DIGITS-CNN/cars/lr-investigations/exponential/1e-2/0.98/caffe_output.log

I0407 21:57:10.735216 23786 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-215708-5173/solver.prototxt
I0407 21:57:10.735435 23786 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0407 21:57:10.735445 23786 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0407 21:57:10.735538 23786 caffe.cpp:218] Using GPUs 2
I0407 21:57:10.759138 23786 caffe.cpp:223] GPU 2: GeForce GTX 1080 Ti
I0407 21:57:11.043913 23786 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99980193
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0407 21:57:11.044699 23786 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0407 21:57:11.045265 23786 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0407 21:57:11.045280 23786 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0407 21:57:11.045420 23786 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"
}
I0407 21:57:11.045507 23786 layer_factory.hpp:77] Creating layer train-data
I0407 21:57:11.046988 23786 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0407 21:57:11.047194 23786 net.cpp:84] Creating Layer train-data
I0407 21:57:11.047204 23786 net.cpp:380] train-data -> data
I0407 21:57:11.047224 23786 net.cpp:380] train-data -> label
I0407 21:57:11.047233 23786 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0407 21:57:11.051882 23786 data_layer.cpp:45] output data size: 128,3,227,227
I0407 21:57:11.172987 23786 net.cpp:122] Setting up train-data
I0407 21:57:11.173010 23786 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0407 21:57:11.173017 23786 net.cpp:129] Top shape: 128 (128)
I0407 21:57:11.173019 23786 net.cpp:137] Memory required for data: 79149056
I0407 21:57:11.173028 23786 layer_factory.hpp:77] Creating layer conv1
I0407 21:57:11.173050 23786 net.cpp:84] Creating Layer conv1
I0407 21:57:11.173056 23786 net.cpp:406] conv1 <- data
I0407 21:57:11.173069 23786 net.cpp:380] conv1 -> conv1
I0407 21:57:11.735087 23786 net.cpp:122] Setting up conv1
I0407 21:57:11.735110 23786 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 21:57:11.735113 23786 net.cpp:137] Memory required for data: 227833856
I0407 21:57:11.735132 23786 layer_factory.hpp:77] Creating layer relu1
I0407 21:57:11.735143 23786 net.cpp:84] Creating Layer relu1
I0407 21:57:11.735147 23786 net.cpp:406] relu1 <- conv1
I0407 21:57:11.735153 23786 net.cpp:367] relu1 -> conv1 (in-place)
I0407 21:57:11.735436 23786 net.cpp:122] Setting up relu1
I0407 21:57:11.735445 23786 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 21:57:11.735448 23786 net.cpp:137] Memory required for data: 376518656
I0407 21:57:11.735451 23786 layer_factory.hpp:77] Creating layer norm1
I0407 21:57:11.735461 23786 net.cpp:84] Creating Layer norm1
I0407 21:57:11.735464 23786 net.cpp:406] norm1 <- conv1
I0407 21:57:11.735491 23786 net.cpp:380] norm1 -> norm1
I0407 21:57:11.735924 23786 net.cpp:122] Setting up norm1
I0407 21:57:11.735934 23786 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 21:57:11.735939 23786 net.cpp:137] Memory required for data: 525203456
I0407 21:57:11.735941 23786 layer_factory.hpp:77] Creating layer pool1
I0407 21:57:11.735949 23786 net.cpp:84] Creating Layer pool1
I0407 21:57:11.735952 23786 net.cpp:406] pool1 <- norm1
I0407 21:57:11.735957 23786 net.cpp:380] pool1 -> pool1
I0407 21:57:11.735992 23786 net.cpp:122] Setting up pool1
I0407 21:57:11.735999 23786 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0407 21:57:11.736002 23786 net.cpp:137] Memory required for data: 561035264
I0407 21:57:11.736006 23786 layer_factory.hpp:77] Creating layer conv2
I0407 21:57:11.736016 23786 net.cpp:84] Creating Layer conv2
I0407 21:57:11.736018 23786 net.cpp:406] conv2 <- pool1
I0407 21:57:11.736023 23786 net.cpp:380] conv2 -> conv2
I0407 21:57:11.742552 23786 net.cpp:122] Setting up conv2
I0407 21:57:11.742565 23786 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 21:57:11.742568 23786 net.cpp:137] Memory required for data: 656586752
I0407 21:57:11.742578 23786 layer_factory.hpp:77] Creating layer relu2
I0407 21:57:11.742585 23786 net.cpp:84] Creating Layer relu2
I0407 21:57:11.742588 23786 net.cpp:406] relu2 <- conv2
I0407 21:57:11.742594 23786 net.cpp:367] relu2 -> conv2 (in-place)
I0407 21:57:11.743084 23786 net.cpp:122] Setting up relu2
I0407 21:57:11.743094 23786 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 21:57:11.743098 23786 net.cpp:137] Memory required for data: 752138240
I0407 21:57:11.743101 23786 layer_factory.hpp:77] Creating layer norm2
I0407 21:57:11.743109 23786 net.cpp:84] Creating Layer norm2
I0407 21:57:11.743113 23786 net.cpp:406] norm2 <- conv2
I0407 21:57:11.743119 23786 net.cpp:380] norm2 -> norm2
I0407 21:57:11.743461 23786 net.cpp:122] Setting up norm2
I0407 21:57:11.743469 23786 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 21:57:11.743472 23786 net.cpp:137] Memory required for data: 847689728
I0407 21:57:11.743475 23786 layer_factory.hpp:77] Creating layer pool2
I0407 21:57:11.743484 23786 net.cpp:84] Creating Layer pool2
I0407 21:57:11.743487 23786 net.cpp:406] pool2 <- norm2
I0407 21:57:11.743492 23786 net.cpp:380] pool2 -> pool2
I0407 21:57:11.743521 23786 net.cpp:122] Setting up pool2
I0407 21:57:11.743527 23786 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 21:57:11.743530 23786 net.cpp:137] Memory required for data: 869840896
I0407 21:57:11.743533 23786 layer_factory.hpp:77] Creating layer conv3
I0407 21:57:11.743542 23786 net.cpp:84] Creating Layer conv3
I0407 21:57:11.743546 23786 net.cpp:406] conv3 <- pool2
I0407 21:57:11.743552 23786 net.cpp:380] conv3 -> conv3
I0407 21:57:11.754475 23786 net.cpp:122] Setting up conv3
I0407 21:57:11.754489 23786 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 21:57:11.754493 23786 net.cpp:137] Memory required for data: 903067648
I0407 21:57:11.754503 23786 layer_factory.hpp:77] Creating layer relu3
I0407 21:57:11.754511 23786 net.cpp:84] Creating Layer relu3
I0407 21:57:11.754514 23786 net.cpp:406] relu3 <- conv3
I0407 21:57:11.754521 23786 net.cpp:367] relu3 -> conv3 (in-place)
I0407 21:57:11.754999 23786 net.cpp:122] Setting up relu3
I0407 21:57:11.755009 23786 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 21:57:11.755012 23786 net.cpp:137] Memory required for data: 936294400
I0407 21:57:11.755015 23786 layer_factory.hpp:77] Creating layer conv4
I0407 21:57:11.755026 23786 net.cpp:84] Creating Layer conv4
I0407 21:57:11.755029 23786 net.cpp:406] conv4 <- conv3
I0407 21:57:11.755036 23786 net.cpp:380] conv4 -> conv4
I0407 21:57:11.765352 23786 net.cpp:122] Setting up conv4
I0407 21:57:11.765367 23786 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 21:57:11.765370 23786 net.cpp:137] Memory required for data: 969521152
I0407 21:57:11.765377 23786 layer_factory.hpp:77] Creating layer relu4
I0407 21:57:11.765385 23786 net.cpp:84] Creating Layer relu4
I0407 21:57:11.765406 23786 net.cpp:406] relu4 <- conv4
I0407 21:57:11.765413 23786 net.cpp:367] relu4 -> conv4 (in-place)
I0407 21:57:11.765744 23786 net.cpp:122] Setting up relu4
I0407 21:57:11.765754 23786 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 21:57:11.765758 23786 net.cpp:137] Memory required for data: 1002747904
I0407 21:57:11.765761 23786 layer_factory.hpp:77] Creating layer conv5
I0407 21:57:11.765771 23786 net.cpp:84] Creating Layer conv5
I0407 21:57:11.765775 23786 net.cpp:406] conv5 <- conv4
I0407 21:57:11.765784 23786 net.cpp:380] conv5 -> conv5
I0407 21:57:11.774065 23786 net.cpp:122] Setting up conv5
I0407 21:57:11.774078 23786 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 21:57:11.774082 23786 net.cpp:137] Memory required for data: 1024899072
I0407 21:57:11.774093 23786 layer_factory.hpp:77] Creating layer relu5
I0407 21:57:11.774099 23786 net.cpp:84] Creating Layer relu5
I0407 21:57:11.774103 23786 net.cpp:406] relu5 <- conv5
I0407 21:57:11.774108 23786 net.cpp:367] relu5 -> conv5 (in-place)
I0407 21:57:11.774588 23786 net.cpp:122] Setting up relu5
I0407 21:57:11.774597 23786 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 21:57:11.774600 23786 net.cpp:137] Memory required for data: 1047050240
I0407 21:57:11.774605 23786 layer_factory.hpp:77] Creating layer pool5
I0407 21:57:11.774611 23786 net.cpp:84] Creating Layer pool5
I0407 21:57:11.774614 23786 net.cpp:406] pool5 <- conv5
I0407 21:57:11.774621 23786 net.cpp:380] pool5 -> pool5
I0407 21:57:11.774657 23786 net.cpp:122] Setting up pool5
I0407 21:57:11.774663 23786 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0407 21:57:11.774667 23786 net.cpp:137] Memory required for data: 1051768832
I0407 21:57:11.774672 23786 layer_factory.hpp:77] Creating layer fc6
I0407 21:57:11.774682 23786 net.cpp:84] Creating Layer fc6
I0407 21:57:11.774685 23786 net.cpp:406] fc6 <- pool5
I0407 21:57:11.774691 23786 net.cpp:380] fc6 -> fc6
I0407 21:57:12.129509 23786 net.cpp:122] Setting up fc6
I0407 21:57:12.129531 23786 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:57:12.129535 23786 net.cpp:137] Memory required for data: 1053865984
I0407 21:57:12.129544 23786 layer_factory.hpp:77] Creating layer relu6
I0407 21:57:12.129554 23786 net.cpp:84] Creating Layer relu6
I0407 21:57:12.129557 23786 net.cpp:406] relu6 <- fc6
I0407 21:57:12.129565 23786 net.cpp:367] relu6 -> fc6 (in-place)
I0407 21:57:12.130213 23786 net.cpp:122] Setting up relu6
I0407 21:57:12.130223 23786 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:57:12.130226 23786 net.cpp:137] Memory required for data: 1055963136
I0407 21:57:12.130230 23786 layer_factory.hpp:77] Creating layer drop6
I0407 21:57:12.130237 23786 net.cpp:84] Creating Layer drop6
I0407 21:57:12.130240 23786 net.cpp:406] drop6 <- fc6
I0407 21:57:12.130246 23786 net.cpp:367] drop6 -> fc6 (in-place)
I0407 21:57:12.130273 23786 net.cpp:122] Setting up drop6
I0407 21:57:12.130278 23786 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:57:12.130281 23786 net.cpp:137] Memory required for data: 1058060288
I0407 21:57:12.130285 23786 layer_factory.hpp:77] Creating layer fc7
I0407 21:57:12.130293 23786 net.cpp:84] Creating Layer fc7
I0407 21:57:12.130296 23786 net.cpp:406] fc7 <- fc6
I0407 21:57:12.130302 23786 net.cpp:380] fc7 -> fc7
I0407 21:57:12.286841 23786 net.cpp:122] Setting up fc7
I0407 21:57:12.286862 23786 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:57:12.286866 23786 net.cpp:137] Memory required for data: 1060157440
I0407 21:57:12.286875 23786 layer_factory.hpp:77] Creating layer relu7
I0407 21:57:12.286885 23786 net.cpp:84] Creating Layer relu7
I0407 21:57:12.286888 23786 net.cpp:406] relu7 <- fc7
I0407 21:57:12.286897 23786 net.cpp:367] relu7 -> fc7 (in-place)
I0407 21:57:12.287520 23786 net.cpp:122] Setting up relu7
I0407 21:57:12.287529 23786 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:57:12.287533 23786 net.cpp:137] Memory required for data: 1062254592
I0407 21:57:12.287536 23786 layer_factory.hpp:77] Creating layer drop7
I0407 21:57:12.287544 23786 net.cpp:84] Creating Layer drop7
I0407 21:57:12.287564 23786 net.cpp:406] drop7 <- fc7
I0407 21:57:12.287570 23786 net.cpp:367] drop7 -> fc7 (in-place)
I0407 21:57:12.287597 23786 net.cpp:122] Setting up drop7
I0407 21:57:12.287602 23786 net.cpp:129] Top shape: 128 4096 (524288)
I0407 21:57:12.287606 23786 net.cpp:137] Memory required for data: 1064351744
I0407 21:57:12.287608 23786 layer_factory.hpp:77] Creating layer fc8
I0407 21:57:12.287616 23786 net.cpp:84] Creating Layer fc8
I0407 21:57:12.287618 23786 net.cpp:406] fc8 <- fc7
I0407 21:57:12.287624 23786 net.cpp:380] fc8 -> fc8
I0407 21:57:12.295269 23786 net.cpp:122] Setting up fc8
I0407 21:57:12.295279 23786 net.cpp:129] Top shape: 128 196 (25088)
I0407 21:57:12.295282 23786 net.cpp:137] Memory required for data: 1064452096
I0407 21:57:12.295290 23786 layer_factory.hpp:77] Creating layer loss
I0407 21:57:12.295296 23786 net.cpp:84] Creating Layer loss
I0407 21:57:12.295300 23786 net.cpp:406] loss <- fc8
I0407 21:57:12.295305 23786 net.cpp:406] loss <- label
I0407 21:57:12.295312 23786 net.cpp:380] loss -> loss
I0407 21:57:12.295321 23786 layer_factory.hpp:77] Creating layer loss
I0407 21:57:12.297945 23786 net.cpp:122] Setting up loss
I0407 21:57:12.297968 23786 net.cpp:129] Top shape: (1)
I0407 21:57:12.297972 23786 net.cpp:132]     with loss weight 1
I0407 21:57:12.297989 23786 net.cpp:137] Memory required for data: 1064452100
I0407 21:57:12.297993 23786 net.cpp:198] loss needs backward computation.
I0407 21:57:12.298000 23786 net.cpp:198] fc8 needs backward computation.
I0407 21:57:12.298003 23786 net.cpp:198] drop7 needs backward computation.
I0407 21:57:12.298007 23786 net.cpp:198] relu7 needs backward computation.
I0407 21:57:12.298010 23786 net.cpp:198] fc7 needs backward computation.
I0407 21:57:12.298013 23786 net.cpp:198] drop6 needs backward computation.
I0407 21:57:12.298017 23786 net.cpp:198] relu6 needs backward computation.
I0407 21:57:12.298020 23786 net.cpp:198] fc6 needs backward computation.
I0407 21:57:12.298024 23786 net.cpp:198] pool5 needs backward computation.
I0407 21:57:12.298027 23786 net.cpp:198] relu5 needs backward computation.
I0407 21:57:12.298032 23786 net.cpp:198] conv5 needs backward computation.
I0407 21:57:12.298035 23786 net.cpp:198] relu4 needs backward computation.
I0407 21:57:12.298038 23786 net.cpp:198] conv4 needs backward computation.
I0407 21:57:12.298043 23786 net.cpp:198] relu3 needs backward computation.
I0407 21:57:12.298045 23786 net.cpp:198] conv3 needs backward computation.
I0407 21:57:12.298049 23786 net.cpp:198] pool2 needs backward computation.
I0407 21:57:12.298053 23786 net.cpp:198] norm2 needs backward computation.
I0407 21:57:12.298058 23786 net.cpp:198] relu2 needs backward computation.
I0407 21:57:12.298061 23786 net.cpp:198] conv2 needs backward computation.
I0407 21:57:12.298064 23786 net.cpp:198] pool1 needs backward computation.
I0407 21:57:12.298069 23786 net.cpp:198] norm1 needs backward computation.
I0407 21:57:12.298071 23786 net.cpp:198] relu1 needs backward computation.
I0407 21:57:12.298075 23786 net.cpp:198] conv1 needs backward computation.
I0407 21:57:12.298079 23786 net.cpp:200] train-data does not need backward computation.
I0407 21:57:12.298082 23786 net.cpp:242] This network produces output loss
I0407 21:57:12.298095 23786 net.cpp:255] Network initialization done.
I0407 21:57:12.298581 23786 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0407 21:57:12.298612 23786 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0407 21:57:12.298751 23786 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"
}
I0407 21:57:12.298852 23786 layer_factory.hpp:77] Creating layer val-data
I0407 21:57:12.300251 23786 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0407 21:57:12.300458 23786 net.cpp:84] Creating Layer val-data
I0407 21:57:12.300467 23786 net.cpp:380] val-data -> data
I0407 21:57:12.300474 23786 net.cpp:380] val-data -> label
I0407 21:57:12.300482 23786 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0407 21:57:12.304298 23786 data_layer.cpp:45] output data size: 32,3,227,227
I0407 21:57:12.342031 23786 net.cpp:122] Setting up val-data
I0407 21:57:12.342051 23786 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0407 21:57:12.342056 23786 net.cpp:129] Top shape: 32 (32)
I0407 21:57:12.342059 23786 net.cpp:137] Memory required for data: 19787264
I0407 21:57:12.342065 23786 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0407 21:57:12.342077 23786 net.cpp:84] Creating Layer label_val-data_1_split
I0407 21:57:12.342082 23786 net.cpp:406] label_val-data_1_split <- label
I0407 21:57:12.342088 23786 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0407 21:57:12.342097 23786 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0407 21:57:12.342151 23786 net.cpp:122] Setting up label_val-data_1_split
I0407 21:57:12.342157 23786 net.cpp:129] Top shape: 32 (32)
I0407 21:57:12.342160 23786 net.cpp:129] Top shape: 32 (32)
I0407 21:57:12.342164 23786 net.cpp:137] Memory required for data: 19787520
I0407 21:57:12.342167 23786 layer_factory.hpp:77] Creating layer conv1
I0407 21:57:12.342178 23786 net.cpp:84] Creating Layer conv1
I0407 21:57:12.342183 23786 net.cpp:406] conv1 <- data
I0407 21:57:12.342188 23786 net.cpp:380] conv1 -> conv1
I0407 21:57:12.351552 23786 net.cpp:122] Setting up conv1
I0407 21:57:12.351564 23786 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 21:57:12.351567 23786 net.cpp:137] Memory required for data: 56958720
I0407 21:57:12.351578 23786 layer_factory.hpp:77] Creating layer relu1
I0407 21:57:12.351585 23786 net.cpp:84] Creating Layer relu1
I0407 21:57:12.351588 23786 net.cpp:406] relu1 <- conv1
I0407 21:57:12.351593 23786 net.cpp:367] relu1 -> conv1 (in-place)
I0407 21:57:12.351884 23786 net.cpp:122] Setting up relu1
I0407 21:57:12.351892 23786 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 21:57:12.351895 23786 net.cpp:137] Memory required for data: 94129920
I0407 21:57:12.351899 23786 layer_factory.hpp:77] Creating layer norm1
I0407 21:57:12.351907 23786 net.cpp:84] Creating Layer norm1
I0407 21:57:12.351910 23786 net.cpp:406] norm1 <- conv1
I0407 21:57:12.351917 23786 net.cpp:380] norm1 -> norm1
I0407 21:57:12.354193 23786 net.cpp:122] Setting up norm1
I0407 21:57:12.354203 23786 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 21:57:12.354207 23786 net.cpp:137] Memory required for data: 131301120
I0407 21:57:12.354210 23786 layer_factory.hpp:77] Creating layer pool1
I0407 21:57:12.354218 23786 net.cpp:84] Creating Layer pool1
I0407 21:57:12.354220 23786 net.cpp:406] pool1 <- norm1
I0407 21:57:12.354225 23786 net.cpp:380] pool1 -> pool1
I0407 21:57:12.354254 23786 net.cpp:122] Setting up pool1
I0407 21:57:12.354259 23786 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0407 21:57:12.354261 23786 net.cpp:137] Memory required for data: 140259072
I0407 21:57:12.354264 23786 layer_factory.hpp:77] Creating layer conv2
I0407 21:57:12.354272 23786 net.cpp:84] Creating Layer conv2
I0407 21:57:12.354276 23786 net.cpp:406] conv2 <- pool1
I0407 21:57:12.354300 23786 net.cpp:380] conv2 -> conv2
I0407 21:57:12.361344 23786 net.cpp:122] Setting up conv2
I0407 21:57:12.361358 23786 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 21:57:12.361362 23786 net.cpp:137] Memory required for data: 164146944
I0407 21:57:12.361372 23786 layer_factory.hpp:77] Creating layer relu2
I0407 21:57:12.361382 23786 net.cpp:84] Creating Layer relu2
I0407 21:57:12.361384 23786 net.cpp:406] relu2 <- conv2
I0407 21:57:12.361390 23786 net.cpp:367] relu2 -> conv2 (in-place)
I0407 21:57:12.361881 23786 net.cpp:122] Setting up relu2
I0407 21:57:12.361891 23786 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 21:57:12.361894 23786 net.cpp:137] Memory required for data: 188034816
I0407 21:57:12.361898 23786 layer_factory.hpp:77] Creating layer norm2
I0407 21:57:12.361907 23786 net.cpp:84] Creating Layer norm2
I0407 21:57:12.361912 23786 net.cpp:406] norm2 <- conv2
I0407 21:57:12.361917 23786 net.cpp:380] norm2 -> norm2
I0407 21:57:12.362430 23786 net.cpp:122] Setting up norm2
I0407 21:57:12.362440 23786 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 21:57:12.362443 23786 net.cpp:137] Memory required for data: 211922688
I0407 21:57:12.362447 23786 layer_factory.hpp:77] Creating layer pool2
I0407 21:57:12.362454 23786 net.cpp:84] Creating Layer pool2
I0407 21:57:12.362458 23786 net.cpp:406] pool2 <- norm2
I0407 21:57:12.362463 23786 net.cpp:380] pool2 -> pool2
I0407 21:57:12.362494 23786 net.cpp:122] Setting up pool2
I0407 21:57:12.362499 23786 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 21:57:12.362502 23786 net.cpp:137] Memory required for data: 217460480
I0407 21:57:12.362505 23786 layer_factory.hpp:77] Creating layer conv3
I0407 21:57:12.362515 23786 net.cpp:84] Creating Layer conv3
I0407 21:57:12.362519 23786 net.cpp:406] conv3 <- pool2
I0407 21:57:12.362524 23786 net.cpp:380] conv3 -> conv3
I0407 21:57:12.375360 23786 net.cpp:122] Setting up conv3
I0407 21:57:12.375380 23786 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 21:57:12.375382 23786 net.cpp:137] Memory required for data: 225767168
I0407 21:57:12.375396 23786 layer_factory.hpp:77] Creating layer relu3
I0407 21:57:12.375403 23786 net.cpp:84] Creating Layer relu3
I0407 21:57:12.375407 23786 net.cpp:406] relu3 <- conv3
I0407 21:57:12.375416 23786 net.cpp:367] relu3 -> conv3 (in-place)
I0407 21:57:12.375917 23786 net.cpp:122] Setting up relu3
I0407 21:57:12.375927 23786 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 21:57:12.375931 23786 net.cpp:137] Memory required for data: 234073856
I0407 21:57:12.375934 23786 layer_factory.hpp:77] Creating layer conv4
I0407 21:57:12.375946 23786 net.cpp:84] Creating Layer conv4
I0407 21:57:12.375950 23786 net.cpp:406] conv4 <- conv3
I0407 21:57:12.375957 23786 net.cpp:380] conv4 -> conv4
I0407 21:57:12.385342 23786 net.cpp:122] Setting up conv4
I0407 21:57:12.385355 23786 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 21:57:12.385358 23786 net.cpp:137] Memory required for data: 242380544
I0407 21:57:12.385366 23786 layer_factory.hpp:77] Creating layer relu4
I0407 21:57:12.385377 23786 net.cpp:84] Creating Layer relu4
I0407 21:57:12.385381 23786 net.cpp:406] relu4 <- conv4
I0407 21:57:12.385386 23786 net.cpp:367] relu4 -> conv4 (in-place)
I0407 21:57:12.385726 23786 net.cpp:122] Setting up relu4
I0407 21:57:12.385733 23786 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 21:57:12.385737 23786 net.cpp:137] Memory required for data: 250687232
I0407 21:57:12.385740 23786 layer_factory.hpp:77] Creating layer conv5
I0407 21:57:12.385751 23786 net.cpp:84] Creating Layer conv5
I0407 21:57:12.385754 23786 net.cpp:406] conv5 <- conv4
I0407 21:57:12.385761 23786 net.cpp:380] conv5 -> conv5
I0407 21:57:12.395800 23786 net.cpp:122] Setting up conv5
I0407 21:57:12.395818 23786 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 21:57:12.395821 23786 net.cpp:137] Memory required for data: 256225024
I0407 21:57:12.395834 23786 layer_factory.hpp:77] Creating layer relu5
I0407 21:57:12.395843 23786 net.cpp:84] Creating Layer relu5
I0407 21:57:12.395846 23786 net.cpp:406] relu5 <- conv5
I0407 21:57:12.395869 23786 net.cpp:367] relu5 -> conv5 (in-place)
I0407 21:57:12.396355 23786 net.cpp:122] Setting up relu5
I0407 21:57:12.396366 23786 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 21:57:12.396369 23786 net.cpp:137] Memory required for data: 261762816
I0407 21:57:12.396373 23786 layer_factory.hpp:77] Creating layer pool5
I0407 21:57:12.396382 23786 net.cpp:84] Creating Layer pool5
I0407 21:57:12.396386 23786 net.cpp:406] pool5 <- conv5
I0407 21:57:12.396391 23786 net.cpp:380] pool5 -> pool5
I0407 21:57:12.396430 23786 net.cpp:122] Setting up pool5
I0407 21:57:12.396436 23786 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0407 21:57:12.396440 23786 net.cpp:137] Memory required for data: 262942464
I0407 21:57:12.396442 23786 layer_factory.hpp:77] Creating layer fc6
I0407 21:57:12.396450 23786 net.cpp:84] Creating Layer fc6
I0407 21:57:12.396452 23786 net.cpp:406] fc6 <- pool5
I0407 21:57:12.396458 23786 net.cpp:380] fc6 -> fc6
I0407 21:57:12.749406 23786 net.cpp:122] Setting up fc6
I0407 21:57:12.749428 23786 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:57:12.749431 23786 net.cpp:137] Memory required for data: 263466752
I0407 21:57:12.749440 23786 layer_factory.hpp:77] Creating layer relu6
I0407 21:57:12.749449 23786 net.cpp:84] Creating Layer relu6
I0407 21:57:12.749452 23786 net.cpp:406] relu6 <- fc6
I0407 21:57:12.749459 23786 net.cpp:367] relu6 -> fc6 (in-place)
I0407 21:57:12.750288 23786 net.cpp:122] Setting up relu6
I0407 21:57:12.750299 23786 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:57:12.750306 23786 net.cpp:137] Memory required for data: 263991040
I0407 21:57:12.750310 23786 layer_factory.hpp:77] Creating layer drop6
I0407 21:57:12.750317 23786 net.cpp:84] Creating Layer drop6
I0407 21:57:12.750321 23786 net.cpp:406] drop6 <- fc6
I0407 21:57:12.750326 23786 net.cpp:367] drop6 -> fc6 (in-place)
I0407 21:57:12.750353 23786 net.cpp:122] Setting up drop6
I0407 21:57:12.750360 23786 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:57:12.750362 23786 net.cpp:137] Memory required for data: 264515328
I0407 21:57:12.750365 23786 layer_factory.hpp:77] Creating layer fc7
I0407 21:57:12.750373 23786 net.cpp:84] Creating Layer fc7
I0407 21:57:12.750376 23786 net.cpp:406] fc7 <- fc6
I0407 21:57:12.750381 23786 net.cpp:380] fc7 -> fc7
I0407 21:57:12.906975 23786 net.cpp:122] Setting up fc7
I0407 21:57:12.906996 23786 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:57:12.907001 23786 net.cpp:137] Memory required for data: 265039616
I0407 21:57:12.907011 23786 layer_factory.hpp:77] Creating layer relu7
I0407 21:57:12.907021 23786 net.cpp:84] Creating Layer relu7
I0407 21:57:12.907025 23786 net.cpp:406] relu7 <- fc7
I0407 21:57:12.907032 23786 net.cpp:367] relu7 -> fc7 (in-place)
I0407 21:57:12.907456 23786 net.cpp:122] Setting up relu7
I0407 21:57:12.907464 23786 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:57:12.907467 23786 net.cpp:137] Memory required for data: 265563904
I0407 21:57:12.907471 23786 layer_factory.hpp:77] Creating layer drop7
I0407 21:57:12.907478 23786 net.cpp:84] Creating Layer drop7
I0407 21:57:12.907481 23786 net.cpp:406] drop7 <- fc7
I0407 21:57:12.907486 23786 net.cpp:367] drop7 -> fc7 (in-place)
I0407 21:57:12.907510 23786 net.cpp:122] Setting up drop7
I0407 21:57:12.907516 23786 net.cpp:129] Top shape: 32 4096 (131072)
I0407 21:57:12.907518 23786 net.cpp:137] Memory required for data: 266088192
I0407 21:57:12.907521 23786 layer_factory.hpp:77] Creating layer fc8
I0407 21:57:12.907527 23786 net.cpp:84] Creating Layer fc8
I0407 21:57:12.907532 23786 net.cpp:406] fc8 <- fc7
I0407 21:57:12.907537 23786 net.cpp:380] fc8 -> fc8
I0407 21:57:12.915232 23786 net.cpp:122] Setting up fc8
I0407 21:57:12.915243 23786 net.cpp:129] Top shape: 32 196 (6272)
I0407 21:57:12.915246 23786 net.cpp:137] Memory required for data: 266113280
I0407 21:57:12.915252 23786 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0407 21:57:12.915261 23786 net.cpp:84] Creating Layer fc8_fc8_0_split
I0407 21:57:12.915264 23786 net.cpp:406] fc8_fc8_0_split <- fc8
I0407 21:57:12.915287 23786 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0407 21:57:12.915293 23786 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0407 21:57:12.915326 23786 net.cpp:122] Setting up fc8_fc8_0_split
I0407 21:57:12.915331 23786 net.cpp:129] Top shape: 32 196 (6272)
I0407 21:57:12.915334 23786 net.cpp:129] Top shape: 32 196 (6272)
I0407 21:57:12.915338 23786 net.cpp:137] Memory required for data: 266163456
I0407 21:57:12.915340 23786 layer_factory.hpp:77] Creating layer accuracy
I0407 21:57:12.915347 23786 net.cpp:84] Creating Layer accuracy
I0407 21:57:12.915350 23786 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0407 21:57:12.915354 23786 net.cpp:406] accuracy <- label_val-data_1_split_0
I0407 21:57:12.915360 23786 net.cpp:380] accuracy -> accuracy
I0407 21:57:12.915367 23786 net.cpp:122] Setting up accuracy
I0407 21:57:12.915371 23786 net.cpp:129] Top shape: (1)
I0407 21:57:12.915374 23786 net.cpp:137] Memory required for data: 266163460
I0407 21:57:12.915377 23786 layer_factory.hpp:77] Creating layer loss
I0407 21:57:12.915382 23786 net.cpp:84] Creating Layer loss
I0407 21:57:12.915385 23786 net.cpp:406] loss <- fc8_fc8_0_split_1
I0407 21:57:12.915390 23786 net.cpp:406] loss <- label_val-data_1_split_1
I0407 21:57:12.915393 23786 net.cpp:380] loss -> loss
I0407 21:57:12.915400 23786 layer_factory.hpp:77] Creating layer loss
I0407 21:57:12.915999 23786 net.cpp:122] Setting up loss
I0407 21:57:12.916008 23786 net.cpp:129] Top shape: (1)
I0407 21:57:12.916011 23786 net.cpp:132]     with loss weight 1
I0407 21:57:12.916021 23786 net.cpp:137] Memory required for data: 266163464
I0407 21:57:12.916025 23786 net.cpp:198] loss needs backward computation.
I0407 21:57:12.916029 23786 net.cpp:200] accuracy does not need backward computation.
I0407 21:57:12.916033 23786 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0407 21:57:12.916036 23786 net.cpp:198] fc8 needs backward computation.
I0407 21:57:12.916040 23786 net.cpp:198] drop7 needs backward computation.
I0407 21:57:12.916043 23786 net.cpp:198] relu7 needs backward computation.
I0407 21:57:12.916046 23786 net.cpp:198] fc7 needs backward computation.
I0407 21:57:12.916049 23786 net.cpp:198] drop6 needs backward computation.
I0407 21:57:12.916052 23786 net.cpp:198] relu6 needs backward computation.
I0407 21:57:12.916055 23786 net.cpp:198] fc6 needs backward computation.
I0407 21:57:12.916059 23786 net.cpp:198] pool5 needs backward computation.
I0407 21:57:12.916062 23786 net.cpp:198] relu5 needs backward computation.
I0407 21:57:12.916066 23786 net.cpp:198] conv5 needs backward computation.
I0407 21:57:12.916069 23786 net.cpp:198] relu4 needs backward computation.
I0407 21:57:12.916072 23786 net.cpp:198] conv4 needs backward computation.
I0407 21:57:12.916076 23786 net.cpp:198] relu3 needs backward computation.
I0407 21:57:12.916079 23786 net.cpp:198] conv3 needs backward computation.
I0407 21:57:12.916083 23786 net.cpp:198] pool2 needs backward computation.
I0407 21:57:12.916086 23786 net.cpp:198] norm2 needs backward computation.
I0407 21:57:12.916092 23786 net.cpp:198] relu2 needs backward computation.
I0407 21:57:12.916095 23786 net.cpp:198] conv2 needs backward computation.
I0407 21:57:12.916098 23786 net.cpp:198] pool1 needs backward computation.
I0407 21:57:12.916102 23786 net.cpp:198] norm1 needs backward computation.
I0407 21:57:12.916105 23786 net.cpp:198] relu1 needs backward computation.
I0407 21:57:12.916108 23786 net.cpp:198] conv1 needs backward computation.
I0407 21:57:12.916112 23786 net.cpp:200] label_val-data_1_split does not need backward computation.
I0407 21:57:12.916116 23786 net.cpp:200] val-data does not need backward computation.
I0407 21:57:12.916119 23786 net.cpp:242] This network produces output accuracy
I0407 21:57:12.916122 23786 net.cpp:242] This network produces output loss
I0407 21:57:12.916138 23786 net.cpp:255] Network initialization done.
I0407 21:57:12.916213 23786 solver.cpp:56] Solver scaffolding done.
I0407 21:57:12.916633 23786 caffe.cpp:248] Starting Optimization
I0407 21:57:12.916641 23786 solver.cpp:272] Solving
I0407 21:57:12.916653 23786 solver.cpp:273] Learning Rate Policy: exp
I0407 21:57:12.917930 23786 solver.cpp:330] Iteration 0, Testing net (#0)
I0407 21:57:12.917940 23786 net.cpp:676] Ignoring source layer train-data
I0407 21:57:12.998108 23786 blocking_queue.cpp:49] Waiting for data
I0407 21:57:17.171612 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:57:17.216266 23786 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0407 21:57:17.216310 23786 solver.cpp:397]     Test net output #1: loss = 5.28092 (* 1 = 5.28092 loss)
I0407 21:57:17.309770 23786 solver.cpp:218] Iteration 0 (-4.62836e-21 iter/s, 4.39293s/12 iters), loss = 5.26969
I0407 21:57:17.311295 23786 solver.cpp:237]     Train net output #0: loss = 5.26969 (* 1 = 5.26969 loss)
I0407 21:57:17.311321 23786 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0407 21:57:21.164052 23786 solver.cpp:218] Iteration 12 (3.11477 iter/s, 3.85261s/12 iters), loss = 5.285
I0407 21:57:21.164098 23786 solver.cpp:237]     Train net output #0: loss = 5.285 (* 1 = 5.285 loss)
I0407 21:57:21.164108 23786 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0407 21:57:26.109633 23786 solver.cpp:218] Iteration 24 (2.42652 iter/s, 4.94536s/12 iters), loss = 5.28623
I0407 21:57:26.109680 23786 solver.cpp:237]     Train net output #0: loss = 5.28623 (* 1 = 5.28623 loss)
I0407 21:57:26.109692 23786 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0407 21:57:31.061951 23786 solver.cpp:218] Iteration 36 (2.42322 iter/s, 4.95209s/12 iters), loss = 5.28625
I0407 21:57:31.062019 23786 solver.cpp:237]     Train net output #0: loss = 5.28625 (* 1 = 5.28625 loss)
I0407 21:57:31.062031 23786 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0407 21:57:36.014266 23786 solver.cpp:218] Iteration 48 (2.42323 iter/s, 4.95207s/12 iters), loss = 5.31085
I0407 21:57:36.014308 23786 solver.cpp:237]     Train net output #0: loss = 5.31085 (* 1 = 5.31085 loss)
I0407 21:57:36.014318 23786 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0407 21:57:40.990445 23786 solver.cpp:218] Iteration 60 (2.4116 iter/s, 4.97595s/12 iters), loss = 5.29938
I0407 21:57:40.990597 23786 solver.cpp:237]     Train net output #0: loss = 5.29938 (* 1 = 5.29938 loss)
I0407 21:57:40.990607 23786 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0407 21:57:46.154282 23786 solver.cpp:218] Iteration 72 (2.32401 iter/s, 5.16349s/12 iters), loss = 5.30157
I0407 21:57:46.154331 23786 solver.cpp:237]     Train net output #0: loss = 5.30157 (* 1 = 5.30157 loss)
I0407 21:57:46.154342 23786 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0407 21:57:51.230226 23786 solver.cpp:218] Iteration 84 (2.3642 iter/s, 5.07571s/12 iters), loss = 5.2995
I0407 21:57:51.230263 23786 solver.cpp:237]     Train net output #0: loss = 5.2995 (* 1 = 5.2995 loss)
I0407 21:57:51.230271 23786 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0407 21:57:56.274119 23786 solver.cpp:218] Iteration 96 (2.37923 iter/s, 5.04366s/12 iters), loss = 5.30115
I0407 21:57:56.274173 23786 solver.cpp:237]     Train net output #0: loss = 5.30115 (* 1 = 5.30115 loss)
I0407 21:57:56.274184 23786 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0407 21:57:57.974885 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:57:58.330736 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0407 21:58:02.954924 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0407 21:58:05.413211 23786 solver.cpp:330] Iteration 102, Testing net (#0)
I0407 21:58:05.413239 23786 net.cpp:676] Ignoring source layer train-data
I0407 21:58:09.790474 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:58:09.867182 23786 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0407 21:58:09.867230 23786 solver.cpp:397]     Test net output #1: loss = 5.28752 (* 1 = 5.28752 loss)
I0407 21:58:11.778731 23786 solver.cpp:218] Iteration 108 (0.773994 iter/s, 15.504s/12 iters), loss = 5.30233
I0407 21:58:11.778874 23786 solver.cpp:237]     Train net output #0: loss = 5.30233 (* 1 = 5.30233 loss)
I0407 21:58:11.778884 23786 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0407 21:58:16.777827 23786 solver.cpp:218] Iteration 120 (2.4006 iter/s, 4.99876s/12 iters), loss = 5.28138
I0407 21:58:16.777874 23786 solver.cpp:237]     Train net output #0: loss = 5.28138 (* 1 = 5.28138 loss)
I0407 21:58:16.777884 23786 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0407 21:58:21.750842 23786 solver.cpp:218] Iteration 132 (2.41314 iter/s, 4.97277s/12 iters), loss = 5.22866
I0407 21:58:21.750895 23786 solver.cpp:237]     Train net output #0: loss = 5.22866 (* 1 = 5.22866 loss)
I0407 21:58:21.750908 23786 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0407 21:58:26.789739 23786 solver.cpp:218] Iteration 144 (2.38159 iter/s, 5.03865s/12 iters), loss = 5.2508
I0407 21:58:26.789793 23786 solver.cpp:237]     Train net output #0: loss = 5.2508 (* 1 = 5.2508 loss)
I0407 21:58:26.789804 23786 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0407 21:58:31.786334 23786 solver.cpp:218] Iteration 156 (2.40175 iter/s, 4.99635s/12 iters), loss = 5.2075
I0407 21:58:31.786386 23786 solver.cpp:237]     Train net output #0: loss = 5.2075 (* 1 = 5.2075 loss)
I0407 21:58:31.786398 23786 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0407 21:58:36.899528 23786 solver.cpp:218] Iteration 168 (2.34698 iter/s, 5.11295s/12 iters), loss = 5.17844
I0407 21:58:36.899574 23786 solver.cpp:237]     Train net output #0: loss = 5.17844 (* 1 = 5.17844 loss)
I0407 21:58:36.899585 23786 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0407 21:58:41.862717 23786 solver.cpp:218] Iteration 180 (2.41792 iter/s, 4.96295s/12 iters), loss = 5.15494
I0407 21:58:41.862828 23786 solver.cpp:237]     Train net output #0: loss = 5.15494 (* 1 = 5.15494 loss)
I0407 21:58:41.862840 23786 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0407 21:58:46.881815 23786 solver.cpp:218] Iteration 192 (2.39101 iter/s, 5.0188s/12 iters), loss = 5.23253
I0407 21:58:46.881858 23786 solver.cpp:237]     Train net output #0: loss = 5.23253 (* 1 = 5.23253 loss)
I0407 21:58:46.881866 23786 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0407 21:58:50.760908 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:58:51.430778 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0407 21:58:56.297650 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0407 21:58:58.660204 23786 solver.cpp:330] Iteration 204, Testing net (#0)
I0407 21:58:58.660230 23786 net.cpp:676] Ignoring source layer train-data
I0407 21:59:02.904619 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:59:03.027354 23786 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0407 21:59:03.027403 23786 solver.cpp:397]     Test net output #1: loss = 5.19032 (* 1 = 5.19032 loss)
I0407 21:59:03.117746 23786 solver.cpp:218] Iteration 204 (0.739131 iter/s, 16.2353s/12 iters), loss = 5.1147
I0407 21:59:03.117791 23786 solver.cpp:237]     Train net output #0: loss = 5.1147 (* 1 = 5.1147 loss)
I0407 21:59:03.117802 23786 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0407 21:59:07.556345 23786 solver.cpp:218] Iteration 216 (2.70369 iter/s, 4.43838s/12 iters), loss = 5.1619
I0407 21:59:07.556396 23786 solver.cpp:237]     Train net output #0: loss = 5.1619 (* 1 = 5.1619 loss)
I0407 21:59:07.556411 23786 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0407 21:59:12.698700 23786 solver.cpp:218] Iteration 228 (2.33368 iter/s, 5.1421s/12 iters), loss = 5.21299
I0407 21:59:12.699113 23786 solver.cpp:237]     Train net output #0: loss = 5.21299 (* 1 = 5.21299 loss)
I0407 21:59:12.699126 23786 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0407 21:59:17.838418 23786 solver.cpp:218] Iteration 240 (2.33504 iter/s, 5.1391s/12 iters), loss = 5.21317
I0407 21:59:17.838466 23786 solver.cpp:237]     Train net output #0: loss = 5.21317 (* 1 = 5.21317 loss)
I0407 21:59:17.838479 23786 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0407 21:59:22.809782 23786 solver.cpp:218] Iteration 252 (2.41394 iter/s, 4.97112s/12 iters), loss = 5.1421
I0407 21:59:22.809839 23786 solver.cpp:237]     Train net output #0: loss = 5.1421 (* 1 = 5.1421 loss)
I0407 21:59:22.809850 23786 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0407 21:59:27.692989 23786 solver.cpp:218] Iteration 264 (2.45753 iter/s, 4.88296s/12 iters), loss = 5.24515
I0407 21:59:27.693040 23786 solver.cpp:237]     Train net output #0: loss = 5.24515 (* 1 = 5.24515 loss)
I0407 21:59:27.693051 23786 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0407 21:59:32.683490 23786 solver.cpp:218] Iteration 276 (2.40469 iter/s, 4.99025s/12 iters), loss = 5.20089
I0407 21:59:32.683542 23786 solver.cpp:237]     Train net output #0: loss = 5.20089 (* 1 = 5.20089 loss)
I0407 21:59:32.683554 23786 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0407 21:59:37.681524 23786 solver.cpp:218] Iteration 288 (2.40106 iter/s, 4.99779s/12 iters), loss = 5.05728
I0407 21:59:37.681574 23786 solver.cpp:237]     Train net output #0: loss = 5.05728 (* 1 = 5.05728 loss)
I0407 21:59:37.681587 23786 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0407 21:59:42.633805 23786 solver.cpp:218] Iteration 300 (2.42325 iter/s, 4.95203s/12 iters), loss = 5.17046
I0407 21:59:42.633857 23786 solver.cpp:237]     Train net output #0: loss = 5.17046 (* 1 = 5.17046 loss)
I0407 21:59:42.633872 23786 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0407 21:59:43.614239 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:59:44.661106 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0407 21:59:49.054514 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0407 21:59:52.161093 23786 solver.cpp:330] Iteration 306, Testing net (#0)
I0407 21:59:52.161118 23786 net.cpp:676] Ignoring source layer train-data
I0407 21:59:56.461123 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 21:59:56.618803 23786 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0407 21:59:56.618847 23786 solver.cpp:397]     Test net output #1: loss = 5.14575 (* 1 = 5.14575 loss)
I0407 21:59:58.614198 23786 solver.cpp:218] Iteration 312 (0.750951 iter/s, 15.9797s/12 iters), loss = 5.11878
I0407 21:59:58.614249 23786 solver.cpp:237]     Train net output #0: loss = 5.11878 (* 1 = 5.11878 loss)
I0407 21:59:58.614259 23786 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0407 22:00:03.626704 23786 solver.cpp:218] Iteration 324 (2.39414 iter/s, 5.01224s/12 iters), loss = 5.18526
I0407 22:00:03.626763 23786 solver.cpp:237]     Train net output #0: loss = 5.18526 (* 1 = 5.18526 loss)
I0407 22:00:03.626775 23786 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0407 22:00:08.608970 23786 solver.cpp:218] Iteration 336 (2.40867 iter/s, 4.98201s/12 iters), loss = 5.12804
I0407 22:00:08.609019 23786 solver.cpp:237]     Train net output #0: loss = 5.12804 (* 1 = 5.12804 loss)
I0407 22:00:08.609030 23786 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0407 22:00:13.633162 23786 solver.cpp:218] Iteration 348 (2.38857 iter/s, 5.02394s/12 iters), loss = 5.11052
I0407 22:00:13.633280 23786 solver.cpp:237]     Train net output #0: loss = 5.11052 (* 1 = 5.11052 loss)
I0407 22:00:13.633293 23786 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0407 22:00:19.042721 23786 solver.cpp:218] Iteration 360 (2.21843 iter/s, 5.40923s/12 iters), loss = 5.13288
I0407 22:00:19.042769 23786 solver.cpp:237]     Train net output #0: loss = 5.13288 (* 1 = 5.13288 loss)
I0407 22:00:19.042781 23786 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0407 22:00:24.218478 23786 solver.cpp:218] Iteration 372 (2.31862 iter/s, 5.1755s/12 iters), loss = 5.07423
I0407 22:00:24.218528 23786 solver.cpp:237]     Train net output #0: loss = 5.07423 (* 1 = 5.07423 loss)
I0407 22:00:24.218539 23786 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0407 22:00:29.294888 23786 solver.cpp:218] Iteration 384 (2.36399 iter/s, 5.07616s/12 iters), loss = 5.09914
I0407 22:00:29.294930 23786 solver.cpp:237]     Train net output #0: loss = 5.09914 (* 1 = 5.09914 loss)
I0407 22:00:29.294940 23786 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0407 22:00:34.327682 23786 solver.cpp:218] Iteration 396 (2.38448 iter/s, 5.03254s/12 iters), loss = 5.05916
I0407 22:00:34.327735 23786 solver.cpp:237]     Train net output #0: loss = 5.05916 (* 1 = 5.05916 loss)
I0407 22:00:34.327749 23786 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0407 22:00:37.433374 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:00:38.847077 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0407 22:00:45.801074 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0407 22:00:48.255690 23786 solver.cpp:330] Iteration 408, Testing net (#0)
I0407 22:00:48.255715 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:00:52.511749 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:00:52.715163 23786 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0407 22:00:52.715206 23786 solver.cpp:397]     Test net output #1: loss = 5.08693 (* 1 = 5.08693 loss)
I0407 22:00:52.805446 23786 solver.cpp:218] Iteration 408 (0.649456 iter/s, 18.477s/12 iters), loss = 5.17479
I0407 22:00:52.805495 23786 solver.cpp:237]     Train net output #0: loss = 5.17479 (* 1 = 5.17479 loss)
I0407 22:00:52.805505 23786 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0407 22:00:57.074424 23786 solver.cpp:218] Iteration 420 (2.81113 iter/s, 4.26875s/12 iters), loss = 5.12759
I0407 22:00:57.074481 23786 solver.cpp:237]     Train net output #0: loss = 5.12759 (* 1 = 5.12759 loss)
I0407 22:00:57.074493 23786 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0407 22:01:02.101024 23786 solver.cpp:218] Iteration 432 (2.38742 iter/s, 5.02634s/12 iters), loss = 5.10383
I0407 22:01:02.101073 23786 solver.cpp:237]     Train net output #0: loss = 5.10383 (* 1 = 5.10383 loss)
I0407 22:01:02.101083 23786 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0407 22:01:07.119766 23786 solver.cpp:218] Iteration 444 (2.39116 iter/s, 5.01849s/12 iters), loss = 5.02432
I0407 22:01:07.119818 23786 solver.cpp:237]     Train net output #0: loss = 5.02432 (* 1 = 5.02432 loss)
I0407 22:01:07.119830 23786 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0407 22:01:12.043486 23786 solver.cpp:218] Iteration 456 (2.4373 iter/s, 4.92347s/12 iters), loss = 5.08182
I0407 22:01:12.043529 23786 solver.cpp:237]     Train net output #0: loss = 5.08182 (* 1 = 5.08182 loss)
I0407 22:01:12.043538 23786 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0407 22:01:17.008610 23786 solver.cpp:218] Iteration 468 (2.41698 iter/s, 4.96488s/12 iters), loss = 5.08558
I0407 22:01:17.008730 23786 solver.cpp:237]     Train net output #0: loss = 5.08558 (* 1 = 5.08558 loss)
I0407 22:01:17.008744 23786 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0407 22:01:21.989580 23786 solver.cpp:218] Iteration 480 (2.40932 iter/s, 4.98065s/12 iters), loss = 5.02689
I0407 22:01:21.989631 23786 solver.cpp:237]     Train net output #0: loss = 5.02689 (* 1 = 5.02689 loss)
I0407 22:01:21.989643 23786 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0407 22:01:26.939354 23786 solver.cpp:218] Iteration 492 (2.42448 iter/s, 4.94952s/12 iters), loss = 5.08482
I0407 22:01:26.939410 23786 solver.cpp:237]     Train net output #0: loss = 5.08482 (* 1 = 5.08482 loss)
I0407 22:01:26.939424 23786 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0407 22:01:31.928067 23786 solver.cpp:218] Iteration 504 (2.40555 iter/s, 4.98846s/12 iters), loss = 5.07716
I0407 22:01:31.928120 23786 solver.cpp:237]     Train net output #0: loss = 5.07716 (* 1 = 5.07716 loss)
I0407 22:01:31.928133 23786 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0407 22:01:32.186854 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:01:33.966305 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0407 22:01:38.446934 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0407 22:01:40.915146 23786 solver.cpp:330] Iteration 510, Testing net (#0)
I0407 22:01:40.915174 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:01:45.152642 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:01:45.390079 23786 solver.cpp:397]     Test net output #0: accuracy = 0.0177696
I0407 22:01:45.390130 23786 solver.cpp:397]     Test net output #1: loss = 5.01703 (* 1 = 5.01703 loss)
I0407 22:01:47.358633 23786 solver.cpp:218] Iteration 516 (0.77771 iter/s, 15.4299s/12 iters), loss = 4.97138
I0407 22:01:47.358763 23786 solver.cpp:237]     Train net output #0: loss = 4.97138 (* 1 = 4.97138 loss)
I0407 22:01:47.358776 23786 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0407 22:01:52.513558 23786 solver.cpp:218] Iteration 528 (2.32802 iter/s, 5.15459s/12 iters), loss = 5.0967
I0407 22:01:52.513612 23786 solver.cpp:237]     Train net output #0: loss = 5.0967 (* 1 = 5.0967 loss)
I0407 22:01:52.513623 23786 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0407 22:01:57.499770 23786 solver.cpp:218] Iteration 540 (2.40676 iter/s, 4.98596s/12 iters), loss = 4.9917
I0407 22:01:57.499825 23786 solver.cpp:237]     Train net output #0: loss = 4.9917 (* 1 = 4.9917 loss)
I0407 22:01:57.499837 23786 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0407 22:02:02.467315 23786 solver.cpp:218] Iteration 552 (2.4158 iter/s, 4.96729s/12 iters), loss = 5.07477
I0407 22:02:02.467365 23786 solver.cpp:237]     Train net output #0: loss = 5.07477 (* 1 = 5.07477 loss)
I0407 22:02:02.467376 23786 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0407 22:02:07.447882 23786 solver.cpp:218] Iteration 564 (2.40949 iter/s, 4.98031s/12 iters), loss = 5.05443
I0407 22:02:07.447938 23786 solver.cpp:237]     Train net output #0: loss = 5.05443 (* 1 = 5.05443 loss)
I0407 22:02:07.447952 23786 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0407 22:02:12.402930 23786 solver.cpp:218] Iteration 576 (2.4219 iter/s, 4.95479s/12 iters), loss = 5.0256
I0407 22:02:12.402976 23786 solver.cpp:237]     Train net output #0: loss = 5.0256 (* 1 = 5.0256 loss)
I0407 22:02:12.402984 23786 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0407 22:02:17.337378 23786 solver.cpp:218] Iteration 588 (2.432 iter/s, 4.93421s/12 iters), loss = 4.83006
I0407 22:02:17.337421 23786 solver.cpp:237]     Train net output #0: loss = 4.83006 (* 1 = 4.83006 loss)
I0407 22:02:17.337431 23786 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0407 22:02:22.328317 23786 solver.cpp:218] Iteration 600 (2.40448 iter/s, 4.99069s/12 iters), loss = 4.94521
I0407 22:02:22.334092 23786 solver.cpp:237]     Train net output #0: loss = 4.94521 (* 1 = 4.94521 loss)
I0407 22:02:22.334105 23786 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0407 22:02:24.726886 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:02:26.942292 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0407 22:02:31.528257 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0407 22:02:33.929131 23786 solver.cpp:330] Iteration 612, Testing net (#0)
I0407 22:02:33.929149 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:02:38.113078 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:02:38.403872 23786 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0407 22:02:38.403920 23786 solver.cpp:397]     Test net output #1: loss = 4.94814 (* 1 = 4.94814 loss)
I0407 22:02:38.494159 23786 solver.cpp:218] Iteration 612 (0.7426 iter/s, 16.1594s/12 iters), loss = 4.91401
I0407 22:02:38.494211 23786 solver.cpp:237]     Train net output #0: loss = 4.91401 (* 1 = 4.91401 loss)
I0407 22:02:38.494222 23786 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0407 22:02:42.574988 23786 solver.cpp:218] Iteration 624 (2.94074 iter/s, 4.08061s/12 iters), loss = 4.93826
I0407 22:02:42.575028 23786 solver.cpp:237]     Train net output #0: loss = 4.93826 (* 1 = 4.93826 loss)
I0407 22:02:42.575037 23786 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0407 22:02:47.604393 23786 solver.cpp:218] Iteration 636 (2.38609 iter/s, 5.02916s/12 iters), loss = 4.81943
I0407 22:02:47.604446 23786 solver.cpp:237]     Train net output #0: loss = 4.81943 (* 1 = 4.81943 loss)
I0407 22:02:47.604458 23786 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0407 22:02:52.527210 23786 solver.cpp:218] Iteration 648 (2.43775 iter/s, 4.92257s/12 iters), loss = 5.14521
I0407 22:02:52.527371 23786 solver.cpp:237]     Train net output #0: loss = 5.14521 (* 1 = 5.14521 loss)
I0407 22:02:52.527386 23786 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0407 22:02:57.518513 23786 solver.cpp:218] Iteration 660 (2.40435 iter/s, 4.99095s/12 iters), loss = 4.91396
I0407 22:02:57.518548 23786 solver.cpp:237]     Train net output #0: loss = 4.91396 (* 1 = 4.91396 loss)
I0407 22:02:57.518556 23786 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0407 22:03:02.518375 23786 solver.cpp:218] Iteration 672 (2.40018 iter/s, 4.99962s/12 iters), loss = 4.80341
I0407 22:03:02.518425 23786 solver.cpp:237]     Train net output #0: loss = 4.80341 (* 1 = 4.80341 loss)
I0407 22:03:02.518438 23786 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0407 22:03:07.545032 23786 solver.cpp:218] Iteration 684 (2.38739 iter/s, 5.02641s/12 iters), loss = 4.76741
I0407 22:03:07.545080 23786 solver.cpp:237]     Train net output #0: loss = 4.76741 (* 1 = 4.76741 loss)
I0407 22:03:07.545094 23786 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0407 22:03:08.335371 23786 blocking_queue.cpp:49] Waiting for data
I0407 22:03:12.567891 23786 solver.cpp:218] Iteration 696 (2.38919 iter/s, 5.02261s/12 iters), loss = 4.80741
I0407 22:03:12.567929 23786 solver.cpp:237]     Train net output #0: loss = 4.80741 (* 1 = 4.80741 loss)
I0407 22:03:12.567937 23786 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0407 22:03:17.188654 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:03:17.567215 23786 solver.cpp:218] Iteration 708 (2.40044 iter/s, 4.99908s/12 iters), loss = 4.93981
I0407 22:03:17.567262 23786 solver.cpp:237]     Train net output #0: loss = 4.93981 (* 1 = 4.93981 loss)
I0407 22:03:17.567273 23786 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0407 22:03:19.601186 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0407 22:03:24.025552 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0407 22:03:27.038689 23786 solver.cpp:330] Iteration 714, Testing net (#0)
I0407 22:03:27.038712 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:03:31.218884 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:03:31.538316 23786 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0407 22:03:31.538367 23786 solver.cpp:397]     Test net output #1: loss = 4.88911 (* 1 = 4.88911 loss)
I0407 22:03:33.369169 23786 solver.cpp:218] Iteration 720 (0.759431 iter/s, 15.8013s/12 iters), loss = 4.98636
I0407 22:03:33.369220 23786 solver.cpp:237]     Train net output #0: loss = 4.98636 (* 1 = 4.98636 loss)
I0407 22:03:33.369230 23786 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0407 22:03:38.329799 23786 solver.cpp:218] Iteration 732 (2.41917 iter/s, 4.96038s/12 iters), loss = 4.65325
I0407 22:03:38.329855 23786 solver.cpp:237]     Train net output #0: loss = 4.65325 (* 1 = 4.65325 loss)
I0407 22:03:38.329869 23786 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0407 22:03:43.406389 23786 solver.cpp:218] Iteration 744 (2.36391 iter/s, 5.07633s/12 iters), loss = 4.83219
I0407 22:03:43.406433 23786 solver.cpp:237]     Train net output #0: loss = 4.83219 (* 1 = 4.83219 loss)
I0407 22:03:43.406442 23786 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0407 22:03:48.441167 23786 solver.cpp:218] Iteration 756 (2.38354 iter/s, 5.03453s/12 iters), loss = 4.93418
I0407 22:03:48.441198 23786 solver.cpp:237]     Train net output #0: loss = 4.93418 (* 1 = 4.93418 loss)
I0407 22:03:48.441206 23786 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0407 22:03:53.639137 23786 solver.cpp:218] Iteration 768 (2.3087 iter/s, 5.19773s/12 iters), loss = 4.83564
I0407 22:03:53.639183 23786 solver.cpp:237]     Train net output #0: loss = 4.83564 (* 1 = 4.83564 loss)
I0407 22:03:53.639191 23786 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0407 22:03:58.708933 23786 solver.cpp:218] Iteration 780 (2.36707 iter/s, 5.06955s/12 iters), loss = 4.82131
I0407 22:03:58.709028 23786 solver.cpp:237]     Train net output #0: loss = 4.82131 (* 1 = 4.82131 loss)
I0407 22:03:58.709038 23786 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0407 22:04:03.692999 23786 solver.cpp:218] Iteration 792 (2.40782 iter/s, 4.98377s/12 iters), loss = 4.60851
I0407 22:04:03.693048 23786 solver.cpp:237]     Train net output #0: loss = 4.60851 (* 1 = 4.60851 loss)
I0407 22:04:03.693060 23786 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0407 22:04:08.738476 23786 solver.cpp:218] Iteration 804 (2.37849 iter/s, 5.04522s/12 iters), loss = 4.78767
I0407 22:04:08.738523 23786 solver.cpp:237]     Train net output #0: loss = 4.78767 (* 1 = 4.78767 loss)
I0407 22:04:08.738534 23786 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0407 22:04:10.497442 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:04:13.266017 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0407 22:04:17.740857 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0407 22:04:20.472980 23786 solver.cpp:330] Iteration 816, Testing net (#0)
I0407 22:04:20.473009 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:04:24.552724 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:04:24.908135 23786 solver.cpp:397]     Test net output #0: accuracy = 0.0373775
I0407 22:04:24.908185 23786 solver.cpp:397]     Test net output #1: loss = 4.79045 (* 1 = 4.79045 loss)
I0407 22:04:24.997232 23786 solver.cpp:218] Iteration 816 (0.738094 iter/s, 16.2581s/12 iters), loss = 4.86711
I0407 22:04:24.997275 23786 solver.cpp:237]     Train net output #0: loss = 4.86711 (* 1 = 4.86711 loss)
I0407 22:04:24.997285 23786 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0407 22:04:29.482549 23786 solver.cpp:218] Iteration 828 (2.67553 iter/s, 4.48509s/12 iters), loss = 4.9208
I0407 22:04:29.482623 23786 solver.cpp:237]     Train net output #0: loss = 4.9208 (* 1 = 4.9208 loss)
I0407 22:04:29.482635 23786 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0407 22:04:34.504164 23786 solver.cpp:218] Iteration 840 (2.3898 iter/s, 5.02134s/12 iters), loss = 4.63075
I0407 22:04:34.504210 23786 solver.cpp:237]     Train net output #0: loss = 4.63075 (* 1 = 4.63075 loss)
I0407 22:04:34.504220 23786 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0407 22:04:39.465803 23786 solver.cpp:218] Iteration 852 (2.41868 iter/s, 4.96139s/12 iters), loss = 4.66689
I0407 22:04:39.465853 23786 solver.cpp:237]     Train net output #0: loss = 4.66689 (* 1 = 4.66689 loss)
I0407 22:04:39.465864 23786 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0407 22:04:44.786305 23786 solver.cpp:218] Iteration 864 (2.25554 iter/s, 5.32024s/12 iters), loss = 4.78565
I0407 22:04:44.786350 23786 solver.cpp:237]     Train net output #0: loss = 4.78565 (* 1 = 4.78565 loss)
I0407 22:04:44.786358 23786 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0407 22:04:49.988498 23786 solver.cpp:218] Iteration 876 (2.30683 iter/s, 5.20194s/12 iters), loss = 4.6657
I0407 22:04:49.988548 23786 solver.cpp:237]     Train net output #0: loss = 4.6657 (* 1 = 4.6657 loss)
I0407 22:04:49.988560 23786 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0407 22:04:55.023824 23786 solver.cpp:218] Iteration 888 (2.38328 iter/s, 5.03507s/12 iters), loss = 4.57777
I0407 22:04:55.023870 23786 solver.cpp:237]     Train net output #0: loss = 4.57777 (* 1 = 4.57777 loss)
I0407 22:04:55.023882 23786 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0407 22:05:00.032831 23786 solver.cpp:218] Iteration 900 (2.3958 iter/s, 5.00876s/12 iters), loss = 4.75789
I0407 22:05:00.039546 23786 solver.cpp:237]     Train net output #0: loss = 4.75789 (* 1 = 4.75789 loss)
I0407 22:05:00.039561 23786 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0407 22:05:03.880371 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:05:04.977473 23786 solver.cpp:218] Iteration 912 (2.43027 iter/s, 4.93773s/12 iters), loss = 4.45025
I0407 22:05:04.977527 23786 solver.cpp:237]     Train net output #0: loss = 4.45025 (* 1 = 4.45025 loss)
I0407 22:05:04.977540 23786 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0407 22:05:06.969918 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0407 22:05:11.578759 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0407 22:05:14.039439 23786 solver.cpp:330] Iteration 918, Testing net (#0)
I0407 22:05:14.039465 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:05:18.128844 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:05:18.530596 23786 solver.cpp:397]     Test net output #0: accuracy = 0.0422794
I0407 22:05:18.530644 23786 solver.cpp:397]     Test net output #1: loss = 4.71643 (* 1 = 4.71643 loss)
I0407 22:05:20.380957 23786 solver.cpp:218] Iteration 924 (0.779077 iter/s, 15.4028s/12 iters), loss = 4.70984
I0407 22:05:20.381008 23786 solver.cpp:237]     Train net output #0: loss = 4.70984 (* 1 = 4.70984 loss)
I0407 22:05:20.381022 23786 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0407 22:05:25.249915 23786 solver.cpp:218] Iteration 936 (2.46472 iter/s, 4.8687s/12 iters), loss = 4.6436
I0407 22:05:25.249984 23786 solver.cpp:237]     Train net output #0: loss = 4.6436 (* 1 = 4.6436 loss)
I0407 22:05:25.249994 23786 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0407 22:05:30.245698 23786 solver.cpp:218] Iteration 948 (2.40215 iter/s, 4.99552s/12 iters), loss = 4.60979
I0407 22:05:30.245802 23786 solver.cpp:237]     Train net output #0: loss = 4.60979 (* 1 = 4.60979 loss)
I0407 22:05:30.245815 23786 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0407 22:05:35.412772 23786 solver.cpp:218] Iteration 960 (2.32254 iter/s, 5.16676s/12 iters), loss = 4.46683
I0407 22:05:35.412822 23786 solver.cpp:237]     Train net output #0: loss = 4.46683 (* 1 = 4.46683 loss)
I0407 22:05:35.412832 23786 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0407 22:05:40.428813 23786 solver.cpp:218] Iteration 972 (2.39244 iter/s, 5.01579s/12 iters), loss = 4.46815
I0407 22:05:40.428866 23786 solver.cpp:237]     Train net output #0: loss = 4.46815 (* 1 = 4.46815 loss)
I0407 22:05:40.428879 23786 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0407 22:05:45.346576 23786 solver.cpp:218] Iteration 984 (2.44026 iter/s, 4.91751s/12 iters), loss = 4.55572
I0407 22:05:45.346626 23786 solver.cpp:237]     Train net output #0: loss = 4.55572 (* 1 = 4.55572 loss)
I0407 22:05:45.346637 23786 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0407 22:05:50.331773 23786 solver.cpp:218] Iteration 996 (2.40725 iter/s, 4.98494s/12 iters), loss = 4.42718
I0407 22:05:50.331821 23786 solver.cpp:237]     Train net output #0: loss = 4.42718 (* 1 = 4.42718 loss)
I0407 22:05:50.331832 23786 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0407 22:05:55.375566 23786 solver.cpp:218] Iteration 1008 (2.37928 iter/s, 5.04354s/12 iters), loss = 4.58713
I0407 22:05:55.375612 23786 solver.cpp:237]     Train net output #0: loss = 4.58713 (* 1 = 4.58713 loss)
I0407 22:05:55.375622 23786 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0407 22:05:56.388527 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:05:59.903537 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0407 22:06:04.986636 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0407 22:06:07.384584 23786 solver.cpp:330] Iteration 1020, Testing net (#0)
I0407 22:06:07.384610 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:06:11.569427 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:06:12.002043 23786 solver.cpp:397]     Test net output #0: accuracy = 0.0637255
I0407 22:06:12.002094 23786 solver.cpp:397]     Test net output #1: loss = 4.51722 (* 1 = 4.51722 loss)
I0407 22:06:12.092571 23786 solver.cpp:218] Iteration 1020 (0.717862 iter/s, 16.7163s/12 iters), loss = 4.39112
I0407 22:06:12.092623 23786 solver.cpp:237]     Train net output #0: loss = 4.39112 (* 1 = 4.39112 loss)
I0407 22:06:12.092635 23786 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0407 22:06:16.386524 23786 solver.cpp:218] Iteration 1032 (2.79478 iter/s, 4.29373s/12 iters), loss = 4.57326
I0407 22:06:16.386567 23786 solver.cpp:237]     Train net output #0: loss = 4.57326 (* 1 = 4.57326 loss)
I0407 22:06:16.386579 23786 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0407 22:06:21.425432 23786 solver.cpp:218] Iteration 1044 (2.38159 iter/s, 5.03866s/12 iters), loss = 4.43434
I0407 22:06:21.425488 23786 solver.cpp:237]     Train net output #0: loss = 4.43434 (* 1 = 4.43434 loss)
I0407 22:06:21.425504 23786 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0407 22:06:26.474509 23786 solver.cpp:218] Iteration 1056 (2.37679 iter/s, 5.04882s/12 iters), loss = 4.63738
I0407 22:06:26.474558 23786 solver.cpp:237]     Train net output #0: loss = 4.63738 (* 1 = 4.63738 loss)
I0407 22:06:26.474570 23786 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0407 22:06:31.527307 23786 solver.cpp:218] Iteration 1068 (2.37504 iter/s, 5.05255s/12 iters), loss = 4.48874
I0407 22:06:31.527354 23786 solver.cpp:237]     Train net output #0: loss = 4.48874 (* 1 = 4.48874 loss)
I0407 22:06:31.527366 23786 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0407 22:06:36.556944 23786 solver.cpp:218] Iteration 1080 (2.38598 iter/s, 5.02938s/12 iters), loss = 4.32402
I0407 22:06:36.557063 23786 solver.cpp:237]     Train net output #0: loss = 4.32402 (* 1 = 4.32402 loss)
I0407 22:06:36.557077 23786 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0407 22:06:41.550909 23786 solver.cpp:218] Iteration 1092 (2.40305 iter/s, 4.99365s/12 iters), loss = 4.31233
I0407 22:06:41.550963 23786 solver.cpp:237]     Train net output #0: loss = 4.31233 (* 1 = 4.31233 loss)
I0407 22:06:41.550974 23786 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0407 22:06:46.660732 23786 solver.cpp:218] Iteration 1104 (2.34854 iter/s, 5.10956s/12 iters), loss = 4.41867
I0407 22:06:46.660773 23786 solver.cpp:237]     Train net output #0: loss = 4.41867 (* 1 = 4.41867 loss)
I0407 22:06:46.660784 23786 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0407 22:06:49.839078 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:06:51.678217 23786 solver.cpp:218] Iteration 1116 (2.39175 iter/s, 5.01725s/12 iters), loss = 4.44577
I0407 22:06:51.678261 23786 solver.cpp:237]     Train net output #0: loss = 4.44577 (* 1 = 4.44577 loss)
I0407 22:06:51.678270 23786 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0407 22:06:53.838060 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0407 22:06:58.304719 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0407 22:07:00.971707 23786 solver.cpp:330] Iteration 1122, Testing net (#0)
I0407 22:07:00.971733 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:07:04.961481 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:07:05.438030 23786 solver.cpp:397]     Test net output #0: accuracy = 0.0643382
I0407 22:07:05.438078 23786 solver.cpp:397]     Test net output #1: loss = 4.47461 (* 1 = 4.47461 loss)
I0407 22:07:07.332715 23786 solver.cpp:218] Iteration 1128 (0.766584 iter/s, 15.6539s/12 iters), loss = 4.36107
I0407 22:07:07.332837 23786 solver.cpp:237]     Train net output #0: loss = 4.36107 (* 1 = 4.36107 loss)
I0407 22:07:07.332850 23786 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0407 22:07:12.308935 23786 solver.cpp:218] Iteration 1140 (2.41162 iter/s, 4.9759s/12 iters), loss = 4.37779
I0407 22:07:12.308984 23786 solver.cpp:237]     Train net output #0: loss = 4.37779 (* 1 = 4.37779 loss)
I0407 22:07:12.308995 23786 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0407 22:07:17.320884 23786 solver.cpp:218] Iteration 1152 (2.3944 iter/s, 5.0117s/12 iters), loss = 4.0005
I0407 22:07:17.320927 23786 solver.cpp:237]     Train net output #0: loss = 4.0005 (* 1 = 4.0005 loss)
I0407 22:07:17.320937 23786 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0407 22:07:22.362607 23786 solver.cpp:218] Iteration 1164 (2.38025 iter/s, 5.04148s/12 iters), loss = 4.23513
I0407 22:07:22.362653 23786 solver.cpp:237]     Train net output #0: loss = 4.23513 (* 1 = 4.23513 loss)
I0407 22:07:22.362663 23786 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0407 22:07:27.381852 23786 solver.cpp:218] Iteration 1176 (2.39092 iter/s, 5.01899s/12 iters), loss = 4.26021
I0407 22:07:27.381904 23786 solver.cpp:237]     Train net output #0: loss = 4.26021 (* 1 = 4.26021 loss)
I0407 22:07:27.381915 23786 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0407 22:07:32.455646 23786 solver.cpp:218] Iteration 1188 (2.36521 iter/s, 5.07354s/12 iters), loss = 4.22197
I0407 22:07:32.455698 23786 solver.cpp:237]     Train net output #0: loss = 4.22197 (* 1 = 4.22197 loss)
I0407 22:07:32.455711 23786 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0407 22:07:37.463416 23786 solver.cpp:218] Iteration 1200 (2.3964 iter/s, 5.00752s/12 iters), loss = 4.29932
I0407 22:07:37.463536 23786 solver.cpp:237]     Train net output #0: loss = 4.29932 (* 1 = 4.29932 loss)
I0407 22:07:37.463549 23786 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0407 22:07:42.541688 23786 solver.cpp:218] Iteration 1212 (2.36316 iter/s, 5.07795s/12 iters), loss = 4.2286
I0407 22:07:42.541746 23786 solver.cpp:237]     Train net output #0: loss = 4.2286 (* 1 = 4.2286 loss)
I0407 22:07:42.541759 23786 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0407 22:07:42.819404 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:07:47.136945 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0407 22:07:51.537206 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0407 22:07:53.952566 23786 solver.cpp:330] Iteration 1224, Testing net (#0)
I0407 22:07:53.952590 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:07:57.905442 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:07:58.417202 23786 solver.cpp:397]     Test net output #0: accuracy = 0.0925245
I0407 22:07:58.417250 23786 solver.cpp:397]     Test net output #1: loss = 4.26147 (* 1 = 4.26147 loss)
I0407 22:07:58.507645 23786 solver.cpp:218] Iteration 1224 (0.751631 iter/s, 15.9653s/12 iters), loss = 4.16606
I0407 22:07:58.507695 23786 solver.cpp:237]     Train net output #0: loss = 4.16606 (* 1 = 4.16606 loss)
I0407 22:07:58.507706 23786 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0407 22:08:02.755447 23786 solver.cpp:218] Iteration 1236 (2.82514 iter/s, 4.24758s/12 iters), loss = 4.20163
I0407 22:08:02.755494 23786 solver.cpp:237]     Train net output #0: loss = 4.20163 (* 1 = 4.20163 loss)
I0407 22:08:02.755506 23786 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0407 22:08:07.743600 23786 solver.cpp:218] Iteration 1248 (2.40582 iter/s, 4.98791s/12 iters), loss = 4.03646
I0407 22:08:07.743705 23786 solver.cpp:237]     Train net output #0: loss = 4.03646 (* 1 = 4.03646 loss)
I0407 22:08:07.743716 23786 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0407 22:08:12.732645 23786 solver.cpp:218] Iteration 1260 (2.40542 iter/s, 4.98874s/12 iters), loss = 4.16232
I0407 22:08:12.732688 23786 solver.cpp:237]     Train net output #0: loss = 4.16232 (* 1 = 4.16232 loss)
I0407 22:08:12.732699 23786 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0407 22:08:17.744504 23786 solver.cpp:218] Iteration 1272 (2.39444 iter/s, 5.01161s/12 iters), loss = 3.96348
I0407 22:08:17.744555 23786 solver.cpp:237]     Train net output #0: loss = 3.96348 (* 1 = 3.96348 loss)
I0407 22:08:17.744567 23786 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0407 22:08:22.808351 23786 solver.cpp:218] Iteration 1284 (2.36986 iter/s, 5.06359s/12 iters), loss = 4.09983
I0407 22:08:22.808396 23786 solver.cpp:237]     Train net output #0: loss = 4.09983 (* 1 = 4.09983 loss)
I0407 22:08:22.808406 23786 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0407 22:08:27.696879 23786 solver.cpp:218] Iteration 1296 (2.45485 iter/s, 4.88828s/12 iters), loss = 3.76088
I0407 22:08:27.696934 23786 solver.cpp:237]     Train net output #0: loss = 3.76088 (* 1 = 3.76088 loss)
I0407 22:08:27.696945 23786 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0407 22:08:32.680559 23786 solver.cpp:218] Iteration 1308 (2.40798 iter/s, 4.98343s/12 iters), loss = 4.17589
I0407 22:08:32.680601 23786 solver.cpp:237]     Train net output #0: loss = 4.17589 (* 1 = 4.17589 loss)
I0407 22:08:32.680610 23786 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0407 22:08:35.157514 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:08:37.633042 23786 solver.cpp:218] Iteration 1320 (2.42315 iter/s, 4.95224s/12 iters), loss = 3.97701
I0407 22:08:37.633085 23786 solver.cpp:237]     Train net output #0: loss = 3.97701 (* 1 = 3.97701 loss)
I0407 22:08:37.633093 23786 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0407 22:08:39.668781 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0407 22:08:45.674626 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0407 22:08:54.434347 23786 solver.cpp:330] Iteration 1326, Testing net (#0)
I0407 22:08:54.434372 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:08:58.344992 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:08:58.901587 23786 solver.cpp:397]     Test net output #0: accuracy = 0.10723
I0407 22:08:58.901643 23786 solver.cpp:397]     Test net output #1: loss = 4.04921 (* 1 = 4.04921 loss)
I0407 22:09:00.905838 23786 solver.cpp:218] Iteration 1332 (0.515644 iter/s, 23.2719s/12 iters), loss = 3.71774
I0407 22:09:00.905905 23786 solver.cpp:237]     Train net output #0: loss = 3.71774 (* 1 = 3.71774 loss)
I0407 22:09:00.905927 23786 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0407 22:09:05.964581 23786 solver.cpp:218] Iteration 1344 (2.37225 iter/s, 5.05848s/12 iters), loss = 3.9405
I0407 22:09:05.964620 23786 solver.cpp:237]     Train net output #0: loss = 3.9405 (* 1 = 3.9405 loss)
I0407 22:09:05.964629 23786 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0407 22:09:10.988162 23786 solver.cpp:218] Iteration 1356 (2.38885 iter/s, 5.02334s/12 iters), loss = 4.00653
I0407 22:09:10.988286 23786 solver.cpp:237]     Train net output #0: loss = 4.00653 (* 1 = 4.00653 loss)
I0407 22:09:10.988299 23786 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0407 22:09:15.996645 23786 solver.cpp:218] Iteration 1368 (2.39609 iter/s, 5.00816s/12 iters), loss = 3.88637
I0407 22:09:15.996699 23786 solver.cpp:237]     Train net output #0: loss = 3.88637 (* 1 = 3.88637 loss)
I0407 22:09:15.996711 23786 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0407 22:09:17.202152 23786 blocking_queue.cpp:49] Waiting for data
I0407 22:09:21.008925 23786 solver.cpp:218] Iteration 1380 (2.39424 iter/s, 5.01202s/12 iters), loss = 3.82025
I0407 22:09:21.008977 23786 solver.cpp:237]     Train net output #0: loss = 3.82025 (* 1 = 3.82025 loss)
I0407 22:09:21.008989 23786 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0407 22:09:26.027150 23786 solver.cpp:218] Iteration 1392 (2.39141 iter/s, 5.01797s/12 iters), loss = 3.98652
I0407 22:09:26.027199 23786 solver.cpp:237]     Train net output #0: loss = 3.98652 (* 1 = 3.98652 loss)
I0407 22:09:26.027209 23786 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0407 22:09:31.050176 23786 solver.cpp:218] Iteration 1404 (2.38912 iter/s, 5.02277s/12 iters), loss = 3.91484
I0407 22:09:31.050228 23786 solver.cpp:237]     Train net output #0: loss = 3.91484 (* 1 = 3.91484 loss)
I0407 22:09:31.050241 23786 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0407 22:09:35.659397 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:09:36.009871 23786 solver.cpp:218] Iteration 1416 (2.41963 iter/s, 4.95944s/12 iters), loss = 3.59869
I0407 22:09:36.009922 23786 solver.cpp:237]     Train net output #0: loss = 3.59869 (* 1 = 3.59869 loss)
I0407 22:09:36.009933 23786 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0407 22:09:40.511319 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0407 22:09:44.834292 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0407 22:09:48.953255 23786 solver.cpp:330] Iteration 1428, Testing net (#0)
I0407 22:09:48.953281 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:09:52.813295 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:09:53.404350 23786 solver.cpp:397]     Test net output #0: accuracy = 0.127451
I0407 22:09:53.404398 23786 solver.cpp:397]     Test net output #1: loss = 3.92408 (* 1 = 3.92408 loss)
I0407 22:09:53.494649 23786 solver.cpp:218] Iteration 1428 (0.68634 iter/s, 17.4841s/12 iters), loss = 3.6912
I0407 22:09:53.494699 23786 solver.cpp:237]     Train net output #0: loss = 3.6912 (* 1 = 3.6912 loss)
I0407 22:09:53.494710 23786 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0407 22:09:58.027424 23786 solver.cpp:218] Iteration 1440 (2.64752 iter/s, 4.53254s/12 iters), loss = 3.78989
I0407 22:09:58.027474 23786 solver.cpp:237]     Train net output #0: loss = 3.78989 (* 1 = 3.78989 loss)
I0407 22:09:58.027487 23786 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0407 22:10:02.929898 23786 solver.cpp:218] Iteration 1452 (2.44787 iter/s, 4.90222s/12 iters), loss = 4.12109
I0407 22:10:02.929949 23786 solver.cpp:237]     Train net output #0: loss = 4.12109 (* 1 = 4.12109 loss)
I0407 22:10:02.929973 23786 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0407 22:10:07.964593 23786 solver.cpp:218] Iteration 1464 (2.38358 iter/s, 5.03444s/12 iters), loss = 3.68653
I0407 22:10:07.964648 23786 solver.cpp:237]     Train net output #0: loss = 3.68653 (* 1 = 3.68653 loss)
I0407 22:10:07.964661 23786 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0407 22:10:12.919658 23786 solver.cpp:218] Iteration 1476 (2.42189 iter/s, 4.95481s/12 iters), loss = 3.8036
I0407 22:10:12.919713 23786 solver.cpp:237]     Train net output #0: loss = 3.8036 (* 1 = 3.8036 loss)
I0407 22:10:12.919725 23786 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0407 22:10:17.907408 23786 solver.cpp:218] Iteration 1488 (2.40602 iter/s, 4.9875s/12 iters), loss = 3.8072
I0407 22:10:17.907536 23786 solver.cpp:237]     Train net output #0: loss = 3.8072 (* 1 = 3.8072 loss)
I0407 22:10:17.907547 23786 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0407 22:10:22.928998 23786 solver.cpp:218] Iteration 1500 (2.38984 iter/s, 5.02126s/12 iters), loss = 3.43024
I0407 22:10:22.929055 23786 solver.cpp:237]     Train net output #0: loss = 3.43024 (* 1 = 3.43024 loss)
I0407 22:10:22.929067 23786 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0407 22:10:28.064054 23786 solver.cpp:218] Iteration 1512 (2.337 iter/s, 5.13479s/12 iters), loss = 3.54963
I0407 22:10:28.064105 23786 solver.cpp:237]     Train net output #0: loss = 3.54963 (* 1 = 3.54963 loss)
I0407 22:10:28.064116 23786 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0407 22:10:29.979087 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:10:33.367986 23786 solver.cpp:218] Iteration 1524 (2.26258 iter/s, 5.30367s/12 iters), loss = 3.60958
I0407 22:10:33.368037 23786 solver.cpp:237]     Train net output #0: loss = 3.60958 (* 1 = 3.60958 loss)
I0407 22:10:33.368048 23786 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0407 22:10:35.420003 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0407 22:10:39.479702 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0407 22:10:43.942764 23786 solver.cpp:330] Iteration 1530, Testing net (#0)
I0407 22:10:43.942788 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:10:47.777356 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:10:48.413146 23786 solver.cpp:397]     Test net output #0: accuracy = 0.137868
I0407 22:10:48.413319 23786 solver.cpp:397]     Test net output #1: loss = 3.82993 (* 1 = 3.82993 loss)
I0407 22:10:50.310361 23786 solver.cpp:218] Iteration 1536 (0.708313 iter/s, 16.9417s/12 iters), loss = 3.42288
I0407 22:10:50.310411 23786 solver.cpp:237]     Train net output #0: loss = 3.42288 (* 1 = 3.42288 loss)
I0407 22:10:50.310432 23786 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0407 22:10:55.335743 23786 solver.cpp:218] Iteration 1548 (2.388 iter/s, 5.02513s/12 iters), loss = 3.14231
I0407 22:10:55.335799 23786 solver.cpp:237]     Train net output #0: loss = 3.14231 (* 1 = 3.14231 loss)
I0407 22:10:55.335811 23786 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0407 22:11:00.316884 23786 solver.cpp:218] Iteration 1560 (2.40921 iter/s, 4.98088s/12 iters), loss = 3.65536
I0407 22:11:00.316941 23786 solver.cpp:237]     Train net output #0: loss = 3.65536 (* 1 = 3.65536 loss)
I0407 22:11:00.316953 23786 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0407 22:11:05.340257 23786 solver.cpp:218] Iteration 1572 (2.38896 iter/s, 5.02311s/12 iters), loss = 3.67146
I0407 22:11:05.340312 23786 solver.cpp:237]     Train net output #0: loss = 3.67146 (* 1 = 3.67146 loss)
I0407 22:11:05.340324 23786 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0407 22:11:10.380863 23786 solver.cpp:218] Iteration 1584 (2.38079 iter/s, 5.04035s/12 iters), loss = 3.41612
I0407 22:11:10.380911 23786 solver.cpp:237]     Train net output #0: loss = 3.41612 (* 1 = 3.41612 loss)
I0407 22:11:10.380920 23786 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0407 22:11:15.378047 23786 solver.cpp:218] Iteration 1596 (2.40147 iter/s, 4.99693s/12 iters), loss = 3.66298
I0407 22:11:15.378104 23786 solver.cpp:237]     Train net output #0: loss = 3.66298 (* 1 = 3.66298 loss)
I0407 22:11:15.378118 23786 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0407 22:11:20.295327 23786 solver.cpp:218] Iteration 1608 (2.4405 iter/s, 4.91702s/12 iters), loss = 3.50817
I0407 22:11:20.295456 23786 solver.cpp:237]     Train net output #0: loss = 3.50817 (* 1 = 3.50817 loss)
I0407 22:11:20.295469 23786 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0407 22:11:24.202977 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:11:25.298388 23786 solver.cpp:218] Iteration 1620 (2.39869 iter/s, 5.00273s/12 iters), loss = 3.3837
I0407 22:11:25.298434 23786 solver.cpp:237]     Train net output #0: loss = 3.3837 (* 1 = 3.3837 loss)
I0407 22:11:25.298444 23786 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0407 22:11:29.847472 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0407 22:11:32.901878 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0407 22:11:42.485123 23786 solver.cpp:330] Iteration 1632, Testing net (#0)
I0407 22:11:42.485157 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:11:46.397130 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:11:47.066624 23786 solver.cpp:397]     Test net output #0: accuracy = 0.146446
I0407 22:11:47.066659 23786 solver.cpp:397]     Test net output #1: loss = 3.80548 (* 1 = 3.80548 loss)
I0407 22:11:47.156975 23786 solver.cpp:218] Iteration 1632 (0.549006 iter/s, 21.8577s/12 iters), loss = 3.4954
I0407 22:11:47.157028 23786 solver.cpp:237]     Train net output #0: loss = 3.4954 (* 1 = 3.4954 loss)
I0407 22:11:47.157039 23786 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0407 22:11:51.699971 23786 solver.cpp:218] Iteration 1644 (2.64157 iter/s, 4.54276s/12 iters), loss = 3.52577
I0407 22:11:51.700124 23786 solver.cpp:237]     Train net output #0: loss = 3.52577 (* 1 = 3.52577 loss)
I0407 22:11:51.700137 23786 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0407 22:11:56.685653 23786 solver.cpp:218] Iteration 1656 (2.40707 iter/s, 4.98532s/12 iters), loss = 3.54936
I0407 22:11:56.685710 23786 solver.cpp:237]     Train net output #0: loss = 3.54936 (* 1 = 3.54936 loss)
I0407 22:11:56.685722 23786 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0407 22:12:01.694958 23786 solver.cpp:218] Iteration 1668 (2.39566 iter/s, 5.00905s/12 iters), loss = 3.06731
I0407 22:12:01.694998 23786 solver.cpp:237]     Train net output #0: loss = 3.06731 (* 1 = 3.06731 loss)
I0407 22:12:01.695008 23786 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0407 22:12:06.659729 23786 solver.cpp:218] Iteration 1680 (2.41715 iter/s, 4.96453s/12 iters), loss = 3.52589
I0407 22:12:06.659773 23786 solver.cpp:237]     Train net output #0: loss = 3.52589 (* 1 = 3.52589 loss)
I0407 22:12:06.659783 23786 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0407 22:12:11.747195 23786 solver.cpp:218] Iteration 1692 (2.35885 iter/s, 5.08722s/12 iters), loss = 3.32482
I0407 22:12:11.747232 23786 solver.cpp:237]     Train net output #0: loss = 3.32482 (* 1 = 3.32482 loss)
I0407 22:12:11.747241 23786 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0407 22:12:16.683559 23786 solver.cpp:218] Iteration 1704 (2.43106 iter/s, 4.93612s/12 iters), loss = 3.25334
I0407 22:12:16.683614 23786 solver.cpp:237]     Train net output #0: loss = 3.25334 (* 1 = 3.25334 loss)
I0407 22:12:16.683624 23786 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0407 22:12:21.742493 23786 solver.cpp:218] Iteration 1716 (2.37216 iter/s, 5.05867s/12 iters), loss = 3.50259
I0407 22:12:21.742619 23786 solver.cpp:237]     Train net output #0: loss = 3.50259 (* 1 = 3.50259 loss)
I0407 22:12:21.742632 23786 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0407 22:12:22.811646 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:12:26.797976 23786 solver.cpp:218] Iteration 1728 (2.37383 iter/s, 5.05513s/12 iters), loss = 3.21193
I0407 22:12:26.798030 23786 solver.cpp:237]     Train net output #0: loss = 3.21193 (* 1 = 3.21193 loss)
I0407 22:12:26.798041 23786 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0407 22:12:28.851555 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0407 22:12:31.814559 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0407 22:12:34.716533 23786 solver.cpp:330] Iteration 1734, Testing net (#0)
I0407 22:12:34.716558 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:12:38.442822 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:12:39.148358 23786 solver.cpp:397]     Test net output #0: accuracy = 0.168505
I0407 22:12:39.148407 23786 solver.cpp:397]     Test net output #1: loss = 3.68098 (* 1 = 3.68098 loss)
I0407 22:12:40.984836 23786 solver.cpp:218] Iteration 1740 (0.845889 iter/s, 14.1863s/12 iters), loss = 3.28576
I0407 22:12:40.984894 23786 solver.cpp:237]     Train net output #0: loss = 3.28576 (* 1 = 3.28576 loss)
I0407 22:12:40.984905 23786 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0407 22:12:45.933521 23786 solver.cpp:218] Iteration 1752 (2.42501 iter/s, 4.94843s/12 iters), loss = 3.27274
I0407 22:12:45.933558 23786 solver.cpp:237]     Train net output #0: loss = 3.27274 (* 1 = 3.27274 loss)
I0407 22:12:45.933568 23786 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0407 22:12:51.143165 23786 solver.cpp:218] Iteration 1764 (2.30353 iter/s, 5.2094s/12 iters), loss = 3.32463
I0407 22:12:51.143209 23786 solver.cpp:237]     Train net output #0: loss = 3.32463 (* 1 = 3.32463 loss)
I0407 22:12:51.143219 23786 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0407 22:12:56.206302 23786 solver.cpp:218] Iteration 1776 (2.37019 iter/s, 5.06289s/12 iters), loss = 3.39274
I0407 22:12:56.206454 23786 solver.cpp:237]     Train net output #0: loss = 3.39274 (* 1 = 3.39274 loss)
I0407 22:12:56.206467 23786 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0407 22:13:01.165419 23786 solver.cpp:218] Iteration 1788 (2.41996 iter/s, 4.95877s/12 iters), loss = 3.04741
I0407 22:13:01.165477 23786 solver.cpp:237]     Train net output #0: loss = 3.04741 (* 1 = 3.04741 loss)
I0407 22:13:01.165489 23786 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0407 22:13:06.176265 23786 solver.cpp:218] Iteration 1800 (2.39493 iter/s, 5.01058s/12 iters), loss = 3.09956
I0407 22:13:06.176317 23786 solver.cpp:237]     Train net output #0: loss = 3.09956 (* 1 = 3.09956 loss)
I0407 22:13:06.176329 23786 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0407 22:13:11.161154 23786 solver.cpp:218] Iteration 1812 (2.4074 iter/s, 4.98463s/12 iters), loss = 3.27701
I0407 22:13:11.161201 23786 solver.cpp:237]     Train net output #0: loss = 3.27701 (* 1 = 3.27701 loss)
I0407 22:13:11.161213 23786 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0407 22:13:14.368165 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:13:16.172940 23786 solver.cpp:218] Iteration 1824 (2.39448 iter/s, 5.01154s/12 iters), loss = 3.51384
I0407 22:13:16.172993 23786 solver.cpp:237]     Train net output #0: loss = 3.51384 (* 1 = 3.51384 loss)
I0407 22:13:16.173005 23786 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0407 22:13:20.725888 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0407 22:13:23.778801 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0407 22:13:26.124943 23786 solver.cpp:330] Iteration 1836, Testing net (#0)
I0407 22:13:26.124967 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:13:29.846311 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:13:30.595918 23786 solver.cpp:397]     Test net output #0: accuracy = 0.208333
I0407 22:13:30.595963 23786 solver.cpp:397]     Test net output #1: loss = 3.52489 (* 1 = 3.52489 loss)
I0407 22:13:30.686331 23786 solver.cpp:218] Iteration 1836 (0.826858 iter/s, 14.5128s/12 iters), loss = 3.36993
I0407 22:13:30.686370 23786 solver.cpp:237]     Train net output #0: loss = 3.36993 (* 1 = 3.36993 loss)
I0407 22:13:30.686381 23786 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0407 22:13:34.762360 23786 solver.cpp:218] Iteration 1848 (2.9442 iter/s, 4.07582s/12 iters), loss = 3.12182
I0407 22:13:34.762406 23786 solver.cpp:237]     Train net output #0: loss = 3.12182 (* 1 = 3.12182 loss)
I0407 22:13:34.762418 23786 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0407 22:13:39.710197 23786 solver.cpp:218] Iteration 1860 (2.42542 iter/s, 4.94759s/12 iters), loss = 3.03385
I0407 22:13:39.710244 23786 solver.cpp:237]     Train net output #0: loss = 3.03385 (* 1 = 3.03385 loss)
I0407 22:13:39.710256 23786 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0407 22:13:44.778959 23786 solver.cpp:218] Iteration 1872 (2.36756 iter/s, 5.06851s/12 iters), loss = 3.05061
I0407 22:13:44.779002 23786 solver.cpp:237]     Train net output #0: loss = 3.05061 (* 1 = 3.05061 loss)
I0407 22:13:44.779012 23786 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0407 22:13:49.793876 23786 solver.cpp:218] Iteration 1884 (2.39298 iter/s, 5.01467s/12 iters), loss = 3.16207
I0407 22:13:49.793915 23786 solver.cpp:237]     Train net output #0: loss = 3.16207 (* 1 = 3.16207 loss)
I0407 22:13:49.793923 23786 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0407 22:13:54.801506 23786 solver.cpp:218] Iteration 1896 (2.39646 iter/s, 5.00739s/12 iters), loss = 3.21068
I0407 22:13:54.801550 23786 solver.cpp:237]     Train net output #0: loss = 3.21068 (* 1 = 3.21068 loss)
I0407 22:13:54.801559 23786 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0407 22:13:59.754452 23786 solver.cpp:218] Iteration 1908 (2.42292 iter/s, 4.95269s/12 iters), loss = 3.10468
I0407 22:13:59.754504 23786 solver.cpp:237]     Train net output #0: loss = 3.10468 (* 1 = 3.10468 loss)
I0407 22:13:59.754515 23786 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0407 22:14:04.774567 23786 solver.cpp:218] Iteration 1920 (2.39051 iter/s, 5.01986s/12 iters), loss = 3.00013
I0407 22:14:04.774677 23786 solver.cpp:237]     Train net output #0: loss = 3.00013 (* 1 = 3.00013 loss)
I0407 22:14:04.774688 23786 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0407 22:14:05.081162 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:14:09.743053 23786 solver.cpp:218] Iteration 1932 (2.41537 iter/s, 4.96818s/12 iters), loss = 2.96929
I0407 22:14:09.743096 23786 solver.cpp:237]     Train net output #0: loss = 2.96929 (* 1 = 2.96929 loss)
I0407 22:14:09.743105 23786 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0407 22:14:11.776726 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0407 22:14:14.747722 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0407 22:14:17.089128 23786 solver.cpp:330] Iteration 1938, Testing net (#0)
I0407 22:14:17.089148 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:14:20.761945 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:14:21.546911 23786 solver.cpp:397]     Test net output #0: accuracy = 0.204044
I0407 22:14:21.546957 23786 solver.cpp:397]     Test net output #1: loss = 3.46448 (* 1 = 3.46448 loss)
I0407 22:14:23.418092 23786 solver.cpp:218] Iteration 1944 (0.877548 iter/s, 13.6745s/12 iters), loss = 2.95765
I0407 22:14:23.418133 23786 solver.cpp:237]     Train net output #0: loss = 2.95765 (* 1 = 2.95765 loss)
I0407 22:14:23.418140 23786 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0407 22:14:28.409921 23786 solver.cpp:218] Iteration 1956 (2.40405 iter/s, 4.99159s/12 iters), loss = 3.17178
I0407 22:14:28.409976 23786 solver.cpp:237]     Train net output #0: loss = 3.17178 (* 1 = 3.17178 loss)
I0407 22:14:28.409988 23786 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0407 22:14:33.353783 23786 solver.cpp:218] Iteration 1968 (2.42738 iter/s, 4.94361s/12 iters), loss = 3.4126
I0407 22:14:33.353826 23786 solver.cpp:237]     Train net output #0: loss = 3.4126 (* 1 = 3.4126 loss)
I0407 22:14:33.353837 23786 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0407 22:14:38.718143 23786 solver.cpp:218] Iteration 1980 (2.2371 iter/s, 5.3641s/12 iters), loss = 2.9535
I0407 22:14:38.718231 23786 solver.cpp:237]     Train net output #0: loss = 2.9535 (* 1 = 2.9535 loss)
I0407 22:14:38.718243 23786 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0407 22:14:44.154052 23786 solver.cpp:218] Iteration 1992 (2.20767 iter/s, 5.4356s/12 iters), loss = 2.97601
I0407 22:14:44.154093 23786 solver.cpp:237]     Train net output #0: loss = 2.97601 (* 1 = 2.97601 loss)
I0407 22:14:44.154100 23786 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0407 22:14:49.119464 23786 solver.cpp:218] Iteration 2004 (2.41684 iter/s, 4.96517s/12 iters), loss = 2.44536
I0407 22:14:49.119514 23786 solver.cpp:237]     Train net output #0: loss = 2.44536 (* 1 = 2.44536 loss)
I0407 22:14:49.119526 23786 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0407 22:14:54.208185 23786 solver.cpp:218] Iteration 2016 (2.35827 iter/s, 5.08847s/12 iters), loss = 2.97795
I0407 22:14:54.208233 23786 solver.cpp:237]     Train net output #0: loss = 2.97795 (* 1 = 2.97795 loss)
I0407 22:14:54.208245 23786 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0407 22:14:56.746433 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:14:59.200103 23786 solver.cpp:218] Iteration 2028 (2.40401 iter/s, 4.99167s/12 iters), loss = 3.10528
I0407 22:14:59.200158 23786 solver.cpp:237]     Train net output #0: loss = 3.10528 (* 1 = 3.10528 loss)
I0407 22:14:59.200170 23786 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0407 22:15:03.780416 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0407 22:15:06.804522 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0407 22:15:10.697381 23786 solver.cpp:330] Iteration 2040, Testing net (#0)
I0407 22:15:10.697468 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:15:14.264284 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:15:15.093636 23786 solver.cpp:397]     Test net output #0: accuracy = 0.213848
I0407 22:15:15.093673 23786 solver.cpp:397]     Test net output #1: loss = 3.40777 (* 1 = 3.40777 loss)
I0407 22:15:15.183887 23786 solver.cpp:218] Iteration 2040 (0.750792 iter/s, 15.9831s/12 iters), loss = 3.02911
I0407 22:15:15.183929 23786 solver.cpp:237]     Train net output #0: loss = 3.02911 (* 1 = 3.02911 loss)
I0407 22:15:15.183938 23786 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0407 22:15:19.581112 23786 solver.cpp:218] Iteration 2052 (2.72913 iter/s, 4.39701s/12 iters), loss = 2.77079
I0407 22:15:19.581156 23786 solver.cpp:237]     Train net output #0: loss = 2.77079 (* 1 = 2.77079 loss)
I0407 22:15:19.581164 23786 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0407 22:15:21.209437 23786 blocking_queue.cpp:49] Waiting for data
I0407 22:15:24.546878 23786 solver.cpp:218] Iteration 2064 (2.41666 iter/s, 4.96552s/12 iters), loss = 2.91579
I0407 22:15:24.546929 23786 solver.cpp:237]     Train net output #0: loss = 2.91579 (* 1 = 2.91579 loss)
I0407 22:15:24.546941 23786 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0407 22:15:29.953049 23786 solver.cpp:218] Iteration 2076 (2.2198 iter/s, 5.4059s/12 iters), loss = 2.95067
I0407 22:15:29.953095 23786 solver.cpp:237]     Train net output #0: loss = 2.95067 (* 1 = 2.95067 loss)
I0407 22:15:29.953105 23786 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0407 22:15:34.997943 23786 solver.cpp:218] Iteration 2088 (2.37876 iter/s, 5.04464s/12 iters), loss = 2.78684
I0407 22:15:34.998005 23786 solver.cpp:237]     Train net output #0: loss = 2.78684 (* 1 = 2.78684 loss)
I0407 22:15:34.998016 23786 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0407 22:15:39.908658 23786 solver.cpp:218] Iteration 2100 (2.44376 iter/s, 4.91046s/12 iters), loss = 2.76463
I0407 22:15:39.908707 23786 solver.cpp:237]     Train net output #0: loss = 2.76463 (* 1 = 2.76463 loss)
I0407 22:15:39.908720 23786 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0407 22:15:44.934485 23786 solver.cpp:218] Iteration 2112 (2.38779 iter/s, 5.02557s/12 iters), loss = 2.97765
I0407 22:15:44.934595 23786 solver.cpp:237]     Train net output #0: loss = 2.97765 (* 1 = 2.97765 loss)
I0407 22:15:44.934607 23786 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0407 22:15:49.532140 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:15:49.849052 23786 solver.cpp:218] Iteration 2124 (2.44187 iter/s, 4.91426s/12 iters), loss = 2.314
I0407 22:15:49.849107 23786 solver.cpp:237]     Train net output #0: loss = 2.314 (* 1 = 2.314 loss)
I0407 22:15:49.849119 23786 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0407 22:15:54.792508 23786 solver.cpp:218] Iteration 2136 (2.42757 iter/s, 4.94321s/12 iters), loss = 2.54644
I0407 22:15:54.792551 23786 solver.cpp:237]     Train net output #0: loss = 2.54644 (* 1 = 2.54644 loss)
I0407 22:15:54.792562 23786 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0407 22:15:56.809203 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0407 22:15:59.821826 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0407 22:16:02.201578 23786 solver.cpp:330] Iteration 2142, Testing net (#0)
I0407 22:16:02.201604 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:16:05.855003 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:16:06.739576 23786 solver.cpp:397]     Test net output #0: accuracy = 0.238971
I0407 22:16:06.739626 23786 solver.cpp:397]     Test net output #1: loss = 3.2057 (* 1 = 3.2057 loss)
I0407 22:16:08.692209 23786 solver.cpp:218] Iteration 2148 (0.863364 iter/s, 13.8991s/12 iters), loss = 2.62362
I0407 22:16:08.692266 23786 solver.cpp:237]     Train net output #0: loss = 2.62362 (* 1 = 2.62362 loss)
I0407 22:16:08.692278 23786 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0407 22:16:14.130918 23786 solver.cpp:218] Iteration 2160 (2.20652 iter/s, 5.43844s/12 iters), loss = 2.91224
I0407 22:16:14.130964 23786 solver.cpp:237]     Train net output #0: loss = 2.91224 (* 1 = 2.91224 loss)
I0407 22:16:14.130975 23786 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0407 22:16:19.242683 23786 solver.cpp:218] Iteration 2172 (2.34764 iter/s, 5.11152s/12 iters), loss = 2.84007
I0407 22:16:19.242817 23786 solver.cpp:237]     Train net output #0: loss = 2.84007 (* 1 = 2.84007 loss)
I0407 22:16:19.242830 23786 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0407 22:16:24.300310 23786 solver.cpp:218] Iteration 2184 (2.37281 iter/s, 5.05729s/12 iters), loss = 2.86887
I0407 22:16:24.300364 23786 solver.cpp:237]     Train net output #0: loss = 2.86887 (* 1 = 2.86887 loss)
I0407 22:16:24.300375 23786 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0407 22:16:29.426024 23786 solver.cpp:218] Iteration 2196 (2.34126 iter/s, 5.12546s/12 iters), loss = 2.65682
I0407 22:16:29.426070 23786 solver.cpp:237]     Train net output #0: loss = 2.65682 (* 1 = 2.65682 loss)
I0407 22:16:29.426082 23786 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0407 22:16:34.394238 23786 solver.cpp:218] Iteration 2208 (2.41548 iter/s, 4.96796s/12 iters), loss = 2.32587
I0407 22:16:34.394292 23786 solver.cpp:237]     Train net output #0: loss = 2.32587 (* 1 = 2.32587 loss)
I0407 22:16:34.394304 23786 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0407 22:16:39.457151 23786 solver.cpp:218] Iteration 2220 (2.3703 iter/s, 5.06265s/12 iters), loss = 2.23747
I0407 22:16:39.457202 23786 solver.cpp:237]     Train net output #0: loss = 2.23747 (* 1 = 2.23747 loss)
I0407 22:16:39.457216 23786 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0407 22:16:41.292692 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:16:44.526929 23786 solver.cpp:218] Iteration 2232 (2.36709 iter/s, 5.06952s/12 iters), loss = 2.6102
I0407 22:16:44.526975 23786 solver.cpp:237]     Train net output #0: loss = 2.6102 (* 1 = 2.6102 loss)
I0407 22:16:44.526984 23786 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0407 22:16:49.095949 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0407 22:16:52.106859 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0407 22:16:54.448370 23786 solver.cpp:330] Iteration 2244, Testing net (#0)
I0407 22:16:54.448390 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:16:57.985795 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:16:58.912636 23786 solver.cpp:397]     Test net output #0: accuracy = 0.246324
I0407 22:16:58.912686 23786 solver.cpp:397]     Test net output #1: loss = 3.22827 (* 1 = 3.22827 loss)
I0407 22:16:59.003309 23786 solver.cpp:218] Iteration 2244 (0.828971 iter/s, 14.4758s/12 iters), loss = 2.56211
I0407 22:16:59.003360 23786 solver.cpp:237]     Train net output #0: loss = 2.56211 (* 1 = 2.56211 loss)
I0407 22:16:59.003371 23786 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0407 22:17:03.172950 23786 solver.cpp:218] Iteration 2256 (2.8781 iter/s, 4.16942s/12 iters), loss = 2.26549
I0407 22:17:03.172996 23786 solver.cpp:237]     Train net output #0: loss = 2.26549 (* 1 = 2.26549 loss)
I0407 22:17:03.173007 23786 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0407 22:17:08.166457 23786 solver.cpp:218] Iteration 2268 (2.40324 iter/s, 4.99326s/12 iters), loss = 2.79132
I0407 22:17:08.166507 23786 solver.cpp:237]     Train net output #0: loss = 2.79132 (* 1 = 2.79132 loss)
I0407 22:17:08.166520 23786 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0407 22:17:13.233510 23786 solver.cpp:218] Iteration 2280 (2.36836 iter/s, 5.0668s/12 iters), loss = 2.60719
I0407 22:17:13.233553 23786 solver.cpp:237]     Train net output #0: loss = 2.60719 (* 1 = 2.60719 loss)
I0407 22:17:13.233564 23786 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0407 22:17:18.278384 23786 solver.cpp:218] Iteration 2292 (2.37877 iter/s, 5.04463s/12 iters), loss = 2.64403
I0407 22:17:18.278435 23786 solver.cpp:237]     Train net output #0: loss = 2.64403 (* 1 = 2.64403 loss)
I0407 22:17:18.278446 23786 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0407 22:17:23.285670 23786 solver.cpp:218] Iteration 2304 (2.39663 iter/s, 5.00703s/12 iters), loss = 2.41741
I0407 22:17:23.285811 23786 solver.cpp:237]     Train net output #0: loss = 2.41741 (* 1 = 2.41741 loss)
I0407 22:17:23.285825 23786 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0407 22:17:28.313928 23786 solver.cpp:218] Iteration 2316 (2.38667 iter/s, 5.02792s/12 iters), loss = 2.38152
I0407 22:17:28.313988 23786 solver.cpp:237]     Train net output #0: loss = 2.38152 (* 1 = 2.38152 loss)
I0407 22:17:28.314000 23786 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0407 22:17:32.563562 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:17:33.614035 23786 solver.cpp:218] Iteration 2328 (2.26422 iter/s, 5.29984s/12 iters), loss = 2.26613
I0407 22:17:33.614070 23786 solver.cpp:237]     Train net output #0: loss = 2.26613 (* 1 = 2.26613 loss)
I0407 22:17:33.614078 23786 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0407 22:17:38.603598 23786 solver.cpp:218] Iteration 2340 (2.40514 iter/s, 4.98932s/12 iters), loss = 2.32617
I0407 22:17:38.603646 23786 solver.cpp:237]     Train net output #0: loss = 2.32617 (* 1 = 2.32617 loss)
I0407 22:17:38.603658 23786 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0407 22:17:40.634774 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0407 22:17:43.705533 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0407 22:17:46.055186 23786 solver.cpp:330] Iteration 2346, Testing net (#0)
I0407 22:17:46.055215 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:17:49.654634 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:17:50.631373 23786 solver.cpp:397]     Test net output #0: accuracy = 0.251838
I0407 22:17:50.631423 23786 solver.cpp:397]     Test net output #1: loss = 3.20574 (* 1 = 3.20574 loss)
I0407 22:17:52.608881 23786 solver.cpp:218] Iteration 2352 (0.856856 iter/s, 14.0047s/12 iters), loss = 2.52396
I0407 22:17:52.608939 23786 solver.cpp:237]     Train net output #0: loss = 2.52396 (* 1 = 2.52396 loss)
I0407 22:17:52.608952 23786 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0407 22:17:57.791523 23786 solver.cpp:218] Iteration 2364 (2.31554 iter/s, 5.18238s/12 iters), loss = 2.30235
I0407 22:17:57.791623 23786 solver.cpp:237]     Train net output #0: loss = 2.30235 (* 1 = 2.30235 loss)
I0407 22:17:57.791635 23786 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0407 22:18:02.793870 23786 solver.cpp:218] Iteration 2376 (2.39902 iter/s, 5.00205s/12 iters), loss = 2.46126
I0407 22:18:02.793910 23786 solver.cpp:237]     Train net output #0: loss = 2.46126 (* 1 = 2.46126 loss)
I0407 22:18:02.793920 23786 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0407 22:18:07.806998 23786 solver.cpp:218] Iteration 2388 (2.39383 iter/s, 5.01288s/12 iters), loss = 2.27926
I0407 22:18:07.807049 23786 solver.cpp:237]     Train net output #0: loss = 2.27926 (* 1 = 2.27926 loss)
I0407 22:18:07.807061 23786 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0407 22:18:12.802353 23786 solver.cpp:218] Iteration 2400 (2.40235 iter/s, 4.9951s/12 iters), loss = 2.25576
I0407 22:18:12.802402 23786 solver.cpp:237]     Train net output #0: loss = 2.25576 (* 1 = 2.25576 loss)
I0407 22:18:12.802413 23786 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0407 22:18:18.119491 23786 solver.cpp:218] Iteration 2412 (2.25697 iter/s, 5.31687s/12 iters), loss = 2.11736
I0407 22:18:18.119541 23786 solver.cpp:237]     Train net output #0: loss = 2.11736 (* 1 = 2.11736 loss)
I0407 22:18:18.119551 23786 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0407 22:18:23.205686 23786 solver.cpp:218] Iteration 2424 (2.35945 iter/s, 5.08594s/12 iters), loss = 2.89624
I0407 22:18:23.205737 23786 solver.cpp:237]     Train net output #0: loss = 2.89624 (* 1 = 2.89624 loss)
I0407 22:18:23.205751 23786 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0407 22:18:24.288250 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:18:28.223862 23786 solver.cpp:218] Iteration 2436 (2.39143 iter/s, 5.01792s/12 iters), loss = 2.31408
I0407 22:18:28.224005 23786 solver.cpp:237]     Train net output #0: loss = 2.31408 (* 1 = 2.31408 loss)
I0407 22:18:28.224020 23786 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0407 22:18:32.796243 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0407 22:18:35.817720 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0407 22:18:38.160315 23786 solver.cpp:330] Iteration 2448, Testing net (#0)
I0407 22:18:38.160337 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:18:41.626766 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:18:42.603940 23786 solver.cpp:397]     Test net output #0: accuracy = 0.270833
I0407 22:18:42.603989 23786 solver.cpp:397]     Test net output #1: loss = 3.1296 (* 1 = 3.1296 loss)
I0407 22:18:42.694262 23786 solver.cpp:218] Iteration 2448 (0.829319 iter/s, 14.4697s/12 iters), loss = 2.35579
I0407 22:18:42.694310 23786 solver.cpp:237]     Train net output #0: loss = 2.35579 (* 1 = 2.35579 loss)
I0407 22:18:42.694322 23786 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0407 22:18:47.175499 23786 solver.cpp:218] Iteration 2460 (2.67797 iter/s, 4.48101s/12 iters), loss = 2.22091
I0407 22:18:47.175542 23786 solver.cpp:237]     Train net output #0: loss = 2.22091 (* 1 = 2.22091 loss)
I0407 22:18:47.175552 23786 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0407 22:18:52.239382 23786 solver.cpp:218] Iteration 2472 (2.36984 iter/s, 5.06363s/12 iters), loss = 2.6131
I0407 22:18:52.239432 23786 solver.cpp:237]     Train net output #0: loss = 2.6131 (* 1 = 2.6131 loss)
I0407 22:18:52.239444 23786 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0407 22:18:57.271944 23786 solver.cpp:218] Iteration 2484 (2.38459 iter/s, 5.03231s/12 iters), loss = 2.04543
I0407 22:18:57.271989 23786 solver.cpp:237]     Train net output #0: loss = 2.04543 (* 1 = 2.04543 loss)
I0407 22:18:57.272001 23786 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0407 22:19:02.291832 23786 solver.cpp:218] Iteration 2496 (2.39061 iter/s, 5.01964s/12 iters), loss = 2.23335
I0407 22:19:02.291949 23786 solver.cpp:237]     Train net output #0: loss = 2.23335 (* 1 = 2.23335 loss)
I0407 22:19:02.291961 23786 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0407 22:19:07.331224 23786 solver.cpp:218] Iteration 2508 (2.38139 iter/s, 5.03908s/12 iters), loss = 2.28499
I0407 22:19:07.331280 23786 solver.cpp:237]     Train net output #0: loss = 2.28499 (* 1 = 2.28499 loss)
I0407 22:19:07.331292 23786 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0407 22:19:12.345852 23786 solver.cpp:218] Iteration 2520 (2.39313 iter/s, 5.01436s/12 iters), loss = 2.14608
I0407 22:19:12.345922 23786 solver.cpp:237]     Train net output #0: loss = 2.14608 (* 1 = 2.14608 loss)
I0407 22:19:12.345938 23786 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0407 22:19:15.496039 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:19:17.294132 23786 solver.cpp:218] Iteration 2532 (2.42521 iter/s, 4.94802s/12 iters), loss = 2.25322
I0407 22:19:17.294181 23786 solver.cpp:237]     Train net output #0: loss = 2.25322 (* 1 = 2.25322 loss)
I0407 22:19:17.294193 23786 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0407 22:19:22.285506 23786 solver.cpp:218] Iteration 2544 (2.40427 iter/s, 4.99112s/12 iters), loss = 2.11183
I0407 22:19:22.285552 23786 solver.cpp:237]     Train net output #0: loss = 2.11183 (* 1 = 2.11183 loss)
I0407 22:19:22.285562 23786 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0407 22:19:24.310542 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0407 22:19:27.601095 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0407 22:19:30.203064 23786 solver.cpp:330] Iteration 2550, Testing net (#0)
I0407 22:19:30.203089 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:19:33.636179 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:19:34.660713 23786 solver.cpp:397]     Test net output #0: accuracy = 0.289216
I0407 22:19:34.660763 23786 solver.cpp:397]     Test net output #1: loss = 3.08766 (* 1 = 3.08766 loss)
I0407 22:19:36.582262 23786 solver.cpp:218] Iteration 2556 (0.839387 iter/s, 14.2962s/12 iters), loss = 1.96606
I0407 22:19:36.582319 23786 solver.cpp:237]     Train net output #0: loss = 1.96606 (* 1 = 1.96606 loss)
I0407 22:19:36.582329 23786 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0407 22:19:41.603415 23786 solver.cpp:218] Iteration 2568 (2.39001 iter/s, 5.02089s/12 iters), loss = 2.23486
I0407 22:19:41.603467 23786 solver.cpp:237]     Train net output #0: loss = 2.23486 (* 1 = 2.23486 loss)
I0407 22:19:41.603479 23786 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0407 22:19:46.616623 23786 solver.cpp:218] Iteration 2580 (2.3938 iter/s, 5.01295s/12 iters), loss = 2.29068
I0407 22:19:46.616675 23786 solver.cpp:237]     Train net output #0: loss = 2.29068 (* 1 = 2.29068 loss)
I0407 22:19:46.616686 23786 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0407 22:19:51.609766 23786 solver.cpp:218] Iteration 2592 (2.40342 iter/s, 4.99289s/12 iters), loss = 2.2896
I0407 22:19:51.609820 23786 solver.cpp:237]     Train net output #0: loss = 2.2896 (* 1 = 2.2896 loss)
I0407 22:19:51.609833 23786 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0407 22:19:56.620728 23786 solver.cpp:218] Iteration 2604 (2.39487 iter/s, 5.0107s/12 iters), loss = 2.28675
I0407 22:19:56.620779 23786 solver.cpp:237]     Train net output #0: loss = 2.28675 (* 1 = 2.28675 loss)
I0407 22:19:56.620791 23786 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0407 22:20:01.618072 23786 solver.cpp:218] Iteration 2616 (2.4014 iter/s, 4.99708s/12 iters), loss = 2.31409
I0407 22:20:01.618126 23786 solver.cpp:237]     Train net output #0: loss = 2.31409 (* 1 = 2.31409 loss)
I0407 22:20:01.618137 23786 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0407 22:20:06.643050 23786 solver.cpp:218] Iteration 2628 (2.38819 iter/s, 5.02472s/12 iters), loss = 2.19057
I0407 22:20:06.643163 23786 solver.cpp:237]     Train net output #0: loss = 2.19057 (* 1 = 2.19057 loss)
I0407 22:20:06.643177 23786 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0407 22:20:07.079221 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:20:11.633111 23786 solver.cpp:218] Iteration 2640 (2.40493 iter/s, 4.98975s/12 iters), loss = 2.23186
I0407 22:20:11.633158 23786 solver.cpp:237]     Train net output #0: loss = 2.23186 (* 1 = 2.23186 loss)
I0407 22:20:11.633170 23786 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0407 22:20:16.147248 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0407 22:20:19.918929 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0407 22:20:22.472033 23786 solver.cpp:330] Iteration 2652, Testing net (#0)
I0407 22:20:22.472059 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:20:25.875156 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:20:26.933539 23786 solver.cpp:397]     Test net output #0: accuracy = 0.299632
I0407 22:20:26.933589 23786 solver.cpp:397]     Test net output #1: loss = 2.98718 (* 1 = 2.98718 loss)
I0407 22:20:27.024276 23786 solver.cpp:218] Iteration 2652 (0.779701 iter/s, 15.3905s/12 iters), loss = 1.94744
I0407 22:20:27.024327 23786 solver.cpp:237]     Train net output #0: loss = 1.94744 (* 1 = 1.94744 loss)
I0407 22:20:27.024338 23786 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0407 22:20:31.322180 23786 solver.cpp:218] Iteration 2664 (2.79221 iter/s, 4.29768s/12 iters), loss = 1.6359
I0407 22:20:31.322225 23786 solver.cpp:237]     Train net output #0: loss = 1.6359 (* 1 = 1.6359 loss)
I0407 22:20:31.322237 23786 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0407 22:20:36.363906 23786 solver.cpp:218] Iteration 2676 (2.38026 iter/s, 5.04148s/12 iters), loss = 2.46829
I0407 22:20:36.363955 23786 solver.cpp:237]     Train net output #0: loss = 2.46829 (* 1 = 2.46829 loss)
I0407 22:20:36.363966 23786 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0407 22:20:41.371440 23786 solver.cpp:218] Iteration 2688 (2.39651 iter/s, 5.00729s/12 iters), loss = 2.23773
I0407 22:20:41.371587 23786 solver.cpp:237]     Train net output #0: loss = 2.23773 (* 1 = 2.23773 loss)
I0407 22:20:41.371601 23786 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0407 22:20:46.396862 23786 solver.cpp:218] Iteration 2700 (2.38803 iter/s, 5.02507s/12 iters), loss = 1.85497
I0407 22:20:46.396909 23786 solver.cpp:237]     Train net output #0: loss = 1.85497 (* 1 = 1.85497 loss)
I0407 22:20:46.396920 23786 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0407 22:20:51.435124 23786 solver.cpp:218] Iteration 2712 (2.38189 iter/s, 5.03801s/12 iters), loss = 1.69422
I0407 22:20:51.435174 23786 solver.cpp:237]     Train net output #0: loss = 1.69422 (* 1 = 1.69422 loss)
I0407 22:20:51.435189 23786 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0407 22:20:56.464674 23786 solver.cpp:218] Iteration 2724 (2.38602 iter/s, 5.0293s/12 iters), loss = 2.09234
I0407 22:20:56.464727 23786 solver.cpp:237]     Train net output #0: loss = 2.09234 (* 1 = 2.09234 loss)
I0407 22:20:56.464740 23786 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0407 22:20:59.019459 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:21:01.386299 23786 solver.cpp:218] Iteration 2736 (2.43834 iter/s, 4.92138s/12 iters), loss = 1.66432
I0407 22:21:01.386345 23786 solver.cpp:237]     Train net output #0: loss = 1.66432 (* 1 = 1.66432 loss)
I0407 22:21:01.386358 23786 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0407 22:21:06.362334 23786 solver.cpp:218] Iteration 2748 (2.41168 iter/s, 4.97579s/12 iters), loss = 1.93644
I0407 22:21:06.362385 23786 solver.cpp:237]     Train net output #0: loss = 1.93644 (* 1 = 1.93644 loss)
I0407 22:21:06.362397 23786 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0407 22:21:08.390717 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0407 22:21:11.359598 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0407 22:21:15.911720 23786 solver.cpp:330] Iteration 2754, Testing net (#0)
I0407 22:21:15.911770 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:21:18.993575 23786 blocking_queue.cpp:49] Waiting for data
I0407 22:21:19.229204 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:21:20.332118 23786 solver.cpp:397]     Test net output #0: accuracy = 0.302696
I0407 22:21:20.332165 23786 solver.cpp:397]     Test net output #1: loss = 2.98269 (* 1 = 2.98269 loss)
I0407 22:21:22.306663 23786 solver.cpp:218] Iteration 2760 (0.75265 iter/s, 15.9437s/12 iters), loss = 1.94218
I0407 22:21:22.306717 23786 solver.cpp:237]     Train net output #0: loss = 1.94218 (* 1 = 1.94218 loss)
I0407 22:21:22.306730 23786 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0407 22:21:27.428356 23786 solver.cpp:218] Iteration 2772 (2.34309 iter/s, 5.12144s/12 iters), loss = 1.86153
I0407 22:21:27.428402 23786 solver.cpp:237]     Train net output #0: loss = 1.86153 (* 1 = 1.86153 loss)
I0407 22:21:27.428413 23786 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0407 22:21:32.485831 23786 solver.cpp:218] Iteration 2784 (2.37285 iter/s, 5.05722s/12 iters), loss = 1.87422
I0407 22:21:32.485882 23786 solver.cpp:237]     Train net output #0: loss = 1.87422 (* 1 = 1.87422 loss)
I0407 22:21:32.485893 23786 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0407 22:21:37.441350 23786 solver.cpp:218] Iteration 2796 (2.42167 iter/s, 4.95527s/12 iters), loss = 1.92075
I0407 22:21:37.441403 23786 solver.cpp:237]     Train net output #0: loss = 1.92075 (* 1 = 1.92075 loss)
I0407 22:21:37.441414 23786 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0407 22:21:42.444578 23786 solver.cpp:218] Iteration 2808 (2.39857 iter/s, 5.00297s/12 iters), loss = 1.85987
I0407 22:21:42.444624 23786 solver.cpp:237]     Train net output #0: loss = 1.85987 (* 1 = 1.85987 loss)
I0407 22:21:42.444634 23786 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0407 22:21:47.697191 23786 solver.cpp:218] Iteration 2820 (2.28469 iter/s, 5.25236s/12 iters), loss = 1.83578
I0407 22:21:47.697317 23786 solver.cpp:237]     Train net output #0: loss = 1.83578 (* 1 = 1.83578 loss)
I0407 22:21:47.697326 23786 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0407 22:21:52.506183 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:21:52.794461 23786 solver.cpp:218] Iteration 2832 (2.35436 iter/s, 5.09693s/12 iters), loss = 1.72473
I0407 22:21:52.794524 23786 solver.cpp:237]     Train net output #0: loss = 1.72473 (* 1 = 1.72473 loss)
I0407 22:21:52.794535 23786 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0407 22:21:57.737391 23786 solver.cpp:218] Iteration 2844 (2.42784 iter/s, 4.94266s/12 iters), loss = 1.73962
I0407 22:21:57.737440 23786 solver.cpp:237]     Train net output #0: loss = 1.73962 (* 1 = 1.73962 loss)
I0407 22:21:57.737452 23786 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0407 22:22:02.284938 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0407 22:22:05.478108 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0407 22:22:07.809429 23786 solver.cpp:330] Iteration 2856, Testing net (#0)
I0407 22:22:07.809448 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:22:11.140444 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:22:12.278561 23786 solver.cpp:397]     Test net output #0: accuracy = 0.330882
I0407 22:22:12.278597 23786 solver.cpp:397]     Test net output #1: loss = 2.87671 (* 1 = 2.87671 loss)
I0407 22:22:12.368808 23786 solver.cpp:218] Iteration 2856 (0.820188 iter/s, 14.6308s/12 iters), loss = 1.70605
I0407 22:22:12.368854 23786 solver.cpp:237]     Train net output #0: loss = 1.70605 (* 1 = 1.70605 loss)
I0407 22:22:12.368863 23786 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0407 22:22:16.676709 23786 solver.cpp:218] Iteration 2868 (2.78572 iter/s, 4.30768s/12 iters), loss = 1.84663
I0407 22:22:16.676764 23786 solver.cpp:237]     Train net output #0: loss = 1.84663 (* 1 = 1.84663 loss)
I0407 22:22:16.676775 23786 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0407 22:22:21.663561 23786 solver.cpp:218] Iteration 2880 (2.40645 iter/s, 4.9866s/12 iters), loss = 1.76085
I0407 22:22:21.663684 23786 solver.cpp:237]     Train net output #0: loss = 1.76085 (* 1 = 1.76085 loss)
I0407 22:22:21.663697 23786 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0407 22:22:26.622707 23786 solver.cpp:218] Iteration 2892 (2.41993 iter/s, 4.95882s/12 iters), loss = 1.80832
I0407 22:22:26.622758 23786 solver.cpp:237]     Train net output #0: loss = 1.80832 (* 1 = 1.80832 loss)
I0407 22:22:26.622771 23786 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0407 22:22:31.628664 23786 solver.cpp:218] Iteration 2904 (2.39727 iter/s, 5.0057s/12 iters), loss = 1.61032
I0407 22:22:31.628713 23786 solver.cpp:237]     Train net output #0: loss = 1.61032 (* 1 = 1.61032 loss)
I0407 22:22:31.628726 23786 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0407 22:22:36.489336 23786 solver.cpp:218] Iteration 2916 (2.46892 iter/s, 4.86042s/12 iters), loss = 1.2726
I0407 22:22:36.489385 23786 solver.cpp:237]     Train net output #0: loss = 1.2726 (* 1 = 1.2726 loss)
I0407 22:22:36.489398 23786 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0407 22:22:41.893026 23786 solver.cpp:218] Iteration 2928 (2.22081 iter/s, 5.40342s/12 iters), loss = 1.40001
I0407 22:22:41.893065 23786 solver.cpp:237]     Train net output #0: loss = 1.40001 (* 1 = 1.40001 loss)
I0407 22:22:41.893074 23786 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0407 22:22:43.731510 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:22:46.939532 23786 solver.cpp:218] Iteration 2940 (2.378 iter/s, 5.04626s/12 iters), loss = 1.66061
I0407 22:22:46.939577 23786 solver.cpp:237]     Train net output #0: loss = 1.66061 (* 1 = 1.66061 loss)
I0407 22:22:46.939589 23786 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0407 22:22:51.923045 23786 solver.cpp:218] Iteration 2952 (2.40806 iter/s, 4.98327s/12 iters), loss = 1.55676
I0407 22:22:51.923182 23786 solver.cpp:237]     Train net output #0: loss = 1.55676 (* 1 = 1.55676 loss)
I0407 22:22:51.923195 23786 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0407 22:22:53.957988 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0407 22:22:57.000087 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0407 22:22:59.383311 23786 solver.cpp:330] Iteration 2958, Testing net (#0)
I0407 22:22:59.383337 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:23:02.703924 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:23:03.889230 23786 solver.cpp:397]     Test net output #0: accuracy = 0.314338
I0407 22:23:03.889276 23786 solver.cpp:397]     Test net output #1: loss = 2.95102 (* 1 = 2.95102 loss)
I0407 22:23:05.777850 23786 solver.cpp:218] Iteration 2964 (0.866168 iter/s, 13.8541s/12 iters), loss = 1.39215
I0407 22:23:05.777904 23786 solver.cpp:237]     Train net output #0: loss = 1.39215 (* 1 = 1.39215 loss)
I0407 22:23:05.777915 23786 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0407 22:23:10.761034 23786 solver.cpp:218] Iteration 2976 (2.40822 iter/s, 4.98293s/12 iters), loss = 1.74234
I0407 22:23:10.761078 23786 solver.cpp:237]     Train net output #0: loss = 1.74234 (* 1 = 1.74234 loss)
I0407 22:23:10.761090 23786 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0407 22:23:15.801018 23786 solver.cpp:218] Iteration 2988 (2.38108 iter/s, 5.03974s/12 iters), loss = 1.52541
I0407 22:23:15.801065 23786 solver.cpp:237]     Train net output #0: loss = 1.52541 (* 1 = 1.52541 loss)
I0407 22:23:15.801077 23786 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0407 22:23:20.775919 23786 solver.cpp:218] Iteration 3000 (2.41223 iter/s, 4.97465s/12 iters), loss = 1.79622
I0407 22:23:20.775966 23786 solver.cpp:237]     Train net output #0: loss = 1.79622 (* 1 = 1.79622 loss)
I0407 22:23:20.775976 23786 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0407 22:23:25.816920 23786 solver.cpp:218] Iteration 3012 (2.3806 iter/s, 5.04075s/12 iters), loss = 1.64049
I0407 22:23:25.817059 23786 solver.cpp:237]     Train net output #0: loss = 1.64049 (* 1 = 1.64049 loss)
I0407 22:23:25.817073 23786 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0407 22:23:30.841251 23786 solver.cpp:218] Iteration 3024 (2.38854 iter/s, 5.02399s/12 iters), loss = 1.38112
I0407 22:23:30.841307 23786 solver.cpp:237]     Train net output #0: loss = 1.38112 (* 1 = 1.38112 loss)
I0407 22:23:30.841320 23786 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0407 22:23:34.835944 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:23:35.848598 23786 solver.cpp:218] Iteration 3036 (2.3966 iter/s, 5.0071s/12 iters), loss = 1.3198
I0407 22:23:35.848623 23786 solver.cpp:237]     Train net output #0: loss = 1.3198 (* 1 = 1.3198 loss)
I0407 22:23:35.848629 23786 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0407 22:23:40.731626 23786 solver.cpp:218] Iteration 3048 (2.45761 iter/s, 4.8828s/12 iters), loss = 1.60489
I0407 22:23:40.731673 23786 solver.cpp:237]     Train net output #0: loss = 1.60489 (* 1 = 1.60489 loss)
I0407 22:23:40.731683 23786 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0407 22:23:45.229415 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0407 22:23:50.645068 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0407 22:23:52.999791 23786 solver.cpp:330] Iteration 3060, Testing net (#0)
I0407 22:23:52.999817 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:23:56.246289 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:23:57.466305 23786 solver.cpp:397]     Test net output #0: accuracy = 0.332721
I0407 22:23:57.466349 23786 solver.cpp:397]     Test net output #1: loss = 2.8977 (* 1 = 2.8977 loss)
I0407 22:23:57.556514 23786 solver.cpp:218] Iteration 3060 (0.713259 iter/s, 16.8242s/12 iters), loss = 1.67968
I0407 22:23:57.556562 23786 solver.cpp:237]     Train net output #0: loss = 1.67968 (* 1 = 1.67968 loss)
I0407 22:23:57.556571 23786 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0407 22:24:01.758291 23786 solver.cpp:218] Iteration 3072 (2.85608 iter/s, 4.20156s/12 iters), loss = 1.57291
I0407 22:24:01.758335 23786 solver.cpp:237]     Train net output #0: loss = 1.57291 (* 1 = 1.57291 loss)
I0407 22:24:01.758343 23786 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0407 22:24:06.763878 23786 solver.cpp:218] Iteration 3084 (2.39744 iter/s, 5.00533s/12 iters), loss = 1.57087
I0407 22:24:06.763942 23786 solver.cpp:237]     Train net output #0: loss = 1.57087 (* 1 = 1.57087 loss)
I0407 22:24:06.763954 23786 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0407 22:24:11.687475 23786 solver.cpp:218] Iteration 3096 (2.43737 iter/s, 4.92334s/12 iters), loss = 1.70846
I0407 22:24:11.687527 23786 solver.cpp:237]     Train net output #0: loss = 1.70846 (* 1 = 1.70846 loss)
I0407 22:24:11.687539 23786 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0407 22:24:16.775094 23786 solver.cpp:218] Iteration 3108 (2.35879 iter/s, 5.08736s/12 iters), loss = 1.44649
I0407 22:24:16.775146 23786 solver.cpp:237]     Train net output #0: loss = 1.44649 (* 1 = 1.44649 loss)
I0407 22:24:16.775158 23786 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0407 22:24:21.765367 23786 solver.cpp:218] Iteration 3120 (2.4048 iter/s, 4.99001s/12 iters), loss = 1.29728
I0407 22:24:21.765416 23786 solver.cpp:237]     Train net output #0: loss = 1.29728 (* 1 = 1.29728 loss)
I0407 22:24:21.765426 23786 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0407 22:24:26.794677 23786 solver.cpp:218] Iteration 3132 (2.38613 iter/s, 5.02906s/12 iters), loss = 1.65432
I0407 22:24:26.794786 23786 solver.cpp:237]     Train net output #0: loss = 1.65432 (* 1 = 1.65432 loss)
I0407 22:24:26.794798 23786 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0407 22:24:27.885380 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:24:31.948537 23786 solver.cpp:218] Iteration 3144 (2.3285 iter/s, 5.15354s/12 iters), loss = 1.45986
I0407 22:24:31.948602 23786 solver.cpp:237]     Train net output #0: loss = 1.45986 (* 1 = 1.45986 loss)
I0407 22:24:31.948618 23786 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0407 22:24:37.116210 23786 solver.cpp:218] Iteration 3156 (2.32225 iter/s, 5.1674s/12 iters), loss = 1.49139
I0407 22:24:37.116266 23786 solver.cpp:237]     Train net output #0: loss = 1.49139 (* 1 = 1.49139 loss)
I0407 22:24:37.116277 23786 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0407 22:24:39.090816 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0407 22:24:44.173547 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0407 22:24:52.536219 23786 solver.cpp:330] Iteration 3162, Testing net (#0)
I0407 22:24:52.536243 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:24:55.729477 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:24:57.306542 23786 solver.cpp:397]     Test net output #0: accuracy = 0.338235
I0407 22:24:57.306663 23786 solver.cpp:397]     Test net output #1: loss = 2.95308 (* 1 = 2.95308 loss)
I0407 22:24:59.289577 23786 solver.cpp:218] Iteration 3168 (0.541212 iter/s, 22.1725s/12 iters), loss = 1.38883
I0407 22:24:59.289633 23786 solver.cpp:237]     Train net output #0: loss = 1.38883 (* 1 = 1.38883 loss)
I0407 22:24:59.289645 23786 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0407 22:25:04.478989 23786 solver.cpp:218] Iteration 3180 (2.31252 iter/s, 5.18915s/12 iters), loss = 1.59795
I0407 22:25:04.479033 23786 solver.cpp:237]     Train net output #0: loss = 1.59795 (* 1 = 1.59795 loss)
I0407 22:25:04.479043 23786 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0407 22:25:09.392254 23786 solver.cpp:218] Iteration 3192 (2.44249 iter/s, 4.91301s/12 iters), loss = 1.60008
I0407 22:25:09.392308 23786 solver.cpp:237]     Train net output #0: loss = 1.60008 (* 1 = 1.60008 loss)
I0407 22:25:09.392320 23786 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0407 22:25:14.848547 23786 solver.cpp:218] Iteration 3204 (2.1994 iter/s, 5.45602s/12 iters), loss = 1.25398
I0407 22:25:14.848584 23786 solver.cpp:237]     Train net output #0: loss = 1.25398 (* 1 = 1.25398 loss)
I0407 22:25:14.848592 23786 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0407 22:25:20.055507 23786 solver.cpp:218] Iteration 3216 (2.30472 iter/s, 5.20671s/12 iters), loss = 1.64318
I0407 22:25:20.055546 23786 solver.cpp:237]     Train net output #0: loss = 1.64318 (* 1 = 1.64318 loss)
I0407 22:25:20.055554 23786 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0407 22:25:25.034214 23786 solver.cpp:218] Iteration 3228 (2.41038 iter/s, 4.97847s/12 iters), loss = 1.10427
I0407 22:25:25.034256 23786 solver.cpp:237]     Train net output #0: loss = 1.10427 (* 1 = 1.10427 loss)
I0407 22:25:25.034267 23786 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0407 22:25:28.298452 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:25:30.058349 23786 solver.cpp:218] Iteration 3240 (2.38859 iter/s, 5.02389s/12 iters), loss = 1.57003
I0407 22:25:30.058390 23786 solver.cpp:237]     Train net output #0: loss = 1.57003 (* 1 = 1.57003 loss)
I0407 22:25:30.058399 23786 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0407 22:25:35.142012 23786 solver.cpp:218] Iteration 3252 (2.36062 iter/s, 5.08342s/12 iters), loss = 1.432
I0407 22:25:35.142055 23786 solver.cpp:237]     Train net output #0: loss = 1.432 (* 1 = 1.432 loss)
I0407 22:25:35.142062 23786 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0407 22:25:39.621052 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0407 22:25:50.506619 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0407 22:25:58.630817 23786 solver.cpp:330] Iteration 3264, Testing net (#0)
I0407 22:25:58.630905 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:26:01.828766 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:26:03.129272 23786 solver.cpp:397]     Test net output #0: accuracy = 0.335172
I0407 22:26:03.129320 23786 solver.cpp:397]     Test net output #1: loss = 3.0182 (* 1 = 3.0182 loss)
I0407 22:26:03.219322 23786 solver.cpp:218] Iteration 3264 (0.427409 iter/s, 28.0762s/12 iters), loss = 1.9348
I0407 22:26:03.219368 23786 solver.cpp:237]     Train net output #0: loss = 1.9348 (* 1 = 1.9348 loss)
I0407 22:26:03.219378 23786 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0407 22:26:07.644290 23786 solver.cpp:218] Iteration 3276 (2.71203 iter/s, 4.42474s/12 iters), loss = 1.07453
I0407 22:26:07.644330 23786 solver.cpp:237]     Train net output #0: loss = 1.07453 (* 1 = 1.07453 loss)
I0407 22:26:07.644337 23786 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0407 22:26:12.644838 23786 solver.cpp:218] Iteration 3288 (2.39986 iter/s, 5.0003s/12 iters), loss = 1.22623
I0407 22:26:12.644892 23786 solver.cpp:237]     Train net output #0: loss = 1.22623 (* 1 = 1.22623 loss)
I0407 22:26:12.644903 23786 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0407 22:26:17.675402 23786 solver.cpp:218] Iteration 3300 (2.38554 iter/s, 5.0303s/12 iters), loss = 1.40001
I0407 22:26:17.675457 23786 solver.cpp:237]     Train net output #0: loss = 1.40001 (* 1 = 1.40001 loss)
I0407 22:26:17.675468 23786 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0407 22:26:22.692915 23786 solver.cpp:218] Iteration 3312 (2.39174 iter/s, 5.01726s/12 iters), loss = 1.36009
I0407 22:26:22.692951 23786 solver.cpp:237]     Train net output #0: loss = 1.36009 (* 1 = 1.36009 loss)
I0407 22:26:22.692960 23786 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0407 22:26:27.750413 23786 solver.cpp:218] Iteration 3324 (2.37283 iter/s, 5.05725s/12 iters), loss = 1.31729
I0407 22:26:27.750468 23786 solver.cpp:237]     Train net output #0: loss = 1.31729 (* 1 = 1.31729 loss)
I0407 22:26:27.750480 23786 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0407 22:26:32.747468 23786 solver.cpp:218] Iteration 3336 (2.40154 iter/s, 4.99679s/12 iters), loss = 1.20299
I0407 22:26:32.749729 23786 solver.cpp:237]     Train net output #0: loss = 1.20299 (* 1 = 1.20299 loss)
I0407 22:26:32.749742 23786 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0407 22:26:33.206367 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:26:37.691644 23786 solver.cpp:218] Iteration 3348 (2.4283 iter/s, 4.94172s/12 iters), loss = 1.19659
I0407 22:26:37.691686 23786 solver.cpp:237]     Train net output #0: loss = 1.19659 (* 1 = 1.19659 loss)
I0407 22:26:37.691695 23786 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0407 22:26:42.683236 23786 solver.cpp:218] Iteration 3360 (2.40416 iter/s, 4.99135s/12 iters), loss = 1.22172
I0407 22:26:42.683274 23786 solver.cpp:237]     Train net output #0: loss = 1.22172 (* 1 = 1.22172 loss)
I0407 22:26:42.683284 23786 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0407 22:26:44.715384 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0407 22:26:51.999701 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0407 22:26:57.639256 23786 solver.cpp:330] Iteration 3366, Testing net (#0)
I0407 22:26:57.639282 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:27:00.761194 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:27:02.096426 23786 solver.cpp:397]     Test net output #0: accuracy = 0.344976
I0407 22:27:02.096474 23786 solver.cpp:397]     Test net output #1: loss = 2.97151 (* 1 = 2.97151 loss)
I0407 22:27:04.058073 23786 solver.cpp:218] Iteration 3372 (0.561431 iter/s, 21.374s/12 iters), loss = 1.19853
I0407 22:27:04.058193 23786 solver.cpp:237]     Train net output #0: loss = 1.19853 (* 1 = 1.19853 loss)
I0407 22:27:04.058207 23786 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0407 22:27:09.110440 23786 solver.cpp:218] Iteration 3384 (2.37527 iter/s, 5.05205s/12 iters), loss = 1.4441
I0407 22:27:09.110483 23786 solver.cpp:237]     Train net output #0: loss = 1.4441 (* 1 = 1.4441 loss)
I0407 22:27:09.110492 23786 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0407 22:27:14.096335 23786 solver.cpp:218] Iteration 3396 (2.40691 iter/s, 4.98564s/12 iters), loss = 1.33914
I0407 22:27:14.096386 23786 solver.cpp:237]     Train net output #0: loss = 1.33914 (* 1 = 1.33914 loss)
I0407 22:27:14.096398 23786 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0407 22:27:19.162248 23786 solver.cpp:218] Iteration 3408 (2.3689 iter/s, 5.06565s/12 iters), loss = 0.990169
I0407 22:27:19.162307 23786 solver.cpp:237]     Train net output #0: loss = 0.990169 (* 1 = 0.990169 loss)
I0407 22:27:19.162319 23786 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0407 22:27:24.218364 23786 solver.cpp:218] Iteration 3420 (2.37349 iter/s, 5.05585s/12 iters), loss = 1.00088
I0407 22:27:24.218430 23786 solver.cpp:237]     Train net output #0: loss = 1.00088 (* 1 = 1.00088 loss)
I0407 22:27:24.218443 23786 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0407 22:27:29.395608 23786 solver.cpp:218] Iteration 3432 (2.31796 iter/s, 5.17697s/12 iters), loss = 1.28398
I0407 22:27:29.395651 23786 solver.cpp:237]     Train net output #0: loss = 1.28398 (* 1 = 1.28398 loss)
I0407 22:27:29.395660 23786 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0407 22:27:32.042295 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:27:34.441042 23786 solver.cpp:218] Iteration 3444 (2.37851 iter/s, 5.04518s/12 iters), loss = 1.08431
I0407 22:27:34.441181 23786 solver.cpp:237]     Train net output #0: loss = 1.08431 (* 1 = 1.08431 loss)
I0407 22:27:34.441193 23786 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0407 22:27:39.422899 23786 solver.cpp:218] Iteration 3456 (2.40891 iter/s, 4.98151s/12 iters), loss = 1.33381
I0407 22:27:39.422950 23786 solver.cpp:237]     Train net output #0: loss = 1.33381 (* 1 = 1.33381 loss)
I0407 22:27:39.422962 23786 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0407 22:27:43.959215 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0407 22:27:50.247548 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0407 22:27:59.095103 23786 solver.cpp:330] Iteration 3468, Testing net (#0)
I0407 22:27:59.095126 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:27:59.519770 23786 blocking_queue.cpp:49] Waiting for data
I0407 22:28:02.159092 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:28:03.728499 23786 solver.cpp:397]     Test net output #0: accuracy = 0.348652
I0407 22:28:03.728549 23786 solver.cpp:397]     Test net output #1: loss = 2.9753 (* 1 = 2.9753 loss)
I0407 22:28:03.816519 23786 solver.cpp:218] Iteration 3468 (0.491952 iter/s, 24.3926s/12 iters), loss = 1.18401
I0407 22:28:03.816570 23786 solver.cpp:237]     Train net output #0: loss = 1.18401 (* 1 = 1.18401 loss)
I0407 22:28:03.816581 23786 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0407 22:28:08.070992 23786 solver.cpp:218] Iteration 3480 (2.82071 iter/s, 4.25425s/12 iters), loss = 1.28341
I0407 22:28:08.071091 23786 solver.cpp:237]     Train net output #0: loss = 1.28341 (* 1 = 1.28341 loss)
I0407 22:28:08.071100 23786 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0407 22:28:13.175129 23786 solver.cpp:218] Iteration 3492 (2.35118 iter/s, 5.10383s/12 iters), loss = 1.08258
I0407 22:28:13.175179 23786 solver.cpp:237]     Train net output #0: loss = 1.08258 (* 1 = 1.08258 loss)
I0407 22:28:13.175190 23786 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0407 22:28:18.153678 23786 solver.cpp:218] Iteration 3504 (2.41046 iter/s, 4.97829s/12 iters), loss = 0.924982
I0407 22:28:18.153718 23786 solver.cpp:237]     Train net output #0: loss = 0.924982 (* 1 = 0.924982 loss)
I0407 22:28:18.153726 23786 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0407 22:28:23.110113 23786 solver.cpp:218] Iteration 3516 (2.42121 iter/s, 4.95619s/12 iters), loss = 1.13295
I0407 22:28:23.110160 23786 solver.cpp:237]     Train net output #0: loss = 1.13295 (* 1 = 1.13295 loss)
I0407 22:28:23.110169 23786 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0407 22:28:28.061218 23786 solver.cpp:218] Iteration 3528 (2.42382 iter/s, 4.95085s/12 iters), loss = 1.1707
I0407 22:28:28.061264 23786 solver.cpp:237]     Train net output #0: loss = 1.1707 (* 1 = 1.1707 loss)
I0407 22:28:28.061275 23786 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0407 22:28:32.811656 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:28:33.067605 23786 solver.cpp:218] Iteration 3540 (2.39706 iter/s, 5.00614s/12 iters), loss = 1.20268
I0407 22:28:33.067651 23786 solver.cpp:237]     Train net output #0: loss = 1.20268 (* 1 = 1.20268 loss)
I0407 22:28:33.067662 23786 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0407 22:28:38.085572 23786 solver.cpp:218] Iteration 3552 (2.39152 iter/s, 5.01772s/12 iters), loss = 1.44138
I0407 22:28:38.085685 23786 solver.cpp:237]     Train net output #0: loss = 1.44138 (* 1 = 1.44138 loss)
I0407 22:28:38.085696 23786 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0407 22:28:43.148345 23786 solver.cpp:218] Iteration 3564 (2.37039 iter/s, 5.06245s/12 iters), loss = 1.1153
I0407 22:28:43.148396 23786 solver.cpp:237]     Train net output #0: loss = 1.1153 (* 1 = 1.1153 loss)
I0407 22:28:43.148407 23786 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0407 22:28:45.188354 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0407 22:28:54.321137 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0407 22:28:58.541893 23786 solver.cpp:330] Iteration 3570, Testing net (#0)
I0407 22:28:58.541920 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:29:01.585872 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:29:03.000080 23786 solver.cpp:397]     Test net output #0: accuracy = 0.354779
I0407 22:29:03.000128 23786 solver.cpp:397]     Test net output #1: loss = 2.97542 (* 1 = 2.97542 loss)
I0407 22:29:04.875346 23786 solver.cpp:218] Iteration 3576 (0.552331 iter/s, 21.7261s/12 iters), loss = 1.14259
I0407 22:29:04.875381 23786 solver.cpp:237]     Train net output #0: loss = 1.14259 (* 1 = 1.14259 loss)
I0407 22:29:04.875389 23786 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0407 22:29:10.045711 23786 solver.cpp:218] Iteration 3588 (2.32103 iter/s, 5.17011s/12 iters), loss = 0.966099
I0407 22:29:10.045823 23786 solver.cpp:237]     Train net output #0: loss = 0.966099 (* 1 = 0.966099 loss)
I0407 22:29:10.045835 23786 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0407 22:29:14.991523 23786 solver.cpp:218] Iteration 3600 (2.42645 iter/s, 4.9455s/12 iters), loss = 1.14531
I0407 22:29:14.991564 23786 solver.cpp:237]     Train net output #0: loss = 1.14531 (* 1 = 1.14531 loss)
I0407 22:29:14.991575 23786 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0407 22:29:19.918082 23786 solver.cpp:218] Iteration 3612 (2.43589 iter/s, 4.92632s/12 iters), loss = 1.30254
I0407 22:29:19.918125 23786 solver.cpp:237]     Train net output #0: loss = 1.30254 (* 1 = 1.30254 loss)
I0407 22:29:19.918136 23786 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0407 22:29:24.856940 23786 solver.cpp:218] Iteration 3624 (2.42983 iter/s, 4.93862s/12 iters), loss = 0.983823
I0407 22:29:24.856976 23786 solver.cpp:237]     Train net output #0: loss = 0.983823 (* 1 = 0.983823 loss)
I0407 22:29:24.856986 23786 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0407 22:29:29.897631 23786 solver.cpp:218] Iteration 3636 (2.38074 iter/s, 5.04045s/12 iters), loss = 1.19004
I0407 22:29:29.897671 23786 solver.cpp:237]     Train net output #0: loss = 1.19004 (* 1 = 1.19004 loss)
I0407 22:29:29.897680 23786 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0407 22:29:31.791455 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:29:34.951503 23786 solver.cpp:218] Iteration 3648 (2.37453 iter/s, 5.05362s/12 iters), loss = 1.11081
I0407 22:29:34.951547 23786 solver.cpp:237]     Train net output #0: loss = 1.11081 (* 1 = 1.11081 loss)
I0407 22:29:34.951557 23786 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0407 22:29:39.999878 23786 solver.cpp:218] Iteration 3660 (2.37712 iter/s, 5.04812s/12 iters), loss = 0.982054
I0407 22:29:39.999933 23786 solver.cpp:237]     Train net output #0: loss = 0.982054 (* 1 = 0.982054 loss)
I0407 22:29:39.999944 23786 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0407 22:29:44.530807 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0407 22:29:49.722811 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0407 22:29:53.856683 23786 solver.cpp:330] Iteration 3672, Testing net (#0)
I0407 22:29:53.856709 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:29:56.835296 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:29:58.295435 23786 solver.cpp:397]     Test net output #0: accuracy = 0.341299
I0407 22:29:58.295485 23786 solver.cpp:397]     Test net output #1: loss = 3.1136 (* 1 = 3.1136 loss)
I0407 22:29:58.385907 23786 solver.cpp:218] Iteration 3672 (0.652697 iter/s, 18.3853s/12 iters), loss = 1.12993
I0407 22:29:58.385974 23786 solver.cpp:237]     Train net output #0: loss = 1.12993 (* 1 = 1.12993 loss)
I0407 22:29:58.385985 23786 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0407 22:30:02.567023 23786 solver.cpp:218] Iteration 3684 (2.8702 iter/s, 4.18089s/12 iters), loss = 1.11092
I0407 22:30:02.567062 23786 solver.cpp:237]     Train net output #0: loss = 1.11092 (* 1 = 1.11092 loss)
I0407 22:30:02.567071 23786 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0407 22:30:07.617255 23786 solver.cpp:218] Iteration 3696 (2.37625 iter/s, 5.04998s/12 iters), loss = 1.0133
I0407 22:30:07.617311 23786 solver.cpp:237]     Train net output #0: loss = 1.0133 (* 1 = 1.0133 loss)
I0407 22:30:07.617322 23786 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0407 22:30:12.630187 23786 solver.cpp:218] Iteration 3708 (2.39393 iter/s, 5.01267s/12 iters), loss = 0.865666
I0407 22:30:12.630228 23786 solver.cpp:237]     Train net output #0: loss = 0.865666 (* 1 = 0.865666 loss)
I0407 22:30:12.630237 23786 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0407 22:30:17.649497 23786 solver.cpp:218] Iteration 3720 (2.39088 iter/s, 5.01906s/12 iters), loss = 1.16554
I0407 22:30:17.649610 23786 solver.cpp:237]     Train net output #0: loss = 1.16554 (* 1 = 1.16554 loss)
I0407 22:30:17.649619 23786 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0407 22:30:22.575590 23786 solver.cpp:218] Iteration 3732 (2.43616 iter/s, 4.92578s/12 iters), loss = 0.807386
I0407 22:30:22.575628 23786 solver.cpp:237]     Train net output #0: loss = 0.807386 (* 1 = 0.807386 loss)
I0407 22:30:22.575637 23786 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0407 22:30:26.572033 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:30:27.576781 23786 solver.cpp:218] Iteration 3744 (2.39954 iter/s, 5.00095s/12 iters), loss = 0.833553
I0407 22:30:27.576825 23786 solver.cpp:237]     Train net output #0: loss = 0.833553 (* 1 = 0.833553 loss)
I0407 22:30:27.576833 23786 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0407 22:30:32.613603 23786 solver.cpp:218] Iteration 3756 (2.38257 iter/s, 5.03657s/12 iters), loss = 1.52465
I0407 22:30:32.613648 23786 solver.cpp:237]     Train net output #0: loss = 1.52465 (* 1 = 1.52465 loss)
I0407 22:30:32.613658 23786 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0407 22:30:37.604068 23786 solver.cpp:218] Iteration 3768 (2.4047 iter/s, 4.99022s/12 iters), loss = 1.17562
I0407 22:30:37.604104 23786 solver.cpp:237]     Train net output #0: loss = 1.17562 (* 1 = 1.17562 loss)
I0407 22:30:37.604112 23786 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0407 22:30:39.624366 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0407 22:30:43.048578 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0407 22:30:49.777815 23786 solver.cpp:330] Iteration 3774, Testing net (#0)
I0407 22:30:49.777875 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:30:52.748401 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:30:54.244020 23786 solver.cpp:397]     Test net output #0: accuracy = 0.365809
I0407 22:30:54.244065 23786 solver.cpp:397]     Test net output #1: loss = 3.05828 (* 1 = 3.05828 loss)
I0407 22:30:56.219189 23786 solver.cpp:218] Iteration 3780 (0.644664 iter/s, 18.6144s/12 iters), loss = 0.862802
I0407 22:30:56.219242 23786 solver.cpp:237]     Train net output #0: loss = 0.862802 (* 1 = 0.862802 loss)
I0407 22:30:56.219254 23786 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0407 22:31:01.290887 23786 solver.cpp:218] Iteration 3792 (2.36619 iter/s, 5.07143s/12 iters), loss = 1.045
I0407 22:31:01.290938 23786 solver.cpp:237]     Train net output #0: loss = 1.045 (* 1 = 1.045 loss)
I0407 22:31:01.290949 23786 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0407 22:31:06.392674 23786 solver.cpp:218] Iteration 3804 (2.35224 iter/s, 5.10153s/12 iters), loss = 0.803693
I0407 22:31:06.392719 23786 solver.cpp:237]     Train net output #0: loss = 0.803693 (* 1 = 0.803693 loss)
I0407 22:31:06.392730 23786 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0407 22:31:11.382164 23786 solver.cpp:218] Iteration 3816 (2.40518 iter/s, 4.98924s/12 iters), loss = 0.977566
I0407 22:31:11.382207 23786 solver.cpp:237]     Train net output #0: loss = 0.977566 (* 1 = 0.977566 loss)
I0407 22:31:11.382216 23786 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0407 22:31:16.372416 23786 solver.cpp:218] Iteration 3828 (2.40481 iter/s, 4.99s/12 iters), loss = 0.774383
I0407 22:31:16.372469 23786 solver.cpp:237]     Train net output #0: loss = 0.774383 (* 1 = 0.774383 loss)
I0407 22:31:16.372481 23786 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0407 22:31:21.388900 23786 solver.cpp:218] Iteration 3840 (2.39224 iter/s, 5.01623s/12 iters), loss = 0.865984
I0407 22:31:21.389041 23786 solver.cpp:237]     Train net output #0: loss = 0.865984 (* 1 = 0.865984 loss)
I0407 22:31:21.389055 23786 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0407 22:31:22.543745 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:31:26.395478 23786 solver.cpp:218] Iteration 3852 (2.39701 iter/s, 5.00624s/12 iters), loss = 1.01542
I0407 22:31:26.395519 23786 solver.cpp:237]     Train net output #0: loss = 1.01542 (* 1 = 1.01542 loss)
I0407 22:31:26.395529 23786 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0407 22:31:31.345722 23786 solver.cpp:218] Iteration 3864 (2.42424 iter/s, 4.95s/12 iters), loss = 1.01451
I0407 22:31:31.345767 23786 solver.cpp:237]     Train net output #0: loss = 1.01451 (* 1 = 1.01451 loss)
I0407 22:31:31.345777 23786 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0407 22:31:35.844511 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0407 22:31:38.792176 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0407 22:31:41.113641 23786 solver.cpp:330] Iteration 3876, Testing net (#0)
I0407 22:31:41.113663 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:31:44.031685 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:31:45.575882 23786 solver.cpp:397]     Test net output #0: accuracy = 0.379902
I0407 22:31:45.575927 23786 solver.cpp:397]     Test net output #1: loss = 3.12065 (* 1 = 3.12065 loss)
I0407 22:31:45.666146 23786 solver.cpp:218] Iteration 3876 (0.838 iter/s, 14.3198s/12 iters), loss = 0.794036
I0407 22:31:45.666191 23786 solver.cpp:237]     Train net output #0: loss = 0.794036 (* 1 = 0.794036 loss)
I0407 22:31:45.666203 23786 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0407 22:31:50.069154 23786 solver.cpp:218] Iteration 3888 (2.72555 iter/s, 4.40278s/12 iters), loss = 0.827676
I0407 22:31:50.069197 23786 solver.cpp:237]     Train net output #0: loss = 0.827676 (* 1 = 0.827676 loss)
I0407 22:31:50.069206 23786 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0407 22:31:55.140393 23786 solver.cpp:218] Iteration 3900 (2.3664 iter/s, 5.07099s/12 iters), loss = 1.03842
I0407 22:31:55.140466 23786 solver.cpp:237]     Train net output #0: loss = 1.03842 (* 1 = 1.03842 loss)
I0407 22:31:55.140477 23786 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0407 22:32:00.291898 23786 solver.cpp:218] Iteration 3912 (2.32954 iter/s, 5.15122s/12 iters), loss = 0.820157
I0407 22:32:00.291945 23786 solver.cpp:237]     Train net output #0: loss = 0.820157 (* 1 = 0.820157 loss)
I0407 22:32:00.291956 23786 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0407 22:32:05.328194 23786 solver.cpp:218] Iteration 3924 (2.38282 iter/s, 5.03604s/12 iters), loss = 0.848589
I0407 22:32:05.328250 23786 solver.cpp:237]     Train net output #0: loss = 0.848589 (* 1 = 0.848589 loss)
I0407 22:32:05.328263 23786 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0407 22:32:10.321604 23786 solver.cpp:218] Iteration 3936 (2.40329 iter/s, 4.99315s/12 iters), loss = 0.955236
I0407 22:32:10.321662 23786 solver.cpp:237]     Train net output #0: loss = 0.955236 (* 1 = 0.955236 loss)
I0407 22:32:10.321676 23786 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0407 22:32:13.782855 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:32:15.407641 23786 solver.cpp:218] Iteration 3948 (2.35952 iter/s, 5.08577s/12 iters), loss = 0.922596
I0407 22:32:15.407696 23786 solver.cpp:237]     Train net output #0: loss = 0.922596 (* 1 = 0.922596 loss)
I0407 22:32:15.407708 23786 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0407 22:32:20.380019 23786 solver.cpp:218] Iteration 3960 (2.41346 iter/s, 4.97212s/12 iters), loss = 0.88975
I0407 22:32:20.380077 23786 solver.cpp:237]     Train net output #0: loss = 0.88975 (* 1 = 0.88975 loss)
I0407 22:32:20.380090 23786 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0407 22:32:25.399309 23786 solver.cpp:218] Iteration 3972 (2.3909 iter/s, 5.01903s/12 iters), loss = 0.851757
I0407 22:32:25.399451 23786 solver.cpp:237]     Train net output #0: loss = 0.851757 (* 1 = 0.851757 loss)
I0407 22:32:25.399462 23786 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0407 22:32:27.469128 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0407 22:32:30.494884 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0407 22:32:33.763046 23786 solver.cpp:330] Iteration 3978, Testing net (#0)
I0407 22:32:33.763068 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:32:36.640990 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:32:38.220383 23786 solver.cpp:397]     Test net output #0: accuracy = 0.378676
I0407 22:32:38.220435 23786 solver.cpp:397]     Test net output #1: loss = 2.90056 (* 1 = 2.90056 loss)
I0407 22:32:40.153388 23786 solver.cpp:218] Iteration 3984 (0.813374 iter/s, 14.7534s/12 iters), loss = 0.56384
I0407 22:32:40.153450 23786 solver.cpp:237]     Train net output #0: loss = 0.56384 (* 1 = 0.56384 loss)
I0407 22:32:40.153463 23786 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0407 22:32:45.122879 23786 solver.cpp:218] Iteration 3996 (2.41486 iter/s, 4.96923s/12 iters), loss = 0.947323
I0407 22:32:45.122921 23786 solver.cpp:237]     Train net output #0: loss = 0.947323 (* 1 = 0.947323 loss)
I0407 22:32:45.122931 23786 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0407 22:32:50.095343 23786 solver.cpp:218] Iteration 4008 (2.41341 iter/s, 4.97222s/12 iters), loss = 0.702867
I0407 22:32:50.095391 23786 solver.cpp:237]     Train net output #0: loss = 0.702867 (* 1 = 0.702867 loss)
I0407 22:32:50.095404 23786 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0407 22:32:55.072191 23786 solver.cpp:218] Iteration 4020 (2.41128 iter/s, 4.9766s/12 iters), loss = 1.06368
I0407 22:32:55.072227 23786 solver.cpp:237]     Train net output #0: loss = 1.06368 (* 1 = 1.06368 loss)
I0407 22:32:55.072234 23786 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0407 22:33:00.097689 23786 solver.cpp:218] Iteration 4032 (2.38794 iter/s, 5.02526s/12 iters), loss = 0.758531
I0407 22:33:00.097785 23786 solver.cpp:237]     Train net output #0: loss = 0.758531 (* 1 = 0.758531 loss)
I0407 22:33:00.097796 23786 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0407 22:33:05.102020 23786 solver.cpp:218] Iteration 4044 (2.39806 iter/s, 5.00404s/12 iters), loss = 1.0208
I0407 22:33:05.102066 23786 solver.cpp:237]     Train net output #0: loss = 1.0208 (* 1 = 1.0208 loss)
I0407 22:33:05.102074 23786 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0407 22:33:05.604923 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:33:10.279312 23786 solver.cpp:218] Iteration 4056 (2.31793 iter/s, 5.17704s/12 iters), loss = 1.10276
I0407 22:33:10.279357 23786 solver.cpp:237]     Train net output #0: loss = 1.10276 (* 1 = 1.10276 loss)
I0407 22:33:10.279369 23786 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0407 22:33:15.267858 23786 solver.cpp:218] Iteration 4068 (2.40563 iter/s, 4.9883s/12 iters), loss = 0.563861
I0407 22:33:15.267901 23786 solver.cpp:237]     Train net output #0: loss = 0.563861 (* 1 = 0.563861 loss)
I0407 22:33:15.267911 23786 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0407 22:33:19.796198 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0407 22:33:24.117591 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0407 22:33:27.937404 23786 solver.cpp:330] Iteration 4080, Testing net (#0)
I0407 22:33:27.937427 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:33:30.783013 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:33:32.396829 23786 solver.cpp:397]     Test net output #0: accuracy = 0.386029
I0407 22:33:32.396863 23786 solver.cpp:397]     Test net output #1: loss = 2.88132 (* 1 = 2.88132 loss)
I0407 22:33:32.487373 23786 solver.cpp:218] Iteration 4080 (0.696913 iter/s, 17.2188s/12 iters), loss = 0.836421
I0407 22:33:32.487428 23786 solver.cpp:237]     Train net output #0: loss = 0.836421 (* 1 = 0.836421 loss)
I0407 22:33:32.487439 23786 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0407 22:33:36.765480 23786 solver.cpp:218] Iteration 4092 (2.80513 iter/s, 4.27788s/12 iters), loss = 0.630575
I0407 22:33:36.765528 23786 solver.cpp:237]     Train net output #0: loss = 0.630575 (* 1 = 0.630575 loss)
I0407 22:33:36.765540 23786 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0407 22:33:41.756985 23786 solver.cpp:218] Iteration 4104 (2.40421 iter/s, 4.99125s/12 iters), loss = 0.932496
I0407 22:33:41.757040 23786 solver.cpp:237]     Train net output #0: loss = 0.932496 (* 1 = 0.932496 loss)
I0407 22:33:41.757051 23786 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0407 22:33:46.780858 23786 solver.cpp:218] Iteration 4116 (2.38872 iter/s, 5.02361s/12 iters), loss = 0.776246
I0407 22:33:46.780910 23786 solver.cpp:237]     Train net output #0: loss = 0.776246 (* 1 = 0.776246 loss)
I0407 22:33:46.780921 23786 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0407 22:33:51.797734 23786 solver.cpp:218] Iteration 4128 (2.39205 iter/s, 5.01662s/12 iters), loss = 0.681962
I0407 22:33:51.797780 23786 solver.cpp:237]     Train net output #0: loss = 0.681962 (* 1 = 0.681962 loss)
I0407 22:33:51.797791 23786 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0407 22:33:56.801311 23786 solver.cpp:218] Iteration 4140 (2.3984 iter/s, 5.00333s/12 iters), loss = 0.957284
I0407 22:33:56.801357 23786 solver.cpp:237]     Train net output #0: loss = 0.957284 (* 1 = 0.957284 loss)
I0407 22:33:56.801367 23786 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0407 22:33:59.461721 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:34:01.785373 23786 solver.cpp:218] Iteration 4152 (2.4078 iter/s, 4.98381s/12 iters), loss = 0.647308
I0407 22:34:01.785495 23786 solver.cpp:237]     Train net output #0: loss = 0.647308 (* 1 = 0.647308 loss)
I0407 22:34:01.785509 23786 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0407 22:34:03.406999 23786 blocking_queue.cpp:49] Waiting for data
I0407 22:34:06.759488 23786 solver.cpp:218] Iteration 4164 (2.41265 iter/s, 4.97379s/12 iters), loss = 0.746029
I0407 22:34:06.759546 23786 solver.cpp:237]     Train net output #0: loss = 0.746029 (* 1 = 0.746029 loss)
I0407 22:34:06.759558 23786 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0407 22:34:11.756919 23786 solver.cpp:218] Iteration 4176 (2.40136 iter/s, 4.99717s/12 iters), loss = 0.411008
I0407 22:34:11.756966 23786 solver.cpp:237]     Train net output #0: loss = 0.411009 (* 1 = 0.411009 loss)
I0407 22:34:11.756978 23786 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0407 22:34:13.827636 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0407 22:34:19.849334 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0407 22:34:22.351763 23786 solver.cpp:330] Iteration 4182, Testing net (#0)
I0407 22:34:22.351791 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:34:25.112744 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:34:26.773773 23786 solver.cpp:397]     Test net output #0: accuracy = 0.39951
I0407 22:34:26.773825 23786 solver.cpp:397]     Test net output #1: loss = 2.87522 (* 1 = 2.87522 loss)
I0407 22:34:28.545310 23786 solver.cpp:218] Iteration 4188 (0.714809 iter/s, 16.7877s/12 iters), loss = 0.648772
I0407 22:34:28.545359 23786 solver.cpp:237]     Train net output #0: loss = 0.648772 (* 1 = 0.648772 loss)
I0407 22:34:28.545370 23786 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0407 22:34:33.647094 23786 solver.cpp:218] Iteration 4200 (2.35224 iter/s, 5.10152s/12 iters), loss = 0.726325
I0407 22:34:33.647279 23786 solver.cpp:237]     Train net output #0: loss = 0.726325 (* 1 = 0.726325 loss)
I0407 22:34:33.647296 23786 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0407 22:34:38.653324 23786 solver.cpp:218] Iteration 4212 (2.3972 iter/s, 5.00585s/12 iters), loss = 0.816091
I0407 22:34:38.653378 23786 solver.cpp:237]     Train net output #0: loss = 0.816091 (* 1 = 0.816091 loss)
I0407 22:34:38.653388 23786 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0407 22:34:43.609602 23786 solver.cpp:218] Iteration 4224 (2.4213 iter/s, 4.95603s/12 iters), loss = 0.746407
I0407 22:34:43.609652 23786 solver.cpp:237]     Train net output #0: loss = 0.746407 (* 1 = 0.746407 loss)
I0407 22:34:43.609665 23786 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0407 22:34:48.532611 23786 solver.cpp:218] Iteration 4236 (2.43766 iter/s, 4.92276s/12 iters), loss = 0.697341
I0407 22:34:48.532657 23786 solver.cpp:237]     Train net output #0: loss = 0.697341 (* 1 = 0.697341 loss)
I0407 22:34:48.532666 23786 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0407 22:34:53.328816 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:34:53.554416 23786 solver.cpp:218] Iteration 4248 (2.3897 iter/s, 5.02155s/12 iters), loss = 0.519999
I0407 22:34:53.554461 23786 solver.cpp:237]     Train net output #0: loss = 0.519999 (* 1 = 0.519999 loss)
I0407 22:34:53.554471 23786 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0407 22:34:58.583685 23786 solver.cpp:218] Iteration 4260 (2.38615 iter/s, 5.02902s/12 iters), loss = 0.686671
I0407 22:34:58.583729 23786 solver.cpp:237]     Train net output #0: loss = 0.686671 (* 1 = 0.686671 loss)
I0407 22:34:58.583739 23786 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0407 22:35:03.655778 23786 solver.cpp:218] Iteration 4272 (2.366 iter/s, 5.07184s/12 iters), loss = 0.642659
I0407 22:35:03.655892 23786 solver.cpp:237]     Train net output #0: loss = 0.642659 (* 1 = 0.642659 loss)
I0407 22:35:03.655902 23786 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0407 22:35:08.274174 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0407 22:35:11.276196 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0407 22:35:13.608983 23786 solver.cpp:330] Iteration 4284, Testing net (#0)
I0407 22:35:13.609005 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:35:16.391898 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:35:18.132418 23786 solver.cpp:397]     Test net output #0: accuracy = 0.375
I0407 22:35:18.132459 23786 solver.cpp:397]     Test net output #1: loss = 3.03983 (* 1 = 3.03983 loss)
I0407 22:35:18.219740 23786 solver.cpp:218] Iteration 4284 (0.82399 iter/s, 14.5633s/12 iters), loss = 0.743086
I0407 22:35:18.219789 23786 solver.cpp:237]     Train net output #0: loss = 0.743086 (* 1 = 0.743086 loss)
I0407 22:35:18.219800 23786 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0407 22:35:22.360910 23786 solver.cpp:218] Iteration 4296 (2.8979 iter/s, 4.14094s/12 iters), loss = 0.814984
I0407 22:35:22.360985 23786 solver.cpp:237]     Train net output #0: loss = 0.814984 (* 1 = 0.814984 loss)
I0407 22:35:22.361001 23786 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0407 22:35:27.335366 23786 solver.cpp:218] Iteration 4308 (2.41245 iter/s, 4.97419s/12 iters), loss = 0.891729
I0407 22:35:27.335445 23786 solver.cpp:237]     Train net output #0: loss = 0.891729 (* 1 = 0.891729 loss)
I0407 22:35:27.335458 23786 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0407 22:35:32.408706 23786 solver.cpp:218] Iteration 4320 (2.36544 iter/s, 5.07306s/12 iters), loss = 0.84953
I0407 22:35:32.408754 23786 solver.cpp:237]     Train net output #0: loss = 0.84953 (* 1 = 0.84953 loss)
I0407 22:35:32.408766 23786 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0407 22:35:37.448949 23786 solver.cpp:218] Iteration 4332 (2.38096 iter/s, 5.03999s/12 iters), loss = 0.768716
I0407 22:35:37.449107 23786 solver.cpp:237]     Train net output #0: loss = 0.768716 (* 1 = 0.768716 loss)
I0407 22:35:37.449121 23786 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0407 22:35:42.408643 23786 solver.cpp:218] Iteration 4344 (2.41968 iter/s, 4.95934s/12 iters), loss = 0.755062
I0407 22:35:42.408696 23786 solver.cpp:237]     Train net output #0: loss = 0.755062 (* 1 = 0.755062 loss)
I0407 22:35:42.408710 23786 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0407 22:35:44.331359 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:35:47.496548 23786 solver.cpp:218] Iteration 4356 (2.35865 iter/s, 5.08765s/12 iters), loss = 0.661121
I0407 22:35:47.496590 23786 solver.cpp:237]     Train net output #0: loss = 0.661121 (* 1 = 0.661121 loss)
I0407 22:35:47.496600 23786 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0407 22:35:52.604607 23786 solver.cpp:218] Iteration 4368 (2.34935 iter/s, 5.10781s/12 iters), loss = 1.12514
I0407 22:35:52.604655 23786 solver.cpp:237]     Train net output #0: loss = 1.12514 (* 1 = 1.12514 loss)
I0407 22:35:52.604665 23786 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0407 22:35:57.646783 23786 solver.cpp:218] Iteration 4380 (2.38004 iter/s, 5.04193s/12 iters), loss = 0.791053
I0407 22:35:57.646833 23786 solver.cpp:237]     Train net output #0: loss = 0.791053 (* 1 = 0.791053 loss)
I0407 22:35:57.646845 23786 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0407 22:35:59.718138 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0407 22:36:03.556582 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0407 22:36:05.909346 23786 solver.cpp:330] Iteration 4386, Testing net (#0)
I0407 22:36:05.909369 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:36:08.496829 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:36:10.237447 23786 solver.cpp:397]     Test net output #0: accuracy = 0.370098
I0407 22:36:10.237495 23786 solver.cpp:397]     Test net output #1: loss = 3.00913 (* 1 = 3.00913 loss)
I0407 22:36:12.219413 23786 solver.cpp:218] Iteration 4392 (0.823496 iter/s, 14.572s/12 iters), loss = 0.922555
I0407 22:36:12.219475 23786 solver.cpp:237]     Train net output #0: loss = 0.922555 (* 1 = 0.922555 loss)
I0407 22:36:12.219488 23786 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0407 22:36:17.218415 23786 solver.cpp:218] Iteration 4404 (2.40061 iter/s, 4.99874s/12 iters), loss = 0.71291
I0407 22:36:17.218467 23786 solver.cpp:237]     Train net output #0: loss = 0.71291 (* 1 = 0.71291 loss)
I0407 22:36:17.218480 23786 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0407 22:36:22.072865 23786 solver.cpp:218] Iteration 4416 (2.47208 iter/s, 4.8542s/12 iters), loss = 0.701738
I0407 22:36:22.072906 23786 solver.cpp:237]     Train net output #0: loss = 0.701738 (* 1 = 0.701738 loss)
I0407 22:36:22.072916 23786 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0407 22:36:27.038736 23786 solver.cpp:218] Iteration 4428 (2.41662 iter/s, 4.96562s/12 iters), loss = 0.556038
I0407 22:36:27.038789 23786 solver.cpp:237]     Train net output #0: loss = 0.556038 (* 1 = 0.556038 loss)
I0407 22:36:27.038800 23786 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0407 22:36:32.063313 23786 solver.cpp:218] Iteration 4440 (2.38838 iter/s, 5.02432s/12 iters), loss = 0.533995
I0407 22:36:32.063361 23786 solver.cpp:237]     Train net output #0: loss = 0.533995 (* 1 = 0.533995 loss)
I0407 22:36:32.063372 23786 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0407 22:36:36.161763 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:36:37.093755 23786 solver.cpp:218] Iteration 4452 (2.3856 iter/s, 5.03019s/12 iters), loss = 0.743115
I0407 22:36:37.093802 23786 solver.cpp:237]     Train net output #0: loss = 0.743115 (* 1 = 0.743115 loss)
I0407 22:36:37.093814 23786 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0407 22:36:42.099432 23786 solver.cpp:218] Iteration 4464 (2.3974 iter/s, 5.00542s/12 iters), loss = 0.482209
I0407 22:36:42.099581 23786 solver.cpp:237]     Train net output #0: loss = 0.482209 (* 1 = 0.482209 loss)
I0407 22:36:42.099596 23786 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0407 22:36:47.139400 23786 solver.cpp:218] Iteration 4476 (2.38113 iter/s, 5.03961s/12 iters), loss = 0.841128
I0407 22:36:47.139457 23786 solver.cpp:237]     Train net output #0: loss = 0.841128 (* 1 = 0.841128 loss)
I0407 22:36:47.139470 23786 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0407 22:36:51.710969 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0407 22:36:54.747612 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0407 22:36:57.089025 23786 solver.cpp:330] Iteration 4488, Testing net (#0)
I0407 22:36:57.089051 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:36:59.761639 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:37:01.536106 23786 solver.cpp:397]     Test net output #0: accuracy = 0.379289
I0407 22:37:01.536151 23786 solver.cpp:397]     Test net output #1: loss = 3.11404 (* 1 = 3.11404 loss)
I0407 22:37:01.626569 23786 solver.cpp:218] Iteration 4488 (0.828355 iter/s, 14.4865s/12 iters), loss = 0.654706
I0407 22:37:01.626646 23786 solver.cpp:237]     Train net output #0: loss = 0.654706 (* 1 = 0.654706 loss)
I0407 22:37:01.626662 23786 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0407 22:37:06.018442 23786 solver.cpp:218] Iteration 4500 (2.73248 iter/s, 4.39162s/12 iters), loss = 0.649758
I0407 22:37:06.018491 23786 solver.cpp:237]     Train net output #0: loss = 0.649758 (* 1 = 0.649758 loss)
I0407 22:37:06.018502 23786 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0407 22:37:11.009665 23786 solver.cpp:218] Iteration 4512 (2.40434 iter/s, 4.99097s/12 iters), loss = 0.660611
I0407 22:37:11.009716 23786 solver.cpp:237]     Train net output #0: loss = 0.660611 (* 1 = 0.660611 loss)
I0407 22:37:11.009727 23786 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0407 22:37:16.005364 23786 solver.cpp:218] Iteration 4524 (2.40219 iter/s, 4.99545s/12 iters), loss = 0.652941
I0407 22:37:16.005483 23786 solver.cpp:237]     Train net output #0: loss = 0.652941 (* 1 = 0.652941 loss)
I0407 22:37:16.005496 23786 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0407 22:37:20.989064 23786 solver.cpp:218] Iteration 4536 (2.408 iter/s, 4.98338s/12 iters), loss = 0.670905
I0407 22:37:20.989120 23786 solver.cpp:237]     Train net output #0: loss = 0.670905 (* 1 = 0.670905 loss)
I0407 22:37:20.989131 23786 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0407 22:37:25.982414 23786 solver.cpp:218] Iteration 4548 (2.40332 iter/s, 4.9931s/12 iters), loss = 0.531072
I0407 22:37:25.982467 23786 solver.cpp:237]     Train net output #0: loss = 0.531072 (* 1 = 0.531072 loss)
I0407 22:37:25.982478 23786 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0407 22:37:27.272552 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:37:30.993816 23786 solver.cpp:218] Iteration 4560 (2.39466 iter/s, 5.01115s/12 iters), loss = 0.486539
I0407 22:37:30.993857 23786 solver.cpp:237]     Train net output #0: loss = 0.486539 (* 1 = 0.486539 loss)
I0407 22:37:30.993867 23786 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0407 22:37:36.035434 23786 solver.cpp:218] Iteration 4572 (2.38031 iter/s, 5.04137s/12 iters), loss = 0.579188
I0407 22:37:36.035487 23786 solver.cpp:237]     Train net output #0: loss = 0.579188 (* 1 = 0.579188 loss)
I0407 22:37:36.035501 23786 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0407 22:37:41.069164 23786 solver.cpp:218] Iteration 4584 (2.38404 iter/s, 5.03347s/12 iters), loss = 0.704574
I0407 22:37:41.069214 23786 solver.cpp:237]     Train net output #0: loss = 0.704574 (* 1 = 0.704574 loss)
I0407 22:37:41.069226 23786 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0407 22:37:43.091444 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0407 22:37:46.092744 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0407 22:37:48.411613 23786 solver.cpp:330] Iteration 4590, Testing net (#0)
I0407 22:37:48.411636 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:37:51.014487 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:37:52.832618 23786 solver.cpp:397]     Test net output #0: accuracy = 0.398284
I0407 22:37:52.832662 23786 solver.cpp:397]     Test net output #1: loss = 3.04374 (* 1 = 3.04374 loss)
I0407 22:37:54.666486 23786 solver.cpp:218] Iteration 4596 (0.882565 iter/s, 13.5967s/12 iters), loss = 0.455723
I0407 22:37:54.666530 23786 solver.cpp:237]     Train net output #0: loss = 0.455723 (* 1 = 0.455723 loss)
I0407 22:37:54.666539 23786 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0407 22:37:59.744009 23786 solver.cpp:218] Iteration 4608 (2.36348 iter/s, 5.07727s/12 iters), loss = 0.675087
I0407 22:37:59.744058 23786 solver.cpp:237]     Train net output #0: loss = 0.675087 (* 1 = 0.675087 loss)
I0407 22:37:59.744069 23786 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0407 22:38:04.806753 23786 solver.cpp:218] Iteration 4620 (2.37038 iter/s, 5.06249s/12 iters), loss = 0.515827
I0407 22:38:04.806797 23786 solver.cpp:237]     Train net output #0: loss = 0.515827 (* 1 = 0.515827 loss)
I0407 22:38:04.806805 23786 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0407 22:38:09.811759 23786 solver.cpp:218] Iteration 4632 (2.39772 iter/s, 5.00476s/12 iters), loss = 0.303703
I0407 22:38:09.811797 23786 solver.cpp:237]     Train net output #0: loss = 0.303703 (* 1 = 0.303703 loss)
I0407 22:38:09.811805 23786 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0407 22:38:14.838251 23786 solver.cpp:218] Iteration 4644 (2.38747 iter/s, 5.02625s/12 iters), loss = 0.599754
I0407 22:38:14.838299 23786 solver.cpp:237]     Train net output #0: loss = 0.599754 (* 1 = 0.599754 loss)
I0407 22:38:14.838311 23786 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0407 22:38:18.235862 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:38:19.857266 23786 solver.cpp:218] Iteration 4656 (2.39103 iter/s, 5.01876s/12 iters), loss = 0.595569
I0407 22:38:19.857321 23786 solver.cpp:237]     Train net output #0: loss = 0.595569 (* 1 = 0.595569 loss)
I0407 22:38:19.857333 23786 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0407 22:38:24.936928 23786 solver.cpp:218] Iteration 4668 (2.36248 iter/s, 5.0794s/12 iters), loss = 0.287285
I0407 22:38:24.936965 23786 solver.cpp:237]     Train net output #0: loss = 0.287285 (* 1 = 0.287285 loss)
I0407 22:38:24.936973 23786 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0407 22:38:29.957217 23786 solver.cpp:218] Iteration 4680 (2.39042 iter/s, 5.02005s/12 iters), loss = 0.666821
I0407 22:38:29.957262 23786 solver.cpp:237]     Train net output #0: loss = 0.666821 (* 1 = 0.666821 loss)
I0407 22:38:29.957273 23786 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0407 22:38:34.558321 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0407 22:38:37.559087 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0407 22:38:39.895747 23786 solver.cpp:330] Iteration 4692, Testing net (#0)
I0407 22:38:39.895772 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:38:42.512558 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:38:44.366744 23786 solver.cpp:397]     Test net output #0: accuracy = 0.408701
I0407 22:38:44.366792 23786 solver.cpp:397]     Test net output #1: loss = 2.9903 (* 1 = 2.9903 loss)
I0407 22:38:44.457352 23786 solver.cpp:218] Iteration 4692 (0.827613 iter/s, 14.4995s/12 iters), loss = 0.444426
I0407 22:38:44.457402 23786 solver.cpp:237]     Train net output #0: loss = 0.444426 (* 1 = 0.444426 loss)
I0407 22:38:44.457413 23786 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0407 22:38:48.778302 23786 solver.cpp:218] Iteration 4704 (2.77731 iter/s, 4.32072s/12 iters), loss = 0.504064
I0407 22:38:48.778443 23786 solver.cpp:237]     Train net output #0: loss = 0.504064 (* 1 = 0.504064 loss)
I0407 22:38:48.778456 23786 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0407 22:38:53.761157 23786 solver.cpp:218] Iteration 4716 (2.40842 iter/s, 4.98251s/12 iters), loss = 0.492578
I0407 22:38:53.761209 23786 solver.cpp:237]     Train net output #0: loss = 0.492578 (* 1 = 0.492578 loss)
I0407 22:38:53.761221 23786 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0407 22:38:58.792034 23786 solver.cpp:218] Iteration 4728 (2.38539 iter/s, 5.03062s/12 iters), loss = 0.544483
I0407 22:38:58.792088 23786 solver.cpp:237]     Train net output #0: loss = 0.544483 (* 1 = 0.544483 loss)
I0407 22:38:58.792100 23786 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0407 22:39:03.782603 23786 solver.cpp:218] Iteration 4740 (2.40466 iter/s, 4.99032s/12 iters), loss = 0.416758
I0407 22:39:03.782653 23786 solver.cpp:237]     Train net output #0: loss = 0.416758 (* 1 = 0.416758 loss)
I0407 22:39:03.782666 23786 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0407 22:39:08.845571 23786 solver.cpp:218] Iteration 4752 (2.37027 iter/s, 5.06271s/12 iters), loss = 0.605776
I0407 22:39:08.845621 23786 solver.cpp:237]     Train net output #0: loss = 0.605776 (* 1 = 0.605776 loss)
I0407 22:39:08.845633 23786 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0407 22:39:09.380149 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:39:13.896893 23786 solver.cpp:218] Iteration 4764 (2.37573 iter/s, 5.05107s/12 iters), loss = 0.565699
I0407 22:39:13.896931 23786 solver.cpp:237]     Train net output #0: loss = 0.565699 (* 1 = 0.565699 loss)
I0407 22:39:13.896939 23786 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0407 22:39:18.899143 23786 solver.cpp:218] Iteration 4776 (2.39904 iter/s, 5.00201s/12 iters), loss = 0.56513
I0407 22:39:18.899902 23786 solver.cpp:237]     Train net output #0: loss = 0.56513 (* 1 = 0.56513 loss)
I0407 22:39:18.899919 23786 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0407 22:39:23.890069 23786 solver.cpp:218] Iteration 4788 (2.40482 iter/s, 4.98997s/12 iters), loss = 0.470389
I0407 22:39:23.890121 23786 solver.cpp:237]     Train net output #0: loss = 0.470389 (* 1 = 0.470389 loss)
I0407 22:39:23.890134 23786 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0407 22:39:25.815119 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0407 22:39:28.786489 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0407 22:39:31.124635 23786 solver.cpp:330] Iteration 4794, Testing net (#0)
I0407 22:39:31.124661 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:39:33.696200 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:39:35.596021 23786 solver.cpp:397]     Test net output #0: accuracy = 0.404412
I0407 22:39:35.596066 23786 solver.cpp:397]     Test net output #1: loss = 3.17949 (* 1 = 3.17949 loss)
I0407 22:39:37.505702 23786 solver.cpp:218] Iteration 4800 (0.881378 iter/s, 13.615s/12 iters), loss = 0.32927
I0407 22:39:37.505754 23786 solver.cpp:237]     Train net output #0: loss = 0.32927 (* 1 = 0.32927 loss)
I0407 22:39:37.505765 23786 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0407 22:39:42.466650 23786 solver.cpp:218] Iteration 4812 (2.41902 iter/s, 4.9607s/12 iters), loss = 0.518115
I0407 22:39:42.466698 23786 solver.cpp:237]     Train net output #0: loss = 0.518115 (* 1 = 0.518115 loss)
I0407 22:39:42.466711 23786 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0407 22:39:47.419968 23786 solver.cpp:218] Iteration 4824 (2.42274 iter/s, 4.95307s/12 iters), loss = 0.489183
I0407 22:39:47.420019 23786 solver.cpp:237]     Train net output #0: loss = 0.489183 (* 1 = 0.489183 loss)
I0407 22:39:47.420032 23786 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0407 22:39:52.456001 23786 solver.cpp:218] Iteration 4836 (2.38295 iter/s, 5.03578s/12 iters), loss = 0.438593
I0407 22:39:52.456203 23786 solver.cpp:237]     Train net output #0: loss = 0.438593 (* 1 = 0.438593 loss)
I0407 22:39:52.456218 23786 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0407 22:39:54.460741 23786 blocking_queue.cpp:49] Waiting for data
I0407 22:39:57.472014 23786 solver.cpp:218] Iteration 4848 (2.39253 iter/s, 5.01561s/12 iters), loss = 0.383921
I0407 22:39:57.472061 23786 solver.cpp:237]     Train net output #0: loss = 0.383921 (* 1 = 0.383921 loss)
I0407 22:39:57.472074 23786 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0407 22:40:00.131880 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:40:02.495301 23786 solver.cpp:218] Iteration 4860 (2.38899 iter/s, 5.02304s/12 iters), loss = 0.336425
I0407 22:40:02.495345 23786 solver.cpp:237]     Train net output #0: loss = 0.336425 (* 1 = 0.336425 loss)
I0407 22:40:02.495357 23786 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0407 22:40:07.516229 23786 solver.cpp:218] Iteration 4872 (2.39012 iter/s, 5.02068s/12 iters), loss = 0.567864
I0407 22:40:07.516286 23786 solver.cpp:237]     Train net output #0: loss = 0.567864 (* 1 = 0.567864 loss)
I0407 22:40:07.516299 23786 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0407 22:40:12.593318 23786 solver.cpp:218] Iteration 4884 (2.36368 iter/s, 5.07683s/12 iters), loss = 0.587739
I0407 22:40:12.593364 23786 solver.cpp:237]     Train net output #0: loss = 0.587739 (* 1 = 0.587739 loss)
I0407 22:40:12.593374 23786 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0407 22:40:17.073735 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0407 22:40:20.454618 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0407 22:40:22.812961 23786 solver.cpp:330] Iteration 4896, Testing net (#0)
I0407 22:40:22.813019 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:40:25.357517 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:40:27.294402 23786 solver.cpp:397]     Test net output #0: accuracy = 0.411765
I0407 22:40:27.294437 23786 solver.cpp:397]     Test net output #1: loss = 2.97131 (* 1 = 2.97131 loss)
I0407 22:40:27.384871 23786 solver.cpp:218] Iteration 4896 (0.811308 iter/s, 14.7909s/12 iters), loss = 0.483745
I0407 22:40:27.384920 23786 solver.cpp:237]     Train net output #0: loss = 0.483745 (* 1 = 0.483745 loss)
I0407 22:40:27.384929 23786 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0407 22:40:31.705962 23786 solver.cpp:218] Iteration 4908 (2.77723 iter/s, 4.32086s/12 iters), loss = 0.332571
I0407 22:40:31.706001 23786 solver.cpp:237]     Train net output #0: loss = 0.332571 (* 1 = 0.332571 loss)
I0407 22:40:31.706010 23786 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0407 22:40:36.750516 23786 solver.cpp:218] Iteration 4920 (2.37892 iter/s, 5.04431s/12 iters), loss = 0.379004
I0407 22:40:36.750568 23786 solver.cpp:237]     Train net output #0: loss = 0.379004 (* 1 = 0.379004 loss)
I0407 22:40:36.750581 23786 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0407 22:40:41.771111 23786 solver.cpp:218] Iteration 4932 (2.39028 iter/s, 5.02034s/12 iters), loss = 0.499475
I0407 22:40:41.771150 23786 solver.cpp:237]     Train net output #0: loss = 0.499475 (* 1 = 0.499475 loss)
I0407 22:40:41.771160 23786 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0407 22:40:46.780078 23786 solver.cpp:218] Iteration 4944 (2.39582 iter/s, 5.00872s/12 iters), loss = 0.340478
I0407 22:40:46.780120 23786 solver.cpp:237]     Train net output #0: loss = 0.340478 (* 1 = 0.340478 loss)
I0407 22:40:46.780130 23786 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0407 22:40:51.632272 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:40:51.827090 23786 solver.cpp:218] Iteration 4956 (2.37775 iter/s, 5.0468s/12 iters), loss = 0.300313
I0407 22:40:51.827128 23786 solver.cpp:237]     Train net output #0: loss = 0.300313 (* 1 = 0.300313 loss)
I0407 22:40:51.827137 23786 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0407 22:40:56.870841 23786 solver.cpp:218] Iteration 4968 (2.37927 iter/s, 5.04356s/12 iters), loss = 0.519535
I0407 22:40:56.870959 23786 solver.cpp:237]     Train net output #0: loss = 0.519535 (* 1 = 0.519535 loss)
I0407 22:40:56.870970 23786 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0407 22:41:01.877494 23786 solver.cpp:218] Iteration 4980 (2.39694 iter/s, 5.00638s/12 iters), loss = 0.439982
I0407 22:41:01.877540 23786 solver.cpp:237]     Train net output #0: loss = 0.439982 (* 1 = 0.439982 loss)
I0407 22:41:01.877552 23786 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0407 22:41:06.866528 23786 solver.cpp:218] Iteration 4992 (2.40537 iter/s, 4.98883s/12 iters), loss = 0.487323
I0407 22:41:06.866575 23786 solver.cpp:237]     Train net output #0: loss = 0.487323 (* 1 = 0.487323 loss)
I0407 22:41:06.866585 23786 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0407 22:41:08.932389 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0407 22:41:11.903189 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0407 22:41:14.255546 23786 solver.cpp:330] Iteration 4998, Testing net (#0)
I0407 22:41:14.255570 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:41:16.748610 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:41:18.722795 23786 solver.cpp:397]     Test net output #0: accuracy = 0.41299
I0407 22:41:18.722843 23786 solver.cpp:397]     Test net output #1: loss = 2.95678 (* 1 = 2.95678 loss)
I0407 22:41:20.614871 23786 solver.cpp:218] Iteration 5004 (0.872862 iter/s, 13.7479s/12 iters), loss = 0.437807
I0407 22:41:20.614928 23786 solver.cpp:237]     Train net output #0: loss = 0.437807 (* 1 = 0.437807 loss)
I0407 22:41:20.614938 23786 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0407 22:41:25.615036 23786 solver.cpp:218] Iteration 5016 (2.40002 iter/s, 4.99995s/12 iters), loss = 0.383132
I0407 22:41:25.615084 23786 solver.cpp:237]     Train net output #0: loss = 0.383132 (* 1 = 0.383132 loss)
I0407 22:41:25.615097 23786 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0407 22:41:30.587568 23786 solver.cpp:218] Iteration 5028 (2.41336 iter/s, 4.97232s/12 iters), loss = 0.227125
I0407 22:41:30.587680 23786 solver.cpp:237]     Train net output #0: loss = 0.227125 (* 1 = 0.227125 loss)
I0407 22:41:30.587693 23786 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0407 22:41:35.606679 23786 solver.cpp:218] Iteration 5040 (2.39099 iter/s, 5.01885s/12 iters), loss = 0.475247
I0407 22:41:35.606720 23786 solver.cpp:237]     Train net output #0: loss = 0.475247 (* 1 = 0.475247 loss)
I0407 22:41:35.606729 23786 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0407 22:41:40.634793 23786 solver.cpp:218] Iteration 5052 (2.38668 iter/s, 5.02791s/12 iters), loss = 0.263422
I0407 22:41:40.634840 23786 solver.cpp:237]     Train net output #0: loss = 0.263422 (* 1 = 0.263422 loss)
I0407 22:41:40.634847 23786 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0407 22:41:42.510499 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:41:45.580257 23786 solver.cpp:218] Iteration 5064 (2.42657 iter/s, 4.94526s/12 iters), loss = 0.459144
I0407 22:41:45.580296 23786 solver.cpp:237]     Train net output #0: loss = 0.459144 (* 1 = 0.459144 loss)
I0407 22:41:45.580303 23786 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0407 22:41:50.620499 23786 solver.cpp:218] Iteration 5076 (2.38093 iter/s, 5.04004s/12 iters), loss = 0.456521
I0407 22:41:50.620543 23786 solver.cpp:237]     Train net output #0: loss = 0.456521 (* 1 = 0.456521 loss)
I0407 22:41:50.620551 23786 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0407 22:41:55.658320 23786 solver.cpp:218] Iteration 5088 (2.38208 iter/s, 5.03761s/12 iters), loss = 0.24009
I0407 22:41:55.658371 23786 solver.cpp:237]     Train net output #0: loss = 0.24009 (* 1 = 0.24009 loss)
I0407 22:41:55.658382 23786 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0407 22:42:00.242302 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0407 22:42:03.966805 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0407 22:42:06.390105 23786 solver.cpp:330] Iteration 5100, Testing net (#0)
I0407 22:42:06.390130 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:42:08.834010 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:42:10.849380 23786 solver.cpp:397]     Test net output #0: accuracy = 0.421569
I0407 22:42:10.849427 23786 solver.cpp:397]     Test net output #1: loss = 2.94256 (* 1 = 2.94256 loss)
I0407 22:42:10.940100 23786 solver.cpp:218] Iteration 5100 (0.785276 iter/s, 15.2813s/12 iters), loss = 0.432484
I0407 22:42:10.940152 23786 solver.cpp:237]     Train net output #0: loss = 0.432484 (* 1 = 0.432484 loss)
I0407 22:42:10.940165 23786 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0407 22:42:15.368552 23786 solver.cpp:218] Iteration 5112 (2.70987 iter/s, 4.42826s/12 iters), loss = 0.392673
I0407 22:42:15.368594 23786 solver.cpp:237]     Train net output #0: loss = 0.392673 (* 1 = 0.392673 loss)
I0407 22:42:15.368603 23786 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0407 22:42:20.264739 23786 solver.cpp:218] Iteration 5124 (2.45099 iter/s, 4.89598s/12 iters), loss = 0.451638
I0407 22:42:20.264787 23786 solver.cpp:237]     Train net output #0: loss = 0.451638 (* 1 = 0.451638 loss)
I0407 22:42:20.264798 23786 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0407 22:42:25.326031 23786 solver.cpp:218] Iteration 5136 (2.37104 iter/s, 5.06108s/12 iters), loss = 0.393915
I0407 22:42:25.326081 23786 solver.cpp:237]     Train net output #0: loss = 0.393915 (* 1 = 0.393915 loss)
I0407 22:42:25.326093 23786 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0407 22:42:30.359479 23786 solver.cpp:218] Iteration 5148 (2.38415 iter/s, 5.03324s/12 iters), loss = 0.232418
I0407 22:42:30.359529 23786 solver.cpp:237]     Train net output #0: loss = 0.232418 (* 1 = 0.232418 loss)
I0407 22:42:30.359541 23786 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0407 22:42:34.444291 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:42:35.387178 23786 solver.cpp:218] Iteration 5160 (2.38688 iter/s, 5.02749s/12 iters), loss = 0.294822
I0407 22:42:35.387226 23786 solver.cpp:237]     Train net output #0: loss = 0.294822 (* 1 = 0.294822 loss)
I0407 22:42:35.387238 23786 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0407 22:42:40.370056 23786 solver.cpp:218] Iteration 5172 (2.40835 iter/s, 4.98267s/12 iters), loss = 0.383978
I0407 22:42:40.370105 23786 solver.cpp:237]     Train net output #0: loss = 0.383978 (* 1 = 0.383978 loss)
I0407 22:42:40.370116 23786 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0407 22:42:45.600499 23786 solver.cpp:218] Iteration 5184 (2.29436 iter/s, 5.23022s/12 iters), loss = 0.361516
I0407 22:42:45.600550 23786 solver.cpp:237]     Train net output #0: loss = 0.361516 (* 1 = 0.361516 loss)
I0407 22:42:45.600562 23786 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0407 22:42:50.769207 23786 solver.cpp:218] Iteration 5196 (2.32176 iter/s, 5.16849s/12 iters), loss = 0.338206
I0407 22:42:50.769253 23786 solver.cpp:237]     Train net output #0: loss = 0.338206 (* 1 = 0.338206 loss)
I0407 22:42:50.769265 23786 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0407 22:42:52.844197 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0407 22:42:57.888837 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0407 22:43:00.232962 23786 solver.cpp:330] Iteration 5202, Testing net (#0)
I0407 22:43:00.232987 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:43:02.654225 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:43:04.704663 23786 solver.cpp:397]     Test net output #0: accuracy = 0.409926
I0407 22:43:04.704820 23786 solver.cpp:397]     Test net output #1: loss = 3.0635 (* 1 = 3.0635 loss)
I0407 22:43:06.581480 23786 solver.cpp:218] Iteration 5208 (0.75893 iter/s, 15.8117s/12 iters), loss = 0.367467
I0407 22:43:06.581537 23786 solver.cpp:237]     Train net output #0: loss = 0.367467 (* 1 = 0.367467 loss)
I0407 22:43:06.581548 23786 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0407 22:43:11.604475 23786 solver.cpp:218] Iteration 5220 (2.38912 iter/s, 5.02277s/12 iters), loss = 0.367943
I0407 22:43:11.604534 23786 solver.cpp:237]     Train net output #0: loss = 0.367943 (* 1 = 0.367943 loss)
I0407 22:43:11.604547 23786 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0407 22:43:16.611799 23786 solver.cpp:218] Iteration 5232 (2.3966 iter/s, 5.00709s/12 iters), loss = 0.242785
I0407 22:43:16.611860 23786 solver.cpp:237]     Train net output #0: loss = 0.242785 (* 1 = 0.242785 loss)
I0407 22:43:16.611871 23786 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0407 22:43:21.640728 23786 solver.cpp:218] Iteration 5244 (2.3863 iter/s, 5.0287s/12 iters), loss = 0.415682
I0407 22:43:21.640775 23786 solver.cpp:237]     Train net output #0: loss = 0.415682 (* 1 = 0.415682 loss)
I0407 22:43:21.640784 23786 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0407 22:43:26.684150 23786 solver.cpp:218] Iteration 5256 (2.37944 iter/s, 5.0432s/12 iters), loss = 0.391381
I0407 22:43:26.684199 23786 solver.cpp:237]     Train net output #0: loss = 0.391381 (* 1 = 0.391381 loss)
I0407 22:43:26.684208 23786 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0407 22:43:28.061580 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:43:31.767421 23786 solver.cpp:218] Iteration 5268 (2.36079 iter/s, 5.08305s/12 iters), loss = 0.262516
I0407 22:43:31.767462 23786 solver.cpp:237]     Train net output #0: loss = 0.262516 (* 1 = 0.262516 loss)
I0407 22:43:31.767472 23786 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0407 22:43:36.916347 23786 solver.cpp:218] Iteration 5280 (2.33068 iter/s, 5.14871s/12 iters), loss = 0.291853
I0407 22:43:36.916460 23786 solver.cpp:237]     Train net output #0: loss = 0.291853 (* 1 = 0.291853 loss)
I0407 22:43:36.916474 23786 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0407 22:43:42.265377 23786 solver.cpp:218] Iteration 5292 (2.24352 iter/s, 5.34874s/12 iters), loss = 0.445046
I0407 22:43:42.265431 23786 solver.cpp:237]     Train net output #0: loss = 0.445046 (* 1 = 0.445046 loss)
I0407 22:43:42.265444 23786 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0407 22:43:46.826391 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0407 22:43:51.648360 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0407 22:43:54.010548 23786 solver.cpp:330] Iteration 5304, Testing net (#0)
I0407 22:43:54.010574 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:43:56.387531 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:43:58.524823 23786 solver.cpp:397]     Test net output #0: accuracy = 0.436887
I0407 22:43:58.524873 23786 solver.cpp:397]     Test net output #1: loss = 3.03093 (* 1 = 3.03093 loss)
I0407 22:43:58.615126 23786 solver.cpp:218] Iteration 5304 (0.733982 iter/s, 16.3492s/12 iters), loss = 0.231029
I0407 22:43:58.615175 23786 solver.cpp:237]     Train net output #0: loss = 0.231029 (* 1 = 0.231029 loss)
I0407 22:43:58.615186 23786 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0407 22:44:03.153569 23786 solver.cpp:218] Iteration 5316 (2.6442 iter/s, 4.53824s/12 iters), loss = 0.346941
I0407 22:44:03.153622 23786 solver.cpp:237]     Train net output #0: loss = 0.346941 (* 1 = 0.346941 loss)
I0407 22:44:03.153635 23786 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0407 22:44:08.498425 23786 solver.cpp:218] Iteration 5328 (2.24525 iter/s, 5.34462s/12 iters), loss = 0.434307
I0407 22:44:08.498950 23786 solver.cpp:237]     Train net output #0: loss = 0.434307 (* 1 = 0.434307 loss)
I0407 22:44:08.498962 23786 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0407 22:44:13.422240 23786 solver.cpp:218] Iteration 5340 (2.43748 iter/s, 4.92312s/12 iters), loss = 0.347822
I0407 22:44:13.422291 23786 solver.cpp:237]     Train net output #0: loss = 0.347822 (* 1 = 0.347822 loss)
I0407 22:44:13.422303 23786 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0407 22:44:18.465260 23786 solver.cpp:218] Iteration 5352 (2.37963 iter/s, 5.0428s/12 iters), loss = 0.330027
I0407 22:44:18.465308 23786 solver.cpp:237]     Train net output #0: loss = 0.330027 (* 1 = 0.330027 loss)
I0407 22:44:18.465320 23786 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0407 22:44:21.866725 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:44:23.455282 23786 solver.cpp:218] Iteration 5364 (2.40491 iter/s, 4.9898s/12 iters), loss = 0.30671
I0407 22:44:23.455338 23786 solver.cpp:237]     Train net output #0: loss = 0.30671 (* 1 = 0.30671 loss)
I0407 22:44:23.455350 23786 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0407 22:44:28.442404 23786 solver.cpp:218] Iteration 5376 (2.40631 iter/s, 4.9869s/12 iters), loss = 0.260262
I0407 22:44:28.442456 23786 solver.cpp:237]     Train net output #0: loss = 0.260262 (* 1 = 0.260262 loss)
I0407 22:44:28.442466 23786 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0407 22:44:33.516377 23786 solver.cpp:218] Iteration 5388 (2.36511 iter/s, 5.07375s/12 iters), loss = 0.201169
I0407 22:44:33.516424 23786 solver.cpp:237]     Train net output #0: loss = 0.201169 (* 1 = 0.201169 loss)
I0407 22:44:33.516435 23786 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0407 22:44:38.480903 23786 solver.cpp:218] Iteration 5400 (2.41726 iter/s, 4.9643s/12 iters), loss = 0.369425
I0407 22:44:38.480958 23786 solver.cpp:237]     Train net output #0: loss = 0.369425 (* 1 = 0.369425 loss)
I0407 22:44:38.480971 23786 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0407 22:44:40.520848 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0407 22:44:44.894268 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0407 22:44:47.810058 23786 solver.cpp:330] Iteration 5406, Testing net (#0)
I0407 22:44:47.810081 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:44:50.142096 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:44:52.277055 23786 solver.cpp:397]     Test net output #0: accuracy = 0.416054
I0407 22:44:52.277104 23786 solver.cpp:397]     Test net output #1: loss = 3.11033 (* 1 = 3.11033 loss)
I0407 22:44:53.941588 23786 solver.cpp:218] Iteration 5412 (0.77619 iter/s, 15.4601s/12 iters), loss = 0.454454
I0407 22:44:53.941640 23786 solver.cpp:237]     Train net output #0: loss = 0.454454 (* 1 = 0.454454 loss)
I0407 22:44:53.941653 23786 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0407 22:44:58.919137 23786 solver.cpp:218] Iteration 5424 (2.41093 iter/s, 4.97732s/12 iters), loss = 0.270656
I0407 22:44:58.919183 23786 solver.cpp:237]     Train net output #0: loss = 0.270656 (* 1 = 0.270656 loss)
I0407 22:44:58.919191 23786 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0407 22:45:04.004236 23786 solver.cpp:218] Iteration 5436 (2.35994 iter/s, 5.08488s/12 iters), loss = 0.195779
I0407 22:45:04.004287 23786 solver.cpp:237]     Train net output #0: loss = 0.195779 (* 1 = 0.195779 loss)
I0407 22:45:04.004297 23786 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0407 22:45:09.094003 23786 solver.cpp:218] Iteration 5448 (2.35779 iter/s, 5.08951s/12 iters), loss = 0.329875
I0407 22:45:09.094069 23786 solver.cpp:237]     Train net output #0: loss = 0.329875 (* 1 = 0.329875 loss)
I0407 22:45:09.094085 23786 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0407 22:45:14.167307 23786 solver.cpp:218] Iteration 5460 (2.36543 iter/s, 5.07307s/12 iters), loss = 0.198689
I0407 22:45:14.167415 23786 solver.cpp:237]     Train net output #0: loss = 0.198689 (* 1 = 0.198689 loss)
I0407 22:45:14.167424 23786 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0407 22:45:14.732215 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:45:19.248375 23786 solver.cpp:218] Iteration 5472 (2.36184 iter/s, 5.08078s/12 iters), loss = 0.413382
I0407 22:45:19.248415 23786 solver.cpp:237]     Train net output #0: loss = 0.413382 (* 1 = 0.413382 loss)
I0407 22:45:19.248425 23786 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0407 22:45:24.269073 23786 solver.cpp:218] Iteration 5484 (2.39021 iter/s, 5.02048s/12 iters), loss = 0.279463
I0407 22:45:24.269134 23786 solver.cpp:237]     Train net output #0: loss = 0.279463 (* 1 = 0.279463 loss)
I0407 22:45:24.269145 23786 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0407 22:45:29.338563 23786 solver.cpp:218] Iteration 5496 (2.36721 iter/s, 5.06925s/12 iters), loss = 0.362206
I0407 22:45:29.338615 23786 solver.cpp:237]     Train net output #0: loss = 0.362206 (* 1 = 0.362206 loss)
I0407 22:45:29.338629 23786 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0407 22:45:33.913225 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0407 22:45:40.532992 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0407 22:45:48.735991 23786 solver.cpp:330] Iteration 5508, Testing net (#0)
I0407 22:45:48.736085 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:45:51.013602 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:45:53.191154 23786 solver.cpp:397]     Test net output #0: accuracy = 0.434436
I0407 22:45:53.191184 23786 solver.cpp:397]     Test net output #1: loss = 2.97191 (* 1 = 2.97191 loss)
I0407 22:45:53.278543 23786 solver.cpp:218] Iteration 5508 (0.501271 iter/s, 23.9391s/12 iters), loss = 0.470912
I0407 22:45:53.278589 23786 solver.cpp:237]     Train net output #0: loss = 0.470912 (* 1 = 0.470912 loss)
I0407 22:45:53.278597 23786 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0407 22:45:57.854691 23786 solver.cpp:218] Iteration 5520 (2.62241 iter/s, 4.57594s/12 iters), loss = 0.356867
I0407 22:45:57.854735 23786 solver.cpp:237]     Train net output #0: loss = 0.356867 (* 1 = 0.356867 loss)
I0407 22:45:57.854744 23786 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0407 22:46:00.508745 23786 blocking_queue.cpp:49] Waiting for data
I0407 22:46:03.287376 23786 solver.cpp:218] Iteration 5532 (2.20895 iter/s, 5.43245s/12 iters), loss = 0.324591
I0407 22:46:03.287431 23786 solver.cpp:237]     Train net output #0: loss = 0.324591 (* 1 = 0.324591 loss)
I0407 22:46:03.287446 23786 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0407 22:46:08.237555 23786 solver.cpp:218] Iteration 5544 (2.42426 iter/s, 4.94995s/12 iters), loss = 0.218017
I0407 22:46:08.237596 23786 solver.cpp:237]     Train net output #0: loss = 0.218017 (* 1 = 0.218017 loss)
I0407 22:46:08.237605 23786 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0407 22:46:13.231190 23786 solver.cpp:218] Iteration 5556 (2.40316 iter/s, 4.99342s/12 iters), loss = 0.227316
I0407 22:46:13.231228 23786 solver.cpp:237]     Train net output #0: loss = 0.227316 (* 1 = 0.227316 loss)
I0407 22:46:13.231236 23786 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0407 22:46:15.935838 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:46:18.217677 23786 solver.cpp:218] Iteration 5568 (2.40661 iter/s, 4.98627s/12 iters), loss = 0.212149
I0407 22:46:18.217725 23786 solver.cpp:237]     Train net output #0: loss = 0.212149 (* 1 = 0.212149 loss)
I0407 22:46:18.217736 23786 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0407 22:46:23.301559 23786 solver.cpp:218] Iteration 5580 (2.36051 iter/s, 5.08365s/12 iters), loss = 0.175144
I0407 22:46:23.301651 23786 solver.cpp:237]     Train net output #0: loss = 0.175144 (* 1 = 0.175144 loss)
I0407 22:46:23.301661 23786 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0407 22:46:28.255414 23786 solver.cpp:218] Iteration 5592 (2.42249 iter/s, 4.95359s/12 iters), loss = 0.13916
I0407 22:46:28.255457 23786 solver.cpp:237]     Train net output #0: loss = 0.13916 (* 1 = 0.13916 loss)
I0407 22:46:28.255466 23786 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0407 22:46:33.349767 23786 solver.cpp:218] Iteration 5604 (2.35566 iter/s, 5.09412s/12 iters), loss = 0.227095
I0407 22:46:33.349822 23786 solver.cpp:237]     Train net output #0: loss = 0.227095 (* 1 = 0.227095 loss)
I0407 22:46:33.349834 23786 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0407 22:46:35.383452 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0407 22:46:43.840509 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0407 22:46:50.433051 23786 solver.cpp:330] Iteration 5610, Testing net (#0)
I0407 22:46:50.433073 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:46:52.665812 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:46:54.878347 23786 solver.cpp:397]     Test net output #0: accuracy = 0.444853
I0407 22:46:54.878492 23786 solver.cpp:397]     Test net output #1: loss = 2.94146 (* 1 = 2.94146 loss)
I0407 22:46:56.812650 23786 solver.cpp:218] Iteration 5616 (0.511464 iter/s, 23.462s/12 iters), loss = 0.238328
I0407 22:46:56.812690 23786 solver.cpp:237]     Train net output #0: loss = 0.238328 (* 1 = 0.238328 loss)
I0407 22:46:56.812700 23786 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0407 22:47:01.880149 23786 solver.cpp:218] Iteration 5628 (2.36814 iter/s, 5.06728s/12 iters), loss = 0.170592
I0407 22:47:01.880199 23786 solver.cpp:237]     Train net output #0: loss = 0.170592 (* 1 = 0.170592 loss)
I0407 22:47:01.880211 23786 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0407 22:47:06.978168 23786 solver.cpp:218] Iteration 5640 (2.35396 iter/s, 5.09779s/12 iters), loss = 0.255004
I0407 22:47:06.978217 23786 solver.cpp:237]     Train net output #0: loss = 0.255004 (* 1 = 0.255004 loss)
I0407 22:47:06.978229 23786 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0407 22:47:11.967916 23786 solver.cpp:218] Iteration 5652 (2.40504 iter/s, 4.98952s/12 iters), loss = 0.369527
I0407 22:47:11.967972 23786 solver.cpp:237]     Train net output #0: loss = 0.369527 (* 1 = 0.369527 loss)
I0407 22:47:11.967988 23786 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0407 22:47:16.926261 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:47:17.095221 23786 solver.cpp:218] Iteration 5664 (2.34052 iter/s, 5.12707s/12 iters), loss = 0.577704
I0407 22:47:17.095278 23786 solver.cpp:237]     Train net output #0: loss = 0.577704 (* 1 = 0.577704 loss)
I0407 22:47:17.095289 23786 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0407 22:47:22.279165 23786 solver.cpp:218] Iteration 5676 (2.31495 iter/s, 5.1837s/12 iters), loss = 0.234215
I0407 22:47:22.279215 23786 solver.cpp:237]     Train net output #0: loss = 0.234215 (* 1 = 0.234215 loss)
I0407 22:47:22.279227 23786 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0407 22:47:27.313743 23786 solver.cpp:218] Iteration 5688 (2.38363 iter/s, 5.03434s/12 iters), loss = 0.442588
I0407 22:47:27.313838 23786 solver.cpp:237]     Train net output #0: loss = 0.442588 (* 1 = 0.442588 loss)
I0407 22:47:27.313850 23786 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0407 22:47:32.307945 23786 solver.cpp:218] Iteration 5700 (2.40292 iter/s, 4.99393s/12 iters), loss = 0.418562
I0407 22:47:32.307999 23786 solver.cpp:237]     Train net output #0: loss = 0.418562 (* 1 = 0.418562 loss)
I0407 22:47:32.308012 23786 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0407 22:47:36.803056 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0407 22:47:42.019439 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0407 22:47:46.244719 23786 solver.cpp:330] Iteration 5712, Testing net (#0)
I0407 22:47:46.244745 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:47:48.454926 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:47:50.699306 23786 solver.cpp:397]     Test net output #0: accuracy = 0.439338
I0407 22:47:50.699353 23786 solver.cpp:397]     Test net output #1: loss = 3.00069 (* 1 = 3.00069 loss)
I0407 22:47:50.789748 23786 solver.cpp:218] Iteration 5712 (0.649311 iter/s, 18.4811s/12 iters), loss = 0.300264
I0407 22:47:50.789796 23786 solver.cpp:237]     Train net output #0: loss = 0.300264 (* 1 = 0.300264 loss)
I0407 22:47:50.789808 23786 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0407 22:47:55.087165 23786 solver.cpp:218] Iteration 5724 (2.79251 iter/s, 4.29721s/12 iters), loss = 0.331538
I0407 22:47:55.087215 23786 solver.cpp:237]     Train net output #0: loss = 0.331538 (* 1 = 0.331538 loss)
I0407 22:47:55.087226 23786 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0407 22:48:00.116868 23786 solver.cpp:218] Iteration 5736 (2.38593 iter/s, 5.02948s/12 iters), loss = 0.375355
I0407 22:48:00.117020 23786 solver.cpp:237]     Train net output #0: loss = 0.375355 (* 1 = 0.375355 loss)
I0407 22:48:00.117033 23786 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0407 22:48:05.197739 23786 solver.cpp:218] Iteration 5748 (2.36195 iter/s, 5.08054s/12 iters), loss = 0.131449
I0407 22:48:05.197786 23786 solver.cpp:237]     Train net output #0: loss = 0.131449 (* 1 = 0.131449 loss)
I0407 22:48:05.197795 23786 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0407 22:48:10.249994 23786 solver.cpp:218] Iteration 5760 (2.37529 iter/s, 5.05201s/12 iters), loss = 0.13245
I0407 22:48:10.250037 23786 solver.cpp:237]     Train net output #0: loss = 0.13245 (* 1 = 0.13245 loss)
I0407 22:48:10.250047 23786 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0407 22:48:12.229066 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:48:15.169935 23786 solver.cpp:218] Iteration 5772 (2.43916 iter/s, 4.91972s/12 iters), loss = 0.175376
I0407 22:48:15.169996 23786 solver.cpp:237]     Train net output #0: loss = 0.175376 (* 1 = 0.175376 loss)
I0407 22:48:15.170007 23786 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0407 22:48:20.217797 23786 solver.cpp:218] Iteration 5784 (2.37736 iter/s, 5.04762s/12 iters), loss = 0.194023
I0407 22:48:20.217849 23786 solver.cpp:237]     Train net output #0: loss = 0.194023 (* 1 = 0.194023 loss)
I0407 22:48:20.217862 23786 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0407 22:48:25.408090 23786 solver.cpp:218] Iteration 5796 (2.31211 iter/s, 5.19006s/12 iters), loss = 0.27372
I0407 22:48:25.408134 23786 solver.cpp:237]     Train net output #0: loss = 0.27372 (* 1 = 0.27372 loss)
I0407 22:48:25.408145 23786 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0407 22:48:30.389796 23786 solver.cpp:218] Iteration 5808 (2.40892 iter/s, 4.98148s/12 iters), loss = 0.18596
I0407 22:48:30.392045 23786 solver.cpp:237]     Train net output #0: loss = 0.18596 (* 1 = 0.18596 loss)
I0407 22:48:30.392056 23786 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0407 22:48:32.398917 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0407 22:48:36.707526 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0407 22:48:42.363101 23786 solver.cpp:330] Iteration 5814, Testing net (#0)
I0407 22:48:42.363130 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:48:44.548305 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:48:46.838603 23786 solver.cpp:397]     Test net output #0: accuracy = 0.430147
I0407 22:48:46.838650 23786 solver.cpp:397]     Test net output #1: loss = 2.99732 (* 1 = 2.99732 loss)
I0407 22:48:48.690062 23786 solver.cpp:218] Iteration 5820 (0.655831 iter/s, 18.2974s/12 iters), loss = 0.340563
I0407 22:48:48.690110 23786 solver.cpp:237]     Train net output #0: loss = 0.340563 (* 1 = 0.340563 loss)
I0407 22:48:48.690121 23786 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0407 22:48:53.683199 23786 solver.cpp:218] Iteration 5832 (2.40341 iter/s, 4.99291s/12 iters), loss = 0.27843
I0407 22:48:53.683243 23786 solver.cpp:237]     Train net output #0: loss = 0.27843 (* 1 = 0.27843 loss)
I0407 22:48:53.683252 23786 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0407 22:48:58.864848 23786 solver.cpp:218] Iteration 5844 (2.31597 iter/s, 5.18142s/12 iters), loss = 0.212924
I0407 22:48:58.864889 23786 solver.cpp:237]     Train net output #0: loss = 0.212924 (* 1 = 0.212924 loss)
I0407 22:48:58.864898 23786 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0407 22:49:03.851554 23786 solver.cpp:218] Iteration 5856 (2.4065 iter/s, 4.98649s/12 iters), loss = 0.107316
I0407 22:49:03.851668 23786 solver.cpp:237]     Train net output #0: loss = 0.107316 (* 1 = 0.107316 loss)
I0407 22:49:03.851680 23786 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0407 22:49:08.075227 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:49:08.889889 23786 solver.cpp:218] Iteration 5868 (2.38188 iter/s, 5.03804s/12 iters), loss = 0.262342
I0407 22:49:08.889938 23786 solver.cpp:237]     Train net output #0: loss = 0.262342 (* 1 = 0.262342 loss)
I0407 22:49:08.889950 23786 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0407 22:49:13.892757 23786 solver.cpp:218] Iteration 5880 (2.39874 iter/s, 5.00264s/12 iters), loss = 0.244992
I0407 22:49:13.892805 23786 solver.cpp:237]     Train net output #0: loss = 0.244992 (* 1 = 0.244992 loss)
I0407 22:49:13.892814 23786 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0407 22:49:18.912770 23786 solver.cpp:218] Iteration 5892 (2.39054 iter/s, 5.01978s/12 iters), loss = 0.313053
I0407 22:49:18.912828 23786 solver.cpp:237]     Train net output #0: loss = 0.313053 (* 1 = 0.313053 loss)
I0407 22:49:18.912840 23786 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0407 22:49:23.934962 23786 solver.cpp:218] Iteration 5904 (2.38951 iter/s, 5.02195s/12 iters), loss = 0.299528
I0407 22:49:23.935009 23786 solver.cpp:237]     Train net output #0: loss = 0.299528 (* 1 = 0.299528 loss)
I0407 22:49:23.935017 23786 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0407 22:49:28.536491 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0407 22:49:36.343336 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0407 22:49:43.611732 23786 solver.cpp:330] Iteration 5916, Testing net (#0)
I0407 22:49:43.611758 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:49:45.780320 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:49:48.107733 23786 solver.cpp:397]     Test net output #0: accuracy = 0.441176
I0407 22:49:48.107772 23786 solver.cpp:397]     Test net output #1: loss = 2.96435 (* 1 = 2.96435 loss)
I0407 22:49:48.198079 23786 solver.cpp:218] Iteration 5916 (0.494596 iter/s, 24.2622s/12 iters), loss = 0.304816
I0407 22:49:48.198132 23786 solver.cpp:237]     Train net output #0: loss = 0.304816 (* 1 = 0.304816 loss)
I0407 22:49:48.198144 23786 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0407 22:49:52.779342 23786 solver.cpp:218] Iteration 5928 (2.61949 iter/s, 4.58104s/12 iters), loss = 0.284551
I0407 22:49:52.779388 23786 solver.cpp:237]     Train net output #0: loss = 0.284551 (* 1 = 0.284551 loss)
I0407 22:49:52.779397 23786 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0407 22:49:57.727557 23786 solver.cpp:218] Iteration 5940 (2.42523 iter/s, 4.94799s/12 iters), loss = 0.28898
I0407 22:49:57.727603 23786 solver.cpp:237]     Train net output #0: loss = 0.28898 (* 1 = 0.28898 loss)
I0407 22:49:57.727614 23786 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0407 22:50:02.751587 23786 solver.cpp:218] Iteration 5952 (2.38863 iter/s, 5.02381s/12 iters), loss = 0.247142
I0407 22:50:02.751623 23786 solver.cpp:237]     Train net output #0: loss = 0.247142 (* 1 = 0.247142 loss)
I0407 22:50:02.751631 23786 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0407 22:50:07.786753 23786 solver.cpp:218] Iteration 5964 (2.38334 iter/s, 5.03495s/12 iters), loss = 0.258398
I0407 22:50:07.786852 23786 solver.cpp:237]     Train net output #0: loss = 0.258398 (* 1 = 0.258398 loss)
I0407 22:50:07.786861 23786 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0407 22:50:09.115720 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:50:12.744454 23786 solver.cpp:218] Iteration 5976 (2.42062 iter/s, 4.95742s/12 iters), loss = 0.228116
I0407 22:50:12.744516 23786 solver.cpp:237]     Train net output #0: loss = 0.228116 (* 1 = 0.228116 loss)
I0407 22:50:12.744529 23786 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0407 22:50:17.663283 23786 solver.cpp:218] Iteration 5988 (2.43972 iter/s, 4.91859s/12 iters), loss = 0.157074
I0407 22:50:17.663336 23786 solver.cpp:237]     Train net output #0: loss = 0.157074 (* 1 = 0.157074 loss)
I0407 22:50:17.663347 23786 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0407 22:50:22.762147 23786 solver.cpp:218] Iteration 6000 (2.35358 iter/s, 5.09862s/12 iters), loss = 0.241336
I0407 22:50:22.762202 23786 solver.cpp:237]     Train net output #0: loss = 0.241336 (* 1 = 0.241336 loss)
I0407 22:50:22.762213 23786 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0407 22:50:28.065346 23786 solver.cpp:218] Iteration 6012 (2.26289 iter/s, 5.30295s/12 iters), loss = 0.0959809
I0407 22:50:28.065397 23786 solver.cpp:237]     Train net output #0: loss = 0.0959809 (* 1 = 0.0959809 loss)
I0407 22:50:28.065407 23786 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0407 22:50:30.289665 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0407 22:50:34.213047 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0407 22:50:37.962131 23786 solver.cpp:330] Iteration 6018, Testing net (#0)
I0407 22:50:37.962239 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:50:40.053292 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:50:42.556452 23786 solver.cpp:397]     Test net output #0: accuracy = 0.431373
I0407 22:50:42.556501 23786 solver.cpp:397]     Test net output #1: loss = 2.9474 (* 1 = 2.9474 loss)
I0407 22:50:44.543725 23786 solver.cpp:218] Iteration 6024 (0.728254 iter/s, 16.4778s/12 iters), loss = 0.242946
I0407 22:50:44.543763 23786 solver.cpp:237]     Train net output #0: loss = 0.242946 (* 1 = 0.242946 loss)
I0407 22:50:44.543771 23786 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0407 22:50:49.489986 23786 solver.cpp:218] Iteration 6036 (2.42618 iter/s, 4.94604s/12 iters), loss = 0.1766
I0407 22:50:49.490041 23786 solver.cpp:237]     Train net output #0: loss = 0.1766 (* 1 = 0.1766 loss)
I0407 22:50:49.490056 23786 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0407 22:50:54.523847 23786 solver.cpp:218] Iteration 6048 (2.38397 iter/s, 5.03362s/12 iters), loss = 0.161271
I0407 22:50:54.523886 23786 solver.cpp:237]     Train net output #0: loss = 0.161271 (* 1 = 0.161271 loss)
I0407 22:50:54.523895 23786 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0407 22:50:59.489274 23786 solver.cpp:218] Iteration 6060 (2.41682 iter/s, 4.9652s/12 iters), loss = 0.18846
I0407 22:50:59.489315 23786 solver.cpp:237]     Train net output #0: loss = 0.18846 (* 1 = 0.18846 loss)
I0407 22:50:59.489324 23786 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0407 22:51:02.985208 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:51:04.529136 23786 solver.cpp:218] Iteration 6072 (2.38113 iter/s, 5.03963s/12 iters), loss = 0.18868
I0407 22:51:04.529183 23786 solver.cpp:237]     Train net output #0: loss = 0.188681 (* 1 = 0.188681 loss)
I0407 22:51:04.529191 23786 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0407 22:51:09.549374 23786 solver.cpp:218] Iteration 6084 (2.39044 iter/s, 5.02s/12 iters), loss = 0.164883
I0407 22:51:09.549504 23786 solver.cpp:237]     Train net output #0: loss = 0.164883 (* 1 = 0.164883 loss)
I0407 22:51:09.549517 23786 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0407 22:51:14.590144 23786 solver.cpp:218] Iteration 6096 (2.38074 iter/s, 5.04045s/12 iters), loss = 0.227613
I0407 22:51:14.590193 23786 solver.cpp:237]     Train net output #0: loss = 0.227613 (* 1 = 0.227613 loss)
I0407 22:51:14.590204 23786 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0407 22:51:19.488011 23786 solver.cpp:218] Iteration 6108 (2.45016 iter/s, 4.89763s/12 iters), loss = 0.292132
I0407 22:51:19.488065 23786 solver.cpp:237]     Train net output #0: loss = 0.292132 (* 1 = 0.292132 loss)
I0407 22:51:19.488075 23786 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0407 22:51:24.307509 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0407 22:51:27.339484 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0407 22:51:30.973052 23786 solver.cpp:330] Iteration 6120, Testing net (#0)
I0407 22:51:30.973083 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:51:33.013167 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:51:35.417788 23786 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0407 22:51:35.417836 23786 solver.cpp:397]     Test net output #1: loss = 2.97471 (* 1 = 2.97471 loss)
I0407 22:51:35.508311 23786 solver.cpp:218] Iteration 6120 (0.749079 iter/s, 16.0197s/12 iters), loss = 0.114227
I0407 22:51:35.508363 23786 solver.cpp:237]     Train net output #0: loss = 0.114227 (* 1 = 0.114227 loss)
I0407 22:51:35.508375 23786 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0407 22:51:40.047092 23786 solver.cpp:218] Iteration 6132 (2.64401 iter/s, 4.53855s/12 iters), loss = 0.166179
I0407 22:51:40.047231 23786 solver.cpp:237]     Train net output #0: loss = 0.166179 (* 1 = 0.166179 loss)
I0407 22:51:40.047241 23786 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0407 22:51:45.115546 23786 solver.cpp:218] Iteration 6144 (2.36774 iter/s, 5.06813s/12 iters), loss = 0.187151
I0407 22:51:45.115581 23786 solver.cpp:237]     Train net output #0: loss = 0.187151 (* 1 = 0.187151 loss)
I0407 22:51:45.115589 23786 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0407 22:51:50.146524 23786 solver.cpp:218] Iteration 6156 (2.38533 iter/s, 5.03076s/12 iters), loss = 0.14319
I0407 22:51:50.146561 23786 solver.cpp:237]     Train net output #0: loss = 0.14319 (* 1 = 0.14319 loss)
I0407 22:51:50.146569 23786 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0407 22:51:55.151940 23786 solver.cpp:218] Iteration 6168 (2.39751 iter/s, 5.00519s/12 iters), loss = 0.247055
I0407 22:51:55.151983 23786 solver.cpp:237]     Train net output #0: loss = 0.247055 (* 1 = 0.247055 loss)
I0407 22:51:55.151993 23786 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0407 22:51:55.745390 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:00.107668 23786 solver.cpp:218] Iteration 6180 (2.42155 iter/s, 4.9555s/12 iters), loss = 0.215211
I0407 22:52:00.107712 23786 solver.cpp:237]     Train net output #0: loss = 0.215211 (* 1 = 0.215211 loss)
I0407 22:52:00.107722 23786 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0407 22:52:05.085769 23786 solver.cpp:218] Iteration 6192 (2.41067 iter/s, 4.97787s/12 iters), loss = 0.152034
I0407 22:52:05.085834 23786 solver.cpp:237]     Train net output #0: loss = 0.152034 (* 1 = 0.152034 loss)
I0407 22:52:05.085850 23786 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0407 22:52:10.117997 23786 solver.cpp:218] Iteration 6204 (2.38475 iter/s, 5.03198s/12 iters), loss = 0.169281
I0407 22:52:10.118129 23786 solver.cpp:237]     Train net output #0: loss = 0.169281 (* 1 = 0.169281 loss)
I0407 22:52:10.118144 23786 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0407 22:52:15.134825 23786 solver.cpp:218] Iteration 6216 (2.3921 iter/s, 5.01651s/12 iters), loss = 0.3291
I0407 22:52:15.134872 23786 solver.cpp:237]     Train net output #0: loss = 0.3291 (* 1 = 0.3291 loss)
I0407 22:52:15.134881 23786 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0407 22:52:17.225737 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0407 22:52:20.256229 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0407 22:52:22.668920 23786 solver.cpp:330] Iteration 6222, Testing net (#0)
I0407 22:52:22.668947 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:52:24.589836 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:25.877772 23786 blocking_queue.cpp:49] Waiting for data
I0407 22:52:27.074112 23786 solver.cpp:397]     Test net output #0: accuracy = 0.450368
I0407 22:52:27.074162 23786 solver.cpp:397]     Test net output #1: loss = 2.98558 (* 1 = 2.98558 loss)
I0407 22:52:28.949106 23786 solver.cpp:218] Iteration 6228 (0.8687 iter/s, 13.8137s/12 iters), loss = 0.212001
I0407 22:52:28.949151 23786 solver.cpp:237]     Train net output #0: loss = 0.212001 (* 1 = 0.212001 loss)
I0407 22:52:28.949162 23786 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0407 22:52:33.939798 23786 solver.cpp:218] Iteration 6240 (2.40459 iter/s, 4.99046s/12 iters), loss = 0.25004
I0407 22:52:33.939848 23786 solver.cpp:237]     Train net output #0: loss = 0.25004 (* 1 = 0.25004 loss)
I0407 22:52:33.939859 23786 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0407 22:52:38.943711 23786 solver.cpp:218] Iteration 6252 (2.39824 iter/s, 5.00368s/12 iters), loss = 0.148102
I0407 22:52:38.943763 23786 solver.cpp:237]     Train net output #0: loss = 0.148102 (* 1 = 0.148102 loss)
I0407 22:52:38.943774 23786 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0407 22:52:43.976541 23786 solver.cpp:218] Iteration 6264 (2.38446 iter/s, 5.03259s/12 iters), loss = 0.108369
I0407 22:52:43.977706 23786 solver.cpp:237]     Train net output #0: loss = 0.108369 (* 1 = 0.108369 loss)
I0407 22:52:43.977720 23786 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0407 22:52:46.728814 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:48.967319 23786 solver.cpp:218] Iteration 6276 (2.40509 iter/s, 4.98943s/12 iters), loss = 0.157652
I0407 22:52:48.967371 23786 solver.cpp:237]     Train net output #0: loss = 0.157652 (* 1 = 0.157652 loss)
I0407 22:52:48.967383 23786 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0407 22:52:54.011905 23786 solver.cpp:218] Iteration 6288 (2.3789 iter/s, 5.04435s/12 iters), loss = 0.297536
I0407 22:52:54.011950 23786 solver.cpp:237]     Train net output #0: loss = 0.297536 (* 1 = 0.297536 loss)
I0407 22:52:54.011961 23786 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0407 22:52:59.062534 23786 solver.cpp:218] Iteration 6300 (2.37605 iter/s, 5.05039s/12 iters), loss = 0.135169
I0407 22:52:59.062587 23786 solver.cpp:237]     Train net output #0: loss = 0.135169 (* 1 = 0.135169 loss)
I0407 22:52:59.062599 23786 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0407 22:53:04.162941 23786 solver.cpp:218] Iteration 6312 (2.35286 iter/s, 5.10017s/12 iters), loss = 0.205765
I0407 22:53:04.162984 23786 solver.cpp:237]     Train net output #0: loss = 0.205765 (* 1 = 0.205765 loss)
I0407 22:53:04.162995 23786 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0407 22:53:08.897125 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0407 22:53:11.925988 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0407 22:53:14.313591 23786 solver.cpp:330] Iteration 6324, Testing net (#0)
I0407 22:53:14.313666 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:53:16.306453 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:53:18.789036 23786 solver.cpp:397]     Test net output #0: accuracy = 0.439338
I0407 22:53:18.789074 23786 solver.cpp:397]     Test net output #1: loss = 3.09817 (* 1 = 3.09817 loss)
I0407 22:53:18.879416 23786 solver.cpp:218] Iteration 6324 (0.815444 iter/s, 14.7159s/12 iters), loss = 0.162368
I0407 22:53:18.879470 23786 solver.cpp:237]     Train net output #0: loss = 0.162368 (* 1 = 0.162368 loss)
I0407 22:53:18.879482 23786 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0407 22:53:23.319573 23786 solver.cpp:218] Iteration 6336 (2.70274 iter/s, 4.43994s/12 iters), loss = 0.14092
I0407 22:53:23.319619 23786 solver.cpp:237]     Train net output #0: loss = 0.14092 (* 1 = 0.14092 loss)
I0407 22:53:23.319629 23786 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0407 22:53:28.343070 23786 solver.cpp:218] Iteration 6348 (2.38889 iter/s, 5.02326s/12 iters), loss = 0.0645294
I0407 22:53:28.343124 23786 solver.cpp:237]     Train net output #0: loss = 0.0645295 (* 1 = 0.0645295 loss)
I0407 22:53:28.343135 23786 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0407 22:53:33.318972 23786 solver.cpp:218] Iteration 6360 (2.41174 iter/s, 4.97566s/12 iters), loss = 0.0650425
I0407 22:53:33.319028 23786 solver.cpp:237]     Train net output #0: loss = 0.0650425 (* 1 = 0.0650425 loss)
I0407 22:53:33.319044 23786 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0407 22:53:38.165267 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:53:38.300779 23786 solver.cpp:218] Iteration 6372 (2.40888 iter/s, 4.98157s/12 iters), loss = 0.236628
I0407 22:53:38.300828 23786 solver.cpp:237]     Train net output #0: loss = 0.236628 (* 1 = 0.236628 loss)
I0407 22:53:38.300840 23786 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0407 22:53:43.276052 23786 solver.cpp:218] Iteration 6384 (2.41204 iter/s, 4.97504s/12 iters), loss = 0.269293
I0407 22:53:43.276096 23786 solver.cpp:237]     Train net output #0: loss = 0.269293 (* 1 = 0.269293 loss)
I0407 22:53:43.276106 23786 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0407 22:53:48.284698 23786 solver.cpp:218] Iteration 6396 (2.39597 iter/s, 5.00841s/12 iters), loss = 0.139214
I0407 22:53:48.284824 23786 solver.cpp:237]     Train net output #0: loss = 0.139214 (* 1 = 0.139214 loss)
I0407 22:53:48.284837 23786 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0407 22:53:53.320942 23786 solver.cpp:218] Iteration 6408 (2.38288 iter/s, 5.03593s/12 iters), loss = 0.173014
I0407 22:53:53.320994 23786 solver.cpp:237]     Train net output #0: loss = 0.173014 (* 1 = 0.173014 loss)
I0407 22:53:53.321007 23786 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0407 22:53:58.321507 23786 solver.cpp:218] Iteration 6420 (2.39984 iter/s, 5.00033s/12 iters), loss = 0.216799
I0407 22:53:58.321550 23786 solver.cpp:237]     Train net output #0: loss = 0.216799 (* 1 = 0.216799 loss)
I0407 22:53:58.321559 23786 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0407 22:54:00.387920 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0407 22:54:03.360134 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0407 22:54:05.725570 23786 solver.cpp:330] Iteration 6426, Testing net (#0)
I0407 22:54:05.725594 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:54:07.578488 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:54:10.141681 23786 solver.cpp:397]     Test net output #0: accuracy = 0.452206
I0407 22:54:10.141731 23786 solver.cpp:397]     Test net output #1: loss = 2.97769 (* 1 = 2.97769 loss)
I0407 22:54:12.020927 23786 solver.cpp:218] Iteration 6432 (0.875984 iter/s, 13.6989s/12 iters), loss = 0.20708
I0407 22:54:12.020967 23786 solver.cpp:237]     Train net output #0: loss = 0.207081 (* 1 = 0.207081 loss)
I0407 22:54:12.020975 23786 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0407 22:54:17.008441 23786 solver.cpp:218] Iteration 6444 (2.40612 iter/s, 4.98728s/12 iters), loss = 0.167214
I0407 22:54:17.008486 23786 solver.cpp:237]     Train net output #0: loss = 0.167214 (* 1 = 0.167214 loss)
I0407 22:54:17.008494 23786 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0407 22:54:21.973574 23786 solver.cpp:218] Iteration 6456 (2.41697 iter/s, 4.9649s/12 iters), loss = 0.149551
I0407 22:54:21.973667 23786 solver.cpp:237]     Train net output #0: loss = 0.149551 (* 1 = 0.149551 loss)
I0407 22:54:21.973678 23786 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0407 22:54:27.035630 23786 solver.cpp:218] Iteration 6468 (2.37071 iter/s, 5.06178s/12 iters), loss = 0.30495
I0407 22:54:27.035672 23786 solver.cpp:237]     Train net output #0: loss = 0.30495 (* 1 = 0.30495 loss)
I0407 22:54:27.035681 23786 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0407 22:54:29.040892 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:54:32.039465 23786 solver.cpp:218] Iteration 6480 (2.39827 iter/s, 5.0036s/12 iters), loss = 0.112137
I0407 22:54:32.039510 23786 solver.cpp:237]     Train net output #0: loss = 0.112137 (* 1 = 0.112137 loss)
I0407 22:54:32.039521 23786 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0407 22:54:37.028136 23786 solver.cpp:218] Iteration 6492 (2.40556 iter/s, 4.98844s/12 iters), loss = 0.12046
I0407 22:54:37.028182 23786 solver.cpp:237]     Train net output #0: loss = 0.12046 (* 1 = 0.12046 loss)
I0407 22:54:37.028192 23786 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0407 22:54:42.112272 23786 solver.cpp:218] Iteration 6504 (2.36039 iter/s, 5.0839s/12 iters), loss = 0.199035
I0407 22:54:42.112319 23786 solver.cpp:237]     Train net output #0: loss = 0.199035 (* 1 = 0.199035 loss)
I0407 22:54:42.112330 23786 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0407 22:54:47.065011 23786 solver.cpp:218] Iteration 6516 (2.42302 iter/s, 4.9525s/12 iters), loss = 0.261211
I0407 22:54:47.065058 23786 solver.cpp:237]     Train net output #0: loss = 0.261211 (* 1 = 0.261211 loss)
I0407 22:54:47.065069 23786 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0407 22:54:51.602185 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0407 22:54:54.668568 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0407 22:54:57.021531 23786 solver.cpp:330] Iteration 6528, Testing net (#0)
I0407 22:54:57.021557 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:54:59.028692 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:01.674794 23786 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0407 22:55:01.674831 23786 solver.cpp:397]     Test net output #1: loss = 3.09426 (* 1 = 3.09426 loss)
I0407 22:55:01.765153 23786 solver.cpp:218] Iteration 6528 (0.816351 iter/s, 14.6996s/12 iters), loss = 0.0863287
I0407 22:55:01.765195 23786 solver.cpp:237]     Train net output #0: loss = 0.0863287 (* 1 = 0.0863287 loss)
I0407 22:55:01.765204 23786 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0407 22:55:05.973502 23786 solver.cpp:218] Iteration 6540 (2.85161 iter/s, 4.20814s/12 iters), loss = 0.23206
I0407 22:55:05.973558 23786 solver.cpp:237]     Train net output #0: loss = 0.23206 (* 1 = 0.23206 loss)
I0407 22:55:05.973570 23786 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0407 22:55:10.971215 23786 solver.cpp:218] Iteration 6552 (2.40122 iter/s, 4.99747s/12 iters), loss = 0.0788796
I0407 22:55:10.971261 23786 solver.cpp:237]     Train net output #0: loss = 0.0788797 (* 1 = 0.0788797 loss)
I0407 22:55:10.971271 23786 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0407 22:55:16.023867 23786 solver.cpp:218] Iteration 6564 (2.3751 iter/s, 5.05242s/12 iters), loss = 0.127893
I0407 22:55:16.023912 23786 solver.cpp:237]     Train net output #0: loss = 0.127893 (* 1 = 0.127893 loss)
I0407 22:55:16.023923 23786 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0407 22:55:20.249663 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:21.021298 23786 solver.cpp:218] Iteration 6576 (2.40135 iter/s, 4.99719s/12 iters), loss = 0.175262
I0407 22:55:21.021351 23786 solver.cpp:237]     Train net output #0: loss = 0.175262 (* 1 = 0.175262 loss)
I0407 22:55:21.021365 23786 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0407 22:55:26.040127 23786 solver.cpp:218] Iteration 6588 (2.39111 iter/s, 5.01858s/12 iters), loss = 0.110305
I0407 22:55:26.040235 23786 solver.cpp:237]     Train net output #0: loss = 0.110305 (* 1 = 0.110305 loss)
I0407 22:55:26.040248 23786 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0407 22:55:31.045064 23786 solver.cpp:218] Iteration 6600 (2.39777 iter/s, 5.00464s/12 iters), loss = 0.159141
I0407 22:55:31.045106 23786 solver.cpp:237]     Train net output #0: loss = 0.159141 (* 1 = 0.159141 loss)
I0407 22:55:31.045116 23786 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0407 22:55:36.031340 23786 solver.cpp:218] Iteration 6612 (2.40672 iter/s, 4.98604s/12 iters), loss = 0.116861
I0407 22:55:36.031399 23786 solver.cpp:237]     Train net output #0: loss = 0.116861 (* 1 = 0.116861 loss)
I0407 22:55:36.031409 23786 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0407 22:55:41.060386 23786 solver.cpp:218] Iteration 6624 (2.38626 iter/s, 5.02879s/12 iters), loss = 0.163788
I0407 22:55:41.060441 23786 solver.cpp:237]     Train net output #0: loss = 0.163788 (* 1 = 0.163788 loss)
I0407 22:55:41.060453 23786 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0407 22:55:43.129411 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0407 22:55:46.138078 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0407 22:55:48.779675 23786 solver.cpp:330] Iteration 6630, Testing net (#0)
I0407 22:55:48.779700 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:55:50.526979 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:53.163180 23786 solver.cpp:397]     Test net output #0: accuracy = 0.435049
I0407 22:55:53.163218 23786 solver.cpp:397]     Test net output #1: loss = 3.15203 (* 1 = 3.15203 loss)
I0407 22:55:54.941105 23786 solver.cpp:218] Iteration 6636 (0.864543 iter/s, 13.8802s/12 iters), loss = 0.101059
I0407 22:55:54.941152 23786 solver.cpp:237]     Train net output #0: loss = 0.101059 (* 1 = 0.101059 loss)
I0407 22:55:54.941164 23786 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0407 22:56:00.371206 23786 solver.cpp:218] Iteration 6648 (2.21001 iter/s, 5.42984s/12 iters), loss = 0.145408
I0407 22:56:00.371330 23786 solver.cpp:237]     Train net output #0: loss = 0.145408 (* 1 = 0.145408 loss)
I0407 22:56:00.371341 23786 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0407 22:56:05.441936 23786 solver.cpp:218] Iteration 6660 (2.36667 iter/s, 5.07042s/12 iters), loss = 0.198716
I0407 22:56:05.442003 23786 solver.cpp:237]     Train net output #0: loss = 0.198716 (* 1 = 0.198716 loss)
I0407 22:56:05.442013 23786 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0407 22:56:10.460510 23786 solver.cpp:218] Iteration 6672 (2.39124 iter/s, 5.01832s/12 iters), loss = 0.189719
I0407 22:56:10.460551 23786 solver.cpp:237]     Train net output #0: loss = 0.189719 (* 1 = 0.189719 loss)
I0407 22:56:10.460561 23786 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0407 22:56:11.836225 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:56:15.479275 23786 solver.cpp:218] Iteration 6684 (2.39114 iter/s, 5.01853s/12 iters), loss = 0.227195
I0407 22:56:15.479324 23786 solver.cpp:237]     Train net output #0: loss = 0.227195 (* 1 = 0.227195 loss)
I0407 22:56:15.479336 23786 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0407 22:56:20.471449 23786 solver.cpp:218] Iteration 6696 (2.40388 iter/s, 4.99193s/12 iters), loss = 0.16329
I0407 22:56:20.471509 23786 solver.cpp:237]     Train net output #0: loss = 0.16329 (* 1 = 0.16329 loss)
I0407 22:56:20.471524 23786 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0407 22:56:25.464450 23786 solver.cpp:218] Iteration 6708 (2.40349 iter/s, 4.99275s/12 iters), loss = 0.214309
I0407 22:56:25.464498 23786 solver.cpp:237]     Train net output #0: loss = 0.214309 (* 1 = 0.214309 loss)
I0407 22:56:25.464505 23786 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0407 22:56:30.454418 23786 solver.cpp:218] Iteration 6720 (2.40494 iter/s, 4.98972s/12 iters), loss = 0.152251
I0407 22:56:30.454511 23786 solver.cpp:237]     Train net output #0: loss = 0.152251 (* 1 = 0.152251 loss)
I0407 22:56:30.454524 23786 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0407 22:56:34.982784 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0407 22:56:38.005532 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0407 22:56:41.485460 23786 solver.cpp:330] Iteration 6732, Testing net (#0)
I0407 22:56:41.485483 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:56:43.279168 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:56:45.958766 23786 solver.cpp:397]     Test net output #0: accuracy = 0.451593
I0407 22:56:45.958815 23786 solver.cpp:397]     Test net output #1: loss = 2.96989 (* 1 = 2.96989 loss)
I0407 22:56:46.049288 23786 solver.cpp:218] Iteration 6732 (0.769516 iter/s, 15.5942s/12 iters), loss = 0.185235
I0407 22:56:46.049341 23786 solver.cpp:237]     Train net output #0: loss = 0.185235 (* 1 = 0.185235 loss)
I0407 22:56:46.049353 23786 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0407 22:56:50.233386 23786 solver.cpp:218] Iteration 6744 (2.86815 iter/s, 4.18388s/12 iters), loss = 0.0716578
I0407 22:56:50.233441 23786 solver.cpp:237]     Train net output #0: loss = 0.0716579 (* 1 = 0.0716579 loss)
I0407 22:56:50.233453 23786 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0407 22:56:55.189455 23786 solver.cpp:218] Iteration 6756 (2.42139 iter/s, 4.95583s/12 iters), loss = 0.101265
I0407 22:56:55.189502 23786 solver.cpp:237]     Train net output #0: loss = 0.101265 (* 1 = 0.101265 loss)
I0407 22:56:55.189512 23786 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0407 22:57:00.311442 23786 solver.cpp:218] Iteration 6768 (2.34295 iter/s, 5.12174s/12 iters), loss = 0.248837
I0407 22:57:00.311501 23786 solver.cpp:237]     Train net output #0: loss = 0.248837 (* 1 = 0.248837 loss)
I0407 22:57:00.311512 23786 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0407 22:57:04.015543 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:05.527361 23786 solver.cpp:218] Iteration 6780 (2.30076 iter/s, 5.21566s/12 iters), loss = 0.100067
I0407 22:57:05.527417 23786 solver.cpp:237]     Train net output #0: loss = 0.100067 (* 1 = 0.100067 loss)
I0407 22:57:05.527429 23786 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0407 22:57:10.553010 23786 solver.cpp:218] Iteration 6792 (2.38787 iter/s, 5.0254s/12 iters), loss = 0.100327
I0407 22:57:10.553068 23786 solver.cpp:237]     Train net output #0: loss = 0.100327 (* 1 = 0.100327 loss)
I0407 22:57:10.553081 23786 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0407 22:57:15.661290 23786 solver.cpp:218] Iteration 6804 (2.34924 iter/s, 5.10803s/12 iters), loss = 0.145547
I0407 22:57:15.661339 23786 solver.cpp:237]     Train net output #0: loss = 0.145547 (* 1 = 0.145547 loss)
I0407 22:57:15.661350 23786 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0407 22:57:20.687438 23786 solver.cpp:218] Iteration 6816 (2.38763 iter/s, 5.02591s/12 iters), loss = 0.138192
I0407 22:57:20.687484 23786 solver.cpp:237]     Train net output #0: loss = 0.138192 (* 1 = 0.138192 loss)
I0407 22:57:20.687494 23786 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0407 22:57:25.683928 23786 solver.cpp:218] Iteration 6828 (2.4018 iter/s, 4.99626s/12 iters), loss = 0.217061
I0407 22:57:25.683971 23786 solver.cpp:237]     Train net output #0: loss = 0.217061 (* 1 = 0.217061 loss)
I0407 22:57:25.683982 23786 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0407 22:57:27.739457 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0407 22:57:30.763326 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0407 22:57:33.113302 23786 solver.cpp:330] Iteration 6834, Testing net (#0)
I0407 22:57:33.113327 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:57:34.924044 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:37.762921 23786 solver.cpp:397]     Test net output #0: accuracy = 0.44424
I0407 22:57:37.762955 23786 solver.cpp:397]     Test net output #1: loss = 2.96009 (* 1 = 2.96009 loss)
I0407 22:57:39.604588 23786 solver.cpp:218] Iteration 6840 (0.862062 iter/s, 13.9201s/12 iters), loss = 0.16302
I0407 22:57:39.604638 23786 solver.cpp:237]     Train net output #0: loss = 0.16302 (* 1 = 0.16302 loss)
I0407 22:57:39.604650 23786 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0407 22:57:44.548219 23786 solver.cpp:218] Iteration 6852 (2.42748 iter/s, 4.94339s/12 iters), loss = 0.0761299
I0407 22:57:44.548274 23786 solver.cpp:237]     Train net output #0: loss = 0.07613 (* 1 = 0.07613 loss)
I0407 22:57:44.548285 23786 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0407 22:57:49.580677 23786 solver.cpp:218] Iteration 6864 (2.38464 iter/s, 5.03221s/12 iters), loss = 0.166731
I0407 22:57:49.580727 23786 solver.cpp:237]     Train net output #0: loss = 0.166731 (* 1 = 0.166731 loss)
I0407 22:57:49.580739 23786 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0407 22:57:54.584998 23786 solver.cpp:218] Iteration 6876 (2.39804 iter/s, 5.00408s/12 iters), loss = 0.14154
I0407 22:57:54.585052 23786 solver.cpp:237]     Train net output #0: loss = 0.14154 (* 1 = 0.14154 loss)
I0407 22:57:54.585063 23786 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0407 22:57:55.221452 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:59.579232 23786 solver.cpp:218] Iteration 6888 (2.40289 iter/s, 4.99399s/12 iters), loss = 0.0820344
I0407 22:57:59.579282 23786 solver.cpp:237]     Train net output #0: loss = 0.0820345 (* 1 = 0.0820345 loss)
I0407 22:57:59.579295 23786 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0407 22:58:04.596721 23786 solver.cpp:218] Iteration 6900 (2.39175 iter/s, 5.01725s/12 iters), loss = 0.156944
I0407 22:58:04.596767 23786 solver.cpp:237]     Train net output #0: loss = 0.156945 (* 1 = 0.156945 loss)
I0407 22:58:04.596778 23786 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0407 22:58:09.629266 23786 solver.cpp:218] Iteration 6912 (2.38459 iter/s, 5.03231s/12 iters), loss = 0.196859
I0407 22:58:09.629384 23786 solver.cpp:237]     Train net output #0: loss = 0.196859 (* 1 = 0.196859 loss)
I0407 22:58:09.629398 23786 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0407 22:58:14.762174 23786 solver.cpp:218] Iteration 6924 (2.338 iter/s, 5.1326s/12 iters), loss = 0.19987
I0407 22:58:14.762223 23786 solver.cpp:237]     Train net output #0: loss = 0.19987 (* 1 = 0.19987 loss)
I0407 22:58:14.762235 23786 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0407 22:58:19.290196 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0407 22:58:22.351583 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0407 22:58:24.673264 23786 solver.cpp:330] Iteration 6936, Testing net (#0)
I0407 22:58:24.673290 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:58:25.332048 23786 blocking_queue.cpp:49] Waiting for data
I0407 22:58:26.411609 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:58:29.133358 23786 solver.cpp:397]     Test net output #0: accuracy = 0.459559
I0407 22:58:29.133404 23786 solver.cpp:397]     Test net output #1: loss = 2.9066 (* 1 = 2.9066 loss)
I0407 22:58:29.223870 23786 solver.cpp:218] Iteration 6936 (0.829812 iter/s, 14.4611s/12 iters), loss = 0.132892
I0407 22:58:29.223922 23786 solver.cpp:237]     Train net output #0: loss = 0.132892 (* 1 = 0.132892 loss)
I0407 22:58:29.223932 23786 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0407 22:58:33.688163 23786 solver.cpp:218] Iteration 6948 (2.68813 iter/s, 4.46407s/12 iters), loss = 0.165575
I0407 22:58:33.688200 23786 solver.cpp:237]     Train net output #0: loss = 0.165575 (* 1 = 0.165575 loss)
I0407 22:58:33.688210 23786 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0407 22:58:38.727716 23786 solver.cpp:218] Iteration 6960 (2.38128 iter/s, 5.03932s/12 iters), loss = 0.0482876
I0407 22:58:38.727767 23786 solver.cpp:237]     Train net output #0: loss = 0.0482877 (* 1 = 0.0482877 loss)
I0407 22:58:38.727778 23786 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0407 22:58:43.749274 23786 solver.cpp:218] Iteration 6972 (2.38981 iter/s, 5.02132s/12 iters), loss = 0.134746
I0407 22:58:43.749354 23786 solver.cpp:237]     Train net output #0: loss = 0.134746 (* 1 = 0.134746 loss)
I0407 22:58:43.749366 23786 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0407 22:58:46.506603 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:58:48.709292 23786 solver.cpp:218] Iteration 6984 (2.41948 iter/s, 4.95975s/12 iters), loss = 0.0610355
I0407 22:58:48.709344 23786 solver.cpp:237]     Train net output #0: loss = 0.0610356 (* 1 = 0.0610356 loss)
I0407 22:58:48.709355 23786 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0407 22:58:53.681404 23786 solver.cpp:218] Iteration 6996 (2.41358 iter/s, 4.97188s/12 iters), loss = 0.126529
I0407 22:58:53.681440 23786 solver.cpp:237]     Train net output #0: loss = 0.126529 (* 1 = 0.126529 loss)
I0407 22:58:53.681449 23786 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0407 22:58:58.679124 23786 solver.cpp:218] Iteration 7008 (2.4012 iter/s, 4.9975s/12 iters), loss = 0.0701569
I0407 22:58:58.679162 23786 solver.cpp:237]     Train net output #0: loss = 0.0701569 (* 1 = 0.0701569 loss)
I0407 22:58:58.679169 23786 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0407 22:59:03.986665 23786 solver.cpp:218] Iteration 7020 (2.26104 iter/s, 5.3073s/12 iters), loss = 0.0977354
I0407 22:59:03.986704 23786 solver.cpp:237]     Train net output #0: loss = 0.0977355 (* 1 = 0.0977355 loss)
I0407 22:59:03.986712 23786 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0407 22:59:08.972335 23786 solver.cpp:218] Iteration 7032 (2.40701 iter/s, 4.98544s/12 iters), loss = 0.220389
I0407 22:59:08.972384 23786 solver.cpp:237]     Train net output #0: loss = 0.220389 (* 1 = 0.220389 loss)
I0407 22:59:08.972396 23786 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0407 22:59:11.005770 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0407 22:59:14.014173 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0407 22:59:16.387068 23786 solver.cpp:330] Iteration 7038, Testing net (#0)
I0407 22:59:16.387092 23786 net.cpp:676] Ignoring source layer train-data
I0407 22:59:18.092808 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:59:20.846922 23786 solver.cpp:397]     Test net output #0: accuracy = 0.449755
I0407 22:59:20.846974 23786 solver.cpp:397]     Test net output #1: loss = 3.01329 (* 1 = 3.01329 loss)
I0407 22:59:22.815457 23786 solver.cpp:218] Iteration 7044 (0.866892 iter/s, 13.8426s/12 iters), loss = 0.180819
I0407 22:59:22.815513 23786 solver.cpp:237]     Train net output #0: loss = 0.180819 (* 1 = 0.180819 loss)
I0407 22:59:22.815526 23786 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0407 22:59:28.006579 23786 solver.cpp:218] Iteration 7056 (2.31175 iter/s, 5.19087s/12 iters), loss = 0.137352
I0407 22:59:28.006626 23786 solver.cpp:237]     Train net output #0: loss = 0.137352 (* 1 = 0.137352 loss)
I0407 22:59:28.006639 23786 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0407 22:59:33.017645 23786 solver.cpp:218] Iteration 7068 (2.39481 iter/s, 5.01083s/12 iters), loss = 0.156065
I0407 22:59:33.017693 23786 solver.cpp:237]     Train net output #0: loss = 0.156065 (* 1 = 0.156065 loss)
I0407 22:59:33.017704 23786 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0407 22:59:37.880590 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:59:37.988649 23786 solver.cpp:218] Iteration 7080 (2.41412 iter/s, 4.97076s/12 iters), loss = 0.110478
I0407 22:59:37.988703 23786 solver.cpp:237]     Train net output #0: loss = 0.110478 (* 1 = 0.110478 loss)
I0407 22:59:37.988714 23786 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0407 22:59:42.961490 23786 solver.cpp:218] Iteration 7092 (2.41323 iter/s, 4.9726s/12 iters), loss = 0.265474
I0407 22:59:42.961540 23786 solver.cpp:237]     Train net output #0: loss = 0.265474 (* 1 = 0.265474 loss)
I0407 22:59:42.961552 23786 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0407 22:59:47.903909 23786 solver.cpp:218] Iteration 7104 (2.42808 iter/s, 4.94218s/12 iters), loss = 0.106027
I0407 22:59:47.903985 23786 solver.cpp:237]     Train net output #0: loss = 0.106027 (* 1 = 0.106027 loss)
I0407 22:59:47.903993 23786 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0407 22:59:52.864929 23786 solver.cpp:218] Iteration 7116 (2.41899 iter/s, 4.96075s/12 iters), loss = 0.0588091
I0407 22:59:52.864975 23786 solver.cpp:237]     Train net output #0: loss = 0.0588092 (* 1 = 0.0588092 loss)
I0407 22:59:52.864984 23786 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0407 22:59:57.811072 23786 solver.cpp:218] Iteration 7128 (2.42625 iter/s, 4.9459s/12 iters), loss = 0.110317
I0407 22:59:57.811128 23786 solver.cpp:237]     Train net output #0: loss = 0.110317 (* 1 = 0.110317 loss)
I0407 22:59:57.811139 23786 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0407 23:00:02.364420 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0407 23:00:05.995540 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0407 23:00:08.329098 23786 solver.cpp:330] Iteration 7140, Testing net (#0)
I0407 23:00:08.329123 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:00:10.197171 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:00:13.053690 23786 solver.cpp:397]     Test net output #0: accuracy = 0.462623
I0407 23:00:13.053721 23786 solver.cpp:397]     Test net output #1: loss = 3.0051 (* 1 = 3.0051 loss)
I0407 23:00:13.144186 23786 solver.cpp:218] Iteration 7140 (0.782651 iter/s, 15.3325s/12 iters), loss = 0.104364
I0407 23:00:13.144229 23786 solver.cpp:237]     Train net output #0: loss = 0.104364 (* 1 = 0.104364 loss)
I0407 23:00:13.144239 23786 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0407 23:00:17.471807 23786 solver.cpp:218] Iteration 7152 (2.77302 iter/s, 4.32741s/12 iters), loss = 0.103066
I0407 23:00:17.471861 23786 solver.cpp:237]     Train net output #0: loss = 0.103066 (* 1 = 0.103066 loss)
I0407 23:00:17.471874 23786 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0407 23:00:22.541200 23786 solver.cpp:218] Iteration 7164 (2.36726 iter/s, 5.06914s/12 iters), loss = 0.0678474
I0407 23:00:22.541355 23786 solver.cpp:237]     Train net output #0: loss = 0.0678475 (* 1 = 0.0678475 loss)
I0407 23:00:22.541369 23786 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0407 23:00:27.595892 23786 solver.cpp:218] Iteration 7176 (2.3742 iter/s, 5.05434s/12 iters), loss = 0.0582692
I0407 23:00:27.595947 23786 solver.cpp:237]     Train net output #0: loss = 0.0582693 (* 1 = 0.0582693 loss)
I0407 23:00:27.595958 23786 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0407 23:00:29.723589 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:00:32.727880 23786 solver.cpp:218] Iteration 7188 (2.33839 iter/s, 5.13173s/12 iters), loss = 0.12393
I0407 23:00:32.727922 23786 solver.cpp:237]     Train net output #0: loss = 0.12393 (* 1 = 0.12393 loss)
I0407 23:00:32.727931 23786 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0407 23:00:37.776789 23786 solver.cpp:218] Iteration 7200 (2.37686 iter/s, 5.04867s/12 iters), loss = 0.109886
I0407 23:00:37.776835 23786 solver.cpp:237]     Train net output #0: loss = 0.109886 (* 1 = 0.109886 loss)
I0407 23:00:37.776844 23786 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0407 23:00:42.754686 23786 solver.cpp:218] Iteration 7212 (2.41077 iter/s, 4.97766s/12 iters), loss = 0.0672098
I0407 23:00:42.754725 23786 solver.cpp:237]     Train net output #0: loss = 0.0672098 (* 1 = 0.0672098 loss)
I0407 23:00:42.754734 23786 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0407 23:00:47.711858 23786 solver.cpp:218] Iteration 7224 (2.42085 iter/s, 4.95694s/12 iters), loss = 0.12557
I0407 23:00:47.711902 23786 solver.cpp:237]     Train net output #0: loss = 0.12557 (* 1 = 0.12557 loss)
I0407 23:00:47.711911 23786 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0407 23:00:52.690565 23786 solver.cpp:218] Iteration 7236 (2.41038 iter/s, 4.97847s/12 iters), loss = 0.0592966
I0407 23:00:52.690634 23786 solver.cpp:237]     Train net output #0: loss = 0.0592967 (* 1 = 0.0592967 loss)
I0407 23:00:52.690644 23786 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0407 23:00:54.714810 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0407 23:00:59.126363 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0407 23:01:01.761849 23786 solver.cpp:330] Iteration 7242, Testing net (#0)
I0407 23:01:01.761874 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:01:03.361807 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:01:06.197710 23786 solver.cpp:397]     Test net output #0: accuracy = 0.461397
I0407 23:01:06.197759 23786 solver.cpp:397]     Test net output #1: loss = 3.00566 (* 1 = 3.00566 loss)
I0407 23:01:08.064388 23786 solver.cpp:218] Iteration 7248 (0.78058 iter/s, 15.3732s/12 iters), loss = 0.0478527
I0407 23:01:08.064443 23786 solver.cpp:237]     Train net output #0: loss = 0.0478528 (* 1 = 0.0478528 loss)
I0407 23:01:08.064456 23786 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0407 23:01:12.999972 23786 solver.cpp:218] Iteration 7260 (2.43144 iter/s, 4.93534s/12 iters), loss = 0.177072
I0407 23:01:13.000023 23786 solver.cpp:237]     Train net output #0: loss = 0.177072 (* 1 = 0.177072 loss)
I0407 23:01:13.000036 23786 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0407 23:01:18.043381 23786 solver.cpp:218] Iteration 7272 (2.37946 iter/s, 5.04316s/12 iters), loss = 0.036117
I0407 23:01:18.043426 23786 solver.cpp:237]     Train net output #0: loss = 0.0361171 (* 1 = 0.0361171 loss)
I0407 23:01:18.043437 23786 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0407 23:01:22.289388 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:01:23.035050 23786 solver.cpp:218] Iteration 7284 (2.40412 iter/s, 4.99143s/12 iters), loss = 0.0680884
I0407 23:01:23.035167 23786 solver.cpp:237]     Train net output #0: loss = 0.0680884 (* 1 = 0.0680884 loss)
I0407 23:01:23.035182 23786 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0407 23:01:28.076217 23786 solver.cpp:218] Iteration 7296 (2.38055 iter/s, 5.04086s/12 iters), loss = 0.0856699
I0407 23:01:28.076265 23786 solver.cpp:237]     Train net output #0: loss = 0.08567 (* 1 = 0.08567 loss)
I0407 23:01:28.076277 23786 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0407 23:01:33.079592 23786 solver.cpp:218] Iteration 7308 (2.3985 iter/s, 5.00313s/12 iters), loss = 0.101446
I0407 23:01:33.079643 23786 solver.cpp:237]     Train net output #0: loss = 0.101446 (* 1 = 0.101446 loss)
I0407 23:01:33.079653 23786 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0407 23:01:37.982128 23786 solver.cpp:218] Iteration 7320 (2.44783 iter/s, 4.9023s/12 iters), loss = 0.15954
I0407 23:01:37.982180 23786 solver.cpp:237]     Train net output #0: loss = 0.15954 (* 1 = 0.15954 loss)
I0407 23:01:37.982192 23786 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0407 23:01:43.028255 23786 solver.cpp:218] Iteration 7332 (2.37818 iter/s, 5.04588s/12 iters), loss = 0.0580539
I0407 23:01:43.028304 23786 solver.cpp:237]     Train net output #0: loss = 0.058054 (* 1 = 0.058054 loss)
I0407 23:01:43.028316 23786 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0407 23:01:47.486697 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0407 23:01:51.899489 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0407 23:01:55.521261 23786 solver.cpp:330] Iteration 7344, Testing net (#0)
I0407 23:01:55.521338 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:01:57.106703 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:01:59.979573 23786 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0407 23:01:59.979610 23786 solver.cpp:397]     Test net output #1: loss = 2.9982 (* 1 = 2.9982 loss)
I0407 23:02:00.070053 23786 solver.cpp:218] Iteration 7344 (0.704179 iter/s, 17.0411s/12 iters), loss = 0.137525
I0407 23:02:00.070111 23786 solver.cpp:237]     Train net output #0: loss = 0.137525 (* 1 = 0.137525 loss)
I0407 23:02:00.070137 23786 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0407 23:02:04.308187 23786 solver.cpp:218] Iteration 7356 (2.83159 iter/s, 4.23791s/12 iters), loss = 0.0820013
I0407 23:02:04.308240 23786 solver.cpp:237]     Train net output #0: loss = 0.0820014 (* 1 = 0.0820014 loss)
I0407 23:02:04.308252 23786 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0407 23:02:09.361662 23786 solver.cpp:218] Iteration 7368 (2.37472 iter/s, 5.05323s/12 iters), loss = 0.177371
I0407 23:02:09.361714 23786 solver.cpp:237]     Train net output #0: loss = 0.177372 (* 1 = 0.177372 loss)
I0407 23:02:09.361726 23786 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0407 23:02:14.287832 23786 solver.cpp:218] Iteration 7380 (2.43609 iter/s, 4.92593s/12 iters), loss = 0.0574903
I0407 23:02:14.287894 23786 solver.cpp:237]     Train net output #0: loss = 0.0574905 (* 1 = 0.0574905 loss)
I0407 23:02:14.287909 23786 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0407 23:02:15.684121 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:02:19.274369 23786 solver.cpp:218] Iteration 7392 (2.4066 iter/s, 4.98629s/12 iters), loss = 0.2235
I0407 23:02:19.274412 23786 solver.cpp:237]     Train net output #0: loss = 0.2235 (* 1 = 0.2235 loss)
I0407 23:02:19.274425 23786 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0407 23:02:24.361943 23786 solver.cpp:218] Iteration 7404 (2.3588 iter/s, 5.08733s/12 iters), loss = 0.212021
I0407 23:02:24.362010 23786 solver.cpp:237]     Train net output #0: loss = 0.212021 (* 1 = 0.212021 loss)
I0407 23:02:24.362022 23786 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0407 23:02:29.453903 23786 solver.cpp:218] Iteration 7416 (2.35678 iter/s, 5.0917s/12 iters), loss = 0.112222
I0407 23:02:29.454031 23786 solver.cpp:237]     Train net output #0: loss = 0.112222 (* 1 = 0.112222 loss)
I0407 23:02:29.454041 23786 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0407 23:02:34.453446 23786 solver.cpp:218] Iteration 7428 (2.40037 iter/s, 4.99922s/12 iters), loss = 0.0900844
I0407 23:02:34.453486 23786 solver.cpp:237]     Train net output #0: loss = 0.0900846 (* 1 = 0.0900846 loss)
I0407 23:02:34.453495 23786 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0407 23:02:39.430649 23786 solver.cpp:218] Iteration 7440 (2.4111 iter/s, 4.97697s/12 iters), loss = 0.0910138
I0407 23:02:39.430693 23786 solver.cpp:237]     Train net output #0: loss = 0.091014 (* 1 = 0.091014 loss)
I0407 23:02:39.430703 23786 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0407 23:02:41.447898 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0407 23:02:45.029601 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0407 23:02:48.761011 23786 solver.cpp:330] Iteration 7446, Testing net (#0)
I0407 23:02:48.761037 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:02:50.305464 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:02:53.220661 23786 solver.cpp:397]     Test net output #0: accuracy = 0.465074
I0407 23:02:53.220708 23786 solver.cpp:397]     Test net output #1: loss = 2.9697 (* 1 = 2.9697 loss)
I0407 23:02:55.149724 23786 solver.cpp:218] Iteration 7452 (0.763434 iter/s, 15.7184s/12 iters), loss = 0.04283
I0407 23:02:55.149785 23786 solver.cpp:237]     Train net output #0: loss = 0.0428301 (* 1 = 0.0428301 loss)
I0407 23:02:55.149797 23786 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0407 23:03:00.193130 23786 solver.cpp:218] Iteration 7464 (2.37946 iter/s, 5.04315s/12 iters), loss = 0.10269
I0407 23:03:00.193243 23786 solver.cpp:237]     Train net output #0: loss = 0.10269 (* 1 = 0.10269 loss)
I0407 23:03:00.193256 23786 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0407 23:03:05.271297 23786 solver.cpp:218] Iteration 7476 (2.3632 iter/s, 5.07786s/12 iters), loss = 0.13362
I0407 23:03:05.271349 23786 solver.cpp:237]     Train net output #0: loss = 0.133621 (* 1 = 0.133621 loss)
I0407 23:03:05.271360 23786 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0407 23:03:08.984493 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:03:10.582455 23786 solver.cpp:218] Iteration 7488 (2.2595 iter/s, 5.3109s/12 iters), loss = 0.0829625
I0407 23:03:10.582512 23786 solver.cpp:237]     Train net output #0: loss = 0.0829626 (* 1 = 0.0829626 loss)
I0407 23:03:10.582525 23786 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0407 23:03:15.635769 23786 solver.cpp:218] Iteration 7500 (2.3748 iter/s, 5.05307s/12 iters), loss = 0.0776388
I0407 23:03:15.635818 23786 solver.cpp:237]     Train net output #0: loss = 0.077639 (* 1 = 0.077639 loss)
I0407 23:03:15.635830 23786 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0407 23:03:20.563467 23786 solver.cpp:218] Iteration 7512 (2.43533 iter/s, 4.92746s/12 iters), loss = 0.057944
I0407 23:03:20.563516 23786 solver.cpp:237]     Train net output #0: loss = 0.0579442 (* 1 = 0.0579442 loss)
I0407 23:03:20.563529 23786 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0407 23:03:25.529968 23786 solver.cpp:218] Iteration 7524 (2.41631 iter/s, 4.96624s/12 iters), loss = 0.0926276
I0407 23:03:25.530019 23786 solver.cpp:237]     Train net output #0: loss = 0.0926277 (* 1 = 0.0926277 loss)
I0407 23:03:25.530030 23786 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0407 23:03:30.484802 23786 solver.cpp:218] Iteration 7536 (2.422 iter/s, 4.95459s/12 iters), loss = 0.147195
I0407 23:03:30.484925 23786 solver.cpp:237]     Train net output #0: loss = 0.147195 (* 1 = 0.147195 loss)
I0407 23:03:30.484935 23786 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0407 23:03:35.019520 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0407 23:03:38.047660 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0407 23:03:41.589444 23786 solver.cpp:330] Iteration 7548, Testing net (#0)
I0407 23:03:41.589470 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:03:43.069969 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:03:46.140987 23786 solver.cpp:397]     Test net output #0: accuracy = 0.462623
I0407 23:03:46.141036 23786 solver.cpp:397]     Test net output #1: loss = 3.01884 (* 1 = 3.01884 loss)
I0407 23:03:46.231729 23786 solver.cpp:218] Iteration 7548 (0.762088 iter/s, 15.7462s/12 iters), loss = 0.0613358
I0407 23:03:46.231784 23786 solver.cpp:237]     Train net output #0: loss = 0.061336 (* 1 = 0.061336 loss)
I0407 23:03:46.231796 23786 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0407 23:03:51.968514 23786 solver.cpp:218] Iteration 7560 (2.09186 iter/s, 5.73651s/12 iters), loss = 0.0994754
I0407 23:03:51.968554 23786 solver.cpp:237]     Train net output #0: loss = 0.0994756 (* 1 = 0.0994756 loss)
I0407 23:03:51.968564 23786 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0407 23:03:57.064978 23786 solver.cpp:218] Iteration 7572 (2.35469 iter/s, 5.09622s/12 iters), loss = 0.126155
I0407 23:03:57.065050 23786 solver.cpp:237]     Train net output #0: loss = 0.126156 (* 1 = 0.126156 loss)
I0407 23:03:57.065070 23786 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0407 23:04:02.011515 23786 solver.cpp:218] Iteration 7584 (2.42606 iter/s, 4.94628s/12 iters), loss = 0.0940135
I0407 23:04:02.011605 23786 solver.cpp:237]     Train net output #0: loss = 0.0940137 (* 1 = 0.0940137 loss)
I0407 23:04:02.011618 23786 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0407 23:04:02.670442 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:04:07.035936 23786 solver.cpp:218] Iteration 7596 (2.38847 iter/s, 5.02414s/12 iters), loss = 0.0834689
I0407 23:04:07.035981 23786 solver.cpp:237]     Train net output #0: loss = 0.083469 (* 1 = 0.083469 loss)
I0407 23:04:07.035990 23786 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0407 23:04:12.008446 23786 solver.cpp:218] Iteration 7608 (2.41338 iter/s, 4.97227s/12 iters), loss = 0.180898
I0407 23:04:12.008495 23786 solver.cpp:237]     Train net output #0: loss = 0.180898 (* 1 = 0.180898 loss)
I0407 23:04:12.008507 23786 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0407 23:04:17.013600 23786 solver.cpp:218] Iteration 7620 (2.39764 iter/s, 5.00491s/12 iters), loss = 0.156067
I0407 23:04:17.013654 23786 solver.cpp:237]     Train net output #0: loss = 0.156068 (* 1 = 0.156068 loss)
I0407 23:04:17.013665 23786 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0407 23:04:19.433697 23786 blocking_queue.cpp:49] Waiting for data
I0407 23:04:21.999495 23786 solver.cpp:218] Iteration 7632 (2.40691 iter/s, 4.98565s/12 iters), loss = 0.202356
I0407 23:04:21.999537 23786 solver.cpp:237]     Train net output #0: loss = 0.202356 (* 1 = 0.202356 loss)
I0407 23:04:21.999547 23786 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0407 23:04:26.928501 23786 solver.cpp:218] Iteration 7644 (2.43468 iter/s, 4.92877s/12 iters), loss = 0.0505629
I0407 23:04:26.928553 23786 solver.cpp:237]     Train net output #0: loss = 0.0505631 (* 1 = 0.0505631 loss)
I0407 23:04:26.928566 23786 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0407 23:04:28.890797 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0407 23:04:31.913082 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0407 23:04:34.960990 23786 solver.cpp:330] Iteration 7650, Testing net (#0)
I0407 23:04:34.961081 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:04:36.316846 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:04:39.332157 23786 solver.cpp:397]     Test net output #0: accuracy = 0.463235
I0407 23:04:39.332204 23786 solver.cpp:397]     Test net output #1: loss = 3.04653 (* 1 = 3.04653 loss)
I0407 23:04:41.131639 23786 solver.cpp:218] Iteration 7656 (0.844918 iter/s, 14.2026s/12 iters), loss = 0.0854038
I0407 23:04:41.131690 23786 solver.cpp:237]     Train net output #0: loss = 0.085404 (* 1 = 0.085404 loss)
I0407 23:04:41.131701 23786 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0407 23:04:46.417718 23786 solver.cpp:218] Iteration 7668 (2.27023 iter/s, 5.28582s/12 iters), loss = 0.156152
I0407 23:04:46.417773 23786 solver.cpp:237]     Train net output #0: loss = 0.156153 (* 1 = 0.156153 loss)
I0407 23:04:46.417784 23786 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0407 23:04:51.443449 23786 solver.cpp:218] Iteration 7680 (2.38783 iter/s, 5.02549s/12 iters), loss = 0.12484
I0407 23:04:51.443490 23786 solver.cpp:237]     Train net output #0: loss = 0.12484 (* 1 = 0.12484 loss)
I0407 23:04:51.443498 23786 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0407 23:04:54.246709 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:04:56.436872 23786 solver.cpp:218] Iteration 7692 (2.40327 iter/s, 4.99319s/12 iters), loss = 0.10184
I0407 23:04:56.436923 23786 solver.cpp:237]     Train net output #0: loss = 0.10184 (* 1 = 0.10184 loss)
I0407 23:04:56.436933 23786 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0407 23:05:01.382987 23786 solver.cpp:218] Iteration 7704 (2.42627 iter/s, 4.94587s/12 iters), loss = 0.0581643
I0407 23:05:01.383044 23786 solver.cpp:237]     Train net output #0: loss = 0.0581644 (* 1 = 0.0581644 loss)
I0407 23:05:01.383056 23786 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0407 23:05:06.401013 23786 solver.cpp:218] Iteration 7716 (2.3915 iter/s, 5.01777s/12 iters), loss = 0.135237
I0407 23:05:06.401132 23786 solver.cpp:237]     Train net output #0: loss = 0.135237 (* 1 = 0.135237 loss)
I0407 23:05:06.401146 23786 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0407 23:05:11.500403 23786 solver.cpp:218] Iteration 7728 (2.35337 iter/s, 5.09908s/12 iters), loss = 0.0175289
I0407 23:05:11.500448 23786 solver.cpp:237]     Train net output #0: loss = 0.017529 (* 1 = 0.017529 loss)
I0407 23:05:11.500460 23786 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0407 23:05:16.698844 23786 solver.cpp:218] Iteration 7740 (2.30849 iter/s, 5.19819s/12 iters), loss = 0.038866
I0407 23:05:16.698899 23786 solver.cpp:237]     Train net output #0: loss = 0.0388662 (* 1 = 0.0388662 loss)
I0407 23:05:16.698911 23786 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0407 23:05:21.283567 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0407 23:05:24.387950 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0407 23:05:26.758157 23786 solver.cpp:330] Iteration 7752, Testing net (#0)
I0407 23:05:26.758186 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:05:28.132673 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:05:31.298779 23786 solver.cpp:397]     Test net output #0: accuracy = 0.465686
I0407 23:05:31.298823 23786 solver.cpp:397]     Test net output #1: loss = 3.01908 (* 1 = 3.01908 loss)
I0407 23:05:31.389252 23786 solver.cpp:218] Iteration 7752 (0.816893 iter/s, 14.6898s/12 iters), loss = 0.0741829
I0407 23:05:31.389297 23786 solver.cpp:237]     Train net output #0: loss = 0.0741831 (* 1 = 0.0741831 loss)
I0407 23:05:31.389305 23786 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0407 23:05:35.720168 23786 solver.cpp:218] Iteration 7764 (2.77092 iter/s, 4.33069s/12 iters), loss = 0.135566
I0407 23:05:35.720221 23786 solver.cpp:237]     Train net output #0: loss = 0.135567 (* 1 = 0.135567 loss)
I0407 23:05:35.720232 23786 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0407 23:05:40.644587 23786 solver.cpp:218] Iteration 7776 (2.43696 iter/s, 4.92418s/12 iters), loss = 0.0798801
I0407 23:05:40.644695 23786 solver.cpp:237]     Train net output #0: loss = 0.0798802 (* 1 = 0.0798802 loss)
I0407 23:05:40.644706 23786 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0407 23:05:45.616199 23786 solver.cpp:218] Iteration 7788 (2.41385 iter/s, 4.97131s/12 iters), loss = 0.117167
I0407 23:05:45.616243 23786 solver.cpp:237]     Train net output #0: loss = 0.117168 (* 1 = 0.117168 loss)
I0407 23:05:45.616253 23786 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0407 23:05:45.627126 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:05:50.559919 23786 solver.cpp:218] Iteration 7800 (2.42744 iter/s, 4.94348s/12 iters), loss = 0.0571776
I0407 23:05:50.559970 23786 solver.cpp:237]     Train net output #0: loss = 0.0571778 (* 1 = 0.0571778 loss)
I0407 23:05:50.559983 23786 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0407 23:05:55.507699 23786 solver.cpp:218] Iteration 7812 (2.42545 iter/s, 4.94753s/12 iters), loss = 0.129094
I0407 23:05:55.507762 23786 solver.cpp:237]     Train net output #0: loss = 0.129095 (* 1 = 0.129095 loss)
I0407 23:05:55.507779 23786 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0407 23:06:00.526316 23786 solver.cpp:218] Iteration 7824 (2.39122 iter/s, 5.01835s/12 iters), loss = 0.0481721
I0407 23:06:00.526367 23786 solver.cpp:237]     Train net output #0: loss = 0.0481723 (* 1 = 0.0481723 loss)
I0407 23:06:00.526377 23786 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0407 23:06:05.536206 23786 solver.cpp:218] Iteration 7836 (2.39538 iter/s, 5.00964s/12 iters), loss = 0.0669065
I0407 23:06:05.536255 23786 solver.cpp:237]     Train net output #0: loss = 0.0669067 (* 1 = 0.0669067 loss)
I0407 23:06:05.536267 23786 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0407 23:06:10.532240 23786 solver.cpp:218] Iteration 7848 (2.40202 iter/s, 4.99579s/12 iters), loss = 0.0796827
I0407 23:06:10.532287 23786 solver.cpp:237]     Train net output #0: loss = 0.0796829 (* 1 = 0.0796829 loss)
I0407 23:06:10.532296 23786 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0407 23:06:12.554494 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0407 23:06:15.601795 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0407 23:06:17.958840 23786 solver.cpp:330] Iteration 7854, Testing net (#0)
I0407 23:06:17.958865 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:06:19.346017 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:22.425025 23786 solver.cpp:397]     Test net output #0: accuracy = 0.476103
I0407 23:06:22.425076 23786 solver.cpp:397]     Test net output #1: loss = 3.02976 (* 1 = 3.02976 loss)
I0407 23:06:24.384517 23786 solver.cpp:218] Iteration 7860 (0.866319 iter/s, 13.8517s/12 iters), loss = 0.0511009
I0407 23:06:24.384568 23786 solver.cpp:237]     Train net output #0: loss = 0.0511011 (* 1 = 0.0511011 loss)
I0407 23:06:24.384582 23786 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0407 23:06:29.388316 23786 solver.cpp:218] Iteration 7872 (2.39829 iter/s, 5.00356s/12 iters), loss = 0.112509
I0407 23:06:29.388367 23786 solver.cpp:237]     Train net output #0: loss = 0.112509 (* 1 = 0.112509 loss)
I0407 23:06:29.388378 23786 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0407 23:06:34.423266 23786 solver.cpp:218] Iteration 7884 (2.38345 iter/s, 5.03471s/12 iters), loss = 0.134869
I0407 23:06:34.423305 23786 solver.cpp:237]     Train net output #0: loss = 0.13487 (* 1 = 0.13487 loss)
I0407 23:06:34.423313 23786 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0407 23:06:36.551326 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:39.421352 23786 solver.cpp:218] Iteration 7896 (2.40103 iter/s, 4.99785s/12 iters), loss = 0.0397129
I0407 23:06:39.421396 23786 solver.cpp:237]     Train net output #0: loss = 0.039713 (* 1 = 0.039713 loss)
I0407 23:06:39.421406 23786 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0407 23:06:44.449458 23786 solver.cpp:218] Iteration 7908 (2.3867 iter/s, 5.02787s/12 iters), loss = 0.075802
I0407 23:06:44.449568 23786 solver.cpp:237]     Train net output #0: loss = 0.0758021 (* 1 = 0.0758021 loss)
I0407 23:06:44.449579 23786 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0407 23:06:49.462486 23786 solver.cpp:218] Iteration 7920 (2.39391 iter/s, 5.01272s/12 iters), loss = 0.0660952
I0407 23:06:49.462538 23786 solver.cpp:237]     Train net output #0: loss = 0.0660954 (* 1 = 0.0660954 loss)
I0407 23:06:49.462549 23786 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0407 23:06:54.493662 23786 solver.cpp:218] Iteration 7932 (2.38524 iter/s, 5.03093s/12 iters), loss = 0.0231361
I0407 23:06:54.493701 23786 solver.cpp:237]     Train net output #0: loss = 0.0231362 (* 1 = 0.0231362 loss)
I0407 23:06:54.493710 23786 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0407 23:06:59.467634 23786 solver.cpp:218] Iteration 7944 (2.41267 iter/s, 4.97374s/12 iters), loss = 0.0294433
I0407 23:06:59.467676 23786 solver.cpp:237]     Train net output #0: loss = 0.0294435 (* 1 = 0.0294435 loss)
I0407 23:06:59.467685 23786 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0407 23:07:03.947093 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0407 23:07:06.968271 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0407 23:07:12.177807 23786 solver.cpp:330] Iteration 7956, Testing net (#0)
I0407 23:07:12.177832 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:07:13.497696 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:07:16.610317 23786 solver.cpp:397]     Test net output #0: accuracy = 0.474265
I0407 23:07:16.610427 23786 solver.cpp:397]     Test net output #1: loss = 2.95305 (* 1 = 2.95305 loss)
I0407 23:07:16.700850 23786 solver.cpp:218] Iteration 7956 (0.696358 iter/s, 17.2325s/12 iters), loss = 0.0720906
I0407 23:07:16.700903 23786 solver.cpp:237]     Train net output #0: loss = 0.0720907 (* 1 = 0.0720907 loss)
I0407 23:07:16.700914 23786 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0407 23:07:21.006965 23786 solver.cpp:218] Iteration 7968 (2.78688 iter/s, 4.30588s/12 iters), loss = 0.144183
I0407 23:07:21.007025 23786 solver.cpp:237]     Train net output #0: loss = 0.144183 (* 1 = 0.144183 loss)
I0407 23:07:21.007040 23786 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0407 23:07:26.048583 23786 solver.cpp:218] Iteration 7980 (2.38031 iter/s, 5.04136s/12 iters), loss = 0.124807
I0407 23:07:26.048635 23786 solver.cpp:237]     Train net output #0: loss = 0.124808 (* 1 = 0.124808 loss)
I0407 23:07:26.048648 23786 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0407 23:07:30.291893 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:07:31.004992 23786 solver.cpp:218] Iteration 7992 (2.42123 iter/s, 4.95616s/12 iters), loss = 0.0731233
I0407 23:07:31.005046 23786 solver.cpp:237]     Train net output #0: loss = 0.0731235 (* 1 = 0.0731235 loss)
I0407 23:07:31.005059 23786 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0407 23:07:36.045912 23786 solver.cpp:218] Iteration 8004 (2.38063 iter/s, 5.04067s/12 iters), loss = 0.0405763
I0407 23:07:36.045974 23786 solver.cpp:237]     Train net output #0: loss = 0.0405765 (* 1 = 0.0405765 loss)
I0407 23:07:36.045985 23786 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0407 23:07:41.056453 23786 solver.cpp:218] Iteration 8016 (2.39507 iter/s, 5.0103s/12 iters), loss = 0.0364289
I0407 23:07:41.056504 23786 solver.cpp:237]     Train net output #0: loss = 0.036429 (* 1 = 0.036429 loss)
I0407 23:07:41.056516 23786 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0407 23:07:46.107975 23786 solver.cpp:218] Iteration 8028 (2.37564 iter/s, 5.05127s/12 iters), loss = 0.162087
I0407 23:07:46.108036 23786 solver.cpp:237]     Train net output #0: loss = 0.162087 (* 1 = 0.162087 loss)
I0407 23:07:46.108052 23786 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0407 23:07:51.137735 23786 solver.cpp:218] Iteration 8040 (2.38592 iter/s, 5.02951s/12 iters), loss = 0.107175
I0407 23:07:51.137831 23786 solver.cpp:237]     Train net output #0: loss = 0.107175 (* 1 = 0.107175 loss)
I0407 23:07:51.137840 23786 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0407 23:07:56.092780 23786 solver.cpp:218] Iteration 8052 (2.42191 iter/s, 4.95476s/12 iters), loss = 0.0598387
I0407 23:07:56.092823 23786 solver.cpp:237]     Train net output #0: loss = 0.0598388 (* 1 = 0.0598388 loss)
I0407 23:07:56.092831 23786 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0407 23:07:58.174610 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0407 23:08:01.246942 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0407 23:08:03.593370 23786 solver.cpp:330] Iteration 8058, Testing net (#0)
I0407 23:08:03.593394 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:08:04.924484 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:08:08.125042 23786 solver.cpp:397]     Test net output #0: accuracy = 0.460172
I0407 23:08:08.125093 23786 solver.cpp:397]     Test net output #1: loss = 2.98389 (* 1 = 2.98389 loss)
I0407 23:08:10.064025 23786 solver.cpp:218] Iteration 8064 (0.858942 iter/s, 13.9707s/12 iters), loss = 0.0732686
I0407 23:08:10.064085 23786 solver.cpp:237]     Train net output #0: loss = 0.0732687 (* 1 = 0.0732687 loss)
I0407 23:08:10.064097 23786 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0407 23:08:15.095636 23786 solver.cpp:218] Iteration 8076 (2.38504 iter/s, 5.03136s/12 iters), loss = 0.0147406
I0407 23:08:15.095679 23786 solver.cpp:237]     Train net output #0: loss = 0.0147408 (* 1 = 0.0147408 loss)
I0407 23:08:15.095688 23786 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0407 23:08:20.104813 23786 solver.cpp:218] Iteration 8088 (2.39572 iter/s, 5.00894s/12 iters), loss = 0.0939019
I0407 23:08:20.104863 23786 solver.cpp:237]     Train net output #0: loss = 0.0939021 (* 1 = 0.0939021 loss)
I0407 23:08:20.104876 23786 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0407 23:08:21.538627 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:08:25.113745 23786 solver.cpp:218] Iteration 8100 (2.39584 iter/s, 5.00868s/12 iters), loss = 0.176753
I0407 23:08:25.113795 23786 solver.cpp:237]     Train net output #0: loss = 0.176753 (* 1 = 0.176753 loss)
I0407 23:08:25.113808 23786 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0407 23:08:30.191721 23786 solver.cpp:218] Iteration 8112 (2.36326 iter/s, 5.07773s/12 iters), loss = 0.112055
I0407 23:08:30.191777 23786 solver.cpp:237]     Train net output #0: loss = 0.112055 (* 1 = 0.112055 loss)
I0407 23:08:30.191790 23786 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0407 23:08:35.259537 23786 solver.cpp:218] Iteration 8124 (2.368 iter/s, 5.06756s/12 iters), loss = 0.0771859
I0407 23:08:35.259594 23786 solver.cpp:237]     Train net output #0: loss = 0.077186 (* 1 = 0.077186 loss)
I0407 23:08:35.259606 23786 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0407 23:08:40.219954 23786 solver.cpp:218] Iteration 8136 (2.41927 iter/s, 4.96017s/12 iters), loss = 0.0916076
I0407 23:08:40.220005 23786 solver.cpp:237]     Train net output #0: loss = 0.0916077 (* 1 = 0.0916077 loss)
I0407 23:08:40.220018 23786 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0407 23:08:45.605523 23786 solver.cpp:218] Iteration 8148 (2.22828 iter/s, 5.38531s/12 iters), loss = 0.0765005
I0407 23:08:45.605572 23786 solver.cpp:237]     Train net output #0: loss = 0.0765006 (* 1 = 0.0765006 loss)
I0407 23:08:45.605584 23786 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0407 23:08:50.127701 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0407 23:08:53.156394 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0407 23:08:55.511211 23786 solver.cpp:330] Iteration 8160, Testing net (#0)
I0407 23:08:55.511237 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:08:56.771638 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:08:59.965298 23786 solver.cpp:397]     Test net output #0: accuracy = 0.471201
I0407 23:08:59.965339 23786 solver.cpp:397]     Test net output #1: loss = 3.02506 (* 1 = 3.02506 loss)
I0407 23:09:00.056043 23786 solver.cpp:218] Iteration 8160 (0.830454 iter/s, 14.4499s/12 iters), loss = 0.0469779
I0407 23:09:00.056097 23786 solver.cpp:237]     Train net output #0: loss = 0.0469781 (* 1 = 0.0469781 loss)
I0407 23:09:00.056108 23786 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0407 23:09:04.391115 23786 solver.cpp:218] Iteration 8172 (2.76826 iter/s, 4.33485s/12 iters), loss = 0.101601
I0407 23:09:04.391165 23786 solver.cpp:237]     Train net output #0: loss = 0.101601 (* 1 = 0.101601 loss)
I0407 23:09:04.391176 23786 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0407 23:09:09.325672 23786 solver.cpp:218] Iteration 8184 (2.43195 iter/s, 4.93431s/12 iters), loss = 0.0917129
I0407 23:09:09.325729 23786 solver.cpp:237]     Train net output #0: loss = 0.091713 (* 1 = 0.091713 loss)
I0407 23:09:09.325743 23786 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0407 23:09:12.969789 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:14.458024 23786 solver.cpp:218] Iteration 8196 (2.33822 iter/s, 5.1321s/12 iters), loss = 0.0526849
I0407 23:09:14.458063 23786 solver.cpp:237]     Train net output #0: loss = 0.052685 (* 1 = 0.052685 loss)
I0407 23:09:14.458072 23786 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0407 23:09:19.605715 23786 solver.cpp:218] Iteration 8208 (2.33125 iter/s, 5.14745s/12 iters), loss = 0.190342
I0407 23:09:19.605773 23786 solver.cpp:237]     Train net output #0: loss = 0.190342 (* 1 = 0.190342 loss)
I0407 23:09:19.605788 23786 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0407 23:09:24.614164 23786 solver.cpp:218] Iteration 8220 (2.39607 iter/s, 5.0082s/12 iters), loss = 0.0532692
I0407 23:09:24.614280 23786 solver.cpp:237]     Train net output #0: loss = 0.0532694 (* 1 = 0.0532694 loss)
I0407 23:09:24.614293 23786 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0407 23:09:29.509528 23786 solver.cpp:218] Iteration 8232 (2.45145 iter/s, 4.89506s/12 iters), loss = 0.0909055
I0407 23:09:29.509582 23786 solver.cpp:237]     Train net output #0: loss = 0.0909057 (* 1 = 0.0909057 loss)
I0407 23:09:29.509593 23786 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0407 23:09:34.680223 23786 solver.cpp:218] Iteration 8244 (2.32088 iter/s, 5.17044s/12 iters), loss = 0.169029
I0407 23:09:34.680274 23786 solver.cpp:237]     Train net output #0: loss = 0.169029 (* 1 = 0.169029 loss)
I0407 23:09:34.680286 23786 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0407 23:09:39.757432 23786 solver.cpp:218] Iteration 8256 (2.36362 iter/s, 5.07696s/12 iters), loss = 0.137329
I0407 23:09:39.757483 23786 solver.cpp:237]     Train net output #0: loss = 0.13733 (* 1 = 0.13733 loss)
I0407 23:09:39.757496 23786 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0407 23:09:41.802250 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0407 23:09:44.843052 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0407 23:09:47.176573 23786 solver.cpp:330] Iteration 8262, Testing net (#0)
I0407 23:09:47.176594 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:09:48.412714 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:51.638690 23786 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0407 23:09:51.638736 23786 solver.cpp:397]     Test net output #1: loss = 3.01956 (* 1 = 3.01956 loss)
I0407 23:09:53.981945 23786 solver.cpp:218] Iteration 8268 (0.843648 iter/s, 14.2239s/12 iters), loss = 0.0348047
I0407 23:09:53.981998 23786 solver.cpp:237]     Train net output #0: loss = 0.0348049 (* 1 = 0.0348049 loss)
I0407 23:09:53.982007 23786 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0407 23:09:59.061518 23786 solver.cpp:218] Iteration 8280 (2.36252 iter/s, 5.07932s/12 iters), loss = 0.0504242
I0407 23:09:59.061666 23786 solver.cpp:237]     Train net output #0: loss = 0.0504244 (* 1 = 0.0504244 loss)
I0407 23:09:59.061678 23786 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0407 23:10:04.089823 23786 solver.cpp:218] Iteration 8292 (2.38665 iter/s, 5.02796s/12 iters), loss = 0.0271725
I0407 23:10:04.089875 23786 solver.cpp:237]     Train net output #0: loss = 0.0271726 (* 1 = 0.0271726 loss)
I0407 23:10:04.089888 23786 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0407 23:10:04.745869 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:10:09.121661 23786 solver.cpp:218] Iteration 8304 (2.38493 iter/s, 5.03159s/12 iters), loss = 0.109613
I0407 23:10:09.121718 23786 solver.cpp:237]     Train net output #0: loss = 0.109613 (* 1 = 0.109613 loss)
I0407 23:10:09.121731 23786 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0407 23:10:11.980727 23786 blocking_queue.cpp:49] Waiting for data
I0407 23:10:14.143061 23786 solver.cpp:218] Iteration 8316 (2.38989 iter/s, 5.02115s/12 iters), loss = 0.112262
I0407 23:10:14.143117 23786 solver.cpp:237]     Train net output #0: loss = 0.112262 (* 1 = 0.112262 loss)
I0407 23:10:14.143129 23786 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0407 23:10:19.114912 23786 solver.cpp:218] Iteration 8328 (2.41371 iter/s, 4.9716s/12 iters), loss = 0.0435615
I0407 23:10:19.114965 23786 solver.cpp:237]     Train net output #0: loss = 0.0435617 (* 1 = 0.0435617 loss)
I0407 23:10:19.114979 23786 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0407 23:10:24.144338 23786 solver.cpp:218] Iteration 8340 (2.38607 iter/s, 5.02918s/12 iters), loss = 0.0579193
I0407 23:10:24.144392 23786 solver.cpp:237]     Train net output #0: loss = 0.0579194 (* 1 = 0.0579194 loss)
I0407 23:10:24.144403 23786 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0407 23:10:29.314118 23786 solver.cpp:218] Iteration 8352 (2.3213 iter/s, 5.16953s/12 iters), loss = 0.0836607
I0407 23:10:29.314193 23786 solver.cpp:237]     Train net output #0: loss = 0.0836608 (* 1 = 0.0836608 loss)
I0407 23:10:29.314204 23786 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0407 23:10:33.837107 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0407 23:10:36.824503 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0407 23:10:41.356307 23786 solver.cpp:330] Iteration 8364, Testing net (#0)
I0407 23:10:41.356333 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:10:42.575553 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:10:45.916880 23786 solver.cpp:397]     Test net output #0: accuracy = 0.471814
I0407 23:10:45.916930 23786 solver.cpp:397]     Test net output #1: loss = 3.03023 (* 1 = 3.03023 loss)
I0407 23:10:46.007091 23786 solver.cpp:218] Iteration 8364 (0.718895 iter/s, 16.6923s/12 iters), loss = 0.0830032
I0407 23:10:46.007139 23786 solver.cpp:237]     Train net output #0: loss = 0.0830034 (* 1 = 0.0830034 loss)
I0407 23:10:46.007150 23786 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0407 23:10:50.549192 23786 solver.cpp:218] Iteration 8376 (2.64208 iter/s, 4.54187s/12 iters), loss = 0.111311
I0407 23:10:50.549253 23786 solver.cpp:237]     Train net output #0: loss = 0.111311 (* 1 = 0.111311 loss)
I0407 23:10:50.549265 23786 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0407 23:10:55.762755 23786 solver.cpp:218] Iteration 8388 (2.3018 iter/s, 5.2133s/12 iters), loss = 0.033634
I0407 23:10:55.762807 23786 solver.cpp:237]     Train net output #0: loss = 0.0336341 (* 1 = 0.0336341 loss)
I0407 23:10:55.762820 23786 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0407 23:10:58.563585 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:11:00.759855 23786 solver.cpp:218] Iteration 8400 (2.40151 iter/s, 4.99685s/12 iters), loss = 0.0726618
I0407 23:11:00.760015 23786 solver.cpp:237]     Train net output #0: loss = 0.0726619 (* 1 = 0.0726619 loss)
I0407 23:11:00.760028 23786 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0407 23:11:05.718497 23786 solver.cpp:218] Iteration 8412 (2.42019 iter/s, 4.95829s/12 iters), loss = 0.0654102
I0407 23:11:05.718559 23786 solver.cpp:237]     Train net output #0: loss = 0.0654103 (* 1 = 0.0654103 loss)
I0407 23:11:05.718575 23786 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0407 23:11:10.733080 23786 solver.cpp:218] Iteration 8424 (2.39314 iter/s, 5.01433s/12 iters), loss = 0.045665
I0407 23:11:10.733126 23786 solver.cpp:237]     Train net output #0: loss = 0.0456651 (* 1 = 0.0456651 loss)
I0407 23:11:10.733136 23786 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0407 23:11:15.644408 23786 solver.cpp:218] Iteration 8436 (2.44345 iter/s, 4.91109s/12 iters), loss = 0.0787578
I0407 23:11:15.644446 23786 solver.cpp:237]     Train net output #0: loss = 0.0787579 (* 1 = 0.0787579 loss)
I0407 23:11:15.644455 23786 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0407 23:11:20.583562 23786 solver.cpp:218] Iteration 8448 (2.42968 iter/s, 4.93892s/12 iters), loss = 0.0820342
I0407 23:11:20.583626 23786 solver.cpp:237]     Train net output #0: loss = 0.0820343 (* 1 = 0.0820343 loss)
I0407 23:11:20.583642 23786 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0407 23:11:25.555577 23786 solver.cpp:218] Iteration 8460 (2.41363 iter/s, 4.97176s/12 iters), loss = 0.0764879
I0407 23:11:25.555625 23786 solver.cpp:237]     Train net output #0: loss = 0.076488 (* 1 = 0.076488 loss)
I0407 23:11:25.555635 23786 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0407 23:11:27.552412 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0407 23:11:30.681320 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0407 23:11:35.146824 23786 solver.cpp:330] Iteration 8466, Testing net (#0)
I0407 23:11:35.146901 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:11:36.460242 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:11:39.784153 23786 solver.cpp:397]     Test net output #0: accuracy = 0.46875
I0407 23:11:39.784204 23786 solver.cpp:397]     Test net output #1: loss = 3.07416 (* 1 = 3.07416 loss)
I0407 23:11:41.744192 23786 solver.cpp:218] Iteration 8472 (0.741291 iter/s, 16.188s/12 iters), loss = 0.0837416
I0407 23:11:41.744241 23786 solver.cpp:237]     Train net output #0: loss = 0.0837418 (* 1 = 0.0837418 loss)
I0407 23:11:41.744254 23786 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0407 23:11:46.767227 23786 solver.cpp:218] Iteration 8484 (2.38911 iter/s, 5.02279s/12 iters), loss = 0.0312469
I0407 23:11:46.767277 23786 solver.cpp:237]     Train net output #0: loss = 0.0312471 (* 1 = 0.0312471 loss)
I0407 23:11:46.767287 23786 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0407 23:11:51.715688 23786 solver.cpp:218] Iteration 8496 (2.42512 iter/s, 4.94822s/12 iters), loss = 0.134373
I0407 23:11:51.715742 23786 solver.cpp:237]     Train net output #0: loss = 0.134373 (* 1 = 0.134373 loss)
I0407 23:11:51.715755 23786 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0407 23:11:51.755924 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:11:56.741840 23786 solver.cpp:218] Iteration 8508 (2.38763 iter/s, 5.0259s/12 iters), loss = 0.12533
I0407 23:11:56.741894 23786 solver.cpp:237]     Train net output #0: loss = 0.125331 (* 1 = 0.125331 loss)
I0407 23:11:56.741906 23786 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0407 23:12:01.708078 23786 solver.cpp:218] Iteration 8520 (2.41644 iter/s, 4.96598s/12 iters), loss = 0.0678763
I0407 23:12:01.708135 23786 solver.cpp:237]     Train net output #0: loss = 0.0678765 (* 1 = 0.0678765 loss)
I0407 23:12:01.708148 23786 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0407 23:12:06.657886 23786 solver.cpp:218] Iteration 8532 (2.42446 iter/s, 4.94956s/12 iters), loss = 0.075806
I0407 23:12:06.658027 23786 solver.cpp:237]     Train net output #0: loss = 0.0758062 (* 1 = 0.0758062 loss)
I0407 23:12:06.658038 23786 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0407 23:12:11.615595 23786 solver.cpp:218] Iteration 8544 (2.42064 iter/s, 4.95737s/12 iters), loss = 0.0261039
I0407 23:12:11.615653 23786 solver.cpp:237]     Train net output #0: loss = 0.0261041 (* 1 = 0.0261041 loss)
I0407 23:12:11.615666 23786 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0407 23:12:16.583074 23786 solver.cpp:218] Iteration 8556 (2.41584 iter/s, 4.96723s/12 iters), loss = 0.112195
I0407 23:12:16.583127 23786 solver.cpp:237]     Train net output #0: loss = 0.112195 (* 1 = 0.112195 loss)
I0407 23:12:16.583137 23786 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0407 23:12:21.100375 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0407 23:12:25.640908 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0407 23:12:30.024261 23786 solver.cpp:330] Iteration 8568, Testing net (#0)
I0407 23:12:30.024286 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:12:31.083864 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:12:34.447189 23786 solver.cpp:397]     Test net output #0: accuracy = 0.481618
I0407 23:12:34.447227 23786 solver.cpp:397]     Test net output #1: loss = 2.95079 (* 1 = 2.95079 loss)
I0407 23:12:34.537830 23786 solver.cpp:218] Iteration 8568 (0.668374 iter/s, 17.954s/12 iters), loss = 0.121213
I0407 23:12:34.537892 23786 solver.cpp:237]     Train net output #0: loss = 0.121213 (* 1 = 0.121213 loss)
I0407 23:12:34.537905 23786 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0407 23:12:38.675033 23786 solver.cpp:218] Iteration 8580 (2.90067 iter/s, 4.13698s/12 iters), loss = 0.113817
I0407 23:12:38.675194 23786 solver.cpp:237]     Train net output #0: loss = 0.113817 (* 1 = 0.113817 loss)
I0407 23:12:38.675215 23786 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0407 23:12:43.513271 23786 solver.cpp:218] Iteration 8592 (2.48041 iter/s, 4.8379s/12 iters), loss = 0.0408626
I0407 23:12:43.513329 23786 solver.cpp:237]     Train net output #0: loss = 0.0408627 (* 1 = 0.0408627 loss)
I0407 23:12:43.513340 23786 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0407 23:12:45.674094 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:12:48.525142 23786 solver.cpp:218] Iteration 8604 (2.39444 iter/s, 5.01162s/12 iters), loss = 0.0327141
I0407 23:12:48.525197 23786 solver.cpp:237]     Train net output #0: loss = 0.0327143 (* 1 = 0.0327143 loss)
I0407 23:12:48.525208 23786 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0407 23:12:53.465626 23786 solver.cpp:218] Iteration 8616 (2.42903 iter/s, 4.94024s/12 iters), loss = 0.0329985
I0407 23:12:53.465672 23786 solver.cpp:237]     Train net output #0: loss = 0.0329987 (* 1 = 0.0329987 loss)
I0407 23:12:53.465683 23786 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0407 23:12:58.447520 23786 solver.cpp:218] Iteration 8628 (2.40884 iter/s, 4.98165s/12 iters), loss = 0.0309739
I0407 23:12:58.447576 23786 solver.cpp:237]     Train net output #0: loss = 0.0309741 (* 1 = 0.0309741 loss)
I0407 23:12:58.447588 23786 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0407 23:13:03.371004 23786 solver.cpp:218] Iteration 8640 (2.43742 iter/s, 4.92324s/12 iters), loss = 0.118257
I0407 23:13:03.371050 23786 solver.cpp:237]     Train net output #0: loss = 0.118258 (* 1 = 0.118258 loss)
I0407 23:13:03.371060 23786 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0407 23:13:08.398655 23786 solver.cpp:218] Iteration 8652 (2.38691 iter/s, 5.02741s/12 iters), loss = 0.0340344
I0407 23:13:08.398703 23786 solver.cpp:237]     Train net output #0: loss = 0.0340346 (* 1 = 0.0340346 loss)
I0407 23:13:08.398715 23786 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0407 23:13:13.421402 23786 solver.cpp:218] Iteration 8664 (2.38925 iter/s, 5.02251s/12 iters), loss = 0.112662
I0407 23:13:13.421547 23786 solver.cpp:237]     Train net output #0: loss = 0.112662 (* 1 = 0.112662 loss)
I0407 23:13:13.421558 23786 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0407 23:13:15.467777 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0407 23:13:20.513571 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0407 23:13:24.780870 23786 solver.cpp:330] Iteration 8670, Testing net (#0)
I0407 23:13:24.780895 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:13:25.816272 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:13:29.215651 23786 solver.cpp:397]     Test net output #0: accuracy = 0.48223
I0407 23:13:29.215701 23786 solver.cpp:397]     Test net output #1: loss = 2.97374 (* 1 = 2.97374 loss)
I0407 23:13:31.205190 23786 solver.cpp:218] Iteration 8676 (0.674803 iter/s, 17.783s/12 iters), loss = 0.0681168
I0407 23:13:31.205247 23786 solver.cpp:237]     Train net output #0: loss = 0.068117 (* 1 = 0.068117 loss)
I0407 23:13:31.205260 23786 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0407 23:13:36.202606 23786 solver.cpp:218] Iteration 8688 (2.40136 iter/s, 4.99717s/12 iters), loss = 0.0608193
I0407 23:13:36.202646 23786 solver.cpp:237]     Train net output #0: loss = 0.0608195 (* 1 = 0.0608195 loss)
I0407 23:13:36.202654 23786 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0407 23:13:40.526487 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:13:41.215632 23786 solver.cpp:218] Iteration 8700 (2.39388 iter/s, 5.01279s/12 iters), loss = 0.0810621
I0407 23:13:41.215677 23786 solver.cpp:237]     Train net output #0: loss = 0.0810623 (* 1 = 0.0810623 loss)
I0407 23:13:41.215687 23786 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0407 23:13:46.248016 23786 solver.cpp:218] Iteration 8712 (2.38467 iter/s, 5.03214s/12 iters), loss = 0.0391005
I0407 23:13:46.248116 23786 solver.cpp:237]     Train net output #0: loss = 0.0391007 (* 1 = 0.0391007 loss)
I0407 23:13:46.248129 23786 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0407 23:13:51.191830 23786 solver.cpp:218] Iteration 8724 (2.42742 iter/s, 4.94353s/12 iters), loss = 0.0145154
I0407 23:13:51.191871 23786 solver.cpp:237]     Train net output #0: loss = 0.0145156 (* 1 = 0.0145156 loss)
I0407 23:13:51.191879 23786 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0407 23:13:56.169142 23786 solver.cpp:218] Iteration 8736 (2.41106 iter/s, 4.97706s/12 iters), loss = 0.0844706
I0407 23:13:56.169204 23786 solver.cpp:237]     Train net output #0: loss = 0.0844708 (* 1 = 0.0844708 loss)
I0407 23:13:56.169217 23786 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0407 23:14:01.188956 23786 solver.cpp:218] Iteration 8748 (2.39065 iter/s, 5.01956s/12 iters), loss = 0.0307924
I0407 23:14:01.188994 23786 solver.cpp:237]     Train net output #0: loss = 0.0307926 (* 1 = 0.0307926 loss)
I0407 23:14:01.189003 23786 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0407 23:14:06.231263 23786 solver.cpp:218] Iteration 8760 (2.37997 iter/s, 5.04207s/12 iters), loss = 0.0829647
I0407 23:14:06.231309 23786 solver.cpp:237]     Train net output #0: loss = 0.0829649 (* 1 = 0.0829649 loss)
I0407 23:14:06.231320 23786 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0407 23:14:10.751255 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0407 23:14:15.164624 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0407 23:14:19.604331 23786 solver.cpp:330] Iteration 8772, Testing net (#0)
I0407 23:14:19.604429 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:14:20.652177 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:14:24.172699 23786 solver.cpp:397]     Test net output #0: accuracy = 0.476716
I0407 23:14:24.172749 23786 solver.cpp:397]     Test net output #1: loss = 3.03251 (* 1 = 3.03251 loss)
I0407 23:14:24.263005 23786 solver.cpp:218] Iteration 8772 (0.66552 iter/s, 18.031s/12 iters), loss = 0.0789544
I0407 23:14:24.263060 23786 solver.cpp:237]     Train net output #0: loss = 0.0789546 (* 1 = 0.0789546 loss)
I0407 23:14:24.263072 23786 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0407 23:14:28.511617 23786 solver.cpp:218] Iteration 8784 (2.8246 iter/s, 4.24839s/12 iters), loss = 0.0495634
I0407 23:14:28.511662 23786 solver.cpp:237]     Train net output #0: loss = 0.0495636 (* 1 = 0.0495636 loss)
I0407 23:14:28.511672 23786 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0407 23:14:33.663159 23786 solver.cpp:218] Iteration 8796 (2.32951 iter/s, 5.1513s/12 iters), loss = 0.0283489
I0407 23:14:33.663197 23786 solver.cpp:237]     Train net output #0: loss = 0.0283491 (* 1 = 0.0283491 loss)
I0407 23:14:33.663206 23786 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0407 23:14:35.108836 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:14:38.665611 23786 solver.cpp:218] Iteration 8808 (2.39894 iter/s, 5.00222s/12 iters), loss = 0.091734
I0407 23:14:38.665655 23786 solver.cpp:237]     Train net output #0: loss = 0.0917342 (* 1 = 0.0917342 loss)
I0407 23:14:38.665664 23786 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0407 23:14:43.680650 23786 solver.cpp:218] Iteration 8820 (2.39292 iter/s, 5.0148s/12 iters), loss = 0.0610799
I0407 23:14:43.680696 23786 solver.cpp:237]     Train net output #0: loss = 0.0610801 (* 1 = 0.0610801 loss)
I0407 23:14:43.680706 23786 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0407 23:14:48.646586 23786 solver.cpp:218] Iteration 8832 (2.41658 iter/s, 4.96569s/12 iters), loss = 0.0319397
I0407 23:14:48.646643 23786 solver.cpp:237]     Train net output #0: loss = 0.0319399 (* 1 = 0.0319399 loss)
I0407 23:14:48.646656 23786 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0407 23:14:53.582111 23786 solver.cpp:218] Iteration 8844 (2.43148 iter/s, 4.93527s/12 iters), loss = 0.0598838
I0407 23:14:53.582221 23786 solver.cpp:237]     Train net output #0: loss = 0.059884 (* 1 = 0.059884 loss)
I0407 23:14:53.582232 23786 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0407 23:14:58.699007 23786 solver.cpp:218] Iteration 8856 (2.34531 iter/s, 5.1166s/12 iters), loss = 0.0271336
I0407 23:14:58.699043 23786 solver.cpp:237]     Train net output #0: loss = 0.0271338 (* 1 = 0.0271338 loss)
I0407 23:14:58.699051 23786 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0407 23:15:03.639824 23786 solver.cpp:218] Iteration 8868 (2.42885 iter/s, 4.9406s/12 iters), loss = 0.0376984
I0407 23:15:03.639874 23786 solver.cpp:237]     Train net output #0: loss = 0.0376986 (* 1 = 0.0376986 loss)
I0407 23:15:03.639885 23786 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0407 23:15:05.699306 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0407 23:15:10.131206 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0407 23:15:12.889550 23786 solver.cpp:330] Iteration 8874, Testing net (#0)
I0407 23:15:12.889576 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:15:13.932337 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:15:17.461472 23786 solver.cpp:397]     Test net output #0: accuracy = 0.476103
I0407 23:15:17.461522 23786 solver.cpp:397]     Test net output #1: loss = 3.03448 (* 1 = 3.03448 loss)
I0407 23:15:19.446913 23786 solver.cpp:218] Iteration 8880 (0.759181 iter/s, 15.8065s/12 iters), loss = 0.0511819
I0407 23:15:19.446965 23786 solver.cpp:237]     Train net output #0: loss = 0.0511821 (* 1 = 0.0511821 loss)
I0407 23:15:19.446977 23786 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0407 23:15:24.645831 23786 solver.cpp:218] Iteration 8892 (2.30828 iter/s, 5.19868s/12 iters), loss = 0.12491
I0407 23:15:24.645952 23786 solver.cpp:237]     Train net output #0: loss = 0.12491 (* 1 = 0.12491 loss)
I0407 23:15:24.645977 23786 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0407 23:15:28.270771 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:15:29.701251 23786 solver.cpp:218] Iteration 8904 (2.37383 iter/s, 5.05512s/12 iters), loss = 0.0616742
I0407 23:15:29.701300 23786 solver.cpp:237]     Train net output #0: loss = 0.0616744 (* 1 = 0.0616744 loss)
I0407 23:15:29.701313 23786 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0407 23:15:34.787668 23786 solver.cpp:218] Iteration 8916 (2.35933 iter/s, 5.08619s/12 iters), loss = 0.0609168
I0407 23:15:34.787709 23786 solver.cpp:237]     Train net output #0: loss = 0.060917 (* 1 = 0.060917 loss)
I0407 23:15:34.787720 23786 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0407 23:15:39.818651 23786 solver.cpp:218] Iteration 8928 (2.38533 iter/s, 5.03076s/12 iters), loss = 0.0917748
I0407 23:15:39.818699 23786 solver.cpp:237]     Train net output #0: loss = 0.091775 (* 1 = 0.091775 loss)
I0407 23:15:39.818708 23786 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0407 23:15:44.801308 23786 solver.cpp:218] Iteration 8940 (2.40846 iter/s, 4.98243s/12 iters), loss = 0.0846166
I0407 23:15:44.801360 23786 solver.cpp:237]     Train net output #0: loss = 0.0846168 (* 1 = 0.0846168 loss)
I0407 23:15:44.801371 23786 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0407 23:15:49.821405 23786 solver.cpp:218] Iteration 8952 (2.3905 iter/s, 5.01987s/12 iters), loss = 0.0880661
I0407 23:15:49.821449 23786 solver.cpp:237]     Train net output #0: loss = 0.0880663 (* 1 = 0.0880663 loss)
I0407 23:15:49.821458 23786 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0407 23:15:55.144845 23786 solver.cpp:218] Iteration 8964 (2.25428 iter/s, 5.3232s/12 iters), loss = 0.101465
I0407 23:15:55.144948 23786 solver.cpp:237]     Train net output #0: loss = 0.101465 (* 1 = 0.101465 loss)
I0407 23:15:55.144958 23786 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0407 23:16:00.071803 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0407 23:16:03.108942 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0407 23:16:07.487059 23786 solver.cpp:330] Iteration 8976, Testing net (#0)
I0407 23:16:07.487087 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:16:08.455698 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:16:12.031952 23786 solver.cpp:397]     Test net output #0: accuracy = 0.476716
I0407 23:16:12.032006 23786 solver.cpp:397]     Test net output #1: loss = 3.01772 (* 1 = 3.01772 loss)
I0407 23:16:12.122455 23786 solver.cpp:218] Iteration 8976 (0.706842 iter/s, 16.9769s/12 iters), loss = 0.0393326
I0407 23:16:12.122511 23786 solver.cpp:237]     Train net output #0: loss = 0.0393328 (* 1 = 0.0393328 loss)
I0407 23:16:12.122521 23786 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0407 23:16:16.657207 23786 solver.cpp:218] Iteration 8988 (2.64636 iter/s, 4.53453s/12 iters), loss = 0.0354934
I0407 23:16:16.657260 23786 solver.cpp:237]     Train net output #0: loss = 0.0354936 (* 1 = 0.0354936 loss)
I0407 23:16:16.657272 23786 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0407 23:16:20.002768 23786 blocking_queue.cpp:49] Waiting for data
I0407 23:16:21.734225 23786 solver.cpp:218] Iteration 9000 (2.3637 iter/s, 5.07678s/12 iters), loss = 0.0993475
I0407 23:16:21.734282 23786 solver.cpp:237]     Train net output #0: loss = 0.0993477 (* 1 = 0.0993477 loss)
I0407 23:16:21.734297 23786 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0407 23:16:22.416592 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:16:26.618599 23786 solver.cpp:218] Iteration 9012 (2.45693 iter/s, 4.88414s/12 iters), loss = 0.043253
I0407 23:16:26.618714 23786 solver.cpp:237]     Train net output #0: loss = 0.0432532 (* 1 = 0.0432532 loss)
I0407 23:16:26.618726 23786 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0407 23:16:31.798619 23786 solver.cpp:218] Iteration 9024 (2.31673 iter/s, 5.17972s/12 iters), loss = 0.0229933
I0407 23:16:31.798669 23786 solver.cpp:237]     Train net output #0: loss = 0.0229935 (* 1 = 0.0229935 loss)
I0407 23:16:31.798681 23786 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0407 23:16:36.779296 23786 solver.cpp:218] Iteration 9036 (2.40943 iter/s, 4.98044s/12 iters), loss = 0.0601841
I0407 23:16:36.779350 23786 solver.cpp:237]     Train net output #0: loss = 0.0601843 (* 1 = 0.0601843 loss)
I0407 23:16:36.779362 23786 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0407 23:16:42.086050 23786 solver.cpp:218] Iteration 9048 (2.26137 iter/s, 5.30651s/12 iters), loss = 0.0668225
I0407 23:16:42.086107 23786 solver.cpp:237]     Train net output #0: loss = 0.0668226 (* 1 = 0.0668226 loss)
I0407 23:16:42.086120 23786 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0407 23:16:47.135948 23786 solver.cpp:218] Iteration 9060 (2.3764 iter/s, 5.04966s/12 iters), loss = 0.066477
I0407 23:16:47.135998 23786 solver.cpp:237]     Train net output #0: loss = 0.0664772 (* 1 = 0.0664772 loss)
I0407 23:16:47.136006 23786 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0407 23:16:52.174733 23786 solver.cpp:218] Iteration 9072 (2.38164 iter/s, 5.03855s/12 iters), loss = 0.0235407
I0407 23:16:52.174787 23786 solver.cpp:237]     Train net output #0: loss = 0.0235408 (* 1 = 0.0235408 loss)
I0407 23:16:52.174798 23786 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0407 23:16:54.407371 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0407 23:16:58.433879 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0407 23:17:05.897682 23786 solver.cpp:330] Iteration 9078, Testing net (#0)
I0407 23:17:05.897709 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:17:06.774968 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:17:10.322696 23786 solver.cpp:397]     Test net output #0: accuracy = 0.46875
I0407 23:17:10.322746 23786 solver.cpp:397]     Test net output #1: loss = 2.99928 (* 1 = 2.99928 loss)
I0407 23:17:12.330888 23786 solver.cpp:218] Iteration 9084 (0.595374 iter/s, 20.1554s/12 iters), loss = 0.0564629
I0407 23:17:12.330943 23786 solver.cpp:237]     Train net output #0: loss = 0.0564631 (* 1 = 0.0564631 loss)
I0407 23:17:12.330955 23786 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0407 23:17:17.428413 23786 solver.cpp:218] Iteration 9096 (2.35419 iter/s, 5.09728s/12 iters), loss = 0.0561882
I0407 23:17:17.428468 23786 solver.cpp:237]     Train net output #0: loss = 0.0561883 (* 1 = 0.0561883 loss)
I0407 23:17:17.428479 23786 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0407 23:17:20.362422 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:17:22.598161 23786 solver.cpp:218] Iteration 9108 (2.3213 iter/s, 5.16951s/12 iters), loss = 0.0665953
I0407 23:17:22.598208 23786 solver.cpp:237]     Train net output #0: loss = 0.0665955 (* 1 = 0.0665955 loss)
I0407 23:17:22.598219 23786 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0407 23:17:27.731422 23786 solver.cpp:218] Iteration 9120 (2.3378 iter/s, 5.13303s/12 iters), loss = 0.0890782
I0407 23:17:27.731457 23786 solver.cpp:237]     Train net output #0: loss = 0.0890784 (* 1 = 0.0890784 loss)
I0407 23:17:27.731467 23786 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0407 23:17:32.763545 23786 solver.cpp:218] Iteration 9132 (2.38478 iter/s, 5.0319s/12 iters), loss = 0.0580016
I0407 23:17:32.763671 23786 solver.cpp:237]     Train net output #0: loss = 0.0580018 (* 1 = 0.0580018 loss)
I0407 23:17:32.763682 23786 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0407 23:17:37.835616 23786 solver.cpp:218] Iteration 9144 (2.36604 iter/s, 5.07176s/12 iters), loss = 0.0480955
I0407 23:17:37.835656 23786 solver.cpp:237]     Train net output #0: loss = 0.0480957 (* 1 = 0.0480957 loss)
I0407 23:17:37.835664 23786 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0407 23:17:42.836169 23786 solver.cpp:218] Iteration 9156 (2.39984 iter/s, 5.00033s/12 iters), loss = 0.0647233
I0407 23:17:42.836218 23786 solver.cpp:237]     Train net output #0: loss = 0.0647235 (* 1 = 0.0647235 loss)
I0407 23:17:42.836230 23786 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0407 23:17:47.797474 23786 solver.cpp:218] Iteration 9168 (2.41883 iter/s, 4.96107s/12 iters), loss = 0.00797513
I0407 23:17:47.797523 23786 solver.cpp:237]     Train net output #0: loss = 0.00797531 (* 1 = 0.00797531 loss)
I0407 23:17:47.797533 23786 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0407 23:17:52.364565 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0407 23:17:58.577481 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0407 23:18:02.890267 23786 solver.cpp:330] Iteration 9180, Testing net (#0)
I0407 23:18:02.890321 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:18:03.852694 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:18:07.431414 23786 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0407 23:18:07.431466 23786 solver.cpp:397]     Test net output #1: loss = 3.0275 (* 1 = 3.0275 loss)
I0407 23:18:07.521260 23786 solver.cpp:218] Iteration 9180 (0.608425 iter/s, 19.7231s/12 iters), loss = 0.00710707
I0407 23:18:07.521311 23786 solver.cpp:237]     Train net output #0: loss = 0.00710725 (* 1 = 0.00710725 loss)
I0407 23:18:07.521322 23786 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0407 23:18:11.735988 23786 solver.cpp:218] Iteration 9192 (2.8473 iter/s, 4.21452s/12 iters), loss = 0.0589528
I0407 23:18:11.736040 23786 solver.cpp:237]     Train net output #0: loss = 0.058953 (* 1 = 0.058953 loss)
I0407 23:18:11.736052 23786 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0407 23:18:16.736765 23786 solver.cpp:218] Iteration 9204 (2.39974 iter/s, 5.00054s/12 iters), loss = 0.0550607
I0407 23:18:16.736824 23786 solver.cpp:237]     Train net output #0: loss = 0.0550609 (* 1 = 0.0550609 loss)
I0407 23:18:16.736836 23786 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0407 23:18:16.804267 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:18:21.983191 23786 solver.cpp:218] Iteration 9216 (2.28738 iter/s, 5.24618s/12 iters), loss = 0.0114857
I0407 23:18:21.983238 23786 solver.cpp:237]     Train net output #0: loss = 0.0114858 (* 1 = 0.0114858 loss)
I0407 23:18:21.983250 23786 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0407 23:18:26.917898 23786 solver.cpp:218] Iteration 9228 (2.43187 iter/s, 4.93448s/12 iters), loss = 0.0565749
I0407 23:18:26.917943 23786 solver.cpp:237]     Train net output #0: loss = 0.0565751 (* 1 = 0.0565751 loss)
I0407 23:18:26.917963 23786 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0407 23:18:31.936357 23786 solver.cpp:218] Iteration 9240 (2.39128 iter/s, 5.01823s/12 iters), loss = 0.0439734
I0407 23:18:31.936398 23786 solver.cpp:237]     Train net output #0: loss = 0.0439736 (* 1 = 0.0439736 loss)
I0407 23:18:31.936405 23786 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0407 23:18:36.907912 23786 solver.cpp:218] Iteration 9252 (2.41384 iter/s, 4.97133s/12 iters), loss = 0.18674
I0407 23:18:36.907996 23786 solver.cpp:237]     Train net output #0: loss = 0.18674 (* 1 = 0.18674 loss)
I0407 23:18:36.908004 23786 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0407 23:18:41.927894 23786 solver.cpp:218] Iteration 9264 (2.39058 iter/s, 5.01971s/12 iters), loss = 0.0237374
I0407 23:18:41.927947 23786 solver.cpp:237]     Train net output #0: loss = 0.0237376 (* 1 = 0.0237376 loss)
I0407 23:18:41.927959 23786 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0407 23:18:46.917551 23786 solver.cpp:218] Iteration 9276 (2.40509 iter/s, 4.98942s/12 iters), loss = 0.109494
I0407 23:18:46.917601 23786 solver.cpp:237]     Train net output #0: loss = 0.109494 (* 1 = 0.109494 loss)
I0407 23:18:46.917613 23786 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0407 23:18:48.963891 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0407 23:18:56.658710 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0407 23:19:01.052493 23786 solver.cpp:330] Iteration 9282, Testing net (#0)
I0407 23:19:01.052515 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:19:01.876592 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:19:05.523558 23786 solver.cpp:397]     Test net output #0: accuracy = 0.469976
I0407 23:19:05.523607 23786 solver.cpp:397]     Test net output #1: loss = 3.05014 (* 1 = 3.05014 loss)
I0407 23:19:07.376605 23786 solver.cpp:218] Iteration 9288 (0.58656 iter/s, 20.4583s/12 iters), loss = 0.0660421
I0407 23:19:07.376763 23786 solver.cpp:237]     Train net output #0: loss = 0.0660422 (* 1 = 0.0660422 loss)
I0407 23:19:07.376777 23786 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0407 23:19:12.410107 23786 solver.cpp:218] Iteration 9300 (2.38419 iter/s, 5.03315s/12 iters), loss = 0.00841654
I0407 23:19:12.410181 23786 solver.cpp:237]     Train net output #0: loss = 0.00841672 (* 1 = 0.00841672 loss)
I0407 23:19:12.410193 23786 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0407 23:19:14.607805 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:19:17.411666 23786 solver.cpp:218] Iteration 9312 (2.39937 iter/s, 5.0013s/12 iters), loss = 0.0508454
I0407 23:19:17.411718 23786 solver.cpp:237]     Train net output #0: loss = 0.0508456 (* 1 = 0.0508456 loss)
I0407 23:19:17.411731 23786 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0407 23:19:22.408860 23786 solver.cpp:218] Iteration 9324 (2.40146 iter/s, 4.99696s/12 iters), loss = 0.0687293
I0407 23:19:22.408910 23786 solver.cpp:237]     Train net output #0: loss = 0.0687295 (* 1 = 0.0687295 loss)
I0407 23:19:22.408921 23786 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0407 23:19:27.589913 23786 solver.cpp:218] Iteration 9336 (2.31624 iter/s, 5.18081s/12 iters), loss = 0.0642737
I0407 23:19:27.589982 23786 solver.cpp:237]     Train net output #0: loss = 0.0642739 (* 1 = 0.0642739 loss)
I0407 23:19:27.589996 23786 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0407 23:19:32.694542 23786 solver.cpp:218] Iteration 9348 (2.35093 iter/s, 5.10437s/12 iters), loss = 0.0571295
I0407 23:19:32.694589 23786 solver.cpp:237]     Train net output #0: loss = 0.0571297 (* 1 = 0.0571297 loss)
I0407 23:19:32.694602 23786 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0407 23:19:37.673467 23786 solver.cpp:218] Iteration 9360 (2.41027 iter/s, 4.97869s/12 iters), loss = 0.0295548
I0407 23:19:37.673600 23786 solver.cpp:237]     Train net output #0: loss = 0.029555 (* 1 = 0.029555 loss)
I0407 23:19:37.673614 23786 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0407 23:19:42.559970 23786 solver.cpp:218] Iteration 9372 (2.4559 iter/s, 4.88619s/12 iters), loss = 0.0497869
I0407 23:19:42.560022 23786 solver.cpp:237]     Train net output #0: loss = 0.0497871 (* 1 = 0.0497871 loss)
I0407 23:19:42.560034 23786 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0407 23:19:47.074153 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0407 23:19:52.649349 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0407 23:19:55.017803 23786 solver.cpp:330] Iteration 9384, Testing net (#0)
I0407 23:19:55.017825 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:19:55.803300 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:19:59.507655 23786 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0407 23:19:59.507707 23786 solver.cpp:397]     Test net output #1: loss = 3.06762 (* 1 = 3.06762 loss)
I0407 23:19:59.598067 23786 solver.cpp:218] Iteration 9384 (0.704331 iter/s, 17.0374s/12 iters), loss = 0.00589711
I0407 23:19:59.598119 23786 solver.cpp:237]     Train net output #0: loss = 0.00589731 (* 1 = 0.00589731 loss)
I0407 23:19:59.598130 23786 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0407 23:20:03.871946 23786 solver.cpp:218] Iteration 9396 (2.8079 iter/s, 4.27366s/12 iters), loss = 0.030176
I0407 23:20:03.871994 23786 solver.cpp:237]     Train net output #0: loss = 0.0301762 (* 1 = 0.0301762 loss)
I0407 23:20:03.872005 23786 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0407 23:20:08.243733 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:20:08.891870 23786 solver.cpp:218] Iteration 9408 (2.39059 iter/s, 5.01969s/12 iters), loss = 0.076337
I0407 23:20:08.891907 23786 solver.cpp:237]     Train net output #0: loss = 0.0763372 (* 1 = 0.0763372 loss)
I0407 23:20:08.891914 23786 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0407 23:20:13.892400 23786 solver.cpp:218] Iteration 9420 (2.39986 iter/s, 5.0003s/12 iters), loss = 0.0660374
I0407 23:20:13.892452 23786 solver.cpp:237]     Train net output #0: loss = 0.0660376 (* 1 = 0.0660376 loss)
I0407 23:20:13.892463 23786 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0407 23:20:18.902987 23786 solver.cpp:218] Iteration 9432 (2.39504 iter/s, 5.01035s/12 iters), loss = 0.019246
I0407 23:20:18.903031 23786 solver.cpp:237]     Train net output #0: loss = 0.0192462 (* 1 = 0.0192462 loss)
I0407 23:20:18.903040 23786 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0407 23:20:23.904330 23786 solver.cpp:218] Iteration 9444 (2.39947 iter/s, 5.00111s/12 iters), loss = 0.0253571
I0407 23:20:23.904381 23786 solver.cpp:237]     Train net output #0: loss = 0.0253573 (* 1 = 0.0253573 loss)
I0407 23:20:23.904392 23786 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0407 23:20:28.943707 23786 solver.cpp:218] Iteration 9456 (2.38136 iter/s, 5.03914s/12 iters), loss = 0.0259331
I0407 23:20:28.943758 23786 solver.cpp:237]     Train net output #0: loss = 0.0259333 (* 1 = 0.0259333 loss)
I0407 23:20:28.943768 23786 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0407 23:20:33.919207 23786 solver.cpp:218] Iteration 9468 (2.41193 iter/s, 4.97526s/12 iters), loss = 0.0232112
I0407 23:20:33.919255 23786 solver.cpp:237]     Train net output #0: loss = 0.0232114 (* 1 = 0.0232114 loss)
I0407 23:20:33.919267 23786 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0407 23:20:38.860340 23786 solver.cpp:218] Iteration 9480 (2.42871 iter/s, 4.9409s/12 iters), loss = 0.00806433
I0407 23:20:38.860411 23786 solver.cpp:237]     Train net output #0: loss = 0.00806454 (* 1 = 0.00806454 loss)
I0407 23:20:38.860421 23786 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0407 23:20:40.872074 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0407 23:20:46.625573 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0407 23:20:48.971268 23786 solver.cpp:330] Iteration 9486, Testing net (#0)
I0407 23:20:48.971293 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:20:49.710083 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:20:53.450549 23786 solver.cpp:397]     Test net output #0: accuracy = 0.464461
I0407 23:20:53.450600 23786 solver.cpp:397]     Test net output #1: loss = 3.09166 (* 1 = 3.09166 loss)
I0407 23:20:55.281978 23786 solver.cpp:218] Iteration 9492 (0.730773 iter/s, 16.421s/12 iters), loss = 0.0182254
I0407 23:20:55.282032 23786 solver.cpp:237]     Train net output #0: loss = 0.0182257 (* 1 = 0.0182257 loss)
I0407 23:20:55.282047 23786 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0407 23:21:00.253764 23786 solver.cpp:218] Iteration 9504 (2.41374 iter/s, 4.97154s/12 iters), loss = 0.1448
I0407 23:21:00.253829 23786 solver.cpp:237]     Train net output #0: loss = 0.144801 (* 1 = 0.144801 loss)
I0407 23:21:00.253844 23786 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0407 23:21:01.687011 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:21:05.162953 23786 solver.cpp:218] Iteration 9516 (2.44452 iter/s, 4.90895s/12 iters), loss = 0.0981091
I0407 23:21:05.162995 23786 solver.cpp:237]     Train net output #0: loss = 0.0981093 (* 1 = 0.0981093 loss)
I0407 23:21:05.163004 23786 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0407 23:21:10.188647 23786 solver.cpp:218] Iteration 9528 (2.38784 iter/s, 5.02545s/12 iters), loss = 0.0345067
I0407 23:21:10.188799 23786 solver.cpp:237]     Train net output #0: loss = 0.0345069 (* 1 = 0.0345069 loss)
I0407 23:21:10.188813 23786 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0407 23:21:15.204798 23786 solver.cpp:218] Iteration 9540 (2.39243 iter/s, 5.01581s/12 iters), loss = 0.0364461
I0407 23:21:15.204849 23786 solver.cpp:237]     Train net output #0: loss = 0.0364463 (* 1 = 0.0364463 loss)
I0407 23:21:15.204857 23786 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0407 23:21:20.181571 23786 solver.cpp:218] Iteration 9552 (2.41132 iter/s, 4.97654s/12 iters), loss = 0.121811
I0407 23:21:20.181619 23786 solver.cpp:237]     Train net output #0: loss = 0.121811 (* 1 = 0.121811 loss)
I0407 23:21:20.181630 23786 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0407 23:21:25.180847 23786 solver.cpp:218] Iteration 9564 (2.40046 iter/s, 4.99904s/12 iters), loss = 0.0737299
I0407 23:21:25.180899 23786 solver.cpp:237]     Train net output #0: loss = 0.0737301 (* 1 = 0.0737301 loss)
I0407 23:21:25.180909 23786 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0407 23:21:30.208212 23786 solver.cpp:218] Iteration 9576 (2.38705 iter/s, 5.02712s/12 iters), loss = 0.0323457
I0407 23:21:30.208268 23786 solver.cpp:237]     Train net output #0: loss = 0.0323459 (* 1 = 0.0323459 loss)
I0407 23:21:30.208281 23786 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0407 23:21:34.753091 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0407 23:21:38.589056 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0407 23:21:40.921509 23786 solver.cpp:330] Iteration 9588, Testing net (#0)
I0407 23:21:40.921586 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:21:41.530304 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:21:45.618882 23786 solver.cpp:397]     Test net output #0: accuracy = 0.471814
I0407 23:21:45.618919 23786 solver.cpp:397]     Test net output #1: loss = 3.01219 (* 1 = 3.01219 loss)
I0407 23:21:45.709249 23786 solver.cpp:218] Iteration 9588 (0.774172 iter/s, 15.5004s/12 iters), loss = 0.0619807
I0407 23:21:45.709300 23786 solver.cpp:237]     Train net output #0: loss = 0.0619809 (* 1 = 0.0619809 loss)
I0407 23:21:45.709311 23786 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0407 23:21:49.904670 23786 solver.cpp:218] Iteration 9600 (2.8604 iter/s, 4.19521s/12 iters), loss = 0.0627065
I0407 23:21:49.904712 23786 solver.cpp:237]     Train net output #0: loss = 0.0627067 (* 1 = 0.0627067 loss)
I0407 23:21:49.904723 23786 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0407 23:21:53.629240 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:21:55.131994 23786 solver.cpp:218] Iteration 9612 (2.29573 iter/s, 5.22709s/12 iters), loss = 0.0739237
I0407 23:21:55.132033 23786 solver.cpp:237]     Train net output #0: loss = 0.0739239 (* 1 = 0.0739239 loss)
I0407 23:21:55.132043 23786 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0407 23:22:01.256683 23786 solver.cpp:218] Iteration 9624 (1.95937 iter/s, 6.12442s/12 iters), loss = 0.0706213
I0407 23:22:01.256736 23786 solver.cpp:237]     Train net output #0: loss = 0.0706215 (* 1 = 0.0706215 loss)
I0407 23:22:01.256748 23786 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0407 23:22:06.240947 23786 solver.cpp:218] Iteration 9636 (2.40769 iter/s, 4.98402s/12 iters), loss = 0.040021
I0407 23:22:06.241003 23786 solver.cpp:237]     Train net output #0: loss = 0.0400212 (* 1 = 0.0400212 loss)
I0407 23:22:06.241014 23786 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0407 23:22:11.200628 23786 solver.cpp:218] Iteration 9648 (2.41963 iter/s, 4.95944s/12 iters), loss = 0.10424
I0407 23:22:11.200763 23786 solver.cpp:237]     Train net output #0: loss = 0.10424 (* 1 = 0.10424 loss)
I0407 23:22:11.200773 23786 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0407 23:22:16.134793 23786 solver.cpp:218] Iteration 9660 (2.43218 iter/s, 4.93384s/12 iters), loss = 0.0851205
I0407 23:22:16.134853 23786 solver.cpp:237]     Train net output #0: loss = 0.0851207 (* 1 = 0.0851207 loss)
I0407 23:22:16.134869 23786 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0407 23:22:21.083770 23786 solver.cpp:218] Iteration 9672 (2.42486 iter/s, 4.94873s/12 iters), loss = 0.0521771
I0407 23:22:21.083822 23786 solver.cpp:237]     Train net output #0: loss = 0.0521772 (* 1 = 0.0521772 loss)
I0407 23:22:21.083834 23786 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0407 23:22:26.036587 23786 solver.cpp:218] Iteration 9684 (2.42298 iter/s, 4.95258s/12 iters), loss = 0.0618399
I0407 23:22:26.036638 23786 solver.cpp:237]     Train net output #0: loss = 0.0618401 (* 1 = 0.0618401 loss)
I0407 23:22:26.036651 23786 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0407 23:22:28.010965 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0407 23:22:31.040753 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0407 23:22:33.376638 23786 solver.cpp:330] Iteration 9690, Testing net (#0)
I0407 23:22:33.376662 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:22:34.056412 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:36.991679 23786 blocking_queue.cpp:49] Waiting for data
I0407 23:22:37.977398 23786 solver.cpp:397]     Test net output #0: accuracy = 0.476103
I0407 23:22:37.977447 23786 solver.cpp:397]     Test net output #1: loss = 3.05562 (* 1 = 3.05562 loss)
I0407 23:22:39.901827 23786 solver.cpp:218] Iteration 9696 (0.865508 iter/s, 13.8647s/12 iters), loss = 0.0274092
I0407 23:22:39.901883 23786 solver.cpp:237]     Train net output #0: loss = 0.0274094 (* 1 = 0.0274094 loss)
I0407 23:22:39.901895 23786 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0407 23:22:44.977461 23786 solver.cpp:218] Iteration 9708 (2.36435 iter/s, 5.07539s/12 iters), loss = 0.0368073
I0407 23:22:44.977550 23786 solver.cpp:237]     Train net output #0: loss = 0.0368075 (* 1 = 0.0368075 loss)
I0407 23:22:44.977560 23786 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0407 23:22:45.799695 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:50.187520 23786 solver.cpp:218] Iteration 9720 (2.30336 iter/s, 5.20977s/12 iters), loss = 0.0200309
I0407 23:22:50.187566 23786 solver.cpp:237]     Train net output #0: loss = 0.0200311 (* 1 = 0.0200311 loss)
I0407 23:22:50.187578 23786 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0407 23:22:55.127455 23786 solver.cpp:218] Iteration 9732 (2.42929 iter/s, 4.93971s/12 iters), loss = 0.00442111
I0407 23:22:55.127498 23786 solver.cpp:237]     Train net output #0: loss = 0.00442129 (* 1 = 0.00442129 loss)
I0407 23:22:55.127507 23786 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0407 23:23:00.071887 23786 solver.cpp:218] Iteration 9744 (2.42709 iter/s, 4.9442s/12 iters), loss = 0.0894206
I0407 23:23:00.071941 23786 solver.cpp:237]     Train net output #0: loss = 0.0894208 (* 1 = 0.0894208 loss)
I0407 23:23:00.071954 23786 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0407 23:23:05.087275 23786 solver.cpp:218] Iteration 9756 (2.39275 iter/s, 5.01515s/12 iters), loss = 0.104585
I0407 23:23:05.087322 23786 solver.cpp:237]     Train net output #0: loss = 0.104585 (* 1 = 0.104585 loss)
I0407 23:23:05.087332 23786 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0407 23:23:10.085965 23786 solver.cpp:218] Iteration 9768 (2.40075 iter/s, 4.99844s/12 iters), loss = 0.0412836
I0407 23:23:10.086012 23786 solver.cpp:237]     Train net output #0: loss = 0.0412838 (* 1 = 0.0412838 loss)
I0407 23:23:10.086024 23786 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0407 23:23:15.046928 23786 solver.cpp:218] Iteration 9780 (2.419 iter/s, 4.96073s/12 iters), loss = 0.0893865
I0407 23:23:15.047588 23786 solver.cpp:237]     Train net output #0: loss = 0.0893866 (* 1 = 0.0893866 loss)
I0407 23:23:15.047600 23786 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0407 23:23:19.565877 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0407 23:23:22.644822 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0407 23:23:24.958001 23786 solver.cpp:330] Iteration 9792, Testing net (#0)
I0407 23:23:24.958024 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:23:25.570833 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:23:29.420158 23786 solver.cpp:397]     Test net output #0: accuracy = 0.474265
I0407 23:23:29.420210 23786 solver.cpp:397]     Test net output #1: loss = 3.07937 (* 1 = 3.07937 loss)
I0407 23:23:29.510701 23786 solver.cpp:218] Iteration 9792 (0.829727 iter/s, 14.4626s/12 iters), loss = 0.0375454
I0407 23:23:29.510756 23786 solver.cpp:237]     Train net output #0: loss = 0.0375456 (* 1 = 0.0375456 loss)
I0407 23:23:29.510766 23786 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0407 23:23:33.677040 23786 solver.cpp:218] Iteration 9804 (2.88038 iter/s, 4.16612s/12 iters), loss = 0.0633918
I0407 23:23:33.677098 23786 solver.cpp:237]     Train net output #0: loss = 0.063392 (* 1 = 0.063392 loss)
I0407 23:23:33.677109 23786 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0407 23:23:36.553321 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:23:38.556485 23786 solver.cpp:218] Iteration 9816 (2.45942 iter/s, 4.87921s/12 iters), loss = 0.101593
I0407 23:23:38.556532 23786 solver.cpp:237]     Train net output #0: loss = 0.101593 (* 1 = 0.101593 loss)
I0407 23:23:38.556545 23786 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0407 23:23:43.620677 23786 solver.cpp:218] Iteration 9828 (2.36969 iter/s, 5.06396s/12 iters), loss = 0.0149628
I0407 23:23:43.620728 23786 solver.cpp:237]     Train net output #0: loss = 0.014963 (* 1 = 0.014963 loss)
I0407 23:23:43.620740 23786 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0407 23:23:48.599651 23786 solver.cpp:218] Iteration 9840 (2.41025 iter/s, 4.97874s/12 iters), loss = 0.0429989
I0407 23:23:48.599763 23786 solver.cpp:237]     Train net output #0: loss = 0.0429991 (* 1 = 0.0429991 loss)
I0407 23:23:48.599777 23786 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0407 23:23:53.618072 23786 solver.cpp:218] Iteration 9852 (2.39134 iter/s, 5.01811s/12 iters), loss = 0.0568741
I0407 23:23:53.618129 23786 solver.cpp:237]     Train net output #0: loss = 0.0568742 (* 1 = 0.0568742 loss)
I0407 23:23:53.618139 23786 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0407 23:23:58.639493 23786 solver.cpp:218] Iteration 9864 (2.38988 iter/s, 5.02117s/12 iters), loss = 0.0621194
I0407 23:23:58.639547 23786 solver.cpp:237]     Train net output #0: loss = 0.0621196 (* 1 = 0.0621196 loss)
I0407 23:23:58.639560 23786 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0407 23:24:03.592722 23786 solver.cpp:218] Iteration 9876 (2.42278 iter/s, 4.95299s/12 iters), loss = 0.0491972
I0407 23:24:03.592774 23786 solver.cpp:237]     Train net output #0: loss = 0.0491974 (* 1 = 0.0491974 loss)
I0407 23:24:03.592785 23786 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0407 23:24:08.583839 23786 solver.cpp:218] Iteration 9888 (2.40439 iter/s, 4.99088s/12 iters), loss = 0.0255384
I0407 23:24:08.583887 23786 solver.cpp:237]     Train net output #0: loss = 0.0255386 (* 1 = 0.0255386 loss)
I0407 23:24:08.583899 23786 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0407 23:24:10.599865 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0407 23:24:13.667948 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0407 23:24:16.024147 23786 solver.cpp:330] Iteration 9894, Testing net (#0)
I0407 23:24:16.024173 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:24:16.589170 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:20.604460 23786 solver.cpp:397]     Test net output #0: accuracy = 0.473652
I0407 23:24:20.604569 23786 solver.cpp:397]     Test net output #1: loss = 3.05929 (* 1 = 3.05929 loss)
I0407 23:24:22.604619 23786 solver.cpp:218] Iteration 9900 (0.855907 iter/s, 14.0202s/12 iters), loss = 0.0645013
I0407 23:24:22.604667 23786 solver.cpp:237]     Train net output #0: loss = 0.0645014 (* 1 = 0.0645014 loss)
I0407 23:24:22.604676 23786 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0407 23:24:27.557482 23786 solver.cpp:218] Iteration 9912 (2.42295 iter/s, 4.95263s/12 iters), loss = 0.0251535
I0407 23:24:27.557523 23786 solver.cpp:237]     Train net output #0: loss = 0.0251537 (* 1 = 0.0251537 loss)
I0407 23:24:27.557529 23786 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0407 23:24:27.670001 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:32.580133 23786 solver.cpp:218] Iteration 9924 (2.38929 iter/s, 5.02242s/12 iters), loss = 0.0122061
I0407 23:24:32.580183 23786 solver.cpp:237]     Train net output #0: loss = 0.0122063 (* 1 = 0.0122063 loss)
I0407 23:24:32.580193 23786 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0407 23:24:37.615869 23786 solver.cpp:218] Iteration 9936 (2.38308 iter/s, 5.0355s/12 iters), loss = 0.0131364
I0407 23:24:37.615913 23786 solver.cpp:237]     Train net output #0: loss = 0.0131365 (* 1 = 0.0131365 loss)
I0407 23:24:37.615924 23786 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0407 23:24:42.975760 23786 solver.cpp:218] Iteration 9948 (2.23895 iter/s, 5.35965s/12 iters), loss = 0.0439087
I0407 23:24:42.975805 23786 solver.cpp:237]     Train net output #0: loss = 0.0439088 (* 1 = 0.0439088 loss)
I0407 23:24:42.975814 23786 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0407 23:24:48.138116 23786 solver.cpp:218] Iteration 9960 (2.32463 iter/s, 5.16212s/12 iters), loss = 0.0375198
I0407 23:24:48.138168 23786 solver.cpp:237]     Train net output #0: loss = 0.03752 (* 1 = 0.03752 loss)
I0407 23:24:48.138180 23786 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0407 23:24:53.087661 23786 solver.cpp:218] Iteration 9972 (2.42458 iter/s, 4.9493s/12 iters), loss = 0.104722
I0407 23:24:53.087761 23786 solver.cpp:237]     Train net output #0: loss = 0.104722 (* 1 = 0.104722 loss)
I0407 23:24:53.087774 23786 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0407 23:24:58.092892 23786 solver.cpp:218] Iteration 9984 (2.39763 iter/s, 5.00495s/12 iters), loss = 0.074819
I0407 23:24:58.092948 23786 solver.cpp:237]     Train net output #0: loss = 0.0748191 (* 1 = 0.0748191 loss)
I0407 23:24:58.092962 23786 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0407 23:25:02.647833 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0407 23:25:05.770200 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0407 23:25:08.161667 23786 solver.cpp:330] Iteration 9996, Testing net (#0)
I0407 23:25:08.161691 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:25:08.680920 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:25:12.628417 23786 solver.cpp:397]     Test net output #0: accuracy = 0.477328
I0407 23:25:12.628468 23786 solver.cpp:397]     Test net output #1: loss = 3.06332 (* 1 = 3.06332 loss)
I0407 23:25:12.719103 23786 solver.cpp:218] Iteration 9996 (0.820478 iter/s, 14.6256s/12 iters), loss = 0.0407598
I0407 23:25:12.719173 23786 solver.cpp:237]     Train net output #0: loss = 0.04076 (* 1 = 0.04076 loss)
I0407 23:25:12.719188 23786 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0407 23:25:16.949831 23786 solver.cpp:218] Iteration 10008 (2.83654 iter/s, 4.2305s/12 iters), loss = 0.0182405
I0407 23:25:16.949867 23786 solver.cpp:237]     Train net output #0: loss = 0.0182407 (* 1 = 0.0182407 loss)
I0407 23:25:16.949875 23786 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0407 23:25:19.244535 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:25:22.043745 23786 solver.cpp:218] Iteration 10020 (2.35586 iter/s, 5.09368s/12 iters), loss = 0.0417817
I0407 23:25:22.043797 23786 solver.cpp:237]     Train net output #0: loss = 0.0417819 (* 1 = 0.0417819 loss)
I0407 23:25:22.043809 23786 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0407 23:25:27.087867 23786 solver.cpp:218] Iteration 10032 (2.37912 iter/s, 5.04388s/12 iters), loss = 0.0381572
I0407 23:25:27.088002 23786 solver.cpp:237]     Train net output #0: loss = 0.0381574 (* 1 = 0.0381574 loss)
I0407 23:25:27.088016 23786 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0407 23:25:32.130515 23786 solver.cpp:218] Iteration 10044 (2.37985 iter/s, 5.04233s/12 iters), loss = 0.0355136
I0407 23:25:32.130564 23786 solver.cpp:237]     Train net output #0: loss = 0.0355137 (* 1 = 0.0355137 loss)
I0407 23:25:32.130576 23786 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0407 23:25:37.393008 23786 solver.cpp:218] Iteration 10056 (2.2804 iter/s, 5.26224s/12 iters), loss = 0.0439784
I0407 23:25:37.393057 23786 solver.cpp:237]     Train net output #0: loss = 0.0439786 (* 1 = 0.0439786 loss)
I0407 23:25:37.393070 23786 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0407 23:25:42.565167 23786 solver.cpp:218] Iteration 10068 (2.32023 iter/s, 5.17191s/12 iters), loss = 0.0520763
I0407 23:25:42.565217 23786 solver.cpp:237]     Train net output #0: loss = 0.0520764 (* 1 = 0.0520764 loss)
I0407 23:25:42.565228 23786 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0407 23:25:47.623212 23786 solver.cpp:218] Iteration 10080 (2.37257 iter/s, 5.0578s/12 iters), loss = 0.0414033
I0407 23:25:47.623262 23786 solver.cpp:237]     Train net output #0: loss = 0.0414035 (* 1 = 0.0414035 loss)
I0407 23:25:47.623275 23786 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0407 23:25:52.672320 23786 solver.cpp:218] Iteration 10092 (2.37677 iter/s, 5.04887s/12 iters), loss = 0.0730482
I0407 23:25:52.672369 23786 solver.cpp:237]     Train net output #0: loss = 0.0730483 (* 1 = 0.0730483 loss)
I0407 23:25:52.672381 23786 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0407 23:25:54.717206 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0407 23:25:57.720764 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0407 23:26:00.074630 23786 solver.cpp:330] Iteration 10098, Testing net (#0)
I0407 23:26:00.074651 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:26:00.469350 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:26:04.601624 23786 solver.cpp:397]     Test net output #0: accuracy = 0.473039
I0407 23:26:04.601680 23786 solver.cpp:397]     Test net output #1: loss = 3.07493 (* 1 = 3.07493 loss)
I0407 23:26:06.597090 23786 solver.cpp:218] Iteration 10104 (0.861808 iter/s, 13.9242s/12 iters), loss = 0.0113856
I0407 23:26:06.597148 23786 solver.cpp:237]     Train net output #0: loss = 0.0113858 (* 1 = 0.0113858 loss)
I0407 23:26:06.597162 23786 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0407 23:26:11.195871 23790 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:26:11.815743 23786 solver.cpp:218] Iteration 10116 (2.29956 iter/s, 5.2184s/12 iters), loss = 0.027047
I0407 23:26:11.815804 23786 solver.cpp:237]     Train net output #0: loss = 0.0270472 (* 1 = 0.0270472 loss)
I0407 23:26:11.815821 23786 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0407 23:26:16.798259 23786 solver.cpp:218] Iteration 10128 (2.40854 iter/s, 4.98227s/12 iters), loss = 0.04582
I0407 23:26:16.798316 23786 solver.cpp:237]     Train net output #0: loss = 0.0458202 (* 1 = 0.0458202 loss)
I0407 23:26:16.798326 23786 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0407 23:26:21.752782 23786 solver.cpp:218] Iteration 10140 (2.42215 iter/s, 4.95428s/12 iters), loss = 0.0288894
I0407 23:26:21.752837 23786 solver.cpp:237]     Train net output #0: loss = 0.0288896 (* 1 = 0.0288896 loss)
I0407 23:26:21.752851 23786 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0407 23:26:26.797040 23786 solver.cpp:218] Iteration 10152 (2.37906 iter/s, 5.04401s/12 iters), loss = 0.0433446
I0407 23:26:26.797094 23786 solver.cpp:237]     Train net output #0: loss = 0.0433448 (* 1 = 0.0433448 loss)
I0407 23:26:26.797106 23786 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0407 23:26:31.740223 23786 solver.cpp:218] Iteration 10164 (2.42771 iter/s, 4.94293s/12 iters), loss = 0.02869
I0407 23:26:31.740377 23786 solver.cpp:237]     Train net output #0: loss = 0.0286902 (* 1 = 0.0286902 loss)
I0407 23:26:31.740391 23786 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0407 23:26:36.754335 23786 solver.cpp:218] Iteration 10176 (2.39341 iter/s, 5.01377s/12 iters), loss = 0.0355027
I0407 23:26:36.754386 23786 solver.cpp:237]     Train net output #0: loss = 0.0355028 (* 1 = 0.0355028 loss)
I0407 23:26:36.754398 23786 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0407 23:26:41.814975 23786 solver.cpp:218] Iteration 10188 (2.37136 iter/s, 5.06039s/12 iters), loss = 0.0236604
I0407 23:26:41.815028 23786 solver.cpp:237]     Train net output #0: loss = 0.0236606 (* 1 = 0.0236606 loss)
I0407 23:26:41.815040 23786 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0407 23:26:46.352147 23786 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0407 23:26:49.347038 23786 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0407 23:26:51.733753 23786 solver.cpp:310] Iteration 10200, loss = 0.0267311
I0407 23:26:51.733783 23786 solver.cpp:330] Iteration 10200, Testing net (#0)
I0407 23:26:51.733791 23786 net.cpp:676] Ignoring source layer train-data
I0407 23:26:52.107110 23791 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:26:56.267374 23786 solver.cpp:397]     Test net output #0: accuracy = 0.476103
I0407 23:26:56.267414 23786 solver.cpp:397]     Test net output #1: loss = 3.04432 (* 1 = 3.04432 loss)
I0407 23:26:56.267423 23786 solver.cpp:315] Optimization Done.
I0407 23:26:56.267429 23786 caffe.cpp:259] Optimization Done.