DIGITS-CNN/cars/lr-investigations/fixed/1e-1/caffe_output.log

I0406 07:07:56.191773  5644 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210406-070754-1bbb/solver.prototxt
I0406 07:07:56.191913  5644 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0406 07:07:56.191917  5644 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0406 07:07:56.191977  5644 caffe.cpp:218] Using GPUs 1
I0406 07:07:56.208690  5644 caffe.cpp:223] GPU 1: GeForce GTX TITAN X
I0406 07:07:56.406143  5644 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.1
display: 12
max_iter: 20400
lr_policy: "fixed"
momentum: 0.9
weight_decay: 0.001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0406 07:07:56.407052  5644 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0406 07:07:56.407693  5644 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0406 07:07:56.407707  5644 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0406 07:07:56.407836  5644 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-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/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: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0406 07:07:56.407922  5644 layer_factory.hpp:77] Creating layer train-data
I0406 07:07:56.409749  5644 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db
I0406 07:07:56.409956  5644 net.cpp:84] Creating Layer train-data
I0406 07:07:56.409968  5644 net.cpp:380] train-data -> data
I0406 07:07:56.409986  5644 net.cpp:380] train-data -> label
I0406 07:07:56.409994  5644 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0406 07:07:56.414346  5644 data_layer.cpp:45] output data size: 128,3,227,227
I0406 07:07:56.555272  5644 net.cpp:122] Setting up train-data
I0406 07:07:56.555292  5644 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0406 07:07:56.555296  5644 net.cpp:129] Top shape: 128 (128)
I0406 07:07:56.555299  5644 net.cpp:137] Memory required for data: 79149056
I0406 07:07:56.555306  5644 layer_factory.hpp:77] Creating layer conv1
I0406 07:07:56.555325  5644 net.cpp:84] Creating Layer conv1
I0406 07:07:56.555330  5644 net.cpp:406] conv1 <- data
I0406 07:07:56.555341  5644 net.cpp:380] conv1 -> conv1
I0406 07:07:56.977953  5644 net.cpp:122] Setting up conv1
I0406 07:07:56.977977  5644 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0406 07:07:56.977980  5644 net.cpp:137] Memory required for data: 227833856
I0406 07:07:56.977999  5644 layer_factory.hpp:77] Creating layer relu1
I0406 07:07:56.978009  5644 net.cpp:84] Creating Layer relu1
I0406 07:07:56.978013  5644 net.cpp:406] relu1 <- conv1
I0406 07:07:56.978018  5644 net.cpp:367] relu1 -> conv1 (in-place)
I0406 07:07:56.978277  5644 net.cpp:122] Setting up relu1
I0406 07:07:56.978287  5644 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0406 07:07:56.978289  5644 net.cpp:137] Memory required for data: 376518656
I0406 07:07:56.978292  5644 layer_factory.hpp:77] Creating layer norm1
I0406 07:07:56.978300  5644 net.cpp:84] Creating Layer norm1
I0406 07:07:56.978303  5644 net.cpp:406] norm1 <- conv1
I0406 07:07:56.978329  5644 net.cpp:380] norm1 -> norm1
I0406 07:07:56.978744  5644 net.cpp:122] Setting up norm1
I0406 07:07:56.978754  5644 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0406 07:07:56.978755  5644 net.cpp:137] Memory required for data: 525203456
I0406 07:07:56.978758  5644 layer_factory.hpp:77] Creating layer pool1
I0406 07:07:56.978765  5644 net.cpp:84] Creating Layer pool1
I0406 07:07:56.978767  5644 net.cpp:406] pool1 <- norm1
I0406 07:07:56.978771  5644 net.cpp:380] pool1 -> pool1
I0406 07:07:56.978802  5644 net.cpp:122] Setting up pool1
I0406 07:07:56.978806  5644 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0406 07:07:56.978808  5644 net.cpp:137] Memory required for data: 561035264
I0406 07:07:56.978811  5644 layer_factory.hpp:77] Creating layer conv2
I0406 07:07:56.978819  5644 net.cpp:84] Creating Layer conv2
I0406 07:07:56.978821  5644 net.cpp:406] conv2 <- pool1
I0406 07:07:56.978826  5644 net.cpp:380] conv2 -> conv2
I0406 07:07:56.986726  5644 net.cpp:122] Setting up conv2
I0406 07:07:56.986742  5644 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0406 07:07:56.986744  5644 net.cpp:137] Memory required for data: 656586752
I0406 07:07:56.986755  5644 layer_factory.hpp:77] Creating layer relu2
I0406 07:07:56.986763  5644 net.cpp:84] Creating Layer relu2
I0406 07:07:56.986766  5644 net.cpp:406] relu2 <- conv2
I0406 07:07:56.986771  5644 net.cpp:367] relu2 -> conv2 (in-place)
I0406 07:07:56.988983  5644 net.cpp:122] Setting up relu2
I0406 07:07:56.988992  5644 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0406 07:07:56.988996  5644 net.cpp:137] Memory required for data: 752138240
I0406 07:07:56.988997  5644 layer_factory.hpp:77] Creating layer norm2
I0406 07:07:56.989004  5644 net.cpp:84] Creating Layer norm2
I0406 07:07:56.989006  5644 net.cpp:406] norm2 <- conv2
I0406 07:07:56.989010  5644 net.cpp:380] norm2 -> norm2
I0406 07:07:56.989269  5644 net.cpp:122] Setting up norm2
I0406 07:07:56.989275  5644 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0406 07:07:56.989277  5644 net.cpp:137] Memory required for data: 847689728
I0406 07:07:56.989280  5644 layer_factory.hpp:77] Creating layer pool2
I0406 07:07:56.989286  5644 net.cpp:84] Creating Layer pool2
I0406 07:07:56.989289  5644 net.cpp:406] pool2 <- norm2
I0406 07:07:56.989292  5644 net.cpp:380] pool2 -> pool2
I0406 07:07:56.989316  5644 net.cpp:122] Setting up pool2
I0406 07:07:56.989320  5644 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0406 07:07:56.989322  5644 net.cpp:137] Memory required for data: 869840896
I0406 07:07:56.989324  5644 layer_factory.hpp:77] Creating layer conv3
I0406 07:07:56.989333  5644 net.cpp:84] Creating Layer conv3
I0406 07:07:56.989336  5644 net.cpp:406] conv3 <- pool2
I0406 07:07:56.989338  5644 net.cpp:380] conv3 -> conv3
I0406 07:07:57.000010  5644 net.cpp:122] Setting up conv3
I0406 07:07:57.000026  5644 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0406 07:07:57.000030  5644 net.cpp:137] Memory required for data: 903067648
I0406 07:07:57.000041  5644 layer_factory.hpp:77] Creating layer relu3
I0406 07:07:57.000047  5644 net.cpp:84] Creating Layer relu3
I0406 07:07:57.000051  5644 net.cpp:406] relu3 <- conv3
I0406 07:07:57.000056  5644 net.cpp:367] relu3 -> conv3 (in-place)
I0406 07:07:57.000449  5644 net.cpp:122] Setting up relu3
I0406 07:07:57.000458  5644 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0406 07:07:57.000459  5644 net.cpp:137] Memory required for data: 936294400
I0406 07:07:57.000463  5644 layer_factory.hpp:77] Creating layer conv4
I0406 07:07:57.000470  5644 net.cpp:84] Creating Layer conv4
I0406 07:07:57.000473  5644 net.cpp:406] conv4 <- conv3
I0406 07:07:57.000478  5644 net.cpp:380] conv4 -> conv4
I0406 07:07:57.009779  5644 net.cpp:122] Setting up conv4
I0406 07:07:57.009799  5644 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0406 07:07:57.009800  5644 net.cpp:137] Memory required for data: 969521152
I0406 07:07:57.009809  5644 layer_factory.hpp:77] Creating layer relu4
I0406 07:07:57.009817  5644 net.cpp:84] Creating Layer relu4
I0406 07:07:57.009821  5644 net.cpp:406] relu4 <- conv4
I0406 07:07:57.009845  5644 net.cpp:367] relu4 -> conv4 (in-place)
I0406 07:07:57.010155  5644 net.cpp:122] Setting up relu4
I0406 07:07:57.010164  5644 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0406 07:07:57.010167  5644 net.cpp:137] Memory required for data: 1002747904
I0406 07:07:57.010169  5644 layer_factory.hpp:77] Creating layer conv5
I0406 07:07:57.010179  5644 net.cpp:84] Creating Layer conv5
I0406 07:07:57.010182  5644 net.cpp:406] conv5 <- conv4
I0406 07:07:57.010186  5644 net.cpp:380] conv5 -> conv5
I0406 07:07:57.018930  5644 net.cpp:122] Setting up conv5
I0406 07:07:57.018950  5644 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0406 07:07:57.018954  5644 net.cpp:137] Memory required for data: 1024899072
I0406 07:07:57.018965  5644 layer_factory.hpp:77] Creating layer relu5
I0406 07:07:57.018975  5644 net.cpp:84] Creating Layer relu5
I0406 07:07:57.018977  5644 net.cpp:406] relu5 <- conv5
I0406 07:07:57.018982  5644 net.cpp:367] relu5 -> conv5 (in-place)
I0406 07:07:57.019443  5644 net.cpp:122] Setting up relu5
I0406 07:07:57.019452  5644 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0406 07:07:57.019454  5644 net.cpp:137] Memory required for data: 1047050240
I0406 07:07:57.019457  5644 layer_factory.hpp:77] Creating layer pool5
I0406 07:07:57.019462  5644 net.cpp:84] Creating Layer pool5
I0406 07:07:57.019465  5644 net.cpp:406] pool5 <- conv5
I0406 07:07:57.019472  5644 net.cpp:380] pool5 -> pool5
I0406 07:07:57.019505  5644 net.cpp:122] Setting up pool5
I0406 07:07:57.019508  5644 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0406 07:07:57.019510  5644 net.cpp:137] Memory required for data: 1051768832
I0406 07:07:57.019512  5644 layer_factory.hpp:77] Creating layer fc6
I0406 07:07:57.019522  5644 net.cpp:84] Creating Layer fc6
I0406 07:07:57.019524  5644 net.cpp:406] fc6 <- pool5
I0406 07:07:57.019528  5644 net.cpp:380] fc6 -> fc6
I0406 07:07:57.386009  5644 net.cpp:122] Setting up fc6
I0406 07:07:57.386032  5644 net.cpp:129] Top shape: 128 4096 (524288)
I0406 07:07:57.386035  5644 net.cpp:137] Memory required for data: 1053865984
I0406 07:07:57.386044  5644 layer_factory.hpp:77] Creating layer relu6
I0406 07:07:57.386057  5644 net.cpp:84] Creating Layer relu6
I0406 07:07:57.386061  5644 net.cpp:406] relu6 <- fc6
I0406 07:07:57.386066  5644 net.cpp:367] relu6 -> fc6 (in-place)
I0406 07:07:57.386662  5644 net.cpp:122] Setting up relu6
I0406 07:07:57.386670  5644 net.cpp:129] Top shape: 128 4096 (524288)
I0406 07:07:57.386673  5644 net.cpp:137] Memory required for data: 1055963136
I0406 07:07:57.386675  5644 layer_factory.hpp:77] Creating layer drop6
I0406 07:07:57.386682  5644 net.cpp:84] Creating Layer drop6
I0406 07:07:57.386683  5644 net.cpp:406] drop6 <- fc6
I0406 07:07:57.386688  5644 net.cpp:367] drop6 -> fc6 (in-place)
I0406 07:07:57.386711  5644 net.cpp:122] Setting up drop6
I0406 07:07:57.386715  5644 net.cpp:129] Top shape: 128 4096 (524288)
I0406 07:07:57.386718  5644 net.cpp:137] Memory required for data: 1058060288
I0406 07:07:57.386719  5644 layer_factory.hpp:77] Creating layer fc7
I0406 07:07:57.386726  5644 net.cpp:84] Creating Layer fc7
I0406 07:07:57.386729  5644 net.cpp:406] fc7 <- fc6
I0406 07:07:57.386734  5644 net.cpp:380] fc7 -> fc7
I0406 07:07:57.572324  5644 net.cpp:122] Setting up fc7
I0406 07:07:57.572346  5644 net.cpp:129] Top shape: 128 4096 (524288)
I0406 07:07:57.572350  5644 net.cpp:137] Memory required for data: 1060157440
I0406 07:07:57.572357  5644 layer_factory.hpp:77] Creating layer relu7
I0406 07:07:57.572367  5644 net.cpp:84] Creating Layer relu7
I0406 07:07:57.572371  5644 net.cpp:406] relu7 <- fc7
I0406 07:07:57.572376  5644 net.cpp:367] relu7 -> fc7 (in-place)
I0406 07:07:57.572759  5644 net.cpp:122] Setting up relu7
I0406 07:07:57.572770  5644 net.cpp:129] Top shape: 128 4096 (524288)
I0406 07:07:57.572774  5644 net.cpp:137] Memory required for data: 1062254592
I0406 07:07:57.572777  5644 layer_factory.hpp:77] Creating layer drop7
I0406 07:07:57.572784  5644 net.cpp:84] Creating Layer drop7
I0406 07:07:57.572788  5644 net.cpp:406] drop7 <- fc7
I0406 07:07:57.572813  5644 net.cpp:367] drop7 -> fc7 (in-place)
I0406 07:07:57.572836  5644 net.cpp:122] Setting up drop7
I0406 07:07:57.572840  5644 net.cpp:129] Top shape: 128 4096 (524288)
I0406 07:07:57.572842  5644 net.cpp:137] Memory required for data: 1064351744
I0406 07:07:57.572844  5644 layer_factory.hpp:77] Creating layer fc8
I0406 07:07:57.572851  5644 net.cpp:84] Creating Layer fc8
I0406 07:07:57.572854  5644 net.cpp:406] fc8 <- fc7
I0406 07:07:57.572857  5644 net.cpp:380] fc8 -> fc8
I0406 07:07:57.580382  5644 net.cpp:122] Setting up fc8
I0406 07:07:57.580401  5644 net.cpp:129] Top shape: 128 196 (25088)
I0406 07:07:57.580404  5644 net.cpp:137] Memory required for data: 1064452096
I0406 07:07:57.580412  5644 layer_factory.hpp:77] Creating layer loss
I0406 07:07:57.580420  5644 net.cpp:84] Creating Layer loss
I0406 07:07:57.580423  5644 net.cpp:406] loss <- fc8
I0406 07:07:57.580427  5644 net.cpp:406] loss <- label
I0406 07:07:57.580435  5644 net.cpp:380] loss -> loss
I0406 07:07:57.580446  5644 layer_factory.hpp:77] Creating layer loss
I0406 07:07:57.581208  5644 net.cpp:122] Setting up loss
I0406 07:07:57.581218  5644 net.cpp:129] Top shape: (1)
I0406 07:07:57.581219  5644 net.cpp:132]     with loss weight 1
I0406 07:07:57.581234  5644 net.cpp:137] Memory required for data: 1064452100
I0406 07:07:57.581238  5644 net.cpp:198] loss needs backward computation.
I0406 07:07:57.581243  5644 net.cpp:198] fc8 needs backward computation.
I0406 07:07:57.581245  5644 net.cpp:198] drop7 needs backward computation.
I0406 07:07:57.581248  5644 net.cpp:198] relu7 needs backward computation.
I0406 07:07:57.581250  5644 net.cpp:198] fc7 needs backward computation.
I0406 07:07:57.581252  5644 net.cpp:198] drop6 needs backward computation.
I0406 07:07:57.581255  5644 net.cpp:198] relu6 needs backward computation.
I0406 07:07:57.581257  5644 net.cpp:198] fc6 needs backward computation.
I0406 07:07:57.581260  5644 net.cpp:198] pool5 needs backward computation.
I0406 07:07:57.581262  5644 net.cpp:198] relu5 needs backward computation.
I0406 07:07:57.581264  5644 net.cpp:198] conv5 needs backward computation.
I0406 07:07:57.581267  5644 net.cpp:198] relu4 needs backward computation.
I0406 07:07:57.581270  5644 net.cpp:198] conv4 needs backward computation.
I0406 07:07:57.581272  5644 net.cpp:198] relu3 needs backward computation.
I0406 07:07:57.581274  5644 net.cpp:198] conv3 needs backward computation.
I0406 07:07:57.581277  5644 net.cpp:198] pool2 needs backward computation.
I0406 07:07:57.581279  5644 net.cpp:198] norm2 needs backward computation.
I0406 07:07:57.581282  5644 net.cpp:198] relu2 needs backward computation.
I0406 07:07:57.581284  5644 net.cpp:198] conv2 needs backward computation.
I0406 07:07:57.581287  5644 net.cpp:198] pool1 needs backward computation.
I0406 07:07:57.581290  5644 net.cpp:198] norm1 needs backward computation.
I0406 07:07:57.581292  5644 net.cpp:198] relu1 needs backward computation.
I0406 07:07:57.581295  5644 net.cpp:198] conv1 needs backward computation.
I0406 07:07:57.581297  5644 net.cpp:200] train-data does not need backward computation.
I0406 07:07:57.581300  5644 net.cpp:242] This network produces output loss
I0406 07:07:57.581312  5644 net.cpp:255] Network initialization done.
I0406 07:07:57.581851  5644 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0406 07:07:57.581879  5644 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0406 07:07:57.582011  5644 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-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/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: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0406 07:07:57.582113  5644 layer_factory.hpp:77] Creating layer val-data
I0406 07:07:57.584072  5644 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db
I0406 07:07:57.584295  5644 net.cpp:84] Creating Layer val-data
I0406 07:07:57.584307  5644 net.cpp:380] val-data -> data
I0406 07:07:57.584314  5644 net.cpp:380] val-data -> label
I0406 07:07:57.584321  5644 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0406 07:07:57.588143  5644 data_layer.cpp:45] output data size: 32,3,227,227
I0406 07:07:57.625741  5644 net.cpp:122] Setting up val-data
I0406 07:07:57.625761  5644 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0406 07:07:57.625764  5644 net.cpp:129] Top shape: 32 (32)
I0406 07:07:57.625766  5644 net.cpp:137] Memory required for data: 19787264
I0406 07:07:57.625771  5644 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0406 07:07:57.625783  5644 net.cpp:84] Creating Layer label_val-data_1_split
I0406 07:07:57.625787  5644 net.cpp:406] label_val-data_1_split <- label
I0406 07:07:57.625793  5644 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0406 07:07:57.625802  5644 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0406 07:07:57.625850  5644 net.cpp:122] Setting up label_val-data_1_split
I0406 07:07:57.625855  5644 net.cpp:129] Top shape: 32 (32)
I0406 07:07:57.625857  5644 net.cpp:129] Top shape: 32 (32)
I0406 07:07:57.625859  5644 net.cpp:137] Memory required for data: 19787520
I0406 07:07:57.625861  5644 layer_factory.hpp:77] Creating layer conv1
I0406 07:07:57.625871  5644 net.cpp:84] Creating Layer conv1
I0406 07:07:57.625874  5644 net.cpp:406] conv1 <- data
I0406 07:07:57.625878  5644 net.cpp:380] conv1 -> conv1
I0406 07:07:57.628376  5644 net.cpp:122] Setting up conv1
I0406 07:07:57.628391  5644 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0406 07:07:57.628393  5644 net.cpp:137] Memory required for data: 56958720
I0406 07:07:57.628403  5644 layer_factory.hpp:77] Creating layer relu1
I0406 07:07:57.628409  5644 net.cpp:84] Creating Layer relu1
I0406 07:07:57.628412  5644 net.cpp:406] relu1 <- conv1
I0406 07:07:57.628417  5644 net.cpp:367] relu1 -> conv1 (in-place)
I0406 07:07:57.628680  5644 net.cpp:122] Setting up relu1
I0406 07:07:57.628687  5644 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0406 07:07:57.628690  5644 net.cpp:137] Memory required for data: 94129920
I0406 07:07:57.628692  5644 layer_factory.hpp:77] Creating layer norm1
I0406 07:07:57.628700  5644 net.cpp:84] Creating Layer norm1
I0406 07:07:57.628701  5644 net.cpp:406] norm1 <- conv1
I0406 07:07:57.628705  5644 net.cpp:380] norm1 -> norm1
I0406 07:07:57.629153  5644 net.cpp:122] Setting up norm1
I0406 07:07:57.629161  5644 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0406 07:07:57.629164  5644 net.cpp:137] Memory required for data: 131301120
I0406 07:07:57.629166  5644 layer_factory.hpp:77] Creating layer pool1
I0406 07:07:57.629173  5644 net.cpp:84] Creating Layer pool1
I0406 07:07:57.629174  5644 net.cpp:406] pool1 <- norm1
I0406 07:07:57.629179  5644 net.cpp:380] pool1 -> pool1
I0406 07:07:57.629204  5644 net.cpp:122] Setting up pool1
I0406 07:07:57.629209  5644 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0406 07:07:57.629210  5644 net.cpp:137] Memory required for data: 140259072
I0406 07:07:57.629212  5644 layer_factory.hpp:77] Creating layer conv2
I0406 07:07:57.629220  5644 net.cpp:84] Creating Layer conv2
I0406 07:07:57.629222  5644 net.cpp:406] conv2 <- pool1
I0406 07:07:57.629248  5644 net.cpp:380] conv2 -> conv2
I0406 07:07:57.635715  5644 net.cpp:122] Setting up conv2
I0406 07:07:57.635735  5644 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0406 07:07:57.635737  5644 net.cpp:137] Memory required for data: 164146944
I0406 07:07:57.635748  5644 layer_factory.hpp:77] Creating layer relu2
I0406 07:07:57.635757  5644 net.cpp:84] Creating Layer relu2
I0406 07:07:57.635761  5644 net.cpp:406] relu2 <- conv2
I0406 07:07:57.635766  5644 net.cpp:367] relu2 -> conv2 (in-place)
I0406 07:07:57.636269  5644 net.cpp:122] Setting up relu2
I0406 07:07:57.636278  5644 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0406 07:07:57.636281  5644 net.cpp:137] Memory required for data: 188034816
I0406 07:07:57.636283  5644 layer_factory.hpp:77] Creating layer norm2
I0406 07:07:57.636293  5644 net.cpp:84] Creating Layer norm2
I0406 07:07:57.636296  5644 net.cpp:406] norm2 <- conv2
I0406 07:07:57.636301  5644 net.cpp:380] norm2 -> norm2
I0406 07:07:57.636807  5644 net.cpp:122] Setting up norm2
I0406 07:07:57.636816  5644 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0406 07:07:57.636818  5644 net.cpp:137] Memory required for data: 211922688
I0406 07:07:57.636821  5644 layer_factory.hpp:77] Creating layer pool2
I0406 07:07:57.636826  5644 net.cpp:84] Creating Layer pool2
I0406 07:07:57.636829  5644 net.cpp:406] pool2 <- norm2
I0406 07:07:57.636834  5644 net.cpp:380] pool2 -> pool2
I0406 07:07:57.636862  5644 net.cpp:122] Setting up pool2
I0406 07:07:57.636865  5644 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0406 07:07:57.636868  5644 net.cpp:137] Memory required for data: 217460480
I0406 07:07:57.636869  5644 layer_factory.hpp:77] Creating layer conv3
I0406 07:07:57.636879  5644 net.cpp:84] Creating Layer conv3
I0406 07:07:57.636893  5644 net.cpp:406] conv3 <- pool2
I0406 07:07:57.636898  5644 net.cpp:380] conv3 -> conv3
I0406 07:07:57.647217  5644 net.cpp:122] Setting up conv3
I0406 07:07:57.647236  5644 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0406 07:07:57.647238  5644 net.cpp:137] Memory required for data: 225767168
I0406 07:07:57.647250  5644 layer_factory.hpp:77] Creating layer relu3
I0406 07:07:57.647259  5644 net.cpp:84] Creating Layer relu3
I0406 07:07:57.647264  5644 net.cpp:406] relu3 <- conv3
I0406 07:07:57.647269  5644 net.cpp:367] relu3 -> conv3 (in-place)
I0406 07:07:57.647753  5644 net.cpp:122] Setting up relu3
I0406 07:07:57.647763  5644 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0406 07:07:57.647764  5644 net.cpp:137] Memory required for data: 234073856
I0406 07:07:57.647768  5644 layer_factory.hpp:77] Creating layer conv4
I0406 07:07:57.647778  5644 net.cpp:84] Creating Layer conv4
I0406 07:07:57.647780  5644 net.cpp:406] conv4 <- conv3
I0406 07:07:57.647785  5644 net.cpp:380] conv4 -> conv4
I0406 07:07:57.657133  5644 net.cpp:122] Setting up conv4
I0406 07:07:57.657151  5644 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0406 07:07:57.657153  5644 net.cpp:137] Memory required for data: 242380544
I0406 07:07:57.657162  5644 layer_factory.hpp:77] Creating layer relu4
I0406 07:07:57.657171  5644 net.cpp:84] Creating Layer relu4
I0406 07:07:57.657174  5644 net.cpp:406] relu4 <- conv4
I0406 07:07:57.657181  5644 net.cpp:367] relu4 -> conv4 (in-place)
I0406 07:07:57.657502  5644 net.cpp:122] Setting up relu4
I0406 07:07:57.657510  5644 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0406 07:07:57.657512  5644 net.cpp:137] Memory required for data: 250687232
I0406 07:07:57.657516  5644 layer_factory.hpp:77] Creating layer conv5
I0406 07:07:57.657526  5644 net.cpp:84] Creating Layer conv5
I0406 07:07:57.657527  5644 net.cpp:406] conv5 <- conv4
I0406 07:07:57.657532  5644 net.cpp:380] conv5 -> conv5
I0406 07:07:57.677639  5644 net.cpp:122] Setting up conv5
I0406 07:07:57.677662  5644 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0406 07:07:57.677665  5644 net.cpp:137] Memory required for data: 256225024
I0406 07:07:57.677683  5644 layer_factory.hpp:77] Creating layer relu5
I0406 07:07:57.677695  5644 net.cpp:84] Creating Layer relu5
I0406 07:07:57.677700  5644 net.cpp:406] relu5 <- conv5
I0406 07:07:57.677733  5644 net.cpp:367] relu5 -> conv5 (in-place)
I0406 07:07:57.679769  5644 net.cpp:122] Setting up relu5
I0406 07:07:57.679783  5644 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0406 07:07:57.679786  5644 net.cpp:137] Memory required for data: 261762816
I0406 07:07:57.679790  5644 layer_factory.hpp:77] Creating layer pool5
I0406 07:07:57.679805  5644 net.cpp:84] Creating Layer pool5
I0406 07:07:57.679809  5644 net.cpp:406] pool5 <- conv5
I0406 07:07:57.679816  5644 net.cpp:380] pool5 -> pool5
I0406 07:07:57.679873  5644 net.cpp:122] Setting up pool5
I0406 07:07:57.679881  5644 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0406 07:07:57.679884  5644 net.cpp:137] Memory required for data: 262942464
I0406 07:07:57.679888  5644 layer_factory.hpp:77] Creating layer fc6
I0406 07:07:57.679898  5644 net.cpp:84] Creating Layer fc6
I0406 07:07:57.679901  5644 net.cpp:406] fc6 <- pool5
I0406 07:07:57.679906  5644 net.cpp:380] fc6 -> fc6
I0406 07:07:58.017792  5644 net.cpp:122] Setting up fc6
I0406 07:07:58.017813  5644 net.cpp:129] Top shape: 32 4096 (131072)
I0406 07:07:58.017817  5644 net.cpp:137] Memory required for data: 263466752
I0406 07:07:58.017825  5644 layer_factory.hpp:77] Creating layer relu6
I0406 07:07:58.017833  5644 net.cpp:84] Creating Layer relu6
I0406 07:07:58.017838  5644 net.cpp:406] relu6 <- fc6
I0406 07:07:58.017843  5644 net.cpp:367] relu6 -> fc6 (in-place)
I0406 07:07:58.018532  5644 net.cpp:122] Setting up relu6
I0406 07:07:58.018541  5644 net.cpp:129] Top shape: 32 4096 (131072)
I0406 07:07:58.018543  5644 net.cpp:137] Memory required for data: 263991040
I0406 07:07:58.018546  5644 layer_factory.hpp:77] Creating layer drop6
I0406 07:07:58.018551  5644 net.cpp:84] Creating Layer drop6
I0406 07:07:58.018554  5644 net.cpp:406] drop6 <- fc6
I0406 07:07:58.018559  5644 net.cpp:367] drop6 -> fc6 (in-place)
I0406 07:07:58.018582  5644 net.cpp:122] Setting up drop6
I0406 07:07:58.018586  5644 net.cpp:129] Top shape: 32 4096 (131072)
I0406 07:07:58.018589  5644 net.cpp:137] Memory required for data: 264515328
I0406 07:07:58.018590  5644 layer_factory.hpp:77] Creating layer fc7
I0406 07:07:58.018596  5644 net.cpp:84] Creating Layer fc7
I0406 07:07:58.018599  5644 net.cpp:406] fc7 <- fc6
I0406 07:07:58.018602  5644 net.cpp:380] fc7 -> fc7
I0406 07:07:58.175339  5644 net.cpp:122] Setting up fc7
I0406 07:07:58.175360  5644 net.cpp:129] Top shape: 32 4096 (131072)
I0406 07:07:58.175364  5644 net.cpp:137] Memory required for data: 265039616
I0406 07:07:58.175371  5644 layer_factory.hpp:77] Creating layer relu7
I0406 07:07:58.175379  5644 net.cpp:84] Creating Layer relu7
I0406 07:07:58.175382  5644 net.cpp:406] relu7 <- fc7
I0406 07:07:58.175388  5644 net.cpp:367] relu7 -> fc7 (in-place)
I0406 07:07:58.175771  5644 net.cpp:122] Setting up relu7
I0406 07:07:58.175778  5644 net.cpp:129] Top shape: 32 4096 (131072)
I0406 07:07:58.175781  5644 net.cpp:137] Memory required for data: 265563904
I0406 07:07:58.175783  5644 layer_factory.hpp:77] Creating layer drop7
I0406 07:07:58.175789  5644 net.cpp:84] Creating Layer drop7
I0406 07:07:58.175791  5644 net.cpp:406] drop7 <- fc7
I0406 07:07:58.175796  5644 net.cpp:367] drop7 -> fc7 (in-place)
I0406 07:07:58.175817  5644 net.cpp:122] Setting up drop7
I0406 07:07:58.175820  5644 net.cpp:129] Top shape: 32 4096 (131072)
I0406 07:07:58.175822  5644 net.cpp:137] Memory required for data: 266088192
I0406 07:07:58.175824  5644 layer_factory.hpp:77] Creating layer fc8
I0406 07:07:58.175829  5644 net.cpp:84] Creating Layer fc8
I0406 07:07:58.175832  5644 net.cpp:406] fc8 <- fc7
I0406 07:07:58.175837  5644 net.cpp:380] fc8 -> fc8
I0406 07:07:58.183147  5644 net.cpp:122] Setting up fc8
I0406 07:07:58.183162  5644 net.cpp:129] Top shape: 32 196 (6272)
I0406 07:07:58.183164  5644 net.cpp:137] Memory required for data: 266113280
I0406 07:07:58.183171  5644 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0406 07:07:58.183177  5644 net.cpp:84] Creating Layer fc8_fc8_0_split
I0406 07:07:58.183182  5644 net.cpp:406] fc8_fc8_0_split <- fc8
I0406 07:07:58.183207  5644 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0406 07:07:58.183213  5644 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0406 07:07:58.183245  5644 net.cpp:122] Setting up fc8_fc8_0_split
I0406 07:07:58.183248  5644 net.cpp:129] Top shape: 32 196 (6272)
I0406 07:07:58.183251  5644 net.cpp:129] Top shape: 32 196 (6272)
I0406 07:07:58.183254  5644 net.cpp:137] Memory required for data: 266163456
I0406 07:07:58.183255  5644 layer_factory.hpp:77] Creating layer accuracy
I0406 07:07:58.183261  5644 net.cpp:84] Creating Layer accuracy
I0406 07:07:58.183264  5644 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0406 07:07:58.183267  5644 net.cpp:406] accuracy <- label_val-data_1_split_0
I0406 07:07:58.183271  5644 net.cpp:380] accuracy -> accuracy
I0406 07:07:58.183277  5644 net.cpp:122] Setting up accuracy
I0406 07:07:58.183280  5644 net.cpp:129] Top shape: (1)
I0406 07:07:58.183282  5644 net.cpp:137] Memory required for data: 266163460
I0406 07:07:58.183284  5644 layer_factory.hpp:77] Creating layer loss
I0406 07:07:58.183288  5644 net.cpp:84] Creating Layer loss
I0406 07:07:58.183290  5644 net.cpp:406] loss <- fc8_fc8_0_split_1
I0406 07:07:58.183293  5644 net.cpp:406] loss <- label_val-data_1_split_1
I0406 07:07:58.183296  5644 net.cpp:380] loss -> loss
I0406 07:07:58.183301  5644 layer_factory.hpp:77] Creating layer loss
I0406 07:07:58.183945  5644 net.cpp:122] Setting up loss
I0406 07:07:58.183954  5644 net.cpp:129] Top shape: (1)
I0406 07:07:58.183955  5644 net.cpp:132]     with loss weight 1
I0406 07:07:58.183964  5644 net.cpp:137] Memory required for data: 266163464
I0406 07:07:58.183966  5644 net.cpp:198] loss needs backward computation.
I0406 07:07:58.183970  5644 net.cpp:200] accuracy does not need backward computation.
I0406 07:07:58.183974  5644 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0406 07:07:58.183975  5644 net.cpp:198] fc8 needs backward computation.
I0406 07:07:58.183977  5644 net.cpp:198] drop7 needs backward computation.
I0406 07:07:58.183979  5644 net.cpp:198] relu7 needs backward computation.
I0406 07:07:58.183981  5644 net.cpp:198] fc7 needs backward computation.
I0406 07:07:58.183984  5644 net.cpp:198] drop6 needs backward computation.
I0406 07:07:58.183986  5644 net.cpp:198] relu6 needs backward computation.
I0406 07:07:58.183988  5644 net.cpp:198] fc6 needs backward computation.
I0406 07:07:58.183991  5644 net.cpp:198] pool5 needs backward computation.
I0406 07:07:58.183993  5644 net.cpp:198] relu5 needs backward computation.
I0406 07:07:58.183995  5644 net.cpp:198] conv5 needs backward computation.
I0406 07:07:58.183998  5644 net.cpp:198] relu4 needs backward computation.
I0406 07:07:58.184000  5644 net.cpp:198] conv4 needs backward computation.
I0406 07:07:58.184002  5644 net.cpp:198] relu3 needs backward computation.
I0406 07:07:58.184005  5644 net.cpp:198] conv3 needs backward computation.
I0406 07:07:58.184007  5644 net.cpp:198] pool2 needs backward computation.
I0406 07:07:58.184011  5644 net.cpp:198] norm2 needs backward computation.
I0406 07:07:58.184013  5644 net.cpp:198] relu2 needs backward computation.
I0406 07:07:58.184015  5644 net.cpp:198] conv2 needs backward computation.
I0406 07:07:58.184018  5644 net.cpp:198] pool1 needs backward computation.
I0406 07:07:58.184020  5644 net.cpp:198] norm1 needs backward computation.
I0406 07:07:58.184023  5644 net.cpp:198] relu1 needs backward computation.
I0406 07:07:58.184026  5644 net.cpp:198] conv1 needs backward computation.
I0406 07:07:58.184028  5644 net.cpp:200] label_val-data_1_split does not need backward computation.
I0406 07:07:58.184031  5644 net.cpp:200] val-data does not need backward computation.
I0406 07:07:58.184033  5644 net.cpp:242] This network produces output accuracy
I0406 07:07:58.184036  5644 net.cpp:242] This network produces output loss
I0406 07:07:58.184051  5644 net.cpp:255] Network initialization done.
I0406 07:07:58.184115  5644 solver.cpp:56] Solver scaffolding done.
I0406 07:07:58.184506  5644 caffe.cpp:248] Starting Optimization
I0406 07:07:58.184512  5644 solver.cpp:272] Solving
I0406 07:07:58.184523  5644 solver.cpp:273] Learning Rate Policy: fixed
I0406 07:07:58.186162  5644 solver.cpp:330] Iteration 0, Testing net (#0)
I0406 07:07:58.186172  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:07:58.296015  5644 blocking_queue.cpp:49] Waiting for data
I0406 07:08:02.414016  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:08:02.461784  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00367647
I0406 07:08:02.461827  5644 solver.cpp:397]     Test net output #1: loss = 5.28126 (* 1 = 5.28126 loss)
I0406 07:08:02.609565  5644 solver.cpp:218] Iteration 0 (2.05085e-38 iter/s, 4.42497s/12 iters), loss = 5.30351
I0406 07:08:02.611136  5644 solver.cpp:237]     Train net output #0: loss = 5.30351 (* 1 = 5.30351 loss)
I0406 07:08:02.611146  5644 sgd_solver.cpp:105] Iteration 0, lr = 0.1
I0406 07:08:06.923724  5644 solver.cpp:218] Iteration 12 (2.78258 iter/s, 4.31254s/12 iters), loss = 5.33566
I0406 07:08:06.923763  5644 solver.cpp:237]     Train net output #0: loss = 5.33566 (* 1 = 5.33566 loss)
I0406 07:08:06.923769  5644 sgd_solver.cpp:105] Iteration 12, lr = 0.1
I0406 07:08:11.960363  5644 solver.cpp:218] Iteration 24 (2.38258 iter/s, 5.03655s/12 iters), loss = 5.26617
I0406 07:08:11.960400  5644 solver.cpp:237]     Train net output #0: loss = 5.26617 (* 1 = 5.26617 loss)
I0406 07:08:11.960407  5644 sgd_solver.cpp:105] Iteration 24, lr = 0.1
I0406 07:08:17.346040  5644 solver.cpp:218] Iteration 36 (2.22817 iter/s, 5.38558s/12 iters), loss = 5.28233
I0406 07:08:17.346076  5644 solver.cpp:237]     Train net output #0: loss = 5.28233 (* 1 = 5.28233 loss)
I0406 07:08:17.346082  5644 sgd_solver.cpp:105] Iteration 36, lr = 0.1
I0406 07:08:22.528445  5644 solver.cpp:218] Iteration 48 (2.31557 iter/s, 5.18231s/12 iters), loss = 5.29802
I0406 07:08:22.528482  5644 solver.cpp:237]     Train net output #0: loss = 5.29802 (* 1 = 5.29802 loss)
I0406 07:08:22.528487  5644 sgd_solver.cpp:105] Iteration 48, lr = 0.1
I0406 07:08:27.800776  5644 solver.cpp:218] Iteration 60 (2.27607 iter/s, 5.27224s/12 iters), loss = 5.28293
I0406 07:08:27.800933  5644 solver.cpp:237]     Train net output #0: loss = 5.28293 (* 1 = 5.28293 loss)
I0406 07:08:27.800940  5644 sgd_solver.cpp:105] Iteration 60, lr = 0.1
I0406 07:08:32.887079  5644 solver.cpp:218] Iteration 72 (2.35938 iter/s, 5.08609s/12 iters), loss = 5.30583
I0406 07:08:32.887125  5644 solver.cpp:237]     Train net output #0: loss = 5.30583 (* 1 = 5.30583 loss)
I0406 07:08:32.887132  5644 sgd_solver.cpp:105] Iteration 72, lr = 0.1
I0406 07:08:38.005187  5644 solver.cpp:218] Iteration 84 (2.34466 iter/s, 5.11801s/12 iters), loss = 5.2921
I0406 07:08:38.005228  5644 solver.cpp:237]     Train net output #0: loss = 5.2921 (* 1 = 5.2921 loss)
I0406 07:08:38.005234  5644 sgd_solver.cpp:105] Iteration 84, lr = 0.1
I0406 07:08:43.371109  5644 solver.cpp:218] Iteration 96 (2.23638 iter/s, 5.36582s/12 iters), loss = 5.27635
I0406 07:08:43.371148  5644 solver.cpp:237]     Train net output #0: loss = 5.27635 (* 1 = 5.27635 loss)
I0406 07:08:43.371153  5644 sgd_solver.cpp:105] Iteration 96, lr = 0.1
I0406 07:08:45.183090  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:08:45.489413  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0406 07:08:48.556530  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0406 07:08:50.861486  5644 solver.cpp:330] Iteration 102, Testing net (#0)
I0406 07:08:50.861505  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:08:55.054889  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:08:55.132575  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 07:08:55.132604  5644 solver.cpp:397]     Test net output #1: loss = 5.2848 (* 1 = 5.2848 loss)
I0406 07:08:57.005558  5644 solver.cpp:218] Iteration 108 (0.880134 iter/s, 13.6343s/12 iters), loss = 5.2829
I0406 07:08:57.005599  5644 solver.cpp:237]     Train net output #0: loss = 5.2829 (* 1 = 5.2829 loss)
I0406 07:08:57.005605  5644 sgd_solver.cpp:105] Iteration 108, lr = 0.1
I0406 07:09:02.095752  5644 solver.cpp:218] Iteration 120 (2.35752 iter/s, 5.0901s/12 iters), loss = 5.26865
I0406 07:09:02.095912  5644 solver.cpp:237]     Train net output #0: loss = 5.26865 (* 1 = 5.26865 loss)
I0406 07:09:02.095919  5644 sgd_solver.cpp:105] Iteration 120, lr = 0.1
I0406 07:09:07.428846  5644 solver.cpp:218] Iteration 132 (2.25019 iter/s, 5.33289s/12 iters), loss = 5.27564
I0406 07:09:07.428880  5644 solver.cpp:237]     Train net output #0: loss = 5.27564 (* 1 = 5.27564 loss)
I0406 07:09:07.428894  5644 sgd_solver.cpp:105] Iteration 132, lr = 0.1
I0406 07:09:12.614966  5644 solver.cpp:218] Iteration 144 (2.31391 iter/s, 5.18603s/12 iters), loss = 5.2717
I0406 07:09:12.615001  5644 solver.cpp:237]     Train net output #0: loss = 5.2717 (* 1 = 5.2717 loss)
I0406 07:09:12.615006  5644 sgd_solver.cpp:105] Iteration 144, lr = 0.1
I0406 07:09:17.812773  5644 solver.cpp:218] Iteration 156 (2.30871 iter/s, 5.19771s/12 iters), loss = 5.29866
I0406 07:09:17.812808  5644 solver.cpp:237]     Train net output #0: loss = 5.29866 (* 1 = 5.29866 loss)
I0406 07:09:17.812814  5644 sgd_solver.cpp:105] Iteration 156, lr = 0.1
I0406 07:09:23.000391  5644 solver.cpp:218] Iteration 168 (2.31324 iter/s, 5.18753s/12 iters), loss = 5.27343
I0406 07:09:23.000433  5644 solver.cpp:237]     Train net output #0: loss = 5.27343 (* 1 = 5.27343 loss)
I0406 07:09:23.000439  5644 sgd_solver.cpp:105] Iteration 168, lr = 0.1
I0406 07:09:28.257993  5644 solver.cpp:218] Iteration 180 (2.28245 iter/s, 5.2575s/12 iters), loss = 5.30013
I0406 07:09:28.258044  5644 solver.cpp:237]     Train net output #0: loss = 5.30013 (* 1 = 5.30013 loss)
I0406 07:09:28.258055  5644 sgd_solver.cpp:105] Iteration 180, lr = 0.1
I0406 07:09:33.238003  5644 solver.cpp:218] Iteration 192 (2.40968 iter/s, 4.97991s/12 iters), loss = 5.26329
I0406 07:09:33.238088  5644 solver.cpp:237]     Train net output #0: loss = 5.26329 (* 1 = 5.26329 loss)
I0406 07:09:33.238095  5644 sgd_solver.cpp:105] Iteration 192, lr = 0.1
I0406 07:09:37.387706  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:09:38.117940  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0406 07:09:41.161206  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0406 07:09:43.453657  5644 solver.cpp:330] Iteration 204, Testing net (#0)
I0406 07:09:43.453676  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:09:47.580451  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:09:47.703977  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:09:47.704015  5644 solver.cpp:397]     Test net output #1: loss = 5.28749 (* 1 = 5.28749 loss)
I0406 07:09:47.841516  5644 solver.cpp:218] Iteration 204 (0.821732 iter/s, 14.6033s/12 iters), loss = 5.26565
I0406 07:09:47.841563  5644 solver.cpp:237]     Train net output #0: loss = 5.26565 (* 1 = 5.26565 loss)
I0406 07:09:47.841570  5644 sgd_solver.cpp:105] Iteration 204, lr = 0.1
I0406 07:09:52.179642  5644 solver.cpp:218] Iteration 216 (2.76624 iter/s, 4.33803s/12 iters), loss = 5.27792
I0406 07:09:52.179687  5644 solver.cpp:237]     Train net output #0: loss = 5.27792 (* 1 = 5.27792 loss)
I0406 07:09:52.179694  5644 sgd_solver.cpp:105] Iteration 216, lr = 0.1
I0406 07:09:57.601672  5644 solver.cpp:218] Iteration 228 (2.21324 iter/s, 5.42193s/12 iters), loss = 5.29325
I0406 07:09:57.601708  5644 solver.cpp:237]     Train net output #0: loss = 5.29325 (* 1 = 5.29325 loss)
I0406 07:09:57.601713  5644 sgd_solver.cpp:105] Iteration 228, lr = 0.1
I0406 07:10:02.805603  5644 solver.cpp:218] Iteration 240 (2.30599 iter/s, 5.20383s/12 iters), loss = 5.27003
I0406 07:10:02.805641  5644 solver.cpp:237]     Train net output #0: loss = 5.27003 (* 1 = 5.27003 loss)
I0406 07:10:02.805647  5644 sgd_solver.cpp:105] Iteration 240, lr = 0.1
I0406 07:10:08.358390  5644 solver.cpp:218] Iteration 252 (2.16111 iter/s, 5.55269s/12 iters), loss = 5.29775
I0406 07:10:08.358501  5644 solver.cpp:237]     Train net output #0: loss = 5.29775 (* 1 = 5.29775 loss)
I0406 07:10:08.358508  5644 sgd_solver.cpp:105] Iteration 252, lr = 0.1
I0406 07:10:13.472993  5644 solver.cpp:218] Iteration 264 (2.3463 iter/s, 5.11443s/12 iters), loss = 5.2709
I0406 07:10:13.473042  5644 solver.cpp:237]     Train net output #0: loss = 5.2709 (* 1 = 5.2709 loss)
I0406 07:10:13.473049  5644 sgd_solver.cpp:105] Iteration 264, lr = 0.1
I0406 07:10:18.557540  5644 solver.cpp:218] Iteration 276 (2.36014 iter/s, 5.08444s/12 iters), loss = 5.26346
I0406 07:10:18.557580  5644 solver.cpp:237]     Train net output #0: loss = 5.26346 (* 1 = 5.26346 loss)
I0406 07:10:18.557586  5644 sgd_solver.cpp:105] Iteration 276, lr = 0.1
I0406 07:10:23.714815  5644 solver.cpp:218] Iteration 288 (2.32685 iter/s, 5.15718s/12 iters), loss = 5.26413
I0406 07:10:23.714854  5644 solver.cpp:237]     Train net output #0: loss = 5.26413 (* 1 = 5.26413 loss)
I0406 07:10:23.714860  5644 sgd_solver.cpp:105] Iteration 288, lr = 0.1
I0406 07:10:28.917857  5644 solver.cpp:218] Iteration 300 (2.30639 iter/s, 5.20294s/12 iters), loss = 5.27603
I0406 07:10:28.917901  5644 solver.cpp:237]     Train net output #0: loss = 5.27603 (* 1 = 5.27603 loss)
I0406 07:10:28.917909  5644 sgd_solver.cpp:105] Iteration 300, lr = 0.1
I0406 07:10:29.843979  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:10:31.045390  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0406 07:10:34.000494  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0406 07:10:36.305584  5644 solver.cpp:330] Iteration 306, Testing net (#0)
I0406 07:10:36.305603  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:10:40.429006  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:10:40.585980  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:10:40.586009  5644 solver.cpp:397]     Test net output #1: loss = 5.28695 (* 1 = 5.28695 loss)
I0406 07:10:42.399758  5644 solver.cpp:218] Iteration 312 (0.890094 iter/s, 13.4817s/12 iters), loss = 5.27674
I0406 07:10:42.399799  5644 solver.cpp:237]     Train net output #0: loss = 5.27674 (* 1 = 5.27674 loss)
I0406 07:10:42.399806  5644 sgd_solver.cpp:105] Iteration 312, lr = 0.1
I0406 07:10:47.503341  5644 solver.cpp:218] Iteration 324 (2.35133 iter/s, 5.10349s/12 iters), loss = 5.28727
I0406 07:10:47.503377  5644 solver.cpp:237]     Train net output #0: loss = 5.28727 (* 1 = 5.28727 loss)
I0406 07:10:47.503382  5644 sgd_solver.cpp:105] Iteration 324, lr = 0.1
I0406 07:10:52.957374  5644 solver.cpp:218] Iteration 336 (2.20025 iter/s, 5.45393s/12 iters), loss = 5.28155
I0406 07:10:52.957422  5644 solver.cpp:237]     Train net output #0: loss = 5.28155 (* 1 = 5.28155 loss)
I0406 07:10:52.957430  5644 sgd_solver.cpp:105] Iteration 336, lr = 0.1
I0406 07:10:58.312397  5644 solver.cpp:218] Iteration 348 (2.24093 iter/s, 5.35492s/12 iters), loss = 5.28142
I0406 07:10:58.312433  5644 solver.cpp:237]     Train net output #0: loss = 5.28142 (* 1 = 5.28142 loss)
I0406 07:10:58.312439  5644 sgd_solver.cpp:105] Iteration 348, lr = 0.1
I0406 07:11:03.632920  5644 solver.cpp:218] Iteration 360 (2.25546 iter/s, 5.32043s/12 iters), loss = 5.2912
I0406 07:11:03.632959  5644 solver.cpp:237]     Train net output #0: loss = 5.2912 (* 1 = 5.2912 loss)
I0406 07:11:03.632966  5644 sgd_solver.cpp:105] Iteration 360, lr = 0.1
I0406 07:11:08.739851  5644 solver.cpp:218] Iteration 372 (2.34979 iter/s, 5.10684s/12 iters), loss = 5.27034
I0406 07:11:08.739889  5644 solver.cpp:237]     Train net output #0: loss = 5.27034 (* 1 = 5.27034 loss)
I0406 07:11:08.739894  5644 sgd_solver.cpp:105] Iteration 372, lr = 0.1
I0406 07:11:14.114367  5644 solver.cpp:218] Iteration 384 (2.2328 iter/s, 5.37442s/12 iters), loss = 5.27559
I0406 07:11:14.114480  5644 solver.cpp:237]     Train net output #0: loss = 5.27559 (* 1 = 5.27559 loss)
I0406 07:11:14.114488  5644 sgd_solver.cpp:105] Iteration 384, lr = 0.1
I0406 07:11:19.377087  5644 solver.cpp:218] Iteration 396 (2.28026 iter/s, 5.26255s/12 iters), loss = 5.29207
I0406 07:11:19.377132  5644 solver.cpp:237]     Train net output #0: loss = 5.29207 (* 1 = 5.29207 loss)
I0406 07:11:19.377138  5644 sgd_solver.cpp:105] Iteration 396, lr = 0.1
I0406 07:11:22.617226  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:11:24.145771  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0406 07:11:27.152160  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0406 07:11:29.457332  5644 solver.cpp:330] Iteration 408, Testing net (#0)
I0406 07:11:29.457352  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:11:33.559995  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:11:33.779436  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:11:33.779465  5644 solver.cpp:397]     Test net output #1: loss = 5.28954 (* 1 = 5.28954 loss)
I0406 07:11:33.914577  5644 solver.cpp:218] Iteration 408 (0.825462 iter/s, 14.5373s/12 iters), loss = 5.26868
I0406 07:11:33.914638  5644 solver.cpp:237]     Train net output #0: loss = 5.26868 (* 1 = 5.26868 loss)
I0406 07:11:33.914645  5644 sgd_solver.cpp:105] Iteration 408, lr = 0.1
I0406 07:11:38.092746  5644 solver.cpp:218] Iteration 420 (2.87215 iter/s, 4.17806s/12 iters), loss = 5.26841
I0406 07:11:38.092783  5644 solver.cpp:237]     Train net output #0: loss = 5.26841 (* 1 = 5.26841 loss)
I0406 07:11:38.092789  5644 sgd_solver.cpp:105] Iteration 420, lr = 0.1
I0406 07:11:43.299098  5644 solver.cpp:218] Iteration 432 (2.30492 iter/s, 5.20626s/12 iters), loss = 5.26899
I0406 07:11:43.299142  5644 solver.cpp:237]     Train net output #0: loss = 5.26899 (* 1 = 5.26899 loss)
I0406 07:11:43.299151  5644 sgd_solver.cpp:105] Iteration 432, lr = 0.1
I0406 07:11:48.584899  5644 solver.cpp:218] Iteration 444 (2.27028 iter/s, 5.2857s/12 iters), loss = 5.27266
I0406 07:11:48.584985  5644 solver.cpp:237]     Train net output #0: loss = 5.27266 (* 1 = 5.27266 loss)
I0406 07:11:48.584990  5644 sgd_solver.cpp:105] Iteration 444, lr = 0.1
I0406 07:11:53.933303  5644 solver.cpp:218] Iteration 456 (2.24372 iter/s, 5.34826s/12 iters), loss = 5.28261
I0406 07:11:53.933341  5644 solver.cpp:237]     Train net output #0: loss = 5.28261 (* 1 = 5.28261 loss)
I0406 07:11:53.933346  5644 sgd_solver.cpp:105] Iteration 456, lr = 0.1
I0406 07:11:59.273849  5644 solver.cpp:218] Iteration 468 (2.247 iter/s, 5.34044s/12 iters), loss = 5.27676
I0406 07:11:59.273888  5644 solver.cpp:237]     Train net output #0: loss = 5.27676 (* 1 = 5.27676 loss)
I0406 07:11:59.273895  5644 sgd_solver.cpp:105] Iteration 468, lr = 0.1
I0406 07:12:04.513422  5644 solver.cpp:218] Iteration 480 (2.29031 iter/s, 5.23947s/12 iters), loss = 5.26452
I0406 07:12:04.513463  5644 solver.cpp:237]     Train net output #0: loss = 5.26452 (* 1 = 5.26452 loss)
I0406 07:12:04.513469  5644 sgd_solver.cpp:105] Iteration 480, lr = 0.1
I0406 07:12:09.846922  5644 solver.cpp:218] Iteration 492 (2.24997 iter/s, 5.3334s/12 iters), loss = 5.28471
I0406 07:12:09.846964  5644 solver.cpp:237]     Train net output #0: loss = 5.28471 (* 1 = 5.28471 loss)
I0406 07:12:09.846971  5644 sgd_solver.cpp:105] Iteration 492, lr = 0.1
I0406 07:12:15.037461  5644 solver.cpp:218] Iteration 504 (2.31194 iter/s, 5.19044s/12 iters), loss = 5.2779
I0406 07:12:15.037497  5644 solver.cpp:237]     Train net output #0: loss = 5.2779 (* 1 = 5.2779 loss)
I0406 07:12:15.037503  5644 sgd_solver.cpp:105] Iteration 504, lr = 0.1
I0406 07:12:15.275077  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:12:17.209511  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0406 07:12:20.262537  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0406 07:12:22.573078  5644 solver.cpp:330] Iteration 510, Testing net (#0)
I0406 07:12:22.573099  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:12:26.626121  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:12:26.862771  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:12:26.862802  5644 solver.cpp:397]     Test net output #1: loss = 5.29133 (* 1 = 5.29133 loss)
I0406 07:12:28.784528  5644 solver.cpp:218] Iteration 516 (0.872924 iter/s, 13.7469s/12 iters), loss = 5.25714
I0406 07:12:28.784570  5644 solver.cpp:237]     Train net output #0: loss = 5.25714 (* 1 = 5.25714 loss)
I0406 07:12:28.784579  5644 sgd_solver.cpp:105] Iteration 516, lr = 0.1
I0406 07:12:34.086745  5644 solver.cpp:218] Iteration 528 (2.26325 iter/s, 5.30211s/12 iters), loss = 5.28712
I0406 07:12:34.086782  5644 solver.cpp:237]     Train net output #0: loss = 5.28712 (* 1 = 5.28712 loss)
I0406 07:12:34.086788  5644 sgd_solver.cpp:105] Iteration 528, lr = 0.1
I0406 07:12:39.431495  5644 solver.cpp:218] Iteration 540 (2.24524 iter/s, 5.34465s/12 iters), loss = 5.27683
I0406 07:12:39.431545  5644 solver.cpp:237]     Train net output #0: loss = 5.27683 (* 1 = 5.27683 loss)
I0406 07:12:39.431553  5644 sgd_solver.cpp:105] Iteration 540, lr = 0.1
I0406 07:12:44.674371  5644 solver.cpp:218] Iteration 552 (2.28887 iter/s, 5.24277s/12 iters), loss = 5.27409
I0406 07:12:44.674408  5644 solver.cpp:237]     Train net output #0: loss = 5.27409 (* 1 = 5.27409 loss)
I0406 07:12:44.674413  5644 sgd_solver.cpp:105] Iteration 552, lr = 0.1
I0406 07:12:49.861862  5644 solver.cpp:218] Iteration 564 (2.3133 iter/s, 5.1874s/12 iters), loss = 5.28489
I0406 07:12:49.861896  5644 solver.cpp:237]     Train net output #0: loss = 5.28489 (* 1 = 5.28489 loss)
I0406 07:12:49.861902  5644 sgd_solver.cpp:105] Iteration 564, lr = 0.1
I0406 07:12:55.225934  5644 solver.cpp:218] Iteration 576 (2.23715 iter/s, 5.36398s/12 iters), loss = 5.25619
I0406 07:12:55.226047  5644 solver.cpp:237]     Train net output #0: loss = 5.25619 (* 1 = 5.25619 loss)
I0406 07:12:55.226054  5644 sgd_solver.cpp:105] Iteration 576, lr = 0.1
I0406 07:13:00.193244  5644 solver.cpp:218] Iteration 588 (2.41588 iter/s, 4.96714s/12 iters), loss = 5.27783
I0406 07:13:00.193281  5644 solver.cpp:237]     Train net output #0: loss = 5.27783 (* 1 = 5.27783 loss)
I0406 07:13:00.193287  5644 sgd_solver.cpp:105] Iteration 588, lr = 0.1
I0406 07:13:05.445650  5644 solver.cpp:218] Iteration 600 (2.28471 iter/s, 5.25231s/12 iters), loss = 5.27275
I0406 07:13:05.445688  5644 solver.cpp:237]     Train net output #0: loss = 5.27275 (* 1 = 5.27275 loss)
I0406 07:13:05.445694  5644 sgd_solver.cpp:105] Iteration 600, lr = 0.1
I0406 07:13:07.947830  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:13:10.260012  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0406 07:13:13.271373  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0406 07:13:15.584867  5644 solver.cpp:330] Iteration 612, Testing net (#0)
I0406 07:13:15.584894  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:13:19.624040  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:13:19.905808  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:13:19.905843  5644 solver.cpp:397]     Test net output #1: loss = 5.29103 (* 1 = 5.29103 loss)
I0406 07:13:20.046159  5644 solver.cpp:218] Iteration 612 (0.821899 iter/s, 14.6003s/12 iters), loss = 5.27997
I0406 07:13:20.047756  5644 solver.cpp:237]     Train net output #0: loss = 5.27997 (* 1 = 5.27997 loss)
I0406 07:13:20.047768  5644 sgd_solver.cpp:105] Iteration 612, lr = 0.1
I0406 07:13:24.496323  5644 solver.cpp:218] Iteration 624 (2.69752 iter/s, 4.44852s/12 iters), loss = 5.27151
I0406 07:13:24.496361  5644 solver.cpp:237]     Train net output #0: loss = 5.27151 (* 1 = 5.27151 loss)
I0406 07:13:24.496366  5644 sgd_solver.cpp:105] Iteration 624, lr = 0.1
I0406 07:13:29.612768  5644 solver.cpp:218] Iteration 636 (2.34542 iter/s, 5.11635s/12 iters), loss = 5.28093
I0406 07:13:29.612880  5644 solver.cpp:237]     Train net output #0: loss = 5.28093 (* 1 = 5.28093 loss)
I0406 07:13:29.612895  5644 sgd_solver.cpp:105] Iteration 636, lr = 0.1
I0406 07:13:34.691959  5644 solver.cpp:218] Iteration 648 (2.36266 iter/s, 5.07903s/12 iters), loss = 5.26803
I0406 07:13:34.691993  5644 solver.cpp:237]     Train net output #0: loss = 5.26803 (* 1 = 5.26803 loss)
I0406 07:13:34.691998  5644 sgd_solver.cpp:105] Iteration 648, lr = 0.1
I0406 07:13:39.982372  5644 solver.cpp:218] Iteration 660 (2.26829 iter/s, 5.29032s/12 iters), loss = 5.26345
I0406 07:13:39.982407  5644 solver.cpp:237]     Train net output #0: loss = 5.26345 (* 1 = 5.26345 loss)
I0406 07:13:39.982414  5644 sgd_solver.cpp:105] Iteration 660, lr = 0.1
I0406 07:13:45.381237  5644 solver.cpp:218] Iteration 672 (2.22273 iter/s, 5.39877s/12 iters), loss = 5.29838
I0406 07:13:45.381275  5644 solver.cpp:237]     Train net output #0: loss = 5.29838 (* 1 = 5.29838 loss)
I0406 07:13:45.381281  5644 sgd_solver.cpp:105] Iteration 672, lr = 0.1
I0406 07:13:50.618080  5644 solver.cpp:218] Iteration 684 (2.2915 iter/s, 5.23675s/12 iters), loss = 5.27208
I0406 07:13:50.618116  5644 solver.cpp:237]     Train net output #0: loss = 5.27208 (* 1 = 5.27208 loss)
I0406 07:13:50.618122  5644 sgd_solver.cpp:105] Iteration 684, lr = 0.1
I0406 07:13:51.307556  5644 blocking_queue.cpp:49] Waiting for data
I0406 07:13:55.727720  5644 solver.cpp:218] Iteration 696 (2.34854 iter/s, 5.10955s/12 iters), loss = 5.29566
I0406 07:13:55.727752  5644 solver.cpp:237]     Train net output #0: loss = 5.29566 (* 1 = 5.29566 loss)
I0406 07:13:55.727757  5644 sgd_solver.cpp:105] Iteration 696, lr = 0.1
I0406 07:14:00.328848  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:14:00.740222  5644 solver.cpp:218] Iteration 708 (2.39406 iter/s, 5.01241s/12 iters), loss = 5.29869
I0406 07:14:00.740274  5644 solver.cpp:237]     Train net output #0: loss = 5.29869 (* 1 = 5.29869 loss)
I0406 07:14:00.740283  5644 sgd_solver.cpp:105] Iteration 708, lr = 0.1
I0406 07:14:02.873037  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0406 07:14:05.873025  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0406 07:14:08.185678  5644 solver.cpp:330] Iteration 714, Testing net (#0)
I0406 07:14:08.185696  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:14:12.150527  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:14:12.463223  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:14:12.463270  5644 solver.cpp:397]     Test net output #1: loss = 5.29017 (* 1 = 5.29017 loss)
I0406 07:14:14.354061  5644 solver.cpp:218] Iteration 720 (0.881468 iter/s, 13.6137s/12 iters), loss = 5.2755
I0406 07:14:14.354104  5644 solver.cpp:237]     Train net output #0: loss = 5.2755 (* 1 = 5.2755 loss)
I0406 07:14:14.354110  5644 sgd_solver.cpp:105] Iteration 720, lr = 0.1
I0406 07:14:19.499425  5644 solver.cpp:218] Iteration 732 (2.33224 iter/s, 5.14526s/12 iters), loss = 5.27109
I0406 07:14:19.499464  5644 solver.cpp:237]     Train net output #0: loss = 5.27109 (* 1 = 5.27109 loss)
I0406 07:14:19.499469  5644 sgd_solver.cpp:105] Iteration 732, lr = 0.1
I0406 07:14:24.703119  5644 solver.cpp:218] Iteration 744 (2.3061 iter/s, 5.2036s/12 iters), loss = 5.28553
I0406 07:14:24.703155  5644 solver.cpp:237]     Train net output #0: loss = 5.28553 (* 1 = 5.28553 loss)
I0406 07:14:24.703159  5644 sgd_solver.cpp:105] Iteration 744, lr = 0.1
I0406 07:14:29.967272  5644 solver.cpp:218] Iteration 756 (2.27961 iter/s, 5.26406s/12 iters), loss = 5.28984
I0406 07:14:29.967309  5644 solver.cpp:237]     Train net output #0: loss = 5.28984 (* 1 = 5.28984 loss)
I0406 07:14:29.967314  5644 sgd_solver.cpp:105] Iteration 756, lr = 0.1
I0406 07:14:35.251451  5644 solver.cpp:218] Iteration 768 (2.27097 iter/s, 5.28408s/12 iters), loss = 5.26666
I0406 07:14:35.251552  5644 solver.cpp:237]     Train net output #0: loss = 5.26666 (* 1 = 5.26666 loss)
I0406 07:14:35.251559  5644 sgd_solver.cpp:105] Iteration 768, lr = 0.1
I0406 07:14:40.700872  5644 solver.cpp:218] Iteration 780 (2.20213 iter/s, 5.44926s/12 iters), loss = 5.28254
I0406 07:14:40.700913  5644 solver.cpp:237]     Train net output #0: loss = 5.28254 (* 1 = 5.28254 loss)
I0406 07:14:40.700919  5644 sgd_solver.cpp:105] Iteration 780, lr = 0.1
I0406 07:14:46.057164  5644 solver.cpp:218] Iteration 792 (2.2404 iter/s, 5.35619s/12 iters), loss = 5.28443
I0406 07:14:46.057205  5644 solver.cpp:237]     Train net output #0: loss = 5.28443 (* 1 = 5.28443 loss)
I0406 07:14:46.057212  5644 sgd_solver.cpp:105] Iteration 792, lr = 0.1
I0406 07:14:51.441856  5644 solver.cpp:218] Iteration 804 (2.22858 iter/s, 5.38459s/12 iters), loss = 5.27072
I0406 07:14:51.441892  5644 solver.cpp:237]     Train net output #0: loss = 5.27072 (* 1 = 5.27072 loss)
I0406 07:14:51.441898  5644 sgd_solver.cpp:105] Iteration 804, lr = 0.1
I0406 07:14:53.282653  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:14:56.094290  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0406 07:14:59.099968  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0406 07:15:01.451383  5644 solver.cpp:330] Iteration 816, Testing net (#0)
I0406 07:15:01.451409  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:15:05.362510  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:15:05.708667  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:15:05.708700  5644 solver.cpp:397]     Test net output #1: loss = 5.28941 (* 1 = 5.28941 loss)
I0406 07:15:05.844199  5644 solver.cpp:218] Iteration 816 (0.833208 iter/s, 14.4022s/12 iters), loss = 5.28438
I0406 07:15:05.844250  5644 solver.cpp:237]     Train net output #0: loss = 5.28438 (* 1 = 5.28438 loss)
I0406 07:15:05.844259  5644 sgd_solver.cpp:105] Iteration 816, lr = 0.1
I0406 07:15:10.074615  5644 solver.cpp:218] Iteration 828 (2.83667 iter/s, 4.23031s/12 iters), loss = 5.25876
I0406 07:15:10.074654  5644 solver.cpp:237]     Train net output #0: loss = 5.25876 (* 1 = 5.25876 loss)
I0406 07:15:10.074659  5644 sgd_solver.cpp:105] Iteration 828, lr = 0.1
I0406 07:15:15.291990  5644 solver.cpp:218] Iteration 840 (2.30005 iter/s, 5.21728s/12 iters), loss = 5.2747
I0406 07:15:15.292024  5644 solver.cpp:237]     Train net output #0: loss = 5.2747 (* 1 = 5.2747 loss)
I0406 07:15:15.292030  5644 sgd_solver.cpp:105] Iteration 840, lr = 0.1
I0406 07:15:20.556228  5644 solver.cpp:218] Iteration 852 (2.27957 iter/s, 5.26414s/12 iters), loss = 5.27378
I0406 07:15:20.556269  5644 solver.cpp:237]     Train net output #0: loss = 5.27378 (* 1 = 5.27378 loss)
I0406 07:15:20.556275  5644 sgd_solver.cpp:105] Iteration 852, lr = 0.1
I0406 07:15:25.939990  5644 solver.cpp:218] Iteration 864 (2.22897 iter/s, 5.38366s/12 iters), loss = 5.28655
I0406 07:15:25.940037  5644 solver.cpp:237]     Train net output #0: loss = 5.28655 (* 1 = 5.28655 loss)
I0406 07:15:25.940043  5644 sgd_solver.cpp:105] Iteration 864, lr = 0.1
I0406 07:15:31.138949  5644 solver.cpp:218] Iteration 876 (2.3082 iter/s, 5.19886s/12 iters), loss = 5.26417
I0406 07:15:31.138991  5644 solver.cpp:237]     Train net output #0: loss = 5.26417 (* 1 = 5.26417 loss)
I0406 07:15:31.138998  5644 sgd_solver.cpp:105] Iteration 876, lr = 0.1
I0406 07:15:36.481501  5644 solver.cpp:218] Iteration 888 (2.24616 iter/s, 5.34245s/12 iters), loss = 5.29315
I0406 07:15:36.481601  5644 solver.cpp:237]     Train net output #0: loss = 5.29315 (* 1 = 5.29315 loss)
I0406 07:15:36.481608  5644 sgd_solver.cpp:105] Iteration 888, lr = 0.1
I0406 07:15:41.549099  5644 solver.cpp:218] Iteration 900 (2.36806 iter/s, 5.06744s/12 iters), loss = 5.26008
I0406 07:15:41.549139  5644 solver.cpp:237]     Train net output #0: loss = 5.26008 (* 1 = 5.26008 loss)
I0406 07:15:41.549146  5644 sgd_solver.cpp:105] Iteration 900, lr = 0.1
I0406 07:15:45.687837  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:15:46.923106  5644 solver.cpp:218] Iteration 912 (2.23301 iter/s, 5.37391s/12 iters), loss = 5.27205
I0406 07:15:46.923146  5644 solver.cpp:237]     Train net output #0: loss = 5.27205 (* 1 = 5.27205 loss)
I0406 07:15:46.923151  5644 sgd_solver.cpp:105] Iteration 912, lr = 0.1
I0406 07:15:48.968269  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0406 07:15:51.997368  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0406 07:15:54.304998  5644 solver.cpp:330] Iteration 918, Testing net (#0)
I0406 07:15:54.305023  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:15:58.216198  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:15:58.608400  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:15:58.608433  5644 solver.cpp:397]     Test net output #1: loss = 5.28943 (* 1 = 5.28943 loss)
I0406 07:16:00.472935  5644 solver.cpp:218] Iteration 924 (0.885631 iter/s, 13.5497s/12 iters), loss = 5.26918
I0406 07:16:00.472977  5644 solver.cpp:237]     Train net output #0: loss = 5.26918 (* 1 = 5.26918 loss)
I0406 07:16:00.472983  5644 sgd_solver.cpp:105] Iteration 924, lr = 0.1
I0406 07:16:05.665215  5644 solver.cpp:218] Iteration 936 (2.31117 iter/s, 5.19218s/12 iters), loss = 5.29108
I0406 07:16:05.665251  5644 solver.cpp:237]     Train net output #0: loss = 5.29108 (* 1 = 5.29108 loss)
I0406 07:16:05.665256  5644 sgd_solver.cpp:105] Iteration 936, lr = 0.1
I0406 07:16:10.716161  5644 solver.cpp:218] Iteration 948 (2.37584 iter/s, 5.05085s/12 iters), loss = 5.27289
I0406 07:16:10.716296  5644 solver.cpp:237]     Train net output #0: loss = 5.27289 (* 1 = 5.27289 loss)
I0406 07:16:10.716303  5644 sgd_solver.cpp:105] Iteration 948, lr = 0.1
I0406 07:16:16.048184  5644 solver.cpp:218] Iteration 960 (2.25063 iter/s, 5.33183s/12 iters), loss = 5.28573
I0406 07:16:16.048221  5644 solver.cpp:237]     Train net output #0: loss = 5.28573 (* 1 = 5.28573 loss)
I0406 07:16:16.048228  5644 sgd_solver.cpp:105] Iteration 960, lr = 0.1
I0406 07:16:21.386679  5644 solver.cpp:218] Iteration 972 (2.24786 iter/s, 5.3384s/12 iters), loss = 5.26917
I0406 07:16:21.386714  5644 solver.cpp:237]     Train net output #0: loss = 5.26917 (* 1 = 5.26917 loss)
I0406 07:16:21.386720  5644 sgd_solver.cpp:105] Iteration 972, lr = 0.1
I0406 07:16:26.619679  5644 solver.cpp:218] Iteration 984 (2.29318 iter/s, 5.2329s/12 iters), loss = 5.27467
I0406 07:16:26.619717  5644 solver.cpp:237]     Train net output #0: loss = 5.27467 (* 1 = 5.27467 loss)
I0406 07:16:26.619729  5644 sgd_solver.cpp:105] Iteration 984, lr = 0.1
I0406 07:16:31.988225  5644 solver.cpp:218] Iteration 996 (2.23528 iter/s, 5.36845s/12 iters), loss = 5.27027
I0406 07:16:31.988270  5644 solver.cpp:237]     Train net output #0: loss = 5.27027 (* 1 = 5.27027 loss)
I0406 07:16:31.988277  5644 sgd_solver.cpp:105] Iteration 996, lr = 0.1
I0406 07:16:37.021797  5644 solver.cpp:218] Iteration 1008 (2.38404 iter/s, 5.03347s/12 iters), loss = 5.28275
I0406 07:16:37.021839  5644 solver.cpp:237]     Train net output #0: loss = 5.28275 (* 1 = 5.28275 loss)
I0406 07:16:37.021844  5644 sgd_solver.cpp:105] Iteration 1008, lr = 0.1
I0406 07:16:38.131022  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:16:41.705750  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0406 07:16:44.762195  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0406 07:16:47.074409  5644 solver.cpp:330] Iteration 1020, Testing net (#0)
I0406 07:16:47.074429  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:16:50.913301  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:16:51.343012  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:16:51.343047  5644 solver.cpp:397]     Test net output #1: loss = 5.28986 (* 1 = 5.28986 loss)
I0406 07:16:51.483340  5644 solver.cpp:218] Iteration 1020 (0.829797 iter/s, 14.4614s/12 iters), loss = 5.2805
I0406 07:16:51.483387  5644 solver.cpp:237]     Train net output #0: loss = 5.2805 (* 1 = 5.2805 loss)
I0406 07:16:51.483395  5644 sgd_solver.cpp:105] Iteration 1020, lr = 0.1
I0406 07:16:55.692476  5644 solver.cpp:218] Iteration 1032 (2.85101 iter/s, 4.20904s/12 iters), loss = 5.29618
I0406 07:16:55.692512  5644 solver.cpp:237]     Train net output #0: loss = 5.29618 (* 1 = 5.29618 loss)
I0406 07:16:55.692517  5644 sgd_solver.cpp:105] Iteration 1032, lr = 0.1
I0406 07:17:00.924226  5644 solver.cpp:218] Iteration 1044 (2.29373 iter/s, 5.23166s/12 iters), loss = 5.27336
I0406 07:17:00.924263  5644 solver.cpp:237]     Train net output #0: loss = 5.27336 (* 1 = 5.27336 loss)
I0406 07:17:00.924268  5644 sgd_solver.cpp:105] Iteration 1044, lr = 0.1
I0406 07:17:06.029700  5644 solver.cpp:218] Iteration 1056 (2.35046 iter/s, 5.10538s/12 iters), loss = 5.27367
I0406 07:17:06.029739  5644 solver.cpp:237]     Train net output #0: loss = 5.27367 (* 1 = 5.27367 loss)
I0406 07:17:06.029744  5644 sgd_solver.cpp:105] Iteration 1056, lr = 0.1
I0406 07:17:11.292177  5644 solver.cpp:218] Iteration 1068 (2.28034 iter/s, 5.26238s/12 iters), loss = 5.28751
I0406 07:17:11.292217  5644 solver.cpp:237]     Train net output #0: loss = 5.28751 (* 1 = 5.28751 loss)
I0406 07:17:11.292222  5644 sgd_solver.cpp:105] Iteration 1068, lr = 0.1
I0406 07:17:16.617215  5644 solver.cpp:218] Iteration 1080 (2.25355 iter/s, 5.32494s/12 iters), loss = 5.25886
I0406 07:17:16.617372  5644 solver.cpp:237]     Train net output #0: loss = 5.25886 (* 1 = 5.25886 loss)
I0406 07:17:16.617380  5644 sgd_solver.cpp:105] Iteration 1080, lr = 0.1
I0406 07:17:21.964098  5644 solver.cpp:218] Iteration 1092 (2.24439 iter/s, 5.34667s/12 iters), loss = 5.27792
I0406 07:17:21.964138  5644 solver.cpp:237]     Train net output #0: loss = 5.27792 (* 1 = 5.27792 loss)
I0406 07:17:21.964143  5644 sgd_solver.cpp:105] Iteration 1092, lr = 0.1
I0406 07:17:27.519501  5644 solver.cpp:218] Iteration 1104 (2.1601 iter/s, 5.5553s/12 iters), loss = 5.29117
I0406 07:17:27.519537  5644 solver.cpp:237]     Train net output #0: loss = 5.29117 (* 1 = 5.29117 loss)
I0406 07:17:27.519542  5644 sgd_solver.cpp:105] Iteration 1104, lr = 0.1
I0406 07:17:31.041712  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:17:33.062333  5644 solver.cpp:218] Iteration 1116 (2.165 iter/s, 5.54273s/12 iters), loss = 5.26574
I0406 07:17:33.062383  5644 solver.cpp:237]     Train net output #0: loss = 5.26574 (* 1 = 5.26574 loss)
I0406 07:17:33.062391  5644 sgd_solver.cpp:105] Iteration 1116, lr = 0.1
I0406 07:17:35.205943  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0406 07:17:38.225272  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0406 07:17:40.525777  5644 solver.cpp:330] Iteration 1122, Testing net (#0)
I0406 07:17:40.525795  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:17:44.331648  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:17:44.800055  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:17:44.800102  5644 solver.cpp:397]     Test net output #1: loss = 5.29009 (* 1 = 5.29009 loss)
I0406 07:17:46.548445  5644 solver.cpp:218] Iteration 1128 (0.889816 iter/s, 13.4859s/12 iters), loss = 5.25213
I0406 07:17:46.548493  5644 solver.cpp:237]     Train net output #0: loss = 5.25213 (* 1 = 5.25213 loss)
I0406 07:17:46.548501  5644 sgd_solver.cpp:105] Iteration 1128, lr = 0.1
I0406 07:17:51.806069  5644 solver.cpp:218] Iteration 1140 (2.28244 iter/s, 5.25752s/12 iters), loss = 5.2736
I0406 07:17:51.806157  5644 solver.cpp:237]     Train net output #0: loss = 5.2736 (* 1 = 5.2736 loss)
I0406 07:17:51.806164  5644 sgd_solver.cpp:105] Iteration 1140, lr = 0.1
I0406 07:17:57.145282  5644 solver.cpp:218] Iteration 1152 (2.24759 iter/s, 5.33906s/12 iters), loss = 5.26756
I0406 07:17:57.145328  5644 solver.cpp:237]     Train net output #0: loss = 5.26756 (* 1 = 5.26756 loss)
I0406 07:17:57.145334  5644 sgd_solver.cpp:105] Iteration 1152, lr = 0.1
I0406 07:18:02.156993  5644 solver.cpp:218] Iteration 1164 (2.39444 iter/s, 5.01161s/12 iters), loss = 5.28701
I0406 07:18:02.157032  5644 solver.cpp:237]     Train net output #0: loss = 5.28701 (* 1 = 5.28701 loss)
I0406 07:18:02.157037  5644 sgd_solver.cpp:105] Iteration 1164, lr = 0.1
I0406 07:18:07.082901  5644 solver.cpp:218] Iteration 1176 (2.43615 iter/s, 4.92581s/12 iters), loss = 5.27112
I0406 07:18:07.082938  5644 solver.cpp:237]     Train net output #0: loss = 5.27112 (* 1 = 5.27112 loss)
I0406 07:18:07.082943  5644 sgd_solver.cpp:105] Iteration 1176, lr = 0.1
I0406 07:18:12.438323  5644 solver.cpp:218] Iteration 1188 (2.24076 iter/s, 5.35532s/12 iters), loss = 5.27105
I0406 07:18:12.438361  5644 solver.cpp:237]     Train net output #0: loss = 5.27105 (* 1 = 5.27105 loss)
I0406 07:18:12.438367  5644 sgd_solver.cpp:105] Iteration 1188, lr = 0.1
I0406 07:18:17.786288  5644 solver.cpp:218] Iteration 1200 (2.24389 iter/s, 5.34786s/12 iters), loss = 5.29326
I0406 07:18:17.786340  5644 solver.cpp:237]     Train net output #0: loss = 5.29326 (* 1 = 5.29326 loss)
I0406 07:18:17.786350  5644 sgd_solver.cpp:105] Iteration 1200, lr = 0.1
I0406 07:18:23.146023  5644 solver.cpp:218] Iteration 1212 (2.23896 iter/s, 5.35963s/12 iters), loss = 5.27511
I0406 07:18:23.146144  5644 solver.cpp:237]     Train net output #0: loss = 5.27511 (* 1 = 5.27511 loss)
I0406 07:18:23.146150  5644 sgd_solver.cpp:105] Iteration 1212, lr = 0.1
I0406 07:18:23.344050  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:18:27.757014  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0406 07:18:30.723582  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0406 07:18:33.027684  5644 solver.cpp:330] Iteration 1224, Testing net (#0)
I0406 07:18:33.027705  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:18:36.835311  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:18:37.363845  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:18:37.363879  5644 solver.cpp:397]     Test net output #1: loss = 5.29126 (* 1 = 5.29126 loss)
I0406 07:18:37.504024  5644 solver.cpp:218] Iteration 1224 (0.835786 iter/s, 14.3577s/12 iters), loss = 5.27303
I0406 07:18:37.504066  5644 solver.cpp:237]     Train net output #0: loss = 5.27303 (* 1 = 5.27303 loss)
I0406 07:18:37.504072  5644 sgd_solver.cpp:105] Iteration 1224, lr = 0.1
I0406 07:18:41.820188  5644 solver.cpp:218] Iteration 1236 (2.78031 iter/s, 4.31607s/12 iters), loss = 5.30438
I0406 07:18:41.820226  5644 solver.cpp:237]     Train net output #0: loss = 5.30438 (* 1 = 5.30438 loss)
I0406 07:18:41.820233  5644 sgd_solver.cpp:105] Iteration 1236, lr = 0.1
I0406 07:18:47.031483  5644 solver.cpp:218] Iteration 1248 (2.30273 iter/s, 5.2112s/12 iters), loss = 5.2654
I0406 07:18:47.031522  5644 solver.cpp:237]     Train net output #0: loss = 5.2654 (* 1 = 5.2654 loss)
I0406 07:18:47.031527  5644 sgd_solver.cpp:105] Iteration 1248, lr = 0.1
I0406 07:18:52.409019  5644 solver.cpp:218] Iteration 1260 (2.23155 iter/s, 5.37744s/12 iters), loss = 5.27514
I0406 07:18:52.409054  5644 solver.cpp:237]     Train net output #0: loss = 5.27514 (* 1 = 5.27514 loss)
I0406 07:18:52.409060  5644 sgd_solver.cpp:105] Iteration 1260, lr = 0.1
I0406 07:18:57.817353  5644 solver.cpp:218] Iteration 1272 (2.21884 iter/s, 5.40824s/12 iters), loss = 5.29122
I0406 07:18:57.817436  5644 solver.cpp:237]     Train net output #0: loss = 5.29122 (* 1 = 5.29122 loss)
I0406 07:18:57.817443  5644 sgd_solver.cpp:105] Iteration 1272, lr = 0.1
I0406 07:19:03.069718  5644 solver.cpp:218] Iteration 1284 (2.28475 iter/s, 5.25222s/12 iters), loss = 5.25769
I0406 07:19:03.069757  5644 solver.cpp:237]     Train net output #0: loss = 5.25769 (* 1 = 5.25769 loss)
I0406 07:19:03.069764  5644 sgd_solver.cpp:105] Iteration 1284, lr = 0.1
I0406 07:19:08.286377  5644 solver.cpp:218] Iteration 1296 (2.30037 iter/s, 5.21656s/12 iters), loss = 5.28558
I0406 07:19:08.286414  5644 solver.cpp:237]     Train net output #0: loss = 5.28558 (* 1 = 5.28558 loss)
I0406 07:19:08.286420  5644 sgd_solver.cpp:105] Iteration 1296, lr = 0.1
I0406 07:19:13.564800  5644 solver.cpp:218] Iteration 1308 (2.27345 iter/s, 5.27832s/12 iters), loss = 5.27313
I0406 07:19:13.564844  5644 solver.cpp:237]     Train net output #0: loss = 5.27313 (* 1 = 5.27313 loss)
I0406 07:19:13.564851  5644 sgd_solver.cpp:105] Iteration 1308, lr = 0.1
I0406 07:19:16.092595  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:19:18.709228  5644 solver.cpp:218] Iteration 1320 (2.33267 iter/s, 5.14433s/12 iters), loss = 5.29075
I0406 07:19:18.709266  5644 solver.cpp:237]     Train net output #0: loss = 5.29075 (* 1 = 5.29075 loss)
I0406 07:19:18.709271  5644 sgd_solver.cpp:105] Iteration 1320, lr = 0.1
I0406 07:19:20.873195  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0406 07:19:23.969085  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0406 07:19:26.299389  5644 solver.cpp:330] Iteration 1326, Testing net (#0)
I0406 07:19:26.299408  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:19:30.005928  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:19:30.558355  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:19:30.558384  5644 solver.cpp:397]     Test net output #1: loss = 5.28995 (* 1 = 5.28995 loss)
I0406 07:19:32.426702  5644 solver.cpp:218] Iteration 1332 (0.874807 iter/s, 13.7173s/12 iters), loss = 5.27582
I0406 07:19:32.426741  5644 solver.cpp:237]     Train net output #0: loss = 5.27582 (* 1 = 5.27582 loss)
I0406 07:19:32.426748  5644 sgd_solver.cpp:105] Iteration 1332, lr = 0.1
I0406 07:19:37.490085  5644 solver.cpp:218] Iteration 1344 (2.37 iter/s, 5.06328s/12 iters), loss = 5.27603
I0406 07:19:37.490124  5644 solver.cpp:237]     Train net output #0: loss = 5.27603 (* 1 = 5.27603 loss)
I0406 07:19:37.490130  5644 sgd_solver.cpp:105] Iteration 1344, lr = 0.1
I0406 07:19:42.692572  5644 solver.cpp:218] Iteration 1356 (2.30663 iter/s, 5.20239s/12 iters), loss = 5.27576
I0406 07:19:42.692608  5644 solver.cpp:237]     Train net output #0: loss = 5.27576 (* 1 = 5.27576 loss)
I0406 07:19:42.692615  5644 sgd_solver.cpp:105] Iteration 1356, lr = 0.1
I0406 07:19:48.080844  5644 solver.cpp:218] Iteration 1368 (2.2271 iter/s, 5.38817s/12 iters), loss = 5.26768
I0406 07:19:48.080900  5644 solver.cpp:237]     Train net output #0: loss = 5.26768 (* 1 = 5.26768 loss)
I0406 07:19:48.080925  5644 sgd_solver.cpp:105] Iteration 1368, lr = 0.1
I0406 07:19:49.369768  5644 blocking_queue.cpp:49] Waiting for data
I0406 07:19:53.116210  5644 solver.cpp:218] Iteration 1380 (2.3832 iter/s, 5.03526s/12 iters), loss = 5.29378
I0406 07:19:53.116253  5644 solver.cpp:237]     Train net output #0: loss = 5.29378 (* 1 = 5.29378 loss)
I0406 07:19:53.116259  5644 sgd_solver.cpp:105] Iteration 1380, lr = 0.1
I0406 07:19:58.093616  5644 solver.cpp:218] Iteration 1392 (2.41094 iter/s, 4.9773s/12 iters), loss = 5.28222
I0406 07:19:58.093657  5644 solver.cpp:237]     Train net output #0: loss = 5.28222 (* 1 = 5.28222 loss)
I0406 07:19:58.093663  5644 sgd_solver.cpp:105] Iteration 1392, lr = 0.1
I0406 07:20:03.170349  5644 solver.cpp:218] Iteration 1404 (2.36377 iter/s, 5.07664s/12 iters), loss = 5.28868
I0406 07:20:03.170435  5644 solver.cpp:237]     Train net output #0: loss = 5.28868 (* 1 = 5.28868 loss)
I0406 07:20:03.170441  5644 sgd_solver.cpp:105] Iteration 1404, lr = 0.1
I0406 07:20:08.081918  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:20:08.464828  5644 solver.cpp:218] Iteration 1416 (2.26657 iter/s, 5.29434s/12 iters), loss = 5.30197
I0406 07:20:08.464864  5644 solver.cpp:237]     Train net output #0: loss = 5.30197 (* 1 = 5.30197 loss)
I0406 07:20:08.464869  5644 sgd_solver.cpp:105] Iteration 1416, lr = 0.1
I0406 07:20:13.325512  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0406 07:20:16.427686  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0406 07:20:18.737087  5644 solver.cpp:330] Iteration 1428, Testing net (#0)
I0406 07:20:18.737107  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:20:22.427156  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:20:23.013319  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:20:23.013343  5644 solver.cpp:397]     Test net output #1: loss = 5.28961 (* 1 = 5.28961 loss)
I0406 07:20:23.149250  5644 solver.cpp:218] Iteration 1428 (0.817202 iter/s, 14.6842s/12 iters), loss = 5.26834
I0406 07:20:23.149307  5644 solver.cpp:237]     Train net output #0: loss = 5.26834 (* 1 = 5.26834 loss)
I0406 07:20:23.149314  5644 sgd_solver.cpp:105] Iteration 1428, lr = 0.1
I0406 07:20:27.327742  5644 solver.cpp:218] Iteration 1440 (2.87192 iter/s, 4.17838s/12 iters), loss = 5.26804
I0406 07:20:27.327780  5644 solver.cpp:237]     Train net output #0: loss = 5.26804 (* 1 = 5.26804 loss)
I0406 07:20:27.327785  5644 sgd_solver.cpp:105] Iteration 1440, lr = 0.1
I0406 07:20:32.765002  5644 solver.cpp:218] Iteration 1452 (2.20703 iter/s, 5.43716s/12 iters), loss = 5.28269
I0406 07:20:32.765040  5644 solver.cpp:237]     Train net output #0: loss = 5.28269 (* 1 = 5.28269 loss)
I0406 07:20:32.765046  5644 sgd_solver.cpp:105] Iteration 1452, lr = 0.1
I0406 07:20:38.078824  5644 solver.cpp:218] Iteration 1464 (2.2583 iter/s, 5.31372s/12 iters), loss = 5.28794
I0406 07:20:38.078940  5644 solver.cpp:237]     Train net output #0: loss = 5.28794 (* 1 = 5.28794 loss)
I0406 07:20:38.078948  5644 sgd_solver.cpp:105] Iteration 1464, lr = 0.1
I0406 07:20:43.361043  5644 solver.cpp:218] Iteration 1476 (2.27185 iter/s, 5.28205s/12 iters), loss = 5.27093
I0406 07:20:43.361081  5644 solver.cpp:237]     Train net output #0: loss = 5.27093 (* 1 = 5.27093 loss)
I0406 07:20:43.361088  5644 sgd_solver.cpp:105] Iteration 1476, lr = 0.1
I0406 07:20:48.512094  5644 solver.cpp:218] Iteration 1488 (2.32967 iter/s, 5.15095s/12 iters), loss = 5.28442
I0406 07:20:48.512128  5644 solver.cpp:237]     Train net output #0: loss = 5.28442 (* 1 = 5.28442 loss)
I0406 07:20:48.512135  5644 sgd_solver.cpp:105] Iteration 1488, lr = 0.1
I0406 07:20:53.775184  5644 solver.cpp:218] Iteration 1500 (2.28007 iter/s, 5.263s/12 iters), loss = 5.27818
I0406 07:20:53.775224  5644 solver.cpp:237]     Train net output #0: loss = 5.27818 (* 1 = 5.27818 loss)
I0406 07:20:53.775230  5644 sgd_solver.cpp:105] Iteration 1500, lr = 0.1
I0406 07:20:58.968261  5644 solver.cpp:218] Iteration 1512 (2.3109 iter/s, 5.19278s/12 iters), loss = 5.27604
I0406 07:20:58.968297  5644 solver.cpp:237]     Train net output #0: loss = 5.27604 (* 1 = 5.27604 loss)
I0406 07:20:58.968302  5644 sgd_solver.cpp:105] Iteration 1512, lr = 0.1
I0406 07:21:00.868233  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:21:04.336827  5644 solver.cpp:218] Iteration 1524 (2.23527 iter/s, 5.36847s/12 iters), loss = 5.29809
I0406 07:21:04.336861  5644 solver.cpp:237]     Train net output #0: loss = 5.29809 (* 1 = 5.29809 loss)
I0406 07:21:04.336866  5644 sgd_solver.cpp:105] Iteration 1524, lr = 0.1
I0406 07:21:06.557435  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0406 07:21:09.557535  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0406 07:21:11.859040  5644 solver.cpp:330] Iteration 1530, Testing net (#0)
I0406 07:21:11.859059  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:21:15.561049  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:21:16.191898  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:21:16.191932  5644 solver.cpp:397]     Test net output #1: loss = 5.28962 (* 1 = 5.28962 loss)
I0406 07:21:18.002944  5644 solver.cpp:218] Iteration 1536 (0.878095 iter/s, 13.6659s/12 iters), loss = 5.25624
I0406 07:21:18.002990  5644 solver.cpp:237]     Train net output #0: loss = 5.25624 (* 1 = 5.25624 loss)
I0406 07:21:18.002995  5644 sgd_solver.cpp:105] Iteration 1536, lr = 0.1
I0406 07:21:23.035408  5644 solver.cpp:218] Iteration 1548 (2.38456 iter/s, 5.03236s/12 iters), loss = 5.28618
I0406 07:21:23.035444  5644 solver.cpp:237]     Train net output #0: loss = 5.28618 (* 1 = 5.28618 loss)
I0406 07:21:23.035449  5644 sgd_solver.cpp:105] Iteration 1548, lr = 0.1
I0406 07:21:28.424505  5644 solver.cpp:218] Iteration 1560 (2.22676 iter/s, 5.389s/12 iters), loss = 5.27369
I0406 07:21:28.424543  5644 solver.cpp:237]     Train net output #0: loss = 5.27369 (* 1 = 5.27369 loss)
I0406 07:21:28.424551  5644 sgd_solver.cpp:105] Iteration 1560, lr = 0.1
I0406 07:21:33.862139  5644 solver.cpp:218] Iteration 1572 (2.20688 iter/s, 5.43754s/12 iters), loss = 5.28062
I0406 07:21:33.862175  5644 solver.cpp:237]     Train net output #0: loss = 5.28062 (* 1 = 5.28062 loss)
I0406 07:21:33.862181  5644 sgd_solver.cpp:105] Iteration 1572, lr = 0.1
I0406 07:21:39.105780  5644 solver.cpp:218] Iteration 1584 (2.28853 iter/s, 5.24355s/12 iters), loss = 5.26308
I0406 07:21:39.105816  5644 solver.cpp:237]     Train net output #0: loss = 5.26308 (* 1 = 5.26308 loss)
I0406 07:21:39.105823  5644 sgd_solver.cpp:105] Iteration 1584, lr = 0.1
I0406 07:21:44.355096  5644 solver.cpp:218] Iteration 1596 (2.28605 iter/s, 5.24922s/12 iters), loss = 5.2816
I0406 07:21:44.355232  5644 solver.cpp:237]     Train net output #0: loss = 5.2816 (* 1 = 5.2816 loss)
I0406 07:21:44.355240  5644 sgd_solver.cpp:105] Iteration 1596, lr = 0.1
I0406 07:21:49.670384  5644 solver.cpp:218] Iteration 1608 (2.25772 iter/s, 5.3151s/12 iters), loss = 5.26333
I0406 07:21:49.670423  5644 solver.cpp:237]     Train net output #0: loss = 5.26333 (* 1 = 5.26333 loss)
I0406 07:21:49.670428  5644 sgd_solver.cpp:105] Iteration 1608, lr = 0.1
I0406 07:21:53.802201  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:21:54.991473  5644 solver.cpp:218] Iteration 1620 (2.25522 iter/s, 5.32099s/12 iters), loss = 5.27536
I0406 07:21:54.991511  5644 solver.cpp:237]     Train net output #0: loss = 5.27536 (* 1 = 5.27536 loss)
I0406 07:21:54.991518  5644 sgd_solver.cpp:105] Iteration 1620, lr = 0.1
I0406 07:21:59.739214  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0406 07:22:02.776660  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0406 07:22:05.108371  5644 solver.cpp:330] Iteration 1632, Testing net (#0)
I0406 07:22:05.108392  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:22:08.708510  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:22:09.371731  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:22:09.371767  5644 solver.cpp:397]     Test net output #1: loss = 5.28997 (* 1 = 5.28997 loss)
I0406 07:22:09.509649  5644 solver.cpp:218] Iteration 1632 (0.82656 iter/s, 14.518s/12 iters), loss = 5.26483
I0406 07:22:09.509706  5644 solver.cpp:237]     Train net output #0: loss = 5.26483 (* 1 = 5.26483 loss)
I0406 07:22:09.509711  5644 sgd_solver.cpp:105] Iteration 1632, lr = 0.1
I0406 07:22:13.784310  5644 solver.cpp:218] Iteration 1644 (2.80732 iter/s, 4.27454s/12 iters), loss = 5.29064
I0406 07:22:13.784363  5644 solver.cpp:237]     Train net output #0: loss = 5.29064 (* 1 = 5.29064 loss)
I0406 07:22:13.784371  5644 sgd_solver.cpp:105] Iteration 1644, lr = 0.1
I0406 07:22:19.219954  5644 solver.cpp:218] Iteration 1656 (2.20769 iter/s, 5.43553s/12 iters), loss = 5.26693
I0406 07:22:19.220050  5644 solver.cpp:237]     Train net output #0: loss = 5.26693 (* 1 = 5.26693 loss)
I0406 07:22:19.220057  5644 sgd_solver.cpp:105] Iteration 1656, lr = 0.1
I0406 07:22:24.588637  5644 solver.cpp:218] Iteration 1668 (2.23525 iter/s, 5.36853s/12 iters), loss = 5.2837
I0406 07:22:24.588687  5644 solver.cpp:237]     Train net output #0: loss = 5.2837 (* 1 = 5.2837 loss)
I0406 07:22:24.588696  5644 sgd_solver.cpp:105] Iteration 1668, lr = 0.1
I0406 07:22:29.907385  5644 solver.cpp:218] Iteration 1680 (2.25622 iter/s, 5.31864s/12 iters), loss = 5.28154
I0406 07:22:29.907430  5644 solver.cpp:237]     Train net output #0: loss = 5.28154 (* 1 = 5.28154 loss)
I0406 07:22:29.907438  5644 sgd_solver.cpp:105] Iteration 1680, lr = 0.1
I0406 07:22:35.238636  5644 solver.cpp:218] Iteration 1692 (2.25092 iter/s, 5.33115s/12 iters), loss = 5.27246
I0406 07:22:35.238675  5644 solver.cpp:237]     Train net output #0: loss = 5.27246 (* 1 = 5.27246 loss)
I0406 07:22:35.238680  5644 sgd_solver.cpp:105] Iteration 1692, lr = 0.1
I0406 07:22:40.419281  5644 solver.cpp:218] Iteration 1704 (2.31635 iter/s, 5.18055s/12 iters), loss = 5.27209
I0406 07:22:40.419319  5644 solver.cpp:237]     Train net output #0: loss = 5.27209 (* 1 = 5.27209 loss)
I0406 07:22:40.419325  5644 sgd_solver.cpp:105] Iteration 1704, lr = 0.1
I0406 07:22:45.785241  5644 solver.cpp:218] Iteration 1716 (2.23636 iter/s, 5.36586s/12 iters), loss = 5.28197
I0406 07:22:45.785281  5644 solver.cpp:237]     Train net output #0: loss = 5.28197 (* 1 = 5.28197 loss)
I0406 07:22:45.785287  5644 sgd_solver.cpp:105] Iteration 1716, lr = 0.1
I0406 07:22:46.882695  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:22:51.036698  5644 solver.cpp:218] Iteration 1728 (2.28512 iter/s, 5.25136s/12 iters), loss = 5.28998
I0406 07:22:51.037885  5644 solver.cpp:237]     Train net output #0: loss = 5.28998 (* 1 = 5.28998 loss)
I0406 07:22:51.037894  5644 sgd_solver.cpp:105] Iteration 1728, lr = 0.1
I0406 07:22:53.022833  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0406 07:22:56.063550  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0406 07:22:58.386958  5644 solver.cpp:330] Iteration 1734, Testing net (#0)
I0406 07:22:58.386983  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:23:02.004448  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:23:02.699431  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:23:02.699458  5644 solver.cpp:397]     Test net output #1: loss = 5.29128 (* 1 = 5.29128 loss)
I0406 07:23:04.556480  5644 solver.cpp:218] Iteration 1740 (0.887675 iter/s, 13.5185s/12 iters), loss = 5.30647
I0406 07:23:04.556522  5644 solver.cpp:237]     Train net output #0: loss = 5.30647 (* 1 = 5.30647 loss)
I0406 07:23:04.556527  5644 sgd_solver.cpp:105] Iteration 1740, lr = 0.1
I0406 07:23:09.964851  5644 solver.cpp:218] Iteration 1752 (2.21883 iter/s, 5.40827s/12 iters), loss = 5.26736
I0406 07:23:09.964903  5644 solver.cpp:237]     Train net output #0: loss = 5.26736 (* 1 = 5.26736 loss)
I0406 07:23:09.964910  5644 sgd_solver.cpp:105] Iteration 1752, lr = 0.1
I0406 07:23:15.353062  5644 solver.cpp:218] Iteration 1764 (2.22713 iter/s, 5.3881s/12 iters), loss = 5.2777
I0406 07:23:15.353102  5644 solver.cpp:237]     Train net output #0: loss = 5.2777 (* 1 = 5.2777 loss)
I0406 07:23:15.353108  5644 sgd_solver.cpp:105] Iteration 1764, lr = 0.1
I0406 07:23:20.524919  5644 solver.cpp:218] Iteration 1776 (2.32029 iter/s, 5.17176s/12 iters), loss = 5.29046
I0406 07:23:20.524953  5644 solver.cpp:237]     Train net output #0: loss = 5.29046 (* 1 = 5.29046 loss)
I0406 07:23:20.524960  5644 sgd_solver.cpp:105] Iteration 1776, lr = 0.1
I0406 07:23:25.801934  5644 solver.cpp:218] Iteration 1788 (2.27405 iter/s, 5.27692s/12 iters), loss = 5.26977
I0406 07:23:25.802060  5644 solver.cpp:237]     Train net output #0: loss = 5.26977 (* 1 = 5.26977 loss)
I0406 07:23:25.802068  5644 sgd_solver.cpp:105] Iteration 1788, lr = 0.1
I0406 07:23:31.170043  5644 solver.cpp:218] Iteration 1800 (2.2355 iter/s, 5.36793s/12 iters), loss = 5.26855
I0406 07:23:31.170083  5644 solver.cpp:237]     Train net output #0: loss = 5.26855 (* 1 = 5.26855 loss)
I0406 07:23:31.170089  5644 sgd_solver.cpp:105] Iteration 1800, lr = 0.1
I0406 07:23:36.445964  5644 solver.cpp:218] Iteration 1812 (2.27453 iter/s, 5.27583s/12 iters), loss = 5.28466
I0406 07:23:36.446003  5644 solver.cpp:237]     Train net output #0: loss = 5.28466 (* 1 = 5.28466 loss)
I0406 07:23:36.446009  5644 sgd_solver.cpp:105] Iteration 1812, lr = 0.1
I0406 07:23:39.723701  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:23:41.781203  5644 solver.cpp:218] Iteration 1824 (2.24924 iter/s, 5.33514s/12 iters), loss = 5.26426
I0406 07:23:41.781244  5644 solver.cpp:237]     Train net output #0: loss = 5.26426 (* 1 = 5.26426 loss)
I0406 07:23:41.781250  5644 sgd_solver.cpp:105] Iteration 1824, lr = 0.1
I0406 07:23:46.233381  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0406 07:23:50.261080  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0406 07:23:52.573572  5644 solver.cpp:330] Iteration 1836, Testing net (#0)
I0406 07:23:52.573593  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:23:56.201818  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:23:56.960072  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:23:56.960099  5644 solver.cpp:397]     Test net output #1: loss = 5.29047 (* 1 = 5.29047 loss)
I0406 07:23:57.095307  5644 solver.cpp:218] Iteration 1836 (0.783601 iter/s, 15.3139s/12 iters), loss = 5.27353
I0406 07:23:57.095355  5644 solver.cpp:237]     Train net output #0: loss = 5.27353 (* 1 = 5.27353 loss)
I0406 07:23:57.095360  5644 sgd_solver.cpp:105] Iteration 1836, lr = 0.1
I0406 07:24:01.356127  5644 solver.cpp:218] Iteration 1848 (2.81642 iter/s, 4.26072s/12 iters), loss = 5.2597
I0406 07:24:01.356168  5644 solver.cpp:237]     Train net output #0: loss = 5.2597 (* 1 = 5.2597 loss)
I0406 07:24:01.356174  5644 sgd_solver.cpp:105] Iteration 1848, lr = 0.1
I0406 07:24:06.697085  5644 solver.cpp:218] Iteration 1860 (2.24683 iter/s, 5.34086s/12 iters), loss = 5.27164
I0406 07:24:06.697125  5644 solver.cpp:237]     Train net output #0: loss = 5.27164 (* 1 = 5.27164 loss)
I0406 07:24:06.697131  5644 sgd_solver.cpp:105] Iteration 1860, lr = 0.1
I0406 07:24:12.037535  5644 solver.cpp:218] Iteration 1872 (2.24704 iter/s, 5.34035s/12 iters), loss = 5.28516
I0406 07:24:12.037578  5644 solver.cpp:237]     Train net output #0: loss = 5.28516 (* 1 = 5.28516 loss)
I0406 07:24:12.037585  5644 sgd_solver.cpp:105] Iteration 1872, lr = 0.1
I0406 07:24:17.194481  5644 solver.cpp:218] Iteration 1884 (2.32701 iter/s, 5.15684s/12 iters), loss = 5.27592
I0406 07:24:17.194522  5644 solver.cpp:237]     Train net output #0: loss = 5.27592 (* 1 = 5.27592 loss)
I0406 07:24:17.194528  5644 sgd_solver.cpp:105] Iteration 1884, lr = 0.1
I0406 07:24:22.415633  5644 solver.cpp:218] Iteration 1896 (2.29839 iter/s, 5.22105s/12 iters), loss = 5.2613
I0406 07:24:22.415681  5644 solver.cpp:237]     Train net output #0: loss = 5.2613 (* 1 = 5.2613 loss)
I0406 07:24:22.415689  5644 sgd_solver.cpp:105] Iteration 1896, lr = 0.1
I0406 07:24:27.702705  5644 solver.cpp:218] Iteration 1908 (2.26973 iter/s, 5.28697s/12 iters), loss = 5.28894
I0406 07:24:27.702805  5644 solver.cpp:237]     Train net output #0: loss = 5.28894 (* 1 = 5.28894 loss)
I0406 07:24:27.702812  5644 sgd_solver.cpp:105] Iteration 1908, lr = 0.1
I0406 07:24:32.760149  5644 solver.cpp:218] Iteration 1920 (2.37281 iter/s, 5.05729s/12 iters), loss = 5.2715
I0406 07:24:32.760182  5644 solver.cpp:237]     Train net output #0: loss = 5.2715 (* 1 = 5.2715 loss)
I0406 07:24:32.760188  5644 sgd_solver.cpp:105] Iteration 1920, lr = 0.1
I0406 07:24:33.047802  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:24:38.093219  5644 solver.cpp:218] Iteration 1932 (2.25015 iter/s, 5.33297s/12 iters), loss = 5.27867
I0406 07:24:38.093271  5644 solver.cpp:237]     Train net output #0: loss = 5.27867 (* 1 = 5.27867 loss)
I0406 07:24:38.093279  5644 sgd_solver.cpp:105] Iteration 1932, lr = 0.1
I0406 07:24:40.137931  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0406 07:24:43.128274  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0406 07:24:45.437410  5644 solver.cpp:330] Iteration 1938, Testing net (#0)
I0406 07:24:45.437433  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:24:49.026355  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:24:49.797132  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:24:49.797168  5644 solver.cpp:397]     Test net output #1: loss = 5.28959 (* 1 = 5.28959 loss)
I0406 07:24:51.551965  5644 solver.cpp:218] Iteration 1944 (0.891625 iter/s, 13.4586s/12 iters), loss = 5.29814
I0406 07:24:51.552008  5644 solver.cpp:237]     Train net output #0: loss = 5.29814 (* 1 = 5.29814 loss)
I0406 07:24:51.552014  5644 sgd_solver.cpp:105] Iteration 1944, lr = 0.1
I0406 07:24:56.958175  5644 solver.cpp:218] Iteration 1956 (2.21971 iter/s, 5.40611s/12 iters), loss = 5.27204
I0406 07:24:56.958215  5644 solver.cpp:237]     Train net output #0: loss = 5.27204 (* 1 = 5.27204 loss)
I0406 07:24:56.958221  5644 sgd_solver.cpp:105] Iteration 1956, lr = 0.1
I0406 07:25:02.030510  5644 solver.cpp:218] Iteration 1968 (2.36582 iter/s, 5.07224s/12 iters), loss = 5.2754
I0406 07:25:02.030619  5644 solver.cpp:237]     Train net output #0: loss = 5.2754 (* 1 = 5.2754 loss)
I0406 07:25:02.030625  5644 sgd_solver.cpp:105] Iteration 1968, lr = 0.1
I0406 07:25:07.014420  5644 solver.cpp:218] Iteration 1980 (2.40783 iter/s, 4.98374s/12 iters), loss = 5.27956
I0406 07:25:07.014454  5644 solver.cpp:237]     Train net output #0: loss = 5.27956 (* 1 = 5.27956 loss)
I0406 07:25:07.014459  5644 sgd_solver.cpp:105] Iteration 1980, lr = 0.1
I0406 07:25:12.408373  5644 solver.cpp:218] Iteration 1992 (2.22475 iter/s, 5.39386s/12 iters), loss = 5.26647
I0406 07:25:12.408404  5644 solver.cpp:237]     Train net output #0: loss = 5.26647 (* 1 = 5.26647 loss)
I0406 07:25:12.408409  5644 sgd_solver.cpp:105] Iteration 1992, lr = 0.1
I0406 07:25:17.693403  5644 solver.cpp:218] Iteration 2004 (2.2706 iter/s, 5.28494s/12 iters), loss = 5.29052
I0406 07:25:17.693436  5644 solver.cpp:237]     Train net output #0: loss = 5.29052 (* 1 = 5.29052 loss)
I0406 07:25:17.693441  5644 sgd_solver.cpp:105] Iteration 2004, lr = 0.1
I0406 07:25:22.874557  5644 solver.cpp:218] Iteration 2016 (2.31613 iter/s, 5.18106s/12 iters), loss = 5.2734
I0406 07:25:22.874598  5644 solver.cpp:237]     Train net output #0: loss = 5.2734 (* 1 = 5.2734 loss)
I0406 07:25:22.874603  5644 sgd_solver.cpp:105] Iteration 2016, lr = 0.1
I0406 07:25:25.518671  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:25:28.213518  5644 solver.cpp:218] Iteration 2028 (2.24767 iter/s, 5.33886s/12 iters), loss = 5.29178
I0406 07:25:28.213554  5644 solver.cpp:237]     Train net output #0: loss = 5.29178 (* 1 = 5.29178 loss)
I0406 07:25:28.213560  5644 sgd_solver.cpp:105] Iteration 2028, lr = 0.1
I0406 07:25:33.042132  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0406 07:25:36.031653  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0406 07:25:38.339031  5644 solver.cpp:330] Iteration 2040, Testing net (#0)
I0406 07:25:38.339051  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:25:41.809304  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:25:42.628343  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:25:42.628374  5644 solver.cpp:397]     Test net output #1: loss = 5.29015 (* 1 = 5.29015 loss)
I0406 07:25:42.767413  5644 solver.cpp:218] Iteration 2040 (0.824531 iter/s, 14.5537s/12 iters), loss = 5.27626
I0406 07:25:42.767454  5644 solver.cpp:237]     Train net output #0: loss = 5.27626 (* 1 = 5.27626 loss)
I0406 07:25:42.767460  5644 sgd_solver.cpp:105] Iteration 2040, lr = 0.1
I0406 07:25:47.160178  5644 solver.cpp:218] Iteration 2052 (2.73182 iter/s, 4.39267s/12 iters), loss = 5.27277
I0406 07:25:47.160215  5644 solver.cpp:237]     Train net output #0: loss = 5.27277 (* 1 = 5.27277 loss)
I0406 07:25:47.160220  5644 sgd_solver.cpp:105] Iteration 2052, lr = 0.1
I0406 07:25:48.968566  5644 blocking_queue.cpp:49] Waiting for data
I0406 07:25:52.446346  5644 solver.cpp:218] Iteration 2064 (2.27012 iter/s, 5.28607s/12 iters), loss = 5.2806
I0406 07:25:52.446378  5644 solver.cpp:237]     Train net output #0: loss = 5.2806 (* 1 = 5.2806 loss)
I0406 07:25:52.446384  5644 sgd_solver.cpp:105] Iteration 2064, lr = 0.1
I0406 07:25:57.844914  5644 solver.cpp:218] Iteration 2076 (2.22285 iter/s, 5.39847s/12 iters), loss = 5.27348
I0406 07:25:57.844954  5644 solver.cpp:237]     Train net output #0: loss = 5.27348 (* 1 = 5.27348 loss)
I0406 07:25:57.844959  5644 sgd_solver.cpp:105] Iteration 2076, lr = 0.1
I0406 07:26:03.139465  5644 solver.cpp:218] Iteration 2088 (2.26652 iter/s, 5.29445s/12 iters), loss = 5.29991
I0406 07:26:03.139601  5644 solver.cpp:237]     Train net output #0: loss = 5.29991 (* 1 = 5.29991 loss)
I0406 07:26:03.139607  5644 sgd_solver.cpp:105] Iteration 2088, lr = 0.1
I0406 07:26:08.224264  5644 solver.cpp:218] Iteration 2100 (2.36006 iter/s, 5.08461s/12 iters), loss = 5.28362
I0406 07:26:08.224298  5644 solver.cpp:237]     Train net output #0: loss = 5.28362 (* 1 = 5.28362 loss)
I0406 07:26:08.224303  5644 sgd_solver.cpp:105] Iteration 2100, lr = 0.1
I0406 07:26:13.626617  5644 solver.cpp:218] Iteration 2112 (2.22129 iter/s, 5.40226s/12 iters), loss = 5.28918
I0406 07:26:13.626658  5644 solver.cpp:237]     Train net output #0: loss = 5.28918 (* 1 = 5.28918 loss)
I0406 07:26:13.626664  5644 sgd_solver.cpp:105] Iteration 2112, lr = 0.1
I0406 07:26:18.438303  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:26:18.793134  5644 solver.cpp:218] Iteration 2124 (2.32269 iter/s, 5.16642s/12 iters), loss = 5.30278
I0406 07:26:18.793186  5644 solver.cpp:237]     Train net output #0: loss = 5.30278 (* 1 = 5.30278 loss)
I0406 07:26:18.793195  5644 sgd_solver.cpp:105] Iteration 2124, lr = 0.1
I0406 07:26:24.133452  5644 solver.cpp:218] Iteration 2136 (2.2471 iter/s, 5.34021s/12 iters), loss = 5.25902
I0406 07:26:24.133488  5644 solver.cpp:237]     Train net output #0: loss = 5.25902 (* 1 = 5.25902 loss)
I0406 07:26:24.133493  5644 sgd_solver.cpp:105] Iteration 2136, lr = 0.1
I0406 07:26:26.250257  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0406 07:26:29.320735  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0406 07:26:32.172677  5644 solver.cpp:330] Iteration 2142, Testing net (#0)
I0406 07:26:32.172700  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:26:35.729503  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:26:36.578725  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:26:36.578773  5644 solver.cpp:397]     Test net output #1: loss = 5.29044 (* 1 = 5.29044 loss)
I0406 07:26:38.493952  5644 solver.cpp:218] Iteration 2148 (0.835635 iter/s, 14.3603s/12 iters), loss = 5.27121
I0406 07:26:38.493990  5644 solver.cpp:237]     Train net output #0: loss = 5.27121 (* 1 = 5.27121 loss)
I0406 07:26:38.493996  5644 sgd_solver.cpp:105] Iteration 2148, lr = 0.1
I0406 07:26:43.908484  5644 solver.cpp:218] Iteration 2160 (2.2163 iter/s, 5.41443s/12 iters), loss = 5.2921
I0406 07:26:43.908532  5644 solver.cpp:237]     Train net output #0: loss = 5.2921 (* 1 = 5.2921 loss)
I0406 07:26:43.908542  5644 sgd_solver.cpp:105] Iteration 2160, lr = 0.1
I0406 07:26:49.203202  5644 solver.cpp:218] Iteration 2172 (2.26646 iter/s, 5.29461s/12 iters), loss = 5.27016
I0406 07:26:49.203239  5644 solver.cpp:237]     Train net output #0: loss = 5.27016 (* 1 = 5.27016 loss)
I0406 07:26:49.203244  5644 sgd_solver.cpp:105] Iteration 2172, lr = 0.1
I0406 07:26:54.533969  5644 solver.cpp:218] Iteration 2184 (2.25112 iter/s, 5.33067s/12 iters), loss = 5.26883
I0406 07:26:54.534006  5644 solver.cpp:237]     Train net output #0: loss = 5.26883 (* 1 = 5.26883 loss)
I0406 07:26:54.534011  5644 sgd_solver.cpp:105] Iteration 2184, lr = 0.1
I0406 07:26:59.622728  5644 solver.cpp:218] Iteration 2196 (2.35818 iter/s, 5.08867s/12 iters), loss = 5.27901
I0406 07:26:59.622762  5644 solver.cpp:237]     Train net output #0: loss = 5.27901 (* 1 = 5.27901 loss)
I0406 07:26:59.622767  5644 sgd_solver.cpp:105] Iteration 2196, lr = 0.1
I0406 07:27:04.908839  5644 solver.cpp:218] Iteration 2208 (2.27014 iter/s, 5.28602s/12 iters), loss = 5.28471
I0406 07:27:04.908879  5644 solver.cpp:237]     Train net output #0: loss = 5.28471 (* 1 = 5.28471 loss)
I0406 07:27:04.908890  5644 sgd_solver.cpp:105] Iteration 2208, lr = 0.1
I0406 07:27:10.145467  5644 solver.cpp:218] Iteration 2220 (2.2916 iter/s, 5.23652s/12 iters), loss = 5.2709
I0406 07:27:10.145587  5644 solver.cpp:237]     Train net output #0: loss = 5.2709 (* 1 = 5.2709 loss)
I0406 07:27:10.145594  5644 sgd_solver.cpp:105] Iteration 2220, lr = 0.1
I0406 07:27:12.001544  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:27:15.294842  5644 solver.cpp:218] Iteration 2232 (2.33046 iter/s, 5.1492s/12 iters), loss = 5.29665
I0406 07:27:15.294878  5644 solver.cpp:237]     Train net output #0: loss = 5.29665 (* 1 = 5.29665 loss)
I0406 07:27:15.294883  5644 sgd_solver.cpp:105] Iteration 2232, lr = 0.1
I0406 07:27:19.963570  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0406 07:27:22.955308  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0406 07:27:25.253355  5644 solver.cpp:330] Iteration 2244, Testing net (#0)
I0406 07:27:25.253377  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:27:28.712057  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:27:29.673233  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 07:27:29.673265  5644 solver.cpp:397]     Test net output #1: loss = 5.29002 (* 1 = 5.29002 loss)
I0406 07:27:29.813119  5644 solver.cpp:218] Iteration 2244 (0.826554 iter/s, 14.5181s/12 iters), loss = 5.25699
I0406 07:27:29.813170  5644 solver.cpp:237]     Train net output #0: loss = 5.25699 (* 1 = 5.25699 loss)
I0406 07:27:29.813179  5644 sgd_solver.cpp:105] Iteration 2244, lr = 0.1
I0406 07:27:34.129748  5644 solver.cpp:218] Iteration 2256 (2.78001 iter/s, 4.31652s/12 iters), loss = 5.28426
I0406 07:27:34.129784  5644 solver.cpp:237]     Train net output #0: loss = 5.28426 (* 1 = 5.28426 loss)
I0406 07:27:34.129789  5644 sgd_solver.cpp:105] Iteration 2256, lr = 0.1
I0406 07:27:39.441526  5644 solver.cpp:218] Iteration 2268 (2.25917 iter/s, 5.31168s/12 iters), loss = 5.26836
I0406 07:27:39.441567  5644 solver.cpp:237]     Train net output #0: loss = 5.26836 (* 1 = 5.26836 loss)
I0406 07:27:39.441573  5644 sgd_solver.cpp:105] Iteration 2268, lr = 0.1
I0406 07:27:44.823630  5644 solver.cpp:218] Iteration 2280 (2.22965 iter/s, 5.382s/12 iters), loss = 5.27763
I0406 07:27:44.823773  5644 solver.cpp:237]     Train net output #0: loss = 5.27763 (* 1 = 5.27763 loss)
I0406 07:27:44.823781  5644 sgd_solver.cpp:105] Iteration 2280, lr = 0.1
I0406 07:27:50.014192  5644 solver.cpp:218] Iteration 2292 (2.31198 iter/s, 5.19037s/12 iters), loss = 5.25836
I0406 07:27:50.014230  5644 solver.cpp:237]     Train net output #0: loss = 5.25836 (* 1 = 5.25836 loss)
I0406 07:27:50.014235  5644 sgd_solver.cpp:105] Iteration 2292, lr = 0.1
I0406 07:27:55.208287  5644 solver.cpp:218] Iteration 2304 (2.31036 iter/s, 5.194s/12 iters), loss = 5.27776
I0406 07:27:55.208325  5644 solver.cpp:237]     Train net output #0: loss = 5.27776 (* 1 = 5.27776 loss)
I0406 07:27:55.208330  5644 sgd_solver.cpp:105] Iteration 2304, lr = 0.1
I0406 07:28:00.140923  5644 solver.cpp:218] Iteration 2316 (2.43282 iter/s, 4.93254s/12 iters), loss = 5.26349
I0406 07:28:00.140960  5644 solver.cpp:237]     Train net output #0: loss = 5.26349 (* 1 = 5.26349 loss)
I0406 07:28:00.140965  5644 sgd_solver.cpp:105] Iteration 2316, lr = 0.1
I0406 07:28:04.362931  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:28:05.572656  5644 solver.cpp:218] Iteration 2328 (2.20928 iter/s, 5.43163s/12 iters), loss = 5.27933
I0406 07:28:05.572703  5644 solver.cpp:237]     Train net output #0: loss = 5.27933 (* 1 = 5.27933 loss)
I0406 07:28:05.572708  5644 sgd_solver.cpp:105] Iteration 2328, lr = 0.1
I0406 07:28:10.911427  5644 solver.cpp:218] Iteration 2340 (2.24775 iter/s, 5.33867s/12 iters), loss = 5.26022
I0406 07:28:10.911463  5644 solver.cpp:237]     Train net output #0: loss = 5.26022 (* 1 = 5.26022 loss)
I0406 07:28:10.911468  5644 sgd_solver.cpp:105] Iteration 2340, lr = 0.1
I0406 07:28:12.931658  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0406 07:28:15.950867  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0406 07:28:18.251222  5644 solver.cpp:330] Iteration 2346, Testing net (#0)
I0406 07:28:18.251245  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:28:21.690618  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:28:22.632767  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 07:28:22.632802  5644 solver.cpp:397]     Test net output #1: loss = 5.28964 (* 1 = 5.28964 loss)
I0406 07:28:24.439816  5644 solver.cpp:218] Iteration 2352 (0.887034 iter/s, 13.5282s/12 iters), loss = 5.2902
I0406 07:28:24.439854  5644 solver.cpp:237]     Train net output #0: loss = 5.2902 (* 1 = 5.2902 loss)
I0406 07:28:24.439860  5644 sgd_solver.cpp:105] Iteration 2352, lr = 0.1
I0406 07:28:29.859359  5644 solver.cpp:218] Iteration 2364 (2.21425 iter/s, 5.41944s/12 iters), loss = 5.27502
I0406 07:28:29.859398  5644 solver.cpp:237]     Train net output #0: loss = 5.27502 (* 1 = 5.27502 loss)
I0406 07:28:29.859405  5644 sgd_solver.cpp:105] Iteration 2364, lr = 0.1
I0406 07:28:35.067471  5644 solver.cpp:218] Iteration 2376 (2.30414 iter/s, 5.20802s/12 iters), loss = 5.28297
I0406 07:28:35.067505  5644 solver.cpp:237]     Train net output #0: loss = 5.28297 (* 1 = 5.28297 loss)
I0406 07:28:35.067512  5644 sgd_solver.cpp:105] Iteration 2376, lr = 0.1
I0406 07:28:40.455171  5644 solver.cpp:218] Iteration 2388 (2.22734 iter/s, 5.3876s/12 iters), loss = 5.2801
I0406 07:28:40.455209  5644 solver.cpp:237]     Train net output #0: loss = 5.2801 (* 1 = 5.2801 loss)
I0406 07:28:40.455214  5644 sgd_solver.cpp:105] Iteration 2388, lr = 0.1
I0406 07:28:45.689553  5644 solver.cpp:218] Iteration 2400 (2.29258 iter/s, 5.23428s/12 iters), loss = 5.27123
I0406 07:28:45.689594  5644 solver.cpp:237]     Train net output #0: loss = 5.27123 (* 1 = 5.27123 loss)
I0406 07:28:45.689599  5644 sgd_solver.cpp:105] Iteration 2400, lr = 0.1
I0406 07:28:51.094493  5644 solver.cpp:218] Iteration 2412 (2.22023 iter/s, 5.40484s/12 iters), loss = 5.26929
I0406 07:28:51.094581  5644 solver.cpp:237]     Train net output #0: loss = 5.26929 (* 1 = 5.26929 loss)
I0406 07:28:51.094588  5644 sgd_solver.cpp:105] Iteration 2412, lr = 0.1
I0406 07:28:56.363873  5644 solver.cpp:218] Iteration 2424 (2.27737 iter/s, 5.26923s/12 iters), loss = 5.27955
I0406 07:28:56.363924  5644 solver.cpp:237]     Train net output #0: loss = 5.27955 (* 1 = 5.27955 loss)
I0406 07:28:56.363932  5644 sgd_solver.cpp:105] Iteration 2424, lr = 0.1
I0406 07:28:57.517212  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:29:01.903889  5644 solver.cpp:218] Iteration 2436 (2.1661 iter/s, 5.5399s/12 iters), loss = 5.2874
I0406 07:29:01.903930  5644 solver.cpp:237]     Train net output #0: loss = 5.2874 (* 1 = 5.2874 loss)
I0406 07:29:01.903936  5644 sgd_solver.cpp:105] Iteration 2436, lr = 0.1
I0406 07:29:06.692186  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0406 07:29:09.710209  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0406 07:29:12.012423  5644 solver.cpp:330] Iteration 2448, Testing net (#0)
I0406 07:29:12.012441  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:29:15.395406  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:29:16.354270  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:29:16.354318  5644 solver.cpp:397]     Test net output #1: loss = 5.29018 (* 1 = 5.29018 loss)
I0406 07:29:16.498903  5644 solver.cpp:218] Iteration 2448 (0.822209 iter/s, 14.5948s/12 iters), loss = 5.30043
I0406 07:29:16.498945  5644 solver.cpp:237]     Train net output #0: loss = 5.30043 (* 1 = 5.30043 loss)
I0406 07:29:16.498951  5644 sgd_solver.cpp:105] Iteration 2448, lr = 0.1
I0406 07:29:20.929558  5644 solver.cpp:218] Iteration 2460 (2.70846 iter/s, 4.43056s/12 iters), loss = 5.26422
I0406 07:29:20.929594  5644 solver.cpp:237]     Train net output #0: loss = 5.26422 (* 1 = 5.26422 loss)
I0406 07:29:20.929598  5644 sgd_solver.cpp:105] Iteration 2460, lr = 0.1
I0406 07:29:26.134351  5644 solver.cpp:218] Iteration 2472 (2.30561 iter/s, 5.20469s/12 iters), loss = 5.27407
I0406 07:29:26.134475  5644 solver.cpp:237]     Train net output #0: loss = 5.27407 (* 1 = 5.27407 loss)
I0406 07:29:26.134482  5644 sgd_solver.cpp:105] Iteration 2472, lr = 0.1
I0406 07:29:31.387912  5644 solver.cpp:218] Iteration 2484 (2.28425 iter/s, 5.25338s/12 iters), loss = 5.2949
I0406 07:29:31.387954  5644 solver.cpp:237]     Train net output #0: loss = 5.2949 (* 1 = 5.2949 loss)
I0406 07:29:31.387961  5644 sgd_solver.cpp:105] Iteration 2484, lr = 0.1
I0406 07:29:36.591703  5644 solver.cpp:218] Iteration 2496 (2.30605 iter/s, 5.2037s/12 iters), loss = 5.27051
I0406 07:29:36.591739  5644 solver.cpp:237]     Train net output #0: loss = 5.27051 (* 1 = 5.27051 loss)
I0406 07:29:36.591745  5644 sgd_solver.cpp:105] Iteration 2496, lr = 0.1
I0406 07:29:41.856868  5644 solver.cpp:218] Iteration 2508 (2.27917 iter/s, 5.26507s/12 iters), loss = 5.26706
I0406 07:29:41.856911  5644 solver.cpp:237]     Train net output #0: loss = 5.26706 (* 1 = 5.26706 loss)
I0406 07:29:41.856917  5644 sgd_solver.cpp:105] Iteration 2508, lr = 0.1
I0406 07:29:47.303369  5644 solver.cpp:218] Iteration 2520 (2.20329 iter/s, 5.4464s/12 iters), loss = 5.28738
I0406 07:29:47.303406  5644 solver.cpp:237]     Train net output #0: loss = 5.28738 (* 1 = 5.28738 loss)
I0406 07:29:47.303411  5644 sgd_solver.cpp:105] Iteration 2520, lr = 0.1
I0406 07:29:50.682152  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:29:52.672667  5644 solver.cpp:218] Iteration 2532 (2.23497 iter/s, 5.3692s/12 iters), loss = 5.25282
I0406 07:29:52.672708  5644 solver.cpp:237]     Train net output #0: loss = 5.25282 (* 1 = 5.25282 loss)
I0406 07:29:52.672714  5644 sgd_solver.cpp:105] Iteration 2532, lr = 0.1
I0406 07:29:58.006245  5644 solver.cpp:218] Iteration 2544 (2.24994 iter/s, 5.33348s/12 iters), loss = 5.27768
I0406 07:29:58.006309  5644 solver.cpp:237]     Train net output #0: loss = 5.27768 (* 1 = 5.27768 loss)
I0406 07:29:58.006314  5644 sgd_solver.cpp:105] Iteration 2544, lr = 0.1
I0406 07:30:00.141254  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0406 07:30:03.187386  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0406 07:30:05.499229  5644 solver.cpp:330] Iteration 2550, Testing net (#0)
I0406 07:30:05.499248  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:30:08.781790  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:30:09.813355  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:30:09.813390  5644 solver.cpp:397]     Test net output #1: loss = 5.29081 (* 1 = 5.29081 loss)
I0406 07:30:11.764369  5644 solver.cpp:218] Iteration 2556 (0.872224 iter/s, 13.7579s/12 iters), loss = 5.26661
I0406 07:30:11.764417  5644 solver.cpp:237]     Train net output #0: loss = 5.26661 (* 1 = 5.26661 loss)
I0406 07:30:11.764425  5644 sgd_solver.cpp:105] Iteration 2556, lr = 0.1
I0406 07:30:17.128561  5644 solver.cpp:218] Iteration 2568 (2.2371 iter/s, 5.36408s/12 iters), loss = 5.27155
I0406 07:30:17.128602  5644 solver.cpp:237]     Train net output #0: loss = 5.27155 (* 1 = 5.27155 loss)
I0406 07:30:17.128608  5644 sgd_solver.cpp:105] Iteration 2568, lr = 0.1
I0406 07:30:22.340809  5644 solver.cpp:218] Iteration 2580 (2.30231 iter/s, 5.21215s/12 iters), loss = 5.28773
I0406 07:30:22.340847  5644 solver.cpp:237]     Train net output #0: loss = 5.28773 (* 1 = 5.28773 loss)
I0406 07:30:22.340852  5644 sgd_solver.cpp:105] Iteration 2580, lr = 0.1
I0406 07:30:27.513624  5644 solver.cpp:218] Iteration 2592 (2.31986 iter/s, 5.17272s/12 iters), loss = 5.26874
I0406 07:30:27.513661  5644 solver.cpp:237]     Train net output #0: loss = 5.26874 (* 1 = 5.26874 loss)
I0406 07:30:27.513666  5644 sgd_solver.cpp:105] Iteration 2592, lr = 0.1
I0406 07:30:32.673529  5644 solver.cpp:218] Iteration 2604 (2.32567 iter/s, 5.15981s/12 iters), loss = 5.26034
I0406 07:30:32.673638  5644 solver.cpp:237]     Train net output #0: loss = 5.26034 (* 1 = 5.26034 loss)
I0406 07:30:32.673645  5644 sgd_solver.cpp:105] Iteration 2604, lr = 0.1
I0406 07:30:37.932094  5644 solver.cpp:218] Iteration 2616 (2.28207 iter/s, 5.25839s/12 iters), loss = 5.28526
I0406 07:30:37.932153  5644 solver.cpp:237]     Train net output #0: loss = 5.28526 (* 1 = 5.28526 loss)
I0406 07:30:37.932163  5644 sgd_solver.cpp:105] Iteration 2616, lr = 0.1
I0406 07:30:43.108124  5644 solver.cpp:218] Iteration 2628 (2.31843 iter/s, 5.17591s/12 iters), loss = 5.27257
I0406 07:30:43.108176  5644 solver.cpp:237]     Train net output #0: loss = 5.27257 (* 1 = 5.27257 loss)
I0406 07:30:43.108186  5644 sgd_solver.cpp:105] Iteration 2628, lr = 0.1
I0406 07:30:43.577399  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:30:48.462353  5644 solver.cpp:218] Iteration 2640 (2.24127 iter/s, 5.35412s/12 iters), loss = 5.2866
I0406 07:30:48.462393  5644 solver.cpp:237]     Train net output #0: loss = 5.2866 (* 1 = 5.2866 loss)
I0406 07:30:48.462399  5644 sgd_solver.cpp:105] Iteration 2640, lr = 0.1
I0406 07:30:53.229749  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0406 07:30:56.267997  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0406 07:30:58.570950  5644 solver.cpp:330] Iteration 2652, Testing net (#0)
I0406 07:30:58.570968  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:31:01.827503  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:31:02.860630  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:31:02.860751  5644 solver.cpp:397]     Test net output #1: loss = 5.29033 (* 1 = 5.29033 loss)
I0406 07:31:03.001456  5644 solver.cpp:218] Iteration 2652 (0.82537 iter/s, 14.5389s/12 iters), loss = 5.29216
I0406 07:31:03.001523  5644 solver.cpp:237]     Train net output #0: loss = 5.29216 (* 1 = 5.29216 loss)
I0406 07:31:03.001531  5644 sgd_solver.cpp:105] Iteration 2652, lr = 0.1
I0406 07:31:07.421051  5644 solver.cpp:218] Iteration 2664 (2.71525 iter/s, 4.41948s/12 iters), loss = 5.27403
I0406 07:31:07.421097  5644 solver.cpp:237]     Train net output #0: loss = 5.27403 (* 1 = 5.27403 loss)
I0406 07:31:07.421104  5644 sgd_solver.cpp:105] Iteration 2664, lr = 0.1
I0406 07:31:12.760994  5644 solver.cpp:218] Iteration 2676 (2.24726 iter/s, 5.33984s/12 iters), loss = 5.27362
I0406 07:31:12.761027  5644 solver.cpp:237]     Train net output #0: loss = 5.27362 (* 1 = 5.27362 loss)
I0406 07:31:12.761032  5644 sgd_solver.cpp:105] Iteration 2676, lr = 0.1
I0406 07:31:18.132086  5644 solver.cpp:218] Iteration 2688 (2.23422 iter/s, 5.37099s/12 iters), loss = 5.26655
I0406 07:31:18.132123  5644 solver.cpp:237]     Train net output #0: loss = 5.26655 (* 1 = 5.26655 loss)
I0406 07:31:18.132129  5644 sgd_solver.cpp:105] Iteration 2688, lr = 0.1
I0406 07:31:23.423139  5644 solver.cpp:218] Iteration 2700 (2.26802 iter/s, 5.29095s/12 iters), loss = 5.28442
I0406 07:31:23.423179  5644 solver.cpp:237]     Train net output #0: loss = 5.28442 (* 1 = 5.28442 loss)
I0406 07:31:23.423185  5644 sgd_solver.cpp:105] Iteration 2700, lr = 0.1
I0406 07:31:28.452915  5644 solver.cpp:218] Iteration 2712 (2.38584 iter/s, 5.02968s/12 iters), loss = 5.28965
I0406 07:31:28.452951  5644 solver.cpp:237]     Train net output #0: loss = 5.28965 (* 1 = 5.28965 loss)
I0406 07:31:28.452956  5644 sgd_solver.cpp:105] Iteration 2712, lr = 0.1
I0406 07:31:33.679419  5644 solver.cpp:218] Iteration 2724 (2.29603 iter/s, 5.22641s/12 iters), loss = 5.27432
I0406 07:31:33.679565  5644 solver.cpp:237]     Train net output #0: loss = 5.27432 (* 1 = 5.27432 loss)
I0406 07:31:33.679574  5644 sgd_solver.cpp:105] Iteration 2724, lr = 0.1
I0406 07:31:36.279422  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:31:38.946705  5644 solver.cpp:218] Iteration 2736 (2.2783 iter/s, 5.26709s/12 iters), loss = 5.29564
I0406 07:31:38.946741  5644 solver.cpp:237]     Train net output #0: loss = 5.29564 (* 1 = 5.29564 loss)
I0406 07:31:38.946748  5644 sgd_solver.cpp:105] Iteration 2736, lr = 0.1
I0406 07:31:44.120872  5644 solver.cpp:218] Iteration 2748 (2.31926 iter/s, 5.17407s/12 iters), loss = 5.27809
I0406 07:31:44.120913  5644 solver.cpp:237]     Train net output #0: loss = 5.27809 (* 1 = 5.27809 loss)
I0406 07:31:44.120919  5644 sgd_solver.cpp:105] Iteration 2748, lr = 0.1
I0406 07:31:46.106176  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0406 07:31:49.119707  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0406 07:31:51.437240  5644 solver.cpp:330] Iteration 2754, Testing net (#0)
I0406 07:31:51.437263  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:31:54.486994  5644 blocking_queue.cpp:49] Waiting for data
I0406 07:31:54.716603  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:31:55.800165  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:31:55.800199  5644 solver.cpp:397]     Test net output #1: loss = 5.2904 (* 1 = 5.2904 loss)
I0406 07:31:57.686669  5644 solver.cpp:218] Iteration 2760 (0.884589 iter/s, 13.5656s/12 iters), loss = 5.27076
I0406 07:31:57.686710  5644 solver.cpp:237]     Train net output #0: loss = 5.27076 (* 1 = 5.27076 loss)
I0406 07:31:57.686717  5644 sgd_solver.cpp:105] Iteration 2760, lr = 0.1
I0406 07:32:03.005795  5644 solver.cpp:218] Iteration 2772 (2.25606 iter/s, 5.31902s/12 iters), loss = 5.28444
I0406 07:32:03.005839  5644 solver.cpp:237]     Train net output #0: loss = 5.28444 (* 1 = 5.28444 loss)
I0406 07:32:03.005847  5644 sgd_solver.cpp:105] Iteration 2772, lr = 0.1
I0406 07:32:08.273250  5644 solver.cpp:218] Iteration 2784 (2.27818 iter/s, 5.26735s/12 iters), loss = 5.27362
I0406 07:32:08.273353  5644 solver.cpp:237]     Train net output #0: loss = 5.27362 (* 1 = 5.27362 loss)
I0406 07:32:08.273365  5644 sgd_solver.cpp:105] Iteration 2784, lr = 0.1
I0406 07:32:13.500018  5644 solver.cpp:218] Iteration 2796 (2.29594 iter/s, 5.22661s/12 iters), loss = 5.29804
I0406 07:32:13.500056  5644 solver.cpp:237]     Train net output #0: loss = 5.29804 (* 1 = 5.29804 loss)
I0406 07:32:13.500061  5644 sgd_solver.cpp:105] Iteration 2796, lr = 0.1
I0406 07:32:18.775949  5644 solver.cpp:218] Iteration 2808 (2.27452 iter/s, 5.27584s/12 iters), loss = 5.28531
I0406 07:32:18.775979  5644 solver.cpp:237]     Train net output #0: loss = 5.28531 (* 1 = 5.28531 loss)
I0406 07:32:18.775983  5644 sgd_solver.cpp:105] Iteration 2808, lr = 0.1
I0406 07:32:24.125139  5644 solver.cpp:218] Iteration 2820 (2.24337 iter/s, 5.3491s/12 iters), loss = 5.28563
I0406 07:32:24.125176  5644 solver.cpp:237]     Train net output #0: loss = 5.28563 (* 1 = 5.28563 loss)
I0406 07:32:24.125182  5644 sgd_solver.cpp:105] Iteration 2820, lr = 0.1
I0406 07:32:29.037588  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:32:29.365362  5644 solver.cpp:218] Iteration 2832 (2.29002 iter/s, 5.24012s/12 iters), loss = 5.30052
I0406 07:32:29.365414  5644 solver.cpp:237]     Train net output #0: loss = 5.30052 (* 1 = 5.30052 loss)
I0406 07:32:29.365424  5644 sgd_solver.cpp:105] Iteration 2832, lr = 0.1
I0406 07:32:34.553445  5644 solver.cpp:218] Iteration 2844 (2.31304 iter/s, 5.18797s/12 iters), loss = 5.26089
I0406 07:32:34.553483  5644 solver.cpp:237]     Train net output #0: loss = 5.26089 (* 1 = 5.26089 loss)
I0406 07:32:34.553489  5644 sgd_solver.cpp:105] Iteration 2844, lr = 0.1
I0406 07:32:39.231031  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0406 07:32:42.247359  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0406 07:32:44.549396  5644 solver.cpp:330] Iteration 2856, Testing net (#0)
I0406 07:32:44.549414  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:32:47.832032  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:32:48.974764  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 07:32:48.974792  5644 solver.cpp:397]     Test net output #1: loss = 5.28945 (* 1 = 5.28945 loss)
I0406 07:32:49.115422  5644 solver.cpp:218] Iteration 2856 (0.824074 iter/s, 14.5618s/12 iters), loss = 5.27137
I0406 07:32:49.115463  5644 solver.cpp:237]     Train net output #0: loss = 5.27137 (* 1 = 5.27137 loss)
I0406 07:32:49.115469  5644 sgd_solver.cpp:105] Iteration 2856, lr = 0.1
I0406 07:32:53.385504  5644 solver.cpp:218] Iteration 2868 (2.81031 iter/s, 4.26999s/12 iters), loss = 5.29147
I0406 07:32:53.385551  5644 solver.cpp:237]     Train net output #0: loss = 5.29147 (* 1 = 5.29147 loss)
I0406 07:32:53.385558  5644 sgd_solver.cpp:105] Iteration 2868, lr = 0.1
I0406 07:32:58.780402  5644 solver.cpp:218] Iteration 2880 (2.22437 iter/s, 5.39479s/12 iters), loss = 5.2716
I0406 07:32:58.780448  5644 solver.cpp:237]     Train net output #0: loss = 5.2716 (* 1 = 5.2716 loss)
I0406 07:32:58.780457  5644 sgd_solver.cpp:105] Iteration 2880, lr = 0.1
I0406 07:33:04.164180  5644 solver.cpp:218] Iteration 2892 (2.22896 iter/s, 5.38367s/12 iters), loss = 5.27308
I0406 07:33:04.164218  5644 solver.cpp:237]     Train net output #0: loss = 5.27308 (* 1 = 5.27308 loss)
I0406 07:33:04.164223  5644 sgd_solver.cpp:105] Iteration 2892, lr = 0.1
I0406 07:33:09.245635  5644 solver.cpp:218] Iteration 2904 (2.36157 iter/s, 5.08136s/12 iters), loss = 5.27383
I0406 07:33:09.245738  5644 solver.cpp:237]     Train net output #0: loss = 5.27383 (* 1 = 5.27383 loss)
I0406 07:33:09.245743  5644 sgd_solver.cpp:105] Iteration 2904, lr = 0.1
I0406 07:33:14.483278  5644 solver.cpp:218] Iteration 2916 (2.29118 iter/s, 5.23748s/12 iters), loss = 5.27725
I0406 07:33:14.483314  5644 solver.cpp:237]     Train net output #0: loss = 5.27725 (* 1 = 5.27725 loss)
I0406 07:33:14.483319  5644 sgd_solver.cpp:105] Iteration 2916, lr = 0.1
I0406 07:33:19.440793  5644 solver.cpp:218] Iteration 2928 (2.42061 iter/s, 4.95743s/12 iters), loss = 5.26778
I0406 07:33:19.440824  5644 solver.cpp:237]     Train net output #0: loss = 5.26778 (* 1 = 5.26778 loss)
I0406 07:33:19.440830  5644 sgd_solver.cpp:105] Iteration 2928, lr = 0.1
I0406 07:33:21.189960  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:33:24.478150  5644 solver.cpp:218] Iteration 2940 (2.38224 iter/s, 5.03727s/12 iters), loss = 5.29915
I0406 07:33:24.478186  5644 solver.cpp:237]     Train net output #0: loss = 5.29915 (* 1 = 5.29915 loss)
I0406 07:33:24.478191  5644 sgd_solver.cpp:105] Iteration 2940, lr = 0.1
I0406 07:33:29.757755  5644 solver.cpp:218] Iteration 2952 (2.27294 iter/s, 5.27951s/12 iters), loss = 5.26349
I0406 07:33:29.757795  5644 solver.cpp:237]     Train net output #0: loss = 5.26349 (* 1 = 5.26349 loss)
I0406 07:33:29.757800  5644 sgd_solver.cpp:105] Iteration 2952, lr = 0.1
I0406 07:33:31.940559  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0406 07:33:35.007800  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0406 07:33:37.317736  5644 solver.cpp:330] Iteration 2958, Testing net (#0)
I0406 07:33:37.317759  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:33:40.432276  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:33:41.605312  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 07:33:41.605350  5644 solver.cpp:397]     Test net output #1: loss = 5.28913 (* 1 = 5.28913 loss)
I0406 07:33:43.424579  5644 solver.cpp:218] Iteration 2964 (0.878049 iter/s, 13.6667s/12 iters), loss = 5.27518
I0406 07:33:43.424618  5644 solver.cpp:237]     Train net output #0: loss = 5.27518 (* 1 = 5.27518 loss)
I0406 07:33:43.424623  5644 sgd_solver.cpp:105] Iteration 2964, lr = 0.1
I0406 07:33:48.639358  5644 solver.cpp:218] Iteration 2976 (2.3012 iter/s, 5.21468s/12 iters), loss = 5.27328
I0406 07:33:48.639397  5644 solver.cpp:237]     Train net output #0: loss = 5.27328 (* 1 = 5.27328 loss)
I0406 07:33:48.639402  5644 sgd_solver.cpp:105] Iteration 2976, lr = 0.1
I0406 07:33:53.928434  5644 solver.cpp:218] Iteration 2988 (2.26887 iter/s, 5.28898s/12 iters), loss = 5.27393
I0406 07:33:53.928476  5644 solver.cpp:237]     Train net output #0: loss = 5.27393 (* 1 = 5.27393 loss)
I0406 07:33:53.928483  5644 sgd_solver.cpp:105] Iteration 2988, lr = 0.1
I0406 07:33:59.206596  5644 solver.cpp:218] Iteration 3000 (2.27356 iter/s, 5.27806s/12 iters), loss = 5.2544
I0406 07:33:59.206632  5644 solver.cpp:237]     Train net output #0: loss = 5.2544 (* 1 = 5.2544 loss)
I0406 07:33:59.206638  5644 sgd_solver.cpp:105] Iteration 3000, lr = 0.1
I0406 07:34:04.381944  5644 solver.cpp:218] Iteration 3012 (2.31873 iter/s, 5.17525s/12 iters), loss = 5.27475
I0406 07:34:04.381981  5644 solver.cpp:237]     Train net output #0: loss = 5.27475 (* 1 = 5.27475 loss)
I0406 07:34:04.381986  5644 sgd_solver.cpp:105] Iteration 3012, lr = 0.1
I0406 07:34:09.673115  5644 solver.cpp:218] Iteration 3024 (2.26797 iter/s, 5.29107s/12 iters), loss = 5.26284
I0406 07:34:09.673153  5644 solver.cpp:237]     Train net output #0: loss = 5.26284 (* 1 = 5.26284 loss)
I0406 07:34:09.673158  5644 sgd_solver.cpp:105] Iteration 3024, lr = 0.1
I0406 07:34:13.853077  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:34:14.988822  5644 solver.cpp:218] Iteration 3036 (2.2575 iter/s, 5.31561s/12 iters), loss = 5.27338
I0406 07:34:14.988858  5644 solver.cpp:237]     Train net output #0: loss = 5.27338 (* 1 = 5.27338 loss)
I0406 07:34:14.988863  5644 sgd_solver.cpp:105] Iteration 3036, lr = 0.1
I0406 07:34:20.091915  5644 solver.cpp:218] Iteration 3048 (2.35156 iter/s, 5.103s/12 iters), loss = 5.25826
I0406 07:34:20.091949  5644 solver.cpp:237]     Train net output #0: loss = 5.25826 (* 1 = 5.25826 loss)
I0406 07:34:20.091954  5644 sgd_solver.cpp:105] Iteration 3048, lr = 0.1
I0406 07:34:24.932670  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0406 07:34:27.959133  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0406 07:34:30.270085  5644 solver.cpp:330] Iteration 3060, Testing net (#0)
I0406 07:34:30.270107  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:34:33.483646  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:34:34.678292  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:34:34.678328  5644 solver.cpp:397]     Test net output #1: loss = 5.28947 (* 1 = 5.28947 loss)
I0406 07:34:34.819087  5644 solver.cpp:218] Iteration 3060 (0.81483 iter/s, 14.727s/12 iters), loss = 5.29341
I0406 07:34:34.819124  5644 solver.cpp:237]     Train net output #0: loss = 5.29341 (* 1 = 5.29341 loss)
I0406 07:34:34.819129  5644 sgd_solver.cpp:105] Iteration 3060, lr = 0.1
I0406 07:34:39.046759  5644 solver.cpp:218] Iteration 3072 (2.8385 iter/s, 4.22758s/12 iters), loss = 5.28062
I0406 07:34:39.046798  5644 solver.cpp:237]     Train net output #0: loss = 5.28062 (* 1 = 5.28062 loss)
I0406 07:34:39.046803  5644 sgd_solver.cpp:105] Iteration 3072, lr = 0.1
I0406 07:34:44.215931  5644 solver.cpp:218] Iteration 3084 (2.3215 iter/s, 5.16907s/12 iters), loss = 5.28162
I0406 07:34:44.216035  5644 solver.cpp:237]     Train net output #0: loss = 5.28162 (* 1 = 5.28162 loss)
I0406 07:34:44.216043  5644 sgd_solver.cpp:105] Iteration 3084, lr = 0.1
I0406 07:34:49.533309  5644 solver.cpp:218] Iteration 3096 (2.25682 iter/s, 5.31721s/12 iters), loss = 5.28475
I0406 07:34:49.533351  5644 solver.cpp:237]     Train net output #0: loss = 5.28475 (* 1 = 5.28475 loss)
I0406 07:34:49.533357  5644 sgd_solver.cpp:105] Iteration 3096, lr = 0.1
I0406 07:34:54.709899  5644 solver.cpp:218] Iteration 3108 (2.31817 iter/s, 5.17649s/12 iters), loss = 5.28284
I0406 07:34:54.709934  5644 solver.cpp:237]     Train net output #0: loss = 5.28284 (* 1 = 5.28284 loss)
I0406 07:34:54.709939  5644 sgd_solver.cpp:105] Iteration 3108, lr = 0.1
I0406 07:35:00.059244  5644 solver.cpp:218] Iteration 3120 (2.24331 iter/s, 5.34924s/12 iters), loss = 5.27041
I0406 07:35:00.059290  5644 solver.cpp:237]     Train net output #0: loss = 5.27041 (* 1 = 5.27041 loss)
I0406 07:35:00.059298  5644 sgd_solver.cpp:105] Iteration 3120, lr = 0.1
I0406 07:35:05.117872  5644 solver.cpp:218] Iteration 3132 (2.37223 iter/s, 5.05853s/12 iters), loss = 5.27649
I0406 07:35:05.117913  5644 solver.cpp:237]     Train net output #0: loss = 5.27649 (* 1 = 5.27649 loss)
I0406 07:35:05.117918  5644 sgd_solver.cpp:105] Iteration 3132, lr = 0.1
I0406 07:35:06.383015  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:35:10.598551  5644 solver.cpp:218] Iteration 3144 (2.18955 iter/s, 5.48057s/12 iters), loss = 5.28657
I0406 07:35:10.598588  5644 solver.cpp:237]     Train net output #0: loss = 5.28657 (* 1 = 5.28657 loss)
I0406 07:35:10.598594  5644 sgd_solver.cpp:105] Iteration 3144, lr = 0.1
I0406 07:35:15.788866  5644 solver.cpp:218] Iteration 3156 (2.31204 iter/s, 5.19022s/12 iters), loss = 5.30084
I0406 07:35:15.789018  5644 solver.cpp:237]     Train net output #0: loss = 5.30084 (* 1 = 5.30084 loss)
I0406 07:35:15.789027  5644 sgd_solver.cpp:105] Iteration 3156, lr = 0.1
I0406 07:35:17.758085  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0406 07:35:20.762106  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0406 07:35:23.069422  5644 solver.cpp:330] Iteration 3162, Testing net (#0)
I0406 07:35:23.069440  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:35:26.116076  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:35:27.383162  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 07:35:27.383188  5644 solver.cpp:397]     Test net output #1: loss = 5.28871 (* 1 = 5.28871 loss)
I0406 07:35:29.221952  5644 solver.cpp:218] Iteration 3168 (0.893335 iter/s, 13.4328s/12 iters), loss = 5.26548
I0406 07:35:29.221993  5644 solver.cpp:237]     Train net output #0: loss = 5.26548 (* 1 = 5.26548 loss)
I0406 07:35:29.221999  5644 sgd_solver.cpp:105] Iteration 3168, lr = 0.1
I0406 07:35:34.347419  5644 solver.cpp:218] Iteration 3180 (2.3413 iter/s, 5.12537s/12 iters), loss = 5.27196
I0406 07:35:34.347458  5644 solver.cpp:237]     Train net output #0: loss = 5.27196 (* 1 = 5.27196 loss)
I0406 07:35:34.347465  5644 sgd_solver.cpp:105] Iteration 3180, lr = 0.1
I0406 07:35:39.767053  5644 solver.cpp:218] Iteration 3192 (2.21421 iter/s, 5.41954s/12 iters), loss = 5.29774
I0406 07:35:39.767089  5644 solver.cpp:237]     Train net output #0: loss = 5.29774 (* 1 = 5.29774 loss)
I0406 07:35:39.767094  5644 sgd_solver.cpp:105] Iteration 3192, lr = 0.1
I0406 07:35:45.050192  5644 solver.cpp:218] Iteration 3204 (2.27142 iter/s, 5.28304s/12 iters), loss = 5.26649
I0406 07:35:45.050233  5644 solver.cpp:237]     Train net output #0: loss = 5.26649 (* 1 = 5.26649 loss)
I0406 07:35:45.050240  5644 sgd_solver.cpp:105] Iteration 3204, lr = 0.1
I0406 07:35:50.366983  5644 solver.cpp:218] Iteration 3216 (2.25704 iter/s, 5.31669s/12 iters), loss = 5.26897
I0406 07:35:50.367084  5644 solver.cpp:237]     Train net output #0: loss = 5.26897 (* 1 = 5.26897 loss)
I0406 07:35:50.367091  5644 sgd_solver.cpp:105] Iteration 3216, lr = 0.1
I0406 07:35:55.685578  5644 solver.cpp:218] Iteration 3228 (2.2563 iter/s, 5.31844s/12 iters), loss = 5.28483
I0406 07:35:55.685616  5644 solver.cpp:237]     Train net output #0: loss = 5.28483 (* 1 = 5.28483 loss)
I0406 07:35:55.685621  5644 sgd_solver.cpp:105] Iteration 3228, lr = 0.1
I0406 07:35:58.969244  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:36:00.926530  5644 solver.cpp:218] Iteration 3240 (2.2897 iter/s, 5.24085s/12 iters), loss = 5.24871
I0406 07:36:00.926566  5644 solver.cpp:237]     Train net output #0: loss = 5.24871 (* 1 = 5.24871 loss)
I0406 07:36:00.926573  5644 sgd_solver.cpp:105] Iteration 3240, lr = 0.1
I0406 07:36:06.253211  5644 solver.cpp:218] Iteration 3252 (2.25285 iter/s, 5.32658s/12 iters), loss = 5.26661
I0406 07:36:06.253250  5644 solver.cpp:237]     Train net output #0: loss = 5.26661 (* 1 = 5.26661 loss)
I0406 07:36:06.253257  5644 sgd_solver.cpp:105] Iteration 3252, lr = 0.1
I0406 07:36:10.779860  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0406 07:36:13.805528  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0406 07:36:16.118245  5644 solver.cpp:330] Iteration 3264, Testing net (#0)
I0406 07:36:16.118263  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:36:19.104715  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:36:20.378806  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:36:20.378916  5644 solver.cpp:397]     Test net output #1: loss = 5.28868 (* 1 = 5.28868 loss)
I0406 07:36:20.519430  5644 solver.cpp:218] Iteration 3264 (0.841158 iter/s, 14.266s/12 iters), loss = 5.2708
I0406 07:36:20.519496  5644 solver.cpp:237]     Train net output #0: loss = 5.2708 (* 1 = 5.2708 loss)
I0406 07:36:20.519505  5644 sgd_solver.cpp:105] Iteration 3264, lr = 0.1
I0406 07:36:24.684096  5644 solver.cpp:218] Iteration 3276 (2.88146 iter/s, 4.16455s/12 iters), loss = 5.26888
I0406 07:36:24.684132  5644 solver.cpp:237]     Train net output #0: loss = 5.26888 (* 1 = 5.26888 loss)
I0406 07:36:24.684139  5644 sgd_solver.cpp:105] Iteration 3276, lr = 0.1
I0406 07:36:29.898792  5644 solver.cpp:218] Iteration 3288 (2.30123 iter/s, 5.2146s/12 iters), loss = 5.28971
I0406 07:36:29.898829  5644 solver.cpp:237]     Train net output #0: loss = 5.28971 (* 1 = 5.28971 loss)
I0406 07:36:29.898834  5644 sgd_solver.cpp:105] Iteration 3288, lr = 0.1
I0406 07:36:35.320705  5644 solver.cpp:218] Iteration 3300 (2.21328 iter/s, 5.42182s/12 iters), loss = 5.2703
I0406 07:36:35.320740  5644 solver.cpp:237]     Train net output #0: loss = 5.2703 (* 1 = 5.2703 loss)
I0406 07:36:35.320746  5644 sgd_solver.cpp:105] Iteration 3300, lr = 0.1
I0406 07:36:40.284047  5644 solver.cpp:218] Iteration 3312 (2.41777 iter/s, 4.96325s/12 iters), loss = 5.26163
I0406 07:36:40.284085  5644 solver.cpp:237]     Train net output #0: loss = 5.26163 (* 1 = 5.26163 loss)
I0406 07:36:40.284091  5644 sgd_solver.cpp:105] Iteration 3312, lr = 0.1
I0406 07:36:45.458611  5644 solver.cpp:218] Iteration 3324 (2.31908 iter/s, 5.17447s/12 iters), loss = 5.28485
I0406 07:36:45.458652  5644 solver.cpp:237]     Train net output #0: loss = 5.28485 (* 1 = 5.28485 loss)
I0406 07:36:45.458657  5644 sgd_solver.cpp:105] Iteration 3324, lr = 0.1
I0406 07:36:50.959219  5644 solver.cpp:218] Iteration 3336 (2.18162 iter/s, 5.50051s/12 iters), loss = 5.26336
I0406 07:36:50.959307  5644 solver.cpp:237]     Train net output #0: loss = 5.26336 (* 1 = 5.26336 loss)
I0406 07:36:50.959313  5644 sgd_solver.cpp:105] Iteration 3336, lr = 0.1
I0406 07:36:51.491915  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:36:56.566979  5644 solver.cpp:218] Iteration 3348 (2.13995 iter/s, 5.60762s/12 iters), loss = 5.28568
I0406 07:36:56.567014  5644 solver.cpp:237]     Train net output #0: loss = 5.28568 (* 1 = 5.28568 loss)
I0406 07:36:56.567019  5644 sgd_solver.cpp:105] Iteration 3348, lr = 0.1
I0406 07:37:01.958425  5644 solver.cpp:218] Iteration 3360 (2.22579 iter/s, 5.39135s/12 iters), loss = 5.28845
I0406 07:37:01.958463  5644 solver.cpp:237]     Train net output #0: loss = 5.28845 (* 1 = 5.28845 loss)
I0406 07:37:01.958469  5644 sgd_solver.cpp:105] Iteration 3360, lr = 0.1
I0406 07:37:04.163053  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0406 07:37:07.167426  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0406 07:37:09.472045  5644 solver.cpp:330] Iteration 3366, Testing net (#0)
I0406 07:37:09.472069  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:37:12.419402  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:37:13.723537  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:37:13.723573  5644 solver.cpp:397]     Test net output #1: loss = 5.28921 (* 1 = 5.28921 loss)
I0406 07:37:15.573016  5644 solver.cpp:218] Iteration 3372 (0.881419 iter/s, 13.6144s/12 iters), loss = 5.27028
I0406 07:37:15.573060  5644 solver.cpp:237]     Train net output #0: loss = 5.27028 (* 1 = 5.27028 loss)
I0406 07:37:15.573067  5644 sgd_solver.cpp:105] Iteration 3372, lr = 0.1
I0406 07:37:20.533824  5644 solver.cpp:218] Iteration 3384 (2.41901 iter/s, 4.96071s/12 iters), loss = 5.27107
I0406 07:37:20.533864  5644 solver.cpp:237]     Train net output #0: loss = 5.27107 (* 1 = 5.27107 loss)
I0406 07:37:20.533869  5644 sgd_solver.cpp:105] Iteration 3384, lr = 0.1
I0406 07:37:25.617161  5644 solver.cpp:218] Iteration 3396 (2.3607 iter/s, 5.08324s/12 iters), loss = 5.25914
I0406 07:37:25.617281  5644 solver.cpp:237]     Train net output #0: loss = 5.25914 (* 1 = 5.25914 loss)
I0406 07:37:25.617288  5644 sgd_solver.cpp:105] Iteration 3396, lr = 0.1
I0406 07:37:30.894490  5644 solver.cpp:218] Iteration 3408 (2.27395 iter/s, 5.27715s/12 iters), loss = 5.28878
I0406 07:37:30.894529  5644 solver.cpp:237]     Train net output #0: loss = 5.28878 (* 1 = 5.28878 loss)
I0406 07:37:30.894536  5644 sgd_solver.cpp:105] Iteration 3408, lr = 0.1
I0406 07:37:36.303282  5644 solver.cpp:218] Iteration 3420 (2.21865 iter/s, 5.40869s/12 iters), loss = 5.28519
I0406 07:37:36.303328  5644 solver.cpp:237]     Train net output #0: loss = 5.28519 (* 1 = 5.28519 loss)
I0406 07:37:36.303336  5644 sgd_solver.cpp:105] Iteration 3420, lr = 0.1
I0406 07:37:41.569348  5644 solver.cpp:218] Iteration 3432 (2.27879 iter/s, 5.26596s/12 iters), loss = 5.27649
I0406 07:37:41.569386  5644 solver.cpp:237]     Train net output #0: loss = 5.27649 (* 1 = 5.27649 loss)
I0406 07:37:41.569391  5644 sgd_solver.cpp:105] Iteration 3432, lr = 0.1
I0406 07:37:44.291168  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:37:46.909416  5644 solver.cpp:218] Iteration 3444 (2.24721 iter/s, 5.33997s/12 iters), loss = 5.29504
I0406 07:37:46.909458  5644 solver.cpp:237]     Train net output #0: loss = 5.29504 (* 1 = 5.29504 loss)
I0406 07:37:46.909464  5644 sgd_solver.cpp:105] Iteration 3444, lr = 0.1
I0406 07:37:52.165755  5644 solver.cpp:218] Iteration 3456 (2.283 iter/s, 5.25624s/12 iters), loss = 5.27736
I0406 07:37:52.165796  5644 solver.cpp:237]     Train net output #0: loss = 5.27736 (* 1 = 5.27736 loss)
I0406 07:37:52.165802  5644 sgd_solver.cpp:105] Iteration 3456, lr = 0.1
I0406 07:37:56.807636  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0406 07:37:59.792632  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0406 07:38:02.133626  5644 solver.cpp:330] Iteration 3468, Testing net (#0)
I0406 07:38:02.133646  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:38:02.545346  5644 blocking_queue.cpp:49] Waiting for data
I0406 07:38:05.086212  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:38:06.436313  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:38:06.436349  5644 solver.cpp:397]     Test net output #1: loss = 5.28894 (* 1 = 5.28894 loss)
I0406 07:38:06.577098  5644 solver.cpp:218] Iteration 3468 (0.832688 iter/s, 14.4112s/12 iters), loss = 5.26496
I0406 07:38:06.577155  5644 solver.cpp:237]     Train net output #0: loss = 5.26496 (* 1 = 5.26496 loss)
I0406 07:38:06.577162  5644 sgd_solver.cpp:105] Iteration 3468, lr = 0.1
I0406 07:38:10.956317  5644 solver.cpp:218] Iteration 3480 (2.74029 iter/s, 4.3791s/12 iters), loss = 5.28504
I0406 07:38:10.956354  5644 solver.cpp:237]     Train net output #0: loss = 5.28504 (* 1 = 5.28504 loss)
I0406 07:38:10.956360  5644 sgd_solver.cpp:105] Iteration 3480, lr = 0.1
I0406 07:38:16.107792  5644 solver.cpp:218] Iteration 3492 (2.32947 iter/s, 5.15138s/12 iters), loss = 5.27324
I0406 07:38:16.107825  5644 solver.cpp:237]     Train net output #0: loss = 5.27324 (* 1 = 5.27324 loss)
I0406 07:38:16.107831  5644 sgd_solver.cpp:105] Iteration 3492, lr = 0.1
I0406 07:38:21.128484  5644 solver.cpp:218] Iteration 3504 (2.39015 iter/s, 5.0206s/12 iters), loss = 5.30038
I0406 07:38:21.128520  5644 solver.cpp:237]     Train net output #0: loss = 5.30038 (* 1 = 5.30038 loss)
I0406 07:38:21.128526  5644 sgd_solver.cpp:105] Iteration 3504, lr = 0.1
I0406 07:38:26.395879  5644 solver.cpp:218] Iteration 3516 (2.27821 iter/s, 5.2673s/12 iters), loss = 5.28968
I0406 07:38:26.395915  5644 solver.cpp:237]     Train net output #0: loss = 5.28968 (* 1 = 5.28968 loss)
I0406 07:38:26.395920  5644 sgd_solver.cpp:105] Iteration 3516, lr = 0.1
I0406 07:38:31.422016  5644 solver.cpp:218] Iteration 3528 (2.38756 iter/s, 5.02605s/12 iters), loss = 5.29372
I0406 07:38:31.422140  5644 solver.cpp:237]     Train net output #0: loss = 5.29372 (* 1 = 5.29372 loss)
I0406 07:38:31.422147  5644 sgd_solver.cpp:105] Iteration 3528, lr = 0.1
I0406 07:38:36.487428  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:38:36.796696  5644 solver.cpp:218] Iteration 3540 (2.23277 iter/s, 5.37449s/12 iters), loss = 5.29771
I0406 07:38:36.796742  5644 solver.cpp:237]     Train net output #0: loss = 5.29771 (* 1 = 5.29771 loss)
I0406 07:38:36.796749  5644 sgd_solver.cpp:105] Iteration 3540, lr = 0.1
I0406 07:38:41.929854  5644 solver.cpp:218] Iteration 3552 (2.33779 iter/s, 5.13305s/12 iters), loss = 5.26543
I0406 07:38:41.929903  5644 solver.cpp:237]     Train net output #0: loss = 5.26543 (* 1 = 5.26543 loss)
I0406 07:38:41.929910  5644 sgd_solver.cpp:105] Iteration 3552, lr = 0.1
I0406 07:38:47.193094  5644 solver.cpp:218] Iteration 3564 (2.28001 iter/s, 5.26314s/12 iters), loss = 5.28304
I0406 07:38:47.193132  5644 solver.cpp:237]     Train net output #0: loss = 5.28304 (* 1 = 5.28304 loss)
I0406 07:38:47.193137  5644 sgd_solver.cpp:105] Iteration 3564, lr = 0.1
I0406 07:38:49.183435  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0406 07:38:52.226981  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0406 07:38:54.538623  5644 solver.cpp:330] Iteration 3570, Testing net (#0)
I0406 07:38:54.538643  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:38:57.551296  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:38:58.931217  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:38:58.931244  5644 solver.cpp:397]     Test net output #1: loss = 5.28815 (* 1 = 5.28815 loss)
I0406 07:39:00.848189  5644 solver.cpp:218] Iteration 3576 (0.878804 iter/s, 13.6549s/12 iters), loss = 5.28806
I0406 07:39:00.848234  5644 solver.cpp:237]     Train net output #0: loss = 5.28806 (* 1 = 5.28806 loss)
I0406 07:39:00.848242  5644 sgd_solver.cpp:105] Iteration 3576, lr = 0.1
I0406 07:39:06.086735  5644 solver.cpp:218] Iteration 3588 (2.29076 iter/s, 5.23844s/12 iters), loss = 5.26777
I0406 07:39:06.086830  5644 solver.cpp:237]     Train net output #0: loss = 5.26777 (* 1 = 5.26777 loss)
I0406 07:39:06.086836  5644 sgd_solver.cpp:105] Iteration 3588, lr = 0.1
I0406 07:39:11.375983  5644 solver.cpp:218] Iteration 3600 (2.26882 iter/s, 5.2891s/12 iters), loss = 5.27521
I0406 07:39:11.376022  5644 solver.cpp:237]     Train net output #0: loss = 5.27521 (* 1 = 5.27521 loss)
I0406 07:39:11.376029  5644 sgd_solver.cpp:105] Iteration 3600, lr = 0.1
I0406 07:39:16.630810  5644 solver.cpp:218] Iteration 3612 (2.28366 iter/s, 5.25473s/12 iters), loss = 5.26754
I0406 07:39:16.630846  5644 solver.cpp:237]     Train net output #0: loss = 5.26754 (* 1 = 5.26754 loss)
I0406 07:39:16.630852  5644 sgd_solver.cpp:105] Iteration 3612, lr = 0.1
I0406 07:39:22.129541  5644 solver.cpp:218] Iteration 3624 (2.18236 iter/s, 5.49863s/12 iters), loss = 5.2871
I0406 07:39:22.129598  5644 solver.cpp:237]     Train net output #0: loss = 5.2871 (* 1 = 5.2871 loss)
I0406 07:39:22.129606  5644 sgd_solver.cpp:105] Iteration 3624, lr = 0.1
I0406 07:39:27.466809  5644 solver.cpp:218] Iteration 3636 (2.24839 iter/s, 5.33716s/12 iters), loss = 5.26728
I0406 07:39:27.466843  5644 solver.cpp:237]     Train net output #0: loss = 5.26728 (* 1 = 5.26728 loss)
I0406 07:39:27.466848  5644 sgd_solver.cpp:105] Iteration 3636, lr = 0.1
I0406 07:39:29.425971  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:39:32.854009  5644 solver.cpp:218] Iteration 3648 (2.22754 iter/s, 5.3871s/12 iters), loss = 5.29918
I0406 07:39:32.854048  5644 solver.cpp:237]     Train net output #0: loss = 5.29918 (* 1 = 5.29918 loss)
I0406 07:39:32.854053  5644 sgd_solver.cpp:105] Iteration 3648, lr = 0.1
I0406 07:39:38.092960  5644 solver.cpp:218] Iteration 3660 (2.29058 iter/s, 5.23885s/12 iters), loss = 5.26268
I0406 07:39:38.093081  5644 solver.cpp:237]     Train net output #0: loss = 5.26268 (* 1 = 5.26268 loss)
I0406 07:39:38.093088  5644 sgd_solver.cpp:105] Iteration 3660, lr = 0.1
I0406 07:39:42.866786  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0406 07:39:45.939229  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0406 07:39:48.248915  5644 solver.cpp:330] Iteration 3672, Testing net (#0)
I0406 07:39:48.248934  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:39:51.121346  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:39:52.561766  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:39:52.561794  5644 solver.cpp:397]     Test net output #1: loss = 5.28845 (* 1 = 5.28845 loss)
I0406 07:39:52.697710  5644 solver.cpp:218] Iteration 3672 (0.821665 iter/s, 14.6045s/12 iters), loss = 5.28381
I0406 07:39:52.697767  5644 solver.cpp:237]     Train net output #0: loss = 5.28381 (* 1 = 5.28381 loss)
I0406 07:39:52.697777  5644 sgd_solver.cpp:105] Iteration 3672, lr = 0.1
I0406 07:39:56.990535  5644 solver.cpp:218] Iteration 3684 (2.79544 iter/s, 4.29271s/12 iters), loss = 5.27502
I0406 07:39:56.990587  5644 solver.cpp:237]     Train net output #0: loss = 5.27502 (* 1 = 5.27502 loss)
I0406 07:39:56.990597  5644 sgd_solver.cpp:105] Iteration 3684, lr = 0.1
I0406 07:40:01.969594  5644 solver.cpp:218] Iteration 3696 (2.41015 iter/s, 4.97895s/12 iters), loss = 5.2735
I0406 07:40:01.969642  5644 solver.cpp:237]     Train net output #0: loss = 5.2735 (* 1 = 5.2735 loss)
I0406 07:40:01.969650  5644 sgd_solver.cpp:105] Iteration 3696, lr = 0.1
I0406 07:40:07.064738  5644 solver.cpp:218] Iteration 3708 (2.35523 iter/s, 5.09504s/12 iters), loss = 5.25463
I0406 07:40:07.064774  5644 solver.cpp:237]     Train net output #0: loss = 5.25463 (* 1 = 5.25463 loss)
I0406 07:40:07.064780  5644 sgd_solver.cpp:105] Iteration 3708, lr = 0.1
I0406 07:40:12.235209  5644 solver.cpp:218] Iteration 3720 (2.32091 iter/s, 5.17038s/12 iters), loss = 5.28205
I0406 07:40:12.235303  5644 solver.cpp:237]     Train net output #0: loss = 5.28205 (* 1 = 5.28205 loss)
I0406 07:40:12.235311  5644 sgd_solver.cpp:105] Iteration 3720, lr = 0.1
I0406 07:40:17.627950  5644 solver.cpp:218] Iteration 3732 (2.22528 iter/s, 5.39258s/12 iters), loss = 5.26376
I0406 07:40:17.627995  5644 solver.cpp:237]     Train net output #0: loss = 5.26376 (* 1 = 5.26376 loss)
I0406 07:40:17.628003  5644 sgd_solver.cpp:105] Iteration 3732, lr = 0.1
I0406 07:40:21.702121  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:40:22.831131  5644 solver.cpp:218] Iteration 3744 (2.30633 iter/s, 5.20308s/12 iters), loss = 5.27407
I0406 07:40:22.831169  5644 solver.cpp:237]     Train net output #0: loss = 5.27407 (* 1 = 5.27407 loss)
I0406 07:40:22.831176  5644 sgd_solver.cpp:105] Iteration 3744, lr = 0.1
I0406 07:40:28.159619  5644 solver.cpp:218] Iteration 3756 (2.25209 iter/s, 5.32839s/12 iters), loss = 5.26934
I0406 07:40:28.159662  5644 solver.cpp:237]     Train net output #0: loss = 5.26934 (* 1 = 5.26934 loss)
I0406 07:40:28.159670  5644 sgd_solver.cpp:105] Iteration 3756, lr = 0.1
I0406 07:40:33.246642  5644 solver.cpp:218] Iteration 3768 (2.35899 iter/s, 5.08692s/12 iters), loss = 5.30886
I0406 07:40:33.246680  5644 solver.cpp:237]     Train net output #0: loss = 5.30886 (* 1 = 5.30886 loss)
I0406 07:40:33.246686  5644 sgd_solver.cpp:105] Iteration 3768, lr = 0.1
I0406 07:40:35.324420  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0406 07:40:38.351598  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0406 07:40:40.663940  5644 solver.cpp:330] Iteration 3774, Testing net (#0)
I0406 07:40:40.663961  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:40:43.459479  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:40:44.919415  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:40:44.919447  5644 solver.cpp:397]     Test net output #1: loss = 5.28822 (* 1 = 5.28822 loss)
I0406 07:40:46.834273  5644 solver.cpp:218] Iteration 3780 (0.883167 iter/s, 13.5875s/12 iters), loss = 5.28192
I0406 07:40:46.834311  5644 solver.cpp:237]     Train net output #0: loss = 5.28192 (* 1 = 5.28192 loss)
I0406 07:40:46.834316  5644 sgd_solver.cpp:105] Iteration 3780, lr = 0.1
I0406 07:40:52.077621  5644 solver.cpp:218] Iteration 3792 (2.28865 iter/s, 5.24326s/12 iters), loss = 5.28647
I0406 07:40:52.077654  5644 solver.cpp:237]     Train net output #0: loss = 5.28647 (* 1 = 5.28647 loss)
I0406 07:40:52.077659  5644 sgd_solver.cpp:105] Iteration 3792, lr = 0.1
I0406 07:40:57.332538  5644 solver.cpp:218] Iteration 3804 (2.28362 iter/s, 5.25482s/12 iters), loss = 5.27719
I0406 07:40:57.332581  5644 solver.cpp:237]     Train net output #0: loss = 5.27719 (* 1 = 5.27719 loss)
I0406 07:40:57.332587  5644 sgd_solver.cpp:105] Iteration 3804, lr = 0.1
I0406 07:41:02.698895  5644 solver.cpp:218] Iteration 3816 (2.2362 iter/s, 5.36625s/12 iters), loss = 5.28592
I0406 07:41:02.698946  5644 solver.cpp:237]     Train net output #0: loss = 5.28592 (* 1 = 5.28592 loss)
I0406 07:41:02.698956  5644 sgd_solver.cpp:105] Iteration 3816, lr = 0.1
I0406 07:41:07.823858  5644 solver.cpp:218] Iteration 3828 (2.34153 iter/s, 5.12486s/12 iters), loss = 5.26942
I0406 07:41:07.823899  5644 solver.cpp:237]     Train net output #0: loss = 5.26942 (* 1 = 5.26942 loss)
I0406 07:41:07.823904  5644 sgd_solver.cpp:105] Iteration 3828, lr = 0.1
I0406 07:41:13.230275  5644 solver.cpp:218] Iteration 3840 (2.21963 iter/s, 5.40631s/12 iters), loss = 5.27421
I0406 07:41:13.230314  5644 solver.cpp:237]     Train net output #0: loss = 5.27421 (* 1 = 5.27421 loss)
I0406 07:41:13.230320  5644 sgd_solver.cpp:105] Iteration 3840, lr = 0.1
I0406 07:41:14.455780  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:41:18.670006  5644 solver.cpp:218] Iteration 3852 (2.20603 iter/s, 5.43963s/12 iters), loss = 5.29101
I0406 07:41:18.670056  5644 solver.cpp:237]     Train net output #0: loss = 5.29101 (* 1 = 5.29101 loss)
I0406 07:41:18.670065  5644 sgd_solver.cpp:105] Iteration 3852, lr = 0.1
I0406 07:41:24.014874  5644 solver.cpp:218] Iteration 3864 (2.24519 iter/s, 5.34477s/12 iters), loss = 5.29375
I0406 07:41:24.014905  5644 solver.cpp:237]     Train net output #0: loss = 5.29375 (* 1 = 5.29375 loss)
I0406 07:41:24.014911  5644 sgd_solver.cpp:105] Iteration 3864, lr = 0.1
I0406 07:41:28.387557  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0406 07:41:31.495489  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0406 07:41:33.805703  5644 solver.cpp:330] Iteration 3876, Testing net (#0)
I0406 07:41:33.805721  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:41:36.561547  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:41:38.081169  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:41:38.081223  5644 solver.cpp:397]     Test net output #1: loss = 5.28766 (* 1 = 5.28766 loss)
I0406 07:41:38.219650  5644 solver.cpp:218] Iteration 3876 (0.844797 iter/s, 14.2046s/12 iters), loss = 5.26744
I0406 07:41:38.219699  5644 solver.cpp:237]     Train net output #0: loss = 5.26744 (* 1 = 5.26744 loss)
I0406 07:41:38.219708  5644 sgd_solver.cpp:105] Iteration 3876, lr = 0.1
I0406 07:41:42.549546  5644 solver.cpp:218] Iteration 3888 (2.77149 iter/s, 4.32979s/12 iters), loss = 5.27614
I0406 07:41:42.549582  5644 solver.cpp:237]     Train net output #0: loss = 5.27614 (* 1 = 5.27614 loss)
I0406 07:41:42.549587  5644 sgd_solver.cpp:105] Iteration 3888, lr = 0.1
I0406 07:41:47.704677  5644 solver.cpp:218] Iteration 3900 (2.32782 iter/s, 5.15504s/12 iters), loss = 5.29892
I0406 07:41:47.704792  5644 solver.cpp:237]     Train net output #0: loss = 5.29892 (* 1 = 5.29892 loss)
I0406 07:41:47.704799  5644 sgd_solver.cpp:105] Iteration 3900, lr = 0.1
I0406 07:41:52.873090  5644 solver.cpp:218] Iteration 3912 (2.32188 iter/s, 5.16823s/12 iters), loss = 5.27155
I0406 07:41:52.873142  5644 solver.cpp:237]     Train net output #0: loss = 5.27155 (* 1 = 5.27155 loss)
I0406 07:41:52.873150  5644 sgd_solver.cpp:105] Iteration 3912, lr = 0.1
I0406 07:41:58.227253  5644 solver.cpp:218] Iteration 3924 (2.2413 iter/s, 5.35405s/12 iters), loss = 5.27973
I0406 07:41:58.227301  5644 solver.cpp:237]     Train net output #0: loss = 5.27973 (* 1 = 5.27973 loss)
I0406 07:41:58.227310  5644 sgd_solver.cpp:105] Iteration 3924, lr = 0.1
I0406 07:42:03.370720  5644 solver.cpp:218] Iteration 3936 (2.3331 iter/s, 5.14336s/12 iters), loss = 5.2946
I0406 07:42:03.370759  5644 solver.cpp:237]     Train net output #0: loss = 5.2946 (* 1 = 5.2946 loss)
I0406 07:42:03.370764  5644 sgd_solver.cpp:105] Iteration 3936, lr = 0.1
I0406 07:42:06.826719  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:42:08.532145  5644 solver.cpp:218] Iteration 3948 (2.32499 iter/s, 5.16132s/12 iters), loss = 5.25252
I0406 07:42:08.532205  5644 solver.cpp:237]     Train net output #0: loss = 5.25252 (* 1 = 5.25252 loss)
I0406 07:42:08.532217  5644 sgd_solver.cpp:105] Iteration 3948, lr = 0.1
I0406 07:42:13.901763  5644 solver.cpp:218] Iteration 3960 (2.23484 iter/s, 5.3695s/12 iters), loss = 5.26594
I0406 07:42:13.901800  5644 solver.cpp:237]     Train net output #0: loss = 5.26594 (* 1 = 5.26594 loss)
I0406 07:42:13.901806  5644 sgd_solver.cpp:105] Iteration 3960, lr = 0.1
I0406 07:42:19.202605  5644 solver.cpp:218] Iteration 3972 (2.26383 iter/s, 5.30075s/12 iters), loss = 5.27964
I0406 07:42:19.202695  5644 solver.cpp:237]     Train net output #0: loss = 5.27964 (* 1 = 5.27964 loss)
I0406 07:42:19.202702  5644 sgd_solver.cpp:105] Iteration 3972, lr = 0.1
I0406 07:42:21.355784  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0406 07:42:24.364877  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0406 07:42:26.684325  5644 solver.cpp:330] Iteration 3978, Testing net (#0)
I0406 07:42:26.684348  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:42:29.386571  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:42:30.926087  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:42:30.926120  5644 solver.cpp:397]     Test net output #1: loss = 5.287 (* 1 = 5.287 loss)
I0406 07:42:32.777940  5644 solver.cpp:218] Iteration 3984 (0.88397 iter/s, 13.5751s/12 iters), loss = 5.26683
I0406 07:42:32.777977  5644 solver.cpp:237]     Train net output #0: loss = 5.26683 (* 1 = 5.26683 loss)
I0406 07:42:32.777983  5644 sgd_solver.cpp:105] Iteration 3984, lr = 0.1
I0406 07:42:38.118181  5644 solver.cpp:218] Iteration 3996 (2.24713 iter/s, 5.34014s/12 iters), loss = 5.29066
I0406 07:42:38.118221  5644 solver.cpp:237]     Train net output #0: loss = 5.29066 (* 1 = 5.29066 loss)
I0406 07:42:38.118227  5644 sgd_solver.cpp:105] Iteration 3996, lr = 0.1
I0406 07:42:43.511687  5644 solver.cpp:218] Iteration 4008 (2.22494 iter/s, 5.39341s/12 iters), loss = 5.26714
I0406 07:42:43.511723  5644 solver.cpp:237]     Train net output #0: loss = 5.26714 (* 1 = 5.26714 loss)
I0406 07:42:43.511729  5644 sgd_solver.cpp:105] Iteration 4008, lr = 0.1
I0406 07:42:48.920574  5644 solver.cpp:218] Iteration 4020 (2.21861 iter/s, 5.40879s/12 iters), loss = 5.26381
I0406 07:42:48.920614  5644 solver.cpp:237]     Train net output #0: loss = 5.26381 (* 1 = 5.26381 loss)
I0406 07:42:48.920619  5644 sgd_solver.cpp:105] Iteration 4020, lr = 0.1
I0406 07:42:54.163480  5644 solver.cpp:218] Iteration 4032 (2.28885 iter/s, 5.2428s/12 iters), loss = 5.28435
I0406 07:42:54.163607  5644 solver.cpp:237]     Train net output #0: loss = 5.28435 (* 1 = 5.28435 loss)
I0406 07:42:54.163614  5644 sgd_solver.cpp:105] Iteration 4032, lr = 0.1
I0406 07:42:59.563493  5644 solver.cpp:218] Iteration 4044 (2.22229 iter/s, 5.39983s/12 iters), loss = 5.27549
I0406 07:42:59.563530  5644 solver.cpp:237]     Train net output #0: loss = 5.27549 (* 1 = 5.27549 loss)
I0406 07:42:59.563535  5644 sgd_solver.cpp:105] Iteration 4044, lr = 0.1
I0406 07:43:00.087656  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:43:04.835666  5644 solver.cpp:218] Iteration 4056 (2.27614 iter/s, 5.27208s/12 iters), loss = 5.28962
I0406 07:43:04.835703  5644 solver.cpp:237]     Train net output #0: loss = 5.28962 (* 1 = 5.28962 loss)
I0406 07:43:04.835709  5644 sgd_solver.cpp:105] Iteration 4056, lr = 0.1
I0406 07:43:10.096055  5644 solver.cpp:218] Iteration 4068 (2.28124 iter/s, 5.26029s/12 iters), loss = 5.28672
I0406 07:43:10.096096  5644 solver.cpp:237]     Train net output #0: loss = 5.28672 (* 1 = 5.28672 loss)
I0406 07:43:10.096102  5644 sgd_solver.cpp:105] Iteration 4068, lr = 0.1
I0406 07:43:14.891957  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0406 07:43:17.972473  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0406 07:43:20.279927  5644 solver.cpp:330] Iteration 4080, Testing net (#0)
I0406 07:43:20.279943  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:43:23.021159  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:43:24.615950  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:43:24.616021  5644 solver.cpp:397]     Test net output #1: loss = 5.28773 (* 1 = 5.28773 loss)
I0406 07:43:24.756244  5644 solver.cpp:218] Iteration 4080 (0.818553 iter/s, 14.66s/12 iters), loss = 5.27531
I0406 07:43:24.756283  5644 solver.cpp:237]     Train net output #0: loss = 5.27531 (* 1 = 5.27531 loss)
I0406 07:43:24.756289  5644 sgd_solver.cpp:105] Iteration 4080, lr = 0.1
I0406 07:43:29.152911  5644 solver.cpp:218] Iteration 4092 (2.7294 iter/s, 4.39657s/12 iters), loss = 5.2655
I0406 07:43:29.152966  5644 solver.cpp:237]     Train net output #0: loss = 5.2655 (* 1 = 5.2655 loss)
I0406 07:43:29.152974  5644 sgd_solver.cpp:105] Iteration 4092, lr = 0.1
I0406 07:43:34.499366  5644 solver.cpp:218] Iteration 4104 (2.24453 iter/s, 5.34634s/12 iters), loss = 5.25334
I0406 07:43:34.499401  5644 solver.cpp:237]     Train net output #0: loss = 5.25334 (* 1 = 5.25334 loss)
I0406 07:43:34.499406  5644 sgd_solver.cpp:105] Iteration 4104, lr = 0.1
I0406 07:43:39.645896  5644 solver.cpp:218] Iteration 4116 (2.33171 iter/s, 5.14644s/12 iters), loss = 5.28426
I0406 07:43:39.645932  5644 solver.cpp:237]     Train net output #0: loss = 5.28426 (* 1 = 5.28426 loss)
I0406 07:43:39.645937  5644 sgd_solver.cpp:105] Iteration 4116, lr = 0.1
I0406 07:43:44.926569  5644 solver.cpp:218] Iteration 4128 (2.27248 iter/s, 5.28057s/12 iters), loss = 5.27811
I0406 07:43:44.926611  5644 solver.cpp:237]     Train net output #0: loss = 5.27811 (* 1 = 5.27811 loss)
I0406 07:43:44.926618  5644 sgd_solver.cpp:105] Iteration 4128, lr = 0.1
I0406 07:43:49.958567  5644 solver.cpp:218] Iteration 4140 (2.38479 iter/s, 5.0319s/12 iters), loss = 5.28297
I0406 07:43:49.958609  5644 solver.cpp:237]     Train net output #0: loss = 5.28297 (* 1 = 5.28297 loss)
I0406 07:43:49.958616  5644 sgd_solver.cpp:105] Iteration 4140, lr = 0.1
I0406 07:43:52.725525  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:43:55.282748  5644 solver.cpp:218] Iteration 4152 (2.25391 iter/s, 5.32408s/12 iters), loss = 5.29233
I0406 07:43:55.282855  5644 solver.cpp:237]     Train net output #0: loss = 5.29233 (* 1 = 5.29233 loss)
I0406 07:43:55.282860  5644 sgd_solver.cpp:105] Iteration 4152, lr = 0.1
I0406 07:43:56.896422  5644 blocking_queue.cpp:49] Waiting for data
I0406 07:44:00.372411  5644 solver.cpp:218] Iteration 4164 (2.3578 iter/s, 5.0895s/12 iters), loss = 5.27034
I0406 07:44:00.372458  5644 solver.cpp:237]     Train net output #0: loss = 5.27034 (* 1 = 5.27034 loss)
I0406 07:44:00.372469  5644 sgd_solver.cpp:105] Iteration 4164, lr = 0.1
I0406 07:44:05.225581  5644 solver.cpp:218] Iteration 4176 (2.47266 iter/s, 4.85307s/12 iters), loss = 5.26402
I0406 07:44:05.225644  5644 solver.cpp:237]     Train net output #0: loss = 5.26402 (* 1 = 5.26402 loss)
I0406 07:44:05.225656  5644 sgd_solver.cpp:105] Iteration 4176, lr = 0.1
I0406 07:44:07.342288  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0406 07:44:10.366871  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0406 07:44:12.671442  5644 solver.cpp:330] Iteration 4182, Testing net (#0)
I0406 07:44:12.671463  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:44:15.390224  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:44:17.005497  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:44:17.005532  5644 solver.cpp:397]     Test net output #1: loss = 5.28725 (* 1 = 5.28725 loss)
I0406 07:44:18.853384  5644 solver.cpp:218] Iteration 4188 (0.880565 iter/s, 13.6276s/12 iters), loss = 5.28798
I0406 07:44:18.853422  5644 solver.cpp:237]     Train net output #0: loss = 5.28798 (* 1 = 5.28798 loss)
I0406 07:44:18.853428  5644 sgd_solver.cpp:105] Iteration 4188, lr = 0.1
I0406 07:44:24.187809  5644 solver.cpp:218] Iteration 4200 (2.24958 iter/s, 5.33432s/12 iters), loss = 5.27736
I0406 07:44:24.187849  5644 solver.cpp:237]     Train net output #0: loss = 5.27736 (* 1 = 5.27736 loss)
I0406 07:44:24.187855  5644 sgd_solver.cpp:105] Iteration 4200, lr = 0.1
I0406 07:44:29.392623  5644 solver.cpp:218] Iteration 4212 (2.3056 iter/s, 5.20471s/12 iters), loss = 5.30289
I0406 07:44:29.392773  5644 solver.cpp:237]     Train net output #0: loss = 5.30289 (* 1 = 5.30289 loss)
I0406 07:44:29.392781  5644 sgd_solver.cpp:105] Iteration 4212, lr = 0.1
I0406 07:44:34.851338  5644 solver.cpp:218] Iteration 4224 (2.1984 iter/s, 5.45851s/12 iters), loss = 5.28978
I0406 07:44:34.851375  5644 solver.cpp:237]     Train net output #0: loss = 5.28978 (* 1 = 5.28978 loss)
I0406 07:44:34.851380  5644 sgd_solver.cpp:105] Iteration 4224, lr = 0.1
I0406 07:44:40.175670  5644 solver.cpp:218] Iteration 4236 (2.25385 iter/s, 5.32423s/12 iters), loss = 5.30026
I0406 07:44:40.175717  5644 solver.cpp:237]     Train net output #0: loss = 5.30026 (* 1 = 5.30026 loss)
I0406 07:44:40.175725  5644 sgd_solver.cpp:105] Iteration 4236, lr = 0.1
I0406 07:44:45.209256  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:44:45.484855  5644 solver.cpp:218] Iteration 4248 (2.26028 iter/s, 5.30908s/12 iters), loss = 5.2947
I0406 07:44:45.484903  5644 solver.cpp:237]     Train net output #0: loss = 5.2947 (* 1 = 5.2947 loss)
I0406 07:44:45.484910  5644 sgd_solver.cpp:105] Iteration 4248, lr = 0.1
I0406 07:44:50.792481  5644 solver.cpp:218] Iteration 4260 (2.26094 iter/s, 5.30752s/12 iters), loss = 5.27066
I0406 07:44:50.792520  5644 solver.cpp:237]     Train net output #0: loss = 5.27066 (* 1 = 5.27066 loss)
I0406 07:44:50.792526  5644 sgd_solver.cpp:105] Iteration 4260, lr = 0.1
I0406 07:44:56.059306  5644 solver.cpp:218] Iteration 4272 (2.27846 iter/s, 5.26672s/12 iters), loss = 5.28257
I0406 07:44:56.059351  5644 solver.cpp:237]     Train net output #0: loss = 5.28257 (* 1 = 5.28257 loss)
I0406 07:44:56.059360  5644 sgd_solver.cpp:105] Iteration 4272, lr = 0.1
I0406 07:45:00.838701  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0406 07:45:03.897677  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0406 07:45:06.196470  5644 solver.cpp:330] Iteration 4284, Testing net (#0)
I0406 07:45:06.196491  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:45:08.823007  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:45:10.506314  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:45:10.506347  5644 solver.cpp:397]     Test net output #1: loss = 5.28824 (* 1 = 5.28824 loss)
I0406 07:45:10.644142  5644 solver.cpp:218] Iteration 4284 (0.822783 iter/s, 14.5846s/12 iters), loss = 5.28965
I0406 07:45:10.644192  5644 solver.cpp:237]     Train net output #0: loss = 5.28965 (* 1 = 5.28965 loss)
I0406 07:45:10.644201  5644 sgd_solver.cpp:105] Iteration 4284, lr = 0.1
I0406 07:45:14.998529  5644 solver.cpp:218] Iteration 4296 (2.75591 iter/s, 4.35428s/12 iters), loss = 5.26315
I0406 07:45:14.998566  5644 solver.cpp:237]     Train net output #0: loss = 5.26315 (* 1 = 5.26315 loss)
I0406 07:45:14.998572  5644 sgd_solver.cpp:105] Iteration 4296, lr = 0.1
I0406 07:45:20.327931  5644 solver.cpp:218] Iteration 4308 (2.2517 iter/s, 5.3293s/12 iters), loss = 5.26515
I0406 07:45:20.327968  5644 solver.cpp:237]     Train net output #0: loss = 5.26515 (* 1 = 5.26515 loss)
I0406 07:45:20.327973  5644 sgd_solver.cpp:105] Iteration 4308, lr = 0.1
I0406 07:45:25.595772  5644 solver.cpp:218] Iteration 4320 (2.27801 iter/s, 5.26774s/12 iters), loss = 5.26948
I0406 07:45:25.595808  5644 solver.cpp:237]     Train net output #0: loss = 5.26948 (* 1 = 5.26948 loss)
I0406 07:45:25.595813  5644 sgd_solver.cpp:105] Iteration 4320, lr = 0.1
I0406 07:45:31.038112  5644 solver.cpp:218] Iteration 4332 (2.20497 iter/s, 5.44225s/12 iters), loss = 5.28016
I0406 07:45:31.038189  5644 solver.cpp:237]     Train net output #0: loss = 5.28016 (* 1 = 5.28016 loss)
I0406 07:45:31.038197  5644 sgd_solver.cpp:105] Iteration 4332, lr = 0.1
I0406 07:45:36.308132  5644 solver.cpp:218] Iteration 4344 (2.27709 iter/s, 5.26988s/12 iters), loss = 5.27644
I0406 07:45:36.308169  5644 solver.cpp:237]     Train net output #0: loss = 5.27644 (* 1 = 5.27644 loss)
I0406 07:45:36.308176  5644 sgd_solver.cpp:105] Iteration 4344, lr = 0.1
I0406 07:45:38.308471  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:45:41.534139  5644 solver.cpp:218] Iteration 4356 (2.29625 iter/s, 5.22591s/12 iters), loss = 5.30295
I0406 07:45:41.534180  5644 solver.cpp:237]     Train net output #0: loss = 5.30295 (* 1 = 5.30295 loss)
I0406 07:45:41.534186  5644 sgd_solver.cpp:105] Iteration 4356, lr = 0.1
I0406 07:45:46.682088  5644 solver.cpp:218] Iteration 4368 (2.33107 iter/s, 5.14785s/12 iters), loss = 5.26329
I0406 07:45:46.682139  5644 solver.cpp:237]     Train net output #0: loss = 5.26329 (* 1 = 5.26329 loss)
I0406 07:45:46.682148  5644 sgd_solver.cpp:105] Iteration 4368, lr = 0.1
I0406 07:45:52.016866  5644 solver.cpp:218] Iteration 4380 (2.24944 iter/s, 5.33467s/12 iters), loss = 5.28446
I0406 07:45:52.016917  5644 solver.cpp:237]     Train net output #0: loss = 5.28446 (* 1 = 5.28446 loss)
I0406 07:45:52.016925  5644 sgd_solver.cpp:105] Iteration 4380, lr = 0.1
I0406 07:45:54.171177  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0406 07:45:57.259615  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0406 07:45:59.570536  5644 solver.cpp:330] Iteration 4386, Testing net (#0)
I0406 07:45:59.570556  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:46:02.202803  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:46:03.901618  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:46:03.901645  5644 solver.cpp:397]     Test net output #1: loss = 5.28805 (* 1 = 5.28805 loss)
I0406 07:46:05.744488  5644 solver.cpp:218] Iteration 4392 (0.874161 iter/s, 13.7274s/12 iters), loss = 5.27597
I0406 07:46:05.744530  5644 solver.cpp:237]     Train net output #0: loss = 5.27597 (* 1 = 5.27597 loss)
I0406 07:46:05.744535  5644 sgd_solver.cpp:105] Iteration 4392, lr = 0.1
I0406 07:46:10.907366  5644 solver.cpp:218] Iteration 4404 (2.32433 iter/s, 5.16278s/12 iters), loss = 5.26937
I0406 07:46:10.907404  5644 solver.cpp:237]     Train net output #0: loss = 5.26937 (* 1 = 5.26937 loss)
I0406 07:46:10.907410  5644 sgd_solver.cpp:105] Iteration 4404, lr = 0.1
I0406 07:46:16.209169  5644 solver.cpp:218] Iteration 4416 (2.26342 iter/s, 5.3017s/12 iters), loss = 5.24796
I0406 07:46:16.209205  5644 solver.cpp:237]     Train net output #0: loss = 5.24796 (* 1 = 5.24796 loss)
I0406 07:46:16.209211  5644 sgd_solver.cpp:105] Iteration 4416, lr = 0.1
I0406 07:46:21.417096  5644 solver.cpp:218] Iteration 4428 (2.30422 iter/s, 5.20783s/12 iters), loss = 5.27859
I0406 07:46:21.417137  5644 solver.cpp:237]     Train net output #0: loss = 5.27859 (* 1 = 5.27859 loss)
I0406 07:46:21.417143  5644 sgd_solver.cpp:105] Iteration 4428, lr = 0.1
I0406 07:46:26.757827  5644 solver.cpp:218] Iteration 4440 (2.24693 iter/s, 5.34063s/12 iters), loss = 5.27232
I0406 07:46:26.757865  5644 solver.cpp:237]     Train net output #0: loss = 5.27232 (* 1 = 5.27232 loss)
I0406 07:46:26.757871  5644 sgd_solver.cpp:105] Iteration 4440, lr = 0.1
I0406 07:46:31.053103  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:46:32.079596  5644 solver.cpp:218] Iteration 4452 (2.25493 iter/s, 5.32167s/12 iters), loss = 5.27716
I0406 07:46:32.079632  5644 solver.cpp:237]     Train net output #0: loss = 5.27716 (* 1 = 5.27716 loss)
I0406 07:46:32.079638  5644 sgd_solver.cpp:105] Iteration 4452, lr = 0.1
I0406 07:46:37.460214  5644 solver.cpp:218] Iteration 4464 (2.23027 iter/s, 5.38052s/12 iters), loss = 5.27479
I0406 07:46:37.460351  5644 solver.cpp:237]     Train net output #0: loss = 5.27479 (* 1 = 5.27479 loss)
I0406 07:46:37.460361  5644 sgd_solver.cpp:105] Iteration 4464, lr = 0.1
I0406 07:46:42.624332  5644 solver.cpp:218] Iteration 4476 (2.32381 iter/s, 5.16393s/12 iters), loss = 5.30923
I0406 07:46:42.624372  5644 solver.cpp:237]     Train net output #0: loss = 5.30923 (* 1 = 5.30923 loss)
I0406 07:46:42.624378  5644 sgd_solver.cpp:105] Iteration 4476, lr = 0.1
I0406 07:46:47.314987  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0406 07:46:50.324280  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0406 07:46:52.621590  5644 solver.cpp:330] Iteration 4488, Testing net (#0)
I0406 07:46:52.621608  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:46:55.224196  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:46:56.952306  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:46:56.952342  5644 solver.cpp:397]     Test net output #1: loss = 5.28751 (* 1 = 5.28751 loss)
I0406 07:46:57.085970  5644 solver.cpp:218] Iteration 4488 (0.829792 iter/s, 14.4615s/12 iters), loss = 5.28387
I0406 07:46:57.086011  5644 solver.cpp:237]     Train net output #0: loss = 5.28387 (* 1 = 5.28387 loss)
I0406 07:46:57.086016  5644 sgd_solver.cpp:105] Iteration 4488, lr = 0.1
I0406 07:47:01.545047  5644 solver.cpp:218] Iteration 4500 (2.6912 iter/s, 4.45898s/12 iters), loss = 5.28604
I0406 07:47:01.545092  5644 solver.cpp:237]     Train net output #0: loss = 5.28604 (* 1 = 5.28604 loss)
I0406 07:47:01.545100  5644 sgd_solver.cpp:105] Iteration 4500, lr = 0.1
I0406 07:47:06.868707  5644 solver.cpp:218] Iteration 4512 (2.25413 iter/s, 5.32356s/12 iters), loss = 5.28156
I0406 07:47:06.868746  5644 solver.cpp:237]     Train net output #0: loss = 5.28156 (* 1 = 5.28156 loss)
I0406 07:47:06.868750  5644 sgd_solver.cpp:105] Iteration 4512, lr = 0.1
I0406 07:47:12.046873  5644 solver.cpp:218] Iteration 4524 (2.31747 iter/s, 5.17807s/12 iters), loss = 5.28208
I0406 07:47:12.046972  5644 solver.cpp:237]     Train net output #0: loss = 5.28208 (* 1 = 5.28208 loss)
I0406 07:47:12.046978  5644 sgd_solver.cpp:105] Iteration 4524, lr = 0.1
I0406 07:47:17.412358  5644 solver.cpp:218] Iteration 4536 (2.23658 iter/s, 5.36532s/12 iters), loss = 5.27389
I0406 07:47:17.412402  5644 solver.cpp:237]     Train net output #0: loss = 5.27389 (* 1 = 5.27389 loss)
I0406 07:47:17.412410  5644 sgd_solver.cpp:105] Iteration 4536, lr = 0.1
I0406 07:47:22.357550  5644 solver.cpp:218] Iteration 4548 (2.42665 iter/s, 4.94509s/12 iters), loss = 5.2733
I0406 07:47:22.357591  5644 solver.cpp:237]     Train net output #0: loss = 5.2733 (* 1 = 5.2733 loss)
I0406 07:47:22.357596  5644 sgd_solver.cpp:105] Iteration 4548, lr = 0.1
I0406 07:47:23.613040  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:47:27.618710  5644 solver.cpp:218] Iteration 4560 (2.28091 iter/s, 5.26106s/12 iters), loss = 5.29081
I0406 07:47:27.618746  5644 solver.cpp:237]     Train net output #0: loss = 5.29081 (* 1 = 5.29081 loss)
I0406 07:47:27.618752  5644 sgd_solver.cpp:105] Iteration 4560, lr = 0.1
I0406 07:47:32.920011  5644 solver.cpp:218] Iteration 4572 (2.26364 iter/s, 5.3012s/12 iters), loss = 5.293
I0406 07:47:32.920050  5644 solver.cpp:237]     Train net output #0: loss = 5.293 (* 1 = 5.293 loss)
I0406 07:47:32.920056  5644 sgd_solver.cpp:105] Iteration 4572, lr = 0.1
I0406 07:47:38.055658  5644 solver.cpp:218] Iteration 4584 (2.33665 iter/s, 5.13555s/12 iters), loss = 5.27538
I0406 07:47:38.055697  5644 solver.cpp:237]     Train net output #0: loss = 5.27538 (* 1 = 5.27538 loss)
I0406 07:47:38.055703  5644 sgd_solver.cpp:105] Iteration 4584, lr = 0.1
I0406 07:47:40.115461  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0406 07:47:43.121903  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0406 07:47:45.461630  5644 solver.cpp:330] Iteration 4590, Testing net (#0)
I0406 07:47:45.461658  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:47:48.011082  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:47:49.906982  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:47:49.907009  5644 solver.cpp:397]     Test net output #1: loss = 5.28812 (* 1 = 5.28812 loss)
I0406 07:47:51.722126  5644 solver.cpp:218] Iteration 4596 (0.878073 iter/s, 13.6663s/12 iters), loss = 5.27524
I0406 07:47:51.722168  5644 solver.cpp:237]     Train net output #0: loss = 5.27524 (* 1 = 5.27524 loss)
I0406 07:47:51.722174  5644 sgd_solver.cpp:105] Iteration 4596, lr = 0.1
I0406 07:47:57.116073  5644 solver.cpp:218] Iteration 4608 (2.22476 iter/s, 5.39385s/12 iters), loss = 5.29474
I0406 07:47:57.116111  5644 solver.cpp:237]     Train net output #0: loss = 5.29474 (* 1 = 5.29474 loss)
I0406 07:47:57.116117  5644 sgd_solver.cpp:105] Iteration 4608, lr = 0.1
I0406 07:48:02.290710  5644 solver.cpp:218] Iteration 4620 (2.31905 iter/s, 5.17454s/12 iters), loss = 5.27459
I0406 07:48:02.290760  5644 solver.cpp:237]     Train net output #0: loss = 5.27459 (* 1 = 5.27459 loss)
I0406 07:48:02.290767  5644 sgd_solver.cpp:105] Iteration 4620, lr = 0.1
I0406 07:48:07.217116  5644 solver.cpp:218] Iteration 4632 (2.4359 iter/s, 4.9263s/12 iters), loss = 5.28498
I0406 07:48:07.217155  5644 solver.cpp:237]     Train net output #0: loss = 5.28498 (* 1 = 5.28498 loss)
I0406 07:48:07.217160  5644 sgd_solver.cpp:105] Iteration 4632, lr = 0.1
I0406 07:48:12.570695  5644 solver.cpp:218] Iteration 4644 (2.24153 iter/s, 5.35348s/12 iters), loss = 5.29689
I0406 07:48:12.570730  5644 solver.cpp:237]     Train net output #0: loss = 5.29689 (* 1 = 5.29689 loss)
I0406 07:48:12.570736  5644 sgd_solver.cpp:105] Iteration 4644, lr = 0.1
I0406 07:48:16.148116  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:48:17.801465  5644 solver.cpp:218] Iteration 4656 (2.29416 iter/s, 5.23067s/12 iters), loss = 5.25037
I0406 07:48:17.801502  5644 solver.cpp:237]     Train net output #0: loss = 5.25037 (* 1 = 5.25037 loss)
I0406 07:48:17.801507  5644 sgd_solver.cpp:105] Iteration 4656, lr = 0.1
I0406 07:48:23.309365  5644 solver.cpp:218] Iteration 4668 (2.17873 iter/s, 5.50779s/12 iters), loss = 5.26285
I0406 07:48:23.309419  5644 solver.cpp:237]     Train net output #0: loss = 5.26285 (* 1 = 5.26285 loss)
I0406 07:48:23.309428  5644 sgd_solver.cpp:105] Iteration 4668, lr = 0.1
I0406 07:48:28.705421  5644 solver.cpp:218] Iteration 4680 (2.22389 iter/s, 5.39594s/12 iters), loss = 5.27653
I0406 07:48:28.705457  5644 solver.cpp:237]     Train net output #0: loss = 5.27653 (* 1 = 5.27653 loss)
I0406 07:48:28.705463  5644 sgd_solver.cpp:105] Iteration 4680, lr = 0.1
I0406 07:48:33.408205  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0406 07:48:36.421890  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0406 07:48:38.733471  5644 solver.cpp:330] Iteration 4692, Testing net (#0)
I0406 07:48:38.733490  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:48:41.258911  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:48:43.096812  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:48:43.096858  5644 solver.cpp:397]     Test net output #1: loss = 5.28703 (* 1 = 5.28703 loss)
I0406 07:48:43.236251  5644 solver.cpp:218] Iteration 4692 (0.82584 iter/s, 14.5307s/12 iters), loss = 5.26451
I0406 07:48:43.236291  5644 solver.cpp:237]     Train net output #0: loss = 5.26451 (* 1 = 5.26451 loss)
I0406 07:48:43.236296  5644 sgd_solver.cpp:105] Iteration 4692, lr = 0.1
I0406 07:48:47.465935  5644 solver.cpp:218] Iteration 4704 (2.83715 iter/s, 4.2296s/12 iters), loss = 5.28939
I0406 07:48:47.466048  5644 solver.cpp:237]     Train net output #0: loss = 5.28939 (* 1 = 5.28939 loss)
I0406 07:48:47.466054  5644 sgd_solver.cpp:105] Iteration 4704, lr = 0.1
I0406 07:48:52.664060  5644 solver.cpp:218] Iteration 4716 (2.3086 iter/s, 5.19795s/12 iters), loss = 5.26113
I0406 07:48:52.664098  5644 solver.cpp:237]     Train net output #0: loss = 5.26113 (* 1 = 5.26113 loss)
I0406 07:48:52.664103  5644 sgd_solver.cpp:105] Iteration 4716, lr = 0.1
I0406 07:48:57.741437  5644 solver.cpp:218] Iteration 4728 (2.36347 iter/s, 5.07728s/12 iters), loss = 5.26446
I0406 07:48:57.741478  5644 solver.cpp:237]     Train net output #0: loss = 5.26446 (* 1 = 5.26446 loss)
I0406 07:48:57.741484  5644 sgd_solver.cpp:105] Iteration 4728, lr = 0.1
I0406 07:49:02.956302  5644 solver.cpp:218] Iteration 4740 (2.30116 iter/s, 5.21476s/12 iters), loss = 5.28489
I0406 07:49:02.956341  5644 solver.cpp:237]     Train net output #0: loss = 5.28489 (* 1 = 5.28489 loss)
I0406 07:49:02.956346  5644 sgd_solver.cpp:105] Iteration 4740, lr = 0.1
I0406 07:49:08.299008  5644 solver.cpp:218] Iteration 4752 (2.24609 iter/s, 5.34261s/12 iters), loss = 5.27998
I0406 07:49:08.299046  5644 solver.cpp:237]     Train net output #0: loss = 5.27998 (* 1 = 5.27998 loss)
I0406 07:49:08.299052  5644 sgd_solver.cpp:105] Iteration 4752, lr = 0.1
I0406 07:49:08.883988  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:49:13.710191  5644 solver.cpp:218] Iteration 4764 (2.21767 iter/s, 5.41108s/12 iters), loss = 5.28712
I0406 07:49:13.710230  5644 solver.cpp:237]     Train net output #0: loss = 5.28712 (* 1 = 5.28712 loss)
I0406 07:49:13.710237  5644 sgd_solver.cpp:105] Iteration 4764, lr = 0.1
I0406 07:49:19.055279  5644 solver.cpp:218] Iteration 4776 (2.24509 iter/s, 5.34499s/12 iters), loss = 5.29071
I0406 07:49:19.055384  5644 solver.cpp:237]     Train net output #0: loss = 5.29071 (* 1 = 5.29071 loss)
I0406 07:49:19.055392  5644 sgd_solver.cpp:105] Iteration 4776, lr = 0.1
I0406 07:49:24.385318  5644 solver.cpp:218] Iteration 4788 (2.25146 iter/s, 5.32988s/12 iters), loss = 5.26903
I0406 07:49:24.385355  5644 solver.cpp:237]     Train net output #0: loss = 5.26903 (* 1 = 5.26903 loss)
I0406 07:49:24.385361  5644 sgd_solver.cpp:105] Iteration 4788, lr = 0.1
I0406 07:49:26.343441  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0406 07:49:29.363184  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0406 07:49:31.677650  5644 solver.cpp:330] Iteration 4794, Testing net (#0)
I0406 07:49:31.677671  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:49:34.146376  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:49:36.001755  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:49:36.001788  5644 solver.cpp:397]     Test net output #1: loss = 5.28637 (* 1 = 5.28637 loss)
I0406 07:49:37.845837  5644 solver.cpp:218] Iteration 4800 (0.891507 iter/s, 13.4603s/12 iters), loss = 5.2706
I0406 07:49:37.845885  5644 solver.cpp:237]     Train net output #0: loss = 5.2706 (* 1 = 5.2706 loss)
I0406 07:49:37.845893  5644 sgd_solver.cpp:105] Iteration 4800, lr = 0.1
I0406 07:49:43.166532  5644 solver.cpp:218] Iteration 4812 (2.25539 iter/s, 5.32059s/12 iters), loss = 5.24841
I0406 07:49:43.166568  5644 solver.cpp:237]     Train net output #0: loss = 5.24841 (* 1 = 5.24841 loss)
I0406 07:49:43.166572  5644 sgd_solver.cpp:105] Iteration 4812, lr = 0.1
I0406 07:49:48.309603  5644 solver.cpp:218] Iteration 4824 (2.33328 iter/s, 5.14298s/12 iters), loss = 5.28835
I0406 07:49:48.309640  5644 solver.cpp:237]     Train net output #0: loss = 5.28835 (* 1 = 5.28835 loss)
I0406 07:49:48.309645  5644 sgd_solver.cpp:105] Iteration 4824, lr = 0.1
I0406 07:49:53.457401  5644 solver.cpp:218] Iteration 4836 (2.33114 iter/s, 5.1477s/12 iters), loss = 5.2802
I0406 07:49:53.457564  5644 solver.cpp:237]     Train net output #0: loss = 5.2802 (* 1 = 5.2802 loss)
I0406 07:49:53.457573  5644 sgd_solver.cpp:105] Iteration 4836, lr = 0.1
I0406 07:49:55.608162  5644 blocking_queue.cpp:49] Waiting for data
I0406 07:49:58.807540  5644 solver.cpp:218] Iteration 4848 (2.24302 iter/s, 5.34992s/12 iters), loss = 5.27463
I0406 07:49:58.807579  5644 solver.cpp:237]     Train net output #0: loss = 5.27463 (* 1 = 5.27463 loss)
I0406 07:49:58.807585  5644 sgd_solver.cpp:105] Iteration 4848, lr = 0.1
I0406 07:50:01.429224  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:50:03.830554  5644 solver.cpp:218] Iteration 4860 (2.38905 iter/s, 5.02292s/12 iters), loss = 5.28196
I0406 07:50:03.830591  5644 solver.cpp:237]     Train net output #0: loss = 5.28196 (* 1 = 5.28196 loss)
I0406 07:50:03.830598  5644 sgd_solver.cpp:105] Iteration 4860, lr = 0.1
I0406 07:50:09.146080  5644 solver.cpp:218] Iteration 4872 (2.25758 iter/s, 5.31543s/12 iters), loss = 5.26907
I0406 07:50:09.146131  5644 solver.cpp:237]     Train net output #0: loss = 5.26907 (* 1 = 5.26907 loss)
I0406 07:50:09.146139  5644 sgd_solver.cpp:105] Iteration 4872, lr = 0.1
I0406 07:50:14.430485  5644 solver.cpp:218] Iteration 4884 (2.27088 iter/s, 5.2843s/12 iters), loss = 5.26437
I0406 07:50:14.430526  5644 solver.cpp:237]     Train net output #0: loss = 5.26437 (* 1 = 5.26437 loss)
I0406 07:50:14.430531  5644 sgd_solver.cpp:105] Iteration 4884, lr = 0.1
I0406 07:50:19.254812  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0406 07:50:22.250582  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0406 07:50:24.554184  5644 solver.cpp:330] Iteration 4896, Testing net (#0)
I0406 07:50:24.554286  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:50:27.006511  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:50:28.892495  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 07:50:28.892530  5644 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0406 07:50:29.033367  5644 solver.cpp:218] Iteration 4896 (0.821766 iter/s, 14.6027s/12 iters), loss = 5.28661
I0406 07:50:29.033423  5644 solver.cpp:237]     Train net output #0: loss = 5.28661 (* 1 = 5.28661 loss)
I0406 07:50:29.033429  5644 sgd_solver.cpp:105] Iteration 4896, lr = 0.1
I0406 07:50:33.292058  5644 solver.cpp:218] Iteration 4908 (2.81784 iter/s, 4.25858s/12 iters), loss = 5.27705
I0406 07:50:33.292098  5644 solver.cpp:237]     Train net output #0: loss = 5.27705 (* 1 = 5.27705 loss)
I0406 07:50:33.292104  5644 sgd_solver.cpp:105] Iteration 4908, lr = 0.1
I0406 07:50:38.407969  5644 solver.cpp:218] Iteration 4920 (2.34567 iter/s, 5.11581s/12 iters), loss = 5.29034
I0406 07:50:38.408007  5644 solver.cpp:237]     Train net output #0: loss = 5.29034 (* 1 = 5.29034 loss)
I0406 07:50:38.408012  5644 sgd_solver.cpp:105] Iteration 4920, lr = 0.1
I0406 07:50:43.703584  5644 solver.cpp:218] Iteration 4932 (2.26607 iter/s, 5.29551s/12 iters), loss = 5.29313
I0406 07:50:43.703639  5644 solver.cpp:237]     Train net output #0: loss = 5.29313 (* 1 = 5.29313 loss)
I0406 07:50:43.703647  5644 sgd_solver.cpp:105] Iteration 4932, lr = 0.1
I0406 07:50:49.028977  5644 solver.cpp:218] Iteration 4944 (2.2534 iter/s, 5.32528s/12 iters), loss = 5.29504
I0406 07:50:49.029014  5644 solver.cpp:237]     Train net output #0: loss = 5.29504 (* 1 = 5.29504 loss)
I0406 07:50:49.029021  5644 sgd_solver.cpp:105] Iteration 4944, lr = 0.1
I0406 07:50:53.959131  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:50:54.211814  5644 solver.cpp:218] Iteration 4956 (2.31538 iter/s, 5.18274s/12 iters), loss = 5.28391
I0406 07:50:54.211855  5644 solver.cpp:237]     Train net output #0: loss = 5.28391 (* 1 = 5.28391 loss)
I0406 07:50:54.211861  5644 sgd_solver.cpp:105] Iteration 4956, lr = 0.1
I0406 07:50:59.404548  5644 solver.cpp:218] Iteration 4968 (2.31096 iter/s, 5.19264s/12 iters), loss = 5.26933
I0406 07:50:59.404664  5644 solver.cpp:237]     Train net output #0: loss = 5.26933 (* 1 = 5.26933 loss)
I0406 07:50:59.404671  5644 sgd_solver.cpp:105] Iteration 4968, lr = 0.1
I0406 07:51:04.648865  5644 solver.cpp:218] Iteration 4980 (2.28827 iter/s, 5.24415s/12 iters), loss = 5.28514
I0406 07:51:04.648906  5644 solver.cpp:237]     Train net output #0: loss = 5.28514 (* 1 = 5.28514 loss)
I0406 07:51:04.648912  5644 sgd_solver.cpp:105] Iteration 4980, lr = 0.1
I0406 07:51:10.068364  5644 solver.cpp:218] Iteration 4992 (2.21427 iter/s, 5.4194s/12 iters), loss = 5.28929
I0406 07:51:10.068399  5644 solver.cpp:237]     Train net output #0: loss = 5.28929 (* 1 = 5.28929 loss)
I0406 07:51:10.068405  5644 sgd_solver.cpp:105] Iteration 4992, lr = 0.1
I0406 07:51:12.212419  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0406 07:51:15.208420  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0406 07:51:17.507681  5644 solver.cpp:330] Iteration 4998, Testing net (#0)
I0406 07:51:17.507700  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:51:19.886803  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:51:21.813050  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:51:21.813086  5644 solver.cpp:397]     Test net output #1: loss = 5.28708 (* 1 = 5.28708 loss)
I0406 07:51:23.769539  5644 solver.cpp:218] Iteration 5004 (0.875848 iter/s, 13.701s/12 iters), loss = 5.26595
I0406 07:51:23.769574  5644 solver.cpp:237]     Train net output #0: loss = 5.26595 (* 1 = 5.26595 loss)
I0406 07:51:23.769579  5644 sgd_solver.cpp:105] Iteration 5004, lr = 0.1
I0406 07:51:29.136206  5644 solver.cpp:218] Iteration 5016 (2.23607 iter/s, 5.36657s/12 iters), loss = 5.26784
I0406 07:51:29.136246  5644 solver.cpp:237]     Train net output #0: loss = 5.26784 (* 1 = 5.26784 loss)
I0406 07:51:29.136252  5644 sgd_solver.cpp:105] Iteration 5016, lr = 0.1
I0406 07:51:34.472405  5644 solver.cpp:218] Iteration 5028 (2.24883 iter/s, 5.3361s/12 iters), loss = 5.27627
I0406 07:51:34.472524  5644 solver.cpp:237]     Train net output #0: loss = 5.27627 (* 1 = 5.27627 loss)
I0406 07:51:34.472533  5644 sgd_solver.cpp:105] Iteration 5028, lr = 0.1
I0406 07:51:39.692752  5644 solver.cpp:218] Iteration 5040 (2.29877 iter/s, 5.22017s/12 iters), loss = 5.28613
I0406 07:51:39.692787  5644 solver.cpp:237]     Train net output #0: loss = 5.28613 (* 1 = 5.28613 loss)
I0406 07:51:39.692792  5644 sgd_solver.cpp:105] Iteration 5040, lr = 0.1
I0406 07:51:45.109524  5644 solver.cpp:218] Iteration 5052 (2.21538 iter/s, 5.41667s/12 iters), loss = 5.27873
I0406 07:51:45.109577  5644 solver.cpp:237]     Train net output #0: loss = 5.27873 (* 1 = 5.27873 loss)
I0406 07:51:45.109586  5644 sgd_solver.cpp:105] Iteration 5052, lr = 0.1
I0406 07:51:47.137370  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:51:50.374428  5644 solver.cpp:218] Iteration 5064 (2.27929 iter/s, 5.2648s/12 iters), loss = 5.30397
I0406 07:51:50.374476  5644 solver.cpp:237]     Train net output #0: loss = 5.30397 (* 1 = 5.30397 loss)
I0406 07:51:50.374485  5644 sgd_solver.cpp:105] Iteration 5064, lr = 0.1
I0406 07:51:55.473664  5644 solver.cpp:218] Iteration 5076 (2.35334 iter/s, 5.09913s/12 iters), loss = 5.25978
I0406 07:51:55.473701  5644 solver.cpp:237]     Train net output #0: loss = 5.25978 (* 1 = 5.25978 loss)
I0406 07:51:55.473707  5644 sgd_solver.cpp:105] Iteration 5076, lr = 0.1
I0406 07:52:00.742355  5644 solver.cpp:218] Iteration 5088 (2.27765 iter/s, 5.2686s/12 iters), loss = 5.28967
I0406 07:52:00.742393  5644 solver.cpp:237]     Train net output #0: loss = 5.28967 (* 1 = 5.28967 loss)
I0406 07:52:00.742399  5644 sgd_solver.cpp:105] Iteration 5088, lr = 0.1
I0406 07:52:05.486382  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0406 07:52:08.498100  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0406 07:52:10.792541  5644 solver.cpp:330] Iteration 5100, Testing net (#0)
I0406 07:52:10.792562  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:52:13.190635  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:52:15.198288  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:52:15.198323  5644 solver.cpp:397]     Test net output #1: loss = 5.28554 (* 1 = 5.28554 loss)
I0406 07:52:15.337380  5644 solver.cpp:218] Iteration 5100 (0.822208 iter/s, 14.5949s/12 iters), loss = 5.26647
I0406 07:52:15.338948  5644 solver.cpp:237]     Train net output #0: loss = 5.26647 (* 1 = 5.26647 loss)
I0406 07:52:15.338959  5644 sgd_solver.cpp:105] Iteration 5100, lr = 0.1
I0406 07:52:19.468688  5644 solver.cpp:218] Iteration 5112 (2.90578 iter/s, 4.12969s/12 iters), loss = 5.26558
I0406 07:52:19.468724  5644 solver.cpp:237]     Train net output #0: loss = 5.26558 (* 1 = 5.26558 loss)
I0406 07:52:19.468730  5644 sgd_solver.cpp:105] Iteration 5112, lr = 0.1
I0406 07:52:24.704496  5644 solver.cpp:218] Iteration 5124 (2.29195 iter/s, 5.23571s/12 iters), loss = 5.25668
I0406 07:52:24.704535  5644 solver.cpp:237]     Train net output #0: loss = 5.25668 (* 1 = 5.25668 loss)
I0406 07:52:24.704541  5644 sgd_solver.cpp:105] Iteration 5124, lr = 0.1
I0406 07:52:29.890833  5644 solver.cpp:218] Iteration 5136 (2.31382 iter/s, 5.18624s/12 iters), loss = 5.27651
I0406 07:52:29.890872  5644 solver.cpp:237]     Train net output #0: loss = 5.27651 (* 1 = 5.27651 loss)
I0406 07:52:29.890877  5644 sgd_solver.cpp:105] Iteration 5136, lr = 0.1
I0406 07:52:35.087338  5644 solver.cpp:218] Iteration 5148 (2.30929 iter/s, 5.19641s/12 iters), loss = 5.27666
I0406 07:52:35.087378  5644 solver.cpp:237]     Train net output #0: loss = 5.27666 (* 1 = 5.27666 loss)
I0406 07:52:35.087384  5644 sgd_solver.cpp:105] Iteration 5148, lr = 0.1
I0406 07:52:39.137151  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:52:40.212451  5644 solver.cpp:218] Iteration 5160 (2.34145 iter/s, 5.12502s/12 iters), loss = 5.27631
I0406 07:52:40.212493  5644 solver.cpp:237]     Train net output #0: loss = 5.27631 (* 1 = 5.27631 loss)
I0406 07:52:40.212498  5644 sgd_solver.cpp:105] Iteration 5160, lr = 0.1
I0406 07:52:45.525456  5644 solver.cpp:218] Iteration 5172 (2.25865 iter/s, 5.3129s/12 iters), loss = 5.27106
I0406 07:52:45.525490  5644 solver.cpp:237]     Train net output #0: loss = 5.27106 (* 1 = 5.27106 loss)
I0406 07:52:45.525496  5644 sgd_solver.cpp:105] Iteration 5172, lr = 0.1
I0406 07:52:50.885877  5644 solver.cpp:218] Iteration 5184 (2.23867 iter/s, 5.36033s/12 iters), loss = 5.30976
I0406 07:52:50.885919  5644 solver.cpp:237]     Train net output #0: loss = 5.30976 (* 1 = 5.30976 loss)
I0406 07:52:50.885927  5644 sgd_solver.cpp:105] Iteration 5184, lr = 0.1
I0406 07:52:55.997961  5644 solver.cpp:218] Iteration 5196 (2.34742 iter/s, 5.11199s/12 iters), loss = 5.29121
I0406 07:52:55.998001  5644 solver.cpp:237]     Train net output #0: loss = 5.29121 (* 1 = 5.29121 loss)
I0406 07:52:55.998008  5644 sgd_solver.cpp:105] Iteration 5196, lr = 0.1
I0406 07:52:58.209318  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0406 07:53:01.222839  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0406 07:53:03.525745  5644 solver.cpp:330] Iteration 5202, Testing net (#0)
I0406 07:53:03.525764  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:53:05.798156  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:53:07.800840  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:53:07.800868  5644 solver.cpp:397]     Test net output #1: loss = 5.28682 (* 1 = 5.28682 loss)
I0406 07:53:09.577883  5644 solver.cpp:218] Iteration 5208 (0.883668 iter/s, 13.5798s/12 iters), loss = 5.29356
I0406 07:53:09.577994  5644 solver.cpp:237]     Train net output #0: loss = 5.29356 (* 1 = 5.29356 loss)
I0406 07:53:09.578001  5644 sgd_solver.cpp:105] Iteration 5208, lr = 0.1
I0406 07:53:14.496387  5644 solver.cpp:218] Iteration 5220 (2.43985 iter/s, 4.91834s/12 iters), loss = 5.27257
I0406 07:53:14.496428  5644 solver.cpp:237]     Train net output #0: loss = 5.27257 (* 1 = 5.27257 loss)
I0406 07:53:14.496434  5644 sgd_solver.cpp:105] Iteration 5220, lr = 0.1
I0406 07:53:19.721751  5644 solver.cpp:218] Iteration 5232 (2.29654 iter/s, 5.22526s/12 iters), loss = 5.27774
I0406 07:53:19.721791  5644 solver.cpp:237]     Train net output #0: loss = 5.27774 (* 1 = 5.27774 loss)
I0406 07:53:19.721797  5644 sgd_solver.cpp:105] Iteration 5232, lr = 0.1
I0406 07:53:25.013195  5644 solver.cpp:218] Iteration 5244 (2.26786 iter/s, 5.29134s/12 iters), loss = 5.27497
I0406 07:53:25.013240  5644 solver.cpp:237]     Train net output #0: loss = 5.27497 (* 1 = 5.27497 loss)
I0406 07:53:25.013249  5644 sgd_solver.cpp:105] Iteration 5244, lr = 0.1
I0406 07:53:30.247011  5644 solver.cpp:218] Iteration 5256 (2.29283 iter/s, 5.23371s/12 iters), loss = 5.27533
I0406 07:53:30.247048  5644 solver.cpp:237]     Train net output #0: loss = 5.27533 (* 1 = 5.27533 loss)
I0406 07:53:30.247053  5644 sgd_solver.cpp:105] Iteration 5256, lr = 0.1
I0406 07:53:31.627943  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:53:35.503329  5644 solver.cpp:218] Iteration 5268 (2.28301 iter/s, 5.25622s/12 iters), loss = 5.29664
I0406 07:53:35.503366  5644 solver.cpp:237]     Train net output #0: loss = 5.29664 (* 1 = 5.29664 loss)
I0406 07:53:35.503372  5644 sgd_solver.cpp:105] Iteration 5268, lr = 0.1
I0406 07:53:40.864722  5644 solver.cpp:218] Iteration 5280 (2.23827 iter/s, 5.36129s/12 iters), loss = 5.28954
I0406 07:53:40.864832  5644 solver.cpp:237]     Train net output #0: loss = 5.28954 (* 1 = 5.28954 loss)
I0406 07:53:40.864841  5644 sgd_solver.cpp:105] Iteration 5280, lr = 0.1
I0406 07:53:45.886225  5644 solver.cpp:218] Iteration 5292 (2.3898 iter/s, 5.02134s/12 iters), loss = 5.27455
I0406 07:53:45.886265  5644 solver.cpp:237]     Train net output #0: loss = 5.27455 (* 1 = 5.27455 loss)
I0406 07:53:45.886270  5644 sgd_solver.cpp:105] Iteration 5292, lr = 0.1
I0406 07:53:50.530848  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0406 07:53:55.221120  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0406 07:53:57.681268  5644 solver.cpp:330] Iteration 5304, Testing net (#0)
I0406 07:53:57.681288  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:53:59.930006  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:54:02.028620  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:54:02.028654  5644 solver.cpp:397]     Test net output #1: loss = 5.28702 (* 1 = 5.28702 loss)
I0406 07:54:02.168429  5644 solver.cpp:218] Iteration 5304 (0.73701 iter/s, 16.282s/12 iters), loss = 5.277
I0406 07:54:02.168473  5644 solver.cpp:237]     Train net output #0: loss = 5.277 (* 1 = 5.277 loss)
I0406 07:54:02.168478  5644 sgd_solver.cpp:105] Iteration 5304, lr = 0.1
I0406 07:54:06.393705  5644 solver.cpp:218] Iteration 5316 (2.84011 iter/s, 4.22518s/12 iters), loss = 5.28846
I0406 07:54:06.393745  5644 solver.cpp:237]     Train net output #0: loss = 5.28846 (* 1 = 5.28846 loss)
I0406 07:54:06.393752  5644 sgd_solver.cpp:105] Iteration 5316, lr = 0.1
I0406 07:54:11.724117  5644 solver.cpp:218] Iteration 5328 (2.25128 iter/s, 5.33031s/12 iters), loss = 5.27098
I0406 07:54:11.724252  5644 solver.cpp:237]     Train net output #0: loss = 5.27098 (* 1 = 5.27098 loss)
I0406 07:54:11.724261  5644 sgd_solver.cpp:105] Iteration 5328, lr = 0.1
I0406 07:54:16.972030  5644 solver.cpp:218] Iteration 5340 (2.28671 iter/s, 5.24772s/12 iters), loss = 5.28608
I0406 07:54:16.972081  5644 solver.cpp:237]     Train net output #0: loss = 5.28608 (* 1 = 5.28608 loss)
I0406 07:54:16.972088  5644 sgd_solver.cpp:105] Iteration 5340, lr = 0.1
I0406 07:54:22.056082  5644 solver.cpp:218] Iteration 5352 (2.36037 iter/s, 5.08394s/12 iters), loss = 5.29619
I0406 07:54:22.056118  5644 solver.cpp:237]     Train net output #0: loss = 5.29619 (* 1 = 5.29619 loss)
I0406 07:54:22.056124  5644 sgd_solver.cpp:105] Iteration 5352, lr = 0.1
I0406 07:54:25.438740  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:54:26.983206  5644 solver.cpp:218] Iteration 5364 (2.43555 iter/s, 4.92703s/12 iters), loss = 5.24812
I0406 07:54:26.983255  5644 solver.cpp:237]     Train net output #0: loss = 5.24812 (* 1 = 5.24812 loss)
I0406 07:54:26.983263  5644 sgd_solver.cpp:105] Iteration 5364, lr = 0.1
I0406 07:54:32.284512  5644 solver.cpp:218] Iteration 5376 (2.26364 iter/s, 5.3012s/12 iters), loss = 5.26834
I0406 07:54:32.284559  5644 solver.cpp:237]     Train net output #0: loss = 5.26834 (* 1 = 5.26834 loss)
I0406 07:54:32.284564  5644 sgd_solver.cpp:105] Iteration 5376, lr = 0.1
I0406 07:54:37.569651  5644 solver.cpp:218] Iteration 5388 (2.27056 iter/s, 5.28503s/12 iters), loss = 5.27966
I0406 07:54:37.569705  5644 solver.cpp:237]     Train net output #0: loss = 5.27966 (* 1 = 5.27966 loss)
I0406 07:54:37.569713  5644 sgd_solver.cpp:105] Iteration 5388, lr = 0.1
I0406 07:54:42.834142  5644 solver.cpp:218] Iteration 5400 (2.27947 iter/s, 5.26438s/12 iters), loss = 5.26653
I0406 07:54:42.834235  5644 solver.cpp:237]     Train net output #0: loss = 5.26653 (* 1 = 5.26653 loss)
I0406 07:54:42.834242  5644 sgd_solver.cpp:105] Iteration 5400, lr = 0.1
I0406 07:54:45.131518  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0406 07:54:48.081964  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0406 07:54:50.399628  5644 solver.cpp:330] Iteration 5406, Testing net (#0)
I0406 07:54:50.399648  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:54:52.645656  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:54:54.730085  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:54:54.730118  5644 solver.cpp:397]     Test net output #1: loss = 5.28684 (* 1 = 5.28684 loss)
I0406 07:54:56.612530  5644 solver.cpp:218] Iteration 5412 (0.870943 iter/s, 13.7782s/12 iters), loss = 5.28122
I0406 07:54:56.612581  5644 solver.cpp:237]     Train net output #0: loss = 5.28122 (* 1 = 5.28122 loss)
I0406 07:54:56.612588  5644 sgd_solver.cpp:105] Iteration 5412, lr = 0.1
I0406 07:55:01.920586  5644 solver.cpp:218] Iteration 5424 (2.26076 iter/s, 5.30794s/12 iters), loss = 5.25906
I0406 07:55:01.920627  5644 solver.cpp:237]     Train net output #0: loss = 5.25906 (* 1 = 5.25906 loss)
I0406 07:55:01.920634  5644 sgd_solver.cpp:105] Iteration 5424, lr = 0.1
I0406 07:55:06.946969  5644 solver.cpp:218] Iteration 5436 (2.38745 iter/s, 5.02628s/12 iters), loss = 5.26956
I0406 07:55:06.947018  5644 solver.cpp:237]     Train net output #0: loss = 5.26956 (* 1 = 5.26956 loss)
I0406 07:55:06.947026  5644 sgd_solver.cpp:105] Iteration 5436, lr = 0.1
I0406 07:55:12.271176  5644 solver.cpp:218] Iteration 5448 (2.2539 iter/s, 5.3241s/12 iters), loss = 5.28226
I0406 07:55:12.271212  5644 solver.cpp:237]     Train net output #0: loss = 5.28226 (* 1 = 5.28226 loss)
I0406 07:55:12.271219  5644 sgd_solver.cpp:105] Iteration 5448, lr = 0.1
I0406 07:55:17.461266  5644 solver.cpp:218] Iteration 5460 (2.31214 iter/s, 5.18999s/12 iters), loss = 5.2841
I0406 07:55:17.461381  5644 solver.cpp:237]     Train net output #0: loss = 5.2841 (* 1 = 5.2841 loss)
I0406 07:55:17.461388  5644 sgd_solver.cpp:105] Iteration 5460, lr = 0.1
I0406 07:55:17.982579  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:55:22.713284  5644 solver.cpp:218] Iteration 5472 (2.28491 iter/s, 5.25185s/12 iters), loss = 5.27722
I0406 07:55:22.713320  5644 solver.cpp:237]     Train net output #0: loss = 5.27722 (* 1 = 5.27722 loss)
I0406 07:55:22.713326  5644 sgd_solver.cpp:105] Iteration 5472, lr = 0.1
I0406 07:55:28.002760  5644 solver.cpp:218] Iteration 5484 (2.2687 iter/s, 5.28938s/12 iters), loss = 5.29284
I0406 07:55:28.002794  5644 solver.cpp:237]     Train net output #0: loss = 5.29284 (* 1 = 5.29284 loss)
I0406 07:55:28.002800  5644 sgd_solver.cpp:105] Iteration 5484, lr = 0.1
I0406 07:55:33.460893  5644 solver.cpp:218] Iteration 5496 (2.1986 iter/s, 5.45803s/12 iters), loss = 5.26823
I0406 07:55:33.460947  5644 solver.cpp:237]     Train net output #0: loss = 5.26823 (* 1 = 5.26823 loss)
I0406 07:55:33.460954  5644 sgd_solver.cpp:105] Iteration 5496, lr = 0.1
I0406 07:55:38.284297  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0406 07:55:41.344696  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0406 07:55:43.670920  5644 solver.cpp:330] Iteration 5508, Testing net (#0)
I0406 07:55:43.670938  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:55:45.901307  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:55:48.060274  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:55:48.060389  5644 solver.cpp:397]     Test net output #1: loss = 5.28566 (* 1 = 5.28566 loss)
I0406 07:55:48.201115  5644 solver.cpp:218] Iteration 5508 (0.81411 iter/s, 14.74s/12 iters), loss = 5.27175
I0406 07:55:48.202713  5644 solver.cpp:237]     Train net output #0: loss = 5.27175 (* 1 = 5.27175 loss)
I0406 07:55:48.202731  5644 sgd_solver.cpp:105] Iteration 5508, lr = 0.1
I0406 07:55:52.510462  5644 solver.cpp:218] Iteration 5520 (2.7857 iter/s, 4.30771s/12 iters), loss = 5.24923
I0406 07:55:52.510509  5644 solver.cpp:237]     Train net output #0: loss = 5.24923 (* 1 = 5.24923 loss)
I0406 07:55:52.510516  5644 sgd_solver.cpp:105] Iteration 5520, lr = 0.1
I0406 07:55:55.079732  5644 blocking_queue.cpp:49] Waiting for data
I0406 07:55:57.808051  5644 solver.cpp:218] Iteration 5532 (2.26523 iter/s, 5.29748s/12 iters), loss = 5.29267
I0406 07:55:57.808099  5644 solver.cpp:237]     Train net output #0: loss = 5.29267 (* 1 = 5.29267 loss)
I0406 07:55:57.808106  5644 sgd_solver.cpp:105] Iteration 5532, lr = 0.1
I0406 07:56:03.075510  5644 solver.cpp:218] Iteration 5544 (2.27818 iter/s, 5.26735s/12 iters), loss = 5.28328
I0406 07:56:03.075548  5644 solver.cpp:237]     Train net output #0: loss = 5.28328 (* 1 = 5.28328 loss)
I0406 07:56:03.075553  5644 sgd_solver.cpp:105] Iteration 5544, lr = 0.1
I0406 07:56:08.548022  5644 solver.cpp:218] Iteration 5556 (2.19282 iter/s, 5.47241s/12 iters), loss = 5.27701
I0406 07:56:08.548058  5644 solver.cpp:237]     Train net output #0: loss = 5.27701 (* 1 = 5.27701 loss)
I0406 07:56:08.548064  5644 sgd_solver.cpp:105] Iteration 5556, lr = 0.1
I0406 07:56:11.235982  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:56:13.609287  5644 solver.cpp:218] Iteration 5568 (2.37099 iter/s, 5.06117s/12 iters), loss = 5.27925
I0406 07:56:13.609326  5644 solver.cpp:237]     Train net output #0: loss = 5.27925 (* 1 = 5.27925 loss)
I0406 07:56:13.609333  5644 sgd_solver.cpp:105] Iteration 5568, lr = 0.1
I0406 07:56:18.711371  5644 solver.cpp:218] Iteration 5580 (2.35202 iter/s, 5.10199s/12 iters), loss = 5.27495
I0406 07:56:18.711500  5644 solver.cpp:237]     Train net output #0: loss = 5.27495 (* 1 = 5.27495 loss)
I0406 07:56:18.711506  5644 sgd_solver.cpp:105] Iteration 5580, lr = 0.1
I0406 07:56:24.107522  5644 solver.cpp:218] Iteration 5592 (2.22388 iter/s, 5.39596s/12 iters), loss = 5.27091
I0406 07:56:24.107566  5644 solver.cpp:237]     Train net output #0: loss = 5.27091 (* 1 = 5.27091 loss)
I0406 07:56:24.107573  5644 sgd_solver.cpp:105] Iteration 5592, lr = 0.1
I0406 07:56:29.446717  5644 solver.cpp:218] Iteration 5604 (2.24757 iter/s, 5.33909s/12 iters), loss = 5.28287
I0406 07:56:29.446765  5644 solver.cpp:237]     Train net output #0: loss = 5.28287 (* 1 = 5.28287 loss)
I0406 07:56:29.446774  5644 sgd_solver.cpp:105] Iteration 5604, lr = 0.1
I0406 07:56:31.503883  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0406 07:56:34.500978  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0406 07:56:36.953186  5644 solver.cpp:330] Iteration 5610, Testing net (#0)
I0406 07:56:36.953207  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:56:39.133599  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:56:41.343390  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:56:41.343426  5644 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0406 07:56:43.272553  5644 solver.cpp:218] Iteration 5616 (0.867951 iter/s, 13.8257s/12 iters), loss = 5.27722
I0406 07:56:43.272588  5644 solver.cpp:237]     Train net output #0: loss = 5.27722 (* 1 = 5.27722 loss)
I0406 07:56:43.272593  5644 sgd_solver.cpp:105] Iteration 5616, lr = 0.1
I0406 07:56:48.529148  5644 solver.cpp:218] Iteration 5628 (2.28289 iter/s, 5.2565s/12 iters), loss = 5.2944
I0406 07:56:48.529202  5644 solver.cpp:237]     Train net output #0: loss = 5.2944 (* 1 = 5.2944 loss)
I0406 07:56:48.529211  5644 sgd_solver.cpp:105] Iteration 5628, lr = 0.1
I0406 07:56:53.802071  5644 solver.cpp:218] Iteration 5640 (2.27583 iter/s, 5.27281s/12 iters), loss = 5.30629
I0406 07:56:53.802175  5644 solver.cpp:237]     Train net output #0: loss = 5.30629 (* 1 = 5.30629 loss)
I0406 07:56:53.802186  5644 sgd_solver.cpp:105] Iteration 5640, lr = 0.1
I0406 07:56:59.037612  5644 solver.cpp:218] Iteration 5652 (2.29209 iter/s, 5.23539s/12 iters), loss = 5.29396
I0406 07:56:59.037648  5644 solver.cpp:237]     Train net output #0: loss = 5.29396 (* 1 = 5.29396 loss)
I0406 07:56:59.037653  5644 sgd_solver.cpp:105] Iteration 5652, lr = 0.1
I0406 07:57:04.007004  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:57:04.228607  5644 solver.cpp:218] Iteration 5664 (2.31174 iter/s, 5.1909s/12 iters), loss = 5.28784
I0406 07:57:04.228644  5644 solver.cpp:237]     Train net output #0: loss = 5.28784 (* 1 = 5.28784 loss)
I0406 07:57:04.228650  5644 sgd_solver.cpp:105] Iteration 5664, lr = 0.1
I0406 07:57:09.576356  5644 solver.cpp:218] Iteration 5676 (2.24398 iter/s, 5.34765s/12 iters), loss = 5.2701
I0406 07:57:09.576396  5644 solver.cpp:237]     Train net output #0: loss = 5.2701 (* 1 = 5.2701 loss)
I0406 07:57:09.576402  5644 sgd_solver.cpp:105] Iteration 5676, lr = 0.1
I0406 07:57:14.870782  5644 solver.cpp:218] Iteration 5688 (2.26658 iter/s, 5.29432s/12 iters), loss = 5.27743
I0406 07:57:14.870831  5644 solver.cpp:237]     Train net output #0: loss = 5.27743 (* 1 = 5.27743 loss)
I0406 07:57:14.870839  5644 sgd_solver.cpp:105] Iteration 5688, lr = 0.1
I0406 07:57:20.145608  5644 solver.cpp:218] Iteration 5700 (2.275 iter/s, 5.27472s/12 iters), loss = 5.28351
I0406 07:57:20.145642  5644 solver.cpp:237]     Train net output #0: loss = 5.28351 (* 1 = 5.28351 loss)
I0406 07:57:20.145648  5644 sgd_solver.cpp:105] Iteration 5700, lr = 0.1
I0406 07:57:24.764807  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0406 07:57:27.725621  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0406 07:57:30.030673  5644 solver.cpp:330] Iteration 5712, Testing net (#0)
I0406 07:57:30.030695  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:57:32.126153  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:57:34.330698  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:57:34.330735  5644 solver.cpp:397]     Test net output #1: loss = 5.28553 (* 1 = 5.28553 loss)
I0406 07:57:34.471122  5644 solver.cpp:218] Iteration 5712 (0.837676 iter/s, 14.3253s/12 iters), loss = 5.26405
I0406 07:57:34.471169  5644 solver.cpp:237]     Train net output #0: loss = 5.26405 (* 1 = 5.26405 loss)
I0406 07:57:34.471179  5644 sgd_solver.cpp:105] Iteration 5712, lr = 0.1
I0406 07:57:38.754537  5644 solver.cpp:218] Iteration 5724 (2.80157 iter/s, 4.28331s/12 iters), loss = 5.25775
I0406 07:57:38.754588  5644 solver.cpp:237]     Train net output #0: loss = 5.25775 (* 1 = 5.25775 loss)
I0406 07:57:38.754596  5644 sgd_solver.cpp:105] Iteration 5724, lr = 0.1
I0406 07:57:44.014395  5644 solver.cpp:218] Iteration 5736 (2.28148 iter/s, 5.25975s/12 iters), loss = 5.27304
I0406 07:57:44.014437  5644 solver.cpp:237]     Train net output #0: loss = 5.27304 (* 1 = 5.27304 loss)
I0406 07:57:44.014444  5644 sgd_solver.cpp:105] Iteration 5736, lr = 0.1
I0406 07:57:49.308499  5644 solver.cpp:218] Iteration 5748 (2.26671 iter/s, 5.29401s/12 iters), loss = 5.28072
I0406 07:57:49.308537  5644 solver.cpp:237]     Train net output #0: loss = 5.28072 (* 1 = 5.28072 loss)
I0406 07:57:49.308542  5644 sgd_solver.cpp:105] Iteration 5748, lr = 0.1
I0406 07:57:54.520877  5644 solver.cpp:218] Iteration 5760 (2.30226 iter/s, 5.21228s/12 iters), loss = 5.27438
I0406 07:57:54.520920  5644 solver.cpp:237]     Train net output #0: loss = 5.27438 (* 1 = 5.27438 loss)
I0406 07:57:54.520925  5644 sgd_solver.cpp:105] Iteration 5760, lr = 0.1
I0406 07:57:56.633491  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:58:00.035907  5644 solver.cpp:218] Iteration 5772 (2.17591 iter/s, 5.51493s/12 iters), loss = 5.29946
I0406 07:58:00.035941  5644 solver.cpp:237]     Train net output #0: loss = 5.29946 (* 1 = 5.29946 loss)
I0406 07:58:00.035948  5644 sgd_solver.cpp:105] Iteration 5772, lr = 0.1
I0406 07:58:05.535522  5644 solver.cpp:218] Iteration 5784 (2.18201 iter/s, 5.49952s/12 iters), loss = 5.26036
I0406 07:58:05.535560  5644 solver.cpp:237]     Train net output #0: loss = 5.26036 (* 1 = 5.26036 loss)
I0406 07:58:05.535567  5644 sgd_solver.cpp:105] Iteration 5784, lr = 0.1
I0406 07:58:10.795465  5644 solver.cpp:218] Iteration 5796 (2.28143 iter/s, 5.25985s/12 iters), loss = 5.2884
I0406 07:58:10.795498  5644 solver.cpp:237]     Train net output #0: loss = 5.2884 (* 1 = 5.2884 loss)
I0406 07:58:10.795503  5644 sgd_solver.cpp:105] Iteration 5796, lr = 0.1
I0406 07:58:16.068380  5644 solver.cpp:218] Iteration 5808 (2.27582 iter/s, 5.27282s/12 iters), loss = 5.27216
I0406 07:58:16.068418  5644 solver.cpp:237]     Train net output #0: loss = 5.27216 (* 1 = 5.27216 loss)
I0406 07:58:16.068423  5644 sgd_solver.cpp:105] Iteration 5808, lr = 0.1
I0406 07:58:18.247881  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0406 07:58:21.252058  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0406 07:58:23.572708  5644 solver.cpp:330] Iteration 5814, Testing net (#0)
I0406 07:58:23.572729  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:58:25.613296  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:58:27.848943  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:58:27.849037  5644 solver.cpp:397]     Test net output #1: loss = 5.2855 (* 1 = 5.2855 loss)
I0406 07:58:29.794450  5644 solver.cpp:218] Iteration 5820 (0.87426 iter/s, 13.7259s/12 iters), loss = 5.25808
I0406 07:58:29.794489  5644 solver.cpp:237]     Train net output #0: loss = 5.25808 (* 1 = 5.25808 loss)
I0406 07:58:29.794495  5644 sgd_solver.cpp:105] Iteration 5820, lr = 0.1
I0406 07:58:35.195010  5644 solver.cpp:218] Iteration 5832 (2.22203 iter/s, 5.40046s/12 iters), loss = 5.25617
I0406 07:58:35.195048  5644 solver.cpp:237]     Train net output #0: loss = 5.25617 (* 1 = 5.25617 loss)
I0406 07:58:35.195055  5644 sgd_solver.cpp:105] Iteration 5832, lr = 0.1
I0406 07:58:40.408880  5644 solver.cpp:218] Iteration 5844 (2.3016 iter/s, 5.21377s/12 iters), loss = 5.2831
I0406 07:58:40.408923  5644 solver.cpp:237]     Train net output #0: loss = 5.2831 (* 1 = 5.2831 loss)
I0406 07:58:40.408929  5644 sgd_solver.cpp:105] Iteration 5844, lr = 0.1
I0406 07:58:45.752615  5644 solver.cpp:218] Iteration 5856 (2.24567 iter/s, 5.34363s/12 iters), loss = 5.27221
I0406 07:58:45.752660  5644 solver.cpp:237]     Train net output #0: loss = 5.27221 (* 1 = 5.27221 loss)
I0406 07:58:45.752667  5644 sgd_solver.cpp:105] Iteration 5856, lr = 0.1
I0406 07:58:50.077281  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:58:50.958441  5644 solver.cpp:218] Iteration 5868 (2.30516 iter/s, 5.20572s/12 iters), loss = 5.27294
I0406 07:58:50.958475  5644 solver.cpp:237]     Train net output #0: loss = 5.27294 (* 1 = 5.27294 loss)
I0406 07:58:50.958480  5644 sgd_solver.cpp:105] Iteration 5868, lr = 0.1
I0406 07:58:56.041944  5644 solver.cpp:218] Iteration 5880 (2.36062 iter/s, 5.08341s/12 iters), loss = 5.27364
I0406 07:58:56.041983  5644 solver.cpp:237]     Train net output #0: loss = 5.27364 (* 1 = 5.27364 loss)
I0406 07:58:56.041990  5644 sgd_solver.cpp:105] Iteration 5880, lr = 0.1
I0406 07:59:01.432410  5644 solver.cpp:218] Iteration 5892 (2.22619 iter/s, 5.39037s/12 iters), loss = 5.31319
I0406 07:59:01.432536  5644 solver.cpp:237]     Train net output #0: loss = 5.31319 (* 1 = 5.31319 loss)
I0406 07:59:01.432543  5644 sgd_solver.cpp:105] Iteration 5892, lr = 0.1
I0406 07:59:06.834486  5644 solver.cpp:218] Iteration 5904 (2.22144 iter/s, 5.40189s/12 iters), loss = 5.29427
I0406 07:59:06.834527  5644 solver.cpp:237]     Train net output #0: loss = 5.29427 (* 1 = 5.29427 loss)
I0406 07:59:06.834534  5644 sgd_solver.cpp:105] Iteration 5904, lr = 0.1
I0406 07:59:11.647343  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0406 07:59:14.666496  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0406 07:59:16.965206  5644 solver.cpp:330] Iteration 5916, Testing net (#0)
I0406 07:59:16.965226  5644 net.cpp:676] Ignoring source layer train-data
I0406 07:59:18.990123  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:59:21.257844  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 07:59:21.257871  5644 solver.cpp:397]     Test net output #1: loss = 5.28509 (* 1 = 5.28509 loss)
I0406 07:59:21.394843  5644 solver.cpp:218] Iteration 5916 (0.824166 iter/s, 14.5602s/12 iters), loss = 5.29371
I0406 07:59:21.394886  5644 solver.cpp:237]     Train net output #0: loss = 5.29371 (* 1 = 5.29371 loss)
I0406 07:59:21.394892  5644 sgd_solver.cpp:105] Iteration 5916, lr = 0.1
I0406 07:59:25.652532  5644 solver.cpp:218] Iteration 5928 (2.81849 iter/s, 4.2576s/12 iters), loss = 5.26424
I0406 07:59:25.652572  5644 solver.cpp:237]     Train net output #0: loss = 5.26424 (* 1 = 5.26424 loss)
I0406 07:59:25.652578  5644 sgd_solver.cpp:105] Iteration 5928, lr = 0.1
I0406 07:59:30.849278  5644 solver.cpp:218] Iteration 5940 (2.30918 iter/s, 5.19665s/12 iters), loss = 5.29025
I0406 07:59:30.849313  5644 solver.cpp:237]     Train net output #0: loss = 5.29025 (* 1 = 5.29025 loss)
I0406 07:59:30.849318  5644 sgd_solver.cpp:105] Iteration 5940, lr = 0.1
I0406 07:59:35.863828  5644 solver.cpp:218] Iteration 5952 (2.39308 iter/s, 5.01446s/12 iters), loss = 5.27458
I0406 07:59:35.863907  5644 solver.cpp:237]     Train net output #0: loss = 5.27458 (* 1 = 5.27458 loss)
I0406 07:59:35.863914  5644 sgd_solver.cpp:105] Iteration 5952, lr = 0.1
I0406 07:59:41.291882  5644 solver.cpp:218] Iteration 5964 (2.21079 iter/s, 5.42791s/12 iters), loss = 5.27769
I0406 07:59:41.291932  5644 solver.cpp:237]     Train net output #0: loss = 5.27769 (* 1 = 5.27769 loss)
I0406 07:59:41.291939  5644 sgd_solver.cpp:105] Iteration 5964, lr = 0.1
I0406 07:59:42.722494  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 07:59:46.704174  5644 solver.cpp:218] Iteration 5976 (2.21722 iter/s, 5.41218s/12 iters), loss = 5.2977
I0406 07:59:46.704211  5644 solver.cpp:237]     Train net output #0: loss = 5.2977 (* 1 = 5.2977 loss)
I0406 07:59:46.704217  5644 sgd_solver.cpp:105] Iteration 5976, lr = 0.1
I0406 07:59:51.900295  5644 solver.cpp:218] Iteration 5988 (2.30946 iter/s, 5.19603s/12 iters), loss = 5.28497
I0406 07:59:51.900333  5644 solver.cpp:237]     Train net output #0: loss = 5.28497 (* 1 = 5.28497 loss)
I0406 07:59:51.900339  5644 sgd_solver.cpp:105] Iteration 5988, lr = 0.1
I0406 07:59:57.079030  5644 solver.cpp:218] Iteration 6000 (2.31721 iter/s, 5.17864s/12 iters), loss = 5.27406
I0406 07:59:57.079068  5644 solver.cpp:237]     Train net output #0: loss = 5.27406 (* 1 = 5.27406 loss)
I0406 07:59:57.079074  5644 sgd_solver.cpp:105] Iteration 6000, lr = 0.1
I0406 08:00:02.453011  5644 solver.cpp:218] Iteration 6012 (2.23302 iter/s, 5.37388s/12 iters), loss = 5.27651
I0406 08:00:02.453054  5644 solver.cpp:237]     Train net output #0: loss = 5.27651 (* 1 = 5.27651 loss)
I0406 08:00:02.453061  5644 sgd_solver.cpp:105] Iteration 6012, lr = 0.1
I0406 08:00:04.586527  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0406 08:00:07.575409  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0406 08:00:09.936810  5644 solver.cpp:330] Iteration 6018, Testing net (#0)
I0406 08:00:09.936832  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:00:11.960481  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:00:14.332276  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:00:14.332306  5644 solver.cpp:397]     Test net output #1: loss = 5.2857 (* 1 = 5.2857 loss)
I0406 08:00:16.278689  5644 solver.cpp:218] Iteration 6024 (0.867961 iter/s, 13.8255s/12 iters), loss = 5.28898
I0406 08:00:16.278738  5644 solver.cpp:237]     Train net output #0: loss = 5.28898 (* 1 = 5.28898 loss)
I0406 08:00:16.278748  5644 sgd_solver.cpp:105] Iteration 6024, lr = 0.1
I0406 08:00:21.588449  5644 solver.cpp:218] Iteration 6036 (2.26004 iter/s, 5.30965s/12 iters), loss = 5.27044
I0406 08:00:21.588491  5644 solver.cpp:237]     Train net output #0: loss = 5.27044 (* 1 = 5.27044 loss)
I0406 08:00:21.588497  5644 sgd_solver.cpp:105] Iteration 6036, lr = 0.1
I0406 08:00:26.923225  5644 solver.cpp:218] Iteration 6048 (2.24944 iter/s, 5.33467s/12 iters), loss = 5.28633
I0406 08:00:26.923269  5644 solver.cpp:237]     Train net output #0: loss = 5.28633 (* 1 = 5.28633 loss)
I0406 08:00:26.923274  5644 sgd_solver.cpp:105] Iteration 6048, lr = 0.1
I0406 08:00:32.038743  5644 solver.cpp:218] Iteration 6060 (2.34585 iter/s, 5.11542s/12 iters), loss = 5.29618
I0406 08:00:32.038779  5644 solver.cpp:237]     Train net output #0: loss = 5.29618 (* 1 = 5.29618 loss)
I0406 08:00:32.038784  5644 sgd_solver.cpp:105] Iteration 6060, lr = 0.1
I0406 08:00:35.745862  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:00:37.294814  5644 solver.cpp:218] Iteration 6072 (2.28311 iter/s, 5.25598s/12 iters), loss = 5.24413
I0406 08:00:37.294852  5644 solver.cpp:237]     Train net output #0: loss = 5.24413 (* 1 = 5.24413 loss)
I0406 08:00:37.294858  5644 sgd_solver.cpp:105] Iteration 6072, lr = 0.1
I0406 08:00:42.523917  5644 solver.cpp:218] Iteration 6084 (2.29489 iter/s, 5.229s/12 iters), loss = 5.27242
I0406 08:00:42.524004  5644 solver.cpp:237]     Train net output #0: loss = 5.27242 (* 1 = 5.27242 loss)
I0406 08:00:42.524011  5644 sgd_solver.cpp:105] Iteration 6084, lr = 0.1
I0406 08:00:47.796980  5644 solver.cpp:218] Iteration 6096 (2.27578 iter/s, 5.27292s/12 iters), loss = 5.28025
I0406 08:00:47.797019  5644 solver.cpp:237]     Train net output #0: loss = 5.28025 (* 1 = 5.28025 loss)
I0406 08:00:47.797024  5644 sgd_solver.cpp:105] Iteration 6096, lr = 0.1
I0406 08:00:53.172845  5644 solver.cpp:218] Iteration 6108 (2.23224 iter/s, 5.37576s/12 iters), loss = 5.26248
I0406 08:00:53.172891  5644 solver.cpp:237]     Train net output #0: loss = 5.26248 (* 1 = 5.26248 loss)
I0406 08:00:53.172897  5644 sgd_solver.cpp:105] Iteration 6108, lr = 0.1
I0406 08:00:57.936203  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0406 08:01:00.961557  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0406 08:01:03.259011  5644 solver.cpp:330] Iteration 6120, Testing net (#0)
I0406 08:01:03.259029  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:01:05.237830  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:01:07.587802  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 08:01:07.587836  5644 solver.cpp:397]     Test net output #1: loss = 5.28451 (* 1 = 5.28451 loss)
I0406 08:01:07.728555  5644 solver.cpp:218] Iteration 6120 (0.824429 iter/s, 14.5555s/12 iters), loss = 5.28496
I0406 08:01:07.728605  5644 solver.cpp:237]     Train net output #0: loss = 5.28496 (* 1 = 5.28496 loss)
I0406 08:01:07.728613  5644 sgd_solver.cpp:105] Iteration 6120, lr = 0.1
I0406 08:01:12.010118  5644 solver.cpp:218] Iteration 6132 (2.80278 iter/s, 4.28146s/12 iters), loss = 5.25412
I0406 08:01:12.010157  5644 solver.cpp:237]     Train net output #0: loss = 5.25412 (* 1 = 5.25412 loss)
I0406 08:01:12.010162  5644 sgd_solver.cpp:105] Iteration 6132, lr = 0.1
I0406 08:01:17.313395  5644 solver.cpp:218] Iteration 6144 (2.2628 iter/s, 5.30317s/12 iters), loss = 5.27366
I0406 08:01:17.313511  5644 solver.cpp:237]     Train net output #0: loss = 5.27366 (* 1 = 5.27366 loss)
I0406 08:01:17.313519  5644 sgd_solver.cpp:105] Iteration 6144, lr = 0.1
I0406 08:01:22.445930  5644 solver.cpp:218] Iteration 6156 (2.33811 iter/s, 5.13236s/12 iters), loss = 5.27988
I0406 08:01:22.445978  5644 solver.cpp:237]     Train net output #0: loss = 5.27988 (* 1 = 5.27988 loss)
I0406 08:01:22.445984  5644 sgd_solver.cpp:105] Iteration 6156, lr = 0.1
I0406 08:01:27.723728  5644 solver.cpp:218] Iteration 6168 (2.27372 iter/s, 5.27769s/12 iters), loss = 5.285
I0406 08:01:27.723767  5644 solver.cpp:237]     Train net output #0: loss = 5.285 (* 1 = 5.285 loss)
I0406 08:01:27.723773  5644 sgd_solver.cpp:105] Iteration 6168, lr = 0.1
I0406 08:01:28.344926  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:01:33.224954  5644 solver.cpp:218] Iteration 6180 (2.18137 iter/s, 5.50112s/12 iters), loss = 5.27489
I0406 08:01:33.224995  5644 solver.cpp:237]     Train net output #0: loss = 5.27489 (* 1 = 5.27489 loss)
I0406 08:01:33.225001  5644 sgd_solver.cpp:105] Iteration 6180, lr = 0.1
I0406 08:01:38.511512  5644 solver.cpp:218] Iteration 6192 (2.26995 iter/s, 5.28646s/12 iters), loss = 5.29157
I0406 08:01:38.511549  5644 solver.cpp:237]     Train net output #0: loss = 5.29157 (* 1 = 5.29157 loss)
I0406 08:01:38.511555  5644 sgd_solver.cpp:105] Iteration 6192, lr = 0.1
I0406 08:01:43.499385  5644 solver.cpp:218] Iteration 6204 (2.40588 iter/s, 4.98778s/12 iters), loss = 5.26497
I0406 08:01:43.499420  5644 solver.cpp:237]     Train net output #0: loss = 5.26497 (* 1 = 5.26497 loss)
I0406 08:01:43.499425  5644 sgd_solver.cpp:105] Iteration 6204, lr = 0.1
I0406 08:01:48.842561  5644 solver.cpp:218] Iteration 6216 (2.24589 iter/s, 5.34308s/12 iters), loss = 5.27056
I0406 08:01:48.842653  5644 solver.cpp:237]     Train net output #0: loss = 5.27056 (* 1 = 5.27056 loss)
I0406 08:01:48.842658  5644 sgd_solver.cpp:105] Iteration 6216, lr = 0.1
I0406 08:01:50.968839  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0406 08:01:54.069501  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0406 08:01:56.381055  5644 solver.cpp:330] Iteration 6222, Testing net (#0)
I0406 08:01:56.381075  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:01:58.337512  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:01:59.588385  5644 blocking_queue.cpp:49] Waiting for data
I0406 08:02:00.738099  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:02:00.738147  5644 solver.cpp:397]     Test net output #1: loss = 5.2855 (* 1 = 5.2855 loss)
I0406 08:02:02.747165  5644 solver.cpp:218] Iteration 6228 (0.863037 iter/s, 13.9044s/12 iters), loss = 5.25567
I0406 08:02:02.747215  5644 solver.cpp:237]     Train net output #0: loss = 5.25567 (* 1 = 5.25567 loss)
I0406 08:02:02.747222  5644 sgd_solver.cpp:105] Iteration 6228, lr = 0.1
I0406 08:02:07.980016  5644 solver.cpp:218] Iteration 6240 (2.29325 iter/s, 5.23275s/12 iters), loss = 5.29464
I0406 08:02:07.980052  5644 solver.cpp:237]     Train net output #0: loss = 5.29464 (* 1 = 5.29464 loss)
I0406 08:02:07.980058  5644 sgd_solver.cpp:105] Iteration 6240, lr = 0.1
I0406 08:02:13.104871  5644 solver.cpp:218] Iteration 6252 (2.34157 iter/s, 5.12476s/12 iters), loss = 5.27741
I0406 08:02:13.104933  5644 solver.cpp:237]     Train net output #0: loss = 5.27741 (* 1 = 5.27741 loss)
I0406 08:02:13.104945  5644 sgd_solver.cpp:105] Iteration 6252, lr = 0.1
I0406 08:02:18.464545  5644 solver.cpp:218] Iteration 6264 (2.239 iter/s, 5.35955s/12 iters), loss = 5.28242
I0406 08:02:18.464596  5644 solver.cpp:237]     Train net output #0: loss = 5.28242 (* 1 = 5.28242 loss)
I0406 08:02:18.464604  5644 sgd_solver.cpp:105] Iteration 6264, lr = 0.1
I0406 08:02:21.277456  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:02:23.683450  5644 solver.cpp:218] Iteration 6276 (2.29938 iter/s, 5.2188s/12 iters), loss = 5.28481
I0406 08:02:23.683486  5644 solver.cpp:237]     Train net output #0: loss = 5.28481 (* 1 = 5.28481 loss)
I0406 08:02:23.683491  5644 sgd_solver.cpp:105] Iteration 6276, lr = 0.1
I0406 08:02:28.758909  5644 solver.cpp:218] Iteration 6288 (2.36436 iter/s, 5.07536s/12 iters), loss = 5.27353
I0406 08:02:28.758949  5644 solver.cpp:237]     Train net output #0: loss = 5.27353 (* 1 = 5.27353 loss)
I0406 08:02:28.758955  5644 sgd_solver.cpp:105] Iteration 6288, lr = 0.1
I0406 08:02:33.854368  5644 solver.cpp:218] Iteration 6300 (2.35508 iter/s, 5.09536s/12 iters), loss = 5.27182
I0406 08:02:33.854406  5644 solver.cpp:237]     Train net output #0: loss = 5.27182 (* 1 = 5.27182 loss)
I0406 08:02:33.854413  5644 sgd_solver.cpp:105] Iteration 6300, lr = 0.1
I0406 08:02:39.352294  5644 solver.cpp:218] Iteration 6312 (2.18268 iter/s, 5.49783s/12 iters), loss = 5.28957
I0406 08:02:39.352331  5644 solver.cpp:237]     Train net output #0: loss = 5.28957 (* 1 = 5.28957 loss)
I0406 08:02:39.352336  5644 sgd_solver.cpp:105] Iteration 6312, lr = 0.1
I0406 08:02:44.099555  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0406 08:02:47.086571  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0406 08:02:49.381868  5644 solver.cpp:330] Iteration 6324, Testing net (#0)
I0406 08:02:49.381888  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:02:51.272192  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:02:53.743556  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:02:53.743664  5644 solver.cpp:397]     Test net output #1: loss = 5.28517 (* 1 = 5.28517 loss)
I0406 08:02:53.884305  5644 solver.cpp:218] Iteration 6324 (0.825773 iter/s, 14.5318s/12 iters), loss = 5.28361
I0406 08:02:53.884354  5644 solver.cpp:237]     Train net output #0: loss = 5.28361 (* 1 = 5.28361 loss)
I0406 08:02:53.884362  5644 sgd_solver.cpp:105] Iteration 6324, lr = 0.1
I0406 08:02:58.325001  5644 solver.cpp:218] Iteration 6336 (2.70234 iter/s, 4.44059s/12 iters), loss = 5.29982
I0406 08:02:58.325040  5644 solver.cpp:237]     Train net output #0: loss = 5.29982 (* 1 = 5.29982 loss)
I0406 08:02:58.325047  5644 sgd_solver.cpp:105] Iteration 6336, lr = 0.1
I0406 08:03:03.732532  5644 solver.cpp:218] Iteration 6348 (2.21917 iter/s, 5.40743s/12 iters), loss = 5.29639
I0406 08:03:03.732573  5644 solver.cpp:237]     Train net output #0: loss = 5.29639 (* 1 = 5.29639 loss)
I0406 08:03:03.732579  5644 sgd_solver.cpp:105] Iteration 6348, lr = 0.1
I0406 08:03:08.918987  5644 solver.cpp:218] Iteration 6360 (2.31376 iter/s, 5.18636s/12 iters), loss = 5.28756
I0406 08:03:08.919026  5644 solver.cpp:237]     Train net output #0: loss = 5.28756 (* 1 = 5.28756 loss)
I0406 08:03:08.919032  5644 sgd_solver.cpp:105] Iteration 6360, lr = 0.1
I0406 08:03:14.055014  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:03:14.246861  5644 solver.cpp:218] Iteration 6372 (2.25235 iter/s, 5.32777s/12 iters), loss = 5.28972
I0406 08:03:14.246902  5644 solver.cpp:237]     Train net output #0: loss = 5.28972 (* 1 = 5.28972 loss)
I0406 08:03:14.246907  5644 sgd_solver.cpp:105] Iteration 6372, lr = 0.1
I0406 08:03:19.669677  5644 solver.cpp:218] Iteration 6384 (2.21291 iter/s, 5.42272s/12 iters), loss = 5.2596
I0406 08:03:19.669715  5644 solver.cpp:237]     Train net output #0: loss = 5.2596 (* 1 = 5.2596 loss)
I0406 08:03:19.669721  5644 sgd_solver.cpp:105] Iteration 6384, lr = 0.1
I0406 08:03:24.907984  5644 solver.cpp:218] Iteration 6396 (2.29086 iter/s, 5.23821s/12 iters), loss = 5.28072
I0406 08:03:24.908126  5644 solver.cpp:237]     Train net output #0: loss = 5.28072 (* 1 = 5.28072 loss)
I0406 08:03:24.908133  5644 sgd_solver.cpp:105] Iteration 6396, lr = 0.1
I0406 08:03:30.277384  5644 solver.cpp:218] Iteration 6408 (2.23497 iter/s, 5.3692s/12 iters), loss = 5.28901
I0406 08:03:30.277423  5644 solver.cpp:237]     Train net output #0: loss = 5.28901 (* 1 = 5.28901 loss)
I0406 08:03:30.277429  5644 sgd_solver.cpp:105] Iteration 6408, lr = 0.1
I0406 08:03:35.508805  5644 solver.cpp:218] Iteration 6420 (2.29388 iter/s, 5.23132s/12 iters), loss = 5.26803
I0406 08:03:35.508853  5644 solver.cpp:237]     Train net output #0: loss = 5.26803 (* 1 = 5.26803 loss)
I0406 08:03:35.508862  5644 sgd_solver.cpp:105] Iteration 6420, lr = 0.1
I0406 08:03:37.595655  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0406 08:03:40.638167  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0406 08:03:42.969832  5644 solver.cpp:330] Iteration 6426, Testing net (#0)
I0406 08:03:42.969858  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:03:44.741283  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:03:47.245920  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:03:47.245954  5644 solver.cpp:397]     Test net output #1: loss = 5.28569 (* 1 = 5.28569 loss)
I0406 08:03:49.221424  5644 solver.cpp:218] Iteration 6432 (0.875118 iter/s, 13.7124s/12 iters), loss = 5.25897
I0406 08:03:49.221465  5644 solver.cpp:237]     Train net output #0: loss = 5.25897 (* 1 = 5.25897 loss)
I0406 08:03:49.221472  5644 sgd_solver.cpp:105] Iteration 6432, lr = 0.1
I0406 08:03:54.680145  5644 solver.cpp:218] Iteration 6444 (2.19836 iter/s, 5.45862s/12 iters), loss = 5.27436
I0406 08:03:54.680197  5644 solver.cpp:237]     Train net output #0: loss = 5.27436 (* 1 = 5.27436 loss)
I0406 08:03:54.680207  5644 sgd_solver.cpp:105] Iteration 6444, lr = 0.1
I0406 08:03:59.977722  5644 solver.cpp:218] Iteration 6456 (2.26523 iter/s, 5.29747s/12 iters), loss = 5.27861
I0406 08:03:59.977826  5644 solver.cpp:237]     Train net output #0: loss = 5.27861 (* 1 = 5.27861 loss)
I0406 08:03:59.977834  5644 sgd_solver.cpp:105] Iteration 6456, lr = 0.1
I0406 08:04:05.083365  5644 solver.cpp:218] Iteration 6468 (2.35041 iter/s, 5.10549s/12 iters), loss = 5.26644
I0406 08:04:05.083403  5644 solver.cpp:237]     Train net output #0: loss = 5.26644 (* 1 = 5.26644 loss)
I0406 08:04:05.083408  5644 sgd_solver.cpp:105] Iteration 6468, lr = 0.1
I0406 08:04:07.133495  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:04:10.430271  5644 solver.cpp:218] Iteration 6480 (2.24433 iter/s, 5.3468s/12 iters), loss = 5.30243
I0406 08:04:10.430325  5644 solver.cpp:237]     Train net output #0: loss = 5.30243 (* 1 = 5.30243 loss)
I0406 08:04:10.430335  5644 sgd_solver.cpp:105] Iteration 6480, lr = 0.1
I0406 08:04:15.703999  5644 solver.cpp:218] Iteration 6492 (2.27548 iter/s, 5.27361s/12 iters), loss = 5.25916
I0406 08:04:15.704044  5644 solver.cpp:237]     Train net output #0: loss = 5.25916 (* 1 = 5.25916 loss)
I0406 08:04:15.704052  5644 sgd_solver.cpp:105] Iteration 6492, lr = 0.1
I0406 08:04:21.064081  5644 solver.cpp:218] Iteration 6504 (2.23882 iter/s, 5.35997s/12 iters), loss = 5.29382
I0406 08:04:21.064131  5644 solver.cpp:237]     Train net output #0: loss = 5.29382 (* 1 = 5.29382 loss)
I0406 08:04:21.064139  5644 sgd_solver.cpp:105] Iteration 6504, lr = 0.1
I0406 08:04:26.411648  5644 solver.cpp:218] Iteration 6516 (2.24406 iter/s, 5.34746s/12 iters), loss = 5.27357
I0406 08:04:26.411687  5644 solver.cpp:237]     Train net output #0: loss = 5.27357 (* 1 = 5.27357 loss)
I0406 08:04:26.411692  5644 sgd_solver.cpp:105] Iteration 6516, lr = 0.1
I0406 08:04:31.178040  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0406 08:04:34.187100  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0406 08:04:36.513783  5644 solver.cpp:330] Iteration 6528, Testing net (#0)
I0406 08:04:36.513801  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:04:38.258716  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:04:40.815793  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:04:40.815821  5644 solver.cpp:397]     Test net output #1: loss = 5.28538 (* 1 = 5.28538 loss)
I0406 08:04:40.962039  5644 solver.cpp:218] Iteration 6528 (0.82473 iter/s, 14.5502s/12 iters), loss = 5.25989
I0406 08:04:40.962080  5644 solver.cpp:237]     Train net output #0: loss = 5.25989 (* 1 = 5.25989 loss)
I0406 08:04:40.962085  5644 sgd_solver.cpp:105] Iteration 6528, lr = 0.1
I0406 08:04:45.377172  5644 solver.cpp:218] Iteration 6540 (2.71798 iter/s, 4.41504s/12 iters), loss = 5.2617
I0406 08:04:45.377208  5644 solver.cpp:237]     Train net output #0: loss = 5.2617 (* 1 = 5.2617 loss)
I0406 08:04:45.377213  5644 sgd_solver.cpp:105] Iteration 6540, lr = 0.1
I0406 08:04:50.511579  5644 solver.cpp:218] Iteration 6552 (2.33722 iter/s, 5.13431s/12 iters), loss = 5.27778
I0406 08:04:50.511615  5644 solver.cpp:237]     Train net output #0: loss = 5.27778 (* 1 = 5.27778 loss)
I0406 08:04:50.511620  5644 sgd_solver.cpp:105] Iteration 6552, lr = 0.1
I0406 08:04:55.609830  5644 solver.cpp:218] Iteration 6564 (2.35379 iter/s, 5.09816s/12 iters), loss = 5.27105
I0406 08:04:55.609869  5644 solver.cpp:237]     Train net output #0: loss = 5.27105 (* 1 = 5.27105 loss)
I0406 08:04:55.609874  5644 sgd_solver.cpp:105] Iteration 6564, lr = 0.1
I0406 08:05:00.143879  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:05:00.986876  5644 solver.cpp:218] Iteration 6576 (2.23175 iter/s, 5.37695s/12 iters), loss = 5.27342
I0406 08:05:00.986914  5644 solver.cpp:237]     Train net output #0: loss = 5.27342 (* 1 = 5.27342 loss)
I0406 08:05:00.986920  5644 sgd_solver.cpp:105] Iteration 6576, lr = 0.1
I0406 08:05:06.265810  5644 solver.cpp:218] Iteration 6588 (2.27323 iter/s, 5.27884s/12 iters), loss = 5.27012
I0406 08:05:06.265929  5644 solver.cpp:237]     Train net output #0: loss = 5.27012 (* 1 = 5.27012 loss)
I0406 08:05:06.265936  5644 sgd_solver.cpp:105] Iteration 6588, lr = 0.1
I0406 08:05:11.591553  5644 solver.cpp:218] Iteration 6600 (2.25328 iter/s, 5.32557s/12 iters), loss = 5.31503
I0406 08:05:11.591591  5644 solver.cpp:237]     Train net output #0: loss = 5.31503 (* 1 = 5.31503 loss)
I0406 08:05:11.591596  5644 sgd_solver.cpp:105] Iteration 6600, lr = 0.1
I0406 08:05:16.885309  5644 solver.cpp:218] Iteration 6612 (2.26686 iter/s, 5.29366s/12 iters), loss = 5.29388
I0406 08:05:16.885345  5644 solver.cpp:237]     Train net output #0: loss = 5.29388 (* 1 = 5.29388 loss)
I0406 08:05:16.885350  5644 sgd_solver.cpp:105] Iteration 6612, lr = 0.1
I0406 08:05:22.072711  5644 solver.cpp:218] Iteration 6624 (2.31334 iter/s, 5.18731s/12 iters), loss = 5.28715
I0406 08:05:22.072758  5644 solver.cpp:237]     Train net output #0: loss = 5.28715 (* 1 = 5.28715 loss)
I0406 08:05:22.072765  5644 sgd_solver.cpp:105] Iteration 6624, lr = 0.1
I0406 08:05:23.878546  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0406 08:05:26.909746  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0406 08:05:29.200628  5644 solver.cpp:330] Iteration 6630, Testing net (#0)
I0406 08:05:29.200649  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:05:30.932523  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:05:33.496812  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:05:33.496861  5644 solver.cpp:397]     Test net output #1: loss = 5.28471 (* 1 = 5.28471 loss)
I0406 08:05:35.346823  5644 solver.cpp:218] Iteration 6636 (0.904027 iter/s, 13.2739s/12 iters), loss = 5.25897
I0406 08:05:35.346876  5644 solver.cpp:237]     Train net output #0: loss = 5.25897 (* 1 = 5.25897 loss)
I0406 08:05:35.346885  5644 sgd_solver.cpp:105] Iteration 6636, lr = 0.1
I0406 08:05:40.244007  5644 solver.cpp:218] Iteration 6648 (2.45044 iter/s, 4.89708s/12 iters), loss = 5.28903
I0406 08:05:40.244132  5644 solver.cpp:237]     Train net output #0: loss = 5.28903 (* 1 = 5.28903 loss)
I0406 08:05:40.244138  5644 sgd_solver.cpp:105] Iteration 6648, lr = 0.1
I0406 08:05:45.595535  5644 solver.cpp:218] Iteration 6660 (2.24243 iter/s, 5.35134s/12 iters), loss = 5.27912
I0406 08:05:45.595587  5644 solver.cpp:237]     Train net output #0: loss = 5.27912 (* 1 = 5.27912 loss)
I0406 08:05:45.595594  5644 sgd_solver.cpp:105] Iteration 6660, lr = 0.1
I0406 08:05:50.739068  5644 solver.cpp:218] Iteration 6672 (2.33307 iter/s, 5.14343s/12 iters), loss = 5.2724
I0406 08:05:50.739104  5644 solver.cpp:237]     Train net output #0: loss = 5.2724 (* 1 = 5.2724 loss)
I0406 08:05:50.739109  5644 sgd_solver.cpp:105] Iteration 6672, lr = 0.1
I0406 08:05:52.122962  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:05:56.020049  5644 solver.cpp:218] Iteration 6684 (2.27234 iter/s, 5.28089s/12 iters), loss = 5.29114
I0406 08:05:56.020087  5644 solver.cpp:237]     Train net output #0: loss = 5.29114 (* 1 = 5.29114 loss)
I0406 08:05:56.020092  5644 sgd_solver.cpp:105] Iteration 6684, lr = 0.1
I0406 08:06:01.371476  5644 solver.cpp:218] Iteration 6696 (2.24243 iter/s, 5.35133s/12 iters), loss = 5.27605
I0406 08:06:01.371516  5644 solver.cpp:237]     Train net output #0: loss = 5.27605 (* 1 = 5.27605 loss)
I0406 08:06:01.371522  5644 sgd_solver.cpp:105] Iteration 6696, lr = 0.1
I0406 08:06:06.568328  5644 solver.cpp:218] Iteration 6708 (2.30913 iter/s, 5.19676s/12 iters), loss = 5.2665
I0406 08:06:06.568367  5644 solver.cpp:237]     Train net output #0: loss = 5.2665 (* 1 = 5.2665 loss)
I0406 08:06:06.568372  5644 sgd_solver.cpp:105] Iteration 6708, lr = 0.1
I0406 08:06:11.730165  5644 solver.cpp:218] Iteration 6720 (2.32479 iter/s, 5.16175s/12 iters), loss = 5.27778
I0406 08:06:11.730235  5644 solver.cpp:237]     Train net output #0: loss = 5.27778 (* 1 = 5.27778 loss)
I0406 08:06:11.730242  5644 sgd_solver.cpp:105] Iteration 6720, lr = 0.1
I0406 08:06:16.243103  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0406 08:06:19.179466  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0406 08:06:21.484014  5644 solver.cpp:330] Iteration 6732, Testing net (#0)
I0406 08:06:21.484035  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:06:23.183614  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:06:25.772352  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:06:25.772388  5644 solver.cpp:397]     Test net output #1: loss = 5.28473 (* 1 = 5.28473 loss)
I0406 08:06:25.910411  5644 solver.cpp:218] Iteration 6732 (0.846259 iter/s, 14.18s/12 iters), loss = 5.29059
I0406 08:06:25.910459  5644 solver.cpp:237]     Train net output #0: loss = 5.29059 (* 1 = 5.29059 loss)
I0406 08:06:25.910465  5644 sgd_solver.cpp:105] Iteration 6732, lr = 0.1
I0406 08:06:30.362401  5644 solver.cpp:218] Iteration 6744 (2.69548 iter/s, 4.4519s/12 iters), loss = 5.27793
I0406 08:06:30.362442  5644 solver.cpp:237]     Train net output #0: loss = 5.27793 (* 1 = 5.27793 loss)
I0406 08:06:30.362447  5644 sgd_solver.cpp:105] Iteration 6744, lr = 0.1
I0406 08:06:35.601646  5644 solver.cpp:218] Iteration 6756 (2.29045 iter/s, 5.23915s/12 iters), loss = 5.28634
I0406 08:06:35.601691  5644 solver.cpp:237]     Train net output #0: loss = 5.28634 (* 1 = 5.28634 loss)
I0406 08:06:35.601698  5644 sgd_solver.cpp:105] Iteration 6756, lr = 0.1
I0406 08:06:40.816570  5644 solver.cpp:218] Iteration 6768 (2.30113 iter/s, 5.21482s/12 iters), loss = 5.29292
I0406 08:06:40.816607  5644 solver.cpp:237]     Train net output #0: loss = 5.29292 (* 1 = 5.29292 loss)
I0406 08:06:40.816613  5644 sgd_solver.cpp:105] Iteration 6768, lr = 0.1
I0406 08:06:44.470131  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:06:46.121410  5644 solver.cpp:218] Iteration 6780 (2.26213 iter/s, 5.30474s/12 iters), loss = 5.24905
I0406 08:06:46.121452  5644 solver.cpp:237]     Train net output #0: loss = 5.24905 (* 1 = 5.24905 loss)
I0406 08:06:46.121459  5644 sgd_solver.cpp:105] Iteration 6780, lr = 0.1
I0406 08:06:51.345959  5644 solver.cpp:218] Iteration 6792 (2.29689 iter/s, 5.22445s/12 iters), loss = 5.2785
I0406 08:06:51.346000  5644 solver.cpp:237]     Train net output #0: loss = 5.2785 (* 1 = 5.2785 loss)
I0406 08:06:51.346006  5644 sgd_solver.cpp:105] Iteration 6792, lr = 0.1
I0406 08:06:56.515643  5644 solver.cpp:218] Iteration 6804 (2.32127 iter/s, 5.16959s/12 iters), loss = 5.28174
I0406 08:06:56.515681  5644 solver.cpp:237]     Train net output #0: loss = 5.28174 (* 1 = 5.28174 loss)
I0406 08:06:56.515686  5644 sgd_solver.cpp:105] Iteration 6804, lr = 0.1
I0406 08:07:01.813896  5644 solver.cpp:218] Iteration 6816 (2.26494 iter/s, 5.29816s/12 iters), loss = 5.26793
I0406 08:07:01.813951  5644 solver.cpp:237]     Train net output #0: loss = 5.26793 (* 1 = 5.26793 loss)
I0406 08:07:01.813958  5644 sgd_solver.cpp:105] Iteration 6816, lr = 0.1
I0406 08:07:07.152410  5644 solver.cpp:218] Iteration 6828 (2.24786 iter/s, 5.3384s/12 iters), loss = 5.28844
I0406 08:07:07.152459  5644 solver.cpp:237]     Train net output #0: loss = 5.28844 (* 1 = 5.28844 loss)
I0406 08:07:07.152467  5644 sgd_solver.cpp:105] Iteration 6828, lr = 0.1
I0406 08:07:09.290324  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0406 08:07:12.289955  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0406 08:07:14.610445  5644 solver.cpp:330] Iteration 6834, Testing net (#0)
I0406 08:07:14.610543  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:07:16.280730  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:07:18.948169  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:07:18.948202  5644 solver.cpp:397]     Test net output #1: loss = 5.28489 (* 1 = 5.28489 loss)
I0406 08:07:20.909335  5644 solver.cpp:218] Iteration 6840 (0.872299 iter/s, 13.7568s/12 iters), loss = 5.25136
I0406 08:07:20.909374  5644 solver.cpp:237]     Train net output #0: loss = 5.25136 (* 1 = 5.25136 loss)
I0406 08:07:20.909380  5644 sgd_solver.cpp:105] Iteration 6840, lr = 0.1
I0406 08:07:26.197003  5644 solver.cpp:218] Iteration 6852 (2.26947 iter/s, 5.28757s/12 iters), loss = 5.27706
I0406 08:07:26.197042  5644 solver.cpp:237]     Train net output #0: loss = 5.27706 (* 1 = 5.27706 loss)
I0406 08:07:26.197048  5644 sgd_solver.cpp:105] Iteration 6852, lr = 0.1
I0406 08:07:31.376915  5644 solver.cpp:218] Iteration 6864 (2.31668 iter/s, 5.17982s/12 iters), loss = 5.27961
I0406 08:07:31.376953  5644 solver.cpp:237]     Train net output #0: loss = 5.27961 (* 1 = 5.27961 loss)
I0406 08:07:31.376960  5644 sgd_solver.cpp:105] Iteration 6864, lr = 0.1
I0406 08:07:36.468505  5644 solver.cpp:218] Iteration 6876 (2.35687 iter/s, 5.0915s/12 iters), loss = 5.28745
I0406 08:07:36.468540  5644 solver.cpp:237]     Train net output #0: loss = 5.28745 (* 1 = 5.28745 loss)
I0406 08:07:36.468546  5644 sgd_solver.cpp:105] Iteration 6876, lr = 0.1
I0406 08:07:37.031653  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:07:41.693898  5644 solver.cpp:218] Iteration 6888 (2.29652 iter/s, 5.2253s/12 iters), loss = 5.27052
I0406 08:07:41.693934  5644 solver.cpp:237]     Train net output #0: loss = 5.27052 (* 1 = 5.27052 loss)
I0406 08:07:41.693939  5644 sgd_solver.cpp:105] Iteration 6888, lr = 0.1
I0406 08:07:46.753293  5644 solver.cpp:218] Iteration 6900 (2.37187 iter/s, 5.0593s/12 iters), loss = 5.28803
I0406 08:07:46.753435  5644 solver.cpp:237]     Train net output #0: loss = 5.28803 (* 1 = 5.28803 loss)
I0406 08:07:46.753445  5644 sgd_solver.cpp:105] Iteration 6900, lr = 0.1
I0406 08:07:51.847270  5644 solver.cpp:218] Iteration 6912 (2.35581 iter/s, 5.09378s/12 iters), loss = 5.26555
I0406 08:07:51.847306  5644 solver.cpp:237]     Train net output #0: loss = 5.26555 (* 1 = 5.26555 loss)
I0406 08:07:51.847311  5644 sgd_solver.cpp:105] Iteration 6912, lr = 0.1
I0406 08:07:57.122561  5644 solver.cpp:218] Iteration 6924 (2.2748 iter/s, 5.2752s/12 iters), loss = 5.26667
I0406 08:07:57.122599  5644 solver.cpp:237]     Train net output #0: loss = 5.26667 (* 1 = 5.26667 loss)
I0406 08:07:57.122606  5644 sgd_solver.cpp:105] Iteration 6924, lr = 0.1
I0406 08:08:01.820899  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0406 08:08:04.854676  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0406 08:08:07.154036  5644 solver.cpp:330] Iteration 6936, Testing net (#0)
I0406 08:08:07.154055  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:08:07.726986  5644 blocking_queue.cpp:49] Waiting for data
I0406 08:08:08.777702  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:08:11.451988  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:08:11.452020  5644 solver.cpp:397]     Test net output #1: loss = 5.28496 (* 1 = 5.28496 loss)
I0406 08:08:11.592706  5644 solver.cpp:218] Iteration 6936 (0.829304 iter/s, 14.47s/12 iters), loss = 5.26172
I0406 08:08:11.592759  5644 solver.cpp:237]     Train net output #0: loss = 5.26172 (* 1 = 5.26172 loss)
I0406 08:08:11.592767  5644 sgd_solver.cpp:105] Iteration 6936, lr = 0.1
I0406 08:08:15.981402  5644 solver.cpp:218] Iteration 6948 (2.73435 iter/s, 4.3886s/12 iters), loss = 5.28186
I0406 08:08:15.981433  5644 solver.cpp:237]     Train net output #0: loss = 5.28186 (* 1 = 5.28186 loss)
I0406 08:08:15.981438  5644 sgd_solver.cpp:105] Iteration 6948, lr = 0.1
I0406 08:08:21.401981  5644 solver.cpp:218] Iteration 6960 (2.21382 iter/s, 5.42049s/12 iters), loss = 5.28302
I0406 08:08:21.402099  5644 solver.cpp:237]     Train net output #0: loss = 5.28302 (* 1 = 5.28302 loss)
I0406 08:08:21.402108  5644 sgd_solver.cpp:105] Iteration 6960, lr = 0.1
I0406 08:08:26.731030  5644 solver.cpp:218] Iteration 6972 (2.25188 iter/s, 5.32887s/12 iters), loss = 5.27884
I0406 08:08:26.731070  5644 solver.cpp:237]     Train net output #0: loss = 5.27884 (* 1 = 5.27884 loss)
I0406 08:08:26.731076  5644 sgd_solver.cpp:105] Iteration 6972, lr = 0.1
I0406 08:08:29.632386  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:08:31.991705  5644 solver.cpp:218] Iteration 6984 (2.28112 iter/s, 5.26058s/12 iters), loss = 5.27881
I0406 08:08:31.991739  5644 solver.cpp:237]     Train net output #0: loss = 5.27881 (* 1 = 5.27881 loss)
I0406 08:08:31.991745  5644 sgd_solver.cpp:105] Iteration 6984, lr = 0.1
I0406 08:08:37.315395  5644 solver.cpp:218] Iteration 6996 (2.25412 iter/s, 5.3236s/12 iters), loss = 5.28709
I0406 08:08:37.315443  5644 solver.cpp:237]     Train net output #0: loss = 5.28709 (* 1 = 5.28709 loss)
I0406 08:08:37.315450  5644 sgd_solver.cpp:105] Iteration 6996, lr = 0.1
I0406 08:08:42.627420  5644 solver.cpp:218] Iteration 7008 (2.25907 iter/s, 5.31192s/12 iters), loss = 5.28327
I0406 08:08:42.627465  5644 solver.cpp:237]     Train net output #0: loss = 5.28327 (* 1 = 5.28327 loss)
I0406 08:08:42.627470  5644 sgd_solver.cpp:105] Iteration 7008, lr = 0.1
I0406 08:08:47.703533  5644 solver.cpp:218] Iteration 7020 (2.36406 iter/s, 5.07601s/12 iters), loss = 5.29482
I0406 08:08:47.703572  5644 solver.cpp:237]     Train net output #0: loss = 5.29482 (* 1 = 5.29482 loss)
I0406 08:08:47.703577  5644 sgd_solver.cpp:105] Iteration 7020, lr = 0.1
I0406 08:08:52.986327  5644 solver.cpp:218] Iteration 7032 (2.27157 iter/s, 5.28269s/12 iters), loss = 5.28185
I0406 08:08:52.986479  5644 solver.cpp:237]     Train net output #0: loss = 5.28185 (* 1 = 5.28185 loss)
I0406 08:08:52.986488  5644 sgd_solver.cpp:105] Iteration 7032, lr = 0.1
I0406 08:08:55.203768  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0406 08:08:58.226872  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0406 08:09:00.572743  5644 solver.cpp:330] Iteration 7038, Testing net (#0)
I0406 08:09:00.572760  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:09:02.175545  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:09:04.886920  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:09:04.886956  5644 solver.cpp:397]     Test net output #1: loss = 5.28529 (* 1 = 5.28529 loss)
I0406 08:09:06.864766  5644 solver.cpp:218] Iteration 7044 (0.864668 iter/s, 13.8782s/12 iters), loss = 5.29223
I0406 08:09:06.864828  5644 solver.cpp:237]     Train net output #0: loss = 5.29223 (* 1 = 5.29223 loss)
I0406 08:09:06.864838  5644 sgd_solver.cpp:105] Iteration 7044, lr = 0.1
I0406 08:09:12.118554  5644 solver.cpp:218] Iteration 7056 (2.28412 iter/s, 5.25367s/12 iters), loss = 5.29231
I0406 08:09:12.118592  5644 solver.cpp:237]     Train net output #0: loss = 5.29231 (* 1 = 5.29231 loss)
I0406 08:09:12.118598  5644 sgd_solver.cpp:105] Iteration 7056, lr = 0.1
I0406 08:09:17.329536  5644 solver.cpp:218] Iteration 7068 (2.30287 iter/s, 5.21088s/12 iters), loss = 5.28474
I0406 08:09:17.329576  5644 solver.cpp:237]     Train net output #0: loss = 5.28474 (* 1 = 5.28474 loss)
I0406 08:09:17.329581  5644 sgd_solver.cpp:105] Iteration 7068, lr = 0.1
I0406 08:09:22.481707  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:09:22.646006  5644 solver.cpp:218] Iteration 7080 (2.25718 iter/s, 5.31637s/12 iters), loss = 5.29347
I0406 08:09:22.646042  5644 solver.cpp:237]     Train net output #0: loss = 5.29347 (* 1 = 5.29347 loss)
I0406 08:09:22.646047  5644 sgd_solver.cpp:105] Iteration 7080, lr = 0.1
I0406 08:09:27.991344  5644 solver.cpp:218] Iteration 7092 (2.24499 iter/s, 5.34525s/12 iters), loss = 5.25588
I0406 08:09:27.991438  5644 solver.cpp:237]     Train net output #0: loss = 5.25588 (* 1 = 5.25588 loss)
I0406 08:09:27.991444  5644 sgd_solver.cpp:105] Iteration 7092, lr = 0.1
I0406 08:09:33.135519  5644 solver.cpp:218] Iteration 7104 (2.3328 iter/s, 5.14402s/12 iters), loss = 5.28674
I0406 08:09:33.135560  5644 solver.cpp:237]     Train net output #0: loss = 5.28674 (* 1 = 5.28674 loss)
I0406 08:09:33.135565  5644 sgd_solver.cpp:105] Iteration 7104, lr = 0.1
I0406 08:09:38.241389  5644 solver.cpp:218] Iteration 7116 (2.35028 iter/s, 5.10577s/12 iters), loss = 5.29116
I0406 08:09:38.241433  5644 solver.cpp:237]     Train net output #0: loss = 5.29116 (* 1 = 5.29116 loss)
I0406 08:09:38.241439  5644 sgd_solver.cpp:105] Iteration 7116, lr = 0.1
I0406 08:09:43.507293  5644 solver.cpp:218] Iteration 7128 (2.27886 iter/s, 5.2658s/12 iters), loss = 5.26636
I0406 08:09:43.507339  5644 solver.cpp:237]     Train net output #0: loss = 5.26636 (* 1 = 5.26636 loss)
I0406 08:09:43.507346  5644 sgd_solver.cpp:105] Iteration 7128, lr = 0.1
I0406 08:09:48.328694  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0406 08:09:51.316787  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0406 08:09:53.625021  5644 solver.cpp:330] Iteration 7140, Testing net (#0)
I0406 08:09:53.625044  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:09:55.232100  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:09:58.152325  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:09:58.152469  5644 solver.cpp:397]     Test net output #1: loss = 5.28531 (* 1 = 5.28531 loss)
I0406 08:09:58.293031  5644 solver.cpp:218] Iteration 7140 (0.811603 iter/s, 14.7856s/12 iters), loss = 5.26999
I0406 08:09:58.293088  5644 solver.cpp:237]     Train net output #0: loss = 5.26999 (* 1 = 5.26999 loss)
I0406 08:09:58.293098  5644 sgd_solver.cpp:105] Iteration 7140, lr = 0.1
I0406 08:10:02.628273  5644 solver.cpp:218] Iteration 7152 (2.76808 iter/s, 4.33514s/12 iters), loss = 5.26671
I0406 08:10:02.628314  5644 solver.cpp:237]     Train net output #0: loss = 5.26671 (* 1 = 5.26671 loss)
I0406 08:10:02.628319  5644 sgd_solver.cpp:105] Iteration 7152, lr = 0.1
I0406 08:10:07.765326  5644 solver.cpp:218] Iteration 7164 (2.33601 iter/s, 5.13696s/12 iters), loss = 5.2777
I0406 08:10:07.765365  5644 solver.cpp:237]     Train net output #0: loss = 5.2777 (* 1 = 5.2777 loss)
I0406 08:10:07.765372  5644 sgd_solver.cpp:105] Iteration 7164, lr = 0.1
I0406 08:10:12.778820  5644 solver.cpp:218] Iteration 7176 (2.39359 iter/s, 5.0134s/12 iters), loss = 5.26876
I0406 08:10:12.778861  5644 solver.cpp:237]     Train net output #0: loss = 5.26876 (* 1 = 5.26876 loss)
I0406 08:10:12.778867  5644 sgd_solver.cpp:105] Iteration 7176, lr = 0.1
I0406 08:10:15.048758  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:10:18.010764  5644 solver.cpp:218] Iteration 7188 (2.29365 iter/s, 5.23184s/12 iters), loss = 5.30238
I0406 08:10:18.010809  5644 solver.cpp:237]     Train net output #0: loss = 5.30238 (* 1 = 5.30238 loss)
I0406 08:10:18.010814  5644 sgd_solver.cpp:105] Iteration 7188, lr = 0.1
I0406 08:10:23.264107  5644 solver.cpp:218] Iteration 7200 (2.28431 iter/s, 5.25324s/12 iters), loss = 5.25428
I0406 08:10:23.264149  5644 solver.cpp:237]     Train net output #0: loss = 5.25428 (* 1 = 5.25428 loss)
I0406 08:10:23.264155  5644 sgd_solver.cpp:105] Iteration 7200, lr = 0.1
I0406 08:10:28.591687  5644 solver.cpp:218] Iteration 7212 (2.25247 iter/s, 5.32748s/12 iters), loss = 5.28959
I0406 08:10:28.591802  5644 solver.cpp:237]     Train net output #0: loss = 5.28959 (* 1 = 5.28959 loss)
I0406 08:10:28.591810  5644 sgd_solver.cpp:105] Iteration 7212, lr = 0.1
I0406 08:10:33.879570  5644 solver.cpp:218] Iteration 7224 (2.26941 iter/s, 5.28772s/12 iters), loss = 5.2655
I0406 08:10:33.879609  5644 solver.cpp:237]     Train net output #0: loss = 5.2655 (* 1 = 5.2655 loss)
I0406 08:10:33.879616  5644 sgd_solver.cpp:105] Iteration 7224, lr = 0.1
I0406 08:10:38.783087  5644 solver.cpp:218] Iteration 7236 (2.44727 iter/s, 4.90342s/12 iters), loss = 5.2512
I0406 08:10:38.783123  5644 solver.cpp:237]     Train net output #0: loss = 5.2512 (* 1 = 5.2512 loss)
I0406 08:10:38.783128  5644 sgd_solver.cpp:105] Iteration 7236, lr = 0.1
I0406 08:10:40.958029  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0406 08:10:43.956784  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0406 08:10:46.736197  5644 solver.cpp:330] Iteration 7242, Testing net (#0)
I0406 08:10:46.736220  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:10:48.239598  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:10:51.196106  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:10:51.196142  5644 solver.cpp:397]     Test net output #1: loss = 5.28526 (* 1 = 5.28526 loss)
I0406 08:10:53.220815  5644 solver.cpp:218] Iteration 7248 (0.831165 iter/s, 14.4376s/12 iters), loss = 5.26581
I0406 08:10:53.220857  5644 solver.cpp:237]     Train net output #0: loss = 5.26581 (* 1 = 5.26581 loss)
I0406 08:10:53.220863  5644 sgd_solver.cpp:105] Iteration 7248, lr = 0.1
I0406 08:10:58.784598  5644 solver.cpp:218] Iteration 7260 (2.15685 iter/s, 5.56368s/12 iters), loss = 5.27614
I0406 08:10:58.784718  5644 solver.cpp:237]     Train net output #0: loss = 5.27614 (* 1 = 5.27614 loss)
I0406 08:10:58.784726  5644 sgd_solver.cpp:105] Iteration 7260, lr = 0.1
I0406 08:11:04.095711  5644 solver.cpp:218] Iteration 7272 (2.25949 iter/s, 5.31094s/12 iters), loss = 5.27495
I0406 08:11:04.095752  5644 solver.cpp:237]     Train net output #0: loss = 5.27495 (* 1 = 5.27495 loss)
I0406 08:11:04.095758  5644 sgd_solver.cpp:105] Iteration 7272, lr = 0.1
I0406 08:11:08.711305  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:11:09.527698  5644 solver.cpp:218] Iteration 7284 (2.20918 iter/s, 5.43189s/12 iters), loss = 5.27631
I0406 08:11:09.527740  5644 solver.cpp:237]     Train net output #0: loss = 5.27631 (* 1 = 5.27631 loss)
I0406 08:11:09.527747  5644 sgd_solver.cpp:105] Iteration 7284, lr = 0.1
I0406 08:11:14.927835  5644 solver.cpp:218] Iteration 7296 (2.2222 iter/s, 5.40004s/12 iters), loss = 5.27516
I0406 08:11:14.927873  5644 solver.cpp:237]     Train net output #0: loss = 5.27516 (* 1 = 5.27516 loss)
I0406 08:11:14.927879  5644 sgd_solver.cpp:105] Iteration 7296, lr = 0.1
I0406 08:11:20.373044  5644 solver.cpp:218] Iteration 7308 (2.20381 iter/s, 5.44511s/12 iters), loss = 5.31151
I0406 08:11:20.373080  5644 solver.cpp:237]     Train net output #0: loss = 5.31151 (* 1 = 5.31151 loss)
I0406 08:11:20.373086  5644 sgd_solver.cpp:105] Iteration 7308, lr = 0.1
I0406 08:11:25.547780  5644 solver.cpp:218] Iteration 7320 (2.319 iter/s, 5.17464s/12 iters), loss = 5.28562
I0406 08:11:25.547832  5644 solver.cpp:237]     Train net output #0: loss = 5.28562 (* 1 = 5.28562 loss)
I0406 08:11:25.547840  5644 sgd_solver.cpp:105] Iteration 7320, lr = 0.1
I0406 08:11:30.879170  5644 solver.cpp:218] Iteration 7332 (2.25086 iter/s, 5.33128s/12 iters), loss = 5.29083
I0406 08:11:30.879254  5644 solver.cpp:237]     Train net output #0: loss = 5.29083 (* 1 = 5.29083 loss)
I0406 08:11:30.879261  5644 sgd_solver.cpp:105] Iteration 7332, lr = 0.1
I0406 08:11:35.592458  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0406 08:11:38.647087  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0406 08:11:40.941676  5644 solver.cpp:330] Iteration 7344, Testing net (#0)
I0406 08:11:40.941696  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:11:42.422849  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:11:45.227661  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:11:45.227699  5644 solver.cpp:397]     Test net output #1: loss = 5.28586 (* 1 = 5.28586 loss)
I0406 08:11:45.368320  5644 solver.cpp:218] Iteration 7344 (0.828218 iter/s, 14.4889s/12 iters), loss = 5.25315
I0406 08:11:45.368373  5644 solver.cpp:237]     Train net output #0: loss = 5.25315 (* 1 = 5.25315 loss)
I0406 08:11:45.368381  5644 sgd_solver.cpp:105] Iteration 7344, lr = 0.1
I0406 08:11:49.853552  5644 solver.cpp:218] Iteration 7356 (2.67551 iter/s, 4.48513s/12 iters), loss = 5.28726
I0406 08:11:49.853601  5644 solver.cpp:237]     Train net output #0: loss = 5.28726 (* 1 = 5.28726 loss)
I0406 08:11:49.853608  5644 sgd_solver.cpp:105] Iteration 7356, lr = 0.1
I0406 08:11:55.123479  5644 solver.cpp:218] Iteration 7368 (2.27712 iter/s, 5.26982s/12 iters), loss = 5.27461
I0406 08:11:55.123518  5644 solver.cpp:237]     Train net output #0: loss = 5.27461 (* 1 = 5.27461 loss)
I0406 08:11:55.123523  5644 sgd_solver.cpp:105] Iteration 7368, lr = 0.1
I0406 08:12:00.371474  5644 solver.cpp:218] Iteration 7380 (2.28663 iter/s, 5.2479s/12 iters), loss = 5.27715
I0406 08:12:00.371511  5644 solver.cpp:237]     Train net output #0: loss = 5.27715 (* 1 = 5.27715 loss)
I0406 08:12:00.371517  5644 sgd_solver.cpp:105] Iteration 7380, lr = 0.1
I0406 08:12:01.798743  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:12:05.679153  5644 solver.cpp:218] Iteration 7392 (2.26092 iter/s, 5.30758s/12 iters), loss = 5.29281
I0406 08:12:05.679198  5644 solver.cpp:237]     Train net output #0: loss = 5.29281 (* 1 = 5.29281 loss)
I0406 08:12:05.679203  5644 sgd_solver.cpp:105] Iteration 7392, lr = 0.1
I0406 08:12:10.929034  5644 solver.cpp:218] Iteration 7404 (2.28581 iter/s, 5.24978s/12 iters), loss = 5.27519
I0406 08:12:10.929096  5644 solver.cpp:237]     Train net output #0: loss = 5.27519 (* 1 = 5.27519 loss)
I0406 08:12:10.929105  5644 sgd_solver.cpp:105] Iteration 7404, lr = 0.1
I0406 08:12:16.216087  5644 solver.cpp:218] Iteration 7416 (2.26974 iter/s, 5.28694s/12 iters), loss = 5.27208
I0406 08:12:16.216143  5644 solver.cpp:237]     Train net output #0: loss = 5.27208 (* 1 = 5.27208 loss)
I0406 08:12:16.216151  5644 sgd_solver.cpp:105] Iteration 7416, lr = 0.1
I0406 08:12:21.511727  5644 solver.cpp:218] Iteration 7428 (2.26606 iter/s, 5.29553s/12 iters), loss = 5.28062
I0406 08:12:21.511775  5644 solver.cpp:237]     Train net output #0: loss = 5.28062 (* 1 = 5.28062 loss)
I0406 08:12:21.511781  5644 sgd_solver.cpp:105] Iteration 7428, lr = 0.1
I0406 08:12:26.881728  5644 solver.cpp:218] Iteration 7440 (2.23468 iter/s, 5.3699s/12 iters), loss = 5.28682
I0406 08:12:26.881767  5644 solver.cpp:237]     Train net output #0: loss = 5.28682 (* 1 = 5.28682 loss)
I0406 08:12:26.881772  5644 sgd_solver.cpp:105] Iteration 7440, lr = 0.1
I0406 08:12:29.061942  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0406 08:12:32.194169  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0406 08:12:34.490723  5644 solver.cpp:330] Iteration 7446, Testing net (#0)
I0406 08:12:34.490742  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:12:35.913758  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:12:38.850802  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:12:38.850838  5644 solver.cpp:397]     Test net output #1: loss = 5.28536 (* 1 = 5.28536 loss)
I0406 08:12:40.709681  5644 solver.cpp:218] Iteration 7452 (0.867818 iter/s, 13.8278s/12 iters), loss = 5.27739
I0406 08:12:40.709723  5644 solver.cpp:237]     Train net output #0: loss = 5.27739 (* 1 = 5.27739 loss)
I0406 08:12:40.709728  5644 sgd_solver.cpp:105] Iteration 7452, lr = 0.1
I0406 08:12:46.004482  5644 solver.cpp:218] Iteration 7464 (2.26642 iter/s, 5.2947s/12 iters), loss = 5.29118
I0406 08:12:46.004536  5644 solver.cpp:237]     Train net output #0: loss = 5.29118 (* 1 = 5.29118 loss)
I0406 08:12:46.004545  5644 sgd_solver.cpp:105] Iteration 7464, lr = 0.1
I0406 08:12:51.474865  5644 solver.cpp:218] Iteration 7476 (2.19368 iter/s, 5.47027s/12 iters), loss = 5.29358
I0406 08:12:51.474921  5644 solver.cpp:237]     Train net output #0: loss = 5.29358 (* 1 = 5.29358 loss)
I0406 08:12:51.474929  5644 sgd_solver.cpp:105] Iteration 7476, lr = 0.1
I0406 08:12:55.111542  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:12:56.590366  5644 solver.cpp:218] Iteration 7488 (2.34586 iter/s, 5.11539s/12 iters), loss = 5.25107
I0406 08:12:56.590413  5644 solver.cpp:237]     Train net output #0: loss = 5.25107 (* 1 = 5.25107 loss)
I0406 08:12:56.590421  5644 sgd_solver.cpp:105] Iteration 7488, lr = 0.1
I0406 08:13:01.918773  5644 solver.cpp:218] Iteration 7500 (2.25212 iter/s, 5.3283s/12 iters), loss = 5.28154
I0406 08:13:01.918814  5644 solver.cpp:237]     Train net output #0: loss = 5.28154 (* 1 = 5.28154 loss)
I0406 08:13:01.918820  5644 sgd_solver.cpp:105] Iteration 7500, lr = 0.1
I0406 08:13:07.243127  5644 solver.cpp:218] Iteration 7512 (2.25384 iter/s, 5.32425s/12 iters), loss = 5.28734
I0406 08:13:07.243239  5644 solver.cpp:237]     Train net output #0: loss = 5.28734 (* 1 = 5.28734 loss)
I0406 08:13:07.243248  5644 sgd_solver.cpp:105] Iteration 7512, lr = 0.1
I0406 08:13:12.622553  5644 solver.cpp:218] Iteration 7524 (2.23079 iter/s, 5.37926s/12 iters), loss = 5.26848
I0406 08:13:12.622592  5644 solver.cpp:237]     Train net output #0: loss = 5.26848 (* 1 = 5.26848 loss)
I0406 08:13:12.622597  5644 sgd_solver.cpp:105] Iteration 7524, lr = 0.1
I0406 08:13:18.022259  5644 solver.cpp:218] Iteration 7536 (2.22238 iter/s, 5.39961s/12 iters), loss = 5.28459
I0406 08:13:18.022300  5644 solver.cpp:237]     Train net output #0: loss = 5.28459 (* 1 = 5.28459 loss)
I0406 08:13:18.022305  5644 sgd_solver.cpp:105] Iteration 7536, lr = 0.1
I0406 08:13:22.718714  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0406 08:13:25.755970  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0406 08:13:28.081640  5644 solver.cpp:330] Iteration 7548, Testing net (#0)
I0406 08:13:28.081657  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:13:29.542922  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:13:32.427551  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:13:32.427583  5644 solver.cpp:397]     Test net output #1: loss = 5.28575 (* 1 = 5.28575 loss)
I0406 08:13:32.568156  5644 solver.cpp:218] Iteration 7548 (0.824984 iter/s, 14.5457s/12 iters), loss = 5.2522
I0406 08:13:32.568197  5644 solver.cpp:237]     Train net output #0: loss = 5.2522 (* 1 = 5.2522 loss)
I0406 08:13:32.568202  5644 sgd_solver.cpp:105] Iteration 7548, lr = 0.1
I0406 08:13:36.950996  5644 solver.cpp:218] Iteration 7560 (2.73801 iter/s, 4.38274s/12 iters), loss = 5.28303
I0406 08:13:36.951045  5644 solver.cpp:237]     Train net output #0: loss = 5.28303 (* 1 = 5.28303 loss)
I0406 08:13:36.951053  5644 sgd_solver.cpp:105] Iteration 7560, lr = 0.1
I0406 08:13:42.219195  5644 solver.cpp:218] Iteration 7572 (2.27786 iter/s, 5.26809s/12 iters), loss = 5.27578
I0406 08:13:42.219341  5644 solver.cpp:237]     Train net output #0: loss = 5.27578 (* 1 = 5.27578 loss)
I0406 08:13:42.219347  5644 sgd_solver.cpp:105] Iteration 7572, lr = 0.1
I0406 08:13:47.525491  5644 solver.cpp:218] Iteration 7584 (2.26155 iter/s, 5.30609s/12 iters), loss = 5.28292
I0406 08:13:47.525547  5644 solver.cpp:237]     Train net output #0: loss = 5.28292 (* 1 = 5.28292 loss)
I0406 08:13:47.525557  5644 sgd_solver.cpp:105] Iteration 7584, lr = 0.1
I0406 08:13:48.189167  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:13:52.835556  5644 solver.cpp:218] Iteration 7596 (2.25991 iter/s, 5.30995s/12 iters), loss = 5.27185
I0406 08:13:52.835610  5644 solver.cpp:237]     Train net output #0: loss = 5.27185 (* 1 = 5.27185 loss)
I0406 08:13:52.835619  5644 sgd_solver.cpp:105] Iteration 7596, lr = 0.1
I0406 08:13:57.936672  5644 solver.cpp:218] Iteration 7608 (2.35248 iter/s, 5.10101s/12 iters), loss = 5.28291
I0406 08:13:57.936723  5644 solver.cpp:237]     Train net output #0: loss = 5.28291 (* 1 = 5.28291 loss)
I0406 08:13:57.936731  5644 sgd_solver.cpp:105] Iteration 7608, lr = 0.1
I0406 08:14:03.331203  5644 solver.cpp:218] Iteration 7620 (2.22452 iter/s, 5.39442s/12 iters), loss = 5.27814
I0406 08:14:03.331243  5644 solver.cpp:237]     Train net output #0: loss = 5.27814 (* 1 = 5.27814 loss)
I0406 08:14:03.331250  5644 sgd_solver.cpp:105] Iteration 7620, lr = 0.1
I0406 08:14:05.966558  5644 blocking_queue.cpp:49] Waiting for data
I0406 08:14:08.723256  5644 solver.cpp:218] Iteration 7632 (2.22554 iter/s, 5.39195s/12 iters), loss = 5.27365
I0406 08:14:08.723304  5644 solver.cpp:237]     Train net output #0: loss = 5.27365 (* 1 = 5.27365 loss)
I0406 08:14:08.723311  5644 sgd_solver.cpp:105] Iteration 7632, lr = 0.1
I0406 08:14:14.025691  5644 solver.cpp:218] Iteration 7644 (2.26316 iter/s, 5.30233s/12 iters), loss = 5.26103
I0406 08:14:14.026775  5644 solver.cpp:237]     Train net output #0: loss = 5.26103 (* 1 = 5.26103 loss)
I0406 08:14:14.026785  5644 sgd_solver.cpp:105] Iteration 7644, lr = 0.1
I0406 08:14:16.044550  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0406 08:14:19.041646  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0406 08:14:21.349931  5644 solver.cpp:330] Iteration 7650, Testing net (#0)
I0406 08:14:21.349952  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:14:22.742761  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:14:25.677651  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:14:25.677687  5644 solver.cpp:397]     Test net output #1: loss = 5.28616 (* 1 = 5.28616 loss)
I0406 08:14:27.493278  5644 solver.cpp:218] Iteration 7656 (0.891107 iter/s, 13.4664s/12 iters), loss = 5.27861
I0406 08:14:27.493316  5644 solver.cpp:237]     Train net output #0: loss = 5.27861 (* 1 = 5.27861 loss)
I0406 08:14:27.493324  5644 sgd_solver.cpp:105] Iteration 7656, lr = 0.1
I0406 08:14:32.771569  5644 solver.cpp:218] Iteration 7668 (2.2735 iter/s, 5.27819s/12 iters), loss = 5.29212
I0406 08:14:32.771607  5644 solver.cpp:237]     Train net output #0: loss = 5.29212 (* 1 = 5.29212 loss)
I0406 08:14:32.771613  5644 sgd_solver.cpp:105] Iteration 7668, lr = 0.1
I0406 08:14:38.010251  5644 solver.cpp:218] Iteration 7680 (2.29069 iter/s, 5.23859s/12 iters), loss = 5.2807
I0406 08:14:38.010309  5644 solver.cpp:237]     Train net output #0: loss = 5.2807 (* 1 = 5.2807 loss)
I0406 08:14:38.010318  5644 sgd_solver.cpp:105] Iteration 7680, lr = 0.1
I0406 08:14:40.959540  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:14:43.371157  5644 solver.cpp:218] Iteration 7692 (2.23847 iter/s, 5.36079s/12 iters), loss = 5.28758
I0406 08:14:43.371196  5644 solver.cpp:237]     Train net output #0: loss = 5.28758 (* 1 = 5.28758 loss)
I0406 08:14:43.371201  5644 sgd_solver.cpp:105] Iteration 7692, lr = 0.1
I0406 08:14:48.788753  5644 solver.cpp:218] Iteration 7704 (2.21505 iter/s, 5.4175s/12 iters), loss = 5.28231
I0406 08:14:48.788904  5644 solver.cpp:237]     Train net output #0: loss = 5.28231 (* 1 = 5.28231 loss)
I0406 08:14:48.788914  5644 sgd_solver.cpp:105] Iteration 7704, lr = 0.1
I0406 08:14:54.000272  5644 solver.cpp:218] Iteration 7716 (2.30268 iter/s, 5.21131s/12 iters), loss = 5.27922
I0406 08:14:54.000319  5644 solver.cpp:237]     Train net output #0: loss = 5.27922 (* 1 = 5.27922 loss)
I0406 08:14:54.000327  5644 sgd_solver.cpp:105] Iteration 7716, lr = 0.1
I0406 08:14:59.367769  5644 solver.cpp:218] Iteration 7728 (2.23572 iter/s, 5.36739s/12 iters), loss = 5.29602
I0406 08:14:59.367807  5644 solver.cpp:237]     Train net output #0: loss = 5.29602 (* 1 = 5.29602 loss)
I0406 08:14:59.367815  5644 sgd_solver.cpp:105] Iteration 7728, lr = 0.1
I0406 08:15:04.615554  5644 solver.cpp:218] Iteration 7740 (2.28672 iter/s, 5.24769s/12 iters), loss = 5.27376
I0406 08:15:04.615594  5644 solver.cpp:237]     Train net output #0: loss = 5.27376 (* 1 = 5.27376 loss)
I0406 08:15:04.615600  5644 sgd_solver.cpp:105] Iteration 7740, lr = 0.1
I0406 08:15:09.482234  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0406 08:15:12.546782  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0406 08:15:14.853504  5644 solver.cpp:330] Iteration 7752, Testing net (#0)
I0406 08:15:14.853528  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:15:16.167670  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:15:19.197504  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:15:19.197580  5644 solver.cpp:397]     Test net output #1: loss = 5.28573 (* 1 = 5.28573 loss)
I0406 08:15:19.335984  5644 solver.cpp:218] Iteration 7752 (0.815203 iter/s, 14.7203s/12 iters), loss = 5.29543
I0406 08:15:19.337569  5644 solver.cpp:237]     Train net output #0: loss = 5.29543 (* 1 = 5.29543 loss)
I0406 08:15:19.337580  5644 sgd_solver.cpp:105] Iteration 7752, lr = 0.1
I0406 08:15:23.880010  5644 solver.cpp:218] Iteration 7764 (2.64178 iter/s, 4.5424s/12 iters), loss = 5.29321
I0406 08:15:23.880050  5644 solver.cpp:237]     Train net output #0: loss = 5.29321 (* 1 = 5.29321 loss)
I0406 08:15:23.880055  5644 sgd_solver.cpp:105] Iteration 7764, lr = 0.1
I0406 08:15:29.106894  5644 solver.cpp:218] Iteration 7776 (2.29587 iter/s, 5.22678s/12 iters), loss = 5.28885
I0406 08:15:29.106940  5644 solver.cpp:237]     Train net output #0: loss = 5.28885 (* 1 = 5.28885 loss)
I0406 08:15:29.106948  5644 sgd_solver.cpp:105] Iteration 7776, lr = 0.1
I0406 08:15:34.293632  5644 solver.cpp:218] Iteration 7788 (2.31364 iter/s, 5.18663s/12 iters), loss = 5.28985
I0406 08:15:34.293680  5644 solver.cpp:237]     Train net output #0: loss = 5.28985 (* 1 = 5.28985 loss)
I0406 08:15:34.293689  5644 sgd_solver.cpp:105] Iteration 7788, lr = 0.1
I0406 08:15:34.300254  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:15:39.715173  5644 solver.cpp:218] Iteration 7800 (2.21344 iter/s, 5.42143s/12 iters), loss = 5.24465
I0406 08:15:39.715210  5644 solver.cpp:237]     Train net output #0: loss = 5.24465 (* 1 = 5.24465 loss)
I0406 08:15:39.715216  5644 sgd_solver.cpp:105] Iteration 7800, lr = 0.1
I0406 08:15:44.974690  5644 solver.cpp:218] Iteration 7812 (2.28162 iter/s, 5.25942s/12 iters), loss = 5.28928
I0406 08:15:44.974731  5644 solver.cpp:237]     Train net output #0: loss = 5.28928 (* 1 = 5.28928 loss)
I0406 08:15:44.974737  5644 sgd_solver.cpp:105] Iteration 7812, lr = 0.1
I0406 08:15:50.162277  5644 solver.cpp:218] Iteration 7824 (2.31326 iter/s, 5.18749s/12 iters), loss = 5.28917
I0406 08:15:50.162389  5644 solver.cpp:237]     Train net output #0: loss = 5.28917 (* 1 = 5.28917 loss)
I0406 08:15:50.162395  5644 sgd_solver.cpp:105] Iteration 7824, lr = 0.1
I0406 08:15:55.677027  5644 solver.cpp:218] Iteration 7836 (2.17605 iter/s, 5.51458s/12 iters), loss = 5.26299
I0406 08:15:55.677063  5644 solver.cpp:237]     Train net output #0: loss = 5.26299 (* 1 = 5.26299 loss)
I0406 08:15:55.677069  5644 sgd_solver.cpp:105] Iteration 7836, lr = 0.1
I0406 08:16:01.147682  5644 solver.cpp:218] Iteration 7848 (2.19356 iter/s, 5.47056s/12 iters), loss = 5.27623
I0406 08:16:01.147730  5644 solver.cpp:237]     Train net output #0: loss = 5.27623 (* 1 = 5.27623 loss)
I0406 08:16:01.147737  5644 sgd_solver.cpp:105] Iteration 7848, lr = 0.1
I0406 08:16:03.214252  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0406 08:16:06.215672  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0406 08:16:08.524041  5644 solver.cpp:330] Iteration 7854, Testing net (#0)
I0406 08:16:08.524060  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:16:09.895068  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:16:12.906183  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:16:12.906219  5644 solver.cpp:397]     Test net output #1: loss = 5.28621 (* 1 = 5.28621 loss)
I0406 08:16:14.902540  5644 solver.cpp:218] Iteration 7860 (0.87243 iter/s, 13.7547s/12 iters), loss = 5.25922
I0406 08:16:14.902588  5644 solver.cpp:237]     Train net output #0: loss = 5.25922 (* 1 = 5.25922 loss)
I0406 08:16:14.902596  5644 sgd_solver.cpp:105] Iteration 7860, lr = 0.1
I0406 08:16:20.163545  5644 solver.cpp:218] Iteration 7872 (2.28098 iter/s, 5.2609s/12 iters), loss = 5.27805
I0406 08:16:20.163628  5644 solver.cpp:237]     Train net output #0: loss = 5.27805 (* 1 = 5.27805 loss)
I0406 08:16:20.163635  5644 sgd_solver.cpp:105] Iteration 7872, lr = 0.1
I0406 08:16:25.232292  5644 solver.cpp:218] Iteration 7884 (2.36752 iter/s, 5.0686s/12 iters), loss = 5.26758
I0406 08:16:25.232352  5644 solver.cpp:237]     Train net output #0: loss = 5.26758 (* 1 = 5.26758 loss)
I0406 08:16:25.232360  5644 sgd_solver.cpp:105] Iteration 7884, lr = 0.1
I0406 08:16:27.546774  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:16:30.620342  5644 solver.cpp:218] Iteration 7896 (2.2272 iter/s, 5.38793s/12 iters), loss = 5.29737
I0406 08:16:30.620388  5644 solver.cpp:237]     Train net output #0: loss = 5.29737 (* 1 = 5.29737 loss)
I0406 08:16:30.620396  5644 sgd_solver.cpp:105] Iteration 7896, lr = 0.1
I0406 08:16:35.741714  5644 solver.cpp:218] Iteration 7908 (2.34317 iter/s, 5.12127s/12 iters), loss = 5.2573
I0406 08:16:35.741752  5644 solver.cpp:237]     Train net output #0: loss = 5.2573 (* 1 = 5.2573 loss)
I0406 08:16:35.741757  5644 sgd_solver.cpp:105] Iteration 7908, lr = 0.1
I0406 08:16:40.856181  5644 solver.cpp:218] Iteration 7920 (2.34633 iter/s, 5.11437s/12 iters), loss = 5.29434
I0406 08:16:40.856221  5644 solver.cpp:237]     Train net output #0: loss = 5.29434 (* 1 = 5.29434 loss)
I0406 08:16:40.856227  5644 sgd_solver.cpp:105] Iteration 7920, lr = 0.1
I0406 08:16:46.217427  5644 solver.cpp:218] Iteration 7932 (2.23833 iter/s, 5.36114s/12 iters), loss = 5.26708
I0406 08:16:46.217473  5644 solver.cpp:237]     Train net output #0: loss = 5.26708 (* 1 = 5.26708 loss)
I0406 08:16:46.217480  5644 sgd_solver.cpp:105] Iteration 7932, lr = 0.1
I0406 08:16:51.548818  5644 solver.cpp:218] Iteration 7944 (2.25086 iter/s, 5.33129s/12 iters), loss = 5.25818
I0406 08:16:51.550259  5644 solver.cpp:237]     Train net output #0: loss = 5.25818 (* 1 = 5.25818 loss)
I0406 08:16:51.550267  5644 sgd_solver.cpp:105] Iteration 7944, lr = 0.1
I0406 08:16:56.180128  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0406 08:16:59.192288  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0406 08:17:01.543398  5644 solver.cpp:330] Iteration 7956, Testing net (#0)
I0406 08:17:01.543418  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:17:02.857995  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:17:05.914038  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:17:05.914068  5644 solver.cpp:397]     Test net output #1: loss = 5.28615 (* 1 = 5.28615 loss)
I0406 08:17:06.052698  5644 solver.cpp:218] Iteration 7956 (0.827455 iter/s, 14.5023s/12 iters), loss = 5.27449
I0406 08:17:06.052750  5644 solver.cpp:237]     Train net output #0: loss = 5.27449 (* 1 = 5.27449 loss)
I0406 08:17:06.052758  5644 sgd_solver.cpp:105] Iteration 7956, lr = 0.1
I0406 08:17:10.515645  5644 solver.cpp:218] Iteration 7968 (2.68887 iter/s, 4.46283s/12 iters), loss = 5.27484
I0406 08:17:10.515707  5644 solver.cpp:237]     Train net output #0: loss = 5.27484 (* 1 = 5.27484 loss)
I0406 08:17:10.515715  5644 sgd_solver.cpp:105] Iteration 7968, lr = 0.1
I0406 08:17:15.771301  5644 solver.cpp:218] Iteration 7980 (2.28331 iter/s, 5.25554s/12 iters), loss = 5.27349
I0406 08:17:15.771342  5644 solver.cpp:237]     Train net output #0: loss = 5.27349 (* 1 = 5.27349 loss)
I0406 08:17:15.771348  5644 sgd_solver.cpp:105] Iteration 7980, lr = 0.1
I0406 08:17:20.334650  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:17:21.120123  5644 solver.cpp:218] Iteration 7992 (2.24353 iter/s, 5.34872s/12 iters), loss = 5.27419
I0406 08:17:21.120177  5644 solver.cpp:237]     Train net output #0: loss = 5.27419 (* 1 = 5.27419 loss)
I0406 08:17:21.120184  5644 sgd_solver.cpp:105] Iteration 7992, lr = 0.1
I0406 08:17:26.542006  5644 solver.cpp:218] Iteration 8004 (2.2133 iter/s, 5.42177s/12 iters), loss = 5.27248
I0406 08:17:26.542124  5644 solver.cpp:237]     Train net output #0: loss = 5.27248 (* 1 = 5.27248 loss)
I0406 08:17:26.542133  5644 sgd_solver.cpp:105] Iteration 8004, lr = 0.1
I0406 08:17:31.884477  5644 solver.cpp:218] Iteration 8016 (2.24622 iter/s, 5.3423s/12 iters), loss = 5.30728
I0406 08:17:31.884524  5644 solver.cpp:237]     Train net output #0: loss = 5.30728 (* 1 = 5.30728 loss)
I0406 08:17:31.884532  5644 sgd_solver.cpp:105] Iteration 8016, lr = 0.1
I0406 08:17:37.027701  5644 solver.cpp:218] Iteration 8028 (2.33321 iter/s, 5.14312s/12 iters), loss = 5.28824
I0406 08:17:37.027755  5644 solver.cpp:237]     Train net output #0: loss = 5.28824 (* 1 = 5.28824 loss)
I0406 08:17:37.027763  5644 sgd_solver.cpp:105] Iteration 8028, lr = 0.1
I0406 08:17:42.309203  5644 solver.cpp:218] Iteration 8040 (2.27213 iter/s, 5.28139s/12 iters), loss = 5.29292
I0406 08:17:42.309254  5644 solver.cpp:237]     Train net output #0: loss = 5.29292 (* 1 = 5.29292 loss)
I0406 08:17:42.309262  5644 sgd_solver.cpp:105] Iteration 8040, lr = 0.1
I0406 08:17:47.544070  5644 solver.cpp:218] Iteration 8052 (2.29237 iter/s, 5.23476s/12 iters), loss = 5.25153
I0406 08:17:47.544109  5644 solver.cpp:237]     Train net output #0: loss = 5.25153 (* 1 = 5.25153 loss)
I0406 08:17:47.544116  5644 sgd_solver.cpp:105] Iteration 8052, lr = 0.1
I0406 08:17:49.726233  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0406 08:17:52.761627  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0406 08:17:55.057546  5644 solver.cpp:330] Iteration 8058, Testing net (#0)
I0406 08:17:55.057565  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:17:56.226301  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:17:59.512531  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:17:59.512671  5644 solver.cpp:397]     Test net output #1: loss = 5.2863 (* 1 = 5.2863 loss)
I0406 08:18:01.469727  5644 solver.cpp:218] Iteration 8064 (0.861729 iter/s, 13.9255s/12 iters), loss = 5.29215
I0406 08:18:01.469776  5644 solver.cpp:237]     Train net output #0: loss = 5.29215 (* 1 = 5.29215 loss)
I0406 08:18:01.469784  5644 sgd_solver.cpp:105] Iteration 8064, lr = 0.1
I0406 08:18:06.876951  5644 solver.cpp:218] Iteration 8076 (2.2193 iter/s, 5.40712s/12 iters), loss = 5.27561
I0406 08:18:06.876992  5644 solver.cpp:237]     Train net output #0: loss = 5.27561 (* 1 = 5.27561 loss)
I0406 08:18:06.876997  5644 sgd_solver.cpp:105] Iteration 8076, lr = 0.1
I0406 08:18:12.113240  5644 solver.cpp:218] Iteration 8088 (2.29174 iter/s, 5.23619s/12 iters), loss = 5.28253
I0406 08:18:12.113276  5644 solver.cpp:237]     Train net output #0: loss = 5.28253 (* 1 = 5.28253 loss)
I0406 08:18:12.113282  5644 sgd_solver.cpp:105] Iteration 8088, lr = 0.1
I0406 08:18:13.427929  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:18:17.191278  5644 solver.cpp:218] Iteration 8100 (2.36316 iter/s, 5.07794s/12 iters), loss = 5.28955
I0406 08:18:17.191318  5644 solver.cpp:237]     Train net output #0: loss = 5.28955 (* 1 = 5.28955 loss)
I0406 08:18:17.191326  5644 sgd_solver.cpp:105] Iteration 8100, lr = 0.1
I0406 08:18:22.503926  5644 solver.cpp:218] Iteration 8112 (2.2588 iter/s, 5.31255s/12 iters), loss = 5.26992
I0406 08:18:22.503974  5644 solver.cpp:237]     Train net output #0: loss = 5.26992 (* 1 = 5.26992 loss)
I0406 08:18:22.503983  5644 sgd_solver.cpp:105] Iteration 8112, lr = 0.1
I0406 08:18:27.979760  5644 solver.cpp:218] Iteration 8124 (2.19149 iter/s, 5.47573s/12 iters), loss = 5.27534
I0406 08:18:27.979805  5644 solver.cpp:237]     Train net output #0: loss = 5.27534 (* 1 = 5.27534 loss)
I0406 08:18:27.979813  5644 sgd_solver.cpp:105] Iteration 8124, lr = 0.1
I0406 08:18:32.874226  5644 solver.cpp:218] Iteration 8136 (2.4518 iter/s, 4.89437s/12 iters), loss = 5.28029
I0406 08:18:32.874366  5644 solver.cpp:237]     Train net output #0: loss = 5.28029 (* 1 = 5.28029 loss)
I0406 08:18:32.874377  5644 sgd_solver.cpp:105] Iteration 8136, lr = 0.1
I0406 08:18:38.230574  5644 solver.cpp:218] Iteration 8148 (2.24041 iter/s, 5.35615s/12 iters), loss = 5.29356
I0406 08:18:38.230629  5644 solver.cpp:237]     Train net output #0: loss = 5.29356 (* 1 = 5.29356 loss)
I0406 08:18:38.230638  5644 sgd_solver.cpp:105] Iteration 8148, lr = 0.1
I0406 08:18:43.066452  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0406 08:18:46.542714  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0406 08:18:48.840260  5644 solver.cpp:330] Iteration 8160, Testing net (#0)
I0406 08:18:48.840278  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:18:50.051434  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:18:53.344357  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:18:53.344393  5644 solver.cpp:397]     Test net output #1: loss = 5.28657 (* 1 = 5.28657 loss)
I0406 08:18:53.484544  5644 solver.cpp:218] Iteration 8160 (0.78669 iter/s, 15.2538s/12 iters), loss = 5.27701
I0406 08:18:53.486160  5644 solver.cpp:237]     Train net output #0: loss = 5.27701 (* 1 = 5.27701 loss)
I0406 08:18:53.486189  5644 sgd_solver.cpp:105] Iteration 8160, lr = 0.1
I0406 08:18:57.970585  5644 solver.cpp:218] Iteration 8172 (2.67594 iter/s, 4.4844s/12 iters), loss = 5.29495
I0406 08:18:57.970633  5644 solver.cpp:237]     Train net output #0: loss = 5.29495 (* 1 = 5.29495 loss)
I0406 08:18:57.970641  5644 sgd_solver.cpp:105] Iteration 8172, lr = 0.1
I0406 08:19:03.479842  5644 solver.cpp:218] Iteration 8184 (2.17819 iter/s, 5.50915s/12 iters), loss = 5.28607
I0406 08:19:03.479950  5644 solver.cpp:237]     Train net output #0: loss = 5.28607 (* 1 = 5.28607 loss)
I0406 08:19:03.479957  5644 sgd_solver.cpp:105] Iteration 8184, lr = 0.1
I0406 08:19:07.198897  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:19:08.788133  5644 solver.cpp:218] Iteration 8196 (2.26069 iter/s, 5.30812s/12 iters), loss = 5.25282
I0406 08:19:08.788182  5644 solver.cpp:237]     Train net output #0: loss = 5.25282 (* 1 = 5.25282 loss)
I0406 08:19:08.788190  5644 sgd_solver.cpp:105] Iteration 8196, lr = 0.1
I0406 08:19:13.952023  5644 solver.cpp:218] Iteration 8208 (2.32388 iter/s, 5.16378s/12 iters), loss = 5.28697
I0406 08:19:13.952061  5644 solver.cpp:237]     Train net output #0: loss = 5.28697 (* 1 = 5.28697 loss)
I0406 08:19:13.952066  5644 sgd_solver.cpp:105] Iteration 8208, lr = 0.1
I0406 08:19:19.047297  5644 solver.cpp:218] Iteration 8220 (2.35517 iter/s, 5.09517s/12 iters), loss = 5.28594
I0406 08:19:19.047343  5644 solver.cpp:237]     Train net output #0: loss = 5.28594 (* 1 = 5.28594 loss)
I0406 08:19:19.047349  5644 sgd_solver.cpp:105] Iteration 8220, lr = 0.1
I0406 08:19:24.307503  5644 solver.cpp:218] Iteration 8232 (2.28132 iter/s, 5.26011s/12 iters), loss = 5.27272
I0406 08:19:24.307538  5644 solver.cpp:237]     Train net output #0: loss = 5.27272 (* 1 = 5.27272 loss)
I0406 08:19:24.307543  5644 sgd_solver.cpp:105] Iteration 8232, lr = 0.1
I0406 08:19:29.676043  5644 solver.cpp:218] Iteration 8244 (2.23528 iter/s, 5.36844s/12 iters), loss = 5.28144
I0406 08:19:29.676084  5644 solver.cpp:237]     Train net output #0: loss = 5.28144 (* 1 = 5.28144 loss)
I0406 08:19:29.676090  5644 sgd_solver.cpp:105] Iteration 8244, lr = 0.1
I0406 08:19:34.896207  5644 solver.cpp:218] Iteration 8256 (2.29882 iter/s, 5.22006s/12 iters), loss = 5.25095
I0406 08:19:34.896323  5644 solver.cpp:237]     Train net output #0: loss = 5.25095 (* 1 = 5.25095 loss)
I0406 08:19:34.896332  5644 sgd_solver.cpp:105] Iteration 8256, lr = 0.1
I0406 08:19:36.912158  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0406 08:19:39.966765  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0406 08:19:42.281913  5644 solver.cpp:330] Iteration 8262, Testing net (#0)
I0406 08:19:42.281934  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:19:43.452596  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:19:46.765774  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:19:46.765807  5644 solver.cpp:397]     Test net output #1: loss = 5.28636 (* 1 = 5.28636 loss)
I0406 08:19:48.767349  5644 solver.cpp:218] Iteration 8268 (0.865121 iter/s, 13.8709s/12 iters), loss = 5.27759
I0406 08:19:48.767390  5644 solver.cpp:237]     Train net output #0: loss = 5.27759 (* 1 = 5.27759 loss)
I0406 08:19:48.767395  5644 sgd_solver.cpp:105] Iteration 8268, lr = 0.1
I0406 08:19:54.099647  5644 solver.cpp:218] Iteration 8280 (2.25048 iter/s, 5.3322s/12 iters), loss = 5.27633
I0406 08:19:54.099695  5644 solver.cpp:237]     Train net output #0: loss = 5.27633 (* 1 = 5.27633 loss)
I0406 08:19:54.099704  5644 sgd_solver.cpp:105] Iteration 8280, lr = 0.1
I0406 08:19:59.509727  5644 solver.cpp:218] Iteration 8292 (2.21813 iter/s, 5.40997s/12 iters), loss = 5.28069
I0406 08:19:59.509768  5644 solver.cpp:237]     Train net output #0: loss = 5.28069 (* 1 = 5.28069 loss)
I0406 08:19:59.509773  5644 sgd_solver.cpp:105] Iteration 8292, lr = 0.1
I0406 08:20:00.169106  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:20:04.524230  5644 solver.cpp:218] Iteration 8304 (2.39311 iter/s, 5.0144s/12 iters), loss = 5.27179
I0406 08:20:04.524268  5644 solver.cpp:237]     Train net output #0: loss = 5.27179 (* 1 = 5.27179 loss)
I0406 08:20:04.524274  5644 sgd_solver.cpp:105] Iteration 8304, lr = 0.1
I0406 08:20:07.518579  5644 blocking_queue.cpp:49] Waiting for data
I0406 08:20:09.838080  5644 solver.cpp:218] Iteration 8316 (2.25829 iter/s, 5.31375s/12 iters), loss = 5.27772
I0406 08:20:09.838138  5644 solver.cpp:237]     Train net output #0: loss = 5.27772 (* 1 = 5.27772 loss)
I0406 08:20:09.838147  5644 sgd_solver.cpp:105] Iteration 8316, lr = 0.1
I0406 08:20:15.232736  5644 solver.cpp:218] Iteration 8328 (2.22447 iter/s, 5.39454s/12 iters), loss = 5.28572
I0406 08:20:15.232784  5644 solver.cpp:237]     Train net output #0: loss = 5.28572 (* 1 = 5.28572 loss)
I0406 08:20:15.232791  5644 sgd_solver.cpp:105] Iteration 8328, lr = 0.1
I0406 08:20:20.434705  5644 solver.cpp:218] Iteration 8340 (2.30687 iter/s, 5.20186s/12 iters), loss = 5.28042
I0406 08:20:20.434744  5644 solver.cpp:237]     Train net output #0: loss = 5.28042 (* 1 = 5.28042 loss)
I0406 08:20:20.434749  5644 sgd_solver.cpp:105] Iteration 8340, lr = 0.1
I0406 08:20:25.506381  5644 solver.cpp:218] Iteration 8352 (2.36613 iter/s, 5.07157s/12 iters), loss = 5.26209
I0406 08:20:25.506438  5644 solver.cpp:237]     Train net output #0: loss = 5.26209 (* 1 = 5.26209 loss)
I0406 08:20:25.506448  5644 sgd_solver.cpp:105] Iteration 8352, lr = 0.1
I0406 08:20:30.132930  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0406 08:20:33.162001  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0406 08:20:35.524516  5644 solver.cpp:330] Iteration 8364, Testing net (#0)
I0406 08:20:35.524538  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:20:36.643270  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:20:39.900336  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:20:39.900450  5644 solver.cpp:397]     Test net output #1: loss = 5.28651 (* 1 = 5.28651 loss)
I0406 08:20:40.036379  5644 solver.cpp:218] Iteration 8364 (0.825888 iter/s, 14.5298s/12 iters), loss = 5.29204
I0406 08:20:40.037986  5644 solver.cpp:237]     Train net output #0: loss = 5.29204 (* 1 = 5.29204 loss)
I0406 08:20:40.037997  5644 sgd_solver.cpp:105] Iteration 8364, lr = 0.1
I0406 08:20:44.431414  5644 solver.cpp:218] Iteration 8376 (2.73138 iter/s, 4.39338s/12 iters), loss = 5.29695
I0406 08:20:44.431452  5644 solver.cpp:237]     Train net output #0: loss = 5.29695 (* 1 = 5.29695 loss)
I0406 08:20:44.431458  5644 sgd_solver.cpp:105] Iteration 8376, lr = 0.1
I0406 08:20:49.848009  5644 solver.cpp:218] Iteration 8388 (2.21545 iter/s, 5.4165s/12 iters), loss = 5.28177
I0406 08:20:49.848067  5644 solver.cpp:237]     Train net output #0: loss = 5.28177 (* 1 = 5.28177 loss)
I0406 08:20:49.848078  5644 sgd_solver.cpp:105] Iteration 8388, lr = 0.1
I0406 08:20:52.747685  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:20:55.144688  5644 solver.cpp:218] Iteration 8400 (2.26562 iter/s, 5.29656s/12 iters), loss = 5.28539
I0406 08:20:55.144727  5644 solver.cpp:237]     Train net output #0: loss = 5.28539 (* 1 = 5.28539 loss)
I0406 08:20:55.144733  5644 sgd_solver.cpp:105] Iteration 8400, lr = 0.1
I0406 08:21:00.244952  5644 solver.cpp:218] Iteration 8412 (2.35287 iter/s, 5.10016s/12 iters), loss = 5.28771
I0406 08:21:00.244992  5644 solver.cpp:237]     Train net output #0: loss = 5.28771 (* 1 = 5.28771 loss)
I0406 08:21:00.244997  5644 sgd_solver.cpp:105] Iteration 8412, lr = 0.1
I0406 08:21:05.694833  5644 solver.cpp:218] Iteration 8424 (2.20192 iter/s, 5.44978s/12 iters), loss = 5.27732
I0406 08:21:05.694875  5644 solver.cpp:237]     Train net output #0: loss = 5.27732 (* 1 = 5.27732 loss)
I0406 08:21:05.694881  5644 sgd_solver.cpp:105] Iteration 8424, lr = 0.1
I0406 08:21:11.004683  5644 solver.cpp:218] Iteration 8436 (2.25999 iter/s, 5.30975s/12 iters), loss = 5.29351
I0406 08:21:11.004801  5644 solver.cpp:237]     Train net output #0: loss = 5.29351 (* 1 = 5.29351 loss)
I0406 08:21:11.004807  5644 sgd_solver.cpp:105] Iteration 8436, lr = 0.1
I0406 08:21:16.322556  5644 solver.cpp:218] Iteration 8448 (2.25662 iter/s, 5.31769s/12 iters), loss = 5.27667
I0406 08:21:16.322603  5644 solver.cpp:237]     Train net output #0: loss = 5.27667 (* 1 = 5.27667 loss)
I0406 08:21:16.322611  5644 sgd_solver.cpp:105] Iteration 8448, lr = 0.1
I0406 08:21:21.345854  5644 solver.cpp:218] Iteration 8460 (2.38892 iter/s, 5.02319s/12 iters), loss = 5.29578
I0406 08:21:21.345899  5644 solver.cpp:237]     Train net output #0: loss = 5.29578 (* 1 = 5.29578 loss)
I0406 08:21:21.345906  5644 sgd_solver.cpp:105] Iteration 8460, lr = 0.1
I0406 08:21:23.517699  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0406 08:21:28.246131  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0406 08:21:30.601904  5644 solver.cpp:330] Iteration 8466, Testing net (#0)
I0406 08:21:30.601922  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:21:31.653090  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:21:34.959542  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:21:34.959578  5644 solver.cpp:397]     Test net output #1: loss = 5.28644 (* 1 = 5.28644 loss)
I0406 08:21:36.762482  5644 solver.cpp:218] Iteration 8472 (0.77839 iter/s, 15.4164s/12 iters), loss = 5.29015
I0406 08:21:36.762519  5644 solver.cpp:237]     Train net output #0: loss = 5.29015 (* 1 = 5.29015 loss)
I0406 08:21:36.762524  5644 sgd_solver.cpp:105] Iteration 8472, lr = 0.1
I0406 08:21:41.953810  5644 solver.cpp:218] Iteration 8484 (2.31159 iter/s, 5.19123s/12 iters), loss = 5.29054
I0406 08:21:41.953910  5644 solver.cpp:237]     Train net output #0: loss = 5.29054 (* 1 = 5.29054 loss)
I0406 08:21:41.953917  5644 sgd_solver.cpp:105] Iteration 8484, lr = 0.1
I0406 08:21:47.193735  5644 solver.cpp:218] Iteration 8496 (2.29018 iter/s, 5.23977s/12 iters), loss = 5.2959
I0406 08:21:47.193792  5644 solver.cpp:237]     Train net output #0: loss = 5.2959 (* 1 = 5.2959 loss)
I0406 08:21:47.193802  5644 sgd_solver.cpp:105] Iteration 8496, lr = 0.1
I0406 08:21:47.228708  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:21:52.604738  5644 solver.cpp:218] Iteration 8508 (2.21775 iter/s, 5.41089s/12 iters), loss = 5.25304
I0406 08:21:52.604780  5644 solver.cpp:237]     Train net output #0: loss = 5.25304 (* 1 = 5.25304 loss)
I0406 08:21:52.604786  5644 sgd_solver.cpp:105] Iteration 8508, lr = 0.1
I0406 08:21:57.966188  5644 solver.cpp:218] Iteration 8520 (2.23824 iter/s, 5.36135s/12 iters), loss = 5.29073
I0406 08:21:57.966238  5644 solver.cpp:237]     Train net output #0: loss = 5.29073 (* 1 = 5.29073 loss)
I0406 08:21:57.966248  5644 sgd_solver.cpp:105] Iteration 8520, lr = 0.1
I0406 08:22:03.299979  5644 solver.cpp:218] Iteration 8532 (2.24985 iter/s, 5.33368s/12 iters), loss = 5.28478
I0406 08:22:03.300020  5644 solver.cpp:237]     Train net output #0: loss = 5.28478 (* 1 = 5.28478 loss)
I0406 08:22:03.300026  5644 sgd_solver.cpp:105] Iteration 8532, lr = 0.1
I0406 08:22:08.655560  5644 solver.cpp:218] Iteration 8544 (2.2407 iter/s, 5.35548s/12 iters), loss = 5.26649
I0406 08:22:08.655601  5644 solver.cpp:237]     Train net output #0: loss = 5.26649 (* 1 = 5.26649 loss)
I0406 08:22:08.655606  5644 sgd_solver.cpp:105] Iteration 8544, lr = 0.1
I0406 08:22:13.862712  5644 solver.cpp:218] Iteration 8556 (2.30457 iter/s, 5.20705s/12 iters), loss = 5.27783
I0406 08:22:13.862802  5644 solver.cpp:237]     Train net output #0: loss = 5.27783 (* 1 = 5.27783 loss)
I0406 08:22:13.862809  5644 sgd_solver.cpp:105] Iteration 8556, lr = 0.1
I0406 08:22:18.734302  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0406 08:22:21.804262  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0406 08:22:24.123473  5644 solver.cpp:330] Iteration 8568, Testing net (#0)
I0406 08:22:24.123493  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:22:25.211421  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:22:28.618088  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:22:28.618113  5644 solver.cpp:397]     Test net output #1: loss = 5.28665 (* 1 = 5.28665 loss)
I0406 08:22:28.752429  5644 solver.cpp:218] Iteration 8568 (0.805937 iter/s, 14.8895s/12 iters), loss = 5.26052
I0406 08:22:28.752465  5644 solver.cpp:237]     Train net output #0: loss = 5.26052 (* 1 = 5.26052 loss)
I0406 08:22:28.752470  5644 sgd_solver.cpp:105] Iteration 8568, lr = 0.1
I0406 08:22:33.183655  5644 solver.cpp:218] Iteration 8580 (2.70811 iter/s, 4.43113s/12 iters), loss = 5.28576
I0406 08:22:33.183710  5644 solver.cpp:237]     Train net output #0: loss = 5.28576 (* 1 = 5.28576 loss)
I0406 08:22:33.183720  5644 sgd_solver.cpp:105] Iteration 8580, lr = 0.1
I0406 08:22:38.447351  5644 solver.cpp:218] Iteration 8592 (2.27982 iter/s, 5.26358s/12 iters), loss = 5.2661
I0406 08:22:38.447402  5644 solver.cpp:237]     Train net output #0: loss = 5.2661 (* 1 = 5.2661 loss)
I0406 08:22:38.447409  5644 sgd_solver.cpp:105] Iteration 8592, lr = 0.1
I0406 08:22:40.733603  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:22:43.803586  5644 solver.cpp:218] Iteration 8604 (2.24043 iter/s, 5.35612s/12 iters), loss = 5.29257
I0406 08:22:43.803629  5644 solver.cpp:237]     Train net output #0: loss = 5.29257 (* 1 = 5.29257 loss)
I0406 08:22:43.803637  5644 sgd_solver.cpp:105] Iteration 8604, lr = 0.1
I0406 08:22:49.200757  5644 solver.cpp:218] Iteration 8616 (2.22343 iter/s, 5.39706s/12 iters), loss = 5.26823
I0406 08:22:49.200893  5644 solver.cpp:237]     Train net output #0: loss = 5.26823 (* 1 = 5.26823 loss)
I0406 08:22:49.200902  5644 sgd_solver.cpp:105] Iteration 8616, lr = 0.1
I0406 08:22:54.447757  5644 solver.cpp:218] Iteration 8628 (2.2871 iter/s, 5.24682s/12 iters), loss = 5.29987
I0406 08:22:54.447800  5644 solver.cpp:237]     Train net output #0: loss = 5.29987 (* 1 = 5.29987 loss)
I0406 08:22:54.447806  5644 sgd_solver.cpp:105] Iteration 8628, lr = 0.1
I0406 08:22:59.725291  5644 solver.cpp:218] Iteration 8640 (2.27383 iter/s, 5.27743s/12 iters), loss = 5.26153
I0406 08:22:59.725342  5644 solver.cpp:237]     Train net output #0: loss = 5.26153 (* 1 = 5.26153 loss)
I0406 08:22:59.725348  5644 sgd_solver.cpp:105] Iteration 8640, lr = 0.1
I0406 08:23:04.920469  5644 solver.cpp:218] Iteration 8652 (2.30988 iter/s, 5.19507s/12 iters), loss = 5.26368
I0406 08:23:04.920511  5644 solver.cpp:237]     Train net output #0: loss = 5.26368 (* 1 = 5.26368 loss)
I0406 08:23:04.920516  5644 sgd_solver.cpp:105] Iteration 8652, lr = 0.1
I0406 08:23:10.226303  5644 solver.cpp:218] Iteration 8664 (2.2617 iter/s, 5.30574s/12 iters), loss = 5.27697
I0406 08:23:10.226341  5644 solver.cpp:237]     Train net output #0: loss = 5.27697 (* 1 = 5.27697 loss)
I0406 08:23:10.226346  5644 sgd_solver.cpp:105] Iteration 8664, lr = 0.1
I0406 08:23:12.147033  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0406 08:23:15.147805  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0406 08:23:17.464565  5644 solver.cpp:330] Iteration 8670, Testing net (#0)
I0406 08:23:17.464582  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:23:18.496698  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:23:21.930276  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:23:21.930384  5644 solver.cpp:397]     Test net output #1: loss = 5.28655 (* 1 = 5.28655 loss)
I0406 08:23:23.805788  5644 solver.cpp:218] Iteration 8676 (0.883697 iter/s, 13.5793s/12 iters), loss = 5.27564
I0406 08:23:23.805830  5644 solver.cpp:237]     Train net output #0: loss = 5.27564 (* 1 = 5.27564 loss)
I0406 08:23:23.805836  5644 sgd_solver.cpp:105] Iteration 8676, lr = 0.1
I0406 08:23:29.003188  5644 solver.cpp:218] Iteration 8688 (2.30889 iter/s, 5.19729s/12 iters), loss = 5.27729
I0406 08:23:29.003237  5644 solver.cpp:237]     Train net output #0: loss = 5.27729 (* 1 = 5.27729 loss)
I0406 08:23:29.003244  5644 sgd_solver.cpp:105] Iteration 8688, lr = 0.1
I0406 08:23:33.464053  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:23:34.195813  5644 solver.cpp:218] Iteration 8700 (2.31102 iter/s, 5.19252s/12 iters), loss = 5.28083
I0406 08:23:34.195855  5644 solver.cpp:237]     Train net output #0: loss = 5.28083 (* 1 = 5.28083 loss)
I0406 08:23:34.195861  5644 sgd_solver.cpp:105] Iteration 8700, lr = 0.1
I0406 08:23:39.329629  5644 solver.cpp:218] Iteration 8712 (2.33749 iter/s, 5.13371s/12 iters), loss = 5.26698
I0406 08:23:39.329668  5644 solver.cpp:237]     Train net output #0: loss = 5.26698 (* 1 = 5.26698 loss)
I0406 08:23:39.329672  5644 sgd_solver.cpp:105] Iteration 8712, lr = 0.1
I0406 08:23:44.735036  5644 solver.cpp:218] Iteration 8724 (2.22004 iter/s, 5.40531s/12 iters), loss = 5.30116
I0406 08:23:44.735074  5644 solver.cpp:237]     Train net output #0: loss = 5.30116 (* 1 = 5.30116 loss)
I0406 08:23:44.735078  5644 sgd_solver.cpp:105] Iteration 8724, lr = 0.1
I0406 08:23:50.158911  5644 solver.cpp:218] Iteration 8736 (2.21248 iter/s, 5.42377s/12 iters), loss = 5.28681
I0406 08:23:50.158962  5644 solver.cpp:237]     Train net output #0: loss = 5.28681 (* 1 = 5.28681 loss)
I0406 08:23:50.158969  5644 sgd_solver.cpp:105] Iteration 8736, lr = 0.1
I0406 08:23:55.257629  5644 solver.cpp:218] Iteration 8748 (2.35358 iter/s, 5.09862s/12 iters), loss = 5.2904
I0406 08:23:55.257742  5644 solver.cpp:237]     Train net output #0: loss = 5.2904 (* 1 = 5.2904 loss)
I0406 08:23:55.257750  5644 sgd_solver.cpp:105] Iteration 8748, lr = 0.1
I0406 08:24:00.517545  5644 solver.cpp:218] Iteration 8760 (2.28148 iter/s, 5.25975s/12 iters), loss = 5.25236
I0406 08:24:00.517586  5644 solver.cpp:237]     Train net output #0: loss = 5.25236 (* 1 = 5.25236 loss)
I0406 08:24:00.517591  5644 sgd_solver.cpp:105] Iteration 8760, lr = 0.1
I0406 08:24:05.322801  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0406 08:24:08.361891  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0406 08:24:10.663873  5644 solver.cpp:330] Iteration 8772, Testing net (#0)
I0406 08:24:10.663892  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:24:11.594169  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:24:15.203753  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:24:15.203789  5644 solver.cpp:397]     Test net output #1: loss = 5.28692 (* 1 = 5.28692 loss)
I0406 08:24:15.344512  5644 solver.cpp:218] Iteration 8772 (0.809346 iter/s, 14.8268s/12 iters), loss = 5.29289
I0406 08:24:15.344565  5644 solver.cpp:237]     Train net output #0: loss = 5.29289 (* 1 = 5.29289 loss)
I0406 08:24:15.344573  5644 sgd_solver.cpp:105] Iteration 8772, lr = 0.1
I0406 08:24:19.849720  5644 solver.cpp:218] Iteration 8784 (2.66365 iter/s, 4.5051s/12 iters), loss = 5.26871
I0406 08:24:19.849761  5644 solver.cpp:237]     Train net output #0: loss = 5.26871 (* 1 = 5.26871 loss)
I0406 08:24:19.849766  5644 sgd_solver.cpp:105] Iteration 8784, lr = 0.1
I0406 08:24:25.160256  5644 solver.cpp:218] Iteration 8796 (2.2597 iter/s, 5.31043s/12 iters), loss = 5.28108
I0406 08:24:25.160306  5644 solver.cpp:237]     Train net output #0: loss = 5.28108 (* 1 = 5.28108 loss)
I0406 08:24:25.160315  5644 sgd_solver.cpp:105] Iteration 8796, lr = 0.1
I0406 08:24:26.765786  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:24:30.515763  5644 solver.cpp:218] Iteration 8808 (2.24073 iter/s, 5.3554s/12 iters), loss = 5.29091
I0406 08:24:30.515801  5644 solver.cpp:237]     Train net output #0: loss = 5.29091 (* 1 = 5.29091 loss)
I0406 08:24:30.515807  5644 sgd_solver.cpp:105] Iteration 8808, lr = 0.1
I0406 08:24:35.539924  5644 solver.cpp:218] Iteration 8820 (2.3885 iter/s, 5.02407s/12 iters), loss = 5.26455
I0406 08:24:35.539963  5644 solver.cpp:237]     Train net output #0: loss = 5.26455 (* 1 = 5.26455 loss)
I0406 08:24:35.539969  5644 sgd_solver.cpp:105] Iteration 8820, lr = 0.1
I0406 08:24:40.873811  5644 solver.cpp:218] Iteration 8832 (2.24981 iter/s, 5.33378s/12 iters), loss = 5.27452
I0406 08:24:40.873854  5644 solver.cpp:237]     Train net output #0: loss = 5.27452 (* 1 = 5.27452 loss)
I0406 08:24:40.873862  5644 sgd_solver.cpp:105] Iteration 8832, lr = 0.1
I0406 08:24:46.102650  5644 solver.cpp:218] Iteration 8844 (2.29501 iter/s, 5.22874s/12 iters), loss = 5.27617
I0406 08:24:46.102689  5644 solver.cpp:237]     Train net output #0: loss = 5.27617 (* 1 = 5.27617 loss)
I0406 08:24:46.102694  5644 sgd_solver.cpp:105] Iteration 8844, lr = 0.1
I0406 08:24:51.408859  5644 solver.cpp:218] Iteration 8856 (2.26155 iter/s, 5.30611s/12 iters), loss = 5.29389
I0406 08:24:51.408915  5644 solver.cpp:237]     Train net output #0: loss = 5.29389 (* 1 = 5.29389 loss)
I0406 08:24:51.408924  5644 sgd_solver.cpp:105] Iteration 8856, lr = 0.1
I0406 08:24:56.690636  5644 solver.cpp:218] Iteration 8868 (2.27201 iter/s, 5.28167s/12 iters), loss = 5.28332
I0406 08:24:56.690671  5644 solver.cpp:237]     Train net output #0: loss = 5.28332 (* 1 = 5.28332 loss)
I0406 08:24:56.690677  5644 sgd_solver.cpp:105] Iteration 8868, lr = 0.1
I0406 08:24:58.775110  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0406 08:25:01.820770  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0406 08:25:04.148766  5644 solver.cpp:330] Iteration 8874, Testing net (#0)
I0406 08:25:04.148789  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:25:05.047233  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:25:08.491992  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:25:08.492027  5644 solver.cpp:397]     Test net output #1: loss = 5.2867 (* 1 = 5.2867 loss)
I0406 08:25:10.406906  5644 solver.cpp:218] Iteration 8880 (0.874884 iter/s, 13.7161s/12 iters), loss = 5.30059
I0406 08:25:10.406960  5644 solver.cpp:237]     Train net output #0: loss = 5.30059 (* 1 = 5.30059 loss)
I0406 08:25:10.406967  5644 sgd_solver.cpp:105] Iteration 8880, lr = 0.1
I0406 08:25:15.785307  5644 solver.cpp:218] Iteration 8892 (2.23119 iter/s, 5.37829s/12 iters), loss = 5.29393
I0406 08:25:15.785348  5644 solver.cpp:237]     Train net output #0: loss = 5.29393 (* 1 = 5.29393 loss)
I0406 08:25:15.785354  5644 sgd_solver.cpp:105] Iteration 8892, lr = 0.1
I0406 08:25:19.469965  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:25:21.041710  5644 solver.cpp:218] Iteration 8904 (2.28297 iter/s, 5.2563s/12 iters), loss = 5.24768
I0406 08:25:21.041760  5644 solver.cpp:237]     Train net output #0: loss = 5.24768 (* 1 = 5.24768 loss)
I0406 08:25:21.041769  5644 sgd_solver.cpp:105] Iteration 8904, lr = 0.1
I0406 08:25:26.427079  5644 solver.cpp:218] Iteration 8916 (2.22831 iter/s, 5.38526s/12 iters), loss = 5.28509
I0406 08:25:26.427137  5644 solver.cpp:237]     Train net output #0: loss = 5.28509 (* 1 = 5.28509 loss)
I0406 08:25:26.427146  5644 sgd_solver.cpp:105] Iteration 8916, lr = 0.1
I0406 08:25:31.795902  5644 solver.cpp:218] Iteration 8928 (2.23517 iter/s, 5.36871s/12 iters), loss = 5.29263
I0406 08:25:31.796010  5644 solver.cpp:237]     Train net output #0: loss = 5.29263 (* 1 = 5.29263 loss)
I0406 08:25:31.796018  5644 sgd_solver.cpp:105] Iteration 8928, lr = 0.1
I0406 08:25:37.113181  5644 solver.cpp:218] Iteration 8940 (2.25686 iter/s, 5.31711s/12 iters), loss = 5.26701
I0406 08:25:37.113235  5644 solver.cpp:237]     Train net output #0: loss = 5.26701 (* 1 = 5.26701 loss)
I0406 08:25:37.113245  5644 sgd_solver.cpp:105] Iteration 8940, lr = 0.1
I0406 08:25:42.540786  5644 solver.cpp:218] Iteration 8952 (2.21096 iter/s, 5.4275s/12 iters), loss = 5.28517
I0406 08:25:42.540824  5644 solver.cpp:237]     Train net output #0: loss = 5.28517 (* 1 = 5.28517 loss)
I0406 08:25:42.540829  5644 sgd_solver.cpp:105] Iteration 8952, lr = 0.1
I0406 08:25:48.062824  5644 solver.cpp:218] Iteration 8964 (2.17315 iter/s, 5.52194s/12 iters), loss = 5.24887
I0406 08:25:48.062865  5644 solver.cpp:237]     Train net output #0: loss = 5.24887 (* 1 = 5.24887 loss)
I0406 08:25:48.062870  5644 sgd_solver.cpp:105] Iteration 8964, lr = 0.1
I0406 08:25:52.877570  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0406 08:25:55.964470  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0406 08:25:58.274564  5644 solver.cpp:330] Iteration 8976, Testing net (#0)
I0406 08:25:58.274583  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:25:59.147070  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:26:02.583782  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:26:02.583914  5644 solver.cpp:397]     Test net output #1: loss = 5.28683 (* 1 = 5.28683 loss)
I0406 08:26:02.724612  5644 solver.cpp:218] Iteration 8976 (0.818464 iter/s, 14.6616s/12 iters), loss = 5.27629
I0406 08:26:02.724651  5644 solver.cpp:237]     Train net output #0: loss = 5.27629 (* 1 = 5.27629 loss)
I0406 08:26:02.724656  5644 sgd_solver.cpp:105] Iteration 8976, lr = 0.1
I0406 08:26:06.969029  5644 solver.cpp:218] Iteration 8988 (2.8273 iter/s, 4.24433s/12 iters), loss = 5.27012
I0406 08:26:06.969081  5644 solver.cpp:237]     Train net output #0: loss = 5.27012 (* 1 = 5.27012 loss)
I0406 08:26:06.969090  5644 sgd_solver.cpp:105] Iteration 8988, lr = 0.1
I0406 08:26:10.365962  5644 blocking_queue.cpp:49] Waiting for data
I0406 08:26:12.080999  5644 solver.cpp:218] Iteration 9000 (2.34748 iter/s, 5.11186s/12 iters), loss = 5.28332
I0406 08:26:12.081049  5644 solver.cpp:237]     Train net output #0: loss = 5.28332 (* 1 = 5.28332 loss)
I0406 08:26:12.081058  5644 sgd_solver.cpp:105] Iteration 9000, lr = 0.1
I0406 08:26:12.815344  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:26:17.468304  5644 solver.cpp:218] Iteration 9012 (2.2275 iter/s, 5.3872s/12 iters), loss = 5.27492
I0406 08:26:17.468343  5644 solver.cpp:237]     Train net output #0: loss = 5.27492 (* 1 = 5.27492 loss)
I0406 08:26:17.468349  5644 sgd_solver.cpp:105] Iteration 9012, lr = 0.1
I0406 08:26:22.775234  5644 solver.cpp:218] Iteration 9024 (2.26124 iter/s, 5.30683s/12 iters), loss = 5.27726
I0406 08:26:22.775270  5644 solver.cpp:237]     Train net output #0: loss = 5.27726 (* 1 = 5.27726 loss)
I0406 08:26:22.775276  5644 sgd_solver.cpp:105] Iteration 9024, lr = 0.1
I0406 08:26:28.195427  5644 solver.cpp:218] Iteration 9036 (2.21398 iter/s, 5.42009s/12 iters), loss = 5.28777
I0406 08:26:28.195466  5644 solver.cpp:237]     Train net output #0: loss = 5.28777 (* 1 = 5.28777 loss)
I0406 08:26:28.195472  5644 sgd_solver.cpp:105] Iteration 9036, lr = 0.1
I0406 08:26:33.628259  5644 solver.cpp:218] Iteration 9048 (2.20883 iter/s, 5.43273s/12 iters), loss = 5.27924
I0406 08:26:33.628358  5644 solver.cpp:237]     Train net output #0: loss = 5.27924 (* 1 = 5.27924 loss)
I0406 08:26:33.628368  5644 sgd_solver.cpp:105] Iteration 9048, lr = 0.1
I0406 08:26:38.816449  5644 solver.cpp:218] Iteration 9060 (2.31301 iter/s, 5.18804s/12 iters), loss = 5.26691
I0406 08:26:38.816489  5644 solver.cpp:237]     Train net output #0: loss = 5.26691 (* 1 = 5.26691 loss)
I0406 08:26:38.816494  5644 sgd_solver.cpp:105] Iteration 9060, lr = 0.1
I0406 08:26:44.154781  5644 solver.cpp:218] Iteration 9072 (2.24794 iter/s, 5.33823s/12 iters), loss = 5.28742
I0406 08:26:44.154822  5644 solver.cpp:237]     Train net output #0: loss = 5.28742 (* 1 = 5.28742 loss)
I0406 08:26:44.154829  5644 sgd_solver.cpp:105] Iteration 9072, lr = 0.1
I0406 08:26:46.137934  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0406 08:26:49.184870  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0406 08:26:51.515472  5644 solver.cpp:330] Iteration 9078, Testing net (#0)
I0406 08:26:51.515491  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:26:52.298285  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:26:55.833087  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:26:55.833123  5644 solver.cpp:397]     Test net output #1: loss = 5.28734 (* 1 = 5.28734 loss)
I0406 08:26:57.811507  5644 solver.cpp:218] Iteration 9084 (0.878699 iter/s, 13.6566s/12 iters), loss = 5.28361
I0406 08:26:57.811547  5644 solver.cpp:237]     Train net output #0: loss = 5.28361 (* 1 = 5.28361 loss)
I0406 08:26:57.811551  5644 sgd_solver.cpp:105] Iteration 9084, lr = 0.1
I0406 08:27:02.957459  5644 solver.cpp:218] Iteration 9096 (2.33197 iter/s, 5.14585s/12 iters), loss = 5.28071
I0406 08:27:02.957507  5644 solver.cpp:237]     Train net output #0: loss = 5.28071 (* 1 = 5.28071 loss)
I0406 08:27:02.957515  5644 sgd_solver.cpp:105] Iteration 9096, lr = 0.1
I0406 08:27:05.816795  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:27:08.033229  5644 solver.cpp:218] Iteration 9108 (2.36422 iter/s, 5.07566s/12 iters), loss = 5.27991
I0406 08:27:08.033269  5644 solver.cpp:237]     Train net output #0: loss = 5.27991 (* 1 = 5.27991 loss)
I0406 08:27:08.033275  5644 sgd_solver.cpp:105] Iteration 9108, lr = 0.1
I0406 08:27:13.216779  5644 solver.cpp:218] Iteration 9120 (2.31506 iter/s, 5.18345s/12 iters), loss = 5.2823
I0406 08:27:13.216830  5644 solver.cpp:237]     Train net output #0: loss = 5.2823 (* 1 = 5.2823 loss)
I0406 08:27:13.216838  5644 sgd_solver.cpp:105] Iteration 9120, lr = 0.1
I0406 08:27:18.318334  5644 solver.cpp:218] Iteration 9132 (2.35227 iter/s, 5.10144s/12 iters), loss = 5.27354
I0406 08:27:18.318387  5644 solver.cpp:237]     Train net output #0: loss = 5.27354 (* 1 = 5.27354 loss)
I0406 08:27:18.318394  5644 sgd_solver.cpp:105] Iteration 9132, lr = 0.1
I0406 08:27:23.466888  5644 solver.cpp:218] Iteration 9144 (2.3308 iter/s, 5.14844s/12 iters), loss = 5.28996
I0406 08:27:23.466936  5644 solver.cpp:237]     Train net output #0: loss = 5.28996 (* 1 = 5.28996 loss)
I0406 08:27:23.466944  5644 sgd_solver.cpp:105] Iteration 9144, lr = 0.1
I0406 08:27:28.880147  5644 solver.cpp:218] Iteration 9156 (2.21682 iter/s, 5.41315s/12 iters), loss = 5.28237
I0406 08:27:28.880198  5644 solver.cpp:237]     Train net output #0: loss = 5.28237 (* 1 = 5.28237 loss)
I0406 08:27:28.880206  5644 sgd_solver.cpp:105] Iteration 9156, lr = 0.1
I0406 08:27:33.984766  5644 solver.cpp:218] Iteration 9168 (2.35086 iter/s, 5.10451s/12 iters), loss = 5.29281
I0406 08:27:33.984822  5644 solver.cpp:237]     Train net output #0: loss = 5.29281 (* 1 = 5.29281 loss)
I0406 08:27:33.984831  5644 sgd_solver.cpp:105] Iteration 9168, lr = 0.1
I0406 08:27:38.886159  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0406 08:27:41.921008  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0406 08:27:44.246516  5644 solver.cpp:330] Iteration 9180, Testing net (#0)
I0406 08:27:44.246541  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:27:45.052959  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:27:48.731700  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:27:48.731734  5644 solver.cpp:397]     Test net output #1: loss = 5.28735 (* 1 = 5.28735 loss)
I0406 08:27:48.872833  5644 solver.cpp:218] Iteration 9180 (0.806025 iter/s, 14.8879s/12 iters), loss = 5.29041
I0406 08:27:48.872870  5644 solver.cpp:237]     Train net output #0: loss = 5.29041 (* 1 = 5.29041 loss)
I0406 08:27:48.872875  5644 sgd_solver.cpp:105] Iteration 9180, lr = 0.1
I0406 08:27:53.317553  5644 solver.cpp:218] Iteration 9192 (2.69989 iter/s, 4.44462s/12 iters), loss = 5.29366
I0406 08:27:53.323755  5644 solver.cpp:237]     Train net output #0: loss = 5.29366 (* 1 = 5.29366 loss)
I0406 08:27:53.323772  5644 sgd_solver.cpp:105] Iteration 9192, lr = 0.1
I0406 08:27:58.705165  5644 solver.cpp:218] Iteration 9204 (2.22991 iter/s, 5.38137s/12 iters), loss = 5.29358
I0406 08:27:58.705201  5644 solver.cpp:237]     Train net output #0: loss = 5.29358 (* 1 = 5.29358 loss)
I0406 08:27:58.705207  5644 sgd_solver.cpp:105] Iteration 9204, lr = 0.1
I0406 08:27:58.755450  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:28:04.108716  5644 solver.cpp:218] Iteration 9216 (2.2208 iter/s, 5.40345s/12 iters), loss = 5.2536
I0406 08:28:04.108754  5644 solver.cpp:237]     Train net output #0: loss = 5.2536 (* 1 = 5.2536 loss)
I0406 08:28:04.108759  5644 sgd_solver.cpp:105] Iteration 9216, lr = 0.1
I0406 08:28:09.491961  5644 solver.cpp:218] Iteration 9228 (2.22918 iter/s, 5.38314s/12 iters), loss = 5.28535
I0406 08:28:09.492095  5644 solver.cpp:237]     Train net output #0: loss = 5.28535 (* 1 = 5.28535 loss)
I0406 08:28:09.492105  5644 sgd_solver.cpp:105] Iteration 9228, lr = 0.1
I0406 08:28:14.586393  5644 solver.cpp:218] Iteration 9240 (2.3556 iter/s, 5.09424s/12 iters), loss = 5.27904
I0406 08:28:14.586439  5644 solver.cpp:237]     Train net output #0: loss = 5.27904 (* 1 = 5.27904 loss)
I0406 08:28:14.586447  5644 sgd_solver.cpp:105] Iteration 9240, lr = 0.1
I0406 08:28:19.915933  5644 solver.cpp:218] Iteration 9252 (2.25165 iter/s, 5.32943s/12 iters), loss = 5.26841
I0406 08:28:19.915982  5644 solver.cpp:237]     Train net output #0: loss = 5.26841 (* 1 = 5.26841 loss)
I0406 08:28:19.915989  5644 sgd_solver.cpp:105] Iteration 9252, lr = 0.1
I0406 08:28:25.251050  5644 solver.cpp:218] Iteration 9264 (2.24929 iter/s, 5.33501s/12 iters), loss = 5.28126
I0406 08:28:25.251107  5644 solver.cpp:237]     Train net output #0: loss = 5.28126 (* 1 = 5.28126 loss)
I0406 08:28:25.251116  5644 sgd_solver.cpp:105] Iteration 9264, lr = 0.1
I0406 08:28:30.539280  5644 solver.cpp:218] Iteration 9276 (2.26924 iter/s, 5.28811s/12 iters), loss = 5.26456
I0406 08:28:30.539340  5644 solver.cpp:237]     Train net output #0: loss = 5.26456 (* 1 = 5.26456 loss)
I0406 08:28:30.539348  5644 sgd_solver.cpp:105] Iteration 9276, lr = 0.1
I0406 08:28:32.673103  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0406 08:28:35.697847  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0406 08:28:37.998893  5644 solver.cpp:330] Iteration 9282, Testing net (#0)
I0406 08:28:37.998914  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:28:38.731256  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:28:42.403172  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:28:42.403267  5644 solver.cpp:397]     Test net output #1: loss = 5.28707 (* 1 = 5.28707 loss)
I0406 08:28:44.351267  5644 solver.cpp:218] Iteration 9288 (0.868822 iter/s, 13.8118s/12 iters), loss = 5.28679
I0406 08:28:44.351306  5644 solver.cpp:237]     Train net output #0: loss = 5.28679 (* 1 = 5.28679 loss)
I0406 08:28:44.351312  5644 sgd_solver.cpp:105] Iteration 9288, lr = 0.1
I0406 08:28:49.777155  5644 solver.cpp:218] Iteration 9300 (2.21166 iter/s, 5.42579s/12 iters), loss = 5.2621
I0406 08:28:49.777191  5644 solver.cpp:237]     Train net output #0: loss = 5.2621 (* 1 = 5.2621 loss)
I0406 08:28:49.777197  5644 sgd_solver.cpp:105] Iteration 9300, lr = 0.1
I0406 08:28:52.145403  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:28:55.016427  5644 solver.cpp:218] Iteration 9312 (2.29044 iter/s, 5.23918s/12 iters), loss = 5.28125
I0406 08:28:55.016465  5644 solver.cpp:237]     Train net output #0: loss = 5.28125 (* 1 = 5.28125 loss)
I0406 08:28:55.016471  5644 sgd_solver.cpp:105] Iteration 9312, lr = 0.1
I0406 08:29:00.195647  5644 solver.cpp:218] Iteration 9324 (2.31699 iter/s, 5.17912s/12 iters), loss = 5.26604
I0406 08:29:00.195689  5644 solver.cpp:237]     Train net output #0: loss = 5.26604 (* 1 = 5.26604 loss)
I0406 08:29:00.195694  5644 sgd_solver.cpp:105] Iteration 9324, lr = 0.1
I0406 08:29:05.358623  5644 solver.cpp:218] Iteration 9336 (2.32429 iter/s, 5.16288s/12 iters), loss = 5.2871
I0406 08:29:05.358671  5644 solver.cpp:237]     Train net output #0: loss = 5.2871 (* 1 = 5.2871 loss)
I0406 08:29:05.358680  5644 sgd_solver.cpp:105] Iteration 9336, lr = 0.1
I0406 08:29:10.320143  5644 solver.cpp:218] Iteration 9348 (2.41867 iter/s, 4.9614s/12 iters), loss = 5.26467
I0406 08:29:10.320194  5644 solver.cpp:237]     Train net output #0: loss = 5.26467 (* 1 = 5.26467 loss)
I0406 08:29:10.320202  5644 sgd_solver.cpp:105] Iteration 9348, lr = 0.1
I0406 08:29:15.683892  5644 solver.cpp:218] Iteration 9360 (2.23729 iter/s, 5.36364s/12 iters), loss = 5.26249
I0406 08:29:15.684032  5644 solver.cpp:237]     Train net output #0: loss = 5.26249 (* 1 = 5.26249 loss)
I0406 08:29:15.684041  5644 sgd_solver.cpp:105] Iteration 9360, lr = 0.1
I0406 08:29:20.829792  5644 solver.cpp:218] Iteration 9372 (2.33204 iter/s, 5.14571s/12 iters), loss = 5.28106
I0406 08:29:20.829828  5644 solver.cpp:237]     Train net output #0: loss = 5.28106 (* 1 = 5.28106 loss)
I0406 08:29:20.829833  5644 sgd_solver.cpp:105] Iteration 9372, lr = 0.1
I0406 08:29:25.189066  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0406 08:29:28.236829  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0406 08:29:30.537007  5644 solver.cpp:330] Iteration 9384, Testing net (#0)
I0406 08:29:30.537027  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:29:31.221441  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:29:34.831125  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:29:34.831158  5644 solver.cpp:397]     Test net output #1: loss = 5.28705 (* 1 = 5.28705 loss)
I0406 08:29:34.968502  5644 solver.cpp:218] Iteration 9384 (0.848744 iter/s, 14.1385s/12 iters), loss = 5.27581
I0406 08:29:34.968549  5644 solver.cpp:237]     Train net output #0: loss = 5.27581 (* 1 = 5.27581 loss)
I0406 08:29:34.968557  5644 sgd_solver.cpp:105] Iteration 9384, lr = 0.1
I0406 08:29:39.236398  5644 solver.cpp:218] Iteration 9396 (2.81175 iter/s, 4.2678s/12 iters), loss = 5.27183
I0406 08:29:39.236438  5644 solver.cpp:237]     Train net output #0: loss = 5.27183 (* 1 = 5.27183 loss)
I0406 08:29:39.236443  5644 sgd_solver.cpp:105] Iteration 9396, lr = 0.1
I0406 08:29:43.812311  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:29:44.564879  5644 solver.cpp:218] Iteration 9408 (2.25209 iter/s, 5.32838s/12 iters), loss = 5.27742
I0406 08:29:44.564921  5644 solver.cpp:237]     Train net output #0: loss = 5.27742 (* 1 = 5.27742 loss)
I0406 08:29:44.564927  5644 sgd_solver.cpp:105] Iteration 9408, lr = 0.1
I0406 08:29:49.876158  5644 solver.cpp:218] Iteration 9420 (2.25939 iter/s, 5.31118s/12 iters), loss = 5.26048
I0406 08:29:49.876255  5644 solver.cpp:237]     Train net output #0: loss = 5.26048 (* 1 = 5.26048 loss)
I0406 08:29:49.876262  5644 sgd_solver.cpp:105] Iteration 9420, lr = 0.1
I0406 08:29:55.255158  5644 solver.cpp:218] Iteration 9432 (2.23096 iter/s, 5.37884s/12 iters), loss = 5.28952
I0406 08:29:55.255198  5644 solver.cpp:237]     Train net output #0: loss = 5.28952 (* 1 = 5.28952 loss)
I0406 08:29:55.255203  5644 sgd_solver.cpp:105] Iteration 9432, lr = 0.1
I0406 08:30:00.431975  5644 solver.cpp:218] Iteration 9444 (2.31807 iter/s, 5.17672s/12 iters), loss = 5.28898
I0406 08:30:00.432024  5644 solver.cpp:237]     Train net output #0: loss = 5.28898 (* 1 = 5.28898 loss)
I0406 08:30:00.432031  5644 sgd_solver.cpp:105] Iteration 9444, lr = 0.1
I0406 08:30:05.750196  5644 solver.cpp:218] Iteration 9456 (2.25644 iter/s, 5.31811s/12 iters), loss = 5.2968
I0406 08:30:05.750244  5644 solver.cpp:237]     Train net output #0: loss = 5.2968 (* 1 = 5.2968 loss)
I0406 08:30:05.750252  5644 sgd_solver.cpp:105] Iteration 9456, lr = 0.1
I0406 08:30:11.113046  5644 solver.cpp:218] Iteration 9468 (2.23766 iter/s, 5.36274s/12 iters), loss = 5.26131
I0406 08:30:11.113082  5644 solver.cpp:237]     Train net output #0: loss = 5.26131 (* 1 = 5.26131 loss)
I0406 08:30:11.113088  5644 sgd_solver.cpp:105] Iteration 9468, lr = 0.1
I0406 08:30:16.230432  5644 solver.cpp:218] Iteration 9480 (2.34499 iter/s, 5.11729s/12 iters), loss = 5.29717
I0406 08:30:16.230476  5644 solver.cpp:237]     Train net output #0: loss = 5.29717 (* 1 = 5.29717 loss)
I0406 08:30:16.230482  5644 sgd_solver.cpp:105] Iteration 9480, lr = 0.1
I0406 08:30:18.225876  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0406 08:30:21.266589  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0406 08:30:23.565676  5644 solver.cpp:330] Iteration 9486, Testing net (#0)
I0406 08:30:23.565694  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:30:24.243279  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:30:27.876510  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:30:27.876546  5644 solver.cpp:397]     Test net output #1: loss = 5.28751 (* 1 = 5.28751 loss)
I0406 08:30:29.730486  5644 solver.cpp:218] Iteration 9492 (0.888897 iter/s, 13.4999s/12 iters), loss = 5.27064
I0406 08:30:29.730542  5644 solver.cpp:237]     Train net output #0: loss = 5.27064 (* 1 = 5.27064 loss)
I0406 08:30:29.730551  5644 sgd_solver.cpp:105] Iteration 9492, lr = 0.1
I0406 08:30:35.077901  5644 solver.cpp:218] Iteration 9504 (2.24412 iter/s, 5.3473s/12 iters), loss = 5.27851
I0406 08:30:35.077939  5644 solver.cpp:237]     Train net output #0: loss = 5.27851 (* 1 = 5.27851 loss)
I0406 08:30:35.077945  5644 sgd_solver.cpp:105] Iteration 9504, lr = 0.1
I0406 08:30:36.534963  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:30:40.193478  5644 solver.cpp:218] Iteration 9516 (2.34582 iter/s, 5.11548s/12 iters), loss = 5.29061
I0406 08:30:40.193524  5644 solver.cpp:237]     Train net output #0: loss = 5.29061 (* 1 = 5.29061 loss)
I0406 08:30:40.193532  5644 sgd_solver.cpp:105] Iteration 9516, lr = 0.1
I0406 08:30:45.195170  5644 solver.cpp:218] Iteration 9528 (2.39924 iter/s, 5.00159s/12 iters), loss = 5.26216
I0406 08:30:45.195219  5644 solver.cpp:237]     Train net output #0: loss = 5.26216 (* 1 = 5.26216 loss)
I0406 08:30:45.195225  5644 sgd_solver.cpp:105] Iteration 9528, lr = 0.1
I0406 08:30:50.234128  5644 solver.cpp:218] Iteration 9540 (2.38149 iter/s, 5.03885s/12 iters), loss = 5.2763
I0406 08:30:50.234179  5644 solver.cpp:237]     Train net output #0: loss = 5.2763 (* 1 = 5.2763 loss)
I0406 08:30:50.234186  5644 sgd_solver.cpp:105] Iteration 9540, lr = 0.1
I0406 08:30:55.517273  5644 solver.cpp:218] Iteration 9552 (2.27142 iter/s, 5.28304s/12 iters), loss = 5.26877
I0406 08:30:55.517365  5644 solver.cpp:237]     Train net output #0: loss = 5.26877 (* 1 = 5.26877 loss)
I0406 08:30:55.517372  5644 sgd_solver.cpp:105] Iteration 9552, lr = 0.1
I0406 08:31:00.852512  5644 solver.cpp:218] Iteration 9564 (2.24926 iter/s, 5.33509s/12 iters), loss = 5.29197
I0406 08:31:00.852550  5644 solver.cpp:237]     Train net output #0: loss = 5.29197 (* 1 = 5.29197 loss)
I0406 08:31:00.852555  5644 sgd_solver.cpp:105] Iteration 9564, lr = 0.1
I0406 08:31:06.361071  5644 solver.cpp:218] Iteration 9576 (2.17847 iter/s, 5.50846s/12 iters), loss = 5.28342
I0406 08:31:06.367297  5644 solver.cpp:237]     Train net output #0: loss = 5.28342 (* 1 = 5.28342 loss)
I0406 08:31:06.367313  5644 sgd_solver.cpp:105] Iteration 9576, lr = 0.1
I0406 08:31:11.134786  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0406 08:31:14.142525  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0406 08:31:16.440232  5644 solver.cpp:330] Iteration 9588, Testing net (#0)
I0406 08:31:16.440251  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:31:17.083076  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:31:20.853416  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:31:20.853443  5644 solver.cpp:397]     Test net output #1: loss = 5.28771 (* 1 = 5.28771 loss)
I0406 08:31:20.991289  5644 solver.cpp:218] Iteration 9588 (0.820576 iter/s, 14.6239s/12 iters), loss = 5.29522
I0406 08:31:20.991338  5644 solver.cpp:237]     Train net output #0: loss = 5.29522 (* 1 = 5.29522 loss)
I0406 08:31:20.991346  5644 sgd_solver.cpp:105] Iteration 9588, lr = 0.1
I0406 08:31:25.266714  5644 solver.cpp:218] Iteration 9600 (2.80681 iter/s, 4.27532s/12 iters), loss = 5.28694
I0406 08:31:25.266755  5644 solver.cpp:237]     Train net output #0: loss = 5.28694 (* 1 = 5.28694 loss)
I0406 08:31:25.266760  5644 sgd_solver.cpp:105] Iteration 9600, lr = 0.1
I0406 08:31:29.123395  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:31:30.689523  5644 solver.cpp:218] Iteration 9612 (2.21292 iter/s, 5.42271s/12 iters), loss = 5.24695
I0406 08:31:30.689570  5644 solver.cpp:237]     Train net output #0: loss = 5.24695 (* 1 = 5.24695 loss)
I0406 08:31:30.689579  5644 sgd_solver.cpp:105] Iteration 9612, lr = 0.1
I0406 08:31:36.087697  5644 solver.cpp:218] Iteration 9624 (2.22302 iter/s, 5.39807s/12 iters), loss = 5.27927
I0406 08:31:36.087738  5644 solver.cpp:237]     Train net output #0: loss = 5.27927 (* 1 = 5.27927 loss)
I0406 08:31:36.087744  5644 sgd_solver.cpp:105] Iteration 9624, lr = 0.1
I0406 08:31:41.317057  5644 solver.cpp:218] Iteration 9636 (2.29478 iter/s, 5.22926s/12 iters), loss = 5.28948
I0406 08:31:41.317095  5644 solver.cpp:237]     Train net output #0: loss = 5.28948 (* 1 = 5.28948 loss)
I0406 08:31:41.317101  5644 sgd_solver.cpp:105] Iteration 9636, lr = 0.1
I0406 08:31:46.687507  5644 solver.cpp:218] Iteration 9648 (2.23449 iter/s, 5.37035s/12 iters), loss = 5.26968
I0406 08:31:46.687546  5644 solver.cpp:237]     Train net output #0: loss = 5.26968 (* 1 = 5.26968 loss)
I0406 08:31:46.687551  5644 sgd_solver.cpp:105] Iteration 9648, lr = 0.1
I0406 08:31:52.041074  5644 solver.cpp:218] Iteration 9660 (2.24154 iter/s, 5.35347s/12 iters), loss = 5.28415
I0406 08:31:52.041112  5644 solver.cpp:237]     Train net output #0: loss = 5.28415 (* 1 = 5.28415 loss)
I0406 08:31:52.041117  5644 sgd_solver.cpp:105] Iteration 9660, lr = 0.1
I0406 08:31:57.395776  5644 solver.cpp:218] Iteration 9672 (2.24106 iter/s, 5.3546s/12 iters), loss = 5.25619
I0406 08:31:57.395817  5644 solver.cpp:237]     Train net output #0: loss = 5.25619 (* 1 = 5.25619 loss)
I0406 08:31:57.395823  5644 sgd_solver.cpp:105] Iteration 9672, lr = 0.1
I0406 08:32:02.808624  5644 solver.cpp:218] Iteration 9684 (2.21699 iter/s, 5.41275s/12 iters), loss = 5.28112
I0406 08:32:02.808725  5644 solver.cpp:237]     Train net output #0: loss = 5.28112 (* 1 = 5.28112 loss)
I0406 08:32:02.808735  5644 sgd_solver.cpp:105] Iteration 9684, lr = 0.1
I0406 08:32:04.947657  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0406 08:32:07.996459  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0406 08:32:10.296913  5644 solver.cpp:330] Iteration 9690, Testing net (#0)
I0406 08:32:10.296933  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:32:10.862084  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:32:13.647964  5644 blocking_queue.cpp:49] Waiting for data
I0406 08:32:14.648229  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:32:14.648262  5644 solver.cpp:397]     Test net output #1: loss = 5.28831 (* 1 = 5.28831 loss)
I0406 08:32:16.503814  5644 solver.cpp:218] Iteration 9696 (0.876234 iter/s, 13.695s/12 iters), loss = 5.26501
I0406 08:32:16.503856  5644 solver.cpp:237]     Train net output #0: loss = 5.26501 (* 1 = 5.26501 loss)
I0406 08:32:16.503861  5644 sgd_solver.cpp:105] Iteration 9696, lr = 0.1
I0406 08:32:21.671926  5644 solver.cpp:218] Iteration 9708 (2.32198 iter/s, 5.16801s/12 iters), loss = 5.27853
I0406 08:32:21.671967  5644 solver.cpp:237]     Train net output #0: loss = 5.27853 (* 1 = 5.27853 loss)
I0406 08:32:21.671972  5644 sgd_solver.cpp:105] Iteration 9708, lr = 0.1
I0406 08:32:22.452854  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:32:27.026401  5644 solver.cpp:218] Iteration 9720 (2.24116 iter/s, 5.35437s/12 iters), loss = 5.27192
I0406 08:32:27.026455  5644 solver.cpp:237]     Train net output #0: loss = 5.27192 (* 1 = 5.27192 loss)
I0406 08:32:27.026464  5644 sgd_solver.cpp:105] Iteration 9720, lr = 0.1
I0406 08:32:32.340926  5644 solver.cpp:218] Iteration 9732 (2.25801 iter/s, 5.31441s/12 iters), loss = 5.27445
I0406 08:32:32.340965  5644 solver.cpp:237]     Train net output #0: loss = 5.27445 (* 1 = 5.27445 loss)
I0406 08:32:32.340970  5644 sgd_solver.cpp:105] Iteration 9732, lr = 0.1
I0406 08:32:37.724234  5644 solver.cpp:218] Iteration 9744 (2.22916 iter/s, 5.38321s/12 iters), loss = 5.28396
I0406 08:32:37.724397  5644 solver.cpp:237]     Train net output #0: loss = 5.28396 (* 1 = 5.28396 loss)
I0406 08:32:37.724406  5644 sgd_solver.cpp:105] Iteration 9744, lr = 0.1
I0406 08:32:43.132103  5644 solver.cpp:218] Iteration 9756 (2.21908 iter/s, 5.40765s/12 iters), loss = 5.27984
I0406 08:32:43.132143  5644 solver.cpp:237]     Train net output #0: loss = 5.27984 (* 1 = 5.27984 loss)
I0406 08:32:43.132148  5644 sgd_solver.cpp:105] Iteration 9756, lr = 0.1
I0406 08:32:48.466874  5644 solver.cpp:218] Iteration 9768 (2.24944 iter/s, 5.33467s/12 iters), loss = 5.27272
I0406 08:32:48.466918  5644 solver.cpp:237]     Train net output #0: loss = 5.27272 (* 1 = 5.27272 loss)
I0406 08:32:48.466926  5644 sgd_solver.cpp:105] Iteration 9768, lr = 0.1
I0406 08:32:53.606559  5644 solver.cpp:218] Iteration 9780 (2.33482 iter/s, 5.13958s/12 iters), loss = 5.28755
I0406 08:32:53.606613  5644 solver.cpp:237]     Train net output #0: loss = 5.28755 (* 1 = 5.28755 loss)
I0406 08:32:53.606621  5644 sgd_solver.cpp:105] Iteration 9780, lr = 0.1
I0406 08:32:58.356045  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0406 08:33:01.421241  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0406 08:33:03.729372  5644 solver.cpp:330] Iteration 9792, Testing net (#0)
I0406 08:33:03.729391  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:33:04.246642  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:33:08.069090  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:33:08.069188  5644 solver.cpp:397]     Test net output #1: loss = 5.28754 (* 1 = 5.28754 loss)
I0406 08:33:08.206326  5644 solver.cpp:218] Iteration 9792 (0.821941 iter/s, 14.5996s/12 iters), loss = 5.27746
I0406 08:33:08.207886  5644 solver.cpp:237]     Train net output #0: loss = 5.27746 (* 1 = 5.27746 loss)
I0406 08:33:08.207901  5644 sgd_solver.cpp:105] Iteration 9792, lr = 0.1
I0406 08:33:12.604101  5644 solver.cpp:218] Iteration 9804 (2.72965 iter/s, 4.39617s/12 iters), loss = 5.28241
I0406 08:33:12.604143  5644 solver.cpp:237]     Train net output #0: loss = 5.28241 (* 1 = 5.28241 loss)
I0406 08:33:12.604148  5644 sgd_solver.cpp:105] Iteration 9804, lr = 0.1
I0406 08:33:15.756407  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:33:17.848381  5644 solver.cpp:218] Iteration 9816 (2.28825 iter/s, 5.24418s/12 iters), loss = 5.27845
I0406 08:33:17.848421  5644 solver.cpp:237]     Train net output #0: loss = 5.27845 (* 1 = 5.27845 loss)
I0406 08:33:17.848428  5644 sgd_solver.cpp:105] Iteration 9816, lr = 0.1
I0406 08:33:23.132113  5644 solver.cpp:218] Iteration 9828 (2.27117 iter/s, 5.28363s/12 iters), loss = 5.28678
I0406 08:33:23.132166  5644 solver.cpp:237]     Train net output #0: loss = 5.28678 (* 1 = 5.28678 loss)
I0406 08:33:23.132175  5644 sgd_solver.cpp:105] Iteration 9828, lr = 0.1
I0406 08:33:28.340538  5644 solver.cpp:218] Iteration 9840 (2.30401 iter/s, 5.20831s/12 iters), loss = 5.27378
I0406 08:33:28.340579  5644 solver.cpp:237]     Train net output #0: loss = 5.27378 (* 1 = 5.27378 loss)
I0406 08:33:28.340585  5644 sgd_solver.cpp:105] Iteration 9840, lr = 0.1
I0406 08:33:33.593106  5644 solver.cpp:218] Iteration 9852 (2.28464 iter/s, 5.25247s/12 iters), loss = 5.28794
I0406 08:33:33.593154  5644 solver.cpp:237]     Train net output #0: loss = 5.28794 (* 1 = 5.28794 loss)
I0406 08:33:33.593163  5644 sgd_solver.cpp:105] Iteration 9852, lr = 0.1
I0406 08:33:38.778867  5644 solver.cpp:218] Iteration 9864 (2.31408 iter/s, 5.18566s/12 iters), loss = 5.27801
I0406 08:33:38.779018  5644 solver.cpp:237]     Train net output #0: loss = 5.27801 (* 1 = 5.27801 loss)
I0406 08:33:38.779027  5644 sgd_solver.cpp:105] Iteration 9864, lr = 0.1
I0406 08:33:44.119982  5644 solver.cpp:218] Iteration 9876 (2.24681 iter/s, 5.34091s/12 iters), loss = 5.28618
I0406 08:33:44.120018  5644 solver.cpp:237]     Train net output #0: loss = 5.28618 (* 1 = 5.28618 loss)
I0406 08:33:44.120023  5644 sgd_solver.cpp:105] Iteration 9876, lr = 0.1
I0406 08:33:49.323014  5644 solver.cpp:218] Iteration 9888 (2.30639 iter/s, 5.20293s/12 iters), loss = 5.29118
I0406 08:33:49.323066  5644 solver.cpp:237]     Train net output #0: loss = 5.29118 (* 1 = 5.29118 loss)
I0406 08:33:49.323074  5644 sgd_solver.cpp:105] Iteration 9888, lr = 0.1
I0406 08:33:51.542517  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0406 08:33:54.611668  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0406 08:33:56.906826  5644 solver.cpp:330] Iteration 9894, Testing net (#0)
I0406 08:33:56.906844  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:33:57.433493  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:34:01.248418  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:34:01.248448  5644 solver.cpp:397]     Test net output #1: loss = 5.28784 (* 1 = 5.28784 loss)
I0406 08:34:03.117410  5644 solver.cpp:218] Iteration 9900 (0.869929 iter/s, 13.7942s/12 iters), loss = 5.29428
I0406 08:34:03.117447  5644 solver.cpp:237]     Train net output #0: loss = 5.29428 (* 1 = 5.29428 loss)
I0406 08:34:03.117452  5644 sgd_solver.cpp:105] Iteration 9900, lr = 0.1
I0406 08:34:08.286739  5644 solver.cpp:218] Iteration 9912 (2.32143 iter/s, 5.16923s/12 iters), loss = 5.28586
I0406 08:34:08.286778  5644 solver.cpp:237]     Train net output #0: loss = 5.28586 (* 1 = 5.28586 loss)
I0406 08:34:08.286784  5644 sgd_solver.cpp:105] Iteration 9912, lr = 0.1
I0406 08:34:08.383590  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:34:13.699653  5644 solver.cpp:218] Iteration 9924 (2.21696 iter/s, 5.41282s/12 iters), loss = 5.25889
I0406 08:34:13.699771  5644 solver.cpp:237]     Train net output #0: loss = 5.25889 (* 1 = 5.25889 loss)
I0406 08:34:13.699779  5644 sgd_solver.cpp:105] Iteration 9924, lr = 0.1
I0406 08:34:18.929345  5644 solver.cpp:218] Iteration 9936 (2.29467 iter/s, 5.22952s/12 iters), loss = 5.28156
I0406 08:34:18.929381  5644 solver.cpp:237]     Train net output #0: loss = 5.28156 (* 1 = 5.28156 loss)
I0406 08:34:18.929386  5644 sgd_solver.cpp:105] Iteration 9936, lr = 0.1
I0406 08:34:24.219902  5644 solver.cpp:218] Iteration 9948 (2.26823 iter/s, 5.29046s/12 iters), loss = 5.28399
I0406 08:34:24.219940  5644 solver.cpp:237]     Train net output #0: loss = 5.28399 (* 1 = 5.28399 loss)
I0406 08:34:24.219945  5644 sgd_solver.cpp:105] Iteration 9948, lr = 0.1
I0406 08:34:29.328575  5644 solver.cpp:218] Iteration 9960 (2.34899 iter/s, 5.10858s/12 iters), loss = 5.27084
I0406 08:34:29.328615  5644 solver.cpp:237]     Train net output #0: loss = 5.27084 (* 1 = 5.27084 loss)
I0406 08:34:29.328621  5644 sgd_solver.cpp:105] Iteration 9960, lr = 0.1
I0406 08:34:34.720876  5644 solver.cpp:218] Iteration 9972 (2.22544 iter/s, 5.3922s/12 iters), loss = 5.28669
I0406 08:34:34.720932  5644 solver.cpp:237]     Train net output #0: loss = 5.28669 (* 1 = 5.28669 loss)
I0406 08:34:34.720940  5644 sgd_solver.cpp:105] Iteration 9972, lr = 0.1
I0406 08:34:40.261013  5644 solver.cpp:218] Iteration 9984 (2.16606 iter/s, 5.54002s/12 iters), loss = 5.26542
I0406 08:34:40.261060  5644 solver.cpp:237]     Train net output #0: loss = 5.26542 (* 1 = 5.26542 loss)
I0406 08:34:40.261070  5644 sgd_solver.cpp:105] Iteration 9984, lr = 0.1
I0406 08:34:45.053560  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0406 08:34:48.082744  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0406 08:34:50.386044  5644 solver.cpp:330] Iteration 9996, Testing net (#0)
I0406 08:34:50.386065  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:34:50.832880  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:34:54.873899  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:34:54.873934  5644 solver.cpp:397]     Test net output #1: loss = 5.28704 (* 1 = 5.28704 loss)
I0406 08:34:55.014596  5644 solver.cpp:218] Iteration 9996 (0.813372 iter/s, 14.7534s/12 iters), loss = 5.2876
I0406 08:34:55.014647  5644 solver.cpp:237]     Train net output #0: loss = 5.2876 (* 1 = 5.2876 loss)
I0406 08:34:55.014654  5644 sgd_solver.cpp:105] Iteration 9996, lr = 0.1
I0406 08:34:59.357867  5644 solver.cpp:218] Iteration 10008 (2.76296 iter/s, 4.34317s/12 iters), loss = 5.2578
I0406 08:34:59.357904  5644 solver.cpp:237]     Train net output #0: loss = 5.2578 (* 1 = 5.2578 loss)
I0406 08:34:59.357910  5644 sgd_solver.cpp:105] Iteration 10008, lr = 0.1
I0406 08:35:01.818838  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:35:04.826442  5644 solver.cpp:218] Iteration 10020 (2.1944 iter/s, 5.46847s/12 iters), loss = 5.28477
I0406 08:35:04.826484  5644 solver.cpp:237]     Train net output #0: loss = 5.28477 (* 1 = 5.28477 loss)
I0406 08:35:04.826490  5644 sgd_solver.cpp:105] Iteration 10020, lr = 0.1
I0406 08:35:09.999889  5644 solver.cpp:218] Iteration 10032 (2.31958 iter/s, 5.17335s/12 iters), loss = 5.27149
I0406 08:35:09.999927  5644 solver.cpp:237]     Train net output #0: loss = 5.27149 (* 1 = 5.27149 loss)
I0406 08:35:09.999933  5644 sgd_solver.cpp:105] Iteration 10032, lr = 0.1
I0406 08:35:15.224753  5644 solver.cpp:218] Iteration 10044 (2.29675 iter/s, 5.22477s/12 iters), loss = 5.29401
I0406 08:35:15.224874  5644 solver.cpp:237]     Train net output #0: loss = 5.29401 (* 1 = 5.29401 loss)
I0406 08:35:15.224880  5644 sgd_solver.cpp:105] Iteration 10044, lr = 0.1
I0406 08:35:20.451138  5644 solver.cpp:218] Iteration 10056 (2.29612 iter/s, 5.22621s/12 iters), loss = 5.26988
I0406 08:35:20.451195  5644 solver.cpp:237]     Train net output #0: loss = 5.26988 (* 1 = 5.26988 loss)
I0406 08:35:20.451203  5644 sgd_solver.cpp:105] Iteration 10056, lr = 0.1
I0406 08:35:25.725642  5644 solver.cpp:218] Iteration 10068 (2.27515 iter/s, 5.27439s/12 iters), loss = 5.26223
I0406 08:35:25.725697  5644 solver.cpp:237]     Train net output #0: loss = 5.26223 (* 1 = 5.26223 loss)
I0406 08:35:25.725706  5644 sgd_solver.cpp:105] Iteration 10068, lr = 0.1
I0406 08:35:30.996070  5644 solver.cpp:218] Iteration 10080 (2.2769 iter/s, 5.27032s/12 iters), loss = 5.29814
I0406 08:35:30.996109  5644 solver.cpp:237]     Train net output #0: loss = 5.29814 (* 1 = 5.29814 loss)
I0406 08:35:30.996114  5644 sgd_solver.cpp:105] Iteration 10080, lr = 0.1
I0406 08:35:36.142171  5644 solver.cpp:218] Iteration 10092 (2.33191 iter/s, 5.146s/12 iters), loss = 5.28612
I0406 08:35:36.142215  5644 solver.cpp:237]     Train net output #0: loss = 5.28612 (* 1 = 5.28612 loss)
I0406 08:35:36.142220  5644 sgd_solver.cpp:105] Iteration 10092, lr = 0.1
I0406 08:35:38.358888  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0406 08:35:41.379698  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0406 08:35:43.679780  5644 solver.cpp:330] Iteration 10098, Testing net (#0)
I0406 08:35:43.679800  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:35:44.140640  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:35:48.023828  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:35:48.023926  5644 solver.cpp:397]     Test net output #1: loss = 5.2879 (* 1 = 5.2879 loss)
I0406 08:35:49.952425  5644 solver.cpp:218] Iteration 10104 (0.86893 iter/s, 13.8101s/12 iters), loss = 5.27805
I0406 08:35:49.952482  5644 solver.cpp:237]     Train net output #0: loss = 5.27805 (* 1 = 5.27805 loss)
I0406 08:35:49.952491  5644 sgd_solver.cpp:105] Iteration 10104, lr = 0.1
I0406 08:35:54.632319  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:35:55.334411  5644 solver.cpp:218] Iteration 10116 (2.22971 iter/s, 5.38187s/12 iters), loss = 5.28716
I0406 08:35:55.334450  5644 solver.cpp:237]     Train net output #0: loss = 5.28716 (* 1 = 5.28716 loss)
I0406 08:35:55.334456  5644 sgd_solver.cpp:105] Iteration 10116, lr = 0.1
I0406 08:36:00.441782  5644 solver.cpp:218] Iteration 10128 (2.34959 iter/s, 5.10727s/12 iters), loss = 5.26766
I0406 08:36:00.441824  5644 solver.cpp:237]     Train net output #0: loss = 5.26766 (* 1 = 5.26766 loss)
I0406 08:36:00.441830  5644 sgd_solver.cpp:105] Iteration 10128, lr = 0.1
I0406 08:36:05.716068  5644 solver.cpp:218] Iteration 10140 (2.27523 iter/s, 5.27418s/12 iters), loss = 5.28159
I0406 08:36:05.716118  5644 solver.cpp:237]     Train net output #0: loss = 5.28159 (* 1 = 5.28159 loss)
I0406 08:36:05.716125  5644 sgd_solver.cpp:105] Iteration 10140, lr = 0.1
I0406 08:36:10.997318  5644 solver.cpp:218] Iteration 10152 (2.27224 iter/s, 5.28114s/12 iters), loss = 5.28776
I0406 08:36:10.997361  5644 solver.cpp:237]     Train net output #0: loss = 5.28776 (* 1 = 5.28776 loss)
I0406 08:36:10.997368  5644 sgd_solver.cpp:105] Iteration 10152, lr = 0.1
I0406 08:36:16.207710  5644 solver.cpp:218] Iteration 10164 (2.30313 iter/s, 5.21029s/12 iters), loss = 5.29457
I0406 08:36:16.207747  5644 solver.cpp:237]     Train net output #0: loss = 5.29457 (* 1 = 5.29457 loss)
I0406 08:36:16.207753  5644 sgd_solver.cpp:105] Iteration 10164, lr = 0.1
I0406 08:36:21.597105  5644 solver.cpp:218] Iteration 10176 (2.22664 iter/s, 5.3893s/12 iters), loss = 5.25828
I0406 08:36:21.597229  5644 solver.cpp:237]     Train net output #0: loss = 5.25828 (* 1 = 5.25828 loss)
I0406 08:36:21.597236  5644 sgd_solver.cpp:105] Iteration 10176, lr = 0.1
I0406 08:36:26.971524  5644 solver.cpp:218] Iteration 10188 (2.23288 iter/s, 5.37424s/12 iters), loss = 5.29282
I0406 08:36:26.971568  5644 solver.cpp:237]     Train net output #0: loss = 5.29282 (* 1 = 5.29282 loss)
I0406 08:36:26.971576  5644 sgd_solver.cpp:105] Iteration 10188, lr = 0.1
I0406 08:36:31.621811  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0406 08:36:34.628036  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0406 08:36:36.938545  5644 solver.cpp:330] Iteration 10200, Testing net (#0)
I0406 08:36:36.938565  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:36:37.339398  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:36:41.392863  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:36:41.392912  5644 solver.cpp:397]     Test net output #1: loss = 5.28739 (* 1 = 5.28739 loss)
I0406 08:36:41.533526  5644 solver.cpp:218] Iteration 10200 (0.824073 iter/s, 14.5618s/12 iters), loss = 5.27162
I0406 08:36:41.533577  5644 solver.cpp:237]     Train net output #0: loss = 5.27162 (* 1 = 5.27162 loss)
I0406 08:36:41.533586  5644 sgd_solver.cpp:105] Iteration 10200, lr = 0.1
I0406 08:36:45.742883  5644 solver.cpp:218] Iteration 10212 (2.85086 iter/s, 4.20926s/12 iters), loss = 5.27158
I0406 08:36:45.742920  5644 solver.cpp:237]     Train net output #0: loss = 5.27158 (* 1 = 5.27158 loss)
I0406 08:36:45.742926  5644 sgd_solver.cpp:105] Iteration 10212, lr = 0.1
I0406 08:36:47.347441  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:36:51.150002  5644 solver.cpp:218] Iteration 10224 (2.21934 iter/s, 5.40702s/12 iters), loss = 5.28711
I0406 08:36:51.150049  5644 solver.cpp:237]     Train net output #0: loss = 5.28711 (* 1 = 5.28711 loss)
I0406 08:36:51.150056  5644 sgd_solver.cpp:105] Iteration 10224, lr = 0.1
I0406 08:36:56.468446  5644 solver.cpp:218] Iteration 10236 (2.25634 iter/s, 5.31834s/12 iters), loss = 5.26182
I0406 08:36:56.468531  5644 solver.cpp:237]     Train net output #0: loss = 5.26182 (* 1 = 5.26182 loss)
I0406 08:36:56.468538  5644 sgd_solver.cpp:105] Iteration 10236, lr = 0.1
I0406 08:37:01.590268  5644 solver.cpp:218] Iteration 10248 (2.34298 iter/s, 5.12168s/12 iters), loss = 5.26907
I0406 08:37:01.590309  5644 solver.cpp:237]     Train net output #0: loss = 5.26907 (* 1 = 5.26907 loss)
I0406 08:37:01.590314  5644 sgd_solver.cpp:105] Iteration 10248, lr = 0.1
I0406 08:37:06.885562  5644 solver.cpp:218] Iteration 10260 (2.26621 iter/s, 5.29519s/12 iters), loss = 5.2712
I0406 08:37:06.885617  5644 solver.cpp:237]     Train net output #0: loss = 5.2712 (* 1 = 5.2712 loss)
I0406 08:37:06.885625  5644 sgd_solver.cpp:105] Iteration 10260, lr = 0.1
I0406 08:37:12.115486  5644 solver.cpp:218] Iteration 10272 (2.29454 iter/s, 5.22981s/12 iters), loss = 5.29461
I0406 08:37:12.115528  5644 solver.cpp:237]     Train net output #0: loss = 5.29461 (* 1 = 5.29461 loss)
I0406 08:37:12.115535  5644 sgd_solver.cpp:105] Iteration 10272, lr = 0.1
I0406 08:37:17.417799  5644 solver.cpp:218] Iteration 10284 (2.26321 iter/s, 5.30221s/12 iters), loss = 5.28314
I0406 08:37:17.417847  5644 solver.cpp:237]     Train net output #0: loss = 5.28314 (* 1 = 5.28314 loss)
I0406 08:37:17.417855  5644 sgd_solver.cpp:105] Iteration 10284, lr = 0.1
I0406 08:37:22.669680  5644 solver.cpp:218] Iteration 10296 (2.28494 iter/s, 5.25178s/12 iters), loss = 5.29727
I0406 08:37:22.669728  5644 solver.cpp:237]     Train net output #0: loss = 5.29727 (* 1 = 5.29727 loss)
I0406 08:37:22.669736  5644 sgd_solver.cpp:105] Iteration 10296, lr = 0.1
I0406 08:37:24.825040  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10302.caffemodel
I0406 08:37:27.840447  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10302.solverstate
I0406 08:37:30.152109  5644 solver.cpp:330] Iteration 10302, Testing net (#0)
I0406 08:37:30.152133  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:37:30.528533  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:37:34.569219  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:37:34.569254  5644 solver.cpp:397]     Test net output #1: loss = 5.28756 (* 1 = 5.28756 loss)
I0406 08:37:36.510588  5644 solver.cpp:218] Iteration 10308 (0.867006 iter/s, 13.8407s/12 iters), loss = 5.27673
I0406 08:37:36.510638  5644 solver.cpp:237]     Train net output #0: loss = 5.27673 (* 1 = 5.27673 loss)
I0406 08:37:36.510646  5644 sgd_solver.cpp:105] Iteration 10308, lr = 0.1
I0406 08:37:40.291678  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:37:41.808840  5644 solver.cpp:218] Iteration 10320 (2.26494 iter/s, 5.29815s/12 iters), loss = 5.2537
I0406 08:37:41.808879  5644 solver.cpp:237]     Train net output #0: loss = 5.2537 (* 1 = 5.2537 loss)
I0406 08:37:41.808894  5644 sgd_solver.cpp:105] Iteration 10320, lr = 0.1
I0406 08:37:47.105129  5644 solver.cpp:218] Iteration 10332 (2.26578 iter/s, 5.29619s/12 iters), loss = 5.27028
I0406 08:37:47.105170  5644 solver.cpp:237]     Train net output #0: loss = 5.27028 (* 1 = 5.27028 loss)
I0406 08:37:47.105176  5644 sgd_solver.cpp:105] Iteration 10332, lr = 0.1
I0406 08:37:52.392868  5644 solver.cpp:218] Iteration 10344 (2.26944 iter/s, 5.28764s/12 iters), loss = 5.29332
I0406 08:37:52.392915  5644 solver.cpp:237]     Train net output #0: loss = 5.29332 (* 1 = 5.29332 loss)
I0406 08:37:52.392920  5644 sgd_solver.cpp:105] Iteration 10344, lr = 0.1
I0406 08:37:57.402653  5644 solver.cpp:218] Iteration 10356 (2.39536 iter/s, 5.00969s/12 iters), loss = 5.27019
I0406 08:37:57.402693  5644 solver.cpp:237]     Train net output #0: loss = 5.27019 (* 1 = 5.27019 loss)
I0406 08:37:57.402699  5644 sgd_solver.cpp:105] Iteration 10356, lr = 0.1
I0406 08:38:02.716064  5644 solver.cpp:218] Iteration 10368 (2.25848 iter/s, 5.31332s/12 iters), loss = 5.28304
I0406 08:38:02.716181  5644 solver.cpp:237]     Train net output #0: loss = 5.28304 (* 1 = 5.28304 loss)
I0406 08:38:02.716189  5644 sgd_solver.cpp:105] Iteration 10368, lr = 0.1
I0406 08:38:08.229995  5644 solver.cpp:218] Iteration 10380 (2.17637 iter/s, 5.51376s/12 iters), loss = 5.26224
I0406 08:38:08.230031  5644 solver.cpp:237]     Train net output #0: loss = 5.26224 (* 1 = 5.26224 loss)
I0406 08:38:08.230036  5644 sgd_solver.cpp:105] Iteration 10380, lr = 0.1
I0406 08:38:13.504555  5644 solver.cpp:218] Iteration 10392 (2.27511 iter/s, 5.27447s/12 iters), loss = 5.28759
I0406 08:38:13.504604  5644 solver.cpp:237]     Train net output #0: loss = 5.28759 (* 1 = 5.28759 loss)
I0406 08:38:13.504612  5644 sgd_solver.cpp:105] Iteration 10392, lr = 0.1
I0406 08:38:18.216069  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10404.caffemodel
I0406 08:38:21.290230  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10404.solverstate
I0406 08:38:23.599593  5644 solver.cpp:330] Iteration 10404, Testing net (#0)
I0406 08:38:23.599617  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:38:23.871058  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:38:24.333703  5644 blocking_queue.cpp:49] Waiting for data
I0406 08:38:27.940371  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:38:27.940404  5644 solver.cpp:397]     Test net output #1: loss = 5.28762 (* 1 = 5.28762 loss)
I0406 08:38:28.080868  5644 solver.cpp:218] Iteration 10404 (0.823263 iter/s, 14.5761s/12 iters), loss = 5.26961
I0406 08:38:28.080922  5644 solver.cpp:237]     Train net output #0: loss = 5.26961 (* 1 = 5.26961 loss)
I0406 08:38:28.080929  5644 sgd_solver.cpp:105] Iteration 10404, lr = 0.1
I0406 08:38:32.402249  5644 solver.cpp:218] Iteration 10416 (2.77696 iter/s, 4.32127s/12 iters), loss = 5.28351
I0406 08:38:32.402290  5644 solver.cpp:237]     Train net output #0: loss = 5.28351 (* 1 = 5.28351 loss)
I0406 08:38:32.402297  5644 sgd_solver.cpp:105] Iteration 10416, lr = 0.1
I0406 08:38:33.377135  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:38:37.775135  5644 solver.cpp:218] Iteration 10428 (2.23348 iter/s, 5.37279s/12 iters), loss = 5.27262
I0406 08:38:37.775177  5644 solver.cpp:237]     Train net output #0: loss = 5.27262 (* 1 = 5.27262 loss)
I0406 08:38:37.775182  5644 sgd_solver.cpp:105] Iteration 10428, lr = 0.1
I0406 08:38:42.842376  5644 solver.cpp:218] Iteration 10440 (2.3682 iter/s, 5.06714s/12 iters), loss = 5.27906
I0406 08:38:42.842417  5644 solver.cpp:237]     Train net output #0: loss = 5.27906 (* 1 = 5.27906 loss)
I0406 08:38:42.842422  5644 sgd_solver.cpp:105] Iteration 10440, lr = 0.1
I0406 08:38:48.247278  5644 solver.cpp:218] Iteration 10452 (2.22025 iter/s, 5.40481s/12 iters), loss = 5.27635
I0406 08:38:48.247315  5644 solver.cpp:237]     Train net output #0: loss = 5.27635 (* 1 = 5.27635 loss)
I0406 08:38:48.247320  5644 sgd_solver.cpp:105] Iteration 10452, lr = 0.1
I0406 08:38:53.467597  5644 solver.cpp:218] Iteration 10464 (2.29875 iter/s, 5.22022s/12 iters), loss = 5.28355
I0406 08:38:53.467635  5644 solver.cpp:237]     Train net output #0: loss = 5.28355 (* 1 = 5.28355 loss)
I0406 08:38:53.467641  5644 sgd_solver.cpp:105] Iteration 10464, lr = 0.1
I0406 08:38:58.959719  5644 solver.cpp:218] Iteration 10476 (2.18499 iter/s, 5.49202s/12 iters), loss = 5.28295
I0406 08:38:58.959767  5644 solver.cpp:237]     Train net output #0: loss = 5.28295 (* 1 = 5.28295 loss)
I0406 08:38:58.959775  5644 sgd_solver.cpp:105] Iteration 10476, lr = 0.1
I0406 08:39:04.135866  5644 solver.cpp:218] Iteration 10488 (2.31837 iter/s, 5.17605s/12 iters), loss = 5.28616
I0406 08:39:04.135957  5644 solver.cpp:237]     Train net output #0: loss = 5.28616 (* 1 = 5.28616 loss)
I0406 08:39:04.135963  5644 sgd_solver.cpp:105] Iteration 10488, lr = 0.1
I0406 08:39:09.454241  5644 solver.cpp:218] Iteration 10500 (2.25639 iter/s, 5.31823s/12 iters), loss = 5.27456
I0406 08:39:09.454293  5644 solver.cpp:237]     Train net output #0: loss = 5.27456 (* 1 = 5.27456 loss)
I0406 08:39:09.454300  5644 sgd_solver.cpp:105] Iteration 10500, lr = 0.1
I0406 08:39:11.635231  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10506.caffemodel
I0406 08:39:14.712864  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10506.solverstate
I0406 08:39:17.010659  5644 solver.cpp:330] Iteration 10506, Testing net (#0)
I0406 08:39:17.010679  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:39:17.307983  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:39:21.340742  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:39:21.340775  5644 solver.cpp:397]     Test net output #1: loss = 5.28758 (* 1 = 5.28758 loss)
I0406 08:39:23.261466  5644 solver.cpp:218] Iteration 10512 (0.869121 iter/s, 13.8071s/12 iters), loss = 5.28564
I0406 08:39:23.261516  5644 solver.cpp:237]     Train net output #0: loss = 5.28564 (* 1 = 5.28564 loss)
I0406 08:39:23.261523  5644 sgd_solver.cpp:105] Iteration 10512, lr = 0.1
I0406 08:39:26.418702  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:39:28.552904  5644 solver.cpp:218] Iteration 10524 (2.26786 iter/s, 5.29133s/12 iters), loss = 5.2796
I0406 08:39:28.552943  5644 solver.cpp:237]     Train net output #0: loss = 5.2796 (* 1 = 5.2796 loss)
I0406 08:39:28.552949  5644 sgd_solver.cpp:105] Iteration 10524, lr = 0.1
I0406 08:39:33.730792  5644 solver.cpp:218] Iteration 10536 (2.31759 iter/s, 5.17778s/12 iters), loss = 5.2854
I0406 08:39:33.730851  5644 solver.cpp:237]     Train net output #0: loss = 5.2854 (* 1 = 5.2854 loss)
I0406 08:39:33.730860  5644 sgd_solver.cpp:105] Iteration 10536, lr = 0.1
I0406 08:39:39.080772  5644 solver.cpp:218] Iteration 10548 (2.24305 iter/s, 5.34987s/12 iters), loss = 5.2756
I0406 08:39:39.080929  5644 solver.cpp:237]     Train net output #0: loss = 5.2756 (* 1 = 5.2756 loss)
I0406 08:39:39.080940  5644 sgd_solver.cpp:105] Iteration 10548, lr = 0.1
I0406 08:39:44.250645  5644 solver.cpp:218] Iteration 10560 (2.32123 iter/s, 5.16967s/12 iters), loss = 5.28131
I0406 08:39:44.250679  5644 solver.cpp:237]     Train net output #0: loss = 5.28131 (* 1 = 5.28131 loss)
I0406 08:39:44.250685  5644 sgd_solver.cpp:105] Iteration 10560, lr = 0.1
I0406 08:39:49.420758  5644 solver.cpp:218] Iteration 10572 (2.32107 iter/s, 5.17003s/12 iters), loss = 5.27675
I0406 08:39:49.420794  5644 solver.cpp:237]     Train net output #0: loss = 5.27675 (* 1 = 5.27675 loss)
I0406 08:39:49.420799  5644 sgd_solver.cpp:105] Iteration 10572, lr = 0.1
I0406 08:39:54.732236  5644 solver.cpp:218] Iteration 10584 (2.2593 iter/s, 5.31138s/12 iters), loss = 5.28623
I0406 08:39:54.732293  5644 solver.cpp:237]     Train net output #0: loss = 5.28623 (* 1 = 5.28623 loss)
I0406 08:39:54.732301  5644 sgd_solver.cpp:105] Iteration 10584, lr = 0.1
I0406 08:40:00.086388  5644 solver.cpp:218] Iteration 10596 (2.2413 iter/s, 5.35404s/12 iters), loss = 5.27981
I0406 08:40:00.086441  5644 solver.cpp:237]     Train net output #0: loss = 5.27981 (* 1 = 5.27981 loss)
I0406 08:40:00.086450  5644 sgd_solver.cpp:105] Iteration 10596, lr = 0.1
I0406 08:40:04.767117  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10608.caffemodel
I0406 08:40:07.857908  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10608.solverstate
I0406 08:40:10.154575  5644 solver.cpp:330] Iteration 10608, Testing net (#0)
I0406 08:40:10.154675  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:40:10.370476  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:40:14.517659  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:40:14.517686  5644 solver.cpp:397]     Test net output #1: loss = 5.28777 (* 1 = 5.28777 loss)
I0406 08:40:14.658212  5644 solver.cpp:218] Iteration 10608 (0.823517 iter/s, 14.5716s/12 iters), loss = 5.29007
I0406 08:40:14.658262  5644 solver.cpp:237]     Train net output #0: loss = 5.29007 (* 1 = 5.29007 loss)
I0406 08:40:14.658269  5644 sgd_solver.cpp:105] Iteration 10608, lr = 0.1
I0406 08:40:18.811255  5644 solver.cpp:218] Iteration 10620 (2.88951 iter/s, 4.15295s/12 iters), loss = 5.28565
I0406 08:40:18.811288  5644 solver.cpp:237]     Train net output #0: loss = 5.28565 (* 1 = 5.28565 loss)
I0406 08:40:18.811293  5644 sgd_solver.cpp:105] Iteration 10620, lr = 0.1
I0406 08:40:18.898388  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:40:24.123716  5644 solver.cpp:218] Iteration 10632 (2.25888 iter/s, 5.31237s/12 iters), loss = 5.2595
I0406 08:40:24.123756  5644 solver.cpp:237]     Train net output #0: loss = 5.2595 (* 1 = 5.2595 loss)
I0406 08:40:24.123762  5644 sgd_solver.cpp:105] Iteration 10632, lr = 0.1
I0406 08:40:29.272570  5644 solver.cpp:218] Iteration 10644 (2.33066 iter/s, 5.14876s/12 iters), loss = 5.28105
I0406 08:40:29.272620  5644 solver.cpp:237]     Train net output #0: loss = 5.28105 (* 1 = 5.28105 loss)
I0406 08:40:29.272629  5644 sgd_solver.cpp:105] Iteration 10644, lr = 0.1
I0406 08:40:34.211024  5644 solver.cpp:218] Iteration 10656 (2.42996 iter/s, 4.93835s/12 iters), loss = 5.28449
I0406 08:40:34.211068  5644 solver.cpp:237]     Train net output #0: loss = 5.28449 (* 1 = 5.28449 loss)
I0406 08:40:34.211074  5644 sgd_solver.cpp:105] Iteration 10656, lr = 0.1
I0406 08:40:39.582801  5644 solver.cpp:218] Iteration 10668 (2.23394 iter/s, 5.37168s/12 iters), loss = 5.27289
I0406 08:40:39.582839  5644 solver.cpp:237]     Train net output #0: loss = 5.27289 (* 1 = 5.27289 loss)
I0406 08:40:39.582844  5644 sgd_solver.cpp:105] Iteration 10668, lr = 0.1
I0406 08:40:44.957383  5644 solver.cpp:218] Iteration 10680 (2.23277 iter/s, 5.37448s/12 iters), loss = 5.27953
I0406 08:40:44.957595  5644 solver.cpp:237]     Train net output #0: loss = 5.27953 (* 1 = 5.27953 loss)
I0406 08:40:44.957605  5644 sgd_solver.cpp:105] Iteration 10680, lr = 0.1
I0406 08:40:50.319542  5644 solver.cpp:218] Iteration 10692 (2.23802 iter/s, 5.36189s/12 iters), loss = 5.26676
I0406 08:40:50.319592  5644 solver.cpp:237]     Train net output #0: loss = 5.26676 (* 1 = 5.26676 loss)
I0406 08:40:50.319602  5644 sgd_solver.cpp:105] Iteration 10692, lr = 0.1
I0406 08:40:55.623324  5644 solver.cpp:218] Iteration 10704 (2.26258 iter/s, 5.30368s/12 iters), loss = 5.29175
I0406 08:40:55.623373  5644 solver.cpp:237]     Train net output #0: loss = 5.29175 (* 1 = 5.29175 loss)
I0406 08:40:55.623383  5644 sgd_solver.cpp:105] Iteration 10704, lr = 0.1
I0406 08:40:57.774062  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10710.caffemodel
I0406 08:41:00.891693  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10710.solverstate
I0406 08:41:03.209712  5644 solver.cpp:330] Iteration 10710, Testing net (#0)
I0406 08:41:03.209733  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:41:03.432196  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:41:07.663743  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:41:07.663779  5644 solver.cpp:397]     Test net output #1: loss = 5.28799 (* 1 = 5.28799 loss)
I0406 08:41:09.407621  5644 solver.cpp:218] Iteration 10716 (0.870566 iter/s, 13.7841s/12 iters), loss = 5.25633
I0406 08:41:09.407673  5644 solver.cpp:237]     Train net output #0: loss = 5.25633 (* 1 = 5.25633 loss)
I0406 08:41:09.407681  5644 sgd_solver.cpp:105] Iteration 10716, lr = 0.1
I0406 08:41:11.740631  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:41:14.700855  5644 solver.cpp:218] Iteration 10728 (2.26709 iter/s, 5.29313s/12 iters), loss = 5.28897
I0406 08:41:14.700896  5644 solver.cpp:237]     Train net output #0: loss = 5.28897 (* 1 = 5.28897 loss)
I0406 08:41:14.700902  5644 sgd_solver.cpp:105] Iteration 10728, lr = 0.1
I0406 08:41:20.097848  5644 solver.cpp:218] Iteration 10740 (2.2235 iter/s, 5.39689s/12 iters), loss = 5.27212
I0406 08:41:20.097934  5644 solver.cpp:237]     Train net output #0: loss = 5.27212 (* 1 = 5.27212 loss)
I0406 08:41:20.097941  5644 sgd_solver.cpp:105] Iteration 10740, lr = 0.1
I0406 08:41:25.396365  5644 solver.cpp:218] Iteration 10752 (2.26485 iter/s, 5.29837s/12 iters), loss = 5.29449
I0406 08:41:25.396414  5644 solver.cpp:237]     Train net output #0: loss = 5.29449 (* 1 = 5.29449 loss)
I0406 08:41:25.396421  5644 sgd_solver.cpp:105] Iteration 10752, lr = 0.1
I0406 08:41:30.587581  5644 solver.cpp:218] Iteration 10764 (2.31164 iter/s, 5.19111s/12 iters), loss = 5.27144
I0406 08:41:30.587621  5644 solver.cpp:237]     Train net output #0: loss = 5.27144 (* 1 = 5.27144 loss)
I0406 08:41:30.587627  5644 sgd_solver.cpp:105] Iteration 10764, lr = 0.1
I0406 08:41:35.861016  5644 solver.cpp:218] Iteration 10776 (2.2756 iter/s, 5.27333s/12 iters), loss = 5.26246
I0406 08:41:35.861070  5644 solver.cpp:237]     Train net output #0: loss = 5.26246 (* 1 = 5.26246 loss)
I0406 08:41:35.861079  5644 sgd_solver.cpp:105] Iteration 10776, lr = 0.1
I0406 08:41:41.226738  5644 solver.cpp:218] Iteration 10788 (2.23647 iter/s, 5.36561s/12 iters), loss = 5.299
I0406 08:41:41.226789  5644 solver.cpp:237]     Train net output #0: loss = 5.299 (* 1 = 5.299 loss)
I0406 08:41:41.226799  5644 sgd_solver.cpp:105] Iteration 10788, lr = 0.1
I0406 08:41:46.453642  5644 solver.cpp:218] Iteration 10800 (2.29586 iter/s, 5.2268s/12 iters), loss = 5.27754
I0406 08:41:46.453691  5644 solver.cpp:237]     Train net output #0: loss = 5.27754 (* 1 = 5.27754 loss)
I0406 08:41:46.453698  5644 sgd_solver.cpp:105] Iteration 10800, lr = 0.1
I0406 08:41:51.126008  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10812.caffemodel
I0406 08:41:54.168311  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10812.solverstate
I0406 08:41:56.486348  5644 solver.cpp:330] Iteration 10812, Testing net (#0)
I0406 08:41:56.486369  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:41:56.623996  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:42:00.746533  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:42:00.746567  5644 solver.cpp:397]     Test net output #1: loss = 5.28813 (* 1 = 5.28813 loss)
I0406 08:42:00.887809  5644 solver.cpp:218] Iteration 10812 (0.831371 iter/s, 14.434s/12 iters), loss = 5.28061
I0406 08:42:00.889374  5644 solver.cpp:237]     Train net output #0: loss = 5.28061 (* 1 = 5.28061 loss)
I0406 08:42:00.889385  5644 sgd_solver.cpp:105] Iteration 10812, lr = 0.1
I0406 08:42:04.551331  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:42:05.221134  5644 solver.cpp:218] Iteration 10824 (2.77026 iter/s, 4.33172s/12 iters), loss = 5.29362
I0406 08:42:05.221181  5644 solver.cpp:237]     Train net output #0: loss = 5.29362 (* 1 = 5.29362 loss)
I0406 08:42:05.221189  5644 sgd_solver.cpp:105] Iteration 10824, lr = 0.1
I0406 08:42:10.505326  5644 solver.cpp:218] Iteration 10836 (2.27097 iter/s, 5.28408s/12 iters), loss = 5.26681
I0406 08:42:10.505375  5644 solver.cpp:237]     Train net output #0: loss = 5.26681 (* 1 = 5.26681 loss)
I0406 08:42:10.505383  5644 sgd_solver.cpp:105] Iteration 10836, lr = 0.1
I0406 08:42:15.620355  5644 solver.cpp:218] Iteration 10848 (2.34607 iter/s, 5.11493s/12 iters), loss = 5.27673
I0406 08:42:15.620391  5644 solver.cpp:237]     Train net output #0: loss = 5.27673 (* 1 = 5.27673 loss)
I0406 08:42:15.620396  5644 sgd_solver.cpp:105] Iteration 10848, lr = 0.1
I0406 08:42:20.914867  5644 solver.cpp:218] Iteration 10860 (2.26654 iter/s, 5.29442s/12 iters), loss = 5.29239
I0406 08:42:20.914911  5644 solver.cpp:237]     Train net output #0: loss = 5.29239 (* 1 = 5.29239 loss)
I0406 08:42:20.914916  5644 sgd_solver.cpp:105] Iteration 10860, lr = 0.1
I0406 08:42:25.964308  5644 solver.cpp:218] Iteration 10872 (2.37655 iter/s, 5.04934s/12 iters), loss = 5.29614
I0406 08:42:25.964434  5644 solver.cpp:237]     Train net output #0: loss = 5.29614 (* 1 = 5.29614 loss)
I0406 08:42:25.964444  5644 sgd_solver.cpp:105] Iteration 10872, lr = 0.1
I0406 08:42:31.253481  5644 solver.cpp:218] Iteration 10884 (2.26886 iter/s, 5.289s/12 iters), loss = 5.25675
I0406 08:42:31.253520  5644 solver.cpp:237]     Train net output #0: loss = 5.25675 (* 1 = 5.25675 loss)
I0406 08:42:31.253525  5644 sgd_solver.cpp:105] Iteration 10884, lr = 0.1
I0406 08:42:36.606228  5644 solver.cpp:218] Iteration 10896 (2.24188 iter/s, 5.35265s/12 iters), loss = 5.28473
I0406 08:42:36.606267  5644 solver.cpp:237]     Train net output #0: loss = 5.28473 (* 1 = 5.28473 loss)
I0406 08:42:36.606272  5644 sgd_solver.cpp:105] Iteration 10896, lr = 0.1
I0406 08:42:41.732445  5644 solver.cpp:218] Iteration 10908 (2.34095 iter/s, 5.12612s/12 iters), loss = 5.27731
I0406 08:42:41.732492  5644 solver.cpp:237]     Train net output #0: loss = 5.27731 (* 1 = 5.27731 loss)
I0406 08:42:41.732499  5644 sgd_solver.cpp:105] Iteration 10908, lr = 0.1
I0406 08:42:43.763690  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10914.caffemodel
I0406 08:42:47.078063  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10914.solverstate
I0406 08:42:49.372282  5644 solver.cpp:330] Iteration 10914, Testing net (#0)
I0406 08:42:49.372304  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:42:49.475669  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:42:53.762770  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:42:53.762811  5644 solver.cpp:397]     Test net output #1: loss = 5.28802 (* 1 = 5.28802 loss)
I0406 08:42:55.711421  5644 solver.cpp:218] Iteration 10920 (0.858442 iter/s, 13.9788s/12 iters), loss = 5.27446
I0406 08:42:55.711468  5644 solver.cpp:237]     Train net output #0: loss = 5.27446 (* 1 = 5.27446 loss)
I0406 08:42:55.711477  5644 sgd_solver.cpp:105] Iteration 10920, lr = 0.1
I0406 08:42:57.132598  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:43:00.849387  5644 solver.cpp:218] Iteration 10932 (2.3356 iter/s, 5.13786s/12 iters), loss = 5.28908
I0406 08:43:00.849428  5644 solver.cpp:237]     Train net output #0: loss = 5.28908 (* 1 = 5.28908 loss)
I0406 08:43:00.849434  5644 sgd_solver.cpp:105] Iteration 10932, lr = 0.1
I0406 08:43:05.907589  5644 solver.cpp:218] Iteration 10944 (2.37243 iter/s, 5.0581s/12 iters), loss = 5.26294
I0406 08:43:05.907640  5644 solver.cpp:237]     Train net output #0: loss = 5.26294 (* 1 = 5.26294 loss)
I0406 08:43:05.907649  5644 sgd_solver.cpp:105] Iteration 10944, lr = 0.1
I0406 08:43:11.073529  5644 solver.cpp:218] Iteration 10956 (2.32296 iter/s, 5.16583s/12 iters), loss = 5.26769
I0406 08:43:11.073570  5644 solver.cpp:237]     Train net output #0: loss = 5.26769 (* 1 = 5.26769 loss)
I0406 08:43:11.073576  5644 sgd_solver.cpp:105] Iteration 10956, lr = 0.1
I0406 08:43:16.189652  5644 solver.cpp:218] Iteration 10968 (2.34557 iter/s, 5.11603s/12 iters), loss = 5.27262
I0406 08:43:16.189693  5644 solver.cpp:237]     Train net output #0: loss = 5.27262 (* 1 = 5.27262 loss)
I0406 08:43:16.189698  5644 sgd_solver.cpp:105] Iteration 10968, lr = 0.1
I0406 08:43:21.536306  5644 solver.cpp:218] Iteration 10980 (2.24444 iter/s, 5.34655s/12 iters), loss = 5.29402
I0406 08:43:21.536352  5644 solver.cpp:237]     Train net output #0: loss = 5.29402 (* 1 = 5.29402 loss)
I0406 08:43:21.536360  5644 sgd_solver.cpp:105] Iteration 10980, lr = 0.1
I0406 08:43:26.758236  5644 solver.cpp:218] Iteration 10992 (2.29805 iter/s, 5.22182s/12 iters), loss = 5.28196
I0406 08:43:26.758286  5644 solver.cpp:237]     Train net output #0: loss = 5.28196 (* 1 = 5.28196 loss)
I0406 08:43:26.758294  5644 sgd_solver.cpp:105] Iteration 10992, lr = 0.1
I0406 08:43:31.728030  5644 solver.cpp:218] Iteration 11004 (2.41464 iter/s, 4.96969s/12 iters), loss = 5.2962
I0406 08:43:31.728125  5644 solver.cpp:237]     Train net output #0: loss = 5.2962 (* 1 = 5.2962 loss)
I0406 08:43:31.728132  5644 sgd_solver.cpp:105] Iteration 11004, lr = 0.1
I0406 08:43:36.420084  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11016.caffemodel
I0406 08:43:39.517457  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11016.solverstate
I0406 08:43:43.052726  5644 solver.cpp:330] Iteration 11016, Testing net (#0)
I0406 08:43:43.052745  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:43:43.101269  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:43:47.329252  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:43:47.329288  5644 solver.cpp:397]     Test net output #1: loss = 5.28775 (* 1 = 5.28775 loss)
I0406 08:43:47.467833  5644 solver.cpp:218] Iteration 11016 (0.762409 iter/s, 15.7396s/12 iters), loss = 5.2715
I0406 08:43:47.467871  5644 solver.cpp:237]     Train net output #0: loss = 5.2715 (* 1 = 5.2715 loss)
I0406 08:43:47.467877  5644 sgd_solver.cpp:105] Iteration 11016, lr = 0.1
I0406 08:43:47.966167  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:43:50.410848  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:43:51.764575  5644 solver.cpp:218] Iteration 11028 (2.79287 iter/s, 4.29666s/12 iters), loss = 5.26014
I0406 08:43:51.764611  5644 solver.cpp:237]     Train net output #0: loss = 5.26014 (* 1 = 5.26014 loss)
I0406 08:43:51.764616  5644 sgd_solver.cpp:105] Iteration 11028, lr = 0.1
I0406 08:43:56.892102  5644 solver.cpp:218] Iteration 11040 (2.34035 iter/s, 5.12743s/12 iters), loss = 5.2704
I0406 08:43:56.892148  5644 solver.cpp:237]     Train net output #0: loss = 5.2704 (* 1 = 5.2704 loss)
I0406 08:43:56.892155  5644 sgd_solver.cpp:105] Iteration 11040, lr = 0.1
I0406 08:44:02.152662  5644 solver.cpp:218] Iteration 11052 (2.28117 iter/s, 5.26046s/12 iters), loss = 5.28388
I0406 08:44:02.152783  5644 solver.cpp:237]     Train net output #0: loss = 5.28388 (* 1 = 5.28388 loss)
I0406 08:44:02.152789  5644 sgd_solver.cpp:105] Iteration 11052, lr = 0.1
I0406 08:44:07.442915  5644 solver.cpp:218] Iteration 11064 (2.2684 iter/s, 5.29008s/12 iters), loss = 5.26563
I0406 08:44:07.442951  5644 solver.cpp:237]     Train net output #0: loss = 5.26563 (* 1 = 5.26563 loss)
I0406 08:44:07.442957  5644 sgd_solver.cpp:105] Iteration 11064, lr = 0.1
I0406 08:44:12.500512  5644 solver.cpp:218] Iteration 11076 (2.37271 iter/s, 5.05751s/12 iters), loss = 5.27555
I0406 08:44:12.500546  5644 solver.cpp:237]     Train net output #0: loss = 5.27555 (* 1 = 5.27555 loss)
I0406 08:44:12.500551  5644 sgd_solver.cpp:105] Iteration 11076, lr = 0.1
I0406 08:44:17.843608  5644 solver.cpp:218] Iteration 11088 (2.24593 iter/s, 5.343s/12 iters), loss = 5.26284
I0406 08:44:17.843657  5644 solver.cpp:237]     Train net output #0: loss = 5.26284 (* 1 = 5.26284 loss)
I0406 08:44:17.843665  5644 sgd_solver.cpp:105] Iteration 11088, lr = 0.1
I0406 08:44:21.289543  5644 blocking_queue.cpp:49] Waiting for data
I0406 08:44:22.938351  5644 solver.cpp:218] Iteration 11100 (2.35542 iter/s, 5.09464s/12 iters), loss = 5.27794
I0406 08:44:22.938390  5644 solver.cpp:237]     Train net output #0: loss = 5.27794 (* 1 = 5.27794 loss)
I0406 08:44:22.938395  5644 sgd_solver.cpp:105] Iteration 11100, lr = 0.1
I0406 08:44:28.126821  5644 solver.cpp:218] Iteration 11112 (2.31286 iter/s, 5.18837s/12 iters), loss = 5.26882
I0406 08:44:28.126876  5644 solver.cpp:237]     Train net output #0: loss = 5.26882 (* 1 = 5.26882 loss)
I0406 08:44:28.126884  5644 sgd_solver.cpp:105] Iteration 11112, lr = 0.1
I0406 08:44:30.197557  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11118.caffemodel
I0406 08:44:33.201978  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11118.solverstate
I0406 08:44:35.536371  5644 solver.cpp:330] Iteration 11118, Testing net (#0)
I0406 08:44:35.536391  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:44:39.823098  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:44:39.823133  5644 solver.cpp:397]     Test net output #1: loss = 5.28784 (* 1 = 5.28784 loss)
I0406 08:44:40.491366  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:44:41.699251  5644 solver.cpp:218] Iteration 11124 (0.884157 iter/s, 13.5723s/12 iters), loss = 5.2819
I0406 08:44:41.699292  5644 solver.cpp:237]     Train net output #0: loss = 5.2819 (* 1 = 5.2819 loss)
I0406 08:44:41.699298  5644 sgd_solver.cpp:105] Iteration 11124, lr = 0.1
I0406 08:44:42.690538  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:44:47.075860  5644 solver.cpp:218] Iteration 11136 (2.23193 iter/s, 5.3765s/12 iters), loss = 5.27183
I0406 08:44:47.075911  5644 solver.cpp:237]     Train net output #0: loss = 5.27183 (* 1 = 5.27183 loss)
I0406 08:44:47.075922  5644 sgd_solver.cpp:105] Iteration 11136, lr = 0.1
I0406 08:44:52.138378  5644 solver.cpp:218] Iteration 11148 (2.37041 iter/s, 5.06242s/12 iters), loss = 5.2872
I0406 08:44:52.138415  5644 solver.cpp:237]     Train net output #0: loss = 5.2872 (* 1 = 5.2872 loss)
I0406 08:44:52.138422  5644 sgd_solver.cpp:105] Iteration 11148, lr = 0.1
I0406 08:44:57.215631  5644 solver.cpp:218] Iteration 11160 (2.36353 iter/s, 5.07716s/12 iters), loss = 5.27784
I0406 08:44:57.215679  5644 solver.cpp:237]     Train net output #0: loss = 5.27784 (* 1 = 5.27784 loss)
I0406 08:44:57.215688  5644 sgd_solver.cpp:105] Iteration 11160, lr = 0.1
I0406 08:45:02.593899  5644 solver.cpp:218] Iteration 11172 (2.23124 iter/s, 5.37816s/12 iters), loss = 5.2851
I0406 08:45:02.593937  5644 solver.cpp:237]     Train net output #0: loss = 5.2851 (* 1 = 5.2851 loss)
I0406 08:45:02.593943  5644 sgd_solver.cpp:105] Iteration 11172, lr = 0.1
I0406 08:45:07.863971  5644 solver.cpp:218] Iteration 11184 (2.27705 iter/s, 5.26997s/12 iters), loss = 5.2926
I0406 08:45:07.864140  5644 solver.cpp:237]     Train net output #0: loss = 5.2926 (* 1 = 5.2926 loss)
I0406 08:45:07.864148  5644 sgd_solver.cpp:105] Iteration 11184, lr = 0.1
I0406 08:45:12.848547  5644 solver.cpp:218] Iteration 11196 (2.40753 iter/s, 4.98436s/12 iters), loss = 5.28495
I0406 08:45:12.848587  5644 solver.cpp:237]     Train net output #0: loss = 5.28495 (* 1 = 5.28495 loss)
I0406 08:45:12.848592  5644 sgd_solver.cpp:105] Iteration 11196, lr = 0.1
I0406 08:45:18.260926  5644 solver.cpp:218] Iteration 11208 (2.21718 iter/s, 5.41229s/12 iters), loss = 5.26917
I0406 08:45:18.260967  5644 solver.cpp:237]     Train net output #0: loss = 5.26917 (* 1 = 5.26917 loss)
I0406 08:45:18.260972  5644 sgd_solver.cpp:105] Iteration 11208, lr = 0.1
I0406 08:45:23.325572  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11220.caffemodel
I0406 08:45:26.357887  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11220.solverstate
I0406 08:45:28.658469  5644 solver.cpp:330] Iteration 11220, Testing net (#0)
I0406 08:45:28.658493  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:45:33.159009  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:45:33.159061  5644 solver.cpp:397]     Test net output #1: loss = 5.28822 (* 1 = 5.28822 loss)
I0406 08:45:33.299723  5644 solver.cpp:218] Iteration 11220 (0.797945 iter/s, 15.0386s/12 iters), loss = 5.28993
I0406 08:45:33.299777  5644 solver.cpp:237]     Train net output #0: loss = 5.28993 (* 1 = 5.28993 loss)
I0406 08:45:33.299784  5644 sgd_solver.cpp:105] Iteration 11220, lr = 0.1
I0406 08:45:33.699224  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:45:35.807072  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:45:37.935006  5644 solver.cpp:218] Iteration 11232 (2.5889 iter/s, 4.63518s/12 iters), loss = 5.27485
I0406 08:45:37.936328  5644 solver.cpp:237]     Train net output #0: loss = 5.27485 (* 1 = 5.27485 loss)
I0406 08:45:37.936337  5644 sgd_solver.cpp:105] Iteration 11232, lr = 0.1
I0406 08:45:43.298660  5644 solver.cpp:218] Iteration 11244 (2.23785 iter/s, 5.36228s/12 iters), loss = 5.28472
I0406 08:45:43.298704  5644 solver.cpp:237]     Train net output #0: loss = 5.28472 (* 1 = 5.28472 loss)
I0406 08:45:43.298709  5644 sgd_solver.cpp:105] Iteration 11244, lr = 0.1
I0406 08:45:48.413391  5644 solver.cpp:218] Iteration 11256 (2.34621 iter/s, 5.11463s/12 iters), loss = 5.27559
I0406 08:45:48.413429  5644 solver.cpp:237]     Train net output #0: loss = 5.27559 (* 1 = 5.27559 loss)
I0406 08:45:48.413435  5644 sgd_solver.cpp:105] Iteration 11256, lr = 0.1
I0406 08:45:53.820803  5644 solver.cpp:218] Iteration 11268 (2.21922 iter/s, 5.40731s/12 iters), loss = 5.28925
I0406 08:45:53.820850  5644 solver.cpp:237]     Train net output #0: loss = 5.28925 (* 1 = 5.28925 loss)
I0406 08:45:53.820858  5644 sgd_solver.cpp:105] Iteration 11268, lr = 0.1
I0406 08:45:59.147780  5644 solver.cpp:218] Iteration 11280 (2.25273 iter/s, 5.32687s/12 iters), loss = 5.28171
I0406 08:45:59.147830  5644 solver.cpp:237]     Train net output #0: loss = 5.28171 (* 1 = 5.28171 loss)
I0406 08:45:59.147838  5644 sgd_solver.cpp:105] Iteration 11280, lr = 0.1
I0406 08:46:04.313199  5644 solver.cpp:218] Iteration 11292 (2.32319 iter/s, 5.16531s/12 iters), loss = 5.28353
I0406 08:46:04.313235  5644 solver.cpp:237]     Train net output #0: loss = 5.28353 (* 1 = 5.28353 loss)
I0406 08:46:04.313241  5644 sgd_solver.cpp:105] Iteration 11292, lr = 0.1
I0406 08:46:09.666184  5644 solver.cpp:218] Iteration 11304 (2.24178 iter/s, 5.35289s/12 iters), loss = 5.27544
I0406 08:46:09.666324  5644 solver.cpp:237]     Train net output #0: loss = 5.27544 (* 1 = 5.27544 loss)
I0406 08:46:09.666333  5644 sgd_solver.cpp:105] Iteration 11304, lr = 0.1
I0406 08:46:15.230520  5644 solver.cpp:218] Iteration 11316 (2.15667 iter/s, 5.56415s/12 iters), loss = 5.28652
I0406 08:46:15.230558  5644 solver.cpp:237]     Train net output #0: loss = 5.28652 (* 1 = 5.28652 loss)
I0406 08:46:15.230563  5644 sgd_solver.cpp:105] Iteration 11316, lr = 0.1
I0406 08:46:17.414832  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11322.caffemodel
I0406 08:46:20.488375  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11322.solverstate
I0406 08:46:22.788775  5644 solver.cpp:330] Iteration 11322, Testing net (#0)
I0406 08:46:22.788795  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:46:27.239821  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:46:27.239854  5644 solver.cpp:397]     Test net output #1: loss = 5.28803 (* 1 = 5.28803 loss)
I0406 08:46:27.616209  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:46:29.232463  5644 solver.cpp:218] Iteration 11328 (0.857034 iter/s, 14.0018s/12 iters), loss = 5.27795
I0406 08:46:29.232517  5644 solver.cpp:237]     Train net output #0: loss = 5.27795 (* 1 = 5.27795 loss)
I0406 08:46:29.232524  5644 sgd_solver.cpp:105] Iteration 11328, lr = 0.1
I0406 08:46:29.387636  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:46:34.461839  5644 solver.cpp:218] Iteration 11340 (2.29478 iter/s, 5.22927s/12 iters), loss = 5.26705
I0406 08:46:34.461890  5644 solver.cpp:237]     Train net output #0: loss = 5.26705 (* 1 = 5.26705 loss)
I0406 08:46:34.461897  5644 sgd_solver.cpp:105] Iteration 11340, lr = 0.1
I0406 08:46:39.870772  5644 solver.cpp:218] Iteration 11352 (2.21859 iter/s, 5.40883s/12 iters), loss = 5.27728
I0406 08:46:39.870841  5644 solver.cpp:237]     Train net output #0: loss = 5.27728 (* 1 = 5.27728 loss)
I0406 08:46:39.870846  5644 sgd_solver.cpp:105] Iteration 11352, lr = 0.1
I0406 08:46:45.026909  5644 solver.cpp:218] Iteration 11364 (2.32738 iter/s, 5.15602s/12 iters), loss = 5.28577
I0406 08:46:45.026945  5644 solver.cpp:237]     Train net output #0: loss = 5.28577 (* 1 = 5.28577 loss)
I0406 08:46:45.026952  5644 sgd_solver.cpp:105] Iteration 11364, lr = 0.1
I0406 08:46:50.419466  5644 solver.cpp:218] Iteration 11376 (2.22533 iter/s, 5.39246s/12 iters), loss = 5.27917
I0406 08:46:50.419523  5644 solver.cpp:237]     Train net output #0: loss = 5.27917 (* 1 = 5.27917 loss)
I0406 08:46:50.419531  5644 sgd_solver.cpp:105] Iteration 11376, lr = 0.1
I0406 08:46:55.759577  5644 solver.cpp:218] Iteration 11388 (2.24719 iter/s, 5.34s/12 iters), loss = 5.2807
I0406 08:46:55.759625  5644 solver.cpp:237]     Train net output #0: loss = 5.2807 (* 1 = 5.2807 loss)
I0406 08:46:55.759634  5644 sgd_solver.cpp:105] Iteration 11388, lr = 0.1
I0406 08:47:00.919956  5644 solver.cpp:218] Iteration 11400 (2.32546 iter/s, 5.16028s/12 iters), loss = 5.26634
I0406 08:47:00.919997  5644 solver.cpp:237]     Train net output #0: loss = 5.26634 (* 1 = 5.26634 loss)
I0406 08:47:00.920003  5644 sgd_solver.cpp:105] Iteration 11400, lr = 0.1
I0406 08:47:06.180295  5644 solver.cpp:218] Iteration 11412 (2.28126 iter/s, 5.26024s/12 iters), loss = 5.28901
I0406 08:47:06.180335  5644 solver.cpp:237]     Train net output #0: loss = 5.28901 (* 1 = 5.28901 loss)
I0406 08:47:06.180339  5644 sgd_solver.cpp:105] Iteration 11412, lr = 0.1
I0406 08:47:10.970291  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11424.caffemodel
I0406 08:47:13.989593  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11424.solverstate
I0406 08:47:16.290423  5644 solver.cpp:330] Iteration 11424, Testing net (#0)
I0406 08:47:16.290443  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:47:20.781376  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:47:20.781414  5644 solver.cpp:397]     Test net output #1: loss = 5.28819 (* 1 = 5.28819 loss)
I0406 08:47:20.922909  5644 solver.cpp:218] Iteration 11424 (0.813976 iter/s, 14.7424s/12 iters), loss = 5.26736
I0406 08:47:20.922948  5644 solver.cpp:237]     Train net output #0: loss = 5.26736 (* 1 = 5.26736 loss)
I0406 08:47:20.922955  5644 sgd_solver.cpp:105] Iteration 11424, lr = 0.1
I0406 08:47:21.228747  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:47:22.481510  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:47:25.269558  5644 solver.cpp:218] Iteration 11436 (2.76081 iter/s, 4.34656s/12 iters), loss = 5.28033
I0406 08:47:25.269608  5644 solver.cpp:237]     Train net output #0: loss = 5.28033 (* 1 = 5.28033 loss)
I0406 08:47:25.269618  5644 sgd_solver.cpp:105] Iteration 11436, lr = 0.1
I0406 08:47:30.340673  5644 solver.cpp:218] Iteration 11448 (2.36639 iter/s, 5.07101s/12 iters), loss = 5.27007
I0406 08:47:30.340715  5644 solver.cpp:237]     Train net output #0: loss = 5.27007 (* 1 = 5.27007 loss)
I0406 08:47:30.340720  5644 sgd_solver.cpp:105] Iteration 11448, lr = 0.1
I0406 08:47:35.476822  5644 solver.cpp:218] Iteration 11460 (2.33643 iter/s, 5.13605s/12 iters), loss = 5.29168
I0406 08:47:35.476859  5644 solver.cpp:237]     Train net output #0: loss = 5.29168 (* 1 = 5.29168 loss)
I0406 08:47:35.476866  5644 sgd_solver.cpp:105] Iteration 11460, lr = 0.1
I0406 08:47:40.816546  5644 solver.cpp:218] Iteration 11472 (2.24735 iter/s, 5.33963s/12 iters), loss = 5.26722
I0406 08:47:40.816591  5644 solver.cpp:237]     Train net output #0: loss = 5.26722 (* 1 = 5.26722 loss)
I0406 08:47:40.816596  5644 sgd_solver.cpp:105] Iteration 11472, lr = 0.1
I0406 08:47:46.130370  5644 solver.cpp:218] Iteration 11484 (2.2583 iter/s, 5.31372s/12 iters), loss = 5.26186
I0406 08:47:46.130465  5644 solver.cpp:237]     Train net output #0: loss = 5.26186 (* 1 = 5.26186 loss)
I0406 08:47:46.130472  5644 sgd_solver.cpp:105] Iteration 11484, lr = 0.1
I0406 08:47:51.503892  5644 solver.cpp:218] Iteration 11496 (2.23324 iter/s, 5.37337s/12 iters), loss = 5.29547
I0406 08:47:51.503931  5644 solver.cpp:237]     Train net output #0: loss = 5.29547 (* 1 = 5.29547 loss)
I0406 08:47:51.503937  5644 sgd_solver.cpp:105] Iteration 11496, lr = 0.1
I0406 08:47:56.999099  5644 solver.cpp:218] Iteration 11508 (2.18376 iter/s, 5.49511s/12 iters), loss = 5.28162
I0406 08:47:56.999136  5644 solver.cpp:237]     Train net output #0: loss = 5.28162 (* 1 = 5.28162 loss)
I0406 08:47:56.999142  5644 sgd_solver.cpp:105] Iteration 11508, lr = 0.1
I0406 08:48:02.427489  5644 solver.cpp:218] Iteration 11520 (2.21064 iter/s, 5.42829s/12 iters), loss = 5.28447
I0406 08:48:02.427546  5644 solver.cpp:237]     Train net output #0: loss = 5.28447 (* 1 = 5.28447 loss)
I0406 08:48:02.427554  5644 sgd_solver.cpp:105] Iteration 11520, lr = 0.1
I0406 08:48:04.556740  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11526.caffemodel
I0406 08:48:07.554316  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11526.solverstate
I0406 08:48:09.860081  5644 solver.cpp:330] Iteration 11526, Testing net (#0)
I0406 08:48:09.860105  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:48:14.263150  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 08:48:14.263181  5644 solver.cpp:397]     Test net output #1: loss = 5.28766 (* 1 = 5.28766 loss)
I0406 08:48:14.367360  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:48:15.511912  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:48:16.161067  5644 solver.cpp:218] Iteration 11532 (0.873782 iter/s, 13.7334s/12 iters), loss = 5.29065
I0406 08:48:16.161185  5644 solver.cpp:237]     Train net output #0: loss = 5.29065 (* 1 = 5.29065 loss)
I0406 08:48:16.161191  5644 sgd_solver.cpp:105] Iteration 11532, lr = 0.1
I0406 08:48:21.248271  5644 solver.cpp:218] Iteration 11544 (2.35894 iter/s, 5.08703s/12 iters), loss = 5.2675
I0406 08:48:21.248318  5644 solver.cpp:237]     Train net output #0: loss = 5.2675 (* 1 = 5.2675 loss)
I0406 08:48:21.248327  5644 sgd_solver.cpp:105] Iteration 11544, lr = 0.1
I0406 08:48:26.423549  5644 solver.cpp:218] Iteration 11556 (2.31876 iter/s, 5.17517s/12 iters), loss = 5.27204
I0406 08:48:26.423599  5644 solver.cpp:237]     Train net output #0: loss = 5.27204 (* 1 = 5.27204 loss)
I0406 08:48:26.423605  5644 sgd_solver.cpp:105] Iteration 11556, lr = 0.1
I0406 08:48:31.687709  5644 solver.cpp:218] Iteration 11568 (2.27961 iter/s, 5.26405s/12 iters), loss = 5.2899
I0406 08:48:31.687762  5644 solver.cpp:237]     Train net output #0: loss = 5.2899 (* 1 = 5.2899 loss)
I0406 08:48:31.687769  5644 sgd_solver.cpp:105] Iteration 11568, lr = 0.1
I0406 08:48:37.031793  5644 solver.cpp:218] Iteration 11580 (2.24552 iter/s, 5.34398s/12 iters), loss = 5.29514
I0406 08:48:37.031842  5644 solver.cpp:237]     Train net output #0: loss = 5.29514 (* 1 = 5.29514 loss)
I0406 08:48:37.031850  5644 sgd_solver.cpp:105] Iteration 11580, lr = 0.1
I0406 08:48:42.281601  5644 solver.cpp:218] Iteration 11592 (2.28584 iter/s, 5.2497s/12 iters), loss = 5.25923
I0406 08:48:42.281649  5644 solver.cpp:237]     Train net output #0: loss = 5.25923 (* 1 = 5.25923 loss)
I0406 08:48:42.281657  5644 sgd_solver.cpp:105] Iteration 11592, lr = 0.1
I0406 08:48:47.486814  5644 solver.cpp:218] Iteration 11604 (2.30543 iter/s, 5.2051s/12 iters), loss = 5.28738
I0406 08:48:47.486935  5644 solver.cpp:237]     Train net output #0: loss = 5.28738 (* 1 = 5.28738 loss)
I0406 08:48:47.486944  5644 sgd_solver.cpp:105] Iteration 11604, lr = 0.1
I0406 08:48:52.745177  5644 solver.cpp:218] Iteration 11616 (2.28216 iter/s, 5.25819s/12 iters), loss = 5.28201
I0406 08:48:52.745214  5644 solver.cpp:237]     Train net output #0: loss = 5.28201 (* 1 = 5.28201 loss)
I0406 08:48:52.745219  5644 sgd_solver.cpp:105] Iteration 11616, lr = 0.1
I0406 08:48:57.374593  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11628.caffemodel
I0406 08:49:00.441171  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11628.solverstate
I0406 08:49:02.767984  5644 solver.cpp:330] Iteration 11628, Testing net (#0)
I0406 08:49:02.768004  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:49:07.073223  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:49:07.073252  5644 solver.cpp:397]     Test net output #1: loss = 5.28824 (* 1 = 5.28824 loss)
I0406 08:49:07.140049  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:49:07.213541  5644 solver.cpp:218] Iteration 11628 (0.829406 iter/s, 14.4682s/12 iters), loss = 5.27814
I0406 08:49:07.215116  5644 solver.cpp:237]     Train net output #0: loss = 5.27814 (* 1 = 5.27814 loss)
I0406 08:49:07.215127  5644 sgd_solver.cpp:105] Iteration 11628, lr = 0.1
I0406 08:49:07.959285  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:49:11.690124  5644 solver.cpp:218] Iteration 11640 (2.68159 iter/s, 4.47496s/12 iters), loss = 5.28458
I0406 08:49:11.690168  5644 solver.cpp:237]     Train net output #0: loss = 5.28458 (* 1 = 5.28458 loss)
I0406 08:49:11.690176  5644 sgd_solver.cpp:105] Iteration 11640, lr = 0.1
I0406 08:49:16.869661  5644 solver.cpp:218] Iteration 11652 (2.31685 iter/s, 5.17944s/12 iters), loss = 5.26421
I0406 08:49:16.869709  5644 solver.cpp:237]     Train net output #0: loss = 5.26421 (* 1 = 5.26421 loss)
I0406 08:49:16.869716  5644 sgd_solver.cpp:105] Iteration 11652, lr = 0.1
I0406 08:49:22.260936  5644 solver.cpp:218] Iteration 11664 (2.22586 iter/s, 5.39117s/12 iters), loss = 5.26927
I0406 08:49:22.261080  5644 solver.cpp:237]     Train net output #0: loss = 5.26927 (* 1 = 5.26927 loss)
I0406 08:49:22.261090  5644 sgd_solver.cpp:105] Iteration 11664, lr = 0.1
I0406 08:49:27.676749  5644 solver.cpp:218] Iteration 11676 (2.21581 iter/s, 5.41562s/12 iters), loss = 5.26859
I0406 08:49:27.676789  5644 solver.cpp:237]     Train net output #0: loss = 5.26859 (* 1 = 5.26859 loss)
I0406 08:49:27.676793  5644 sgd_solver.cpp:105] Iteration 11676, lr = 0.1
I0406 08:49:32.913179  5644 solver.cpp:218] Iteration 11688 (2.29168 iter/s, 5.23633s/12 iters), loss = 5.29486
I0406 08:49:32.913224  5644 solver.cpp:237]     Train net output #0: loss = 5.29486 (* 1 = 5.29486 loss)
I0406 08:49:32.913233  5644 sgd_solver.cpp:105] Iteration 11688, lr = 0.1
I0406 08:49:38.115140  5644 solver.cpp:218] Iteration 11700 (2.30687 iter/s, 5.20186s/12 iters), loss = 5.27636
I0406 08:49:38.115182  5644 solver.cpp:237]     Train net output #0: loss = 5.27636 (* 1 = 5.27636 loss)
I0406 08:49:38.115188  5644 sgd_solver.cpp:105] Iteration 11700, lr = 0.1
I0406 08:49:43.647939  5644 solver.cpp:218] Iteration 11712 (2.16892 iter/s, 5.5327s/12 iters), loss = 5.29511
I0406 08:49:43.647982  5644 solver.cpp:237]     Train net output #0: loss = 5.29511 (* 1 = 5.29511 loss)
I0406 08:49:43.647990  5644 sgd_solver.cpp:105] Iteration 11712, lr = 0.1
I0406 08:49:48.933138  5644 solver.cpp:218] Iteration 11724 (2.27053 iter/s, 5.2851s/12 iters), loss = 5.2623
I0406 08:49:48.933195  5644 solver.cpp:237]     Train net output #0: loss = 5.2623 (* 1 = 5.2623 loss)
I0406 08:49:48.933204  5644 sgd_solver.cpp:105] Iteration 11724, lr = 0.1
I0406 08:49:51.114912  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11730.caffemodel
I0406 08:49:54.194151  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11730.solverstate
I0406 08:49:56.531574  5644 solver.cpp:330] Iteration 11730, Testing net (#0)
I0406 08:49:56.531594  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:50:00.882489  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:50:00.912382  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:50:00.912411  5644 solver.cpp:397]     Test net output #1: loss = 5.28851 (* 1 = 5.28851 loss)
I0406 08:50:01.622423  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:50:02.875689  5644 solver.cpp:218] Iteration 11736 (0.860686 iter/s, 13.9424s/12 iters), loss = 5.26278
I0406 08:50:02.875725  5644 solver.cpp:237]     Train net output #0: loss = 5.26278 (* 1 = 5.26278 loss)
I0406 08:50:02.875731  5644 sgd_solver.cpp:105] Iteration 11736, lr = 0.1
I0406 08:50:08.178344  5644 solver.cpp:218] Iteration 11748 (2.26306 iter/s, 5.30256s/12 iters), loss = 5.26915
I0406 08:50:08.178395  5644 solver.cpp:237]     Train net output #0: loss = 5.26915 (* 1 = 5.26915 loss)
I0406 08:50:08.178403  5644 sgd_solver.cpp:105] Iteration 11748, lr = 0.1
I0406 08:50:13.363214  5644 solver.cpp:218] Iteration 11760 (2.31447 iter/s, 5.18476s/12 iters), loss = 5.28378
I0406 08:50:13.363262  5644 solver.cpp:237]     Train net output #0: loss = 5.28378 (* 1 = 5.28378 loss)
I0406 08:50:13.363270  5644 sgd_solver.cpp:105] Iteration 11760, lr = 0.1
I0406 08:50:18.671106  5644 solver.cpp:218] Iteration 11772 (2.26083 iter/s, 5.30778s/12 iters), loss = 5.26787
I0406 08:50:18.671159  5644 solver.cpp:237]     Train net output #0: loss = 5.26787 (* 1 = 5.26787 loss)
I0406 08:50:18.671167  5644 sgd_solver.cpp:105] Iteration 11772, lr = 0.1
I0406 08:50:22.463240  5644 blocking_queue.cpp:49] Waiting for data
I0406 08:50:23.954869  5644 solver.cpp:218] Iteration 11784 (2.27116 iter/s, 5.28365s/12 iters), loss = 5.27905
I0406 08:50:23.954917  5644 solver.cpp:237]     Train net output #0: loss = 5.27905 (* 1 = 5.27905 loss)
I0406 08:50:23.954926  5644 sgd_solver.cpp:105] Iteration 11784, lr = 0.1
I0406 08:50:29.126330  5644 solver.cpp:218] Iteration 11796 (2.32047 iter/s, 5.17136s/12 iters), loss = 5.26566
I0406 08:50:29.126457  5644 solver.cpp:237]     Train net output #0: loss = 5.26566 (* 1 = 5.26566 loss)
I0406 08:50:29.126466  5644 sgd_solver.cpp:105] Iteration 11796, lr = 0.1
I0406 08:50:34.616261  5644 solver.cpp:218] Iteration 11808 (2.18589 iter/s, 5.48975s/12 iters), loss = 5.27804
I0406 08:50:34.616302  5644 solver.cpp:237]     Train net output #0: loss = 5.27804 (* 1 = 5.27804 loss)
I0406 08:50:34.616307  5644 sgd_solver.cpp:105] Iteration 11808, lr = 0.1
I0406 08:50:39.848675  5644 solver.cpp:218] Iteration 11820 (2.29344 iter/s, 5.23232s/12 iters), loss = 5.2689
I0406 08:50:39.848716  5644 solver.cpp:237]     Train net output #0: loss = 5.2689 (* 1 = 5.2689 loss)
I0406 08:50:39.848721  5644 sgd_solver.cpp:105] Iteration 11820, lr = 0.1
I0406 08:50:44.621032  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11832.caffemodel
I0406 08:50:47.556859  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11832.solverstate
I0406 08:50:49.858553  5644 solver.cpp:330] Iteration 11832, Testing net (#0)
I0406 08:50:49.858572  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:50:54.148442  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:50:54.210261  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 08:50:54.210301  5644 solver.cpp:397]     Test net output #1: loss = 5.28839 (* 1 = 5.28839 loss)
I0406 08:50:54.349071  5644 solver.cpp:218] Iteration 11832 (0.827573 iter/s, 14.5002s/12 iters), loss = 5.27569
I0406 08:50:54.349108  5644 solver.cpp:237]     Train net output #0: loss = 5.27569 (* 1 = 5.27569 loss)
I0406 08:50:54.349113  5644 sgd_solver.cpp:105] Iteration 11832, lr = 0.1
I0406 08:50:54.431913  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:50:58.855633  5644 solver.cpp:218] Iteration 11844 (2.66284 iter/s, 4.50647s/12 iters), loss = 5.27083
I0406 08:50:58.855667  5644 solver.cpp:237]     Train net output #0: loss = 5.27083 (* 1 = 5.27083 loss)
I0406 08:50:58.855672  5644 sgd_solver.cpp:105] Iteration 11844, lr = 0.1
I0406 08:51:03.973387  5644 solver.cpp:218] Iteration 11856 (2.34482 iter/s, 5.11766s/12 iters), loss = 5.28681
I0406 08:51:03.973476  5644 solver.cpp:237]     Train net output #0: loss = 5.28681 (* 1 = 5.28681 loss)
I0406 08:51:03.973482  5644 sgd_solver.cpp:105] Iteration 11856, lr = 0.1
I0406 08:51:09.233489  5644 solver.cpp:218] Iteration 11868 (2.28139 iter/s, 5.25996s/12 iters), loss = 5.27789
I0406 08:51:09.233531  5644 solver.cpp:237]     Train net output #0: loss = 5.27789 (* 1 = 5.27789 loss)
I0406 08:51:09.233538  5644 sgd_solver.cpp:105] Iteration 11868, lr = 0.1
I0406 08:51:14.356367  5644 solver.cpp:218] Iteration 11880 (2.34248 iter/s, 5.12278s/12 iters), loss = 5.28326
I0406 08:51:14.356423  5644 solver.cpp:237]     Train net output #0: loss = 5.28326 (* 1 = 5.28326 loss)
I0406 08:51:14.356432  5644 sgd_solver.cpp:105] Iteration 11880, lr = 0.1
I0406 08:51:19.665935  5644 solver.cpp:218] Iteration 11892 (2.26012 iter/s, 5.30946s/12 iters), loss = 5.29953
I0406 08:51:19.665973  5644 solver.cpp:237]     Train net output #0: loss = 5.29953 (* 1 = 5.29953 loss)
I0406 08:51:19.665979  5644 sgd_solver.cpp:105] Iteration 11892, lr = 0.1
I0406 08:51:24.948158  5644 solver.cpp:218] Iteration 11904 (2.27181 iter/s, 5.28212s/12 iters), loss = 5.27247
I0406 08:51:24.948199  5644 solver.cpp:237]     Train net output #0: loss = 5.27247 (* 1 = 5.27247 loss)
I0406 08:51:24.948204  5644 sgd_solver.cpp:105] Iteration 11904, lr = 0.1
I0406 08:51:30.235648  5644 solver.cpp:218] Iteration 11916 (2.26955 iter/s, 5.28739s/12 iters), loss = 5.27322
I0406 08:51:30.235688  5644 solver.cpp:237]     Train net output #0: loss = 5.27322 (* 1 = 5.27322 loss)
I0406 08:51:30.235694  5644 sgd_solver.cpp:105] Iteration 11916, lr = 0.1
I0406 08:51:35.487139  5644 solver.cpp:218] Iteration 11928 (2.28511 iter/s, 5.2514s/12 iters), loss = 5.29162
I0406 08:51:35.487239  5644 solver.cpp:237]     Train net output #0: loss = 5.29162 (* 1 = 5.29162 loss)
I0406 08:51:35.487246  5644 sgd_solver.cpp:105] Iteration 11928, lr = 0.1
I0406 08:51:37.511622  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_11934.caffemodel
I0406 08:51:38.366335  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:51:40.470408  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_11934.solverstate
I0406 08:51:42.772085  5644 solver.cpp:330] Iteration 11934, Testing net (#0)
I0406 08:51:42.772105  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:51:46.984695  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:51:47.090936  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:51:47.090967  5644 solver.cpp:397]     Test net output #1: loss = 5.28852 (* 1 = 5.28852 loss)
I0406 08:51:49.044481  5644 solver.cpp:218] Iteration 11940 (0.885144 iter/s, 13.5571s/12 iters), loss = 5.27788
I0406 08:51:49.044533  5644 solver.cpp:237]     Train net output #0: loss = 5.27788 (* 1 = 5.27788 loss)
I0406 08:51:49.044540  5644 sgd_solver.cpp:105] Iteration 11940, lr = 0.1
I0406 08:51:54.468217  5644 solver.cpp:218] Iteration 11952 (2.21254 iter/s, 5.42363s/12 iters), loss = 5.28633
I0406 08:51:54.468264  5644 solver.cpp:237]     Train net output #0: loss = 5.28633 (* 1 = 5.28633 loss)
I0406 08:51:54.468272  5644 sgd_solver.cpp:105] Iteration 11952, lr = 0.1
I0406 08:51:59.833789  5644 solver.cpp:218] Iteration 11964 (2.23653 iter/s, 5.36546s/12 iters), loss = 5.27643
I0406 08:51:59.833848  5644 solver.cpp:237]     Train net output #0: loss = 5.27643 (* 1 = 5.27643 loss)
I0406 08:51:59.833856  5644 sgd_solver.cpp:105] Iteration 11964, lr = 0.1
I0406 08:52:05.204597  5644 solver.cpp:218] Iteration 11976 (2.23435 iter/s, 5.3707s/12 iters), loss = 5.28556
I0406 08:52:05.204638  5644 solver.cpp:237]     Train net output #0: loss = 5.28556 (* 1 = 5.28556 loss)
I0406 08:52:05.204644  5644 sgd_solver.cpp:105] Iteration 11976, lr = 0.1
I0406 08:52:10.476985  5644 solver.cpp:218] Iteration 11988 (2.27605 iter/s, 5.27229s/12 iters), loss = 5.28716
I0406 08:52:10.477105  5644 solver.cpp:237]     Train net output #0: loss = 5.28716 (* 1 = 5.28716 loss)
I0406 08:52:10.477111  5644 sgd_solver.cpp:105] Iteration 11988, lr = 0.1
I0406 08:52:15.790031  5644 solver.cpp:218] Iteration 12000 (2.25867 iter/s, 5.31287s/12 iters), loss = 5.28034
I0406 08:52:15.790078  5644 solver.cpp:237]     Train net output #0: loss = 5.28034 (* 1 = 5.28034 loss)
I0406 08:52:15.790086  5644 sgd_solver.cpp:105] Iteration 12000, lr = 0.1
I0406 08:52:21.149255  5644 solver.cpp:218] Iteration 12012 (2.23917 iter/s, 5.35912s/12 iters), loss = 5.27248
I0406 08:52:21.149304  5644 solver.cpp:237]     Train net output #0: loss = 5.27248 (* 1 = 5.27248 loss)
I0406 08:52:21.149312  5644 sgd_solver.cpp:105] Iteration 12012, lr = 0.1
I0406 08:52:26.461371  5644 solver.cpp:218] Iteration 12024 (2.25903 iter/s, 5.31201s/12 iters), loss = 5.29315
I0406 08:52:26.461418  5644 solver.cpp:237]     Train net output #0: loss = 5.29315 (* 1 = 5.29315 loss)
I0406 08:52:26.461426  5644 sgd_solver.cpp:105] Iteration 12024, lr = 0.1
I0406 08:52:30.952814  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12036.caffemodel
I0406 08:52:31.575395  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:52:33.998697  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12036.solverstate
I0406 08:52:36.301898  5644 solver.cpp:330] Iteration 12036, Testing net (#0)
I0406 08:52:36.301916  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:52:40.517285  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:52:40.654328  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:52:40.654354  5644 solver.cpp:397]     Test net output #1: loss = 5.28873 (* 1 = 5.28873 loss)
I0406 08:52:40.794147  5644 solver.cpp:218] Iteration 12036 (0.837252 iter/s, 14.3326s/12 iters), loss = 5.27763
I0406 08:52:40.794195  5644 solver.cpp:237]     Train net output #0: loss = 5.27763 (* 1 = 5.27763 loss)
I0406 08:52:40.794203  5644 sgd_solver.cpp:105] Iteration 12036, lr = 0.1
I0406 08:52:45.157071  5644 solver.cpp:218] Iteration 12048 (2.75051 iter/s, 4.36282s/12 iters), loss = 5.26543
I0406 08:52:45.157109  5644 solver.cpp:237]     Train net output #0: loss = 5.26543 (* 1 = 5.26543 loss)
I0406 08:52:45.157116  5644 sgd_solver.cpp:105] Iteration 12048, lr = 0.1
I0406 08:52:50.546737  5644 solver.cpp:218] Iteration 12060 (2.22652 iter/s, 5.38957s/12 iters), loss = 5.28021
I0406 08:52:50.546789  5644 solver.cpp:237]     Train net output #0: loss = 5.28021 (* 1 = 5.28021 loss)
I0406 08:52:50.546797  5644 sgd_solver.cpp:105] Iteration 12060, lr = 0.1
I0406 08:52:55.919224  5644 solver.cpp:218] Iteration 12072 (2.23365 iter/s, 5.37238s/12 iters), loss = 5.28521
I0406 08:52:55.919266  5644 solver.cpp:237]     Train net output #0: loss = 5.28521 (* 1 = 5.28521 loss)
I0406 08:52:55.919271  5644 sgd_solver.cpp:105] Iteration 12072, lr = 0.1
I0406 08:53:01.328593  5644 solver.cpp:218] Iteration 12084 (2.21842 iter/s, 5.40927s/12 iters), loss = 5.28287
I0406 08:53:01.328632  5644 solver.cpp:237]     Train net output #0: loss = 5.28287 (* 1 = 5.28287 loss)
I0406 08:53:01.328637  5644 sgd_solver.cpp:105] Iteration 12084, lr = 0.1
I0406 08:53:06.754766  5644 solver.cpp:218] Iteration 12096 (2.21154 iter/s, 5.42607s/12 iters), loss = 5.2828
I0406 08:53:06.754818  5644 solver.cpp:237]     Train net output #0: loss = 5.2828 (* 1 = 5.2828 loss)
I0406 08:53:06.754827  5644 sgd_solver.cpp:105] Iteration 12096, lr = 0.1
I0406 08:53:12.139186  5644 solver.cpp:218] Iteration 12108 (2.2287 iter/s, 5.38431s/12 iters), loss = 5.27277
I0406 08:53:12.139349  5644 solver.cpp:237]     Train net output #0: loss = 5.27277 (* 1 = 5.27277 loss)
I0406 08:53:12.139358  5644 sgd_solver.cpp:105] Iteration 12108, lr = 0.1
I0406 08:53:17.349298  5644 solver.cpp:218] Iteration 12120 (2.30331 iter/s, 5.20989s/12 iters), loss = 5.29478
I0406 08:53:17.349336  5644 solver.cpp:237]     Train net output #0: loss = 5.29478 (* 1 = 5.29478 loss)
I0406 08:53:17.349341  5644 sgd_solver.cpp:105] Iteration 12120, lr = 0.1
I0406 08:53:22.544806  5644 solver.cpp:218] Iteration 12132 (2.30973 iter/s, 5.19541s/12 iters), loss = 5.26472
I0406 08:53:22.544845  5644 solver.cpp:237]     Train net output #0: loss = 5.26472 (* 1 = 5.26472 loss)
I0406 08:53:22.544852  5644 sgd_solver.cpp:105] Iteration 12132, lr = 0.1
I0406 08:53:24.748641  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12138.caffemodel
I0406 08:53:25.139233  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:53:27.894716  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12138.solverstate
I0406 08:53:30.187222  5644 solver.cpp:330] Iteration 12138, Testing net (#0)
I0406 08:53:30.187242  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:53:34.383025  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:53:34.553658  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:53:34.553694  5644 solver.cpp:397]     Test net output #1: loss = 5.28906 (* 1 = 5.28906 loss)
I0406 08:53:36.538914  5644 solver.cpp:218] Iteration 12144 (0.857514 iter/s, 13.9939s/12 iters), loss = 5.2802
I0406 08:53:36.538954  5644 solver.cpp:237]     Train net output #0: loss = 5.2802 (* 1 = 5.2802 loss)
I0406 08:53:36.538959  5644 sgd_solver.cpp:105] Iteration 12144, lr = 0.1
I0406 08:53:41.915603  5644 solver.cpp:218] Iteration 12156 (2.2319 iter/s, 5.37659s/12 iters), loss = 5.26998
I0406 08:53:41.915650  5644 solver.cpp:237]     Train net output #0: loss = 5.26998 (* 1 = 5.26998 loss)
I0406 08:53:41.915658  5644 sgd_solver.cpp:105] Iteration 12156, lr = 0.1
I0406 08:53:47.258013  5644 solver.cpp:218] Iteration 12168 (2.24622 iter/s, 5.34231s/12 iters), loss = 5.28731
I0406 08:53:47.258118  5644 solver.cpp:237]     Train net output #0: loss = 5.28731 (* 1 = 5.28731 loss)
I0406 08:53:47.258126  5644 sgd_solver.cpp:105] Iteration 12168, lr = 0.1
I0406 08:53:52.431974  5644 solver.cpp:218] Iteration 12180 (2.31937 iter/s, 5.17381s/12 iters), loss = 5.26672
I0406 08:53:52.432010  5644 solver.cpp:237]     Train net output #0: loss = 5.26672 (* 1 = 5.26672 loss)
I0406 08:53:52.432016  5644 sgd_solver.cpp:105] Iteration 12180, lr = 0.1
I0406 08:53:57.601864  5644 solver.cpp:218] Iteration 12192 (2.32118 iter/s, 5.16979s/12 iters), loss = 5.257
I0406 08:53:57.601915  5644 solver.cpp:237]     Train net output #0: loss = 5.257 (* 1 = 5.257 loss)
I0406 08:53:57.601924  5644 sgd_solver.cpp:105] Iteration 12192, lr = 0.1
I0406 08:54:02.790005  5644 solver.cpp:218] Iteration 12204 (2.31301 iter/s, 5.18804s/12 iters), loss = 5.29495
I0406 08:54:02.790040  5644 solver.cpp:237]     Train net output #0: loss = 5.29495 (* 1 = 5.29495 loss)
I0406 08:54:02.790046  5644 sgd_solver.cpp:105] Iteration 12204, lr = 0.1
I0406 08:54:07.972797  5644 solver.cpp:218] Iteration 12216 (2.3154 iter/s, 5.1827s/12 iters), loss = 5.28893
I0406 08:54:07.972849  5644 solver.cpp:237]     Train net output #0: loss = 5.28893 (* 1 = 5.28893 loss)
I0406 08:54:07.972858  5644 sgd_solver.cpp:105] Iteration 12216, lr = 0.1
I0406 08:54:13.359854  5644 solver.cpp:218] Iteration 12228 (2.22761 iter/s, 5.38695s/12 iters), loss = 5.27975
I0406 08:54:13.359905  5644 solver.cpp:237]     Train net output #0: loss = 5.27975 (* 1 = 5.27975 loss)
I0406 08:54:13.359912  5644 sgd_solver.cpp:105] Iteration 12228, lr = 0.1
I0406 08:54:17.915689  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:54:18.013876  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12240.caffemodel
I0406 08:54:21.071513  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12240.solverstate
I0406 08:54:23.406262  5644 solver.cpp:330] Iteration 12240, Testing net (#0)
I0406 08:54:23.406283  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:54:27.535955  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:54:27.754621  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:54:27.754657  5644 solver.cpp:397]     Test net output #1: loss = 5.28864 (* 1 = 5.28864 loss)
I0406 08:54:27.895207  5644 solver.cpp:218] Iteration 12240 (0.825583 iter/s, 14.5352s/12 iters), loss = 5.29292
I0406 08:54:27.896771  5644 solver.cpp:237]     Train net output #0: loss = 5.29292 (* 1 = 5.29292 loss)
I0406 08:54:27.896783  5644 sgd_solver.cpp:105] Iteration 12240, lr = 0.1
I0406 08:54:32.182672  5644 solver.cpp:218] Iteration 12252 (2.7999 iter/s, 4.28586s/12 iters), loss = 5.2696
I0406 08:54:32.182713  5644 solver.cpp:237]     Train net output #0: loss = 5.2696 (* 1 = 5.2696 loss)
I0406 08:54:32.182718  5644 sgd_solver.cpp:105] Iteration 12252, lr = 0.1
I0406 08:54:37.473963  5644 solver.cpp:218] Iteration 12264 (2.26792 iter/s, 5.29119s/12 iters), loss = 5.26465
I0406 08:54:37.474014  5644 solver.cpp:237]     Train net output #0: loss = 5.26465 (* 1 = 5.26465 loss)
I0406 08:54:37.474021  5644 sgd_solver.cpp:105] Iteration 12264, lr = 0.1
I0406 08:54:42.896611  5644 solver.cpp:218] Iteration 12276 (2.21299 iter/s, 5.42254s/12 iters), loss = 5.28545
I0406 08:54:42.896668  5644 solver.cpp:237]     Train net output #0: loss = 5.28545 (* 1 = 5.28545 loss)
I0406 08:54:42.896677  5644 sgd_solver.cpp:105] Iteration 12276, lr = 0.1
I0406 08:54:47.839968  5644 solver.cpp:218] Iteration 12288 (2.42755 iter/s, 4.94325s/12 iters), loss = 5.29239
I0406 08:54:47.840005  5644 solver.cpp:237]     Train net output #0: loss = 5.29239 (* 1 = 5.29239 loss)
I0406 08:54:47.840010  5644 sgd_solver.cpp:105] Iteration 12288, lr = 0.1
I0406 08:54:53.181375  5644 solver.cpp:218] Iteration 12300 (2.24664 iter/s, 5.34131s/12 iters), loss = 5.25673
I0406 08:54:53.181494  5644 solver.cpp:237]     Train net output #0: loss = 5.25673 (* 1 = 5.25673 loss)
I0406 08:54:53.181502  5644 sgd_solver.cpp:105] Iteration 12300, lr = 0.1
I0406 08:54:58.477771  5644 solver.cpp:218] Iteration 12312 (2.26577 iter/s, 5.29622s/12 iters), loss = 5.28617
I0406 08:54:58.477820  5644 solver.cpp:237]     Train net output #0: loss = 5.28617 (* 1 = 5.28617 loss)
I0406 08:54:58.477828  5644 sgd_solver.cpp:105] Iteration 12312, lr = 0.1
I0406 08:55:03.567032  5644 solver.cpp:218] Iteration 12324 (2.35795 iter/s, 5.08916s/12 iters), loss = 5.28178
I0406 08:55:03.567078  5644 solver.cpp:237]     Train net output #0: loss = 5.28178 (* 1 = 5.28178 loss)
I0406 08:55:03.567085  5644 sgd_solver.cpp:105] Iteration 12324, lr = 0.1
I0406 08:55:08.800031  5644 solver.cpp:218] Iteration 12336 (2.29318 iter/s, 5.2329s/12 iters), loss = 5.27168
I0406 08:55:08.800081  5644 solver.cpp:237]     Train net output #0: loss = 5.27168 (* 1 = 5.27168 loss)
I0406 08:55:08.800089  5644 sgd_solver.cpp:105] Iteration 12336, lr = 0.1
I0406 08:55:10.637166  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:55:10.970312  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12342.caffemodel
I0406 08:55:14.021239  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12342.solverstate
I0406 08:55:16.324499  5644 solver.cpp:330] Iteration 12342, Testing net (#0)
I0406 08:55:16.324518  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:55:20.557051  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:55:20.813380  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:55:20.813410  5644 solver.cpp:397]     Test net output #1: loss = 5.28905 (* 1 = 5.28905 loss)
I0406 08:55:22.644906  5644 solver.cpp:218] Iteration 12348 (0.866757 iter/s, 13.8447s/12 iters), loss = 5.28047
I0406 08:55:22.644943  5644 solver.cpp:237]     Train net output #0: loss = 5.28047 (* 1 = 5.28047 loss)
I0406 08:55:22.644949  5644 sgd_solver.cpp:105] Iteration 12348, lr = 0.1
I0406 08:55:27.852872  5644 solver.cpp:218] Iteration 12360 (2.3042 iter/s, 5.20787s/12 iters), loss = 5.26125
I0406 08:55:27.853006  5644 solver.cpp:237]     Train net output #0: loss = 5.26125 (* 1 = 5.26125 loss)
I0406 08:55:27.853013  5644 sgd_solver.cpp:105] Iteration 12360, lr = 0.1
I0406 08:55:33.120676  5644 solver.cpp:218] Iteration 12372 (2.27807 iter/s, 5.26761s/12 iters), loss = 5.27409
I0406 08:55:33.120723  5644 solver.cpp:237]     Train net output #0: loss = 5.27409 (* 1 = 5.27409 loss)
I0406 08:55:33.120730  5644 sgd_solver.cpp:105] Iteration 12372, lr = 0.1
I0406 08:55:38.355825  5644 solver.cpp:218] Iteration 12384 (2.29224 iter/s, 5.23504s/12 iters), loss = 5.27408
I0406 08:55:38.355875  5644 solver.cpp:237]     Train net output #0: loss = 5.27408 (* 1 = 5.27408 loss)
I0406 08:55:38.355883  5644 sgd_solver.cpp:105] Iteration 12384, lr = 0.1
I0406 08:55:43.886518  5644 solver.cpp:218] Iteration 12396 (2.16975 iter/s, 5.53059s/12 iters), loss = 5.29755
I0406 08:55:43.886555  5644 solver.cpp:237]     Train net output #0: loss = 5.29755 (* 1 = 5.29755 loss)
I0406 08:55:43.886561  5644 sgd_solver.cpp:105] Iteration 12396, lr = 0.1
I0406 08:55:49.217391  5644 solver.cpp:218] Iteration 12408 (2.25108 iter/s, 5.33077s/12 iters), loss = 5.26824
I0406 08:55:49.217442  5644 solver.cpp:237]     Train net output #0: loss = 5.26824 (* 1 = 5.26824 loss)
I0406 08:55:49.217449  5644 sgd_solver.cpp:105] Iteration 12408, lr = 0.1
I0406 08:55:54.410854  5644 solver.cpp:218] Iteration 12420 (2.31064 iter/s, 5.19336s/12 iters), loss = 5.29051
I0406 08:55:54.410905  5644 solver.cpp:237]     Train net output #0: loss = 5.29051 (* 1 = 5.29051 loss)
I0406 08:55:54.410913  5644 sgd_solver.cpp:105] Iteration 12420, lr = 0.1
I0406 08:55:59.594359  5644 solver.cpp:218] Iteration 12432 (2.31508 iter/s, 5.1834s/12 iters), loss = 5.26377
I0406 08:55:59.594451  5644 solver.cpp:237]     Train net output #0: loss = 5.26377 (* 1 = 5.26377 loss)
I0406 08:55:59.594458  5644 sgd_solver.cpp:105] Iteration 12432, lr = 0.1
I0406 08:56:03.706774  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:56:04.443256  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12444.caffemodel
I0406 08:56:07.466128  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12444.solverstate
I0406 08:56:09.770040  5644 solver.cpp:330] Iteration 12444, Testing net (#0)
I0406 08:56:09.770061  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:56:13.835808  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:56:14.132236  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:56:14.132272  5644 solver.cpp:397]     Test net output #1: loss = 5.28893 (* 1 = 5.28893 loss)
I0406 08:56:14.272284  5644 solver.cpp:218] Iteration 12444 (0.817567 iter/s, 14.6777s/12 iters), loss = 5.26372
I0406 08:56:14.272334  5644 solver.cpp:237]     Train net output #0: loss = 5.26372 (* 1 = 5.26372 loss)
I0406 08:56:14.272342  5644 sgd_solver.cpp:105] Iteration 12444, lr = 0.1
I0406 08:56:18.716387  5644 solver.cpp:218] Iteration 12456 (2.70027 iter/s, 4.444s/12 iters), loss = 5.27461
I0406 08:56:18.716428  5644 solver.cpp:237]     Train net output #0: loss = 5.27461 (* 1 = 5.27461 loss)
I0406 08:56:18.716434  5644 sgd_solver.cpp:105] Iteration 12456, lr = 0.1
I0406 08:56:22.996927  5644 blocking_queue.cpp:49] Waiting for data
I0406 08:56:23.973505  5644 solver.cpp:218] Iteration 12468 (2.28266 iter/s, 5.25702s/12 iters), loss = 5.28845
I0406 08:56:23.973546  5644 solver.cpp:237]     Train net output #0: loss = 5.28845 (* 1 = 5.28845 loss)
I0406 08:56:23.973551  5644 sgd_solver.cpp:105] Iteration 12468, lr = 0.1
I0406 08:56:28.964586  5644 solver.cpp:218] Iteration 12480 (2.40434 iter/s, 4.99098s/12 iters), loss = 5.26727
I0406 08:56:28.964637  5644 solver.cpp:237]     Train net output #0: loss = 5.26727 (* 1 = 5.26727 loss)
I0406 08:56:28.964645  5644 sgd_solver.cpp:105] Iteration 12480, lr = 0.1
I0406 08:56:34.340445  5644 solver.cpp:218] Iteration 12492 (2.23225 iter/s, 5.37575s/12 iters), loss = 5.28685
I0406 08:56:34.340596  5644 solver.cpp:237]     Train net output #0: loss = 5.28685 (* 1 = 5.28685 loss)
I0406 08:56:34.340605  5644 sgd_solver.cpp:105] Iteration 12492, lr = 0.1
I0406 08:56:39.793576  5644 solver.cpp:218] Iteration 12504 (2.20065 iter/s, 5.45293s/12 iters), loss = 5.26817
I0406 08:56:39.793613  5644 solver.cpp:237]     Train net output #0: loss = 5.26817 (* 1 = 5.26817 loss)
I0406 08:56:39.793618  5644 sgd_solver.cpp:105] Iteration 12504, lr = 0.1
I0406 08:56:45.095832  5644 solver.cpp:218] Iteration 12516 (2.26323 iter/s, 5.30216s/12 iters), loss = 5.27576
I0406 08:56:45.095886  5644 solver.cpp:237]     Train net output #0: loss = 5.27576 (* 1 = 5.27576 loss)
I0406 08:56:45.095894  5644 sgd_solver.cpp:105] Iteration 12516, lr = 0.1
I0406 08:56:50.527230  5644 solver.cpp:218] Iteration 12528 (2.20942 iter/s, 5.43129s/12 iters), loss = 5.2696
I0406 08:56:50.527281  5644 solver.cpp:237]     Train net output #0: loss = 5.2696 (* 1 = 5.2696 loss)
I0406 08:56:50.527289  5644 sgd_solver.cpp:105] Iteration 12528, lr = 0.1
I0406 08:56:55.743851  5644 solver.cpp:218] Iteration 12540 (2.30039 iter/s, 5.21651s/12 iters), loss = 5.27672
I0406 08:56:55.743906  5644 solver.cpp:237]     Train net output #0: loss = 5.27672 (* 1 = 5.27672 loss)
I0406 08:56:55.743916  5644 sgd_solver.cpp:105] Iteration 12540, lr = 0.1
I0406 08:56:56.751037  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:56:57.922417  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12546.caffemodel
I0406 08:57:01.086494  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12546.solverstate
I0406 08:57:03.423616  5644 solver.cpp:330] Iteration 12546, Testing net (#0)
I0406 08:57:03.423635  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:57:07.483613  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:57:07.810786  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:57:07.810819  5644 solver.cpp:397]     Test net output #1: loss = 5.28885 (* 1 = 5.28885 loss)
I0406 08:57:09.603760  5644 solver.cpp:218] Iteration 12552 (0.865818 iter/s, 13.8597s/12 iters), loss = 5.27226
I0406 08:57:09.603812  5644 solver.cpp:237]     Train net output #0: loss = 5.27226 (* 1 = 5.27226 loss)
I0406 08:57:09.603821  5644 sgd_solver.cpp:105] Iteration 12552, lr = 0.1
I0406 08:57:14.896277  5644 solver.cpp:218] Iteration 12564 (2.2674 iter/s, 5.29241s/12 iters), loss = 5.29001
I0406 08:57:14.896315  5644 solver.cpp:237]     Train net output #0: loss = 5.29001 (* 1 = 5.29001 loss)
I0406 08:57:14.896322  5644 sgd_solver.cpp:105] Iteration 12564, lr = 0.1
I0406 08:57:19.941267  5644 solver.cpp:218] Iteration 12576 (2.37864 iter/s, 5.04489s/12 iters), loss = 5.27523
I0406 08:57:19.941316  5644 solver.cpp:237]     Train net output #0: loss = 5.27523 (* 1 = 5.27523 loss)
I0406 08:57:19.941326  5644 sgd_solver.cpp:105] Iteration 12576, lr = 0.1
I0406 08:57:25.172931  5644 solver.cpp:218] Iteration 12588 (2.29377 iter/s, 5.23156s/12 iters), loss = 5.28071
I0406 08:57:25.172972  5644 solver.cpp:237]     Train net output #0: loss = 5.28071 (* 1 = 5.28071 loss)
I0406 08:57:25.172977  5644 sgd_solver.cpp:105] Iteration 12588, lr = 0.1
I0406 08:57:30.221205  5644 solver.cpp:218] Iteration 12600 (2.3771 iter/s, 5.04817s/12 iters), loss = 5.30112
I0406 08:57:30.221261  5644 solver.cpp:237]     Train net output #0: loss = 5.30112 (* 1 = 5.30112 loss)
I0406 08:57:30.221271  5644 sgd_solver.cpp:105] Iteration 12600, lr = 0.1
I0406 08:57:35.507320  5644 solver.cpp:218] Iteration 12612 (2.27015 iter/s, 5.286s/12 iters), loss = 5.27006
I0406 08:57:35.507370  5644 solver.cpp:237]     Train net output #0: loss = 5.27006 (* 1 = 5.27006 loss)
I0406 08:57:35.507378  5644 sgd_solver.cpp:105] Iteration 12612, lr = 0.1
I0406 08:57:40.415771  5644 solver.cpp:218] Iteration 12624 (2.44481 iter/s, 4.90835s/12 iters), loss = 5.27369
I0406 08:57:40.415886  5644 solver.cpp:237]     Train net output #0: loss = 5.27369 (* 1 = 5.27369 loss)
I0406 08:57:40.415894  5644 sgd_solver.cpp:105] Iteration 12624, lr = 0.1
I0406 08:57:46.005095  5644 solver.cpp:218] Iteration 12636 (2.14702 iter/s, 5.58915s/12 iters), loss = 5.29504
I0406 08:57:46.005142  5644 solver.cpp:237]     Train net output #0: loss = 5.29504 (* 1 = 5.29504 loss)
I0406 08:57:46.005151  5644 sgd_solver.cpp:105] Iteration 12636, lr = 0.1
I0406 08:57:49.238481  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:57:50.880650  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12648.caffemodel
I0406 08:57:53.908974  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12648.solverstate
I0406 08:57:56.219101  5644 solver.cpp:330] Iteration 12648, Testing net (#0)
I0406 08:57:56.219120  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:58:00.200726  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:58:00.574188  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:58:00.574223  5644 solver.cpp:397]     Test net output #1: loss = 5.28924 (* 1 = 5.28924 loss)
I0406 08:58:00.711230  5644 solver.cpp:218] Iteration 12648 (0.815996 iter/s, 14.706s/12 iters), loss = 5.27447
I0406 08:58:00.711282  5644 solver.cpp:237]     Train net output #0: loss = 5.27447 (* 1 = 5.27447 loss)
I0406 08:58:00.711290  5644 sgd_solver.cpp:105] Iteration 12648, lr = 0.1
I0406 08:58:05.108155  5644 solver.cpp:218] Iteration 12660 (2.72924 iter/s, 4.39683s/12 iters), loss = 5.28288
I0406 08:58:05.108194  5644 solver.cpp:237]     Train net output #0: loss = 5.28288 (* 1 = 5.28288 loss)
I0406 08:58:05.108201  5644 sgd_solver.cpp:105] Iteration 12660, lr = 0.1
I0406 08:58:10.241178  5644 solver.cpp:218] Iteration 12672 (2.33785 iter/s, 5.13293s/12 iters), loss = 5.27335
I0406 08:58:10.241231  5644 solver.cpp:237]     Train net output #0: loss = 5.27335 (* 1 = 5.27335 loss)
I0406 08:58:10.241240  5644 sgd_solver.cpp:105] Iteration 12672, lr = 0.1
I0406 08:58:15.587998  5644 solver.cpp:218] Iteration 12684 (2.24437 iter/s, 5.34671s/12 iters), loss = 5.27796
I0406 08:58:15.588119  5644 solver.cpp:237]     Train net output #0: loss = 5.27796 (* 1 = 5.27796 loss)
I0406 08:58:15.588129  5644 sgd_solver.cpp:105] Iteration 12684, lr = 0.1
I0406 08:58:20.809649  5644 solver.cpp:218] Iteration 12696 (2.2982 iter/s, 5.22148s/12 iters), loss = 5.28723
I0406 08:58:20.809693  5644 solver.cpp:237]     Train net output #0: loss = 5.28723 (* 1 = 5.28723 loss)
I0406 08:58:20.809700  5644 sgd_solver.cpp:105] Iteration 12696, lr = 0.1
I0406 08:58:26.094604  5644 solver.cpp:218] Iteration 12708 (2.27064 iter/s, 5.28485s/12 iters), loss = 5.27811
I0406 08:58:26.094655  5644 solver.cpp:237]     Train net output #0: loss = 5.27811 (* 1 = 5.27811 loss)
I0406 08:58:26.094663  5644 sgd_solver.cpp:105] Iteration 12708, lr = 0.1
I0406 08:58:31.086587  5644 solver.cpp:218] Iteration 12720 (2.4039 iter/s, 4.99188s/12 iters), loss = 5.26774
I0406 08:58:31.086625  5644 solver.cpp:237]     Train net output #0: loss = 5.26774 (* 1 = 5.26774 loss)
I0406 08:58:31.086632  5644 sgd_solver.cpp:105] Iteration 12720, lr = 0.1
I0406 08:58:35.987229  5644 solver.cpp:218] Iteration 12732 (2.4487 iter/s, 4.90055s/12 iters), loss = 5.29006
I0406 08:58:35.987267  5644 solver.cpp:237]     Train net output #0: loss = 5.29006 (* 1 = 5.29006 loss)
I0406 08:58:35.987273  5644 sgd_solver.cpp:105] Iteration 12732, lr = 0.1
I0406 08:58:41.328331  5644 solver.cpp:218] Iteration 12744 (2.24677 iter/s, 5.34101s/12 iters), loss = 5.27974
I0406 08:58:41.328368  5644 solver.cpp:237]     Train net output #0: loss = 5.27974 (* 1 = 5.27974 loss)
I0406 08:58:41.328374  5644 sgd_solver.cpp:105] Iteration 12744, lr = 0.1
I0406 08:58:41.560019  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:58:43.506255  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12750.caffemodel
I0406 08:58:46.563386  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12750.solverstate
I0406 08:58:48.914166  5644 solver.cpp:330] Iteration 12750, Testing net (#0)
I0406 08:58:48.914186  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:58:52.852303  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:58:53.312862  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:58:53.312901  5644 solver.cpp:397]     Test net output #1: loss = 5.28897 (* 1 = 5.28897 loss)
I0406 08:58:55.272219  5644 solver.cpp:218] Iteration 12756 (0.860602 iter/s, 13.9437s/12 iters), loss = 5.26526
I0406 08:58:55.272275  5644 solver.cpp:237]     Train net output #0: loss = 5.26526 (* 1 = 5.26526 loss)
I0406 08:58:55.272284  5644 sgd_solver.cpp:105] Iteration 12756, lr = 0.1
I0406 08:59:00.607762  5644 solver.cpp:218] Iteration 12768 (2.24912 iter/s, 5.33543s/12 iters), loss = 5.28442
I0406 08:59:00.607813  5644 solver.cpp:237]     Train net output #0: loss = 5.28442 (* 1 = 5.28442 loss)
I0406 08:59:00.607821  5644 sgd_solver.cpp:105] Iteration 12768, lr = 0.1
I0406 08:59:06.025228  5644 solver.cpp:218] Iteration 12780 (2.2151 iter/s, 5.41735s/12 iters), loss = 5.28022
I0406 08:59:06.025288  5644 solver.cpp:237]     Train net output #0: loss = 5.28022 (* 1 = 5.28022 loss)
I0406 08:59:06.025296  5644 sgd_solver.cpp:105] Iteration 12780, lr = 0.1
I0406 08:59:11.333757  5644 solver.cpp:218] Iteration 12792 (2.26056 iter/s, 5.30841s/12 iters), loss = 5.28079
I0406 08:59:11.333806  5644 solver.cpp:237]     Train net output #0: loss = 5.28079 (* 1 = 5.28079 loss)
I0406 08:59:11.333812  5644 sgd_solver.cpp:105] Iteration 12792, lr = 0.1
I0406 08:59:16.773779  5644 solver.cpp:218] Iteration 12804 (2.20592 iter/s, 5.43991s/12 iters), loss = 5.28054
I0406 08:59:16.773896  5644 solver.cpp:237]     Train net output #0: loss = 5.28054 (* 1 = 5.28054 loss)
I0406 08:59:16.773906  5644 sgd_solver.cpp:105] Iteration 12804, lr = 0.1
I0406 08:59:21.909904  5644 solver.cpp:218] Iteration 12816 (2.33647 iter/s, 5.13595s/12 iters), loss = 5.26839
I0406 08:59:21.909953  5644 solver.cpp:237]     Train net output #0: loss = 5.26839 (* 1 = 5.26839 loss)
I0406 08:59:21.909961  5644 sgd_solver.cpp:105] Iteration 12816, lr = 0.1
I0406 08:59:27.230360  5644 solver.cpp:218] Iteration 12828 (2.25549 iter/s, 5.32035s/12 iters), loss = 5.28513
I0406 08:59:27.230417  5644 solver.cpp:237]     Train net output #0: loss = 5.28513 (* 1 = 5.28513 loss)
I0406 08:59:27.230427  5644 sgd_solver.cpp:105] Iteration 12828, lr = 0.1
I0406 08:59:32.568389  5644 solver.cpp:218] Iteration 12840 (2.24807 iter/s, 5.33791s/12 iters), loss = 5.26565
I0406 08:59:32.568429  5644 solver.cpp:237]     Train net output #0: loss = 5.26565 (* 1 = 5.26565 loss)
I0406 08:59:32.568434  5644 sgd_solver.cpp:105] Iteration 12840, lr = 0.1
I0406 08:59:35.146471  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:59:37.500283  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12852.caffemodel
I0406 08:59:40.567467  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12852.solverstate
I0406 08:59:43.464267  5644 solver.cpp:330] Iteration 12852, Testing net (#0)
I0406 08:59:43.464285  5644 net.cpp:676] Ignoring source layer train-data
I0406 08:59:47.394959  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 08:59:47.846135  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 08:59:47.846169  5644 solver.cpp:397]     Test net output #1: loss = 5.28945 (* 1 = 5.28945 loss)
I0406 08:59:47.982612  5644 solver.cpp:218] Iteration 12852 (0.778511 iter/s, 15.414s/12 iters), loss = 5.28379
I0406 08:59:47.982674  5644 solver.cpp:237]     Train net output #0: loss = 5.28379 (* 1 = 5.28379 loss)
I0406 08:59:47.982682  5644 sgd_solver.cpp:105] Iteration 12852, lr = 0.1
I0406 08:59:52.269721  5644 solver.cpp:218] Iteration 12864 (2.79916 iter/s, 4.287s/12 iters), loss = 5.26991
I0406 08:59:52.269770  5644 solver.cpp:237]     Train net output #0: loss = 5.26991 (* 1 = 5.26991 loss)
I0406 08:59:52.269778  5644 sgd_solver.cpp:105] Iteration 12864, lr = 0.1
I0406 08:59:57.678678  5644 solver.cpp:218] Iteration 12876 (2.21859 iter/s, 5.40885s/12 iters), loss = 5.28206
I0406 08:59:57.678727  5644 solver.cpp:237]     Train net output #0: loss = 5.28206 (* 1 = 5.28206 loss)
I0406 08:59:57.678736  5644 sgd_solver.cpp:105] Iteration 12876, lr = 0.1
I0406 09:00:03.037238  5644 solver.cpp:218] Iteration 12888 (2.23945 iter/s, 5.35846s/12 iters), loss = 5.26734
I0406 09:00:03.037276  5644 solver.cpp:237]     Train net output #0: loss = 5.26734 (* 1 = 5.26734 loss)
I0406 09:00:03.037281  5644 sgd_solver.cpp:105] Iteration 12888, lr = 0.1
I0406 09:00:08.453608  5644 solver.cpp:218] Iteration 12900 (2.21555 iter/s, 5.41627s/12 iters), loss = 5.26284
I0406 09:00:08.453658  5644 solver.cpp:237]     Train net output #0: loss = 5.26284 (* 1 = 5.26284 loss)
I0406 09:00:08.453666  5644 sgd_solver.cpp:105] Iteration 12900, lr = 0.1
I0406 09:00:13.749521  5644 solver.cpp:218] Iteration 12912 (2.26594 iter/s, 5.2958s/12 iters), loss = 5.29992
I0406 09:00:13.749567  5644 solver.cpp:237]     Train net output #0: loss = 5.29992 (* 1 = 5.29992 loss)
I0406 09:00:13.749575  5644 sgd_solver.cpp:105] Iteration 12912, lr = 0.1
I0406 09:00:19.166209  5644 solver.cpp:218] Iteration 12924 (2.21542 iter/s, 5.41658s/12 iters), loss = 5.28458
I0406 09:00:19.166716  5644 solver.cpp:237]     Train net output #0: loss = 5.28458 (* 1 = 5.28458 loss)
I0406 09:00:19.166726  5644 sgd_solver.cpp:105] Iteration 12924, lr = 0.1
I0406 09:00:24.353200  5644 solver.cpp:218] Iteration 12936 (2.31373 iter/s, 5.18644s/12 iters), loss = 5.28399
I0406 09:00:24.353235  5644 solver.cpp:237]     Train net output #0: loss = 5.28399 (* 1 = 5.28399 loss)
I0406 09:00:24.353241  5644 sgd_solver.cpp:105] Iteration 12936, lr = 0.1
I0406 09:00:28.973450  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:00:29.542240  5644 solver.cpp:218] Iteration 12948 (2.31261 iter/s, 5.18895s/12 iters), loss = 5.29945
I0406 09:00:29.542287  5644 solver.cpp:237]     Train net output #0: loss = 5.29945 (* 1 = 5.29945 loss)
I0406 09:00:29.542295  5644 sgd_solver.cpp:105] Iteration 12948, lr = 0.1
I0406 09:00:31.714879  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_12954.caffemodel
I0406 09:00:34.700999  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_12954.solverstate
I0406 09:00:37.010643  5644 solver.cpp:330] Iteration 12954, Testing net (#0)
I0406 09:00:37.010660  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:00:40.873005  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:00:41.393291  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:00:41.393328  5644 solver.cpp:397]     Test net output #1: loss = 5.28928 (* 1 = 5.28928 loss)
I0406 09:00:43.471200  5644 solver.cpp:218] Iteration 12960 (0.861525 iter/s, 13.9288s/12 iters), loss = 5.27212
I0406 09:00:43.471240  5644 solver.cpp:237]     Train net output #0: loss = 5.27212 (* 1 = 5.27212 loss)
I0406 09:00:43.471246  5644 sgd_solver.cpp:105] Iteration 12960, lr = 0.1
I0406 09:00:48.779055  5644 solver.cpp:218] Iteration 12972 (2.26084 iter/s, 5.30776s/12 iters), loss = 5.26764
I0406 09:00:48.779106  5644 solver.cpp:237]     Train net output #0: loss = 5.26764 (* 1 = 5.26764 loss)
I0406 09:00:48.779116  5644 sgd_solver.cpp:105] Iteration 12972, lr = 0.1
I0406 09:00:54.042594  5644 solver.cpp:218] Iteration 12984 (2.27988 iter/s, 5.26343s/12 iters), loss = 5.2821
I0406 09:00:54.042739  5644 solver.cpp:237]     Train net output #0: loss = 5.2821 (* 1 = 5.2821 loss)
I0406 09:00:54.042748  5644 sgd_solver.cpp:105] Iteration 12984, lr = 0.1
I0406 09:00:59.392920  5644 solver.cpp:218] Iteration 12996 (2.24294 iter/s, 5.35013s/12 iters), loss = 5.29259
I0406 09:00:59.392958  5644 solver.cpp:237]     Train net output #0: loss = 5.29259 (* 1 = 5.29259 loss)
I0406 09:00:59.392963  5644 sgd_solver.cpp:105] Iteration 12996, lr = 0.1
I0406 09:01:04.622283  5644 solver.cpp:218] Iteration 13008 (2.29477 iter/s, 5.22928s/12 iters), loss = 5.25902
I0406 09:01:04.622318  5644 solver.cpp:237]     Train net output #0: loss = 5.25902 (* 1 = 5.25902 loss)
I0406 09:01:04.622323  5644 sgd_solver.cpp:105] Iteration 13008, lr = 0.1
I0406 09:01:09.937597  5644 solver.cpp:218] Iteration 13020 (2.25768 iter/s, 5.3152s/12 iters), loss = 5.2903
I0406 09:01:09.937659  5644 solver.cpp:237]     Train net output #0: loss = 5.2903 (* 1 = 5.2903 loss)
I0406 09:01:09.937669  5644 sgd_solver.cpp:105] Iteration 13020, lr = 0.1
I0406 09:01:14.922853  5644 solver.cpp:218] Iteration 13032 (2.40715 iter/s, 4.98515s/12 iters), loss = 5.27387
I0406 09:01:14.922886  5644 solver.cpp:237]     Train net output #0: loss = 5.27387 (* 1 = 5.27387 loss)
I0406 09:01:14.922891  5644 sgd_solver.cpp:105] Iteration 13032, lr = 0.1
I0406 09:01:20.250788  5644 solver.cpp:218] Iteration 13044 (2.25232 iter/s, 5.32784s/12 iters), loss = 5.27459
I0406 09:01:20.250833  5644 solver.cpp:237]     Train net output #0: loss = 5.27459 (* 1 = 5.27459 loss)
I0406 09:01:20.250840  5644 sgd_solver.cpp:105] Iteration 13044, lr = 0.1
I0406 09:01:22.145931  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:01:25.076294  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13056.caffemodel
I0406 09:01:28.142899  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13056.solverstate
I0406 09:01:30.441176  5644 solver.cpp:330] Iteration 13056, Testing net (#0)
I0406 09:01:30.441195  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:01:34.169773  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:01:34.684974  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:01:34.685009  5644 solver.cpp:397]     Test net output #1: loss = 5.28916 (* 1 = 5.28916 loss)
I0406 09:01:34.821287  5644 solver.cpp:218] Iteration 13056 (0.823592 iter/s, 14.5703s/12 iters), loss = 5.28099
I0406 09:01:34.821339  5644 solver.cpp:237]     Train net output #0: loss = 5.28099 (* 1 = 5.28099 loss)
I0406 09:01:34.821346  5644 sgd_solver.cpp:105] Iteration 13056, lr = 0.1
I0406 09:01:39.089991  5644 solver.cpp:218] Iteration 13068 (2.81123 iter/s, 4.2686s/12 iters), loss = 5.26443
I0406 09:01:39.090039  5644 solver.cpp:237]     Train net output #0: loss = 5.26443 (* 1 = 5.26443 loss)
I0406 09:01:39.090046  5644 sgd_solver.cpp:105] Iteration 13068, lr = 0.1
I0406 09:01:44.373457  5644 solver.cpp:218] Iteration 13080 (2.27128 iter/s, 5.28336s/12 iters), loss = 5.27316
I0406 09:01:44.373499  5644 solver.cpp:237]     Train net output #0: loss = 5.27316 (* 1 = 5.27316 loss)
I0406 09:01:44.373507  5644 sgd_solver.cpp:105] Iteration 13080, lr = 0.1
I0406 09:01:49.639410  5644 solver.cpp:218] Iteration 13092 (2.27883 iter/s, 5.26585s/12 iters), loss = 5.26761
I0406 09:01:49.639449  5644 solver.cpp:237]     Train net output #0: loss = 5.26761 (* 1 = 5.26761 loss)
I0406 09:01:49.639456  5644 sgd_solver.cpp:105] Iteration 13092, lr = 0.1
I0406 09:01:54.810781  5644 solver.cpp:218] Iteration 13104 (2.32051 iter/s, 5.17128s/12 iters), loss = 5.29227
I0406 09:01:54.810822  5644 solver.cpp:237]     Train net output #0: loss = 5.29227 (* 1 = 5.29227 loss)
I0406 09:01:54.810827  5644 sgd_solver.cpp:105] Iteration 13104, lr = 0.1
I0406 09:02:00.176700  5644 solver.cpp:218] Iteration 13116 (2.23638 iter/s, 5.36582s/12 iters), loss = 5.2679
I0406 09:02:00.176812  5644 solver.cpp:237]     Train net output #0: loss = 5.2679 (* 1 = 5.2679 loss)
I0406 09:02:00.176818  5644 sgd_solver.cpp:105] Iteration 13116, lr = 0.1
I0406 09:02:05.359957  5644 solver.cpp:218] Iteration 13128 (2.31522 iter/s, 5.18309s/12 iters), loss = 5.29355
I0406 09:02:05.360009  5644 solver.cpp:237]     Train net output #0: loss = 5.29355 (* 1 = 5.29355 loss)
I0406 09:02:05.360018  5644 sgd_solver.cpp:105] Iteration 13128, lr = 0.1
I0406 09:02:10.608211  5644 solver.cpp:218] Iteration 13140 (2.28652 iter/s, 5.24814s/12 iters), loss = 5.26167
I0406 09:02:10.608259  5644 solver.cpp:237]     Train net output #0: loss = 5.26167 (* 1 = 5.26167 loss)
I0406 09:02:10.608268  5644 sgd_solver.cpp:105] Iteration 13140, lr = 0.1
I0406 09:02:14.541365  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:02:15.791924  5644 solver.cpp:218] Iteration 13152 (2.31499 iter/s, 5.18361s/12 iters), loss = 5.27119
I0406 09:02:15.791965  5644 solver.cpp:237]     Train net output #0: loss = 5.27119 (* 1 = 5.27119 loss)
I0406 09:02:15.791971  5644 sgd_solver.cpp:105] Iteration 13152, lr = 0.1
I0406 09:02:17.846501  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13158.caffemodel
I0406 09:02:20.883499  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13158.solverstate
I0406 09:02:23.221947  5644 solver.cpp:330] Iteration 13158, Testing net (#0)
I0406 09:02:23.221966  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:02:26.840807  5644 blocking_queue.cpp:49] Waiting for data
I0406 09:02:27.068625  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:02:27.633168  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:02:27.633196  5644 solver.cpp:397]     Test net output #1: loss = 5.28946 (* 1 = 5.28946 loss)
I0406 09:02:29.627787  5644 solver.cpp:218] Iteration 13164 (0.867321 iter/s, 13.8357s/12 iters), loss = 5.27147
I0406 09:02:29.627827  5644 solver.cpp:237]     Train net output #0: loss = 5.27147 (* 1 = 5.27147 loss)
I0406 09:02:29.627833  5644 sgd_solver.cpp:105] Iteration 13164, lr = 0.1
I0406 09:02:34.916836  5644 solver.cpp:218] Iteration 13176 (2.26888 iter/s, 5.28895s/12 iters), loss = 5.28363
I0406 09:02:34.916924  5644 solver.cpp:237]     Train net output #0: loss = 5.28363 (* 1 = 5.28363 loss)
I0406 09:02:34.916931  5644 sgd_solver.cpp:105] Iteration 13176, lr = 0.1
I0406 09:02:40.000617  5644 solver.cpp:218] Iteration 13188 (2.36052 iter/s, 5.08363s/12 iters), loss = 5.26952
I0406 09:02:40.000669  5644 solver.cpp:237]     Train net output #0: loss = 5.26952 (* 1 = 5.26952 loss)
I0406 09:02:40.000677  5644 sgd_solver.cpp:105] Iteration 13188, lr = 0.1
I0406 09:02:45.407603  5644 solver.cpp:218] Iteration 13200 (2.2194 iter/s, 5.40688s/12 iters), loss = 5.28374
I0406 09:02:45.407660  5644 solver.cpp:237]     Train net output #0: loss = 5.28374 (* 1 = 5.28374 loss)
I0406 09:02:45.407670  5644 sgd_solver.cpp:105] Iteration 13200, lr = 0.1
I0406 09:02:50.664784  5644 solver.cpp:218] Iteration 13212 (2.28264 iter/s, 5.25707s/12 iters), loss = 5.27419
I0406 09:02:50.664835  5644 solver.cpp:237]     Train net output #0: loss = 5.27419 (* 1 = 5.27419 loss)
I0406 09:02:50.664844  5644 sgd_solver.cpp:105] Iteration 13212, lr = 0.1
I0406 09:02:55.997134  5644 solver.cpp:218] Iteration 13224 (2.25046 iter/s, 5.33224s/12 iters), loss = 5.2731
I0406 09:02:55.997185  5644 solver.cpp:237]     Train net output #0: loss = 5.2731 (* 1 = 5.2731 loss)
I0406 09:02:55.997191  5644 sgd_solver.cpp:105] Iteration 13224, lr = 0.1
I0406 09:03:01.323529  5644 solver.cpp:218] Iteration 13236 (2.25298 iter/s, 5.32628s/12 iters), loss = 5.27802
I0406 09:03:01.323588  5644 solver.cpp:237]     Train net output #0: loss = 5.27802 (* 1 = 5.27802 loss)
I0406 09:03:01.323598  5644 sgd_solver.cpp:105] Iteration 13236, lr = 0.1
I0406 09:03:06.692759  5644 solver.cpp:218] Iteration 13248 (2.235 iter/s, 5.36912s/12 iters), loss = 5.27287
I0406 09:03:06.692868  5644 solver.cpp:237]     Train net output #0: loss = 5.27287 (* 1 = 5.27287 loss)
I0406 09:03:06.692875  5644 sgd_solver.cpp:105] Iteration 13248, lr = 0.1
I0406 09:03:07.642345  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:03:11.476276  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13260.caffemodel
I0406 09:03:14.522804  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13260.solverstate
I0406 09:03:16.832180  5644 solver.cpp:330] Iteration 13260, Testing net (#0)
I0406 09:03:16.832203  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:03:20.537109  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:03:21.130642  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:03:21.130678  5644 solver.cpp:397]     Test net output #1: loss = 5.2896 (* 1 = 5.2896 loss)
I0406 09:03:21.270407  5644 solver.cpp:218] Iteration 13260 (0.823192 iter/s, 14.5774s/12 iters), loss = 5.27937
I0406 09:03:21.270457  5644 solver.cpp:237]     Train net output #0: loss = 5.27937 (* 1 = 5.27937 loss)
I0406 09:03:21.270464  5644 sgd_solver.cpp:105] Iteration 13260, lr = 0.1
I0406 09:03:25.700119  5644 solver.cpp:218] Iteration 13272 (2.70904 iter/s, 4.42961s/12 iters), loss = 5.28713
I0406 09:03:25.700173  5644 solver.cpp:237]     Train net output #0: loss = 5.28713 (* 1 = 5.28713 loss)
I0406 09:03:25.700182  5644 sgd_solver.cpp:105] Iteration 13272, lr = 0.1
I0406 09:03:31.019145  5644 solver.cpp:218] Iteration 13284 (2.2561 iter/s, 5.31892s/12 iters), loss = 5.27268
I0406 09:03:31.019186  5644 solver.cpp:237]     Train net output #0: loss = 5.27268 (* 1 = 5.27268 loss)
I0406 09:03:31.019191  5644 sgd_solver.cpp:105] Iteration 13284, lr = 0.1
I0406 09:03:36.127677  5644 solver.cpp:218] Iteration 13296 (2.34906 iter/s, 5.10843s/12 iters), loss = 5.27829
I0406 09:03:36.127718  5644 solver.cpp:237]     Train net output #0: loss = 5.27829 (* 1 = 5.27829 loss)
I0406 09:03:36.127724  5644 sgd_solver.cpp:105] Iteration 13296, lr = 0.1
I0406 09:03:41.669293  5644 solver.cpp:218] Iteration 13308 (2.16547 iter/s, 5.54151s/12 iters), loss = 5.29766
I0406 09:03:41.669373  5644 solver.cpp:237]     Train net output #0: loss = 5.29766 (* 1 = 5.29766 loss)
I0406 09:03:41.669380  5644 sgd_solver.cpp:105] Iteration 13308, lr = 0.1
I0406 09:03:46.991681  5644 solver.cpp:218] Iteration 13320 (2.25468 iter/s, 5.32225s/12 iters), loss = 5.2684
I0406 09:03:46.991719  5644 solver.cpp:237]     Train net output #0: loss = 5.2684 (* 1 = 5.2684 loss)
I0406 09:03:46.991724  5644 sgd_solver.cpp:105] Iteration 13320, lr = 0.1
I0406 09:03:52.365962  5644 solver.cpp:218] Iteration 13332 (2.2329 iter/s, 5.37419s/12 iters), loss = 5.27139
I0406 09:03:52.365998  5644 solver.cpp:237]     Train net output #0: loss = 5.27139 (* 1 = 5.27139 loss)
I0406 09:03:52.366004  5644 sgd_solver.cpp:105] Iteration 13332, lr = 0.1
I0406 09:03:57.579370  5644 solver.cpp:218] Iteration 13344 (2.3018 iter/s, 5.21331s/12 iters), loss = 5.29413
I0406 09:03:57.579417  5644 solver.cpp:237]     Train net output #0: loss = 5.29413 (* 1 = 5.29413 loss)
I0406 09:03:57.579425  5644 sgd_solver.cpp:105] Iteration 13344, lr = 0.1
I0406 09:04:00.818949  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:04:02.865483  5644 solver.cpp:218] Iteration 13356 (2.27014 iter/s, 5.28601s/12 iters), loss = 5.27006
I0406 09:04:02.865520  5644 solver.cpp:237]     Train net output #0: loss = 5.27006 (* 1 = 5.27006 loss)
I0406 09:04:02.865525  5644 sgd_solver.cpp:105] Iteration 13356, lr = 0.1
I0406 09:04:04.910907  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13362.caffemodel
I0406 09:04:07.962327  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13362.solverstate
I0406 09:04:10.267834  5644 solver.cpp:330] Iteration 13362, Testing net (#0)
I0406 09:04:10.267858  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:04:13.949785  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:04:14.622838  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:04:14.622871  5644 solver.cpp:397]     Test net output #1: loss = 5.28932 (* 1 = 5.28932 loss)
I0406 09:04:16.591972  5644 solver.cpp:218] Iteration 13368 (0.874232 iter/s, 13.7263s/12 iters), loss = 5.27852
I0406 09:04:16.592015  5644 solver.cpp:237]     Train net output #0: loss = 5.27852 (* 1 = 5.27852 loss)
I0406 09:04:16.592020  5644 sgd_solver.cpp:105] Iteration 13368, lr = 0.1
I0406 09:04:21.961858  5644 solver.cpp:218] Iteration 13380 (2.23473 iter/s, 5.36978s/12 iters), loss = 5.27354
I0406 09:04:21.961911  5644 solver.cpp:237]     Train net output #0: loss = 5.27354 (* 1 = 5.27354 loss)
I0406 09:04:21.961920  5644 sgd_solver.cpp:105] Iteration 13380, lr = 0.1
I0406 09:04:27.276865  5644 solver.cpp:218] Iteration 13392 (2.2578 iter/s, 5.3149s/12 iters), loss = 5.27549
I0406 09:04:27.276908  5644 solver.cpp:237]     Train net output #0: loss = 5.27549 (* 1 = 5.27549 loss)
I0406 09:04:27.276916  5644 sgd_solver.cpp:105] Iteration 13392, lr = 0.1
I0406 09:04:32.289959  5644 solver.cpp:218] Iteration 13404 (2.39378 iter/s, 5.013s/12 iters), loss = 5.287
I0406 09:04:32.289999  5644 solver.cpp:237]     Train net output #0: loss = 5.287 (* 1 = 5.287 loss)
I0406 09:04:32.290004  5644 sgd_solver.cpp:105] Iteration 13404, lr = 0.1
I0406 09:04:37.397475  5644 solver.cpp:218] Iteration 13416 (2.34952 iter/s, 5.10742s/12 iters), loss = 5.27174
I0406 09:04:37.397518  5644 solver.cpp:237]     Train net output #0: loss = 5.27174 (* 1 = 5.27174 loss)
I0406 09:04:37.397524  5644 sgd_solver.cpp:105] Iteration 13416, lr = 0.1
I0406 09:04:42.784202  5644 solver.cpp:218] Iteration 13428 (2.22774 iter/s, 5.38662s/12 iters), loss = 5.26572
I0406 09:04:42.784250  5644 solver.cpp:237]     Train net output #0: loss = 5.26572 (* 1 = 5.26572 loss)
I0406 09:04:42.784258  5644 sgd_solver.cpp:105] Iteration 13428, lr = 0.1
I0406 09:04:47.977497  5644 solver.cpp:218] Iteration 13440 (2.31072 iter/s, 5.19319s/12 iters), loss = 5.28398
I0406 09:04:47.977596  5644 solver.cpp:237]     Train net output #0: loss = 5.28398 (* 1 = 5.28398 loss)
I0406 09:04:47.977602  5644 sgd_solver.cpp:105] Iteration 13440, lr = 0.1
I0406 09:04:53.353130  5644 solver.cpp:218] Iteration 13452 (2.23236 iter/s, 5.37548s/12 iters), loss = 5.28063
I0406 09:04:53.353168  5644 solver.cpp:237]     Train net output #0: loss = 5.28063 (* 1 = 5.28063 loss)
I0406 09:04:53.353173  5644 sgd_solver.cpp:105] Iteration 13452, lr = 0.1
I0406 09:04:53.580682  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:04:58.083308  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13464.caffemodel
I0406 09:05:01.216401  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13464.solverstate
I0406 09:05:03.540380  5644 solver.cpp:330] Iteration 13464, Testing net (#0)
I0406 09:05:03.540401  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:05:07.265897  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:05:07.946221  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:05:07.946256  5644 solver.cpp:397]     Test net output #1: loss = 5.28953 (* 1 = 5.28953 loss)
I0406 09:05:08.084889  5644 solver.cpp:218] Iteration 13464 (0.814576 iter/s, 14.7316s/12 iters), loss = 5.26653
I0406 09:05:08.084929  5644 solver.cpp:237]     Train net output #0: loss = 5.26653 (* 1 = 5.26653 loss)
I0406 09:05:08.084936  5644 sgd_solver.cpp:105] Iteration 13464, lr = 0.1
I0406 09:05:12.395570  5644 solver.cpp:218] Iteration 13476 (2.78384 iter/s, 4.31059s/12 iters), loss = 5.29683
I0406 09:05:12.395618  5644 solver.cpp:237]     Train net output #0: loss = 5.29683 (* 1 = 5.29683 loss)
I0406 09:05:12.395627  5644 sgd_solver.cpp:105] Iteration 13476, lr = 0.1
I0406 09:05:17.659763  5644 solver.cpp:218] Iteration 13488 (2.2796 iter/s, 5.26409s/12 iters), loss = 5.27254
I0406 09:05:17.659806  5644 solver.cpp:237]     Train net output #0: loss = 5.27254 (* 1 = 5.27254 loss)
I0406 09:05:17.659812  5644 sgd_solver.cpp:105] Iteration 13488, lr = 0.1
I0406 09:05:22.773082  5644 solver.cpp:218] Iteration 13500 (2.34686 iter/s, 5.11322s/12 iters), loss = 5.28368
I0406 09:05:22.773195  5644 solver.cpp:237]     Train net output #0: loss = 5.28368 (* 1 = 5.28368 loss)
I0406 09:05:22.773202  5644 sgd_solver.cpp:105] Iteration 13500, lr = 0.1
I0406 09:05:27.980345  5644 solver.cpp:218] Iteration 13512 (2.30455 iter/s, 5.20709s/12 iters), loss = 5.28614
I0406 09:05:27.980383  5644 solver.cpp:237]     Train net output #0: loss = 5.28614 (* 1 = 5.28614 loss)
I0406 09:05:27.980388  5644 sgd_solver.cpp:105] Iteration 13512, lr = 0.1
I0406 09:05:33.288744  5644 solver.cpp:218] Iteration 13524 (2.26061 iter/s, 5.3083s/12 iters), loss = 5.26186
I0406 09:05:33.288796  5644 solver.cpp:237]     Train net output #0: loss = 5.26186 (* 1 = 5.26186 loss)
I0406 09:05:33.288805  5644 sgd_solver.cpp:105] Iteration 13524, lr = 0.1
I0406 09:05:38.326908  5644 solver.cpp:218] Iteration 13536 (2.38187 iter/s, 5.03806s/12 iters), loss = 5.28439
I0406 09:05:38.326952  5644 solver.cpp:237]     Train net output #0: loss = 5.28439 (* 1 = 5.28439 loss)
I0406 09:05:38.326959  5644 sgd_solver.cpp:105] Iteration 13536, lr = 0.1
I0406 09:05:43.594164  5644 solver.cpp:218] Iteration 13548 (2.27827 iter/s, 5.26715s/12 iters), loss = 5.26484
I0406 09:05:43.594215  5644 solver.cpp:237]     Train net output #0: loss = 5.26484 (* 1 = 5.26484 loss)
I0406 09:05:43.594223  5644 sgd_solver.cpp:105] Iteration 13548, lr = 0.1
I0406 09:05:46.091795  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:05:48.983913  5644 solver.cpp:218] Iteration 13560 (2.22649 iter/s, 5.38964s/12 iters), loss = 5.28169
I0406 09:05:48.983954  5644 solver.cpp:237]     Train net output #0: loss = 5.28169 (* 1 = 5.28169 loss)
I0406 09:05:48.983960  5644 sgd_solver.cpp:105] Iteration 13560, lr = 0.1
I0406 09:05:50.979677  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13566.caffemodel
I0406 09:05:54.091325  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13566.solverstate
I0406 09:05:57.608729  5644 solver.cpp:330] Iteration 13566, Testing net (#0)
I0406 09:05:57.608750  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:06:01.512346  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:06:02.234031  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:06:02.234066  5644 solver.cpp:397]     Test net output #1: loss = 5.28993 (* 1 = 5.28993 loss)
I0406 09:06:04.224699  5644 solver.cpp:218] Iteration 13572 (0.78737 iter/s, 15.2406s/12 iters), loss = 5.26879
I0406 09:06:04.224756  5644 solver.cpp:237]     Train net output #0: loss = 5.26879 (* 1 = 5.26879 loss)
I0406 09:06:04.224764  5644 sgd_solver.cpp:105] Iteration 13572, lr = 0.1
I0406 09:06:09.309703  5644 solver.cpp:218] Iteration 13584 (2.35993 iter/s, 5.08489s/12 iters), loss = 5.27491
I0406 09:06:09.309756  5644 solver.cpp:237]     Train net output #0: loss = 5.27491 (* 1 = 5.27491 loss)
I0406 09:06:09.309763  5644 sgd_solver.cpp:105] Iteration 13584, lr = 0.1
I0406 09:06:14.448671  5644 solver.cpp:218] Iteration 13596 (2.33515 iter/s, 5.13886s/12 iters), loss = 5.26754
I0406 09:06:14.448719  5644 solver.cpp:237]     Train net output #0: loss = 5.26754 (* 1 = 5.26754 loss)
I0406 09:06:14.448726  5644 sgd_solver.cpp:105] Iteration 13596, lr = 0.1
I0406 09:06:19.517894  5644 solver.cpp:218] Iteration 13608 (2.36727 iter/s, 5.06913s/12 iters), loss = 5.26372
I0406 09:06:19.517930  5644 solver.cpp:237]     Train net output #0: loss = 5.26372 (* 1 = 5.26372 loss)
I0406 09:06:19.517935  5644 sgd_solver.cpp:105] Iteration 13608, lr = 0.1
I0406 09:06:24.826385  5644 solver.cpp:218] Iteration 13620 (2.26057 iter/s, 5.3084s/12 iters), loss = 5.2918
I0406 09:06:24.826504  5644 solver.cpp:237]     Train net output #0: loss = 5.2918 (* 1 = 5.2918 loss)
I0406 09:06:24.826511  5644 sgd_solver.cpp:105] Iteration 13620, lr = 0.1
I0406 09:06:29.973317  5644 solver.cpp:218] Iteration 13632 (2.33157 iter/s, 5.14676s/12 iters), loss = 5.27818
I0406 09:06:29.973366  5644 solver.cpp:237]     Train net output #0: loss = 5.27818 (* 1 = 5.27818 loss)
I0406 09:06:29.973374  5644 sgd_solver.cpp:105] Iteration 13632, lr = 0.1
I0406 09:06:35.311636  5644 solver.cpp:218] Iteration 13644 (2.24794 iter/s, 5.33821s/12 iters), loss = 5.29683
I0406 09:06:35.311691  5644 solver.cpp:237]     Train net output #0: loss = 5.29683 (* 1 = 5.29683 loss)
I0406 09:06:35.311699  5644 sgd_solver.cpp:105] Iteration 13644, lr = 0.1
I0406 09:06:40.234099  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:06:40.645092  5644 solver.cpp:218] Iteration 13656 (2.25 iter/s, 5.33334s/12 iters), loss = 5.29911
I0406 09:06:40.645133  5644 solver.cpp:237]     Train net output #0: loss = 5.29911 (* 1 = 5.29911 loss)
I0406 09:06:40.645138  5644 sgd_solver.cpp:105] Iteration 13656, lr = 0.1
I0406 09:06:45.496529  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13668.caffemodel
I0406 09:06:48.541348  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13668.solverstate
I0406 09:06:50.860636  5644 solver.cpp:330] Iteration 13668, Testing net (#0)
I0406 09:06:50.860656  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:06:54.452127  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:06:55.258049  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:06:55.258733  5644 solver.cpp:397]     Test net output #1: loss = 5.28965 (* 1 = 5.28965 loss)
I0406 09:06:55.398756  5644 solver.cpp:218] Iteration 13668 (0.813367 iter/s, 14.7535s/12 iters), loss = 5.27462
I0406 09:06:55.398800  5644 solver.cpp:237]     Train net output #0: loss = 5.27462 (* 1 = 5.27462 loss)
I0406 09:06:55.398805  5644 sgd_solver.cpp:105] Iteration 13668, lr = 0.1
I0406 09:06:59.664530  5644 solver.cpp:218] Iteration 13680 (2.81315 iter/s, 4.26569s/12 iters), loss = 5.26767
I0406 09:06:59.664568  5644 solver.cpp:237]     Train net output #0: loss = 5.26767 (* 1 = 5.26767 loss)
I0406 09:06:59.664574  5644 sgd_solver.cpp:105] Iteration 13680, lr = 0.1
I0406 09:07:04.929579  5644 solver.cpp:218] Iteration 13692 (2.27922 iter/s, 5.26495s/12 iters), loss = 5.2826
I0406 09:07:04.929617  5644 solver.cpp:237]     Train net output #0: loss = 5.2826 (* 1 = 5.2826 loss)
I0406 09:07:04.929622  5644 sgd_solver.cpp:105] Iteration 13692, lr = 0.1
I0406 09:07:09.973685  5644 solver.cpp:218] Iteration 13704 (2.37906 iter/s, 5.04401s/12 iters), loss = 5.28865
I0406 09:07:09.973732  5644 solver.cpp:237]     Train net output #0: loss = 5.28865 (* 1 = 5.28865 loss)
I0406 09:07:09.973742  5644 sgd_solver.cpp:105] Iteration 13704, lr = 0.1
I0406 09:07:15.247033  5644 solver.cpp:218] Iteration 13716 (2.27564 iter/s, 5.27325s/12 iters), loss = 5.26684
I0406 09:07:15.247072  5644 solver.cpp:237]     Train net output #0: loss = 5.26684 (* 1 = 5.26684 loss)
I0406 09:07:15.247078  5644 sgd_solver.cpp:105] Iteration 13716, lr = 0.1
I0406 09:07:20.456048  5644 solver.cpp:218] Iteration 13728 (2.30374 iter/s, 5.20892s/12 iters), loss = 5.28239
I0406 09:07:20.456089  5644 solver.cpp:237]     Train net output #0: loss = 5.28239 (* 1 = 5.28239 loss)
I0406 09:07:20.456094  5644 sgd_solver.cpp:105] Iteration 13728, lr = 0.1
I0406 09:07:25.778416  5644 solver.cpp:218] Iteration 13740 (2.25468 iter/s, 5.32227s/12 iters), loss = 5.28394
I0406 09:07:25.778537  5644 solver.cpp:237]     Train net output #0: loss = 5.28394 (* 1 = 5.28394 loss)
I0406 09:07:25.778545  5644 sgd_solver.cpp:105] Iteration 13740, lr = 0.1
I0406 09:07:31.064173  5644 solver.cpp:218] Iteration 13752 (2.27033 iter/s, 5.28558s/12 iters), loss = 5.27178
I0406 09:07:31.064224  5644 solver.cpp:237]     Train net output #0: loss = 5.27178 (* 1 = 5.27178 loss)
I0406 09:07:31.064235  5644 sgd_solver.cpp:105] Iteration 13752, lr = 0.1
I0406 09:07:32.773759  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:07:36.304448  5644 solver.cpp:218] Iteration 13764 (2.29 iter/s, 5.24017s/12 iters), loss = 5.28511
I0406 09:07:36.304486  5644 solver.cpp:237]     Train net output #0: loss = 5.28511 (* 1 = 5.28511 loss)
I0406 09:07:36.304491  5644 sgd_solver.cpp:105] Iteration 13764, lr = 0.1
I0406 09:07:38.425209  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13770.caffemodel
I0406 09:07:41.439692  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13770.solverstate
I0406 09:07:44.062000  5644 solver.cpp:330] Iteration 13770, Testing net (#0)
I0406 09:07:44.062021  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:07:47.637596  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:07:48.447674  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:07:48.447708  5644 solver.cpp:397]     Test net output #1: loss = 5.28948 (* 1 = 5.28948 loss)
I0406 09:07:50.313578  5644 solver.cpp:218] Iteration 13776 (0.856595 iter/s, 14.009s/12 iters), loss = 5.25846
I0406 09:07:50.313629  5644 solver.cpp:237]     Train net output #0: loss = 5.25846 (* 1 = 5.25846 loss)
I0406 09:07:50.313637  5644 sgd_solver.cpp:105] Iteration 13776, lr = 0.1
I0406 09:07:55.640100  5644 solver.cpp:218] Iteration 13788 (2.25292 iter/s, 5.32641s/12 iters), loss = 5.27419
I0406 09:07:55.640136  5644 solver.cpp:237]     Train net output #0: loss = 5.27419 (* 1 = 5.27419 loss)
I0406 09:07:55.640141  5644 sgd_solver.cpp:105] Iteration 13788, lr = 0.1
I0406 09:08:01.000322  5644 solver.cpp:218] Iteration 13800 (2.23875 iter/s, 5.36013s/12 iters), loss = 5.27236
I0406 09:08:01.000439  5644 solver.cpp:237]     Train net output #0: loss = 5.27236 (* 1 = 5.27236 loss)
I0406 09:08:01.000448  5644 sgd_solver.cpp:105] Iteration 13800, lr = 0.1
I0406 09:08:06.346067  5644 solver.cpp:218] Iteration 13812 (2.24485 iter/s, 5.34557s/12 iters), loss = 5.28621
I0406 09:08:06.346109  5644 solver.cpp:237]     Train net output #0: loss = 5.28621 (* 1 = 5.28621 loss)
I0406 09:08:06.346115  5644 sgd_solver.cpp:105] Iteration 13812, lr = 0.1
I0406 09:08:11.726953  5644 solver.cpp:218] Iteration 13824 (2.23016 iter/s, 5.38078s/12 iters), loss = 5.26381
I0406 09:08:11.726994  5644 solver.cpp:237]     Train net output #0: loss = 5.26381 (* 1 = 5.26381 loss)
I0406 09:08:11.726999  5644 sgd_solver.cpp:105] Iteration 13824, lr = 0.1
I0406 09:08:17.128970  5644 solver.cpp:218] Iteration 13836 (2.22143 iter/s, 5.40191s/12 iters), loss = 5.29256
I0406 09:08:17.129020  5644 solver.cpp:237]     Train net output #0: loss = 5.29256 (* 1 = 5.29256 loss)
I0406 09:08:17.129029  5644 sgd_solver.cpp:105] Iteration 13836, lr = 0.1
I0406 09:08:22.491333  5644 solver.cpp:218] Iteration 13848 (2.23787 iter/s, 5.36225s/12 iters), loss = 5.2598
I0406 09:08:22.491385  5644 solver.cpp:237]     Train net output #0: loss = 5.2598 (* 1 = 5.2598 loss)
I0406 09:08:22.491394  5644 sgd_solver.cpp:105] Iteration 13848, lr = 0.1
I0406 09:08:26.359149  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:08:27.640275  5644 solver.cpp:218] Iteration 13860 (2.33062 iter/s, 5.14884s/12 iters), loss = 5.27338
I0406 09:08:27.640314  5644 solver.cpp:237]     Train net output #0: loss = 5.27338 (* 1 = 5.27338 loss)
I0406 09:08:27.640319  5644 sgd_solver.cpp:105] Iteration 13860, lr = 0.1
I0406 09:08:32.298844  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13872.caffemodel
I0406 09:08:35.320752  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13872.solverstate
I0406 09:08:37.632378  5644 solver.cpp:330] Iteration 13872, Testing net (#0)
I0406 09:08:37.632398  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:08:38.605301  5644 blocking_queue.cpp:49] Waiting for data
I0406 09:08:41.130674  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:08:42.020280  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:08:42.020315  5644 solver.cpp:397]     Test net output #1: loss = 5.29006 (* 1 = 5.29006 loss)
I0406 09:08:42.160172  5644 solver.cpp:218] Iteration 13872 (0.826462 iter/s, 14.5197s/12 iters), loss = 5.26927
I0406 09:08:42.160219  5644 solver.cpp:237]     Train net output #0: loss = 5.26927 (* 1 = 5.26927 loss)
I0406 09:08:42.160225  5644 sgd_solver.cpp:105] Iteration 13872, lr = 0.1
I0406 09:08:46.780098  5644 solver.cpp:218] Iteration 13884 (2.5975 iter/s, 4.61983s/12 iters), loss = 5.29081
I0406 09:08:46.780153  5644 solver.cpp:237]     Train net output #0: loss = 5.29081 (* 1 = 5.29081 loss)
I0406 09:08:46.780161  5644 sgd_solver.cpp:105] Iteration 13884, lr = 0.1
I0406 09:08:51.915599  5644 solver.cpp:218] Iteration 13896 (2.33672 iter/s, 5.13539s/12 iters), loss = 5.27189
I0406 09:08:51.915635  5644 solver.cpp:237]     Train net output #0: loss = 5.27189 (* 1 = 5.27189 loss)
I0406 09:08:51.915642  5644 sgd_solver.cpp:105] Iteration 13896, lr = 0.1
I0406 09:08:57.224126  5644 solver.cpp:218] Iteration 13908 (2.26055 iter/s, 5.30843s/12 iters), loss = 5.28473
I0406 09:08:57.224174  5644 solver.cpp:237]     Train net output #0: loss = 5.28473 (* 1 = 5.28473 loss)
I0406 09:08:57.224181  5644 sgd_solver.cpp:105] Iteration 13908, lr = 0.1
I0406 09:09:02.708681  5644 solver.cpp:218] Iteration 13920 (2.188 iter/s, 5.48446s/12 iters), loss = 5.26966
I0406 09:09:02.708783  5644 solver.cpp:237]     Train net output #0: loss = 5.26966 (* 1 = 5.26966 loss)
I0406 09:09:02.708791  5644 sgd_solver.cpp:105] Iteration 13920, lr = 0.1
I0406 09:09:07.934633  5644 solver.cpp:218] Iteration 13932 (2.2963 iter/s, 5.22579s/12 iters), loss = 5.27511
I0406 09:09:07.934671  5644 solver.cpp:237]     Train net output #0: loss = 5.27511 (* 1 = 5.27511 loss)
I0406 09:09:07.934677  5644 sgd_solver.cpp:105] Iteration 13932, lr = 0.1
I0406 09:09:12.957124  5644 solver.cpp:218] Iteration 13944 (2.3893 iter/s, 5.0224s/12 iters), loss = 5.27036
I0406 09:09:12.957161  5644 solver.cpp:237]     Train net output #0: loss = 5.27036 (* 1 = 5.27036 loss)
I0406 09:09:12.957166  5644 sgd_solver.cpp:105] Iteration 13944, lr = 0.1
I0406 09:09:18.170454  5644 solver.cpp:218] Iteration 13956 (2.30183 iter/s, 5.21323s/12 iters), loss = 5.28297
I0406 09:09:18.170506  5644 solver.cpp:237]     Train net output #0: loss = 5.28297 (* 1 = 5.28297 loss)
I0406 09:09:18.170514  5644 sgd_solver.cpp:105] Iteration 13956, lr = 0.1
I0406 09:09:19.231585  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:09:23.440721  5644 solver.cpp:218] Iteration 13968 (2.27697 iter/s, 5.27016s/12 iters), loss = 5.28019
I0406 09:09:23.440773  5644 solver.cpp:237]     Train net output #0: loss = 5.28019 (* 1 = 5.28019 loss)
I0406 09:09:23.440780  5644 sgd_solver.cpp:105] Iteration 13968, lr = 0.1
I0406 09:09:25.572377  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_13974.caffemodel
I0406 09:09:28.637073  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_13974.solverstate
I0406 09:09:30.933527  5644 solver.cpp:330] Iteration 13974, Testing net (#0)
I0406 09:09:30.933547  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:09:34.377595  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:09:35.262094  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 09:09:35.262133  5644 solver.cpp:397]     Test net output #1: loss = 5.28931 (* 1 = 5.28931 loss)
I0406 09:09:37.088289  5644 solver.cpp:218] Iteration 13980 (0.879289 iter/s, 13.6474s/12 iters), loss = 5.295
I0406 09:09:37.088340  5644 solver.cpp:237]     Train net output #0: loss = 5.295 (* 1 = 5.295 loss)
I0406 09:09:37.088346  5644 sgd_solver.cpp:105] Iteration 13980, lr = 0.1
I0406 09:09:42.551129  5644 solver.cpp:218] Iteration 13992 (2.1967 iter/s, 5.46273s/12 iters), loss = 5.27335
I0406 09:09:42.551173  5644 solver.cpp:237]     Train net output #0: loss = 5.27335 (* 1 = 5.27335 loss)
I0406 09:09:42.551179  5644 sgd_solver.cpp:105] Iteration 13992, lr = 0.1
I0406 09:09:47.792416  5644 solver.cpp:218] Iteration 14004 (2.28956 iter/s, 5.24118s/12 iters), loss = 5.27443
I0406 09:09:47.792455  5644 solver.cpp:237]     Train net output #0: loss = 5.27443 (* 1 = 5.27443 loss)
I0406 09:09:47.792461  5644 sgd_solver.cpp:105] Iteration 14004, lr = 0.1
I0406 09:09:53.070606  5644 solver.cpp:218] Iteration 14016 (2.27355 iter/s, 5.2781s/12 iters), loss = 5.28873
I0406 09:09:53.070644  5644 solver.cpp:237]     Train net output #0: loss = 5.28873 (* 1 = 5.28873 loss)
I0406 09:09:53.070649  5644 sgd_solver.cpp:105] Iteration 14016, lr = 0.1
I0406 09:09:58.416133  5644 solver.cpp:218] Iteration 14028 (2.24559 iter/s, 5.3438s/12 iters), loss = 5.26563
I0406 09:09:58.416172  5644 solver.cpp:237]     Train net output #0: loss = 5.26563 (* 1 = 5.26563 loss)
I0406 09:09:58.416178  5644 sgd_solver.cpp:105] Iteration 14028, lr = 0.1
I0406 09:10:03.624639  5644 solver.cpp:218] Iteration 14040 (2.30397 iter/s, 5.20841s/12 iters), loss = 5.26607
I0406 09:10:03.624684  5644 solver.cpp:237]     Train net output #0: loss = 5.26607 (* 1 = 5.26607 loss)
I0406 09:10:03.624691  5644 sgd_solver.cpp:105] Iteration 14040, lr = 0.1
I0406 09:10:08.933383  5644 solver.cpp:218] Iteration 14052 (2.26046 iter/s, 5.30865s/12 iters), loss = 5.28961
I0406 09:10:08.933509  5644 solver.cpp:237]     Train net output #0: loss = 5.28961 (* 1 = 5.28961 loss)
I0406 09:10:08.933516  5644 sgd_solver.cpp:105] Iteration 14052, lr = 0.1
I0406 09:10:12.282618  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:10:14.379171  5644 solver.cpp:218] Iteration 14064 (2.20361 iter/s, 5.4456s/12 iters), loss = 5.26528
I0406 09:10:14.379211  5644 solver.cpp:237]     Train net output #0: loss = 5.26528 (* 1 = 5.26528 loss)
I0406 09:10:14.379217  5644 sgd_solver.cpp:105] Iteration 14064, lr = 0.1
I0406 09:10:19.293324  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14076.caffemodel
I0406 09:10:22.388784  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14076.solverstate
I0406 09:10:24.763535  5644 solver.cpp:330] Iteration 14076, Testing net (#0)
I0406 09:10:24.763561  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:10:28.566079  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:10:29.528160  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 09:10:29.528199  5644 solver.cpp:397]     Test net output #1: loss = 5.2896 (* 1 = 5.2896 loss)
I0406 09:10:29.670889  5644 solver.cpp:218] Iteration 14076 (0.784748 iter/s, 15.2915s/12 iters), loss = 5.2752
I0406 09:10:29.672466  5644 solver.cpp:237]     Train net output #0: loss = 5.2752 (* 1 = 5.2752 loss)
I0406 09:10:29.672480  5644 sgd_solver.cpp:105] Iteration 14076, lr = 0.1
I0406 09:10:34.224102  5644 solver.cpp:218] Iteration 14088 (2.63644 iter/s, 4.55159s/12 iters), loss = 5.26677
I0406 09:10:34.224153  5644 solver.cpp:237]     Train net output #0: loss = 5.26677 (* 1 = 5.26677 loss)
I0406 09:10:34.224160  5644 sgd_solver.cpp:105] Iteration 14088, lr = 0.1
I0406 09:10:39.601455  5644 solver.cpp:218] Iteration 14100 (2.23163 iter/s, 5.37725s/12 iters), loss = 5.27875
I0406 09:10:39.601552  5644 solver.cpp:237]     Train net output #0: loss = 5.27875 (* 1 = 5.27875 loss)
I0406 09:10:39.601560  5644 sgd_solver.cpp:105] Iteration 14100, lr = 0.1
I0406 09:10:44.654124  5644 solver.cpp:218] Iteration 14112 (2.37505 iter/s, 5.05252s/12 iters), loss = 5.28094
I0406 09:10:44.654162  5644 solver.cpp:237]     Train net output #0: loss = 5.28094 (* 1 = 5.28094 loss)
I0406 09:10:44.654167  5644 sgd_solver.cpp:105] Iteration 14112, lr = 0.1
I0406 09:10:49.826587  5644 solver.cpp:218] Iteration 14124 (2.32002 iter/s, 5.17236s/12 iters), loss = 5.27145
I0406 09:10:49.826645  5644 solver.cpp:237]     Train net output #0: loss = 5.27145 (* 1 = 5.27145 loss)
I0406 09:10:49.826653  5644 sgd_solver.cpp:105] Iteration 14124, lr = 0.1
I0406 09:10:55.169561  5644 solver.cpp:218] Iteration 14136 (2.24599 iter/s, 5.34286s/12 iters), loss = 5.27215
I0406 09:10:55.169601  5644 solver.cpp:237]     Train net output #0: loss = 5.27215 (* 1 = 5.27215 loss)
I0406 09:10:55.169607  5644 sgd_solver.cpp:105] Iteration 14136, lr = 0.1
I0406 09:11:00.523401  5644 solver.cpp:218] Iteration 14148 (2.24142 iter/s, 5.35374s/12 iters), loss = 5.2919
I0406 09:11:00.523443  5644 solver.cpp:237]     Train net output #0: loss = 5.2919 (* 1 = 5.2919 loss)
I0406 09:11:00.523449  5644 sgd_solver.cpp:105] Iteration 14148, lr = 0.1
I0406 09:11:05.845919  5644 solver.cpp:218] Iteration 14160 (2.25461 iter/s, 5.32242s/12 iters), loss = 5.27894
I0406 09:11:05.845957  5644 solver.cpp:237]     Train net output #0: loss = 5.27894 (* 1 = 5.27894 loss)
I0406 09:11:05.845963  5644 sgd_solver.cpp:105] Iteration 14160, lr = 0.1
I0406 09:11:06.153898  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:11:11.135094  5644 solver.cpp:218] Iteration 14172 (2.26882 iter/s, 5.28908s/12 iters), loss = 5.27088
I0406 09:11:11.135202  5644 solver.cpp:237]     Train net output #0: loss = 5.27088 (* 1 = 5.27088 loss)
I0406 09:11:11.135210  5644 sgd_solver.cpp:105] Iteration 14172, lr = 0.1
I0406 09:11:13.396034  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14178.caffemodel
I0406 09:11:16.463050  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14178.solverstate
I0406 09:11:18.771710  5644 solver.cpp:330] Iteration 14178, Testing net (#0)
I0406 09:11:18.771729  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:11:22.099064  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:11:23.038955  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:11:23.038988  5644 solver.cpp:397]     Test net output #1: loss = 5.28969 (* 1 = 5.28969 loss)
I0406 09:11:24.891074  5644 solver.cpp:218] Iteration 14184 (0.872362 iter/s, 13.7557s/12 iters), loss = 5.29817
I0406 09:11:24.891113  5644 solver.cpp:237]     Train net output #0: loss = 5.29817 (* 1 = 5.29817 loss)
I0406 09:11:24.891119  5644 sgd_solver.cpp:105] Iteration 14184, lr = 0.1
I0406 09:11:30.041317  5644 solver.cpp:218] Iteration 14196 (2.33003 iter/s, 5.15015s/12 iters), loss = 5.26731
I0406 09:11:30.041360  5644 solver.cpp:237]     Train net output #0: loss = 5.26731 (* 1 = 5.26731 loss)
I0406 09:11:30.041366  5644 sgd_solver.cpp:105] Iteration 14196, lr = 0.1
I0406 09:11:35.301606  5644 solver.cpp:218] Iteration 14208 (2.28129 iter/s, 5.26019s/12 iters), loss = 5.27626
I0406 09:11:35.301646  5644 solver.cpp:237]     Train net output #0: loss = 5.27626 (* 1 = 5.27626 loss)
I0406 09:11:35.301652  5644 sgd_solver.cpp:105] Iteration 14208, lr = 0.1
I0406 09:11:40.432866  5644 solver.cpp:218] Iteration 14220 (2.33865 iter/s, 5.13116s/12 iters), loss = 5.29023
I0406 09:11:40.432915  5644 solver.cpp:237]     Train net output #0: loss = 5.29023 (* 1 = 5.29023 loss)
I0406 09:11:40.432921  5644 sgd_solver.cpp:105] Iteration 14220, lr = 0.1
I0406 09:11:45.540518  5644 solver.cpp:218] Iteration 14232 (2.34947 iter/s, 5.10754s/12 iters), loss = 5.25809
I0406 09:11:45.540632  5644 solver.cpp:237]     Train net output #0: loss = 5.25809 (* 1 = 5.25809 loss)
I0406 09:11:45.540642  5644 sgd_solver.cpp:105] Iteration 14232, lr = 0.1
I0406 09:11:50.917531  5644 solver.cpp:218] Iteration 14244 (2.23179 iter/s, 5.37684s/12 iters), loss = 5.28617
I0406 09:11:50.917570  5644 solver.cpp:237]     Train net output #0: loss = 5.28617 (* 1 = 5.28617 loss)
I0406 09:11:50.917577  5644 sgd_solver.cpp:105] Iteration 14244, lr = 0.1
I0406 09:11:56.241652  5644 solver.cpp:218] Iteration 14256 (2.25394 iter/s, 5.32402s/12 iters), loss = 5.27208
I0406 09:11:56.241706  5644 solver.cpp:237]     Train net output #0: loss = 5.27208 (* 1 = 5.27208 loss)
I0406 09:11:56.241715  5644 sgd_solver.cpp:105] Iteration 14256, lr = 0.1
I0406 09:11:58.976094  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:12:01.769148  5644 solver.cpp:218] Iteration 14268 (2.17101 iter/s, 5.52738s/12 iters), loss = 5.292
I0406 09:12:01.769203  5644 solver.cpp:237]     Train net output #0: loss = 5.292 (* 1 = 5.292 loss)
I0406 09:12:01.769212  5644 sgd_solver.cpp:105] Iteration 14268, lr = 0.1
I0406 09:12:06.594553  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14280.caffemodel
I0406 09:12:09.624686  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14280.solverstate
I0406 09:12:11.927649  5644 solver.cpp:330] Iteration 14280, Testing net (#0)
I0406 09:12:11.927668  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:12:15.354713  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:12:16.337420  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:12:16.337584  5644 solver.cpp:397]     Test net output #1: loss = 5.29002 (* 1 = 5.29002 loss)
I0406 09:12:16.473047  5644 solver.cpp:218] Iteration 14280 (0.81612 iter/s, 14.7037s/12 iters), loss = 5.27436
I0406 09:12:16.473084  5644 solver.cpp:237]     Train net output #0: loss = 5.27436 (* 1 = 5.27436 loss)
I0406 09:12:16.473089  5644 sgd_solver.cpp:105] Iteration 14280, lr = 0.1
I0406 09:12:20.690030  5644 solver.cpp:218] Iteration 14292 (2.8457 iter/s, 4.21689s/12 iters), loss = 5.2759
I0406 09:12:20.690069  5644 solver.cpp:237]     Train net output #0: loss = 5.2759 (* 1 = 5.2759 loss)
I0406 09:12:20.690074  5644 sgd_solver.cpp:105] Iteration 14292, lr = 0.1
I0406 09:12:25.787533  5644 solver.cpp:218] Iteration 14304 (2.35414 iter/s, 5.0974s/12 iters), loss = 5.27517
I0406 09:12:25.787583  5644 solver.cpp:237]     Train net output #0: loss = 5.27517 (* 1 = 5.27517 loss)
I0406 09:12:25.787591  5644 sgd_solver.cpp:105] Iteration 14304, lr = 0.1
I0406 09:12:31.126096  5644 solver.cpp:218] Iteration 14316 (2.24784 iter/s, 5.33846s/12 iters), loss = 5.26716
I0406 09:12:31.126135  5644 solver.cpp:237]     Train net output #0: loss = 5.26716 (* 1 = 5.26716 loss)
I0406 09:12:31.126142  5644 sgd_solver.cpp:105] Iteration 14316, lr = 0.1
I0406 09:12:36.365334  5644 solver.cpp:218] Iteration 14328 (2.29045 iter/s, 5.23914s/12 iters), loss = 5.29333
I0406 09:12:36.365375  5644 solver.cpp:237]     Train net output #0: loss = 5.29333 (* 1 = 5.29333 loss)
I0406 09:12:36.365379  5644 sgd_solver.cpp:105] Iteration 14328, lr = 0.1
I0406 09:12:41.801172  5644 solver.cpp:218] Iteration 14340 (2.20761 iter/s, 5.43574s/12 iters), loss = 5.28199
I0406 09:12:41.801213  5644 solver.cpp:237]     Train net output #0: loss = 5.28199 (* 1 = 5.28199 loss)
I0406 09:12:41.801218  5644 sgd_solver.cpp:105] Iteration 14340, lr = 0.1
I0406 09:12:47.151701  5644 solver.cpp:218] Iteration 14352 (2.24281 iter/s, 5.35043s/12 iters), loss = 5.28859
I0406 09:12:47.151823  5644 solver.cpp:237]     Train net output #0: loss = 5.28859 (* 1 = 5.28859 loss)
I0406 09:12:47.151831  5644 sgd_solver.cpp:105] Iteration 14352, lr = 0.1
I0406 09:12:51.909212  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:12:52.300572  5644 solver.cpp:218] Iteration 14364 (2.33069 iter/s, 5.1487s/12 iters), loss = 5.30224
I0406 09:12:52.300611  5644 solver.cpp:237]     Train net output #0: loss = 5.30224 (* 1 = 5.30224 loss)
I0406 09:12:52.300617  5644 sgd_solver.cpp:105] Iteration 14364, lr = 0.1
I0406 09:12:57.708901  5644 solver.cpp:218] Iteration 14376 (2.21884 iter/s, 5.40823s/12 iters), loss = 5.26792
I0406 09:12:57.708940  5644 solver.cpp:237]     Train net output #0: loss = 5.26792 (* 1 = 5.26792 loss)
I0406 09:12:57.708945  5644 sgd_solver.cpp:105] Iteration 14376, lr = 0.1
I0406 09:12:59.868908  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14382.caffemodel
I0406 09:13:02.916831  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14382.solverstate
I0406 09:13:05.232089  5644 solver.cpp:330] Iteration 14382, Testing net (#0)
I0406 09:13:05.232111  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:13:08.500145  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:13:09.514626  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:13:09.514662  5644 solver.cpp:397]     Test net output #1: loss = 5.29008 (* 1 = 5.29008 loss)
I0406 09:13:11.420670  5644 solver.cpp:218] Iteration 14388 (0.875171 iter/s, 13.7116s/12 iters), loss = 5.26757
I0406 09:13:11.420707  5644 solver.cpp:237]     Train net output #0: loss = 5.26757 (* 1 = 5.26757 loss)
I0406 09:13:11.420712  5644 sgd_solver.cpp:105] Iteration 14388, lr = 0.1
I0406 09:13:16.592777  5644 solver.cpp:218] Iteration 14400 (2.32018 iter/s, 5.17201s/12 iters), loss = 5.28291
I0406 09:13:16.592823  5644 solver.cpp:237]     Train net output #0: loss = 5.28291 (* 1 = 5.28291 loss)
I0406 09:13:16.592831  5644 sgd_solver.cpp:105] Iteration 14400, lr = 0.1
I0406 09:13:21.708961  5644 solver.cpp:218] Iteration 14412 (2.34555 iter/s, 5.11608s/12 iters), loss = 5.28798
I0406 09:13:21.709122  5644 solver.cpp:237]     Train net output #0: loss = 5.28798 (* 1 = 5.28798 loss)
I0406 09:13:21.709133  5644 sgd_solver.cpp:105] Iteration 14412, lr = 0.1
I0406 09:13:26.819873  5644 solver.cpp:218] Iteration 14424 (2.34802 iter/s, 5.1107s/12 iters), loss = 5.27002
I0406 09:13:26.819913  5644 solver.cpp:237]     Train net output #0: loss = 5.27002 (* 1 = 5.27002 loss)
I0406 09:13:26.819919  5644 sgd_solver.cpp:105] Iteration 14424, lr = 0.1
I0406 09:13:31.945925  5644 solver.cpp:218] Iteration 14436 (2.34103 iter/s, 5.12596s/12 iters), loss = 5.28289
I0406 09:13:31.945966  5644 solver.cpp:237]     Train net output #0: loss = 5.28289 (* 1 = 5.28289 loss)
I0406 09:13:31.945971  5644 sgd_solver.cpp:105] Iteration 14436, lr = 0.1
I0406 09:13:37.355687  5644 solver.cpp:218] Iteration 14448 (2.21825 iter/s, 5.40966s/12 iters), loss = 5.27784
I0406 09:13:37.355732  5644 solver.cpp:237]     Train net output #0: loss = 5.27784 (* 1 = 5.27784 loss)
I0406 09:13:37.355741  5644 sgd_solver.cpp:105] Iteration 14448, lr = 0.1
I0406 09:13:42.551882  5644 solver.cpp:218] Iteration 14460 (2.30943 iter/s, 5.19609s/12 iters), loss = 5.27615
I0406 09:13:42.551932  5644 solver.cpp:237]     Train net output #0: loss = 5.27615 (* 1 = 5.27615 loss)
I0406 09:13:42.551941  5644 sgd_solver.cpp:105] Iteration 14460, lr = 0.1
I0406 09:13:44.322209  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:13:47.750124  5644 solver.cpp:218] Iteration 14472 (2.30852 iter/s, 5.19813s/12 iters), loss = 5.29812
I0406 09:13:47.750185  5644 solver.cpp:237]     Train net output #0: loss = 5.29812 (* 1 = 5.29812 loss)
I0406 09:13:47.750193  5644 sgd_solver.cpp:105] Iteration 14472, lr = 0.1
I0406 09:13:52.565773  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14484.caffemodel
I0406 09:13:55.610034  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14484.solverstate
I0406 09:13:57.927520  5644 solver.cpp:330] Iteration 14484, Testing net (#0)
I0406 09:13:57.927539  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:14:01.279280  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:14:02.412782  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:14:02.412814  5644 solver.cpp:397]     Test net output #1: loss = 5.28979 (* 1 = 5.28979 loss)
I0406 09:14:02.545720  5644 solver.cpp:218] Iteration 14484 (0.811062 iter/s, 14.7954s/12 iters), loss = 5.25607
I0406 09:14:02.545769  5644 solver.cpp:237]     Train net output #0: loss = 5.25607 (* 1 = 5.25607 loss)
I0406 09:14:02.545776  5644 sgd_solver.cpp:105] Iteration 14484, lr = 0.1
I0406 09:14:06.875098  5644 solver.cpp:218] Iteration 14496 (2.77182 iter/s, 4.32928s/12 iters), loss = 5.28631
I0406 09:14:06.875134  5644 solver.cpp:237]     Train net output #0: loss = 5.28631 (* 1 = 5.28631 loss)
I0406 09:14:06.875139  5644 sgd_solver.cpp:105] Iteration 14496, lr = 0.1
I0406 09:14:12.098050  5644 solver.cpp:218] Iteration 14508 (2.29759 iter/s, 5.22286s/12 iters), loss = 5.27282
I0406 09:14:12.098096  5644 solver.cpp:237]     Train net output #0: loss = 5.27282 (* 1 = 5.27282 loss)
I0406 09:14:12.098104  5644 sgd_solver.cpp:105] Iteration 14508, lr = 0.1
I0406 09:14:17.211225  5644 solver.cpp:218] Iteration 14520 (2.34693 iter/s, 5.11307s/12 iters), loss = 5.27988
I0406 09:14:17.211277  5644 solver.cpp:237]     Train net output #0: loss = 5.27988 (* 1 = 5.27988 loss)
I0406 09:14:17.211284  5644 sgd_solver.cpp:105] Iteration 14520, lr = 0.1
I0406 09:14:22.392941  5644 solver.cpp:218] Iteration 14532 (2.31588 iter/s, 5.18161s/12 iters), loss = 5.26332
I0406 09:14:22.392982  5644 solver.cpp:237]     Train net output #0: loss = 5.26332 (* 1 = 5.26332 loss)
I0406 09:14:22.392987  5644 sgd_solver.cpp:105] Iteration 14532, lr = 0.1
I0406 09:14:27.769496  5644 solver.cpp:218] Iteration 14544 (2.23195 iter/s, 5.37645s/12 iters), loss = 5.28037
I0406 09:14:27.769659  5644 solver.cpp:237]     Train net output #0: loss = 5.28037 (* 1 = 5.28037 loss)
I0406 09:14:27.769668  5644 sgd_solver.cpp:105] Iteration 14544, lr = 0.1
I0406 09:14:33.112002  5644 solver.cpp:218] Iteration 14556 (2.24623 iter/s, 5.34229s/12 iters), loss = 5.26326
I0406 09:14:33.112058  5644 solver.cpp:237]     Train net output #0: loss = 5.26326 (* 1 = 5.26326 loss)
I0406 09:14:33.112067  5644 sgd_solver.cpp:105] Iteration 14556, lr = 0.1
I0406 09:14:37.278168  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:14:37.484107  5644 blocking_queue.cpp:49] Waiting for data
I0406 09:14:38.486281  5644 solver.cpp:218] Iteration 14568 (2.23291 iter/s, 5.37416s/12 iters), loss = 5.2754
I0406 09:14:38.486335  5644 solver.cpp:237]     Train net output #0: loss = 5.2754 (* 1 = 5.2754 loss)
I0406 09:14:38.486344  5644 sgd_solver.cpp:105] Iteration 14568, lr = 0.1
I0406 09:14:43.809382  5644 solver.cpp:218] Iteration 14580 (2.25437 iter/s, 5.323s/12 iters), loss = 5.2648
I0406 09:14:43.809424  5644 solver.cpp:237]     Train net output #0: loss = 5.2648 (* 1 = 5.2648 loss)
I0406 09:14:43.809429  5644 sgd_solver.cpp:105] Iteration 14580, lr = 0.1
I0406 09:14:45.935729  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14586.caffemodel
I0406 09:14:48.960239  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14586.solverstate
I0406 09:14:51.274780  5644 solver.cpp:330] Iteration 14586, Testing net (#0)
I0406 09:14:51.274799  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:14:54.493227  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:14:55.588023  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 09:14:55.588061  5644 solver.cpp:397]     Test net output #1: loss = 5.28963 (* 1 = 5.28963 loss)
I0406 09:14:57.499624  5644 solver.cpp:218] Iteration 14592 (0.876547 iter/s, 13.6901s/12 iters), loss = 5.29062
I0406 09:14:57.499673  5644 solver.cpp:237]     Train net output #0: loss = 5.29062 (* 1 = 5.29062 loss)
I0406 09:14:57.499682  5644 sgd_solver.cpp:105] Iteration 14592, lr = 0.1
I0406 09:15:02.672955  5644 solver.cpp:218] Iteration 14604 (2.31964 iter/s, 5.17322s/12 iters), loss = 5.26658
I0406 09:15:02.673060  5644 solver.cpp:237]     Train net output #0: loss = 5.26658 (* 1 = 5.26658 loss)
I0406 09:15:02.673069  5644 sgd_solver.cpp:105] Iteration 14604, lr = 0.1
I0406 09:15:07.999116  5644 solver.cpp:218] Iteration 14616 (2.2531 iter/s, 5.326s/12 iters), loss = 5.28322
I0406 09:15:07.999161  5644 solver.cpp:237]     Train net output #0: loss = 5.28322 (* 1 = 5.28322 loss)
I0406 09:15:07.999169  5644 sgd_solver.cpp:105] Iteration 14616, lr = 0.1
I0406 09:15:13.136564  5644 solver.cpp:218] Iteration 14628 (2.33584 iter/s, 5.13734s/12 iters), loss = 5.27603
I0406 09:15:13.136624  5644 solver.cpp:237]     Train net output #0: loss = 5.27603 (* 1 = 5.27603 loss)
I0406 09:15:13.136633  5644 sgd_solver.cpp:105] Iteration 14628, lr = 0.1
I0406 09:15:18.476783  5644 solver.cpp:218] Iteration 14640 (2.24715 iter/s, 5.3401s/12 iters), loss = 5.27244
I0406 09:15:18.476831  5644 solver.cpp:237]     Train net output #0: loss = 5.27244 (* 1 = 5.27244 loss)
I0406 09:15:18.476838  5644 sgd_solver.cpp:105] Iteration 14640, lr = 0.1
I0406 09:15:23.679230  5644 solver.cpp:218] Iteration 14652 (2.30665 iter/s, 5.20235s/12 iters), loss = 5.27353
I0406 09:15:23.679265  5644 solver.cpp:237]     Train net output #0: loss = 5.27353 (* 1 = 5.27353 loss)
I0406 09:15:23.679270  5644 sgd_solver.cpp:105] Iteration 14652, lr = 0.1
I0406 09:15:29.007818  5644 solver.cpp:218] Iteration 14664 (2.25204 iter/s, 5.32849s/12 iters), loss = 5.2809
I0406 09:15:29.007875  5644 solver.cpp:237]     Train net output #0: loss = 5.2809 (* 1 = 5.2809 loss)
I0406 09:15:29.007884  5644 sgd_solver.cpp:105] Iteration 14664, lr = 0.1
I0406 09:15:30.099023  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:15:34.356354  5644 solver.cpp:218] Iteration 14676 (2.24365 iter/s, 5.34842s/12 iters), loss = 5.28653
I0406 09:15:34.356489  5644 solver.cpp:237]     Train net output #0: loss = 5.28653 (* 1 = 5.28653 loss)
I0406 09:15:34.356498  5644 sgd_solver.cpp:105] Iteration 14676, lr = 0.1
I0406 09:15:39.035706  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14688.caffemodel
I0406 09:15:42.846876  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14688.solverstate
I0406 09:15:45.154899  5644 solver.cpp:330] Iteration 14688, Testing net (#0)
I0406 09:15:45.154922  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:15:48.409255  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:15:49.565891  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 09:15:49.565932  5644 solver.cpp:397]     Test net output #1: loss = 5.2897 (* 1 = 5.2897 loss)
I0406 09:15:49.704605  5644 solver.cpp:218] Iteration 14688 (0.781862 iter/s, 15.348s/12 iters), loss = 5.29916
I0406 09:15:49.704653  5644 solver.cpp:237]     Train net output #0: loss = 5.29916 (* 1 = 5.29916 loss)
I0406 09:15:49.704660  5644 sgd_solver.cpp:105] Iteration 14688, lr = 0.1
I0406 09:15:54.083962  5644 solver.cpp:218] Iteration 14700 (2.74019 iter/s, 4.37926s/12 iters), loss = 5.26732
I0406 09:15:54.084010  5644 solver.cpp:237]     Train net output #0: loss = 5.26732 (* 1 = 5.26732 loss)
I0406 09:15:54.084018  5644 sgd_solver.cpp:105] Iteration 14700, lr = 0.1
I0406 09:15:59.260953  5644 solver.cpp:218] Iteration 14712 (2.31799 iter/s, 5.17689s/12 iters), loss = 5.27624
I0406 09:15:59.260990  5644 solver.cpp:237]     Train net output #0: loss = 5.27624 (* 1 = 5.27624 loss)
I0406 09:15:59.260996  5644 sgd_solver.cpp:105] Iteration 14712, lr = 0.1
I0406 09:16:04.462654  5644 solver.cpp:218] Iteration 14724 (2.30698 iter/s, 5.2016s/12 iters), loss = 5.28971
I0406 09:16:04.462759  5644 solver.cpp:237]     Train net output #0: loss = 5.28971 (* 1 = 5.28971 loss)
I0406 09:16:04.462769  5644 sgd_solver.cpp:105] Iteration 14724, lr = 0.1
I0406 09:16:09.688130  5644 solver.cpp:218] Iteration 14736 (2.29651 iter/s, 5.22532s/12 iters), loss = 5.27022
I0406 09:16:09.688189  5644 solver.cpp:237]     Train net output #0: loss = 5.27022 (* 1 = 5.27022 loss)
I0406 09:16:09.688199  5644 sgd_solver.cpp:105] Iteration 14736, lr = 0.1
I0406 09:16:14.880193  5644 solver.cpp:218] Iteration 14748 (2.31127 iter/s, 5.19195s/12 iters), loss = 5.26763
I0406 09:16:14.880236  5644 solver.cpp:237]     Train net output #0: loss = 5.26763 (* 1 = 5.26763 loss)
I0406 09:16:14.880244  5644 sgd_solver.cpp:105] Iteration 14748, lr = 0.1
I0406 09:16:20.038847  5644 solver.cpp:218] Iteration 14760 (2.32623 iter/s, 5.15856s/12 iters), loss = 5.28527
I0406 09:16:20.038885  5644 solver.cpp:237]     Train net output #0: loss = 5.28527 (* 1 = 5.28527 loss)
I0406 09:16:20.038892  5644 sgd_solver.cpp:105] Iteration 14760, lr = 0.1
I0406 09:16:23.314224  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:16:25.118786  5644 solver.cpp:218] Iteration 14772 (2.36228 iter/s, 5.07984s/12 iters), loss = 5.2651
I0406 09:16:25.118834  5644 solver.cpp:237]     Train net output #0: loss = 5.2651 (* 1 = 5.2651 loss)
I0406 09:16:25.118844  5644 sgd_solver.cpp:105] Iteration 14772, lr = 0.1
I0406 09:16:30.101769  5644 solver.cpp:218] Iteration 14784 (2.40825 iter/s, 4.98288s/12 iters), loss = 5.27379
I0406 09:16:30.101816  5644 solver.cpp:237]     Train net output #0: loss = 5.27379 (* 1 = 5.27379 loss)
I0406 09:16:30.101824  5644 sgd_solver.cpp:105] Iteration 14784, lr = 0.1
I0406 09:16:32.143149  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14790.caffemodel
I0406 09:16:35.161731  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14790.solverstate
I0406 09:16:37.485009  5644 solver.cpp:330] Iteration 14790, Testing net (#0)
I0406 09:16:37.485028  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:16:40.621703  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:16:41.840863  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:16:41.840910  5644 solver.cpp:397]     Test net output #1: loss = 5.28997 (* 1 = 5.28997 loss)
I0406 09:16:43.756067  5644 solver.cpp:218] Iteration 14796 (0.878855 iter/s, 13.6541s/12 iters), loss = 5.25942
I0406 09:16:43.756114  5644 solver.cpp:237]     Train net output #0: loss = 5.25942 (* 1 = 5.25942 loss)
I0406 09:16:43.756119  5644 sgd_solver.cpp:105] Iteration 14796, lr = 0.1
I0406 09:16:49.003316  5644 solver.cpp:218] Iteration 14808 (2.28696 iter/s, 5.24714s/12 iters), loss = 5.27151
I0406 09:16:49.003361  5644 solver.cpp:237]     Train net output #0: loss = 5.27151 (* 1 = 5.27151 loss)
I0406 09:16:49.003369  5644 sgd_solver.cpp:105] Iteration 14808, lr = 0.1
I0406 09:16:54.181149  5644 solver.cpp:218] Iteration 14820 (2.31762 iter/s, 5.17773s/12 iters), loss = 5.28295
I0406 09:16:54.181192  5644 solver.cpp:237]     Train net output #0: loss = 5.28295 (* 1 = 5.28295 loss)
I0406 09:16:54.181200  5644 sgd_solver.cpp:105] Iteration 14820, lr = 0.1
I0406 09:16:59.418035  5644 solver.cpp:218] Iteration 14832 (2.29148 iter/s, 5.23679s/12 iters), loss = 5.2753
I0406 09:16:59.418072  5644 solver.cpp:237]     Train net output #0: loss = 5.2753 (* 1 = 5.2753 loss)
I0406 09:16:59.418078  5644 sgd_solver.cpp:105] Iteration 14832, lr = 0.1
I0406 09:17:04.671669  5644 solver.cpp:218] Iteration 14844 (2.28418 iter/s, 5.25354s/12 iters), loss = 5.26116
I0406 09:17:04.671726  5644 solver.cpp:237]     Train net output #0: loss = 5.26116 (* 1 = 5.26116 loss)
I0406 09:17:04.671736  5644 sgd_solver.cpp:105] Iteration 14844, lr = 0.1
I0406 09:17:09.962142  5644 solver.cpp:218] Iteration 14856 (2.26828 iter/s, 5.29036s/12 iters), loss = 5.28949
I0406 09:17:09.962244  5644 solver.cpp:237]     Train net output #0: loss = 5.28949 (* 1 = 5.28949 loss)
I0406 09:17:09.962251  5644 sgd_solver.cpp:105] Iteration 14856, lr = 0.1
I0406 09:17:15.084507  5644 solver.cpp:218] Iteration 14868 (2.34274 iter/s, 5.12221s/12 iters), loss = 5.27178
I0406 09:17:15.084548  5644 solver.cpp:237]     Train net output #0: loss = 5.27178 (* 1 = 5.27178 loss)
I0406 09:17:15.084553  5644 sgd_solver.cpp:105] Iteration 14868, lr = 0.1
I0406 09:17:15.371428  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:17:20.525363  5644 solver.cpp:218] Iteration 14880 (2.20558 iter/s, 5.44076s/12 iters), loss = 5.27864
I0406 09:17:20.525401  5644 solver.cpp:237]     Train net output #0: loss = 5.27864 (* 1 = 5.27864 loss)
I0406 09:17:20.525408  5644 sgd_solver.cpp:105] Iteration 14880, lr = 0.1
I0406 09:17:25.208014  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14892.caffemodel
I0406 09:17:28.334846  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14892.solverstate
I0406 09:17:30.640554  5644 solver.cpp:330] Iteration 14892, Testing net (#0)
I0406 09:17:30.640573  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:17:33.706487  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:17:35.080189  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:17:35.080224  5644 solver.cpp:397]     Test net output #1: loss = 5.29009 (* 1 = 5.29009 loss)
I0406 09:17:35.222123  5644 solver.cpp:218] Iteration 14892 (0.816516 iter/s, 14.6966s/12 iters), loss = 5.29756
I0406 09:17:35.223685  5644 solver.cpp:237]     Train net output #0: loss = 5.29756 (* 1 = 5.29756 loss)
I0406 09:17:35.223695  5644 sgd_solver.cpp:105] Iteration 14892, lr = 0.1
I0406 09:17:39.593739  5644 solver.cpp:218] Iteration 14904 (2.74599 iter/s, 4.37001s/12 iters), loss = 5.272
I0406 09:17:39.593773  5644 solver.cpp:237]     Train net output #0: loss = 5.272 (* 1 = 5.272 loss)
I0406 09:17:39.593780  5644 sgd_solver.cpp:105] Iteration 14904, lr = 0.1
I0406 09:17:44.794463  5644 solver.cpp:218] Iteration 14916 (2.30741 iter/s, 5.20063s/12 iters), loss = 5.27483
I0406 09:17:44.794593  5644 solver.cpp:237]     Train net output #0: loss = 5.27483 (* 1 = 5.27483 loss)
I0406 09:17:44.794601  5644 sgd_solver.cpp:105] Iteration 14916, lr = 0.1
I0406 09:17:50.009999  5644 solver.cpp:218] Iteration 14928 (2.3009 iter/s, 5.21535s/12 iters), loss = 5.27908
I0406 09:17:50.010036  5644 solver.cpp:237]     Train net output #0: loss = 5.27908 (* 1 = 5.27908 loss)
I0406 09:17:50.010041  5644 sgd_solver.cpp:105] Iteration 14928, lr = 0.1
I0406 09:17:55.327548  5644 solver.cpp:218] Iteration 14940 (2.25672 iter/s, 5.31745s/12 iters), loss = 5.26662
I0406 09:17:55.327592  5644 solver.cpp:237]     Train net output #0: loss = 5.26662 (* 1 = 5.26662 loss)
I0406 09:17:55.327600  5644 sgd_solver.cpp:105] Iteration 14940, lr = 0.1
I0406 09:18:00.632377  5644 solver.cpp:218] Iteration 14952 (2.26213 iter/s, 5.30473s/12 iters), loss = 5.2903
I0406 09:18:00.632428  5644 solver.cpp:237]     Train net output #0: loss = 5.2903 (* 1 = 5.2903 loss)
I0406 09:18:00.632436  5644 sgd_solver.cpp:105] Iteration 14952, lr = 0.1
I0406 09:18:05.958770  5644 solver.cpp:218] Iteration 14964 (2.25298 iter/s, 5.32628s/12 iters), loss = 5.27148
I0406 09:18:05.958827  5644 solver.cpp:237]     Train net output #0: loss = 5.27148 (* 1 = 5.27148 loss)
I0406 09:18:05.958834  5644 sgd_solver.cpp:105] Iteration 14964, lr = 0.1
I0406 09:18:08.663985  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:18:11.243476  5644 solver.cpp:218] Iteration 14976 (2.27075 iter/s, 5.28459s/12 iters), loss = 5.29185
I0406 09:18:11.243527  5644 solver.cpp:237]     Train net output #0: loss = 5.29185 (* 1 = 5.29185 loss)
I0406 09:18:11.243535  5644 sgd_solver.cpp:105] Iteration 14976, lr = 0.1
I0406 09:18:16.437014  5644 solver.cpp:218] Iteration 14988 (2.31061 iter/s, 5.19343s/12 iters), loss = 5.27642
I0406 09:18:16.437124  5644 solver.cpp:237]     Train net output #0: loss = 5.27642 (* 1 = 5.27642 loss)
I0406 09:18:16.437132  5644 sgd_solver.cpp:105] Iteration 14988, lr = 0.1
I0406 09:18:18.621008  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_14994.caffemodel
I0406 09:18:21.669679  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_14994.solverstate
I0406 09:18:24.012770  5644 solver.cpp:330] Iteration 14994, Testing net (#0)
I0406 09:18:24.012794  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:18:27.228960  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:18:28.494912  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 09:18:28.494940  5644 solver.cpp:397]     Test net output #1: loss = 5.2894 (* 1 = 5.2894 loss)
I0406 09:18:30.526960  5644 solver.cpp:218] Iteration 15000 (0.851685 iter/s, 14.0897s/12 iters), loss = 5.27337
I0406 09:18:30.527019  5644 solver.cpp:237]     Train net output #0: loss = 5.27337 (* 1 = 5.27337 loss)
I0406 09:18:30.527027  5644 sgd_solver.cpp:105] Iteration 15000, lr = 0.1
I0406 09:18:35.949360  5644 solver.cpp:218] Iteration 15012 (2.21309 iter/s, 5.42229s/12 iters), loss = 5.28003
I0406 09:18:35.949405  5644 solver.cpp:237]     Train net output #0: loss = 5.28003 (* 1 = 5.28003 loss)
I0406 09:18:35.949414  5644 sgd_solver.cpp:105] Iteration 15012, lr = 0.1
I0406 09:18:41.272672  5644 solver.cpp:218] Iteration 15024 (2.25428 iter/s, 5.32321s/12 iters), loss = 5.27307
I0406 09:18:41.272717  5644 solver.cpp:237]     Train net output #0: loss = 5.27307 (* 1 = 5.27307 loss)
I0406 09:18:41.272723  5644 sgd_solver.cpp:105] Iteration 15024, lr = 0.1
I0406 09:18:46.545837  5644 solver.cpp:218] Iteration 15036 (2.27572 iter/s, 5.27306s/12 iters), loss = 5.29938
I0406 09:18:46.546068  5644 solver.cpp:237]     Train net output #0: loss = 5.29938 (* 1 = 5.29938 loss)
I0406 09:18:46.546075  5644 sgd_solver.cpp:105] Iteration 15036, lr = 0.1
I0406 09:18:51.910449  5644 solver.cpp:218] Iteration 15048 (2.237 iter/s, 5.36432s/12 iters), loss = 5.28382
I0406 09:18:51.910495  5644 solver.cpp:237]     Train net output #0: loss = 5.28382 (* 1 = 5.28382 loss)
I0406 09:18:51.910503  5644 sgd_solver.cpp:105] Iteration 15048, lr = 0.1
I0406 09:18:57.278826  5644 solver.cpp:218] Iteration 15060 (2.23536 iter/s, 5.36827s/12 iters), loss = 5.28956
I0406 09:18:57.278874  5644 solver.cpp:237]     Train net output #0: loss = 5.28956 (* 1 = 5.28956 loss)
I0406 09:18:57.278883  5644 sgd_solver.cpp:105] Iteration 15060, lr = 0.1
I0406 09:19:02.067140  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:19:02.422087  5644 solver.cpp:218] Iteration 15072 (2.3332 iter/s, 5.14316s/12 iters), loss = 5.30294
I0406 09:19:02.422127  5644 solver.cpp:237]     Train net output #0: loss = 5.30294 (* 1 = 5.30294 loss)
I0406 09:19:02.422133  5644 sgd_solver.cpp:105] Iteration 15072, lr = 0.1
I0406 09:19:07.880872  5644 solver.cpp:218] Iteration 15084 (2.19833 iter/s, 5.45869s/12 iters), loss = 5.25895
I0406 09:19:07.880924  5644 solver.cpp:237]     Train net output #0: loss = 5.25895 (* 1 = 5.25895 loss)
I0406 09:19:07.880932  5644 sgd_solver.cpp:105] Iteration 15084, lr = 0.1
I0406 09:19:12.538370  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15096.caffemodel
I0406 09:19:15.583318  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15096.solverstate
I0406 09:19:17.883010  5644 solver.cpp:330] Iteration 15096, Testing net (#0)
I0406 09:19:17.883091  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:19:20.898152  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:19:22.219287  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:19:22.219323  5644 solver.cpp:397]     Test net output #1: loss = 5.28973 (* 1 = 5.28973 loss)
I0406 09:19:22.354794  5644 solver.cpp:218] Iteration 15096 (0.829088 iter/s, 14.4737s/12 iters), loss = 5.27149
I0406 09:19:22.354843  5644 solver.cpp:237]     Train net output #0: loss = 5.27149 (* 1 = 5.27149 loss)
I0406 09:19:22.354851  5644 sgd_solver.cpp:105] Iteration 15096, lr = 0.1
I0406 09:19:26.906924  5644 solver.cpp:218] Iteration 15108 (2.63619 iter/s, 4.55203s/12 iters), loss = 5.29227
I0406 09:19:26.906966  5644 solver.cpp:237]     Train net output #0: loss = 5.29227 (* 1 = 5.29227 loss)
I0406 09:19:26.906975  5644 sgd_solver.cpp:105] Iteration 15108, lr = 0.1
I0406 09:19:32.015911  5644 solver.cpp:218] Iteration 15120 (2.34885 iter/s, 5.10889s/12 iters), loss = 5.27085
I0406 09:19:32.015957  5644 solver.cpp:237]     Train net output #0: loss = 5.27085 (* 1 = 5.27085 loss)
I0406 09:19:32.015966  5644 sgd_solver.cpp:105] Iteration 15120, lr = 0.1
I0406 09:19:37.249114  5644 solver.cpp:218] Iteration 15132 (2.2931 iter/s, 5.2331s/12 iters), loss = 5.26946
I0406 09:19:37.249173  5644 solver.cpp:237]     Train net output #0: loss = 5.26946 (* 1 = 5.26946 loss)
I0406 09:19:37.249182  5644 sgd_solver.cpp:105] Iteration 15132, lr = 0.1
I0406 09:19:42.584208  5644 solver.cpp:218] Iteration 15144 (2.24931 iter/s, 5.33498s/12 iters), loss = 5.27795
I0406 09:19:42.584264  5644 solver.cpp:237]     Train net output #0: loss = 5.27795 (* 1 = 5.27795 loss)
I0406 09:19:42.584272  5644 sgd_solver.cpp:105] Iteration 15144, lr = 0.1
I0406 09:19:47.926785  5644 solver.cpp:218] Iteration 15156 (2.24615 iter/s, 5.34247s/12 iters), loss = 5.28395
I0406 09:19:47.926904  5644 solver.cpp:237]     Train net output #0: loss = 5.28395 (* 1 = 5.28395 loss)
I0406 09:19:47.926911  5644 sgd_solver.cpp:105] Iteration 15156, lr = 0.1
I0406 09:19:53.145336  5644 solver.cpp:218] Iteration 15168 (2.29957 iter/s, 5.21837s/12 iters), loss = 5.2721
I0406 09:19:53.145387  5644 solver.cpp:237]     Train net output #0: loss = 5.2721 (* 1 = 5.2721 loss)
I0406 09:19:53.145396  5644 sgd_solver.cpp:105] Iteration 15168, lr = 0.1
I0406 09:19:54.930729  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:19:58.444931  5644 solver.cpp:218] Iteration 15180 (2.26437 iter/s, 5.29948s/12 iters), loss = 5.29649
I0406 09:19:58.444973  5644 solver.cpp:237]     Train net output #0: loss = 5.29649 (* 1 = 5.29649 loss)
I0406 09:19:58.444979  5644 sgd_solver.cpp:105] Iteration 15180, lr = 0.1
I0406 09:20:03.583853  5644 solver.cpp:218] Iteration 15192 (2.33517 iter/s, 5.13882s/12 iters), loss = 5.2574
I0406 09:20:03.583894  5644 solver.cpp:237]     Train net output #0: loss = 5.2574 (* 1 = 5.2574 loss)
I0406 09:20:03.583900  5644 sgd_solver.cpp:105] Iteration 15192, lr = 0.1
I0406 09:20:05.759766  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15198.caffemodel
I0406 09:20:08.774452  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15198.solverstate
I0406 09:20:11.089365  5644 solver.cpp:330] Iteration 15198, Testing net (#0)
I0406 09:20:11.089385  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:20:14.040422  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:20:15.376233  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:20:15.376266  5644 solver.cpp:397]     Test net output #1: loss = 5.28983 (* 1 = 5.28983 loss)
I0406 09:20:17.278988  5644 solver.cpp:218] Iteration 15204 (0.876234 iter/s, 13.695s/12 iters), loss = 5.28483
I0406 09:20:17.279029  5644 solver.cpp:237]     Train net output #0: loss = 5.28483 (* 1 = 5.28483 loss)
I0406 09:20:17.279036  5644 sgd_solver.cpp:105] Iteration 15204, lr = 0.1
I0406 09:20:22.555053  5644 solver.cpp:218] Iteration 15216 (2.27447 iter/s, 5.27596s/12 iters), loss = 5.26825
I0406 09:20:22.555191  5644 solver.cpp:237]     Train net output #0: loss = 5.26825 (* 1 = 5.26825 loss)
I0406 09:20:22.555200  5644 sgd_solver.cpp:105] Iteration 15216, lr = 0.1
I0406 09:20:27.829916  5644 solver.cpp:218] Iteration 15228 (2.27502 iter/s, 5.27467s/12 iters), loss = 5.27596
I0406 09:20:27.829962  5644 solver.cpp:237]     Train net output #0: loss = 5.27596 (* 1 = 5.27596 loss)
I0406 09:20:27.829970  5644 sgd_solver.cpp:105] Iteration 15228, lr = 0.1
I0406 09:20:32.929474  5644 solver.cpp:218] Iteration 15240 (2.35319 iter/s, 5.09946s/12 iters), loss = 5.25713
I0406 09:20:32.929512  5644 solver.cpp:237]     Train net output #0: loss = 5.25713 (* 1 = 5.25713 loss)
I0406 09:20:32.929517  5644 sgd_solver.cpp:105] Iteration 15240, lr = 0.1
I0406 09:20:37.714107  5644 blocking_queue.cpp:49] Waiting for data
I0406 09:20:38.250689  5644 solver.cpp:218] Iteration 15252 (2.25517 iter/s, 5.32112s/12 iters), loss = 5.27546
I0406 09:20:38.250735  5644 solver.cpp:237]     Train net output #0: loss = 5.27546 (* 1 = 5.27546 loss)
I0406 09:20:38.250742  5644 sgd_solver.cpp:105] Iteration 15252, lr = 0.1
I0406 09:20:43.285384  5644 solver.cpp:218] Iteration 15264 (2.38351 iter/s, 5.0346s/12 iters), loss = 5.26272
I0406 09:20:43.285425  5644 solver.cpp:237]     Train net output #0: loss = 5.26272 (* 1 = 5.26272 loss)
I0406 09:20:43.285430  5644 sgd_solver.cpp:105] Iteration 15264, lr = 0.1
I0406 09:20:47.535576  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:20:48.699350  5644 solver.cpp:218] Iteration 15276 (2.21653 iter/s, 5.41387s/12 iters), loss = 5.27953
I0406 09:20:48.699390  5644 solver.cpp:237]     Train net output #0: loss = 5.27953 (* 1 = 5.27953 loss)
I0406 09:20:48.699396  5644 sgd_solver.cpp:105] Iteration 15276, lr = 0.1
I0406 09:20:53.943207  5644 solver.cpp:218] Iteration 15288 (2.28844 iter/s, 5.24375s/12 iters), loss = 5.26007
I0406 09:20:53.943368  5644 solver.cpp:237]     Train net output #0: loss = 5.26007 (* 1 = 5.26007 loss)
I0406 09:20:53.943379  5644 sgd_solver.cpp:105] Iteration 15288, lr = 0.1
I0406 09:20:58.707346  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15300.caffemodel
I0406 09:21:01.732120  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15300.solverstate
I0406 09:21:04.039492  5644 solver.cpp:330] Iteration 15300, Testing net (#0)
I0406 09:21:04.039512  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:21:07.061642  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:21:08.422498  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:21:08.422528  5644 solver.cpp:397]     Test net output #1: loss = 5.28904 (* 1 = 5.28904 loss)
I0406 09:21:08.558621  5644 solver.cpp:218] Iteration 15300 (0.821067 iter/s, 14.6151s/12 iters), loss = 5.29026
I0406 09:21:08.558661  5644 solver.cpp:237]     Train net output #0: loss = 5.29026 (* 1 = 5.29026 loss)
I0406 09:21:08.558667  5644 sgd_solver.cpp:105] Iteration 15300, lr = 0.1
I0406 09:21:12.911856  5644 solver.cpp:218] Iteration 15312 (2.75663 iter/s, 4.35314s/12 iters), loss = 5.27478
I0406 09:21:12.911904  5644 solver.cpp:237]     Train net output #0: loss = 5.27478 (* 1 = 5.27478 loss)
I0406 09:21:12.911912  5644 sgd_solver.cpp:105] Iteration 15312, lr = 0.1
I0406 09:21:18.178848  5644 solver.cpp:218] Iteration 15324 (2.27839 iter/s, 5.26689s/12 iters), loss = 5.28238
I0406 09:21:18.178902  5644 solver.cpp:237]     Train net output #0: loss = 5.28238 (* 1 = 5.28238 loss)
I0406 09:21:18.178911  5644 sgd_solver.cpp:105] Iteration 15324, lr = 0.1
I0406 09:21:23.594048  5644 solver.cpp:218] Iteration 15336 (2.21603 iter/s, 5.41509s/12 iters), loss = 5.27997
I0406 09:21:23.594086  5644 solver.cpp:237]     Train net output #0: loss = 5.27997 (* 1 = 5.27997 loss)
I0406 09:21:23.594092  5644 sgd_solver.cpp:105] Iteration 15336, lr = 0.1
I0406 09:21:28.829586  5644 solver.cpp:218] Iteration 15348 (2.29207 iter/s, 5.23544s/12 iters), loss = 5.27124
I0406 09:21:28.829690  5644 solver.cpp:237]     Train net output #0: loss = 5.27124 (* 1 = 5.27124 loss)
I0406 09:21:28.829697  5644 sgd_solver.cpp:105] Iteration 15348, lr = 0.1
I0406 09:21:33.888074  5644 solver.cpp:218] Iteration 15360 (2.37232 iter/s, 5.05833s/12 iters), loss = 5.26912
I0406 09:21:33.888115  5644 solver.cpp:237]     Train net output #0: loss = 5.26912 (* 1 = 5.26912 loss)
I0406 09:21:33.888121  5644 sgd_solver.cpp:105] Iteration 15360, lr = 0.1
I0406 09:21:39.142123  5644 solver.cpp:218] Iteration 15372 (2.28399 iter/s, 5.25395s/12 iters), loss = 5.27872
I0406 09:21:39.142163  5644 solver.cpp:237]     Train net output #0: loss = 5.27872 (* 1 = 5.27872 loss)
I0406 09:21:39.142168  5644 sgd_solver.cpp:105] Iteration 15372, lr = 0.1
I0406 09:21:40.256445  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:21:44.437515  5644 solver.cpp:218] Iteration 15384 (2.26616 iter/s, 5.2953s/12 iters), loss = 5.28668
I0406 09:21:44.437551  5644 solver.cpp:237]     Train net output #0: loss = 5.28668 (* 1 = 5.28668 loss)
I0406 09:21:44.437556  5644 sgd_solver.cpp:105] Iteration 15384, lr = 0.1
I0406 09:21:49.734647  5644 solver.cpp:218] Iteration 15396 (2.26542 iter/s, 5.29703s/12 iters), loss = 5.29824
I0406 09:21:49.734699  5644 solver.cpp:237]     Train net output #0: loss = 5.29824 (* 1 = 5.29824 loss)
I0406 09:21:49.734707  5644 sgd_solver.cpp:105] Iteration 15396, lr = 0.1
I0406 09:21:51.867669  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15402.caffemodel
I0406 09:21:54.922564  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15402.solverstate
I0406 09:21:57.223991  5644 solver.cpp:330] Iteration 15402, Testing net (#0)
I0406 09:21:57.224011  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:22:00.133114  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:22:01.574681  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:22:01.574717  5644 solver.cpp:397]     Test net output #1: loss = 5.28892 (* 1 = 5.28892 loss)
I0406 09:22:03.384060  5644 solver.cpp:218] Iteration 15408 (0.87917 iter/s, 13.6492s/12 iters), loss = 5.26409
I0406 09:22:03.384109  5644 solver.cpp:237]     Train net output #0: loss = 5.26409 (* 1 = 5.26409 loss)
I0406 09:22:03.384116  5644 sgd_solver.cpp:105] Iteration 15408, lr = 0.1
I0406 09:22:08.461737  5644 solver.cpp:218] Iteration 15420 (2.36334 iter/s, 5.07757s/12 iters), loss = 5.27284
I0406 09:22:08.461791  5644 solver.cpp:237]     Train net output #0: loss = 5.27284 (* 1 = 5.27284 loss)
I0406 09:22:08.461799  5644 sgd_solver.cpp:105] Iteration 15420, lr = 0.1
I0406 09:22:13.826026  5644 solver.cpp:218] Iteration 15432 (2.23706 iter/s, 5.36418s/12 iters), loss = 5.29467
I0406 09:22:13.826079  5644 solver.cpp:237]     Train net output #0: loss = 5.29467 (* 1 = 5.29467 loss)
I0406 09:22:13.826088  5644 sgd_solver.cpp:105] Iteration 15432, lr = 0.1
I0406 09:22:19.151948  5644 solver.cpp:218] Iteration 15444 (2.25318 iter/s, 5.32581s/12 iters), loss = 5.27003
I0406 09:22:19.151986  5644 solver.cpp:237]     Train net output #0: loss = 5.27003 (* 1 = 5.27003 loss)
I0406 09:22:19.151993  5644 sgd_solver.cpp:105] Iteration 15444, lr = 0.1
I0406 09:22:24.620733  5644 solver.cpp:218] Iteration 15456 (2.19431 iter/s, 5.46868s/12 iters), loss = 5.26511
I0406 09:22:24.620784  5644 solver.cpp:237]     Train net output #0: loss = 5.26511 (* 1 = 5.26511 loss)
I0406 09:22:24.620792  5644 sgd_solver.cpp:105] Iteration 15456, lr = 0.1
I0406 09:22:29.863509  5644 solver.cpp:218] Iteration 15468 (2.28891 iter/s, 5.24267s/12 iters), loss = 5.28768
I0406 09:22:29.863567  5644 solver.cpp:237]     Train net output #0: loss = 5.28768 (* 1 = 5.28768 loss)
I0406 09:22:29.863576  5644 sgd_solver.cpp:105] Iteration 15468, lr = 0.1
I0406 09:22:33.233027  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:22:35.202162  5644 solver.cpp:218] Iteration 15480 (2.24781 iter/s, 5.33854s/12 iters), loss = 5.25206
I0406 09:22:35.202219  5644 solver.cpp:237]     Train net output #0: loss = 5.25206 (* 1 = 5.25206 loss)
I0406 09:22:35.202229  5644 sgd_solver.cpp:105] Iteration 15480, lr = 0.1
I0406 09:22:40.386981  5644 solver.cpp:218] Iteration 15492 (2.3145 iter/s, 5.18471s/12 iters), loss = 5.27835
I0406 09:22:40.387022  5644 solver.cpp:237]     Train net output #0: loss = 5.27835 (* 1 = 5.27835 loss)
I0406 09:22:40.387029  5644 sgd_solver.cpp:105] Iteration 15492, lr = 0.1
I0406 09:22:45.280095  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15504.caffemodel
I0406 09:22:48.318687  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15504.solverstate
I0406 09:22:50.648317  5644 solver.cpp:330] Iteration 15504, Testing net (#0)
I0406 09:22:50.648340  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:22:53.502053  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:22:54.947369  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:22:54.947405  5644 solver.cpp:397]     Test net output #1: loss = 5.28937 (* 1 = 5.28937 loss)
I0406 09:22:55.085021  5644 solver.cpp:218] Iteration 15504 (0.816445 iter/s, 14.6979s/12 iters), loss = 5.26515
I0406 09:22:55.085059  5644 solver.cpp:237]     Train net output #0: loss = 5.26515 (* 1 = 5.26515 loss)
I0406 09:22:55.085064  5644 sgd_solver.cpp:105] Iteration 15504, lr = 0.1
I0406 09:22:59.312184  5644 solver.cpp:218] Iteration 15516 (2.83885 iter/s, 4.22707s/12 iters), loss = 5.27341
I0406 09:22:59.312228  5644 solver.cpp:237]     Train net output #0: loss = 5.27341 (* 1 = 5.27341 loss)
I0406 09:22:59.312234  5644 sgd_solver.cpp:105] Iteration 15516, lr = 0.1
I0406 09:23:04.395434  5644 solver.cpp:218] Iteration 15528 (2.36074 iter/s, 5.08315s/12 iters), loss = 5.28543
I0406 09:23:04.395526  5644 solver.cpp:237]     Train net output #0: loss = 5.28543 (* 1 = 5.28543 loss)
I0406 09:23:04.395534  5644 sgd_solver.cpp:105] Iteration 15528, lr = 0.1
I0406 09:23:09.392578  5644 solver.cpp:218] Iteration 15540 (2.40144 iter/s, 4.997s/12 iters), loss = 5.26755
I0406 09:23:09.392619  5644 solver.cpp:237]     Train net output #0: loss = 5.26755 (* 1 = 5.26755 loss)
I0406 09:23:09.392625  5644 sgd_solver.cpp:105] Iteration 15540, lr = 0.1
I0406 09:23:14.776985  5644 solver.cpp:218] Iteration 15552 (2.2287 iter/s, 5.3843s/12 iters), loss = 5.2607
I0406 09:23:14.777031  5644 solver.cpp:237]     Train net output #0: loss = 5.2607 (* 1 = 5.2607 loss)
I0406 09:23:14.777040  5644 sgd_solver.cpp:105] Iteration 15552, lr = 0.1
I0406 09:23:20.215741  5644 solver.cpp:218] Iteration 15564 (2.20643 iter/s, 5.43866s/12 iters), loss = 5.28458
I0406 09:23:20.215778  5644 solver.cpp:237]     Train net output #0: loss = 5.28458 (* 1 = 5.28458 loss)
I0406 09:23:20.215785  5644 sgd_solver.cpp:105] Iteration 15564, lr = 0.1
I0406 09:23:25.397039  5644 solver.cpp:218] Iteration 15576 (2.31607 iter/s, 5.1812s/12 iters), loss = 5.2738
I0406 09:23:25.397097  5644 solver.cpp:237]     Train net output #0: loss = 5.2738 (* 1 = 5.2738 loss)
I0406 09:23:25.397105  5644 sgd_solver.cpp:105] Iteration 15576, lr = 0.1
I0406 09:23:25.795575  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:23:30.498908  5644 solver.cpp:218] Iteration 15588 (2.35213 iter/s, 5.10176s/12 iters), loss = 5.28597
I0406 09:23:30.498950  5644 solver.cpp:237]     Train net output #0: loss = 5.28597 (* 1 = 5.28597 loss)
I0406 09:23:30.498955  5644 sgd_solver.cpp:105] Iteration 15588, lr = 0.1
I0406 09:23:35.833693  5644 solver.cpp:218] Iteration 15600 (2.24943 iter/s, 5.33469s/12 iters), loss = 5.2917
I0406 09:23:35.833835  5644 solver.cpp:237]     Train net output #0: loss = 5.2917 (* 1 = 5.2917 loss)
I0406 09:23:35.833873  5644 sgd_solver.cpp:105] Iteration 15600, lr = 0.1
I0406 09:23:38.062544  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15606.caffemodel
I0406 09:23:41.882683  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15606.solverstate
I0406 09:23:44.255192  5644 solver.cpp:330] Iteration 15606, Testing net (#0)
I0406 09:23:44.255209  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:23:47.159242  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:23:48.661306  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:23:48.661342  5644 solver.cpp:397]     Test net output #1: loss = 5.2889 (* 1 = 5.2889 loss)
I0406 09:23:50.608114  5644 solver.cpp:218] Iteration 15612 (0.812229 iter/s, 14.7742s/12 iters), loss = 5.27429
I0406 09:23:50.608166  5644 solver.cpp:237]     Train net output #0: loss = 5.27429 (* 1 = 5.27429 loss)
I0406 09:23:50.608173  5644 sgd_solver.cpp:105] Iteration 15612, lr = 0.1
I0406 09:23:55.947204  5644 solver.cpp:218] Iteration 15624 (2.24762 iter/s, 5.33898s/12 iters), loss = 5.27558
I0406 09:23:55.947244  5644 solver.cpp:237]     Train net output #0: loss = 5.27558 (* 1 = 5.27558 loss)
I0406 09:23:55.947250  5644 sgd_solver.cpp:105] Iteration 15624, lr = 0.1
I0406 09:24:01.309597  5644 solver.cpp:218] Iteration 15636 (2.23785 iter/s, 5.36229s/12 iters), loss = 5.26603
I0406 09:24:01.309646  5644 solver.cpp:237]     Train net output #0: loss = 5.26603 (* 1 = 5.26603 loss)
I0406 09:24:01.309654  5644 sgd_solver.cpp:105] Iteration 15636, lr = 0.1
I0406 09:24:06.688228  5644 solver.cpp:218] Iteration 15648 (2.23109 iter/s, 5.37853s/12 iters), loss = 5.28358
I0406 09:24:06.688304  5644 solver.cpp:237]     Train net output #0: loss = 5.28358 (* 1 = 5.28358 loss)
I0406 09:24:06.688311  5644 sgd_solver.cpp:105] Iteration 15648, lr = 0.1
I0406 09:24:11.847959  5644 solver.cpp:218] Iteration 15660 (2.32576 iter/s, 5.1596s/12 iters), loss = 5.28979
I0406 09:24:11.847998  5644 solver.cpp:237]     Train net output #0: loss = 5.28979 (* 1 = 5.28979 loss)
I0406 09:24:11.848004  5644 sgd_solver.cpp:105] Iteration 15660, lr = 0.1
I0406 09:24:16.945773  5644 solver.cpp:218] Iteration 15672 (2.35399 iter/s, 5.09772s/12 iters), loss = 5.27281
I0406 09:24:16.945813  5644 solver.cpp:237]     Train net output #0: loss = 5.27281 (* 1 = 5.27281 loss)
I0406 09:24:16.945818  5644 sgd_solver.cpp:105] Iteration 15672, lr = 0.1
I0406 09:24:19.770434  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:24:22.366484  5644 solver.cpp:218] Iteration 15684 (2.21377 iter/s, 5.42061s/12 iters), loss = 5.29556
I0406 09:24:22.366526  5644 solver.cpp:237]     Train net output #0: loss = 5.29556 (* 1 = 5.29556 loss)
I0406 09:24:22.366533  5644 sgd_solver.cpp:105] Iteration 15684, lr = 0.1
I0406 09:24:27.856106  5644 solver.cpp:218] Iteration 15696 (2.18598 iter/s, 5.48952s/12 iters), loss = 5.27856
I0406 09:24:27.856150  5644 solver.cpp:237]     Train net output #0: loss = 5.27856 (* 1 = 5.27856 loss)
I0406 09:24:27.856158  5644 sgd_solver.cpp:105] Iteration 15696, lr = 0.1
I0406 09:24:32.643550  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15708.caffemodel
I0406 09:24:35.672668  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15708.solverstate
I0406 09:24:37.983940  5644 solver.cpp:330] Iteration 15708, Testing net (#0)
I0406 09:24:37.984055  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:24:40.903230  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:24:42.552933  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:24:42.552963  5644 solver.cpp:397]     Test net output #1: loss = 5.28826 (* 1 = 5.28826 loss)
I0406 09:24:42.684867  5644 solver.cpp:218] Iteration 15708 (0.809248 iter/s, 14.8286s/12 iters), loss = 5.27085
I0406 09:24:42.684929  5644 solver.cpp:237]     Train net output #0: loss = 5.27085 (* 1 = 5.27085 loss)
I0406 09:24:42.684938  5644 sgd_solver.cpp:105] Iteration 15708, lr = 0.1
I0406 09:24:47.097831  5644 solver.cpp:218] Iteration 15720 (2.71933 iter/s, 4.41285s/12 iters), loss = 5.28328
I0406 09:24:47.097880  5644 solver.cpp:237]     Train net output #0: loss = 5.28328 (* 1 = 5.28328 loss)
I0406 09:24:47.097888  5644 sgd_solver.cpp:105] Iteration 15720, lr = 0.1
I0406 09:24:52.421816  5644 solver.cpp:218] Iteration 15732 (2.254 iter/s, 5.32388s/12 iters), loss = 5.27393
I0406 09:24:52.421869  5644 solver.cpp:237]     Train net output #0: loss = 5.27393 (* 1 = 5.27393 loss)
I0406 09:24:52.421876  5644 sgd_solver.cpp:105] Iteration 15732, lr = 0.1
I0406 09:24:57.721899  5644 solver.cpp:218] Iteration 15744 (2.26416 iter/s, 5.29997s/12 iters), loss = 5.2969
I0406 09:24:57.721940  5644 solver.cpp:237]     Train net output #0: loss = 5.2969 (* 1 = 5.2969 loss)
I0406 09:24:57.721946  5644 sgd_solver.cpp:105] Iteration 15744, lr = 0.1
I0406 09:25:02.897243  5644 solver.cpp:218] Iteration 15756 (2.31873 iter/s, 5.17525s/12 iters), loss = 5.28729
I0406 09:25:02.897281  5644 solver.cpp:237]     Train net output #0: loss = 5.28729 (* 1 = 5.28729 loss)
I0406 09:25:02.897286  5644 sgd_solver.cpp:105] Iteration 15756, lr = 0.1
I0406 09:25:08.152346  5644 solver.cpp:218] Iteration 15768 (2.28354 iter/s, 5.25501s/12 iters), loss = 5.28669
I0406 09:25:08.152493  5644 solver.cpp:237]     Train net output #0: loss = 5.28669 (* 1 = 5.28669 loss)
I0406 09:25:08.152505  5644 sgd_solver.cpp:105] Iteration 15768, lr = 0.1
I0406 09:25:12.923346  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:25:13.256592  5644 solver.cpp:218] Iteration 15780 (2.35107 iter/s, 5.10405s/12 iters), loss = 5.2999
I0406 09:25:13.256641  5644 solver.cpp:237]     Train net output #0: loss = 5.2999 (* 1 = 5.2999 loss)
I0406 09:25:13.256649  5644 sgd_solver.cpp:105] Iteration 15780, lr = 0.1
I0406 09:25:18.126252  5644 solver.cpp:218] Iteration 15792 (2.46429 iter/s, 4.86956s/12 iters), loss = 5.25978
I0406 09:25:18.126296  5644 solver.cpp:237]     Train net output #0: loss = 5.25978 (* 1 = 5.25978 loss)
I0406 09:25:18.126305  5644 sgd_solver.cpp:105] Iteration 15792, lr = 0.1
I0406 09:25:23.111325  5644 solver.cpp:218] Iteration 15804 (2.40724 iter/s, 4.98497s/12 iters), loss = 5.27149
I0406 09:25:23.111374  5644 solver.cpp:237]     Train net output #0: loss = 5.27149 (* 1 = 5.27149 loss)
I0406 09:25:23.111382  5644 sgd_solver.cpp:105] Iteration 15804, lr = 0.1
I0406 09:25:25.065423  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15810.caffemodel
I0406 09:25:28.152182  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15810.solverstate
I0406 09:25:30.462841  5644 solver.cpp:330] Iteration 15810, Testing net (#0)
I0406 09:25:30.462864  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:25:33.357569  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:25:34.911892  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:25:34.911929  5644 solver.cpp:397]     Test net output #1: loss = 5.28833 (* 1 = 5.28833 loss)
I0406 09:25:36.636488  5644 solver.cpp:218] Iteration 15816 (0.887246 iter/s, 13.525s/12 iters), loss = 5.29246
I0406 09:25:36.636538  5644 solver.cpp:237]     Train net output #0: loss = 5.29246 (* 1 = 5.29246 loss)
I0406 09:25:36.636544  5644 sgd_solver.cpp:105] Iteration 15816, lr = 0.1
I0406 09:25:42.132894  5644 solver.cpp:218] Iteration 15828 (2.18329 iter/s, 5.49629s/12 iters), loss = 5.27088
I0406 09:25:42.133031  5644 solver.cpp:237]     Train net output #0: loss = 5.27088 (* 1 = 5.27088 loss)
I0406 09:25:42.133039  5644 sgd_solver.cpp:105] Iteration 15828, lr = 0.1
I0406 09:25:47.341181  5644 solver.cpp:218] Iteration 15840 (2.3041 iter/s, 5.2081s/12 iters), loss = 5.27211
I0406 09:25:47.341219  5644 solver.cpp:237]     Train net output #0: loss = 5.27211 (* 1 = 5.27211 loss)
I0406 09:25:47.341224  5644 sgd_solver.cpp:105] Iteration 15840, lr = 0.1
I0406 09:25:52.463776  5644 solver.cpp:218] Iteration 15852 (2.34261 iter/s, 5.1225s/12 iters), loss = 5.27416
I0406 09:25:52.463824  5644 solver.cpp:237]     Train net output #0: loss = 5.27416 (* 1 = 5.27416 loss)
I0406 09:25:52.463830  5644 sgd_solver.cpp:105] Iteration 15852, lr = 0.1
I0406 09:25:57.805198  5644 solver.cpp:218] Iteration 15864 (2.24664 iter/s, 5.34132s/12 iters), loss = 5.27637
I0406 09:25:57.805245  5644 solver.cpp:237]     Train net output #0: loss = 5.27637 (* 1 = 5.27637 loss)
I0406 09:25:57.805253  5644 sgd_solver.cpp:105] Iteration 15864, lr = 0.1
I0406 09:26:03.093475  5644 solver.cpp:218] Iteration 15876 (2.26921 iter/s, 5.28817s/12 iters), loss = 5.26828
I0406 09:26:03.093516  5644 solver.cpp:237]     Train net output #0: loss = 5.26828 (* 1 = 5.26828 loss)
I0406 09:26:03.093523  5644 sgd_solver.cpp:105] Iteration 15876, lr = 0.1
I0406 09:26:05.047804  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:26:08.452620  5644 solver.cpp:218] Iteration 15888 (2.2392 iter/s, 5.35905s/12 iters), loss = 5.29774
I0406 09:26:08.452674  5644 solver.cpp:237]     Train net output #0: loss = 5.29774 (* 1 = 5.29774 loss)
I0406 09:26:08.452684  5644 sgd_solver.cpp:105] Iteration 15888, lr = 0.1
I0406 09:26:13.721079  5644 solver.cpp:218] Iteration 15900 (2.27775 iter/s, 5.26835s/12 iters), loss = 5.26185
I0406 09:26:13.721535  5644 solver.cpp:237]     Train net output #0: loss = 5.26185 (* 1 = 5.26185 loss)
I0406 09:26:13.721544  5644 sgd_solver.cpp:105] Iteration 15900, lr = 0.1
I0406 09:26:18.555178  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_15912.caffemodel
I0406 09:26:21.622891  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_15912.solverstate
I0406 09:26:23.944802  5644 solver.cpp:330] Iteration 15912, Testing net (#0)
I0406 09:26:23.944824  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:26:26.808054  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:26:28.407166  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:26:28.407199  5644 solver.cpp:397]     Test net output #1: loss = 5.2888 (* 1 = 5.2888 loss)
I0406 09:26:28.543226  5644 solver.cpp:218] Iteration 15912 (0.809631 iter/s, 14.8216s/12 iters), loss = 5.2753
I0406 09:26:28.543273  5644 solver.cpp:237]     Train net output #0: loss = 5.2753 (* 1 = 5.2753 loss)
I0406 09:26:28.543280  5644 sgd_solver.cpp:105] Iteration 15912, lr = 0.1
I0406 09:26:33.050143  5644 solver.cpp:218] Iteration 15924 (2.66263 iter/s, 4.50682s/12 iters), loss = 5.27341
I0406 09:26:33.050180  5644 solver.cpp:237]     Train net output #0: loss = 5.27341 (* 1 = 5.27341 loss)
I0406 09:26:33.050187  5644 sgd_solver.cpp:105] Iteration 15924, lr = 0.1
I0406 09:26:38.300424  5644 solver.cpp:218] Iteration 15936 (2.28563 iter/s, 5.25018s/12 iters), loss = 5.27309
I0406 09:26:38.300480  5644 solver.cpp:237]     Train net output #0: loss = 5.27309 (* 1 = 5.27309 loss)
I0406 09:26:38.300489  5644 sgd_solver.cpp:105] Iteration 15936, lr = 0.1
I0406 09:26:38.300745  5644 blocking_queue.cpp:49] Waiting for data
I0406 09:26:43.563280  5644 solver.cpp:218] Iteration 15948 (2.28018 iter/s, 5.26275s/12 iters), loss = 5.25391
I0406 09:26:43.563318  5644 solver.cpp:237]     Train net output #0: loss = 5.25391 (* 1 = 5.25391 loss)
I0406 09:26:43.563323  5644 sgd_solver.cpp:105] Iteration 15948, lr = 0.1
I0406 09:26:48.779670  5644 solver.cpp:218] Iteration 15960 (2.30048 iter/s, 5.2163s/12 iters), loss = 5.27225
I0406 09:26:48.779778  5644 solver.cpp:237]     Train net output #0: loss = 5.27225 (* 1 = 5.27225 loss)
I0406 09:26:48.779784  5644 sgd_solver.cpp:105] Iteration 15960, lr = 0.1
I0406 09:26:54.082581  5644 solver.cpp:218] Iteration 15972 (2.26298 iter/s, 5.30274s/12 iters), loss = 5.26258
I0406 09:26:54.082628  5644 solver.cpp:237]     Train net output #0: loss = 5.26258 (* 1 = 5.26258 loss)
I0406 09:26:54.082635  5644 sgd_solver.cpp:105] Iteration 15972, lr = 0.1
I0406 09:26:58.443831  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:26:59.575471  5644 solver.cpp:218] Iteration 15984 (2.18468 iter/s, 5.49278s/12 iters), loss = 5.27393
I0406 09:26:59.575517  5644 solver.cpp:237]     Train net output #0: loss = 5.27393 (* 1 = 5.27393 loss)
I0406 09:26:59.575525  5644 sgd_solver.cpp:105] Iteration 15984, lr = 0.1
I0406 09:27:04.747337  5644 solver.cpp:218] Iteration 15996 (2.32029 iter/s, 5.17176s/12 iters), loss = 5.25804
I0406 09:27:04.747381  5644 solver.cpp:237]     Train net output #0: loss = 5.25804 (* 1 = 5.25804 loss)
I0406 09:27:04.747390  5644 sgd_solver.cpp:105] Iteration 15996, lr = 0.1
I0406 09:27:09.742624  5644 solver.cpp:218] Iteration 16008 (2.40231 iter/s, 4.99519s/12 iters), loss = 5.29249
I0406 09:27:09.742666  5644 solver.cpp:237]     Train net output #0: loss = 5.29249 (* 1 = 5.29249 loss)
I0406 09:27:09.742671  5644 sgd_solver.cpp:105] Iteration 16008, lr = 0.1
I0406 09:27:11.786705  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16014.caffemodel
I0406 09:27:14.792707  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16014.solverstate
I0406 09:27:17.104544  5644 solver.cpp:330] Iteration 16014, Testing net (#0)
I0406 09:27:17.104568  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:27:19.795028  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:27:21.477730  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:27:21.477767  5644 solver.cpp:397]     Test net output #1: loss = 5.28833 (* 1 = 5.28833 loss)
I0406 09:27:23.427520  5644 solver.cpp:218] Iteration 16020 (0.87689 iter/s, 13.6847s/12 iters), loss = 5.28068
I0406 09:27:23.427578  5644 solver.cpp:237]     Train net output #0: loss = 5.28068 (* 1 = 5.28068 loss)
I0406 09:27:23.427588  5644 sgd_solver.cpp:105] Iteration 16020, lr = 0.1
I0406 09:27:28.661965  5644 solver.cpp:218] Iteration 16032 (2.29256 iter/s, 5.23433s/12 iters), loss = 5.28089
I0406 09:27:28.662012  5644 solver.cpp:237]     Train net output #0: loss = 5.28089 (* 1 = 5.28089 loss)
I0406 09:27:28.662020  5644 sgd_solver.cpp:105] Iteration 16032, lr = 0.1
I0406 09:27:34.082664  5644 solver.cpp:218] Iteration 16044 (2.21378 iter/s, 5.42059s/12 iters), loss = 5.28607
I0406 09:27:34.082718  5644 solver.cpp:237]     Train net output #0: loss = 5.28607 (* 1 = 5.28607 loss)
I0406 09:27:34.082727  5644 sgd_solver.cpp:105] Iteration 16044, lr = 0.1
I0406 09:27:39.559585  5644 solver.cpp:218] Iteration 16056 (2.19106 iter/s, 5.47681s/12 iters), loss = 5.28253
I0406 09:27:39.559633  5644 solver.cpp:237]     Train net output #0: loss = 5.28253 (* 1 = 5.28253 loss)
I0406 09:27:39.559640  5644 sgd_solver.cpp:105] Iteration 16056, lr = 0.1
I0406 09:27:45.201439  5644 solver.cpp:218] Iteration 16068 (2.127 iter/s, 5.64175s/12 iters), loss = 5.27069
I0406 09:27:45.201478  5644 solver.cpp:237]     Train net output #0: loss = 5.27069 (* 1 = 5.27069 loss)
I0406 09:27:45.201483  5644 sgd_solver.cpp:105] Iteration 16068, lr = 0.1
I0406 09:27:50.405603  5644 solver.cpp:218] Iteration 16080 (2.30589 iter/s, 5.20407s/12 iters), loss = 5.27494
I0406 09:27:50.405740  5644 solver.cpp:237]     Train net output #0: loss = 5.27494 (* 1 = 5.27494 loss)
I0406 09:27:50.405750  5644 sgd_solver.cpp:105] Iteration 16080, lr = 0.1
I0406 09:27:51.564540  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:27:56.001875  5644 solver.cpp:218] Iteration 16092 (2.14436 iter/s, 5.59608s/12 iters), loss = 5.28397
I0406 09:27:56.001930  5644 solver.cpp:237]     Train net output #0: loss = 5.28397 (* 1 = 5.28397 loss)
I0406 09:27:56.001938  5644 sgd_solver.cpp:105] Iteration 16092, lr = 0.1
I0406 09:28:01.523695  5644 solver.cpp:218] Iteration 16104 (2.17324 iter/s, 5.52171s/12 iters), loss = 5.29612
I0406 09:28:01.523746  5644 solver.cpp:237]     Train net output #0: loss = 5.29612 (* 1 = 5.29612 loss)
I0406 09:28:01.523754  5644 sgd_solver.cpp:105] Iteration 16104, lr = 0.1
I0406 09:28:06.430470  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16116.caffemodel
I0406 09:28:09.610839  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16116.solverstate
I0406 09:28:13.044876  5644 solver.cpp:330] Iteration 16116, Testing net (#0)
I0406 09:28:13.044909  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:28:15.921085  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:28:17.885556  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:28:17.885591  5644 solver.cpp:397]     Test net output #1: loss = 5.28877 (* 1 = 5.28877 loss)
I0406 09:28:18.029767  5644 solver.cpp:218] Iteration 16116 (0.727014 iter/s, 16.5059s/12 iters), loss = 5.26539
I0406 09:28:18.031343  5644 solver.cpp:237]     Train net output #0: loss = 5.26539 (* 1 = 5.26539 loss)
I0406 09:28:18.031360  5644 sgd_solver.cpp:105] Iteration 16116, lr = 0.1
I0406 09:28:22.830761  5644 solver.cpp:218] Iteration 16128 (2.50033 iter/s, 4.79937s/12 iters), loss = 5.26999
I0406 09:28:22.830883  5644 solver.cpp:237]     Train net output #0: loss = 5.26999 (* 1 = 5.26999 loss)
I0406 09:28:22.830894  5644 sgd_solver.cpp:105] Iteration 16128, lr = 0.1
I0406 09:28:28.503228  5644 solver.cpp:218] Iteration 16140 (2.11555 iter/s, 5.67229s/12 iters), loss = 5.2958
I0406 09:28:28.509434  5644 solver.cpp:237]     Train net output #0: loss = 5.2958 (* 1 = 5.2958 loss)
I0406 09:28:28.509454  5644 sgd_solver.cpp:105] Iteration 16140, lr = 0.1
I0406 09:28:34.043118  5644 solver.cpp:218] Iteration 16152 (2.16855 iter/s, 5.53364s/12 iters), loss = 5.26565
I0406 09:28:34.043167  5644 solver.cpp:237]     Train net output #0: loss = 5.26565 (* 1 = 5.26565 loss)
I0406 09:28:34.043179  5644 sgd_solver.cpp:105] Iteration 16152, lr = 0.1
I0406 09:28:39.694101  5644 solver.cpp:218] Iteration 16164 (2.12356 iter/s, 5.65088s/12 iters), loss = 5.26863
I0406 09:28:39.694147  5644 solver.cpp:237]     Train net output #0: loss = 5.26863 (* 1 = 5.26863 loss)
I0406 09:28:39.694154  5644 sgd_solver.cpp:105] Iteration 16164, lr = 0.1
I0406 09:28:45.308275  5644 solver.cpp:218] Iteration 16176 (2.13749 iter/s, 5.61407s/12 iters), loss = 5.28607
I0406 09:28:45.308324  5644 solver.cpp:237]     Train net output #0: loss = 5.28607 (* 1 = 5.28607 loss)
I0406 09:28:45.308332  5644 sgd_solver.cpp:105] Iteration 16176, lr = 0.1
I0406 09:28:48.895807  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:28:50.954123  5644 solver.cpp:218] Iteration 16188 (2.1255 iter/s, 5.64574s/12 iters), loss = 5.24773
I0406 09:28:50.954164  5644 solver.cpp:237]     Train net output #0: loss = 5.24773 (* 1 = 5.24773 loss)
I0406 09:28:50.954170  5644 sgd_solver.cpp:105] Iteration 16188, lr = 0.1
I0406 09:28:56.685248  5644 solver.cpp:218] Iteration 16200 (2.09387 iter/s, 5.73102s/12 iters), loss = 5.26763
I0406 09:28:56.685384  5644 solver.cpp:237]     Train net output #0: loss = 5.26763 (* 1 = 5.26763 loss)
I0406 09:28:56.685392  5644 sgd_solver.cpp:105] Iteration 16200, lr = 0.1
I0406 09:29:02.423133  5644 solver.cpp:218] Iteration 16212 (2.09143 iter/s, 5.73769s/12 iters), loss = 5.26957
I0406 09:29:02.423179  5644 solver.cpp:237]     Train net output #0: loss = 5.26957 (* 1 = 5.26957 loss)
I0406 09:29:02.423187  5644 sgd_solver.cpp:105] Iteration 16212, lr = 0.1
I0406 09:29:04.797328  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16218.caffemodel
I0406 09:29:07.894059  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16218.solverstate
I0406 09:29:10.209009  5644 solver.cpp:330] Iteration 16218, Testing net (#0)
I0406 09:29:10.209033  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:29:12.908668  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:29:14.940230  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:29:14.940263  5644 solver.cpp:397]     Test net output #1: loss = 5.2879 (* 1 = 5.2879 loss)
I0406 09:29:16.750337  5644 solver.cpp:218] Iteration 16224 (0.837578 iter/s, 14.327s/12 iters), loss = 5.27241
I0406 09:29:16.750388  5644 solver.cpp:237]     Train net output #0: loss = 5.27241 (* 1 = 5.27241 loss)
I0406 09:29:16.750396  5644 sgd_solver.cpp:105] Iteration 16224, lr = 0.1
I0406 09:29:22.478220  5644 solver.cpp:218] Iteration 16236 (2.09506 iter/s, 5.72777s/12 iters), loss = 5.28564
I0406 09:29:22.484434  5644 solver.cpp:237]     Train net output #0: loss = 5.28564 (* 1 = 5.28564 loss)
I0406 09:29:22.484453  5644 sgd_solver.cpp:105] Iteration 16236, lr = 0.1
I0406 09:29:28.163702  5644 solver.cpp:218] Iteration 16248 (2.11296 iter/s, 5.67923s/12 iters), loss = 5.26774
I0406 09:29:28.163785  5644 solver.cpp:237]     Train net output #0: loss = 5.26774 (* 1 = 5.26774 loss)
I0406 09:29:28.163791  5644 sgd_solver.cpp:105] Iteration 16248, lr = 0.1
I0406 09:29:33.761580  5644 solver.cpp:218] Iteration 16260 (2.14373 iter/s, 5.59773s/12 iters), loss = 5.26382
I0406 09:29:33.761629  5644 solver.cpp:237]     Train net output #0: loss = 5.26382 (* 1 = 5.26382 loss)
I0406 09:29:33.761636  5644 sgd_solver.cpp:105] Iteration 16260, lr = 0.1
I0406 09:29:39.492614  5644 solver.cpp:218] Iteration 16272 (2.09391 iter/s, 5.73091s/12 iters), loss = 5.28486
I0406 09:29:39.492671  5644 solver.cpp:237]     Train net output #0: loss = 5.28486 (* 1 = 5.28486 loss)
I0406 09:29:39.492681  5644 sgd_solver.cpp:105] Iteration 16272, lr = 0.1
I0406 09:29:44.979761  5644 solver.cpp:218] Iteration 16284 (2.18698 iter/s, 5.48703s/12 iters), loss = 5.2649
I0406 09:29:44.979810  5644 solver.cpp:237]     Train net output #0: loss = 5.2649 (* 1 = 5.2649 loss)
I0406 09:29:44.979820  5644 sgd_solver.cpp:105] Iteration 16284, lr = 0.1
I0406 09:29:45.537528  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:29:50.771756  5644 solver.cpp:218] Iteration 16296 (2.07186 iter/s, 5.79188s/12 iters), loss = 5.28304
I0406 09:29:50.771804  5644 solver.cpp:237]     Train net output #0: loss = 5.28304 (* 1 = 5.28304 loss)
I0406 09:29:50.771812  5644 sgd_solver.cpp:105] Iteration 16296, lr = 0.1
I0406 09:29:56.283659  5644 solver.cpp:218] Iteration 16308 (2.17715 iter/s, 5.51179s/12 iters), loss = 5.2873
I0406 09:29:56.283720  5644 solver.cpp:237]     Train net output #0: loss = 5.2873 (* 1 = 5.2873 loss)
I0406 09:29:56.283728  5644 sgd_solver.cpp:105] Iteration 16308, lr = 0.1
I0406 09:30:01.461094  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16320.caffemodel
I0406 09:30:04.593003  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16320.solverstate
I0406 09:30:06.901782  5644 solver.cpp:330] Iteration 16320, Testing net (#0)
I0406 09:30:06.901803  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:30:09.742074  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:30:11.794971  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:30:11.795001  5644 solver.cpp:397]     Test net output #1: loss = 5.28821 (* 1 = 5.28821 loss)
I0406 09:30:11.935493  5644 solver.cpp:218] Iteration 16320 (0.766693 iter/s, 15.6516s/12 iters), loss = 5.27143
I0406 09:30:11.935550  5644 solver.cpp:237]     Train net output #0: loss = 5.27143 (* 1 = 5.27143 loss)
I0406 09:30:11.935560  5644 sgd_solver.cpp:105] Iteration 16320, lr = 0.1
I0406 09:30:16.663478  5644 solver.cpp:218] Iteration 16332 (2.53814 iter/s, 4.72787s/12 iters), loss = 5.27189
I0406 09:30:16.663518  5644 solver.cpp:237]     Train net output #0: loss = 5.27189 (* 1 = 5.27189 loss)
I0406 09:30:16.663524  5644 sgd_solver.cpp:105] Iteration 16332, lr = 0.1
I0406 09:30:22.283915  5644 solver.cpp:218] Iteration 16344 (2.1351 iter/s, 5.62034s/12 iters), loss = 5.2595
I0406 09:30:22.283958  5644 solver.cpp:237]     Train net output #0: loss = 5.2595 (* 1 = 5.2595 loss)
I0406 09:30:22.283967  5644 sgd_solver.cpp:105] Iteration 16344, lr = 0.1
I0406 09:30:27.922263  5644 solver.cpp:218] Iteration 16356 (2.12832 iter/s, 5.63825s/12 iters), loss = 5.28706
I0406 09:30:27.922309  5644 solver.cpp:237]     Train net output #0: loss = 5.28706 (* 1 = 5.28706 loss)
I0406 09:30:27.922317  5644 sgd_solver.cpp:105] Iteration 16356, lr = 0.1
I0406 09:30:33.470670  5644 solver.cpp:218] Iteration 16368 (2.16282 iter/s, 5.5483s/12 iters), loss = 5.28464
I0406 09:30:33.470818  5644 solver.cpp:237]     Train net output #0: loss = 5.28464 (* 1 = 5.28464 loss)
I0406 09:30:33.470827  5644 sgd_solver.cpp:105] Iteration 16368, lr = 0.1
I0406 09:30:39.211148  5644 solver.cpp:218] Iteration 16380 (2.09049 iter/s, 5.74027s/12 iters), loss = 5.27487
I0406 09:30:39.211189  5644 solver.cpp:237]     Train net output #0: loss = 5.27487 (* 1 = 5.27487 loss)
I0406 09:30:39.211194  5644 sgd_solver.cpp:105] Iteration 16380, lr = 0.1
I0406 09:30:42.142256  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:30:44.823997  5644 solver.cpp:218] Iteration 16392 (2.13799 iter/s, 5.61274s/12 iters), loss = 5.29467
I0406 09:30:44.824052  5644 solver.cpp:237]     Train net output #0: loss = 5.29467 (* 1 = 5.29467 loss)
I0406 09:30:44.824062  5644 sgd_solver.cpp:105] Iteration 16392, lr = 0.1
I0406 09:30:50.373723  5644 solver.cpp:218] Iteration 16404 (2.16231 iter/s, 5.54961s/12 iters), loss = 5.27803
I0406 09:30:50.373777  5644 solver.cpp:237]     Train net output #0: loss = 5.27803 (* 1 = 5.27803 loss)
I0406 09:30:50.373786  5644 sgd_solver.cpp:105] Iteration 16404, lr = 0.1
I0406 09:30:56.134824  5644 solver.cpp:218] Iteration 16416 (2.08298 iter/s, 5.76099s/12 iters), loss = 5.26452
I0406 09:30:56.134876  5644 solver.cpp:237]     Train net output #0: loss = 5.26452 (* 1 = 5.26452 loss)
I0406 09:30:56.134884  5644 sgd_solver.cpp:105] Iteration 16416, lr = 0.1
I0406 09:30:58.503579  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16422.caffemodel
I0406 09:31:03.101501  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16422.solverstate
I0406 09:31:05.424050  5644 solver.cpp:330] Iteration 16422, Testing net (#0)
I0406 09:31:05.424136  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:31:08.310503  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:31:10.459163  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:31:10.459198  5644 solver.cpp:397]     Test net output #1: loss = 5.28804 (* 1 = 5.28804 loss)
I0406 09:31:12.405067  5644 solver.cpp:218] Iteration 16428 (0.737552 iter/s, 16.27s/12 iters), loss = 5.28416
I0406 09:31:12.405125  5644 solver.cpp:237]     Train net output #0: loss = 5.28416 (* 1 = 5.28416 loss)
I0406 09:31:12.405134  5644 sgd_solver.cpp:105] Iteration 16428, lr = 0.1
I0406 09:31:18.107596  5644 solver.cpp:218] Iteration 16440 (2.10437 iter/s, 5.70241s/12 iters), loss = 5.27391
I0406 09:31:18.107651  5644 solver.cpp:237]     Train net output #0: loss = 5.27391 (* 1 = 5.27391 loss)
I0406 09:31:18.107661  5644 sgd_solver.cpp:105] Iteration 16440, lr = 0.1
I0406 09:31:23.546491  5644 solver.cpp:218] Iteration 16452 (2.20638 iter/s, 5.43878s/12 iters), loss = 5.29953
I0406 09:31:23.546532  5644 solver.cpp:237]     Train net output #0: loss = 5.29953 (* 1 = 5.29953 loss)
I0406 09:31:23.546538  5644 sgd_solver.cpp:105] Iteration 16452, lr = 0.1
I0406 09:31:29.139914  5644 solver.cpp:218] Iteration 16464 (2.14542 iter/s, 5.59332s/12 iters), loss = 5.29163
I0406 09:31:29.139964  5644 solver.cpp:237]     Train net output #0: loss = 5.29163 (* 1 = 5.29163 loss)
I0406 09:31:29.139973  5644 sgd_solver.cpp:105] Iteration 16464, lr = 0.1
I0406 09:31:34.606268  5644 solver.cpp:218] Iteration 16476 (2.19529 iter/s, 5.46624s/12 iters), loss = 5.29371
I0406 09:31:34.606320  5644 solver.cpp:237]     Train net output #0: loss = 5.29371 (* 1 = 5.29371 loss)
I0406 09:31:34.606328  5644 sgd_solver.cpp:105] Iteration 16476, lr = 0.1
I0406 09:31:39.682691  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:31:39.984848  5644 solver.cpp:218] Iteration 16488 (2.23112 iter/s, 5.37847s/12 iters), loss = 5.29685
I0406 09:31:39.984905  5644 solver.cpp:237]     Train net output #0: loss = 5.29685 (* 1 = 5.29685 loss)
I0406 09:31:39.984915  5644 sgd_solver.cpp:105] Iteration 16488, lr = 0.1
I0406 09:31:45.592483  5644 solver.cpp:218] Iteration 16500 (2.13998 iter/s, 5.60752s/12 iters), loss = 5.26488
I0406 09:31:45.592525  5644 solver.cpp:237]     Train net output #0: loss = 5.26488 (* 1 = 5.26488 loss)
I0406 09:31:45.592532  5644 sgd_solver.cpp:105] Iteration 16500, lr = 0.1
I0406 09:31:51.055510  5644 solver.cpp:218] Iteration 16512 (2.19663 iter/s, 5.46292s/12 iters), loss = 5.28262
I0406 09:31:51.055560  5644 solver.cpp:237]     Train net output #0: loss = 5.28262 (* 1 = 5.28262 loss)
I0406 09:31:51.055569  5644 sgd_solver.cpp:105] Iteration 16512, lr = 0.1
I0406 09:31:56.122555  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16524.caffemodel
I0406 09:31:59.150748  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16524.solverstate
I0406 09:32:01.468263  5644 solver.cpp:330] Iteration 16524, Testing net (#0)
I0406 09:32:01.468288  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:32:04.351558  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:32:06.515383  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:32:06.515424  5644 solver.cpp:397]     Test net output #1: loss = 5.28798 (* 1 = 5.28798 loss)
I0406 09:32:06.659174  5644 solver.cpp:218] Iteration 16524 (0.769059 iter/s, 15.6035s/12 iters), loss = 5.28997
I0406 09:32:06.659215  5644 solver.cpp:237]     Train net output #0: loss = 5.28997 (* 1 = 5.28997 loss)
I0406 09:32:06.659220  5644 sgd_solver.cpp:105] Iteration 16524, lr = 0.1
I0406 09:32:11.278844  5644 solver.cpp:218] Iteration 16536 (2.59764 iter/s, 4.61957s/12 iters), loss = 5.26666
I0406 09:32:11.278945  5644 solver.cpp:237]     Train net output #0: loss = 5.26666 (* 1 = 5.26666 loss)
I0406 09:32:11.278952  5644 sgd_solver.cpp:105] Iteration 16536, lr = 0.1
I0406 09:32:16.776721  5644 solver.cpp:218] Iteration 16548 (2.18273 iter/s, 5.49771s/12 iters), loss = 5.2755
I0406 09:32:16.776777  5644 solver.cpp:237]     Train net output #0: loss = 5.2755 (* 1 = 5.2755 loss)
I0406 09:32:16.776787  5644 sgd_solver.cpp:105] Iteration 16548, lr = 0.1
I0406 09:32:22.378849  5644 solver.cpp:218] Iteration 16560 (2.14209 iter/s, 5.60201s/12 iters), loss = 5.26811
I0406 09:32:22.378898  5644 solver.cpp:237]     Train net output #0: loss = 5.26811 (* 1 = 5.26811 loss)
I0406 09:32:22.378906  5644 sgd_solver.cpp:105] Iteration 16560, lr = 0.1
I0406 09:32:28.135793  5644 solver.cpp:218] Iteration 16572 (2.08448 iter/s, 5.75683s/12 iters), loss = 5.28499
I0406 09:32:28.135834  5644 solver.cpp:237]     Train net output #0: loss = 5.28499 (* 1 = 5.28499 loss)
I0406 09:32:28.135840  5644 sgd_solver.cpp:105] Iteration 16572, lr = 0.1
I0406 09:32:33.945662  5644 solver.cpp:218] Iteration 16584 (2.06549 iter/s, 5.80976s/12 iters), loss = 5.2678
I0406 09:32:33.945720  5644 solver.cpp:237]     Train net output #0: loss = 5.2678 (* 1 = 5.2678 loss)
I0406 09:32:33.945729  5644 sgd_solver.cpp:105] Iteration 16584, lr = 0.1
I0406 09:32:36.061554  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:32:39.464386  5644 solver.cpp:218] Iteration 16596 (2.17446 iter/s, 5.51861s/12 iters), loss = 5.29827
I0406 09:32:39.464444  5644 solver.cpp:237]     Train net output #0: loss = 5.29827 (* 1 = 5.29827 loss)
I0406 09:32:39.464453  5644 sgd_solver.cpp:105] Iteration 16596, lr = 0.1
I0406 09:32:44.981175  5644 solver.cpp:218] Iteration 16608 (2.17522 iter/s, 5.51668s/12 iters), loss = 5.26207
I0406 09:32:44.981317  5644 solver.cpp:237]     Train net output #0: loss = 5.26207 (* 1 = 5.26207 loss)
I0406 09:32:44.981326  5644 sgd_solver.cpp:105] Iteration 16608, lr = 0.1
I0406 09:32:50.675351  5644 solver.cpp:218] Iteration 16620 (2.10749 iter/s, 5.69397s/12 iters), loss = 5.28433
I0406 09:32:50.675397  5644 solver.cpp:237]     Train net output #0: loss = 5.28433 (* 1 = 5.28433 loss)
I0406 09:32:50.675405  5644 sgd_solver.cpp:105] Iteration 16620, lr = 0.1
I0406 09:32:52.819448  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16626.caffemodel
I0406 09:32:56.074689  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16626.solverstate
I0406 09:32:58.429746  5644 solver.cpp:330] Iteration 16626, Testing net (#0)
I0406 09:32:58.429769  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:33:01.175734  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:33:02.714841  5644 blocking_queue.cpp:49] Waiting for data
I0406 09:33:03.424154  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 09:33:03.424190  5644 solver.cpp:397]     Test net output #1: loss = 5.28719 (* 1 = 5.28719 loss)
I0406 09:33:05.547487  5644 solver.cpp:218] Iteration 16632 (0.806888 iter/s, 14.872s/12 iters), loss = 5.27429
I0406 09:33:05.547531  5644 solver.cpp:237]     Train net output #0: loss = 5.27429 (* 1 = 5.27429 loss)
I0406 09:33:05.547538  5644 sgd_solver.cpp:105] Iteration 16632, lr = 0.1
I0406 09:33:11.166637  5644 solver.cpp:218] Iteration 16644 (2.1356 iter/s, 5.61904s/12 iters), loss = 5.27249
I0406 09:33:11.166692  5644 solver.cpp:237]     Train net output #0: loss = 5.27249 (* 1 = 5.27249 loss)
I0406 09:33:11.166702  5644 sgd_solver.cpp:105] Iteration 16644, lr = 0.1
I0406 09:33:16.812419  5644 solver.cpp:218] Iteration 16656 (2.12552 iter/s, 5.64567s/12 iters), loss = 5.25464
I0406 09:33:16.813035  5644 solver.cpp:237]     Train net output #0: loss = 5.25464 (* 1 = 5.25464 loss)
I0406 09:33:16.813045  5644 sgd_solver.cpp:105] Iteration 16656, lr = 0.1
I0406 09:33:22.639158  5644 solver.cpp:218] Iteration 16668 (2.05971 iter/s, 5.82606s/12 iters), loss = 5.27838
I0406 09:33:22.639210  5644 solver.cpp:237]     Train net output #0: loss = 5.27838 (* 1 = 5.27838 loss)
I0406 09:33:22.639217  5644 sgd_solver.cpp:105] Iteration 16668, lr = 0.1
I0406 09:33:28.351608  5644 solver.cpp:218] Iteration 16680 (2.10072 iter/s, 5.71234s/12 iters), loss = 5.26606
I0406 09:33:28.351646  5644 solver.cpp:237]     Train net output #0: loss = 5.26606 (* 1 = 5.26606 loss)
I0406 09:33:28.351652  5644 sgd_solver.cpp:105] Iteration 16680, lr = 0.1
I0406 09:33:32.889468  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:33:34.072525  5644 solver.cpp:218] Iteration 16692 (2.0976 iter/s, 5.72082s/12 iters), loss = 5.27405
I0406 09:33:34.072572  5644 solver.cpp:237]     Train net output #0: loss = 5.27405 (* 1 = 5.27405 loss)
I0406 09:33:34.072580  5644 sgd_solver.cpp:105] Iteration 16692, lr = 0.1
I0406 09:33:39.658231  5644 solver.cpp:218] Iteration 16704 (2.14838 iter/s, 5.5856s/12 iters), loss = 5.26677
I0406 09:33:39.664474  5644 solver.cpp:237]     Train net output #0: loss = 5.26677 (* 1 = 5.26677 loss)
I0406 09:33:39.664503  5644 sgd_solver.cpp:105] Iteration 16704, lr = 0.1
I0406 09:33:45.534373  5644 solver.cpp:218] Iteration 16716 (2.04434 iter/s, 5.86987s/12 iters), loss = 5.30586
I0406 09:33:45.534423  5644 solver.cpp:237]     Train net output #0: loss = 5.30586 (* 1 = 5.30586 loss)
I0406 09:33:45.534433  5644 sgd_solver.cpp:105] Iteration 16716, lr = 0.1
I0406 09:33:50.487943  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16728.caffemodel
I0406 09:33:53.947491  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16728.solverstate
I0406 09:33:56.378561  5644 solver.cpp:330] Iteration 16728, Testing net (#0)
I0406 09:33:56.378582  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:33:59.094076  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:34:01.244310  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:34:01.244349  5644 solver.cpp:397]     Test net output #1: loss = 5.28772 (* 1 = 5.28772 loss)
I0406 09:34:01.386029  5644 solver.cpp:218] Iteration 16728 (0.757028 iter/s, 15.8515s/12 iters), loss = 5.28083
I0406 09:34:01.386085  5644 solver.cpp:237]     Train net output #0: loss = 5.28083 (* 1 = 5.28083 loss)
I0406 09:34:01.386094  5644 sgd_solver.cpp:105] Iteration 16728, lr = 0.1
I0406 09:34:06.168844  5644 solver.cpp:218] Iteration 16740 (2.50904 iter/s, 4.7827s/12 iters), loss = 5.28384
I0406 09:34:06.168915  5644 solver.cpp:237]     Train net output #0: loss = 5.28384 (* 1 = 5.28384 loss)
I0406 09:34:06.168926  5644 sgd_solver.cpp:105] Iteration 16740, lr = 0.1
I0406 09:34:11.839675  5644 solver.cpp:218] Iteration 16752 (2.11614 iter/s, 5.6707s/12 iters), loss = 5.27993
I0406 09:34:11.839715  5644 solver.cpp:237]     Train net output #0: loss = 5.27993 (* 1 = 5.27993 loss)
I0406 09:34:11.839721  5644 sgd_solver.cpp:105] Iteration 16752, lr = 0.1
I0406 09:34:17.521677  5644 solver.cpp:218] Iteration 16764 (2.11197 iter/s, 5.6819s/12 iters), loss = 5.28569
I0406 09:34:17.521734  5644 solver.cpp:237]     Train net output #0: loss = 5.28569 (* 1 = 5.28569 loss)
I0406 09:34:17.521744  5644 sgd_solver.cpp:105] Iteration 16764, lr = 0.1
I0406 09:34:22.791059  5644 solver.cpp:218] Iteration 16776 (2.27736 iter/s, 5.26927s/12 iters), loss = 5.2694
I0406 09:34:22.791182  5644 solver.cpp:237]     Train net output #0: loss = 5.2694 (* 1 = 5.2694 loss)
I0406 09:34:22.791188  5644 sgd_solver.cpp:105] Iteration 16776, lr = 0.1
I0406 09:34:28.497478  5644 solver.cpp:218] Iteration 16788 (2.10296 iter/s, 5.70624s/12 iters), loss = 5.27335
I0406 09:34:28.497525  5644 solver.cpp:237]     Train net output #0: loss = 5.27335 (* 1 = 5.27335 loss)
I0406 09:34:28.497534  5644 sgd_solver.cpp:105] Iteration 16788, lr = 0.1
I0406 09:34:29.709997  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:34:34.193626  5644 solver.cpp:218] Iteration 16800 (2.10673 iter/s, 5.69604s/12 iters), loss = 5.28898
I0406 09:34:34.193673  5644 solver.cpp:237]     Train net output #0: loss = 5.28898 (* 1 = 5.28898 loss)
I0406 09:34:34.193681  5644 sgd_solver.cpp:105] Iteration 16800, lr = 0.1
I0406 09:34:39.830425  5644 solver.cpp:218] Iteration 16812 (2.12891 iter/s, 5.63669s/12 iters), loss = 5.2926
I0406 09:34:39.830471  5644 solver.cpp:237]     Train net output #0: loss = 5.2926 (* 1 = 5.2926 loss)
I0406 09:34:39.830478  5644 sgd_solver.cpp:105] Iteration 16812, lr = 0.1
I0406 09:34:45.254595  5644 solver.cpp:218] Iteration 16824 (2.21236 iter/s, 5.42407s/12 iters), loss = 5.26748
I0406 09:34:45.254637  5644 solver.cpp:237]     Train net output #0: loss = 5.26748 (* 1 = 5.26748 loss)
I0406 09:34:45.254643  5644 sgd_solver.cpp:105] Iteration 16824, lr = 0.1
I0406 09:34:47.649665  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16830.caffemodel
I0406 09:34:50.711005  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16830.solverstate
I0406 09:34:53.545389  5644 solver.cpp:330] Iteration 16830, Testing net (#0)
I0406 09:34:53.545501  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:34:56.192247  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:34:58.485605  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:34:58.485646  5644 solver.cpp:397]     Test net output #1: loss = 5.28725 (* 1 = 5.28725 loss)
I0406 09:35:00.540738  5644 solver.cpp:218] Iteration 16836 (0.785034 iter/s, 15.286s/12 iters), loss = 5.27551
I0406 09:35:00.540789  5644 solver.cpp:237]     Train net output #0: loss = 5.27551 (* 1 = 5.27551 loss)
I0406 09:35:00.540796  5644 sgd_solver.cpp:105] Iteration 16836, lr = 0.1
I0406 09:35:06.301527  5644 solver.cpp:218] Iteration 16848 (2.08309 iter/s, 5.76067s/12 iters), loss = 5.29803
I0406 09:35:06.301574  5644 solver.cpp:237]     Train net output #0: loss = 5.29803 (* 1 = 5.29803 loss)
I0406 09:35:06.301582  5644 sgd_solver.cpp:105] Iteration 16848, lr = 0.1
I0406 09:35:12.033650  5644 solver.cpp:218] Iteration 16860 (2.0935 iter/s, 5.73202s/12 iters), loss = 5.27172
I0406 09:35:12.033696  5644 solver.cpp:237]     Train net output #0: loss = 5.27172 (* 1 = 5.27172 loss)
I0406 09:35:12.033702  5644 sgd_solver.cpp:105] Iteration 16860, lr = 0.1
I0406 09:35:17.620313  5644 solver.cpp:218] Iteration 16872 (2.14802 iter/s, 5.58655s/12 iters), loss = 5.27582
I0406 09:35:17.620368  5644 solver.cpp:237]     Train net output #0: loss = 5.27582 (* 1 = 5.27582 loss)
I0406 09:35:17.620378  5644 sgd_solver.cpp:105] Iteration 16872, lr = 0.1
I0406 09:35:22.894464  5644 solver.cpp:218] Iteration 16884 (2.27529 iter/s, 5.27405s/12 iters), loss = 5.29418
I0406 09:35:22.894506  5644 solver.cpp:237]     Train net output #0: loss = 5.29418 (* 1 = 5.29418 loss)
I0406 09:35:22.894515  5644 sgd_solver.cpp:105] Iteration 16884, lr = 0.1
I0406 09:35:26.470544  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:35:28.397975  5644 solver.cpp:218] Iteration 16896 (2.18047 iter/s, 5.50341s/12 iters), loss = 5.25117
I0406 09:35:28.398017  5644 solver.cpp:237]     Train net output #0: loss = 5.25117 (* 1 = 5.25117 loss)
I0406 09:35:28.398026  5644 sgd_solver.cpp:105] Iteration 16896, lr = 0.1
I0406 09:35:33.947947  5644 solver.cpp:218] Iteration 16908 (2.16221 iter/s, 5.54987s/12 iters), loss = 5.26955
I0406 09:35:33.947990  5644 solver.cpp:237]     Train net output #0: loss = 5.26955 (* 1 = 5.26955 loss)
I0406 09:35:33.947996  5644 sgd_solver.cpp:105] Iteration 16908, lr = 0.1
I0406 09:35:39.667119  5644 solver.cpp:218] Iteration 16920 (2.09825 iter/s, 5.71906s/12 iters), loss = 5.27636
I0406 09:35:39.667171  5644 solver.cpp:237]     Train net output #0: loss = 5.27636 (* 1 = 5.27636 loss)
I0406 09:35:39.667178  5644 sgd_solver.cpp:105] Iteration 16920, lr = 0.1
I0406 09:35:44.844684  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_16932.caffemodel
I0406 09:35:48.022511  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_16932.solverstate
I0406 09:35:50.328328  5644 solver.cpp:330] Iteration 16932, Testing net (#0)
I0406 09:35:50.328348  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:35:52.795239  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:35:55.158967  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:35:55.158995  5644 solver.cpp:397]     Test net output #1: loss = 5.2871 (* 1 = 5.2871 loss)
I0406 09:35:55.292655  5644 solver.cpp:218] Iteration 16932 (0.767983 iter/s, 15.6253s/12 iters), loss = 5.27033
I0406 09:35:55.298887  5644 solver.cpp:237]     Train net output #0: loss = 5.27033 (* 1 = 5.27033 loss)
I0406 09:35:55.298900  5644 sgd_solver.cpp:105] Iteration 16932, lr = 0.1
I0406 09:36:00.067351  5644 solver.cpp:218] Iteration 16944 (2.51656 iter/s, 4.76842s/12 iters), loss = 5.2858
I0406 09:36:00.067466  5644 solver.cpp:237]     Train net output #0: loss = 5.2858 (* 1 = 5.2858 loss)
I0406 09:36:00.067474  5644 sgd_solver.cpp:105] Iteration 16944, lr = 0.1
I0406 09:36:05.889945  5644 solver.cpp:218] Iteration 16956 (2.061 iter/s, 5.82242s/12 iters), loss = 5.2633
I0406 09:36:05.889986  5644 solver.cpp:237]     Train net output #0: loss = 5.2633 (* 1 = 5.2633 loss)
I0406 09:36:05.889991  5644 sgd_solver.cpp:105] Iteration 16956, lr = 0.1
I0406 09:36:11.572751  5644 solver.cpp:218] Iteration 16968 (2.11167 iter/s, 5.6827s/12 iters), loss = 5.26494
I0406 09:36:11.572790  5644 solver.cpp:237]     Train net output #0: loss = 5.26494 (* 1 = 5.26494 loss)
I0406 09:36:11.572796  5644 sgd_solver.cpp:105] Iteration 16968, lr = 0.1
I0406 09:36:17.154595  5644 solver.cpp:218] Iteration 16980 (2.14987 iter/s, 5.58174s/12 iters), loss = 5.28368
I0406 09:36:17.154634  5644 solver.cpp:237]     Train net output #0: loss = 5.28368 (* 1 = 5.28368 loss)
I0406 09:36:17.154640  5644 sgd_solver.cpp:105] Iteration 16980, lr = 0.1
I0406 09:36:22.962317  5644 solver.cpp:218] Iteration 16992 (2.06625 iter/s, 5.80762s/12 iters), loss = 5.27542
I0406 09:36:22.962365  5644 solver.cpp:237]     Train net output #0: loss = 5.27542 (* 1 = 5.27542 loss)
I0406 09:36:22.962373  5644 sgd_solver.cpp:105] Iteration 16992, lr = 0.1
I0406 09:36:23.507331  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:36:28.589282  5644 solver.cpp:218] Iteration 17004 (2.13263 iter/s, 5.62686s/12 iters), loss = 5.28947
I0406 09:36:28.589331  5644 solver.cpp:237]     Train net output #0: loss = 5.28947 (* 1 = 5.28947 loss)
I0406 09:36:28.589339  5644 sgd_solver.cpp:105] Iteration 17004, lr = 0.1
I0406 09:36:34.139164  5644 solver.cpp:218] Iteration 17016 (2.16225 iter/s, 5.54977s/12 iters), loss = 5.28602
I0406 09:36:34.139336  5644 solver.cpp:237]     Train net output #0: loss = 5.28602 (* 1 = 5.28602 loss)
I0406 09:36:34.139346  5644 sgd_solver.cpp:105] Iteration 17016, lr = 0.1
I0406 09:36:39.775935  5644 solver.cpp:218] Iteration 17028 (2.12896 iter/s, 5.63654s/12 iters), loss = 5.27626
I0406 09:36:39.775976  5644 solver.cpp:237]     Train net output #0: loss = 5.27626 (* 1 = 5.27626 loss)
I0406 09:36:39.775982  5644 sgd_solver.cpp:105] Iteration 17028, lr = 0.1
I0406 09:36:41.933276  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17034.caffemodel
I0406 09:36:45.028491  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17034.solverstate
I0406 09:36:47.411279  5644 solver.cpp:330] Iteration 17034, Testing net (#0)
I0406 09:36:47.411298  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:36:49.922749  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:36:52.345283  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:36:52.345319  5644 solver.cpp:397]     Test net output #1: loss = 5.28722 (* 1 = 5.28722 loss)
I0406 09:36:54.399551  5644 solver.cpp:218] Iteration 17040 (0.8206 iter/s, 14.6234s/12 iters), loss = 5.26781
I0406 09:36:54.399596  5644 solver.cpp:237]     Train net output #0: loss = 5.26781 (* 1 = 5.26781 loss)
I0406 09:36:54.399605  5644 sgd_solver.cpp:105] Iteration 17040, lr = 0.1
I0406 09:37:00.020617  5644 solver.cpp:218] Iteration 17052 (2.13487 iter/s, 5.62096s/12 iters), loss = 5.2552
I0406 09:37:00.020655  5644 solver.cpp:237]     Train net output #0: loss = 5.2552 (* 1 = 5.2552 loss)
I0406 09:37:00.020661  5644 sgd_solver.cpp:105] Iteration 17052, lr = 0.1
I0406 09:37:05.602022  5644 solver.cpp:218] Iteration 17064 (2.15003 iter/s, 5.58131s/12 iters), loss = 5.28351
I0406 09:37:05.602133  5644 solver.cpp:237]     Train net output #0: loss = 5.28351 (* 1 = 5.28351 loss)
I0406 09:37:05.602142  5644 sgd_solver.cpp:105] Iteration 17064, lr = 0.1
I0406 09:37:11.210548  5644 solver.cpp:218] Iteration 17076 (2.13967 iter/s, 5.60835s/12 iters), loss = 5.27835
I0406 09:37:11.210595  5644 solver.cpp:237]     Train net output #0: loss = 5.27835 (* 1 = 5.27835 loss)
I0406 09:37:11.210603  5644 sgd_solver.cpp:105] Iteration 17076, lr = 0.1
I0406 09:37:16.796854  5644 solver.cpp:218] Iteration 17088 (2.14815 iter/s, 5.5862s/12 iters), loss = 5.2753
I0406 09:37:16.796913  5644 solver.cpp:237]     Train net output #0: loss = 5.2753 (* 1 = 5.2753 loss)
I0406 09:37:16.796922  5644 sgd_solver.cpp:105] Iteration 17088, lr = 0.1
I0406 09:37:19.994761  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:37:22.723821  5644 solver.cpp:218] Iteration 17100 (2.02469 iter/s, 5.92684s/12 iters), loss = 5.29309
I0406 09:37:22.730048  5644 solver.cpp:237]     Train net output #0: loss = 5.29309 (* 1 = 5.29309 loss)
I0406 09:37:22.730068  5644 sgd_solver.cpp:105] Iteration 17100, lr = 0.1
I0406 09:37:28.136593  5644 solver.cpp:218] Iteration 17112 (2.21955 iter/s, 5.40651s/12 iters), loss = 5.27344
I0406 09:37:28.136638  5644 solver.cpp:237]     Train net output #0: loss = 5.27344 (* 1 = 5.27344 loss)
I0406 09:37:28.136646  5644 sgd_solver.cpp:105] Iteration 17112, lr = 0.1
I0406 09:37:33.414574  5644 solver.cpp:218] Iteration 17124 (2.27364 iter/s, 5.27787s/12 iters), loss = 5.26453
I0406 09:37:33.414620  5644 solver.cpp:237]     Train net output #0: loss = 5.26453 (* 1 = 5.26453 loss)
I0406 09:37:33.414628  5644 sgd_solver.cpp:105] Iteration 17124, lr = 0.1
I0406 09:37:38.527820  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17136.caffemodel
I0406 09:37:41.670204  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17136.solverstate
I0406 09:37:44.071357  5644 solver.cpp:330] Iteration 17136, Testing net (#0)
I0406 09:37:44.071377  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:37:46.661497  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:37:48.981878  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:37:48.981914  5644 solver.cpp:397]     Test net output #1: loss = 5.28761 (* 1 = 5.28761 loss)
I0406 09:37:49.122560  5644 solver.cpp:218] Iteration 17136 (0.763952 iter/s, 15.7078s/12 iters), loss = 5.28356
I0406 09:37:49.122771  5644 solver.cpp:237]     Train net output #0: loss = 5.28356 (* 1 = 5.28356 loss)
I0406 09:37:49.122782  5644 sgd_solver.cpp:105] Iteration 17136, lr = 0.1
I0406 09:37:53.941741  5644 solver.cpp:218] Iteration 17148 (2.49019 iter/s, 4.81892s/12 iters), loss = 5.27887
I0406 09:37:53.941793  5644 solver.cpp:237]     Train net output #0: loss = 5.27887 (* 1 = 5.27887 loss)
I0406 09:37:53.941802  5644 sgd_solver.cpp:105] Iteration 17148, lr = 0.1
I0406 09:37:59.528980  5644 solver.cpp:218] Iteration 17160 (2.14779 iter/s, 5.58713s/12 iters), loss = 5.29781
I0406 09:37:59.529016  5644 solver.cpp:237]     Train net output #0: loss = 5.29781 (* 1 = 5.29781 loss)
I0406 09:37:59.529022  5644 sgd_solver.cpp:105] Iteration 17160, lr = 0.1
I0406 09:38:05.055795  5644 solver.cpp:218] Iteration 17172 (2.17127 iter/s, 5.52671s/12 iters), loss = 5.29261
I0406 09:38:05.055850  5644 solver.cpp:237]     Train net output #0: loss = 5.29261 (* 1 = 5.29261 loss)
I0406 09:38:05.055858  5644 sgd_solver.cpp:105] Iteration 17172, lr = 0.1
I0406 09:38:10.767282  5644 solver.cpp:218] Iteration 17184 (2.10107 iter/s, 5.71137s/12 iters), loss = 5.30059
I0406 09:38:10.767407  5644 solver.cpp:237]     Train net output #0: loss = 5.30059 (* 1 = 5.30059 loss)
I0406 09:38:10.767416  5644 sgd_solver.cpp:105] Iteration 17184, lr = 0.1
I0406 09:38:15.945170  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:38:16.220599  5644 solver.cpp:218] Iteration 17196 (2.20057 iter/s, 5.45314s/12 iters), loss = 5.29429
I0406 09:38:16.220638  5644 solver.cpp:237]     Train net output #0: loss = 5.29429 (* 1 = 5.29429 loss)
I0406 09:38:16.220644  5644 sgd_solver.cpp:105] Iteration 17196, lr = 0.1
I0406 09:38:21.739684  5644 solver.cpp:218] Iteration 17208 (2.17431 iter/s, 5.51899s/12 iters), loss = 5.27053
I0406 09:38:21.739734  5644 solver.cpp:237]     Train net output #0: loss = 5.27053 (* 1 = 5.27053 loss)
I0406 09:38:21.739742  5644 sgd_solver.cpp:105] Iteration 17208, lr = 0.1
I0406 09:38:27.266983  5644 solver.cpp:218] Iteration 17220 (2.17109 iter/s, 5.52719s/12 iters), loss = 5.28135
I0406 09:38:27.267035  5644 solver.cpp:237]     Train net output #0: loss = 5.28135 (* 1 = 5.28135 loss)
I0406 09:38:27.267042  5644 sgd_solver.cpp:105] Iteration 17220, lr = 0.1
I0406 09:38:32.602592  5644 solver.cpp:218] Iteration 17232 (2.24909 iter/s, 5.3355s/12 iters), loss = 5.28853
I0406 09:38:32.602653  5644 solver.cpp:237]     Train net output #0: loss = 5.28853 (* 1 = 5.28853 loss)
I0406 09:38:32.602661  5644 sgd_solver.cpp:105] Iteration 17232, lr = 0.1
I0406 09:38:34.982225  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17238.caffemodel
I0406 09:38:38.072700  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17238.solverstate
I0406 09:38:40.429513  5644 solver.cpp:330] Iteration 17238, Testing net (#0)
I0406 09:38:40.429533  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:38:42.810411  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:38:45.027034  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:38:45.027071  5644 solver.cpp:397]     Test net output #1: loss = 5.28709 (* 1 = 5.28709 loss)
I0406 09:38:47.121814  5644 solver.cpp:218] Iteration 17244 (0.826501 iter/s, 14.519s/12 iters), loss = 5.26328
I0406 09:38:47.121855  5644 solver.cpp:237]     Train net output #0: loss = 5.26328 (* 1 = 5.26328 loss)
I0406 09:38:47.121861  5644 sgd_solver.cpp:105] Iteration 17244, lr = 0.1
I0406 09:38:52.719646  5644 solver.cpp:218] Iteration 17256 (2.14373 iter/s, 5.59773s/12 iters), loss = 5.26554
I0406 09:38:52.719683  5644 solver.cpp:237]     Train net output #0: loss = 5.26554 (* 1 = 5.26554 loss)
I0406 09:38:52.719689  5644 sgd_solver.cpp:105] Iteration 17256, lr = 0.1
I0406 09:38:58.638726  5644 solver.cpp:218] Iteration 17268 (2.02738 iter/s, 5.91897s/12 iters), loss = 5.26997
I0406 09:38:58.638779  5644 solver.cpp:237]     Train net output #0: loss = 5.26997 (* 1 = 5.26997 loss)
I0406 09:38:58.638787  5644 sgd_solver.cpp:105] Iteration 17268, lr = 0.1
I0406 09:39:04.272959  5644 solver.cpp:218] Iteration 17280 (2.12988 iter/s, 5.63412s/12 iters), loss = 5.28061
I0406 09:39:04.273015  5644 solver.cpp:237]     Train net output #0: loss = 5.28061 (* 1 = 5.28061 loss)
I0406 09:39:04.273023  5644 sgd_solver.cpp:105] Iteration 17280, lr = 0.1
I0406 09:39:10.019924  5644 solver.cpp:218] Iteration 17292 (2.0881 iter/s, 5.74685s/12 iters), loss = 5.27661
I0406 09:39:10.019982  5644 solver.cpp:237]     Train net output #0: loss = 5.27661 (* 1 = 5.27661 loss)
I0406 09:39:10.019991  5644 sgd_solver.cpp:105] Iteration 17292, lr = 0.1
I0406 09:39:12.100466  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:39:15.326647  5644 solver.cpp:218] Iteration 17304 (2.26133 iter/s, 5.30661s/12 iters), loss = 5.303
I0406 09:39:15.326771  5644 solver.cpp:237]     Train net output #0: loss = 5.303 (* 1 = 5.303 loss)
I0406 09:39:15.326778  5644 sgd_solver.cpp:105] Iteration 17304, lr = 0.1
I0406 09:39:20.773655  5644 solver.cpp:218] Iteration 17316 (2.20312 iter/s, 5.44683s/12 iters), loss = 5.26315
I0406 09:39:20.773696  5644 solver.cpp:237]     Train net output #0: loss = 5.26315 (* 1 = 5.26315 loss)
I0406 09:39:20.773701  5644 sgd_solver.cpp:105] Iteration 17316, lr = 0.1
I0406 09:39:26.520601  5644 solver.cpp:218] Iteration 17328 (2.0881 iter/s, 5.74685s/12 iters), loss = 5.28412
I0406 09:39:26.520634  5644 solver.cpp:237]     Train net output #0: loss = 5.28412 (* 1 = 5.28412 loss)
I0406 09:39:26.520640  5644 sgd_solver.cpp:105] Iteration 17328, lr = 0.1
I0406 09:39:31.275946  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17340.caffemodel
I0406 09:39:34.490243  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17340.solverstate
I0406 09:39:36.880945  5644 solver.cpp:330] Iteration 17340, Testing net (#0)
I0406 09:39:36.880964  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:39:38.269305  5644 blocking_queue.cpp:49] Waiting for data
I0406 09:39:39.386365  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:39:41.852567  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:39:41.852605  5644 solver.cpp:397]     Test net output #1: loss = 5.28725 (* 1 = 5.28725 loss)
I0406 09:39:41.998733  5644 solver.cpp:218] Iteration 17340 (0.775296 iter/s, 15.478s/12 iters), loss = 5.27588
I0406 09:39:42.004954  5644 solver.cpp:237]     Train net output #0: loss = 5.27588 (* 1 = 5.27588 loss)
I0406 09:39:42.004973  5644 sgd_solver.cpp:105] Iteration 17340, lr = 0.1
I0406 09:39:46.695598  5644 solver.cpp:218] Iteration 17352 (2.5583 iter/s, 4.69061s/12 iters), loss = 5.26934
I0406 09:39:46.695742  5644 solver.cpp:237]     Train net output #0: loss = 5.26934 (* 1 = 5.26934 loss)
I0406 09:39:46.695749  5644 sgd_solver.cpp:105] Iteration 17352, lr = 0.1
I0406 09:39:52.235648  5644 solver.cpp:218] Iteration 17364 (2.16612 iter/s, 5.53985s/12 iters), loss = 5.24811
I0406 09:39:52.235683  5644 solver.cpp:237]     Train net output #0: loss = 5.24811 (* 1 = 5.24811 loss)
I0406 09:39:52.235688  5644 sgd_solver.cpp:105] Iteration 17364, lr = 0.1
I0406 09:39:57.930886  5644 solver.cpp:218] Iteration 17376 (2.10706 iter/s, 5.69514s/12 iters), loss = 5.27902
I0406 09:39:57.930935  5644 solver.cpp:237]     Train net output #0: loss = 5.27902 (* 1 = 5.27902 loss)
I0406 09:39:57.930943  5644 sgd_solver.cpp:105] Iteration 17376, lr = 0.1
I0406 09:40:03.404287  5644 solver.cpp:218] Iteration 17388 (2.19246 iter/s, 5.47329s/12 iters), loss = 5.27242
I0406 09:40:03.404326  5644 solver.cpp:237]     Train net output #0: loss = 5.27242 (* 1 = 5.27242 loss)
I0406 09:40:03.404331  5644 sgd_solver.cpp:105] Iteration 17388, lr = 0.1
I0406 09:40:08.091271  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:40:09.203784  5644 solver.cpp:218] Iteration 17400 (2.06918 iter/s, 5.79939s/12 iters), loss = 5.27702
I0406 09:40:09.203833  5644 solver.cpp:237]     Train net output #0: loss = 5.27702 (* 1 = 5.27702 loss)
I0406 09:40:09.203841  5644 sgd_solver.cpp:105] Iteration 17400, lr = 0.1
I0406 09:40:15.087803  5644 solver.cpp:218] Iteration 17412 (2.03946 iter/s, 5.88391s/12 iters), loss = 5.27485
I0406 09:40:15.087846  5644 solver.cpp:237]     Train net output #0: loss = 5.27485 (* 1 = 5.27485 loss)
I0406 09:40:15.087854  5644 sgd_solver.cpp:105] Iteration 17412, lr = 0.1
I0406 09:40:20.494824  5644 solver.cpp:218] Iteration 17424 (2.21938 iter/s, 5.40692s/12 iters), loss = 5.30938
I0406 09:40:20.501026  5644 solver.cpp:237]     Train net output #0: loss = 5.30938 (* 1 = 5.30938 loss)
I0406 09:40:20.501044  5644 sgd_solver.cpp:105] Iteration 17424, lr = 0.1
I0406 09:40:26.257712  5644 solver.cpp:218] Iteration 17436 (2.08455 iter/s, 5.75664s/12 iters), loss = 5.28397
I0406 09:40:26.257758  5644 solver.cpp:237]     Train net output #0: loss = 5.28397 (* 1 = 5.28397 loss)
I0406 09:40:26.257767  5644 sgd_solver.cpp:105] Iteration 17436, lr = 0.1
I0406 09:40:28.536744  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17442.caffemodel
I0406 09:40:31.729710  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17442.solverstate
I0406 09:40:34.036279  5644 solver.cpp:330] Iteration 17442, Testing net (#0)
I0406 09:40:34.036300  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:40:36.367851  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:40:38.802130  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:40:38.802171  5644 solver.cpp:397]     Test net output #1: loss = 5.28669 (* 1 = 5.28669 loss)
I0406 09:40:40.646311  5644 solver.cpp:218] Iteration 17448 (0.834004 iter/s, 14.3884s/12 iters), loss = 5.2864
I0406 09:40:40.646368  5644 solver.cpp:237]     Train net output #0: loss = 5.2864 (* 1 = 5.2864 loss)
I0406 09:40:40.646375  5644 sgd_solver.cpp:105] Iteration 17448, lr = 0.1
I0406 09:40:46.337678  5644 solver.cpp:218] Iteration 17460 (2.1085 iter/s, 5.69125s/12 iters), loss = 5.28159
I0406 09:40:46.337724  5644 solver.cpp:237]     Train net output #0: loss = 5.28159 (* 1 = 5.28159 loss)
I0406 09:40:46.337731  5644 sgd_solver.cpp:105] Iteration 17460, lr = 0.1
I0406 09:40:51.878096  5644 solver.cpp:218] Iteration 17472 (2.16594 iter/s, 5.54031s/12 iters), loss = 5.28216
I0406 09:40:51.878223  5644 solver.cpp:237]     Train net output #0: loss = 5.28216 (* 1 = 5.28216 loss)
I0406 09:40:51.878230  5644 sgd_solver.cpp:105] Iteration 17472, lr = 0.1
I0406 09:40:57.631469  5644 solver.cpp:218] Iteration 17484 (2.0858 iter/s, 5.75319s/12 iters), loss = 5.27377
I0406 09:40:57.631517  5644 solver.cpp:237]     Train net output #0: loss = 5.27377 (* 1 = 5.27377 loss)
I0406 09:40:57.631527  5644 sgd_solver.cpp:105] Iteration 17484, lr = 0.1
I0406 09:41:03.148927  5644 solver.cpp:218] Iteration 17496 (2.17916 iter/s, 5.50671s/12 iters), loss = 5.27345
I0406 09:41:03.148980  5644 solver.cpp:237]     Train net output #0: loss = 5.27345 (* 1 = 5.27345 loss)
I0406 09:41:03.148988  5644 sgd_solver.cpp:105] Iteration 17496, lr = 0.1
I0406 09:41:04.502094  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:41:08.987529  5644 solver.cpp:218] Iteration 17508 (2.05533 iter/s, 5.83848s/12 iters), loss = 5.2902
I0406 09:41:08.987581  5644 solver.cpp:237]     Train net output #0: loss = 5.2902 (* 1 = 5.2902 loss)
I0406 09:41:08.987588  5644 sgd_solver.cpp:105] Iteration 17508, lr = 0.1
I0406 09:41:14.773069  5644 solver.cpp:218] Iteration 17520 (2.07418 iter/s, 5.78543s/12 iters), loss = 5.29317
I0406 09:41:14.773120  5644 solver.cpp:237]     Train net output #0: loss = 5.29317 (* 1 = 5.29317 loss)
I0406 09:41:14.773130  5644 sgd_solver.cpp:105] Iteration 17520, lr = 0.1
I0406 09:41:20.352851  5644 solver.cpp:218] Iteration 17532 (2.15066 iter/s, 5.57967s/12 iters), loss = 5.27554
I0406 09:41:20.352908  5644 solver.cpp:237]     Train net output #0: loss = 5.27554 (* 1 = 5.27554 loss)
I0406 09:41:20.352917  5644 sgd_solver.cpp:105] Iteration 17532, lr = 0.1
I0406 09:41:25.359584  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17544.caffemodel
I0406 09:41:28.592296  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17544.solverstate
I0406 09:41:30.920982  5644 solver.cpp:330] Iteration 17544, Testing net (#0)
I0406 09:41:30.921006  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:41:33.135776  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:41:35.720964  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:41:35.720999  5644 solver.cpp:397]     Test net output #1: loss = 5.28696 (* 1 = 5.28696 loss)
I0406 09:41:35.861467  5644 solver.cpp:218] Iteration 17544 (0.773774 iter/s, 15.5084s/12 iters), loss = 5.27536
I0406 09:41:35.861510  5644 solver.cpp:237]     Train net output #0: loss = 5.27536 (* 1 = 5.27536 loss)
I0406 09:41:35.861515  5644 sgd_solver.cpp:105] Iteration 17544, lr = 0.1
I0406 09:41:40.646883  5644 solver.cpp:218] Iteration 17556 (2.50767 iter/s, 4.78532s/12 iters), loss = 5.29481
I0406 09:41:40.646935  5644 solver.cpp:237]     Train net output #0: loss = 5.29481 (* 1 = 5.29481 loss)
I0406 09:41:40.646943  5644 sgd_solver.cpp:105] Iteration 17556, lr = 0.1
I0406 09:41:46.309729  5644 solver.cpp:218] Iteration 17568 (2.11912 iter/s, 5.66274s/12 iters), loss = 5.27479
I0406 09:41:46.309778  5644 solver.cpp:237]     Train net output #0: loss = 5.27479 (* 1 = 5.27479 loss)
I0406 09:41:46.309785  5644 sgd_solver.cpp:105] Iteration 17568, lr = 0.1
I0406 09:41:51.862843  5644 solver.cpp:218] Iteration 17580 (2.16099 iter/s, 5.553s/12 iters), loss = 5.28365
I0406 09:41:51.862895  5644 solver.cpp:237]     Train net output #0: loss = 5.28365 (* 1 = 5.28365 loss)
I0406 09:41:51.862905  5644 sgd_solver.cpp:105] Iteration 17580, lr = 0.1
I0406 09:41:57.638813  5644 solver.cpp:218] Iteration 17592 (2.07761 iter/s, 5.77585s/12 iters), loss = 5.29738
I0406 09:41:57.645027  5644 solver.cpp:237]     Train net output #0: loss = 5.29738 (* 1 = 5.29738 loss)
I0406 09:41:57.645053  5644 sgd_solver.cpp:105] Iteration 17592, lr = 0.1
I0406 09:42:01.451541  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:42:03.230798  5644 solver.cpp:218] Iteration 17604 (2.14833 iter/s, 5.58573s/12 iters), loss = 5.24968
I0406 09:42:03.230854  5644 solver.cpp:237]     Train net output #0: loss = 5.24968 (* 1 = 5.24968 loss)
I0406 09:42:03.230863  5644 sgd_solver.cpp:105] Iteration 17604, lr = 0.1
I0406 09:42:08.633152  5644 solver.cpp:218] Iteration 17616 (2.2213 iter/s, 5.40224s/12 iters), loss = 5.26406
I0406 09:42:08.633196  5644 solver.cpp:237]     Train net output #0: loss = 5.26406 (* 1 = 5.26406 loss)
I0406 09:42:08.633201  5644 sgd_solver.cpp:105] Iteration 17616, lr = 0.1
I0406 09:42:14.206919  5644 solver.cpp:218] Iteration 17628 (2.15298 iter/s, 5.57366s/12 iters), loss = 5.27612
I0406 09:42:14.206961  5644 solver.cpp:237]     Train net output #0: loss = 5.27612 (* 1 = 5.27612 loss)
I0406 09:42:14.206967  5644 sgd_solver.cpp:105] Iteration 17628, lr = 0.1
I0406 09:42:19.733314  5644 solver.cpp:218] Iteration 17640 (2.17144 iter/s, 5.52629s/12 iters), loss = 5.26762
I0406 09:42:19.733350  5644 solver.cpp:237]     Train net output #0: loss = 5.26762 (* 1 = 5.26762 loss)
I0406 09:42:19.733356  5644 sgd_solver.cpp:105] Iteration 17640, lr = 0.1
I0406 09:42:21.984630  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17646.caffemodel
I0406 09:42:26.315981  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17646.solverstate
I0406 09:42:28.733924  5644 solver.cpp:330] Iteration 17646, Testing net (#0)
I0406 09:42:28.734048  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:42:31.372371  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:42:33.838778  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:42:33.838814  5644 solver.cpp:397]     Test net output #1: loss = 5.28617 (* 1 = 5.28617 loss)
I0406 09:42:35.750918  5644 solver.cpp:218] Iteration 17652 (0.749184 iter/s, 16.0174s/12 iters), loss = 5.28626
I0406 09:42:35.750957  5644 solver.cpp:237]     Train net output #0: loss = 5.28626 (* 1 = 5.28626 loss)
I0406 09:42:35.750963  5644 sgd_solver.cpp:105] Iteration 17652, lr = 0.1
I0406 09:42:41.483387  5644 solver.cpp:218] Iteration 17664 (2.09338 iter/s, 5.73237s/12 iters), loss = 5.25917
I0406 09:42:41.483438  5644 solver.cpp:237]     Train net output #0: loss = 5.25917 (* 1 = 5.25917 loss)
I0406 09:42:41.483445  5644 sgd_solver.cpp:105] Iteration 17664, lr = 0.1
I0406 09:42:47.064859  5644 solver.cpp:218] Iteration 17676 (2.15001 iter/s, 5.58136s/12 iters), loss = 5.26476
I0406 09:42:47.064918  5644 solver.cpp:237]     Train net output #0: loss = 5.26476 (* 1 = 5.26476 loss)
I0406 09:42:47.064927  5644 sgd_solver.cpp:105] Iteration 17676, lr = 0.1
I0406 09:42:52.544648  5644 solver.cpp:218] Iteration 17688 (2.18991 iter/s, 5.47967s/12 iters), loss = 5.28599
I0406 09:42:52.544687  5644 solver.cpp:237]     Train net output #0: loss = 5.28599 (* 1 = 5.28599 loss)
I0406 09:42:52.544692  5644 sgd_solver.cpp:105] Iteration 17688, lr = 0.1
I0406 09:42:58.304255  5644 solver.cpp:218] Iteration 17700 (2.08351 iter/s, 5.75951s/12 iters), loss = 5.27956
I0406 09:42:58.304303  5644 solver.cpp:237]     Train net output #0: loss = 5.27956 (* 1 = 5.27956 loss)
I0406 09:42:58.304311  5644 sgd_solver.cpp:105] Iteration 17700, lr = 0.1
I0406 09:42:58.865756  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:43:03.832511  5644 solver.cpp:218] Iteration 17712 (2.17071 iter/s, 5.52815s/12 iters), loss = 5.28579
I0406 09:43:03.832551  5644 solver.cpp:237]     Train net output #0: loss = 5.28579 (* 1 = 5.28579 loss)
I0406 09:43:03.832558  5644 sgd_solver.cpp:105] Iteration 17712, lr = 0.1
I0406 09:43:09.522658  5644 solver.cpp:218] Iteration 17724 (2.10895 iter/s, 5.69004s/12 iters), loss = 5.28863
I0406 09:43:09.522707  5644 solver.cpp:237]     Train net output #0: loss = 5.28863 (* 1 = 5.28863 loss)
I0406 09:43:09.522716  5644 sgd_solver.cpp:105] Iteration 17724, lr = 0.1
I0406 09:43:15.267241  5644 solver.cpp:218] Iteration 17736 (2.08897 iter/s, 5.74447s/12 iters), loss = 5.26998
I0406 09:43:15.267297  5644 solver.cpp:237]     Train net output #0: loss = 5.26998 (* 1 = 5.26998 loss)
I0406 09:43:15.267305  5644 sgd_solver.cpp:105] Iteration 17736, lr = 0.1
I0406 09:43:20.456720  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17748.caffemodel
I0406 09:43:24.466969  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17748.solverstate
I0406 09:43:26.849412  5644 solver.cpp:330] Iteration 17748, Testing net (#0)
I0406 09:43:26.849431  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:43:29.136090  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:43:31.895488  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:43:31.895514  5644 solver.cpp:397]     Test net output #1: loss = 5.28671 (* 1 = 5.28671 loss)
I0406 09:43:32.036378  5644 solver.cpp:218] Iteration 17748 (0.715609 iter/s, 16.7689s/12 iters), loss = 5.27313
I0406 09:43:32.037943  5644 solver.cpp:237]     Train net output #0: loss = 5.27313 (* 1 = 5.27313 loss)
I0406 09:43:32.037956  5644 sgd_solver.cpp:105] Iteration 17748, lr = 0.1
I0406 09:43:36.879565  5644 solver.cpp:218] Iteration 17760 (2.47853 iter/s, 4.84157s/12 iters), loss = 5.25127
I0406 09:43:36.879618  5644 solver.cpp:237]     Train net output #0: loss = 5.25127 (* 1 = 5.25127 loss)
I0406 09:43:36.879626  5644 sgd_solver.cpp:105] Iteration 17760, lr = 0.1
I0406 09:43:42.333551  5644 solver.cpp:218] Iteration 17772 (2.20027 iter/s, 5.45387s/12 iters), loss = 5.28747
I0406 09:43:42.339813  5644 solver.cpp:237]     Train net output #0: loss = 5.28747 (* 1 = 5.28747 loss)
I0406 09:43:42.339836  5644 sgd_solver.cpp:105] Iteration 17772, lr = 0.1
I0406 09:43:47.930150  5644 solver.cpp:218] Iteration 17784 (2.14658 iter/s, 5.5903s/12 iters), loss = 5.27926
I0406 09:43:47.930189  5644 solver.cpp:237]     Train net output #0: loss = 5.27926 (* 1 = 5.27926 loss)
I0406 09:43:47.930194  5644 sgd_solver.cpp:105] Iteration 17784, lr = 0.1
I0406 09:43:53.613487  5644 solver.cpp:218] Iteration 17796 (2.11147 iter/s, 5.68323s/12 iters), loss = 5.27369
I0406 09:43:53.613538  5644 solver.cpp:237]     Train net output #0: loss = 5.27369 (* 1 = 5.27369 loss)
I0406 09:43:53.613545  5644 sgd_solver.cpp:105] Iteration 17796, lr = 0.1
I0406 09:43:56.508508  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:43:59.069695  5644 solver.cpp:218] Iteration 17808 (2.19937 iter/s, 5.4561s/12 iters), loss = 5.28225
I0406 09:43:59.069738  5644 solver.cpp:237]     Train net output #0: loss = 5.28225 (* 1 = 5.28225 loss)
I0406 09:43:59.069746  5644 sgd_solver.cpp:105] Iteration 17808, lr = 0.1
I0406 09:44:04.664775  5644 solver.cpp:218] Iteration 17820 (2.14478 iter/s, 5.59498s/12 iters), loss = 5.26947
I0406 09:44:04.664878  5644 solver.cpp:237]     Train net output #0: loss = 5.26947 (* 1 = 5.26947 loss)
I0406 09:44:04.664894  5644 sgd_solver.cpp:105] Iteration 17820, lr = 0.1
I0406 09:44:10.387879  5644 solver.cpp:218] Iteration 17832 (2.09682 iter/s, 5.72294s/12 iters), loss = 5.26445
I0406 09:44:10.387928  5644 solver.cpp:237]     Train net output #0: loss = 5.26445 (* 1 = 5.26445 loss)
I0406 09:44:10.387935  5644 sgd_solver.cpp:105] Iteration 17832, lr = 0.1
I0406 09:44:16.022575  5644 solver.cpp:218] Iteration 17844 (2.1297 iter/s, 5.63458s/12 iters), loss = 5.28597
I0406 09:44:16.022624  5644 solver.cpp:237]     Train net output #0: loss = 5.28597 (* 1 = 5.28597 loss)
I0406 09:44:16.022632  5644 sgd_solver.cpp:105] Iteration 17844, lr = 0.1
I0406 09:44:18.184762  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17850.caffemodel
I0406 09:44:22.239027  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17850.solverstate
I0406 09:44:24.590646  5644 solver.cpp:330] Iteration 17850, Testing net (#0)
I0406 09:44:24.590665  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:44:26.902686  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:44:29.611982  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0406 09:44:29.612020  5644 solver.cpp:397]     Test net output #1: loss = 5.28579 (* 1 = 5.28579 loss)
I0406 09:44:31.724586  5644 solver.cpp:218] Iteration 17856 (0.764242 iter/s, 15.7018s/12 iters), loss = 5.27663
I0406 09:44:31.724642  5644 solver.cpp:237]     Train net output #0: loss = 5.27663 (* 1 = 5.27663 loss)
I0406 09:44:31.724650  5644 sgd_solver.cpp:105] Iteration 17856, lr = 0.1
I0406 09:44:37.049094  5644 solver.cpp:218] Iteration 17868 (2.25378 iter/s, 5.3244s/12 iters), loss = 5.28976
I0406 09:44:37.049299  5644 solver.cpp:237]     Train net output #0: loss = 5.28976 (* 1 = 5.28976 loss)
I0406 09:44:37.049307  5644 sgd_solver.cpp:105] Iteration 17868, lr = 0.1
I0406 09:44:42.883006  5644 solver.cpp:218] Iteration 17880 (2.05703 iter/s, 5.83365s/12 iters), loss = 5.2951
I0406 09:44:42.883057  5644 solver.cpp:237]     Train net output #0: loss = 5.2951 (* 1 = 5.2951 loss)
I0406 09:44:42.883065  5644 sgd_solver.cpp:105] Iteration 17880, lr = 0.1
I0406 09:44:48.479914  5644 solver.cpp:218] Iteration 17892 (2.14408 iter/s, 5.5968s/12 iters), loss = 5.29457
I0406 09:44:48.479950  5644 solver.cpp:237]     Train net output #0: loss = 5.29457 (* 1 = 5.29457 loss)
I0406 09:44:48.479956  5644 sgd_solver.cpp:105] Iteration 17892, lr = 0.1
I0406 09:44:53.835146  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:44:54.085073  5644 solver.cpp:218] Iteration 17904 (2.14092 iter/s, 5.60506s/12 iters), loss = 5.28293
I0406 09:44:54.085122  5644 solver.cpp:237]     Train net output #0: loss = 5.28293 (* 1 = 5.28293 loss)
I0406 09:44:54.085132  5644 sgd_solver.cpp:105] Iteration 17904, lr = 0.1
I0406 09:44:59.491065  5644 solver.cpp:218] Iteration 17916 (2.2198 iter/s, 5.40588s/12 iters), loss = 5.26835
I0406 09:44:59.491111  5644 solver.cpp:237]     Train net output #0: loss = 5.26835 (* 1 = 5.26835 loss)
I0406 09:44:59.491118  5644 sgd_solver.cpp:105] Iteration 17916, lr = 0.1
I0406 09:45:05.024857  5644 solver.cpp:218] Iteration 17928 (2.16854 iter/s, 5.53369s/12 iters), loss = 5.28393
I0406 09:45:05.024901  5644 solver.cpp:237]     Train net output #0: loss = 5.28393 (* 1 = 5.28393 loss)
I0406 09:45:05.024907  5644 sgd_solver.cpp:105] Iteration 17928, lr = 0.1
I0406 09:45:10.721094  5644 solver.cpp:218] Iteration 17940 (2.10669 iter/s, 5.69613s/12 iters), loss = 5.29086
I0406 09:45:10.721222  5644 solver.cpp:237]     Train net output #0: loss = 5.29086 (* 1 = 5.29086 loss)
I0406 09:45:10.721231  5644 sgd_solver.cpp:105] Iteration 17940, lr = 0.1
I0406 09:45:15.858618  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_17952.caffemodel
I0406 09:45:19.495055  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_17952.solverstate
I0406 09:45:21.834267  5644 solver.cpp:330] Iteration 17952, Testing net (#0)
I0406 09:45:21.834291  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:45:24.159727  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:45:26.934813  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:45:26.934849  5644 solver.cpp:397]     Test net output #1: loss = 5.28578 (* 1 = 5.28578 loss)
I0406 09:45:27.074527  5644 solver.cpp:218] Iteration 17952 (0.733803 iter/s, 16.3532s/12 iters), loss = 5.26518
I0406 09:45:27.074574  5644 solver.cpp:237]     Train net output #0: loss = 5.26518 (* 1 = 5.26518 loss)
I0406 09:45:27.074582  5644 sgd_solver.cpp:105] Iteration 17952, lr = 0.1
I0406 09:45:31.671550  5644 solver.cpp:218] Iteration 17964 (2.61044 iter/s, 4.59692s/12 iters), loss = 5.2686
I0406 09:45:31.671587  5644 solver.cpp:237]     Train net output #0: loss = 5.2686 (* 1 = 5.2686 loss)
I0406 09:45:31.671593  5644 sgd_solver.cpp:105] Iteration 17964, lr = 0.1
I0406 09:45:37.300123  5644 solver.cpp:218] Iteration 17976 (2.13202 iter/s, 5.62847s/12 iters), loss = 5.27507
I0406 09:45:37.300182  5644 solver.cpp:237]     Train net output #0: loss = 5.27507 (* 1 = 5.27507 loss)
I0406 09:45:37.300190  5644 sgd_solver.cpp:105] Iteration 17976, lr = 0.1
I0406 09:45:43.039456  5644 solver.cpp:218] Iteration 17988 (2.09088 iter/s, 5.73921s/12 iters), loss = 5.2834
I0406 09:45:43.039579  5644 solver.cpp:237]     Train net output #0: loss = 5.2834 (* 1 = 5.2834 loss)
I0406 09:45:43.039588  5644 sgd_solver.cpp:105] Iteration 17988, lr = 0.1
I0406 09:45:48.741555  5644 solver.cpp:218] Iteration 18000 (2.10456 iter/s, 5.70192s/12 iters), loss = 5.27897
I0406 09:45:48.741611  5644 solver.cpp:237]     Train net output #0: loss = 5.27897 (* 1 = 5.27897 loss)
I0406 09:45:48.741619  5644 sgd_solver.cpp:105] Iteration 18000, lr = 0.1
I0406 09:45:51.017457  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:45:54.605777  5644 solver.cpp:218] Iteration 18012 (2.04635 iter/s, 5.86411s/12 iters), loss = 5.30308
I0406 09:45:54.605825  5644 solver.cpp:237]     Train net output #0: loss = 5.30308 (* 1 = 5.30308 loss)
I0406 09:45:54.605834  5644 sgd_solver.cpp:105] Iteration 18012, lr = 0.1
I0406 09:45:59.949517  5644 solver.cpp:218] Iteration 18024 (2.24566 iter/s, 5.34363s/12 iters), loss = 5.26005
I0406 09:45:59.949563  5644 solver.cpp:237]     Train net output #0: loss = 5.26005 (* 1 = 5.26005 loss)
I0406 09:45:59.949573  5644 sgd_solver.cpp:105] Iteration 18024, lr = 0.1
I0406 09:46:05.787729  5644 solver.cpp:218] Iteration 18036 (2.05546 iter/s, 5.8381s/12 iters), loss = 5.29111
I0406 09:46:05.787784  5644 solver.cpp:237]     Train net output #0: loss = 5.29111 (* 1 = 5.29111 loss)
I0406 09:46:05.787793  5644 sgd_solver.cpp:105] Iteration 18036, lr = 0.1
I0406 09:46:05.788056  5644 blocking_queue.cpp:49] Waiting for data
I0406 09:46:11.258379  5644 solver.cpp:218] Iteration 18048 (2.19357 iter/s, 5.47054s/12 iters), loss = 5.26776
I0406 09:46:11.258435  5644 solver.cpp:237]     Train net output #0: loss = 5.26776 (* 1 = 5.26776 loss)
I0406 09:46:11.258443  5644 sgd_solver.cpp:105] Iteration 18048, lr = 0.1
I0406 09:46:13.443706  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18054.caffemodel
I0406 09:46:16.680248  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18054.solverstate
I0406 09:46:19.007411  5644 solver.cpp:330] Iteration 18054, Testing net (#0)
I0406 09:46:19.007432  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:46:20.986547  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:46:23.755082  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:46:23.755123  5644 solver.cpp:397]     Test net output #1: loss = 5.28675 (* 1 = 5.28675 loss)
I0406 09:46:25.709234  5644 solver.cpp:218] Iteration 18060 (0.830411 iter/s, 14.4507s/12 iters), loss = 5.2648
I0406 09:46:25.709293  5644 solver.cpp:237]     Train net output #0: loss = 5.2648 (* 1 = 5.2648 loss)
I0406 09:46:25.709302  5644 sgd_solver.cpp:105] Iteration 18060, lr = 0.1
I0406 09:46:31.439184  5644 solver.cpp:218] Iteration 18072 (2.0943 iter/s, 5.72983s/12 iters), loss = 5.25152
I0406 09:46:31.439234  5644 solver.cpp:237]     Train net output #0: loss = 5.25152 (* 1 = 5.25152 loss)
I0406 09:46:31.439241  5644 sgd_solver.cpp:105] Iteration 18072, lr = 0.1
I0406 09:46:36.903791  5644 solver.cpp:218] Iteration 18084 (2.19599 iter/s, 5.4645s/12 iters), loss = 5.27431
I0406 09:46:36.903841  5644 solver.cpp:237]     Train net output #0: loss = 5.27431 (* 1 = 5.27431 loss)
I0406 09:46:36.903847  5644 sgd_solver.cpp:105] Iteration 18084, lr = 0.1
I0406 09:46:42.609807  5644 solver.cpp:218] Iteration 18096 (2.10308 iter/s, 5.7059s/12 iters), loss = 5.27633
I0406 09:46:42.609856  5644 solver.cpp:237]     Train net output #0: loss = 5.27633 (* 1 = 5.27633 loss)
I0406 09:46:42.609864  5644 sgd_solver.cpp:105] Iteration 18096, lr = 0.1
I0406 09:46:47.053028  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:46:48.010308  5644 solver.cpp:218] Iteration 18108 (2.22206 iter/s, 5.40039s/12 iters), loss = 5.27581
I0406 09:46:48.010358  5644 solver.cpp:237]     Train net output #0: loss = 5.27581 (* 1 = 5.27581 loss)
I0406 09:46:48.010368  5644 sgd_solver.cpp:105] Iteration 18108, lr = 0.1
I0406 09:46:53.664574  5644 solver.cpp:218] Iteration 18120 (2.12233 iter/s, 5.65416s/12 iters), loss = 5.271
I0406 09:46:53.664614  5644 solver.cpp:237]     Train net output #0: loss = 5.271 (* 1 = 5.271 loss)
I0406 09:46:53.664620  5644 sgd_solver.cpp:105] Iteration 18120, lr = 0.1
I0406 09:46:59.257339  5644 solver.cpp:218] Iteration 18132 (2.14567 iter/s, 5.59266s/12 iters), loss = 5.30838
I0406 09:46:59.257377  5644 solver.cpp:237]     Train net output #0: loss = 5.30838 (* 1 = 5.30838 loss)
I0406 09:46:59.257383  5644 sgd_solver.cpp:105] Iteration 18132, lr = 0.1
I0406 09:47:04.931727  5644 solver.cpp:218] Iteration 18144 (2.1148 iter/s, 5.67429s/12 iters), loss = 5.28961
I0406 09:47:04.931767  5644 solver.cpp:237]     Train net output #0: loss = 5.28961 (* 1 = 5.28961 loss)
I0406 09:47:04.931773  5644 sgd_solver.cpp:105] Iteration 18144, lr = 0.1
I0406 09:47:10.054895  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18156.caffemodel
I0406 09:47:14.109935  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18156.solverstate
I0406 09:47:16.485586  5644 solver.cpp:330] Iteration 18156, Testing net (#0)
I0406 09:47:16.485607  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:47:18.734926  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:47:21.773594  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:47:21.773631  5644 solver.cpp:397]     Test net output #1: loss = 5.2862 (* 1 = 5.2862 loss)
I0406 09:47:21.918506  5644 solver.cpp:218] Iteration 18156 (0.70644 iter/s, 16.9866s/12 iters), loss = 5.29278
I0406 09:47:21.920090  5644 solver.cpp:237]     Train net output #0: loss = 5.29278 (* 1 = 5.29278 loss)
I0406 09:47:21.920104  5644 sgd_solver.cpp:105] Iteration 18156, lr = 0.1
I0406 09:47:26.613951  5644 solver.cpp:218] Iteration 18168 (2.55655 iter/s, 4.69382s/12 iters), loss = 5.27134
I0406 09:47:26.613992  5644 solver.cpp:237]     Train net output #0: loss = 5.27134 (* 1 = 5.27134 loss)
I0406 09:47:26.613998  5644 sgd_solver.cpp:105] Iteration 18168, lr = 0.1
I0406 09:47:32.348850  5644 solver.cpp:218] Iteration 18180 (2.09249 iter/s, 5.7348s/12 iters), loss = 5.27808
I0406 09:47:32.348912  5644 solver.cpp:237]     Train net output #0: loss = 5.27808 (* 1 = 5.27808 loss)
I0406 09:47:32.348919  5644 sgd_solver.cpp:105] Iteration 18180, lr = 0.1
I0406 09:47:37.964531  5644 solver.cpp:218] Iteration 18192 (2.13692 iter/s, 5.61556s/12 iters), loss = 5.27517
I0406 09:47:37.970777  5644 solver.cpp:237]     Train net output #0: loss = 5.27517 (* 1 = 5.27517 loss)
I0406 09:47:37.970796  5644 sgd_solver.cpp:105] Iteration 18192, lr = 0.1
I0406 09:47:43.698544  5644 solver.cpp:218] Iteration 18204 (2.09507 iter/s, 5.72772s/12 iters), loss = 5.27562
I0406 09:47:43.698587  5644 solver.cpp:237]     Train net output #0: loss = 5.27562 (* 1 = 5.27562 loss)
I0406 09:47:43.698596  5644 sgd_solver.cpp:105] Iteration 18204, lr = 0.1
I0406 09:47:45.091811  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:47:49.347862  5644 solver.cpp:218] Iteration 18216 (2.12419 iter/s, 5.64922s/12 iters), loss = 5.29737
I0406 09:47:49.349840  5644 solver.cpp:237]     Train net output #0: loss = 5.29737 (* 1 = 5.29737 loss)
I0406 09:47:49.349851  5644 sgd_solver.cpp:105] Iteration 18216, lr = 0.1
I0406 09:47:55.197366  5644 solver.cpp:218] Iteration 18228 (2.05217 iter/s, 5.84747s/12 iters), loss = 5.28949
I0406 09:47:55.197403  5644 solver.cpp:237]     Train net output #0: loss = 5.28949 (* 1 = 5.28949 loss)
I0406 09:47:55.197409  5644 sgd_solver.cpp:105] Iteration 18228, lr = 0.1
I0406 09:48:00.574587  5644 solver.cpp:218] Iteration 18240 (2.23167 iter/s, 5.37713s/12 iters), loss = 5.27477
I0406 09:48:00.574632  5644 solver.cpp:237]     Train net output #0: loss = 5.27477 (* 1 = 5.27477 loss)
I0406 09:48:00.574640  5644 sgd_solver.cpp:105] Iteration 18240, lr = 0.1
I0406 09:48:06.288620  5644 solver.cpp:218] Iteration 18252 (2.10013 iter/s, 5.71393s/12 iters), loss = 5.27676
I0406 09:48:06.288657  5644 solver.cpp:237]     Train net output #0: loss = 5.27676 (* 1 = 5.27676 loss)
I0406 09:48:06.288664  5644 sgd_solver.cpp:105] Iteration 18252, lr = 0.1
I0406 09:48:08.456192  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18258.caffemodel
I0406 09:48:12.350668  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18258.solverstate
I0406 09:48:15.550868  5644 solver.cpp:330] Iteration 18258, Testing net (#0)
I0406 09:48:15.550889  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:48:17.506716  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:48:20.486428  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:48:20.486552  5644 solver.cpp:397]     Test net output #1: loss = 5.28585 (* 1 = 5.28585 loss)
I0406 09:48:22.550951  5644 solver.cpp:218] Iteration 18264 (0.73791 iter/s, 16.2621s/12 iters), loss = 5.28845
I0406 09:48:22.551003  5644 solver.cpp:237]     Train net output #0: loss = 5.28845 (* 1 = 5.28845 loss)
I0406 09:48:22.551012  5644 sgd_solver.cpp:105] Iteration 18264, lr = 0.1
I0406 09:48:28.305546  5644 solver.cpp:218] Iteration 18276 (2.08533 iter/s, 5.75448s/12 iters), loss = 5.27084
I0406 09:48:28.305584  5644 solver.cpp:237]     Train net output #0: loss = 5.27084 (* 1 = 5.27084 loss)
I0406 09:48:28.305590  5644 sgd_solver.cpp:105] Iteration 18276, lr = 0.1
I0406 09:48:33.971001  5644 solver.cpp:218] Iteration 18288 (2.11814 iter/s, 5.66536s/12 iters), loss = 5.28638
I0406 09:48:33.971040  5644 solver.cpp:237]     Train net output #0: loss = 5.28638 (* 1 = 5.28638 loss)
I0406 09:48:33.971046  5644 sgd_solver.cpp:105] Iteration 18288, lr = 0.1
I0406 09:48:39.804759  5644 solver.cpp:218] Iteration 18300 (2.05703 iter/s, 5.83366s/12 iters), loss = 5.29608
I0406 09:48:39.804805  5644 solver.cpp:237]     Train net output #0: loss = 5.29608 (* 1 = 5.29608 loss)
I0406 09:48:39.804813  5644 sgd_solver.cpp:105] Iteration 18300, lr = 0.1
I0406 09:48:43.569929  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:48:45.345247  5644 solver.cpp:218] Iteration 18312 (2.16592 iter/s, 5.54038s/12 iters), loss = 5.24827
I0406 09:48:45.345296  5644 solver.cpp:237]     Train net output #0: loss = 5.24827 (* 1 = 5.24827 loss)
I0406 09:48:45.345304  5644 sgd_solver.cpp:105] Iteration 18312, lr = 0.1
I0406 09:48:51.086064  5644 solver.cpp:218] Iteration 18324 (2.09033 iter/s, 5.74071s/12 iters), loss = 5.26843
I0406 09:48:51.086858  5644 solver.cpp:237]     Train net output #0: loss = 5.26843 (* 1 = 5.26843 loss)
I0406 09:48:51.086867  5644 sgd_solver.cpp:105] Iteration 18324, lr = 0.1
I0406 09:48:56.664642  5644 solver.cpp:218] Iteration 18336 (2.15141 iter/s, 5.57774s/12 iters), loss = 5.27966
I0406 09:48:56.664686  5644 solver.cpp:237]     Train net output #0: loss = 5.27966 (* 1 = 5.27966 loss)
I0406 09:48:56.664693  5644 sgd_solver.cpp:105] Iteration 18336, lr = 0.1
I0406 09:49:02.422672  5644 solver.cpp:218] Iteration 18348 (2.08409 iter/s, 5.75792s/12 iters), loss = 5.26648
I0406 09:49:02.422726  5644 solver.cpp:237]     Train net output #0: loss = 5.26648 (* 1 = 5.26648 loss)
I0406 09:49:02.422735  5644 sgd_solver.cpp:105] Iteration 18348, lr = 0.1
I0406 09:49:07.421840  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18360.caffemodel
I0406 09:49:10.457384  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18360.solverstate
I0406 09:49:12.854884  5644 solver.cpp:330] Iteration 18360, Testing net (#0)
I0406 09:49:12.854912  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:49:14.870026  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:49:17.849922  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:49:17.849952  5644 solver.cpp:397]     Test net output #1: loss = 5.28566 (* 1 = 5.28566 loss)
I0406 09:49:17.986168  5644 solver.cpp:218] Iteration 18360 (0.771044 iter/s, 15.5633s/12 iters), loss = 5.28109
I0406 09:49:17.986222  5644 solver.cpp:237]     Train net output #0: loss = 5.28109 (* 1 = 5.28109 loss)
I0406 09:49:17.986230  5644 sgd_solver.cpp:105] Iteration 18360, lr = 0.1
I0406 09:49:23.027875  5644 solver.cpp:218] Iteration 18372 (2.3802 iter/s, 5.0416s/12 iters), loss = 5.25875
I0406 09:49:23.027997  5644 solver.cpp:237]     Train net output #0: loss = 5.25875 (* 1 = 5.25875 loss)
I0406 09:49:23.028004  5644 sgd_solver.cpp:105] Iteration 18372, lr = 0.1
I0406 09:49:28.481815  5644 solver.cpp:218] Iteration 18384 (2.20032 iter/s, 5.45376s/12 iters), loss = 5.26963
I0406 09:49:28.481863  5644 solver.cpp:237]     Train net output #0: loss = 5.26963 (* 1 = 5.26963 loss)
I0406 09:49:28.481871  5644 sgd_solver.cpp:105] Iteration 18384, lr = 0.1
I0406 09:49:33.856333  5644 solver.cpp:218] Iteration 18396 (2.2328 iter/s, 5.37441s/12 iters), loss = 5.28277
I0406 09:49:33.856382  5644 solver.cpp:237]     Train net output #0: loss = 5.28277 (* 1 = 5.28277 loss)
I0406 09:49:33.856391  5644 sgd_solver.cpp:105] Iteration 18396, lr = 0.1
I0406 09:49:39.487340  5644 solver.cpp:218] Iteration 18408 (2.1311 iter/s, 5.6309s/12 iters), loss = 5.28416
I0406 09:49:39.487377  5644 solver.cpp:237]     Train net output #0: loss = 5.28416 (* 1 = 5.28416 loss)
I0406 09:49:39.487383  5644 sgd_solver.cpp:105] Iteration 18408, lr = 0.1
I0406 09:49:40.040518  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:49:45.091027  5644 solver.cpp:218] Iteration 18420 (2.14148 iter/s, 5.60359s/12 iters), loss = 5.27724
I0406 09:49:45.091074  5644 solver.cpp:237]     Train net output #0: loss = 5.27724 (* 1 = 5.27724 loss)
I0406 09:49:45.091081  5644 sgd_solver.cpp:105] Iteration 18420, lr = 0.1
I0406 09:49:50.928812  5644 solver.cpp:218] Iteration 18432 (2.05561 iter/s, 5.83768s/12 iters), loss = 5.28966
I0406 09:49:50.928859  5644 solver.cpp:237]     Train net output #0: loss = 5.28966 (* 1 = 5.28966 loss)
I0406 09:49:50.928867  5644 sgd_solver.cpp:105] Iteration 18432, lr = 0.1
I0406 09:49:56.481720  5644 solver.cpp:218] Iteration 18444 (2.16107 iter/s, 5.5528s/12 iters), loss = 5.26996
I0406 09:49:56.481829  5644 solver.cpp:237]     Train net output #0: loss = 5.26996 (* 1 = 5.26996 loss)
I0406 09:49:56.481838  5644 sgd_solver.cpp:105] Iteration 18444, lr = 0.1
I0406 09:50:02.555389  5644 solver.cpp:218] Iteration 18456 (1.9758 iter/s, 6.0735s/12 iters), loss = 5.27522
I0406 09:50:02.555449  5644 solver.cpp:237]     Train net output #0: loss = 5.27522 (* 1 = 5.27522 loss)
I0406 09:50:02.555456  5644 sgd_solver.cpp:105] Iteration 18456, lr = 0.1
I0406 09:50:04.741660  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18462.caffemodel
I0406 09:50:07.811969  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18462.solverstate
I0406 09:50:10.130484  5644 solver.cpp:330] Iteration 18462, Testing net (#0)
I0406 09:50:10.130502  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:50:12.088071  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:50:15.019520  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:50:15.019547  5644 solver.cpp:397]     Test net output #1: loss = 5.28568 (* 1 = 5.28568 loss)
I0406 09:50:17.101958  5644 solver.cpp:218] Iteration 18468 (0.824948 iter/s, 14.5464s/12 iters), loss = 5.25201
I0406 09:50:17.102010  5644 solver.cpp:237]     Train net output #0: loss = 5.25201 (* 1 = 5.25201 loss)
I0406 09:50:17.102017  5644 sgd_solver.cpp:105] Iteration 18468, lr = 0.1
I0406 09:50:22.990794  5644 solver.cpp:218] Iteration 18480 (2.03779 iter/s, 5.88873s/12 iters), loss = 5.29105
I0406 09:50:22.990830  5644 solver.cpp:237]     Train net output #0: loss = 5.29105 (* 1 = 5.29105 loss)
I0406 09:50:22.990836  5644 sgd_solver.cpp:105] Iteration 18480, lr = 0.1
I0406 09:50:28.564481  5644 solver.cpp:218] Iteration 18492 (2.15301 iter/s, 5.57359s/12 iters), loss = 5.2829
I0406 09:50:28.565522  5644 solver.cpp:237]     Train net output #0: loss = 5.2829 (* 1 = 5.2829 loss)
I0406 09:50:28.565534  5644 sgd_solver.cpp:105] Iteration 18492, lr = 0.1
I0406 09:50:34.164114  5644 solver.cpp:218] Iteration 18504 (2.14341 iter/s, 5.59854s/12 iters), loss = 5.27575
I0406 09:50:34.164165  5644 solver.cpp:237]     Train net output #0: loss = 5.27575 (* 1 = 5.27575 loss)
I0406 09:50:34.164173  5644 sgd_solver.cpp:105] Iteration 18504, lr = 0.1
I0406 09:50:37.224201  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:50:39.959900  5644 solver.cpp:218] Iteration 18516 (2.07051 iter/s, 5.79568s/12 iters), loss = 5.2796
I0406 09:50:39.959955  5644 solver.cpp:237]     Train net output #0: loss = 5.2796 (* 1 = 5.2796 loss)
I0406 09:50:39.959964  5644 sgd_solver.cpp:105] Iteration 18516, lr = 0.1
I0406 09:50:45.444591  5644 solver.cpp:218] Iteration 18528 (2.18795 iter/s, 5.48458s/12 iters), loss = 5.27481
I0406 09:50:45.444643  5644 solver.cpp:237]     Train net output #0: loss = 5.27481 (* 1 = 5.27481 loss)
I0406 09:50:45.444650  5644 sgd_solver.cpp:105] Iteration 18528, lr = 0.1
I0406 09:50:51.359079  5644 solver.cpp:218] Iteration 18540 (2.02895 iter/s, 5.91438s/12 iters), loss = 5.27072
I0406 09:50:51.359124  5644 solver.cpp:237]     Train net output #0: loss = 5.27072 (* 1 = 5.27072 loss)
I0406 09:50:51.359133  5644 sgd_solver.cpp:105] Iteration 18540, lr = 0.1
I0406 09:50:56.918932  5644 solver.cpp:218] Iteration 18552 (2.15837 iter/s, 5.55974s/12 iters), loss = 5.28243
I0406 09:50:56.918987  5644 solver.cpp:237]     Train net output #0: loss = 5.28243 (* 1 = 5.28243 loss)
I0406 09:50:56.918996  5644 sgd_solver.cpp:105] Iteration 18552, lr = 0.1
I0406 09:51:01.852810  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18564.caffemodel
I0406 09:51:04.962599  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18564.solverstate
I0406 09:51:07.288111  5644 solver.cpp:330] Iteration 18564, Testing net (#0)
I0406 09:51:07.288130  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:51:09.098285  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:51:12.096768  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:51:12.096804  5644 solver.cpp:397]     Test net output #1: loss = 5.28537 (* 1 = 5.28537 loss)
I0406 09:51:12.226050  5644 solver.cpp:218] Iteration 18564 (0.783958 iter/s, 15.3069s/12 iters), loss = 5.27749
I0406 09:51:12.232264  5644 solver.cpp:237]     Train net output #0: loss = 5.27749 (* 1 = 5.27749 loss)
I0406 09:51:12.232281  5644 sgd_solver.cpp:105] Iteration 18564, lr = 0.1
I0406 09:51:16.948818  5644 solver.cpp:218] Iteration 18576 (2.54425 iter/s, 4.71652s/12 iters), loss = 5.29468
I0406 09:51:16.948858  5644 solver.cpp:237]     Train net output #0: loss = 5.29468 (* 1 = 5.29468 loss)
I0406 09:51:16.948863  5644 sgd_solver.cpp:105] Iteration 18576, lr = 0.1
I0406 09:51:22.248497  5644 solver.cpp:218] Iteration 18588 (2.26433 iter/s, 5.29958s/12 iters), loss = 5.30742
I0406 09:51:22.248534  5644 solver.cpp:237]     Train net output #0: loss = 5.30742 (* 1 = 5.30742 loss)
I0406 09:51:22.248540  5644 sgd_solver.cpp:105] Iteration 18588, lr = 0.1
I0406 09:51:27.764819  5644 solver.cpp:218] Iteration 18600 (2.1754 iter/s, 5.51623s/12 iters), loss = 5.29394
I0406 09:51:27.764860  5644 solver.cpp:237]     Train net output #0: loss = 5.29394 (* 1 = 5.29394 loss)
I0406 09:51:27.764866  5644 sgd_solver.cpp:105] Iteration 18600, lr = 0.1
I0406 09:51:32.831841  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:51:33.050640  5644 solver.cpp:218] Iteration 18612 (2.27027 iter/s, 5.28572s/12 iters), loss = 5.28686
I0406 09:51:33.050698  5644 solver.cpp:237]     Train net output #0: loss = 5.28686 (* 1 = 5.28686 loss)
I0406 09:51:33.050706  5644 sgd_solver.cpp:105] Iteration 18612, lr = 0.1
I0406 09:51:38.245290  5644 solver.cpp:218] Iteration 18624 (2.31012 iter/s, 5.19454s/12 iters), loss = 5.26777
I0406 09:51:38.245332  5644 solver.cpp:237]     Train net output #0: loss = 5.26777 (* 1 = 5.26777 loss)
I0406 09:51:38.245340  5644 sgd_solver.cpp:105] Iteration 18624, lr = 0.1
I0406 09:51:43.585747  5644 solver.cpp:218] Iteration 18636 (2.24704 iter/s, 5.34036s/12 iters), loss = 5.27712
I0406 09:51:43.585786  5644 solver.cpp:237]     Train net output #0: loss = 5.27712 (* 1 = 5.27712 loss)
I0406 09:51:43.585793  5644 sgd_solver.cpp:105] Iteration 18636, lr = 0.1
I0406 09:51:48.915105  5644 solver.cpp:218] Iteration 18648 (2.25172 iter/s, 5.32926s/12 iters), loss = 5.28618
I0406 09:51:48.915145  5644 solver.cpp:237]     Train net output #0: loss = 5.28618 (* 1 = 5.28618 loss)
I0406 09:51:48.915151  5644 sgd_solver.cpp:105] Iteration 18648, lr = 0.1
I0406 09:51:53.918344  5644 solver.cpp:218] Iteration 18660 (2.39849 iter/s, 5.00314s/12 iters), loss = 5.26199
I0406 09:51:53.918383  5644 solver.cpp:237]     Train net output #0: loss = 5.26199 (* 1 = 5.26199 loss)
I0406 09:51:53.918390  5644 sgd_solver.cpp:105] Iteration 18660, lr = 0.1
I0406 09:51:56.148447  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18666.caffemodel
I0406 09:51:59.150734  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18666.solverstate
I0406 09:52:01.486101  5644 solver.cpp:330] Iteration 18666, Testing net (#0)
I0406 09:52:01.486121  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:52:03.147938  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:52:05.940266  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:52:05.940294  5644 solver.cpp:397]     Test net output #1: loss = 5.28534 (* 1 = 5.28534 loss)
I0406 09:52:07.887603  5644 solver.cpp:218] Iteration 18672 (0.859039 iter/s, 13.9691s/12 iters), loss = 5.25876
I0406 09:52:07.887645  5644 solver.cpp:237]     Train net output #0: loss = 5.25876 (* 1 = 5.25876 loss)
I0406 09:52:07.887650  5644 sgd_solver.cpp:105] Iteration 18672, lr = 0.1
I0406 09:52:13.231262  5644 solver.cpp:218] Iteration 18684 (2.2457 iter/s, 5.34355s/12 iters), loss = 5.27374
I0406 09:52:13.231317  5644 solver.cpp:237]     Train net output #0: loss = 5.27374 (* 1 = 5.27374 loss)
I0406 09:52:13.231325  5644 sgd_solver.cpp:105] Iteration 18684, lr = 0.1
I0406 09:52:18.475513  5644 solver.cpp:218] Iteration 18696 (2.28827 iter/s, 5.24414s/12 iters), loss = 5.28018
I0406 09:52:18.475550  5644 solver.cpp:237]     Train net output #0: loss = 5.28018 (* 1 = 5.28018 loss)
I0406 09:52:18.475556  5644 sgd_solver.cpp:105] Iteration 18696, lr = 0.1
I0406 09:52:23.504513  5644 solver.cpp:218] Iteration 18708 (2.3862 iter/s, 5.02891s/12 iters), loss = 5.27392
I0406 09:52:23.504552  5644 solver.cpp:237]     Train net output #0: loss = 5.27392 (* 1 = 5.27392 loss)
I0406 09:52:23.504559  5644 sgd_solver.cpp:105] Iteration 18708, lr = 0.1
I0406 09:52:25.580384  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:52:29.065317  5644 solver.cpp:218] Iteration 18720 (2.158 iter/s, 5.56071s/12 iters), loss = 5.29873
I0406 09:52:29.065357  5644 solver.cpp:237]     Train net output #0: loss = 5.29873 (* 1 = 5.29873 loss)
I0406 09:52:29.065363  5644 sgd_solver.cpp:105] Iteration 18720, lr = 0.1
I0406 09:52:29.425799  5644 blocking_queue.cpp:49] Waiting for data
I0406 09:52:34.353514  5644 solver.cpp:218] Iteration 18732 (2.26925 iter/s, 5.2881s/12 iters), loss = 5.25963
I0406 09:52:34.353633  5644 solver.cpp:237]     Train net output #0: loss = 5.25963 (* 1 = 5.25963 loss)
I0406 09:52:34.353642  5644 sgd_solver.cpp:105] Iteration 18732, lr = 0.1
I0406 09:52:39.683677  5644 solver.cpp:218] Iteration 18744 (2.25141 iter/s, 5.32998s/12 iters), loss = 5.28845
I0406 09:52:39.683732  5644 solver.cpp:237]     Train net output #0: loss = 5.28845 (* 1 = 5.28845 loss)
I0406 09:52:39.683743  5644 sgd_solver.cpp:105] Iteration 18744, lr = 0.1
I0406 09:52:44.997288  5644 solver.cpp:218] Iteration 18756 (2.2584 iter/s, 5.3135s/12 iters), loss = 5.27215
I0406 09:52:44.997324  5644 solver.cpp:237]     Train net output #0: loss = 5.27215 (* 1 = 5.27215 loss)
I0406 09:52:44.997329  5644 sgd_solver.cpp:105] Iteration 18756, lr = 0.1
I0406 09:52:49.769896  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18768.caffemodel
I0406 09:52:52.801321  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18768.solverstate
I0406 09:52:55.115722  5644 solver.cpp:330] Iteration 18768, Testing net (#0)
I0406 09:52:55.115742  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:52:56.747344  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:52:59.566516  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:52:59.566551  5644 solver.cpp:397]     Test net output #1: loss = 5.28544 (* 1 = 5.28544 loss)
I0406 09:52:59.706130  5644 solver.cpp:218] Iteration 18768 (0.815845 iter/s, 14.7087s/12 iters), loss = 5.25881
I0406 09:52:59.706194  5644 solver.cpp:237]     Train net output #0: loss = 5.25881 (* 1 = 5.25881 loss)
I0406 09:52:59.706203  5644 sgd_solver.cpp:105] Iteration 18768, lr = 0.1
I0406 09:53:03.977669  5644 solver.cpp:218] Iteration 18780 (2.80937 iter/s, 4.27143s/12 iters), loss = 5.25538
I0406 09:53:03.977707  5644 solver.cpp:237]     Train net output #0: loss = 5.25538 (* 1 = 5.25538 loss)
I0406 09:53:03.977712  5644 sgd_solver.cpp:105] Iteration 18780, lr = 0.1
I0406 09:53:09.332043  5644 solver.cpp:218] Iteration 18792 (2.2412 iter/s, 5.35428s/12 iters), loss = 5.28239
I0406 09:53:09.332183  5644 solver.cpp:237]     Train net output #0: loss = 5.28239 (* 1 = 5.28239 loss)
I0406 09:53:09.332192  5644 sgd_solver.cpp:105] Iteration 18792, lr = 0.1
I0406 09:53:14.669708  5644 solver.cpp:218] Iteration 18804 (2.24826 iter/s, 5.33747s/12 iters), loss = 5.27194
I0406 09:53:14.669766  5644 solver.cpp:237]     Train net output #0: loss = 5.27194 (* 1 = 5.27194 loss)
I0406 09:53:14.669775  5644 sgd_solver.cpp:105] Iteration 18804, lr = 0.1
I0406 09:53:19.008486  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:53:19.882902  5644 solver.cpp:218] Iteration 18816 (2.3019 iter/s, 5.21308s/12 iters), loss = 5.27391
I0406 09:53:19.882942  5644 solver.cpp:237]     Train net output #0: loss = 5.27391 (* 1 = 5.27391 loss)
I0406 09:53:19.882948  5644 sgd_solver.cpp:105] Iteration 18816, lr = 0.1
I0406 09:53:25.214346  5644 solver.cpp:218] Iteration 18828 (2.25084 iter/s, 5.33134s/12 iters), loss = 5.27271
I0406 09:53:25.214401  5644 solver.cpp:237]     Train net output #0: loss = 5.27271 (* 1 = 5.27271 loss)
I0406 09:53:25.214411  5644 sgd_solver.cpp:105] Iteration 18828, lr = 0.1
I0406 09:53:30.470651  5644 solver.cpp:218] Iteration 18840 (2.28302 iter/s, 5.25619s/12 iters), loss = 5.31103
I0406 09:53:30.470706  5644 solver.cpp:237]     Train net output #0: loss = 5.31103 (* 1 = 5.31103 loss)
I0406 09:53:30.470715  5644 sgd_solver.cpp:105] Iteration 18840, lr = 0.1
I0406 09:53:35.597088  5644 solver.cpp:218] Iteration 18852 (2.34086 iter/s, 5.12633s/12 iters), loss = 5.29214
I0406 09:53:35.597136  5644 solver.cpp:237]     Train net output #0: loss = 5.29214 (* 1 = 5.29214 loss)
I0406 09:53:35.597144  5644 sgd_solver.cpp:105] Iteration 18852, lr = 0.1
I0406 09:53:40.904850  5644 solver.cpp:218] Iteration 18864 (2.26088 iter/s, 5.30766s/12 iters), loss = 5.28942
I0406 09:53:40.904958  5644 solver.cpp:237]     Train net output #0: loss = 5.28942 (* 1 = 5.28942 loss)
I0406 09:53:40.904964  5644 sgd_solver.cpp:105] Iteration 18864, lr = 0.1
I0406 09:53:42.950453  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18870.caffemodel
I0406 09:53:45.980916  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18870.solverstate
I0406 09:53:48.331110  5644 solver.cpp:330] Iteration 18870, Testing net (#0)
I0406 09:53:48.331128  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:53:49.946684  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:53:52.657721  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:53:52.657758  5644 solver.cpp:397]     Test net output #1: loss = 5.2853 (* 1 = 5.2853 loss)
I0406 09:53:54.693583  5644 solver.cpp:218] Iteration 18876 (0.87029 iter/s, 13.7885s/12 iters), loss = 5.26584
I0406 09:53:54.693640  5644 solver.cpp:237]     Train net output #0: loss = 5.26584 (* 1 = 5.26584 loss)
I0406 09:53:54.693650  5644 sgd_solver.cpp:105] Iteration 18876, lr = 0.1
I0406 09:54:00.094400  5644 solver.cpp:218] Iteration 18888 (2.22193 iter/s, 5.4007s/12 iters), loss = 5.2882
I0406 09:54:00.094449  5644 solver.cpp:237]     Train net output #0: loss = 5.2882 (* 1 = 5.2882 loss)
I0406 09:54:00.094456  5644 sgd_solver.cpp:105] Iteration 18888, lr = 0.1
I0406 09:54:05.072979  5644 solver.cpp:218] Iteration 18900 (2.41037 iter/s, 4.97848s/12 iters), loss = 5.27412
I0406 09:54:05.073017  5644 solver.cpp:237]     Train net output #0: loss = 5.27412 (* 1 = 5.27412 loss)
I0406 09:54:05.073022  5644 sgd_solver.cpp:105] Iteration 18900, lr = 0.1
I0406 09:54:10.338500  5644 solver.cpp:218] Iteration 18912 (2.27902 iter/s, 5.26542s/12 iters), loss = 5.27735
I0406 09:54:10.338553  5644 solver.cpp:237]     Train net output #0: loss = 5.27735 (* 1 = 5.27735 loss)
I0406 09:54:10.338562  5644 sgd_solver.cpp:105] Iteration 18912, lr = 0.1
I0406 09:54:11.743774  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:54:15.689208  5644 solver.cpp:218] Iteration 18924 (2.24274 iter/s, 5.3506s/12 iters), loss = 5.29739
I0406 09:54:15.689250  5644 solver.cpp:237]     Train net output #0: loss = 5.29739 (* 1 = 5.29739 loss)
I0406 09:54:15.689256  5644 sgd_solver.cpp:105] Iteration 18924, lr = 0.1
I0406 09:54:20.826076  5644 solver.cpp:218] Iteration 18936 (2.3361 iter/s, 5.13678s/12 iters), loss = 5.2849
I0406 09:54:20.826112  5644 solver.cpp:237]     Train net output #0: loss = 5.2849 (* 1 = 5.2849 loss)
I0406 09:54:20.826117  5644 sgd_solver.cpp:105] Iteration 18936, lr = 0.1
I0406 09:54:26.360443  5644 solver.cpp:218] Iteration 18948 (2.16831 iter/s, 5.53427s/12 iters), loss = 5.27421
I0406 09:54:26.360491  5644 solver.cpp:237]     Train net output #0: loss = 5.27421 (* 1 = 5.27421 loss)
I0406 09:54:26.360498  5644 sgd_solver.cpp:105] Iteration 18948, lr = 0.1
I0406 09:54:31.687767  5644 solver.cpp:218] Iteration 18960 (2.25258 iter/s, 5.32721s/12 iters), loss = 5.27639
I0406 09:54:31.687822  5644 solver.cpp:237]     Train net output #0: loss = 5.27639 (* 1 = 5.27639 loss)
I0406 09:54:31.687830  5644 sgd_solver.cpp:105] Iteration 18960, lr = 0.1
I0406 09:54:36.535059  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_18972.caffemodel
I0406 09:54:39.550906  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_18972.solverstate
I0406 09:54:41.843858  5644 solver.cpp:330] Iteration 18972, Testing net (#0)
I0406 09:54:41.843920  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:54:43.402854  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:54:46.189520  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:54:46.189556  5644 solver.cpp:397]     Test net output #1: loss = 5.28529 (* 1 = 5.28529 loss)
I0406 09:54:46.326977  5644 solver.cpp:218] Iteration 18972 (0.819726 iter/s, 14.639s/12 iters), loss = 5.28852
I0406 09:54:46.327029  5644 solver.cpp:237]     Train net output #0: loss = 5.28852 (* 1 = 5.28852 loss)
I0406 09:54:46.327037  5644 sgd_solver.cpp:105] Iteration 18972, lr = 0.1
I0406 09:54:50.679163  5644 solver.cpp:218] Iteration 18984 (2.7573 iter/s, 4.35208s/12 iters), loss = 5.27092
I0406 09:54:50.679220  5644 solver.cpp:237]     Train net output #0: loss = 5.27092 (* 1 = 5.27092 loss)
I0406 09:54:50.679229  5644 sgd_solver.cpp:105] Iteration 18984, lr = 0.1
I0406 09:54:56.004387  5644 solver.cpp:218] Iteration 18996 (2.25347 iter/s, 5.32511s/12 iters), loss = 5.28588
I0406 09:54:56.004446  5644 solver.cpp:237]     Train net output #0: loss = 5.28588 (* 1 = 5.28588 loss)
I0406 09:54:56.004456  5644 sgd_solver.cpp:105] Iteration 18996, lr = 0.1
I0406 09:55:01.173192  5644 solver.cpp:218] Iteration 19008 (2.32167 iter/s, 5.16869s/12 iters), loss = 5.29618
I0406 09:55:01.173233  5644 solver.cpp:237]     Train net output #0: loss = 5.29618 (* 1 = 5.29618 loss)
I0406 09:55:01.173238  5644 sgd_solver.cpp:105] Iteration 19008, lr = 0.1
I0406 09:55:04.937530  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:55:06.664186  5644 solver.cpp:218] Iteration 19020 (2.18544 iter/s, 5.4909s/12 iters), loss = 5.24413
I0406 09:55:06.664224  5644 solver.cpp:237]     Train net output #0: loss = 5.24413 (* 1 = 5.24413 loss)
I0406 09:55:06.664230  5644 sgd_solver.cpp:105] Iteration 19020, lr = 0.1
I0406 09:55:12.119074  5644 solver.cpp:218] Iteration 19032 (2.1999 iter/s, 5.45479s/12 iters), loss = 5.2732
I0406 09:55:12.119220  5644 solver.cpp:237]     Train net output #0: loss = 5.2732 (* 1 = 5.2732 loss)
I0406 09:55:12.119227  5644 sgd_solver.cpp:105] Iteration 19032, lr = 0.1
I0406 09:55:17.387816  5644 solver.cpp:218] Iteration 19044 (2.27767 iter/s, 5.26854s/12 iters), loss = 5.2797
I0406 09:55:17.387871  5644 solver.cpp:237]     Train net output #0: loss = 5.2797 (* 1 = 5.2797 loss)
I0406 09:55:17.387878  5644 sgd_solver.cpp:105] Iteration 19044, lr = 0.1
I0406 09:55:22.648403  5644 solver.cpp:218] Iteration 19056 (2.28116 iter/s, 5.26048s/12 iters), loss = 5.26558
I0406 09:55:22.648442  5644 solver.cpp:237]     Train net output #0: loss = 5.26558 (* 1 = 5.26558 loss)
I0406 09:55:22.648447  5644 sgd_solver.cpp:105] Iteration 19056, lr = 0.1
I0406 09:55:28.270951  5644 solver.cpp:218] Iteration 19068 (2.1343 iter/s, 5.62245s/12 iters), loss = 5.28349
I0406 09:55:28.270993  5644 solver.cpp:237]     Train net output #0: loss = 5.28349 (* 1 = 5.28349 loss)
I0406 09:55:28.271000  5644 sgd_solver.cpp:105] Iteration 19068, lr = 0.1
I0406 09:55:30.377557  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19074.caffemodel
I0406 09:55:33.446954  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19074.solverstate
I0406 09:55:35.885887  5644 solver.cpp:330] Iteration 19074, Testing net (#0)
I0406 09:55:35.885905  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:55:37.478662  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:55:43.666368  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:55:43.684949  5644 solver.cpp:397]     Test net output #1: loss = 5.28538 (* 1 = 5.28538 loss)
I0406 09:55:47.082947  5644 solver.cpp:218] Iteration 19080 (0.637898 iter/s, 18.8118s/12 iters), loss = 5.25222
I0406 09:55:47.083001  5644 solver.cpp:237]     Train net output #0: loss = 5.25222 (* 1 = 5.25222 loss)
I0406 09:55:47.083009  5644 sgd_solver.cpp:105] Iteration 19080, lr = 0.1
I0406 09:55:55.630867  5644 solver.cpp:218] Iteration 19092 (1.40387 iter/s, 8.54778s/12 iters), loss = 5.27382
I0406 09:55:55.630924  5644 solver.cpp:237]     Train net output #0: loss = 5.27382 (* 1 = 5.27382 loss)
I0406 09:55:55.630934  5644 sgd_solver.cpp:105] Iteration 19092, lr = 0.1
I0406 09:56:03.790033  5644 solver.cpp:218] Iteration 19104 (1.47076 iter/s, 8.15903s/12 iters), loss = 5.27997
I0406 09:56:03.790083  5644 solver.cpp:237]     Train net output #0: loss = 5.27997 (* 1 = 5.27997 loss)
I0406 09:56:03.790092  5644 sgd_solver.cpp:105] Iteration 19104, lr = 0.1
I0406 09:56:10.894021  5644 solver.cpp:218] Iteration 19116 (1.68922 iter/s, 7.10386s/12 iters), loss = 5.2847
I0406 09:56:10.894068  5644 solver.cpp:237]     Train net output #0: loss = 5.2847 (* 1 = 5.2847 loss)
I0406 09:56:10.894075  5644 sgd_solver.cpp:105] Iteration 19116, lr = 0.1
I0406 09:56:11.476068  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:56:16.087373  5644 solver.cpp:218] Iteration 19128 (2.31069 iter/s, 5.19324s/12 iters), loss = 5.27501
I0406 09:56:16.087479  5644 solver.cpp:237]     Train net output #0: loss = 5.27501 (* 1 = 5.27501 loss)
I0406 09:56:16.087487  5644 sgd_solver.cpp:105] Iteration 19128, lr = 0.1
I0406 09:56:21.272496  5644 solver.cpp:218] Iteration 19140 (2.31438 iter/s, 5.18496s/12 iters), loss = 5.29083
I0406 09:56:21.272539  5644 solver.cpp:237]     Train net output #0: loss = 5.29083 (* 1 = 5.29083 loss)
I0406 09:56:21.272550  5644 sgd_solver.cpp:105] Iteration 19140, lr = 0.1
I0406 09:56:26.590382  5644 solver.cpp:218] Iteration 19152 (2.25658 iter/s, 5.31778s/12 iters), loss = 5.26542
I0406 09:56:26.590440  5644 solver.cpp:237]     Train net output #0: loss = 5.26542 (* 1 = 5.26542 loss)
I0406 09:56:26.590448  5644 sgd_solver.cpp:105] Iteration 19152, lr = 0.1
I0406 09:56:31.867401  5644 solver.cpp:218] Iteration 19164 (2.27406 iter/s, 5.27691s/12 iters), loss = 5.27236
I0406 09:56:31.867437  5644 solver.cpp:237]     Train net output #0: loss = 5.27236 (* 1 = 5.27236 loss)
I0406 09:56:31.867444  5644 sgd_solver.cpp:105] Iteration 19164, lr = 0.1
I0406 09:56:36.569958  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19176.caffemodel
I0406 09:56:39.679411  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19176.solverstate
I0406 09:56:41.990279  5644 solver.cpp:330] Iteration 19176, Testing net (#0)
I0406 09:56:41.990299  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:56:43.767024  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:56:46.707552  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:56:46.707690  5644 solver.cpp:397]     Test net output #1: loss = 5.28579 (* 1 = 5.28579 loss)
I0406 09:56:46.847265  5644 solver.cpp:218] Iteration 19176 (0.801085 iter/s, 14.9797s/12 iters), loss = 5.25882
I0406 09:56:46.847316  5644 solver.cpp:237]     Train net output #0: loss = 5.25882 (* 1 = 5.25882 loss)
I0406 09:56:46.847324  5644 sgd_solver.cpp:105] Iteration 19176, lr = 0.1
I0406 09:56:51.076391  5644 solver.cpp:218] Iteration 19188 (2.83753 iter/s, 4.22902s/12 iters), loss = 5.29121
I0406 09:56:51.076431  5644 solver.cpp:237]     Train net output #0: loss = 5.29121 (* 1 = 5.29121 loss)
I0406 09:56:51.076436  5644 sgd_solver.cpp:105] Iteration 19188, lr = 0.1
I0406 09:56:56.366761  5644 solver.cpp:218] Iteration 19200 (2.26831 iter/s, 5.29028s/12 iters), loss = 5.27774
I0406 09:56:56.366798  5644 solver.cpp:237]     Train net output #0: loss = 5.27774 (* 1 = 5.27774 loss)
I0406 09:56:56.366804  5644 sgd_solver.cpp:105] Iteration 19200, lr = 0.1
I0406 09:57:01.399111  5644 solver.cpp:218] Iteration 19212 (2.38462 iter/s, 5.03225s/12 iters), loss = 5.27751
I0406 09:57:01.399152  5644 solver.cpp:237]     Train net output #0: loss = 5.27751 (* 1 = 5.27751 loss)
I0406 09:57:01.399158  5644 sgd_solver.cpp:105] Iteration 19212, lr = 0.1
I0406 09:57:04.385030  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:57:06.877790  5644 solver.cpp:218] Iteration 19224 (2.19035 iter/s, 5.47858s/12 iters), loss = 5.28789
I0406 09:57:06.877827  5644 solver.cpp:237]     Train net output #0: loss = 5.28789 (* 1 = 5.28789 loss)
I0406 09:57:06.877833  5644 sgd_solver.cpp:105] Iteration 19224, lr = 0.1
I0406 09:57:12.087002  5644 solver.cpp:218] Iteration 19236 (2.30365 iter/s, 5.20911s/12 iters), loss = 5.2754
I0406 09:57:12.087060  5644 solver.cpp:237]     Train net output #0: loss = 5.2754 (* 1 = 5.2754 loss)
I0406 09:57:12.087069  5644 sgd_solver.cpp:105] Iteration 19236, lr = 0.1
I0406 09:57:17.450529  5644 solver.cpp:218] Iteration 19248 (2.23738 iter/s, 5.36341s/12 iters), loss = 5.27146
I0406 09:57:17.456789  5644 solver.cpp:237]     Train net output #0: loss = 5.27146 (* 1 = 5.27146 loss)
I0406 09:57:17.456805  5644 sgd_solver.cpp:105] Iteration 19248, lr = 0.1
I0406 09:57:22.824404  5644 solver.cpp:218] Iteration 19260 (2.23565 iter/s, 5.36757s/12 iters), loss = 5.28979
I0406 09:57:22.824450  5644 solver.cpp:237]     Train net output #0: loss = 5.28979 (* 1 = 5.28979 loss)
I0406 09:57:22.824456  5644 sgd_solver.cpp:105] Iteration 19260, lr = 0.1
I0406 09:57:28.310011  5644 solver.cpp:218] Iteration 19272 (2.18758 iter/s, 5.48551s/12 iters), loss = 5.28385
I0406 09:57:28.310047  5644 solver.cpp:237]     Train net output #0: loss = 5.28385 (* 1 = 5.28385 loss)
I0406 09:57:28.310053  5644 sgd_solver.cpp:105] Iteration 19272, lr = 0.1
I0406 09:57:30.439299  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19278.caffemodel
I0406 09:57:33.494048  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19278.solverstate
I0406 09:57:35.808714  5644 solver.cpp:330] Iteration 19278, Testing net (#0)
I0406 09:57:35.808738  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:57:37.248544  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:57:40.165932  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:57:40.165968  5644 solver.cpp:397]     Test net output #1: loss = 5.28515 (* 1 = 5.28515 loss)
I0406 09:57:42.062780  5644 solver.cpp:218] Iteration 19284 (0.872562 iter/s, 13.7526s/12 iters), loss = 5.29919
I0406 09:57:42.062822  5644 solver.cpp:237]     Train net output #0: loss = 5.29919 (* 1 = 5.29919 loss)
I0406 09:57:42.062829  5644 sgd_solver.cpp:105] Iteration 19284, lr = 0.1
I0406 09:57:47.389637  5644 solver.cpp:218] Iteration 19296 (2.25278 iter/s, 5.32675s/12 iters), loss = 5.29645
I0406 09:57:47.389678  5644 solver.cpp:237]     Train net output #0: loss = 5.29645 (* 1 = 5.29645 loss)
I0406 09:57:47.389685  5644 sgd_solver.cpp:105] Iteration 19296, lr = 0.1
I0406 09:57:52.701700  5644 solver.cpp:218] Iteration 19308 (2.25905 iter/s, 5.31196s/12 iters), loss = 5.28688
I0406 09:57:52.701839  5644 solver.cpp:237]     Train net output #0: loss = 5.28688 (* 1 = 5.28688 loss)
I0406 09:57:52.701848  5644 sgd_solver.cpp:105] Iteration 19308, lr = 0.1
I0406 09:57:57.808056  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:57:57.999514  5644 solver.cpp:218] Iteration 19320 (2.26517 iter/s, 5.29762s/12 iters), loss = 5.28927
I0406 09:57:57.999554  5644 solver.cpp:237]     Train net output #0: loss = 5.28927 (* 1 = 5.28927 loss)
I0406 09:57:57.999559  5644 sgd_solver.cpp:105] Iteration 19320, lr = 0.1
I0406 09:58:03.154399  5644 solver.cpp:218] Iteration 19332 (2.32793 iter/s, 5.15479s/12 iters), loss = 5.25969
I0406 09:58:03.154451  5644 solver.cpp:237]     Train net output #0: loss = 5.25969 (* 1 = 5.25969 loss)
I0406 09:58:03.154460  5644 sgd_solver.cpp:105] Iteration 19332, lr = 0.1
I0406 09:58:08.407615  5644 solver.cpp:218] Iteration 19344 (2.28436 iter/s, 5.25311s/12 iters), loss = 5.28081
I0406 09:58:08.407666  5644 solver.cpp:237]     Train net output #0: loss = 5.28081 (* 1 = 5.28081 loss)
I0406 09:58:08.407676  5644 sgd_solver.cpp:105] Iteration 19344, lr = 0.1
I0406 09:58:13.649137  5644 solver.cpp:218] Iteration 19356 (2.28946 iter/s, 5.24142s/12 iters), loss = 5.28893
I0406 09:58:13.649176  5644 solver.cpp:237]     Train net output #0: loss = 5.28893 (* 1 = 5.28893 loss)
I0406 09:58:13.649183  5644 sgd_solver.cpp:105] Iteration 19356, lr = 0.1
I0406 09:58:19.059764  5644 solver.cpp:218] Iteration 19368 (2.2179 iter/s, 5.41053s/12 iters), loss = 5.26831
I0406 09:58:19.059808  5644 solver.cpp:237]     Train net output #0: loss = 5.26831 (* 1 = 5.26831 loss)
I0406 09:58:19.059813  5644 sgd_solver.cpp:105] Iteration 19368, lr = 0.1
I0406 09:58:23.736838  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19380.caffemodel
I0406 09:58:26.865828  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19380.solverstate
I0406 09:58:29.183383  5644 solver.cpp:330] Iteration 19380, Testing net (#0)
I0406 09:58:29.183405  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:58:30.680662  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:58:33.752662  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:58:33.752699  5644 solver.cpp:397]     Test net output #1: loss = 5.28597 (* 1 = 5.28597 loss)
I0406 09:58:33.892724  5644 solver.cpp:218] Iteration 19380 (0.809019 iter/s, 14.8328s/12 iters), loss = 5.25925
I0406 09:58:33.892771  5644 solver.cpp:237]     Train net output #0: loss = 5.25925 (* 1 = 5.25925 loss)
I0406 09:58:33.892779  5644 sgd_solver.cpp:105] Iteration 19380, lr = 0.1
I0406 09:58:38.354744  5644 solver.cpp:218] Iteration 19392 (2.68942 iter/s, 4.46193s/12 iters), loss = 5.27444
I0406 09:58:38.354784  5644 solver.cpp:237]     Train net output #0: loss = 5.27444 (* 1 = 5.27444 loss)
I0406 09:58:38.354789  5644 sgd_solver.cpp:105] Iteration 19392, lr = 0.1
I0406 09:58:43.603478  5644 solver.cpp:218] Iteration 19404 (2.28631 iter/s, 5.24864s/12 iters), loss = 5.2786
I0406 09:58:43.603516  5644 solver.cpp:237]     Train net output #0: loss = 5.2786 (* 1 = 5.2786 loss)
I0406 09:58:43.603523  5644 sgd_solver.cpp:105] Iteration 19404, lr = 0.1
I0406 09:58:44.496778  5644 blocking_queue.cpp:49] Waiting for data
I0406 09:58:48.965291  5644 solver.cpp:218] Iteration 19416 (2.23809 iter/s, 5.36171s/12 iters), loss = 5.26643
I0406 09:58:48.965332  5644 solver.cpp:237]     Train net output #0: loss = 5.26643 (* 1 = 5.26643 loss)
I0406 09:58:48.965338  5644 sgd_solver.cpp:105] Iteration 19416, lr = 0.1
I0406 09:58:51.046393  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:58:54.316610  5644 solver.cpp:218] Iteration 19428 (2.24248 iter/s, 5.35122s/12 iters), loss = 5.30233
I0406 09:58:54.316759  5644 solver.cpp:237]     Train net output #0: loss = 5.30233 (* 1 = 5.30233 loss)
I0406 09:58:54.316769  5644 sgd_solver.cpp:105] Iteration 19428, lr = 0.1
I0406 09:58:59.375300  5644 solver.cpp:218] Iteration 19440 (2.37225 iter/s, 5.05849s/12 iters), loss = 5.25918
I0406 09:58:59.375339  5644 solver.cpp:237]     Train net output #0: loss = 5.25918 (* 1 = 5.25918 loss)
I0406 09:58:59.375345  5644 sgd_solver.cpp:105] Iteration 19440, lr = 0.1
I0406 09:59:04.694751  5644 solver.cpp:218] Iteration 19452 (2.25591 iter/s, 5.31935s/12 iters), loss = 5.29408
I0406 09:59:04.694797  5644 solver.cpp:237]     Train net output #0: loss = 5.29408 (* 1 = 5.29408 loss)
I0406 09:59:04.694805  5644 sgd_solver.cpp:105] Iteration 19452, lr = 0.1
I0406 09:59:09.852463  5644 solver.cpp:218] Iteration 19464 (2.32666 iter/s, 5.15761s/12 iters), loss = 5.27371
I0406 09:59:09.852514  5644 solver.cpp:237]     Train net output #0: loss = 5.27371 (* 1 = 5.27371 loss)
I0406 09:59:09.852522  5644 sgd_solver.cpp:105] Iteration 19464, lr = 0.1
I0406 09:59:15.200965  5644 solver.cpp:218] Iteration 19476 (2.24366 iter/s, 5.3484s/12 iters), loss = 5.25998
I0406 09:59:15.201002  5644 solver.cpp:237]     Train net output #0: loss = 5.25998 (* 1 = 5.25998 loss)
I0406 09:59:15.201009  5644 sgd_solver.cpp:105] Iteration 19476, lr = 0.1
I0406 09:59:17.454162  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19482.caffemodel
I0406 09:59:22.023022  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19482.solverstate
I0406 09:59:24.425840  5644 solver.cpp:330] Iteration 19482, Testing net (#0)
I0406 09:59:24.425916  5644 net.cpp:676] Ignoring source layer train-data
I0406 09:59:25.807375  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:59:29.013751  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 09:59:29.013784  5644 solver.cpp:397]     Test net output #1: loss = 5.28527 (* 1 = 5.28527 loss)
I0406 09:59:30.838977  5644 solver.cpp:218] Iteration 19488 (0.767369 iter/s, 15.6378s/12 iters), loss = 5.26155
I0406 09:59:30.839018  5644 solver.cpp:237]     Train net output #0: loss = 5.26155 (* 1 = 5.26155 loss)
I0406 09:59:30.839023  5644 sgd_solver.cpp:105] Iteration 19488, lr = 0.1
I0406 09:59:35.948071  5644 solver.cpp:218] Iteration 19500 (2.3488 iter/s, 5.10899s/12 iters), loss = 5.27746
I0406 09:59:35.948118  5644 solver.cpp:237]     Train net output #0: loss = 5.27746 (* 1 = 5.27746 loss)
I0406 09:59:35.948127  5644 sgd_solver.cpp:105] Iteration 19500, lr = 0.1
I0406 09:59:41.376425  5644 solver.cpp:218] Iteration 19512 (2.21066 iter/s, 5.42825s/12 iters), loss = 5.2707
I0406 09:59:41.376473  5644 solver.cpp:237]     Train net output #0: loss = 5.2707 (* 1 = 5.2707 loss)
I0406 09:59:41.376482  5644 sgd_solver.cpp:105] Iteration 19512, lr = 0.1
I0406 09:59:46.096560  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 09:59:47.106377  5644 solver.cpp:218] Iteration 19524 (2.0943 iter/s, 5.72984s/12 iters), loss = 5.27345
I0406 09:59:47.106431  5644 solver.cpp:237]     Train net output #0: loss = 5.27345 (* 1 = 5.27345 loss)
I0406 09:59:47.106438  5644 sgd_solver.cpp:105] Iteration 19524, lr = 0.1
I0406 09:59:52.673010  5644 solver.cpp:218] Iteration 19536 (2.15574 iter/s, 5.56652s/12 iters), loss = 5.27055
I0406 09:59:52.673069  5644 solver.cpp:237]     Train net output #0: loss = 5.27055 (* 1 = 5.27055 loss)
I0406 09:59:52.673077  5644 sgd_solver.cpp:105] Iteration 19536, lr = 0.1
I0406 09:59:58.225234  5644 solver.cpp:218] Iteration 19548 (2.16134 iter/s, 5.55211s/12 iters), loss = 5.31406
I0406 09:59:58.225384  5644 solver.cpp:237]     Train net output #0: loss = 5.31406 (* 1 = 5.31406 loss)
I0406 09:59:58.225395  5644 sgd_solver.cpp:105] Iteration 19548, lr = 0.1
I0406 10:00:03.767490  5644 solver.cpp:218] Iteration 19560 (2.16526 iter/s, 5.54205s/12 iters), loss = 5.29288
I0406 10:00:03.767544  5644 solver.cpp:237]     Train net output #0: loss = 5.29288 (* 1 = 5.29288 loss)
I0406 10:00:03.767554  5644 sgd_solver.cpp:105] Iteration 19560, lr = 0.1
I0406 10:00:09.202653  5644 solver.cpp:218] Iteration 19572 (2.20789 iter/s, 5.43505s/12 iters), loss = 5.28504
I0406 10:00:09.202705  5644 solver.cpp:237]     Train net output #0: loss = 5.28504 (* 1 = 5.28504 loss)
I0406 10:00:09.202713  5644 sgd_solver.cpp:105] Iteration 19572, lr = 0.1
I0406 10:00:13.986974  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19584.caffemodel
I0406 10:00:18.033785  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19584.solverstate
I0406 10:00:20.372541  5644 solver.cpp:330] Iteration 19584, Testing net (#0)
I0406 10:00:20.372560  5644 net.cpp:676] Ignoring source layer train-data
I0406 10:00:21.817715  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:00:24.938067  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 10:00:24.938097  5644 solver.cpp:397]     Test net output #1: loss = 5.28552 (* 1 = 5.28552 loss)
I0406 10:00:25.078038  5644 solver.cpp:218] Iteration 19584 (0.755896 iter/s, 15.8752s/12 iters), loss = 5.25913
I0406 10:00:25.078088  5644 solver.cpp:237]     Train net output #0: loss = 5.25913 (* 1 = 5.25913 loss)
I0406 10:00:25.078095  5644 sgd_solver.cpp:105] Iteration 19584, lr = 0.1
I0406 10:00:29.483214  5644 solver.cpp:218] Iteration 19596 (2.72413 iter/s, 4.40508s/12 iters), loss = 5.28737
I0406 10:00:29.483335  5644 solver.cpp:237]     Train net output #0: loss = 5.28737 (* 1 = 5.28737 loss)
I0406 10:00:29.483343  5644 sgd_solver.cpp:105] Iteration 19596, lr = 0.1
I0406 10:00:34.938186  5644 solver.cpp:218] Iteration 19608 (2.1999 iter/s, 5.4548s/12 iters), loss = 5.27947
I0406 10:00:34.938238  5644 solver.cpp:237]     Train net output #0: loss = 5.27947 (* 1 = 5.27947 loss)
I0406 10:00:34.938247  5644 sgd_solver.cpp:105] Iteration 19608, lr = 0.1
I0406 10:00:40.537135  5644 solver.cpp:218] Iteration 19620 (2.1433 iter/s, 5.59884s/12 iters), loss = 5.27205
I0406 10:00:40.537185  5644 solver.cpp:237]     Train net output #0: loss = 5.27205 (* 1 = 5.27205 loss)
I0406 10:00:40.537194  5644 sgd_solver.cpp:105] Iteration 19620, lr = 0.1
I0406 10:00:42.126875  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:00:46.217563  5644 solver.cpp:218] Iteration 19632 (2.11256 iter/s, 5.68032s/12 iters), loss = 5.29064
I0406 10:00:46.217607  5644 solver.cpp:237]     Train net output #0: loss = 5.29064 (* 1 = 5.29064 loss)
I0406 10:00:46.217612  5644 sgd_solver.cpp:105] Iteration 19632, lr = 0.1
I0406 10:00:51.881892  5644 solver.cpp:218] Iteration 19644 (2.11856 iter/s, 5.66422s/12 iters), loss = 5.27558
I0406 10:00:51.881947  5644 solver.cpp:237]     Train net output #0: loss = 5.27558 (* 1 = 5.27558 loss)
I0406 10:00:51.881955  5644 sgd_solver.cpp:105] Iteration 19644, lr = 0.1
I0406 10:00:57.112257  5644 solver.cpp:218] Iteration 19656 (2.29434 iter/s, 5.23026s/12 iters), loss = 5.26714
I0406 10:00:57.112310  5644 solver.cpp:237]     Train net output #0: loss = 5.26714 (* 1 = 5.26714 loss)
I0406 10:00:57.112318  5644 sgd_solver.cpp:105] Iteration 19656, lr = 0.1
I0406 10:01:02.625313  5644 solver.cpp:218] Iteration 19668 (2.17669 iter/s, 5.51295s/12 iters), loss = 5.27673
I0406 10:01:02.625442  5644 solver.cpp:237]     Train net output #0: loss = 5.27673 (* 1 = 5.27673 loss)
I0406 10:01:02.625450  5644 sgd_solver.cpp:105] Iteration 19668, lr = 0.1
I0406 10:01:07.891640  5644 solver.cpp:218] Iteration 19680 (2.27871 iter/s, 5.26615s/12 iters), loss = 5.28869
I0406 10:01:07.891680  5644 solver.cpp:237]     Train net output #0: loss = 5.28869 (* 1 = 5.28869 loss)
I0406 10:01:07.891685  5644 sgd_solver.cpp:105] Iteration 19680, lr = 0.1
I0406 10:01:09.893991  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19686.caffemodel
I0406 10:01:14.356213  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19686.solverstate
I0406 10:01:16.699319  5644 solver.cpp:330] Iteration 19686, Testing net (#0)
I0406 10:01:16.699342  5644 net.cpp:676] Ignoring source layer train-data
I0406 10:01:18.134339  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:01:21.399737  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 10:01:21.399776  5644 solver.cpp:397]     Test net output #1: loss = 5.28578 (* 1 = 5.28578 loss)
I0406 10:01:23.390738  5644 solver.cpp:218] Iteration 19692 (0.774247 iter/s, 15.4989s/12 iters), loss = 5.2784
I0406 10:01:23.390787  5644 solver.cpp:237]     Train net output #0: loss = 5.2784 (* 1 = 5.2784 loss)
I0406 10:01:23.390795  5644 sgd_solver.cpp:105] Iteration 19692, lr = 0.1
I0406 10:01:28.883028  5644 solver.cpp:218] Iteration 19704 (2.18492 iter/s, 5.49218s/12 iters), loss = 5.2856
I0406 10:01:28.883080  5644 solver.cpp:237]     Train net output #0: loss = 5.2856 (* 1 = 5.2856 loss)
I0406 10:01:28.883088  5644 sgd_solver.cpp:105] Iteration 19704, lr = 0.1
I0406 10:01:34.318848  5644 solver.cpp:218] Iteration 19716 (2.20763 iter/s, 5.43571s/12 iters), loss = 5.29333
I0406 10:01:34.319000  5644 solver.cpp:237]     Train net output #0: loss = 5.29333 (* 1 = 5.29333 loss)
I0406 10:01:34.319008  5644 sgd_solver.cpp:105] Iteration 19716, lr = 0.1
I0406 10:01:38.312361  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:01:40.144362  5644 solver.cpp:218] Iteration 19728 (2.05998 iter/s, 5.8253s/12 iters), loss = 5.24842
I0406 10:01:40.144416  5644 solver.cpp:237]     Train net output #0: loss = 5.24842 (* 1 = 5.24842 loss)
I0406 10:01:40.144425  5644 sgd_solver.cpp:105] Iteration 19728, lr = 0.1
I0406 10:01:45.522994  5644 solver.cpp:218] Iteration 19740 (2.2311 iter/s, 5.37852s/12 iters), loss = 5.27856
I0406 10:01:45.523046  5644 solver.cpp:237]     Train net output #0: loss = 5.27856 (* 1 = 5.27856 loss)
I0406 10:01:45.523056  5644 sgd_solver.cpp:105] Iteration 19740, lr = 0.1
I0406 10:01:50.926018  5644 solver.cpp:218] Iteration 19752 (2.22102 iter/s, 5.40292s/12 iters), loss = 5.28198
I0406 10:01:50.926056  5644 solver.cpp:237]     Train net output #0: loss = 5.28198 (* 1 = 5.28198 loss)
I0406 10:01:50.926062  5644 sgd_solver.cpp:105] Iteration 19752, lr = 0.1
I0406 10:01:56.297593  5644 solver.cpp:218] Iteration 19764 (2.23402 iter/s, 5.37148s/12 iters), loss = 5.27023
I0406 10:01:56.297633  5644 solver.cpp:237]     Train net output #0: loss = 5.27023 (* 1 = 5.27023 loss)
I0406 10:01:56.297638  5644 sgd_solver.cpp:105] Iteration 19764, lr = 0.1
I0406 10:02:01.653308  5644 solver.cpp:218] Iteration 19776 (2.24064 iter/s, 5.35562s/12 iters), loss = 5.28658
I0406 10:02:01.653358  5644 solver.cpp:237]     Train net output #0: loss = 5.28658 (* 1 = 5.28658 loss)
I0406 10:02:01.653367  5644 sgd_solver.cpp:105] Iteration 19776, lr = 0.1
I0406 10:02:06.617213  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19788.caffemodel
I0406 10:02:11.145635  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19788.solverstate
I0406 10:02:13.452013  5644 solver.cpp:330] Iteration 19788, Testing net (#0)
I0406 10:02:13.452037  5644 net.cpp:676] Ignoring source layer train-data
I0406 10:02:14.808914  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:02:18.224931  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 10:02:18.224968  5644 solver.cpp:397]     Test net output #1: loss = 5.2859 (* 1 = 5.2859 loss)
I0406 10:02:18.367533  5644 solver.cpp:218] Iteration 19788 (0.71796 iter/s, 16.714s/12 iters), loss = 5.25108
I0406 10:02:18.370568  5644 solver.cpp:237]     Train net output #0: loss = 5.25108 (* 1 = 5.25108 loss)
I0406 10:02:18.370587  5644 sgd_solver.cpp:105] Iteration 19788, lr = 0.1
I0406 10:02:22.579286  5644 solver.cpp:218] Iteration 19800 (2.85125 iter/s, 4.20869s/12 iters), loss = 5.27734
I0406 10:02:22.579324  5644 solver.cpp:237]     Train net output #0: loss = 5.27734 (* 1 = 5.27734 loss)
I0406 10:02:22.579330  5644 sgd_solver.cpp:105] Iteration 19800, lr = 0.1
I0406 10:02:27.992581  5644 solver.cpp:218] Iteration 19812 (2.2168 iter/s, 5.4132s/12 iters), loss = 5.27998
I0406 10:02:27.992622  5644 solver.cpp:237]     Train net output #0: loss = 5.27998 (* 1 = 5.27998 loss)
I0406 10:02:27.992628  5644 sgd_solver.cpp:105] Iteration 19812, lr = 0.1
I0406 10:02:33.644291  5644 solver.cpp:218] Iteration 19824 (2.12329 iter/s, 5.65161s/12 iters), loss = 5.28657
I0406 10:02:33.650504  5644 solver.cpp:237]     Train net output #0: loss = 5.28657 (* 1 = 5.28657 loss)
I0406 10:02:33.650527  5644 sgd_solver.cpp:105] Iteration 19824, lr = 0.1
I0406 10:02:34.325318  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:02:39.357082  5644 solver.cpp:218] Iteration 19836 (2.10285 iter/s, 5.70653s/12 iters), loss = 5.27108
I0406 10:02:39.357223  5644 solver.cpp:237]     Train net output #0: loss = 5.27108 (* 1 = 5.27108 loss)
I0406 10:02:39.357232  5644 sgd_solver.cpp:105] Iteration 19836, lr = 0.1
I0406 10:02:44.804062  5644 solver.cpp:218] Iteration 19848 (2.20314 iter/s, 5.44678s/12 iters), loss = 5.28697
I0406 10:02:44.804103  5644 solver.cpp:237]     Train net output #0: loss = 5.28697 (* 1 = 5.28697 loss)
I0406 10:02:44.804111  5644 sgd_solver.cpp:105] Iteration 19848, lr = 0.1
I0406 10:02:50.380554  5644 solver.cpp:218] Iteration 19860 (2.15193 iter/s, 5.57639s/12 iters), loss = 5.26572
I0406 10:02:50.380609  5644 solver.cpp:237]     Train net output #0: loss = 5.26572 (* 1 = 5.26572 loss)
I0406 10:02:50.380617  5644 sgd_solver.cpp:105] Iteration 19860, lr = 0.1
I0406 10:02:55.904628  5644 solver.cpp:218] Iteration 19872 (2.17235 iter/s, 5.52396s/12 iters), loss = 5.26936
I0406 10:02:55.904676  5644 solver.cpp:237]     Train net output #0: loss = 5.26936 (* 1 = 5.26936 loss)
I0406 10:02:55.904685  5644 sgd_solver.cpp:105] Iteration 19872, lr = 0.1
I0406 10:03:01.246264  5644 solver.cpp:218] Iteration 19884 (2.24655 iter/s, 5.34153s/12 iters), loss = 5.26436
I0406 10:03:01.246307  5644 solver.cpp:237]     Train net output #0: loss = 5.26436 (* 1 = 5.26436 loss)
I0406 10:03:01.246313  5644 sgd_solver.cpp:105] Iteration 19884, lr = 0.1
I0406 10:03:03.495589  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19890.caffemodel
I0406 10:03:07.694828  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19890.solverstate
I0406 10:03:10.008860  5644 solver.cpp:330] Iteration 19890, Testing net (#0)
I0406 10:03:10.008944  5644 net.cpp:676] Ignoring source layer train-data
I0406 10:03:11.275317  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:03:14.588244  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 10:03:14.588284  5644 solver.cpp:397]     Test net output #1: loss = 5.28614 (* 1 = 5.28614 loss)
I0406 10:03:16.607030  5644 solver.cpp:218] Iteration 19896 (0.78122 iter/s, 15.3606s/12 iters), loss = 5.27925
I0406 10:03:16.607074  5644 solver.cpp:237]     Train net output #0: loss = 5.27925 (* 1 = 5.27925 loss)
I0406 10:03:16.607079  5644 sgd_solver.cpp:105] Iteration 19896, lr = 0.1
I0406 10:03:22.213236  5644 solver.cpp:218] Iteration 19908 (2.14053 iter/s, 5.6061s/12 iters), loss = 5.28162
I0406 10:03:22.213289  5644 solver.cpp:237]     Train net output #0: loss = 5.28162 (* 1 = 5.28162 loss)
I0406 10:03:22.213297  5644 sgd_solver.cpp:105] Iteration 19908, lr = 0.1
I0406 10:03:27.416571  5644 solver.cpp:218] Iteration 19920 (2.30626 iter/s, 5.20323s/12 iters), loss = 5.27625
I0406 10:03:27.416625  5644 solver.cpp:237]     Train net output #0: loss = 5.27625 (* 1 = 5.27625 loss)
I0406 10:03:27.416635  5644 sgd_solver.cpp:105] Iteration 19920, lr = 0.1
I0406 10:03:30.401870  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:03:32.909675  5644 solver.cpp:218] Iteration 19932 (2.1846 iter/s, 5.493s/12 iters), loss = 5.28126
I0406 10:03:32.909723  5644 solver.cpp:237]     Train net output #0: loss = 5.28126 (* 1 = 5.28126 loss)
I0406 10:03:32.909730  5644 sgd_solver.cpp:105] Iteration 19932, lr = 0.1
I0406 10:03:38.581167  5644 solver.cpp:218] Iteration 19944 (2.11589 iter/s, 5.67138s/12 iters), loss = 5.28729
I0406 10:03:38.581218  5644 solver.cpp:237]     Train net output #0: loss = 5.28729 (* 1 = 5.28729 loss)
I0406 10:03:38.581224  5644 sgd_solver.cpp:105] Iteration 19944, lr = 0.1
I0406 10:03:44.298748  5644 solver.cpp:218] Iteration 19956 (2.09883 iter/s, 5.71747s/12 iters), loss = 5.28277
I0406 10:03:44.298894  5644 solver.cpp:237]     Train net output #0: loss = 5.28277 (* 1 = 5.28277 loss)
I0406 10:03:44.298903  5644 sgd_solver.cpp:105] Iteration 19956, lr = 0.1
I0406 10:03:49.613306  5644 solver.cpp:218] Iteration 19968 (2.25803 iter/s, 5.31436s/12 iters), loss = 5.29456
I0406 10:03:49.613344  5644 solver.cpp:237]     Train net output #0: loss = 5.29456 (* 1 = 5.29456 loss)
I0406 10:03:49.613351  5644 sgd_solver.cpp:105] Iteration 19968, lr = 0.1
I0406 10:03:55.074957  5644 solver.cpp:218] Iteration 19980 (2.19718 iter/s, 5.46155s/12 iters), loss = 5.28198
I0406 10:03:55.075011  5644 solver.cpp:237]     Train net output #0: loss = 5.28198 (* 1 = 5.28198 loss)
I0406 10:03:55.075021  5644 sgd_solver.cpp:105] Iteration 19980, lr = 0.1
I0406 10:04:00.028874  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_19992.caffemodel
I0406 10:04:03.467958  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_19992.solverstate
I0406 10:04:05.778621  5644 solver.cpp:330] Iteration 19992, Testing net (#0)
I0406 10:04:05.778646  5644 net.cpp:676] Ignoring source layer train-data
I0406 10:04:07.096866  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:04:10.349064  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 10:04:10.349105  5644 solver.cpp:397]     Test net output #1: loss = 5.28608 (* 1 = 5.28608 loss)
I0406 10:04:10.483376  5644 solver.cpp:218] Iteration 19992 (0.778804 iter/s, 15.4082s/12 iters), loss = 5.29177
I0406 10:04:10.483438  5644 solver.cpp:237]     Train net output #0: loss = 5.29177 (* 1 = 5.29177 loss)
I0406 10:04:10.483448  5644 sgd_solver.cpp:105] Iteration 19992, lr = 0.1
I0406 10:04:15.076419  5644 solver.cpp:218] Iteration 20004 (2.61271 iter/s, 4.59293s/12 iters), loss = 5.29229
I0406 10:04:15.076529  5644 solver.cpp:237]     Train net output #0: loss = 5.29229 (* 1 = 5.29229 loss)
I0406 10:04:15.076539  5644 sgd_solver.cpp:105] Iteration 20004, lr = 0.1
I0406 10:04:20.485394  5644 solver.cpp:218] Iteration 20016 (2.2186 iter/s, 5.40881s/12 iters), loss = 5.28448
I0406 10:04:20.485445  5644 solver.cpp:237]     Train net output #0: loss = 5.28448 (* 1 = 5.28448 loss)
I0406 10:04:20.485452  5644 sgd_solver.cpp:105] Iteration 20016, lr = 0.1
I0406 10:04:25.789441  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:04:25.956151  5644 solver.cpp:218] Iteration 20028 (2.19353 iter/s, 5.47065s/12 iters), loss = 5.29322
I0406 10:04:25.962370  5644 solver.cpp:237]     Train net output #0: loss = 5.29322 (* 1 = 5.29322 loss)
I0406 10:04:25.962394  5644 sgd_solver.cpp:105] Iteration 20028, lr = 0.1
I0406 10:04:31.687063  5644 solver.cpp:218] Iteration 20040 (2.0962 iter/s, 5.72466s/12 iters), loss = 5.25589
I0406 10:04:31.687101  5644 solver.cpp:237]     Train net output #0: loss = 5.25589 (* 1 = 5.25589 loss)
I0406 10:04:31.687160  5644 sgd_solver.cpp:105] Iteration 20040, lr = 0.1
I0406 10:04:39.152925  5644 solver.cpp:218] Iteration 20052 (1.60883 iter/s, 7.45883s/12 iters), loss = 5.28671
I0406 10:04:39.152973  5644 solver.cpp:237]     Train net output #0: loss = 5.28671 (* 1 = 5.28671 loss)
I0406 10:04:39.152981  5644 sgd_solver.cpp:105] Iteration 20052, lr = 0.1
I0406 10:04:48.383584  5644 solver.cpp:218] Iteration 20064 (1.30004 iter/s, 9.23052s/12 iters), loss = 5.29107
I0406 10:04:48.389797  5644 solver.cpp:237]     Train net output #0: loss = 5.29107 (* 1 = 5.29107 loss)
I0406 10:04:48.389838  5644 sgd_solver.cpp:105] Iteration 20064, lr = 0.1
I0406 10:04:57.793922  5644 solver.cpp:218] Iteration 20076 (1.27604 iter/s, 9.40406s/12 iters), loss = 5.2665
I0406 10:04:57.793977  5644 solver.cpp:237]     Train net output #0: loss = 5.2665 (* 1 = 5.2665 loss)
I0406 10:04:57.793985  5644 sgd_solver.cpp:105] Iteration 20076, lr = 0.1
I0406 10:05:05.736928  5644 solver.cpp:218] Iteration 20088 (1.51201 iter/s, 7.93647s/12 iters), loss = 5.27014
I0406 10:05:05.736979  5644 solver.cpp:237]     Train net output #0: loss = 5.27014 (* 1 = 5.27014 loss)
I0406 10:05:05.736987  5644 sgd_solver.cpp:105] Iteration 20088, lr = 0.1
I0406 10:05:08.751797  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20094.caffemodel
I0406 10:05:12.957587  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20094.solverstate
I0406 10:05:16.214805  5644 solver.cpp:330] Iteration 20094, Testing net (#0)
I0406 10:05:16.214830  5644 net.cpp:676] Ignoring source layer train-data
I0406 10:05:18.465585  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:05:23.937191  5644 blocking_queue.cpp:49] Waiting for data
I0406 10:05:24.627951  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 10:05:24.627991  5644 solver.cpp:397]     Test net output #1: loss = 5.28615 (* 1 = 5.28615 loss)
I0406 10:05:27.572939  5644 solver.cpp:218] Iteration 20100 (0.549678 iter/s, 21.831s/12 iters), loss = 5.26672
I0406 10:05:27.573009  5644 solver.cpp:237]     Train net output #0: loss = 5.26672 (* 1 = 5.26672 loss)
I0406 10:05:27.573017  5644 sgd_solver.cpp:105] Iteration 20100, lr = 0.1
I0406 10:05:35.585988  5644 solver.cpp:218] Iteration 20112 (1.49758 iter/s, 8.0129s/12 iters), loss = 5.27774
I0406 10:05:35.586041  5644 solver.cpp:237]     Train net output #0: loss = 5.27774 (* 1 = 5.27774 loss)
I0406 10:05:35.586050  5644 sgd_solver.cpp:105] Iteration 20112, lr = 0.1
I0406 10:05:42.078027  5644 solver.cpp:218] Iteration 20124 (1.84845 iter/s, 6.49192s/12 iters), loss = 5.2686
I0406 10:05:42.078075  5644 solver.cpp:237]     Train net output #0: loss = 5.2686 (* 1 = 5.2686 loss)
I0406 10:05:42.078083  5644 sgd_solver.cpp:105] Iteration 20124, lr = 0.1
I0406 10:05:44.770323  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:05:48.779141  5644 solver.cpp:218] Iteration 20136 (1.79078 iter/s, 6.701s/12 iters), loss = 5.30228
I0406 10:05:48.779248  5644 solver.cpp:237]     Train net output #0: loss = 5.30228 (* 1 = 5.30228 loss)
I0406 10:05:48.779258  5644 sgd_solver.cpp:105] Iteration 20136, lr = 0.1
I0406 10:05:55.466197  5644 solver.cpp:218] Iteration 20148 (1.79456 iter/s, 6.68688s/12 iters), loss = 5.25451
I0406 10:05:55.466246  5644 solver.cpp:237]     Train net output #0: loss = 5.25451 (* 1 = 5.25451 loss)
I0406 10:05:55.466254  5644 sgd_solver.cpp:105] Iteration 20148, lr = 0.1
I0406 10:06:02.194195  5644 solver.cpp:218] Iteration 20160 (1.78362 iter/s, 6.72788s/12 iters), loss = 5.28962
I0406 10:06:02.194248  5644 solver.cpp:237]     Train net output #0: loss = 5.28962 (* 1 = 5.28962 loss)
I0406 10:06:02.194257  5644 sgd_solver.cpp:105] Iteration 20160, lr = 0.1
I0406 10:06:08.786268  5644 solver.cpp:218] Iteration 20172 (1.8204 iter/s, 6.59195s/12 iters), loss = 5.26537
I0406 10:06:08.786317  5644 solver.cpp:237]     Train net output #0: loss = 5.26537 (* 1 = 5.26537 loss)
I0406 10:06:08.786325  5644 sgd_solver.cpp:105] Iteration 20172, lr = 0.1
I0406 10:06:15.524927  5644 solver.cpp:218] Iteration 20184 (1.7826 iter/s, 6.73176s/12 iters), loss = 5.25133
I0406 10:06:15.524973  5644 solver.cpp:237]     Train net output #0: loss = 5.25133 (* 1 = 5.25133 loss)
I0406 10:06:15.524981  5644 sgd_solver.cpp:105] Iteration 20184, lr = 0.1
I0406 10:06:21.319540  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20196.caffemodel
I0406 10:06:25.737826  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20196.solverstate
I0406 10:06:28.106999  5644 solver.cpp:330] Iteration 20196, Testing net (#0)
I0406 10:06:28.107025  5644 net.cpp:676] Ignoring source layer train-data
I0406 10:06:29.179729  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:06:32.925485  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 10:06:32.925523  5644 solver.cpp:397]     Test net output #1: loss = 5.28607 (* 1 = 5.28607 loss)
I0406 10:06:33.065317  5644 solver.cpp:218] Iteration 20196 (0.684143 iter/s, 17.5402s/12 iters), loss = 5.26431
I0406 10:06:33.065359  5644 solver.cpp:237]     Train net output #0: loss = 5.26431 (* 1 = 5.26431 loss)
I0406 10:06:33.065366  5644 sgd_solver.cpp:105] Iteration 20196, lr = 0.1
I0406 10:06:37.519901  5644 solver.cpp:218] Iteration 20208 (2.69391 iter/s, 4.45449s/12 iters), loss = 5.27646
I0406 10:06:37.519963  5644 solver.cpp:237]     Train net output #0: loss = 5.27646 (* 1 = 5.27646 loss)
I0406 10:06:37.519971  5644 sgd_solver.cpp:105] Iteration 20208, lr = 0.1
I0406 10:06:43.611282  5644 solver.cpp:218] Iteration 20220 (1.97089 iter/s, 6.08861s/12 iters), loss = 5.27457
I0406 10:06:43.616952  5644 solver.cpp:237]     Train net output #0: loss = 5.27457 (* 1 = 5.27457 loss)
I0406 10:06:43.616974  5644 sgd_solver.cpp:105] Iteration 20220, lr = 0.1
I0406 10:06:53.376915  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:06:55.576943  5644 solver.cpp:218] Iteration 20232 (1.00416 iter/s, 11.9503s/12 iters), loss = 5.27529
I0406 10:06:55.577004  5644 solver.cpp:237]     Train net output #0: loss = 5.27529 (* 1 = 5.27529 loss)
I0406 10:06:55.577013  5644 sgd_solver.cpp:105] Iteration 20232, lr = 0.1
I0406 10:07:06.582077  5644 solver.cpp:218] Iteration 20244 (1.09042 iter/s, 11.005s/12 iters), loss = 5.27497
I0406 10:07:06.582130  5644 solver.cpp:237]     Train net output #0: loss = 5.27497 (* 1 = 5.27497 loss)
I0406 10:07:06.582139  5644 sgd_solver.cpp:105] Iteration 20244, lr = 0.1
I0406 10:07:19.112812  5644 solver.cpp:218] Iteration 20256 (0.957658 iter/s, 12.5306s/12 iters), loss = 5.31013
I0406 10:07:19.112869  5644 solver.cpp:237]     Train net output #0: loss = 5.31013 (* 1 = 5.31013 loss)
I0406 10:07:19.112877  5644 sgd_solver.cpp:105] Iteration 20256, lr = 0.1
I0406 10:07:26.420933  5644 solver.cpp:218] Iteration 20268 (1.64276 iter/s, 7.30478s/12 iters), loss = 5.2846
I0406 10:07:26.421056  5644 solver.cpp:237]     Train net output #0: loss = 5.2846 (* 1 = 5.2846 loss)
I0406 10:07:26.421066  5644 sgd_solver.cpp:105] Iteration 20268, lr = 0.1
I0406 10:07:33.064285  5644 solver.cpp:218] Iteration 20280 (1.80637 iter/s, 6.64316s/12 iters), loss = 5.29024
I0406 10:07:33.064339  5644 solver.cpp:237]     Train net output #0: loss = 5.29024 (* 1 = 5.29024 loss)
I0406 10:07:33.064347  5644 sgd_solver.cpp:105] Iteration 20280, lr = 0.1
I0406 10:07:39.701864  5644 solver.cpp:218] Iteration 20292 (1.80792 iter/s, 6.63745s/12 iters), loss = 5.25352
I0406 10:07:39.701920  5644 solver.cpp:237]     Train net output #0: loss = 5.25352 (* 1 = 5.25352 loss)
I0406 10:07:39.701928  5644 sgd_solver.cpp:105] Iteration 20292, lr = 0.1
I0406 10:07:42.403672  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20298.caffemodel
I0406 10:07:48.359480  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20298.solverstate
I0406 10:07:51.036646  5644 solver.cpp:330] Iteration 20298, Testing net (#0)
I0406 10:07:51.036670  5644 net.cpp:676] Ignoring source layer train-data
I0406 10:07:52.520450  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:07:57.368268  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 10:07:57.375643  5644 solver.cpp:397]     Test net output #1: loss = 5.28583 (* 1 = 5.28583 loss)
I0406 10:07:59.939520  5644 solver.cpp:218] Iteration 20304 (0.592961 iter/s, 20.2374s/12 iters), loss = 5.28664
I0406 10:07:59.939577  5644 solver.cpp:237]     Train net output #0: loss = 5.28664 (* 1 = 5.28664 loss)
I0406 10:07:59.939585  5644 sgd_solver.cpp:105] Iteration 20304, lr = 0.1
I0406 10:08:06.676734  5644 solver.cpp:218] Iteration 20316 (1.78118 iter/s, 6.73709s/12 iters), loss = 5.27431
I0406 10:08:06.676784  5644 solver.cpp:237]     Train net output #0: loss = 5.27431 (* 1 = 5.27431 loss)
I0406 10:08:06.676792  5644 sgd_solver.cpp:105] Iteration 20316, lr = 0.1
I0406 10:08:13.189812  5644 solver.cpp:218] Iteration 20328 (1.84248 iter/s, 6.51296s/12 iters), loss = 5.277
I0406 10:08:13.189868  5644 solver.cpp:237]     Train net output #0: loss = 5.277 (* 1 = 5.277 loss)
I0406 10:08:13.189877  5644 sgd_solver.cpp:105] Iteration 20328, lr = 0.1
I0406 10:08:15.085662  5669 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:08:20.026051  5644 solver.cpp:218] Iteration 20340 (1.75538 iter/s, 6.83611s/12 iters), loss = 5.29288
I0406 10:08:20.026099  5644 solver.cpp:237]     Train net output #0: loss = 5.29288 (* 1 = 5.29288 loss)
I0406 10:08:20.026106  5644 sgd_solver.cpp:105] Iteration 20340, lr = 0.1
I0406 10:08:26.867617  5644 solver.cpp:218] Iteration 20352 (1.75402 iter/s, 6.84145s/12 iters), loss = 5.27486
I0406 10:08:26.867669  5644 solver.cpp:237]     Train net output #0: loss = 5.27486 (* 1 = 5.27486 loss)
I0406 10:08:26.867678  5644 sgd_solver.cpp:105] Iteration 20352, lr = 0.1
I0406 10:08:33.359028  5644 solver.cpp:218] Iteration 20364 (1.84863 iter/s, 6.49129s/12 iters), loss = 5.27244
I0406 10:08:33.359184  5644 solver.cpp:237]     Train net output #0: loss = 5.27244 (* 1 = 5.27244 loss)
I0406 10:08:33.359194  5644 sgd_solver.cpp:105] Iteration 20364, lr = 0.1
I0406 10:08:38.742318  5644 solver.cpp:218] Iteration 20376 (2.2292 iter/s, 5.38309s/12 iters), loss = 5.28008
I0406 10:08:38.742354  5644 solver.cpp:237]     Train net output #0: loss = 5.28008 (* 1 = 5.28008 loss)
I0406 10:08:38.742359  5644 sgd_solver.cpp:105] Iteration 20376, lr = 0.1
I0406 10:08:44.063230  5644 solver.cpp:218] Iteration 20388 (2.25529 iter/s, 5.32081s/12 iters), loss = 5.28596
I0406 10:08:44.063284  5644 solver.cpp:237]     Train net output #0: loss = 5.28596 (* 1 = 5.28596 loss)
I0406 10:08:44.063292  5644 sgd_solver.cpp:105] Iteration 20388, lr = 0.1
I0406 10:08:49.409129  5644 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_20400.caffemodel
I0406 10:08:54.590314  5644 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_20400.solverstate
I0406 10:08:57.066660  5644 solver.cpp:310] Iteration 20400, loss = 5.27764
I0406 10:08:57.066687  5644 solver.cpp:330] Iteration 20400, Testing net (#0)
I0406 10:08:57.066690  5644 net.cpp:676] Ignoring source layer train-data
I0406 10:08:58.219993  5694 data_layer.cpp:73] Restarting data prefetching from start.
I0406 10:09:01.726511  5644 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0406 10:09:01.726547  5644 solver.cpp:397]     Test net output #1: loss = 5.28611 (* 1 = 5.28611 loss)
I0406 10:09:01.726553  5644 solver.cpp:315] Optimization Done.
I0406 10:09:01.726557  5644 caffe.cpp:259] Optimization Done.