DIGITS-CNN/cars/lr-investigations/step-down/1e-2/33_0.75/caffe_output.log

I0407 08:24:53.446125 18909 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210407-082451-6191/solver.prototxt
I0407 08:24:53.446290 18909 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0407 08:24:53.446295 18909 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0407 08:24:53.446359 18909 caffe.cpp:218] Using GPUs 2
I0407 08:24:53.463285 18909 caffe.cpp:223] GPU 2: GeForce GTX TITAN X
I0407 08:24:53.690966 18909 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "step"
gamma: 0.75
momentum: 0.9
weight_decay: 0.0001
stepsize: 3366
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0407 08:24:53.691808 18909 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0407 08:24:53.692472 18909 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0407 08:24:53.692487 18909 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0407 08:24:53.692615 18909 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"
}
I0407 08:24:53.692700 18909 layer_factory.hpp:77] Creating layer train-data
I0407 08:24:53.694433 18909 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db
I0407 08:24:53.694662 18909 net.cpp:84] Creating Layer train-data
I0407 08:24:53.694671 18909 net.cpp:380] train-data -> data
I0407 08:24:53.694690 18909 net.cpp:380] train-data -> label
I0407 08:24:53.694700 18909 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0407 08:24:53.699160 18909 data_layer.cpp:45] output data size: 128,3,227,227
I0407 08:24:53.829603 18909 net.cpp:122] Setting up train-data
I0407 08:24:53.829623 18909 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0407 08:24:53.829627 18909 net.cpp:129] Top shape: 128 (128)
I0407 08:24:53.829630 18909 net.cpp:137] Memory required for data: 79149056
I0407 08:24:53.829638 18909 layer_factory.hpp:77] Creating layer conv1
I0407 08:24:53.829658 18909 net.cpp:84] Creating Layer conv1
I0407 08:24:53.829661 18909 net.cpp:406] conv1 <- data
I0407 08:24:53.829672 18909 net.cpp:380] conv1 -> conv1
I0407 08:24:54.242549 18909 net.cpp:122] Setting up conv1
I0407 08:24:54.242569 18909 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 08:24:54.242573 18909 net.cpp:137] Memory required for data: 227833856
I0407 08:24:54.242590 18909 layer_factory.hpp:77] Creating layer relu1
I0407 08:24:54.242599 18909 net.cpp:84] Creating Layer relu1
I0407 08:24:54.242602 18909 net.cpp:406] relu1 <- conv1
I0407 08:24:54.242606 18909 net.cpp:367] relu1 -> conv1 (in-place)
I0407 08:24:54.242861 18909 net.cpp:122] Setting up relu1
I0407 08:24:54.242869 18909 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 08:24:54.242871 18909 net.cpp:137] Memory required for data: 376518656
I0407 08:24:54.242873 18909 layer_factory.hpp:77] Creating layer norm1
I0407 08:24:54.242882 18909 net.cpp:84] Creating Layer norm1
I0407 08:24:54.242902 18909 net.cpp:406] norm1 <- conv1
I0407 08:24:54.242905 18909 net.cpp:380] norm1 -> norm1
I0407 08:24:54.243310 18909 net.cpp:122] Setting up norm1
I0407 08:24:54.243319 18909 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 08:24:54.243321 18909 net.cpp:137] Memory required for data: 525203456
I0407 08:24:54.243324 18909 layer_factory.hpp:77] Creating layer pool1
I0407 08:24:54.243330 18909 net.cpp:84] Creating Layer pool1
I0407 08:24:54.243333 18909 net.cpp:406] pool1 <- norm1
I0407 08:24:54.243337 18909 net.cpp:380] pool1 -> pool1
I0407 08:24:54.243367 18909 net.cpp:122] Setting up pool1
I0407 08:24:54.243372 18909 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0407 08:24:54.243374 18909 net.cpp:137] Memory required for data: 561035264
I0407 08:24:54.243376 18909 layer_factory.hpp:77] Creating layer conv2
I0407 08:24:54.243386 18909 net.cpp:84] Creating Layer conv2
I0407 08:24:54.243388 18909 net.cpp:406] conv2 <- pool1
I0407 08:24:54.243391 18909 net.cpp:380] conv2 -> conv2
I0407 08:24:54.249159 18909 net.cpp:122] Setting up conv2
I0407 08:24:54.249174 18909 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 08:24:54.249176 18909 net.cpp:137] Memory required for data: 656586752
I0407 08:24:54.249184 18909 layer_factory.hpp:77] Creating layer relu2
I0407 08:24:54.249191 18909 net.cpp:84] Creating Layer relu2
I0407 08:24:54.249193 18909 net.cpp:406] relu2 <- conv2
I0407 08:24:54.249199 18909 net.cpp:367] relu2 -> conv2 (in-place)
I0407 08:24:54.249675 18909 net.cpp:122] Setting up relu2
I0407 08:24:54.249684 18909 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 08:24:54.249686 18909 net.cpp:137] Memory required for data: 752138240
I0407 08:24:54.249688 18909 layer_factory.hpp:77] Creating layer norm2
I0407 08:24:54.249696 18909 net.cpp:84] Creating Layer norm2
I0407 08:24:54.249699 18909 net.cpp:406] norm2 <- conv2
I0407 08:24:54.249703 18909 net.cpp:380] norm2 -> norm2
I0407 08:24:54.250052 18909 net.cpp:122] Setting up norm2
I0407 08:24:54.250059 18909 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 08:24:54.250061 18909 net.cpp:137] Memory required for data: 847689728
I0407 08:24:54.250064 18909 layer_factory.hpp:77] Creating layer pool2
I0407 08:24:54.250072 18909 net.cpp:84] Creating Layer pool2
I0407 08:24:54.250073 18909 net.cpp:406] pool2 <- norm2
I0407 08:24:54.250078 18909 net.cpp:380] pool2 -> pool2
I0407 08:24:54.250105 18909 net.cpp:122] Setting up pool2
I0407 08:24:54.250109 18909 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 08:24:54.250111 18909 net.cpp:137] Memory required for data: 869840896
I0407 08:24:54.250113 18909 layer_factory.hpp:77] Creating layer conv3
I0407 08:24:54.250123 18909 net.cpp:84] Creating Layer conv3
I0407 08:24:54.250124 18909 net.cpp:406] conv3 <- pool2
I0407 08:24:54.250128 18909 net.cpp:380] conv3 -> conv3
I0407 08:24:54.260059 18909 net.cpp:122] Setting up conv3
I0407 08:24:54.260077 18909 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 08:24:54.260079 18909 net.cpp:137] Memory required for data: 903067648
I0407 08:24:54.260092 18909 layer_factory.hpp:77] Creating layer relu3
I0407 08:24:54.260099 18909 net.cpp:84] Creating Layer relu3
I0407 08:24:54.260102 18909 net.cpp:406] relu3 <- conv3
I0407 08:24:54.260107 18909 net.cpp:367] relu3 -> conv3 (in-place)
I0407 08:24:54.260579 18909 net.cpp:122] Setting up relu3
I0407 08:24:54.260587 18909 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 08:24:54.260591 18909 net.cpp:137] Memory required for data: 936294400
I0407 08:24:54.260592 18909 layer_factory.hpp:77] Creating layer conv4
I0407 08:24:54.260603 18909 net.cpp:84] Creating Layer conv4
I0407 08:24:54.260605 18909 net.cpp:406] conv4 <- conv3
I0407 08:24:54.260610 18909 net.cpp:380] conv4 -> conv4
I0407 08:24:54.270813 18909 net.cpp:122] Setting up conv4
I0407 08:24:54.270829 18909 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 08:24:54.270831 18909 net.cpp:137] Memory required for data: 969521152
I0407 08:24:54.270839 18909 layer_factory.hpp:77] Creating layer relu4
I0407 08:24:54.270848 18909 net.cpp:84] Creating Layer relu4
I0407 08:24:54.270869 18909 net.cpp:406] relu4 <- conv4
I0407 08:24:54.270874 18909 net.cpp:367] relu4 -> conv4 (in-place)
I0407 08:24:54.271193 18909 net.cpp:122] Setting up relu4
I0407 08:24:54.271200 18909 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 08:24:54.271203 18909 net.cpp:137] Memory required for data: 1002747904
I0407 08:24:54.271205 18909 layer_factory.hpp:77] Creating layer conv5
I0407 08:24:54.271215 18909 net.cpp:84] Creating Layer conv5
I0407 08:24:54.271217 18909 net.cpp:406] conv5 <- conv4
I0407 08:24:54.271224 18909 net.cpp:380] conv5 -> conv5
I0407 08:24:54.291848 18909 net.cpp:122] Setting up conv5
I0407 08:24:54.291865 18909 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 08:24:54.291868 18909 net.cpp:137] Memory required for data: 1024899072
I0407 08:24:54.291880 18909 layer_factory.hpp:77] Creating layer relu5
I0407 08:24:54.291889 18909 net.cpp:84] Creating Layer relu5
I0407 08:24:54.291893 18909 net.cpp:406] relu5 <- conv5
I0407 08:24:54.291898 18909 net.cpp:367] relu5 -> conv5 (in-place)
I0407 08:24:54.292445 18909 net.cpp:122] Setting up relu5
I0407 08:24:54.292454 18909 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 08:24:54.292456 18909 net.cpp:137] Memory required for data: 1047050240
I0407 08:24:54.292459 18909 layer_factory.hpp:77] Creating layer pool5
I0407 08:24:54.292466 18909 net.cpp:84] Creating Layer pool5
I0407 08:24:54.292469 18909 net.cpp:406] pool5 <- conv5
I0407 08:24:54.292474 18909 net.cpp:380] pool5 -> pool5
I0407 08:24:54.292508 18909 net.cpp:122] Setting up pool5
I0407 08:24:54.292513 18909 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0407 08:24:54.292515 18909 net.cpp:137] Memory required for data: 1051768832
I0407 08:24:54.292517 18909 layer_factory.hpp:77] Creating layer fc6
I0407 08:24:54.292526 18909 net.cpp:84] Creating Layer fc6
I0407 08:24:54.292528 18909 net.cpp:406] fc6 <- pool5
I0407 08:24:54.292532 18909 net.cpp:380] fc6 -> fc6
I0407 08:24:54.624992 18909 net.cpp:122] Setting up fc6
I0407 08:24:54.625012 18909 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:24:54.625015 18909 net.cpp:137] Memory required for data: 1053865984
I0407 08:24:54.625023 18909 layer_factory.hpp:77] Creating layer relu6
I0407 08:24:54.625031 18909 net.cpp:84] Creating Layer relu6
I0407 08:24:54.625034 18909 net.cpp:406] relu6 <- fc6
I0407 08:24:54.625041 18909 net.cpp:367] relu6 -> fc6 (in-place)
I0407 08:24:54.625700 18909 net.cpp:122] Setting up relu6
I0407 08:24:54.625710 18909 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:24:54.625711 18909 net.cpp:137] Memory required for data: 1055963136
I0407 08:24:54.625715 18909 layer_factory.hpp:77] Creating layer drop6
I0407 08:24:54.625720 18909 net.cpp:84] Creating Layer drop6
I0407 08:24:54.625722 18909 net.cpp:406] drop6 <- fc6
I0407 08:24:54.625726 18909 net.cpp:367] drop6 -> fc6 (in-place)
I0407 08:24:54.625751 18909 net.cpp:122] Setting up drop6
I0407 08:24:54.625756 18909 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:24:54.625757 18909 net.cpp:137] Memory required for data: 1058060288
I0407 08:24:54.625759 18909 layer_factory.hpp:77] Creating layer fc7
I0407 08:24:54.625766 18909 net.cpp:84] Creating Layer fc7
I0407 08:24:54.625767 18909 net.cpp:406] fc7 <- fc6
I0407 08:24:54.625772 18909 net.cpp:380] fc7 -> fc7
I0407 08:24:54.777706 18909 net.cpp:122] Setting up fc7
I0407 08:24:54.777724 18909 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:24:54.777727 18909 net.cpp:137] Memory required for data: 1060157440
I0407 08:24:54.777735 18909 layer_factory.hpp:77] Creating layer relu7
I0407 08:24:54.777743 18909 net.cpp:84] Creating Layer relu7
I0407 08:24:54.777746 18909 net.cpp:406] relu7 <- fc7
I0407 08:24:54.777751 18909 net.cpp:367] relu7 -> fc7 (in-place)
I0407 08:24:54.778127 18909 net.cpp:122] Setting up relu7
I0407 08:24:54.778136 18909 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:24:54.778137 18909 net.cpp:137] Memory required for data: 1062254592
I0407 08:24:54.778139 18909 layer_factory.hpp:77] Creating layer drop7
I0407 08:24:54.778144 18909 net.cpp:84] Creating Layer drop7
I0407 08:24:54.778163 18909 net.cpp:406] drop7 <- fc7
I0407 08:24:54.778168 18909 net.cpp:367] drop7 -> fc7 (in-place)
I0407 08:24:54.778188 18909 net.cpp:122] Setting up drop7
I0407 08:24:54.778192 18909 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:24:54.778194 18909 net.cpp:137] Memory required for data: 1064351744
I0407 08:24:54.778196 18909 layer_factory.hpp:77] Creating layer fc8
I0407 08:24:54.778204 18909 net.cpp:84] Creating Layer fc8
I0407 08:24:54.778206 18909 net.cpp:406] fc8 <- fc7
I0407 08:24:54.778210 18909 net.cpp:380] fc8 -> fc8
I0407 08:24:54.785758 18909 net.cpp:122] Setting up fc8
I0407 08:24:54.785776 18909 net.cpp:129] Top shape: 128 196 (25088)
I0407 08:24:54.785778 18909 net.cpp:137] Memory required for data: 1064452096
I0407 08:24:54.785786 18909 layer_factory.hpp:77] Creating layer loss
I0407 08:24:54.785794 18909 net.cpp:84] Creating Layer loss
I0407 08:24:54.785796 18909 net.cpp:406] loss <- fc8
I0407 08:24:54.785800 18909 net.cpp:406] loss <- label
I0407 08:24:54.785806 18909 net.cpp:380] loss -> loss
I0407 08:24:54.785815 18909 layer_factory.hpp:77] Creating layer loss
I0407 08:24:54.786468 18909 net.cpp:122] Setting up loss
I0407 08:24:54.786474 18909 net.cpp:129] Top shape: (1)
I0407 08:24:54.786478 18909 net.cpp:132]     with loss weight 1
I0407 08:24:54.786499 18909 net.cpp:137] Memory required for data: 1064452100
I0407 08:24:54.786501 18909 net.cpp:198] loss needs backward computation.
I0407 08:24:54.786507 18909 net.cpp:198] fc8 needs backward computation.
I0407 08:24:54.786509 18909 net.cpp:198] drop7 needs backward computation.
I0407 08:24:54.786511 18909 net.cpp:198] relu7 needs backward computation.
I0407 08:24:54.786514 18909 net.cpp:198] fc7 needs backward computation.
I0407 08:24:54.786515 18909 net.cpp:198] drop6 needs backward computation.
I0407 08:24:54.786518 18909 net.cpp:198] relu6 needs backward computation.
I0407 08:24:54.786520 18909 net.cpp:198] fc6 needs backward computation.
I0407 08:24:54.786523 18909 net.cpp:198] pool5 needs backward computation.
I0407 08:24:54.786525 18909 net.cpp:198] relu5 needs backward computation.
I0407 08:24:54.786527 18909 net.cpp:198] conv5 needs backward computation.
I0407 08:24:54.786530 18909 net.cpp:198] relu4 needs backward computation.
I0407 08:24:54.786532 18909 net.cpp:198] conv4 needs backward computation.
I0407 08:24:54.786535 18909 net.cpp:198] relu3 needs backward computation.
I0407 08:24:54.786536 18909 net.cpp:198] conv3 needs backward computation.
I0407 08:24:54.786540 18909 net.cpp:198] pool2 needs backward computation.
I0407 08:24:54.786541 18909 net.cpp:198] norm2 needs backward computation.
I0407 08:24:54.786543 18909 net.cpp:198] relu2 needs backward computation.
I0407 08:24:54.786545 18909 net.cpp:198] conv2 needs backward computation.
I0407 08:24:54.786548 18909 net.cpp:198] pool1 needs backward computation.
I0407 08:24:54.786550 18909 net.cpp:198] norm1 needs backward computation.
I0407 08:24:54.786554 18909 net.cpp:198] relu1 needs backward computation.
I0407 08:24:54.786556 18909 net.cpp:198] conv1 needs backward computation.
I0407 08:24:54.786558 18909 net.cpp:200] train-data does not need backward computation.
I0407 08:24:54.786561 18909 net.cpp:242] This network produces output loss
I0407 08:24:54.786572 18909 net.cpp:255] Network initialization done.
I0407 08:24:54.787091 18909 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0407 08:24:54.787119 18909 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0407 08:24:54.787248 18909 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"
}
I0407 08:24:54.787345 18909 layer_factory.hpp:77] Creating layer val-data
I0407 08:24:54.789166 18909 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db
I0407 08:24:54.789405 18909 net.cpp:84] Creating Layer val-data
I0407 08:24:54.789414 18909 net.cpp:380] val-data -> data
I0407 08:24:54.789422 18909 net.cpp:380] val-data -> label
I0407 08:24:54.789427 18909 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0407 08:24:54.793125 18909 data_layer.cpp:45] output data size: 32,3,227,227
I0407 08:24:54.839465 18909 net.cpp:122] Setting up val-data
I0407 08:24:54.839483 18909 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0407 08:24:54.839488 18909 net.cpp:129] Top shape: 32 (32)
I0407 08:24:54.839489 18909 net.cpp:137] Memory required for data: 19787264
I0407 08:24:54.839494 18909 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0407 08:24:54.839505 18909 net.cpp:84] Creating Layer label_val-data_1_split
I0407 08:24:54.839509 18909 net.cpp:406] label_val-data_1_split <- label
I0407 08:24:54.839514 18909 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0407 08:24:54.839521 18909 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0407 08:24:54.839573 18909 net.cpp:122] Setting up label_val-data_1_split
I0407 08:24:54.839578 18909 net.cpp:129] Top shape: 32 (32)
I0407 08:24:54.839581 18909 net.cpp:129] Top shape: 32 (32)
I0407 08:24:54.839582 18909 net.cpp:137] Memory required for data: 19787520
I0407 08:24:54.839584 18909 layer_factory.hpp:77] Creating layer conv1
I0407 08:24:54.839594 18909 net.cpp:84] Creating Layer conv1
I0407 08:24:54.839597 18909 net.cpp:406] conv1 <- data
I0407 08:24:54.839601 18909 net.cpp:380] conv1 -> conv1
I0407 08:24:54.851976 18909 net.cpp:122] Setting up conv1
I0407 08:24:54.851999 18909 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 08:24:54.852003 18909 net.cpp:137] Memory required for data: 56958720
I0407 08:24:54.852020 18909 layer_factory.hpp:77] Creating layer relu1
I0407 08:24:54.852030 18909 net.cpp:84] Creating Layer relu1
I0407 08:24:54.852035 18909 net.cpp:406] relu1 <- conv1
I0407 08:24:54.852042 18909 net.cpp:367] relu1 -> conv1 (in-place)
I0407 08:24:54.852435 18909 net.cpp:122] Setting up relu1
I0407 08:24:54.852447 18909 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 08:24:54.852449 18909 net.cpp:137] Memory required for data: 94129920
I0407 08:24:54.852453 18909 layer_factory.hpp:77] Creating layer norm1
I0407 08:24:54.852464 18909 net.cpp:84] Creating Layer norm1
I0407 08:24:54.852468 18909 net.cpp:406] norm1 <- conv1
I0407 08:24:54.852475 18909 net.cpp:380] norm1 -> norm1
I0407 08:24:54.853088 18909 net.cpp:122] Setting up norm1
I0407 08:24:54.853101 18909 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 08:24:54.853104 18909 net.cpp:137] Memory required for data: 131301120
I0407 08:24:54.853108 18909 layer_factory.hpp:77] Creating layer pool1
I0407 08:24:54.853116 18909 net.cpp:84] Creating Layer pool1
I0407 08:24:54.853121 18909 net.cpp:406] pool1 <- norm1
I0407 08:24:54.853127 18909 net.cpp:380] pool1 -> pool1
I0407 08:24:54.853168 18909 net.cpp:122] Setting up pool1
I0407 08:24:54.853174 18909 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0407 08:24:54.853178 18909 net.cpp:137] Memory required for data: 140259072
I0407 08:24:54.853181 18909 layer_factory.hpp:77] Creating layer conv2
I0407 08:24:54.853193 18909 net.cpp:84] Creating Layer conv2
I0407 08:24:54.853196 18909 net.cpp:406] conv2 <- pool1
I0407 08:24:54.853230 18909 net.cpp:380] conv2 -> conv2
I0407 08:24:54.862079 18909 net.cpp:122] Setting up conv2
I0407 08:24:54.862102 18909 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 08:24:54.862105 18909 net.cpp:137] Memory required for data: 164146944
I0407 08:24:54.862121 18909 layer_factory.hpp:77] Creating layer relu2
I0407 08:24:54.862130 18909 net.cpp:84] Creating Layer relu2
I0407 08:24:54.862134 18909 net.cpp:406] relu2 <- conv2
I0407 08:24:54.862141 18909 net.cpp:367] relu2 -> conv2 (in-place)
I0407 08:24:54.862807 18909 net.cpp:122] Setting up relu2
I0407 08:24:54.862819 18909 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 08:24:54.862823 18909 net.cpp:137] Memory required for data: 188034816
I0407 08:24:54.862826 18909 layer_factory.hpp:77] Creating layer norm2
I0407 08:24:54.862843 18909 net.cpp:84] Creating Layer norm2
I0407 08:24:54.862848 18909 net.cpp:406] norm2 <- conv2
I0407 08:24:54.862854 18909 net.cpp:380] norm2 -> norm2
I0407 08:24:54.863541 18909 net.cpp:122] Setting up norm2
I0407 08:24:54.863554 18909 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 08:24:54.863557 18909 net.cpp:137] Memory required for data: 211922688
I0407 08:24:54.863561 18909 layer_factory.hpp:77] Creating layer pool2
I0407 08:24:54.863569 18909 net.cpp:84] Creating Layer pool2
I0407 08:24:54.863574 18909 net.cpp:406] pool2 <- norm2
I0407 08:24:54.863579 18909 net.cpp:380] pool2 -> pool2
I0407 08:24:54.863618 18909 net.cpp:122] Setting up pool2
I0407 08:24:54.863624 18909 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 08:24:54.863628 18909 net.cpp:137] Memory required for data: 217460480
I0407 08:24:54.863631 18909 layer_factory.hpp:77] Creating layer conv3
I0407 08:24:54.863644 18909 net.cpp:84] Creating Layer conv3
I0407 08:24:54.863648 18909 net.cpp:406] conv3 <- pool2
I0407 08:24:54.863656 18909 net.cpp:380] conv3 -> conv3
I0407 08:24:54.879355 18909 net.cpp:122] Setting up conv3
I0407 08:24:54.879384 18909 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 08:24:54.879387 18909 net.cpp:137] Memory required for data: 225767168
I0407 08:24:54.879403 18909 layer_factory.hpp:77] Creating layer relu3
I0407 08:24:54.879415 18909 net.cpp:84] Creating Layer relu3
I0407 08:24:54.879420 18909 net.cpp:406] relu3 <- conv3
I0407 08:24:54.879429 18909 net.cpp:367] relu3 -> conv3 (in-place)
I0407 08:24:54.880100 18909 net.cpp:122] Setting up relu3
I0407 08:24:54.880113 18909 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 08:24:54.880117 18909 net.cpp:137] Memory required for data: 234073856
I0407 08:24:54.880122 18909 layer_factory.hpp:77] Creating layer conv4
I0407 08:24:54.880136 18909 net.cpp:84] Creating Layer conv4
I0407 08:24:54.880141 18909 net.cpp:406] conv4 <- conv3
I0407 08:24:54.880148 18909 net.cpp:380] conv4 -> conv4
I0407 08:24:54.894243 18909 net.cpp:122] Setting up conv4
I0407 08:24:54.894265 18909 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 08:24:54.894269 18909 net.cpp:137] Memory required for data: 242380544
I0407 08:24:54.894280 18909 layer_factory.hpp:77] Creating layer relu4
I0407 08:24:54.894290 18909 net.cpp:84] Creating Layer relu4
I0407 08:24:54.894295 18909 net.cpp:406] relu4 <- conv4
I0407 08:24:54.894304 18909 net.cpp:367] relu4 -> conv4 (in-place)
I0407 08:24:54.894779 18909 net.cpp:122] Setting up relu4
I0407 08:24:54.894791 18909 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 08:24:54.894795 18909 net.cpp:137] Memory required for data: 250687232
I0407 08:24:54.894799 18909 layer_factory.hpp:77] Creating layer conv5
I0407 08:24:54.894812 18909 net.cpp:84] Creating Layer conv5
I0407 08:24:54.894817 18909 net.cpp:406] conv5 <- conv4
I0407 08:24:54.894825 18909 net.cpp:380] conv5 -> conv5
I0407 08:24:54.907004 18909 net.cpp:122] Setting up conv5
I0407 08:24:54.907027 18909 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 08:24:54.907032 18909 net.cpp:137] Memory required for data: 256225024
I0407 08:24:54.907048 18909 layer_factory.hpp:77] Creating layer relu5
I0407 08:24:54.907059 18909 net.cpp:84] Creating Layer relu5
I0407 08:24:54.907088 18909 net.cpp:406] relu5 <- conv5
I0407 08:24:54.907097 18909 net.cpp:367] relu5 -> conv5 (in-place)
I0407 08:24:54.909761 18909 net.cpp:122] Setting up relu5
I0407 08:24:54.909775 18909 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 08:24:54.909778 18909 net.cpp:137] Memory required for data: 261762816
I0407 08:24:54.909783 18909 layer_factory.hpp:77] Creating layer pool5
I0407 08:24:54.909798 18909 net.cpp:84] Creating Layer pool5
I0407 08:24:54.909802 18909 net.cpp:406] pool5 <- conv5
I0407 08:24:54.909809 18909 net.cpp:380] pool5 -> pool5
I0407 08:24:54.909862 18909 net.cpp:122] Setting up pool5
I0407 08:24:54.909869 18909 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0407 08:24:54.909873 18909 net.cpp:137] Memory required for data: 262942464
I0407 08:24:54.909876 18909 layer_factory.hpp:77] Creating layer fc6
I0407 08:24:54.909888 18909 net.cpp:84] Creating Layer fc6
I0407 08:24:54.909891 18909 net.cpp:406] fc6 <- pool5
I0407 08:24:54.909898 18909 net.cpp:380] fc6 -> fc6
I0407 08:24:55.242172 18909 net.cpp:122] Setting up fc6
I0407 08:24:55.242194 18909 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:24:55.242197 18909 net.cpp:137] Memory required for data: 263466752
I0407 08:24:55.242205 18909 layer_factory.hpp:77] Creating layer relu6
I0407 08:24:55.242214 18909 net.cpp:84] Creating Layer relu6
I0407 08:24:55.242218 18909 net.cpp:406] relu6 <- fc6
I0407 08:24:55.242223 18909 net.cpp:367] relu6 -> fc6 (in-place)
I0407 08:24:55.242905 18909 net.cpp:122] Setting up relu6
I0407 08:24:55.242915 18909 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:24:55.242918 18909 net.cpp:137] Memory required for data: 263991040
I0407 08:24:55.242920 18909 layer_factory.hpp:77] Creating layer drop6
I0407 08:24:55.242925 18909 net.cpp:84] Creating Layer drop6
I0407 08:24:55.242928 18909 net.cpp:406] drop6 <- fc6
I0407 08:24:55.242931 18909 net.cpp:367] drop6 -> fc6 (in-place)
I0407 08:24:55.242955 18909 net.cpp:122] Setting up drop6
I0407 08:24:55.242959 18909 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:24:55.242961 18909 net.cpp:137] Memory required for data: 264515328
I0407 08:24:55.242964 18909 layer_factory.hpp:77] Creating layer fc7
I0407 08:24:55.242970 18909 net.cpp:84] Creating Layer fc7
I0407 08:24:55.242972 18909 net.cpp:406] fc7 <- fc6
I0407 08:24:55.242976 18909 net.cpp:380] fc7 -> fc7
I0407 08:24:55.390293 18909 net.cpp:122] Setting up fc7
I0407 08:24:55.390312 18909 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:24:55.390316 18909 net.cpp:137] Memory required for data: 265039616
I0407 08:24:55.390323 18909 layer_factory.hpp:77] Creating layer relu7
I0407 08:24:55.390331 18909 net.cpp:84] Creating Layer relu7
I0407 08:24:55.390336 18909 net.cpp:406] relu7 <- fc7
I0407 08:24:55.390341 18909 net.cpp:367] relu7 -> fc7 (in-place)
I0407 08:24:55.390722 18909 net.cpp:122] Setting up relu7
I0407 08:24:55.390731 18909 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:24:55.390734 18909 net.cpp:137] Memory required for data: 265563904
I0407 08:24:55.390736 18909 layer_factory.hpp:77] Creating layer drop7
I0407 08:24:55.390741 18909 net.cpp:84] Creating Layer drop7
I0407 08:24:55.390744 18909 net.cpp:406] drop7 <- fc7
I0407 08:24:55.390748 18909 net.cpp:367] drop7 -> fc7 (in-place)
I0407 08:24:55.390770 18909 net.cpp:122] Setting up drop7
I0407 08:24:55.390774 18909 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:24:55.390776 18909 net.cpp:137] Memory required for data: 266088192
I0407 08:24:55.390779 18909 layer_factory.hpp:77] Creating layer fc8
I0407 08:24:55.390785 18909 net.cpp:84] Creating Layer fc8
I0407 08:24:55.390787 18909 net.cpp:406] fc8 <- fc7
I0407 08:24:55.390791 18909 net.cpp:380] fc8 -> fc8
I0407 08:24:55.399226 18909 net.cpp:122] Setting up fc8
I0407 08:24:55.399243 18909 net.cpp:129] Top shape: 32 196 (6272)
I0407 08:24:55.399245 18909 net.cpp:137] Memory required for data: 266113280
I0407 08:24:55.399252 18909 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0407 08:24:55.399260 18909 net.cpp:84] Creating Layer fc8_fc8_0_split
I0407 08:24:55.399263 18909 net.cpp:406] fc8_fc8_0_split <- fc8
I0407 08:24:55.399288 18909 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0407 08:24:55.399296 18909 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0407 08:24:55.399329 18909 net.cpp:122] Setting up fc8_fc8_0_split
I0407 08:24:55.399333 18909 net.cpp:129] Top shape: 32 196 (6272)
I0407 08:24:55.399335 18909 net.cpp:129] Top shape: 32 196 (6272)
I0407 08:24:55.399338 18909 net.cpp:137] Memory required for data: 266163456
I0407 08:24:55.399339 18909 layer_factory.hpp:77] Creating layer accuracy
I0407 08:24:55.399345 18909 net.cpp:84] Creating Layer accuracy
I0407 08:24:55.399348 18909 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0407 08:24:55.399350 18909 net.cpp:406] accuracy <- label_val-data_1_split_0
I0407 08:24:55.399355 18909 net.cpp:380] accuracy -> accuracy
I0407 08:24:55.399361 18909 net.cpp:122] Setting up accuracy
I0407 08:24:55.399364 18909 net.cpp:129] Top shape: (1)
I0407 08:24:55.399365 18909 net.cpp:137] Memory required for data: 266163460
I0407 08:24:55.399367 18909 layer_factory.hpp:77] Creating layer loss
I0407 08:24:55.399371 18909 net.cpp:84] Creating Layer loss
I0407 08:24:55.399374 18909 net.cpp:406] loss <- fc8_fc8_0_split_1
I0407 08:24:55.399376 18909 net.cpp:406] loss <- label_val-data_1_split_1
I0407 08:24:55.399380 18909 net.cpp:380] loss -> loss
I0407 08:24:55.399386 18909 layer_factory.hpp:77] Creating layer loss
I0407 08:24:55.400666 18909 net.cpp:122] Setting up loss
I0407 08:24:55.400676 18909 net.cpp:129] Top shape: (1)
I0407 08:24:55.400678 18909 net.cpp:132]     with loss weight 1
I0407 08:24:55.400686 18909 net.cpp:137] Memory required for data: 266163464
I0407 08:24:55.400688 18909 net.cpp:198] loss needs backward computation.
I0407 08:24:55.400692 18909 net.cpp:200] accuracy does not need backward computation.
I0407 08:24:55.400694 18909 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0407 08:24:55.400696 18909 net.cpp:198] fc8 needs backward computation.
I0407 08:24:55.400699 18909 net.cpp:198] drop7 needs backward computation.
I0407 08:24:55.400701 18909 net.cpp:198] relu7 needs backward computation.
I0407 08:24:55.400703 18909 net.cpp:198] fc7 needs backward computation.
I0407 08:24:55.400705 18909 net.cpp:198] drop6 needs backward computation.
I0407 08:24:55.400707 18909 net.cpp:198] relu6 needs backward computation.
I0407 08:24:55.400709 18909 net.cpp:198] fc6 needs backward computation.
I0407 08:24:55.400712 18909 net.cpp:198] pool5 needs backward computation.
I0407 08:24:55.400714 18909 net.cpp:198] relu5 needs backward computation.
I0407 08:24:55.400717 18909 net.cpp:198] conv5 needs backward computation.
I0407 08:24:55.400718 18909 net.cpp:198] relu4 needs backward computation.
I0407 08:24:55.400722 18909 net.cpp:198] conv4 needs backward computation.
I0407 08:24:55.400723 18909 net.cpp:198] relu3 needs backward computation.
I0407 08:24:55.400725 18909 net.cpp:198] conv3 needs backward computation.
I0407 08:24:55.400728 18909 net.cpp:198] pool2 needs backward computation.
I0407 08:24:55.400730 18909 net.cpp:198] norm2 needs backward computation.
I0407 08:24:55.400732 18909 net.cpp:198] relu2 needs backward computation.
I0407 08:24:55.400734 18909 net.cpp:198] conv2 needs backward computation.
I0407 08:24:55.400736 18909 net.cpp:198] pool1 needs backward computation.
I0407 08:24:55.400739 18909 net.cpp:198] norm1 needs backward computation.
I0407 08:24:55.400741 18909 net.cpp:198] relu1 needs backward computation.
I0407 08:24:55.400743 18909 net.cpp:198] conv1 needs backward computation.
I0407 08:24:55.400746 18909 net.cpp:200] label_val-data_1_split does not need backward computation.
I0407 08:24:55.400749 18909 net.cpp:200] val-data does not need backward computation.
I0407 08:24:55.400750 18909 net.cpp:242] This network produces output accuracy
I0407 08:24:55.400753 18909 net.cpp:242] This network produces output loss
I0407 08:24:55.400771 18909 net.cpp:255] Network initialization done.
I0407 08:24:55.400835 18909 solver.cpp:56] Solver scaffolding done.
I0407 08:24:55.401229 18909 caffe.cpp:248] Starting Optimization
I0407 08:24:55.401237 18909 solver.cpp:272] Solving
I0407 08:24:55.401250 18909 solver.cpp:273] Learning Rate Policy: step
I0407 08:24:55.413601 18909 solver.cpp:330] Iteration 0, Testing net (#0)
I0407 08:24:55.413612 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:24:55.518435 18909 blocking_queue.cpp:49] Waiting for data
I0407 08:24:59.684993 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:24:59.733503 18909 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0407 08:24:59.733533 18909 solver.cpp:397]     Test net output #1: loss = 5.27729 (* 1 = 5.27729 loss)
I0407 08:24:59.883035 18909 solver.cpp:218] Iteration 0 (-2.00847e-21 iter/s, 4.4817s/12 iters), loss = 5.26914
I0407 08:24:59.884596 18909 solver.cpp:237]     Train net output #0: loss = 5.26914 (* 1 = 5.26914 loss)
I0407 08:24:59.884634 18909 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0407 08:25:04.045926 18909 solver.cpp:218] Iteration 12 (2.88372 iter/s, 4.16129s/12 iters), loss = 5.27688
I0407 08:25:04.045977 18909 solver.cpp:237]     Train net output #0: loss = 5.27688 (* 1 = 5.27688 loss)
I0407 08:25:04.045986 18909 sgd_solver.cpp:105] Iteration 12, lr = 0.01
I0407 08:25:09.246789 18909 solver.cpp:218] Iteration 24 (2.30736 iter/s, 5.20076s/12 iters), loss = 5.27994
I0407 08:25:09.246832 18909 solver.cpp:237]     Train net output #0: loss = 5.27994 (* 1 = 5.27994 loss)
I0407 08:25:09.246840 18909 sgd_solver.cpp:105] Iteration 24, lr = 0.01
I0407 08:25:14.514860 18909 solver.cpp:218] Iteration 36 (2.27792 iter/s, 5.26796s/12 iters), loss = 5.28565
I0407 08:25:14.514911 18909 solver.cpp:237]     Train net output #0: loss = 5.28565 (* 1 = 5.28565 loss)
I0407 08:25:14.514920 18909 sgd_solver.cpp:105] Iteration 36, lr = 0.01
I0407 08:25:19.541229 18909 solver.cpp:218] Iteration 48 (2.38747 iter/s, 5.02625s/12 iters), loss = 5.28967
I0407 08:25:19.541270 18909 solver.cpp:237]     Train net output #0: loss = 5.28967 (* 1 = 5.28967 loss)
I0407 08:25:19.541275 18909 sgd_solver.cpp:105] Iteration 48, lr = 0.01
I0407 08:25:24.643591 18909 solver.cpp:218] Iteration 60 (2.35191 iter/s, 5.10224s/12 iters), loss = 5.27589
I0407 08:25:24.643736 18909 solver.cpp:237]     Train net output #0: loss = 5.27589 (* 1 = 5.27589 loss)
I0407 08:25:24.643745 18909 sgd_solver.cpp:105] Iteration 60, lr = 0.01
I0407 08:25:29.449539 18909 solver.cpp:218] Iteration 72 (2.49702 iter/s, 4.80573s/12 iters), loss = 5.32654
I0407 08:25:29.449573 18909 solver.cpp:237]     Train net output #0: loss = 5.32654 (* 1 = 5.32654 loss)
I0407 08:25:29.449579 18909 sgd_solver.cpp:105] Iteration 72, lr = 0.01
I0407 08:25:34.305043 18909 solver.cpp:218] Iteration 84 (2.47148 iter/s, 4.85539s/12 iters), loss = 5.29483
I0407 08:25:34.305080 18909 solver.cpp:237]     Train net output #0: loss = 5.29483 (* 1 = 5.29483 loss)
I0407 08:25:34.305088 18909 sgd_solver.cpp:105] Iteration 84, lr = 0.01
I0407 08:25:39.588158 18909 solver.cpp:218] Iteration 96 (2.27144 iter/s, 5.28299s/12 iters), loss = 5.28604
I0407 08:25:39.588201 18909 solver.cpp:237]     Train net output #0: loss = 5.28604 (* 1 = 5.28604 loss)
I0407 08:25:39.588207 18909 sgd_solver.cpp:105] Iteration 96, lr = 0.01
I0407 08:25:41.406484 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:25:41.709051 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0407 08:25:44.851994 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0407 08:25:47.154729 18909 solver.cpp:330] Iteration 102, Testing net (#0)
I0407 08:25:47.154748 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:25:51.381033 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:25:51.458832 18909 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0407 08:25:51.458860 18909 solver.cpp:397]     Test net output #1: loss = 5.29078 (* 1 = 5.29078 loss)
I0407 08:25:53.396776 18909 solver.cpp:218] Iteration 108 (0.869039 iter/s, 13.8084s/12 iters), loss = 5.28906
I0407 08:25:53.396818 18909 solver.cpp:237]     Train net output #0: loss = 5.28906 (* 1 = 5.28906 loss)
I0407 08:25:53.396824 18909 sgd_solver.cpp:105] Iteration 108, lr = 0.01
I0407 08:25:58.709408 18909 solver.cpp:218] Iteration 120 (2.25882 iter/s, 5.31251s/12 iters), loss = 5.26041
I0407 08:25:58.709528 18909 solver.cpp:237]     Train net output #0: loss = 5.26041 (* 1 = 5.26041 loss)
I0407 08:25:58.709537 18909 sgd_solver.cpp:105] Iteration 120, lr = 0.01
I0407 08:26:03.975231 18909 solver.cpp:218] Iteration 132 (2.27892 iter/s, 5.26565s/12 iters), loss = 5.27281
I0407 08:26:03.975282 18909 solver.cpp:237]     Train net output #0: loss = 5.27281 (* 1 = 5.27281 loss)
I0407 08:26:03.975291 18909 sgd_solver.cpp:105] Iteration 132, lr = 0.01
I0407 08:26:09.138406 18909 solver.cpp:218] Iteration 144 (2.32417 iter/s, 5.16313s/12 iters), loss = 5.26176
I0407 08:26:09.138448 18909 solver.cpp:237]     Train net output #0: loss = 5.26176 (* 1 = 5.26176 loss)
I0407 08:26:09.138455 18909 sgd_solver.cpp:105] Iteration 144, lr = 0.01
I0407 08:26:14.071825 18909 solver.cpp:218] Iteration 156 (2.4324 iter/s, 4.9334s/12 iters), loss = 5.27569
I0407 08:26:14.071867 18909 solver.cpp:237]     Train net output #0: loss = 5.27569 (* 1 = 5.27569 loss)
I0407 08:26:14.071874 18909 sgd_solver.cpp:105] Iteration 156, lr = 0.01
I0407 08:26:18.995432 18909 solver.cpp:218] Iteration 168 (2.43724 iter/s, 4.9236s/12 iters), loss = 5.22227
I0407 08:26:18.995467 18909 solver.cpp:237]     Train net output #0: loss = 5.22227 (* 1 = 5.22227 loss)
I0407 08:26:18.995473 18909 sgd_solver.cpp:105] Iteration 168, lr = 0.01
I0407 08:26:24.269377 18909 solver.cpp:218] Iteration 180 (2.27534 iter/s, 5.27394s/12 iters), loss = 5.25716
I0407 08:26:24.269415 18909 solver.cpp:237]     Train net output #0: loss = 5.25716 (* 1 = 5.25716 loss)
I0407 08:26:24.269423 18909 sgd_solver.cpp:105] Iteration 180, lr = 0.01
I0407 08:26:29.394939 18909 solver.cpp:218] Iteration 192 (2.34121 iter/s, 5.12555s/12 iters), loss = 5.10588
I0407 08:26:29.395032 18909 solver.cpp:237]     Train net output #0: loss = 5.10588 (* 1 = 5.10588 loss)
I0407 08:26:29.395041 18909 sgd_solver.cpp:105] Iteration 192, lr = 0.01
I0407 08:26:33.652668 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:26:34.351610 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0407 08:26:37.370923 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0407 08:26:39.678953 18909 solver.cpp:330] Iteration 204, Testing net (#0)
I0407 08:26:39.678973 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:26:43.873870 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:26:43.997277 18909 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0407 08:26:43.997314 18909 solver.cpp:397]     Test net output #1: loss = 5.18832 (* 1 = 5.18832 loss)
I0407 08:26:44.135816 18909 solver.cpp:218] Iteration 204 (0.814063 iter/s, 14.7409s/12 iters), loss = 5.15635
I0407 08:26:44.135870 18909 solver.cpp:237]     Train net output #0: loss = 5.15635 (* 1 = 5.15635 loss)
I0407 08:26:44.135876 18909 sgd_solver.cpp:105] Iteration 204, lr = 0.01
I0407 08:26:48.464151 18909 solver.cpp:218] Iteration 216 (2.77245 iter/s, 4.3283s/12 iters), loss = 5.23689
I0407 08:26:48.464195 18909 solver.cpp:237]     Train net output #0: loss = 5.23689 (* 1 = 5.23689 loss)
I0407 08:26:48.464203 18909 sgd_solver.cpp:105] Iteration 216, lr = 0.01
I0407 08:26:53.334950 18909 solver.cpp:218] Iteration 228 (2.46368 iter/s, 4.87076s/12 iters), loss = 5.20522
I0407 08:26:53.334995 18909 solver.cpp:237]     Train net output #0: loss = 5.20522 (* 1 = 5.20522 loss)
I0407 08:26:53.335001 18909 sgd_solver.cpp:105] Iteration 228, lr = 0.01
I0407 08:26:58.550078 18909 solver.cpp:218] Iteration 240 (2.30101 iter/s, 5.2151s/12 iters), loss = 5.17738
I0407 08:26:58.550122 18909 solver.cpp:237]     Train net output #0: loss = 5.17738 (* 1 = 5.17738 loss)
I0407 08:26:58.550129 18909 sgd_solver.cpp:105] Iteration 240, lr = 0.01
I0407 08:27:03.659448 18909 solver.cpp:218] Iteration 252 (2.34864 iter/s, 5.10933s/12 iters), loss = 5.22431
I0407 08:27:03.659593 18909 solver.cpp:237]     Train net output #0: loss = 5.22431 (* 1 = 5.22431 loss)
I0407 08:27:03.659602 18909 sgd_solver.cpp:105] Iteration 252, lr = 0.01
I0407 08:27:08.734985 18909 solver.cpp:218] Iteration 264 (2.36435 iter/s, 5.0754s/12 iters), loss = 5.09872
I0407 08:27:08.735038 18909 solver.cpp:237]     Train net output #0: loss = 5.09872 (* 1 = 5.09872 loss)
I0407 08:27:08.735049 18909 sgd_solver.cpp:105] Iteration 264, lr = 0.01
I0407 08:27:13.729303 18909 solver.cpp:218] Iteration 276 (2.40275 iter/s, 4.99427s/12 iters), loss = 5.10072
I0407 08:27:13.729344 18909 solver.cpp:237]     Train net output #0: loss = 5.10072 (* 1 = 5.10072 loss)
I0407 08:27:13.729351 18909 sgd_solver.cpp:105] Iteration 276, lr = 0.01
I0407 08:27:18.769338 18909 solver.cpp:218] Iteration 288 (2.38095 iter/s, 5.04s/12 iters), loss = 5.15828
I0407 08:27:18.769381 18909 solver.cpp:237]     Train net output #0: loss = 5.15828 (* 1 = 5.15828 loss)
I0407 08:27:18.769389 18909 sgd_solver.cpp:105] Iteration 288, lr = 0.01
I0407 08:27:23.714174 18909 solver.cpp:218] Iteration 300 (2.4268 iter/s, 4.94479s/12 iters), loss = 5.23955
I0407 08:27:23.714212 18909 solver.cpp:237]     Train net output #0: loss = 5.23955 (* 1 = 5.23955 loss)
I0407 08:27:23.714221 18909 sgd_solver.cpp:105] Iteration 300, lr = 0.01
I0407 08:27:24.763775 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:27:25.884775 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0407 08:27:30.228065 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0407 08:27:32.531729 18909 solver.cpp:330] Iteration 306, Testing net (#0)
I0407 08:27:32.531749 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:27:36.806000 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:27:36.963181 18909 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0407 08:27:36.963224 18909 solver.cpp:397]     Test net output #1: loss = 5.15591 (* 1 = 5.15591 loss)
I0407 08:27:38.839409 18909 solver.cpp:218] Iteration 312 (0.793377 iter/s, 15.1252s/12 iters), loss = 5.13614
I0407 08:27:38.839457 18909 solver.cpp:237]     Train net output #0: loss = 5.13614 (* 1 = 5.13614 loss)
I0407 08:27:38.839464 18909 sgd_solver.cpp:105] Iteration 312, lr = 0.01
I0407 08:27:43.876626 18909 solver.cpp:218] Iteration 324 (2.38229 iter/s, 5.03717s/12 iters), loss = 5.20538
I0407 08:27:43.876662 18909 solver.cpp:237]     Train net output #0: loss = 5.20538 (* 1 = 5.20538 loss)
I0407 08:27:43.876668 18909 sgd_solver.cpp:105] Iteration 324, lr = 0.01
I0407 08:27:49.121111 18909 solver.cpp:218] Iteration 336 (2.28813 iter/s, 5.24445s/12 iters), loss = 5.13817
I0407 08:27:49.121153 18909 solver.cpp:237]     Train net output #0: loss = 5.13817 (* 1 = 5.13817 loss)
I0407 08:27:49.121160 18909 sgd_solver.cpp:105] Iteration 336, lr = 0.01
I0407 08:27:54.196094 18909 solver.cpp:218] Iteration 348 (2.36456 iter/s, 5.07494s/12 iters), loss = 5.10442
I0407 08:27:54.196139 18909 solver.cpp:237]     Train net output #0: loss = 5.10442 (* 1 = 5.10442 loss)
I0407 08:27:54.196146 18909 sgd_solver.cpp:105] Iteration 348, lr = 0.01
I0407 08:27:59.464751 18909 solver.cpp:218] Iteration 360 (2.27764 iter/s, 5.2686s/12 iters), loss = 5.16098
I0407 08:27:59.464798 18909 solver.cpp:237]     Train net output #0: loss = 5.16098 (* 1 = 5.16098 loss)
I0407 08:27:59.464807 18909 sgd_solver.cpp:105] Iteration 360, lr = 0.01
I0407 08:28:04.669459 18909 solver.cpp:218] Iteration 372 (2.30563 iter/s, 5.20465s/12 iters), loss = 5.13183
I0407 08:28:04.669505 18909 solver.cpp:237]     Train net output #0: loss = 5.13183 (* 1 = 5.13183 loss)
I0407 08:28:04.669512 18909 sgd_solver.cpp:105] Iteration 372, lr = 0.01
I0407 08:28:09.890156 18909 solver.cpp:218] Iteration 384 (2.29857 iter/s, 5.22065s/12 iters), loss = 5.18489
I0407 08:28:09.890293 18909 solver.cpp:237]     Train net output #0: loss = 5.18489 (* 1 = 5.18489 loss)
I0407 08:28:09.890300 18909 sgd_solver.cpp:105] Iteration 384, lr = 0.01
I0407 08:28:15.169957 18909 solver.cpp:218] Iteration 396 (2.27287 iter/s, 5.27966s/12 iters), loss = 5.09436
I0407 08:28:15.170007 18909 solver.cpp:237]     Train net output #0: loss = 5.09436 (* 1 = 5.09436 loss)
I0407 08:28:15.170017 18909 sgd_solver.cpp:105] Iteration 396, lr = 0.01
I0407 08:28:18.312538 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:28:19.817685 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0407 08:28:24.775660 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0407 08:28:27.087543 18909 solver.cpp:330] Iteration 408, Testing net (#0)
I0407 08:28:27.087563 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:28:31.241082 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:28:31.446645 18909 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0407 08:28:31.446689 18909 solver.cpp:397]     Test net output #1: loss = 5.10432 (* 1 = 5.10432 loss)
I0407 08:28:31.588129 18909 solver.cpp:218] Iteration 408 (0.730899 iter/s, 16.4181s/12 iters), loss = 5.0937
I0407 08:28:31.588189 18909 solver.cpp:237]     Train net output #0: loss = 5.0937 (* 1 = 5.0937 loss)
I0407 08:28:31.588201 18909 sgd_solver.cpp:105] Iteration 408, lr = 0.01
I0407 08:28:35.943629 18909 solver.cpp:218] Iteration 420 (2.75518 iter/s, 4.35543s/12 iters), loss = 5.05254
I0407 08:28:35.943671 18909 solver.cpp:237]     Train net output #0: loss = 5.05254 (* 1 = 5.05254 loss)
I0407 08:28:35.943679 18909 sgd_solver.cpp:105] Iteration 420, lr = 0.01
I0407 08:28:41.239965 18909 solver.cpp:218] Iteration 432 (2.26574 iter/s, 5.29628s/12 iters), loss = 5.04143
I0407 08:28:41.240077 18909 solver.cpp:237]     Train net output #0: loss = 5.04143 (* 1 = 5.04143 loss)
I0407 08:28:41.240087 18909 sgd_solver.cpp:105] Iteration 432, lr = 0.01
I0407 08:28:46.353274 18909 solver.cpp:218] Iteration 444 (2.34687 iter/s, 5.1132s/12 iters), loss = 5.10649
I0407 08:28:46.353314 18909 solver.cpp:237]     Train net output #0: loss = 5.10649 (* 1 = 5.10649 loss)
I0407 08:28:46.353322 18909 sgd_solver.cpp:105] Iteration 444, lr = 0.01
I0407 08:28:51.327653 18909 solver.cpp:218] Iteration 456 (2.41239 iter/s, 4.97433s/12 iters), loss = 5.12691
I0407 08:28:51.327693 18909 solver.cpp:237]     Train net output #0: loss = 5.12691 (* 1 = 5.12691 loss)
I0407 08:28:51.327700 18909 sgd_solver.cpp:105] Iteration 456, lr = 0.01
I0407 08:28:56.553457 18909 solver.cpp:218] Iteration 468 (2.29632 iter/s, 5.22575s/12 iters), loss = 5.05974
I0407 08:28:56.553504 18909 solver.cpp:237]     Train net output #0: loss = 5.05974 (* 1 = 5.05974 loss)
I0407 08:28:56.553512 18909 sgd_solver.cpp:105] Iteration 468, lr = 0.01
I0407 08:29:01.820207 18909 solver.cpp:218] Iteration 480 (2.27847 iter/s, 5.26669s/12 iters), loss = 5.00257
I0407 08:29:01.820247 18909 solver.cpp:237]     Train net output #0: loss = 5.00257 (* 1 = 5.00257 loss)
I0407 08:29:01.820253 18909 sgd_solver.cpp:105] Iteration 480, lr = 0.01
I0407 08:29:07.055286 18909 solver.cpp:218] Iteration 492 (2.29225 iter/s, 5.23503s/12 iters), loss = 5.10296
I0407 08:29:07.055332 18909 solver.cpp:237]     Train net output #0: loss = 5.10296 (* 1 = 5.10296 loss)
I0407 08:29:07.055341 18909 sgd_solver.cpp:105] Iteration 492, lr = 0.01
I0407 08:29:12.343286 18909 solver.cpp:218] Iteration 504 (2.26931 iter/s, 5.28795s/12 iters), loss = 5.01734
I0407 08:29:12.343406 18909 solver.cpp:237]     Train net output #0: loss = 5.01734 (* 1 = 5.01734 loss)
I0407 08:29:12.343415 18909 sgd_solver.cpp:105] Iteration 504, lr = 0.01
I0407 08:29:12.579136 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:29:14.478782 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0407 08:29:18.908002 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0407 08:29:21.322856 18909 solver.cpp:330] Iteration 510, Testing net (#0)
I0407 08:29:21.322877 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:29:25.563838 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:29:25.816754 18909 solver.cpp:397]     Test net output #0: accuracy = 0.0245098
I0407 08:29:25.816787 18909 solver.cpp:397]     Test net output #1: loss = 5.04254 (* 1 = 5.04254 loss)
I0407 08:29:27.699345 18909 solver.cpp:218] Iteration 516 (0.781456 iter/s, 15.3559s/12 iters), loss = 5.01999
I0407 08:29:27.699389 18909 solver.cpp:237]     Train net output #0: loss = 5.01999 (* 1 = 5.01999 loss)
I0407 08:29:27.699395 18909 sgd_solver.cpp:105] Iteration 516, lr = 0.01
I0407 08:29:32.887181 18909 solver.cpp:218] Iteration 528 (2.31313 iter/s, 5.18778s/12 iters), loss = 5.07922
I0407 08:29:32.887218 18909 solver.cpp:237]     Train net output #0: loss = 5.07922 (* 1 = 5.07922 loss)
I0407 08:29:32.887225 18909 sgd_solver.cpp:105] Iteration 528, lr = 0.01
I0407 08:29:38.194589 18909 solver.cpp:218] Iteration 540 (2.26101 iter/s, 5.30736s/12 iters), loss = 4.97795
I0407 08:29:38.194630 18909 solver.cpp:237]     Train net output #0: loss = 4.97795 (* 1 = 4.97795 loss)
I0407 08:29:38.194638 18909 sgd_solver.cpp:105] Iteration 540, lr = 0.01
I0407 08:29:43.387176 18909 solver.cpp:218] Iteration 552 (2.31101 iter/s, 5.19253s/12 iters), loss = 5.0968
I0407 08:29:43.387318 18909 solver.cpp:237]     Train net output #0: loss = 5.0968 (* 1 = 5.0968 loss)
I0407 08:29:43.387329 18909 sgd_solver.cpp:105] Iteration 552, lr = 0.01
I0407 08:29:48.828922 18909 solver.cpp:218] Iteration 564 (2.20524 iter/s, 5.44159s/12 iters), loss = 4.98079
I0407 08:29:48.828977 18909 solver.cpp:237]     Train net output #0: loss = 4.98079 (* 1 = 4.98079 loss)
I0407 08:29:48.828986 18909 sgd_solver.cpp:105] Iteration 564, lr = 0.01
I0407 08:29:54.102026 18909 solver.cpp:218] Iteration 576 (2.27573 iter/s, 5.27304s/12 iters), loss = 4.86677
I0407 08:29:54.102067 18909 solver.cpp:237]     Train net output #0: loss = 4.86677 (* 1 = 4.86677 loss)
I0407 08:29:54.102074 18909 sgd_solver.cpp:105] Iteration 576, lr = 0.01
I0407 08:29:59.238054 18909 solver.cpp:218] Iteration 588 (2.33646 iter/s, 5.13597s/12 iters), loss = 4.95149
I0407 08:29:59.238095 18909 solver.cpp:237]     Train net output #0: loss = 4.95149 (* 1 = 4.95149 loss)
I0407 08:29:59.238102 18909 sgd_solver.cpp:105] Iteration 588, lr = 0.01
I0407 08:30:04.526620 18909 solver.cpp:218] Iteration 600 (2.26907 iter/s, 5.28851s/12 iters), loss = 4.97912
I0407 08:30:04.526664 18909 solver.cpp:237]     Train net output #0: loss = 4.97912 (* 1 = 4.97912 loss)
I0407 08:30:04.526670 18909 sgd_solver.cpp:105] Iteration 600, lr = 0.01
I0407 08:30:06.929245 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:30:09.309361 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0407 08:30:13.617555 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0407 08:30:16.687422 18909 solver.cpp:330] Iteration 612, Testing net (#0)
I0407 08:30:16.687448 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:30:20.684433 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:30:20.987848 18909 solver.cpp:397]     Test net output #0: accuracy = 0.0324755
I0407 08:30:20.987876 18909 solver.cpp:397]     Test net output #1: loss = 4.94865 (* 1 = 4.94865 loss)
I0407 08:30:21.118643 18909 solver.cpp:218] Iteration 612 (0.723241 iter/s, 16.592s/12 iters), loss = 4.94902
I0407 08:30:21.118683 18909 solver.cpp:237]     Train net output #0: loss = 4.94902 (* 1 = 4.94902 loss)
I0407 08:30:21.118690 18909 sgd_solver.cpp:105] Iteration 612, lr = 0.01
I0407 08:30:25.481866 18909 solver.cpp:218] Iteration 624 (2.7503 iter/s, 4.36317s/12 iters), loss = 4.89958
I0407 08:30:25.481915 18909 solver.cpp:237]     Train net output #0: loss = 4.89958 (* 1 = 4.89958 loss)
I0407 08:30:25.481925 18909 sgd_solver.cpp:105] Iteration 624, lr = 0.01
I0407 08:30:30.499366 18909 solver.cpp:218] Iteration 636 (2.39166 iter/s, 5.01744s/12 iters), loss = 4.92922
I0407 08:30:30.499405 18909 solver.cpp:237]     Train net output #0: loss = 4.92922 (* 1 = 4.92922 loss)
I0407 08:30:30.499413 18909 sgd_solver.cpp:105] Iteration 636, lr = 0.01
I0407 08:30:35.663058 18909 solver.cpp:218] Iteration 648 (2.32394 iter/s, 5.16364s/12 iters), loss = 4.80869
I0407 08:30:35.663108 18909 solver.cpp:237]     Train net output #0: loss = 4.80869 (* 1 = 4.80869 loss)
I0407 08:30:35.663118 18909 sgd_solver.cpp:105] Iteration 648, lr = 0.01
I0407 08:30:40.713500 18909 solver.cpp:218] Iteration 660 (2.37606 iter/s, 5.05038s/12 iters), loss = 4.8592
I0407 08:30:40.713539 18909 solver.cpp:237]     Train net output #0: loss = 4.8592 (* 1 = 4.8592 loss)
I0407 08:30:40.713546 18909 sgd_solver.cpp:105] Iteration 660, lr = 0.01
I0407 08:30:46.049242 18909 solver.cpp:218] Iteration 672 (2.249 iter/s, 5.33569s/12 iters), loss = 4.89137
I0407 08:30:46.049369 18909 solver.cpp:237]     Train net output #0: loss = 4.89137 (* 1 = 4.89137 loss)
I0407 08:30:46.049377 18909 sgd_solver.cpp:105] Iteration 672, lr = 0.01
I0407 08:30:51.396562 18909 solver.cpp:218] Iteration 684 (2.24417 iter/s, 5.34719s/12 iters), loss = 4.84526
I0407 08:30:51.396596 18909 solver.cpp:237]     Train net output #0: loss = 4.84526 (* 1 = 4.84526 loss)
I0407 08:30:51.396602 18909 sgd_solver.cpp:105] Iteration 684, lr = 0.01
I0407 08:30:52.143029 18909 blocking_queue.cpp:49] Waiting for data
I0407 08:30:56.440551 18909 solver.cpp:218] Iteration 696 (2.37909 iter/s, 5.04394s/12 iters), loss = 4.84902
I0407 08:30:56.440595 18909 solver.cpp:237]     Train net output #0: loss = 4.84902 (* 1 = 4.84902 loss)
I0407 08:30:56.440603 18909 sgd_solver.cpp:105] Iteration 696, lr = 0.01
I0407 08:31:00.991729 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:31:01.401067 18909 solver.cpp:218] Iteration 708 (2.41913 iter/s, 4.96046s/12 iters), loss = 4.91176
I0407 08:31:01.401104 18909 solver.cpp:237]     Train net output #0: loss = 4.91176 (* 1 = 4.91176 loss)
I0407 08:31:01.401110 18909 sgd_solver.cpp:105] Iteration 708, lr = 0.01
I0407 08:31:03.470782 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0407 08:31:07.834085 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0407 08:31:12.326385 18909 solver.cpp:330] Iteration 714, Testing net (#0)
I0407 08:31:12.326406 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:31:16.552656 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:31:16.866866 18909 solver.cpp:397]     Test net output #0: accuracy = 0.0422794
I0407 08:31:16.866896 18909 solver.cpp:397]     Test net output #1: loss = 4.89296 (* 1 = 4.89296 loss)
I0407 08:31:18.704764 18909 solver.cpp:218] Iteration 720 (0.693495 iter/s, 17.3037s/12 iters), loss = 4.8486
I0407 08:31:18.704805 18909 solver.cpp:237]     Train net output #0: loss = 4.8486 (* 1 = 4.8486 loss)
I0407 08:31:18.704813 18909 sgd_solver.cpp:105] Iteration 720, lr = 0.01
I0407 08:31:23.795153 18909 solver.cpp:218] Iteration 732 (2.35741 iter/s, 5.09034s/12 iters), loss = 4.76957
I0407 08:31:23.795200 18909 solver.cpp:237]     Train net output #0: loss = 4.76957 (* 1 = 4.76957 loss)
I0407 08:31:23.795209 18909 sgd_solver.cpp:105] Iteration 732, lr = 0.01
I0407 08:31:28.896593 18909 solver.cpp:218] Iteration 744 (2.3523 iter/s, 5.10138s/12 iters), loss = 4.74374
I0407 08:31:28.896636 18909 solver.cpp:237]     Train net output #0: loss = 4.74374 (* 1 = 4.74374 loss)
I0407 08:31:28.896643 18909 sgd_solver.cpp:105] Iteration 744, lr = 0.01
I0407 08:31:34.018491 18909 solver.cpp:218] Iteration 756 (2.34291 iter/s, 5.12184s/12 iters), loss = 4.70226
I0407 08:31:34.018543 18909 solver.cpp:237]     Train net output #0: loss = 4.70226 (* 1 = 4.70226 loss)
I0407 08:31:34.018554 18909 sgd_solver.cpp:105] Iteration 756, lr = 0.01
I0407 08:31:39.367693 18909 solver.cpp:218] Iteration 768 (2.24335 iter/s, 5.34914s/12 iters), loss = 4.84433
I0407 08:31:39.367728 18909 solver.cpp:237]     Train net output #0: loss = 4.84433 (* 1 = 4.84433 loss)
I0407 08:31:39.367734 18909 sgd_solver.cpp:105] Iteration 768, lr = 0.01
I0407 08:31:44.672726 18909 solver.cpp:218] Iteration 780 (2.26202 iter/s, 5.30499s/12 iters), loss = 4.69793
I0407 08:31:44.672768 18909 solver.cpp:237]     Train net output #0: loss = 4.69793 (* 1 = 4.69793 loss)
I0407 08:31:44.672775 18909 sgd_solver.cpp:105] Iteration 780, lr = 0.01
I0407 08:31:49.846870 18909 solver.cpp:218] Iteration 792 (2.31925 iter/s, 5.17409s/12 iters), loss = 4.90535
I0407 08:31:49.847014 18909 solver.cpp:237]     Train net output #0: loss = 4.90535 (* 1 = 4.90535 loss)
I0407 08:31:49.847023 18909 sgd_solver.cpp:105] Iteration 792, lr = 0.01
I0407 08:31:55.204731 18909 solver.cpp:218] Iteration 804 (2.23977 iter/s, 5.3577s/12 iters), loss = 4.81005
I0407 08:31:55.204772 18909 solver.cpp:237]     Train net output #0: loss = 4.81005 (* 1 = 4.81005 loss)
I0407 08:31:55.204779 18909 sgd_solver.cpp:105] Iteration 804, lr = 0.01
I0407 08:31:56.976091 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:32:00.013485 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0407 08:32:04.413039 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0407 08:32:08.910208 18909 solver.cpp:330] Iteration 816, Testing net (#0)
I0407 08:32:08.910229 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:32:12.942332 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:32:13.288581 18909 solver.cpp:397]     Test net output #0: accuracy = 0.0557598
I0407 08:32:13.288609 18909 solver.cpp:397]     Test net output #1: loss = 4.80912 (* 1 = 4.80912 loss)
I0407 08:32:13.429860 18909 solver.cpp:218] Iteration 816 (0.658433 iter/s, 18.2251s/12 iters), loss = 4.8184
I0407 08:32:13.429903 18909 solver.cpp:237]     Train net output #0: loss = 4.8184 (* 1 = 4.8184 loss)
I0407 08:32:13.429908 18909 sgd_solver.cpp:105] Iteration 816, lr = 0.01
I0407 08:32:17.881173 18909 solver.cpp:218] Iteration 828 (2.69587 iter/s, 4.45126s/12 iters), loss = 4.70654
I0407 08:32:17.881217 18909 solver.cpp:237]     Train net output #0: loss = 4.70654 (* 1 = 4.70654 loss)
I0407 08:32:17.881224 18909 sgd_solver.cpp:105] Iteration 828, lr = 0.01
I0407 08:32:23.019165 18909 solver.cpp:218] Iteration 840 (2.33557 iter/s, 5.13794s/12 iters), loss = 4.55595
I0407 08:32:23.019268 18909 solver.cpp:237]     Train net output #0: loss = 4.55595 (* 1 = 4.55595 loss)
I0407 08:32:23.019276 18909 sgd_solver.cpp:105] Iteration 840, lr = 0.01
I0407 08:32:28.321099 18909 solver.cpp:218] Iteration 852 (2.26337 iter/s, 5.30182s/12 iters), loss = 4.64583
I0407 08:32:28.321144 18909 solver.cpp:237]     Train net output #0: loss = 4.64583 (* 1 = 4.64583 loss)
I0407 08:32:28.321153 18909 sgd_solver.cpp:105] Iteration 852, lr = 0.01
I0407 08:32:33.670987 18909 solver.cpp:218] Iteration 864 (2.24306 iter/s, 5.34983s/12 iters), loss = 4.76054
I0407 08:32:33.671027 18909 solver.cpp:237]     Train net output #0: loss = 4.76054 (* 1 = 4.76054 loss)
I0407 08:32:33.671034 18909 sgd_solver.cpp:105] Iteration 864, lr = 0.01
I0407 08:32:38.985553 18909 solver.cpp:218] Iteration 876 (2.25797 iter/s, 5.31451s/12 iters), loss = 4.71744
I0407 08:32:38.985595 18909 solver.cpp:237]     Train net output #0: loss = 4.71744 (* 1 = 4.71744 loss)
I0407 08:32:38.985602 18909 sgd_solver.cpp:105] Iteration 876, lr = 0.01
I0407 08:32:44.305387 18909 solver.cpp:218] Iteration 888 (2.25573 iter/s, 5.31978s/12 iters), loss = 4.69972
I0407 08:32:44.305430 18909 solver.cpp:237]     Train net output #0: loss = 4.69972 (* 1 = 4.69972 loss)
I0407 08:32:44.305438 18909 sgd_solver.cpp:105] Iteration 888, lr = 0.01
I0407 08:32:49.412542 18909 solver.cpp:218] Iteration 900 (2.34967 iter/s, 5.1071s/12 iters), loss = 4.62027
I0407 08:32:49.412600 18909 solver.cpp:237]     Train net output #0: loss = 4.62027 (* 1 = 4.62027 loss)
I0407 08:32:49.412611 18909 sgd_solver.cpp:105] Iteration 900, lr = 0.01
I0407 08:32:53.511698 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:32:54.727604 18909 solver.cpp:218] Iteration 912 (2.25776 iter/s, 5.31499s/12 iters), loss = 4.63908
I0407 08:32:54.727649 18909 solver.cpp:237]     Train net output #0: loss = 4.63908 (* 1 = 4.63908 loss)
I0407 08:32:54.727655 18909 sgd_solver.cpp:105] Iteration 912, lr = 0.01
I0407 08:32:56.780287 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0407 08:33:02.489454 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0407 08:33:06.242282 18909 solver.cpp:330] Iteration 918, Testing net (#0)
I0407 08:33:06.242300 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:33:10.223320 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:33:10.627023 18909 solver.cpp:397]     Test net output #0: accuracy = 0.0557598
I0407 08:33:10.627055 18909 solver.cpp:397]     Test net output #1: loss = 4.62184 (* 1 = 4.62184 loss)
I0407 08:33:12.562079 18909 solver.cpp:218] Iteration 924 (0.672856 iter/s, 17.8344s/12 iters), loss = 4.54606
I0407 08:33:12.562124 18909 solver.cpp:237]     Train net output #0: loss = 4.54606 (* 1 = 4.54606 loss)
I0407 08:33:12.562130 18909 sgd_solver.cpp:105] Iteration 924, lr = 0.01
I0407 08:33:17.579496 18909 solver.cpp:218] Iteration 936 (2.39169 iter/s, 5.01736s/12 iters), loss = 4.59142
I0407 08:33:17.579535 18909 solver.cpp:237]     Train net output #0: loss = 4.59142 (* 1 = 4.59142 loss)
I0407 08:33:17.579540 18909 sgd_solver.cpp:105] Iteration 936, lr = 0.01
I0407 08:33:22.609216 18909 solver.cpp:218] Iteration 948 (2.38584 iter/s, 5.02967s/12 iters), loss = 4.63493
I0407 08:33:22.609254 18909 solver.cpp:237]     Train net output #0: loss = 4.63493 (* 1 = 4.63493 loss)
I0407 08:33:22.609261 18909 sgd_solver.cpp:105] Iteration 948, lr = 0.01
I0407 08:33:27.921353 18909 solver.cpp:218] Iteration 960 (2.259 iter/s, 5.31208s/12 iters), loss = 4.53025
I0407 08:33:27.921452 18909 solver.cpp:237]     Train net output #0: loss = 4.53025 (* 1 = 4.53025 loss)
I0407 08:33:27.921459 18909 sgd_solver.cpp:105] Iteration 960, lr = 0.01
I0407 08:33:33.161481 18909 solver.cpp:218] Iteration 972 (2.29007 iter/s, 5.24002s/12 iters), loss = 4.35052
I0407 08:33:33.161518 18909 solver.cpp:237]     Train net output #0: loss = 4.35052 (* 1 = 4.35052 loss)
I0407 08:33:33.161525 18909 sgd_solver.cpp:105] Iteration 972, lr = 0.01
I0407 08:33:38.537822 18909 solver.cpp:218] Iteration 984 (2.23202 iter/s, 5.37629s/12 iters), loss = 4.46782
I0407 08:33:38.537868 18909 solver.cpp:237]     Train net output #0: loss = 4.46782 (* 1 = 4.46782 loss)
I0407 08:33:38.537876 18909 sgd_solver.cpp:105] Iteration 984, lr = 0.01
I0407 08:33:43.800648 18909 solver.cpp:218] Iteration 996 (2.28017 iter/s, 5.26277s/12 iters), loss = 4.37728
I0407 08:33:43.800690 18909 solver.cpp:237]     Train net output #0: loss = 4.37728 (* 1 = 4.37728 loss)
I0407 08:33:43.800698 18909 sgd_solver.cpp:105] Iteration 996, lr = 0.01
I0407 08:33:49.185688 18909 solver.cpp:218] Iteration 1008 (2.22842 iter/s, 5.38498s/12 iters), loss = 4.48882
I0407 08:33:49.185734 18909 solver.cpp:237]     Train net output #0: loss = 4.48882 (* 1 = 4.48882 loss)
I0407 08:33:49.185742 18909 sgd_solver.cpp:105] Iteration 1008, lr = 0.01
I0407 08:33:50.253244 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:33:53.917649 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0407 08:33:57.282928 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0407 08:34:01.089439 18909 solver.cpp:330] Iteration 1020, Testing net (#0)
I0407 08:34:01.089540 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:34:05.031641 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:34:05.454741 18909 solver.cpp:397]     Test net output #0: accuracy = 0.0667892
I0407 08:34:05.454771 18909 solver.cpp:397]     Test net output #1: loss = 4.48766 (* 1 = 4.48766 loss)
I0407 08:34:05.585134 18909 solver.cpp:218] Iteration 1020 (0.731734 iter/s, 16.3994s/12 iters), loss = 4.43421
I0407 08:34:05.585173 18909 solver.cpp:237]     Train net output #0: loss = 4.43421 (* 1 = 4.43421 loss)
I0407 08:34:05.585180 18909 sgd_solver.cpp:105] Iteration 1020, lr = 0.01
I0407 08:34:09.936709 18909 solver.cpp:218] Iteration 1032 (2.75765 iter/s, 4.35152s/12 iters), loss = 4.69683
I0407 08:34:09.936755 18909 solver.cpp:237]     Train net output #0: loss = 4.69683 (* 1 = 4.69683 loss)
I0407 08:34:09.936764 18909 sgd_solver.cpp:105] Iteration 1032, lr = 0.01
I0407 08:34:15.045397 18909 solver.cpp:218] Iteration 1044 (2.34897 iter/s, 5.10863s/12 iters), loss = 4.42711
I0407 08:34:15.045440 18909 solver.cpp:237]     Train net output #0: loss = 4.42711 (* 1 = 4.42711 loss)
I0407 08:34:15.045447 18909 sgd_solver.cpp:105] Iteration 1044, lr = 0.01
I0407 08:34:20.224536 18909 solver.cpp:218] Iteration 1056 (2.31701 iter/s, 5.17909s/12 iters), loss = 4.45181
I0407 08:34:20.224575 18909 solver.cpp:237]     Train net output #0: loss = 4.45181 (* 1 = 4.45181 loss)
I0407 08:34:20.224582 18909 sgd_solver.cpp:105] Iteration 1056, lr = 0.01
I0407 08:34:25.488938 18909 solver.cpp:218] Iteration 1068 (2.27948 iter/s, 5.26435s/12 iters), loss = 4.17971
I0407 08:34:25.488987 18909 solver.cpp:237]     Train net output #0: loss = 4.17971 (* 1 = 4.17971 loss)
I0407 08:34:25.488994 18909 sgd_solver.cpp:105] Iteration 1068, lr = 0.01
I0407 08:34:30.525874 18909 solver.cpp:218] Iteration 1080 (2.38243 iter/s, 5.03688s/12 iters), loss = 4.18678
I0407 08:34:30.525913 18909 solver.cpp:237]     Train net output #0: loss = 4.18678 (* 1 = 4.18678 loss)
I0407 08:34:30.525920 18909 sgd_solver.cpp:105] Iteration 1080, lr = 0.01
I0407 08:34:35.719673 18909 solver.cpp:218] Iteration 1092 (2.31047 iter/s, 5.19374s/12 iters), loss = 4.44617
I0407 08:34:35.719830 18909 solver.cpp:237]     Train net output #0: loss = 4.44617 (* 1 = 4.44617 loss)
I0407 08:34:35.719841 18909 sgd_solver.cpp:105] Iteration 1092, lr = 0.01
I0407 08:34:41.096765 18909 solver.cpp:218] Iteration 1104 (2.23176 iter/s, 5.37693s/12 iters), loss = 4.34587
I0407 08:34:41.096807 18909 solver.cpp:237]     Train net output #0: loss = 4.34587 (* 1 = 4.34587 loss)
I0407 08:34:41.096814 18909 sgd_solver.cpp:105] Iteration 1104, lr = 0.01
I0407 08:34:44.491161 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:34:46.457331 18909 solver.cpp:218] Iteration 1116 (2.23859 iter/s, 5.36052s/12 iters), loss = 4.27684
I0407 08:34:46.457368 18909 solver.cpp:237]     Train net output #0: loss = 4.27684 (* 1 = 4.27684 loss)
I0407 08:34:46.457374 18909 sgd_solver.cpp:105] Iteration 1116, lr = 0.01
I0407 08:34:48.552853 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0407 08:34:51.634845 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0407 08:34:55.531057 18909 solver.cpp:330] Iteration 1122, Testing net (#0)
I0407 08:34:55.531085 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:34:59.428814 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:34:59.964624 18909 solver.cpp:397]     Test net output #0: accuracy = 0.0851716
I0407 08:34:59.964653 18909 solver.cpp:397]     Test net output #1: loss = 4.40637 (* 1 = 4.40637 loss)
I0407 08:35:01.782903 18909 solver.cpp:218] Iteration 1128 (0.783007 iter/s, 15.3255s/12 iters), loss = 4.28853
I0407 08:35:01.782943 18909 solver.cpp:237]     Train net output #0: loss = 4.28853 (* 1 = 4.28853 loss)
I0407 08:35:01.782950 18909 sgd_solver.cpp:105] Iteration 1128, lr = 0.01
I0407 08:35:06.920579 18909 solver.cpp:218] Iteration 1140 (2.33571 iter/s, 5.13762s/12 iters), loss = 4.24658
I0407 08:35:06.920670 18909 solver.cpp:237]     Train net output #0: loss = 4.24658 (* 1 = 4.24658 loss)
I0407 08:35:06.920676 18909 sgd_solver.cpp:105] Iteration 1140, lr = 0.01
I0407 08:35:12.229413 18909 solver.cpp:218] Iteration 1152 (2.26043 iter/s, 5.30873s/12 iters), loss = 4.44317
I0407 08:35:12.229454 18909 solver.cpp:237]     Train net output #0: loss = 4.44317 (* 1 = 4.44317 loss)
I0407 08:35:12.229460 18909 sgd_solver.cpp:105] Iteration 1152, lr = 0.01
I0407 08:35:17.384754 18909 solver.cpp:218] Iteration 1164 (2.32771 iter/s, 5.15528s/12 iters), loss = 4.35612
I0407 08:35:17.384794 18909 solver.cpp:237]     Train net output #0: loss = 4.35612 (* 1 = 4.35612 loss)
I0407 08:35:17.384801 18909 sgd_solver.cpp:105] Iteration 1164, lr = 0.01
I0407 08:35:22.567008 18909 solver.cpp:218] Iteration 1176 (2.31562 iter/s, 5.1822s/12 iters), loss = 4.18856
I0407 08:35:22.567049 18909 solver.cpp:237]     Train net output #0: loss = 4.18856 (* 1 = 4.18856 loss)
I0407 08:35:22.567057 18909 sgd_solver.cpp:105] Iteration 1176, lr = 0.01
I0407 08:35:27.702397 18909 solver.cpp:218] Iteration 1188 (2.33675 iter/s, 5.13533s/12 iters), loss = 4.16631
I0407 08:35:27.702448 18909 solver.cpp:237]     Train net output #0: loss = 4.16631 (* 1 = 4.16631 loss)
I0407 08:35:27.702457 18909 sgd_solver.cpp:105] Iteration 1188, lr = 0.01
I0407 08:35:32.648262 18909 solver.cpp:218] Iteration 1200 (2.4263 iter/s, 4.9458s/12 iters), loss = 3.92867
I0407 08:35:32.648305 18909 solver.cpp:237]     Train net output #0: loss = 3.92867 (* 1 = 3.92867 loss)
I0407 08:35:32.648313 18909 sgd_solver.cpp:105] Iteration 1200, lr = 0.01
I0407 08:35:37.944211 18909 solver.cpp:218] Iteration 1212 (2.26591 iter/s, 5.2959s/12 iters), loss = 4.08321
I0407 08:35:37.944345 18909 solver.cpp:237]     Train net output #0: loss = 4.08321 (* 1 = 4.08321 loss)
I0407 08:35:37.944355 18909 sgd_solver.cpp:105] Iteration 1212, lr = 0.01
I0407 08:35:38.193097 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:35:42.877211 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0407 08:35:45.807178 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0407 08:35:49.204154 18909 solver.cpp:330] Iteration 1224, Testing net (#0)
I0407 08:35:49.204175 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:35:53.000830 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:35:53.501348 18909 solver.cpp:397]     Test net output #0: accuracy = 0.0919118
I0407 08:35:53.501380 18909 solver.cpp:397]     Test net output #1: loss = 4.26581 (* 1 = 4.26581 loss)
I0407 08:35:53.637244 18909 solver.cpp:218] Iteration 1224 (0.764677 iter/s, 15.6929s/12 iters), loss = 4.1267
I0407 08:35:53.638801 18909 solver.cpp:237]     Train net output #0: loss = 4.1267 (* 1 = 4.1267 loss)
I0407 08:35:53.638814 18909 sgd_solver.cpp:105] Iteration 1224, lr = 0.01
I0407 08:35:57.783308 18909 solver.cpp:218] Iteration 1236 (2.89541 iter/s, 4.1445s/12 iters), loss = 4.35856
I0407 08:35:57.783361 18909 solver.cpp:237]     Train net output #0: loss = 4.35856 (* 1 = 4.35856 loss)
I0407 08:35:57.783371 18909 sgd_solver.cpp:105] Iteration 1236, lr = 0.01
I0407 08:36:02.914430 18909 solver.cpp:218] Iteration 1248 (2.3387 iter/s, 5.13106s/12 iters), loss = 4.06863
I0407 08:36:02.914469 18909 solver.cpp:237]     Train net output #0: loss = 4.06863 (* 1 = 4.06863 loss)
I0407 08:36:02.914476 18909 sgd_solver.cpp:105] Iteration 1248, lr = 0.01
I0407 08:36:07.972375 18909 solver.cpp:218] Iteration 1260 (2.37253 iter/s, 5.05789s/12 iters), loss = 4.15148
I0407 08:36:07.972484 18909 solver.cpp:237]     Train net output #0: loss = 4.15148 (* 1 = 4.15148 loss)
I0407 08:36:07.972492 18909 sgd_solver.cpp:105] Iteration 1260, lr = 0.01
I0407 08:36:13.258155 18909 solver.cpp:218] Iteration 1272 (2.27029 iter/s, 5.28566s/12 iters), loss = 4.13936
I0407 08:36:13.258198 18909 solver.cpp:237]     Train net output #0: loss = 4.13936 (* 1 = 4.13936 loss)
I0407 08:36:13.258205 18909 sgd_solver.cpp:105] Iteration 1272, lr = 0.01
I0407 08:36:18.509671 18909 solver.cpp:218] Iteration 1284 (2.28508 iter/s, 5.25146s/12 iters), loss = 3.88732
I0407 08:36:18.509714 18909 solver.cpp:237]     Train net output #0: loss = 3.88732 (* 1 = 3.88732 loss)
I0407 08:36:18.509721 18909 sgd_solver.cpp:105] Iteration 1284, lr = 0.01
I0407 08:36:23.790560 18909 solver.cpp:218] Iteration 1296 (2.27237 iter/s, 5.28083s/12 iters), loss = 4.14673
I0407 08:36:23.790602 18909 solver.cpp:237]     Train net output #0: loss = 4.14673 (* 1 = 4.14673 loss)
I0407 08:36:23.790609 18909 sgd_solver.cpp:105] Iteration 1296, lr = 0.01
I0407 08:36:28.913527 18909 solver.cpp:218] Iteration 1308 (2.34242 iter/s, 5.12291s/12 iters), loss = 3.98784
I0407 08:36:28.913585 18909 solver.cpp:237]     Train net output #0: loss = 3.98784 (* 1 = 3.98784 loss)
I0407 08:36:28.913596 18909 sgd_solver.cpp:105] Iteration 1308, lr = 0.01
I0407 08:36:31.534595 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:36:34.159427 18909 solver.cpp:218] Iteration 1320 (2.28753 iter/s, 5.24583s/12 iters), loss = 4.08556
I0407 08:36:34.159483 18909 solver.cpp:237]     Train net output #0: loss = 4.08556 (* 1 = 4.08556 loss)
I0407 08:36:34.159493 18909 sgd_solver.cpp:105] Iteration 1320, lr = 0.01
I0407 08:36:36.221477 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0407 08:36:39.322978 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0407 08:36:42.041958 18909 solver.cpp:330] Iteration 1326, Testing net (#0)
I0407 08:36:42.041977 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:36:45.880625 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:36:46.441977 18909 solver.cpp:397]     Test net output #0: accuracy = 0.115809
I0407 08:36:46.442008 18909 solver.cpp:397]     Test net output #1: loss = 4.0344 (* 1 = 4.0344 loss)
I0407 08:36:48.378702 18909 solver.cpp:218] Iteration 1332 (0.843928 iter/s, 14.2192s/12 iters), loss = 3.92272
I0407 08:36:48.378754 18909 solver.cpp:237]     Train net output #0: loss = 3.92272 (* 1 = 3.92272 loss)
I0407 08:36:48.378762 18909 sgd_solver.cpp:105] Iteration 1332, lr = 0.01
I0407 08:36:53.601661 18909 solver.cpp:218] Iteration 1344 (2.29758 iter/s, 5.2229s/12 iters), loss = 3.96452
I0407 08:36:53.601701 18909 solver.cpp:237]     Train net output #0: loss = 3.96452 (* 1 = 3.96452 loss)
I0407 08:36:53.601707 18909 sgd_solver.cpp:105] Iteration 1344, lr = 0.01
I0407 08:36:58.850152 18909 solver.cpp:218] Iteration 1356 (2.28639 iter/s, 5.24844s/12 iters), loss = 3.66031
I0407 08:36:58.850198 18909 solver.cpp:237]     Train net output #0: loss = 3.66031 (* 1 = 3.66031 loss)
I0407 08:36:58.850206 18909 sgd_solver.cpp:105] Iteration 1356, lr = 0.01
I0407 08:37:04.243183 18909 solver.cpp:218] Iteration 1368 (2.22512 iter/s, 5.39297s/12 iters), loss = 3.70912
I0407 08:37:04.243227 18909 solver.cpp:237]     Train net output #0: loss = 3.70912 (* 1 = 3.70912 loss)
I0407 08:37:04.243233 18909 sgd_solver.cpp:105] Iteration 1368, lr = 0.01
I0407 08:37:05.502876 18909 blocking_queue.cpp:49] Waiting for data
I0407 08:37:09.368405 18909 solver.cpp:218] Iteration 1380 (2.34139 iter/s, 5.12516s/12 iters), loss = 4.03042
I0407 08:37:09.368518 18909 solver.cpp:237]     Train net output #0: loss = 4.03042 (* 1 = 4.03042 loss)
I0407 08:37:09.368526 18909 sgd_solver.cpp:105] Iteration 1380, lr = 0.01
I0407 08:37:14.375452 18909 solver.cpp:218] Iteration 1392 (2.39668 iter/s, 5.00693s/12 iters), loss = 3.82196
I0407 08:37:14.375492 18909 solver.cpp:237]     Train net output #0: loss = 3.82196 (* 1 = 3.82196 loss)
I0407 08:37:14.375499 18909 sgd_solver.cpp:105] Iteration 1392, lr = 0.01
I0407 08:37:19.664686 18909 solver.cpp:218] Iteration 1404 (2.26878 iter/s, 5.28918s/12 iters), loss = 3.76069
I0407 08:37:19.664736 18909 solver.cpp:237]     Train net output #0: loss = 3.76069 (* 1 = 3.76069 loss)
I0407 08:37:19.664746 18909 sgd_solver.cpp:105] Iteration 1404, lr = 0.01
I0407 08:37:24.322531 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:37:24.705174 18909 solver.cpp:218] Iteration 1416 (2.38075 iter/s, 5.04042s/12 iters), loss = 4.07466
I0407 08:37:24.705229 18909 solver.cpp:237]     Train net output #0: loss = 4.07466 (* 1 = 4.07466 loss)
I0407 08:37:24.705238 18909 sgd_solver.cpp:105] Iteration 1416, lr = 0.01
I0407 08:37:29.396196 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0407 08:37:32.400676 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0407 08:37:34.994663 18909 solver.cpp:330] Iteration 1428, Testing net (#0)
I0407 08:37:34.994680 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:37:38.687460 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:37:39.267755 18909 solver.cpp:397]     Test net output #0: accuracy = 0.128676
I0407 08:37:39.267791 18909 solver.cpp:397]     Test net output #1: loss = 3.90214 (* 1 = 3.90214 loss)
I0407 08:37:39.408813 18909 solver.cpp:218] Iteration 1428 (0.816127 iter/s, 14.7036s/12 iters), loss = 3.85901
I0407 08:37:39.408943 18909 solver.cpp:237]     Train net output #0: loss = 3.85901 (* 1 = 3.85901 loss)
I0407 08:37:39.408951 18909 sgd_solver.cpp:105] Iteration 1428, lr = 0.01
I0407 08:37:43.606820 18909 solver.cpp:218] Iteration 1440 (2.8586 iter/s, 4.19786s/12 iters), loss = 3.86206
I0407 08:37:43.606863 18909 solver.cpp:237]     Train net output #0: loss = 3.86206 (* 1 = 3.86206 loss)
I0407 08:37:43.606870 18909 sgd_solver.cpp:105] Iteration 1440, lr = 0.01
I0407 08:37:48.578547 18909 solver.cpp:218] Iteration 1452 (2.41368 iter/s, 4.97167s/12 iters), loss = 3.7143
I0407 08:37:48.578593 18909 solver.cpp:237]     Train net output #0: loss = 3.7143 (* 1 = 3.7143 loss)
I0407 08:37:48.578599 18909 sgd_solver.cpp:105] Iteration 1452, lr = 0.01
I0407 08:37:53.803596 18909 solver.cpp:218] Iteration 1464 (2.29665 iter/s, 5.22499s/12 iters), loss = 3.59421
I0407 08:37:53.803650 18909 solver.cpp:237]     Train net output #0: loss = 3.59421 (* 1 = 3.59421 loss)
I0407 08:37:53.803663 18909 sgd_solver.cpp:105] Iteration 1464, lr = 0.01
I0407 08:37:58.940379 18909 solver.cpp:218] Iteration 1476 (2.33612 iter/s, 5.13672s/12 iters), loss = 3.72046
I0407 08:37:58.940423 18909 solver.cpp:237]     Train net output #0: loss = 3.72046 (* 1 = 3.72046 loss)
I0407 08:37:58.940430 18909 sgd_solver.cpp:105] Iteration 1476, lr = 0.01
I0407 08:38:04.165300 18909 solver.cpp:218] Iteration 1488 (2.29671 iter/s, 5.22486s/12 iters), loss = 3.8068
I0407 08:38:04.165346 18909 solver.cpp:237]     Train net output #0: loss = 3.8068 (* 1 = 3.8068 loss)
I0407 08:38:04.165354 18909 sgd_solver.cpp:105] Iteration 1488, lr = 0.01
I0407 08:38:09.291998 18909 solver.cpp:218] Iteration 1500 (2.34072 iter/s, 5.12664s/12 iters), loss = 3.85763
I0407 08:38:09.292040 18909 solver.cpp:237]     Train net output #0: loss = 3.85763 (* 1 = 3.85763 loss)
I0407 08:38:09.292048 18909 sgd_solver.cpp:105] Iteration 1500, lr = 0.01
I0407 08:38:14.504731 18909 solver.cpp:218] Iteration 1512 (2.30208 iter/s, 5.21267s/12 iters), loss = 3.64358
I0407 08:38:14.504858 18909 solver.cpp:237]     Train net output #0: loss = 3.64358 (* 1 = 3.64358 loss)
I0407 08:38:14.504868 18909 sgd_solver.cpp:105] Iteration 1512, lr = 0.01
I0407 08:38:16.385906 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:38:19.708743 18909 solver.cpp:218] Iteration 1524 (2.30597 iter/s, 5.20387s/12 iters), loss = 3.67542
I0407 08:38:19.708788 18909 solver.cpp:237]     Train net output #0: loss = 3.67542 (* 1 = 3.67542 loss)
I0407 08:38:19.708796 18909 sgd_solver.cpp:105] Iteration 1524, lr = 0.01
I0407 08:38:21.891424 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0407 08:38:24.896236 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0407 08:38:27.199142 18909 solver.cpp:330] Iteration 1530, Testing net (#0)
I0407 08:38:27.199164 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:38:30.950256 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:38:31.577188 18909 solver.cpp:397]     Test net output #0: accuracy = 0.132353
I0407 08:38:31.577224 18909 solver.cpp:397]     Test net output #1: loss = 3.87442 (* 1 = 3.87442 loss)
I0407 08:38:33.434428 18909 solver.cpp:218] Iteration 1536 (0.874277 iter/s, 13.7256s/12 iters), loss = 3.3001
I0407 08:38:33.434489 18909 solver.cpp:237]     Train net output #0: loss = 3.3001 (* 1 = 3.3001 loss)
I0407 08:38:33.434501 18909 sgd_solver.cpp:105] Iteration 1536, lr = 0.01
I0407 08:38:38.562131 18909 solver.cpp:218] Iteration 1548 (2.34026 iter/s, 5.12764s/12 iters), loss = 3.57126
I0407 08:38:38.562182 18909 solver.cpp:237]     Train net output #0: loss = 3.57126 (* 1 = 3.57126 loss)
I0407 08:38:38.562191 18909 sgd_solver.cpp:105] Iteration 1548, lr = 0.01
I0407 08:38:43.874150 18909 solver.cpp:218] Iteration 1560 (2.25906 iter/s, 5.31195s/12 iters), loss = 3.59829
I0407 08:38:43.874202 18909 solver.cpp:237]     Train net output #0: loss = 3.59829 (* 1 = 3.59829 loss)
I0407 08:38:43.874212 18909 sgd_solver.cpp:105] Iteration 1560, lr = 0.01
I0407 08:38:48.830854 18909 solver.cpp:218] Iteration 1572 (2.421 iter/s, 4.95664s/12 iters), loss = 3.62817
I0407 08:38:48.831001 18909 solver.cpp:237]     Train net output #0: loss = 3.62817 (* 1 = 3.62817 loss)
I0407 08:38:48.831010 18909 sgd_solver.cpp:105] Iteration 1572, lr = 0.01
I0407 08:38:54.154482 18909 solver.cpp:218] Iteration 1584 (2.25417 iter/s, 5.32347s/12 iters), loss = 3.83293
I0407 08:38:54.154527 18909 solver.cpp:237]     Train net output #0: loss = 3.83293 (* 1 = 3.83293 loss)
I0407 08:38:54.154534 18909 sgd_solver.cpp:105] Iteration 1584, lr = 0.01
I0407 08:38:59.586376 18909 solver.cpp:218] Iteration 1596 (2.2092 iter/s, 5.43183s/12 iters), loss = 3.62613
I0407 08:38:59.586421 18909 solver.cpp:237]     Train net output #0: loss = 3.62613 (* 1 = 3.62613 loss)
I0407 08:38:59.586428 18909 sgd_solver.cpp:105] Iteration 1596, lr = 0.01
I0407 08:39:04.814441 18909 solver.cpp:218] Iteration 1608 (2.29533 iter/s, 5.228s/12 iters), loss = 3.32705
I0407 08:39:04.814487 18909 solver.cpp:237]     Train net output #0: loss = 3.32705 (* 1 = 3.32705 loss)
I0407 08:39:04.814493 18909 sgd_solver.cpp:105] Iteration 1608, lr = 0.01
I0407 08:39:08.920547 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:39:10.133181 18909 solver.cpp:218] Iteration 1620 (2.2562 iter/s, 5.31869s/12 iters), loss = 3.6479
I0407 08:39:10.133226 18909 solver.cpp:237]     Train net output #0: loss = 3.6479 (* 1 = 3.6479 loss)
I0407 08:39:10.133234 18909 sgd_solver.cpp:105] Iteration 1620, lr = 0.01
I0407 08:39:14.813300 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0407 08:39:17.802495 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0407 08:39:20.248541 18909 solver.cpp:330] Iteration 1632, Testing net (#0)
I0407 08:39:20.248611 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:39:23.899135 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:39:24.560593 18909 solver.cpp:397]     Test net output #0: accuracy = 0.172181
I0407 08:39:24.560627 18909 solver.cpp:397]     Test net output #1: loss = 3.66804 (* 1 = 3.66804 loss)
I0407 08:39:24.699092 18909 solver.cpp:218] Iteration 1632 (0.823844 iter/s, 14.5659s/12 iters), loss = 3.57315
I0407 08:39:24.699163 18909 solver.cpp:237]     Train net output #0: loss = 3.57315 (* 1 = 3.57315 loss)
I0407 08:39:24.699172 18909 sgd_solver.cpp:105] Iteration 1632, lr = 0.01
I0407 08:39:28.898574 18909 solver.cpp:218] Iteration 1644 (2.85755 iter/s, 4.19939s/12 iters), loss = 3.81998
I0407 08:39:28.898619 18909 solver.cpp:237]     Train net output #0: loss = 3.81998 (* 1 = 3.81998 loss)
I0407 08:39:28.898625 18909 sgd_solver.cpp:105] Iteration 1644, lr = 0.01
I0407 08:39:34.172888 18909 solver.cpp:218] Iteration 1656 (2.2752 iter/s, 5.27425s/12 iters), loss = 3.59701
I0407 08:39:34.172932 18909 solver.cpp:237]     Train net output #0: loss = 3.59701 (* 1 = 3.59701 loss)
I0407 08:39:34.172940 18909 sgd_solver.cpp:105] Iteration 1656, lr = 0.01
I0407 08:39:39.157470 18909 solver.cpp:218] Iteration 1668 (2.40745 iter/s, 4.98453s/12 iters), loss = 3.41643
I0407 08:39:39.157511 18909 solver.cpp:237]     Train net output #0: loss = 3.41643 (* 1 = 3.41643 loss)
I0407 08:39:39.157519 18909 sgd_solver.cpp:105] Iteration 1668, lr = 0.01
I0407 08:39:43.862179 18909 solver.cpp:218] Iteration 1680 (2.55067 iter/s, 4.70465s/12 iters), loss = 3.29045
I0407 08:39:43.862229 18909 solver.cpp:237]     Train net output #0: loss = 3.29045 (* 1 = 3.29045 loss)
I0407 08:39:43.862238 18909 sgd_solver.cpp:105] Iteration 1680, lr = 0.01
I0407 08:39:49.003814 18909 solver.cpp:218] Iteration 1692 (2.33392 iter/s, 5.14157s/12 iters), loss = 3.30254
I0407 08:39:49.003856 18909 solver.cpp:237]     Train net output #0: loss = 3.30254 (* 1 = 3.30254 loss)
I0407 08:39:49.003863 18909 sgd_solver.cpp:105] Iteration 1692, lr = 0.01
I0407 08:39:54.261868 18909 solver.cpp:218] Iteration 1704 (2.28224 iter/s, 5.258s/12 iters), loss = 3.1161
I0407 08:39:54.262001 18909 solver.cpp:237]     Train net output #0: loss = 3.1161 (* 1 = 3.1161 loss)
I0407 08:39:54.262009 18909 sgd_solver.cpp:105] Iteration 1704, lr = 0.01
I0407 08:39:59.559721 18909 solver.cpp:218] Iteration 1716 (2.26513 iter/s, 5.29771s/12 iters), loss = 3.50673
I0407 08:39:59.559756 18909 solver.cpp:237]     Train net output #0: loss = 3.50673 (* 1 = 3.50673 loss)
I0407 08:39:59.559762 18909 sgd_solver.cpp:105] Iteration 1716, lr = 0.01
I0407 08:40:00.810302 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:40:04.957506 18909 solver.cpp:218] Iteration 1728 (2.22315 iter/s, 5.39774s/12 iters), loss = 3.19633
I0407 08:40:04.957551 18909 solver.cpp:237]     Train net output #0: loss = 3.19633 (* 1 = 3.19633 loss)
I0407 08:40:04.957557 18909 sgd_solver.cpp:105] Iteration 1728, lr = 0.01
I0407 08:40:06.904307 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0407 08:40:09.915884 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0407 08:40:12.225296 18909 solver.cpp:330] Iteration 1734, Testing net (#0)
I0407 08:40:12.225313 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:40:15.942760 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:40:16.657688 18909 solver.cpp:397]     Test net output #0: accuracy = 0.166054
I0407 08:40:16.657716 18909 solver.cpp:397]     Test net output #1: loss = 3.60102 (* 1 = 3.60102 loss)
I0407 08:40:18.614526 18909 solver.cpp:218] Iteration 1740 (0.878672 iter/s, 13.657s/12 iters), loss = 3.34369
I0407 08:40:18.614573 18909 solver.cpp:237]     Train net output #0: loss = 3.34369 (* 1 = 3.34369 loss)
I0407 08:40:18.614583 18909 sgd_solver.cpp:105] Iteration 1740, lr = 0.01
I0407 08:40:23.965765 18909 solver.cpp:218] Iteration 1752 (2.2425 iter/s, 5.35117s/12 iters), loss = 3.2193
I0407 08:40:23.965818 18909 solver.cpp:237]     Train net output #0: loss = 3.2193 (* 1 = 3.2193 loss)
I0407 08:40:23.965827 18909 sgd_solver.cpp:105] Iteration 1752, lr = 0.01
I0407 08:40:29.294097 18909 solver.cpp:218] Iteration 1764 (2.25214 iter/s, 5.32827s/12 iters), loss = 3.59592
I0407 08:40:29.294194 18909 solver.cpp:237]     Train net output #0: loss = 3.59592 (* 1 = 3.59592 loss)
I0407 08:40:29.294201 18909 sgd_solver.cpp:105] Iteration 1764, lr = 0.01
I0407 08:40:34.610877 18909 solver.cpp:218] Iteration 1776 (2.25705 iter/s, 5.31667s/12 iters), loss = 2.97091
I0407 08:40:34.610919 18909 solver.cpp:237]     Train net output #0: loss = 2.97091 (* 1 = 2.97091 loss)
I0407 08:40:34.610925 18909 sgd_solver.cpp:105] Iteration 1776, lr = 0.01
I0407 08:40:39.779012 18909 solver.cpp:218] Iteration 1788 (2.32195 iter/s, 5.16808s/12 iters), loss = 3.0157
I0407 08:40:39.779055 18909 solver.cpp:237]     Train net output #0: loss = 3.0157 (* 1 = 3.0157 loss)
I0407 08:40:39.779062 18909 sgd_solver.cpp:105] Iteration 1788, lr = 0.01
I0407 08:40:45.103153 18909 solver.cpp:218] Iteration 1800 (2.25391 iter/s, 5.32409s/12 iters), loss = 2.9545
I0407 08:40:45.103195 18909 solver.cpp:237]     Train net output #0: loss = 2.9545 (* 1 = 2.9545 loss)
I0407 08:40:45.103202 18909 sgd_solver.cpp:105] Iteration 1800, lr = 0.01
I0407 08:40:50.408190 18909 solver.cpp:218] Iteration 1812 (2.26202 iter/s, 5.30498s/12 iters), loss = 3.04894
I0407 08:40:50.408244 18909 solver.cpp:237]     Train net output #0: loss = 3.04894 (* 1 = 3.04894 loss)
I0407 08:40:50.408257 18909 sgd_solver.cpp:105] Iteration 1812, lr = 0.01
I0407 08:40:53.684351 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:40:55.573658 18909 solver.cpp:218] Iteration 1824 (2.32316 iter/s, 5.16538s/12 iters), loss = 3.52252
I0407 08:40:55.573709 18909 solver.cpp:237]     Train net output #0: loss = 3.52252 (* 1 = 3.52252 loss)
I0407 08:40:55.573719 18909 sgd_solver.cpp:105] Iteration 1824, lr = 0.01
I0407 08:41:00.089745 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0407 08:41:03.018630 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0407 08:41:05.327972 18909 solver.cpp:330] Iteration 1836, Testing net (#0)
I0407 08:41:05.327992 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:41:08.986714 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:41:09.725006 18909 solver.cpp:397]     Test net output #0: accuracy = 0.185662
I0407 08:41:09.725039 18909 solver.cpp:397]     Test net output #1: loss = 3.47883 (* 1 = 3.47883 loss)
I0407 08:41:09.867164 18909 solver.cpp:218] Iteration 1836 (0.839546 iter/s, 14.2934s/12 iters), loss = 3.09731
I0407 08:41:09.867233 18909 solver.cpp:237]     Train net output #0: loss = 3.09731 (* 1 = 3.09731 loss)
I0407 08:41:09.867249 18909 sgd_solver.cpp:105] Iteration 1836, lr = 0.01
I0407 08:41:14.055732 18909 solver.cpp:218] Iteration 1848 (2.86499 iter/s, 4.18849s/12 iters), loss = 3.16304
I0407 08:41:14.055771 18909 solver.cpp:237]     Train net output #0: loss = 3.16304 (* 1 = 3.16304 loss)
I0407 08:41:14.055779 18909 sgd_solver.cpp:105] Iteration 1848, lr = 0.01
I0407 08:41:19.175828 18909 solver.cpp:218] Iteration 1860 (2.34373 iter/s, 5.12005s/12 iters), loss = 3.14732
I0407 08:41:19.175861 18909 solver.cpp:237]     Train net output #0: loss = 3.14732 (* 1 = 3.14732 loss)
I0407 08:41:19.175868 18909 sgd_solver.cpp:105] Iteration 1860, lr = 0.01
I0407 08:41:24.407356 18909 solver.cpp:218] Iteration 1872 (2.29381 iter/s, 5.23148s/12 iters), loss = 3.12606
I0407 08:41:24.407402 18909 solver.cpp:237]     Train net output #0: loss = 3.12606 (* 1 = 3.12606 loss)
I0407 08:41:24.407408 18909 sgd_solver.cpp:105] Iteration 1872, lr = 0.01
I0407 08:41:29.518249 18909 solver.cpp:218] Iteration 1884 (2.34795 iter/s, 5.11084s/12 iters), loss = 3.12018
I0407 08:41:29.518287 18909 solver.cpp:237]     Train net output #0: loss = 3.12018 (* 1 = 3.12018 loss)
I0407 08:41:29.518294 18909 sgd_solver.cpp:105] Iteration 1884, lr = 0.01
I0407 08:41:34.819631 18909 solver.cpp:218] Iteration 1896 (2.26358 iter/s, 5.30133s/12 iters), loss = 3.18176
I0407 08:41:34.819733 18909 solver.cpp:237]     Train net output #0: loss = 3.18176 (* 1 = 3.18176 loss)
I0407 08:41:34.819741 18909 sgd_solver.cpp:105] Iteration 1896, lr = 0.01
I0407 08:41:40.066103 18909 solver.cpp:218] Iteration 1908 (2.2873 iter/s, 5.24636s/12 iters), loss = 2.992
I0407 08:41:40.066144 18909 solver.cpp:237]     Train net output #0: loss = 2.992 (* 1 = 2.992 loss)
I0407 08:41:40.066151 18909 sgd_solver.cpp:105] Iteration 1908, lr = 0.01
I0407 08:41:45.111968 18909 solver.cpp:218] Iteration 1920 (2.37821 iter/s, 5.04581s/12 iters), loss = 3.16304
I0407 08:41:45.112010 18909 solver.cpp:237]     Train net output #0: loss = 3.16304 (* 1 = 3.16304 loss)
I0407 08:41:45.112016 18909 sgd_solver.cpp:105] Iteration 1920, lr = 0.01
I0407 08:41:45.398311 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:41:50.101686 18909 solver.cpp:218] Iteration 1932 (2.40497 iter/s, 4.98966s/12 iters), loss = 3.07386
I0407 08:41:50.101729 18909 solver.cpp:237]     Train net output #0: loss = 3.07386 (* 1 = 3.07386 loss)
I0407 08:41:50.101737 18909 sgd_solver.cpp:105] Iteration 1932, lr = 0.01
I0407 08:41:52.198303 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0407 08:41:55.189647 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0407 08:41:57.487771 18909 solver.cpp:330] Iteration 1938, Testing net (#0)
I0407 08:41:57.487787 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:42:00.988802 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:42:01.776553 18909 solver.cpp:397]     Test net output #0: accuracy = 0.206495
I0407 08:42:01.776585 18909 solver.cpp:397]     Test net output #1: loss = 3.42484 (* 1 = 3.42484 loss)
I0407 08:42:03.679085 18909 solver.cpp:218] Iteration 1944 (0.883825 iter/s, 13.5774s/12 iters), loss = 3.35422
I0407 08:42:03.679128 18909 solver.cpp:237]     Train net output #0: loss = 3.35422 (* 1 = 3.35422 loss)
I0407 08:42:03.679136 18909 sgd_solver.cpp:105] Iteration 1944, lr = 0.01
I0407 08:42:08.860303 18909 solver.cpp:218] Iteration 1956 (2.31608 iter/s, 5.18116s/12 iters), loss = 3.02731
I0407 08:42:08.860450 18909 solver.cpp:237]     Train net output #0: loss = 3.02731 (* 1 = 3.02731 loss)
I0407 08:42:08.860460 18909 sgd_solver.cpp:105] Iteration 1956, lr = 0.01
I0407 08:42:13.950778 18909 solver.cpp:218] Iteration 1968 (2.35742 iter/s, 5.09032s/12 iters), loss = 3.24317
I0407 08:42:13.950816 18909 solver.cpp:237]     Train net output #0: loss = 3.24317 (* 1 = 3.24317 loss)
I0407 08:42:13.950824 18909 sgd_solver.cpp:105] Iteration 1968, lr = 0.01
I0407 08:42:19.186180 18909 solver.cpp:218] Iteration 1980 (2.29211 iter/s, 5.23535s/12 iters), loss = 2.81377
I0407 08:42:19.186228 18909 solver.cpp:237]     Train net output #0: loss = 2.81377 (* 1 = 2.81377 loss)
I0407 08:42:19.186237 18909 sgd_solver.cpp:105] Iteration 1980, lr = 0.01
I0407 08:42:24.352289 18909 solver.cpp:218] Iteration 1992 (2.32286 iter/s, 5.16605s/12 iters), loss = 2.78403
I0407 08:42:24.352339 18909 solver.cpp:237]     Train net output #0: loss = 2.78403 (* 1 = 2.78403 loss)
I0407 08:42:24.352346 18909 sgd_solver.cpp:105] Iteration 1992, lr = 0.01
I0407 08:42:29.711570 18909 solver.cpp:218] Iteration 2004 (2.23913 iter/s, 5.35922s/12 iters), loss = 2.93398
I0407 08:42:29.711609 18909 solver.cpp:237]     Train net output #0: loss = 2.93398 (* 1 = 2.93398 loss)
I0407 08:42:29.711616 18909 sgd_solver.cpp:105] Iteration 2004, lr = 0.01
I0407 08:42:34.939903 18909 solver.cpp:218] Iteration 2016 (2.29521 iter/s, 5.22828s/12 iters), loss = 3.17681
I0407 08:42:34.939949 18909 solver.cpp:237]     Train net output #0: loss = 3.17681 (* 1 = 3.17681 loss)
I0407 08:42:34.939956 18909 sgd_solver.cpp:105] Iteration 2016, lr = 0.01
I0407 08:42:37.536924 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:42:40.137146 18909 solver.cpp:218] Iteration 2028 (2.30894 iter/s, 5.19718s/12 iters), loss = 2.92145
I0407 08:42:40.137251 18909 solver.cpp:237]     Train net output #0: loss = 2.92145 (* 1 = 2.92145 loss)
I0407 08:42:40.137259 18909 sgd_solver.cpp:105] Iteration 2028, lr = 0.01
I0407 08:42:44.697496 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0407 08:42:47.694605 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0407 08:42:50.017326 18909 solver.cpp:330] Iteration 2040, Testing net (#0)
I0407 08:42:50.017349 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:42:53.487653 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:42:54.302345 18909 solver.cpp:397]     Test net output #0: accuracy = 0.1875
I0407 08:42:54.302381 18909 solver.cpp:397]     Test net output #1: loss = 3.56858 (* 1 = 3.56858 loss)
I0407 08:42:54.442399 18909 solver.cpp:218] Iteration 2040 (0.838859 iter/s, 14.3051s/12 iters), loss = 2.93901
I0407 08:42:54.442441 18909 solver.cpp:237]     Train net output #0: loss = 2.93901 (* 1 = 2.93901 loss)
I0407 08:42:54.442449 18909 sgd_solver.cpp:105] Iteration 2040, lr = 0.01
I0407 08:42:58.675029 18909 solver.cpp:218] Iteration 2052 (2.83515 iter/s, 4.23258s/12 iters), loss = 3.07523
I0407 08:42:58.675071 18909 solver.cpp:237]     Train net output #0: loss = 3.07523 (* 1 = 3.07523 loss)
I0407 08:42:58.675081 18909 sgd_solver.cpp:105] Iteration 2052, lr = 0.01
I0407 08:43:00.286960 18909 blocking_queue.cpp:49] Waiting for data
I0407 08:43:03.978462 18909 solver.cpp:218] Iteration 2064 (2.26271 iter/s, 5.30338s/12 iters), loss = 2.74338
I0407 08:43:03.978507 18909 solver.cpp:237]     Train net output #0: loss = 2.74338 (* 1 = 2.74338 loss)
I0407 08:43:03.978513 18909 sgd_solver.cpp:105] Iteration 2064, lr = 0.01
I0407 08:43:09.209365 18909 solver.cpp:218] Iteration 2076 (2.29408 iter/s, 5.23085s/12 iters), loss = 2.76833
I0407 08:43:09.209408 18909 solver.cpp:237]     Train net output #0: loss = 2.76833 (* 1 = 2.76833 loss)
I0407 08:43:09.209414 18909 sgd_solver.cpp:105] Iteration 2076, lr = 0.01
I0407 08:43:14.374475 18909 solver.cpp:218] Iteration 2088 (2.32331 iter/s, 5.16505s/12 iters), loss = 2.7922
I0407 08:43:14.374614 18909 solver.cpp:237]     Train net output #0: loss = 2.7922 (* 1 = 2.7922 loss)
I0407 08:43:14.374622 18909 sgd_solver.cpp:105] Iteration 2088, lr = 0.01
I0407 08:43:19.232098 18909 solver.cpp:218] Iteration 2100 (2.47043 iter/s, 4.85746s/12 iters), loss = 2.99698
I0407 08:43:19.232147 18909 solver.cpp:237]     Train net output #0: loss = 2.99698 (* 1 = 2.99698 loss)
I0407 08:43:19.232156 18909 sgd_solver.cpp:105] Iteration 2100, lr = 0.01
I0407 08:43:24.164584 18909 solver.cpp:218] Iteration 2112 (2.43288 iter/s, 4.93243s/12 iters), loss = 2.93332
I0407 08:43:24.164624 18909 solver.cpp:237]     Train net output #0: loss = 2.93332 (* 1 = 2.93332 loss)
I0407 08:43:24.164633 18909 sgd_solver.cpp:105] Iteration 2112, lr = 0.01
I0407 08:43:29.069640 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:43:29.423715 18909 solver.cpp:218] Iteration 2124 (2.28177 iter/s, 5.25908s/12 iters), loss = 3.15727
I0407 08:43:29.423758 18909 solver.cpp:237]     Train net output #0: loss = 3.15727 (* 1 = 3.15727 loss)
I0407 08:43:29.423765 18909 sgd_solver.cpp:105] Iteration 2124, lr = 0.01
I0407 08:43:34.813022 18909 solver.cpp:218] Iteration 2136 (2.22665 iter/s, 5.38925s/12 iters), loss = 2.8694
I0407 08:43:34.813062 18909 solver.cpp:237]     Train net output #0: loss = 2.8694 (* 1 = 2.8694 loss)
I0407 08:43:34.813069 18909 sgd_solver.cpp:105] Iteration 2136, lr = 0.01
I0407 08:43:36.981134 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0407 08:43:40.283490 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0407 08:43:42.605515 18909 solver.cpp:330] Iteration 2142, Testing net (#0)
I0407 08:43:42.605535 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:43:46.072984 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:43:46.959872 18909 solver.cpp:397]     Test net output #0: accuracy = 0.193627
I0407 08:43:46.959919 18909 solver.cpp:397]     Test net output #1: loss = 3.50335 (* 1 = 3.50335 loss)
I0407 08:43:48.804417 18909 solver.cpp:218] Iteration 2148 (0.857673 iter/s, 13.9913s/12 iters), loss = 3.00605
I0407 08:43:48.804461 18909 solver.cpp:237]     Train net output #0: loss = 3.00605 (* 1 = 3.00605 loss)
I0407 08:43:48.804468 18909 sgd_solver.cpp:105] Iteration 2148, lr = 0.01
I0407 08:43:53.927563 18909 solver.cpp:218] Iteration 2160 (2.34233 iter/s, 5.1231s/12 iters), loss = 2.6693
I0407 08:43:53.927605 18909 solver.cpp:237]     Train net output #0: loss = 2.6693 (* 1 = 2.6693 loss)
I0407 08:43:53.927613 18909 sgd_solver.cpp:105] Iteration 2160, lr = 0.01
I0407 08:43:59.123142 18909 solver.cpp:218] Iteration 2172 (2.30968 iter/s, 5.19552s/12 iters), loss = 2.76629
I0407 08:43:59.123186 18909 solver.cpp:237]     Train net output #0: loss = 2.76629 (* 1 = 2.76629 loss)
I0407 08:43:59.123193 18909 sgd_solver.cpp:105] Iteration 2172, lr = 0.01
I0407 08:44:04.348997 18909 solver.cpp:218] Iteration 2184 (2.2963 iter/s, 5.2258s/12 iters), loss = 2.73041
I0407 08:44:04.349041 18909 solver.cpp:237]     Train net output #0: loss = 2.73041 (* 1 = 2.73041 loss)
I0407 08:44:04.349048 18909 sgd_solver.cpp:105] Iteration 2184, lr = 0.01
I0407 08:44:09.703661 18909 solver.cpp:218] Iteration 2196 (2.24106 iter/s, 5.35461s/12 iters), loss = 2.53279
I0407 08:44:09.703704 18909 solver.cpp:237]     Train net output #0: loss = 2.53279 (* 1 = 2.53279 loss)
I0407 08:44:09.703713 18909 sgd_solver.cpp:105] Iteration 2196, lr = 0.01
I0407 08:44:14.968674 18909 solver.cpp:218] Iteration 2208 (2.27922 iter/s, 5.26496s/12 iters), loss = 2.40554
I0407 08:44:14.968711 18909 solver.cpp:237]     Train net output #0: loss = 2.40554 (* 1 = 2.40554 loss)
I0407 08:44:14.968719 18909 sgd_solver.cpp:105] Iteration 2208, lr = 0.01
I0407 08:44:20.004679 18909 solver.cpp:218] Iteration 2220 (2.38286 iter/s, 5.03596s/12 iters), loss = 2.57502
I0407 08:44:20.004812 18909 solver.cpp:237]     Train net output #0: loss = 2.57502 (* 1 = 2.57502 loss)
I0407 08:44:20.004818 18909 sgd_solver.cpp:105] Iteration 2220, lr = 0.01
I0407 08:44:21.749486 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:44:24.926707 18909 solver.cpp:218] Iteration 2232 (2.43809 iter/s, 4.92188s/12 iters), loss = 2.64302
I0407 08:44:24.926753 18909 solver.cpp:237]     Train net output #0: loss = 2.64302 (* 1 = 2.64302 loss)
I0407 08:44:24.926761 18909 sgd_solver.cpp:105] Iteration 2232, lr = 0.01
I0407 08:44:29.691283 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0407 08:44:33.274477 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0407 08:44:35.604383 18909 solver.cpp:330] Iteration 2244, Testing net (#0)
I0407 08:44:35.604406 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:44:39.011862 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:44:39.971120 18909 solver.cpp:397]     Test net output #0: accuracy = 0.23223
I0407 08:44:39.971150 18909 solver.cpp:397]     Test net output #1: loss = 3.33331 (* 1 = 3.33331 loss)
I0407 08:44:40.112272 18909 solver.cpp:218] Iteration 2244 (0.790227 iter/s, 15.1855s/12 iters), loss = 2.43635
I0407 08:44:40.112327 18909 solver.cpp:237]     Train net output #0: loss = 2.43635 (* 1 = 2.43635 loss)
I0407 08:44:40.112335 18909 sgd_solver.cpp:105] Iteration 2244, lr = 0.01
I0407 08:44:44.534963 18909 solver.cpp:218] Iteration 2256 (2.71332 iter/s, 4.42262s/12 iters), loss = 2.90881
I0407 08:44:44.535006 18909 solver.cpp:237]     Train net output #0: loss = 2.90881 (* 1 = 2.90881 loss)
I0407 08:44:44.535013 18909 sgd_solver.cpp:105] Iteration 2256, lr = 0.01
I0407 08:44:49.837266 18909 solver.cpp:218] Iteration 2268 (2.26319 iter/s, 5.30224s/12 iters), loss = 2.48006
I0407 08:44:49.837314 18909 solver.cpp:237]     Train net output #0: loss = 2.48006 (* 1 = 2.48006 loss)
I0407 08:44:49.837322 18909 sgd_solver.cpp:105] Iteration 2268, lr = 0.01
I0407 08:44:55.072234 18909 solver.cpp:218] Iteration 2280 (2.2923 iter/s, 5.23491s/12 iters), loss = 2.62445
I0407 08:44:55.072341 18909 solver.cpp:237]     Train net output #0: loss = 2.62445 (* 1 = 2.62445 loss)
I0407 08:44:55.072348 18909 sgd_solver.cpp:105] Iteration 2280, lr = 0.01
I0407 08:45:00.477701 18909 solver.cpp:218] Iteration 2292 (2.22002 iter/s, 5.40535s/12 iters), loss = 2.8514
I0407 08:45:00.477741 18909 solver.cpp:237]     Train net output #0: loss = 2.8514 (* 1 = 2.8514 loss)
I0407 08:45:00.477747 18909 sgd_solver.cpp:105] Iteration 2292, lr = 0.01
I0407 08:45:05.521572 18909 solver.cpp:218] Iteration 2304 (2.37915 iter/s, 5.04382s/12 iters), loss = 2.66288
I0407 08:45:05.521627 18909 solver.cpp:237]     Train net output #0: loss = 2.66288 (* 1 = 2.66288 loss)
I0407 08:45:05.521639 18909 sgd_solver.cpp:105] Iteration 2304, lr = 0.01
I0407 08:45:10.826191 18909 solver.cpp:218] Iteration 2316 (2.26221 iter/s, 5.30456s/12 iters), loss = 2.47747
I0407 08:45:10.826239 18909 solver.cpp:237]     Train net output #0: loss = 2.47747 (* 1 = 2.47747 loss)
I0407 08:45:10.826247 18909 sgd_solver.cpp:105] Iteration 2316, lr = 0.01
I0407 08:45:14.904224 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:45:16.064785 18909 solver.cpp:218] Iteration 2328 (2.29072 iter/s, 5.23853s/12 iters), loss = 2.28245
I0407 08:45:16.064837 18909 solver.cpp:237]     Train net output #0: loss = 2.28245 (* 1 = 2.28245 loss)
I0407 08:45:16.064846 18909 sgd_solver.cpp:105] Iteration 2328, lr = 0.01
I0407 08:45:21.027252 18909 solver.cpp:218] Iteration 2340 (2.41818 iter/s, 4.96241s/12 iters), loss = 2.57201
I0407 08:45:21.027298 18909 solver.cpp:237]     Train net output #0: loss = 2.57201 (* 1 = 2.57201 loss)
I0407 08:45:21.027304 18909 sgd_solver.cpp:105] Iteration 2340, lr = 0.01
I0407 08:45:23.228871 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0407 08:45:26.731387 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0407 08:45:29.055281 18909 solver.cpp:330] Iteration 2346, Testing net (#0)
I0407 08:45:29.055306 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:45:32.411362 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:45:33.343928 18909 solver.cpp:397]     Test net output #0: accuracy = 0.22549
I0407 08:45:33.343967 18909 solver.cpp:397]     Test net output #1: loss = 3.39779 (* 1 = 3.39779 loss)
I0407 08:45:35.194123 18909 solver.cpp:218] Iteration 2352 (0.84705 iter/s, 14.1668s/12 iters), loss = 2.57304
I0407 08:45:35.194157 18909 solver.cpp:237]     Train net output #0: loss = 2.57304 (* 1 = 2.57304 loss)
I0407 08:45:35.194164 18909 sgd_solver.cpp:105] Iteration 2352, lr = 0.01
I0407 08:45:40.295898 18909 solver.cpp:218] Iteration 2364 (2.35215 iter/s, 5.10172s/12 iters), loss = 2.25285
I0407 08:45:40.295955 18909 solver.cpp:237]     Train net output #0: loss = 2.25285 (* 1 = 2.25285 loss)
I0407 08:45:40.295965 18909 sgd_solver.cpp:105] Iteration 2364, lr = 0.01
I0407 08:45:45.627336 18909 solver.cpp:218] Iteration 2376 (2.25083 iter/s, 5.33137s/12 iters), loss = 2.66739
I0407 08:45:45.627372 18909 solver.cpp:237]     Train net output #0: loss = 2.66739 (* 1 = 2.66739 loss)
I0407 08:45:45.627380 18909 sgd_solver.cpp:105] Iteration 2376, lr = 0.01
I0407 08:45:51.016963 18909 solver.cpp:218] Iteration 2388 (2.22652 iter/s, 5.38958s/12 iters), loss = 2.48572
I0407 08:45:51.017026 18909 solver.cpp:237]     Train net output #0: loss = 2.48572 (* 1 = 2.48572 loss)
I0407 08:45:51.017037 18909 sgd_solver.cpp:105] Iteration 2388, lr = 0.01
I0407 08:45:56.202379 18909 solver.cpp:218] Iteration 2400 (2.31422 iter/s, 5.18534s/12 iters), loss = 2.52155
I0407 08:45:56.202420 18909 solver.cpp:237]     Train net output #0: loss = 2.52155 (* 1 = 2.52155 loss)
I0407 08:45:56.202427 18909 sgd_solver.cpp:105] Iteration 2400, lr = 0.01
I0407 08:46:01.451120 18909 solver.cpp:218] Iteration 2412 (2.28629 iter/s, 5.24869s/12 iters), loss = 2.43355
I0407 08:46:01.451223 18909 solver.cpp:237]     Train net output #0: loss = 2.43355 (* 1 = 2.43355 loss)
I0407 08:46:01.451231 18909 sgd_solver.cpp:105] Iteration 2412, lr = 0.01
I0407 08:46:06.689493 18909 solver.cpp:218] Iteration 2424 (2.29084 iter/s, 5.23826s/12 iters), loss = 2.51327
I0407 08:46:06.689533 18909 solver.cpp:237]     Train net output #0: loss = 2.51327 (* 1 = 2.51327 loss)
I0407 08:46:06.689541 18909 sgd_solver.cpp:105] Iteration 2424, lr = 0.01
I0407 08:46:07.794122 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:46:11.993356 18909 solver.cpp:218] Iteration 2436 (2.26252 iter/s, 5.30381s/12 iters), loss = 2.25521
I0407 08:46:11.993398 18909 solver.cpp:237]     Train net output #0: loss = 2.25521 (* 1 = 2.25521 loss)
I0407 08:46:11.993405 18909 sgd_solver.cpp:105] Iteration 2436, lr = 0.01
I0407 08:46:16.630040 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0407 08:46:20.465237 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0407 08:46:22.792834 18909 solver.cpp:330] Iteration 2448, Testing net (#0)
I0407 08:46:22.792855 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:46:26.168071 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:46:27.131814 18909 solver.cpp:397]     Test net output #0: accuracy = 0.257353
I0407 08:46:27.131841 18909 solver.cpp:397]     Test net output #1: loss = 3.27876 (* 1 = 3.27876 loss)
I0407 08:46:27.267554 18909 solver.cpp:218] Iteration 2448 (0.785641 iter/s, 15.2742s/12 iters), loss = 2.65092
I0407 08:46:27.269131 18909 solver.cpp:237]     Train net output #0: loss = 2.65092 (* 1 = 2.65092 loss)
I0407 08:46:27.269142 18909 sgd_solver.cpp:105] Iteration 2448, lr = 0.01
I0407 08:46:31.561810 18909 solver.cpp:218] Iteration 2460 (2.79546 iter/s, 4.29267s/12 iters), loss = 2.64007
I0407 08:46:31.562016 18909 solver.cpp:237]     Train net output #0: loss = 2.64007 (* 1 = 2.64007 loss)
I0407 08:46:31.562027 18909 sgd_solver.cpp:105] Iteration 2460, lr = 0.01
I0407 08:46:36.664909 18909 solver.cpp:218] Iteration 2472 (2.35161 iter/s, 5.10289s/12 iters), loss = 2.58532
I0407 08:46:36.664949 18909 solver.cpp:237]     Train net output #0: loss = 2.58532 (* 1 = 2.58532 loss)
I0407 08:46:36.664956 18909 sgd_solver.cpp:105] Iteration 2472, lr = 0.01
I0407 08:46:41.850811 18909 solver.cpp:218] Iteration 2484 (2.31399 iter/s, 5.18585s/12 iters), loss = 1.8716
I0407 08:46:41.850852 18909 solver.cpp:237]     Train net output #0: loss = 1.8716 (* 1 = 1.8716 loss)
I0407 08:46:41.850857 18909 sgd_solver.cpp:105] Iteration 2484, lr = 0.01
I0407 08:46:46.873422 18909 solver.cpp:218] Iteration 2496 (2.38923 iter/s, 5.02254s/12 iters), loss = 2.12852
I0407 08:46:46.873481 18909 solver.cpp:237]     Train net output #0: loss = 2.12852 (* 1 = 2.12852 loss)
I0407 08:46:46.873493 18909 sgd_solver.cpp:105] Iteration 2496, lr = 0.01
I0407 08:46:52.048174 18909 solver.cpp:218] Iteration 2508 (2.31898 iter/s, 5.17468s/12 iters), loss = 2.35961
I0407 08:46:52.048218 18909 solver.cpp:237]     Train net output #0: loss = 2.35961 (* 1 = 2.35961 loss)
I0407 08:46:52.048225 18909 sgd_solver.cpp:105] Iteration 2508, lr = 0.01
I0407 08:46:57.092680 18909 solver.cpp:218] Iteration 2520 (2.37885 iter/s, 5.04445s/12 iters), loss = 2.0332
I0407 08:46:57.092725 18909 solver.cpp:237]     Train net output #0: loss = 2.0332 (* 1 = 2.0332 loss)
I0407 08:46:57.092731 18909 sgd_solver.cpp:105] Iteration 2520, lr = 0.01
I0407 08:47:00.464593 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:47:02.436739 18909 solver.cpp:218] Iteration 2532 (2.24551 iter/s, 5.34401s/12 iters), loss = 1.97736
I0407 08:47:02.436854 18909 solver.cpp:237]     Train net output #0: loss = 1.97736 (* 1 = 1.97736 loss)
I0407 08:47:02.436862 18909 sgd_solver.cpp:105] Iteration 2532, lr = 0.01
I0407 08:47:07.680091 18909 solver.cpp:218] Iteration 2544 (2.28867 iter/s, 5.24323s/12 iters), loss = 2.11883
I0407 08:47:07.680138 18909 solver.cpp:237]     Train net output #0: loss = 2.11883 (* 1 = 2.11883 loss)
I0407 08:47:07.680146 18909 sgd_solver.cpp:105] Iteration 2544, lr = 0.01
I0407 08:47:09.662534 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0407 08:47:14.193246 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0407 08:47:16.551986 18909 solver.cpp:330] Iteration 2550, Testing net (#0)
I0407 08:47:16.552004 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:47:19.846909 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:47:20.848598 18909 solver.cpp:397]     Test net output #0: accuracy = 0.25
I0407 08:47:20.848640 18909 solver.cpp:397]     Test net output #1: loss = 3.24308 (* 1 = 3.24308 loss)
I0407 08:47:22.787698 18909 solver.cpp:218] Iteration 2556 (0.794304 iter/s, 15.1076s/12 iters), loss = 2.38268
I0407 08:47:22.787734 18909 solver.cpp:237]     Train net output #0: loss = 2.38268 (* 1 = 2.38268 loss)
I0407 08:47:22.787741 18909 sgd_solver.cpp:105] Iteration 2556, lr = 0.01
I0407 08:47:28.133337 18909 solver.cpp:218] Iteration 2568 (2.24484 iter/s, 5.34558s/12 iters), loss = 2.0532
I0407 08:47:28.133388 18909 solver.cpp:237]     Train net output #0: loss = 2.0532 (* 1 = 2.0532 loss)
I0407 08:47:28.133396 18909 sgd_solver.cpp:105] Iteration 2568, lr = 0.01
I0407 08:47:33.243041 18909 solver.cpp:218] Iteration 2580 (2.3485 iter/s, 5.10965s/12 iters), loss = 2.36438
I0407 08:47:33.243167 18909 solver.cpp:237]     Train net output #0: loss = 2.36438 (* 1 = 2.36438 loss)
I0407 08:47:33.243175 18909 sgd_solver.cpp:105] Iteration 2580, lr = 0.01
I0407 08:47:38.390725 18909 solver.cpp:218] Iteration 2592 (2.33121 iter/s, 5.14754s/12 iters), loss = 2.21738
I0407 08:47:38.390771 18909 solver.cpp:237]     Train net output #0: loss = 2.21738 (* 1 = 2.21738 loss)
I0407 08:47:38.390779 18909 sgd_solver.cpp:105] Iteration 2592, lr = 0.01
I0407 08:47:43.426760 18909 solver.cpp:218] Iteration 2604 (2.38285 iter/s, 5.03598s/12 iters), loss = 2.27526
I0407 08:47:43.426805 18909 solver.cpp:237]     Train net output #0: loss = 2.27526 (* 1 = 2.27526 loss)
I0407 08:47:43.426815 18909 sgd_solver.cpp:105] Iteration 2604, lr = 0.01
I0407 08:47:48.634521 18909 solver.cpp:218] Iteration 2616 (2.30428 iter/s, 5.2077s/12 iters), loss = 2.0353
I0407 08:47:48.634565 18909 solver.cpp:237]     Train net output #0: loss = 2.0353 (* 1 = 2.0353 loss)
I0407 08:47:48.634572 18909 sgd_solver.cpp:105] Iteration 2616, lr = 0.01
I0407 08:47:53.504789 18909 solver.cpp:218] Iteration 2628 (2.46396 iter/s, 4.87021s/12 iters), loss = 1.80986
I0407 08:47:53.504834 18909 solver.cpp:237]     Train net output #0: loss = 1.80986 (* 1 = 1.80986 loss)
I0407 08:47:53.504842 18909 sgd_solver.cpp:105] Iteration 2628, lr = 0.01
I0407 08:47:53.970247 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:47:58.730860 18909 solver.cpp:218] Iteration 2640 (2.29621 iter/s, 5.22601s/12 iters), loss = 2.4848
I0407 08:47:58.730901 18909 solver.cpp:237]     Train net output #0: loss = 2.4848 (* 1 = 2.4848 loss)
I0407 08:47:58.730908 18909 sgd_solver.cpp:105] Iteration 2640, lr = 0.01
I0407 08:48:03.481338 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0407 08:48:07.694335 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0407 08:48:11.873165 18909 solver.cpp:330] Iteration 2652, Testing net (#0)
I0407 08:48:11.873186 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:48:15.188526 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:48:16.243554 18909 solver.cpp:397]     Test net output #0: accuracy = 0.276348
I0407 08:48:16.243587 18909 solver.cpp:397]     Test net output #1: loss = 3.16688 (* 1 = 3.16688 loss)
I0407 08:48:16.384773 18909 solver.cpp:218] Iteration 2652 (0.679738 iter/s, 17.6539s/12 iters), loss = 2.46868
I0407 08:48:16.384838 18909 solver.cpp:237]     Train net output #0: loss = 2.46868 (* 1 = 2.46868 loss)
I0407 08:48:16.384851 18909 sgd_solver.cpp:105] Iteration 2652, lr = 0.01
I0407 08:48:20.630594 18909 solver.cpp:218] Iteration 2664 (2.82636 iter/s, 4.24574s/12 iters), loss = 2.40093
I0407 08:48:20.630642 18909 solver.cpp:237]     Train net output #0: loss = 2.40093 (* 1 = 2.40093 loss)
I0407 08:48:20.630651 18909 sgd_solver.cpp:105] Iteration 2664, lr = 0.01
I0407 08:48:25.678526 18909 solver.cpp:218] Iteration 2676 (2.37724 iter/s, 5.04787s/12 iters), loss = 2.39632
I0407 08:48:25.678565 18909 solver.cpp:237]     Train net output #0: loss = 2.39632 (* 1 = 2.39632 loss)
I0407 08:48:25.678571 18909 sgd_solver.cpp:105] Iteration 2676, lr = 0.01
I0407 08:48:30.833968 18909 solver.cpp:218] Iteration 2688 (2.32766 iter/s, 5.15539s/12 iters), loss = 2.33116
I0407 08:48:30.834008 18909 solver.cpp:237]     Train net output #0: loss = 2.33116 (* 1 = 2.33116 loss)
I0407 08:48:30.834015 18909 sgd_solver.cpp:105] Iteration 2688, lr = 0.01
I0407 08:48:35.983392 18909 solver.cpp:218] Iteration 2700 (2.33038 iter/s, 5.14937s/12 iters), loss = 2.11049
I0407 08:48:35.983494 18909 solver.cpp:237]     Train net output #0: loss = 2.11049 (* 1 = 2.11049 loss)
I0407 08:48:35.983501 18909 sgd_solver.cpp:105] Iteration 2700, lr = 0.01
I0407 08:48:41.346761 18909 solver.cpp:218] Iteration 2712 (2.23745 iter/s, 5.36326s/12 iters), loss = 2.22989
I0407 08:48:41.346802 18909 solver.cpp:237]     Train net output #0: loss = 2.22989 (* 1 = 2.22989 loss)
I0407 08:48:41.346808 18909 sgd_solver.cpp:105] Iteration 2712, lr = 0.01
I0407 08:48:46.524515 18909 solver.cpp:218] Iteration 2724 (2.31763 iter/s, 5.1777s/12 iters), loss = 2.66377
I0407 08:48:46.524559 18909 solver.cpp:237]     Train net output #0: loss = 2.66377 (* 1 = 2.66377 loss)
I0407 08:48:46.524565 18909 sgd_solver.cpp:105] Iteration 2724, lr = 0.01
I0407 08:48:49.285071 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:48:51.900947 18909 solver.cpp:218] Iteration 2736 (2.23199 iter/s, 5.37637s/12 iters), loss = 2.34164
I0407 08:48:51.900995 18909 solver.cpp:237]     Train net output #0: loss = 2.34164 (* 1 = 2.34164 loss)
I0407 08:48:51.901001 18909 sgd_solver.cpp:105] Iteration 2736, lr = 0.01
I0407 08:48:57.128388 18909 solver.cpp:218] Iteration 2748 (2.2956 iter/s, 5.22738s/12 iters), loss = 2.17624
I0407 08:48:57.128428 18909 solver.cpp:237]     Train net output #0: loss = 2.17624 (* 1 = 2.17624 loss)
I0407 08:48:57.128435 18909 sgd_solver.cpp:105] Iteration 2748, lr = 0.01
I0407 08:48:59.239233 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0407 08:49:03.605988 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0407 08:49:08.172111 18909 solver.cpp:330] Iteration 2754, Testing net (#0)
I0407 08:49:08.172205 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:49:11.135991 18909 blocking_queue.cpp:49] Waiting for data
I0407 08:49:11.368783 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:49:12.451671 18909 solver.cpp:397]     Test net output #0: accuracy = 0.267157
I0407 08:49:12.451721 18909 solver.cpp:397]     Test net output #1: loss = 3.19212 (* 1 = 3.19212 loss)
I0407 08:49:14.291599 18909 solver.cpp:218] Iteration 2760 (0.699172 iter/s, 17.1632s/12 iters), loss = 2.40462
I0407 08:49:14.291640 18909 solver.cpp:237]     Train net output #0: loss = 2.40462 (* 1 = 2.40462 loss)
I0407 08:49:14.291646 18909 sgd_solver.cpp:105] Iteration 2760, lr = 0.01
I0407 08:49:19.370707 18909 solver.cpp:218] Iteration 2772 (2.36264 iter/s, 5.07905s/12 iters), loss = 1.978
I0407 08:49:19.370748 18909 solver.cpp:237]     Train net output #0: loss = 1.978 (* 1 = 1.978 loss)
I0407 08:49:19.370754 18909 sgd_solver.cpp:105] Iteration 2772, lr = 0.01
I0407 08:49:24.640534 18909 solver.cpp:218] Iteration 2784 (2.27714 iter/s, 5.26977s/12 iters), loss = 2.12126
I0407 08:49:24.640575 18909 solver.cpp:237]     Train net output #0: loss = 2.12126 (* 1 = 2.12126 loss)
I0407 08:49:24.640583 18909 sgd_solver.cpp:105] Iteration 2784, lr = 0.01
I0407 08:49:29.767505 18909 solver.cpp:218] Iteration 2796 (2.34059 iter/s, 5.12692s/12 iters), loss = 2.2206
I0407 08:49:29.767545 18909 solver.cpp:237]     Train net output #0: loss = 2.2206 (* 1 = 2.2206 loss)
I0407 08:49:29.767551 18909 sgd_solver.cpp:105] Iteration 2796, lr = 0.01
I0407 08:49:35.110628 18909 solver.cpp:218] Iteration 2808 (2.2459 iter/s, 5.34307s/12 iters), loss = 1.92523
I0407 08:49:35.110672 18909 solver.cpp:237]     Train net output #0: loss = 1.92523 (* 1 = 1.92523 loss)
I0407 08:49:35.110679 18909 sgd_solver.cpp:105] Iteration 2808, lr = 0.01
I0407 08:49:40.407631 18909 solver.cpp:218] Iteration 2820 (2.26546 iter/s, 5.29694s/12 iters), loss = 2.07824
I0407 08:49:40.407745 18909 solver.cpp:237]     Train net output #0: loss = 2.07824 (* 1 = 2.07824 loss)
I0407 08:49:40.407754 18909 sgd_solver.cpp:105] Iteration 2820, lr = 0.01
I0407 08:49:45.503518 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:49:45.824492 18909 solver.cpp:218] Iteration 2832 (2.21535 iter/s, 5.41674s/12 iters), loss = 2.08259
I0407 08:49:45.824537 18909 solver.cpp:237]     Train net output #0: loss = 2.08259 (* 1 = 2.08259 loss)
I0407 08:49:45.824543 18909 sgd_solver.cpp:105] Iteration 2832, lr = 0.01
I0407 08:49:51.102432 18909 solver.cpp:218] Iteration 2844 (2.27364 iter/s, 5.27788s/12 iters), loss = 2.1771
I0407 08:49:51.102475 18909 solver.cpp:237]     Train net output #0: loss = 2.1771 (* 1 = 2.1771 loss)
I0407 08:49:51.102481 18909 sgd_solver.cpp:105] Iteration 2844, lr = 0.01
I0407 08:49:55.719430 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0407 08:50:00.228973 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0407 08:50:03.986274 18909 solver.cpp:330] Iteration 2856, Testing net (#0)
I0407 08:50:03.986290 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:50:07.199664 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:50:08.312101 18909 solver.cpp:397]     Test net output #0: accuracy = 0.289216
I0407 08:50:08.312139 18909 solver.cpp:397]     Test net output #1: loss = 3.00608 (* 1 = 3.00608 loss)
I0407 08:50:08.453001 18909 solver.cpp:218] Iteration 2856 (0.691622 iter/s, 17.3505s/12 iters), loss = 1.80395
I0407 08:50:08.453042 18909 solver.cpp:237]     Train net output #0: loss = 1.80395 (* 1 = 1.80395 loss)
I0407 08:50:08.453048 18909 sgd_solver.cpp:105] Iteration 2856, lr = 0.01
I0407 08:50:12.757339 18909 solver.cpp:218] Iteration 2868 (2.78792 iter/s, 4.30428s/12 iters), loss = 2.07658
I0407 08:50:12.757459 18909 solver.cpp:237]     Train net output #0: loss = 2.07658 (* 1 = 2.07658 loss)
I0407 08:50:12.757468 18909 sgd_solver.cpp:105] Iteration 2868, lr = 0.01
I0407 08:50:18.046439 18909 solver.cpp:218] Iteration 2880 (2.26887 iter/s, 5.28897s/12 iters), loss = 2.20526
I0407 08:50:18.046479 18909 solver.cpp:237]     Train net output #0: loss = 2.20526 (* 1 = 2.20526 loss)
I0407 08:50:18.046486 18909 sgd_solver.cpp:105] Iteration 2880, lr = 0.01
I0407 08:50:23.168161 18909 solver.cpp:218] Iteration 2892 (2.34299 iter/s, 5.12167s/12 iters), loss = 2.12059
I0407 08:50:23.168202 18909 solver.cpp:237]     Train net output #0: loss = 2.12059 (* 1 = 2.12059 loss)
I0407 08:50:23.168210 18909 sgd_solver.cpp:105] Iteration 2892, lr = 0.01
I0407 08:50:28.420154 18909 solver.cpp:218] Iteration 2904 (2.28487 iter/s, 5.25194s/12 iters), loss = 1.98397
I0407 08:50:28.420197 18909 solver.cpp:237]     Train net output #0: loss = 1.98397 (* 1 = 1.98397 loss)
I0407 08:50:28.420204 18909 sgd_solver.cpp:105] Iteration 2904, lr = 0.01
I0407 08:50:33.451157 18909 solver.cpp:218] Iteration 2916 (2.38524 iter/s, 5.03095s/12 iters), loss = 1.67152
I0407 08:50:33.451197 18909 solver.cpp:237]     Train net output #0: loss = 1.67152 (* 1 = 1.67152 loss)
I0407 08:50:33.451203 18909 sgd_solver.cpp:105] Iteration 2916, lr = 0.01
I0407 08:50:38.654767 18909 solver.cpp:218] Iteration 2928 (2.30611 iter/s, 5.20356s/12 iters), loss = 2.07739
I0407 08:50:38.654814 18909 solver.cpp:237]     Train net output #0: loss = 2.07739 (* 1 = 2.07739 loss)
I0407 08:50:38.654822 18909 sgd_solver.cpp:105] Iteration 2928, lr = 0.01
I0407 08:50:40.483709 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:50:43.908747 18909 solver.cpp:218] Iteration 2940 (2.28401 iter/s, 5.25392s/12 iters), loss = 2.08847
I0407 08:50:43.908854 18909 solver.cpp:237]     Train net output #0: loss = 2.08847 (* 1 = 2.08847 loss)
I0407 08:50:43.908861 18909 sgd_solver.cpp:105] Iteration 2940, lr = 0.01
I0407 08:50:49.176204 18909 solver.cpp:218] Iteration 2952 (2.27819 iter/s, 5.26734s/12 iters), loss = 1.67035
I0407 08:50:49.176247 18909 solver.cpp:237]     Train net output #0: loss = 1.67035 (* 1 = 1.67035 loss)
I0407 08:50:49.176255 18909 sgd_solver.cpp:105] Iteration 2952, lr = 0.01
I0407 08:50:51.295105 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0407 08:50:54.340978 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0407 08:50:58.589184 18909 solver.cpp:330] Iteration 2958, Testing net (#0)
I0407 08:50:58.589205 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:51:01.933691 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:51:03.094729 18909 solver.cpp:397]     Test net output #0: accuracy = 0.272672
I0407 08:51:03.094763 18909 solver.cpp:397]     Test net output #1: loss = 3.05308 (* 1 = 3.05308 loss)
I0407 08:51:04.988615 18909 solver.cpp:218] Iteration 2964 (0.7589 iter/s, 15.8124s/12 iters), loss = 1.85784
I0407 08:51:04.988663 18909 solver.cpp:237]     Train net output #0: loss = 1.85784 (* 1 = 1.85784 loss)
I0407 08:51:04.988670 18909 sgd_solver.cpp:105] Iteration 2964, lr = 0.01
I0407 08:51:10.161931 18909 solver.cpp:218] Iteration 2976 (2.31962 iter/s, 5.17325s/12 iters), loss = 2.04021
I0407 08:51:10.161975 18909 solver.cpp:237]     Train net output #0: loss = 2.04021 (* 1 = 2.04021 loss)
I0407 08:51:10.161983 18909 sgd_solver.cpp:105] Iteration 2976, lr = 0.01
I0407 08:51:15.495272 18909 solver.cpp:218] Iteration 2988 (2.25002 iter/s, 5.33329s/12 iters), loss = 2.19275
I0407 08:51:15.495416 18909 solver.cpp:237]     Train net output #0: loss = 2.19275 (* 1 = 2.19275 loss)
I0407 08:51:15.495424 18909 sgd_solver.cpp:105] Iteration 2988, lr = 0.01
I0407 08:51:20.675276 18909 solver.cpp:218] Iteration 3000 (2.31667 iter/s, 5.17985s/12 iters), loss = 1.94793
I0407 08:51:20.675318 18909 solver.cpp:237]     Train net output #0: loss = 1.94793 (* 1 = 1.94793 loss)
I0407 08:51:20.675325 18909 sgd_solver.cpp:105] Iteration 3000, lr = 0.01
I0407 08:51:25.896103 18909 solver.cpp:218] Iteration 3012 (2.29851 iter/s, 5.22077s/12 iters), loss = 1.83215
I0407 08:51:25.896144 18909 solver.cpp:237]     Train net output #0: loss = 1.83215 (* 1 = 1.83215 loss)
I0407 08:51:25.896152 18909 sgd_solver.cpp:105] Iteration 3012, lr = 0.01
I0407 08:51:30.866384 18909 solver.cpp:218] Iteration 3024 (2.41438 iter/s, 4.97023s/12 iters), loss = 1.64171
I0407 08:51:30.866421 18909 solver.cpp:237]     Train net output #0: loss = 1.64171 (* 1 = 1.64171 loss)
I0407 08:51:30.866428 18909 sgd_solver.cpp:105] Iteration 3024, lr = 0.01
I0407 08:51:35.158339 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:51:36.293049 18909 solver.cpp:218] Iteration 3036 (2.21132 iter/s, 5.42661s/12 iters), loss = 2.44861
I0407 08:51:36.293092 18909 solver.cpp:237]     Train net output #0: loss = 2.44861 (* 1 = 2.44861 loss)
I0407 08:51:36.293099 18909 sgd_solver.cpp:105] Iteration 3036, lr = 0.01
I0407 08:51:41.384120 18909 solver.cpp:218] Iteration 3048 (2.35709 iter/s, 5.09102s/12 iters), loss = 1.8686
I0407 08:51:41.384164 18909 solver.cpp:237]     Train net output #0: loss = 1.8686 (* 1 = 1.8686 loss)
I0407 08:51:41.384172 18909 sgd_solver.cpp:105] Iteration 3048, lr = 0.01
I0407 08:51:45.883678 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0407 08:51:48.898365 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0407 08:51:52.561473 18909 solver.cpp:330] Iteration 3060, Testing net (#0)
I0407 08:51:52.561497 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:51:55.694394 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:51:56.888916 18909 solver.cpp:397]     Test net output #0: accuracy = 0.294118
I0407 08:51:56.888949 18909 solver.cpp:397]     Test net output #1: loss = 3.03055 (* 1 = 3.03055 loss)
I0407 08:51:57.029963 18909 solver.cpp:218] Iteration 3060 (0.766979 iter/s, 15.6458s/12 iters), loss = 2.05575
I0407 08:51:57.030001 18909 solver.cpp:237]     Train net output #0: loss = 2.05575 (* 1 = 2.05575 loss)
I0407 08:51:57.030007 18909 sgd_solver.cpp:105] Iteration 3060, lr = 0.01
I0407 08:52:01.350841 18909 solver.cpp:218] Iteration 3072 (2.77725 iter/s, 4.32082s/12 iters), loss = 1.67569
I0407 08:52:01.350888 18909 solver.cpp:237]     Train net output #0: loss = 1.67569 (* 1 = 1.67569 loss)
I0407 08:52:01.350895 18909 sgd_solver.cpp:105] Iteration 3072, lr = 0.01
I0407 08:52:06.368022 18909 solver.cpp:218] Iteration 3084 (2.39181 iter/s, 5.01713s/12 iters), loss = 1.84075
I0407 08:52:06.368060 18909 solver.cpp:237]     Train net output #0: loss = 1.84075 (* 1 = 1.84075 loss)
I0407 08:52:06.368068 18909 sgd_solver.cpp:105] Iteration 3084, lr = 0.01
I0407 08:52:11.308408 18909 solver.cpp:218] Iteration 3096 (2.42899 iter/s, 4.94033s/12 iters), loss = 1.81027
I0407 08:52:11.308459 18909 solver.cpp:237]     Train net output #0: loss = 1.81027 (* 1 = 1.81027 loss)
I0407 08:52:11.308467 18909 sgd_solver.cpp:105] Iteration 3096, lr = 0.01
I0407 08:52:16.407213 18909 solver.cpp:218] Iteration 3108 (2.35352 iter/s, 5.09874s/12 iters), loss = 1.86016
I0407 08:52:16.407351 18909 solver.cpp:237]     Train net output #0: loss = 1.86016 (* 1 = 1.86016 loss)
I0407 08:52:16.407358 18909 sgd_solver.cpp:105] Iteration 3108, lr = 0.01
I0407 08:52:21.779764 18909 solver.cpp:218] Iteration 3120 (2.23364 iter/s, 5.3724s/12 iters), loss = 1.5661
I0407 08:52:21.779801 18909 solver.cpp:237]     Train net output #0: loss = 1.5661 (* 1 = 1.5661 loss)
I0407 08:52:21.779808 18909 sgd_solver.cpp:105] Iteration 3120, lr = 0.01
I0407 08:52:26.788580 18909 solver.cpp:218] Iteration 3132 (2.3958 iter/s, 5.00877s/12 iters), loss = 2.00919
I0407 08:52:26.788623 18909 solver.cpp:237]     Train net output #0: loss = 2.00919 (* 1 = 2.00919 loss)
I0407 08:52:26.788630 18909 sgd_solver.cpp:105] Iteration 3132, lr = 0.01
I0407 08:52:27.857321 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:52:32.046677 18909 solver.cpp:218] Iteration 3144 (2.28222 iter/s, 5.25804s/12 iters), loss = 1.82658
I0407 08:52:32.046732 18909 solver.cpp:237]     Train net output #0: loss = 1.82658 (* 1 = 1.82658 loss)
I0407 08:52:32.046741 18909 sgd_solver.cpp:105] Iteration 3144, lr = 0.01
I0407 08:52:37.201274 18909 solver.cpp:218] Iteration 3156 (2.32805 iter/s, 5.15452s/12 iters), loss = 2.06181
I0407 08:52:37.201321 18909 solver.cpp:237]     Train net output #0: loss = 2.06181 (* 1 = 2.06181 loss)
I0407 08:52:37.201328 18909 sgd_solver.cpp:105] Iteration 3156, lr = 0.01
I0407 08:52:39.264878 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0407 08:52:42.282070 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0407 08:52:44.580585 18909 solver.cpp:330] Iteration 3162, Testing net (#0)
I0407 08:52:44.580605 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:52:47.667594 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:52:48.974282 18909 solver.cpp:397]     Test net output #0: accuracy = 0.284314
I0407 08:52:48.974313 18909 solver.cpp:397]     Test net output #1: loss = 3.06301 (* 1 = 3.06301 loss)
I0407 08:52:50.857430 18909 solver.cpp:218] Iteration 3168 (0.878728 iter/s, 13.6561s/12 iters), loss = 1.80512
I0407 08:52:50.857467 18909 solver.cpp:237]     Train net output #0: loss = 1.80512 (* 1 = 1.80512 loss)
I0407 08:52:50.857473 18909 sgd_solver.cpp:105] Iteration 3168, lr = 0.01
I0407 08:52:56.058137 18909 solver.cpp:218] Iteration 3180 (2.3074 iter/s, 5.20065s/12 iters), loss = 2.30743
I0407 08:52:56.058189 18909 solver.cpp:237]     Train net output #0: loss = 2.30743 (* 1 = 2.30743 loss)
I0407 08:52:56.058199 18909 sgd_solver.cpp:105] Iteration 3180, lr = 0.01
I0407 08:53:01.211863 18909 solver.cpp:218] Iteration 3192 (2.32844 iter/s, 5.15366s/12 iters), loss = 1.59202
I0407 08:53:01.211907 18909 solver.cpp:237]     Train net output #0: loss = 1.59202 (* 1 = 1.59202 loss)
I0407 08:53:01.211915 18909 sgd_solver.cpp:105] Iteration 3192, lr = 0.01
I0407 08:53:06.639534 18909 solver.cpp:218] Iteration 3204 (2.21092 iter/s, 5.42762s/12 iters), loss = 1.90446
I0407 08:53:06.639577 18909 solver.cpp:237]     Train net output #0: loss = 1.90446 (* 1 = 1.90446 loss)
I0407 08:53:06.639586 18909 sgd_solver.cpp:105] Iteration 3204, lr = 0.01
I0407 08:53:11.926613 18909 solver.cpp:218] Iteration 3216 (2.26971 iter/s, 5.28702s/12 iters), loss = 1.89727
I0407 08:53:11.926658 18909 solver.cpp:237]     Train net output #0: loss = 1.89727 (* 1 = 1.89727 loss)
I0407 08:53:11.926667 18909 sgd_solver.cpp:105] Iteration 3216, lr = 0.01
I0407 08:53:17.217800 18909 solver.cpp:218] Iteration 3228 (2.26795 iter/s, 5.29113s/12 iters), loss = 1.62532
I0407 08:53:17.217844 18909 solver.cpp:237]     Train net output #0: loss = 1.62532 (* 1 = 1.62532 loss)
I0407 08:53:17.217852 18909 sgd_solver.cpp:105] Iteration 3228, lr = 0.01
I0407 08:53:20.455715 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:53:22.338833 18909 solver.cpp:218] Iteration 3240 (2.3433 iter/s, 5.12098s/12 iters), loss = 1.59093
I0407 08:53:22.338874 18909 solver.cpp:237]     Train net output #0: loss = 1.59093 (* 1 = 1.59093 loss)
I0407 08:53:22.338881 18909 sgd_solver.cpp:105] Iteration 3240, lr = 0.01
I0407 08:53:27.568737 18909 solver.cpp:218] Iteration 3252 (2.29452 iter/s, 5.22985s/12 iters), loss = 2.07068
I0407 08:53:27.568774 18909 solver.cpp:237]     Train net output #0: loss = 2.07068 (* 1 = 2.07068 loss)
I0407 08:53:27.568781 18909 sgd_solver.cpp:105] Iteration 3252, lr = 0.01
I0407 08:53:32.258224 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0407 08:53:35.261322 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0407 08:53:37.575068 18909 solver.cpp:330] Iteration 3264, Testing net (#0)
I0407 08:53:37.575086 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:53:40.595942 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:53:41.880007 18909 solver.cpp:397]     Test net output #0: accuracy = 0.292892
I0407 08:53:41.880051 18909 solver.cpp:397]     Test net output #1: loss = 3.06301 (* 1 = 3.06301 loss)
I0407 08:53:42.010805 18909 solver.cpp:218] Iteration 3264 (0.830908 iter/s, 14.442s/12 iters), loss = 1.79622
I0407 08:53:42.010849 18909 solver.cpp:237]     Train net output #0: loss = 1.79622 (* 1 = 1.79622 loss)
I0407 08:53:42.010856 18909 sgd_solver.cpp:105] Iteration 3264, lr = 0.01
I0407 08:53:46.265254 18909 solver.cpp:218] Iteration 3276 (2.82061 iter/s, 4.2544s/12 iters), loss = 1.8918
I0407 08:53:46.265295 18909 solver.cpp:237]     Train net output #0: loss = 1.8918 (* 1 = 1.8918 loss)
I0407 08:53:46.265301 18909 sgd_solver.cpp:105] Iteration 3276, lr = 0.01
I0407 08:53:51.489774 18909 solver.cpp:218] Iteration 3288 (2.29688 iter/s, 5.22448s/12 iters), loss = 1.6951
I0407 08:53:51.489890 18909 solver.cpp:237]     Train net output #0: loss = 1.6951 (* 1 = 1.6951 loss)
I0407 08:53:51.489898 18909 sgd_solver.cpp:105] Iteration 3288, lr = 0.01
I0407 08:53:56.603929 18909 solver.cpp:218] Iteration 3300 (2.34649 iter/s, 5.11403s/12 iters), loss = 1.79948
I0407 08:53:56.603969 18909 solver.cpp:237]     Train net output #0: loss = 1.79948 (* 1 = 1.79948 loss)
I0407 08:53:56.603976 18909 sgd_solver.cpp:105] Iteration 3300, lr = 0.01
I0407 08:54:01.846103 18909 solver.cpp:218] Iteration 3312 (2.28915 iter/s, 5.24212s/12 iters), loss = 1.87399
I0407 08:54:01.846144 18909 solver.cpp:237]     Train net output #0: loss = 1.87399 (* 1 = 1.87399 loss)
I0407 08:54:01.846151 18909 sgd_solver.cpp:105] Iteration 3312, lr = 0.01
I0407 08:54:07.094355 18909 solver.cpp:218] Iteration 3324 (2.2865 iter/s, 5.2482s/12 iters), loss = 1.70353
I0407 08:54:07.094393 18909 solver.cpp:237]     Train net output #0: loss = 1.70353 (* 1 = 1.70353 loss)
I0407 08:54:07.094400 18909 sgd_solver.cpp:105] Iteration 3324, lr = 0.01
I0407 08:54:12.253026 18909 solver.cpp:218] Iteration 3336 (2.3262 iter/s, 5.15862s/12 iters), loss = 1.93005
I0407 08:54:12.253069 18909 solver.cpp:237]     Train net output #0: loss = 1.93005 (* 1 = 1.93005 loss)
I0407 08:54:12.253077 18909 sgd_solver.cpp:105] Iteration 3336, lr = 0.01
I0407 08:54:12.756201 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:54:17.552083 18909 solver.cpp:218] Iteration 3348 (2.26458 iter/s, 5.299s/12 iters), loss = 1.89086
I0407 08:54:17.552122 18909 solver.cpp:237]     Train net output #0: loss = 1.89086 (* 1 = 1.89086 loss)
I0407 08:54:17.552129 18909 sgd_solver.cpp:105] Iteration 3348, lr = 0.01
I0407 08:54:22.814090 18909 solver.cpp:218] Iteration 3360 (2.28052 iter/s, 5.26195s/12 iters), loss = 1.69471
I0407 08:54:22.814218 18909 solver.cpp:237]     Train net output #0: loss = 1.69471 (* 1 = 1.69471 loss)
I0407 08:54:22.814227 18909 sgd_solver.cpp:105] Iteration 3360, lr = 0.01
I0407 08:54:24.874722 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0407 08:54:27.894078 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0407 08:54:30.206312 18909 solver.cpp:330] Iteration 3366, Testing net (#0)
I0407 08:54:30.206333 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:54:33.194635 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:54:34.527006 18909 solver.cpp:397]     Test net output #0: accuracy = 0.304534
I0407 08:54:34.527051 18909 solver.cpp:397]     Test net output #1: loss = 3.06667 (* 1 = 3.06667 loss)
I0407 08:54:36.415789 18909 solver.cpp:218] Iteration 3372 (0.882251 iter/s, 13.6016s/12 iters), loss = 1.74559
I0407 08:54:36.415827 18909 solver.cpp:237]     Train net output #0: loss = 1.74559 (* 1 = 1.74559 loss)
I0407 08:54:36.415833 18909 sgd_solver.cpp:105] Iteration 3372, lr = 0.0075
I0407 08:54:41.478353 18909 solver.cpp:218] Iteration 3384 (2.37036 iter/s, 5.06252s/12 iters), loss = 1.58993
I0407 08:54:41.478385 18909 solver.cpp:237]     Train net output #0: loss = 1.58993 (* 1 = 1.58993 loss)
I0407 08:54:41.478391 18909 sgd_solver.cpp:105] Iteration 3384, lr = 0.0075
I0407 08:54:46.619673 18909 solver.cpp:218] Iteration 3396 (2.33405 iter/s, 5.14127s/12 iters), loss = 1.65351
I0407 08:54:46.619714 18909 solver.cpp:237]     Train net output #0: loss = 1.65351 (* 1 = 1.65351 loss)
I0407 08:54:46.619722 18909 sgd_solver.cpp:105] Iteration 3396, lr = 0.0075
I0407 08:54:51.813627 18909 solver.cpp:218] Iteration 3408 (2.3104 iter/s, 5.1939s/12 iters), loss = 1.4544
I0407 08:54:51.813660 18909 solver.cpp:237]     Train net output #0: loss = 1.4544 (* 1 = 1.4544 loss)
I0407 08:54:51.813668 18909 sgd_solver.cpp:105] Iteration 3408, lr = 0.0075
I0407 08:54:56.958395 18909 solver.cpp:218] Iteration 3420 (2.33249 iter/s, 5.14472s/12 iters), loss = 1.72355
I0407 08:54:56.958552 18909 solver.cpp:237]     Train net output #0: loss = 1.72355 (* 1 = 1.72355 loss)
I0407 08:54:56.958562 18909 sgd_solver.cpp:105] Iteration 3420, lr = 0.0075
I0407 08:55:02.283514 18909 solver.cpp:218] Iteration 3432 (2.25354 iter/s, 5.32496s/12 iters), loss = 1.52179
I0407 08:55:02.283556 18909 solver.cpp:237]     Train net output #0: loss = 1.52179 (* 1 = 1.52179 loss)
I0407 08:55:02.283563 18909 sgd_solver.cpp:105] Iteration 3432, lr = 0.0075
I0407 08:55:05.038265 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:55:07.566911 18909 solver.cpp:218] Iteration 3444 (2.27129 iter/s, 5.28334s/12 iters), loss = 1.88339
I0407 08:55:07.566956 18909 solver.cpp:237]     Train net output #0: loss = 1.88339 (* 1 = 1.88339 loss)
I0407 08:55:07.566964 18909 sgd_solver.cpp:105] Iteration 3444, lr = 0.0075
I0407 08:55:12.934366 18909 solver.cpp:218] Iteration 3456 (2.23572 iter/s, 5.3674s/12 iters), loss = 1.56278
I0407 08:55:12.934409 18909 solver.cpp:237]     Train net output #0: loss = 1.56278 (* 1 = 1.56278 loss)
I0407 08:55:12.934417 18909 sgd_solver.cpp:105] Iteration 3456, lr = 0.0075
I0407 08:55:17.637266 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0407 08:55:20.656450 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0407 08:55:22.960994 18909 solver.cpp:330] Iteration 3468, Testing net (#0)
I0407 08:55:22.961012 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:55:23.384145 18909 blocking_queue.cpp:49] Waiting for data
I0407 08:55:25.912369 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:55:27.263276 18909 solver.cpp:397]     Test net output #0: accuracy = 0.329044
I0407 08:55:27.263384 18909 solver.cpp:397]     Test net output #1: loss = 3.03669 (* 1 = 3.03669 loss)
I0407 08:55:27.394168 18909 solver.cpp:218] Iteration 3468 (0.829889 iter/s, 14.4598s/12 iters), loss = 1.7317
I0407 08:55:27.394237 18909 solver.cpp:237]     Train net output #0: loss = 1.7317 (* 1 = 1.7317 loss)
I0407 08:55:27.394246 18909 sgd_solver.cpp:105] Iteration 3468, lr = 0.0075
I0407 08:55:31.638842 18909 solver.cpp:218] Iteration 3480 (2.82712 iter/s, 4.2446s/12 iters), loss = 1.43127
I0407 08:55:31.638878 18909 solver.cpp:237]     Train net output #0: loss = 1.43127 (* 1 = 1.43127 loss)
I0407 08:55:31.638885 18909 sgd_solver.cpp:105] Iteration 3480, lr = 0.0075
I0407 08:55:36.849762 18909 solver.cpp:218] Iteration 3492 (2.30288 iter/s, 5.21087s/12 iters), loss = 1.33127
I0407 08:55:36.849803 18909 solver.cpp:237]     Train net output #0: loss = 1.33127 (* 1 = 1.33127 loss)
I0407 08:55:36.849809 18909 sgd_solver.cpp:105] Iteration 3492, lr = 0.0075
I0407 08:55:42.105931 18909 solver.cpp:218] Iteration 3504 (2.28306 iter/s, 5.25611s/12 iters), loss = 1.5337
I0407 08:55:42.105974 18909 solver.cpp:237]     Train net output #0: loss = 1.5337 (* 1 = 1.5337 loss)
I0407 08:55:42.105981 18909 sgd_solver.cpp:105] Iteration 3504, lr = 0.0075
I0407 08:55:47.293396 18909 solver.cpp:218] Iteration 3516 (2.31329 iter/s, 5.18741s/12 iters), loss = 1.17514
I0407 08:55:47.293439 18909 solver.cpp:237]     Train net output #0: loss = 1.17514 (* 1 = 1.17514 loss)
I0407 08:55:47.293447 18909 sgd_solver.cpp:105] Iteration 3516, lr = 0.0075
I0407 08:55:52.425321 18909 solver.cpp:218] Iteration 3528 (2.33833 iter/s, 5.13187s/12 iters), loss = 1.24258
I0407 08:55:52.425364 18909 solver.cpp:237]     Train net output #0: loss = 1.24258 (* 1 = 1.24258 loss)
I0407 08:55:52.425370 18909 sgd_solver.cpp:105] Iteration 3528, lr = 0.0075
I0407 08:55:57.435446 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:55:57.728919 18909 solver.cpp:218] Iteration 3540 (2.26264 iter/s, 5.30355s/12 iters), loss = 1.19826
I0407 08:55:57.728950 18909 solver.cpp:237]     Train net output #0: loss = 1.19826 (* 1 = 1.19826 loss)
I0407 08:55:57.728955 18909 sgd_solver.cpp:105] Iteration 3540, lr = 0.0075
I0407 08:56:03.028204 18909 solver.cpp:218] Iteration 3552 (2.26447 iter/s, 5.29924s/12 iters), loss = 1.42551
I0407 08:56:03.028242 18909 solver.cpp:237]     Train net output #0: loss = 1.42551 (* 1 = 1.42551 loss)
I0407 08:56:03.028249 18909 sgd_solver.cpp:105] Iteration 3552, lr = 0.0075
I0407 08:56:08.055550 18909 solver.cpp:218] Iteration 3564 (2.38697 iter/s, 5.02729s/12 iters), loss = 1.35197
I0407 08:56:08.055596 18909 solver.cpp:237]     Train net output #0: loss = 1.35197 (* 1 = 1.35197 loss)
I0407 08:56:08.055604 18909 sgd_solver.cpp:105] Iteration 3564, lr = 0.0075
I0407 08:56:10.402330 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0407 08:56:13.377952 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0407 08:56:15.709666 18909 solver.cpp:330] Iteration 3570, Testing net (#0)
I0407 08:56:15.709690 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:56:18.669243 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:56:20.085443 18909 solver.cpp:397]     Test net output #0: accuracy = 0.340074
I0407 08:56:20.085479 18909 solver.cpp:397]     Test net output #1: loss = 3.03079 (* 1 = 3.03079 loss)
I0407 08:56:22.074489 18909 solver.cpp:218] Iteration 3576 (0.855988 iter/s, 14.0189s/12 iters), loss = 1.6089
I0407 08:56:22.074543 18909 solver.cpp:237]     Train net output #0: loss = 1.6089 (* 1 = 1.6089 loss)
I0407 08:56:22.074553 18909 sgd_solver.cpp:105] Iteration 3576, lr = 0.0075
I0407 08:56:27.113951 18909 solver.cpp:218] Iteration 3588 (2.38124 iter/s, 5.03939s/12 iters), loss = 1.18872
I0407 08:56:27.114001 18909 solver.cpp:237]     Train net output #0: loss = 1.18872 (* 1 = 1.18872 loss)
I0407 08:56:27.114009 18909 sgd_solver.cpp:105] Iteration 3588, lr = 0.0075
I0407 08:56:32.371522 18909 solver.cpp:218] Iteration 3600 (2.28245 iter/s, 5.25751s/12 iters), loss = 1.07532
I0407 08:56:32.371630 18909 solver.cpp:237]     Train net output #0: loss = 1.07532 (* 1 = 1.07532 loss)
I0407 08:56:32.371639 18909 sgd_solver.cpp:105] Iteration 3600, lr = 0.0075
I0407 08:56:37.613302 18909 solver.cpp:218] Iteration 3612 (2.28935 iter/s, 5.24166s/12 iters), loss = 1.31659
I0407 08:56:37.613349 18909 solver.cpp:237]     Train net output #0: loss = 1.31659 (* 1 = 1.31659 loss)
I0407 08:56:37.613356 18909 sgd_solver.cpp:105] Iteration 3612, lr = 0.0075
I0407 08:56:42.760286 18909 solver.cpp:218] Iteration 3624 (2.33149 iter/s, 5.14693s/12 iters), loss = 0.993782
I0407 08:56:42.760322 18909 solver.cpp:237]     Train net output #0: loss = 0.993782 (* 1 = 0.993782 loss)
I0407 08:56:42.760329 18909 sgd_solver.cpp:105] Iteration 3624, lr = 0.0075
I0407 08:56:48.089210 18909 solver.cpp:218] Iteration 3636 (2.25188 iter/s, 5.32887s/12 iters), loss = 1.38346
I0407 08:56:48.089251 18909 solver.cpp:237]     Train net output #0: loss = 1.38346 (* 1 = 1.38346 loss)
I0407 08:56:48.089258 18909 sgd_solver.cpp:105] Iteration 3636, lr = 0.0075
I0407 08:56:49.993260 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:56:53.365195 18909 solver.cpp:218] Iteration 3648 (2.27448 iter/s, 5.27593s/12 iters), loss = 0.96667
I0407 08:56:53.365238 18909 solver.cpp:237]     Train net output #0: loss = 0.96667 (* 1 = 0.96667 loss)
I0407 08:56:53.365247 18909 sgd_solver.cpp:105] Iteration 3648, lr = 0.0075
I0407 08:56:58.668052 18909 solver.cpp:218] Iteration 3660 (2.26295 iter/s, 5.3028s/12 iters), loss = 1.49067
I0407 08:56:58.668097 18909 solver.cpp:237]     Train net output #0: loss = 1.49067 (* 1 = 1.49067 loss)
I0407 08:56:58.668105 18909 sgd_solver.cpp:105] Iteration 3660, lr = 0.0075
I0407 08:57:03.386319 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0407 08:57:06.445355 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0407 08:57:08.750068 18909 solver.cpp:330] Iteration 3672, Testing net (#0)
I0407 08:57:08.750090 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:57:11.652298 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:57:13.080188 18909 solver.cpp:397]     Test net output #0: accuracy = 0.352328
I0407 08:57:13.080219 18909 solver.cpp:397]     Test net output #1: loss = 2.90975 (* 1 = 2.90975 loss)
I0407 08:57:13.219848 18909 solver.cpp:218] Iteration 3672 (0.824643 iter/s, 14.5517s/12 iters), loss = 1.2141
I0407 08:57:13.219892 18909 solver.cpp:237]     Train net output #0: loss = 1.2141 (* 1 = 1.2141 loss)
I0407 08:57:13.219899 18909 sgd_solver.cpp:105] Iteration 3672, lr = 0.0075
I0407 08:57:17.433444 18909 solver.cpp:218] Iteration 3684 (2.84796 iter/s, 4.21354s/12 iters), loss = 1.12262
I0407 08:57:17.433485 18909 solver.cpp:237]     Train net output #0: loss = 1.12262 (* 1 = 1.12262 loss)
I0407 08:57:17.433492 18909 sgd_solver.cpp:105] Iteration 3684, lr = 0.0075
I0407 08:57:22.338029 18909 solver.cpp:218] Iteration 3696 (2.44672 iter/s, 4.90453s/12 iters), loss = 1.19671
I0407 08:57:22.338073 18909 solver.cpp:237]     Train net output #0: loss = 1.19671 (* 1 = 1.19671 loss)
I0407 08:57:22.338078 18909 sgd_solver.cpp:105] Iteration 3696, lr = 0.0075
I0407 08:57:27.643903 18909 solver.cpp:218] Iteration 3708 (2.26167 iter/s, 5.30582s/12 iters), loss = 1.1063
I0407 08:57:27.643947 18909 solver.cpp:237]     Train net output #0: loss = 1.1063 (* 1 = 1.1063 loss)
I0407 08:57:27.643955 18909 sgd_solver.cpp:105] Iteration 3708, lr = 0.0075
I0407 08:57:32.999987 18909 solver.cpp:218] Iteration 3720 (2.24047 iter/s, 5.35603s/12 iters), loss = 1.12608
I0407 08:57:33.000028 18909 solver.cpp:237]     Train net output #0: loss = 1.12608 (* 1 = 1.12608 loss)
I0407 08:57:33.000034 18909 sgd_solver.cpp:105] Iteration 3720, lr = 0.0075
I0407 08:57:38.376013 18909 solver.cpp:218] Iteration 3732 (2.23215 iter/s, 5.37597s/12 iters), loss = 0.964908
I0407 08:57:38.376118 18909 solver.cpp:237]     Train net output #0: loss = 0.964908 (* 1 = 0.964908 loss)
I0407 08:57:38.376127 18909 sgd_solver.cpp:105] Iteration 3732, lr = 0.0075
I0407 08:57:42.618598 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:57:43.734673 18909 solver.cpp:218] Iteration 3744 (2.23942 iter/s, 5.35854s/12 iters), loss = 1.30128
I0407 08:57:43.734720 18909 solver.cpp:237]     Train net output #0: loss = 1.30128 (* 1 = 1.30128 loss)
I0407 08:57:43.734728 18909 sgd_solver.cpp:105] Iteration 3744, lr = 0.0075
I0407 08:57:48.933629 18909 solver.cpp:218] Iteration 3756 (2.30818 iter/s, 5.19889s/12 iters), loss = 1.0275
I0407 08:57:48.933686 18909 solver.cpp:237]     Train net output #0: loss = 1.0275 (* 1 = 1.0275 loss)
I0407 08:57:48.933696 18909 sgd_solver.cpp:105] Iteration 3756, lr = 0.0075
I0407 08:57:54.054332 18909 solver.cpp:218] Iteration 3768 (2.34346 iter/s, 5.12064s/12 iters), loss = 1.4046
I0407 08:57:54.054374 18909 solver.cpp:237]     Train net output #0: loss = 1.4046 (* 1 = 1.4046 loss)
I0407 08:57:54.054381 18909 sgd_solver.cpp:105] Iteration 3768, lr = 0.0075
I0407 08:57:56.121235 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0407 08:57:59.226228 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0407 08:58:01.541841 18909 solver.cpp:330] Iteration 3774, Testing net (#0)
I0407 08:58:01.541864 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:58:04.436339 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:58:05.910517 18909 solver.cpp:397]     Test net output #0: accuracy = 0.359069
I0407 08:58:05.910554 18909 solver.cpp:397]     Test net output #1: loss = 2.96636 (* 1 = 2.96636 loss)
I0407 08:58:07.736847 18909 solver.cpp:218] Iteration 3780 (0.877035 iter/s, 13.6825s/12 iters), loss = 1.15302
I0407 08:58:07.736912 18909 solver.cpp:237]     Train net output #0: loss = 1.15302 (* 1 = 1.15302 loss)
I0407 08:58:07.736922 18909 sgd_solver.cpp:105] Iteration 3780, lr = 0.0075
I0407 08:58:12.634629 18909 solver.cpp:218] Iteration 3792 (2.45013 iter/s, 4.8977s/12 iters), loss = 1.0254
I0407 08:58:12.634774 18909 solver.cpp:237]     Train net output #0: loss = 1.0254 (* 1 = 1.0254 loss)
I0407 08:58:12.634783 18909 sgd_solver.cpp:105] Iteration 3792, lr = 0.0075
I0407 08:58:17.614732 18909 solver.cpp:218] Iteration 3804 (2.40966 iter/s, 4.97995s/12 iters), loss = 1.08293
I0407 08:58:17.614779 18909 solver.cpp:237]     Train net output #0: loss = 1.08293 (* 1 = 1.08293 loss)
I0407 08:58:17.614789 18909 sgd_solver.cpp:105] Iteration 3804, lr = 0.0075
I0407 08:58:22.756608 18909 solver.cpp:218] Iteration 3816 (2.3338 iter/s, 5.14182s/12 iters), loss = 1.08485
I0407 08:58:22.756647 18909 solver.cpp:237]     Train net output #0: loss = 1.08485 (* 1 = 1.08485 loss)
I0407 08:58:22.756654 18909 sgd_solver.cpp:105] Iteration 3816, lr = 0.0075
I0407 08:58:27.727666 18909 solver.cpp:218] Iteration 3828 (2.414 iter/s, 4.97101s/12 iters), loss = 0.873985
I0407 08:58:27.727725 18909 solver.cpp:237]     Train net output #0: loss = 0.873985 (* 1 = 0.873985 loss)
I0407 08:58:27.727736 18909 sgd_solver.cpp:105] Iteration 3828, lr = 0.0075
I0407 08:58:32.994033 18909 solver.cpp:218] Iteration 3840 (2.27864 iter/s, 5.2663s/12 iters), loss = 0.944611
I0407 08:58:32.994074 18909 solver.cpp:237]     Train net output #0: loss = 0.944611 (* 1 = 0.944611 loss)
I0407 08:58:32.994081 18909 sgd_solver.cpp:105] Iteration 3840, lr = 0.0075
I0407 08:58:34.063532 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:58:38.137094 18909 solver.cpp:218] Iteration 3852 (2.33327 iter/s, 5.14301s/12 iters), loss = 0.947152
I0407 08:58:38.137138 18909 solver.cpp:237]     Train net output #0: loss = 0.947152 (* 1 = 0.947152 loss)
I0407 08:58:38.137145 18909 sgd_solver.cpp:105] Iteration 3852, lr = 0.0075
I0407 08:58:43.448843 18909 solver.cpp:218] Iteration 3864 (2.25917 iter/s, 5.31169s/12 iters), loss = 1.16657
I0407 08:58:43.448971 18909 solver.cpp:237]     Train net output #0: loss = 1.16657 (* 1 = 1.16657 loss)
I0407 08:58:43.448982 18909 sgd_solver.cpp:105] Iteration 3864, lr = 0.0075
I0407 08:58:47.871356 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0407 08:58:52.631271 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0407 08:58:54.949409 18909 solver.cpp:330] Iteration 3876, Testing net (#0)
I0407 08:58:54.949429 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:58:57.733754 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:58:59.242161 18909 solver.cpp:397]     Test net output #0: accuracy = 0.364583
I0407 08:58:59.242195 18909 solver.cpp:397]     Test net output #1: loss = 2.97277 (* 1 = 2.97277 loss)
I0407 08:58:59.383061 18909 solver.cpp:218] Iteration 3876 (0.753102 iter/s, 15.9341s/12 iters), loss = 1.24788
I0407 08:58:59.384625 18909 solver.cpp:237]     Train net output #0: loss = 1.24788 (* 1 = 1.24788 loss)
I0407 08:58:59.384639 18909 sgd_solver.cpp:105] Iteration 3876, lr = 0.0075
I0407 08:59:03.717058 18909 solver.cpp:218] Iteration 3888 (2.76981 iter/s, 4.33243s/12 iters), loss = 1.05002
I0407 08:59:03.717108 18909 solver.cpp:237]     Train net output #0: loss = 1.05002 (* 1 = 1.05002 loss)
I0407 08:59:03.717118 18909 sgd_solver.cpp:105] Iteration 3888, lr = 0.0075
I0407 08:59:08.937734 18909 solver.cpp:218] Iteration 3900 (2.29858 iter/s, 5.22062s/12 iters), loss = 1.02323
I0407 08:59:08.937780 18909 solver.cpp:237]     Train net output #0: loss = 1.02323 (* 1 = 1.02323 loss)
I0407 08:59:08.937788 18909 sgd_solver.cpp:105] Iteration 3900, lr = 0.0075
I0407 08:59:14.026305 18909 solver.cpp:218] Iteration 3912 (2.35825 iter/s, 5.08852s/12 iters), loss = 0.905362
I0407 08:59:14.026446 18909 solver.cpp:237]     Train net output #0: loss = 0.905362 (* 1 = 0.905362 loss)
I0407 08:59:14.026454 18909 sgd_solver.cpp:105] Iteration 3912, lr = 0.0075
I0407 08:59:19.217384 18909 solver.cpp:218] Iteration 3924 (2.31173 iter/s, 5.19093s/12 iters), loss = 1.46318
I0407 08:59:19.217427 18909 solver.cpp:237]     Train net output #0: loss = 1.46318 (* 1 = 1.46318 loss)
I0407 08:59:19.217434 18909 sgd_solver.cpp:105] Iteration 3924, lr = 0.0075
I0407 08:59:24.564013 18909 solver.cpp:218] Iteration 3936 (2.24443 iter/s, 5.34657s/12 iters), loss = 1.03497
I0407 08:59:24.564054 18909 solver.cpp:237]     Train net output #0: loss = 1.03497 (* 1 = 1.03497 loss)
I0407 08:59:24.564060 18909 sgd_solver.cpp:105] Iteration 3936, lr = 0.0075
I0407 08:59:27.859062 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:59:29.623901 18909 solver.cpp:218] Iteration 3948 (2.37162 iter/s, 5.05983s/12 iters), loss = 1.12972
I0407 08:59:29.623960 18909 solver.cpp:237]     Train net output #0: loss = 1.12972 (* 1 = 1.12972 loss)
I0407 08:59:29.623970 18909 sgd_solver.cpp:105] Iteration 3948, lr = 0.0075
I0407 08:59:34.577888 18909 solver.cpp:218] Iteration 3960 (2.42232 iter/s, 4.95392s/12 iters), loss = 1.03734
I0407 08:59:34.577929 18909 solver.cpp:237]     Train net output #0: loss = 1.03734 (* 1 = 1.03734 loss)
I0407 08:59:34.577936 18909 sgd_solver.cpp:105] Iteration 3960, lr = 0.0075
I0407 08:59:39.660624 18909 solver.cpp:218] Iteration 3972 (2.36096 iter/s, 5.08269s/12 iters), loss = 0.938465
I0407 08:59:39.660665 18909 solver.cpp:237]     Train net output #0: loss = 0.938465 (* 1 = 0.938465 loss)
I0407 08:59:39.660671 18909 sgd_solver.cpp:105] Iteration 3972, lr = 0.0075
I0407 08:59:41.775164 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0407 08:59:46.242806 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0407 08:59:49.215728 18909 solver.cpp:330] Iteration 3978, Testing net (#0)
I0407 08:59:49.215747 18909 net.cpp:676] Ignoring source layer train-data
I0407 08:59:52.086280 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:59:53.625763 18909 solver.cpp:397]     Test net output #0: accuracy = 0.340074
I0407 08:59:53.625798 18909 solver.cpp:397]     Test net output #1: loss = 3.17033 (* 1 = 3.17033 loss)
I0407 08:59:55.376561 18909 solver.cpp:218] Iteration 3984 (0.763558 iter/s, 15.7159s/12 iters), loss = 0.937658
I0407 08:59:55.376603 18909 solver.cpp:237]     Train net output #0: loss = 0.937658 (* 1 = 0.937658 loss)
I0407 08:59:55.376610 18909 sgd_solver.cpp:105] Iteration 3984, lr = 0.0075
I0407 09:00:00.519299 18909 solver.cpp:218] Iteration 3996 (2.33341 iter/s, 5.14268s/12 iters), loss = 0.983468
I0407 09:00:00.519345 18909 solver.cpp:237]     Train net output #0: loss = 0.983468 (* 1 = 0.983468 loss)
I0407 09:00:00.519352 18909 sgd_solver.cpp:105] Iteration 3996, lr = 0.0075
I0407 09:00:05.520803 18909 solver.cpp:218] Iteration 4008 (2.39931 iter/s, 5.00145s/12 iters), loss = 0.990386
I0407 09:00:05.520838 18909 solver.cpp:237]     Train net output #0: loss = 0.990386 (* 1 = 0.990386 loss)
I0407 09:00:05.520844 18909 sgd_solver.cpp:105] Iteration 4008, lr = 0.0075
I0407 09:00:10.764935 18909 solver.cpp:218] Iteration 4020 (2.28829 iter/s, 5.24409s/12 iters), loss = 1.1304
I0407 09:00:10.764971 18909 solver.cpp:237]     Train net output #0: loss = 1.1304 (* 1 = 1.1304 loss)
I0407 09:00:10.764978 18909 sgd_solver.cpp:105] Iteration 4020, lr = 0.0075
I0407 09:00:16.023064 18909 solver.cpp:218] Iteration 4032 (2.2822 iter/s, 5.25808s/12 iters), loss = 1.03331
I0407 09:00:16.023128 18909 solver.cpp:237]     Train net output #0: loss = 1.03331 (* 1 = 1.03331 loss)
I0407 09:00:16.023140 18909 sgd_solver.cpp:105] Iteration 4032, lr = 0.0075
I0407 09:00:21.161548 18909 solver.cpp:218] Iteration 4044 (2.33535 iter/s, 5.13842s/12 iters), loss = 1.27142
I0407 09:00:21.161653 18909 solver.cpp:237]     Train net output #0: loss = 1.27142 (* 1 = 1.27142 loss)
I0407 09:00:21.161659 18909 sgd_solver.cpp:105] Iteration 4044, lr = 0.0075
I0407 09:00:21.683612 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:00:26.471684 18909 solver.cpp:218] Iteration 4056 (2.25988 iter/s, 5.31002s/12 iters), loss = 1.17647
I0407 09:00:26.471721 18909 solver.cpp:237]     Train net output #0: loss = 1.17647 (* 1 = 1.17647 loss)
I0407 09:00:26.471729 18909 sgd_solver.cpp:105] Iteration 4056, lr = 0.0075
I0407 09:00:31.878566 18909 solver.cpp:218] Iteration 4068 (2.21941 iter/s, 5.40683s/12 iters), loss = 1.01778
I0407 09:00:31.878603 18909 solver.cpp:237]     Train net output #0: loss = 1.01778 (* 1 = 1.01778 loss)
I0407 09:00:31.878610 18909 sgd_solver.cpp:105] Iteration 4068, lr = 0.0075
I0407 09:00:36.611243 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0407 09:00:41.433400 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0407 09:00:46.171864 18909 solver.cpp:330] Iteration 4080, Testing net (#0)
I0407 09:00:46.171883 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:00:48.923456 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:00:50.582633 18909 solver.cpp:397]     Test net output #0: accuracy = 0.349877
I0407 09:00:50.582667 18909 solver.cpp:397]     Test net output #1: loss = 3.0175 (* 1 = 3.0175 loss)
I0407 09:00:50.717730 18909 solver.cpp:218] Iteration 4080 (0.636972 iter/s, 18.8391s/12 iters), loss = 1.30267
I0407 09:00:50.717773 18909 solver.cpp:237]     Train net output #0: loss = 1.30267 (* 1 = 1.30267 loss)
I0407 09:00:50.717780 18909 sgd_solver.cpp:105] Iteration 4080, lr = 0.0075
I0407 09:00:55.055447 18909 solver.cpp:218] Iteration 4092 (2.76647 iter/s, 4.33766s/12 iters), loss = 0.664144
I0407 09:00:55.055583 18909 solver.cpp:237]     Train net output #0: loss = 0.664144 (* 1 = 0.664144 loss)
I0407 09:00:55.055593 18909 sgd_solver.cpp:105] Iteration 4092, lr = 0.0075
I0407 09:01:00.172415 18909 solver.cpp:218] Iteration 4104 (2.34521 iter/s, 5.11682s/12 iters), loss = 1.15508
I0407 09:01:00.172462 18909 solver.cpp:237]     Train net output #0: loss = 1.15508 (* 1 = 1.15508 loss)
I0407 09:01:00.172470 18909 sgd_solver.cpp:105] Iteration 4104, lr = 0.0075
I0407 09:01:05.292665 18909 solver.cpp:218] Iteration 4116 (2.34366 iter/s, 5.1202s/12 iters), loss = 1.07515
I0407 09:01:05.292695 18909 solver.cpp:237]     Train net output #0: loss = 1.07515 (* 1 = 1.07515 loss)
I0407 09:01:05.292702 18909 sgd_solver.cpp:105] Iteration 4116, lr = 0.0075
I0407 09:01:10.459903 18909 solver.cpp:218] Iteration 4128 (2.32234 iter/s, 5.16719s/12 iters), loss = 1.0703
I0407 09:01:10.459942 18909 solver.cpp:237]     Train net output #0: loss = 1.0703 (* 1 = 1.0703 loss)
I0407 09:01:10.459949 18909 sgd_solver.cpp:105] Iteration 4128, lr = 0.0075
I0407 09:01:15.654232 18909 solver.cpp:218] Iteration 4140 (2.31023 iter/s, 5.19428s/12 iters), loss = 1.28691
I0407 09:01:15.654273 18909 solver.cpp:237]     Train net output #0: loss = 1.28691 (* 1 = 1.28691 loss)
I0407 09:01:15.654279 18909 sgd_solver.cpp:105] Iteration 4140, lr = 0.0075
I0407 09:01:18.437749 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:01:20.781065 18909 solver.cpp:218] Iteration 4152 (2.34065 iter/s, 5.12678s/12 iters), loss = 0.940004
I0407 09:01:20.781111 18909 solver.cpp:237]     Train net output #0: loss = 0.940004 (* 1 = 0.940004 loss)
I0407 09:01:20.781118 18909 sgd_solver.cpp:105] Iteration 4152, lr = 0.0075
I0407 09:01:22.481479 18909 blocking_queue.cpp:49] Waiting for data
I0407 09:01:26.188387 18909 solver.cpp:218] Iteration 4164 (2.21924 iter/s, 5.40727s/12 iters), loss = 1.04913
I0407 09:01:26.188535 18909 solver.cpp:237]     Train net output #0: loss = 1.04913 (* 1 = 1.04913 loss)
I0407 09:01:26.188544 18909 sgd_solver.cpp:105] Iteration 4164, lr = 0.0075
I0407 09:01:31.303887 18909 solver.cpp:218] Iteration 4176 (2.34588 iter/s, 5.11534s/12 iters), loss = 1.03425
I0407 09:01:31.303928 18909 solver.cpp:237]     Train net output #0: loss = 1.03425 (* 1 = 1.03425 loss)
I0407 09:01:31.303936 18909 sgd_solver.cpp:105] Iteration 4176, lr = 0.0075
I0407 09:01:33.366183 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0407 09:01:37.801595 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0407 09:01:42.785327 18909 solver.cpp:330] Iteration 4182, Testing net (#0)
I0407 09:01:42.785358 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:01:45.492543 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:01:47.108495 18909 solver.cpp:397]     Test net output #0: accuracy = 0.356005
I0407 09:01:47.108523 18909 solver.cpp:397]     Test net output #1: loss = 2.93019 (* 1 = 2.93019 loss)
I0407 09:01:48.896194 18909 solver.cpp:218] Iteration 4188 (0.682118 iter/s, 17.5923s/12 iters), loss = 1.08528
I0407 09:01:48.896239 18909 solver.cpp:237]     Train net output #0: loss = 1.08528 (* 1 = 1.08528 loss)
I0407 09:01:48.896245 18909 sgd_solver.cpp:105] Iteration 4188, lr = 0.0075
I0407 09:01:54.062582 18909 solver.cpp:218] Iteration 4200 (2.32273 iter/s, 5.16633s/12 iters), loss = 0.871497
I0407 09:01:54.062619 18909 solver.cpp:237]     Train net output #0: loss = 0.871497 (* 1 = 0.871497 loss)
I0407 09:01:54.062625 18909 sgd_solver.cpp:105] Iteration 4200, lr = 0.0075
I0407 09:01:59.341028 18909 solver.cpp:218] Iteration 4212 (2.27342 iter/s, 5.2784s/12 iters), loss = 1.13575
I0407 09:01:59.341122 18909 solver.cpp:237]     Train net output #0: loss = 1.13575 (* 1 = 1.13575 loss)
I0407 09:01:59.341130 18909 sgd_solver.cpp:105] Iteration 4212, lr = 0.0075
I0407 09:02:04.754338 18909 solver.cpp:218] Iteration 4224 (2.2168 iter/s, 5.4132s/12 iters), loss = 1.29587
I0407 09:02:04.754382 18909 solver.cpp:237]     Train net output #0: loss = 1.29587 (* 1 = 1.29587 loss)
I0407 09:02:04.754390 18909 sgd_solver.cpp:105] Iteration 4224, lr = 0.0075
I0407 09:02:09.959323 18909 solver.cpp:218] Iteration 4236 (2.3055 iter/s, 5.20494s/12 iters), loss = 0.853384
I0407 09:02:09.959357 18909 solver.cpp:237]     Train net output #0: loss = 0.853384 (* 1 = 0.853384 loss)
I0407 09:02:09.959363 18909 sgd_solver.cpp:105] Iteration 4236, lr = 0.0075
I0407 09:02:14.829854 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:02:15.099591 18909 solver.cpp:218] Iteration 4248 (2.33453 iter/s, 5.14022s/12 iters), loss = 0.949922
I0407 09:02:15.099634 18909 solver.cpp:237]     Train net output #0: loss = 0.949922 (* 1 = 0.949922 loss)
I0407 09:02:15.099642 18909 sgd_solver.cpp:105] Iteration 4248, lr = 0.0075
I0407 09:02:20.349236 18909 solver.cpp:218] Iteration 4260 (2.28589 iter/s, 5.24959s/12 iters), loss = 0.931967
I0407 09:02:20.349277 18909 solver.cpp:237]     Train net output #0: loss = 0.931967 (* 1 = 0.931967 loss)
I0407 09:02:20.349284 18909 sgd_solver.cpp:105] Iteration 4260, lr = 0.0075
I0407 09:02:25.422926 18909 solver.cpp:218] Iteration 4272 (2.36517 iter/s, 5.07364s/12 iters), loss = 0.683998
I0407 09:02:25.422967 18909 solver.cpp:237]     Train net output #0: loss = 0.683998 (* 1 = 0.683998 loss)
I0407 09:02:25.422974 18909 sgd_solver.cpp:105] Iteration 4272, lr = 0.0075
I0407 09:02:30.060811 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0407 09:02:33.110190 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0407 09:02:38.232913 18909 solver.cpp:330] Iteration 4284, Testing net (#0)
I0407 09:02:38.232944 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:02:40.873529 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:02:42.519332 18909 solver.cpp:397]     Test net output #0: accuracy = 0.357843
I0407 09:02:42.519362 18909 solver.cpp:397]     Test net output #1: loss = 2.98381 (* 1 = 2.98381 loss)
I0407 09:02:42.659924 18909 solver.cpp:218] Iteration 4284 (0.696179 iter/s, 17.237s/12 iters), loss = 1.01486
I0407 09:02:42.661514 18909 solver.cpp:237]     Train net output #0: loss = 1.01486 (* 1 = 1.01486 loss)
I0407 09:02:42.661530 18909 sgd_solver.cpp:105] Iteration 4284, lr = 0.0075
I0407 09:02:46.855626 18909 solver.cpp:218] Iteration 4296 (2.86115 iter/s, 4.19412s/12 iters), loss = 1.00717
I0407 09:02:46.855665 18909 solver.cpp:237]     Train net output #0: loss = 1.00717 (* 1 = 1.00717 loss)
I0407 09:02:46.855671 18909 sgd_solver.cpp:105] Iteration 4296, lr = 0.0075
I0407 09:02:51.884608 18909 solver.cpp:218] Iteration 4308 (2.3862 iter/s, 5.02892s/12 iters), loss = 0.869925
I0407 09:02:51.884660 18909 solver.cpp:237]     Train net output #0: loss = 0.869925 (* 1 = 0.869925 loss)
I0407 09:02:51.884670 18909 sgd_solver.cpp:105] Iteration 4308, lr = 0.0075
I0407 09:02:56.967839 18909 solver.cpp:218] Iteration 4320 (2.36073 iter/s, 5.08317s/12 iters), loss = 0.802411
I0407 09:02:56.967881 18909 solver.cpp:237]     Train net output #0: loss = 0.802411 (* 1 = 0.802411 loss)
I0407 09:02:56.967888 18909 sgd_solver.cpp:105] Iteration 4320, lr = 0.0075
I0407 09:03:02.207326 18909 solver.cpp:218] Iteration 4332 (2.29032 iter/s, 5.23943s/12 iters), loss = 0.985945
I0407 09:03:02.207840 18909 solver.cpp:237]     Train net output #0: loss = 0.985945 (* 1 = 0.985945 loss)
I0407 09:03:02.207849 18909 sgd_solver.cpp:105] Iteration 4332, lr = 0.0075
I0407 09:03:07.438159 18909 solver.cpp:218] Iteration 4344 (2.29432 iter/s, 5.23031s/12 iters), loss = 0.879566
I0407 09:03:07.438199 18909 solver.cpp:237]     Train net output #0: loss = 0.879566 (* 1 = 0.879566 loss)
I0407 09:03:07.438206 18909 sgd_solver.cpp:105] Iteration 4344, lr = 0.0075
I0407 09:03:09.464913 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:03:12.816144 18909 solver.cpp:218] Iteration 4356 (2.23134 iter/s, 5.37793s/12 iters), loss = 0.675248
I0407 09:03:12.816186 18909 solver.cpp:237]     Train net output #0: loss = 0.675248 (* 1 = 0.675248 loss)
I0407 09:03:12.816193 18909 sgd_solver.cpp:105] Iteration 4356, lr = 0.0075
I0407 09:03:18.129079 18909 solver.cpp:218] Iteration 4368 (2.25866 iter/s, 5.31288s/12 iters), loss = 0.688681
I0407 09:03:18.129122 18909 solver.cpp:237]     Train net output #0: loss = 0.688681 (* 1 = 0.688681 loss)
I0407 09:03:18.129132 18909 sgd_solver.cpp:105] Iteration 4368, lr = 0.0075
I0407 09:03:23.300576 18909 solver.cpp:218] Iteration 4380 (2.32044 iter/s, 5.17144s/12 iters), loss = 0.74488
I0407 09:03:23.300616 18909 solver.cpp:237]     Train net output #0: loss = 0.74488 (* 1 = 0.74488 loss)
I0407 09:03:23.300622 18909 sgd_solver.cpp:105] Iteration 4380, lr = 0.0075
I0407 09:03:25.404467 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0407 09:03:28.433748 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0407 09:03:32.973129 18909 solver.cpp:330] Iteration 4386, Testing net (#0)
I0407 09:03:32.973233 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:03:35.590725 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:03:37.287932 18909 solver.cpp:397]     Test net output #0: accuracy = 0.356005
I0407 09:03:37.287958 18909 solver.cpp:397]     Test net output #1: loss = 3.06065 (* 1 = 3.06065 loss)
I0407 09:03:39.246153 18909 solver.cpp:218] Iteration 4392 (0.752562 iter/s, 15.9455s/12 iters), loss = 0.986954
I0407 09:03:39.246194 18909 solver.cpp:237]     Train net output #0: loss = 0.986954 (* 1 = 0.986954 loss)
I0407 09:03:39.246202 18909 sgd_solver.cpp:105] Iteration 4392, lr = 0.0075
I0407 09:03:43.999640 18909 solver.cpp:218] Iteration 4404 (2.52449 iter/s, 4.75343s/12 iters), loss = 0.704119
I0407 09:03:43.999694 18909 solver.cpp:237]     Train net output #0: loss = 0.704119 (* 1 = 0.704119 loss)
I0407 09:03:43.999704 18909 sgd_solver.cpp:105] Iteration 4404, lr = 0.0075
I0407 09:03:48.909111 18909 solver.cpp:218] Iteration 4416 (2.44429 iter/s, 4.90941s/12 iters), loss = 0.849975
I0407 09:03:48.909157 18909 solver.cpp:237]     Train net output #0: loss = 0.849975 (* 1 = 0.849975 loss)
I0407 09:03:48.909164 18909 sgd_solver.cpp:105] Iteration 4416, lr = 0.0075
I0407 09:03:53.945780 18909 solver.cpp:218] Iteration 4428 (2.38255 iter/s, 5.03661s/12 iters), loss = 0.888192
I0407 09:03:53.945823 18909 solver.cpp:237]     Train net output #0: loss = 0.888192 (* 1 = 0.888192 loss)
I0407 09:03:53.945830 18909 sgd_solver.cpp:105] Iteration 4428, lr = 0.0075
I0407 09:03:59.070227 18909 solver.cpp:218] Iteration 4440 (2.34174 iter/s, 5.12439s/12 iters), loss = 0.719877
I0407 09:03:59.070274 18909 solver.cpp:237]     Train net output #0: loss = 0.719877 (* 1 = 0.719877 loss)
I0407 09:03:59.070281 18909 sgd_solver.cpp:105] Iteration 4440, lr = 0.0075
I0407 09:04:03.349423 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:04:04.409894 18909 solver.cpp:218] Iteration 4452 (2.24736 iter/s, 5.33961s/12 iters), loss = 0.881093
I0407 09:04:04.409936 18909 solver.cpp:237]     Train net output #0: loss = 0.881093 (* 1 = 0.881093 loss)
I0407 09:04:04.409943 18909 sgd_solver.cpp:105] Iteration 4452, lr = 0.0075
I0407 09:04:09.496322 18909 solver.cpp:218] Iteration 4464 (2.35925 iter/s, 5.08637s/12 iters), loss = 0.970719
I0407 09:04:09.496363 18909 solver.cpp:237]     Train net output #0: loss = 0.970719 (* 1 = 0.970719 loss)
I0407 09:04:09.496371 18909 sgd_solver.cpp:105] Iteration 4464, lr = 0.0075
I0407 09:04:14.586140 18909 solver.cpp:218] Iteration 4476 (2.35767 iter/s, 5.08976s/12 iters), loss = 0.757568
I0407 09:04:14.586202 18909 solver.cpp:237]     Train net output #0: loss = 0.757568 (* 1 = 0.757568 loss)
I0407 09:04:14.586213 18909 sgd_solver.cpp:105] Iteration 4476, lr = 0.0075
I0407 09:04:19.106967 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0407 09:04:22.127082 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0407 09:04:24.443948 18909 solver.cpp:330] Iteration 4488, Testing net (#0)
I0407 09:04:24.443967 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:04:26.966641 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:04:28.703778 18909 solver.cpp:397]     Test net output #0: accuracy = 0.368873
I0407 09:04:28.703811 18909 solver.cpp:397]     Test net output #1: loss = 2.92694 (* 1 = 2.92694 loss)
I0407 09:04:28.838364 18909 solver.cpp:218] Iteration 4488 (0.841978 iter/s, 14.2522s/12 iters), loss = 1.01116
I0407 09:04:28.838407 18909 solver.cpp:237]     Train net output #0: loss = 1.01116 (* 1 = 1.01116 loss)
I0407 09:04:28.838413 18909 sgd_solver.cpp:105] Iteration 4488, lr = 0.0075
I0407 09:04:33.237087 18909 solver.cpp:218] Iteration 4500 (2.7281 iter/s, 4.39866s/12 iters), loss = 0.892297
I0407 09:04:33.237138 18909 solver.cpp:237]     Train net output #0: loss = 0.892297 (* 1 = 0.892297 loss)
I0407 09:04:33.237147 18909 sgd_solver.cpp:105] Iteration 4500, lr = 0.0075
I0407 09:04:38.508462 18909 solver.cpp:218] Iteration 4512 (2.27647 iter/s, 5.27132s/12 iters), loss = 0.890731
I0407 09:04:38.508611 18909 solver.cpp:237]     Train net output #0: loss = 0.890731 (* 1 = 0.890731 loss)
I0407 09:04:38.508620 18909 sgd_solver.cpp:105] Iteration 4512, lr = 0.0075
I0407 09:04:43.793597 18909 solver.cpp:218] Iteration 4524 (2.27059 iter/s, 5.28498s/12 iters), loss = 0.772103
I0407 09:04:43.793639 18909 solver.cpp:237]     Train net output #0: loss = 0.772103 (* 1 = 0.772103 loss)
I0407 09:04:43.793645 18909 sgd_solver.cpp:105] Iteration 4524, lr = 0.0075
I0407 09:04:49.150319 18909 solver.cpp:218] Iteration 4536 (2.2402 iter/s, 5.35667s/12 iters), loss = 0.776296
I0407 09:04:49.150367 18909 solver.cpp:237]     Train net output #0: loss = 0.776296 (* 1 = 0.776296 loss)
I0407 09:04:49.150375 18909 sgd_solver.cpp:105] Iteration 4536, lr = 0.0075
I0407 09:04:54.267220 18909 solver.cpp:218] Iteration 4548 (2.3452 iter/s, 5.11684s/12 iters), loss = 0.607005
I0407 09:04:54.267263 18909 solver.cpp:237]     Train net output #0: loss = 0.607005 (* 1 = 0.607005 loss)
I0407 09:04:54.267271 18909 sgd_solver.cpp:105] Iteration 4548, lr = 0.0075
I0407 09:04:55.523279 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:04:59.463119 18909 solver.cpp:218] Iteration 4560 (2.30954 iter/s, 5.19585s/12 iters), loss = 0.663034
I0407 09:04:59.463158 18909 solver.cpp:237]     Train net output #0: loss = 0.663034 (* 1 = 0.663034 loss)
I0407 09:04:59.463165 18909 sgd_solver.cpp:105] Iteration 4560, lr = 0.0075
I0407 09:05:04.663460 18909 solver.cpp:218] Iteration 4572 (2.30756 iter/s, 5.20029s/12 iters), loss = 0.747478
I0407 09:05:04.663507 18909 solver.cpp:237]     Train net output #0: loss = 0.747478 (* 1 = 0.747478 loss)
I0407 09:05:04.663516 18909 sgd_solver.cpp:105] Iteration 4572, lr = 0.0075
I0407 09:05:09.499915 18909 solver.cpp:218] Iteration 4584 (2.48118 iter/s, 4.8364s/12 iters), loss = 0.841175
I0407 09:05:09.500054 18909 solver.cpp:237]     Train net output #0: loss = 0.841175 (* 1 = 0.841175 loss)
I0407 09:05:09.500067 18909 sgd_solver.cpp:105] Iteration 4584, lr = 0.0075
I0407 09:05:11.491297 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0407 09:05:14.521628 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0407 09:05:16.849433 18909 solver.cpp:330] Iteration 4590, Testing net (#0)
I0407 09:05:16.849462 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:05:19.435261 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:05:21.243232 18909 solver.cpp:397]     Test net output #0: accuracy = 0.369485
I0407 09:05:21.243263 18909 solver.cpp:397]     Test net output #1: loss = 2.9457 (* 1 = 2.9457 loss)
I0407 09:05:23.062556 18909 solver.cpp:218] Iteration 4596 (0.884793 iter/s, 13.5625s/12 iters), loss = 0.884498
I0407 09:05:23.062598 18909 solver.cpp:237]     Train net output #0: loss = 0.884498 (* 1 = 0.884498 loss)
I0407 09:05:23.062605 18909 sgd_solver.cpp:105] Iteration 4596, lr = 0.0075
I0407 09:05:28.295006 18909 solver.cpp:218] Iteration 4608 (2.29341 iter/s, 5.23239s/12 iters), loss = 0.890222
I0407 09:05:28.295049 18909 solver.cpp:237]     Train net output #0: loss = 0.890222 (* 1 = 0.890222 loss)
I0407 09:05:28.295058 18909 sgd_solver.cpp:105] Iteration 4608, lr = 0.0075
I0407 09:05:33.559672 18909 solver.cpp:218] Iteration 4620 (2.27937 iter/s, 5.26461s/12 iters), loss = 0.78398
I0407 09:05:33.559711 18909 solver.cpp:237]     Train net output #0: loss = 0.78398 (* 1 = 0.78398 loss)
I0407 09:05:33.559718 18909 sgd_solver.cpp:105] Iteration 4620, lr = 0.0075
I0407 09:05:38.758816 18909 solver.cpp:218] Iteration 4632 (2.3081 iter/s, 5.19909s/12 iters), loss = 0.729499
I0407 09:05:38.758862 18909 solver.cpp:237]     Train net output #0: loss = 0.729499 (* 1 = 0.729499 loss)
I0407 09:05:38.758868 18909 sgd_solver.cpp:105] Iteration 4632, lr = 0.0075
I0407 09:05:43.996783 18909 solver.cpp:218] Iteration 4644 (2.29099 iter/s, 5.23791s/12 iters), loss = 0.788841
I0407 09:05:43.996927 18909 solver.cpp:237]     Train net output #0: loss = 0.788841 (* 1 = 0.788841 loss)
I0407 09:05:43.996935 18909 sgd_solver.cpp:105] Iteration 4644, lr = 0.0075
I0407 09:05:47.324028 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:05:48.949359 18909 solver.cpp:218] Iteration 4656 (2.42306 iter/s, 4.95242s/12 iters), loss = 1.34682
I0407 09:05:48.949402 18909 solver.cpp:237]     Train net output #0: loss = 1.34682 (* 1 = 1.34682 loss)
I0407 09:05:48.949409 18909 sgd_solver.cpp:105] Iteration 4656, lr = 0.0075
I0407 09:05:54.243060 18909 solver.cpp:218] Iteration 4668 (2.26687 iter/s, 5.29365s/12 iters), loss = 0.60337
I0407 09:05:54.243104 18909 solver.cpp:237]     Train net output #0: loss = 0.60337 (* 1 = 0.60337 loss)
I0407 09:05:54.243111 18909 sgd_solver.cpp:105] Iteration 4668, lr = 0.0075
I0407 09:05:59.406746 18909 solver.cpp:218] Iteration 4680 (2.32395 iter/s, 5.16363s/12 iters), loss = 0.715676
I0407 09:05:59.406781 18909 solver.cpp:237]     Train net output #0: loss = 0.715676 (* 1 = 0.715676 loss)
I0407 09:05:59.406788 18909 sgd_solver.cpp:105] Iteration 4680, lr = 0.0075
I0407 09:06:04.111312 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0407 09:06:07.157095 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0407 09:06:09.497275 18909 solver.cpp:330] Iteration 4692, Testing net (#0)
I0407 09:06:09.497295 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:06:12.007503 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:06:13.844782 18909 solver.cpp:397]     Test net output #0: accuracy = 0.376226
I0407 09:06:13.844810 18909 solver.cpp:397]     Test net output #1: loss = 2.91796 (* 1 = 2.91796 loss)
I0407 09:06:13.985780 18909 solver.cpp:218] Iteration 4692 (0.823102 iter/s, 14.579s/12 iters), loss = 0.512291
I0407 09:06:13.985822 18909 solver.cpp:237]     Train net output #0: loss = 0.512291 (* 1 = 0.512291 loss)
I0407 09:06:13.985829 18909 sgd_solver.cpp:105] Iteration 4692, lr = 0.0075
I0407 09:06:18.237632 18909 solver.cpp:218] Iteration 4704 (2.82234 iter/s, 4.25179s/12 iters), loss = 0.771275
I0407 09:06:18.237743 18909 solver.cpp:237]     Train net output #0: loss = 0.771275 (* 1 = 0.771275 loss)
I0407 09:06:18.237752 18909 sgd_solver.cpp:105] Iteration 4704, lr = 0.0075
I0407 09:06:23.290203 18909 solver.cpp:218] Iteration 4716 (2.37508 iter/s, 5.05246s/12 iters), loss = 0.855019
I0407 09:06:23.290241 18909 solver.cpp:237]     Train net output #0: loss = 0.855019 (* 1 = 0.855019 loss)
I0407 09:06:23.290247 18909 sgd_solver.cpp:105] Iteration 4716, lr = 0.0075
I0407 09:06:28.344552 18909 solver.cpp:218] Iteration 4728 (2.37422 iter/s, 5.0543s/12 iters), loss = 0.97865
I0407 09:06:28.344609 18909 solver.cpp:237]     Train net output #0: loss = 0.97865 (* 1 = 0.97865 loss)
I0407 09:06:28.344619 18909 sgd_solver.cpp:105] Iteration 4728, lr = 0.0075
I0407 09:06:33.468806 18909 solver.cpp:218] Iteration 4740 (2.34184 iter/s, 5.12419s/12 iters), loss = 0.710836
I0407 09:06:33.468863 18909 solver.cpp:237]     Train net output #0: loss = 0.710836 (* 1 = 0.710836 loss)
I0407 09:06:33.468873 18909 sgd_solver.cpp:105] Iteration 4740, lr = 0.0075
I0407 09:06:38.732852 18909 solver.cpp:218] Iteration 4752 (2.27965 iter/s, 5.26397s/12 iters), loss = 0.843112
I0407 09:06:38.732918 18909 solver.cpp:237]     Train net output #0: loss = 0.843112 (* 1 = 0.843112 loss)
I0407 09:06:38.732928 18909 sgd_solver.cpp:105] Iteration 4752, lr = 0.0075
I0407 09:06:39.279430 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:06:44.007164 18909 solver.cpp:218] Iteration 4764 (2.27521 iter/s, 5.27423s/12 iters), loss = 0.871007
I0407 09:06:44.007216 18909 solver.cpp:237]     Train net output #0: loss = 0.871007 (* 1 = 0.871007 loss)
I0407 09:06:44.007227 18909 sgd_solver.cpp:105] Iteration 4764, lr = 0.0075
I0407 09:06:49.301934 18909 solver.cpp:218] Iteration 4776 (2.26642 iter/s, 5.2947s/12 iters), loss = 0.798244
I0407 09:06:49.302052 18909 solver.cpp:237]     Train net output #0: loss = 0.798244 (* 1 = 0.798244 loss)
I0407 09:06:49.302062 18909 sgd_solver.cpp:105] Iteration 4776, lr = 0.0075
I0407 09:06:54.606707 18909 solver.cpp:218] Iteration 4788 (2.26216 iter/s, 5.30465s/12 iters), loss = 0.82707
I0407 09:06:54.606746 18909 solver.cpp:237]     Train net output #0: loss = 0.82707 (* 1 = 0.82707 loss)
I0407 09:06:54.606752 18909 sgd_solver.cpp:105] Iteration 4788, lr = 0.0075
I0407 09:06:56.655473 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0407 09:06:59.707448 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0407 09:07:02.019870 18909 solver.cpp:330] Iteration 4794, Testing net (#0)
I0407 09:07:02.019888 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:07:04.475586 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:07:06.336504 18909 solver.cpp:397]     Test net output #0: accuracy = 0.400735
I0407 09:07:06.336545 18909 solver.cpp:397]     Test net output #1: loss = 2.89551 (* 1 = 2.89551 loss)
I0407 09:07:08.207720 18909 solver.cpp:218] Iteration 4800 (0.88229 iter/s, 13.601s/12 iters), loss = 0.808756
I0407 09:07:08.207763 18909 solver.cpp:237]     Train net output #0: loss = 0.808756 (* 1 = 0.808756 loss)
I0407 09:07:08.207772 18909 sgd_solver.cpp:105] Iteration 4800, lr = 0.0075
I0407 09:07:13.435212 18909 solver.cpp:218] Iteration 4812 (2.29558 iter/s, 5.22744s/12 iters), loss = 0.766835
I0407 09:07:13.435251 18909 solver.cpp:237]     Train net output #0: loss = 0.766835 (* 1 = 0.766835 loss)
I0407 09:07:13.435259 18909 sgd_solver.cpp:105] Iteration 4812, lr = 0.0075
I0407 09:07:18.584300 18909 solver.cpp:218] Iteration 4824 (2.33053 iter/s, 5.14904s/12 iters), loss = 0.731855
I0407 09:07:18.584340 18909 solver.cpp:237]     Train net output #0: loss = 0.731855 (* 1 = 0.731855 loss)
I0407 09:07:18.584347 18909 sgd_solver.cpp:105] Iteration 4824, lr = 0.0075
I0407 09:07:23.932073 18909 solver.cpp:218] Iteration 4836 (2.24395 iter/s, 5.34772s/12 iters), loss = 0.834285
I0407 09:07:23.932252 18909 solver.cpp:237]     Train net output #0: loss = 0.834285 (* 1 = 0.834285 loss)
I0407 09:07:23.932268 18909 sgd_solver.cpp:105] Iteration 4836, lr = 0.0075
I0407 09:07:26.167035 18909 blocking_queue.cpp:49] Waiting for data
I0407 09:07:29.401837 18909 solver.cpp:218] Iteration 4848 (2.19395 iter/s, 5.46958s/12 iters), loss = 1.019
I0407 09:07:29.401880 18909 solver.cpp:237]     Train net output #0: loss = 1.019 (* 1 = 1.019 loss)
I0407 09:07:29.401887 18909 sgd_solver.cpp:105] Iteration 4848, lr = 0.0075
I0407 09:07:32.156874 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:07:34.399542 18909 solver.cpp:218] Iteration 4860 (2.40113 iter/s, 4.99765s/12 iters), loss = 0.683903
I0407 09:07:34.399582 18909 solver.cpp:237]     Train net output #0: loss = 0.683903 (* 1 = 0.683903 loss)
I0407 09:07:34.399590 18909 sgd_solver.cpp:105] Iteration 4860, lr = 0.0075
I0407 09:07:39.514894 18909 solver.cpp:218] Iteration 4872 (2.3459 iter/s, 5.1153s/12 iters), loss = 0.688409
I0407 09:07:39.514940 18909 solver.cpp:237]     Train net output #0: loss = 0.688409 (* 1 = 0.688409 loss)
I0407 09:07:39.514947 18909 sgd_solver.cpp:105] Iteration 4872, lr = 0.0075
I0407 09:07:44.654048 18909 solver.cpp:218] Iteration 4884 (2.33504 iter/s, 5.1391s/12 iters), loss = 0.735153
I0407 09:07:44.654093 18909 solver.cpp:237]     Train net output #0: loss = 0.735153 (* 1 = 0.735153 loss)
I0407 09:07:44.654099 18909 sgd_solver.cpp:105] Iteration 4884, lr = 0.0075
I0407 09:07:49.325937 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0407 09:07:52.351207 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0407 09:07:54.670504 18909 solver.cpp:330] Iteration 4896, Testing net (#0)
I0407 09:07:54.670624 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:07:57.070116 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:07:58.961844 18909 solver.cpp:397]     Test net output #0: accuracy = 0.382966
I0407 09:07:58.961889 18909 solver.cpp:397]     Test net output #1: loss = 2.93271 (* 1 = 2.93271 loss)
I0407 09:07:59.097479 18909 solver.cpp:218] Iteration 4896 (0.83083 iter/s, 14.4434s/12 iters), loss = 0.805658
I0407 09:07:59.097525 18909 solver.cpp:237]     Train net output #0: loss = 0.805658 (* 1 = 0.805658 loss)
I0407 09:07:59.097534 18909 sgd_solver.cpp:105] Iteration 4896, lr = 0.0075
I0407 09:08:03.439288 18909 solver.cpp:218] Iteration 4908 (2.76386 iter/s, 4.34176s/12 iters), loss = 0.637037
I0407 09:08:03.439325 18909 solver.cpp:237]     Train net output #0: loss = 0.637037 (* 1 = 0.637037 loss)
I0407 09:08:03.439332 18909 sgd_solver.cpp:105] Iteration 4908, lr = 0.0075
I0407 09:08:08.419438 18909 solver.cpp:218] Iteration 4920 (2.40959 iter/s, 4.9801s/12 iters), loss = 0.747441
I0407 09:08:08.419484 18909 solver.cpp:237]     Train net output #0: loss = 0.747441 (* 1 = 0.747441 loss)
I0407 09:08:08.419492 18909 sgd_solver.cpp:105] Iteration 4920, lr = 0.0075
I0407 09:08:13.545207 18909 solver.cpp:218] Iteration 4932 (2.34114 iter/s, 5.12571s/12 iters), loss = 0.625203
I0407 09:08:13.545253 18909 solver.cpp:237]     Train net output #0: loss = 0.625203 (* 1 = 0.625203 loss)
I0407 09:08:13.545260 18909 sgd_solver.cpp:105] Iteration 4932, lr = 0.0075
I0407 09:08:18.846627 18909 solver.cpp:218] Iteration 4944 (2.26357 iter/s, 5.30137s/12 iters), loss = 0.656303
I0407 09:08:18.846668 18909 solver.cpp:237]     Train net output #0: loss = 0.656303 (* 1 = 0.656303 loss)
I0407 09:08:18.846675 18909 sgd_solver.cpp:105] Iteration 4944, lr = 0.0075
I0407 09:08:23.846832 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:08:24.092769 18909 solver.cpp:218] Iteration 4956 (2.28742 iter/s, 5.24609s/12 iters), loss = 0.902723
I0407 09:08:24.092813 18909 solver.cpp:237]     Train net output #0: loss = 0.902723 (* 1 = 0.902723 loss)
I0407 09:08:24.092820 18909 sgd_solver.cpp:105] Iteration 4956, lr = 0.0075
I0407 09:08:29.179009 18909 solver.cpp:218] Iteration 4968 (2.35933 iter/s, 5.08618s/12 iters), loss = 0.647595
I0407 09:08:29.179133 18909 solver.cpp:237]     Train net output #0: loss = 0.647595 (* 1 = 0.647595 loss)
I0407 09:08:29.179141 18909 sgd_solver.cpp:105] Iteration 4968, lr = 0.0075
I0407 09:08:34.267084 18909 solver.cpp:218] Iteration 4980 (2.35852 iter/s, 5.08794s/12 iters), loss = 0.535918
I0407 09:08:34.267136 18909 solver.cpp:237]     Train net output #0: loss = 0.535918 (* 1 = 0.535918 loss)
I0407 09:08:34.267144 18909 sgd_solver.cpp:105] Iteration 4980, lr = 0.0075
I0407 09:08:39.675374 18909 solver.cpp:218] Iteration 4992 (2.21884 iter/s, 5.40823s/12 iters), loss = 0.880998
I0407 09:08:39.675415 18909 solver.cpp:237]     Train net output #0: loss = 0.880998 (* 1 = 0.880998 loss)
I0407 09:08:39.675423 18909 sgd_solver.cpp:105] Iteration 4992, lr = 0.0075
I0407 09:08:41.816429 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0407 09:08:44.883062 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0407 09:08:47.208345 18909 solver.cpp:330] Iteration 4998, Testing net (#0)
I0407 09:08:47.208364 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:08:49.570835 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:08:51.660631 18909 solver.cpp:397]     Test net output #0: accuracy = 0.376838
I0407 09:08:51.660681 18909 solver.cpp:397]     Test net output #1: loss = 3.02967 (* 1 = 3.02967 loss)
I0407 09:08:53.625285 18909 solver.cpp:218] Iteration 5004 (0.860223 iter/s, 13.9499s/12 iters), loss = 0.634831
I0407 09:08:53.625329 18909 solver.cpp:237]     Train net output #0: loss = 0.634831 (* 1 = 0.634831 loss)
I0407 09:08:53.625336 18909 sgd_solver.cpp:105] Iteration 5004, lr = 0.0075
I0407 09:08:58.708112 18909 solver.cpp:218] Iteration 5016 (2.36091 iter/s, 5.08278s/12 iters), loss = 0.555428
I0407 09:08:58.708156 18909 solver.cpp:237]     Train net output #0: loss = 0.555428 (* 1 = 0.555428 loss)
I0407 09:08:58.708164 18909 sgd_solver.cpp:105] Iteration 5016, lr = 0.0075
I0407 09:09:03.860496 18909 solver.cpp:218] Iteration 5028 (2.32904 iter/s, 5.15233s/12 iters), loss = 0.551916
I0407 09:09:03.860652 18909 solver.cpp:237]     Train net output #0: loss = 0.551916 (* 1 = 0.551916 loss)
I0407 09:09:03.860661 18909 sgd_solver.cpp:105] Iteration 5028, lr = 0.0075
I0407 09:09:09.041569 18909 solver.cpp:218] Iteration 5040 (2.31619 iter/s, 5.18091s/12 iters), loss = 0.669032
I0407 09:09:09.041610 18909 solver.cpp:237]     Train net output #0: loss = 0.669032 (* 1 = 0.669032 loss)
I0407 09:09:09.041618 18909 sgd_solver.cpp:105] Iteration 5040, lr = 0.0075
I0407 09:09:14.317759 18909 solver.cpp:218] Iteration 5052 (2.27439 iter/s, 5.27614s/12 iters), loss = 0.616898
I0407 09:09:14.317796 18909 solver.cpp:237]     Train net output #0: loss = 0.616898 (* 1 = 0.616898 loss)
I0407 09:09:14.317803 18909 sgd_solver.cpp:105] Iteration 5052, lr = 0.0075
I0407 09:09:16.254892 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:09:19.466871 18909 solver.cpp:218] Iteration 5064 (2.33052 iter/s, 5.14906s/12 iters), loss = 0.454311
I0407 09:09:19.466908 18909 solver.cpp:237]     Train net output #0: loss = 0.454311 (* 1 = 0.454311 loss)
I0407 09:09:19.466917 18909 sgd_solver.cpp:105] Iteration 5064, lr = 0.0075
I0407 09:09:24.587448 18909 solver.cpp:218] Iteration 5076 (2.34351 iter/s, 5.12053s/12 iters), loss = 0.556705
I0407 09:09:24.587491 18909 solver.cpp:237]     Train net output #0: loss = 0.556705 (* 1 = 0.556705 loss)
I0407 09:09:24.587498 18909 sgd_solver.cpp:105] Iteration 5076, lr = 0.0075
I0407 09:09:29.835821 18909 solver.cpp:218] Iteration 5088 (2.28644 iter/s, 5.24832s/12 iters), loss = 0.611163
I0407 09:09:29.835865 18909 solver.cpp:237]     Train net output #0: loss = 0.611163 (* 1 = 0.611163 loss)
I0407 09:09:29.835873 18909 sgd_solver.cpp:105] Iteration 5088, lr = 0.0075
I0407 09:09:34.562556 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0407 09:09:37.553658 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0407 09:09:39.866351 18909 solver.cpp:330] Iteration 5100, Testing net (#0)
I0407 09:09:39.866379 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:09:42.233687 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:09:44.199792 18909 solver.cpp:397]     Test net output #0: accuracy = 0.383578
I0407 09:09:44.199836 18909 solver.cpp:397]     Test net output #1: loss = 3.00782 (* 1 = 3.00782 loss)
I0407 09:09:44.330646 18909 solver.cpp:218] Iteration 5100 (0.827885 iter/s, 14.4948s/12 iters), loss = 0.67757
I0407 09:09:44.330724 18909 solver.cpp:237]     Train net output #0: loss = 0.67757 (* 1 = 0.67757 loss)
I0407 09:09:44.330739 18909 sgd_solver.cpp:105] Iteration 5100, lr = 0.0075
I0407 09:09:48.419960 18909 solver.cpp:218] Iteration 5112 (2.93454 iter/s, 4.08923s/12 iters), loss = 0.708504
I0407 09:09:48.420008 18909 solver.cpp:237]     Train net output #0: loss = 0.708504 (* 1 = 0.708504 loss)
I0407 09:09:48.420017 18909 sgd_solver.cpp:105] Iteration 5112, lr = 0.0075
I0407 09:09:53.625309 18909 solver.cpp:218] Iteration 5124 (2.30535 iter/s, 5.20529s/12 iters), loss = 0.774969
I0407 09:09:53.625351 18909 solver.cpp:237]     Train net output #0: loss = 0.774969 (* 1 = 0.774969 loss)
I0407 09:09:53.625358 18909 sgd_solver.cpp:105] Iteration 5124, lr = 0.0075
I0407 09:09:58.948938 18909 solver.cpp:218] Iteration 5136 (2.25413 iter/s, 5.32357s/12 iters), loss = 0.817518
I0407 09:09:58.948983 18909 solver.cpp:237]     Train net output #0: loss = 0.817518 (* 1 = 0.817518 loss)
I0407 09:09:58.948989 18909 sgd_solver.cpp:105] Iteration 5136, lr = 0.0075
I0407 09:10:04.053422 18909 solver.cpp:218] Iteration 5148 (2.3509 iter/s, 5.10443s/12 iters), loss = 0.749845
I0407 09:10:04.053472 18909 solver.cpp:237]     Train net output #0: loss = 0.749845 (* 1 = 0.749845 loss)
I0407 09:10:04.053479 18909 sgd_solver.cpp:105] Iteration 5148, lr = 0.0075
I0407 09:10:08.273522 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:10:09.327509 18909 solver.cpp:218] Iteration 5160 (2.2753 iter/s, 5.27403s/12 iters), loss = 0.58623
I0407 09:10:09.327553 18909 solver.cpp:237]     Train net output #0: loss = 0.58623 (* 1 = 0.58623 loss)
I0407 09:10:09.327559 18909 sgd_solver.cpp:105] Iteration 5160, lr = 0.0075
I0407 09:10:14.503969 18909 solver.cpp:218] Iteration 5172 (2.31821 iter/s, 5.17641s/12 iters), loss = 0.897655
I0407 09:10:14.504009 18909 solver.cpp:237]     Train net output #0: loss = 0.897655 (* 1 = 0.897655 loss)
I0407 09:10:14.504016 18909 sgd_solver.cpp:105] Iteration 5172, lr = 0.0075
I0407 09:10:19.562238 18909 solver.cpp:218] Iteration 5184 (2.37238 iter/s, 5.05822s/12 iters), loss = 1.02256
I0407 09:10:19.562281 18909 solver.cpp:237]     Train net output #0: loss = 1.02256 (* 1 = 1.02256 loss)
I0407 09:10:19.562288 18909 sgd_solver.cpp:105] Iteration 5184, lr = 0.0075
I0407 09:10:24.900012 18909 solver.cpp:218] Iteration 5196 (2.24815 iter/s, 5.33772s/12 iters), loss = 0.63007
I0407 09:10:24.900050 18909 solver.cpp:237]     Train net output #0: loss = 0.63007 (* 1 = 0.63007 loss)
I0407 09:10:24.900056 18909 sgd_solver.cpp:105] Iteration 5196, lr = 0.0075
I0407 09:10:27.031152 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0407 09:10:30.070214 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0407 09:10:32.375844 18909 solver.cpp:330] Iteration 5202, Testing net (#0)
I0407 09:10:32.375864 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:10:34.724721 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:10:36.753857 18909 solver.cpp:397]     Test net output #0: accuracy = 0.384804
I0407 09:10:36.753893 18909 solver.cpp:397]     Test net output #1: loss = 2.93068 (* 1 = 2.93068 loss)
I0407 09:10:38.556918 18909 solver.cpp:218] Iteration 5208 (0.878679 iter/s, 13.6569s/12 iters), loss = 0.554407
I0407 09:10:38.557056 18909 solver.cpp:237]     Train net output #0: loss = 0.554407 (* 1 = 0.554407 loss)
I0407 09:10:38.557066 18909 sgd_solver.cpp:105] Iteration 5208, lr = 0.0075
I0407 09:10:43.804271 18909 solver.cpp:218] Iteration 5220 (2.28693 iter/s, 5.24721s/12 iters), loss = 0.519366
I0407 09:10:43.804327 18909 solver.cpp:237]     Train net output #0: loss = 0.519366 (* 1 = 0.519366 loss)
I0407 09:10:43.804337 18909 sgd_solver.cpp:105] Iteration 5220, lr = 0.0075
I0407 09:10:49.134593 18909 solver.cpp:218] Iteration 5232 (2.2513 iter/s, 5.33026s/12 iters), loss = 0.889194
I0407 09:10:49.134640 18909 solver.cpp:237]     Train net output #0: loss = 0.889194 (* 1 = 0.889194 loss)
I0407 09:10:49.134649 18909 sgd_solver.cpp:105] Iteration 5232, lr = 0.0075
I0407 09:10:54.479604 18909 solver.cpp:218] Iteration 5244 (2.24511 iter/s, 5.34495s/12 iters), loss = 0.71483
I0407 09:10:54.479658 18909 solver.cpp:237]     Train net output #0: loss = 0.71483 (* 1 = 0.71483 loss)
I0407 09:10:54.479668 18909 sgd_solver.cpp:105] Iteration 5244, lr = 0.0075
I0407 09:10:59.841775 18909 solver.cpp:218] Iteration 5256 (2.23793 iter/s, 5.36211s/12 iters), loss = 0.628869
I0407 09:10:59.841818 18909 solver.cpp:237]     Train net output #0: loss = 0.628869 (* 1 = 0.628869 loss)
I0407 09:10:59.841825 18909 sgd_solver.cpp:105] Iteration 5256, lr = 0.0075
I0407 09:11:01.309870 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:11:05.017958 18909 solver.cpp:218] Iteration 5268 (2.31833 iter/s, 5.17613s/12 iters), loss = 0.589184
I0407 09:11:05.018005 18909 solver.cpp:237]     Train net output #0: loss = 0.589184 (* 1 = 0.589184 loss)
I0407 09:11:05.018015 18909 sgd_solver.cpp:105] Iteration 5268, lr = 0.0075
I0407 09:11:10.042558 18909 solver.cpp:218] Iteration 5280 (2.38828 iter/s, 5.02454s/12 iters), loss = 0.694608
I0407 09:11:10.042647 18909 solver.cpp:237]     Train net output #0: loss = 0.694608 (* 1 = 0.694608 loss)
I0407 09:11:10.042655 18909 sgd_solver.cpp:105] Iteration 5280, lr = 0.0075
I0407 09:11:15.140708 18909 solver.cpp:218] Iteration 5292 (2.35384 iter/s, 5.09805s/12 iters), loss = 0.663682
I0407 09:11:15.140753 18909 solver.cpp:237]     Train net output #0: loss = 0.663682 (* 1 = 0.663682 loss)
I0407 09:11:15.140760 18909 sgd_solver.cpp:105] Iteration 5292, lr = 0.0075
I0407 09:11:19.907752 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0407 09:11:22.983572 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0407 09:11:25.291469 18909 solver.cpp:330] Iteration 5304, Testing net (#0)
I0407 09:11:25.291491 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:11:27.508752 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:11:29.558902 18909 solver.cpp:397]     Test net output #0: accuracy = 0.377451
I0407 09:11:29.558936 18909 solver.cpp:397]     Test net output #1: loss = 2.96781 (* 1 = 2.96781 loss)
I0407 09:11:29.694751 18909 solver.cpp:218] Iteration 5304 (0.824516 iter/s, 14.554s/12 iters), loss = 0.574747
I0407 09:11:29.694797 18909 solver.cpp:237]     Train net output #0: loss = 0.574747 (* 1 = 0.574747 loss)
I0407 09:11:29.694805 18909 sgd_solver.cpp:105] Iteration 5304, lr = 0.0075
I0407 09:11:34.002720 18909 solver.cpp:218] Iteration 5316 (2.78557 iter/s, 4.30791s/12 iters), loss = 0.495699
I0407 09:11:34.002764 18909 solver.cpp:237]     Train net output #0: loss = 0.495699 (* 1 = 0.495699 loss)
I0407 09:11:34.002772 18909 sgd_solver.cpp:105] Iteration 5316, lr = 0.0075
I0407 09:11:39.221218 18909 solver.cpp:218] Iteration 5328 (2.29954 iter/s, 5.21844s/12 iters), loss = 0.699801
I0407 09:11:39.221257 18909 solver.cpp:237]     Train net output #0: loss = 0.699801 (* 1 = 0.699801 loss)
I0407 09:11:39.221264 18909 sgd_solver.cpp:105] Iteration 5328, lr = 0.0075
I0407 09:11:44.451462 18909 solver.cpp:218] Iteration 5340 (2.29437 iter/s, 5.23019s/12 iters), loss = 0.602265
I0407 09:11:44.451663 18909 solver.cpp:237]     Train net output #0: loss = 0.602265 (* 1 = 0.602265 loss)
I0407 09:11:44.451673 18909 sgd_solver.cpp:105] Iteration 5340, lr = 0.0075
I0407 09:11:49.595697 18909 solver.cpp:218] Iteration 5352 (2.3328 iter/s, 5.14403s/12 iters), loss = 0.705663
I0407 09:11:49.595739 18909 solver.cpp:237]     Train net output #0: loss = 0.705663 (* 1 = 0.705663 loss)
I0407 09:11:49.595746 18909 sgd_solver.cpp:105] Iteration 5352, lr = 0.0075
I0407 09:11:52.885476 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:11:54.559900 18909 solver.cpp:218] Iteration 5364 (2.41733 iter/s, 4.96415s/12 iters), loss = 0.71458
I0407 09:11:54.559947 18909 solver.cpp:237]     Train net output #0: loss = 0.71458 (* 1 = 0.71458 loss)
I0407 09:11:54.559955 18909 sgd_solver.cpp:105] Iteration 5364, lr = 0.0075
I0407 09:11:59.889370 18909 solver.cpp:218] Iteration 5376 (2.25165 iter/s, 5.32941s/12 iters), loss = 0.6785
I0407 09:11:59.889417 18909 solver.cpp:237]     Train net output #0: loss = 0.6785 (* 1 = 0.6785 loss)
I0407 09:11:59.889423 18909 sgd_solver.cpp:105] Iteration 5376, lr = 0.0075
I0407 09:12:05.009814 18909 solver.cpp:218] Iteration 5388 (2.34358 iter/s, 5.12038s/12 iters), loss = 0.478132
I0407 09:12:05.009857 18909 solver.cpp:237]     Train net output #0: loss = 0.478132 (* 1 = 0.478132 loss)
I0407 09:12:05.009865 18909 sgd_solver.cpp:105] Iteration 5388, lr = 0.0075
I0407 09:12:10.097112 18909 solver.cpp:218] Iteration 5400 (2.35884 iter/s, 5.08725s/12 iters), loss = 0.517009
I0407 09:12:10.097153 18909 solver.cpp:237]     Train net output #0: loss = 0.517009 (* 1 = 0.517009 loss)
I0407 09:12:10.097159 18909 sgd_solver.cpp:105] Iteration 5400, lr = 0.0075
I0407 09:12:12.055056 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0407 09:12:15.100328 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0407 09:12:17.426429 18909 solver.cpp:330] Iteration 5406, Testing net (#0)
I0407 09:12:17.426450 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:12:19.661351 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:12:21.878228 18909 solver.cpp:397]     Test net output #0: accuracy = 0.376838
I0407 09:12:21.878270 18909 solver.cpp:397]     Test net output #1: loss = 3.04641 (* 1 = 3.04641 loss)
I0407 09:12:23.722762 18909 solver.cpp:218] Iteration 5412 (0.880695 iter/s, 13.6256s/12 iters), loss = 0.421359
I0407 09:12:23.722823 18909 solver.cpp:237]     Train net output #0: loss = 0.421359 (* 1 = 0.421359 loss)
I0407 09:12:23.722833 18909 sgd_solver.cpp:105] Iteration 5412, lr = 0.0075
I0407 09:12:28.812083 18909 solver.cpp:218] Iteration 5424 (2.35791 iter/s, 5.08925s/12 iters), loss = 0.55378
I0407 09:12:28.812126 18909 solver.cpp:237]     Train net output #0: loss = 0.55378 (* 1 = 0.55378 loss)
I0407 09:12:28.812134 18909 sgd_solver.cpp:105] Iteration 5424, lr = 0.0075
I0407 09:12:33.852993 18909 solver.cpp:218] Iteration 5436 (2.38055 iter/s, 5.04086s/12 iters), loss = 0.696992
I0407 09:12:33.853029 18909 solver.cpp:237]     Train net output #0: loss = 0.696992 (* 1 = 0.696992 loss)
I0407 09:12:33.853035 18909 sgd_solver.cpp:105] Iteration 5436, lr = 0.0075
I0407 09:12:39.048712 18909 solver.cpp:218] Iteration 5448 (2.30961 iter/s, 5.19567s/12 iters), loss = 0.61326
I0407 09:12:39.048753 18909 solver.cpp:237]     Train net output #0: loss = 0.61326 (* 1 = 0.61326 loss)
I0407 09:12:39.048760 18909 sgd_solver.cpp:105] Iteration 5448, lr = 0.0075
I0407 09:12:44.244240 18909 solver.cpp:218] Iteration 5460 (2.30971 iter/s, 5.19547s/12 iters), loss = 0.67075
I0407 09:12:44.244287 18909 solver.cpp:237]     Train net output #0: loss = 0.67075 (* 1 = 0.67075 loss)
I0407 09:12:44.244294 18909 sgd_solver.cpp:105] Iteration 5460, lr = 0.0075
I0407 09:12:44.761328 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:12:49.470439 18909 solver.cpp:218] Iteration 5472 (2.29615 iter/s, 5.22615s/12 iters), loss = 0.688058
I0407 09:12:49.470564 18909 solver.cpp:237]     Train net output #0: loss = 0.688058 (* 1 = 0.688058 loss)
I0407 09:12:49.470572 18909 sgd_solver.cpp:105] Iteration 5472, lr = 0.0075
I0407 09:12:54.935915 18909 solver.cpp:218] Iteration 5484 (2.19565 iter/s, 5.46535s/12 iters), loss = 0.620179
I0407 09:12:54.935959 18909 solver.cpp:237]     Train net output #0: loss = 0.620179 (* 1 = 0.620179 loss)
I0407 09:12:54.935967 18909 sgd_solver.cpp:105] Iteration 5484, lr = 0.0075
I0407 09:13:00.193441 18909 solver.cpp:218] Iteration 5496 (2.28247 iter/s, 5.25747s/12 iters), loss = 0.757366
I0407 09:13:00.193498 18909 solver.cpp:237]     Train net output #0: loss = 0.757366 (* 1 = 0.757366 loss)
I0407 09:13:00.193507 18909 sgd_solver.cpp:105] Iteration 5496, lr = 0.0075
I0407 09:13:05.129420 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0407 09:13:08.095355 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0407 09:13:10.441990 18909 solver.cpp:330] Iteration 5508, Testing net (#0)
I0407 09:13:10.442018 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:13:12.645679 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:13:14.777096 18909 solver.cpp:397]     Test net output #0: accuracy = 0.393382
I0407 09:13:14.777132 18909 solver.cpp:397]     Test net output #1: loss = 2.92933 (* 1 = 2.92933 loss)
I0407 09:13:14.912438 18909 solver.cpp:218] Iteration 5508 (0.815276 iter/s, 14.7189s/12 iters), loss = 0.557848
I0407 09:13:14.912494 18909 solver.cpp:237]     Train net output #0: loss = 0.557848 (* 1 = 0.557848 loss)
I0407 09:13:14.912503 18909 sgd_solver.cpp:105] Iteration 5508, lr = 0.0075
I0407 09:13:19.325345 18909 solver.cpp:218] Iteration 5520 (2.71934 iter/s, 4.41283s/12 iters), loss = 0.511502
I0407 09:13:19.325403 18909 solver.cpp:237]     Train net output #0: loss = 0.511502 (* 1 = 0.511502 loss)
I0407 09:13:19.325414 18909 sgd_solver.cpp:105] Iteration 5520, lr = 0.0075
I0407 09:13:21.808619 18909 blocking_queue.cpp:49] Waiting for data
I0407 09:13:24.402725 18909 solver.cpp:218] Iteration 5532 (2.36346 iter/s, 5.07731s/12 iters), loss = 0.733274
I0407 09:13:24.402770 18909 solver.cpp:237]     Train net output #0: loss = 0.733274 (* 1 = 0.733274 loss)
I0407 09:13:24.402777 18909 sgd_solver.cpp:105] Iteration 5532, lr = 0.0075
I0407 09:13:29.519187 18909 solver.cpp:218] Iteration 5544 (2.3454 iter/s, 5.1164s/12 iters), loss = 0.586127
I0407 09:13:29.519232 18909 solver.cpp:237]     Train net output #0: loss = 0.586127 (* 1 = 0.586127 loss)
I0407 09:13:29.519239 18909 sgd_solver.cpp:105] Iteration 5544, lr = 0.0075
I0407 09:13:34.526218 18909 solver.cpp:218] Iteration 5556 (2.39666 iter/s, 5.00698s/12 iters), loss = 0.83539
I0407 09:13:34.526260 18909 solver.cpp:237]     Train net output #0: loss = 0.83539 (* 1 = 0.83539 loss)
I0407 09:13:34.526268 18909 sgd_solver.cpp:105] Iteration 5556, lr = 0.0075
I0407 09:13:37.334686 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:13:39.861212 18909 solver.cpp:218] Iteration 5568 (2.24932 iter/s, 5.33494s/12 iters), loss = 0.585889
I0407 09:13:39.861251 18909 solver.cpp:237]     Train net output #0: loss = 0.585889 (* 1 = 0.585889 loss)
I0407 09:13:39.861258 18909 sgd_solver.cpp:105] Iteration 5568, lr = 0.0075
I0407 09:13:45.107592 18909 solver.cpp:218] Iteration 5580 (2.28731 iter/s, 5.24633s/12 iters), loss = 0.398424
I0407 09:13:45.107633 18909 solver.cpp:237]     Train net output #0: loss = 0.398424 (* 1 = 0.398424 loss)
I0407 09:13:45.107640 18909 sgd_solver.cpp:105] Iteration 5580, lr = 0.0075
I0407 09:13:50.347739 18909 solver.cpp:218] Iteration 5592 (2.29004 iter/s, 5.24009s/12 iters), loss = 0.68935
I0407 09:13:50.347780 18909 solver.cpp:237]     Train net output #0: loss = 0.68935 (* 1 = 0.68935 loss)
I0407 09:13:50.347787 18909 sgd_solver.cpp:105] Iteration 5592, lr = 0.0075
I0407 09:13:55.689018 18909 solver.cpp:218] Iteration 5604 (2.24667 iter/s, 5.34123s/12 iters), loss = 0.617857
I0407 09:13:55.689175 18909 solver.cpp:237]     Train net output #0: loss = 0.617857 (* 1 = 0.617857 loss)
I0407 09:13:55.689184 18909 sgd_solver.cpp:105] Iteration 5604, lr = 0.0075
I0407 09:13:57.854391 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0407 09:14:00.901065 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0407 09:14:03.201722 18909 solver.cpp:330] Iteration 5610, Testing net (#0)
I0407 09:14:03.201740 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:14:05.392529 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:14:07.559535 18909 solver.cpp:397]     Test net output #0: accuracy = 0.378064
I0407 09:14:07.559561 18909 solver.cpp:397]     Test net output #1: loss = 3.0789 (* 1 = 3.0789 loss)
I0407 09:14:09.311472 18909 solver.cpp:218] Iteration 5616 (0.880909 iter/s, 13.6223s/12 iters), loss = 0.573074
I0407 09:14:09.311518 18909 solver.cpp:237]     Train net output #0: loss = 0.573074 (* 1 = 0.573074 loss)
I0407 09:14:09.311525 18909 sgd_solver.cpp:105] Iteration 5616, lr = 0.0075
I0407 09:14:14.606227 18909 solver.cpp:218] Iteration 5628 (2.26642 iter/s, 5.2947s/12 iters), loss = 0.397502
I0407 09:14:14.606267 18909 solver.cpp:237]     Train net output #0: loss = 0.397502 (* 1 = 0.397502 loss)
I0407 09:14:14.606274 18909 sgd_solver.cpp:105] Iteration 5628, lr = 0.0075
I0407 09:14:19.753165 18909 solver.cpp:218] Iteration 5640 (2.33151 iter/s, 5.14689s/12 iters), loss = 0.677046
I0407 09:14:19.753253 18909 solver.cpp:237]     Train net output #0: loss = 0.677046 (* 1 = 0.677046 loss)
I0407 09:14:19.753262 18909 sgd_solver.cpp:105] Iteration 5640, lr = 0.0075
I0407 09:14:24.941454 18909 solver.cpp:218] Iteration 5652 (2.31295 iter/s, 5.18819s/12 iters), loss = 0.686481
I0407 09:14:24.941498 18909 solver.cpp:237]     Train net output #0: loss = 0.686481 (* 1 = 0.686481 loss)
I0407 09:14:24.941504 18909 sgd_solver.cpp:105] Iteration 5652, lr = 0.0075
I0407 09:14:30.003077 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:14:30.217314 18909 solver.cpp:218] Iteration 5664 (2.27453 iter/s, 5.27581s/12 iters), loss = 0.485666
I0407 09:14:30.217357 18909 solver.cpp:237]     Train net output #0: loss = 0.485666 (* 1 = 0.485666 loss)
I0407 09:14:30.217365 18909 sgd_solver.cpp:105] Iteration 5664, lr = 0.0075
I0407 09:14:35.496120 18909 solver.cpp:218] Iteration 5676 (2.27326 iter/s, 5.27875s/12 iters), loss = 0.661848
I0407 09:14:35.496167 18909 solver.cpp:237]     Train net output #0: loss = 0.661848 (* 1 = 0.661848 loss)
I0407 09:14:35.496177 18909 sgd_solver.cpp:105] Iteration 5676, lr = 0.0075
I0407 09:14:40.823469 18909 solver.cpp:218] Iteration 5688 (2.25255 iter/s, 5.32729s/12 iters), loss = 0.63738
I0407 09:14:40.823511 18909 solver.cpp:237]     Train net output #0: loss = 0.63738 (* 1 = 0.63738 loss)
I0407 09:14:40.823518 18909 sgd_solver.cpp:105] Iteration 5688, lr = 0.0075
I0407 09:14:46.081475 18909 solver.cpp:218] Iteration 5700 (2.28226 iter/s, 5.25795s/12 iters), loss = 0.904269
I0407 09:14:46.081529 18909 solver.cpp:237]     Train net output #0: loss = 0.904269 (* 1 = 0.904269 loss)
I0407 09:14:46.081540 18909 sgd_solver.cpp:105] Iteration 5700, lr = 0.0075
I0407 09:14:50.823405 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0407 09:14:53.846683 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0407 09:14:56.216985 18909 solver.cpp:330] Iteration 5712, Testing net (#0)
I0407 09:14:56.217006 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:14:58.301872 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:15:00.495174 18909 solver.cpp:397]     Test net output #0: accuracy = 0.38848
I0407 09:15:00.495348 18909 solver.cpp:397]     Test net output #1: loss = 3.03556 (* 1 = 3.03556 loss)
I0407 09:15:00.631337 18909 solver.cpp:218] Iteration 5712 (0.824753 iter/s, 14.5498s/12 iters), loss = 0.687978
I0407 09:15:00.631386 18909 solver.cpp:237]     Train net output #0: loss = 0.687978 (* 1 = 0.687978 loss)
I0407 09:15:00.631395 18909 sgd_solver.cpp:105] Iteration 5712, lr = 0.0075
I0407 09:15:04.918665 18909 solver.cpp:218] Iteration 5724 (2.79899 iter/s, 4.28727s/12 iters), loss = 0.432191
I0407 09:15:04.918705 18909 solver.cpp:237]     Train net output #0: loss = 0.432191 (* 1 = 0.432191 loss)
I0407 09:15:04.918712 18909 sgd_solver.cpp:105] Iteration 5724, lr = 0.0075
I0407 09:15:10.168732 18909 solver.cpp:218] Iteration 5736 (2.28571 iter/s, 5.25002s/12 iters), loss = 0.418508
I0407 09:15:10.168774 18909 solver.cpp:237]     Train net output #0: loss = 0.418508 (* 1 = 0.418508 loss)
I0407 09:15:10.168781 18909 sgd_solver.cpp:105] Iteration 5736, lr = 0.0075
I0407 09:15:15.301465 18909 solver.cpp:218] Iteration 5748 (2.33796 iter/s, 5.13267s/12 iters), loss = 0.520263
I0407 09:15:15.301504 18909 solver.cpp:237]     Train net output #0: loss = 0.520263 (* 1 = 0.520263 loss)
I0407 09:15:15.301512 18909 sgd_solver.cpp:105] Iteration 5748, lr = 0.0075
I0407 09:15:20.241019 18909 solver.cpp:218] Iteration 5760 (2.42939 iter/s, 4.93951s/12 iters), loss = 0.428419
I0407 09:15:20.241057 18909 solver.cpp:237]     Train net output #0: loss = 0.428419 (* 1 = 0.428419 loss)
I0407 09:15:20.241063 18909 sgd_solver.cpp:105] Iteration 5760, lr = 0.0075
I0407 09:15:22.281038 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:15:25.556058 18909 solver.cpp:218] Iteration 5772 (2.25776 iter/s, 5.31499s/12 iters), loss = 0.643615
I0407 09:15:25.556100 18909 solver.cpp:237]     Train net output #0: loss = 0.643615 (* 1 = 0.643615 loss)
I0407 09:15:25.556108 18909 sgd_solver.cpp:105] Iteration 5772, lr = 0.0075
I0407 09:15:30.832808 18909 solver.cpp:218] Iteration 5784 (2.27415 iter/s, 5.2767s/12 iters), loss = 0.67449
I0407 09:15:30.832903 18909 solver.cpp:237]     Train net output #0: loss = 0.67449 (* 1 = 0.67449 loss)
I0407 09:15:30.832911 18909 sgd_solver.cpp:105] Iteration 5784, lr = 0.0075
I0407 09:15:36.076740 18909 solver.cpp:218] Iteration 5796 (2.28841 iter/s, 5.24383s/12 iters), loss = 0.579372
I0407 09:15:36.076786 18909 solver.cpp:237]     Train net output #0: loss = 0.579372 (* 1 = 0.579372 loss)
I0407 09:15:36.076792 18909 sgd_solver.cpp:105] Iteration 5796, lr = 0.0075
I0407 09:15:41.243134 18909 solver.cpp:218] Iteration 5808 (2.32273 iter/s, 5.16634s/12 iters), loss = 0.473914
I0407 09:15:41.243180 18909 solver.cpp:237]     Train net output #0: loss = 0.473914 (* 1 = 0.473914 loss)
I0407 09:15:41.243188 18909 sgd_solver.cpp:105] Iteration 5808, lr = 0.0075
I0407 09:15:43.183692 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0407 09:15:46.255352 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0407 09:15:48.556108 18909 solver.cpp:330] Iteration 5814, Testing net (#0)
I0407 09:15:48.556128 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:15:50.635548 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:15:52.870038 18909 solver.cpp:397]     Test net output #0: accuracy = 0.391544
I0407 09:15:52.870088 18909 solver.cpp:397]     Test net output #1: loss = 2.95603 (* 1 = 2.95603 loss)
I0407 09:15:54.711639 18909 solver.cpp:218] Iteration 5820 (0.890971 iter/s, 13.4685s/12 iters), loss = 0.506015
I0407 09:15:54.711683 18909 solver.cpp:237]     Train net output #0: loss = 0.506015 (* 1 = 0.506015 loss)
I0407 09:15:54.711690 18909 sgd_solver.cpp:105] Iteration 5820, lr = 0.0075
I0407 09:15:59.897452 18909 solver.cpp:218] Iteration 5832 (2.31403 iter/s, 5.18576s/12 iters), loss = 0.511748
I0407 09:15:59.897491 18909 solver.cpp:237]     Train net output #0: loss = 0.511748 (* 1 = 0.511748 loss)
I0407 09:15:59.897498 18909 sgd_solver.cpp:105] Iteration 5832, lr = 0.0075
I0407 09:16:04.844316 18909 solver.cpp:218] Iteration 5844 (2.42581 iter/s, 4.94681s/12 iters), loss = 0.363746
I0407 09:16:04.844452 18909 solver.cpp:237]     Train net output #0: loss = 0.363746 (* 1 = 0.363746 loss)
I0407 09:16:04.844460 18909 sgd_solver.cpp:105] Iteration 5844, lr = 0.0075
I0407 09:16:10.146814 18909 solver.cpp:218] Iteration 5856 (2.26314 iter/s, 5.30236s/12 iters), loss = 0.390991
I0407 09:16:10.146860 18909 solver.cpp:237]     Train net output #0: loss = 0.390991 (* 1 = 0.390991 loss)
I0407 09:16:10.146868 18909 sgd_solver.cpp:105] Iteration 5856, lr = 0.0075
I0407 09:16:14.451309 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:16:15.307210 18909 solver.cpp:218] Iteration 5868 (2.32543 iter/s, 5.16034s/12 iters), loss = 0.562269
I0407 09:16:15.307256 18909 solver.cpp:237]     Train net output #0: loss = 0.562269 (* 1 = 0.562269 loss)
I0407 09:16:15.307266 18909 sgd_solver.cpp:105] Iteration 5868, lr = 0.0075
I0407 09:16:20.505381 18909 solver.cpp:218] Iteration 5880 (2.30853 iter/s, 5.19811s/12 iters), loss = 0.731516
I0407 09:16:20.505440 18909 solver.cpp:237]     Train net output #0: loss = 0.731516 (* 1 = 0.731516 loss)
I0407 09:16:20.505450 18909 sgd_solver.cpp:105] Iteration 5880, lr = 0.0075
I0407 09:16:25.769635 18909 solver.cpp:218] Iteration 5892 (2.27955 iter/s, 5.26419s/12 iters), loss = 0.492674
I0407 09:16:25.769675 18909 solver.cpp:237]     Train net output #0: loss = 0.492674 (* 1 = 0.492674 loss)
I0407 09:16:25.769681 18909 sgd_solver.cpp:105] Iteration 5892, lr = 0.0075
I0407 09:16:30.974262 18909 solver.cpp:218] Iteration 5904 (2.30567 iter/s, 5.20457s/12 iters), loss = 0.388936
I0407 09:16:30.974323 18909 solver.cpp:237]     Train net output #0: loss = 0.388936 (* 1 = 0.388936 loss)
I0407 09:16:30.974335 18909 sgd_solver.cpp:105] Iteration 5904, lr = 0.0075
I0407 09:16:35.717682 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0407 09:16:38.745476 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0407 09:16:41.073179 18909 solver.cpp:330] Iteration 5916, Testing net (#0)
I0407 09:16:41.073200 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:16:43.097322 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:16:45.420416 18909 solver.cpp:397]     Test net output #0: accuracy = 0.376226
I0407 09:16:45.420450 18909 solver.cpp:397]     Test net output #1: loss = 3.08595 (* 1 = 3.08595 loss)
I0407 09:16:45.561173 18909 solver.cpp:218] Iteration 5916 (0.822658 iter/s, 14.5869s/12 iters), loss = 0.583881
I0407 09:16:45.561216 18909 solver.cpp:237]     Train net output #0: loss = 0.583881 (* 1 = 0.583881 loss)
I0407 09:16:45.561223 18909 sgd_solver.cpp:105] Iteration 5916, lr = 0.0075
I0407 09:16:49.830442 18909 solver.cpp:218] Iteration 5928 (2.81082 iter/s, 4.26922s/12 iters), loss = 0.817043
I0407 09:16:49.830482 18909 solver.cpp:237]     Train net output #0: loss = 0.817043 (* 1 = 0.817043 loss)
I0407 09:16:49.830488 18909 sgd_solver.cpp:105] Iteration 5928, lr = 0.0075
I0407 09:16:54.946187 18909 solver.cpp:218] Iteration 5940 (2.34572 iter/s, 5.11569s/12 iters), loss = 0.610843
I0407 09:16:54.946225 18909 solver.cpp:237]     Train net output #0: loss = 0.610843 (* 1 = 0.610843 loss)
I0407 09:16:54.946233 18909 sgd_solver.cpp:105] Iteration 5940, lr = 0.0075
I0407 09:16:59.806146 18909 solver.cpp:218] Iteration 5952 (2.46918 iter/s, 4.85991s/12 iters), loss = 0.547577
I0407 09:16:59.806190 18909 solver.cpp:237]     Train net output #0: loss = 0.547577 (* 1 = 0.547577 loss)
I0407 09:16:59.806197 18909 sgd_solver.cpp:105] Iteration 5952, lr = 0.0075
I0407 09:17:05.122444 18909 solver.cpp:218] Iteration 5964 (2.25723 iter/s, 5.31625s/12 iters), loss = 0.683249
I0407 09:17:05.122483 18909 solver.cpp:237]     Train net output #0: loss = 0.683249 (* 1 = 0.683249 loss)
I0407 09:17:05.122490 18909 sgd_solver.cpp:105] Iteration 5964, lr = 0.0075
I0407 09:17:06.546416 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:17:10.451325 18909 solver.cpp:218] Iteration 5976 (2.2519 iter/s, 5.32882s/12 iters), loss = 0.538545
I0407 09:17:10.451368 18909 solver.cpp:237]     Train net output #0: loss = 0.538545 (* 1 = 0.538545 loss)
I0407 09:17:10.451376 18909 sgd_solver.cpp:105] Iteration 5976, lr = 0.0075
I0407 09:17:15.732849 18909 solver.cpp:218] Iteration 5988 (2.2721 iter/s, 5.28146s/12 iters), loss = 0.526032
I0407 09:17:15.732913 18909 solver.cpp:237]     Train net output #0: loss = 0.526032 (* 1 = 0.526032 loss)
I0407 09:17:15.732923 18909 sgd_solver.cpp:105] Iteration 5988, lr = 0.0075
I0407 09:17:20.829659 18909 solver.cpp:218] Iteration 6000 (2.35445 iter/s, 5.09674s/12 iters), loss = 0.535947
I0407 09:17:20.829704 18909 solver.cpp:237]     Train net output #0: loss = 0.535947 (* 1 = 0.535947 loss)
I0407 09:17:20.829711 18909 sgd_solver.cpp:105] Iteration 6000, lr = 0.0075
I0407 09:17:26.096010 18909 solver.cpp:218] Iteration 6012 (2.27865 iter/s, 5.26629s/12 iters), loss = 0.589924
I0407 09:17:26.096055 18909 solver.cpp:237]     Train net output #0: loss = 0.589924 (* 1 = 0.589924 loss)
I0407 09:17:26.096065 18909 sgd_solver.cpp:105] Iteration 6012, lr = 0.0075
I0407 09:17:28.076558 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0407 09:17:31.085695 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0407 09:17:33.425632 18909 solver.cpp:330] Iteration 6018, Testing net (#0)
I0407 09:17:33.425650 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:17:35.490341 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:17:37.860873 18909 solver.cpp:397]     Test net output #0: accuracy = 0.384804
I0407 09:17:37.861014 18909 solver.cpp:397]     Test net output #1: loss = 3.0437 (* 1 = 3.0437 loss)
I0407 09:17:39.744738 18909 solver.cpp:218] Iteration 6024 (0.879206 iter/s, 13.6487s/12 iters), loss = 0.285
I0407 09:17:39.744801 18909 solver.cpp:237]     Train net output #0: loss = 0.285 (* 1 = 0.285 loss)
I0407 09:17:39.744810 18909 sgd_solver.cpp:105] Iteration 6024, lr = 0.0075
I0407 09:17:44.964196 18909 solver.cpp:218] Iteration 6036 (2.29912 iter/s, 5.21939s/12 iters), loss = 0.549387
I0407 09:17:44.964246 18909 solver.cpp:237]     Train net output #0: loss = 0.549387 (* 1 = 0.549387 loss)
I0407 09:17:44.964252 18909 sgd_solver.cpp:105] Iteration 6036, lr = 0.0075
I0407 09:17:50.203867 18909 solver.cpp:218] Iteration 6048 (2.29025 iter/s, 5.23961s/12 iters), loss = 0.510609
I0407 09:17:50.203905 18909 solver.cpp:237]     Train net output #0: loss = 0.510609 (* 1 = 0.510609 loss)
I0407 09:17:50.203912 18909 sgd_solver.cpp:105] Iteration 6048, lr = 0.0075
I0407 09:17:55.260741 18909 solver.cpp:218] Iteration 6060 (2.37303 iter/s, 5.05682s/12 iters), loss = 0.624052
I0407 09:17:55.260784 18909 solver.cpp:237]     Train net output #0: loss = 0.624052 (* 1 = 0.624052 loss)
I0407 09:17:55.260792 18909 sgd_solver.cpp:105] Iteration 6060, lr = 0.0075
I0407 09:17:58.983722 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:18:00.561008 18909 solver.cpp:218] Iteration 6072 (2.26406 iter/s, 5.30021s/12 iters), loss = 0.331762
I0407 09:18:00.561053 18909 solver.cpp:237]     Train net output #0: loss = 0.331762 (* 1 = 0.331762 loss)
I0407 09:18:00.561060 18909 sgd_solver.cpp:105] Iteration 6072, lr = 0.0075
I0407 09:18:05.636086 18909 solver.cpp:218] Iteration 6084 (2.36452 iter/s, 5.07502s/12 iters), loss = 0.627137
I0407 09:18:05.636121 18909 solver.cpp:237]     Train net output #0: loss = 0.627137 (* 1 = 0.627137 loss)
I0407 09:18:05.636126 18909 sgd_solver.cpp:105] Iteration 6084, lr = 0.0075
I0407 09:18:10.839107 18909 solver.cpp:218] Iteration 6096 (2.30637 iter/s, 5.20298s/12 iters), loss = 0.537154
I0407 09:18:10.839241 18909 solver.cpp:237]     Train net output #0: loss = 0.537154 (* 1 = 0.537154 loss)
I0407 09:18:10.839249 18909 sgd_solver.cpp:105] Iteration 6096, lr = 0.0075
I0407 09:18:16.077153 18909 solver.cpp:218] Iteration 6108 (2.29099 iter/s, 5.2379s/12 iters), loss = 0.465075
I0407 09:18:16.077199 18909 solver.cpp:237]     Train net output #0: loss = 0.465075 (* 1 = 0.465075 loss)
I0407 09:18:16.077208 18909 sgd_solver.cpp:105] Iteration 6108, lr = 0.0075
I0407 09:18:20.721305 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0407 09:18:24.200022 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0407 09:18:26.515533 18909 solver.cpp:330] Iteration 6120, Testing net (#0)
I0407 09:18:26.515558 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:18:28.605229 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:18:31.168347 18909 solver.cpp:397]     Test net output #0: accuracy = 0.372549
I0407 09:18:31.168370 18909 solver.cpp:397]     Test net output #1: loss = 3.1191 (* 1 = 3.1191 loss)
I0407 09:18:31.308892 18909 solver.cpp:218] Iteration 6120 (0.787831 iter/s, 15.2317s/12 iters), loss = 0.443712
I0407 09:18:31.308943 18909 solver.cpp:237]     Train net output #0: loss = 0.443712 (* 1 = 0.443712 loss)
I0407 09:18:31.308949 18909 sgd_solver.cpp:105] Iteration 6120, lr = 0.0075
I0407 09:18:35.629664 18909 solver.cpp:218] Iteration 6132 (2.77732 iter/s, 4.32071s/12 iters), loss = 0.463564
I0407 09:18:35.629701 18909 solver.cpp:237]     Train net output #0: loss = 0.463564 (* 1 = 0.463564 loss)
I0407 09:18:35.629709 18909 sgd_solver.cpp:105] Iteration 6132, lr = 0.0075
I0407 09:18:40.827005 18909 solver.cpp:218] Iteration 6144 (2.3089 iter/s, 5.19729s/12 iters), loss = 0.363448
I0407 09:18:40.827045 18909 solver.cpp:237]     Train net output #0: loss = 0.363448 (* 1 = 0.363448 loss)
I0407 09:18:40.827054 18909 sgd_solver.cpp:105] Iteration 6144, lr = 0.0075
I0407 09:18:46.065747 18909 solver.cpp:218] Iteration 6156 (2.29065 iter/s, 5.23869s/12 iters), loss = 0.553091
I0407 09:18:46.065829 18909 solver.cpp:237]     Train net output #0: loss = 0.553091 (* 1 = 0.553091 loss)
I0407 09:18:46.065837 18909 sgd_solver.cpp:105] Iteration 6156, lr = 0.0075
I0407 09:18:51.503684 18909 solver.cpp:218] Iteration 6168 (2.20676 iter/s, 5.43784s/12 iters), loss = 0.731338
I0407 09:18:51.503739 18909 solver.cpp:237]     Train net output #0: loss = 0.731338 (* 1 = 0.731338 loss)
I0407 09:18:51.503749 18909 sgd_solver.cpp:105] Iteration 6168, lr = 0.0075
I0407 09:18:52.115320 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:18:56.792732 18909 solver.cpp:218] Iteration 6180 (2.26887 iter/s, 5.28898s/12 iters), loss = 0.570859
I0407 09:18:56.792770 18909 solver.cpp:237]     Train net output #0: loss = 0.570859 (* 1 = 0.570859 loss)
I0407 09:18:56.792778 18909 sgd_solver.cpp:105] Iteration 6180, lr = 0.0075
I0407 09:19:02.195377 18909 solver.cpp:218] Iteration 6192 (2.22115 iter/s, 5.4026s/12 iters), loss = 0.719276
I0407 09:19:02.195415 18909 solver.cpp:237]     Train net output #0: loss = 0.719276 (* 1 = 0.719276 loss)
I0407 09:19:02.195421 18909 sgd_solver.cpp:105] Iteration 6192, lr = 0.0075
I0407 09:19:07.464484 18909 solver.cpp:218] Iteration 6204 (2.27745 iter/s, 5.26906s/12 iters), loss = 0.718909
I0407 09:19:07.464522 18909 solver.cpp:237]     Train net output #0: loss = 0.718909 (* 1 = 0.718909 loss)
I0407 09:19:07.464529 18909 sgd_solver.cpp:105] Iteration 6204, lr = 0.0075
I0407 09:19:12.948603 18909 solver.cpp:218] Iteration 6216 (2.18816 iter/s, 5.48407s/12 iters), loss = 0.455441
I0407 09:19:12.948654 18909 solver.cpp:237]     Train net output #0: loss = 0.455441 (* 1 = 0.455441 loss)
I0407 09:19:12.948663 18909 sgd_solver.cpp:105] Iteration 6216, lr = 0.0075
I0407 09:19:14.986476 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0407 09:19:18.488683 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0407 09:19:20.812180 18909 solver.cpp:330] Iteration 6222, Testing net (#0)
I0407 09:19:20.812203 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:19:22.753994 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:19:24.014688 18909 blocking_queue.cpp:49] Waiting for data
I0407 09:19:25.180404 18909 solver.cpp:397]     Test net output #0: accuracy = 0.389706
I0407 09:19:25.180433 18909 solver.cpp:397]     Test net output #1: loss = 3.11036 (* 1 = 3.11036 loss)
I0407 09:19:27.191170 18909 solver.cpp:218] Iteration 6228 (0.842548 iter/s, 14.2425s/12 iters), loss = 0.695772
I0407 09:19:27.191212 18909 solver.cpp:237]     Train net output #0: loss = 0.695772 (* 1 = 0.695772 loss)
I0407 09:19:27.191218 18909 sgd_solver.cpp:105] Iteration 6228, lr = 0.0075
I0407 09:19:32.358578 18909 solver.cpp:218] Iteration 6240 (2.32227 iter/s, 5.16735s/12 iters), loss = 0.463976
I0407 09:19:32.358625 18909 solver.cpp:237]     Train net output #0: loss = 0.463976 (* 1 = 0.463976 loss)
I0407 09:19:32.358633 18909 sgd_solver.cpp:105] Iteration 6240, lr = 0.0075
I0407 09:19:37.408072 18909 solver.cpp:218] Iteration 6252 (2.3765 iter/s, 5.04944s/12 iters), loss = 0.767884
I0407 09:19:37.408113 18909 solver.cpp:237]     Train net output #0: loss = 0.767884 (* 1 = 0.767884 loss)
I0407 09:19:37.408118 18909 sgd_solver.cpp:105] Iteration 6252, lr = 0.0075
I0407 09:19:42.548003 18909 solver.cpp:218] Iteration 6264 (2.33469 iter/s, 5.13988s/12 iters), loss = 0.652997
I0407 09:19:42.548045 18909 solver.cpp:237]     Train net output #0: loss = 0.652997 (* 1 = 0.652997 loss)
I0407 09:19:42.548051 18909 sgd_solver.cpp:105] Iteration 6264, lr = 0.0075
I0407 09:19:45.328737 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:19:47.756618 18909 solver.cpp:218] Iteration 6276 (2.3039 iter/s, 5.20856s/12 iters), loss = 0.410192
I0407 09:19:47.756662 18909 solver.cpp:237]     Train net output #0: loss = 0.410192 (* 1 = 0.410192 loss)
I0407 09:19:47.756669 18909 sgd_solver.cpp:105] Iteration 6276, lr = 0.0075
I0407 09:19:52.779266 18909 solver.cpp:218] Iteration 6288 (2.38921 iter/s, 5.02259s/12 iters), loss = 0.831363
I0407 09:19:52.779378 18909 solver.cpp:237]     Train net output #0: loss = 0.831363 (* 1 = 0.831363 loss)
I0407 09:19:52.779386 18909 sgd_solver.cpp:105] Iteration 6288, lr = 0.0075
I0407 09:19:58.009688 18909 solver.cpp:218] Iteration 6300 (2.29432 iter/s, 5.2303s/12 iters), loss = 0.615826
I0407 09:19:58.009727 18909 solver.cpp:237]     Train net output #0: loss = 0.615826 (* 1 = 0.615826 loss)
I0407 09:19:58.009732 18909 sgd_solver.cpp:105] Iteration 6300, lr = 0.0075
I0407 09:20:03.388778 18909 solver.cpp:218] Iteration 6312 (2.23088 iter/s, 5.37904s/12 iters), loss = 0.53934
I0407 09:20:03.388820 18909 solver.cpp:237]     Train net output #0: loss = 0.53934 (* 1 = 0.53934 loss)
I0407 09:20:03.388828 18909 sgd_solver.cpp:105] Iteration 6312, lr = 0.0075
I0407 09:20:08.141109 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0407 09:20:11.816934 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0407 09:20:14.166821 18909 solver.cpp:330] Iteration 6324, Testing net (#0)
I0407 09:20:14.166841 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:20:16.090948 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:20:18.515950 18909 solver.cpp:397]     Test net output #0: accuracy = 0.368873
I0407 09:20:18.515983 18909 solver.cpp:397]     Test net output #1: loss = 3.20594 (* 1 = 3.20594 loss)
I0407 09:20:18.656709 18909 solver.cpp:218] Iteration 6324 (0.785963 iter/s, 15.2679s/12 iters), loss = 0.44757
I0407 09:20:18.656749 18909 solver.cpp:237]     Train net output #0: loss = 0.44757 (* 1 = 0.44757 loss)
I0407 09:20:18.656756 18909 sgd_solver.cpp:105] Iteration 6324, lr = 0.0075
I0407 09:20:23.133518 18909 solver.cpp:218] Iteration 6336 (2.68051 iter/s, 4.47676s/12 iters), loss = 0.443885
I0407 09:20:23.133638 18909 solver.cpp:237]     Train net output #0: loss = 0.443885 (* 1 = 0.443885 loss)
I0407 09:20:23.133646 18909 sgd_solver.cpp:105] Iteration 6336, lr = 0.0075
I0407 09:20:28.296191 18909 solver.cpp:218] Iteration 6348 (2.32444 iter/s, 5.16254s/12 iters), loss = 0.656215
I0407 09:20:28.296237 18909 solver.cpp:237]     Train net output #0: loss = 0.656215 (* 1 = 0.656215 loss)
I0407 09:20:28.296245 18909 sgd_solver.cpp:105] Iteration 6348, lr = 0.0075
I0407 09:20:33.499326 18909 solver.cpp:218] Iteration 6360 (2.30633 iter/s, 5.20307s/12 iters), loss = 0.566362
I0407 09:20:33.499370 18909 solver.cpp:237]     Train net output #0: loss = 0.566362 (* 1 = 0.566362 loss)
I0407 09:20:33.499378 18909 sgd_solver.cpp:105] Iteration 6360, lr = 0.0075
I0407 09:20:38.509785 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:20:38.695281 18909 solver.cpp:218] Iteration 6372 (2.30951 iter/s, 5.19591s/12 iters), loss = 0.627591
I0407 09:20:38.695318 18909 solver.cpp:237]     Train net output #0: loss = 0.627591 (* 1 = 0.627591 loss)
I0407 09:20:38.695325 18909 sgd_solver.cpp:105] Iteration 6372, lr = 0.0075
I0407 09:20:44.091137 18909 solver.cpp:218] Iteration 6384 (2.22395 iter/s, 5.39581s/12 iters), loss = 0.396767
I0407 09:20:44.091181 18909 solver.cpp:237]     Train net output #0: loss = 0.396767 (* 1 = 0.396767 loss)
I0407 09:20:44.091188 18909 sgd_solver.cpp:105] Iteration 6384, lr = 0.0075
I0407 09:20:49.134068 18909 solver.cpp:218] Iteration 6396 (2.37959 iter/s, 5.04288s/12 iters), loss = 0.479671
I0407 09:20:49.134106 18909 solver.cpp:237]     Train net output #0: loss = 0.479671 (* 1 = 0.479671 loss)
I0407 09:20:49.134114 18909 sgd_solver.cpp:105] Iteration 6396, lr = 0.0075
I0407 09:20:54.514120 18909 solver.cpp:218] Iteration 6408 (2.23048 iter/s, 5.38s/12 iters), loss = 0.779567
I0407 09:20:54.514264 18909 solver.cpp:237]     Train net output #0: loss = 0.779567 (* 1 = 0.779567 loss)
I0407 09:20:54.514274 18909 sgd_solver.cpp:105] Iteration 6408, lr = 0.0075
I0407 09:20:59.787827 18909 solver.cpp:218] Iteration 6420 (2.2755 iter/s, 5.27356s/12 iters), loss = 0.570013
I0407 09:20:59.787870 18909 solver.cpp:237]     Train net output #0: loss = 0.570013 (* 1 = 0.570013 loss)
I0407 09:20:59.787878 18909 sgd_solver.cpp:105] Iteration 6420, lr = 0.0075
I0407 09:21:01.923629 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0407 09:21:05.589895 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0407 09:21:08.037782 18909 solver.cpp:330] Iteration 6426, Testing net (#0)
I0407 09:21:08.037806 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:21:09.861238 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:21:12.377430 18909 solver.cpp:397]     Test net output #0: accuracy = 0.372549
I0407 09:21:12.377468 18909 solver.cpp:397]     Test net output #1: loss = 3.173 (* 1 = 3.173 loss)
I0407 09:21:14.193275 18909 solver.cpp:218] Iteration 6432 (0.833021 iter/s, 14.4054s/12 iters), loss = 0.482697
I0407 09:21:14.193320 18909 solver.cpp:237]     Train net output #0: loss = 0.482697 (* 1 = 0.482697 loss)
I0407 09:21:14.193327 18909 sgd_solver.cpp:105] Iteration 6432, lr = 0.0075
I0407 09:21:19.295528 18909 solver.cpp:218] Iteration 6444 (2.35193 iter/s, 5.1022s/12 iters), loss = 0.56175
I0407 09:21:19.295568 18909 solver.cpp:237]     Train net output #0: loss = 0.56175 (* 1 = 0.56175 loss)
I0407 09:21:19.295576 18909 sgd_solver.cpp:105] Iteration 6444, lr = 0.0075
I0407 09:21:24.506661 18909 solver.cpp:218] Iteration 6456 (2.30279 iter/s, 5.21108s/12 iters), loss = 0.465126
I0407 09:21:24.506702 18909 solver.cpp:237]     Train net output #0: loss = 0.465126 (* 1 = 0.465126 loss)
I0407 09:21:24.506711 18909 sgd_solver.cpp:105] Iteration 6456, lr = 0.0075
I0407 09:21:29.579052 18909 solver.cpp:218] Iteration 6468 (2.36577 iter/s, 5.07234s/12 iters), loss = 0.494729
I0407 09:21:29.579192 18909 solver.cpp:237]     Train net output #0: loss = 0.494729 (* 1 = 0.494729 loss)
I0407 09:21:29.579200 18909 sgd_solver.cpp:105] Iteration 6468, lr = 0.0075
I0407 09:21:31.692610 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:21:35.014060 18909 solver.cpp:218] Iteration 6480 (2.20797 iter/s, 5.43486s/12 iters), loss = 0.598301
I0407 09:21:35.014094 18909 solver.cpp:237]     Train net output #0: loss = 0.598301 (* 1 = 0.598301 loss)
I0407 09:21:35.014101 18909 sgd_solver.cpp:105] Iteration 6480, lr = 0.0075
I0407 09:21:40.397187 18909 solver.cpp:218] Iteration 6492 (2.22921 iter/s, 5.38308s/12 iters), loss = 0.502671
I0407 09:21:40.397231 18909 solver.cpp:237]     Train net output #0: loss = 0.502671 (* 1 = 0.502671 loss)
I0407 09:21:40.397238 18909 sgd_solver.cpp:105] Iteration 6492, lr = 0.0075
I0407 09:21:45.577587 18909 solver.cpp:218] Iteration 6504 (2.31645 iter/s, 5.18034s/12 iters), loss = 0.403871
I0407 09:21:45.577630 18909 solver.cpp:237]     Train net output #0: loss = 0.403871 (* 1 = 0.403871 loss)
I0407 09:21:45.577638 18909 sgd_solver.cpp:105] Iteration 6504, lr = 0.0075
I0407 09:21:50.851176 18909 solver.cpp:218] Iteration 6516 (2.27551 iter/s, 5.27353s/12 iters), loss = 0.729688
I0407 09:21:50.851218 18909 solver.cpp:237]     Train net output #0: loss = 0.729688 (* 1 = 0.729688 loss)
I0407 09:21:50.851227 18909 sgd_solver.cpp:105] Iteration 6516, lr = 0.0075
I0407 09:21:55.479184 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0407 09:21:58.913913 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0407 09:22:01.230238 18909 solver.cpp:330] Iteration 6528, Testing net (#0)
I0407 09:22:01.230296 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:22:02.992995 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:22:05.510522 18909 solver.cpp:397]     Test net output #0: accuracy = 0.375613
I0407 09:22:05.510557 18909 solver.cpp:397]     Test net output #1: loss = 3.17691 (* 1 = 3.17691 loss)
I0407 09:22:05.647996 18909 solver.cpp:218] Iteration 6528 (0.810987 iter/s, 14.7968s/12 iters), loss = 0.626187
I0407 09:22:05.648037 18909 solver.cpp:237]     Train net output #0: loss = 0.626187 (* 1 = 0.626187 loss)
I0407 09:22:05.648046 18909 sgd_solver.cpp:105] Iteration 6528, lr = 0.0075
I0407 09:22:09.814312 18909 solver.cpp:218] Iteration 6540 (2.88028 iter/s, 4.16626s/12 iters), loss = 0.325392
I0407 09:22:09.814357 18909 solver.cpp:237]     Train net output #0: loss = 0.325392 (* 1 = 0.325392 loss)
I0407 09:22:09.814363 18909 sgd_solver.cpp:105] Iteration 6540, lr = 0.0075
I0407 09:22:14.807616 18909 solver.cpp:218] Iteration 6552 (2.40324 iter/s, 4.99325s/12 iters), loss = 0.364593
I0407 09:22:14.807660 18909 solver.cpp:237]     Train net output #0: loss = 0.364593 (* 1 = 0.364593 loss)
I0407 09:22:14.807668 18909 sgd_solver.cpp:105] Iteration 6552, lr = 0.0075
I0407 09:22:19.878429 18909 solver.cpp:218] Iteration 6564 (2.36651 iter/s, 5.07076s/12 iters), loss = 0.403276
I0407 09:22:19.878477 18909 solver.cpp:237]     Train net output #0: loss = 0.403276 (* 1 = 0.403276 loss)
I0407 09:22:19.878485 18909 sgd_solver.cpp:105] Iteration 6564, lr = 0.0075
I0407 09:22:24.242220 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:22:25.073616 18909 solver.cpp:218] Iteration 6576 (2.30986 iter/s, 5.19512s/12 iters), loss = 0.547237
I0407 09:22:25.073660 18909 solver.cpp:237]     Train net output #0: loss = 0.547237 (* 1 = 0.547237 loss)
I0407 09:22:25.073668 18909 sgd_solver.cpp:105] Iteration 6576, lr = 0.0075
I0407 09:22:30.288843 18909 solver.cpp:218] Iteration 6588 (2.30098 iter/s, 5.21517s/12 iters), loss = 0.470756
I0407 09:22:30.288879 18909 solver.cpp:237]     Train net output #0: loss = 0.470756 (* 1 = 0.470756 loss)
I0407 09:22:30.288892 18909 sgd_solver.cpp:105] Iteration 6588, lr = 0.0075
I0407 09:22:35.556740 18909 solver.cpp:218] Iteration 6600 (2.27797 iter/s, 5.26785s/12 iters), loss = 0.640289
I0407 09:22:35.556875 18909 solver.cpp:237]     Train net output #0: loss = 0.640289 (* 1 = 0.640289 loss)
I0407 09:22:35.556888 18909 sgd_solver.cpp:105] Iteration 6600, lr = 0.0075
I0407 09:22:40.856225 18909 solver.cpp:218] Iteration 6612 (2.26443 iter/s, 5.29934s/12 iters), loss = 0.578665
I0407 09:22:40.856271 18909 solver.cpp:237]     Train net output #0: loss = 0.578665 (* 1 = 0.578665 loss)
I0407 09:22:40.856278 18909 sgd_solver.cpp:105] Iteration 6612, lr = 0.0075
I0407 09:22:46.004036 18909 solver.cpp:218] Iteration 6624 (2.33112 iter/s, 5.14775s/12 iters), loss = 0.320783
I0407 09:22:46.004092 18909 solver.cpp:237]     Train net output #0: loss = 0.320783 (* 1 = 0.320783 loss)
I0407 09:22:46.004102 18909 sgd_solver.cpp:105] Iteration 6624, lr = 0.0075
I0407 09:22:48.069053 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0407 09:22:53.431874 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0407 09:22:56.097087 18909 solver.cpp:330] Iteration 6630, Testing net (#0)
I0407 09:22:56.097108 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:22:57.983381 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:23:00.546763 18909 solver.cpp:397]     Test net output #0: accuracy = 0.403799
I0407 09:23:00.546793 18909 solver.cpp:397]     Test net output #1: loss = 3.04366 (* 1 = 3.04366 loss)
I0407 09:23:02.506475 18909 solver.cpp:218] Iteration 6636 (0.727168 iter/s, 16.5024s/12 iters), loss = 0.642502
I0407 09:23:02.506525 18909 solver.cpp:237]     Train net output #0: loss = 0.642502 (* 1 = 0.642502 loss)
I0407 09:23:02.506532 18909 sgd_solver.cpp:105] Iteration 6636, lr = 0.0075
I0407 09:23:07.499742 18909 solver.cpp:218] Iteration 6648 (2.40327 iter/s, 4.99321s/12 iters), loss = 0.499126
I0407 09:23:07.499883 18909 solver.cpp:237]     Train net output #0: loss = 0.499126 (* 1 = 0.499126 loss)
I0407 09:23:07.499891 18909 sgd_solver.cpp:105] Iteration 6648, lr = 0.0075
I0407 09:23:12.583089 18909 solver.cpp:218] Iteration 6660 (2.36072 iter/s, 5.08319s/12 iters), loss = 0.556012
I0407 09:23:12.583148 18909 solver.cpp:237]     Train net output #0: loss = 0.556012 (* 1 = 0.556012 loss)
I0407 09:23:12.583159 18909 sgd_solver.cpp:105] Iteration 6660, lr = 0.0075
I0407 09:23:17.655776 18909 solver.cpp:218] Iteration 6672 (2.36564 iter/s, 5.07262s/12 iters), loss = 0.573209
I0407 09:23:17.655820 18909 solver.cpp:237]     Train net output #0: loss = 0.573209 (* 1 = 0.573209 loss)
I0407 09:23:17.655827 18909 sgd_solver.cpp:105] Iteration 6672, lr = 0.0075
I0407 09:23:19.069213 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:23:23.050429 18909 solver.cpp:218] Iteration 6684 (2.22445 iter/s, 5.39459s/12 iters), loss = 0.517253
I0407 09:23:23.050472 18909 solver.cpp:237]     Train net output #0: loss = 0.517253 (* 1 = 0.517253 loss)
I0407 09:23:23.050478 18909 sgd_solver.cpp:105] Iteration 6684, lr = 0.0075
I0407 09:23:28.506549 18909 solver.cpp:218] Iteration 6696 (2.19939 iter/s, 5.45607s/12 iters), loss = 0.487679
I0407 09:23:28.506600 18909 solver.cpp:237]     Train net output #0: loss = 0.487679 (* 1 = 0.487679 loss)
I0407 09:23:28.506610 18909 sgd_solver.cpp:105] Iteration 6696, lr = 0.0075
I0407 09:23:33.813784 18909 solver.cpp:218] Iteration 6708 (2.26109 iter/s, 5.30717s/12 iters), loss = 0.721708
I0407 09:23:33.813853 18909 solver.cpp:237]     Train net output #0: loss = 0.721708 (* 1 = 0.721708 loss)
I0407 09:23:33.813864 18909 sgd_solver.cpp:105] Iteration 6708, lr = 0.0075
I0407 09:23:38.943289 18909 solver.cpp:218] Iteration 6720 (2.33944 iter/s, 5.12943s/12 iters), loss = 0.422882
I0407 09:23:38.943431 18909 solver.cpp:237]     Train net output #0: loss = 0.422882 (* 1 = 0.422882 loss)
I0407 09:23:38.943440 18909 sgd_solver.cpp:105] Iteration 6720, lr = 0.0075
I0407 09:23:43.417901 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0407 09:23:48.936679 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0407 09:23:51.429008 18909 solver.cpp:330] Iteration 6732, Testing net (#0)
I0407 09:23:51.429026 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:23:53.218485 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:23:55.869603 18909 solver.cpp:397]     Test net output #0: accuracy = 0.400735
I0407 09:23:55.869637 18909 solver.cpp:397]     Test net output #1: loss = 3.01456 (* 1 = 3.01456 loss)
I0407 09:23:56.010331 18909 solver.cpp:218] Iteration 6732 (0.703115 iter/s, 17.0669s/12 iters), loss = 0.305626
I0407 09:23:56.010381 18909 solver.cpp:237]     Train net output #0: loss = 0.305626 (* 1 = 0.305626 loss)
I0407 09:23:56.010390 18909 sgd_solver.cpp:105] Iteration 6732, lr = 0.005625
I0407 09:24:00.229698 18909 solver.cpp:218] Iteration 6744 (2.84408 iter/s, 4.21929s/12 iters), loss = 0.51884
I0407 09:24:00.229758 18909 solver.cpp:237]     Train net output #0: loss = 0.51884 (* 1 = 0.51884 loss)
I0407 09:24:00.229768 18909 sgd_solver.cpp:105] Iteration 6744, lr = 0.005625
I0407 09:24:05.348471 18909 solver.cpp:218] Iteration 6756 (2.34434 iter/s, 5.11871s/12 iters), loss = 0.51582
I0407 09:24:05.348528 18909 solver.cpp:237]     Train net output #0: loss = 0.51582 (* 1 = 0.51582 loss)
I0407 09:24:05.348538 18909 sgd_solver.cpp:105] Iteration 6756, lr = 0.005625
I0407 09:24:10.483454 18909 solver.cpp:218] Iteration 6768 (2.33694 iter/s, 5.13492s/12 iters), loss = 0.533038
I0407 09:24:10.483590 18909 solver.cpp:237]     Train net output #0: loss = 0.533038 (* 1 = 0.533038 loss)
I0407 09:24:10.483603 18909 sgd_solver.cpp:105] Iteration 6768, lr = 0.005625
I0407 09:24:14.148906 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:24:15.811872 18909 solver.cpp:218] Iteration 6780 (2.25214 iter/s, 5.32827s/12 iters), loss = 0.546592
I0407 09:24:15.811921 18909 solver.cpp:237]     Train net output #0: loss = 0.546592 (* 1 = 0.546592 loss)
I0407 09:24:15.811929 18909 sgd_solver.cpp:105] Iteration 6780, lr = 0.005625
I0407 09:24:21.100917 18909 solver.cpp:218] Iteration 6792 (2.26887 iter/s, 5.28898s/12 iters), loss = 0.576128
I0407 09:24:21.100980 18909 solver.cpp:237]     Train net output #0: loss = 0.576128 (* 1 = 0.576128 loss)
I0407 09:24:21.100991 18909 sgd_solver.cpp:105] Iteration 6792, lr = 0.005625
I0407 09:24:26.262871 18909 solver.cpp:218] Iteration 6804 (2.32473 iter/s, 5.16188s/12 iters), loss = 0.386531
I0407 09:24:26.262915 18909 solver.cpp:237]     Train net output #0: loss = 0.386531 (* 1 = 0.386531 loss)
I0407 09:24:26.262923 18909 sgd_solver.cpp:105] Iteration 6804, lr = 0.005625
I0407 09:24:31.403618 18909 solver.cpp:218] Iteration 6816 (2.33432 iter/s, 5.14069s/12 iters), loss = 0.362186
I0407 09:24:31.403662 18909 solver.cpp:237]     Train net output #0: loss = 0.362186 (* 1 = 0.362186 loss)
I0407 09:24:31.403669 18909 sgd_solver.cpp:105] Iteration 6816, lr = 0.005625
I0407 09:24:36.625059 18909 solver.cpp:218] Iteration 6828 (2.29824 iter/s, 5.22139s/12 iters), loss = 0.278663
I0407 09:24:36.625102 18909 solver.cpp:237]     Train net output #0: loss = 0.278663 (* 1 = 0.278663 loss)
I0407 09:24:36.625108 18909 sgd_solver.cpp:105] Iteration 6828, lr = 0.005625
I0407 09:24:38.676601 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0407 09:24:43.322432 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0407 09:24:45.625321 18909 solver.cpp:330] Iteration 6834, Testing net (#0)
I0407 09:24:45.625341 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:24:47.265457 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:24:49.906615 18909 solver.cpp:397]     Test net output #0: accuracy = 0.407476
I0407 09:24:49.906644 18909 solver.cpp:397]     Test net output #1: loss = 3.0746 (* 1 = 3.0746 loss)
I0407 09:24:51.716488 18909 solver.cpp:218] Iteration 6840 (0.795156 iter/s, 15.0914s/12 iters), loss = 0.44101
I0407 09:24:51.716531 18909 solver.cpp:237]     Train net output #0: loss = 0.44101 (* 1 = 0.44101 loss)
I0407 09:24:51.716538 18909 sgd_solver.cpp:105] Iteration 6840, lr = 0.005625
I0407 09:24:56.977624 18909 solver.cpp:218] Iteration 6852 (2.2809 iter/s, 5.26108s/12 iters), loss = 0.248074
I0407 09:24:56.977669 18909 solver.cpp:237]     Train net output #0: loss = 0.248074 (* 1 = 0.248074 loss)
I0407 09:24:56.977676 18909 sgd_solver.cpp:105] Iteration 6852, lr = 0.005625
I0407 09:25:02.221283 18909 solver.cpp:218] Iteration 6864 (2.2885 iter/s, 5.24361s/12 iters), loss = 0.562145
I0407 09:25:02.221326 18909 solver.cpp:237]     Train net output #0: loss = 0.562145 (* 1 = 0.562145 loss)
I0407 09:25:02.221333 18909 sgd_solver.cpp:105] Iteration 6864, lr = 0.005625
I0407 09:25:07.578928 18909 solver.cpp:218] Iteration 6876 (2.23981 iter/s, 5.35759s/12 iters), loss = 0.363155
I0407 09:25:07.578971 18909 solver.cpp:237]     Train net output #0: loss = 0.363155 (* 1 = 0.363155 loss)
I0407 09:25:07.578979 18909 sgd_solver.cpp:105] Iteration 6876, lr = 0.005625
I0407 09:25:08.217388 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:25:12.755818 18909 solver.cpp:218] Iteration 6888 (2.31802 iter/s, 5.17684s/12 iters), loss = 0.497604
I0407 09:25:12.755858 18909 solver.cpp:237]     Train net output #0: loss = 0.497604 (* 1 = 0.497604 loss)
I0407 09:25:12.755864 18909 sgd_solver.cpp:105] Iteration 6888, lr = 0.005625
I0407 09:25:17.796422 18909 solver.cpp:218] Iteration 6900 (2.38069 iter/s, 5.04055s/12 iters), loss = 0.34116
I0407 09:25:17.798430 18909 solver.cpp:237]     Train net output #0: loss = 0.34116 (* 1 = 0.34116 loss)
I0407 09:25:17.798439 18909 sgd_solver.cpp:105] Iteration 6900, lr = 0.005625
I0407 09:25:23.163759 18909 solver.cpp:218] Iteration 6912 (2.23658 iter/s, 5.36532s/12 iters), loss = 0.422052
I0407 09:25:23.163803 18909 solver.cpp:237]     Train net output #0: loss = 0.422052 (* 1 = 0.422052 loss)
I0407 09:25:23.163810 18909 sgd_solver.cpp:105] Iteration 6912, lr = 0.005625
I0407 09:25:28.377636 18909 solver.cpp:218] Iteration 6924 (2.30158 iter/s, 5.21382s/12 iters), loss = 0.545312
I0407 09:25:28.377681 18909 solver.cpp:237]     Train net output #0: loss = 0.545312 (* 1 = 0.545312 loss)
I0407 09:25:28.377687 18909 sgd_solver.cpp:105] Iteration 6924, lr = 0.005625
I0407 09:25:33.270337 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0407 09:25:38.178755 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0407 09:25:40.523031 18909 solver.cpp:330] Iteration 6936, Testing net (#0)
I0407 09:25:40.523057 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:25:41.088801 18909 blocking_queue.cpp:49] Waiting for data
I0407 09:25:42.112319 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:25:44.912473 18909 solver.cpp:397]     Test net output #0: accuracy = 0.414828
I0407 09:25:44.912523 18909 solver.cpp:397]     Test net output #1: loss = 2.9347 (* 1 = 2.9347 loss)
I0407 09:25:45.053721 18909 solver.cpp:218] Iteration 6936 (0.719596 iter/s, 16.676s/12 iters), loss = 0.34186
I0407 09:25:45.053777 18909 solver.cpp:237]     Train net output #0: loss = 0.34186 (* 1 = 0.34186 loss)
I0407 09:25:45.053788 18909 sgd_solver.cpp:105] Iteration 6936, lr = 0.005625
I0407 09:25:49.276479 18909 solver.cpp:218] Iteration 6948 (2.84179 iter/s, 4.22269s/12 iters), loss = 0.322713
I0407 09:25:49.276619 18909 solver.cpp:237]     Train net output #0: loss = 0.322713 (* 1 = 0.322713 loss)
I0407 09:25:49.276628 18909 sgd_solver.cpp:105] Iteration 6948, lr = 0.005625
I0407 09:25:54.492280 18909 solver.cpp:218] Iteration 6960 (2.30077 iter/s, 5.21565s/12 iters), loss = 0.301025
I0407 09:25:54.492331 18909 solver.cpp:237]     Train net output #0: loss = 0.301025 (* 1 = 0.301025 loss)
I0407 09:25:54.492341 18909 sgd_solver.cpp:105] Iteration 6960, lr = 0.005625
I0407 09:25:59.708591 18909 solver.cpp:218] Iteration 6972 (2.3005 iter/s, 5.21625s/12 iters), loss = 0.424632
I0407 09:25:59.708633 18909 solver.cpp:237]     Train net output #0: loss = 0.424632 (* 1 = 0.424632 loss)
I0407 09:25:59.708640 18909 sgd_solver.cpp:105] Iteration 6972, lr = 0.005625
I0407 09:26:02.707109 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:26:05.079453 18909 solver.cpp:218] Iteration 6984 (2.2343 iter/s, 5.3708s/12 iters), loss = 0.311531
I0407 09:26:05.079499 18909 solver.cpp:237]     Train net output #0: loss = 0.311531 (* 1 = 0.311531 loss)
I0407 09:26:05.079506 18909 sgd_solver.cpp:105] Iteration 6984, lr = 0.005625
I0407 09:26:10.326923 18909 solver.cpp:218] Iteration 6996 (2.28684 iter/s, 5.24741s/12 iters), loss = 0.32044
I0407 09:26:10.326968 18909 solver.cpp:237]     Train net output #0: loss = 0.32044 (* 1 = 0.32044 loss)
I0407 09:26:10.326975 18909 sgd_solver.cpp:105] Iteration 6996, lr = 0.005625
I0407 09:26:15.586377 18909 solver.cpp:218] Iteration 7008 (2.28163 iter/s, 5.25939s/12 iters), loss = 0.456121
I0407 09:26:15.586422 18909 solver.cpp:237]     Train net output #0: loss = 0.456121 (* 1 = 0.456121 loss)
I0407 09:26:15.586431 18909 sgd_solver.cpp:105] Iteration 7008, lr = 0.005625
I0407 09:26:20.707715 18909 solver.cpp:218] Iteration 7020 (2.34316 iter/s, 5.12128s/12 iters), loss = 0.505585
I0407 09:26:20.707829 18909 solver.cpp:237]     Train net output #0: loss = 0.505585 (* 1 = 0.505585 loss)
I0407 09:26:20.707839 18909 sgd_solver.cpp:105] Iteration 7020, lr = 0.005625
I0407 09:26:25.758437 18909 solver.cpp:218] Iteration 7032 (2.37596 iter/s, 5.0506s/12 iters), loss = 0.424981
I0407 09:26:25.758486 18909 solver.cpp:237]     Train net output #0: loss = 0.424981 (* 1 = 0.424981 loss)
I0407 09:26:25.758494 18909 sgd_solver.cpp:105] Iteration 7032, lr = 0.005625
I0407 09:26:28.013756 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0407 09:26:32.850987 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0407 09:26:35.159360 18909 solver.cpp:330] Iteration 7038, Testing net (#0)
I0407 09:26:35.159379 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:26:36.822669 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:26:39.517033 18909 solver.cpp:397]     Test net output #0: accuracy = 0.403799
I0407 09:26:39.517068 18909 solver.cpp:397]     Test net output #1: loss = 3.04119 (* 1 = 3.04119 loss)
I0407 09:26:41.395906 18909 solver.cpp:218] Iteration 7044 (0.76739 iter/s, 15.6374s/12 iters), loss = 0.211778
I0407 09:26:41.395949 18909 solver.cpp:237]     Train net output #0: loss = 0.211778 (* 1 = 0.211778 loss)
I0407 09:26:41.395957 18909 sgd_solver.cpp:105] Iteration 7044, lr = 0.005625
I0407 09:26:46.452929 18909 solver.cpp:218] Iteration 7056 (2.37296 iter/s, 5.05697s/12 iters), loss = 0.489384
I0407 09:26:46.452967 18909 solver.cpp:237]     Train net output #0: loss = 0.489384 (* 1 = 0.489384 loss)
I0407 09:26:46.452975 18909 sgd_solver.cpp:105] Iteration 7056, lr = 0.005625
I0407 09:26:51.662437 18909 solver.cpp:218] Iteration 7068 (2.3035 iter/s, 5.20946s/12 iters), loss = 0.259383
I0407 09:26:51.662523 18909 solver.cpp:237]     Train net output #0: loss = 0.259383 (* 1 = 0.259383 loss)
I0407 09:26:51.662529 18909 sgd_solver.cpp:105] Iteration 7068, lr = 0.005625
I0407 09:26:56.713378 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:26:56.875368 18909 solver.cpp:218] Iteration 7080 (2.30201 iter/s, 5.21283s/12 iters), loss = 0.294561
I0407 09:26:56.875411 18909 solver.cpp:237]     Train net output #0: loss = 0.294561 (* 1 = 0.294561 loss)
I0407 09:26:56.875418 18909 sgd_solver.cpp:105] Iteration 7080, lr = 0.005625
I0407 09:27:02.101917 18909 solver.cpp:218] Iteration 7092 (2.296 iter/s, 5.22649s/12 iters), loss = 0.41455
I0407 09:27:02.101964 18909 solver.cpp:237]     Train net output #0: loss = 0.41455 (* 1 = 0.41455 loss)
I0407 09:27:02.101974 18909 sgd_solver.cpp:105] Iteration 7092, lr = 0.005625
I0407 09:27:07.179414 18909 solver.cpp:218] Iteration 7104 (2.3634 iter/s, 5.07744s/12 iters), loss = 0.358198
I0407 09:27:07.179456 18909 solver.cpp:237]     Train net output #0: loss = 0.358198 (* 1 = 0.358198 loss)
I0407 09:27:07.179463 18909 sgd_solver.cpp:105] Iteration 7104, lr = 0.005625
I0407 09:27:12.398569 18909 solver.cpp:218] Iteration 7116 (2.29925 iter/s, 5.2191s/12 iters), loss = 0.467294
I0407 09:27:12.398614 18909 solver.cpp:237]     Train net output #0: loss = 0.467294 (* 1 = 0.467294 loss)
I0407 09:27:12.398622 18909 sgd_solver.cpp:105] Iteration 7116, lr = 0.005625
I0407 09:27:17.592062 18909 solver.cpp:218] Iteration 7128 (2.31061 iter/s, 5.19344s/12 iters), loss = 0.253499
I0407 09:27:17.592103 18909 solver.cpp:237]     Train net output #0: loss = 0.253499 (* 1 = 0.253499 loss)
I0407 09:27:17.592110 18909 sgd_solver.cpp:105] Iteration 7128, lr = 0.005625
I0407 09:27:22.239125 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0407 09:27:27.247184 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0407 09:27:29.564316 18909 solver.cpp:330] Iteration 7140, Testing net (#0)
I0407 09:27:29.564334 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:27:31.081272 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:27:33.820284 18909 solver.cpp:397]     Test net output #0: accuracy = 0.417892
I0407 09:27:33.820317 18909 solver.cpp:397]     Test net output #1: loss = 3.06299 (* 1 = 3.06299 loss)
I0407 09:27:33.960628 18909 solver.cpp:218] Iteration 7140 (0.733114 iter/s, 16.3685s/12 iters), loss = 0.245332
I0407 09:27:33.960698 18909 solver.cpp:237]     Train net output #0: loss = 0.245332 (* 1 = 0.245332 loss)
I0407 09:27:33.960706 18909 sgd_solver.cpp:105] Iteration 7140, lr = 0.005625
I0407 09:27:38.187829 18909 solver.cpp:218] Iteration 7152 (2.83881 iter/s, 4.22712s/12 iters), loss = 0.472608
I0407 09:27:38.187863 18909 solver.cpp:237]     Train net output #0: loss = 0.472608 (* 1 = 0.472608 loss)
I0407 09:27:38.187870 18909 sgd_solver.cpp:105] Iteration 7152, lr = 0.005625
I0407 09:27:43.314182 18909 solver.cpp:218] Iteration 7164 (2.34087 iter/s, 5.12631s/12 iters), loss = 0.472102
I0407 09:27:43.314224 18909 solver.cpp:237]     Train net output #0: loss = 0.472102 (* 1 = 0.472102 loss)
I0407 09:27:43.314230 18909 sgd_solver.cpp:105] Iteration 7164, lr = 0.005625
I0407 09:27:48.283164 18909 solver.cpp:218] Iteration 7176 (2.41501 iter/s, 4.96893s/12 iters), loss = 0.261663
I0407 09:27:48.283210 18909 solver.cpp:237]     Train net output #0: loss = 0.261663 (* 1 = 0.261663 loss)
I0407 09:27:48.283218 18909 sgd_solver.cpp:105] Iteration 7176, lr = 0.005625
I0407 09:27:50.521879 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:27:53.568648 18909 solver.cpp:218] Iteration 7188 (2.2704 iter/s, 5.28542s/12 iters), loss = 0.429956
I0407 09:27:53.568758 18909 solver.cpp:237]     Train net output #0: loss = 0.429956 (* 1 = 0.429956 loss)
I0407 09:27:53.568765 18909 sgd_solver.cpp:105] Iteration 7188, lr = 0.005625
I0407 09:27:58.943545 18909 solver.cpp:218] Iteration 7200 (2.23265 iter/s, 5.37479s/12 iters), loss = 0.274663
I0407 09:27:58.943580 18909 solver.cpp:237]     Train net output #0: loss = 0.274663 (* 1 = 0.274663 loss)
I0407 09:27:58.943586 18909 sgd_solver.cpp:105] Iteration 7200, lr = 0.005625
I0407 09:28:04.403636 18909 solver.cpp:218] Iteration 7212 (2.19779 iter/s, 5.46004s/12 iters), loss = 0.203687
I0407 09:28:04.403681 18909 solver.cpp:237]     Train net output #0: loss = 0.203687 (* 1 = 0.203687 loss)
I0407 09:28:04.403687 18909 sgd_solver.cpp:105] Iteration 7212, lr = 0.005625
I0407 09:28:09.721698 18909 solver.cpp:218] Iteration 7224 (2.25648 iter/s, 5.31801s/12 iters), loss = 0.252773
I0407 09:28:09.721737 18909 solver.cpp:237]     Train net output #0: loss = 0.252773 (* 1 = 0.252773 loss)
I0407 09:28:09.721745 18909 sgd_solver.cpp:105] Iteration 7224, lr = 0.005625
I0407 09:28:14.912415 18909 solver.cpp:218] Iteration 7236 (2.31184 iter/s, 5.19067s/12 iters), loss = 0.189507
I0407 09:28:14.912452 18909 solver.cpp:237]     Train net output #0: loss = 0.189507 (* 1 = 0.189507 loss)
I0407 09:28:14.912458 18909 sgd_solver.cpp:105] Iteration 7236, lr = 0.005625
I0407 09:28:17.099151 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0407 09:28:21.575598 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0407 09:28:24.004953 18909 solver.cpp:330] Iteration 7242, Testing net (#0)
I0407 09:28:24.005067 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:28:25.578845 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:28:28.347136 18909 solver.cpp:397]     Test net output #0: accuracy = 0.42402
I0407 09:28:28.347170 18909 solver.cpp:397]     Test net output #1: loss = 3.00307 (* 1 = 3.00307 loss)
I0407 09:28:30.158579 18909 solver.cpp:218] Iteration 7248 (0.787085 iter/s, 15.2461s/12 iters), loss = 0.465436
I0407 09:28:30.158619 18909 solver.cpp:237]     Train net output #0: loss = 0.465436 (* 1 = 0.465436 loss)
I0407 09:28:30.158627 18909 sgd_solver.cpp:105] Iteration 7248, lr = 0.005625
I0407 09:28:35.482621 18909 solver.cpp:218] Iteration 7260 (2.25395 iter/s, 5.32399s/12 iters), loss = 0.318769
I0407 09:28:35.482661 18909 solver.cpp:237]     Train net output #0: loss = 0.318769 (* 1 = 0.318769 loss)
I0407 09:28:35.482668 18909 sgd_solver.cpp:105] Iteration 7260, lr = 0.005625
I0407 09:28:40.817973 18909 solver.cpp:218] Iteration 7272 (2.24917 iter/s, 5.3353s/12 iters), loss = 0.309951
I0407 09:28:40.818015 18909 solver.cpp:237]     Train net output #0: loss = 0.309951 (* 1 = 0.309951 loss)
I0407 09:28:40.818022 18909 sgd_solver.cpp:105] Iteration 7272, lr = 0.005625
I0407 09:28:45.380342 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:28:46.165014 18909 solver.cpp:218] Iteration 7284 (2.24425 iter/s, 5.34699s/12 iters), loss = 0.348565
I0407 09:28:46.165055 18909 solver.cpp:237]     Train net output #0: loss = 0.348565 (* 1 = 0.348565 loss)
I0407 09:28:46.165063 18909 sgd_solver.cpp:105] Iteration 7284, lr = 0.005625
I0407 09:28:51.463702 18909 solver.cpp:218] Iteration 7296 (2.26473 iter/s, 5.29863s/12 iters), loss = 0.276324
I0407 09:28:51.463742 18909 solver.cpp:237]     Train net output #0: loss = 0.276324 (* 1 = 0.276324 loss)
I0407 09:28:51.463748 18909 sgd_solver.cpp:105] Iteration 7296, lr = 0.005625
I0407 09:28:56.747776 18909 solver.cpp:218] Iteration 7308 (2.271 iter/s, 5.28402s/12 iters), loss = 0.248291
I0407 09:28:56.747900 18909 solver.cpp:237]     Train net output #0: loss = 0.248291 (* 1 = 0.248291 loss)
I0407 09:28:56.747910 18909 sgd_solver.cpp:105] Iteration 7308, lr = 0.005625
I0407 09:29:02.208636 18909 solver.cpp:218] Iteration 7320 (2.19751 iter/s, 5.46073s/12 iters), loss = 0.206842
I0407 09:29:02.208678 18909 solver.cpp:237]     Train net output #0: loss = 0.206842 (* 1 = 0.206842 loss)
I0407 09:29:02.208685 18909 sgd_solver.cpp:105] Iteration 7320, lr = 0.005625
I0407 09:29:07.504650 18909 solver.cpp:218] Iteration 7332 (2.26588 iter/s, 5.29596s/12 iters), loss = 0.202051
I0407 09:29:07.504694 18909 solver.cpp:237]     Train net output #0: loss = 0.202051 (* 1 = 0.202051 loss)
I0407 09:29:07.504700 18909 sgd_solver.cpp:105] Iteration 7332, lr = 0.005625
I0407 09:29:12.448272 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0407 09:29:17.442889 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0407 09:29:19.778285 18909 solver.cpp:330] Iteration 7344, Testing net (#0)
I0407 09:29:19.778309 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:29:21.298053 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:29:24.186393 18909 solver.cpp:397]     Test net output #0: accuracy = 0.415441
I0407 09:29:24.186424 18909 solver.cpp:397]     Test net output #1: loss = 2.99108 (* 1 = 2.99108 loss)
I0407 09:29:24.327303 18909 solver.cpp:218] Iteration 7344 (0.713326 iter/s, 16.8226s/12 iters), loss = 0.480422
I0407 09:29:24.327375 18909 solver.cpp:237]     Train net output #0: loss = 0.480422 (* 1 = 0.480422 loss)
I0407 09:29:24.327384 18909 sgd_solver.cpp:105] Iteration 7344, lr = 0.005625
I0407 09:29:28.761519 18909 solver.cpp:218] Iteration 7356 (2.70628 iter/s, 4.43413s/12 iters), loss = 0.319154
I0407 09:29:28.761654 18909 solver.cpp:237]     Train net output #0: loss = 0.319154 (* 1 = 0.319154 loss)
I0407 09:29:28.761663 18909 sgd_solver.cpp:105] Iteration 7356, lr = 0.005625
I0407 09:29:33.834453 18909 solver.cpp:218] Iteration 7368 (2.36556 iter/s, 5.07279s/12 iters), loss = 0.277412
I0407 09:29:33.834488 18909 solver.cpp:237]     Train net output #0: loss = 0.277412 (* 1 = 0.277412 loss)
I0407 09:29:33.834494 18909 sgd_solver.cpp:105] Iteration 7368, lr = 0.005625
I0407 09:29:38.885766 18909 solver.cpp:218] Iteration 7380 (2.37564 iter/s, 5.05127s/12 iters), loss = 0.261359
I0407 09:29:38.885802 18909 solver.cpp:237]     Train net output #0: loss = 0.261359 (* 1 = 0.261359 loss)
I0407 09:29:38.885809 18909 sgd_solver.cpp:105] Iteration 7380, lr = 0.005625
I0407 09:29:40.328688 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:29:44.288434 18909 solver.cpp:218] Iteration 7392 (2.22114 iter/s, 5.40262s/12 iters), loss = 0.266044
I0407 09:29:44.288476 18909 solver.cpp:237]     Train net output #0: loss = 0.266044 (* 1 = 0.266044 loss)
I0407 09:29:44.288484 18909 sgd_solver.cpp:105] Iteration 7392, lr = 0.005625
I0407 09:29:49.512354 18909 solver.cpp:218] Iteration 7404 (2.29715 iter/s, 5.22386s/12 iters), loss = 0.2858
I0407 09:29:49.512398 18909 solver.cpp:237]     Train net output #0: loss = 0.2858 (* 1 = 0.2858 loss)
I0407 09:29:49.512405 18909 sgd_solver.cpp:105] Iteration 7404, lr = 0.005625
I0407 09:29:54.846768 18909 solver.cpp:218] Iteration 7416 (2.24957 iter/s, 5.33436s/12 iters), loss = 0.237602
I0407 09:29:54.846808 18909 solver.cpp:237]     Train net output #0: loss = 0.237602 (* 1 = 0.237602 loss)
I0407 09:29:54.846815 18909 sgd_solver.cpp:105] Iteration 7416, lr = 0.005625
I0407 09:30:00.149544 18909 solver.cpp:218] Iteration 7428 (2.26299 iter/s, 5.30272s/12 iters), loss = 0.301051
I0407 09:30:00.149665 18909 solver.cpp:237]     Train net output #0: loss = 0.301051 (* 1 = 0.301051 loss)
I0407 09:30:00.149674 18909 sgd_solver.cpp:105] Iteration 7428, lr = 0.005625
I0407 09:30:05.505475 18909 solver.cpp:218] Iteration 7440 (2.24056 iter/s, 5.3558s/12 iters), loss = 0.198457
I0407 09:30:05.505518 18909 solver.cpp:237]     Train net output #0: loss = 0.198457 (* 1 = 0.198457 loss)
I0407 09:30:05.505525 18909 sgd_solver.cpp:105] Iteration 7440, lr = 0.005625
I0407 09:30:07.601573 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0407 09:30:12.008152 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0407 09:30:14.328737 18909 solver.cpp:330] Iteration 7446, Testing net (#0)
I0407 09:30:14.328755 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:30:15.798522 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:30:18.677362 18909 solver.cpp:397]     Test net output #0: accuracy = 0.435049
I0407 09:30:18.677412 18909 solver.cpp:397]     Test net output #1: loss = 2.99829 (* 1 = 2.99829 loss)
I0407 09:30:20.584571 18909 solver.cpp:218] Iteration 7452 (0.795806 iter/s, 15.0791s/12 iters), loss = 0.290827
I0407 09:30:20.584610 18909 solver.cpp:237]     Train net output #0: loss = 0.290827 (* 1 = 0.290827 loss)
I0407 09:30:20.584617 18909 sgd_solver.cpp:105] Iteration 7452, lr = 0.005625
I0407 09:30:25.785892 18909 solver.cpp:218] Iteration 7464 (2.30713 iter/s, 5.20127s/12 iters), loss = 0.347845
I0407 09:30:25.785933 18909 solver.cpp:237]     Train net output #0: loss = 0.347845 (* 1 = 0.347845 loss)
I0407 09:30:25.785941 18909 sgd_solver.cpp:105] Iteration 7464, lr = 0.005625
I0407 09:30:31.079104 18909 solver.cpp:218] Iteration 7476 (2.26708 iter/s, 5.29316s/12 iters), loss = 0.361665
I0407 09:30:31.079253 18909 solver.cpp:237]     Train net output #0: loss = 0.361665 (* 1 = 0.361665 loss)
I0407 09:30:31.079262 18909 sgd_solver.cpp:105] Iteration 7476, lr = 0.005625
I0407 09:30:34.741719 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:30:36.345309 18909 solver.cpp:218] Iteration 7488 (2.27875 iter/s, 5.26604s/12 iters), loss = 0.428409
I0407 09:30:36.345356 18909 solver.cpp:237]     Train net output #0: loss = 0.428409 (* 1 = 0.428409 loss)
I0407 09:30:36.345363 18909 sgd_solver.cpp:105] Iteration 7488, lr = 0.005625
I0407 09:30:41.545826 18909 solver.cpp:218] Iteration 7500 (2.30749 iter/s, 5.20046s/12 iters), loss = 0.270878
I0407 09:30:41.545868 18909 solver.cpp:237]     Train net output #0: loss = 0.270878 (* 1 = 0.270878 loss)
I0407 09:30:41.545876 18909 sgd_solver.cpp:105] Iteration 7500, lr = 0.005625
I0407 09:30:46.663331 18909 solver.cpp:218] Iteration 7512 (2.34492 iter/s, 5.11746s/12 iters), loss = 0.204243
I0407 09:30:46.663372 18909 solver.cpp:237]     Train net output #0: loss = 0.204243 (* 1 = 0.204243 loss)
I0407 09:30:46.663378 18909 sgd_solver.cpp:105] Iteration 7512, lr = 0.005625
I0407 09:30:51.961963 18909 solver.cpp:218] Iteration 7524 (2.26476 iter/s, 5.29858s/12 iters), loss = 0.129723
I0407 09:30:51.962018 18909 solver.cpp:237]     Train net output #0: loss = 0.129723 (* 1 = 0.129723 loss)
I0407 09:30:51.962028 18909 sgd_solver.cpp:105] Iteration 7524, lr = 0.005625
I0407 09:30:57.168460 18909 solver.cpp:218] Iteration 7536 (2.30484 iter/s, 5.20643s/12 iters), loss = 0.225219
I0407 09:30:57.168516 18909 solver.cpp:237]     Train net output #0: loss = 0.225219 (* 1 = 0.225219 loss)
I0407 09:30:57.168526 18909 sgd_solver.cpp:105] Iteration 7536, lr = 0.005625
I0407 09:31:01.724112 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0407 09:31:06.369804 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0407 09:31:08.792101 18909 solver.cpp:330] Iteration 7548, Testing net (#0)
I0407 09:31:08.792125 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:31:10.270416 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:31:13.188527 18909 solver.cpp:397]     Test net output #0: accuracy = 0.409926
I0407 09:31:13.188555 18909 solver.cpp:397]     Test net output #1: loss = 3.10785 (* 1 = 3.10785 loss)
I0407 09:31:13.319926 18909 solver.cpp:218] Iteration 7548 (0.74297 iter/s, 16.1514s/12 iters), loss = 0.186569
I0407 09:31:13.319980 18909 solver.cpp:237]     Train net output #0: loss = 0.186569 (* 1 = 0.186569 loss)
I0407 09:31:13.319990 18909 sgd_solver.cpp:105] Iteration 7548, lr = 0.005625
I0407 09:31:17.506117 18909 solver.cpp:218] Iteration 7560 (2.86661 iter/s, 4.18612s/12 iters), loss = 0.380522
I0407 09:31:17.506158 18909 solver.cpp:237]     Train net output #0: loss = 0.380522 (* 1 = 0.380522 loss)
I0407 09:31:17.506165 18909 sgd_solver.cpp:105] Iteration 7560, lr = 0.005625
I0407 09:31:22.717955 18909 solver.cpp:218] Iteration 7572 (2.30247 iter/s, 5.21178s/12 iters), loss = 0.250777
I0407 09:31:22.717995 18909 solver.cpp:237]     Train net output #0: loss = 0.250777 (* 1 = 0.250777 loss)
I0407 09:31:22.718003 18909 sgd_solver.cpp:105] Iteration 7572, lr = 0.005625
I0407 09:31:27.915546 18909 solver.cpp:218] Iteration 7584 (2.30879 iter/s, 5.19753s/12 iters), loss = 0.174987
I0407 09:31:27.915588 18909 solver.cpp:237]     Train net output #0: loss = 0.174987 (* 1 = 0.174987 loss)
I0407 09:31:27.915596 18909 sgd_solver.cpp:105] Iteration 7584, lr = 0.005625
I0407 09:31:28.565348 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:31:33.299273 18909 solver.cpp:218] Iteration 7596 (2.22896 iter/s, 5.38367s/12 iters), loss = 0.36935
I0407 09:31:33.299404 18909 solver.cpp:237]     Train net output #0: loss = 0.36935 (* 1 = 0.36935 loss)
I0407 09:31:33.299412 18909 sgd_solver.cpp:105] Iteration 7596, lr = 0.005625
I0407 09:31:38.485653 18909 solver.cpp:218] Iteration 7608 (2.31381 iter/s, 5.18624s/12 iters), loss = 0.187944
I0407 09:31:38.485694 18909 solver.cpp:237]     Train net output #0: loss = 0.187944 (* 1 = 0.187944 loss)
I0407 09:31:38.485702 18909 sgd_solver.cpp:105] Iteration 7608, lr = 0.005625
I0407 09:31:43.704036 18909 solver.cpp:218] Iteration 7620 (2.29959 iter/s, 5.21832s/12 iters), loss = 0.401818
I0407 09:31:43.704090 18909 solver.cpp:237]     Train net output #0: loss = 0.401818 (* 1 = 0.401818 loss)
I0407 09:31:43.704100 18909 sgd_solver.cpp:105] Iteration 7620, lr = 0.005625
I0407 09:31:46.265600 18909 blocking_queue.cpp:49] Waiting for data
I0407 09:31:48.769737 18909 solver.cpp:218] Iteration 7632 (2.3689 iter/s, 5.06563s/12 iters), loss = 0.280554
I0407 09:31:48.769784 18909 solver.cpp:237]     Train net output #0: loss = 0.280554 (* 1 = 0.280554 loss)
I0407 09:31:48.769791 18909 sgd_solver.cpp:105] Iteration 7632, lr = 0.005625
I0407 09:31:53.789608 18909 solver.cpp:218] Iteration 7644 (2.39053 iter/s, 5.01981s/12 iters), loss = 0.18287
I0407 09:31:53.789649 18909 solver.cpp:237]     Train net output #0: loss = 0.18287 (* 1 = 0.18287 loss)
I0407 09:31:53.789656 18909 sgd_solver.cpp:105] Iteration 7644, lr = 0.005625
I0407 09:31:55.918885 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0407 09:32:00.348215 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0407 09:32:03.057031 18909 solver.cpp:330] Iteration 7650, Testing net (#0)
I0407 09:32:03.057051 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:32:04.380867 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:32:07.317329 18909 solver.cpp:397]     Test net output #0: accuracy = 0.411765
I0407 09:32:07.317361 18909 solver.cpp:397]     Test net output #1: loss = 3.06508 (* 1 = 3.06508 loss)
I0407 09:32:09.079025 18909 solver.cpp:218] Iteration 7656 (0.784859 iter/s, 15.2894s/12 iters), loss = 0.259964
I0407 09:32:09.079067 18909 solver.cpp:237]     Train net output #0: loss = 0.259964 (* 1 = 0.259964 loss)
I0407 09:32:09.079075 18909 sgd_solver.cpp:105] Iteration 7656, lr = 0.005625
I0407 09:32:14.272830 18909 solver.cpp:218] Iteration 7668 (2.31047 iter/s, 5.19375s/12 iters), loss = 0.180365
I0407 09:32:14.272872 18909 solver.cpp:237]     Train net output #0: loss = 0.180365 (* 1 = 0.180365 loss)
I0407 09:32:14.272878 18909 sgd_solver.cpp:105] Iteration 7668, lr = 0.005625
I0407 09:32:19.449699 18909 solver.cpp:218] Iteration 7680 (2.31803 iter/s, 5.17682s/12 iters), loss = 0.199943
I0407 09:32:19.449743 18909 solver.cpp:237]     Train net output #0: loss = 0.199943 (* 1 = 0.199943 loss)
I0407 09:32:19.449750 18909 sgd_solver.cpp:105] Iteration 7680, lr = 0.005625
I0407 09:32:22.397647 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:32:24.736901 18909 solver.cpp:218] Iteration 7692 (2.26966 iter/s, 5.28714s/12 iters), loss = 0.303151
I0407 09:32:24.736945 18909 solver.cpp:237]     Train net output #0: loss = 0.303151 (* 1 = 0.303151 loss)
I0407 09:32:24.736953 18909 sgd_solver.cpp:105] Iteration 7692, lr = 0.005625
I0407 09:32:29.687458 18909 solver.cpp:218] Iteration 7704 (2.424 iter/s, 4.9505s/12 iters), loss = 0.310388
I0407 09:32:29.687497 18909 solver.cpp:237]     Train net output #0: loss = 0.310388 (* 1 = 0.310388 loss)
I0407 09:32:29.687505 18909 sgd_solver.cpp:105] Iteration 7704, lr = 0.005625
I0407 09:32:34.871855 18909 solver.cpp:218] Iteration 7716 (2.31466 iter/s, 5.18434s/12 iters), loss = 0.504867
I0407 09:32:34.871970 18909 solver.cpp:237]     Train net output #0: loss = 0.504867 (* 1 = 0.504867 loss)
I0407 09:32:34.871978 18909 sgd_solver.cpp:105] Iteration 7716, lr = 0.005625
I0407 09:32:39.979693 18909 solver.cpp:218] Iteration 7728 (2.34939 iter/s, 5.10771s/12 iters), loss = 0.129383
I0407 09:32:39.979737 18909 solver.cpp:237]     Train net output #0: loss = 0.129383 (* 1 = 0.129383 loss)
I0407 09:32:39.979745 18909 sgd_solver.cpp:105] Iteration 7728, lr = 0.005625
I0407 09:32:45.224835 18909 solver.cpp:218] Iteration 7740 (2.28786 iter/s, 5.24509s/12 iters), loss = 0.145579
I0407 09:32:45.224875 18909 solver.cpp:237]     Train net output #0: loss = 0.145579 (* 1 = 0.145579 loss)
I0407 09:32:45.224889 18909 sgd_solver.cpp:105] Iteration 7740, lr = 0.005625
I0407 09:32:49.995726 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0407 09:32:54.379142 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0407 09:32:57.518779 18909 solver.cpp:330] Iteration 7752, Testing net (#0)
I0407 09:32:57.518805 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:32:58.877022 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:33:01.838366 18909 solver.cpp:397]     Test net output #0: accuracy = 0.403799
I0407 09:33:01.838402 18909 solver.cpp:397]     Test net output #1: loss = 3.18528 (* 1 = 3.18528 loss)
I0407 09:33:01.978210 18909 solver.cpp:218] Iteration 7752 (0.716276 iter/s, 16.7533s/12 iters), loss = 0.226276
I0407 09:33:01.978255 18909 solver.cpp:237]     Train net output #0: loss = 0.226276 (* 1 = 0.226276 loss)
I0407 09:33:01.978263 18909 sgd_solver.cpp:105] Iteration 7752, lr = 0.005625
I0407 09:33:06.244251 18909 solver.cpp:218] Iteration 7764 (2.81295 iter/s, 4.26598s/12 iters), loss = 0.501565
I0407 09:33:06.244346 18909 solver.cpp:237]     Train net output #0: loss = 0.501565 (* 1 = 0.501565 loss)
I0407 09:33:06.244354 18909 sgd_solver.cpp:105] Iteration 7764, lr = 0.005625
I0407 09:33:11.252337 18909 solver.cpp:218] Iteration 7776 (2.39618 iter/s, 5.00798s/12 iters), loss = 0.367811
I0407 09:33:11.252384 18909 solver.cpp:237]     Train net output #0: loss = 0.367811 (* 1 = 0.367811 loss)
I0407 09:33:11.252393 18909 sgd_solver.cpp:105] Iteration 7776, lr = 0.005625
I0407 09:33:16.439018 18909 solver.cpp:218] Iteration 7788 (2.31365 iter/s, 5.18662s/12 iters), loss = 0.113237
I0407 09:33:16.439064 18909 solver.cpp:237]     Train net output #0: loss = 0.113237 (* 1 = 0.113237 loss)
I0407 09:33:16.439071 18909 sgd_solver.cpp:105] Iteration 7788, lr = 0.005625
I0407 09:33:16.445807 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:33:21.777563 18909 solver.cpp:218] Iteration 7800 (2.24783 iter/s, 5.33849s/12 iters), loss = 0.25601
I0407 09:33:21.777606 18909 solver.cpp:237]     Train net output #0: loss = 0.256009 (* 1 = 0.256009 loss)
I0407 09:33:21.777613 18909 sgd_solver.cpp:105] Iteration 7800, lr = 0.005625
I0407 09:33:27.148208 18909 solver.cpp:218] Iteration 7812 (2.23439 iter/s, 5.37059s/12 iters), loss = 0.202636
I0407 09:33:27.148259 18909 solver.cpp:237]     Train net output #0: loss = 0.202636 (* 1 = 0.202636 loss)
I0407 09:33:27.148267 18909 sgd_solver.cpp:105] Iteration 7812, lr = 0.005625
I0407 09:33:32.342458 18909 solver.cpp:218] Iteration 7824 (2.31028 iter/s, 5.19419s/12 iters), loss = 0.187029
I0407 09:33:32.342506 18909 solver.cpp:237]     Train net output #0: loss = 0.187029 (* 1 = 0.187029 loss)
I0407 09:33:32.342514 18909 sgd_solver.cpp:105] Iteration 7824, lr = 0.005625
I0407 09:33:37.589516 18909 solver.cpp:218] Iteration 7836 (2.28702 iter/s, 5.247s/12 iters), loss = 0.164357
I0407 09:33:37.589640 18909 solver.cpp:237]     Train net output #0: loss = 0.164357 (* 1 = 0.164357 loss)
I0407 09:33:37.589648 18909 sgd_solver.cpp:105] Iteration 7836, lr = 0.005625
I0407 09:33:42.669394 18909 solver.cpp:218] Iteration 7848 (2.36233 iter/s, 5.07974s/12 iters), loss = 0.487799
I0407 09:33:42.669440 18909 solver.cpp:237]     Train net output #0: loss = 0.487799 (* 1 = 0.487799 loss)
I0407 09:33:42.669448 18909 sgd_solver.cpp:105] Iteration 7848, lr = 0.005625
I0407 09:33:44.712956 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0407 09:33:49.142530 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0407 09:33:52.236481 18909 solver.cpp:330] Iteration 7854, Testing net (#0)
I0407 09:33:52.236502 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:33:53.576217 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:33:56.590081 18909 solver.cpp:397]     Test net output #0: accuracy = 0.423407
I0407 09:33:56.590111 18909 solver.cpp:397]     Test net output #1: loss = 3.1398 (* 1 = 3.1398 loss)
I0407 09:33:58.478188 18909 solver.cpp:218] Iteration 7860 (0.759074 iter/s, 15.8087s/12 iters), loss = 0.231913
I0407 09:33:58.478250 18909 solver.cpp:237]     Train net output #0: loss = 0.231913 (* 1 = 0.231913 loss)
I0407 09:33:58.478260 18909 sgd_solver.cpp:105] Iteration 7860, lr = 0.005625
I0407 09:34:03.664983 18909 solver.cpp:218] Iteration 7872 (2.3136 iter/s, 5.18673s/12 iters), loss = 0.229792
I0407 09:34:03.665030 18909 solver.cpp:237]     Train net output #0: loss = 0.229792 (* 1 = 0.229792 loss)
I0407 09:34:03.665040 18909 sgd_solver.cpp:105] Iteration 7872, lr = 0.005625
I0407 09:34:08.846913 18909 solver.cpp:218] Iteration 7884 (2.31577 iter/s, 5.18187s/12 iters), loss = 0.188151
I0407 09:34:08.847101 18909 solver.cpp:237]     Train net output #0: loss = 0.188151 (* 1 = 0.188151 loss)
I0407 09:34:08.847113 18909 sgd_solver.cpp:105] Iteration 7884, lr = 0.005625
I0407 09:34:11.067432 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:34:13.961644 18909 solver.cpp:218] Iteration 7896 (2.34625 iter/s, 5.11454s/12 iters), loss = 0.209415
I0407 09:34:13.961689 18909 solver.cpp:237]     Train net output #0: loss = 0.209415 (* 1 = 0.209415 loss)
I0407 09:34:13.961699 18909 sgd_solver.cpp:105] Iteration 7896, lr = 0.005625
I0407 09:34:19.347750 18909 solver.cpp:218] Iteration 7908 (2.22798 iter/s, 5.38605s/12 iters), loss = 0.375408
I0407 09:34:19.347803 18909 solver.cpp:237]     Train net output #0: loss = 0.375408 (* 1 = 0.375408 loss)
I0407 09:34:19.347813 18909 sgd_solver.cpp:105] Iteration 7908, lr = 0.005625
I0407 09:34:24.490937 18909 solver.cpp:218] Iteration 7920 (2.33321 iter/s, 5.14312s/12 iters), loss = 0.180082
I0407 09:34:24.490995 18909 solver.cpp:237]     Train net output #0: loss = 0.180082 (* 1 = 0.180082 loss)
I0407 09:34:24.491004 18909 sgd_solver.cpp:105] Iteration 7920, lr = 0.005625
I0407 09:34:29.692591 18909 solver.cpp:218] Iteration 7932 (2.30699 iter/s, 5.20159s/12 iters), loss = 0.221935
I0407 09:34:29.692633 18909 solver.cpp:237]     Train net output #0: loss = 0.221935 (* 1 = 0.221935 loss)
I0407 09:34:29.692641 18909 sgd_solver.cpp:105] Iteration 7932, lr = 0.005625
I0407 09:34:34.807685 18909 solver.cpp:218] Iteration 7944 (2.34602 iter/s, 5.11504s/12 iters), loss = 0.418384
I0407 09:34:34.807734 18909 solver.cpp:237]     Train net output #0: loss = 0.418384 (* 1 = 0.418384 loss)
I0407 09:34:34.807742 18909 sgd_solver.cpp:105] Iteration 7944, lr = 0.005625
I0407 09:34:39.496907 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0407 09:34:43.914242 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0407 09:34:47.145591 18909 solver.cpp:330] Iteration 7956, Testing net (#0)
I0407 09:34:47.145612 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:34:48.518070 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:34:51.603652 18909 solver.cpp:397]     Test net output #0: accuracy = 0.420956
I0407 09:34:51.603694 18909 solver.cpp:397]     Test net output #1: loss = 3.12395 (* 1 = 3.12395 loss)
I0407 09:34:51.740422 18909 solver.cpp:218] Iteration 7956 (0.708688 iter/s, 16.9327s/12 iters), loss = 0.321575
I0407 09:34:51.741982 18909 solver.cpp:237]     Train net output #0: loss = 0.321575 (* 1 = 0.321575 loss)
I0407 09:34:51.741998 18909 sgd_solver.cpp:105] Iteration 7956, lr = 0.005625
I0407 09:34:56.153515 18909 solver.cpp:218] Iteration 7968 (2.72014 iter/s, 4.41153s/12 iters), loss = 0.217609
I0407 09:34:56.153555 18909 solver.cpp:237]     Train net output #0: loss = 0.217609 (* 1 = 0.217609 loss)
I0407 09:34:56.153563 18909 sgd_solver.cpp:105] Iteration 7968, lr = 0.005625
I0407 09:35:01.442775 18909 solver.cpp:218] Iteration 7980 (2.26877 iter/s, 5.28921s/12 iters), loss = 0.296229
I0407 09:35:01.442819 18909 solver.cpp:237]     Train net output #0: loss = 0.296229 (* 1 = 0.296229 loss)
I0407 09:35:01.442826 18909 sgd_solver.cpp:105] Iteration 7980, lr = 0.005625
I0407 09:35:05.702455 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:35:06.483996 18909 solver.cpp:218] Iteration 7992 (2.3804 iter/s, 5.04116s/12 iters), loss = 0.273584
I0407 09:35:06.484041 18909 solver.cpp:237]     Train net output #0: loss = 0.273584 (* 1 = 0.273584 loss)
I0407 09:35:06.484048 18909 sgd_solver.cpp:105] Iteration 7992, lr = 0.005625
I0407 09:35:11.429091 18909 solver.cpp:218] Iteration 8004 (2.42667 iter/s, 4.94504s/12 iters), loss = 0.271677
I0407 09:35:11.429564 18909 solver.cpp:237]     Train net output #0: loss = 0.271677 (* 1 = 0.271677 loss)
I0407 09:35:11.429574 18909 sgd_solver.cpp:105] Iteration 8004, lr = 0.005625
I0407 09:35:16.342511 18909 solver.cpp:218] Iteration 8016 (2.44253 iter/s, 4.91294s/12 iters), loss = 0.289144
I0407 09:35:16.342552 18909 solver.cpp:237]     Train net output #0: loss = 0.289144 (* 1 = 0.289144 loss)
I0407 09:35:16.342558 18909 sgd_solver.cpp:105] Iteration 8016, lr = 0.005625
I0407 09:35:21.635011 18909 solver.cpp:218] Iteration 8028 (2.26739 iter/s, 5.29244s/12 iters), loss = 0.305915
I0407 09:35:21.635068 18909 solver.cpp:237]     Train net output #0: loss = 0.305915 (* 1 = 0.305915 loss)
I0407 09:35:21.635079 18909 sgd_solver.cpp:105] Iteration 8028, lr = 0.005625
I0407 09:35:26.806394 18909 solver.cpp:218] Iteration 8040 (2.32049 iter/s, 5.17132s/12 iters), loss = 0.164493
I0407 09:35:26.806438 18909 solver.cpp:237]     Train net output #0: loss = 0.164493 (* 1 = 0.164493 loss)
I0407 09:35:26.806444 18909 sgd_solver.cpp:105] Iteration 8040, lr = 0.005625
I0407 09:35:32.142421 18909 solver.cpp:218] Iteration 8052 (2.24889 iter/s, 5.33597s/12 iters), loss = 0.26348
I0407 09:35:32.142467 18909 solver.cpp:237]     Train net output #0: loss = 0.26348 (* 1 = 0.26348 loss)
I0407 09:35:32.142473 18909 sgd_solver.cpp:105] Iteration 8052, lr = 0.005625
I0407 09:35:34.215658 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0407 09:35:38.602816 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0407 09:35:41.950814 18909 solver.cpp:330] Iteration 8058, Testing net (#0)
I0407 09:35:41.950891 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:35:43.106519 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:35:46.204782 18909 solver.cpp:397]     Test net output #0: accuracy = 0.430147
I0407 09:35:46.205207 18909 solver.cpp:397]     Test net output #1: loss = 3.03304 (* 1 = 3.03304 loss)
I0407 09:35:48.128789 18909 solver.cpp:218] Iteration 8064 (0.750642 iter/s, 15.9863s/12 iters), loss = 0.461672
I0407 09:35:48.128832 18909 solver.cpp:237]     Train net output #0: loss = 0.461672 (* 1 = 0.461672 loss)
I0407 09:35:48.128841 18909 sgd_solver.cpp:105] Iteration 8064, lr = 0.005625
I0407 09:35:53.099891 18909 solver.cpp:218] Iteration 8076 (2.41398 iter/s, 4.97105s/12 iters), loss = 0.252532
I0407 09:35:53.099928 18909 solver.cpp:237]     Train net output #0: loss = 0.252532 (* 1 = 0.252532 loss)
I0407 09:35:53.099936 18909 sgd_solver.cpp:105] Iteration 8076, lr = 0.005625
I0407 09:35:58.290076 18909 solver.cpp:218] Iteration 8088 (2.31208 iter/s, 5.19013s/12 iters), loss = 0.272297
I0407 09:35:58.290120 18909 solver.cpp:237]     Train net output #0: loss = 0.272297 (* 1 = 0.272297 loss)
I0407 09:35:58.290128 18909 sgd_solver.cpp:105] Iteration 8088, lr = 0.005625
I0407 09:35:59.755726 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:36:03.444545 18909 solver.cpp:218] Iteration 8100 (2.3281 iter/s, 5.15442s/12 iters), loss = 0.205077
I0407 09:36:03.444586 18909 solver.cpp:237]     Train net output #0: loss = 0.205077 (* 1 = 0.205077 loss)
I0407 09:36:03.444592 18909 sgd_solver.cpp:105] Iteration 8100, lr = 0.005625
I0407 09:36:08.534972 18909 solver.cpp:218] Iteration 8112 (2.35739 iter/s, 5.09038s/12 iters), loss = 0.232268
I0407 09:36:08.535012 18909 solver.cpp:237]     Train net output #0: loss = 0.232268 (* 1 = 0.232268 loss)
I0407 09:36:08.535018 18909 sgd_solver.cpp:105] Iteration 8112, lr = 0.005625
I0407 09:36:13.848917 18909 solver.cpp:218] Iteration 8124 (2.25823 iter/s, 5.31389s/12 iters), loss = 0.276885
I0407 09:36:13.849043 18909 solver.cpp:237]     Train net output #0: loss = 0.276885 (* 1 = 0.276885 loss)
I0407 09:36:13.849052 18909 sgd_solver.cpp:105] Iteration 8124, lr = 0.005625
I0407 09:36:19.003058 18909 solver.cpp:218] Iteration 8136 (2.32829 iter/s, 5.15401s/12 iters), loss = 0.252619
I0407 09:36:19.003098 18909 solver.cpp:237]     Train net output #0: loss = 0.252619 (* 1 = 0.252619 loss)
I0407 09:36:19.003104 18909 sgd_solver.cpp:105] Iteration 8136, lr = 0.005625
I0407 09:36:24.235940 18909 solver.cpp:218] Iteration 8148 (2.29322 iter/s, 5.23283s/12 iters), loss = 0.288456
I0407 09:36:24.235981 18909 solver.cpp:237]     Train net output #0: loss = 0.288456 (* 1 = 0.288456 loss)
I0407 09:36:24.235988 18909 sgd_solver.cpp:105] Iteration 8148, lr = 0.005625
I0407 09:36:29.045956 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0407 09:36:33.948506 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0407 09:36:38.212651 18909 solver.cpp:330] Iteration 8160, Testing net (#0)
I0407 09:36:38.212671 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:36:39.434691 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:36:42.561657 18909 solver.cpp:397]     Test net output #0: accuracy = 0.429534
I0407 09:36:42.561683 18909 solver.cpp:397]     Test net output #1: loss = 3.1183 (* 1 = 3.1183 loss)
I0407 09:36:42.702569 18909 solver.cpp:218] Iteration 8160 (0.649822 iter/s, 18.4666s/12 iters), loss = 0.260766
I0407 09:36:42.702625 18909 solver.cpp:237]     Train net output #0: loss = 0.260766 (* 1 = 0.260766 loss)
I0407 09:36:42.702633 18909 sgd_solver.cpp:105] Iteration 8160, lr = 0.005625
I0407 09:36:46.979223 18909 solver.cpp:218] Iteration 8172 (2.80597 iter/s, 4.27659s/12 iters), loss = 0.208847
I0407 09:36:46.979331 18909 solver.cpp:237]     Train net output #0: loss = 0.208847 (* 1 = 0.208847 loss)
I0407 09:36:46.979339 18909 sgd_solver.cpp:105] Iteration 8172, lr = 0.005625
I0407 09:36:52.185521 18909 solver.cpp:218] Iteration 8184 (2.30495 iter/s, 5.20618s/12 iters), loss = 0.219262
I0407 09:36:52.185565 18909 solver.cpp:237]     Train net output #0: loss = 0.219262 (* 1 = 0.219262 loss)
I0407 09:36:52.185573 18909 sgd_solver.cpp:105] Iteration 8184, lr = 0.005625
I0407 09:36:55.771492 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:36:57.311161 18909 solver.cpp:218] Iteration 8196 (2.3412 iter/s, 5.12558s/12 iters), loss = 0.233021
I0407 09:36:57.311221 18909 solver.cpp:237]     Train net output #0: loss = 0.233021 (* 1 = 0.233021 loss)
I0407 09:36:57.311231 18909 sgd_solver.cpp:105] Iteration 8196, lr = 0.005625
I0407 09:37:02.441479 18909 solver.cpp:218] Iteration 8208 (2.33907 iter/s, 5.13025s/12 iters), loss = 0.311603
I0407 09:37:02.441540 18909 solver.cpp:237]     Train net output #0: loss = 0.311603 (* 1 = 0.311603 loss)
I0407 09:37:02.441550 18909 sgd_solver.cpp:105] Iteration 8208, lr = 0.005625
I0407 09:37:07.840138 18909 solver.cpp:218] Iteration 8220 (2.2228 iter/s, 5.39859s/12 iters), loss = 0.301721
I0407 09:37:07.840195 18909 solver.cpp:237]     Train net output #0: loss = 0.301721 (* 1 = 0.301721 loss)
I0407 09:37:07.840205 18909 sgd_solver.cpp:105] Iteration 8220, lr = 0.005625
I0407 09:37:13.126957 18909 solver.cpp:218] Iteration 8232 (2.26983 iter/s, 5.28675s/12 iters), loss = 0.306118
I0407 09:37:13.127020 18909 solver.cpp:237]     Train net output #0: loss = 0.306118 (* 1 = 0.306118 loss)
I0407 09:37:13.127032 18909 sgd_solver.cpp:105] Iteration 8232, lr = 0.005625
I0407 09:37:18.272212 18909 solver.cpp:218] Iteration 8244 (2.33227 iter/s, 5.14519s/12 iters), loss = 0.134083
I0407 09:37:18.272341 18909 solver.cpp:237]     Train net output #0: loss = 0.134083 (* 1 = 0.134083 loss)
I0407 09:37:18.272349 18909 sgd_solver.cpp:105] Iteration 8244, lr = 0.005625
I0407 09:37:23.391866 18909 solver.cpp:218] Iteration 8256 (2.34397 iter/s, 5.11951s/12 iters), loss = 0.222741
I0407 09:37:23.391906 18909 solver.cpp:237]     Train net output #0: loss = 0.222741 (* 1 = 0.222741 loss)
I0407 09:37:23.391913 18909 sgd_solver.cpp:105] Iteration 8256, lr = 0.005625
I0407 09:37:25.545218 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0407 09:37:30.369927 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0407 09:37:34.463104 18909 solver.cpp:330] Iteration 8262, Testing net (#0)
I0407 09:37:34.463124 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:37:35.627883 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:37:38.782112 18909 solver.cpp:397]     Test net output #0: accuracy = 0.435049
I0407 09:37:38.782140 18909 solver.cpp:397]     Test net output #1: loss = 3.09974 (* 1 = 3.09974 loss)
I0407 09:37:40.748224 18909 solver.cpp:218] Iteration 8268 (0.691391 iter/s, 17.3563s/12 iters), loss = 0.257752
I0407 09:37:40.748267 18909 solver.cpp:237]     Train net output #0: loss = 0.257752 (* 1 = 0.257752 loss)
I0407 09:37:40.748275 18909 sgd_solver.cpp:105] Iteration 8268, lr = 0.005625
I0407 09:37:46.006603 18909 solver.cpp:218] Iteration 8280 (2.2821 iter/s, 5.25833s/12 iters), loss = 0.282895
I0407 09:37:46.006640 18909 solver.cpp:237]     Train net output #0: loss = 0.282895 (* 1 = 0.282895 loss)
I0407 09:37:46.006647 18909 sgd_solver.cpp:105] Iteration 8280, lr = 0.005625
I0407 09:37:51.307678 18909 solver.cpp:218] Iteration 8292 (2.26371 iter/s, 5.30103s/12 iters), loss = 0.3463
I0407 09:37:51.307765 18909 solver.cpp:237]     Train net output #0: loss = 0.3463 (* 1 = 0.3463 loss)
I0407 09:37:51.307773 18909 sgd_solver.cpp:105] Iteration 8292, lr = 0.005625
I0407 09:37:52.028936 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:37:56.410215 18909 solver.cpp:218] Iteration 8304 (2.35182 iter/s, 5.10244s/12 iters), loss = 0.146025
I0407 09:37:56.410271 18909 solver.cpp:237]     Train net output #0: loss = 0.146025 (* 1 = 0.146025 loss)
I0407 09:37:56.410280 18909 sgd_solver.cpp:105] Iteration 8304, lr = 0.005625
I0407 09:37:59.234477 18909 blocking_queue.cpp:49] Waiting for data
I0407 09:38:01.585093 18909 solver.cpp:218] Iteration 8316 (2.31892 iter/s, 5.17481s/12 iters), loss = 0.259592
I0407 09:38:01.585139 18909 solver.cpp:237]     Train net output #0: loss = 0.259592 (* 1 = 0.259592 loss)
I0407 09:38:01.585145 18909 sgd_solver.cpp:105] Iteration 8316, lr = 0.005625
I0407 09:38:06.791254 18909 solver.cpp:218] Iteration 8328 (2.30499 iter/s, 5.2061s/12 iters), loss = 0.249315
I0407 09:38:06.791309 18909 solver.cpp:237]     Train net output #0: loss = 0.249315 (* 1 = 0.249315 loss)
I0407 09:38:06.791318 18909 sgd_solver.cpp:105] Iteration 8328, lr = 0.005625
I0407 09:38:12.023990 18909 solver.cpp:218] Iteration 8340 (2.29329 iter/s, 5.23266s/12 iters), loss = 0.279948
I0407 09:38:12.024046 18909 solver.cpp:237]     Train net output #0: loss = 0.279948 (* 1 = 0.279948 loss)
I0407 09:38:12.024055 18909 sgd_solver.cpp:105] Iteration 8340, lr = 0.005625
I0407 09:38:17.116537 18909 solver.cpp:218] Iteration 8352 (2.35642 iter/s, 5.09248s/12 iters), loss = 0.363171
I0407 09:38:17.116591 18909 solver.cpp:237]     Train net output #0: loss = 0.363171 (* 1 = 0.363171 loss)
I0407 09:38:17.116600 18909 sgd_solver.cpp:105] Iteration 8352, lr = 0.005625
I0407 09:38:21.639915 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0407 09:38:26.118021 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0407 09:38:29.494321 18909 solver.cpp:330] Iteration 8364, Testing net (#0)
I0407 09:38:29.494340 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:38:30.590116 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:38:33.880682 18909 solver.cpp:397]     Test net output #0: accuracy = 0.429534
I0407 09:38:33.880718 18909 solver.cpp:397]     Test net output #1: loss = 2.9599 (* 1 = 2.9599 loss)
I0407 09:38:34.018894 18909 solver.cpp:218] Iteration 8364 (0.709963 iter/s, 16.9023s/12 iters), loss = 0.126699
I0407 09:38:34.018935 18909 solver.cpp:237]     Train net output #0: loss = 0.126699 (* 1 = 0.126699 loss)
I0407 09:38:34.018942 18909 sgd_solver.cpp:105] Iteration 8364, lr = 0.005625
I0407 09:38:38.220233 18909 solver.cpp:218] Iteration 8376 (2.85627 iter/s, 4.20128s/12 iters), loss = 0.308438
I0407 09:38:38.220275 18909 solver.cpp:237]     Train net output #0: loss = 0.308438 (* 1 = 0.308438 loss)
I0407 09:38:38.220283 18909 sgd_solver.cpp:105] Iteration 8376, lr = 0.005625
I0407 09:38:43.476584 18909 solver.cpp:218] Iteration 8388 (2.28298 iter/s, 5.25629s/12 iters), loss = 0.18553
I0407 09:38:43.476640 18909 solver.cpp:237]     Train net output #0: loss = 0.18553 (* 1 = 0.18553 loss)
I0407 09:38:43.476650 18909 sgd_solver.cpp:105] Iteration 8388, lr = 0.005625
I0407 09:38:46.278936 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:38:48.612311 18909 solver.cpp:218] Iteration 8400 (2.3366 iter/s, 5.13566s/12 iters), loss = 0.190734
I0407 09:38:48.612354 18909 solver.cpp:237]     Train net output #0: loss = 0.190734 (* 1 = 0.190734 loss)
I0407 09:38:48.612361 18909 sgd_solver.cpp:105] Iteration 8400, lr = 0.005625
I0407 09:38:53.703012 18909 solver.cpp:218] Iteration 8412 (2.35726 iter/s, 5.09065s/12 iters), loss = 0.314253
I0407 09:38:53.703112 18909 solver.cpp:237]     Train net output #0: loss = 0.314253 (* 1 = 0.314253 loss)
I0407 09:38:53.703120 18909 sgd_solver.cpp:105] Iteration 8412, lr = 0.005625
I0407 09:38:58.917630 18909 solver.cpp:218] Iteration 8424 (2.30127 iter/s, 5.2145s/12 iters), loss = 0.253597
I0407 09:38:58.917675 18909 solver.cpp:237]     Train net output #0: loss = 0.253597 (* 1 = 0.253597 loss)
I0407 09:38:58.917683 18909 sgd_solver.cpp:105] Iteration 8424, lr = 0.005625
I0407 09:39:04.233742 18909 solver.cpp:218] Iteration 8436 (2.25731 iter/s, 5.31606s/12 iters), loss = 0.385681
I0407 09:39:04.233784 18909 solver.cpp:237]     Train net output #0: loss = 0.385681 (* 1 = 0.385681 loss)
I0407 09:39:04.233791 18909 sgd_solver.cpp:105] Iteration 8436, lr = 0.005625
I0407 09:39:09.519325 18909 solver.cpp:218] Iteration 8448 (2.27035 iter/s, 5.28552s/12 iters), loss = 0.557669
I0407 09:39:09.519374 18909 solver.cpp:237]     Train net output #0: loss = 0.557669 (* 1 = 0.557669 loss)
I0407 09:39:09.519381 18909 sgd_solver.cpp:105] Iteration 8448, lr = 0.005625
I0407 09:39:14.567080 18909 solver.cpp:218] Iteration 8460 (2.37732 iter/s, 5.04769s/12 iters), loss = 0.213799
I0407 09:39:14.567122 18909 solver.cpp:237]     Train net output #0: loss = 0.213799 (* 1 = 0.213799 loss)
I0407 09:39:14.567129 18909 sgd_solver.cpp:105] Iteration 8460, lr = 0.005625
I0407 09:39:16.768817 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0407 09:39:21.243180 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0407 09:39:24.877246 18909 solver.cpp:330] Iteration 8466, Testing net (#0)
I0407 09:39:24.877324 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:39:25.942864 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:39:29.280905 18909 solver.cpp:397]     Test net output #0: accuracy = 0.440564
I0407 09:39:29.280956 18909 solver.cpp:397]     Test net output #1: loss = 3.02625 (* 1 = 3.02625 loss)
I0407 09:39:31.172194 18909 solver.cpp:218] Iteration 8472 (0.722671 iter/s, 16.6051s/12 iters), loss = 0.17695
I0407 09:39:31.172238 18909 solver.cpp:237]     Train net output #0: loss = 0.17695 (* 1 = 0.17695 loss)
I0407 09:39:31.172245 18909 sgd_solver.cpp:105] Iteration 8472, lr = 0.005625
I0407 09:39:36.359061 18909 solver.cpp:218] Iteration 8484 (2.31357 iter/s, 5.1868s/12 iters), loss = 0.251825
I0407 09:39:36.359119 18909 solver.cpp:237]     Train net output #0: loss = 0.251825 (* 1 = 0.251825 loss)
I0407 09:39:36.359129 18909 sgd_solver.cpp:105] Iteration 8484, lr = 0.005625
I0407 09:39:41.626307 18909 solver.cpp:218] Iteration 8496 (2.27826 iter/s, 5.26718s/12 iters), loss = 0.137
I0407 09:39:41.626348 18909 solver.cpp:237]     Train net output #0: loss = 0.137 (* 1 = 0.137 loss)
I0407 09:39:41.626355 18909 sgd_solver.cpp:105] Iteration 8496, lr = 0.005625
I0407 09:39:41.661119 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:39:46.816956 18909 solver.cpp:218] Iteration 8508 (2.31187 iter/s, 5.1906s/12 iters), loss = 0.170881
I0407 09:39:46.816994 18909 solver.cpp:237]     Train net output #0: loss = 0.170881 (* 1 = 0.170881 loss)
I0407 09:39:46.817000 18909 sgd_solver.cpp:105] Iteration 8508, lr = 0.005625
I0407 09:39:52.078408 18909 solver.cpp:218] Iteration 8520 (2.28076 iter/s, 5.2614s/12 iters), loss = 0.177371
I0407 09:39:52.078454 18909 solver.cpp:237]     Train net output #0: loss = 0.177371 (* 1 = 0.177371 loss)
I0407 09:39:52.078460 18909 sgd_solver.cpp:105] Iteration 8520, lr = 0.005625
I0407 09:39:57.501907 18909 solver.cpp:218] Iteration 8532 (2.21262 iter/s, 5.42345s/12 iters), loss = 0.349598
I0407 09:39:57.502013 18909 solver.cpp:237]     Train net output #0: loss = 0.349598 (* 1 = 0.349598 loss)
I0407 09:39:57.502022 18909 sgd_solver.cpp:105] Iteration 8532, lr = 0.005625
I0407 09:40:02.797627 18909 solver.cpp:218] Iteration 8544 (2.26603 iter/s, 5.29561s/12 iters), loss = 0.400722
I0407 09:40:02.797667 18909 solver.cpp:237]     Train net output #0: loss = 0.400722 (* 1 = 0.400722 loss)
I0407 09:40:02.797673 18909 sgd_solver.cpp:105] Iteration 8544, lr = 0.005625
I0407 09:40:08.220908 18909 solver.cpp:218] Iteration 8556 (2.21271 iter/s, 5.42322s/12 iters), loss = 0.150132
I0407 09:40:08.220952 18909 solver.cpp:237]     Train net output #0: loss = 0.150132 (* 1 = 0.150132 loss)
I0407 09:40:08.220958 18909 sgd_solver.cpp:105] Iteration 8556, lr = 0.005625
I0407 09:40:13.142616 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0407 09:40:17.483776 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0407 09:40:21.192162 18909 solver.cpp:330] Iteration 8568, Testing net (#0)
I0407 09:40:21.192188 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:40:22.187347 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:40:25.536914 18909 solver.cpp:397]     Test net output #0: accuracy = 0.426471
I0407 09:40:25.536944 18909 solver.cpp:397]     Test net output #1: loss = 3.03139 (* 1 = 3.03139 loss)
I0407 09:40:25.677852 18909 solver.cpp:218] Iteration 8568 (0.687407 iter/s, 17.4569s/12 iters), loss = 0.221441
I0407 09:40:25.677899 18909 solver.cpp:237]     Train net output #0: loss = 0.221441 (* 1 = 0.221441 loss)
I0407 09:40:25.677908 18909 sgd_solver.cpp:105] Iteration 8568, lr = 0.005625
I0407 09:40:30.059316 18909 solver.cpp:218] Iteration 8580 (2.73885 iter/s, 4.3814s/12 iters), loss = 0.196016
I0407 09:40:30.059469 18909 solver.cpp:237]     Train net output #0: loss = 0.196016 (* 1 = 0.196016 loss)
I0407 09:40:30.059480 18909 sgd_solver.cpp:105] Iteration 8580, lr = 0.005625
I0407 09:40:35.214211 18909 solver.cpp:218] Iteration 8592 (2.32796 iter/s, 5.15474s/12 iters), loss = 0.178595
I0407 09:40:35.214251 18909 solver.cpp:237]     Train net output #0: loss = 0.178594 (* 1 = 0.178594 loss)
I0407 09:40:35.214259 18909 sgd_solver.cpp:105] Iteration 8592, lr = 0.005625
I0407 09:40:37.340574 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:40:40.223839 18909 solver.cpp:218] Iteration 8604 (2.39542 iter/s, 5.00957s/12 iters), loss = 0.328247
I0407 09:40:40.223894 18909 solver.cpp:237]     Train net output #0: loss = 0.328247 (* 1 = 0.328247 loss)
I0407 09:40:40.223904 18909 sgd_solver.cpp:105] Iteration 8604, lr = 0.005625
I0407 09:40:45.390594 18909 solver.cpp:218] Iteration 8616 (2.32257 iter/s, 5.16669s/12 iters), loss = 0.180544
I0407 09:40:45.390630 18909 solver.cpp:237]     Train net output #0: loss = 0.180544 (* 1 = 0.180544 loss)
I0407 09:40:45.390637 18909 sgd_solver.cpp:105] Iteration 8616, lr = 0.005625
I0407 09:40:50.443785 18909 solver.cpp:218] Iteration 8628 (2.37476 iter/s, 5.05315s/12 iters), loss = 0.232184
I0407 09:40:50.443816 18909 solver.cpp:237]     Train net output #0: loss = 0.232184 (* 1 = 0.232184 loss)
I0407 09:40:50.443822 18909 sgd_solver.cpp:105] Iteration 8628, lr = 0.005625
I0407 09:40:55.835254 18909 solver.cpp:218] Iteration 8640 (2.22576 iter/s, 5.39142s/12 iters), loss = 0.18369
I0407 09:40:55.835314 18909 solver.cpp:237]     Train net output #0: loss = 0.18369 (* 1 = 0.18369 loss)
I0407 09:40:55.835323 18909 sgd_solver.cpp:105] Iteration 8640, lr = 0.005625
I0407 09:41:00.897866 18909 solver.cpp:218] Iteration 8652 (2.37035 iter/s, 5.06254s/12 iters), loss = 0.210544
I0407 09:41:00.898039 18909 solver.cpp:237]     Train net output #0: loss = 0.210544 (* 1 = 0.210544 loss)
I0407 09:41:00.898051 18909 sgd_solver.cpp:105] Iteration 8652, lr = 0.005625
I0407 09:41:06.081899 18909 solver.cpp:218] Iteration 8664 (2.31488 iter/s, 5.18385s/12 iters), loss = 0.262643
I0407 09:41:06.081956 18909 solver.cpp:237]     Train net output #0: loss = 0.262643 (* 1 = 0.262643 loss)
I0407 09:41:06.081966 18909 sgd_solver.cpp:105] Iteration 8664, lr = 0.005625
I0407 09:41:08.199262 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0407 09:41:13.210189 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0407 09:41:17.408215 18909 solver.cpp:330] Iteration 8670, Testing net (#0)
I0407 09:41:17.408232 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:41:18.348302 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:41:21.719856 18909 solver.cpp:397]     Test net output #0: accuracy = 0.443627
I0407 09:41:21.719892 18909 solver.cpp:397]     Test net output #1: loss = 3.14066 (* 1 = 3.14066 loss)
I0407 09:41:23.643312 18909 solver.cpp:218] Iteration 8676 (0.683318 iter/s, 17.5614s/12 iters), loss = 0.236924
I0407 09:41:23.643357 18909 solver.cpp:237]     Train net output #0: loss = 0.236924 (* 1 = 0.236924 loss)
I0407 09:41:23.643363 18909 sgd_solver.cpp:105] Iteration 8676, lr = 0.005625
I0407 09:41:28.701241 18909 solver.cpp:218] Iteration 8688 (2.37254 iter/s, 5.05787s/12 iters), loss = 0.302341
I0407 09:41:28.701292 18909 solver.cpp:237]     Train net output #0: loss = 0.302341 (* 1 = 0.302341 loss)
I0407 09:41:28.701300 18909 sgd_solver.cpp:105] Iteration 8688, lr = 0.005625
I0407 09:41:33.288094 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:41:34.039355 18909 solver.cpp:218] Iteration 8700 (2.24801 iter/s, 5.33805s/12 iters), loss = 0.342909
I0407 09:41:34.039398 18909 solver.cpp:237]     Train net output #0: loss = 0.342909 (* 1 = 0.342909 loss)
I0407 09:41:34.039405 18909 sgd_solver.cpp:105] Iteration 8700, lr = 0.005625
I0407 09:41:39.332006 18909 solver.cpp:218] Iteration 8712 (2.26732 iter/s, 5.2926s/12 iters), loss = 0.138496
I0407 09:41:39.332044 18909 solver.cpp:237]     Train net output #0: loss = 0.138496 (* 1 = 0.138496 loss)
I0407 09:41:39.332051 18909 sgd_solver.cpp:105] Iteration 8712, lr = 0.005625
I0407 09:41:44.696658 18909 solver.cpp:218] Iteration 8724 (2.23689 iter/s, 5.3646s/12 iters), loss = 0.223032
I0407 09:41:44.696698 18909 solver.cpp:237]     Train net output #0: loss = 0.223032 (* 1 = 0.223032 loss)
I0407 09:41:44.696705 18909 sgd_solver.cpp:105] Iteration 8724, lr = 0.005625
I0407 09:41:49.947311 18909 solver.cpp:218] Iteration 8736 (2.28545 iter/s, 5.2506s/12 iters), loss = 0.240716
I0407 09:41:49.947351 18909 solver.cpp:237]     Train net output #0: loss = 0.240716 (* 1 = 0.240716 loss)
I0407 09:41:49.947358 18909 sgd_solver.cpp:105] Iteration 8736, lr = 0.005625
I0407 09:41:55.127774 18909 solver.cpp:218] Iteration 8748 (2.31642 iter/s, 5.18041s/12 iters), loss = 0.226418
I0407 09:41:55.127815 18909 solver.cpp:237]     Train net output #0: loss = 0.226418 (* 1 = 0.226418 loss)
I0407 09:41:55.127821 18909 sgd_solver.cpp:105] Iteration 8748, lr = 0.005625
I0407 09:42:00.312176 18909 solver.cpp:218] Iteration 8760 (2.31466 iter/s, 5.18434s/12 iters), loss = 0.220798
I0407 09:42:00.312237 18909 solver.cpp:237]     Train net output #0: loss = 0.220798 (* 1 = 0.220798 loss)
I0407 09:42:00.312247 18909 sgd_solver.cpp:105] Iteration 8760, lr = 0.005625
I0407 09:42:04.908416 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0407 09:42:09.457545 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0407 09:42:13.387979 18909 solver.cpp:330] Iteration 8772, Testing net (#0)
I0407 09:42:13.387997 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:42:14.296495 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:42:17.682286 18909 solver.cpp:397]     Test net output #0: accuracy = 0.438726
I0407 09:42:17.682335 18909 solver.cpp:397]     Test net output #1: loss = 3.14062 (* 1 = 3.14062 loss)
I0407 09:42:17.820281 18909 solver.cpp:218] Iteration 8772 (0.685399 iter/s, 17.5081s/12 iters), loss = 0.2359
I0407 09:42:17.820323 18909 solver.cpp:237]     Train net output #0: loss = 0.2359 (* 1 = 0.2359 loss)
I0407 09:42:17.820330 18909 sgd_solver.cpp:105] Iteration 8772, lr = 0.005625
I0407 09:42:22.069214 18909 solver.cpp:218] Iteration 8784 (2.82428 iter/s, 4.24887s/12 iters), loss = 0.362929
I0407 09:42:22.069267 18909 solver.cpp:237]     Train net output #0: loss = 0.362929 (* 1 = 0.362929 loss)
I0407 09:42:22.069276 18909 sgd_solver.cpp:105] Iteration 8784, lr = 0.005625
I0407 09:42:27.178284 18909 solver.cpp:218] Iteration 8796 (2.34879 iter/s, 5.10901s/12 iters), loss = 0.254943
I0407 09:42:27.178328 18909 solver.cpp:237]     Train net output #0: loss = 0.254943 (* 1 = 0.254943 loss)
I0407 09:42:27.178337 18909 sgd_solver.cpp:105] Iteration 8796, lr = 0.005625
I0407 09:42:28.620656 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:42:32.457252 18909 solver.cpp:218] Iteration 8808 (2.2732 iter/s, 5.27891s/12 iters), loss = 0.253114
I0407 09:42:32.457307 18909 solver.cpp:237]     Train net output #0: loss = 0.253114 (* 1 = 0.253114 loss)
I0407 09:42:32.457316 18909 sgd_solver.cpp:105] Iteration 8808, lr = 0.005625
I0407 09:42:37.524258 18909 solver.cpp:218] Iteration 8820 (2.36829 iter/s, 5.06694s/12 iters), loss = 0.178188
I0407 09:42:37.524343 18909 solver.cpp:237]     Train net output #0: loss = 0.178188 (* 1 = 0.178188 loss)
I0407 09:42:37.524351 18909 sgd_solver.cpp:105] Iteration 8820, lr = 0.005625
I0407 09:42:42.631518 18909 solver.cpp:218] Iteration 8832 (2.34964 iter/s, 5.10716s/12 iters), loss = 0.250362
I0407 09:42:42.631561 18909 solver.cpp:237]     Train net output #0: loss = 0.250362 (* 1 = 0.250362 loss)
I0407 09:42:42.631569 18909 sgd_solver.cpp:105] Iteration 8832, lr = 0.005625
I0407 09:42:47.944144 18909 solver.cpp:218] Iteration 8844 (2.25879 iter/s, 5.31257s/12 iters), loss = 0.356022
I0407 09:42:47.944197 18909 solver.cpp:237]     Train net output #0: loss = 0.356022 (* 1 = 0.356022 loss)
I0407 09:42:47.944206 18909 sgd_solver.cpp:105] Iteration 8844, lr = 0.005625
I0407 09:42:53.188422 18909 solver.cpp:218] Iteration 8856 (2.28824 iter/s, 5.24421s/12 iters), loss = 0.0999126
I0407 09:42:53.188482 18909 solver.cpp:237]     Train net output #0: loss = 0.0999126 (* 1 = 0.0999126 loss)
I0407 09:42:53.188491 18909 sgd_solver.cpp:105] Iteration 8856, lr = 0.005625
I0407 09:42:58.319381 18909 solver.cpp:218] Iteration 8868 (2.33878 iter/s, 5.13089s/12 iters), loss = 0.305329
I0407 09:42:58.319427 18909 solver.cpp:237]     Train net output #0: loss = 0.305329 (* 1 = 0.305329 loss)
I0407 09:42:58.319433 18909 sgd_solver.cpp:105] Iteration 8868, lr = 0.005625
I0407 09:43:00.407538 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0407 09:43:03.521708 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0407 09:43:07.749156 18909 solver.cpp:330] Iteration 8874, Testing net (#0)
I0407 09:43:07.749259 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:43:08.610695 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:43:12.065737 18909 solver.cpp:397]     Test net output #0: accuracy = 0.439338
I0407 09:43:12.065768 18909 solver.cpp:397]     Test net output #1: loss = 3.07308 (* 1 = 3.07308 loss)
I0407 09:43:13.981765 18909 solver.cpp:218] Iteration 8880 (0.766169 iter/s, 15.6623s/12 iters), loss = 0.360523
I0407 09:43:13.981829 18909 solver.cpp:237]     Train net output #0: loss = 0.360523 (* 1 = 0.360523 loss)
I0407 09:43:13.981839 18909 sgd_solver.cpp:105] Iteration 8880, lr = 0.005625
I0407 09:43:19.121464 18909 solver.cpp:218] Iteration 8892 (2.3348 iter/s, 5.13962s/12 iters), loss = 0.242488
I0407 09:43:19.121515 18909 solver.cpp:237]     Train net output #0: loss = 0.242488 (* 1 = 0.242488 loss)
I0407 09:43:19.121526 18909 sgd_solver.cpp:105] Iteration 8892, lr = 0.005625
I0407 09:43:22.929644 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:43:24.491350 18909 solver.cpp:218] Iteration 8904 (2.23471 iter/s, 5.36983s/12 iters), loss = 0.231132
I0407 09:43:24.491400 18909 solver.cpp:237]     Train net output #0: loss = 0.231132 (* 1 = 0.231132 loss)
I0407 09:43:24.491408 18909 sgd_solver.cpp:105] Iteration 8904, lr = 0.005625
I0407 09:43:29.731673 18909 solver.cpp:218] Iteration 8916 (2.28996 iter/s, 5.24027s/12 iters), loss = 0.166732
I0407 09:43:29.731720 18909 solver.cpp:237]     Train net output #0: loss = 0.166732 (* 1 = 0.166732 loss)
I0407 09:43:29.731727 18909 sgd_solver.cpp:105] Iteration 8916, lr = 0.005625
I0407 09:43:35.178653 18909 solver.cpp:218] Iteration 8928 (2.20308 iter/s, 5.44693s/12 iters), loss = 0.174096
I0407 09:43:35.178694 18909 solver.cpp:237]     Train net output #0: loss = 0.174096 (* 1 = 0.174096 loss)
I0407 09:43:35.178699 18909 sgd_solver.cpp:105] Iteration 8928, lr = 0.005625
I0407 09:43:40.316579 18909 solver.cpp:218] Iteration 8940 (2.3356 iter/s, 5.13788s/12 iters), loss = 0.188179
I0407 09:43:40.316689 18909 solver.cpp:237]     Train net output #0: loss = 0.188179 (* 1 = 0.188179 loss)
I0407 09:43:40.316699 18909 sgd_solver.cpp:105] Iteration 8940, lr = 0.005625
I0407 09:43:45.561726 18909 solver.cpp:218] Iteration 8952 (2.28788 iter/s, 5.24503s/12 iters), loss = 0.155267
I0407 09:43:45.561769 18909 solver.cpp:237]     Train net output #0: loss = 0.155267 (* 1 = 0.155267 loss)
I0407 09:43:45.561776 18909 sgd_solver.cpp:105] Iteration 8952, lr = 0.005625
I0407 09:43:50.848793 18909 solver.cpp:218] Iteration 8964 (2.26971 iter/s, 5.28701s/12 iters), loss = 0.163042
I0407 09:43:50.848840 18909 solver.cpp:237]     Train net output #0: loss = 0.163042 (* 1 = 0.163042 loss)
I0407 09:43:50.848847 18909 sgd_solver.cpp:105] Iteration 8964, lr = 0.005625
I0407 09:43:55.588168 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0407 09:43:58.654796 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0407 09:44:02.125771 18909 solver.cpp:330] Iteration 8976, Testing net (#0)
I0407 09:44:02.125789 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:44:02.992620 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:44:06.451434 18909 solver.cpp:397]     Test net output #0: accuracy = 0.442402
I0407 09:44:06.451469 18909 solver.cpp:397]     Test net output #1: loss = 3.03595 (* 1 = 3.03595 loss)
I0407 09:44:06.582075 18909 solver.cpp:218] Iteration 8976 (0.762717 iter/s, 15.7332s/12 iters), loss = 0.0846191
I0407 09:44:06.582124 18909 solver.cpp:237]     Train net output #0: loss = 0.0846191 (* 1 = 0.0846191 loss)
I0407 09:44:06.582134 18909 sgd_solver.cpp:105] Iteration 8976, lr = 0.005625
I0407 09:44:10.898892 18909 solver.cpp:218] Iteration 8988 (2.77987 iter/s, 4.31675s/12 iters), loss = 0.206145
I0407 09:44:10.899027 18909 solver.cpp:237]     Train net output #0: loss = 0.206145 (* 1 = 0.206145 loss)
I0407 09:44:10.899036 18909 sgd_solver.cpp:105] Iteration 8988, lr = 0.005625
I0407 09:44:14.112601 18909 blocking_queue.cpp:49] Waiting for data
I0407 09:44:15.902319 18909 solver.cpp:218] Iteration 9000 (2.39843 iter/s, 5.00328s/12 iters), loss = 0.203864
I0407 09:44:15.902382 18909 solver.cpp:237]     Train net output #0: loss = 0.203864 (* 1 = 0.203864 loss)
I0407 09:44:15.902393 18909 sgd_solver.cpp:105] Iteration 9000, lr = 0.005625
I0407 09:44:16.629146 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:44:21.242692 18909 solver.cpp:218] Iteration 9012 (2.24706 iter/s, 5.34031s/12 iters), loss = 0.210013
I0407 09:44:21.242733 18909 solver.cpp:237]     Train net output #0: loss = 0.210013 (* 1 = 0.210013 loss)
I0407 09:44:21.242738 18909 sgd_solver.cpp:105] Iteration 9012, lr = 0.005625
I0407 09:44:26.583853 18909 solver.cpp:218] Iteration 9024 (2.24673 iter/s, 5.3411s/12 iters), loss = 0.266402
I0407 09:44:26.583911 18909 solver.cpp:237]     Train net output #0: loss = 0.266402 (* 1 = 0.266402 loss)
I0407 09:44:26.583921 18909 sgd_solver.cpp:105] Iteration 9024, lr = 0.005625
I0407 09:44:31.896721 18909 solver.cpp:218] Iteration 9036 (2.2587 iter/s, 5.3128s/12 iters), loss = 0.160049
I0407 09:44:31.896781 18909 solver.cpp:237]     Train net output #0: loss = 0.160049 (* 1 = 0.160049 loss)
I0407 09:44:31.896791 18909 sgd_solver.cpp:105] Iteration 9036, lr = 0.005625
I0407 09:44:37.204862 18909 solver.cpp:218] Iteration 9048 (2.26071 iter/s, 5.30807s/12 iters), loss = 0.150856
I0407 09:44:37.204921 18909 solver.cpp:237]     Train net output #0: loss = 0.150856 (* 1 = 0.150856 loss)
I0407 09:44:37.204931 18909 sgd_solver.cpp:105] Iteration 9048, lr = 0.005625
I0407 09:44:42.509016 18909 solver.cpp:218] Iteration 9060 (2.26241 iter/s, 5.30408s/12 iters), loss = 0.102405
I0407 09:44:42.509111 18909 solver.cpp:237]     Train net output #0: loss = 0.102405 (* 1 = 0.102405 loss)
I0407 09:44:42.509120 18909 sgd_solver.cpp:105] Iteration 9060, lr = 0.005625
I0407 09:44:47.870797 18909 solver.cpp:218] Iteration 9072 (2.23811 iter/s, 5.36167s/12 iters), loss = 0.329855
I0407 09:44:47.870846 18909 solver.cpp:237]     Train net output #0: loss = 0.329855 (* 1 = 0.329855 loss)
I0407 09:44:47.870853 18909 sgd_solver.cpp:105] Iteration 9072, lr = 0.005625
I0407 09:44:49.819635 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0407 09:44:52.858330 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0407 09:44:55.159922 18909 solver.cpp:330] Iteration 9078, Testing net (#0)
I0407 09:44:55.159940 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:44:56.052620 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:44:59.730196 18909 solver.cpp:397]     Test net output #0: accuracy = 0.433824
I0407 09:44:59.730226 18909 solver.cpp:397]     Test net output #1: loss = 2.96483 (* 1 = 2.96483 loss)
I0407 09:45:01.523880 18909 solver.cpp:218] Iteration 9084 (0.878926 iter/s, 13.653s/12 iters), loss = 0.173201
I0407 09:45:01.523931 18909 solver.cpp:237]     Train net output #0: loss = 0.173201 (* 1 = 0.173201 loss)
I0407 09:45:01.523938 18909 sgd_solver.cpp:105] Iteration 9084, lr = 0.005625
I0407 09:45:06.573477 18909 solver.cpp:218] Iteration 9096 (2.37646 iter/s, 5.04953s/12 iters), loss = 0.254661
I0407 09:45:06.573519 18909 solver.cpp:237]     Train net output #0: loss = 0.254661 (* 1 = 0.254661 loss)
I0407 09:45:06.573526 18909 sgd_solver.cpp:105] Iteration 9096, lr = 0.005625
I0407 09:45:09.616093 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:45:11.874336 18909 solver.cpp:218] Iteration 9108 (2.26381 iter/s, 5.3008s/12 iters), loss = 0.201404
I0407 09:45:11.874397 18909 solver.cpp:237]     Train net output #0: loss = 0.201404 (* 1 = 0.201404 loss)
I0407 09:45:11.874406 18909 sgd_solver.cpp:105] Iteration 9108, lr = 0.005625
I0407 09:45:17.099834 18909 solver.cpp:218] Iteration 9120 (2.29646 iter/s, 5.22543s/12 iters), loss = 0.246664
I0407 09:45:17.099961 18909 solver.cpp:237]     Train net output #0: loss = 0.246664 (* 1 = 0.246664 loss)
I0407 09:45:17.099969 18909 sgd_solver.cpp:105] Iteration 9120, lr = 0.005625
I0407 09:45:22.303951 18909 solver.cpp:218] Iteration 9132 (2.30593 iter/s, 5.20398s/12 iters), loss = 0.261723
I0407 09:45:22.303992 18909 solver.cpp:237]     Train net output #0: loss = 0.261723 (* 1 = 0.261723 loss)
I0407 09:45:22.303998 18909 sgd_solver.cpp:105] Iteration 9132, lr = 0.005625
I0407 09:45:27.407775 18909 solver.cpp:218] Iteration 9144 (2.3512 iter/s, 5.10377s/12 iters), loss = 0.194845
I0407 09:45:27.407819 18909 solver.cpp:237]     Train net output #0: loss = 0.194845 (* 1 = 0.194845 loss)
I0407 09:45:27.407826 18909 sgd_solver.cpp:105] Iteration 9144, lr = 0.005625
I0407 09:45:32.716081 18909 solver.cpp:218] Iteration 9156 (2.26063 iter/s, 5.30825s/12 iters), loss = 0.163679
I0407 09:45:32.716128 18909 solver.cpp:237]     Train net output #0: loss = 0.163679 (* 1 = 0.163679 loss)
I0407 09:45:32.716136 18909 sgd_solver.cpp:105] Iteration 9156, lr = 0.005625
I0407 09:45:37.853251 18909 solver.cpp:218] Iteration 9168 (2.33594 iter/s, 5.13711s/12 iters), loss = 0.222489
I0407 09:45:37.853296 18909 solver.cpp:237]     Train net output #0: loss = 0.222489 (* 1 = 0.222489 loss)
I0407 09:45:37.853304 18909 sgd_solver.cpp:105] Iteration 9168, lr = 0.005625
I0407 09:45:42.660089 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0407 09:45:45.665778 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0407 09:45:47.990618 18909 solver.cpp:330] Iteration 9180, Testing net (#0)
I0407 09:45:47.990691 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:45:48.739960 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:45:52.254101 18909 solver.cpp:397]     Test net output #0: accuracy = 0.432598
I0407 09:45:52.254137 18909 solver.cpp:397]     Test net output #1: loss = 3.08599 (* 1 = 3.08599 loss)
I0407 09:45:52.394673 18909 solver.cpp:218] Iteration 9180 (0.825231 iter/s, 14.5414s/12 iters), loss = 0.442023
I0407 09:45:52.394724 18909 solver.cpp:237]     Train net output #0: loss = 0.442023 (* 1 = 0.442023 loss)
I0407 09:45:52.394731 18909 sgd_solver.cpp:105] Iteration 9180, lr = 0.005625
I0407 09:45:56.693307 18909 solver.cpp:218] Iteration 9192 (2.79163 iter/s, 4.29856s/12 iters), loss = 0.104886
I0407 09:45:56.693363 18909 solver.cpp:237]     Train net output #0: loss = 0.104886 (* 1 = 0.104886 loss)
I0407 09:45:56.693374 18909 sgd_solver.cpp:105] Iteration 9192, lr = 0.005625
I0407 09:46:02.243548 18909 solver.cpp:218] Iteration 9204 (2.1621 iter/s, 5.55017s/12 iters), loss = 0.107819
I0407 09:46:02.243603 18909 solver.cpp:237]     Train net output #0: loss = 0.107819 (* 1 = 0.107819 loss)
I0407 09:46:02.243613 18909 sgd_solver.cpp:105] Iteration 9204, lr = 0.005625
I0407 09:46:02.305769 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:46:07.585045 18909 solver.cpp:218] Iteration 9216 (2.24659 iter/s, 5.34143s/12 iters), loss = 0.372836
I0407 09:46:07.585100 18909 solver.cpp:237]     Train net output #0: loss = 0.372836 (* 1 = 0.372836 loss)
I0407 09:46:07.585110 18909 sgd_solver.cpp:105] Iteration 9216, lr = 0.005625
I0407 09:46:12.951727 18909 solver.cpp:218] Iteration 9228 (2.23605 iter/s, 5.36661s/12 iters), loss = 0.212301
I0407 09:46:12.951786 18909 solver.cpp:237]     Train net output #0: loss = 0.212301 (* 1 = 0.212301 loss)
I0407 09:46:12.951799 18909 sgd_solver.cpp:105] Iteration 9228, lr = 0.005625
I0407 09:46:17.814852 18909 solver.cpp:218] Iteration 9240 (2.46758 iter/s, 4.86306s/12 iters), loss = 0.178775
I0407 09:46:17.814895 18909 solver.cpp:237]     Train net output #0: loss = 0.178775 (* 1 = 0.178775 loss)
I0407 09:46:17.814903 18909 sgd_solver.cpp:105] Iteration 9240, lr = 0.005625
I0407 09:46:22.957826 18909 solver.cpp:218] Iteration 9252 (2.3333 iter/s, 5.14292s/12 iters), loss = 0.104574
I0407 09:46:22.957937 18909 solver.cpp:237]     Train net output #0: loss = 0.104574 (* 1 = 0.104574 loss)
I0407 09:46:22.957945 18909 sgd_solver.cpp:105] Iteration 9252, lr = 0.005625
I0407 09:46:28.176661 18909 solver.cpp:218] Iteration 9264 (2.29942 iter/s, 5.21871s/12 iters), loss = 0.303381
I0407 09:46:28.176707 18909 solver.cpp:237]     Train net output #0: loss = 0.303381 (* 1 = 0.303381 loss)
I0407 09:46:28.176715 18909 sgd_solver.cpp:105] Iteration 9264, lr = 0.005625
I0407 09:46:33.498973 18909 solver.cpp:218] Iteration 9276 (2.25469 iter/s, 5.32225s/12 iters), loss = 0.191831
I0407 09:46:33.499022 18909 solver.cpp:237]     Train net output #0: loss = 0.191831 (* 1 = 0.191831 loss)
I0407 09:46:33.499028 18909 sgd_solver.cpp:105] Iteration 9276, lr = 0.005625
I0407 09:46:35.589277 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0407 09:46:38.612206 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0407 09:46:40.947947 18909 solver.cpp:330] Iteration 9282, Testing net (#0)
I0407 09:46:40.947968 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:46:41.667068 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:46:45.341190 18909 solver.cpp:397]     Test net output #0: accuracy = 0.431373
I0407 09:46:45.341231 18909 solver.cpp:397]     Test net output #1: loss = 3.02929 (* 1 = 3.02929 loss)
I0407 09:46:47.271342 18909 solver.cpp:218] Iteration 9288 (0.871313 iter/s, 13.7723s/12 iters), loss = 0.0795449
I0407 09:46:47.271382 18909 solver.cpp:237]     Train net output #0: loss = 0.0795449 (* 1 = 0.0795449 loss)
I0407 09:46:47.271391 18909 sgd_solver.cpp:105] Iteration 9288, lr = 0.005625
I0407 09:46:52.347396 18909 solver.cpp:218] Iteration 9300 (2.36406 iter/s, 5.076s/12 iters), loss = 0.27729
I0407 09:46:52.347437 18909 solver.cpp:237]     Train net output #0: loss = 0.27729 (* 1 = 0.27729 loss)
I0407 09:46:52.347445 18909 sgd_solver.cpp:105] Iteration 9300, lr = 0.005625
I0407 09:46:54.666744 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:46:57.450927 18909 solver.cpp:218] Iteration 9312 (2.35134 iter/s, 5.10348s/12 iters), loss = 0.338448
I0407 09:46:57.450968 18909 solver.cpp:237]     Train net output #0: loss = 0.338448 (* 1 = 0.338448 loss)
I0407 09:46:57.450974 18909 sgd_solver.cpp:105] Iteration 9312, lr = 0.005625
I0407 09:47:02.634285 18909 solver.cpp:218] Iteration 9324 (2.31512 iter/s, 5.18331s/12 iters), loss = 0.135123
I0407 09:47:02.634326 18909 solver.cpp:237]     Train net output #0: loss = 0.135123 (* 1 = 0.135123 loss)
I0407 09:47:02.634332 18909 sgd_solver.cpp:105] Iteration 9324, lr = 0.005625
I0407 09:47:07.777781 18909 solver.cpp:218] Iteration 9336 (2.33307 iter/s, 5.14344s/12 iters), loss = 0.237445
I0407 09:47:07.777832 18909 solver.cpp:237]     Train net output #0: loss = 0.237445 (* 1 = 0.237445 loss)
I0407 09:47:07.777842 18909 sgd_solver.cpp:105] Iteration 9336, lr = 0.005625
I0407 09:47:12.882704 18909 solver.cpp:218] Iteration 9348 (2.3507 iter/s, 5.10486s/12 iters), loss = 0.124948
I0407 09:47:12.882751 18909 solver.cpp:237]     Train net output #0: loss = 0.124948 (* 1 = 0.124948 loss)
I0407 09:47:12.882758 18909 sgd_solver.cpp:105] Iteration 9348, lr = 0.005625
I0407 09:47:18.288916 18909 solver.cpp:218] Iteration 9360 (2.21969 iter/s, 5.40615s/12 iters), loss = 0.263526
I0407 09:47:18.288964 18909 solver.cpp:237]     Train net output #0: loss = 0.263526 (* 1 = 0.263526 loss)
I0407 09:47:18.288971 18909 sgd_solver.cpp:105] Iteration 9360, lr = 0.005625
I0407 09:47:23.514544 18909 solver.cpp:218] Iteration 9372 (2.2964 iter/s, 5.22556s/12 iters), loss = 0.261916
I0407 09:47:23.514590 18909 solver.cpp:237]     Train net output #0: loss = 0.261916 (* 1 = 0.261916 loss)
I0407 09:47:23.514600 18909 sgd_solver.cpp:105] Iteration 9372, lr = 0.005625
I0407 09:47:28.058113 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0407 09:47:31.118124 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0407 09:47:33.450984 18909 solver.cpp:330] Iteration 9384, Testing net (#0)
I0407 09:47:33.451012 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:47:34.170610 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:47:37.852901 18909 solver.cpp:397]     Test net output #0: accuracy = 0.448529
I0407 09:47:37.852934 18909 solver.cpp:397]     Test net output #1: loss = 3.11885 (* 1 = 3.11885 loss)
I0407 09:47:37.989893 18909 solver.cpp:218] Iteration 9384 (0.828998 iter/s, 14.4753s/12 iters), loss = 0.178703
I0407 09:47:37.991454 18909 solver.cpp:237]     Train net output #0: loss = 0.178703 (* 1 = 0.178703 loss)
I0407 09:47:37.991468 18909 sgd_solver.cpp:105] Iteration 9384, lr = 0.005625
I0407 09:47:42.341339 18909 solver.cpp:218] Iteration 9396 (2.7587 iter/s, 4.34988s/12 iters), loss = 0.23714
I0407 09:47:42.341405 18909 solver.cpp:237]     Train net output #0: loss = 0.23714 (* 1 = 0.23714 loss)
I0407 09:47:42.341416 18909 sgd_solver.cpp:105] Iteration 9396, lr = 0.005625
I0407 09:47:46.937065 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:47:47.666149 18909 solver.cpp:218] Iteration 9408 (2.25363 iter/s, 5.32473s/12 iters), loss = 0.16089
I0407 09:47:47.666205 18909 solver.cpp:237]     Train net output #0: loss = 0.16089 (* 1 = 0.16089 loss)
I0407 09:47:47.666216 18909 sgd_solver.cpp:105] Iteration 9408, lr = 0.005625
I0407 09:47:52.912724 18909 solver.cpp:218] Iteration 9420 (2.28724 iter/s, 5.2465s/12 iters), loss = 0.16894
I0407 09:47:52.912787 18909 solver.cpp:237]     Train net output #0: loss = 0.16894 (* 1 = 0.16894 loss)
I0407 09:47:52.912798 18909 sgd_solver.cpp:105] Iteration 9420, lr = 0.005625
I0407 09:47:58.257464 18909 solver.cpp:218] Iteration 9432 (2.24523 iter/s, 5.34467s/12 iters), loss = 0.172854
I0407 09:47:58.257566 18909 solver.cpp:237]     Train net output #0: loss = 0.172854 (* 1 = 0.172854 loss)
I0407 09:47:58.257575 18909 sgd_solver.cpp:105] Iteration 9432, lr = 0.005625
I0407 09:48:03.280361 18909 solver.cpp:218] Iteration 9444 (2.38911 iter/s, 5.02279s/12 iters), loss = 0.0997476
I0407 09:48:03.280412 18909 solver.cpp:237]     Train net output #0: loss = 0.0997476 (* 1 = 0.0997476 loss)
I0407 09:48:03.280421 18909 sgd_solver.cpp:105] Iteration 9444, lr = 0.005625
I0407 09:48:08.335934 18909 solver.cpp:218] Iteration 9456 (2.37365 iter/s, 5.05551s/12 iters), loss = 0.220058
I0407 09:48:08.335983 18909 solver.cpp:237]     Train net output #0: loss = 0.220058 (* 1 = 0.220058 loss)
I0407 09:48:08.335989 18909 sgd_solver.cpp:105] Iteration 9456, lr = 0.005625
I0407 09:48:13.374708 18909 solver.cpp:218] Iteration 9468 (2.38156 iter/s, 5.03871s/12 iters), loss = 0.193556
I0407 09:48:13.374764 18909 solver.cpp:237]     Train net output #0: loss = 0.193556 (* 1 = 0.193556 loss)
I0407 09:48:13.374774 18909 sgd_solver.cpp:105] Iteration 9468, lr = 0.005625
I0407 09:48:18.400725 18909 solver.cpp:218] Iteration 9480 (2.38761 iter/s, 5.02595s/12 iters), loss = 0.293617
I0407 09:48:18.400786 18909 solver.cpp:237]     Train net output #0: loss = 0.293617 (* 1 = 0.293617 loss)
I0407 09:48:18.400800 18909 sgd_solver.cpp:105] Iteration 9480, lr = 0.005625
I0407 09:48:20.349926 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0407 09:48:23.332769 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0407 09:48:25.631737 18909 solver.cpp:330] Iteration 9486, Testing net (#0)
I0407 09:48:25.631757 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:48:26.278945 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:48:29.919787 18909 solver.cpp:397]     Test net output #0: accuracy = 0.442402
I0407 09:48:29.919919 18909 solver.cpp:397]     Test net output #1: loss = 3.14199 (* 1 = 3.14199 loss)
I0407 09:48:31.710043 18909 solver.cpp:218] Iteration 9492 (0.901628 iter/s, 13.3093s/12 iters), loss = 0.0977814
I0407 09:48:31.710091 18909 solver.cpp:237]     Train net output #0: loss = 0.0977814 (* 1 = 0.0977814 loss)
I0407 09:48:31.710098 18909 sgd_solver.cpp:105] Iteration 9492, lr = 0.005625
I0407 09:48:36.885200 18909 solver.cpp:218] Iteration 9504 (2.3188 iter/s, 5.17509s/12 iters), loss = 0.226936
I0407 09:48:36.885254 18909 solver.cpp:237]     Train net output #0: loss = 0.226936 (* 1 = 0.226936 loss)
I0407 09:48:36.885263 18909 sgd_solver.cpp:105] Iteration 9504, lr = 0.005625
I0407 09:48:38.358412 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:48:42.041029 18909 solver.cpp:218] Iteration 9516 (2.32749 iter/s, 5.15577s/12 iters), loss = 0.259434
I0407 09:48:42.041071 18909 solver.cpp:237]     Train net output #0: loss = 0.259434 (* 1 = 0.259434 loss)
I0407 09:48:42.041079 18909 sgd_solver.cpp:105] Iteration 9516, lr = 0.005625
I0407 09:48:47.247258 18909 solver.cpp:218] Iteration 9528 (2.30495 iter/s, 5.20618s/12 iters), loss = 0.146199
I0407 09:48:47.247296 18909 solver.cpp:237]     Train net output #0: loss = 0.146199 (* 1 = 0.146199 loss)
I0407 09:48:47.247303 18909 sgd_solver.cpp:105] Iteration 9528, lr = 0.005625
I0407 09:48:52.722577 18909 solver.cpp:218] Iteration 9540 (2.19168 iter/s, 5.47526s/12 iters), loss = 0.470241
I0407 09:48:52.722635 18909 solver.cpp:237]     Train net output #0: loss = 0.470241 (* 1 = 0.470241 loss)
I0407 09:48:52.722645 18909 sgd_solver.cpp:105] Iteration 9540, lr = 0.005625
I0407 09:48:58.062920 18909 solver.cpp:218] Iteration 9552 (2.24708 iter/s, 5.34027s/12 iters), loss = 0.16219
I0407 09:48:58.062981 18909 solver.cpp:237]     Train net output #0: loss = 0.16219 (* 1 = 0.16219 loss)
I0407 09:48:58.062992 18909 sgd_solver.cpp:105] Iteration 9552, lr = 0.005625
I0407 09:49:03.354588 18909 solver.cpp:218] Iteration 9564 (2.26775 iter/s, 5.2916s/12 iters), loss = 0.209062
I0407 09:49:03.354717 18909 solver.cpp:237]     Train net output #0: loss = 0.209062 (* 1 = 0.209062 loss)
I0407 09:49:03.354727 18909 sgd_solver.cpp:105] Iteration 9564, lr = 0.005625
I0407 09:49:08.326486 18909 solver.cpp:218] Iteration 9576 (2.41363 iter/s, 4.97176s/12 iters), loss = 0.16189
I0407 09:49:08.326535 18909 solver.cpp:237]     Train net output #0: loss = 0.16189 (* 1 = 0.16189 loss)
I0407 09:49:08.326545 18909 sgd_solver.cpp:105] Iteration 9576, lr = 0.005625
I0407 09:49:13.204504 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0407 09:49:16.253572 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0407 09:49:18.598901 18909 solver.cpp:330] Iteration 9588, Testing net (#0)
I0407 09:49:18.598920 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:49:19.231242 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:49:23.049615 18909 solver.cpp:397]     Test net output #0: accuracy = 0.442402
I0407 09:49:23.049644 18909 solver.cpp:397]     Test net output #1: loss = 3.0502 (* 1 = 3.0502 loss)
I0407 09:49:23.187623 18909 solver.cpp:218] Iteration 9588 (0.807478 iter/s, 14.8611s/12 iters), loss = 0.199413
I0407 09:49:23.187685 18909 solver.cpp:237]     Train net output #0: loss = 0.199413 (* 1 = 0.199413 loss)
I0407 09:49:23.187693 18909 sgd_solver.cpp:105] Iteration 9588, lr = 0.005625
I0407 09:49:27.436623 18909 solver.cpp:218] Iteration 9600 (2.82424 iter/s, 4.24892s/12 iters), loss = 0.123588
I0407 09:49:27.436667 18909 solver.cpp:237]     Train net output #0: loss = 0.123588 (* 1 = 0.123588 loss)
I0407 09:49:27.436676 18909 sgd_solver.cpp:105] Iteration 9600, lr = 0.005625
I0407 09:49:31.208060 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:49:32.758841 18909 solver.cpp:218] Iteration 9612 (2.25473 iter/s, 5.32216s/12 iters), loss = 0.185798
I0407 09:49:32.758883 18909 solver.cpp:237]     Train net output #0: loss = 0.185798 (* 1 = 0.185798 loss)
I0407 09:49:32.758890 18909 sgd_solver.cpp:105] Iteration 9612, lr = 0.005625
I0407 09:49:38.057530 18909 solver.cpp:218] Iteration 9624 (2.26473 iter/s, 5.29864s/12 iters), loss = 0.122547
I0407 09:49:38.057662 18909 solver.cpp:237]     Train net output #0: loss = 0.122547 (* 1 = 0.122547 loss)
I0407 09:49:38.057670 18909 sgd_solver.cpp:105] Iteration 9624, lr = 0.005625
I0407 09:49:43.448112 18909 solver.cpp:218] Iteration 9636 (2.22616 iter/s, 5.39044s/12 iters), loss = 0.141388
I0407 09:49:43.448172 18909 solver.cpp:237]     Train net output #0: loss = 0.141388 (* 1 = 0.141388 loss)
I0407 09:49:43.448184 18909 sgd_solver.cpp:105] Iteration 9636, lr = 0.005625
I0407 09:49:48.729271 18909 solver.cpp:218] Iteration 9648 (2.27226 iter/s, 5.28108s/12 iters), loss = 0.159064
I0407 09:49:48.729324 18909 solver.cpp:237]     Train net output #0: loss = 0.159064 (* 1 = 0.159064 loss)
I0407 09:49:48.729333 18909 sgd_solver.cpp:105] Iteration 9648, lr = 0.005625
I0407 09:49:54.068056 18909 solver.cpp:218] Iteration 9660 (2.24773 iter/s, 5.33872s/12 iters), loss = 0.240868
I0407 09:49:54.068096 18909 solver.cpp:237]     Train net output #0: loss = 0.240868 (* 1 = 0.240868 loss)
I0407 09:49:54.068104 18909 sgd_solver.cpp:105] Iteration 9660, lr = 0.005625
I0407 09:49:59.228662 18909 solver.cpp:218] Iteration 9672 (2.32533 iter/s, 5.16055s/12 iters), loss = 0.341613
I0407 09:49:59.228704 18909 solver.cpp:237]     Train net output #0: loss = 0.341613 (* 1 = 0.341613 loss)
I0407 09:49:59.228711 18909 sgd_solver.cpp:105] Iteration 9672, lr = 0.005625
I0407 09:50:04.509424 18909 solver.cpp:218] Iteration 9684 (2.27242 iter/s, 5.28071s/12 iters), loss = 0.129969
I0407 09:50:04.509475 18909 solver.cpp:237]     Train net output #0: loss = 0.129969 (* 1 = 0.129969 loss)
I0407 09:50:04.509485 18909 sgd_solver.cpp:105] Iteration 9684, lr = 0.005625
I0407 09:50:06.620254 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0407 09:50:09.656975 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0407 09:50:12.018877 18909 solver.cpp:330] Iteration 9690, Testing net (#0)
I0407 09:50:12.018895 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:50:12.572157 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:50:15.312074 18909 blocking_queue.cpp:49] Waiting for data
I0407 09:50:16.334609 18909 solver.cpp:397]     Test net output #0: accuracy = 0.444853
I0407 09:50:16.334645 18909 solver.cpp:397]     Test net output #1: loss = 3.07648 (* 1 = 3.07648 loss)
I0407 09:50:18.151141 18909 solver.cpp:218] Iteration 9696 (0.879658 iter/s, 13.6417s/12 iters), loss = 0.208744
I0407 09:50:18.151182 18909 solver.cpp:237]     Train net output #0: loss = 0.208744 (* 1 = 0.208744 loss)
I0407 09:50:18.151190 18909 sgd_solver.cpp:105] Iteration 9696, lr = 0.005625
I0407 09:50:23.328569 18909 solver.cpp:218] Iteration 9708 (2.31778 iter/s, 5.17737s/12 iters), loss = 0.167196
I0407 09:50:23.328626 18909 solver.cpp:237]     Train net output #0: loss = 0.167196 (* 1 = 0.167196 loss)
I0407 09:50:23.328636 18909 sgd_solver.cpp:105] Iteration 9708, lr = 0.005625
I0407 09:50:24.073689 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:50:28.617316 18909 solver.cpp:218] Iteration 9720 (2.269 iter/s, 5.28868s/12 iters), loss = 0.160203
I0407 09:50:28.617360 18909 solver.cpp:237]     Train net output #0: loss = 0.160203 (* 1 = 0.160203 loss)
I0407 09:50:28.617368 18909 sgd_solver.cpp:105] Iteration 9720, lr = 0.005625
I0407 09:50:33.987702 18909 solver.cpp:218] Iteration 9732 (2.2345 iter/s, 5.37033s/12 iters), loss = 0.158915
I0407 09:50:33.987754 18909 solver.cpp:237]     Train net output #0: loss = 0.158915 (* 1 = 0.158915 loss)
I0407 09:50:33.987763 18909 sgd_solver.cpp:105] Iteration 9732, lr = 0.005625
I0407 09:50:39.369347 18909 solver.cpp:218] Iteration 9744 (2.22983 iter/s, 5.38159s/12 iters), loss = 0.129627
I0407 09:50:39.369385 18909 solver.cpp:237]     Train net output #0: loss = 0.129627 (* 1 = 0.129627 loss)
I0407 09:50:39.369392 18909 sgd_solver.cpp:105] Iteration 9744, lr = 0.005625
I0407 09:50:44.705960 18909 solver.cpp:218] Iteration 9756 (2.24864 iter/s, 5.33656s/12 iters), loss = 0.226508
I0407 09:50:44.706081 18909 solver.cpp:237]     Train net output #0: loss = 0.226508 (* 1 = 0.226508 loss)
I0407 09:50:44.706089 18909 sgd_solver.cpp:105] Iteration 9756, lr = 0.005625
I0407 09:50:49.957700 18909 solver.cpp:218] Iteration 9768 (2.28502 iter/s, 5.2516s/12 iters), loss = 0.0920589
I0407 09:50:49.957742 18909 solver.cpp:237]     Train net output #0: loss = 0.0920589 (* 1 = 0.0920589 loss)
I0407 09:50:49.957749 18909 sgd_solver.cpp:105] Iteration 9768, lr = 0.005625
I0407 09:50:55.221223 18909 solver.cpp:218] Iteration 9780 (2.27986 iter/s, 5.26347s/12 iters), loss = 0.234899
I0407 09:50:55.221261 18909 solver.cpp:237]     Train net output #0: loss = 0.234899 (* 1 = 0.234899 loss)
I0407 09:50:55.221266 18909 sgd_solver.cpp:105] Iteration 9780, lr = 0.005625
I0407 09:50:59.977290 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0407 09:51:03.042450 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0407 09:51:05.393352 18909 solver.cpp:330] Iteration 9792, Testing net (#0)
I0407 09:51:05.393375 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:51:05.947010 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:51:09.816021 18909 solver.cpp:397]     Test net output #0: accuracy = 0.443627
I0407 09:51:09.816059 18909 solver.cpp:397]     Test net output #1: loss = 3.14378 (* 1 = 3.14378 loss)
I0407 09:51:09.947239 18909 solver.cpp:218] Iteration 9792 (0.814887 iter/s, 14.726s/12 iters), loss = 0.289478
I0407 09:51:09.947299 18909 solver.cpp:237]     Train net output #0: loss = 0.289478 (* 1 = 0.289478 loss)
I0407 09:51:09.947309 18909 sgd_solver.cpp:105] Iteration 9792, lr = 0.005625
I0407 09:51:14.195281 18909 solver.cpp:218] Iteration 9804 (2.82488 iter/s, 4.24797s/12 iters), loss = 0.202162
I0407 09:51:14.195329 18909 solver.cpp:237]     Train net output #0: loss = 0.202162 (* 1 = 0.202162 loss)
I0407 09:51:14.195339 18909 sgd_solver.cpp:105] Iteration 9804, lr = 0.005625
I0407 09:51:17.246909 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:51:19.428167 18909 solver.cpp:218] Iteration 9816 (2.29322 iter/s, 5.23283s/12 iters), loss = 0.0929877
I0407 09:51:19.428228 18909 solver.cpp:237]     Train net output #0: loss = 0.0929878 (* 1 = 0.0929878 loss)
I0407 09:51:19.428241 18909 sgd_solver.cpp:105] Iteration 9816, lr = 0.005625
I0407 09:51:24.778620 18909 solver.cpp:218] Iteration 9828 (2.24283 iter/s, 5.35037s/12 iters), loss = 0.295189
I0407 09:51:24.778676 18909 solver.cpp:237]     Train net output #0: loss = 0.295189 (* 1 = 0.295189 loss)
I0407 09:51:24.778687 18909 sgd_solver.cpp:105] Iteration 9828, lr = 0.005625
I0407 09:51:30.175743 18909 solver.cpp:218] Iteration 9840 (2.22343 iter/s, 5.39706s/12 iters), loss = 0.200449
I0407 09:51:30.175786 18909 solver.cpp:237]     Train net output #0: loss = 0.200449 (* 1 = 0.200449 loss)
I0407 09:51:30.175791 18909 sgd_solver.cpp:105] Iteration 9840, lr = 0.005625
I0407 09:51:35.404920 18909 solver.cpp:218] Iteration 9852 (2.29484 iter/s, 5.22912s/12 iters), loss = 0.165602
I0407 09:51:35.404963 18909 solver.cpp:237]     Train net output #0: loss = 0.165602 (* 1 = 0.165602 loss)
I0407 09:51:35.404970 18909 sgd_solver.cpp:105] Iteration 9852, lr = 0.005625
I0407 09:51:40.432570 18909 solver.cpp:218] Iteration 9864 (2.38683 iter/s, 5.02759s/12 iters), loss = 0.228029
I0407 09:51:40.432616 18909 solver.cpp:237]     Train net output #0: loss = 0.228029 (* 1 = 0.228029 loss)
I0407 09:51:40.432624 18909 sgd_solver.cpp:105] Iteration 9864, lr = 0.005625
I0407 09:51:45.797204 18909 solver.cpp:218] Iteration 9876 (2.2369 iter/s, 5.36457s/12 iters), loss = 0.279353
I0407 09:51:45.797256 18909 solver.cpp:237]     Train net output #0: loss = 0.279353 (* 1 = 0.279353 loss)
I0407 09:51:45.797264 18909 sgd_solver.cpp:105] Iteration 9876, lr = 0.005625
I0407 09:51:51.068944 18909 solver.cpp:218] Iteration 9888 (2.27632 iter/s, 5.27167s/12 iters), loss = 0.120874
I0407 09:51:51.069080 18909 solver.cpp:237]     Train net output #0: loss = 0.120874 (* 1 = 0.120874 loss)
I0407 09:51:51.069090 18909 sgd_solver.cpp:105] Iteration 9888, lr = 0.005625
I0407 09:51:53.206029 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0407 09:51:56.204481 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0407 09:51:58.530241 18909 solver.cpp:330] Iteration 9894, Testing net (#0)
I0407 09:51:58.530262 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:51:59.062603 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:52:02.988929 18909 solver.cpp:397]     Test net output #0: accuracy = 0.448529
I0407 09:52:02.988965 18909 solver.cpp:397]     Test net output #1: loss = 3.07265 (* 1 = 3.07265 loss)
I0407 09:52:04.890026 18909 solver.cpp:218] Iteration 9900 (0.868247 iter/s, 13.8209s/12 iters), loss = 0.161064
I0407 09:52:04.890071 18909 solver.cpp:237]     Train net output #0: loss = 0.161064 (* 1 = 0.161064 loss)
I0407 09:52:04.890079 18909 sgd_solver.cpp:105] Iteration 9900, lr = 0.005625
I0407 09:52:10.028599 18909 solver.cpp:218] Iteration 9912 (2.33531 iter/s, 5.13851s/12 iters), loss = 0.244818
I0407 09:52:10.028635 18909 solver.cpp:237]     Train net output #0: loss = 0.244818 (* 1 = 0.244818 loss)
I0407 09:52:10.028642 18909 sgd_solver.cpp:105] Iteration 9912, lr = 0.005625
I0407 09:52:10.117044 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:52:15.260073 18909 solver.cpp:218] Iteration 9924 (2.29383 iter/s, 5.23142s/12 iters), loss = 0.249776
I0407 09:52:15.260123 18909 solver.cpp:237]     Train net output #0: loss = 0.249776 (* 1 = 0.249776 loss)
I0407 09:52:15.260131 18909 sgd_solver.cpp:105] Iteration 9924, lr = 0.005625
I0407 09:52:20.578462 18909 solver.cpp:218] Iteration 9936 (2.25635 iter/s, 5.31832s/12 iters), loss = 0.18153
I0407 09:52:20.578518 18909 solver.cpp:237]     Train net output #0: loss = 0.18153 (* 1 = 0.18153 loss)
I0407 09:52:20.578527 18909 sgd_solver.cpp:105] Iteration 9936, lr = 0.005625
I0407 09:52:26.145967 18909 solver.cpp:218] Iteration 9948 (2.15539 iter/s, 5.56743s/12 iters), loss = 0.137598
I0407 09:52:26.146155 18909 solver.cpp:237]     Train net output #0: loss = 0.137598 (* 1 = 0.137598 loss)
I0407 09:52:26.146167 18909 sgd_solver.cpp:105] Iteration 9948, lr = 0.005625
I0407 09:52:31.456761 18909 solver.cpp:218] Iteration 9960 (2.25963 iter/s, 5.31059s/12 iters), loss = 0.188014
I0407 09:52:31.456810 18909 solver.cpp:237]     Train net output #0: loss = 0.188014 (* 1 = 0.188014 loss)
I0407 09:52:31.456820 18909 sgd_solver.cpp:105] Iteration 9960, lr = 0.005625
I0407 09:52:36.446873 18909 solver.cpp:218] Iteration 9972 (2.40478 iter/s, 4.99005s/12 iters), loss = 0.21529
I0407 09:52:36.446918 18909 solver.cpp:237]     Train net output #0: loss = 0.21529 (* 1 = 0.21529 loss)
I0407 09:52:36.446925 18909 sgd_solver.cpp:105] Iteration 9972, lr = 0.005625
I0407 09:52:41.690053 18909 solver.cpp:218] Iteration 9984 (2.28871 iter/s, 5.24312s/12 iters), loss = 0.24727
I0407 09:52:41.690105 18909 solver.cpp:237]     Train net output #0: loss = 0.24727 (* 1 = 0.24727 loss)
I0407 09:52:41.690115 18909 sgd_solver.cpp:105] Iteration 9984, lr = 0.005625
I0407 09:52:46.425839 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0407 09:52:49.373229 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0407 09:52:51.699702 18909 solver.cpp:330] Iteration 9996, Testing net (#0)
I0407 09:52:51.699720 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:52:52.141175 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:52:56.009310 18909 solver.cpp:397]     Test net output #0: accuracy = 0.444853
I0407 09:52:56.009344 18909 solver.cpp:397]     Test net output #1: loss = 3.04074 (* 1 = 3.04074 loss)
I0407 09:52:56.145615 18909 solver.cpp:218] Iteration 9996 (0.830133 iter/s, 14.4555s/12 iters), loss = 0.209176
I0407 09:52:56.145659 18909 solver.cpp:237]     Train net output #0: loss = 0.209176 (* 1 = 0.209176 loss)
I0407 09:52:56.145668 18909 sgd_solver.cpp:105] Iteration 9996, lr = 0.005625
I0407 09:53:00.425194 18909 solver.cpp:218] Iteration 10008 (2.80405 iter/s, 4.27952s/12 iters), loss = 0.23389
I0407 09:53:00.425318 18909 solver.cpp:237]     Train net output #0: loss = 0.23389 (* 1 = 0.23389 loss)
I0407 09:53:00.425325 18909 sgd_solver.cpp:105] Iteration 10008, lr = 0.005625
I0407 09:53:02.794463 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:53:05.810750 18909 solver.cpp:218] Iteration 10020 (2.22824 iter/s, 5.38542s/12 iters), loss = 0.198896
I0407 09:53:05.810791 18909 solver.cpp:237]     Train net output #0: loss = 0.198896 (* 1 = 0.198896 loss)
I0407 09:53:05.810796 18909 sgd_solver.cpp:105] Iteration 10020, lr = 0.005625
I0407 09:53:10.996168 18909 solver.cpp:218] Iteration 10032 (2.31421 iter/s, 5.18536s/12 iters), loss = 0.0884832
I0407 09:53:10.996234 18909 solver.cpp:237]     Train net output #0: loss = 0.0884833 (* 1 = 0.0884833 loss)
I0407 09:53:10.996244 18909 sgd_solver.cpp:105] Iteration 10032, lr = 0.005625
I0407 09:53:16.291173 18909 solver.cpp:218] Iteration 10044 (2.26632 iter/s, 5.29493s/12 iters), loss = 0.237768
I0407 09:53:16.291219 18909 solver.cpp:237]     Train net output #0: loss = 0.237768 (* 1 = 0.237768 loss)
I0407 09:53:16.291226 18909 sgd_solver.cpp:105] Iteration 10044, lr = 0.005625
I0407 09:53:21.331506 18909 solver.cpp:218] Iteration 10056 (2.38082 iter/s, 5.04027s/12 iters), loss = 0.153537
I0407 09:53:21.331564 18909 solver.cpp:237]     Train net output #0: loss = 0.153537 (* 1 = 0.153537 loss)
I0407 09:53:21.331575 18909 sgd_solver.cpp:105] Iteration 10056, lr = 0.005625
I0407 09:53:26.674291 18909 solver.cpp:218] Iteration 10068 (2.24605 iter/s, 5.34272s/12 iters), loss = 0.199101
I0407 09:53:26.674352 18909 solver.cpp:237]     Train net output #0: loss = 0.199101 (* 1 = 0.199101 loss)
I0407 09:53:26.674363 18909 sgd_solver.cpp:105] Iteration 10068, lr = 0.005625
I0407 09:53:32.122265 18909 solver.cpp:218] Iteration 10080 (2.20268 iter/s, 5.4479s/12 iters), loss = 0.222721
I0407 09:53:32.122383 18909 solver.cpp:237]     Train net output #0: loss = 0.222721 (* 1 = 0.222721 loss)
I0407 09:53:32.122393 18909 sgd_solver.cpp:105] Iteration 10080, lr = 0.005625
I0407 09:53:37.413115 18909 solver.cpp:218] Iteration 10092 (2.26812 iter/s, 5.29073s/12 iters), loss = 0.216733
I0407 09:53:37.413158 18909 solver.cpp:237]     Train net output #0: loss = 0.216733 (* 1 = 0.216733 loss)
I0407 09:53:37.413167 18909 sgd_solver.cpp:105] Iteration 10092, lr = 0.005625
I0407 09:53:39.501103 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0407 09:53:42.554699 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0407 09:53:44.869838 18909 solver.cpp:330] Iteration 10098, Testing net (#0)
I0407 09:53:44.869858 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:53:45.284348 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:53:49.188485 18909 solver.cpp:397]     Test net output #0: accuracy = 0.452819
I0407 09:53:49.188524 18909 solver.cpp:397]     Test net output #1: loss = 3.05775 (* 1 = 3.05775 loss)
I0407 09:53:51.089424 18909 solver.cpp:218] Iteration 10104 (0.877433 iter/s, 13.6763s/12 iters), loss = 0.182965
I0407 09:53:51.089476 18909 solver.cpp:237]     Train net output #0: loss = 0.182965 (* 1 = 0.182965 loss)
I0407 09:53:51.089488 18909 sgd_solver.cpp:105] Iteration 10104, lr = 0.00421875
I0407 09:53:55.572046 18933 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:53:56.230600 18909 solver.cpp:218] Iteration 10116 (2.33412 iter/s, 5.14111s/12 iters), loss = 0.0951014
I0407 09:53:56.230641 18909 solver.cpp:237]     Train net output #0: loss = 0.0951014 (* 1 = 0.0951014 loss)
I0407 09:53:56.230649 18909 sgd_solver.cpp:105] Iteration 10116, lr = 0.00421875
I0407 09:54:01.375913 18909 solver.cpp:218] Iteration 10128 (2.33225 iter/s, 5.14526s/12 iters), loss = 0.177293
I0407 09:54:01.375957 18909 solver.cpp:237]     Train net output #0: loss = 0.177293 (* 1 = 0.177293 loss)
I0407 09:54:01.375965 18909 sgd_solver.cpp:105] Iteration 10128, lr = 0.00421875
I0407 09:54:06.580251 18909 solver.cpp:218] Iteration 10140 (2.30579 iter/s, 5.20428s/12 iters), loss = 0.19273
I0407 09:54:06.580380 18909 solver.cpp:237]     Train net output #0: loss = 0.19273 (* 1 = 0.19273 loss)
I0407 09:54:06.580391 18909 sgd_solver.cpp:105] Iteration 10140, lr = 0.00421875
I0407 09:54:11.807340 18909 solver.cpp:218] Iteration 10152 (2.29579 iter/s, 5.22695s/12 iters), loss = 0.278018
I0407 09:54:11.807399 18909 solver.cpp:237]     Train net output #0: loss = 0.278018 (* 1 = 0.278018 loss)
I0407 09:54:11.807410 18909 sgd_solver.cpp:105] Iteration 10152, lr = 0.00421875
I0407 09:54:17.149788 18909 solver.cpp:218] Iteration 10164 (2.24619 iter/s, 5.34238s/12 iters), loss = 0.250607
I0407 09:54:17.149852 18909 solver.cpp:237]     Train net output #0: loss = 0.250607 (* 1 = 0.250607 loss)
I0407 09:54:17.149863 18909 sgd_solver.cpp:105] Iteration 10164, lr = 0.00421875
I0407 09:54:22.536154 18909 solver.cpp:218] Iteration 10176 (2.22788 iter/s, 5.38629s/12 iters), loss = 0.162603
I0407 09:54:22.536212 18909 solver.cpp:237]     Train net output #0: loss = 0.162603 (* 1 = 0.162603 loss)
I0407 09:54:22.536222 18909 sgd_solver.cpp:105] Iteration 10176, lr = 0.00421875
I0407 09:54:27.689064 18909 solver.cpp:218] Iteration 10188 (2.32881 iter/s, 5.15285s/12 iters), loss = 0.192119
I0407 09:54:27.689106 18909 solver.cpp:237]     Train net output #0: loss = 0.192119 (* 1 = 0.192119 loss)
I0407 09:54:27.689113 18909 sgd_solver.cpp:105] Iteration 10188, lr = 0.00421875
I0407 09:54:32.519857 18909 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0407 09:54:35.598585 18909 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0407 09:54:37.962512 18909 solver.cpp:310] Iteration 10200, loss = 0.128787
I0407 09:54:37.962589 18909 solver.cpp:330] Iteration 10200, Testing net (#0)
I0407 09:54:37.962596 18909 net.cpp:676] Ignoring source layer train-data
I0407 09:54:38.349792 18961 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:54:42.375211 18909 solver.cpp:397]     Test net output #0: accuracy = 0.463848
I0407 09:54:42.375262 18909 solver.cpp:397]     Test net output #1: loss = 3.0052 (* 1 = 3.0052 loss)
I0407 09:54:42.375272 18909 solver.cpp:315] Optimization Done.
I0407 09:54:42.375279 18909 caffe.cpp:259] Optimization Done.