I0408 19:15:20.740890 24089 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210408-191518-c216/solver.prototxt
I0408 19:15:20.741106 24089 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0408 19:15:20.741117 24089 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0408 19:15:20.741207 24089 caffe.cpp:218] Using GPUs 2
I0408 19:15:20.767989 24089 caffe.cpp:223] GPU 2: GeForce GTX 1080 Ti
I0408 19:15:21.058737 24089 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.999236
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0408 19:15:21.059535 24089 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0408 19:15:21.060204 24089 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0408 19:15:21.060220 24089 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0408 19:15:21.060360 24089 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 19:15:21.060451 24089 layer_factory.hpp:77] Creating layer train-data
I0408 19:15:21.070441 24089 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0408 19:15:21.070734 24089 net.cpp:84] Creating Layer train-data
I0408 19:15:21.070744 24089 net.cpp:380] train-data -> data
I0408 19:15:21.070765 24089 net.cpp:380] train-data -> label
I0408 19:15:21.070775 24089 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0408 19:15:21.075762 24089 data_layer.cpp:45] output data size: 128,3,227,227
I0408 19:15:21.201184 24089 net.cpp:122] Setting up train-data
I0408 19:15:21.201207 24089 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0408 19:15:21.201213 24089 net.cpp:129] Top shape: 128 (128)
I0408 19:15:21.201216 24089 net.cpp:137] Memory required for data: 79149056
I0408 19:15:21.201226 24089 layer_factory.hpp:77] Creating layer conv1
I0408 19:15:21.201248 24089 net.cpp:84] Creating Layer conv1
I0408 19:15:21.201253 24089 net.cpp:406] conv1 <- data
I0408 19:15:21.201267 24089 net.cpp:380] conv1 -> conv1
I0408 19:15:21.809502 24089 net.cpp:122] Setting up conv1
I0408 19:15:21.809525 24089 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 19:15:21.809528 24089 net.cpp:137] Memory required for data: 227833856
I0408 19:15:21.809547 24089 layer_factory.hpp:77] Creating layer relu1
I0408 19:15:21.809556 24089 net.cpp:84] Creating Layer relu1
I0408 19:15:21.809561 24089 net.cpp:406] relu1 <- conv1
I0408 19:15:21.809566 24089 net.cpp:367] relu1 -> conv1 (in-place)
I0408 19:15:21.809851 24089 net.cpp:122] Setting up relu1
I0408 19:15:21.809859 24089 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 19:15:21.809862 24089 net.cpp:137] Memory required for data: 376518656
I0408 19:15:21.809866 24089 layer_factory.hpp:77] Creating layer norm1
I0408 19:15:21.809875 24089 net.cpp:84] Creating Layer norm1
I0408 19:15:21.809880 24089 net.cpp:406] norm1 <- conv1
I0408 19:15:21.809904 24089 net.cpp:380] norm1 -> norm1
I0408 19:15:21.810354 24089 net.cpp:122] Setting up norm1
I0408 19:15:21.810365 24089 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 19:15:21.810369 24089 net.cpp:137] Memory required for data: 525203456
I0408 19:15:21.810372 24089 layer_factory.hpp:77] Creating layer pool1
I0408 19:15:21.810380 24089 net.cpp:84] Creating Layer pool1
I0408 19:15:21.810384 24089 net.cpp:406] pool1 <- norm1
I0408 19:15:21.810389 24089 net.cpp:380] pool1 -> pool1
I0408 19:15:21.810425 24089 net.cpp:122] Setting up pool1
I0408 19:15:21.810431 24089 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0408 19:15:21.810434 24089 net.cpp:137] Memory required for data: 561035264
I0408 19:15:21.810437 24089 layer_factory.hpp:77] Creating layer conv2
I0408 19:15:21.810447 24089 net.cpp:84] Creating Layer conv2
I0408 19:15:21.810451 24089 net.cpp:406] conv2 <- pool1
I0408 19:15:21.810456 24089 net.cpp:380] conv2 -> conv2
I0408 19:15:21.817020 24089 net.cpp:122] Setting up conv2
I0408 19:15:21.817036 24089 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 19:15:21.817039 24089 net.cpp:137] Memory required for data: 656586752
I0408 19:15:21.817049 24089 layer_factory.hpp:77] Creating layer relu2
I0408 19:15:21.817057 24089 net.cpp:84] Creating Layer relu2
I0408 19:15:21.817060 24089 net.cpp:406] relu2 <- conv2
I0408 19:15:21.817066 24089 net.cpp:367] relu2 -> conv2 (in-place)
I0408 19:15:21.817484 24089 net.cpp:122] Setting up relu2
I0408 19:15:21.817493 24089 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 19:15:21.817497 24089 net.cpp:137] Memory required for data: 752138240
I0408 19:15:21.817500 24089 layer_factory.hpp:77] Creating layer norm2
I0408 19:15:21.817508 24089 net.cpp:84] Creating Layer norm2
I0408 19:15:21.817512 24089 net.cpp:406] norm2 <- conv2
I0408 19:15:21.817517 24089 net.cpp:380] norm2 -> norm2
I0408 19:15:21.817806 24089 net.cpp:122] Setting up norm2
I0408 19:15:21.817813 24089 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 19:15:21.817817 24089 net.cpp:137] Memory required for data: 847689728
I0408 19:15:21.817821 24089 layer_factory.hpp:77] Creating layer pool2
I0408 19:15:21.817827 24089 net.cpp:84] Creating Layer pool2
I0408 19:15:21.817831 24089 net.cpp:406] pool2 <- norm2
I0408 19:15:21.817836 24089 net.cpp:380] pool2 -> pool2
I0408 19:15:21.817862 24089 net.cpp:122] Setting up pool2
I0408 19:15:21.817867 24089 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 19:15:21.817870 24089 net.cpp:137] Memory required for data: 869840896
I0408 19:15:21.817873 24089 layer_factory.hpp:77] Creating layer conv3
I0408 19:15:21.817883 24089 net.cpp:84] Creating Layer conv3
I0408 19:15:21.817885 24089 net.cpp:406] conv3 <- pool2
I0408 19:15:21.817890 24089 net.cpp:380] conv3 -> conv3
I0408 19:15:21.830991 24089 net.cpp:122] Setting up conv3
I0408 19:15:21.831007 24089 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 19:15:21.831012 24089 net.cpp:137] Memory required for data: 903067648
I0408 19:15:21.831022 24089 layer_factory.hpp:77] Creating layer relu3
I0408 19:15:21.831030 24089 net.cpp:84] Creating Layer relu3
I0408 19:15:21.831034 24089 net.cpp:406] relu3 <- conv3
I0408 19:15:21.831039 24089 net.cpp:367] relu3 -> conv3 (in-place)
I0408 19:15:21.831459 24089 net.cpp:122] Setting up relu3
I0408 19:15:21.831470 24089 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 19:15:21.831473 24089 net.cpp:137] Memory required for data: 936294400
I0408 19:15:21.831477 24089 layer_factory.hpp:77] Creating layer conv4
I0408 19:15:21.831487 24089 net.cpp:84] Creating Layer conv4
I0408 19:15:21.831491 24089 net.cpp:406] conv4 <- conv3
I0408 19:15:21.831498 24089 net.cpp:380] conv4 -> conv4
I0408 19:15:21.841739 24089 net.cpp:122] Setting up conv4
I0408 19:15:21.841753 24089 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 19:15:21.841758 24089 net.cpp:137] Memory required for data: 969521152
I0408 19:15:21.841765 24089 layer_factory.hpp:77] Creating layer relu4
I0408 19:15:21.841773 24089 net.cpp:84] Creating Layer relu4
I0408 19:15:21.841792 24089 net.cpp:406] relu4 <- conv4
I0408 19:15:21.841800 24089 net.cpp:367] relu4 -> conv4 (in-place)
I0408 19:15:21.842154 24089 net.cpp:122] Setting up relu4
I0408 19:15:21.842161 24089 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 19:15:21.842165 24089 net.cpp:137] Memory required for data: 1002747904
I0408 19:15:21.842168 24089 layer_factory.hpp:77] Creating layer conv5
I0408 19:15:21.842180 24089 net.cpp:84] Creating Layer conv5
I0408 19:15:21.842183 24089 net.cpp:406] conv5 <- conv4
I0408 19:15:21.842190 24089 net.cpp:380] conv5 -> conv5
I0408 19:15:21.850508 24089 net.cpp:122] Setting up conv5
I0408 19:15:21.850523 24089 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 19:15:21.850528 24089 net.cpp:137] Memory required for data: 1024899072
I0408 19:15:21.850538 24089 layer_factory.hpp:77] Creating layer relu5
I0408 19:15:21.850544 24089 net.cpp:84] Creating Layer relu5
I0408 19:15:21.850549 24089 net.cpp:406] relu5 <- conv5
I0408 19:15:21.850556 24089 net.cpp:367] relu5 -> conv5 (in-place)
I0408 19:15:21.851037 24089 net.cpp:122] Setting up relu5
I0408 19:15:21.851047 24089 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 19:15:21.851049 24089 net.cpp:137] Memory required for data: 1047050240
I0408 19:15:21.851053 24089 layer_factory.hpp:77] Creating layer pool5
I0408 19:15:21.851060 24089 net.cpp:84] Creating Layer pool5
I0408 19:15:21.851064 24089 net.cpp:406] pool5 <- conv5
I0408 19:15:21.851069 24089 net.cpp:380] pool5 -> pool5
I0408 19:15:21.851106 24089 net.cpp:122] Setting up pool5
I0408 19:15:21.851112 24089 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0408 19:15:21.851115 24089 net.cpp:137] Memory required for data: 1051768832
I0408 19:15:21.851119 24089 layer_factory.hpp:77] Creating layer fc6
I0408 19:15:21.851127 24089 net.cpp:84] Creating Layer fc6
I0408 19:15:21.851131 24089 net.cpp:406] fc6 <- pool5
I0408 19:15:21.851137 24089 net.cpp:380] fc6 -> fc6
I0408 19:15:22.205451 24089 net.cpp:122] Setting up fc6
I0408 19:15:22.205472 24089 net.cpp:129] Top shape: 128 4096 (524288)
I0408 19:15:22.205476 24089 net.cpp:137] Memory required for data: 1053865984
I0408 19:15:22.205485 24089 layer_factory.hpp:77] Creating layer relu6
I0408 19:15:22.205493 24089 net.cpp:84] Creating Layer relu6
I0408 19:15:22.205498 24089 net.cpp:406] relu6 <- fc6
I0408 19:15:22.205504 24089 net.cpp:367] relu6 -> fc6 (in-place)
I0408 19:15:22.206142 24089 net.cpp:122] Setting up relu6
I0408 19:15:22.206151 24089 net.cpp:129] Top shape: 128 4096 (524288)
I0408 19:15:22.206156 24089 net.cpp:137] Memory required for data: 1055963136
I0408 19:15:22.206158 24089 layer_factory.hpp:77] Creating layer drop6
I0408 19:15:22.206166 24089 net.cpp:84] Creating Layer drop6
I0408 19:15:22.206171 24089 net.cpp:406] drop6 <- fc6
I0408 19:15:22.206174 24089 net.cpp:367] drop6 -> fc6 (in-place)
I0408 19:15:22.206202 24089 net.cpp:122] Setting up drop6
I0408 19:15:22.206207 24089 net.cpp:129] Top shape: 128 4096 (524288)
I0408 19:15:22.206210 24089 net.cpp:137] Memory required for data: 1058060288
I0408 19:15:22.206214 24089 layer_factory.hpp:77] Creating layer fc7
I0408 19:15:22.206220 24089 net.cpp:84] Creating Layer fc7
I0408 19:15:22.206224 24089 net.cpp:406] fc7 <- fc6
I0408 19:15:22.206229 24089 net.cpp:380] fc7 -> fc7
I0408 19:15:22.364225 24089 net.cpp:122] Setting up fc7
I0408 19:15:22.364245 24089 net.cpp:129] Top shape: 128 4096 (524288)
I0408 19:15:22.364248 24089 net.cpp:137] Memory required for data: 1060157440
I0408 19:15:22.364259 24089 layer_factory.hpp:77] Creating layer relu7
I0408 19:15:22.364269 24089 net.cpp:84] Creating Layer relu7
I0408 19:15:22.364272 24089 net.cpp:406] relu7 <- fc7
I0408 19:15:22.364279 24089 net.cpp:367] relu7 -> fc7 (in-place)
I0408 19:15:22.364893 24089 net.cpp:122] Setting up relu7
I0408 19:15:22.364902 24089 net.cpp:129] Top shape: 128 4096 (524288)
I0408 19:15:22.364907 24089 net.cpp:137] Memory required for data: 1062254592
I0408 19:15:22.364909 24089 layer_factory.hpp:77] Creating layer drop7
I0408 19:15:22.364917 24089 net.cpp:84] Creating Layer drop7
I0408 19:15:22.364938 24089 net.cpp:406] drop7 <- fc7
I0408 19:15:22.364943 24089 net.cpp:367] drop7 -> fc7 (in-place)
I0408 19:15:22.364967 24089 net.cpp:122] Setting up drop7
I0408 19:15:22.364972 24089 net.cpp:129] Top shape: 128 4096 (524288)
I0408 19:15:22.364975 24089 net.cpp:137] Memory required for data: 1064351744
I0408 19:15:22.364979 24089 layer_factory.hpp:77] Creating layer fc8
I0408 19:15:22.364986 24089 net.cpp:84] Creating Layer fc8
I0408 19:15:22.364989 24089 net.cpp:406] fc8 <- fc7
I0408 19:15:22.364995 24089 net.cpp:380] fc8 -> fc8
I0408 19:15:22.372648 24089 net.cpp:122] Setting up fc8
I0408 19:15:22.372659 24089 net.cpp:129] Top shape: 128 196 (25088)
I0408 19:15:22.372663 24089 net.cpp:137] Memory required for data: 1064452096
I0408 19:15:22.372668 24089 layer_factory.hpp:77] Creating layer loss
I0408 19:15:22.372676 24089 net.cpp:84] Creating Layer loss
I0408 19:15:22.372680 24089 net.cpp:406] loss <- fc8
I0408 19:15:22.372684 24089 net.cpp:406] loss <- label
I0408 19:15:22.372691 24089 net.cpp:380] loss -> loss
I0408 19:15:22.372699 24089 layer_factory.hpp:77] Creating layer loss
I0408 19:15:22.373327 24089 net.cpp:122] Setting up loss
I0408 19:15:22.373337 24089 net.cpp:129] Top shape: (1)
I0408 19:15:22.373339 24089 net.cpp:132]     with loss weight 1
I0408 19:15:22.373358 24089 net.cpp:137] Memory required for data: 1064452100
I0408 19:15:22.373360 24089 net.cpp:198] loss needs backward computation.
I0408 19:15:22.373368 24089 net.cpp:198] fc8 needs backward computation.
I0408 19:15:22.373371 24089 net.cpp:198] drop7 needs backward computation.
I0408 19:15:22.373374 24089 net.cpp:198] relu7 needs backward computation.
I0408 19:15:22.373378 24089 net.cpp:198] fc7 needs backward computation.
I0408 19:15:22.373380 24089 net.cpp:198] drop6 needs backward computation.
I0408 19:15:22.373383 24089 net.cpp:198] relu6 needs backward computation.
I0408 19:15:22.373387 24089 net.cpp:198] fc6 needs backward computation.
I0408 19:15:22.373390 24089 net.cpp:198] pool5 needs backward computation.
I0408 19:15:22.373394 24089 net.cpp:198] relu5 needs backward computation.
I0408 19:15:22.373397 24089 net.cpp:198] conv5 needs backward computation.
I0408 19:15:22.373401 24089 net.cpp:198] relu4 needs backward computation.
I0408 19:15:22.373405 24089 net.cpp:198] conv4 needs backward computation.
I0408 19:15:22.373409 24089 net.cpp:198] relu3 needs backward computation.
I0408 19:15:22.373411 24089 net.cpp:198] conv3 needs backward computation.
I0408 19:15:22.373415 24089 net.cpp:198] pool2 needs backward computation.
I0408 19:15:22.373418 24089 net.cpp:198] norm2 needs backward computation.
I0408 19:15:22.373422 24089 net.cpp:198] relu2 needs backward computation.
I0408 19:15:22.373425 24089 net.cpp:198] conv2 needs backward computation.
I0408 19:15:22.373430 24089 net.cpp:198] pool1 needs backward computation.
I0408 19:15:22.373433 24089 net.cpp:198] norm1 needs backward computation.
I0408 19:15:22.373436 24089 net.cpp:198] relu1 needs backward computation.
I0408 19:15:22.373440 24089 net.cpp:198] conv1 needs backward computation.
I0408 19:15:22.373445 24089 net.cpp:200] train-data does not need backward computation.
I0408 19:15:22.373447 24089 net.cpp:242] This network produces output loss
I0408 19:15:22.373462 24089 net.cpp:255] Network initialization done.
I0408 19:15:22.373988 24089 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0408 19:15:22.374019 24089 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0408 19:15:22.374163 24089 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 19:15:22.374260 24089 layer_factory.hpp:77] Creating layer val-data
I0408 19:15:22.386791 24089 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0408 19:15:22.386950 24089 net.cpp:84] Creating Layer val-data
I0408 19:15:22.386960 24089 net.cpp:380] val-data -> data
I0408 19:15:22.386968 24089 net.cpp:380] val-data -> label
I0408 19:15:22.386976 24089 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0408 19:15:22.390853 24089 data_layer.cpp:45] output data size: 32,3,227,227
I0408 19:15:22.426885 24089 net.cpp:122] Setting up val-data
I0408 19:15:22.426904 24089 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0408 19:15:22.426910 24089 net.cpp:129] Top shape: 32 (32)
I0408 19:15:22.426913 24089 net.cpp:137] Memory required for data: 19787264
I0408 19:15:22.426919 24089 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0408 19:15:22.426931 24089 net.cpp:84] Creating Layer label_val-data_1_split
I0408 19:15:22.426935 24089 net.cpp:406] label_val-data_1_split <- label
I0408 19:15:22.426942 24089 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0408 19:15:22.426950 24089 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0408 19:15:22.427016 24089 net.cpp:122] Setting up label_val-data_1_split
I0408 19:15:22.427021 24089 net.cpp:129] Top shape: 32 (32)
I0408 19:15:22.427026 24089 net.cpp:129] Top shape: 32 (32)
I0408 19:15:22.427028 24089 net.cpp:137] Memory required for data: 19787520
I0408 19:15:22.427031 24089 layer_factory.hpp:77] Creating layer conv1
I0408 19:15:22.427043 24089 net.cpp:84] Creating Layer conv1
I0408 19:15:22.427047 24089 net.cpp:406] conv1 <- data
I0408 19:15:22.427052 24089 net.cpp:380] conv1 -> conv1
I0408 19:15:22.428925 24089 net.cpp:122] Setting up conv1
I0408 19:15:22.428936 24089 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 19:15:22.428939 24089 net.cpp:137] Memory required for data: 56958720
I0408 19:15:22.428949 24089 layer_factory.hpp:77] Creating layer relu1
I0408 19:15:22.428956 24089 net.cpp:84] Creating Layer relu1
I0408 19:15:22.428959 24089 net.cpp:406] relu1 <- conv1
I0408 19:15:22.428964 24089 net.cpp:367] relu1 -> conv1 (in-place)
I0408 19:15:22.429247 24089 net.cpp:122] Setting up relu1
I0408 19:15:22.429255 24089 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 19:15:22.429258 24089 net.cpp:137] Memory required for data: 94129920
I0408 19:15:22.429262 24089 layer_factory.hpp:77] Creating layer norm1
I0408 19:15:22.429270 24089 net.cpp:84] Creating Layer norm1
I0408 19:15:22.429273 24089 net.cpp:406] norm1 <- conv1
I0408 19:15:22.429278 24089 net.cpp:380] norm1 -> norm1
I0408 19:15:22.429728 24089 net.cpp:122] Setting up norm1
I0408 19:15:22.429738 24089 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 19:15:22.429740 24089 net.cpp:137] Memory required for data: 131301120
I0408 19:15:22.429744 24089 layer_factory.hpp:77] Creating layer pool1
I0408 19:15:22.429750 24089 net.cpp:84] Creating Layer pool1
I0408 19:15:22.429754 24089 net.cpp:406] pool1 <- norm1
I0408 19:15:22.429759 24089 net.cpp:380] pool1 -> pool1
I0408 19:15:22.429786 24089 net.cpp:122] Setting up pool1
I0408 19:15:22.429791 24089 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0408 19:15:22.429795 24089 net.cpp:137] Memory required for data: 140259072
I0408 19:15:22.429798 24089 layer_factory.hpp:77] Creating layer conv2
I0408 19:15:22.429806 24089 net.cpp:84] Creating Layer conv2
I0408 19:15:22.429809 24089 net.cpp:406] conv2 <- pool1
I0408 19:15:22.429834 24089 net.cpp:380] conv2 -> conv2
I0408 19:15:22.436936 24089 net.cpp:122] Setting up conv2
I0408 19:15:22.436952 24089 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 19:15:22.436956 24089 net.cpp:137] Memory required for data: 164146944
I0408 19:15:22.436965 24089 layer_factory.hpp:77] Creating layer relu2
I0408 19:15:22.436972 24089 net.cpp:84] Creating Layer relu2
I0408 19:15:22.436977 24089 net.cpp:406] relu2 <- conv2
I0408 19:15:22.436982 24089 net.cpp:367] relu2 -> conv2 (in-place)
I0408 19:15:22.437507 24089 net.cpp:122] Setting up relu2
I0408 19:15:22.437518 24089 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 19:15:22.437521 24089 net.cpp:137] Memory required for data: 188034816
I0408 19:15:22.437525 24089 layer_factory.hpp:77] Creating layer norm2
I0408 19:15:22.437534 24089 net.cpp:84] Creating Layer norm2
I0408 19:15:22.437537 24089 net.cpp:406] norm2 <- conv2
I0408 19:15:22.437544 24089 net.cpp:380] norm2 -> norm2
I0408 19:15:22.438077 24089 net.cpp:122] Setting up norm2
I0408 19:15:22.438088 24089 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 19:15:22.438091 24089 net.cpp:137] Memory required for data: 211922688
I0408 19:15:22.438097 24089 layer_factory.hpp:77] Creating layer pool2
I0408 19:15:22.438104 24089 net.cpp:84] Creating Layer pool2
I0408 19:15:22.438108 24089 net.cpp:406] pool2 <- norm2
I0408 19:15:22.438113 24089 net.cpp:380] pool2 -> pool2
I0408 19:15:22.438144 24089 net.cpp:122] Setting up pool2
I0408 19:15:22.438149 24089 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 19:15:22.438153 24089 net.cpp:137] Memory required for data: 217460480
I0408 19:15:22.438155 24089 layer_factory.hpp:77] Creating layer conv3
I0408 19:15:22.438164 24089 net.cpp:84] Creating Layer conv3
I0408 19:15:22.438169 24089 net.cpp:406] conv3 <- pool2
I0408 19:15:22.438174 24089 net.cpp:380] conv3 -> conv3
I0408 19:15:22.449136 24089 net.cpp:122] Setting up conv3
I0408 19:15:22.449154 24089 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 19:15:22.449158 24089 net.cpp:137] Memory required for data: 225767168
I0408 19:15:22.449169 24089 layer_factory.hpp:77] Creating layer relu3
I0408 19:15:22.449178 24089 net.cpp:84] Creating Layer relu3
I0408 19:15:22.449182 24089 net.cpp:406] relu3 <- conv3
I0408 19:15:22.449190 24089 net.cpp:367] relu3 -> conv3 (in-place)
I0408 19:15:22.449693 24089 net.cpp:122] Setting up relu3
I0408 19:15:22.449702 24089 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 19:15:22.449707 24089 net.cpp:137] Memory required for data: 234073856
I0408 19:15:22.449710 24089 layer_factory.hpp:77] Creating layer conv4
I0408 19:15:22.449723 24089 net.cpp:84] Creating Layer conv4
I0408 19:15:22.449726 24089 net.cpp:406] conv4 <- conv3
I0408 19:15:22.449733 24089 net.cpp:380] conv4 -> conv4
I0408 19:15:22.461220 24089 net.cpp:122] Setting up conv4
I0408 19:15:22.461236 24089 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 19:15:22.461239 24089 net.cpp:137] Memory required for data: 242380544
I0408 19:15:22.461247 24089 layer_factory.hpp:77] Creating layer relu4
I0408 19:15:22.461256 24089 net.cpp:84] Creating Layer relu4
I0408 19:15:22.461261 24089 net.cpp:406] relu4 <- conv4
I0408 19:15:22.461266 24089 net.cpp:367] relu4 -> conv4 (in-place)
I0408 19:15:22.461611 24089 net.cpp:122] Setting up relu4
I0408 19:15:22.461618 24089 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 19:15:22.461621 24089 net.cpp:137] Memory required for data: 250687232
I0408 19:15:22.461625 24089 layer_factory.hpp:77] Creating layer conv5
I0408 19:15:22.461635 24089 net.cpp:84] Creating Layer conv5
I0408 19:15:22.461639 24089 net.cpp:406] conv5 <- conv4
I0408 19:15:22.461645 24089 net.cpp:380] conv5 -> conv5
I0408 19:15:22.471190 24089 net.cpp:122] Setting up conv5
I0408 19:15:22.471206 24089 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 19:15:22.471210 24089 net.cpp:137] Memory required for data: 256225024
I0408 19:15:22.471222 24089 layer_factory.hpp:77] Creating layer relu5
I0408 19:15:22.471230 24089 net.cpp:84] Creating Layer relu5
I0408 19:15:22.471235 24089 net.cpp:406] relu5 <- conv5
I0408 19:15:22.471258 24089 net.cpp:367] relu5 -> conv5 (in-place)
I0408 19:15:22.471746 24089 net.cpp:122] Setting up relu5
I0408 19:15:22.471755 24089 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 19:15:22.471760 24089 net.cpp:137] Memory required for data: 261762816
I0408 19:15:22.471762 24089 layer_factory.hpp:77] Creating layer pool5
I0408 19:15:22.471773 24089 net.cpp:84] Creating Layer pool5
I0408 19:15:22.471777 24089 net.cpp:406] pool5 <- conv5
I0408 19:15:22.471783 24089 net.cpp:380] pool5 -> pool5
I0408 19:15:22.471820 24089 net.cpp:122] Setting up pool5
I0408 19:15:22.471827 24089 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0408 19:15:22.471829 24089 net.cpp:137] Memory required for data: 262942464
I0408 19:15:22.471832 24089 layer_factory.hpp:77] Creating layer fc6
I0408 19:15:22.471840 24089 net.cpp:84] Creating Layer fc6
I0408 19:15:22.471843 24089 net.cpp:406] fc6 <- pool5
I0408 19:15:22.471849 24089 net.cpp:380] fc6 -> fc6
I0408 19:15:22.829941 24089 net.cpp:122] Setting up fc6
I0408 19:15:22.829972 24089 net.cpp:129] Top shape: 32 4096 (131072)
I0408 19:15:22.829977 24089 net.cpp:137] Memory required for data: 263466752
I0408 19:15:22.829986 24089 layer_factory.hpp:77] Creating layer relu6
I0408 19:15:22.829995 24089 net.cpp:84] Creating Layer relu6
I0408 19:15:22.830000 24089 net.cpp:406] relu6 <- fc6
I0408 19:15:22.830008 24089 net.cpp:367] relu6 -> fc6 (in-place)
I0408 19:15:22.830838 24089 net.cpp:122] Setting up relu6
I0408 19:15:22.830848 24089 net.cpp:129] Top shape: 32 4096 (131072)
I0408 19:15:22.830852 24089 net.cpp:137] Memory required for data: 263991040
I0408 19:15:22.830857 24089 layer_factory.hpp:77] Creating layer drop6
I0408 19:15:22.830864 24089 net.cpp:84] Creating Layer drop6
I0408 19:15:22.830868 24089 net.cpp:406] drop6 <- fc6
I0408 19:15:22.830874 24089 net.cpp:367] drop6 -> fc6 (in-place)
I0408 19:15:22.830899 24089 net.cpp:122] Setting up drop6
I0408 19:15:22.830904 24089 net.cpp:129] Top shape: 32 4096 (131072)
I0408 19:15:22.830907 24089 net.cpp:137] Memory required for data: 264515328
I0408 19:15:22.830911 24089 layer_factory.hpp:77] Creating layer fc7
I0408 19:15:22.830919 24089 net.cpp:84] Creating Layer fc7
I0408 19:15:22.830921 24089 net.cpp:406] fc7 <- fc6
I0408 19:15:22.830929 24089 net.cpp:380] fc7 -> fc7
I0408 19:15:22.988240 24089 net.cpp:122] Setting up fc7
I0408 19:15:22.988262 24089 net.cpp:129] Top shape: 32 4096 (131072)
I0408 19:15:22.988266 24089 net.cpp:137] Memory required for data: 265039616
I0408 19:15:22.988274 24089 layer_factory.hpp:77] Creating layer relu7
I0408 19:15:22.988283 24089 net.cpp:84] Creating Layer relu7
I0408 19:15:22.988288 24089 net.cpp:406] relu7 <- fc7
I0408 19:15:22.988294 24089 net.cpp:367] relu7 -> fc7 (in-place)
I0408 19:15:22.988726 24089 net.cpp:122] Setting up relu7
I0408 19:15:22.988734 24089 net.cpp:129] Top shape: 32 4096 (131072)
I0408 19:15:22.988737 24089 net.cpp:137] Memory required for data: 265563904
I0408 19:15:22.988741 24089 layer_factory.hpp:77] Creating layer drop7
I0408 19:15:22.988749 24089 net.cpp:84] Creating Layer drop7
I0408 19:15:22.988751 24089 net.cpp:406] drop7 <- fc7
I0408 19:15:22.988757 24089 net.cpp:367] drop7 -> fc7 (in-place)
I0408 19:15:22.988781 24089 net.cpp:122] Setting up drop7
I0408 19:15:22.988786 24089 net.cpp:129] Top shape: 32 4096 (131072)
I0408 19:15:22.988790 24089 net.cpp:137] Memory required for data: 266088192
I0408 19:15:22.988793 24089 layer_factory.hpp:77] Creating layer fc8
I0408 19:15:22.988801 24089 net.cpp:84] Creating Layer fc8
I0408 19:15:22.988804 24089 net.cpp:406] fc8 <- fc7
I0408 19:15:22.988811 24089 net.cpp:380] fc8 -> fc8
I0408 19:15:22.996587 24089 net.cpp:122] Setting up fc8
I0408 19:15:22.996596 24089 net.cpp:129] Top shape: 32 196 (6272)
I0408 19:15:22.996599 24089 net.cpp:137] Memory required for data: 266113280
I0408 19:15:22.996605 24089 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0408 19:15:22.996613 24089 net.cpp:84] Creating Layer fc8_fc8_0_split
I0408 19:15:22.996616 24089 net.cpp:406] fc8_fc8_0_split <- fc8
I0408 19:15:22.996639 24089 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0408 19:15:22.996646 24089 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0408 19:15:22.996678 24089 net.cpp:122] Setting up fc8_fc8_0_split
I0408 19:15:22.996682 24089 net.cpp:129] Top shape: 32 196 (6272)
I0408 19:15:22.996686 24089 net.cpp:129] Top shape: 32 196 (6272)
I0408 19:15:22.996690 24089 net.cpp:137] Memory required for data: 266163456
I0408 19:15:22.996693 24089 layer_factory.hpp:77] Creating layer accuracy
I0408 19:15:22.996701 24089 net.cpp:84] Creating Layer accuracy
I0408 19:15:22.996706 24089 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0408 19:15:22.996709 24089 net.cpp:406] accuracy <- label_val-data_1_split_0
I0408 19:15:22.996714 24089 net.cpp:380] accuracy -> accuracy
I0408 19:15:22.996722 24089 net.cpp:122] Setting up accuracy
I0408 19:15:22.996726 24089 net.cpp:129] Top shape: (1)
I0408 19:15:22.996731 24089 net.cpp:137] Memory required for data: 266163460
I0408 19:15:22.996733 24089 layer_factory.hpp:77] Creating layer loss
I0408 19:15:22.996739 24089 net.cpp:84] Creating Layer loss
I0408 19:15:22.996743 24089 net.cpp:406] loss <- fc8_fc8_0_split_1
I0408 19:15:22.996747 24089 net.cpp:406] loss <- label_val-data_1_split_1
I0408 19:15:22.996752 24089 net.cpp:380] loss -> loss
I0408 19:15:22.996759 24089 layer_factory.hpp:77] Creating layer loss
I0408 19:15:22.997354 24089 net.cpp:122] Setting up loss
I0408 19:15:22.997364 24089 net.cpp:129] Top shape: (1)
I0408 19:15:22.997367 24089 net.cpp:132]     with loss weight 1
I0408 19:15:22.997376 24089 net.cpp:137] Memory required for data: 266163464
I0408 19:15:22.997380 24089 net.cpp:198] loss needs backward computation.
I0408 19:15:22.997385 24089 net.cpp:200] accuracy does not need backward computation.
I0408 19:15:22.997390 24089 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0408 19:15:22.997392 24089 net.cpp:198] fc8 needs backward computation.
I0408 19:15:22.997395 24089 net.cpp:198] drop7 needs backward computation.
I0408 19:15:22.997400 24089 net.cpp:198] relu7 needs backward computation.
I0408 19:15:22.997403 24089 net.cpp:198] fc7 needs backward computation.
I0408 19:15:22.997407 24089 net.cpp:198] drop6 needs backward computation.
I0408 19:15:22.997411 24089 net.cpp:198] relu6 needs backward computation.
I0408 19:15:22.997416 24089 net.cpp:198] fc6 needs backward computation.
I0408 19:15:22.997419 24089 net.cpp:198] pool5 needs backward computation.
I0408 19:15:22.997422 24089 net.cpp:198] relu5 needs backward computation.
I0408 19:15:22.997426 24089 net.cpp:198] conv5 needs backward computation.
I0408 19:15:22.997431 24089 net.cpp:198] relu4 needs backward computation.
I0408 19:15:22.997434 24089 net.cpp:198] conv4 needs backward computation.
I0408 19:15:22.997438 24089 net.cpp:198] relu3 needs backward computation.
I0408 19:15:22.997442 24089 net.cpp:198] conv3 needs backward computation.
I0408 19:15:22.997445 24089 net.cpp:198] pool2 needs backward computation.
I0408 19:15:22.997449 24089 net.cpp:198] norm2 needs backward computation.
I0408 19:15:22.997453 24089 net.cpp:198] relu2 needs backward computation.
I0408 19:15:22.997457 24089 net.cpp:198] conv2 needs backward computation.
I0408 19:15:22.997462 24089 net.cpp:198] pool1 needs backward computation.
I0408 19:15:22.997464 24089 net.cpp:198] norm1 needs backward computation.
I0408 19:15:22.997468 24089 net.cpp:198] relu1 needs backward computation.
I0408 19:15:22.997473 24089 net.cpp:198] conv1 needs backward computation.
I0408 19:15:22.997476 24089 net.cpp:200] label_val-data_1_split does not need backward computation.
I0408 19:15:22.997480 24089 net.cpp:200] val-data does not need backward computation.
I0408 19:15:22.997483 24089 net.cpp:242] This network produces output accuracy
I0408 19:15:22.997488 24089 net.cpp:242] This network produces output loss
I0408 19:15:22.997505 24089 net.cpp:255] Network initialization done.
I0408 19:15:22.997582 24089 solver.cpp:56] Solver scaffolding done.
I0408 19:15:22.998026 24089 caffe.cpp:248] Starting Optimization
I0408 19:15:22.998035 24089 solver.cpp:272] Solving
I0408 19:15:22.998047 24089 solver.cpp:273] Learning Rate Policy: exp
I0408 19:15:22.999326 24089 solver.cpp:330] Iteration 0, Testing net (#0)
I0408 19:15:22.999336 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:15:23.088994 24089 blocking_queue.cpp:49] Waiting for data
I0408 19:15:27.756264 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:15:27.801216 24089 solver.cpp:397]     Test net output #0: accuracy = 0.00490196
I0408 19:15:27.801265 24089 solver.cpp:397]     Test net output #1: loss = 5.27881 (* 1 = 5.27881 loss)
I0408 19:15:27.897914 24089 solver.cpp:218] Iteration 0 (-4.97146e-30 iter/s, 4.89964s/12 iters), loss = 5.28028
I0408 19:15:27.899425 24089 solver.cpp:237]     Train net output #0: loss = 5.28028 (* 1 = 5.28028 loss)
I0408 19:15:27.899446 24089 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0408 19:15:31.787674 24089 solver.cpp:218] Iteration 12 (3.08635 iter/s, 3.88808s/12 iters), loss = 5.26641
I0408 19:15:31.787722 24089 solver.cpp:237]     Train net output #0: loss = 5.26641 (* 1 = 5.26641 loss)
I0408 19:15:31.787735 24089 sgd_solver.cpp:105] Iteration 12, lr = 0.0099087
I0408 19:15:36.785838 24089 solver.cpp:218] Iteration 24 (2.401 iter/s, 4.99792s/12 iters), loss = 5.28024
I0408 19:15:36.785888 24089 solver.cpp:237]     Train net output #0: loss = 5.28024 (* 1 = 5.28024 loss)
I0408 19:15:36.785900 24089 sgd_solver.cpp:105] Iteration 24, lr = 0.00981824
I0408 19:15:41.696559 24089 solver.cpp:218] Iteration 36 (2.44376 iter/s, 4.91047s/12 iters), loss = 5.31257
I0408 19:15:41.696619 24089 solver.cpp:237]     Train net output #0: loss = 5.31257 (* 1 = 5.31257 loss)
I0408 19:15:41.696632 24089 sgd_solver.cpp:105] Iteration 36, lr = 0.0097286
I0408 19:15:46.878265 24089 solver.cpp:218] Iteration 48 (2.31595 iter/s, 5.18145s/12 iters), loss = 5.30435
I0408 19:15:46.878314 24089 solver.cpp:237]     Train net output #0: loss = 5.30435 (* 1 = 5.30435 loss)
I0408 19:15:46.878325 24089 sgd_solver.cpp:105] Iteration 48, lr = 0.00963978
I0408 19:15:52.151235 24089 solver.cpp:218] Iteration 60 (2.27587 iter/s, 5.27271s/12 iters), loss = 5.28648
I0408 19:15:52.151432 24089 solver.cpp:237]     Train net output #0: loss = 5.28648 (* 1 = 5.28648 loss)
I0408 19:15:52.151441 24089 sgd_solver.cpp:105] Iteration 60, lr = 0.00955177
I0408 19:15:57.197569 24089 solver.cpp:218] Iteration 72 (2.37815 iter/s, 5.04593s/12 iters), loss = 5.29779
I0408 19:15:57.197611 24089 solver.cpp:237]     Train net output #0: loss = 5.29779 (* 1 = 5.29779 loss)
I0408 19:15:57.197620 24089 sgd_solver.cpp:105] Iteration 72, lr = 0.00946457
I0408 19:16:02.175309 24089 solver.cpp:218] Iteration 84 (2.41085 iter/s, 4.97749s/12 iters), loss = 5.28472
I0408 19:16:02.175360 24089 solver.cpp:237]     Train net output #0: loss = 5.28472 (* 1 = 5.28472 loss)
I0408 19:16:02.175371 24089 sgd_solver.cpp:105] Iteration 84, lr = 0.00937816
I0408 19:16:07.143632 24089 solver.cpp:218] Iteration 96 (2.41542 iter/s, 4.96807s/12 iters), loss = 5.31077
I0408 19:16:07.143680 24089 solver.cpp:237]     Train net output #0: loss = 5.31077 (* 1 = 5.31077 loss)
I0408 19:16:07.143692 24089 sgd_solver.cpp:105] Iteration 96, lr = 0.00929254
I0408 19:16:08.842464 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:16:09.154413 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0408 19:16:15.724547 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0408 19:16:21.561511 24089 solver.cpp:330] Iteration 102, Testing net (#0)
I0408 19:16:21.561537 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:16:26.037396 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:16:26.114864 24089 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 19:16:26.114909 24089 solver.cpp:397]     Test net output #1: loss = 5.28721 (* 1 = 5.28721 loss)
I0408 19:16:28.082687 24089 solver.cpp:218] Iteration 108 (0.573115 iter/s, 20.9382s/12 iters), loss = 5.30223
I0408 19:16:28.082731 24089 solver.cpp:237]     Train net output #0: loss = 5.30223 (* 1 = 5.30223 loss)
I0408 19:16:28.082738 24089 sgd_solver.cpp:105] Iteration 108, lr = 0.0092077
I0408 19:16:33.110464 24089 solver.cpp:218] Iteration 120 (2.38686 iter/s, 5.02753s/12 iters), loss = 5.26665
I0408 19:16:33.110514 24089 solver.cpp:237]     Train net output #0: loss = 5.26665 (* 1 = 5.26665 loss)
I0408 19:16:33.110525 24089 sgd_solver.cpp:105] Iteration 120, lr = 0.00912364
I0408 19:16:38.258983 24089 solver.cpp:218] Iteration 132 (2.33089 iter/s, 5.14825s/12 iters), loss = 5.22995
I0408 19:16:38.259042 24089 solver.cpp:237]     Train net output #0: loss = 5.22995 (* 1 = 5.22995 loss)
I0408 19:16:38.259052 24089 sgd_solver.cpp:105] Iteration 132, lr = 0.00904034
I0408 19:16:43.314463 24089 solver.cpp:218] Iteration 144 (2.37379 iter/s, 5.05521s/12 iters), loss = 5.27497
I0408 19:16:43.314518 24089 solver.cpp:237]     Train net output #0: loss = 5.27497 (* 1 = 5.27497 loss)
I0408 19:16:43.314530 24089 sgd_solver.cpp:105] Iteration 144, lr = 0.00895781
I0408 19:16:48.432961 24089 solver.cpp:218] Iteration 156 (2.34456 iter/s, 5.11823s/12 iters), loss = 5.22623
I0408 19:16:48.433012 24089 solver.cpp:237]     Train net output #0: loss = 5.22623 (* 1 = 5.22623 loss)
I0408 19:16:48.433023 24089 sgd_solver.cpp:105] Iteration 156, lr = 0.00887602
I0408 19:16:53.613338 24089 solver.cpp:218] Iteration 168 (2.31655 iter/s, 5.18011s/12 iters), loss = 5.19044
I0408 19:16:53.613391 24089 solver.cpp:237]     Train net output #0: loss = 5.19044 (* 1 = 5.19044 loss)
I0408 19:16:53.613402 24089 sgd_solver.cpp:105] Iteration 168, lr = 0.00879499
I0408 19:16:58.811728 24089 solver.cpp:218] Iteration 180 (2.30853 iter/s, 5.19812s/12 iters), loss = 5.16412
I0408 19:16:58.811856 24089 solver.cpp:237]     Train net output #0: loss = 5.16412 (* 1 = 5.16412 loss)
I0408 19:16:58.811869 24089 sgd_solver.cpp:105] Iteration 180, lr = 0.00871469
I0408 19:17:04.096653 24089 solver.cpp:218] Iteration 192 (2.27076 iter/s, 5.28458s/12 iters), loss = 5.23211
I0408 19:17:04.096707 24089 solver.cpp:237]     Train net output #0: loss = 5.23211 (* 1 = 5.23211 loss)
I0408 19:17:04.096720 24089 sgd_solver.cpp:105] Iteration 192, lr = 0.00863513
I0408 19:17:07.953171 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:17:08.642107 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0408 19:17:12.516311 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0408 19:17:17.897114 24089 solver.cpp:330] Iteration 204, Testing net (#0)
I0408 19:17:17.897141 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:17:22.291452 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:17:22.416400 24089 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0408 19:17:22.416450 24089 solver.cpp:397]     Test net output #1: loss = 5.18219 (* 1 = 5.18219 loss)
I0408 19:17:22.504179 24089 solver.cpp:218] Iteration 204 (0.651935 iter/s, 18.4068s/12 iters), loss = 5.10488
I0408 19:17:22.504227 24089 solver.cpp:237]     Train net output #0: loss = 5.10488 (* 1 = 5.10488 loss)
I0408 19:17:22.504238 24089 sgd_solver.cpp:105] Iteration 204, lr = 0.00855629
I0408 19:17:26.887135 24089 solver.cpp:218] Iteration 216 (2.73802 iter/s, 4.38273s/12 iters), loss = 5.16266
I0408 19:17:26.887187 24089 solver.cpp:237]     Train net output #0: loss = 5.16266 (* 1 = 5.16266 loss)
I0408 19:17:26.887198 24089 sgd_solver.cpp:105] Iteration 216, lr = 0.00847818
I0408 19:17:31.902388 24089 solver.cpp:218] Iteration 228 (2.39282 iter/s, 5.01499s/12 iters), loss = 5.19023
I0408 19:17:31.902499 24089 solver.cpp:237]     Train net output #0: loss = 5.19023 (* 1 = 5.19023 loss)
I0408 19:17:31.902511 24089 sgd_solver.cpp:105] Iteration 228, lr = 0.00840077
I0408 19:17:37.008934 24089 solver.cpp:218] Iteration 240 (2.35007 iter/s, 5.10623s/12 iters), loss = 5.21561
I0408 19:17:37.008976 24089 solver.cpp:237]     Train net output #0: loss = 5.21561 (* 1 = 5.21561 loss)
I0408 19:17:37.008985 24089 sgd_solver.cpp:105] Iteration 240, lr = 0.00832408
I0408 19:17:42.198540 24089 solver.cpp:218] Iteration 252 (2.31243 iter/s, 5.18935s/12 iters), loss = 5.12315
I0408 19:17:42.198606 24089 solver.cpp:237]     Train net output #0: loss = 5.12315 (* 1 = 5.12315 loss)
I0408 19:17:42.198619 24089 sgd_solver.cpp:105] Iteration 252, lr = 0.00824808
I0408 19:17:47.377986 24089 solver.cpp:218] Iteration 264 (2.31697 iter/s, 5.17917s/12 iters), loss = 5.25404
I0408 19:17:47.378029 24089 solver.cpp:237]     Train net output #0: loss = 5.25404 (* 1 = 5.25404 loss)
I0408 19:17:47.378039 24089 sgd_solver.cpp:105] Iteration 264, lr = 0.00817278
I0408 19:17:52.395941 24089 solver.cpp:218] Iteration 276 (2.39153 iter/s, 5.0177s/12 iters), loss = 5.19256
I0408 19:17:52.395998 24089 solver.cpp:237]     Train net output #0: loss = 5.19256 (* 1 = 5.19256 loss)
I0408 19:17:52.396009 24089 sgd_solver.cpp:105] Iteration 276, lr = 0.00809816
I0408 19:17:57.369315 24089 solver.cpp:218] Iteration 288 (2.41298 iter/s, 4.97311s/12 iters), loss = 5.01488
I0408 19:17:57.369372 24089 solver.cpp:237]     Train net output #0: loss = 5.01488 (* 1 = 5.01488 loss)
I0408 19:17:57.369385 24089 sgd_solver.cpp:105] Iteration 288, lr = 0.00802423
I0408 19:18:02.416805 24089 solver.cpp:218] Iteration 300 (2.37755 iter/s, 5.04722s/12 iters), loss = 5.15194
I0408 19:18:02.416981 24089 solver.cpp:237]     Train net output #0: loss = 5.15194 (* 1 = 5.15194 loss)
I0408 19:18:02.417001 24089 sgd_solver.cpp:105] Iteration 300, lr = 0.00795097
I0408 19:18:03.393913 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:18:04.439229 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0408 19:18:07.475421 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0408 19:18:11.096918 24089 solver.cpp:330] Iteration 306, Testing net (#0)
I0408 19:18:11.096940 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:18:15.500016 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:18:15.659584 24089 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0408 19:18:15.659629 24089 solver.cpp:397]     Test net output #1: loss = 5.12458 (* 1 = 5.12458 loss)
I0408 19:18:17.765314 24089 solver.cpp:218] Iteration 312 (0.781874 iter/s, 15.3477s/12 iters), loss = 5.07871
I0408 19:18:17.765367 24089 solver.cpp:237]     Train net output #0: loss = 5.07871 (* 1 = 5.07871 loss)
I0408 19:18:17.765378 24089 sgd_solver.cpp:105] Iteration 312, lr = 0.00787838
I0408 19:18:23.210999 24089 solver.cpp:218] Iteration 324 (2.20369 iter/s, 5.44541s/12 iters), loss = 5.14515
I0408 19:18:23.211046 24089 solver.cpp:237]     Train net output #0: loss = 5.14515 (* 1 = 5.14515 loss)
I0408 19:18:23.211057 24089 sgd_solver.cpp:105] Iteration 324, lr = 0.00780645
I0408 19:18:28.170593 24089 solver.cpp:218] Iteration 336 (2.41967 iter/s, 4.95934s/12 iters), loss = 5.08855
I0408 19:18:28.170639 24089 solver.cpp:237]     Train net output #0: loss = 5.08855 (* 1 = 5.08855 loss)
I0408 19:18:28.170650 24089 sgd_solver.cpp:105] Iteration 336, lr = 0.00773518
I0408 19:18:33.131116 24089 solver.cpp:218] Iteration 348 (2.41922 iter/s, 4.96027s/12 iters), loss = 5.0465
I0408 19:18:33.131228 24089 solver.cpp:237]     Train net output #0: loss = 5.0465 (* 1 = 5.0465 loss)
I0408 19:18:33.131240 24089 sgd_solver.cpp:105] Iteration 348, lr = 0.00766456
I0408 19:18:38.080601 24089 solver.cpp:218] Iteration 360 (2.42465 iter/s, 4.94917s/12 iters), loss = 5.12009
I0408 19:18:38.080658 24089 solver.cpp:237]     Train net output #0: loss = 5.12009 (* 1 = 5.12009 loss)
I0408 19:18:38.080672 24089 sgd_solver.cpp:105] Iteration 360, lr = 0.00759458
I0408 19:18:43.013460 24089 solver.cpp:218] Iteration 372 (2.4328 iter/s, 4.9326s/12 iters), loss = 5.07306
I0408 19:18:43.013517 24089 solver.cpp:237]     Train net output #0: loss = 5.07306 (* 1 = 5.07306 loss)
I0408 19:18:43.013530 24089 sgd_solver.cpp:105] Iteration 372, lr = 0.00752525
I0408 19:18:47.943061 24089 solver.cpp:218] Iteration 384 (2.4344 iter/s, 4.92934s/12 iters), loss = 5.05883
I0408 19:18:47.943114 24089 solver.cpp:237]     Train net output #0: loss = 5.05883 (* 1 = 5.05883 loss)
I0408 19:18:47.943126 24089 sgd_solver.cpp:105] Iteration 384, lr = 0.00745655
I0408 19:18:53.047668 24089 solver.cpp:218] Iteration 396 (2.35094 iter/s, 5.10434s/12 iters), loss = 5.04293
I0408 19:18:53.047720 24089 solver.cpp:237]     Train net output #0: loss = 5.04293 (* 1 = 5.04293 loss)
I0408 19:18:53.047732 24089 sgd_solver.cpp:105] Iteration 396, lr = 0.00738847
I0408 19:18:56.097242 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:18:57.502645 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0408 19:19:00.523654 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0408 19:19:02.862046 24089 solver.cpp:330] Iteration 408, Testing net (#0)
I0408 19:19:02.862073 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:19:07.375543 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:19:07.579335 24089 solver.cpp:397]     Test net output #0: accuracy = 0.0153186
I0408 19:19:07.579386 24089 solver.cpp:397]     Test net output #1: loss = 5.07051 (* 1 = 5.07051 loss)
I0408 19:19:07.669221 24089 solver.cpp:218] Iteration 408 (0.820741 iter/s, 14.6209s/12 iters), loss = 5.14621
I0408 19:19:07.669276 24089 solver.cpp:237]     Train net output #0: loss = 5.14621 (* 1 = 5.14621 loss)
I0408 19:19:07.669288 24089 sgd_solver.cpp:105] Iteration 408, lr = 0.00732101
I0408 19:19:11.924588 24089 solver.cpp:218] Iteration 420 (2.82012 iter/s, 4.25513s/12 iters), loss = 5.11161
I0408 19:19:11.924643 24089 solver.cpp:237]     Train net output #0: loss = 5.11161 (* 1 = 5.11161 loss)
I0408 19:19:11.924654 24089 sgd_solver.cpp:105] Iteration 420, lr = 0.00725418
I0408 19:19:16.876216 24089 solver.cpp:218] Iteration 432 (2.42358 iter/s, 4.95136s/12 iters), loss = 5.0595
I0408 19:19:16.876266 24089 solver.cpp:237]     Train net output #0: loss = 5.0595 (* 1 = 5.0595 loss)
I0408 19:19:16.876276 24089 sgd_solver.cpp:105] Iteration 432, lr = 0.00718795
I0408 19:19:21.869189 24089 solver.cpp:218] Iteration 444 (2.4035 iter/s, 4.99271s/12 iters), loss = 4.97852
I0408 19:19:21.869237 24089 solver.cpp:237]     Train net output #0: loss = 4.97852 (* 1 = 4.97852 loss)
I0408 19:19:21.869248 24089 sgd_solver.cpp:105] Iteration 444, lr = 0.00712232
I0408 19:19:27.107789 24089 solver.cpp:218] Iteration 456 (2.2908 iter/s, 5.23834s/12 iters), loss = 5.06481
I0408 19:19:27.107841 24089 solver.cpp:237]     Train net output #0: loss = 5.06481 (* 1 = 5.06481 loss)
I0408 19:19:27.107853 24089 sgd_solver.cpp:105] Iteration 456, lr = 0.0070573
I0408 19:19:32.114553 24089 solver.cpp:218] Iteration 468 (2.39688 iter/s, 5.00651s/12 iters), loss = 5.08812
I0408 19:19:32.114593 24089 solver.cpp:237]     Train net output #0: loss = 5.08812 (* 1 = 5.08812 loss)
I0408 19:19:32.114601 24089 sgd_solver.cpp:105] Iteration 468, lr = 0.00699287
I0408 19:19:37.164113 24089 solver.cpp:218] Iteration 480 (2.37656 iter/s, 5.04931s/12 iters), loss = 4.98205
I0408 19:19:37.164172 24089 solver.cpp:237]     Train net output #0: loss = 4.98205 (* 1 = 4.98205 loss)
I0408 19:19:37.164186 24089 sgd_solver.cpp:105] Iteration 480, lr = 0.00692902
I0408 19:19:42.249579 24089 solver.cpp:218] Iteration 492 (2.35979 iter/s, 5.0852s/12 iters), loss = 5.05449
I0408 19:19:42.250254 24089 solver.cpp:237]     Train net output #0: loss = 5.05449 (* 1 = 5.05449 loss)
I0408 19:19:42.250267 24089 sgd_solver.cpp:105] Iteration 492, lr = 0.00686576
I0408 19:19:47.672920 24089 solver.cpp:218] Iteration 504 (2.21302 iter/s, 5.42245s/12 iters), loss = 5.07559
I0408 19:19:47.672974 24089 solver.cpp:237]     Train net output #0: loss = 5.07559 (* 1 = 5.07559 loss)
I0408 19:19:47.672986 24089 sgd_solver.cpp:105] Iteration 504, lr = 0.00680308
I0408 19:19:47.935786 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:19:49.713300 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0408 19:19:53.528614 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0408 19:19:55.857873 24089 solver.cpp:330] Iteration 510, Testing net (#0)
I0408 19:19:55.857899 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:20:00.145318 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:20:00.386670 24089 solver.cpp:397]     Test net output #0: accuracy = 0.0196078
I0408 19:20:00.386723 24089 solver.cpp:397]     Test net output #1: loss = 5.00933 (* 1 = 5.00933 loss)
I0408 19:20:02.080890 24089 solver.cpp:218] Iteration 516 (0.832908 iter/s, 14.4074s/12 iters), loss = 4.95652
I0408 19:20:02.080946 24089 solver.cpp:237]     Train net output #0: loss = 4.95652 (* 1 = 4.95652 loss)
I0408 19:20:02.080960 24089 sgd_solver.cpp:105] Iteration 516, lr = 0.00674097
I0408 19:20:07.127363 24089 solver.cpp:218] Iteration 528 (2.37802 iter/s, 5.04621s/12 iters), loss = 5.04848
I0408 19:20:07.127414 24089 solver.cpp:237]     Train net output #0: loss = 5.04848 (* 1 = 5.04848 loss)
I0408 19:20:07.127426 24089 sgd_solver.cpp:105] Iteration 528, lr = 0.00667943
I0408 19:20:12.361416 24089 solver.cpp:218] Iteration 540 (2.29279 iter/s, 5.23379s/12 iters), loss = 4.90376
I0408 19:20:12.361563 24089 solver.cpp:237]     Train net output #0: loss = 4.90376 (* 1 = 4.90376 loss)
I0408 19:20:12.361577 24089 sgd_solver.cpp:105] Iteration 540, lr = 0.00661845
I0408 19:20:17.561383 24089 solver.cpp:218] Iteration 552 (2.30787 iter/s, 5.19961s/12 iters), loss = 5.02003
I0408 19:20:17.561435 24089 solver.cpp:237]     Train net output #0: loss = 5.02003 (* 1 = 5.02003 loss)
I0408 19:20:17.561448 24089 sgd_solver.cpp:105] Iteration 552, lr = 0.00655802
I0408 19:20:22.610164 24089 solver.cpp:218] Iteration 564 (2.37693 iter/s, 5.04853s/12 iters), loss = 4.96203
I0408 19:20:22.610205 24089 solver.cpp:237]     Train net output #0: loss = 4.96203 (* 1 = 4.96203 loss)
I0408 19:20:22.610214 24089 sgd_solver.cpp:105] Iteration 564, lr = 0.00649815
I0408 19:20:27.595731 24089 solver.cpp:218] Iteration 576 (2.40707 iter/s, 4.98531s/12 iters), loss = 5.01339
I0408 19:20:27.595782 24089 solver.cpp:237]     Train net output #0: loss = 5.01339 (* 1 = 5.01339 loss)
I0408 19:20:27.595793 24089 sgd_solver.cpp:105] Iteration 576, lr = 0.00643882
I0408 19:20:32.582680 24089 solver.cpp:218] Iteration 588 (2.4064 iter/s, 4.98669s/12 iters), loss = 4.86009
I0408 19:20:32.582731 24089 solver.cpp:237]     Train net output #0: loss = 4.86009 (* 1 = 4.86009 loss)
I0408 19:20:32.582744 24089 sgd_solver.cpp:105] Iteration 588, lr = 0.00638004
I0408 19:20:37.603024 24089 solver.cpp:218] Iteration 600 (2.3904 iter/s, 5.02009s/12 iters), loss = 4.95672
I0408 19:20:37.603063 24089 solver.cpp:237]     Train net output #0: loss = 4.95672 (* 1 = 4.95672 loss)
I0408 19:20:37.603072 24089 sgd_solver.cpp:105] Iteration 600, lr = 0.00632179
I0408 19:20:39.987052 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:20:42.143146 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0408 19:20:46.044268 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0408 19:20:48.380628 24089 solver.cpp:330] Iteration 612, Testing net (#0)
I0408 19:20:48.380653 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:20:52.770438 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:20:53.055976 24089 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0408 19:20:53.056026 24089 solver.cpp:397]     Test net output #1: loss = 4.94694 (* 1 = 4.94694 loss)
I0408 19:20:53.146381 24089 solver.cpp:218] Iteration 612 (0.772066 iter/s, 15.5427s/12 iters), loss = 4.95521
I0408 19:20:53.146422 24089 solver.cpp:237]     Train net output #0: loss = 4.95521 (* 1 = 4.95521 loss)
I0408 19:20:53.146431 24089 sgd_solver.cpp:105] Iteration 612, lr = 0.00626407
I0408 19:20:57.493937 24089 solver.cpp:218] Iteration 624 (2.76032 iter/s, 4.34732s/12 iters), loss = 4.82734
I0408 19:20:57.494022 24089 solver.cpp:237]     Train net output #0: loss = 4.82734 (* 1 = 4.82734 loss)
I0408 19:20:57.494037 24089 sgd_solver.cpp:105] Iteration 624, lr = 0.00620688
I0408 19:21:02.586923 24089 solver.cpp:218] Iteration 636 (2.35632 iter/s, 5.09269s/12 iters), loss = 4.80673
I0408 19:21:02.586977 24089 solver.cpp:237]     Train net output #0: loss = 4.80673 (* 1 = 4.80673 loss)
I0408 19:21:02.586988 24089 sgd_solver.cpp:105] Iteration 636, lr = 0.00615022
I0408 19:21:07.590579 24089 solver.cpp:218] Iteration 648 (2.39837 iter/s, 5.0034s/12 iters), loss = 5.05186
I0408 19:21:07.590633 24089 solver.cpp:237]     Train net output #0: loss = 5.05186 (* 1 = 5.05186 loss)
I0408 19:21:07.590646 24089 sgd_solver.cpp:105] Iteration 648, lr = 0.00609407
I0408 19:21:12.648303 24089 solver.cpp:218] Iteration 660 (2.37273 iter/s, 5.05746s/12 iters), loss = 4.91693
I0408 19:21:12.648352 24089 solver.cpp:237]     Train net output #0: loss = 4.91693 (* 1 = 4.91693 loss)
I0408 19:21:12.648365 24089 sgd_solver.cpp:105] Iteration 660, lr = 0.00603843
I0408 19:21:17.884096 24089 solver.cpp:218] Iteration 672 (2.29203 iter/s, 5.23553s/12 iters), loss = 4.83121
I0408 19:21:17.884263 24089 solver.cpp:237]     Train net output #0: loss = 4.83121 (* 1 = 4.83121 loss)
I0408 19:21:17.884277 24089 sgd_solver.cpp:105] Iteration 672, lr = 0.0059833
I0408 19:21:22.853709 24089 solver.cpp:218] Iteration 684 (2.41485 iter/s, 4.96924s/12 iters), loss = 4.71762
I0408 19:21:22.853760 24089 solver.cpp:237]     Train net output #0: loss = 4.71762 (* 1 = 4.71762 loss)
I0408 19:21:22.853772 24089 sgd_solver.cpp:105] Iteration 684, lr = 0.00592868
I0408 19:21:23.640645 24089 blocking_queue.cpp:49] Waiting for data
I0408 19:21:27.928488 24089 solver.cpp:218] Iteration 696 (2.36476 iter/s, 5.07452s/12 iters), loss = 4.84972
I0408 19:21:27.928540 24089 solver.cpp:237]     Train net output #0: loss = 4.84972 (* 1 = 4.84972 loss)
I0408 19:21:27.928553 24089 sgd_solver.cpp:105] Iteration 696, lr = 0.00587455
I0408 19:21:32.629107 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:21:33.011502 24089 solver.cpp:218] Iteration 708 (2.36093 iter/s, 5.08275s/12 iters), loss = 5.01574
I0408 19:21:33.011555 24089 solver.cpp:237]     Train net output #0: loss = 5.01574 (* 1 = 5.01574 loss)
I0408 19:21:33.011567 24089 sgd_solver.cpp:105] Iteration 708, lr = 0.00582092
I0408 19:21:35.170742 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0408 19:21:38.178529 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0408 19:21:40.485639 24089 solver.cpp:330] Iteration 714, Testing net (#0)
I0408 19:21:40.485661 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:21:44.684979 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:21:45.019920 24089 solver.cpp:397]     Test net output #0: accuracy = 0.0330882
I0408 19:21:45.019961 24089 solver.cpp:397]     Test net output #1: loss = 4.89845 (* 1 = 4.89845 loss)
I0408 19:21:46.847424 24089 solver.cpp:218] Iteration 720 (0.867345 iter/s, 13.8353s/12 iters), loss = 5.04603
I0408 19:21:46.847476 24089 solver.cpp:237]     Train net output #0: loss = 5.04603 (* 1 = 5.04603 loss)
I0408 19:21:46.847487 24089 sgd_solver.cpp:105] Iteration 720, lr = 0.00576777
I0408 19:21:51.945708 24089 solver.cpp:218] Iteration 732 (2.35385 iter/s, 5.09802s/12 iters), loss = 4.67162
I0408 19:21:51.945785 24089 solver.cpp:237]     Train net output #0: loss = 4.67162 (* 1 = 4.67162 loss)
I0408 19:21:51.945797 24089 sgd_solver.cpp:105] Iteration 732, lr = 0.00571511
I0408 19:21:56.964843 24089 solver.cpp:218] Iteration 744 (2.39099 iter/s, 5.01885s/12 iters), loss = 4.91671
I0408 19:21:56.964891 24089 solver.cpp:237]     Train net output #0: loss = 4.91671 (* 1 = 4.91671 loss)
I0408 19:21:56.964903 24089 sgd_solver.cpp:105] Iteration 744, lr = 0.00566294
I0408 19:22:01.983958 24089 solver.cpp:218] Iteration 756 (2.39098 iter/s, 5.01886s/12 iters), loss = 4.92781
I0408 19:22:01.984012 24089 solver.cpp:237]     Train net output #0: loss = 4.92781 (* 1 = 4.92781 loss)
I0408 19:22:01.984025 24089 sgd_solver.cpp:105] Iteration 756, lr = 0.00561124
I0408 19:22:06.972288 24089 solver.cpp:218] Iteration 768 (2.40574 iter/s, 4.98807s/12 iters), loss = 4.82052
I0408 19:22:06.972338 24089 solver.cpp:237]     Train net output #0: loss = 4.82052 (* 1 = 4.82052 loss)
I0408 19:22:06.972352 24089 sgd_solver.cpp:105] Iteration 768, lr = 0.00556001
I0408 19:22:12.020188 24089 solver.cpp:218] Iteration 780 (2.37735 iter/s, 5.04765s/12 iters), loss = 4.91372
I0408 19:22:12.020229 24089 solver.cpp:237]     Train net output #0: loss = 4.91372 (* 1 = 4.91372 loss)
I0408 19:22:12.020241 24089 sgd_solver.cpp:105] Iteration 780, lr = 0.00550925
I0408 19:22:17.013586 24089 solver.cpp:218] Iteration 792 (2.40329 iter/s, 4.99315s/12 iters), loss = 4.71581
I0408 19:22:17.013641 24089 solver.cpp:237]     Train net output #0: loss = 4.71581 (* 1 = 4.71581 loss)
I0408 19:22:17.013653 24089 sgd_solver.cpp:105] Iteration 792, lr = 0.00545895
I0408 19:22:22.011118 24089 solver.cpp:218] Iteration 804 (2.40131 iter/s, 4.99728s/12 iters), loss = 4.8382
I0408 19:22:22.011250 24089 solver.cpp:237]     Train net output #0: loss = 4.8382 (* 1 = 4.8382 loss)
I0408 19:22:22.011262 24089 sgd_solver.cpp:105] Iteration 804, lr = 0.00540911
I0408 19:22:23.792789 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:22:26.611066 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0408 19:22:29.693037 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0408 19:22:32.036424 24089 solver.cpp:330] Iteration 816, Testing net (#0)
I0408 19:22:32.036444 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:22:36.152765 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:22:36.509436 24089 solver.cpp:397]     Test net output #0: accuracy = 0.0373775
I0408 19:22:36.509474 24089 solver.cpp:397]     Test net output #1: loss = 4.83871 (* 1 = 4.83871 loss)
I0408 19:22:36.599792 24089 solver.cpp:218] Iteration 816 (0.822596 iter/s, 14.588s/12 iters), loss = 4.9669
I0408 19:22:36.599844 24089 solver.cpp:237]     Train net output #0: loss = 4.9669 (* 1 = 4.9669 loss)
I0408 19:22:36.599855 24089 sgd_solver.cpp:105] Iteration 816, lr = 0.00535973
I0408 19:22:40.936324 24089 solver.cpp:218] Iteration 828 (2.76734 iter/s, 4.3363s/12 iters), loss = 4.85917
I0408 19:22:40.936374 24089 solver.cpp:237]     Train net output #0: loss = 4.85917 (* 1 = 4.85917 loss)
I0408 19:22:40.936384 24089 sgd_solver.cpp:105] Iteration 828, lr = 0.00531079
I0408 19:22:45.888515 24089 solver.cpp:218] Iteration 840 (2.42329 iter/s, 4.95194s/12 iters), loss = 4.60773
I0408 19:22:45.888561 24089 solver.cpp:237]     Train net output #0: loss = 4.60773 (* 1 = 4.60773 loss)
I0408 19:22:45.888571 24089 sgd_solver.cpp:105] Iteration 840, lr = 0.00526231
I0408 19:22:50.892634 24089 solver.cpp:218] Iteration 852 (2.39814 iter/s, 5.00387s/12 iters), loss = 4.73936
I0408 19:22:50.892674 24089 solver.cpp:237]     Train net output #0: loss = 4.73936 (* 1 = 4.73936 loss)
I0408 19:22:50.892683 24089 sgd_solver.cpp:105] Iteration 852, lr = 0.00521426
I0408 19:22:55.975689 24089 solver.cpp:218] Iteration 864 (2.3609 iter/s, 5.08281s/12 iters), loss = 4.67321
I0408 19:22:55.975791 24089 solver.cpp:237]     Train net output #0: loss = 4.67321 (* 1 = 4.67321 loss)
I0408 19:22:55.975805 24089 sgd_solver.cpp:105] Iteration 864, lr = 0.00516666
I0408 19:23:00.931532 24089 solver.cpp:218] Iteration 876 (2.42153 iter/s, 4.95554s/12 iters), loss = 4.76463
I0408 19:23:00.931581 24089 solver.cpp:237]     Train net output #0: loss = 4.76463 (* 1 = 4.76463 loss)
I0408 19:23:00.931594 24089 sgd_solver.cpp:105] Iteration 876, lr = 0.00511949
I0408 19:23:05.931704 24089 solver.cpp:218] Iteration 888 (2.40004 iter/s, 4.99992s/12 iters), loss = 4.73125
I0408 19:23:05.931756 24089 solver.cpp:237]     Train net output #0: loss = 4.73125 (* 1 = 4.73125 loss)
I0408 19:23:05.931768 24089 sgd_solver.cpp:105] Iteration 888, lr = 0.00507275
I0408 19:23:11.189821 24089 solver.cpp:218] Iteration 900 (2.2823 iter/s, 5.25786s/12 iters), loss = 4.77704
I0408 19:23:11.189860 24089 solver.cpp:237]     Train net output #0: loss = 4.77704 (* 1 = 4.77704 loss)
I0408 19:23:11.189868 24089 sgd_solver.cpp:105] Iteration 900, lr = 0.00502644
I0408 19:23:15.187570 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:23:16.409222 24089 solver.cpp:218] Iteration 912 (2.29922 iter/s, 5.21915s/12 iters), loss = 4.49605
I0408 19:23:16.409262 24089 solver.cpp:237]     Train net output #0: loss = 4.49605 (* 1 = 4.49605 loss)
I0408 19:23:16.409271 24089 sgd_solver.cpp:105] Iteration 912, lr = 0.00498055
I0408 19:23:18.666958 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0408 19:23:21.980970 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0408 19:23:25.719521 24089 solver.cpp:330] Iteration 918, Testing net (#0)
I0408 19:23:25.719544 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:23:29.692593 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:23:30.093892 24089 solver.cpp:397]     Test net output #0: accuracy = 0.0453431
I0408 19:23:30.093942 24089 solver.cpp:397]     Test net output #1: loss = 4.71854 (* 1 = 4.71854 loss)
I0408 19:23:32.068384 24089 solver.cpp:218] Iteration 924 (0.766357 iter/s, 15.6585s/12 iters), loss = 4.7505
I0408 19:23:32.068440 24089 solver.cpp:237]     Train net output #0: loss = 4.7505 (* 1 = 4.7505 loss)
I0408 19:23:32.068452 24089 sgd_solver.cpp:105] Iteration 924, lr = 0.00493508
I0408 19:23:37.213336 24089 solver.cpp:218] Iteration 936 (2.3325 iter/s, 5.14469s/12 iters), loss = 4.72084
I0408 19:23:37.213387 24089 solver.cpp:237]     Train net output #0: loss = 4.72084 (* 1 = 4.72084 loss)
I0408 19:23:37.213399 24089 sgd_solver.cpp:105] Iteration 936, lr = 0.00489002
I0408 19:23:42.262279 24089 solver.cpp:218] Iteration 948 (2.37686 iter/s, 5.04869s/12 iters), loss = 4.60719
I0408 19:23:42.262322 24089 solver.cpp:237]     Train net output #0: loss = 4.60719 (* 1 = 4.60719 loss)
I0408 19:23:42.262332 24089 sgd_solver.cpp:105] Iteration 948, lr = 0.00484537
I0408 19:23:47.328292 24089 solver.cpp:218] Iteration 960 (2.36884 iter/s, 5.06576s/12 iters), loss = 4.4955
I0408 19:23:47.328338 24089 solver.cpp:237]     Train net output #0: loss = 4.4955 (* 1 = 4.4955 loss)
I0408 19:23:47.328351 24089 sgd_solver.cpp:105] Iteration 960, lr = 0.00480114
I0408 19:23:52.516429 24089 solver.cpp:218] Iteration 972 (2.31309 iter/s, 5.18788s/12 iters), loss = 4.60655
I0408 19:23:52.516484 24089 solver.cpp:237]     Train net output #0: loss = 4.60655 (* 1 = 4.60655 loss)
I0408 19:23:52.516499 24089 sgd_solver.cpp:105] Iteration 972, lr = 0.0047573
I0408 19:23:57.772294 24089 solver.cpp:218] Iteration 984 (2.28328 iter/s, 5.25559s/12 iters), loss = 4.6091
I0408 19:23:57.772349 24089 solver.cpp:237]     Train net output #0: loss = 4.6091 (* 1 = 4.6091 loss)
I0408 19:23:57.772363 24089 sgd_solver.cpp:105] Iteration 984, lr = 0.00471387
I0408 19:24:02.785161 24089 solver.cpp:218] Iteration 996 (2.39396 iter/s, 5.01261s/12 iters), loss = 4.45021
I0408 19:24:02.785257 24089 solver.cpp:237]     Train net output #0: loss = 4.45021 (* 1 = 4.45021 loss)
I0408 19:24:02.785269 24089 sgd_solver.cpp:105] Iteration 996, lr = 0.00467084
I0408 19:24:08.150027 24089 solver.cpp:218] Iteration 1008 (2.23691 iter/s, 5.36455s/12 iters), loss = 4.60339
I0408 19:24:08.150081 24089 solver.cpp:237]     Train net output #0: loss = 4.60339 (* 1 = 4.60339 loss)
I0408 19:24:08.150094 24089 sgd_solver.cpp:105] Iteration 1008, lr = 0.00462819
I0408 19:24:09.200393 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:24:12.935498 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0408 19:24:15.916707 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0408 19:24:18.265936 24089 solver.cpp:330] Iteration 1020, Testing net (#0)
I0408 19:24:18.265975 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:24:22.256204 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:24:22.690023 24089 solver.cpp:397]     Test net output #0: accuracy = 0.0557598
I0408 19:24:22.690069 24089 solver.cpp:397]     Test net output #1: loss = 4.68301 (* 1 = 4.68301 loss)
I0408 19:24:22.780668 24089 solver.cpp:218] Iteration 1020 (0.820232 iter/s, 14.63s/12 iters), loss = 4.55897
I0408 19:24:22.780721 24089 solver.cpp:237]     Train net output #0: loss = 4.55897 (* 1 = 4.55897 loss)
I0408 19:24:22.780732 24089 sgd_solver.cpp:105] Iteration 1020, lr = 0.00458594
I0408 19:24:27.087922 24089 solver.cpp:218] Iteration 1032 (2.78615 iter/s, 4.30702s/12 iters), loss = 4.43839
I0408 19:24:27.087978 24089 solver.cpp:237]     Train net output #0: loss = 4.43839 (* 1 = 4.43839 loss)
I0408 19:24:27.087990 24089 sgd_solver.cpp:105] Iteration 1032, lr = 0.00454407
I0408 19:24:32.173647 24089 solver.cpp:218] Iteration 1044 (2.35967 iter/s, 5.08546s/12 iters), loss = 4.65555
I0408 19:24:32.173686 24089 solver.cpp:237]     Train net output #0: loss = 4.65555 (* 1 = 4.65555 loss)
I0408 19:24:32.173696 24089 sgd_solver.cpp:105] Iteration 1044, lr = 0.00450258
I0408 19:24:37.428503 24089 solver.cpp:218] Iteration 1056 (2.28371 iter/s, 5.2546s/12 iters), loss = 4.64704
I0408 19:24:37.428656 24089 solver.cpp:237]     Train net output #0: loss = 4.64704 (* 1 = 4.64704 loss)
I0408 19:24:37.428670 24089 sgd_solver.cpp:105] Iteration 1056, lr = 0.00446148
I0408 19:24:42.537652 24089 solver.cpp:218] Iteration 1068 (2.34889 iter/s, 5.10879s/12 iters), loss = 4.45738
I0408 19:24:42.537698 24089 solver.cpp:237]     Train net output #0: loss = 4.45738 (* 1 = 4.45738 loss)
I0408 19:24:42.537708 24089 sgd_solver.cpp:105] Iteration 1068, lr = 0.00442074
I0408 19:24:47.664697 24089 solver.cpp:218] Iteration 1080 (2.34065 iter/s, 5.12679s/12 iters), loss = 4.57892
I0408 19:24:47.664737 24089 solver.cpp:237]     Train net output #0: loss = 4.57892 (* 1 = 4.57892 loss)
I0408 19:24:47.664745 24089 sgd_solver.cpp:105] Iteration 1080, lr = 0.00438038
I0408 19:24:52.793577 24089 solver.cpp:218] Iteration 1092 (2.33981 iter/s, 5.12863s/12 iters), loss = 4.37931
I0408 19:24:52.793622 24089 solver.cpp:237]     Train net output #0: loss = 4.37931 (* 1 = 4.37931 loss)
I0408 19:24:52.793632 24089 sgd_solver.cpp:105] Iteration 1092, lr = 0.00434039
I0408 19:24:57.888206 24089 solver.cpp:218] Iteration 1104 (2.35554 iter/s, 5.09437s/12 iters), loss = 4.43338
I0408 19:24:57.888254 24089 solver.cpp:237]     Train net output #0: loss = 4.43338 (* 1 = 4.43338 loss)
I0408 19:24:57.888264 24089 sgd_solver.cpp:105] Iteration 1104, lr = 0.00430077
I0408 19:25:01.086907 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:25:02.923995 24089 solver.cpp:218] Iteration 1116 (2.38306 iter/s, 5.03554s/12 iters), loss = 4.4547
I0408 19:25:02.924033 24089 solver.cpp:237]     Train net output #0: loss = 4.4547 (* 1 = 4.4547 loss)
I0408 19:25:02.924042 24089 sgd_solver.cpp:105] Iteration 1116, lr = 0.0042615
I0408 19:25:04.996286 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0408 19:25:07.981786 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0408 19:25:11.691094 24089 solver.cpp:330] Iteration 1122, Testing net (#0)
I0408 19:25:11.691123 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:25:15.698840 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:25:16.176882 24089 solver.cpp:397]     Test net output #0: accuracy = 0.0680147
I0408 19:25:16.176934 24089 solver.cpp:397]     Test net output #1: loss = 4.5385 (* 1 = 4.5385 loss)
I0408 19:25:18.176021 24089 solver.cpp:218] Iteration 1128 (0.786814 iter/s, 15.2514s/12 iters), loss = 4.44791
I0408 19:25:18.176077 24089 solver.cpp:237]     Train net output #0: loss = 4.44791 (* 1 = 4.44791 loss)
I0408 19:25:18.176090 24089 sgd_solver.cpp:105] Iteration 1128, lr = 0.00422259
I0408 19:25:23.238443 24089 solver.cpp:218] Iteration 1140 (2.37053 iter/s, 5.06216s/12 iters), loss = 4.38601
I0408 19:25:23.238493 24089 solver.cpp:237]     Train net output #0: loss = 4.38601 (* 1 = 4.38601 loss)
I0408 19:25:23.238504 24089 sgd_solver.cpp:105] Iteration 1140, lr = 0.00418404
I0408 19:25:28.237423 24089 solver.cpp:218] Iteration 1152 (2.40061 iter/s, 4.99872s/12 iters), loss = 4.19942
I0408 19:25:28.237474 24089 solver.cpp:237]     Train net output #0: loss = 4.19942 (* 1 = 4.19942 loss)
I0408 19:25:28.237486 24089 sgd_solver.cpp:105] Iteration 1152, lr = 0.00414584
I0408 19:25:33.247443 24089 solver.cpp:218] Iteration 1164 (2.39532 iter/s, 5.00976s/12 iters), loss = 4.44176
I0408 19:25:33.247500 24089 solver.cpp:237]     Train net output #0: loss = 4.44176 (* 1 = 4.44176 loss)
I0408 19:25:33.247512 24089 sgd_solver.cpp:105] Iteration 1164, lr = 0.00410799
I0408 19:25:38.411689 24089 solver.cpp:218] Iteration 1176 (2.32379 iter/s, 5.16398s/12 iters), loss = 4.35917
I0408 19:25:38.411831 24089 solver.cpp:237]     Train net output #0: loss = 4.35917 (* 1 = 4.35917 loss)
I0408 19:25:38.411844 24089 sgd_solver.cpp:105] Iteration 1176, lr = 0.00407049
I0408 19:25:43.547552 24089 solver.cpp:218] Iteration 1188 (2.33667 iter/s, 5.13551s/12 iters), loss = 4.41114
I0408 19:25:43.547600 24089 solver.cpp:237]     Train net output #0: loss = 4.41114 (* 1 = 4.41114 loss)
I0408 19:25:43.547612 24089 sgd_solver.cpp:105] Iteration 1188, lr = 0.00403333
I0408 19:25:48.921396 24089 solver.cpp:218] Iteration 1200 (2.23315 iter/s, 5.37357s/12 iters), loss = 4.46144
I0408 19:25:48.921447 24089 solver.cpp:237]     Train net output #0: loss = 4.46144 (* 1 = 4.46144 loss)
I0408 19:25:48.921458 24089 sgd_solver.cpp:105] Iteration 1200, lr = 0.0039965
I0408 19:25:54.042119 24089 solver.cpp:218] Iteration 1212 (2.34354 iter/s, 5.12046s/12 iters), loss = 4.3064
I0408 19:25:54.042169 24089 solver.cpp:237]     Train net output #0: loss = 4.3064 (* 1 = 4.3064 loss)
I0408 19:25:54.042183 24089 sgd_solver.cpp:105] Iteration 1212, lr = 0.00396002
I0408 19:25:54.331248 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:25:58.885083 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0408 19:26:04.785014 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0408 19:26:09.198882 24089 solver.cpp:330] Iteration 1224, Testing net (#0)
I0408 19:26:09.198936 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:26:13.150615 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:26:13.662698 24089 solver.cpp:397]     Test net output #0: accuracy = 0.0698529
I0408 19:26:13.662744 24089 solver.cpp:397]     Test net output #1: loss = 4.40628 (* 1 = 4.40628 loss)
I0408 19:26:13.753587 24089 solver.cpp:218] Iteration 1224 (0.608808 iter/s, 19.7106s/12 iters), loss = 4.32411
I0408 19:26:13.753636 24089 solver.cpp:237]     Train net output #0: loss = 4.32411 (* 1 = 4.32411 loss)
I0408 19:26:13.753648 24089 sgd_solver.cpp:105] Iteration 1224, lr = 0.00392386
I0408 19:26:18.228060 24089 solver.cpp:218] Iteration 1236 (2.68202 iter/s, 4.47424s/12 iters), loss = 4.36253
I0408 19:26:18.228102 24089 solver.cpp:237]     Train net output #0: loss = 4.36253 (* 1 = 4.36253 loss)
I0408 19:26:18.228112 24089 sgd_solver.cpp:105] Iteration 1236, lr = 0.00388804
I0408 19:26:23.295727 24089 solver.cpp:218] Iteration 1248 (2.36807 iter/s, 5.06741s/12 iters), loss = 3.99462
I0408 19:26:23.295775 24089 solver.cpp:237]     Train net output #0: loss = 3.99462 (* 1 = 3.99462 loss)
I0408 19:26:23.295786 24089 sgd_solver.cpp:105] Iteration 1248, lr = 0.00385254
I0408 19:26:28.509104 24089 solver.cpp:218] Iteration 1260 (2.30189 iter/s, 5.21311s/12 iters), loss = 4.15796
I0408 19:26:28.509153 24089 solver.cpp:237]     Train net output #0: loss = 4.15796 (* 1 = 4.15796 loss)
I0408 19:26:28.509166 24089 sgd_solver.cpp:105] Iteration 1260, lr = 0.00381737
I0408 19:26:33.547585 24089 solver.cpp:218] Iteration 1272 (2.38179 iter/s, 5.03822s/12 iters), loss = 4.09477
I0408 19:26:33.547637 24089 solver.cpp:237]     Train net output #0: loss = 4.09477 (* 1 = 4.09477 loss)
I0408 19:26:33.547652 24089 sgd_solver.cpp:105] Iteration 1272, lr = 0.00378252
I0408 19:26:38.566187 24089 solver.cpp:218] Iteration 1284 (2.39123 iter/s, 5.01834s/12 iters), loss = 4.22349
I0408 19:26:38.566249 24089 solver.cpp:237]     Train net output #0: loss = 4.22349 (* 1 = 4.22349 loss)
I0408 19:26:38.566262 24089 sgd_solver.cpp:105] Iteration 1284, lr = 0.00374798
I0408 19:26:43.646934 24089 solver.cpp:218] Iteration 1296 (2.36198 iter/s, 5.08048s/12 iters), loss = 4.07766
I0408 19:26:43.647068 24089 solver.cpp:237]     Train net output #0: loss = 4.07766 (* 1 = 4.07766 loss)
I0408 19:26:43.647079 24089 sgd_solver.cpp:105] Iteration 1296, lr = 0.00371377
I0408 19:26:48.756096 24089 solver.cpp:218] Iteration 1308 (2.34888 iter/s, 5.10882s/12 iters), loss = 4.18603
I0408 19:26:48.756145 24089 solver.cpp:237]     Train net output #0: loss = 4.18603 (* 1 = 4.18603 loss)
I0408 19:26:48.756157 24089 sgd_solver.cpp:105] Iteration 1308, lr = 0.00367986
I0408 19:26:51.269644 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:26:53.788262 24089 solver.cpp:218] Iteration 1320 (2.38478 iter/s, 5.0319s/12 iters), loss = 4.12164
I0408 19:26:53.788313 24089 solver.cpp:237]     Train net output #0: loss = 4.12164 (* 1 = 4.12164 loss)
I0408 19:26:53.788324 24089 sgd_solver.cpp:105] Iteration 1320, lr = 0.00364627
I0408 19:26:55.908713 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0408 19:27:04.912583 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0408 19:27:09.026015 24089 solver.cpp:330] Iteration 1326, Testing net (#0)
I0408 19:27:09.026036 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:27:12.882294 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:27:13.441612 24089 solver.cpp:397]     Test net output #0: accuracy = 0.0955882
I0408 19:27:13.441661 24089 solver.cpp:397]     Test net output #1: loss = 4.22232 (* 1 = 4.22232 loss)
I0408 19:27:15.201872 24089 solver.cpp:218] Iteration 1332 (0.560415 iter/s, 21.4127s/12 iters), loss = 4.03566
I0408 19:27:15.202036 24089 solver.cpp:237]     Train net output #0: loss = 4.03566 (* 1 = 4.03566 loss)
I0408 19:27:15.202049 24089 sgd_solver.cpp:105] Iteration 1332, lr = 0.00361298
I0408 19:27:20.266357 24089 solver.cpp:218] Iteration 1344 (2.36962 iter/s, 5.06411s/12 iters), loss = 3.92754
I0408 19:27:20.266413 24089 solver.cpp:237]     Train net output #0: loss = 3.92754 (* 1 = 3.92754 loss)
I0408 19:27:20.266425 24089 sgd_solver.cpp:105] Iteration 1344, lr = 0.00357999
I0408 19:27:25.370672 24089 solver.cpp:218] Iteration 1356 (2.35107 iter/s, 5.10405s/12 iters), loss = 4.15509
I0408 19:27:25.370725 24089 solver.cpp:237]     Train net output #0: loss = 4.15509 (* 1 = 4.15509 loss)
I0408 19:27:25.370738 24089 sgd_solver.cpp:105] Iteration 1356, lr = 0.00354731
I0408 19:27:30.393905 24089 solver.cpp:218] Iteration 1368 (2.38902 iter/s, 5.02297s/12 iters), loss = 4.13027
I0408 19:27:30.393977 24089 solver.cpp:237]     Train net output #0: loss = 4.13027 (* 1 = 4.13027 loss)
I0408 19:27:30.393990 24089 sgd_solver.cpp:105] Iteration 1368, lr = 0.00351492
I0408 19:27:31.597681 24089 blocking_queue.cpp:49] Waiting for data
I0408 19:27:35.426046 24089 solver.cpp:218] Iteration 1380 (2.3848 iter/s, 5.03188s/12 iters), loss = 3.90029
I0408 19:27:35.426100 24089 solver.cpp:237]     Train net output #0: loss = 3.90029 (* 1 = 3.90029 loss)
I0408 19:27:35.426111 24089 sgd_solver.cpp:105] Iteration 1380, lr = 0.00348283
I0408 19:27:40.382493 24089 solver.cpp:218] Iteration 1392 (2.42122 iter/s, 4.95619s/12 iters), loss = 4.25107
I0408 19:27:40.382548 24089 solver.cpp:237]     Train net output #0: loss = 4.25107 (* 1 = 4.25107 loss)
I0408 19:27:40.382560 24089 sgd_solver.cpp:105] Iteration 1392, lr = 0.00345103
I0408 19:27:45.593571 24089 solver.cpp:218] Iteration 1404 (2.30291 iter/s, 5.21081s/12 iters), loss = 3.99142
I0408 19:27:45.602041 24089 solver.cpp:237]     Train net output #0: loss = 3.99142 (* 1 = 3.99142 loss)
I0408 19:27:45.602056 24089 sgd_solver.cpp:105] Iteration 1404, lr = 0.00341953
I0408 19:27:50.389524 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:27:50.772974 24089 solver.cpp:218] Iteration 1416 (2.32076 iter/s, 5.17073s/12 iters), loss = 3.76617
I0408 19:27:50.773013 24089 solver.cpp:237]     Train net output #0: loss = 3.76617 (* 1 = 3.76617 loss)
I0408 19:27:50.773025 24089 sgd_solver.cpp:105] Iteration 1416, lr = 0.00338831
I0408 19:27:55.551465 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0408 19:28:05.099488 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0408 19:28:08.939345 24089 solver.cpp:330] Iteration 1428, Testing net (#0)
I0408 19:28:08.939370 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:28:12.811841 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:28:13.403656 24089 solver.cpp:397]     Test net output #0: accuracy = 0.106618
I0408 19:28:13.403687 24089 solver.cpp:397]     Test net output #1: loss = 4.11426 (* 1 = 4.11426 loss)
I0408 19:28:13.493889 24089 solver.cpp:218] Iteration 1428 (0.528169 iter/s, 22.72s/12 iters), loss = 3.98283
I0408 19:28:13.493930 24089 solver.cpp:237]     Train net output #0: loss = 3.98283 (* 1 = 3.98283 loss)
I0408 19:28:13.493939 24089 sgd_solver.cpp:105] Iteration 1428, lr = 0.00335737
I0408 19:28:17.787312 24089 solver.cpp:218] Iteration 1440 (2.79512 iter/s, 4.29319s/12 iters), loss = 4.13513
I0408 19:28:17.787452 24089 solver.cpp:237]     Train net output #0: loss = 4.13513 (* 1 = 4.13513 loss)
I0408 19:28:17.787467 24089 sgd_solver.cpp:105] Iteration 1440, lr = 0.00332672
I0408 19:28:22.808156 24089 solver.cpp:218] Iteration 1452 (2.3902 iter/s, 5.02049s/12 iters), loss = 4.10311
I0408 19:28:22.808214 24089 solver.cpp:237]     Train net output #0: loss = 4.10311 (* 1 = 4.10311 loss)
I0408 19:28:22.808226 24089 sgd_solver.cpp:105] Iteration 1452, lr = 0.00329635
I0408 19:28:27.856626 24089 solver.cpp:218] Iteration 1464 (2.37708 iter/s, 5.04821s/12 iters), loss = 3.85101
I0408 19:28:27.856669 24089 solver.cpp:237]     Train net output #0: loss = 3.85101 (* 1 = 3.85101 loss)
I0408 19:28:27.856679 24089 sgd_solver.cpp:105] Iteration 1464, lr = 0.00326625
I0408 19:28:33.177508 24089 solver.cpp:218] Iteration 1476 (2.25538 iter/s, 5.32062s/12 iters), loss = 4.07515
I0408 19:28:33.177557 24089 solver.cpp:237]     Train net output #0: loss = 4.07515 (* 1 = 4.07515 loss)
I0408 19:28:33.177567 24089 sgd_solver.cpp:105] Iteration 1476, lr = 0.00323643
I0408 19:28:38.495095 24089 solver.cpp:218] Iteration 1488 (2.25678 iter/s, 5.31731s/12 iters), loss = 4.00241
I0408 19:28:38.495144 24089 solver.cpp:237]     Train net output #0: loss = 4.00241 (* 1 = 4.00241 loss)
I0408 19:28:38.495154 24089 sgd_solver.cpp:105] Iteration 1488, lr = 0.00320689
I0408 19:28:43.501725 24089 solver.cpp:218] Iteration 1500 (2.39695 iter/s, 5.00637s/12 iters), loss = 3.70767
I0408 19:28:43.501768 24089 solver.cpp:237]     Train net output #0: loss = 3.70767 (* 1 = 3.70767 loss)
I0408 19:28:43.501776 24089 sgd_solver.cpp:105] Iteration 1500, lr = 0.00317761
I0408 19:28:48.520893 24089 solver.cpp:218] Iteration 1512 (2.39095 iter/s, 5.01892s/12 iters), loss = 3.84629
I0408 19:28:48.521103 24089 solver.cpp:237]     Train net output #0: loss = 3.84629 (* 1 = 3.84629 loss)
I0408 19:28:48.521122 24089 sgd_solver.cpp:105] Iteration 1512, lr = 0.0031486
I0408 19:28:50.278239 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:28:53.459111 24089 solver.cpp:218] Iteration 1524 (2.43022 iter/s, 4.93781s/12 iters), loss = 3.9745
I0408 19:28:53.459161 24089 solver.cpp:237]     Train net output #0: loss = 3.9745 (* 1 = 3.9745 loss)
I0408 19:28:53.459172 24089 sgd_solver.cpp:105] Iteration 1524, lr = 0.00311985
I0408 19:28:55.503394 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0408 19:28:59.485546 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0408 19:29:04.299137 24089 solver.cpp:330] Iteration 1530, Testing net (#0)
I0408 19:29:04.299165 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:29:08.134639 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:29:08.781476 24089 solver.cpp:397]     Test net output #0: accuracy = 0.119485
I0408 19:29:08.781518 24089 solver.cpp:397]     Test net output #1: loss = 4.07042 (* 1 = 4.07042 loss)
I0408 19:29:10.773254 24089 solver.cpp:218] Iteration 1536 (0.693104 iter/s, 17.3134s/12 iters), loss = 3.87379
I0408 19:29:10.773305 24089 solver.cpp:237]     Train net output #0: loss = 3.87379 (* 1 = 3.87379 loss)
I0408 19:29:10.773315 24089 sgd_solver.cpp:105] Iteration 1536, lr = 0.00309137
I0408 19:29:15.828408 24089 solver.cpp:218] Iteration 1548 (2.37394 iter/s, 5.05489s/12 iters), loss = 3.60978
I0408 19:29:15.828465 24089 solver.cpp:237]     Train net output #0: loss = 3.60978 (* 1 = 3.60978 loss)
I0408 19:29:15.828480 24089 sgd_solver.cpp:105] Iteration 1548, lr = 0.00306314
I0408 19:29:20.826721 24089 solver.cpp:218] Iteration 1560 (2.40094 iter/s, 4.99805s/12 iters), loss = 3.73384
I0408 19:29:20.826860 24089 solver.cpp:237]     Train net output #0: loss = 3.73384 (* 1 = 3.73384 loss)
I0408 19:29:20.826874 24089 sgd_solver.cpp:105] Iteration 1560, lr = 0.00303518
I0408 19:29:25.870122 24089 solver.cpp:218] Iteration 1572 (2.37951 iter/s, 5.04306s/12 iters), loss = 3.78377
I0408 19:29:25.870172 24089 solver.cpp:237]     Train net output #0: loss = 3.78377 (* 1 = 3.78377 loss)
I0408 19:29:25.870184 24089 sgd_solver.cpp:105] Iteration 1572, lr = 0.00300747
I0408 19:29:30.880743 24089 solver.cpp:218] Iteration 1584 (2.39504 iter/s, 5.01036s/12 iters), loss = 3.75512
I0408 19:29:30.880798 24089 solver.cpp:237]     Train net output #0: loss = 3.75512 (* 1 = 3.75512 loss)
I0408 19:29:30.880808 24089 sgd_solver.cpp:105] Iteration 1584, lr = 0.00298001
I0408 19:29:35.932237 24089 solver.cpp:218] Iteration 1596 (2.37566 iter/s, 5.05123s/12 iters), loss = 3.7745
I0408 19:29:35.932287 24089 solver.cpp:237]     Train net output #0: loss = 3.7745 (* 1 = 3.7745 loss)
I0408 19:29:35.932298 24089 sgd_solver.cpp:105] Iteration 1596, lr = 0.0029528
I0408 19:29:40.890053 24089 solver.cpp:218] Iteration 1608 (2.42055 iter/s, 4.95756s/12 iters), loss = 3.60839
I0408 19:29:40.890101 24089 solver.cpp:237]     Train net output #0: loss = 3.60839 (* 1 = 3.60839 loss)
I0408 19:29:40.890112 24089 sgd_solver.cpp:105] Iteration 1608, lr = 0.00292585
I0408 19:29:44.888973 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:29:46.070161 24089 solver.cpp:218] Iteration 1620 (2.31667 iter/s, 5.17984s/12 iters), loss = 3.68995
I0408 19:29:46.070209 24089 solver.cpp:237]     Train net output #0: loss = 3.68995 (* 1 = 3.68995 loss)
I0408 19:29:46.070221 24089 sgd_solver.cpp:105] Iteration 1620, lr = 0.00289913
I0408 19:29:51.185137 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0408 19:29:54.217519 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0408 19:29:56.547981 24089 solver.cpp:330] Iteration 1632, Testing net (#0)
I0408 19:29:56.548008 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:30:00.217686 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:30:00.890602 24089 solver.cpp:397]     Test net output #0: accuracy = 0.139093
I0408 19:30:00.890653 24089 solver.cpp:397]     Test net output #1: loss = 3.92919 (* 1 = 3.92919 loss)
I0408 19:30:00.981276 24089 solver.cpp:218] Iteration 1632 (0.804803 iter/s, 14.9105s/12 iters), loss = 3.78324
I0408 19:30:00.981326 24089 solver.cpp:237]     Train net output #0: loss = 3.78324 (* 1 = 3.78324 loss)
I0408 19:30:00.981338 24089 sgd_solver.cpp:105] Iteration 1632, lr = 0.00287267
I0408 19:30:05.410385 24089 solver.cpp:218] Iteration 1644 (2.7095 iter/s, 4.42887s/12 iters), loss = 3.71509
I0408 19:30:05.410439 24089 solver.cpp:237]     Train net output #0: loss = 3.71509 (* 1 = 3.71509 loss)
I0408 19:30:05.410451 24089 sgd_solver.cpp:105] Iteration 1644, lr = 0.00284644
I0408 19:30:10.377435 24089 solver.cpp:218] Iteration 1656 (2.41604 iter/s, 4.96679s/12 iters), loss = 3.71454
I0408 19:30:10.377475 24089 solver.cpp:237]     Train net output #0: loss = 3.71454 (* 1 = 3.71454 loss)
I0408 19:30:10.377485 24089 sgd_solver.cpp:105] Iteration 1656, lr = 0.00282045
I0408 19:30:15.428839 24089 solver.cpp:218] Iteration 1668 (2.37569 iter/s, 5.05116s/12 iters), loss = 3.33832
I0408 19:30:15.428884 24089 solver.cpp:237]     Train net output #0: loss = 3.33832 (* 1 = 3.33832 loss)
I0408 19:30:15.428892 24089 sgd_solver.cpp:105] Iteration 1668, lr = 0.0027947
I0408 19:30:20.495229 24089 solver.cpp:218] Iteration 1680 (2.36867 iter/s, 5.06613s/12 iters), loss = 3.53534
I0408 19:30:20.495281 24089 solver.cpp:237]     Train net output #0: loss = 3.53534 (* 1 = 3.53534 loss)
I0408 19:30:20.495293 24089 sgd_solver.cpp:105] Iteration 1680, lr = 0.00276919
I0408 19:30:25.497360 24089 solver.cpp:218] Iteration 1692 (2.3991 iter/s, 5.00187s/12 iters), loss = 3.78598
I0408 19:30:25.497505 24089 solver.cpp:237]     Train net output #0: loss = 3.78598 (* 1 = 3.78598 loss)
I0408 19:30:25.497519 24089 sgd_solver.cpp:105] Iteration 1692, lr = 0.00274391
I0408 19:30:30.552228 24089 solver.cpp:218] Iteration 1704 (2.37411 iter/s, 5.05452s/12 iters), loss = 3.53889
I0408 19:30:30.552279 24089 solver.cpp:237]     Train net output #0: loss = 3.53889 (* 1 = 3.53889 loss)
I0408 19:30:30.552290 24089 sgd_solver.cpp:105] Iteration 1704, lr = 0.00271885
I0408 19:30:35.741542 24089 solver.cpp:218] Iteration 1716 (2.31256 iter/s, 5.18905s/12 iters), loss = 3.59894
I0408 19:30:35.741590 24089 solver.cpp:237]     Train net output #0: loss = 3.59894 (* 1 = 3.59894 loss)
I0408 19:30:35.741601 24089 sgd_solver.cpp:105] Iteration 1716, lr = 0.00269403
I0408 19:30:36.783825 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:30:40.824985 24089 solver.cpp:218] Iteration 1728 (2.36073 iter/s, 5.08318s/12 iters), loss = 3.44404
I0408 19:30:40.825040 24089 solver.cpp:237]     Train net output #0: loss = 3.44404 (* 1 = 3.44404 loss)
I0408 19:30:40.825053 24089 sgd_solver.cpp:105] Iteration 1728, lr = 0.00266944
I0408 19:30:42.905468 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0408 19:30:45.882377 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0408 19:30:48.201429 24089 solver.cpp:330] Iteration 1734, Testing net (#0)
I0408 19:30:48.201457 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:30:51.908828 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:30:52.615005 24089 solver.cpp:397]     Test net output #0: accuracy = 0.15625
I0408 19:30:52.615052 24089 solver.cpp:397]     Test net output #1: loss = 3.79385 (* 1 = 3.79385 loss)
I0408 19:30:54.506533 24089 solver.cpp:218] Iteration 1740 (0.877132 iter/s, 13.681s/12 iters), loss = 3.72068
I0408 19:30:54.506580 24089 solver.cpp:237]     Train net output #0: loss = 3.72068 (* 1 = 3.72068 loss)
I0408 19:30:54.506590 24089 sgd_solver.cpp:105] Iteration 1740, lr = 0.00264506
I0408 19:30:59.883070 24089 solver.cpp:218] Iteration 1752 (2.23203 iter/s, 5.37627s/12 iters), loss = 3.52715
I0408 19:30:59.883167 24089 solver.cpp:237]     Train net output #0: loss = 3.52715 (* 1 = 3.52715 loss)
I0408 19:30:59.883177 24089 sgd_solver.cpp:105] Iteration 1752, lr = 0.00262092
I0408 19:31:05.000717 24089 solver.cpp:218] Iteration 1764 (2.34497 iter/s, 5.11734s/12 iters), loss = 3.64149
I0408 19:31:05.000774 24089 solver.cpp:237]     Train net output #0: loss = 3.64149 (* 1 = 3.64149 loss)
I0408 19:31:05.000787 24089 sgd_solver.cpp:105] Iteration 1764, lr = 0.00259699
I0408 19:31:10.052564 24089 solver.cpp:218] Iteration 1776 (2.37549 iter/s, 5.05158s/12 iters), loss = 3.60433
I0408 19:31:10.052614 24089 solver.cpp:237]     Train net output #0: loss = 3.60433 (* 1 = 3.60433 loss)
I0408 19:31:10.052625 24089 sgd_solver.cpp:105] Iteration 1776, lr = 0.00257328
I0408 19:31:15.124085 24089 solver.cpp:218] Iteration 1788 (2.36627 iter/s, 5.07127s/12 iters), loss = 3.48771
I0408 19:31:15.124126 24089 solver.cpp:237]     Train net output #0: loss = 3.48771 (* 1 = 3.48771 loss)
I0408 19:31:15.124135 24089 sgd_solver.cpp:105] Iteration 1788, lr = 0.00254978
I0408 19:31:20.114773 24089 solver.cpp:218] Iteration 1800 (2.4046 iter/s, 4.99044s/12 iters), loss = 3.42105
I0408 19:31:20.114825 24089 solver.cpp:237]     Train net output #0: loss = 3.42105 (* 1 = 3.42105 loss)
I0408 19:31:20.114837 24089 sgd_solver.cpp:105] Iteration 1800, lr = 0.00252651
I0408 19:31:25.227375 24089 solver.cpp:218] Iteration 1812 (2.34727 iter/s, 5.11233s/12 iters), loss = 3.40818
I0408 19:31:25.227433 24089 solver.cpp:237]     Train net output #0: loss = 3.40818 (* 1 = 3.40818 loss)
I0408 19:31:25.227447 24089 sgd_solver.cpp:105] Iteration 1812, lr = 0.00250344
I0408 19:31:28.341308 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:31:30.144134 24089 solver.cpp:218] Iteration 1824 (2.44076 iter/s, 4.9165s/12 iters), loss = 3.5842
I0408 19:31:30.144261 24089 solver.cpp:237]     Train net output #0: loss = 3.5842 (* 1 = 3.5842 loss)
I0408 19:31:30.144271 24089 sgd_solver.cpp:105] Iteration 1824, lr = 0.00248058
I0408 19:31:34.758031 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0408 19:31:37.750628 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0408 19:31:40.077903 24089 solver.cpp:330] Iteration 1836, Testing net (#0)
I0408 19:31:40.077931 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:31:43.718295 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:31:44.472996 24089 solver.cpp:397]     Test net output #0: accuracy = 0.175245
I0408 19:31:44.473040 24089 solver.cpp:397]     Test net output #1: loss = 3.70612 (* 1 = 3.70612 loss)
I0408 19:31:44.562377 24089 solver.cpp:218] Iteration 1836 (0.832319 iter/s, 14.4175s/12 iters), loss = 3.39251
I0408 19:31:44.562417 24089 solver.cpp:237]     Train net output #0: loss = 3.39251 (* 1 = 3.39251 loss)
I0408 19:31:44.562427 24089 sgd_solver.cpp:105] Iteration 1836, lr = 0.00245794
I0408 19:31:48.657083 24089 solver.cpp:218] Iteration 1848 (2.93077 iter/s, 4.09449s/12 iters), loss = 3.50762
I0408 19:31:48.657124 24089 solver.cpp:237]     Train net output #0: loss = 3.50762 (* 1 = 3.50762 loss)
I0408 19:31:48.657135 24089 sgd_solver.cpp:105] Iteration 1848, lr = 0.0024355
I0408 19:31:53.707346 24089 solver.cpp:218] Iteration 1860 (2.37623 iter/s, 5.05001s/12 iters), loss = 3.25525
I0408 19:31:53.707386 24089 solver.cpp:237]     Train net output #0: loss = 3.25525 (* 1 = 3.25525 loss)
I0408 19:31:53.707396 24089 sgd_solver.cpp:105] Iteration 1860, lr = 0.00241326
I0408 19:31:58.704435 24089 solver.cpp:218] Iteration 1872 (2.40152 iter/s, 4.99684s/12 iters), loss = 3.53458
I0408 19:31:58.704489 24089 solver.cpp:237]     Train net output #0: loss = 3.53458 (* 1 = 3.53458 loss)
I0408 19:31:58.704502 24089 sgd_solver.cpp:105] Iteration 1872, lr = 0.00239123
I0408 19:32:03.791783 24089 solver.cpp:218] Iteration 1884 (2.35892 iter/s, 5.08708s/12 iters), loss = 3.3548
I0408 19:32:03.791877 24089 solver.cpp:237]     Train net output #0: loss = 3.3548 (* 1 = 3.3548 loss)
I0408 19:32:03.791885 24089 sgd_solver.cpp:105] Iteration 1884, lr = 0.0023694
I0408 19:32:08.847466 24089 solver.cpp:218] Iteration 1896 (2.37371 iter/s, 5.05538s/12 iters), loss = 3.37964
I0408 19:32:08.847510 24089 solver.cpp:237]     Train net output #0: loss = 3.37964 (* 1 = 3.37964 loss)
I0408 19:32:08.847519 24089 sgd_solver.cpp:105] Iteration 1896, lr = 0.00234777
I0408 19:32:14.001121 24089 solver.cpp:218] Iteration 1908 (2.32856 iter/s, 5.15339s/12 iters), loss = 3.34802
I0408 19:32:14.001174 24089 solver.cpp:237]     Train net output #0: loss = 3.34802 (* 1 = 3.34802 loss)
I0408 19:32:14.001185 24089 sgd_solver.cpp:105] Iteration 1908, lr = 0.00232633
I0408 19:32:19.477938 24089 solver.cpp:218] Iteration 1920 (2.19117 iter/s, 5.47653s/12 iters), loss = 3.09463
I0408 19:32:19.478004 24089 solver.cpp:237]     Train net output #0: loss = 3.09463 (* 1 = 3.09463 loss)
I0408 19:32:19.478016 24089 sgd_solver.cpp:105] Iteration 1920, lr = 0.00230509
I0408 19:32:19.793190 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:32:24.671759 24089 solver.cpp:218] Iteration 1932 (2.31056 iter/s, 5.19354s/12 iters), loss = 3.16354
I0408 19:32:24.671808 24089 solver.cpp:237]     Train net output #0: loss = 3.16354 (* 1 = 3.16354 loss)
I0408 19:32:24.671819 24089 sgd_solver.cpp:105] Iteration 1932, lr = 0.00228405
I0408 19:32:26.780346 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0408 19:32:29.871070 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0408 19:32:32.188815 24089 solver.cpp:330] Iteration 1938, Testing net (#0)
I0408 19:32:32.188840 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:32:35.841177 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:32:36.628324 24089 solver.cpp:397]     Test net output #0: accuracy = 0.182598
I0408 19:32:36.628366 24089 solver.cpp:397]     Test net output #1: loss = 3.70224 (* 1 = 3.70224 loss)
I0408 19:32:38.434690 24089 solver.cpp:218] Iteration 1944 (0.871945 iter/s, 13.7623s/12 iters), loss = 3.18403
I0408 19:32:38.434749 24089 solver.cpp:237]     Train net output #0: loss = 3.18403 (* 1 = 3.18403 loss)
I0408 19:32:38.434760 24089 sgd_solver.cpp:105] Iteration 1944, lr = 0.00226319
I0408 19:32:43.524328 24089 solver.cpp:218] Iteration 1956 (2.35786 iter/s, 5.08937s/12 iters), loss = 3.05817
I0408 19:32:43.524377 24089 solver.cpp:237]     Train net output #0: loss = 3.05817 (* 1 = 3.05817 loss)
I0408 19:32:43.524387 24089 sgd_solver.cpp:105] Iteration 1956, lr = 0.00224253
I0408 19:32:48.596088 24089 solver.cpp:218] Iteration 1968 (2.36616 iter/s, 5.07151s/12 iters), loss = 3.23642
I0408 19:32:48.596124 24089 solver.cpp:237]     Train net output #0: loss = 3.23642 (* 1 = 3.23642 loss)
I0408 19:32:48.596133 24089 sgd_solver.cpp:105] Iteration 1968, lr = 0.00222206
I0408 19:32:53.668648 24089 solver.cpp:218] Iteration 1980 (2.36578 iter/s, 5.07231s/12 iters), loss = 3.12755
I0408 19:32:53.668694 24089 solver.cpp:237]     Train net output #0: loss = 3.12755 (* 1 = 3.12755 loss)
I0408 19:32:53.668705 24089 sgd_solver.cpp:105] Iteration 1980, lr = 0.00220177
I0408 19:32:58.838150 24089 solver.cpp:218] Iteration 1992 (2.32142 iter/s, 5.16924s/12 iters), loss = 3.23232
I0408 19:32:58.838193 24089 solver.cpp:237]     Train net output #0: loss = 3.23232 (* 1 = 3.23232 loss)
I0408 19:32:58.838204 24089 sgd_solver.cpp:105] Iteration 1992, lr = 0.00218167
I0408 19:33:04.017292 24089 solver.cpp:218] Iteration 2004 (2.3171 iter/s, 5.17888s/12 iters), loss = 3.01446
I0408 19:33:04.017343 24089 solver.cpp:237]     Train net output #0: loss = 3.01446 (* 1 = 3.01446 loss)
I0408 19:33:04.017355 24089 sgd_solver.cpp:105] Iteration 2004, lr = 0.00216175
I0408 19:33:09.203536 24089 solver.cpp:218] Iteration 2016 (2.31393 iter/s, 5.18598s/12 iters), loss = 3.17079
I0408 19:33:09.203644 24089 solver.cpp:237]     Train net output #0: loss = 3.17079 (* 1 = 3.17079 loss)
I0408 19:33:09.203656 24089 sgd_solver.cpp:105] Iteration 2016, lr = 0.00214202
I0408 19:33:11.759953 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:33:14.230264 24089 solver.cpp:218] Iteration 2028 (2.38739 iter/s, 5.02641s/12 iters), loss = 2.87571
I0408 19:33:14.230317 24089 solver.cpp:237]     Train net output #0: loss = 2.87571 (* 1 = 2.87571 loss)
I0408 19:33:14.230329 24089 sgd_solver.cpp:105] Iteration 2028, lr = 0.00212246
I0408 19:33:18.745189 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0408 19:33:21.961796 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0408 19:33:24.383798 24089 solver.cpp:330] Iteration 2040, Testing net (#0)
I0408 19:33:24.383826 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:33:28.030642 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:33:28.861929 24089 solver.cpp:397]     Test net output #0: accuracy = 0.199142
I0408 19:33:28.861989 24089 solver.cpp:397]     Test net output #1: loss = 3.51351 (* 1 = 3.51351 loss)
I0408 19:33:28.952550 24089 solver.cpp:218] Iteration 2040 (0.815126 iter/s, 14.7216s/12 iters), loss = 3.05998
I0408 19:33:28.952600 24089 solver.cpp:237]     Train net output #0: loss = 3.05998 (* 1 = 3.05998 loss)
I0408 19:33:28.952610 24089 sgd_solver.cpp:105] Iteration 2040, lr = 0.00210308
I0408 19:33:33.577210 24089 solver.cpp:218] Iteration 2052 (2.59492 iter/s, 4.62442s/12 iters), loss = 3.21749
I0408 19:33:33.577250 24089 solver.cpp:237]     Train net output #0: loss = 3.21749 (* 1 = 3.21749 loss)
I0408 19:33:33.577260 24089 sgd_solver.cpp:105] Iteration 2052, lr = 0.00208388
I0408 19:33:35.384871 24089 blocking_queue.cpp:49] Waiting for data
I0408 19:33:38.940394 24089 solver.cpp:218] Iteration 2064 (2.23759 iter/s, 5.36292s/12 iters), loss = 3.13494
I0408 19:33:38.940452 24089 solver.cpp:237]     Train net output #0: loss = 3.13494 (* 1 = 3.13494 loss)
I0408 19:33:38.940467 24089 sgd_solver.cpp:105] Iteration 2064, lr = 0.00206486
I0408 19:33:44.100731 24089 solver.cpp:218] Iteration 2076 (2.32555 iter/s, 5.16007s/12 iters), loss = 3.17525
I0408 19:33:44.100847 24089 solver.cpp:237]     Train net output #0: loss = 3.17525 (* 1 = 3.17525 loss)
I0408 19:33:44.100857 24089 sgd_solver.cpp:105] Iteration 2076, lr = 0.00204601
I0408 19:33:49.245246 24089 solver.cpp:218] Iteration 2088 (2.33273 iter/s, 5.14418s/12 iters), loss = 3.11623
I0408 19:33:49.245304 24089 solver.cpp:237]     Train net output #0: loss = 3.11623 (* 1 = 3.11623 loss)
I0408 19:33:49.245317 24089 sgd_solver.cpp:105] Iteration 2088, lr = 0.00202733
I0408 19:33:54.302206 24089 solver.cpp:218] Iteration 2100 (2.37309 iter/s, 5.05669s/12 iters), loss = 2.78592
I0408 19:33:54.302259 24089 solver.cpp:237]     Train net output #0: loss = 2.78592 (* 1 = 2.78592 loss)
I0408 19:33:54.302270 24089 sgd_solver.cpp:105] Iteration 2100, lr = 0.00200882
I0408 19:33:59.500845 24089 solver.cpp:218] Iteration 2112 (2.30841 iter/s, 5.19837s/12 iters), loss = 3.09561
I0408 19:33:59.500888 24089 solver.cpp:237]     Train net output #0: loss = 3.09561 (* 1 = 3.09561 loss)
I0408 19:33:59.500897 24089 sgd_solver.cpp:105] Iteration 2112, lr = 0.00199048
I0408 19:34:05.110195 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:34:05.458765 24089 solver.cpp:218] Iteration 2124 (2.01423 iter/s, 5.95763s/12 iters), loss = 2.74712
I0408 19:34:05.458814 24089 solver.cpp:237]     Train net output #0: loss = 2.74712 (* 1 = 2.74712 loss)
I0408 19:34:05.458825 24089 sgd_solver.cpp:105] Iteration 2124, lr = 0.0019723
I0408 19:34:10.634310 24089 solver.cpp:218] Iteration 2136 (2.31871 iter/s, 5.17528s/12 iters), loss = 2.6773
I0408 19:34:10.634356 24089 solver.cpp:237]     Train net output #0: loss = 2.6773 (* 1 = 2.6773 loss)
I0408 19:34:10.634366 24089 sgd_solver.cpp:105] Iteration 2136, lr = 0.0019543
I0408 19:34:12.740267 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0408 19:34:15.755451 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0408 19:34:18.069737 24089 solver.cpp:330] Iteration 2142, Testing net (#0)
I0408 19:34:18.069766 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:34:21.694785 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:34:22.561172 24089 solver.cpp:397]     Test net output #0: accuracy = 0.21875
I0408 19:34:22.561223 24089 solver.cpp:397]     Test net output #1: loss = 3.4064 (* 1 = 3.4064 loss)
I0408 19:34:24.436679 24089 solver.cpp:218] Iteration 2148 (0.869453 iter/s, 13.8018s/12 iters), loss = 2.94796
I0408 19:34:24.436738 24089 solver.cpp:237]     Train net output #0: loss = 2.94796 (* 1 = 2.94796 loss)
I0408 19:34:24.436748 24089 sgd_solver.cpp:105] Iteration 2148, lr = 0.00193646
I0408 19:34:29.569011 24089 solver.cpp:218] Iteration 2160 (2.33824 iter/s, 5.13206s/12 iters), loss = 2.96478
I0408 19:34:29.569068 24089 solver.cpp:237]     Train net output #0: loss = 2.96478 (* 1 = 2.96478 loss)
I0408 19:34:29.569082 24089 sgd_solver.cpp:105] Iteration 2160, lr = 0.00191878
I0408 19:34:34.500104 24089 solver.cpp:218] Iteration 2172 (2.43367 iter/s, 4.93083s/12 iters), loss = 2.82169
I0408 19:34:34.500162 24089 solver.cpp:237]     Train net output #0: loss = 2.82169 (* 1 = 2.82169 loss)
I0408 19:34:34.500175 24089 sgd_solver.cpp:105] Iteration 2172, lr = 0.00190126
I0408 19:34:39.447224 24089 solver.cpp:218] Iteration 2184 (2.42578 iter/s, 4.94686s/12 iters), loss = 2.79969
I0408 19:34:39.447280 24089 solver.cpp:237]     Train net output #0: loss = 2.79969 (* 1 = 2.79969 loss)
I0408 19:34:39.447293 24089 sgd_solver.cpp:105] Iteration 2184, lr = 0.0018839
I0408 19:34:44.385835 24089 solver.cpp:218] Iteration 2196 (2.42996 iter/s, 4.93835s/12 iters), loss = 2.94983
I0408 19:34:44.385890 24089 solver.cpp:237]     Train net output #0: loss = 2.94983 (* 1 = 2.94983 loss)
I0408 19:34:44.385903 24089 sgd_solver.cpp:105] Iteration 2196, lr = 0.0018667
I0408 19:34:49.951651 24089 solver.cpp:218] Iteration 2208 (2.15613 iter/s, 5.56553s/12 iters), loss = 2.82566
I0408 19:34:49.951790 24089 solver.cpp:237]     Train net output #0: loss = 2.82566 (* 1 = 2.82566 loss)
I0408 19:34:49.951803 24089 sgd_solver.cpp:105] Iteration 2208, lr = 0.00184966
I0408 19:34:55.343107 24089 solver.cpp:218] Iteration 2220 (2.22589 iter/s, 5.39109s/12 iters), loss = 2.68311
I0408 19:34:55.343161 24089 solver.cpp:237]     Train net output #0: loss = 2.68311 (* 1 = 2.68311 loss)
I0408 19:34:55.343173 24089 sgd_solver.cpp:105] Iteration 2220, lr = 0.00183277
I0408 19:34:57.210664 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:35:00.466861 24089 solver.cpp:218] Iteration 2232 (2.34215 iter/s, 5.12349s/12 iters), loss = 2.90929
I0408 19:35:00.466904 24089 solver.cpp:237]     Train net output #0: loss = 2.90929 (* 1 = 2.90929 loss)
I0408 19:35:00.466914 24089 sgd_solver.cpp:105] Iteration 2232, lr = 0.00181604
I0408 19:35:04.984097 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0408 19:35:08.041165 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0408 19:35:10.380364 24089 solver.cpp:330] Iteration 2244, Testing net (#0)
I0408 19:35:10.380391 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:35:13.953222 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:35:14.864256 24089 solver.cpp:397]     Test net output #0: accuracy = 0.216299
I0408 19:35:14.864302 24089 solver.cpp:397]     Test net output #1: loss = 3.44187 (* 1 = 3.44187 loss)
I0408 19:35:14.956521 24089 solver.cpp:218] Iteration 2244 (0.828212 iter/s, 14.489s/12 iters), loss = 2.8449
I0408 19:35:14.956569 24089 solver.cpp:237]     Train net output #0: loss = 2.8449 (* 1 = 2.8449 loss)
I0408 19:35:14.956579 24089 sgd_solver.cpp:105] Iteration 2244, lr = 0.00179946
I0408 19:35:19.424324 24089 solver.cpp:218] Iteration 2256 (2.68603 iter/s, 4.46757s/12 iters), loss = 2.6321
I0408 19:35:19.424377 24089 solver.cpp:237]     Train net output #0: loss = 2.6321 (* 1 = 2.6321 loss)
I0408 19:35:19.424389 24089 sgd_solver.cpp:105] Iteration 2256, lr = 0.00178303
I0408 19:35:24.521554 24089 solver.cpp:218] Iteration 2268 (2.35434 iter/s, 5.09696s/12 iters), loss = 2.76162
I0408 19:35:24.521636 24089 solver.cpp:237]     Train net output #0: loss = 2.76162 (* 1 = 2.76162 loss)
I0408 19:35:24.521648 24089 sgd_solver.cpp:105] Iteration 2268, lr = 0.00176675
I0408 19:35:29.682147 24089 solver.cpp:218] Iteration 2280 (2.32545 iter/s, 5.1603s/12 iters), loss = 2.62134
I0408 19:35:29.682202 24089 solver.cpp:237]     Train net output #0: loss = 2.62134 (* 1 = 2.62134 loss)
I0408 19:35:29.682215 24089 sgd_solver.cpp:105] Iteration 2280, lr = 0.00175062
I0408 19:35:34.774904 24089 solver.cpp:218] Iteration 2292 (2.35641 iter/s, 5.09249s/12 iters), loss = 2.61866
I0408 19:35:34.774960 24089 solver.cpp:237]     Train net output #0: loss = 2.61866 (* 1 = 2.61866 loss)
I0408 19:35:34.774971 24089 sgd_solver.cpp:105] Iteration 2292, lr = 0.00173464
I0408 19:35:40.038662 24089 solver.cpp:218] Iteration 2304 (2.27986 iter/s, 5.26348s/12 iters), loss = 2.63336
I0408 19:35:40.038719 24089 solver.cpp:237]     Train net output #0: loss = 2.63336 (* 1 = 2.63336 loss)
I0408 19:35:40.038733 24089 sgd_solver.cpp:105] Iteration 2304, lr = 0.0017188
I0408 19:35:45.231511 24089 solver.cpp:218] Iteration 2316 (2.31099 iter/s, 5.19257s/12 iters), loss = 2.83091
I0408 19:35:45.231564 24089 solver.cpp:237]     Train net output #0: loss = 2.83091 (* 1 = 2.83091 loss)
I0408 19:35:45.231575 24089 sgd_solver.cpp:105] Iteration 2316, lr = 0.00170311
I0408 19:35:49.441378 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:35:50.482309 24089 solver.cpp:218] Iteration 2328 (2.28549 iter/s, 5.25053s/12 iters), loss = 2.47673
I0408 19:35:50.482362 24089 solver.cpp:237]     Train net output #0: loss = 2.47673 (* 1 = 2.47673 loss)
I0408 19:35:50.482375 24089 sgd_solver.cpp:105] Iteration 2328, lr = 0.00168756
I0408 19:35:55.518180 24089 solver.cpp:218] Iteration 2340 (2.38303 iter/s, 5.03561s/12 iters), loss = 2.54304
I0408 19:35:55.518326 24089 solver.cpp:237]     Train net output #0: loss = 2.54304 (* 1 = 2.54304 loss)
I0408 19:35:55.518338 24089 sgd_solver.cpp:105] Iteration 2340, lr = 0.00167215
I0408 19:35:57.592650 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0408 19:36:02.616317 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0408 19:36:08.327879 24089 solver.cpp:330] Iteration 2346, Testing net (#0)
I0408 19:36:08.327903 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:36:11.945250 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:36:13.017241 24089 solver.cpp:397]     Test net output #0: accuracy = 0.229167
I0408 19:36:13.017293 24089 solver.cpp:397]     Test net output #1: loss = 3.35271 (* 1 = 3.35271 loss)
I0408 19:36:15.033396 24089 solver.cpp:218] Iteration 2352 (0.614933 iter/s, 19.5143s/12 iters), loss = 2.74609
I0408 19:36:15.033440 24089 solver.cpp:237]     Train net output #0: loss = 2.74609 (* 1 = 2.74609 loss)
I0408 19:36:15.033449 24089 sgd_solver.cpp:105] Iteration 2352, lr = 0.00165689
I0408 19:36:20.110697 24089 solver.cpp:218] Iteration 2364 (2.36358 iter/s, 5.07704s/12 iters), loss = 2.62052
I0408 19:36:20.110757 24089 solver.cpp:237]     Train net output #0: loss = 2.62052 (* 1 = 2.62052 loss)
I0408 19:36:20.110769 24089 sgd_solver.cpp:105] Iteration 2364, lr = 0.00164176
I0408 19:36:25.384162 24089 solver.cpp:218] Iteration 2376 (2.27566 iter/s, 5.27318s/12 iters), loss = 2.45496
I0408 19:36:25.384222 24089 solver.cpp:237]     Train net output #0: loss = 2.45496 (* 1 = 2.45496 loss)
I0408 19:36:25.384233 24089 sgd_solver.cpp:105] Iteration 2376, lr = 0.00162677
I0408 19:36:30.862879 24089 solver.cpp:218] Iteration 2388 (2.19041 iter/s, 5.47843s/12 iters), loss = 2.72483
I0408 19:36:30.862987 24089 solver.cpp:237]     Train net output #0: loss = 2.72483 (* 1 = 2.72483 loss)
I0408 19:36:30.862998 24089 sgd_solver.cpp:105] Iteration 2388, lr = 0.00161192
I0408 19:36:36.196130 24089 solver.cpp:218] Iteration 2400 (2.25018 iter/s, 5.33292s/12 iters), loss = 2.56559
I0408 19:36:36.196190 24089 solver.cpp:237]     Train net output #0: loss = 2.56559 (* 1 = 2.56559 loss)
I0408 19:36:36.196204 24089 sgd_solver.cpp:105] Iteration 2400, lr = 0.0015972
I0408 19:36:41.489760 24089 solver.cpp:218] Iteration 2412 (2.26699 iter/s, 5.29335s/12 iters), loss = 2.5823
I0408 19:36:41.489811 24089 solver.cpp:237]     Train net output #0: loss = 2.5823 (* 1 = 2.5823 loss)
I0408 19:36:41.489822 24089 sgd_solver.cpp:105] Iteration 2412, lr = 0.00158262
I0408 19:36:46.606876 24089 solver.cpp:218] Iteration 2424 (2.34519 iter/s, 5.11686s/12 iters), loss = 2.68139
I0408 19:36:46.606915 24089 solver.cpp:237]     Train net output #0: loss = 2.68139 (* 1 = 2.68139 loss)
I0408 19:36:46.606925 24089 sgd_solver.cpp:105] Iteration 2424, lr = 0.00156817
I0408 19:36:47.695052 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:36:51.802081 24089 solver.cpp:218] Iteration 2436 (2.30994 iter/s, 5.19495s/12 iters), loss = 2.34789
I0408 19:36:51.802124 24089 solver.cpp:237]     Train net output #0: loss = 2.34789 (* 1 = 2.34789 loss)
I0408 19:36:51.802134 24089 sgd_solver.cpp:105] Iteration 2436, lr = 0.00155386
I0408 19:36:56.435851 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0408 19:37:00.201783 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0408 19:37:05.977731 24089 solver.cpp:330] Iteration 2448, Testing net (#0)
I0408 19:37:05.977821 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:37:09.470638 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:37:10.449647 24089 solver.cpp:397]     Test net output #0: accuracy = 0.228554
I0408 19:37:10.449687 24089 solver.cpp:397]     Test net output #1: loss = 3.2978 (* 1 = 3.2978 loss)
I0408 19:37:10.540375 24089 solver.cpp:218] Iteration 2448 (0.640427 iter/s, 18.7375s/12 iters), loss = 2.50501
I0408 19:37:10.540434 24089 solver.cpp:237]     Train net output #0: loss = 2.50501 (* 1 = 2.50501 loss)
I0408 19:37:10.540446 24089 sgd_solver.cpp:105] Iteration 2448, lr = 0.00153967
I0408 19:37:14.881989 24089 solver.cpp:218] Iteration 2460 (2.76412 iter/s, 4.34135s/12 iters), loss = 2.44545
I0408 19:37:14.882041 24089 solver.cpp:237]     Train net output #0: loss = 2.44545 (* 1 = 2.44545 loss)
I0408 19:37:14.882055 24089 sgd_solver.cpp:105] Iteration 2460, lr = 0.00152561
I0408 19:37:19.786547 24089 solver.cpp:218] Iteration 2472 (2.44683 iter/s, 4.9043s/12 iters), loss = 2.5278
I0408 19:37:19.786597 24089 solver.cpp:237]     Train net output #0: loss = 2.5278 (* 1 = 2.5278 loss)
I0408 19:37:19.786609 24089 sgd_solver.cpp:105] Iteration 2472, lr = 0.00151168
I0408 19:37:24.787505 24089 solver.cpp:218] Iteration 2484 (2.39966 iter/s, 5.0007s/12 iters), loss = 2.39701
I0408 19:37:24.787555 24089 solver.cpp:237]     Train net output #0: loss = 2.39701 (* 1 = 2.39701 loss)
I0408 19:37:24.787567 24089 sgd_solver.cpp:105] Iteration 2484, lr = 0.00149788
I0408 19:37:29.765106 24089 solver.cpp:218] Iteration 2496 (2.41092 iter/s, 4.97734s/12 iters), loss = 2.4217
I0408 19:37:29.765156 24089 solver.cpp:237]     Train net output #0: loss = 2.4217 (* 1 = 2.4217 loss)
I0408 19:37:29.765168 24089 sgd_solver.cpp:105] Iteration 2496, lr = 0.00148421
I0408 19:37:34.799120 24089 solver.cpp:218] Iteration 2508 (2.38391 iter/s, 5.03376s/12 iters), loss = 2.41125
I0408 19:37:34.799176 24089 solver.cpp:237]     Train net output #0: loss = 2.41125 (* 1 = 2.41125 loss)
I0408 19:37:34.799190 24089 sgd_solver.cpp:105] Iteration 2508, lr = 0.00147066
I0408 19:37:39.948799 24089 solver.cpp:218] Iteration 2520 (2.33036 iter/s, 5.14941s/12 iters), loss = 2.43839
I0408 19:37:39.948875 24089 solver.cpp:237]     Train net output #0: loss = 2.43839 (* 1 = 2.43839 loss)
I0408 19:37:39.948887 24089 sgd_solver.cpp:105] Iteration 2520, lr = 0.00145723
I0408 19:37:43.217691 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:37:45.022186 24089 solver.cpp:218] Iteration 2532 (2.36542 iter/s, 5.0731s/12 iters), loss = 2.5497
I0408 19:37:45.022233 24089 solver.cpp:237]     Train net output #0: loss = 2.5497 (* 1 = 2.5497 loss)
I0408 19:37:45.022249 24089 sgd_solver.cpp:105] Iteration 2532, lr = 0.00144393
I0408 19:37:50.029991 24089 solver.cpp:218] Iteration 2544 (2.39638 iter/s, 5.00755s/12 iters), loss = 2.45954
I0408 19:37:50.030037 24089 solver.cpp:237]     Train net output #0: loss = 2.45954 (* 1 = 2.45954 loss)
I0408 19:37:50.030050 24089 sgd_solver.cpp:105] Iteration 2544, lr = 0.00143074
I0408 19:37:52.126121 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0408 19:38:00.281797 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0408 19:38:02.586665 24089 solver.cpp:330] Iteration 2550, Testing net (#0)
I0408 19:38:02.586686 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:38:06.077737 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:38:07.121333 24089 solver.cpp:397]     Test net output #0: accuracy = 0.260417
I0408 19:38:07.121381 24089 solver.cpp:397]     Test net output #1: loss = 3.27419 (* 1 = 3.27419 loss)
I0408 19:38:09.113493 24089 solver.cpp:218] Iteration 2556 (0.628842 iter/s, 19.0827s/12 iters), loss = 2.67602
I0408 19:38:09.113550 24089 solver.cpp:237]     Train net output #0: loss = 2.67602 (* 1 = 2.67602 loss)
I0408 19:38:09.113562 24089 sgd_solver.cpp:105] Iteration 2556, lr = 0.00141768
I0408 19:38:14.579604 24089 solver.cpp:218] Iteration 2568 (2.19546 iter/s, 5.46583s/12 iters), loss = 2.34638
I0408 19:38:14.579728 24089 solver.cpp:237]     Train net output #0: loss = 2.34638 (* 1 = 2.34638 loss)
I0408 19:38:14.579739 24089 sgd_solver.cpp:105] Iteration 2568, lr = 0.00140474
I0408 19:38:19.647068 24089 solver.cpp:218] Iteration 2580 (2.3682 iter/s, 5.06713s/12 iters), loss = 2.33799
I0408 19:38:19.647114 24089 solver.cpp:237]     Train net output #0: loss = 2.33799 (* 1 = 2.33799 loss)
I0408 19:38:19.647125 24089 sgd_solver.cpp:105] Iteration 2580, lr = 0.00139191
I0408 19:38:24.710750 24089 solver.cpp:218] Iteration 2592 (2.36993 iter/s, 5.06343s/12 iters), loss = 2.45616
I0408 19:38:24.710793 24089 solver.cpp:237]     Train net output #0: loss = 2.45616 (* 1 = 2.45616 loss)
I0408 19:38:24.710803 24089 sgd_solver.cpp:105] Iteration 2592, lr = 0.00137921
I0408 19:38:29.824019 24089 solver.cpp:218] Iteration 2604 (2.34695 iter/s, 5.11301s/12 iters), loss = 2.37526
I0408 19:38:29.824064 24089 solver.cpp:237]     Train net output #0: loss = 2.37526 (* 1 = 2.37526 loss)
I0408 19:38:29.824074 24089 sgd_solver.cpp:105] Iteration 2604, lr = 0.00136661
I0408 19:38:34.900342 24089 solver.cpp:218] Iteration 2616 (2.36404 iter/s, 5.07606s/12 iters), loss = 2.27003
I0408 19:38:34.900394 24089 solver.cpp:237]     Train net output #0: loss = 2.27003 (* 1 = 2.27003 loss)
I0408 19:38:34.900408 24089 sgd_solver.cpp:105] Iteration 2616, lr = 0.00135414
I0408 19:38:40.386482 24089 solver.cpp:218] Iteration 2628 (2.18744 iter/s, 5.48586s/12 iters), loss = 2.34325
I0408 19:38:40.386534 24089 solver.cpp:237]     Train net output #0: loss = 2.34325 (* 1 = 2.34325 loss)
I0408 19:38:40.386546 24089 sgd_solver.cpp:105] Iteration 2628, lr = 0.00134177
I0408 19:38:40.824184 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:38:45.431221 24089 solver.cpp:218] Iteration 2640 (2.37884 iter/s, 5.04448s/12 iters), loss = 2.32897
I0408 19:38:45.431331 24089 solver.cpp:237]     Train net output #0: loss = 2.32897 (* 1 = 2.32897 loss)
I0408 19:38:45.431344 24089 sgd_solver.cpp:105] Iteration 2640, lr = 0.00132952
I0408 19:38:49.977783 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0408 19:38:55.144100 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0408 19:38:58.703408 24089 solver.cpp:330] Iteration 2652, Testing net (#0)
I0408 19:38:58.703433 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:39:02.155573 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:39:03.234091 24089 solver.cpp:397]     Test net output #0: accuracy = 0.28125
I0408 19:39:03.234140 24089 solver.cpp:397]     Test net output #1: loss = 3.11263 (* 1 = 3.11263 loss)
I0408 19:39:03.324770 24089 solver.cpp:218] Iteration 2652 (0.670663 iter/s, 17.8927s/12 iters), loss = 2.19484
I0408 19:39:03.324842 24089 solver.cpp:237]     Train net output #0: loss = 2.19484 (* 1 = 2.19484 loss)
I0408 19:39:03.324858 24089 sgd_solver.cpp:105] Iteration 2652, lr = 0.00131739
I0408 19:39:07.669281 24089 solver.cpp:218] Iteration 2664 (2.76227 iter/s, 4.34426s/12 iters), loss = 1.98072
I0408 19:39:07.669340 24089 solver.cpp:237]     Train net output #0: loss = 1.98072 (* 1 = 1.98072 loss)
I0408 19:39:07.669355 24089 sgd_solver.cpp:105] Iteration 2664, lr = 0.00130536
I0408 19:39:12.688424 24089 solver.cpp:218] Iteration 2676 (2.39097 iter/s, 5.01888s/12 iters), loss = 2.19884
I0408 19:39:12.688465 24089 solver.cpp:237]     Train net output #0: loss = 2.19884 (* 1 = 2.19884 loss)
I0408 19:39:12.688474 24089 sgd_solver.cpp:105] Iteration 2676, lr = 0.00129344
I0408 19:39:17.737022 24089 solver.cpp:218] Iteration 2688 (2.37702 iter/s, 5.04833s/12 iters), loss = 2.29601
I0408 19:39:17.737198 24089 solver.cpp:237]     Train net output #0: loss = 2.29601 (* 1 = 2.29601 loss)
I0408 19:39:17.737213 24089 sgd_solver.cpp:105] Iteration 2688, lr = 0.00128163
I0408 19:39:22.713023 24089 solver.cpp:218] Iteration 2700 (2.41176 iter/s, 4.97563s/12 iters), loss = 2.284
I0408 19:39:22.713070 24089 solver.cpp:237]     Train net output #0: loss = 2.284 (* 1 = 2.284 loss)
I0408 19:39:22.713081 24089 sgd_solver.cpp:105] Iteration 2700, lr = 0.00126993
I0408 19:39:27.817061 24089 solver.cpp:218] Iteration 2712 (2.3512 iter/s, 5.10378s/12 iters), loss = 1.99849
I0408 19:39:27.817113 24089 solver.cpp:237]     Train net output #0: loss = 1.99849 (* 1 = 1.99849 loss)
I0408 19:39:27.817126 24089 sgd_solver.cpp:105] Iteration 2712, lr = 0.00125834
I0408 19:39:32.890123 24089 solver.cpp:218] Iteration 2724 (2.36556 iter/s, 5.0728s/12 iters), loss = 2.16422
I0408 19:39:32.890177 24089 solver.cpp:237]     Train net output #0: loss = 2.16422 (* 1 = 2.16422 loss)
I0408 19:39:32.890190 24089 sgd_solver.cpp:105] Iteration 2724, lr = 0.00124685
I0408 19:39:35.505673 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:39:38.011678 24089 solver.cpp:218] Iteration 2736 (2.34316 iter/s, 5.12129s/12 iters), loss = 1.98514
I0408 19:39:38.011729 24089 solver.cpp:237]     Train net output #0: loss = 1.98514 (* 1 = 1.98514 loss)
I0408 19:39:38.011741 24089 sgd_solver.cpp:105] Iteration 2736, lr = 0.00123547
I0408 19:39:43.373759 24089 solver.cpp:218] Iteration 2748 (2.23805 iter/s, 5.36181s/12 iters), loss = 2.06012
I0408 19:39:43.373800 24089 solver.cpp:237]     Train net output #0: loss = 2.06012 (* 1 = 2.06012 loss)
I0408 19:39:43.373809 24089 sgd_solver.cpp:105] Iteration 2748, lr = 0.00122419
I0408 19:39:45.406100 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0408 19:39:48.409859 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0408 19:39:50.698266 24089 solver.cpp:330] Iteration 2754, Testing net (#0)
I0408 19:39:50.698290 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:39:53.834296 24089 blocking_queue.cpp:49] Waiting for data
I0408 19:39:54.070504 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:39:55.177124 24089 solver.cpp:397]     Test net output #0: accuracy = 0.265931
I0408 19:39:55.177173 24089 solver.cpp:397]     Test net output #1: loss = 3.17677 (* 1 = 3.17677 loss)
I0408 19:39:57.142715 24089 solver.cpp:218] Iteration 2760 (0.871563 iter/s, 13.7684s/12 iters), loss = 2.09027
I0408 19:39:57.142758 24089 solver.cpp:237]     Train net output #0: loss = 2.09027 (* 1 = 2.09027 loss)
I0408 19:39:57.142771 24089 sgd_solver.cpp:105] Iteration 2760, lr = 0.00121301
I0408 19:40:02.134843 24089 solver.cpp:218] Iteration 2772 (2.40391 iter/s, 4.99188s/12 iters), loss = 2.0458
I0408 19:40:02.134891 24089 solver.cpp:237]     Train net output #0: loss = 2.0458 (* 1 = 2.0458 loss)
I0408 19:40:02.134903 24089 sgd_solver.cpp:105] Iteration 2772, lr = 0.00120194
I0408 19:40:07.118373 24089 solver.cpp:218] Iteration 2784 (2.40806 iter/s, 4.98327s/12 iters), loss = 2.29997
I0408 19:40:07.118424 24089 solver.cpp:237]     Train net output #0: loss = 2.29997 (* 1 = 2.29997 loss)
I0408 19:40:07.118438 24089 sgd_solver.cpp:105] Iteration 2784, lr = 0.00119096
I0408 19:40:12.034081 24089 solver.cpp:218] Iteration 2796 (2.44128 iter/s, 4.91545s/12 iters), loss = 2.17715
I0408 19:40:12.034140 24089 solver.cpp:237]     Train net output #0: loss = 2.17715 (* 1 = 2.17715 loss)
I0408 19:40:12.034152 24089 sgd_solver.cpp:105] Iteration 2796, lr = 0.00118009
I0408 19:40:16.993506 24089 solver.cpp:218] Iteration 2808 (2.41976 iter/s, 4.95916s/12 iters), loss = 1.86241
I0408 19:40:16.993556 24089 solver.cpp:237]     Train net output #0: loss = 1.86241 (* 1 = 1.86241 loss)
I0408 19:40:16.993567 24089 sgd_solver.cpp:105] Iteration 2808, lr = 0.00116932
I0408 19:40:22.025635 24089 solver.cpp:218] Iteration 2820 (2.3848 iter/s, 5.03186s/12 iters), loss = 2.01586
I0408 19:40:22.025797 24089 solver.cpp:237]     Train net output #0: loss = 2.01586 (* 1 = 2.01586 loss)
I0408 19:40:22.025810 24089 sgd_solver.cpp:105] Iteration 2820, lr = 0.00115864
I0408 19:40:26.768182 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:40:27.053912 24089 solver.cpp:218] Iteration 2832 (2.38668 iter/s, 5.02791s/12 iters), loss = 1.88047
I0408 19:40:27.053951 24089 solver.cpp:237]     Train net output #0: loss = 1.88047 (* 1 = 1.88047 loss)
I0408 19:40:27.053970 24089 sgd_solver.cpp:105] Iteration 2832, lr = 0.00114806
I0408 19:40:32.145797 24089 solver.cpp:218] Iteration 2844 (2.35681 iter/s, 5.09163s/12 iters), loss = 1.95375
I0408 19:40:32.145851 24089 solver.cpp:237]     Train net output #0: loss = 1.95375 (* 1 = 1.95375 loss)
I0408 19:40:32.145864 24089 sgd_solver.cpp:105] Iteration 2844, lr = 0.00113758
I0408 19:40:36.763096 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0408 19:40:40.706359 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0408 19:40:43.029090 24089 solver.cpp:330] Iteration 2856, Testing net (#0)
I0408 19:40:43.029117 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:40:46.587838 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:40:47.840199 24089 solver.cpp:397]     Test net output #0: accuracy = 0.28125
I0408 19:40:47.840229 24089 solver.cpp:397]     Test net output #1: loss = 3.12121 (* 1 = 3.12121 loss)
I0408 19:40:47.930665 24089 solver.cpp:218] Iteration 2856 (0.760255 iter/s, 15.7842s/12 iters), loss = 2.03454
I0408 19:40:47.930706 24089 solver.cpp:237]     Train net output #0: loss = 2.03454 (* 1 = 2.03454 loss)
I0408 19:40:47.930716 24089 sgd_solver.cpp:105] Iteration 2856, lr = 0.00112719
I0408 19:40:52.059336 24089 solver.cpp:218] Iteration 2868 (2.90666 iter/s, 4.12845s/12 iters), loss = 2.16742
I0408 19:40:52.075729 24089 solver.cpp:237]     Train net output #0: loss = 2.16742 (* 1 = 2.16742 loss)
I0408 19:40:52.075745 24089 sgd_solver.cpp:105] Iteration 2868, lr = 0.0011169
I0408 19:40:57.095844 24089 solver.cpp:218] Iteration 2880 (2.39048 iter/s, 5.01992s/12 iters), loss = 1.94439
I0408 19:40:57.095883 24089 solver.cpp:237]     Train net output #0: loss = 1.94439 (* 1 = 1.94439 loss)
I0408 19:40:57.095891 24089 sgd_solver.cpp:105] Iteration 2880, lr = 0.00110671
I0408 19:41:02.026809 24089 solver.cpp:218] Iteration 2892 (2.43372 iter/s, 4.93072s/12 iters), loss = 2.01897
I0408 19:41:02.026860 24089 solver.cpp:237]     Train net output #0: loss = 2.01897 (* 1 = 2.01897 loss)
I0408 19:41:02.026872 24089 sgd_solver.cpp:105] Iteration 2892, lr = 0.0010966
I0408 19:41:07.122989 24089 solver.cpp:218] Iteration 2904 (2.35483 iter/s, 5.09592s/12 iters), loss = 2.07751
I0408 19:41:07.123035 24089 solver.cpp:237]     Train net output #0: loss = 2.07751 (* 1 = 2.07751 loss)
I0408 19:41:07.123047 24089 sgd_solver.cpp:105] Iteration 2904, lr = 0.00108659
I0408 19:41:12.105720 24089 solver.cpp:218] Iteration 2916 (2.40844 iter/s, 4.98248s/12 iters), loss = 1.93
I0408 19:41:12.105769 24089 solver.cpp:237]     Train net output #0: loss = 1.93 (* 1 = 1.93 loss)
I0408 19:41:12.105780 24089 sgd_solver.cpp:105] Iteration 2916, lr = 0.00107667
I0408 19:41:17.166412 24089 solver.cpp:218] Iteration 2928 (2.37134 iter/s, 5.06043s/12 iters), loss = 1.75799
I0408 19:41:17.166466 24089 solver.cpp:237]     Train net output #0: loss = 1.75799 (* 1 = 1.75799 loss)
I0408 19:41:17.166479 24089 sgd_solver.cpp:105] Iteration 2928, lr = 0.00106684
I0408 19:41:18.995613 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:41:22.118007 24089 solver.cpp:218] Iteration 2940 (2.42359 iter/s, 4.95134s/12 iters), loss = 1.97134
I0408 19:41:22.118165 24089 solver.cpp:237]     Train net output #0: loss = 1.97134 (* 1 = 1.97134 loss)
I0408 19:41:22.118180 24089 sgd_solver.cpp:105] Iteration 2940, lr = 0.0010571
I0408 19:41:27.131809 24089 solver.cpp:218] Iteration 2952 (2.39357 iter/s, 5.01343s/12 iters), loss = 2.01561
I0408 19:41:27.131861 24089 solver.cpp:237]     Train net output #0: loss = 2.01561 (* 1 = 2.01561 loss)
I0408 19:41:27.131872 24089 sgd_solver.cpp:105] Iteration 2952, lr = 0.00104745
I0408 19:41:29.156438 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0408 19:41:32.189430 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0408 19:41:37.397634 24089 solver.cpp:330] Iteration 2958, Testing net (#0)
I0408 19:41:37.397656 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:41:40.682525 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:41:41.866353 24089 solver.cpp:397]     Test net output #0: accuracy = 0.306373
I0408 19:41:41.866400 24089 solver.cpp:397]     Test net output #1: loss = 3.04877 (* 1 = 3.04877 loss)
I0408 19:41:43.924141 24089 solver.cpp:218] Iteration 2964 (0.714643 iter/s, 16.7916s/12 iters), loss = 1.91012
I0408 19:41:43.924193 24089 solver.cpp:237]     Train net output #0: loss = 1.91012 (* 1 = 1.91012 loss)
I0408 19:41:43.924206 24089 sgd_solver.cpp:105] Iteration 2964, lr = 0.00103789
I0408 19:41:48.997004 24089 solver.cpp:218] Iteration 2976 (2.36565 iter/s, 5.0726s/12 iters), loss = 1.76819
I0408 19:41:48.997051 24089 solver.cpp:237]     Train net output #0: loss = 1.76819 (* 1 = 1.76819 loss)
I0408 19:41:48.997063 24089 sgd_solver.cpp:105] Iteration 2976, lr = 0.00102841
I0408 19:41:54.075628 24089 solver.cpp:218] Iteration 2988 (2.36297 iter/s, 5.07837s/12 iters), loss = 1.85446
I0408 19:41:54.076834 24089 solver.cpp:237]     Train net output #0: loss = 1.85446 (* 1 = 1.85446 loss)
I0408 19:41:54.076845 24089 sgd_solver.cpp:105] Iteration 2988, lr = 0.00101902
I0408 19:41:59.119843 24089 solver.cpp:218] Iteration 3000 (2.37963 iter/s, 5.0428s/12 iters), loss = 1.88412
I0408 19:41:59.119884 24089 solver.cpp:237]     Train net output #0: loss = 1.88412 (* 1 = 1.88412 loss)
I0408 19:41:59.119892 24089 sgd_solver.cpp:105] Iteration 3000, lr = 0.00100972
I0408 19:42:04.149297 24089 solver.cpp:218] Iteration 3012 (2.38607 iter/s, 5.0292s/12 iters), loss = 1.70872
I0408 19:42:04.149348 24089 solver.cpp:237]     Train net output #0: loss = 1.70872 (* 1 = 1.70872 loss)
I0408 19:42:04.149358 24089 sgd_solver.cpp:105] Iteration 3012, lr = 0.0010005
I0408 19:42:09.228453 24089 solver.cpp:218] Iteration 3024 (2.36272 iter/s, 5.0789s/12 iters), loss = 1.7763
I0408 19:42:09.228502 24089 solver.cpp:237]     Train net output #0: loss = 1.7763 (* 1 = 1.7763 loss)
I0408 19:42:09.228513 24089 sgd_solver.cpp:105] Iteration 3024, lr = 0.000991366
I0408 19:42:13.161231 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:42:14.188645 24089 solver.cpp:218] Iteration 3036 (2.41939 iter/s, 4.95993s/12 iters), loss = 1.71266
I0408 19:42:14.188691 24089 solver.cpp:237]     Train net output #0: loss = 1.71266 (* 1 = 1.71266 loss)
I0408 19:42:14.188701 24089 sgd_solver.cpp:105] Iteration 3036, lr = 0.000982315
I0408 19:42:19.148381 24089 solver.cpp:218] Iteration 3048 (2.41961 iter/s, 4.95948s/12 iters), loss = 1.91908
I0408 19:42:19.148420 24089 solver.cpp:237]     Train net output #0: loss = 1.91908 (* 1 = 1.91908 loss)
I0408 19:42:19.148429 24089 sgd_solver.cpp:105] Iteration 3048, lr = 0.000973347
I0408 19:42:23.718416 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0408 19:42:26.720121 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0408 19:42:29.052969 24089 solver.cpp:330] Iteration 3060, Testing net (#0)
I0408 19:42:29.052994 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:42:32.543998 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:42:33.893123 24089 solver.cpp:397]     Test net output #0: accuracy = 0.300245
I0408 19:42:33.893173 24089 solver.cpp:397]     Test net output #1: loss = 3.02415 (* 1 = 3.02415 loss)
I0408 19:42:33.983821 24089 solver.cpp:218] Iteration 3060 (0.808909 iter/s, 14.8348s/12 iters), loss = 2.02409
I0408 19:42:33.983866 24089 solver.cpp:237]     Train net output #0: loss = 2.02409 (* 1 = 2.02409 loss)
I0408 19:42:33.983877 24089 sgd_solver.cpp:105] Iteration 3060, lr = 0.000964461
I0408 19:42:38.298139 24089 solver.cpp:218] Iteration 3072 (2.78159 iter/s, 4.31408s/12 iters), loss = 1.93012
I0408 19:42:38.298202 24089 solver.cpp:237]     Train net output #0: loss = 1.93012 (* 1 = 1.93012 loss)
I0408 19:42:38.298214 24089 sgd_solver.cpp:105] Iteration 3072, lr = 0.000955655
I0408 19:42:43.245080 24089 solver.cpp:218] Iteration 3084 (2.42587 iter/s, 4.94667s/12 iters), loss = 1.82219
I0408 19:42:43.245129 24089 solver.cpp:237]     Train net output #0: loss = 1.82219 (* 1 = 1.82219 loss)
I0408 19:42:43.245142 24089 sgd_solver.cpp:105] Iteration 3084, lr = 0.000946931
I0408 19:42:48.089638 24089 solver.cpp:218] Iteration 3096 (2.47713 iter/s, 4.84431s/12 iters), loss = 1.7231
I0408 19:42:48.089675 24089 solver.cpp:237]     Train net output #0: loss = 1.7231 (* 1 = 1.7231 loss)
I0408 19:42:48.089684 24089 sgd_solver.cpp:105] Iteration 3096, lr = 0.000938285
I0408 19:42:52.992409 24089 solver.cpp:218] Iteration 3108 (2.44772 iter/s, 4.90253s/12 iters), loss = 1.76178
I0408 19:42:52.992460 24089 solver.cpp:237]     Train net output #0: loss = 1.76178 (* 1 = 1.76178 loss)
I0408 19:42:52.992471 24089 sgd_solver.cpp:105] Iteration 3108, lr = 0.000929719
I0408 19:42:58.171236 24089 solver.cpp:218] Iteration 3120 (2.31725 iter/s, 5.17856s/12 iters), loss = 1.61228
I0408 19:42:58.171340 24089 solver.cpp:237]     Train net output #0: loss = 1.61228 (* 1 = 1.61228 loss)
I0408 19:42:58.171352 24089 sgd_solver.cpp:105] Iteration 3120, lr = 0.000921231
I0408 19:43:03.228767 24089 solver.cpp:218] Iteration 3132 (2.37285 iter/s, 5.05722s/12 iters), loss = 1.83536
I0408 19:43:03.228818 24089 solver.cpp:237]     Train net output #0: loss = 1.83536 (* 1 = 1.83536 loss)
I0408 19:43:03.228832 24089 sgd_solver.cpp:105] Iteration 3132, lr = 0.00091282
I0408 19:43:04.356145 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:43:08.275310 24089 solver.cpp:218] Iteration 3144 (2.37799 iter/s, 5.04628s/12 iters), loss = 1.67858
I0408 19:43:08.275362 24089 solver.cpp:237]     Train net output #0: loss = 1.67858 (* 1 = 1.67858 loss)
I0408 19:43:08.275373 24089 sgd_solver.cpp:105] Iteration 3144, lr = 0.000904487
I0408 19:43:13.283239 24089 solver.cpp:218] Iteration 3156 (2.39632 iter/s, 5.00767s/12 iters), loss = 1.64357
I0408 19:43:13.283293 24089 solver.cpp:237]     Train net output #0: loss = 1.64357 (* 1 = 1.64357 loss)
I0408 19:43:13.283305 24089 sgd_solver.cpp:105] Iteration 3156, lr = 0.000896229
I0408 19:43:15.261155 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0408 19:43:18.299664 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0408 19:43:21.616678 24089 solver.cpp:330] Iteration 3162, Testing net (#0)
I0408 19:43:21.616699 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:43:24.790585 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:43:26.057888 24089 solver.cpp:397]     Test net output #0: accuracy = 0.303922
I0408 19:43:26.057936 24089 solver.cpp:397]     Test net output #1: loss = 2.99936 (* 1 = 2.99936 loss)
I0408 19:43:28.080896 24089 solver.cpp:218] Iteration 3168 (0.810975 iter/s, 14.797s/12 iters), loss = 1.48836
I0408 19:43:28.080942 24089 solver.cpp:237]     Train net output #0: loss = 1.48836 (* 1 = 1.48836 loss)
I0408 19:43:28.080950 24089 sgd_solver.cpp:105] Iteration 3168, lr = 0.000888047
I0408 19:43:33.217847 24089 solver.cpp:218] Iteration 3180 (2.33614 iter/s, 5.13669s/12 iters), loss = 1.72616
I0408 19:43:33.227231 24089 solver.cpp:237]     Train net output #0: loss = 1.72616 (* 1 = 1.72616 loss)
I0408 19:43:33.227247 24089 sgd_solver.cpp:105] Iteration 3180, lr = 0.000879939
I0408 19:43:38.190812 24089 solver.cpp:218] Iteration 3192 (2.41771 iter/s, 4.96338s/12 iters), loss = 1.6014
I0408 19:43:38.190858 24089 solver.cpp:237]     Train net output #0: loss = 1.6014 (* 1 = 1.6014 loss)
I0408 19:43:38.190866 24089 sgd_solver.cpp:105] Iteration 3192, lr = 0.000871905
I0408 19:43:43.208600 24089 solver.cpp:218] Iteration 3204 (2.39162 iter/s, 5.01753s/12 iters), loss = 1.6305
I0408 19:43:43.208647 24089 solver.cpp:237]     Train net output #0: loss = 1.6305 (* 1 = 1.6305 loss)
I0408 19:43:43.208658 24089 sgd_solver.cpp:105] Iteration 3204, lr = 0.000863945
I0408 19:43:48.213953 24089 solver.cpp:218] Iteration 3216 (2.39756 iter/s, 5.00509s/12 iters), loss = 1.73114
I0408 19:43:48.214007 24089 solver.cpp:237]     Train net output #0: loss = 1.73114 (* 1 = 1.73114 loss)
I0408 19:43:48.214018 24089 sgd_solver.cpp:105] Iteration 3216, lr = 0.000856058
I0408 19:43:53.290969 24089 solver.cpp:218] Iteration 3228 (2.36372 iter/s, 5.07675s/12 iters), loss = 1.45067
I0408 19:43:53.291009 24089 solver.cpp:237]     Train net output #0: loss = 1.45067 (* 1 = 1.45067 loss)
I0408 19:43:53.291018 24089 sgd_solver.cpp:105] Iteration 3228, lr = 0.000848242
I0408 19:43:56.594451 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:43:58.373275 24089 solver.cpp:218] Iteration 3240 (2.36125 iter/s, 5.08205s/12 iters), loss = 1.38132
I0408 19:43:58.373327 24089 solver.cpp:237]     Train net output #0: loss = 1.38132 (* 1 = 1.38132 loss)
I0408 19:43:58.373342 24089 sgd_solver.cpp:105] Iteration 3240, lr = 0.000840498
I0408 19:44:03.462069 24089 solver.cpp:218] Iteration 3252 (2.35825 iter/s, 5.08852s/12 iters), loss = 1.50016
I0408 19:44:03.462185 24089 solver.cpp:237]     Train net output #0: loss = 1.50016 (* 1 = 1.50016 loss)
I0408 19:44:03.462199 24089 sgd_solver.cpp:105] Iteration 3252, lr = 0.000832824
I0408 19:44:08.001503 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0408 19:44:12.390908 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0408 19:44:16.982146 24089 solver.cpp:330] Iteration 3264, Testing net (#0)
I0408 19:44:16.982172 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:44:20.146351 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:44:21.451205 24089 solver.cpp:397]     Test net output #0: accuracy = 0.310662
I0408 19:44:21.451248 24089 solver.cpp:397]     Test net output #1: loss = 3.01218 (* 1 = 3.01218 loss)
I0408 19:44:21.541739 24089 solver.cpp:218] Iteration 3264 (0.66376 iter/s, 18.0788s/12 iters), loss = 1.48865
I0408 19:44:21.541787 24089 solver.cpp:237]     Train net output #0: loss = 1.48865 (* 1 = 1.48865 loss)
I0408 19:44:21.541797 24089 sgd_solver.cpp:105] Iteration 3264, lr = 0.000825221
I0408 19:44:25.647933 24089 solver.cpp:218] Iteration 3276 (2.92257 iter/s, 4.10597s/12 iters), loss = 1.51191
I0408 19:44:25.647985 24089 solver.cpp:237]     Train net output #0: loss = 1.51191 (* 1 = 1.51191 loss)
I0408 19:44:25.647997 24089 sgd_solver.cpp:105] Iteration 3276, lr = 0.000817687
I0408 19:44:30.699399 24089 solver.cpp:218] Iteration 3288 (2.37565 iter/s, 5.05126s/12 iters), loss = 1.37168
I0408 19:44:30.699456 24089 solver.cpp:237]     Train net output #0: loss = 1.37168 (* 1 = 1.37168 loss)
I0408 19:44:30.699470 24089 sgd_solver.cpp:105] Iteration 3288, lr = 0.000810221
I0408 19:44:35.746068 24089 solver.cpp:218] Iteration 3300 (2.3779 iter/s, 5.04646s/12 iters), loss = 1.55934
I0408 19:44:35.746170 24089 solver.cpp:237]     Train net output #0: loss = 1.55934 (* 1 = 1.55934 loss)
I0408 19:44:35.746181 24089 sgd_solver.cpp:105] Iteration 3300, lr = 0.000802824
I0408 19:44:40.801232 24089 solver.cpp:218] Iteration 3312 (2.37393 iter/s, 5.05491s/12 iters), loss = 1.45009
I0408 19:44:40.801285 24089 solver.cpp:237]     Train net output #0: loss = 1.45009 (* 1 = 1.45009 loss)
I0408 19:44:40.801297 24089 sgd_solver.cpp:105] Iteration 3312, lr = 0.000795495
I0408 19:44:45.862556 24089 solver.cpp:218] Iteration 3324 (2.37102 iter/s, 5.06112s/12 iters), loss = 1.39154
I0408 19:44:45.862604 24089 solver.cpp:237]     Train net output #0: loss = 1.39154 (* 1 = 1.39154 loss)
I0408 19:44:45.862617 24089 sgd_solver.cpp:105] Iteration 3324, lr = 0.000788232
I0408 19:44:50.859110 24089 solver.cpp:218] Iteration 3336 (2.40175 iter/s, 4.99636s/12 iters), loss = 1.38607
I0408 19:44:50.859160 24089 solver.cpp:237]     Train net output #0: loss = 1.38607 (* 1 = 1.38607 loss)
I0408 19:44:50.859174 24089 sgd_solver.cpp:105] Iteration 3336, lr = 0.000781036
I0408 19:44:51.331241 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:44:55.910676 24089 solver.cpp:218] Iteration 3348 (2.3756 iter/s, 5.05136s/12 iters), loss = 1.42831
I0408 19:44:55.910719 24089 solver.cpp:237]     Train net output #0: loss = 1.42831 (* 1 = 1.42831 loss)
I0408 19:44:55.910728 24089 sgd_solver.cpp:105] Iteration 3348, lr = 0.000773905
I0408 19:45:00.931226 24089 solver.cpp:218] Iteration 3360 (2.39027 iter/s, 5.02035s/12 iters), loss = 1.32563
I0408 19:45:00.931277 24089 solver.cpp:237]     Train net output #0: loss = 1.32563 (* 1 = 1.32563 loss)
I0408 19:45:00.931289 24089 sgd_solver.cpp:105] Iteration 3360, lr = 0.00076684
I0408 19:45:02.943131 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0408 19:45:05.915226 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0408 19:45:08.238332 24089 solver.cpp:330] Iteration 3366, Testing net (#0)
I0408 19:45:08.238358 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:45:11.369204 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:45:12.708528 24089 solver.cpp:397]     Test net output #0: accuracy = 0.321078
I0408 19:45:12.708577 24089 solver.cpp:397]     Test net output #1: loss = 3.02891 (* 1 = 3.02891 loss)
I0408 19:45:14.574200 24089 solver.cpp:218] Iteration 3372 (0.879602 iter/s, 13.6425s/12 iters), loss = 1.45326
I0408 19:45:14.574244 24089 solver.cpp:237]     Train net output #0: loss = 1.45326 (* 1 = 1.45326 loss)
I0408 19:45:14.574254 24089 sgd_solver.cpp:105] Iteration 3372, lr = 0.000759839
I0408 19:45:19.690834 24089 solver.cpp:218] Iteration 3384 (2.34538 iter/s, 5.11643s/12 iters), loss = 1.24509
I0408 19:45:19.690876 24089 solver.cpp:237]     Train net output #0: loss = 1.24509 (* 1 = 1.24509 loss)
I0408 19:45:19.690884 24089 sgd_solver.cpp:105] Iteration 3384, lr = 0.000752902
I0408 19:45:24.865444 24089 solver.cpp:218] Iteration 3396 (2.31911 iter/s, 5.1744s/12 iters), loss = 1.3036
I0408 19:45:24.865502 24089 solver.cpp:237]     Train net output #0: loss = 1.3036 (* 1 = 1.3036 loss)
I0408 19:45:24.865514 24089 sgd_solver.cpp:105] Iteration 3396, lr = 0.000746028
I0408 19:45:29.943543 24089 solver.cpp:218] Iteration 3408 (2.36319 iter/s, 5.07789s/12 iters), loss = 1.48408
I0408 19:45:29.943590 24089 solver.cpp:237]     Train net output #0: loss = 1.48408 (* 1 = 1.48408 loss)
I0408 19:45:29.943601 24089 sgd_solver.cpp:105] Iteration 3408, lr = 0.000739217
I0408 19:45:35.027890 24089 solver.cpp:218] Iteration 3420 (2.36028 iter/s, 5.08414s/12 iters), loss = 1.21141
I0408 19:45:35.027945 24089 solver.cpp:237]     Train net output #0: loss = 1.21141 (* 1 = 1.21141 loss)
I0408 19:45:35.027957 24089 sgd_solver.cpp:105] Iteration 3420, lr = 0.000732468
I0408 19:45:40.153842 24089 solver.cpp:218] Iteration 3432 (2.34113 iter/s, 5.12574s/12 iters), loss = 1.29108
I0408 19:45:40.153987 24089 solver.cpp:237]     Train net output #0: loss = 1.29108 (* 1 = 1.29108 loss)
I0408 19:45:40.154003 24089 sgd_solver.cpp:105] Iteration 3432, lr = 0.000725781
I0408 19:45:42.861776 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:45:45.374409 24089 solver.cpp:218] Iteration 3444 (2.29873 iter/s, 5.22027s/12 iters), loss = 1.22092
I0408 19:45:45.374444 24089 solver.cpp:237]     Train net output #0: loss = 1.22092 (* 1 = 1.22092 loss)
I0408 19:45:45.374452 24089 sgd_solver.cpp:105] Iteration 3444, lr = 0.000719154
I0408 19:45:50.460261 24089 solver.cpp:218] Iteration 3456 (2.35958 iter/s, 5.08565s/12 iters), loss = 1.37511
I0408 19:45:50.460307 24089 solver.cpp:237]     Train net output #0: loss = 1.37511 (* 1 = 1.37511 loss)
I0408 19:45:50.460316 24089 sgd_solver.cpp:105] Iteration 3456, lr = 0.000712589
I0408 19:45:54.936342 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0408 19:45:57.990262 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0408 19:46:00.302577 24089 solver.cpp:330] Iteration 3468, Testing net (#0)
I0408 19:46:00.302601 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:46:00.774849 24089 blocking_queue.cpp:49] Waiting for data
I0408 19:46:03.405745 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:46:04.794842 24089 solver.cpp:397]     Test net output #0: accuracy = 0.324142
I0408 19:46:04.794890 24089 solver.cpp:397]     Test net output #1: loss = 2.99392 (* 1 = 2.99392 loss)
I0408 19:46:04.882594 24089 solver.cpp:218] Iteration 3468 (0.83207 iter/s, 14.4219s/12 iters), loss = 1.53157
I0408 19:46:04.882648 24089 solver.cpp:237]     Train net output #0: loss = 1.53157 (* 1 = 1.53157 loss)
I0408 19:46:04.882660 24089 sgd_solver.cpp:105] Iteration 3468, lr = 0.000706083
I0408 19:46:09.229130 24089 solver.cpp:218] Iteration 3480 (2.76094 iter/s, 4.34635s/12 iters), loss = 1.27788
I0408 19:46:09.229169 24089 solver.cpp:237]     Train net output #0: loss = 1.27788 (* 1 = 1.27788 loss)
I0408 19:46:09.229178 24089 sgd_solver.cpp:105] Iteration 3480, lr = 0.000699637
I0408 19:46:14.240069 24089 solver.cpp:218] Iteration 3492 (2.39486 iter/s, 5.01074s/12 iters), loss = 1.503
I0408 19:46:14.240170 24089 solver.cpp:237]     Train net output #0: loss = 1.503 (* 1 = 1.503 loss)
I0408 19:46:14.240180 24089 sgd_solver.cpp:105] Iteration 3492, lr = 0.000693249
I0408 19:46:19.290191 24089 solver.cpp:218] Iteration 3504 (2.3763 iter/s, 5.04986s/12 iters), loss = 1.52589
I0408 19:46:19.290230 24089 solver.cpp:237]     Train net output #0: loss = 1.52589 (* 1 = 1.52589 loss)
I0408 19:46:19.290239 24089 sgd_solver.cpp:105] Iteration 3504, lr = 0.00068692
I0408 19:46:24.367692 24089 solver.cpp:218] Iteration 3516 (2.36346 iter/s, 5.07729s/12 iters), loss = 1.02418
I0408 19:46:24.367746 24089 solver.cpp:237]     Train net output #0: loss = 1.02418 (* 1 = 1.02418 loss)
I0408 19:46:24.367758 24089 sgd_solver.cpp:105] Iteration 3516, lr = 0.000680649
I0408 19:46:29.404808 24089 solver.cpp:218] Iteration 3528 (2.38242 iter/s, 5.0369s/12 iters), loss = 1.21519
I0408 19:46:29.404847 24089 solver.cpp:237]     Train net output #0: loss = 1.21519 (* 1 = 1.21519 loss)
I0408 19:46:29.404857 24089 sgd_solver.cpp:105] Iteration 3528, lr = 0.000674435
I0408 19:46:34.195770 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:46:34.456243 24089 solver.cpp:218] Iteration 3540 (2.37566 iter/s, 5.05123s/12 iters), loss = 1.01722
I0408 19:46:34.456288 24089 solver.cpp:237]     Train net output #0: loss = 1.01722 (* 1 = 1.01722 loss)
I0408 19:46:34.456300 24089 sgd_solver.cpp:105] Iteration 3540, lr = 0.000668277
I0408 19:46:39.494688 24089 solver.cpp:218] Iteration 3552 (2.38179 iter/s, 5.03823s/12 iters), loss = 1.17601
I0408 19:46:39.494735 24089 solver.cpp:237]     Train net output #0: loss = 1.17601 (* 1 = 1.17601 loss)
I0408 19:46:39.494746 24089 sgd_solver.cpp:105] Iteration 3552, lr = 0.000662176
I0408 19:46:44.679487 24089 solver.cpp:218] Iteration 3564 (2.31456 iter/s, 5.18458s/12 iters), loss = 1.47697
I0408 19:46:44.679576 24089 solver.cpp:237]     Train net output #0: loss = 1.47697 (* 1 = 1.47697 loss)
I0408 19:46:44.679586 24089 sgd_solver.cpp:105] Iteration 3564, lr = 0.00065613
I0408 19:46:46.804589 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0408 19:46:49.893757 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0408 19:46:52.217408 24089 solver.cpp:330] Iteration 3570, Testing net (#0)
I0408 19:46:52.217435 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:46:55.279556 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:46:56.701148 24089 solver.cpp:397]     Test net output #0: accuracy = 0.344363
I0408 19:46:56.701193 24089 solver.cpp:397]     Test net output #1: loss = 2.92831 (* 1 = 2.92831 loss)
I0408 19:46:58.561880 24089 solver.cpp:218] Iteration 3576 (0.864437 iter/s, 13.8819s/12 iters), loss = 1.25502
I0408 19:46:58.561933 24089 solver.cpp:237]     Train net output #0: loss = 1.25502 (* 1 = 1.25502 loss)
I0408 19:46:58.561944 24089 sgd_solver.cpp:105] Iteration 3576, lr = 0.00065014
I0408 19:47:03.490233 24089 solver.cpp:218] Iteration 3588 (2.435 iter/s, 4.92814s/12 iters), loss = 1.03601
I0408 19:47:03.490286 24089 solver.cpp:237]     Train net output #0: loss = 1.03601 (* 1 = 1.03601 loss)
I0408 19:47:03.490298 24089 sgd_solver.cpp:105] Iteration 3588, lr = 0.000644205
I0408 19:47:08.486198 24089 solver.cpp:218] Iteration 3600 (2.40204 iter/s, 4.99575s/12 iters), loss = 1.5788
I0408 19:47:08.486239 24089 solver.cpp:237]     Train net output #0: loss = 1.5788 (* 1 = 1.5788 loss)
I0408 19:47:08.486248 24089 sgd_solver.cpp:105] Iteration 3600, lr = 0.000638323
I0408 19:47:13.486948 24089 solver.cpp:218] Iteration 3612 (2.39974 iter/s, 5.00054s/12 iters), loss = 1.06891
I0408 19:47:13.487001 24089 solver.cpp:237]     Train net output #0: loss = 1.06891 (* 1 = 1.06891 loss)
I0408 19:47:13.487015 24089 sgd_solver.cpp:105] Iteration 3612, lr = 0.000632495
I0408 19:47:18.456791 24089 solver.cpp:218] Iteration 3624 (2.41467 iter/s, 4.96963s/12 iters), loss = 1.08919
I0408 19:47:18.456938 24089 solver.cpp:237]     Train net output #0: loss = 1.08919 (* 1 = 1.08919 loss)
I0408 19:47:18.456951 24089 sgd_solver.cpp:105] Iteration 3624, lr = 0.000626721
I0408 19:47:23.507309 24089 solver.cpp:218] Iteration 3636 (2.37614 iter/s, 5.0502s/12 iters), loss = 1.27137
I0408 19:47:23.507361 24089 solver.cpp:237]     Train net output #0: loss = 1.27137 (* 1 = 1.27137 loss)
I0408 19:47:23.507372 24089 sgd_solver.cpp:105] Iteration 3636, lr = 0.000620999
I0408 19:47:25.378716 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:47:28.493641 24089 solver.cpp:218] Iteration 3648 (2.40669 iter/s, 4.98611s/12 iters), loss = 1.01237
I0408 19:47:28.493693 24089 solver.cpp:237]     Train net output #0: loss = 1.01237 (* 1 = 1.01237 loss)
I0408 19:47:28.493705 24089 sgd_solver.cpp:105] Iteration 3648, lr = 0.00061533
I0408 19:47:33.560441 24089 solver.cpp:218] Iteration 3660 (2.36846 iter/s, 5.06658s/12 iters), loss = 1.16566
I0408 19:47:33.560487 24089 solver.cpp:237]     Train net output #0: loss = 1.16566 (* 1 = 1.16566 loss)
I0408 19:47:33.560497 24089 sgd_solver.cpp:105] Iteration 3660, lr = 0.000609712
I0408 19:47:38.071419 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0408 19:47:43.299964 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0408 19:47:45.626475 24089 solver.cpp:330] Iteration 3672, Testing net (#0)
I0408 19:47:45.626500 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:47:48.629101 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:47:50.099505 24089 solver.cpp:397]     Test net output #0: accuracy = 0.355392
I0408 19:47:50.099537 24089 solver.cpp:397]     Test net output #1: loss = 2.88971 (* 1 = 2.88971 loss)
I0408 19:47:50.187016 24089 solver.cpp:218] Iteration 3672 (0.721761 iter/s, 16.626s/12 iters), loss = 0.988426
I0408 19:47:50.187063 24089 solver.cpp:237]     Train net output #0: loss = 0.988426 (* 1 = 0.988426 loss)
I0408 19:47:50.187072 24089 sgd_solver.cpp:105] Iteration 3672, lr = 0.000604145
I0408 19:47:54.426443 24089 solver.cpp:218] Iteration 3684 (2.8307 iter/s, 4.23924s/12 iters), loss = 1.14736
I0408 19:47:54.426491 24089 solver.cpp:237]     Train net output #0: loss = 1.14736 (* 1 = 1.14736 loss)
I0408 19:47:54.426501 24089 sgd_solver.cpp:105] Iteration 3684, lr = 0.00059863
I0408 19:47:59.490298 24089 solver.cpp:218] Iteration 3696 (2.36984 iter/s, 5.06364s/12 iters), loss = 1.08654
I0408 19:47:59.490348 24089 solver.cpp:237]     Train net output #0: loss = 1.08654 (* 1 = 1.08654 loss)
I0408 19:47:59.490360 24089 sgd_solver.cpp:105] Iteration 3696, lr = 0.000593164
I0408 19:48:04.477684 24089 solver.cpp:218] Iteration 3708 (2.40617 iter/s, 4.98717s/12 iters), loss = 1.31181
I0408 19:48:04.477727 24089 solver.cpp:237]     Train net output #0: loss = 1.31181 (* 1 = 1.31181 loss)
I0408 19:48:04.477736 24089 sgd_solver.cpp:105] Iteration 3708, lr = 0.000587749
I0408 19:48:09.512547 24089 solver.cpp:218] Iteration 3720 (2.38348 iter/s, 5.03465s/12 iters), loss = 1.06849
I0408 19:48:09.512591 24089 solver.cpp:237]     Train net output #0: loss = 1.06849 (* 1 = 1.06849 loss)
I0408 19:48:09.512600 24089 sgd_solver.cpp:105] Iteration 3720, lr = 0.000582383
I0408 19:48:14.515637 24089 solver.cpp:218] Iteration 3732 (2.39862 iter/s, 5.00287s/12 iters), loss = 0.944358
I0408 19:48:14.515683 24089 solver.cpp:237]     Train net output #0: loss = 0.944358 (* 1 = 0.944358 loss)
I0408 19:48:14.515692 24089 sgd_solver.cpp:105] Iteration 3732, lr = 0.000577066
I0408 19:48:18.565053 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:48:19.567050 24089 solver.cpp:218] Iteration 3744 (2.37568 iter/s, 5.05119s/12 iters), loss = 1.01755
I0408 19:48:19.567200 24089 solver.cpp:237]     Train net output #0: loss = 1.01755 (* 1 = 1.01755 loss)
I0408 19:48:19.567214 24089 sgd_solver.cpp:105] Iteration 3744, lr = 0.000571797
I0408 19:48:24.637888 24089 solver.cpp:218] Iteration 3756 (2.36662 iter/s, 5.07052s/12 iters), loss = 1.02035
I0408 19:48:24.637935 24089 solver.cpp:237]     Train net output #0: loss = 1.02035 (* 1 = 1.02035 loss)
I0408 19:48:24.637946 24089 sgd_solver.cpp:105] Iteration 3756, lr = 0.000566577
I0408 19:48:29.718415 24089 solver.cpp:218] Iteration 3768 (2.36206 iter/s, 5.08031s/12 iters), loss = 1.0757
I0408 19:48:29.718466 24089 solver.cpp:237]     Train net output #0: loss = 1.0757 (* 1 = 1.0757 loss)
I0408 19:48:29.718479 24089 sgd_solver.cpp:105] Iteration 3768, lr = 0.000561404
I0408 19:48:31.753780 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0408 19:48:36.678586 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0408 19:48:42.477358 24089 solver.cpp:330] Iteration 3774, Testing net (#0)
I0408 19:48:42.477383 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:48:45.467730 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:48:46.997364 24089 solver.cpp:397]     Test net output #0: accuracy = 0.356005
I0408 19:48:46.997412 24089 solver.cpp:397]     Test net output #1: loss = 2.89178 (* 1 = 2.89178 loss)
I0408 19:48:48.961024 24089 solver.cpp:218] Iteration 3780 (0.623638 iter/s, 19.2419s/12 iters), loss = 0.829235
I0408 19:48:48.961091 24089 solver.cpp:237]     Train net output #0: loss = 0.829235 (* 1 = 0.829235 loss)
I0408 19:48:48.961108 24089 sgd_solver.cpp:105] Iteration 3780, lr = 0.000556279
I0408 19:48:54.281819 24089 solver.cpp:218] Iteration 3792 (2.25541 iter/s, 5.32055s/12 iters), loss = 0.967322
I0408 19:48:54.281934 24089 solver.cpp:237]     Train net output #0: loss = 0.967322 (* 1 = 0.967322 loss)
I0408 19:48:54.281947 24089 sgd_solver.cpp:105] Iteration 3792, lr = 0.0005512
I0408 19:48:59.333061 24089 solver.cpp:218] Iteration 3804 (2.37579 iter/s, 5.05096s/12 iters), loss = 1.36097
I0408 19:48:59.333109 24089 solver.cpp:237]     Train net output #0: loss = 1.36097 (* 1 = 1.36097 loss)
I0408 19:48:59.333122 24089 sgd_solver.cpp:105] Iteration 3804, lr = 0.000546168
I0408 19:49:04.450176 24089 solver.cpp:218] Iteration 3816 (2.34517 iter/s, 5.11689s/12 iters), loss = 1.06478
I0408 19:49:04.450224 24089 solver.cpp:237]     Train net output #0: loss = 1.06478 (* 1 = 1.06478 loss)
I0408 19:49:04.450235 24089 sgd_solver.cpp:105] Iteration 3816, lr = 0.000541182
I0408 19:49:09.550988 24089 solver.cpp:218] Iteration 3828 (2.35267 iter/s, 5.10059s/12 iters), loss = 0.911845
I0408 19:49:09.551044 24089 solver.cpp:237]     Train net output #0: loss = 0.911845 (* 1 = 0.911845 loss)
I0408 19:49:09.551056 24089 sgd_solver.cpp:105] Iteration 3828, lr = 0.000536241
I0408 19:49:14.625387 24089 solver.cpp:218] Iteration 3840 (2.36492 iter/s, 5.07416s/12 iters), loss = 1.1935
I0408 19:49:14.625437 24089 solver.cpp:237]     Train net output #0: loss = 1.1935 (* 1 = 1.1935 loss)
I0408 19:49:14.625449 24089 sgd_solver.cpp:105] Iteration 3840, lr = 0.000531345
I0408 19:49:15.770598 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:49:19.660715 24089 solver.cpp:218] Iteration 3852 (2.38327 iter/s, 5.0351s/12 iters), loss = 1.02898
I0408 19:49:19.660765 24089 solver.cpp:237]     Train net output #0: loss = 1.02898 (* 1 = 1.02898 loss)
I0408 19:49:19.660778 24089 sgd_solver.cpp:105] Iteration 3852, lr = 0.000526494
I0408 19:49:24.615404 24089 solver.cpp:218] Iteration 3864 (2.42206 iter/s, 4.95447s/12 iters), loss = 0.989692
I0408 19:49:24.615566 24089 solver.cpp:237]     Train net output #0: loss = 0.989692 (* 1 = 0.989692 loss)
I0408 19:49:24.615579 24089 sgd_solver.cpp:105] Iteration 3864, lr = 0.000521687
I0408 19:49:29.189158 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0408 19:49:34.338287 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0408 19:49:40.107034 24089 solver.cpp:330] Iteration 3876, Testing net (#0)
I0408 19:49:40.107061 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:49:43.030696 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:49:44.578193 24089 solver.cpp:397]     Test net output #0: accuracy = 0.360294
I0408 19:49:44.578244 24089 solver.cpp:397]     Test net output #1: loss = 2.90613 (* 1 = 2.90613 loss)
I0408 19:49:44.668902 24089 solver.cpp:218] Iteration 3876 (0.598424 iter/s, 20.0527s/12 iters), loss = 0.886521
I0408 19:49:44.668952 24089 solver.cpp:237]     Train net output #0: loss = 0.886521 (* 1 = 0.886521 loss)
I0408 19:49:44.668964 24089 sgd_solver.cpp:105] Iteration 3876, lr = 0.000516924
I0408 19:49:49.155791 24089 solver.cpp:218] Iteration 3888 (2.67458 iter/s, 4.48668s/12 iters), loss = 1.04563
I0408 19:49:49.155843 24089 solver.cpp:237]     Train net output #0: loss = 1.04563 (* 1 = 1.04563 loss)
I0408 19:49:49.155855 24089 sgd_solver.cpp:105] Iteration 3888, lr = 0.000512205
I0408 19:49:54.175837 24089 solver.cpp:218] Iteration 3900 (2.39052 iter/s, 5.01982s/12 iters), loss = 1.03882
I0408 19:49:54.175880 24089 solver.cpp:237]     Train net output #0: loss = 1.03882 (* 1 = 1.03882 loss)
I0408 19:49:54.175889 24089 sgd_solver.cpp:105] Iteration 3900, lr = 0.000507529
I0408 19:49:59.231130 24089 solver.cpp:218] Iteration 3912 (2.37385 iter/s, 5.05507s/12 iters), loss = 0.879836
I0408 19:49:59.231259 24089 solver.cpp:237]     Train net output #0: loss = 0.879836 (* 1 = 0.879836 loss)
I0408 19:49:59.231273 24089 sgd_solver.cpp:105] Iteration 3912, lr = 0.000502895
I0408 19:50:04.303176 24089 solver.cpp:218] Iteration 3924 (2.36605 iter/s, 5.07174s/12 iters), loss = 0.979702
I0408 19:50:04.303225 24089 solver.cpp:237]     Train net output #0: loss = 0.979702 (* 1 = 0.979702 loss)
I0408 19:50:04.303238 24089 sgd_solver.cpp:105] Iteration 3924, lr = 0.000498304
I0408 19:50:09.304963 24089 solver.cpp:218] Iteration 3936 (2.39925 iter/s, 5.00156s/12 iters), loss = 0.77545
I0408 19:50:09.305012 24089 solver.cpp:237]     Train net output #0: loss = 0.77545 (* 1 = 0.77545 loss)
I0408 19:50:09.305024 24089 sgd_solver.cpp:105] Iteration 3936, lr = 0.000493755
I0408 19:50:12.702337 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:50:14.357023 24089 solver.cpp:218] Iteration 3948 (2.37537 iter/s, 5.05183s/12 iters), loss = 1.04138
I0408 19:50:14.357072 24089 solver.cpp:237]     Train net output #0: loss = 1.04138 (* 1 = 1.04138 loss)
I0408 19:50:14.357084 24089 sgd_solver.cpp:105] Iteration 3948, lr = 0.000489247
I0408 19:50:19.337921 24089 solver.cpp:218] Iteration 3960 (2.40931 iter/s, 4.98068s/12 iters), loss = 0.864222
I0408 19:50:19.337983 24089 solver.cpp:237]     Train net output #0: loss = 0.864222 (* 1 = 0.864222 loss)
I0408 19:50:19.337996 24089 sgd_solver.cpp:105] Iteration 3960, lr = 0.00048478
I0408 19:50:24.409070 24089 solver.cpp:218] Iteration 3972 (2.36644 iter/s, 5.07091s/12 iters), loss = 1.11037
I0408 19:50:24.409121 24089 solver.cpp:237]     Train net output #0: loss = 1.11037 (* 1 = 1.11037 loss)
I0408 19:50:24.409132 24089 sgd_solver.cpp:105] Iteration 3972, lr = 0.000480354
I0408 19:50:26.460273 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0408 19:50:32.847359 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0408 19:50:41.980146 24089 solver.cpp:330] Iteration 3978, Testing net (#0)
I0408 19:50:41.980170 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:50:44.857590 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:50:46.463346 24089 solver.cpp:397]     Test net output #0: accuracy = 0.353554
I0408 19:50:46.463394 24089 solver.cpp:397]     Test net output #1: loss = 2.95623 (* 1 = 2.95623 loss)
I0408 19:50:48.360693 24089 solver.cpp:218] Iteration 3984 (0.501028 iter/s, 23.9508s/12 iters), loss = 0.848535
I0408 19:50:48.360754 24089 solver.cpp:237]     Train net output #0: loss = 0.848535 (* 1 = 0.848535 loss)
I0408 19:50:48.360769 24089 sgd_solver.cpp:105] Iteration 3984, lr = 0.000475969
I0408 19:50:53.278964 24089 solver.cpp:218] Iteration 3996 (2.44 iter/s, 4.91804s/12 iters), loss = 0.776248
I0408 19:50:53.279021 24089 solver.cpp:237]     Train net output #0: loss = 0.776248 (* 1 = 0.776248 loss)
I0408 19:50:53.279034 24089 sgd_solver.cpp:105] Iteration 3996, lr = 0.000471623
I0408 19:50:58.256978 24089 solver.cpp:218] Iteration 4008 (2.41071 iter/s, 4.97778s/12 iters), loss = 0.887154
I0408 19:50:58.257021 24089 solver.cpp:237]     Train net output #0: loss = 0.887154 (* 1 = 0.887154 loss)
I0408 19:50:58.257030 24089 sgd_solver.cpp:105] Iteration 4008, lr = 0.000467317
I0408 19:51:03.295473 24089 solver.cpp:218] Iteration 4020 (2.38177 iter/s, 5.03827s/12 iters), loss = 0.729342
I0408 19:51:03.295573 24089 solver.cpp:237]     Train net output #0: loss = 0.729342 (* 1 = 0.729342 loss)
I0408 19:51:03.295584 24089 sgd_solver.cpp:105] Iteration 4020, lr = 0.000463051
I0408 19:51:08.425900 24089 solver.cpp:218] Iteration 4032 (2.33911 iter/s, 5.13015s/12 iters), loss = 0.706442
I0408 19:51:08.425945 24089 solver.cpp:237]     Train net output #0: loss = 0.706442 (* 1 = 0.706442 loss)
I0408 19:51:08.425969 24089 sgd_solver.cpp:105] Iteration 4032, lr = 0.000458823
I0408 19:51:13.475996 24089 solver.cpp:218] Iteration 4044 (2.3763 iter/s, 5.04987s/12 iters), loss = 1.00484
I0408 19:51:13.476038 24089 solver.cpp:237]     Train net output #0: loss = 1.00484 (* 1 = 1.00484 loss)
I0408 19:51:13.476048 24089 sgd_solver.cpp:105] Iteration 4044, lr = 0.000454634
I0408 19:51:13.996997 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:51:18.517724 24089 solver.cpp:218] Iteration 4056 (2.38024 iter/s, 5.0415s/12 iters), loss = 0.916393
I0408 19:51:18.517768 24089 solver.cpp:237]     Train net output #0: loss = 0.916393 (* 1 = 0.916393 loss)
I0408 19:51:18.517778 24089 sgd_solver.cpp:105] Iteration 4056, lr = 0.000450484
I0408 19:51:23.540439 24089 solver.cpp:218] Iteration 4068 (2.38925 iter/s, 5.02249s/12 iters), loss = 0.853268
I0408 19:51:23.540482 24089 solver.cpp:237]     Train net output #0: loss = 0.853268 (* 1 = 0.853268 loss)
I0408 19:51:23.540490 24089 sgd_solver.cpp:105] Iteration 4068, lr = 0.000446371
I0408 19:51:28.100613 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0408 19:51:31.150862 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0408 19:51:35.932770 24089 solver.cpp:330] Iteration 4080, Testing net (#0)
I0408 19:51:35.932884 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:51:38.897940 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:51:40.515115 24089 solver.cpp:397]     Test net output #0: accuracy = 0.359681
I0408 19:51:40.515166 24089 solver.cpp:397]     Test net output #1: loss = 2.94138 (* 1 = 2.94138 loss)
I0408 19:51:40.605695 24089 solver.cpp:218] Iteration 4080 (0.703209 iter/s, 17.0646s/12 iters), loss = 0.974843
I0408 19:51:40.605749 24089 solver.cpp:237]     Train net output #0: loss = 0.974843 (* 1 = 0.974843 loss)
I0408 19:51:40.605762 24089 sgd_solver.cpp:105] Iteration 4080, lr = 0.000442296
I0408 19:51:44.892907 24089 solver.cpp:218] Iteration 4092 (2.79916 iter/s, 4.287s/12 iters), loss = 0.831614
I0408 19:51:44.892961 24089 solver.cpp:237]     Train net output #0: loss = 0.831614 (* 1 = 0.831614 loss)
I0408 19:51:44.892973 24089 sgd_solver.cpp:105] Iteration 4092, lr = 0.000438258
I0408 19:51:49.968983 24089 solver.cpp:218] Iteration 4104 (2.36414 iter/s, 5.07584s/12 iters), loss = 0.885535
I0408 19:51:49.969035 24089 solver.cpp:237]     Train net output #0: loss = 0.885535 (* 1 = 0.885535 loss)
I0408 19:51:49.969046 24089 sgd_solver.cpp:105] Iteration 4104, lr = 0.000434256
I0408 19:51:55.109022 24089 solver.cpp:218] Iteration 4116 (2.33472 iter/s, 5.13981s/12 iters), loss = 0.78438
I0408 19:51:55.109066 24089 solver.cpp:237]     Train net output #0: loss = 0.78438 (* 1 = 0.78438 loss)
I0408 19:51:55.109076 24089 sgd_solver.cpp:105] Iteration 4116, lr = 0.000430292
I0408 19:52:00.061937 24089 solver.cpp:218] Iteration 4128 (2.42292 iter/s, 4.95269s/12 iters), loss = 0.812844
I0408 19:52:00.061998 24089 solver.cpp:237]     Train net output #0: loss = 0.812844 (* 1 = 0.812844 loss)
I0408 19:52:00.062011 24089 sgd_solver.cpp:105] Iteration 4128, lr = 0.000426363
I0408 19:52:05.104036 24089 solver.cpp:218] Iteration 4140 (2.38008 iter/s, 5.04186s/12 iters), loss = 0.895193
I0408 19:52:05.104084 24089 solver.cpp:237]     Train net output #0: loss = 0.895193 (* 1 = 0.895193 loss)
I0408 19:52:05.104096 24089 sgd_solver.cpp:105] Iteration 4140, lr = 0.000422471
I0408 19:52:07.975572 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:52:10.472788 24089 solver.cpp:218] Iteration 4152 (2.23526 iter/s, 5.36851s/12 iters), loss = 0.875935
I0408 19:52:10.472842 24089 solver.cpp:237]     Train net output #0: loss = 0.875935 (* 1 = 0.875935 loss)
I0408 19:52:10.472856 24089 sgd_solver.cpp:105] Iteration 4152, lr = 0.000418614
I0408 19:52:12.128706 24089 blocking_queue.cpp:49] Waiting for data
I0408 19:52:15.419054 24089 solver.cpp:218] Iteration 4164 (2.42619 iter/s, 4.94603s/12 iters), loss = 0.814054
I0408 19:52:15.419121 24089 solver.cpp:237]     Train net output #0: loss = 0.814054 (* 1 = 0.814054 loss)
I0408 19:52:15.419137 24089 sgd_solver.cpp:105] Iteration 4164, lr = 0.000414792
I0408 19:52:20.432209 24089 solver.cpp:218] Iteration 4176 (2.39382 iter/s, 5.01291s/12 iters), loss = 0.940636
I0408 19:52:20.432263 24089 solver.cpp:237]     Train net output #0: loss = 0.940636 (* 1 = 0.940636 loss)
I0408 19:52:20.432276 24089 sgd_solver.cpp:105] Iteration 4176, lr = 0.000411005
I0408 19:52:22.511003 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0408 19:52:27.776135 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0408 19:52:31.977494 24089 solver.cpp:330] Iteration 4182, Testing net (#0)
I0408 19:52:31.977524 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:52:34.855113 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:52:36.518201 24089 solver.cpp:397]     Test net output #0: accuracy = 0.373774
I0408 19:52:36.518252 24089 solver.cpp:397]     Test net output #1: loss = 2.90657 (* 1 = 2.90657 loss)
I0408 19:52:38.636343 24089 solver.cpp:218] Iteration 4188 (0.659215 iter/s, 18.2035s/12 iters), loss = 0.90368
I0408 19:52:38.636467 24089 solver.cpp:237]     Train net output #0: loss = 0.90368 (* 1 = 0.90368 loss)
I0408 19:52:38.636477 24089 sgd_solver.cpp:105] Iteration 4188, lr = 0.000407253
I0408 19:52:43.640154 24089 solver.cpp:218] Iteration 4200 (2.39832 iter/s, 5.0035s/12 iters), loss = 0.879181
I0408 19:52:43.640208 24089 solver.cpp:237]     Train net output #0: loss = 0.879181 (* 1 = 0.879181 loss)
I0408 19:52:43.640221 24089 sgd_solver.cpp:105] Iteration 4200, lr = 0.000403535
I0408 19:52:48.709172 24089 solver.cpp:218] Iteration 4212 (2.36743 iter/s, 5.06878s/12 iters), loss = 0.75468
I0408 19:52:48.709221 24089 solver.cpp:237]     Train net output #0: loss = 0.75468 (* 1 = 0.75468 loss)
I0408 19:52:48.709233 24089 sgd_solver.cpp:105] Iteration 4212, lr = 0.00039985
I0408 19:52:53.698792 24089 solver.cpp:218] Iteration 4224 (2.4051 iter/s, 4.98939s/12 iters), loss = 0.686508
I0408 19:52:53.698834 24089 solver.cpp:237]     Train net output #0: loss = 0.686508 (* 1 = 0.686508 loss)
I0408 19:52:53.698844 24089 sgd_solver.cpp:105] Iteration 4224, lr = 0.0003962
I0408 19:52:58.776537 24089 solver.cpp:218] Iteration 4236 (2.36336 iter/s, 5.07751s/12 iters), loss = 0.736143
I0408 19:52:58.776587 24089 solver.cpp:237]     Train net output #0: loss = 0.736143 (* 1 = 0.736143 loss)
I0408 19:52:58.776597 24089 sgd_solver.cpp:105] Iteration 4236, lr = 0.000392583
I0408 19:53:03.641685 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:53:03.870894 24089 solver.cpp:218] Iteration 4248 (2.35566 iter/s, 5.09412s/12 iters), loss = 0.854256
I0408 19:53:03.870944 24089 solver.cpp:237]     Train net output #0: loss = 0.854256 (* 1 = 0.854256 loss)
I0408 19:53:03.870955 24089 sgd_solver.cpp:105] Iteration 4248, lr = 0.000388999
I0408 19:53:08.862648 24089 solver.cpp:218] Iteration 4260 (2.40408 iter/s, 4.99152s/12 iters), loss = 0.836308
I0408 19:53:08.862725 24089 solver.cpp:237]     Train net output #0: loss = 0.836308 (* 1 = 0.836308 loss)
I0408 19:53:08.862740 24089 sgd_solver.cpp:105] Iteration 4260, lr = 0.000385447
I0408 19:53:13.796648 24089 solver.cpp:218] Iteration 4272 (2.43223 iter/s, 4.93374s/12 iters), loss = 0.837147
I0408 19:53:13.796694 24089 solver.cpp:237]     Train net output #0: loss = 0.837147 (* 1 = 0.837147 loss)
I0408 19:53:13.796705 24089 sgd_solver.cpp:105] Iteration 4272, lr = 0.000381928
I0408 19:53:18.689942 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0408 19:53:23.068819 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0408 19:53:25.395825 24089 solver.cpp:330] Iteration 4284, Testing net (#0)
I0408 19:53:25.395853 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:53:28.166203 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:53:29.862298 24089 solver.cpp:397]     Test net output #0: accuracy = 0.376838
I0408 19:53:29.862349 24089 solver.cpp:397]     Test net output #1: loss = 2.91594 (* 1 = 2.91594 loss)
I0408 19:53:29.952952 24089 solver.cpp:218] Iteration 4284 (0.742772 iter/s, 16.1557s/12 iters), loss = 0.855058
I0408 19:53:29.953008 24089 solver.cpp:237]     Train net output #0: loss = 0.855058 (* 1 = 0.855058 loss)
I0408 19:53:29.953019 24089 sgd_solver.cpp:105] Iteration 4284, lr = 0.000378441
I0408 19:53:34.539718 24089 solver.cpp:218] Iteration 4296 (2.61635 iter/s, 4.58654s/12 iters), loss = 0.900933
I0408 19:53:34.539762 24089 solver.cpp:237]     Train net output #0: loss = 0.900933 (* 1 = 0.900933 loss)
I0408 19:53:34.539772 24089 sgd_solver.cpp:105] Iteration 4296, lr = 0.000374986
I0408 19:53:39.765552 24089 solver.cpp:218] Iteration 4308 (2.29639 iter/s, 5.22559s/12 iters), loss = 0.915132
I0408 19:53:39.765659 24089 solver.cpp:237]     Train net output #0: loss = 0.915132 (* 1 = 0.915132 loss)
I0408 19:53:39.765672 24089 sgd_solver.cpp:105] Iteration 4308, lr = 0.000371563
I0408 19:53:44.786072 24089 solver.cpp:218] Iteration 4320 (2.39033 iter/s, 5.02023s/12 iters), loss = 0.845165
I0408 19:53:44.786125 24089 solver.cpp:237]     Train net output #0: loss = 0.845165 (* 1 = 0.845165 loss)
I0408 19:53:44.786137 24089 sgd_solver.cpp:105] Iteration 4320, lr = 0.00036817
I0408 19:53:49.868078 24089 solver.cpp:218] Iteration 4332 (2.36138 iter/s, 5.08177s/12 iters), loss = 0.595657
I0408 19:53:49.868127 24089 solver.cpp:237]     Train net output #0: loss = 0.595657 (* 1 = 0.595657 loss)
I0408 19:53:49.868140 24089 sgd_solver.cpp:105] Iteration 4332, lr = 0.000364809
I0408 19:53:54.879910 24089 solver.cpp:218] Iteration 4344 (2.39445 iter/s, 5.0116s/12 iters), loss = 1.05033
I0408 19:53:54.879959 24089 solver.cpp:237]     Train net output #0: loss = 1.05033 (* 1 = 1.05033 loss)
I0408 19:53:54.879971 24089 sgd_solver.cpp:105] Iteration 4344, lr = 0.000361478
I0408 19:53:56.795452 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:53:59.928306 24089 solver.cpp:218] Iteration 4356 (2.3771 iter/s, 5.04816s/12 iters), loss = 0.726241
I0408 19:53:59.928359 24089 solver.cpp:237]     Train net output #0: loss = 0.726241 (* 1 = 0.726241 loss)
I0408 19:53:59.928370 24089 sgd_solver.cpp:105] Iteration 4356, lr = 0.000358178
I0408 19:54:04.996394 24089 solver.cpp:218] Iteration 4368 (2.36787 iter/s, 5.06785s/12 iters), loss = 0.771438
I0408 19:54:04.996452 24089 solver.cpp:237]     Train net output #0: loss = 0.771438 (* 1 = 0.771438 loss)
I0408 19:54:04.996465 24089 sgd_solver.cpp:105] Iteration 4368, lr = 0.000354908
I0408 19:54:10.233413 24089 solver.cpp:218] Iteration 4380 (2.29149 iter/s, 5.23677s/12 iters), loss = 0.641464
I0408 19:54:10.233558 24089 solver.cpp:237]     Train net output #0: loss = 0.641464 (* 1 = 0.641464 loss)
I0408 19:54:10.233572 24089 sgd_solver.cpp:105] Iteration 4380, lr = 0.000351668
I0408 19:54:12.289261 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0408 19:54:15.335263 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0408 19:54:17.662901 24089 solver.cpp:330] Iteration 4386, Testing net (#0)
I0408 19:54:17.662930 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:54:20.524401 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:54:22.268052 24089 solver.cpp:397]     Test net output #0: accuracy = 0.373774
I0408 19:54:22.268095 24089 solver.cpp:397]     Test net output #1: loss = 2.95061 (* 1 = 2.95061 loss)
I0408 19:54:24.047399 24089 solver.cpp:218] Iteration 4392 (0.868725 iter/s, 13.8134s/12 iters), loss = 0.697688
I0408 19:54:24.047444 24089 solver.cpp:237]     Train net output #0: loss = 0.697688 (* 1 = 0.697688 loss)
I0408 19:54:24.047453 24089 sgd_solver.cpp:105] Iteration 4392, lr = 0.000348457
I0408 19:54:29.052701 24089 solver.cpp:218] Iteration 4404 (2.39757 iter/s, 5.00506s/12 iters), loss = 0.78107
I0408 19:54:29.052760 24089 solver.cpp:237]     Train net output #0: loss = 0.78107 (* 1 = 0.78107 loss)
I0408 19:54:29.052774 24089 sgd_solver.cpp:105] Iteration 4404, lr = 0.000345276
I0408 19:54:34.356935 24089 solver.cpp:218] Iteration 4416 (2.26245 iter/s, 5.30398s/12 iters), loss = 0.751669
I0408 19:54:34.356983 24089 solver.cpp:237]     Train net output #0: loss = 0.751669 (* 1 = 0.751669 loss)
I0408 19:54:34.356993 24089 sgd_solver.cpp:105] Iteration 4416, lr = 0.000342124
I0408 19:54:39.387787 24089 solver.cpp:218] Iteration 4428 (2.38539 iter/s, 5.03062s/12 iters), loss = 0.726407
I0408 19:54:39.387831 24089 solver.cpp:237]     Train net output #0: loss = 0.726407 (* 1 = 0.726407 loss)
I0408 19:54:39.387840 24089 sgd_solver.cpp:105] Iteration 4428, lr = 0.000339
I0408 19:54:44.441169 24089 solver.cpp:218] Iteration 4440 (2.37475 iter/s, 5.05315s/12 iters), loss = 0.700346
I0408 19:54:44.441231 24089 solver.cpp:237]     Train net output #0: loss = 0.700346 (* 1 = 0.700346 loss)
I0408 19:54:44.441241 24089 sgd_solver.cpp:105] Iteration 4440, lr = 0.000335905
I0408 19:54:48.636658 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:54:49.685493 24089 solver.cpp:218] Iteration 4452 (2.2883 iter/s, 5.24407s/12 iters), loss = 0.664565
I0408 19:54:49.685540 24089 solver.cpp:237]     Train net output #0: loss = 0.664565 (* 1 = 0.664565 loss)
I0408 19:54:49.685550 24089 sgd_solver.cpp:105] Iteration 4452, lr = 0.000332839
I0408 19:54:54.944571 24089 solver.cpp:218] Iteration 4464 (2.28187 iter/s, 5.25883s/12 iters), loss = 0.788414
I0408 19:54:54.944614 24089 solver.cpp:237]     Train net output #0: loss = 0.788414 (* 1 = 0.788414 loss)
I0408 19:54:54.944624 24089 sgd_solver.cpp:105] Iteration 4464, lr = 0.0003298
I0408 19:54:59.938247 24089 solver.cpp:218] Iteration 4476 (2.40315 iter/s, 4.99344s/12 iters), loss = 0.805279
I0408 19:54:59.938297 24089 solver.cpp:237]     Train net output #0: loss = 0.805279 (* 1 = 0.805279 loss)
I0408 19:54:59.938309 24089 sgd_solver.cpp:105] Iteration 4476, lr = 0.000326789
I0408 19:55:04.620388 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0408 19:55:07.704227 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0408 19:55:10.012348 24089 solver.cpp:330] Iteration 4488, Testing net (#0)
I0408 19:55:10.012373 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:55:12.686378 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:55:14.461318 24089 solver.cpp:397]     Test net output #0: accuracy = 0.365809
I0408 19:55:14.461436 24089 solver.cpp:397]     Test net output #1: loss = 2.97764 (* 1 = 2.97764 loss)
I0408 19:55:14.551739 24089 solver.cpp:218] Iteration 4488 (0.821191 iter/s, 14.6129s/12 iters), loss = 0.734727
I0408 19:55:14.551782 24089 solver.cpp:237]     Train net output #0: loss = 0.734727 (* 1 = 0.734727 loss)
I0408 19:55:14.551791 24089 sgd_solver.cpp:105] Iteration 4488, lr = 0.000323805
I0408 19:55:18.890751 24089 solver.cpp:218] Iteration 4500 (2.76574 iter/s, 4.3388s/12 iters), loss = 0.76671
I0408 19:55:18.890803 24089 solver.cpp:237]     Train net output #0: loss = 0.76671 (* 1 = 0.76671 loss)
I0408 19:55:18.890815 24089 sgd_solver.cpp:105] Iteration 4500, lr = 0.000320849
I0408 19:55:24.028825 24089 solver.cpp:218] Iteration 4512 (2.33562 iter/s, 5.13783s/12 iters), loss = 0.462069
I0408 19:55:24.028877 24089 solver.cpp:237]     Train net output #0: loss = 0.462069 (* 1 = 0.462069 loss)
I0408 19:55:24.028889 24089 sgd_solver.cpp:105] Iteration 4512, lr = 0.00031792
I0408 19:55:29.135233 24089 solver.cpp:218] Iteration 4524 (2.3501 iter/s, 5.10617s/12 iters), loss = 0.635967
I0408 19:55:29.135277 24089 solver.cpp:237]     Train net output #0: loss = 0.635967 (* 1 = 0.635967 loss)
I0408 19:55:29.135284 24089 sgd_solver.cpp:105] Iteration 4524, lr = 0.000315017
I0408 19:55:34.190311 24089 solver.cpp:218] Iteration 4536 (2.37396 iter/s, 5.05485s/12 iters), loss = 0.752808
I0408 19:55:34.190351 24089 solver.cpp:237]     Train net output #0: loss = 0.752808 (* 1 = 0.752808 loss)
I0408 19:55:34.190359 24089 sgd_solver.cpp:105] Iteration 4536, lr = 0.000312141
I0408 19:55:39.251941 24089 solver.cpp:218] Iteration 4548 (2.37089 iter/s, 5.0614s/12 iters), loss = 0.620436
I0408 19:55:39.251991 24089 solver.cpp:237]     Train net output #0: loss = 0.620436 (* 1 = 0.620436 loss)
I0408 19:55:39.252002 24089 sgd_solver.cpp:105] Iteration 4548, lr = 0.000309291
I0408 19:55:40.532120 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:55:44.466665 24089 solver.cpp:218] Iteration 4560 (2.30129 iter/s, 5.21447s/12 iters), loss = 0.541682
I0408 19:55:44.466796 24089 solver.cpp:237]     Train net output #0: loss = 0.541682 (* 1 = 0.541682 loss)
I0408 19:55:44.466809 24089 sgd_solver.cpp:105] Iteration 4560, lr = 0.000306468
I0408 19:55:49.547765 24089 solver.cpp:218] Iteration 4572 (2.36184 iter/s, 5.08078s/12 iters), loss = 0.6514
I0408 19:55:49.547814 24089 solver.cpp:237]     Train net output #0: loss = 0.6514 (* 1 = 0.6514 loss)
I0408 19:55:49.547827 24089 sgd_solver.cpp:105] Iteration 4572, lr = 0.00030367
I0408 19:55:54.604722 24089 solver.cpp:218] Iteration 4584 (2.37308 iter/s, 5.05672s/12 iters), loss = 0.621551
I0408 19:55:54.604773 24089 solver.cpp:237]     Train net output #0: loss = 0.621551 (* 1 = 0.621551 loss)
I0408 19:55:54.604785 24089 sgd_solver.cpp:105] Iteration 4584, lr = 0.000300897
I0408 19:55:56.635720 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0408 19:55:59.664165 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0408 19:56:02.006678 24089 solver.cpp:330] Iteration 4590, Testing net (#0)
I0408 19:56:02.006705 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:56:04.664822 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:56:06.487732 24089 solver.cpp:397]     Test net output #0: accuracy = 0.36826
I0408 19:56:06.487783 24089 solver.cpp:397]     Test net output #1: loss = 2.96024 (* 1 = 2.96024 loss)
I0408 19:56:08.310065 24089 solver.cpp:218] Iteration 4596 (0.875606 iter/s, 13.7048s/12 iters), loss = 0.493538
I0408 19:56:08.310129 24089 solver.cpp:237]     Train net output #0: loss = 0.493538 (* 1 = 0.493538 loss)
I0408 19:56:08.310142 24089 sgd_solver.cpp:105] Iteration 4596, lr = 0.00029815
I0408 19:56:13.248772 24089 solver.cpp:218] Iteration 4608 (2.42991 iter/s, 4.93846s/12 iters), loss = 0.672973
I0408 19:56:13.248827 24089 solver.cpp:237]     Train net output #0: loss = 0.672973 (* 1 = 0.672973 loss)
I0408 19:56:13.248840 24089 sgd_solver.cpp:105] Iteration 4608, lr = 0.000295428
I0408 19:56:18.204015 24089 solver.cpp:218] Iteration 4620 (2.4218 iter/s, 4.955s/12 iters), loss = 0.562575
I0408 19:56:18.204205 24089 solver.cpp:237]     Train net output #0: loss = 0.562575 (* 1 = 0.562575 loss)
I0408 19:56:18.204226 24089 sgd_solver.cpp:105] Iteration 4620, lr = 0.000292731
I0408 19:56:23.502914 24089 solver.cpp:218] Iteration 4632 (2.26478 iter/s, 5.29853s/12 iters), loss = 0.624379
I0408 19:56:23.502954 24089 solver.cpp:237]     Train net output #0: loss = 0.624379 (* 1 = 0.624379 loss)
I0408 19:56:23.502962 24089 sgd_solver.cpp:105] Iteration 4632, lr = 0.000290058
I0408 19:56:28.843686 24089 solver.cpp:218] Iteration 4644 (2.24697 iter/s, 5.34053s/12 iters), loss = 0.449626
I0408 19:56:28.843734 24089 solver.cpp:237]     Train net output #0: loss = 0.449626 (* 1 = 0.449626 loss)
I0408 19:56:28.843746 24089 sgd_solver.cpp:105] Iteration 4644, lr = 0.00028741
I0408 19:56:32.608307 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:56:34.346220 24089 solver.cpp:218] Iteration 4656 (2.18092 iter/s, 5.50227s/12 iters), loss = 0.670519
I0408 19:56:34.346267 24089 solver.cpp:237]     Train net output #0: loss = 0.670519 (* 1 = 0.670519 loss)
I0408 19:56:34.346278 24089 sgd_solver.cpp:105] Iteration 4656, lr = 0.000284786
I0408 19:56:39.490777 24089 solver.cpp:218] Iteration 4668 (2.33267 iter/s, 5.14432s/12 iters), loss = 0.631989
I0408 19:56:39.490823 24089 solver.cpp:237]     Train net output #0: loss = 0.631989 (* 1 = 0.631989 loss)
I0408 19:56:39.490834 24089 sgd_solver.cpp:105] Iteration 4668, lr = 0.000282186
I0408 19:56:44.567314 24089 solver.cpp:218] Iteration 4680 (2.36393 iter/s, 5.0763s/12 iters), loss = 0.468385
I0408 19:56:44.567363 24089 solver.cpp:237]     Train net output #0: loss = 0.468385 (* 1 = 0.468385 loss)
I0408 19:56:44.567371 24089 sgd_solver.cpp:105] Iteration 4680, lr = 0.00027961
I0408 19:56:49.197664 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0408 19:56:54.081760 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0408 19:56:57.921494 24089 solver.cpp:330] Iteration 4692, Testing net (#0)
I0408 19:56:57.921519 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:57:00.674130 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:57:02.529644 24089 solver.cpp:397]     Test net output #0: accuracy = 0.379902
I0408 19:57:02.529693 24089 solver.cpp:397]     Test net output #1: loss = 2.95119 (* 1 = 2.95119 loss)
I0408 19:57:02.620323 24089 solver.cpp:218] Iteration 4692 (0.664735 iter/s, 18.0523s/12 iters), loss = 0.797683
I0408 19:57:02.620369 24089 solver.cpp:237]     Train net output #0: loss = 0.797683 (* 1 = 0.797683 loss)
I0408 19:57:02.620380 24089 sgd_solver.cpp:105] Iteration 4692, lr = 0.000277057
I0408 19:57:06.998030 24089 solver.cpp:218] Iteration 4704 (2.7413 iter/s, 4.37749s/12 iters), loss = 0.563672
I0408 19:57:06.998085 24089 solver.cpp:237]     Train net output #0: loss = 0.563672 (* 1 = 0.563672 loss)
I0408 19:57:06.998096 24089 sgd_solver.cpp:105] Iteration 4704, lr = 0.000274528
I0408 19:57:12.057588 24089 solver.cpp:218] Iteration 4716 (2.37186 iter/s, 5.05931s/12 iters), loss = 0.598687
I0408 19:57:12.057638 24089 solver.cpp:237]     Train net output #0: loss = 0.598687 (* 1 = 0.598687 loss)
I0408 19:57:12.057651 24089 sgd_solver.cpp:105] Iteration 4716, lr = 0.000272021
I0408 19:57:17.125281 24089 solver.cpp:218] Iteration 4728 (2.36805 iter/s, 5.06745s/12 iters), loss = 0.497211
I0408 19:57:17.125335 24089 solver.cpp:237]     Train net output #0: loss = 0.497211 (* 1 = 0.497211 loss)
I0408 19:57:17.125347 24089 sgd_solver.cpp:105] Iteration 4728, lr = 0.000269538
I0408 19:57:22.220749 24089 solver.cpp:218] Iteration 4740 (2.35515 iter/s, 5.09522s/12 iters), loss = 0.729839
I0408 19:57:22.220873 24089 solver.cpp:237]     Train net output #0: loss = 0.729839 (* 1 = 0.729839 loss)
I0408 19:57:22.220887 24089 sgd_solver.cpp:105] Iteration 4740, lr = 0.000267077
I0408 19:57:27.198060 24089 solver.cpp:218] Iteration 4752 (2.41109 iter/s, 4.977s/12 iters), loss = 0.659121
I0408 19:57:27.198115 24089 solver.cpp:237]     Train net output #0: loss = 0.659121 (* 1 = 0.659121 loss)
I0408 19:57:27.198127 24089 sgd_solver.cpp:105] Iteration 4752, lr = 0.000264639
I0408 19:57:27.735671 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:57:32.247992 24089 solver.cpp:218] Iteration 4764 (2.37638 iter/s, 5.04969s/12 iters), loss = 0.686805
I0408 19:57:32.248042 24089 solver.cpp:237]     Train net output #0: loss = 0.686805 (* 1 = 0.686805 loss)
I0408 19:57:32.248054 24089 sgd_solver.cpp:105] Iteration 4764, lr = 0.000262223
I0408 19:57:37.302393 24089 solver.cpp:218] Iteration 4776 (2.37428 iter/s, 5.05416s/12 iters), loss = 0.624456
I0408 19:57:37.302448 24089 solver.cpp:237]     Train net output #0: loss = 0.624456 (* 1 = 0.624456 loss)
I0408 19:57:37.302459 24089 sgd_solver.cpp:105] Iteration 4776, lr = 0.000259829
I0408 19:57:42.364202 24089 solver.cpp:218] Iteration 4788 (2.37081 iter/s, 5.06156s/12 iters), loss = 0.76101
I0408 19:57:42.364253 24089 solver.cpp:237]     Train net output #0: loss = 0.76101 (* 1 = 0.76101 loss)
I0408 19:57:42.364264 24089 sgd_solver.cpp:105] Iteration 4788, lr = 0.000257457
I0408 19:57:44.376642 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0408 19:57:49.571918 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0408 19:57:51.896627 24089 solver.cpp:330] Iteration 4794, Testing net (#0)
I0408 19:57:51.896654 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:57:54.424347 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:57:56.332674 24089 solver.cpp:397]     Test net output #0: accuracy = 0.379289
I0408 19:57:56.332724 24089 solver.cpp:397]     Test net output #1: loss = 2.99402 (* 1 = 2.99402 loss)
I0408 19:57:58.251526 24089 solver.cpp:218] Iteration 4800 (0.755349 iter/s, 15.8867s/12 iters), loss = 0.640514
I0408 19:57:58.251577 24089 solver.cpp:237]     Train net output #0: loss = 0.640514 (* 1 = 0.640514 loss)
I0408 19:57:58.251590 24089 sgd_solver.cpp:105] Iteration 4800, lr = 0.000255106
I0408 19:58:03.211521 24089 solver.cpp:218] Iteration 4812 (2.41947 iter/s, 4.95976s/12 iters), loss = 0.511422
I0408 19:58:03.211563 24089 solver.cpp:237]     Train net output #0: loss = 0.511422 (* 1 = 0.511422 loss)
I0408 19:58:03.211575 24089 sgd_solver.cpp:105] Iteration 4812, lr = 0.000252777
I0408 19:58:08.458626 24089 solver.cpp:218] Iteration 4824 (2.28708 iter/s, 5.24686s/12 iters), loss = 0.73034
I0408 19:58:08.458678 24089 solver.cpp:237]     Train net output #0: loss = 0.73034 (* 1 = 0.73034 loss)
I0408 19:58:08.458691 24089 sgd_solver.cpp:105] Iteration 4824, lr = 0.000250469
I0408 19:58:13.832437 24089 solver.cpp:218] Iteration 4836 (2.23316 iter/s, 5.37356s/12 iters), loss = 0.486358
I0408 19:58:13.832487 24089 solver.cpp:237]     Train net output #0: loss = 0.486358 (* 1 = 0.486358 loss)
I0408 19:58:13.832500 24089 sgd_solver.cpp:105] Iteration 4836, lr = 0.000248183
I0408 19:58:15.900152 24089 blocking_queue.cpp:49] Waiting for data
I0408 19:58:18.889451 24089 solver.cpp:218] Iteration 4848 (2.37306 iter/s, 5.05677s/12 iters), loss = 0.469408
I0408 19:58:18.889501 24089 solver.cpp:237]     Train net output #0: loss = 0.469408 (* 1 = 0.469408 loss)
I0408 19:58:18.889513 24089 sgd_solver.cpp:105] Iteration 4848, lr = 0.000245917
I0408 19:58:21.571985 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:58:23.981082 24089 solver.cpp:218] Iteration 4860 (2.35692 iter/s, 5.09139s/12 iters), loss = 0.524389
I0408 19:58:23.981128 24089 solver.cpp:237]     Train net output #0: loss = 0.524389 (* 1 = 0.524389 loss)
I0408 19:58:23.981137 24089 sgd_solver.cpp:105] Iteration 4860, lr = 0.000243672
I0408 19:58:29.006829 24089 solver.cpp:218] Iteration 4872 (2.38782 iter/s, 5.02551s/12 iters), loss = 0.474421
I0408 19:58:29.006964 24089 solver.cpp:237]     Train net output #0: loss = 0.474421 (* 1 = 0.474421 loss)
I0408 19:58:29.006974 24089 sgd_solver.cpp:105] Iteration 4872, lr = 0.000241447
I0408 19:58:34.338749 24089 solver.cpp:218] Iteration 4884 (2.25074 iter/s, 5.33158s/12 iters), loss = 0.570858
I0408 19:58:34.338804 24089 solver.cpp:237]     Train net output #0: loss = 0.570858 (* 1 = 0.570858 loss)
I0408 19:58:34.338814 24089 sgd_solver.cpp:105] Iteration 4884, lr = 0.000239243
I0408 19:58:38.910750 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0408 19:58:42.778506 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0408 19:58:45.894853 24089 solver.cpp:330] Iteration 4896, Testing net (#0)
I0408 19:58:45.894878 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:58:48.480729 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:58:50.474071 24089 solver.cpp:397]     Test net output #0: accuracy = 0.375613
I0408 19:58:50.474109 24089 solver.cpp:397]     Test net output #1: loss = 2.9871 (* 1 = 2.9871 loss)
I0408 19:58:50.564649 24089 solver.cpp:218] Iteration 4896 (0.739588 iter/s, 16.2253s/12 iters), loss = 0.657843
I0408 19:58:50.564694 24089 solver.cpp:237]     Train net output #0: loss = 0.657843 (* 1 = 0.657843 loss)
I0408 19:58:50.564704 24089 sgd_solver.cpp:105] Iteration 4896, lr = 0.000237058
I0408 19:58:55.151072 24089 solver.cpp:218] Iteration 4908 (2.61655 iter/s, 4.5862s/12 iters), loss = 0.508736
I0408 19:58:55.151115 24089 solver.cpp:237]     Train net output #0: loss = 0.508736 (* 1 = 0.508736 loss)
I0408 19:58:55.151124 24089 sgd_solver.cpp:105] Iteration 4908, lr = 0.000234894
I0408 19:59:00.305411 24089 solver.cpp:218] Iteration 4920 (2.32825 iter/s, 5.1541s/12 iters), loss = 0.53784
I0408 19:59:00.305531 24089 solver.cpp:237]     Train net output #0: loss = 0.53784 (* 1 = 0.53784 loss)
I0408 19:59:00.305544 24089 sgd_solver.cpp:105] Iteration 4920, lr = 0.00023275
I0408 19:59:05.441224 24089 solver.cpp:218] Iteration 4932 (2.33668 iter/s, 5.1355s/12 iters), loss = 0.504109
I0408 19:59:05.441275 24089 solver.cpp:237]     Train net output #0: loss = 0.504109 (* 1 = 0.504109 loss)
I0408 19:59:05.441287 24089 sgd_solver.cpp:105] Iteration 4932, lr = 0.000230625
I0408 19:59:10.420326 24089 solver.cpp:218] Iteration 4944 (2.41019 iter/s, 4.97886s/12 iters), loss = 0.628162
I0408 19:59:10.420378 24089 solver.cpp:237]     Train net output #0: loss = 0.628162 (* 1 = 0.628162 loss)
I0408 19:59:10.420389 24089 sgd_solver.cpp:105] Iteration 4944, lr = 0.000228519
I0408 19:59:15.212296 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:59:15.416049 24089 solver.cpp:218] Iteration 4956 (2.40217 iter/s, 4.99548s/12 iters), loss = 0.501801
I0408 19:59:15.416090 24089 solver.cpp:237]     Train net output #0: loss = 0.501801 (* 1 = 0.501801 loss)
I0408 19:59:15.416100 24089 sgd_solver.cpp:105] Iteration 4956, lr = 0.000226433
I0408 19:59:20.454931 24089 solver.cpp:218] Iteration 4968 (2.3816 iter/s, 5.03864s/12 iters), loss = 0.56314
I0408 19:59:20.454985 24089 solver.cpp:237]     Train net output #0: loss = 0.56314 (* 1 = 0.56314 loss)
I0408 19:59:20.454998 24089 sgd_solver.cpp:105] Iteration 4968, lr = 0.000224365
I0408 19:59:25.644081 24089 solver.cpp:218] Iteration 4980 (2.31263 iter/s, 5.1889s/12 iters), loss = 0.510791
I0408 19:59:25.644130 24089 solver.cpp:237]     Train net output #0: loss = 0.510791 (* 1 = 0.510791 loss)
I0408 19:59:25.644142 24089 sgd_solver.cpp:105] Iteration 4980, lr = 0.000222317
I0408 19:59:30.694044 24089 solver.cpp:218] Iteration 4992 (2.37637 iter/s, 5.04971s/12 iters), loss = 0.671683
I0408 19:59:30.696604 24089 solver.cpp:237]     Train net output #0: loss = 0.671683 (* 1 = 0.671683 loss)
I0408 19:59:30.696619 24089 sgd_solver.cpp:105] Iteration 4992, lr = 0.000220287
I0408 19:59:32.750808 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0408 19:59:35.720232 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0408 19:59:38.044495 24089 solver.cpp:330] Iteration 4998, Testing net (#0)
I0408 19:59:38.044523 24089 net.cpp:676] Ignoring source layer train-data
I0408 19:59:40.538306 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 19:59:42.511658 24089 solver.cpp:397]     Test net output #0: accuracy = 0.387868
I0408 19:59:42.511710 24089 solver.cpp:397]     Test net output #1: loss = 3.00751 (* 1 = 3.00751 loss)
I0408 19:59:44.488404 24089 solver.cpp:218] Iteration 5004 (0.870114 iter/s, 13.7913s/12 iters), loss = 0.482759
I0408 19:59:44.488454 24089 solver.cpp:237]     Train net output #0: loss = 0.482759 (* 1 = 0.482759 loss)
I0408 19:59:44.488466 24089 sgd_solver.cpp:105] Iteration 5004, lr = 0.000218276
I0408 19:59:49.562685 24089 solver.cpp:218] Iteration 5016 (2.36498 iter/s, 5.07403s/12 iters), loss = 0.667622
I0408 19:59:49.562732 24089 solver.cpp:237]     Train net output #0: loss = 0.667622 (* 1 = 0.667622 loss)
I0408 19:59:49.562743 24089 sgd_solver.cpp:105] Iteration 5016, lr = 0.000216283
I0408 19:59:54.653918 24089 solver.cpp:218] Iteration 5028 (2.3571 iter/s, 5.09099s/12 iters), loss = 0.414306
I0408 19:59:54.653970 24089 solver.cpp:237]     Train net output #0: loss = 0.414306 (* 1 = 0.414306 loss)
I0408 19:59:54.653980 24089 sgd_solver.cpp:105] Iteration 5028, lr = 0.000214309
I0408 19:59:59.817983 24089 solver.cpp:218] Iteration 5040 (2.32386 iter/s, 5.16383s/12 iters), loss = 0.499038
I0408 19:59:59.818029 24089 solver.cpp:237]     Train net output #0: loss = 0.499038 (* 1 = 0.499038 loss)
I0408 19:59:59.818043 24089 sgd_solver.cpp:105] Iteration 5040, lr = 0.000212352
I0408 20:00:05.212698 24089 solver.cpp:218] Iteration 5052 (2.2245 iter/s, 5.39446s/12 iters), loss = 0.558548
I0408 20:00:05.212813 24089 solver.cpp:237]     Train net output #0: loss = 0.558548 (* 1 = 0.558548 loss)
I0408 20:00:05.212827 24089 sgd_solver.cpp:105] Iteration 5052, lr = 0.000210414
I0408 20:00:07.363214 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:00:10.601851 24089 solver.cpp:218] Iteration 5064 (2.22683 iter/s, 5.38883s/12 iters), loss = 0.502608
I0408 20:00:10.601900 24089 solver.cpp:237]     Train net output #0: loss = 0.502608 (* 1 = 0.502608 loss)
I0408 20:00:10.601912 24089 sgd_solver.cpp:105] Iteration 5064, lr = 0.000208493
I0408 20:00:15.846912 24089 solver.cpp:218] Iteration 5076 (2.28798 iter/s, 5.2448s/12 iters), loss = 0.534155
I0408 20:00:15.846957 24089 solver.cpp:237]     Train net output #0: loss = 0.534155 (* 1 = 0.534155 loss)
I0408 20:00:15.846967 24089 sgd_solver.cpp:105] Iteration 5076, lr = 0.000206589
I0408 20:00:21.201413 24089 solver.cpp:218] Iteration 5088 (2.24121 iter/s, 5.35425s/12 iters), loss = 0.639827
I0408 20:00:21.201455 24089 solver.cpp:237]     Train net output #0: loss = 0.639827 (* 1 = 0.639827 loss)
I0408 20:00:21.201463 24089 sgd_solver.cpp:105] Iteration 5088, lr = 0.000204703
I0408 20:00:25.821593 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0408 20:00:28.816045 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0408 20:00:31.139433 24089 solver.cpp:330] Iteration 5100, Testing net (#0)
I0408 20:00:31.139459 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:00:33.587332 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:00:35.606271 24089 solver.cpp:397]     Test net output #0: accuracy = 0.385417
I0408 20:00:35.606426 24089 solver.cpp:397]     Test net output #1: loss = 3.00362 (* 1 = 3.00362 loss)
I0408 20:00:35.697340 24089 solver.cpp:218] Iteration 5100 (0.827852 iter/s, 14.4953s/12 iters), loss = 0.482044
I0408 20:00:35.697386 24089 solver.cpp:237]     Train net output #0: loss = 0.482044 (* 1 = 0.482044 loss)
I0408 20:00:35.697394 24089 sgd_solver.cpp:105] Iteration 5100, lr = 0.000202834
I0408 20:00:39.979691 24089 solver.cpp:218] Iteration 5112 (2.80234 iter/s, 4.28214s/12 iters), loss = 0.538747
I0408 20:00:39.979737 24089 solver.cpp:237]     Train net output #0: loss = 0.538747 (* 1 = 0.538747 loss)
I0408 20:00:39.979746 24089 sgd_solver.cpp:105] Iteration 5112, lr = 0.000200982
I0408 20:00:44.985329 24089 solver.cpp:218] Iteration 5124 (2.39741 iter/s, 5.00539s/12 iters), loss = 0.467969
I0408 20:00:44.985379 24089 solver.cpp:237]     Train net output #0: loss = 0.467969 (* 1 = 0.467969 loss)
I0408 20:00:44.985390 24089 sgd_solver.cpp:105] Iteration 5124, lr = 0.000199147
I0408 20:00:50.042901 24089 solver.cpp:218] Iteration 5136 (2.3728 iter/s, 5.05732s/12 iters), loss = 0.481282
I0408 20:00:50.042955 24089 solver.cpp:237]     Train net output #0: loss = 0.481282 (* 1 = 0.481282 loss)
I0408 20:00:50.042969 24089 sgd_solver.cpp:105] Iteration 5136, lr = 0.000197329
I0408 20:00:55.178947 24089 solver.cpp:218] Iteration 5148 (2.33654 iter/s, 5.1358s/12 iters), loss = 0.476492
I0408 20:00:55.178987 24089 solver.cpp:237]     Train net output #0: loss = 0.476492 (* 1 = 0.476492 loss)
I0408 20:00:55.178997 24089 sgd_solver.cpp:105] Iteration 5148, lr = 0.000195528
I0408 20:00:59.242978 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:01:00.193137 24089 solver.cpp:218] Iteration 5160 (2.39332 iter/s, 5.01395s/12 iters), loss = 0.523683
I0408 20:01:00.193189 24089 solver.cpp:237]     Train net output #0: loss = 0.523683 (* 1 = 0.523683 loss)
I0408 20:01:00.193200 24089 sgd_solver.cpp:105] Iteration 5160, lr = 0.000193742
I0408 20:01:05.276633 24089 solver.cpp:218] Iteration 5172 (2.3607 iter/s, 5.08325s/12 iters), loss = 0.632144
I0408 20:01:05.276677 24089 solver.cpp:237]     Train net output #0: loss = 0.632144 (* 1 = 0.632144 loss)
I0408 20:01:05.276687 24089 sgd_solver.cpp:105] Iteration 5172, lr = 0.000191974
I0408 20:01:10.301754 24089 solver.cpp:218] Iteration 5184 (2.38812 iter/s, 5.02488s/12 iters), loss = 0.521182
I0408 20:01:10.301851 24089 solver.cpp:237]     Train net output #0: loss = 0.521182 (* 1 = 0.521182 loss)
I0408 20:01:10.301862 24089 sgd_solver.cpp:105] Iteration 5184, lr = 0.000190221
I0408 20:01:15.272729 24089 solver.cpp:218] Iteration 5196 (2.41415 iter/s, 4.97069s/12 iters), loss = 0.490814
I0408 20:01:15.272773 24089 solver.cpp:237]     Train net output #0: loss = 0.490814 (* 1 = 0.490814 loss)
I0408 20:01:15.272783 24089 sgd_solver.cpp:105] Iteration 5196, lr = 0.000188484
I0408 20:01:17.347101 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0408 20:01:22.143410 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0408 20:01:25.375661 24089 solver.cpp:330] Iteration 5202, Testing net (#0)
I0408 20:01:25.375682 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:01:27.780788 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:01:30.013168 24089 solver.cpp:397]     Test net output #0: accuracy = 0.392157
I0408 20:01:30.013216 24089 solver.cpp:397]     Test net output #1: loss = 2.99665 (* 1 = 2.99665 loss)
I0408 20:01:32.005431 24089 solver.cpp:218] Iteration 5208 (0.717187 iter/s, 16.732s/12 iters), loss = 0.537528
I0408 20:01:32.005479 24089 solver.cpp:237]     Train net output #0: loss = 0.537528 (* 1 = 0.537528 loss)
I0408 20:01:32.005491 24089 sgd_solver.cpp:105] Iteration 5208, lr = 0.000186764
I0408 20:01:37.272027 24089 solver.cpp:218] Iteration 5220 (2.27862 iter/s, 5.26634s/12 iters), loss = 0.383017
I0408 20:01:37.272079 24089 solver.cpp:237]     Train net output #0: loss = 0.383017 (* 1 = 0.383017 loss)
I0408 20:01:37.272092 24089 sgd_solver.cpp:105] Iteration 5220, lr = 0.000185058
I0408 20:01:42.557425 24089 solver.cpp:218] Iteration 5232 (2.27052 iter/s, 5.28514s/12 iters), loss = 0.627874
I0408 20:01:42.557544 24089 solver.cpp:237]     Train net output #0: loss = 0.627874 (* 1 = 0.627874 loss)
I0408 20:01:42.557554 24089 sgd_solver.cpp:105] Iteration 5232, lr = 0.000183369
I0408 20:01:47.890931 24089 solver.cpp:218] Iteration 5244 (2.25007 iter/s, 5.33318s/12 iters), loss = 0.566053
I0408 20:01:47.890985 24089 solver.cpp:237]     Train net output #0: loss = 0.566053 (* 1 = 0.566053 loss)
I0408 20:01:47.890997 24089 sgd_solver.cpp:105] Iteration 5244, lr = 0.000181695
I0408 20:01:53.364605 24089 solver.cpp:218] Iteration 5256 (2.19242 iter/s, 5.47341s/12 iters), loss = 0.6087
I0408 20:01:53.364651 24089 solver.cpp:237]     Train net output #0: loss = 0.6087 (* 1 = 0.6087 loss)
I0408 20:01:53.364661 24089 sgd_solver.cpp:105] Iteration 5256, lr = 0.000180036
I0408 20:01:54.775655 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:01:58.730756 24089 solver.cpp:218] Iteration 5268 (2.23635 iter/s, 5.36589s/12 iters), loss = 0.446754
I0408 20:01:58.730805 24089 solver.cpp:237]     Train net output #0: loss = 0.446754 (* 1 = 0.446754 loss)
I0408 20:01:58.730818 24089 sgd_solver.cpp:105] Iteration 5268, lr = 0.000178392
I0408 20:02:03.810338 24089 solver.cpp:218] Iteration 5280 (2.36251 iter/s, 5.07933s/12 iters), loss = 0.431902
I0408 20:02:03.810386 24089 solver.cpp:237]     Train net output #0: loss = 0.431902 (* 1 = 0.431902 loss)
I0408 20:02:03.810400 24089 sgd_solver.cpp:105] Iteration 5280, lr = 0.000176764
I0408 20:02:08.843812 24089 solver.cpp:218] Iteration 5292 (2.38416 iter/s, 5.03323s/12 iters), loss = 0.483343
I0408 20:02:08.843861 24089 solver.cpp:237]     Train net output #0: loss = 0.483343 (* 1 = 0.483343 loss)
I0408 20:02:08.843873 24089 sgd_solver.cpp:105] Iteration 5292, lr = 0.00017515
I0408 20:02:13.409787 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0408 20:02:18.385637 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0408 20:02:22.444713 24089 solver.cpp:330] Iteration 5304, Testing net (#0)
I0408 20:02:22.444741 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:02:24.893370 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:02:26.994400 24089 solver.cpp:397]     Test net output #0: accuracy = 0.390931
I0408 20:02:26.994451 24089 solver.cpp:397]     Test net output #1: loss = 2.98836 (* 1 = 2.98836 loss)
I0408 20:02:27.085194 24089 solver.cpp:218] Iteration 5304 (0.657871 iter/s, 18.2407s/12 iters), loss = 0.615902
I0408 20:02:27.085242 24089 solver.cpp:237]     Train net output #0: loss = 0.615902 (* 1 = 0.615902 loss)
I0408 20:02:27.085253 24089 sgd_solver.cpp:105] Iteration 5304, lr = 0.000173551
I0408 20:02:31.333823 24089 solver.cpp:218] Iteration 5316 (2.82458 iter/s, 4.24841s/12 iters), loss = 0.423897
I0408 20:02:31.333874 24089 solver.cpp:237]     Train net output #0: loss = 0.423897 (* 1 = 0.423897 loss)
I0408 20:02:31.333889 24089 sgd_solver.cpp:105] Iteration 5316, lr = 0.000171966
I0408 20:02:36.278111 24089 solver.cpp:218] Iteration 5328 (2.42716 iter/s, 4.94404s/12 iters), loss = 0.420108
I0408 20:02:36.278164 24089 solver.cpp:237]     Train net output #0: loss = 0.420108 (* 1 = 0.420108 loss)
I0408 20:02:36.278177 24089 sgd_solver.cpp:105] Iteration 5328, lr = 0.000170396
I0408 20:02:41.256765 24089 solver.cpp:218] Iteration 5340 (2.41041 iter/s, 4.97841s/12 iters), loss = 0.345634
I0408 20:02:41.256815 24089 solver.cpp:237]     Train net output #0: loss = 0.345634 (* 1 = 0.345634 loss)
I0408 20:02:41.256826 24089 sgd_solver.cpp:105] Iteration 5340, lr = 0.000168841
I0408 20:02:46.178194 24089 solver.cpp:218] Iteration 5352 (2.43844 iter/s, 4.92119s/12 iters), loss = 0.565247
I0408 20:02:46.178328 24089 solver.cpp:237]     Train net output #0: loss = 0.565247 (* 1 = 0.565247 loss)
I0408 20:02:46.178341 24089 sgd_solver.cpp:105] Iteration 5352, lr = 0.000167299
I0408 20:02:49.626449 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:02:51.204753 24089 solver.cpp:218] Iteration 5364 (2.38748 iter/s, 5.02623s/12 iters), loss = 0.50436
I0408 20:02:51.204803 24089 solver.cpp:237]     Train net output #0: loss = 0.50436 (* 1 = 0.50436 loss)
I0408 20:02:51.204815 24089 sgd_solver.cpp:105] Iteration 5364, lr = 0.000165772
I0408 20:02:56.273859 24089 solver.cpp:218] Iteration 5376 (2.3674 iter/s, 5.06886s/12 iters), loss = 0.514933
I0408 20:02:56.273911 24089 solver.cpp:237]     Train net output #0: loss = 0.514933 (* 1 = 0.514933 loss)
I0408 20:02:56.273924 24089 sgd_solver.cpp:105] Iteration 5376, lr = 0.000164258
I0408 20:03:01.304787 24089 solver.cpp:218] Iteration 5388 (2.38536 iter/s, 5.03068s/12 iters), loss = 0.554698
I0408 20:03:01.304834 24089 solver.cpp:237]     Train net output #0: loss = 0.554698 (* 1 = 0.554698 loss)
I0408 20:03:01.304847 24089 sgd_solver.cpp:105] Iteration 5388, lr = 0.000162759
I0408 20:03:06.274335 24089 solver.cpp:218] Iteration 5400 (2.41483 iter/s, 4.9693s/12 iters), loss = 0.522291
I0408 20:03:06.274394 24089 solver.cpp:237]     Train net output #0: loss = 0.522291 (* 1 = 0.522291 loss)
I0408 20:03:06.274407 24089 sgd_solver.cpp:105] Iteration 5400, lr = 0.000161273
I0408 20:03:08.405560 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0408 20:03:17.507158 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0408 20:03:24.782158 24089 solver.cpp:330] Iteration 5406, Testing net (#0)
I0408 20:03:24.782188 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:03:27.095893 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:03:29.232596 24089 solver.cpp:397]     Test net output #0: accuracy = 0.400735
I0408 20:03:29.232663 24089 solver.cpp:397]     Test net output #1: loss = 3.01573 (* 1 = 3.01573 loss)
I0408 20:03:31.208348 24089 solver.cpp:218] Iteration 5412 (0.481289 iter/s, 24.933s/12 iters), loss = 0.403998
I0408 20:03:31.208395 24089 solver.cpp:237]     Train net output #0: loss = 0.403998 (* 1 = 0.403998 loss)
I0408 20:03:31.208405 24089 sgd_solver.cpp:105] Iteration 5412, lr = 0.0001598
I0408 20:03:36.222807 24089 solver.cpp:218] Iteration 5424 (2.39319 iter/s, 5.01422s/12 iters), loss = 0.30746
I0408 20:03:36.222854 24089 solver.cpp:237]     Train net output #0: loss = 0.30746 (* 1 = 0.30746 loss)
I0408 20:03:36.222865 24089 sgd_solver.cpp:105] Iteration 5424, lr = 0.000158341
I0408 20:03:41.504743 24089 solver.cpp:218] Iteration 5436 (2.27201 iter/s, 5.28168s/12 iters), loss = 0.550073
I0408 20:03:41.504799 24089 solver.cpp:237]     Train net output #0: loss = 0.550073 (* 1 = 0.550073 loss)
I0408 20:03:41.504812 24089 sgd_solver.cpp:105] Iteration 5436, lr = 0.000156896
I0408 20:03:46.597661 24089 solver.cpp:218] Iteration 5448 (2.35633 iter/s, 5.09266s/12 iters), loss = 0.691411
I0408 20:03:46.597713 24089 solver.cpp:237]     Train net output #0: loss = 0.691411 (* 1 = 0.691411 loss)
I0408 20:03:46.597724 24089 sgd_solver.cpp:105] Iteration 5448, lr = 0.000155463
I0408 20:03:51.762157 24089 solver.cpp:218] Iteration 5460 (2.32367 iter/s, 5.16424s/12 iters), loss = 0.485293
I0408 20:03:51.762301 24089 solver.cpp:237]     Train net output #0: loss = 0.485293 (* 1 = 0.485293 loss)
I0408 20:03:51.762315 24089 sgd_solver.cpp:105] Iteration 5460, lr = 0.000154044
I0408 20:03:52.333559 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:03:56.857574 24089 solver.cpp:218] Iteration 5472 (2.35522 iter/s, 5.09508s/12 iters), loss = 0.459402
I0408 20:03:56.857627 24089 solver.cpp:237]     Train net output #0: loss = 0.459402 (* 1 = 0.459402 loss)
I0408 20:03:56.857640 24089 sgd_solver.cpp:105] Iteration 5472, lr = 0.000152638
I0408 20:04:01.939119 24089 solver.cpp:218] Iteration 5484 (2.3616 iter/s, 5.08129s/12 iters), loss = 0.475237
I0408 20:04:01.939170 24089 solver.cpp:237]     Train net output #0: loss = 0.475237 (* 1 = 0.475237 loss)
I0408 20:04:01.939182 24089 sgd_solver.cpp:105] Iteration 5484, lr = 0.000151244
I0408 20:04:06.902510 24089 solver.cpp:218] Iteration 5496 (2.41782 iter/s, 4.96314s/12 iters), loss = 0.492247
I0408 20:04:06.902561 24089 solver.cpp:237]     Train net output #0: loss = 0.492247 (* 1 = 0.492247 loss)
I0408 20:04:06.902573 24089 sgd_solver.cpp:105] Iteration 5496, lr = 0.000149863
I0408 20:04:11.478586 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0408 20:04:14.618247 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0408 20:04:22.374714 24089 solver.cpp:330] Iteration 5508, Testing net (#0)
I0408 20:04:22.374775 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:04:24.979802 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:04:27.160219 24089 solver.cpp:397]     Test net output #0: accuracy = 0.389093
I0408 20:04:27.160271 24089 solver.cpp:397]     Test net output #1: loss = 3.04701 (* 1 = 3.04701 loss)
I0408 20:04:27.248701 24089 solver.cpp:218] Iteration 5508 (0.589815 iter/s, 20.3454s/12 iters), loss = 0.518165
I0408 20:04:27.248751 24089 solver.cpp:237]     Train net output #0: loss = 0.518165 (* 1 = 0.518165 loss)
I0408 20:04:27.248762 24089 sgd_solver.cpp:105] Iteration 5508, lr = 0.000148495
I0408 20:04:31.893131 24089 solver.cpp:218] Iteration 5520 (2.58387 iter/s, 4.64419s/12 iters), loss = 0.335659
I0408 20:04:31.893183 24089 solver.cpp:237]     Train net output #0: loss = 0.335659 (* 1 = 0.335659 loss)
I0408 20:04:31.893194 24089 sgd_solver.cpp:105] Iteration 5520, lr = 0.000147139
I0408 20:04:34.542876 24089 blocking_queue.cpp:49] Waiting for data
I0408 20:04:37.213496 24089 solver.cpp:218] Iteration 5532 (2.25559 iter/s, 5.32011s/12 iters), loss = 0.37572
I0408 20:04:37.213534 24089 solver.cpp:237]     Train net output #0: loss = 0.37572 (* 1 = 0.37572 loss)
I0408 20:04:37.213543 24089 sgd_solver.cpp:105] Iteration 5532, lr = 0.000145796
I0408 20:04:42.233314 24089 solver.cpp:218] Iteration 5544 (2.39064 iter/s, 5.01957s/12 iters), loss = 0.37174
I0408 20:04:42.233371 24089 solver.cpp:237]     Train net output #0: loss = 0.37174 (* 1 = 0.37174 loss)
I0408 20:04:42.233382 24089 sgd_solver.cpp:105] Iteration 5544, lr = 0.000144465
I0408 20:04:47.332633 24089 solver.cpp:218] Iteration 5556 (2.35338 iter/s, 5.09906s/12 iters), loss = 0.308358
I0408 20:04:47.332687 24089 solver.cpp:237]     Train net output #0: loss = 0.308358 (* 1 = 0.308358 loss)
I0408 20:04:47.332700 24089 sgd_solver.cpp:105] Iteration 5556, lr = 0.000143146
I0408 20:04:50.071368 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:04:52.382709 24089 solver.cpp:218] Iteration 5568 (2.37632 iter/s, 5.04982s/12 iters), loss = 0.358886
I0408 20:04:52.382797 24089 solver.cpp:237]     Train net output #0: loss = 0.358886 (* 1 = 0.358886 loss)
I0408 20:04:52.382810 24089 sgd_solver.cpp:105] Iteration 5568, lr = 0.000141839
I0408 20:04:57.456532 24089 solver.cpp:218] Iteration 5580 (2.36522 iter/s, 5.07353s/12 iters), loss = 0.398924
I0408 20:04:57.456594 24089 solver.cpp:237]     Train net output #0: loss = 0.398924 (* 1 = 0.398924 loss)
I0408 20:04:57.456607 24089 sgd_solver.cpp:105] Iteration 5580, lr = 0.000140544
I0408 20:05:02.443974 24089 solver.cpp:218] Iteration 5592 (2.40617 iter/s, 4.98719s/12 iters), loss = 0.592646
I0408 20:05:02.444025 24089 solver.cpp:237]     Train net output #0: loss = 0.592646 (* 1 = 0.592646 loss)
I0408 20:05:02.444036 24089 sgd_solver.cpp:105] Iteration 5592, lr = 0.000139261
I0408 20:05:07.479717 24089 solver.cpp:218] Iteration 5604 (2.38308 iter/s, 5.03549s/12 iters), loss = 0.518245
I0408 20:05:07.479769 24089 solver.cpp:237]     Train net output #0: loss = 0.518245 (* 1 = 0.518245 loss)
I0408 20:05:07.479781 24089 sgd_solver.cpp:105] Iteration 5604, lr = 0.00013799
I0408 20:05:09.540709 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0408 20:05:14.890352 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0408 20:05:19.332346 24089 solver.cpp:330] Iteration 5610, Testing net (#0)
I0408 20:05:19.332373 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:05:21.604092 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:05:23.821542 24089 solver.cpp:397]     Test net output #0: accuracy = 0.393382
I0408 20:05:23.821705 24089 solver.cpp:397]     Test net output #1: loss = 3.02971 (* 1 = 3.02971 loss)
I0408 20:05:25.716629 24089 solver.cpp:218] Iteration 5616 (0.658033 iter/s, 18.2362s/12 iters), loss = 0.442914
I0408 20:05:25.716681 24089 solver.cpp:237]     Train net output #0: loss = 0.442914 (* 1 = 0.442914 loss)
I0408 20:05:25.716696 24089 sgd_solver.cpp:105] Iteration 5616, lr = 0.00013673
I0408 20:05:30.720069 24089 solver.cpp:218] Iteration 5628 (2.39847 iter/s, 5.00319s/12 iters), loss = 0.497014
I0408 20:05:30.720126 24089 solver.cpp:237]     Train net output #0: loss = 0.497014 (* 1 = 0.497014 loss)
I0408 20:05:30.720139 24089 sgd_solver.cpp:105] Iteration 5628, lr = 0.000135482
I0408 20:05:35.743103 24089 solver.cpp:218] Iteration 5640 (2.38912 iter/s, 5.02278s/12 iters), loss = 0.570402
I0408 20:05:35.743150 24089 solver.cpp:237]     Train net output #0: loss = 0.570402 (* 1 = 0.570402 loss)
I0408 20:05:35.743161 24089 sgd_solver.cpp:105] Iteration 5640, lr = 0.000134245
I0408 20:05:40.758438 24089 solver.cpp:218] Iteration 5652 (2.39278 iter/s, 5.01509s/12 iters), loss = 0.547592
I0408 20:05:40.758488 24089 solver.cpp:237]     Train net output #0: loss = 0.547592 (* 1 = 0.547592 loss)
I0408 20:05:40.758500 24089 sgd_solver.cpp:105] Iteration 5652, lr = 0.000133019
I0408 20:05:45.854259 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:05:46.032697 24089 solver.cpp:218] Iteration 5664 (2.27531 iter/s, 5.274s/12 iters), loss = 0.539675
I0408 20:05:46.032752 24089 solver.cpp:237]     Train net output #0: loss = 0.539675 (* 1 = 0.539675 loss)
I0408 20:05:46.032763 24089 sgd_solver.cpp:105] Iteration 5664, lr = 0.000131805
I0408 20:05:51.362011 24089 solver.cpp:218] Iteration 5676 (2.25181 iter/s, 5.32905s/12 iters), loss = 0.425043
I0408 20:05:51.362058 24089 solver.cpp:237]     Train net output #0: loss = 0.425043 (* 1 = 0.425043 loss)
I0408 20:05:51.362067 24089 sgd_solver.cpp:105] Iteration 5676, lr = 0.000130601
I0408 20:05:56.407389 24089 solver.cpp:218] Iteration 5688 (2.37853 iter/s, 5.04512s/12 iters), loss = 0.401131
I0408 20:05:56.407497 24089 solver.cpp:237]     Train net output #0: loss = 0.401131 (* 1 = 0.401131 loss)
I0408 20:05:56.407508 24089 sgd_solver.cpp:105] Iteration 5688, lr = 0.000129409
I0408 20:06:01.919746 24089 solver.cpp:218] Iteration 5700 (2.17706 iter/s, 5.51203s/12 iters), loss = 0.490994
I0408 20:06:01.919797 24089 solver.cpp:237]     Train net output #0: loss = 0.490994 (* 1 = 0.490994 loss)
I0408 20:06:01.919808 24089 sgd_solver.cpp:105] Iteration 5700, lr = 0.000128227
I0408 20:06:06.868083 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0408 20:06:09.900023 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0408 20:06:12.231573 24089 solver.cpp:330] Iteration 5712, Testing net (#0)
I0408 20:06:12.231599 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:06:14.552124 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:06:16.802685 24089 solver.cpp:397]     Test net output #0: accuracy = 0.393995
I0408 20:06:16.802737 24089 solver.cpp:397]     Test net output #1: loss = 3.01668 (* 1 = 3.01668 loss)
I0408 20:06:16.893322 24089 solver.cpp:218] Iteration 5712 (0.801445 iter/s, 14.973s/12 iters), loss = 0.432801
I0408 20:06:16.893371 24089 solver.cpp:237]     Train net output #0: loss = 0.432801 (* 1 = 0.432801 loss)
I0408 20:06:16.893383 24089 sgd_solver.cpp:105] Iteration 5712, lr = 0.000127057
I0408 20:06:21.198844 24089 solver.cpp:218] Iteration 5724 (2.78726 iter/s, 4.30531s/12 iters), loss = 0.567526
I0408 20:06:21.198880 24089 solver.cpp:237]     Train net output #0: loss = 0.567526 (* 1 = 0.567526 loss)
I0408 20:06:21.198889 24089 sgd_solver.cpp:105] Iteration 5724, lr = 0.000125897
I0408 20:06:26.257640 24089 solver.cpp:218] Iteration 5736 (2.37222 iter/s, 5.05856s/12 iters), loss = 0.399731
I0408 20:06:26.257684 24089 solver.cpp:237]     Train net output #0: loss = 0.399731 (* 1 = 0.399731 loss)
I0408 20:06:26.257695 24089 sgd_solver.cpp:105] Iteration 5736, lr = 0.000124747
I0408 20:06:31.334419 24089 solver.cpp:218] Iteration 5748 (2.36382 iter/s, 5.07653s/12 iters), loss = 0.444542
I0408 20:06:31.334569 24089 solver.cpp:237]     Train net output #0: loss = 0.444542 (* 1 = 0.444542 loss)
I0408 20:06:31.334583 24089 sgd_solver.cpp:105] Iteration 5748, lr = 0.000123608
I0408 20:06:36.372850 24089 solver.cpp:218] Iteration 5760 (2.38186 iter/s, 5.03808s/12 iters), loss = 0.519436
I0408 20:06:36.372905 24089 solver.cpp:237]     Train net output #0: loss = 0.519436 (* 1 = 0.519436 loss)
I0408 20:06:36.372915 24089 sgd_solver.cpp:105] Iteration 5760, lr = 0.00012248
I0408 20:06:38.739538 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:06:41.766670 24089 solver.cpp:218] Iteration 5772 (2.22488 iter/s, 5.39355s/12 iters), loss = 0.490351
I0408 20:06:41.766723 24089 solver.cpp:237]     Train net output #0: loss = 0.490351 (* 1 = 0.490351 loss)
I0408 20:06:41.766736 24089 sgd_solver.cpp:105] Iteration 5772, lr = 0.000121362
I0408 20:06:46.857355 24089 solver.cpp:218] Iteration 5784 (2.35736 iter/s, 5.09043s/12 iters), loss = 0.345262
I0408 20:06:46.857409 24089 solver.cpp:237]     Train net output #0: loss = 0.345262 (* 1 = 0.345262 loss)
I0408 20:06:46.857421 24089 sgd_solver.cpp:105] Iteration 5784, lr = 0.000120254
I0408 20:06:51.858691 24089 solver.cpp:218] Iteration 5796 (2.39948 iter/s, 5.00108s/12 iters), loss = 0.364192
I0408 20:06:51.858747 24089 solver.cpp:237]     Train net output #0: loss = 0.364192 (* 1 = 0.364192 loss)
I0408 20:06:51.858758 24089 sgd_solver.cpp:105] Iteration 5796, lr = 0.000119156
I0408 20:06:56.897454 24089 solver.cpp:218] Iteration 5808 (2.38166 iter/s, 5.0385s/12 iters), loss = 0.493167
I0408 20:06:56.897501 24089 solver.cpp:237]     Train net output #0: loss = 0.493167 (* 1 = 0.493167 loss)
I0408 20:06:56.897511 24089 sgd_solver.cpp:105] Iteration 5808, lr = 0.000118068
I0408 20:06:59.130877 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0408 20:07:02.132638 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0408 20:07:04.559033 24089 solver.cpp:330] Iteration 5814, Testing net (#0)
I0408 20:07:04.559060 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:07:06.706756 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:07:09.122129 24089 solver.cpp:397]     Test net output #0: accuracy = 0.39277
I0408 20:07:09.122166 24089 solver.cpp:397]     Test net output #1: loss = 3.03376 (* 1 = 3.03376 loss)
I0408 20:07:11.038540 24089 solver.cpp:218] Iteration 5820 (0.848626 iter/s, 14.1405s/12 iters), loss = 0.422298
I0408 20:07:11.038589 24089 solver.cpp:237]     Train net output #0: loss = 0.422298 (* 1 = 0.422298 loss)
I0408 20:07:11.038599 24089 sgd_solver.cpp:105] Iteration 5820, lr = 0.00011699
I0408 20:07:16.066303 24089 solver.cpp:218] Iteration 5832 (2.38687 iter/s, 5.02751s/12 iters), loss = 0.256474
I0408 20:07:16.066346 24089 solver.cpp:237]     Train net output #0: loss = 0.256474 (* 1 = 0.256474 loss)
I0408 20:07:16.066354 24089 sgd_solver.cpp:105] Iteration 5832, lr = 0.000115922
I0408 20:07:21.153993 24089 solver.cpp:218] Iteration 5844 (2.35876 iter/s, 5.08743s/12 iters), loss = 0.408685
I0408 20:07:21.154045 24089 solver.cpp:237]     Train net output #0: loss = 0.408685 (* 1 = 0.408685 loss)
I0408 20:07:21.154057 24089 sgd_solver.cpp:105] Iteration 5844, lr = 0.000114864
I0408 20:07:26.225525 24089 solver.cpp:218] Iteration 5856 (2.36627 iter/s, 5.07128s/12 iters), loss = 0.323473
I0408 20:07:26.225569 24089 solver.cpp:237]     Train net output #0: loss = 0.323473 (* 1 = 0.323473 loss)
I0408 20:07:26.225577 24089 sgd_solver.cpp:105] Iteration 5856, lr = 0.000113815
I0408 20:07:30.420146 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:07:31.232439 24089 solver.cpp:218] Iteration 5868 (2.3968 iter/s, 5.00667s/12 iters), loss = 0.466882
I0408 20:07:31.232499 24089 solver.cpp:237]     Train net output #0: loss = 0.466882 (* 1 = 0.466882 loss)
I0408 20:07:31.232513 24089 sgd_solver.cpp:105] Iteration 5868, lr = 0.000112776
I0408 20:07:36.275570 24089 solver.cpp:218] Iteration 5880 (2.37959 iter/s, 5.04288s/12 iters), loss = 0.414638
I0408 20:07:36.275708 24089 solver.cpp:237]     Train net output #0: loss = 0.414638 (* 1 = 0.414638 loss)
I0408 20:07:36.275719 24089 sgd_solver.cpp:105] Iteration 5880, lr = 0.000111746
I0408 20:07:41.355584 24089 solver.cpp:218] Iteration 5892 (2.36235 iter/s, 5.07968s/12 iters), loss = 0.518323
I0408 20:07:41.355628 24089 solver.cpp:237]     Train net output #0: loss = 0.518323 (* 1 = 0.518323 loss)
I0408 20:07:41.355638 24089 sgd_solver.cpp:105] Iteration 5892, lr = 0.000110726
I0408 20:07:46.482060 24089 solver.cpp:218] Iteration 5904 (2.3409 iter/s, 5.12623s/12 iters), loss = 0.355609
I0408 20:07:46.482111 24089 solver.cpp:237]     Train net output #0: loss = 0.355609 (* 1 = 0.355609 loss)
I0408 20:07:46.482123 24089 sgd_solver.cpp:105] Iteration 5904, lr = 0.000109715
I0408 20:07:51.073516 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0408 20:07:55.546170 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0408 20:07:59.478829 24089 solver.cpp:330] Iteration 5916, Testing net (#0)
I0408 20:07:59.478855 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:08:01.770323 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:08:04.144889 24089 solver.cpp:397]     Test net output #0: accuracy = 0.40625
I0408 20:08:04.144938 24089 solver.cpp:397]     Test net output #1: loss = 3.03612 (* 1 = 3.03612 loss)
I0408 20:08:04.235339 24089 solver.cpp:218] Iteration 5916 (0.675959 iter/s, 17.7526s/12 iters), loss = 0.41571
I0408 20:08:04.235395 24089 solver.cpp:237]     Train net output #0: loss = 0.41571 (* 1 = 0.41571 loss)
I0408 20:08:04.235409 24089 sgd_solver.cpp:105] Iteration 5916, lr = 0.000108713
I0408 20:08:08.491418 24089 solver.cpp:218] Iteration 5928 (2.81964 iter/s, 4.25586s/12 iters), loss = 0.580881
I0408 20:08:08.491516 24089 solver.cpp:237]     Train net output #0: loss = 0.580881 (* 1 = 0.580881 loss)
I0408 20:08:08.491528 24089 sgd_solver.cpp:105] Iteration 5928, lr = 0.000107721
I0408 20:08:13.516726 24089 solver.cpp:218] Iteration 5940 (2.38805 iter/s, 5.02501s/12 iters), loss = 0.323765
I0408 20:08:13.516779 24089 solver.cpp:237]     Train net output #0: loss = 0.323765 (* 1 = 0.323765 loss)
I0408 20:08:13.516791 24089 sgd_solver.cpp:105] Iteration 5940, lr = 0.000106737
I0408 20:08:18.413426 24089 solver.cpp:218] Iteration 5952 (2.45076 iter/s, 4.89645s/12 iters), loss = 0.427417
I0408 20:08:18.413472 24089 solver.cpp:237]     Train net output #0: loss = 0.427417 (* 1 = 0.427417 loss)
I0408 20:08:18.413481 24089 sgd_solver.cpp:105] Iteration 5952, lr = 0.000105763
I0408 20:08:23.421540 24089 solver.cpp:218] Iteration 5964 (2.39623 iter/s, 5.00786s/12 iters), loss = 0.348156
I0408 20:08:23.421594 24089 solver.cpp:237]     Train net output #0: loss = 0.348156 (* 1 = 0.348156 loss)
I0408 20:08:23.421607 24089 sgd_solver.cpp:105] Iteration 5964, lr = 0.000104797
I0408 20:08:24.779822 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:08:28.514204 24089 solver.cpp:218] Iteration 5976 (2.35645 iter/s, 5.09241s/12 iters), loss = 0.382097
I0408 20:08:28.514247 24089 solver.cpp:237]     Train net output #0: loss = 0.382097 (* 1 = 0.382097 loss)
I0408 20:08:28.514257 24089 sgd_solver.cpp:105] Iteration 5976, lr = 0.000103841
I0408 20:08:33.496599 24089 solver.cpp:218] Iteration 5988 (2.4086 iter/s, 4.98215s/12 iters), loss = 0.468334
I0408 20:08:33.496641 24089 solver.cpp:237]     Train net output #0: loss = 0.468334 (* 1 = 0.468334 loss)
I0408 20:08:33.496651 24089 sgd_solver.cpp:105] Iteration 5988, lr = 0.000102893
I0408 20:08:38.520365 24089 solver.cpp:218] Iteration 6000 (2.38876 iter/s, 5.02352s/12 iters), loss = 0.568841
I0408 20:08:38.520512 24089 solver.cpp:237]     Train net output #0: loss = 0.568841 (* 1 = 0.568841 loss)
I0408 20:08:38.520524 24089 sgd_solver.cpp:105] Iteration 6000, lr = 0.000101953
I0408 20:08:43.592634 24089 solver.cpp:218] Iteration 6012 (2.36597 iter/s, 5.07192s/12 iters), loss = 0.422766
I0408 20:08:43.592684 24089 solver.cpp:237]     Train net output #0: loss = 0.422766 (* 1 = 0.422766 loss)
I0408 20:08:43.592694 24089 sgd_solver.cpp:105] Iteration 6012, lr = 0.000101022
I0408 20:08:45.639159 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0408 20:08:48.642793 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0408 20:08:50.971931 24089 solver.cpp:330] Iteration 6018, Testing net (#0)
I0408 20:08:50.971958 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:08:53.266788 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:08:55.639921 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0408 20:08:55.639962 24089 solver.cpp:397]     Test net output #1: loss = 3.03135 (* 1 = 3.03135 loss)
I0408 20:08:57.570294 24089 solver.cpp:218] Iteration 6024 (0.858549 iter/s, 13.9771s/12 iters), loss = 0.387164
I0408 20:08:57.570353 24089 solver.cpp:237]     Train net output #0: loss = 0.387164 (* 1 = 0.387164 loss)
I0408 20:08:57.570364 24089 sgd_solver.cpp:105] Iteration 6024, lr = 0.0001001
I0408 20:09:02.619801 24089 solver.cpp:218] Iteration 6036 (2.37659 iter/s, 5.04925s/12 iters), loss = 0.477694
I0408 20:09:02.619853 24089 solver.cpp:237]     Train net output #0: loss = 0.477694 (* 1 = 0.477694 loss)
I0408 20:09:02.619863 24089 sgd_solver.cpp:105] Iteration 6036, lr = 9.91862e-05
I0408 20:09:07.649731 24089 solver.cpp:218] Iteration 6048 (2.38584 iter/s, 5.02968s/12 iters), loss = 0.30477
I0408 20:09:07.649788 24089 solver.cpp:237]     Train net output #0: loss = 0.30477 (* 1 = 0.30477 loss)
I0408 20:09:07.649801 24089 sgd_solver.cpp:105] Iteration 6048, lr = 9.82807e-05
I0408 20:09:12.709575 24089 solver.cpp:218] Iteration 6060 (2.37174 iter/s, 5.05959s/12 iters), loss = 0.372251
I0408 20:09:12.709693 24089 solver.cpp:237]     Train net output #0: loss = 0.372251 (* 1 = 0.372251 loss)
I0408 20:09:12.709707 24089 sgd_solver.cpp:105] Iteration 6060, lr = 9.73834e-05
I0408 20:09:16.157907 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:09:17.728428 24089 solver.cpp:218] Iteration 6072 (2.39113 iter/s, 5.01854s/12 iters), loss = 0.492829
I0408 20:09:17.728474 24089 solver.cpp:237]     Train net output #0: loss = 0.492829 (* 1 = 0.492829 loss)
I0408 20:09:17.728485 24089 sgd_solver.cpp:105] Iteration 6072, lr = 9.64943e-05
I0408 20:09:22.762195 24089 solver.cpp:218] Iteration 6084 (2.38402 iter/s, 5.03351s/12 iters), loss = 0.42713
I0408 20:09:22.762250 24089 solver.cpp:237]     Train net output #0: loss = 0.42713 (* 1 = 0.42713 loss)
I0408 20:09:22.762262 24089 sgd_solver.cpp:105] Iteration 6084, lr = 9.56134e-05
I0408 20:09:28.038100 24089 solver.cpp:218] Iteration 6096 (2.2746 iter/s, 5.27565s/12 iters), loss = 0.397063
I0408 20:09:28.038136 24089 solver.cpp:237]     Train net output #0: loss = 0.397063 (* 1 = 0.397063 loss)
I0408 20:09:28.038146 24089 sgd_solver.cpp:105] Iteration 6096, lr = 9.47405e-05
I0408 20:09:33.033380 24089 solver.cpp:218] Iteration 6108 (2.40239 iter/s, 4.99504s/12 iters), loss = 0.503687
I0408 20:09:33.033432 24089 solver.cpp:237]     Train net output #0: loss = 0.503687 (* 1 = 0.503687 loss)
I0408 20:09:33.033442 24089 sgd_solver.cpp:105] Iteration 6108, lr = 9.38755e-05
I0408 20:09:37.712381 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0408 20:09:40.725184 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0408 20:09:43.051678 24089 solver.cpp:330] Iteration 6120, Testing net (#0)
I0408 20:09:43.051800 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:09:45.083055 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:09:47.501600 24089 solver.cpp:397]     Test net output #0: accuracy = 0.40625
I0408 20:09:47.501650 24089 solver.cpp:397]     Test net output #1: loss = 3.05434 (* 1 = 3.05434 loss)
I0408 20:09:47.591555 24089 solver.cpp:218] Iteration 6120 (0.824313 iter/s, 14.5576s/12 iters), loss = 0.275401
I0408 20:09:47.591611 24089 solver.cpp:237]     Train net output #0: loss = 0.275401 (* 1 = 0.275401 loss)
I0408 20:09:47.591624 24089 sgd_solver.cpp:105] Iteration 6120, lr = 9.30184e-05
I0408 20:09:51.844169 24089 solver.cpp:218] Iteration 6132 (2.82195 iter/s, 4.25239s/12 iters), loss = 0.42772
I0408 20:09:51.844223 24089 solver.cpp:237]     Train net output #0: loss = 0.42772 (* 1 = 0.42772 loss)
I0408 20:09:51.844236 24089 sgd_solver.cpp:105] Iteration 6132, lr = 9.21692e-05
I0408 20:09:56.898135 24089 solver.cpp:218] Iteration 6144 (2.37449 iter/s, 5.05371s/12 iters), loss = 0.457337
I0408 20:09:56.898187 24089 solver.cpp:237]     Train net output #0: loss = 0.457337 (* 1 = 0.457337 loss)
I0408 20:09:56.898200 24089 sgd_solver.cpp:105] Iteration 6144, lr = 9.13277e-05
I0408 20:10:01.946897 24089 solver.cpp:218] Iteration 6156 (2.37694 iter/s, 5.04851s/12 iters), loss = 0.398009
I0408 20:10:01.946954 24089 solver.cpp:237]     Train net output #0: loss = 0.398009 (* 1 = 0.398009 loss)
I0408 20:10:01.946966 24089 sgd_solver.cpp:105] Iteration 6156, lr = 9.04939e-05
I0408 20:10:06.960867 24089 solver.cpp:218] Iteration 6168 (2.39344 iter/s, 5.01371s/12 iters), loss = 0.304904
I0408 20:10:06.960919 24089 solver.cpp:237]     Train net output #0: loss = 0.304904 (* 1 = 0.304904 loss)
I0408 20:10:06.960932 24089 sgd_solver.cpp:105] Iteration 6168, lr = 8.96678e-05
I0408 20:10:07.571628 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:10:12.241925 24089 solver.cpp:218] Iteration 6180 (2.27239 iter/s, 5.28079s/12 iters), loss = 0.567073
I0408 20:10:12.241984 24089 solver.cpp:237]     Train net output #0: loss = 0.567073 (* 1 = 0.567073 loss)
I0408 20:10:12.241994 24089 sgd_solver.cpp:105] Iteration 6180, lr = 8.88491e-05
I0408 20:10:17.699885 24089 solver.cpp:218] Iteration 6192 (2.19873 iter/s, 5.45768s/12 iters), loss = 0.342285
I0408 20:10:17.700009 24089 solver.cpp:237]     Train net output #0: loss = 0.342285 (* 1 = 0.342285 loss)
I0408 20:10:17.700023 24089 sgd_solver.cpp:105] Iteration 6192, lr = 8.80379e-05
I0408 20:10:22.817728 24089 solver.cpp:218] Iteration 6204 (2.34489 iter/s, 5.11752s/12 iters), loss = 0.371871
I0408 20:10:22.817776 24089 solver.cpp:237]     Train net output #0: loss = 0.371871 (* 1 = 0.371871 loss)
I0408 20:10:22.817785 24089 sgd_solver.cpp:105] Iteration 6204, lr = 8.72342e-05
I0408 20:10:28.118149 24089 solver.cpp:218] Iteration 6216 (2.26408 iter/s, 5.30016s/12 iters), loss = 0.541242
I0408 20:10:28.118192 24089 solver.cpp:237]     Train net output #0: loss = 0.541242 (* 1 = 0.541242 loss)
I0408 20:10:28.118201 24089 sgd_solver.cpp:105] Iteration 6216, lr = 8.64378e-05
I0408 20:10:30.360321 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0408 20:10:33.460323 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0408 20:10:35.853016 24089 solver.cpp:330] Iteration 6222, Testing net (#0)
I0408 20:10:35.853042 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:10:37.830024 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:10:39.106298 24089 blocking_queue.cpp:49] Waiting for data
I0408 20:10:40.280597 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405637
I0408 20:10:40.280642 24089 solver.cpp:397]     Test net output #1: loss = 3.05765 (* 1 = 3.05765 loss)
I0408 20:10:42.109434 24089 solver.cpp:218] Iteration 6228 (0.857712 iter/s, 13.9907s/12 iters), loss = 0.355131
I0408 20:10:42.109503 24089 solver.cpp:237]     Train net output #0: loss = 0.355131 (* 1 = 0.355131 loss)
I0408 20:10:42.109515 24089 sgd_solver.cpp:105] Iteration 6228, lr = 8.56486e-05
I0408 20:10:47.049363 24089 solver.cpp:218] Iteration 6240 (2.42931 iter/s, 4.93967s/12 iters), loss = 0.357253
I0408 20:10:47.049417 24089 solver.cpp:237]     Train net output #0: loss = 0.357253 (* 1 = 0.357253 loss)
I0408 20:10:47.049428 24089 sgd_solver.cpp:105] Iteration 6240, lr = 8.48667e-05
I0408 20:10:52.112144 24089 solver.cpp:218] Iteration 6252 (2.37036 iter/s, 5.06253s/12 iters), loss = 0.460792
I0408 20:10:52.112262 24089 solver.cpp:237]     Train net output #0: loss = 0.460792 (* 1 = 0.460792 loss)
I0408 20:10:52.112270 24089 sgd_solver.cpp:105] Iteration 6252, lr = 8.40918e-05
I0408 20:10:57.292490 24089 solver.cpp:218] Iteration 6264 (2.3166 iter/s, 5.18002s/12 iters), loss = 0.264629
I0408 20:10:57.292541 24089 solver.cpp:237]     Train net output #0: loss = 0.264629 (* 1 = 0.264629 loss)
I0408 20:10:57.292551 24089 sgd_solver.cpp:105] Iteration 6264, lr = 8.33241e-05
I0408 20:11:00.055133 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:11:02.380241 24089 solver.cpp:218] Iteration 6276 (2.35872 iter/s, 5.0875s/12 iters), loss = 0.449321
I0408 20:11:02.380292 24089 solver.cpp:237]     Train net output #0: loss = 0.449321 (* 1 = 0.449321 loss)
I0408 20:11:02.380306 24089 sgd_solver.cpp:105] Iteration 6276, lr = 8.25634e-05
I0408 20:11:07.388883 24089 solver.cpp:218] Iteration 6288 (2.39598 iter/s, 5.00839s/12 iters), loss = 0.487852
I0408 20:11:07.388929 24089 solver.cpp:237]     Train net output #0: loss = 0.487852 (* 1 = 0.487852 loss)
I0408 20:11:07.388939 24089 sgd_solver.cpp:105] Iteration 6288, lr = 8.18096e-05
I0408 20:11:12.426455 24089 solver.cpp:218] Iteration 6300 (2.38222 iter/s, 5.03733s/12 iters), loss = 0.527201
I0408 20:11:12.426501 24089 solver.cpp:237]     Train net output #0: loss = 0.527201 (* 1 = 0.527201 loss)
I0408 20:11:12.426510 24089 sgd_solver.cpp:105] Iteration 6300, lr = 8.10627e-05
I0408 20:11:17.440009 24089 solver.cpp:218] Iteration 6312 (2.39363 iter/s, 5.01331s/12 iters), loss = 0.488427
I0408 20:11:17.440053 24089 solver.cpp:237]     Train net output #0: loss = 0.488427 (* 1 = 0.488427 loss)
I0408 20:11:17.440063 24089 sgd_solver.cpp:105] Iteration 6312, lr = 8.03226e-05
I0408 20:11:22.055424 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0408 20:11:25.067900 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0408 20:11:27.400933 24089 solver.cpp:330] Iteration 6324, Testing net (#0)
I0408 20:11:27.400959 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:11:29.444474 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:11:32.176793 24089 solver.cpp:397]     Test net output #0: accuracy = 0.401348
I0408 20:11:32.176843 24089 solver.cpp:397]     Test net output #1: loss = 3.05922 (* 1 = 3.05922 loss)
I0408 20:11:32.267216 24089 solver.cpp:218] Iteration 6324 (0.809357 iter/s, 14.8266s/12 iters), loss = 0.340395
I0408 20:11:32.267261 24089 solver.cpp:237]     Train net output #0: loss = 0.340395 (* 1 = 0.340395 loss)
I0408 20:11:32.267273 24089 sgd_solver.cpp:105] Iteration 6324, lr = 7.95893e-05
I0408 20:11:36.807688 24089 solver.cpp:218] Iteration 6336 (2.64303 iter/s, 4.54025s/12 iters), loss = 0.388551
I0408 20:11:36.807739 24089 solver.cpp:237]     Train net output #0: loss = 0.388551 (* 1 = 0.388551 loss)
I0408 20:11:36.807751 24089 sgd_solver.cpp:105] Iteration 6336, lr = 7.88627e-05
I0408 20:11:41.978102 24089 solver.cpp:218] Iteration 6348 (2.32101 iter/s, 5.17015s/12 iters), loss = 0.420344
I0408 20:11:41.978157 24089 solver.cpp:237]     Train net output #0: loss = 0.420344 (* 1 = 0.420344 loss)
I0408 20:11:41.978169 24089 sgd_solver.cpp:105] Iteration 6348, lr = 7.81427e-05
I0408 20:11:47.201450 24089 solver.cpp:218] Iteration 6360 (2.29749 iter/s, 5.22309s/12 iters), loss = 0.402884
I0408 20:11:47.201499 24089 solver.cpp:237]     Train net output #0: loss = 0.402884 (* 1 = 0.402884 loss)
I0408 20:11:47.201510 24089 sgd_solver.cpp:105] Iteration 6360, lr = 7.74293e-05
I0408 20:11:52.348199 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:11:52.484061 24089 solver.cpp:218] Iteration 6372 (2.27171 iter/s, 5.28236s/12 iters), loss = 0.340726
I0408 20:11:52.484105 24089 solver.cpp:237]     Train net output #0: loss = 0.340726 (* 1 = 0.340726 loss)
I0408 20:11:52.484114 24089 sgd_solver.cpp:105] Iteration 6372, lr = 7.67224e-05
I0408 20:11:57.529757 24089 solver.cpp:218] Iteration 6384 (2.37838 iter/s, 5.04545s/12 iters), loss = 0.216984
I0408 20:11:57.538028 24089 solver.cpp:237]     Train net output #0: loss = 0.216984 (* 1 = 0.216984 loss)
I0408 20:11:57.538039 24089 sgd_solver.cpp:105] Iteration 6384, lr = 7.60219e-05
I0408 20:12:02.439538 24089 solver.cpp:218] Iteration 6396 (2.44832 iter/s, 4.90132s/12 iters), loss = 0.343601
I0408 20:12:02.439576 24089 solver.cpp:237]     Train net output #0: loss = 0.343601 (* 1 = 0.343601 loss)
I0408 20:12:02.439586 24089 sgd_solver.cpp:105] Iteration 6396, lr = 7.53278e-05
I0408 20:12:07.380308 24089 solver.cpp:218] Iteration 6408 (2.42889 iter/s, 4.94053s/12 iters), loss = 0.352612
I0408 20:12:07.380348 24089 solver.cpp:237]     Train net output #0: loss = 0.352612 (* 1 = 0.352612 loss)
I0408 20:12:07.380357 24089 sgd_solver.cpp:105] Iteration 6408, lr = 7.46401e-05
I0408 20:12:12.359061 24089 solver.cpp:218] Iteration 6420 (2.41036 iter/s, 4.9785s/12 iters), loss = 0.412216
I0408 20:12:12.359123 24089 solver.cpp:237]     Train net output #0: loss = 0.412216 (* 1 = 0.412216 loss)
I0408 20:12:12.359143 24089 sgd_solver.cpp:105] Iteration 6420, lr = 7.39587e-05
I0408 20:12:14.438386 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0408 20:12:17.485843 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0408 20:12:19.811168 24089 solver.cpp:330] Iteration 6426, Testing net (#0)
I0408 20:12:19.811194 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:12:21.751332 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:12:24.286478 24089 solver.cpp:397]     Test net output #0: accuracy = 0.402574
I0408 20:12:24.286530 24089 solver.cpp:397]     Test net output #1: loss = 3.06787 (* 1 = 3.06787 loss)
I0408 20:12:26.180871 24089 solver.cpp:218] Iteration 6432 (0.86823 iter/s, 13.8212s/12 iters), loss = 0.276425
I0408 20:12:26.180932 24089 solver.cpp:237]     Train net output #0: loss = 0.276425 (* 1 = 0.276425 loss)
I0408 20:12:26.180944 24089 sgd_solver.cpp:105] Iteration 6432, lr = 7.32835e-05
I0408 20:12:31.253635 24089 solver.cpp:218] Iteration 6444 (2.3657 iter/s, 5.0725s/12 iters), loss = 0.544725
I0408 20:12:31.253711 24089 solver.cpp:237]     Train net output #0: loss = 0.544725 (* 1 = 0.544725 loss)
I0408 20:12:31.253722 24089 sgd_solver.cpp:105] Iteration 6444, lr = 7.26144e-05
I0408 20:12:36.322293 24089 solver.cpp:218] Iteration 6456 (2.36762 iter/s, 5.06838s/12 iters), loss = 0.317484
I0408 20:12:36.322338 24089 solver.cpp:237]     Train net output #0: loss = 0.317484 (* 1 = 0.317484 loss)
I0408 20:12:36.322350 24089 sgd_solver.cpp:105] Iteration 6456, lr = 7.19514e-05
I0408 20:12:41.338905 24089 solver.cpp:218] Iteration 6468 (2.39217 iter/s, 5.01636s/12 iters), loss = 0.49256
I0408 20:12:41.338958 24089 solver.cpp:237]     Train net output #0: loss = 0.49256 (* 1 = 0.49256 loss)
I0408 20:12:41.338968 24089 sgd_solver.cpp:105] Iteration 6468, lr = 7.12945e-05
I0408 20:12:43.356477 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:12:46.364246 24089 solver.cpp:218] Iteration 6480 (2.38802 iter/s, 5.02509s/12 iters), loss = 0.503905
I0408 20:12:46.364292 24089 solver.cpp:237]     Train net output #0: loss = 0.503905 (* 1 = 0.503905 loss)
I0408 20:12:46.364303 24089 sgd_solver.cpp:105] Iteration 6480, lr = 7.06436e-05
I0408 20:12:51.456872 24089 solver.cpp:218] Iteration 6492 (2.35646 iter/s, 5.09237s/12 iters), loss = 0.255263
I0408 20:12:51.456926 24089 solver.cpp:237]     Train net output #0: loss = 0.255263 (* 1 = 0.255263 loss)
I0408 20:12:51.456938 24089 sgd_solver.cpp:105] Iteration 6492, lr = 6.99987e-05
I0408 20:12:56.454474 24089 solver.cpp:218] Iteration 6504 (2.40128 iter/s, 4.99734s/12 iters), loss = 0.34623
I0408 20:12:56.454524 24089 solver.cpp:237]     Train net output #0: loss = 0.34623 (* 1 = 0.34623 loss)
I0408 20:12:56.454535 24089 sgd_solver.cpp:105] Iteration 6504, lr = 6.93596e-05
I0408 20:13:01.487812 24089 solver.cpp:218] Iteration 6516 (2.38422 iter/s, 5.03309s/12 iters), loss = 0.329539
I0408 20:13:01.492425 24089 solver.cpp:237]     Train net output #0: loss = 0.329539 (* 1 = 0.329539 loss)
I0408 20:13:01.492439 24089 sgd_solver.cpp:105] Iteration 6516, lr = 6.87264e-05
I0408 20:13:06.288422 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0408 20:13:09.317474 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0408 20:13:11.643573 24089 solver.cpp:330] Iteration 6528, Testing net (#0)
I0408 20:13:11.643599 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:13:13.544680 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:13:16.112627 24089 solver.cpp:397]     Test net output #0: accuracy = 0.404412
I0408 20:13:16.112676 24089 solver.cpp:397]     Test net output #1: loss = 3.06488 (* 1 = 3.06488 loss)
I0408 20:13:16.203083 24089 solver.cpp:218] Iteration 6528 (0.815766 iter/s, 14.7101s/12 iters), loss = 0.310267
I0408 20:13:16.203141 24089 solver.cpp:237]     Train net output #0: loss = 0.310267 (* 1 = 0.310267 loss)
I0408 20:13:16.203155 24089 sgd_solver.cpp:105] Iteration 6528, lr = 6.80989e-05
I0408 20:13:20.445979 24089 solver.cpp:218] Iteration 6540 (2.82842 iter/s, 4.24265s/12 iters), loss = 0.253882
I0408 20:13:20.446024 24089 solver.cpp:237]     Train net output #0: loss = 0.253882 (* 1 = 0.253882 loss)
I0408 20:13:20.446033 24089 sgd_solver.cpp:105] Iteration 6540, lr = 6.74772e-05
I0408 20:13:25.521414 24089 solver.cpp:218] Iteration 6552 (2.36445 iter/s, 5.07518s/12 iters), loss = 0.322611
I0408 20:13:25.521471 24089 solver.cpp:237]     Train net output #0: loss = 0.322611 (* 1 = 0.322611 loss)
I0408 20:13:25.521482 24089 sgd_solver.cpp:105] Iteration 6552, lr = 6.68612e-05
I0408 20:13:30.574968 24089 solver.cpp:218] Iteration 6564 (2.37469 iter/s, 5.0533s/12 iters), loss = 0.433307
I0408 20:13:30.575016 24089 solver.cpp:237]     Train net output #0: loss = 0.433307 (* 1 = 0.433307 loss)
I0408 20:13:30.575026 24089 sgd_solver.cpp:105] Iteration 6564, lr = 6.62507e-05
I0408 20:13:34.904305 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:13:35.684521 24089 solver.cpp:218] Iteration 6576 (2.34866 iter/s, 5.1093s/12 iters), loss = 0.342512
I0408 20:13:35.684563 24089 solver.cpp:237]     Train net output #0: loss = 0.342512 (* 1 = 0.342512 loss)
I0408 20:13:35.684571 24089 sgd_solver.cpp:105] Iteration 6576, lr = 6.56459e-05
I0408 20:13:40.679617 24089 solver.cpp:218] Iteration 6588 (2.40248 iter/s, 4.99485s/12 iters), loss = 0.415265
I0408 20:13:40.679672 24089 solver.cpp:237]     Train net output #0: loss = 0.415265 (* 1 = 0.415265 loss)
I0408 20:13:40.679682 24089 sgd_solver.cpp:105] Iteration 6588, lr = 6.50466e-05
I0408 20:13:45.746318 24089 solver.cpp:218] Iteration 6600 (2.36853 iter/s, 5.06644s/12 iters), loss = 0.35581
I0408 20:13:45.746382 24089 solver.cpp:237]     Train net output #0: loss = 0.35581 (* 1 = 0.35581 loss)
I0408 20:13:45.746398 24089 sgd_solver.cpp:105] Iteration 6600, lr = 6.44527e-05
I0408 20:13:50.811306 24089 solver.cpp:218] Iteration 6612 (2.36933 iter/s, 5.06472s/12 iters), loss = 0.415314
I0408 20:13:50.811364 24089 solver.cpp:237]     Train net output #0: loss = 0.415314 (* 1 = 0.415314 loss)
I0408 20:13:50.811378 24089 sgd_solver.cpp:105] Iteration 6612, lr = 6.38643e-05
I0408 20:13:55.869004 24089 solver.cpp:218] Iteration 6624 (2.37275 iter/s, 5.05743s/12 iters), loss = 0.385638
I0408 20:13:55.869056 24089 solver.cpp:237]     Train net output #0: loss = 0.385638 (* 1 = 0.385638 loss)
I0408 20:13:55.869067 24089 sgd_solver.cpp:105] Iteration 6624, lr = 6.32812e-05
I0408 20:13:57.912046 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0408 20:14:05.544066 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0408 20:14:12.525625 24089 solver.cpp:330] Iteration 6630, Testing net (#0)
I0408 20:14:12.525653 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:14:14.528043 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:14:17.134654 24089 solver.cpp:397]     Test net output #0: accuracy = 0.409314
I0408 20:14:17.134701 24089 solver.cpp:397]     Test net output #1: loss = 3.05105 (* 1 = 3.05105 loss)
I0408 20:14:19.086601 24089 solver.cpp:218] Iteration 6636 (0.51687 iter/s, 23.2167s/12 iters), loss = 0.307692
I0408 20:14:19.086650 24089 solver.cpp:237]     Train net output #0: loss = 0.307692 (* 1 = 0.307692 loss)
I0408 20:14:19.086663 24089 sgd_solver.cpp:105] Iteration 6636, lr = 6.27035e-05
I0408 20:14:24.164255 24089 solver.cpp:218] Iteration 6648 (2.36342 iter/s, 5.0774s/12 iters), loss = 0.410105
I0408 20:14:24.164309 24089 solver.cpp:237]     Train net output #0: loss = 0.410105 (* 1 = 0.410105 loss)
I0408 20:14:24.164320 24089 sgd_solver.cpp:105] Iteration 6648, lr = 6.2131e-05
I0408 20:14:29.406565 24089 solver.cpp:218] Iteration 6660 (2.28918 iter/s, 5.24205s/12 iters), loss = 0.561088
I0408 20:14:29.406617 24089 solver.cpp:237]     Train net output #0: loss = 0.561088 (* 1 = 0.561088 loss)
I0408 20:14:29.406627 24089 sgd_solver.cpp:105] Iteration 6660, lr = 6.15638e-05
I0408 20:14:34.474803 24089 solver.cpp:218] Iteration 6672 (2.36781 iter/s, 5.06797s/12 iters), loss = 0.457771
I0408 20:14:34.474853 24089 solver.cpp:237]     Train net output #0: loss = 0.457771 (* 1 = 0.457771 loss)
I0408 20:14:34.474862 24089 sgd_solver.cpp:105] Iteration 6672, lr = 6.10017e-05
I0408 20:14:35.820344 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:14:39.473881 24089 solver.cpp:218] Iteration 6684 (2.40056 iter/s, 4.99883s/12 iters), loss = 0.286448
I0408 20:14:39.473925 24089 solver.cpp:237]     Train net output #0: loss = 0.286448 (* 1 = 0.286448 loss)
I0408 20:14:39.473934 24089 sgd_solver.cpp:105] Iteration 6684, lr = 6.04448e-05
I0408 20:14:44.538472 24089 solver.cpp:218] Iteration 6696 (2.36951 iter/s, 5.06434s/12 iters), loss = 0.326474
I0408 20:14:44.538516 24089 solver.cpp:237]     Train net output #0: loss = 0.326474 (* 1 = 0.326474 loss)
I0408 20:14:44.538523 24089 sgd_solver.cpp:105] Iteration 6696, lr = 5.98929e-05
I0408 20:14:49.696054 24089 solver.cpp:218] Iteration 6708 (2.32678 iter/s, 5.15733s/12 iters), loss = 0.257486
I0408 20:14:49.696096 24089 solver.cpp:237]     Train net output #0: loss = 0.257486 (* 1 = 0.257486 loss)
I0408 20:14:49.696105 24089 sgd_solver.cpp:105] Iteration 6708, lr = 5.93461e-05
I0408 20:14:54.740221 24089 solver.cpp:218] Iteration 6720 (2.37911 iter/s, 5.04391s/12 iters), loss = 0.342484
I0408 20:14:54.740278 24089 solver.cpp:237]     Train net output #0: loss = 0.342484 (* 1 = 0.342484 loss)
I0408 20:14:54.740290 24089 sgd_solver.cpp:105] Iteration 6720, lr = 5.88043e-05
I0408 20:14:59.316794 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0408 20:15:05.623214 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0408 20:15:08.135680 24089 solver.cpp:330] Iteration 6732, Testing net (#0)
I0408 20:15:08.135792 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:15:09.956867 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:15:12.604544 24089 solver.cpp:397]     Test net output #0: accuracy = 0.401348
I0408 20:15:12.604593 24089 solver.cpp:397]     Test net output #1: loss = 3.07665 (* 1 = 3.07665 loss)
I0408 20:15:12.695180 24089 solver.cpp:218] Iteration 6732 (0.668367 iter/s, 17.9542s/12 iters), loss = 0.377429
I0408 20:15:12.695231 24089 solver.cpp:237]     Train net output #0: loss = 0.377429 (* 1 = 0.377429 loss)
I0408 20:15:12.695243 24089 sgd_solver.cpp:105] Iteration 6732, lr = 5.82674e-05
I0408 20:15:17.051152 24089 solver.cpp:218] Iteration 6744 (2.75498 iter/s, 4.35575s/12 iters), loss = 0.369563
I0408 20:15:17.051200 24089 solver.cpp:237]     Train net output #0: loss = 0.369563 (* 1 = 0.369563 loss)
I0408 20:15:17.051213 24089 sgd_solver.cpp:105] Iteration 6744, lr = 5.77355e-05
I0408 20:15:22.148836 24089 solver.cpp:218] Iteration 6756 (2.35413 iter/s, 5.09743s/12 iters), loss = 0.373281
I0408 20:15:22.148888 24089 solver.cpp:237]     Train net output #0: loss = 0.373281 (* 1 = 0.373281 loss)
I0408 20:15:22.148900 24089 sgd_solver.cpp:105] Iteration 6756, lr = 5.72084e-05
I0408 20:15:27.244057 24089 solver.cpp:218] Iteration 6768 (2.35527 iter/s, 5.09496s/12 iters), loss = 0.329717
I0408 20:15:27.244102 24089 solver.cpp:237]     Train net output #0: loss = 0.329717 (* 1 = 0.329717 loss)
I0408 20:15:27.244110 24089 sgd_solver.cpp:105] Iteration 6768, lr = 5.66861e-05
I0408 20:15:30.748664 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:15:32.214212 24089 solver.cpp:218] Iteration 6780 (2.41453 iter/s, 4.96991s/12 iters), loss = 0.253858
I0408 20:15:32.214264 24089 solver.cpp:237]     Train net output #0: loss = 0.253858 (* 1 = 0.253858 loss)
I0408 20:15:32.214277 24089 sgd_solver.cpp:105] Iteration 6780, lr = 5.61685e-05
I0408 20:15:37.224669 24089 solver.cpp:218] Iteration 6792 (2.39511 iter/s, 5.0102s/12 iters), loss = 0.425104
I0408 20:15:37.224725 24089 solver.cpp:237]     Train net output #0: loss = 0.425104 (* 1 = 0.425104 loss)
I0408 20:15:37.224737 24089 sgd_solver.cpp:105] Iteration 6792, lr = 5.56557e-05
I0408 20:15:42.287978 24089 solver.cpp:218] Iteration 6804 (2.37012 iter/s, 5.06304s/12 iters), loss = 0.2644
I0408 20:15:42.288118 24089 solver.cpp:237]     Train net output #0: loss = 0.2644 (* 1 = 0.2644 loss)
I0408 20:15:42.288132 24089 sgd_solver.cpp:105] Iteration 6804, lr = 5.51476e-05
I0408 20:15:47.532917 24089 solver.cpp:218] Iteration 6816 (2.28807 iter/s, 5.2446s/12 iters), loss = 0.378662
I0408 20:15:47.532964 24089 solver.cpp:237]     Train net output #0: loss = 0.378662 (* 1 = 0.378662 loss)
I0408 20:15:47.532976 24089 sgd_solver.cpp:105] Iteration 6816, lr = 5.46441e-05
I0408 20:15:52.807487 24089 solver.cpp:218] Iteration 6828 (2.27518 iter/s, 5.27431s/12 iters), loss = 0.307706
I0408 20:15:52.807535 24089 solver.cpp:237]     Train net output #0: loss = 0.307706 (* 1 = 0.307706 loss)
I0408 20:15:52.807547 24089 sgd_solver.cpp:105] Iteration 6828, lr = 5.41453e-05
I0408 20:15:54.869664 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0408 20:15:59.948172 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0408 20:16:06.183141 24089 solver.cpp:330] Iteration 6834, Testing net (#0)
I0408 20:16:06.183167 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:16:08.074486 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:16:10.799319 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0408 20:16:10.799367 24089 solver.cpp:397]     Test net output #1: loss = 3.07385 (* 1 = 3.07385 loss)
I0408 20:16:12.770074 24089 solver.cpp:218] Iteration 6840 (0.601149 iter/s, 19.9618s/12 iters), loss = 0.460304
I0408 20:16:12.770227 24089 solver.cpp:237]     Train net output #0: loss = 0.460304 (* 1 = 0.460304 loss)
I0408 20:16:12.770242 24089 sgd_solver.cpp:105] Iteration 6840, lr = 5.36509e-05
I0408 20:16:17.837180 24089 solver.cpp:218] Iteration 6852 (2.36838 iter/s, 5.06675s/12 iters), loss = 0.415629
I0408 20:16:17.837236 24089 solver.cpp:237]     Train net output #0: loss = 0.415629 (* 1 = 0.415629 loss)
I0408 20:16:17.837249 24089 sgd_solver.cpp:105] Iteration 6852, lr = 5.31611e-05
I0408 20:16:22.957598 24089 solver.cpp:218] Iteration 6864 (2.34368 iter/s, 5.12016s/12 iters), loss = 0.404407
I0408 20:16:22.957644 24089 solver.cpp:237]     Train net output #0: loss = 0.404407 (* 1 = 0.404407 loss)
I0408 20:16:22.957655 24089 sgd_solver.cpp:105] Iteration 6864, lr = 5.26758e-05
I0408 20:16:28.040800 24089 solver.cpp:218] Iteration 6876 (2.36084 iter/s, 5.08295s/12 iters), loss = 0.424367
I0408 20:16:28.040859 24089 solver.cpp:237]     Train net output #0: loss = 0.424367 (* 1 = 0.424367 loss)
I0408 20:16:28.040872 24089 sgd_solver.cpp:105] Iteration 6876, lr = 5.21948e-05
I0408 20:16:28.676178 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:16:33.118221 24089 solver.cpp:218] Iteration 6888 (2.36353 iter/s, 5.07714s/12 iters), loss = 0.417895
I0408 20:16:33.118269 24089 solver.cpp:237]     Train net output #0: loss = 0.417895 (* 1 = 0.417895 loss)
I0408 20:16:33.118281 24089 sgd_solver.cpp:105] Iteration 6888, lr = 5.17183e-05
I0408 20:16:38.173763 24089 solver.cpp:218] Iteration 6900 (2.37375 iter/s, 5.05529s/12 iters), loss = 0.253832
I0408 20:16:38.173818 24089 solver.cpp:237]     Train net output #0: loss = 0.253832 (* 1 = 0.253832 loss)
I0408 20:16:38.173831 24089 sgd_solver.cpp:105] Iteration 6900, lr = 5.12461e-05
I0408 20:16:43.275147 24089 solver.cpp:218] Iteration 6912 (2.35242 iter/s, 5.10113s/12 iters), loss = 0.364188
I0408 20:16:43.275259 24089 solver.cpp:237]     Train net output #0: loss = 0.364188 (* 1 = 0.364188 loss)
I0408 20:16:43.275271 24089 sgd_solver.cpp:105] Iteration 6912, lr = 5.07783e-05
I0408 20:16:48.302485 24089 solver.cpp:218] Iteration 6924 (2.3871 iter/s, 5.02703s/12 iters), loss = 0.376103
I0408 20:16:48.302534 24089 solver.cpp:237]     Train net output #0: loss = 0.376103 (* 1 = 0.376103 loss)
I0408 20:16:48.302546 24089 sgd_solver.cpp:105] Iteration 6924, lr = 5.03147e-05
I0408 20:16:52.892920 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0408 20:17:02.133842 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0408 20:17:05.942821 24089 solver.cpp:330] Iteration 6936, Testing net (#0)
I0408 20:17:05.942849 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:17:06.605757 24089 blocking_queue.cpp:49] Waiting for data
I0408 20:17:07.695741 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:17:10.420220 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0408 20:17:10.420269 24089 solver.cpp:397]     Test net output #1: loss = 3.06896 (* 1 = 3.06896 loss)
I0408 20:17:10.511050 24089 solver.cpp:218] Iteration 6936 (0.540354 iter/s, 22.2077s/12 iters), loss = 0.339857
I0408 20:17:10.511107 24089 solver.cpp:237]     Train net output #0: loss = 0.339857 (* 1 = 0.339857 loss)
I0408 20:17:10.511121 24089 sgd_solver.cpp:105] Iteration 6936, lr = 4.98553e-05
I0408 20:17:14.923772 24089 solver.cpp:218] Iteration 6948 (2.71956 iter/s, 4.41248s/12 iters), loss = 0.372685
I0408 20:17:14.923882 24089 solver.cpp:237]     Train net output #0: loss = 0.372685 (* 1 = 0.372685 loss)
I0408 20:17:14.923897 24089 sgd_solver.cpp:105] Iteration 6948, lr = 4.94002e-05
I0408 20:17:19.969386 24089 solver.cpp:218] Iteration 6960 (2.37845 iter/s, 5.0453s/12 iters), loss = 0.323268
I0408 20:17:19.969432 24089 solver.cpp:237]     Train net output #0: loss = 0.323268 (* 1 = 0.323268 loss)
I0408 20:17:19.969444 24089 sgd_solver.cpp:105] Iteration 6960, lr = 4.89492e-05
I0408 20:17:25.005829 24089 solver.cpp:218] Iteration 6972 (2.38275 iter/s, 5.0362s/12 iters), loss = 0.237371
I0408 20:17:25.005877 24089 solver.cpp:237]     Train net output #0: loss = 0.237371 (* 1 = 0.237371 loss)
I0408 20:17:25.005888 24089 sgd_solver.cpp:105] Iteration 6972, lr = 4.85023e-05
I0408 20:17:27.775157 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:17:30.030179 24089 solver.cpp:218] Iteration 6984 (2.38849 iter/s, 5.0241s/12 iters), loss = 0.286384
I0408 20:17:30.030227 24089 solver.cpp:237]     Train net output #0: loss = 0.286384 (* 1 = 0.286384 loss)
I0408 20:17:30.030239 24089 sgd_solver.cpp:105] Iteration 6984, lr = 4.80594e-05
I0408 20:17:35.085040 24089 solver.cpp:218] Iteration 6996 (2.37407 iter/s, 5.05461s/12 iters), loss = 0.317308
I0408 20:17:35.085088 24089 solver.cpp:237]     Train net output #0: loss = 0.317308 (* 1 = 0.317308 loss)
I0408 20:17:35.085100 24089 sgd_solver.cpp:105] Iteration 6996, lr = 4.76207e-05
I0408 20:17:39.960495 24089 solver.cpp:218] Iteration 7008 (2.46143 iter/s, 4.87521s/12 iters), loss = 0.407264
I0408 20:17:39.960546 24089 solver.cpp:237]     Train net output #0: loss = 0.407264 (* 1 = 0.407264 loss)
I0408 20:17:39.960558 24089 sgd_solver.cpp:105] Iteration 7008, lr = 4.71859e-05
I0408 20:17:44.871451 24089 solver.cpp:218] Iteration 7020 (2.44364 iter/s, 4.91072s/12 iters), loss = 0.462106
I0408 20:17:44.871490 24089 solver.cpp:237]     Train net output #0: loss = 0.462106 (* 1 = 0.462106 loss)
I0408 20:17:44.871497 24089 sgd_solver.cpp:105] Iteration 7020, lr = 4.67551e-05
I0408 20:17:49.832509 24089 solver.cpp:218] Iteration 7032 (2.41895 iter/s, 4.96082s/12 iters), loss = 0.363796
I0408 20:17:49.832656 24089 solver.cpp:237]     Train net output #0: loss = 0.363796 (* 1 = 0.363796 loss)
I0408 20:17:49.832669 24089 sgd_solver.cpp:105] Iteration 7032, lr = 4.63283e-05
I0408 20:17:51.969117 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0408 20:17:54.962954 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0408 20:17:57.372670 24089 solver.cpp:330] Iteration 7038, Testing net (#0)
I0408 20:17:57.372696 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:17:59.086506 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:18:01.843154 24089 solver.cpp:397]     Test net output #0: accuracy = 0.401348
I0408 20:18:01.843189 24089 solver.cpp:397]     Test net output #1: loss = 3.06595 (* 1 = 3.06595 loss)
I0408 20:18:03.674540 24089 solver.cpp:218] Iteration 7044 (0.866967 iter/s, 13.8414s/12 iters), loss = 0.201871
I0408 20:18:03.674593 24089 solver.cpp:237]     Train net output #0: loss = 0.201871 (* 1 = 0.201871 loss)
I0408 20:18:03.674604 24089 sgd_solver.cpp:105] Iteration 7044, lr = 4.59053e-05
I0408 20:18:08.676111 24089 solver.cpp:218] Iteration 7056 (2.39937 iter/s, 5.00132s/12 iters), loss = 0.438437
I0408 20:18:08.676154 24089 solver.cpp:237]     Train net output #0: loss = 0.438437 (* 1 = 0.438437 loss)
I0408 20:18:08.676164 24089 sgd_solver.cpp:105] Iteration 7056, lr = 4.54862e-05
I0408 20:18:13.766837 24089 solver.cpp:218] Iteration 7068 (2.35734 iter/s, 5.09048s/12 iters), loss = 0.3211
I0408 20:18:13.766887 24089 solver.cpp:237]     Train net output #0: loss = 0.3211 (* 1 = 0.3211 loss)
I0408 20:18:13.766898 24089 sgd_solver.cpp:105] Iteration 7068, lr = 4.50709e-05
I0408 20:18:18.624680 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:18:18.734517 24089 solver.cpp:218] Iteration 7080 (2.41573 iter/s, 4.96743s/12 iters), loss = 0.255584
I0408 20:18:18.734558 24089 solver.cpp:237]     Train net output #0: loss = 0.255584 (* 1 = 0.255584 loss)
I0408 20:18:18.734570 24089 sgd_solver.cpp:105] Iteration 7080, lr = 4.46594e-05
I0408 20:18:23.748251 24089 solver.cpp:218] Iteration 7092 (2.39354 iter/s, 5.01349s/12 iters), loss = 0.335515
I0408 20:18:23.748349 24089 solver.cpp:237]     Train net output #0: loss = 0.335515 (* 1 = 0.335515 loss)
I0408 20:18:23.748358 24089 sgd_solver.cpp:105] Iteration 7092, lr = 4.42517e-05
I0408 20:18:28.756593 24089 solver.cpp:218] Iteration 7104 (2.39614 iter/s, 5.00805s/12 iters), loss = 0.439368
I0408 20:18:28.756636 24089 solver.cpp:237]     Train net output #0: loss = 0.439368 (* 1 = 0.439368 loss)
I0408 20:18:28.756644 24089 sgd_solver.cpp:105] Iteration 7104, lr = 4.38477e-05
I0408 20:18:33.942895 24089 solver.cpp:218] Iteration 7116 (2.3139 iter/s, 5.18605s/12 iters), loss = 0.357005
I0408 20:18:33.942936 24089 solver.cpp:237]     Train net output #0: loss = 0.357005 (* 1 = 0.357005 loss)
I0408 20:18:33.942946 24089 sgd_solver.cpp:105] Iteration 7116, lr = 4.34474e-05
I0408 20:18:39.081871 24089 solver.cpp:218] Iteration 7128 (2.33519 iter/s, 5.13878s/12 iters), loss = 0.314667
I0408 20:18:39.081909 24089 solver.cpp:237]     Train net output #0: loss = 0.314667 (* 1 = 0.314667 loss)
I0408 20:18:39.081918 24089 sgd_solver.cpp:105] Iteration 7128, lr = 4.30507e-05
I0408 20:18:43.928603 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0408 20:18:46.933092 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0408 20:18:50.323629 24089 solver.cpp:330] Iteration 7140, Testing net (#0)
I0408 20:18:50.323660 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:18:51.990960 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:18:54.790813 24089 solver.cpp:397]     Test net output #0: accuracy = 0.402574
I0408 20:18:54.790966 24089 solver.cpp:397]     Test net output #1: loss = 3.10049 (* 1 = 3.10049 loss)
I0408 20:18:54.881702 24089 solver.cpp:218] Iteration 7140 (0.759525 iter/s, 15.7993s/12 iters), loss = 0.28402
I0408 20:18:54.881747 24089 solver.cpp:237]     Train net output #0: loss = 0.28402 (* 1 = 0.28402 loss)
I0408 20:18:54.881757 24089 sgd_solver.cpp:105] Iteration 7140, lr = 4.26577e-05
I0408 20:18:59.199811 24089 solver.cpp:218] Iteration 7152 (2.77911 iter/s, 4.31793s/12 iters), loss = 0.420613
I0408 20:18:59.199858 24089 solver.cpp:237]     Train net output #0: loss = 0.420613 (* 1 = 0.420613 loss)
I0408 20:18:59.199869 24089 sgd_solver.cpp:105] Iteration 7152, lr = 4.22682e-05
I0408 20:19:04.270232 24089 solver.cpp:218] Iteration 7164 (2.36676 iter/s, 5.07022s/12 iters), loss = 0.295908
I0408 20:19:04.270277 24089 solver.cpp:237]     Train net output #0: loss = 0.295908 (* 1 = 0.295908 loss)
I0408 20:19:04.270288 24089 sgd_solver.cpp:105] Iteration 7164, lr = 4.18823e-05
I0408 20:19:09.362915 24089 solver.cpp:218] Iteration 7176 (2.35642 iter/s, 5.09248s/12 iters), loss = 0.447234
I0408 20:19:09.362987 24089 solver.cpp:237]     Train net output #0: loss = 0.447234 (* 1 = 0.447234 loss)
I0408 20:19:09.363003 24089 sgd_solver.cpp:105] Iteration 7176, lr = 4.15e-05
I0408 20:19:11.516093 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:19:14.425575 24089 solver.cpp:218] Iteration 7188 (2.3704 iter/s, 5.06244s/12 iters), loss = 0.269888
I0408 20:19:14.425626 24089 solver.cpp:237]     Train net output #0: loss = 0.269888 (* 1 = 0.269888 loss)
I0408 20:19:14.425638 24089 sgd_solver.cpp:105] Iteration 7188, lr = 4.11211e-05
I0408 20:19:19.437248 24089 solver.cpp:218] Iteration 7200 (2.39451 iter/s, 5.01147s/12 iters), loss = 0.31275
I0408 20:19:19.437288 24089 solver.cpp:237]     Train net output #0: loss = 0.31275 (* 1 = 0.31275 loss)
I0408 20:19:19.437295 24089 sgd_solver.cpp:105] Iteration 7200, lr = 4.07457e-05
I0408 20:19:24.439741 24089 solver.cpp:218] Iteration 7212 (2.3989 iter/s, 5.0023s/12 iters), loss = 0.189609
I0408 20:19:24.439779 24089 solver.cpp:237]     Train net output #0: loss = 0.189609 (* 1 = 0.189609 loss)
I0408 20:19:24.439787 24089 sgd_solver.cpp:105] Iteration 7212, lr = 4.03737e-05
I0408 20:19:29.575256 24089 solver.cpp:218] Iteration 7224 (2.33676 iter/s, 5.13532s/12 iters), loss = 0.297432
I0408 20:19:29.575373 24089 solver.cpp:237]     Train net output #0: loss = 0.297432 (* 1 = 0.297432 loss)
I0408 20:19:29.575383 24089 sgd_solver.cpp:105] Iteration 7224, lr = 4.00051e-05
I0408 20:19:34.645100 24089 solver.cpp:218] Iteration 7236 (2.36706 iter/s, 5.06958s/12 iters), loss = 0.274882
I0408 20:19:34.645143 24089 solver.cpp:237]     Train net output #0: loss = 0.274882 (* 1 = 0.274882 loss)
I0408 20:19:34.645153 24089 sgd_solver.cpp:105] Iteration 7236, lr = 3.96398e-05
I0408 20:19:36.696877 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0408 20:19:39.729558 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0408 20:19:42.081403 24089 solver.cpp:330] Iteration 7242, Testing net (#0)
I0408 20:19:42.081429 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:19:43.693617 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:19:46.534852 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0408 20:19:46.534889 24089 solver.cpp:397]     Test net output #1: loss = 3.08124 (* 1 = 3.08124 loss)
I0408 20:19:48.503823 24089 solver.cpp:218] Iteration 7248 (0.865909 iter/s, 13.8583s/12 iters), loss = 0.319137
I0408 20:19:48.503871 24089 solver.cpp:237]     Train net output #0: loss = 0.319137 (* 1 = 0.319137 loss)
I0408 20:19:48.503882 24089 sgd_solver.cpp:105] Iteration 7248, lr = 3.92779e-05
I0408 20:19:53.984872 24089 solver.cpp:218] Iteration 7260 (2.18945 iter/s, 5.48083s/12 iters), loss = 0.36352
I0408 20:19:53.984922 24089 solver.cpp:237]     Train net output #0: loss = 0.36352 (* 1 = 0.36352 loss)
I0408 20:19:53.984935 24089 sgd_solver.cpp:105] Iteration 7260, lr = 3.89193e-05
I0408 20:19:59.072316 24089 solver.cpp:218] Iteration 7272 (2.35885 iter/s, 5.08723s/12 iters), loss = 0.346935
I0408 20:19:59.072366 24089 solver.cpp:237]     Train net output #0: loss = 0.346935 (* 1 = 0.346935 loss)
I0408 20:19:59.072378 24089 sgd_solver.cpp:105] Iteration 7272, lr = 3.8564e-05
I0408 20:20:03.346357 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:20:04.118069 24089 solver.cpp:218] Iteration 7284 (2.37833 iter/s, 5.04555s/12 iters), loss = 0.388394
I0408 20:20:04.118104 24089 solver.cpp:237]     Train net output #0: loss = 0.388394 (* 1 = 0.388394 loss)
I0408 20:20:04.118113 24089 sgd_solver.cpp:105] Iteration 7284, lr = 3.82119e-05
I0408 20:20:09.141676 24089 solver.cpp:218] Iteration 7296 (2.38881 iter/s, 5.02341s/12 iters), loss = 0.361213
I0408 20:20:09.141723 24089 solver.cpp:237]     Train net output #0: loss = 0.361213 (* 1 = 0.361213 loss)
I0408 20:20:09.141736 24089 sgd_solver.cpp:105] Iteration 7296, lr = 3.78631e-05
I0408 20:20:14.171909 24089 solver.cpp:218] Iteration 7308 (2.38567 iter/s, 5.03003s/12 iters), loss = 0.417412
I0408 20:20:14.171957 24089 solver.cpp:237]     Train net output #0: loss = 0.417412 (* 1 = 0.417412 loss)
I0408 20:20:14.171967 24089 sgd_solver.cpp:105] Iteration 7308, lr = 3.75174e-05
I0408 20:20:19.237095 24089 solver.cpp:218] Iteration 7320 (2.36921 iter/s, 5.06498s/12 iters), loss = 0.395983
I0408 20:20:19.237147 24089 solver.cpp:237]     Train net output #0: loss = 0.395983 (* 1 = 0.395983 loss)
I0408 20:20:19.237159 24089 sgd_solver.cpp:105] Iteration 7320, lr = 3.71749e-05
I0408 20:20:24.265015 24089 solver.cpp:218] Iteration 7332 (2.38677 iter/s, 5.02771s/12 iters), loss = 0.358749
I0408 20:20:24.265067 24089 solver.cpp:237]     Train net output #0: loss = 0.358749 (* 1 = 0.358749 loss)
I0408 20:20:24.265080 24089 sgd_solver.cpp:105] Iteration 7332, lr = 3.68355e-05
I0408 20:20:28.705549 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0408 20:20:31.961050 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0408 20:20:34.260226 24089 solver.cpp:330] Iteration 7344, Testing net (#0)
I0408 20:20:34.260272 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:20:35.845691 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:20:38.857758 24089 solver.cpp:397]     Test net output #0: accuracy = 0.403799
I0408 20:20:38.857800 24089 solver.cpp:397]     Test net output #1: loss = 3.09181 (* 1 = 3.09181 loss)
I0408 20:20:38.948468 24089 solver.cpp:218] Iteration 7344 (0.817275 iter/s, 14.6829s/12 iters), loss = 0.260758
I0408 20:20:38.948516 24089 solver.cpp:237]     Train net output #0: loss = 0.260758 (* 1 = 0.260758 loss)
I0408 20:20:38.948527 24089 sgd_solver.cpp:105] Iteration 7344, lr = 3.64992e-05
I0408 20:20:43.140233 24089 solver.cpp:218] Iteration 7356 (2.86288 iter/s, 4.19158s/12 iters), loss = 0.47223
I0408 20:20:43.140283 24089 solver.cpp:237]     Train net output #0: loss = 0.47223 (* 1 = 0.47223 loss)
I0408 20:20:43.140296 24089 sgd_solver.cpp:105] Iteration 7356, lr = 3.61659e-05
I0408 20:20:48.236290 24089 solver.cpp:218] Iteration 7368 (2.35486 iter/s, 5.09584s/12 iters), loss = 0.30373
I0408 20:20:48.236341 24089 solver.cpp:237]     Train net output #0: loss = 0.30373 (* 1 = 0.30373 loss)
I0408 20:20:48.236353 24089 sgd_solver.cpp:105] Iteration 7368, lr = 3.58357e-05
I0408 20:20:53.489539 24089 solver.cpp:218] Iteration 7380 (2.2844 iter/s, 5.25303s/12 iters), loss = 0.333062
I0408 20:20:53.489598 24089 solver.cpp:237]     Train net output #0: loss = 0.333062 (* 1 = 0.333062 loss)
I0408 20:20:53.489612 24089 sgd_solver.cpp:105] Iteration 7380, lr = 3.55086e-05
I0408 20:20:54.843888 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:20:58.530877 24089 solver.cpp:218] Iteration 7392 (2.38042 iter/s, 5.04112s/12 iters), loss = 0.351257
I0408 20:20:58.530926 24089 solver.cpp:237]     Train net output #0: loss = 0.351257 (* 1 = 0.351257 loss)
I0408 20:20:58.530938 24089 sgd_solver.cpp:105] Iteration 7392, lr = 3.51844e-05
I0408 20:21:03.547785 24089 solver.cpp:218] Iteration 7404 (2.39201 iter/s, 5.0167s/12 iters), loss = 0.461401
I0408 20:21:03.547834 24089 solver.cpp:237]     Train net output #0: loss = 0.461401 (* 1 = 0.461401 loss)
I0408 20:21:03.547847 24089 sgd_solver.cpp:105] Iteration 7404, lr = 3.48632e-05
I0408 20:21:08.561295 24089 solver.cpp:218] Iteration 7416 (2.39363 iter/s, 5.0133s/12 iters), loss = 0.361481
I0408 20:21:08.561440 24089 solver.cpp:237]     Train net output #0: loss = 0.361481 (* 1 = 0.361481 loss)
I0408 20:21:08.561455 24089 sgd_solver.cpp:105] Iteration 7416, lr = 3.45449e-05
I0408 20:21:13.633247 24089 solver.cpp:218] Iteration 7428 (2.3661 iter/s, 5.07165s/12 iters), loss = 0.328436
I0408 20:21:13.633291 24089 solver.cpp:237]     Train net output #0: loss = 0.328436 (* 1 = 0.328436 loss)
I0408 20:21:13.633302 24089 sgd_solver.cpp:105] Iteration 7428, lr = 3.42295e-05
I0408 20:21:18.554031 24089 solver.cpp:218] Iteration 7440 (2.43874 iter/s, 4.92058s/12 iters), loss = 0.330486
I0408 20:21:18.554080 24089 solver.cpp:237]     Train net output #0: loss = 0.330486 (* 1 = 0.330486 loss)
I0408 20:21:18.554093 24089 sgd_solver.cpp:105] Iteration 7440, lr = 3.3917e-05
I0408 20:21:20.546089 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0408 20:21:25.852739 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0408 20:21:28.160148 24089 solver.cpp:330] Iteration 7446, Testing net (#0)
I0408 20:21:28.160171 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:21:29.705379 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:21:32.773200 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0408 20:21:32.773233 24089 solver.cpp:397]     Test net output #1: loss = 3.09468 (* 1 = 3.09468 loss)
I0408 20:21:34.578727 24089 solver.cpp:218] Iteration 7452 (0.748869 iter/s, 16.0242s/12 iters), loss = 0.302833
I0408 20:21:34.578770 24089 solver.cpp:237]     Train net output #0: loss = 0.302833 (* 1 = 0.302833 loss)
I0408 20:21:34.578780 24089 sgd_solver.cpp:105] Iteration 7452, lr = 3.36073e-05
I0408 20:21:39.637866 24089 solver.cpp:218] Iteration 7464 (2.37204 iter/s, 5.05893s/12 iters), loss = 0.311574
I0408 20:21:39.637987 24089 solver.cpp:237]     Train net output #0: loss = 0.311574 (* 1 = 0.311574 loss)
I0408 20:21:39.638001 24089 sgd_solver.cpp:105] Iteration 7464, lr = 3.33005e-05
I0408 20:21:44.715281 24089 solver.cpp:218] Iteration 7476 (2.36354 iter/s, 5.07713s/12 iters), loss = 0.344117
I0408 20:21:44.715319 24089 solver.cpp:237]     Train net output #0: loss = 0.344117 (* 1 = 0.344117 loss)
I0408 20:21:44.715329 24089 sgd_solver.cpp:105] Iteration 7476, lr = 3.29965e-05
I0408 20:21:48.220754 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:21:49.687641 24089 solver.cpp:218] Iteration 7488 (2.41344 iter/s, 4.97216s/12 iters), loss = 0.341364
I0408 20:21:49.687686 24089 solver.cpp:237]     Train net output #0: loss = 0.341364 (* 1 = 0.341364 loss)
I0408 20:21:49.687700 24089 sgd_solver.cpp:105] Iteration 7488, lr = 3.26952e-05
I0408 20:21:54.734747 24089 solver.cpp:218] Iteration 7500 (2.3777 iter/s, 5.0469s/12 iters), loss = 0.45258
I0408 20:21:54.734791 24089 solver.cpp:237]     Train net output #0: loss = 0.45258 (* 1 = 0.45258 loss)
I0408 20:21:54.734802 24089 sgd_solver.cpp:105] Iteration 7500, lr = 3.23967e-05
I0408 20:21:59.685658 24089 solver.cpp:218] Iteration 7512 (2.4239 iter/s, 4.95071s/12 iters), loss = 0.321114
I0408 20:21:59.685705 24089 solver.cpp:237]     Train net output #0: loss = 0.321114 (* 1 = 0.321114 loss)
I0408 20:21:59.685716 24089 sgd_solver.cpp:105] Iteration 7512, lr = 3.2101e-05
I0408 20:22:04.828902 24089 solver.cpp:218] Iteration 7524 (2.33326 iter/s, 5.14303s/12 iters), loss = 0.344017
I0408 20:22:04.828949 24089 solver.cpp:237]     Train net output #0: loss = 0.344017 (* 1 = 0.344017 loss)
I0408 20:22:04.828961 24089 sgd_solver.cpp:105] Iteration 7524, lr = 3.18079e-05
I0408 20:22:10.029839 24089 solver.cpp:218] Iteration 7536 (2.30737 iter/s, 5.20072s/12 iters), loss = 0.412006
I0408 20:22:10.031857 24089 solver.cpp:237]     Train net output #0: loss = 0.412006 (* 1 = 0.412006 loss)
I0408 20:22:10.031870 24089 sgd_solver.cpp:105] Iteration 7536, lr = 3.15175e-05
I0408 20:22:14.621168 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0408 20:22:21.244417 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0408 20:22:23.563609 24089 solver.cpp:330] Iteration 7548, Testing net (#0)
I0408 20:22:23.563634 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:22:25.081797 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:22:28.038496 24089 solver.cpp:397]     Test net output #0: accuracy = 0.410539
I0408 20:22:28.038545 24089 solver.cpp:397]     Test net output #1: loss = 3.08277 (* 1 = 3.08277 loss)
I0408 20:22:28.129115 24089 solver.cpp:218] Iteration 7548 (0.663105 iter/s, 18.0967s/12 iters), loss = 0.388744
I0408 20:22:28.129168 24089 solver.cpp:237]     Train net output #0: loss = 0.388744 (* 1 = 0.388744 loss)
I0408 20:22:28.129179 24089 sgd_solver.cpp:105] Iteration 7548, lr = 3.12297e-05
I0408 20:22:32.593763 24089 solver.cpp:218] Iteration 7560 (2.68791 iter/s, 4.46444s/12 iters), loss = 0.224334
I0408 20:22:32.593811 24089 solver.cpp:237]     Train net output #0: loss = 0.224334 (* 1 = 0.224334 loss)
I0408 20:22:32.593822 24089 sgd_solver.cpp:105] Iteration 7560, lr = 3.09446e-05
I0408 20:22:37.609860 24089 solver.cpp:218] Iteration 7572 (2.3924 iter/s, 5.01588s/12 iters), loss = 0.315179
I0408 20:22:37.609896 24089 solver.cpp:237]     Train net output #0: loss = 0.315179 (* 1 = 0.315179 loss)
I0408 20:22:37.609906 24089 sgd_solver.cpp:105] Iteration 7572, lr = 3.06621e-05
I0408 20:22:42.579619 24089 solver.cpp:218] Iteration 7584 (2.4147 iter/s, 4.96956s/12 iters), loss = 0.489109
I0408 20:22:42.579697 24089 solver.cpp:237]     Train net output #0: loss = 0.489109 (* 1 = 0.489109 loss)
I0408 20:22:42.579711 24089 sgd_solver.cpp:105] Iteration 7584, lr = 3.03822e-05
I0408 20:22:43.234117 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:22:47.650439 24089 solver.cpp:218] Iteration 7596 (2.36659 iter/s, 5.07058s/12 iters), loss = 0.443902
I0408 20:22:47.650478 24089 solver.cpp:237]     Train net output #0: loss = 0.443902 (* 1 = 0.443902 loss)
I0408 20:22:47.650486 24089 sgd_solver.cpp:105] Iteration 7596, lr = 3.01048e-05
I0408 20:22:52.688869 24089 solver.cpp:218] Iteration 7608 (2.38179 iter/s, 5.03822s/12 iters), loss = 0.292065
I0408 20:22:52.688908 24089 solver.cpp:237]     Train net output #0: loss = 0.292065 (* 1 = 0.292065 loss)
I0408 20:22:52.688918 24089 sgd_solver.cpp:105] Iteration 7608, lr = 2.98299e-05
I0408 20:22:57.703899 24089 solver.cpp:218] Iteration 7620 (2.39291 iter/s, 5.01482s/12 iters), loss = 0.30872
I0408 20:22:57.703940 24089 solver.cpp:237]     Train net output #0: loss = 0.30872 (* 1 = 0.30872 loss)
I0408 20:22:57.703948 24089 sgd_solver.cpp:105] Iteration 7620, lr = 2.95576e-05
I0408 20:23:00.104404 24089 blocking_queue.cpp:49] Waiting for data
I0408 20:23:02.632793 24089 solver.cpp:218] Iteration 7632 (2.43473 iter/s, 4.92869s/12 iters), loss = 0.459646
I0408 20:23:02.632835 24089 solver.cpp:237]     Train net output #0: loss = 0.459646 (* 1 = 0.459646 loss)
I0408 20:23:02.632845 24089 sgd_solver.cpp:105] Iteration 7632, lr = 2.92878e-05
I0408 20:23:07.676234 24089 solver.cpp:218] Iteration 7644 (2.37943 iter/s, 5.04323s/12 iters), loss = 0.420999
I0408 20:23:07.676282 24089 solver.cpp:237]     Train net output #0: loss = 0.420999 (* 1 = 0.420999 loss)
I0408 20:23:07.676293 24089 sgd_solver.cpp:105] Iteration 7644, lr = 2.90204e-05
I0408 20:23:09.676651 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0408 20:23:13.629426 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0408 20:23:18.186168 24089 solver.cpp:330] Iteration 7650, Testing net (#0)
I0408 20:23:18.186197 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:23:19.661175 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:23:22.661868 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0408 20:23:22.661918 24089 solver.cpp:397]     Test net output #1: loss = 3.08993 (* 1 = 3.08993 loss)
I0408 20:23:24.642596 24089 solver.cpp:218] Iteration 7656 (0.707306 iter/s, 16.9658s/12 iters), loss = 0.420638
I0408 20:23:24.642647 24089 solver.cpp:237]     Train net output #0: loss = 0.420638 (* 1 = 0.420638 loss)
I0408 20:23:24.642660 24089 sgd_solver.cpp:105] Iteration 7656, lr = 2.87554e-05
I0408 20:23:30.122412 24089 solver.cpp:218] Iteration 7668 (2.18995 iter/s, 5.47958s/12 iters), loss = 0.229606
I0408 20:23:30.122464 24089 solver.cpp:237]     Train net output #0: loss = 0.229606 (* 1 = 0.229606 loss)
I0408 20:23:30.122476 24089 sgd_solver.cpp:105] Iteration 7668, lr = 2.84929e-05
I0408 20:23:35.345523 24089 solver.cpp:218] Iteration 7680 (2.29758 iter/s, 5.22288s/12 iters), loss = 0.444687
I0408 20:23:35.345584 24089 solver.cpp:237]     Train net output #0: loss = 0.444687 (* 1 = 0.444687 loss)
I0408 20:23:35.345597 24089 sgd_solver.cpp:105] Iteration 7680, lr = 2.82328e-05
I0408 20:23:38.141757 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:23:40.422829 24089 solver.cpp:218] Iteration 7692 (2.36356 iter/s, 5.07708s/12 iters), loss = 0.436646
I0408 20:23:40.422878 24089 solver.cpp:237]     Train net output #0: loss = 0.436646 (* 1 = 0.436646 loss)
I0408 20:23:40.422888 24089 sgd_solver.cpp:105] Iteration 7692, lr = 2.7975e-05
I0408 20:23:45.423436 24089 solver.cpp:218] Iteration 7704 (2.39981 iter/s, 5.00039s/12 iters), loss = 0.355224
I0408 20:23:45.423528 24089 solver.cpp:237]     Train net output #0: loss = 0.355224 (* 1 = 0.355224 loss)
I0408 20:23:45.423540 24089 sgd_solver.cpp:105] Iteration 7704, lr = 2.77196e-05
I0408 20:23:50.532357 24089 solver.cpp:218] Iteration 7716 (2.34895 iter/s, 5.10866s/12 iters), loss = 0.343823
I0408 20:23:50.532405 24089 solver.cpp:237]     Train net output #0: loss = 0.343823 (* 1 = 0.343823 loss)
I0408 20:23:50.532418 24089 sgd_solver.cpp:105] Iteration 7716, lr = 2.74665e-05
I0408 20:23:55.631242 24089 solver.cpp:218] Iteration 7728 (2.35356 iter/s, 5.09867s/12 iters), loss = 0.321659
I0408 20:23:55.631290 24089 solver.cpp:237]     Train net output #0: loss = 0.321659 (* 1 = 0.321659 loss)
I0408 20:23:55.631302 24089 sgd_solver.cpp:105] Iteration 7728, lr = 2.72158e-05
I0408 20:24:00.754439 24089 solver.cpp:218] Iteration 7740 (2.34239 iter/s, 5.12298s/12 iters), loss = 0.351056
I0408 20:24:00.754487 24089 solver.cpp:237]     Train net output #0: loss = 0.351056 (* 1 = 0.351056 loss)
I0408 20:24:00.754498 24089 sgd_solver.cpp:105] Iteration 7740, lr = 2.69673e-05
I0408 20:24:05.337672 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0408 20:24:08.402685 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0408 20:24:12.236836 24089 solver.cpp:330] Iteration 7752, Testing net (#0)
I0408 20:24:12.236865 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:24:13.674111 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:24:16.708606 24089 solver.cpp:397]     Test net output #0: accuracy = 0.407476
I0408 20:24:16.708753 24089 solver.cpp:397]     Test net output #1: loss = 3.09899 (* 1 = 3.09899 loss)
I0408 20:24:16.799311 24089 solver.cpp:218] Iteration 7752 (0.747929 iter/s, 16.0443s/12 iters), loss = 0.339755
I0408 20:24:16.799350 24089 solver.cpp:237]     Train net output #0: loss = 0.339755 (* 1 = 0.339755 loss)
I0408 20:24:16.799360 24089 sgd_solver.cpp:105] Iteration 7752, lr = 2.67211e-05
I0408 20:24:21.235414 24089 solver.cpp:218] Iteration 7764 (2.70519 iter/s, 4.43591s/12 iters), loss = 0.311485
I0408 20:24:21.235455 24089 solver.cpp:237]     Train net output #0: loss = 0.311485 (* 1 = 0.311485 loss)
I0408 20:24:21.235464 24089 sgd_solver.cpp:105] Iteration 7764, lr = 2.64771e-05
I0408 20:24:26.290975 24089 solver.cpp:218] Iteration 7776 (2.37372 iter/s, 5.05535s/12 iters), loss = 0.333517
I0408 20:24:26.291021 24089 solver.cpp:237]     Train net output #0: loss = 0.333517 (* 1 = 0.333517 loss)
I0408 20:24:26.291033 24089 sgd_solver.cpp:105] Iteration 7776, lr = 2.62354e-05
I0408 20:24:31.337899 24089 solver.cpp:218] Iteration 7788 (2.37779 iter/s, 5.04671s/12 iters), loss = 0.493011
I0408 20:24:31.337944 24089 solver.cpp:237]     Train net output #0: loss = 0.493011 (* 1 = 0.493011 loss)
I0408 20:24:31.337970 24089 sgd_solver.cpp:105] Iteration 7788, lr = 2.59959e-05
I0408 20:24:31.348745 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:24:36.411813 24089 solver.cpp:218] Iteration 7800 (2.36514 iter/s, 5.0737s/12 iters), loss = 0.390123
I0408 20:24:36.411861 24089 solver.cpp:237]     Train net output #0: loss = 0.390123 (* 1 = 0.390123 loss)
I0408 20:24:36.411873 24089 sgd_solver.cpp:105] Iteration 7800, lr = 2.57585e-05
I0408 20:24:41.434835 24089 solver.cpp:218] Iteration 7812 (2.38911 iter/s, 5.0228s/12 iters), loss = 0.312651
I0408 20:24:41.434888 24089 solver.cpp:237]     Train net output #0: loss = 0.312651 (* 1 = 0.312651 loss)
I0408 20:24:41.434900 24089 sgd_solver.cpp:105] Iteration 7812, lr = 2.55234e-05
I0408 20:24:46.512573 24089 solver.cpp:218] Iteration 7824 (2.36336 iter/s, 5.07751s/12 iters), loss = 0.263145
I0408 20:24:46.512620 24089 solver.cpp:237]     Train net output #0: loss = 0.263145 (* 1 = 0.263145 loss)
I0408 20:24:46.512631 24089 sgd_solver.cpp:105] Iteration 7824, lr = 2.52904e-05
I0408 20:24:51.563971 24089 solver.cpp:218] Iteration 7836 (2.37568 iter/s, 5.05118s/12 iters), loss = 0.34921
I0408 20:24:51.564090 24089 solver.cpp:237]     Train net output #0: loss = 0.34921 (* 1 = 0.34921 loss)
I0408 20:24:51.564103 24089 sgd_solver.cpp:105] Iteration 7836, lr = 2.50595e-05
I0408 20:24:56.656316 24089 solver.cpp:218] Iteration 7848 (2.35661 iter/s, 5.09206s/12 iters), loss = 0.416455
I0408 20:24:56.656363 24089 solver.cpp:237]     Train net output #0: loss = 0.416455 (* 1 = 0.416455 loss)
I0408 20:24:56.656375 24089 sgd_solver.cpp:105] Iteration 7848, lr = 2.48307e-05
I0408 20:24:58.680054 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0408 20:25:01.681448 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0408 20:25:04.009542 24089 solver.cpp:330] Iteration 7854, Testing net (#0)
I0408 20:25:04.009567 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:25:05.345465 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:25:08.581931 24089 solver.cpp:397]     Test net output #0: accuracy = 0.401961
I0408 20:25:08.581996 24089 solver.cpp:397]     Test net output #1: loss = 3.10477 (* 1 = 3.10477 loss)
I0408 20:25:10.611115 24089 solver.cpp:218] Iteration 7860 (0.85995 iter/s, 13.9543s/12 iters), loss = 0.395553
I0408 20:25:10.611168 24089 solver.cpp:237]     Train net output #0: loss = 0.395553 (* 1 = 0.395553 loss)
I0408 20:25:10.611181 24089 sgd_solver.cpp:105] Iteration 7860, lr = 2.4604e-05
I0408 20:25:15.892637 24089 solver.cpp:218] Iteration 7872 (2.27217 iter/s, 5.28129s/12 iters), loss = 0.292436
I0408 20:25:15.892685 24089 solver.cpp:237]     Train net output #0: loss = 0.292436 (* 1 = 0.292436 loss)
I0408 20:25:15.892697 24089 sgd_solver.cpp:105] Iteration 7872, lr = 2.43794e-05
I0408 20:25:20.933316 24089 solver.cpp:218] Iteration 7884 (2.38073 iter/s, 5.04046s/12 iters), loss = 0.345813
I0408 20:25:20.933370 24089 solver.cpp:237]     Train net output #0: loss = 0.345813 (* 1 = 0.345813 loss)
I0408 20:25:20.933383 24089 sgd_solver.cpp:105] Iteration 7884, lr = 2.41568e-05
I0408 20:25:23.104924 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:25:25.980353 24089 solver.cpp:218] Iteration 7896 (2.37774 iter/s, 5.04681s/12 iters), loss = 0.301041
I0408 20:25:25.980402 24089 solver.cpp:237]     Train net output #0: loss = 0.301041 (* 1 = 0.301041 loss)
I0408 20:25:25.980413 24089 sgd_solver.cpp:105] Iteration 7896, lr = 2.39362e-05
I0408 20:25:31.070546 24089 solver.cpp:218] Iteration 7908 (2.35758 iter/s, 5.08997s/12 iters), loss = 0.22872
I0408 20:25:31.070595 24089 solver.cpp:237]     Train net output #0: loss = 0.22872 (* 1 = 0.22872 loss)
I0408 20:25:31.070607 24089 sgd_solver.cpp:105] Iteration 7908, lr = 2.37177e-05
I0408 20:25:36.476752 24089 solver.cpp:218] Iteration 7920 (2.21977 iter/s, 5.40597s/12 iters), loss = 0.321484
I0408 20:25:36.476804 24089 solver.cpp:237]     Train net output #0: loss = 0.321484 (* 1 = 0.321484 loss)
I0408 20:25:36.476815 24089 sgd_solver.cpp:105] Iteration 7920, lr = 2.35012e-05
I0408 20:25:41.608834 24089 solver.cpp:218] Iteration 7932 (2.33833 iter/s, 5.13186s/12 iters), loss = 0.338748
I0408 20:25:41.608881 24089 solver.cpp:237]     Train net output #0: loss = 0.338748 (* 1 = 0.338748 loss)
I0408 20:25:41.608892 24089 sgd_solver.cpp:105] Iteration 7932, lr = 2.32866e-05
I0408 20:25:46.667270 24089 solver.cpp:218] Iteration 7944 (2.37238 iter/s, 5.05822s/12 iters), loss = 0.240832
I0408 20:25:46.667333 24089 solver.cpp:237]     Train net output #0: loss = 0.240832 (* 1 = 0.240832 loss)
I0408 20:25:46.667343 24089 sgd_solver.cpp:105] Iteration 7944, lr = 2.3074e-05
I0408 20:25:51.265097 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0408 20:25:54.275995 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0408 20:25:58.425567 24089 solver.cpp:330] Iteration 7956, Testing net (#0)
I0408 20:25:58.425592 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:25:59.770459 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:26:02.885166 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405637
I0408 20:26:02.885215 24089 solver.cpp:397]     Test net output #1: loss = 3.09565 (* 1 = 3.09565 loss)
I0408 20:26:02.976138 24089 solver.cpp:218] Iteration 7956 (0.735823 iter/s, 16.3083s/12 iters), loss = 0.302112
I0408 20:26:02.976213 24089 solver.cpp:237]     Train net output #0: loss = 0.302112 (* 1 = 0.302112 loss)
I0408 20:26:02.976230 24089 sgd_solver.cpp:105] Iteration 7956, lr = 2.28633e-05
I0408 20:26:07.454433 24089 solver.cpp:218] Iteration 7968 (2.67973 iter/s, 4.47807s/12 iters), loss = 0.342759
I0408 20:26:07.454483 24089 solver.cpp:237]     Train net output #0: loss = 0.342759 (* 1 = 0.342759 loss)
I0408 20:26:07.454496 24089 sgd_solver.cpp:105] Iteration 7968, lr = 2.26546e-05
I0408 20:26:12.870913 24089 solver.cpp:218] Iteration 7980 (2.21556 iter/s, 5.41624s/12 iters), loss = 0.304769
I0408 20:26:12.870960 24089 solver.cpp:237]     Train net output #0: loss = 0.304769 (* 1 = 0.304769 loss)
I0408 20:26:12.870971 24089 sgd_solver.cpp:105] Iteration 7980, lr = 2.24478e-05
I0408 20:26:17.145445 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:26:17.862663 24089 solver.cpp:218] Iteration 7992 (2.40407 iter/s, 4.99153s/12 iters), loss = 0.36628
I0408 20:26:17.862710 24089 solver.cpp:237]     Train net output #0: loss = 0.36628 (* 1 = 0.36628 loss)
I0408 20:26:17.862722 24089 sgd_solver.cpp:105] Iteration 7992, lr = 2.22428e-05
I0408 20:26:22.982637 24089 solver.cpp:218] Iteration 8004 (2.34386 iter/s, 5.11975s/12 iters), loss = 0.386211
I0408 20:26:22.982688 24089 solver.cpp:237]     Train net output #0: loss = 0.386211 (* 1 = 0.386211 loss)
I0408 20:26:22.982702 24089 sgd_solver.cpp:105] Iteration 8004, lr = 2.20398e-05
I0408 20:26:28.039880 24089 solver.cpp:218] Iteration 8016 (2.37294 iter/s, 5.05702s/12 iters), loss = 0.288338
I0408 20:26:28.040001 24089 solver.cpp:237]     Train net output #0: loss = 0.288338 (* 1 = 0.288338 loss)
I0408 20:26:28.040014 24089 sgd_solver.cpp:105] Iteration 8016, lr = 2.18386e-05
I0408 20:26:33.072413 24089 solver.cpp:218] Iteration 8028 (2.38462 iter/s, 5.03224s/12 iters), loss = 0.272938
I0408 20:26:33.072459 24089 solver.cpp:237]     Train net output #0: loss = 0.272938 (* 1 = 0.272938 loss)
I0408 20:26:33.072470 24089 sgd_solver.cpp:105] Iteration 8028, lr = 2.16392e-05
I0408 20:26:38.163383 24089 solver.cpp:218] Iteration 8040 (2.35722 iter/s, 5.09075s/12 iters), loss = 0.343503
I0408 20:26:38.163432 24089 solver.cpp:237]     Train net output #0: loss = 0.343503 (* 1 = 0.343503 loss)
I0408 20:26:38.163444 24089 sgd_solver.cpp:105] Iteration 8040, lr = 2.14416e-05
I0408 20:26:43.167882 24089 solver.cpp:218] Iteration 8052 (2.39795 iter/s, 5.00428s/12 iters), loss = 0.284604
I0408 20:26:43.167932 24089 solver.cpp:237]     Train net output #0: loss = 0.284604 (* 1 = 0.284604 loss)
I0408 20:26:43.167944 24089 sgd_solver.cpp:105] Iteration 8052, lr = 2.12459e-05
I0408 20:26:45.237569 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0408 20:26:54.234483 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0408 20:26:57.063915 24089 solver.cpp:330] Iteration 8058, Testing net (#0)
I0408 20:26:57.063941 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:26:58.364454 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:27:01.525678 24089 solver.cpp:397]     Test net output #0: accuracy = 0.403186
I0408 20:27:01.525722 24089 solver.cpp:397]     Test net output #1: loss = 3.11695 (* 1 = 3.11695 loss)
I0408 20:27:03.251327 24089 solver.cpp:218] Iteration 8064 (0.597528 iter/s, 20.0827s/12 iters), loss = 0.296174
I0408 20:27:03.251377 24089 solver.cpp:237]     Train net output #0: loss = 0.296174 (* 1 = 0.296174 loss)
I0408 20:27:03.251389 24089 sgd_solver.cpp:105] Iteration 8064, lr = 2.10519e-05
I0408 20:27:08.253839 24089 solver.cpp:218] Iteration 8076 (2.39891 iter/s, 5.00228s/12 iters), loss = 0.271698
I0408 20:27:08.253877 24089 solver.cpp:237]     Train net output #0: loss = 0.271698 (* 1 = 0.271698 loss)
I0408 20:27:08.253886 24089 sgd_solver.cpp:105] Iteration 8076, lr = 2.08597e-05
I0408 20:27:13.309047 24089 solver.cpp:218] Iteration 8088 (2.37389 iter/s, 5.05499s/12 iters), loss = 0.239949
I0408 20:27:13.309108 24089 solver.cpp:237]     Train net output #0: loss = 0.239949 (* 1 = 0.239949 loss)
I0408 20:27:13.309126 24089 sgd_solver.cpp:105] Iteration 8088, lr = 2.06692e-05
I0408 20:27:14.728574 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:27:18.342366 24089 solver.cpp:218] Iteration 8100 (2.38423 iter/s, 5.03308s/12 iters), loss = 0.412046
I0408 20:27:18.342418 24089 solver.cpp:237]     Train net output #0: loss = 0.412046 (* 1 = 0.412046 loss)
I0408 20:27:18.342430 24089 sgd_solver.cpp:105] Iteration 8100, lr = 2.04805e-05
I0408 20:27:23.291790 24089 solver.cpp:218] Iteration 8112 (2.42463 iter/s, 4.9492s/12 iters), loss = 0.327668
I0408 20:27:23.291827 24089 solver.cpp:237]     Train net output #0: loss = 0.327668 (* 1 = 0.327668 loss)
I0408 20:27:23.291836 24089 sgd_solver.cpp:105] Iteration 8112, lr = 2.02936e-05
I0408 20:27:28.353865 24089 solver.cpp:218] Iteration 8124 (2.37067 iter/s, 5.06185s/12 iters), loss = 0.260022
I0408 20:27:28.353929 24089 solver.cpp:237]     Train net output #0: loss = 0.260022 (* 1 = 0.260022 loss)
I0408 20:27:28.353946 24089 sgd_solver.cpp:105] Iteration 8124, lr = 2.01083e-05
I0408 20:27:33.443217 24089 solver.cpp:218] Iteration 8136 (2.35797 iter/s, 5.08912s/12 iters), loss = 0.331456
I0408 20:27:33.443334 24089 solver.cpp:237]     Train net output #0: loss = 0.331456 (* 1 = 0.331456 loss)
I0408 20:27:33.443344 24089 sgd_solver.cpp:105] Iteration 8136, lr = 1.99247e-05
I0408 20:27:38.522882 24089 solver.cpp:218] Iteration 8148 (2.3625 iter/s, 5.07937s/12 iters), loss = 0.360521
I0408 20:27:38.522924 24089 solver.cpp:237]     Train net output #0: loss = 0.360521 (* 1 = 0.360521 loss)
I0408 20:27:38.522933 24089 sgd_solver.cpp:105] Iteration 8148, lr = 1.97428e-05
I0408 20:27:43.181744 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0408 20:27:46.218624 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0408 20:27:48.588582 24089 solver.cpp:330] Iteration 8160, Testing net (#0)
I0408 20:27:48.588608 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:27:49.857270 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:27:53.054836 24089 solver.cpp:397]     Test net output #0: accuracy = 0.404412
I0408 20:27:53.054884 24089 solver.cpp:397]     Test net output #1: loss = 3.09344 (* 1 = 3.09344 loss)
I0408 20:27:53.145658 24089 solver.cpp:218] Iteration 8160 (0.820668 iter/s, 14.6222s/12 iters), loss = 0.345762
I0408 20:27:53.145704 24089 solver.cpp:237]     Train net output #0: loss = 0.345762 (* 1 = 0.345762 loss)
I0408 20:27:53.145714 24089 sgd_solver.cpp:105] Iteration 8160, lr = 1.95625e-05
I0408 20:27:57.712585 24089 solver.cpp:218] Iteration 8172 (2.62771 iter/s, 4.56672s/12 iters), loss = 0.33809
I0408 20:27:57.712632 24089 solver.cpp:237]     Train net output #0: loss = 0.33809 (* 1 = 0.33809 loss)
I0408 20:27:57.712643 24089 sgd_solver.cpp:105] Iteration 8172, lr = 1.93839e-05
I0408 20:28:02.842550 24089 solver.cpp:218] Iteration 8184 (2.3393 iter/s, 5.12974s/12 iters), loss = 0.40306
I0408 20:28:02.842592 24089 solver.cpp:237]     Train net output #0: loss = 0.40306 (* 1 = 0.40306 loss)
I0408 20:28:02.842600 24089 sgd_solver.cpp:105] Iteration 8184, lr = 1.9207e-05
I0408 20:28:06.420159 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:28:07.870488 24089 solver.cpp:218] Iteration 8196 (2.38677 iter/s, 5.02772s/12 iters), loss = 0.363849
I0408 20:28:07.870535 24089 solver.cpp:237]     Train net output #0: loss = 0.363849 (* 1 = 0.363849 loss)
I0408 20:28:07.870546 24089 sgd_solver.cpp:105] Iteration 8196, lr = 1.90316e-05
I0408 20:28:12.925359 24089 solver.cpp:218] Iteration 8208 (2.37406 iter/s, 5.05464s/12 iters), loss = 0.256096
I0408 20:28:12.925438 24089 solver.cpp:237]     Train net output #0: loss = 0.256096 (* 1 = 0.256096 loss)
I0408 20:28:12.925460 24089 sgd_solver.cpp:105] Iteration 8208, lr = 1.88579e-05
I0408 20:28:17.882341 24089 solver.cpp:218] Iteration 8220 (2.42095 iter/s, 4.95674s/12 iters), loss = 0.413607
I0408 20:28:17.882387 24089 solver.cpp:237]     Train net output #0: loss = 0.413607 (* 1 = 0.413607 loss)
I0408 20:28:17.882398 24089 sgd_solver.cpp:105] Iteration 8220, lr = 1.86857e-05
I0408 20:28:22.865561 24089 solver.cpp:218] Iteration 8232 (2.40819 iter/s, 4.98301s/12 iters), loss = 0.334379
I0408 20:28:22.865594 24089 solver.cpp:237]     Train net output #0: loss = 0.334379 (* 1 = 0.334379 loss)
I0408 20:28:22.865603 24089 sgd_solver.cpp:105] Iteration 8232, lr = 1.85151e-05
I0408 20:28:27.951084 24089 solver.cpp:218] Iteration 8244 (2.35974 iter/s, 5.08531s/12 iters), loss = 0.301201
I0408 20:28:27.951128 24089 solver.cpp:237]     Train net output #0: loss = 0.301201 (* 1 = 0.301201 loss)
I0408 20:28:27.951139 24089 sgd_solver.cpp:105] Iteration 8244, lr = 1.83461e-05
I0408 20:28:32.942615 24089 solver.cpp:218] Iteration 8256 (2.40418 iter/s, 4.99131s/12 iters), loss = 0.386108
I0408 20:28:32.942667 24089 solver.cpp:237]     Train net output #0: loss = 0.386108 (* 1 = 0.386108 loss)
I0408 20:28:32.942679 24089 sgd_solver.cpp:105] Iteration 8256, lr = 1.81786e-05
I0408 20:28:35.237579 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0408 20:28:38.277284 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0408 20:28:40.605547 24089 solver.cpp:330] Iteration 8262, Testing net (#0)
I0408 20:28:40.605576 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:28:41.838624 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:28:45.067589 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0408 20:28:45.067633 24089 solver.cpp:397]     Test net output #1: loss = 3.09727 (* 1 = 3.09727 loss)
I0408 20:28:47.058660 24089 solver.cpp:218] Iteration 8268 (0.850128 iter/s, 14.1155s/12 iters), loss = 0.305285
I0408 20:28:47.058702 24089 solver.cpp:237]     Train net output #0: loss = 0.305285 (* 1 = 0.305285 loss)
I0408 20:28:47.058712 24089 sgd_solver.cpp:105] Iteration 8268, lr = 1.80126e-05
I0408 20:28:52.282333 24089 solver.cpp:218] Iteration 8280 (2.29733 iter/s, 5.22345s/12 iters), loss = 0.311071
I0408 20:28:52.282368 24089 solver.cpp:237]     Train net output #0: loss = 0.311071 (* 1 = 0.311071 loss)
I0408 20:28:52.282377 24089 sgd_solver.cpp:105] Iteration 8280, lr = 1.78482e-05
I0408 20:28:57.418905 24089 solver.cpp:218] Iteration 8292 (2.33629 iter/s, 5.13635s/12 iters), loss = 0.396386
I0408 20:28:57.418942 24089 solver.cpp:237]     Train net output #0: loss = 0.396386 (* 1 = 0.396386 loss)
I0408 20:28:57.418951 24089 sgd_solver.cpp:105] Iteration 8292, lr = 1.76852e-05
I0408 20:28:58.077143 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:29:02.452024 24089 solver.cpp:218] Iteration 8304 (2.38431 iter/s, 5.0329s/12 iters), loss = 0.479314
I0408 20:29:02.452070 24089 solver.cpp:237]     Train net output #0: loss = 0.479314 (* 1 = 0.479314 loss)
I0408 20:29:02.452082 24089 sgd_solver.cpp:105] Iteration 8304, lr = 1.75237e-05
I0408 20:29:05.384861 24089 blocking_queue.cpp:49] Waiting for data
I0408 20:29:08.534034 24089 solver.cpp:218] Iteration 8316 (1.97745 iter/s, 6.06843s/12 iters), loss = 0.329732
I0408 20:29:08.546064 24089 solver.cpp:237]     Train net output #0: loss = 0.329732 (* 1 = 0.329732 loss)
I0408 20:29:08.546083 24089 sgd_solver.cpp:105] Iteration 8316, lr = 1.73638e-05
I0408 20:29:16.656889 24089 solver.cpp:218] Iteration 8328 (1.4819 iter/s, 8.09772s/12 iters), loss = 0.314435
I0408 20:29:16.656949 24089 solver.cpp:237]     Train net output #0: loss = 0.314435 (* 1 = 0.314435 loss)
I0408 20:29:16.656962 24089 sgd_solver.cpp:105] Iteration 8328, lr = 1.72052e-05
I0408 20:29:24.263429 24089 solver.cpp:218] Iteration 8340 (1.57766 iter/s, 7.60621s/12 iters), loss = 0.319578
I0408 20:29:24.274621 24089 solver.cpp:237]     Train net output #0: loss = 0.319578 (* 1 = 0.319578 loss)
I0408 20:29:24.274646 24089 sgd_solver.cpp:105] Iteration 8340, lr = 1.70482e-05
I0408 20:29:30.521816 24089 solver.cpp:218] Iteration 8352 (1.92093 iter/s, 6.24698s/12 iters), loss = 0.358542
I0408 20:29:30.521876 24089 solver.cpp:237]     Train net output #0: loss = 0.358542 (* 1 = 0.358542 loss)
I0408 20:29:30.521889 24089 sgd_solver.cpp:105] Iteration 8352, lr = 1.68925e-05
I0408 20:29:36.234679 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0408 20:29:43.521189 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0408 20:29:46.238759 24089 solver.cpp:330] Iteration 8364, Testing net (#0)
I0408 20:29:46.238783 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:29:47.595075 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:29:51.641635 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405637
I0408 20:29:51.641674 24089 solver.cpp:397]     Test net output #1: loss = 3.10454 (* 1 = 3.10454 loss)
I0408 20:29:51.734724 24089 solver.cpp:218] Iteration 8364 (0.565714 iter/s, 21.2121s/12 iters), loss = 0.349417
I0408 20:29:51.734782 24089 solver.cpp:237]     Train net output #0: loss = 0.349417 (* 1 = 0.349417 loss)
I0408 20:29:51.734794 24089 sgd_solver.cpp:105] Iteration 8364, lr = 1.67383e-05
I0408 20:29:56.503914 24089 solver.cpp:218] Iteration 8376 (2.51627 iter/s, 4.76895s/12 iters), loss = 0.29971
I0408 20:29:56.503965 24089 solver.cpp:237]     Train net output #0: loss = 0.29971 (* 1 = 0.29971 loss)
I0408 20:29:56.503975 24089 sgd_solver.cpp:105] Iteration 8376, lr = 1.65855e-05
I0408 20:30:02.099287 24089 solver.cpp:218] Iteration 8388 (2.14473 iter/s, 5.59511s/12 iters), loss = 0.412796
I0408 20:30:02.099341 24089 solver.cpp:237]     Train net output #0: loss = 0.412796 (* 1 = 0.412796 loss)
I0408 20:30:02.099354 24089 sgd_solver.cpp:105] Iteration 8388, lr = 1.64341e-05
I0408 20:30:05.474370 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:30:08.116763 24089 solver.cpp:218] Iteration 8400 (1.99428 iter/s, 6.01721s/12 iters), loss = 0.276235
I0408 20:30:08.116808 24089 solver.cpp:237]     Train net output #0: loss = 0.276235 (* 1 = 0.276235 loss)
I0408 20:30:08.116818 24089 sgd_solver.cpp:105] Iteration 8400, lr = 1.6284e-05
I0408 20:30:14.711681 24089 solver.cpp:218] Iteration 8412 (1.81966 iter/s, 6.59463s/12 iters), loss = 0.316971
I0408 20:30:14.721227 24089 solver.cpp:237]     Train net output #0: loss = 0.316971 (* 1 = 0.316971 loss)
I0408 20:30:14.721246 24089 sgd_solver.cpp:105] Iteration 8412, lr = 1.61353e-05
I0408 20:30:20.829602 24089 solver.cpp:218] Iteration 8424 (1.96458 iter/s, 6.10817s/12 iters), loss = 0.298713
I0408 20:30:20.829658 24089 solver.cpp:237]     Train net output #0: loss = 0.298713 (* 1 = 0.298713 loss)
I0408 20:30:20.829669 24089 sgd_solver.cpp:105] Iteration 8424, lr = 1.5988e-05
I0408 20:30:26.859596 24089 solver.cpp:218] Iteration 8436 (1.99014 iter/s, 6.02972s/12 iters), loss = 0.259889
I0408 20:30:26.859652 24089 solver.cpp:237]     Train net output #0: loss = 0.259889 (* 1 = 0.259889 loss)
I0408 20:30:26.859663 24089 sgd_solver.cpp:105] Iteration 8436, lr = 1.58421e-05
I0408 20:30:31.937880 24089 solver.cpp:218] Iteration 8448 (2.36311 iter/s, 5.07804s/12 iters), loss = 0.415561
I0408 20:30:31.937927 24089 solver.cpp:237]     Train net output #0: loss = 0.415561 (* 1 = 0.415561 loss)
I0408 20:30:31.937938 24089 sgd_solver.cpp:105] Iteration 8448, lr = 1.56974e-05
I0408 20:30:37.008033 24089 solver.cpp:218] Iteration 8460 (2.3669 iter/s, 5.06993s/12 iters), loss = 0.316937
I0408 20:30:37.008075 24089 solver.cpp:237]     Train net output #0: loss = 0.316937 (* 1 = 0.316937 loss)
I0408 20:30:37.008085 24089 sgd_solver.cpp:105] Iteration 8460, lr = 1.55541e-05
I0408 20:30:39.062742 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0408 20:30:42.067636 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0408 20:30:44.377175 24089 solver.cpp:330] Iteration 8466, Testing net (#0)
I0408 20:30:44.377205 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:30:45.827597 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:30:49.299598 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405637
I0408 20:30:49.299646 24089 solver.cpp:397]     Test net output #1: loss = 3.09836 (* 1 = 3.09836 loss)
I0408 20:30:51.241395 24089 solver.cpp:218] Iteration 8472 (0.843121 iter/s, 14.2328s/12 iters), loss = 0.431018
I0408 20:30:51.241444 24089 solver.cpp:237]     Train net output #0: loss = 0.431018 (* 1 = 0.431018 loss)
I0408 20:30:51.241456 24089 sgd_solver.cpp:105] Iteration 8472, lr = 1.54121e-05
I0408 20:30:56.309363 24089 solver.cpp:218] Iteration 8484 (2.36792 iter/s, 5.06774s/12 iters), loss = 0.184015
I0408 20:30:56.309413 24089 solver.cpp:237]     Train net output #0: loss = 0.184015 (* 1 = 0.184015 loss)
I0408 20:30:56.309425 24089 sgd_solver.cpp:105] Iteration 8484, lr = 1.52714e-05
I0408 20:31:01.327184 24089 solver.cpp:218] Iteration 8496 (2.39158 iter/s, 5.01759s/12 iters), loss = 0.405977
I0408 20:31:01.327220 24089 solver.cpp:237]     Train net output #0: loss = 0.405977 (* 1 = 0.405977 loss)
I0408 20:31:01.327229 24089 sgd_solver.cpp:105] Iteration 8496, lr = 1.5132e-05
I0408 20:31:01.375710 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:31:06.393605 24089 solver.cpp:218] Iteration 8508 (2.36864 iter/s, 5.0662s/12 iters), loss = 0.356294
I0408 20:31:06.393654 24089 solver.cpp:237]     Train net output #0: loss = 0.356294 (* 1 = 0.356294 loss)
I0408 20:31:06.393666 24089 sgd_solver.cpp:105] Iteration 8508, lr = 1.49938e-05
I0408 20:31:11.417325 24089 solver.cpp:218] Iteration 8520 (2.38878 iter/s, 5.02349s/12 iters), loss = 0.403776
I0408 20:31:11.417359 24089 solver.cpp:237]     Train net output #0: loss = 0.403776 (* 1 = 0.403776 loss)
I0408 20:31:11.417367 24089 sgd_solver.cpp:105] Iteration 8520, lr = 1.48569e-05
I0408 20:31:16.446301 24089 solver.cpp:218] Iteration 8532 (2.38628 iter/s, 5.02876s/12 iters), loss = 0.368116
I0408 20:31:16.446458 24089 solver.cpp:237]     Train net output #0: loss = 0.368116 (* 1 = 0.368116 loss)
I0408 20:31:16.446473 24089 sgd_solver.cpp:105] Iteration 8532, lr = 1.47213e-05
I0408 20:31:21.731366 24089 solver.cpp:218] Iteration 8544 (2.27069 iter/s, 5.28473s/12 iters), loss = 0.390978
I0408 20:31:21.731405 24089 solver.cpp:237]     Train net output #0: loss = 0.390978 (* 1 = 0.390978 loss)
I0408 20:31:21.731413 24089 sgd_solver.cpp:105] Iteration 8544, lr = 1.45869e-05
I0408 20:31:27.128518 24089 solver.cpp:218] Iteration 8556 (2.22349 iter/s, 5.39692s/12 iters), loss = 0.424406
I0408 20:31:27.128566 24089 solver.cpp:237]     Train net output #0: loss = 0.424406 (* 1 = 0.424406 loss)
I0408 20:31:27.128576 24089 sgd_solver.cpp:105] Iteration 8556, lr = 1.44537e-05
I0408 20:31:31.756179 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0408 20:31:34.776029 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0408 20:31:37.102600 24089 solver.cpp:330] Iteration 8568, Testing net (#0)
I0408 20:31:37.102628 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:31:38.206007 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:31:41.564558 24089 solver.cpp:397]     Test net output #0: accuracy = 0.407476
I0408 20:31:41.564604 24089 solver.cpp:397]     Test net output #1: loss = 3.12085 (* 1 = 3.12085 loss)
I0408 20:31:41.654893 24089 solver.cpp:218] Iteration 8568 (0.826115 iter/s, 14.5258s/12 iters), loss = 0.300264
I0408 20:31:41.654940 24089 solver.cpp:237]     Train net output #0: loss = 0.300264 (* 1 = 0.300264 loss)
I0408 20:31:41.654951 24089 sgd_solver.cpp:105] Iteration 8568, lr = 1.43218e-05
I0408 20:31:45.938238 24089 solver.cpp:218] Iteration 8580 (2.80168 iter/s, 4.28314s/12 iters), loss = 0.274257
I0408 20:31:45.938287 24089 solver.cpp:237]     Train net output #0: loss = 0.274257 (* 1 = 0.274257 loss)
I0408 20:31:45.938299 24089 sgd_solver.cpp:105] Iteration 8580, lr = 1.4191e-05
I0408 20:31:50.973589 24089 solver.cpp:218] Iteration 8592 (2.38326 iter/s, 5.03512s/12 iters), loss = 0.321475
I0408 20:31:50.973695 24089 solver.cpp:237]     Train net output #0: loss = 0.321475 (* 1 = 0.321475 loss)
I0408 20:31:50.973711 24089 sgd_solver.cpp:105] Iteration 8592, lr = 1.40615e-05
I0408 20:31:53.093410 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:31:55.854992 24089 solver.cpp:218] Iteration 8604 (2.45845 iter/s, 4.88113s/12 iters), loss = 0.317688
I0408 20:31:55.855027 24089 solver.cpp:237]     Train net output #0: loss = 0.317688 (* 1 = 0.317688 loss)
I0408 20:31:55.855036 24089 sgd_solver.cpp:105] Iteration 8604, lr = 1.39331e-05
I0408 20:32:00.945716 24089 solver.cpp:218] Iteration 8616 (2.35733 iter/s, 5.0905s/12 iters), loss = 0.303468
I0408 20:32:00.945765 24089 solver.cpp:237]     Train net output #0: loss = 0.303468 (* 1 = 0.303468 loss)
I0408 20:32:00.945776 24089 sgd_solver.cpp:105] Iteration 8616, lr = 1.38059e-05
I0408 20:32:06.012739 24089 solver.cpp:218] Iteration 8628 (2.36836 iter/s, 5.06679s/12 iters), loss = 0.259613
I0408 20:32:06.012781 24089 solver.cpp:237]     Train net output #0: loss = 0.259613 (* 1 = 0.259613 loss)
I0408 20:32:06.012792 24089 sgd_solver.cpp:105] Iteration 8628, lr = 1.36798e-05
I0408 20:32:10.981421 24089 solver.cpp:218] Iteration 8640 (2.41524 iter/s, 4.96846s/12 iters), loss = 0.238156
I0408 20:32:10.981465 24089 solver.cpp:237]     Train net output #0: loss = 0.238156 (* 1 = 0.238156 loss)
I0408 20:32:10.981474 24089 sgd_solver.cpp:105] Iteration 8640, lr = 1.35549e-05
I0408 20:32:16.065451 24089 solver.cpp:218] Iteration 8652 (2.36044 iter/s, 5.0838s/12 iters), loss = 0.344763
I0408 20:32:16.065487 24089 solver.cpp:237]     Train net output #0: loss = 0.344763 (* 1 = 0.344763 loss)
I0408 20:32:16.065495 24089 sgd_solver.cpp:105] Iteration 8652, lr = 1.34312e-05
I0408 20:32:21.108518 24089 solver.cpp:218] Iteration 8664 (2.37961 iter/s, 5.04285s/12 iters), loss = 0.286029
I0408 20:32:21.108669 24089 solver.cpp:237]     Train net output #0: loss = 0.286029 (* 1 = 0.286029 loss)
I0408 20:32:21.108681 24089 sgd_solver.cpp:105] Iteration 8664, lr = 1.33086e-05
I0408 20:32:23.154875 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0408 20:32:27.154454 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0408 20:32:29.486037 24089 solver.cpp:330] Iteration 8670, Testing net (#0)
I0408 20:32:29.486064 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:32:30.571038 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:32:33.964625 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0408 20:32:33.964675 24089 solver.cpp:397]     Test net output #1: loss = 3.12397 (* 1 = 3.12397 loss)
I0408 20:32:35.790810 24089 solver.cpp:218] Iteration 8676 (0.817348 iter/s, 14.6816s/12 iters), loss = 0.320655
I0408 20:32:35.790859 24089 solver.cpp:237]     Train net output #0: loss = 0.320655 (* 1 = 0.320655 loss)
I0408 20:32:35.790870 24089 sgd_solver.cpp:105] Iteration 8676, lr = 1.31871e-05
I0408 20:32:41.044543 24089 solver.cpp:218] Iteration 8688 (2.28419 iter/s, 5.25349s/12 iters), loss = 0.43117
I0408 20:32:41.044589 24089 solver.cpp:237]     Train net output #0: loss = 0.43117 (* 1 = 0.43117 loss)
I0408 20:32:41.044601 24089 sgd_solver.cpp:105] Iteration 8688, lr = 1.30667e-05
I0408 20:32:45.381177 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:32:46.059818 24089 solver.cpp:218] Iteration 8700 (2.3928 iter/s, 5.01504s/12 iters), loss = 0.304549
I0408 20:32:46.059865 24089 solver.cpp:237]     Train net output #0: loss = 0.304549 (* 1 = 0.304549 loss)
I0408 20:32:46.059878 24089 sgd_solver.cpp:105] Iteration 8700, lr = 1.29474e-05
I0408 20:32:51.064065 24089 solver.cpp:218] Iteration 8712 (2.39807 iter/s, 5.00402s/12 iters), loss = 0.347001
I0408 20:32:51.064112 24089 solver.cpp:237]     Train net output #0: loss = 0.347001 (* 1 = 0.347001 loss)
I0408 20:32:51.064123 24089 sgd_solver.cpp:105] Iteration 8712, lr = 1.28292e-05
I0408 20:32:56.116452 24089 solver.cpp:218] Iteration 8724 (2.37522 iter/s, 5.05216s/12 iters), loss = 0.319649
I0408 20:32:56.116586 24089 solver.cpp:237]     Train net output #0: loss = 0.319649 (* 1 = 0.319649 loss)
I0408 20:32:56.116605 24089 sgd_solver.cpp:105] Iteration 8724, lr = 1.2712e-05
I0408 20:33:01.120564 24089 solver.cpp:218] Iteration 8736 (2.39818 iter/s, 5.0038s/12 iters), loss = 0.312311
I0408 20:33:01.120610 24089 solver.cpp:237]     Train net output #0: loss = 0.312311 (* 1 = 0.312311 loss)
I0408 20:33:01.120621 24089 sgd_solver.cpp:105] Iteration 8736, lr = 1.2596e-05
I0408 20:33:06.180764 24089 solver.cpp:218] Iteration 8748 (2.37156 iter/s, 5.05997s/12 iters), loss = 0.255662
I0408 20:33:06.180814 24089 solver.cpp:237]     Train net output #0: loss = 0.255662 (* 1 = 0.255662 loss)
I0408 20:33:06.180825 24089 sgd_solver.cpp:105] Iteration 8748, lr = 1.2481e-05
I0408 20:33:11.148352 24089 solver.cpp:218] Iteration 8760 (2.41577 iter/s, 4.96736s/12 iters), loss = 0.367806
I0408 20:33:11.148402 24089 solver.cpp:237]     Train net output #0: loss = 0.367806 (* 1 = 0.367806 loss)
I0408 20:33:11.148413 24089 sgd_solver.cpp:105] Iteration 8760, lr = 1.2367e-05
I0408 20:33:15.731343 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0408 20:33:20.136143 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0408 20:33:24.575641 24089 solver.cpp:330] Iteration 8772, Testing net (#0)
I0408 20:33:24.575667 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:33:25.607679 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:33:29.046432 24089 solver.cpp:397]     Test net output #0: accuracy = 0.407476
I0408 20:33:29.046597 24089 solver.cpp:397]     Test net output #1: loss = 3.11971 (* 1 = 3.11971 loss)
I0408 20:33:29.136966 24089 solver.cpp:218] Iteration 8772 (0.667114 iter/s, 17.9879s/12 iters), loss = 0.330087
I0408 20:33:29.137014 24089 solver.cpp:237]     Train net output #0: loss = 0.330087 (* 1 = 0.330087 loss)
I0408 20:33:29.137027 24089 sgd_solver.cpp:105] Iteration 8772, lr = 1.22541e-05
I0408 20:33:33.475448 24089 solver.cpp:218] Iteration 8784 (2.76608 iter/s, 4.33827s/12 iters), loss = 0.481881
I0408 20:33:33.475498 24089 solver.cpp:237]     Train net output #0: loss = 0.481881 (* 1 = 0.481881 loss)
I0408 20:33:33.475509 24089 sgd_solver.cpp:105] Iteration 8784, lr = 1.21422e-05
I0408 20:33:38.517107 24089 solver.cpp:218] Iteration 8796 (2.38028 iter/s, 5.04142s/12 iters), loss = 0.295713
I0408 20:33:38.517156 24089 solver.cpp:237]     Train net output #0: loss = 0.295713 (* 1 = 0.295713 loss)
I0408 20:33:38.517168 24089 sgd_solver.cpp:105] Iteration 8796, lr = 1.20314e-05
I0408 20:33:39.963757 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:33:43.481792 24089 solver.cpp:218] Iteration 8808 (2.41718 iter/s, 4.96445s/12 iters), loss = 0.279272
I0408 20:33:43.481842 24089 solver.cpp:237]     Train net output #0: loss = 0.279272 (* 1 = 0.279272 loss)
I0408 20:33:43.481853 24089 sgd_solver.cpp:105] Iteration 8808, lr = 1.19216e-05
I0408 20:33:48.530773 24089 solver.cpp:218] Iteration 8820 (2.37683 iter/s, 5.04874s/12 iters), loss = 0.35465
I0408 20:33:48.530827 24089 solver.cpp:237]     Train net output #0: loss = 0.35465 (* 1 = 0.35465 loss)
I0408 20:33:48.530841 24089 sgd_solver.cpp:105] Iteration 8820, lr = 1.18127e-05
I0408 20:33:53.689134 24089 solver.cpp:218] Iteration 8832 (2.32643 iter/s, 5.15812s/12 iters), loss = 0.389461
I0408 20:33:53.689188 24089 solver.cpp:237]     Train net output #0: loss = 0.389461 (* 1 = 0.389461 loss)
I0408 20:33:53.689200 24089 sgd_solver.cpp:105] Iteration 8832, lr = 1.17049e-05
I0408 20:33:58.785354 24089 solver.cpp:218] Iteration 8844 (2.3548 iter/s, 5.09598s/12 iters), loss = 0.280406
I0408 20:33:58.785406 24089 solver.cpp:237]     Train net output #0: loss = 0.280406 (* 1 = 0.280406 loss)
I0408 20:33:58.785418 24089 sgd_solver.cpp:105] Iteration 8844, lr = 1.1598e-05
I0408 20:34:03.944828 24089 solver.cpp:218] Iteration 8856 (2.32593 iter/s, 5.15923s/12 iters), loss = 0.30499
I0408 20:34:03.948911 24089 solver.cpp:237]     Train net output #0: loss = 0.30499 (* 1 = 0.30499 loss)
I0408 20:34:03.948925 24089 sgd_solver.cpp:105] Iteration 8856, lr = 1.14921e-05
I0408 20:34:08.986014 24089 solver.cpp:218] Iteration 8868 (2.38241 iter/s, 5.03692s/12 iters), loss = 0.37346
I0408 20:34:08.986063 24089 solver.cpp:237]     Train net output #0: loss = 0.37346 (* 1 = 0.37346 loss)
I0408 20:34:08.986074 24089 sgd_solver.cpp:105] Iteration 8868, lr = 1.13872e-05
I0408 20:34:11.028203 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0408 20:34:14.054392 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0408 20:34:16.394398 24089 solver.cpp:330] Iteration 8874, Testing net (#0)
I0408 20:34:16.394425 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:34:17.382221 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:34:20.872826 24089 solver.cpp:397]     Test net output #0: accuracy = 0.408701
I0408 20:34:20.872876 24089 solver.cpp:397]     Test net output #1: loss = 3.0991 (* 1 = 3.0991 loss)
I0408 20:34:22.863144 24089 solver.cpp:218] Iteration 8880 (0.864765 iter/s, 13.8766s/12 iters), loss = 0.455693
I0408 20:34:22.863194 24089 solver.cpp:237]     Train net output #0: loss = 0.455693 (* 1 = 0.455693 loss)
I0408 20:34:22.863204 24089 sgd_solver.cpp:105] Iteration 8880, lr = 1.12832e-05
I0408 20:34:28.070066 24089 solver.cpp:218] Iteration 8892 (2.30473 iter/s, 5.20668s/12 iters), loss = 0.25459
I0408 20:34:28.070119 24089 solver.cpp:237]     Train net output #0: loss = 0.25459 (* 1 = 0.25459 loss)
I0408 20:34:28.070132 24089 sgd_solver.cpp:105] Iteration 8892, lr = 1.11802e-05
I0408 20:34:31.697373 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:34:33.118304 24089 solver.cpp:218] Iteration 8904 (2.37718 iter/s, 5.048s/12 iters), loss = 0.374339
I0408 20:34:33.118350 24089 solver.cpp:237]     Train net output #0: loss = 0.374339 (* 1 = 0.374339 loss)
I0408 20:34:33.118360 24089 sgd_solver.cpp:105] Iteration 8904, lr = 1.10781e-05
I0408 20:34:38.172485 24089 solver.cpp:218] Iteration 8916 (2.37438 iter/s, 5.05394s/12 iters), loss = 0.377787
I0408 20:34:38.172606 24089 solver.cpp:237]     Train net output #0: loss = 0.377787 (* 1 = 0.377787 loss)
I0408 20:34:38.172618 24089 sgd_solver.cpp:105] Iteration 8916, lr = 1.0977e-05
I0408 20:34:43.215612 24089 solver.cpp:218] Iteration 8928 (2.37962 iter/s, 5.04282s/12 iters), loss = 0.315514
I0408 20:34:43.215675 24089 solver.cpp:237]     Train net output #0: loss = 0.315514 (* 1 = 0.315514 loss)
I0408 20:34:43.215689 24089 sgd_solver.cpp:105] Iteration 8928, lr = 1.08768e-05
I0408 20:34:48.137676 24089 solver.cpp:218] Iteration 8940 (2.43812 iter/s, 4.92182s/12 iters), loss = 0.542149
I0408 20:34:48.137746 24089 solver.cpp:237]     Train net output #0: loss = 0.542149 (* 1 = 0.542149 loss)
I0408 20:34:48.137761 24089 sgd_solver.cpp:105] Iteration 8940, lr = 1.07775e-05
I0408 20:34:53.164674 24089 solver.cpp:218] Iteration 8952 (2.38723 iter/s, 5.02675s/12 iters), loss = 0.289193
I0408 20:34:53.164721 24089 solver.cpp:237]     Train net output #0: loss = 0.289193 (* 1 = 0.289193 loss)
I0408 20:34:53.164731 24089 sgd_solver.cpp:105] Iteration 8952, lr = 1.06791e-05
I0408 20:34:58.253760 24089 solver.cpp:218] Iteration 8964 (2.3581 iter/s, 5.08885s/12 iters), loss = 0.383237
I0408 20:34:58.253810 24089 solver.cpp:237]     Train net output #0: loss = 0.383237 (* 1 = 0.383237 loss)
I0408 20:34:58.253823 24089 sgd_solver.cpp:105] Iteration 8964, lr = 1.05816e-05
I0408 20:35:02.838477 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0408 20:35:05.856715 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0408 20:35:08.171591 24089 solver.cpp:330] Iteration 8976, Testing net (#0)
I0408 20:35:08.171617 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:35:09.132148 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:35:12.653434 24089 solver.cpp:397]     Test net output #0: accuracy = 0.40625
I0408 20:35:12.653482 24089 solver.cpp:397]     Test net output #1: loss = 3.11217 (* 1 = 3.11217 loss)
I0408 20:35:12.744117 24089 solver.cpp:218] Iteration 8976 (0.828169 iter/s, 14.4898s/12 iters), loss = 0.251813
I0408 20:35:12.744166 24089 solver.cpp:237]     Train net output #0: loss = 0.251813 (* 1 = 0.251813 loss)
I0408 20:35:12.744177 24089 sgd_solver.cpp:105] Iteration 8976, lr = 1.0485e-05
I0408 20:35:17.259361 24089 solver.cpp:218] Iteration 8988 (2.65779 iter/s, 4.51502s/12 iters), loss = 0.291965
I0408 20:35:17.259413 24089 solver.cpp:237]     Train net output #0: loss = 0.291965 (* 1 = 0.291965 loss)
I0408 20:35:17.259424 24089 sgd_solver.cpp:105] Iteration 8988, lr = 1.03893e-05
I0408 20:35:20.558414 24089 blocking_queue.cpp:49] Waiting for data
I0408 20:35:22.302762 24089 solver.cpp:218] Iteration 9000 (2.37946 iter/s, 5.04316s/12 iters), loss = 0.265885
I0408 20:35:22.302816 24089 solver.cpp:237]     Train net output #0: loss = 0.265885 (* 1 = 0.265885 loss)
I0408 20:35:22.302831 24089 sgd_solver.cpp:105] Iteration 9000, lr = 1.02944e-05
I0408 20:35:22.996577 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:35:27.372272 24089 solver.cpp:218] Iteration 9012 (2.36721 iter/s, 5.06926s/12 iters), loss = 0.402672
I0408 20:35:27.372325 24089 solver.cpp:237]     Train net output #0: loss = 0.402672 (* 1 = 0.402672 loss)
I0408 20:35:27.372337 24089 sgd_solver.cpp:105] Iteration 9012, lr = 1.02004e-05
I0408 20:35:32.542946 24089 solver.cpp:218] Iteration 9024 (2.32089 iter/s, 5.17043s/12 iters), loss = 0.278738
I0408 20:35:32.542985 24089 solver.cpp:237]     Train net output #0: loss = 0.278738 (* 1 = 0.278738 loss)
I0408 20:35:32.542994 24089 sgd_solver.cpp:105] Iteration 9024, lr = 1.01073e-05
I0408 20:35:37.624684 24089 solver.cpp:218] Iteration 9036 (2.36151 iter/s, 5.0815s/12 iters), loss = 0.315887
I0408 20:35:37.624733 24089 solver.cpp:237]     Train net output #0: loss = 0.315887 (* 1 = 0.315887 loss)
I0408 20:35:37.624743 24089 sgd_solver.cpp:105] Iteration 9036, lr = 1.0015e-05
I0408 20:35:42.663825 24089 solver.cpp:218] Iteration 9048 (2.38147 iter/s, 5.0389s/12 iters), loss = 0.301208
I0408 20:35:42.663997 24089 solver.cpp:237]     Train net output #0: loss = 0.301208 (* 1 = 0.301208 loss)
I0408 20:35:42.664012 24089 sgd_solver.cpp:105] Iteration 9048, lr = 9.92359e-06
I0408 20:35:47.592643 24089 solver.cpp:218] Iteration 9060 (2.43483 iter/s, 4.92847s/12 iters), loss = 0.406042
I0408 20:35:47.592680 24089 solver.cpp:237]     Train net output #0: loss = 0.406042 (* 1 = 0.406042 loss)
I0408 20:35:47.592689 24089 sgd_solver.cpp:105] Iteration 9060, lr = 9.83299e-06
I0408 20:35:52.681648 24089 solver.cpp:218] Iteration 9072 (2.35813 iter/s, 5.08878s/12 iters), loss = 0.347607
I0408 20:35:52.681700 24089 solver.cpp:237]     Train net output #0: loss = 0.347607 (* 1 = 0.347607 loss)
I0408 20:35:52.681713 24089 sgd_solver.cpp:105] Iteration 9072, lr = 9.74322e-06
I0408 20:35:54.751953 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0408 20:35:57.779130 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0408 20:36:00.154979 24089 solver.cpp:330] Iteration 9078, Testing net (#0)
I0408 20:36:00.155004 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:36:01.135311 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:36:04.719017 24089 solver.cpp:397]     Test net output #0: accuracy = 0.407476
I0408 20:36:04.719064 24089 solver.cpp:397]     Test net output #1: loss = 3.11459 (* 1 = 3.11459 loss)
I0408 20:36:06.637128 24089 solver.cpp:218] Iteration 9084 (0.859911 iter/s, 13.9549s/12 iters), loss = 0.331105
I0408 20:36:06.637176 24089 solver.cpp:237]     Train net output #0: loss = 0.331105 (* 1 = 0.331105 loss)
I0408 20:36:06.637187 24089 sgd_solver.cpp:105] Iteration 9084, lr = 9.65426e-06
I0408 20:36:11.698050 24089 solver.cpp:218] Iteration 9096 (2.37122 iter/s, 5.06068s/12 iters), loss = 0.356943
I0408 20:36:11.698117 24089 solver.cpp:237]     Train net output #0: loss = 0.356943 (* 1 = 0.356943 loss)
I0408 20:36:11.698132 24089 sgd_solver.cpp:105] Iteration 9096, lr = 9.56612e-06
I0408 20:36:14.715041 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:36:16.855455 24089 solver.cpp:218] Iteration 9108 (2.32686 iter/s, 5.15715s/12 iters), loss = 0.299606
I0408 20:36:16.855497 24089 solver.cpp:237]     Train net output #0: loss = 0.299606 (* 1 = 0.299606 loss)
I0408 20:36:16.855509 24089 sgd_solver.cpp:105] Iteration 9108, lr = 9.47879e-06
I0408 20:36:22.024056 24089 solver.cpp:218] Iteration 9120 (2.32181 iter/s, 5.16837s/12 iters), loss = 0.31966
I0408 20:36:22.024101 24089 solver.cpp:237]     Train net output #0: loss = 0.31966 (* 1 = 0.31966 loss)
I0408 20:36:22.024113 24089 sgd_solver.cpp:105] Iteration 9120, lr = 9.39225e-06
I0408 20:36:27.074609 24089 solver.cpp:218] Iteration 9132 (2.37609 iter/s, 5.05032s/12 iters), loss = 0.426572
I0408 20:36:27.074662 24089 solver.cpp:237]     Train net output #0: loss = 0.426572 (* 1 = 0.426572 loss)
I0408 20:36:27.074674 24089 sgd_solver.cpp:105] Iteration 9132, lr = 9.3065e-06
I0408 20:36:32.172551 24089 solver.cpp:218] Iteration 9144 (2.354 iter/s, 5.0977s/12 iters), loss = 0.299381
I0408 20:36:32.172600 24089 solver.cpp:237]     Train net output #0: loss = 0.299381 (* 1 = 0.299381 loss)
I0408 20:36:32.172610 24089 sgd_solver.cpp:105] Iteration 9144, lr = 9.22153e-06
I0408 20:36:37.254254 24089 solver.cpp:218] Iteration 9156 (2.36152 iter/s, 5.08147s/12 iters), loss = 0.487083
I0408 20:36:37.254295 24089 solver.cpp:237]     Train net output #0: loss = 0.487083 (* 1 = 0.487083 loss)
I0408 20:36:37.254303 24089 sgd_solver.cpp:105] Iteration 9156, lr = 9.13734e-06
I0408 20:36:42.316856 24089 solver.cpp:218] Iteration 9168 (2.37043 iter/s, 5.06237s/12 iters), loss = 0.364496
I0408 20:36:42.316903 24089 solver.cpp:237]     Train net output #0: loss = 0.364496 (* 1 = 0.364496 loss)
I0408 20:36:42.316915 24089 sgd_solver.cpp:105] Iteration 9168, lr = 9.05392e-06
I0408 20:36:47.191565 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0408 20:36:50.240387 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0408 20:36:52.548736 24089 solver.cpp:330] Iteration 9180, Testing net (#0)
I0408 20:36:52.548758 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:36:53.403076 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:36:57.214306 24089 solver.cpp:397]     Test net output #0: accuracy = 0.409314
I0408 20:36:57.214344 24089 solver.cpp:397]     Test net output #1: loss = 3.10514 (* 1 = 3.10514 loss)
I0408 20:36:57.304672 24089 solver.cpp:218] Iteration 9180 (0.800681 iter/s, 14.9872s/12 iters), loss = 0.431158
I0408 20:36:57.304708 24089 solver.cpp:237]     Train net output #0: loss = 0.431158 (* 1 = 0.431158 loss)
I0408 20:36:57.304716 24089 sgd_solver.cpp:105] Iteration 9180, lr = 8.97126e-06
I0408 20:37:01.669991 24089 solver.cpp:218] Iteration 9192 (2.74907 iter/s, 4.36511s/12 iters), loss = 0.305113
I0408 20:37:01.670042 24089 solver.cpp:237]     Train net output #0: loss = 0.305113 (* 1 = 0.305113 loss)
I0408 20:37:01.670054 24089 sgd_solver.cpp:105] Iteration 9192, lr = 8.88936e-06
I0408 20:37:06.804121 24089 solver.cpp:218] Iteration 9204 (2.33741 iter/s, 5.13389s/12 iters), loss = 0.296701
I0408 20:37:06.804168 24089 solver.cpp:237]     Train net output #0: loss = 0.296701 (* 1 = 0.296701 loss)
I0408 20:37:06.804179 24089 sgd_solver.cpp:105] Iteration 9204, lr = 8.8082e-06
I0408 20:37:06.885526 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:37:11.795725 24089 solver.cpp:218] Iteration 9216 (2.40415 iter/s, 4.99137s/12 iters), loss = 0.325411
I0408 20:37:11.795773 24089 solver.cpp:237]     Train net output #0: loss = 0.325411 (* 1 = 0.325411 loss)
I0408 20:37:11.795783 24089 sgd_solver.cpp:105] Iteration 9216, lr = 8.72778e-06
I0408 20:37:16.818130 24089 solver.cpp:218] Iteration 9228 (2.38941 iter/s, 5.02217s/12 iters), loss = 0.259874
I0408 20:37:16.818178 24089 solver.cpp:237]     Train net output #0: loss = 0.259874 (* 1 = 0.259874 loss)
I0408 20:37:16.818190 24089 sgd_solver.cpp:105] Iteration 9228, lr = 8.6481e-06
I0408 20:37:21.899276 24089 solver.cpp:218] Iteration 9240 (2.36178 iter/s, 5.08091s/12 iters), loss = 0.231672
I0408 20:37:21.899384 24089 solver.cpp:237]     Train net output #0: loss = 0.231672 (* 1 = 0.231672 loss)
I0408 20:37:21.899396 24089 sgd_solver.cpp:105] Iteration 9240, lr = 8.56915e-06
I0408 20:37:26.898608 24089 solver.cpp:218] Iteration 9252 (2.40046 iter/s, 4.99904s/12 iters), loss = 0.404806
I0408 20:37:26.898656 24089 solver.cpp:237]     Train net output #0: loss = 0.404806 (* 1 = 0.404806 loss)
I0408 20:37:26.898667 24089 sgd_solver.cpp:105] Iteration 9252, lr = 8.49091e-06
I0408 20:37:31.967854 24089 solver.cpp:218] Iteration 9264 (2.36733 iter/s, 5.06901s/12 iters), loss = 0.290335
I0408 20:37:31.967901 24089 solver.cpp:237]     Train net output #0: loss = 0.290335 (* 1 = 0.290335 loss)
I0408 20:37:31.967911 24089 sgd_solver.cpp:105] Iteration 9264, lr = 8.41339e-06
I0408 20:37:37.035943 24089 solver.cpp:218] Iteration 9276 (2.36787 iter/s, 5.06785s/12 iters), loss = 0.363225
I0408 20:37:37.035993 24089 solver.cpp:237]     Train net output #0: loss = 0.363225 (* 1 = 0.363225 loss)
I0408 20:37:37.036005 24089 sgd_solver.cpp:105] Iteration 9276, lr = 8.33658e-06
I0408 20:37:39.068886 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0408 20:37:42.121495 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0408 20:37:45.308863 24089 solver.cpp:330] Iteration 9282, Testing net (#0)
I0408 20:37:45.308892 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:37:46.187821 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:37:49.862159 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405637
I0408 20:37:49.862208 24089 solver.cpp:397]     Test net output #1: loss = 3.14059 (* 1 = 3.14059 loss)
I0408 20:37:51.809345 24089 solver.cpp:218] Iteration 9288 (0.812302 iter/s, 14.7728s/12 iters), loss = 0.353876
I0408 20:37:51.809386 24089 solver.cpp:237]     Train net output #0: loss = 0.353876 (* 1 = 0.353876 loss)
I0408 20:37:51.809396 24089 sgd_solver.cpp:105] Iteration 9288, lr = 8.26047e-06
I0408 20:37:56.844813 24089 solver.cpp:218] Iteration 9300 (2.3832 iter/s, 5.03524s/12 iters), loss = 0.32502
I0408 20:37:56.844933 24089 solver.cpp:237]     Train net output #0: loss = 0.32502 (* 1 = 0.32502 loss)
I0408 20:37:56.844942 24089 sgd_solver.cpp:105] Iteration 9300, lr = 8.18505e-06
I0408 20:37:59.071681 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:38:01.877398 24089 solver.cpp:218] Iteration 9312 (2.3846 iter/s, 5.03228s/12 iters), loss = 0.240362
I0408 20:38:01.877442 24089 solver.cpp:237]     Train net output #0: loss = 0.240362 (* 1 = 0.240362 loss)
I0408 20:38:01.877454 24089 sgd_solver.cpp:105] Iteration 9312, lr = 8.11033e-06
I0408 20:38:06.922462 24089 solver.cpp:218] Iteration 9324 (2.37867 iter/s, 5.04483s/12 iters), loss = 0.317595
I0408 20:38:06.922509 24089 solver.cpp:237]     Train net output #0: loss = 0.317595 (* 1 = 0.317595 loss)
I0408 20:38:06.922521 24089 sgd_solver.cpp:105] Iteration 9324, lr = 8.03628e-06
I0408 20:38:11.948108 24089 solver.cpp:218] Iteration 9336 (2.38787 iter/s, 5.02541s/12 iters), loss = 0.228093
I0408 20:38:11.948158 24089 solver.cpp:237]     Train net output #0: loss = 0.228093 (* 1 = 0.228093 loss)
I0408 20:38:11.948168 24089 sgd_solver.cpp:105] Iteration 9336, lr = 7.96291e-06
I0408 20:38:16.917008 24089 solver.cpp:218] Iteration 9348 (2.41513 iter/s, 4.96867s/12 iters), loss = 0.367036
I0408 20:38:16.917047 24089 solver.cpp:237]     Train net output #0: loss = 0.367036 (* 1 = 0.367036 loss)
I0408 20:38:16.917054 24089 sgd_solver.cpp:105] Iteration 9348, lr = 7.89021e-06
I0408 20:38:21.997951 24089 solver.cpp:218] Iteration 9360 (2.36187 iter/s, 5.08071s/12 iters), loss = 0.335677
I0408 20:38:21.997997 24089 solver.cpp:237]     Train net output #0: loss = 0.335677 (* 1 = 0.335677 loss)
I0408 20:38:21.998005 24089 sgd_solver.cpp:105] Iteration 9360, lr = 7.81818e-06
I0408 20:38:26.936038 24089 solver.cpp:218] Iteration 9372 (2.43021 iter/s, 4.93785s/12 iters), loss = 0.383694
I0408 20:38:26.936156 24089 solver.cpp:237]     Train net output #0: loss = 0.383694 (* 1 = 0.383694 loss)
I0408 20:38:26.936169 24089 sgd_solver.cpp:105] Iteration 9372, lr = 7.7468e-06
I0408 20:38:31.452205 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0408 20:38:34.443372 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0408 20:38:36.785174 24089 solver.cpp:330] Iteration 9384, Testing net (#0)
I0408 20:38:36.785199 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:38:37.652355 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:38:41.383574 24089 solver.cpp:397]     Test net output #0: accuracy = 0.407476
I0408 20:38:41.383621 24089 solver.cpp:397]     Test net output #1: loss = 3.10734 (* 1 = 3.10734 loss)
I0408 20:38:41.473654 24089 solver.cpp:218] Iteration 9384 (0.825481 iter/s, 14.537s/12 iters), loss = 0.309343
I0408 20:38:41.473701 24089 solver.cpp:237]     Train net output #0: loss = 0.309343 (* 1 = 0.309343 loss)
I0408 20:38:41.473711 24089 sgd_solver.cpp:105] Iteration 9384, lr = 7.67608e-06
I0408 20:38:45.686157 24089 solver.cpp:218] Iteration 9396 (2.8488 iter/s, 4.21229s/12 iters), loss = 0.205752
I0408 20:38:45.686203 24089 solver.cpp:237]     Train net output #0: loss = 0.205752 (* 1 = 0.205752 loss)
I0408 20:38:45.686215 24089 sgd_solver.cpp:105] Iteration 9396, lr = 7.606e-06
I0408 20:38:50.052868 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:38:50.712862 24089 solver.cpp:218] Iteration 9408 (2.38736 iter/s, 5.02647s/12 iters), loss = 0.241455
I0408 20:38:50.712905 24089 solver.cpp:237]     Train net output #0: loss = 0.241455 (* 1 = 0.241455 loss)
I0408 20:38:50.712916 24089 sgd_solver.cpp:105] Iteration 9408, lr = 7.53655e-06
I0408 20:38:55.675316 24089 solver.cpp:218] Iteration 9420 (2.41827 iter/s, 4.96222s/12 iters), loss = 0.21233
I0408 20:38:55.675364 24089 solver.cpp:237]     Train net output #0: loss = 0.21233 (* 1 = 0.21233 loss)
I0408 20:38:55.675375 24089 sgd_solver.cpp:105] Iteration 9420, lr = 7.46775e-06
I0408 20:39:00.636857 24089 solver.cpp:218] Iteration 9432 (2.41872 iter/s, 4.96131s/12 iters), loss = 0.253315
I0408 20:39:00.637001 24089 solver.cpp:237]     Train net output #0: loss = 0.253315 (* 1 = 0.253315 loss)
I0408 20:39:00.637015 24089 sgd_solver.cpp:105] Iteration 9432, lr = 7.39957e-06
I0408 20:39:05.646018 24089 solver.cpp:218] Iteration 9444 (2.39577 iter/s, 5.00883s/12 iters), loss = 0.212562
I0408 20:39:05.646070 24089 solver.cpp:237]     Train net output #0: loss = 0.212562 (* 1 = 0.212562 loss)
I0408 20:39:05.646082 24089 sgd_solver.cpp:105] Iteration 9444, lr = 7.33201e-06
I0408 20:39:10.751062 24089 solver.cpp:218] Iteration 9456 (2.35073 iter/s, 5.1048s/12 iters), loss = 0.276356
I0408 20:39:10.751111 24089 solver.cpp:237]     Train net output #0: loss = 0.276356 (* 1 = 0.276356 loss)
I0408 20:39:10.751123 24089 sgd_solver.cpp:105] Iteration 9456, lr = 7.26507e-06
I0408 20:39:15.918391 24089 solver.cpp:218] Iteration 9468 (2.32239 iter/s, 5.16709s/12 iters), loss = 0.307627
I0408 20:39:15.918442 24089 solver.cpp:237]     Train net output #0: loss = 0.307627 (* 1 = 0.307627 loss)
I0408 20:39:15.918452 24089 sgd_solver.cpp:105] Iteration 9468, lr = 7.19875e-06
I0408 20:39:20.954255 24089 solver.cpp:218] Iteration 9480 (2.38302 iter/s, 5.03562s/12 iters), loss = 0.33277
I0408 20:39:20.954301 24089 solver.cpp:237]     Train net output #0: loss = 0.33277 (* 1 = 0.33277 loss)
I0408 20:39:20.954313 24089 sgd_solver.cpp:105] Iteration 9480, lr = 7.13302e-06
I0408 20:39:23.015868 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0408 20:39:27.432268 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0408 20:39:29.772719 24089 solver.cpp:330] Iteration 9486, Testing net (#0)
I0408 20:39:29.772745 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:39:30.472626 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:39:34.333163 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0408 20:39:34.333282 24089 solver.cpp:397]     Test net output #1: loss = 3.11626 (* 1 = 3.11626 loss)
I0408 20:39:36.274567 24089 solver.cpp:218] Iteration 9492 (0.783305 iter/s, 15.3197s/12 iters), loss = 0.288923
I0408 20:39:36.274616 24089 solver.cpp:237]     Train net output #0: loss = 0.288923 (* 1 = 0.288923 loss)
I0408 20:39:36.274627 24089 sgd_solver.cpp:105] Iteration 9492, lr = 7.0679e-06
I0408 20:39:41.242666 24089 solver.cpp:218] Iteration 9504 (2.41552 iter/s, 4.96787s/12 iters), loss = 0.32058
I0408 20:39:41.242702 24089 solver.cpp:237]     Train net output #0: loss = 0.32058 (* 1 = 0.32058 loss)
I0408 20:39:41.242710 24089 sgd_solver.cpp:105] Iteration 9504, lr = 7.00337e-06
I0408 20:39:42.740788 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:39:46.325156 24089 solver.cpp:218] Iteration 9516 (2.36116 iter/s, 5.08226s/12 iters), loss = 0.281908
I0408 20:39:46.325206 24089 solver.cpp:237]     Train net output #0: loss = 0.281908 (* 1 = 0.281908 loss)
I0408 20:39:46.325219 24089 sgd_solver.cpp:105] Iteration 9516, lr = 6.93943e-06
I0408 20:39:51.319723 24089 solver.cpp:218] Iteration 9528 (2.40272 iter/s, 4.99433s/12 iters), loss = 0.245257
I0408 20:39:51.319769 24089 solver.cpp:237]     Train net output #0: loss = 0.245257 (* 1 = 0.245257 loss)
I0408 20:39:51.319780 24089 sgd_solver.cpp:105] Iteration 9528, lr = 6.87608e-06
I0408 20:39:56.337924 24089 solver.cpp:218] Iteration 9540 (2.39141 iter/s, 5.01796s/12 iters), loss = 0.202212
I0408 20:39:56.337983 24089 solver.cpp:237]     Train net output #0: loss = 0.202212 (* 1 = 0.202212 loss)
I0408 20:39:56.337996 24089 sgd_solver.cpp:105] Iteration 9540, lr = 6.8133e-06
I0408 20:40:01.292397 24089 solver.cpp:218] Iteration 9552 (2.42217 iter/s, 4.95423s/12 iters), loss = 0.359646
I0408 20:40:01.292441 24089 solver.cpp:237]     Train net output #0: loss = 0.359646 (* 1 = 0.359646 loss)
I0408 20:40:01.292452 24089 sgd_solver.cpp:105] Iteration 9552, lr = 6.7511e-06
I0408 20:40:06.194723 24089 solver.cpp:218] Iteration 9564 (2.44793 iter/s, 4.9021s/12 iters), loss = 0.416725
I0408 20:40:06.194871 24089 solver.cpp:237]     Train net output #0: loss = 0.416725 (* 1 = 0.416725 loss)
I0408 20:40:06.194885 24089 sgd_solver.cpp:105] Iteration 9564, lr = 6.68946e-06
I0408 20:40:11.200014 24089 solver.cpp:218] Iteration 9576 (2.39762 iter/s, 5.00496s/12 iters), loss = 0.361691
I0408 20:40:11.200063 24089 solver.cpp:237]     Train net output #0: loss = 0.361691 (* 1 = 0.361691 loss)
I0408 20:40:11.200075 24089 sgd_solver.cpp:105] Iteration 9576, lr = 6.62839e-06
I0408 20:40:15.800580 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0408 20:40:18.902705 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0408 20:40:23.375048 24089 solver.cpp:330] Iteration 9588, Testing net (#0)
I0408 20:40:23.375077 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:40:24.073333 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:40:27.835654 24089 solver.cpp:397]     Test net output #0: accuracy = 0.408701
I0408 20:40:27.835701 24089 solver.cpp:397]     Test net output #1: loss = 3.10754 (* 1 = 3.10754 loss)
I0408 20:40:27.926129 24089 solver.cpp:218] Iteration 9588 (0.717469 iter/s, 16.7255s/12 iters), loss = 0.293893
I0408 20:40:27.926178 24089 solver.cpp:237]     Train net output #0: loss = 0.293893 (* 1 = 0.293893 loss)
I0408 20:40:27.926189 24089 sgd_solver.cpp:105] Iteration 9588, lr = 6.56787e-06
I0408 20:40:32.240937 24089 solver.cpp:218] Iteration 9600 (2.78126 iter/s, 4.31459s/12 iters), loss = 0.206092
I0408 20:40:32.240983 24089 solver.cpp:237]     Train net output #0: loss = 0.206092 (* 1 = 0.206092 loss)
I0408 20:40:32.240994 24089 sgd_solver.cpp:105] Iteration 9600, lr = 6.50791e-06
I0408 20:40:35.861094 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:40:37.253904 24089 solver.cpp:218] Iteration 9612 (2.3939 iter/s, 5.01273s/12 iters), loss = 0.235499
I0408 20:40:37.254034 24089 solver.cpp:237]     Train net output #0: loss = 0.235499 (* 1 = 0.235499 loss)
I0408 20:40:37.254046 24089 sgd_solver.cpp:105] Iteration 9612, lr = 6.4485e-06
I0408 20:40:42.287106 24089 solver.cpp:218] Iteration 9624 (2.38432 iter/s, 5.03288s/12 iters), loss = 0.258306
I0408 20:40:42.287160 24089 solver.cpp:237]     Train net output #0: loss = 0.258306 (* 1 = 0.258306 loss)
I0408 20:40:42.287173 24089 sgd_solver.cpp:105] Iteration 9624, lr = 6.38962e-06
I0408 20:40:47.296988 24089 solver.cpp:218] Iteration 9636 (2.39538 iter/s, 5.00964s/12 iters), loss = 0.20493
I0408 20:40:47.297035 24089 solver.cpp:237]     Train net output #0: loss = 0.20493 (* 1 = 0.20493 loss)
I0408 20:40:47.297047 24089 sgd_solver.cpp:105] Iteration 9636, lr = 6.33129e-06
I0408 20:40:52.375818 24089 solver.cpp:218] Iteration 9648 (2.36286 iter/s, 5.07859s/12 iters), loss = 0.381635
I0408 20:40:52.375870 24089 solver.cpp:237]     Train net output #0: loss = 0.381635 (* 1 = 0.381635 loss)
I0408 20:40:52.375882 24089 sgd_solver.cpp:105] Iteration 9648, lr = 6.27349e-06
I0408 20:40:57.684422 24089 solver.cpp:218] Iteration 9660 (2.26059 iter/s, 5.30835s/12 iters), loss = 0.407065
I0408 20:40:57.684468 24089 solver.cpp:237]     Train net output #0: loss = 0.407065 (* 1 = 0.407065 loss)
I0408 20:40:57.684480 24089 sgd_solver.cpp:105] Iteration 9660, lr = 6.21621e-06
I0408 20:41:02.636611 24089 solver.cpp:218] Iteration 9672 (2.42329 iter/s, 4.95195s/12 iters), loss = 0.351268
I0408 20:41:02.636662 24089 solver.cpp:237]     Train net output #0: loss = 0.351268 (* 1 = 0.351268 loss)
I0408 20:41:02.636672 24089 sgd_solver.cpp:105] Iteration 9672, lr = 6.15946e-06
I0408 20:41:07.681910 24089 solver.cpp:218] Iteration 9684 (2.37856 iter/s, 5.04506s/12 iters), loss = 0.371846
I0408 20:41:07.684231 24089 solver.cpp:237]     Train net output #0: loss = 0.371846 (* 1 = 0.371846 loss)
I0408 20:41:07.684242 24089 sgd_solver.cpp:105] Iteration 9684, lr = 6.10322e-06
I0408 20:41:09.728611 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0408 20:41:12.709007 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0408 20:41:15.069368 24089 solver.cpp:330] Iteration 9690, Testing net (#0)
I0408 20:41:15.069394 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:41:15.724781 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:41:18.626205 24089 blocking_queue.cpp:49] Waiting for data
I0408 20:41:19.636040 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0408 20:41:19.636087 24089 solver.cpp:397]     Test net output #1: loss = 3.11956 (* 1 = 3.11956 loss)
I0408 20:41:21.546341 24089 solver.cpp:218] Iteration 9696 (0.8657 iter/s, 13.8616s/12 iters), loss = 0.285007
I0408 20:41:21.546392 24089 solver.cpp:237]     Train net output #0: loss = 0.285007 (* 1 = 0.285007 loss)
I0408 20:41:21.546401 24089 sgd_solver.cpp:105] Iteration 9696, lr = 6.0475e-06
I0408 20:41:26.555435 24089 solver.cpp:218] Iteration 9708 (2.39576 iter/s, 5.00885s/12 iters), loss = 0.486287
I0408 20:41:26.555485 24089 solver.cpp:237]     Train net output #0: loss = 0.486287 (* 1 = 0.486287 loss)
I0408 20:41:26.555497 24089 sgd_solver.cpp:105] Iteration 9708, lr = 5.99229e-06
I0408 20:41:27.312391 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:41:31.535565 24089 solver.cpp:218] Iteration 9720 (2.40969 iter/s, 4.97989s/12 iters), loss = 0.566231
I0408 20:41:31.535612 24089 solver.cpp:237]     Train net output #0: loss = 0.566231 (* 1 = 0.566231 loss)
I0408 20:41:31.535624 24089 sgd_solver.cpp:105] Iteration 9720, lr = 5.93758e-06
I0408 20:41:36.552125 24089 solver.cpp:218] Iteration 9732 (2.39219 iter/s, 5.01632s/12 iters), loss = 0.35903
I0408 20:41:36.552163 24089 solver.cpp:237]     Train net output #0: loss = 0.35903 (* 1 = 0.35903 loss)
I0408 20:41:36.552171 24089 sgd_solver.cpp:105] Iteration 9732, lr = 5.88337e-06
I0408 20:41:41.522526 24089 solver.cpp:218] Iteration 9744 (2.4144 iter/s, 4.97017s/12 iters), loss = 0.285124
I0408 20:41:41.522852 24089 solver.cpp:237]     Train net output #0: loss = 0.285124 (* 1 = 0.285124 loss)
I0408 20:41:41.522863 24089 sgd_solver.cpp:105] Iteration 9744, lr = 5.82966e-06
I0408 20:41:46.533521 24089 solver.cpp:218] Iteration 9756 (2.39498 iter/s, 5.01048s/12 iters), loss = 0.27897
I0408 20:41:46.533569 24089 solver.cpp:237]     Train net output #0: loss = 0.27897 (* 1 = 0.27897 loss)
I0408 20:41:46.533581 24089 sgd_solver.cpp:105] Iteration 9756, lr = 5.77644e-06
I0408 20:41:51.559587 24089 solver.cpp:218] Iteration 9768 (2.38767 iter/s, 5.02583s/12 iters), loss = 0.302429
I0408 20:41:51.559633 24089 solver.cpp:237]     Train net output #0: loss = 0.302429 (* 1 = 0.302429 loss)
I0408 20:41:51.559643 24089 sgd_solver.cpp:105] Iteration 9768, lr = 5.7237e-06
I0408 20:41:56.674387 24089 solver.cpp:218] Iteration 9780 (2.34624 iter/s, 5.11456s/12 iters), loss = 0.348312
I0408 20:41:56.674432 24089 solver.cpp:237]     Train net output #0: loss = 0.348312 (* 1 = 0.348312 loss)
I0408 20:41:56.674443 24089 sgd_solver.cpp:105] Iteration 9780, lr = 5.67144e-06
I0408 20:42:01.530527 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0408 20:42:04.622107 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0408 20:42:07.023042 24089 solver.cpp:330] Iteration 9792, Testing net (#0)
I0408 20:42:07.023067 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:42:07.634322 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:42:11.483908 24089 solver.cpp:397]     Test net output #0: accuracy = 0.408088
I0408 20:42:11.483958 24089 solver.cpp:397]     Test net output #1: loss = 3.09727 (* 1 = 3.09727 loss)
I0408 20:42:11.574838 24089 solver.cpp:218] Iteration 9792 (0.805377 iter/s, 14.8999s/12 iters), loss = 0.281198
I0408 20:42:11.574988 24089 solver.cpp:237]     Train net output #0: loss = 0.281198 (* 1 = 0.281198 loss)
I0408 20:42:11.575002 24089 sgd_solver.cpp:105] Iteration 9792, lr = 5.61967e-06
I0408 20:42:16.074944 24089 solver.cpp:218] Iteration 9804 (2.66679 iter/s, 4.49979s/12 iters), loss = 0.268605
I0408 20:42:16.074991 24089 solver.cpp:237]     Train net output #0: loss = 0.268605 (* 1 = 0.268605 loss)
I0408 20:42:16.075002 24089 sgd_solver.cpp:105] Iteration 9804, lr = 5.56836e-06
I0408 20:42:19.058141 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:42:21.094274 24089 solver.cpp:218] Iteration 9816 (2.39087 iter/s, 5.01909s/12 iters), loss = 0.260027
I0408 20:42:21.094319 24089 solver.cpp:237]     Train net output #0: loss = 0.260027 (* 1 = 0.260027 loss)
I0408 20:42:21.094331 24089 sgd_solver.cpp:105] Iteration 9816, lr = 5.51752e-06
I0408 20:42:26.134236 24089 solver.cpp:218] Iteration 9828 (2.38108 iter/s, 5.03973s/12 iters), loss = 0.383308
I0408 20:42:26.134284 24089 solver.cpp:237]     Train net output #0: loss = 0.383308 (* 1 = 0.383308 loss)
I0408 20:42:26.134294 24089 sgd_solver.cpp:105] Iteration 9828, lr = 5.46715e-06
I0408 20:42:31.088994 24089 solver.cpp:218] Iteration 9840 (2.42203 iter/s, 4.95452s/12 iters), loss = 0.254965
I0408 20:42:31.089040 24089 solver.cpp:237]     Train net output #0: loss = 0.254965 (* 1 = 0.254965 loss)
I0408 20:42:31.089052 24089 sgd_solver.cpp:105] Iteration 9840, lr = 5.41724e-06
I0408 20:42:36.123314 24089 solver.cpp:218] Iteration 9852 (2.38375 iter/s, 5.03408s/12 iters), loss = 0.280969
I0408 20:42:36.123369 24089 solver.cpp:237]     Train net output #0: loss = 0.280969 (* 1 = 0.280969 loss)
I0408 20:42:36.123381 24089 sgd_solver.cpp:105] Iteration 9852, lr = 5.36778e-06
I0408 20:42:41.217284 24089 solver.cpp:218] Iteration 9864 (2.35584 iter/s, 5.09372s/12 iters), loss = 0.303197
I0408 20:42:41.217332 24089 solver.cpp:237]     Train net output #0: loss = 0.303197 (* 1 = 0.303197 loss)
I0408 20:42:41.217344 24089 sgd_solver.cpp:105] Iteration 9864, lr = 5.31877e-06
I0408 20:42:46.230377 24089 solver.cpp:218] Iteration 9876 (2.39385 iter/s, 5.01285s/12 iters), loss = 0.425078
I0408 20:42:46.230512 24089 solver.cpp:237]     Train net output #0: loss = 0.425078 (* 1 = 0.425078 loss)
I0408 20:42:46.230526 24089 sgd_solver.cpp:105] Iteration 9876, lr = 5.27021e-06
I0408 20:42:51.221621 24089 solver.cpp:218] Iteration 9888 (2.40437 iter/s, 4.99092s/12 iters), loss = 0.433966
I0408 20:42:51.221664 24089 solver.cpp:237]     Train net output #0: loss = 0.433966 (* 1 = 0.433966 loss)
I0408 20:42:51.221675 24089 sgd_solver.cpp:105] Iteration 9888, lr = 5.2221e-06
I0408 20:42:53.273005 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0408 20:42:57.124948 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0408 20:42:59.456171 24089 solver.cpp:330] Iteration 9894, Testing net (#0)
I0408 20:42:59.456207 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:43:00.033782 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:43:03.922662 24089 solver.cpp:397]     Test net output #0: accuracy = 0.405637
I0408 20:43:03.922710 24089 solver.cpp:397]     Test net output #1: loss = 3.11461 (* 1 = 3.11461 loss)
I0408 20:43:05.946527 24089 solver.cpp:218] Iteration 9900 (0.814978 iter/s, 14.7243s/12 iters), loss = 0.187166
I0408 20:43:05.946573 24089 solver.cpp:237]     Train net output #0: loss = 0.187166 (* 1 = 0.187166 loss)
I0408 20:43:05.946583 24089 sgd_solver.cpp:105] Iteration 9900, lr = 5.17442e-06
I0408 20:43:11.116533 24089 solver.cpp:218] Iteration 9912 (2.32119 iter/s, 5.16977s/12 iters), loss = 0.296571
I0408 20:43:11.116570 24089 solver.cpp:237]     Train net output #0: loss = 0.296571 (* 1 = 0.296571 loss)
I0408 20:43:11.116578 24089 sgd_solver.cpp:105] Iteration 9912, lr = 5.12718e-06
I0408 20:43:11.214844 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:43:16.143625 24089 solver.cpp:218] Iteration 9924 (2.38718 iter/s, 5.02686s/12 iters), loss = 0.283496
I0408 20:43:16.143673 24089 solver.cpp:237]     Train net output #0: loss = 0.283496 (* 1 = 0.283496 loss)
I0408 20:43:16.143687 24089 sgd_solver.cpp:105] Iteration 9924, lr = 5.08037e-06
I0408 20:43:21.184752 24089 solver.cpp:218] Iteration 9936 (2.38053 iter/s, 5.04089s/12 iters), loss = 0.279655
I0408 20:43:21.184906 24089 solver.cpp:237]     Train net output #0: loss = 0.279655 (* 1 = 0.279655 loss)
I0408 20:43:21.184919 24089 sgd_solver.cpp:105] Iteration 9936, lr = 5.03399e-06
I0408 20:43:26.242224 24089 solver.cpp:218] Iteration 9948 (2.37289 iter/s, 5.05713s/12 iters), loss = 0.354058
I0408 20:43:26.242281 24089 solver.cpp:237]     Train net output #0: loss = 0.354058 (* 1 = 0.354058 loss)
I0408 20:43:26.242297 24089 sgd_solver.cpp:105] Iteration 9948, lr = 4.98803e-06
I0408 20:43:31.302418 24089 solver.cpp:218] Iteration 9960 (2.37157 iter/s, 5.05995s/12 iters), loss = 0.324384
I0408 20:43:31.302467 24089 solver.cpp:237]     Train net output #0: loss = 0.324384 (* 1 = 0.324384 loss)
I0408 20:43:31.302479 24089 sgd_solver.cpp:105] Iteration 9960, lr = 4.94249e-06
I0408 20:43:36.706219 24089 solver.cpp:218] Iteration 9972 (2.22076 iter/s, 5.40355s/12 iters), loss = 0.333115
I0408 20:43:36.706265 24089 solver.cpp:237]     Train net output #0: loss = 0.333115 (* 1 = 0.333115 loss)
I0408 20:43:36.706276 24089 sgd_solver.cpp:105] Iteration 9972, lr = 4.89737e-06
I0408 20:43:41.806342 24089 solver.cpp:218] Iteration 9984 (2.353 iter/s, 5.09988s/12 iters), loss = 0.287031
I0408 20:43:41.806389 24089 solver.cpp:237]     Train net output #0: loss = 0.287031 (* 1 = 0.287031 loss)
I0408 20:43:41.806401 24089 sgd_solver.cpp:105] Iteration 9984, lr = 4.85265e-06
I0408 20:43:46.298569 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0408 20:43:50.444722 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0408 20:43:53.123126 24089 solver.cpp:330] Iteration 9996, Testing net (#0)
I0408 20:43:53.123205 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:43:53.630410 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:43:57.563086 24089 solver.cpp:397]     Test net output #0: accuracy = 0.406863
I0408 20:43:57.563131 24089 solver.cpp:397]     Test net output #1: loss = 3.09966 (* 1 = 3.09966 loss)
I0408 20:43:57.653789 24089 solver.cpp:218] Iteration 9996 (0.75725 iter/s, 15.8468s/12 iters), loss = 0.288441
I0408 20:43:57.653833 24089 solver.cpp:237]     Train net output #0: loss = 0.288441 (* 1 = 0.288441 loss)
I0408 20:43:57.653844 24089 sgd_solver.cpp:105] Iteration 9996, lr = 4.80835e-06
I0408 20:44:01.892086 24089 solver.cpp:218] Iteration 10008 (2.83147 iter/s, 4.23809s/12 iters), loss = 0.355759
I0408 20:44:01.892135 24089 solver.cpp:237]     Train net output #0: loss = 0.355759 (* 1 = 0.355759 loss)
I0408 20:44:01.892148 24089 sgd_solver.cpp:105] Iteration 10008, lr = 4.76445e-06
I0408 20:44:04.168670 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:44:07.099844 24089 solver.cpp:218] Iteration 10020 (2.30437 iter/s, 5.20751s/12 iters), loss = 0.234471
I0408 20:44:07.099892 24089 solver.cpp:237]     Train net output #0: loss = 0.234471 (* 1 = 0.234471 loss)
I0408 20:44:07.099905 24089 sgd_solver.cpp:105] Iteration 10020, lr = 4.72095e-06
I0408 20:44:12.270006 24089 solver.cpp:218] Iteration 10032 (2.32112 iter/s, 5.16992s/12 iters), loss = 0.280258
I0408 20:44:12.270053 24089 solver.cpp:237]     Train net output #0: loss = 0.280258 (* 1 = 0.280258 loss)
I0408 20:44:12.270066 24089 sgd_solver.cpp:105] Iteration 10032, lr = 4.67785e-06
I0408 20:44:17.244218 24089 solver.cpp:218] Iteration 10044 (2.41256 iter/s, 4.97397s/12 iters), loss = 0.316318
I0408 20:44:17.244268 24089 solver.cpp:237]     Train net output #0: loss = 0.316318 (* 1 = 0.316318 loss)
I0408 20:44:17.244279 24089 sgd_solver.cpp:105] Iteration 10044, lr = 4.63515e-06
I0408 20:44:22.215355 24089 solver.cpp:218] Iteration 10056 (2.41405 iter/s, 4.97089s/12 iters), loss = 0.296885
I0408 20:44:22.215409 24089 solver.cpp:237]     Train net output #0: loss = 0.296885 (* 1 = 0.296885 loss)
I0408 20:44:22.215420 24089 sgd_solver.cpp:105] Iteration 10056, lr = 4.59283e-06
I0408 20:44:27.288138 24089 solver.cpp:218] Iteration 10068 (2.36568 iter/s, 5.07254s/12 iters), loss = 0.465521
I0408 20:44:27.290933 24089 solver.cpp:237]     Train net output #0: loss = 0.465521 (* 1 = 0.465521 loss)
I0408 20:44:27.290946 24089 sgd_solver.cpp:105] Iteration 10068, lr = 4.5509e-06
I0408 20:44:32.315232 24089 solver.cpp:218] Iteration 10080 (2.38848 iter/s, 5.02411s/12 iters), loss = 0.254874
I0408 20:44:32.315291 24089 solver.cpp:237]     Train net output #0: loss = 0.254874 (* 1 = 0.254874 loss)
I0408 20:44:32.315306 24089 sgd_solver.cpp:105] Iteration 10080, lr = 4.50935e-06
I0408 20:44:37.315272 24089 solver.cpp:218] Iteration 10092 (2.4001 iter/s, 4.9998s/12 iters), loss = 0.29364
I0408 20:44:37.315312 24089 solver.cpp:237]     Train net output #0: loss = 0.29364 (* 1 = 0.29364 loss)
I0408 20:44:37.315322 24089 sgd_solver.cpp:105] Iteration 10092, lr = 4.46818e-06
I0408 20:44:39.350106 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0408 20:44:42.986953 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0408 20:44:45.321874 24089 solver.cpp:330] Iteration 10098, Testing net (#0)
I0408 20:44:45.321902 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:44:45.798163 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:44:49.745297 24089 solver.cpp:397]     Test net output #0: accuracy = 0.406863
I0408 20:44:49.745347 24089 solver.cpp:397]     Test net output #1: loss = 3.1171 (* 1 = 3.1171 loss)
I0408 20:44:51.732092 24089 solver.cpp:218] Iteration 10104 (0.832394 iter/s, 14.4162s/12 iters), loss = 0.234728
I0408 20:44:51.732144 24089 solver.cpp:237]     Train net output #0: loss = 0.234728 (* 1 = 0.234728 loss)
I0408 20:44:51.732156 24089 sgd_solver.cpp:105] Iteration 10104, lr = 4.42739e-06
I0408 20:44:56.115034 24093 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:44:56.738155 24089 solver.cpp:218] Iteration 10116 (2.39721 iter/s, 5.00582s/12 iters), loss = 0.327497
I0408 20:44:56.738202 24089 solver.cpp:237]     Train net output #0: loss = 0.327497 (* 1 = 0.327497 loss)
I0408 20:44:56.738214 24089 sgd_solver.cpp:105] Iteration 10116, lr = 4.38696e-06
I0408 20:45:01.701901 24089 solver.cpp:218] Iteration 10128 (2.41764 iter/s, 4.96351s/12 iters), loss = 0.308207
I0408 20:45:01.702042 24089 solver.cpp:237]     Train net output #0: loss = 0.308207 (* 1 = 0.308207 loss)
I0408 20:45:01.702054 24089 sgd_solver.cpp:105] Iteration 10128, lr = 4.34691e-06
I0408 20:45:06.676088 24089 solver.cpp:218] Iteration 10140 (2.41262 iter/s, 4.97386s/12 iters), loss = 0.43384
I0408 20:45:06.676138 24089 solver.cpp:237]     Train net output #0: loss = 0.43384 (* 1 = 0.43384 loss)
I0408 20:45:06.676149 24089 sgd_solver.cpp:105] Iteration 10140, lr = 4.30723e-06
I0408 20:45:11.698664 24089 solver.cpp:218] Iteration 10152 (2.38933 iter/s, 5.02234s/12 iters), loss = 0.22485
I0408 20:45:11.698700 24089 solver.cpp:237]     Train net output #0: loss = 0.22485 (* 1 = 0.22485 loss)
I0408 20:45:11.698709 24089 sgd_solver.cpp:105] Iteration 10152, lr = 4.2679e-06
I0408 20:45:16.745738 24089 solver.cpp:218] Iteration 10164 (2.37773 iter/s, 5.04684s/12 iters), loss = 0.311649
I0408 20:45:16.745784 24089 solver.cpp:237]     Train net output #0: loss = 0.311649 (* 1 = 0.311649 loss)
I0408 20:45:16.745796 24089 sgd_solver.cpp:105] Iteration 10164, lr = 4.22894e-06
I0408 20:45:21.758594 24089 solver.cpp:218] Iteration 10176 (2.39396 iter/s, 5.01261s/12 iters), loss = 0.223948
I0408 20:45:21.758642 24089 solver.cpp:237]     Train net output #0: loss = 0.223948 (* 1 = 0.223948 loss)
I0408 20:45:21.758654 24089 sgd_solver.cpp:105] Iteration 10176, lr = 4.19033e-06
I0408 20:45:26.772998 24089 solver.cpp:218] Iteration 10188 (2.39322 iter/s, 5.01416s/12 iters), loss = 0.332837
I0408 20:45:26.773048 24089 solver.cpp:237]     Train net output #0: loss = 0.332837 (* 1 = 0.332837 loss)
I0408 20:45:26.773061 24089 sgd_solver.cpp:105] Iteration 10188, lr = 4.15207e-06
I0408 20:45:31.342286 24089 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0408 20:45:34.355918 24089 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0408 20:45:36.720223 24089 solver.cpp:310] Iteration 10200, loss = 0.224724
I0408 20:45:36.720255 24089 solver.cpp:330] Iteration 10200, Testing net (#0)
I0408 20:45:36.720263 24089 net.cpp:676] Ignoring source layer train-data
I0408 20:45:37.107928 24094 data_layer.cpp:73] Restarting data prefetching from start.
I0408 20:45:41.130990 24089 solver.cpp:397]     Test net output #0: accuracy = 0.408088
I0408 20:45:41.131038 24089 solver.cpp:397]     Test net output #1: loss = 3.12549 (* 1 = 3.12549 loss)
I0408 20:45:41.131049 24089 solver.cpp:315] Optimization Done.
I0408 20:45:41.131057 24089 caffe.cpp:259] Optimization Done.