DIGITS-CNN/cars/lr-investigations/exponential/1e-1/0.9/caffe_output.log

I0408 07:39:39.957782 31856 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210408-073938-9033/solver.prototxt
I0408 07:39:39.957949 31856 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0408 07:39:39.957969 31856 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0408 07:39:39.958046 31856 caffe.cpp:218] Using GPUs 0
I0408 07:39:39.980113 31856 caffe.cpp:223] GPU 0: GeForce GTX 1080 Ti
I0408 07:39:40.238166 31856 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.1
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99896759
momentum: 0.9
weight_decay: 0.001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 0
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0408 07:39:40.238898 31856 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0408 07:39:40.239447 31856 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0408 07:39:40.239462 31856 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0408 07:39:40.239603 31856 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 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"
}
I0408 07:39:40.239688 31856 layer_factory.hpp:77] Creating layer train-data
I0408 07:39:40.241751 31856 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0408 07:39:40.241991 31856 net.cpp:84] Creating Layer train-data
I0408 07:39:40.242005 31856 net.cpp:380] train-data -> data
I0408 07:39:40.242031 31856 net.cpp:380] train-data -> label
I0408 07:39:40.242045 31856 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0408 07:39:40.249358 31856 data_layer.cpp:45] output data size: 128,3,227,227
I0408 07:39:40.382967 31856 net.cpp:122] Setting up train-data
I0408 07:39:40.382990 31856 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0408 07:39:40.382995 31856 net.cpp:129] Top shape: 128 (128)
I0408 07:39:40.382999 31856 net.cpp:137] Memory required for data: 79149056
I0408 07:39:40.383009 31856 layer_factory.hpp:77] Creating layer conv1
I0408 07:39:40.383030 31856 net.cpp:84] Creating Layer conv1
I0408 07:39:40.383036 31856 net.cpp:406] conv1 <- data
I0408 07:39:40.383049 31856 net.cpp:380] conv1 -> conv1
I0408 07:39:40.925521 31856 net.cpp:122] Setting up conv1
I0408 07:39:40.925542 31856 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 07:39:40.925546 31856 net.cpp:137] Memory required for data: 227833856
I0408 07:39:40.925566 31856 layer_factory.hpp:77] Creating layer relu1
I0408 07:39:40.925576 31856 net.cpp:84] Creating Layer relu1
I0408 07:39:40.925581 31856 net.cpp:406] relu1 <- conv1
I0408 07:39:40.925586 31856 net.cpp:367] relu1 -> conv1 (in-place)
I0408 07:39:40.925873 31856 net.cpp:122] Setting up relu1
I0408 07:39:40.925881 31856 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 07:39:40.925884 31856 net.cpp:137] Memory required for data: 376518656
I0408 07:39:40.925889 31856 layer_factory.hpp:77] Creating layer norm1
I0408 07:39:40.925897 31856 net.cpp:84] Creating Layer norm1
I0408 07:39:40.925900 31856 net.cpp:406] norm1 <- conv1
I0408 07:39:40.925925 31856 net.cpp:380] norm1 -> norm1
I0408 07:39:40.926416 31856 net.cpp:122] Setting up norm1
I0408 07:39:40.926427 31856 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 07:39:40.926431 31856 net.cpp:137] Memory required for data: 525203456
I0408 07:39:40.926435 31856 layer_factory.hpp:77] Creating layer pool1
I0408 07:39:40.926442 31856 net.cpp:84] Creating Layer pool1
I0408 07:39:40.926446 31856 net.cpp:406] pool1 <- norm1
I0408 07:39:40.926452 31856 net.cpp:380] pool1 -> pool1
I0408 07:39:40.926487 31856 net.cpp:122] Setting up pool1
I0408 07:39:40.926493 31856 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0408 07:39:40.926497 31856 net.cpp:137] Memory required for data: 561035264
I0408 07:39:40.926501 31856 layer_factory.hpp:77] Creating layer conv2
I0408 07:39:40.926512 31856 net.cpp:84] Creating Layer conv2
I0408 07:39:40.926514 31856 net.cpp:406] conv2 <- pool1
I0408 07:39:40.926519 31856 net.cpp:380] conv2 -> conv2
I0408 07:39:40.933037 31856 net.cpp:122] Setting up conv2
I0408 07:39:40.933049 31856 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 07:39:40.933053 31856 net.cpp:137] Memory required for data: 656586752
I0408 07:39:40.933063 31856 layer_factory.hpp:77] Creating layer relu2
I0408 07:39:40.933069 31856 net.cpp:84] Creating Layer relu2
I0408 07:39:40.933073 31856 net.cpp:406] relu2 <- conv2
I0408 07:39:40.933079 31856 net.cpp:367] relu2 -> conv2 (in-place)
I0408 07:39:40.933502 31856 net.cpp:122] Setting up relu2
I0408 07:39:40.933511 31856 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 07:39:40.933516 31856 net.cpp:137] Memory required for data: 752138240
I0408 07:39:40.933518 31856 layer_factory.hpp:77] Creating layer norm2
I0408 07:39:40.933526 31856 net.cpp:84] Creating Layer norm2
I0408 07:39:40.933529 31856 net.cpp:406] norm2 <- conv2
I0408 07:39:40.933535 31856 net.cpp:380] norm2 -> norm2
I0408 07:39:40.933818 31856 net.cpp:122] Setting up norm2
I0408 07:39:40.933826 31856 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 07:39:40.933830 31856 net.cpp:137] Memory required for data: 847689728
I0408 07:39:40.933833 31856 layer_factory.hpp:77] Creating layer pool2
I0408 07:39:40.933841 31856 net.cpp:84] Creating Layer pool2
I0408 07:39:40.933845 31856 net.cpp:406] pool2 <- norm2
I0408 07:39:40.933851 31856 net.cpp:380] pool2 -> pool2
I0408 07:39:40.933876 31856 net.cpp:122] Setting up pool2
I0408 07:39:40.933881 31856 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 07:39:40.933884 31856 net.cpp:137] Memory required for data: 869840896
I0408 07:39:40.933887 31856 layer_factory.hpp:77] Creating layer conv3
I0408 07:39:40.933895 31856 net.cpp:84] Creating Layer conv3
I0408 07:39:40.933899 31856 net.cpp:406] conv3 <- pool2
I0408 07:39:40.933904 31856 net.cpp:380] conv3 -> conv3
I0408 07:39:40.943643 31856 net.cpp:122] Setting up conv3
I0408 07:39:40.943655 31856 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 07:39:40.943658 31856 net.cpp:137] Memory required for data: 903067648
I0408 07:39:40.943667 31856 layer_factory.hpp:77] Creating layer relu3
I0408 07:39:40.943675 31856 net.cpp:84] Creating Layer relu3
I0408 07:39:40.943677 31856 net.cpp:406] relu3 <- conv3
I0408 07:39:40.943682 31856 net.cpp:367] relu3 -> conv3 (in-place)
I0408 07:39:40.944100 31856 net.cpp:122] Setting up relu3
I0408 07:39:40.944109 31856 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 07:39:40.944113 31856 net.cpp:137] Memory required for data: 936294400
I0408 07:39:40.944116 31856 layer_factory.hpp:77] Creating layer conv4
I0408 07:39:40.944125 31856 net.cpp:84] Creating Layer conv4
I0408 07:39:40.944128 31856 net.cpp:406] conv4 <- conv3
I0408 07:39:40.944134 31856 net.cpp:380] conv4 -> conv4
I0408 07:39:40.954257 31856 net.cpp:122] Setting up conv4
I0408 07:39:40.954270 31856 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 07:39:40.954273 31856 net.cpp:137] Memory required for data: 969521152
I0408 07:39:40.954280 31856 layer_factory.hpp:77] Creating layer relu4
I0408 07:39:40.954288 31856 net.cpp:84] Creating Layer relu4
I0408 07:39:40.954308 31856 net.cpp:406] relu4 <- conv4
I0408 07:39:40.954313 31856 net.cpp:367] relu4 -> conv4 (in-place)
I0408 07:39:40.954648 31856 net.cpp:122] Setting up relu4
I0408 07:39:40.954655 31856 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 07:39:40.954658 31856 net.cpp:137] Memory required for data: 1002747904
I0408 07:39:40.954663 31856 layer_factory.hpp:77] Creating layer conv5
I0408 07:39:40.954671 31856 net.cpp:84] Creating Layer conv5
I0408 07:39:40.954675 31856 net.cpp:406] conv5 <- conv4
I0408 07:39:40.954681 31856 net.cpp:380] conv5 -> conv5
I0408 07:39:40.966033 31856 net.cpp:122] Setting up conv5
I0408 07:39:40.966045 31856 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 07:39:40.966049 31856 net.cpp:137] Memory required for data: 1024899072
I0408 07:39:40.966061 31856 layer_factory.hpp:77] Creating layer relu5
I0408 07:39:40.966068 31856 net.cpp:84] Creating Layer relu5
I0408 07:39:40.966073 31856 net.cpp:406] relu5 <- conv5
I0408 07:39:40.966078 31856 net.cpp:367] relu5 -> conv5 (in-place)
I0408 07:39:40.966560 31856 net.cpp:122] Setting up relu5
I0408 07:39:40.966571 31856 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 07:39:40.966574 31856 net.cpp:137] Memory required for data: 1047050240
I0408 07:39:40.966578 31856 layer_factory.hpp:77] Creating layer pool5
I0408 07:39:40.966584 31856 net.cpp:84] Creating Layer pool5
I0408 07:39:40.966588 31856 net.cpp:406] pool5 <- conv5
I0408 07:39:40.966595 31856 net.cpp:380] pool5 -> pool5
I0408 07:39:40.966631 31856 net.cpp:122] Setting up pool5
I0408 07:39:40.966637 31856 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0408 07:39:40.966640 31856 net.cpp:137] Memory required for data: 1051768832
I0408 07:39:40.966643 31856 layer_factory.hpp:77] Creating layer fc6
I0408 07:39:40.966655 31856 net.cpp:84] Creating Layer fc6
I0408 07:39:40.966657 31856 net.cpp:406] fc6 <- pool5
I0408 07:39:40.966662 31856 net.cpp:380] fc6 -> fc6
I0408 07:39:41.321069 31856 net.cpp:122] Setting up fc6
I0408 07:39:41.321091 31856 net.cpp:129] Top shape: 128 4096 (524288)
I0408 07:39:41.321095 31856 net.cpp:137] Memory required for data: 1053865984
I0408 07:39:41.321105 31856 layer_factory.hpp:77] Creating layer relu6
I0408 07:39:41.321115 31856 net.cpp:84] Creating Layer relu6
I0408 07:39:41.321118 31856 net.cpp:406] relu6 <- fc6
I0408 07:39:41.321125 31856 net.cpp:367] relu6 -> fc6 (in-place)
I0408 07:39:41.321755 31856 net.cpp:122] Setting up relu6
I0408 07:39:41.321765 31856 net.cpp:129] Top shape: 128 4096 (524288)
I0408 07:39:41.321768 31856 net.cpp:137] Memory required for data: 1055963136
I0408 07:39:41.321772 31856 layer_factory.hpp:77] Creating layer drop6
I0408 07:39:41.321779 31856 net.cpp:84] Creating Layer drop6
I0408 07:39:41.321784 31856 net.cpp:406] drop6 <- fc6
I0408 07:39:41.321789 31856 net.cpp:367] drop6 -> fc6 (in-place)
I0408 07:39:41.321818 31856 net.cpp:122] Setting up drop6
I0408 07:39:41.321823 31856 net.cpp:129] Top shape: 128 4096 (524288)
I0408 07:39:41.321827 31856 net.cpp:137] Memory required for data: 1058060288
I0408 07:39:41.321830 31856 layer_factory.hpp:77] Creating layer fc7
I0408 07:39:41.321839 31856 net.cpp:84] Creating Layer fc7
I0408 07:39:41.321842 31856 net.cpp:406] fc7 <- fc6
I0408 07:39:41.321847 31856 net.cpp:380] fc7 -> fc7
I0408 07:39:41.478929 31856 net.cpp:122] Setting up fc7
I0408 07:39:41.478951 31856 net.cpp:129] Top shape: 128 4096 (524288)
I0408 07:39:41.478955 31856 net.cpp:137] Memory required for data: 1060157440
I0408 07:39:41.478965 31856 layer_factory.hpp:77] Creating layer relu7
I0408 07:39:41.478974 31856 net.cpp:84] Creating Layer relu7
I0408 07:39:41.478978 31856 net.cpp:406] relu7 <- fc7
I0408 07:39:41.478984 31856 net.cpp:367] relu7 -> fc7 (in-place)
I0408 07:39:41.479599 31856 net.cpp:122] Setting up relu7
I0408 07:39:41.479609 31856 net.cpp:129] Top shape: 128 4096 (524288)
I0408 07:39:41.479612 31856 net.cpp:137] Memory required for data: 1062254592
I0408 07:39:41.479616 31856 layer_factory.hpp:77] Creating layer drop7
I0408 07:39:41.479624 31856 net.cpp:84] Creating Layer drop7
I0408 07:39:41.479645 31856 net.cpp:406] drop7 <- fc7
I0408 07:39:41.479652 31856 net.cpp:367] drop7 -> fc7 (in-place)
I0408 07:39:41.479676 31856 net.cpp:122] Setting up drop7
I0408 07:39:41.479682 31856 net.cpp:129] Top shape: 128 4096 (524288)
I0408 07:39:41.479686 31856 net.cpp:137] Memory required for data: 1064351744
I0408 07:39:41.479688 31856 layer_factory.hpp:77] Creating layer fc8
I0408 07:39:41.479697 31856 net.cpp:84] Creating Layer fc8
I0408 07:39:41.479701 31856 net.cpp:406] fc8 <- fc7
I0408 07:39:41.479707 31856 net.cpp:380] fc8 -> fc8
I0408 07:39:41.487897 31856 net.cpp:122] Setting up fc8
I0408 07:39:41.487907 31856 net.cpp:129] Top shape: 128 196 (25088)
I0408 07:39:41.487910 31856 net.cpp:137] Memory required for data: 1064452096
I0408 07:39:41.487916 31856 layer_factory.hpp:77] Creating layer loss
I0408 07:39:41.487924 31856 net.cpp:84] Creating Layer loss
I0408 07:39:41.487928 31856 net.cpp:406] loss <- fc8
I0408 07:39:41.487933 31856 net.cpp:406] loss <- label
I0408 07:39:41.487939 31856 net.cpp:380] loss -> loss
I0408 07:39:41.487948 31856 layer_factory.hpp:77] Creating layer loss
I0408 07:39:41.488543 31856 net.cpp:122] Setting up loss
I0408 07:39:41.488552 31856 net.cpp:129] Top shape: (1)
I0408 07:39:41.488555 31856 net.cpp:132]     with loss weight 1
I0408 07:39:41.488572 31856 net.cpp:137] Memory required for data: 1064452100
I0408 07:39:41.488576 31856 net.cpp:198] loss needs backward computation.
I0408 07:39:41.488584 31856 net.cpp:198] fc8 needs backward computation.
I0408 07:39:41.488587 31856 net.cpp:198] drop7 needs backward computation.
I0408 07:39:41.488590 31856 net.cpp:198] relu7 needs backward computation.
I0408 07:39:41.488593 31856 net.cpp:198] fc7 needs backward computation.
I0408 07:39:41.488597 31856 net.cpp:198] drop6 needs backward computation.
I0408 07:39:41.488601 31856 net.cpp:198] relu6 needs backward computation.
I0408 07:39:41.488605 31856 net.cpp:198] fc6 needs backward computation.
I0408 07:39:41.488608 31856 net.cpp:198] pool5 needs backward computation.
I0408 07:39:41.488612 31856 net.cpp:198] relu5 needs backward computation.
I0408 07:39:41.488615 31856 net.cpp:198] conv5 needs backward computation.
I0408 07:39:41.488620 31856 net.cpp:198] relu4 needs backward computation.
I0408 07:39:41.488622 31856 net.cpp:198] conv4 needs backward computation.
I0408 07:39:41.488626 31856 net.cpp:198] relu3 needs backward computation.
I0408 07:39:41.488629 31856 net.cpp:198] conv3 needs backward computation.
I0408 07:39:41.488633 31856 net.cpp:198] pool2 needs backward computation.
I0408 07:39:41.488636 31856 net.cpp:198] norm2 needs backward computation.
I0408 07:39:41.488641 31856 net.cpp:198] relu2 needs backward computation.
I0408 07:39:41.488643 31856 net.cpp:198] conv2 needs backward computation.
I0408 07:39:41.488647 31856 net.cpp:198] pool1 needs backward computation.
I0408 07:39:41.488651 31856 net.cpp:198] norm1 needs backward computation.
I0408 07:39:41.488654 31856 net.cpp:198] relu1 needs backward computation.
I0408 07:39:41.488657 31856 net.cpp:198] conv1 needs backward computation.
I0408 07:39:41.488662 31856 net.cpp:200] train-data does not need backward computation.
I0408 07:39:41.488665 31856 net.cpp:242] This network produces output loss
I0408 07:39:41.488679 31856 net.cpp:255] Network initialization done.
I0408 07:39:41.489208 31856 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0408 07:39:41.489238 31856 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0408 07:39:41.489377 31856 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 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"
}
I0408 07:39:41.489471 31856 layer_factory.hpp:77] Creating layer val-data
I0408 07:39:41.491112 31856 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0408 07:39:41.491314 31856 net.cpp:84] Creating Layer val-data
I0408 07:39:41.491324 31856 net.cpp:380] val-data -> data
I0408 07:39:41.491333 31856 net.cpp:380] val-data -> label
I0408 07:39:41.491339 31856 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0408 07:39:41.495204 31856 data_layer.cpp:45] output data size: 32,3,227,227
I0408 07:39:41.547219 31856 net.cpp:122] Setting up val-data
I0408 07:39:41.547238 31856 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0408 07:39:41.547243 31856 net.cpp:129] Top shape: 32 (32)
I0408 07:39:41.547246 31856 net.cpp:137] Memory required for data: 19787264
I0408 07:39:41.547251 31856 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0408 07:39:41.547263 31856 net.cpp:84] Creating Layer label_val-data_1_split
I0408 07:39:41.547267 31856 net.cpp:406] label_val-data_1_split <- label
I0408 07:39:41.547274 31856 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0408 07:39:41.547282 31856 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0408 07:39:41.547331 31856 net.cpp:122] Setting up label_val-data_1_split
I0408 07:39:41.547336 31856 net.cpp:129] Top shape: 32 (32)
I0408 07:39:41.547340 31856 net.cpp:129] Top shape: 32 (32)
I0408 07:39:41.547343 31856 net.cpp:137] Memory required for data: 19787520
I0408 07:39:41.547348 31856 layer_factory.hpp:77] Creating layer conv1
I0408 07:39:41.547358 31856 net.cpp:84] Creating Layer conv1
I0408 07:39:41.547361 31856 net.cpp:406] conv1 <- data
I0408 07:39:41.547367 31856 net.cpp:380] conv1 -> conv1
I0408 07:39:41.556671 31856 net.cpp:122] Setting up conv1
I0408 07:39:41.556682 31856 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 07:39:41.556686 31856 net.cpp:137] Memory required for data: 56958720
I0408 07:39:41.556696 31856 layer_factory.hpp:77] Creating layer relu1
I0408 07:39:41.556702 31856 net.cpp:84] Creating Layer relu1
I0408 07:39:41.556706 31856 net.cpp:406] relu1 <- conv1
I0408 07:39:41.556711 31856 net.cpp:367] relu1 -> conv1 (in-place)
I0408 07:39:41.557004 31856 net.cpp:122] Setting up relu1
I0408 07:39:41.557013 31856 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 07:39:41.557016 31856 net.cpp:137] Memory required for data: 94129920
I0408 07:39:41.557019 31856 layer_factory.hpp:77] Creating layer norm1
I0408 07:39:41.557027 31856 net.cpp:84] Creating Layer norm1
I0408 07:39:41.557031 31856 net.cpp:406] norm1 <- conv1
I0408 07:39:41.557036 31856 net.cpp:380] norm1 -> norm1
I0408 07:39:41.557487 31856 net.cpp:122] Setting up norm1
I0408 07:39:41.557497 31856 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 07:39:41.557500 31856 net.cpp:137] Memory required for data: 131301120
I0408 07:39:41.557504 31856 layer_factory.hpp:77] Creating layer pool1
I0408 07:39:41.557510 31856 net.cpp:84] Creating Layer pool1
I0408 07:39:41.557514 31856 net.cpp:406] pool1 <- norm1
I0408 07:39:41.557519 31856 net.cpp:380] pool1 -> pool1
I0408 07:39:41.557547 31856 net.cpp:122] Setting up pool1
I0408 07:39:41.557552 31856 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0408 07:39:41.557555 31856 net.cpp:137] Memory required for data: 140259072
I0408 07:39:41.557559 31856 layer_factory.hpp:77] Creating layer conv2
I0408 07:39:41.557566 31856 net.cpp:84] Creating Layer conv2
I0408 07:39:41.557569 31856 net.cpp:406] conv2 <- pool1
I0408 07:39:41.557592 31856 net.cpp:380] conv2 -> conv2
I0408 07:39:41.566066 31856 net.cpp:122] Setting up conv2
I0408 07:39:41.566078 31856 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 07:39:41.566082 31856 net.cpp:137] Memory required for data: 164146944
I0408 07:39:41.566092 31856 layer_factory.hpp:77] Creating layer relu2
I0408 07:39:41.566098 31856 net.cpp:84] Creating Layer relu2
I0408 07:39:41.566102 31856 net.cpp:406] relu2 <- conv2
I0408 07:39:41.566107 31856 net.cpp:367] relu2 -> conv2 (in-place)
I0408 07:39:41.566609 31856 net.cpp:122] Setting up relu2
I0408 07:39:41.566618 31856 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 07:39:41.566622 31856 net.cpp:137] Memory required for data: 188034816
I0408 07:39:41.566625 31856 layer_factory.hpp:77] Creating layer norm2
I0408 07:39:41.566635 31856 net.cpp:84] Creating Layer norm2
I0408 07:39:41.566638 31856 net.cpp:406] norm2 <- conv2
I0408 07:39:41.566645 31856 net.cpp:380] norm2 -> norm2
I0408 07:39:41.567157 31856 net.cpp:122] Setting up norm2
I0408 07:39:41.567165 31856 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 07:39:41.567169 31856 net.cpp:137] Memory required for data: 211922688
I0408 07:39:41.567173 31856 layer_factory.hpp:77] Creating layer pool2
I0408 07:39:41.567180 31856 net.cpp:84] Creating Layer pool2
I0408 07:39:41.567184 31856 net.cpp:406] pool2 <- norm2
I0408 07:39:41.567189 31856 net.cpp:380] pool2 -> pool2
I0408 07:39:41.567221 31856 net.cpp:122] Setting up pool2
I0408 07:39:41.567226 31856 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 07:39:41.567229 31856 net.cpp:137] Memory required for data: 217460480
I0408 07:39:41.567232 31856 layer_factory.hpp:77] Creating layer conv3
I0408 07:39:41.567241 31856 net.cpp:84] Creating Layer conv3
I0408 07:39:41.567245 31856 net.cpp:406] conv3 <- pool2
I0408 07:39:41.567251 31856 net.cpp:380] conv3 -> conv3
I0408 07:39:41.578121 31856 net.cpp:122] Setting up conv3
I0408 07:39:41.578135 31856 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 07:39:41.578140 31856 net.cpp:137] Memory required for data: 225767168
I0408 07:39:41.578150 31856 layer_factory.hpp:77] Creating layer relu3
I0408 07:39:41.578158 31856 net.cpp:84] Creating Layer relu3
I0408 07:39:41.578162 31856 net.cpp:406] relu3 <- conv3
I0408 07:39:41.578168 31856 net.cpp:367] relu3 -> conv3 (in-place)
I0408 07:39:41.578680 31856 net.cpp:122] Setting up relu3
I0408 07:39:41.578688 31856 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 07:39:41.578691 31856 net.cpp:137] Memory required for data: 234073856
I0408 07:39:41.578696 31856 layer_factory.hpp:77] Creating layer conv4
I0408 07:39:41.578706 31856 net.cpp:84] Creating Layer conv4
I0408 07:39:41.578709 31856 net.cpp:406] conv4 <- conv3
I0408 07:39:41.578716 31856 net.cpp:380] conv4 -> conv4
I0408 07:39:41.588099 31856 net.cpp:122] Setting up conv4
I0408 07:39:41.588110 31856 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 07:39:41.588114 31856 net.cpp:137] Memory required for data: 242380544
I0408 07:39:41.588120 31856 layer_factory.hpp:77] Creating layer relu4
I0408 07:39:41.588127 31856 net.cpp:84] Creating Layer relu4
I0408 07:39:41.588130 31856 net.cpp:406] relu4 <- conv4
I0408 07:39:41.588140 31856 net.cpp:367] relu4 -> conv4 (in-place)
I0408 07:39:41.588479 31856 net.cpp:122] Setting up relu4
I0408 07:39:41.588487 31856 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 07:39:41.588490 31856 net.cpp:137] Memory required for data: 250687232
I0408 07:39:41.588495 31856 layer_factory.hpp:77] Creating layer conv5
I0408 07:39:41.588505 31856 net.cpp:84] Creating Layer conv5
I0408 07:39:41.588508 31856 net.cpp:406] conv5 <- conv4
I0408 07:39:41.588515 31856 net.cpp:380] conv5 -> conv5
I0408 07:39:41.599427 31856 net.cpp:122] Setting up conv5
I0408 07:39:41.599442 31856 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 07:39:41.599445 31856 net.cpp:137] Memory required for data: 256225024
I0408 07:39:41.599457 31856 layer_factory.hpp:77] Creating layer relu5
I0408 07:39:41.599465 31856 net.cpp:84] Creating Layer relu5
I0408 07:39:41.599469 31856 net.cpp:406] relu5 <- conv5
I0408 07:39:41.599493 31856 net.cpp:367] relu5 -> conv5 (in-place)
I0408 07:39:41.599979 31856 net.cpp:122] Setting up relu5
I0408 07:39:41.599988 31856 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 07:39:41.599992 31856 net.cpp:137] Memory required for data: 261762816
I0408 07:39:41.599995 31856 layer_factory.hpp:77] Creating layer pool5
I0408 07:39:41.600006 31856 net.cpp:84] Creating Layer pool5
I0408 07:39:41.600010 31856 net.cpp:406] pool5 <- conv5
I0408 07:39:41.600016 31856 net.cpp:380] pool5 -> pool5
I0408 07:39:41.600055 31856 net.cpp:122] Setting up pool5
I0408 07:39:41.600060 31856 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0408 07:39:41.600064 31856 net.cpp:137] Memory required for data: 262942464
I0408 07:39:41.600067 31856 layer_factory.hpp:77] Creating layer fc6
I0408 07:39:41.600075 31856 net.cpp:84] Creating Layer fc6
I0408 07:39:41.600077 31856 net.cpp:406] fc6 <- pool5
I0408 07:39:41.600082 31856 net.cpp:380] fc6 -> fc6
I0408 07:39:41.954593 31856 net.cpp:122] Setting up fc6
I0408 07:39:41.954614 31856 net.cpp:129] Top shape: 32 4096 (131072)
I0408 07:39:41.954618 31856 net.cpp:137] Memory required for data: 263466752
I0408 07:39:41.954628 31856 layer_factory.hpp:77] Creating layer relu6
I0408 07:39:41.954638 31856 net.cpp:84] Creating Layer relu6
I0408 07:39:41.954641 31856 net.cpp:406] relu6 <- fc6
I0408 07:39:41.954649 31856 net.cpp:367] relu6 -> fc6 (in-place)
I0408 07:39:41.955479 31856 net.cpp:122] Setting up relu6
I0408 07:39:41.955489 31856 net.cpp:129] Top shape: 32 4096 (131072)
I0408 07:39:41.955492 31856 net.cpp:137] Memory required for data: 263991040
I0408 07:39:41.955497 31856 layer_factory.hpp:77] Creating layer drop6
I0408 07:39:41.955504 31856 net.cpp:84] Creating Layer drop6
I0408 07:39:41.955508 31856 net.cpp:406] drop6 <- fc6
I0408 07:39:41.955513 31856 net.cpp:367] drop6 -> fc6 (in-place)
I0408 07:39:41.955541 31856 net.cpp:122] Setting up drop6
I0408 07:39:41.955547 31856 net.cpp:129] Top shape: 32 4096 (131072)
I0408 07:39:41.955550 31856 net.cpp:137] Memory required for data: 264515328
I0408 07:39:41.955554 31856 layer_factory.hpp:77] Creating layer fc7
I0408 07:39:41.955560 31856 net.cpp:84] Creating Layer fc7
I0408 07:39:41.955564 31856 net.cpp:406] fc7 <- fc6
I0408 07:39:41.955569 31856 net.cpp:380] fc7 -> fc7
I0408 07:39:42.112435 31856 net.cpp:122] Setting up fc7
I0408 07:39:42.112457 31856 net.cpp:129] Top shape: 32 4096 (131072)
I0408 07:39:42.112462 31856 net.cpp:137] Memory required for data: 265039616
I0408 07:39:42.112470 31856 layer_factory.hpp:77] Creating layer relu7
I0408 07:39:42.112480 31856 net.cpp:84] Creating Layer relu7
I0408 07:39:42.112485 31856 net.cpp:406] relu7 <- fc7
I0408 07:39:42.112491 31856 net.cpp:367] relu7 -> fc7 (in-place)
I0408 07:39:42.112921 31856 net.cpp:122] Setting up relu7
I0408 07:39:42.112929 31856 net.cpp:129] Top shape: 32 4096 (131072)
I0408 07:39:42.112933 31856 net.cpp:137] Memory required for data: 265563904
I0408 07:39:42.112937 31856 layer_factory.hpp:77] Creating layer drop7
I0408 07:39:42.112943 31856 net.cpp:84] Creating Layer drop7
I0408 07:39:42.112947 31856 net.cpp:406] drop7 <- fc7
I0408 07:39:42.112953 31856 net.cpp:367] drop7 -> fc7 (in-place)
I0408 07:39:42.112977 31856 net.cpp:122] Setting up drop7
I0408 07:39:42.112980 31856 net.cpp:129] Top shape: 32 4096 (131072)
I0408 07:39:42.112984 31856 net.cpp:137] Memory required for data: 266088192
I0408 07:39:42.112987 31856 layer_factory.hpp:77] Creating layer fc8
I0408 07:39:42.112995 31856 net.cpp:84] Creating Layer fc8
I0408 07:39:42.112999 31856 net.cpp:406] fc8 <- fc7
I0408 07:39:42.113005 31856 net.cpp:380] fc8 -> fc8
I0408 07:39:42.120700 31856 net.cpp:122] Setting up fc8
I0408 07:39:42.120710 31856 net.cpp:129] Top shape: 32 196 (6272)
I0408 07:39:42.120713 31856 net.cpp:137] Memory required for data: 266113280
I0408 07:39:42.120719 31856 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0408 07:39:42.120725 31856 net.cpp:84] Creating Layer fc8_fc8_0_split
I0408 07:39:42.120729 31856 net.cpp:406] fc8_fc8_0_split <- fc8
I0408 07:39:42.120754 31856 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0408 07:39:42.120760 31856 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0408 07:39:42.120790 31856 net.cpp:122] Setting up fc8_fc8_0_split
I0408 07:39:42.120795 31856 net.cpp:129] Top shape: 32 196 (6272)
I0408 07:39:42.120800 31856 net.cpp:129] Top shape: 32 196 (6272)
I0408 07:39:42.120802 31856 net.cpp:137] Memory required for data: 266163456
I0408 07:39:42.120805 31856 layer_factory.hpp:77] Creating layer accuracy
I0408 07:39:42.120811 31856 net.cpp:84] Creating Layer accuracy
I0408 07:39:42.120815 31856 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0408 07:39:42.120820 31856 net.cpp:406] accuracy <- label_val-data_1_split_0
I0408 07:39:42.120824 31856 net.cpp:380] accuracy -> accuracy
I0408 07:39:42.120832 31856 net.cpp:122] Setting up accuracy
I0408 07:39:42.120836 31856 net.cpp:129] Top shape: (1)
I0408 07:39:42.120839 31856 net.cpp:137] Memory required for data: 266163460
I0408 07:39:42.120842 31856 layer_factory.hpp:77] Creating layer loss
I0408 07:39:42.120847 31856 net.cpp:84] Creating Layer loss
I0408 07:39:42.120851 31856 net.cpp:406] loss <- fc8_fc8_0_split_1
I0408 07:39:42.120855 31856 net.cpp:406] loss <- label_val-data_1_split_1
I0408 07:39:42.120859 31856 net.cpp:380] loss -> loss
I0408 07:39:42.120867 31856 layer_factory.hpp:77] Creating layer loss
I0408 07:39:42.121454 31856 net.cpp:122] Setting up loss
I0408 07:39:42.121462 31856 net.cpp:129] Top shape: (1)
I0408 07:39:42.121465 31856 net.cpp:132]     with loss weight 1
I0408 07:39:42.121476 31856 net.cpp:137] Memory required for data: 266163464
I0408 07:39:42.121480 31856 net.cpp:198] loss needs backward computation.
I0408 07:39:42.121485 31856 net.cpp:200] accuracy does not need backward computation.
I0408 07:39:42.121490 31856 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0408 07:39:42.121492 31856 net.cpp:198] fc8 needs backward computation.
I0408 07:39:42.121495 31856 net.cpp:198] drop7 needs backward computation.
I0408 07:39:42.121498 31856 net.cpp:198] relu7 needs backward computation.
I0408 07:39:42.121501 31856 net.cpp:198] fc7 needs backward computation.
I0408 07:39:42.121505 31856 net.cpp:198] drop6 needs backward computation.
I0408 07:39:42.121508 31856 net.cpp:198] relu6 needs backward computation.
I0408 07:39:42.121511 31856 net.cpp:198] fc6 needs backward computation.
I0408 07:39:42.121515 31856 net.cpp:198] pool5 needs backward computation.
I0408 07:39:42.121520 31856 net.cpp:198] relu5 needs backward computation.
I0408 07:39:42.121522 31856 net.cpp:198] conv5 needs backward computation.
I0408 07:39:42.121526 31856 net.cpp:198] relu4 needs backward computation.
I0408 07:39:42.121529 31856 net.cpp:198] conv4 needs backward computation.
I0408 07:39:42.121532 31856 net.cpp:198] relu3 needs backward computation.
I0408 07:39:42.121536 31856 net.cpp:198] conv3 needs backward computation.
I0408 07:39:42.121539 31856 net.cpp:198] pool2 needs backward computation.
I0408 07:39:42.121543 31856 net.cpp:198] norm2 needs backward computation.
I0408 07:39:42.121546 31856 net.cpp:198] relu2 needs backward computation.
I0408 07:39:42.121549 31856 net.cpp:198] conv2 needs backward computation.
I0408 07:39:42.121553 31856 net.cpp:198] pool1 needs backward computation.
I0408 07:39:42.121556 31856 net.cpp:198] norm1 needs backward computation.
I0408 07:39:42.121560 31856 net.cpp:198] relu1 needs backward computation.
I0408 07:39:42.121563 31856 net.cpp:198] conv1 needs backward computation.
I0408 07:39:42.121567 31856 net.cpp:200] label_val-data_1_split does not need backward computation.
I0408 07:39:42.121572 31856 net.cpp:200] val-data does not need backward computation.
I0408 07:39:42.121573 31856 net.cpp:242] This network produces output accuracy
I0408 07:39:42.121577 31856 net.cpp:242] This network produces output loss
I0408 07:39:42.121594 31856 net.cpp:255] Network initialization done.
I0408 07:39:42.121662 31856 solver.cpp:56] Solver scaffolding done.
I0408 07:39:42.122084 31856 caffe.cpp:248] Starting Optimization
I0408 07:39:42.122093 31856 solver.cpp:272] Solving
I0408 07:39:42.122105 31856 solver.cpp:273] Learning Rate Policy: exp
I0408 07:39:42.123391 31856 solver.cpp:330] Iteration 0, Testing net (#0)
I0408 07:39:42.123401 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:39:42.201939 31856 blocking_queue.cpp:49] Waiting for data
I0408 07:39:46.508920 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:39:46.553926 31856 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0408 07:39:46.553982 31856 solver.cpp:397]     Test net output #1: loss = 5.28147 (* 1 = 5.28147 loss)
I0408 07:39:46.655428 31856 solver.cpp:218] Iteration 0 (2.81634e+37 iter/s, 4.53312s/12 iters), loss = 5.2668
I0408 07:39:46.656940 31856 solver.cpp:237]     Train net output #0: loss = 5.2668 (* 1 = 5.2668 loss)
I0408 07:39:46.656958 31856 sgd_solver.cpp:105] Iteration 0, lr = 0.1
I0408 07:39:50.715729 31856 solver.cpp:218] Iteration 12 (2.95666 iter/s, 4.05863s/12 iters), loss = 5.33498
I0408 07:39:50.715775 31856 solver.cpp:237]     Train net output #0: loss = 5.33498 (* 1 = 5.33498 loss)
I0408 07:39:50.715787 31856 sgd_solver.cpp:105] Iteration 12, lr = 0.0987681
I0408 07:39:55.664290 31856 solver.cpp:218] Iteration 24 (2.42506 iter/s, 4.94834s/12 iters), loss = 5.31827
I0408 07:39:55.664324 31856 solver.cpp:237]     Train net output #0: loss = 5.31827 (* 1 = 5.31827 loss)
I0408 07:39:55.664332 31856 sgd_solver.cpp:105] Iteration 24, lr = 0.0975514
I0408 07:40:00.598390 31856 solver.cpp:218] Iteration 36 (2.43217 iter/s, 4.93387s/12 iters), loss = 5.30907
I0408 07:40:00.598469 31856 solver.cpp:237]     Train net output #0: loss = 5.30907 (* 1 = 5.30907 loss)
I0408 07:40:00.598486 31856 sgd_solver.cpp:105] Iteration 36, lr = 0.0963497
I0408 07:40:05.611399 31856 solver.cpp:218] Iteration 48 (2.39389 iter/s, 5.01275s/12 iters), loss = 5.29501
I0408 07:40:05.611444 31856 solver.cpp:237]     Train net output #0: loss = 5.29501 (* 1 = 5.29501 loss)
I0408 07:40:05.611456 31856 sgd_solver.cpp:105] Iteration 48, lr = 0.0951628
I0408 07:40:10.632692 31856 solver.cpp:218] Iteration 60 (2.38993 iter/s, 5.02107s/12 iters), loss = 5.29672
I0408 07:40:10.632872 31856 solver.cpp:237]     Train net output #0: loss = 5.29672 (* 1 = 5.29672 loss)
I0408 07:40:10.632886 31856 sgd_solver.cpp:105] Iteration 60, lr = 0.0939905
I0408 07:40:15.576858 31856 solver.cpp:218] Iteration 72 (2.42728 iter/s, 4.94381s/12 iters), loss = 5.29647
I0408 07:40:15.576903 31856 solver.cpp:237]     Train net output #0: loss = 5.29647 (* 1 = 5.29647 loss)
I0408 07:40:15.576913 31856 sgd_solver.cpp:105] Iteration 72, lr = 0.0928326
I0408 07:40:20.449589 31856 solver.cpp:218] Iteration 84 (2.4628 iter/s, 4.87251s/12 iters), loss = 5.28835
I0408 07:40:20.449625 31856 solver.cpp:237]     Train net output #0: loss = 5.28835 (* 1 = 5.28835 loss)
I0408 07:40:20.449633 31856 sgd_solver.cpp:105] Iteration 84, lr = 0.091689
I0408 07:40:25.463470 31856 solver.cpp:218] Iteration 96 (2.39346 iter/s, 5.01366s/12 iters), loss = 5.29692
I0408 07:40:25.463523 31856 solver.cpp:237]     Train net output #0: loss = 5.29692 (* 1 = 5.29692 loss)
I0408 07:40:25.463536 31856 sgd_solver.cpp:105] Iteration 96, lr = 0.0905595
I0408 07:40:27.178887 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:40:27.532021 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0408 07:40:30.594744 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0408 07:40:32.882464 31856 solver.cpp:330] Iteration 102, Testing net (#0)
I0408 07:40:32.882488 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:40:37.222254 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:40:37.299163 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:40:37.299209 31856 solver.cpp:397]     Test net output #1: loss = 5.28295 (* 1 = 5.28295 loss)
I0408 07:40:39.151324 31856 solver.cpp:218] Iteration 108 (0.876723 iter/s, 13.6873s/12 iters), loss = 5.29993
I0408 07:40:39.151361 31856 solver.cpp:237]     Train net output #0: loss = 5.29993 (* 1 = 5.29993 loss)
I0408 07:40:39.151369 31856 sgd_solver.cpp:105] Iteration 108, lr = 0.0894439
I0408 07:40:44.105979 31856 solver.cpp:218] Iteration 120 (2.42207 iter/s, 4.95444s/12 iters), loss = 5.28416
I0408 07:40:44.106108 31856 solver.cpp:237]     Train net output #0: loss = 5.28416 (* 1 = 5.28416 loss)
I0408 07:40:44.106122 31856 sgd_solver.cpp:105] Iteration 120, lr = 0.0883421
I0408 07:40:49.201555 31856 solver.cpp:218] Iteration 132 (2.35512 iter/s, 5.09527s/12 iters), loss = 5.25178
I0408 07:40:49.201593 31856 solver.cpp:237]     Train net output #0: loss = 5.25178 (* 1 = 5.25178 loss)
I0408 07:40:49.201602 31856 sgd_solver.cpp:105] Iteration 132, lr = 0.0872538
I0408 07:40:54.218611 31856 solver.cpp:218] Iteration 144 (2.39195 iter/s, 5.01683s/12 iters), loss = 5.29956
I0408 07:40:54.218664 31856 solver.cpp:237]     Train net output #0: loss = 5.29956 (* 1 = 5.29956 loss)
I0408 07:40:54.218677 31856 sgd_solver.cpp:105] Iteration 144, lr = 0.086179
I0408 07:40:59.175700 31856 solver.cpp:218] Iteration 156 (2.42088 iter/s, 4.95687s/12 iters), loss = 5.26616
I0408 07:40:59.175732 31856 solver.cpp:237]     Train net output #0: loss = 5.26616 (* 1 = 5.26616 loss)
I0408 07:40:59.175740 31856 sgd_solver.cpp:105] Iteration 156, lr = 0.0851173
I0408 07:41:04.153755 31856 solver.cpp:218] Iteration 168 (2.41068 iter/s, 4.97784s/12 iters), loss = 5.26727
I0408 07:41:04.153801 31856 solver.cpp:237]     Train net output #0: loss = 5.26727 (* 1 = 5.26727 loss)
I0408 07:41:04.153812 31856 sgd_solver.cpp:105] Iteration 168, lr = 0.0840688
I0408 07:41:09.166651 31856 solver.cpp:218] Iteration 180 (2.39393 iter/s, 5.01268s/12 iters), loss = 5.26742
I0408 07:41:09.166687 31856 solver.cpp:237]     Train net output #0: loss = 5.26742 (* 1 = 5.26742 loss)
I0408 07:41:09.166697 31856 sgd_solver.cpp:105] Iteration 180, lr = 0.0830332
I0408 07:41:14.096338 31856 solver.cpp:218] Iteration 192 (2.43434 iter/s, 4.92947s/12 iters), loss = 5.27359
I0408 07:41:14.096387 31856 solver.cpp:237]     Train net output #0: loss = 5.27359 (* 1 = 5.27359 loss)
I0408 07:41:14.096398 31856 sgd_solver.cpp:105] Iteration 192, lr = 0.0820103
I0408 07:41:18.063547 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:41:18.733198 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0408 07:41:21.693964 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0408 07:41:24.017093 31856 solver.cpp:330] Iteration 204, Testing net (#0)
I0408 07:41:24.017117 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:41:28.366246 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:41:28.489265 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:41:28.489312 31856 solver.cpp:397]     Test net output #1: loss = 5.28525 (* 1 = 5.28525 loss)
I0408 07:41:28.578266 31856 solver.cpp:218] Iteration 204 (0.82865 iter/s, 14.4814s/12 iters), loss = 5.26982
I0408 07:41:28.578316 31856 solver.cpp:237]     Train net output #0: loss = 5.26982 (* 1 = 5.26982 loss)
I0408 07:41:28.578328 31856 sgd_solver.cpp:105] Iteration 204, lr = 0.081
I0408 07:41:32.791604 31856 solver.cpp:218] Iteration 216 (2.84823 iter/s, 4.21314s/12 iters), loss = 5.28074
I0408 07:41:32.791640 31856 solver.cpp:237]     Train net output #0: loss = 5.28074 (* 1 = 5.28074 loss)
I0408 07:41:32.791649 31856 sgd_solver.cpp:105] Iteration 216, lr = 0.0800022
I0408 07:41:37.777174 31856 solver.cpp:218] Iteration 228 (2.40705 iter/s, 4.98536s/12 iters), loss = 5.26192
I0408 07:41:37.777221 31856 solver.cpp:237]     Train net output #0: loss = 5.26192 (* 1 = 5.26192 loss)
I0408 07:41:37.777233 31856 sgd_solver.cpp:105] Iteration 228, lr = 0.0790167
I0408 07:41:42.742178 31856 solver.cpp:218] Iteration 240 (2.41702 iter/s, 4.96478s/12 iters), loss = 5.28366
I0408 07:41:42.742224 31856 solver.cpp:237]     Train net output #0: loss = 5.28366 (* 1 = 5.28366 loss)
I0408 07:41:42.742236 31856 sgd_solver.cpp:105] Iteration 240, lr = 0.0780433
I0408 07:41:47.727130 31856 solver.cpp:218] Iteration 252 (2.40735 iter/s, 4.98473s/12 iters), loss = 5.26868
I0408 07:41:47.727175 31856 solver.cpp:237]     Train net output #0: loss = 5.26868 (* 1 = 5.26868 loss)
I0408 07:41:47.727188 31856 sgd_solver.cpp:105] Iteration 252, lr = 0.0770819
I0408 07:41:52.683199 31856 solver.cpp:218] Iteration 264 (2.42138 iter/s, 4.95585s/12 iters), loss = 5.26947
I0408 07:41:52.683341 31856 solver.cpp:237]     Train net output #0: loss = 5.26947 (* 1 = 5.26947 loss)
I0408 07:41:52.683354 31856 sgd_solver.cpp:105] Iteration 264, lr = 0.0761323
I0408 07:41:57.657167 31856 solver.cpp:218] Iteration 276 (2.41271 iter/s, 4.97365s/12 iters), loss = 5.29241
I0408 07:41:57.657214 31856 solver.cpp:237]     Train net output #0: loss = 5.29241 (* 1 = 5.29241 loss)
I0408 07:41:57.657227 31856 sgd_solver.cpp:105] Iteration 276, lr = 0.0751944
I0408 07:42:02.617693 31856 solver.cpp:218] Iteration 288 (2.41921 iter/s, 4.9603s/12 iters), loss = 5.2836
I0408 07:42:02.617745 31856 solver.cpp:237]     Train net output #0: loss = 5.2836 (* 1 = 5.2836 loss)
I0408 07:42:02.617755 31856 sgd_solver.cpp:105] Iteration 288, lr = 0.0742681
I0408 07:42:07.568352 31856 solver.cpp:218] Iteration 300 (2.42403 iter/s, 4.95044s/12 iters), loss = 5.283
I0408 07:42:07.568388 31856 solver.cpp:237]     Train net output #0: loss = 5.283 (* 1 = 5.283 loss)
I0408 07:42:07.568395 31856 sgd_solver.cpp:105] Iteration 300, lr = 0.0733532
I0408 07:42:08.549046 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:42:09.605105 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0408 07:42:12.591753 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0408 07:42:14.916762 31856 solver.cpp:330] Iteration 306, Testing net (#0)
I0408 07:42:14.916785 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:42:19.215107 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:42:19.372702 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:42:19.372747 31856 solver.cpp:397]     Test net output #1: loss = 5.28542 (* 1 = 5.28542 loss)
I0408 07:42:21.217432 31856 solver.cpp:218] Iteration 312 (0.879212 iter/s, 13.6486s/12 iters), loss = 5.28521
I0408 07:42:21.217470 31856 solver.cpp:237]     Train net output #0: loss = 5.28521 (* 1 = 5.28521 loss)
I0408 07:42:21.217478 31856 sgd_solver.cpp:105] Iteration 312, lr = 0.0724496
I0408 07:42:26.198144 31856 solver.cpp:218] Iteration 324 (2.4094 iter/s, 4.9805s/12 iters), loss = 5.24894
I0408 07:42:26.198247 31856 solver.cpp:237]     Train net output #0: loss = 5.24894 (* 1 = 5.24894 loss)
I0408 07:42:26.198261 31856 sgd_solver.cpp:105] Iteration 324, lr = 0.0715571
I0408 07:42:31.194491 31856 solver.cpp:218] Iteration 336 (2.40189 iter/s, 4.99607s/12 iters), loss = 5.26241
I0408 07:42:31.194535 31856 solver.cpp:237]     Train net output #0: loss = 5.26241 (* 1 = 5.26241 loss)
I0408 07:42:31.194546 31856 sgd_solver.cpp:105] Iteration 336, lr = 0.0706756
I0408 07:42:36.153450 31856 solver.cpp:218] Iteration 348 (2.41997 iter/s, 4.95874s/12 iters), loss = 5.26733
I0408 07:42:36.153493 31856 solver.cpp:237]     Train net output #0: loss = 5.26733 (* 1 = 5.26733 loss)
I0408 07:42:36.153504 31856 sgd_solver.cpp:105] Iteration 348, lr = 0.069805
I0408 07:42:41.699478 31856 solver.cpp:218] Iteration 360 (2.1638 iter/s, 5.5458s/12 iters), loss = 5.29264
I0408 07:42:41.699527 31856 solver.cpp:237]     Train net output #0: loss = 5.29264 (* 1 = 5.29264 loss)
I0408 07:42:41.699539 31856 sgd_solver.cpp:105] Iteration 360, lr = 0.0689451
I0408 07:42:46.822773 31856 solver.cpp:218] Iteration 372 (2.34234 iter/s, 5.12308s/12 iters), loss = 5.27355
I0408 07:42:46.822818 31856 solver.cpp:237]     Train net output #0: loss = 5.27355 (* 1 = 5.27355 loss)
I0408 07:42:46.822830 31856 sgd_solver.cpp:105] Iteration 372, lr = 0.0680957
I0408 07:42:51.789793 31856 solver.cpp:218] Iteration 384 (2.41604 iter/s, 4.96681s/12 iters), loss = 5.27866
I0408 07:42:51.789824 31856 solver.cpp:237]     Train net output #0: loss = 5.27866 (* 1 = 5.27866 loss)
I0408 07:42:51.789832 31856 sgd_solver.cpp:105] Iteration 384, lr = 0.0672569
I0408 07:42:56.872190 31856 solver.cpp:218] Iteration 396 (2.36119 iter/s, 5.08219s/12 iters), loss = 5.27202
I0408 07:42:56.872310 31856 solver.cpp:237]     Train net output #0: loss = 5.27202 (* 1 = 5.27202 loss)
I0408 07:42:56.872323 31856 sgd_solver.cpp:105] Iteration 396, lr = 0.0664283
I0408 07:43:00.002873 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:43:01.420270 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0408 07:43:04.410343 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0408 07:43:06.729014 31856 solver.cpp:330] Iteration 408, Testing net (#0)
I0408 07:43:06.729040 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:43:10.995857 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:43:11.199187 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:43:11.199234 31856 solver.cpp:397]     Test net output #1: loss = 5.28681 (* 1 = 5.28681 loss)
I0408 07:43:11.286504 31856 solver.cpp:218] Iteration 408 (0.832539 iter/s, 14.4137s/12 iters), loss = 5.28324
I0408 07:43:11.286543 31856 solver.cpp:237]     Train net output #0: loss = 5.28324 (* 1 = 5.28324 loss)
I0408 07:43:11.286553 31856 sgd_solver.cpp:105] Iteration 408, lr = 0.06561
I0408 07:43:15.606535 31856 solver.cpp:218] Iteration 420 (2.77788 iter/s, 4.31985s/12 iters), loss = 5.27509
I0408 07:43:15.606582 31856 solver.cpp:237]     Train net output #0: loss = 5.27509 (* 1 = 5.27509 loss)
I0408 07:43:15.606595 31856 sgd_solver.cpp:105] Iteration 420, lr = 0.0648018
I0408 07:43:20.614491 31856 solver.cpp:218] Iteration 432 (2.39629 iter/s, 5.00774s/12 iters), loss = 5.26868
I0408 07:43:20.614539 31856 solver.cpp:237]     Train net output #0: loss = 5.26868 (* 1 = 5.26868 loss)
I0408 07:43:20.614550 31856 sgd_solver.cpp:105] Iteration 432, lr = 0.0640035
I0408 07:43:25.596621 31856 solver.cpp:218] Iteration 444 (2.40871 iter/s, 4.98191s/12 iters), loss = 5.289
I0408 07:43:25.596670 31856 solver.cpp:237]     Train net output #0: loss = 5.289 (* 1 = 5.289 loss)
I0408 07:43:25.596681 31856 sgd_solver.cpp:105] Iteration 444, lr = 0.0632151
I0408 07:43:30.549934 31856 solver.cpp:218] Iteration 456 (2.42273 iter/s, 4.9531s/12 iters), loss = 5.2839
I0408 07:43:30.550068 31856 solver.cpp:237]     Train net output #0: loss = 5.2839 (* 1 = 5.2839 loss)
I0408 07:43:30.550082 31856 sgd_solver.cpp:105] Iteration 456, lr = 0.0624363
I0408 07:43:35.528946 31856 solver.cpp:218] Iteration 468 (2.41026 iter/s, 4.97872s/12 iters), loss = 5.28698
I0408 07:43:35.528993 31856 solver.cpp:237]     Train net output #0: loss = 5.28698 (* 1 = 5.28698 loss)
I0408 07:43:35.529004 31856 sgd_solver.cpp:105] Iteration 468, lr = 0.0616672
I0408 07:43:40.442513 31856 solver.cpp:218] Iteration 480 (2.44232 iter/s, 4.91336s/12 iters), loss = 5.26481
I0408 07:43:40.442565 31856 solver.cpp:237]     Train net output #0: loss = 5.26481 (* 1 = 5.26481 loss)
I0408 07:43:40.442577 31856 sgd_solver.cpp:105] Iteration 480, lr = 0.0609075
I0408 07:43:45.412904 31856 solver.cpp:218] Iteration 492 (2.4144 iter/s, 4.97018s/12 iters), loss = 5.29332
I0408 07:43:45.412948 31856 solver.cpp:237]     Train net output #0: loss = 5.29332 (* 1 = 5.29332 loss)
I0408 07:43:45.412959 31856 sgd_solver.cpp:105] Iteration 492, lr = 0.0601572
I0408 07:43:50.389490 31856 solver.cpp:218] Iteration 504 (2.41139 iter/s, 4.97638s/12 iters), loss = 5.26921
I0408 07:43:50.389547 31856 solver.cpp:237]     Train net output #0: loss = 5.26921 (* 1 = 5.26921 loss)
I0408 07:43:50.389564 31856 sgd_solver.cpp:105] Iteration 504, lr = 0.0594161
I0408 07:43:50.630673 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:43:52.344296 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0408 07:43:55.365569 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0408 07:43:57.690620 31856 solver.cpp:330] Iteration 510, Testing net (#0)
I0408 07:43:57.690644 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:44:01.912189 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:44:02.150765 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0408 07:44:02.150813 31856 solver.cpp:397]     Test net output #1: loss = 5.28651 (* 1 = 5.28651 loss)
I0408 07:44:04.113329 31856 solver.cpp:218] Iteration 516 (0.874421 iter/s, 13.7234s/12 iters), loss = 5.28023
I0408 07:44:04.113371 31856 solver.cpp:237]     Train net output #0: loss = 5.28023 (* 1 = 5.28023 loss)
I0408 07:44:04.113381 31856 sgd_solver.cpp:105] Iteration 516, lr = 0.0586842
I0408 07:44:09.004204 31856 solver.cpp:218] Iteration 528 (2.45365 iter/s, 4.89067s/12 iters), loss = 5.27449
I0408 07:44:09.004249 31856 solver.cpp:237]     Train net output #0: loss = 5.27449 (* 1 = 5.27449 loss)
I0408 07:44:09.004261 31856 sgd_solver.cpp:105] Iteration 528, lr = 0.0579613
I0408 07:44:13.914484 31856 solver.cpp:218] Iteration 540 (2.44395 iter/s, 4.91008s/12 iters), loss = 5.27706
I0408 07:44:13.914527 31856 solver.cpp:237]     Train net output #0: loss = 5.27706 (* 1 = 5.27706 loss)
I0408 07:44:13.914539 31856 sgd_solver.cpp:105] Iteration 540, lr = 0.0572473
I0408 07:44:18.869307 31856 solver.cpp:218] Iteration 552 (2.42198 iter/s, 4.95462s/12 iters), loss = 5.27326
I0408 07:44:18.869352 31856 solver.cpp:237]     Train net output #0: loss = 5.27326 (* 1 = 5.27326 loss)
I0408 07:44:18.869364 31856 sgd_solver.cpp:105] Iteration 552, lr = 0.056542
I0408 07:44:23.918439 31856 solver.cpp:218] Iteration 564 (2.37674 iter/s, 5.04893s/12 iters), loss = 5.25699
I0408 07:44:23.918483 31856 solver.cpp:237]     Train net output #0: loss = 5.25699 (* 1 = 5.25699 loss)
I0408 07:44:23.918496 31856 sgd_solver.cpp:105] Iteration 564, lr = 0.0558455
I0408 07:44:28.857138 31856 solver.cpp:218] Iteration 576 (2.42989 iter/s, 4.9385s/12 iters), loss = 5.27907
I0408 07:44:28.857184 31856 solver.cpp:237]     Train net output #0: loss = 5.27907 (* 1 = 5.27907 loss)
I0408 07:44:28.857195 31856 sgd_solver.cpp:105] Iteration 576, lr = 0.0551576
I0408 07:44:33.869105 31856 solver.cpp:218] Iteration 588 (2.39437 iter/s, 5.01177s/12 iters), loss = 5.26557
I0408 07:44:33.869199 31856 solver.cpp:237]     Train net output #0: loss = 5.26557 (* 1 = 5.26557 loss)
I0408 07:44:33.869208 31856 sgd_solver.cpp:105] Iteration 588, lr = 0.0544781
I0408 07:44:38.866616 31856 solver.cpp:218] Iteration 600 (2.40132 iter/s, 4.99726s/12 iters), loss = 5.26111
I0408 07:44:38.866660 31856 solver.cpp:237]     Train net output #0: loss = 5.26111 (* 1 = 5.26111 loss)
I0408 07:44:38.866672 31856 sgd_solver.cpp:105] Iteration 600, lr = 0.053807
I0408 07:44:41.269970 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:44:43.418272 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0408 07:44:46.444160 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0408 07:44:48.770545 31856 solver.cpp:330] Iteration 612, Testing net (#0)
I0408 07:44:48.770570 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:44:52.957783 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:44:53.242952 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:44:53.243000 31856 solver.cpp:397]     Test net output #1: loss = 5.28661 (* 1 = 5.28661 loss)
I0408 07:44:53.332844 31856 solver.cpp:218] Iteration 612 (0.829546 iter/s, 14.4658s/12 iters), loss = 5.27657
I0408 07:44:53.332890 31856 solver.cpp:237]     Train net output #0: loss = 5.27657 (* 1 = 5.27657 loss)
I0408 07:44:53.332901 31856 sgd_solver.cpp:105] Iteration 612, lr = 0.0531441
I0408 07:44:57.686480 31856 solver.cpp:218] Iteration 624 (2.75643 iter/s, 4.35345s/12 iters), loss = 5.29212
I0408 07:44:57.686525 31856 solver.cpp:237]     Train net output #0: loss = 5.29212 (* 1 = 5.29212 loss)
I0408 07:44:57.686537 31856 sgd_solver.cpp:105] Iteration 624, lr = 0.0524895
I0408 07:45:02.857689 31856 solver.cpp:218] Iteration 636 (2.32064 iter/s, 5.17099s/12 iters), loss = 5.28739
I0408 07:45:02.857740 31856 solver.cpp:237]     Train net output #0: loss = 5.28739 (* 1 = 5.28739 loss)
I0408 07:45:02.857753 31856 sgd_solver.cpp:105] Iteration 636, lr = 0.0518428
I0408 07:45:07.835764 31856 solver.cpp:218] Iteration 648 (2.41067 iter/s, 4.97787s/12 iters), loss = 5.27454
I0408 07:45:07.835893 31856 solver.cpp:237]     Train net output #0: loss = 5.27454 (* 1 = 5.27454 loss)
I0408 07:45:07.835904 31856 sgd_solver.cpp:105] Iteration 648, lr = 0.0512042
I0408 07:45:12.880250 31856 solver.cpp:218] Iteration 660 (2.37897 iter/s, 5.0442s/12 iters), loss = 5.26884
I0408 07:45:12.880295 31856 solver.cpp:237]     Train net output #0: loss = 5.26884 (* 1 = 5.26884 loss)
I0408 07:45:12.880304 31856 sgd_solver.cpp:105] Iteration 660, lr = 0.0505734
I0408 07:45:17.933501 31856 solver.cpp:218] Iteration 672 (2.3748 iter/s, 5.05305s/12 iters), loss = 5.27694
I0408 07:45:17.933538 31856 solver.cpp:237]     Train net output #0: loss = 5.27694 (* 1 = 5.27694 loss)
I0408 07:45:17.933548 31856 sgd_solver.cpp:105] Iteration 672, lr = 0.0499504
I0408 07:45:22.986377 31856 solver.cpp:218] Iteration 684 (2.37498 iter/s, 5.05267s/12 iters), loss = 5.27601
I0408 07:45:22.986426 31856 solver.cpp:237]     Train net output #0: loss = 5.27601 (* 1 = 5.27601 loss)
I0408 07:45:22.986438 31856 sgd_solver.cpp:105] Iteration 684, lr = 0.0493351
I0408 07:45:23.812376 31856 blocking_queue.cpp:49] Waiting for data
I0408 07:45:27.989174 31856 solver.cpp:218] Iteration 696 (2.39876 iter/s, 5.00259s/12 iters), loss = 5.27049
I0408 07:45:27.989223 31856 solver.cpp:237]     Train net output #0: loss = 5.27049 (* 1 = 5.27049 loss)
I0408 07:45:27.989234 31856 sgd_solver.cpp:105] Iteration 696, lr = 0.0487273
I0408 07:45:32.612839 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:45:32.989018 31856 solver.cpp:218] Iteration 708 (2.40018 iter/s, 4.99963s/12 iters), loss = 5.25811
I0408 07:45:32.989063 31856 solver.cpp:237]     Train net output #0: loss = 5.25811 (* 1 = 5.25811 loss)
I0408 07:45:32.989073 31856 sgd_solver.cpp:105] Iteration 708, lr = 0.0481271
I0408 07:45:35.031399 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0408 07:45:38.025775 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0408 07:45:40.360303 31856 solver.cpp:330] Iteration 714, Testing net (#0)
I0408 07:45:40.360328 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:45:44.501019 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:45:44.820711 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:45:44.820760 31856 solver.cpp:397]     Test net output #1: loss = 5.28761 (* 1 = 5.28761 loss)
I0408 07:45:46.816815 31856 solver.cpp:218] Iteration 720 (0.867846 iter/s, 13.8273s/12 iters), loss = 5.27272
I0408 07:45:46.816864 31856 solver.cpp:237]     Train net output #0: loss = 5.27272 (* 1 = 5.27272 loss)
I0408 07:45:46.816876 31856 sgd_solver.cpp:105] Iteration 720, lr = 0.0475342
I0408 07:45:52.155390 31856 solver.cpp:218] Iteration 732 (2.24788 iter/s, 5.33836s/12 iters), loss = 5.27948
I0408 07:45:52.155438 31856 solver.cpp:237]     Train net output #0: loss = 5.27948 (* 1 = 5.27948 loss)
I0408 07:45:52.155450 31856 sgd_solver.cpp:105] Iteration 732, lr = 0.0469486
I0408 07:45:57.152434 31856 solver.cpp:218] Iteration 744 (2.40152 iter/s, 4.99684s/12 iters), loss = 5.27799
I0408 07:45:57.152483 31856 solver.cpp:237]     Train net output #0: loss = 5.27799 (* 1 = 5.27799 loss)
I0408 07:45:57.152496 31856 sgd_solver.cpp:105] Iteration 744, lr = 0.0463703
I0408 07:46:02.025661 31856 solver.cpp:218] Iteration 756 (2.46254 iter/s, 4.87302s/12 iters), loss = 5.27614
I0408 07:46:02.025712 31856 solver.cpp:237]     Train net output #0: loss = 5.27614 (* 1 = 5.27614 loss)
I0408 07:46:02.025724 31856 sgd_solver.cpp:105] Iteration 756, lr = 0.0457991
I0408 07:46:07.016105 31856 solver.cpp:218] Iteration 768 (2.40469 iter/s, 4.99024s/12 iters), loss = 5.27993
I0408 07:46:07.016155 31856 solver.cpp:237]     Train net output #0: loss = 5.27993 (* 1 = 5.27993 loss)
I0408 07:46:07.016166 31856 sgd_solver.cpp:105] Iteration 768, lr = 0.0452349
I0408 07:46:12.147337 31856 solver.cpp:218] Iteration 780 (2.33871 iter/s, 5.13102s/12 iters), loss = 5.26595
I0408 07:46:12.147471 31856 solver.cpp:237]     Train net output #0: loss = 5.26595 (* 1 = 5.26595 loss)
I0408 07:46:12.147485 31856 sgd_solver.cpp:105] Iteration 780, lr = 0.0446776
I0408 07:46:17.147652 31856 solver.cpp:218] Iteration 792 (2.39998 iter/s, 5.00003s/12 iters), loss = 5.26701
I0408 07:46:17.147686 31856 solver.cpp:237]     Train net output #0: loss = 5.26701 (* 1 = 5.26701 loss)
I0408 07:46:17.147694 31856 sgd_solver.cpp:105] Iteration 792, lr = 0.0441272
I0408 07:46:22.119532 31856 solver.cpp:218] Iteration 804 (2.41367 iter/s, 4.97169s/12 iters), loss = 5.28888
I0408 07:46:22.119581 31856 solver.cpp:237]     Train net output #0: loss = 5.28888 (* 1 = 5.28888 loss)
I0408 07:46:22.119593 31856 sgd_solver.cpp:105] Iteration 804, lr = 0.0435837
I0408 07:46:23.962733 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:46:26.778221 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0408 07:46:29.806574 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0408 07:46:32.133385 31856 solver.cpp:330] Iteration 816, Testing net (#0)
I0408 07:46:32.133410 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:46:36.245563 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:46:36.600198 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:46:36.600248 31856 solver.cpp:397]     Test net output #1: loss = 5.28754 (* 1 = 5.28754 loss)
I0408 07:46:36.690506 31856 solver.cpp:218] Iteration 816 (0.823582 iter/s, 14.5705s/12 iters), loss = 5.27764
I0408 07:46:36.690557 31856 solver.cpp:237]     Train net output #0: loss = 5.27764 (* 1 = 5.27764 loss)
I0408 07:46:36.690568 31856 sgd_solver.cpp:105] Iteration 816, lr = 0.0430468
I0408 07:46:40.928126 31856 solver.cpp:218] Iteration 828 (2.8319 iter/s, 4.23744s/12 iters), loss = 5.28256
I0408 07:46:40.928176 31856 solver.cpp:237]     Train net output #0: loss = 5.28256 (* 1 = 5.28256 loss)
I0408 07:46:40.928189 31856 sgd_solver.cpp:105] Iteration 828, lr = 0.0425165
I0408 07:46:45.871578 31856 solver.cpp:218] Iteration 840 (2.42755 iter/s, 4.94325s/12 iters), loss = 5.22944
I0408 07:46:45.874416 31856 solver.cpp:237]     Train net output #0: loss = 5.22944 (* 1 = 5.22944 loss)
I0408 07:46:45.874433 31856 sgd_solver.cpp:105] Iteration 840, lr = 0.0419927
I0408 07:46:50.863304 31856 solver.cpp:218] Iteration 852 (2.40542 iter/s, 4.98874s/12 iters), loss = 5.30188
I0408 07:46:50.863353 31856 solver.cpp:237]     Train net output #0: loss = 5.30188 (* 1 = 5.30188 loss)
I0408 07:46:50.863364 31856 sgd_solver.cpp:105] Iteration 852, lr = 0.0414754
I0408 07:46:55.891186 31856 solver.cpp:218] Iteration 864 (2.38679 iter/s, 5.02768s/12 iters), loss = 5.26192
I0408 07:46:55.891232 31856 solver.cpp:237]     Train net output #0: loss = 5.26192 (* 1 = 5.26192 loss)
I0408 07:46:55.891242 31856 sgd_solver.cpp:105] Iteration 864, lr = 0.0409645
I0408 07:47:00.956457 31856 solver.cpp:218] Iteration 876 (2.36917 iter/s, 5.06507s/12 iters), loss = 5.26986
I0408 07:47:00.956511 31856 solver.cpp:237]     Train net output #0: loss = 5.26986 (* 1 = 5.26986 loss)
I0408 07:47:00.956524 31856 sgd_solver.cpp:105] Iteration 876, lr = 0.0404598
I0408 07:47:05.987921 31856 solver.cpp:218] Iteration 888 (2.38509 iter/s, 5.03125s/12 iters), loss = 5.2656
I0408 07:47:05.987957 31856 solver.cpp:237]     Train net output #0: loss = 5.2656 (* 1 = 5.2656 loss)
I0408 07:47:05.987965 31856 sgd_solver.cpp:105] Iteration 888, lr = 0.0399614
I0408 07:47:10.969987 31856 solver.cpp:218] Iteration 900 (2.40873 iter/s, 4.98187s/12 iters), loss = 5.27382
I0408 07:47:10.970032 31856 solver.cpp:237]     Train net output #0: loss = 5.27382 (* 1 = 5.27382 loss)
I0408 07:47:10.970043 31856 sgd_solver.cpp:105] Iteration 900, lr = 0.0394692
I0408 07:47:14.765868 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:47:15.885040 31856 solver.cpp:218] Iteration 912 (2.44158 iter/s, 4.91485s/12 iters), loss = 5.25793
I0408 07:47:15.885174 31856 solver.cpp:237]     Train net output #0: loss = 5.25793 (* 1 = 5.25793 loss)
I0408 07:47:15.885183 31856 sgd_solver.cpp:105] Iteration 912, lr = 0.0389829
I0408 07:47:17.865926 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0408 07:47:20.864789 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0408 07:47:23.180239 31856 solver.cpp:330] Iteration 918, Testing net (#0)
I0408 07:47:23.180263 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:47:27.180380 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:47:27.582005 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:47:27.582041 31856 solver.cpp:397]     Test net output #1: loss = 5.2874 (* 1 = 5.2874 loss)
I0408 07:47:29.492398 31856 solver.cpp:218] Iteration 924 (0.881911 iter/s, 13.6068s/12 iters), loss = 5.28481
I0408 07:47:29.492432 31856 solver.cpp:237]     Train net output #0: loss = 5.28481 (* 1 = 5.28481 loss)
I0408 07:47:29.492441 31856 sgd_solver.cpp:105] Iteration 924, lr = 0.0385027
I0408 07:47:34.502605 31856 solver.cpp:218] Iteration 936 (2.3952 iter/s, 5.01001s/12 iters), loss = 5.26078
I0408 07:47:34.502652 31856 solver.cpp:237]     Train net output #0: loss = 5.26078 (* 1 = 5.26078 loss)
I0408 07:47:34.502663 31856 sgd_solver.cpp:105] Iteration 936, lr = 0.0380284
I0408 07:47:39.531824 31856 solver.cpp:218] Iteration 948 (2.38615 iter/s, 5.02901s/12 iters), loss = 5.28849
I0408 07:47:39.531870 31856 solver.cpp:237]     Train net output #0: loss = 5.28849 (* 1 = 5.28849 loss)
I0408 07:47:39.531883 31856 sgd_solver.cpp:105] Iteration 948, lr = 0.0375599
I0408 07:47:44.534422 31856 solver.cpp:218] Iteration 960 (2.39885 iter/s, 5.00239s/12 iters), loss = 5.26028
I0408 07:47:44.534469 31856 solver.cpp:237]     Train net output #0: loss = 5.26028 (* 1 = 5.26028 loss)
I0408 07:47:44.534482 31856 sgd_solver.cpp:105] Iteration 960, lr = 0.0370972
I0408 07:47:49.540148 31856 solver.cpp:218] Iteration 972 (2.39736 iter/s, 5.00551s/12 iters), loss = 5.27398
I0408 07:47:49.540299 31856 solver.cpp:237]     Train net output #0: loss = 5.27398 (* 1 = 5.27398 loss)
I0408 07:47:49.540314 31856 sgd_solver.cpp:105] Iteration 972, lr = 0.0366402
I0408 07:47:54.565762 31856 solver.cpp:218] Iteration 984 (2.38791 iter/s, 5.02531s/12 iters), loss = 5.29183
I0408 07:47:54.565799 31856 solver.cpp:237]     Train net output #0: loss = 5.29183 (* 1 = 5.29183 loss)
I0408 07:47:54.565809 31856 sgd_solver.cpp:105] Iteration 984, lr = 0.0361889
I0408 07:47:59.534030 31856 solver.cpp:218] Iteration 996 (2.41542 iter/s, 4.96807s/12 iters), loss = 5.27692
I0408 07:47:59.534078 31856 solver.cpp:237]     Train net output #0: loss = 5.27692 (* 1 = 5.27692 loss)
I0408 07:47:59.534090 31856 sgd_solver.cpp:105] Iteration 996, lr = 0.0357431
I0408 07:48:04.564409 31856 solver.cpp:218] Iteration 1008 (2.38561 iter/s, 5.03017s/12 iters), loss = 5.28939
I0408 07:48:04.564453 31856 solver.cpp:237]     Train net output #0: loss = 5.28939 (* 1 = 5.28939 loss)
I0408 07:48:04.564465 31856 sgd_solver.cpp:105] Iteration 1008, lr = 0.0353028
I0408 07:48:05.578002 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:48:09.093708 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0408 07:48:12.185070 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0408 07:48:14.499219 31856 solver.cpp:330] Iteration 1020, Testing net (#0)
I0408 07:48:14.499245 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:48:18.586706 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:48:19.024741 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:48:19.024788 31856 solver.cpp:397]     Test net output #1: loss = 5.28723 (* 1 = 5.28723 loss)
I0408 07:48:19.115025 31856 solver.cpp:218] Iteration 1020 (0.824735 iter/s, 14.5501s/12 iters), loss = 5.28737
I0408 07:48:19.115067 31856 solver.cpp:237]     Train net output #0: loss = 5.28737 (* 1 = 5.28737 loss)
I0408 07:48:19.115077 31856 sgd_solver.cpp:105] Iteration 1020, lr = 0.0348679
I0408 07:48:23.664100 31856 solver.cpp:218] Iteration 1032 (2.63801 iter/s, 4.54889s/12 iters), loss = 5.24805
I0408 07:48:23.664245 31856 solver.cpp:237]     Train net output #0: loss = 5.24805 (* 1 = 5.24805 loss)
I0408 07:48:23.664258 31856 sgd_solver.cpp:105] Iteration 1032, lr = 0.0344383
I0408 07:48:28.758426 31856 solver.cpp:218] Iteration 1044 (2.3557 iter/s, 5.09402s/12 iters), loss = 5.25736
I0408 07:48:28.758473 31856 solver.cpp:237]     Train net output #0: loss = 5.25736 (* 1 = 5.25736 loss)
I0408 07:48:28.758486 31856 sgd_solver.cpp:105] Iteration 1044, lr = 0.0340141
I0408 07:48:33.767923 31856 solver.cpp:218] Iteration 1056 (2.39555 iter/s, 5.00929s/12 iters), loss = 5.26391
I0408 07:48:33.767977 31856 solver.cpp:237]     Train net output #0: loss = 5.26391 (* 1 = 5.26391 loss)
I0408 07:48:33.767989 31856 sgd_solver.cpp:105] Iteration 1056, lr = 0.0335951
I0408 07:48:38.786708 31856 solver.cpp:218] Iteration 1068 (2.39112 iter/s, 5.01857s/12 iters), loss = 5.28826
I0408 07:48:38.786756 31856 solver.cpp:237]     Train net output #0: loss = 5.28826 (* 1 = 5.28826 loss)
I0408 07:48:38.786768 31856 sgd_solver.cpp:105] Iteration 1068, lr = 0.0331812
I0408 07:48:43.778952 31856 solver.cpp:218] Iteration 1080 (2.40383 iter/s, 4.99203s/12 iters), loss = 5.26928
I0408 07:48:43.779009 31856 solver.cpp:237]     Train net output #0: loss = 5.26928 (* 1 = 5.26928 loss)
I0408 07:48:43.779022 31856 sgd_solver.cpp:105] Iteration 1080, lr = 0.0327725
I0408 07:48:48.798550 31856 solver.cpp:218] Iteration 1092 (2.39073 iter/s, 5.01939s/12 iters), loss = 5.2832
I0408 07:48:48.798596 31856 solver.cpp:237]     Train net output #0: loss = 5.2832 (* 1 = 5.2832 loss)
I0408 07:48:48.798607 31856 sgd_solver.cpp:105] Iteration 1092, lr = 0.0323688
I0408 07:48:53.839665 31856 solver.cpp:218] Iteration 1104 (2.38052 iter/s, 5.04091s/12 iters), loss = 5.27281
I0408 07:48:53.839745 31856 solver.cpp:237]     Train net output #0: loss = 5.27281 (* 1 = 5.27281 loss)
I0408 07:48:53.839756 31856 sgd_solver.cpp:105] Iteration 1104, lr = 0.03197
I0408 07:48:56.992406 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:48:58.821897 31856 solver.cpp:218] Iteration 1116 (2.40867 iter/s, 4.982s/12 iters), loss = 5.27318
I0408 07:48:58.821943 31856 solver.cpp:237]     Train net output #0: loss = 5.27318 (* 1 = 5.27318 loss)
I0408 07:48:58.821966 31856 sgd_solver.cpp:105] Iteration 1116, lr = 0.0315762
I0408 07:49:00.851161 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0408 07:49:03.880844 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0408 07:49:06.229163 31856 solver.cpp:330] Iteration 1122, Testing net (#0)
I0408 07:49:06.229182 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:49:10.115803 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:49:10.592509 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:49:10.592555 31856 solver.cpp:397]     Test net output #1: loss = 5.28719 (* 1 = 5.28719 loss)
I0408 07:49:12.590222 31856 solver.cpp:218] Iteration 1128 (0.871594 iter/s, 13.7679s/12 iters), loss = 5.27309
I0408 07:49:12.590270 31856 solver.cpp:237]     Train net output #0: loss = 5.27309 (* 1 = 5.27309 loss)
I0408 07:49:12.590282 31856 sgd_solver.cpp:105] Iteration 1128, lr = 0.0311872
I0408 07:49:17.688853 31856 solver.cpp:218] Iteration 1140 (2.35367 iter/s, 5.09842s/12 iters), loss = 5.26606
I0408 07:49:17.688899 31856 solver.cpp:237]     Train net output #0: loss = 5.26606 (* 1 = 5.26606 loss)
I0408 07:49:17.688910 31856 sgd_solver.cpp:105] Iteration 1140, lr = 0.030803
I0408 07:49:22.682353 31856 solver.cpp:218] Iteration 1152 (2.40322 iter/s, 4.9933s/12 iters), loss = 5.28002
I0408 07:49:22.682399 31856 solver.cpp:237]     Train net output #0: loss = 5.28002 (* 1 = 5.28002 loss)
I0408 07:49:22.682411 31856 sgd_solver.cpp:105] Iteration 1152, lr = 0.0304236
I0408 07:49:27.666371 31856 solver.cpp:218] Iteration 1164 (2.40779 iter/s, 4.98382s/12 iters), loss = 5.27333
I0408 07:49:27.666489 31856 solver.cpp:237]     Train net output #0: loss = 5.27333 (* 1 = 5.27333 loss)
I0408 07:49:27.666499 31856 sgd_solver.cpp:105] Iteration 1164, lr = 0.0300488
I0408 07:49:32.678524 31856 solver.cpp:218] Iteration 1176 (2.39432 iter/s, 5.01187s/12 iters), loss = 5.28834
I0408 07:49:32.678584 31856 solver.cpp:237]     Train net output #0: loss = 5.28834 (* 1 = 5.28834 loss)
I0408 07:49:32.678596 31856 sgd_solver.cpp:105] Iteration 1176, lr = 0.0296786
I0408 07:49:37.670977 31856 solver.cpp:218] Iteration 1188 (2.40373 iter/s, 4.99224s/12 iters), loss = 5.27003
I0408 07:49:37.671025 31856 solver.cpp:237]     Train net output #0: loss = 5.27003 (* 1 = 5.27003 loss)
I0408 07:49:37.671036 31856 sgd_solver.cpp:105] Iteration 1188, lr = 0.029313
I0408 07:49:42.666337 31856 solver.cpp:218] Iteration 1200 (2.40233 iter/s, 4.99516s/12 iters), loss = 5.28962
I0408 07:49:42.666376 31856 solver.cpp:237]     Train net output #0: loss = 5.28962 (* 1 = 5.28962 loss)
I0408 07:49:42.666385 31856 sgd_solver.cpp:105] Iteration 1200, lr = 0.0289519
I0408 07:49:47.697098 31856 solver.cpp:218] Iteration 1212 (2.38542 iter/s, 5.03056s/12 iters), loss = 5.26632
I0408 07:49:47.697142 31856 solver.cpp:237]     Train net output #0: loss = 5.26632 (* 1 = 5.26632 loss)
I0408 07:49:47.697154 31856 sgd_solver.cpp:105] Iteration 1212, lr = 0.0285952
I0408 07:49:47.974186 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:49:52.153074 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0408 07:49:55.161722 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0408 07:49:57.459547 31856 solver.cpp:330] Iteration 1224, Testing net (#0)
I0408 07:49:57.459569 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:50:01.430933 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:50:01.954469 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0408 07:50:01.954515 31856 solver.cpp:397]     Test net output #1: loss = 5.28717 (* 1 = 5.28717 loss)
I0408 07:50:02.044698 31856 solver.cpp:218] Iteration 1224 (0.836404 iter/s, 14.3471s/12 iters), loss = 5.28364
I0408 07:50:02.044751 31856 solver.cpp:237]     Train net output #0: loss = 5.28364 (* 1 = 5.28364 loss)
I0408 07:50:02.044762 31856 sgd_solver.cpp:105] Iteration 1224, lr = 0.028243
I0408 07:50:06.539321 31856 solver.cpp:218] Iteration 1236 (2.66997 iter/s, 4.49443s/12 iters), loss = 5.2717
I0408 07:50:06.539363 31856 solver.cpp:237]     Train net output #0: loss = 5.2717 (* 1 = 5.2717 loss)
I0408 07:50:06.539373 31856 sgd_solver.cpp:105] Iteration 1236, lr = 0.0278951
I0408 07:50:11.567061 31856 solver.cpp:218] Iteration 1248 (2.38685 iter/s, 5.02754s/12 iters), loss = 5.27928
I0408 07:50:11.567104 31856 solver.cpp:237]     Train net output #0: loss = 5.27928 (* 1 = 5.27928 loss)
I0408 07:50:11.567116 31856 sgd_solver.cpp:105] Iteration 1248, lr = 0.0275514
I0408 07:50:16.557530 31856 solver.cpp:218] Iteration 1260 (2.40468 iter/s, 4.99027s/12 iters), loss = 5.27115
I0408 07:50:16.557577 31856 solver.cpp:237]     Train net output #0: loss = 5.27115 (* 1 = 5.27115 loss)
I0408 07:50:16.557588 31856 sgd_solver.cpp:105] Iteration 1260, lr = 0.027212
I0408 07:50:21.559259 31856 solver.cpp:218] Iteration 1272 (2.39927 iter/s, 5.00152s/12 iters), loss = 5.2444
I0408 07:50:21.559306 31856 solver.cpp:237]     Train net output #0: loss = 5.2444 (* 1 = 5.2444 loss)
I0408 07:50:21.559320 31856 sgd_solver.cpp:105] Iteration 1272, lr = 0.0268768
I0408 07:50:26.560036 31856 solver.cpp:218] Iteration 1284 (2.39972 iter/s, 5.00058s/12 iters), loss = 5.28323
I0408 07:50:26.560073 31856 solver.cpp:237]     Train net output #0: loss = 5.28323 (* 1 = 5.28323 loss)
I0408 07:50:26.560084 31856 sgd_solver.cpp:105] Iteration 1284, lr = 0.0265457
I0408 07:50:31.465169 31856 solver.cpp:218] Iteration 1296 (2.44651 iter/s, 4.90494s/12 iters), loss = 5.26807
I0408 07:50:31.465272 31856 solver.cpp:237]     Train net output #0: loss = 5.26807 (* 1 = 5.26807 loss)
I0408 07:50:31.465283 31856 sgd_solver.cpp:105] Iteration 1296, lr = 0.0262187
I0408 07:50:36.480300 31856 solver.cpp:218] Iteration 1308 (2.39288 iter/s, 5.01488s/12 iters), loss = 5.25252
I0408 07:50:36.480337 31856 solver.cpp:237]     Train net output #0: loss = 5.25252 (* 1 = 5.25252 loss)
I0408 07:50:36.480346 31856 sgd_solver.cpp:105] Iteration 1308, lr = 0.0258957
I0408 07:50:38.973259 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:50:41.422797 31856 solver.cpp:218] Iteration 1320 (2.42802 iter/s, 4.9423s/12 iters), loss = 5.27524
I0408 07:50:41.422844 31856 solver.cpp:237]     Train net output #0: loss = 5.27524 (* 1 = 5.27524 loss)
I0408 07:50:41.422856 31856 sgd_solver.cpp:105] Iteration 1320, lr = 0.0255767
I0408 07:50:43.440116 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0408 07:50:46.465788 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0408 07:50:48.821497 31856 solver.cpp:330] Iteration 1326, Testing net (#0)
I0408 07:50:48.821517 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:50:52.731935 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:50:53.289760 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:50:53.289808 31856 solver.cpp:397]     Test net output #1: loss = 5.28769 (* 1 = 5.28769 loss)
I0408 07:50:55.264642 31856 solver.cpp:218] Iteration 1332 (0.866965 iter/s, 13.8414s/12 iters), loss = 5.28784
I0408 07:50:55.264694 31856 solver.cpp:237]     Train net output #0: loss = 5.28784 (* 1 = 5.28784 loss)
I0408 07:50:55.264708 31856 sgd_solver.cpp:105] Iteration 1332, lr = 0.0252616
I0408 07:51:00.425369 31856 solver.cpp:218] Iteration 1344 (2.32535 iter/s, 5.16052s/12 iters), loss = 5.28661
I0408 07:51:00.425410 31856 solver.cpp:237]     Train net output #0: loss = 5.28661 (* 1 = 5.28661 loss)
I0408 07:51:00.425420 31856 sgd_solver.cpp:105] Iteration 1344, lr = 0.0249504
I0408 07:51:05.410478 31856 solver.cpp:218] Iteration 1356 (2.40726 iter/s, 4.98491s/12 iters), loss = 5.2752
I0408 07:51:05.410588 31856 solver.cpp:237]     Train net output #0: loss = 5.2752 (* 1 = 5.2752 loss)
I0408 07:51:05.410602 31856 sgd_solver.cpp:105] Iteration 1356, lr = 0.0246431
I0408 07:51:10.437546 31856 solver.cpp:218] Iteration 1368 (2.3872 iter/s, 5.02681s/12 iters), loss = 5.26895
I0408 07:51:10.437582 31856 solver.cpp:237]     Train net output #0: loss = 5.26895 (* 1 = 5.26895 loss)
I0408 07:51:10.437590 31856 sgd_solver.cpp:105] Iteration 1368, lr = 0.0243395
I0408 07:51:11.645582 31856 blocking_queue.cpp:49] Waiting for data
I0408 07:51:15.436029 31856 solver.cpp:218] Iteration 1380 (2.40082 iter/s, 4.99829s/12 iters), loss = 5.27378
I0408 07:51:15.436075 31856 solver.cpp:237]     Train net output #0: loss = 5.27378 (* 1 = 5.27378 loss)
I0408 07:51:15.436086 31856 sgd_solver.cpp:105] Iteration 1380, lr = 0.0240397
I0408 07:51:20.401940 31856 solver.cpp:218] Iteration 1392 (2.41657 iter/s, 4.96571s/12 iters), loss = 5.27181
I0408 07:51:20.401999 31856 solver.cpp:237]     Train net output #0: loss = 5.27181 (* 1 = 5.27181 loss)
I0408 07:51:20.402010 31856 sgd_solver.cpp:105] Iteration 1392, lr = 0.0237435
I0408 07:51:25.449156 31856 solver.cpp:218] Iteration 1404 (2.37765 iter/s, 5.04701s/12 iters), loss = 5.27616
I0408 07:51:25.449193 31856 solver.cpp:237]     Train net output #0: loss = 5.27616 (* 1 = 5.27616 loss)
I0408 07:51:25.449200 31856 sgd_solver.cpp:105] Iteration 1404, lr = 0.023451
I0408 07:51:30.013347 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:51:30.359678 31856 solver.cpp:218] Iteration 1416 (2.44383 iter/s, 4.91033s/12 iters), loss = 5.25988
I0408 07:51:30.359724 31856 solver.cpp:237]     Train net output #0: loss = 5.25988 (* 1 = 5.25988 loss)
I0408 07:51:30.359735 31856 sgd_solver.cpp:105] Iteration 1416, lr = 0.0231622
I0408 07:51:34.932807 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0408 07:51:37.917521 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0408 07:51:40.230288 31856 solver.cpp:330] Iteration 1428, Testing net (#0)
I0408 07:51:40.230310 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:51:44.172572 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:51:44.770495 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:51:44.770542 31856 solver.cpp:397]     Test net output #1: loss = 5.2872 (* 1 = 5.2872 loss)
I0408 07:51:44.860641 31856 solver.cpp:218] Iteration 1428 (0.827558 iter/s, 14.5005s/12 iters), loss = 5.27449
I0408 07:51:44.860687 31856 solver.cpp:237]     Train net output #0: loss = 5.27449 (* 1 = 5.27449 loss)
I0408 07:51:44.860697 31856 sgd_solver.cpp:105] Iteration 1428, lr = 0.0228768
I0408 07:51:49.384742 31856 solver.cpp:218] Iteration 1440 (2.65257 iter/s, 4.52392s/12 iters), loss = 5.28489
I0408 07:51:49.384789 31856 solver.cpp:237]     Train net output #0: loss = 5.28489 (* 1 = 5.28489 loss)
I0408 07:51:49.384801 31856 sgd_solver.cpp:105] Iteration 1440, lr = 0.022595
I0408 07:51:54.379912 31856 solver.cpp:218] Iteration 1452 (2.40242 iter/s, 4.99496s/12 iters), loss = 5.28029
I0408 07:51:54.379961 31856 solver.cpp:237]     Train net output #0: loss = 5.28029 (* 1 = 5.28029 loss)
I0408 07:51:54.379972 31856 sgd_solver.cpp:105] Iteration 1452, lr = 0.0223167
I0408 07:51:59.374670 31856 solver.cpp:218] Iteration 1464 (2.40261 iter/s, 4.99456s/12 iters), loss = 5.2763
I0408 07:51:59.374707 31856 solver.cpp:237]     Train net output #0: loss = 5.2763 (* 1 = 5.2763 loss)
I0408 07:51:59.374716 31856 sgd_solver.cpp:105] Iteration 1464, lr = 0.0220417
I0408 07:52:04.341657 31856 solver.cpp:218] Iteration 1476 (2.41604 iter/s, 4.9668s/12 iters), loss = 5.27634
I0408 07:52:04.341703 31856 solver.cpp:237]     Train net output #0: loss = 5.27634 (* 1 = 5.27634 loss)
I0408 07:52:04.341714 31856 sgd_solver.cpp:105] Iteration 1476, lr = 0.0217702
I0408 07:52:09.358603 31856 solver.cpp:218] Iteration 1488 (2.39199 iter/s, 5.01675s/12 iters), loss = 5.2517
I0408 07:52:09.358707 31856 solver.cpp:237]     Train net output #0: loss = 5.2517 (* 1 = 5.2517 loss)
I0408 07:52:09.358721 31856 sgd_solver.cpp:105] Iteration 1488, lr = 0.021502
I0408 07:52:14.312783 31856 solver.cpp:218] Iteration 1500 (2.42232 iter/s, 4.95393s/12 iters), loss = 5.26898
I0408 07:52:14.312834 31856 solver.cpp:237]     Train net output #0: loss = 5.26898 (* 1 = 5.26898 loss)
I0408 07:52:14.312846 31856 sgd_solver.cpp:105] Iteration 1500, lr = 0.0212372
I0408 07:52:19.332715 31856 solver.cpp:218] Iteration 1512 (2.39056 iter/s, 5.01973s/12 iters), loss = 5.28582
I0408 07:52:19.332754 31856 solver.cpp:237]     Train net output #0: loss = 5.28582 (* 1 = 5.28582 loss)
I0408 07:52:19.332764 31856 sgd_solver.cpp:105] Iteration 1512, lr = 0.0209755
I0408 07:52:21.118330 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:52:24.289999 31856 solver.cpp:218] Iteration 1524 (2.42077 iter/s, 4.95709s/12 iters), loss = 5.2738
I0408 07:52:24.290043 31856 solver.cpp:237]     Train net output #0: loss = 5.2738 (* 1 = 5.2738 loss)
I0408 07:52:24.290055 31856 sgd_solver.cpp:105] Iteration 1524, lr = 0.0207171
I0408 07:52:26.339627 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0408 07:52:29.368022 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0408 07:52:31.689699 31856 solver.cpp:330] Iteration 1530, Testing net (#0)
I0408 07:52:31.689723 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:52:35.526577 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:52:36.176578 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:52:36.176625 31856 solver.cpp:397]     Test net output #1: loss = 5.28695 (* 1 = 5.28695 loss)
I0408 07:52:38.138628 31856 solver.cpp:218] Iteration 1536 (0.86654 iter/s, 13.8482s/12 iters), loss = 5.27619
I0408 07:52:38.138676 31856 solver.cpp:237]     Train net output #0: loss = 5.27619 (* 1 = 5.27619 loss)
I0408 07:52:38.138689 31856 sgd_solver.cpp:105] Iteration 1536, lr = 0.0204619
I0408 07:52:43.111807 31856 solver.cpp:218] Iteration 1548 (2.41304 iter/s, 4.97298s/12 iters), loss = 5.23117
I0408 07:52:43.111966 31856 solver.cpp:237]     Train net output #0: loss = 5.23117 (* 1 = 5.23117 loss)
I0408 07:52:43.111979 31856 sgd_solver.cpp:105] Iteration 1548, lr = 0.0202099
I0408 07:52:48.146845 31856 solver.cpp:218] Iteration 1560 (2.38344 iter/s, 5.03473s/12 iters), loss = 5.29209
I0408 07:52:48.146885 31856 solver.cpp:237]     Train net output #0: loss = 5.29209 (* 1 = 5.29209 loss)
I0408 07:52:48.146895 31856 sgd_solver.cpp:105] Iteration 1560, lr = 0.0199609
I0408 07:52:53.175092 31856 solver.cpp:218] Iteration 1572 (2.38661 iter/s, 5.02805s/12 iters), loss = 5.25757
I0408 07:52:53.175137 31856 solver.cpp:237]     Train net output #0: loss = 5.25757 (* 1 = 5.25757 loss)
I0408 07:52:53.175149 31856 sgd_solver.cpp:105] Iteration 1572, lr = 0.019715
I0408 07:52:58.112462 31856 solver.cpp:218] Iteration 1584 (2.43054 iter/s, 4.93718s/12 iters), loss = 5.26636
I0408 07:52:58.112507 31856 solver.cpp:237]     Train net output #0: loss = 5.26636 (* 1 = 5.26636 loss)
I0408 07:52:58.112520 31856 sgd_solver.cpp:105] Iteration 1584, lr = 0.0194721
I0408 07:53:03.125162 31856 solver.cpp:218] Iteration 1596 (2.39402 iter/s, 5.01249s/12 iters), loss = 5.26744
I0408 07:53:03.125206 31856 solver.cpp:237]     Train net output #0: loss = 5.26744 (* 1 = 5.26744 loss)
I0408 07:53:03.125218 31856 sgd_solver.cpp:105] Iteration 1596, lr = 0.0192323
I0408 07:53:08.188580 31856 solver.cpp:218] Iteration 1608 (2.37003 iter/s, 5.06322s/12 iters), loss = 5.26641
I0408 07:53:08.188622 31856 solver.cpp:237]     Train net output #0: loss = 5.26641 (* 1 = 5.26641 loss)
I0408 07:53:08.188633 31856 sgd_solver.cpp:105] Iteration 1608, lr = 0.0189953
I0408 07:53:12.133883 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:53:13.203768 31856 solver.cpp:218] Iteration 1620 (2.39283 iter/s, 5.01499s/12 iters), loss = 5.25448
I0408 07:53:13.204844 31856 solver.cpp:237]     Train net output #0: loss = 5.25448 (* 1 = 5.25448 loss)
I0408 07:53:13.204857 31856 sgd_solver.cpp:105] Iteration 1620, lr = 0.0187613
I0408 07:53:17.786801 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0408 07:53:20.812237 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0408 07:53:23.135349 31856 solver.cpp:330] Iteration 1632, Testing net (#0)
I0408 07:53:23.135373 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:53:26.944607 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:53:27.702716 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:53:27.702764 31856 solver.cpp:397]     Test net output #1: loss = 5.28707 (* 1 = 5.28707 loss)
I0408 07:53:27.792800 31856 solver.cpp:218] Iteration 1632 (0.82262 iter/s, 14.5875s/12 iters), loss = 5.2887
I0408 07:53:27.792850 31856 solver.cpp:237]     Train net output #0: loss = 5.2887 (* 1 = 5.2887 loss)
I0408 07:53:27.792860 31856 sgd_solver.cpp:105] Iteration 1632, lr = 0.0185302
I0408 07:53:32.272929 31856 solver.cpp:218] Iteration 1644 (2.67861 iter/s, 4.47994s/12 iters), loss = 5.25396
I0408 07:53:32.272976 31856 solver.cpp:237]     Train net output #0: loss = 5.25396 (* 1 = 5.25396 loss)
I0408 07:53:32.272989 31856 sgd_solver.cpp:105] Iteration 1644, lr = 0.018302
I0408 07:53:37.239146 31856 solver.cpp:218] Iteration 1656 (2.41642 iter/s, 4.96602s/12 iters), loss = 5.29288
I0408 07:53:37.239187 31856 solver.cpp:237]     Train net output #0: loss = 5.29288 (* 1 = 5.29288 loss)
I0408 07:53:37.239195 31856 sgd_solver.cpp:105] Iteration 1656, lr = 0.0180765
I0408 07:53:42.206677 31856 solver.cpp:218] Iteration 1668 (2.41578 iter/s, 4.96734s/12 iters), loss = 5.25971
I0408 07:53:42.206722 31856 solver.cpp:237]     Train net output #0: loss = 5.25971 (* 1 = 5.25971 loss)
I0408 07:53:42.206734 31856 sgd_solver.cpp:105] Iteration 1668, lr = 0.0178538
I0408 07:53:47.237643 31856 solver.cpp:218] Iteration 1680 (2.38532 iter/s, 5.03077s/12 iters), loss = 5.27378
I0408 07:53:47.237803 31856 solver.cpp:237]     Train net output #0: loss = 5.27378 (* 1 = 5.27378 loss)
I0408 07:53:47.237818 31856 sgd_solver.cpp:105] Iteration 1680, lr = 0.0176339
I0408 07:53:52.461763 31856 solver.cpp:218] Iteration 1692 (2.29718 iter/s, 5.2238s/12 iters), loss = 5.28875
I0408 07:53:52.461807 31856 solver.cpp:237]     Train net output #0: loss = 5.28875 (* 1 = 5.28875 loss)
I0408 07:53:52.461818 31856 sgd_solver.cpp:105] Iteration 1692, lr = 0.0174167
I0408 07:53:57.698137 31856 solver.cpp:218] Iteration 1704 (2.29175 iter/s, 5.23617s/12 iters), loss = 5.26765
I0408 07:53:57.698186 31856 solver.cpp:237]     Train net output #0: loss = 5.26765 (* 1 = 5.26765 loss)
I0408 07:53:57.698199 31856 sgd_solver.cpp:105] Iteration 1704, lr = 0.0172021
I0408 07:54:02.721616 31856 solver.cpp:218] Iteration 1716 (2.38888 iter/s, 5.02328s/12 iters), loss = 5.28061
I0408 07:54:02.721662 31856 solver.cpp:237]     Train net output #0: loss = 5.28061 (* 1 = 5.28061 loss)
I0408 07:54:02.721673 31856 sgd_solver.cpp:105] Iteration 1716, lr = 0.0169902
I0408 07:54:03.776299 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:54:07.758786 31856 solver.cpp:218] Iteration 1728 (2.38238 iter/s, 5.03698s/12 iters), loss = 5.28511
I0408 07:54:07.758821 31856 solver.cpp:237]     Train net output #0: loss = 5.28511 (* 1 = 5.28511 loss)
I0408 07:54:07.758828 31856 sgd_solver.cpp:105] Iteration 1728, lr = 0.0167809
I0408 07:54:09.834604 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0408 07:54:12.799048 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0408 07:54:15.197979 31856 solver.cpp:330] Iteration 1734, Testing net (#0)
I0408 07:54:15.198004 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:54:18.962846 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:54:19.667923 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:54:19.667973 31856 solver.cpp:397]     Test net output #1: loss = 5.28739 (* 1 = 5.28739 loss)
I0408 07:54:21.625869 31856 solver.cpp:218] Iteration 1740 (0.865386 iter/s, 13.8666s/12 iters), loss = 5.25418
I0408 07:54:21.625926 31856 solver.cpp:237]     Train net output #0: loss = 5.25418 (* 1 = 5.25418 loss)
I0408 07:54:21.625937 31856 sgd_solver.cpp:105] Iteration 1740, lr = 0.0165742
I0408 07:54:26.802873 31856 solver.cpp:218] Iteration 1752 (2.31804 iter/s, 5.17679s/12 iters), loss = 5.26494
I0408 07:54:26.802918 31856 solver.cpp:237]     Train net output #0: loss = 5.26494 (* 1 = 5.26494 loss)
I0408 07:54:26.802929 31856 sgd_solver.cpp:105] Iteration 1752, lr = 0.01637
I0408 07:54:31.840972 31856 solver.cpp:218] Iteration 1764 (2.38195 iter/s, 5.0379s/12 iters), loss = 5.26516
I0408 07:54:31.841012 31856 solver.cpp:237]     Train net output #0: loss = 5.26516 (* 1 = 5.26516 loss)
I0408 07:54:31.841022 31856 sgd_solver.cpp:105] Iteration 1764, lr = 0.0161683
I0408 07:54:36.853570 31856 solver.cpp:218] Iteration 1776 (2.39406 iter/s, 5.0124s/12 iters), loss = 5.28106
I0408 07:54:36.853622 31856 solver.cpp:237]     Train net output #0: loss = 5.28106 (* 1 = 5.28106 loss)
I0408 07:54:36.853633 31856 sgd_solver.cpp:105] Iteration 1776, lr = 0.0159692
I0408 07:54:41.848414 31856 solver.cpp:218] Iteration 1788 (2.40258 iter/s, 4.99464s/12 iters), loss = 5.26388
I0408 07:54:41.848461 31856 solver.cpp:237]     Train net output #0: loss = 5.26388 (* 1 = 5.26388 loss)
I0408 07:54:41.848472 31856 sgd_solver.cpp:105] Iteration 1788, lr = 0.0157724
I0408 07:54:46.881067 31856 solver.cpp:218] Iteration 1800 (2.38452 iter/s, 5.03245s/12 iters), loss = 5.27917
I0408 07:54:46.881111 31856 solver.cpp:237]     Train net output #0: loss = 5.27917 (* 1 = 5.27917 loss)
I0408 07:54:46.881122 31856 sgd_solver.cpp:105] Iteration 1800, lr = 0.0155781
I0408 07:54:51.936967 31856 solver.cpp:218] Iteration 1812 (2.37356 iter/s, 5.0557s/12 iters), loss = 5.26887
I0408 07:54:51.937077 31856 solver.cpp:237]     Train net output #0: loss = 5.26887 (* 1 = 5.26887 loss)
I0408 07:54:51.937088 31856 sgd_solver.cpp:105] Iteration 1812, lr = 0.0153862
I0408 07:54:55.161865 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:54:56.969811 31856 solver.cpp:218] Iteration 1824 (2.38446 iter/s, 5.03258s/12 iters), loss = 5.27338
I0408 07:54:56.969861 31856 solver.cpp:237]     Train net output #0: loss = 5.27338 (* 1 = 5.27338 loss)
I0408 07:54:56.969874 31856 sgd_solver.cpp:105] Iteration 1824, lr = 0.0151967
I0408 07:55:01.500442 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0408 07:55:04.497344 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0408 07:55:06.835839 31856 solver.cpp:330] Iteration 1836, Testing net (#0)
I0408 07:55:06.835865 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:55:10.551635 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:55:11.301118 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:55:11.301164 31856 solver.cpp:397]     Test net output #1: loss = 5.28679 (* 1 = 5.28679 loss)
I0408 07:55:11.391268 31856 solver.cpp:218] Iteration 1836 (0.83212 iter/s, 14.421s/12 iters), loss = 5.27689
I0408 07:55:11.391319 31856 solver.cpp:237]     Train net output #0: loss = 5.27689 (* 1 = 5.27689 loss)
I0408 07:55:11.391331 31856 sgd_solver.cpp:105] Iteration 1836, lr = 0.0150095
I0408 07:55:15.697244 31856 solver.cpp:218] Iteration 1848 (2.78694 iter/s, 4.30579s/12 iters), loss = 5.27107
I0408 07:55:15.697291 31856 solver.cpp:237]     Train net output #0: loss = 5.27107 (* 1 = 5.27107 loss)
I0408 07:55:15.697304 31856 sgd_solver.cpp:105] Iteration 1848, lr = 0.0148246
I0408 07:55:20.731840 31856 solver.cpp:218] Iteration 1860 (2.3836 iter/s, 5.0344s/12 iters), loss = 5.28237
I0408 07:55:20.731887 31856 solver.cpp:237]     Train net output #0: loss = 5.28237 (* 1 = 5.28237 loss)
I0408 07:55:20.731900 31856 sgd_solver.cpp:105] Iteration 1860, lr = 0.014642
I0408 07:55:25.773897 31856 solver.cpp:218] Iteration 1872 (2.38007 iter/s, 5.04186s/12 iters), loss = 5.26855
I0408 07:55:25.774008 31856 solver.cpp:237]     Train net output #0: loss = 5.26855 (* 1 = 5.26855 loss)
I0408 07:55:25.774020 31856 sgd_solver.cpp:105] Iteration 1872, lr = 0.0144616
I0408 07:55:30.772915 31856 solver.cpp:218] Iteration 1884 (2.40059 iter/s, 4.99876s/12 iters), loss = 5.28526
I0408 07:55:30.772961 31856 solver.cpp:237]     Train net output #0: loss = 5.28526 (* 1 = 5.28526 loss)
I0408 07:55:30.772972 31856 sgd_solver.cpp:105] Iteration 1884, lr = 0.0142834
I0408 07:55:35.976665 31856 solver.cpp:218] Iteration 1896 (2.30612 iter/s, 5.20355s/12 iters), loss = 5.26527
I0408 07:55:35.976708 31856 solver.cpp:237]     Train net output #0: loss = 5.26527 (* 1 = 5.26527 loss)
I0408 07:55:35.976719 31856 sgd_solver.cpp:105] Iteration 1896, lr = 0.0141075
I0408 07:55:41.038765 31856 solver.cpp:218] Iteration 1908 (2.37065 iter/s, 5.0619s/12 iters), loss = 5.28352
I0408 07:55:41.038810 31856 solver.cpp:237]     Train net output #0: loss = 5.28352 (* 1 = 5.28352 loss)
I0408 07:55:41.038820 31856 sgd_solver.cpp:105] Iteration 1908, lr = 0.0139337
I0408 07:55:46.079782 31856 solver.cpp:218] Iteration 1920 (2.38057 iter/s, 5.04082s/12 iters), loss = 5.27528
I0408 07:55:46.079828 31856 solver.cpp:237]     Train net output #0: loss = 5.27528 (* 1 = 5.27528 loss)
I0408 07:55:46.079839 31856 sgd_solver.cpp:105] Iteration 1920, lr = 0.0137621
I0408 07:55:46.386561 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:55:51.043612 31856 solver.cpp:218] Iteration 1932 (2.41758 iter/s, 4.96363s/12 iters), loss = 5.28033
I0408 07:55:51.043658 31856 solver.cpp:237]     Train net output #0: loss = 5.28033 (* 1 = 5.28033 loss)
I0408 07:55:51.043669 31856 sgd_solver.cpp:105] Iteration 1932, lr = 0.0135925
I0408 07:55:53.104212 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0408 07:55:56.102324 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0408 07:55:58.404997 31856 solver.cpp:330] Iteration 1938, Testing net (#0)
I0408 07:55:58.405023 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:56:01.951529 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:56:02.738528 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:56:02.738572 31856 solver.cpp:397]     Test net output #1: loss = 5.28724 (* 1 = 5.28724 loss)
I0408 07:56:04.723791 31856 solver.cpp:218] Iteration 1944 (0.877209 iter/s, 13.6797s/12 iters), loss = 5.27066
I0408 07:56:04.723839 31856 solver.cpp:237]     Train net output #0: loss = 5.27066 (* 1 = 5.27066 loss)
I0408 07:56:04.723850 31856 sgd_solver.cpp:105] Iteration 1944, lr = 0.0134251
I0408 07:56:09.830379 31856 solver.cpp:218] Iteration 1956 (2.35 iter/s, 5.10639s/12 iters), loss = 5.2801
I0408 07:56:09.830427 31856 solver.cpp:237]     Train net output #0: loss = 5.2801 (* 1 = 5.2801 loss)
I0408 07:56:09.830438 31856 sgd_solver.cpp:105] Iteration 1956, lr = 0.0132597
I0408 07:56:14.853121 31856 solver.cpp:218] Iteration 1968 (2.38923 iter/s, 5.02254s/12 iters), loss = 5.27207
I0408 07:56:14.853155 31856 solver.cpp:237]     Train net output #0: loss = 5.27207 (* 1 = 5.27207 loss)
I0408 07:56:14.853164 31856 sgd_solver.cpp:105] Iteration 1968, lr = 0.0130964
I0408 07:56:19.815860 31856 solver.cpp:218] Iteration 1980 (2.41811 iter/s, 4.96255s/12 iters), loss = 5.25455
I0408 07:56:19.815914 31856 solver.cpp:237]     Train net output #0: loss = 5.25455 (* 1 = 5.25455 loss)
I0408 07:56:19.815928 31856 sgd_solver.cpp:105] Iteration 1980, lr = 0.012935
I0408 07:56:24.811852 31856 solver.cpp:218] Iteration 1992 (2.40202 iter/s, 4.99579s/12 iters), loss = 5.28091
I0408 07:56:24.811892 31856 solver.cpp:237]     Train net output #0: loss = 5.28091 (* 1 = 5.28091 loss)
I0408 07:56:24.811904 31856 sgd_solver.cpp:105] Iteration 1992, lr = 0.0127757
I0408 07:56:29.812636 31856 solver.cpp:218] Iteration 2004 (2.39971 iter/s, 5.0006s/12 iters), loss = 5.27636
I0408 07:56:29.812732 31856 solver.cpp:237]     Train net output #0: loss = 5.27636 (* 1 = 5.27636 loss)
I0408 07:56:29.812744 31856 sgd_solver.cpp:105] Iteration 2004, lr = 0.0126183
I0408 07:56:34.886811 31856 solver.cpp:218] Iteration 2016 (2.36503 iter/s, 5.07393s/12 iters), loss = 5.25365
I0408 07:56:34.886848 31856 solver.cpp:237]     Train net output #0: loss = 5.25365 (* 1 = 5.25365 loss)
I0408 07:56:34.886857 31856 sgd_solver.cpp:105] Iteration 2016, lr = 0.0124629
I0408 07:56:37.420694 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:56:39.877785 31856 solver.cpp:218] Iteration 2028 (2.40443 iter/s, 4.99079s/12 iters), loss = 5.27657
I0408 07:56:39.877827 31856 solver.cpp:237]     Train net output #0: loss = 5.27657 (* 1 = 5.27657 loss)
I0408 07:56:39.877837 31856 sgd_solver.cpp:105] Iteration 2028, lr = 0.0123093
I0408 07:56:44.542753 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0408 07:56:47.558302 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0408 07:56:49.884771 31856 solver.cpp:330] Iteration 2040, Testing net (#0)
I0408 07:56:49.884797 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:56:53.531903 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:56:54.361114 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0408 07:56:54.361153 31856 solver.cpp:397]     Test net output #1: loss = 5.28694 (* 1 = 5.28694 loss)
I0408 07:56:54.450245 31856 solver.cpp:218] Iteration 2040 (0.823497 iter/s, 14.572s/12 iters), loss = 5.28236
I0408 07:56:54.450297 31856 solver.cpp:237]     Train net output #0: loss = 5.28236 (* 1 = 5.28236 loss)
I0408 07:56:54.450309 31856 sgd_solver.cpp:105] Iteration 2040, lr = 0.0121577
I0408 07:56:58.707707 31856 solver.cpp:218] Iteration 2052 (2.8187 iter/s, 4.25728s/12 iters), loss = 5.28371
I0408 07:56:58.707758 31856 solver.cpp:237]     Train net output #0: loss = 5.28371 (* 1 = 5.28371 loss)
I0408 07:56:58.707769 31856 sgd_solver.cpp:105] Iteration 2052, lr = 0.0120079
I0408 07:57:00.317699 31856 blocking_queue.cpp:49] Waiting for data
I0408 07:57:03.656080 31856 solver.cpp:218] Iteration 2064 (2.42514 iter/s, 4.94817s/12 iters), loss = 5.27464
I0408 07:57:03.656129 31856 solver.cpp:237]     Train net output #0: loss = 5.27464 (* 1 = 5.27464 loss)
I0408 07:57:03.656141 31856 sgd_solver.cpp:105] Iteration 2064, lr = 0.01186
I0408 07:57:08.727556 31856 solver.cpp:218] Iteration 2076 (2.36627 iter/s, 5.07128s/12 iters), loss = 5.27832
I0408 07:57:08.727594 31856 solver.cpp:237]     Train net output #0: loss = 5.27832 (* 1 = 5.27832 loss)
I0408 07:57:08.727602 31856 sgd_solver.cpp:105] Iteration 2076, lr = 0.0117139
I0408 07:57:13.747721 31856 solver.cpp:218] Iteration 2088 (2.39045 iter/s, 5.01997s/12 iters), loss = 5.2746
I0408 07:57:13.747781 31856 solver.cpp:237]     Train net output #0: loss = 5.2746 (* 1 = 5.2746 loss)
I0408 07:57:13.747795 31856 sgd_solver.cpp:105] Iteration 2088, lr = 0.0115696
I0408 07:57:18.703567 31856 solver.cpp:218] Iteration 2100 (2.42148 iter/s, 4.95564s/12 iters), loss = 5.27026
I0408 07:57:18.703613 31856 solver.cpp:237]     Train net output #0: loss = 5.27026 (* 1 = 5.27026 loss)
I0408 07:57:18.703624 31856 sgd_solver.cpp:105] Iteration 2100, lr = 0.0114271
I0408 07:57:23.682942 31856 solver.cpp:218] Iteration 2112 (2.41003 iter/s, 4.97918s/12 iters), loss = 5.28009
I0408 07:57:23.682986 31856 solver.cpp:237]     Train net output #0: loss = 5.28009 (* 1 = 5.28009 loss)
I0408 07:57:23.682997 31856 sgd_solver.cpp:105] Iteration 2112, lr = 0.0112863
I0408 07:57:28.362546 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:57:28.677412 31856 solver.cpp:218] Iteration 2124 (2.40275 iter/s, 4.99427s/12 iters), loss = 5.25763
I0408 07:57:28.677455 31856 solver.cpp:237]     Train net output #0: loss = 5.25763 (* 1 = 5.25763 loss)
I0408 07:57:28.677466 31856 sgd_solver.cpp:105] Iteration 2124, lr = 0.0111473
I0408 07:57:33.958818 31856 solver.cpp:218] Iteration 2136 (2.27221 iter/s, 5.28121s/12 iters), loss = 5.27448
I0408 07:57:33.958901 31856 solver.cpp:237]     Train net output #0: loss = 5.27448 (* 1 = 5.27448 loss)
I0408 07:57:33.958914 31856 sgd_solver.cpp:105] Iteration 2136, lr = 0.0110099
I0408 07:57:36.094588 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0408 07:57:39.158179 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0408 07:57:41.463755 31856 solver.cpp:330] Iteration 2142, Testing net (#0)
I0408 07:57:41.463783 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:57:45.074801 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:57:45.937942 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:57:45.938004 31856 solver.cpp:397]     Test net output #1: loss = 5.28715 (* 1 = 5.28715 loss)
I0408 07:57:47.913049 31856 solver.cpp:218] Iteration 2148 (0.859984 iter/s, 13.9538s/12 iters), loss = 5.28133
I0408 07:57:47.913100 31856 solver.cpp:237]     Train net output #0: loss = 5.28133 (* 1 = 5.28133 loss)
I0408 07:57:47.913110 31856 sgd_solver.cpp:105] Iteration 2148, lr = 0.0108743
I0408 07:57:53.075068 31856 solver.cpp:218] Iteration 2160 (2.32476 iter/s, 5.16182s/12 iters), loss = 5.28616
I0408 07:57:53.075111 31856 solver.cpp:237]     Train net output #0: loss = 5.28616 (* 1 = 5.28616 loss)
I0408 07:57:53.075122 31856 sgd_solver.cpp:105] Iteration 2160, lr = 0.0107404
I0408 07:57:58.094192 31856 solver.cpp:218] Iteration 2172 (2.39095 iter/s, 5.01893s/12 iters), loss = 5.27486
I0408 07:57:58.094239 31856 solver.cpp:237]     Train net output #0: loss = 5.27486 (* 1 = 5.27486 loss)
I0408 07:57:58.094251 31856 sgd_solver.cpp:105] Iteration 2172, lr = 0.010608
I0408 07:58:03.127097 31856 solver.cpp:218] Iteration 2184 (2.3844 iter/s, 5.0327s/12 iters), loss = 5.27135
I0408 07:58:03.127142 31856 solver.cpp:237]     Train net output #0: loss = 5.27135 (* 1 = 5.27135 loss)
I0408 07:58:03.127153 31856 sgd_solver.cpp:105] Iteration 2184, lr = 0.0104774
I0408 07:58:08.107820 31856 solver.cpp:218] Iteration 2196 (2.40939 iter/s, 4.98052s/12 iters), loss = 5.25074
I0408 07:58:08.107973 31856 solver.cpp:237]     Train net output #0: loss = 5.25074 (* 1 = 5.25074 loss)
I0408 07:58:08.107986 31856 sgd_solver.cpp:105] Iteration 2196, lr = 0.0103483
I0408 07:58:13.151659 31856 solver.cpp:218] Iteration 2208 (2.37928 iter/s, 5.04354s/12 iters), loss = 5.2694
I0408 07:58:13.151707 31856 solver.cpp:237]     Train net output #0: loss = 5.2694 (* 1 = 5.2694 loss)
I0408 07:58:13.151719 31856 sgd_solver.cpp:105] Iteration 2208, lr = 0.0102208
I0408 07:58:18.348774 31856 solver.cpp:218] Iteration 2220 (2.30906 iter/s, 5.19692s/12 iters), loss = 5.28266
I0408 07:58:18.348810 31856 solver.cpp:237]     Train net output #0: loss = 5.28266 (* 1 = 5.28266 loss)
I0408 07:58:18.348817 31856 sgd_solver.cpp:105] Iteration 2220, lr = 0.0100949
I0408 07:58:20.303956 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:58:23.485124 31856 solver.cpp:218] Iteration 2232 (2.33638 iter/s, 5.13616s/12 iters), loss = 5.28489
I0408 07:58:23.485170 31856 solver.cpp:237]     Train net output #0: loss = 5.28489 (* 1 = 5.28489 loss)
I0408 07:58:23.485183 31856 sgd_solver.cpp:105] Iteration 2232, lr = 0.00997055
I0408 07:58:28.082458 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0408 07:58:31.052436 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0408 07:58:33.351107 31856 solver.cpp:330] Iteration 2244, Testing net (#0)
I0408 07:58:33.351131 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:58:36.916347 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:58:37.824314 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:58:37.824355 31856 solver.cpp:397]     Test net output #1: loss = 5.28706 (* 1 = 5.28706 loss)
I0408 07:58:37.914448 31856 solver.cpp:218] Iteration 2244 (0.831666 iter/s, 14.4289s/12 iters), loss = 5.27832
I0408 07:58:37.914495 31856 solver.cpp:237]     Train net output #0: loss = 5.27832 (* 1 = 5.27832 loss)
I0408 07:58:37.914505 31856 sgd_solver.cpp:105] Iteration 2244, lr = 0.00984773
I0408 07:58:42.084966 31856 solver.cpp:218] Iteration 2256 (2.87746 iter/s, 4.17034s/12 iters), loss = 5.24022
I0408 07:58:42.087437 31856 solver.cpp:237]     Train net output #0: loss = 5.24022 (* 1 = 5.24022 loss)
I0408 07:58:42.087452 31856 sgd_solver.cpp:105] Iteration 2256, lr = 0.00972642
I0408 07:58:47.068490 31856 solver.cpp:218] Iteration 2268 (2.4092 iter/s, 4.98091s/12 iters), loss = 5.28563
I0408 07:58:47.068533 31856 solver.cpp:237]     Train net output #0: loss = 5.28563 (* 1 = 5.28563 loss)
I0408 07:58:47.068544 31856 sgd_solver.cpp:105] Iteration 2268, lr = 0.0096066
I0408 07:58:52.116134 31856 solver.cpp:218] Iteration 2280 (2.37744 iter/s, 5.04745s/12 iters), loss = 5.25348
I0408 07:58:52.116189 31856 solver.cpp:237]     Train net output #0: loss = 5.25348 (* 1 = 5.25348 loss)
I0408 07:58:52.116204 31856 sgd_solver.cpp:105] Iteration 2280, lr = 0.00948826
I0408 07:58:57.089413 31856 solver.cpp:218] Iteration 2292 (2.413 iter/s, 4.97307s/12 iters), loss = 5.27254
I0408 07:58:57.089458 31856 solver.cpp:237]     Train net output #0: loss = 5.27254 (* 1 = 5.27254 loss)
I0408 07:58:57.089470 31856 sgd_solver.cpp:105] Iteration 2292, lr = 0.00937137
I0408 07:59:02.127133 31856 solver.cpp:218] Iteration 2304 (2.38212 iter/s, 5.03752s/12 iters), loss = 5.27018
I0408 07:59:02.127184 31856 solver.cpp:237]     Train net output #0: loss = 5.27018 (* 1 = 5.27018 loss)
I0408 07:59:02.127197 31856 sgd_solver.cpp:105] Iteration 2304, lr = 0.00925593
I0408 07:59:07.146349 31856 solver.cpp:218] Iteration 2316 (2.39091 iter/s, 5.01901s/12 iters), loss = 5.26122
I0408 07:59:07.146395 31856 solver.cpp:237]     Train net output #0: loss = 5.26122 (* 1 = 5.26122 loss)
I0408 07:59:07.146407 31856 sgd_solver.cpp:105] Iteration 2316, lr = 0.0091419
I0408 07:59:11.159482 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:59:12.212113 31856 solver.cpp:218] Iteration 2328 (2.36893 iter/s, 5.06557s/12 iters), loss = 5.25865
I0408 07:59:12.212270 31856 solver.cpp:237]     Train net output #0: loss = 5.25865 (* 1 = 5.25865 loss)
I0408 07:59:12.212283 31856 sgd_solver.cpp:105] Iteration 2328, lr = 0.00902929
I0408 07:59:17.243355 31856 solver.cpp:218] Iteration 2340 (2.38524 iter/s, 5.03093s/12 iters), loss = 5.29202
I0408 07:59:17.243407 31856 solver.cpp:237]     Train net output #0: loss = 5.29202 (* 1 = 5.29202 loss)
I0408 07:59:17.243418 31856 sgd_solver.cpp:105] Iteration 2340, lr = 0.00891806
I0408 07:59:19.289551 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0408 07:59:22.309226 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0408 07:59:24.636312 31856 solver.cpp:330] Iteration 2346, Testing net (#0)
I0408 07:59:24.636344 31856 net.cpp:676] Ignoring source layer train-data
I0408 07:59:28.168236 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:59:29.111135 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:59:29.111181 31856 solver.cpp:397]     Test net output #1: loss = 5.28738 (* 1 = 5.28738 loss)
I0408 07:59:31.081810 31856 solver.cpp:218] Iteration 2352 (0.867177 iter/s, 13.838s/12 iters), loss = 5.25647
I0408 07:59:31.081862 31856 solver.cpp:237]     Train net output #0: loss = 5.25647 (* 1 = 5.25647 loss)
I0408 07:59:31.081874 31856 sgd_solver.cpp:105] Iteration 2352, lr = 0.0088082
I0408 07:59:36.084288 31856 solver.cpp:218] Iteration 2364 (2.39891 iter/s, 5.00228s/12 iters), loss = 5.30544
I0408 07:59:36.084338 31856 solver.cpp:237]     Train net output #0: loss = 5.30544 (* 1 = 5.30544 loss)
I0408 07:59:36.084350 31856 sgd_solver.cpp:105] Iteration 2364, lr = 0.00869969
I0408 07:59:41.105362 31856 solver.cpp:218] Iteration 2376 (2.39002 iter/s, 5.02087s/12 iters), loss = 5.26027
I0408 07:59:41.105409 31856 solver.cpp:237]     Train net output #0: loss = 5.26027 (* 1 = 5.26027 loss)
I0408 07:59:41.105420 31856 sgd_solver.cpp:105] Iteration 2376, lr = 0.00859252
I0408 07:59:46.065176 31856 solver.cpp:218] Iteration 2388 (2.41954 iter/s, 4.95962s/12 iters), loss = 5.27404
I0408 07:59:46.066583 31856 solver.cpp:237]     Train net output #0: loss = 5.27404 (* 1 = 5.27404 loss)
I0408 07:59:46.066601 31856 sgd_solver.cpp:105] Iteration 2388, lr = 0.00848667
I0408 07:59:51.103801 31856 solver.cpp:218] Iteration 2400 (2.38234 iter/s, 5.03707s/12 iters), loss = 5.2832
I0408 07:59:51.103854 31856 solver.cpp:237]     Train net output #0: loss = 5.2832 (* 1 = 5.2832 loss)
I0408 07:59:51.103866 31856 sgd_solver.cpp:105] Iteration 2400, lr = 0.00838212
I0408 07:59:56.129665 31856 solver.cpp:218] Iteration 2412 (2.38775 iter/s, 5.02566s/12 iters), loss = 5.26963
I0408 07:59:56.129711 31856 solver.cpp:237]     Train net output #0: loss = 5.26963 (* 1 = 5.26963 loss)
I0408 07:59:56.129724 31856 sgd_solver.cpp:105] Iteration 2412, lr = 0.00827887
I0408 08:00:01.173768 31856 solver.cpp:218] Iteration 2424 (2.37911 iter/s, 5.0439s/12 iters), loss = 5.27672
I0408 08:00:01.173816 31856 solver.cpp:237]     Train net output #0: loss = 5.27672 (* 1 = 5.27672 loss)
I0408 08:00:01.173828 31856 sgd_solver.cpp:105] Iteration 2424, lr = 0.00817688
I0408 08:00:02.269979 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:00:06.229383 31856 solver.cpp:218] Iteration 2436 (2.37369 iter/s, 5.05542s/12 iters), loss = 5.27968
I0408 08:00:06.229431 31856 solver.cpp:237]     Train net output #0: loss = 5.27968 (* 1 = 5.27968 loss)
I0408 08:00:06.229442 31856 sgd_solver.cpp:105] Iteration 2436, lr = 0.00807615
I0408 08:00:10.845404 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0408 08:00:13.814189 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0408 08:00:16.122295 31856 solver.cpp:330] Iteration 2448, Testing net (#0)
I0408 08:00:16.122416 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:00:19.726774 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:00:20.705483 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:00:20.705529 31856 solver.cpp:397]     Test net output #1: loss = 5.2871 (* 1 = 5.2871 loss)
I0408 08:00:20.795570 31856 solver.cpp:218] Iteration 2448 (0.823852 iter/s, 14.5657s/12 iters), loss = 5.25584
I0408 08:00:20.795621 31856 solver.cpp:237]     Train net output #0: loss = 5.25584 (* 1 = 5.25584 loss)
I0408 08:00:20.795632 31856 sgd_solver.cpp:105] Iteration 2448, lr = 0.00797666
I0408 08:00:24.927350 31856 solver.cpp:218] Iteration 2460 (2.90444 iter/s, 4.1316s/12 iters), loss = 5.26353
I0408 08:00:24.927397 31856 solver.cpp:237]     Train net output #0: loss = 5.26353 (* 1 = 5.26353 loss)
I0408 08:00:24.927408 31856 sgd_solver.cpp:105] Iteration 2460, lr = 0.0078784
I0408 08:00:29.665330 31856 solver.cpp:218] Iteration 2472 (2.53283 iter/s, 4.73779s/12 iters), loss = 5.27083
I0408 08:00:29.665382 31856 solver.cpp:237]     Train net output #0: loss = 5.27083 (* 1 = 5.27083 loss)
I0408 08:00:29.665395 31856 sgd_solver.cpp:105] Iteration 2472, lr = 0.00778135
I0408 08:00:34.626003 31856 solver.cpp:218] Iteration 2484 (2.41912 iter/s, 4.96047s/12 iters), loss = 5.27539
I0408 08:00:34.626047 31856 solver.cpp:237]     Train net output #0: loss = 5.27539 (* 1 = 5.27539 loss)
I0408 08:00:34.626058 31856 sgd_solver.cpp:105] Iteration 2484, lr = 0.00768549
I0408 08:00:39.650667 31856 solver.cpp:218] Iteration 2496 (2.38831 iter/s, 5.02447s/12 iters), loss = 5.26982
I0408 08:00:39.650699 31856 solver.cpp:237]     Train net output #0: loss = 5.26982 (* 1 = 5.26982 loss)
I0408 08:00:39.650707 31856 sgd_solver.cpp:105] Iteration 2496, lr = 0.00759081
I0408 08:00:44.600577 31856 solver.cpp:218] Iteration 2508 (2.42438 iter/s, 4.94972s/12 iters), loss = 5.28824
I0408 08:00:44.600627 31856 solver.cpp:237]     Train net output #0: loss = 5.28824 (* 1 = 5.28824 loss)
I0408 08:00:44.600639 31856 sgd_solver.cpp:105] Iteration 2508, lr = 0.0074973
I0408 08:00:49.675268 31856 solver.cpp:218] Iteration 2520 (2.36477 iter/s, 5.0745s/12 iters), loss = 5.27605
I0408 08:00:49.675355 31856 solver.cpp:237]     Train net output #0: loss = 5.27605 (* 1 = 5.27605 loss)
I0408 08:00:49.675364 31856 sgd_solver.cpp:105] Iteration 2520, lr = 0.00740494
I0408 08:00:52.834082 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:00:54.573863 31856 solver.cpp:218] Iteration 2532 (2.4498 iter/s, 4.89836s/12 iters), loss = 5.28309
I0408 08:00:54.573900 31856 solver.cpp:237]     Train net output #0: loss = 5.28309 (* 1 = 5.28309 loss)
I0408 08:00:54.573909 31856 sgd_solver.cpp:105] Iteration 2532, lr = 0.00731372
I0408 08:00:59.630755 31856 solver.cpp:218] Iteration 2544 (2.37309 iter/s, 5.0567s/12 iters), loss = 5.27364
I0408 08:00:59.630802 31856 solver.cpp:237]     Train net output #0: loss = 5.27364 (* 1 = 5.27364 loss)
I0408 08:00:59.630813 31856 sgd_solver.cpp:105] Iteration 2544, lr = 0.00722363
I0408 08:01:01.698560 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0408 08:01:04.746521 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0408 08:01:07.062657 31856 solver.cpp:330] Iteration 2550, Testing net (#0)
I0408 08:01:07.062680 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:01:10.494936 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:01:11.517621 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:01:11.517669 31856 solver.cpp:397]     Test net output #1: loss = 5.28701 (* 1 = 5.28701 loss)
I0408 08:01:13.516783 31856 solver.cpp:218] Iteration 2556 (0.864205 iter/s, 13.8856s/12 iters), loss = 5.27907
I0408 08:01:13.516834 31856 solver.cpp:237]     Train net output #0: loss = 5.27907 (* 1 = 5.27907 loss)
I0408 08:01:13.516845 31856 sgd_solver.cpp:105] Iteration 2556, lr = 0.00713464
I0408 08:01:18.895067 31856 solver.cpp:218] Iteration 2568 (2.23128 iter/s, 5.37807s/12 iters), loss = 5.2862
I0408 08:01:18.895112 31856 solver.cpp:237]     Train net output #0: loss = 5.2862 (* 1 = 5.2862 loss)
I0408 08:01:18.895123 31856 sgd_solver.cpp:105] Iteration 2568, lr = 0.00704675
I0408 08:01:23.904915 31856 solver.cpp:218] Iteration 2580 (2.39538 iter/s, 5.00965s/12 iters), loss = 5.26715
I0408 08:01:23.905097 31856 solver.cpp:237]     Train net output #0: loss = 5.26715 (* 1 = 5.26715 loss)
I0408 08:01:23.905114 31856 sgd_solver.cpp:105] Iteration 2580, lr = 0.00695994
I0408 08:01:28.910599 31856 solver.cpp:218] Iteration 2592 (2.39743 iter/s, 5.00536s/12 iters), loss = 5.28705
I0408 08:01:28.910637 31856 solver.cpp:237]     Train net output #0: loss = 5.28705 (* 1 = 5.28705 loss)
I0408 08:01:28.910645 31856 sgd_solver.cpp:105] Iteration 2592, lr = 0.0068742
I0408 08:01:33.887663 31856 solver.cpp:218] Iteration 2604 (2.41115 iter/s, 4.97687s/12 iters), loss = 5.25873
I0408 08:01:33.887717 31856 solver.cpp:237]     Train net output #0: loss = 5.25873 (* 1 = 5.25873 loss)
I0408 08:01:33.887729 31856 sgd_solver.cpp:105] Iteration 2604, lr = 0.00678952
I0408 08:01:38.825851 31856 solver.cpp:218] Iteration 2616 (2.43014 iter/s, 4.93799s/12 iters), loss = 5.27992
I0408 08:01:38.825896 31856 solver.cpp:237]     Train net output #0: loss = 5.27992 (* 1 = 5.27992 loss)
I0408 08:01:38.825907 31856 sgd_solver.cpp:105] Iteration 2616, lr = 0.00670588
I0408 08:01:43.863003 31856 solver.cpp:218] Iteration 2628 (2.38239 iter/s, 5.03696s/12 iters), loss = 5.27877
I0408 08:01:43.863046 31856 solver.cpp:237]     Train net output #0: loss = 5.27877 (* 1 = 5.27877 loss)
I0408 08:01:43.863057 31856 sgd_solver.cpp:105] Iteration 2628, lr = 0.00662327
I0408 08:01:44.291226 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:01:48.877213 31856 solver.cpp:218] Iteration 2640 (2.39329 iter/s, 5.01402s/12 iters), loss = 5.27972
I0408 08:01:48.877249 31856 solver.cpp:237]     Train net output #0: loss = 5.27972 (* 1 = 5.27972 loss)
I0408 08:01:48.877259 31856 sgd_solver.cpp:105] Iteration 2640, lr = 0.00654168
I0408 08:01:53.448668 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0408 08:01:56.418642 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0408 08:01:58.847200 31856 solver.cpp:330] Iteration 2652, Testing net (#0)
I0408 08:01:58.847225 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:02:02.407630 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:02:03.467496 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:02:03.467545 31856 solver.cpp:397]     Test net output #1: loss = 5.2872 (* 1 = 5.2872 loss)
I0408 08:02:03.557631 31856 solver.cpp:218] Iteration 2652 (0.817441 iter/s, 14.68s/12 iters), loss = 5.26843
I0408 08:02:03.557680 31856 solver.cpp:237]     Train net output #0: loss = 5.26843 (* 1 = 5.26843 loss)
I0408 08:02:03.557691 31856 sgd_solver.cpp:105] Iteration 2652, lr = 0.0064611
I0408 08:02:07.906095 31856 solver.cpp:218] Iteration 2664 (2.75971 iter/s, 4.34828s/12 iters), loss = 5.27957
I0408 08:02:07.906144 31856 solver.cpp:237]     Train net output #0: loss = 5.27957 (* 1 = 5.27957 loss)
I0408 08:02:07.906157 31856 sgd_solver.cpp:105] Iteration 2664, lr = 0.0063815
I0408 08:02:12.857254 31856 solver.cpp:218] Iteration 2676 (2.42377 iter/s, 4.95096s/12 iters), loss = 5.26826
I0408 08:02:12.857300 31856 solver.cpp:237]     Train net output #0: loss = 5.26826 (* 1 = 5.26826 loss)
I0408 08:02:12.857311 31856 sgd_solver.cpp:105] Iteration 2676, lr = 0.00630289
I0408 08:02:17.853932 31856 solver.cpp:218] Iteration 2688 (2.40169 iter/s, 4.99648s/12 iters), loss = 5.25805
I0408 08:02:17.853998 31856 solver.cpp:237]     Train net output #0: loss = 5.25805 (* 1 = 5.25805 loss)
I0408 08:02:17.854012 31856 sgd_solver.cpp:105] Iteration 2688, lr = 0.00622525
I0408 08:02:22.865034 31856 solver.cpp:218] Iteration 2700 (2.39479 iter/s, 5.01088s/12 iters), loss = 5.28102
I0408 08:02:22.865087 31856 solver.cpp:237]     Train net output #0: loss = 5.28102 (* 1 = 5.28102 loss)
I0408 08:02:22.865098 31856 sgd_solver.cpp:105] Iteration 2700, lr = 0.00614856
I0408 08:02:27.842916 31856 solver.cpp:218] Iteration 2712 (2.41076 iter/s, 4.97768s/12 iters), loss = 5.28191
I0408 08:02:27.843039 31856 solver.cpp:237]     Train net output #0: loss = 5.28191 (* 1 = 5.28191 loss)
I0408 08:02:27.843048 31856 sgd_solver.cpp:105] Iteration 2712, lr = 0.00607282
I0408 08:02:32.883822 31856 solver.cpp:218] Iteration 2724 (2.38065 iter/s, 5.04064s/12 iters), loss = 5.25704
I0408 08:02:32.883867 31856 solver.cpp:237]     Train net output #0: loss = 5.25704 (* 1 = 5.25704 loss)
I0408 08:02:32.883878 31856 sgd_solver.cpp:105] Iteration 2724, lr = 0.00599801
I0408 08:02:35.461216 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:02:37.856808 31856 solver.cpp:218] Iteration 2736 (2.41313 iter/s, 4.97279s/12 iters), loss = 5.27976
I0408 08:02:37.856853 31856 solver.cpp:237]     Train net output #0: loss = 5.27976 (* 1 = 5.27976 loss)
I0408 08:02:37.856863 31856 sgd_solver.cpp:105] Iteration 2736, lr = 0.00592412
I0408 08:02:42.833467 31856 solver.cpp:218] Iteration 2748 (2.41135 iter/s, 4.97647s/12 iters), loss = 5.27673
I0408 08:02:42.833510 31856 solver.cpp:237]     Train net output #0: loss = 5.27673 (* 1 = 5.27673 loss)
I0408 08:02:42.833521 31856 sgd_solver.cpp:105] Iteration 2748, lr = 0.00585114
I0408 08:02:44.882509 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0408 08:02:47.896185 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0408 08:02:50.225663 31856 solver.cpp:330] Iteration 2754, Testing net (#0)
I0408 08:02:50.225690 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:02:53.326213 31856 blocking_queue.cpp:49] Waiting for data
I0408 08:02:53.562350 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:02:54.668500 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0408 08:02:54.668540 31856 solver.cpp:397]     Test net output #1: loss = 5.28703 (* 1 = 5.28703 loss)
I0408 08:02:56.510478 31856 solver.cpp:218] Iteration 2760 (0.877412 iter/s, 13.6766s/12 iters), loss = 5.2787
I0408 08:02:56.510524 31856 solver.cpp:237]     Train net output #0: loss = 5.2787 (* 1 = 5.2787 loss)
I0408 08:02:56.510535 31856 sgd_solver.cpp:105] Iteration 2760, lr = 0.00577906
I0408 08:03:01.525264 31856 solver.cpp:218] Iteration 2772 (2.39302 iter/s, 5.01459s/12 iters), loss = 5.27724
I0408 08:03:01.525377 31856 solver.cpp:237]     Train net output #0: loss = 5.27724 (* 1 = 5.27724 loss)
I0408 08:03:01.525388 31856 sgd_solver.cpp:105] Iteration 2772, lr = 0.00570787
I0408 08:03:06.589394 31856 solver.cpp:218] Iteration 2784 (2.36973 iter/s, 5.06386s/12 iters), loss = 5.27798
I0408 08:03:06.589442 31856 solver.cpp:237]     Train net output #0: loss = 5.27798 (* 1 = 5.27798 loss)
I0408 08:03:06.589454 31856 sgd_solver.cpp:105] Iteration 2784, lr = 0.00563755
I0408 08:03:11.545820 31856 solver.cpp:218] Iteration 2796 (2.42119 iter/s, 4.95623s/12 iters), loss = 5.2694
I0408 08:03:11.545868 31856 solver.cpp:237]     Train net output #0: loss = 5.2694 (* 1 = 5.2694 loss)
I0408 08:03:11.545881 31856 sgd_solver.cpp:105] Iteration 2796, lr = 0.00556811
I0408 08:03:16.467557 31856 solver.cpp:218] Iteration 2808 (2.43826 iter/s, 4.92154s/12 iters), loss = 5.2632
I0408 08:03:16.467603 31856 solver.cpp:237]     Train net output #0: loss = 5.2632 (* 1 = 5.2632 loss)
I0408 08:03:16.467615 31856 sgd_solver.cpp:105] Iteration 2808, lr = 0.00549951
I0408 08:03:21.419183 31856 solver.cpp:218] Iteration 2820 (2.42354 iter/s, 4.95143s/12 iters), loss = 5.27605
I0408 08:03:21.419225 31856 solver.cpp:237]     Train net output #0: loss = 5.27605 (* 1 = 5.27605 loss)
I0408 08:03:21.419236 31856 sgd_solver.cpp:105] Iteration 2820, lr = 0.00543177
I0408 08:03:26.157158 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:03:26.443996 31856 solver.cpp:218] Iteration 2832 (2.38824 iter/s, 5.02462s/12 iters), loss = 5.26093
I0408 08:03:26.444043 31856 solver.cpp:237]     Train net output #0: loss = 5.26093 (* 1 = 5.26093 loss)
I0408 08:03:26.444054 31856 sgd_solver.cpp:105] Iteration 2832, lr = 0.00536485
I0408 08:03:31.679652 31856 solver.cpp:218] Iteration 2844 (2.29206 iter/s, 5.23546s/12 iters), loss = 5.26854
I0408 08:03:31.679764 31856 solver.cpp:237]     Train net output #0: loss = 5.26854 (* 1 = 5.26854 loss)
I0408 08:03:31.679776 31856 sgd_solver.cpp:105] Iteration 2844, lr = 0.00529876
I0408 08:03:36.275094 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0408 08:03:39.268036 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0408 08:03:41.596624 31856 solver.cpp:330] Iteration 2856, Testing net (#0)
I0408 08:03:41.596650 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:03:44.813886 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:03:45.959398 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:03:45.959443 31856 solver.cpp:397]     Test net output #1: loss = 5.28725 (* 1 = 5.28725 loss)
I0408 08:03:46.049568 31856 solver.cpp:218] Iteration 2856 (0.835109 iter/s, 14.3694s/12 iters), loss = 5.29032
I0408 08:03:46.049643 31856 solver.cpp:237]     Train net output #0: loss = 5.29032 (* 1 = 5.29032 loss)
I0408 08:03:46.049659 31856 sgd_solver.cpp:105] Iteration 2856, lr = 0.00523349
I0408 08:03:50.169209 31856 solver.cpp:218] Iteration 2868 (2.91301 iter/s, 4.11945s/12 iters), loss = 5.27956
I0408 08:03:50.169245 31856 solver.cpp:237]     Train net output #0: loss = 5.27956 (* 1 = 5.27956 loss)
I0408 08:03:50.169255 31856 sgd_solver.cpp:105] Iteration 2868, lr = 0.00516902
I0408 08:03:55.063998 31856 solver.cpp:218] Iteration 2880 (2.45168 iter/s, 4.89461s/12 iters), loss = 5.27905
I0408 08:03:55.064035 31856 solver.cpp:237]     Train net output #0: loss = 5.27905 (* 1 = 5.27905 loss)
I0408 08:03:55.064044 31856 sgd_solver.cpp:105] Iteration 2880, lr = 0.00510534
I0408 08:03:59.990983 31856 solver.cpp:218] Iteration 2892 (2.43566 iter/s, 4.9268s/12 iters), loss = 5.27191
I0408 08:03:59.991021 31856 solver.cpp:237]     Train net output #0: loss = 5.27191 (* 1 = 5.27191 loss)
I0408 08:03:59.991029 31856 sgd_solver.cpp:105] Iteration 2892, lr = 0.00504245
I0408 08:04:04.994119 31856 solver.cpp:218] Iteration 2904 (2.39859 iter/s, 5.00295s/12 iters), loss = 5.25381
I0408 08:04:04.994210 31856 solver.cpp:237]     Train net output #0: loss = 5.25381 (* 1 = 5.25381 loss)
I0408 08:04:04.994220 31856 sgd_solver.cpp:105] Iteration 2904, lr = 0.00498033
I0408 08:04:09.977519 31856 solver.cpp:218] Iteration 2916 (2.40811 iter/s, 4.98317s/12 iters), loss = 5.27025
I0408 08:04:09.977555 31856 solver.cpp:237]     Train net output #0: loss = 5.27025 (* 1 = 5.27025 loss)
I0408 08:04:09.977564 31856 sgd_solver.cpp:105] Iteration 2916, lr = 0.00491898
I0408 08:04:14.963368 31856 solver.cpp:218] Iteration 2928 (2.40691 iter/s, 4.98565s/12 iters), loss = 5.27863
I0408 08:04:14.963430 31856 solver.cpp:237]     Train net output #0: loss = 5.27863 (* 1 = 5.27863 loss)
I0408 08:04:14.963446 31856 sgd_solver.cpp:105] Iteration 2928, lr = 0.00485839
I0408 08:04:16.823374 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:04:19.984349 31856 solver.cpp:218] Iteration 2940 (2.39007 iter/s, 5.02078s/12 iters), loss = 5.28258
I0408 08:04:19.984385 31856 solver.cpp:237]     Train net output #0: loss = 5.28258 (* 1 = 5.28258 loss)
I0408 08:04:19.984392 31856 sgd_solver.cpp:105] Iteration 2940, lr = 0.00479854
I0408 08:04:24.983307 31856 solver.cpp:218] Iteration 2952 (2.40059 iter/s, 4.99877s/12 iters), loss = 5.28079
I0408 08:04:24.983353 31856 solver.cpp:237]     Train net output #0: loss = 5.28079 (* 1 = 5.28079 loss)
I0408 08:04:24.983363 31856 sgd_solver.cpp:105] Iteration 2952, lr = 0.00473942
I0408 08:04:27.019115 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0408 08:04:29.941658 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0408 08:04:32.245548 31856 solver.cpp:330] Iteration 2958, Testing net (#0)
I0408 08:04:32.245574 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:04:35.520767 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:04:36.706836 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:04:36.706883 31856 solver.cpp:397]     Test net output #1: loss = 5.28697 (* 1 = 5.28697 loss)
I0408 08:04:38.524425 31856 solver.cpp:218] Iteration 2964 (0.886218 iter/s, 13.5407s/12 iters), loss = 5.24092
I0408 08:04:38.524472 31856 solver.cpp:237]     Train net output #0: loss = 5.24092 (* 1 = 5.24092 loss)
I0408 08:04:38.524483 31856 sgd_solver.cpp:105] Iteration 2964, lr = 0.00468104
I0408 08:04:43.491489 31856 solver.cpp:218] Iteration 2976 (2.41601 iter/s, 4.96687s/12 iters), loss = 5.2834
I0408 08:04:43.491539 31856 solver.cpp:237]     Train net output #0: loss = 5.2834 (* 1 = 5.2834 loss)
I0408 08:04:43.491550 31856 sgd_solver.cpp:105] Iteration 2976, lr = 0.00462337
I0408 08:04:48.490908 31856 solver.cpp:218] Iteration 2988 (2.40037 iter/s, 4.99922s/12 iters), loss = 5.26222
I0408 08:04:48.490953 31856 solver.cpp:237]     Train net output #0: loss = 5.26222 (* 1 = 5.26222 loss)
I0408 08:04:48.490965 31856 sgd_solver.cpp:105] Iteration 2988, lr = 0.00456642
I0408 08:04:53.509644 31856 solver.cpp:218] Iteration 3000 (2.39113 iter/s, 5.01854s/12 iters), loss = 5.26789
I0408 08:04:53.509685 31856 solver.cpp:237]     Train net output #0: loss = 5.26789 (* 1 = 5.26789 loss)
I0408 08:04:53.509696 31856 sgd_solver.cpp:105] Iteration 3000, lr = 0.00451017
I0408 08:04:58.512076 31856 solver.cpp:218] Iteration 3012 (2.39893 iter/s, 5.00223s/12 iters), loss = 5.27262
I0408 08:04:58.512120 31856 solver.cpp:237]     Train net output #0: loss = 5.27262 (* 1 = 5.27262 loss)
I0408 08:04:58.512130 31856 sgd_solver.cpp:105] Iteration 3012, lr = 0.00445461
I0408 08:05:03.508736 31856 solver.cpp:218] Iteration 3024 (2.40169 iter/s, 4.99647s/12 iters), loss = 5.25644
I0408 08:05:03.508769 31856 solver.cpp:237]     Train net output #0: loss = 5.25644 (* 1 = 5.25644 loss)
I0408 08:05:03.508778 31856 sgd_solver.cpp:105] Iteration 3024, lr = 0.00439973
I0408 08:05:07.393584 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:05:08.435508 31856 solver.cpp:218] Iteration 3036 (2.43576 iter/s, 4.92659s/12 iters), loss = 5.25798
I0408 08:05:08.435556 31856 solver.cpp:237]     Train net output #0: loss = 5.25798 (* 1 = 5.25798 loss)
I0408 08:05:08.435568 31856 sgd_solver.cpp:105] Iteration 3036, lr = 0.00434553
I0408 08:05:13.536279 31856 solver.cpp:218] Iteration 3048 (2.35268 iter/s, 5.10057s/12 iters), loss = 5.2902
I0408 08:05:13.536329 31856 solver.cpp:237]     Train net output #0: loss = 5.2902 (* 1 = 5.2902 loss)
I0408 08:05:13.536340 31856 sgd_solver.cpp:105] Iteration 3048, lr = 0.004292
I0408 08:05:18.070724 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0408 08:05:21.048480 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0408 08:05:23.340708 31856 solver.cpp:330] Iteration 3060, Testing net (#0)
I0408 08:05:23.340728 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:05:27.107467 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:05:28.324355 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0408 08:05:28.324404 31856 solver.cpp:397]     Test net output #1: loss = 5.28675 (* 1 = 5.28675 loss)
I0408 08:05:28.415472 31856 solver.cpp:218] Iteration 3060 (0.806521 iter/s, 14.8787s/12 iters), loss = 5.25563
I0408 08:05:28.415513 31856 solver.cpp:237]     Train net output #0: loss = 5.25563 (* 1 = 5.25563 loss)
I0408 08:05:28.415524 31856 sgd_solver.cpp:105] Iteration 3060, lr = 0.00423913
I0408 08:05:32.799336 31856 solver.cpp:218] Iteration 3072 (2.73742 iter/s, 4.38369s/12 iters), loss = 5.30648
I0408 08:05:32.799382 31856 solver.cpp:237]     Train net output #0: loss = 5.30648 (* 1 = 5.30648 loss)
I0408 08:05:32.799394 31856 sgd_solver.cpp:105] Iteration 3072, lr = 0.00418691
I0408 08:05:37.778105 31856 solver.cpp:218] Iteration 3084 (2.41033 iter/s, 4.97858s/12 iters), loss = 5.27295
I0408 08:05:37.778265 31856 solver.cpp:237]     Train net output #0: loss = 5.27295 (* 1 = 5.27295 loss)
I0408 08:05:37.778278 31856 sgd_solver.cpp:105] Iteration 3084, lr = 0.00413533
I0408 08:05:42.654536 31856 solver.cpp:218] Iteration 3096 (2.46097 iter/s, 4.87613s/12 iters), loss = 5.27199
I0408 08:05:42.654584 31856 solver.cpp:237]     Train net output #0: loss = 5.27199 (* 1 = 5.27199 loss)
I0408 08:05:42.654595 31856 sgd_solver.cpp:105] Iteration 3096, lr = 0.00408439
I0408 08:05:47.630491 31856 solver.cpp:218] Iteration 3108 (2.41169 iter/s, 4.97576s/12 iters), loss = 5.27868
I0408 08:05:47.630539 31856 solver.cpp:237]     Train net output #0: loss = 5.27868 (* 1 = 5.27868 loss)
I0408 08:05:47.630553 31856 sgd_solver.cpp:105] Iteration 3108, lr = 0.00403407
I0408 08:05:52.562834 31856 solver.cpp:218] Iteration 3120 (2.43302 iter/s, 4.93215s/12 iters), loss = 5.2652
I0408 08:05:52.562885 31856 solver.cpp:237]     Train net output #0: loss = 5.2652 (* 1 = 5.2652 loss)
I0408 08:05:52.562898 31856 sgd_solver.cpp:105] Iteration 3120, lr = 0.00398438
I0408 08:05:57.933444 31856 solver.cpp:218] Iteration 3132 (2.23447 iter/s, 5.3704s/12 iters), loss = 5.275
I0408 08:05:57.933492 31856 solver.cpp:237]     Train net output #0: loss = 5.275 (* 1 = 5.275 loss)
I0408 08:05:57.933504 31856 sgd_solver.cpp:105] Iteration 3132, lr = 0.00393529
I0408 08:05:59.157765 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:06:03.406208 31856 solver.cpp:218] Iteration 3144 (2.19276 iter/s, 5.47255s/12 iters), loss = 5.28133
I0408 08:06:03.406258 31856 solver.cpp:237]     Train net output #0: loss = 5.28133 (* 1 = 5.28133 loss)
I0408 08:06:03.406270 31856 sgd_solver.cpp:105] Iteration 3144, lr = 0.00388681
I0408 08:06:08.653625 31856 solver.cpp:218] Iteration 3156 (2.28693 iter/s, 5.24721s/12 iters), loss = 5.249
I0408 08:06:08.653702 31856 solver.cpp:237]     Train net output #0: loss = 5.249 (* 1 = 5.249 loss)
I0408 08:06:08.653713 31856 sgd_solver.cpp:105] Iteration 3156, lr = 0.00383893
I0408 08:06:10.710423 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0408 08:06:13.737799 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0408 08:06:16.064016 31856 solver.cpp:330] Iteration 3162, Testing net (#0)
I0408 08:06:16.064041 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:06:19.361613 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:06:20.626188 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:06:20.626235 31856 solver.cpp:397]     Test net output #1: loss = 5.2869 (* 1 = 5.2869 loss)
I0408 08:06:22.608144 31856 solver.cpp:218] Iteration 3168 (0.859966 iter/s, 13.954s/12 iters), loss = 5.2681
I0408 08:06:22.608194 31856 solver.cpp:237]     Train net output #0: loss = 5.2681 (* 1 = 5.2681 loss)
I0408 08:06:22.608206 31856 sgd_solver.cpp:105] Iteration 3168, lr = 0.00379164
I0408 08:06:27.823179 31856 solver.cpp:218] Iteration 3180 (2.30113 iter/s, 5.21483s/12 iters), loss = 5.27133
I0408 08:06:27.823218 31856 solver.cpp:237]     Train net output #0: loss = 5.27133 (* 1 = 5.27133 loss)
I0408 08:06:27.823228 31856 sgd_solver.cpp:105] Iteration 3180, lr = 0.00374493
I0408 08:06:32.691828 31856 solver.cpp:218] Iteration 3192 (2.46484 iter/s, 4.86846s/12 iters), loss = 5.27778
I0408 08:06:32.691875 31856 solver.cpp:237]     Train net output #0: loss = 5.27778 (* 1 = 5.27778 loss)
I0408 08:06:32.691887 31856 sgd_solver.cpp:105] Iteration 3192, lr = 0.0036988
I0408 08:06:37.708830 31856 solver.cpp:218] Iteration 3204 (2.39196 iter/s, 5.01681s/12 iters), loss = 5.26192
I0408 08:06:37.708868 31856 solver.cpp:237]     Train net output #0: loss = 5.26192 (* 1 = 5.26192 loss)
I0408 08:06:37.708878 31856 sgd_solver.cpp:105] Iteration 3204, lr = 0.00365324
I0408 08:06:42.826093 31856 solver.cpp:218] Iteration 3216 (2.34509 iter/s, 5.11707s/12 iters), loss = 5.28825
I0408 08:06:42.829064 31856 solver.cpp:237]     Train net output #0: loss = 5.28825 (* 1 = 5.28825 loss)
I0408 08:06:42.829078 31856 sgd_solver.cpp:105] Iteration 3216, lr = 0.00360823
I0408 08:06:47.841595 31856 solver.cpp:218] Iteration 3228 (2.39407 iter/s, 5.01238s/12 iters), loss = 5.27716
I0408 08:06:47.841650 31856 solver.cpp:237]     Train net output #0: loss = 5.27716 (* 1 = 5.27716 loss)
I0408 08:06:47.841665 31856 sgd_solver.cpp:105] Iteration 3228, lr = 0.00356378
I0408 08:06:51.085475 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:06:52.842403 31856 solver.cpp:218] Iteration 3240 (2.39971 iter/s, 5.00061s/12 iters), loss = 5.28199
I0408 08:06:52.842442 31856 solver.cpp:237]     Train net output #0: loss = 5.28199 (* 1 = 5.28199 loss)
I0408 08:06:52.842451 31856 sgd_solver.cpp:105] Iteration 3240, lr = 0.00351988
I0408 08:06:57.914880 31856 solver.cpp:218] Iteration 3252 (2.3658 iter/s, 5.07228s/12 iters), loss = 5.26957
I0408 08:06:57.914930 31856 solver.cpp:237]     Train net output #0: loss = 5.26957 (* 1 = 5.26957 loss)
I0408 08:06:57.914942 31856 sgd_solver.cpp:105] Iteration 3252, lr = 0.00347652
I0408 08:07:02.433461 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0408 08:07:05.476982 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0408 08:07:07.789440 31856 solver.cpp:330] Iteration 3264, Testing net (#0)
I0408 08:07:07.789463 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:07:10.949798 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:07:12.260018 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:07:12.260066 31856 solver.cpp:397]     Test net output #1: loss = 5.28742 (* 1 = 5.28742 loss)
I0408 08:07:12.350056 31856 solver.cpp:218] Iteration 3264 (0.831329 iter/s, 14.4347s/12 iters), loss = 5.27563
I0408 08:07:12.350104 31856 solver.cpp:237]     Train net output #0: loss = 5.27563 (* 1 = 5.27563 loss)
I0408 08:07:12.350116 31856 sgd_solver.cpp:105] Iteration 3264, lr = 0.00343369
I0408 08:07:16.592995 31856 solver.cpp:218] Iteration 3276 (2.82835 iter/s, 4.24276s/12 iters), loss = 5.28837
I0408 08:07:16.593114 31856 solver.cpp:237]     Train net output #0: loss = 5.28837 (* 1 = 5.28837 loss)
I0408 08:07:16.593127 31856 sgd_solver.cpp:105] Iteration 3276, lr = 0.00339139
I0408 08:07:21.528760 31856 solver.cpp:218] Iteration 3288 (2.43136 iter/s, 4.9355s/12 iters), loss = 5.26016
I0408 08:07:21.528800 31856 solver.cpp:237]     Train net output #0: loss = 5.26016 (* 1 = 5.26016 loss)
I0408 08:07:21.528810 31856 sgd_solver.cpp:105] Iteration 3288, lr = 0.00334962
I0408 08:07:26.532629 31856 solver.cpp:218] Iteration 3300 (2.39823 iter/s, 5.00368s/12 iters), loss = 5.28246
I0408 08:07:26.532667 31856 solver.cpp:237]     Train net output #0: loss = 5.28246 (* 1 = 5.28246 loss)
I0408 08:07:26.532676 31856 sgd_solver.cpp:105] Iteration 3300, lr = 0.00330835
I0408 08:07:31.559514 31856 solver.cpp:218] Iteration 3312 (2.38726 iter/s, 5.02669s/12 iters), loss = 5.25531
I0408 08:07:31.559563 31856 solver.cpp:237]     Train net output #0: loss = 5.25531 (* 1 = 5.25531 loss)
I0408 08:07:31.559577 31856 sgd_solver.cpp:105] Iteration 3312, lr = 0.0032676
I0408 08:07:36.532510 31856 solver.cpp:218] Iteration 3324 (2.41313 iter/s, 4.9728s/12 iters), loss = 5.28158
I0408 08:07:36.532552 31856 solver.cpp:237]     Train net output #0: loss = 5.28158 (* 1 = 5.28158 loss)
I0408 08:07:36.532564 31856 sgd_solver.cpp:105] Iteration 3324, lr = 0.00322734
I0408 08:07:41.491703 31856 solver.cpp:218] Iteration 3336 (2.41984 iter/s, 4.959s/12 iters), loss = 5.27364
I0408 08:07:41.491748 31856 solver.cpp:237]     Train net output #0: loss = 5.27364 (* 1 = 5.27364 loss)
I0408 08:07:41.491761 31856 sgd_solver.cpp:105] Iteration 3336, lr = 0.00318759
I0408 08:07:41.952008 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:07:46.418462 31856 solver.cpp:218] Iteration 3348 (2.43577 iter/s, 4.92657s/12 iters), loss = 5.27664
I0408 08:07:46.418504 31856 solver.cpp:237]     Train net output #0: loss = 5.27664 (* 1 = 5.27664 loss)
I0408 08:07:46.418515 31856 sgd_solver.cpp:105] Iteration 3348, lr = 0.00314832
I0408 08:07:51.493618 31856 solver.cpp:218] Iteration 3360 (2.36455 iter/s, 5.07496s/12 iters), loss = 5.26633
I0408 08:07:51.493803 31856 solver.cpp:237]     Train net output #0: loss = 5.26633 (* 1 = 5.26633 loss)
I0408 08:07:51.493825 31856 sgd_solver.cpp:105] Iteration 3360, lr = 0.00310954
I0408 08:07:53.526051 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0408 08:07:56.582269 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0408 08:07:58.967006 31856 solver.cpp:330] Iteration 3366, Testing net (#0)
I0408 08:07:58.967025 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:08:02.093070 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:08:03.429793 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:08:03.429836 31856 solver.cpp:397]     Test net output #1: loss = 5.28712 (* 1 = 5.28712 loss)
I0408 08:08:05.351694 31856 solver.cpp:218] Iteration 3372 (0.865956 iter/s, 13.8575s/12 iters), loss = 5.28592
I0408 08:08:05.351739 31856 solver.cpp:237]     Train net output #0: loss = 5.28592 (* 1 = 5.28592 loss)
I0408 08:08:05.351749 31856 sgd_solver.cpp:105] Iteration 3372, lr = 0.00307123
I0408 08:08:10.312674 31856 solver.cpp:218] Iteration 3384 (2.41897 iter/s, 4.96079s/12 iters), loss = 5.26577
I0408 08:08:10.312718 31856 solver.cpp:237]     Train net output #0: loss = 5.26577 (* 1 = 5.26577 loss)
I0408 08:08:10.312729 31856 sgd_solver.cpp:105] Iteration 3384, lr = 0.0030334
I0408 08:08:15.281788 31856 solver.cpp:218] Iteration 3396 (2.41501 iter/s, 4.96892s/12 iters), loss = 5.2616
I0408 08:08:15.281831 31856 solver.cpp:237]     Train net output #0: loss = 5.2616 (* 1 = 5.2616 loss)
I0408 08:08:15.281841 31856 sgd_solver.cpp:105] Iteration 3396, lr = 0.00299603
I0408 08:08:20.116418 31856 solver.cpp:218] Iteration 3408 (2.48219 iter/s, 4.83444s/12 iters), loss = 5.28758
I0408 08:08:20.116472 31856 solver.cpp:237]     Train net output #0: loss = 5.28758 (* 1 = 5.28758 loss)
I0408 08:08:20.116487 31856 sgd_solver.cpp:105] Iteration 3408, lr = 0.00295912
I0408 08:08:25.012210 31856 solver.cpp:218] Iteration 3420 (2.45118 iter/s, 4.8956s/12 iters), loss = 5.27702
I0408 08:08:25.013720 31856 solver.cpp:237]     Train net output #0: loss = 5.27702 (* 1 = 5.27702 loss)
I0408 08:08:25.013734 31856 sgd_solver.cpp:105] Iteration 3420, lr = 0.00292267
I0408 08:08:30.093207 31856 solver.cpp:218] Iteration 3432 (2.36251 iter/s, 5.07934s/12 iters), loss = 5.26053
I0408 08:08:30.093251 31856 solver.cpp:237]     Train net output #0: loss = 5.26053 (* 1 = 5.26053 loss)
I0408 08:08:30.093263 31856 sgd_solver.cpp:105] Iteration 3432, lr = 0.00288666
I0408 08:08:32.710695 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:08:35.149653 31856 solver.cpp:218] Iteration 3444 (2.3733 iter/s, 5.05625s/12 iters), loss = 5.27387
I0408 08:08:35.149701 31856 solver.cpp:237]     Train net output #0: loss = 5.27387 (* 1 = 5.27387 loss)
I0408 08:08:35.149713 31856 sgd_solver.cpp:105] Iteration 3444, lr = 0.0028511
I0408 08:08:40.177898 31856 solver.cpp:218] Iteration 3456 (2.38661 iter/s, 5.02805s/12 iters), loss = 5.27125
I0408 08:08:40.177944 31856 solver.cpp:237]     Train net output #0: loss = 5.27125 (* 1 = 5.27125 loss)
I0408 08:08:40.177968 31856 sgd_solver.cpp:105] Iteration 3456, lr = 0.00281598
I0408 08:08:44.756426 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0408 08:08:47.754783 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0408 08:08:50.081413 31856 solver.cpp:330] Iteration 3468, Testing net (#0)
I0408 08:08:50.081439 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:08:50.539821 31856 blocking_queue.cpp:49] Waiting for data
I0408 08:08:53.151266 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:08:54.533324 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:08:54.533367 31856 solver.cpp:397]     Test net output #1: loss = 5.28736 (* 1 = 5.28736 loss)
I0408 08:08:54.621208 31856 solver.cpp:218] Iteration 3468 (0.830861 iter/s, 14.4429s/12 iters), loss = 5.27316
I0408 08:08:54.621258 31856 solver.cpp:237]     Train net output #0: loss = 5.27316 (* 1 = 5.27316 loss)
I0408 08:08:54.621268 31856 sgd_solver.cpp:105] Iteration 3468, lr = 0.00278129
I0408 08:08:58.752691 31856 solver.cpp:218] Iteration 3480 (2.90465 iter/s, 4.13131s/12 iters), loss = 5.2787
I0408 08:08:58.752838 31856 solver.cpp:237]     Train net output #0: loss = 5.2787 (* 1 = 5.2787 loss)
I0408 08:08:58.752852 31856 sgd_solver.cpp:105] Iteration 3480, lr = 0.00274703
I0408 08:09:03.780181 31856 solver.cpp:218] Iteration 3492 (2.38702 iter/s, 5.0272s/12 iters), loss = 5.28809
I0408 08:09:03.780228 31856 solver.cpp:237]     Train net output #0: loss = 5.28809 (* 1 = 5.28809 loss)
I0408 08:09:03.780239 31856 sgd_solver.cpp:105] Iteration 3492, lr = 0.00271319
I0408 08:09:08.722959 31856 solver.cpp:218] Iteration 3504 (2.42788 iter/s, 4.94258s/12 iters), loss = 5.27186
I0408 08:09:08.723004 31856 solver.cpp:237]     Train net output #0: loss = 5.27186 (* 1 = 5.27186 loss)
I0408 08:09:08.723016 31856 sgd_solver.cpp:105] Iteration 3504, lr = 0.00267977
I0408 08:09:13.661814 31856 solver.cpp:218] Iteration 3516 (2.42981 iter/s, 4.93866s/12 iters), loss = 5.26324
I0408 08:09:13.661867 31856 solver.cpp:237]     Train net output #0: loss = 5.26324 (* 1 = 5.26324 loss)
I0408 08:09:13.661880 31856 sgd_solver.cpp:105] Iteration 3516, lr = 0.00264675
I0408 08:09:18.577342 31856 solver.cpp:218] Iteration 3528 (2.44134 iter/s, 4.91533s/12 iters), loss = 5.27081
I0408 08:09:18.577387 31856 solver.cpp:237]     Train net output #0: loss = 5.27081 (* 1 = 5.27081 loss)
I0408 08:09:18.577397 31856 sgd_solver.cpp:105] Iteration 3528, lr = 0.00261415
I0408 08:09:23.354784 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:09:23.613461 31856 solver.cpp:218] Iteration 3540 (2.38288 iter/s, 5.03592s/12 iters), loss = 5.25647
I0408 08:09:23.613504 31856 solver.cpp:237]     Train net output #0: loss = 5.25647 (* 1 = 5.25647 loss)
I0408 08:09:23.613514 31856 sgd_solver.cpp:105] Iteration 3540, lr = 0.00258195
I0408 08:09:28.624119 31856 solver.cpp:218] Iteration 3552 (2.39499 iter/s, 5.01047s/12 iters), loss = 5.26694
I0408 08:09:28.624161 31856 solver.cpp:237]     Train net output #0: loss = 5.26694 (* 1 = 5.26694 loss)
I0408 08:09:28.624171 31856 sgd_solver.cpp:105] Iteration 3552, lr = 0.00255014
I0408 08:09:33.615391 31856 solver.cpp:218] Iteration 3564 (2.40429 iter/s, 4.99108s/12 iters), loss = 5.29196
I0408 08:09:33.615471 31856 solver.cpp:237]     Train net output #0: loss = 5.29196 (* 1 = 5.29196 loss)
I0408 08:09:33.615483 31856 sgd_solver.cpp:105] Iteration 3564, lr = 0.00251873
I0408 08:09:35.677266 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0408 08:09:38.721004 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0408 08:09:41.071208 31856 solver.cpp:330] Iteration 3570, Testing net (#0)
I0408 08:09:41.071233 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:09:44.115823 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:09:45.529778 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:09:45.529822 31856 solver.cpp:397]     Test net output #1: loss = 5.28726 (* 1 = 5.28726 loss)
I0408 08:09:47.446386 31856 solver.cpp:218] Iteration 3576 (0.867646 iter/s, 13.8305s/12 iters), loss = 5.28202
I0408 08:09:47.446430 31856 solver.cpp:237]     Train net output #0: loss = 5.28202 (* 1 = 5.28202 loss)
I0408 08:09:47.446440 31856 sgd_solver.cpp:105] Iteration 3576, lr = 0.0024877
I0408 08:09:52.469318 31856 solver.cpp:218] Iteration 3588 (2.38913 iter/s, 5.02274s/12 iters), loss = 5.27651
I0408 08:09:52.469354 31856 solver.cpp:237]     Train net output #0: loss = 5.27651 (* 1 = 5.27651 loss)
I0408 08:09:52.469362 31856 sgd_solver.cpp:105] Iteration 3588, lr = 0.00245705
I0408 08:09:57.501215 31856 solver.cpp:218] Iteration 3600 (2.38488 iter/s, 5.03171s/12 iters), loss = 5.26353
I0408 08:09:57.501260 31856 solver.cpp:237]     Train net output #0: loss = 5.26353 (* 1 = 5.26353 loss)
I0408 08:09:57.501271 31856 sgd_solver.cpp:105] Iteration 3600, lr = 0.00242678
I0408 08:10:02.554610 31856 solver.cpp:218] Iteration 3612 (2.37473 iter/s, 5.0532s/12 iters), loss = 5.24015
I0408 08:10:02.554656 31856 solver.cpp:237]     Train net output #0: loss = 5.24015 (* 1 = 5.24015 loss)
I0408 08:10:02.554667 31856 sgd_solver.cpp:105] Iteration 3612, lr = 0.00239689
I0408 08:10:07.613833 31856 solver.cpp:218] Iteration 3624 (2.372 iter/s, 5.05903s/12 iters), loss = 5.27581
I0408 08:10:07.613987 31856 solver.cpp:237]     Train net output #0: loss = 5.27581 (* 1 = 5.27581 loss)
I0408 08:10:07.614001 31856 sgd_solver.cpp:105] Iteration 3624, lr = 0.00236736
I0408 08:10:12.801914 31856 solver.cpp:218] Iteration 3636 (2.31313 iter/s, 5.18778s/12 iters), loss = 5.27931
I0408 08:10:12.801967 31856 solver.cpp:237]     Train net output #0: loss = 5.27931 (* 1 = 5.27931 loss)
I0408 08:10:12.801980 31856 sgd_solver.cpp:105] Iteration 3636, lr = 0.0023382
I0408 08:10:14.681524 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:10:17.828225 31856 solver.cpp:218] Iteration 3648 (2.38753 iter/s, 5.02611s/12 iters), loss = 5.28537
I0408 08:10:17.828276 31856 solver.cpp:237]     Train net output #0: loss = 5.28537 (* 1 = 5.28537 loss)
I0408 08:10:17.828287 31856 sgd_solver.cpp:105] Iteration 3648, lr = 0.0023094
I0408 08:10:22.893208 31856 solver.cpp:218] Iteration 3660 (2.3693 iter/s, 5.06478s/12 iters), loss = 5.27778
I0408 08:10:22.893254 31856 solver.cpp:237]     Train net output #0: loss = 5.27778 (* 1 = 5.27778 loss)
I0408 08:10:22.893265 31856 sgd_solver.cpp:105] Iteration 3660, lr = 0.00228095
I0408 08:10:27.453229 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0408 08:10:30.468611 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0408 08:10:32.795749 31856 solver.cpp:330] Iteration 3672, Testing net (#0)
I0408 08:10:32.795774 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:10:35.760123 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:10:37.220682 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:10:37.220729 31856 solver.cpp:397]     Test net output #1: loss = 5.28666 (* 1 = 5.28666 loss)
I0408 08:10:37.307847 31856 solver.cpp:218] Iteration 3672 (0.832513 iter/s, 14.4142s/12 iters), loss = 5.25026
I0408 08:10:37.307895 31856 solver.cpp:237]     Train net output #0: loss = 5.25026 (* 1 = 5.25026 loss)
I0408 08:10:37.307906 31856 sgd_solver.cpp:105] Iteration 3672, lr = 0.00225285
I0408 08:10:41.500181 31856 solver.cpp:218] Iteration 3684 (2.86249 iter/s, 4.19216s/12 iters), loss = 5.2703
I0408 08:10:41.500360 31856 solver.cpp:237]     Train net output #0: loss = 5.2703 (* 1 = 5.2703 loss)
I0408 08:10:41.500377 31856 sgd_solver.cpp:105] Iteration 3684, lr = 0.00222509
I0408 08:10:46.481310 31856 solver.cpp:218] Iteration 3696 (2.40925 iter/s, 4.98081s/12 iters), loss = 5.26012
I0408 08:10:46.481359 31856 solver.cpp:237]     Train net output #0: loss = 5.26012 (* 1 = 5.26012 loss)
I0408 08:10:46.481370 31856 sgd_solver.cpp:105] Iteration 3696, lr = 0.00219768
I0408 08:10:51.470810 31856 solver.cpp:218] Iteration 3708 (2.40515 iter/s, 4.9893s/12 iters), loss = 5.26836
I0408 08:10:51.470854 31856 solver.cpp:237]     Train net output #0: loss = 5.26836 (* 1 = 5.26836 loss)
I0408 08:10:51.470865 31856 sgd_solver.cpp:105] Iteration 3708, lr = 0.00217061
I0408 08:10:56.654841 31856 solver.cpp:218] Iteration 3720 (2.31489 iter/s, 5.18383s/12 iters), loss = 5.26798
I0408 08:10:56.654889 31856 solver.cpp:237]     Train net output #0: loss = 5.26798 (* 1 = 5.26798 loss)
I0408 08:10:56.654901 31856 sgd_solver.cpp:105] Iteration 3720, lr = 0.00214387
I0408 08:11:01.993892 31856 solver.cpp:218] Iteration 3732 (2.24768 iter/s, 5.33884s/12 iters), loss = 5.25443
I0408 08:11:01.993944 31856 solver.cpp:237]     Train net output #0: loss = 5.25443 (* 1 = 5.25443 loss)
I0408 08:11:01.993971 31856 sgd_solver.cpp:105] Iteration 3732, lr = 0.00211746
I0408 08:11:06.341945 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:11:07.431241 31856 solver.cpp:218] Iteration 3744 (2.20704 iter/s, 5.43714s/12 iters), loss = 5.25531
I0408 08:11:07.431293 31856 solver.cpp:237]     Train net output #0: loss = 5.25531 (* 1 = 5.25531 loss)
I0408 08:11:07.431305 31856 sgd_solver.cpp:105] Iteration 3744, lr = 0.00209138
I0408 08:11:12.556280 31856 solver.cpp:218] Iteration 3756 (2.34154 iter/s, 5.12484s/12 iters), loss = 5.27856
I0408 08:11:12.556354 31856 solver.cpp:237]     Train net output #0: loss = 5.27856 (* 1 = 5.27856 loss)
I0408 08:11:12.556366 31856 sgd_solver.cpp:105] Iteration 3756, lr = 0.00206561
I0408 08:11:17.465306 31856 solver.cpp:218] Iteration 3768 (2.44459 iter/s, 4.90881s/12 iters), loss = 5.25977
I0408 08:11:17.465356 31856 solver.cpp:237]     Train net output #0: loss = 5.25977 (* 1 = 5.25977 loss)
I0408 08:11:17.465368 31856 sgd_solver.cpp:105] Iteration 3768, lr = 0.00204017
I0408 08:11:19.518615 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0408 08:11:24.099261 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0408 08:11:27.677464 31856 solver.cpp:330] Iteration 3774, Testing net (#0)
I0408 08:11:27.677482 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:11:30.638794 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:11:32.136936 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:11:32.136984 31856 solver.cpp:397]     Test net output #1: loss = 5.28754 (* 1 = 5.28754 loss)
I0408 08:11:34.095803 31856 solver.cpp:218] Iteration 3780 (0.721588 iter/s, 16.63s/12 iters), loss = 5.31049
I0408 08:11:34.095858 31856 solver.cpp:237]     Train net output #0: loss = 5.31049 (* 1 = 5.31049 loss)
I0408 08:11:34.095872 31856 sgd_solver.cpp:105] Iteration 3780, lr = 0.00201504
I0408 08:11:39.095232 31856 solver.cpp:218] Iteration 3792 (2.40037 iter/s, 4.99923s/12 iters), loss = 5.27583
I0408 08:11:39.095279 31856 solver.cpp:237]     Train net output #0: loss = 5.27583 (* 1 = 5.27583 loss)
I0408 08:11:39.095293 31856 sgd_solver.cpp:105] Iteration 3792, lr = 0.00199021
I0408 08:11:44.024853 31856 solver.cpp:218] Iteration 3804 (2.43436 iter/s, 4.92943s/12 iters), loss = 5.26875
I0408 08:11:44.024953 31856 solver.cpp:237]     Train net output #0: loss = 5.26875 (* 1 = 5.26875 loss)
I0408 08:11:44.024961 31856 sgd_solver.cpp:105] Iteration 3804, lr = 0.0019657
I0408 08:11:49.129474 31856 solver.cpp:218] Iteration 3816 (2.35093 iter/s, 5.10437s/12 iters), loss = 5.27129
I0408 08:11:49.129519 31856 solver.cpp:237]     Train net output #0: loss = 5.27129 (* 1 = 5.27129 loss)
I0408 08:11:49.129532 31856 sgd_solver.cpp:105] Iteration 3816, lr = 0.00194148
I0408 08:11:54.329046 31856 solver.cpp:218] Iteration 3828 (2.30797 iter/s, 5.19937s/12 iters), loss = 5.26259
I0408 08:11:54.329095 31856 solver.cpp:237]     Train net output #0: loss = 5.26259 (* 1 = 5.26259 loss)
I0408 08:11:54.329106 31856 sgd_solver.cpp:105] Iteration 3828, lr = 0.00191756
I0408 08:11:59.361461 31856 solver.cpp:218] Iteration 3840 (2.38464 iter/s, 5.03222s/12 iters), loss = 5.26788
I0408 08:11:59.361508 31856 solver.cpp:237]     Train net output #0: loss = 5.26788 (* 1 = 5.26788 loss)
I0408 08:11:59.361521 31856 sgd_solver.cpp:105] Iteration 3840, lr = 0.00189394
I0408 08:12:00.512135 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:12:04.339393 31856 solver.cpp:218] Iteration 3852 (2.41073 iter/s, 4.97774s/12 iters), loss = 5.27466
I0408 08:12:04.339442 31856 solver.cpp:237]     Train net output #0: loss = 5.27466 (* 1 = 5.27466 loss)
I0408 08:12:04.339470 31856 sgd_solver.cpp:105] Iteration 3852, lr = 0.00187061
I0408 08:12:09.264854 31856 solver.cpp:218] Iteration 3864 (2.43642 iter/s, 4.92527s/12 iters), loss = 5.25342
I0408 08:12:09.264899 31856 solver.cpp:237]     Train net output #0: loss = 5.25342 (* 1 = 5.25342 loss)
I0408 08:12:09.264911 31856 sgd_solver.cpp:105] Iteration 3864, lr = 0.00184757
I0408 08:12:13.826205 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0408 08:12:16.802299 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0408 08:12:19.129022 31856 solver.cpp:330] Iteration 3876, Testing net (#0)
I0408 08:12:19.129047 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:12:22.040495 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:12:23.585999 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:12:23.586047 31856 solver.cpp:397]     Test net output #1: loss = 5.28705 (* 1 = 5.28705 loss)
I0408 08:12:23.676237 31856 solver.cpp:218] Iteration 3876 (0.832701 iter/s, 14.4109s/12 iters), loss = 5.27115
I0408 08:12:23.676278 31856 solver.cpp:237]     Train net output #0: loss = 5.27115 (* 1 = 5.27115 loss)
I0408 08:12:23.676290 31856 sgd_solver.cpp:105] Iteration 3876, lr = 0.00182481
I0408 08:12:27.892892 31856 solver.cpp:218] Iteration 3888 (2.84597 iter/s, 4.21649s/12 iters), loss = 5.269
I0408 08:12:27.892936 31856 solver.cpp:237]     Train net output #0: loss = 5.269 (* 1 = 5.269 loss)
I0408 08:12:27.892948 31856 sgd_solver.cpp:105] Iteration 3888, lr = 0.00180233
I0408 08:12:32.850759 31856 solver.cpp:218] Iteration 3900 (2.42049 iter/s, 4.95767s/12 iters), loss = 5.2744
I0408 08:12:32.850803 31856 solver.cpp:237]     Train net output #0: loss = 5.2744 (* 1 = 5.2744 loss)
I0408 08:12:32.850816 31856 sgd_solver.cpp:105] Iteration 3900, lr = 0.00178012
I0408 08:12:37.804169 31856 solver.cpp:218] Iteration 3912 (2.42267 iter/s, 4.95322s/12 iters), loss = 5.2588
I0408 08:12:37.804215 31856 solver.cpp:237]     Train net output #0: loss = 5.2588 (* 1 = 5.2588 loss)
I0408 08:12:37.804226 31856 sgd_solver.cpp:105] Iteration 3912, lr = 0.0017582
I0408 08:12:42.786600 31856 solver.cpp:218] Iteration 3924 (2.40856 iter/s, 4.98224s/12 iters), loss = 5.29162
I0408 08:12:42.786638 31856 solver.cpp:237]     Train net output #0: loss = 5.29162 (* 1 = 5.29162 loss)
I0408 08:12:42.786646 31856 sgd_solver.cpp:105] Iteration 3924, lr = 0.00173654
I0408 08:12:47.766916 31856 solver.cpp:218] Iteration 3936 (2.40958 iter/s, 4.98013s/12 iters), loss = 5.2731
I0408 08:12:47.767032 31856 solver.cpp:237]     Train net output #0: loss = 5.2731 (* 1 = 5.2731 loss)
I0408 08:12:47.767045 31856 sgd_solver.cpp:105] Iteration 3936, lr = 0.00171514
I0408 08:12:51.082257 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:12:52.690369 31856 solver.cpp:218] Iteration 3948 (2.43744 iter/s, 4.92319s/12 iters), loss = 5.28461
I0408 08:12:52.690415 31856 solver.cpp:237]     Train net output #0: loss = 5.28461 (* 1 = 5.28461 loss)
I0408 08:12:52.690428 31856 sgd_solver.cpp:105] Iteration 3948, lr = 0.00169402
I0408 08:12:57.522502 31856 solver.cpp:218] Iteration 3960 (2.48347 iter/s, 4.83194s/12 iters), loss = 5.27012
I0408 08:12:57.522547 31856 solver.cpp:237]     Train net output #0: loss = 5.27012 (* 1 = 5.27012 loss)
I0408 08:12:57.522559 31856 sgd_solver.cpp:105] Iteration 3960, lr = 0.00167315
I0408 08:13:02.421311 31856 solver.cpp:218] Iteration 3972 (2.44967 iter/s, 4.89862s/12 iters), loss = 5.28064
I0408 08:13:02.421360 31856 solver.cpp:237]     Train net output #0: loss = 5.28064 (* 1 = 5.28064 loss)
I0408 08:13:02.421371 31856 sgd_solver.cpp:105] Iteration 3972, lr = 0.00165254
I0408 08:13:04.423765 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0408 08:13:07.494132 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0408 08:13:09.826508 31856 solver.cpp:330] Iteration 3978, Testing net (#0)
I0408 08:13:09.826534 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:13:12.805182 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:13:14.384181 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:13:14.384209 31856 solver.cpp:397]     Test net output #1: loss = 5.28687 (* 1 = 5.28687 loss)
I0408 08:13:16.163450 31856 solver.cpp:218] Iteration 3984 (0.873254 iter/s, 13.7417s/12 iters), loss = 5.28052
I0408 08:13:16.163484 31856 solver.cpp:237]     Train net output #0: loss = 5.28052 (* 1 = 5.28052 loss)
I0408 08:13:16.163491 31856 sgd_solver.cpp:105] Iteration 3984, lr = 0.00163218
I0408 08:13:21.201885 31856 solver.cpp:218] Iteration 3996 (2.38178 iter/s, 5.03825s/12 iters), loss = 5.26201
I0408 08:13:21.202051 31856 solver.cpp:237]     Train net output #0: loss = 5.26201 (* 1 = 5.26201 loss)
I0408 08:13:21.202065 31856 sgd_solver.cpp:105] Iteration 3996, lr = 0.00161207
I0408 08:13:26.239078 31856 solver.cpp:218] Iteration 4008 (2.38243 iter/s, 5.03688s/12 iters), loss = 5.28664
I0408 08:13:26.239122 31856 solver.cpp:237]     Train net output #0: loss = 5.28664 (* 1 = 5.28664 loss)
I0408 08:13:26.239133 31856 sgd_solver.cpp:105] Iteration 4008, lr = 0.00159221
I0408 08:13:31.683607 31856 solver.cpp:218] Iteration 4020 (2.20413 iter/s, 5.44432s/12 iters), loss = 5.25445
I0408 08:13:31.683655 31856 solver.cpp:237]     Train net output #0: loss = 5.25445 (* 1 = 5.25445 loss)
I0408 08:13:31.683667 31856 sgd_solver.cpp:105] Iteration 4020, lr = 0.0015726
I0408 08:13:36.686658 31856 solver.cpp:218] Iteration 4032 (2.39863 iter/s, 5.00285s/12 iters), loss = 5.27359
I0408 08:13:36.686708 31856 solver.cpp:237]     Train net output #0: loss = 5.27359 (* 1 = 5.27359 loss)
I0408 08:13:36.686720 31856 sgd_solver.cpp:105] Iteration 4032, lr = 0.00155323
I0408 08:13:41.705494 31856 solver.cpp:218] Iteration 4044 (2.39109 iter/s, 5.01864s/12 iters), loss = 5.27273
I0408 08:13:41.705541 31856 solver.cpp:237]     Train net output #0: loss = 5.27273 (* 1 = 5.27273 loss)
I0408 08:13:41.705552 31856 sgd_solver.cpp:105] Iteration 4044, lr = 0.00153409
I0408 08:13:42.231335 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:13:46.687966 31856 solver.cpp:218] Iteration 4056 (2.40854 iter/s, 4.98227s/12 iters), loss = 5.27383
I0408 08:13:46.688025 31856 solver.cpp:237]     Train net output #0: loss = 5.27383 (* 1 = 5.27383 loss)
I0408 08:13:46.688037 31856 sgd_solver.cpp:105] Iteration 4056, lr = 0.00151519
I0408 08:13:51.686702 31856 solver.cpp:218] Iteration 4068 (2.40071 iter/s, 4.99853s/12 iters), loss = 5.27101
I0408 08:13:51.686810 31856 solver.cpp:237]     Train net output #0: loss = 5.27101 (* 1 = 5.27101 loss)
I0408 08:13:51.686822 31856 sgd_solver.cpp:105] Iteration 4068, lr = 0.00149653
I0408 08:13:56.186504 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0408 08:13:59.225401 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0408 08:14:01.542419 31856 solver.cpp:330] Iteration 4080, Testing net (#0)
I0408 08:14:01.542441 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:14:04.345679 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:14:05.960391 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:14:05.960440 31856 solver.cpp:397]     Test net output #1: loss = 5.28741 (* 1 = 5.28741 loss)
I0408 08:14:06.048626 31856 solver.cpp:218] Iteration 4080 (0.835572 iter/s, 14.3614s/12 iters), loss = 5.28599
I0408 08:14:06.048669 31856 solver.cpp:237]     Train net output #0: loss = 5.28599 (* 1 = 5.28599 loss)
I0408 08:14:06.048681 31856 sgd_solver.cpp:105] Iteration 4080, lr = 0.00147809
I0408 08:14:10.427610 31856 solver.cpp:218] Iteration 4092 (2.74047 iter/s, 4.37881s/12 iters), loss = 5.26395
I0408 08:14:10.427657 31856 solver.cpp:237]     Train net output #0: loss = 5.26395 (* 1 = 5.26395 loss)
I0408 08:14:10.427668 31856 sgd_solver.cpp:105] Iteration 4092, lr = 0.00145989
I0408 08:14:15.330201 31856 solver.cpp:218] Iteration 4104 (2.44778 iter/s, 4.9024s/12 iters), loss = 5.26227
I0408 08:14:15.330250 31856 solver.cpp:237]     Train net output #0: loss = 5.26227 (* 1 = 5.26227 loss)
I0408 08:14:15.330260 31856 sgd_solver.cpp:105] Iteration 4104, lr = 0.0014419
I0408 08:14:20.291883 31856 solver.cpp:218] Iteration 4116 (2.41863 iter/s, 4.96149s/12 iters), loss = 5.29287
I0408 08:14:20.291927 31856 solver.cpp:237]     Train net output #0: loss = 5.29287 (* 1 = 5.29287 loss)
I0408 08:14:20.291939 31856 sgd_solver.cpp:105] Iteration 4116, lr = 0.00142414
I0408 08:14:25.246508 31856 solver.cpp:218] Iteration 4128 (2.42207 iter/s, 4.95444s/12 iters), loss = 5.26629
I0408 08:14:25.247174 31856 solver.cpp:237]     Train net output #0: loss = 5.26629 (* 1 = 5.26629 loss)
I0408 08:14:25.247185 31856 sgd_solver.cpp:105] Iteration 4128, lr = 0.00140659
I0408 08:14:30.314545 31856 solver.cpp:218] Iteration 4140 (2.36816 iter/s, 5.06723s/12 iters), loss = 5.25894
I0408 08:14:30.314592 31856 solver.cpp:237]     Train net output #0: loss = 5.25894 (* 1 = 5.25894 loss)
I0408 08:14:30.314605 31856 sgd_solver.cpp:105] Iteration 4140, lr = 0.00138927
I0408 08:14:33.125234 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:14:35.652468 31856 solver.cpp:218] Iteration 4152 (2.24815 iter/s, 5.33772s/12 iters), loss = 5.26901
I0408 08:14:35.652513 31856 solver.cpp:237]     Train net output #0: loss = 5.26901 (* 1 = 5.26901 loss)
I0408 08:14:35.652524 31856 sgd_solver.cpp:105] Iteration 4152, lr = 0.00137215
I0408 08:14:37.422857 31856 blocking_queue.cpp:49] Waiting for data
I0408 08:14:40.871410 31856 solver.cpp:218] Iteration 4164 (2.2994 iter/s, 5.21874s/12 iters), loss = 5.26413
I0408 08:14:40.871454 31856 solver.cpp:237]     Train net output #0: loss = 5.26413 (* 1 = 5.26413 loss)
I0408 08:14:40.871466 31856 sgd_solver.cpp:105] Iteration 4164, lr = 0.00135525
I0408 08:14:46.147222 31856 solver.cpp:218] Iteration 4176 (2.27462 iter/s, 5.27561s/12 iters), loss = 5.26617
I0408 08:14:46.147266 31856 solver.cpp:237]     Train net output #0: loss = 5.26617 (* 1 = 5.26617 loss)
I0408 08:14:46.147277 31856 sgd_solver.cpp:105] Iteration 4176, lr = 0.00133855
I0408 08:14:48.167953 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0408 08:14:51.191880 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0408 08:14:53.553222 31856 solver.cpp:330] Iteration 4182, Testing net (#0)
I0408 08:14:53.553247 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:14:56.503535 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:14:58.164508 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0408 08:14:58.164556 31856 solver.cpp:397]     Test net output #1: loss = 5.28703 (* 1 = 5.28703 loss)
I0408 08:15:00.169193 31856 solver.cpp:218] Iteration 4188 (0.855827 iter/s, 14.0215s/12 iters), loss = 5.26887
I0408 08:15:00.169234 31856 solver.cpp:237]     Train net output #0: loss = 5.26887 (* 1 = 5.26887 loss)
I0408 08:15:00.169245 31856 sgd_solver.cpp:105] Iteration 4188, lr = 0.00132207
I0408 08:15:05.613548 31856 solver.cpp:218] Iteration 4200 (2.2042 iter/s, 5.44415s/12 iters), loss = 5.28391
I0408 08:15:05.613585 31856 solver.cpp:237]     Train net output #0: loss = 5.28391 (* 1 = 5.28391 loss)
I0408 08:15:05.613593 31856 sgd_solver.cpp:105] Iteration 4200, lr = 0.00130578
I0408 08:15:10.869695 31856 solver.cpp:218] Iteration 4212 (2.28313 iter/s, 5.25595s/12 iters), loss = 5.27055
I0408 08:15:10.869743 31856 solver.cpp:237]     Train net output #0: loss = 5.27055 (* 1 = 5.27055 loss)
I0408 08:15:10.869755 31856 sgd_solver.cpp:105] Iteration 4212, lr = 0.00128969
I0408 08:15:15.993841 31856 solver.cpp:218] Iteration 4224 (2.34195 iter/s, 5.12395s/12 iters), loss = 5.26237
I0408 08:15:15.993878 31856 solver.cpp:237]     Train net output #0: loss = 5.26237 (* 1 = 5.26237 loss)
I0408 08:15:15.993887 31856 sgd_solver.cpp:105] Iteration 4224, lr = 0.00127381
I0408 08:15:20.964406 31856 solver.cpp:218] Iteration 4236 (2.4143 iter/s, 4.97038s/12 iters), loss = 5.26641
I0408 08:15:20.964450 31856 solver.cpp:237]     Train net output #0: loss = 5.26641 (* 1 = 5.26641 loss)
I0408 08:15:20.964463 31856 sgd_solver.cpp:105] Iteration 4236, lr = 0.00125811
I0408 08:15:25.737701 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:15:25.963163 31856 solver.cpp:218] Iteration 4248 (2.40069 iter/s, 4.99857s/12 iters), loss = 5.24341
I0408 08:15:25.963210 31856 solver.cpp:237]     Train net output #0: loss = 5.24341 (* 1 = 5.24341 loss)
I0408 08:15:25.963222 31856 sgd_solver.cpp:105] Iteration 4248, lr = 0.00124262
I0408 08:15:31.101481 31856 solver.cpp:218] Iteration 4260 (2.33548 iter/s, 5.13812s/12 iters), loss = 5.26711
I0408 08:15:31.101619 31856 solver.cpp:237]     Train net output #0: loss = 5.26711 (* 1 = 5.26711 loss)
I0408 08:15:31.101630 31856 sgd_solver.cpp:105] Iteration 4260, lr = 0.00122731
I0408 08:15:36.180191 31856 solver.cpp:218] Iteration 4272 (2.36294 iter/s, 5.07842s/12 iters), loss = 5.29105
I0408 08:15:36.180229 31856 solver.cpp:237]     Train net output #0: loss = 5.29105 (* 1 = 5.29105 loss)
I0408 08:15:36.180239 31856 sgd_solver.cpp:105] Iteration 4272, lr = 0.00121219
I0408 08:15:40.684162 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0408 08:15:43.676412 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0408 08:15:45.978169 31856 solver.cpp:330] Iteration 4284, Testing net (#0)
I0408 08:15:45.978189 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:15:48.628232 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:15:50.334605 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:15:50.334653 31856 solver.cpp:397]     Test net output #1: loss = 5.28715 (* 1 = 5.28715 loss)
I0408 08:15:50.424913 31856 solver.cpp:218] Iteration 4284 (0.842444 iter/s, 14.2443s/12 iters), loss = 5.27596
I0408 08:15:50.424954 31856 solver.cpp:237]     Train net output #0: loss = 5.27596 (* 1 = 5.27596 loss)
I0408 08:15:50.424965 31856 sgd_solver.cpp:105] Iteration 4284, lr = 0.00119726
I0408 08:15:54.918377 31856 solver.cpp:218] Iteration 4296 (2.67065 iter/s, 4.49329s/12 iters), loss = 5.27531
I0408 08:15:54.918419 31856 solver.cpp:237]     Train net output #0: loss = 5.27531 (* 1 = 5.27531 loss)
I0408 08:15:54.918429 31856 sgd_solver.cpp:105] Iteration 4296, lr = 0.00118251
I0408 08:15:59.899575 31856 solver.cpp:218] Iteration 4308 (2.40915 iter/s, 4.981s/12 iters), loss = 5.26137
I0408 08:15:59.899621 31856 solver.cpp:237]     Train net output #0: loss = 5.26137 (* 1 = 5.26137 loss)
I0408 08:15:59.899633 31856 sgd_solver.cpp:105] Iteration 4308, lr = 0.00116794
I0408 08:16:04.923708 31856 solver.cpp:218] Iteration 4320 (2.38856 iter/s, 5.02394s/12 iters), loss = 5.24646
I0408 08:16:04.923825 31856 solver.cpp:237]     Train net output #0: loss = 5.24646 (* 1 = 5.24646 loss)
I0408 08:16:04.923837 31856 sgd_solver.cpp:105] Iteration 4320, lr = 0.00115355
I0408 08:16:09.904603 31856 solver.cpp:218] Iteration 4332 (2.40933 iter/s, 4.98063s/12 iters), loss = 5.27581
I0408 08:16:09.904646 31856 solver.cpp:237]     Train net output #0: loss = 5.27581 (* 1 = 5.27581 loss)
I0408 08:16:09.904659 31856 sgd_solver.cpp:105] Iteration 4332, lr = 0.00113934
I0408 08:16:14.914938 31856 solver.cpp:218] Iteration 4344 (2.39514 iter/s, 5.01014s/12 iters), loss = 5.27909
I0408 08:16:14.914984 31856 solver.cpp:237]     Train net output #0: loss = 5.27909 (* 1 = 5.27909 loss)
I0408 08:16:14.914996 31856 sgd_solver.cpp:105] Iteration 4344, lr = 0.00112531
I0408 08:16:16.828635 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:16:19.956326 31856 solver.cpp:218] Iteration 4356 (2.38039 iter/s, 5.04119s/12 iters), loss = 5.28846
I0408 08:16:19.956373 31856 solver.cpp:237]     Train net output #0: loss = 5.28846 (* 1 = 5.28846 loss)
I0408 08:16:19.956385 31856 sgd_solver.cpp:105] Iteration 4356, lr = 0.00111144
I0408 08:16:24.997064 31856 solver.cpp:218] Iteration 4368 (2.3807 iter/s, 5.04054s/12 iters), loss = 5.27646
I0408 08:16:24.997110 31856 solver.cpp:237]     Train net output #0: loss = 5.27646 (* 1 = 5.27646 loss)
I0408 08:16:24.997123 31856 sgd_solver.cpp:105] Iteration 4368, lr = 0.00109775
I0408 08:16:29.895555 31856 solver.cpp:218] Iteration 4380 (2.44983 iter/s, 4.8983s/12 iters), loss = 5.2595
I0408 08:16:29.895601 31856 solver.cpp:237]     Train net output #0: loss = 5.2595 (* 1 = 5.2595 loss)
I0408 08:16:29.895612 31856 sgd_solver.cpp:105] Iteration 4380, lr = 0.00108423
I0408 08:16:31.925308 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0408 08:16:34.959112 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0408 08:16:37.294392 31856 solver.cpp:330] Iteration 4386, Testing net (#0)
I0408 08:16:37.294417 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:16:40.051759 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:16:41.829286 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:16:41.829334 31856 solver.cpp:397]     Test net output #1: loss = 5.2871 (* 1 = 5.2871 loss)
I0408 08:16:43.670310 31856 solver.cpp:218] Iteration 4392 (0.871186 iter/s, 13.7743s/12 iters), loss = 5.26739
I0408 08:16:43.670359 31856 solver.cpp:237]     Train net output #0: loss = 5.26739 (* 1 = 5.26739 loss)
I0408 08:16:43.670370 31856 sgd_solver.cpp:105] Iteration 4392, lr = 0.00107087
I0408 08:16:49.009461 31856 solver.cpp:218] Iteration 4404 (2.24763 iter/s, 5.33895s/12 iters), loss = 5.26185
I0408 08:16:49.009510 31856 solver.cpp:237]     Train net output #0: loss = 5.26185 (* 1 = 5.26185 loss)
I0408 08:16:49.009521 31856 sgd_solver.cpp:105] Iteration 4404, lr = 0.00105768
I0408 08:16:53.893509 31856 solver.cpp:218] Iteration 4416 (2.45708 iter/s, 4.88385s/12 iters), loss = 5.26467
I0408 08:16:53.893558 31856 solver.cpp:237]     Train net output #0: loss = 5.26467 (* 1 = 5.26467 loss)
I0408 08:16:53.893568 31856 sgd_solver.cpp:105] Iteration 4416, lr = 0.00104465
I0408 08:16:58.886545 31856 solver.cpp:218] Iteration 4428 (2.40344 iter/s, 4.99284s/12 iters), loss = 5.26716
I0408 08:16:58.886591 31856 solver.cpp:237]     Train net output #0: loss = 5.26716 (* 1 = 5.26716 loss)
I0408 08:16:58.886602 31856 sgd_solver.cpp:105] Iteration 4428, lr = 0.00103178
I0408 08:17:04.039417 31856 solver.cpp:218] Iteration 4440 (2.32889 iter/s, 5.15267s/12 iters), loss = 5.26113
I0408 08:17:04.039470 31856 solver.cpp:237]     Train net output #0: loss = 5.26113 (* 1 = 5.26113 loss)
I0408 08:17:04.039482 31856 sgd_solver.cpp:105] Iteration 4440, lr = 0.00101907
I0408 08:17:08.134152 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:17:09.071727 31856 solver.cpp:218] Iteration 4452 (2.38469 iter/s, 5.03211s/12 iters), loss = 5.25571
I0408 08:17:09.071782 31856 solver.cpp:237]     Train net output #0: loss = 5.25571 (* 1 = 5.25571 loss)
I0408 08:17:09.071799 31856 sgd_solver.cpp:105] Iteration 4452, lr = 0.00100652
I0408 08:17:14.135815 31856 solver.cpp:218] Iteration 4464 (2.36972 iter/s, 5.06388s/12 iters), loss = 5.27866
I0408 08:17:14.135860 31856 solver.cpp:237]     Train net output #0: loss = 5.27866 (* 1 = 5.27866 loss)
I0408 08:17:14.135871 31856 sgd_solver.cpp:105] Iteration 4464, lr = 0.000994119
I0408 08:17:19.115731 31856 solver.cpp:218] Iteration 4476 (2.40977 iter/s, 4.97972s/12 iters), loss = 5.25854
I0408 08:17:19.115767 31856 solver.cpp:237]     Train net output #0: loss = 5.25854 (* 1 = 5.25854 loss)
I0408 08:17:19.115775 31856 sgd_solver.cpp:105] Iteration 4476, lr = 0.000981873
I0408 08:17:23.632690 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0408 08:17:26.633458 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0408 08:17:28.955699 31856 solver.cpp:330] Iteration 4488, Testing net (#0)
I0408 08:17:28.955722 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:17:31.645552 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:17:33.454525 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0408 08:17:33.454571 31856 solver.cpp:397]     Test net output #1: loss = 5.28696 (* 1 = 5.28696 loss)
I0408 08:17:33.544689 31856 solver.cpp:218] Iteration 4488 (0.831687 iter/s, 14.4285s/12 iters), loss = 5.30947
I0408 08:17:33.544737 31856 solver.cpp:237]     Train net output #0: loss = 5.30947 (* 1 = 5.30947 loss)
I0408 08:17:33.544749 31856 sgd_solver.cpp:105] Iteration 4488, lr = 0.000969778
I0408 08:17:37.929811 31856 solver.cpp:218] Iteration 4500 (2.73664 iter/s, 4.38495s/12 iters), loss = 5.27019
I0408 08:17:37.929847 31856 solver.cpp:237]     Train net output #0: loss = 5.27019 (* 1 = 5.27019 loss)
I0408 08:17:37.929855 31856 sgd_solver.cpp:105] Iteration 4500, lr = 0.000957831
I0408 08:17:42.915627 31856 solver.cpp:218] Iteration 4512 (2.40692 iter/s, 4.98563s/12 iters), loss = 5.26876
I0408 08:17:42.915763 31856 solver.cpp:237]     Train net output #0: loss = 5.26876 (* 1 = 5.26876 loss)
I0408 08:17:42.915777 31856 sgd_solver.cpp:105] Iteration 4512, lr = 0.000946032
I0408 08:17:47.975008 31856 solver.cpp:218] Iteration 4524 (2.37196 iter/s, 5.0591s/12 iters), loss = 5.27407
I0408 08:17:47.975049 31856 solver.cpp:237]     Train net output #0: loss = 5.27407 (* 1 = 5.27407 loss)
I0408 08:17:47.975059 31856 sgd_solver.cpp:105] Iteration 4524, lr = 0.000934378
I0408 08:17:52.990605 31856 solver.cpp:218] Iteration 4536 (2.39263 iter/s, 5.01541s/12 iters), loss = 5.26724
I0408 08:17:52.990643 31856 solver.cpp:237]     Train net output #0: loss = 5.26724 (* 1 = 5.26724 loss)
I0408 08:17:52.990651 31856 sgd_solver.cpp:105] Iteration 4536, lr = 0.000922867
I0408 08:17:58.067797 31856 solver.cpp:218] Iteration 4548 (2.3636 iter/s, 5.077s/12 iters), loss = 5.26443
I0408 08:17:58.067849 31856 solver.cpp:237]     Train net output #0: loss = 5.26443 (* 1 = 5.26443 loss)
I0408 08:17:58.067865 31856 sgd_solver.cpp:105] Iteration 4548, lr = 0.000911499
I0408 08:17:59.335331 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:18:03.126446 31856 solver.cpp:218] Iteration 4560 (2.37227 iter/s, 5.05845s/12 iters), loss = 5.27379
I0408 08:18:03.126487 31856 solver.cpp:237]     Train net output #0: loss = 5.27379 (* 1 = 5.27379 loss)
I0408 08:18:03.126497 31856 sgd_solver.cpp:105] Iteration 4560, lr = 0.00090027
I0408 08:18:08.027798 31856 solver.cpp:218] Iteration 4572 (2.4484 iter/s, 4.90116s/12 iters), loss = 5.26223
I0408 08:18:08.027843 31856 solver.cpp:237]     Train net output #0: loss = 5.26223 (* 1 = 5.26223 loss)
I0408 08:18:08.027853 31856 sgd_solver.cpp:105] Iteration 4572, lr = 0.00088918
I0408 08:18:13.028721 31856 solver.cpp:218] Iteration 4584 (2.39965 iter/s, 5.00073s/12 iters), loss = 5.27472
I0408 08:18:13.028807 31856 solver.cpp:237]     Train net output #0: loss = 5.27472 (* 1 = 5.27472 loss)
I0408 08:18:13.028818 31856 sgd_solver.cpp:105] Iteration 4584, lr = 0.000878226
I0408 08:18:15.069969 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0408 08:18:18.057869 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0408 08:18:20.379843 31856 solver.cpp:330] Iteration 4590, Testing net (#0)
I0408 08:18:20.379868 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:18:23.039176 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:18:24.892537 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:18:24.892585 31856 solver.cpp:397]     Test net output #1: loss = 5.28692 (* 1 = 5.28692 loss)
I0408 08:18:26.893581 31856 solver.cpp:218] Iteration 4596 (0.865527 iter/s, 13.8644s/12 iters), loss = 5.27181
I0408 08:18:26.893630 31856 solver.cpp:237]     Train net output #0: loss = 5.27181 (* 1 = 5.27181 loss)
I0408 08:18:26.893641 31856 sgd_solver.cpp:105] Iteration 4596, lr = 0.000867407
I0408 08:18:32.213546 31856 solver.cpp:218] Iteration 4608 (2.25574 iter/s, 5.31976s/12 iters), loss = 5.27292
I0408 08:18:32.213591 31856 solver.cpp:237]     Train net output #0: loss = 5.27292 (* 1 = 5.27292 loss)
I0408 08:18:32.213603 31856 sgd_solver.cpp:105] Iteration 4608, lr = 0.000856722
I0408 08:18:37.184370 31856 solver.cpp:218] Iteration 4620 (2.41418 iter/s, 4.97063s/12 iters), loss = 5.26217
I0408 08:18:37.184417 31856 solver.cpp:237]     Train net output #0: loss = 5.26217 (* 1 = 5.26217 loss)
I0408 08:18:37.184429 31856 sgd_solver.cpp:105] Iteration 4620, lr = 0.000846168
I0408 08:18:42.234959 31856 solver.cpp:218] Iteration 4632 (2.37605 iter/s, 5.05039s/12 iters), loss = 5.29309
I0408 08:18:42.235006 31856 solver.cpp:237]     Train net output #0: loss = 5.29309 (* 1 = 5.29309 loss)
I0408 08:18:42.235018 31856 sgd_solver.cpp:105] Iteration 4632, lr = 0.000835744
I0408 08:18:47.222837 31856 solver.cpp:218] Iteration 4644 (2.40593 iter/s, 4.98768s/12 iters), loss = 5.26663
I0408 08:18:47.222970 31856 solver.cpp:237]     Train net output #0: loss = 5.26663 (* 1 = 5.26663 loss)
I0408 08:18:47.222980 31856 sgd_solver.cpp:105] Iteration 4644, lr = 0.000825449
I0408 08:18:50.595933 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:18:52.238929 31856 solver.cpp:218] Iteration 4656 (2.39244 iter/s, 5.01581s/12 iters), loss = 5.27936
I0408 08:18:52.238976 31856 solver.cpp:237]     Train net output #0: loss = 5.27936 (* 1 = 5.27936 loss)
I0408 08:18:52.238988 31856 sgd_solver.cpp:105] Iteration 4656, lr = 0.00081528
I0408 08:18:57.387390 31856 solver.cpp:218] Iteration 4668 (2.33088 iter/s, 5.14827s/12 iters), loss = 5.2645
I0408 08:18:57.387432 31856 solver.cpp:237]     Train net output #0: loss = 5.2645 (* 1 = 5.2645 loss)
I0408 08:18:57.387441 31856 sgd_solver.cpp:105] Iteration 4668, lr = 0.000805237
I0408 08:19:02.730449 31856 solver.cpp:218] Iteration 4680 (2.24599 iter/s, 5.34286s/12 iters), loss = 5.27471
I0408 08:19:02.730499 31856 solver.cpp:237]     Train net output #0: loss = 5.27471 (* 1 = 5.27471 loss)
I0408 08:19:02.730510 31856 sgd_solver.cpp:105] Iteration 4680, lr = 0.000795317
I0408 08:19:07.204169 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0408 08:19:10.248431 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0408 08:19:12.580566 31856 solver.cpp:330] Iteration 4692, Testing net (#0)
I0408 08:19:12.580592 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:19:15.165493 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:19:17.062335 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:19:17.062383 31856 solver.cpp:397]     Test net output #1: loss = 5.28726 (* 1 = 5.28726 loss)
I0408 08:19:17.152213 31856 solver.cpp:218] Iteration 4692 (0.832102 iter/s, 14.4213s/12 iters), loss = 5.26888
I0408 08:19:17.152267 31856 solver.cpp:237]     Train net output #0: loss = 5.26888 (* 1 = 5.26888 loss)
I0408 08:19:17.152278 31856 sgd_solver.cpp:105] Iteration 4692, lr = 0.00078552
I0408 08:19:21.244905 31856 solver.cpp:218] Iteration 4704 (2.93218 iter/s, 4.09252s/12 iters), loss = 5.26472
I0408 08:19:21.245028 31856 solver.cpp:237]     Train net output #0: loss = 5.26472 (* 1 = 5.26472 loss)
I0408 08:19:21.245043 31856 sgd_solver.cpp:105] Iteration 4704, lr = 0.000775843
I0408 08:19:26.265419 31856 solver.cpp:218] Iteration 4716 (2.39032 iter/s, 5.02024s/12 iters), loss = 5.27974
I0408 08:19:26.265465 31856 solver.cpp:237]     Train net output #0: loss = 5.27974 (* 1 = 5.27974 loss)
I0408 08:19:26.265476 31856 sgd_solver.cpp:105] Iteration 4716, lr = 0.000766286
I0408 08:19:31.265692 31856 solver.cpp:218] Iteration 4728 (2.39996 iter/s, 5.00007s/12 iters), loss = 5.26192
I0408 08:19:31.265741 31856 solver.cpp:237]     Train net output #0: loss = 5.26192 (* 1 = 5.26192 loss)
I0408 08:19:31.265753 31856 sgd_solver.cpp:105] Iteration 4728, lr = 0.000756846
I0408 08:19:36.253203 31856 solver.cpp:218] Iteration 4740 (2.4061 iter/s, 4.98731s/12 iters), loss = 5.2759
I0408 08:19:36.253249 31856 solver.cpp:237]     Train net output #0: loss = 5.2759 (* 1 = 5.2759 loss)
I0408 08:19:36.253262 31856 sgd_solver.cpp:105] Iteration 4740, lr = 0.000747523
I0408 08:19:41.214516 31856 solver.cpp:218] Iteration 4752 (2.41881 iter/s, 4.96112s/12 iters), loss = 5.28081
I0408 08:19:41.214562 31856 solver.cpp:237]     Train net output #0: loss = 5.28081 (* 1 = 5.28081 loss)
I0408 08:19:41.214573 31856 sgd_solver.cpp:105] Iteration 4752, lr = 0.000738314
I0408 08:19:41.762053 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:19:46.299183 31856 solver.cpp:218] Iteration 4764 (2.36013 iter/s, 5.08447s/12 iters), loss = 5.27837
I0408 08:19:46.299232 31856 solver.cpp:237]     Train net output #0: loss = 5.27837 (* 1 = 5.27837 loss)
I0408 08:19:46.299243 31856 sgd_solver.cpp:105] Iteration 4764, lr = 0.000729219
I0408 08:19:51.338255 31856 solver.cpp:218] Iteration 4776 (2.38148 iter/s, 5.03888s/12 iters), loss = 5.2649
I0408 08:19:51.338855 31856 solver.cpp:237]     Train net output #0: loss = 5.2649 (* 1 = 5.2649 loss)
I0408 08:19:51.338867 31856 sgd_solver.cpp:105] Iteration 4776, lr = 0.000720236
I0408 08:19:56.395038 31856 solver.cpp:218] Iteration 4788 (2.3734 iter/s, 5.05604s/12 iters), loss = 5.2927
I0408 08:19:56.395082 31856 solver.cpp:237]     Train net output #0: loss = 5.2927 (* 1 = 5.2927 loss)
I0408 08:19:56.395093 31856 sgd_solver.cpp:105] Iteration 4788, lr = 0.000711363
I0408 08:19:58.422904 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0408 08:20:01.631139 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0408 08:20:04.013340 31856 solver.cpp:330] Iteration 4794, Testing net (#0)
I0408 08:20:04.013367 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:20:06.651443 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:20:08.552278 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0408 08:20:08.552325 31856 solver.cpp:397]     Test net output #1: loss = 5.28654 (* 1 = 5.28654 loss)
I0408 08:20:10.536404 31856 solver.cpp:218] Iteration 4800 (0.848601 iter/s, 14.1409s/12 iters), loss = 5.27294
I0408 08:20:10.536451 31856 solver.cpp:237]     Train net output #0: loss = 5.27294 (* 1 = 5.27294 loss)
I0408 08:20:10.536463 31856 sgd_solver.cpp:105] Iteration 4800, lr = 0.0007026
I0408 08:20:15.549914 31856 solver.cpp:218] Iteration 4812 (2.39363 iter/s, 5.01332s/12 iters), loss = 5.26398
I0408 08:20:15.549968 31856 solver.cpp:237]     Train net output #0: loss = 5.26398 (* 1 = 5.26398 loss)
I0408 08:20:15.549979 31856 sgd_solver.cpp:105] Iteration 4812, lr = 0.000693945
I0408 08:20:20.558399 31856 solver.cpp:218] Iteration 4824 (2.39603 iter/s, 5.00829s/12 iters), loss = 5.29131
I0408 08:20:20.558445 31856 solver.cpp:237]     Train net output #0: loss = 5.29131 (* 1 = 5.29131 loss)
I0408 08:20:20.558457 31856 sgd_solver.cpp:105] Iteration 4824, lr = 0.000685396
I0408 08:20:25.570704 31856 solver.cpp:218] Iteration 4836 (2.3942 iter/s, 5.01211s/12 iters), loss = 5.26538
I0408 08:20:25.570830 31856 solver.cpp:237]     Train net output #0: loss = 5.26538 (* 1 = 5.26538 loss)
I0408 08:20:25.570844 31856 sgd_solver.cpp:105] Iteration 4836, lr = 0.000676953
I0408 08:20:27.655308 31856 blocking_queue.cpp:49] Waiting for data
I0408 08:20:30.650008 31856 solver.cpp:218] Iteration 4848 (2.36266 iter/s, 5.07903s/12 iters), loss = 5.26721
I0408 08:20:30.650054 31856 solver.cpp:237]     Train net output #0: loss = 5.26721 (* 1 = 5.26721 loss)
I0408 08:20:30.650066 31856 sgd_solver.cpp:105] Iteration 4848, lr = 0.000668614
I0408 08:20:33.528647 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:20:35.999159 31856 solver.cpp:218] Iteration 4860 (2.24343 iter/s, 5.34895s/12 iters), loss = 5.26852
I0408 08:20:35.999203 31856 solver.cpp:237]     Train net output #0: loss = 5.26852 (* 1 = 5.26852 loss)
I0408 08:20:35.999213 31856 sgd_solver.cpp:105] Iteration 4860, lr = 0.000660377
I0408 08:20:41.052572 31856 solver.cpp:218] Iteration 4872 (2.37473 iter/s, 5.05322s/12 iters), loss = 5.26361
I0408 08:20:41.052626 31856 solver.cpp:237]     Train net output #0: loss = 5.26361 (* 1 = 5.26361 loss)
I0408 08:20:41.052640 31856 sgd_solver.cpp:105] Iteration 4872, lr = 0.000652242
I0408 08:20:46.483731 31856 solver.cpp:218] Iteration 4884 (2.20956 iter/s, 5.43095s/12 iters), loss = 5.26859
I0408 08:20:46.483774 31856 solver.cpp:237]     Train net output #0: loss = 5.26859 (* 1 = 5.26859 loss)
I0408 08:20:46.483785 31856 sgd_solver.cpp:105] Iteration 4884, lr = 0.000644207
I0408 08:20:50.973150 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0408 08:20:53.999819 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0408 08:20:56.346607 31856 solver.cpp:330] Iteration 4896, Testing net (#0)
I0408 08:20:56.346731 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:20:58.818188 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:21:00.751926 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:21:00.751967 31856 solver.cpp:397]     Test net output #1: loss = 5.28741 (* 1 = 5.28741 loss)
I0408 08:21:00.842087 31856 solver.cpp:218] Iteration 4896 (0.835776 iter/s, 14.3579s/12 iters), loss = 5.26845
I0408 08:21:00.842130 31856 solver.cpp:237]     Train net output #0: loss = 5.26845 (* 1 = 5.26845 loss)
I0408 08:21:00.842140 31856 sgd_solver.cpp:105] Iteration 4896, lr = 0.000636271
I0408 08:21:05.042646 31856 solver.cpp:218] Iteration 4908 (2.85688 iter/s, 4.20039s/12 iters), loss = 5.28929
I0408 08:21:05.042682 31856 solver.cpp:237]     Train net output #0: loss = 5.28929 (* 1 = 5.28929 loss)
I0408 08:21:05.042691 31856 sgd_solver.cpp:105] Iteration 4908, lr = 0.000628433
I0408 08:21:10.085234 31856 solver.cpp:218] Iteration 4920 (2.37982 iter/s, 5.0424s/12 iters), loss = 5.26948
I0408 08:21:10.085269 31856 solver.cpp:237]     Train net output #0: loss = 5.26948 (* 1 = 5.26948 loss)
I0408 08:21:10.085278 31856 sgd_solver.cpp:105] Iteration 4920, lr = 0.000620692
I0408 08:21:15.145346 31856 solver.cpp:218] Iteration 4932 (2.37158 iter/s, 5.05993s/12 iters), loss = 5.26461
I0408 08:21:15.145390 31856 solver.cpp:237]     Train net output #0: loss = 5.26461 (* 1 = 5.26461 loss)
I0408 08:21:15.145399 31856 sgd_solver.cpp:105] Iteration 4932, lr = 0.000613045
I0408 08:21:20.111129 31856 solver.cpp:218] Iteration 4944 (2.41663 iter/s, 4.9656s/12 iters), loss = 5.26457
I0408 08:21:20.111164 31856 solver.cpp:237]     Train net output #0: loss = 5.26457 (* 1 = 5.26457 loss)
I0408 08:21:20.111171 31856 sgd_solver.cpp:105] Iteration 4944, lr = 0.000605493
I0408 08:21:24.962771 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:21:25.159588 31856 solver.cpp:218] Iteration 4956 (2.37705 iter/s, 5.04828s/12 iters), loss = 5.2496
I0408 08:21:25.159621 31856 solver.cpp:237]     Train net output #0: loss = 5.2496 (* 1 = 5.2496 loss)
I0408 08:21:25.159627 31856 sgd_solver.cpp:105] Iteration 4956, lr = 0.000598034
I0408 08:21:30.202685 31856 solver.cpp:218] Iteration 4968 (2.37958 iter/s, 5.04292s/12 iters), loss = 5.26272
I0408 08:21:30.202791 31856 solver.cpp:237]     Train net output #0: loss = 5.26272 (* 1 = 5.26272 loss)
I0408 08:21:30.202801 31856 sgd_solver.cpp:105] Iteration 4968, lr = 0.000590667
I0408 08:21:35.160974 31856 solver.cpp:218] Iteration 4980 (2.42031 iter/s, 4.95804s/12 iters), loss = 5.29241
I0408 08:21:35.161007 31856 solver.cpp:237]     Train net output #0: loss = 5.29241 (* 1 = 5.29241 loss)
I0408 08:21:35.161015 31856 sgd_solver.cpp:105] Iteration 4980, lr = 0.000583391
I0408 08:21:40.153481 31856 solver.cpp:218] Iteration 4992 (2.40369 iter/s, 4.99232s/12 iters), loss = 5.28425
I0408 08:21:40.153532 31856 solver.cpp:237]     Train net output #0: loss = 5.28425 (* 1 = 5.28425 loss)
I0408 08:21:40.153543 31856 sgd_solver.cpp:105] Iteration 4992, lr = 0.000576204
I0408 08:21:42.222117 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0408 08:21:48.279425 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0408 08:21:50.598757 31856 solver.cpp:330] Iteration 4998, Testing net (#0)
I0408 08:21:50.598781 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:21:53.087986 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:21:55.059887 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:21:55.059937 31856 solver.cpp:397]     Test net output #1: loss = 5.28721 (* 1 = 5.28721 loss)
I0408 08:21:56.967106 31856 solver.cpp:218] Iteration 5004 (0.713729 iter/s, 16.8131s/12 iters), loss = 5.27927
I0408 08:21:56.967154 31856 solver.cpp:237]     Train net output #0: loss = 5.27927 (* 1 = 5.27927 loss)
I0408 08:21:56.967164 31856 sgd_solver.cpp:105] Iteration 5004, lr = 0.000569106
I0408 08:22:01.956389 31856 solver.cpp:218] Iteration 5016 (2.40525 iter/s, 4.98909s/12 iters), loss = 5.26479
I0408 08:22:01.956861 31856 solver.cpp:237]     Train net output #0: loss = 5.26479 (* 1 = 5.26479 loss)
I0408 08:22:01.956876 31856 sgd_solver.cpp:105] Iteration 5016, lr = 0.000562095
I0408 08:22:07.016110 31856 solver.cpp:218] Iteration 5028 (2.37196 iter/s, 5.0591s/12 iters), loss = 5.24728
I0408 08:22:07.016153 31856 solver.cpp:237]     Train net output #0: loss = 5.24728 (* 1 = 5.24728 loss)
I0408 08:22:07.016165 31856 sgd_solver.cpp:105] Iteration 5028, lr = 0.000555171
I0408 08:22:11.976807 31856 solver.cpp:218] Iteration 5040 (2.41911 iter/s, 4.96051s/12 iters), loss = 5.28556
I0408 08:22:11.976852 31856 solver.cpp:237]     Train net output #0: loss = 5.28556 (* 1 = 5.28556 loss)
I0408 08:22:11.976862 31856 sgd_solver.cpp:105] Iteration 5040, lr = 0.000548332
I0408 08:22:16.961540 31856 solver.cpp:218] Iteration 5052 (2.40745 iter/s, 4.98454s/12 iters), loss = 5.27038
I0408 08:22:16.961588 31856 solver.cpp:237]     Train net output #0: loss = 5.27038 (* 1 = 5.27038 loss)
I0408 08:22:16.961601 31856 sgd_solver.cpp:105] Iteration 5052, lr = 0.000541577
I0408 08:22:18.892551 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:22:21.900645 31856 solver.cpp:218] Iteration 5064 (2.42969 iter/s, 4.93891s/12 iters), loss = 5.2884
I0408 08:22:21.900701 31856 solver.cpp:237]     Train net output #0: loss = 5.2884 (* 1 = 5.2884 loss)
I0408 08:22:21.900712 31856 sgd_solver.cpp:105] Iteration 5064, lr = 0.000534906
I0408 08:22:26.884719 31856 solver.cpp:218] Iteration 5076 (2.40777 iter/s, 4.98387s/12 iters), loss = 5.27301
I0408 08:22:26.884768 31856 solver.cpp:237]     Train net output #0: loss = 5.27301 (* 1 = 5.27301 loss)
I0408 08:22:26.884779 31856 sgd_solver.cpp:105] Iteration 5076, lr = 0.000528316
I0408 08:22:31.832551 31856 solver.cpp:218] Iteration 5088 (2.4254 iter/s, 4.94764s/12 iters), loss = 5.26227
I0408 08:22:31.832594 31856 solver.cpp:237]     Train net output #0: loss = 5.26227 (* 1 = 5.26227 loss)
I0408 08:22:31.832605 31856 sgd_solver.cpp:105] Iteration 5088, lr = 0.000521808
I0408 08:22:36.330077 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0408 08:22:43.862828 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0408 08:22:46.489369 31856 solver.cpp:330] Iteration 5100, Testing net (#0)
I0408 08:22:46.489395 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:22:48.905293 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:22:50.922255 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:22:50.922292 31856 solver.cpp:397]     Test net output #1: loss = 5.28689 (* 1 = 5.28689 loss)
I0408 08:22:51.013396 31856 solver.cpp:218] Iteration 5100 (0.625643 iter/s, 19.1803s/12 iters), loss = 5.26598
I0408 08:22:51.013432 31856 solver.cpp:237]     Train net output #0: loss = 5.26598 (* 1 = 5.26598 loss)
I0408 08:22:51.013440 31856 sgd_solver.cpp:105] Iteration 5100, lr = 0.00051538
I0408 08:22:55.224987 31856 solver.cpp:218] Iteration 5112 (2.84939 iter/s, 4.21143s/12 iters), loss = 5.26247
I0408 08:22:55.225023 31856 solver.cpp:237]     Train net output #0: loss = 5.26247 (* 1 = 5.26247 loss)
I0408 08:22:55.225030 31856 sgd_solver.cpp:105] Iteration 5112, lr = 0.000509031
I0408 08:23:00.128245 31856 solver.cpp:218] Iteration 5124 (2.44744 iter/s, 4.90308s/12 iters), loss = 5.27297
I0408 08:23:00.128283 31856 solver.cpp:237]     Train net output #0: loss = 5.27297 (* 1 = 5.27297 loss)
I0408 08:23:00.128290 31856 sgd_solver.cpp:105] Iteration 5124, lr = 0.00050276
I0408 08:23:05.158582 31856 solver.cpp:218] Iteration 5136 (2.38562 iter/s, 5.03015s/12 iters), loss = 5.26645
I0408 08:23:05.158630 31856 solver.cpp:237]     Train net output #0: loss = 5.26645 (* 1 = 5.26645 loss)
I0408 08:23:05.158641 31856 sgd_solver.cpp:105] Iteration 5136, lr = 0.000496567
I0408 08:23:10.159900 31856 solver.cpp:218] Iteration 5148 (2.39946 iter/s, 5.00112s/12 iters), loss = 5.26092
I0408 08:23:10.160056 31856 solver.cpp:237]     Train net output #0: loss = 5.26092 (* 1 = 5.26092 loss)
I0408 08:23:10.160071 31856 sgd_solver.cpp:105] Iteration 5148, lr = 0.00049045
I0408 08:23:14.286023 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:23:15.233453 31856 solver.cpp:218] Iteration 5160 (2.36535 iter/s, 5.07325s/12 iters), loss = 5.25554
I0408 08:23:15.233492 31856 solver.cpp:237]     Train net output #0: loss = 5.25554 (* 1 = 5.25554 loss)
I0408 08:23:15.233501 31856 sgd_solver.cpp:105] Iteration 5160, lr = 0.000484408
I0408 08:23:20.201985 31856 solver.cpp:218] Iteration 5172 (2.41529 iter/s, 4.96834s/12 iters), loss = 5.27472
I0408 08:23:20.202039 31856 solver.cpp:237]     Train net output #0: loss = 5.27472 (* 1 = 5.27472 loss)
I0408 08:23:20.202051 31856 sgd_solver.cpp:105] Iteration 5172, lr = 0.000478441
I0408 08:23:25.231050 31856 solver.cpp:218] Iteration 5184 (2.38622 iter/s, 5.02887s/12 iters), loss = 5.27005
I0408 08:23:25.231089 31856 solver.cpp:237]     Train net output #0: loss = 5.27005 (* 1 = 5.27005 loss)
I0408 08:23:25.231098 31856 sgd_solver.cpp:105] Iteration 5184, lr = 0.000472547
I0408 08:23:30.208915 31856 solver.cpp:218] Iteration 5196 (2.41076 iter/s, 4.97767s/12 iters), loss = 5.3075
I0408 08:23:30.208961 31856 solver.cpp:237]     Train net output #0: loss = 5.3075 (* 1 = 5.3075 loss)
I0408 08:23:30.208972 31856 sgd_solver.cpp:105] Iteration 5196, lr = 0.000466726
I0408 08:23:32.246233 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0408 08:23:38.480288 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0408 08:23:43.451155 31856 solver.cpp:330] Iteration 5202, Testing net (#0)
I0408 08:23:43.451210 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:23:45.863445 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:23:47.955145 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:23:47.955195 31856 solver.cpp:397]     Test net output #1: loss = 5.28707 (* 1 = 5.28707 loss)
I0408 08:23:49.899955 31856 solver.cpp:218] Iteration 5208 (0.609433 iter/s, 19.6904s/12 iters), loss = 5.27162
I0408 08:23:49.900004 31856 solver.cpp:237]     Train net output #0: loss = 5.27162 (* 1 = 5.27162 loss)
I0408 08:23:49.900017 31856 sgd_solver.cpp:105] Iteration 5208, lr = 0.000460976
I0408 08:23:54.950297 31856 solver.cpp:218] Iteration 5220 (2.37617 iter/s, 5.05014s/12 iters), loss = 5.27265
I0408 08:23:54.950345 31856 solver.cpp:237]     Train net output #0: loss = 5.27265 (* 1 = 5.27265 loss)
I0408 08:23:54.950357 31856 sgd_solver.cpp:105] Iteration 5220, lr = 0.000455297
I0408 08:23:59.997090 31856 solver.cpp:218] Iteration 5232 (2.37784 iter/s, 5.0466s/12 iters), loss = 5.27799
I0408 08:23:59.997129 31856 solver.cpp:237]     Train net output #0: loss = 5.27799 (* 1 = 5.27799 loss)
I0408 08:23:59.997139 31856 sgd_solver.cpp:105] Iteration 5232, lr = 0.000449689
I0408 08:24:05.012473 31856 solver.cpp:218] Iteration 5244 (2.39273 iter/s, 5.0152s/12 iters), loss = 5.27132
I0408 08:24:05.012518 31856 solver.cpp:237]     Train net output #0: loss = 5.27132 (* 1 = 5.27132 loss)
I0408 08:24:05.012529 31856 sgd_solver.cpp:105] Iteration 5244, lr = 0.000444149
I0408 08:24:10.029275 31856 solver.cpp:218] Iteration 5256 (2.39206 iter/s, 5.01661s/12 iters), loss = 5.25962
I0408 08:24:10.029325 31856 solver.cpp:237]     Train net output #0: loss = 5.25962 (* 1 = 5.25962 loss)
I0408 08:24:10.029337 31856 sgd_solver.cpp:105] Iteration 5256, lr = 0.000438678
I0408 08:24:11.346323 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:24:15.062913 31856 solver.cpp:218] Iteration 5268 (2.38406 iter/s, 5.03344s/12 iters), loss = 5.27651
I0408 08:24:15.063033 31856 solver.cpp:237]     Train net output #0: loss = 5.27651 (* 1 = 5.27651 loss)
I0408 08:24:15.063045 31856 sgd_solver.cpp:105] Iteration 5268, lr = 0.000433274
I0408 08:24:20.099422 31856 solver.cpp:218] Iteration 5280 (2.38273 iter/s, 5.03624s/12 iters), loss = 5.26632
I0408 08:24:20.099469 31856 solver.cpp:237]     Train net output #0: loss = 5.26632 (* 1 = 5.26632 loss)
I0408 08:24:20.099480 31856 sgd_solver.cpp:105] Iteration 5280, lr = 0.000427936
I0408 08:24:25.141034 31856 solver.cpp:218] Iteration 5292 (2.38028 iter/s, 5.04142s/12 iters), loss = 5.27894
I0408 08:24:25.141075 31856 solver.cpp:237]     Train net output #0: loss = 5.27894 (* 1 = 5.27894 loss)
I0408 08:24:25.141085 31856 sgd_solver.cpp:105] Iteration 5292, lr = 0.000422664
I0408 08:24:29.636158 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0408 08:24:34.206511 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0408 08:24:37.674746 31856 solver.cpp:330] Iteration 5304, Testing net (#0)
I0408 08:24:37.674773 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:24:40.038568 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:24:42.162220 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:24:42.162267 31856 solver.cpp:397]     Test net output #1: loss = 5.28683 (* 1 = 5.28683 loss)
I0408 08:24:42.252178 31856 solver.cpp:218] Iteration 5304 (0.701319 iter/s, 17.1106s/12 iters), loss = 5.27125
I0408 08:24:42.252228 31856 solver.cpp:237]     Train net output #0: loss = 5.27125 (* 1 = 5.27125 loss)
I0408 08:24:42.252238 31856 sgd_solver.cpp:105] Iteration 5304, lr = 0.000417458
I0408 08:24:46.818768 31856 solver.cpp:218] Iteration 5316 (2.62789 iter/s, 4.5664s/12 iters), loss = 5.27025
I0408 08:24:46.818874 31856 solver.cpp:237]     Train net output #0: loss = 5.27025 (* 1 = 5.27025 loss)
I0408 08:24:46.818886 31856 sgd_solver.cpp:105] Iteration 5316, lr = 0.000412315
I0408 08:24:52.247807 31856 solver.cpp:218] Iteration 5328 (2.21044 iter/s, 5.42877s/12 iters), loss = 5.25715
I0408 08:24:52.247856 31856 solver.cpp:237]     Train net output #0: loss = 5.25715 (* 1 = 5.25715 loss)
I0408 08:24:52.247869 31856 sgd_solver.cpp:105] Iteration 5328, lr = 0.000407236
I0408 08:24:57.283730 31856 solver.cpp:218] Iteration 5340 (2.38297 iter/s, 5.03572s/12 iters), loss = 5.30027
I0408 08:24:57.283776 31856 solver.cpp:237]     Train net output #0: loss = 5.30027 (* 1 = 5.30027 loss)
I0408 08:24:57.283787 31856 sgd_solver.cpp:105] Iteration 5340, lr = 0.000402219
I0408 08:25:02.204160 31856 solver.cpp:218] Iteration 5352 (2.43891 iter/s, 4.92023s/12 iters), loss = 5.27341
I0408 08:25:02.204205 31856 solver.cpp:237]     Train net output #0: loss = 5.27341 (* 1 = 5.27341 loss)
I0408 08:25:02.204216 31856 sgd_solver.cpp:105] Iteration 5352, lr = 0.000397264
I0408 08:25:05.619758 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:25:07.214589 31856 solver.cpp:218] Iteration 5364 (2.3951 iter/s, 5.01024s/12 iters), loss = 5.27567
I0408 08:25:07.214635 31856 solver.cpp:237]     Train net output #0: loss = 5.27567 (* 1 = 5.27567 loss)
I0408 08:25:07.214648 31856 sgd_solver.cpp:105] Iteration 5364, lr = 0.000392371
I0408 08:25:12.235154 31856 solver.cpp:218] Iteration 5376 (2.39026 iter/s, 5.02037s/12 iters), loss = 5.26452
I0408 08:25:12.235200 31856 solver.cpp:237]     Train net output #0: loss = 5.26452 (* 1 = 5.26452 loss)
I0408 08:25:12.235213 31856 sgd_solver.cpp:105] Iteration 5376, lr = 0.000387537
I0408 08:25:17.193181 31856 solver.cpp:218] Iteration 5388 (2.42041 iter/s, 4.95783s/12 iters), loss = 5.26585
I0408 08:25:17.193332 31856 solver.cpp:237]     Train net output #0: loss = 5.26585 (* 1 = 5.26585 loss)
I0408 08:25:17.193346 31856 sgd_solver.cpp:105] Iteration 5388, lr = 0.000382763
I0408 08:25:22.146234 31856 solver.cpp:218] Iteration 5400 (2.42289 iter/s, 4.95276s/12 iters), loss = 5.2699
I0408 08:25:22.146271 31856 solver.cpp:237]     Train net output #0: loss = 5.2699 (* 1 = 5.2699 loss)
I0408 08:25:22.146279 31856 sgd_solver.cpp:105] Iteration 5400, lr = 0.000378048
I0408 08:25:24.205652 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0408 08:25:28.586903 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0408 08:25:32.262493 31856 solver.cpp:330] Iteration 5406, Testing net (#0)
I0408 08:25:32.262519 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:25:34.570287 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:25:36.702948 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:25:36.702996 31856 solver.cpp:397]     Test net output #1: loss = 5.28696 (* 1 = 5.28696 loss)
I0408 08:25:38.697834 31856 solver.cpp:218] Iteration 5412 (0.725028 iter/s, 16.5511s/12 iters), loss = 5.26291
I0408 08:25:38.697880 31856 solver.cpp:237]     Train net output #0: loss = 5.26291 (* 1 = 5.26291 loss)
I0408 08:25:38.697890 31856 sgd_solver.cpp:105] Iteration 5412, lr = 0.000373391
I0408 08:25:44.014827 31856 solver.cpp:218] Iteration 5424 (2.257 iter/s, 5.31678s/12 iters), loss = 5.27876
I0408 08:25:44.014875 31856 solver.cpp:237]     Train net output #0: loss = 5.27876 (* 1 = 5.27876 loss)
I0408 08:25:44.014886 31856 sgd_solver.cpp:105] Iteration 5424, lr = 0.000368791
I0408 08:25:49.077756 31856 solver.cpp:218] Iteration 5436 (2.37026 iter/s, 5.06273s/12 iters), loss = 5.26606
I0408 08:25:49.079154 31856 solver.cpp:237]     Train net output #0: loss = 5.26606 (* 1 = 5.26606 loss)
I0408 08:25:49.079164 31856 sgd_solver.cpp:105] Iteration 5436, lr = 0.000364248
I0408 08:25:54.073807 31856 solver.cpp:218] Iteration 5448 (2.40264 iter/s, 4.99451s/12 iters), loss = 5.27781
I0408 08:25:54.073844 31856 solver.cpp:237]     Train net output #0: loss = 5.27781 (* 1 = 5.27781 loss)
I0408 08:25:54.073853 31856 sgd_solver.cpp:105] Iteration 5448, lr = 0.000359761
I0408 08:25:59.092623 31856 solver.cpp:218] Iteration 5460 (2.39109 iter/s, 5.01862s/12 iters), loss = 5.27862
I0408 08:25:59.092674 31856 solver.cpp:237]     Train net output #0: loss = 5.27862 (* 1 = 5.27862 loss)
I0408 08:25:59.092684 31856 sgd_solver.cpp:105] Iteration 5460, lr = 0.000355329
I0408 08:25:59.649374 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:26:04.106410 31856 solver.cpp:218] Iteration 5472 (2.3935 iter/s, 5.01359s/12 iters), loss = 5.27856
I0408 08:26:04.106459 31856 solver.cpp:237]     Train net output #0: loss = 5.27856 (* 1 = 5.27856 loss)
I0408 08:26:04.106472 31856 sgd_solver.cpp:105] Iteration 5472, lr = 0.000350952
I0408 08:26:09.132288 31856 solver.cpp:218] Iteration 5484 (2.38774 iter/s, 5.02568s/12 iters), loss = 5.27356
I0408 08:26:09.132336 31856 solver.cpp:237]     Train net output #0: loss = 5.27356 (* 1 = 5.27356 loss)
I0408 08:26:09.132347 31856 sgd_solver.cpp:105] Iteration 5484, lr = 0.000346628
I0408 08:26:14.188673 31856 solver.cpp:218] Iteration 5496 (2.37333 iter/s, 5.05618s/12 iters), loss = 5.29129
I0408 08:26:14.188721 31856 solver.cpp:237]     Train net output #0: loss = 5.29129 (* 1 = 5.29129 loss)
I0408 08:26:14.188733 31856 sgd_solver.cpp:105] Iteration 5496, lr = 0.000342358
I0408 08:26:18.732049 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0408 08:26:22.996979 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0408 08:26:26.751452 31856 solver.cpp:330] Iteration 5508, Testing net (#0)
I0408 08:26:26.751478 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:26:29.033572 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:26:31.216464 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:26:31.216513 31856 solver.cpp:397]     Test net output #1: loss = 5.28793 (* 1 = 5.28793 loss)
I0408 08:26:31.306938 31856 solver.cpp:218] Iteration 5508 (0.701027 iter/s, 17.1177s/12 iters), loss = 5.27653
I0408 08:26:31.306980 31856 solver.cpp:237]     Train net output #0: loss = 5.27653 (* 1 = 5.27653 loss)
I0408 08:26:31.306989 31856 sgd_solver.cpp:105] Iteration 5508, lr = 0.000338141
I0408 08:26:35.745813 31856 solver.cpp:218] Iteration 5520 (2.70349 iter/s, 4.4387s/12 iters), loss = 5.273
I0408 08:26:35.745857 31856 solver.cpp:237]     Train net output #0: loss = 5.273 (* 1 = 5.273 loss)
I0408 08:26:35.745868 31856 sgd_solver.cpp:105] Iteration 5520, lr = 0.000333975
I0408 08:26:38.135560 31856 blocking_queue.cpp:49] Waiting for data
I0408 08:26:40.715823 31856 solver.cpp:218] Iteration 5532 (2.41458 iter/s, 4.96982s/12 iters), loss = 5.28623
I0408 08:26:40.715870 31856 solver.cpp:237]     Train net output #0: loss = 5.28623 (* 1 = 5.28623 loss)
I0408 08:26:40.715881 31856 sgd_solver.cpp:105] Iteration 5532, lr = 0.000329861
I0408 08:26:45.736137 31856 solver.cpp:218] Iteration 5544 (2.39038 iter/s, 5.02012s/12 iters), loss = 5.25877
I0408 08:26:45.736183 31856 solver.cpp:237]     Train net output #0: loss = 5.25877 (* 1 = 5.25877 loss)
I0408 08:26:45.736196 31856 sgd_solver.cpp:105] Iteration 5544, lr = 0.000325798
I0408 08:26:50.742660 31856 solver.cpp:218] Iteration 5556 (2.39697 iter/s, 5.00633s/12 iters), loss = 5.26951
I0408 08:26:50.742702 31856 solver.cpp:237]     Train net output #0: loss = 5.26951 (* 1 = 5.26951 loss)
I0408 08:26:50.742712 31856 sgd_solver.cpp:105] Iteration 5556, lr = 0.000321784
I0408 08:26:53.448225 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:26:55.743741 31856 solver.cpp:218] Iteration 5568 (2.39958 iter/s, 5.00088s/12 iters), loss = 5.2774
I0408 08:26:55.743788 31856 solver.cpp:237]     Train net output #0: loss = 5.2774 (* 1 = 5.2774 loss)
I0408 08:26:55.743798 31856 sgd_solver.cpp:105] Iteration 5568, lr = 0.00031782
I0408 08:27:00.802386 31856 solver.cpp:218] Iteration 5580 (2.37227 iter/s, 5.05845s/12 iters), loss = 5.25926
I0408 08:27:00.802428 31856 solver.cpp:237]     Train net output #0: loss = 5.25926 (* 1 = 5.25926 loss)
I0408 08:27:00.802438 31856 sgd_solver.cpp:105] Iteration 5580, lr = 0.000313905
I0408 08:27:05.781857 31856 solver.cpp:218] Iteration 5592 (2.40999 iter/s, 4.97928s/12 iters), loss = 5.27233
I0408 08:27:05.781903 31856 solver.cpp:237]     Train net output #0: loss = 5.27233 (* 1 = 5.27233 loss)
I0408 08:27:05.781913 31856 sgd_solver.cpp:105] Iteration 5592, lr = 0.000310038
I0408 08:27:10.752493 31856 solver.cpp:218] Iteration 5604 (2.41427 iter/s, 4.97044s/12 iters), loss = 5.26388
I0408 08:27:10.752539 31856 solver.cpp:237]     Train net output #0: loss = 5.26388 (* 1 = 5.26388 loss)
I0408 08:27:10.752550 31856 sgd_solver.cpp:105] Iteration 5604, lr = 0.000306219
I0408 08:27:12.796082 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0408 08:27:19.541404 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0408 08:27:25.091243 31856 solver.cpp:330] Iteration 5610, Testing net (#0)
I0408 08:27:25.091429 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:27:27.350395 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:27:29.561661 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:27:29.561710 31856 solver.cpp:397]     Test net output #1: loss = 5.28725 (* 1 = 5.28725 loss)
I0408 08:27:31.488431 31856 solver.cpp:218] Iteration 5616 (0.578723 iter/s, 20.7353s/12 iters), loss = 5.2934
I0408 08:27:31.488484 31856 solver.cpp:237]     Train net output #0: loss = 5.2934 (* 1 = 5.2934 loss)
I0408 08:27:31.488497 31856 sgd_solver.cpp:105] Iteration 5616, lr = 0.000302446
I0408 08:27:36.444010 31856 solver.cpp:218] Iteration 5628 (2.42161 iter/s, 4.95537s/12 iters), loss = 5.27258
I0408 08:27:36.444058 31856 solver.cpp:237]     Train net output #0: loss = 5.27258 (* 1 = 5.27258 loss)
I0408 08:27:36.444070 31856 sgd_solver.cpp:105] Iteration 5628, lr = 0.000298721
I0408 08:27:41.441591 31856 solver.cpp:218] Iteration 5640 (2.40126 iter/s, 4.99738s/12 iters), loss = 5.2624
I0408 08:27:41.441637 31856 solver.cpp:237]     Train net output #0: loss = 5.2624 (* 1 = 5.2624 loss)
I0408 08:27:41.441648 31856 sgd_solver.cpp:105] Iteration 5640, lr = 0.000295041
I0408 08:27:46.460428 31856 solver.cpp:218] Iteration 5652 (2.39109 iter/s, 5.01864s/12 iters), loss = 5.26667
I0408 08:27:46.460474 31856 solver.cpp:237]     Train net output #0: loss = 5.26667 (* 1 = 5.26667 loss)
I0408 08:27:46.460486 31856 sgd_solver.cpp:105] Iteration 5652, lr = 0.000291406
I0408 08:27:51.306908 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:27:51.476466 31856 solver.cpp:218] Iteration 5664 (2.39242 iter/s, 5.01584s/12 iters), loss = 5.25095
I0408 08:27:51.476516 31856 solver.cpp:237]     Train net output #0: loss = 5.25095 (* 1 = 5.25095 loss)
I0408 08:27:51.476526 31856 sgd_solver.cpp:105] Iteration 5664, lr = 0.000287816
I0408 08:27:56.487118 31856 solver.cpp:218] Iteration 5676 (2.395 iter/s, 5.01045s/12 iters), loss = 5.26333
I0408 08:27:56.487236 31856 solver.cpp:237]     Train net output #0: loss = 5.26333 (* 1 = 5.26333 loss)
I0408 08:27:56.487248 31856 sgd_solver.cpp:105] Iteration 5676, lr = 0.000284271
I0408 08:28:01.484450 31856 solver.cpp:218] Iteration 5688 (2.40141 iter/s, 4.99707s/12 iters), loss = 5.29503
I0408 08:28:01.484499 31856 solver.cpp:237]     Train net output #0: loss = 5.29503 (* 1 = 5.29503 loss)
I0408 08:28:01.484510 31856 sgd_solver.cpp:105] Iteration 5688, lr = 0.000280769
I0408 08:28:06.395375 31856 solver.cpp:218] Iteration 5700 (2.44363 iter/s, 4.91073s/12 iters), loss = 5.28679
I0408 08:28:06.395413 31856 solver.cpp:237]     Train net output #0: loss = 5.28679 (* 1 = 5.28679 loss)
I0408 08:28:06.395422 31856 sgd_solver.cpp:105] Iteration 5700, lr = 0.00027731
I0408 08:28:10.977236 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0408 08:28:15.239032 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0408 08:28:19.052924 31856 solver.cpp:330] Iteration 5712, Testing net (#0)
I0408 08:28:19.052955 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:28:21.275388 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:28:23.523371 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:28:23.523419 31856 solver.cpp:397]     Test net output #1: loss = 5.28721 (* 1 = 5.28721 loss)
I0408 08:28:23.613318 31856 solver.cpp:218] Iteration 5712 (0.696969 iter/s, 17.2174s/12 iters), loss = 5.27875
I0408 08:28:23.613369 31856 solver.cpp:237]     Train net output #0: loss = 5.27875 (* 1 = 5.27875 loss)
I0408 08:28:23.613381 31856 sgd_solver.cpp:105] Iteration 5712, lr = 0.000273894
I0408 08:28:27.789666 31856 solver.cpp:218] Iteration 5724 (2.87345 iter/s, 4.17617s/12 iters), loss = 5.26615
I0408 08:28:27.789872 31856 solver.cpp:237]     Train net output #0: loss = 5.26615 (* 1 = 5.26615 loss)
I0408 08:28:27.789886 31856 sgd_solver.cpp:105] Iteration 5724, lr = 0.00027052
I0408 08:28:32.742962 31856 solver.cpp:218] Iteration 5736 (2.4228 iter/s, 4.95294s/12 iters), loss = 5.24368
I0408 08:28:32.743013 31856 solver.cpp:237]     Train net output #0: loss = 5.24368 (* 1 = 5.24368 loss)
I0408 08:28:32.743026 31856 sgd_solver.cpp:105] Iteration 5736, lr = 0.000267188
I0408 08:28:37.684002 31856 solver.cpp:218] Iteration 5748 (2.42874 iter/s, 4.94084s/12 iters), loss = 5.27716
I0408 08:28:37.684048 31856 solver.cpp:237]     Train net output #0: loss = 5.27716 (* 1 = 5.27716 loss)
I0408 08:28:37.684059 31856 sgd_solver.cpp:105] Iteration 5748, lr = 0.000263896
I0408 08:28:42.696979 31856 solver.cpp:218] Iteration 5760 (2.39388 iter/s, 5.01278s/12 iters), loss = 5.26596
I0408 08:28:42.697026 31856 solver.cpp:237]     Train net output #0: loss = 5.26596 (* 1 = 5.26596 loss)
I0408 08:28:42.697037 31856 sgd_solver.cpp:105] Iteration 5760, lr = 0.000260645
I0408 08:28:44.655763 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:28:47.544885 31856 solver.cpp:218] Iteration 5772 (2.47539 iter/s, 4.84771s/12 iters), loss = 5.29126
I0408 08:28:47.544931 31856 solver.cpp:237]     Train net output #0: loss = 5.29126 (* 1 = 5.29126 loss)
I0408 08:28:47.544943 31856 sgd_solver.cpp:105] Iteration 5772, lr = 0.000257434
I0408 08:28:52.603996 31856 solver.cpp:218] Iteration 5784 (2.37205 iter/s, 5.05891s/12 iters), loss = 5.27078
I0408 08:28:52.604045 31856 solver.cpp:237]     Train net output #0: loss = 5.27078 (* 1 = 5.27078 loss)
I0408 08:28:52.604058 31856 sgd_solver.cpp:105] Iteration 5784, lr = 0.000254263
I0408 08:28:57.569401 31856 solver.cpp:218] Iteration 5796 (2.41682 iter/s, 4.9652s/12 iters), loss = 5.26909
I0408 08:28:57.569468 31856 solver.cpp:237]     Train net output #0: loss = 5.26909 (* 1 = 5.26909 loss)
I0408 08:28:57.569484 31856 sgd_solver.cpp:105] Iteration 5796, lr = 0.000251131
I0408 08:29:02.580991 31856 solver.cpp:218] Iteration 5808 (2.39455 iter/s, 5.01138s/12 iters), loss = 5.26432
I0408 08:29:02.581095 31856 solver.cpp:237]     Train net output #0: loss = 5.26432 (* 1 = 5.26432 loss)
I0408 08:29:02.581106 31856 sgd_solver.cpp:105] Iteration 5808, lr = 0.000248037
I0408 08:29:04.601794 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0408 08:29:08.372917 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0408 08:29:12.077076 31856 solver.cpp:330] Iteration 5814, Testing net (#0)
I0408 08:29:12.077108 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:29:14.253326 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:29:16.545621 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:29:16.545670 31856 solver.cpp:397]     Test net output #1: loss = 5.28712 (* 1 = 5.28712 loss)
I0408 08:29:18.521483 31856 solver.cpp:218] Iteration 5820 (0.752826 iter/s, 15.9399s/12 iters), loss = 5.27327
I0408 08:29:18.521533 31856 solver.cpp:237]     Train net output #0: loss = 5.27327 (* 1 = 5.27327 loss)
I0408 08:29:18.521545 31856 sgd_solver.cpp:105] Iteration 5820, lr = 0.000244982
I0408 08:29:23.501401 31856 solver.cpp:218] Iteration 5832 (2.40977 iter/s, 4.97972s/12 iters), loss = 5.27279
I0408 08:29:23.501448 31856 solver.cpp:237]     Train net output #0: loss = 5.27279 (* 1 = 5.27279 loss)
I0408 08:29:23.501459 31856 sgd_solver.cpp:105] Iteration 5832, lr = 0.000241964
I0408 08:29:28.444058 31856 solver.cpp:218] Iteration 5844 (2.42794 iter/s, 4.94246s/12 iters), loss = 5.26236
I0408 08:29:28.444103 31856 solver.cpp:237]     Train net output #0: loss = 5.26236 (* 1 = 5.26236 loss)
I0408 08:29:28.444113 31856 sgd_solver.cpp:105] Iteration 5844, lr = 0.000238983
I0408 08:29:33.377364 31856 solver.cpp:218] Iteration 5856 (2.43254 iter/s, 4.93311s/12 iters), loss = 5.25979
I0408 08:29:33.377454 31856 solver.cpp:237]     Train net output #0: loss = 5.25979 (* 1 = 5.25979 loss)
I0408 08:29:33.377466 31856 sgd_solver.cpp:105] Iteration 5856, lr = 0.000236039
I0408 08:29:37.603065 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:29:38.402926 31856 solver.cpp:218] Iteration 5868 (2.3879 iter/s, 5.02534s/12 iters), loss = 5.2529
I0408 08:29:38.402971 31856 solver.cpp:237]     Train net output #0: loss = 5.2529 (* 1 = 5.2529 loss)
I0408 08:29:38.402982 31856 sgd_solver.cpp:105] Iteration 5868, lr = 0.000233131
I0408 08:29:43.652956 31856 solver.cpp:218] Iteration 5880 (2.28579 iter/s, 5.24983s/12 iters), loss = 5.2759
I0408 08:29:43.653004 31856 solver.cpp:237]     Train net output #0: loss = 5.2759 (* 1 = 5.2759 loss)
I0408 08:29:43.653017 31856 sgd_solver.cpp:105] Iteration 5880, lr = 0.000230259
I0408 08:29:48.685593 31856 solver.cpp:218] Iteration 5892 (2.38453 iter/s, 5.03244s/12 iters), loss = 5.26898
I0408 08:29:48.685638 31856 solver.cpp:237]     Train net output #0: loss = 5.26898 (* 1 = 5.26898 loss)
I0408 08:29:48.685650 31856 sgd_solver.cpp:105] Iteration 5892, lr = 0.000227423
I0408 08:29:53.604403 31856 solver.cpp:218] Iteration 5904 (2.43971 iter/s, 4.91862s/12 iters), loss = 5.30501
I0408 08:29:53.604447 31856 solver.cpp:237]     Train net output #0: loss = 5.30501 (* 1 = 5.30501 loss)
I0408 08:29:53.604458 31856 sgd_solver.cpp:105] Iteration 5904, lr = 0.000224621
I0408 08:29:58.174218 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0408 08:30:01.172680 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0408 08:30:05.287922 31856 solver.cpp:330] Iteration 5916, Testing net (#0)
I0408 08:30:05.288044 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:30:07.416592 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:30:09.746134 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:30:09.746181 31856 solver.cpp:397]     Test net output #1: loss = 5.28715 (* 1 = 5.28715 loss)
I0408 08:30:09.836124 31856 solver.cpp:218] Iteration 5916 (0.739316 iter/s, 16.2312s/12 iters), loss = 5.26645
I0408 08:30:09.836166 31856 solver.cpp:237]     Train net output #0: loss = 5.26645 (* 1 = 5.26645 loss)
I0408 08:30:09.836176 31856 sgd_solver.cpp:105] Iteration 5916, lr = 0.000221854
I0408 08:30:14.353368 31856 solver.cpp:218] Iteration 5928 (2.65659 iter/s, 4.51706s/12 iters), loss = 5.26957
I0408 08:30:14.353417 31856 solver.cpp:237]     Train net output #0: loss = 5.26957 (* 1 = 5.26957 loss)
I0408 08:30:14.353430 31856 sgd_solver.cpp:105] Iteration 5928, lr = 0.000219121
I0408 08:30:19.829887 31856 solver.cpp:218] Iteration 5940 (2.19126 iter/s, 5.47631s/12 iters), loss = 5.28103
I0408 08:30:19.829936 31856 solver.cpp:237]     Train net output #0: loss = 5.28103 (* 1 = 5.28103 loss)
I0408 08:30:19.829948 31856 sgd_solver.cpp:105] Iteration 5940, lr = 0.000216422
I0408 08:30:25.262960 31856 solver.cpp:218] Iteration 5952 (2.20878 iter/s, 5.43287s/12 iters), loss = 5.27442
I0408 08:30:25.263003 31856 solver.cpp:237]     Train net output #0: loss = 5.27442 (* 1 = 5.27442 loss)
I0408 08:30:25.263015 31856 sgd_solver.cpp:105] Iteration 5952, lr = 0.000213756
I0408 08:30:30.730512 31856 solver.cpp:218] Iteration 5964 (2.19485 iter/s, 5.46735s/12 iters), loss = 5.25822
I0408 08:30:30.730548 31856 solver.cpp:237]     Train net output #0: loss = 5.25822 (* 1 = 5.25822 loss)
I0408 08:30:30.730556 31856 sgd_solver.cpp:105] Iteration 5964, lr = 0.000211123
I0408 08:30:32.174274 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:30:36.171356 31856 solver.cpp:218] Iteration 5976 (2.20562 iter/s, 5.44064s/12 iters), loss = 5.27623
I0408 08:30:36.171480 31856 solver.cpp:237]     Train net output #0: loss = 5.27623 (* 1 = 5.27623 loss)
I0408 08:30:36.171494 31856 sgd_solver.cpp:105] Iteration 5976, lr = 0.000208522
I0408 08:30:41.251492 31856 solver.cpp:218] Iteration 5988 (2.36227 iter/s, 5.07986s/12 iters), loss = 5.263
I0408 08:30:41.251539 31856 solver.cpp:237]     Train net output #0: loss = 5.263 (* 1 = 5.263 loss)
I0408 08:30:41.251551 31856 sgd_solver.cpp:105] Iteration 5988, lr = 0.000205953
I0408 08:30:46.484025 31856 solver.cpp:218] Iteration 6000 (2.29343 iter/s, 5.23233s/12 iters), loss = 5.28042
I0408 08:30:46.484069 31856 solver.cpp:237]     Train net output #0: loss = 5.28042 (* 1 = 5.28042 loss)
I0408 08:30:46.484081 31856 sgd_solver.cpp:105] Iteration 6000, lr = 0.000203416
I0408 08:30:51.515977 31856 solver.cpp:218] Iteration 6012 (2.38485 iter/s, 5.03176s/12 iters), loss = 5.26817
I0408 08:30:51.516012 31856 solver.cpp:237]     Train net output #0: loss = 5.26817 (* 1 = 5.26817 loss)
I0408 08:30:51.516021 31856 sgd_solver.cpp:105] Iteration 6012, lr = 0.00020091
I0408 08:30:53.484241 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0408 08:30:58.512954 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0408 08:31:02.829905 31856 solver.cpp:330] Iteration 6018, Testing net (#0)
I0408 08:31:02.829936 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:31:04.930430 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:31:07.302688 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:31:07.302848 31856 solver.cpp:397]     Test net output #1: loss = 5.28716 (* 1 = 5.28716 loss)
I0408 08:31:09.287739 31856 solver.cpp:218] Iteration 6024 (0.675249 iter/s, 17.7712s/12 iters), loss = 5.26701
I0408 08:31:09.287791 31856 solver.cpp:237]     Train net output #0: loss = 5.26701 (* 1 = 5.26701 loss)
I0408 08:31:09.287802 31856 sgd_solver.cpp:105] Iteration 6024, lr = 0.000198435
I0408 08:31:14.413326 31856 solver.cpp:218] Iteration 6036 (2.34129 iter/s, 5.12538s/12 iters), loss = 5.259
I0408 08:31:14.413370 31856 solver.cpp:237]     Train net output #0: loss = 5.259 (* 1 = 5.259 loss)
I0408 08:31:14.413381 31856 sgd_solver.cpp:105] Iteration 6036, lr = 0.000195991
I0408 08:31:19.377472 31856 solver.cpp:218] Iteration 6048 (2.41743 iter/s, 4.96395s/12 iters), loss = 5.30244
I0408 08:31:19.377521 31856 solver.cpp:237]     Train net output #0: loss = 5.30244 (* 1 = 5.30244 loss)
I0408 08:31:19.377532 31856 sgd_solver.cpp:105] Iteration 6048, lr = 0.000193576
I0408 08:31:24.393673 31856 solver.cpp:218] Iteration 6060 (2.39234 iter/s, 5.016s/12 iters), loss = 5.27673
I0408 08:31:24.393723 31856 solver.cpp:237]     Train net output #0: loss = 5.27673 (* 1 = 5.27673 loss)
I0408 08:31:24.393736 31856 sgd_solver.cpp:105] Iteration 6060, lr = 0.000191192
I0408 08:31:27.885787 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:31:29.421008 31856 solver.cpp:218] Iteration 6072 (2.38704 iter/s, 5.02714s/12 iters), loss = 5.27317
I0408 08:31:29.421051 31856 solver.cpp:237]     Train net output #0: loss = 5.27317 (* 1 = 5.27317 loss)
I0408 08:31:29.421061 31856 sgd_solver.cpp:105] Iteration 6072, lr = 0.000188836
I0408 08:31:34.396701 31856 solver.cpp:218] Iteration 6084 (2.41182 iter/s, 4.9755s/12 iters), loss = 5.25783
I0408 08:31:34.396745 31856 solver.cpp:237]     Train net output #0: loss = 5.25783 (* 1 = 5.25783 loss)
I0408 08:31:34.396757 31856 sgd_solver.cpp:105] Iteration 6084, lr = 0.00018651
I0408 08:31:39.438273 31856 solver.cpp:218] Iteration 6096 (2.3803 iter/s, 5.04138s/12 iters), loss = 5.2612
I0408 08:31:39.438393 31856 solver.cpp:237]     Train net output #0: loss = 5.2612 (* 1 = 5.2612 loss)
I0408 08:31:39.438407 31856 sgd_solver.cpp:105] Iteration 6096, lr = 0.000184213
I0408 08:31:44.468767 31856 solver.cpp:218] Iteration 6108 (2.38558 iter/s, 5.03023s/12 iters), loss = 5.27362
I0408 08:31:44.468813 31856 solver.cpp:237]     Train net output #0: loss = 5.27362 (* 1 = 5.27362 loss)
I0408 08:31:44.468825 31856 sgd_solver.cpp:105] Iteration 6108, lr = 0.000181943
I0408 08:31:48.989610 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0408 08:31:54.047430 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0408 08:32:00.288918 31856 solver.cpp:330] Iteration 6120, Testing net (#0)
I0408 08:32:00.288952 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:32:02.328774 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:32:04.731139 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:32:04.731190 31856 solver.cpp:397]     Test net output #1: loss = 5.28711 (* 1 = 5.28711 loss)
I0408 08:32:04.821106 31856 solver.cpp:218] Iteration 6120 (0.589631 iter/s, 20.3517s/12 iters), loss = 5.26495
I0408 08:32:04.821144 31856 solver.cpp:237]     Train net output #0: loss = 5.26495 (* 1 = 5.26495 loss)
I0408 08:32:04.821156 31856 sgd_solver.cpp:105] Iteration 6120, lr = 0.000179702
I0408 08:32:09.282296 31856 solver.cpp:218] Iteration 6132 (2.68997 iter/s, 4.46102s/12 iters), loss = 5.27358
I0408 08:32:09.282351 31856 solver.cpp:237]     Train net output #0: loss = 5.27358 (* 1 = 5.27358 loss)
I0408 08:32:09.282362 31856 sgd_solver.cpp:105] Iteration 6132, lr = 0.000177488
I0408 08:32:14.264307 31856 solver.cpp:218] Iteration 6144 (2.40876 iter/s, 4.98181s/12 iters), loss = 5.26869
I0408 08:32:14.264421 31856 solver.cpp:237]     Train net output #0: loss = 5.26869 (* 1 = 5.26869 loss)
I0408 08:32:14.264433 31856 sgd_solver.cpp:105] Iteration 6144, lr = 0.000175302
I0408 08:32:19.510242 31856 solver.cpp:218] Iteration 6156 (2.2876 iter/s, 5.24567s/12 iters), loss = 5.27874
I0408 08:32:19.510296 31856 solver.cpp:237]     Train net output #0: loss = 5.27874 (* 1 = 5.27874 loss)
I0408 08:32:19.510308 31856 sgd_solver.cpp:105] Iteration 6156, lr = 0.000173142
I0408 08:32:24.508692 31856 solver.cpp:218] Iteration 6168 (2.40084 iter/s, 4.99825s/12 iters), loss = 5.28867
I0408 08:32:24.508736 31856 solver.cpp:237]     Train net output #0: loss = 5.28867 (* 1 = 5.28867 loss)
I0408 08:32:24.508749 31856 sgd_solver.cpp:105] Iteration 6168, lr = 0.000171009
I0408 08:32:25.115917 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:32:29.473476 31856 solver.cpp:218] Iteration 6180 (2.41712 iter/s, 4.96459s/12 iters), loss = 5.28204
I0408 08:32:29.473523 31856 solver.cpp:237]     Train net output #0: loss = 5.28204 (* 1 = 5.28204 loss)
I0408 08:32:29.473536 31856 sgd_solver.cpp:105] Iteration 6180, lr = 0.000168903
I0408 08:32:34.489178 31856 solver.cpp:218] Iteration 6192 (2.39258 iter/s, 5.0155s/12 iters), loss = 5.26769
I0408 08:32:34.489231 31856 solver.cpp:237]     Train net output #0: loss = 5.26769 (* 1 = 5.26769 loss)
I0408 08:32:34.489243 31856 sgd_solver.cpp:105] Iteration 6192, lr = 0.000166822
I0408 08:32:39.465016 31856 solver.cpp:218] Iteration 6204 (2.41175 iter/s, 4.97564s/12 iters), loss = 5.28615
I0408 08:32:39.465063 31856 solver.cpp:237]     Train net output #0: loss = 5.28615 (* 1 = 5.28615 loss)
I0408 08:32:39.465075 31856 sgd_solver.cpp:105] Iteration 6204, lr = 0.000164767
I0408 08:32:44.499848 31856 solver.cpp:218] Iteration 6216 (2.38349 iter/s, 5.03463s/12 iters), loss = 5.27901
I0408 08:32:44.499954 31856 solver.cpp:237]     Train net output #0: loss = 5.27901 (* 1 = 5.27901 loss)
I0408 08:32:44.499967 31856 sgd_solver.cpp:105] Iteration 6216, lr = 0.000162737
I0408 08:32:46.542634 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0408 08:32:51.374707 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0408 08:33:03.638099 31856 solver.cpp:330] Iteration 6222, Testing net (#0)
I0408 08:33:03.638125 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:33:05.651926 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:33:06.928434 31856 blocking_queue.cpp:49] Waiting for data
I0408 08:33:08.125928 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:33:08.125969 31856 solver.cpp:397]     Test net output #1: loss = 5.28692 (* 1 = 5.28692 loss)
I0408 08:33:10.051580 31856 solver.cpp:218] Iteration 6228 (0.469651 iter/s, 25.5509s/12 iters), loss = 5.27525
I0408 08:33:10.051630 31856 solver.cpp:237]     Train net output #0: loss = 5.27525 (* 1 = 5.27525 loss)
I0408 08:33:10.051641 31856 sgd_solver.cpp:105] Iteration 6228, lr = 0.000160733
I0408 08:33:14.997277 31856 solver.cpp:218] Iteration 6240 (2.42645 iter/s, 4.9455s/12 iters), loss = 5.28251
I0408 08:33:14.997404 31856 solver.cpp:237]     Train net output #0: loss = 5.28251 (* 1 = 5.28251 loss)
I0408 08:33:14.997416 31856 sgd_solver.cpp:105] Iteration 6240, lr = 0.000158753
I0408 08:33:20.036082 31856 solver.cpp:218] Iteration 6252 (2.38165 iter/s, 5.03853s/12 iters), loss = 5.25843
I0408 08:33:20.036128 31856 solver.cpp:237]     Train net output #0: loss = 5.25843 (* 1 = 5.25843 loss)
I0408 08:33:20.036139 31856 sgd_solver.cpp:105] Iteration 6252, lr = 0.000156797
I0408 08:33:24.962867 31856 solver.cpp:218] Iteration 6264 (2.43576 iter/s, 4.92659s/12 iters), loss = 5.26486
I0408 08:33:24.962913 31856 solver.cpp:237]     Train net output #0: loss = 5.26486 (* 1 = 5.26486 loss)
I0408 08:33:24.962924 31856 sgd_solver.cpp:105] Iteration 6264, lr = 0.000154865
I0408 08:33:27.722807 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:33:30.014328 31856 solver.cpp:218] Iteration 6276 (2.37564 iter/s, 5.05126s/12 iters), loss = 5.27758
I0408 08:33:30.014374 31856 solver.cpp:237]     Train net output #0: loss = 5.27758 (* 1 = 5.27758 loss)
I0408 08:33:30.014385 31856 sgd_solver.cpp:105] Iteration 6276, lr = 0.000152958
I0408 08:33:35.022528 31856 solver.cpp:218] Iteration 6288 (2.39616 iter/s, 5.00801s/12 iters), loss = 5.2589
I0408 08:33:35.022567 31856 solver.cpp:237]     Train net output #0: loss = 5.2589 (* 1 = 5.2589 loss)
I0408 08:33:35.022578 31856 sgd_solver.cpp:105] Iteration 6288, lr = 0.000151073
I0408 08:33:40.029140 31856 solver.cpp:218] Iteration 6300 (2.39692 iter/s, 5.00642s/12 iters), loss = 5.27028
I0408 08:33:40.029191 31856 solver.cpp:237]     Train net output #0: loss = 5.27028 (* 1 = 5.27028 loss)
I0408 08:33:40.029202 31856 sgd_solver.cpp:105] Iteration 6300, lr = 0.000149212
I0408 08:33:45.067075 31856 solver.cpp:218] Iteration 6312 (2.38202 iter/s, 5.03773s/12 iters), loss = 5.26128
I0408 08:33:45.067792 31856 solver.cpp:237]     Train net output #0: loss = 5.26128 (* 1 = 5.26128 loss)
I0408 08:33:45.067806 31856 sgd_solver.cpp:105] Iteration 6312, lr = 0.000147374
I0408 08:33:49.572382 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0408 08:34:01.033111 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0408 08:34:09.976727 31856 solver.cpp:330] Iteration 6324, Testing net (#0)
I0408 08:34:09.976755 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:34:11.965463 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:34:14.466908 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:34:14.466958 31856 solver.cpp:397]     Test net output #1: loss = 5.28704 (* 1 = 5.28704 loss)
I0408 08:34:14.557128 31856 solver.cpp:218] Iteration 6324 (0.406938 iter/s, 29.4885s/12 iters), loss = 5.29891
I0408 08:34:14.557178 31856 solver.cpp:237]     Train net output #0: loss = 5.29891 (* 1 = 5.29891 loss)
I0408 08:34:14.557188 31856 sgd_solver.cpp:105] Iteration 6324, lr = 0.000145559
I0408 08:34:18.919700 31856 solver.cpp:218] Iteration 6336 (2.75079 iter/s, 4.36239s/12 iters), loss = 5.26937
I0408 08:34:18.919804 31856 solver.cpp:237]     Train net output #0: loss = 5.26937 (* 1 = 5.26937 loss)
I0408 08:34:18.919817 31856 sgd_solver.cpp:105] Iteration 6336, lr = 0.000143766
I0408 08:34:23.951431 31856 solver.cpp:218] Iteration 6348 (2.38499 iter/s, 5.03148s/12 iters), loss = 5.26166
I0408 08:34:23.951475 31856 solver.cpp:237]     Train net output #0: loss = 5.26166 (* 1 = 5.26166 loss)
I0408 08:34:23.951488 31856 sgd_solver.cpp:105] Iteration 6348, lr = 0.000141995
I0408 08:34:28.980100 31856 solver.cpp:218] Iteration 6360 (2.38641 iter/s, 5.02847s/12 iters), loss = 5.26789
I0408 08:34:28.980149 31856 solver.cpp:237]     Train net output #0: loss = 5.26789 (* 1 = 5.26789 loss)
I0408 08:34:28.980161 31856 sgd_solver.cpp:105] Iteration 6360, lr = 0.000140245
I0408 08:34:33.853119 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:34:33.988922 31856 solver.cpp:218] Iteration 6372 (2.39587 iter/s, 5.00862s/12 iters), loss = 5.25188
I0408 08:34:33.988970 31856 solver.cpp:237]     Train net output #0: loss = 5.25188 (* 1 = 5.25188 loss)
I0408 08:34:33.988981 31856 sgd_solver.cpp:105] Iteration 6372, lr = 0.000138518
I0408 08:34:39.034200 31856 solver.cpp:218] Iteration 6384 (2.37855 iter/s, 5.04508s/12 iters), loss = 5.26763
I0408 08:34:39.034245 31856 solver.cpp:237]     Train net output #0: loss = 5.26763 (* 1 = 5.26763 loss)
I0408 08:34:39.034256 31856 sgd_solver.cpp:105] Iteration 6384, lr = 0.000136811
I0408 08:34:44.051092 31856 solver.cpp:218] Iteration 6396 (2.39201 iter/s, 5.0167s/12 iters), loss = 5.29585
I0408 08:34:44.051141 31856 solver.cpp:237]     Train net output #0: loss = 5.29585 (* 1 = 5.29585 loss)
I0408 08:34:44.051152 31856 sgd_solver.cpp:105] Iteration 6396, lr = 0.000135126
I0408 08:34:48.977924 31856 solver.cpp:218] Iteration 6408 (2.43574 iter/s, 4.92664s/12 iters), loss = 5.28337
I0408 08:34:48.978085 31856 solver.cpp:237]     Train net output #0: loss = 5.28337 (* 1 = 5.28337 loss)
I0408 08:34:48.978097 31856 sgd_solver.cpp:105] Iteration 6408, lr = 0.000133461
I0408 08:34:53.883149 31856 solver.cpp:218] Iteration 6420 (2.44652 iter/s, 4.90492s/12 iters), loss = 5.27793
I0408 08:34:53.883198 31856 solver.cpp:237]     Train net output #0: loss = 5.27793 (* 1 = 5.27793 loss)
I0408 08:34:53.883208 31856 sgd_solver.cpp:105] Iteration 6420, lr = 0.000131817
I0408 08:34:55.871040 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0408 08:35:05.049584 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0408 08:35:10.199743 31856 solver.cpp:330] Iteration 6426, Testing net (#0)
I0408 08:35:10.199775 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:35:12.137136 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:35:14.664614 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:35:14.664662 31856 solver.cpp:397]     Test net output #1: loss = 5.28699 (* 1 = 5.28699 loss)
I0408 08:35:16.517031 31856 solver.cpp:218] Iteration 6432 (0.530195 iter/s, 22.6332s/12 iters), loss = 5.26945
I0408 08:35:16.517067 31856 solver.cpp:237]     Train net output #0: loss = 5.26945 (* 1 = 5.26945 loss)
I0408 08:35:16.517076 31856 sgd_solver.cpp:105] Iteration 6432, lr = 0.000130193
I0408 08:35:21.503538 31856 solver.cpp:218] Iteration 6444 (2.40659 iter/s, 4.98631s/12 iters), loss = 5.24495
I0408 08:35:21.503638 31856 solver.cpp:237]     Train net output #0: loss = 5.24495 (* 1 = 5.24495 loss)
I0408 08:35:21.503650 31856 sgd_solver.cpp:105] Iteration 6444, lr = 0.00012859
I0408 08:35:26.684852 31856 solver.cpp:218] Iteration 6456 (2.31613 iter/s, 5.18106s/12 iters), loss = 5.27473
I0408 08:35:26.684896 31856 solver.cpp:237]     Train net output #0: loss = 5.27473 (* 1 = 5.27473 loss)
I0408 08:35:26.684908 31856 sgd_solver.cpp:105] Iteration 6456, lr = 0.000127005
I0408 08:35:31.851472 31856 solver.cpp:218] Iteration 6468 (2.32269 iter/s, 5.16642s/12 iters), loss = 5.26529
I0408 08:35:31.851518 31856 solver.cpp:237]     Train net output #0: loss = 5.26529 (* 1 = 5.26529 loss)
I0408 08:35:31.851531 31856 sgd_solver.cpp:105] Iteration 6468, lr = 0.000125441
I0408 08:35:33.825182 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:35:36.835888 31856 solver.cpp:218] Iteration 6480 (2.4076 iter/s, 4.98422s/12 iters), loss = 5.28835
I0408 08:35:36.835935 31856 solver.cpp:237]     Train net output #0: loss = 5.28835 (* 1 = 5.28835 loss)
I0408 08:35:36.835947 31856 sgd_solver.cpp:105] Iteration 6480, lr = 0.000123896
I0408 08:35:41.839684 31856 solver.cpp:218] Iteration 6492 (2.39827 iter/s, 5.0036s/12 iters), loss = 5.27008
I0408 08:35:41.839730 31856 solver.cpp:237]     Train net output #0: loss = 5.27008 (* 1 = 5.27008 loss)
I0408 08:35:41.839740 31856 sgd_solver.cpp:105] Iteration 6492, lr = 0.000122369
I0408 08:35:46.799554 31856 solver.cpp:218] Iteration 6504 (2.41951 iter/s, 4.95967s/12 iters), loss = 5.27083
I0408 08:35:46.799602 31856 solver.cpp:237]     Train net output #0: loss = 5.27083 (* 1 = 5.27083 loss)
I0408 08:35:46.799613 31856 sgd_solver.cpp:105] Iteration 6504, lr = 0.000120862
I0408 08:35:51.678465 31856 solver.cpp:218] Iteration 6516 (2.45966 iter/s, 4.87872s/12 iters), loss = 5.26443
I0408 08:35:51.678580 31856 solver.cpp:237]     Train net output #0: loss = 5.26443 (* 1 = 5.26443 loss)
I0408 08:35:51.678589 31856 sgd_solver.cpp:105] Iteration 6516, lr = 0.000119373
I0408 08:35:56.153190 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0408 08:36:00.310091 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0408 08:36:04.063900 31856 solver.cpp:330] Iteration 6528, Testing net (#0)
I0408 08:36:04.063949 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:36:05.955984 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:36:08.517688 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:36:08.517735 31856 solver.cpp:397]     Test net output #1: loss = 5.28705 (* 1 = 5.28705 loss)
I0408 08:36:08.607977 31856 solver.cpp:218] Iteration 6528 (0.708847 iter/s, 16.9289s/12 iters), loss = 5.27093
I0408 08:36:08.608034 31856 solver.cpp:237]     Train net output #0: loss = 5.27093 (* 1 = 5.27093 loss)
I0408 08:36:08.608047 31856 sgd_solver.cpp:105] Iteration 6528, lr = 0.000117903
I0408 08:36:13.151530 31856 solver.cpp:218] Iteration 6540 (2.64122 iter/s, 4.54336s/12 iters), loss = 5.27185
I0408 08:36:13.151577 31856 solver.cpp:237]     Train net output #0: loss = 5.27185 (* 1 = 5.27185 loss)
I0408 08:36:13.151589 31856 sgd_solver.cpp:105] Iteration 6540, lr = 0.00011645
I0408 08:36:18.558313 31856 solver.cpp:218] Iteration 6552 (2.21952 iter/s, 5.40657s/12 iters), loss = 5.26745
I0408 08:36:18.558362 31856 solver.cpp:237]     Train net output #0: loss = 5.26745 (* 1 = 5.26745 loss)
I0408 08:36:18.558373 31856 sgd_solver.cpp:105] Iteration 6552, lr = 0.000115016
I0408 08:36:23.663033 31856 solver.cpp:218] Iteration 6564 (2.35086 iter/s, 5.10452s/12 iters), loss = 5.25892
I0408 08:36:23.663165 31856 solver.cpp:237]     Train net output #0: loss = 5.25892 (* 1 = 5.25892 loss)
I0408 08:36:23.663182 31856 sgd_solver.cpp:105] Iteration 6564, lr = 0.000113599
I0408 08:36:27.836601 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:36:28.606572 31856 solver.cpp:218] Iteration 6576 (2.42755 iter/s, 4.94327s/12 iters), loss = 5.2558
I0408 08:36:28.606607 31856 solver.cpp:237]     Train net output #0: loss = 5.2558 (* 1 = 5.2558 loss)
I0408 08:36:28.606614 31856 sgd_solver.cpp:105] Iteration 6576, lr = 0.000112199
I0408 08:36:33.715179 31856 solver.cpp:218] Iteration 6588 (2.34907 iter/s, 5.10841s/12 iters), loss = 5.27867
I0408 08:36:33.715234 31856 solver.cpp:237]     Train net output #0: loss = 5.27867 (* 1 = 5.27867 loss)
I0408 08:36:33.715246 31856 sgd_solver.cpp:105] Iteration 6588, lr = 0.000110817
I0408 08:36:38.939280 31856 solver.cpp:218] Iteration 6600 (2.29714 iter/s, 5.22389s/12 iters), loss = 5.27212
I0408 08:36:38.939328 31856 solver.cpp:237]     Train net output #0: loss = 5.27212 (* 1 = 5.27212 loss)
I0408 08:36:38.939340 31856 sgd_solver.cpp:105] Iteration 6600, lr = 0.000109452
I0408 08:36:43.975272 31856 solver.cpp:218] Iteration 6612 (2.38294 iter/s, 5.03579s/12 iters), loss = 5.30373
I0408 08:36:43.975320 31856 solver.cpp:237]     Train net output #0: loss = 5.30373 (* 1 = 5.30373 loss)
I0408 08:36:43.975332 31856 sgd_solver.cpp:105] Iteration 6612, lr = 0.000108104
I0408 08:36:49.109773 31856 solver.cpp:218] Iteration 6624 (2.33722 iter/s, 5.13429s/12 iters), loss = 5.26938
I0408 08:36:49.109822 31856 solver.cpp:237]     Train net output #0: loss = 5.26938 (* 1 = 5.26938 loss)
I0408 08:36:49.109833 31856 sgd_solver.cpp:105] Iteration 6624, lr = 0.000106772
I0408 08:36:51.360141 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0408 08:36:56.036026 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0408 08:37:00.053010 31856 solver.cpp:330] Iteration 6630, Testing net (#0)
I0408 08:37:00.053037 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:37:01.913363 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:37:04.508641 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:37:04.508690 31856 solver.cpp:397]     Test net output #1: loss = 5.28715 (* 1 = 5.28715 loss)
I0408 08:37:06.469079 31856 solver.cpp:218] Iteration 6636 (0.691294 iter/s, 17.3588s/12 iters), loss = 5.27327
I0408 08:37:06.469132 31856 solver.cpp:237]     Train net output #0: loss = 5.27327 (* 1 = 5.27327 loss)
I0408 08:37:06.469144 31856 sgd_solver.cpp:105] Iteration 6636, lr = 0.000105457
I0408 08:37:11.466054 31856 solver.cpp:218] Iteration 6648 (2.40155 iter/s, 4.99677s/12 iters), loss = 5.27655
I0408 08:37:11.466097 31856 solver.cpp:237]     Train net output #0: loss = 5.27655 (* 1 = 5.27655 loss)
I0408 08:37:11.466107 31856 sgd_solver.cpp:105] Iteration 6648, lr = 0.000104158
I0408 08:37:16.417171 31856 solver.cpp:218] Iteration 6660 (2.42379 iter/s, 4.95092s/12 iters), loss = 5.28053
I0408 08:37:16.417217 31856 solver.cpp:237]     Train net output #0: loss = 5.28053 (* 1 = 5.28053 loss)
I0408 08:37:16.417228 31856 sgd_solver.cpp:105] Iteration 6660, lr = 0.000102874
I0408 08:37:21.426993 31856 solver.cpp:218] Iteration 6672 (2.39539 iter/s, 5.00962s/12 iters), loss = 5.26397
I0408 08:37:21.427039 31856 solver.cpp:237]     Train net output #0: loss = 5.26397 (* 1 = 5.26397 loss)
I0408 08:37:21.427052 31856 sgd_solver.cpp:105] Iteration 6672, lr = 0.000101607
I0408 08:37:22.791127 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:37:26.419983 31856 solver.cpp:218] Iteration 6684 (2.40346 iter/s, 4.99279s/12 iters), loss = 5.27397
I0408 08:37:26.420100 31856 solver.cpp:237]     Train net output #0: loss = 5.27397 (* 1 = 5.27397 loss)
I0408 08:37:26.420114 31856 sgd_solver.cpp:105] Iteration 6684, lr = 0.000100355
I0408 08:37:31.451886 31856 solver.cpp:218] Iteration 6696 (2.38491 iter/s, 5.03164s/12 iters), loss = 5.26766
I0408 08:37:31.451936 31856 solver.cpp:237]     Train net output #0: loss = 5.26766 (* 1 = 5.26766 loss)
I0408 08:37:31.451946 31856 sgd_solver.cpp:105] Iteration 6696, lr = 9.91192e-05
I0408 08:37:36.483538 31856 solver.cpp:218] Iteration 6708 (2.385 iter/s, 5.03145s/12 iters), loss = 5.27405
I0408 08:37:36.483587 31856 solver.cpp:237]     Train net output #0: loss = 5.27405 (* 1 = 5.27405 loss)
I0408 08:37:36.483599 31856 sgd_solver.cpp:105] Iteration 6708, lr = 9.78982e-05
I0408 08:37:41.460268 31856 solver.cpp:218] Iteration 6720 (2.41132 iter/s, 4.97653s/12 iters), loss = 5.26954
I0408 08:37:41.460319 31856 solver.cpp:237]     Train net output #0: loss = 5.26954 (* 1 = 5.26954 loss)
I0408 08:37:41.460330 31856 sgd_solver.cpp:105] Iteration 6720, lr = 9.66922e-05
I0408 08:37:46.008770 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0408 08:37:50.916662 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0408 08:37:55.086716 31856 solver.cpp:330] Iteration 6732, Testing net (#0)
I0408 08:37:55.086750 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:37:56.890884 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:37:59.528328 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:37:59.528378 31856 solver.cpp:397]     Test net output #1: loss = 5.28689 (* 1 = 5.28689 loss)
I0408 08:37:59.618430 31856 solver.cpp:218] Iteration 6732 (0.66088 iter/s, 18.1576s/12 iters), loss = 5.26925
I0408 08:37:59.618485 31856 solver.cpp:237]     Train net output #0: loss = 5.26925 (* 1 = 5.26925 loss)
I0408 08:37:59.618496 31856 sgd_solver.cpp:105] Iteration 6732, lr = 9.55011e-05
I0408 08:38:03.867974 31856 solver.cpp:218] Iteration 6744 (2.82395 iter/s, 4.24937s/12 iters), loss = 5.25978
I0408 08:38:03.868010 31856 solver.cpp:237]     Train net output #0: loss = 5.25978 (* 1 = 5.25978 loss)
I0408 08:38:03.868017 31856 sgd_solver.cpp:105] Iteration 6744, lr = 9.43246e-05
I0408 08:38:08.859201 31856 solver.cpp:218] Iteration 6756 (2.40431 iter/s, 4.99104s/12 iters), loss = 5.29068
I0408 08:38:08.859239 31856 solver.cpp:237]     Train net output #0: loss = 5.29068 (* 1 = 5.29068 loss)
I0408 08:38:08.859248 31856 sgd_solver.cpp:105] Iteration 6756, lr = 9.31626e-05
I0408 08:38:13.834928 31856 solver.cpp:218] Iteration 6768 (2.4118 iter/s, 4.97554s/12 iters), loss = 5.27266
I0408 08:38:13.834969 31856 solver.cpp:237]     Train net output #0: loss = 5.27266 (* 1 = 5.27266 loss)
I0408 08:38:13.834980 31856 sgd_solver.cpp:105] Iteration 6768, lr = 9.2015e-05
I0408 08:38:17.317270 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:38:18.841006 31856 solver.cpp:218] Iteration 6780 (2.39718 iter/s, 5.00589s/12 iters), loss = 5.27532
I0408 08:38:18.841048 31856 solver.cpp:237]     Train net output #0: loss = 5.27532 (* 1 = 5.27532 loss)
I0408 08:38:18.841056 31856 sgd_solver.cpp:105] Iteration 6780, lr = 9.08815e-05
I0408 08:38:23.845202 31856 solver.cpp:218] Iteration 6792 (2.39808 iter/s, 5.004s/12 iters), loss = 5.25896
I0408 08:38:23.845239 31856 solver.cpp:237]     Train net output #0: loss = 5.25896 (* 1 = 5.25896 loss)
I0408 08:38:23.845248 31856 sgd_solver.cpp:105] Iteration 6792, lr = 8.97619e-05
I0408 08:38:28.830596 31856 solver.cpp:218] Iteration 6804 (2.40712 iter/s, 4.9852s/12 iters), loss = 5.26528
I0408 08:38:28.830767 31856 solver.cpp:237]     Train net output #0: loss = 5.26528 (* 1 = 5.26528 loss)
I0408 08:38:28.830780 31856 sgd_solver.cpp:105] Iteration 6804, lr = 8.86561e-05
I0408 08:38:33.815203 31856 solver.cpp:218] Iteration 6816 (2.40756 iter/s, 4.98429s/12 iters), loss = 5.27901
I0408 08:38:33.815248 31856 solver.cpp:237]     Train net output #0: loss = 5.27901 (* 1 = 5.27901 loss)
I0408 08:38:33.815258 31856 sgd_solver.cpp:105] Iteration 6816, lr = 8.7564e-05
I0408 08:38:38.872809 31856 solver.cpp:218] Iteration 6828 (2.37276 iter/s, 5.05741s/12 iters), loss = 5.267
I0408 08:38:38.872856 31856 solver.cpp:237]     Train net output #0: loss = 5.267 (* 1 = 5.267 loss)
I0408 08:38:38.872867 31856 sgd_solver.cpp:105] Iteration 6828, lr = 8.64853e-05
I0408 08:38:40.897768 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0408 08:38:44.914863 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0408 08:38:51.393932 31856 solver.cpp:330] Iteration 6834, Testing net (#0)
I0408 08:38:51.393981 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:38:53.181411 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:38:55.857043 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:38:55.857087 31856 solver.cpp:397]     Test net output #1: loss = 5.28768 (* 1 = 5.28768 loss)
I0408 08:38:57.845520 31856 solver.cpp:218] Iteration 6840 (0.632507 iter/s, 18.9721s/12 iters), loss = 5.27437
I0408 08:38:57.845561 31856 solver.cpp:237]     Train net output #0: loss = 5.27437 (* 1 = 5.27437 loss)
I0408 08:38:57.845568 31856 sgd_solver.cpp:105] Iteration 6840, lr = 8.54199e-05
I0408 08:39:03.064308 31856 solver.cpp:218] Iteration 6852 (2.29947 iter/s, 5.2186s/12 iters), loss = 5.27489
I0408 08:39:03.064411 31856 solver.cpp:237]     Train net output #0: loss = 5.27489 (* 1 = 5.27489 loss)
I0408 08:39:03.064420 31856 sgd_solver.cpp:105] Iteration 6852, lr = 8.43676e-05
I0408 08:39:08.039532 31856 solver.cpp:218] Iteration 6864 (2.41208 iter/s, 4.97497s/12 iters), loss = 5.27704
I0408 08:39:08.039592 31856 solver.cpp:237]     Train net output #0: loss = 5.27704 (* 1 = 5.27704 loss)
I0408 08:39:08.039606 31856 sgd_solver.cpp:105] Iteration 6864, lr = 8.33283e-05
I0408 08:39:13.033634 31856 solver.cpp:218] Iteration 6876 (2.40294 iter/s, 4.99389s/12 iters), loss = 5.28383
I0408 08:39:13.033681 31856 solver.cpp:237]     Train net output #0: loss = 5.28383 (* 1 = 5.28383 loss)
I0408 08:39:13.033692 31856 sgd_solver.cpp:105] Iteration 6876, lr = 8.23018e-05
I0408 08:39:13.664005 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:39:18.037385 31856 solver.cpp:218] Iteration 6888 (2.3983 iter/s, 5.00355s/12 iters), loss = 5.2813
I0408 08:39:18.037434 31856 solver.cpp:237]     Train net output #0: loss = 5.2813 (* 1 = 5.2813 loss)
I0408 08:39:18.037446 31856 sgd_solver.cpp:105] Iteration 6888, lr = 8.1288e-05
I0408 08:39:23.025682 31856 solver.cpp:218] Iteration 6900 (2.40573 iter/s, 4.9881s/12 iters), loss = 5.26732
I0408 08:39:23.025732 31856 solver.cpp:237]     Train net output #0: loss = 5.26732 (* 1 = 5.26732 loss)
I0408 08:39:23.025743 31856 sgd_solver.cpp:105] Iteration 6900, lr = 8.02866e-05
I0408 08:39:28.029680 31856 solver.cpp:218] Iteration 6912 (2.39818 iter/s, 5.0038s/12 iters), loss = 5.28519
I0408 08:39:28.029727 31856 solver.cpp:237]     Train net output #0: loss = 5.28519 (* 1 = 5.28519 loss)
I0408 08:39:28.029738 31856 sgd_solver.cpp:105] Iteration 6912, lr = 7.92975e-05
I0408 08:39:33.011185 31856 solver.cpp:218] Iteration 6924 (2.409 iter/s, 4.98131s/12 iters), loss = 5.28133
I0408 08:39:33.011224 31856 solver.cpp:237]     Train net output #0: loss = 5.28133 (* 1 = 5.28133 loss)
I0408 08:39:33.011234 31856 sgd_solver.cpp:105] Iteration 6924, lr = 7.83207e-05
I0408 08:39:37.529435 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0408 08:39:40.565460 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0408 08:39:47.387514 31856 solver.cpp:330] Iteration 6936, Testing net (#0)
I0408 08:39:47.387539 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:39:48.050453 31856 blocking_queue.cpp:49] Waiting for data
I0408 08:39:49.126756 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:39:51.848417 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:39:51.848461 31856 solver.cpp:397]     Test net output #1: loss = 5.28739 (* 1 = 5.28739 loss)
I0408 08:39:51.936344 31856 solver.cpp:218] Iteration 6936 (0.634096 iter/s, 18.9246s/12 iters), loss = 5.28225
I0408 08:39:51.936415 31856 solver.cpp:237]     Train net output #0: loss = 5.28225 (* 1 = 5.28225 loss)
I0408 08:39:51.936431 31856 sgd_solver.cpp:105] Iteration 6936, lr = 7.73559e-05
I0408 08:39:56.147521 31856 solver.cpp:218] Iteration 6948 (2.8497 iter/s, 4.21097s/12 iters), loss = 5.2773
I0408 08:39:56.147568 31856 solver.cpp:237]     Train net output #0: loss = 5.2773 (* 1 = 5.2773 loss)
I0408 08:39:56.147580 31856 sgd_solver.cpp:105] Iteration 6948, lr = 7.64029e-05
I0408 08:40:01.429113 31856 solver.cpp:218] Iteration 6960 (2.27213 iter/s, 5.28139s/12 iters), loss = 5.26388
I0408 08:40:01.429157 31856 solver.cpp:237]     Train net output #0: loss = 5.26388 (* 1 = 5.26388 loss)
I0408 08:40:01.429168 31856 sgd_solver.cpp:105] Iteration 6960, lr = 7.54617e-05
I0408 08:40:06.579435 31856 solver.cpp:218] Iteration 6972 (2.33004 iter/s, 5.15013s/12 iters), loss = 5.26824
I0408 08:40:06.579480 31856 solver.cpp:237]     Train net output #0: loss = 5.26824 (* 1 = 5.26824 loss)
I0408 08:40:06.579491 31856 sgd_solver.cpp:105] Iteration 6972, lr = 7.45321e-05
I0408 08:40:09.348379 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:40:11.598305 31856 solver.cpp:218] Iteration 6984 (2.39107 iter/s, 5.01868s/12 iters), loss = 5.27665
I0408 08:40:11.598351 31856 solver.cpp:237]     Train net output #0: loss = 5.27665 (* 1 = 5.27665 loss)
I0408 08:40:11.598362 31856 sgd_solver.cpp:105] Iteration 6984, lr = 7.3614e-05
I0408 08:40:16.553599 31856 solver.cpp:218] Iteration 6996 (2.42175 iter/s, 4.9551s/12 iters), loss = 5.25668
I0408 08:40:16.553645 31856 solver.cpp:237]     Train net output #0: loss = 5.25668 (* 1 = 5.25668 loss)
I0408 08:40:16.553658 31856 sgd_solver.cpp:105] Iteration 6996, lr = 7.27072e-05
I0408 08:40:21.561523 31856 solver.cpp:218] Iteration 7008 (2.3963 iter/s, 5.00772s/12 iters), loss = 5.26024
I0408 08:40:21.561568 31856 solver.cpp:237]     Train net output #0: loss = 5.26024 (* 1 = 5.26024 loss)
I0408 08:40:21.561578 31856 sgd_solver.cpp:105] Iteration 7008, lr = 7.18115e-05
I0408 08:40:26.562875 31856 solver.cpp:218] Iteration 7020 (2.39944 iter/s, 5.00116s/12 iters), loss = 5.2587
I0408 08:40:26.562920 31856 solver.cpp:237]     Train net output #0: loss = 5.2587 (* 1 = 5.2587 loss)
I0408 08:40:26.562932 31856 sgd_solver.cpp:105] Iteration 7020, lr = 7.09268e-05
I0408 08:40:31.519807 31856 solver.cpp:218] Iteration 7032 (2.42095 iter/s, 4.95674s/12 iters), loss = 5.30295
I0408 08:40:31.519855 31856 solver.cpp:237]     Train net output #0: loss = 5.30295 (* 1 = 5.30295 loss)
I0408 08:40:31.519865 31856 sgd_solver.cpp:105] Iteration 7032, lr = 7.00531e-05
I0408 08:40:33.578599 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0408 08:40:38.859251 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0408 08:40:43.781785 31856 solver.cpp:330] Iteration 7038, Testing net (#0)
I0408 08:40:43.781939 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:40:45.643352 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:40:48.443270 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:40:48.443317 31856 solver.cpp:397]     Test net output #1: loss = 5.28709 (* 1 = 5.28709 loss)
I0408 08:40:50.377007 31856 solver.cpp:218] Iteration 7044 (0.636381 iter/s, 18.8566s/12 iters), loss = 5.27118
I0408 08:40:50.377053 31856 solver.cpp:237]     Train net output #0: loss = 5.27118 (* 1 = 5.27118 loss)
I0408 08:40:50.377065 31856 sgd_solver.cpp:105] Iteration 7044, lr = 6.91901e-05
I0408 08:40:55.694428 31856 solver.cpp:218] Iteration 7056 (2.25682 iter/s, 5.31722s/12 iters), loss = 5.27151
I0408 08:40:55.694464 31856 solver.cpp:237]     Train net output #0: loss = 5.27151 (* 1 = 5.27151 loss)
I0408 08:40:55.694473 31856 sgd_solver.cpp:105] Iteration 7056, lr = 6.83378e-05
I0408 08:41:00.586652 31856 solver.cpp:218] Iteration 7068 (2.45296 iter/s, 4.89204s/12 iters), loss = 5.26666
I0408 08:41:00.586699 31856 solver.cpp:237]     Train net output #0: loss = 5.26666 (* 1 = 5.26666 loss)
I0408 08:41:00.586710 31856 sgd_solver.cpp:105] Iteration 7068, lr = 6.7496e-05
I0408 08:41:05.573952 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:41:05.685356 31856 solver.cpp:218] Iteration 7080 (2.35363 iter/s, 5.09851s/12 iters), loss = 5.24278
I0408 08:41:05.685403 31856 solver.cpp:237]     Train net output #0: loss = 5.24278 (* 1 = 5.24278 loss)
I0408 08:41:05.685415 31856 sgd_solver.cpp:105] Iteration 7080, lr = 6.66645e-05
I0408 08:41:10.699599 31856 solver.cpp:218] Iteration 7092 (2.39328 iter/s, 5.01405s/12 iters), loss = 5.26778
I0408 08:41:10.699647 31856 solver.cpp:237]     Train net output #0: loss = 5.26778 (* 1 = 5.26778 loss)
I0408 08:41:10.699658 31856 sgd_solver.cpp:105] Iteration 7092, lr = 6.58433e-05
I0408 08:41:15.526029 31856 solver.cpp:218] Iteration 7104 (2.48641 iter/s, 4.82624s/12 iters), loss = 5.29524
I0408 08:41:15.526108 31856 solver.cpp:237]     Train net output #0: loss = 5.29524 (* 1 = 5.29524 loss)
I0408 08:41:15.526121 31856 sgd_solver.cpp:105] Iteration 7104, lr = 6.50321e-05
I0408 08:41:20.335762 31856 solver.cpp:218] Iteration 7116 (2.49506 iter/s, 4.80951s/12 iters), loss = 5.27569
I0408 08:41:20.335809 31856 solver.cpp:237]     Train net output #0: loss = 5.27569 (* 1 = 5.27569 loss)
I0408 08:41:20.335821 31856 sgd_solver.cpp:105] Iteration 7116, lr = 6.4231e-05
I0408 08:41:25.171137 31856 solver.cpp:218] Iteration 7128 (2.48181 iter/s, 4.83519s/12 iters), loss = 5.27428
I0408 08:41:25.171172 31856 solver.cpp:237]     Train net output #0: loss = 5.27428 (* 1 = 5.27428 loss)
I0408 08:41:25.171180 31856 sgd_solver.cpp:105] Iteration 7128, lr = 6.34398e-05
I0408 08:41:29.623260 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0408 08:41:32.572762 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0408 08:41:36.727291 31856 solver.cpp:330] Iteration 7140, Testing net (#0)
I0408 08:41:36.727324 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:41:38.395905 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:41:41.196177 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:41:41.196220 31856 solver.cpp:397]     Test net output #1: loss = 5.28728 (* 1 = 5.28728 loss)
I0408 08:41:41.283409 31856 solver.cpp:218] Iteration 7140 (0.744797 iter/s, 16.1118s/12 iters), loss = 5.26276
I0408 08:41:41.283450 31856 solver.cpp:237]     Train net output #0: loss = 5.26276 (* 1 = 5.26276 loss)
I0408 08:41:41.283460 31856 sgd_solver.cpp:105] Iteration 7140, lr = 6.26583e-05
I0408 08:41:45.560871 31856 solver.cpp:218] Iteration 7152 (2.80552 iter/s, 4.27729s/12 iters), loss = 5.24631
I0408 08:41:45.561017 31856 solver.cpp:237]     Train net output #0: loss = 5.24631 (* 1 = 5.24631 loss)
I0408 08:41:45.561029 31856 sgd_solver.cpp:105] Iteration 7152, lr = 6.18864e-05
I0408 08:41:50.562386 31856 solver.cpp:218] Iteration 7164 (2.39941 iter/s, 5.00122s/12 iters), loss = 5.27232
I0408 08:41:50.562433 31856 solver.cpp:237]     Train net output #0: loss = 5.27232 (* 1 = 5.27232 loss)
I0408 08:41:50.562445 31856 sgd_solver.cpp:105] Iteration 7164, lr = 6.1124e-05
I0408 08:41:55.554733 31856 solver.cpp:218] Iteration 7176 (2.40377 iter/s, 4.99215s/12 iters), loss = 5.25706
I0408 08:41:55.554769 31856 solver.cpp:237]     Train net output #0: loss = 5.25706 (* 1 = 5.25706 loss)
I0408 08:41:55.554776 31856 sgd_solver.cpp:105] Iteration 7176, lr = 6.0371e-05
I0408 08:41:57.670866 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:42:00.615773 31856 solver.cpp:218] Iteration 7188 (2.37115 iter/s, 5.06085s/12 iters), loss = 5.2753
I0408 08:42:00.615823 31856 solver.cpp:237]     Train net output #0: loss = 5.2753 (* 1 = 5.2753 loss)
I0408 08:42:00.615834 31856 sgd_solver.cpp:105] Iteration 7188, lr = 5.96273e-05
I0408 08:42:05.600311 31856 solver.cpp:218] Iteration 7200 (2.40754 iter/s, 4.98434s/12 iters), loss = 5.27112
I0408 08:42:05.600356 31856 solver.cpp:237]     Train net output #0: loss = 5.27112 (* 1 = 5.27112 loss)
I0408 08:42:05.600368 31856 sgd_solver.cpp:105] Iteration 7200, lr = 5.88928e-05
I0408 08:42:10.610838 31856 solver.cpp:218] Iteration 7212 (2.39505 iter/s, 5.01033s/12 iters), loss = 5.27952
I0408 08:42:10.610885 31856 solver.cpp:237]     Train net output #0: loss = 5.27952 (* 1 = 5.27952 loss)
I0408 08:42:10.610896 31856 sgd_solver.cpp:105] Iteration 7212, lr = 5.81673e-05
I0408 08:42:15.600697 31856 solver.cpp:218] Iteration 7224 (2.40497 iter/s, 4.98966s/12 iters), loss = 5.26399
I0408 08:42:15.600807 31856 solver.cpp:237]     Train net output #0: loss = 5.26399 (* 1 = 5.26399 loss)
I0408 08:42:15.600821 31856 sgd_solver.cpp:105] Iteration 7224, lr = 5.74508e-05
I0408 08:42:20.543416 31856 solver.cpp:218] Iteration 7236 (2.42794 iter/s, 4.94246s/12 iters), loss = 5.27437
I0408 08:42:20.543468 31856 solver.cpp:237]     Train net output #0: loss = 5.27437 (* 1 = 5.27437 loss)
I0408 08:42:20.543480 31856 sgd_solver.cpp:105] Iteration 7236, lr = 5.6743e-05
I0408 08:42:22.550143 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0408 08:42:25.525992 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0408 08:42:28.805523 31856 solver.cpp:330] Iteration 7242, Testing net (#0)
I0408 08:42:28.805554 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:42:30.420169 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:42:33.259168 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:42:33.259215 31856 solver.cpp:397]     Test net output #1: loss = 5.28691 (* 1 = 5.28691 loss)
I0408 08:42:35.250488 31856 solver.cpp:218] Iteration 7248 (0.81596 iter/s, 14.7066s/12 iters), loss = 5.27382
I0408 08:42:35.250535 31856 solver.cpp:237]     Train net output #0: loss = 5.27382 (* 1 = 5.27382 loss)
I0408 08:42:35.250547 31856 sgd_solver.cpp:105] Iteration 7248, lr = 5.6044e-05
I0408 08:42:40.512401 31856 solver.cpp:218] Iteration 7260 (2.28063 iter/s, 5.26171s/12 iters), loss = 5.27217
I0408 08:42:40.512446 31856 solver.cpp:237]     Train net output #0: loss = 5.27217 (* 1 = 5.27217 loss)
I0408 08:42:40.512459 31856 sgd_solver.cpp:105] Iteration 7260, lr = 5.53536e-05
I0408 08:42:45.801918 31856 solver.cpp:218] Iteration 7272 (2.26873 iter/s, 5.28931s/12 iters), loss = 5.2523
I0408 08:42:45.802076 31856 solver.cpp:237]     Train net output #0: loss = 5.2523 (* 1 = 5.2523 loss)
I0408 08:42:45.802090 31856 sgd_solver.cpp:105] Iteration 7272, lr = 5.46717e-05
I0408 08:42:50.095338 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:42:50.823228 31856 solver.cpp:218] Iteration 7284 (2.38996 iter/s, 5.021s/12 iters), loss = 5.25683
I0408 08:42:50.823274 31856 solver.cpp:237]     Train net output #0: loss = 5.25683 (* 1 = 5.25683 loss)
I0408 08:42:50.823285 31856 sgd_solver.cpp:105] Iteration 7284, lr = 5.39982e-05
I0408 08:42:55.846709 31856 solver.cpp:218] Iteration 7296 (2.38887 iter/s, 5.02329s/12 iters), loss = 5.28154
I0408 08:42:55.846746 31856 solver.cpp:237]     Train net output #0: loss = 5.28154 (* 1 = 5.28154 loss)
I0408 08:42:55.846755 31856 sgd_solver.cpp:105] Iteration 7296, lr = 5.3333e-05
I0408 08:43:00.876673 31856 solver.cpp:218] Iteration 7308 (2.38579 iter/s, 5.02977s/12 iters), loss = 5.28244
I0408 08:43:00.876720 31856 solver.cpp:237]     Train net output #0: loss = 5.28244 (* 1 = 5.28244 loss)
I0408 08:43:00.876732 31856 sgd_solver.cpp:105] Iteration 7308, lr = 5.2676e-05
I0408 08:43:05.881160 31856 solver.cpp:218] Iteration 7320 (2.39794 iter/s, 5.00429s/12 iters), loss = 5.29419
I0408 08:43:05.881206 31856 solver.cpp:237]     Train net output #0: loss = 5.29419 (* 1 = 5.29419 loss)
I0408 08:43:05.881217 31856 sgd_solver.cpp:105] Iteration 7320, lr = 5.20271e-05
I0408 08:43:11.024396 31856 solver.cpp:218] Iteration 7332 (2.33325 iter/s, 5.14304s/12 iters), loss = 5.2685
I0408 08:43:11.024441 31856 solver.cpp:237]     Train net output #0: loss = 5.2685 (* 1 = 5.2685 loss)
I0408 08:43:11.024452 31856 sgd_solver.cpp:105] Iteration 7332, lr = 5.13862e-05
I0408 08:43:15.579389 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0408 08:43:19.382066 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0408 08:43:24.234423 31856 solver.cpp:330] Iteration 7344, Testing net (#0)
I0408 08:43:24.234457 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:43:25.828020 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:43:28.700372 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:43:28.700420 31856 solver.cpp:397]     Test net output #1: loss = 5.2878 (* 1 = 5.2878 loss)
I0408 08:43:28.790499 31856 solver.cpp:218] Iteration 7344 (0.675464 iter/s, 17.7656s/12 iters), loss = 5.27624
I0408 08:43:28.790547 31856 solver.cpp:237]     Train net output #0: loss = 5.27624 (* 1 = 5.27624 loss)
I0408 08:43:28.790558 31856 sgd_solver.cpp:105] Iteration 7344, lr = 5.07532e-05
I0408 08:43:33.322620 31856 solver.cpp:218] Iteration 7356 (2.64788 iter/s, 4.53193s/12 iters), loss = 5.28379
I0408 08:43:33.322669 31856 solver.cpp:237]     Train net output #0: loss = 5.28379 (* 1 = 5.28379 loss)
I0408 08:43:33.322681 31856 sgd_solver.cpp:105] Iteration 7356, lr = 5.0128e-05
I0408 08:43:38.559653 31856 solver.cpp:218] Iteration 7368 (2.29147 iter/s, 5.23682s/12 iters), loss = 5.2769
I0408 08:43:38.559701 31856 solver.cpp:237]     Train net output #0: loss = 5.2769 (* 1 = 5.2769 loss)
I0408 08:43:38.559715 31856 sgd_solver.cpp:105] Iteration 7368, lr = 4.95105e-05
I0408 08:43:43.714134 31856 solver.cpp:218] Iteration 7380 (2.32816 iter/s, 5.15428s/12 iters), loss = 5.26271
I0408 08:43:43.714190 31856 solver.cpp:237]     Train net output #0: loss = 5.26271 (* 1 = 5.26271 loss)
I0408 08:43:43.714206 31856 sgd_solver.cpp:105] Iteration 7380, lr = 4.89006e-05
I0408 08:43:45.202994 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:43:48.872630 31856 solver.cpp:218] Iteration 7392 (2.32635 iter/s, 5.15829s/12 iters), loss = 5.27243
I0408 08:43:48.872674 31856 solver.cpp:237]     Train net output #0: loss = 5.27243 (* 1 = 5.27243 loss)
I0408 08:43:48.872685 31856 sgd_solver.cpp:105] Iteration 7392, lr = 4.82982e-05
I0408 08:43:53.859195 31856 solver.cpp:218] Iteration 7404 (2.40656 iter/s, 4.98637s/12 iters), loss = 5.26877
I0408 08:43:53.859359 31856 solver.cpp:237]     Train net output #0: loss = 5.26877 (* 1 = 5.26877 loss)
I0408 08:43:53.859373 31856 sgd_solver.cpp:105] Iteration 7404, lr = 4.77032e-05
I0408 08:43:58.867375 31856 solver.cpp:218] Iteration 7416 (2.39623 iter/s, 5.00787s/12 iters), loss = 5.26708
I0408 08:43:58.867419 31856 solver.cpp:237]     Train net output #0: loss = 5.26708 (* 1 = 5.26708 loss)
I0408 08:43:58.867430 31856 sgd_solver.cpp:105] Iteration 7416, lr = 4.71155e-05
I0408 08:44:03.945618 31856 solver.cpp:218] Iteration 7428 (2.36312 iter/s, 5.07804s/12 iters), loss = 5.27867
I0408 08:44:03.945665 31856 solver.cpp:237]     Train net output #0: loss = 5.27867 (* 1 = 5.27867 loss)
I0408 08:44:03.945678 31856 sgd_solver.cpp:105] Iteration 7428, lr = 4.65351e-05
I0408 08:44:09.015604 31856 solver.cpp:218] Iteration 7440 (2.36696 iter/s, 5.06979s/12 iters), loss = 5.25891
I0408 08:44:09.015655 31856 solver.cpp:237]     Train net output #0: loss = 5.25891 (* 1 = 5.25891 loss)
I0408 08:44:09.015667 31856 sgd_solver.cpp:105] Iteration 7440, lr = 4.59619e-05
I0408 08:44:11.057065 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0408 08:44:14.065387 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0408 08:44:17.932736 31856 solver.cpp:330] Iteration 7446, Testing net (#0)
I0408 08:44:17.932765 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:44:19.437243 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:44:22.356680 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:44:22.356727 31856 solver.cpp:397]     Test net output #1: loss = 5.28712 (* 1 = 5.28712 loss)
I0408 08:44:24.143452 31856 solver.cpp:218] Iteration 7452 (0.793264 iter/s, 15.1274s/12 iters), loss = 5.26522
I0408 08:44:24.143537 31856 solver.cpp:237]     Train net output #0: loss = 5.26522 (* 1 = 5.26522 loss)
I0408 08:44:24.143548 31856 sgd_solver.cpp:105] Iteration 7452, lr = 4.53957e-05
I0408 08:44:29.171411 31856 solver.cpp:218] Iteration 7464 (2.38676 iter/s, 5.02773s/12 iters), loss = 5.28723
I0408 08:44:29.171460 31856 solver.cpp:237]     Train net output #0: loss = 5.28723 (* 1 = 5.28723 loss)
I0408 08:44:29.171471 31856 sgd_solver.cpp:105] Iteration 7464, lr = 4.48364e-05
I0408 08:44:34.093706 31856 solver.cpp:218] Iteration 7476 (2.43799 iter/s, 4.9221s/12 iters), loss = 5.27591
I0408 08:44:34.093757 31856 solver.cpp:237]     Train net output #0: loss = 5.27591 (* 1 = 5.27591 loss)
I0408 08:44:34.093768 31856 sgd_solver.cpp:105] Iteration 7476, lr = 4.42841e-05
I0408 08:44:37.635160 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:44:39.105484 31856 solver.cpp:218] Iteration 7488 (2.39446 iter/s, 5.01156s/12 iters), loss = 5.269
I0408 08:44:39.105542 31856 solver.cpp:237]     Train net output #0: loss = 5.269 (* 1 = 5.269 loss)
I0408 08:44:39.105558 31856 sgd_solver.cpp:105] Iteration 7488, lr = 4.37386e-05
I0408 08:44:44.118577 31856 solver.cpp:218] Iteration 7500 (2.39383 iter/s, 5.01289s/12 iters), loss = 5.25796
I0408 08:44:44.118618 31856 solver.cpp:237]     Train net output #0: loss = 5.25796 (* 1 = 5.25796 loss)
I0408 08:44:44.118628 31856 sgd_solver.cpp:105] Iteration 7500, lr = 4.31998e-05
I0408 08:44:49.082751 31856 solver.cpp:218] Iteration 7512 (2.41741 iter/s, 4.96398s/12 iters), loss = 5.26148
I0408 08:44:49.082798 31856 solver.cpp:237]     Train net output #0: loss = 5.26148 (* 1 = 5.26148 loss)
I0408 08:44:49.082808 31856 sgd_solver.cpp:105] Iteration 7512, lr = 4.26676e-05
I0408 08:44:54.090484 31856 solver.cpp:218] Iteration 7524 (2.39639 iter/s, 5.00754s/12 iters), loss = 5.26904
I0408 08:44:54.090528 31856 solver.cpp:237]     Train net output #0: loss = 5.26904 (* 1 = 5.26904 loss)
I0408 08:44:54.090539 31856 sgd_solver.cpp:105] Iteration 7524, lr = 4.2142e-05
I0408 08:44:59.045950 31856 solver.cpp:218] Iteration 7536 (2.42166 iter/s, 4.95528s/12 iters), loss = 5.262
I0408 08:44:59.046101 31856 solver.cpp:237]     Train net output #0: loss = 5.262 (* 1 = 5.262 loss)
I0408 08:44:59.046113 31856 sgd_solver.cpp:105] Iteration 7536, lr = 4.16229e-05
I0408 08:45:03.586653 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0408 08:45:06.620826 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0408 08:45:10.759707 31856 solver.cpp:330] Iteration 7548, Testing net (#0)
I0408 08:45:10.759739 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:45:12.269523 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:45:15.223788 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:45:15.223834 31856 solver.cpp:397]     Test net output #1: loss = 5.28724 (* 1 = 5.28724 loss)
I0408 08:45:15.314013 31856 solver.cpp:218] Iteration 7548 (0.737669 iter/s, 16.2675s/12 iters), loss = 5.28059
I0408 08:45:15.314047 31856 solver.cpp:237]     Train net output #0: loss = 5.28059 (* 1 = 5.28059 loss)
I0408 08:45:15.314059 31856 sgd_solver.cpp:105] Iteration 7548, lr = 4.11101e-05
I0408 08:45:19.658248 31856 solver.cpp:218] Iteration 7560 (2.76239 iter/s, 4.34407s/12 iters), loss = 5.27054
I0408 08:45:19.658303 31856 solver.cpp:237]     Train net output #0: loss = 5.27054 (* 1 = 5.27054 loss)
I0408 08:45:19.658315 31856 sgd_solver.cpp:105] Iteration 7560, lr = 4.06037e-05
I0408 08:45:24.679785 31856 solver.cpp:218] Iteration 7572 (2.38981 iter/s, 5.02133s/12 iters), loss = 5.2807
I0408 08:45:24.679841 31856 solver.cpp:237]     Train net output #0: loss = 5.2807 (* 1 = 5.2807 loss)
I0408 08:45:24.679855 31856 sgd_solver.cpp:105] Iteration 7572, lr = 4.01035e-05
I0408 08:45:29.629496 31856 solver.cpp:218] Iteration 7584 (2.42448 iter/s, 4.94951s/12 iters), loss = 5.28763
I0408 08:45:29.629612 31856 solver.cpp:237]     Train net output #0: loss = 5.28763 (* 1 = 5.28763 loss)
I0408 08:45:29.629624 31856 sgd_solver.cpp:105] Iteration 7584, lr = 3.96095e-05
I0408 08:45:30.285974 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:45:34.665886 31856 solver.cpp:218] Iteration 7596 (2.38278 iter/s, 5.03613s/12 iters), loss = 5.27923
I0408 08:45:34.665930 31856 solver.cpp:237]     Train net output #0: loss = 5.27923 (* 1 = 5.27923 loss)
I0408 08:45:34.665940 31856 sgd_solver.cpp:105] Iteration 7596, lr = 3.91215e-05
I0408 08:45:39.644840 31856 solver.cpp:218] Iteration 7608 (2.41024 iter/s, 4.97876s/12 iters), loss = 5.26302
I0408 08:45:39.644886 31856 solver.cpp:237]     Train net output #0: loss = 5.26302 (* 1 = 5.26302 loss)
I0408 08:45:39.644897 31856 sgd_solver.cpp:105] Iteration 7608, lr = 3.86396e-05
I0408 08:45:44.636204 31856 solver.cpp:218] Iteration 7620 (2.40424 iter/s, 4.99117s/12 iters), loss = 5.2791
I0408 08:45:44.636238 31856 solver.cpp:237]     Train net output #0: loss = 5.2791 (* 1 = 5.2791 loss)
I0408 08:45:44.636248 31856 sgd_solver.cpp:105] Iteration 7620, lr = 3.81636e-05
I0408 08:45:47.089852 31856 blocking_queue.cpp:49] Waiting for data
I0408 08:45:49.664572 31856 solver.cpp:218] Iteration 7632 (2.38655 iter/s, 5.02818s/12 iters), loss = 5.27995
I0408 08:45:49.664614 31856 solver.cpp:237]     Train net output #0: loss = 5.27995 (* 1 = 5.27995 loss)
I0408 08:45:49.664628 31856 sgd_solver.cpp:105] Iteration 7632, lr = 3.76935e-05
I0408 08:45:54.659454 31856 solver.cpp:218] Iteration 7644 (2.40255 iter/s, 4.99469s/12 iters), loss = 5.28318
I0408 08:45:54.659495 31856 solver.cpp:237]     Train net output #0: loss = 5.28318 (* 1 = 5.28318 loss)
I0408 08:45:54.659507 31856 sgd_solver.cpp:105] Iteration 7644, lr = 3.72291e-05
I0408 08:45:56.670603 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0408 08:45:59.647850 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0408 08:46:01.990314 31856 solver.cpp:330] Iteration 7650, Testing net (#0)
I0408 08:46:01.990334 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:46:03.455940 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:46:06.468837 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:46:06.468883 31856 solver.cpp:397]     Test net output #1: loss = 5.28741 (* 1 = 5.28741 loss)
I0408 08:46:08.462092 31856 solver.cpp:218] Iteration 7656 (0.869426 iter/s, 13.8022s/12 iters), loss = 5.27408
I0408 08:46:08.462150 31856 solver.cpp:237]     Train net output #0: loss = 5.27408 (* 1 = 5.27408 loss)
I0408 08:46:08.462167 31856 sgd_solver.cpp:105] Iteration 7656, lr = 3.67705e-05
I0408 08:46:13.499574 31856 solver.cpp:218] Iteration 7668 (2.38224 iter/s, 5.03728s/12 iters), loss = 5.26723
I0408 08:46:13.499614 31856 solver.cpp:237]     Train net output #0: loss = 5.26723 (* 1 = 5.26723 loss)
I0408 08:46:13.499627 31856 sgd_solver.cpp:105] Iteration 7668, lr = 3.63175e-05
I0408 08:46:18.498876 31856 solver.cpp:218] Iteration 7680 (2.40043 iter/s, 4.99911s/12 iters), loss = 5.26314
I0408 08:46:18.498922 31856 solver.cpp:237]     Train net output #0: loss = 5.26314 (* 1 = 5.26314 loss)
I0408 08:46:18.498934 31856 sgd_solver.cpp:105] Iteration 7680, lr = 3.58701e-05
I0408 08:46:21.293712 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:46:23.466922 31856 solver.cpp:218] Iteration 7692 (2.41553 iter/s, 4.96785s/12 iters), loss = 5.27055
I0408 08:46:23.466969 31856 solver.cpp:237]     Train net output #0: loss = 5.27055 (* 1 = 5.27055 loss)
I0408 08:46:23.466979 31856 sgd_solver.cpp:105] Iteration 7692, lr = 3.54283e-05
I0408 08:46:28.505157 31856 solver.cpp:218] Iteration 7704 (2.38188 iter/s, 5.03804s/12 iters), loss = 5.25349
I0408 08:46:28.505201 31856 solver.cpp:237]     Train net output #0: loss = 5.25349 (* 1 = 5.25349 loss)
I0408 08:46:28.505213 31856 sgd_solver.cpp:105] Iteration 7704, lr = 3.49918e-05
I0408 08:46:33.510476 31856 solver.cpp:218] Iteration 7716 (2.39754 iter/s, 5.00513s/12 iters), loss = 5.2545
I0408 08:46:33.511895 31856 solver.cpp:237]     Train net output #0: loss = 5.2545 (* 1 = 5.2545 loss)
I0408 08:46:33.511906 31856 sgd_solver.cpp:105] Iteration 7716, lr = 3.45608e-05
I0408 08:46:38.538281 31856 solver.cpp:218] Iteration 7728 (2.38747 iter/s, 5.02624s/12 iters), loss = 5.25733
I0408 08:46:38.538327 31856 solver.cpp:237]     Train net output #0: loss = 5.25733 (* 1 = 5.25733 loss)
I0408 08:46:38.538339 31856 sgd_solver.cpp:105] Iteration 7728, lr = 3.4135e-05
I0408 08:46:43.552760 31856 solver.cpp:218] Iteration 7740 (2.39316 iter/s, 5.01428s/12 iters), loss = 5.29873
I0408 08:46:43.552806 31856 solver.cpp:237]     Train net output #0: loss = 5.29873 (* 1 = 5.29873 loss)
I0408 08:46:43.552817 31856 sgd_solver.cpp:105] Iteration 7740, lr = 3.37145e-05
I0408 08:46:48.138260 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0408 08:46:51.142380 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0408 08:46:53.471781 31856 solver.cpp:330] Iteration 7752, Testing net (#0)
I0408 08:46:53.471808 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:46:54.780815 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:46:57.828110 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:46:57.828156 31856 solver.cpp:397]     Test net output #1: loss = 5.28703 (* 1 = 5.28703 loss)
I0408 08:46:57.918592 31856 solver.cpp:218] Iteration 7752 (0.835342 iter/s, 14.3654s/12 iters), loss = 5.26764
I0408 08:46:57.918660 31856 solver.cpp:237]     Train net output #0: loss = 5.26764 (* 1 = 5.26764 loss)
I0408 08:46:57.918676 31856 sgd_solver.cpp:105] Iteration 7752, lr = 3.32992e-05
I0408 08:47:02.132846 31856 solver.cpp:218] Iteration 7764 (2.84761 iter/s, 4.21407s/12 iters), loss = 5.27559
I0408 08:47:02.132894 31856 solver.cpp:237]     Train net output #0: loss = 5.27559 (* 1 = 5.27559 loss)
I0408 08:47:02.132905 31856 sgd_solver.cpp:105] Iteration 7764, lr = 3.2889e-05
I0408 08:47:07.056807 31856 solver.cpp:218] Iteration 7776 (2.43716 iter/s, 4.92377s/12 iters), loss = 5.27074
I0408 08:47:07.056937 31856 solver.cpp:237]     Train net output #0: loss = 5.27074 (* 1 = 5.27074 loss)
I0408 08:47:07.056948 31856 sgd_solver.cpp:105] Iteration 7776, lr = 3.24838e-05
I0408 08:47:11.971122 31856 solver.cpp:218] Iteration 7788 (2.44198 iter/s, 4.91404s/12 iters), loss = 5.24541
I0408 08:47:11.971170 31856 solver.cpp:237]     Train net output #0: loss = 5.24541 (* 1 = 5.24541 loss)
I0408 08:47:11.971181 31856 sgd_solver.cpp:105] Iteration 7788, lr = 3.20837e-05
I0408 08:47:11.979290 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:47:16.903786 31856 solver.cpp:218] Iteration 7800 (2.43286 iter/s, 4.93247s/12 iters), loss = 5.26898
I0408 08:47:16.903838 31856 solver.cpp:237]     Train net output #0: loss = 5.26898 (* 1 = 5.26898 loss)
I0408 08:47:16.903851 31856 sgd_solver.cpp:105] Iteration 7800, lr = 3.16884e-05
I0408 08:47:21.984059 31856 solver.cpp:218] Iteration 7812 (2.36217 iter/s, 5.08006s/12 iters), loss = 5.29511
I0408 08:47:21.984107 31856 solver.cpp:237]     Train net output #0: loss = 5.29511 (* 1 = 5.29511 loss)
I0408 08:47:21.984119 31856 sgd_solver.cpp:105] Iteration 7812, lr = 3.12981e-05
I0408 08:47:26.996714 31856 solver.cpp:218] Iteration 7824 (2.39404 iter/s, 5.01245s/12 iters), loss = 5.27339
I0408 08:47:26.996759 31856 solver.cpp:237]     Train net output #0: loss = 5.27339 (* 1 = 5.27339 loss)
I0408 08:47:26.996768 31856 sgd_solver.cpp:105] Iteration 7824, lr = 3.09125e-05
I0408 08:47:31.992081 31856 solver.cpp:218] Iteration 7836 (2.40232 iter/s, 4.99517s/12 iters), loss = 5.27355
I0408 08:47:31.992130 31856 solver.cpp:237]     Train net output #0: loss = 5.27355 (* 1 = 5.27355 loss)
I0408 08:47:31.992141 31856 sgd_solver.cpp:105] Iteration 7836, lr = 3.05317e-05
I0408 08:47:36.942806 31856 solver.cpp:218] Iteration 7848 (2.42398 iter/s, 4.95053s/12 iters), loss = 5.25766
I0408 08:47:36.942852 31856 solver.cpp:237]     Train net output #0: loss = 5.25766 (* 1 = 5.25766 loss)
I0408 08:47:36.942863 31856 sgd_solver.cpp:105] Iteration 7848, lr = 3.01556e-05
I0408 08:47:39.024211 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0408 08:47:42.048595 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0408 08:47:45.225278 31856 solver.cpp:330] Iteration 7854, Testing net (#0)
I0408 08:47:45.225303 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:47:46.500293 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:47:49.639971 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:47:49.640015 31856 solver.cpp:397]     Test net output #1: loss = 5.2869 (* 1 = 5.2869 loss)
I0408 08:47:51.547420 31856 solver.cpp:218] Iteration 7860 (0.821684 iter/s, 14.6042s/12 iters), loss = 5.24296
I0408 08:47:51.547463 31856 solver.cpp:237]     Train net output #0: loss = 5.24296 (* 1 = 5.24296 loss)
I0408 08:47:51.547475 31856 sgd_solver.cpp:105] Iteration 7860, lr = 2.97841e-05
I0408 08:47:56.521724 31856 solver.cpp:218] Iteration 7872 (2.41249 iter/s, 4.97411s/12 iters), loss = 5.26503
I0408 08:47:56.521765 31856 solver.cpp:237]     Train net output #0: loss = 5.26503 (* 1 = 5.26503 loss)
I0408 08:47:56.521778 31856 sgd_solver.cpp:105] Iteration 7872, lr = 2.94172e-05
I0408 08:48:01.524030 31856 solver.cpp:218] Iteration 7884 (2.39899 iter/s, 5.00212s/12 iters), loss = 5.2568
I0408 08:48:01.524075 31856 solver.cpp:237]     Train net output #0: loss = 5.2568 (* 1 = 5.2568 loss)
I0408 08:48:01.524086 31856 sgd_solver.cpp:105] Iteration 7884, lr = 2.90548e-05
I0408 08:48:03.612108 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:48:06.490579 31856 solver.cpp:218] Iteration 7896 (2.41626 iter/s, 4.96636s/12 iters), loss = 5.27637
I0408 08:48:06.490614 31856 solver.cpp:237]     Train net output #0: loss = 5.27637 (* 1 = 5.27637 loss)
I0408 08:48:06.490623 31856 sgd_solver.cpp:105] Iteration 7896, lr = 2.86969e-05
I0408 08:48:11.894500 31856 solver.cpp:218] Iteration 7908 (2.22069 iter/s, 5.40372s/12 iters), loss = 5.26969
I0408 08:48:11.894644 31856 solver.cpp:237]     Train net output #0: loss = 5.26969 (* 1 = 5.26969 loss)
I0408 08:48:11.894656 31856 sgd_solver.cpp:105] Iteration 7908, lr = 2.83434e-05
I0408 08:48:16.918831 31856 solver.cpp:218] Iteration 7920 (2.38851 iter/s, 5.02404s/12 iters), loss = 5.28534
I0408 08:48:16.918877 31856 solver.cpp:237]     Train net output #0: loss = 5.28534 (* 1 = 5.28534 loss)
I0408 08:48:16.918887 31856 sgd_solver.cpp:105] Iteration 7920, lr = 2.79942e-05
I0408 08:48:21.877888 31856 solver.cpp:218] Iteration 7932 (2.41991 iter/s, 4.95886s/12 iters), loss = 5.26237
I0408 08:48:21.877933 31856 solver.cpp:237]     Train net output #0: loss = 5.26237 (* 1 = 5.26237 loss)
I0408 08:48:21.877944 31856 sgd_solver.cpp:105] Iteration 7932, lr = 2.76494e-05
I0408 08:48:26.903316 31856 solver.cpp:218] Iteration 7944 (2.38795 iter/s, 5.02523s/12 iters), loss = 5.26686
I0408 08:48:26.903362 31856 solver.cpp:237]     Train net output #0: loss = 5.26686 (* 1 = 5.26686 loss)
I0408 08:48:26.903373 31856 sgd_solver.cpp:105] Iteration 7944, lr = 2.73088e-05
I0408 08:48:31.412077 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0408 08:48:34.443064 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0408 08:48:36.837735 31856 solver.cpp:330] Iteration 7956, Testing net (#0)
I0408 08:48:36.837761 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:48:38.115895 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:48:41.291661 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:48:41.291704 31856 solver.cpp:397]     Test net output #1: loss = 5.28741 (* 1 = 5.28741 loss)
I0408 08:48:41.381744 31856 solver.cpp:218] Iteration 7956 (0.828845 iter/s, 14.478s/12 iters), loss = 5.27502
I0408 08:48:41.381794 31856 solver.cpp:237]     Train net output #0: loss = 5.27502 (* 1 = 5.27502 loss)
I0408 08:48:41.381805 31856 sgd_solver.cpp:105] Iteration 7956, lr = 2.69723e-05
I0408 08:48:45.636000 31856 solver.cpp:218] Iteration 7968 (2.82082 iter/s, 4.25408s/12 iters), loss = 5.27444
I0408 08:48:45.636108 31856 solver.cpp:237]     Train net output #0: loss = 5.27444 (* 1 = 5.27444 loss)
I0408 08:48:45.636121 31856 sgd_solver.cpp:105] Iteration 7968, lr = 2.66401e-05
I0408 08:48:50.625751 31856 solver.cpp:218] Iteration 7980 (2.40505 iter/s, 4.98949s/12 iters), loss = 5.25334
I0408 08:48:50.625802 31856 solver.cpp:237]     Train net output #0: loss = 5.25334 (* 1 = 5.25334 loss)
I0408 08:48:50.625814 31856 sgd_solver.cpp:105] Iteration 7980, lr = 2.63119e-05
I0408 08:48:54.848601 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:48:55.566182 31856 solver.cpp:218] Iteration 7992 (2.42903 iter/s, 4.94024s/12 iters), loss = 5.25601
I0408 08:48:55.566226 31856 solver.cpp:237]     Train net output #0: loss = 5.25601 (* 1 = 5.25601 loss)
I0408 08:48:55.566237 31856 sgd_solver.cpp:105] Iteration 7992, lr = 2.59878e-05
I0408 08:49:00.573817 31856 solver.cpp:218] Iteration 8004 (2.39644 iter/s, 5.00743s/12 iters), loss = 5.27789
I0408 08:49:00.573875 31856 solver.cpp:237]     Train net output #0: loss = 5.27789 (* 1 = 5.27789 loss)
I0408 08:49:00.573889 31856 sgd_solver.cpp:105] Iteration 8004, lr = 2.56676e-05
I0408 08:49:05.586632 31856 solver.cpp:218] Iteration 8016 (2.39396 iter/s, 5.01261s/12 iters), loss = 5.2775
I0408 08:49:05.586676 31856 solver.cpp:237]     Train net output #0: loss = 5.2775 (* 1 = 5.2775 loss)
I0408 08:49:05.586688 31856 sgd_solver.cpp:105] Iteration 8016, lr = 2.53514e-05
I0408 08:49:10.688979 31856 solver.cpp:218] Iteration 8028 (2.35195 iter/s, 5.10215s/12 iters), loss = 5.2951
I0408 08:49:10.689029 31856 solver.cpp:237]     Train net output #0: loss = 5.2951 (* 1 = 5.2951 loss)
I0408 08:49:10.689043 31856 sgd_solver.cpp:105] Iteration 8028, lr = 2.50391e-05
I0408 08:49:16.024108 31856 solver.cpp:218] Iteration 8040 (2.24933 iter/s, 5.33492s/12 iters), loss = 5.26355
I0408 08:49:16.024220 31856 solver.cpp:237]     Train net output #0: loss = 5.26355 (* 1 = 5.26355 loss)
I0408 08:49:16.024230 31856 sgd_solver.cpp:105] Iteration 8040, lr = 2.47307e-05
I0408 08:49:21.068666 31856 solver.cpp:218] Iteration 8052 (2.37893 iter/s, 5.04429s/12 iters), loss = 5.27936
I0408 08:49:21.068717 31856 solver.cpp:237]     Train net output #0: loss = 5.27936 (* 1 = 5.27936 loss)
I0408 08:49:21.068728 31856 sgd_solver.cpp:105] Iteration 8052, lr = 2.4426e-05
I0408 08:49:23.146718 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0408 08:49:29.219810 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0408 08:49:31.549947 31856 solver.cpp:330] Iteration 8058, Testing net (#0)
I0408 08:49:31.549986 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:49:32.869027 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:49:36.093781 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:49:36.093828 31856 solver.cpp:397]     Test net output #1: loss = 5.28732 (* 1 = 5.28732 loss)
I0408 08:49:38.068310 31856 solver.cpp:218] Iteration 8064 (0.705919 iter/s, 16.9991s/12 iters), loss = 5.27921
I0408 08:49:38.068356 31856 solver.cpp:237]     Train net output #0: loss = 5.27921 (* 1 = 5.27921 loss)
I0408 08:49:38.068367 31856 sgd_solver.cpp:105] Iteration 8064, lr = 2.41251e-05
I0408 08:49:43.149144 31856 solver.cpp:218] Iteration 8076 (2.36191 iter/s, 5.08064s/12 iters), loss = 5.27575
I0408 08:49:43.149192 31856 solver.cpp:237]     Train net output #0: loss = 5.27575 (* 1 = 5.27575 loss)
I0408 08:49:43.149204 31856 sgd_solver.cpp:105] Iteration 8076, lr = 2.38279e-05
I0408 08:49:48.158771 31856 solver.cpp:218] Iteration 8088 (2.39548 iter/s, 5.00943s/12 iters), loss = 5.26155
I0408 08:49:48.158876 31856 solver.cpp:237]     Train net output #0: loss = 5.26155 (* 1 = 5.26155 loss)
I0408 08:49:48.158890 31856 sgd_solver.cpp:105] Iteration 8088, lr = 2.35344e-05
I0408 08:49:49.566314 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:49:53.134150 31856 solver.cpp:218] Iteration 8100 (2.412 iter/s, 4.97513s/12 iters), loss = 5.26146
I0408 08:49:53.134196 31856 solver.cpp:237]     Train net output #0: loss = 5.26146 (* 1 = 5.26146 loss)
I0408 08:49:53.134207 31856 sgd_solver.cpp:105] Iteration 8100, lr = 2.32445e-05
I0408 08:49:58.164921 31856 solver.cpp:218] Iteration 8112 (2.38541 iter/s, 5.03058s/12 iters), loss = 5.26607
I0408 08:49:58.164954 31856 solver.cpp:237]     Train net output #0: loss = 5.26607 (* 1 = 5.26607 loss)
I0408 08:49:58.164963 31856 sgd_solver.cpp:105] Iteration 8112, lr = 2.29581e-05
I0408 08:50:03.170632 31856 solver.cpp:218] Iteration 8124 (2.39735 iter/s, 5.00553s/12 iters), loss = 5.27074
I0408 08:50:03.170677 31856 solver.cpp:237]     Train net output #0: loss = 5.27074 (* 1 = 5.27074 loss)
I0408 08:50:03.170688 31856 sgd_solver.cpp:105] Iteration 8124, lr = 2.26753e-05
I0408 08:50:08.123742 31856 solver.cpp:218] Iteration 8136 (2.42281 iter/s, 4.95292s/12 iters), loss = 5.28358
I0408 08:50:08.123788 31856 solver.cpp:237]     Train net output #0: loss = 5.28358 (* 1 = 5.28358 loss)
I0408 08:50:08.123800 31856 sgd_solver.cpp:105] Iteration 8136, lr = 2.2396e-05
I0408 08:50:13.151865 31856 solver.cpp:218] Iteration 8148 (2.38667 iter/s, 5.02793s/12 iters), loss = 5.25016
I0408 08:50:13.151916 31856 solver.cpp:237]     Train net output #0: loss = 5.25016 (* 1 = 5.25016 loss)
I0408 08:50:13.151927 31856 sgd_solver.cpp:105] Iteration 8148, lr = 2.21201e-05
I0408 08:50:17.682551 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0408 08:50:22.743531 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0408 08:50:27.235399 31856 solver.cpp:330] Iteration 8160, Testing net (#0)
I0408 08:50:27.235422 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:50:28.506871 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:50:31.700246 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:50:31.700294 31856 solver.cpp:397]     Test net output #1: loss = 5.28708 (* 1 = 5.28708 loss)
I0408 08:50:31.790799 31856 solver.cpp:218] Iteration 8160 (0.643834 iter/s, 18.6384s/12 iters), loss = 5.26329
I0408 08:50:31.790854 31856 solver.cpp:237]     Train net output #0: loss = 5.26329 (* 1 = 5.26329 loss)
I0408 08:50:31.790866 31856 sgd_solver.cpp:105] Iteration 8160, lr = 2.18476e-05
I0408 08:50:36.161566 31856 solver.cpp:218] Iteration 8172 (2.74563 iter/s, 4.37058s/12 iters), loss = 5.28575
I0408 08:50:36.161604 31856 solver.cpp:237]     Train net output #0: loss = 5.28575 (* 1 = 5.28575 loss)
I0408 08:50:36.161613 31856 sgd_solver.cpp:105] Iteration 8172, lr = 2.15785e-05
I0408 08:50:41.189648 31856 solver.cpp:218] Iteration 8184 (2.38669 iter/s, 5.02789s/12 iters), loss = 5.2721
I0408 08:50:41.189687 31856 solver.cpp:237]     Train net output #0: loss = 5.2721 (* 1 = 5.2721 loss)
I0408 08:50:41.189695 31856 sgd_solver.cpp:105] Iteration 8184, lr = 2.13126e-05
I0408 08:50:44.746634 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:50:46.185623 31856 solver.cpp:218] Iteration 8196 (2.40203 iter/s, 4.99578s/12 iters), loss = 5.27324
I0408 08:50:46.185673 31856 solver.cpp:237]     Train net output #0: loss = 5.27324 (* 1 = 5.27324 loss)
I0408 08:50:46.185686 31856 sgd_solver.cpp:105] Iteration 8196, lr = 2.10501e-05
I0408 08:50:51.238514 31856 solver.cpp:218] Iteration 8208 (2.37497 iter/s, 5.05269s/12 iters), loss = 5.25733
I0408 08:50:51.238569 31856 solver.cpp:237]     Train net output #0: loss = 5.25733 (* 1 = 5.25733 loss)
I0408 08:50:51.238584 31856 sgd_solver.cpp:105] Iteration 8208, lr = 2.07908e-05
I0408 08:50:56.301054 31856 solver.cpp:218] Iteration 8220 (2.37045 iter/s, 5.06234s/12 iters), loss = 5.26323
I0408 08:50:56.301136 31856 solver.cpp:237]     Train net output #0: loss = 5.26323 (* 1 = 5.26323 loss)
I0408 08:50:56.301148 31856 sgd_solver.cpp:105] Iteration 8220, lr = 2.05347e-05
I0408 08:51:01.188735 31856 solver.cpp:218] Iteration 8232 (2.45526 iter/s, 4.88746s/12 iters), loss = 5.26829
I0408 08:51:01.188776 31856 solver.cpp:237]     Train net output #0: loss = 5.26829 (* 1 = 5.26829 loss)
I0408 08:51:01.188783 31856 sgd_solver.cpp:105] Iteration 8232, lr = 2.02817e-05
I0408 08:51:06.268967 31856 solver.cpp:218] Iteration 8244 (2.36219 iter/s, 5.08004s/12 iters), loss = 5.25167
I0408 08:51:06.269011 31856 solver.cpp:237]     Train net output #0: loss = 5.25167 (* 1 = 5.25167 loss)
I0408 08:51:06.269021 31856 sgd_solver.cpp:105] Iteration 8244, lr = 2.00319e-05
I0408 08:51:11.344231 31856 solver.cpp:218] Iteration 8256 (2.3645 iter/s, 5.07506s/12 iters), loss = 5.27196
I0408 08:51:11.344305 31856 solver.cpp:237]     Train net output #0: loss = 5.27196 (* 1 = 5.27196 loss)
I0408 08:51:11.344326 31856 sgd_solver.cpp:105] Iteration 8256, lr = 1.97851e-05
I0408 08:51:13.356040 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0408 08:51:16.454377 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0408 08:51:20.879000 31856 solver.cpp:330] Iteration 8262, Testing net (#0)
I0408 08:51:20.879027 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:51:22.111038 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:51:25.391914 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0408 08:51:25.391961 31856 solver.cpp:397]     Test net output #1: loss = 5.28719 (* 1 = 5.28719 loss)
I0408 08:51:27.369757 31856 solver.cpp:218] Iteration 8268 (0.74883 iter/s, 16.025s/12 iters), loss = 5.27796
I0408 08:51:27.369882 31856 solver.cpp:237]     Train net output #0: loss = 5.27796 (* 1 = 5.27796 loss)
I0408 08:51:27.369896 31856 sgd_solver.cpp:105] Iteration 8268, lr = 1.95414e-05
I0408 08:51:32.395354 31856 solver.cpp:218] Iteration 8280 (2.3879 iter/s, 5.02533s/12 iters), loss = 5.28487
I0408 08:51:32.395401 31856 solver.cpp:237]     Train net output #0: loss = 5.28487 (* 1 = 5.28487 loss)
I0408 08:51:32.395414 31856 sgd_solver.cpp:105] Iteration 8280, lr = 1.93006e-05
I0408 08:51:37.397495 31856 solver.cpp:218] Iteration 8292 (2.39907 iter/s, 5.00194s/12 iters), loss = 5.2902
I0408 08:51:37.397543 31856 solver.cpp:237]     Train net output #0: loss = 5.2902 (* 1 = 5.2902 loss)
I0408 08:51:37.397554 31856 sgd_solver.cpp:105] Iteration 8292, lr = 1.90629e-05
I0408 08:51:38.087636 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:51:42.330049 31856 solver.cpp:218] Iteration 8304 (2.43291 iter/s, 4.93236s/12 iters), loss = 5.27839
I0408 08:51:42.330092 31856 solver.cpp:237]     Train net output #0: loss = 5.27839 (* 1 = 5.27839 loss)
I0408 08:51:42.330104 31856 sgd_solver.cpp:105] Iteration 8304, lr = 1.8828e-05
I0408 08:51:45.249523 31856 blocking_queue.cpp:49] Waiting for data
I0408 08:51:47.412607 31856 solver.cpp:218] Iteration 8316 (2.36111 iter/s, 5.08236s/12 iters), loss = 5.26927
I0408 08:51:47.412652 31856 solver.cpp:237]     Train net output #0: loss = 5.26927 (* 1 = 5.26927 loss)
I0408 08:51:47.412664 31856 sgd_solver.cpp:105] Iteration 8316, lr = 1.85961e-05
I0408 08:51:52.321768 31856 solver.cpp:218] Iteration 8328 (2.44451 iter/s, 4.90896s/12 iters), loss = 5.28121
I0408 08:51:52.321817 31856 solver.cpp:237]     Train net output #0: loss = 5.28121 (* 1 = 5.28121 loss)
I0408 08:51:52.321830 31856 sgd_solver.cpp:105] Iteration 8328, lr = 1.8367e-05
I0408 08:51:57.313102 31856 solver.cpp:218] Iteration 8340 (2.40426 iter/s, 4.99114s/12 iters), loss = 5.27303
I0408 08:51:57.313150 31856 solver.cpp:237]     Train net output #0: loss = 5.27303 (* 1 = 5.27303 loss)
I0408 08:51:57.313163 31856 sgd_solver.cpp:105] Iteration 8340, lr = 1.81408e-05
I0408 08:52:02.336320 31856 solver.cpp:218] Iteration 8352 (2.389 iter/s, 5.02302s/12 iters), loss = 5.28909
I0408 08:52:02.336438 31856 solver.cpp:237]     Train net output #0: loss = 5.28909 (* 1 = 5.28909 loss)
I0408 08:52:02.336452 31856 sgd_solver.cpp:105] Iteration 8352, lr = 1.79173e-05
I0408 08:52:06.846696 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0408 08:52:10.489913 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0408 08:52:13.838871 31856 solver.cpp:330] Iteration 8364, Testing net (#0)
I0408 08:52:13.838898 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:52:14.990803 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:52:18.329499 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:52:18.329546 31856 solver.cpp:397]     Test net output #1: loss = 5.28688 (* 1 = 5.28688 loss)
I0408 08:52:18.419768 31856 solver.cpp:218] Iteration 8364 (0.746135 iter/s, 16.0829s/12 iters), loss = 5.26542
I0408 08:52:18.419813 31856 solver.cpp:237]     Train net output #0: loss = 5.26542 (* 1 = 5.26542 loss)
I0408 08:52:18.419826 31856 sgd_solver.cpp:105] Iteration 8364, lr = 1.76966e-05
I0408 08:52:22.872148 31856 solver.cpp:218] Iteration 8376 (2.6953 iter/s, 4.4522s/12 iters), loss = 5.26503
I0408 08:52:22.872197 31856 solver.cpp:237]     Train net output #0: loss = 5.26503 (* 1 = 5.26503 loss)
I0408 08:52:22.872211 31856 sgd_solver.cpp:105] Iteration 8376, lr = 1.74786e-05
I0408 08:52:27.848223 31856 solver.cpp:218] Iteration 8388 (2.41163 iter/s, 4.97588s/12 iters), loss = 5.25938
I0408 08:52:27.848269 31856 solver.cpp:237]     Train net output #0: loss = 5.25938 (* 1 = 5.25938 loss)
I0408 08:52:27.848280 31856 sgd_solver.cpp:105] Iteration 8388, lr = 1.72632e-05
I0408 08:52:30.674094 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:52:32.862841 31856 solver.cpp:218] Iteration 8400 (2.3931 iter/s, 5.01442s/12 iters), loss = 5.26411
I0408 08:52:32.865913 31856 solver.cpp:237]     Train net output #0: loss = 5.26411 (* 1 = 5.26411 loss)
I0408 08:52:32.865926 31856 sgd_solver.cpp:105] Iteration 8400, lr = 1.70506e-05
I0408 08:52:38.032812 31856 solver.cpp:218] Iteration 8412 (2.32255 iter/s, 5.16674s/12 iters), loss = 5.2495
I0408 08:52:38.032868 31856 solver.cpp:237]     Train net output #0: loss = 5.2495 (* 1 = 5.2495 loss)
I0408 08:52:38.032881 31856 sgd_solver.cpp:105] Iteration 8412, lr = 1.68405e-05
I0408 08:52:43.113428 31856 solver.cpp:218] Iteration 8424 (2.36202 iter/s, 5.08041s/12 iters), loss = 5.25389
I0408 08:52:43.113473 31856 solver.cpp:237]     Train net output #0: loss = 5.25389 (* 1 = 5.25389 loss)
I0408 08:52:43.113484 31856 sgd_solver.cpp:105] Iteration 8424, lr = 1.66331e-05
I0408 08:52:48.122983 31856 solver.cpp:218] Iteration 8436 (2.39552 iter/s, 5.00935s/12 iters), loss = 5.25338
I0408 08:52:48.123035 31856 solver.cpp:237]     Train net output #0: loss = 5.25338 (* 1 = 5.25338 loss)
I0408 08:52:48.123047 31856 sgd_solver.cpp:105] Iteration 8436, lr = 1.64282e-05
I0408 08:52:53.110321 31856 solver.cpp:218] Iteration 8448 (2.40619 iter/s, 4.98714s/12 iters), loss = 5.29528
I0408 08:52:53.110364 31856 solver.cpp:237]     Train net output #0: loss = 5.29528 (* 1 = 5.29528 loss)
I0408 08:52:53.110374 31856 sgd_solver.cpp:105] Iteration 8448, lr = 1.62258e-05
I0408 08:52:58.053081 31856 solver.cpp:218] Iteration 8460 (2.42789 iter/s, 4.94257s/12 iters), loss = 5.27318
I0408 08:52:58.053124 31856 solver.cpp:237]     Train net output #0: loss = 5.27318 (* 1 = 5.27318 loss)
I0408 08:52:58.053136 31856 sgd_solver.cpp:105] Iteration 8460, lr = 1.60259e-05
I0408 08:53:00.050755 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0408 08:53:03.082363 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0408 08:53:05.492235 31856 solver.cpp:330] Iteration 8466, Testing net (#0)
I0408 08:53:05.492255 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:53:06.538030 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:53:09.905580 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:53:09.905618 31856 solver.cpp:397]     Test net output #1: loss = 5.28689 (* 1 = 5.28689 loss)
I0408 08:53:11.870040 31856 solver.cpp:218] Iteration 8472 (0.868525 iter/s, 13.8165s/12 iters), loss = 5.2733
I0408 08:53:11.870087 31856 solver.cpp:237]     Train net output #0: loss = 5.2733 (* 1 = 5.2733 loss)
I0408 08:53:11.870097 31856 sgd_solver.cpp:105] Iteration 8472, lr = 1.58285e-05
I0408 08:53:16.910061 31856 solver.cpp:218] Iteration 8484 (2.38103 iter/s, 5.03983s/12 iters), loss = 5.27145
I0408 08:53:16.910097 31856 solver.cpp:237]     Train net output #0: loss = 5.27145 (* 1 = 5.27145 loss)
I0408 08:53:16.910106 31856 sgd_solver.cpp:105] Iteration 8484, lr = 1.56335e-05
I0408 08:53:21.932498 31856 solver.cpp:218] Iteration 8496 (2.38937 iter/s, 5.02225s/12 iters), loss = 5.25529
I0408 08:53:21.932543 31856 solver.cpp:237]     Train net output #0: loss = 5.25529 (* 1 = 5.25529 loss)
I0408 08:53:21.932555 31856 sgd_solver.cpp:105] Iteration 8496, lr = 1.54409e-05
I0408 08:53:21.983283 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:53:26.978572 31856 solver.cpp:218] Iteration 8508 (2.37818 iter/s, 5.04587s/12 iters), loss = 5.2761
I0408 08:53:26.978622 31856 solver.cpp:237]     Train net output #0: loss = 5.2761 (* 1 = 5.2761 loss)
I0408 08:53:26.978633 31856 sgd_solver.cpp:105] Iteration 8508, lr = 1.52507e-05
I0408 08:53:32.022629 31856 solver.cpp:218] Iteration 8520 (2.37913 iter/s, 5.04386s/12 iters), loss = 5.29444
I0408 08:53:32.022665 31856 solver.cpp:237]     Train net output #0: loss = 5.29444 (* 1 = 5.29444 loss)
I0408 08:53:32.022672 31856 sgd_solver.cpp:105] Iteration 8520, lr = 1.50628e-05
I0408 08:53:37.123838 31856 solver.cpp:218] Iteration 8532 (2.35247 iter/s, 5.10102s/12 iters), loss = 5.2704
I0408 08:53:37.123944 31856 solver.cpp:237]     Train net output #0: loss = 5.2704 (* 1 = 5.2704 loss)
I0408 08:53:37.123957 31856 sgd_solver.cpp:105] Iteration 8532, lr = 1.48773e-05
I0408 08:53:42.128823 31856 solver.cpp:218] Iteration 8544 (2.39773 iter/s, 5.00473s/12 iters), loss = 5.27256
I0408 08:53:42.128868 31856 solver.cpp:237]     Train net output #0: loss = 5.27256 (* 1 = 5.27256 loss)
I0408 08:53:42.128880 31856 sgd_solver.cpp:105] Iteration 8544, lr = 1.4694e-05
I0408 08:53:47.077891 31856 solver.cpp:218] Iteration 8556 (2.42479 iter/s, 4.94888s/12 iters), loss = 5.2572
I0408 08:53:47.077937 31856 solver.cpp:237]     Train net output #0: loss = 5.2572 (* 1 = 5.2572 loss)
I0408 08:53:47.077948 31856 sgd_solver.cpp:105] Iteration 8556, lr = 1.4513e-05
I0408 08:53:51.588027 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0408 08:53:55.989632 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0408 08:54:00.380049 31856 solver.cpp:330] Iteration 8568, Testing net (#0)
I0408 08:54:00.380071 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:54:01.494544 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:54:04.852289 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:54:04.852337 31856 solver.cpp:397]     Test net output #1: loss = 5.28698 (* 1 = 5.28698 loss)
I0408 08:54:04.941992 31856 solver.cpp:218] Iteration 8568 (0.671759 iter/s, 17.8636s/12 iters), loss = 5.24644
I0408 08:54:04.942041 31856 solver.cpp:237]     Train net output #0: loss = 5.24644 (* 1 = 5.24644 loss)
I0408 08:54:04.942051 31856 sgd_solver.cpp:105] Iteration 8568, lr = 1.43342e-05
I0408 08:54:09.371335 31856 solver.cpp:218] Iteration 8580 (2.70932 iter/s, 4.42916s/12 iters), loss = 5.26307
I0408 08:54:09.371479 31856 solver.cpp:237]     Train net output #0: loss = 5.26307 (* 1 = 5.26307 loss)
I0408 08:54:09.371492 31856 sgd_solver.cpp:105] Iteration 8580, lr = 1.41576e-05
I0408 08:54:14.346295 31856 solver.cpp:218] Iteration 8592 (2.41222 iter/s, 4.97467s/12 iters), loss = 5.25045
I0408 08:54:14.346347 31856 solver.cpp:237]     Train net output #0: loss = 5.25045 (* 1 = 5.25045 loss)
I0408 08:54:14.346359 31856 sgd_solver.cpp:105] Iteration 8592, lr = 1.39832e-05
I0408 08:54:16.549679 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:54:19.357064 31856 solver.cpp:218] Iteration 8604 (2.39494 iter/s, 5.01056s/12 iters), loss = 5.27025
I0408 08:54:19.357111 31856 solver.cpp:237]     Train net output #0: loss = 5.27025 (* 1 = 5.27025 loss)
I0408 08:54:19.357122 31856 sgd_solver.cpp:105] Iteration 8604, lr = 1.3811e-05
I0408 08:54:24.318481 31856 solver.cpp:218] Iteration 8616 (2.41876 iter/s, 4.96122s/12 iters), loss = 5.26551
I0408 08:54:24.318529 31856 solver.cpp:237]     Train net output #0: loss = 5.26551 (* 1 = 5.26551 loss)
I0408 08:54:24.318539 31856 sgd_solver.cpp:105] Iteration 8616, lr = 1.36408e-05
I0408 08:54:29.323014 31856 solver.cpp:218] Iteration 8628 (2.39792 iter/s, 5.00433s/12 iters), loss = 5.28581
I0408 08:54:29.323065 31856 solver.cpp:237]     Train net output #0: loss = 5.28581 (* 1 = 5.28581 loss)
I0408 08:54:29.323078 31856 sgd_solver.cpp:105] Iteration 8628, lr = 1.34728e-05
I0408 08:54:34.239068 31856 solver.cpp:218] Iteration 8640 (2.44108 iter/s, 4.91586s/12 iters), loss = 5.26403
I0408 08:54:34.239114 31856 solver.cpp:237]     Train net output #0: loss = 5.26403 (* 1 = 5.26403 loss)
I0408 08:54:34.239125 31856 sgd_solver.cpp:105] Iteration 8640, lr = 1.33068e-05
I0408 08:54:39.219059 31856 solver.cpp:218] Iteration 8652 (2.40974 iter/s, 4.9798s/12 iters), loss = 5.26539
I0408 08:54:39.219106 31856 solver.cpp:237]     Train net output #0: loss = 5.26539 (* 1 = 5.26539 loss)
I0408 08:54:39.219118 31856 sgd_solver.cpp:105] Iteration 8652, lr = 1.31429e-05
I0408 08:54:44.209853 31856 solver.cpp:218] Iteration 8664 (2.40452 iter/s, 4.9906s/12 iters), loss = 5.27423
I0408 08:54:44.209978 31856 solver.cpp:237]     Train net output #0: loss = 5.27423 (* 1 = 5.27423 loss)
I0408 08:54:44.209991 31856 sgd_solver.cpp:105] Iteration 8664, lr = 1.2981e-05
I0408 08:54:46.262665 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0408 08:54:52.733355 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0408 08:54:55.040185 31856 solver.cpp:330] Iteration 8670, Testing net (#0)
I0408 08:54:55.040210 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:54:56.073137 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:54:59.463732 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:54:59.463780 31856 solver.cpp:397]     Test net output #1: loss = 5.2873 (* 1 = 5.2873 loss)
I0408 08:55:01.323647 31856 solver.cpp:218] Iteration 8676 (0.701213 iter/s, 17.1132s/12 iters), loss = 5.27521
I0408 08:55:01.323688 31856 solver.cpp:237]     Train net output #0: loss = 5.27521 (* 1 = 5.27521 loss)
I0408 08:55:01.323698 31856 sgd_solver.cpp:105] Iteration 8676, lr = 1.28211e-05
I0408 08:55:06.409981 31856 solver.cpp:218] Iteration 8688 (2.35935 iter/s, 5.08614s/12 iters), loss = 5.26095
I0408 08:55:06.410028 31856 solver.cpp:237]     Train net output #0: loss = 5.26095 (* 1 = 5.26095 loss)
I0408 08:55:06.410039 31856 sgd_solver.cpp:105] Iteration 8688, lr = 1.26632e-05
I0408 08:55:10.737715 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:55:11.424824 31856 solver.cpp:218] Iteration 8700 (2.39299 iter/s, 5.01464s/12 iters), loss = 5.26391
I0408 08:55:11.424875 31856 solver.cpp:237]     Train net output #0: loss = 5.26391 (* 1 = 5.26391 loss)
I0408 08:55:11.424887 31856 sgd_solver.cpp:105] Iteration 8700, lr = 1.25072e-05
I0408 08:55:16.431349 31856 solver.cpp:218] Iteration 8712 (2.39697 iter/s, 5.00633s/12 iters), loss = 5.28038
I0408 08:55:16.431499 31856 solver.cpp:237]     Train net output #0: loss = 5.28038 (* 1 = 5.28038 loss)
I0408 08:55:16.431514 31856 sgd_solver.cpp:105] Iteration 8712, lr = 1.23531e-05
I0408 08:55:21.427927 31856 solver.cpp:218] Iteration 8724 (2.40177 iter/s, 4.99632s/12 iters), loss = 5.28134
I0408 08:55:21.427970 31856 solver.cpp:237]     Train net output #0: loss = 5.28134 (* 1 = 5.28134 loss)
I0408 08:55:21.427981 31856 sgd_solver.cpp:105] Iteration 8724, lr = 1.22009e-05
I0408 08:55:26.476982 31856 solver.cpp:218] Iteration 8736 (2.37675 iter/s, 5.04891s/12 iters), loss = 5.29521
I0408 08:55:26.477027 31856 solver.cpp:237]     Train net output #0: loss = 5.29521 (* 1 = 5.29521 loss)
I0408 08:55:26.477038 31856 sgd_solver.cpp:105] Iteration 8736, lr = 1.20506e-05
I0408 08:55:31.559438 31856 solver.cpp:218] Iteration 8748 (2.36114 iter/s, 5.0823s/12 iters), loss = 5.27071
I0408 08:55:31.559485 31856 solver.cpp:237]     Train net output #0: loss = 5.27071 (* 1 = 5.27071 loss)
I0408 08:55:31.559497 31856 sgd_solver.cpp:105] Iteration 8748, lr = 1.19022e-05
I0408 08:55:36.567903 31856 solver.cpp:218] Iteration 8760 (2.39602 iter/s, 5.00831s/12 iters), loss = 5.27712
I0408 08:55:36.567945 31856 solver.cpp:237]     Train net output #0: loss = 5.27712 (* 1 = 5.27712 loss)
I0408 08:55:36.567957 31856 sgd_solver.cpp:105] Iteration 8760, lr = 1.17555e-05
I0408 08:55:41.077539 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0408 08:55:44.144997 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0408 08:55:46.456769 31856 solver.cpp:330] Iteration 8772, Testing net (#0)
I0408 08:55:46.456823 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:55:47.524367 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:55:51.141866 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:55:51.141916 31856 solver.cpp:397]     Test net output #1: loss = 5.28736 (* 1 = 5.28736 loss)
I0408 08:55:51.232086 31856 solver.cpp:218] Iteration 8772 (0.818339 iter/s, 14.6638s/12 iters), loss = 5.28024
I0408 08:55:51.232127 31856 solver.cpp:237]     Train net output #0: loss = 5.28024 (* 1 = 5.28024 loss)
I0408 08:55:51.232137 31856 sgd_solver.cpp:105] Iteration 8772, lr = 1.16107e-05
I0408 08:55:55.490233 31856 solver.cpp:218] Iteration 8784 (2.81822 iter/s, 4.25801s/12 iters), loss = 5.27487
I0408 08:55:55.490275 31856 solver.cpp:237]     Train net output #0: loss = 5.27487 (* 1 = 5.27487 loss)
I0408 08:55:55.490283 31856 sgd_solver.cpp:105] Iteration 8784, lr = 1.14677e-05
I0408 08:56:00.534837 31856 solver.cpp:218] Iteration 8796 (2.37885 iter/s, 5.04445s/12 iters), loss = 5.25725
I0408 08:56:00.534873 31856 solver.cpp:237]     Train net output #0: loss = 5.25725 (* 1 = 5.25725 loss)
I0408 08:56:00.534879 31856 sgd_solver.cpp:105] Iteration 8796, lr = 1.13264e-05
I0408 08:56:01.951273 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:56:05.417076 31856 solver.cpp:218] Iteration 8808 (2.45796 iter/s, 4.88209s/12 iters), loss = 5.26222
I0408 08:56:05.417117 31856 solver.cpp:237]     Train net output #0: loss = 5.26222 (* 1 = 5.26222 loss)
I0408 08:56:05.417127 31856 sgd_solver.cpp:105] Iteration 8808, lr = 1.11869e-05
I0408 08:56:10.593487 31856 solver.cpp:218] Iteration 8820 (2.31828 iter/s, 5.17625s/12 iters), loss = 5.26531
I0408 08:56:10.593533 31856 solver.cpp:237]     Train net output #0: loss = 5.26531 (* 1 = 5.26531 loss)
I0408 08:56:10.593545 31856 sgd_solver.cpp:105] Iteration 8820, lr = 1.10491e-05
I0408 08:56:15.573408 31856 solver.cpp:218] Iteration 8832 (2.40975 iter/s, 4.97977s/12 iters), loss = 5.26308
I0408 08:56:15.573452 31856 solver.cpp:237]     Train net output #0: loss = 5.26308 (* 1 = 5.26308 loss)
I0408 08:56:15.573464 31856 sgd_solver.cpp:105] Iteration 8832, lr = 1.0913e-05
I0408 08:56:20.635355 31856 solver.cpp:218] Iteration 8844 (2.3707 iter/s, 5.06179s/12 iters), loss = 5.29705
I0408 08:56:20.635505 31856 solver.cpp:237]     Train net output #0: loss = 5.29705 (* 1 = 5.29705 loss)
I0408 08:56:20.635519 31856 sgd_solver.cpp:105] Iteration 8844, lr = 1.07785e-05
I0408 08:56:25.650602 31856 solver.cpp:218] Iteration 8856 (2.39283 iter/s, 5.01499s/12 iters), loss = 5.256
I0408 08:56:25.650650 31856 solver.cpp:237]     Train net output #0: loss = 5.256 (* 1 = 5.256 loss)
I0408 08:56:25.650661 31856 sgd_solver.cpp:105] Iteration 8856, lr = 1.06458e-05
I0408 08:56:30.688580 31856 solver.cpp:218] Iteration 8868 (2.38198 iter/s, 5.03782s/12 iters), loss = 5.25959
I0408 08:56:30.688616 31856 solver.cpp:237]     Train net output #0: loss = 5.25959 (* 1 = 5.25959 loss)
I0408 08:56:30.688625 31856 sgd_solver.cpp:105] Iteration 8868, lr = 1.05146e-05
I0408 08:56:32.706318 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0408 08:56:36.200352 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0408 08:56:38.497848 31856 solver.cpp:330] Iteration 8874, Testing net (#0)
I0408 08:56:38.497871 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:56:39.493105 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:56:42.964637 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:56:42.964684 31856 solver.cpp:397]     Test net output #1: loss = 5.28714 (* 1 = 5.28714 loss)
I0408 08:56:44.890081 31856 solver.cpp:218] Iteration 8880 (0.845001 iter/s, 14.2012s/12 iters), loss = 5.28003
I0408 08:56:44.890122 31856 solver.cpp:237]     Train net output #0: loss = 5.28003 (* 1 = 5.28003 loss)
I0408 08:56:44.890131 31856 sgd_solver.cpp:105] Iteration 8880, lr = 1.03851e-05
I0408 08:56:49.914938 31856 solver.cpp:218] Iteration 8892 (2.3882 iter/s, 5.0247s/12 iters), loss = 5.27811
I0408 08:56:49.914990 31856 solver.cpp:237]     Train net output #0: loss = 5.27811 (* 1 = 5.27811 loss)
I0408 08:56:49.915001 31856 sgd_solver.cpp:105] Iteration 8892, lr = 1.02572e-05
I0408 08:56:53.507752 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:56:54.932598 31856 solver.cpp:218] Iteration 8904 (2.39163 iter/s, 5.0175s/12 iters), loss = 5.27573
I0408 08:56:54.932636 31856 solver.cpp:237]     Train net output #0: loss = 5.27573 (* 1 = 5.27573 loss)
I0408 08:56:54.932644 31856 sgd_solver.cpp:105] Iteration 8904, lr = 1.01308e-05
I0408 08:56:59.916484 31856 solver.cpp:218] Iteration 8916 (2.40783 iter/s, 4.98373s/12 iters), loss = 5.26429
I0408 08:56:59.916524 31856 solver.cpp:237]     Train net output #0: loss = 5.26429 (* 1 = 5.26429 loss)
I0408 08:56:59.916533 31856 sgd_solver.cpp:105] Iteration 8916, lr = 1.0006e-05
I0408 08:57:04.932602 31856 solver.cpp:218] Iteration 8928 (2.39236 iter/s, 5.01596s/12 iters), loss = 5.26061
I0408 08:57:04.932646 31856 solver.cpp:237]     Train net output #0: loss = 5.26061 (* 1 = 5.26061 loss)
I0408 08:57:04.932655 31856 sgd_solver.cpp:105] Iteration 8928, lr = 9.88273e-06
I0408 08:57:09.992895 31856 solver.cpp:218] Iteration 8940 (2.37148 iter/s, 5.06013s/12 iters), loss = 5.26569
I0408 08:57:09.992945 31856 solver.cpp:237]     Train net output #0: loss = 5.26569 (* 1 = 5.26569 loss)
I0408 08:57:09.992957 31856 sgd_solver.cpp:105] Iteration 8940, lr = 9.76099e-06
I0408 08:57:15.188652 31856 solver.cpp:218] Iteration 8952 (2.30965 iter/s, 5.19559s/12 iters), loss = 5.25627
I0408 08:57:15.188689 31856 solver.cpp:237]     Train net output #0: loss = 5.25627 (* 1 = 5.25627 loss)
I0408 08:57:15.188696 31856 sgd_solver.cpp:105] Iteration 8952, lr = 9.64075e-06
I0408 08:57:20.196198 31856 solver.cpp:218] Iteration 8964 (2.39646 iter/s, 5.00739s/12 iters), loss = 5.27683
I0408 08:57:20.196245 31856 solver.cpp:237]     Train net output #0: loss = 5.27683 (* 1 = 5.27683 loss)
I0408 08:57:20.196256 31856 sgd_solver.cpp:105] Iteration 8964, lr = 9.52198e-06
I0408 08:57:24.708294 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0408 08:57:27.734350 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0408 08:57:30.047071 31856 solver.cpp:330] Iteration 8976, Testing net (#0)
I0408 08:57:30.047094 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:57:31.008754 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:57:34.515709 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:57:34.515748 31856 solver.cpp:397]     Test net output #1: loss = 5.28722 (* 1 = 5.28722 loss)
I0408 08:57:34.605576 31856 solver.cpp:218] Iteration 8976 (0.832812 iter/s, 14.409s/12 iters), loss = 5.27655
I0408 08:57:34.605619 31856 solver.cpp:237]     Train net output #0: loss = 5.27655 (* 1 = 5.27655 loss)
I0408 08:57:34.605630 31856 sgd_solver.cpp:105] Iteration 8976, lr = 9.40468e-06
I0408 08:57:38.836081 31856 solver.cpp:218] Iteration 8988 (2.83664 iter/s, 4.23036s/12 iters), loss = 5.28454
I0408 08:57:38.836114 31856 solver.cpp:237]     Train net output #0: loss = 5.28454 (* 1 = 5.28454 loss)
I0408 08:57:38.836122 31856 sgd_solver.cpp:105] Iteration 8988, lr = 9.28883e-06
I0408 08:57:42.122444 31856 blocking_queue.cpp:49] Waiting for data
I0408 08:57:43.831961 31856 solver.cpp:218] Iteration 9000 (2.40205 iter/s, 4.99572s/12 iters), loss = 5.2885
I0408 08:57:43.832017 31856 solver.cpp:237]     Train net output #0: loss = 5.2885 (* 1 = 5.2885 loss)
I0408 08:57:43.832031 31856 sgd_solver.cpp:105] Iteration 9000, lr = 9.1744e-06
I0408 08:57:44.511685 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:57:48.786942 31856 solver.cpp:218] Iteration 9012 (2.42189 iter/s, 4.95481s/12 iters), loss = 5.28496
I0408 08:57:48.786988 31856 solver.cpp:237]     Train net output #0: loss = 5.28496 (* 1 = 5.28496 loss)
I0408 08:57:48.786999 31856 sgd_solver.cpp:105] Iteration 9012, lr = 9.06138e-06
I0408 08:57:53.783836 31856 solver.cpp:218] Iteration 9024 (2.40157 iter/s, 4.99673s/12 iters), loss = 5.26387
I0408 08:57:53.783890 31856 solver.cpp:237]     Train net output #0: loss = 5.26387 (* 1 = 5.26387 loss)
I0408 08:57:53.783900 31856 sgd_solver.cpp:105] Iteration 9024, lr = 8.94976e-06
I0408 08:57:58.768981 31856 solver.cpp:218] Iteration 9036 (2.40723 iter/s, 4.98497s/12 iters), loss = 5.27226
I0408 08:57:58.769114 31856 solver.cpp:237]     Train net output #0: loss = 5.27226 (* 1 = 5.27226 loss)
I0408 08:57:58.769129 31856 sgd_solver.cpp:105] Iteration 9036, lr = 8.83951e-06
I0408 08:58:03.701268 31856 solver.cpp:218] Iteration 9048 (2.43307 iter/s, 4.93205s/12 iters), loss = 5.27474
I0408 08:58:03.701313 31856 solver.cpp:237]     Train net output #0: loss = 5.27474 (* 1 = 5.27474 loss)
I0408 08:58:03.701324 31856 sgd_solver.cpp:105] Iteration 9048, lr = 8.73062e-06
I0408 08:58:08.640205 31856 solver.cpp:218] Iteration 9060 (2.42975 iter/s, 4.93878s/12 iters), loss = 5.29116
I0408 08:58:08.640250 31856 solver.cpp:237]     Train net output #0: loss = 5.29116 (* 1 = 5.29116 loss)
I0408 08:58:08.640264 31856 sgd_solver.cpp:105] Iteration 9060, lr = 8.62306e-06
I0408 08:58:13.994189 31856 solver.cpp:218] Iteration 9072 (2.24139 iter/s, 5.35381s/12 iters), loss = 5.26795
I0408 08:58:13.994237 31856 solver.cpp:237]     Train net output #0: loss = 5.26795 (* 1 = 5.26795 loss)
I0408 08:58:13.994249 31856 sgd_solver.cpp:105] Iteration 9072, lr = 8.51684e-06
I0408 08:58:16.037851 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0408 08:58:19.076822 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0408 08:58:21.406308 31856 solver.cpp:330] Iteration 9078, Testing net (#0)
I0408 08:58:21.406333 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:58:22.318631 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:58:25.874812 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:58:25.874855 31856 solver.cpp:397]     Test net output #1: loss = 5.28741 (* 1 = 5.28741 loss)
I0408 08:58:27.836671 31856 solver.cpp:218] Iteration 9084 (0.866919 iter/s, 13.8421s/12 iters), loss = 5.25864
I0408 08:58:27.836719 31856 solver.cpp:237]     Train net output #0: loss = 5.25864 (* 1 = 5.25864 loss)
I0408 08:58:27.836730 31856 sgd_solver.cpp:105] Iteration 9084, lr = 8.41192e-06
I0408 08:58:33.019454 31856 solver.cpp:218] Iteration 9096 (2.31543 iter/s, 5.18262s/12 iters), loss = 5.26389
I0408 08:58:33.019569 31856 solver.cpp:237]     Train net output #0: loss = 5.26389 (* 1 = 5.26389 loss)
I0408 08:58:33.019578 31856 sgd_solver.cpp:105] Iteration 9096, lr = 8.3083e-06
I0408 08:58:35.969852 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:58:38.041662 31856 solver.cpp:218] Iteration 9108 (2.3895 iter/s, 5.02198s/12 iters), loss = 5.26132
I0408 08:58:38.041695 31856 solver.cpp:237]     Train net output #0: loss = 5.26132 (* 1 = 5.26132 loss)
I0408 08:58:38.041703 31856 sgd_solver.cpp:105] Iteration 9108, lr = 8.20595e-06
I0408 08:58:43.058965 31856 solver.cpp:218] Iteration 9120 (2.3918 iter/s, 5.01715s/12 iters), loss = 5.24945
I0408 08:58:43.059000 31856 solver.cpp:237]     Train net output #0: loss = 5.24945 (* 1 = 5.24945 loss)
I0408 08:58:43.059008 31856 sgd_solver.cpp:105] Iteration 9120, lr = 8.10486e-06
I0408 08:58:48.071537 31856 solver.cpp:218] Iteration 9132 (2.39405 iter/s, 5.01242s/12 iters), loss = 5.25006
I0408 08:58:48.071571 31856 solver.cpp:237]     Train net output #0: loss = 5.25006 (* 1 = 5.25006 loss)
I0408 08:58:48.071579 31856 sgd_solver.cpp:105] Iteration 9132, lr = 8.00502e-06
I0408 08:58:52.980702 31856 solver.cpp:218] Iteration 9144 (2.44448 iter/s, 4.90902s/12 iters), loss = 5.25822
I0408 08:58:52.980738 31856 solver.cpp:237]     Train net output #0: loss = 5.25822 (* 1 = 5.25822 loss)
I0408 08:58:52.980749 31856 sgd_solver.cpp:105] Iteration 9144, lr = 7.9064e-06
I0408 08:58:57.982769 31856 solver.cpp:218] Iteration 9156 (2.39908 iter/s, 5.00191s/12 iters), loss = 5.28851
I0408 08:58:57.982811 31856 solver.cpp:237]     Train net output #0: loss = 5.28851 (* 1 = 5.28851 loss)
I0408 08:58:57.982823 31856 sgd_solver.cpp:105] Iteration 9156, lr = 7.80901e-06
I0408 08:59:03.202567 31856 solver.cpp:218] Iteration 9168 (2.29901 iter/s, 5.21963s/12 iters), loss = 5.27189
I0408 08:59:03.202700 31856 solver.cpp:237]     Train net output #0: loss = 5.27189 (* 1 = 5.27189 loss)
I0408 08:59:03.202714 31856 sgd_solver.cpp:105] Iteration 9168, lr = 7.71281e-06
I0408 08:59:07.886910 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0408 08:59:11.815214 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0408 08:59:14.196368 31856 solver.cpp:330] Iteration 9180, Testing net (#0)
I0408 08:59:14.196394 31856 net.cpp:676] Ignoring source layer train-data
I0408 08:59:15.067965 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:59:18.672610 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 08:59:18.672657 31856 solver.cpp:397]     Test net output #1: loss = 5.28768 (* 1 = 5.28768 loss)
I0408 08:59:18.762990 31856 solver.cpp:218] Iteration 9180 (0.771211 iter/s, 15.5599s/12 iters), loss = 5.27377
I0408 08:59:18.763046 31856 solver.cpp:237]     Train net output #0: loss = 5.27377 (* 1 = 5.27377 loss)
I0408 08:59:18.763059 31856 sgd_solver.cpp:105] Iteration 9180, lr = 7.6178e-06
I0408 08:59:23.025578 31856 solver.cpp:218] Iteration 9192 (2.8153 iter/s, 4.26243s/12 iters), loss = 5.27354
I0408 08:59:23.025624 31856 solver.cpp:237]     Train net output #0: loss = 5.27354 (* 1 = 5.27354 loss)
I0408 08:59:23.025635 31856 sgd_solver.cpp:105] Iteration 9192, lr = 7.52395e-06
I0408 08:59:28.032235 31856 solver.cpp:218] Iteration 9204 (2.39689 iter/s, 5.00649s/12 iters), loss = 5.26429
I0408 08:59:28.032280 31856 solver.cpp:237]     Train net output #0: loss = 5.26429 (* 1 = 5.26429 loss)
I0408 08:59:28.032294 31856 sgd_solver.cpp:105] Iteration 9204, lr = 7.43127e-06
I0408 08:59:28.111913 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:59:33.047488 31856 solver.cpp:218] Iteration 9216 (2.39278 iter/s, 5.01509s/12 iters), loss = 5.277
I0408 08:59:33.047535 31856 solver.cpp:237]     Train net output #0: loss = 5.277 (* 1 = 5.277 loss)
I0408 08:59:33.047546 31856 sgd_solver.cpp:105] Iteration 9216, lr = 7.33972e-06
I0408 08:59:38.022394 31856 solver.cpp:218] Iteration 9228 (2.41219 iter/s, 4.97474s/12 iters), loss = 5.28558
I0408 08:59:38.022537 31856 solver.cpp:237]     Train net output #0: loss = 5.28558 (* 1 = 5.28558 loss)
I0408 08:59:38.022550 31856 sgd_solver.cpp:105] Iteration 9228, lr = 7.24931e-06
I0408 08:59:43.066913 31856 solver.cpp:218] Iteration 9240 (2.37894 iter/s, 5.04426s/12 iters), loss = 5.26094
I0408 08:59:43.066962 31856 solver.cpp:237]     Train net output #0: loss = 5.26094 (* 1 = 5.26094 loss)
I0408 08:59:43.066973 31856 sgd_solver.cpp:105] Iteration 9240, lr = 7.16e-06
I0408 08:59:48.095489 31856 solver.cpp:218] Iteration 9252 (2.38644 iter/s, 5.0284s/12 iters), loss = 5.27424
I0408 08:59:48.095535 31856 solver.cpp:237]     Train net output #0: loss = 5.27424 (* 1 = 5.27424 loss)
I0408 08:59:48.095546 31856 sgd_solver.cpp:105] Iteration 9252, lr = 7.0718e-06
I0408 08:59:53.124303 31856 solver.cpp:218] Iteration 9264 (2.38633 iter/s, 5.02865s/12 iters), loss = 5.26008
I0408 08:59:53.124347 31856 solver.cpp:237]     Train net output #0: loss = 5.26008 (* 1 = 5.26008 loss)
I0408 08:59:53.124358 31856 sgd_solver.cpp:105] Iteration 9264, lr = 6.98468e-06
I0408 08:59:58.079828 31856 solver.cpp:218] Iteration 9276 (2.42162 iter/s, 4.95536s/12 iters), loss = 5.24941
I0408 08:59:58.079870 31856 solver.cpp:237]     Train net output #0: loss = 5.24941 (* 1 = 5.24941 loss)
I0408 08:59:58.079881 31856 sgd_solver.cpp:105] Iteration 9276, lr = 6.89864e-06
I0408 09:00:00.109980 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0408 09:00:03.296897 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0408 09:00:05.641863 31856 solver.cpp:330] Iteration 9282, Testing net (#0)
I0408 09:00:05.641893 31856 net.cpp:676] Ignoring source layer train-data
I0408 09:00:06.466164 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:00:10.119437 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 09:00:10.119522 31856 solver.cpp:397]     Test net output #1: loss = 5.2872 (* 1 = 5.2872 loss)
I0408 09:00:12.086474 31856 solver.cpp:218] Iteration 9288 (0.856759 iter/s, 14.0063s/12 iters), loss = 5.2656
I0408 09:00:12.086519 31856 solver.cpp:237]     Train net output #0: loss = 5.2656 (* 1 = 5.2656 loss)
I0408 09:00:12.086530 31856 sgd_solver.cpp:105] Iteration 9288, lr = 6.81366e-06
I0408 09:00:17.238180 31856 solver.cpp:218] Iteration 9300 (2.3294 iter/s, 5.15154s/12 iters), loss = 5.25134
I0408 09:00:17.238216 31856 solver.cpp:237]     Train net output #0: loss = 5.25134 (* 1 = 5.25134 loss)
I0408 09:00:17.238225 31856 sgd_solver.cpp:105] Iteration 9300, lr = 6.72972e-06
I0408 09:00:19.457482 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:00:22.278131 31856 solver.cpp:218] Iteration 9312 (2.38105 iter/s, 5.03979s/12 iters), loss = 5.27227
I0408 09:00:22.278170 31856 solver.cpp:237]     Train net output #0: loss = 5.27227 (* 1 = 5.27227 loss)
I0408 09:00:22.278179 31856 sgd_solver.cpp:105] Iteration 9312, lr = 6.64682e-06
I0408 09:00:27.272737 31856 solver.cpp:218] Iteration 9324 (2.40267 iter/s, 4.99444s/12 iters), loss = 5.27901
I0408 09:00:27.272773 31856 solver.cpp:237]     Train net output #0: loss = 5.27901 (* 1 = 5.27901 loss)
I0408 09:00:27.272781 31856 sgd_solver.cpp:105] Iteration 9324, lr = 6.56494e-06
I0408 09:00:32.317744 31856 solver.cpp:218] Iteration 9336 (2.37867 iter/s, 5.04484s/12 iters), loss = 5.28583
I0408 09:00:32.317778 31856 solver.cpp:237]     Train net output #0: loss = 5.28583 (* 1 = 5.28583 loss)
I0408 09:00:32.317786 31856 sgd_solver.cpp:105] Iteration 9336, lr = 6.48407e-06
I0408 09:00:37.308511 31856 solver.cpp:218] Iteration 9348 (2.40452 iter/s, 4.99061s/12 iters), loss = 5.27109
I0408 09:00:37.308545 31856 solver.cpp:237]     Train net output #0: loss = 5.27109 (* 1 = 5.27109 loss)
I0408 09:00:37.308553 31856 sgd_solver.cpp:105] Iteration 9348, lr = 6.40419e-06
I0408 09:00:42.355978 31856 solver.cpp:218] Iteration 9360 (2.37751 iter/s, 5.04731s/12 iters), loss = 5.27066
I0408 09:00:42.356102 31856 solver.cpp:237]     Train net output #0: loss = 5.27066 (* 1 = 5.27066 loss)
I0408 09:00:42.356117 31856 sgd_solver.cpp:105] Iteration 9360, lr = 6.3253e-06
I0408 09:00:47.267294 31856 solver.cpp:218] Iteration 9372 (2.44346 iter/s, 4.91107s/12 iters), loss = 5.27286
I0408 09:00:47.267343 31856 solver.cpp:237]     Train net output #0: loss = 5.27286 (* 1 = 5.27286 loss)
I0408 09:00:47.267354 31856 sgd_solver.cpp:105] Iteration 9372, lr = 6.24738e-06
I0408 09:00:51.632822 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0408 09:00:54.673830 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0408 09:00:56.978931 31856 solver.cpp:330] Iteration 9384, Testing net (#0)
I0408 09:00:56.978951 31856 net.cpp:676] Ignoring source layer train-data
I0408 09:00:57.683883 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:01:01.344441 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 09:01:01.344489 31856 solver.cpp:397]     Test net output #1: loss = 5.28724 (* 1 = 5.28724 loss)
I0408 09:01:01.434633 31856 solver.cpp:218] Iteration 9384 (0.847042 iter/s, 14.167s/12 iters), loss = 5.27746
I0408 09:01:01.434685 31856 solver.cpp:237]     Train net output #0: loss = 5.27746 (* 1 = 5.27746 loss)
I0408 09:01:01.434696 31856 sgd_solver.cpp:105] Iteration 9384, lr = 6.17042e-06
I0408 09:01:05.711956 31856 solver.cpp:218] Iteration 9396 (2.8056 iter/s, 4.27716s/12 iters), loss = 5.26684
I0408 09:01:05.712005 31856 solver.cpp:237]     Train net output #0: loss = 5.26684 (* 1 = 5.26684 loss)
I0408 09:01:05.712018 31856 sgd_solver.cpp:105] Iteration 9396, lr = 6.0944e-06
I0408 09:01:10.092221 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:01:10.753863 31856 solver.cpp:218] Iteration 9408 (2.38014 iter/s, 5.04172s/12 iters), loss = 5.26801
I0408 09:01:10.753911 31856 solver.cpp:237]     Train net output #0: loss = 5.26801 (* 1 = 5.26801 loss)
I0408 09:01:10.753922 31856 sgd_solver.cpp:105] Iteration 9408, lr = 6.01933e-06
I0408 09:01:15.721993 31856 solver.cpp:218] Iteration 9420 (2.41548 iter/s, 4.96796s/12 iters), loss = 5.27607
I0408 09:01:15.722113 31856 solver.cpp:237]     Train net output #0: loss = 5.27607 (* 1 = 5.27607 loss)
I0408 09:01:15.722123 31856 sgd_solver.cpp:105] Iteration 9420, lr = 5.94518e-06
I0408 09:01:20.757194 31856 solver.cpp:218] Iteration 9432 (2.38334 iter/s, 5.03496s/12 iters), loss = 5.28352
I0408 09:01:20.757238 31856 solver.cpp:237]     Train net output #0: loss = 5.28352 (* 1 = 5.28352 loss)
I0408 09:01:20.757248 31856 sgd_solver.cpp:105] Iteration 9432, lr = 5.87194e-06
I0408 09:01:25.781625 31856 solver.cpp:218] Iteration 9444 (2.38841 iter/s, 5.02426s/12 iters), loss = 5.2866
I0408 09:01:25.781673 31856 solver.cpp:237]     Train net output #0: loss = 5.2866 (* 1 = 5.2866 loss)
I0408 09:01:25.781685 31856 sgd_solver.cpp:105] Iteration 9444, lr = 5.7996e-06
I0408 09:01:30.809556 31856 solver.cpp:218] Iteration 9456 (2.38675 iter/s, 5.02776s/12 iters), loss = 5.26484
I0408 09:01:30.809605 31856 solver.cpp:237]     Train net output #0: loss = 5.26484 (* 1 = 5.26484 loss)
I0408 09:01:30.809617 31856 sgd_solver.cpp:105] Iteration 9456, lr = 5.72816e-06
I0408 09:01:35.805493 31856 solver.cpp:218] Iteration 9468 (2.40204 iter/s, 4.99576s/12 iters), loss = 5.27829
I0408 09:01:35.805544 31856 solver.cpp:237]     Train net output #0: loss = 5.27829 (* 1 = 5.27829 loss)
I0408 09:01:35.805557 31856 sgd_solver.cpp:105] Iteration 9468, lr = 5.65759e-06
I0408 09:01:40.828867 31856 solver.cpp:218] Iteration 9480 (2.38892 iter/s, 5.0232s/12 iters), loss = 5.27684
I0408 09:01:40.828915 31856 solver.cpp:237]     Train net output #0: loss = 5.27684 (* 1 = 5.27684 loss)
I0408 09:01:40.828927 31856 sgd_solver.cpp:105] Iteration 9480, lr = 5.5879e-06
I0408 09:01:42.989866 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0408 09:01:45.999363 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0408 09:01:48.320194 31856 solver.cpp:330] Iteration 9486, Testing net (#0)
I0408 09:01:48.320216 31856 net.cpp:676] Ignoring source layer train-data
I0408 09:01:49.055205 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:01:52.781108 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 09:01:52.781152 31856 solver.cpp:397]     Test net output #1: loss = 5.28669 (* 1 = 5.28669 loss)
I0408 09:01:54.683440 31856 solver.cpp:218] Iteration 9492 (0.866164 iter/s, 13.8542s/12 iters), loss = 5.26855
I0408 09:01:54.683490 31856 solver.cpp:237]     Train net output #0: loss = 5.26855 (* 1 = 5.26855 loss)
I0408 09:01:54.683501 31856 sgd_solver.cpp:105] Iteration 9492, lr = 5.51906e-06
I0408 09:01:59.696362 31856 solver.cpp:218] Iteration 9504 (2.3939 iter/s, 5.01275s/12 iters), loss = 5.25821
I0408 09:01:59.696408 31856 solver.cpp:237]     Train net output #0: loss = 5.25821 (* 1 = 5.25821 loss)
I0408 09:01:59.696419 31856 sgd_solver.cpp:105] Iteration 9504, lr = 5.45107e-06
I0408 09:02:01.179227 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:02:04.636168 31856 solver.cpp:218] Iteration 9516 (2.42933 iter/s, 4.93963s/12 iters), loss = 5.26214
I0408 09:02:04.636217 31856 solver.cpp:237]     Train net output #0: loss = 5.26214 (* 1 = 5.26214 loss)
I0408 09:02:04.636229 31856 sgd_solver.cpp:105] Iteration 9516, lr = 5.38392e-06
I0408 09:02:09.758335 31856 solver.cpp:218] Iteration 9528 (2.34284 iter/s, 5.12199s/12 iters), loss = 5.26318
I0408 09:02:09.758383 31856 solver.cpp:237]     Train net output #0: loss = 5.26318 (* 1 = 5.26318 loss)
I0408 09:02:09.758395 31856 sgd_solver.cpp:105] Iteration 9528, lr = 5.3176e-06
I0408 09:02:15.013711 31856 solver.cpp:218] Iteration 9540 (2.28346 iter/s, 5.25519s/12 iters), loss = 5.24617
I0408 09:02:15.013761 31856 solver.cpp:237]     Train net output #0: loss = 5.24617 (* 1 = 5.24617 loss)
I0408 09:02:15.013773 31856 sgd_solver.cpp:105] Iteration 9540, lr = 5.25209e-06
I0408 09:02:20.057660 31856 solver.cpp:218] Iteration 9552 (2.37917 iter/s, 5.04377s/12 iters), loss = 5.29982
I0408 09:02:20.057809 31856 solver.cpp:237]     Train net output #0: loss = 5.29982 (* 1 = 5.29982 loss)
I0408 09:02:20.057824 31856 sgd_solver.cpp:105] Iteration 9552, lr = 5.18739e-06
I0408 09:02:25.075131 31856 solver.cpp:218] Iteration 9564 (2.39177 iter/s, 5.01719s/12 iters), loss = 5.25591
I0408 09:02:25.075181 31856 solver.cpp:237]     Train net output #0: loss = 5.25591 (* 1 = 5.25591 loss)
I0408 09:02:25.075193 31856 sgd_solver.cpp:105] Iteration 9564, lr = 5.12349e-06
I0408 09:02:30.087968 31856 solver.cpp:218] Iteration 9576 (2.39394 iter/s, 5.01266s/12 iters), loss = 5.26153
I0408 09:02:30.088008 31856 solver.cpp:237]     Train net output #0: loss = 5.26153 (* 1 = 5.26153 loss)
I0408 09:02:30.088018 31856 sgd_solver.cpp:105] Iteration 9576, lr = 5.06038e-06
I0408 09:02:34.619431 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0408 09:02:37.591339 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0408 09:02:41.509209 31856 solver.cpp:330] Iteration 9588, Testing net (#0)
I0408 09:02:41.509235 31856 net.cpp:676] Ignoring source layer train-data
I0408 09:02:42.199905 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:02:45.959395 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 09:02:45.959441 31856 solver.cpp:397]     Test net output #1: loss = 5.28699 (* 1 = 5.28699 loss)
I0408 09:02:46.049629 31856 solver.cpp:218] Iteration 9588 (0.751821 iter/s, 15.9612s/12 iters), loss = 5.27469
I0408 09:02:46.049679 31856 solver.cpp:237]     Train net output #0: loss = 5.27469 (* 1 = 5.27469 loss)
I0408 09:02:46.049690 31856 sgd_solver.cpp:105] Iteration 9588, lr = 4.99804e-06
I0408 09:02:50.620990 31856 solver.cpp:218] Iteration 9600 (2.62513 iter/s, 4.5712s/12 iters), loss = 5.27371
I0408 09:02:50.621054 31856 solver.cpp:237]     Train net output #0: loss = 5.27371 (* 1 = 5.27371 loss)
I0408 09:02:50.621063 31856 sgd_solver.cpp:105] Iteration 9600, lr = 4.93647e-06
I0408 09:02:54.419924 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:02:55.800562 31856 solver.cpp:218] Iteration 9612 (2.31688 iter/s, 5.17938s/12 iters), loss = 5.27087
I0408 09:02:55.800598 31856 solver.cpp:237]     Train net output #0: loss = 5.27087 (* 1 = 5.27087 loss)
I0408 09:02:55.800606 31856 sgd_solver.cpp:105] Iteration 9612, lr = 4.87566e-06
I0408 09:03:00.844604 31856 solver.cpp:218] Iteration 9624 (2.37912 iter/s, 5.04388s/12 iters), loss = 5.26701
I0408 09:03:00.844638 31856 solver.cpp:237]     Train net output #0: loss = 5.26701 (* 1 = 5.26701 loss)
I0408 09:03:00.844646 31856 sgd_solver.cpp:105] Iteration 9624, lr = 4.81559e-06
I0408 09:03:05.752003 31856 solver.cpp:218] Iteration 9636 (2.44537 iter/s, 4.90724s/12 iters), loss = 5.25556
I0408 09:03:05.752038 31856 solver.cpp:237]     Train net output #0: loss = 5.25556 (* 1 = 5.25556 loss)
I0408 09:03:05.752048 31856 sgd_solver.cpp:105] Iteration 9636, lr = 4.75627e-06
I0408 09:03:10.722998 31856 solver.cpp:218] Iteration 9648 (2.41409 iter/s, 4.97082s/12 iters), loss = 5.26643
I0408 09:03:10.723048 31856 solver.cpp:237]     Train net output #0: loss = 5.26643 (* 1 = 5.26643 loss)
I0408 09:03:10.723062 31856 sgd_solver.cpp:105] Iteration 9648, lr = 4.69768e-06
I0408 09:03:16.156813 31856 solver.cpp:218] Iteration 9660 (2.20847 iter/s, 5.43362s/12 iters), loss = 5.25275
I0408 09:03:16.156862 31856 solver.cpp:237]     Train net output #0: loss = 5.25275 (* 1 = 5.25275 loss)
I0408 09:03:16.156872 31856 sgd_solver.cpp:105] Iteration 9660, lr = 4.63981e-06
I0408 09:03:21.487090 31856 solver.cpp:218] Iteration 9672 (2.25137 iter/s, 5.33009s/12 iters), loss = 5.27225
I0408 09:03:21.487187 31856 solver.cpp:237]     Train net output #0: loss = 5.27225 (* 1 = 5.27225 loss)
I0408 09:03:21.487200 31856 sgd_solver.cpp:105] Iteration 9672, lr = 4.58265e-06
I0408 09:03:26.504118 31856 solver.cpp:218] Iteration 9684 (2.39196 iter/s, 5.0168s/12 iters), loss = 5.28898
I0408 09:03:26.504164 31856 solver.cpp:237]     Train net output #0: loss = 5.28898 (* 1 = 5.28898 loss)
I0408 09:03:26.504176 31856 sgd_solver.cpp:105] Iteration 9684, lr = 4.5262e-06
I0408 09:03:28.541606 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0408 09:03:31.579082 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0408 09:03:35.984899 31856 solver.cpp:330] Iteration 9690, Testing net (#0)
I0408 09:03:35.984925 31856 net.cpp:676] Ignoring source layer train-data
I0408 09:03:36.617101 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:03:39.428148 31856 blocking_queue.cpp:49] Waiting for data
I0408 09:03:40.415380 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 09:03:40.415427 31856 solver.cpp:397]     Test net output #1: loss = 5.28718 (* 1 = 5.28718 loss)
I0408 09:03:42.414461 31856 solver.cpp:218] Iteration 9696 (0.754247 iter/s, 15.9099s/12 iters), loss = 5.28634
I0408 09:03:42.414507 31856 solver.cpp:237]     Train net output #0: loss = 5.28634 (* 1 = 5.28634 loss)
I0408 09:03:42.414518 31856 sgd_solver.cpp:105] Iteration 9696, lr = 4.47044e-06
I0408 09:03:47.868088 31856 solver.cpp:218] Iteration 9708 (2.20045 iter/s, 5.45343s/12 iters), loss = 5.28858
I0408 09:03:47.868136 31856 solver.cpp:237]     Train net output #0: loss = 5.28858 (* 1 = 5.28858 loss)
I0408 09:03:47.868149 31856 sgd_solver.cpp:105] Iteration 9708, lr = 4.41537e-06
I0408 09:03:48.681924 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:03:53.010084 31856 solver.cpp:218] Iteration 9720 (2.33381 iter/s, 5.14182s/12 iters), loss = 5.28778
I0408 09:03:53.010229 31856 solver.cpp:237]     Train net output #0: loss = 5.28778 (* 1 = 5.28778 loss)
I0408 09:03:53.010243 31856 sgd_solver.cpp:105] Iteration 9720, lr = 4.36098e-06
I0408 09:03:57.953311 31856 solver.cpp:218] Iteration 9732 (2.4277 iter/s, 4.94296s/12 iters), loss = 5.26257
I0408 09:03:57.953357 31856 solver.cpp:237]     Train net output #0: loss = 5.26257 (* 1 = 5.26257 loss)
I0408 09:03:57.953368 31856 sgd_solver.cpp:105] Iteration 9732, lr = 4.30726e-06
I0408 09:04:02.929603 31856 solver.cpp:218] Iteration 9744 (2.41152 iter/s, 4.97612s/12 iters), loss = 5.26781
I0408 09:04:02.929642 31856 solver.cpp:237]     Train net output #0: loss = 5.26781 (* 1 = 5.26781 loss)
I0408 09:04:02.929652 31856 sgd_solver.cpp:105] Iteration 9744, lr = 4.2542e-06
I0408 09:04:07.857525 31856 solver.cpp:218] Iteration 9756 (2.43519 iter/s, 4.92775s/12 iters), loss = 5.27377
I0408 09:04:07.857564 31856 solver.cpp:237]     Train net output #0: loss = 5.27377 (* 1 = 5.27377 loss)
I0408 09:04:07.857573 31856 sgd_solver.cpp:105] Iteration 9756, lr = 4.20179e-06
I0408 09:04:12.815142 31856 solver.cpp:218] Iteration 9768 (2.4206 iter/s, 4.95745s/12 iters), loss = 5.28808
I0408 09:04:12.815186 31856 solver.cpp:237]     Train net output #0: loss = 5.28808 (* 1 = 5.28808 loss)
I0408 09:04:12.815197 31856 sgd_solver.cpp:105] Iteration 9768, lr = 4.15003e-06
I0408 09:04:17.830371 31856 solver.cpp:218] Iteration 9780 (2.3928 iter/s, 5.01506s/12 iters), loss = 5.27007
I0408 09:04:17.830420 31856 solver.cpp:237]     Train net output #0: loss = 5.27007 (* 1 = 5.27007 loss)
I0408 09:04:17.830432 31856 sgd_solver.cpp:105] Iteration 9780, lr = 4.09891e-06
I0408 09:04:22.429347 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0408 09:04:25.513645 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0408 09:04:31.520833 31856 solver.cpp:330] Iteration 9792, Testing net (#0)
I0408 09:04:31.520853 31856 net.cpp:676] Ignoring source layer train-data
I0408 09:04:32.130005 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:04:36.103689 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 09:04:36.103736 31856 solver.cpp:397]     Test net output #1: loss = 5.28696 (* 1 = 5.28696 loss)
I0408 09:04:36.193759 31856 solver.cpp:218] Iteration 9792 (0.653492 iter/s, 18.3629s/12 iters), loss = 5.2523
I0408 09:04:36.193811 31856 solver.cpp:237]     Train net output #0: loss = 5.2523 (* 1 = 5.2523 loss)
I0408 09:04:36.193823 31856 sgd_solver.cpp:105] Iteration 9792, lr = 4.04841e-06
I0408 09:04:40.603487 31856 solver.cpp:218] Iteration 9804 (2.72136 iter/s, 4.40956s/12 iters), loss = 5.2723
I0408 09:04:40.603523 31856 solver.cpp:237]     Train net output #0: loss = 5.2723 (* 1 = 5.2723 loss)
I0408 09:04:40.603531 31856 sgd_solver.cpp:105] Iteration 9804, lr = 3.99854e-06
I0408 09:04:43.535784 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:04:45.532711 31856 solver.cpp:218] Iteration 9816 (2.43454 iter/s, 4.92905s/12 iters), loss = 5.263
I0408 09:04:45.532758 31856 solver.cpp:237]     Train net output #0: loss = 5.263 (* 1 = 5.263 loss)
I0408 09:04:45.532770 31856 sgd_solver.cpp:105] Iteration 9816, lr = 3.94928e-06
I0408 09:04:50.450686 31856 solver.cpp:218] Iteration 9828 (2.44012 iter/s, 4.9178s/12 iters), loss = 5.25487
I0408 09:04:50.450736 31856 solver.cpp:237]     Train net output #0: loss = 5.25487 (* 1 = 5.25487 loss)
I0408 09:04:50.450747 31856 sgd_solver.cpp:105] Iteration 9828, lr = 3.90063e-06
I0408 09:04:55.388732 31856 solver.cpp:218] Iteration 9840 (2.4302 iter/s, 4.93786s/12 iters), loss = 5.24878
I0408 09:04:55.388793 31856 solver.cpp:237]     Train net output #0: loss = 5.24878 (* 1 = 5.24878 loss)
I0408 09:04:55.388809 31856 sgd_solver.cpp:105] Iteration 9840, lr = 3.85258e-06
I0408 09:05:00.494423 31856 solver.cpp:218] Iteration 9852 (2.35041 iter/s, 5.1055s/12 iters), loss = 5.26752
I0408 09:05:00.494547 31856 solver.cpp:237]     Train net output #0: loss = 5.26752 (* 1 = 5.26752 loss)
I0408 09:05:00.494560 31856 sgd_solver.cpp:105] Iteration 9852, lr = 3.80512e-06
I0408 09:05:05.523126 31856 solver.cpp:218] Iteration 9864 (2.38642 iter/s, 5.02845s/12 iters), loss = 5.28879
I0408 09:05:05.523177 31856 solver.cpp:237]     Train net output #0: loss = 5.28879 (* 1 = 5.28879 loss)
I0408 09:05:05.523190 31856 sgd_solver.cpp:105] Iteration 9864, lr = 3.75825e-06
I0408 09:05:10.459913 31856 solver.cpp:218] Iteration 9876 (2.43082 iter/s, 4.93661s/12 iters), loss = 5.27085
I0408 09:05:10.459957 31856 solver.cpp:237]     Train net output #0: loss = 5.27085 (* 1 = 5.27085 loss)
I0408 09:05:10.459970 31856 sgd_solver.cpp:105] Iteration 9876, lr = 3.71195e-06
I0408 09:05:15.462586 31856 solver.cpp:218] Iteration 9888 (2.3988 iter/s, 5.0025s/12 iters), loss = 5.27578
I0408 09:05:15.462630 31856 solver.cpp:237]     Train net output #0: loss = 5.27578 (* 1 = 5.27578 loss)
I0408 09:05:15.462643 31856 sgd_solver.cpp:105] Iteration 9888, lr = 3.66622e-06
I0408 09:05:17.489048 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0408 09:05:21.765326 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0408 09:05:26.217485 31856 solver.cpp:330] Iteration 9894, Testing net (#0)
I0408 09:05:26.217511 31856 net.cpp:676] Ignoring source layer train-data
I0408 09:05:26.781911 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:05:30.678977 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 09:05:30.679061 31856 solver.cpp:397]     Test net output #1: loss = 5.2873 (* 1 = 5.2873 loss)
I0408 09:05:32.690793 31856 solver.cpp:218] Iteration 9900 (0.696551 iter/s, 17.2277s/12 iters), loss = 5.27517
I0408 09:05:32.690840 31856 solver.cpp:237]     Train net output #0: loss = 5.27517 (* 1 = 5.27517 loss)
I0408 09:05:32.690850 31856 sgd_solver.cpp:105] Iteration 9900, lr = 3.62106e-06
I0408 09:05:37.720953 31856 solver.cpp:218] Iteration 9912 (2.3857 iter/s, 5.02998s/12 iters), loss = 5.25691
I0408 09:05:37.720999 31856 solver.cpp:237]     Train net output #0: loss = 5.25691 (* 1 = 5.25691 loss)
I0408 09:05:37.721010 31856 sgd_solver.cpp:105] Iteration 9912, lr = 3.57645e-06
I0408 09:05:37.833786 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:05:42.656177 31856 solver.cpp:218] Iteration 9924 (2.43159 iter/s, 4.93504s/12 iters), loss = 5.2709
I0408 09:05:42.656236 31856 solver.cpp:237]     Train net output #0: loss = 5.2709 (* 1 = 5.2709 loss)
I0408 09:05:42.656250 31856 sgd_solver.cpp:105] Iteration 9924, lr = 3.53239e-06
I0408 09:05:47.661108 31856 solver.cpp:218] Iteration 9936 (2.39773 iter/s, 5.00474s/12 iters), loss = 5.28969
I0408 09:05:47.661157 31856 solver.cpp:237]     Train net output #0: loss = 5.28969 (* 1 = 5.28969 loss)
I0408 09:05:47.661170 31856 sgd_solver.cpp:105] Iteration 9936, lr = 3.48888e-06
I0408 09:05:52.666942 31856 solver.cpp:218] Iteration 9948 (2.39729 iter/s, 5.00565s/12 iters), loss = 5.25891
I0408 09:05:52.666993 31856 solver.cpp:237]     Train net output #0: loss = 5.25891 (* 1 = 5.25891 loss)
I0408 09:05:52.667006 31856 sgd_solver.cpp:105] Iteration 9948, lr = 3.4459e-06
I0408 09:05:57.634860 31856 solver.cpp:218] Iteration 9960 (2.41559 iter/s, 4.96774s/12 iters), loss = 5.26932
I0408 09:05:57.634908 31856 solver.cpp:237]     Train net output #0: loss = 5.26932 (* 1 = 5.26932 loss)
I0408 09:05:57.634922 31856 sgd_solver.cpp:105] Iteration 9960, lr = 3.40345e-06
I0408 09:06:02.634408 31856 solver.cpp:218] Iteration 9972 (2.4003 iter/s, 4.99937s/12 iters), loss = 5.26147
I0408 09:06:02.634500 31856 solver.cpp:237]     Train net output #0: loss = 5.26147 (* 1 = 5.26147 loss)
I0408 09:06:02.634510 31856 sgd_solver.cpp:105] Iteration 9972, lr = 3.36152e-06
I0408 09:06:07.754818 31856 solver.cpp:218] Iteration 9984 (2.34367 iter/s, 5.12018s/12 iters), loss = 5.24644
I0408 09:06:07.754863 31856 solver.cpp:237]     Train net output #0: loss = 5.24644 (* 1 = 5.24644 loss)
I0408 09:06:07.754873 31856 sgd_solver.cpp:105] Iteration 9984, lr = 3.32011e-06
I0408 09:06:12.387825 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0408 09:06:17.112663 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0408 09:06:19.586570 31856 solver.cpp:330] Iteration 9996, Testing net (#0)
I0408 09:06:19.586594 31856 net.cpp:676] Ignoring source layer train-data
I0408 09:06:20.096267 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:06:24.046828 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 09:06:24.046876 31856 solver.cpp:397]     Test net output #1: loss = 5.28777 (* 1 = 5.28777 loss)
I0408 09:06:24.137099 31856 solver.cpp:218] Iteration 9996 (0.732519 iter/s, 16.3818s/12 iters), loss = 5.2697
I0408 09:06:24.137145 31856 solver.cpp:237]     Train net output #0: loss = 5.2697 (* 1 = 5.2697 loss)
I0408 09:06:24.137156 31856 sgd_solver.cpp:105] Iteration 9996, lr = 3.27921e-06
I0408 09:06:28.266820 31856 solver.cpp:218] Iteration 10008 (2.90588 iter/s, 4.12956s/12 iters), loss = 5.24409
I0408 09:06:28.266860 31856 solver.cpp:237]     Train net output #0: loss = 5.24409 (* 1 = 5.24409 loss)
I0408 09:06:28.266870 31856 sgd_solver.cpp:105] Iteration 10008, lr = 3.23882e-06
I0408 09:06:30.497330 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:06:33.174449 31856 solver.cpp:218] Iteration 10020 (2.44526 iter/s, 4.90746s/12 iters), loss = 5.26818
I0408 09:06:33.174530 31856 solver.cpp:237]     Train net output #0: loss = 5.26818 (* 1 = 5.26818 loss)
I0408 09:06:33.174538 31856 sgd_solver.cpp:105] Iteration 10020, lr = 3.19892e-06
I0408 09:06:38.084575 31856 solver.cpp:218] Iteration 10032 (2.44404 iter/s, 4.90991s/12 iters), loss = 5.27718
I0408 09:06:38.084627 31856 solver.cpp:237]     Train net output #0: loss = 5.27718 (* 1 = 5.27718 loss)
I0408 09:06:38.084640 31856 sgd_solver.cpp:105] Iteration 10032, lr = 3.15951e-06
I0408 09:06:43.101198 31856 solver.cpp:218] Iteration 10044 (2.39214 iter/s, 5.01644s/12 iters), loss = 5.28402
I0408 09:06:43.101240 31856 solver.cpp:237]     Train net output #0: loss = 5.28402 (* 1 = 5.28402 loss)
I0408 09:06:43.101251 31856 sgd_solver.cpp:105] Iteration 10044, lr = 3.12059e-06
I0408 09:06:48.080215 31856 solver.cpp:218] Iteration 10056 (2.4102 iter/s, 4.97884s/12 iters), loss = 5.2772
I0408 09:06:48.080260 31856 solver.cpp:237]     Train net output #0: loss = 5.2772 (* 1 = 5.2772 loss)
I0408 09:06:48.080271 31856 sgd_solver.cpp:105] Iteration 10056, lr = 3.08215e-06
I0408 09:06:53.102612 31856 solver.cpp:218] Iteration 10068 (2.38938 iter/s, 5.02222s/12 iters), loss = 5.27441
I0408 09:06:53.102663 31856 solver.cpp:237]     Train net output #0: loss = 5.27441 (* 1 = 5.27441 loss)
I0408 09:06:53.102674 31856 sgd_solver.cpp:105] Iteration 10068, lr = 3.04418e-06
I0408 09:06:58.105274 31856 solver.cpp:218] Iteration 10080 (2.39881 iter/s, 5.00248s/12 iters), loss = 5.26209
I0408 09:06:58.105317 31856 solver.cpp:237]     Train net output #0: loss = 5.26209 (* 1 = 5.26209 loss)
I0408 09:06:58.105329 31856 sgd_solver.cpp:105] Iteration 10080, lr = 3.00668e-06
I0408 09:07:03.116189 31856 solver.cpp:218] Iteration 10092 (2.39486 iter/s, 5.01074s/12 iters), loss = 5.27589
I0408 09:07:03.116236 31856 solver.cpp:237]     Train net output #0: loss = 5.27589 (* 1 = 5.27589 loss)
I0408 09:07:03.116247 31856 sgd_solver.cpp:105] Iteration 10092, lr = 2.96964e-06
I0408 09:07:05.134809 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0408 09:07:09.718092 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0408 09:07:17.456862 31856 solver.cpp:330] Iteration 10098, Testing net (#0)
I0408 09:07:17.456888 31856 net.cpp:676] Ignoring source layer train-data
I0408 09:07:17.938643 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:07:21.923045 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 09:07:21.923094 31856 solver.cpp:397]     Test net output #1: loss = 5.28737 (* 1 = 5.28737 loss)
I0408 09:07:23.916204 31856 solver.cpp:218] Iteration 10104 (0.576939 iter/s, 20.7994s/12 iters), loss = 5.27224
I0408 09:07:23.916252 31856 solver.cpp:237]     Train net output #0: loss = 5.27224 (* 1 = 5.27224 loss)
I0408 09:07:23.916263 31856 sgd_solver.cpp:105] Iteration 10104, lr = 2.93306e-06
I0408 09:07:28.576673 31860 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:07:29.207114 31856 solver.cpp:218] Iteration 10116 (2.26812 iter/s, 5.29072s/12 iters), loss = 5.2591
I0408 09:07:29.207170 31856 solver.cpp:237]     Train net output #0: loss = 5.2591 (* 1 = 5.2591 loss)
I0408 09:07:29.207185 31856 sgd_solver.cpp:105] Iteration 10116, lr = 2.89693e-06
I0408 09:07:34.210388 31856 solver.cpp:218] Iteration 10128 (2.39852 iter/s, 5.00309s/12 iters), loss = 5.27369
I0408 09:07:34.210422 31856 solver.cpp:237]     Train net output #0: loss = 5.27369 (* 1 = 5.27369 loss)
I0408 09:07:34.210430 31856 sgd_solver.cpp:105] Iteration 10128, lr = 2.86124e-06
I0408 09:07:39.179365 31856 solver.cpp:218] Iteration 10140 (2.41507 iter/s, 4.9688s/12 iters), loss = 5.28258
I0408 09:07:39.179531 31856 solver.cpp:237]     Train net output #0: loss = 5.28258 (* 1 = 5.28258 loss)
I0408 09:07:39.179543 31856 sgd_solver.cpp:105] Iteration 10140, lr = 2.82599e-06
I0408 09:07:44.191846 31856 solver.cpp:218] Iteration 10152 (2.39417 iter/s, 5.01218s/12 iters), loss = 5.27522
I0408 09:07:44.191888 31856 solver.cpp:237]     Train net output #0: loss = 5.27522 (* 1 = 5.27522 loss)
I0408 09:07:44.191900 31856 sgd_solver.cpp:105] Iteration 10152, lr = 2.79118e-06
I0408 09:07:49.258858 31856 solver.cpp:218] Iteration 10164 (2.36834 iter/s, 5.06683s/12 iters), loss = 5.26389
I0408 09:07:49.258903 31856 solver.cpp:237]     Train net output #0: loss = 5.26389 (* 1 = 5.26389 loss)
I0408 09:07:49.258914 31856 sgd_solver.cpp:105] Iteration 10164, lr = 2.7568e-06
I0408 09:07:54.282733 31856 solver.cpp:218] Iteration 10176 (2.38868 iter/s, 5.02369s/12 iters), loss = 5.27613
I0408 09:07:54.282778 31856 solver.cpp:237]     Train net output #0: loss = 5.27613 (* 1 = 5.27613 loss)
I0408 09:07:54.282788 31856 sgd_solver.cpp:105] Iteration 10176, lr = 2.72283e-06
I0408 09:07:59.234032 31856 solver.cpp:218] Iteration 10188 (2.42369 iter/s, 4.95112s/12 iters), loss = 5.27791
I0408 09:07:59.234076 31856 solver.cpp:237]     Train net output #0: loss = 5.27791 (* 1 = 5.27791 loss)
I0408 09:07:59.234087 31856 sgd_solver.cpp:105] Iteration 10188, lr = 2.68929e-06
I0408 09:08:03.731838 31856 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0408 09:08:06.782351 31856 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0408 09:08:11.263018 31856 solver.cpp:310] Iteration 10200, loss = 5.26336
I0408 09:08:11.263136 31856 solver.cpp:330] Iteration 10200, Testing net (#0)
I0408 09:08:11.263145 31856 net.cpp:676] Ignoring source layer train-data
I0408 09:08:11.696835 31861 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:08:15.726521 31856 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 09:08:15.726567 31856 solver.cpp:397]     Test net output #1: loss = 5.28671 (* 1 = 5.28671 loss)
I0408 09:08:15.726578 31856 solver.cpp:315] Optimization Done.
I0408 09:08:15.726585 31856 caffe.cpp:259] Optimization Done.