39:08.423918  4221 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0409 23:39:13.378902  4221 solver.cpp:218] Iteration 6672 (2.4219 iter/s, 4.95479s/12 iters), loss = 0.208284
I0409 23:39:13.378957  4221 solver.cpp:237]     Train net output #0: loss = 0.208285 (* 1 = 0.208285 loss)
I0409 23:39:13.378969  4221 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0409 23:39:14.839071  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:39:18.385510  4221 solver.cpp:218] Iteration 6684 (2.39696 iter/s, 5.00634s/12 iters), loss = 0.25295
I0409 23:39:18.385567  4221 solver.cpp:237]     Train net output #0: loss = 0.25295 (* 1 = 0.25295 loss)
I0409 23:39:18.385579  4221 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0409 23:39:23.149088  4221 solver.cpp:218] Iteration 6696 (2.51925 iter/s, 4.76332s/12 iters), loss = 0.370788
I0409 23:39:23.149142  4221 solver.cpp:237]     Train net output #0: loss = 0.370788 (* 1 = 0.370788 loss)
I0409 23:39:23.149155  4221 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0409 23:39:27.962358  4221 solver.cpp:218] Iteration 6708 (2.49324 iter/s, 4.81301s/12 iters), loss = 0.236746
I0409 23:39:27.962458  4221 solver.cpp:237]     Train net output #0: loss = 0.236746 (* 1 = 0.236746 loss)
I0409 23:39:27.962468  4221 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0409 23:39:32.781020  4221 solver.cpp:218] Iteration 6720 (2.49048 iter/s, 4.81836s/12 iters), loss = 0.238688
I0409 23:39:32.781073  4221 solver.cpp:237]     Train net output #0: loss = 0.238688 (* 1 = 0.238688 loss)
I0409 23:39:32.781085  4221 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0409 23:39:37.157251  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0409 23:39:37.869452  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0409 23:39:38.374557  4221 solver.cpp:330] Iteration 6732, Testing net (#0)
I0409 23:39:38.374579  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:39:40.194969  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:39:42.945350  4221 solver.cpp:397]     Test net output #0: accuracy = 0.41973
I0409 23:39:42.945391  4221 solver.cpp:397]     Test net output #1: loss = 3.70581 (* 1 = 3.70581 loss)
I0409 23:39:43.028525  4221 solver.cpp:218] Iteration 6732 (1.17107 iter/s, 10.247s/12 iters), loss = 0.195334
I0409 23:39:43.028578  4221 solver.cpp:237]     Train net output #0: loss = 0.195334 (* 1 = 0.195334 loss)
I0409 23:39:43.028587  4221 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0409 23:39:47.099953  4221 solver.cpp:218] Iteration 6744 (2.94754 iter/s, 4.07119s/12 iters), loss = 0.254974
I0409 23:39:47.100010  4221 solver.cpp:237]     Train net output #0: loss = 0.254974 (* 1 = 0.254974 loss)
I0409 23:39:47.100023  4221 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0409 23:39:51.896620  4221 solver.cpp:218] Iteration 6756 (2.50188 iter/s, 4.7964s/12 iters), loss = 0.256197
I0409 23:39:51.896668  4221 solver.cpp:237]     Train net output #0: loss = 0.256197 (* 1 = 0.256197 loss)
I0409 23:39:51.896677  4221 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0409 23:39:56.732770  4221 solver.cpp:218] Iteration 6768 (2.48145 iter/s, 4.83589s/12 iters), loss = 0.156292
I0409 23:39:56.732828  4221 solver.cpp:237]     Train net output #0: loss = 0.156292 (* 1 = 0.156292 loss)
I0409 23:39:56.732841  4221 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0409 23:40:00.076810  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:40:01.541324  4221 solver.cpp:218] Iteration 6780 (2.49569 iter/s, 4.80828s/12 iters), loss = 0.302449
I0409 23:40:01.541384  4221 solver.cpp:237]     Train net output #0: loss = 0.302449 (* 1 = 0.302449 loss)
I0409 23:40:01.541396  4221 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0409 23:40:06.422123  4221 solver.cpp:218] Iteration 6792 (2.45875 iter/s, 4.88053s/12 iters), loss = 0.158738
I0409 23:40:06.422183  4221 solver.cpp:237]     Train net output #0: loss = 0.158738 (* 1 = 0.158738 loss)
I0409 23:40:06.422195  4221 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0409 23:40:11.212625  4221 solver.cpp:218] Iteration 6804 (2.5051 iter/s, 4.79023s/12 iters), loss = 0.305778
I0409 23:40:11.212698  4221 solver.cpp:237]     Train net output #0: loss = 0.305778 (* 1 = 0.305778 loss)
I0409 23:40:11.212716  4221 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0409 23:40:16.026161  4221 solver.cpp:218] Iteration 6816 (2.49311 iter/s, 4.81326s/12 iters), loss = 0.186738
I0409 23:40:16.026219  4221 solver.cpp:237]     Train net output #0: loss = 0.186739 (* 1 = 0.186739 loss)
I0409 23:40:16.026232  4221 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0409 23:40:20.875917  4221 solver.cpp:218] Iteration 6828 (2.47449 iter/s, 4.84949s/12 iters), loss = 0.22517
I0409 23:40:20.875962  4221 solver.cpp:237]     Train net output #0: loss = 0.22517 (* 1 = 0.22517 loss)
I0409 23:40:20.875970  4221 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0409 23:40:22.843765  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0409 23:40:23.549625  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0409 23:40:24.051388  4221 solver.cpp:330] Iteration 6834, Testing net (#0)
I0409 23:40:24.051419  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:40:25.826551  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:40:28.678952  4221 solver.cpp:397]     Test net output #0: accuracy = 0.413603
I0409 23:40:28.679003  4221 solver.cpp:397]     Test net output #1: loss = 3.50285 (* 1 = 3.50285 loss)
I0409 23:40:30.417933  4221 solver.cpp:218] Iteration 6840 (1.25765 iter/s, 9.54157s/12 iters), loss = 0.170048
I0409 23:40:30.418061  4221 solver.cpp:237]     Train net output #0: loss = 0.170048 (* 1 = 0.170048 loss)
I0409 23:40:30.418074  4221 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0409 23:40:35.524590  4221 solver.cpp:218] Iteration 6852 (2.35003 iter/s, 5.10631s/12 iters), loss = 0.223947
I0409 23:40:35.524650  4221 solver.cpp:237]     Train net output #0: loss = 0.223947 (* 1 = 0.223947 loss)
I0409 23:40:35.524663  4221 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0409 23:40:40.356159  4221 solver.cpp:218] Iteration 6864 (2.4838 iter/s, 4.83131s/12 iters), loss = 0.219684
I0409 23:40:40.356200  4221 solver.cpp:237]     Train net output #0: loss = 0.219684 (* 1 = 0.219684 loss)
I0409 23:40:40.356209  4221 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0409 23:40:45.145036  4221 solver.cpp:218] Iteration 6876 (2.50594 iter/s, 4.78863s/12 iters), loss = 0.24368
I0409 23:40:45.145123  4221 solver.cpp:237]     Train net output #0: loss = 0.24368 (* 1 = 0.24368 loss)
I0409 23:40:45.145133  4221 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0409 23:40:45.742604  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:40:49.944075  4221 solver.cpp:218] Iteration 6888 (2.50065 iter/s, 4.79874s/12 iters), loss = 0.128262
I0409 23:40:49.944131  4221 solver.cpp:237]     Train net output #0: loss = 0.128262 (* 1 = 0.128262 loss)
I0409 23:40:49.944144  4221 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0409 23:40:54.977398  4221 solver.cpp:218] Iteration 6900 (2.38424 iter/s, 5.03305s/12 iters), loss = 0.19551
I0409 23:40:54.977450  4221 solver.cpp:237]     Train net output #0: loss = 0.19551 (* 1 = 0.19551 loss)
I0409 23:40:54.977461  4221 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0409 23:40:59.876482  4221 solver.cpp:218] Iteration 6912 (2.44957 iter/s, 4.89882s/12 iters), loss = 0.342614
I0409 23:40:59.876541  4221 solver.cpp:237]     Train net output #0: loss = 0.342614 (* 1 = 0.342614 loss)
I0409 23:40:59.876554  4221 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0409 23:41:04.750459  4221 solver.cpp:218] Iteration 6924 (2.46219 iter/s, 4.87371s/12 iters), loss = 0.257678
I0409 23:41:04.750591  4221 solver.cpp:237]     Train net output #0: loss = 0.257678 (* 1 = 0.257678 loss)
I0409 23:41:04.750603  4221 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0409 23:41:09.194020  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0409 23:41:10.028283  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0409 23:41:11.819867  4221 solver.cpp:330] Iteration 6936, Testing net (#0)
I0409 23:41:11.819897  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:41:12.187919  4221 blocking_queue.cpp:49] Waiting for data
I0409 23:41:13.563094  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:41:16.396059  4221 solver.cpp:397]     Test net output #0: accuracy = 0.428309
I0409 23:41:16.396108  4221 solver.cpp:397]     Test net output #1: loss = 3.5908 (* 1 = 3.5908 loss)
I0409 23:41:16.477499  4221 solver.cpp:218] Iteration 6936 (1.02333 iter/s, 11.7264s/12 iters), loss = 0.216227
I0409 23:41:16.477558  4221 solver.cpp:237]     Train net output #0: loss = 0.216227 (* 1 = 0.216227 loss)
I0409 23:41:16.477569  4221 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0409 23:41:20.562753  4221 solver.cpp:218] Iteration 6948 (2.93756 iter/s, 4.08502s/12 iters), loss = 0.296967
I0409 23:41:20.562803  4221 solver.cpp:237]     Train net output #0: loss = 0.296967 (* 1 = 0.296967 loss)
I0409 23:41:20.562813  4221 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0409 23:41:25.364877  4221 solver.cpp:218] Iteration 6960 (2.49903 iter/s, 4.80187s/12 iters), loss = 0.132421
I0409 23:41:25.364933  4221 solver.cpp:237]     Train net output #0: loss = 0.132421 (* 1 = 0.132421 loss)
I0409 23:41:25.364946  4221 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0409 23:41:30.207470  4221 solver.cpp:218] Iteration 6972 (2.47815 iter/s, 4.84232s/12 iters), loss = 0.20033
I0409 23:41:30.207530  4221 solver.cpp:237]     Train net output #0: loss = 0.20033 (* 1 = 0.20033 loss)
I0409 23:41:30.207542  4221 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0409 23:41:33.069409  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:41:35.279556  4221 solver.cpp:218] Iteration 6984 (2.36602 iter/s, 5.0718s/12 iters), loss = 0.149064
I0409 23:41:35.279976  4221 solver.cpp:237]     Train net output #0: loss = 0.149064 (* 1 = 0.149064 loss)
I0409 23:41:35.279992  4221 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0409 23:41:40.169189  4221 solver.cpp:218] Iteration 6996 (2.45449 iter/s, 4.88901s/12 iters), loss = 0.264527
I0409 23:41:40.169246  4221 solver.cpp:237]     Train net output #0: loss = 0.264527 (* 1 = 0.264527 loss)
I0409 23:41:40.169258  4221 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0409 23:41:45.105660  4221 solver.cpp:218] Iteration 7008 (2.43102 iter/s, 4.9362s/12 iters), loss = 0.27411
I0409 23:41:45.105707  4221 solver.cpp:237]     Train net output #0: loss = 0.27411 (* 1 = 0.27411 loss)
I0409 23:41:45.105717  4221 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0409 23:41:50.124558  4221 solver.cpp:218] Iteration 7020 (2.39109 iter/s, 5.01863s/12 iters), loss = 0.228096
I0409 23:41:50.124609  4221 solver.cpp:237]     Train net output #0: loss = 0.228096 (* 1 = 0.228096 loss)
I0409 23:41:50.124619  4221 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0409 23:41:54.987407  4221 solver.cpp:218] Iteration 7032 (2.46782 iter/s, 4.86259s/12 iters), loss = 0.197289
I0409 23:41:54.987460  4221 solver.cpp:237]     Train net output #0: loss = 0.197289 (* 1 = 0.197289 loss)
I0409 23:41:54.987473  4221 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0409 23:41:56.947278  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0409 23:41:57.657882  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0409 23:41:58.145700  4221 solver.cpp:330] Iteration 7038, Testing net (#0)
I0409 23:41:58.145722  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:41:59.702132  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:42:02.517629  4221 solver.cpp:397]     Test net output #0: accuracy = 0.423407
I0409 23:42:02.517674  4221 solver.cpp:397]     Test net output #1: loss = 3.58667 (* 1 = 3.58667 loss)
I0409 23:42:04.226073  4221 solver.cpp:218] Iteration 7044 (1.29895 iter/s, 9.23823s/12 iters), loss = 0.0856219
I0409 23:42:04.226123  4221 solver.cpp:237]     Train net output #0: loss = 0.0856219 (* 1 = 0.0856219 loss)
I0409 23:42:04.226133  4221 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0409 23:42:09.125373  4221 solver.cpp:218] Iteration 7056 (2.44946 iter/s, 4.89904s/12 iters), loss = 0.222594
I0409 23:42:09.125546  4221 solver.cpp:237]     Train net output #0: loss = 0.222594 (* 1 = 0.222594 loss)
I0409 23:42:09.125560  4221 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0409 23:42:13.988792  4221 solver.cpp:218] Iteration 7068 (2.46759 iter/s, 4.86304s/12 iters), loss = 0.203515
I0409 23:42:13.988847  4221 solver.cpp:237]     Train net output #0: loss = 0.203515 (* 1 = 0.203515 loss)
I0409 23:42:13.988858  4221 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0409 23:42:18.698956  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:42:18.800647  4221 solver.cpp:218] Iteration 7080 (2.49398 iter/s, 4.8116s/12 iters), loss = 0.195622
I0409 23:42:18.800689  4221 solver.cpp:237]     Train net output #0: loss = 0.195622 (* 1 = 0.195622 loss)
I0409 23:42:18.800698  4221 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0409 23:42:23.703722  4221 solver.cpp:218] Iteration 7092 (2.44757 iter/s, 4.90282s/12 iters), loss = 0.187305
I0409 23:42:23.703776  4221 solver.cpp:237]     Train net output #0: loss = 0.187305 (* 1 = 0.187305 loss)
I0409 23:42:23.703788  4221 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0409 23:42:28.551427  4221 solver.cpp:218] Iteration 7104 (2.47553 iter/s, 4.84744s/12 iters), loss = 0.207072
I0409 23:42:28.551474  4221 solver.cpp:237]     Train net output #0: loss = 0.207072 (* 1 = 0.207072 loss)
I0409 23:42:28.551483  4221 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0409 23:42:33.513514  4221 solver.cpp:218] Iteration 7116 (2.41847 iter/s, 4.96182s/12 iters), loss = 0.25504
I0409 23:42:33.513564  4221 solver.cpp:237]     Train net output #0: loss = 0.25504 (* 1 = 0.25504 loss)
I0409 23:42:33.513576  4221 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0409 23:42:38.515635  4221 solver.cpp:218] Iteration 7128 (2.39911 iter/s, 5.00186s/12 iters), loss = 0.150411
I0409 23:42:38.515687  4221 solver.cpp:237]     Train net output #0: loss = 0.150411 (* 1 = 0.150411 loss)
I0409 23:42:38.515700  4221 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0409 23:42:43.041154  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0409 23:42:43.789930  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0409 23:42:44.903354  4221 solver.cpp:330] Iteration 7140, Testing net (#0)
I0409 23:42:44.903380  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:42:46.575174  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:42:49.469573  4221 solver.cpp:397]     Test net output #0: accuracy = 0.425245
I0409 23:42:49.469621  4221 solver.cpp:397]     Test net output #1: loss = 3.58864 (* 1 = 3.58864 loss)
I0409 23:42:49.551988  4221 solver.cpp:218] Iteration 7140 (1.08737 iter/s, 11.0358s/12 iters), loss = 0.295782
I0409 23:42:49.552045  4221 solver.cpp:237]     Train net output #0: loss = 0.295782 (* 1 = 0.295782 loss)
I0409 23:42:49.552057  4221 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0409 23:42:53.760305  4221 solver.cpp:218] Iteration 7152 (2.85166 iter/s, 4.20807s/12 iters), loss = 0.241706
I0409 23:42:53.760363  4221 solver.cpp:237]     Train net output #0: loss = 0.241706 (* 1 = 0.241706 loss)
I0409 23:42:53.760375  4221 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0409 23:42:58.580034  4221 solver.cpp:218] Iteration 7164 (2.4899 iter/s, 4.81947s/12 iters), loss = 0.26219
I0409 23:42:58.580080  4221 solver.cpp:237]     Train net output #0: loss = 0.26219 (* 1 = 0.26219 loss)
I0409 23:42:58.580090  4221 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0409 23:43:03.523739  4221 solver.cpp:218] Iteration 7176 (2.42746 iter/s, 4.94345s/12 iters), loss = 0.119598
I0409 23:43:03.523780  4221 solver.cpp:237]     Train net output #0: loss = 0.119598 (* 1 = 0.119598 loss)
I0409 23:43:03.523789  4221 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0409 23:43:05.538329  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:43:08.306746  4221 solver.cpp:218] Iteration 7188 (2.50902 iter/s, 4.78275s/12 iters), loss = 0.209205
I0409 23:43:08.306803  4221 solver.cpp:237]     Train net output #0: loss = 0.209205 (* 1 = 0.209205 loss)
I0409 23:43:08.306816  4221 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0409 23:43:13.122157  4221 solver.cpp:218] Iteration 7200 (2.49214 iter/s, 4.81515s/12 iters), loss = 0.11195
I0409 23:43:13.122318  4221 solver.cpp:237]     Train net output #0: loss = 0.11195 (* 1 = 0.11195 loss)
I0409 23:43:13.122331  4221 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0409 23:43:17.964982  4221 solver.cpp:218] Iteration 7212 (2.47808 iter/s, 4.84245s/12 iters), loss = 0.154869
I0409 23:43:17.965040  4221 solver.cpp:237]     Train net output #0: loss = 0.154869 (* 1 = 0.154869 loss)
I0409 23:43:17.965052  4221 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0409 23:43:22.784576  4221 solver.cpp:218] Iteration 7224 (2.48998 iter/s, 4.81932s/12 iters), loss = 0.190166
I0409 23:43:22.784638  4221 solver.cpp:237]     Train net output #0: loss = 0.190166 (* 1 = 0.190166 loss)
I0409 23:43:22.784651  4221 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0409 23:43:27.636211  4221 solver.cpp:218] Iteration 7236 (2.47353 iter/s, 4.85136s/12 iters), loss = 0.18287
I0409 23:43:27.636274  4221 solver.cpp:237]     Train net output #0: loss = 0.18287 (* 1 = 0.18287 loss)
I0409 23:43:27.636287  4221 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0409 23:43:29.611148  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0409 23:43:32.021302  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0409 23:43:32.515560  4221 solver.cpp:330] Iteration 7242, Testing net (#0)
I0409 23:43:32.515583  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:43:34.129616  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:43:37.019914  4221 solver.cpp:397]     Test net output #0: accuracy = 0.431373
I0409 23:43:37.019944  4221 solver.cpp:397]     Test net output #1: loss = 3.62476 (* 1 = 3.62476 loss)
I0409 23:43:38.855073  4221 solver.cpp:218] Iteration 7248 (1.06968 iter/s, 11.2183s/12 iters), loss = 0.230446
I0409 23:43:38.855139  4221 solver.cpp:237]     Train net output #0: loss = 0.230446 (* 1 = 0.230446 loss)
I0409 23:43:38.855152  4221 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0409 23:43:43.734396  4221 solver.cpp:218] Iteration 7260 (2.4595 iter/s, 4.87904s/12 iters), loss = 0.174228
I0409 23:43:43.734570  4221 solver.cpp:237]     Train net output #0: loss = 0.174228 (* 1 = 0.174228 loss)
I0409 23:43:43.734587  4221 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0409 23:43:48.607103  4221 solver.cpp:218] Iteration 7272 (2.46288 iter/s, 4.87234s/12 iters), loss = 0.178889
I0409 23:43:48.607141  4221 solver.cpp:237]     Train net output #0: loss = 0.178889 (* 1 = 0.178889 loss)
I0409 23:43:48.607148  4221 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0409 23:43:52.746004  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:43:53.477151  4221 solver.cpp:218] Iteration 7284 (2.46417 iter/s, 4.8698s/12 iters), loss = 0.210989
I0409 23:43:53.477196  4221 solver.cpp:237]     Train net output #0: loss = 0.210989 (* 1 = 0.210989 loss)
I0409 23:43:53.477206  4221 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0409 23:43:58.300009  4221 solver.cpp:218] Iteration 7296 (2.48828 iter/s, 4.8226s/12 iters), loss = 0.158258
I0409 23:43:58.300055  4221 solver.cpp:237]     Train net output #0: loss = 0.158258 (* 1 = 0.158258 loss)
I0409 23:43:58.300065  4221 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0409 23:44:03.087886  4221 solver.cpp:218] Iteration 7308 (2.50646 iter/s, 4.78762s/12 iters), loss = 0.22389
I0409 23:44:03.087944  4221 solver.cpp:237]     Train net output #0: loss = 0.22389 (* 1 = 0.22389 loss)
I0409 23:44:03.087956  4221 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0409 23:44:07.886925  4221 solver.cpp:218] Iteration 7320 (2.50064 iter/s, 4.79877s/12 iters), loss = 0.138757
I0409 23:44:07.886981  4221 solver.cpp:237]     Train net output #0: loss = 0.138757 (* 1 = 0.138757 loss)
I0409 23:44:07.886994  4221 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0409 23:44:12.702056  4221 solver.cpp:218] Iteration 7332 (2.49228 iter/s, 4.81487s/12 iters), loss = 0.141361
I0409 23:44:12.702111  4221 solver.cpp:237]     Train net output #0: loss = 0.141361 (* 1 = 0.141361 loss)
I0409 23:44:12.702123  4221 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0409 23:44:17.059878  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0409 23:44:19.444988  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0409 23:44:21.853157  4221 solver.cpp:330] Iteration 7344, Testing net (#0)
I0409 23:44:21.853181  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:44:23.397946  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:44:26.266232  4221 solver.cpp:397]     Test net output #0: accuracy = 0.431985
I0409 23:44:26.266278  4221 solver.cpp:397]     Test net output #1: loss = 3.55367 (* 1 = 3.55367 loss)
I0409 23:44:26.349520  4221 solver.cpp:218] Iteration 7344 (0.879324 iter/s, 13.6468s/12 iters), loss = 0.216986
I0409 23:44:26.349575  4221 solver.cpp:237]     Train net output #0: loss = 0.216986 (* 1 = 0.216986 loss)
I0409 23:44:26.349587  4221 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0409 23:44:30.449719  4221 solver.cpp:218] Iteration 7356 (2.92685 iter/s, 4.09997s/12 iters), loss = 0.146443
I0409 23:44:30.449767  4221 solver.cpp:237]     Train net output #0: loss = 0.146443 (* 1 = 0.146443 loss)
I0409 23:44:30.449776  4221 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0409 23:44:35.326414  4221 solver.cpp:218] Iteration 7368 (2.46081 iter/s, 4.87643s/12 iters), loss = 0.145381
I0409 23:44:35.326470  4221 solver.cpp:237]     Train net output #0: loss = 0.145381 (* 1 = 0.145381 loss)
I0409 23:44:35.326483  4221 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0409 23:44:40.161522  4221 solver.cpp:218] Iteration 7380 (2.48198 iter/s, 4.83484s/12 iters), loss = 0.110986
I0409 23:44:40.161581  4221 solver.cpp:237]     Train net output #0: loss = 0.110986 (* 1 = 0.110986 loss)
I0409 23:44:40.161593  4221 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0409 23:44:41.521893  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:44:45.038826  4221 solver.cpp:218] Iteration 7392 (2.46051 iter/s, 4.87703s/12 iters), loss = 0.198342
I0409 23:44:45.038887  4221 solver.cpp:237]     Train net output #0: loss = 0.198342 (* 1 = 0.198342 loss)
I0409 23:44:45.038902  4221 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0409 23:44:49.919385  4221 solver.cpp:218] Iteration 7404 (2.45887 iter/s, 4.88029s/12 iters), loss = 0.131322
I0409 23:44:49.919495  4221 solver.cpp:237]     Train net output #0: loss = 0.131322 (* 1 = 0.131322 loss)
I0409 23:44:49.919507  4221 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0409 23:44:54.992954  4221 solver.cpp:218] Iteration 7416 (2.36535 iter/s, 5.07324s/12 iters), loss = 0.18395
I0409 23:44:54.993003  4221 solver.cpp:237]     Train net output #0: loss = 0.18395 (* 1 = 0.18395 loss)
I0409 23:44:54.993013  4221 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0409 23:44:59.935809  4221 solver.cpp:218] Iteration 7428 (2.42788 iter/s, 4.94259s/12 iters), loss = 0.228775
I0409 23:44:59.935863  4221 solver.cpp:237]     Train net output #0: loss = 0.228775 (* 1 = 0.228775 loss)
I0409 23:44:59.935874  4221 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0409 23:45:04.857369  4221 solver.cpp:218] Iteration 7440 (2.43838 iter/s, 4.9213s/12 iters), loss = 0.100797
I0409 23:45:04.857411  4221 solver.cpp:237]     Train net output #0: loss = 0.100797 (* 1 = 0.100797 loss)
I0409 23:45:04.857420  4221 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0409 23:45:06.859800  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0409 23:45:07.543231  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0409 23:45:08.042204  4221 solver.cpp:330] Iteration 7446, Testing net (#0)
I0409 23:45:08.042235  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:45:09.585579  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:45:12.501632  4221 solver.cpp:397]     Test net output #0: accuracy = 0.436274
I0409 23:45:12.501689  4221 solver.cpp:397]     Test net output #1: loss = 3.57592 (* 1 = 3.57592 loss)
I0409 23:45:14.450847  4221 solver.cpp:218] Iteration 7452 (1.25091 iter/s, 9.59304s/12 iters), loss = 0.154159
I0409 23:45:14.450893  4221 solver.cpp:237]     Train net output #0: loss = 0.154159 (* 1 = 0.154159 loss)
I0409 23:45:14.450904  4221 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0409 23:45:19.335875  4221 solver.cpp:218] Iteration 7464 (2.45661 iter/s, 4.88477s/12 iters), loss = 0.266225
I0409 23:45:19.335924  4221 solver.cpp:237]     Train net output #0: loss = 0.266225 (* 1 = 0.266225 loss)
I0409 23:45:19.335934  4221 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0409 23:45:24.197726  4221 solver.cpp:218] Iteration 7476 (2.46833 iter/s, 4.86159s/12 iters), loss = 0.172313
I0409 23:45:24.197855  4221 solver.cpp:237]     Train net output #0: loss = 0.172313 (* 1 = 0.172313 loss)
I0409 23:45:24.197870  4221 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0409 23:45:27.690956  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:45:29.134011  4221 solver.cpp:218] Iteration 7488 (2.43115 iter/s, 4.93594s/12 iters), loss = 0.197218
I0409 23:45:29.134070  4221 solver.cpp:237]     Train net output #0: loss = 0.197218 (* 1 = 0.197218 loss)
I0409 23:45:29.134090  4221 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0409 23:45:34.000396  4221 solver.cpp:218] Iteration 7500 (2.46603 iter/s, 4.86611s/12 iters), loss = 0.154208
I0409 23:45:34.000452  4221 solver.cpp:237]     Train net output #0: loss = 0.154208 (* 1 = 0.154208 loss)
I0409 23:45:34.000465  4221 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0409 23:45:38.888743  4221 solver.cpp:218] Iteration 7512 (2.45495 iter/s, 4.88808s/12 iters), loss = 0.152351
I0409 23:45:38.888794  4221 solver.cpp:237]     Train net output #0: loss = 0.152351 (* 1 = 0.152351 loss)
I0409 23:45:38.888804  4221 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0409 23:45:43.756574  4221 solver.cpp:218] Iteration 7524 (2.4653 iter/s, 4.86757s/12 iters), loss = 0.135477
I0409 23:45:43.756633  4221 solver.cpp:237]     Train net output #0: loss = 0.135477 (* 1 = 0.135477 loss)
I0409 23:45:43.756646  4221 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0409 23:45:48.830137  4221 solver.cpp:218] Iteration 7536 (2.36533 iter/s, 5.07329s/12 iters), loss = 0.315345
I0409 23:45:48.830181  4221 solver.cpp:237]     Train net output #0: loss = 0.315345 (* 1 = 0.315345 loss)
I0409 23:45:48.830190  4221 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0409 23:45:53.279167  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0409 23:45:53.973368  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0409 23:45:54.457132  4221 solver.cpp:330] Iteration 7548, Testing net (#0)
I0409 23:45:54.457242  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:45:55.819618  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:45:59.031566  4221 solver.cpp:397]     Test net output #0: accuracy = 0.43076
I0409 23:45:59.031602  4221 solver.cpp:397]     Test net output #1: loss = 3.62098 (* 1 = 3.62098 loss)
I0409 23:45:59.115063  4221 solver.cpp:218] Iteration 7548 (1.16681 iter/s, 10.2844s/12 iters), loss = 0.209072
I0409 23:45:59.115146  4221 solver.cpp:237]     Train net output #0: loss = 0.209072 (* 1 = 0.209072 loss)
I0409 23:45:59.115159  4221 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0409 23:46:03.265357  4221 solver.cpp:218] Iteration 7560 (2.89155 iter/s, 4.15003s/12 iters), loss = 0.134265
I0409 23:46:03.265421  4221 solver.cpp:237]     Train net output #0: loss = 0.134265 (* 1 = 0.134265 loss)
I0409 23:46:03.265435  4221 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0409 23:46:08.065549  4221 solver.cpp:218] Iteration 7572 (2.50004 iter/s, 4.79993s/12 iters), loss = 0.132997
I0409 23:46:08.065603  4221 solver.cpp:237]     Train net output #0: loss = 0.132997 (* 1 = 0.132997 loss)
I0409 23:46:08.065613  4221 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0409 23:46:12.882725  4221 solver.cpp:218] Iteration 7584 (2.49122 iter/s, 4.81691s/12 iters), loss = 0.11544
I0409 23:46:12.882787  4221 solver.cpp:237]     Train net output #0: loss = 0.11544 (* 1 = 0.11544 loss)
I0409 23:46:12.882800  4221 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0409 23:46:13.508564  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:46:17.697394  4221 solver.cpp:218] Iteration 7596 (2.49252 iter/s, 4.8144s/12 iters), loss = 0.140876
I0409 23:46:17.697435  4221 solver.cpp:237]     Train net output #0: loss = 0.140876 (* 1 = 0.140876 loss)
I0409 23:46:17.697443  4221 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0409 23:46:22.602507  4221 solver.cpp:218] Iteration 7608 (2.44655 iter/s, 4.90486s/12 iters), loss = 0.200055
I0409 23:46:22.602562  4221 solver.cpp:237]     Train net output #0: loss = 0.200055 (* 1 = 0.200055 loss)
I0409 23:46:22.602573  4221 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0409 23:46:27.420639  4221 solver.cpp:218] Iteration 7620 (2.49073 iter/s, 4.81787s/12 iters), loss = 0.200589
I0409 23:46:27.420743  4221 solver.cpp:237]     Train net output #0: loss = 0.200589 (* 1 = 0.200589 loss)
I0409 23:46:27.420754  4221 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0409 23:46:28.552729  4221 blocking_queue.cpp:49] Waiting for data
I0409 23:46:32.223140  4221 solver.cpp:218] Iteration 7632 (2.49886 iter/s, 4.80219s/12 iters), loss = 0.18504
I0409 23:46:32.223189  4221 solver.cpp:237]     Train net output #0: loss = 0.18504 (* 1 = 0.18504 loss)
I0409 23:46:32.223201  4221 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0409 23:46:37.046639  4221 solver.cpp:218] Iteration 7644 (2.48796 iter/s, 4.82324s/12 iters), loss = 0.154344
I0409 23:46:37.046697  4221 solver.cpp:237]     Train net output #0: loss = 0.154344 (* 1 = 0.154344 loss)
I0409 23:46:37.046710  4221 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0409 23:46:39.001904  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0409 23:46:39.660601  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0409 23:46:40.140445  4221 solver.cpp:330] Iteration 7650, Testing net (#0)
I0409 23:46:40.140470  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:46:41.474118  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:46:44.565770  4221 solver.cpp:397]     Test net output #0: accuracy = 0.4375
I0409 23:46:44.565816  4221 solver.cpp:397]     Test net output #1: loss = 3.51767 (* 1 = 3.51767 loss)
I0409 23:46:46.396759  4221 solver.cpp:218] Iteration 7656 (1.28347 iter/s, 9.34968s/12 iters), loss = 0.136833
I0409 23:46:46.396806  4221 solver.cpp:237]     Train net output #0: loss = 0.136833 (* 1 = 0.136833 loss)
I0409 23:46:46.396817  4221 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0409 23:46:51.210040  4221 solver.cpp:218] Iteration 7668 (2.49324 iter/s, 4.81302s/12 iters), loss = 0.144081
I0409 23:46:51.210098  4221 solver.cpp:237]     Train net output #0: loss = 0.144081 (* 1 = 0.144081 loss)
I0409 23:46:51.210110  4221 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0409 23:46:56.000118  4221 solver.cpp:218] Iteration 7680 (2.50532 iter/s, 4.78982s/12 iters), loss = 0.118507
I0409 23:46:56.000164  4221 solver.cpp:237]     Train net output #0: loss = 0.118507 (* 1 = 0.118507 loss)
I0409 23:46:56.000171  4221 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0409 23:46:58.689349  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:47:00.838258  4221 solver.cpp:218] Iteration 7692 (2.48043 iter/s, 4.83788s/12 iters), loss = 0.132491
I0409 23:47:00.838315  4221 solver.cpp:237]     Train net output #0: loss = 0.132491 (* 1 = 0.132491 loss)
I0409 23:47:00.838327  4221 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0409 23:47:05.701223  4221 solver.cpp:218] Iteration 7704 (2.46777 iter/s, 4.8627s/12 iters), loss = 0.188354
I0409 23:47:05.701274  4221 solver.cpp:237]     Train net output #0: loss = 0.188354 (* 1 = 0.188354 loss)
I0409 23:47:05.701287  4221 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0409 23:47:10.522553  4221 solver.cpp:218] Iteration 7716 (2.48908 iter/s, 4.82107s/12 iters), loss = 0.21645
I0409 23:47:10.522614  4221 solver.cpp:237]     Train net output #0: loss = 0.21645 (* 1 = 0.21645 loss)
I0409 23:47:10.522624  4221 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0409 23:47:15.341753  4221 solver.cpp:218] Iteration 7728 (2.49018 iter/s, 4.81893s/12 iters), loss = 0.145048
I0409 23:47:15.341800  4221 solver.cpp:237]     Train net output #0: loss = 0.145048 (* 1 = 0.145048 loss)
I0409 23:47:15.341810  4221 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0409 23:47:20.147509  4221 solver.cpp:218] Iteration 7740 (2.49714 iter/s, 4.80549s/12 iters), loss = 0.189418
I0409 23:47:20.147570  4221 solver.cpp:237]     Train net output #0: loss = 0.189418 (* 1 = 0.189418 loss)
I0409 23:47:20.147581  4221 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0409 23:47:24.943166  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0409 23:47:25.684469  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0409 23:47:26.178691  4221 solver.cpp:330] Iteration 7752, Testing net (#0)
I0409 23:47:26.178717  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:47:27.512080  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:47:30.566226  4221 solver.cpp:397]     Test net output #0: accuracy = 0.439338
I0409 23:47:30.568657  4221 solver.cpp:397]     Test net output #1: loss = 3.58564 (* 1 = 3.58564 loss)
I0409 23:47:30.651991  4221 solver.cpp:218] Iteration 7752 (1.14242 iter/s, 10.504s/12 iters), loss = 0.225997
I0409 23:47:30.652050  4221 solver.cpp:237]     Train net output #0: loss = 0.225997 (* 1 = 0.225997 loss)
I0409 23:47:30.652062  4221 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0409 23:47:34.852553  4221 solver.cpp:218] Iteration 7764 (2.85692 iter/s, 4.20032s/12 iters), loss = 0.124082
I0409 23:47:34.852607  4221 solver.cpp:237]     Train net output #0: loss = 0.124082 (* 1 = 0.124082 loss)
I0409 23:47:34.852618  4221 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0409 23:47:39.665993  4221 solver.cpp:218] Iteration 7776 (2.49316 iter/s, 4.81318s/12 iters), loss = 0.0587718
I0409 23:47:39.666043  4221 solver.cpp:237]     Train net output #0: loss = 0.0587718 (* 1 = 0.0587718 loss)
I0409 23:47:39.666054  4221 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0409 23:47:44.458467  4221 solver.cpp:218] Iteration 7788 (2.50406 iter/s, 4.79221s/12 iters), loss = 0.159454
I0409 23:47:44.458526  4221 solver.cpp:237]     Train net output #0: loss = 0.159454 (* 1 = 0.159454 loss)
I0409 23:47:44.458537  4221 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0409 23:47:44.466572  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:47:49.265856  4221 solver.cpp:218] Iteration 7800 (2.4963 iter/s, 4.80712s/12 iters), loss = 0.158543
I0409 23:47:49.265900  4221 solver.cpp:237]     Train net output #0: loss = 0.158543 (* 1 = 0.158543 loss)
I0409 23:47:49.265908  4221 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0409 23:47:54.184811  4221 solver.cpp:218] Iteration 7812 (2.43967 iter/s, 4.9187s/12 iters), loss = 0.171012
I0409 23:47:54.184860  4221 solver.cpp:237]     Train net output #0: loss = 0.171012 (* 1 = 0.171012 loss)
I0409 23:47:54.184872  4221 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0409 23:47:59.054162  4221 solver.cpp:218] Iteration 7824 (2.46452 iter/s, 4.8691s/12 iters), loss = 0.144704
I0409 23:47:59.054199  4221 solver.cpp:237]     Train net output #0: loss = 0.144704 (* 1 = 0.144704 loss)
I0409 23:47:59.054208  4221 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0409 23:48:03.869987  4221 solver.cpp:218] Iteration 7836 (2.49192 iter/s, 4.81556s/12 iters), loss = 0.0394592
I0409 23:48:03.870121  4221 solver.cpp:237]     Train net output #0: loss = 0.0394592 (* 1 = 0.0394592 loss)
I0409 23:48:03.870136  4221 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0409 23:48:08.717403  4221 solver.cpp:218] Iteration 7848 (2.47572 iter/s, 4.84708s/12 iters), loss = 0.137545
I0409 23:48:08.717450  4221 solver.cpp:237]     Train net output #0: loss = 0.137545 (* 1 = 0.137545 loss)
I0409 23:48:08.717458  4221 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0409 23:48:10.837282  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0409 23:48:12.013841  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0409 23:48:14.346698  4221 solver.cpp:330] Iteration 7854, Testing net (#0)
I0409 23:48:14.346724  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:48:15.722798  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:48:18.836905  4221 solver.cpp:397]     Test net output #0: accuracy = 0.425245
I0409 23:48:18.836941  4221 solver.cpp:397]     Test net output #1: loss = 3.59865 (* 1 = 3.59865 loss)
I0409 23:48:20.673919  4221 solver.cpp:218] Iteration 7860 (1.00368 iter/s, 11.956s/12 iters), loss = 0.132149
I0409 23:48:20.673990  4221 solver.cpp:237]     Train net output #0: loss = 0.132149 (* 1 = 0.132149 loss)
I0409 23:48:20.674003  4221 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0409 23:48:25.531540  4221 solver.cpp:218] Iteration 7872 (2.47049 iter/s, 4.85734s/12 iters), loss = 0.0925284
I0409 23:48:25.531584  4221 solver.cpp:237]     Train net output #0: loss = 0.0925284 (* 1 = 0.0925284 loss)
I0409 23:48:25.531594  4221 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0409 23:48:30.433341  4221 solver.cpp:218] Iteration 7884 (2.44821 iter/s, 4.90155s/12 iters), loss = 0.162672
I0409 23:48:30.433384  4221 solver.cpp:237]     Train net output #0: loss = 0.162672 (* 1 = 0.162672 loss)
I0409 23:48:30.433391  4221 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0409 23:48:32.530242  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:48:35.309146  4221 solver.cpp:218] Iteration 7896 (2.46126 iter/s, 4.87555s/12 iters), loss = 0.13625
I0409 23:48:35.309507  4221 solver.cpp:237]     Train net output #0: loss = 0.13625 (* 1 = 0.13625 loss)
I0409 23:48:35.309517  4221 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0409 23:48:40.240113  4221 solver.cpp:218] Iteration 7908 (2.43388 iter/s, 4.93039s/12 iters), loss = 0.07567
I0409 23:48:40.240164  4221 solver.cpp:237]     Train net output #0: loss = 0.07567 (* 1 = 0.07567 loss)
I0409 23:48:40.240175  4221 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0409 23:48:45.053694  4221 solver.cpp:218] Iteration 7920 (2.49308 iter/s, 4.81333s/12 iters), loss = 0.254592
I0409 23:48:45.053738  4221 solver.cpp:237]     Train net output #0: loss = 0.254592 (* 1 = 0.254592 loss)
I0409 23:48:45.053747  4221 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0409 23:48:49.858495  4221 solver.cpp:218] Iteration 7932 (2.49763 iter/s, 4.80455s/12 iters), loss = 0.133601
I0409 23:48:49.858541  4221 solver.cpp:237]     Train net output #0: loss = 0.133601 (* 1 = 0.133601 loss)
I0409 23:48:49.858552  4221 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0409 23:48:54.752969  4221 solver.cpp:218] Iteration 7944 (2.45187 iter/s, 4.89422s/12 iters), loss = 0.250585
I0409 23:48:54.753011  4221 solver.cpp:237]     Train net output #0: loss = 0.250585 (* 1 = 0.250585 loss)
I0409 23:48:54.753021  4221 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0409 23:48:59.122354  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0409 23:48:59.802969  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0409 23:49:00.301012  4221 solver.cpp:330] Iteration 7956, Testing net (#0)
I0409 23:49:00.301043  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:49:01.652319  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:49:04.947365  4221 solver.cpp:397]     Test net output #0: accuracy = 0.434436
I0409 23:49:04.947415  4221 solver.cpp:397]     Test net output #1: loss = 3.60373 (* 1 = 3.60373 loss)
I0409 23:49:05.030719  4221 solver.cpp:218] Iteration 7956 (1.16763 iter/s, 10.2773s/12 iters), loss = 0.305111
I0409 23:49:05.030778  4221 solver.cpp:237]     Train net output #0: loss = 0.305111 (* 1 = 0.305111 loss)
I0409 23:49:05.030791  4221 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0409 23:49:09.138706  4221 solver.cpp:218] Iteration 7968 (2.92131 iter/s, 4.10775s/12 iters), loss = 0.242799
I0409 23:49:09.138788  4221 solver.cpp:237]     Train net output #0: loss = 0.242799 (* 1 = 0.242799 loss)
I0409 23:49:09.138799  4221 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0409 23:49:13.969844  4221 solver.cpp:218] Iteration 7980 (2.48404 iter/s, 4.83085s/12 iters), loss = 0.124109
I0409 23:49:13.969892  4221 solver.cpp:237]     Train net output #0: loss = 0.124109 (* 1 = 0.124109 loss)
I0409 23:49:13.969903  4221 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0409 23:49:18.098800  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:49:18.790841  4221 solver.cpp:218] Iteration 7992 (2.48925 iter/s, 4.82074s/12 iters), loss = 0.160937
I0409 23:49:18.790889  4221 solver.cpp:237]     Train net output #0: loss = 0.160937 (* 1 = 0.160937 loss)
I0409 23:49:18.790897  4221 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0409 23:49:23.619796  4221 solver.cpp:218] Iteration 8004 (2.48514 iter/s, 4.8287s/12 iters), loss = 0.16588
I0409 23:49:23.619843  4221 solver.cpp:237]     Train net output #0: loss = 0.16588 (* 1 = 0.16588 loss)
I0409 23:49:23.619853  4221 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0409 23:49:28.450035  4221 solver.cpp:218] Iteration 8016 (2.48448 iter/s, 4.82998s/12 iters), loss = 0.0936862
I0409 23:49:28.450098  4221 solver.cpp:237]     Train net output #0: loss = 0.0936862 (* 1 = 0.0936862 loss)
I0409 23:49:28.450109  4221 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0409 23:49:33.259573  4221 solver.cpp:218] Iteration 8028 (2.49519 iter/s, 4.80926s/12 iters), loss = 0.110877
I0409 23:49:33.259637  4221 solver.cpp:237]     Train net output #0: loss = 0.110877 (* 1 = 0.110877 loss)
I0409 23:49:33.259651  4221 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0409 23:49:38.126963  4221 solver.cpp:218] Iteration 8040 (2.46553 iter/s, 4.86712s/12 iters), loss = 0.204049
I0409 23:49:38.127004  4221 solver.cpp:237]     Train net output #0: loss = 0.204049 (* 1 = 0.204049 loss)
I0409 23:49:38.127013  4221 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0409 23:49:42.929675  4221 solver.cpp:218] Iteration 8052 (2.49872 iter/s, 4.80246s/12 iters), loss = 0.119665
I0409 23:49:42.929831  4221 solver.cpp:237]     Train net output #0: loss = 0.119665 (* 1 = 0.119665 loss)
I0409 23:49:42.929844  4221 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0409 23:49:44.893632  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0409 23:49:45.599575  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0409 23:49:46.089715  4221 solver.cpp:330] Iteration 8058, Testing net (#0)
I0409 23:49:46.089740  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:49:47.348166  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:49:50.540285  4221 solver.cpp:397]     Test net output #0: accuracy = 0.44424
I0409 23:49:50.540318  4221 solver.cpp:397]     Test net output #1: loss = 3.59645 (* 1 = 3.59645 loss)
I0409 23:49:52.389019  4221 solver.cpp:218] Iteration 8064 (1.26866 iter/s, 9.4588s/12 iters), loss = 0.101307
I0409 23:49:52.389063  4221 solver.cpp:237]     Train net output #0: loss = 0.101307 (* 1 = 0.101307 loss)
I0409 23:49:52.389075  4221 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0409 23:49:57.203042  4221 solver.cpp:218] Iteration 8076 (2.49285 iter/s, 4.81376s/12 iters), loss = 0.110349
I0409 23:49:57.203102  4221 solver.cpp:237]     Train net output #0: loss = 0.110349 (* 1 = 0.110349 loss)
I0409 23:49:57.203114  4221 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0409 23:50:02.041775  4221 solver.cpp:218] Iteration 8088 (2.48013 iter/s, 4.83846s/12 iters), loss = 0.120256
I0409 23:50:02.041826  4221 solver.cpp:237]     Train net output #0: loss = 0.120256 (* 1 = 0.120256 loss)
I0409 23:50:02.041834  4221 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0409 23:50:03.402938  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:50:06.883899  4221 solver.cpp:218] Iteration 8100 (2.47838 iter/s, 4.84186s/12 iters), loss = 0.0576071
I0409 23:50:06.883947  4221 solver.cpp:237]     Train net output #0: loss = 0.0576071 (* 1 = 0.0576071 loss)
I0409 23:50:06.883957  4221 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0409 23:50:11.726315  4221 solver.cpp:218] Iteration 8112 (2.47823 iter/s, 4.84216s/12 iters), loss = 0.141782
I0409 23:50:11.726368  4221 solver.cpp:237]     Train net output #0: loss = 0.141782 (* 1 = 0.141782 loss)
I0409 23:50:11.726378  4221 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0409 23:50:16.497476  4221 solver.cpp:218] Iteration 8124 (2.51525 iter/s, 4.7709s/12 iters), loss = 0.0744115
I0409 23:50:16.497587  4221 solver.cpp:237]     Train net output #0: loss = 0.0744115 (* 1 = 0.0744115 loss)
I0409 23:50:16.497599  4221 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0409 23:50:21.331701  4221 solver.cpp:218] Iteration 8136 (2.48246 iter/s, 4.83391s/12 iters), loss = 0.21022
I0409 23:50:21.331743  4221 solver.cpp:237]     Train net output #0: loss = 0.21022 (* 1 = 0.21022 loss)
I0409 23:50:21.331750  4221 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0409 23:50:26.196110  4221 solver.cpp:218] Iteration 8148 (2.46703 iter/s, 4.86415s/12 iters), loss = 0.132646
I0409 23:50:26.196168  4221 solver.cpp:237]     Train net output #0: loss = 0.132646 (* 1 = 0.132646 loss)
I0409 23:50:26.196180  4221 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0409 23:50:30.611250  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0409 23:50:32.391036  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0409 23:50:33.638859  4221 solver.cpp:330] Iteration 8160, Testing net (#0)
I0409 23:50:33.638881  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:50:34.926765  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:50:38.180724  4221 solver.cpp:397]     Test net output #0: accuracy = 0.435049
I0409 23:50:38.180763  4221 solver.cpp:397]     Test net output #1: loss = 3.60836 (* 1 = 3.60836 loss)
I0409 23:50:38.264464  4221 solver.cpp:218] Iteration 8160 (0.994383 iter/s, 12.0678s/12 iters), loss = 0.0836188
I0409 23:50:38.264541  4221 solver.cpp:237]     Train net output #0: loss = 0.0836188 (* 1 = 0.0836188 loss)
I0409 23:50:38.264556  4221 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0409 23:50:42.350512  4221 solver.cpp:218] Iteration 8172 (2.93701 iter/s, 4.08579s/12 iters), loss = 0.0411174
I0409 23:50:42.350565  4221 solver.cpp:237]     Train net output #0: loss = 0.0411174 (* 1 = 0.0411174 loss)
I0409 23:50:42.350577  4221 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0409 23:50:47.299314  4221 solver.cpp:218] Iteration 8184 (2.42496 iter/s, 4.94854s/12 iters), loss = 0.157954
I0409 23:50:47.299412  4221 solver.cpp:237]     Train net output #0: loss = 0.157954 (* 1 = 0.157954 loss)
I0409 23:50:47.299422  4221 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0409 23:50:50.781782  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:50:52.215811  4221 solver.cpp:218] Iteration 8196 (2.44092 iter/s, 4.91618s/12 iters), loss = 0.124511
I0409 23:50:52.215869  4221 solver.cpp:237]     Train net output #0: loss = 0.124511 (* 1 = 0.124511 loss)
I0409 23:50:52.215881  4221 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0409 23:50:57.016291  4221 solver.cpp:218] Iteration 8208 (2.49989 iter/s, 4.80021s/12 iters), loss = 0.0890383
I0409 23:50:57.016350  4221 solver.cpp:237]     Train net output #0: loss = 0.0890383 (* 1 = 0.0890383 loss)
I0409 23:50:57.016366  4221 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0409 23:51:01.800505  4221 solver.cpp:218] Iteration 8220 (2.50839 iter/s, 4.78395s/12 iters), loss = 0.170584
I0409 23:51:01.800549  4221 solver.cpp:237]     Train net output #0: loss = 0.170584 (* 1 = 0.170584 loss)
I0409 23:51:01.800557  4221 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0409 23:51:06.618831  4221 solver.cpp:218] Iteration 8232 (2.49063 iter/s, 4.81807s/12 iters), loss = 0.138016
I0409 23:51:06.618887  4221 solver.cpp:237]     Train net output #0: loss = 0.138016 (* 1 = 0.138016 loss)
I0409 23:51:06.618899  4221 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0409 23:51:11.531154  4221 solver.cpp:218] Iteration 8244 (2.44297 iter/s, 4.91205s/12 iters), loss = 0.210531
I0409 23:51:11.531211  4221 solver.cpp:237]     Train net output #0: loss = 0.210531 (* 1 = 0.210531 loss)
I0409 23:51:11.531224  4221 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0409 23:51:16.506376  4221 solver.cpp:218] Iteration 8256 (2.41209 iter/s, 4.97495s/12 iters), loss = 0.0989067
I0409 23:51:16.506435  4221 solver.cpp:237]     Train net output #0: loss = 0.0989067 (* 1 = 0.0989067 loss)
I0409 23:51:16.506446  4221 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0409 23:51:18.476528  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0409 23:51:20.724195  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0409 23:51:21.263162  4221 solver.cpp:330] Iteration 8262, Testing net (#0)
I0409 23:51:21.263185  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:51:22.370327  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:51:25.648463  4221 solver.cpp:397]     Test net output #0: accuracy = 0.439951
I0409 23:51:25.648507  4221 solver.cpp:397]     Test net output #1: loss = 3.56412 (* 1 = 3.56412 loss)
I0409 23:51:27.534420  4221 solver.cpp:218] Iteration 8268 (1.08819 iter/s, 11.0275s/12 iters), loss = 0.0784357
I0409 23:51:27.534477  4221 solver.cpp:237]     Train net output #0: loss = 0.0784357 (* 1 = 0.0784357 loss)
I0409 23:51:27.534488  4221 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0409 23:51:32.374545  4221 solver.cpp:218] Iteration 8280 (2.47941 iter/s, 4.83986s/12 iters), loss = 0.186161
I0409 23:51:32.374600  4221 solver.cpp:237]     Train net output #0: loss = 0.186161 (* 1 = 0.186161 loss)
I0409 23:51:32.374611  4221 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0409 23:51:37.334986  4221 solver.cpp:218] Iteration 8292 (2.41927 iter/s, 4.96017s/12 iters), loss = 0.207908
I0409 23:51:37.335031  4221 solver.cpp:237]     Train net output #0: loss = 0.207908 (* 1 = 0.207908 loss)
I0409 23:51:37.335039  4221 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0409 23:51:38.023083  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:51:42.266873  4221 solver.cpp:218] Iteration 8304 (2.43328 iter/s, 4.93162s/12 iters), loss = 0.234659
I0409 23:51:42.266933  4221 solver.cpp:237]     Train net output #0: loss = 0.234659 (* 1 = 0.234659 loss)
I0409 23:51:42.266944  4221 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0409 23:51:44.062458  4221 blocking_queue.cpp:49] Waiting for data
I0409 23:51:47.437003  4221 solver.cpp:218] Iteration 8316 (2.32115 iter/s, 5.16985s/12 iters), loss = 0.08843
I0409 23:51:47.437053  4221 solver.cpp:237]     Train net output #0: loss = 0.08843 (* 1 = 0.08843 loss)
I0409 23:51:47.437065  4221 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0409 23:51:52.304080  4221 solver.cpp:218] Iteration 8328 (2.46568 iter/s, 4.86682s/12 iters), loss = 0.144358
I0409 23:51:52.304172  4221 solver.cpp:237]     Train net output #0: loss = 0.144358 (* 1 = 0.144358 loss)
I0409 23:51:52.304179  4221 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0409 23:51:57.234413  4221 solver.cpp:218] Iteration 8340 (2.43406 iter/s, 4.93003s/12 iters), loss = 0.146581
I0409 23:51:57.234467  4221 solver.cpp:237]     Train net output #0: loss = 0.146581 (* 1 = 0.146581 loss)
I0409 23:51:57.234477  4221 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0409 23:52:02.202669  4221 solver.cpp:218] Iteration 8352 (2.41547 iter/s, 4.96799s/12 iters), loss = 0.0995088
I0409 23:52:02.202720  4221 solver.cpp:237]     Train net output #0: loss = 0.0995088 (* 1 = 0.0995088 loss)
I0409 23:52:02.202730  4221 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0409 23:52:06.691920  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0409 23:52:07.399294  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0409 23:52:07.896837  4221 solver.cpp:330] Iteration 8364, Testing net (#0)
I0409 23:52:07.896862  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:52:08.988750  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:52:12.297942  4221 solver.cpp:397]     Test net output #0: accuracy = 0.441176
I0409 23:52:12.298012  4221 solver.cpp:397]     Test net output #1: loss = 3.63398 (* 1 = 3.63398 loss)
I0409 23:52:12.381922  4221 solver.cpp:218] Iteration 8364 (1.17892 iter/s, 10.1788s/12 iters), loss = 0.114685
I0409 23:52:12.382000  4221 solver.cpp:237]     Train net output #0: loss = 0.114685 (* 1 = 0.114685 loss)
I0409 23:52:12.382014  4221 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0409 23:52:16.432498  4221 solver.cpp:218] Iteration 8376 (2.96273 iter/s, 4.05032s/12 iters), loss = 0.0722024
I0409 23:52:16.432557  4221 solver.cpp:237]     Train net output #0: loss = 0.0722024 (* 1 = 0.0722024 loss)
I0409 23:52:16.432570  4221 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0409 23:52:21.271358  4221 solver.cpp:218] Iteration 8388 (2.48006 iter/s, 4.83859s/12 iters), loss = 0.244462
I0409 23:52:21.271411  4221 solver.cpp:237]     Train net output #0: loss = 0.244462 (* 1 = 0.244462 loss)
I0409 23:52:21.271422  4221 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0409 23:52:24.004650  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:52:26.124743  4221 solver.cpp:218] Iteration 8400 (2.47264 iter/s, 4.85312s/12 iters), loss = 0.108122
I0409 23:52:26.124804  4221 solver.cpp:237]     Train net output #0: loss = 0.108122 (* 1 = 0.108122 loss)
I0409 23:52:26.124815  4221 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0409 23:52:30.990126  4221 solver.cpp:218] Iteration 8412 (2.46654 iter/s, 4.86511s/12 iters), loss = 0.179023
I0409 23:52:30.990177  4221 solver.cpp:237]     Train net output #0: loss = 0.179023 (* 1 = 0.179023 loss)
I0409 23:52:30.990190  4221 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0409 23:52:35.838210  4221 solver.cpp:218] Iteration 8424 (2.47534 iter/s, 4.84782s/12 iters), loss = 0.0707992
I0409 23:52:35.838258  4221 solver.cpp:237]     Train net output #0: loss = 0.0707992 (* 1 = 0.0707992 loss)
I0409 23:52:35.838268  4221 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0409 23:52:40.650547  4221 solver.cpp:218] Iteration 8436 (2.49373 iter/s, 4.81208s/12 iters), loss = 0.115104
I0409 23:52:40.650591  4221 solver.cpp:237]     Train net output #0: loss = 0.115104 (* 1 = 0.115104 loss)
I0409 23:52:40.650600  4221 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0409 23:52:45.492763  4221 solver.cpp:218] Iteration 8448 (2.47834 iter/s, 4.84196s/12 iters), loss = 0.198136
I0409 23:52:45.492823  4221 solver.cpp:237]     Train net output #0: loss = 0.198136 (* 1 = 0.198136 loss)
I0409 23:52:45.492835  4221 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0409 23:52:50.342134  4221 solver.cpp:218] Iteration 8460 (2.47469 iter/s, 4.8491s/12 iters), loss = 0.213667
I0409 23:52:50.342192  4221 solver.cpp:237]     Train net output #0: loss = 0.213667 (* 1 = 0.213667 loss)
I0409 23:52:50.342206  4221 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0409 23:52:52.285104  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0409 23:52:52.948336  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0409 23:52:53.442242  4221 solver.cpp:330] Iteration 8466, Testing net (#0)
I0409 23:52:53.442281  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:52:54.489866  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:52:57.795214  4221 solver.cpp:397]     Test net output #0: accuracy = 0.441176
I0409 23:52:57.795253  4221 solver.cpp:397]     Test net output #1: loss = 3.64294 (* 1 = 3.64294 loss)
I0409 23:52:59.544593  4221 solver.cpp:218] Iteration 8472 (1.30406 iter/s, 9.20201s/12 iters), loss = 0.229339
I0409 23:52:59.544648  4221 solver.cpp:237]     Train net output #0: loss = 0.229339 (* 1 = 0.229339 loss)
I0409 23:52:59.544661  4221 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0409 23:53:04.349026  4221 solver.cpp:218] Iteration 8484 (2.49783 iter/s, 4.80417s/12 iters), loss = 0.198943
I0409 23:53:04.349076  4221 solver.cpp:237]     Train net output #0: loss = 0.198943 (* 1 = 0.198943 loss)
I0409 23:53:04.349088  4221 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0409 23:53:09.313925  4221 solver.cpp:218] Iteration 8496 (2.41709 iter/s, 4.96464s/12 iters), loss = 0.198914
I0409 23:53:09.313980  4221 solver.cpp:237]     Train net output #0: loss = 0.198914 (* 1 = 0.198914 loss)
I0409 23:53:09.313990  4221 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0409 23:53:09.360869  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:53:14.182979  4221 solver.cpp:218] Iteration 8508 (2.46468 iter/s, 4.86879s/12 iters), loss = 0.145573
I0409 23:53:14.183037  4221 solver.cpp:237]     Train net output #0: loss = 0.145573 (* 1 = 0.145573 loss)
I0409 23:53:14.183048  4221 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0409 23:53:19.026932  4221 solver.cpp:218] Iteration 8520 (2.47745 iter/s, 4.84368s/12 iters), loss = 0.145563
I0409 23:53:19.026990  4221 solver.cpp:237]     Train net output #0: loss = 0.145563 (* 1 = 0.145563 loss)
I0409 23:53:19.027002  4221 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0409 23:53:23.849222  4221 solver.cpp:218] Iteration 8532 (2.48858 iter/s, 4.82202s/12 iters), loss = 0.0557325
I0409 23:53:23.849272  4221 solver.cpp:237]     Train net output #0: loss = 0.0557325 (* 1 = 0.0557325 loss)
I0409 23:53:23.849282  4221 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0409 23:53:28.716099  4221 solver.cpp:218] Iteration 8544 (2.46578 iter/s, 4.86662s/12 iters), loss = 0.0705072
I0409 23:53:28.716230  4221 solver.cpp:237]     Train net output #0: loss = 0.0705072 (* 1 = 0.0705072 loss)
I0409 23:53:28.716243  4221 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0409 23:53:33.577350  4221 solver.cpp:218] Iteration 8556 (2.46867 iter/s, 4.86091s/12 iters), loss = 0.0704781
I0409 23:53:33.577409  4221 solver.cpp:237]     Train net output #0: loss = 0.0704781 (* 1 = 0.0704781 loss)
I0409 23:53:33.577421  4221 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0409 23:53:38.399729  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0409 23:53:40.654259  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0409 23:53:41.463238  4221 solver.cpp:330] Iteration 8568, Testing net (#0)
I0409 23:53:41.463263  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:53:42.545634  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:53:45.941737  4221 solver.cpp:397]     Test net output #0: accuracy = 0.433211
I0409 23:53:45.941774  4221 solver.cpp:397]     Test net output #1: loss = 3.64708 (* 1 = 3.64708 loss)
I0409 23:53:46.024997  4221 solver.cpp:218] Iteration 8568 (0.964082 iter/s, 12.4471s/12 iters), loss = 0.138566
I0409 23:53:46.025048  4221 solver.cpp:237]     Train net output #0: loss = 0.138566 (* 1 = 0.138566 loss)
I0409 23:53:46.025058  4221 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0409 23:53:50.129184  4221 solver.cpp:218] Iteration 8580 (2.92401 iter/s, 4.10395s/12 iters), loss = 0.167211
I0409 23:53:50.129230  4221 solver.cpp:237]     Train net output #0: loss = 0.167211 (* 1 = 0.167211 loss)
I0409 23:53:50.129240  4221 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0409 23:53:55.024302  4221 solver.cpp:218] Iteration 8592 (2.45155 iter/s, 4.89486s/12 iters), loss = 0.0514045
I0409 23:53:55.024354  4221 solver.cpp:237]     Train net output #0: loss = 0.0514045 (* 1 = 0.0514045 loss)
I0409 23:53:55.024366  4221 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0409 23:53:57.166775  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:54:00.116426  4221 solver.cpp:218] Iteration 8604 (2.35671 iter/s, 5.09185s/12 iters), loss = 0.0470827
I0409 23:54:00.116529  4221 solver.cpp:237]     Train net output #0: loss = 0.0470827 (* 1 = 0.0470827 loss)
I0409 23:54:00.116542  4221 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0409 23:54:05.023903  4221 solver.cpp:218] Iteration 8616 (2.4454 iter/s, 4.90717s/12 iters), loss = 0.02355
I0409 23:54:05.023950  4221 solver.cpp:237]     Train net output #0: loss = 0.02355 (* 1 = 0.02355 loss)
I0409 23:54:05.023960  4221 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0409 23:54:09.886281  4221 solver.cpp:218] Iteration 8628 (2.46806 iter/s, 4.86212s/12 iters), loss = 0.0470131
I0409 23:54:09.886328  4221 solver.cpp:237]     Train net output #0: loss = 0.0470131 (* 1 = 0.0470131 loss)
I0409 23:54:09.886338  4221 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0409 23:54:14.736182  4221 solver.cpp:218] Iteration 8640 (2.47441 iter/s, 4.84964s/12 iters), loss = 0.0427775
I0409 23:54:14.736224  4221 solver.cpp:237]     Train net output #0: loss = 0.0427775 (* 1 = 0.0427775 loss)
I0409 23:54:14.736233  4221 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0409 23:54:19.636571  4221 solver.cpp:218] Iteration 8652 (2.44891 iter/s, 4.90013s/12 iters), loss = 0.144457
I0409 23:54:19.636616  4221 solver.cpp:237]     Train net output #0: loss = 0.144457 (* 1 = 0.144457 loss)
I0409 23:54:19.636626  4221 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0409 23:54:24.554662  4221 solver.cpp:218] Iteration 8664 (2.4401 iter/s, 4.91783s/12 iters), loss = 0.194542
I0409 23:54:24.554716  4221 solver.cpp:237]     Train net output #0: loss = 0.194543 (* 1 = 0.194543 loss)
I0409 23:54:24.554728  4221 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0409 23:54:26.568629  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0409 23:54:27.602437  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0409 23:54:29.214511  4221 solver.cpp:330] Iteration 8670, Testing net (#0)
I0409 23:54:29.214534  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:54:30.194173  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:54:33.894284  4221 solver.cpp:397]     Test net output #0: accuracy = 0.45098
I0409 23:54:33.894320  4221 solver.cpp:397]     Test net output #1: loss = 3.57218 (* 1 = 3.57218 loss)
I0409 23:54:35.769881  4221 solver.cpp:218] Iteration 8676 (1.07002 iter/s, 11.2147s/12 iters), loss = 0.244128
I0409 23:54:35.769924  4221 solver.cpp:237]     Train net output #0: loss = 0.244128 (* 1 = 0.244128 loss)
I0409 23:54:35.769933  4221 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0409 23:54:40.701423  4221 solver.cpp:218] Iteration 8688 (2.43344 iter/s, 4.93128s/12 iters), loss = 0.140979
I0409 23:54:40.701478  4221 solver.cpp:237]     Train net output #0: loss = 0.140979 (* 1 = 0.140979 loss)
I0409 23:54:40.701491  4221 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0409 23:54:44.998208  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:54:45.700906  4221 solver.cpp:218] Iteration 8700 (2.40038 iter/s, 4.99921s/12 iters), loss = 0.188856
I0409 23:54:45.700965  4221 solver.cpp:237]     Train net output #0: loss = 0.188856 (* 1 = 0.188856 loss)
I0409 23:54:45.700978  4221 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0409 23:54:50.548914  4221 solver.cpp:218] Iteration 8712 (2.47538 iter/s, 4.84774s/12 iters), loss = 0.129435
I0409 23:54:50.548957  4221 solver.cpp:237]     Train net output #0: loss = 0.129435 (* 1 = 0.129435 loss)
I0409 23:54:50.548966  4221 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0409 23:54:55.412855  4221 solver.cpp:218] Iteration 8724 (2.46727 iter/s, 4.86368s/12 iters), loss = 0.0724701
I0409 23:54:55.412911  4221 solver.cpp:237]     Train net output #0: loss = 0.0724701 (* 1 = 0.0724701 loss)
I0409 23:54:55.412925  4221 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0409 23:55:00.242245  4221 solver.cpp:218] Iteration 8736 (2.48492 iter/s, 4.82913s/12 iters), loss = 0.0535261
I0409 23:55:00.242344  4221 solver.cpp:237]     Train net output #0: loss = 0.0535261 (* 1 = 0.0535261 loss)
I0409 23:55:00.242354  4221 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0409 23:55:05.128023  4221 solver.cpp:218] Iteration 8748 (2.45627 iter/s, 4.88546s/12 iters), loss = 0.0283711
I0409 23:55:05.128084  4221 solver.cpp:237]     Train net output #0: loss = 0.0283712 (* 1 = 0.0283712 loss)
I0409 23:55:05.128098  4221 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0409 23:55:09.996587  4221 solver.cpp:218] Iteration 8760 (2.46493 iter/s, 4.8683s/12 iters), loss = 0.10044
I0409 23:55:09.996640  4221 solver.cpp:237]     Train net output #0: loss = 0.10044 (* 1 = 0.10044 loss)
I0409 23:55:09.996651  4221 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0409 23:55:14.486550  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0409 23:55:15.170516  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0409 23:55:16.186693  4221 solver.cpp:330] Iteration 8772, Testing net (#0)
I0409 23:55:16.186719  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:55:17.248940  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:55:20.698943  4221 solver.cpp:397]     Test net output #0: accuracy = 0.45098
I0409 23:55:20.698992  4221 solver.cpp:397]     Test net output #1: loss = 3.50167 (* 1 = 3.50167 loss)
I0409 23:55:20.780916  4221 solver.cpp:218] Iteration 8772 (1.11278 iter/s, 10.7838s/12 iters), loss = 0.0967658
I0409 23:55:20.780967  4221 solver.cpp:237]     Train net output #0: loss = 0.0967658 (* 1 = 0.0967658 loss)
I0409 23:55:20.780978  4221 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0409 23:55:24.976024  4221 solver.cpp:218] Iteration 8784 (2.86064 iter/s, 4.19487s/12 iters), loss = 0.125381
I0409 23:55:24.976078  4221 solver.cpp:237]     Train net output #0: loss = 0.125381 (* 1 = 0.125381 loss)
I0409 23:55:24.976089  4221 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0409 23:55:29.855330  4221 solver.cpp:218] Iteration 8796 (2.4595 iter/s, 4.87904s/12 iters), loss = 0.158459
I0409 23:55:29.855386  4221 solver.cpp:237]     Train net output #0: loss = 0.158459 (* 1 = 0.158459 loss)
I0409 23:55:29.855396  4221 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0409 23:55:31.264261  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:55:34.734516  4221 solver.cpp:218] Iteration 8808 (2.45956 iter/s, 4.87892s/12 iters), loss = 0.0718486
I0409 23:55:34.734575  4221 solver.cpp:237]     Train net output #0: loss = 0.0718487 (* 1 = 0.0718487 loss)
I0409 23:55:34.734588  4221 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0409 23:55:39.853022  4221 solver.cpp:218] Iteration 8820 (2.34456 iter/s, 5.11823s/12 iters), loss = 0.153048
I0409 23:55:39.853072  4221 solver.cpp:237]     Train net output #0: loss = 0.153048 (* 1 = 0.153048 loss)
I0409 23:55:39.853085  4221 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0409 23:55:44.634363  4221 solver.cpp:218] Iteration 8832 (2.5099 iter/s, 4.78107s/12 iters), loss = 0.11091
I0409 23:55:44.634423  4221 solver.cpp:237]     Train net output #0: loss = 0.11091 (* 1 = 0.11091 loss)
I0409 23:55:44.634436  4221 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0409 23:55:49.396252  4221 solver.cpp:218] Iteration 8844 (2.52015 iter/s, 4.76162s/12 iters), loss = 0.176308
I0409 23:55:49.396311  4221 solver.cpp:237]     Train net output #0: loss = 0.176308 (* 1 = 0.176308 loss)
I0409 23:55:49.396322  4221 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0409 23:55:54.276474  4221 solver.cpp:218] Iteration 8856 (2.45904 iter/s, 4.87995s/12 iters), loss = 0.163313
I0409 23:55:54.276517  4221 solver.cpp:237]     Train net output #0: loss = 0.163313 (* 1 = 0.163313 loss)
I0409 23:55:54.276525  4221 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0409 23:55:59.098858  4221 solver.cpp:218] Iteration 8868 (2.48853 iter/s, 4.82213s/12 iters), loss = 0.0594289
I0409 23:55:59.098920  4221 solver.cpp:237]     Train net output #0: loss = 0.0594289 (* 1 = 0.0594289 loss)
I0409 23:55:59.098933  4221 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0409 23:56:01.060945  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0409 23:56:01.774029  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0409 23:56:02.308835  4221 solver.cpp:330] Iteration 8874, Testing net (#0)
I0409 23:56:02.308863  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:56:03.313338  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:56:06.824824  4221 solver.cpp:397]     Test net output #0: accuracy = 0.45527
I0409 23:56:06.824875  4221 solver.cpp:397]     Test net output #1: loss = 3.59823 (* 1 = 3.59823 loss)
I0409 23:56:08.716404  4221 solver.cpp:218] Iteration 8880 (1.24778 iter/s, 9.61708s/12 iters), loss = 0.138058
I0409 23:56:08.716456  4221 solver.cpp:237]     Train net output #0: loss = 0.138058 (* 1 = 0.138058 loss)
I0409 23:56:08.716467  4221 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0409 23:56:13.576937  4221 solver.cpp:218] Iteration 8892 (2.469 iter/s, 4.86027s/12 iters), loss = 0.100353
I0409 23:56:13.576990  4221 solver.cpp:237]     Train net output #0: loss = 0.100353 (* 1 = 0.100353 loss)
I0409 23:56:13.577003  4221 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0409 23:56:17.037803  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:56:18.419997  4221 solver.cpp:218] Iteration 8904 (2.47791 iter/s, 4.84279s/12 iters), loss = 0.133083
I0409 23:56:18.420055  4221 solver.cpp:237]     Train net output #0: loss = 0.133083 (* 1 = 0.133083 loss)
I0409 23:56:18.420068  4221 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0409 23:56:23.236667  4221 solver.cpp:218] Iteration 8916 (2.49149 iter/s, 4.8164s/12 iters), loss = 0.157719
I0409 23:56:23.236737  4221 solver.cpp:237]     Train net output #0: loss = 0.157719 (* 1 = 0.157719 loss)
I0409 23:56:23.236752  4221 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0409 23:56:28.058147  4221 solver.cpp:218] Iteration 8928 (2.489 iter/s, 4.82121s/12 iters), loss = 0.144075
I0409 23:56:28.058198  4221 solver.cpp:237]     Train net output #0: loss = 0.144076 (* 1 = 0.144076 loss)
I0409 23:56:28.058212  4221 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0409 23:56:32.930090  4221 solver.cpp:218] Iteration 8940 (2.46322 iter/s, 4.87168s/12 iters), loss = 0.104919
I0409 23:56:32.930244  4221 solver.cpp:237]     Train net output #0: loss = 0.104919 (* 1 = 0.104919 loss)
I0409 23:56:32.930255  4221 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0409 23:56:37.777693  4221 solver.cpp:218] Iteration 8952 (2.47563 iter/s, 4.84725s/12 iters), loss = 0.177171
I0409 23:56:37.777736  4221 solver.cpp:237]     Train net output #0: loss = 0.177171 (* 1 = 0.177171 loss)
I0409 23:56:37.777745  4221 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0409 23:56:42.583922  4221 solver.cpp:218] Iteration 8964 (2.49689 iter/s, 4.80598s/12 iters), loss = 0.0883841
I0409 23:56:42.583974  4221 solver.cpp:237]     Train net output #0: loss = 0.0883842 (* 1 = 0.0883842 loss)
I0409 23:56:42.583986  4221 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0409 23:56:46.956403  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0409 23:56:47.661285  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0409 23:56:48.147424  4221 solver.cpp:330] Iteration 8976, Testing net (#0)
I0409 23:56:48.147454  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:56:49.065652  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:56:52.786033  4221 solver.cpp:397]     Test net output #0: accuracy = 0.456495
I0409 23:56:52.786084  4221 solver.cpp:397]     Test net output #1: loss = 3.64634 (* 1 = 3.64634 loss)
I0409 23:56:52.869359  4221 solver.cpp:218] Iteration 8976 (1.16675 iter/s, 10.285s/12 iters), loss = 0.0436887
I0409 23:56:52.869415  4221 solver.cpp:237]     Train net output #0: loss = 0.0436888 (* 1 = 0.0436888 loss)
I0409 23:56:52.869426  4221 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0409 23:56:56.897071  4221 solver.cpp:218] Iteration 8988 (2.97953 iter/s, 4.02749s/12 iters), loss = 0.0533534
I0409 23:56:56.897111  4221 solver.cpp:237]     Train net output #0: loss = 0.0533535 (* 1 = 0.0533535 loss)
I0409 23:56:56.897122  4221 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0409 23:56:58.905822  4221 blocking_queue.cpp:49] Waiting for data
I0409 23:57:01.830070  4221 solver.cpp:218] Iteration 9000 (2.43272 iter/s, 4.93275s/12 iters), loss = 0.129487
I0409 23:57:01.830123  4221 solver.cpp:237]     Train net output #0: loss = 0.129487 (* 1 = 0.129487 loss)
I0409 23:57:01.830134  4221 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0409 23:57:02.543305  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:57:06.788272  4221 solver.cpp:218] Iteration 9012 (2.42036 iter/s, 4.95793s/12 iters), loss = 0.125857
I0409 23:57:06.788403  4221 solver.cpp:237]     Train net output #0: loss = 0.125857 (* 1 = 0.125857 loss)
I0409 23:57:06.788417  4221 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0409 23:57:11.633334  4221 solver.cpp:218] Iteration 9024 (2.47692 iter/s, 4.84472s/12 iters), loss = 0.105557
I0409 23:57:11.633391  4221 solver.cpp:237]     Train net output #0: loss = 0.105557 (* 1 = 0.105557 loss)
I0409 23:57:11.633404  4221 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0409 23:57:16.534279  4221 solver.cpp:218] Iteration 9036 (2.44864 iter/s, 4.90068s/12 iters), loss = 0.0374462
I0409 23:57:16.534333  4221 solver.cpp:237]     Train net output #0: loss = 0.0374462 (* 1 = 0.0374462 loss)
I0409 23:57:16.534346  4221 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0409 23:57:21.417691  4221 solver.cpp:218] Iteration 9048 (2.45743 iter/s, 4.88314s/12 iters), loss = 0.133817
I0409 23:57:21.417752  4221 solver.cpp:237]     Train net output #0: loss = 0.133817 (* 1 = 0.133817 loss)
I0409 23:57:21.417763  4221 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0409 23:57:26.307538  4221 solver.cpp:218] Iteration 9060 (2.4542 iter/s, 4.88957s/12 iters), loss = 0.0766303
I0409 23:57:26.307595  4221 solver.cpp:237]     Train net output #0: loss = 0.0766303 (* 1 = 0.0766303 loss)
I0409 23:57:26.307606  4221 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0409 23:57:31.174294  4221 solver.cpp:218] Iteration 9072 (2.46585 iter/s, 4.86648s/12 iters), loss = 0.129839
I0409 23:57:31.174365  4221 solver.cpp:237]     Train net output #0: loss = 0.129839 (* 1 = 0.129839 loss)
I0409 23:57:31.174374  4221 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0409 23:57:33.154333  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0409 23:57:33.804682  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0409 23:57:34.300505  4221 solver.cpp:330] Iteration 9078, Testing net (#0)
I0409 23:57:34.300534  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:57:35.227706  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:57:39.081130  4221 solver.cpp:397]     Test net output #0: accuracy = 0.438726
I0409 23:57:39.081280  4221 solver.cpp:397]     Test net output #1: loss = 3.62645 (* 1 = 3.62645 loss)
I0409 23:57:40.867200  4221 solver.cpp:218] Iteration 9084 (1.23808 iter/s, 9.69243s/12 iters), loss = 0.0668187
I0409 23:57:40.867251  4221 solver.cpp:237]     Train net output #0: loss = 0.0668187 (* 1 = 0.0668187 loss)
I0409 23:57:40.867264  4221 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0409 23:57:45.805615  4221 solver.cpp:218] Iteration 9096 (2.43006 iter/s, 4.93815s/12 iters), loss = 0.102859
I0409 23:57:45.805668  4221 solver.cpp:237]     Train net output #0: loss = 0.102859 (* 1 = 0.102859 loss)
I0409 23:57:45.805680  4221 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0409 23:57:48.674607  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:57:50.700402  4221 solver.cpp:218] Iteration 9108 (2.45172 iter/s, 4.89452s/12 iters), loss = 0.122066
I0409 23:57:50.700453  4221 solver.cpp:237]     Train net output #0: loss = 0.122066 (* 1 = 0.122066 loss)
I0409 23:57:50.700464  4221 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0409 23:57:55.608347  4221 solver.cpp:218] Iteration 9120 (2.44515 iter/s, 4.90768s/12 iters), loss = 0.149859
I0409 23:57:55.608402  4221 solver.cpp:237]     Train net output #0: loss = 0.149859 (* 1 = 0.149859 loss)
I0409 23:57:55.608413  4221 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0409 23:58:00.514961  4221 solver.cpp:218] Iteration 9132 (2.44581 iter/s, 4.90635s/12 iters), loss = 0.134589
I0409 23:58:00.515010  4221 solver.cpp:237]     Train net output #0: loss = 0.134589 (* 1 = 0.134589 loss)
I0409 23:58:00.515022  4221 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0409 23:58:05.539302  4221 solver.cpp:218] Iteration 9144 (2.3885 iter/s, 5.02407s/12 iters), loss = 0.107897
I0409 23:58:05.539355  4221 solver.cpp:237]     Train net output #0: loss = 0.107897 (* 1 = 0.107897 loss)
I0409 23:58:05.539366  4221 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0409 23:58:10.428069  4221 solver.cpp:218] Iteration 9156 (2.45474 iter/s, 4.8885s/12 iters), loss = 0.137261
I0409 23:58:10.428220  4221 solver.cpp:237]     Train net output #0: loss = 0.137261 (* 1 = 0.137261 loss)
I0409 23:58:10.428234  4221 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0409 23:58:15.419857  4221 solver.cpp:218] Iteration 9168 (2.40412 iter/s, 4.99142s/12 iters), loss = 0.10382
I0409 23:58:15.419919  4221 solver.cpp:237]     Train net output #0: loss = 0.10382 (* 1 = 0.10382 loss)
I0409 23:58:15.419930  4221 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0409 23:58:20.034112  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0409 23:58:20.849350  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0409 23:58:22.425833  4221 solver.cpp:330] Iteration 9180, Testing net (#0)
I0409 23:58:22.425858  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:58:23.296926  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:58:26.872903  4221 solver.cpp:397]     Test net output #0: accuracy = 0.44424
I0409 23:58:26.872953  4221 solver.cpp:397]     Test net output #1: loss = 3.59802 (* 1 = 3.59802 loss)
I0409 23:58:26.956080  4221 solver.cpp:218] Iteration 9180 (1.04025 iter/s, 11.5357s/12 iters), loss = 0.109185
I0409 23:58:26.956136  4221 solver.cpp:237]     Train net output #0: loss = 0.109185 (* 1 = 0.109185 loss)
I0409 23:58:26.956147  4221 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0409 23:58:30.982887  4221 solver.cpp:218] Iteration 9192 (2.9802 iter/s, 4.02657s/12 iters), loss = 0.150417
I0409 23:58:30.982949  4221 solver.cpp:237]     Train net output #0: loss = 0.150417 (* 1 = 0.150417 loss)
I0409 23:58:30.982962  4221 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0409 23:58:35.979480  4221 solver.cpp:218] Iteration 9204 (2.40177 iter/s, 4.99631s/12 iters), loss = 0.0683024
I0409 23:58:35.979539  4221 solver.cpp:237]     Train net output #0: loss = 0.0683024 (* 1 = 0.0683024 loss)
I0409 23:58:35.979552  4221 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0409 23:58:36.047503  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:58:40.779458  4221 solver.cpp:218] Iteration 9216 (2.50015 iter/s, 4.79972s/12 iters), loss = 0.108963
I0409 23:58:40.779565  4221 solver.cpp:237]     Train net output #0: loss = 0.108963 (* 1 = 0.108963 loss)
I0409 23:58:40.779577  4221 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0409 23:58:45.546072  4221 solver.cpp:218] Iteration 9228 (2.51768 iter/s, 4.7663s/12 iters), loss = 0.0625652
I0409 23:58:45.546135  4221 solver.cpp:237]     Train net output #0: loss = 0.0625653 (* 1 = 0.0625653 loss)
I0409 23:58:45.546147  4221 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0409 23:58:50.452160  4221 solver.cpp:218] Iteration 9240 (2.44608 iter/s, 4.90581s/12 iters), loss = 0.182343
I0409 23:58:50.452208  4221 solver.cpp:237]     Train net output #0: loss = 0.182343 (* 1 = 0.182343 loss)
I0409 23:58:50.452216  4221 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0409 23:58:55.500360  4221 solver.cpp:218] Iteration 9252 (2.37721 iter/s, 5.04794s/12 iters), loss = 0.126206
I0409 23:58:55.500414  4221 solver.cpp:237]     Train net output #0: loss = 0.126206 (* 1 = 0.126206 loss)
I0409 23:58:55.500425  4221 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0409 23:59:00.410024  4221 solver.cpp:218] Iteration 9264 (2.44429 iter/s, 4.90939s/12 iters), loss = 0.124499
I0409 23:59:00.410089  4221 solver.cpp:237]     Train net output #0: loss = 0.124499 (* 1 = 0.124499 loss)
I0409 23:59:00.410105  4221 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0409 23:59:05.335500  4221 solver.cpp:218] Iteration 9276 (2.43645 iter/s, 4.9252s/12 iters), loss = 0.0959589
I0409 23:59:05.335561  4221 solver.cpp:237]     Train net output #0: loss = 0.0959589 (* 1 = 0.0959589 loss)
I0409 23:59:05.335572  4221 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0409 23:59:07.490468  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0409 23:59:08.866367  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0409 23:59:10.517010  4221 solver.cpp:330] Iteration 9282, Testing net (#0)
I0409 23:59:10.517035  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:59:11.369675  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:59:15.007179  4221 solver.cpp:397]     Test net output #0: accuracy = 0.450368
I0409 23:59:15.007225  4221 solver.cpp:397]     Test net output #1: loss = 3.64085 (* 1 = 3.64085 loss)
I0409 23:59:16.908634  4221 solver.cpp:218] Iteration 9288 (1.03693 iter/s, 11.5726s/12 iters), loss = 0.0421764
I0409 23:59:16.908689  4221 solver.cpp:237]     Train net output #0: loss = 0.0421765 (* 1 = 0.0421765 loss)
I0409 23:59:16.908699  4221 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0409 23:59:21.904031  4221 solver.cpp:218] Iteration 9300 (2.40234 iter/s, 4.99513s/12 iters), loss = 0.0564887
I0409 23:59:21.904076  4221 solver.cpp:237]     Train net output #0: loss = 0.0564887 (* 1 = 0.0564887 loss)
I0409 23:59:21.904086  4221 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0409 23:59:24.034085  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:59:26.724756  4221 solver.cpp:218] Iteration 9312 (2.48939 iter/s, 4.82046s/12 iters), loss = 0.0818096
I0409 23:59:26.724817  4221 solver.cpp:237]     Train net output #0: loss = 0.0818096 (* 1 = 0.0818096 loss)
I0409 23:59:26.724829  4221 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0409 23:59:31.551589  4221 solver.cpp:218] Iteration 9324 (2.48624 iter/s, 4.82656s/12 iters), loss = 0.0558225
I0409 23:59:31.551643  4221 solver.cpp:237]     Train net output #0: loss = 0.0558225 (* 1 = 0.0558225 loss)
I0409 23:59:31.551656  4221 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0409 23:59:36.423911  4221 solver.cpp:218] Iteration 9336 (2.46302 iter/s, 4.87206s/12 iters), loss = 0.0815454
I0409 23:59:36.423961  4221 solver.cpp:237]     Train net output #0: loss = 0.0815454 (* 1 = 0.0815454 loss)
I0409 23:59:36.423974  4221 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0409 23:59:41.305264  4221 solver.cpp:218] Iteration 9348 (2.45847 iter/s, 4.88109s/12 iters), loss = 0.221949
I0409 23:59:41.305318  4221 solver.cpp:237]     Train net output #0: loss = 0.221949 (* 1 = 0.221949 loss)
I0409 23:59:41.305330  4221 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0409 23:59:46.184140  4221 solver.cpp:218] Iteration 9360 (2.45972 iter/s, 4.87861s/12 iters), loss = 0.0917783
I0409 23:59:46.184252  4221 solver.cpp:237]     Train net output #0: loss = 0.0917784 (* 1 = 0.0917784 loss)
I0409 23:59:46.184263  4221 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0409 23:59:51.033110  4221 solver.cpp:218] Iteration 9372 (2.47491 iter/s, 4.84865s/12 iters), loss = 0.0737103
I0409 23:59:51.033159  4221 solver.cpp:237]     Train net output #0: loss = 0.0737104 (* 1 = 0.0737104 loss)
I0409 23:59:51.033171  4221 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0409 23:59:55.398255  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0409 23:59:56.075104  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0409 23:59:56.595118  4221 solver.cpp:330] Iteration 9384, Testing net (#0)
I0409 23:59:56.595137  4221 net.cpp:676] Ignoring source layer train-data
I0409 23:59:57.287099  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:00.966595  4221 solver.cpp:397]     Test net output #0: accuracy = 0.453431
I0410 00:00:00.966641  4221 solver.cpp:397]     Test net output #1: loss = 3.59052 (* 1 = 3.59052 loss)
I0410 00:00:01.049908  4221 solver.cpp:218] Iteration 9384 (1.19804 iter/s, 10.0163s/12 iters), loss = 0.139603
I0410 00:00:01.049981  4221 solver.cpp:237]     Train net output #0: loss = 0.139603 (* 1 = 0.139603 loss)
I0410 00:00:01.049993  4221 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 00:00:05.173460  4221 solver.cpp:218] Iteration 9396 (2.91028 iter/s, 4.12332s/12 iters), loss = 0.0647017
I0410 00:00:05.173509  4221 solver.cpp:237]     Train net output #0: loss = 0.0647018 (* 1 = 0.0647018 loss)
I0410 00:00:05.173518  4221 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 00:00:09.425043  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:10.068032  4221 solver.cpp:218] Iteration 9408 (2.45183 iter/s, 4.8943s/12 iters), loss = 0.0650173
I0410 00:00:10.068089  4221 solver.cpp:237]     Train net output #0: loss = 0.0650174 (* 1 = 0.0650174 loss)
I0410 00:00:10.068099  4221 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 00:00:15.133400  4221 solver.cpp:218] Iteration 9420 (2.36916 iter/s, 5.06509s/12 iters), loss = 0.132631
I0410 00:00:15.133456  4221 solver.cpp:237]     Train net output #0: loss = 0.132631 (* 1 = 0.132631 loss)
I0410 00:00:15.133469  4221 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 00:00:19.921399  4221 solver.cpp:218] Iteration 9432 (2.5064 iter/s, 4.78773s/12 iters), loss = 0.0900565
I0410 00:00:19.921523  4221 solver.cpp:237]     Train net output #0: loss = 0.0900566 (* 1 = 0.0900566 loss)
I0410 00:00:19.921536  4221 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 00:00:24.763831  4221 solver.cpp:218] Iteration 9444 (2.47826 iter/s, 4.8421s/12 iters), loss = 0.0325093
I0410 00:00:24.763882  4221 solver.cpp:237]     Train net output #0: loss = 0.0325094 (* 1 = 0.0325094 loss)
I0410 00:00:24.763893  4221 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 00:00:29.780143  4221 solver.cpp:218] Iteration 9456 (2.39233 iter/s, 5.01604s/12 iters), loss = 0.0414006
I0410 00:00:29.780200  4221 solver.cpp:237]     Train net output #0: loss = 0.0414007 (* 1 = 0.0414007 loss)
I0410 00:00:29.780212  4221 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 00:00:34.571266  4221 solver.cpp:218] Iteration 9468 (2.50477 iter/s, 4.79086s/12 iters), loss = 0.129873
I0410 00:00:34.571307  4221 solver.cpp:237]     Train net output #0: loss = 0.129873 (* 1 = 0.129873 loss)
I0410 00:00:34.571316  4221 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 00:00:39.437999  4221 solver.cpp:218] Iteration 9480 (2.46587 iter/s, 4.86644s/12 iters), loss = 0.0360533
I0410 00:00:39.438066  4221 solver.cpp:237]     Train net output #0: loss = 0.0360534 (* 1 = 0.0360534 loss)
I0410 00:00:39.438082  4221 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 00:00:41.446321  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 00:00:42.137645  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 00:00:42.619952  4221 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 00:00:42.619976  4221 net.cpp:676] Ignoring source layer train-data
I0410 00:00:43.361001  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:47.134603  4221 solver.cpp:397]     Test net output #0: accuracy = 0.456495
I0410 00:00:47.134652  4221 solver.cpp:397]     Test net output #1: loss = 3.61173 (* 1 = 3.61173 loss)
I0410 00:00:49.048877  4221 solver.cpp:218] Iteration 9492 (1.24865 iter/s, 9.61041s/12 iters), loss = 0.182035
I0410 00:00:49.048929  4221 solver.cpp:237]     Train net output #0: loss = 0.182035 (* 1 = 0.182035 loss)
I0410 00:00:49.048941  4221 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 00:00:53.893874  4221 solver.cpp:218] Iteration 9504 (2.47692 iter/s, 4.84473s/12 iters), loss = 0.0339427
I0410 00:00:53.894008  4221 solver.cpp:237]     Train net output #0: loss = 0.0339428 (* 1 = 0.0339428 loss)
I0410 00:00:53.894021  4221 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 00:00:55.299417  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:58.675858  4221 solver.cpp:218] Iteration 9516 (2.50959 iter/s, 4.78165s/12 iters), loss = 0.115617
I0410 00:00:58.675910  4221 solver.cpp:237]     Train net output #0: loss = 0.115618 (* 1 = 0.115618 loss)
I0410 00:00:58.675921  4221 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 00:01:03.464558  4221 solver.cpp:218] Iteration 9528 (2.50604 iter/s, 4.78844s/12 iters), loss = 0.0746661
I0410 00:01:03.464618  4221 solver.cpp:237]     Train net output #0: loss = 0.0746662 (* 1 = 0.0746662 loss)
I0410 00:01:03.464632  4221 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 00:01:08.230171  4221 solver.cpp:218] Iteration 9540 (2.51818 iter/s, 4.76534s/12 iters), loss = 0.103624
I0410 00:01:08.230237  4221 solver.cpp:237]     Train net output #0: loss = 0.103624 (* 1 = 0.103624 loss)
I0410 00:01:08.230249  4221 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 00:01:13.080766  4221 solver.cpp:218] Iteration 9552 (2.47406 iter/s, 4.85032s/12 iters), loss = 0.0885757
I0410 00:01:13.080808  4221 solver.cpp:237]     Train net output #0: loss = 0.0885758 (* 1 = 0.0885758 loss)
I0410 00:01:13.080816  4221 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 00:01:17.934530  4221 solver.cpp:218] Iteration 9564 (2.47244 iter/s, 4.85351s/12 iters), loss = 0.130377
I0410 00:01:17.934587  4221 solver.cpp:237]     Train net output #0: loss = 0.130377 (* 1 = 0.130377 loss)
I0410 00:01:17.934599  4221 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 00:01:22.743710  4221 solver.cpp:218] Iteration 9576 (2.49537 iter/s, 4.80891s/12 iters), loss = 0.0622642
I0410 00:01:22.743767  4221 solver.cpp:237]     Train net output #0: loss = 0.0622643 (* 1 = 0.0622643 loss)
I0410 00:01:22.743777  4221 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 00:01:27.134626  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 00:01:29.885275  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 00:01:31.877378  4221 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 00:01:31.877401  4221 net.cpp:676] Ignoring source layer train-data
I0410 00:01:32.572108  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:01:36.447460  4221 solver.cpp:397]     Test net output #0: accuracy = 0.446078
I0410 00:01:36.447494  4221 solver.cpp:397]     Test net output #1: loss = 3.66892 (* 1 = 3.66892 loss)
I0410 00:01:36.530702  4221 solver.cpp:218] Iteration 9588 (0.870425 iter/s, 13.7864s/12 iters), loss = 0.0465582
I0410 00:01:36.530745  4221 solver.cpp:237]     Train net output #0: loss = 0.0465583 (* 1 = 0.0465583 loss)
I0410 00:01:36.530755  4221 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 00:01:40.739360  4221 solver.cpp:218] Iteration 9600 (2.85142 iter/s, 4.20843s/12 iters), loss = 0.0879691
I0410 00:01:40.739414  4221 solver.cpp:237]     Train net output #0: loss = 0.0879692 (* 1 = 0.0879692 loss)
I0410 00:01:40.739425  4221 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 00:01:44.246325  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:01:45.593322  4221 solver.cpp:218] Iteration 9612 (2.47234 iter/s, 4.8537s/12 iters), loss = 0.0927441
I0410 00:01:45.593376  4221 solver.cpp:237]     Train net output #0: loss = 0.0927442 (* 1 = 0.0927442 loss)
I0410 00:01:45.593387  4221 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 00:01:50.482916  4221 solver.cpp:218] Iteration 9624 (2.45432 iter/s, 4.88933s/12 iters), loss = 0.105807
I0410 00:01:50.482964  4221 solver.cpp:237]     Train net output #0: loss = 0.105807 (* 1 = 0.105807 loss)
I0410 00:01:50.482973  4221 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 00:01:55.365008  4221 solver.cpp:218] Iteration 9636 (2.45809 iter/s, 4.88183s/12 iters), loss = 0.165955
I0410 00:01:55.365061  4221 solver.cpp:237]     Train net output #0: loss = 0.165955 (* 1 = 0.165955 loss)
I0410 00:01:55.365072  4221 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 00:02:00.394461  4221 solver.cpp:218] Iteration 9648 (2.38608 iter/s, 5.02918s/12 iters), loss = 0.112713
I0410 00:02:00.394621  4221 solver.cpp:237]     Train net output #0: loss = 0.112713 (* 1 = 0.112713 loss)
I0410 00:02:00.394634  4221 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 00:02:05.314888  4221 solver.cpp:218] Iteration 9660 (2.43899 iter/s, 4.92006s/12 iters), loss = 0.084957
I0410 00:02:05.314931  4221 solver.cpp:237]     Train net output #0: loss = 0.0849572 (* 1 = 0.0849572 loss)
I0410 00:02:05.314941  4221 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 00:02:10.257932  4221 solver.cpp:218] Iteration 9672 (2.42778 iter/s, 4.94279s/12 iters), loss = 0.067867
I0410 00:02:10.258009  4221 solver.cpp:237]     Train net output #0: loss = 0.0678672 (* 1 = 0.0678672 loss)
I0410 00:02:10.258021  4221 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 00:02:15.063364  4221 solver.cpp:218] Iteration 9684 (2.49732 iter/s, 4.80515s/12 iters), loss = 0.0964353
I0410 00:02:15.063419  4221 solver.cpp:237]     Train net output #0: loss = 0.0964354 (* 1 = 0.0964354 loss)
I0410 00:02:15.063432  4221 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 00:02:17.043468  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 00:02:19.695065  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 00:02:20.302333  4221 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 00:02:20.302356  4221 net.cpp:676] Ignoring source layer train-data
I0410 00:02:20.948364  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:02:23.634032  4221 blocking_queue.cpp:49] Waiting for data
I0410 00:02:24.888607  4221 solver.cpp:397]     Test net output #0: accuracy = 0.44424
I0410 00:02:24.888636  4221 solver.cpp:397]     Test net output #1: loss = 3.62057 (* 1 = 3.62057 loss)
I0410 00:02:26.750525  4221 solver.cpp:218] Iteration 9696 (1.02682 iter/s, 11.6866s/12 iters), loss = 0.05923
I0410 00:02:26.750572  4221 solver.cpp:237]     Train net output #0: loss = 0.0592301 (* 1 = 0.0592301 loss)
I0410 00:02:26.750582  4221 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 00:02:31.600821  4221 solver.cpp:218] Iteration 9708 (2.47421 iter/s, 4.85004s/12 iters), loss = 0.0408765
I0410 00:02:31.600914  4221 solver.cpp:237]     Train net output #0: loss = 0.0408766 (* 1 = 0.0408766 loss)
I0410 00:02:31.600924  4221 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 00:02:32.376245  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:02:36.531013  4221 solver.cpp:218] Iteration 9720 (2.43414 iter/s, 4.92988s/12 iters), loss = 0.0747396
I0410 00:02:36.531067  4221 solver.cpp:237]     Train net output #0: loss = 0.0747397 (* 1 = 0.0747397 loss)
I0410 00:02:36.531078  4221 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 00:02:41.445327  4221 solver.cpp:218] Iteration 9732 (2.44198 iter/s, 4.91404s/12 iters), loss = 0.0592839
I0410 00:02:41.445374  4221 solver.cpp:237]     Train net output #0: loss = 0.0592841 (* 1 = 0.0592841 loss)
I0410 00:02:41.445384  4221 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 00:02:46.267382  4221 solver.cpp:218] Iteration 9744 (2.4887 iter/s, 4.8218s/12 iters), loss = 0.0359808
I0410 00:02:46.267431  4221 solver.cpp:237]     Train net output #0: loss = 0.035981 (* 1 = 0.035981 loss)
I0410 00:02:46.267441  4221 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 00:02:51.121122  4221 solver.cpp:218] Iteration 9756 (2.47245 iter/s, 4.85348s/12 iters), loss = 0.169189
I0410 00:02:51.121167  4221 solver.cpp:237]     Train net output #0: loss = 0.16919 (* 1 = 0.16919 loss)
I0410 00:02:51.121177  4221 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 00:02:56.104146  4221 solver.cpp:218] Iteration 9768 (2.4083 iter/s, 4.98276s/12 iters), loss = 0.0548992
I0410 00:02:56.104202  4221 solver.cpp:237]     Train net output #0: loss = 0.0548994 (* 1 = 0.0548994 loss)
I0410 00:02:56.104212  4221 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 00:03:00.895505  4221 solver.cpp:218] Iteration 9780 (2.50465 iter/s, 4.7911s/12 iters), loss = 0.063427
I0410 00:03:00.895552  4221 solver.cpp:237]     Train net output #0: loss = 0.0634272 (* 1 = 0.0634272 loss)
I0410 00:03:00.895561  4221 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 00:03:05.302227  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 00:03:06.319083  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 00:03:07.179646  4221 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 00:03:07.179668  4221 net.cpp:676] Ignoring source layer train-data
I0410 00:03:07.680737  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:03:11.485545  4221 solver.cpp:397]     Test net output #0: accuracy = 0.455882
I0410 00:03:11.485594  4221 solver.cpp:397]     Test net output #1: loss = 3.61416 (* 1 = 3.61416 loss)
I0410 00:03:11.569041  4221 solver.cpp:218] Iteration 9792 (1.12433 iter/s, 10.673s/12 iters), loss = 0.0234258
I0410 00:03:11.569087  4221 solver.cpp:237]     Train net output #0: loss = 0.023426 (* 1 = 0.023426 loss)
I0410 00:03:11.569098  4221 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 00:03:15.760047  4221 solver.cpp:218] Iteration 9804 (2.86343 iter/s, 4.19077s/12 iters), loss = 0.151635
I0410 00:03:15.760108  4221 solver.cpp:237]     Train net output #0: loss = 0.151635 (* 1 = 0.151635 loss)
I0410 00:03:15.760120  4221 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 00:03:18.627348  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:03:20.610623  4221 solver.cpp:218] Iteration 9816 (2.47407 iter/s, 4.8503s/12 iters), loss = 0.0177192
I0410 00:03:20.610683  4221 solver.cpp:237]     Train net output #0: loss = 0.0177194 (* 1 = 0.0177194 loss)
I0410 00:03:20.610697  4221 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 00:03:25.432122  4221 solver.cpp:218] Iteration 9828 (2.48899 iter/s, 4.82123s/12 iters), loss = 0.03854
I0410 00:03:25.432173  4221 solver.cpp:237]     Train net output #0: loss = 0.0385401 (* 1 = 0.0385401 loss)
I0410 00:03:25.432183  4221 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 00:03:30.286154  4221 solver.cpp:218] Iteration 9840 (2.4723 iter/s, 4.85377s/12 iters), loss = 0.0366895
I0410 00:03:30.286195  4221 solver.cpp:237]     Train net output #0: loss = 0.0366897 (* 1 = 0.0366897 loss)
I0410 00:03:30.286204  4221 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 00:03:35.154664  4221 solver.cpp:218] Iteration 9852 (2.46495 iter/s, 4.86825s/12 iters), loss = 0.068602
I0410 00:03:35.154726  4221 solver.cpp:237]     Train net output #0: loss = 0.0686022 (* 1 = 0.0686022 loss)
I0410 00:03:35.154737  4221 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 00:03:40.032099  4221 solver.cpp:218] Iteration 9864 (2.46045 iter/s, 4.87717s/12 iters), loss = 0.0598735
I0410 00:03:40.032218  4221 solver.cpp:237]     Train net output #0: loss = 0.0598737 (* 1 = 0.0598737 loss)
I0410 00:03:40.032230  4221 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 00:03:44.896708  4221 solver.cpp:218] Iteration 9876 (2.46696 iter/s, 4.86428s/12 iters), loss = 0.0679954
I0410 00:03:44.896762  4221 solver.cpp:237]     Train net output #0: loss = 0.0679955 (* 1 = 0.0679955 loss)
I0410 00:03:44.896775  4221 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 00:03:49.765491  4221 solver.cpp:218] Iteration 9888 (2.46481 iter/s, 4.86852s/12 iters), loss = 0.087371
I0410 00:03:49.765533  4221 solver.cpp:237]     Train net output #0: loss = 0.0873711 (* 1 = 0.0873711 loss)
I0410 00:03:49.765542  4221 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 00:03:51.773068  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 00:03:52.695927  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 00:03:53.658159  4221 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 00:03:53.658186  4221 net.cpp:676] Ignoring source layer train-data
I0410 00:03:54.166380  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:03:58.008605  4221 solver.cpp:397]     Test net output #0: accuracy = 0.454044
I0410 00:03:58.008652  4221 solver.cpp:397]     Test net output #1: loss = 3.66783 (* 1 = 3.66783 loss)
I0410 00:03:59.863652  4221 solver.cpp:218] Iteration 9900 (1.18839 iter/s, 10.0977s/12 iters), loss = 0.132628
I0410 00:03:59.863700  4221 solver.cpp:237]     Train net output #0: loss = 0.132628 (* 1 = 0.132628 loss)
I0410 00:03:59.863709  4221 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 00:04:04.740722  4221 solver.cpp:218] Iteration 9912 (2.46063 iter/s, 4.87681s/12 iters), loss = 0.0280204
I0410 00:04:04.740772  4221 solver.cpp:237]     Train net output #0: loss = 0.0280205 (* 1 = 0.0280205 loss)
I0410 00:04:04.740782  4221 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 00:04:04.838805  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:04:09.745388  4221 solver.cpp:218] Iteration 9924 (2.39789 iter/s, 5.0044s/12 iters), loss = 0.08367
I0410 00:04:09.745434  4221 solver.cpp:237]     Train net output #0: loss = 0.0836702 (* 1 = 0.0836702 loss)
I0410 00:04:09.745445  4221 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 00:04:14.571642  4221 solver.cpp:218] Iteration 9936 (2.48653 iter/s, 4.826s/12 iters), loss = 0.0964301
I0410 00:04:14.571772  4221 solver.cpp:237]     Train net output #0: loss = 0.0964303 (* 1 = 0.0964303 loss)
I0410 00:04:14.571784  4221 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 00:04:19.425371  4221 solver.cpp:218] Iteration 9948 (2.4725 iter/s, 4.85339s/12 iters), loss = 0.105331
I0410 00:04:19.425415  4221 solver.cpp:237]     Train net output #0: loss = 0.105331 (* 1 = 0.105331 loss)
I0410 00:04:19.425423  4221 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 00:04:24.255009  4221 solver.cpp:218] Iteration 9960 (2.48479 iter/s, 4.82938s/12 iters), loss = 0.103335
I0410 00:04:24.255057  4221 solver.cpp:237]     Train net output #0: loss = 0.103336 (* 1 = 0.103336 loss)
I0410 00:04:24.255066  4221 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 00:04:29.162122  4221 solver.cpp:218] Iteration 9972 (2.44556 iter/s, 4.90685s/12 iters), loss = 0.0728573
I0410 00:04:29.162170  4221 solver.cpp:237]     Train net output #0: loss = 0.0728574 (* 1 = 0.0728574 loss)
I0410 00:04:29.162179  4221 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 00:04:34.031471  4221 solver.cpp:218] Iteration 9984 (2.46453 iter/s, 4.86909s/12 iters), loss = 0.108933
I0410 00:04:34.031518  4221 solver.cpp:237]     Train net output #0: loss = 0.108933 (* 1 = 0.108933 loss)
I0410 00:04:34.031527  4221 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 00:04:38.580258  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 00:04:39.660601  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 00:04:40.511588  4221 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 00:04:40.511618  4221 net.cpp:676] Ignoring source layer train-data
I0410 00:04:41.008662  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:04:44.932246  4221 solver.cpp:397]     Test net output #0: accuracy = 0.458333
I0410 00:04:44.932366  4221 solver.cpp:397]     Test net output #1: loss = 3.64024 (* 1 = 3.64024 loss)
I0410 00:04:45.015547  4221 solver.cpp:218] Iteration 9996 (1.09254 iter/s, 10.9836s/12 iters), loss = 0.0388397
I0410 00:04:45.015588  4221 solver.cpp:237]     Train net output #0: loss = 0.0388398 (* 1 = 0.0388398 loss)
I0410 00:04:45.015599  4221 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 00:04:49.301013  4221 solver.cpp:218] Iteration 10008 (2.80031 iter/s, 4.28524s/12 iters), loss = 0.14537
I0410 00:04:49.301055  4221 solver.cpp:237]     Train net output #0: loss = 0.14537 (* 1 = 0.14537 loss)
I0410 00:04:49.301066  4221 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 00:04:51.486532  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:04:54.126907  4221 solver.cpp:218] Iteration 10020 (2.48672 iter/s, 4.82564s/12 iters), loss = 0.0337345
I0410 00:04:54.126961  4221 solver.cpp:237]     Train net output #0: loss = 0.0337346 (* 1 = 0.0337346 loss)
I0410 00:04:54.126973  4221 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 00:04:58.967103  4221 solver.cpp:218] Iteration 10032 (2.47937 iter/s, 4.83993s/12 iters), loss = 0.0361255
I0410 00:04:58.967151  4221 solver.cpp:237]     Train net output #0: loss = 0.0361256 (* 1 = 0.0361256 loss)
I0410 00:04:58.967161  4221 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 00:05:03.787003  4221 solver.cpp:218] Iteration 10044 (2.48981 iter/s, 4.81964s/12 iters), loss = 0.127606
I0410 00:05:03.787055  4221 solver.cpp:237]     Train net output #0: loss = 0.127606 (* 1 = 0.127606 loss)
I0410 00:05:03.787065  4221 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 00:05:08.828894  4221 solver.cpp:218] Iteration 10056 (2.38019 iter/s, 5.04162s/12 iters), loss = 0.040962
I0410 00:05:08.828950  4221 solver.cpp:237]     Train net output #0: loss = 0.0409622 (* 1 = 0.0409622 loss)
I0410 00:05:08.828963  4221 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 00:05:13.620505  4221 solver.cpp:218] Iteration 10068 (2.50451 iter/s, 4.79135s/12 iters), loss = 0.0978475
I0410 00:05:13.620551  4221 solver.cpp:237]     Train net output #0: loss = 0.0978476 (* 1 = 0.0978476 loss)
I0410 00:05:13.620561  4221 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 00:05:18.418433  4221 solver.cpp:218] Iteration 10080 (2.50122 iter/s, 4.79767s/12 iters), loss = 0.0410353
I0410 00:05:18.418620  4221 solver.cpp:237]     Train net output #0: loss = 0.0410355 (* 1 = 0.0410355 loss)
I0410 00:05:18.418635  4221 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 00:05:23.256103  4221 solver.cpp:218] Iteration 10092 (2.48074 iter/s, 4.83727s/12 iters), loss = 0.0291979
I0410 00:05:23.256157  4221 solver.cpp:237]     Train net output #0: loss = 0.029198 (* 1 = 0.029198 loss)
I0410 00:05:23.256168  4221 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 00:05:25.211663  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 00:05:26.201364  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 00:05:27.498437  4221 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 00:05:27.498461  4221 net.cpp:676] Ignoring source layer train-data
I0410 00:05:27.958642  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:05:32.184610  4221 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0410 00:05:32.184659  4221 solver.cpp:397]     Test net output #1: loss = 3.66135 (* 1 = 3.66135 loss)
I0410 00:05:34.033543  4221 solver.cpp:218] Iteration 10104 (1.11349 iter/s, 10.7769s/12 iters), loss = 0.0453892
I0410 00:05:34.033589  4221 solver.cpp:237]     Train net output #0: loss = 0.0453893 (* 1 = 0.0453893 loss)
I0410 00:05:34.033598  4221 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 00:05:38.240303  4233 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:05:38.846437  4221 solver.cpp:218] Iteration 10116 (2.49343 iter/s, 4.81264s/12 iters), loss = 0.195028
I0410 00:05:38.846484  4221 solver.cpp:237]     Train net output #0: loss = 0.195028 (* 1 = 0.195028 loss)
I0410 00:05:38.846496  4221 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 00:05:43.693703  4221 solver.cpp:218] Iteration 10128 (2.47576 iter/s, 4.847s/12 iters), loss = 0.098644
I0410 00:05:43.693760  4221 solver.cpp:237]     Train net output #0: loss = 0.0986442 (* 1 = 0.0986442 loss)
I0410 00:05:43.693773  4221 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 00:05:48.508123  4221 solver.cpp:218] Iteration 10140 (2.49265 iter/s, 4.81416s/12 iters), loss = 0.0416593
I0410 00:05:48.508224  4221 solver.cpp:237]     Train net output #0: loss = 0.0416594 (* 1 = 0.0416594 loss)
I0410 00:05:48.508234  4221 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 00:05:53.347687  4221 solver.cpp:218] Iteration 10152 (2.47972 iter/s, 4.83925s/12 iters), loss = 0.0147403
I0410 00:05:53.347738  4221 solver.cpp:237]     Train net output #0: loss = 0.0147405 (* 1 = 0.0147405 loss)
I0410 00:05:53.347750  4221 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 00:05:58.173467  4221 solver.cpp:218] Iteration 10164 (2.48678 iter/s, 4.82552s/12 iters), loss = 0.0319597
I0410 00:05:58.173513  4221 solver.cpp:237]     Train net output #0: loss = 0.0319598 (* 1 = 0.0319598 loss)
I0410 00:05:58.173522  4221 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 00:06:03.062872  4221 solver.cpp:218] Iteration 10176 (2.45442 iter/s, 4.88914s/12 iters), loss = 0.0205677
I0410 00:06:03.062930  4221 solver.cpp:237]     Train net output #0: loss = 0.0205679 (* 1 = 0.0205679 loss)
I0410 00:06:03.062943  4221 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 00:06:07.892206  4221 solver.cpp:218] Iteration 10188 (2.48495 iter/s, 4.82907s/12 iters), loss = 0.0382411
I0410 00:06:07.892249  4221 solver.cpp:237]     Train net output #0: loss = 0.0382412 (* 1 = 0.0382412 loss)
I0410 00:06:07.892258  4221 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 00:06:12.353565  4221 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 00:06:13.642715  4221 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 00:06:15.381127  4221 solver.cpp:310] Iteration 10200, loss = 0.106558
I0410 00:06:15.381162  4221 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 00:06:15.381170  4221 net.cpp:676] Ignoring source layer train-data
I0410 00:06:15.806259  4234 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:06:19.898706  4221 solver.cpp:397]     Test net output #0: accuracy = 0.452206
I0410 00:06:19.898815  4221 solver.cpp:397]     Test net output #1: loss = 3.66147 (* 1 = 3.66147 loss)
I0410 00:06:19.898821  4221 solver.cpp:315] Optimization Done.
I0410 00:06:19.898824  4221 caffe.cpp:259] Optimization Done.