I0409 21:17:01.967762 24944 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-200205-7a01/solver.prototxt
I0409 21:17:01.967952 24944 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0409 21:17:01.967959 24944 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0409 21:17:01.968047 24944 caffe.cpp:218] Using GPUs 2
I0409 21:17:01.987784 24944 caffe.cpp:223] GPU 2: GeForce GTX 1080 Ti
I0409 21:17:02.254348 24944 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"
I0409 21:17:02.255306 24944 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0409 21:17:02.256224 24944 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0409 21:17:02.256240 24944 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0409 21:17:02.256392 24944 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: 8192
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: 8192
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: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.5"
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"
}
I0409 21:17:02.256484 24944 layer_factory.hpp:77] Creating layer train-data
I0409 21:17:02.258920 24944 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0409 21:17:02.259143 24944 net.cpp:84] Creating Layer train-data
I0409 21:17:02.259153 24944 net.cpp:380] train-data -> data
I0409 21:17:02.259176 24944 net.cpp:380] train-data -> label
I0409 21:17:02.259187 24944 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 21:17:02.264106 24944 data_layer.cpp:45] output data size: 128,3,227,227
I0409 21:17:02.393231 24944 net.cpp:122] Setting up train-data
I0409 21:17:02.393255 24944 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0409 21:17:02.393261 24944 net.cpp:129] Top shape: 128 (128)
I0409 21:17:02.393265 24944 net.cpp:137] Memory required for data: 79149056
I0409 21:17:02.393275 24944 layer_factory.hpp:77] Creating layer conv1
I0409 21:17:02.393296 24944 net.cpp:84] Creating Layer conv1
I0409 21:17:02.393302 24944 net.cpp:406] conv1 <- data
I0409 21:17:02.393316 24944 net.cpp:380] conv1 -> conv1
I0409 21:17:02.972707 24944 net.cpp:122] Setting up conv1
I0409 21:17:02.972728 24944 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 21:17:02.972731 24944 net.cpp:137] Memory required for data: 227833856
I0409 21:17:02.972751 24944 layer_factory.hpp:77] Creating layer relu1
I0409 21:17:02.972796 24944 net.cpp:84] Creating Layer relu1
I0409 21:17:02.972801 24944 net.cpp:406] relu1 <- conv1
I0409 21:17:02.972807 24944 net.cpp:367] relu1 -> conv1 (in-place)
I0409 21:17:02.973093 24944 net.cpp:122] Setting up relu1
I0409 21:17:02.973102 24944 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 21:17:02.973106 24944 net.cpp:137] Memory required for data: 376518656
I0409 21:17:02.973109 24944 layer_factory.hpp:77] Creating layer norm1
I0409 21:17:02.973119 24944 net.cpp:84] Creating Layer norm1
I0409 21:17:02.973121 24944 net.cpp:406] norm1 <- conv1
I0409 21:17:02.973127 24944 net.cpp:380] norm1 -> norm1
I0409 21:17:02.973558 24944 net.cpp:122] Setting up norm1
I0409 21:17:02.973567 24944 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 21:17:02.973572 24944 net.cpp:137] Memory required for data: 525203456
I0409 21:17:02.973575 24944 layer_factory.hpp:77] Creating layer pool1
I0409 21:17:02.973583 24944 net.cpp:84] Creating Layer pool1
I0409 21:17:02.973587 24944 net.cpp:406] pool1 <- norm1
I0409 21:17:02.973592 24944 net.cpp:380] pool1 -> pool1
I0409 21:17:02.973628 24944 net.cpp:122] Setting up pool1
I0409 21:17:02.973634 24944 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0409 21:17:02.973636 24944 net.cpp:137] Memory required for data: 561035264
I0409 21:17:02.973640 24944 layer_factory.hpp:77] Creating layer conv2
I0409 21:17:02.973649 24944 net.cpp:84] Creating Layer conv2
I0409 21:17:02.973652 24944 net.cpp:406] conv2 <- pool1
I0409 21:17:02.973657 24944 net.cpp:380] conv2 -> conv2
I0409 21:17:02.980592 24944 net.cpp:122] Setting up conv2
I0409 21:17:02.980609 24944 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 21:17:02.980613 24944 net.cpp:137] Memory required for data: 656586752
I0409 21:17:02.980623 24944 layer_factory.hpp:77] Creating layer relu2
I0409 21:17:02.980631 24944 net.cpp:84] Creating Layer relu2
I0409 21:17:02.980635 24944 net.cpp:406] relu2 <- conv2
I0409 21:17:02.980641 24944 net.cpp:367] relu2 -> conv2 (in-place)
I0409 21:17:02.981129 24944 net.cpp:122] Setting up relu2
I0409 21:17:02.981138 24944 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 21:17:02.981142 24944 net.cpp:137] Memory required for data: 752138240
I0409 21:17:02.981145 24944 layer_factory.hpp:77] Creating layer norm2
I0409 21:17:02.981153 24944 net.cpp:84] Creating Layer norm2
I0409 21:17:02.981158 24944 net.cpp:406] norm2 <- conv2
I0409 21:17:02.981163 24944 net.cpp:380] norm2 -> norm2
I0409 21:17:02.981514 24944 net.cpp:122] Setting up norm2
I0409 21:17:02.981523 24944 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 21:17:02.981525 24944 net.cpp:137] Memory required for data: 847689728
I0409 21:17:02.981529 24944 layer_factory.hpp:77] Creating layer pool2
I0409 21:17:02.981539 24944 net.cpp:84] Creating Layer pool2
I0409 21:17:02.981541 24944 net.cpp:406] pool2 <- norm2
I0409 21:17:02.981546 24944 net.cpp:380] pool2 -> pool2
I0409 21:17:02.981575 24944 net.cpp:122] Setting up pool2
I0409 21:17:02.981580 24944 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 21:17:02.981583 24944 net.cpp:137] Memory required for data: 869840896
I0409 21:17:02.981586 24944 layer_factory.hpp:77] Creating layer conv3
I0409 21:17:02.981596 24944 net.cpp:84] Creating Layer conv3
I0409 21:17:02.981599 24944 net.cpp:406] conv3 <- pool2
I0409 21:17:02.981606 24944 net.cpp:380] conv3 -> conv3
I0409 21:17:02.991652 24944 net.cpp:122] Setting up conv3
I0409 21:17:02.991669 24944 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 21:17:02.991673 24944 net.cpp:137] Memory required for data: 903067648
I0409 21:17:02.991683 24944 layer_factory.hpp:77] Creating layer relu3
I0409 21:17:02.991691 24944 net.cpp:84] Creating Layer relu3
I0409 21:17:02.991694 24944 net.cpp:406] relu3 <- conv3
I0409 21:17:02.991701 24944 net.cpp:367] relu3 -> conv3 (in-place)
I0409 21:17:02.992185 24944 net.cpp:122] Setting up relu3
I0409 21:17:02.992195 24944 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 21:17:02.992198 24944 net.cpp:137] Memory required for data: 936294400
I0409 21:17:02.992202 24944 layer_factory.hpp:77] Creating layer conv4
I0409 21:17:02.992233 24944 net.cpp:84] Creating Layer conv4
I0409 21:17:02.992236 24944 net.cpp:406] conv4 <- conv3
I0409 21:17:02.992242 24944 net.cpp:380] conv4 -> conv4
I0409 21:17:03.002665 24944 net.cpp:122] Setting up conv4
I0409 21:17:03.002681 24944 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 21:17:03.002684 24944 net.cpp:137] Memory required for data: 969521152
I0409 21:17:03.002693 24944 layer_factory.hpp:77] Creating layer relu4
I0409 21:17:03.002702 24944 net.cpp:84] Creating Layer relu4
I0409 21:17:03.002707 24944 net.cpp:406] relu4 <- conv4
I0409 21:17:03.002714 24944 net.cpp:367] relu4 -> conv4 (in-place)
I0409 21:17:03.003057 24944 net.cpp:122] Setting up relu4
I0409 21:17:03.003067 24944 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 21:17:03.003070 24944 net.cpp:137] Memory required for data: 1002747904
I0409 21:17:03.003074 24944 layer_factory.hpp:77] Creating layer conv5
I0409 21:17:03.003087 24944 net.cpp:84] Creating Layer conv5
I0409 21:17:03.003089 24944 net.cpp:406] conv5 <- conv4
I0409 21:17:03.003095 24944 net.cpp:380] conv5 -> conv5
I0409 21:17:03.011471 24944 net.cpp:122] Setting up conv5
I0409 21:17:03.011487 24944 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 21:17:03.011490 24944 net.cpp:137] Memory required for data: 1024899072
I0409 21:17:03.011502 24944 layer_factory.hpp:77] Creating layer relu5
I0409 21:17:03.011509 24944 net.cpp:84] Creating Layer relu5
I0409 21:17:03.011513 24944 net.cpp:406] relu5 <- conv5
I0409 21:17:03.011520 24944 net.cpp:367] relu5 -> conv5 (in-place)
I0409 21:17:03.012001 24944 net.cpp:122] Setting up relu5
I0409 21:17:03.012010 24944 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 21:17:03.012014 24944 net.cpp:137] Memory required for data: 1047050240
I0409 21:17:03.012018 24944 layer_factory.hpp:77] Creating layer pool5
I0409 21:17:03.012027 24944 net.cpp:84] Creating Layer pool5
I0409 21:17:03.012030 24944 net.cpp:406] pool5 <- conv5
I0409 21:17:03.012037 24944 net.cpp:380] pool5 -> pool5
I0409 21:17:03.012073 24944 net.cpp:122] Setting up pool5
I0409 21:17:03.012079 24944 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0409 21:17:03.012082 24944 net.cpp:137] Memory required for data: 1051768832
I0409 21:17:03.012085 24944 layer_factory.hpp:77] Creating layer fc6
I0409 21:17:03.012095 24944 net.cpp:84] Creating Layer fc6
I0409 21:17:03.012099 24944 net.cpp:406] fc6 <- pool5
I0409 21:17:03.012104 24944 net.cpp:380] fc6 -> fc6
I0409 21:17:03.718580 24944 net.cpp:122] Setting up fc6
I0409 21:17:03.718600 24944 net.cpp:129] Top shape: 128 8192 (1048576)
I0409 21:17:03.718605 24944 net.cpp:137] Memory required for data: 1055963136
I0409 21:17:03.718613 24944 layer_factory.hpp:77] Creating layer relu6
I0409 21:17:03.718622 24944 net.cpp:84] Creating Layer relu6
I0409 21:17:03.718627 24944 net.cpp:406] relu6 <- fc6
I0409 21:17:03.718634 24944 net.cpp:367] relu6 -> fc6 (in-place)
I0409 21:17:03.719278 24944 net.cpp:122] Setting up relu6
I0409 21:17:03.719287 24944 net.cpp:129] Top shape: 128 8192 (1048576)
I0409 21:17:03.719290 24944 net.cpp:137] Memory required for data: 1060157440
I0409 21:17:03.719295 24944 layer_factory.hpp:77] Creating layer drop6
I0409 21:17:03.719300 24944 net.cpp:84] Creating Layer drop6
I0409 21:17:03.719305 24944 net.cpp:406] drop6 <- fc6
I0409 21:17:03.719311 24944 net.cpp:367] drop6 -> fc6 (in-place)
I0409 21:17:03.719336 24944 net.cpp:122] Setting up drop6
I0409 21:17:03.719341 24944 net.cpp:129] Top shape: 128 8192 (1048576)
I0409 21:17:03.719344 24944 net.cpp:137] Memory required for data: 1064351744
I0409 21:17:03.719347 24944 layer_factory.hpp:77] Creating layer fc7
I0409 21:17:03.719355 24944 net.cpp:84] Creating Layer fc7
I0409 21:17:03.719359 24944 net.cpp:406] fc7 <- fc6
I0409 21:17:03.719365 24944 net.cpp:380] fc7 -> fc7
I0409 21:17:04.348618 24944 net.cpp:122] Setting up fc7
I0409 21:17:04.348639 24944 net.cpp:129] Top shape: 128 8192 (1048576)
I0409 21:17:04.348642 24944 net.cpp:137] Memory required for data: 1068546048
I0409 21:17:04.348652 24944 layer_factory.hpp:77] Creating layer relu7
I0409 21:17:04.348680 24944 net.cpp:84] Creating Layer relu7
I0409 21:17:04.348685 24944 net.cpp:406] relu7 <- fc7
I0409 21:17:04.348692 24944 net.cpp:367] relu7 -> fc7 (in-place)
I0409 21:17:04.349295 24944 net.cpp:122] Setting up relu7
I0409 21:17:04.349305 24944 net.cpp:129] Top shape: 128 8192 (1048576)
I0409 21:17:04.349309 24944 net.cpp:137] Memory required for data: 1072740352
I0409 21:17:04.349314 24944 layer_factory.hpp:77] Creating layer drop7
I0409 21:17:04.349323 24944 net.cpp:84] Creating Layer drop7
I0409 21:17:04.349326 24944 net.cpp:406] drop7 <- fc7
I0409 21:17:04.349332 24944 net.cpp:367] drop7 -> fc7 (in-place)
I0409 21:17:04.349359 24944 net.cpp:122] Setting up drop7
I0409 21:17:04.349364 24944 net.cpp:129] Top shape: 128 8192 (1048576)
I0409 21:17:04.349367 24944 net.cpp:137] Memory required for data: 1076934656
I0409 21:17:04.349370 24944 layer_factory.hpp:77] Creating layer fc7.5
I0409 21:17:04.349377 24944 net.cpp:84] Creating Layer fc7.5
I0409 21:17:04.349380 24944 net.cpp:406] fc7.5 <- fc7
I0409 21:17:04.349386 24944 net.cpp:380] fc7.5 -> fc7.5
I0409 21:17:04.978307 24944 net.cpp:122] Setting up fc7.5
I0409 21:17:04.978330 24944 net.cpp:129] Top shape: 128 8192 (1048576)
I0409 21:17:04.978334 24944 net.cpp:137] Memory required for data: 1081128960
I0409 21:17:04.978344 24944 layer_factory.hpp:77] Creating layer relu7.5
I0409 21:17:04.978353 24944 net.cpp:84] Creating Layer relu7.5
I0409 21:17:04.978358 24944 net.cpp:406] relu7.5 <- fc7.5
I0409 21:17:04.978366 24944 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0409 21:17:04.979012 24944 net.cpp:122] Setting up relu7.5
I0409 21:17:04.979020 24944 net.cpp:129] Top shape: 128 8192 (1048576)
I0409 21:17:04.979024 24944 net.cpp:137] Memory required for data: 1085323264
I0409 21:17:04.979027 24944 layer_factory.hpp:77] Creating layer drop7.5
I0409 21:17:04.979034 24944 net.cpp:84] Creating Layer drop7.5
I0409 21:17:04.979038 24944 net.cpp:406] drop7.5 <- fc7.5
I0409 21:17:04.979044 24944 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0409 21:17:04.979066 24944 net.cpp:122] Setting up drop7.5
I0409 21:17:04.979072 24944 net.cpp:129] Top shape: 128 8192 (1048576)
I0409 21:17:04.979076 24944 net.cpp:137] Memory required for data: 1089517568
I0409 21:17:04.979079 24944 layer_factory.hpp:77] Creating layer fc8
I0409 21:17:04.979086 24944 net.cpp:84] Creating Layer fc8
I0409 21:17:04.979089 24944 net.cpp:406] fc8 <- fc7.5
I0409 21:17:04.979096 24944 net.cpp:380] fc8 -> fc8
I0409 21:17:04.996618 24944 net.cpp:122] Setting up fc8
I0409 21:17:04.996636 24944 net.cpp:129] Top shape: 128 196 (25088)
I0409 21:17:04.996640 24944 net.cpp:137] Memory required for data: 1089617920
I0409 21:17:04.996654 24944 layer_factory.hpp:77] Creating layer loss
I0409 21:17:04.996663 24944 net.cpp:84] Creating Layer loss
I0409 21:17:04.996667 24944 net.cpp:406] loss <- fc8
I0409 21:17:04.996673 24944 net.cpp:406] loss <- label
I0409 21:17:04.996685 24944 net.cpp:380] loss -> loss
I0409 21:17:04.996697 24944 layer_factory.hpp:77] Creating layer loss
I0409 21:17:04.997377 24944 net.cpp:122] Setting up loss
I0409 21:17:04.997387 24944 net.cpp:129] Top shape: (1)
I0409 21:17:04.997390 24944 net.cpp:132]     with loss weight 1
I0409 21:17:04.997408 24944 net.cpp:137] Memory required for data: 1089617924
I0409 21:17:04.997412 24944 net.cpp:198] loss needs backward computation.
I0409 21:17:04.997421 24944 net.cpp:198] fc8 needs backward computation.
I0409 21:17:04.997423 24944 net.cpp:198] drop7.5 needs backward computation.
I0409 21:17:04.997426 24944 net.cpp:198] relu7.5 needs backward computation.
I0409 21:17:04.997431 24944 net.cpp:198] fc7.5 needs backward computation.
I0409 21:17:04.997433 24944 net.cpp:198] drop7 needs backward computation.
I0409 21:17:04.997437 24944 net.cpp:198] relu7 needs backward computation.
I0409 21:17:04.997440 24944 net.cpp:198] fc7 needs backward computation.
I0409 21:17:04.997445 24944 net.cpp:198] drop6 needs backward computation.
I0409 21:17:04.997449 24944 net.cpp:198] relu6 needs backward computation.
I0409 21:17:04.997453 24944 net.cpp:198] fc6 needs backward computation.
I0409 21:17:04.997474 24944 net.cpp:198] pool5 needs backward computation.
I0409 21:17:04.997478 24944 net.cpp:198] relu5 needs backward computation.
I0409 21:17:04.997483 24944 net.cpp:198] conv5 needs backward computation.
I0409 21:17:04.997486 24944 net.cpp:198] relu4 needs backward computation.
I0409 21:17:04.997490 24944 net.cpp:198] conv4 needs backward computation.
I0409 21:17:04.997494 24944 net.cpp:198] relu3 needs backward computation.
I0409 21:17:04.997498 24944 net.cpp:198] conv3 needs backward computation.
I0409 21:17:04.997503 24944 net.cpp:198] pool2 needs backward computation.
I0409 21:17:04.997506 24944 net.cpp:198] norm2 needs backward computation.
I0409 21:17:04.997509 24944 net.cpp:198] relu2 needs backward computation.
I0409 21:17:04.997514 24944 net.cpp:198] conv2 needs backward computation.
I0409 21:17:04.997517 24944 net.cpp:198] pool1 needs backward computation.
I0409 21:17:04.997520 24944 net.cpp:198] norm1 needs backward computation.
I0409 21:17:04.997524 24944 net.cpp:198] relu1 needs backward computation.
I0409 21:17:04.997527 24944 net.cpp:198] conv1 needs backward computation.
I0409 21:17:04.997534 24944 net.cpp:200] train-data does not need backward computation.
I0409 21:17:04.997536 24944 net.cpp:242] This network produces output loss
I0409 21:17:04.997551 24944 net.cpp:255] Network initialization done.
I0409 21:17:05.031546 24944 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0409 21:17:05.031586 24944 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0409 21:17:05.031756 24944 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: 8192
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: 8192
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: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.5"
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"
}
I0409 21:17:05.031868 24944 layer_factory.hpp:77] Creating layer val-data
I0409 21:17:05.330286 24944 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0409 21:17:05.330693 24944 net.cpp:84] Creating Layer val-data
I0409 21:17:05.330724 24944 net.cpp:380] val-data -> data
I0409 21:17:05.330745 24944 net.cpp:380] val-data -> label
I0409 21:17:05.330760 24944 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 21:17:05.377247 24944 data_layer.cpp:45] output data size: 32,3,227,227
I0409 21:17:05.423035 24944 net.cpp:122] Setting up val-data
I0409 21:17:05.423063 24944 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0409 21:17:05.423071 24944 net.cpp:129] Top shape: 32 (32)
I0409 21:17:05.423076 24944 net.cpp:137] Memory required for data: 19787264
I0409 21:17:05.423110 24944 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0409 21:17:05.423128 24944 net.cpp:84] Creating Layer label_val-data_1_split
I0409 21:17:05.423135 24944 net.cpp:406] label_val-data_1_split <- label
I0409 21:17:05.423143 24944 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0409 21:17:05.423157 24944 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0409 21:17:05.423223 24944 net.cpp:122] Setting up label_val-data_1_split
I0409 21:17:05.423230 24944 net.cpp:129] Top shape: 32 (32)
I0409 21:17:05.423236 24944 net.cpp:129] Top shape: 32 (32)
I0409 21:17:05.423240 24944 net.cpp:137] Memory required for data: 19787520
I0409 21:17:05.423245 24944 layer_factory.hpp:77] Creating layer conv1
I0409 21:17:05.423262 24944 net.cpp:84] Creating Layer conv1
I0409 21:17:05.423267 24944 net.cpp:406] conv1 <- data
I0409 21:17:05.423276 24944 net.cpp:380] conv1 -> conv1
I0409 21:17:05.427137 24944 net.cpp:122] Setting up conv1
I0409 21:17:05.427155 24944 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 21:17:05.427160 24944 net.cpp:137] Memory required for data: 56958720
I0409 21:17:05.427175 24944 layer_factory.hpp:77] Creating layer relu1
I0409 21:17:05.427186 24944 net.cpp:84] Creating Layer relu1
I0409 21:17:05.427191 24944 net.cpp:406] relu1 <- conv1
I0409 21:17:05.427198 24944 net.cpp:367] relu1 -> conv1 (in-place)
I0409 21:17:05.427891 24944 net.cpp:122] Setting up relu1
I0409 21:17:05.427906 24944 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 21:17:05.427911 24944 net.cpp:137] Memory required for data: 94129920
I0409 21:17:05.427917 24944 layer_factory.hpp:77] Creating layer norm1
I0409 21:17:05.427928 24944 net.cpp:84] Creating Layer norm1
I0409 21:17:05.427933 24944 net.cpp:406] norm1 <- conv1
I0409 21:17:05.427942 24944 net.cpp:380] norm1 -> norm1
I0409 21:17:05.429980 24944 net.cpp:122] Setting up norm1
I0409 21:17:05.429996 24944 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 21:17:05.430001 24944 net.cpp:137] Memory required for data: 131301120
I0409 21:17:05.430006 24944 layer_factory.hpp:77] Creating layer pool1
I0409 21:17:05.430016 24944 net.cpp:84] Creating Layer pool1
I0409 21:17:05.430022 24944 net.cpp:406] pool1 <- norm1
I0409 21:17:05.430030 24944 net.cpp:380] pool1 -> pool1
I0409 21:17:05.430076 24944 net.cpp:122] Setting up pool1
I0409 21:17:05.430084 24944 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0409 21:17:05.430089 24944 net.cpp:137] Memory required for data: 140259072
I0409 21:17:05.430094 24944 layer_factory.hpp:77] Creating layer conv2
I0409 21:17:05.430106 24944 net.cpp:84] Creating Layer conv2
I0409 21:17:05.430111 24944 net.cpp:406] conv2 <- pool1
I0409 21:17:05.430119 24944 net.cpp:380] conv2 -> conv2
I0409 21:17:05.439756 24944 net.cpp:122] Setting up conv2
I0409 21:17:05.439774 24944 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 21:17:05.439779 24944 net.cpp:137] Memory required for data: 164146944
I0409 21:17:05.439792 24944 layer_factory.hpp:77] Creating layer relu2
I0409 21:17:05.439803 24944 net.cpp:84] Creating Layer relu2
I0409 21:17:05.439810 24944 net.cpp:406] relu2 <- conv2
I0409 21:17:05.439817 24944 net.cpp:367] relu2 -> conv2 (in-place)
I0409 21:17:05.440572 24944 net.cpp:122] Setting up relu2
I0409 21:17:05.440585 24944 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 21:17:05.440590 24944 net.cpp:137] Memory required for data: 188034816
I0409 21:17:05.440595 24944 layer_factory.hpp:77] Creating layer norm2
I0409 21:17:05.440609 24944 net.cpp:84] Creating Layer norm2
I0409 21:17:05.440614 24944 net.cpp:406] norm2 <- conv2
I0409 21:17:05.440623 24944 net.cpp:380] norm2 -> norm2
I0409 21:17:05.441191 24944 net.cpp:122] Setting up norm2
I0409 21:17:05.441205 24944 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 21:17:05.441210 24944 net.cpp:137] Memory required for data: 211922688
I0409 21:17:05.441215 24944 layer_factory.hpp:77] Creating layer pool2
I0409 21:17:05.441226 24944 net.cpp:84] Creating Layer pool2
I0409 21:17:05.441231 24944 net.cpp:406] pool2 <- norm2
I0409 21:17:05.441260 24944 net.cpp:380] pool2 -> pool2
I0409 21:17:05.441308 24944 net.cpp:122] Setting up pool2
I0409 21:17:05.441315 24944 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 21:17:05.441320 24944 net.cpp:137] Memory required for data: 217460480
I0409 21:17:05.441325 24944 layer_factory.hpp:77] Creating layer conv3
I0409 21:17:05.441339 24944 net.cpp:84] Creating Layer conv3
I0409 21:17:05.441344 24944 net.cpp:406] conv3 <- pool2
I0409 21:17:05.441352 24944 net.cpp:380] conv3 -> conv3
I0409 21:17:05.458020 24944 net.cpp:122] Setting up conv3
I0409 21:17:05.458041 24944 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 21:17:05.458046 24944 net.cpp:137] Memory required for data: 225767168
I0409 21:17:05.458062 24944 layer_factory.hpp:77] Creating layer relu3
I0409 21:17:05.458073 24944 net.cpp:84] Creating Layer relu3
I0409 21:17:05.458079 24944 net.cpp:406] relu3 <- conv3
I0409 21:17:05.458089 24944 net.cpp:367] relu3 -> conv3 (in-place)
I0409 21:17:05.458591 24944 net.cpp:122] Setting up relu3
I0409 21:17:05.458602 24944 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 21:17:05.458607 24944 net.cpp:137] Memory required for data: 234073856
I0409 21:17:05.458612 24944 layer_factory.hpp:77] Creating layer conv4
I0409 21:17:05.458626 24944 net.cpp:84] Creating Layer conv4
I0409 21:17:05.458631 24944 net.cpp:406] conv4 <- conv3
I0409 21:17:05.458640 24944 net.cpp:380] conv4 -> conv4
I0409 21:17:05.472045 24944 net.cpp:122] Setting up conv4
I0409 21:17:05.472064 24944 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 21:17:05.472069 24944 net.cpp:137] Memory required for data: 242380544
I0409 21:17:05.472079 24944 layer_factory.hpp:77] Creating layer relu4
I0409 21:17:05.472090 24944 net.cpp:84] Creating Layer relu4
I0409 21:17:05.472095 24944 net.cpp:406] relu4 <- conv4
I0409 21:17:05.472102 24944 net.cpp:367] relu4 -> conv4 (in-place)
I0409 21:17:05.472761 24944 net.cpp:122] Setting up relu4
I0409 21:17:05.472772 24944 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 21:17:05.472777 24944 net.cpp:137] Memory required for data: 250687232
I0409 21:17:05.472782 24944 layer_factory.hpp:77] Creating layer conv5
I0409 21:17:05.472797 24944 net.cpp:84] Creating Layer conv5
I0409 21:17:05.472801 24944 net.cpp:406] conv5 <- conv4
I0409 21:17:05.472810 24944 net.cpp:380] conv5 -> conv5
I0409 21:17:05.484277 24944 net.cpp:122] Setting up conv5
I0409 21:17:05.484297 24944 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 21:17:05.484302 24944 net.cpp:137] Memory required for data: 256225024
I0409 21:17:05.484315 24944 layer_factory.hpp:77] Creating layer relu5
I0409 21:17:05.484324 24944 net.cpp:84] Creating Layer relu5
I0409 21:17:05.484329 24944 net.cpp:406] relu5 <- conv5
I0409 21:17:05.484338 24944 net.cpp:367] relu5 -> conv5 (in-place)
I0409 21:17:05.485211 24944 net.cpp:122] Setting up relu5
I0409 21:17:05.485224 24944 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 21:17:05.485227 24944 net.cpp:137] Memory required for data: 261762816
I0409 21:17:05.485232 24944 layer_factory.hpp:77] Creating layer pool5
I0409 21:17:05.485244 24944 net.cpp:84] Creating Layer pool5
I0409 21:17:05.485249 24944 net.cpp:406] pool5 <- conv5
I0409 21:17:05.485257 24944 net.cpp:380] pool5 -> pool5
I0409 21:17:05.485306 24944 net.cpp:122] Setting up pool5
I0409 21:17:05.485312 24944 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0409 21:17:05.485316 24944 net.cpp:137] Memory required for data: 262942464
I0409 21:17:05.485321 24944 layer_factory.hpp:77] Creating layer fc6
I0409 21:17:05.485330 24944 net.cpp:84] Creating Layer fc6
I0409 21:17:05.485334 24944 net.cpp:406] fc6 <- pool5
I0409 21:17:05.485340 24944 net.cpp:380] fc6 -> fc6
I0409 21:17:06.327471 24944 net.cpp:122] Setting up fc6
I0409 21:17:06.327497 24944 net.cpp:129] Top shape: 32 8192 (262144)
I0409 21:17:06.327502 24944 net.cpp:137] Memory required for data: 263991040
I0409 21:17:06.327512 24944 layer_factory.hpp:77] Creating layer relu6
I0409 21:17:06.327522 24944 net.cpp:84] Creating Layer relu6
I0409 21:17:06.327526 24944 net.cpp:406] relu6 <- fc6
I0409 21:17:06.327555 24944 net.cpp:367] relu6 -> fc6 (in-place)
I0409 21:17:06.328008 24944 net.cpp:122] Setting up relu6
I0409 21:17:06.328017 24944 net.cpp:129] Top shape: 32 8192 (262144)
I0409 21:17:06.328022 24944 net.cpp:137] Memory required for data: 265039616
I0409 21:17:06.328027 24944 layer_factory.hpp:77] Creating layer drop6
I0409 21:17:06.328033 24944 net.cpp:84] Creating Layer drop6
I0409 21:17:06.328037 24944 net.cpp:406] drop6 <- fc6
I0409 21:17:06.328045 24944 net.cpp:367] drop6 -> fc6 (in-place)
I0409 21:17:06.328070 24944 net.cpp:122] Setting up drop6
I0409 21:17:06.328075 24944 net.cpp:129] Top shape: 32 8192 (262144)
I0409 21:17:06.328079 24944 net.cpp:137] Memory required for data: 266088192
I0409 21:17:06.328083 24944 layer_factory.hpp:77] Creating layer fc7
I0409 21:17:06.328094 24944 net.cpp:84] Creating Layer fc7
I0409 21:17:06.328097 24944 net.cpp:406] fc7 <- fc6
I0409 21:17:06.328102 24944 net.cpp:380] fc7 -> fc7
I0409 21:17:06.979394 24944 net.cpp:122] Setting up fc7
I0409 21:17:06.979418 24944 net.cpp:129] Top shape: 32 8192 (262144)
I0409 21:17:06.979421 24944 net.cpp:137] Memory required for data: 267136768
I0409 21:17:06.979430 24944 layer_factory.hpp:77] Creating layer relu7
I0409 21:17:06.979440 24944 net.cpp:84] Creating Layer relu7
I0409 21:17:06.979444 24944 net.cpp:406] relu7 <- fc7
I0409 21:17:06.979452 24944 net.cpp:367] relu7 -> fc7 (in-place)
I0409 21:17:06.981775 24944 net.cpp:122] Setting up relu7
I0409 21:17:06.981783 24944 net.cpp:129] Top shape: 32 8192 (262144)
I0409 21:17:06.981787 24944 net.cpp:137] Memory required for data: 268185344
I0409 21:17:06.981791 24944 layer_factory.hpp:77] Creating layer drop7
I0409 21:17:06.981798 24944 net.cpp:84] Creating Layer drop7
I0409 21:17:06.981802 24944 net.cpp:406] drop7 <- fc7
I0409 21:17:06.981807 24944 net.cpp:367] drop7 -> fc7 (in-place)
I0409 21:17:06.981833 24944 net.cpp:122] Setting up drop7
I0409 21:17:06.981838 24944 net.cpp:129] Top shape: 32 8192 (262144)
I0409 21:17:06.981842 24944 net.cpp:137] Memory required for data: 269233920
I0409 21:17:06.981844 24944 layer_factory.hpp:77] Creating layer fc7.5
I0409 21:17:06.981851 24944 net.cpp:84] Creating Layer fc7.5
I0409 21:17:06.981854 24944 net.cpp:406] fc7.5 <- fc7
I0409 21:17:06.981860 24944 net.cpp:380] fc7.5 -> fc7.5
I0409 21:17:07.613565 24944 net.cpp:122] Setting up fc7.5
I0409 21:17:07.613590 24944 net.cpp:129] Top shape: 32 8192 (262144)
I0409 21:17:07.613592 24944 net.cpp:137] Memory required for data: 270282496
I0409 21:17:07.613602 24944 layer_factory.hpp:77] Creating layer relu7.5
I0409 21:17:07.613613 24944 net.cpp:84] Creating Layer relu7.5
I0409 21:17:07.613617 24944 net.cpp:406] relu7.5 <- fc7.5
I0409 21:17:07.613623 24944 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0409 21:17:07.614297 24944 net.cpp:122] Setting up relu7.5
I0409 21:17:07.614310 24944 net.cpp:129] Top shape: 32 8192 (262144)
I0409 21:17:07.614312 24944 net.cpp:137] Memory required for data: 271331072
I0409 21:17:07.614316 24944 layer_factory.hpp:77] Creating layer drop7.5
I0409 21:17:07.614323 24944 net.cpp:84] Creating Layer drop7.5
I0409 21:17:07.614327 24944 net.cpp:406] drop7.5 <- fc7.5
I0409 21:17:07.614332 24944 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0409 21:17:07.614358 24944 net.cpp:122] Setting up drop7.5
I0409 21:17:07.614363 24944 net.cpp:129] Top shape: 32 8192 (262144)
I0409 21:17:07.614367 24944 net.cpp:137] Memory required for data: 272379648
I0409 21:17:07.614369 24944 layer_factory.hpp:77] Creating layer fc8
I0409 21:17:07.614378 24944 net.cpp:84] Creating Layer fc8
I0409 21:17:07.614382 24944 net.cpp:406] fc8 <- fc7.5
I0409 21:17:07.614387 24944 net.cpp:380] fc8 -> fc8
I0409 21:17:07.629686 24944 net.cpp:122] Setting up fc8
I0409 21:17:07.629709 24944 net.cpp:129] Top shape: 32 196 (6272)
I0409 21:17:07.629714 24944 net.cpp:137] Memory required for data: 272404736
I0409 21:17:07.629726 24944 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0409 21:17:07.629736 24944 net.cpp:84] Creating Layer fc8_fc8_0_split
I0409 21:17:07.629740 24944 net.cpp:406] fc8_fc8_0_split <- fc8
I0409 21:17:07.629766 24944 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0409 21:17:07.629776 24944 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0409 21:17:07.629813 24944 net.cpp:122] Setting up fc8_fc8_0_split
I0409 21:17:07.629818 24944 net.cpp:129] Top shape: 32 196 (6272)
I0409 21:17:07.629822 24944 net.cpp:129] Top shape: 32 196 (6272)
I0409 21:17:07.629825 24944 net.cpp:137] Memory required for data: 272454912
I0409 21:17:07.629828 24944 layer_factory.hpp:77] Creating layer accuracy
I0409 21:17:07.629834 24944 net.cpp:84] Creating Layer accuracy
I0409 21:17:07.629838 24944 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0409 21:17:07.629843 24944 net.cpp:406] accuracy <- label_val-data_1_split_0
I0409 21:17:07.629848 24944 net.cpp:380] accuracy -> accuracy
I0409 21:17:07.629855 24944 net.cpp:122] Setting up accuracy
I0409 21:17:07.629859 24944 net.cpp:129] Top shape: (1)
I0409 21:17:07.629863 24944 net.cpp:137] Memory required for data: 272454916
I0409 21:17:07.629866 24944 layer_factory.hpp:77] Creating layer loss
I0409 21:17:07.629871 24944 net.cpp:84] Creating Layer loss
I0409 21:17:07.629875 24944 net.cpp:406] loss <- fc8_fc8_0_split_1
I0409 21:17:07.629879 24944 net.cpp:406] loss <- label_val-data_1_split_1
I0409 21:17:07.629886 24944 net.cpp:380] loss -> loss
I0409 21:17:07.629892 24944 layer_factory.hpp:77] Creating layer loss
I0409 21:17:07.634371 24944 net.cpp:122] Setting up loss
I0409 21:17:07.634382 24944 net.cpp:129] Top shape: (1)
I0409 21:17:07.634387 24944 net.cpp:132]     with loss weight 1
I0409 21:17:07.634397 24944 net.cpp:137] Memory required for data: 272454920
I0409 21:17:07.634402 24944 net.cpp:198] loss needs backward computation.
I0409 21:17:07.634407 24944 net.cpp:200] accuracy does not need backward computation.
I0409 21:17:07.634410 24944 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0409 21:17:07.634414 24944 net.cpp:198] fc8 needs backward computation.
I0409 21:17:07.634418 24944 net.cpp:198] drop7.5 needs backward computation.
I0409 21:17:07.634421 24944 net.cpp:198] relu7.5 needs backward computation.
I0409 21:17:07.634424 24944 net.cpp:198] fc7.5 needs backward computation.
I0409 21:17:07.634428 24944 net.cpp:198] drop7 needs backward computation.
I0409 21:17:07.634431 24944 net.cpp:198] relu7 needs backward computation.
I0409 21:17:07.634434 24944 net.cpp:198] fc7 needs backward computation.
I0409 21:17:07.634438 24944 net.cpp:198] drop6 needs backward computation.
I0409 21:17:07.634440 24944 net.cpp:198] relu6 needs backward computation.
I0409 21:17:07.634444 24944 net.cpp:198] fc6 needs backward computation.
I0409 21:17:07.634447 24944 net.cpp:198] pool5 needs backward computation.
I0409 21:17:07.634452 24944 net.cpp:198] relu5 needs backward computation.
I0409 21:17:07.634455 24944 net.cpp:198] conv5 needs backward computation.
I0409 21:17:07.634459 24944 net.cpp:198] relu4 needs backward computation.
I0409 21:17:07.634462 24944 net.cpp:198] conv4 needs backward computation.
I0409 21:17:07.634465 24944 net.cpp:198] relu3 needs backward computation.
I0409 21:17:07.634469 24944 net.cpp:198] conv3 needs backward computation.
I0409 21:17:07.634472 24944 net.cpp:198] pool2 needs backward computation.
I0409 21:17:07.634475 24944 net.cpp:198] norm2 needs backward computation.
I0409 21:17:07.634479 24944 net.cpp:198] relu2 needs backward computation.
I0409 21:17:07.634482 24944 net.cpp:198] conv2 needs backward computation.
I0409 21:17:07.634486 24944 net.cpp:198] pool1 needs backward computation.
I0409 21:17:07.634490 24944 net.cpp:198] norm1 needs backward computation.
I0409 21:17:07.634493 24944 net.cpp:198] relu1 needs backward computation.
I0409 21:17:07.634496 24944 net.cpp:198] conv1 needs backward computation.
I0409 21:17:07.634501 24944 net.cpp:200] label_val-data_1_split does not need backward computation.
I0409 21:17:07.634505 24944 net.cpp:200] val-data does not need backward computation.
I0409 21:17:07.634508 24944 net.cpp:242] This network produces output accuracy
I0409 21:17:07.634512 24944 net.cpp:242] This network produces output loss
I0409 21:17:07.634541 24944 net.cpp:255] Network initialization done.
I0409 21:17:07.634624 24944 solver.cpp:56] Solver scaffolding done.
I0409 21:17:07.635092 24944 caffe.cpp:248] Starting Optimization
I0409 21:17:07.635099 24944 solver.cpp:272] Solving
I0409 21:17:07.635103 24944 solver.cpp:273] Learning Rate Policy: exp
I0409 21:17:07.637132 24944 solver.cpp:330] Iteration 0, Testing net (#0)
I0409 21:17:07.637142 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:17:07.863914 24944 blocking_queue.cpp:49] Waiting for data
I0409 21:17:12.295958 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:17:12.345691 24944 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0409 21:17:12.345736 24944 solver.cpp:397]     Test net output #1: loss = 5.28505 (* 1 = 5.28505 loss)
I0409 21:17:12.483089 24944 solver.cpp:218] Iteration 0 (0 iter/s, 4.84777s/12 iters), loss = 5.26989
I0409 21:17:12.484601 24944 solver.cpp:237]     Train net output #0: loss = 5.26989 (* 1 = 5.26989 loss)
I0409 21:17:12.484619 24944 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0409 21:17:16.420572 24944 solver.cpp:218] Iteration 12 (3.04893 iter/s, 3.93581s/12 iters), loss = 5.31609
I0409 21:17:16.420611 24944 solver.cpp:237]     Train net output #0: loss = 5.31609 (* 1 = 5.31609 loss)
I0409 21:17:16.420622 24944 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0409 21:17:21.090884 24944 solver.cpp:218] Iteration 24 (2.56954 iter/s, 4.6701s/12 iters), loss = 5.33475
I0409 21:17:21.090922 24944 solver.cpp:237]     Train net output #0: loss = 5.33475 (* 1 = 5.33475 loss)
I0409 21:17:21.090930 24944 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0409 21:17:25.832947 24944 solver.cpp:218] Iteration 36 (2.53066 iter/s, 4.74184s/12 iters), loss = 5.33667
I0409 21:17:25.832991 24944 solver.cpp:237]     Train net output #0: loss = 5.33667 (* 1 = 5.33667 loss)
I0409 21:17:25.833001 24944 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0409 21:17:30.578416 24944 solver.cpp:218] Iteration 48 (2.52885 iter/s, 4.74525s/12 iters), loss = 5.35758
I0409 21:17:30.578455 24944 solver.cpp:237]     Train net output #0: loss = 5.35758 (* 1 = 5.35758 loss)
I0409 21:17:30.578464 24944 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0409 21:17:35.461592 24944 solver.cpp:218] Iteration 60 (2.45753 iter/s, 4.88295s/12 iters), loss = 5.35414
I0409 21:17:35.461755 24944 solver.cpp:237]     Train net output #0: loss = 5.35414 (* 1 = 5.35414 loss)
I0409 21:17:35.461766 24944 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0409 21:17:40.155918 24944 solver.cpp:218] Iteration 72 (2.55646 iter/s, 4.69399s/12 iters), loss = 5.34601
I0409 21:17:40.155966 24944 solver.cpp:237]     Train net output #0: loss = 5.34601 (* 1 = 5.34601 loss)
I0409 21:17:40.155977 24944 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0409 21:17:44.932989 24944 solver.cpp:218] Iteration 84 (2.51212 iter/s, 4.77684s/12 iters), loss = 5.32759
I0409 21:17:44.933038 24944 solver.cpp:237]     Train net output #0: loss = 5.32759 (* 1 = 5.32759 loss)
I0409 21:17:44.933048 24944 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0409 21:17:49.888424 24944 solver.cpp:218] Iteration 96 (2.4217 iter/s, 4.9552s/12 iters), loss = 5.33443
I0409 21:17:49.888470 24944 solver.cpp:237]     Train net output #0: loss = 5.33443 (* 1 = 5.33443 loss)
I0409 21:17:49.888481 24944 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0409 21:17:51.463719 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:17:51.800343 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0409 21:18:02.696244 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0409 21:18:24.036494 24944 solver.cpp:330] Iteration 102, Testing net (#0)
I0409 21:18:24.036578 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:18:28.460171 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:18:28.539640 24944 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 21:18:28.539671 24944 solver.cpp:397]     Test net output #1: loss = 5.3051 (* 1 = 5.3051 loss)
I0409 21:18:30.374140 24944 solver.cpp:218] Iteration 108 (0.296412 iter/s, 40.4842s/12 iters), loss = 5.33689
I0409 21:18:30.374204 24944 solver.cpp:237]     Train net output #0: loss = 5.33689 (* 1 = 5.33689 loss)
I0409 21:18:30.374215 24944 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0409 21:18:35.553212 24944 solver.cpp:218] Iteration 120 (2.31713 iter/s, 5.17881s/12 iters), loss = 5.27992
I0409 21:18:35.553256 24944 solver.cpp:237]     Train net output #0: loss = 5.27992 (* 1 = 5.27992 loss)
I0409 21:18:35.553265 24944 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0409 21:18:40.368036 24944 solver.cpp:218] Iteration 132 (2.49242 iter/s, 4.8146s/12 iters), loss = 5.23823
I0409 21:18:40.368081 24944 solver.cpp:237]     Train net output #0: loss = 5.23823 (* 1 = 5.23823 loss)
I0409 21:18:40.368093 24944 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0409 21:18:45.440830 24944 solver.cpp:218] Iteration 144 (2.36567 iter/s, 5.07255s/12 iters), loss = 5.32309
I0409 21:18:45.440879 24944 solver.cpp:237]     Train net output #0: loss = 5.32309 (* 1 = 5.32309 loss)
I0409 21:18:45.440891 24944 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0409 21:18:50.419801 24944 solver.cpp:218] Iteration 156 (2.41025 iter/s, 4.97874s/12 iters), loss = 5.28024
I0409 21:18:50.419847 24944 solver.cpp:237]     Train net output #0: loss = 5.28024 (* 1 = 5.28024 loss)
I0409 21:18:50.419857 24944 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0409 21:18:55.599004 24944 solver.cpp:218] Iteration 168 (2.31707 iter/s, 5.17896s/12 iters), loss = 5.26207
I0409 21:18:55.599110 24944 solver.cpp:237]     Train net output #0: loss = 5.26207 (* 1 = 5.26207 loss)
I0409 21:18:55.599123 24944 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0409 21:19:00.878069 24944 solver.cpp:218] Iteration 180 (2.27326 iter/s, 5.27876s/12 iters), loss = 5.28702
I0409 21:19:00.878113 24944 solver.cpp:237]     Train net output #0: loss = 5.28702 (* 1 = 5.28702 loss)
I0409 21:19:00.878123 24944 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0409 21:19:06.136739 24944 solver.cpp:218] Iteration 192 (2.28205 iter/s, 5.25843s/12 iters), loss = 5.28943
I0409 21:19:06.136790 24944 solver.cpp:237]     Train net output #0: loss = 5.28943 (* 1 = 5.28943 loss)
I0409 21:19:06.136801 24944 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0409 21:19:09.855584 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:19:10.512033 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0409 21:19:31.418254 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0409 21:19:44.193528 24944 solver.cpp:330] Iteration 204, Testing net (#0)
I0409 21:19:44.193549 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:19:48.698429 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:19:48.824744 24944 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 21:19:48.824795 24944 solver.cpp:397]     Test net output #1: loss = 5.29448 (* 1 = 5.29448 loss)
I0409 21:19:48.941480 24944 solver.cpp:218] Iteration 204 (0.280353 iter/s, 42.8032s/12 iters), loss = 5.26561
I0409 21:19:48.943064 24944 solver.cpp:237]     Train net output #0: loss = 5.26561 (* 1 = 5.26561 loss)
I0409 21:19:48.943087 24944 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0409 21:19:53.311892 24944 solver.cpp:218] Iteration 216 (2.74683 iter/s, 4.36867s/12 iters), loss = 5.27922
I0409 21:19:53.311944 24944 solver.cpp:237]     Train net output #0: loss = 5.27922 (* 1 = 5.27922 loss)
I0409 21:19:53.311955 24944 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0409 21:19:58.551936 24944 solver.cpp:218] Iteration 228 (2.29017 iter/s, 5.23979s/12 iters), loss = 5.27733
I0409 21:19:58.551976 24944 solver.cpp:237]     Train net output #0: loss = 5.27733 (* 1 = 5.27733 loss)
I0409 21:19:58.551985 24944 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0409 21:20:03.763114 24944 solver.cpp:218] Iteration 240 (2.30285 iter/s, 5.21093s/12 iters), loss = 5.32022
I0409 21:20:03.763259 24944 solver.cpp:237]     Train net output #0: loss = 5.32022 (* 1 = 5.32022 loss)
I0409 21:20:03.763273 24944 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0409 21:20:08.516592 24944 solver.cpp:218] Iteration 252 (2.52464 iter/s, 4.75315s/12 iters), loss = 5.28371
I0409 21:20:08.516644 24944 solver.cpp:237]     Train net output #0: loss = 5.28371 (* 1 = 5.28371 loss)
I0409 21:20:08.516654 24944 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0409 21:20:13.002326 24944 solver.cpp:218] Iteration 264 (2.67528 iter/s, 4.48551s/12 iters), loss = 5.28043
I0409 21:20:13.002378 24944 solver.cpp:237]     Train net output #0: loss = 5.28043 (* 1 = 5.28043 loss)
I0409 21:20:13.002389 24944 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0409 21:20:17.494655 24944 solver.cpp:218] Iteration 276 (2.67136 iter/s, 4.4921s/12 iters), loss = 5.29671
I0409 21:20:17.494707 24944 solver.cpp:237]     Train net output #0: loss = 5.29671 (* 1 = 5.29671 loss)
I0409 21:20:17.494719 24944 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0409 21:20:21.980204 24944 solver.cpp:218] Iteration 288 (2.67539 iter/s, 4.48532s/12 iters), loss = 5.27438
I0409 21:20:21.980249 24944 solver.cpp:237]     Train net output #0: loss = 5.27438 (* 1 = 5.27438 loss)
I0409 21:20:21.980260 24944 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0409 21:20:26.925652 24944 solver.cpp:218] Iteration 300 (2.42659 iter/s, 4.94521s/12 iters), loss = 5.27416
I0409 21:20:26.925701 24944 solver.cpp:237]     Train net output #0: loss = 5.27416 (* 1 = 5.27416 loss)
I0409 21:20:26.925711 24944 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0409 21:20:27.799283 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:20:28.863221 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0409 21:20:43.639423 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0409 21:20:52.102257 24944 solver.cpp:330] Iteration 306, Testing net (#0)
I0409 21:20:52.102279 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:20:56.465966 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:20:56.628788 24944 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0409 21:20:56.628829 24944 solver.cpp:397]     Test net output #1: loss = 5.22573 (* 1 = 5.22573 loss)
I0409 21:20:58.504465 24944 solver.cpp:218] Iteration 312 (0.380014 iter/s, 31.5777s/12 iters), loss = 5.21214
I0409 21:20:58.504518 24944 solver.cpp:237]     Train net output #0: loss = 5.21214 (* 1 = 5.21214 loss)
I0409 21:20:58.504529 24944 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0409 21:21:03.701068 24944 solver.cpp:218] Iteration 324 (2.3093 iter/s, 5.19638s/12 iters), loss = 5.24058
I0409 21:21:03.701117 24944 solver.cpp:237]     Train net output #0: loss = 5.24058 (* 1 = 5.24058 loss)
I0409 21:21:03.701128 24944 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0409 21:21:08.833581 24944 solver.cpp:218] Iteration 336 (2.33813 iter/s, 5.1323s/12 iters), loss = 5.22664
I0409 21:21:08.833631 24944 solver.cpp:237]     Train net output #0: loss = 5.22664 (* 1 = 5.22664 loss)
I0409 21:21:08.833642 24944 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0409 21:21:13.990541 24944 solver.cpp:218] Iteration 348 (2.32705 iter/s, 5.15674s/12 iters), loss = 5.18698
I0409 21:21:13.990643 24944 solver.cpp:237]     Train net output #0: loss = 5.18698 (* 1 = 5.18698 loss)
I0409 21:21:13.990653 24944 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0409 21:21:18.984867 24944 solver.cpp:218] Iteration 360 (2.40285 iter/s, 4.99407s/12 iters), loss = 5.24404
I0409 21:21:18.984903 24944 solver.cpp:237]     Train net output #0: loss = 5.24404 (* 1 = 5.24404 loss)
I0409 21:21:18.984912 24944 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0409 21:21:24.193289 24944 solver.cpp:218] Iteration 372 (2.30405 iter/s, 5.20821s/12 iters), loss = 5.17978
I0409 21:21:24.193333 24944 solver.cpp:237]     Train net output #0: loss = 5.17978 (* 1 = 5.17978 loss)
I0409 21:21:24.193343 24944 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0409 21:21:29.462980 24944 solver.cpp:218] Iteration 384 (2.27727 iter/s, 5.26947s/12 iters), loss = 5.17184
I0409 21:21:29.463033 24944 solver.cpp:237]     Train net output #0: loss = 5.17184 (* 1 = 5.17184 loss)
I0409 21:21:29.463044 24944 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0409 21:21:34.485781 24944 solver.cpp:218] Iteration 396 (2.38921 iter/s, 5.02258s/12 iters), loss = 5.11355
I0409 21:21:34.485826 24944 solver.cpp:237]     Train net output #0: loss = 5.11355 (* 1 = 5.11355 loss)
I0409 21:21:34.485836 24944 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0409 21:21:37.557752 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:21:39.052449 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0409 21:21:50.532742 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0409 21:22:09.446404 24944 solver.cpp:330] Iteration 408, Testing net (#0)
I0409 21:22:09.446427 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:22:13.758358 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:22:13.965885 24944 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0409 21:22:13.965932 24944 solver.cpp:397]     Test net output #1: loss = 5.17233 (* 1 = 5.17233 loss)
I0409 21:22:14.080448 24944 solver.cpp:218] Iteration 408 (0.303081 iter/s, 39.5934s/12 iters), loss = 5.24778
I0409 21:22:14.082001 24944 solver.cpp:237]     Train net output #0: loss = 5.24778 (* 1 = 5.24778 loss)
I0409 21:22:14.082015 24944 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0409 21:22:18.424674 24944 solver.cpp:218] Iteration 420 (2.76336 iter/s, 4.34254s/12 iters), loss = 5.25852
I0409 21:22:18.424727 24944 solver.cpp:237]     Train net output #0: loss = 5.25852 (* 1 = 5.25852 loss)
I0409 21:22:18.424738 24944 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0409 21:22:23.587468 24944 solver.cpp:218] Iteration 432 (2.32442 iter/s, 5.16257s/12 iters), loss = 5.16691
I0409 21:22:23.587543 24944 solver.cpp:237]     Train net output #0: loss = 5.16691 (* 1 = 5.16691 loss)
I0409 21:22:23.587554 24944 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0409 21:22:28.745705 24944 solver.cpp:218] Iteration 444 (2.32649 iter/s, 5.15799s/12 iters), loss = 5.16421
I0409 21:22:28.745757 24944 solver.cpp:237]     Train net output #0: loss = 5.16421 (* 1 = 5.16421 loss)
I0409 21:22:28.745769 24944 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0409 21:22:33.773761 24944 solver.cpp:218] Iteration 456 (2.38671 iter/s, 5.02783s/12 iters), loss = 5.20049
I0409 21:22:33.773810 24944 solver.cpp:237]     Train net output #0: loss = 5.20049 (* 1 = 5.20049 loss)
I0409 21:22:33.773821 24944 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0409 21:22:38.378521 24944 solver.cpp:218] Iteration 468 (2.60611 iter/s, 4.60456s/12 iters), loss = 5.16279
I0409 21:22:38.378564 24944 solver.cpp:237]     Train net output #0: loss = 5.16279 (* 1 = 5.16279 loss)
I0409 21:22:38.378576 24944 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0409 21:22:43.026960 24944 solver.cpp:218] Iteration 480 (2.58163 iter/s, 4.64823s/12 iters), loss = 5.11687
I0409 21:22:43.027009 24944 solver.cpp:237]     Train net output #0: loss = 5.11687 (* 1 = 5.11687 loss)
I0409 21:22:43.027020 24944 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0409 21:22:47.914415 24944 solver.cpp:218] Iteration 492 (2.45537 iter/s, 4.88724s/12 iters), loss = 5.16252
I0409 21:22:47.914461 24944 solver.cpp:237]     Train net output #0: loss = 5.16252 (* 1 = 5.16252 loss)
I0409 21:22:47.914474 24944 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0409 21:22:53.151154 24944 solver.cpp:218] Iteration 504 (2.2916 iter/s, 5.23651s/12 iters), loss = 5.16152
I0409 21:22:53.151201 24944 solver.cpp:237]     Train net output #0: loss = 5.16152 (* 1 = 5.16152 loss)
I0409 21:22:53.151213 24944 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0409 21:22:53.392714 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:22:55.255674 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0409 21:23:20.931666 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0409 21:23:34.303227 24944 solver.cpp:330] Iteration 510, Testing net (#0)
I0409 21:23:34.303275 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:23:38.576952 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:23:38.820502 24944 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0409 21:23:38.820540 24944 solver.cpp:397]     Test net output #1: loss = 5.14899 (* 1 = 5.14899 loss)
I0409 21:23:40.751199 24944 solver.cpp:218] Iteration 516 (0.252109 iter/s, 47.5985s/12 iters), loss = 5.13872
I0409 21:23:40.751237 24944 solver.cpp:237]     Train net output #0: loss = 5.13872 (* 1 = 5.13872 loss)
I0409 21:23:40.751247 24944 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0409 21:23:45.284440 24944 solver.cpp:218] Iteration 528 (2.64724 iter/s, 4.53303s/12 iters), loss = 5.2109
I0409 21:23:45.284494 24944 solver.cpp:237]     Train net output #0: loss = 5.2109 (* 1 = 5.2109 loss)
I0409 21:23:45.284505 24944 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0409 21:23:49.830539 24944 solver.cpp:218] Iteration 540 (2.63975 iter/s, 4.54589s/12 iters), loss = 5.13774
I0409 21:23:49.830581 24944 solver.cpp:237]     Train net output #0: loss = 5.13774 (* 1 = 5.13774 loss)
I0409 21:23:49.830590 24944 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0409 21:23:54.673631 24944 solver.cpp:218] Iteration 552 (2.47787 iter/s, 4.84288s/12 iters), loss = 5.11481
I0409 21:23:54.673681 24944 solver.cpp:237]     Train net output #0: loss = 5.11481 (* 1 = 5.11481 loss)
I0409 21:23:54.673691 24944 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0409 21:23:59.554193 24944 solver.cpp:218] Iteration 564 (2.45884 iter/s, 4.88034s/12 iters), loss = 5.16427
I0409 21:23:59.554236 24944 solver.cpp:237]     Train net output #0: loss = 5.16427 (* 1 = 5.16427 loss)
I0409 21:23:59.554246 24944 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0409 21:24:04.797616 24944 solver.cpp:218] Iteration 576 (2.28868 iter/s, 5.24319s/12 iters), loss = 5.11719
I0409 21:24:04.797725 24944 solver.cpp:237]     Train net output #0: loss = 5.11719 (* 1 = 5.11719 loss)
I0409 21:24:04.797736 24944 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0409 21:24:10.040850 24944 solver.cpp:218] Iteration 588 (2.28879 iter/s, 5.24294s/12 iters), loss = 5.0927
I0409 21:24:10.040899 24944 solver.cpp:237]     Train net output #0: loss = 5.0927 (* 1 = 5.0927 loss)
I0409 21:24:10.040908 24944 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0409 21:24:15.247305 24944 solver.cpp:218] Iteration 600 (2.30493 iter/s, 5.20623s/12 iters), loss = 5.16072
I0409 21:24:15.247344 24944 solver.cpp:237]     Train net output #0: loss = 5.16072 (* 1 = 5.16072 loss)
I0409 21:24:15.247351 24944 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0409 21:24:17.488035 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:24:19.604578 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0409 21:24:32.666112 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0409 21:24:41.236271 24944 solver.cpp:330] Iteration 612, Testing net (#0)
I0409 21:24:41.236338 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:24:45.422787 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:24:45.713052 24944 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0409 21:24:45.713097 24944 solver.cpp:397]     Test net output #1: loss = 5.13459 (* 1 = 5.13459 loss)
I0409 21:24:45.834429 24944 solver.cpp:218] Iteration 612 (0.392335 iter/s, 30.5861s/12 iters), loss = 5.14303
I0409 21:24:45.836760 24944 solver.cpp:237]     Train net output #0: loss = 5.14303 (* 1 = 5.14303 loss)
I0409 21:24:45.836778 24944 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0409 21:24:49.980732 24944 solver.cpp:218] Iteration 624 (2.89587 iter/s, 4.14384s/12 iters), loss = 5.14842
I0409 21:24:49.980782 24944 solver.cpp:237]     Train net output #0: loss = 5.14842 (* 1 = 5.14842 loss)
I0409 21:24:49.980792 24944 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0409 21:24:54.813691 24944 solver.cpp:218] Iteration 636 (2.48306 iter/s, 4.83274s/12 iters), loss = 5.04495
I0409 21:24:54.813735 24944 solver.cpp:237]     Train net output #0: loss = 5.04495 (* 1 = 5.04495 loss)
I0409 21:24:54.813745 24944 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0409 21:24:59.732400 24944 solver.cpp:218] Iteration 648 (2.43977 iter/s, 4.91849s/12 iters), loss = 5.13442
I0409 21:24:59.732446 24944 solver.cpp:237]     Train net output #0: loss = 5.13442 (* 1 = 5.13442 loss)
I0409 21:24:59.732456 24944 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0409 21:25:04.896772 24944 solver.cpp:218] Iteration 660 (2.32371 iter/s, 5.16414s/12 iters), loss = 5.12155
I0409 21:25:04.896821 24944 solver.cpp:237]     Train net output #0: loss = 5.12155 (* 1 = 5.12155 loss)
I0409 21:25:04.896829 24944 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0409 21:25:10.142349 24944 solver.cpp:218] Iteration 672 (2.28774 iter/s, 5.24535s/12 iters), loss = 5.08875
I0409 21:25:10.142393 24944 solver.cpp:237]     Train net output #0: loss = 5.08875 (* 1 = 5.08875 loss)
I0409 21:25:10.142401 24944 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0409 21:25:15.066185 24944 solver.cpp:218] Iteration 684 (2.43723 iter/s, 4.92362s/12 iters), loss = 4.94884
I0409 21:25:15.066324 24944 solver.cpp:237]     Train net output #0: loss = 4.94884 (* 1 = 4.94884 loss)
I0409 21:25:15.066334 24944 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0409 21:25:15.803761 24944 blocking_queue.cpp:49] Waiting for data
I0409 21:25:19.852202 24944 solver.cpp:218] Iteration 696 (2.50747 iter/s, 4.78571s/12 iters), loss = 5.06192
I0409 21:25:19.852252 24944 solver.cpp:237]     Train net output #0: loss = 5.06192 (* 1 = 5.06192 loss)
I0409 21:25:19.852262 24944 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0409 21:25:24.089087 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:25:24.507141 24944 solver.cpp:218] Iteration 708 (2.57803 iter/s, 4.65473s/12 iters), loss = 5.09588
I0409 21:25:24.507185 24944 solver.cpp:237]     Train net output #0: loss = 5.09588 (* 1 = 5.09588 loss)
I0409 21:25:24.507197 24944 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0409 21:25:26.396747 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0409 21:25:39.769781 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0409 21:25:51.132699 24944 solver.cpp:330] Iteration 714, Testing net (#0)
I0409 21:25:51.132776 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:25:55.380234 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:25:55.704596 24944 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0409 21:25:55.704628 24944 solver.cpp:397]     Test net output #1: loss = 5.06308 (* 1 = 5.06308 loss)
I0409 21:25:57.486745 24944 solver.cpp:218] Iteration 720 (0.363874 iter/s, 32.9785s/12 iters), loss = 5.13786
I0409 21:25:57.486789 24944 solver.cpp:237]     Train net output #0: loss = 5.13786 (* 1 = 5.13786 loss)
I0409 21:25:57.486797 24944 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0409 21:26:02.279517 24944 solver.cpp:218] Iteration 732 (2.50388 iter/s, 4.79255s/12 iters), loss = 4.99993
I0409 21:26:02.279572 24944 solver.cpp:237]     Train net output #0: loss = 4.99993 (* 1 = 4.99993 loss)
I0409 21:26:02.279582 24944 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0409 21:26:07.424877 24944 solver.cpp:218] Iteration 744 (2.33231 iter/s, 5.14512s/12 iters), loss = 5.01101
I0409 21:26:07.424934 24944 solver.cpp:237]     Train net output #0: loss = 5.01101 (* 1 = 5.01101 loss)
I0409 21:26:07.424945 24944 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0409 21:26:12.347666 24944 solver.cpp:218] Iteration 756 (2.43775 iter/s, 4.92256s/12 iters), loss = 5.08146
I0409 21:26:12.347712 24944 solver.cpp:237]     Train net output #0: loss = 5.08146 (* 1 = 5.08146 loss)
I0409 21:26:12.347720 24944 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0409 21:26:17.453135 24944 solver.cpp:218] Iteration 768 (2.35053 iter/s, 5.10524s/12 iters), loss = 5.06376
I0409 21:26:17.453176 24944 solver.cpp:237]     Train net output #0: loss = 5.06376 (* 1 = 5.06376 loss)
I0409 21:26:17.453186 24944 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0409 21:26:22.590590 24944 solver.cpp:218] Iteration 780 (2.33589 iter/s, 5.13723s/12 iters), loss = 5.06038
I0409 21:26:22.590735 24944 solver.cpp:237]     Train net output #0: loss = 5.06038 (* 1 = 5.06038 loss)
I0409 21:26:22.590749 24944 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0409 21:26:27.761109 24944 solver.cpp:218] Iteration 792 (2.321 iter/s, 5.17019s/12 iters), loss = 4.94632
I0409 21:26:27.761159 24944 solver.cpp:237]     Train net output #0: loss = 4.94632 (* 1 = 4.94632 loss)
I0409 21:26:27.761169 24944 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0409 21:26:32.715291 24944 solver.cpp:218] Iteration 804 (2.42231 iter/s, 4.95395s/12 iters), loss = 5.00352
I0409 21:26:32.715334 24944 solver.cpp:237]     Train net output #0: loss = 5.00352 (* 1 = 5.00352 loss)
I0409 21:26:32.715344 24944 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0409 21:26:34.407821 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:26:36.982024 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0409 21:26:48.334599 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0409 21:27:05.879631 24944 solver.cpp:330] Iteration 816, Testing net (#0)
I0409 21:27:05.879700 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:27:10.203640 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:27:10.568508 24944 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0409 21:27:10.568539 24944 solver.cpp:397]     Test net output #1: loss = 4.9991 (* 1 = 4.9991 loss)
I0409 21:27:10.689442 24944 solver.cpp:218] Iteration 816 (0.316015 iter/s, 37.9729s/12 iters), loss = 5.10423
I0409 21:27:10.691299 24944 solver.cpp:237]     Train net output #0: loss = 5.10423 (* 1 = 5.10423 loss)
I0409 21:27:10.691310 24944 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0409 21:27:15.008822 24944 solver.cpp:218] Iteration 828 (2.77947 iter/s, 4.31737s/12 iters), loss = 5.12568
I0409 21:27:15.008864 24944 solver.cpp:237]     Train net output #0: loss = 5.12568 (* 1 = 5.12568 loss)
I0409 21:27:15.008872 24944 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0409 21:27:19.962771 24944 solver.cpp:218] Iteration 840 (2.42242 iter/s, 4.95373s/12 iters), loss = 4.88612
I0409 21:27:19.962817 24944 solver.cpp:237]     Train net output #0: loss = 4.88612 (* 1 = 4.88612 loss)
I0409 21:27:19.962827 24944 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0409 21:27:24.864416 24944 solver.cpp:218] Iteration 852 (2.44827 iter/s, 4.90142s/12 iters), loss = 4.92798
I0409 21:27:24.864470 24944 solver.cpp:237]     Train net output #0: loss = 4.92798 (* 1 = 4.92798 loss)
I0409 21:27:24.864480 24944 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0409 21:27:29.874840 24944 solver.cpp:218] Iteration 864 (2.39512 iter/s, 5.01019s/12 iters), loss = 4.98967
I0409 21:27:29.874881 24944 solver.cpp:237]     Train net output #0: loss = 4.98967 (* 1 = 4.98967 loss)
I0409 21:27:29.874888 24944 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0409 21:27:34.981688 24944 solver.cpp:218] Iteration 876 (2.34989 iter/s, 5.10662s/12 iters), loss = 5.00996
I0409 21:27:34.981730 24944 solver.cpp:237]     Train net output #0: loss = 5.00996 (* 1 = 5.00996 loss)
I0409 21:27:34.981739 24944 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0409 21:27:40.212844 24944 solver.cpp:218] Iteration 888 (2.29405 iter/s, 5.23093s/12 iters), loss = 4.89113
I0409 21:27:40.212962 24944 solver.cpp:237]     Train net output #0: loss = 4.89113 (* 1 = 4.89113 loss)
I0409 21:27:40.212972 24944 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0409 21:27:45.238330 24944 solver.cpp:218] Iteration 900 (2.38797 iter/s, 5.02519s/12 iters), loss = 5.01221
I0409 21:27:45.238375 24944 solver.cpp:237]     Train net output #0: loss = 5.01221 (* 1 = 5.01221 loss)
I0409 21:27:45.238384 24944 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0409 21:27:48.948187 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:27:50.124291 24944 solver.cpp:218] Iteration 912 (2.45613 iter/s, 4.88574s/12 iters), loss = 4.83205
I0409 21:27:50.124339 24944 solver.cpp:237]     Train net output #0: loss = 4.83205 (* 1 = 4.83205 loss)
I0409 21:27:50.124349 24944 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0409 21:27:51.924324 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0409 21:28:04.447585 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0409 21:28:17.238683 24944 solver.cpp:330] Iteration 918, Testing net (#0)
I0409 21:28:17.238761 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:28:21.365602 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:28:21.778456 24944 solver.cpp:397]     Test net output #0: accuracy = 0.0257353
I0409 21:28:21.778514 24944 solver.cpp:397]     Test net output #1: loss = 4.91441 (* 1 = 4.91441 loss)
I0409 21:28:23.432562 24944 solver.cpp:218] Iteration 924 (0.360283 iter/s, 33.3071s/12 iters), loss = 4.9534
I0409 21:28:23.432631 24944 solver.cpp:237]     Train net output #0: loss = 4.9534 (* 1 = 4.9534 loss)
I0409 21:28:23.432643 24944 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0409 21:28:28.218503 24944 solver.cpp:218] Iteration 936 (2.50747 iter/s, 4.7857s/12 iters), loss = 4.92849
I0409 21:28:28.218546 24944 solver.cpp:237]     Train net output #0: loss = 4.92849 (* 1 = 4.92849 loss)
I0409 21:28:28.218554 24944 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0409 21:28:32.931102 24944 solver.cpp:218] Iteration 948 (2.54648 iter/s, 4.71238s/12 iters), loss = 4.89325
I0409 21:28:32.931159 24944 solver.cpp:237]     Train net output #0: loss = 4.89325 (* 1 = 4.89325 loss)
I0409 21:28:32.931172 24944 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0409 21:28:38.232086 24944 solver.cpp:218] Iteration 960 (2.26384 iter/s, 5.30073s/12 iters), loss = 4.84528
I0409 21:28:38.232142 24944 solver.cpp:237]     Train net output #0: loss = 4.84528 (* 1 = 4.84528 loss)
I0409 21:28:38.232151 24944 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0409 21:28:43.454046 24944 solver.cpp:218] Iteration 972 (2.2981 iter/s, 5.22171s/12 iters), loss = 4.93278
I0409 21:28:43.454102 24944 solver.cpp:237]     Train net output #0: loss = 4.93278 (* 1 = 4.93278 loss)
I0409 21:28:43.454114 24944 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0409 21:28:48.588820 24944 solver.cpp:218] Iteration 984 (2.33712 iter/s, 5.13453s/12 iters), loss = 4.83719
I0409 21:28:48.588948 24944 solver.cpp:237]     Train net output #0: loss = 4.83719 (* 1 = 4.83719 loss)
I0409 21:28:48.588958 24944 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0409 21:28:54.171190 24944 solver.cpp:218] Iteration 996 (2.14975 iter/s, 5.58204s/12 iters), loss = 4.81588
I0409 21:28:54.171234 24944 solver.cpp:237]     Train net output #0: loss = 4.81588 (* 1 = 4.81588 loss)
I0409 21:28:54.171243 24944 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0409 21:28:59.516350 24944 solver.cpp:218] Iteration 1008 (2.24512 iter/s, 5.34492s/12 iters), loss = 4.85296
I0409 21:28:59.516392 24944 solver.cpp:237]     Train net output #0: loss = 4.85296 (* 1 = 4.85296 loss)
I0409 21:28:59.516400 24944 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0409 21:29:00.561452 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:29:03.895763 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0409 21:29:25.112798 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0409 21:29:34.009560 24944 solver.cpp:330] Iteration 1020, Testing net (#0)
I0409 21:29:34.009580 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:29:38.191668 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:29:38.631706 24944 solver.cpp:397]     Test net output #0: accuracy = 0.0343137
I0409 21:29:38.631744 24944 solver.cpp:397]     Test net output #1: loss = 4.89208 (* 1 = 4.89208 loss)
I0409 21:29:38.752945 24944 solver.cpp:218] Iteration 1020 (0.305848 iter/s, 39.2352s/12 iters), loss = 4.80212
I0409 21:29:38.754470 24944 solver.cpp:237]     Train net output #0: loss = 4.80212 (* 1 = 4.80212 loss)
I0409 21:29:38.754480 24944 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0409 21:29:42.932098 24944 solver.cpp:218] Iteration 1032 (2.87255 iter/s, 4.17747s/12 iters), loss = 4.82385
I0409 21:29:42.932149 24944 solver.cpp:237]     Train net output #0: loss = 4.82385 (* 1 = 4.82385 loss)
I0409 21:29:42.932159 24944 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0409 21:29:48.729442 24944 solver.cpp:218] Iteration 1044 (2.07001 iter/s, 5.79708s/12 iters), loss = 4.89395
I0409 21:29:48.729494 24944 solver.cpp:237]     Train net output #0: loss = 4.89395 (* 1 = 4.89395 loss)
I0409 21:29:48.729506 24944 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0409 21:29:53.620123 24944 solver.cpp:218] Iteration 1056 (2.45376 iter/s, 4.89045s/12 iters), loss = 4.77523
I0409 21:29:53.620168 24944 solver.cpp:237]     Train net output #0: loss = 4.77523 (* 1 = 4.77523 loss)
I0409 21:29:53.620175 24944 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0409 21:29:59.182603 24944 solver.cpp:218] Iteration 1068 (2.15741 iter/s, 5.56222s/12 iters), loss = 4.86341
I0409 21:29:59.182739 24944 solver.cpp:237]     Train net output #0: loss = 4.86341 (* 1 = 4.86341 loss)
I0409 21:29:59.182752 24944 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0409 21:30:05.130805 24944 solver.cpp:218] Iteration 1080 (2.01753 iter/s, 5.94785s/12 iters), loss = 4.72571
I0409 21:30:05.130861 24944 solver.cpp:237]     Train net output #0: loss = 4.72571 (* 1 = 4.72571 loss)
I0409 21:30:05.130872 24944 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0409 21:30:09.704632 24944 solver.cpp:218] Iteration 1092 (2.62376 iter/s, 4.57359s/12 iters), loss = 4.81667
I0409 21:30:09.704686 24944 solver.cpp:237]     Train net output #0: loss = 4.81667 (* 1 = 4.81667 loss)
I0409 21:30:09.704696 24944 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0409 21:30:14.570869 24944 solver.cpp:218] Iteration 1104 (2.46609 iter/s, 4.86601s/12 iters), loss = 4.72614
I0409 21:30:14.570909 24944 solver.cpp:237]     Train net output #0: loss = 4.72614 (* 1 = 4.72614 loss)
I0409 21:30:14.570916 24944 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0409 21:30:17.558221 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:30:19.499114 24944 solver.cpp:218] Iteration 1116 (2.43506 iter/s, 4.92802s/12 iters), loss = 4.79024
I0409 21:30:19.499168 24944 solver.cpp:237]     Train net output #0: loss = 4.79024 (* 1 = 4.79024 loss)
I0409 21:30:19.499178 24944 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0409 21:30:21.460886 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0409 21:30:40.689870 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0409 21:30:55.443305 24944 solver.cpp:330] Iteration 1122, Testing net (#0)
I0409 21:30:55.443327 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:30:59.477849 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:30:59.982622 24944 solver.cpp:397]     Test net output #0: accuracy = 0.033701
I0409 21:30:59.982666 24944 solver.cpp:397]     Test net output #1: loss = 4.80915 (* 1 = 4.80915 loss)
I0409 21:31:02.216459 24944 solver.cpp:218] Iteration 1128 (0.280926 iter/s, 42.7158s/12 iters), loss = 4.82736
I0409 21:31:02.216517 24944 solver.cpp:237]     Train net output #0: loss = 4.82736 (* 1 = 4.82736 loss)
I0409 21:31:02.216528 24944 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0409 21:31:08.167652 24944 solver.cpp:218] Iteration 1140 (2.0165 iter/s, 5.95092s/12 iters), loss = 4.87082
I0409 21:31:08.167708 24944 solver.cpp:237]     Train net output #0: loss = 4.87082 (* 1 = 4.87082 loss)
I0409 21:31:08.167719 24944 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0409 21:31:13.659238 24944 solver.cpp:218] Iteration 1152 (2.18526 iter/s, 5.49133s/12 iters), loss = 4.74145
I0409 21:31:13.659392 24944 solver.cpp:237]     Train net output #0: loss = 4.74145 (* 1 = 4.74145 loss)
I0409 21:31:13.659404 24944 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0409 21:31:19.043339 24944 solver.cpp:218] Iteration 1164 (2.22893 iter/s, 5.38375s/12 iters), loss = 4.68419
I0409 21:31:19.043383 24944 solver.cpp:237]     Train net output #0: loss = 4.68419 (* 1 = 4.68419 loss)
I0409 21:31:19.043392 24944 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0409 21:31:23.886674 24944 solver.cpp:218] Iteration 1176 (2.47775 iter/s, 4.84311s/12 iters), loss = 4.62506
I0409 21:31:23.886723 24944 solver.cpp:237]     Train net output #0: loss = 4.62506 (* 1 = 4.62506 loss)
I0409 21:31:23.886734 24944 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0409 21:31:29.465499 24944 solver.cpp:218] Iteration 1188 (2.15109 iter/s, 5.57857s/12 iters), loss = 4.63356
I0409 21:31:29.465556 24944 solver.cpp:237]     Train net output #0: loss = 4.63356 (* 1 = 4.63356 loss)
I0409 21:31:29.465566 24944 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0409 21:31:34.767935 24944 solver.cpp:218] Iteration 1200 (2.26322 iter/s, 5.30218s/12 iters), loss = 4.64633
I0409 21:31:34.767989 24944 solver.cpp:237]     Train net output #0: loss = 4.64633 (* 1 = 4.64633 loss)
I0409 21:31:34.767999 24944 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0409 21:31:39.960275 24944 solver.cpp:218] Iteration 1212 (2.3112 iter/s, 5.1921s/12 iters), loss = 4.71629
I0409 21:31:39.960317 24944 solver.cpp:237]     Train net output #0: loss = 4.71629 (* 1 = 4.71629 loss)
I0409 21:31:39.960325 24944 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0409 21:31:40.205448 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:31:45.116135 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0409 21:31:56.282301 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0409 21:32:05.006168 24944 solver.cpp:330] Iteration 1224, Testing net (#0)
I0409 21:32:05.006196 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:32:09.306913 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:32:09.938774 24944 solver.cpp:397]     Test net output #0: accuracy = 0.0410539
I0409 21:32:09.938814 24944 solver.cpp:397]     Test net output #1: loss = 4.71489 (* 1 = 4.71489 loss)
I0409 21:32:10.059927 24944 solver.cpp:218] Iteration 1224 (0.39869 iter/s, 30.0986s/12 iters), loss = 4.68029
I0409 21:32:10.059983 24944 solver.cpp:237]     Train net output #0: loss = 4.68029 (* 1 = 4.68029 loss)
I0409 21:32:10.059993 24944 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0409 21:32:14.456988 24944 solver.cpp:218] Iteration 1236 (2.72923 iter/s, 4.39684s/12 iters), loss = 4.64107
I0409 21:32:14.457033 24944 solver.cpp:237]     Train net output #0: loss = 4.64107 (* 1 = 4.64107 loss)
I0409 21:32:14.457041 24944 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0409 21:32:19.784171 24944 solver.cpp:218] Iteration 1248 (2.2527 iter/s, 5.32694s/12 iters), loss = 4.4594
I0409 21:32:19.784283 24944 solver.cpp:237]     Train net output #0: loss = 4.4594 (* 1 = 4.4594 loss)
I0409 21:32:19.784296 24944 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0409 21:32:25.205185 24944 solver.cpp:218] Iteration 1260 (2.21373 iter/s, 5.42071s/12 iters), loss = 4.64269
I0409 21:32:25.205233 24944 solver.cpp:237]     Train net output #0: loss = 4.64269 (* 1 = 4.64269 loss)
I0409 21:32:25.205245 24944 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0409 21:32:30.084585 24944 solver.cpp:218] Iteration 1272 (2.45944 iter/s, 4.87917s/12 iters), loss = 4.54997
I0409 21:32:30.084643 24944 solver.cpp:237]     Train net output #0: loss = 4.54997 (* 1 = 4.54997 loss)
I0409 21:32:30.084652 24944 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0409 21:32:35.353776 24944 solver.cpp:218] Iteration 1284 (2.2775 iter/s, 5.26894s/12 iters), loss = 4.56098
I0409 21:32:35.353829 24944 solver.cpp:237]     Train net output #0: loss = 4.56098 (* 1 = 4.56098 loss)
I0409 21:32:35.353840 24944 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0409 21:32:40.729648 24944 solver.cpp:218] Iteration 1296 (2.2323 iter/s, 5.37561s/12 iters), loss = 4.53076
I0409 21:32:40.729704 24944 solver.cpp:237]     Train net output #0: loss = 4.53076 (* 1 = 4.53076 loss)
I0409 21:32:40.729717 24944 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0409 21:32:45.583556 24944 solver.cpp:218] Iteration 1308 (2.47235 iter/s, 4.85367s/12 iters), loss = 4.51735
I0409 21:32:45.583611 24944 solver.cpp:237]     Train net output #0: loss = 4.51735 (* 1 = 4.51735 loss)
I0409 21:32:45.583621 24944 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0409 21:32:48.078078 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:32:50.539198 24944 solver.cpp:218] Iteration 1320 (2.4216 iter/s, 4.9554s/12 iters), loss = 4.4877
I0409 21:32:50.539340 24944 solver.cpp:237]     Train net output #0: loss = 4.4877 (* 1 = 4.4877 loss)
I0409 21:32:50.539350 24944 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0409 21:32:52.458237 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0409 21:33:05.761374 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0409 21:33:23.166935 24944 solver.cpp:330] Iteration 1326, Testing net (#0)
I0409 21:33:23.167008 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:33:27.182281 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:33:27.803464 24944 solver.cpp:397]     Test net output #0: accuracy = 0.0545343
I0409 21:33:27.803498 24944 solver.cpp:397]     Test net output #1: loss = 4.54771 (* 1 = 4.54771 loss)
I0409 21:33:29.756234 24944 solver.cpp:218] Iteration 1332 (0.306001 iter/s, 39.2155s/12 iters), loss = 4.40453
I0409 21:33:29.756283 24944 solver.cpp:237]     Train net output #0: loss = 4.40453 (* 1 = 4.40453 loss)
I0409 21:33:29.756291 24944 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0409 21:33:34.549445 24944 solver.cpp:218] Iteration 1344 (2.50366 iter/s, 4.79298s/12 iters), loss = 4.44314
I0409 21:33:34.549489 24944 solver.cpp:237]     Train net output #0: loss = 4.44314 (* 1 = 4.44314 loss)
I0409 21:33:34.549497 24944 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0409 21:33:39.311040 24944 solver.cpp:218] Iteration 1356 (2.52028 iter/s, 4.76137s/12 iters), loss = 4.54396
I0409 21:33:39.311094 24944 solver.cpp:237]     Train net output #0: loss = 4.54396 (* 1 = 4.54396 loss)
I0409 21:33:39.311105 24944 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0409 21:33:44.795864 24944 solver.cpp:218] Iteration 1368 (2.18796 iter/s, 5.48457s/12 iters), loss = 4.54952
I0409 21:33:44.795907 24944 solver.cpp:237]     Train net output #0: loss = 4.54952 (* 1 = 4.54952 loss)
I0409 21:33:44.795917 24944 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0409 21:33:46.317116 24944 blocking_queue.cpp:49] Waiting for data
I0409 21:33:50.254907 24944 solver.cpp:218] Iteration 1380 (2.19829 iter/s, 5.4588s/12 iters), loss = 4.32063
I0409 21:33:50.254951 24944 solver.cpp:237]     Train net output #0: loss = 4.32063 (* 1 = 4.32063 loss)
I0409 21:33:50.254959 24944 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0409 21:33:55.442433 24944 solver.cpp:218] Iteration 1392 (2.31335 iter/s, 5.18729s/12 iters), loss = 4.55183
I0409 21:33:55.442605 24944 solver.cpp:237]     Train net output #0: loss = 4.55183 (* 1 = 4.55183 loss)
I0409 21:33:55.442617 24944 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0409 21:33:59.952103 24944 solver.cpp:218] Iteration 1404 (2.66115 iter/s, 4.50933s/12 iters), loss = 4.37826
I0409 21:33:59.952158 24944 solver.cpp:237]     Train net output #0: loss = 4.37826 (* 1 = 4.37826 loss)
I0409 21:33:59.952167 24944 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0409 21:34:04.100883 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:34:04.614774 24944 solver.cpp:218] Iteration 1416 (2.57376 iter/s, 4.66244s/12 iters), loss = 4.25871
I0409 21:34:04.614825 24944 solver.cpp:237]     Train net output #0: loss = 4.25871 (* 1 = 4.25871 loss)
I0409 21:34:04.614835 24944 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0409 21:34:09.865312 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0409 21:34:25.756305 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0409 21:34:46.139376 24944 solver.cpp:330] Iteration 1428, Testing net (#0)
I0409 21:34:46.139400 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:34:50.120353 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:34:50.730867 24944 solver.cpp:397]     Test net output #0: accuracy = 0.0637255
I0409 21:34:50.730901 24944 solver.cpp:397]     Test net output #1: loss = 4.36521 (* 1 = 4.36521 loss)
I0409 21:34:50.853376 24944 solver.cpp:218] Iteration 1428 (0.259533 iter/s, 46.237s/12 iters), loss = 4.33453
I0409 21:34:50.853435 24944 solver.cpp:237]     Train net output #0: loss = 4.33453 (* 1 = 4.33453 loss)
I0409 21:34:50.853446 24944 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0409 21:34:54.825994 24944 solver.cpp:218] Iteration 1440 (3.02086 iter/s, 3.97238s/12 iters), loss = 4.35441
I0409 21:34:54.826050 24944 solver.cpp:237]     Train net output #0: loss = 4.35441 (* 1 = 4.35441 loss)
I0409 21:34:54.826059 24944 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0409 21:34:59.306120 24944 solver.cpp:218] Iteration 1452 (2.67863 iter/s, 4.4799s/12 iters), loss = 4.5419
I0409 21:34:59.306236 24944 solver.cpp:237]     Train net output #0: loss = 4.5419 (* 1 = 4.5419 loss)
I0409 21:34:59.306246 24944 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0409 21:35:04.306290 24944 solver.cpp:218] Iteration 1464 (2.40006 iter/s, 4.99987s/12 iters), loss = 4.36352
I0409 21:35:04.306349 24944 solver.cpp:237]     Train net output #0: loss = 4.36352 (* 1 = 4.36352 loss)
I0409 21:35:04.306360 24944 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0409 21:35:09.194351 24944 solver.cpp:218] Iteration 1476 (2.45508 iter/s, 4.88782s/12 iters), loss = 4.28614
I0409 21:35:09.194403 24944 solver.cpp:237]     Train net output #0: loss = 4.28614 (* 1 = 4.28614 loss)
I0409 21:35:09.194412 24944 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0409 21:35:14.212653 24944 solver.cpp:218] Iteration 1488 (2.39136 iter/s, 5.01806s/12 iters), loss = 4.30878
I0409 21:35:14.212703 24944 solver.cpp:237]     Train net output #0: loss = 4.30878 (* 1 = 4.30878 loss)
I0409 21:35:14.212714 24944 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0409 21:35:18.945497 24944 solver.cpp:218] Iteration 1500 (2.5356 iter/s, 4.73261s/12 iters), loss = 4.05843
I0409 21:35:18.945549 24944 solver.cpp:237]     Train net output #0: loss = 4.05843 (* 1 = 4.05843 loss)
I0409 21:35:18.945561 24944 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0409 21:35:23.938863 24944 solver.cpp:218] Iteration 1512 (2.4033 iter/s, 4.99313s/12 iters), loss = 4.36413
I0409 21:35:23.938908 24944 solver.cpp:237]     Train net output #0: loss = 4.36413 (* 1 = 4.36413 loss)
I0409 21:35:23.938916 24944 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0409 21:35:26.145725 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:35:29.365232 24944 solver.cpp:218] Iteration 1524 (2.21152 iter/s, 5.42612s/12 iters), loss = 4.15373
I0409 21:35:29.365378 24944 solver.cpp:237]     Train net output #0: loss = 4.15373 (* 1 = 4.15373 loss)
I0409 21:35:29.365387 24944 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0409 21:35:31.339051 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0409 21:35:49.205716 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0409 21:35:59.358497 24944 solver.cpp:330] Iteration 1530, Testing net (#0)
I0409 21:35:59.358520 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:36:03.497691 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:36:04.156193 24944 solver.cpp:397]     Test net output #0: accuracy = 0.0839461
I0409 21:36:04.156232 24944 solver.cpp:397]     Test net output #1: loss = 4.23135 (* 1 = 4.23135 loss)
I0409 21:36:06.016858 24944 solver.cpp:218] Iteration 1536 (0.32742 iter/s, 36.6502s/12 iters), loss = 4.17786
I0409 21:36:06.016914 24944 solver.cpp:237]     Train net output #0: loss = 4.17786 (* 1 = 4.17786 loss)
I0409 21:36:06.016925 24944 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0409 21:36:10.950268 24944 solver.cpp:218] Iteration 1548 (2.43252 iter/s, 4.93317s/12 iters), loss = 3.8428
I0409 21:36:10.950323 24944 solver.cpp:237]     Train net output #0: loss = 3.8428 (* 1 = 3.8428 loss)
I0409 21:36:10.950333 24944 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0409 21:36:15.493741 24944 solver.cpp:218] Iteration 1560 (2.64128 iter/s, 4.54325s/12 iters), loss = 4.26938
I0409 21:36:15.493793 24944 solver.cpp:237]     Train net output #0: loss = 4.26938 (* 1 = 4.26938 loss)
I0409 21:36:15.493804 24944 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0409 21:36:20.687211 24944 solver.cpp:218] Iteration 1572 (2.3107 iter/s, 5.19322s/12 iters), loss = 4.00265
I0409 21:36:20.687258 24944 solver.cpp:237]     Train net output #0: loss = 4.00265 (* 1 = 4.00265 loss)
I0409 21:36:20.687265 24944 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0409 21:36:25.738152 24944 solver.cpp:218] Iteration 1584 (2.37591 iter/s, 5.0507s/12 iters), loss = 4.20355
I0409 21:36:25.738210 24944 solver.cpp:237]     Train net output #0: loss = 4.20355 (* 1 = 4.20355 loss)
I0409 21:36:25.738221 24944 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0409 21:36:30.322525 24944 solver.cpp:218] Iteration 1596 (2.61772 iter/s, 4.58415s/12 iters), loss = 4.19937
I0409 21:36:30.322576 24944 solver.cpp:237]     Train net output #0: loss = 4.19937 (* 1 = 4.19937 loss)
I0409 21:36:30.322587 24944 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0409 21:36:35.425007 24944 solver.cpp:218] Iteration 1608 (2.35191 iter/s, 5.10224s/12 iters), loss = 4.16503
I0409 21:36:35.425112 24944 solver.cpp:237]     Train net output #0: loss = 4.16503 (* 1 = 4.16503 loss)
I0409 21:36:35.425122 24944 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0409 21:36:39.001850 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:36:40.077515 24944 solver.cpp:218] Iteration 1620 (2.57941 iter/s, 4.65223s/12 iters), loss = 4.20639
I0409 21:36:40.077564 24944 solver.cpp:237]     Train net output #0: loss = 4.20639 (* 1 = 4.20639 loss)
I0409 21:36:40.077572 24944 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0409 21:36:44.674841 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0409 21:37:08.458564 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0409 21:37:18.249521 24944 solver.cpp:330] Iteration 1632, Testing net (#0)
I0409 21:37:18.249543 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:37:22.169386 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:37:22.848826 24944 solver.cpp:397]     Test net output #0: accuracy = 0.0827206
I0409 21:37:22.848856 24944 solver.cpp:397]     Test net output #1: loss = 4.1257 (* 1 = 4.1257 loss)
I0409 21:37:22.969810 24944 solver.cpp:218] Iteration 1632 (0.279781 iter/s, 42.8908s/12 iters), loss = 4.08002
I0409 21:37:22.971323 24944 solver.cpp:237]     Train net output #0: loss = 4.08002 (* 1 = 4.08002 loss)
I0409 21:37:22.971333 24944 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0409 21:37:26.941970 24944 solver.cpp:218] Iteration 1644 (3.02231 iter/s, 3.97048s/12 iters), loss = 3.82388
I0409 21:37:26.942023 24944 solver.cpp:237]     Train net output #0: loss = 3.82388 (* 1 = 3.82388 loss)
I0409 21:37:26.942034 24944 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0409 21:37:31.511096 24944 solver.cpp:218] Iteration 1656 (2.62645 iter/s, 4.5689s/12 iters), loss = 4.1039
I0409 21:37:31.511149 24944 solver.cpp:237]     Train net output #0: loss = 4.1039 (* 1 = 4.1039 loss)
I0409 21:37:31.511160 24944 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0409 21:37:36.017524 24944 solver.cpp:218] Iteration 1668 (2.663 iter/s, 4.5062s/12 iters), loss = 3.86659
I0409 21:37:36.017567 24944 solver.cpp:237]     Train net output #0: loss = 3.86659 (* 1 = 3.86659 loss)
I0409 21:37:36.017575 24944 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0409 21:37:40.672232 24944 solver.cpp:218] Iteration 1680 (2.57816 iter/s, 4.65449s/12 iters), loss = 3.9655
I0409 21:37:40.672399 24944 solver.cpp:237]     Train net output #0: loss = 3.9655 (* 1 = 3.9655 loss)
I0409 21:37:40.672415 24944 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0409 21:37:45.591953 24944 solver.cpp:218] Iteration 1692 (2.43933 iter/s, 4.91937s/12 iters), loss = 3.895
I0409 21:37:45.592008 24944 solver.cpp:237]     Train net output #0: loss = 3.895 (* 1 = 3.895 loss)
I0409 21:37:45.592020 24944 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0409 21:37:50.513329 24944 solver.cpp:218] Iteration 1704 (2.43846 iter/s, 4.92114s/12 iters), loss = 3.8746
I0409 21:37:50.513363 24944 solver.cpp:237]     Train net output #0: loss = 3.8746 (* 1 = 3.8746 loss)
I0409 21:37:50.513371 24944 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0409 21:37:55.431975 24944 solver.cpp:218] Iteration 1716 (2.4398 iter/s, 4.91843s/12 iters), loss = 4.00397
I0409 21:37:55.432013 24944 solver.cpp:237]     Train net output #0: loss = 4.00397 (* 1 = 4.00397 loss)
I0409 21:37:55.432021 24944 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0409 21:37:56.311506 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:38:00.303889 24944 solver.cpp:218] Iteration 1728 (2.46321 iter/s, 4.87169s/12 iters), loss = 3.80334
I0409 21:38:00.303939 24944 solver.cpp:237]     Train net output #0: loss = 3.80334 (* 1 = 3.80334 loss)
I0409 21:38:00.303951 24944 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0409 21:38:02.227771 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0409 21:38:15.180927 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0409 21:38:23.845440 24944 solver.cpp:330] Iteration 1734, Testing net (#0)
I0409 21:38:23.845463 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:38:27.993235 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:38:28.707149 24944 solver.cpp:397]     Test net output #0: accuracy = 0.110907
I0409 21:38:28.707188 24944 solver.cpp:397]     Test net output #1: loss = 3.93872 (* 1 = 3.93872 loss)
I0409 21:38:30.414139 24944 solver.cpp:218] Iteration 1740 (0.39855 iter/s, 30.1091s/12 iters), loss = 3.91974
I0409 21:38:30.414194 24944 solver.cpp:237]     Train net output #0: loss = 3.91974 (* 1 = 3.91974 loss)
I0409 21:38:30.414206 24944 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0409 21:38:35.343899 24944 solver.cpp:218] Iteration 1752 (2.43431 iter/s, 4.92952s/12 iters), loss = 3.95841
I0409 21:38:35.343946 24944 solver.cpp:237]     Train net output #0: loss = 3.95841 (* 1 = 3.95841 loss)
I0409 21:38:35.343955 24944 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0409 21:38:39.998311 24944 solver.cpp:218] Iteration 1764 (2.57832 iter/s, 4.65419s/12 iters), loss = 3.91712
I0409 21:38:39.998358 24944 solver.cpp:237]     Train net output #0: loss = 3.91712 (* 1 = 3.91712 loss)
I0409 21:38:39.998368 24944 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0409 21:38:44.601882 24944 solver.cpp:218] Iteration 1776 (2.6068 iter/s, 4.60335s/12 iters), loss = 3.93265
I0409 21:38:44.601923 24944 solver.cpp:237]     Train net output #0: loss = 3.93265 (* 1 = 3.93265 loss)
I0409 21:38:44.601933 24944 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0409 21:38:49.298069 24944 solver.cpp:218] Iteration 1788 (2.55538 iter/s, 4.69597s/12 iters), loss = 3.81252
I0409 21:38:49.298158 24944 solver.cpp:237]     Train net output #0: loss = 3.81252 (* 1 = 3.81252 loss)
I0409 21:38:49.298167 24944 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0409 21:38:53.867430 24944 solver.cpp:218] Iteration 1800 (2.62634 iter/s, 4.5691s/12 iters), loss = 3.6949
I0409 21:38:53.867470 24944 solver.cpp:237]     Train net output #0: loss = 3.6949 (* 1 = 3.6949 loss)
I0409 21:38:53.867480 24944 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0409 21:38:58.747776 24944 solver.cpp:218] Iteration 1812 (2.45896 iter/s, 4.88012s/12 iters), loss = 3.73283
I0409 21:38:58.747819 24944 solver.cpp:237]     Train net output #0: loss = 3.73283 (* 1 = 3.73283 loss)
I0409 21:38:58.747828 24944 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0409 21:39:01.685618 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:39:03.376039 24944 solver.cpp:218] Iteration 1824 (2.59289 iter/s, 4.62804s/12 iters), loss = 3.91696
I0409 21:39:03.376087 24944 solver.cpp:237]     Train net output #0: loss = 3.91696 (* 1 = 3.91696 loss)
I0409 21:39:03.376096 24944 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0409 21:39:07.635008 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0409 21:39:22.041507 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0409 21:39:32.563014 24944 solver.cpp:330] Iteration 1836, Testing net (#0)
I0409 21:39:32.563035 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:39:36.337812 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:39:37.113026 24944 solver.cpp:397]     Test net output #0: accuracy = 0.112745
I0409 21:39:37.113075 24944 solver.cpp:397]     Test net output #1: loss = 3.89553 (* 1 = 3.89553 loss)
I0409 21:39:37.234242 24944 solver.cpp:218] Iteration 1836 (0.354432 iter/s, 33.857s/12 iters), loss = 3.84692
I0409 21:39:37.235769 24944 solver.cpp:237]     Train net output #0: loss = 3.84692 (* 1 = 3.84692 loss)
I0409 21:39:37.235782 24944 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0409 21:39:41.579620 24944 solver.cpp:218] Iteration 1848 (2.76263 iter/s, 4.34369s/12 iters), loss = 4.02002
I0409 21:39:41.579663 24944 solver.cpp:237]     Train net output #0: loss = 4.02002 (* 1 = 4.02002 loss)
I0409 21:39:41.579674 24944 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0409 21:39:46.414809 24944 solver.cpp:218] Iteration 1860 (2.48192 iter/s, 4.83497s/12 iters), loss = 3.74667
I0409 21:39:46.414849 24944 solver.cpp:237]     Train net output #0: loss = 3.74667 (* 1 = 3.74667 loss)
I0409 21:39:46.414856 24944 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0409 21:39:50.994737 24944 solver.cpp:218] Iteration 1872 (2.62025 iter/s, 4.57971s/12 iters), loss = 3.59962
I0409 21:39:50.994784 24944 solver.cpp:237]     Train net output #0: loss = 3.59962 (* 1 = 3.59962 loss)
I0409 21:39:50.994796 24944 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0409 21:39:55.672781 24944 solver.cpp:218] Iteration 1884 (2.5653 iter/s, 4.67782s/12 iters), loss = 3.71679
I0409 21:39:55.672870 24944 solver.cpp:237]     Train net output #0: loss = 3.71679 (* 1 = 3.71679 loss)
I0409 21:39:55.672880 24944 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0409 21:40:00.869200 24944 solver.cpp:218] Iteration 1896 (2.30941 iter/s, 5.19613s/12 iters), loss = 3.74246
I0409 21:40:00.869253 24944 solver.cpp:237]     Train net output #0: loss = 3.74246 (* 1 = 3.74246 loss)
I0409 21:40:00.869264 24944 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0409 21:40:05.729622 24944 solver.cpp:218] Iteration 1908 (2.46904 iter/s, 4.86019s/12 iters), loss = 3.65632
I0409 21:40:05.729669 24944 solver.cpp:237]     Train net output #0: loss = 3.65632 (* 1 = 3.65632 loss)
I0409 21:40:05.729679 24944 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0409 21:40:11.018093 24944 solver.cpp:218] Iteration 1920 (2.26919 iter/s, 5.28823s/12 iters), loss = 3.51931
I0409 21:40:11.018132 24944 solver.cpp:237]     Train net output #0: loss = 3.51931 (* 1 = 3.51931 loss)
I0409 21:40:11.018141 24944 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0409 21:40:11.258339 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:40:15.734316 24944 solver.cpp:218] Iteration 1932 (2.54453 iter/s, 4.716s/12 iters), loss = 3.78331
I0409 21:40:15.734360 24944 solver.cpp:237]     Train net output #0: loss = 3.78331 (* 1 = 3.78331 loss)
I0409 21:40:15.734369 24944 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0409 21:40:17.697193 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0409 21:40:33.585387 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0409 21:40:42.094099 24944 solver.cpp:330] Iteration 1938, Testing net (#0)
I0409 21:40:42.094118 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:40:45.815623 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:40:46.613606 24944 solver.cpp:397]     Test net output #0: accuracy = 0.137255
I0409 21:40:46.613656 24944 solver.cpp:397]     Test net output #1: loss = 3.78535 (* 1 = 3.78535 loss)
I0409 21:40:48.350718 24944 solver.cpp:218] Iteration 1944 (0.367926 iter/s, 32.6152s/12 iters), loss = 3.48637
I0409 21:40:48.350767 24944 solver.cpp:237]     Train net output #0: loss = 3.48637 (* 1 = 3.48637 loss)
I0409 21:40:48.350777 24944 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0409 21:40:53.317111 24944 solver.cpp:218] Iteration 1956 (2.41635 iter/s, 4.96616s/12 iters), loss = 3.52016
I0409 21:40:53.317158 24944 solver.cpp:237]     Train net output #0: loss = 3.52016 (* 1 = 3.52016 loss)
I0409 21:40:53.317168 24944 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0409 21:40:58.451268 24944 solver.cpp:218] Iteration 1968 (2.3374 iter/s, 5.13392s/12 iters), loss = 3.5034
I0409 21:40:58.451308 24944 solver.cpp:237]     Train net output #0: loss = 3.5034 (* 1 = 3.5034 loss)
I0409 21:40:58.451316 24944 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0409 21:41:03.024936 24944 solver.cpp:218] Iteration 1980 (2.62384 iter/s, 4.57345s/12 iters), loss = 3.49634
I0409 21:41:03.024988 24944 solver.cpp:237]     Train net output #0: loss = 3.49634 (* 1 = 3.49634 loss)
I0409 21:41:03.024999 24944 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0409 21:41:07.968003 24944 solver.cpp:218] Iteration 1992 (2.42776 iter/s, 4.94283s/12 iters), loss = 3.72158
I0409 21:41:07.968102 24944 solver.cpp:237]     Train net output #0: loss = 3.72158 (* 1 = 3.72158 loss)
I0409 21:41:07.968112 24944 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0409 21:41:13.004449 24944 solver.cpp:218] Iteration 2004 (2.38277 iter/s, 5.03616s/12 iters), loss = 3.53721
I0409 21:41:13.004489 24944 solver.cpp:237]     Train net output #0: loss = 3.53721 (* 1 = 3.53721 loss)
I0409 21:41:13.004498 24944 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0409 21:41:17.721671 24944 solver.cpp:218] Iteration 2016 (2.54399 iter/s, 4.717s/12 iters), loss = 3.68376
I0409 21:41:17.721719 24944 solver.cpp:237]     Train net output #0: loss = 3.68376 (* 1 = 3.68376 loss)
I0409 21:41:17.721729 24944 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0409 21:41:20.253413 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:41:22.890470 24944 solver.cpp:218] Iteration 2028 (2.32173 iter/s, 5.16855s/12 iters), loss = 3.41807
I0409 21:41:22.890523 24944 solver.cpp:237]     Train net output #0: loss = 3.41807 (* 1 = 3.41807 loss)
I0409 21:41:22.890534 24944 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0409 21:41:27.599709 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0409 21:41:38.167053 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0409 21:41:51.487844 24944 solver.cpp:330] Iteration 2040, Testing net (#0)
I0409 21:41:51.487867 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:41:55.205102 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:41:56.053812 24944 solver.cpp:397]     Test net output #0: accuracy = 0.153186
I0409 21:41:56.053861 24944 solver.cpp:397]     Test net output #1: loss = 3.60201 (* 1 = 3.60201 loss)
I0409 21:41:56.172821 24944 solver.cpp:218] Iteration 2040 (0.360565 iter/s, 33.2811s/12 iters), loss = 3.45434
I0409 21:41:56.174342 24944 solver.cpp:237]     Train net output #0: loss = 3.45434 (* 1 = 3.45434 loss)
I0409 21:41:56.174356 24944 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0409 21:42:00.116305 24944 solver.cpp:218] Iteration 2052 (3.04428 iter/s, 3.94182s/12 iters), loss = 3.57001
I0409 21:42:00.116349 24944 solver.cpp:237]     Train net output #0: loss = 3.57001 (* 1 = 3.57001 loss)
I0409 21:42:00.116358 24944 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0409 21:42:01.812898 24944 blocking_queue.cpp:49] Waiting for data
I0409 21:42:05.354838 24944 solver.cpp:218] Iteration 2064 (2.29082 iter/s, 5.23829s/12 iters), loss = 3.57432
I0409 21:42:05.354883 24944 solver.cpp:237]     Train net output #0: loss = 3.57432 (* 1 = 3.57432 loss)
I0409 21:42:05.354893 24944 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0409 21:42:10.554442 24944 solver.cpp:218] Iteration 2076 (2.30797 iter/s, 5.19937s/12 iters), loss = 3.49863
I0409 21:42:10.554531 24944 solver.cpp:237]     Train net output #0: loss = 3.49863 (* 1 = 3.49863 loss)
I0409 21:42:10.554541 24944 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0409 21:42:15.646528 24944 solver.cpp:218] Iteration 2088 (2.35673 iter/s, 5.09181s/12 iters), loss = 3.52004
I0409 21:42:15.646567 24944 solver.cpp:237]     Train net output #0: loss = 3.52004 (* 1 = 3.52004 loss)
I0409 21:42:15.646576 24944 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0409 21:42:20.697559 24944 solver.cpp:218] Iteration 2100 (2.37586 iter/s, 5.0508s/12 iters), loss = 3.55411
I0409 21:42:20.697607 24944 solver.cpp:237]     Train net output #0: loss = 3.55411 (* 1 = 3.55411 loss)
I0409 21:42:20.697615 24944 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0409 21:42:25.441092 24944 solver.cpp:218] Iteration 2112 (2.52988 iter/s, 4.74331s/12 iters), loss = 3.42861
I0409 21:42:25.441134 24944 solver.cpp:237]     Train net output #0: loss = 3.42861 (* 1 = 3.42861 loss)
I0409 21:42:25.441144 24944 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0409 21:42:29.618185 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:42:29.970176 24944 solver.cpp:218] Iteration 2124 (2.64967 iter/s, 4.52887s/12 iters), loss = 3.15624
I0409 21:42:29.970218 24944 solver.cpp:237]     Train net output #0: loss = 3.15624 (* 1 = 3.15624 loss)
I0409 21:42:29.970228 24944 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0409 21:42:34.789644 24944 solver.cpp:218] Iteration 2136 (2.49002 iter/s, 4.81924s/12 iters), loss = 3.23844
I0409 21:42:34.789690 24944 solver.cpp:237]     Train net output #0: loss = 3.23844 (* 1 = 3.23844 loss)
I0409 21:42:34.789701 24944 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0409 21:42:37.003444 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0409 21:42:50.099769 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0409 21:43:07.708784 24944 solver.cpp:330] Iteration 2142, Testing net (#0)
I0409 21:43:07.708806 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:43:11.321837 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:43:12.199213 24944 solver.cpp:397]     Test net output #0: accuracy = 0.162377
I0409 21:43:12.199261 24944 solver.cpp:397]     Test net output #1: loss = 3.52565 (* 1 = 3.52565 loss)
I0409 21:43:13.902781 24944 solver.cpp:218] Iteration 2148 (0.306813 iter/s, 39.1117s/12 iters), loss = 3.31987
I0409 21:43:13.902824 24944 solver.cpp:237]     Train net output #0: loss = 3.31987 (* 1 = 3.31987 loss)
I0409 21:43:13.902832 24944 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0409 21:43:18.460251 24944 solver.cpp:218] Iteration 2160 (2.63316 iter/s, 4.55726s/12 iters), loss = 3.61467
I0409 21:43:18.460295 24944 solver.cpp:237]     Train net output #0: loss = 3.61467 (* 1 = 3.61467 loss)
I0409 21:43:18.460305 24944 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0409 21:43:23.495790 24944 solver.cpp:218] Iteration 2172 (2.38317 iter/s, 5.0353s/12 iters), loss = 3.24078
I0409 21:43:23.495929 24944 solver.cpp:237]     Train net output #0: loss = 3.24078 (* 1 = 3.24078 loss)
I0409 21:43:23.495940 24944 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0409 21:43:28.647361 24944 solver.cpp:218] Iteration 2184 (2.32953 iter/s, 5.15124s/12 iters), loss = 3.06968
I0409 21:43:28.647413 24944 solver.cpp:237]     Train net output #0: loss = 3.06968 (* 1 = 3.06968 loss)
I0409 21:43:28.647423 24944 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0409 21:43:33.638186 24944 solver.cpp:218] Iteration 2196 (2.40453 iter/s, 4.99058s/12 iters), loss = 3.28606
I0409 21:43:33.638238 24944 solver.cpp:237]     Train net output #0: loss = 3.28606 (* 1 = 3.28606 loss)
I0409 21:43:33.638248 24944 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0409 21:43:38.848062 24944 solver.cpp:218] Iteration 2208 (2.30343 iter/s, 5.20963s/12 iters), loss = 2.96717
I0409 21:43:38.848110 24944 solver.cpp:237]     Train net output #0: loss = 2.96717 (* 1 = 2.96717 loss)
I0409 21:43:38.848120 24944 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0409 21:43:44.089231 24944 solver.cpp:218] Iteration 2220 (2.28967 iter/s, 5.24093s/12 iters), loss = 3.44237
I0409 21:43:44.089277 24944 solver.cpp:237]     Train net output #0: loss = 3.44237 (* 1 = 3.44237 loss)
I0409 21:43:44.089287 24944 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0409 21:43:45.942975 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:43:49.262392 24944 solver.cpp:218] Iteration 2232 (2.31977 iter/s, 5.17292s/12 iters), loss = 3.28758
I0409 21:43:49.262437 24944 solver.cpp:237]     Train net output #0: loss = 3.28758 (* 1 = 3.28758 loss)
I0409 21:43:49.262447 24944 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0409 21:43:54.009191 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0409 21:44:10.852095 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0409 21:44:20.836431 24944 solver.cpp:330] Iteration 2244, Testing net (#0)
I0409 21:44:20.836458 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:44:24.385298 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:44:25.324082 24944 solver.cpp:397]     Test net output #0: accuracy = 0.185662
I0409 21:44:25.324129 24944 solver.cpp:397]     Test net output #1: loss = 3.44727 (* 1 = 3.44727 loss)
I0409 21:44:25.438174 24944 solver.cpp:218] Iteration 2244 (0.331726 iter/s, 36.1745s/12 iters), loss = 3.2419
I0409 21:44:25.439699 24944 solver.cpp:237]     Train net output #0: loss = 3.2419 (* 1 = 3.2419 loss)
I0409 21:44:25.439713 24944 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0409 21:44:29.791200 24944 solver.cpp:218] Iteration 2256 (2.75777 iter/s, 4.35134s/12 iters), loss = 2.89879
I0409 21:44:29.791254 24944 solver.cpp:237]     Train net output #0: loss = 2.89879 (* 1 = 2.89879 loss)
I0409 21:44:29.791265 24944 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0409 21:44:34.938117 24944 solver.cpp:218] Iteration 2268 (2.3316 iter/s, 5.14667s/12 iters), loss = 3.32786
I0409 21:44:34.938158 24944 solver.cpp:237]     Train net output #0: loss = 3.32786 (* 1 = 3.32786 loss)
I0409 21:44:34.938166 24944 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0409 21:44:40.334097 24944 solver.cpp:218] Iteration 2280 (2.22398 iter/s, 5.39574s/12 iters), loss = 3.0281
I0409 21:44:40.334146 24944 solver.cpp:237]     Train net output #0: loss = 3.0281 (* 1 = 3.0281 loss)
I0409 21:44:40.334154 24944 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0409 21:44:45.325384 24944 solver.cpp:218] Iteration 2292 (2.40431 iter/s, 4.99105s/12 iters), loss = 2.88639
I0409 21:44:45.325428 24944 solver.cpp:237]     Train net output #0: loss = 2.88639 (* 1 = 2.88639 loss)
I0409 21:44:45.325435 24944 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0409 21:44:50.347280 24944 solver.cpp:218] Iteration 2304 (2.38965 iter/s, 5.02166s/12 iters), loss = 3.3318
I0409 21:44:50.347328 24944 solver.cpp:237]     Train net output #0: loss = 3.3318 (* 1 = 3.3318 loss)
I0409 21:44:50.347338 24944 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0409 21:44:55.452620 24944 solver.cpp:218] Iteration 2316 (2.35059 iter/s, 5.1051s/12 iters), loss = 3.0585
I0409 21:44:55.452747 24944 solver.cpp:237]     Train net output #0: loss = 3.0585 (* 1 = 3.0585 loss)
I0409 21:44:55.452759 24944 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0409 21:44:59.505074 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:45:00.598963 24944 solver.cpp:218] Iteration 2328 (2.3319 iter/s, 5.14602s/12 iters), loss = 3.2584
I0409 21:45:00.599009 24944 solver.cpp:237]     Train net output #0: loss = 3.2584 (* 1 = 3.2584 loss)
I0409 21:45:00.599020 24944 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0409 21:45:05.543656 24944 solver.cpp:218] Iteration 2340 (2.42696 iter/s, 4.94446s/12 iters), loss = 3.18645
I0409 21:45:05.543704 24944 solver.cpp:237]     Train net output #0: loss = 3.18645 (* 1 = 3.18645 loss)
I0409 21:45:05.543712 24944 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0409 21:45:07.540783 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0409 21:45:18.139533 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0409 21:45:26.659155 24944 solver.cpp:330] Iteration 2346, Testing net (#0)
I0409 21:45:26.659236 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:45:30.210229 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:45:31.163910 24944 solver.cpp:397]     Test net output #0: accuracy = 0.189951
I0409 21:45:31.163949 24944 solver.cpp:397]     Test net output #1: loss = 3.46256 (* 1 = 3.46256 loss)
I0409 21:45:32.995697 24944 solver.cpp:218] Iteration 2352 (0.437142 iter/s, 27.451s/12 iters), loss = 3.23109
I0409 21:45:32.995741 24944 solver.cpp:237]     Train net output #0: loss = 3.23109 (* 1 = 3.23109 loss)
I0409 21:45:32.995751 24944 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0409 21:45:38.106479 24944 solver.cpp:218] Iteration 2364 (2.34809 iter/s, 5.11055s/12 iters), loss = 2.88221
I0409 21:45:38.106520 24944 solver.cpp:237]     Train net output #0: loss = 2.88221 (* 1 = 2.88221 loss)
I0409 21:45:38.106529 24944 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0409 21:45:42.916393 24944 solver.cpp:218] Iteration 2376 (2.49497 iter/s, 4.80969s/12 iters), loss = 3.0839
I0409 21:45:42.916450 24944 solver.cpp:237]     Train net output #0: loss = 3.0839 (* 1 = 3.0839 loss)
I0409 21:45:42.916460 24944 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0409 21:45:47.964011 24944 solver.cpp:218] Iteration 2388 (2.37748 iter/s, 5.04737s/12 iters), loss = 2.83696
I0409 21:45:47.964061 24944 solver.cpp:237]     Train net output #0: loss = 2.83696 (* 1 = 2.83696 loss)
I0409 21:45:47.964071 24944 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0409 21:45:53.148973 24944 solver.cpp:218] Iteration 2400 (2.3145 iter/s, 5.18472s/12 iters), loss = 2.67499
I0409 21:45:53.149022 24944 solver.cpp:237]     Train net output #0: loss = 2.67499 (* 1 = 2.67499 loss)
I0409 21:45:53.149032 24944 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0409 21:45:58.281430 24944 solver.cpp:218] Iteration 2412 (2.33817 iter/s, 5.13222s/12 iters), loss = 2.69295
I0409 21:45:58.281528 24944 solver.cpp:237]     Train net output #0: loss = 2.69295 (* 1 = 2.69295 loss)
I0409 21:45:58.281538 24944 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0409 21:46:03.410871 24944 solver.cpp:218] Iteration 2424 (2.33957 iter/s, 5.12915s/12 iters), loss = 3.20402
I0409 21:46:03.410914 24944 solver.cpp:237]     Train net output #0: loss = 3.20402 (* 1 = 3.20402 loss)
I0409 21:46:03.410923 24944 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0409 21:46:04.315999 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:46:07.889164 24944 solver.cpp:218] Iteration 2436 (2.67972 iter/s, 4.47808s/12 iters), loss = 3.04576
I0409 21:46:07.889212 24944 solver.cpp:237]     Train net output #0: loss = 3.04576 (* 1 = 3.04576 loss)
I0409 21:46:07.889225 24944 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0409 21:46:12.400240 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0409 21:46:23.134145 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0409 21:46:31.627128 24944 solver.cpp:330] Iteration 2448, Testing net (#0)
I0409 21:46:31.627202 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:46:35.155992 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:46:36.306650 24944 solver.cpp:397]     Test net output #0: accuracy = 0.183211
I0409 21:46:36.306689 24944 solver.cpp:397]     Test net output #1: loss = 3.45069 (* 1 = 3.45069 loss)
I0409 21:46:36.422221 24944 solver.cpp:218] Iteration 2448 (0.42058 iter/s, 28.532s/12 iters), loss = 3.11696
I0409 21:46:36.423743 24944 solver.cpp:237]     Train net output #0: loss = 3.11696 (* 1 = 3.11696 loss)
I0409 21:46:36.423754 24944 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0409 21:46:40.733317 24944 solver.cpp:218] Iteration 2460 (2.7846 iter/s, 4.30942s/12 iters), loss = 2.81728
I0409 21:46:40.733355 24944 solver.cpp:237]     Train net output #0: loss = 2.81728 (* 1 = 2.81728 loss)
I0409 21:46:40.733366 24944 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0409 21:46:45.565575 24944 solver.cpp:218] Iteration 2472 (2.48343 iter/s, 4.83203s/12 iters), loss = 2.89903
I0409 21:46:45.565629 24944 solver.cpp:237]     Train net output #0: loss = 2.89903 (* 1 = 2.89903 loss)
I0409 21:46:45.565639 24944 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0409 21:46:50.486723 24944 solver.cpp:218] Iteration 2484 (2.43857 iter/s, 4.92091s/12 iters), loss = 2.85939
I0409 21:46:50.486768 24944 solver.cpp:237]     Train net output #0: loss = 2.85939 (* 1 = 2.85939 loss)
I0409 21:46:50.486778 24944 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0409 21:46:55.505196 24944 solver.cpp:218] Iteration 2496 (2.39128 iter/s, 5.01824s/12 iters), loss = 2.89682
I0409 21:46:55.505249 24944 solver.cpp:237]     Train net output #0: loss = 2.89682 (* 1 = 2.89682 loss)
I0409 21:46:55.505259 24944 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0409 21:47:00.707866 24944 solver.cpp:218] Iteration 2508 (2.30662 iter/s, 5.20243s/12 iters), loss = 2.84891
I0409 21:47:00.707908 24944 solver.cpp:237]     Train net output #0: loss = 2.84891 (* 1 = 2.84891 loss)
I0409 21:47:00.707917 24944 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0409 21:47:05.545809 24944 solver.cpp:218] Iteration 2520 (2.48051 iter/s, 4.83772s/12 iters), loss = 2.60329
I0409 21:47:05.545907 24944 solver.cpp:237]     Train net output #0: loss = 2.60329 (* 1 = 2.60329 loss)
I0409 21:47:05.545915 24944 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0409 21:47:08.805706 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:47:10.617360 24944 solver.cpp:218] Iteration 2532 (2.36627 iter/s, 5.07126s/12 iters), loss = 3.05925
I0409 21:47:10.617403 24944 solver.cpp:237]     Train net output #0: loss = 3.05925 (* 1 = 3.05925 loss)
I0409 21:47:10.617413 24944 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0409 21:47:15.760344 24944 solver.cpp:218] Iteration 2544 (2.33338 iter/s, 5.14275s/12 iters), loss = 2.75069
I0409 21:47:15.760380 24944 solver.cpp:237]     Train net output #0: loss = 2.75069 (* 1 = 2.75069 loss)
I0409 21:47:15.760390 24944 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0409 21:47:17.640868 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0409 21:47:28.306851 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0409 21:47:41.946028 24944 solver.cpp:330] Iteration 2550, Testing net (#0)
I0409 21:47:41.946105 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:47:45.676298 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:47:46.759130 24944 solver.cpp:397]     Test net output #0: accuracy = 0.229779
I0409 21:47:46.759162 24944 solver.cpp:397]     Test net output #1: loss = 3.23194 (* 1 = 3.23194 loss)
I0409 21:47:48.450013 24944 solver.cpp:218] Iteration 2556 (0.367102 iter/s, 32.6885s/12 iters), loss = 2.90984
I0409 21:47:48.450058 24944 solver.cpp:237]     Train net output #0: loss = 2.90984 (* 1 = 2.90984 loss)
I0409 21:47:48.450067 24944 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0409 21:47:53.306635 24944 solver.cpp:218] Iteration 2568 (2.47097 iter/s, 4.8564s/12 iters), loss = 2.77571
I0409 21:47:53.306674 24944 solver.cpp:237]     Train net output #0: loss = 2.77571 (* 1 = 2.77571 loss)
I0409 21:47:53.306682 24944 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0409 21:47:58.381619 24944 solver.cpp:218] Iteration 2580 (2.36465 iter/s, 5.07475s/12 iters), loss = 2.83857
I0409 21:47:58.381664 24944 solver.cpp:237]     Train net output #0: loss = 2.83857 (* 1 = 2.83857 loss)
I0409 21:47:58.381673 24944 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0409 21:48:03.437247 24944 solver.cpp:218] Iteration 2592 (2.3737 iter/s, 5.05539s/12 iters), loss = 2.87322
I0409 21:48:03.437288 24944 solver.cpp:237]     Train net output #0: loss = 2.87322 (* 1 = 2.87322 loss)
I0409 21:48:03.437299 24944 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0409 21:48:08.332880 24944 solver.cpp:218] Iteration 2604 (2.45128 iter/s, 4.8954s/12 iters), loss = 3.01917
I0409 21:48:08.332931 24944 solver.cpp:237]     Train net output #0: loss = 3.01917 (* 1 = 3.01917 loss)
I0409 21:48:08.332942 24944 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0409 21:48:13.517050 24944 solver.cpp:218] Iteration 2616 (2.31485 iter/s, 5.18392s/12 iters), loss = 2.95512
I0409 21:48:13.517164 24944 solver.cpp:237]     Train net output #0: loss = 2.95512 (* 1 = 2.95512 loss)
I0409 21:48:13.517177 24944 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0409 21:48:18.265239 24944 solver.cpp:218] Iteration 2628 (2.52743 iter/s, 4.7479s/12 iters), loss = 2.70632
I0409 21:48:18.265290 24944 solver.cpp:237]     Train net output #0: loss = 2.70632 (* 1 = 2.70632 loss)
I0409 21:48:18.265301 24944 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0409 21:48:18.592742 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:48:22.910873 24944 solver.cpp:218] Iteration 2640 (2.5832 iter/s, 4.64541s/12 iters), loss = 2.81685
I0409 21:48:22.910920 24944 solver.cpp:237]     Train net output #0: loss = 2.81685 (* 1 = 2.81685 loss)
I0409 21:48:22.910931 24944 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0409 21:48:27.361039 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0409 21:48:38.158463 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0409 21:48:47.455624 24944 solver.cpp:330] Iteration 2652, Testing net (#0)
I0409 21:48:47.455673 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:48:50.888837 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:48:51.959417 24944 solver.cpp:397]     Test net output #0: accuracy = 0.224265
I0409 21:48:51.959466 24944 solver.cpp:397]     Test net output #1: loss = 3.15881 (* 1 = 3.15881 loss)
I0409 21:48:52.080585 24944 solver.cpp:218] Iteration 2652 (0.411401 iter/s, 29.1686s/12 iters), loss = 2.6124
I0409 21:48:52.083623 24944 solver.cpp:237]     Train net output #0: loss = 2.6124 (* 1 = 2.6124 loss)
I0409 21:48:52.083637 24944 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0409 21:48:56.500890 24944 solver.cpp:218] Iteration 2664 (2.71671 iter/s, 4.41711s/12 iters), loss = 2.6825
I0409 21:48:56.500938 24944 solver.cpp:237]     Train net output #0: loss = 2.6825 (* 1 = 2.6825 loss)
I0409 21:48:56.500949 24944 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0409 21:49:01.676854 24944 solver.cpp:218] Iteration 2676 (2.31852 iter/s, 5.17572s/12 iters), loss = 2.8641
I0409 21:49:01.676901 24944 solver.cpp:237]     Train net output #0: loss = 2.8641 (* 1 = 2.8641 loss)
I0409 21:49:01.676913 24944 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0409 21:49:06.413185 24944 solver.cpp:218] Iteration 2688 (2.53373 iter/s, 4.7361s/12 iters), loss = 2.51217
I0409 21:49:06.413239 24944 solver.cpp:237]     Train net output #0: loss = 2.51217 (* 1 = 2.51217 loss)
I0409 21:49:06.413249 24944 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0409 21:49:11.094240 24944 solver.cpp:218] Iteration 2700 (2.56365 iter/s, 4.68083s/12 iters), loss = 2.8193
I0409 21:49:11.094285 24944 solver.cpp:237]     Train net output #0: loss = 2.8193 (* 1 = 2.8193 loss)
I0409 21:49:11.094295 24944 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0409 21:49:16.130146 24944 solver.cpp:218] Iteration 2712 (2.38301 iter/s, 5.03566s/12 iters), loss = 2.52109
I0409 21:49:16.130194 24944 solver.cpp:237]     Train net output #0: loss = 2.52109 (* 1 = 2.52109 loss)
I0409 21:49:16.130203 24944 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0409 21:49:21.251982 24944 solver.cpp:218] Iteration 2724 (2.34302 iter/s, 5.1216s/12 iters), loss = 2.68672
I0409 21:49:21.252135 24944 solver.cpp:237]     Train net output #0: loss = 2.68672 (* 1 = 2.68672 loss)
I0409 21:49:21.252147 24944 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0409 21:49:23.878923 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:49:26.407442 24944 solver.cpp:218] Iteration 2736 (2.32778 iter/s, 5.15512s/12 iters), loss = 2.06905
I0409 21:49:26.407488 24944 solver.cpp:237]     Train net output #0: loss = 2.06905 (* 1 = 2.06905 loss)
I0409 21:49:26.407497 24944 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0409 21:49:31.653656 24944 solver.cpp:218] Iteration 2748 (2.28747 iter/s, 5.24597s/12 iters), loss = 2.61468
I0409 21:49:31.653700 24944 solver.cpp:237]     Train net output #0: loss = 2.61468 (* 1 = 2.61468 loss)
I0409 21:49:31.653709 24944 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0409 21:49:33.773597 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0409 21:49:44.483536 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0409 21:50:06.945243 24944 solver.cpp:330] Iteration 2754, Testing net (#0)
I0409 21:50:06.945314 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:50:10.244776 24944 blocking_queue.cpp:49] Waiting for data
I0409 21:50:10.506783 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:50:11.668354 24944 solver.cpp:397]     Test net output #0: accuracy = 0.266544
I0409 21:50:11.668406 24944 solver.cpp:397]     Test net output #1: loss = 3.01377 (* 1 = 3.01377 loss)
I0409 21:50:13.354992 24944 solver.cpp:218] Iteration 2760 (0.287771 iter/s, 41.6998s/12 iters), loss = 2.70781
I0409 21:50:13.355037 24944 solver.cpp:237]     Train net output #0: loss = 2.70781 (* 1 = 2.70781 loss)
I0409 21:50:13.355047 24944 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0409 21:50:17.996457 24944 solver.cpp:218] Iteration 2772 (2.58552 iter/s, 4.64124s/12 iters), loss = 2.43723
I0409 21:50:17.996505 24944 solver.cpp:237]     Train net output #0: loss = 2.43723 (* 1 = 2.43723 loss)
I0409 21:50:17.996515 24944 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0409 21:50:22.903446 24944 solver.cpp:218] Iteration 2784 (2.44561 iter/s, 4.90676s/12 iters), loss = 2.69817
I0409 21:50:22.903491 24944 solver.cpp:237]     Train net output #0: loss = 2.69817 (* 1 = 2.69817 loss)
I0409 21:50:22.903501 24944 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0409 21:50:27.502485 24944 solver.cpp:218] Iteration 2796 (2.60937 iter/s, 4.59882s/12 iters), loss = 2.38104
I0409 21:50:27.502532 24944 solver.cpp:237]     Train net output #0: loss = 2.38104 (* 1 = 2.38104 loss)
I0409 21:50:27.502542 24944 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0409 21:50:32.159143 24944 solver.cpp:218] Iteration 2808 (2.57708 iter/s, 4.65643s/12 iters), loss = 2.4049
I0409 21:50:32.159189 24944 solver.cpp:237]     Train net output #0: loss = 2.4049 (* 1 = 2.4049 loss)
I0409 21:50:32.159200 24944 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0409 21:50:36.928696 24944 solver.cpp:218] Iteration 2820 (2.51608 iter/s, 4.76933s/12 iters), loss = 2.34544
I0409 21:50:36.928750 24944 solver.cpp:237]     Train net output #0: loss = 2.34544 (* 1 = 2.34544 loss)
I0409 21:50:36.928761 24944 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0409 21:50:41.349999 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:50:41.680630 24944 solver.cpp:218] Iteration 2832 (2.52541 iter/s, 4.7517s/12 iters), loss = 2.33144
I0409 21:50:41.680678 24944 solver.cpp:237]     Train net output #0: loss = 2.33144 (* 1 = 2.33144 loss)
I0409 21:50:41.680688 24944 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0409 21:50:46.542356 24944 solver.cpp:218] Iteration 2844 (2.46838 iter/s, 4.86149s/12 iters), loss = 2.56123
I0409 21:50:46.542403 24944 solver.cpp:237]     Train net output #0: loss = 2.56123 (* 1 = 2.56123 loss)
I0409 21:50:46.542412 24944 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0409 21:50:50.781862 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0409 21:51:17.654623 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0409 21:51:30.278173 24944 solver.cpp:330] Iteration 2856, Testing net (#0)
I0409 21:51:30.278200 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:51:33.529075 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:51:34.682200 24944 solver.cpp:397]     Test net output #0: accuracy = 0.256127
I0409 21:51:34.682248 24944 solver.cpp:397]     Test net output #1: loss = 3.03158 (* 1 = 3.03158 loss)
I0409 21:51:34.803347 24944 solver.cpp:218] Iteration 2856 (0.248657 iter/s, 48.2592s/12 iters), loss = 2.31813
I0409 21:51:34.804883 24944 solver.cpp:237]     Train net output #0: loss = 2.31813 (* 1 = 2.31813 loss)
I0409 21:51:34.804900 24944 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0409 21:51:38.848654 24944 solver.cpp:218] Iteration 2868 (2.96763 iter/s, 4.04363s/12 iters), loss = 2.57522
I0409 21:51:38.848695 24944 solver.cpp:237]     Train net output #0: loss = 2.57522 (* 1 = 2.57522 loss)
I0409 21:51:38.848703 24944 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0409 21:51:43.999555 24944 solver.cpp:218] Iteration 2880 (2.3298 iter/s, 5.15066s/12 iters), loss = 2.38814
I0409 21:51:43.999598 24944 solver.cpp:237]     Train net output #0: loss = 2.38814 (* 1 = 2.38814 loss)
I0409 21:51:43.999608 24944 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0409 21:51:49.223677 24944 solver.cpp:218] Iteration 2892 (2.29715 iter/s, 5.22388s/12 iters), loss = 2.36226
I0409 21:51:49.223780 24944 solver.cpp:237]     Train net output #0: loss = 2.36226 (* 1 = 2.36226 loss)
I0409 21:51:49.223791 24944 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0409 21:51:54.124953 24944 solver.cpp:218] Iteration 2904 (2.44848 iter/s, 4.90099s/12 iters), loss = 2.52422
I0409 21:51:54.124994 24944 solver.cpp:237]     Train net output #0: loss = 2.52422 (* 1 = 2.52422 loss)
I0409 21:51:54.125001 24944 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0409 21:51:59.304078 24944 solver.cpp:218] Iteration 2916 (2.3171 iter/s, 5.17889s/12 iters), loss = 2.30334
I0409 21:51:59.304132 24944 solver.cpp:237]     Train net output #0: loss = 2.30334 (* 1 = 2.30334 loss)
I0409 21:51:59.304144 24944 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0409 21:52:04.553040 24944 solver.cpp:218] Iteration 2928 (2.28628 iter/s, 5.2487s/12 iters), loss = 2.31915
I0409 21:52:04.553090 24944 solver.cpp:237]     Train net output #0: loss = 2.31915 (* 1 = 2.31915 loss)
I0409 21:52:04.553102 24944 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0409 21:52:06.475674 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:52:09.755812 24944 solver.cpp:218] Iteration 2940 (2.30657 iter/s, 5.20253s/12 iters), loss = 2.09593
I0409 21:52:09.755858 24944 solver.cpp:237]     Train net output #0: loss = 2.09593 (* 1 = 2.09593 loss)
I0409 21:52:09.755869 24944 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0409 21:52:15.015064 24944 solver.cpp:218] Iteration 2952 (2.2818 iter/s, 5.25901s/12 iters), loss = 2.34678
I0409 21:52:15.015108 24944 solver.cpp:237]     Train net output #0: loss = 2.34678 (* 1 = 2.34678 loss)
I0409 21:52:15.015117 24944 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0409 21:52:17.099527 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0409 21:52:27.787961 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0409 21:52:37.606003 24944 solver.cpp:330] Iteration 2958, Testing net (#0)
I0409 21:52:37.606026 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:52:40.824649 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:52:42.031760 24944 solver.cpp:397]     Test net output #0: accuracy = 0.300245
I0409 21:52:42.031795 24944 solver.cpp:397]     Test net output #1: loss = 2.91592 (* 1 = 2.91592 loss)
I0409 21:52:43.884068 24944 solver.cpp:218] Iteration 2964 (0.415686 iter/s, 28.8679s/12 iters), loss = 2.26986
I0409 21:52:43.884121 24944 solver.cpp:237]     Train net output #0: loss = 2.26986 (* 1 = 2.26986 loss)
I0409 21:52:43.884133 24944 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0409 21:52:48.723860 24944 solver.cpp:218] Iteration 2976 (2.47956 iter/s, 4.83956s/12 iters), loss = 2.23646
I0409 21:52:48.723901 24944 solver.cpp:237]     Train net output #0: loss = 2.23646 (* 1 = 2.23646 loss)
I0409 21:52:48.723909 24944 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0409 21:52:53.550649 24944 solver.cpp:218] Iteration 2988 (2.48624 iter/s, 4.82656s/12 iters), loss = 2.18171
I0409 21:52:53.550695 24944 solver.cpp:237]     Train net output #0: loss = 2.18171 (* 1 = 2.18171 loss)
I0409 21:52:53.550704 24944 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0409 21:52:58.722853 24944 solver.cpp:218] Iteration 3000 (2.3202 iter/s, 5.17196s/12 iters), loss = 2.37468
I0409 21:52:58.723587 24944 solver.cpp:237]     Train net output #0: loss = 2.37468 (* 1 = 2.37468 loss)
I0409 21:52:58.723598 24944 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0409 21:53:03.362329 24944 solver.cpp:218] Iteration 3012 (2.58701 iter/s, 4.63857s/12 iters), loss = 2.35909
I0409 21:53:03.362373 24944 solver.cpp:237]     Train net output #0: loss = 2.35909 (* 1 = 2.35909 loss)
I0409 21:53:03.362383 24944 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0409 21:53:08.066138 24944 solver.cpp:218] Iteration 3024 (2.55124 iter/s, 4.70359s/12 iters), loss = 2.00548
I0409 21:53:08.066180 24944 solver.cpp:237]     Train net output #0: loss = 2.00548 (* 1 = 2.00548 loss)
I0409 21:53:08.066190 24944 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0409 21:53:11.750049 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:53:12.774055 24944 solver.cpp:218] Iteration 3036 (2.54902 iter/s, 4.7077s/12 iters), loss = 2.12996
I0409 21:53:12.774104 24944 solver.cpp:237]     Train net output #0: loss = 2.12996 (* 1 = 2.12996 loss)
I0409 21:53:12.774116 24944 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0409 21:53:17.571063 24944 solver.cpp:218] Iteration 3048 (2.50168 iter/s, 4.79677s/12 iters), loss = 2.31328
I0409 21:53:17.571111 24944 solver.cpp:237]     Train net output #0: loss = 2.31328 (* 1 = 2.31328 loss)
I0409 21:53:17.571122 24944 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0409 21:53:22.309548 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0409 21:53:33.686079 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0409 21:53:46.497153 24944 solver.cpp:330] Iteration 3060, Testing net (#0)
I0409 21:53:46.497175 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:53:49.782860 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:53:51.015935 24944 solver.cpp:397]     Test net output #0: accuracy = 0.303922
I0409 21:53:51.015974 24944 solver.cpp:397]     Test net output #1: loss = 2.80936 (* 1 = 2.80936 loss)
I0409 21:53:51.136960 24944 solver.cpp:218] Iteration 3060 (0.357519 iter/s, 33.5647s/12 iters), loss = 2.1784
I0409 21:53:51.138494 24944 solver.cpp:237]     Train net output #0: loss = 2.1784 (* 1 = 2.1784 loss)
I0409 21:53:51.138506 24944 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0409 21:53:55.205726 24944 solver.cpp:218] Iteration 3072 (2.95051 iter/s, 4.06709s/12 iters), loss = 2.02191
I0409 21:53:55.205754 24944 solver.cpp:237]     Train net output #0: loss = 2.02191 (* 1 = 2.02191 loss)
I0409 21:53:55.205761 24944 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0409 21:53:59.779042 24944 solver.cpp:218] Iteration 3084 (2.62404 iter/s, 4.57311s/12 iters), loss = 2.29144
I0409 21:53:59.779088 24944 solver.cpp:237]     Train net output #0: loss = 2.29144 (* 1 = 2.29144 loss)
I0409 21:53:59.779099 24944 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0409 21:54:04.649586 24944 solver.cpp:218] Iteration 3096 (2.46391 iter/s, 4.87032s/12 iters), loss = 2.09155
I0409 21:54:04.649695 24944 solver.cpp:237]     Train net output #0: loss = 2.09155 (* 1 = 2.09155 loss)
I0409 21:54:04.649706 24944 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0409 21:54:09.887044 24944 solver.cpp:218] Iteration 3108 (2.29132 iter/s, 5.23715s/12 iters), loss = 2.00717
I0409 21:54:09.887099 24944 solver.cpp:237]     Train net output #0: loss = 2.00717 (* 1 = 2.00717 loss)
I0409 21:54:09.887109 24944 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0409 21:54:14.715624 24944 solver.cpp:218] Iteration 3120 (2.48533 iter/s, 4.82834s/12 iters), loss = 1.73206
I0409 21:54:14.715672 24944 solver.cpp:237]     Train net output #0: loss = 1.73206 (* 1 = 1.73206 loss)
I0409 21:54:14.715683 24944 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0409 21:54:19.419152 24944 solver.cpp:218] Iteration 3132 (2.5514 iter/s, 4.7033s/12 iters), loss = 2.14796
I0409 21:54:19.419203 24944 solver.cpp:237]     Train net output #0: loss = 2.14796 (* 1 = 2.14796 loss)
I0409 21:54:19.419214 24944 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0409 21:54:20.393038 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:54:23.994695 24944 solver.cpp:218] Iteration 3144 (2.62277 iter/s, 4.57532s/12 iters), loss = 1.78334
I0409 21:54:23.994740 24944 solver.cpp:237]     Train net output #0: loss = 1.78334 (* 1 = 1.78334 loss)
I0409 21:54:23.994750 24944 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0409 21:54:29.111862 24944 solver.cpp:218] Iteration 3156 (2.34516 iter/s, 5.11693s/12 iters), loss = 2.13586
I0409 21:54:29.111909 24944 solver.cpp:237]     Train net output #0: loss = 2.13586 (* 1 = 2.13586 loss)
I0409 21:54:29.111919 24944 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0409 21:54:31.197208 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0409 21:54:44.405887 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0409 21:54:52.906719 24944 solver.cpp:330] Iteration 3162, Testing net (#0)
I0409 21:54:52.906744 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:54:56.275065 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:54:57.592590 24944 solver.cpp:397]     Test net output #0: accuracy = 0.326593
I0409 21:54:57.592630 24944 solver.cpp:397]     Test net output #1: loss = 2.66352 (* 1 = 2.66352 loss)
I0409 21:54:59.418732 24944 solver.cpp:218] Iteration 3168 (0.395964 iter/s, 30.3058s/12 iters), loss = 1.79864
I0409 21:54:59.418776 24944 solver.cpp:237]     Train net output #0: loss = 1.79864 (* 1 = 1.79864 loss)
I0409 21:54:59.418785 24944 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0409 21:55:04.423542 24944 solver.cpp:218] Iteration 3180 (2.3978 iter/s, 5.00459s/12 iters), loss = 2.17527
I0409 21:55:04.423588 24944 solver.cpp:237]     Train net output #0: loss = 2.17527 (* 1 = 2.17527 loss)
I0409 21:55:04.423597 24944 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0409 21:55:09.624056 24944 solver.cpp:218] Iteration 3192 (2.30757 iter/s, 5.20028s/12 iters), loss = 1.79448
I0409 21:55:09.624105 24944 solver.cpp:237]     Train net output #0: loss = 1.79448 (* 1 = 1.79448 loss)
I0409 21:55:09.624116 24944 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0409 21:55:14.761662 24944 solver.cpp:218] Iteration 3204 (2.33582 iter/s, 5.13737s/12 iters), loss = 1.93423
I0409 21:55:14.761791 24944 solver.cpp:237]     Train net output #0: loss = 1.93423 (* 1 = 1.93423 loss)
I0409 21:55:14.761801 24944 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0409 21:55:19.776094 24944 solver.cpp:218] Iteration 3216 (2.39324 iter/s, 5.01412s/12 iters), loss = 2.08267
I0409 21:55:19.776149 24944 solver.cpp:237]     Train net output #0: loss = 2.08267 (* 1 = 2.08267 loss)
I0409 21:55:19.776162 24944 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0409 21:55:24.929930 24944 solver.cpp:218] Iteration 3228 (2.32847 iter/s, 5.1536s/12 iters), loss = 1.73621
I0409 21:55:24.929984 24944 solver.cpp:237]     Train net output #0: loss = 1.73621 (* 1 = 1.73621 loss)
I0409 21:55:24.929992 24944 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0409 21:55:28.292865 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:55:30.164564 24944 solver.cpp:218] Iteration 3240 (2.29253 iter/s, 5.23439s/12 iters), loss = 1.78105
I0409 21:55:30.164614 24944 solver.cpp:237]     Train net output #0: loss = 1.78105 (* 1 = 1.78105 loss)
I0409 21:55:30.164625 24944 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0409 21:55:35.186722 24944 solver.cpp:218] Iteration 3252 (2.38952 iter/s, 5.02192s/12 iters), loss = 1.90952
I0409 21:55:35.186781 24944 solver.cpp:237]     Train net output #0: loss = 1.90952 (* 1 = 1.90952 loss)
I0409 21:55:35.186796 24944 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0409 21:55:39.871840 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0409 21:55:51.101383 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0409 21:56:03.912365 24944 solver.cpp:330] Iteration 3264, Testing net (#0)
I0409 21:56:03.912386 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:56:07.186045 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:56:08.503868 24944 solver.cpp:397]     Test net output #0: accuracy = 0.36152
I0409 21:56:08.503916 24944 solver.cpp:397]     Test net output #1: loss = 2.57046 (* 1 = 2.57046 loss)
I0409 21:56:08.625008 24944 solver.cpp:218] Iteration 3264 (0.358883 iter/s, 33.4371s/12 iters), loss = 2.01295
I0409 21:56:08.626539 24944 solver.cpp:237]     Train net output #0: loss = 2.01295 (* 1 = 2.01295 loss)
I0409 21:56:08.626557 24944 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0409 21:56:12.954514 24944 solver.cpp:218] Iteration 3276 (2.77275 iter/s, 4.32783s/12 iters), loss = 1.69271
I0409 21:56:12.954555 24944 solver.cpp:237]     Train net output #0: loss = 1.69271 (* 1 = 1.69271 loss)
I0409 21:56:12.954564 24944 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0409 21:56:18.040004 24944 solver.cpp:218] Iteration 3288 (2.35976 iter/s, 5.08526s/12 iters), loss = 2.05229
I0409 21:56:18.040053 24944 solver.cpp:237]     Train net output #0: loss = 2.05229 (* 1 = 2.05229 loss)
I0409 21:56:18.040063 24944 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0409 21:56:23.008009 24944 solver.cpp:218] Iteration 3300 (2.41557 iter/s, 4.96778s/12 iters), loss = 1.889
I0409 21:56:23.008131 24944 solver.cpp:237]     Train net output #0: loss = 1.889 (* 1 = 1.889 loss)
I0409 21:56:23.008143 24944 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0409 21:56:27.879756 24944 solver.cpp:218] Iteration 3312 (2.46333 iter/s, 4.87145s/12 iters), loss = 2.23911
I0409 21:56:27.879806 24944 solver.cpp:237]     Train net output #0: loss = 2.23911 (* 1 = 2.23911 loss)
I0409 21:56:27.879817 24944 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0409 21:56:33.030912 24944 solver.cpp:218] Iteration 3324 (2.32968 iter/s, 5.15092s/12 iters), loss = 1.76799
I0409 21:56:33.030962 24944 solver.cpp:237]     Train net output #0: loss = 1.76799 (* 1 = 1.76799 loss)
I0409 21:56:33.030974 24944 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0409 21:56:38.028708 24944 solver.cpp:218] Iteration 3336 (2.40117 iter/s, 4.99757s/12 iters), loss = 1.64758
I0409 21:56:38.028751 24944 solver.cpp:237]     Train net output #0: loss = 1.64758 (* 1 = 1.64758 loss)
I0409 21:56:38.028760 24944 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0409 21:56:38.433562 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:56:42.788218 24944 solver.cpp:218] Iteration 3348 (2.52138 iter/s, 4.75929s/12 iters), loss = 2.09755
I0409 21:56:42.788264 24944 solver.cpp:237]     Train net output #0: loss = 2.09755 (* 1 = 2.09755 loss)
I0409 21:56:42.788273 24944 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0409 21:56:47.474802 24944 solver.cpp:218] Iteration 3360 (2.56062 iter/s, 4.68637s/12 iters), loss = 1.78744
I0409 21:56:47.474848 24944 solver.cpp:237]     Train net output #0: loss = 1.78744 (* 1 = 1.78744 loss)
I0409 21:56:47.474858 24944 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0409 21:56:49.346290 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0409 21:57:00.008932 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0409 21:57:11.019901 24944 solver.cpp:330] Iteration 3366, Testing net (#0)
I0409 21:57:11.019922 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:57:14.161252 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:57:15.540772 24944 solver.cpp:397]     Test net output #0: accuracy = 0.370711
I0409 21:57:15.540824 24944 solver.cpp:397]     Test net output #1: loss = 2.58201 (* 1 = 2.58201 loss)
I0409 21:57:17.394131 24944 solver.cpp:218] Iteration 3372 (0.401093 iter/s, 29.9183s/12 iters), loss = 1.54789
I0409 21:57:17.394178 24944 solver.cpp:237]     Train net output #0: loss = 1.54789 (* 1 = 1.54789 loss)
I0409 21:57:17.394188 24944 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0409 21:57:22.593910 24944 solver.cpp:218] Iteration 3384 (2.3079 iter/s, 5.19954s/12 iters), loss = 1.93324
I0409 21:57:22.593952 24944 solver.cpp:237]     Train net output #0: loss = 1.93324 (* 1 = 1.93324 loss)
I0409 21:57:22.594010 24944 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0409 21:57:27.352288 24944 solver.cpp:218] Iteration 3396 (2.52198 iter/s, 4.75816s/12 iters), loss = 1.77305
I0409 21:57:27.352335 24944 solver.cpp:237]     Train net output #0: loss = 1.77305 (* 1 = 1.77305 loss)
I0409 21:57:27.352346 24944 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0409 21:57:32.731703 24944 solver.cpp:218] Iteration 3408 (2.23083 iter/s, 5.37917s/12 iters), loss = 1.72351
I0409 21:57:32.731809 24944 solver.cpp:237]     Train net output #0: loss = 1.72351 (* 1 = 1.72351 loss)
I0409 21:57:32.731822 24944 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0409 21:57:37.906420 24944 solver.cpp:218] Iteration 3420 (2.3191 iter/s, 5.17442s/12 iters), loss = 1.54475
I0409 21:57:37.906469 24944 solver.cpp:237]     Train net output #0: loss = 1.54475 (* 1 = 1.54475 loss)
I0409 21:57:37.906481 24944 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0409 21:57:43.025624 24944 solver.cpp:218] Iteration 3432 (2.34422 iter/s, 5.11897s/12 iters), loss = 1.78075
I0409 21:57:43.025669 24944 solver.cpp:237]     Train net output #0: loss = 1.78075 (* 1 = 1.78075 loss)
I0409 21:57:43.025681 24944 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0409 21:57:45.546347 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:57:47.969007 24944 solver.cpp:218] Iteration 3444 (2.4276 iter/s, 4.94316s/12 iters), loss = 1.4152
I0409 21:57:47.969053 24944 solver.cpp:237]     Train net output #0: loss = 1.4152 (* 1 = 1.4152 loss)
I0409 21:57:47.969063 24944 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0409 21:57:53.177105 24944 solver.cpp:218] Iteration 3456 (2.30421 iter/s, 5.20786s/12 iters), loss = 1.85808
I0409 21:57:53.177151 24944 solver.cpp:237]     Train net output #0: loss = 1.85808 (* 1 = 1.85808 loss)
I0409 21:57:53.177160 24944 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0409 21:57:57.442437 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0409 21:58:08.251981 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0409 21:58:38.115061 24944 solver.cpp:330] Iteration 3468, Testing net (#0)
I0409 21:58:38.115083 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:58:38.598807 24944 blocking_queue.cpp:49] Waiting for data
I0409 21:58:41.450963 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:58:42.961833 24944 solver.cpp:397]     Test net output #0: accuracy = 0.366422
I0409 21:58:42.961879 24944 solver.cpp:397]     Test net output #1: loss = 2.61714 (* 1 = 2.61714 loss)
I0409 21:58:43.083288 24944 solver.cpp:218] Iteration 3468 (0.24046 iter/s, 49.9044s/12 iters), loss = 1.78067
I0409 21:58:43.084813 24944 solver.cpp:237]     Train net output #0: loss = 1.78067 (* 1 = 1.78067 loss)
I0409 21:58:43.084825 24944 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0409 21:58:47.392967 24944 solver.cpp:218] Iteration 3480 (2.78552 iter/s, 4.30799s/12 iters), loss = 1.60833
I0409 21:58:47.393018 24944 solver.cpp:237]     Train net output #0: loss = 1.60833 (* 1 = 1.60833 loss)
I0409 21:58:47.393028 24944 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0409 21:58:52.063637 24944 solver.cpp:218] Iteration 3492 (2.56935 iter/s, 4.67044s/12 iters), loss = 1.65744
I0409 21:58:52.063683 24944 solver.cpp:237]     Train net output #0: loss = 1.65744 (* 1 = 1.65744 loss)
I0409 21:58:52.063694 24944 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0409 21:58:56.698671 24944 solver.cpp:218] Iteration 3504 (2.5891 iter/s, 4.63482s/12 iters), loss = 1.68893
I0409 21:58:56.698714 24944 solver.cpp:237]     Train net output #0: loss = 1.68893 (* 1 = 1.68893 loss)
I0409 21:58:56.698724 24944 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0409 21:59:01.907016 24944 solver.cpp:218] Iteration 3516 (2.3041 iter/s, 5.20811s/12 iters), loss = 1.72651
I0409 21:59:01.907073 24944 solver.cpp:237]     Train net output #0: loss = 1.72651 (* 1 = 1.72651 loss)
I0409 21:59:01.907084 24944 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0409 21:59:07.114262 24944 solver.cpp:218] Iteration 3528 (2.30459 iter/s, 5.20699s/12 iters), loss = 1.91198
I0409 21:59:07.114316 24944 solver.cpp:237]     Train net output #0: loss = 1.91198 (* 1 = 1.91198 loss)
I0409 21:59:07.114327 24944 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0409 21:59:11.828451 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:59:12.130625 24944 solver.cpp:218] Iteration 3540 (2.39229 iter/s, 5.01612s/12 iters), loss = 1.53707
I0409 21:59:12.130673 24944 solver.cpp:237]     Train net output #0: loss = 1.53707 (* 1 = 1.53707 loss)
I0409 21:59:12.130686 24944 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0409 21:59:17.289938 24944 solver.cpp:218] Iteration 3552 (2.326 iter/s, 5.15907s/12 iters), loss = 1.61749
I0409 21:59:17.290004 24944 solver.cpp:237]     Train net output #0: loss = 1.61749 (* 1 = 1.61749 loss)
I0409 21:59:17.290014 24944 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0409 21:59:21.878396 24944 solver.cpp:218] Iteration 3564 (2.61539 iter/s, 4.58822s/12 iters), loss = 1.33419
I0409 21:59:21.878443 24944 solver.cpp:237]     Train net output #0: loss = 1.33419 (* 1 = 1.33419 loss)
I0409 21:59:21.878450 24944 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0409 21:59:23.746279 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0409 21:59:37.982306 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0409 21:59:46.649237 24944 solver.cpp:330] Iteration 3570, Testing net (#0)
I0409 21:59:46.649312 24944 net.cpp:676] Ignoring source layer train-data
I0409 21:59:49.754325 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:59:51.188711 24944 solver.cpp:397]     Test net output #0: accuracy = 0.38848
I0409 21:59:51.188760 24944 solver.cpp:397]     Test net output #1: loss = 2.58473 (* 1 = 2.58473 loss)
I0409 21:59:53.031646 24944 solver.cpp:218] Iteration 3576 (0.385206 iter/s, 31.1521s/12 iters), loss = 1.71474
I0409 21:59:53.031700 24944 solver.cpp:237]     Train net output #0: loss = 1.71474 (* 1 = 1.71474 loss)
I0409 21:59:53.031711 24944 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0409 21:59:57.895484 24944 solver.cpp:218] Iteration 3588 (2.46731 iter/s, 4.8636s/12 iters), loss = 1.50269
I0409 21:59:57.895524 24944 solver.cpp:237]     Train net output #0: loss = 1.50269 (* 1 = 1.50269 loss)
I0409 21:59:57.895534 24944 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0409 22:00:02.554811 24944 solver.cpp:218] Iteration 3600 (2.5756 iter/s, 4.65911s/12 iters), loss = 1.89011
I0409 22:00:02.554863 24944 solver.cpp:237]     Train net output #0: loss = 1.89011 (* 1 = 1.89011 loss)
I0409 22:00:02.554875 24944 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0409 22:00:07.280105 24944 solver.cpp:218] Iteration 3612 (2.53965 iter/s, 4.72506s/12 iters), loss = 1.4538
I0409 22:00:07.280156 24944 solver.cpp:237]     Train net output #0: loss = 1.4538 (* 1 = 1.4538 loss)
I0409 22:00:07.280166 24944 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0409 22:00:11.966137 24944 solver.cpp:218] Iteration 3624 (2.56093 iter/s, 4.6858s/12 iters), loss = 1.78754
I0409 22:00:11.966187 24944 solver.cpp:237]     Train net output #0: loss = 1.78754 (* 1 = 1.78754 loss)
I0409 22:00:11.966197 24944 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0409 22:00:16.989787 24944 solver.cpp:218] Iteration 3636 (2.38881 iter/s, 5.02341s/12 iters), loss = 1.51823
I0409 22:00:16.989886 24944 solver.cpp:237]     Train net output #0: loss = 1.51823 (* 1 = 1.51823 loss)
I0409 22:00:16.989897 24944 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0409 22:00:18.910497 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:00:22.188686 24944 solver.cpp:218] Iteration 3648 (2.30831 iter/s, 5.19861s/12 iters), loss = 1.50338
I0409 22:00:22.188726 24944 solver.cpp:237]     Train net output #0: loss = 1.50338 (* 1 = 1.50338 loss)
I0409 22:00:22.188735 24944 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0409 22:00:27.334476 24944 solver.cpp:218] Iteration 3660 (2.33211 iter/s, 5.14556s/12 iters), loss = 1.62721
I0409 22:00:27.334524 24944 solver.cpp:237]     Train net output #0: loss = 1.62721 (* 1 = 1.62721 loss)
I0409 22:00:27.334533 24944 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0409 22:00:32.058420 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0409 22:00:44.506152 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0409 22:01:01.616847 24944 solver.cpp:330] Iteration 3672, Testing net (#0)
I0409 22:01:01.616914 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:01:04.613220 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:01:06.095281 24944 solver.cpp:397]     Test net output #0: accuracy = 0.385417
I0409 22:01:06.095326 24944 solver.cpp:397]     Test net output #1: loss = 2.61496 (* 1 = 2.61496 loss)
I0409 22:01:06.216488 24944 solver.cpp:218] Iteration 3672 (0.308637 iter/s, 38.8806s/12 iters), loss = 1.28538
I0409 22:01:06.218045 24944 solver.cpp:237]     Train net output #0: loss = 1.28538 (* 1 = 1.28538 loss)
I0409 22:01:06.218056 24944 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0409 22:01:10.181521 24944 solver.cpp:218] Iteration 3684 (3.02776 iter/s, 3.96333s/12 iters), loss = 1.5544
I0409 22:01:10.181571 24944 solver.cpp:237]     Train net output #0: loss = 1.5544 (* 1 = 1.5544 loss)
I0409 22:01:10.181582 24944 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0409 22:01:14.825129 24944 solver.cpp:218] Iteration 3696 (2.58432 iter/s, 4.64339s/12 iters), loss = 1.13716
I0409 22:01:14.825170 24944 solver.cpp:237]     Train net output #0: loss = 1.13716 (* 1 = 1.13716 loss)
I0409 22:01:14.825181 24944 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0409 22:01:19.462311 24944 solver.cpp:218] Iteration 3708 (2.5879 iter/s, 4.63696s/12 iters), loss = 1.73496
I0409 22:01:19.462373 24944 solver.cpp:237]     Train net output #0: loss = 1.73496 (* 1 = 1.73496 loss)
I0409 22:01:19.462385 24944 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0409 22:01:24.034335 24944 solver.cpp:218] Iteration 3720 (2.62479 iter/s, 4.57179s/12 iters), loss = 1.72723
I0409 22:01:24.034381 24944 solver.cpp:237]     Train net output #0: loss = 1.72723 (* 1 = 1.72723 loss)
I0409 22:01:24.034389 24944 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0409 22:01:28.644249 24944 solver.cpp:218] Iteration 3732 (2.60321 iter/s, 4.60969s/12 iters), loss = 1.23981
I0409 22:01:28.644296 24944 solver.cpp:237]     Train net output #0: loss = 1.23981 (* 1 = 1.23981 loss)
I0409 22:01:28.644305 24944 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0409 22:01:32.361138 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:01:33.312662 24944 solver.cpp:218] Iteration 3744 (2.57059 iter/s, 4.66819s/12 iters), loss = 1.47147
I0409 22:01:33.312716 24944 solver.cpp:237]     Train net output #0: loss = 1.47147 (* 1 = 1.47147 loss)
I0409 22:01:33.312726 24944 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0409 22:01:37.921934 24944 solver.cpp:218] Iteration 3756 (2.60358 iter/s, 4.60905s/12 iters), loss = 1.71446
I0409 22:01:37.922010 24944 solver.cpp:237]     Train net output #0: loss = 1.71446 (* 1 = 1.71446 loss)
I0409 22:01:37.922020 24944 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0409 22:01:42.641225 24944 solver.cpp:218] Iteration 3768 (2.54289 iter/s, 4.71904s/12 iters), loss = 1.46208
I0409 22:01:42.641273 24944 solver.cpp:237]     Train net output #0: loss = 1.46208 (* 1 = 1.46208 loss)
I0409 22:01:42.641283 24944 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0409 22:01:44.434885 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0409 22:01:56.897670 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0409 22:02:07.423099 24944 solver.cpp:330] Iteration 3774, Testing net (#0)
I0409 22:02:07.423172 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:02:10.441012 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:02:12.148535 24944 solver.cpp:397]     Test net output #0: accuracy = 0.404412
I0409 22:02:12.148586 24944 solver.cpp:397]     Test net output #1: loss = 2.46283 (* 1 = 2.46283 loss)
I0409 22:02:13.958562 24944 solver.cpp:218] Iteration 3780 (0.383188 iter/s, 31.3162s/12 iters), loss = 1.38542
I0409 22:02:13.958616 24944 solver.cpp:237]     Train net output #0: loss = 1.38542 (* 1 = 1.38542 loss)
I0409 22:02:13.958628 24944 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0409 22:02:18.909942 24944 solver.cpp:218] Iteration 3792 (2.42368 iter/s, 4.95114s/12 iters), loss = 1.49795
I0409 22:02:18.910006 24944 solver.cpp:237]     Train net output #0: loss = 1.49795 (* 1 = 1.49795 loss)
I0409 22:02:18.910017 24944 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0409 22:02:23.463480 24944 solver.cpp:218] Iteration 3804 (2.63545 iter/s, 4.55331s/12 iters), loss = 1.67458
I0409 22:02:23.463528 24944 solver.cpp:237]     Train net output #0: loss = 1.67458 (* 1 = 1.67458 loss)
I0409 22:02:23.463538 24944 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0409 22:02:28.441522 24944 solver.cpp:218] Iteration 3816 (2.4107 iter/s, 4.97781s/12 iters), loss = 1.21259
I0409 22:02:28.441565 24944 solver.cpp:237]     Train net output #0: loss = 1.21259 (* 1 = 1.21259 loss)
I0409 22:02:28.441573 24944 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0409 22:02:33.651854 24944 solver.cpp:218] Iteration 3828 (2.30322 iter/s, 5.2101s/12 iters), loss = 1.2802
I0409 22:02:33.651892 24944 solver.cpp:237]     Train net output #0: loss = 1.2802 (* 1 = 1.2802 loss)
I0409 22:02:33.651901 24944 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0409 22:02:38.791893 24944 solver.cpp:218] Iteration 3840 (2.33472 iter/s, 5.1398s/12 iters), loss = 1.26489
I0409 22:02:38.792042 24944 solver.cpp:237]     Train net output #0: loss = 1.26489 (* 1 = 1.26489 loss)
I0409 22:02:38.792055 24944 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0409 22:02:39.792143 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:02:43.347373 24944 solver.cpp:218] Iteration 3852 (2.63437 iter/s, 4.55517s/12 iters), loss = 1.43595
I0409 22:02:43.347416 24944 solver.cpp:237]     Train net output #0: loss = 1.43595 (* 1 = 1.43595 loss)
I0409 22:02:43.347429 24944 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0409 22:02:47.915714 24944 solver.cpp:218] Iteration 3864 (2.6269 iter/s, 4.56812s/12 iters), loss = 1.61275
I0409 22:02:47.915760 24944 solver.cpp:237]     Train net output #0: loss = 1.61275 (* 1 = 1.61275 loss)
I0409 22:02:47.915768 24944 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0409 22:02:52.015715 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0409 22:03:04.706251 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0409 22:03:23.004603 24944 solver.cpp:330] Iteration 3876, Testing net (#0)
I0409 22:03:23.004683 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:03:25.902580 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:03:27.478250 24944 solver.cpp:397]     Test net output #0: accuracy = 0.38848
I0409 22:03:27.478299 24944 solver.cpp:397]     Test net output #1: loss = 2.59801 (* 1 = 2.59801 loss)
I0409 22:03:27.599331 24944 solver.cpp:218] Iteration 3876 (0.302403 iter/s, 39.6822s/12 iters), loss = 1.18608
I0409 22:03:27.600865 24944 solver.cpp:237]     Train net output #0: loss = 1.18608 (* 1 = 1.18608 loss)
I0409 22:03:27.600878 24944 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0409 22:03:31.812121 24944 solver.cpp:218] Iteration 3888 (2.84961 iter/s, 4.2111s/12 iters), loss = 1.19529
I0409 22:03:31.812163 24944 solver.cpp:237]     Train net output #0: loss = 1.19529 (* 1 = 1.19529 loss)
I0409 22:03:31.812173 24944 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0409 22:03:36.856602 24944 solver.cpp:218] Iteration 3900 (2.37895 iter/s, 5.04425s/12 iters), loss = 1.50781
I0409 22:03:36.856652 24944 solver.cpp:237]     Train net output #0: loss = 1.50781 (* 1 = 1.50781 loss)
I0409 22:03:36.856663 24944 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0409 22:03:42.021996 24944 solver.cpp:218] Iteration 3912 (2.32326 iter/s, 5.16515s/12 iters), loss = 1.18216
I0409 22:03:42.022047 24944 solver.cpp:237]     Train net output #0: loss = 1.18216 (* 1 = 1.18216 loss)
I0409 22:03:42.022055 24944 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0409 22:03:47.222853 24944 solver.cpp:218] Iteration 3924 (2.30742 iter/s, 5.20061s/12 iters), loss = 1.17571
I0409 22:03:47.222901 24944 solver.cpp:237]     Train net output #0: loss = 1.17571 (* 1 = 1.17571 loss)
I0409 22:03:47.222913 24944 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0409 22:03:52.457520 24944 solver.cpp:218] Iteration 3936 (2.29252 iter/s, 5.23442s/12 iters), loss = 1.21277
I0409 22:03:52.457567 24944 solver.cpp:237]     Train net output #0: loss = 1.21277 (* 1 = 1.21277 loss)
I0409 22:03:52.457581 24944 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0409 22:03:55.915338 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:03:57.685714 24944 solver.cpp:218] Iteration 3948 (2.29536 iter/s, 5.22795s/12 iters), loss = 1.40016
I0409 22:03:57.685765 24944 solver.cpp:237]     Train net output #0: loss = 1.40016 (* 1 = 1.40016 loss)
I0409 22:03:57.685777 24944 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0409 22:04:02.592996 24944 solver.cpp:218] Iteration 3960 (2.44546 iter/s, 4.90705s/12 iters), loss = 1.39451
I0409 22:04:02.593044 24944 solver.cpp:237]     Train net output #0: loss = 1.39451 (* 1 = 1.39451 loss)
I0409 22:04:02.593055 24944 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0409 22:04:07.289053 24944 solver.cpp:218] Iteration 3972 (2.55546 iter/s, 4.69583s/12 iters), loss = 1.30265
I0409 22:04:07.289104 24944 solver.cpp:237]     Train net output #0: loss = 1.30265 (* 1 = 1.30265 loss)
I0409 22:04:07.289115 24944 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0409 22:04:09.218231 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0409 22:04:29.119784 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0409 22:04:40.392964 24944 solver.cpp:330] Iteration 3978, Testing net (#0)
I0409 22:04:40.392989 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:04:43.270555 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:04:44.894646 24944 solver.cpp:397]     Test net output #0: accuracy = 0.400735
I0409 22:04:44.894691 24944 solver.cpp:397]     Test net output #1: loss = 2.55859 (* 1 = 2.55859 loss)
I0409 22:04:46.713275 24944 solver.cpp:218] Iteration 3984 (0.304392 iter/s, 39.4228s/12 iters), loss = 1.4034
I0409 22:04:46.713320 24944 solver.cpp:237]     Train net output #0: loss = 1.4034 (* 1 = 1.4034 loss)
I0409 22:04:46.713330 24944 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0409 22:04:51.365087 24944 solver.cpp:218] Iteration 3996 (2.57976 iter/s, 4.65159s/12 iters), loss = 1.16519
I0409 22:04:51.365134 24944 solver.cpp:237]     Train net output #0: loss = 1.16519 (* 1 = 1.16519 loss)
I0409 22:04:51.365144 24944 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0409 22:04:56.073748 24944 solver.cpp:218] Iteration 4008 (2.54862 iter/s, 4.70844s/12 iters), loss = 1.34113
I0409 22:04:56.073791 24944 solver.cpp:237]     Train net output #0: loss = 1.34113 (* 1 = 1.34113 loss)
I0409 22:04:56.073801 24944 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0409 22:05:00.644313 24944 solver.cpp:218] Iteration 4020 (2.62562 iter/s, 4.57035s/12 iters), loss = 1.53404
I0409 22:05:00.644467 24944 solver.cpp:237]     Train net output #0: loss = 1.53404 (* 1 = 1.53404 loss)
I0409 22:05:00.644476 24944 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0409 22:05:05.918279 24944 solver.cpp:218] Iteration 4032 (2.27548 iter/s, 5.27362s/12 iters), loss = 1.46977
I0409 22:05:05.918324 24944 solver.cpp:237]     Train net output #0: loss = 1.46977 (* 1 = 1.46977 loss)
I0409 22:05:05.918335 24944 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0409 22:05:11.079916 24944 solver.cpp:218] Iteration 4044 (2.32495 iter/s, 5.1614s/12 iters), loss = 1.04674
I0409 22:05:11.079958 24944 solver.cpp:237]     Train net output #0: loss = 1.04674 (* 1 = 1.04674 loss)
I0409 22:05:11.079968 24944 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0409 22:05:11.560721 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:05:16.146966 24944 solver.cpp:218] Iteration 4056 (2.36835 iter/s, 5.06682s/12 iters), loss = 1.19978
I0409 22:05:16.147006 24944 solver.cpp:237]     Train net output #0: loss = 1.19978 (* 1 = 1.19978 loss)
I0409 22:05:16.147015 24944 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0409 22:05:21.333173 24944 solver.cpp:218] Iteration 4068 (2.31393 iter/s, 5.18597s/12 iters), loss = 1.25222
I0409 22:05:21.333217 24944 solver.cpp:237]     Train net output #0: loss = 1.25222 (* 1 = 1.25222 loss)
I0409 22:05:21.333226 24944 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0409 22:05:25.942368 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0409 22:05:47.107514 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0409 22:05:55.682183 24944 solver.cpp:330] Iteration 4080, Testing net (#0)
I0409 22:05:55.682205 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:05:58.514008 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:06:00.186218 24944 solver.cpp:397]     Test net output #0: accuracy = 0.410539
I0409 22:06:00.186251 24944 solver.cpp:397]     Test net output #1: loss = 2.54653 (* 1 = 2.54653 loss)
I0409 22:06:00.307222 24944 solver.cpp:218] Iteration 4080 (0.307908 iter/s, 38.9727s/12 iters), loss = 1.23438
I0409 22:06:00.308751 24944 solver.cpp:237]     Train net output #0: loss = 1.23438 (* 1 = 1.23438 loss)
I0409 22:06:00.308761 24944 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0409 22:06:04.369170 24944 solver.cpp:218] Iteration 4092 (2.95547 iter/s, 4.06027s/12 iters), loss = 1.27111
I0409 22:06:04.369215 24944 solver.cpp:237]     Train net output #0: loss = 1.27111 (* 1 = 1.27111 loss)
I0409 22:06:04.369225 24944 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0409 22:06:09.258286 24944 solver.cpp:218] Iteration 4104 (2.45455 iter/s, 4.88887s/12 iters), loss = 1.26639
I0409 22:06:09.258358 24944 solver.cpp:237]     Train net output #0: loss = 1.26639 (* 1 = 1.26639 loss)
I0409 22:06:09.258369 24944 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0409 22:06:13.953403 24944 solver.cpp:218] Iteration 4116 (2.55598 iter/s, 4.69487s/12 iters), loss = 1.29099
I0409 22:06:13.953457 24944 solver.cpp:237]     Train net output #0: loss = 1.29099 (* 1 = 1.29099 loss)
I0409 22:06:13.953469 24944 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0409 22:06:18.650211 24944 solver.cpp:218] Iteration 4128 (2.55505 iter/s, 4.69657s/12 iters), loss = 0.975139
I0409 22:06:18.650321 24944 solver.cpp:237]     Train net output #0: loss = 0.975139 (* 1 = 0.975139 loss)
I0409 22:06:18.650333 24944 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0409 22:06:23.368484 24944 solver.cpp:218] Iteration 4140 (2.54346 iter/s, 4.71799s/12 iters), loss = 1.00139
I0409 22:06:23.368537 24944 solver.cpp:237]     Train net output #0: loss = 1.00139 (* 1 = 1.00139 loss)
I0409 22:06:23.368549 24944 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0409 22:06:25.701310 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:06:28.205790 24944 solver.cpp:218] Iteration 4152 (2.48084 iter/s, 4.83707s/12 iters), loss = 0.992649
I0409 22:06:28.205842 24944 solver.cpp:237]     Train net output #0: loss = 0.992649 (* 1 = 0.992649 loss)
I0409 22:06:28.205853 24944 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0409 22:06:29.731148 24944 blocking_queue.cpp:49] Waiting for data
I0409 22:06:33.092094 24944 solver.cpp:218] Iteration 4164 (2.45596 iter/s, 4.88607s/12 iters), loss = 1.25418
I0409 22:06:33.092147 24944 solver.cpp:237]     Train net output #0: loss = 1.25418 (* 1 = 1.25418 loss)
I0409 22:06:33.092159 24944 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0409 22:06:37.945291 24944 solver.cpp:218] Iteration 4176 (2.47272 iter/s, 4.85296s/12 iters), loss = 1.32189
I0409 22:06:37.945338 24944 solver.cpp:237]     Train net output #0: loss = 1.32189 (* 1 = 1.32189 loss)
I0409 22:06:37.945348 24944 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0409 22:06:39.922050 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0409 22:06:50.883379 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0409 22:07:06.765609 24944 solver.cpp:330] Iteration 4182, Testing net (#0)
I0409 22:07:06.765631 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:07:09.498762 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:07:11.196295 24944 solver.cpp:397]     Test net output #0: accuracy = 0.415441
I0409 22:07:11.196331 24944 solver.cpp:397]     Test net output #1: loss = 2.55227 (* 1 = 2.55227 loss)
I0409 22:07:13.067819 24944 solver.cpp:218] Iteration 4188 (0.341673 iter/s, 35.1213s/12 iters), loss = 0.89547
I0409 22:07:13.067878 24944 solver.cpp:237]     Train net output #0: loss = 0.89547 (* 1 = 0.89547 loss)
I0409 22:07:13.067890 24944 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0409 22:07:18.241122 24944 solver.cpp:218] Iteration 4200 (2.31971 iter/s, 5.17305s/12 iters), loss = 1.33348
I0409 22:07:18.241163 24944 solver.cpp:237]     Train net output #0: loss = 1.33348 (* 1 = 1.33348 loss)
I0409 22:07:18.241173 24944 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0409 22:07:23.289033 24944 solver.cpp:218] Iteration 4212 (2.37733 iter/s, 5.04768s/12 iters), loss = 1.03474
I0409 22:07:23.289132 24944 solver.cpp:237]     Train net output #0: loss = 1.03474 (* 1 = 1.03474 loss)
I0409 22:07:23.289142 24944 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0409 22:07:27.891192 24944 solver.cpp:218] Iteration 4224 (2.60763 iter/s, 4.60189s/12 iters), loss = 0.871141
I0409 22:07:27.891242 24944 solver.cpp:237]     Train net output #0: loss = 0.871141 (* 1 = 0.871141 loss)
I0409 22:07:27.891253 24944 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0409 22:07:32.537536 24944 solver.cpp:218] Iteration 4236 (2.5828 iter/s, 4.64612s/12 iters), loss = 1.05533
I0409 22:07:32.537577 24944 solver.cpp:237]     Train net output #0: loss = 1.05533 (* 1 = 1.05533 loss)
I0409 22:07:32.537586 24944 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0409 22:07:37.127797 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:07:37.387301 24944 solver.cpp:218] Iteration 4248 (2.47446 iter/s, 4.84954s/12 iters), loss = 0.933997
I0409 22:07:37.387353 24944 solver.cpp:237]     Train net output #0: loss = 0.933997 (* 1 = 0.933997 loss)
I0409 22:07:37.387364 24944 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0409 22:07:42.097791 24944 solver.cpp:218] Iteration 4260 (2.54763 iter/s, 4.71026s/12 iters), loss = 1.05159
I0409 22:07:42.097836 24944 solver.cpp:237]     Train net output #0: loss = 1.05159 (* 1 = 1.05159 loss)
I0409 22:07:42.097844 24944 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0409 22:07:47.055564 24944 solver.cpp:218] Iteration 4272 (2.42055 iter/s, 4.95754s/12 iters), loss = 0.97735
I0409 22:07:47.055615 24944 solver.cpp:237]     Train net output #0: loss = 0.97735 (* 1 = 0.97735 loss)
I0409 22:07:47.055625 24944 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0409 22:07:51.364786 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0409 22:08:13.959869 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0409 22:08:22.402256 24944 solver.cpp:330] Iteration 4284, Testing net (#0)
I0409 22:08:22.402278 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:08:25.266053 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:08:27.015623 24944 solver.cpp:397]     Test net output #0: accuracy = 0.441789
I0409 22:08:27.015668 24944 solver.cpp:397]     Test net output #1: loss = 2.52181 (* 1 = 2.52181 loss)
I0409 22:08:27.136471 24944 solver.cpp:218] Iteration 4284 (0.299405 iter/s, 40.0795s/12 iters), loss = 1.14866
I0409 22:08:27.137998 24944 solver.cpp:237]     Train net output #0: loss = 1.14866 (* 1 = 1.14866 loss)
I0409 22:08:27.138013 24944 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0409 22:08:30.964262 24944 solver.cpp:218] Iteration 4296 (3.13634 iter/s, 3.82612s/12 iters), loss = 1.05436
I0409 22:08:30.964315 24944 solver.cpp:237]     Train net output #0: loss = 1.05436 (* 1 = 1.05436 loss)
I0409 22:08:30.964326 24944 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0409 22:08:36.045078 24944 solver.cpp:218] Iteration 4308 (2.36194 iter/s, 5.08057s/12 iters), loss = 1.05443
I0409 22:08:36.045120 24944 solver.cpp:237]     Train net output #0: loss = 1.05443 (* 1 = 1.05443 loss)
I0409 22:08:36.045128 24944 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0409 22:08:41.077765 24944 solver.cpp:218] Iteration 4320 (2.38452 iter/s, 5.03245s/12 iters), loss = 0.962148
I0409 22:08:41.077817 24944 solver.cpp:237]     Train net output #0: loss = 0.962148 (* 1 = 0.962148 loss)
I0409 22:08:41.077829 24944 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0409 22:08:46.300966 24944 solver.cpp:218] Iteration 4332 (2.29755 iter/s, 5.22295s/12 iters), loss = 1.08773
I0409 22:08:46.301095 24944 solver.cpp:237]     Train net output #0: loss = 1.08773 (* 1 = 1.08773 loss)
I0409 22:08:46.301105 24944 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0409 22:08:51.343849 24944 solver.cpp:218] Iteration 4344 (2.37974 iter/s, 5.04257s/12 iters), loss = 0.934734
I0409 22:08:51.343897 24944 solver.cpp:237]     Train net output #0: loss = 0.934734 (* 1 = 0.934734 loss)
I0409 22:08:51.343909 24944 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0409 22:08:53.139987 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:08:56.151971 24944 solver.cpp:218] Iteration 4356 (2.4959 iter/s, 4.80789s/12 iters), loss = 1.2082
I0409 22:08:56.152017 24944 solver.cpp:237]     Train net output #0: loss = 1.2082 (* 1 = 1.2082 loss)
I0409 22:08:56.152026 24944 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0409 22:09:00.880450 24944 solver.cpp:218] Iteration 4368 (2.53793 iter/s, 4.72825s/12 iters), loss = 1.1997
I0409 22:09:00.880491 24944 solver.cpp:237]     Train net output #0: loss = 1.1997 (* 1 = 1.1997 loss)
I0409 22:09:00.880502 24944 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0409 22:09:05.362941 24944 solver.cpp:218] Iteration 4380 (2.67721 iter/s, 4.48228s/12 iters), loss = 0.79847
I0409 22:09:05.362996 24944 solver.cpp:237]     Train net output #0: loss = 0.79847 (* 1 = 0.79847 loss)
I0409 22:09:05.363008 24944 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0409 22:09:07.180714 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0409 22:09:20.150734 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0409 22:09:30.386173 24944 solver.cpp:330] Iteration 4386, Testing net (#0)
I0409 22:09:30.386199 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:09:33.110247 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:09:34.880475 24944 solver.cpp:397]     Test net output #0: accuracy = 0.42402
I0409 22:09:34.880523 24944 solver.cpp:397]     Test net output #1: loss = 2.505 (* 1 = 2.505 loss)
I0409 22:09:36.694625 24944 solver.cpp:218] Iteration 4392 (0.383013 iter/s, 31.3305s/12 iters), loss = 0.915739
I0409 22:09:36.694677 24944 solver.cpp:237]     Train net output #0: loss = 0.915739 (* 1 = 0.915739 loss)
I0409 22:09:36.694689 24944 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0409 22:09:41.857319 24944 solver.cpp:218] Iteration 4404 (2.32448 iter/s, 5.16245s/12 iters), loss = 0.825144
I0409 22:09:41.857372 24944 solver.cpp:237]     Train net output #0: loss = 0.825144 (* 1 = 0.825144 loss)
I0409 22:09:41.857381 24944 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0409 22:09:46.823066 24944 solver.cpp:218] Iteration 4416 (2.41667 iter/s, 4.96551s/12 iters), loss = 0.98866
I0409 22:09:46.823117 24944 solver.cpp:237]     Train net output #0: loss = 0.98866 (* 1 = 0.98866 loss)
I0409 22:09:46.823129 24944 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0409 22:09:52.007118 24944 solver.cpp:218] Iteration 4428 (2.3149 iter/s, 5.18381s/12 iters), loss = 0.774092
I0409 22:09:52.007201 24944 solver.cpp:237]     Train net output #0: loss = 0.774092 (* 1 = 0.774092 loss)
I0409 22:09:52.007213 24944 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0409 22:09:56.923502 24944 solver.cpp:218] Iteration 4440 (2.44095 iter/s, 4.91612s/12 iters), loss = 0.888122
I0409 22:09:56.923547 24944 solver.cpp:237]     Train net output #0: loss = 0.888122 (* 1 = 0.888122 loss)
I0409 22:09:56.923555 24944 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0409 22:10:00.981245 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:10:01.971390 24944 solver.cpp:218] Iteration 4452 (2.37734 iter/s, 5.04765s/12 iters), loss = 0.844055
I0409 22:10:01.971441 24944 solver.cpp:237]     Train net output #0: loss = 0.844055 (* 1 = 0.844055 loss)
I0409 22:10:01.971453 24944 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0409 22:10:06.966043 24944 solver.cpp:218] Iteration 4464 (2.40268 iter/s, 4.99442s/12 iters), loss = 0.981902
I0409 22:10:06.966089 24944 solver.cpp:237]     Train net output #0: loss = 0.981902 (* 1 = 0.981902 loss)
I0409 22:10:06.966099 24944 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0409 22:10:12.125200 24944 solver.cpp:218] Iteration 4476 (2.32607 iter/s, 5.15892s/12 iters), loss = 1.17042
I0409 22:10:12.125247 24944 solver.cpp:237]     Train net output #0: loss = 1.17042 (* 1 = 1.17042 loss)
I0409 22:10:12.125257 24944 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0409 22:10:16.840891 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0409 22:10:27.481097 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0409 22:10:44.997288 24944 solver.cpp:330] Iteration 4488, Testing net (#0)
I0409 22:10:44.997310 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:10:47.713697 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:10:49.506757 24944 solver.cpp:397]     Test net output #0: accuracy = 0.433211
I0409 22:10:49.506803 24944 solver.cpp:397]     Test net output #1: loss = 2.50471 (* 1 = 2.50471 loss)
I0409 22:10:49.628172 24944 solver.cpp:218] Iteration 4488 (0.319986 iter/s, 37.5016s/12 iters), loss = 0.823181
I0409 22:10:49.629704 24944 solver.cpp:237]     Train net output #0: loss = 0.823181 (* 1 = 0.823181 loss)
I0409 22:10:49.629719 24944 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0409 22:10:53.621991 24944 solver.cpp:218] Iteration 4500 (3.00591 iter/s, 3.99214s/12 iters), loss = 1.04596
I0409 22:10:53.622035 24944 solver.cpp:237]     Train net output #0: loss = 1.04596 (* 1 = 1.04596 loss)
I0409 22:10:53.622045 24944 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0409 22:10:58.283663 24944 solver.cpp:218] Iteration 4512 (2.57431 iter/s, 4.66145s/12 iters), loss = 0.713646
I0409 22:10:58.286339 24944 solver.cpp:237]     Train net output #0: loss = 0.713646 (* 1 = 0.713646 loss)
I0409 22:10:58.286357 24944 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0409 22:11:02.927255 24944 solver.cpp:218] Iteration 4524 (2.58579 iter/s, 4.64075s/12 iters), loss = 0.960651
I0409 22:11:02.927306 24944 solver.cpp:237]     Train net output #0: loss = 0.960651 (* 1 = 0.960651 loss)
I0409 22:11:02.927317 24944 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0409 22:11:07.838896 24944 solver.cpp:218] Iteration 4536 (2.44329 iter/s, 4.91141s/12 iters), loss = 0.759149
I0409 22:11:07.838943 24944 solver.cpp:237]     Train net output #0: loss = 0.759149 (* 1 = 0.759149 loss)
I0409 22:11:07.838954 24944 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0409 22:11:12.535601 24944 solver.cpp:218] Iteration 4548 (2.5551 iter/s, 4.69649s/12 iters), loss = 1.0587
I0409 22:11:12.535636 24944 solver.cpp:237]     Train net output #0: loss = 1.0587 (* 1 = 1.0587 loss)
I0409 22:11:12.535645 24944 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0409 22:11:13.690114 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:11:17.217806 24944 solver.cpp:218] Iteration 4560 (2.56301 iter/s, 4.68199s/12 iters), loss = 0.87493
I0409 22:11:17.217849 24944 solver.cpp:237]     Train net output #0: loss = 0.87493 (* 1 = 0.87493 loss)
I0409 22:11:17.217856 24944 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0409 22:11:22.423192 24944 solver.cpp:218] Iteration 4572 (2.30541 iter/s, 5.20515s/12 iters), loss = 0.843824
I0409 22:11:22.423244 24944 solver.cpp:237]     Train net output #0: loss = 0.843824 (* 1 = 0.843824 loss)
I0409 22:11:22.423255 24944 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0409 22:11:27.613346 24944 solver.cpp:218] Iteration 4584 (2.31218 iter/s, 5.18991s/12 iters), loss = 0.712209
I0409 22:11:27.613391 24944 solver.cpp:237]     Train net output #0: loss = 0.712209 (* 1 = 0.712209 loss)
I0409 22:11:27.613399 24944 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0409 22:11:29.468935 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0409 22:11:46.635736 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0409 22:11:58.678697 24944 solver.cpp:330] Iteration 4590, Testing net (#0)
I0409 22:11:58.678720 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:12:01.324565 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:12:03.218865 24944 solver.cpp:397]     Test net output #0: accuracy = 0.441789
I0409 22:12:03.218914 24944 solver.cpp:397]     Test net output #1: loss = 2.5475 (* 1 = 2.5475 loss)
I0409 22:12:05.110311 24944 solver.cpp:218] Iteration 4596 (0.320038 iter/s, 37.4956s/12 iters), loss = 0.709493
I0409 22:12:05.110358 24944 solver.cpp:237]     Train net output #0: loss = 0.709493 (* 1 = 0.709493 loss)
I0409 22:12:05.110366 24944 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0409 22:12:09.990620 24944 solver.cpp:218] Iteration 4608 (2.45898 iter/s, 4.88008s/12 iters), loss = 0.991088
I0409 22:12:09.990664 24944 solver.cpp:237]     Train net output #0: loss = 0.991088 (* 1 = 0.991088 loss)
I0409 22:12:09.990674 24944 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0409 22:12:15.138407 24944 solver.cpp:218] Iteration 4620 (2.33121 iter/s, 5.14755s/12 iters), loss = 0.638167
I0409 22:12:15.138460 24944 solver.cpp:237]     Train net output #0: loss = 0.638167 (* 1 = 0.638167 loss)
I0409 22:12:15.138473 24944 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0409 22:12:20.336556 24944 solver.cpp:218] Iteration 4632 (2.30863 iter/s, 5.1979s/12 iters), loss = 0.665117
I0409 22:12:20.336616 24944 solver.cpp:237]     Train net output #0: loss = 0.665117 (* 1 = 0.665117 loss)
I0409 22:12:20.336627 24944 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0409 22:12:25.370626 24944 solver.cpp:218] Iteration 4644 (2.38387 iter/s, 5.03382s/12 iters), loss = 0.75242
I0409 22:12:25.370676 24944 solver.cpp:237]     Train net output #0: loss = 0.75242 (* 1 = 0.75242 loss)
I0409 22:12:25.370687 24944 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0409 22:12:28.802588 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:12:30.351497 24944 solver.cpp:218] Iteration 4656 (2.40933 iter/s, 4.98064s/12 iters), loss = 0.676202
I0409 22:12:30.351541 24944 solver.cpp:237]     Train net output #0: loss = 0.676202 (* 1 = 0.676202 loss)
I0409 22:12:30.351549 24944 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0409 22:12:34.956589 24944 solver.cpp:218] Iteration 4668 (2.60593 iter/s, 4.60488s/12 iters), loss = 0.736319
I0409 22:12:34.956658 24944 solver.cpp:237]     Train net output #0: loss = 0.736319 (* 1 = 0.736319 loss)
I0409 22:12:34.956670 24944 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0409 22:12:39.598915 24944 solver.cpp:218] Iteration 4680 (2.58505 iter/s, 4.64208s/12 iters), loss = 0.770517
I0409 22:12:39.598968 24944 solver.cpp:237]     Train net output #0: loss = 0.770517 (* 1 = 0.770517 loss)
I0409 22:12:39.598978 24944 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0409 22:12:43.954131 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0409 22:12:56.648854 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0409 22:13:11.844986 24944 solver.cpp:330] Iteration 4692, Testing net (#0)
I0409 22:13:11.845027 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:13:14.404743 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:13:16.293897 24944 solver.cpp:397]     Test net output #0: accuracy = 0.435662
I0409 22:13:16.293933 24944 solver.cpp:397]     Test net output #1: loss = 2.56341 (* 1 = 2.56341 loss)
I0409 22:13:16.415144 24944 solver.cpp:218] Iteration 4692 (0.325955 iter/s, 36.8149s/12 iters), loss = 0.738142
I0409 22:13:16.416667 24944 solver.cpp:237]     Train net output #0: loss = 0.738142 (* 1 = 0.738142 loss)
I0409 22:13:16.416682 24944 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0409 22:13:20.616297 24944 solver.cpp:218] Iteration 4704 (2.8575 iter/s, 4.19948s/12 iters), loss = 0.56556
I0409 22:13:20.616340 24944 solver.cpp:237]     Train net output #0: loss = 0.56556 (* 1 = 0.56556 loss)
I0409 22:13:20.616350 24944 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0409 22:13:25.517987 24944 solver.cpp:218] Iteration 4716 (2.44825 iter/s, 4.90146s/12 iters), loss = 0.699735
I0409 22:13:25.518038 24944 solver.cpp:237]     Train net output #0: loss = 0.699735 (* 1 = 0.699735 loss)
I0409 22:13:25.518049 24944 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0409 22:13:30.257540 24944 solver.cpp:218] Iteration 4728 (2.53201 iter/s, 4.73932s/12 iters), loss = 0.890923
I0409 22:13:30.257586 24944 solver.cpp:237]     Train net output #0: loss = 0.890923 (* 1 = 0.890923 loss)
I0409 22:13:30.257594 24944 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0409 22:13:34.880430 24944 solver.cpp:218] Iteration 4740 (2.5959 iter/s, 4.62267s/12 iters), loss = 0.961183
I0409 22:13:34.880475 24944 solver.cpp:237]     Train net output #0: loss = 0.961183 (* 1 = 0.961183 loss)
I0409 22:13:34.880483 24944 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0409 22:13:39.893148 24944 solver.cpp:218] Iteration 4752 (2.39402 iter/s, 5.01248s/12 iters), loss = 0.857315
I0409 22:13:39.893193 24944 solver.cpp:237]     Train net output #0: loss = 0.857315 (* 1 = 0.857315 loss)
I0409 22:13:39.893203 24944 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0409 22:13:40.378808 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:13:45.087440 24944 solver.cpp:218] Iteration 4764 (2.31034 iter/s, 5.19405s/12 iters), loss = 0.817877
I0409 22:13:45.087577 24944 solver.cpp:237]     Train net output #0: loss = 0.817877 (* 1 = 0.817877 loss)
I0409 22:13:45.087590 24944 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0409 22:13:50.276063 24944 solver.cpp:218] Iteration 4776 (2.3129 iter/s, 5.18829s/12 iters), loss = 0.778378
I0409 22:13:50.276116 24944 solver.cpp:237]     Train net output #0: loss = 0.778378 (* 1 = 0.778378 loss)
I0409 22:13:50.276127 24944 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0409 22:13:55.324179 24944 solver.cpp:218] Iteration 4788 (2.37724 iter/s, 5.04787s/12 iters), loss = 0.748414
I0409 22:13:55.324234 24944 solver.cpp:237]     Train net output #0: loss = 0.748414 (* 1 = 0.748414 loss)
I0409 22:13:55.324246 24944 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0409 22:13:57.244042 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0409 22:14:14.716748 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0409 22:14:23.210211 24944 solver.cpp:330] Iteration 4794, Testing net (#0)
I0409 22:14:23.210259 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:14:25.819674 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:14:27.763248 24944 solver.cpp:397]     Test net output #0: accuracy = 0.469976
I0409 22:14:27.763296 24944 solver.cpp:397]     Test net output #1: loss = 2.46147 (* 1 = 2.46147 loss)
I0409 22:14:29.538864 24944 solver.cpp:218] Iteration 4800 (0.350739 iter/s, 34.2134s/12 iters), loss = 0.79852
I0409 22:14:29.538913 24944 solver.cpp:237]     Train net output #0: loss = 0.79852 (* 1 = 0.79852 loss)
I0409 22:14:29.538924 24944 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0409 22:14:34.648959 24944 solver.cpp:218] Iteration 4812 (2.3484 iter/s, 5.10985s/12 iters), loss = 0.925549
I0409 22:14:34.649009 24944 solver.cpp:237]     Train net output #0: loss = 0.925549 (* 1 = 0.925549 loss)
I0409 22:14:34.649020 24944 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0409 22:14:39.349841 24944 solver.cpp:218] Iteration 4824 (2.55284 iter/s, 4.70065s/12 iters), loss = 0.757857
I0409 22:14:39.349893 24944 solver.cpp:237]     Train net output #0: loss = 0.757857 (* 1 = 0.757857 loss)
I0409 22:14:39.349905 24944 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0409 22:14:44.424510 24944 solver.cpp:218] Iteration 4836 (2.3648 iter/s, 5.07443s/12 iters), loss = 0.791592
I0409 22:14:44.424559 24944 solver.cpp:237]     Train net output #0: loss = 0.791592 (* 1 = 0.791592 loss)
I0409 22:14:44.424572 24944 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0409 22:14:46.476747 24944 blocking_queue.cpp:49] Waiting for data
I0409 22:14:49.587898 24944 solver.cpp:218] Iteration 4848 (2.32416 iter/s, 5.16315s/12 iters), loss = 0.787682
I0409 22:14:49.587946 24944 solver.cpp:237]     Train net output #0: loss = 0.787682 (* 1 = 0.787682 loss)
I0409 22:14:49.587957 24944 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0409 22:14:52.298503 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:14:54.698567 24944 solver.cpp:218] Iteration 4860 (2.34814 iter/s, 5.11043s/12 iters), loss = 0.667326
I0409 22:14:54.698711 24944 solver.cpp:237]     Train net output #0: loss = 0.667326 (* 1 = 0.667326 loss)
I0409 22:14:54.698724 24944 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0409 22:14:59.595590 24944 solver.cpp:218] Iteration 4872 (2.45063 iter/s, 4.8967s/12 iters), loss = 0.73467
I0409 22:14:59.595643 24944 solver.cpp:237]     Train net output #0: loss = 0.73467 (* 1 = 0.73467 loss)
I0409 22:14:59.595656 24944 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0409 22:15:04.552738 24944 solver.cpp:218] Iteration 4884 (2.42086 iter/s, 4.95691s/12 iters), loss = 0.655858
I0409 22:15:04.552786 24944 solver.cpp:237]     Train net output #0: loss = 0.655858 (* 1 = 0.655858 loss)
I0409 22:15:04.552799 24944 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0409 22:15:09.248986 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0409 22:15:19.917425 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0409 22:15:32.894158 24944 solver.cpp:330] Iteration 4896, Testing net (#0)
I0409 22:15:32.894245 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:15:35.439785 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:15:37.413918 24944 solver.cpp:397]     Test net output #0: accuracy = 0.447917
I0409 22:15:37.413990 24944 solver.cpp:397]     Test net output #1: loss = 2.62513 (* 1 = 2.62513 loss)
I0409 22:15:37.535070 24944 solver.cpp:218] Iteration 4896 (0.363844 iter/s, 32.9811s/12 iters), loss = 0.589376
I0409 22:15:37.536640 24944 solver.cpp:237]     Train net output #0: loss = 0.589376 (* 1 = 0.589376 loss)
I0409 22:15:37.536653 24944 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0409 22:15:41.814455 24944 solver.cpp:218] Iteration 4908 (2.80528 iter/s, 4.27765s/12 iters), loss = 0.618818
I0409 22:15:41.814507 24944 solver.cpp:237]     Train net output #0: loss = 0.618818 (* 1 = 0.618818 loss)
I0409 22:15:41.814519 24944 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0409 22:15:47.048607 24944 solver.cpp:218] Iteration 4920 (2.29274 iter/s, 5.2339s/12 iters), loss = 0.622557
I0409 22:15:47.048655 24944 solver.cpp:237]     Train net output #0: loss = 0.622557 (* 1 = 0.622557 loss)
I0409 22:15:47.048666 24944 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0409 22:15:52.236654 24944 solver.cpp:218] Iteration 4932 (2.31312 iter/s, 5.1878s/12 iters), loss = 0.623255
I0409 22:15:52.236706 24944 solver.cpp:237]     Train net output #0: loss = 0.623255 (* 1 = 0.623255 loss)
I0409 22:15:52.236718 24944 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0409 22:15:57.341882 24944 solver.cpp:218] Iteration 4944 (2.35065 iter/s, 5.10498s/12 iters), loss = 0.570603
I0409 22:15:57.341938 24944 solver.cpp:237]     Train net output #0: loss = 0.570603 (* 1 = 0.570603 loss)
I0409 22:15:57.341948 24944 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0409 22:16:01.796072 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:16:02.020998 24944 solver.cpp:218] Iteration 4956 (2.56471 iter/s, 4.67889s/12 iters), loss = 0.632485
I0409 22:16:02.021039 24944 solver.cpp:237]     Train net output #0: loss = 0.632485 (* 1 = 0.632485 loss)
I0409 22:16:02.021047 24944 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0409 22:16:07.038338 24944 solver.cpp:218] Iteration 4968 (2.39181 iter/s, 5.01711s/12 iters), loss = 0.580209
I0409 22:16:07.040035 24944 solver.cpp:237]     Train net output #0: loss = 0.580209 (* 1 = 0.580209 loss)
I0409 22:16:07.040047 24944 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0409 22:16:12.299625 24944 solver.cpp:218] Iteration 4980 (2.28163 iter/s, 5.2594s/12 iters), loss = 0.702135
I0409 22:16:12.299674 24944 solver.cpp:237]     Train net output #0: loss = 0.702135 (* 1 = 0.702135 loss)
I0409 22:16:12.299685 24944 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0409 22:16:17.117403 24944 solver.cpp:218] Iteration 4992 (2.49089 iter/s, 4.81755s/12 iters), loss = 0.620795
I0409 22:16:17.117455 24944 solver.cpp:237]     Train net output #0: loss = 0.620795 (* 1 = 0.620795 loss)
I0409 22:16:17.117465 24944 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0409 22:16:19.193749 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0409 22:16:34.952508 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0409 22:16:45.856729 24944 solver.cpp:330] Iteration 4998, Testing net (#0)
I0409 22:16:45.866014 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:16:48.355506 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:16:50.345516 24944 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0409 22:16:50.345547 24944 solver.cpp:397]     Test net output #1: loss = 2.58557 (* 1 = 2.58557 loss)
I0409 22:16:52.227039 24944 solver.cpp:218] Iteration 5004 (0.341799 iter/s, 35.1084s/12 iters), loss = 0.54143
I0409 22:16:52.227088 24944 solver.cpp:237]     Train net output #0: loss = 0.54143 (* 1 = 0.54143 loss)
I0409 22:16:52.227098 24944 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0409 22:16:56.899328 24944 solver.cpp:218] Iteration 5016 (2.56846 iter/s, 4.67206s/12 iters), loss = 0.683083
I0409 22:16:56.899369 24944 solver.cpp:237]     Train net output #0: loss = 0.683083 (* 1 = 0.683083 loss)
I0409 22:16:56.899379 24944 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0409 22:17:01.551340 24944 solver.cpp:218] Iteration 5028 (2.57965 iter/s, 4.65179s/12 iters), loss = 0.723888
I0409 22:17:01.551386 24944 solver.cpp:237]     Train net output #0: loss = 0.723888 (* 1 = 0.723888 loss)
I0409 22:17:01.551395 24944 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0409 22:17:06.279832 24944 solver.cpp:218] Iteration 5040 (2.53793 iter/s, 4.72827s/12 iters), loss = 0.718831
I0409 22:17:06.279881 24944 solver.cpp:237]     Train net output #0: loss = 0.718831 (* 1 = 0.718831 loss)
I0409 22:17:06.279891 24944 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0409 22:17:10.977344 24944 solver.cpp:218] Iteration 5052 (2.55467 iter/s, 4.69728s/12 iters), loss = 0.490367
I0409 22:17:10.977396 24944 solver.cpp:237]     Train net output #0: loss = 0.490367 (* 1 = 0.490367 loss)
I0409 22:17:10.977407 24944 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0409 22:17:12.779397 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:17:15.736469 24944 solver.cpp:218] Iteration 5064 (2.5216 iter/s, 4.75889s/12 iters), loss = 0.542041
I0409 22:17:15.736521 24944 solver.cpp:237]     Train net output #0: loss = 0.542041 (* 1 = 0.542041 loss)
I0409 22:17:15.736533 24944 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0409 22:17:20.520921 24944 solver.cpp:218] Iteration 5076 (2.50825 iter/s, 4.78422s/12 iters), loss = 0.545479
I0409 22:17:20.521025 24944 solver.cpp:237]     Train net output #0: loss = 0.545479 (* 1 = 0.545479 loss)
I0409 22:17:20.521039 24944 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0409 22:17:25.358534 24944 solver.cpp:218] Iteration 5088 (2.48071 iter/s, 4.83733s/12 iters), loss = 0.61705
I0409 22:17:25.358574 24944 solver.cpp:237]     Train net output #0: loss = 0.61705 (* 1 = 0.61705 loss)
I0409 22:17:25.358583 24944 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0409 22:17:29.815017 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0409 22:17:44.334856 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0409 22:17:58.246448 24944 solver.cpp:330] Iteration 5100, Testing net (#0)
I0409 22:17:58.246520 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:18:00.892410 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:18:02.932147 24944 solver.cpp:397]     Test net output #0: accuracy = 0.484681
I0409 22:18:02.932189 24944 solver.cpp:397]     Test net output #1: loss = 2.59126 (* 1 = 2.59126 loss)
I0409 22:18:03.052541 24944 solver.cpp:218] Iteration 5100 (0.318365 iter/s, 37.6926s/12 iters), loss = 0.61909
I0409 22:18:03.054062 24944 solver.cpp:237]     Train net output #0: loss = 0.61909 (* 1 = 0.61909 loss)
I0409 22:18:03.054075 24944 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0409 22:18:07.142807 24944 solver.cpp:218] Iteration 5112 (2.935 iter/s, 4.08859s/12 iters), loss = 0.48766
I0409 22:18:07.142851 24944 solver.cpp:237]     Train net output #0: loss = 0.48766 (* 1 = 0.48766 loss)
I0409 22:18:07.142860 24944 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0409 22:18:12.334693 24944 solver.cpp:218] Iteration 5124 (2.31141 iter/s, 5.19165s/12 iters), loss = 0.779641
I0409 22:18:12.334734 24944 solver.cpp:237]     Train net output #0: loss = 0.779641 (* 1 = 0.779641 loss)
I0409 22:18:12.334743 24944 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0409 22:18:17.338932 24944 solver.cpp:218] Iteration 5136 (2.39808 iter/s, 5.004s/12 iters), loss = 0.447277
I0409 22:18:17.338977 24944 solver.cpp:237]     Train net output #0: loss = 0.447277 (* 1 = 0.447277 loss)
I0409 22:18:17.338986 24944 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0409 22:18:21.984421 24944 solver.cpp:218] Iteration 5148 (2.58328 iter/s, 4.64526s/12 iters), loss = 0.418719
I0409 22:18:21.984465 24944 solver.cpp:237]     Train net output #0: loss = 0.418719 (* 1 = 0.418719 loss)
I0409 22:18:21.984477 24944 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0409 22:18:25.743041 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:18:26.754063 24944 solver.cpp:218] Iteration 5160 (2.51603 iter/s, 4.76941s/12 iters), loss = 0.635724
I0409 22:18:26.754125 24944 solver.cpp:237]     Train net output #0: loss = 0.635724 (* 1 = 0.635724 loss)
I0409 22:18:26.754139 24944 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0409 22:18:31.715724 24944 solver.cpp:218] Iteration 5172 (2.41867 iter/s, 4.96141s/12 iters), loss = 0.618067
I0409 22:18:31.715835 24944 solver.cpp:237]     Train net output #0: loss = 0.618067 (* 1 = 0.618067 loss)
I0409 22:18:31.715844 24944 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0409 22:18:36.888974 24944 solver.cpp:218] Iteration 5184 (2.31976 iter/s, 5.17294s/12 iters), loss = 0.486002
I0409 22:18:36.889029 24944 solver.cpp:237]     Train net output #0: loss = 0.486002 (* 1 = 0.486002 loss)
I0409 22:18:36.889040 24944 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0409 22:18:41.890977 24944 solver.cpp:218] Iteration 5196 (2.39916 iter/s, 5.00176s/12 iters), loss = 0.464422
I0409 22:18:41.891023 24944 solver.cpp:237]     Train net output #0: loss = 0.464422 (* 1 = 0.464422 loss)
I0409 22:18:41.891033 24944 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0409 22:18:43.970698 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0409 22:19:01.712322 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0409 22:19:11.413420 24944 solver.cpp:330] Iteration 5202, Testing net (#0)
I0409 22:19:11.413489 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:19:13.808095 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:19:15.883988 24944 solver.cpp:397]     Test net output #0: accuracy = 0.494485
I0409 22:19:15.884038 24944 solver.cpp:397]     Test net output #1: loss = 2.44269 (* 1 = 2.44269 loss)
I0409 22:19:17.661711 24944 solver.cpp:218] Iteration 5208 (0.335482 iter/s, 35.7694s/12 iters), loss = 0.346574
I0409 22:19:17.661767 24944 solver.cpp:237]     Train net output #0: loss = 0.346574 (* 1 = 0.346574 loss)
I0409 22:19:17.661778 24944 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0409 22:19:22.843334 24944 solver.cpp:218] Iteration 5220 (2.31599 iter/s, 5.18137s/12 iters), loss = 0.390278
I0409 22:19:22.843380 24944 solver.cpp:237]     Train net output #0: loss = 0.390278 (* 1 = 0.390278 loss)
I0409 22:19:22.843389 24944 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0409 22:19:27.995082 24944 solver.cpp:218] Iteration 5232 (2.32941 iter/s, 5.15151s/12 iters), loss = 0.547104
I0409 22:19:27.995126 24944 solver.cpp:237]     Train net output #0: loss = 0.547104 (* 1 = 0.547104 loss)
I0409 22:19:27.995136 24944 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0409 22:19:33.226701 24944 solver.cpp:218] Iteration 5244 (2.29385 iter/s, 5.23137s/12 iters), loss = 0.62871
I0409 22:19:33.226753 24944 solver.cpp:237]     Train net output #0: loss = 0.62871 (* 1 = 0.62871 loss)
I0409 22:19:33.226764 24944 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0409 22:19:38.438958 24944 solver.cpp:218] Iteration 5256 (2.30238 iter/s, 5.21201s/12 iters), loss = 0.31981
I0409 22:19:38.439016 24944 solver.cpp:237]     Train net output #0: loss = 0.31981 (* 1 = 0.31981 loss)
I0409 22:19:38.439029 24944 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0409 22:19:39.694962 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:19:43.235558 24944 solver.cpp:218] Iteration 5268 (2.5019 iter/s, 4.79636s/12 iters), loss = 0.448201
I0409 22:19:43.235663 24944 solver.cpp:237]     Train net output #0: loss = 0.448201 (* 1 = 0.448201 loss)
I0409 22:19:43.235673 24944 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0409 22:19:48.364490 24944 solver.cpp:218] Iteration 5280 (2.33981 iter/s, 5.12863s/12 iters), loss = 0.547932
I0409 22:19:48.364538 24944 solver.cpp:237]     Train net output #0: loss = 0.547932 (* 1 = 0.547932 loss)
I0409 22:19:48.364547 24944 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0409 22:19:53.155391 24944 solver.cpp:218] Iteration 5292 (2.50487 iter/s, 4.79067s/12 iters), loss = 0.425378
I0409 22:19:53.155442 24944 solver.cpp:237]     Train net output #0: loss = 0.425378 (* 1 = 0.425378 loss)
I0409 22:19:53.155453 24944 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0409 22:19:57.511744 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0409 22:20:08.183171 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0409 22:20:19.580263 24944 solver.cpp:330] Iteration 5304, Testing net (#0)
I0409 22:20:19.580307 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:20:22.038226 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:20:24.164541 24944 solver.cpp:397]     Test net output #0: accuracy = 0.489583
I0409 22:20:24.164585 24944 solver.cpp:397]     Test net output #1: loss = 2.46418 (* 1 = 2.46418 loss)
I0409 22:20:24.282738 24944 solver.cpp:218] Iteration 5304 (0.385527 iter/s, 31.1262s/12 iters), loss = 0.456593
I0409 22:20:24.284257 24944 solver.cpp:237]     Train net output #0: loss = 0.456593 (* 1 = 0.456593 loss)
I0409 22:20:24.284269 24944 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0409 22:20:28.353359 24944 solver.cpp:218] Iteration 5316 (2.94916 iter/s, 4.06895s/12 iters), loss = 0.453504
I0409 22:20:28.353405 24944 solver.cpp:237]     Train net output #0: loss = 0.453504 (* 1 = 0.453504 loss)
I0409 22:20:28.353415 24944 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0409 22:20:32.949113 24944 solver.cpp:218] Iteration 5328 (2.61123 iter/s, 4.59553s/12 iters), loss = 0.571127
I0409 22:20:32.949162 24944 solver.cpp:237]     Train net output #0: loss = 0.571127 (* 1 = 0.571127 loss)
I0409 22:20:32.949173 24944 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0409 22:20:37.748458 24944 solver.cpp:218] Iteration 5340 (2.50046 iter/s, 4.79912s/12 iters), loss = 0.525569
I0409 22:20:37.748493 24944 solver.cpp:237]     Train net output #0: loss = 0.525569 (* 1 = 0.525569 loss)
I0409 22:20:37.748502 24944 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0409 22:20:42.552839 24944 solver.cpp:218] Iteration 5352 (2.49784 iter/s, 4.80416s/12 iters), loss = 0.429402
I0409 22:20:42.552889 24944 solver.cpp:237]     Train net output #0: loss = 0.429402 (* 1 = 0.429402 loss)
I0409 22:20:42.552898 24944 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0409 22:20:45.948436 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:20:47.648627 24944 solver.cpp:218] Iteration 5364 (2.355 iter/s, 5.09554s/12 iters), loss = 0.554599
I0409 22:20:47.648692 24944 solver.cpp:237]     Train net output #0: loss = 0.554599 (* 1 = 0.554599 loss)
I0409 22:20:47.648707 24944 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0409 22:20:52.699185 24944 solver.cpp:218] Iteration 5376 (2.37609 iter/s, 5.05031s/12 iters), loss = 0.556618
I0409 22:20:52.699299 24944 solver.cpp:237]     Train net output #0: loss = 0.556618 (* 1 = 0.556618 loss)
I0409 22:20:52.699311 24944 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0409 22:20:57.735509 24944 solver.cpp:218] Iteration 5388 (2.38283 iter/s, 5.03602s/12 iters), loss = 0.512544
I0409 22:20:57.735558 24944 solver.cpp:237]     Train net output #0: loss = 0.512544 (* 1 = 0.512544 loss)
I0409 22:20:57.735569 24944 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0409 22:21:02.675946 24944 solver.cpp:218] Iteration 5400 (2.42905 iter/s, 4.9402s/12 iters), loss = 0.341322
I0409 22:21:02.676000 24944 solver.cpp:237]     Train net output #0: loss = 0.341322 (* 1 = 0.341322 loss)
I0409 22:21:02.676012 24944 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0409 22:21:04.634641 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0409 22:21:15.711607 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0409 22:21:30.878206 24944 solver.cpp:330] Iteration 5406, Testing net (#0)
I0409 22:21:30.878278 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:21:33.236829 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:21:35.564467 24944 solver.cpp:397]     Test net output #0: accuracy = 0.47549
I0409 22:21:35.564517 24944 solver.cpp:397]     Test net output #1: loss = 2.52318 (* 1 = 2.52318 loss)
I0409 22:21:37.442411 24944 solver.cpp:218] Iteration 5412 (0.345173 iter/s, 34.7652s/12 iters), loss = 0.265537
I0409 22:21:37.442464 24944 solver.cpp:237]     Train net output #0: loss = 0.265537 (* 1 = 0.265537 loss)
I0409 22:21:37.442476 24944 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0409 22:21:42.225097 24944 solver.cpp:218] Iteration 5424 (2.50917 iter/s, 4.78245s/12 iters), loss = 0.318767
I0409 22:21:42.225153 24944 solver.cpp:237]     Train net output #0: loss = 0.318767 (* 1 = 0.318767 loss)
I0409 22:21:42.225165 24944 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0409 22:21:46.952502 24944 solver.cpp:218] Iteration 5436 (2.53852 iter/s, 4.72717s/12 iters), loss = 0.45003
I0409 22:21:46.952550 24944 solver.cpp:237]     Train net output #0: loss = 0.45003 (* 1 = 0.45003 loss)
I0409 22:21:46.952561 24944 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0409 22:21:52.155966 24944 solver.cpp:218] Iteration 5448 (2.30626 iter/s, 5.20322s/12 iters), loss = 0.610229
I0409 22:21:52.156011 24944 solver.cpp:237]     Train net output #0: loss = 0.610229 (* 1 = 0.610229 loss)
I0409 22:21:52.156021 24944 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0409 22:21:56.850253 24944 solver.cpp:218] Iteration 5460 (2.55642 iter/s, 4.69407s/12 iters), loss = 0.558417
I0409 22:21:56.850301 24944 solver.cpp:237]     Train net output #0: loss = 0.558417 (* 1 = 0.558417 loss)
I0409 22:21:56.850311 24944 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0409 22:21:57.332207 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:22:01.625708 24944 solver.cpp:218] Iteration 5472 (2.51297 iter/s, 4.77523s/12 iters), loss = 0.34415
I0409 22:22:01.625840 24944 solver.cpp:237]     Train net output #0: loss = 0.34415 (* 1 = 0.34415 loss)
I0409 22:22:01.625851 24944 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0409 22:22:06.320926 24944 solver.cpp:218] Iteration 5484 (2.55596 iter/s, 4.69491s/12 iters), loss = 0.381345
I0409 22:22:06.320974 24944 solver.cpp:237]     Train net output #0: loss = 0.381345 (* 1 = 0.381345 loss)
I0409 22:22:06.320986 24944 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0409 22:22:10.980762 24944 solver.cpp:218] Iteration 5496 (2.57532 iter/s, 4.65961s/12 iters), loss = 0.460446
I0409 22:22:10.980821 24944 solver.cpp:237]     Train net output #0: loss = 0.460446 (* 1 = 0.460446 loss)
I0409 22:22:10.980832 24944 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0409 22:22:15.190340 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0409 22:22:28.685007 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0409 22:22:42.720443 24944 solver.cpp:330] Iteration 5508, Testing net (#0)
I0409 22:22:42.720492 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:22:45.126607 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:22:47.357379 24944 solver.cpp:397]     Test net output #0: accuracy = 0.509804
I0409 22:22:47.357429 24944 solver.cpp:397]     Test net output #1: loss = 2.40386 (* 1 = 2.40386 loss)
I0409 22:22:47.478369 24944 solver.cpp:218] Iteration 5508 (0.328801 iter/s, 36.4963s/12 iters), loss = 0.518524
I0409 22:22:47.479892 24944 solver.cpp:237]     Train net output #0: loss = 0.518524 (* 1 = 0.518524 loss)
I0409 22:22:47.479902 24944 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0409 22:22:51.553130 24944 solver.cpp:218] Iteration 5520 (2.94617 iter/s, 4.07308s/12 iters), loss = 0.313818
I0409 22:22:51.553175 24944 solver.cpp:237]     Train net output #0: loss = 0.313818 (* 1 = 0.313818 loss)
I0409 22:22:51.553185 24944 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0409 22:22:53.905230 24944 blocking_queue.cpp:49] Waiting for data
I0409 22:22:56.380120 24944 solver.cpp:218] Iteration 5532 (2.48614 iter/s, 4.82676s/12 iters), loss = 0.46411
I0409 22:22:56.380162 24944 solver.cpp:237]     Train net output #0: loss = 0.46411 (* 1 = 0.46411 loss)
I0409 22:22:56.380172 24944 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0409 22:23:01.110105 24944 solver.cpp:218] Iteration 5544 (2.53713 iter/s, 4.72976s/12 iters), loss = 0.449112
I0409 22:23:01.110150 24944 solver.cpp:237]     Train net output #0: loss = 0.449112 (* 1 = 0.449112 loss)
I0409 22:23:01.110162 24944 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0409 22:23:05.755970 24944 solver.cpp:218] Iteration 5556 (2.58306 iter/s, 4.64565s/12 iters), loss = 0.373702
I0409 22:23:05.756021 24944 solver.cpp:237]     Train net output #0: loss = 0.373702 (* 1 = 0.373702 loss)
I0409 22:23:05.756031 24944 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0409 22:23:08.188519 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:23:10.418323 24944 solver.cpp:218] Iteration 5568 (2.57393 iter/s, 4.66212s/12 iters), loss = 0.615131
I0409 22:23:10.418370 24944 solver.cpp:237]     Train net output #0: loss = 0.615131 (* 1 = 0.615131 loss)
I0409 22:23:10.418381 24944 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0409 22:23:15.301353 24944 solver.cpp:218] Iteration 5580 (2.45761 iter/s, 4.88279s/12 iters), loss = 0.398032
I0409 22:23:15.301477 24944 solver.cpp:237]     Train net output #0: loss = 0.398032 (* 1 = 0.398032 loss)
I0409 22:23:15.301491 24944 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0409 22:23:20.432294 24944 solver.cpp:218] Iteration 5592 (2.33889 iter/s, 5.13063s/12 iters), loss = 0.366072
I0409 22:23:20.432339 24944 solver.cpp:237]     Train net output #0: loss = 0.366072 (* 1 = 0.366072 loss)
I0409 22:23:20.432350 24944 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0409 22:23:25.345463 24944 solver.cpp:218] Iteration 5604 (2.44253 iter/s, 4.91294s/12 iters), loss = 0.376492
I0409 22:23:25.345510 24944 solver.cpp:237]     Train net output #0: loss = 0.376492 (* 1 = 0.376492 loss)
I0409 22:23:25.345518 24944 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0409 22:23:27.463330 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0409 22:23:41.566599 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0409 22:23:57.783102 24944 solver.cpp:330] Iteration 5610, Testing net (#0)
I0409 22:23:57.783205 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:24:00.047922 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:24:02.284462 24944 solver.cpp:397]     Test net output #0: accuracy = 0.504289
I0409 22:24:02.284509 24944 solver.cpp:397]     Test net output #1: loss = 2.49123 (* 1 = 2.49123 loss)
I0409 22:24:04.136718 24944 solver.cpp:218] Iteration 5616 (0.309359 iter/s, 38.7898s/12 iters), loss = 0.343155
I0409 22:24:04.136760 24944 solver.cpp:237]     Train net output #0: loss = 0.343155 (* 1 = 0.343155 loss)
I0409 22:24:04.136770 24944 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0409 22:24:09.380228 24944 solver.cpp:218] Iteration 5628 (2.28865 iter/s, 5.24327s/12 iters), loss = 0.496122
I0409 22:24:09.380277 24944 solver.cpp:237]     Train net output #0: loss = 0.496122 (* 1 = 0.496122 loss)
I0409 22:24:09.380287 24944 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0409 22:24:14.558360 24944 solver.cpp:218] Iteration 5640 (2.31755 iter/s, 5.17789s/12 iters), loss = 0.373895
I0409 22:24:14.558398 24944 solver.cpp:237]     Train net output #0: loss = 0.373895 (* 1 = 0.373895 loss)
I0409 22:24:14.558408 24944 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0409 22:24:19.620857 24944 solver.cpp:218] Iteration 5652 (2.37048 iter/s, 5.06226s/12 iters), loss = 0.315759
I0409 22:24:19.620911 24944 solver.cpp:237]     Train net output #0: loss = 0.315759 (* 1 = 0.315759 loss)
I0409 22:24:19.620923 24944 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0409 22:24:24.140506 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:24:24.352093 24944 solver.cpp:218] Iteration 5664 (2.53646 iter/s, 4.731s/12 iters), loss = 0.201141
I0409 22:24:24.352144 24944 solver.cpp:237]     Train net output #0: loss = 0.201141 (* 1 = 0.201141 loss)
I0409 22:24:24.352154 24944 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0409 22:24:29.486212 24944 solver.cpp:218] Iteration 5676 (2.33741 iter/s, 5.13388s/12 iters), loss = 0.373941
I0409 22:24:29.486287 24944 solver.cpp:237]     Train net output #0: loss = 0.373941 (* 1 = 0.373941 loss)
I0409 22:24:29.486297 24944 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0409 22:24:34.114138 24944 solver.cpp:218] Iteration 5688 (2.59309 iter/s, 4.62768s/12 iters), loss = 0.343448
I0409 22:24:34.114181 24944 solver.cpp:237]     Train net output #0: loss = 0.343448 (* 1 = 0.343448 loss)
I0409 22:24:34.114190 24944 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0409 22:24:38.764392 24944 solver.cpp:218] Iteration 5700 (2.58063 iter/s, 4.65004s/12 iters), loss = 0.432829
I0409 22:24:38.764437 24944 solver.cpp:237]     Train net output #0: loss = 0.432829 (* 1 = 0.432829 loss)
I0409 22:24:38.764448 24944 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0409 22:24:43.371345 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0409 22:24:58.504717 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0409 22:25:07.037182 24944 solver.cpp:330] Iteration 5712, Testing net (#0)
I0409 22:25:07.037230 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:25:09.244313 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:25:11.513388 24944 solver.cpp:397]     Test net output #0: accuracy = 0.490809
I0409 22:25:11.513418 24944 solver.cpp:397]     Test net output #1: loss = 2.52236 (* 1 = 2.52236 loss)
I0409 22:25:11.634719 24944 solver.cpp:218] Iteration 5712 (0.365084 iter/s, 32.8691s/12 iters), loss = 0.630531
I0409 22:25:11.636240 24944 solver.cpp:237]     Train net output #0: loss = 0.630531 (* 1 = 0.630531 loss)
I0409 22:25:11.636250 24944 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0409 22:25:15.723230 24944 solver.cpp:218] Iteration 5724 (2.93626 iter/s, 4.08684s/12 iters), loss = 0.325796
I0409 22:25:15.723280 24944 solver.cpp:237]     Train net output #0: loss = 0.325796 (* 1 = 0.325796 loss)
I0409 22:25:15.723290 24944 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0409 22:25:20.416481 24944 solver.cpp:218] Iteration 5736 (2.55699 iter/s, 4.69302s/12 iters), loss = 0.377982
I0409 22:25:20.416520 24944 solver.cpp:237]     Train net output #0: loss = 0.377982 (* 1 = 0.377982 loss)
I0409 22:25:20.416529 24944 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0409 22:25:25.204591 24944 solver.cpp:218] Iteration 5748 (2.50632 iter/s, 4.78789s/12 iters), loss = 0.339652
I0409 22:25:25.204633 24944 solver.cpp:237]     Train net output #0: loss = 0.339652 (* 1 = 0.339652 loss)
I0409 22:25:25.204644 24944 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0409 22:25:30.463908 24944 solver.cpp:218] Iteration 5760 (2.28177 iter/s, 5.25908s/12 iters), loss = 0.287897
I0409 22:25:30.463953 24944 solver.cpp:237]     Train net output #0: loss = 0.287897 (* 1 = 0.287897 loss)
I0409 22:25:30.463963 24944 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0409 22:25:32.502915 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:25:35.698153 24944 solver.cpp:218] Iteration 5772 (2.2927 iter/s, 5.23401s/12 iters), loss = 0.390987
I0409 22:25:35.698192 24944 solver.cpp:237]     Train net output #0: loss = 0.390987 (* 1 = 0.390987 loss)
I0409 22:25:35.698201 24944 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0409 22:25:40.911798 24944 solver.cpp:218] Iteration 5784 (2.30176 iter/s, 5.21341s/12 iters), loss = 0.44541
I0409 22:25:40.911947 24944 solver.cpp:237]     Train net output #0: loss = 0.44541 (* 1 = 0.44541 loss)
I0409 22:25:40.911958 24944 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0409 22:25:46.120669 24944 solver.cpp:218] Iteration 5796 (2.30391 iter/s, 5.20854s/12 iters), loss = 0.316059
I0409 22:25:46.120712 24944 solver.cpp:237]     Train net output #0: loss = 0.316059 (* 1 = 0.316059 loss)
I0409 22:25:46.120721 24944 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0409 22:25:51.296211 24944 solver.cpp:218] Iteration 5808 (2.3187 iter/s, 5.1753s/12 iters), loss = 0.376427
I0409 22:25:51.296262 24944 solver.cpp:237]     Train net output #0: loss = 0.376427 (* 1 = 0.376427 loss)
I0409 22:25:51.296272 24944 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0409 22:25:53.393646 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0409 22:26:03.988291 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0409 22:26:13.283414 24944 solver.cpp:330] Iteration 5814, Testing net (#0)
I0409 22:26:13.283488 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:26:15.550931 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:26:17.864892 24944 solver.cpp:397]     Test net output #0: accuracy = 0.487745
I0409 22:26:17.864941 24944 solver.cpp:397]     Test net output #1: loss = 2.60473 (* 1 = 2.60473 loss)
I0409 22:26:19.848517 24944 solver.cpp:218] Iteration 5820 (0.420297 iter/s, 28.5513s/12 iters), loss = 0.19038
I0409 22:26:19.848563 24944 solver.cpp:237]     Train net output #0: loss = 0.19038 (* 1 = 0.19038 loss)
I0409 22:26:19.848572 24944 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0409 22:26:24.918253 24944 solver.cpp:218] Iteration 5832 (2.3671 iter/s, 5.0695s/12 iters), loss = 0.237222
I0409 22:26:24.918299 24944 solver.cpp:237]     Train net output #0: loss = 0.237222 (* 1 = 0.237222 loss)
I0409 22:26:24.918310 24944 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0409 22:26:30.147984 24944 solver.cpp:218] Iteration 5844 (2.29468 iter/s, 5.22949s/12 iters), loss = 0.243833
I0409 22:26:30.148034 24944 solver.cpp:237]     Train net output #0: loss = 0.243833 (* 1 = 0.243833 loss)
I0409 22:26:30.148046 24944 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0409 22:26:34.928561 24944 solver.cpp:218] Iteration 5856 (2.51028 iter/s, 4.78034s/12 iters), loss = 0.418134
I0409 22:26:34.928617 24944 solver.cpp:237]     Train net output #0: loss = 0.418134 (* 1 = 0.418134 loss)
I0409 22:26:34.928627 24944 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0409 22:26:38.740571 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:26:39.553300 24944 solver.cpp:218] Iteration 5868 (2.59487 iter/s, 4.62451s/12 iters), loss = 0.300643
I0409 22:26:39.553349 24944 solver.cpp:237]     Train net output #0: loss = 0.300643 (* 1 = 0.300643 loss)
I0409 22:26:39.553359 24944 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0409 22:26:44.386576 24944 solver.cpp:218] Iteration 5880 (2.48291 iter/s, 4.83305s/12 iters), loss = 0.231246
I0409 22:26:44.386703 24944 solver.cpp:237]     Train net output #0: loss = 0.231246 (* 1 = 0.231246 loss)
I0409 22:26:44.386713 24944 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0409 22:26:49.539937 24944 solver.cpp:218] Iteration 5892 (2.32872 iter/s, 5.15305s/12 iters), loss = 0.409071
I0409 22:26:49.539980 24944 solver.cpp:237]     Train net output #0: loss = 0.409071 (* 1 = 0.409071 loss)
I0409 22:26:49.539990 24944 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0409 22:26:54.470737 24944 solver.cpp:218] Iteration 5904 (2.43379 iter/s, 4.93057s/12 iters), loss = 0.277986
I0409 22:26:54.470784 24944 solver.cpp:237]     Train net output #0: loss = 0.277986 (* 1 = 0.277986 loss)
I0409 22:26:54.470794 24944 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0409 22:26:58.957720 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0409 22:27:10.247434 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0409 22:27:22.794270 24944 solver.cpp:330] Iteration 5916, Testing net (#0)
I0409 22:27:22.794334 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:27:24.946372 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:27:27.297133 24944 solver.cpp:397]     Test net output #0: accuracy = 0.516544
I0409 22:27:27.297184 24944 solver.cpp:397]     Test net output #1: loss = 2.46352 (* 1 = 2.46352 loss)
I0409 22:27:27.416399 24944 solver.cpp:218] Iteration 5916 (0.364249 iter/s, 32.9445s/12 iters), loss = 0.298293
I0409 22:27:27.417920 24944 solver.cpp:237]     Train net output #0: loss = 0.298293 (* 1 = 0.298293 loss)
I0409 22:27:27.417933 24944 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0409 22:27:31.419495 24944 solver.cpp:218] Iteration 5928 (2.99893 iter/s, 4.00143s/12 iters), loss = 0.252786
I0409 22:27:31.419544 24944 solver.cpp:237]     Train net output #0: loss = 0.252786 (* 1 = 0.252786 loss)
I0409 22:27:31.419554 24944 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0409 22:27:36.187716 24944 solver.cpp:218] Iteration 5940 (2.51678 iter/s, 4.768s/12 iters), loss = 0.321392
I0409 22:27:36.187752 24944 solver.cpp:237]     Train net output #0: loss = 0.321392 (* 1 = 0.321392 loss)
I0409 22:27:36.187760 24944 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0409 22:27:40.905776 24944 solver.cpp:218] Iteration 5952 (2.54354 iter/s, 4.71784s/12 iters), loss = 0.308211
I0409 22:27:40.905817 24944 solver.cpp:237]     Train net output #0: loss = 0.308211 (* 1 = 0.308211 loss)
I0409 22:27:40.905827 24944 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0409 22:27:45.726799 24944 solver.cpp:218] Iteration 5964 (2.48922 iter/s, 4.82079s/12 iters), loss = 0.285146
I0409 22:27:45.726856 24944 solver.cpp:237]     Train net output #0: loss = 0.285146 (* 1 = 0.285146 loss)
I0409 22:27:45.726868 24944 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0409 22:27:46.892632 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:27:50.350751 24944 solver.cpp:218] Iteration 5976 (2.59531 iter/s, 4.62372s/12 iters), loss = 0.263753
I0409 22:27:50.350800 24944 solver.cpp:237]     Train net output #0: loss = 0.263753 (* 1 = 0.263753 loss)
I0409 22:27:50.350811 24944 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0409 22:27:55.018920 24944 solver.cpp:218] Iteration 5988 (2.57073 iter/s, 4.66794s/12 iters), loss = 0.280553
I0409 22:27:55.019058 24944 solver.cpp:237]     Train net output #0: loss = 0.280553 (* 1 = 0.280553 loss)
I0409 22:27:55.019068 24944 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0409 22:27:59.576290 24944 solver.cpp:218] Iteration 6000 (2.63328 iter/s, 4.55706s/12 iters), loss = 0.424844
I0409 22:27:59.576333 24944 solver.cpp:237]     Train net output #0: loss = 0.424844 (* 1 = 0.424844 loss)
I0409 22:27:59.576342 24944 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0409 22:28:04.162482 24944 solver.cpp:218] Iteration 6012 (2.61667 iter/s, 4.58597s/12 iters), loss = 0.328673
I0409 22:28:04.162529 24944 solver.cpp:237]     Train net output #0: loss = 0.328673 (* 1 = 0.328673 loss)
I0409 22:28:04.162539 24944 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0409 22:28:06.195704 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0409 22:28:16.822808 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0409 22:28:34.272470 24944 solver.cpp:330] Iteration 6018, Testing net (#0)
I0409 22:28:34.272552 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:28:36.334493 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:28:38.733768 24944 solver.cpp:397]     Test net output #0: accuracy = 0.512255
I0409 22:28:38.733806 24944 solver.cpp:397]     Test net output #1: loss = 2.41758 (* 1 = 2.41758 loss)
I0409 22:28:40.574069 24944 solver.cpp:218] Iteration 6024 (0.329577 iter/s, 36.4103s/12 iters), loss = 0.377087
I0409 22:28:40.574121 24944 solver.cpp:237]     Train net output #0: loss = 0.377087 (* 1 = 0.377087 loss)
I0409 22:28:40.574131 24944 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0409 22:28:45.729501 24944 solver.cpp:218] Iteration 6036 (2.32775 iter/s, 5.15518s/12 iters), loss = 0.282286
I0409 22:28:45.729554 24944 solver.cpp:237]     Train net output #0: loss = 0.282286 (* 1 = 0.282286 loss)
I0409 22:28:45.729565 24944 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0409 22:28:50.549188 24944 solver.cpp:218] Iteration 6048 (2.48991 iter/s, 4.81946s/12 iters), loss = 0.355501
I0409 22:28:50.549233 24944 solver.cpp:237]     Train net output #0: loss = 0.355501 (* 1 = 0.355501 loss)
I0409 22:28:50.549242 24944 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0409 22:28:55.652812 24944 solver.cpp:218] Iteration 6060 (2.35139 iter/s, 5.10337s/12 iters), loss = 0.396156
I0409 22:28:55.652887 24944 solver.cpp:237]     Train net output #0: loss = 0.396156 (* 1 = 0.396156 loss)
I0409 22:28:55.652904 24944 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0409 22:28:59.237195 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:29:00.893764 24944 solver.cpp:218] Iteration 6072 (2.28978 iter/s, 5.24069s/12 iters), loss = 0.400773
I0409 22:29:00.893813 24944 solver.cpp:237]     Train net output #0: loss = 0.400773 (* 1 = 0.400773 loss)
I0409 22:29:00.893826 24944 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0409 22:29:05.570744 24944 solver.cpp:218] Iteration 6084 (2.56588 iter/s, 4.67676s/12 iters), loss = 0.286553
I0409 22:29:05.570865 24944 solver.cpp:237]     Train net output #0: loss = 0.286553 (* 1 = 0.286553 loss)
I0409 22:29:05.570879 24944 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0409 22:29:10.185528 24944 solver.cpp:218] Iteration 6096 (2.6005 iter/s, 4.61449s/12 iters), loss = 0.28016
I0409 22:29:10.185570 24944 solver.cpp:237]     Train net output #0: loss = 0.28016 (* 1 = 0.28016 loss)
I0409 22:29:10.185580 24944 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0409 22:29:14.777945 24944 solver.cpp:218] Iteration 6108 (2.61312 iter/s, 4.5922s/12 iters), loss = 0.252911
I0409 22:29:14.778005 24944 solver.cpp:237]     Train net output #0: loss = 0.252911 (* 1 = 0.252911 loss)
I0409 22:29:14.778015 24944 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0409 22:29:19.039674 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0409 22:29:46.567943 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0409 22:30:00.485474 24944 solver.cpp:330] Iteration 6120, Testing net (#0)
I0409 22:30:00.485499 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:30:02.551293 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:30:04.984683 24944 solver.cpp:397]     Test net output #0: accuracy = 0.505515
I0409 22:30:04.984719 24944 solver.cpp:397]     Test net output #1: loss = 2.46905 (* 1 = 2.46905 loss)
I0409 22:30:05.105520 24944 solver.cpp:218] Iteration 6120 (0.238446 iter/s, 50.3258s/12 iters), loss = 0.293481
I0409 22:30:05.107090 24944 solver.cpp:237]     Train net output #0: loss = 0.293481 (* 1 = 0.293481 loss)
I0409 22:30:05.107106 24944 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0409 22:30:09.441367 24944 solver.cpp:218] Iteration 6132 (2.76873 iter/s, 4.33412s/12 iters), loss = 0.170626
I0409 22:30:09.441423 24944 solver.cpp:237]     Train net output #0: loss = 0.170626 (* 1 = 0.170626 loss)
I0409 22:30:09.441435 24944 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0409 22:30:14.605548 24944 solver.cpp:218] Iteration 6144 (2.32381 iter/s, 5.16394s/12 iters), loss = 0.294179
I0409 22:30:14.605587 24944 solver.cpp:237]     Train net output #0: loss = 0.294179 (* 1 = 0.294179 loss)
I0409 22:30:14.605595 24944 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0409 22:30:19.772778 24944 solver.cpp:218] Iteration 6156 (2.32243 iter/s, 5.167s/12 iters), loss = 0.166492
I0409 22:30:19.772886 24944 solver.cpp:237]     Train net output #0: loss = 0.166492 (* 1 = 0.166492 loss)
I0409 22:30:19.772897 24944 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0409 22:30:24.748224 24944 solver.cpp:218] Iteration 6168 (2.41198 iter/s, 4.97516s/12 iters), loss = 0.274468
I0409 22:30:24.748260 24944 solver.cpp:237]     Train net output #0: loss = 0.274468 (* 1 = 0.274468 loss)
I0409 22:30:24.748268 24944 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0409 22:30:25.317781 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:30:29.797343 24944 solver.cpp:218] Iteration 6180 (2.37676 iter/s, 5.04889s/12 iters), loss = 0.310542
I0409 22:30:29.797397 24944 solver.cpp:237]     Train net output #0: loss = 0.310542 (* 1 = 0.310542 loss)
I0409 22:30:29.797411 24944 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0409 22:30:34.646010 24944 solver.cpp:218] Iteration 6192 (2.47503 iter/s, 4.84843s/12 iters), loss = 0.413723
I0409 22:30:34.646067 24944 solver.cpp:237]     Train net output #0: loss = 0.413723 (* 1 = 0.413723 loss)
I0409 22:30:34.646080 24944 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0409 22:30:39.152218 24944 solver.cpp:218] Iteration 6204 (2.66313 iter/s, 4.50598s/12 iters), loss = 0.19288
I0409 22:30:39.152268 24944 solver.cpp:237]     Train net output #0: loss = 0.19288 (* 1 = 0.19288 loss)
I0409 22:30:39.152279 24944 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0409 22:30:44.434197 24944 solver.cpp:218] Iteration 6216 (2.27198 iter/s, 5.28173s/12 iters), loss = 0.319461
I0409 22:30:44.434244 24944 solver.cpp:237]     Train net output #0: loss = 0.319461 (* 1 = 0.319461 loss)
I0409 22:30:44.434254 24944 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0409 22:30:46.528024 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0409 22:31:17.099108 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0409 22:31:25.598976 24944 solver.cpp:330] Iteration 6222, Testing net (#0)
I0409 22:31:25.599000 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:31:27.665977 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:31:28.977295 24944 blocking_queue.cpp:49] Waiting for data
I0409 22:31:30.185730 24944 solver.cpp:397]     Test net output #0: accuracy = 0.510417
I0409 22:31:30.185772 24944 solver.cpp:397]     Test net output #1: loss = 2.52651 (* 1 = 2.52651 loss)
I0409 22:31:32.017153 24944 solver.cpp:218] Iteration 6228 (0.2522 iter/s, 47.5813s/12 iters), loss = 0.219064
I0409 22:31:32.017202 24944 solver.cpp:237]     Train net output #0: loss = 0.219064 (* 1 = 0.219064 loss)
I0409 22:31:32.017210 24944 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0409 22:31:36.846465 24944 solver.cpp:218] Iteration 6240 (2.48495 iter/s, 4.82908s/12 iters), loss = 0.28798
I0409 22:31:36.846518 24944 solver.cpp:237]     Train net output #0: loss = 0.28798 (* 1 = 0.28798 loss)
I0409 22:31:36.846529 24944 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0409 22:31:41.542081 24944 solver.cpp:218] Iteration 6252 (2.5557 iter/s, 4.69539s/12 iters), loss = 0.258541
I0409 22:31:41.542130 24944 solver.cpp:237]     Train net output #0: loss = 0.258541 (* 1 = 0.258541 loss)
I0409 22:31:41.542140 24944 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0409 22:31:46.326756 24944 solver.cpp:218] Iteration 6264 (2.50813 iter/s, 4.78444s/12 iters), loss = 0.198894
I0409 22:31:46.326807 24944 solver.cpp:237]     Train net output #0: loss = 0.198894 (* 1 = 0.198894 loss)
I0409 22:31:46.326818 24944 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0409 22:31:49.132431 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:31:51.530786 24944 solver.cpp:218] Iteration 6276 (2.30601 iter/s, 5.20378s/12 iters), loss = 0.227618
I0409 22:31:51.530838 24944 solver.cpp:237]     Train net output #0: loss = 0.227618 (* 1 = 0.227618 loss)
I0409 22:31:51.530850 24944 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0409 22:31:56.567088 24944 solver.cpp:218] Iteration 6288 (2.38281 iter/s, 5.03606s/12 iters), loss = 0.217064
I0409 22:31:56.567142 24944 solver.cpp:237]     Train net output #0: loss = 0.217064 (* 1 = 0.217064 loss)
I0409 22:31:56.567153 24944 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0409 22:32:01.654819 24944 solver.cpp:218] Iteration 6300 (2.35873 iter/s, 5.08749s/12 iters), loss = 0.311134
I0409 22:32:01.654870 24944 solver.cpp:237]     Train net output #0: loss = 0.311134 (* 1 = 0.311134 loss)
I0409 22:32:01.654879 24944 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0409 22:32:06.725399 24944 solver.cpp:218] Iteration 6312 (2.3667 iter/s, 5.07034s/12 iters), loss = 0.194493
I0409 22:32:06.725443 24944 solver.cpp:237]     Train net output #0: loss = 0.194493 (* 1 = 0.194493 loss)
I0409 22:32:06.725451 24944 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0409 22:32:11.125927 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0409 22:32:23.958496 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0409 22:32:36.556999 24944 solver.cpp:330] Iteration 6324, Testing net (#0)
I0409 22:32:36.557022 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:32:38.547688 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:32:41.057772 24944 solver.cpp:397]     Test net output #0: accuracy = 0.511642
I0409 22:32:41.057798 24944 solver.cpp:397]     Test net output #1: loss = 2.48096 (* 1 = 2.48096 loss)
I0409 22:32:41.177404 24944 solver.cpp:218] Iteration 6324 (0.348323 iter/s, 34.4508s/12 iters), loss = 0.203498
I0409 22:32:41.178928 24944 solver.cpp:237]     Train net output #0: loss = 0.203498 (* 1 = 0.203498 loss)
I0409 22:32:41.178941 24944 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0409 22:32:45.149367 24944 solver.cpp:218] Iteration 6336 (3.02244 iter/s, 3.9703s/12 iters), loss = 0.18651
I0409 22:32:45.149410 24944 solver.cpp:237]     Train net output #0: loss = 0.18651 (* 1 = 0.18651 loss)
I0409 22:32:45.149420 24944 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0409 22:32:49.788223 24944 solver.cpp:218] Iteration 6348 (2.58697 iter/s, 4.63864s/12 iters), loss = 0.252006
I0409 22:32:49.788270 24944 solver.cpp:237]     Train net output #0: loss = 0.252006 (* 1 = 0.252006 loss)
I0409 22:32:49.788280 24944 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0409 22:32:54.725455 24944 solver.cpp:218] Iteration 6360 (2.43063 iter/s, 4.937s/12 iters), loss = 0.222457
I0409 22:32:54.725576 24944 solver.cpp:237]     Train net output #0: loss = 0.222457 (* 1 = 0.222457 loss)
I0409 22:32:54.725586 24944 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0409 22:32:59.481808 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:32:59.658010 24944 solver.cpp:218] Iteration 6372 (2.43297 iter/s, 4.93225s/12 iters), loss = 0.234939
I0409 22:32:59.658056 24944 solver.cpp:237]     Train net output #0: loss = 0.234939 (* 1 = 0.234939 loss)
I0409 22:32:59.658064 24944 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0409 22:33:04.480412 24944 solver.cpp:218] Iteration 6384 (2.4885 iter/s, 4.82218s/12 iters), loss = 0.288807
I0409 22:33:04.480463 24944 solver.cpp:237]     Train net output #0: loss = 0.288807 (* 1 = 0.288807 loss)
I0409 22:33:04.480474 24944 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0409 22:33:09.305603 24944 solver.cpp:218] Iteration 6396 (2.48707 iter/s, 4.82496s/12 iters), loss = 0.254072
I0409 22:33:09.305656 24944 solver.cpp:237]     Train net output #0: loss = 0.254072 (* 1 = 0.254072 loss)
I0409 22:33:09.305668 24944 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0409 22:33:14.519378 24944 solver.cpp:218] Iteration 6408 (2.30171 iter/s, 5.21353s/12 iters), loss = 0.23699
I0409 22:33:14.519426 24944 solver.cpp:237]     Train net output #0: loss = 0.23699 (* 1 = 0.23699 loss)
I0409 22:33:14.519435 24944 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0409 22:33:19.526437 24944 solver.cpp:218] Iteration 6420 (2.39673 iter/s, 5.00683s/12 iters), loss = 0.314973
I0409 22:33:19.526479 24944 solver.cpp:237]     Train net output #0: loss = 0.314973 (* 1 = 0.314973 loss)
I0409 22:33:19.526487 24944 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0409 22:33:21.653302 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0409 22:33:59.192451 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0409 22:34:11.156108 24944 solver.cpp:330] Iteration 6426, Testing net (#0)
I0409 22:34:11.156131 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:34:13.110327 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:34:15.667889 24944 solver.cpp:397]     Test net output #0: accuracy = 0.51348
I0409 22:34:15.667935 24944 solver.cpp:397]     Test net output #1: loss = 2.54114 (* 1 = 2.54114 loss)
I0409 22:34:17.552142 24944 solver.cpp:218] Iteration 6432 (0.206812 iter/s, 58.0236s/12 iters), loss = 0.192802
I0409 22:34:17.552192 24944 solver.cpp:237]     Train net output #0: loss = 0.192802 (* 1 = 0.192802 loss)
I0409 22:34:17.552201 24944 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0409 22:34:22.710032 24944 solver.cpp:218] Iteration 6444 (2.32664 iter/s, 5.15765s/12 iters), loss = 0.207967
I0409 22:34:22.710098 24944 solver.cpp:237]     Train net output #0: loss = 0.207967 (* 1 = 0.207967 loss)
I0409 22:34:22.710108 24944 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0409 22:34:27.541299 24944 solver.cpp:218] Iteration 6456 (2.48395 iter/s, 4.83102s/12 iters), loss = 0.208652
I0409 22:34:27.541339 24944 solver.cpp:237]     Train net output #0: loss = 0.208652 (* 1 = 0.208652 loss)
I0409 22:34:27.541348 24944 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0409 22:34:32.450428 24944 solver.cpp:218] Iteration 6468 (2.44454 iter/s, 4.90891s/12 iters), loss = 0.128977
I0409 22:34:32.450517 24944 solver.cpp:237]     Train net output #0: loss = 0.128977 (* 1 = 0.128977 loss)
I0409 22:34:32.450528 24944 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0409 22:34:34.284498 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:34:37.138419 24944 solver.cpp:218] Iteration 6480 (2.55988 iter/s, 4.68772s/12 iters), loss = 0.157491
I0409 22:34:37.138464 24944 solver.cpp:237]     Train net output #0: loss = 0.157491 (* 1 = 0.157491 loss)
I0409 22:34:37.138473 24944 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0409 22:34:42.307900 24944 solver.cpp:218] Iteration 6492 (2.32142 iter/s, 5.16924s/12 iters), loss = 0.134755
I0409 22:34:42.307945 24944 solver.cpp:237]     Train net output #0: loss = 0.134755 (* 1 = 0.134755 loss)
I0409 22:34:42.307953 24944 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0409 22:34:47.335167 24944 solver.cpp:218] Iteration 6504 (2.38709 iter/s, 5.02703s/12 iters), loss = 0.31801
I0409 22:34:47.335216 24944 solver.cpp:237]     Train net output #0: loss = 0.31801 (* 1 = 0.31801 loss)
I0409 22:34:47.335227 24944 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0409 22:34:51.906777 24944 solver.cpp:218] Iteration 6516 (2.62503 iter/s, 4.57138s/12 iters), loss = 0.103313
I0409 22:34:51.906829 24944 solver.cpp:237]     Train net output #0: loss = 0.103313 (* 1 = 0.103313 loss)
I0409 22:34:51.906841 24944 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0409 22:34:56.253696 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0409 22:35:06.797921 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0409 22:35:19.278504 24944 solver.cpp:330] Iteration 6528, Testing net (#0)
I0409 22:35:19.278528 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:35:21.295226 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:35:24.276424 24944 solver.cpp:397]     Test net output #0: accuracy = 0.511642
I0409 22:35:24.276468 24944 solver.cpp:397]     Test net output #1: loss = 2.5424 (* 1 = 2.5424 loss)
I0409 22:35:24.397629 24944 solver.cpp:218] Iteration 6528 (0.369348 iter/s, 32.4897s/12 iters), loss = 0.307933
I0409 22:35:24.399163 24944 solver.cpp:237]     Train net output #0: loss = 0.307933 (* 1 = 0.307933 loss)
I0409 22:35:24.399175 24944 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0409 22:35:28.602986 24944 solver.cpp:218] Iteration 6540 (2.85465 iter/s, 4.20367s/12 iters), loss = 0.199357
I0409 22:35:28.603029 24944 solver.cpp:237]     Train net output #0: loss = 0.199357 (* 1 = 0.199357 loss)
I0409 22:35:28.603039 24944 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0409 22:35:33.558620 24944 solver.cpp:218] Iteration 6552 (2.4216 iter/s, 4.9554s/12 iters), loss = 0.193629
I0409 22:35:33.558689 24944 solver.cpp:237]     Train net output #0: loss = 0.193629 (* 1 = 0.193629 loss)
I0409 22:35:33.558704 24944 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0409 22:35:38.468652 24944 solver.cpp:218] Iteration 6564 (2.4441 iter/s, 4.90977s/12 iters), loss = 0.179022
I0409 22:35:38.468784 24944 solver.cpp:237]     Train net output #0: loss = 0.179022 (* 1 = 0.179022 loss)
I0409 22:35:38.468798 24944 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0409 22:35:42.780891 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:35:43.601950 24944 solver.cpp:218] Iteration 6576 (2.33782 iter/s, 5.13298s/12 iters), loss = 0.354525
I0409 22:35:43.602025 24944 solver.cpp:237]     Train net output #0: loss = 0.354525 (* 1 = 0.354525 loss)
I0409 22:35:43.602033 24944 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0409 22:35:48.196065 24944 solver.cpp:218] Iteration 6588 (2.61218 iter/s, 4.59387s/12 iters), loss = 0.183937
I0409 22:35:48.196111 24944 solver.cpp:237]     Train net output #0: loss = 0.183937 (* 1 = 0.183937 loss)
I0409 22:35:48.196120 24944 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0409 22:35:52.853610 24944 solver.cpp:218] Iteration 6600 (2.57659 iter/s, 4.65732s/12 iters), loss = 0.132777
I0409 22:35:52.853660 24944 solver.cpp:237]     Train net output #0: loss = 0.132777 (* 1 = 0.132777 loss)
I0409 22:35:52.853672 24944 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0409 22:35:57.409780 24944 solver.cpp:218] Iteration 6612 (2.63392 iter/s, 4.55594s/12 iters), loss = 0.128793
I0409 22:35:57.409835 24944 solver.cpp:237]     Train net output #0: loss = 0.128793 (* 1 = 0.128793 loss)
I0409 22:35:57.409848 24944 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0409 22:36:02.537633 24944 solver.cpp:218] Iteration 6624 (2.34027 iter/s, 5.12761s/12 iters), loss = 0.148862
I0409 22:36:02.537685 24944 solver.cpp:237]     Train net output #0: loss = 0.148862 (* 1 = 0.148862 loss)
I0409 22:36:02.537695 24944 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0409 22:36:04.564772 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0409 22:36:28.680269 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0409 22:36:49.171574 24944 solver.cpp:330] Iteration 6630, Testing net (#0)
I0409 22:36:49.171597 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:36:51.045634 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:36:53.740386 24944 solver.cpp:397]     Test net output #0: accuracy = 0.510417
I0409 22:36:53.740432 24944 solver.cpp:397]     Test net output #1: loss = 2.59857 (* 1 = 2.59857 loss)
I0409 22:36:55.442776 24944 solver.cpp:218] Iteration 6636 (0.226829 iter/s, 52.9033s/12 iters), loss = 0.226762
I0409 22:36:55.442821 24944 solver.cpp:237]     Train net output #0: loss = 0.226762 (* 1 = 0.226762 loss)
I0409 22:36:55.442831 24944 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0409 22:37:00.388931 24944 solver.cpp:218] Iteration 6648 (2.42624 iter/s, 4.94592s/12 iters), loss = 0.114541
I0409 22:37:00.389004 24944 solver.cpp:237]     Train net output #0: loss = 0.114541 (* 1 = 0.114541 loss)
I0409 22:37:00.389014 24944 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0409 22:37:05.416028 24944 solver.cpp:218] Iteration 6660 (2.38719 iter/s, 5.02684s/12 iters), loss = 0.177406
I0409 22:37:05.416074 24944 solver.cpp:237]     Train net output #0: loss = 0.177406 (* 1 = 0.177406 loss)
I0409 22:37:05.416082 24944 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0409 22:37:10.118933 24944 solver.cpp:218] Iteration 6672 (2.55174 iter/s, 4.70268s/12 iters), loss = 0.179702
I0409 22:37:10.118988 24944 solver.cpp:237]     Train net output #0: loss = 0.179702 (* 1 = 0.179702 loss)
I0409 22:37:10.118999 24944 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0409 22:37:11.436918 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:37:15.098546 24944 solver.cpp:218] Iteration 6684 (2.40994 iter/s, 4.97937s/12 iters), loss = 0.186309
I0409 22:37:15.098590 24944 solver.cpp:237]     Train net output #0: loss = 0.186309 (* 1 = 0.186309 loss)
I0409 22:37:15.098603 24944 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0409 22:37:19.820479 24944 solver.cpp:218] Iteration 6696 (2.54146 iter/s, 4.7217s/12 iters), loss = 0.291732
I0409 22:37:19.820544 24944 solver.cpp:237]     Train net output #0: loss = 0.291732 (* 1 = 0.291732 loss)
I0409 22:37:19.820559 24944 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0409 22:37:24.656919 24944 solver.cpp:218] Iteration 6708 (2.48129 iter/s, 4.8362s/12 iters), loss = 0.204664
I0409 22:37:24.656961 24944 solver.cpp:237]     Train net output #0: loss = 0.204664 (* 1 = 0.204664 loss)
I0409 22:37:24.656970 24944 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0409 22:37:29.765615 24944 solver.cpp:218] Iteration 6720 (2.34904 iter/s, 5.10846s/12 iters), loss = 0.186234
I0409 22:37:29.765656 24944 solver.cpp:237]     Train net output #0: loss = 0.186234 (* 1 = 0.186234 loss)
I0409 22:37:29.765666 24944 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0409 22:37:34.199642 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0409 22:37:51.853650 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0409 22:38:04.094539 24944 solver.cpp:330] Iteration 6732, Testing net (#0)
I0409 22:38:04.094563 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:38:05.955963 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:38:08.707348 24944 solver.cpp:397]     Test net output #0: accuracy = 0.509191
I0409 22:38:08.707381 24944 solver.cpp:397]     Test net output #1: loss = 2.58854 (* 1 = 2.58854 loss)
I0409 22:38:08.827783 24944 solver.cpp:218] Iteration 6732 (0.307214 iter/s, 39.0608s/12 iters), loss = 0.177934
I0409 22:38:08.829394 24944 solver.cpp:237]     Train net output #0: loss = 0.177934 (* 1 = 0.177934 loss)
I0409 22:38:08.829406 24944 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0409 22:38:13.148416 24944 solver.cpp:218] Iteration 6744 (2.77851 iter/s, 4.31886s/12 iters), loss = 0.162803
I0409 22:38:13.148463 24944 solver.cpp:237]     Train net output #0: loss = 0.162803 (* 1 = 0.162803 loss)
I0409 22:38:13.148475 24944 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0409 22:38:18.402123 24944 solver.cpp:218] Iteration 6756 (2.28421 iter/s, 5.25346s/12 iters), loss = 0.229432
I0409 22:38:18.402179 24944 solver.cpp:237]     Train net output #0: loss = 0.229432 (* 1 = 0.229432 loss)
I0409 22:38:18.402189 24944 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0409 22:38:23.436587 24944 solver.cpp:218] Iteration 6768 (2.38369 iter/s, 5.03422s/12 iters), loss = 0.251154
I0409 22:38:23.436636 24944 solver.cpp:237]     Train net output #0: loss = 0.251154 (* 1 = 0.251154 loss)
I0409 22:38:23.436647 24944 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0409 22:38:27.052923 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:38:28.621043 24944 solver.cpp:218] Iteration 6780 (2.31472 iter/s, 5.18421s/12 iters), loss = 0.0962057
I0409 22:38:28.621091 24944 solver.cpp:237]     Train net output #0: loss = 0.0962057 (* 1 = 0.0962057 loss)
I0409 22:38:28.621101 24944 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0409 22:38:33.866170 24944 solver.cpp:218] Iteration 6792 (2.28794 iter/s, 5.24488s/12 iters), loss = 0.119983
I0409 22:38:33.866219 24944 solver.cpp:237]     Train net output #0: loss = 0.119983 (* 1 = 0.119983 loss)
I0409 22:38:33.866228 24944 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0409 22:38:39.046906 24944 solver.cpp:218] Iteration 6804 (2.31638 iter/s, 5.1805s/12 iters), loss = 0.170434
I0409 22:38:39.050034 24944 solver.cpp:237]     Train net output #0: loss = 0.170434 (* 1 = 0.170434 loss)
I0409 22:38:39.050045 24944 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0409 22:38:44.438820 24944 solver.cpp:218] Iteration 6816 (2.22693 iter/s, 5.38859s/12 iters), loss = 0.305425
I0409 22:38:44.438863 24944 solver.cpp:237]     Train net output #0: loss = 0.305425 (* 1 = 0.305425 loss)
I0409 22:38:44.438872 24944 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0409 22:38:49.638701 24944 solver.cpp:218] Iteration 6828 (2.30785 iter/s, 5.19964s/12 iters), loss = 0.140294
I0409 22:38:49.638761 24944 solver.cpp:237]     Train net output #0: loss = 0.140294 (* 1 = 0.140294 loss)
I0409 22:38:49.638772 24944 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0409 22:38:51.734544 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0409 22:39:02.416280 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0409 22:39:16.105136 24944 solver.cpp:330] Iteration 6834, Testing net (#0)
I0409 22:39:16.105185 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:39:17.905786 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:39:20.621459 24944 solver.cpp:397]     Test net output #0: accuracy = 0.531863
I0409 22:39:20.621495 24944 solver.cpp:397]     Test net output #1: loss = 2.50245 (* 1 = 2.50245 loss)
I0409 22:39:22.469883 24944 solver.cpp:218] Iteration 6840 (0.36552 iter/s, 32.83s/12 iters), loss = 0.109724
I0409 22:39:22.469928 24944 solver.cpp:237]     Train net output #0: loss = 0.109724 (* 1 = 0.109724 loss)
I0409 22:39:22.469938 24944 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0409 22:39:27.710590 24944 solver.cpp:218] Iteration 6852 (2.28987 iter/s, 5.24047s/12 iters), loss = 0.130259
I0409 22:39:27.710626 24944 solver.cpp:237]     Train net output #0: loss = 0.130259 (* 1 = 0.130259 loss)
I0409 22:39:27.710635 24944 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0409 22:39:32.878593 24944 solver.cpp:218] Iteration 6864 (2.32208 iter/s, 5.16777s/12 iters), loss = 0.115961
I0409 22:39:32.878641 24944 solver.cpp:237]     Train net output #0: loss = 0.115961 (* 1 = 0.115961 loss)
I0409 22:39:32.878651 24944 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0409 22:39:38.126914 24944 solver.cpp:218] Iteration 6876 (2.28655 iter/s, 5.24807s/12 iters), loss = 0.139041
I0409 22:39:38.126963 24944 solver.cpp:237]     Train net output #0: loss = 0.139041 (* 1 = 0.139041 loss)
I0409 22:39:38.126973 24944 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0409 22:39:38.746393 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:39:43.227583 24944 solver.cpp:218] Iteration 6888 (2.35274 iter/s, 5.10043s/12 iters), loss = 0.072125
I0409 22:39:43.227628 24944 solver.cpp:237]     Train net output #0: loss = 0.072125 (* 1 = 0.072125 loss)
I0409 22:39:43.227639 24944 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0409 22:39:48.450376 24944 solver.cpp:218] Iteration 6900 (2.29773 iter/s, 5.22255s/12 iters), loss = 0.173716
I0409 22:39:48.450495 24944 solver.cpp:237]     Train net output #0: loss = 0.173716 (* 1 = 0.173716 loss)
I0409 22:39:48.450506 24944 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0409 22:39:53.567230 24944 solver.cpp:218] Iteration 6912 (2.34533 iter/s, 5.11655s/12 iters), loss = 0.23342
I0409 22:39:53.567274 24944 solver.cpp:237]     Train net output #0: loss = 0.23342 (* 1 = 0.23342 loss)
I0409 22:39:53.567283 24944 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0409 22:39:58.597681 24944 solver.cpp:218] Iteration 6924 (2.38558 iter/s, 5.03022s/12 iters), loss = 0.210175
I0409 22:39:58.597725 24944 solver.cpp:237]     Train net output #0: loss = 0.210175 (* 1 = 0.210175 loss)
I0409 22:39:58.597735 24944 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0409 22:40:03.313575 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0409 22:40:24.046909 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0409 22:40:52.806311 24944 solver.cpp:330] Iteration 6936, Testing net (#0)
I0409 22:40:52.806332 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:40:53.504292 24944 blocking_queue.cpp:49] Waiting for data
I0409 22:40:54.597887 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:40:57.352630 24944 solver.cpp:397]     Test net output #0: accuracy = 0.518382
I0409 22:40:57.352671 24944 solver.cpp:397]     Test net output #1: loss = 2.61602 (* 1 = 2.61602 loss)
I0409 22:40:57.473512 24944 solver.cpp:218] Iteration 6936 (0.203826 iter/s, 58.8737s/12 iters), loss = 0.165496
I0409 22:40:57.475031 24944 solver.cpp:237]     Train net output #0: loss = 0.165496 (* 1 = 0.165496 loss)
I0409 22:40:57.475042 24944 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0409 22:41:01.680879 24944 solver.cpp:218] Iteration 6948 (2.85328 iter/s, 4.20569s/12 iters), loss = 0.225394
I0409 22:41:01.680922 24944 solver.cpp:237]     Train net output #0: loss = 0.225394 (* 1 = 0.225394 loss)
I0409 22:41:01.680932 24944 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0409 22:41:06.623375 24944 solver.cpp:218] Iteration 6960 (2.42804 iter/s, 4.94227s/12 iters), loss = 0.103701
I0409 22:41:06.623421 24944 solver.cpp:237]     Train net output #0: loss = 0.103701 (* 1 = 0.103701 loss)
I0409 22:41:06.623433 24944 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0409 22:41:11.586735 24944 solver.cpp:218] Iteration 6972 (2.41783 iter/s, 4.96313s/12 iters), loss = 0.134661
I0409 22:41:11.586776 24944 solver.cpp:237]     Train net output #0: loss = 0.134661 (* 1 = 0.134661 loss)
I0409 22:41:11.586786 24944 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0409 22:41:14.433163 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:41:16.790484 24944 solver.cpp:218] Iteration 6984 (2.30614 iter/s, 5.2035s/12 iters), loss = 0.122737
I0409 22:41:16.790539 24944 solver.cpp:237]     Train net output #0: loss = 0.122737 (* 1 = 0.122737 loss)
I0409 22:41:16.790551 24944 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0409 22:41:21.941990 24944 solver.cpp:218] Iteration 6996 (2.32953 iter/s, 5.15126s/12 iters), loss = 0.133439
I0409 22:41:21.942040 24944 solver.cpp:237]     Train net output #0: loss = 0.133439 (* 1 = 0.133439 loss)
I0409 22:41:21.942051 24944 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0409 22:41:26.824393 24944 solver.cpp:218] Iteration 7008 (2.45792 iter/s, 4.88217s/12 iters), loss = 0.135268
I0409 22:41:26.824519 24944 solver.cpp:237]     Train net output #0: loss = 0.135268 (* 1 = 0.135268 loss)
I0409 22:41:26.824530 24944 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0409 22:41:31.258973 24944 solver.cpp:218] Iteration 7020 (2.70619 iter/s, 4.43428s/12 iters), loss = 0.141873
I0409 22:41:31.259033 24944 solver.cpp:237]     Train net output #0: loss = 0.141873 (* 1 = 0.141873 loss)
I0409 22:41:31.259045 24944 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0409 22:41:36.194134 24944 solver.cpp:218] Iteration 7032 (2.43165 iter/s, 4.93492s/12 iters), loss = 0.155919
I0409 22:41:36.194180 24944 solver.cpp:237]     Train net output #0: loss = 0.155919 (* 1 = 0.155919 loss)
I0409 22:41:36.194192 24944 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0409 22:41:38.073437 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0409 22:42:09.856282 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0409 22:42:19.195722 24944 solver.cpp:330] Iteration 7038, Testing net (#0)
I0409 22:42:19.195744 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:42:20.898610 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:42:23.744904 24944 solver.cpp:397]     Test net output #0: accuracy = 0.530637
I0409 22:42:23.744968 24944 solver.cpp:397]     Test net output #1: loss = 2.55349 (* 1 = 2.55349 loss)
I0409 22:42:25.652160 24944 solver.cpp:218] Iteration 7044 (0.242639 iter/s, 49.4562s/12 iters), loss = 0.0856919
I0409 22:42:25.652204 24944 solver.cpp:237]     Train net output #0: loss = 0.0856919 (* 1 = 0.0856919 loss)
I0409 22:42:25.652215 24944 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0409 22:42:30.867244 24944 solver.cpp:218] Iteration 7056 (2.30112 iter/s, 5.21484s/12 iters), loss = 0.103089
I0409 22:42:30.867286 24944 solver.cpp:237]     Train net output #0: loss = 0.103089 (* 1 = 0.103089 loss)
I0409 22:42:30.867295 24944 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0409 22:42:36.094597 24944 solver.cpp:218] Iteration 7068 (2.29572 iter/s, 5.22712s/12 iters), loss = 0.160072
I0409 22:42:36.094633 24944 solver.cpp:237]     Train net output #0: loss = 0.160072 (* 1 = 0.160072 loss)
I0409 22:42:36.094642 24944 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0409 22:42:40.855582 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:42:40.993722 24944 solver.cpp:218] Iteration 7080 (2.44953 iter/s, 4.89891s/12 iters), loss = 0.131595
I0409 22:42:40.993752 24944 solver.cpp:237]     Train net output #0: loss = 0.131595 (* 1 = 0.131595 loss)
I0409 22:42:40.993760 24944 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0409 22:42:46.115322 24944 solver.cpp:218] Iteration 7092 (2.34312 iter/s, 5.12137s/12 iters), loss = 0.195847
I0409 22:42:46.115381 24944 solver.cpp:237]     Train net output #0: loss = 0.195847 (* 1 = 0.195847 loss)
I0409 22:42:46.115396 24944 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0409 22:42:51.302608 24944 solver.cpp:218] Iteration 7104 (2.31346 iter/s, 5.18703s/12 iters), loss = 0.175953
I0409 22:42:51.302644 24944 solver.cpp:237]     Train net output #0: loss = 0.175953 (* 1 = 0.175953 loss)
I0409 22:42:51.302654 24944 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0409 22:42:56.308523 24944 solver.cpp:218] Iteration 7116 (2.39728 iter/s, 5.00568s/12 iters), loss = 0.14675
I0409 22:42:56.308586 24944 solver.cpp:237]     Train net output #0: loss = 0.14675 (* 1 = 0.14675 loss)
I0409 22:42:56.308598 24944 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0409 22:43:01.456530 24944 solver.cpp:218] Iteration 7128 (2.33111 iter/s, 5.14775s/12 iters), loss = 0.166239
I0409 22:43:01.456581 24944 solver.cpp:237]     Train net output #0: loss = 0.166239 (* 1 = 0.166239 loss)
I0409 22:43:01.456593 24944 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0409 22:43:06.193325 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0409 22:43:21.523898 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0409 22:43:31.578397 24944 solver.cpp:330] Iteration 7140, Testing net (#0)
I0409 22:43:31.578423 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:43:33.295483 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:43:36.255313 24944 solver.cpp:397]     Test net output #0: accuracy = 0.539828
I0409 22:43:36.255360 24944 solver.cpp:397]     Test net output #1: loss = 2.51878 (* 1 = 2.51878 loss)
I0409 22:43:36.370246 24944 solver.cpp:218] Iteration 7140 (0.343717 iter/s, 34.9124s/12 iters), loss = 0.105395
I0409 22:43:36.371771 24944 solver.cpp:237]     Train net output #0: loss = 0.105395 (* 1 = 0.105395 loss)
I0409 22:43:36.371783 24944 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0409 22:43:40.386945 24944 solver.cpp:218] Iteration 7152 (2.98878 iter/s, 4.01502s/12 iters), loss = 0.137358
I0409 22:43:40.386997 24944 solver.cpp:237]     Train net output #0: loss = 0.137358 (* 1 = 0.137358 loss)
I0409 22:43:40.387008 24944 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0409 22:43:45.004369 24944 solver.cpp:218] Iteration 7164 (2.59898 iter/s, 4.6172s/12 iters), loss = 0.189568
I0409 22:43:45.004411 24944 solver.cpp:237]     Train net output #0: loss = 0.189568 (* 1 = 0.189568 loss)
I0409 22:43:45.004420 24944 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0409 22:43:50.173156 24944 solver.cpp:218] Iteration 7176 (2.32173 iter/s, 5.16855s/12 iters), loss = 0.157318
I0409 22:43:50.173197 24944 solver.cpp:237]     Train net output #0: loss = 0.157318 (* 1 = 0.157318 loss)
I0409 22:43:50.173205 24944 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0409 22:43:52.206142 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:43:55.078567 24944 solver.cpp:218] Iteration 7188 (2.44639 iter/s, 4.90518s/12 iters), loss = 0.137359
I0409 22:43:55.078608 24944 solver.cpp:237]     Train net output #0: loss = 0.137359 (* 1 = 0.137359 loss)
I0409 22:43:55.078617 24944 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0409 22:43:59.776232 24944 solver.cpp:218] Iteration 7200 (2.55459 iter/s, 4.69743s/12 iters), loss = 0.233176
I0409 22:43:59.776288 24944 solver.cpp:237]     Train net output #0: loss = 0.233176 (* 1 = 0.233176 loss)
I0409 22:43:59.776299 24944 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0409 22:44:04.328974 24944 solver.cpp:218] Iteration 7212 (2.63591 iter/s, 4.55251s/12 iters), loss = 0.151164
I0409 22:44:04.329027 24944 solver.cpp:237]     Train net output #0: loss = 0.151164 (* 1 = 0.151164 loss)
I0409 22:44:04.329037 24944 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0409 22:44:08.979822 24944 solver.cpp:218] Iteration 7224 (2.5803 iter/s, 4.65062s/12 iters), loss = 0.0857435
I0409 22:44:08.979871 24944 solver.cpp:237]     Train net output #0: loss = 0.0857435 (* 1 = 0.0857435 loss)
I0409 22:44:08.979882 24944 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0409 22:44:13.738111 24944 solver.cpp:218] Iteration 7236 (2.52204 iter/s, 4.75806s/12 iters), loss = 0.130195
I0409 22:44:13.738166 24944 solver.cpp:237]     Train net output #0: loss = 0.130195 (* 1 = 0.130195 loss)
I0409 22:44:13.738178 24944 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0409 22:44:15.825536 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0409 22:44:31.458459 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0409 22:44:49.511054 24944 solver.cpp:330] Iteration 7242, Testing net (#0)
I0409 22:44:49.511075 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:44:51.182230 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:44:54.125479 24944 solver.cpp:397]     Test net output #0: accuracy = 0.528186
I0409 22:44:54.125525 24944 solver.cpp:397]     Test net output #1: loss = 2.51686 (* 1 = 2.51686 loss)
I0409 22:44:56.048075 24944 solver.cpp:218] Iteration 7248 (0.283631 iter/s, 42.3084s/12 iters), loss = 0.0928973
I0409 22:44:56.048123 24944 solver.cpp:237]     Train net output #0: loss = 0.0928973 (* 1 = 0.0928973 loss)
I0409 22:44:56.048132 24944 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0409 22:45:00.842151 24944 solver.cpp:218] Iteration 7260 (2.50321 iter/s, 4.79384s/12 iters), loss = 0.147337
I0409 22:45:00.842206 24944 solver.cpp:237]     Train net output #0: loss = 0.147337 (* 1 = 0.147337 loss)
I0409 22:45:00.842218 24944 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0409 22:45:05.591094 24944 solver.cpp:218] Iteration 7272 (2.527 iter/s, 4.74871s/12 iters), loss = 0.15955
I0409 22:45:05.591169 24944 solver.cpp:237]     Train net output #0: loss = 0.15955 (* 1 = 0.15955 loss)
I0409 22:45:05.591179 24944 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0409 22:45:09.506040 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:45:10.253176 24944 solver.cpp:218] Iteration 7284 (2.57409 iter/s, 4.66183s/12 iters), loss = 0.163716
I0409 22:45:10.253223 24944 solver.cpp:237]     Train net output #0: loss = 0.163716 (* 1 = 0.163716 loss)
I0409 22:45:10.253233 24944 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0409 22:45:15.035077 24944 solver.cpp:218] Iteration 7296 (2.50958 iter/s, 4.78168s/12 iters), loss = 0.191156
I0409 22:45:15.035120 24944 solver.cpp:237]     Train net output #0: loss = 0.191156 (* 1 = 0.191156 loss)
I0409 22:45:15.035130 24944 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0409 22:45:19.806180 24944 solver.cpp:218] Iteration 7308 (2.51526 iter/s, 4.77088s/12 iters), loss = 0.118704
I0409 22:45:19.806227 24944 solver.cpp:237]     Train net output #0: loss = 0.118704 (* 1 = 0.118704 loss)
I0409 22:45:19.806239 24944 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0409 22:45:24.369318 24944 solver.cpp:218] Iteration 7320 (2.62989 iter/s, 4.56292s/12 iters), loss = 0.0914513
I0409 22:45:24.369365 24944 solver.cpp:237]     Train net output #0: loss = 0.0914513 (* 1 = 0.0914513 loss)
I0409 22:45:24.369374 24944 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0409 22:45:29.260558 24944 solver.cpp:218] Iteration 7332 (2.45348 iter/s, 4.89101s/12 iters), loss = 0.122299
I0409 22:45:29.260608 24944 solver.cpp:237]     Train net output #0: loss = 0.122299 (* 1 = 0.122299 loss)
I0409 22:45:29.260618 24944 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0409 22:45:33.789149 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0409 22:45:55.651011 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0409 22:46:18.034066 24944 solver.cpp:330] Iteration 7344, Testing net (#0)
I0409 22:46:18.034097 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:46:19.630961 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:46:22.536433 24944 solver.cpp:397]     Test net output #0: accuracy = 0.547181
I0409 22:46:22.536481 24944 solver.cpp:397]     Test net output #1: loss = 2.55567 (* 1 = 2.55567 loss)
I0409 22:46:22.654328 24944 solver.cpp:218] Iteration 7344 (0.224753 iter/s, 53.3919s/12 iters), loss = 0.161289
I0409 22:46:22.655920 24944 solver.cpp:237]     Train net output #0: loss = 0.161289 (* 1 = 0.161289 loss)
I0409 22:46:22.655930 24944 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0409 22:46:27.050930 24944 solver.cpp:218] Iteration 7356 (2.73047 iter/s, 4.39485s/12 iters), loss = 0.0497297
I0409 22:46:27.051056 24944 solver.cpp:237]     Train net output #0: loss = 0.0497297 (* 1 = 0.0497297 loss)
I0409 22:46:27.051066 24944 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0409 22:46:32.271816 24944 solver.cpp:218] Iteration 7368 (2.2986 iter/s, 5.22057s/12 iters), loss = 0.219714
I0409 22:46:32.271859 24944 solver.cpp:237]     Train net output #0: loss = 0.219714 (* 1 = 0.219714 loss)
I0409 22:46:32.271869 24944 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0409 22:46:37.457363 24944 solver.cpp:218] Iteration 7380 (2.31423 iter/s, 5.1853s/12 iters), loss = 0.207741
I0409 22:46:37.457420 24944 solver.cpp:237]     Train net output #0: loss = 0.207741 (* 1 = 0.207741 loss)
I0409 22:46:37.457432 24944 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0409 22:46:38.765866 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:46:42.236853 24944 solver.cpp:218] Iteration 7392 (2.51085 iter/s, 4.77926s/12 iters), loss = 0.126592
I0409 22:46:42.236901 24944 solver.cpp:237]     Train net output #0: loss = 0.126592 (* 1 = 0.126592 loss)
I0409 22:46:42.236912 24944 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0409 22:46:47.425269 24944 solver.cpp:218] Iteration 7404 (2.31295 iter/s, 5.18817s/12 iters), loss = 0.122608
I0409 22:46:47.425315 24944 solver.cpp:237]     Train net output #0: loss = 0.122608 (* 1 = 0.122608 loss)
I0409 22:46:47.425324 24944 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0409 22:46:52.262241 24944 solver.cpp:218] Iteration 7416 (2.481 iter/s, 4.83675s/12 iters), loss = 0.0789736
I0409 22:46:52.262282 24944 solver.cpp:237]     Train net output #0: loss = 0.0789736 (* 1 = 0.0789736 loss)
I0409 22:46:52.262291 24944 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0409 22:46:56.954118 24944 solver.cpp:218] Iteration 7428 (2.55773 iter/s, 4.69166s/12 iters), loss = 0.150918
I0409 22:46:56.954164 24944 solver.cpp:237]     Train net output #0: loss = 0.150918 (* 1 = 0.150918 loss)
I0409 22:46:56.954174 24944 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0409 22:47:01.757262 24944 solver.cpp:218] Iteration 7440 (2.49848 iter/s, 4.80292s/12 iters), loss = 0.0937544
I0409 22:47:01.757356 24944 solver.cpp:237]     Train net output #0: loss = 0.0937544 (* 1 = 0.0937544 loss)
I0409 22:47:01.757367 24944 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0409 22:47:03.842195 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0409 22:47:18.963024 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0409 22:47:48.477447 24944 solver.cpp:330] Iteration 7446, Testing net (#0)
I0409 22:47:48.477494 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:47:50.027825 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:47:52.981986 24944 solver.cpp:397]     Test net output #0: accuracy = 0.529412
I0409 22:47:52.982031 24944 solver.cpp:397]     Test net output #1: loss = 2.54639 (* 1 = 2.54639 loss)
I0409 22:47:54.793582 24944 solver.cpp:218] Iteration 7452 (0.226268 iter/s, 53.0344s/12 iters), loss = 0.0934078
I0409 22:47:54.793634 24944 solver.cpp:237]     Train net output #0: loss = 0.0934078 (* 1 = 0.0934078 loss)
I0409 22:47:54.793648 24944 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0409 22:47:59.562153 24944 solver.cpp:218] Iteration 7464 (2.5166 iter/s, 4.76834s/12 iters), loss = 0.16167
I0409 22:47:59.562203 24944 solver.cpp:237]     Train net output #0: loss = 0.16167 (* 1 = 0.16167 loss)
I0409 22:47:59.562223 24944 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0409 22:48:04.344154 24944 solver.cpp:218] Iteration 7476 (2.50953 iter/s, 4.78177s/12 iters), loss = 0.10302
I0409 22:48:04.344200 24944 solver.cpp:237]     Train net output #0: loss = 0.10302 (* 1 = 0.10302 loss)
I0409 22:48:04.344211 24944 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0409 22:48:07.648306 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:48:09.095923 24944 solver.cpp:218] Iteration 7488 (2.5255 iter/s, 4.75154s/12 iters), loss = 0.160105
I0409 22:48:09.095974 24944 solver.cpp:237]     Train net output #0: loss = 0.160105 (* 1 = 0.160105 loss)
I0409 22:48:09.095985 24944 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0409 22:48:13.708719 24944 solver.cpp:218] Iteration 7500 (2.60159 iter/s, 4.61257s/12 iters), loss = 0.0926744
I0409 22:48:13.708765 24944 solver.cpp:237]     Train net output #0: loss = 0.0926744 (* 1 = 0.0926744 loss)
I0409 22:48:13.708775 24944 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0409 22:48:18.416285 24944 solver.cpp:218] Iteration 7512 (2.54921 iter/s, 4.70734s/12 iters), loss = 0.0736247
I0409 22:48:18.416333 24944 solver.cpp:237]     Train net output #0: loss = 0.0736247 (* 1 = 0.0736247 loss)
I0409 22:48:18.416344 24944 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0409 22:48:23.549696 24944 solver.cpp:218] Iteration 7524 (2.33774 iter/s, 5.13317s/12 iters), loss = 0.182182
I0409 22:48:23.549834 24944 solver.cpp:237]     Train net output #0: loss = 0.182182 (* 1 = 0.182182 loss)
I0409 22:48:23.549846 24944 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0409 22:48:28.349113 24944 solver.cpp:218] Iteration 7536 (2.50047 iter/s, 4.7991s/12 iters), loss = 0.141474
I0409 22:48:28.349165 24944 solver.cpp:237]     Train net output #0: loss = 0.141474 (* 1 = 0.141474 loss)
I0409 22:48:28.349177 24944 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0409 22:48:32.707979 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0409 22:48:44.455315 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0409 22:48:55.366322 24944 solver.cpp:330] Iteration 7548, Testing net (#0)
I0409 22:48:55.366395 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:48:57.043686 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:49:00.062417 24944 solver.cpp:397]     Test net output #0: accuracy = 0.545343
I0409 22:49:00.062456 24944 solver.cpp:397]     Test net output #1: loss = 2.5093 (* 1 = 2.5093 loss)
I0409 22:49:00.181988 24944 solver.cpp:218] Iteration 7548 (0.376983 iter/s, 31.8317s/12 iters), loss = 0.0896156
I0409 22:49:00.183502 24944 solver.cpp:237]     Train net output #0: loss = 0.0896156 (* 1 = 0.0896156 loss)
I0409 22:49:00.183514 24944 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0409 22:49:04.409313 24944 solver.cpp:218] Iteration 7560 (2.8398 iter/s, 4.22565s/12 iters), loss = 0.136275
I0409 22:49:04.409370 24944 solver.cpp:237]     Train net output #0: loss = 0.136275 (* 1 = 0.136275 loss)
I0409 22:49:04.409384 24944 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0409 22:49:09.527097 24944 solver.cpp:218] Iteration 7572 (2.34488 iter/s, 5.11754s/12 iters), loss = 0.136884
I0409 22:49:09.527146 24944 solver.cpp:237]     Train net output #0: loss = 0.136884 (* 1 = 0.136884 loss)
I0409 22:49:09.527155 24944 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0409 22:49:14.193472 24944 solver.cpp:218] Iteration 7584 (2.57171 iter/s, 4.66615s/12 iters), loss = 0.0902354
I0409 22:49:14.193522 24944 solver.cpp:237]     Train net output #0: loss = 0.0902354 (* 1 = 0.0902354 loss)
I0409 22:49:14.193534 24944 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0409 22:49:14.776351 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:49:19.299324 24944 solver.cpp:218] Iteration 7596 (2.35035 iter/s, 5.10561s/12 iters), loss = 0.0799325
I0409 22:49:19.299363 24944 solver.cpp:237]     Train net output #0: loss = 0.0799325 (* 1 = 0.0799325 loss)
I0409 22:49:19.299371 24944 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0409 22:49:24.484982 24944 solver.cpp:218] Iteration 7608 (2.31418 iter/s, 5.18542s/12 iters), loss = 0.060664
I0409 22:49:24.485033 24944 solver.cpp:237]     Train net output #0: loss = 0.060664 (* 1 = 0.060664 loss)
I0409 22:49:24.485044 24944 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0409 22:49:29.706195 24944 solver.cpp:218] Iteration 7620 (2.29842 iter/s, 5.22097s/12 iters), loss = 0.13569
I0409 22:49:29.706334 24944 solver.cpp:237]     Train net output #0: loss = 0.13569 (* 1 = 0.13569 loss)
I0409 22:49:29.706347 24944 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0409 22:49:32.202821 24944 blocking_queue.cpp:49] Waiting for data
I0409 22:49:34.843853 24944 solver.cpp:218] Iteration 7632 (2.33584 iter/s, 5.13733s/12 iters), loss = 0.124889
I0409 22:49:34.843899 24944 solver.cpp:237]     Train net output #0: loss = 0.124889 (* 1 = 0.124889 loss)
I0409 22:49:34.843907 24944 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0409 22:49:40.058311 24944 solver.cpp:218] Iteration 7644 (2.3014 iter/s, 5.21422s/12 iters), loss = 0.131123
I0409 22:49:40.058369 24944 solver.cpp:237]     Train net output #0: loss = 0.131123 (* 1 = 0.131123 loss)
I0409 22:49:40.058382 24944 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0409 22:49:41.959023 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0409 22:49:59.752548 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0409 22:50:25.520102 24944 solver.cpp:330] Iteration 7650, Testing net (#0)
I0409 22:50:25.520124 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:50:26.975764 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:50:30.034533 24944 solver.cpp:397]     Test net output #0: accuracy = 0.534314
I0409 22:50:30.034615 24944 solver.cpp:397]     Test net output #1: loss = 2.61255 (* 1 = 2.61255 loss)
I0409 22:50:31.902451 24944 solver.cpp:218] Iteration 7656 (0.231471 iter/s, 51.8423s/12 iters), loss = 0.0920708
I0409 22:50:31.902498 24944 solver.cpp:237]     Train net output #0: loss = 0.0920709 (* 1 = 0.0920709 loss)
I0409 22:50:31.902508 24944 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0409 22:50:36.594184 24944 solver.cpp:218] Iteration 7668 (2.55781 iter/s, 4.69151s/12 iters), loss = 0.0548949
I0409 22:50:36.594238 24944 solver.cpp:237]     Train net output #0: loss = 0.0548949 (* 1 = 0.0548949 loss)
I0409 22:50:36.594249 24944 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0409 22:50:41.648605 24944 solver.cpp:218] Iteration 7680 (2.37427 iter/s, 5.05418s/12 iters), loss = 0.111393
I0409 22:50:41.648651 24944 solver.cpp:237]     Train net output #0: loss = 0.111393 (* 1 = 0.111393 loss)
I0409 22:50:41.648660 24944 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0409 22:50:44.194056 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:50:46.357046 24944 solver.cpp:218] Iteration 7692 (2.54874 iter/s, 4.70822s/12 iters), loss = 0.0705401
I0409 22:50:46.357092 24944 solver.cpp:237]     Train net output #0: loss = 0.0705402 (* 1 = 0.0705402 loss)
I0409 22:50:46.357103 24944 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0409 22:50:50.963701 24944 solver.cpp:218] Iteration 7704 (2.60505 iter/s, 4.60643s/12 iters), loss = 0.0793749
I0409 22:50:50.963750 24944 solver.cpp:237]     Train net output #0: loss = 0.0793749 (* 1 = 0.0793749 loss)
I0409 22:50:50.963760 24944 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0409 22:50:55.640303 24944 solver.cpp:218] Iteration 7716 (2.56609 iter/s, 4.67638s/12 iters), loss = 0.0934223
I0409 22:50:55.640345 24944 solver.cpp:237]     Train net output #0: loss = 0.0934223 (* 1 = 0.0934223 loss)
I0409 22:50:55.640354 24944 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0409 22:51:00.404999 24944 solver.cpp:218] Iteration 7728 (2.51865 iter/s, 4.76447s/12 iters), loss = 0.0602526
I0409 22:51:00.405112 24944 solver.cpp:237]     Train net output #0: loss = 0.0602526 (* 1 = 0.0602526 loss)
I0409 22:51:00.405123 24944 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0409 22:51:05.259152 24944 solver.cpp:218] Iteration 7740 (2.47226 iter/s, 4.85386s/12 iters), loss = 0.0722337
I0409 22:51:05.259203 24944 solver.cpp:237]     Train net output #0: loss = 0.0722337 (* 1 = 0.0722337 loss)
I0409 22:51:05.259214 24944 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0409 22:51:09.903182 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0409 22:51:23.009454 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0409 22:51:35.391100 24944 solver.cpp:330] Iteration 7752, Testing net (#0)
I0409 22:51:35.391182 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:51:36.807366 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:51:39.876212 24944 solver.cpp:397]     Test net output #0: accuracy = 0.550858
I0409 22:51:39.876261 24944 solver.cpp:397]     Test net output #1: loss = 2.54155 (* 1 = 2.54155 loss)
I0409 22:51:39.997313 24944 solver.cpp:218] Iteration 7752 (0.345454 iter/s, 34.7369s/12 iters), loss = 0.045918
I0409 22:51:39.998838 24944 solver.cpp:237]     Train net output #0: loss = 0.045918 (* 1 = 0.045918 loss)
I0409 22:51:39.998852 24944 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0409 22:51:43.915951 24944 solver.cpp:218] Iteration 7764 (3.06359 iter/s, 3.91697s/12 iters), loss = 0.13716
I0409 22:51:43.915999 24944 solver.cpp:237]     Train net output #0: loss = 0.13716 (* 1 = 0.13716 loss)
I0409 22:51:43.916010 24944 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0409 22:51:48.556032 24944 solver.cpp:218] Iteration 7776 (2.58629 iter/s, 4.63986s/12 iters), loss = 0.0546971
I0409 22:51:48.556077 24944 solver.cpp:237]     Train net output #0: loss = 0.054697 (* 1 = 0.054697 loss)
I0409 22:51:48.556087 24944 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0409 22:51:53.221951 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:51:53.277285 24944 solver.cpp:218] Iteration 7788 (2.54182 iter/s, 4.72103s/12 iters), loss = 0.0575461
I0409 22:51:53.277335 24944 solver.cpp:237]     Train net output #0: loss = 0.0575461 (* 1 = 0.0575461 loss)
I0409 22:51:53.277344 24944 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0409 22:51:58.673347 24944 solver.cpp:218] Iteration 7800 (2.22395 iter/s, 5.39581s/12 iters), loss = 0.0915499
I0409 22:51:58.673396 24944 solver.cpp:237]     Train net output #0: loss = 0.0915499 (* 1 = 0.0915499 loss)
I0409 22:51:58.673408 24944 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0409 22:52:03.423429 24944 solver.cpp:218] Iteration 7812 (2.52639 iter/s, 4.74986s/12 iters), loss = 0.0996659
I0409 22:52:03.423478 24944 solver.cpp:237]     Train net output #0: loss = 0.0996659 (* 1 = 0.0996659 loss)
I0409 22:52:03.423489 24944 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0409 22:52:08.127229 24944 solver.cpp:218] Iteration 7824 (2.55125 iter/s, 4.70357s/12 iters), loss = 0.0571529
I0409 22:52:08.127308 24944 solver.cpp:237]     Train net output #0: loss = 0.0571529 (* 1 = 0.0571529 loss)
I0409 22:52:08.127317 24944 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0409 22:52:13.059018 24944 solver.cpp:218] Iteration 7836 (2.43332 iter/s, 4.93153s/12 iters), loss = 0.113627
I0409 22:52:13.059067 24944 solver.cpp:237]     Train net output #0: loss = 0.113627 (* 1 = 0.113627 loss)
I0409 22:52:13.059078 24944 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0409 22:52:18.003096 24944 solver.cpp:218] Iteration 7848 (2.42726 iter/s, 4.94384s/12 iters), loss = 0.0767578
I0409 22:52:18.003140 24944 solver.cpp:237]     Train net output #0: loss = 0.0767578 (* 1 = 0.0767578 loss)
I0409 22:52:18.003149 24944 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0409 22:52:19.979421 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0409 22:52:48.396664 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0409 22:53:02.217777 24944 solver.cpp:330] Iteration 7854, Testing net (#0)
I0409 22:53:02.217803 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:53:03.597415 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:53:07.104254 24944 solver.cpp:397]     Test net output #0: accuracy = 0.545956
I0409 22:53:07.104290 24944 solver.cpp:397]     Test net output #1: loss = 2.49399 (* 1 = 2.49399 loss)
I0409 22:53:08.907986 24944 solver.cpp:218] Iteration 7860 (0.235742 iter/s, 50.9031s/12 iters), loss = 0.0864869
I0409 22:53:08.908041 24944 solver.cpp:237]     Train net output #0: loss = 0.0864869 (* 1 = 0.0864869 loss)
I0409 22:53:08.908052 24944 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0409 22:53:13.803388 24944 solver.cpp:218] Iteration 7872 (2.4514 iter/s, 4.89516s/12 iters), loss = 0.170564
I0409 22:53:13.803442 24944 solver.cpp:237]     Train net output #0: loss = 0.170564 (* 1 = 0.170564 loss)
I0409 22:53:13.803453 24944 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0409 22:53:18.539822 24944 solver.cpp:218] Iteration 7884 (2.53368 iter/s, 4.7362s/12 iters), loss = 0.0452378
I0409 22:53:18.539994 24944 solver.cpp:237]     Train net output #0: loss = 0.0452378 (* 1 = 0.0452378 loss)
I0409 22:53:18.540007 24944 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0409 22:53:20.533545 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:53:23.578068 24944 solver.cpp:218] Iteration 7896 (2.38195 iter/s, 5.03789s/12 iters), loss = 0.12811
I0409 22:53:23.578114 24944 solver.cpp:237]     Train net output #0: loss = 0.12811 (* 1 = 0.12811 loss)
I0409 22:53:23.578121 24944 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0409 22:53:28.641698 24944 solver.cpp:218] Iteration 7908 (2.36996 iter/s, 5.06339s/12 iters), loss = 0.0476601
I0409 22:53:28.641757 24944 solver.cpp:237]     Train net output #0: loss = 0.0476601 (* 1 = 0.0476601 loss)
I0409 22:53:28.641772 24944 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0409 22:53:33.496160 24944 solver.cpp:218] Iteration 7920 (2.47208 iter/s, 4.85422s/12 iters), loss = 0.121859
I0409 22:53:33.496208 24944 solver.cpp:237]     Train net output #0: loss = 0.121859 (* 1 = 0.121859 loss)
I0409 22:53:33.496219 24944 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0409 22:53:38.170419 24944 solver.cpp:218] Iteration 7932 (2.56738 iter/s, 4.67403s/12 iters), loss = 0.0872917
I0409 22:53:38.170462 24944 solver.cpp:237]     Train net output #0: loss = 0.0872917 (* 1 = 0.0872917 loss)
I0409 22:53:38.170471 24944 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0409 22:53:43.526901 24944 solver.cpp:218] Iteration 7944 (2.24038 iter/s, 5.35623s/12 iters), loss = 0.0468745
I0409 22:53:43.526953 24944 solver.cpp:237]     Train net output #0: loss = 0.0468745 (* 1 = 0.0468745 loss)
I0409 22:53:43.526964 24944 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0409 22:53:47.897352 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0409 22:53:59.392347 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0409 22:54:14.961918 24944 solver.cpp:330] Iteration 7956, Testing net (#0)
I0409 22:54:14.961941 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:54:16.219969 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:54:19.420116 24944 solver.cpp:397]     Test net output #0: accuracy = 0.533088
I0409 22:54:19.420152 24944 solver.cpp:397]     Test net output #1: loss = 2.62429 (* 1 = 2.62429 loss)
I0409 22:54:19.540817 24944 solver.cpp:218] Iteration 7956 (0.333217 iter/s, 36.0126s/12 iters), loss = 0.0710129
I0409 22:54:19.540855 24944 solver.cpp:237]     Train net output #0: loss = 0.0710129 (* 1 = 0.0710129 loss)
I0409 22:54:19.540863 24944 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0409 22:54:23.997516 24944 solver.cpp:218] Iteration 7968 (2.6927 iter/s, 4.45649s/12 iters), loss = 0.0860036
I0409 22:54:23.997561 24944 solver.cpp:237]     Train net output #0: loss = 0.0860036 (* 1 = 0.0860036 loss)
I0409 22:54:23.997570 24944 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0409 22:54:29.056932 24944 solver.cpp:218] Iteration 7980 (2.37193 iter/s, 5.05917s/12 iters), loss = 0.129024
I0409 22:54:29.056991 24944 solver.cpp:237]     Train net output #0: loss = 0.129024 (* 1 = 0.129024 loss)
I0409 22:54:29.057003 24944 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0409 22:54:32.982678 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:54:33.734452 24944 solver.cpp:218] Iteration 7992 (2.56559 iter/s, 4.67728s/12 iters), loss = 0.0860502
I0409 22:54:33.734501 24944 solver.cpp:237]     Train net output #0: loss = 0.0860502 (* 1 = 0.0860502 loss)
I0409 22:54:33.734509 24944 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0409 22:54:38.488111 24944 solver.cpp:218] Iteration 8004 (2.5245 iter/s, 4.75342s/12 iters), loss = 0.0871543
I0409 22:54:38.488168 24944 solver.cpp:237]     Train net output #0: loss = 0.0871543 (* 1 = 0.0871543 loss)
I0409 22:54:38.488178 24944 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0409 22:54:43.223249 24944 solver.cpp:218] Iteration 8016 (2.53437 iter/s, 4.7349s/12 iters), loss = 0.025575
I0409 22:54:43.223299 24944 solver.cpp:237]     Train net output #0: loss = 0.025575 (* 1 = 0.025575 loss)
I0409 22:54:43.223309 24944 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0409 22:54:47.942178 24944 solver.cpp:218] Iteration 8028 (2.54307 iter/s, 4.7187s/12 iters), loss = 0.172054
I0409 22:54:47.942219 24944 solver.cpp:237]     Train net output #0: loss = 0.172054 (* 1 = 0.172054 loss)
I0409 22:54:47.942227 24944 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0409 22:54:53.234982 24944 solver.cpp:218] Iteration 8040 (2.26733 iter/s, 5.29256s/12 iters), loss = 0.0706002
I0409 22:54:53.235030 24944 solver.cpp:237]     Train net output #0: loss = 0.0706003 (* 1 = 0.0706003 loss)
I0409 22:54:53.235039 24944 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0409 22:54:58.619935 24944 solver.cpp:218] Iteration 8052 (2.22854 iter/s, 5.3847s/12 iters), loss = 0.102569
I0409 22:54:58.619984 24944 solver.cpp:237]     Train net output #0: loss = 0.102569 (* 1 = 0.102569 loss)
I0409 22:54:58.619993 24944 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0409 22:55:00.650946 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0409 22:55:12.478294 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0409 22:55:21.093235 24944 solver.cpp:330] Iteration 8058, Testing net (#0)
I0409 22:55:21.093256 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:55:22.516043 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:55:25.743026 24944 solver.cpp:397]     Test net output #0: accuracy = 0.549632
I0409 22:55:25.743062 24944 solver.cpp:397]     Test net output #1: loss = 2.60998 (* 1 = 2.60998 loss)
I0409 22:55:27.509876 24944 solver.cpp:218] Iteration 8064 (0.415385 iter/s, 28.8889s/12 iters), loss = 0.0557651
I0409 22:55:27.509930 24944 solver.cpp:237]     Train net output #0: loss = 0.0557651 (* 1 = 0.0557651 loss)
I0409 22:55:27.509943 24944 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0409 22:55:32.382030 24944 solver.cpp:218] Iteration 8076 (2.4631 iter/s, 4.87192s/12 iters), loss = 0.117336
I0409 22:55:32.382076 24944 solver.cpp:237]     Train net output #0: loss = 0.117336 (* 1 = 0.117336 loss)
I0409 22:55:32.382084 24944 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0409 22:55:37.228834 24944 solver.cpp:218] Iteration 8088 (2.47598 iter/s, 4.84657s/12 iters), loss = 0.114771
I0409 22:55:37.228893 24944 solver.cpp:237]     Train net output #0: loss = 0.114771 (* 1 = 0.114771 loss)
I0409 22:55:37.228904 24944 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0409 22:55:38.566675 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:55:42.452452 24944 solver.cpp:218] Iteration 8100 (2.29737 iter/s, 5.22337s/12 iters), loss = 0.0760954
I0409 22:55:42.452491 24944 solver.cpp:237]     Train net output #0: loss = 0.0760954 (* 1 = 0.0760954 loss)
I0409 22:55:42.452499 24944 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0409 22:55:47.264708 24944 solver.cpp:218] Iteration 8112 (2.49375 iter/s, 4.81203s/12 iters), loss = 0.072661
I0409 22:55:47.264784 24944 solver.cpp:237]     Train net output #0: loss = 0.072661 (* 1 = 0.072661 loss)
I0409 22:55:47.264796 24944 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0409 22:55:52.053012 24944 solver.cpp:218] Iteration 8124 (2.50624 iter/s, 4.78805s/12 iters), loss = 0.0923565
I0409 22:55:52.053054 24944 solver.cpp:237]     Train net output #0: loss = 0.0923564 (* 1 = 0.0923564 loss)
I0409 22:55:52.053062 24944 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0409 22:55:56.713567 24944 solver.cpp:218] Iteration 8136 (2.57492 iter/s, 4.66033s/12 iters), loss = 0.137382
I0409 22:55:56.713613 24944 solver.cpp:237]     Train net output #0: loss = 0.137382 (* 1 = 0.137382 loss)
I0409 22:55:56.713622 24944 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0409 22:56:01.573901 24944 solver.cpp:218] Iteration 8148 (2.46908 iter/s, 4.86011s/12 iters), loss = 0.0740528
I0409 22:56:01.573976 24944 solver.cpp:237]     Train net output #0: loss = 0.0740528 (* 1 = 0.0740528 loss)
I0409 22:56:01.573988 24944 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0409 22:56:05.619455 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0409 22:56:16.255417 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0409 22:56:36.608623 24944 solver.cpp:330] Iteration 8160, Testing net (#0)
I0409 22:56:36.608718 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:56:37.783241 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:56:41.039340 24944 solver.cpp:397]     Test net output #0: accuracy = 0.553922
I0409 22:56:41.039389 24944 solver.cpp:397]     Test net output #1: loss = 2.54955 (* 1 = 2.54955 loss)
I0409 22:56:41.160398 24944 solver.cpp:218] Iteration 8160 (0.303145 iter/s, 39.5851s/12 iters), loss = 0.0821759
I0409 22:56:41.161916 24944 solver.cpp:237]     Train net output #0: loss = 0.0821759 (* 1 = 0.0821759 loss)
I0409 22:56:41.161927 24944 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0409 22:56:45.076139 24944 solver.cpp:218] Iteration 8172 (3.06586 iter/s, 3.91407s/12 iters), loss = 0.0875788
I0409 22:56:45.076198 24944 solver.cpp:237]     Train net output #0: loss = 0.0875787 (* 1 = 0.0875787 loss)
I0409 22:56:45.076210 24944 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0409 22:56:49.802028 24944 solver.cpp:218] Iteration 8184 (2.53933 iter/s, 4.72565s/12 iters), loss = 0.0428345
I0409 22:56:49.802073 24944 solver.cpp:237]     Train net output #0: loss = 0.0428345 (* 1 = 0.0428345 loss)
I0409 22:56:49.802083 24944 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0409 22:56:53.261480 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:56:54.728199 24944 solver.cpp:218] Iteration 8196 (2.43609 iter/s, 4.92593s/12 iters), loss = 0.0694407
I0409 22:56:54.728255 24944 solver.cpp:237]     Train net output #0: loss = 0.0694406 (* 1 = 0.0694406 loss)
I0409 22:56:54.728266 24944 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0409 22:56:59.679729 24944 solver.cpp:218] Iteration 8208 (2.42361 iter/s, 4.95129s/12 iters), loss = 0.0279662
I0409 22:56:59.679769 24944 solver.cpp:237]     Train net output #0: loss = 0.0279662 (* 1 = 0.0279662 loss)
I0409 22:56:59.679776 24944 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0409 22:57:04.927561 24944 solver.cpp:218] Iteration 8220 (2.28677 iter/s, 5.24759s/12 iters), loss = 0.0552177
I0409 22:57:04.927618 24944 solver.cpp:237]     Train net output #0: loss = 0.0552177 (* 1 = 0.0552177 loss)
I0409 22:57:04.927626 24944 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0409 22:57:09.684834 24944 solver.cpp:218] Iteration 8232 (2.52258 iter/s, 4.75703s/12 iters), loss = 0.0842566
I0409 22:57:09.684952 24944 solver.cpp:237]     Train net output #0: loss = 0.0842566 (* 1 = 0.0842566 loss)
I0409 22:57:09.684965 24944 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0409 22:57:14.316848 24944 solver.cpp:218] Iteration 8244 (2.59083 iter/s, 4.63172s/12 iters), loss = 0.101717
I0409 22:57:14.316902 24944 solver.cpp:237]     Train net output #0: loss = 0.101717 (* 1 = 0.101717 loss)
I0409 22:57:14.316913 24944 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0409 22:57:19.274775 24944 solver.cpp:218] Iteration 8256 (2.42048 iter/s, 4.95769s/12 iters), loss = 0.0506683
I0409 22:57:19.274827 24944 solver.cpp:237]     Train net output #0: loss = 0.0506683 (* 1 = 0.0506683 loss)
I0409 22:57:19.274837 24944 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0409 22:57:21.137702 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0409 22:57:38.050500 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0409 22:57:49.810616 24944 solver.cpp:330] Iteration 8262, Testing net (#0)
I0409 22:57:49.810708 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:57:50.957684 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:57:54.382295 24944 solver.cpp:397]     Test net output #0: accuracy = 0.558211
I0409 22:57:54.382349 24944 solver.cpp:397]     Test net output #1: loss = 2.59156 (* 1 = 2.59156 loss)
I0409 22:57:56.137048 24944 solver.cpp:218] Iteration 8268 (0.325548 iter/s, 36.8609s/12 iters), loss = 0.040904
I0409 22:57:56.137091 24944 solver.cpp:237]     Train net output #0: loss = 0.040904 (* 1 = 0.040904 loss)
I0409 22:57:56.137100 24944 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0409 22:58:01.046468 24944 solver.cpp:218] Iteration 8280 (2.4444 iter/s, 4.90919s/12 iters), loss = 0.0450588
I0409 22:58:01.046512 24944 solver.cpp:237]     Train net output #0: loss = 0.0450588 (* 1 = 0.0450588 loss)
I0409 22:58:01.046521 24944 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0409 22:58:05.792261 24944 solver.cpp:218] Iteration 8292 (2.52868 iter/s, 4.74556s/12 iters), loss = 0.176877
I0409 22:58:05.792311 24944 solver.cpp:237]     Train net output #0: loss = 0.176877 (* 1 = 0.176877 loss)
I0409 22:58:05.792321 24944 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0409 22:58:06.357189 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:58:10.517354 24944 solver.cpp:218] Iteration 8304 (2.53976 iter/s, 4.72486s/12 iters), loss = 0.0872783
I0409 22:58:10.517408 24944 solver.cpp:237]     Train net output #0: loss = 0.0872783 (* 1 = 0.0872783 loss)
I0409 22:58:10.517421 24944 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0409 22:58:13.225126 24944 blocking_queue.cpp:49] Waiting for data
I0409 22:58:15.232024 24944 solver.cpp:218] Iteration 8316 (2.54537 iter/s, 4.71444s/12 iters), loss = 0.0468189
I0409 22:58:15.232070 24944 solver.cpp:237]     Train net output #0: loss = 0.0468189 (* 1 = 0.0468189 loss)
I0409 22:58:15.232080 24944 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0409 22:58:19.835208 24944 solver.cpp:218] Iteration 8328 (2.60702 iter/s, 4.60296s/12 iters), loss = 0.0905862
I0409 22:58:19.858053 24944 solver.cpp:237]     Train net output #0: loss = 0.0905862 (* 1 = 0.0905862 loss)
I0409 22:58:19.858065 24944 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0409 22:58:24.524094 24944 solver.cpp:218] Iteration 8340 (2.57187 iter/s, 4.66587s/12 iters), loss = 0.122946
I0409 22:58:24.524147 24944 solver.cpp:237]     Train net output #0: loss = 0.122946 (* 1 = 0.122946 loss)
I0409 22:58:24.524156 24944 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0409 22:58:29.263402 24944 solver.cpp:218] Iteration 8352 (2.53214 iter/s, 4.73908s/12 iters), loss = 0.0292687
I0409 22:58:29.263445 24944 solver.cpp:237]     Train net output #0: loss = 0.0292687 (* 1 = 0.0292687 loss)
I0409 22:58:29.263454 24944 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0409 22:58:33.498365 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0409 22:58:48.535625 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0409 22:59:05.156157 24944 solver.cpp:330] Iteration 8364, Testing net (#0)
I0409 22:59:05.156234 24944 net.cpp:676] Ignoring source layer train-data
I0409 22:59:06.401007 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:59:09.780354 24944 solver.cpp:397]     Test net output #0: accuracy = 0.543505
I0409 22:59:09.780387 24944 solver.cpp:397]     Test net output #1: loss = 2.55717 (* 1 = 2.55717 loss)
I0409 22:59:09.899161 24944 solver.cpp:218] Iteration 8364 (0.295317 iter/s, 40.6343s/12 iters), loss = 0.0568906
I0409 22:59:09.900683 24944 solver.cpp:237]     Train net output #0: loss = 0.0568906 (* 1 = 0.0568906 loss)
I0409 22:59:09.900693 24944 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0409 22:59:14.206589 24944 solver.cpp:218] Iteration 8376 (2.78698 iter/s, 4.30574s/12 iters), loss = 0.0946929
I0409 22:59:14.206636 24944 solver.cpp:237]     Train net output #0: loss = 0.0946929 (* 1 = 0.0946929 loss)
I0409 22:59:14.206645 24944 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0409 22:59:18.886132 24944 solver.cpp:218] Iteration 8388 (2.56448 iter/s, 4.67932s/12 iters), loss = 0.119594
I0409 22:59:18.886186 24944 solver.cpp:237]     Train net output #0: loss = 0.119594 (* 1 = 0.119594 loss)
I0409 22:59:18.886198 24944 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0409 22:59:21.370374 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:59:23.373796 24944 solver.cpp:218] Iteration 8400 (2.67413 iter/s, 4.48743s/12 iters), loss = 0.0617913
I0409 22:59:23.373857 24944 solver.cpp:237]     Train net output #0: loss = 0.0617914 (* 1 = 0.0617914 loss)
I0409 22:59:23.373868 24944 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0409 22:59:28.155300 24944 solver.cpp:218] Iteration 8412 (2.5098 iter/s, 4.78126s/12 iters), loss = 0.126906
I0409 22:59:28.155355 24944 solver.cpp:237]     Train net output #0: loss = 0.126906 (* 1 = 0.126906 loss)
I0409 22:59:28.155364 24944 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0409 22:59:33.076611 24944 solver.cpp:218] Iteration 8424 (2.43849 iter/s, 4.92107s/12 iters), loss = 0.132984
I0409 22:59:33.076654 24944 solver.cpp:237]     Train net output #0: loss = 0.132984 (* 1 = 0.132984 loss)
I0409 22:59:33.076663 24944 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0409 22:59:37.646612 24944 solver.cpp:218] Iteration 8436 (2.62595 iter/s, 4.56978s/12 iters), loss = 0.0398283
I0409 22:59:37.646747 24944 solver.cpp:237]     Train net output #0: loss = 0.0398283 (* 1 = 0.0398283 loss)
I0409 22:59:37.646757 24944 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0409 22:59:42.338125 24944 solver.cpp:218] Iteration 8448 (2.55798 iter/s, 4.69121s/12 iters), loss = 0.0254067
I0409 22:59:42.338162 24944 solver.cpp:237]     Train net output #0: loss = 0.0254067 (* 1 = 0.0254067 loss)
I0409 22:59:42.338171 24944 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0409 22:59:46.936379 24944 solver.cpp:218] Iteration 8460 (2.60981 iter/s, 4.59804s/12 iters), loss = 0.10295
I0409 22:59:46.936436 24944 solver.cpp:237]     Train net output #0: loss = 0.10295 (* 1 = 0.10295 loss)
I0409 22:59:46.936446 24944 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0409 22:59:48.853091 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0409 23:00:02.981398 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0409 23:00:11.476248 24944 solver.cpp:330] Iteration 8466, Testing net (#0)
I0409 23:00:11.476320 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:00:12.566702 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:00:16.062052 24944 solver.cpp:397]     Test net output #0: accuracy = 0.558211
I0409 23:00:16.062103 24944 solver.cpp:397]     Test net output #1: loss = 2.47672 (* 1 = 2.47672 loss)
I0409 23:00:17.932741 24944 solver.cpp:218] Iteration 8472 (0.387156 iter/s, 30.9952s/12 iters), loss = 0.090501
I0409 23:00:17.932773 24944 solver.cpp:237]     Train net output #0: loss = 0.0905011 (* 1 = 0.0905011 loss)
I0409 23:00:17.932780 24944 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0409 23:00:22.805034 24944 solver.cpp:218] Iteration 8484 (2.46302 iter/s, 4.87207s/12 iters), loss = 0.152598
I0409 23:00:22.805085 24944 solver.cpp:237]     Train net output #0: loss = 0.152598 (* 1 = 0.152598 loss)
I0409 23:00:22.805096 24944 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0409 23:00:27.736989 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:00:27.763298 24944 solver.cpp:218] Iteration 8496 (2.42032 iter/s, 4.95802s/12 iters), loss = 0.0323983
I0409 23:00:27.763355 24944 solver.cpp:237]     Train net output #0: loss = 0.0323984 (* 1 = 0.0323984 loss)
I0409 23:00:27.763368 24944 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0409 23:00:32.625234 24944 solver.cpp:218] Iteration 8508 (2.46828 iter/s, 4.86169s/12 iters), loss = 0.0515323
I0409 23:00:32.625288 24944 solver.cpp:237]     Train net output #0: loss = 0.0515323 (* 1 = 0.0515323 loss)
I0409 23:00:32.625298 24944 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0409 23:00:37.547353 24944 solver.cpp:218] Iteration 8520 (2.43809 iter/s, 4.92188s/12 iters), loss = 0.0378969
I0409 23:00:37.547397 24944 solver.cpp:237]     Train net output #0: loss = 0.0378969 (* 1 = 0.0378969 loss)
I0409 23:00:37.547406 24944 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0409 23:00:42.454545 24944 solver.cpp:218] Iteration 8532 (2.44551 iter/s, 4.90696s/12 iters), loss = 0.0979105
I0409 23:00:42.454699 24944 solver.cpp:237]     Train net output #0: loss = 0.0979105 (* 1 = 0.0979105 loss)
I0409 23:00:42.454710 24944 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0409 23:00:47.185988 24944 solver.cpp:218] Iteration 8544 (2.53641 iter/s, 4.7311s/12 iters), loss = 0.120034
I0409 23:00:47.186033 24944 solver.cpp:237]     Train net output #0: loss = 0.120034 (* 1 = 0.120034 loss)
I0409 23:00:47.186041 24944 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0409 23:00:51.868407 24944 solver.cpp:218] Iteration 8556 (2.5629 iter/s, 4.68219s/12 iters), loss = 0.0564928
I0409 23:00:51.868450 24944 solver.cpp:237]     Train net output #0: loss = 0.0564928 (* 1 = 0.0564928 loss)
I0409 23:00:51.868461 24944 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0409 23:00:55.997025 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0409 23:01:06.549361 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0409 23:01:15.048856 24944 solver.cpp:330] Iteration 8568, Testing net (#0)
I0409 23:01:15.048928 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:01:16.067940 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:01:19.641688 24944 solver.cpp:397]     Test net output #0: accuracy = 0.552696
I0409 23:01:19.641738 24944 solver.cpp:397]     Test net output #1: loss = 2.63191 (* 1 = 2.63191 loss)
I0409 23:01:19.763077 24944 solver.cpp:218] Iteration 8568 (0.430206 iter/s, 27.8936s/12 iters), loss = 0.0511544
I0409 23:01:19.764616 24944 solver.cpp:237]     Train net output #0: loss = 0.0511544 (* 1 = 0.0511544 loss)
I0409 23:01:19.764634 24944 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0409 23:01:23.748124 24944 solver.cpp:218] Iteration 8580 (3.01252 iter/s, 3.98337s/12 iters), loss = 0.0584971
I0409 23:01:23.748170 24944 solver.cpp:237]     Train net output #0: loss = 0.0584971 (* 1 = 0.0584971 loss)
I0409 23:01:23.748178 24944 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0409 23:01:28.704880 24944 solver.cpp:218] Iteration 8592 (2.42105 iter/s, 4.95653s/12 iters), loss = 0.0459478
I0409 23:01:28.704922 24944 solver.cpp:237]     Train net output #0: loss = 0.0459478 (* 1 = 0.0459478 loss)
I0409 23:01:28.704931 24944 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0409 23:01:30.916450 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:01:33.869786 24944 solver.cpp:218] Iteration 8604 (2.32348 iter/s, 5.16467s/12 iters), loss = 0.0626612
I0409 23:01:33.869829 24944 solver.cpp:237]     Train net output #0: loss = 0.0626612 (* 1 = 0.0626612 loss)
I0409 23:01:33.869837 24944 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0409 23:01:38.531894 24944 solver.cpp:218] Iteration 8616 (2.57407 iter/s, 4.66188s/12 iters), loss = 0.0209022
I0409 23:01:38.531936 24944 solver.cpp:237]     Train net output #0: loss = 0.0209022 (* 1 = 0.0209022 loss)
I0409 23:01:38.531944 24944 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0409 23:01:43.174825 24944 solver.cpp:218] Iteration 8628 (2.5847 iter/s, 4.64271s/12 iters), loss = 0.0181522
I0409 23:01:43.174878 24944 solver.cpp:237]     Train net output #0: loss = 0.0181523 (* 1 = 0.0181523 loss)
I0409 23:01:43.174888 24944 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0409 23:01:47.768271 24944 solver.cpp:218] Iteration 8640 (2.61255 iter/s, 4.59322s/12 iters), loss = 0.0722547
I0409 23:01:47.768404 24944 solver.cpp:237]     Train net output #0: loss = 0.0722548 (* 1 = 0.0722548 loss)
I0409 23:01:47.768414 24944 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0409 23:01:52.448784 24944 solver.cpp:218] Iteration 8652 (2.56399 iter/s, 4.6802s/12 iters), loss = 0.0424611
I0409 23:01:52.448833 24944 solver.cpp:237]     Train net output #0: loss = 0.0424611 (* 1 = 0.0424611 loss)
I0409 23:01:52.448843 24944 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0409 23:01:57.192749 24944 solver.cpp:218] Iteration 8664 (2.52965 iter/s, 4.74374s/12 iters), loss = 0.103166
I0409 23:01:57.192801 24944 solver.cpp:237]     Train net output #0: loss = 0.103166 (* 1 = 0.103166 loss)
I0409 23:01:57.192812 24944 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0409 23:01:59.132434 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0409 23:02:09.984001 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0409 23:02:23.894361 24944 solver.cpp:330] Iteration 8670, Testing net (#0)
I0409 23:02:23.894421 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:02:24.880844 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:02:28.364552 24944 solver.cpp:397]     Test net output #0: accuracy = 0.552083
I0409 23:02:28.364601 24944 solver.cpp:397]     Test net output #1: loss = 2.60045 (* 1 = 2.60045 loss)
I0409 23:02:30.175554 24944 solver.cpp:218] Iteration 8676 (0.363839 iter/s, 32.9816s/12 iters), loss = 0.0562683
I0409 23:02:30.175609 24944 solver.cpp:237]     Train net output #0: loss = 0.0562683 (* 1 = 0.0562683 loss)
I0409 23:02:30.175621 24944 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0409 23:02:34.889039 24944 solver.cpp:218] Iteration 8688 (2.54601 iter/s, 4.71325s/12 iters), loss = 0.0673014
I0409 23:02:34.889086 24944 solver.cpp:237]     Train net output #0: loss = 0.0673014 (* 1 = 0.0673014 loss)
I0409 23:02:34.889096 24944 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0409 23:02:38.888053 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:02:39.577144 24944 solver.cpp:218] Iteration 8700 (2.55979 iter/s, 4.68788s/12 iters), loss = 0.0210628
I0409 23:02:39.577188 24944 solver.cpp:237]     Train net output #0: loss = 0.0210629 (* 1 = 0.0210629 loss)
I0409 23:02:39.577198 24944 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0409 23:02:44.250924 24944 solver.cpp:218] Iteration 8712 (2.56764 iter/s, 4.67356s/12 iters), loss = 0.121263
I0409 23:02:44.250967 24944 solver.cpp:237]     Train net output #0: loss = 0.121263 (* 1 = 0.121263 loss)
I0409 23:02:44.250977 24944 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0409 23:02:48.960216 24944 solver.cpp:218] Iteration 8724 (2.54827 iter/s, 4.70907s/12 iters), loss = 0.0525233
I0409 23:02:48.960260 24944 solver.cpp:237]     Train net output #0: loss = 0.0525233 (* 1 = 0.0525233 loss)
I0409 23:02:48.960269 24944 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0409 23:02:53.615440 24944 solver.cpp:218] Iteration 8736 (2.57787 iter/s, 4.65501s/12 iters), loss = 0.0563072
I0409 23:02:53.615475 24944 solver.cpp:237]     Train net output #0: loss = 0.0563073 (* 1 = 0.0563073 loss)
I0409 23:02:53.615483 24944 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0409 23:02:58.713193 24944 solver.cpp:218] Iteration 8748 (2.35408 iter/s, 5.09752s/12 iters), loss = 0.0532037
I0409 23:02:58.713317 24944 solver.cpp:237]     Train net output #0: loss = 0.0532037 (* 1 = 0.0532037 loss)
I0409 23:02:58.713327 24944 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0409 23:03:03.743541 24944 solver.cpp:218] Iteration 8760 (2.38567 iter/s, 5.03003s/12 iters), loss = 0.064476
I0409 23:03:03.743594 24944 solver.cpp:237]     Train net output #0: loss = 0.064476 (* 1 = 0.064476 loss)
I0409 23:03:03.743605 24944 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0409 23:03:07.817319 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0409 23:03:18.643584 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0409 23:03:35.906735 24944 solver.cpp:330] Iteration 8772, Testing net (#0)
I0409 23:03:35.906822 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:03:36.874661 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:03:40.353284 24944 solver.cpp:397]     Test net output #0: accuracy = 0.55576
I0409 23:03:40.353334 24944 solver.cpp:397]     Test net output #1: loss = 2.56171 (* 1 = 2.56171 loss)
I0409 23:03:40.474479 24944 solver.cpp:218] Iteration 8772 (0.326712 iter/s, 36.7296s/12 iters), loss = 0.125326
I0409 23:03:40.476006 24944 solver.cpp:237]     Train net output #0: loss = 0.125326 (* 1 = 0.125326 loss)
I0409 23:03:40.476019 24944 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0409 23:03:44.566103 24944 solver.cpp:218] Iteration 8784 (2.93403 iter/s, 4.08993s/12 iters), loss = 0.0335017
I0409 23:03:44.566152 24944 solver.cpp:237]     Train net output #0: loss = 0.0335018 (* 1 = 0.0335018 loss)
I0409 23:03:44.566164 24944 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0409 23:03:49.342526 24944 solver.cpp:218] Iteration 8796 (2.51246 iter/s, 4.77619s/12 iters), loss = 0.0778315
I0409 23:03:49.342577 24944 solver.cpp:237]     Train net output #0: loss = 0.0778315 (* 1 = 0.0778315 loss)
I0409 23:03:49.342588 24944 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0409 23:03:50.613374 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:03:54.147300 24944 solver.cpp:218] Iteration 8808 (2.49764 iter/s, 4.80454s/12 iters), loss = 0.116306
I0409 23:03:54.147352 24944 solver.cpp:237]     Train net output #0: loss = 0.116306 (* 1 = 0.116306 loss)
I0409 23:03:54.147362 24944 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0409 23:03:58.991430 24944 solver.cpp:218] Iteration 8820 (2.47735 iter/s, 4.84389s/12 iters), loss = 0.0806134
I0409 23:03:58.991482 24944 solver.cpp:237]     Train net output #0: loss = 0.0806134 (* 1 = 0.0806134 loss)
I0409 23:03:58.991490 24944 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0409 23:04:03.827517 24944 solver.cpp:218] Iteration 8832 (2.48147 iter/s, 4.83585s/12 iters), loss = 0.0202672
I0409 23:04:03.827566 24944 solver.cpp:237]     Train net output #0: loss = 0.0202672 (* 1 = 0.0202672 loss)
I0409 23:04:03.827577 24944 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0409 23:04:08.921664 24944 solver.cpp:218] Iteration 8844 (2.35576 iter/s, 5.09391s/12 iters), loss = 0.021782
I0409 23:04:08.921775 24944 solver.cpp:237]     Train net output #0: loss = 0.021782 (* 1 = 0.021782 loss)
I0409 23:04:08.921789 24944 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0409 23:04:13.830529 24944 solver.cpp:218] Iteration 8856 (2.4447 iter/s, 4.90858s/12 iters), loss = 0.0210195
I0409 23:04:13.830574 24944 solver.cpp:237]     Train net output #0: loss = 0.0210195 (* 1 = 0.0210195 loss)
I0409 23:04:13.830583 24944 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0409 23:04:18.422797 24944 solver.cpp:218] Iteration 8868 (2.61321 iter/s, 4.59205s/12 iters), loss = 0.0362724
I0409 23:04:18.422849 24944 solver.cpp:237]     Train net output #0: loss = 0.0362724 (* 1 = 0.0362724 loss)
I0409 23:04:18.422860 24944 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0409 23:04:20.294303 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0409 23:04:43.899211 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0409 23:04:52.449232 24944 solver.cpp:330] Iteration 8874, Testing net (#0)
I0409 23:04:52.449255 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:04:53.525674 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:04:57.081770 24944 solver.cpp:397]     Test net output #0: accuracy = 0.560049
I0409 23:04:57.081817 24944 solver.cpp:397]     Test net output #1: loss = 2.45683 (* 1 = 2.45683 loss)
I0409 23:04:58.758090 24944 solver.cpp:218] Iteration 8880 (0.297517 iter/s, 40.3338s/12 iters), loss = 0.0771816
I0409 23:04:58.758154 24944 solver.cpp:237]     Train net output #0: loss = 0.0771817 (* 1 = 0.0771817 loss)
I0409 23:04:58.758165 24944 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0409 23:05:03.632783 24944 solver.cpp:218] Iteration 8892 (2.46182 iter/s, 4.87445s/12 iters), loss = 0.0422019
I0409 23:05:03.632825 24944 solver.cpp:237]     Train net output #0: loss = 0.0422019 (* 1 = 0.0422019 loss)
I0409 23:05:03.632834 24944 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0409 23:05:06.909250 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:05:08.391429 24944 solver.cpp:218] Iteration 8904 (2.52184 iter/s, 4.75842s/12 iters), loss = 0.0829707
I0409 23:05:08.391477 24944 solver.cpp:237]     Train net output #0: loss = 0.0829708 (* 1 = 0.0829708 loss)
I0409 23:05:08.391487 24944 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0409 23:05:13.454773 24944 solver.cpp:218] Iteration 8916 (2.37009 iter/s, 5.0631s/12 iters), loss = 0.0662344
I0409 23:05:13.454823 24944 solver.cpp:237]     Train net output #0: loss = 0.0662345 (* 1 = 0.0662345 loss)
I0409 23:05:13.454833 24944 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0409 23:05:18.629680 24944 solver.cpp:218] Iteration 8928 (2.31899 iter/s, 5.17466s/12 iters), loss = 0.0969653
I0409 23:05:18.631268 24944 solver.cpp:237]     Train net output #0: loss = 0.0969653 (* 1 = 0.0969653 loss)
I0409 23:05:18.631281 24944 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0409 23:05:23.827708 24944 solver.cpp:218] Iteration 8940 (2.30936 iter/s, 5.19625s/12 iters), loss = 0.0728032
I0409 23:05:23.827759 24944 solver.cpp:237]     Train net output #0: loss = 0.0728032 (* 1 = 0.0728032 loss)
I0409 23:05:23.827770 24944 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0409 23:05:29.064148 24944 solver.cpp:218] Iteration 8952 (2.29174 iter/s, 5.2362s/12 iters), loss = 0.0878036
I0409 23:05:29.064189 24944 solver.cpp:237]     Train net output #0: loss = 0.0878037 (* 1 = 0.0878037 loss)
I0409 23:05:29.064198 24944 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0409 23:05:34.234642 24944 solver.cpp:218] Iteration 8964 (2.32097 iter/s, 5.17025s/12 iters), loss = 0.0525348
I0409 23:05:34.234691 24944 solver.cpp:237]     Train net output #0: loss = 0.0525348 (* 1 = 0.0525348 loss)
I0409 23:05:34.234700 24944 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0409 23:05:38.869426 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0409 23:05:51.581349 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0409 23:06:00.108271 24944 solver.cpp:330] Iteration 8976, Testing net (#0)
I0409 23:06:00.108291 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:06:01.067267 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:06:04.794701 24944 solver.cpp:397]     Test net output #0: accuracy = 0.5625
I0409 23:06:04.794731 24944 solver.cpp:397]     Test net output #1: loss = 2.56989 (* 1 = 2.56989 loss)
I0409 23:06:04.915585 24944 solver.cpp:218] Iteration 8976 (0.391137 iter/s, 30.6798s/12 iters), loss = 0.064306
I0409 23:06:04.917191 24944 solver.cpp:237]     Train net output #0: loss = 0.0643061 (* 1 = 0.0643061 loss)
I0409 23:06:04.917201 24944 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0409 23:06:09.176751 24944 solver.cpp:218] Iteration 8988 (2.8173 iter/s, 4.2594s/12 iters), loss = 0.0357303
I0409 23:06:09.176802 24944 solver.cpp:237]     Train net output #0: loss = 0.0357304 (* 1 = 0.0357304 loss)
I0409 23:06:09.176812 24944 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0409 23:06:12.610873 24944 blocking_queue.cpp:49] Waiting for data
I0409 23:06:14.364017 24944 solver.cpp:218] Iteration 9000 (2.31347 iter/s, 5.18702s/12 iters), loss = 0.0490871
I0409 23:06:14.364060 24944 solver.cpp:237]     Train net output #0: loss = 0.0490871 (* 1 = 0.0490871 loss)
I0409 23:06:14.364069 24944 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0409 23:06:15.047256 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:06:18.954036 24944 solver.cpp:218] Iteration 9012 (2.61449 iter/s, 4.5898s/12 iters), loss = 0.109902
I0409 23:06:18.954092 24944 solver.cpp:237]     Train net output #0: loss = 0.109902 (* 1 = 0.109902 loss)
I0409 23:06:18.954104 24944 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0409 23:06:23.443578 24944 solver.cpp:218] Iteration 9024 (2.67301 iter/s, 4.48931s/12 iters), loss = 0.102855
I0409 23:06:23.444114 24944 solver.cpp:237]     Train net output #0: loss = 0.102855 (* 1 = 0.102855 loss)
I0409 23:06:23.444128 24944 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0409 23:06:28.330708 24944 solver.cpp:218] Iteration 9036 (2.45579 iter/s, 4.88641s/12 iters), loss = 0.0403745
I0409 23:06:28.330751 24944 solver.cpp:237]     Train net output #0: loss = 0.0403746 (* 1 = 0.0403746 loss)
I0409 23:06:28.330760 24944 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0409 23:06:33.261266 24944 solver.cpp:218] Iteration 9048 (2.43391 iter/s, 4.93033s/12 iters), loss = 0.0740503
I0409 23:06:33.261312 24944 solver.cpp:237]     Train net output #0: loss = 0.0740504 (* 1 = 0.0740504 loss)
I0409 23:06:33.261322 24944 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0409 23:06:38.099866 24944 solver.cpp:218] Iteration 9060 (2.48017 iter/s, 4.83837s/12 iters), loss = 0.0340419
I0409 23:06:38.099920 24944 solver.cpp:237]     Train net output #0: loss = 0.034042 (* 1 = 0.034042 loss)
I0409 23:06:38.099931 24944 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0409 23:06:42.845495 24944 solver.cpp:218] Iteration 9072 (2.52877 iter/s, 4.7454s/12 iters), loss = 0.0348738
I0409 23:06:42.845539 24944 solver.cpp:237]     Train net output #0: loss = 0.0348738 (* 1 = 0.0348738 loss)
I0409 23:06:42.845551 24944 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0409 23:06:44.646229 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0409 23:06:55.532655 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0409 23:07:04.867933 24944 solver.cpp:330] Iteration 9078, Testing net (#0)
I0409 23:07:04.867954 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:07:05.805498 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:07:09.455188 24944 solver.cpp:397]     Test net output #0: accuracy = 0.556985
I0409 23:07:09.455224 24944 solver.cpp:397]     Test net output #1: loss = 2.63761 (* 1 = 2.63761 loss)
I0409 23:07:11.154213 24944 solver.cpp:218] Iteration 9084 (0.423913 iter/s, 28.3077s/12 iters), loss = 0.0213601
I0409 23:07:11.154278 24944 solver.cpp:237]     Train net output #0: loss = 0.0213602 (* 1 = 0.0213602 loss)
I0409 23:07:11.154292 24944 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0409 23:07:16.003541 24944 solver.cpp:218] Iteration 9096 (2.47469 iter/s, 4.84909s/12 iters), loss = 0.0307222
I0409 23:07:16.003592 24944 solver.cpp:237]     Train net output #0: loss = 0.0307223 (* 1 = 0.0307223 loss)
I0409 23:07:16.003603 24944 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0409 23:07:18.984652 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:07:21.195418 24944 solver.cpp:218] Iteration 9108 (2.31141 iter/s, 5.19164s/12 iters), loss = 0.0310069
I0409 23:07:21.195456 24944 solver.cpp:237]     Train net output #0: loss = 0.0310069 (* 1 = 0.0310069 loss)
I0409 23:07:21.195466 24944 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0409 23:07:26.079746 24944 solver.cpp:218] Iteration 9120 (2.45695 iter/s, 4.88411s/12 iters), loss = 0.152999
I0409 23:07:26.079860 24944 solver.cpp:237]     Train net output #0: loss = 0.152999 (* 1 = 0.152999 loss)
I0409 23:07:26.079872 24944 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0409 23:07:30.799659 24944 solver.cpp:218] Iteration 9132 (2.54258 iter/s, 4.71962s/12 iters), loss = 0.00736168
I0409 23:07:30.799705 24944 solver.cpp:237]     Train net output #0: loss = 0.00736169 (* 1 = 0.00736169 loss)
I0409 23:07:30.799715 24944 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0409 23:07:35.697710 24944 solver.cpp:218] Iteration 9144 (2.45007 iter/s, 4.89782s/12 iters), loss = 0.0637709
I0409 23:07:35.697757 24944 solver.cpp:237]     Train net output #0: loss = 0.0637709 (* 1 = 0.0637709 loss)
I0409 23:07:35.697767 24944 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0409 23:07:40.727141 24944 solver.cpp:218] Iteration 9156 (2.38607 iter/s, 5.02919s/12 iters), loss = 0.0283127
I0409 23:07:40.727192 24944 solver.cpp:237]     Train net output #0: loss = 0.0283127 (* 1 = 0.0283127 loss)
I0409 23:07:40.727202 24944 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0409 23:07:45.882254 24944 solver.cpp:218] Iteration 9168 (2.3279 iter/s, 5.15487s/12 iters), loss = 0.0197448
I0409 23:07:45.882304 24944 solver.cpp:237]     Train net output #0: loss = 0.0197448 (* 1 = 0.0197448 loss)
I0409 23:07:45.882315 24944 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0409 23:07:50.318790 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0409 23:08:01.189549 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0409 23:08:10.565239 24944 solver.cpp:330] Iteration 9180, Testing net (#0)
I0409 23:08:10.565263 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:08:11.447297 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:08:15.076862 24944 solver.cpp:397]     Test net output #0: accuracy = 0.560662
I0409 23:08:15.076894 24944 solver.cpp:397]     Test net output #1: loss = 2.60339 (* 1 = 2.60339 loss)
I0409 23:08:15.198012 24944 solver.cpp:218] Iteration 9180 (0.409351 iter/s, 29.3147s/12 iters), loss = 0.119509
I0409 23:08:15.199537 24944 solver.cpp:237]     Train net output #0: loss = 0.119509 (* 1 = 0.119509 loss)
I0409 23:08:15.199549 24944 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0409 23:08:19.315593 24944 solver.cpp:218] Iteration 9192 (2.91552 iter/s, 4.1159s/12 iters), loss = 0.0343309
I0409 23:08:19.315639 24944 solver.cpp:237]     Train net output #0: loss = 0.034331 (* 1 = 0.034331 loss)
I0409 23:08:19.315650 24944 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0409 23:08:24.378072 24944 solver.cpp:218] Iteration 9204 (2.37049 iter/s, 5.06224s/12 iters), loss = 0.016978
I0409 23:08:24.378118 24944 solver.cpp:237]     Train net output #0: loss = 0.016978 (* 1 = 0.016978 loss)
I0409 23:08:24.378127 24944 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0409 23:08:24.379092 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:08:29.437175 24944 solver.cpp:218] Iteration 9216 (2.37207 iter/s, 5.05887s/12 iters), loss = 0.0629173
I0409 23:08:29.437223 24944 solver.cpp:237]     Train net output #0: loss = 0.0629173 (* 1 = 0.0629173 loss)
I0409 23:08:29.437232 24944 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0409 23:08:34.393044 24944 solver.cpp:218] Iteration 9228 (2.42149 iter/s, 4.95563s/12 iters), loss = 0.0505079
I0409 23:08:34.393131 24944 solver.cpp:237]     Train net output #0: loss = 0.0505079 (* 1 = 0.0505079 loss)
I0409 23:08:34.393143 24944 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0409 23:08:39.062875 24944 solver.cpp:218] Iteration 9240 (2.56983 iter/s, 4.66957s/12 iters), loss = 0.0537704
I0409 23:08:39.062918 24944 solver.cpp:237]     Train net output #0: loss = 0.0537704 (* 1 = 0.0537704 loss)
I0409 23:08:39.062927 24944 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0409 23:08:44.163683 24944 solver.cpp:218] Iteration 9252 (2.35268 iter/s, 5.10057s/12 iters), loss = 0.0600909
I0409 23:08:44.163733 24944 solver.cpp:237]     Train net output #0: loss = 0.0600909 (* 1 = 0.0600909 loss)
I0409 23:08:44.163744 24944 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0409 23:08:49.130731 24944 solver.cpp:218] Iteration 9264 (2.41604 iter/s, 4.96681s/12 iters), loss = 0.0248653
I0409 23:08:49.130781 24944 solver.cpp:237]     Train net output #0: loss = 0.0248653 (* 1 = 0.0248653 loss)
I0409 23:08:49.130791 24944 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0409 23:08:54.234087 24944 solver.cpp:218] Iteration 9276 (2.35151 iter/s, 5.10311s/12 iters), loss = 0.0463614
I0409 23:08:54.234136 24944 solver.cpp:237]     Train net output #0: loss = 0.0463614 (* 1 = 0.0463614 loss)
I0409 23:08:54.234148 24944 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0409 23:08:56.220212 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0409 23:09:06.993052 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0409 23:09:15.920773 24944 solver.cpp:330] Iteration 9282, Testing net (#0)
I0409 23:09:15.920796 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:09:16.660002 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:09:20.368824 24944 solver.cpp:397]     Test net output #0: accuracy = 0.563725
I0409 23:09:20.368873 24944 solver.cpp:397]     Test net output #1: loss = 2.58365 (* 1 = 2.58365 loss)
I0409 23:09:22.067553 24944 solver.cpp:218] Iteration 9288 (0.431152 iter/s, 27.8324s/12 iters), loss = 0.0250147
I0409 23:09:22.067602 24944 solver.cpp:237]     Train net output #0: loss = 0.0250147 (* 1 = 0.0250147 loss)
I0409 23:09:22.067613 24944 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0409 23:09:27.288982 24944 solver.cpp:218] Iteration 9300 (2.29833 iter/s, 5.22119s/12 iters), loss = 0.0599033
I0409 23:09:27.289036 24944 solver.cpp:237]     Train net output #0: loss = 0.0599033 (* 1 = 0.0599033 loss)
I0409 23:09:27.289048 24944 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0409 23:09:29.586309 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:09:32.487412 24944 solver.cpp:218] Iteration 9312 (2.3085 iter/s, 5.19818s/12 iters), loss = 0.0490941
I0409 23:09:32.487459 24944 solver.cpp:237]     Train net output #0: loss = 0.0490941 (* 1 = 0.0490941 loss)
I0409 23:09:32.487471 24944 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0409 23:09:37.643888 24944 solver.cpp:218] Iteration 9324 (2.32728 iter/s, 5.15624s/12 iters), loss = 0.0522822
I0409 23:09:37.644007 24944 solver.cpp:237]     Train net output #0: loss = 0.0522822 (* 1 = 0.0522822 loss)
I0409 23:09:37.644019 24944 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0409 23:09:42.839131 24944 solver.cpp:218] Iteration 9336 (2.30994 iter/s, 5.19493s/12 iters), loss = 0.0323972
I0409 23:09:42.839184 24944 solver.cpp:237]     Train net output #0: loss = 0.0323972 (* 1 = 0.0323972 loss)
I0409 23:09:42.839195 24944 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0409 23:09:47.995393 24944 solver.cpp:218] Iteration 9348 (2.32738 iter/s, 5.15602s/12 iters), loss = 0.0451042
I0409 23:09:47.995434 24944 solver.cpp:237]     Train net output #0: loss = 0.0451042 (* 1 = 0.0451042 loss)
I0409 23:09:47.995445 24944 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0409 23:09:53.084048 24944 solver.cpp:218] Iteration 9360 (2.35829 iter/s, 5.08842s/12 iters), loss = 0.0257235
I0409 23:09:53.084097 24944 solver.cpp:237]     Train net output #0: loss = 0.0257235 (* 1 = 0.0257235 loss)
I0409 23:09:53.084110 24944 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0409 23:09:58.325251 24944 solver.cpp:218] Iteration 9372 (2.28966 iter/s, 5.24096s/12 iters), loss = 0.118473
I0409 23:09:58.325297 24944 solver.cpp:237]     Train net output #0: loss = 0.118473 (* 1 = 0.118473 loss)
I0409 23:09:58.325307 24944 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0409 23:10:03.152410 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0409 23:10:13.863581 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0409 23:10:26.241205 24944 solver.cpp:330] Iteration 9384, Testing net (#0)
I0409 23:10:26.241230 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:10:27.038576 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:10:30.820497 24944 solver.cpp:397]     Test net output #0: accuracy = 0.560049
I0409 23:10:30.820540 24944 solver.cpp:397]     Test net output #1: loss = 2.62009 (* 1 = 2.62009 loss)
I0409 23:10:30.938181 24944 solver.cpp:218] Iteration 9384 (0.367966 iter/s, 32.6117s/12 iters), loss = 0.0195447
I0409 23:10:30.939707 24944 solver.cpp:237]     Train net output #0: loss = 0.0195447 (* 1 = 0.0195447 loss)
I0409 23:10:30.939718 24944 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0409 23:10:35.071905 24944 solver.cpp:218] Iteration 9396 (2.90413 iter/s, 4.13205s/12 iters), loss = 0.0611392
I0409 23:10:35.071956 24944 solver.cpp:237]     Train net output #0: loss = 0.0611392 (* 1 = 0.0611392 loss)
I0409 23:10:35.071967 24944 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0409 23:10:39.278982 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:10:39.987846 24944 solver.cpp:218] Iteration 9408 (2.44115 iter/s, 4.91571s/12 iters), loss = 0.0543902
I0409 23:10:39.987896 24944 solver.cpp:237]     Train net output #0: loss = 0.0543902 (* 1 = 0.0543902 loss)
I0409 23:10:39.987907 24944 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0409 23:10:45.127388 24944 solver.cpp:218] Iteration 9420 (2.33495 iter/s, 5.1393s/12 iters), loss = 0.0684184
I0409 23:10:45.127468 24944 solver.cpp:237]     Train net output #0: loss = 0.0684184 (* 1 = 0.0684184 loss)
I0409 23:10:45.127478 24944 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0409 23:10:49.803004 24944 solver.cpp:218] Iteration 9432 (2.56665 iter/s, 4.67536s/12 iters), loss = 0.0172852
I0409 23:10:49.803050 24944 solver.cpp:237]     Train net output #0: loss = 0.0172852 (* 1 = 0.0172852 loss)
I0409 23:10:49.803059 24944 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0409 23:10:54.487141 24944 solver.cpp:218] Iteration 9444 (2.56196 iter/s, 4.68391s/12 iters), loss = 0.041222
I0409 23:10:54.487185 24944 solver.cpp:237]     Train net output #0: loss = 0.041222 (* 1 = 0.041222 loss)
I0409 23:10:54.487195 24944 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0409 23:10:59.275415 24944 solver.cpp:218] Iteration 9456 (2.50624 iter/s, 4.78805s/12 iters), loss = 0.0730155
I0409 23:10:59.275470 24944 solver.cpp:237]     Train net output #0: loss = 0.0730155 (* 1 = 0.0730155 loss)
I0409 23:10:59.275481 24944 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0409 23:11:03.945363 24944 solver.cpp:218] Iteration 9468 (2.56975 iter/s, 4.66972s/12 iters), loss = 0.0305397
I0409 23:11:03.945415 24944 solver.cpp:237]     Train net output #0: loss = 0.0305397 (* 1 = 0.0305397 loss)
I0409 23:11:03.945426 24944 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0409 23:11:08.723601 24944 solver.cpp:218] Iteration 9480 (2.51151 iter/s, 4.77801s/12 iters), loss = 0.0150866
I0409 23:11:08.723655 24944 solver.cpp:237]     Train net output #0: loss = 0.0150866 (* 1 = 0.0150866 loss)
I0409 23:11:08.723666 24944 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0409 23:11:10.936400 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0409 23:11:23.951910 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0409 23:11:38.583118 24944 solver.cpp:330] Iteration 9486, Testing net (#0)
I0409 23:11:38.583142 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:11:39.313881 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:11:43.136029 24944 solver.cpp:397]     Test net output #0: accuracy = 0.564951
I0409 23:11:43.136057 24944 solver.cpp:397]     Test net output #1: loss = 2.51593 (* 1 = 2.51593 loss)
I0409 23:11:44.998518 24944 solver.cpp:218] Iteration 9492 (0.330819 iter/s, 36.2736s/12 iters), loss = 0.116685
I0409 23:11:44.998569 24944 solver.cpp:237]     Train net output #0: loss = 0.116685 (* 1 = 0.116685 loss)
I0409 23:11:44.998580 24944 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0409 23:11:50.207829 24944 solver.cpp:218] Iteration 9504 (2.30368 iter/s, 5.20907s/12 iters), loss = 0.022647
I0409 23:11:50.207878 24944 solver.cpp:237]     Train net output #0: loss = 0.022647 (* 1 = 0.022647 loss)
I0409 23:11:50.207890 24944 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0409 23:11:51.715402 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:11:55.406178 24944 solver.cpp:218] Iteration 9516 (2.30853 iter/s, 5.1981s/12 iters), loss = 0.0264295
I0409 23:11:55.406328 24944 solver.cpp:237]     Train net output #0: loss = 0.0264295 (* 1 = 0.0264295 loss)
I0409 23:11:55.406342 24944 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0409 23:12:00.661249 24944 solver.cpp:218] Iteration 9528 (2.28366 iter/s, 5.25473s/12 iters), loss = 0.0215577
I0409 23:12:00.661295 24944 solver.cpp:237]     Train net output #0: loss = 0.0215576 (* 1 = 0.0215576 loss)
I0409 23:12:00.661304 24944 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0409 23:12:05.460242 24944 solver.cpp:218] Iteration 9540 (2.50064 iter/s, 4.79876s/12 iters), loss = 0.0682711
I0409 23:12:05.460297 24944 solver.cpp:237]     Train net output #0: loss = 0.0682711 (* 1 = 0.0682711 loss)
I0409 23:12:05.460309 24944 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0409 23:12:10.426756 24944 solver.cpp:218] Iteration 9552 (2.4163 iter/s, 4.96627s/12 iters), loss = 0.0914905
I0409 23:12:10.426800 24944 solver.cpp:237]     Train net output #0: loss = 0.0914905 (* 1 = 0.0914905 loss)
I0409 23:12:10.426807 24944 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0409 23:12:15.101092 24944 solver.cpp:218] Iteration 9564 (2.56733 iter/s, 4.67411s/12 iters), loss = 0.0184077
I0409 23:12:15.101140 24944 solver.cpp:237]     Train net output #0: loss = 0.0184077 (* 1 = 0.0184077 loss)
I0409 23:12:15.101148 24944 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0409 23:12:19.851656 24944 solver.cpp:218] Iteration 9576 (2.52614 iter/s, 4.75034s/12 iters), loss = 0.0249142
I0409 23:12:19.851706 24944 solver.cpp:237]     Train net output #0: loss = 0.0249142 (* 1 = 0.0249142 loss)
I0409 23:12:19.851716 24944 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0409 23:12:24.294899 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0409 23:12:39.605152 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0409 23:12:48.807318 24944 solver.cpp:330] Iteration 9588, Testing net (#0)
I0409 23:12:48.807340 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:12:49.462574 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:12:53.279222 24944 solver.cpp:397]     Test net output #0: accuracy = 0.557598
I0409 23:12:53.279271 24944 solver.cpp:397]     Test net output #1: loss = 2.59575 (* 1 = 2.59575 loss)
I0409 23:12:53.398361 24944 solver.cpp:218] Iteration 9588 (0.357723 iter/s, 33.5455s/12 iters), loss = 0.0176756
I0409 23:12:53.399946 24944 solver.cpp:237]     Train net output #0: loss = 0.0176756 (* 1 = 0.0176756 loss)
I0409 23:12:53.399957 24944 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0409 23:12:57.503856 24944 solver.cpp:218] Iteration 9600 (2.92415 iter/s, 4.10375s/12 iters), loss = 0.0077612
I0409 23:12:57.503906 24944 solver.cpp:237]     Train net output #0: loss = 0.00776121 (* 1 = 0.00776121 loss)
I0409 23:12:57.503916 24944 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0409 23:13:01.093612 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:13:02.580484 24944 solver.cpp:218] Iteration 9612 (2.36389 iter/s, 5.07639s/12 iters), loss = 0.0308031
I0409 23:13:02.580529 24944 solver.cpp:237]     Train net output #0: loss = 0.0308031 (* 1 = 0.0308031 loss)
I0409 23:13:02.580538 24944 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0409 23:13:07.761008 24944 solver.cpp:218] Iteration 9624 (2.31648 iter/s, 5.18028s/12 iters), loss = 0.0236337
I0409 23:13:07.761063 24944 solver.cpp:237]     Train net output #0: loss = 0.0236337 (* 1 = 0.0236337 loss)
I0409 23:13:07.761075 24944 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0409 23:13:12.585798 24944 solver.cpp:218] Iteration 9636 (2.48727 iter/s, 4.82456s/12 iters), loss = 0.0317909
I0409 23:13:12.585909 24944 solver.cpp:237]     Train net output #0: loss = 0.0317909 (* 1 = 0.0317909 loss)
I0409 23:13:12.585918 24944 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0409 23:13:17.598115 24944 solver.cpp:218] Iteration 9648 (2.39425 iter/s, 5.01202s/12 iters), loss = 0.0934072
I0409 23:13:17.598161 24944 solver.cpp:237]     Train net output #0: loss = 0.0934072 (* 1 = 0.0934072 loss)
I0409 23:13:17.598172 24944 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0409 23:13:22.426056 24944 solver.cpp:218] Iteration 9660 (2.48565 iter/s, 4.82771s/12 iters), loss = 0.0360977
I0409 23:13:22.426100 24944 solver.cpp:237]     Train net output #0: loss = 0.0360977 (* 1 = 0.0360977 loss)
I0409 23:13:22.426110 24944 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0409 23:13:27.547276 24944 solver.cpp:218] Iteration 9672 (2.3433 iter/s, 5.12098s/12 iters), loss = 0.0176814
I0409 23:13:27.547323 24944 solver.cpp:237]     Train net output #0: loss = 0.0176814 (* 1 = 0.0176814 loss)
I0409 23:13:27.547333 24944 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0409 23:13:32.774682 24944 solver.cpp:218] Iteration 9684 (2.2957 iter/s, 5.22716s/12 iters), loss = 0.0334243
I0409 23:13:32.774729 24944 solver.cpp:237]     Train net output #0: loss = 0.0334243 (* 1 = 0.0334243 loss)
I0409 23:13:32.774740 24944 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0409 23:13:34.841836 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0409 23:13:45.506350 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0409 23:13:55.531652 24944 solver.cpp:330] Iteration 9690, Testing net (#0)
I0409 23:13:55.531673 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:13:56.219287 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:13:59.104483 24944 blocking_queue.cpp:49] Waiting for data
I0409 23:14:00.099866 24944 solver.cpp:397]     Test net output #0: accuracy = 0.557598
I0409 23:14:00.099910 24944 solver.cpp:397]     Test net output #1: loss = 2.59005 (* 1 = 2.59005 loss)
I0409 23:14:01.939411 24944 solver.cpp:218] Iteration 9696 (0.411471 iter/s, 29.1637s/12 iters), loss = 0.00986355
I0409 23:14:01.939460 24944 solver.cpp:237]     Train net output #0: loss = 0.00986356 (* 1 = 0.00986356 loss)
I0409 23:14:01.939469 24944 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0409 23:14:07.219097 24944 solver.cpp:218] Iteration 9708 (2.27297 iter/s, 5.27944s/12 iters), loss = 0.0382335
I0409 23:14:07.219148 24944 solver.cpp:237]     Train net output #0: loss = 0.0382335 (* 1 = 0.0382335 loss)
I0409 23:14:07.219159 24944 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0409 23:14:07.975980 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:14:12.270552 24944 solver.cpp:218] Iteration 9720 (2.37567 iter/s, 5.05121s/12 iters), loss = 0.108248
I0409 23:14:12.270606 24944 solver.cpp:237]     Train net output #0: loss = 0.108248 (* 1 = 0.108248 loss)
I0409 23:14:12.270617 24944 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0409 23:14:17.354601 24944 solver.cpp:218] Iteration 9732 (2.36044 iter/s, 5.0838s/12 iters), loss = 0.0548905
I0409 23:14:17.354707 24944 solver.cpp:237]     Train net output #0: loss = 0.0548905 (* 1 = 0.0548905 loss)
I0409 23:14:17.354717 24944 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0409 23:14:22.334146 24944 solver.cpp:218] Iteration 9744 (2.41 iter/s, 4.97925s/12 iters), loss = 0.0734482
I0409 23:14:22.334195 24944 solver.cpp:237]     Train net output #0: loss = 0.0734482 (* 1 = 0.0734482 loss)
I0409 23:14:22.334208 24944 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0409 23:14:27.528693 24944 solver.cpp:218] Iteration 9756 (2.31022 iter/s, 5.1943s/12 iters), loss = 0.0700863
I0409 23:14:27.528743 24944 solver.cpp:237]     Train net output #0: loss = 0.0700863 (* 1 = 0.0700863 loss)
I0409 23:14:27.528755 24944 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0409 23:14:32.673772 24944 solver.cpp:218] Iteration 9768 (2.33243 iter/s, 5.14484s/12 iters), loss = 0.0237233
I0409 23:14:32.673817 24944 solver.cpp:237]     Train net output #0: loss = 0.0237233 (* 1 = 0.0237233 loss)
I0409 23:14:32.673826 24944 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0409 23:14:37.831637 24944 solver.cpp:218] Iteration 9780 (2.32665 iter/s, 5.15763s/12 iters), loss = 0.0755269
I0409 23:14:37.831676 24944 solver.cpp:237]     Train net output #0: loss = 0.075527 (* 1 = 0.075527 loss)
I0409 23:14:37.831684 24944 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0409 23:14:42.421873 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0409 23:14:53.201385 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0409 23:15:01.928786 24944 solver.cpp:330] Iteration 9792, Testing net (#0)
I0409 23:15:01.928810 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:15:02.554971 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:15:06.711804 24944 solver.cpp:397]     Test net output #0: accuracy = 0.569853
I0409 23:15:06.711844 24944 solver.cpp:397]     Test net output #1: loss = 2.53166 (* 1 = 2.53166 loss)
I0409 23:15:06.832823 24944 solver.cpp:218] Iteration 9792 (0.413791 iter/s, 29.0001s/12 iters), loss = 0.0161179
I0409 23:15:06.832864 24944 solver.cpp:237]     Train net output #0: loss = 0.016118 (* 1 = 0.016118 loss)
I0409 23:15:06.832872 24944 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0409 23:15:11.029371 24944 solver.cpp:218] Iteration 9804 (2.85964 iter/s, 4.19634s/12 iters), loss = 0.0718815
I0409 23:15:11.029417 24944 solver.cpp:237]     Train net output #0: loss = 0.0718816 (* 1 = 0.0718816 loss)
I0409 23:15:11.029426 24944 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0409 23:15:14.080515 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:15:16.260664 24944 solver.cpp:218] Iteration 9816 (2.294 iter/s, 5.23105s/12 iters), loss = 0.0134113
I0409 23:15:16.260720 24944 solver.cpp:237]     Train net output #0: loss = 0.0134113 (* 1 = 0.0134113 loss)
I0409 23:15:16.260731 24944 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0409 23:15:21.218693 24944 solver.cpp:218] Iteration 9828 (2.42043 iter/s, 4.95779s/12 iters), loss = 0.0268351
I0409 23:15:21.218739 24944 solver.cpp:237]     Train net output #0: loss = 0.0268351 (* 1 = 0.0268351 loss)
I0409 23:15:21.218747 24944 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0409 23:15:25.831732 24944 solver.cpp:218] Iteration 9840 (2.60145 iter/s, 4.61282s/12 iters), loss = 0.015889
I0409 23:15:25.831841 24944 solver.cpp:237]     Train net output #0: loss = 0.0158891 (* 1 = 0.0158891 loss)
I0409 23:15:25.831851 24944 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0409 23:15:30.767738 24944 solver.cpp:218] Iteration 9852 (2.43126 iter/s, 4.93571s/12 iters), loss = 0.0311448
I0409 23:15:30.767788 24944 solver.cpp:237]     Train net output #0: loss = 0.0311449 (* 1 = 0.0311449 loss)
I0409 23:15:30.767799 24944 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0409 23:15:35.602677 24944 solver.cpp:218] Iteration 9864 (2.48205 iter/s, 4.83471s/12 iters), loss = 0.00669439
I0409 23:15:35.602725 24944 solver.cpp:237]     Train net output #0: loss = 0.00669443 (* 1 = 0.00669443 loss)
I0409 23:15:35.602732 24944 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0409 23:15:40.754081 24944 solver.cpp:218] Iteration 9876 (2.32957 iter/s, 5.15116s/12 iters), loss = 0.0404132
I0409 23:15:40.754127 24944 solver.cpp:237]     Train net output #0: loss = 0.0404133 (* 1 = 0.0404133 loss)
I0409 23:15:40.754137 24944 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0409 23:15:45.945122 24944 solver.cpp:218] Iteration 9888 (2.31178 iter/s, 5.1908s/12 iters), loss = 0.0193396
I0409 23:15:45.945171 24944 solver.cpp:237]     Train net output #0: loss = 0.0193396 (* 1 = 0.0193396 loss)
I0409 23:15:45.945183 24944 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0409 23:15:48.075980 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0409 23:15:59.264726 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0409 23:16:07.773340 24944 solver.cpp:330] Iteration 9894, Testing net (#0)
I0409 23:16:07.773361 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:16:08.353758 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:16:12.331432 24944 solver.cpp:397]     Test net output #0: accuracy = 0.572304
I0409 23:16:12.331475 24944 solver.cpp:397]     Test net output #1: loss = 2.53369 (* 1 = 2.53369 loss)
I0409 23:16:14.061499 24944 solver.cpp:218] Iteration 9900 (0.426813 iter/s, 28.1153s/12 iters), loss = 0.0751231
I0409 23:16:14.061550 24944 solver.cpp:237]     Train net output #0: loss = 0.0751231 (* 1 = 0.0751231 loss)
I0409 23:16:14.061559 24944 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0409 23:16:19.151228 24944 solver.cpp:218] Iteration 9912 (2.3578 iter/s, 5.08948s/12 iters), loss = 0.0389786
I0409 23:16:19.151276 24944 solver.cpp:237]     Train net output #0: loss = 0.0389786 (* 1 = 0.0389786 loss)
I0409 23:16:19.151286 24944 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0409 23:16:19.227245 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:16:24.383813 24944 solver.cpp:218] Iteration 9924 (2.29343 iter/s, 5.23234s/12 iters), loss = 0.0888722
I0409 23:16:24.383855 24944 solver.cpp:237]     Train net output #0: loss = 0.0888723 (* 1 = 0.0888723 loss)
I0409 23:16:24.383865 24944 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0409 23:16:29.578346 24944 solver.cpp:218] Iteration 9936 (2.31023 iter/s, 5.19429s/12 iters), loss = 0.0356584
I0409 23:16:29.578478 24944 solver.cpp:237]     Train net output #0: loss = 0.0356584 (* 1 = 0.0356584 loss)
I0409 23:16:29.578495 24944 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0409 23:16:34.322212 24944 solver.cpp:218] Iteration 9948 (2.52974 iter/s, 4.74356s/12 iters), loss = 0.0289381
I0409 23:16:34.322266 24944 solver.cpp:237]     Train net output #0: loss = 0.0289381 (* 1 = 0.0289381 loss)
I0409 23:16:34.322278 24944 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0409 23:16:39.096138 24944 solver.cpp:218] Iteration 9960 (2.51378 iter/s, 4.77369s/12 iters), loss = 0.0122863
I0409 23:16:39.096190 24944 solver.cpp:237]     Train net output #0: loss = 0.0122863 (* 1 = 0.0122863 loss)
I0409 23:16:39.096201 24944 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0409 23:16:43.961208 24944 solver.cpp:218] Iteration 9972 (2.46668 iter/s, 4.86483s/12 iters), loss = 0.048761
I0409 23:16:43.961251 24944 solver.cpp:237]     Train net output #0: loss = 0.048761 (* 1 = 0.048761 loss)
I0409 23:16:43.961261 24944 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0409 23:16:48.726166 24944 solver.cpp:218] Iteration 9984 (2.5185 iter/s, 4.76474s/12 iters), loss = 0.00947206
I0409 23:16:48.726209 24944 solver.cpp:237]     Train net output #0: loss = 0.00947211 (* 1 = 0.00947211 loss)
I0409 23:16:48.726217 24944 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0409 23:16:53.245923 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0409 23:17:04.468323 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0409 23:17:13.540009 24944 solver.cpp:330] Iteration 9996, Testing net (#0)
I0409 23:17:13.540030 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:17:14.010516 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:17:18.024924 24944 solver.cpp:397]     Test net output #0: accuracy = 0.570466
I0409 23:17:18.024961 24944 solver.cpp:397]     Test net output #1: loss = 2.55607 (* 1 = 2.55607 loss)
I0409 23:17:18.139195 24944 solver.cpp:218] Iteration 9996 (0.407997 iter/s, 29.412s/12 iters), loss = 0.0668121
I0409 23:17:18.140722 24944 solver.cpp:237]     Train net output #0: loss = 0.0668122 (* 1 = 0.0668122 loss)
I0409 23:17:18.140735 24944 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0409 23:17:22.242934 24944 solver.cpp:218] Iteration 10008 (2.92536 iter/s, 4.10206s/12 iters), loss = 0.0283626
I0409 23:17:22.242986 24944 solver.cpp:237]     Train net output #0: loss = 0.0283626 (* 1 = 0.0283626 loss)
I0409 23:17:22.242998 24944 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0409 23:17:24.550557 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:17:27.450578 24944 solver.cpp:218] Iteration 10020 (2.30442 iter/s, 5.2074s/12 iters), loss = 0.0234716
I0409 23:17:27.450628 24944 solver.cpp:237]     Train net output #0: loss = 0.0234717 (* 1 = 0.0234717 loss)
I0409 23:17:27.450639 24944 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0409 23:17:32.291234 24944 solver.cpp:218] Iteration 10032 (2.47912 iter/s, 4.84042s/12 iters), loss = 0.0359635
I0409 23:17:32.291282 24944 solver.cpp:237]     Train net output #0: loss = 0.0359635 (* 1 = 0.0359635 loss)
I0409 23:17:32.291293 24944 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0409 23:17:37.148001 24944 solver.cpp:218] Iteration 10044 (2.4709 iter/s, 4.85653s/12 iters), loss = 0.0252138
I0409 23:17:37.148129 24944 solver.cpp:237]     Train net output #0: loss = 0.0252139 (* 1 = 0.0252139 loss)
I0409 23:17:37.148138 24944 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0409 23:17:42.100244 24944 solver.cpp:218] Iteration 10056 (2.4233 iter/s, 4.95193s/12 iters), loss = 0.00619274
I0409 23:17:42.100292 24944 solver.cpp:237]     Train net output #0: loss = 0.00619281 (* 1 = 0.00619281 loss)
I0409 23:17:42.100302 24944 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0409 23:17:46.903756 24944 solver.cpp:218] Iteration 10068 (2.49829 iter/s, 4.80328s/12 iters), loss = 0.0451936
I0409 23:17:46.903802 24944 solver.cpp:237]     Train net output #0: loss = 0.0451936 (* 1 = 0.0451936 loss)
I0409 23:17:46.903810 24944 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0409 23:17:51.838104 24944 solver.cpp:218] Iteration 10080 (2.43205 iter/s, 4.93411s/12 iters), loss = 0.0549729
I0409 23:17:51.838150 24944 solver.cpp:237]     Train net output #0: loss = 0.054973 (* 1 = 0.054973 loss)
I0409 23:17:51.838160 24944 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0409 23:17:56.855160 24944 solver.cpp:218] Iteration 10092 (2.39195 iter/s, 5.01682s/12 iters), loss = 0.0322235
I0409 23:17:56.855207 24944 solver.cpp:237]     Train net output #0: loss = 0.0322235 (* 1 = 0.0322235 loss)
I0409 23:17:56.855217 24944 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0409 23:17:58.809173 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0409 23:18:09.492961 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0409 23:18:26.579342 24944 solver.cpp:330] Iteration 10098, Testing net (#0)
I0409 23:18:26.579361 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:18:27.019595 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:18:31.047209 24944 solver.cpp:397]     Test net output #0: accuracy = 0.560049
I0409 23:18:31.047261 24944 solver.cpp:397]     Test net output #1: loss = 2.64984 (* 1 = 2.64984 loss)
I0409 23:18:32.829679 24944 solver.cpp:218] Iteration 10104 (0.333582 iter/s, 35.9732s/12 iters), loss = 0.0271396
I0409 23:18:32.829730 24944 solver.cpp:237]     Train net output #0: loss = 0.0271397 (* 1 = 0.0271397 loss)
I0409 23:18:32.829739 24944 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0409 23:18:36.772190 24948 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:18:37.428115 24944 solver.cpp:218] Iteration 10116 (2.60971 iter/s, 4.59821s/12 iters), loss = 0.0347903
I0409 23:18:37.428170 24944 solver.cpp:237]     Train net output #0: loss = 0.0347904 (* 1 = 0.0347904 loss)
I0409 23:18:37.428181 24944 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0409 23:18:42.255002 24944 solver.cpp:218] Iteration 10128 (2.4862 iter/s, 4.82665s/12 iters), loss = 0.0533622
I0409 23:18:42.255138 24944 solver.cpp:237]     Train net output #0: loss = 0.0533623 (* 1 = 0.0533623 loss)
I0409 23:18:42.255151 24944 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0409 23:18:46.766706 24944 solver.cpp:218] Iteration 10140 (2.65993 iter/s, 4.5114s/12 iters), loss = 0.0191511
I0409 23:18:46.766760 24944 solver.cpp:237]     Train net output #0: loss = 0.0191511 (* 1 = 0.0191511 loss)
I0409 23:18:46.766772 24944 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0409 23:18:51.722898 24944 solver.cpp:218] Iteration 10152 (2.42133 iter/s, 4.95595s/12 iters), loss = 0.00321881
I0409 23:18:51.722941 24944 solver.cpp:237]     Train net output #0: loss = 0.00321887 (* 1 = 0.00321887 loss)
I0409 23:18:51.722950 24944 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0409 23:18:56.582911 24944 solver.cpp:218] Iteration 10164 (2.46924 iter/s, 4.85979s/12 iters), loss = 0.0290551
I0409 23:18:56.582960 24944 solver.cpp:237]     Train net output #0: loss = 0.0290552 (* 1 = 0.0290552 loss)
I0409 23:18:56.582971 24944 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0409 23:19:01.277307 24944 solver.cpp:218] Iteration 10176 (2.55636 iter/s, 4.69417s/12 iters), loss = 0.0306444
I0409 23:19:01.277354 24944 solver.cpp:237]     Train net output #0: loss = 0.0306444 (* 1 = 0.0306444 loss)
I0409 23:19:01.277365 24944 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0409 23:19:06.131664 24944 solver.cpp:218] Iteration 10188 (2.47212 iter/s, 4.85413s/12 iters), loss = 0.0879232
I0409 23:19:06.131716 24944 solver.cpp:237]     Train net output #0: loss = 0.0879233 (* 1 = 0.0879233 loss)
I0409 23:19:06.131726 24944 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0409 23:19:10.625134 24944 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0409 23:19:30.922446 24944 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0409 23:19:40.545835 24944 solver.cpp:310] Iteration 10200, loss = 0.023094
I0409 23:19:40.545876 24944 solver.cpp:330] Iteration 10200, Testing net (#0)
I0409 23:19:40.545886 24944 net.cpp:676] Ignoring source layer train-data
I0409 23:19:40.926028 24949 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:19:44.928431 24944 solver.cpp:397]     Test net output #0: accuracy = 0.558824
I0409 23:19:44.928474 24944 solver.cpp:397]     Test net output #1: loss = 2.54834 (* 1 = 2.54834 loss)
I0409 23:19:44.928483 24944 solver.cpp:315] Optimization Done.
I0409 23:19:44.928488 24944 caffe.cpp:259] Optimization Done.