DIGITS-CNN/cars/architecture-investigations/conv/layers/layer3.5/kernel/5/caffe_output.log

I0428 13:41:13.988245 27120 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210428-120239-d954/solver.prototxt
I0428 13:41:13.990576 27120 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0428 13:41:13.990581 27120 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0428 13:41:13.990658 27120 caffe.cpp:218] Using GPUs 2
I0428 13:41:14.032722 27120 caffe.cpp:223] GPU 2: GeForce RTX 2080
I0428 13:41:14.373330 27120 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"
I0428 13:41:14.374853 27120 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0428 13:41:14.375876 27120 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0428 13:41:14.375890 27120 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0428 13:41:14.376022 27120 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-AMB-2/digits/jobs/20210419-113214-d311/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113214-d311/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: "conv3.5"
type: "Convolution"
bottom: "conv3"
top: "conv3.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 5
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3.5"
type: "ReLU"
bottom: "conv3.5"
top: "conv3.5"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3.5"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0428 13:41:14.376117 27120 layer_factory.hpp:77] Creating layer train-data
I0428 13:41:14.387595 27120 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113214-d311/train_db
I0428 13:41:14.388924 27120 net.cpp:84] Creating Layer train-data
I0428 13:41:14.388939 27120 net.cpp:380] train-data -> data
I0428 13:41:14.388959 27120 net.cpp:380] train-data -> label
I0428 13:41:14.388970 27120 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113214-d311/mean.binaryproto
I0428 13:41:14.393935 27120 data_layer.cpp:45] output data size: 128,3,227,227
I0428 13:41:14.522536 27120 net.cpp:122] Setting up train-data
I0428 13:41:14.522559 27120 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0428 13:41:14.522564 27120 net.cpp:129] Top shape: 128 (128)
I0428 13:41:14.522567 27120 net.cpp:137] Memory required for data: 79149056
I0428 13:41:14.522576 27120 layer_factory.hpp:77] Creating layer conv1
I0428 13:41:14.522619 27120 net.cpp:84] Creating Layer conv1
I0428 13:41:14.522625 27120 net.cpp:406] conv1 <- data
I0428 13:41:14.522637 27120 net.cpp:380] conv1 -> conv1
I0428 13:41:15.444555 27120 net.cpp:122] Setting up conv1
I0428 13:41:15.444576 27120 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0428 13:41:15.444579 27120 net.cpp:137] Memory required for data: 227833856
I0428 13:41:15.444597 27120 layer_factory.hpp:77] Creating layer relu1
I0428 13:41:15.444607 27120 net.cpp:84] Creating Layer relu1
I0428 13:41:15.444610 27120 net.cpp:406] relu1 <- conv1
I0428 13:41:15.444636 27120 net.cpp:367] relu1 -> conv1 (in-place)
I0428 13:41:15.444959 27120 net.cpp:122] Setting up relu1
I0428 13:41:15.444968 27120 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0428 13:41:15.444972 27120 net.cpp:137] Memory required for data: 376518656
I0428 13:41:15.444974 27120 layer_factory.hpp:77] Creating layer norm1
I0428 13:41:15.444983 27120 net.cpp:84] Creating Layer norm1
I0428 13:41:15.444986 27120 net.cpp:406] norm1 <- conv1
I0428 13:41:15.444991 27120 net.cpp:380] norm1 -> norm1
I0428 13:41:15.445502 27120 net.cpp:122] Setting up norm1
I0428 13:41:15.445513 27120 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0428 13:41:15.445515 27120 net.cpp:137] Memory required for data: 525203456
I0428 13:41:15.445518 27120 layer_factory.hpp:77] Creating layer pool1
I0428 13:41:15.445525 27120 net.cpp:84] Creating Layer pool1
I0428 13:41:15.445528 27120 net.cpp:406] pool1 <- norm1
I0428 13:41:15.445533 27120 net.cpp:380] pool1 -> pool1
I0428 13:41:15.445564 27120 net.cpp:122] Setting up pool1
I0428 13:41:15.445570 27120 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0428 13:41:15.445572 27120 net.cpp:137] Memory required for data: 561035264
I0428 13:41:15.445575 27120 layer_factory.hpp:77] Creating layer conv2
I0428 13:41:15.445585 27120 net.cpp:84] Creating Layer conv2
I0428 13:41:15.445587 27120 net.cpp:406] conv2 <- pool1
I0428 13:41:15.445592 27120 net.cpp:380] conv2 -> conv2
I0428 13:41:15.453315 27120 net.cpp:122] Setting up conv2
I0428 13:41:15.453330 27120 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0428 13:41:15.453333 27120 net.cpp:137] Memory required for data: 656586752
I0428 13:41:15.453343 27120 layer_factory.hpp:77] Creating layer relu2
I0428 13:41:15.453351 27120 net.cpp:84] Creating Layer relu2
I0428 13:41:15.453356 27120 net.cpp:406] relu2 <- conv2
I0428 13:41:15.453361 27120 net.cpp:367] relu2 -> conv2 (in-place)
I0428 13:41:15.453910 27120 net.cpp:122] Setting up relu2
I0428 13:41:15.453922 27120 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0428 13:41:15.453924 27120 net.cpp:137] Memory required for data: 752138240
I0428 13:41:15.453927 27120 layer_factory.hpp:77] Creating layer norm2
I0428 13:41:15.453934 27120 net.cpp:84] Creating Layer norm2
I0428 13:41:15.453938 27120 net.cpp:406] norm2 <- conv2
I0428 13:41:15.453943 27120 net.cpp:380] norm2 -> norm2
I0428 13:41:15.454324 27120 net.cpp:122] Setting up norm2
I0428 13:41:15.454334 27120 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0428 13:41:15.454335 27120 net.cpp:137] Memory required for data: 847689728
I0428 13:41:15.454339 27120 layer_factory.hpp:77] Creating layer pool2
I0428 13:41:15.454347 27120 net.cpp:84] Creating Layer pool2
I0428 13:41:15.454350 27120 net.cpp:406] pool2 <- norm2
I0428 13:41:15.454355 27120 net.cpp:380] pool2 -> pool2
I0428 13:41:15.454382 27120 net.cpp:122] Setting up pool2
I0428 13:41:15.454387 27120 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0428 13:41:15.454391 27120 net.cpp:137] Memory required for data: 869840896
I0428 13:41:15.454393 27120 layer_factory.hpp:77] Creating layer conv3
I0428 13:41:15.454402 27120 net.cpp:84] Creating Layer conv3
I0428 13:41:15.454406 27120 net.cpp:406] conv3 <- pool2
I0428 13:41:15.454411 27120 net.cpp:380] conv3 -> conv3
I0428 13:41:15.465153 27120 net.cpp:122] Setting up conv3
I0428 13:41:15.465173 27120 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0428 13:41:15.465175 27120 net.cpp:137] Memory required for data: 903067648
I0428 13:41:15.465185 27120 layer_factory.hpp:77] Creating layer relu3
I0428 13:41:15.465193 27120 net.cpp:84] Creating Layer relu3
I0428 13:41:15.465196 27120 net.cpp:406] relu3 <- conv3
I0428 13:41:15.465204 27120 net.cpp:367] relu3 -> conv3 (in-place)
I0428 13:41:15.465788 27120 net.cpp:122] Setting up relu3
I0428 13:41:15.465798 27120 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0428 13:41:15.465801 27120 net.cpp:137] Memory required for data: 936294400
I0428 13:41:15.465804 27120 layer_factory.hpp:77] Creating layer conv3.5
I0428 13:41:15.465814 27120 net.cpp:84] Creating Layer conv3.5
I0428 13:41:15.465837 27120 net.cpp:406] conv3.5 <- conv3
I0428 13:41:15.465843 27120 net.cpp:380] conv3.5 -> conv3.5
I0428 13:41:15.504078 27120 net.cpp:122] Setting up conv3.5
I0428 13:41:15.504096 27120 net.cpp:129] Top shape: 128 384 11 11 (5947392)
I0428 13:41:15.504098 27120 net.cpp:137] Memory required for data: 960083968
I0428 13:41:15.504106 27120 layer_factory.hpp:77] Creating layer relu3.5
I0428 13:41:15.504114 27120 net.cpp:84] Creating Layer relu3.5
I0428 13:41:15.504118 27120 net.cpp:406] relu3.5 <- conv3.5
I0428 13:41:15.504125 27120 net.cpp:367] relu3.5 -> conv3.5 (in-place)
I0428 13:41:15.504670 27120 net.cpp:122] Setting up relu3.5
I0428 13:41:15.504681 27120 net.cpp:129] Top shape: 128 384 11 11 (5947392)
I0428 13:41:15.504684 27120 net.cpp:137] Memory required for data: 983873536
I0428 13:41:15.504688 27120 layer_factory.hpp:77] Creating layer conv4
I0428 13:41:15.504698 27120 net.cpp:84] Creating Layer conv4
I0428 13:41:15.504701 27120 net.cpp:406] conv4 <- conv3.5
I0428 13:41:15.504706 27120 net.cpp:380] conv4 -> conv4
I0428 13:41:15.514925 27120 net.cpp:122] Setting up conv4
I0428 13:41:15.514941 27120 net.cpp:129] Top shape: 128 384 11 11 (5947392)
I0428 13:41:15.514945 27120 net.cpp:137] Memory required for data: 1007663104
I0428 13:41:15.514957 27120 layer_factory.hpp:77] Creating layer relu4
I0428 13:41:15.514963 27120 net.cpp:84] Creating Layer relu4
I0428 13:41:15.514967 27120 net.cpp:406] relu4 <- conv4
I0428 13:41:15.514972 27120 net.cpp:367] relu4 -> conv4 (in-place)
I0428 13:41:15.515341 27120 net.cpp:122] Setting up relu4
I0428 13:41:15.515352 27120 net.cpp:129] Top shape: 128 384 11 11 (5947392)
I0428 13:41:15.515354 27120 net.cpp:137] Memory required for data: 1031452672
I0428 13:41:15.515357 27120 layer_factory.hpp:77] Creating layer conv5
I0428 13:41:15.515367 27120 net.cpp:84] Creating Layer conv5
I0428 13:41:15.515372 27120 net.cpp:406] conv5 <- conv4
I0428 13:41:15.515377 27120 net.cpp:380] conv5 -> conv5
I0428 13:41:15.524399 27120 net.cpp:122] Setting up conv5
I0428 13:41:15.524417 27120 net.cpp:129] Top shape: 128 256 11 11 (3964928)
I0428 13:41:15.524420 27120 net.cpp:137] Memory required for data: 1047312384
I0428 13:41:15.524428 27120 layer_factory.hpp:77] Creating layer relu5
I0428 13:41:15.524438 27120 net.cpp:84] Creating Layer relu5
I0428 13:41:15.524443 27120 net.cpp:406] relu5 <- conv5
I0428 13:41:15.524448 27120 net.cpp:367] relu5 -> conv5 (in-place)
I0428 13:41:15.525007 27120 net.cpp:122] Setting up relu5
I0428 13:41:15.525017 27120 net.cpp:129] Top shape: 128 256 11 11 (3964928)
I0428 13:41:15.525019 27120 net.cpp:137] Memory required for data: 1063172096
I0428 13:41:15.525022 27120 layer_factory.hpp:77] Creating layer pool5
I0428 13:41:15.525028 27120 net.cpp:84] Creating Layer pool5
I0428 13:41:15.525032 27120 net.cpp:406] pool5 <- conv5
I0428 13:41:15.525038 27120 net.cpp:380] pool5 -> pool5
I0428 13:41:15.525072 27120 net.cpp:122] Setting up pool5
I0428 13:41:15.525077 27120 net.cpp:129] Top shape: 128 256 5 5 (819200)
I0428 13:41:15.525080 27120 net.cpp:137] Memory required for data: 1066448896
I0428 13:41:15.525082 27120 layer_factory.hpp:77] Creating layer fc6
I0428 13:41:15.525089 27120 net.cpp:84] Creating Layer fc6
I0428 13:41:15.525091 27120 net.cpp:406] fc6 <- pool5
I0428 13:41:15.525097 27120 net.cpp:380] fc6 -> fc6
I0428 13:41:15.774029 27120 net.cpp:122] Setting up fc6
I0428 13:41:15.774050 27120 net.cpp:129] Top shape: 128 4096 (524288)
I0428 13:41:15.774053 27120 net.cpp:137] Memory required for data: 1068546048
I0428 13:41:15.774062 27120 layer_factory.hpp:77] Creating layer relu6
I0428 13:41:15.774072 27120 net.cpp:84] Creating Layer relu6
I0428 13:41:15.774076 27120 net.cpp:406] relu6 <- fc6
I0428 13:41:15.774082 27120 net.cpp:367] relu6 -> fc6 (in-place)
I0428 13:41:15.774991 27120 net.cpp:122] Setting up relu6
I0428 13:41:15.775002 27120 net.cpp:129] Top shape: 128 4096 (524288)
I0428 13:41:15.775004 27120 net.cpp:137] Memory required for data: 1070643200
I0428 13:41:15.775007 27120 layer_factory.hpp:77] Creating layer drop6
I0428 13:41:15.775032 27120 net.cpp:84] Creating Layer drop6
I0428 13:41:15.775035 27120 net.cpp:406] drop6 <- fc6
I0428 13:41:15.775039 27120 net.cpp:367] drop6 -> fc6 (in-place)
I0428 13:41:15.775069 27120 net.cpp:122] Setting up drop6
I0428 13:41:15.775074 27120 net.cpp:129] Top shape: 128 4096 (524288)
I0428 13:41:15.775077 27120 net.cpp:137] Memory required for data: 1072740352
I0428 13:41:15.775080 27120 layer_factory.hpp:77] Creating layer fc7
I0428 13:41:15.775087 27120 net.cpp:84] Creating Layer fc7
I0428 13:41:15.775090 27120 net.cpp:406] fc7 <- fc6
I0428 13:41:15.775094 27120 net.cpp:380] fc7 -> fc7
I0428 13:41:15.934293 27120 net.cpp:122] Setting up fc7
I0428 13:41:15.934314 27120 net.cpp:129] Top shape: 128 4096 (524288)
I0428 13:41:15.934317 27120 net.cpp:137] Memory required for data: 1074837504
I0428 13:41:15.934326 27120 layer_factory.hpp:77] Creating layer relu7
I0428 13:41:15.934334 27120 net.cpp:84] Creating Layer relu7
I0428 13:41:15.934338 27120 net.cpp:406] relu7 <- fc7
I0428 13:41:15.934346 27120 net.cpp:367] relu7 -> fc7 (in-place)
I0428 13:41:15.935103 27120 net.cpp:122] Setting up relu7
I0428 13:41:15.935113 27120 net.cpp:129] Top shape: 128 4096 (524288)
I0428 13:41:15.935117 27120 net.cpp:137] Memory required for data: 1076934656
I0428 13:41:15.935119 27120 layer_factory.hpp:77] Creating layer drop7
I0428 13:41:15.935127 27120 net.cpp:84] Creating Layer drop7
I0428 13:41:15.935130 27120 net.cpp:406] drop7 <- fc7
I0428 13:41:15.935134 27120 net.cpp:367] drop7 -> fc7 (in-place)
I0428 13:41:15.935158 27120 net.cpp:122] Setting up drop7
I0428 13:41:15.935163 27120 net.cpp:129] Top shape: 128 4096 (524288)
I0428 13:41:15.935166 27120 net.cpp:137] Memory required for data: 1079031808
I0428 13:41:15.935168 27120 layer_factory.hpp:77] Creating layer fc8
I0428 13:41:15.935176 27120 net.cpp:84] Creating Layer fc8
I0428 13:41:15.935179 27120 net.cpp:406] fc8 <- fc7
I0428 13:41:15.935184 27120 net.cpp:380] fc8 -> fc8
I0428 13:41:15.942922 27120 net.cpp:122] Setting up fc8
I0428 13:41:15.942934 27120 net.cpp:129] Top shape: 128 196 (25088)
I0428 13:41:15.942935 27120 net.cpp:137] Memory required for data: 1079132160
I0428 13:41:15.942947 27120 layer_factory.hpp:77] Creating layer loss
I0428 13:41:15.942955 27120 net.cpp:84] Creating Layer loss
I0428 13:41:15.942957 27120 net.cpp:406] loss <- fc8
I0428 13:41:15.942961 27120 net.cpp:406] loss <- label
I0428 13:41:15.942968 27120 net.cpp:380] loss -> loss
I0428 13:41:15.942978 27120 layer_factory.hpp:77] Creating layer loss
I0428 13:41:15.943486 27120 net.cpp:122] Setting up loss
I0428 13:41:15.943495 27120 net.cpp:129] Top shape: (1)
I0428 13:41:15.943497 27120 net.cpp:132]     with loss weight 1
I0428 13:41:15.943514 27120 net.cpp:137] Memory required for data: 1079132164
I0428 13:41:15.943517 27120 net.cpp:198] loss needs backward computation.
I0428 13:41:15.943523 27120 net.cpp:198] fc8 needs backward computation.
I0428 13:41:15.943526 27120 net.cpp:198] drop7 needs backward computation.
I0428 13:41:15.943529 27120 net.cpp:198] relu7 needs backward computation.
I0428 13:41:15.943532 27120 net.cpp:198] fc7 needs backward computation.
I0428 13:41:15.943534 27120 net.cpp:198] drop6 needs backward computation.
I0428 13:41:15.943537 27120 net.cpp:198] relu6 needs backward computation.
I0428 13:41:15.943540 27120 net.cpp:198] fc6 needs backward computation.
I0428 13:41:15.943543 27120 net.cpp:198] pool5 needs backward computation.
I0428 13:41:15.943547 27120 net.cpp:198] relu5 needs backward computation.
I0428 13:41:15.943548 27120 net.cpp:198] conv5 needs backward computation.
I0428 13:41:15.943552 27120 net.cpp:198] relu4 needs backward computation.
I0428 13:41:15.943554 27120 net.cpp:198] conv4 needs backward computation.
I0428 13:41:15.943557 27120 net.cpp:198] relu3.5 needs backward computation.
I0428 13:41:15.943560 27120 net.cpp:198] conv3.5 needs backward computation.
I0428 13:41:15.943563 27120 net.cpp:198] relu3 needs backward computation.
I0428 13:41:15.943567 27120 net.cpp:198] conv3 needs backward computation.
I0428 13:41:15.943569 27120 net.cpp:198] pool2 needs backward computation.
I0428 13:41:15.943588 27120 net.cpp:198] norm2 needs backward computation.
I0428 13:41:15.943593 27120 net.cpp:198] relu2 needs backward computation.
I0428 13:41:15.943595 27120 net.cpp:198] conv2 needs backward computation.
I0428 13:41:15.943598 27120 net.cpp:198] pool1 needs backward computation.
I0428 13:41:15.943600 27120 net.cpp:198] norm1 needs backward computation.
I0428 13:41:15.943603 27120 net.cpp:198] relu1 needs backward computation.
I0428 13:41:15.943606 27120 net.cpp:198] conv1 needs backward computation.
I0428 13:41:15.943614 27120 net.cpp:200] train-data does not need backward computation.
I0428 13:41:15.943616 27120 net.cpp:242] This network produces output loss
I0428 13:41:15.943629 27120 net.cpp:255] Network initialization done.
I0428 13:41:15.944180 27120 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0428 13:41:15.944211 27120 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0428 13:41:15.944351 27120 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-AMB-2/digits/jobs/20210419-113214-d311/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113214-d311/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: "conv3.5"
type: "Convolution"
bottom: "conv3"
top: "conv3.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 5
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3.5"
type: "ReLU"
bottom: "conv3.5"
top: "conv3.5"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3.5"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0428 13:41:15.944447 27120 layer_factory.hpp:77] Creating layer val-data
I0428 13:41:16.022473 27120 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113214-d311/val_db
I0428 13:41:16.023334 27120 net.cpp:84] Creating Layer val-data
I0428 13:41:16.023358 27120 net.cpp:380] val-data -> data
I0428 13:41:16.023376 27120 net.cpp:380] val-data -> label
I0428 13:41:16.023389 27120 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AMB-2/digits/jobs/20210419-113214-d311/mean.binaryproto
I0428 13:41:16.030102 27120 data_layer.cpp:45] output data size: 32,3,227,227
I0428 13:41:16.065634 27120 net.cpp:122] Setting up val-data
I0428 13:41:16.065654 27120 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0428 13:41:16.065659 27120 net.cpp:129] Top shape: 32 (32)
I0428 13:41:16.065661 27120 net.cpp:137] Memory required for data: 19787264
I0428 13:41:16.065667 27120 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0428 13:41:16.065678 27120 net.cpp:84] Creating Layer label_val-data_1_split
I0428 13:41:16.065682 27120 net.cpp:406] label_val-data_1_split <- label
I0428 13:41:16.065688 27120 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0428 13:41:16.065698 27120 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0428 13:41:16.065738 27120 net.cpp:122] Setting up label_val-data_1_split
I0428 13:41:16.065743 27120 net.cpp:129] Top shape: 32 (32)
I0428 13:41:16.065747 27120 net.cpp:129] Top shape: 32 (32)
I0428 13:41:16.065748 27120 net.cpp:137] Memory required for data: 19787520
I0428 13:41:16.065771 27120 layer_factory.hpp:77] Creating layer conv1
I0428 13:41:16.065783 27120 net.cpp:84] Creating Layer conv1
I0428 13:41:16.065786 27120 net.cpp:406] conv1 <- data
I0428 13:41:16.065791 27120 net.cpp:380] conv1 -> conv1
I0428 13:41:16.070188 27120 net.cpp:122] Setting up conv1
I0428 13:41:16.070200 27120 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0428 13:41:16.070204 27120 net.cpp:137] Memory required for data: 56958720
I0428 13:41:16.070214 27120 layer_factory.hpp:77] Creating layer relu1
I0428 13:41:16.070220 27120 net.cpp:84] Creating Layer relu1
I0428 13:41:16.070223 27120 net.cpp:406] relu1 <- conv1
I0428 13:41:16.070228 27120 net.cpp:367] relu1 -> conv1 (in-place)
I0428 13:41:16.070737 27120 net.cpp:122] Setting up relu1
I0428 13:41:16.070749 27120 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0428 13:41:16.070751 27120 net.cpp:137] Memory required for data: 94129920
I0428 13:41:16.070755 27120 layer_factory.hpp:77] Creating layer norm1
I0428 13:41:16.070763 27120 net.cpp:84] Creating Layer norm1
I0428 13:41:16.070766 27120 net.cpp:406] norm1 <- conv1
I0428 13:41:16.070771 27120 net.cpp:380] norm1 -> norm1
I0428 13:41:16.071108 27120 net.cpp:122] Setting up norm1
I0428 13:41:16.071117 27120 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0428 13:41:16.071120 27120 net.cpp:137] Memory required for data: 131301120
I0428 13:41:16.071123 27120 layer_factory.hpp:77] Creating layer pool1
I0428 13:41:16.071130 27120 net.cpp:84] Creating Layer pool1
I0428 13:41:16.071133 27120 net.cpp:406] pool1 <- norm1
I0428 13:41:16.071137 27120 net.cpp:380] pool1 -> pool1
I0428 13:41:16.071164 27120 net.cpp:122] Setting up pool1
I0428 13:41:16.071169 27120 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0428 13:41:16.071172 27120 net.cpp:137] Memory required for data: 140259072
I0428 13:41:16.071175 27120 layer_factory.hpp:77] Creating layer conv2
I0428 13:41:16.071183 27120 net.cpp:84] Creating Layer conv2
I0428 13:41:16.071187 27120 net.cpp:406] conv2 <- pool1
I0428 13:41:16.071192 27120 net.cpp:380] conv2 -> conv2
I0428 13:41:16.079622 27120 net.cpp:122] Setting up conv2
I0428 13:41:16.079634 27120 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0428 13:41:16.079638 27120 net.cpp:137] Memory required for data: 164146944
I0428 13:41:16.079645 27120 layer_factory.hpp:77] Creating layer relu2
I0428 13:41:16.079651 27120 net.cpp:84] Creating Layer relu2
I0428 13:41:16.079654 27120 net.cpp:406] relu2 <- conv2
I0428 13:41:16.079660 27120 net.cpp:367] relu2 -> conv2 (in-place)
I0428 13:41:16.081372 27120 net.cpp:122] Setting up relu2
I0428 13:41:16.081382 27120 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0428 13:41:16.081385 27120 net.cpp:137] Memory required for data: 188034816
I0428 13:41:16.081388 27120 layer_factory.hpp:77] Creating layer norm2
I0428 13:41:16.081398 27120 net.cpp:84] Creating Layer norm2
I0428 13:41:16.081401 27120 net.cpp:406] norm2 <- conv2
I0428 13:41:16.081406 27120 net.cpp:380] norm2 -> norm2
I0428 13:41:16.082037 27120 net.cpp:122] Setting up norm2
I0428 13:41:16.082047 27120 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0428 13:41:16.082051 27120 net.cpp:137] Memory required for data: 211922688
I0428 13:41:16.082054 27120 layer_factory.hpp:77] Creating layer pool2
I0428 13:41:16.082062 27120 net.cpp:84] Creating Layer pool2
I0428 13:41:16.082065 27120 net.cpp:406] pool2 <- norm2
I0428 13:41:16.082072 27120 net.cpp:380] pool2 -> pool2
I0428 13:41:16.082098 27120 net.cpp:122] Setting up pool2
I0428 13:41:16.082105 27120 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0428 13:41:16.082108 27120 net.cpp:137] Memory required for data: 217460480
I0428 13:41:16.082110 27120 layer_factory.hpp:77] Creating layer conv3
I0428 13:41:16.082118 27120 net.cpp:84] Creating Layer conv3
I0428 13:41:16.082121 27120 net.cpp:406] conv3 <- pool2
I0428 13:41:16.082127 27120 net.cpp:380] conv3 -> conv3
I0428 13:41:16.092556 27120 net.cpp:122] Setting up conv3
I0428 13:41:16.092571 27120 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0428 13:41:16.092573 27120 net.cpp:137] Memory required for data: 225767168
I0428 13:41:16.092603 27120 layer_factory.hpp:77] Creating layer relu3
I0428 13:41:16.092610 27120 net.cpp:84] Creating Layer relu3
I0428 13:41:16.092613 27120 net.cpp:406] relu3 <- conv3
I0428 13:41:16.092622 27120 net.cpp:367] relu3 -> conv3 (in-place)
I0428 13:41:16.093194 27120 net.cpp:122] Setting up relu3
I0428 13:41:16.093206 27120 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0428 13:41:16.093209 27120 net.cpp:137] Memory required for data: 234073856
I0428 13:41:16.093212 27120 layer_factory.hpp:77] Creating layer conv3.5
I0428 13:41:16.093223 27120 net.cpp:84] Creating Layer conv3.5
I0428 13:41:16.093227 27120 net.cpp:406] conv3.5 <- conv3
I0428 13:41:16.093233 27120 net.cpp:380] conv3.5 -> conv3.5
I0428 13:41:16.130398 27120 net.cpp:122] Setting up conv3.5
I0428 13:41:16.130419 27120 net.cpp:129] Top shape: 32 384 11 11 (1486848)
I0428 13:41:16.130422 27120 net.cpp:137] Memory required for data: 240021248
I0428 13:41:16.130431 27120 layer_factory.hpp:77] Creating layer relu3.5
I0428 13:41:16.130439 27120 net.cpp:84] Creating Layer relu3.5
I0428 13:41:16.130443 27120 net.cpp:406] relu3.5 <- conv3.5
I0428 13:41:16.130450 27120 net.cpp:367] relu3.5 -> conv3.5 (in-place)
I0428 13:41:16.131181 27120 net.cpp:122] Setting up relu3.5
I0428 13:41:16.131191 27120 net.cpp:129] Top shape: 32 384 11 11 (1486848)
I0428 13:41:16.131194 27120 net.cpp:137] Memory required for data: 245968640
I0428 13:41:16.131197 27120 layer_factory.hpp:77] Creating layer conv4
I0428 13:41:16.131207 27120 net.cpp:84] Creating Layer conv4
I0428 13:41:16.131211 27120 net.cpp:406] conv4 <- conv3.5
I0428 13:41:16.131217 27120 net.cpp:380] conv4 -> conv4
I0428 13:41:16.142575 27120 net.cpp:122] Setting up conv4
I0428 13:41:16.142592 27120 net.cpp:129] Top shape: 32 384 11 11 (1486848)
I0428 13:41:16.142602 27120 net.cpp:137] Memory required for data: 251916032
I0428 13:41:16.142616 27120 layer_factory.hpp:77] Creating layer relu4
I0428 13:41:16.142623 27120 net.cpp:84] Creating Layer relu4
I0428 13:41:16.142628 27120 net.cpp:406] relu4 <- conv4
I0428 13:41:16.142633 27120 net.cpp:367] relu4 -> conv4 (in-place)
I0428 13:41:16.143240 27120 net.cpp:122] Setting up relu4
I0428 13:41:16.143249 27120 net.cpp:129] Top shape: 32 384 11 11 (1486848)
I0428 13:41:16.143252 27120 net.cpp:137] Memory required for data: 257863424
I0428 13:41:16.143255 27120 layer_factory.hpp:77] Creating layer conv5
I0428 13:41:16.143270 27120 net.cpp:84] Creating Layer conv5
I0428 13:41:16.143272 27120 net.cpp:406] conv5 <- conv4
I0428 13:41:16.143278 27120 net.cpp:380] conv5 -> conv5
I0428 13:41:16.152783 27120 net.cpp:122] Setting up conv5
I0428 13:41:16.152803 27120 net.cpp:129] Top shape: 32 256 11 11 (991232)
I0428 13:41:16.152806 27120 net.cpp:137] Memory required for data: 261828352
I0428 13:41:16.152814 27120 layer_factory.hpp:77] Creating layer relu5
I0428 13:41:16.152822 27120 net.cpp:84] Creating Layer relu5
I0428 13:41:16.152824 27120 net.cpp:406] relu5 <- conv5
I0428 13:41:16.152832 27120 net.cpp:367] relu5 -> conv5 (in-place)
I0428 13:41:16.153214 27120 net.cpp:122] Setting up relu5
I0428 13:41:16.153223 27120 net.cpp:129] Top shape: 32 256 11 11 (991232)
I0428 13:41:16.153225 27120 net.cpp:137] Memory required for data: 265793280
I0428 13:41:16.153228 27120 layer_factory.hpp:77] Creating layer pool5
I0428 13:41:16.153236 27120 net.cpp:84] Creating Layer pool5
I0428 13:41:16.153239 27120 net.cpp:406] pool5 <- conv5
I0428 13:41:16.153244 27120 net.cpp:380] pool5 -> pool5
I0428 13:41:16.153280 27120 net.cpp:122] Setting up pool5
I0428 13:41:16.153285 27120 net.cpp:129] Top shape: 32 256 5 5 (204800)
I0428 13:41:16.153288 27120 net.cpp:137] Memory required for data: 266612480
I0428 13:41:16.153291 27120 layer_factory.hpp:77] Creating layer fc6
I0428 13:41:16.153297 27120 net.cpp:84] Creating Layer fc6
I0428 13:41:16.153301 27120 net.cpp:406] fc6 <- pool5
I0428 13:41:16.153306 27120 net.cpp:380] fc6 -> fc6
I0428 13:41:16.401963 27120 net.cpp:122] Setting up fc6
I0428 13:41:16.401983 27120 net.cpp:129] Top shape: 32 4096 (131072)
I0428 13:41:16.402004 27120 net.cpp:137] Memory required for data: 267136768
I0428 13:41:16.402014 27120 layer_factory.hpp:77] Creating layer relu6
I0428 13:41:16.402021 27120 net.cpp:84] Creating Layer relu6
I0428 13:41:16.402024 27120 net.cpp:406] relu6 <- fc6
I0428 13:41:16.402034 27120 net.cpp:367] relu6 -> fc6 (in-place)
I0428 13:41:16.402803 27120 net.cpp:122] Setting up relu6
I0428 13:41:16.402813 27120 net.cpp:129] Top shape: 32 4096 (131072)
I0428 13:41:16.402817 27120 net.cpp:137] Memory required for data: 267661056
I0428 13:41:16.402819 27120 layer_factory.hpp:77] Creating layer drop6
I0428 13:41:16.402827 27120 net.cpp:84] Creating Layer drop6
I0428 13:41:16.402829 27120 net.cpp:406] drop6 <- fc6
I0428 13:41:16.402835 27120 net.cpp:367] drop6 -> fc6 (in-place)
I0428 13:41:16.402859 27120 net.cpp:122] Setting up drop6
I0428 13:41:16.402866 27120 net.cpp:129] Top shape: 32 4096 (131072)
I0428 13:41:16.402868 27120 net.cpp:137] Memory required for data: 268185344
I0428 13:41:16.402871 27120 layer_factory.hpp:77] Creating layer fc7
I0428 13:41:16.402877 27120 net.cpp:84] Creating Layer fc7
I0428 13:41:16.402880 27120 net.cpp:406] fc7 <- fc6
I0428 13:41:16.402886 27120 net.cpp:380] fc7 -> fc7
I0428 13:41:16.571383 27120 net.cpp:122] Setting up fc7
I0428 13:41:16.571405 27120 net.cpp:129] Top shape: 32 4096 (131072)
I0428 13:41:16.571408 27120 net.cpp:137] Memory required for data: 268709632
I0428 13:41:16.571417 27120 layer_factory.hpp:77] Creating layer relu7
I0428 13:41:16.571425 27120 net.cpp:84] Creating Layer relu7
I0428 13:41:16.571429 27120 net.cpp:406] relu7 <- fc7
I0428 13:41:16.571435 27120 net.cpp:367] relu7 -> fc7 (in-place)
I0428 13:41:16.572188 27120 net.cpp:122] Setting up relu7
I0428 13:41:16.572198 27120 net.cpp:129] Top shape: 32 4096 (131072)
I0428 13:41:16.572201 27120 net.cpp:137] Memory required for data: 269233920
I0428 13:41:16.572204 27120 layer_factory.hpp:77] Creating layer drop7
I0428 13:41:16.572211 27120 net.cpp:84] Creating Layer drop7
I0428 13:41:16.572216 27120 net.cpp:406] drop7 <- fc7
I0428 13:41:16.572219 27120 net.cpp:367] drop7 -> fc7 (in-place)
I0428 13:41:16.572244 27120 net.cpp:122] Setting up drop7
I0428 13:41:16.572248 27120 net.cpp:129] Top shape: 32 4096 (131072)
I0428 13:41:16.572252 27120 net.cpp:137] Memory required for data: 269758208
I0428 13:41:16.572254 27120 layer_factory.hpp:77] Creating layer fc8
I0428 13:41:16.572260 27120 net.cpp:84] Creating Layer fc8
I0428 13:41:16.572263 27120 net.cpp:406] fc8 <- fc7
I0428 13:41:16.572269 27120 net.cpp:380] fc8 -> fc8
I0428 13:41:16.580018 27120 net.cpp:122] Setting up fc8
I0428 13:41:16.580027 27120 net.cpp:129] Top shape: 32 196 (6272)
I0428 13:41:16.580030 27120 net.cpp:137] Memory required for data: 269783296
I0428 13:41:16.580044 27120 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0428 13:41:16.580049 27120 net.cpp:84] Creating Layer fc8_fc8_0_split
I0428 13:41:16.580052 27120 net.cpp:406] fc8_fc8_0_split <- fc8
I0428 13:41:16.580057 27120 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0428 13:41:16.580065 27120 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0428 13:41:16.580096 27120 net.cpp:122] Setting up fc8_fc8_0_split
I0428 13:41:16.580099 27120 net.cpp:129] Top shape: 32 196 (6272)
I0428 13:41:16.580102 27120 net.cpp:129] Top shape: 32 196 (6272)
I0428 13:41:16.580106 27120 net.cpp:137] Memory required for data: 269833472
I0428 13:41:16.580107 27120 layer_factory.hpp:77] Creating layer accuracy
I0428 13:41:16.580116 27120 net.cpp:84] Creating Layer accuracy
I0428 13:41:16.580118 27120 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0428 13:41:16.580122 27120 net.cpp:406] accuracy <- label_val-data_1_split_0
I0428 13:41:16.580127 27120 net.cpp:380] accuracy -> accuracy
I0428 13:41:16.580132 27120 net.cpp:122] Setting up accuracy
I0428 13:41:16.580137 27120 net.cpp:129] Top shape: (1)
I0428 13:41:16.580138 27120 net.cpp:137] Memory required for data: 269833476
I0428 13:41:16.580142 27120 layer_factory.hpp:77] Creating layer loss
I0428 13:41:16.580145 27120 net.cpp:84] Creating Layer loss
I0428 13:41:16.580168 27120 net.cpp:406] loss <- fc8_fc8_0_split_1
I0428 13:41:16.580173 27120 net.cpp:406] loss <- label_val-data_1_split_1
I0428 13:41:16.580176 27120 net.cpp:380] loss -> loss
I0428 13:41:16.580183 27120 layer_factory.hpp:77] Creating layer loss
I0428 13:41:16.580893 27120 net.cpp:122] Setting up loss
I0428 13:41:16.580901 27120 net.cpp:129] Top shape: (1)
I0428 13:41:16.580904 27120 net.cpp:132]     with loss weight 1
I0428 13:41:16.580912 27120 net.cpp:137] Memory required for data: 269833480
I0428 13:41:16.580916 27120 net.cpp:198] loss needs backward computation.
I0428 13:41:16.580920 27120 net.cpp:200] accuracy does not need backward computation.
I0428 13:41:16.580924 27120 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0428 13:41:16.580926 27120 net.cpp:198] fc8 needs backward computation.
I0428 13:41:16.580929 27120 net.cpp:198] drop7 needs backward computation.
I0428 13:41:16.580932 27120 net.cpp:198] relu7 needs backward computation.
I0428 13:41:16.580935 27120 net.cpp:198] fc7 needs backward computation.
I0428 13:41:16.580937 27120 net.cpp:198] drop6 needs backward computation.
I0428 13:41:16.580940 27120 net.cpp:198] relu6 needs backward computation.
I0428 13:41:16.580943 27120 net.cpp:198] fc6 needs backward computation.
I0428 13:41:16.580947 27120 net.cpp:198] pool5 needs backward computation.
I0428 13:41:16.580951 27120 net.cpp:198] relu5 needs backward computation.
I0428 13:41:16.580955 27120 net.cpp:198] conv5 needs backward computation.
I0428 13:41:16.580957 27120 net.cpp:198] relu4 needs backward computation.
I0428 13:41:16.580960 27120 net.cpp:198] conv4 needs backward computation.
I0428 13:41:16.580963 27120 net.cpp:198] relu3.5 needs backward computation.
I0428 13:41:16.580966 27120 net.cpp:198] conv3.5 needs backward computation.
I0428 13:41:16.580969 27120 net.cpp:198] relu3 needs backward computation.
I0428 13:41:16.580972 27120 net.cpp:198] conv3 needs backward computation.
I0428 13:41:16.580976 27120 net.cpp:198] pool2 needs backward computation.
I0428 13:41:16.580978 27120 net.cpp:198] norm2 needs backward computation.
I0428 13:41:16.580981 27120 net.cpp:198] relu2 needs backward computation.
I0428 13:41:16.580984 27120 net.cpp:198] conv2 needs backward computation.
I0428 13:41:16.580987 27120 net.cpp:198] pool1 needs backward computation.
I0428 13:41:16.580991 27120 net.cpp:198] norm1 needs backward computation.
I0428 13:41:16.580992 27120 net.cpp:198] relu1 needs backward computation.
I0428 13:41:16.580996 27120 net.cpp:198] conv1 needs backward computation.
I0428 13:41:16.580999 27120 net.cpp:200] label_val-data_1_split does not need backward computation.
I0428 13:41:16.581002 27120 net.cpp:200] val-data does not need backward computation.
I0428 13:41:16.581005 27120 net.cpp:242] This network produces output accuracy
I0428 13:41:16.581008 27120 net.cpp:242] This network produces output loss
I0428 13:41:16.581025 27120 net.cpp:255] Network initialization done.
I0428 13:41:16.581095 27120 solver.cpp:56] Solver scaffolding done.
I0428 13:41:16.581480 27120 caffe.cpp:248] Starting Optimization
I0428 13:41:16.581490 27120 solver.cpp:272] Solving
I0428 13:41:16.581492 27120 solver.cpp:273] Learning Rate Policy: exp
I0428 13:41:16.583281 27120 solver.cpp:330] Iteration 0, Testing net (#0)
I0428 13:41:16.583290 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:41:16.679885 27120 blocking_queue.cpp:49] Waiting for data
I0428 13:41:21.292943 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:41:21.344424 27120 solver.cpp:397]     Test net output #0: accuracy = 0.00306373
I0428 13:41:21.344452 27120 solver.cpp:397]     Test net output #1: loss = 5.28029 (* 1 = 5.28029 loss)
I0428 13:41:21.483274 27120 solver.cpp:218] Iteration 0 (-1.90741e-33 iter/s, 4.90177s/12 iters), loss = 5.27545
I0428 13:41:21.484814 27120 solver.cpp:237]     Train net output #0: loss = 5.27545 (* 1 = 5.27545 loss)
I0428 13:41:21.484843 27120 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0428 13:41:25.745779 27120 solver.cpp:218] Iteration 12 (2.81626 iter/s, 4.26097s/12 iters), loss = 5.29774
I0428 13:41:25.745851 27120 solver.cpp:237]     Train net output #0: loss = 5.29774 (* 1 = 5.29774 loss)
I0428 13:41:25.745859 27120 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0428 13:41:31.016283 27120 solver.cpp:218] Iteration 24 (2.27685 iter/s, 5.27045s/12 iters), loss = 5.28517
I0428 13:41:31.016330 27120 solver.cpp:237]     Train net output #0: loss = 5.28517 (* 1 = 5.28517 loss)
I0428 13:41:31.016338 27120 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0428 13:41:36.390252 27120 solver.cpp:218] Iteration 36 (2.233 iter/s, 5.37394s/12 iters), loss = 5.30776
I0428 13:41:36.390300 27120 solver.cpp:237]     Train net output #0: loss = 5.30776 (* 1 = 5.30776 loss)
I0428 13:41:36.390309 27120 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0428 13:41:41.765578 27120 solver.cpp:218] Iteration 48 (2.23244 iter/s, 5.37529s/12 iters), loss = 5.31879
I0428 13:41:41.765622 27120 solver.cpp:237]     Train net output #0: loss = 5.31879 (* 1 = 5.31879 loss)
I0428 13:41:41.765630 27120 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0428 13:41:47.148476 27120 solver.cpp:218] Iteration 60 (2.22929 iter/s, 5.38287s/12 iters), loss = 5.27409
I0428 13:41:47.148625 27120 solver.cpp:237]     Train net output #0: loss = 5.27409 (* 1 = 5.27409 loss)
I0428 13:41:47.148635 27120 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0428 13:41:52.429883 27120 solver.cpp:218] Iteration 72 (2.27218 iter/s, 5.28128s/12 iters), loss = 5.29434
I0428 13:41:52.429924 27120 solver.cpp:237]     Train net output #0: loss = 5.29434 (* 1 = 5.29434 loss)
I0428 13:41:52.429932 27120 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0428 13:41:57.914062 27120 solver.cpp:218] Iteration 84 (2.18812 iter/s, 5.48416s/12 iters), loss = 5.28472
I0428 13:41:57.914105 27120 solver.cpp:237]     Train net output #0: loss = 5.28472 (* 1 = 5.28472 loss)
I0428 13:41:57.914114 27120 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0428 13:42:03.330524 27120 solver.cpp:218] Iteration 96 (2.21548 iter/s, 5.41644s/12 iters), loss = 5.29896
I0428 13:42:03.330567 27120 solver.cpp:237]     Train net output #0: loss = 5.29896 (* 1 = 5.29896 loss)
I0428 13:42:03.330575 27120 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0428 13:42:05.186187 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:42:05.504395 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0428 13:42:11.170464 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0428 13:42:13.439947 27120 solver.cpp:330] Iteration 102, Testing net (#0)
I0428 13:42:13.439970 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:42:18.518059 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:42:18.611582 27120 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0428 13:42:18.611618 27120 solver.cpp:397]     Test net output #1: loss = 5.28909 (* 1 = 5.28909 loss)
I0428 13:42:20.567879 27120 solver.cpp:218] Iteration 108 (0.69616 iter/s, 17.2374s/12 iters), loss = 5.30555
I0428 13:42:20.567927 27120 solver.cpp:237]     Train net output #0: loss = 5.30555 (* 1 = 5.30555 loss)
I0428 13:42:20.567936 27120 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0428 13:42:25.873154 27120 solver.cpp:218] Iteration 120 (2.26191 iter/s, 5.30524s/12 iters), loss = 5.28634
I0428 13:42:25.873199 27120 solver.cpp:237]     Train net output #0: loss = 5.28634 (* 1 = 5.28634 loss)
I0428 13:42:25.873209 27120 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0428 13:42:31.241951 27120 solver.cpp:218] Iteration 132 (2.23515 iter/s, 5.36877s/12 iters), loss = 5.28553
I0428 13:42:31.241992 27120 solver.cpp:237]     Train net output #0: loss = 5.28553 (* 1 = 5.28553 loss)
I0428 13:42:31.242000 27120 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0428 13:42:36.582172 27120 solver.cpp:218] Iteration 144 (2.24711 iter/s, 5.3402s/12 iters), loss = 5.30375
I0428 13:42:36.582216 27120 solver.cpp:237]     Train net output #0: loss = 5.30375 (* 1 = 5.30375 loss)
I0428 13:42:36.582224 27120 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0428 13:42:41.997258 27120 solver.cpp:218] Iteration 156 (2.21604 iter/s, 5.41507s/12 iters), loss = 5.29077
I0428 13:42:41.997295 27120 solver.cpp:237]     Train net output #0: loss = 5.29077 (* 1 = 5.29077 loss)
I0428 13:42:41.997303 27120 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0428 13:42:47.313791 27120 solver.cpp:218] Iteration 168 (2.25712 iter/s, 5.31652s/12 iters), loss = 5.2911
I0428 13:42:47.313832 27120 solver.cpp:237]     Train net output #0: loss = 5.2911 (* 1 = 5.2911 loss)
I0428 13:42:47.313839 27120 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0428 13:42:52.725973 27120 solver.cpp:218] Iteration 180 (2.21723 iter/s, 5.41216s/12 iters), loss = 5.29348
I0428 13:42:52.726091 27120 solver.cpp:237]     Train net output #0: loss = 5.29348 (* 1 = 5.29348 loss)
I0428 13:42:52.726100 27120 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0428 13:42:58.141813 27120 solver.cpp:218] Iteration 192 (2.21576 iter/s, 5.41575s/12 iters), loss = 5.27379
I0428 13:42:58.141856 27120 solver.cpp:237]     Train net output #0: loss = 5.27379 (* 1 = 5.27379 loss)
I0428 13:42:58.141865 27120 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0428 13:43:02.463726 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:43:03.228852 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0428 13:43:08.717136 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0428 13:43:11.564127 27120 solver.cpp:330] Iteration 204, Testing net (#0)
I0428 13:43:11.564153 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:43:16.569209 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:43:16.718376 27120 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0428 13:43:16.718415 27120 solver.cpp:397]     Test net output #1: loss = 5.24852 (* 1 = 5.24852 loss)
I0428 13:43:16.855238 27120 solver.cpp:218] Iteration 204 (0.641248 iter/s, 18.7135s/12 iters), loss = 5.27526
I0428 13:43:16.855288 27120 solver.cpp:237]     Train net output #0: loss = 5.27526 (* 1 = 5.27526 loss)
I0428 13:43:16.855298 27120 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0428 13:43:21.364692 27120 solver.cpp:218] Iteration 216 (2.66109 iter/s, 4.50942s/12 iters), loss = 5.21062
I0428 13:43:21.364737 27120 solver.cpp:237]     Train net output #0: loss = 5.21062 (* 1 = 5.21062 loss)
I0428 13:43:21.364748 27120 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0428 13:43:26.647624 27120 solver.cpp:218] Iteration 228 (2.27148 iter/s, 5.28291s/12 iters), loss = 5.23639
I0428 13:43:26.647708 27120 solver.cpp:237]     Train net output #0: loss = 5.23639 (* 1 = 5.23639 loss)
I0428 13:43:26.647718 27120 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0428 13:43:32.033046 27120 solver.cpp:218] Iteration 240 (2.22826 iter/s, 5.38536s/12 iters), loss = 5.11422
I0428 13:43:32.033089 27120 solver.cpp:237]     Train net output #0: loss = 5.11422 (* 1 = 5.11422 loss)
I0428 13:43:32.033097 27120 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0428 13:43:37.427901 27120 solver.cpp:218] Iteration 252 (2.22435 iter/s, 5.39483s/12 iters), loss = 5.17053
I0428 13:43:37.427950 27120 solver.cpp:237]     Train net output #0: loss = 5.17053 (* 1 = 5.17053 loss)
I0428 13:43:37.427958 27120 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0428 13:43:42.811501 27120 solver.cpp:218] Iteration 264 (2.229 iter/s, 5.38358s/12 iters), loss = 5.16573
I0428 13:43:42.811542 27120 solver.cpp:237]     Train net output #0: loss = 5.16573 (* 1 = 5.16573 loss)
I0428 13:43:42.811550 27120 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0428 13:43:48.195554 27120 solver.cpp:218] Iteration 276 (2.22881 iter/s, 5.38404s/12 iters), loss = 5.23206
I0428 13:43:48.195598 27120 solver.cpp:237]     Train net output #0: loss = 5.23206 (* 1 = 5.23206 loss)
I0428 13:43:48.195607 27120 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0428 13:43:53.474948 27120 solver.cpp:218] Iteration 288 (2.273 iter/s, 5.27937s/12 iters), loss = 5.20281
I0428 13:43:53.474997 27120 solver.cpp:237]     Train net output #0: loss = 5.20281 (* 1 = 5.20281 loss)
I0428 13:43:53.475005 27120 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0428 13:43:58.787562 27120 solver.cpp:218] Iteration 300 (2.25879 iter/s, 5.31259s/12 iters), loss = 5.15345
I0428 13:43:58.787690 27120 solver.cpp:237]     Train net output #0: loss = 5.15345 (* 1 = 5.15345 loss)
I0428 13:43:58.787700 27120 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0428 13:43:59.860392 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:44:00.978361 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0428 13:44:04.501298 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0428 13:44:07.724421 27120 solver.cpp:330] Iteration 306, Testing net (#0)
I0428 13:44:07.724440 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:44:12.800765 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:44:12.987344 27120 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0428 13:44:12.987377 27120 solver.cpp:397]     Test net output #1: loss = 5.16002 (* 1 = 5.16002 loss)
I0428 13:44:15.010282 27120 solver.cpp:218] Iteration 312 (0.739704 iter/s, 16.2227s/12 iters), loss = 5.1883
I0428 13:44:15.010318 27120 solver.cpp:237]     Train net output #0: loss = 5.1883 (* 1 = 5.1883 loss)
I0428 13:44:15.010326 27120 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0428 13:44:20.420054 27120 solver.cpp:218] Iteration 324 (2.21821 iter/s, 5.40976s/12 iters), loss = 5.13756
I0428 13:44:20.420099 27120 solver.cpp:237]     Train net output #0: loss = 5.13756 (* 1 = 5.13756 loss)
I0428 13:44:20.420107 27120 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0428 13:44:25.784880 27120 solver.cpp:218] Iteration 336 (2.2368 iter/s, 5.36481s/12 iters), loss = 5.09314
I0428 13:44:25.784917 27120 solver.cpp:237]     Train net output #0: loss = 5.09314 (* 1 = 5.09314 loss)
I0428 13:44:25.784925 27120 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0428 13:44:31.177271 27120 solver.cpp:218] Iteration 348 (2.22536 iter/s, 5.39238s/12 iters), loss = 5.14538
I0428 13:44:31.177369 27120 solver.cpp:237]     Train net output #0: loss = 5.14538 (* 1 = 5.14538 loss)
I0428 13:44:31.177379 27120 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0428 13:44:36.549144 27120 solver.cpp:218] Iteration 360 (2.23389 iter/s, 5.3718s/12 iters), loss = 5.08664
I0428 13:44:36.549185 27120 solver.cpp:237]     Train net output #0: loss = 5.08664 (* 1 = 5.08664 loss)
I0428 13:44:36.549193 27120 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0428 13:44:41.929446 27120 solver.cpp:218] Iteration 372 (2.23036 iter/s, 5.38029s/12 iters), loss = 5.10249
I0428 13:44:41.929487 27120 solver.cpp:237]     Train net output #0: loss = 5.10249 (* 1 = 5.10249 loss)
I0428 13:44:41.929497 27120 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0428 13:44:47.135787 27120 solver.cpp:218] Iteration 384 (2.30489 iter/s, 5.20632s/12 iters), loss = 5.11084
I0428 13:44:47.135829 27120 solver.cpp:237]     Train net output #0: loss = 5.11084 (* 1 = 5.11084 loss)
I0428 13:44:47.135838 27120 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0428 13:44:52.448361 27120 solver.cpp:218] Iteration 396 (2.2588 iter/s, 5.31255s/12 iters), loss = 5.17859
I0428 13:44:52.448415 27120 solver.cpp:237]     Train net output #0: loss = 5.17859 (* 1 = 5.17859 loss)
I0428 13:44:52.448424 27120 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0428 13:44:55.819370 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:44:57.342010 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0428 13:45:03.654831 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0428 13:45:14.031906 27120 solver.cpp:330] Iteration 408, Testing net (#0)
I0428 13:45:14.031929 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:45:18.982365 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:45:19.228220 27120 solver.cpp:397]     Test net output #0: accuracy = 0.0122549
I0428 13:45:19.228251 27120 solver.cpp:397]     Test net output #1: loss = 5.11411 (* 1 = 5.11411 loss)
I0428 13:45:19.366334 27120 solver.cpp:218] Iteration 408 (0.445796 iter/s, 26.9181s/12 iters), loss = 5.05932
I0428 13:45:19.366380 27120 solver.cpp:237]     Train net output #0: loss = 5.05932 (* 1 = 5.05932 loss)
I0428 13:45:19.366389 27120 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0428 13:45:23.855407 27120 solver.cpp:218] Iteration 420 (2.67318 iter/s, 4.48904s/12 iters), loss = 5.09112
I0428 13:45:23.855451 27120 solver.cpp:237]     Train net output #0: loss = 5.09112 (* 1 = 5.09112 loss)
I0428 13:45:23.855460 27120 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0428 13:45:29.226824 27120 solver.cpp:218] Iteration 432 (2.23406 iter/s, 5.37139s/12 iters), loss = 5.08678
I0428 13:45:29.226873 27120 solver.cpp:237]     Train net output #0: loss = 5.08678 (* 1 = 5.08678 loss)
I0428 13:45:29.226883 27120 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0428 13:45:34.595429 27120 solver.cpp:218] Iteration 444 (2.23523 iter/s, 5.36858s/12 iters), loss = 5.14564
I0428 13:45:34.595502 27120 solver.cpp:237]     Train net output #0: loss = 5.14564 (* 1 = 5.14564 loss)
I0428 13:45:34.595511 27120 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0428 13:45:39.973223 27120 solver.cpp:218] Iteration 456 (2.23142 iter/s, 5.37775s/12 iters), loss = 5.07381
I0428 13:45:39.973263 27120 solver.cpp:237]     Train net output #0: loss = 5.07381 (* 1 = 5.07381 loss)
I0428 13:45:39.973271 27120 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0428 13:45:45.357175 27120 solver.cpp:218] Iteration 468 (2.22885 iter/s, 5.38394s/12 iters), loss = 5.08385
I0428 13:45:45.357219 27120 solver.cpp:237]     Train net output #0: loss = 5.08385 (* 1 = 5.08385 loss)
I0428 13:45:45.357229 27120 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0428 13:45:50.737532 27120 solver.cpp:218] Iteration 480 (2.23035 iter/s, 5.38033s/12 iters), loss = 5.09018
I0428 13:45:50.737577 27120 solver.cpp:237]     Train net output #0: loss = 5.09018 (* 1 = 5.09018 loss)
I0428 13:45:50.737586 27120 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0428 13:45:56.127012 27120 solver.cpp:218] Iteration 492 (2.22657 iter/s, 5.38945s/12 iters), loss = 5.05375
I0428 13:45:56.127058 27120 solver.cpp:237]     Train net output #0: loss = 5.05375 (* 1 = 5.05375 loss)
I0428 13:45:56.127066 27120 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0428 13:46:01.529215 27120 solver.cpp:218] Iteration 504 (2.22133 iter/s, 5.40217s/12 iters), loss = 5.07641
I0428 13:46:01.529264 27120 solver.cpp:237]     Train net output #0: loss = 5.07641 (* 1 = 5.07641 loss)
I0428 13:46:01.529273 27120 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0428 13:46:01.775710 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:46:03.697544 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0428 13:46:13.439599 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0428 13:46:20.730896 27120 solver.cpp:330] Iteration 510, Testing net (#0)
I0428 13:46:20.730914 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:46:25.554939 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:46:25.838762 27120 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0428 13:46:25.838793 27120 solver.cpp:397]     Test net output #1: loss = 5.07821 (* 1 = 5.07821 loss)
I0428 13:46:27.860128 27120 solver.cpp:218] Iteration 516 (0.455736 iter/s, 26.331s/12 iters), loss = 5.01246
I0428 13:46:27.860172 27120 solver.cpp:237]     Train net output #0: loss = 5.01246 (* 1 = 5.01246 loss)
I0428 13:46:27.860181 27120 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0428 13:46:33.318145 27120 solver.cpp:218] Iteration 528 (2.19861 iter/s, 5.45799s/12 iters), loss = 5.05585
I0428 13:46:33.318186 27120 solver.cpp:237]     Train net output #0: loss = 5.05585 (* 1 = 5.05585 loss)
I0428 13:46:33.318194 27120 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0428 13:46:38.741822 27120 solver.cpp:218] Iteration 540 (2.21253 iter/s, 5.42366s/12 iters), loss = 5.0953
I0428 13:46:38.741863 27120 solver.cpp:237]     Train net output #0: loss = 5.0953 (* 1 = 5.0953 loss)
I0428 13:46:38.741871 27120 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0428 13:46:44.186416 27120 solver.cpp:218] Iteration 552 (2.20403 iter/s, 5.44458s/12 iters), loss = 5.03356
I0428 13:46:44.186566 27120 solver.cpp:237]     Train net output #0: loss = 5.03356 (* 1 = 5.03356 loss)
I0428 13:46:44.186576 27120 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0428 13:46:49.536763 27120 solver.cpp:218] Iteration 564 (2.2429 iter/s, 5.35022s/12 iters), loss = 5.03116
I0428 13:46:49.536803 27120 solver.cpp:237]     Train net output #0: loss = 5.03116 (* 1 = 5.03116 loss)
I0428 13:46:49.536811 27120 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0428 13:46:54.959057 27120 solver.cpp:218] Iteration 576 (2.21309 iter/s, 5.42227s/12 iters), loss = 5.04631
I0428 13:46:54.959113 27120 solver.cpp:237]     Train net output #0: loss = 5.04631 (* 1 = 5.04631 loss)
I0428 13:46:54.959123 27120 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0428 13:47:00.370612 27120 solver.cpp:218] Iteration 588 (2.21749 iter/s, 5.41152s/12 iters), loss = 5.02182
I0428 13:47:00.370654 27120 solver.cpp:237]     Train net output #0: loss = 5.02182 (* 1 = 5.02182 loss)
I0428 13:47:00.370662 27120 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0428 13:47:05.756050 27120 solver.cpp:218] Iteration 600 (2.22824 iter/s, 5.38542s/12 iters), loss = 5.09221
I0428 13:47:05.756090 27120 solver.cpp:237]     Train net output #0: loss = 5.09221 (* 1 = 5.09221 loss)
I0428 13:47:05.756098 27120 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0428 13:47:08.304352 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:47:10.620932 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0428 13:47:15.835297 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0428 13:47:20.462261 27120 solver.cpp:330] Iteration 612, Testing net (#0)
I0428 13:47:20.462286 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:47:25.222909 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:47:25.542716 27120 solver.cpp:397]     Test net output #0: accuracy = 0.0189951
I0428 13:47:25.542762 27120 solver.cpp:397]     Test net output #1: loss = 5.03012 (* 1 = 5.03012 loss)
I0428 13:47:25.679585 27120 solver.cpp:218] Iteration 612 (0.6023 iter/s, 19.9236s/12 iters), loss = 5.04198
I0428 13:47:25.679653 27120 solver.cpp:237]     Train net output #0: loss = 5.04198 (* 1 = 5.04198 loss)
I0428 13:47:25.679663 27120 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0428 13:47:30.169829 27120 solver.cpp:218] Iteration 624 (2.67249 iter/s, 4.49019s/12 iters), loss = 5.04153
I0428 13:47:30.169876 27120 solver.cpp:237]     Train net output #0: loss = 5.04153 (* 1 = 5.04153 loss)
I0428 13:47:30.169884 27120 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0428 13:47:35.552487 27120 solver.cpp:218] Iteration 636 (2.22939 iter/s, 5.38263s/12 iters), loss = 5.06577
I0428 13:47:35.552531 27120 solver.cpp:237]     Train net output #0: loss = 5.06577 (* 1 = 5.06577 loss)
I0428 13:47:35.552539 27120 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0428 13:47:40.949626 27120 solver.cpp:218] Iteration 648 (2.22341 iter/s, 5.39711s/12 iters), loss = 5.01722
I0428 13:47:40.949676 27120 solver.cpp:237]     Train net output #0: loss = 5.01722 (* 1 = 5.01722 loss)
I0428 13:47:40.949685 27120 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0428 13:47:46.326582 27120 solver.cpp:218] Iteration 660 (2.23176 iter/s, 5.37693s/12 iters), loss = 5.01946
I0428 13:47:46.326747 27120 solver.cpp:237]     Train net output #0: loss = 5.01946 (* 1 = 5.01946 loss)
I0428 13:47:46.326757 27120 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0428 13:47:51.712266 27120 solver.cpp:218] Iteration 672 (2.22819 iter/s, 5.38554s/12 iters), loss = 4.97097
I0428 13:47:51.712313 27120 solver.cpp:237]     Train net output #0: loss = 4.97097 (* 1 = 4.97097 loss)
I0428 13:47:51.712322 27120 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0428 13:47:57.087072 27120 solver.cpp:218] Iteration 684 (2.23265 iter/s, 5.37478s/12 iters), loss = 5.00853
I0428 13:47:57.087111 27120 solver.cpp:237]     Train net output #0: loss = 5.00853 (* 1 = 5.00853 loss)
I0428 13:47:57.087119 27120 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0428 13:47:57.909631 27120 blocking_queue.cpp:49] Waiting for data
I0428 13:48:02.501991 27120 solver.cpp:218] Iteration 696 (2.21611 iter/s, 5.4149s/12 iters), loss = 4.92074
I0428 13:48:02.502036 27120 solver.cpp:237]     Train net output #0: loss = 4.92074 (* 1 = 4.92074 loss)
I0428 13:48:02.502045 27120 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0428 13:48:07.531524 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:48:07.952306 27120 solver.cpp:218] Iteration 708 (2.20172 iter/s, 5.45029s/12 iters), loss = 4.93663
I0428 13:48:07.952344 27120 solver.cpp:237]     Train net output #0: loss = 4.93663 (* 1 = 4.93663 loss)
I0428 13:48:07.952353 27120 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0428 13:48:10.149631 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0428 13:48:12.766516 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0428 13:48:15.575562 27120 solver.cpp:330] Iteration 714, Testing net (#0)
I0428 13:48:15.575580 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:48:20.141881 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:48:20.496515 27120 solver.cpp:397]     Test net output #0: accuracy = 0.0159314
I0428 13:48:20.496544 27120 solver.cpp:397]     Test net output #1: loss = 4.99881 (* 1 = 4.99881 loss)
I0428 13:48:22.475163 27120 solver.cpp:218] Iteration 720 (0.82628 iter/s, 14.5229s/12 iters), loss = 4.84233
I0428 13:48:22.475205 27120 solver.cpp:237]     Train net output #0: loss = 4.84233 (* 1 = 4.84233 loss)
I0428 13:48:22.475214 27120 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0428 13:48:27.830035 27120 solver.cpp:218] Iteration 732 (2.24096 iter/s, 5.35486s/12 iters), loss = 4.92547
I0428 13:48:27.830080 27120 solver.cpp:237]     Train net output #0: loss = 4.92547 (* 1 = 4.92547 loss)
I0428 13:48:27.830089 27120 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0428 13:48:33.169422 27120 solver.cpp:218] Iteration 744 (2.24746 iter/s, 5.33937s/12 iters), loss = 4.90502
I0428 13:48:33.169467 27120 solver.cpp:237]     Train net output #0: loss = 4.90502 (* 1 = 4.90502 loss)
I0428 13:48:33.169476 27120 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0428 13:48:38.555253 27120 solver.cpp:218] Iteration 756 (2.22808 iter/s, 5.38581s/12 iters), loss = 4.96924
I0428 13:48:38.555289 27120 solver.cpp:237]     Train net output #0: loss = 4.96924 (* 1 = 4.96924 loss)
I0428 13:48:38.555299 27120 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0428 13:48:43.951535 27120 solver.cpp:218] Iteration 768 (2.22376 iter/s, 5.39627s/12 iters), loss = 4.83041
I0428 13:48:43.951578 27120 solver.cpp:237]     Train net output #0: loss = 4.83041 (* 1 = 4.83041 loss)
I0428 13:48:43.951586 27120 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0428 13:48:49.310693 27120 solver.cpp:218] Iteration 780 (2.23917 iter/s, 5.35914s/12 iters), loss = 4.83565
I0428 13:48:49.310737 27120 solver.cpp:237]     Train net output #0: loss = 4.83565 (* 1 = 4.83565 loss)
I0428 13:48:49.310746 27120 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0428 13:48:54.550343 27120 solver.cpp:218] Iteration 792 (2.29024 iter/s, 5.23963s/12 iters), loss = 4.89186
I0428 13:48:54.550478 27120 solver.cpp:237]     Train net output #0: loss = 4.89186 (* 1 = 4.89186 loss)
I0428 13:48:54.550489 27120 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0428 13:48:59.943859 27120 solver.cpp:218] Iteration 804 (2.22494 iter/s, 5.39341s/12 iters), loss = 4.94262
I0428 13:48:59.943902 27120 solver.cpp:237]     Train net output #0: loss = 4.94262 (* 1 = 4.94262 loss)
I0428 13:48:59.943910 27120 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0428 13:49:01.824748 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:49:04.810680 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0428 13:49:11.026559 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0428 13:49:14.337059 27120 solver.cpp:330] Iteration 816, Testing net (#0)
I0428 13:49:14.337080 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:49:19.000092 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:49:19.418299 27120 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0428 13:49:19.418340 27120 solver.cpp:397]     Test net output #1: loss = 4.89741 (* 1 = 4.89741 loss)
I0428 13:49:19.555575 27120 solver.cpp:218] Iteration 816 (0.611876 iter/s, 19.6118s/12 iters), loss = 4.9117
I0428 13:49:19.555621 27120 solver.cpp:237]     Train net output #0: loss = 4.9117 (* 1 = 4.9117 loss)
I0428 13:49:19.555630 27120 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0428 13:49:24.127876 27120 solver.cpp:218] Iteration 828 (2.62452 iter/s, 4.57227s/12 iters), loss = 4.80131
I0428 13:49:24.127919 27120 solver.cpp:237]     Train net output #0: loss = 4.80131 (* 1 = 4.80131 loss)
I0428 13:49:24.127928 27120 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0428 13:49:29.405288 27120 solver.cpp:218] Iteration 840 (2.27385 iter/s, 5.27739s/12 iters), loss = 4.86317
I0428 13:49:29.405378 27120 solver.cpp:237]     Train net output #0: loss = 4.86317 (* 1 = 4.86317 loss)
I0428 13:49:29.405388 27120 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0428 13:49:34.778134 27120 solver.cpp:218] Iteration 852 (2.23348 iter/s, 5.37278s/12 iters), loss = 4.95569
I0428 13:49:34.778177 27120 solver.cpp:237]     Train net output #0: loss = 4.95569 (* 1 = 4.95569 loss)
I0428 13:49:34.778184 27120 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0428 13:49:40.139389 27120 solver.cpp:218] Iteration 864 (2.23829 iter/s, 5.36123s/12 iters), loss = 4.84884
I0428 13:49:40.139429 27120 solver.cpp:237]     Train net output #0: loss = 4.84884 (* 1 = 4.84884 loss)
I0428 13:49:40.139436 27120 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0428 13:49:45.505342 27120 solver.cpp:218] Iteration 876 (2.23633 iter/s, 5.36594s/12 iters), loss = 4.94564
I0428 13:49:45.505388 27120 solver.cpp:237]     Train net output #0: loss = 4.94564 (* 1 = 4.94564 loss)
I0428 13:49:45.505396 27120 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0428 13:49:50.944172 27120 solver.cpp:218] Iteration 888 (2.20636 iter/s, 5.43881s/12 iters), loss = 4.87494
I0428 13:49:50.944223 27120 solver.cpp:237]     Train net output #0: loss = 4.87494 (* 1 = 4.87494 loss)
I0428 13:49:50.944232 27120 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0428 13:49:56.374156 27120 solver.cpp:218] Iteration 900 (2.20996 iter/s, 5.42996s/12 iters), loss = 4.86996
I0428 13:49:56.374202 27120 solver.cpp:237]     Train net output #0: loss = 4.86996 (* 1 = 4.86996 loss)
I0428 13:49:56.374212 27120 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0428 13:50:00.566880 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:50:01.788060 27120 solver.cpp:218] Iteration 912 (2.21652 iter/s, 5.41388s/12 iters), loss = 4.8651
I0428 13:50:01.788107 27120 solver.cpp:237]     Train net output #0: loss = 4.8651 (* 1 = 4.8651 loss)
I0428 13:50:01.788116 27120 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0428 13:50:03.980207 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0428 13:50:12.672720 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0428 13:50:22.392366 27120 solver.cpp:330] Iteration 918, Testing net (#0)
I0428 13:50:22.392388 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:50:27.008671 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:50:27.480298 27120 solver.cpp:397]     Test net output #0: accuracy = 0.033701
I0428 13:50:27.480334 27120 solver.cpp:397]     Test net output #1: loss = 4.79842 (* 1 = 4.79842 loss)
I0428 13:50:29.550920 27120 solver.cpp:218] Iteration 924 (0.43223 iter/s, 27.763s/12 iters), loss = 4.73045
I0428 13:50:29.550963 27120 solver.cpp:237]     Train net output #0: loss = 4.73045 (* 1 = 4.73045 loss)
I0428 13:50:29.550972 27120 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0428 13:50:34.902468 27120 solver.cpp:218] Iteration 936 (2.24235 iter/s, 5.35152s/12 iters), loss = 4.75116
I0428 13:50:34.902623 27120 solver.cpp:237]     Train net output #0: loss = 4.75116 (* 1 = 4.75116 loss)
I0428 13:50:34.902634 27120 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0428 13:50:40.293989 27120 solver.cpp:218] Iteration 948 (2.22577 iter/s, 5.39139s/12 iters), loss = 4.60251
I0428 13:50:40.294036 27120 solver.cpp:237]     Train net output #0: loss = 4.60251 (* 1 = 4.60251 loss)
I0428 13:50:40.294046 27120 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0428 13:50:45.670202 27120 solver.cpp:218] Iteration 960 (2.23207 iter/s, 5.37618s/12 iters), loss = 4.69381
I0428 13:50:45.670243 27120 solver.cpp:237]     Train net output #0: loss = 4.69381 (* 1 = 4.69381 loss)
I0428 13:50:45.670251 27120 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0428 13:50:51.047816 27120 solver.cpp:218] Iteration 972 (2.23148 iter/s, 5.37759s/12 iters), loss = 4.6496
I0428 13:50:51.047863 27120 solver.cpp:237]     Train net output #0: loss = 4.6496 (* 1 = 4.6496 loss)
I0428 13:50:51.047871 27120 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0428 13:50:56.426715 27120 solver.cpp:218] Iteration 984 (2.23095 iter/s, 5.37887s/12 iters), loss = 4.58297
I0428 13:50:56.426759 27120 solver.cpp:237]     Train net output #0: loss = 4.58297 (* 1 = 4.58297 loss)
I0428 13:50:56.426767 27120 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0428 13:51:01.722364 27120 solver.cpp:218] Iteration 996 (2.26602 iter/s, 5.29563s/12 iters), loss = 4.74375
I0428 13:51:01.722404 27120 solver.cpp:237]     Train net output #0: loss = 4.74375 (* 1 = 4.74375 loss)
I0428 13:51:01.722412 27120 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0428 13:51:07.116982 27120 solver.cpp:218] Iteration 1008 (2.22445 iter/s, 5.3946s/12 iters), loss = 4.65083
I0428 13:51:07.117110 27120 solver.cpp:237]     Train net output #0: loss = 4.65083 (* 1 = 4.65083 loss)
I0428 13:51:07.117120 27120 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0428 13:51:08.194705 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:51:11.974179 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0428 13:51:16.981542 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0428 13:51:28.729537 27120 solver.cpp:330] Iteration 1020, Testing net (#0)
I0428 13:51:28.729557 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:51:33.339216 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:51:33.855018 27120 solver.cpp:397]     Test net output #0: accuracy = 0.0447304
I0428 13:51:33.855046 27120 solver.cpp:397]     Test net output #1: loss = 4.6621 (* 1 = 4.6621 loss)
I0428 13:51:33.993407 27120 solver.cpp:218] Iteration 1020 (0.446487 iter/s, 26.8765s/12 iters), loss = 4.7429
I0428 13:51:33.993458 27120 solver.cpp:237]     Train net output #0: loss = 4.7429 (* 1 = 4.7429 loss)
I0428 13:51:33.993467 27120 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0428 13:51:38.467684 27120 solver.cpp:218] Iteration 1032 (2.68202 iter/s, 4.47424s/12 iters), loss = 4.53916
I0428 13:51:38.467840 27120 solver.cpp:237]     Train net output #0: loss = 4.53916 (* 1 = 4.53916 loss)
I0428 13:51:38.467857 27120 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0428 13:51:43.776265 27120 solver.cpp:218] Iteration 1044 (2.26055 iter/s, 5.30844s/12 iters), loss = 4.57965
I0428 13:51:43.776307 27120 solver.cpp:237]     Train net output #0: loss = 4.57965 (* 1 = 4.57965 loss)
I0428 13:51:43.776316 27120 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0428 13:51:49.143041 27120 solver.cpp:218] Iteration 1056 (2.23599 iter/s, 5.36675s/12 iters), loss = 4.59765
I0428 13:51:49.143083 27120 solver.cpp:237]     Train net output #0: loss = 4.59765 (* 1 = 4.59765 loss)
I0428 13:51:49.143091 27120 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0428 13:51:54.505926 27120 solver.cpp:218] Iteration 1068 (2.23761 iter/s, 5.36286s/12 iters), loss = 4.64969
I0428 13:51:54.505973 27120 solver.cpp:237]     Train net output #0: loss = 4.64969 (* 1 = 4.64969 loss)
I0428 13:51:54.505983 27120 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0428 13:51:59.861093 27120 solver.cpp:218] Iteration 1080 (2.24084 iter/s, 5.35514s/12 iters), loss = 4.63603
I0428 13:51:59.861133 27120 solver.cpp:237]     Train net output #0: loss = 4.63603 (* 1 = 4.63603 loss)
I0428 13:51:59.861142 27120 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0428 13:52:05.243700 27120 solver.cpp:218] Iteration 1092 (2.22941 iter/s, 5.38259s/12 iters), loss = 4.42755
I0428 13:52:05.243741 27120 solver.cpp:237]     Train net output #0: loss = 4.42755 (* 1 = 4.42755 loss)
I0428 13:52:05.243749 27120 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0428 13:52:10.642069 27120 solver.cpp:218] Iteration 1104 (2.2229 iter/s, 5.39836s/12 iters), loss = 4.64562
I0428 13:52:10.642156 27120 solver.cpp:237]     Train net output #0: loss = 4.64562 (* 1 = 4.64562 loss)
I0428 13:52:10.642165 27120 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0428 13:52:14.022812 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:52:16.041654 27120 solver.cpp:218] Iteration 1116 (2.22242 iter/s, 5.39952s/12 iters), loss = 4.24447
I0428 13:52:16.041702 27120 solver.cpp:237]     Train net output #0: loss = 4.24447 (* 1 = 4.24447 loss)
I0428 13:52:16.041710 27120 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0428 13:52:18.237155 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0428 13:52:23.489603 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0428 13:52:25.929335 27120 solver.cpp:330] Iteration 1122, Testing net (#0)
I0428 13:52:25.929356 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:52:30.479202 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:52:31.051674 27120 solver.cpp:397]     Test net output #0: accuracy = 0.0514706
I0428 13:52:31.051707 27120 solver.cpp:397]     Test net output #1: loss = 4.58235 (* 1 = 4.58235 loss)
I0428 13:52:33.054641 27120 solver.cpp:218] Iteration 1128 (0.705341 iter/s, 17.0131s/12 iters), loss = 4.42562
I0428 13:52:33.054683 27120 solver.cpp:237]     Train net output #0: loss = 4.42562 (* 1 = 4.42562 loss)
I0428 13:52:33.054692 27120 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0428 13:52:38.410073 27120 solver.cpp:218] Iteration 1140 (2.24072 iter/s, 5.35541s/12 iters), loss = 4.62597
I0428 13:52:38.410111 27120 solver.cpp:237]     Train net output #0: loss = 4.62597 (* 1 = 4.62597 loss)
I0428 13:52:38.410120 27120 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0428 13:52:43.795670 27120 solver.cpp:218] Iteration 1152 (2.22817 iter/s, 5.38558s/12 iters), loss = 4.62194
I0428 13:52:43.795797 27120 solver.cpp:237]     Train net output #0: loss = 4.62194 (* 1 = 4.62194 loss)
I0428 13:52:43.795807 27120 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0428 13:52:49.118824 27120 solver.cpp:218] Iteration 1164 (2.25435 iter/s, 5.32305s/12 iters), loss = 4.31721
I0428 13:52:49.118865 27120 solver.cpp:237]     Train net output #0: loss = 4.31721 (* 1 = 4.31721 loss)
I0428 13:52:49.118873 27120 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0428 13:52:54.483029 27120 solver.cpp:218] Iteration 1176 (2.23706 iter/s, 5.36419s/12 iters), loss = 4.47993
I0428 13:52:54.483069 27120 solver.cpp:237]     Train net output #0: loss = 4.47993 (* 1 = 4.47993 loss)
I0428 13:52:54.483078 27120 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0428 13:52:59.857131 27120 solver.cpp:218] Iteration 1188 (2.23294 iter/s, 5.37409s/12 iters), loss = 4.3817
I0428 13:52:59.857174 27120 solver.cpp:237]     Train net output #0: loss = 4.3817 (* 1 = 4.3817 loss)
I0428 13:52:59.857183 27120 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0428 13:53:05.236971 27120 solver.cpp:218] Iteration 1200 (2.23056 iter/s, 5.37982s/12 iters), loss = 4.26593
I0428 13:53:05.237015 27120 solver.cpp:237]     Train net output #0: loss = 4.26593 (* 1 = 4.26593 loss)
I0428 13:53:05.237025 27120 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0428 13:53:10.546221 27120 solver.cpp:218] Iteration 1212 (2.26021 iter/s, 5.30923s/12 iters), loss = 4.32227
I0428 13:53:10.546267 27120 solver.cpp:237]     Train net output #0: loss = 4.32227 (* 1 = 4.32227 loss)
I0428 13:53:10.546274 27120 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0428 13:53:10.822830 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:53:15.328404 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0428 13:53:18.928073 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0428 13:53:23.140724 27120 solver.cpp:330] Iteration 1224, Testing net (#0)
I0428 13:53:23.140743 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:53:27.715049 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:53:28.365147 27120 solver.cpp:397]     Test net output #0: accuracy = 0.0606618
I0428 13:53:28.365176 27120 solver.cpp:397]     Test net output #1: loss = 4.44242 (* 1 = 4.44242 loss)
I0428 13:53:28.501893 27120 solver.cpp:218] Iteration 1224 (0.66831 iter/s, 17.9558s/12 iters), loss = 4.38093
I0428 13:53:28.501933 27120 solver.cpp:237]     Train net output #0: loss = 4.38093 (* 1 = 4.38093 loss)
I0428 13:53:28.501942 27120 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0428 13:53:33.015902 27120 solver.cpp:218] Iteration 1236 (2.6584 iter/s, 4.51399s/12 iters), loss = 4.51631
I0428 13:53:33.015947 27120 solver.cpp:237]     Train net output #0: loss = 4.51631 (* 1 = 4.51631 loss)
I0428 13:53:33.015956 27120 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0428 13:53:38.414250 27120 solver.cpp:218] Iteration 1248 (2.22291 iter/s, 5.39833s/12 iters), loss = 4.35064
I0428 13:53:38.414289 27120 solver.cpp:237]     Train net output #0: loss = 4.35064 (* 1 = 4.35064 loss)
I0428 13:53:38.414299 27120 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0428 13:53:43.836622 27120 solver.cpp:218] Iteration 1260 (2.21306 iter/s, 5.42235s/12 iters), loss = 4.31905
I0428 13:53:43.836675 27120 solver.cpp:237]     Train net output #0: loss = 4.31905 (* 1 = 4.31905 loss)
I0428 13:53:43.836686 27120 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0428 13:53:49.201490 27120 solver.cpp:218] Iteration 1272 (2.23678 iter/s, 5.36484s/12 iters), loss = 4.21294
I0428 13:53:49.201597 27120 solver.cpp:237]     Train net output #0: loss = 4.21294 (* 1 = 4.21294 loss)
I0428 13:53:49.201607 27120 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0428 13:53:54.512423 27120 solver.cpp:218] Iteration 1284 (2.25953 iter/s, 5.31085s/12 iters), loss = 4.38863
I0428 13:53:54.512467 27120 solver.cpp:237]     Train net output #0: loss = 4.38863 (* 1 = 4.38863 loss)
I0428 13:53:54.512476 27120 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0428 13:53:59.806886 27120 solver.cpp:218] Iteration 1296 (2.26653 iter/s, 5.29444s/12 iters), loss = 4.36114
I0428 13:53:59.806931 27120 solver.cpp:237]     Train net output #0: loss = 4.36114 (* 1 = 4.36114 loss)
I0428 13:53:59.806941 27120 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0428 13:54:05.018204 27120 solver.cpp:218] Iteration 1308 (2.30269 iter/s, 5.2113s/12 iters), loss = 4.06757
I0428 13:54:05.018254 27120 solver.cpp:237]     Train net output #0: loss = 4.06757 (* 1 = 4.06757 loss)
I0428 13:54:05.018262 27120 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0428 13:54:07.729173 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:54:10.412034 27120 solver.cpp:218] Iteration 1320 (2.22477 iter/s, 5.39381s/12 iters), loss = 4.00061
I0428 13:54:10.412078 27120 solver.cpp:237]     Train net output #0: loss = 4.00061 (* 1 = 4.00061 loss)
I0428 13:54:10.412087 27120 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0428 13:54:12.577407 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0428 13:54:15.996623 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0428 13:54:18.875761 27120 solver.cpp:330] Iteration 1326, Testing net (#0)
I0428 13:54:18.875778 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:54:23.146787 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:54:23.806840 27120 solver.cpp:397]     Test net output #0: accuracy = 0.0949755
I0428 13:54:23.806869 27120 solver.cpp:397]     Test net output #1: loss = 4.22961 (* 1 = 4.22961 loss)
I0428 13:54:25.829527 27120 solver.cpp:218] Iteration 1332 (0.778334 iter/s, 15.4175s/12 iters), loss = 4.28574
I0428 13:54:25.829589 27120 solver.cpp:237]     Train net output #0: loss = 4.28574 (* 1 = 4.28574 loss)
I0428 13:54:25.829603 27120 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0428 13:54:31.265692 27120 solver.cpp:218] Iteration 1344 (2.20745 iter/s, 5.43613s/12 iters), loss = 4.00045
I0428 13:54:31.265731 27120 solver.cpp:237]     Train net output #0: loss = 4.00045 (* 1 = 4.00045 loss)
I0428 13:54:31.265740 27120 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0428 13:54:36.677258 27120 solver.cpp:218] Iteration 1356 (2.21748 iter/s, 5.41155s/12 iters), loss = 4.24963
I0428 13:54:36.677304 27120 solver.cpp:237]     Train net output #0: loss = 4.24963 (* 1 = 4.24963 loss)
I0428 13:54:36.677314 27120 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0428 13:54:41.953624 27120 solver.cpp:218] Iteration 1368 (2.2743 iter/s, 5.27634s/12 iters), loss = 4.16029
I0428 13:54:41.953668 27120 solver.cpp:237]     Train net output #0: loss = 4.16029 (* 1 = 4.16029 loss)
I0428 13:54:41.953676 27120 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0428 13:54:43.216641 27120 blocking_queue.cpp:49] Waiting for data
I0428 13:54:47.346947 27120 solver.cpp:218] Iteration 1380 (2.22498 iter/s, 5.3933s/12 iters), loss = 4.09452
I0428 13:54:47.346993 27120 solver.cpp:237]     Train net output #0: loss = 4.09452 (* 1 = 4.09452 loss)
I0428 13:54:47.347002 27120 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0428 13:54:52.558641 27120 solver.cpp:218] Iteration 1392 (2.30253 iter/s, 5.21167s/12 iters), loss = 4.014
I0428 13:54:52.558687 27120 solver.cpp:237]     Train net output #0: loss = 4.014 (* 1 = 4.014 loss)
I0428 13:54:52.558696 27120 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0428 13:54:57.966200 27120 solver.cpp:218] Iteration 1404 (2.21912 iter/s, 5.40754s/12 iters), loss = 3.83238
I0428 13:54:57.966285 27120 solver.cpp:237]     Train net output #0: loss = 3.83238 (* 1 = 3.83238 loss)
I0428 13:54:57.966295 27120 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0428 13:55:02.973404 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:55:03.358243 27120 solver.cpp:218] Iteration 1416 (2.22553 iter/s, 5.39199s/12 iters), loss = 4.03106
I0428 13:55:03.358283 27120 solver.cpp:237]     Train net output #0: loss = 4.03106 (* 1 = 4.03106 loss)
I0428 13:55:03.358290 27120 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0428 13:55:08.144604 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0428 13:55:10.760159 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0428 13:55:13.442212 27120 solver.cpp:330] Iteration 1428, Testing net (#0)
I0428 13:55:13.442240 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:55:17.565977 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:55:18.237352 27120 solver.cpp:397]     Test net output #0: accuracy = 0.0839461
I0428 13:55:18.237390 27120 solver.cpp:397]     Test net output #1: loss = 4.15631 (* 1 = 4.15631 loss)
I0428 13:55:18.373018 27120 solver.cpp:218] Iteration 1428 (0.799209 iter/s, 15.0148s/12 iters), loss = 4.16957
I0428 13:55:18.373088 27120 solver.cpp:237]     Train net output #0: loss = 4.16957 (* 1 = 4.16957 loss)
I0428 13:55:18.373098 27120 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0428 13:55:22.827186 27120 solver.cpp:218] Iteration 1440 (2.69413 iter/s, 4.45413s/12 iters), loss = 3.86511
I0428 13:55:22.827219 27120 solver.cpp:237]     Train net output #0: loss = 3.86511 (* 1 = 3.86511 loss)
I0428 13:55:22.827227 27120 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0428 13:55:28.223737 27120 solver.cpp:218] Iteration 1452 (2.22365 iter/s, 5.39654s/12 iters), loss = 3.75678
I0428 13:55:28.223899 27120 solver.cpp:237]     Train net output #0: loss = 3.75678 (* 1 = 3.75678 loss)
I0428 13:55:28.223909 27120 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0428 13:55:33.714864 27120 solver.cpp:218] Iteration 1464 (2.1854 iter/s, 5.49099s/12 iters), loss = 4.09103
I0428 13:55:33.714903 27120 solver.cpp:237]     Train net output #0: loss = 4.09103 (* 1 = 4.09103 loss)
I0428 13:55:33.714912 27120 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0428 13:55:39.102679 27120 solver.cpp:218] Iteration 1476 (2.22726 iter/s, 5.3878s/12 iters), loss = 3.8229
I0428 13:55:39.102723 27120 solver.cpp:237]     Train net output #0: loss = 3.8229 (* 1 = 3.8229 loss)
I0428 13:55:39.102732 27120 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0428 13:55:44.472558 27120 solver.cpp:218] Iteration 1488 (2.2347 iter/s, 5.36986s/12 iters), loss = 3.88853
I0428 13:55:44.472602 27120 solver.cpp:237]     Train net output #0: loss = 3.88853 (* 1 = 3.88853 loss)
I0428 13:55:44.472611 27120 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0428 13:55:49.858882 27120 solver.cpp:218] Iteration 1500 (2.22788 iter/s, 5.38629s/12 iters), loss = 3.90982
I0428 13:55:49.858952 27120 solver.cpp:237]     Train net output #0: loss = 3.90982 (* 1 = 3.90982 loss)
I0428 13:55:49.858968 27120 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0428 13:55:55.246014 27120 solver.cpp:218] Iteration 1512 (2.22755 iter/s, 5.38709s/12 iters), loss = 3.82979
I0428 13:55:55.246059 27120 solver.cpp:237]     Train net output #0: loss = 3.82979 (* 1 = 3.82979 loss)
I0428 13:55:55.246068 27120 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0428 13:55:57.154116 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:56:00.636569 27120 solver.cpp:218] Iteration 1524 (2.22613 iter/s, 5.39053s/12 iters), loss = 4.02665
I0428 13:56:00.636658 27120 solver.cpp:237]     Train net output #0: loss = 4.02665 (* 1 = 4.02665 loss)
I0428 13:56:00.636668 27120 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0428 13:56:02.801900 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0428 13:56:06.178876 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0428 13:56:09.547093 27120 solver.cpp:330] Iteration 1530, Testing net (#0)
I0428 13:56:09.547114 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:56:13.897943 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:56:14.661298 27120 solver.cpp:397]     Test net output #0: accuracy = 0.120098
I0428 13:56:14.661335 27120 solver.cpp:397]     Test net output #1: loss = 3.89238 (* 1 = 3.89238 loss)
I0428 13:56:16.674445 27120 solver.cpp:218] Iteration 1536 (0.748227 iter/s, 16.0379s/12 iters), loss = 3.84126
I0428 13:56:16.674490 27120 solver.cpp:237]     Train net output #0: loss = 3.84126 (* 1 = 3.84126 loss)
I0428 13:56:16.674499 27120 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0428 13:56:22.035673 27120 solver.cpp:218] Iteration 1548 (2.2383 iter/s, 5.36121s/12 iters), loss = 3.75053
I0428 13:56:22.035715 27120 solver.cpp:237]     Train net output #0: loss = 3.75053 (* 1 = 3.75053 loss)
I0428 13:56:22.035723 27120 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0428 13:56:27.383770 27120 solver.cpp:218] Iteration 1560 (2.24379 iter/s, 5.34808s/12 iters), loss = 3.93701
I0428 13:56:27.383811 27120 solver.cpp:237]     Train net output #0: loss = 3.93701 (* 1 = 3.93701 loss)
I0428 13:56:27.383821 27120 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0428 13:56:32.777107 27120 solver.cpp:218] Iteration 1572 (2.22497 iter/s, 5.39332s/12 iters), loss = 3.75063
I0428 13:56:32.777242 27120 solver.cpp:237]     Train net output #0: loss = 3.75063 (* 1 = 3.75063 loss)
I0428 13:56:32.777253 27120 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0428 13:56:38.130450 27120 solver.cpp:218] Iteration 1584 (2.24163 iter/s, 5.35324s/12 iters), loss = 4.03597
I0428 13:56:38.130492 27120 solver.cpp:237]     Train net output #0: loss = 4.03597 (* 1 = 4.03597 loss)
I0428 13:56:38.130501 27120 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0428 13:56:43.513329 27120 solver.cpp:218] Iteration 1596 (2.2293 iter/s, 5.38287s/12 iters), loss = 3.85804
I0428 13:56:43.513372 27120 solver.cpp:237]     Train net output #0: loss = 3.85804 (* 1 = 3.85804 loss)
I0428 13:56:43.513382 27120 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0428 13:56:48.890385 27120 solver.cpp:218] Iteration 1608 (2.23171 iter/s, 5.37704s/12 iters), loss = 3.58203
I0428 13:56:48.890424 27120 solver.cpp:237]     Train net output #0: loss = 3.58203 (* 1 = 3.58203 loss)
I0428 13:56:48.890434 27120 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0428 13:56:53.113370 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:56:54.297694 27120 solver.cpp:218] Iteration 1620 (2.21922 iter/s, 5.4073s/12 iters), loss = 3.7408
I0428 13:56:54.297741 27120 solver.cpp:237]     Train net output #0: loss = 3.7408 (* 1 = 3.7408 loss)
I0428 13:56:54.297750 27120 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0428 13:56:59.163547 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0428 13:57:03.872617 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0428 13:57:06.763782 27120 solver.cpp:330] Iteration 1632, Testing net (#0)
I0428 13:57:06.763801 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:57:11.087383 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:57:11.883431 27120 solver.cpp:397]     Test net output #0: accuracy = 0.147059
I0428 13:57:11.883471 27120 solver.cpp:397]     Test net output #1: loss = 3.74206 (* 1 = 3.74206 loss)
I0428 13:57:12.012980 27120 solver.cpp:218] Iteration 1632 (0.677378 iter/s, 17.7154s/12 iters), loss = 3.73935
I0428 13:57:12.013025 27120 solver.cpp:237]     Train net output #0: loss = 3.73935 (* 1 = 3.73935 loss)
I0428 13:57:12.013034 27120 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0428 13:57:16.457922 27120 solver.cpp:218] Iteration 1644 (2.69971 iter/s, 4.44492s/12 iters), loss = 3.92513
I0428 13:57:16.457967 27120 solver.cpp:237]     Train net output #0: loss = 3.92513 (* 1 = 3.92513 loss)
I0428 13:57:16.457974 27120 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0428 13:57:21.818114 27120 solver.cpp:218] Iteration 1656 (2.23873 iter/s, 5.36018s/12 iters), loss = 3.69287
I0428 13:57:21.818150 27120 solver.cpp:237]     Train net output #0: loss = 3.69287 (* 1 = 3.69287 loss)
I0428 13:57:21.818157 27120 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0428 13:57:27.207123 27120 solver.cpp:218] Iteration 1668 (2.22676 iter/s, 5.389s/12 iters), loss = 3.78099
I0428 13:57:27.207165 27120 solver.cpp:237]     Train net output #0: loss = 3.78099 (* 1 = 3.78099 loss)
I0428 13:57:27.207173 27120 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0428 13:57:32.611809 27120 solver.cpp:218] Iteration 1680 (2.2203 iter/s, 5.40467s/12 iters), loss = 3.35168
I0428 13:57:32.611850 27120 solver.cpp:237]     Train net output #0: loss = 3.35168 (* 1 = 3.35168 loss)
I0428 13:57:32.611858 27120 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0428 13:57:37.974161 27120 solver.cpp:218] Iteration 1692 (2.23783 iter/s, 5.36234s/12 iters), loss = 3.38974
I0428 13:57:37.974287 27120 solver.cpp:237]     Train net output #0: loss = 3.38974 (* 1 = 3.38974 loss)
I0428 13:57:37.974296 27120 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0428 13:57:43.340492 27120 solver.cpp:218] Iteration 1704 (2.23621 iter/s, 5.36623s/12 iters), loss = 3.62417
I0428 13:57:43.340534 27120 solver.cpp:237]     Train net output #0: loss = 3.62417 (* 1 = 3.62417 loss)
I0428 13:57:43.340543 27120 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0428 13:57:48.728102 27120 solver.cpp:218] Iteration 1716 (2.22734 iter/s, 5.3876s/12 iters), loss = 3.46723
I0428 13:57:48.728144 27120 solver.cpp:237]     Train net output #0: loss = 3.46723 (* 1 = 3.46723 loss)
I0428 13:57:48.728152 27120 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0428 13:57:49.837021 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:57:54.036829 27120 solver.cpp:218] Iteration 1728 (2.26043 iter/s, 5.30871s/12 iters), loss = 3.58257
I0428 13:57:54.036870 27120 solver.cpp:237]     Train net output #0: loss = 3.58257 (* 1 = 3.58257 loss)
I0428 13:57:54.036878 27120 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0428 13:57:56.193403 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0428 13:58:00.143517 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0428 13:58:03.023938 27120 solver.cpp:330] Iteration 1734, Testing net (#0)
I0428 13:58:03.023959 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:58:07.292287 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:58:08.134459 27120 solver.cpp:397]     Test net output #0: accuracy = 0.16299
I0428 13:58:08.134609 27120 solver.cpp:397]     Test net output #1: loss = 3.64118 (* 1 = 3.64118 loss)
I0428 13:58:10.179710 27120 solver.cpp:218] Iteration 1740 (0.743358 iter/s, 16.143s/12 iters), loss = 3.55343
I0428 13:58:10.179756 27120 solver.cpp:237]     Train net output #0: loss = 3.55343 (* 1 = 3.55343 loss)
I0428 13:58:10.179765 27120 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0428 13:58:15.534127 27120 solver.cpp:218] Iteration 1752 (2.24115 iter/s, 5.3544s/12 iters), loss = 3.53232
I0428 13:58:15.534162 27120 solver.cpp:237]     Train net output #0: loss = 3.53232 (* 1 = 3.53232 loss)
I0428 13:58:15.534168 27120 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0428 13:58:20.920799 27120 solver.cpp:218] Iteration 1764 (2.22772 iter/s, 5.38666s/12 iters), loss = 3.51472
I0428 13:58:20.920840 27120 solver.cpp:237]     Train net output #0: loss = 3.51472 (* 1 = 3.51472 loss)
I0428 13:58:20.920850 27120 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0428 13:58:26.350142 27120 solver.cpp:218] Iteration 1776 (2.21022 iter/s, 5.42932s/12 iters), loss = 3.38362
I0428 13:58:26.350185 27120 solver.cpp:237]     Train net output #0: loss = 3.38362 (* 1 = 3.38362 loss)
I0428 13:58:26.350193 27120 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0428 13:58:31.771016 27120 solver.cpp:218] Iteration 1788 (2.21367 iter/s, 5.42085s/12 iters), loss = 3.1607
I0428 13:58:31.771061 27120 solver.cpp:237]     Train net output #0: loss = 3.1607 (* 1 = 3.1607 loss)
I0428 13:58:31.771070 27120 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0428 13:58:37.133450 27120 solver.cpp:218] Iteration 1800 (2.2378 iter/s, 5.36242s/12 iters), loss = 3.32539
I0428 13:58:37.133489 27120 solver.cpp:237]     Train net output #0: loss = 3.32539 (* 1 = 3.32539 loss)
I0428 13:58:37.133498 27120 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0428 13:58:42.517765 27120 solver.cpp:218] Iteration 1812 (2.2287 iter/s, 5.3843s/12 iters), loss = 3.30623
I0428 13:58:42.517923 27120 solver.cpp:237]     Train net output #0: loss = 3.30623 (* 1 = 3.30623 loss)
I0428 13:58:42.517932 27120 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0428 13:58:45.930147 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:58:47.913444 27120 solver.cpp:218] Iteration 1824 (2.22405 iter/s, 5.39555s/12 iters), loss = 3.16047
I0428 13:58:47.913487 27120 solver.cpp:237]     Train net output #0: loss = 3.16047 (* 1 = 3.16047 loss)
I0428 13:58:47.913496 27120 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0428 13:58:52.663168 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0428 13:58:56.024694 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0428 13:59:01.889721 27120 solver.cpp:330] Iteration 1836, Testing net (#0)
I0428 13:59:01.889744 27120 net.cpp:676] Ignoring source layer train-data
I0428 13:59:06.098623 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:59:06.998411 27120 solver.cpp:397]     Test net output #0: accuracy = 0.185662
I0428 13:59:06.998443 27120 solver.cpp:397]     Test net output #1: loss = 3.57773 (* 1 = 3.57773 loss)
I0428 13:59:07.136476 27120 solver.cpp:218] Iteration 1836 (0.624248 iter/s, 19.2231s/12 iters), loss = 3.28701
I0428 13:59:07.136550 27120 solver.cpp:237]     Train net output #0: loss = 3.28701 (* 1 = 3.28701 loss)
I0428 13:59:07.136562 27120 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0428 13:59:11.557376 27120 solver.cpp:218] Iteration 1848 (2.71441 iter/s, 4.42085s/12 iters), loss = 3.48051
I0428 13:59:11.557417 27120 solver.cpp:237]     Train net output #0: loss = 3.48051 (* 1 = 3.48051 loss)
I0428 13:59:11.557426 27120 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0428 13:59:16.950944 27120 solver.cpp:218] Iteration 1860 (2.22488 iter/s, 5.39355s/12 iters), loss = 3.13884
I0428 13:59:16.951032 27120 solver.cpp:237]     Train net output #0: loss = 3.13884 (* 1 = 3.13884 loss)
I0428 13:59:16.951042 27120 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0428 13:59:22.460886 27120 solver.cpp:218] Iteration 1872 (2.17791 iter/s, 5.50988s/12 iters), loss = 3.45192
I0428 13:59:22.460928 27120 solver.cpp:237]     Train net output #0: loss = 3.45192 (* 1 = 3.45192 loss)
I0428 13:59:22.460937 27120 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0428 13:59:27.878957 27120 solver.cpp:218] Iteration 1884 (2.21482 iter/s, 5.41805s/12 iters), loss = 3.4463
I0428 13:59:27.879004 27120 solver.cpp:237]     Train net output #0: loss = 3.4463 (* 1 = 3.4463 loss)
I0428 13:59:27.879012 27120 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0428 13:59:33.261471 27120 solver.cpp:218] Iteration 1896 (2.22945 iter/s, 5.38249s/12 iters), loss = 3.11693
I0428 13:59:33.261513 27120 solver.cpp:237]     Train net output #0: loss = 3.11693 (* 1 = 3.11693 loss)
I0428 13:59:33.261521 27120 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0428 13:59:38.657452 27120 solver.cpp:218] Iteration 1908 (2.22388 iter/s, 5.39597s/12 iters), loss = 3.21714
I0428 13:59:38.657498 27120 solver.cpp:237]     Train net output #0: loss = 3.21714 (* 1 = 3.21714 loss)
I0428 13:59:38.657507 27120 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0428 13:59:43.945780 27120 solver.cpp:218] Iteration 1920 (2.26916 iter/s, 5.2883s/12 iters), loss = 3.11687
I0428 13:59:43.945823 27120 solver.cpp:237]     Train net output #0: loss = 3.11687 (* 1 = 3.11687 loss)
I0428 13:59:43.945833 27120 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0428 13:59:44.252300 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 13:59:49.334187 27120 solver.cpp:218] Iteration 1932 (2.22701 iter/s, 5.38839s/12 iters), loss = 3.22381
I0428 13:59:49.334344 27120 solver.cpp:237]     Train net output #0: loss = 3.22381 (* 1 = 3.22381 loss)
I0428 13:59:49.334354 27120 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0428 13:59:51.474088 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0428 13:59:54.092312 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0428 13:59:56.328279 27120 solver.cpp:330] Iteration 1938, Testing net (#0)
I0428 13:59:56.328299 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:00:00.507414 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:00:01.443828 27120 solver.cpp:397]     Test net output #0: accuracy = 0.184436
I0428 14:00:01.443862 27120 solver.cpp:397]     Test net output #1: loss = 3.51579 (* 1 = 3.51579 loss)
I0428 14:00:03.422765 27120 solver.cpp:218] Iteration 1944 (0.851757 iter/s, 14.0885s/12 iters), loss = 3.29096
I0428 14:00:03.422809 27120 solver.cpp:237]     Train net output #0: loss = 3.29096 (* 1 = 3.29096 loss)
I0428 14:00:03.422818 27120 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0428 14:00:08.800361 27120 solver.cpp:218] Iteration 1956 (2.23149 iter/s, 5.37758s/12 iters), loss = 3.21694
I0428 14:00:08.800398 27120 solver.cpp:237]     Train net output #0: loss = 3.21694 (* 1 = 3.21694 loss)
I0428 14:00:08.800406 27120 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0428 14:00:14.109877 27120 solver.cpp:218] Iteration 1968 (2.2601 iter/s, 5.30951s/12 iters), loss = 3.25824
I0428 14:00:14.109920 27120 solver.cpp:237]     Train net output #0: loss = 3.25824 (* 1 = 3.25824 loss)
I0428 14:00:14.109928 27120 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0428 14:00:19.473505 27120 solver.cpp:218] Iteration 1980 (2.2373 iter/s, 5.36361s/12 iters), loss = 2.84794
I0428 14:00:19.473637 27120 solver.cpp:237]     Train net output #0: loss = 2.84794 (* 1 = 2.84794 loss)
I0428 14:00:19.473647 27120 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0428 14:00:24.844205 27120 solver.cpp:218] Iteration 1992 (2.23439 iter/s, 5.3706s/12 iters), loss = 3.14848
I0428 14:00:24.844244 27120 solver.cpp:237]     Train net output #0: loss = 3.14848 (* 1 = 3.14848 loss)
I0428 14:00:24.844251 27120 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0428 14:00:30.146121 27120 solver.cpp:218] Iteration 2004 (2.26334 iter/s, 5.30191s/12 iters), loss = 3.43449
I0428 14:00:30.146160 27120 solver.cpp:237]     Train net output #0: loss = 3.43449 (* 1 = 3.43449 loss)
I0428 14:00:30.146167 27120 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0428 14:00:35.564752 27120 solver.cpp:218] Iteration 2016 (2.21459 iter/s, 5.41862s/12 iters), loss = 2.93237
I0428 14:00:35.564798 27120 solver.cpp:237]     Train net output #0: loss = 2.93237 (* 1 = 2.93237 loss)
I0428 14:00:35.564808 27120 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0428 14:00:38.314826 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:00:40.967846 27120 solver.cpp:218] Iteration 2028 (2.22096 iter/s, 5.40308s/12 iters), loss = 3.17352
I0428 14:00:40.967885 27120 solver.cpp:237]     Train net output #0: loss = 3.17352 (* 1 = 3.17352 loss)
I0428 14:00:40.967892 27120 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0428 14:00:45.711688 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0428 14:00:48.307404 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0428 14:00:50.357826 27120 solver.cpp:330] Iteration 2040, Testing net (#0)
I0428 14:00:50.357903 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:00:54.557866 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:00:55.549194 27120 solver.cpp:397]     Test net output #0: accuracy = 0.209559
I0428 14:00:55.549230 27120 solver.cpp:397]     Test net output #1: loss = 3.40475 (* 1 = 3.40475 loss)
I0428 14:00:55.686211 27120 solver.cpp:218] Iteration 2040 (0.815304 iter/s, 14.7184s/12 iters), loss = 3.10059
I0428 14:00:55.686276 27120 solver.cpp:237]     Train net output #0: loss = 3.10059 (* 1 = 3.10059 loss)
I0428 14:00:55.686286 27120 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0428 14:01:00.136845 27120 solver.cpp:218] Iteration 2052 (2.69627 iter/s, 4.45059s/12 iters), loss = 2.82148
I0428 14:01:00.136888 27120 solver.cpp:237]     Train net output #0: loss = 2.82148 (* 1 = 2.82148 loss)
I0428 14:01:00.136896 27120 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0428 14:01:01.883672 27120 blocking_queue.cpp:49] Waiting for data
I0428 14:01:05.560400 27120 solver.cpp:218] Iteration 2064 (2.21258 iter/s, 5.42354s/12 iters), loss = 2.91681
I0428 14:01:05.560443 27120 solver.cpp:237]     Train net output #0: loss = 2.91681 (* 1 = 2.91681 loss)
I0428 14:01:05.560452 27120 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0428 14:01:10.923131 27120 solver.cpp:218] Iteration 2076 (2.23767 iter/s, 5.36272s/12 iters), loss = 3.03555
I0428 14:01:10.923173 27120 solver.cpp:237]     Train net output #0: loss = 3.03555 (* 1 = 3.03555 loss)
I0428 14:01:10.923182 27120 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0428 14:01:16.212417 27120 solver.cpp:218] Iteration 2088 (2.26874 iter/s, 5.28927s/12 iters), loss = 2.83325
I0428 14:01:16.212457 27120 solver.cpp:237]     Train net output #0: loss = 2.83325 (* 1 = 2.83325 loss)
I0428 14:01:16.212466 27120 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0428 14:01:21.533627 27120 solver.cpp:218] Iteration 2100 (2.25513 iter/s, 5.32119s/12 iters), loss = 2.62445
I0428 14:01:21.533777 27120 solver.cpp:237]     Train net output #0: loss = 2.62445 (* 1 = 2.62445 loss)
I0428 14:01:21.533787 27120 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0428 14:01:26.932857 27120 solver.cpp:218] Iteration 2112 (2.22259 iter/s, 5.39911s/12 iters), loss = 2.92207
I0428 14:01:26.932901 27120 solver.cpp:237]     Train net output #0: loss = 2.92207 (* 1 = 2.92207 loss)
I0428 14:01:26.932910 27120 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0428 14:01:31.968384 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:01:32.323889 27120 solver.cpp:218] Iteration 2124 (2.22593 iter/s, 5.39102s/12 iters), loss = 2.76857
I0428 14:01:32.323922 27120 solver.cpp:237]     Train net output #0: loss = 2.76857 (* 1 = 2.76857 loss)
I0428 14:01:32.323930 27120 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0428 14:01:37.706666 27120 solver.cpp:218] Iteration 2136 (2.22933 iter/s, 5.38277s/12 iters), loss = 2.6711
I0428 14:01:37.706714 27120 solver.cpp:237]     Train net output #0: loss = 2.6711 (* 1 = 2.6711 loss)
I0428 14:01:37.706722 27120 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0428 14:01:39.865993 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0428 14:01:43.273563 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0428 14:01:45.325353 27120 solver.cpp:330] Iteration 2142, Testing net (#0)
I0428 14:01:45.325372 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:01:49.436520 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:01:50.465270 27120 solver.cpp:397]     Test net output #0: accuracy = 0.238358
I0428 14:01:50.465306 27120 solver.cpp:397]     Test net output #1: loss = 3.25733 (* 1 = 3.25733 loss)
I0428 14:01:52.507339 27120 solver.cpp:218] Iteration 2148 (0.810771 iter/s, 14.8007s/12 iters), loss = 3.04611
I0428 14:01:52.507439 27120 solver.cpp:237]     Train net output #0: loss = 3.04611 (* 1 = 3.04611 loss)
I0428 14:01:52.507449 27120 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0428 14:01:57.912354 27120 solver.cpp:218] Iteration 2160 (2.22019 iter/s, 5.40494s/12 iters), loss = 2.74795
I0428 14:01:57.912397 27120 solver.cpp:237]     Train net output #0: loss = 2.74795 (* 1 = 2.74795 loss)
I0428 14:01:57.912405 27120 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0428 14:02:03.425318 27120 solver.cpp:218] Iteration 2172 (2.17669 iter/s, 5.51295s/12 iters), loss = 2.947
I0428 14:02:03.425357 27120 solver.cpp:237]     Train net output #0: loss = 2.947 (* 1 = 2.947 loss)
I0428 14:02:03.425365 27120 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0428 14:02:08.928855 27120 solver.cpp:218] Iteration 2184 (2.18042 iter/s, 5.50353s/12 iters), loss = 2.71355
I0428 14:02:08.928900 27120 solver.cpp:237]     Train net output #0: loss = 2.71355 (* 1 = 2.71355 loss)
I0428 14:02:08.928910 27120 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0428 14:02:14.299984 27120 solver.cpp:218] Iteration 2196 (2.23417 iter/s, 5.37111s/12 iters), loss = 2.75833
I0428 14:02:14.300026 27120 solver.cpp:237]     Train net output #0: loss = 2.75833 (* 1 = 2.75833 loss)
I0428 14:02:14.300035 27120 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0428 14:02:19.724581 27120 solver.cpp:218] Iteration 2208 (2.21215 iter/s, 5.42458s/12 iters), loss = 2.66617
I0428 14:02:19.724624 27120 solver.cpp:237]     Train net output #0: loss = 2.66617 (* 1 = 2.66617 loss)
I0428 14:02:19.724632 27120 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0428 14:02:25.246873 27120 solver.cpp:218] Iteration 2220 (2.17302 iter/s, 5.52228s/12 iters), loss = 2.63787
I0428 14:02:25.247014 27120 solver.cpp:237]     Train net output #0: loss = 2.63787 (* 1 = 2.63787 loss)
I0428 14:02:25.247023 27120 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0428 14:02:27.186058 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:02:30.648267 27120 solver.cpp:218] Iteration 2232 (2.22169 iter/s, 5.40128s/12 iters), loss = 2.6526
I0428 14:02:30.648311 27120 solver.cpp:237]     Train net output #0: loss = 2.6526 (* 1 = 2.6526 loss)
I0428 14:02:30.648320 27120 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0428 14:02:35.478826 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0428 14:02:38.264005 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0428 14:02:41.020097 27120 solver.cpp:330] Iteration 2244, Testing net (#0)
I0428 14:02:41.020114 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:02:45.084056 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:02:46.180627 27120 solver.cpp:397]     Test net output #0: accuracy = 0.265931
I0428 14:02:46.180656 27120 solver.cpp:397]     Test net output #1: loss = 3.09551 (* 1 = 3.09551 loss)
I0428 14:02:46.314513 27120 solver.cpp:218] Iteration 2244 (0.765975 iter/s, 15.6663s/12 iters), loss = 2.6637
I0428 14:02:46.314560 27120 solver.cpp:237]     Train net output #0: loss = 2.6637 (* 1 = 2.6637 loss)
I0428 14:02:46.314569 27120 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0428 14:02:50.819713 27120 solver.cpp:218] Iteration 2256 (2.66361 iter/s, 4.50517s/12 iters), loss = 2.57681
I0428 14:02:50.819761 27120 solver.cpp:237]     Train net output #0: loss = 2.57681 (* 1 = 2.57681 loss)
I0428 14:02:50.819772 27120 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0428 14:02:56.227809 27120 solver.cpp:218] Iteration 2268 (2.2189 iter/s, 5.40808s/12 iters), loss = 3.0383
I0428 14:02:56.227886 27120 solver.cpp:237]     Train net output #0: loss = 3.0383 (* 1 = 3.0383 loss)
I0428 14:02:56.227896 27120 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0428 14:03:01.618115 27120 solver.cpp:218] Iteration 2280 (2.22624 iter/s, 5.39026s/12 iters), loss = 2.52364
I0428 14:03:01.618156 27120 solver.cpp:237]     Train net output #0: loss = 2.52364 (* 1 = 2.52364 loss)
I0428 14:03:01.618165 27120 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0428 14:03:06.988322 27120 solver.cpp:218] Iteration 2292 (2.23456 iter/s, 5.37019s/12 iters), loss = 3.00157
I0428 14:03:06.988365 27120 solver.cpp:237]     Train net output #0: loss = 3.00157 (* 1 = 3.00157 loss)
I0428 14:03:06.988374 27120 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0428 14:03:12.368427 27120 solver.cpp:218] Iteration 2304 (2.23045 iter/s, 5.38009s/12 iters), loss = 2.797
I0428 14:03:12.368474 27120 solver.cpp:237]     Train net output #0: loss = 2.797 (* 1 = 2.797 loss)
I0428 14:03:12.368484 27120 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0428 14:03:17.736644 27120 solver.cpp:218] Iteration 2316 (2.23539 iter/s, 5.3682s/12 iters), loss = 2.55482
I0428 14:03:17.736685 27120 solver.cpp:237]     Train net output #0: loss = 2.55482 (* 1 = 2.55482 loss)
I0428 14:03:17.736693 27120 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0428 14:03:21.918632 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:03:23.071316 27120 solver.cpp:218] Iteration 2328 (2.24944 iter/s, 5.33466s/12 iters), loss = 2.52782
I0428 14:03:23.071362 27120 solver.cpp:237]     Train net output #0: loss = 2.52782 (* 1 = 2.52782 loss)
I0428 14:03:23.071372 27120 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0428 14:03:28.431910 27120 solver.cpp:218] Iteration 2340 (2.23857 iter/s, 5.36058s/12 iters), loss = 2.33835
I0428 14:03:28.432078 27120 solver.cpp:237]     Train net output #0: loss = 2.33835 (* 1 = 2.33835 loss)
I0428 14:03:28.432088 27120 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0428 14:03:30.585135 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0428 14:03:33.187479 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0428 14:03:35.234966 27120 solver.cpp:330] Iteration 2346, Testing net (#0)
I0428 14:03:35.234997 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:03:39.250321 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:03:40.364012 27120 solver.cpp:397]     Test net output #0: accuracy = 0.273897
I0428 14:03:40.364039 27120 solver.cpp:397]     Test net output #1: loss = 2.99853 (* 1 = 2.99853 loss)
I0428 14:03:42.353492 27120 solver.cpp:218] Iteration 2352 (0.861975 iter/s, 13.9215s/12 iters), loss = 2.54089
I0428 14:03:42.353536 27120 solver.cpp:237]     Train net output #0: loss = 2.54089 (* 1 = 2.54089 loss)
I0428 14:03:42.353544 27120 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0428 14:03:47.757185 27120 solver.cpp:218] Iteration 2364 (2.22071 iter/s, 5.40367s/12 iters), loss = 2.57315
I0428 14:03:47.757233 27120 solver.cpp:237]     Train net output #0: loss = 2.57315 (* 1 = 2.57315 loss)
I0428 14:03:47.757242 27120 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0428 14:03:53.132390 27120 solver.cpp:218] Iteration 2376 (2.23248 iter/s, 5.37519s/12 iters), loss = 2.76353
I0428 14:03:53.132421 27120 solver.cpp:237]     Train net output #0: loss = 2.76353 (* 1 = 2.76353 loss)
I0428 14:03:53.132431 27120 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0428 14:03:58.505622 27120 solver.cpp:218] Iteration 2388 (2.2333 iter/s, 5.37322s/12 iters), loss = 2.22522
I0428 14:03:58.505731 27120 solver.cpp:237]     Train net output #0: loss = 2.22522 (* 1 = 2.22522 loss)
I0428 14:03:58.505741 27120 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0428 14:04:03.885426 27120 solver.cpp:218] Iteration 2400 (2.2306 iter/s, 5.37972s/12 iters), loss = 2.55728
I0428 14:04:03.885473 27120 solver.cpp:237]     Train net output #0: loss = 2.55728 (* 1 = 2.55728 loss)
I0428 14:04:03.885481 27120 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0428 14:04:09.244324 27120 solver.cpp:218] Iteration 2412 (2.23928 iter/s, 5.35887s/12 iters), loss = 2.26108
I0428 14:04:09.244390 27120 solver.cpp:237]     Train net output #0: loss = 2.26108 (* 1 = 2.26108 loss)
I0428 14:04:09.244403 27120 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0428 14:04:14.756902 27120 solver.cpp:218] Iteration 2424 (2.17685 iter/s, 5.51255s/12 iters), loss = 2.36366
I0428 14:04:14.756947 27120 solver.cpp:237]     Train net output #0: loss = 2.36366 (* 1 = 2.36366 loss)
I0428 14:04:14.756955 27120 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0428 14:04:15.941350 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:04:20.288893 27120 solver.cpp:218] Iteration 2436 (2.16921 iter/s, 5.53197s/12 iters), loss = 2.64129
I0428 14:04:20.288939 27120 solver.cpp:237]     Train net output #0: loss = 2.64129 (* 1 = 2.64129 loss)
I0428 14:04:20.288949 27120 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0428 14:04:25.135042 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0428 14:04:27.760304 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0428 14:04:29.800451 27120 solver.cpp:330] Iteration 2448, Testing net (#0)
I0428 14:04:29.800590 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:04:33.758137 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:04:34.909034 27120 solver.cpp:397]     Test net output #0: accuracy = 0.298407
I0428 14:04:34.909065 27120 solver.cpp:397]     Test net output #1: loss = 2.90479 (* 1 = 2.90479 loss)
I0428 14:04:35.045792 27120 solver.cpp:218] Iteration 2448 (0.813175 iter/s, 14.757s/12 iters), loss = 2.43653
I0428 14:04:35.045843 27120 solver.cpp:237]     Train net output #0: loss = 2.43653 (* 1 = 2.43653 loss)
I0428 14:04:35.045853 27120 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0428 14:04:39.592530 27120 solver.cpp:218] Iteration 2460 (2.63927 iter/s, 4.54671s/12 iters), loss = 2.57811
I0428 14:04:39.592572 27120 solver.cpp:237]     Train net output #0: loss = 2.57811 (* 1 = 2.57811 loss)
I0428 14:04:39.592581 27120 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0428 14:04:45.027189 27120 solver.cpp:218] Iteration 2472 (2.20806 iter/s, 5.43465s/12 iters), loss = 2.18851
I0428 14:04:45.027231 27120 solver.cpp:237]     Train net output #0: loss = 2.18851 (* 1 = 2.18851 loss)
I0428 14:04:45.027240 27120 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0428 14:04:50.301206 27120 solver.cpp:218] Iteration 2484 (2.27531 iter/s, 5.274s/12 iters), loss = 2.46314
I0428 14:04:50.301251 27120 solver.cpp:237]     Train net output #0: loss = 2.46314 (* 1 = 2.46314 loss)
I0428 14:04:50.301260 27120 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0428 14:04:55.648111 27120 solver.cpp:218] Iteration 2496 (2.2443 iter/s, 5.34689s/12 iters), loss = 2.27218
I0428 14:04:55.648149 27120 solver.cpp:237]     Train net output #0: loss = 2.27218 (* 1 = 2.27218 loss)
I0428 14:04:55.648159 27120 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0428 14:05:01.012161 27120 solver.cpp:218] Iteration 2508 (2.23712 iter/s, 5.36404s/12 iters), loss = 2.38792
I0428 14:05:01.012241 27120 solver.cpp:237]     Train net output #0: loss = 2.38792 (* 1 = 2.38792 loss)
I0428 14:05:01.012250 27120 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0428 14:05:06.382668 27120 solver.cpp:218] Iteration 2520 (2.23445 iter/s, 5.37046s/12 iters), loss = 2.3746
I0428 14:05:06.382712 27120 solver.cpp:237]     Train net output #0: loss = 2.3746 (* 1 = 2.3746 loss)
I0428 14:05:06.382720 27120 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0428 14:05:09.826896 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:05:11.791013 27120 solver.cpp:218] Iteration 2532 (2.2188 iter/s, 5.40833s/12 iters), loss = 2.11344
I0428 14:05:11.791060 27120 solver.cpp:237]     Train net output #0: loss = 2.11344 (* 1 = 2.11344 loss)
I0428 14:05:11.791069 27120 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0428 14:05:17.155663 27120 solver.cpp:218] Iteration 2544 (2.23687 iter/s, 5.36463s/12 iters), loss = 2.22848
I0428 14:05:17.155706 27120 solver.cpp:237]     Train net output #0: loss = 2.22848 (* 1 = 2.22848 loss)
I0428 14:05:17.155714 27120 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0428 14:05:19.320286 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0428 14:05:23.154978 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0428 14:05:25.731566 27120 solver.cpp:330] Iteration 2550, Testing net (#0)
I0428 14:05:25.731585 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:05:29.673466 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:05:30.883303 27120 solver.cpp:397]     Test net output #0: accuracy = 0.320466
I0428 14:05:30.883335 27120 solver.cpp:397]     Test net output #1: loss = 2.85507 (* 1 = 2.85507 loss)
I0428 14:05:32.912732 27120 solver.cpp:218] Iteration 2556 (0.761559 iter/s, 15.7571s/12 iters), loss = 2.27998
I0428 14:05:32.912847 27120 solver.cpp:237]     Train net output #0: loss = 2.27998 (* 1 = 2.27998 loss)
I0428 14:05:32.912858 27120 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0428 14:05:38.260812 27120 solver.cpp:218] Iteration 2568 (2.24383 iter/s, 5.348s/12 iters), loss = 2.09437
I0428 14:05:38.260845 27120 solver.cpp:237]     Train net output #0: loss = 2.09437 (* 1 = 2.09437 loss)
I0428 14:05:38.260854 27120 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0428 14:05:43.635082 27120 solver.cpp:218] Iteration 2580 (2.23287 iter/s, 5.37425s/12 iters), loss = 2.56982
I0428 14:05:43.635154 27120 solver.cpp:237]     Train net output #0: loss = 2.56982 (* 1 = 2.56982 loss)
I0428 14:05:43.635169 27120 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0428 14:05:49.016508 27120 solver.cpp:218] Iteration 2592 (2.22991 iter/s, 5.38139s/12 iters), loss = 2.22675
I0428 14:05:49.016549 27120 solver.cpp:237]     Train net output #0: loss = 2.22675 (* 1 = 2.22675 loss)
I0428 14:05:49.016557 27120 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0428 14:05:54.371529 27120 solver.cpp:218] Iteration 2604 (2.2409 iter/s, 5.355s/12 iters), loss = 1.85009
I0428 14:05:54.371577 27120 solver.cpp:237]     Train net output #0: loss = 1.85009 (* 1 = 1.85009 loss)
I0428 14:05:54.371585 27120 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0428 14:05:59.779742 27120 solver.cpp:218] Iteration 2616 (2.21886 iter/s, 5.40819s/12 iters), loss = 2.22834
I0428 14:05:59.779786 27120 solver.cpp:237]     Train net output #0: loss = 2.22834 (* 1 = 2.22834 loss)
I0428 14:05:59.779796 27120 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0428 14:06:05.164749 27120 solver.cpp:218] Iteration 2628 (2.22842 iter/s, 5.38498s/12 iters), loss = 2.40601
I0428 14:06:05.164907 27120 solver.cpp:237]     Train net output #0: loss = 2.40601 (* 1 = 2.40601 loss)
I0428 14:06:05.164917 27120 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0428 14:06:05.633358 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:06:10.558097 27120 solver.cpp:218] Iteration 2640 (2.22502 iter/s, 5.39322s/12 iters), loss = 2.17983
I0428 14:06:10.558140 27120 solver.cpp:237]     Train net output #0: loss = 2.17983 (* 1 = 2.17983 loss)
I0428 14:06:10.558148 27120 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0428 14:06:15.303858 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0428 14:06:19.216987 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0428 14:06:23.638521 27120 solver.cpp:330] Iteration 2652, Testing net (#0)
I0428 14:06:23.638541 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:06:27.498843 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:06:28.684073 27120 solver.cpp:397]     Test net output #0: accuracy = 0.296569
I0428 14:06:28.684113 27120 solver.cpp:397]     Test net output #1: loss = 2.96908 (* 1 = 2.96908 loss)
I0428 14:06:28.820911 27120 solver.cpp:218] Iteration 2652 (0.657069 iter/s, 18.2629s/12 iters), loss = 2.31436
I0428 14:06:28.820978 27120 solver.cpp:237]     Train net output #0: loss = 2.31436 (* 1 = 2.31436 loss)
I0428 14:06:28.820991 27120 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0428 14:06:33.310860 27120 solver.cpp:218] Iteration 2664 (2.67266 iter/s, 4.48991s/12 iters), loss = 2.3573
I0428 14:06:33.310909 27120 solver.cpp:237]     Train net output #0: loss = 2.3573 (* 1 = 2.3573 loss)
I0428 14:06:33.310917 27120 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0428 14:06:38.532805 27120 solver.cpp:218] Iteration 2676 (2.29801 iter/s, 5.22192s/12 iters), loss = 2.23412
I0428 14:06:38.532955 27120 solver.cpp:237]     Train net output #0: loss = 2.23412 (* 1 = 2.23412 loss)
I0428 14:06:38.532969 27120 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0428 14:06:43.808214 27120 solver.cpp:218] Iteration 2688 (2.27475 iter/s, 5.2753s/12 iters), loss = 1.85517
I0428 14:06:43.808259 27120 solver.cpp:237]     Train net output #0: loss = 1.85517 (* 1 = 1.85517 loss)
I0428 14:06:43.808269 27120 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0428 14:06:49.180707 27120 solver.cpp:218] Iteration 2700 (2.2336 iter/s, 5.37249s/12 iters), loss = 2.18705
I0428 14:06:49.180752 27120 solver.cpp:237]     Train net output #0: loss = 2.18705 (* 1 = 2.18705 loss)
I0428 14:06:49.180759 27120 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0428 14:06:54.578142 27120 solver.cpp:218] Iteration 2712 (2.22328 iter/s, 5.39743s/12 iters), loss = 2.02959
I0428 14:06:54.578187 27120 solver.cpp:237]     Train net output #0: loss = 2.02959 (* 1 = 2.02959 loss)
I0428 14:06:54.578195 27120 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0428 14:06:59.964927 27120 solver.cpp:218] Iteration 2724 (2.22768 iter/s, 5.38677s/12 iters), loss = 1.77624
I0428 14:06:59.964970 27120 solver.cpp:237]     Train net output #0: loss = 1.77624 (* 1 = 1.77624 loss)
I0428 14:06:59.964979 27120 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0428 14:07:02.731339 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:07:05.362473 27120 solver.cpp:218] Iteration 2736 (2.22324 iter/s, 5.39754s/12 iters), loss = 1.94616
I0428 14:07:05.362517 27120 solver.cpp:237]     Train net output #0: loss = 1.94616 (* 1 = 1.94616 loss)
I0428 14:07:05.362525 27120 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0428 14:07:10.748174 27120 solver.cpp:218] Iteration 2748 (2.22813 iter/s, 5.38569s/12 iters), loss = 2.09518
I0428 14:07:10.748337 27120 solver.cpp:237]     Train net output #0: loss = 2.09518 (* 1 = 2.09518 loss)
I0428 14:07:10.748347 27120 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0428 14:07:12.901983 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0428 14:07:17.471773 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0428 14:07:22.144673 27120 solver.cpp:330] Iteration 2754, Testing net (#0)
I0428 14:07:22.144693 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:07:25.640290 27120 blocking_queue.cpp:49] Waiting for data
I0428 14:07:25.932384 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:07:27.234377 27120 solver.cpp:397]     Test net output #0: accuracy = 0.315564
I0428 14:07:27.234428 27120 solver.cpp:397]     Test net output #1: loss = 2.83509 (* 1 = 2.83509 loss)
I0428 14:07:29.254716 27120 solver.cpp:218] Iteration 2760 (0.648419 iter/s, 18.5065s/12 iters), loss = 2.09348
I0428 14:07:29.254761 27120 solver.cpp:237]     Train net output #0: loss = 2.09348 (* 1 = 2.09348 loss)
I0428 14:07:29.254770 27120 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0428 14:07:34.630760 27120 solver.cpp:218] Iteration 2772 (2.23213 iter/s, 5.37603s/12 iters), loss = 2.09209
I0428 14:07:34.630808 27120 solver.cpp:237]     Train net output #0: loss = 2.09209 (* 1 = 2.09209 loss)
I0428 14:07:34.630818 27120 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0428 14:07:39.968772 27120 solver.cpp:218] Iteration 2784 (2.24803 iter/s, 5.338s/12 iters), loss = 2.30583
I0428 14:07:39.968811 27120 solver.cpp:237]     Train net output #0: loss = 2.30583 (* 1 = 2.30583 loss)
I0428 14:07:39.968820 27120 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0428 14:07:45.318980 27120 solver.cpp:218] Iteration 2796 (2.2429 iter/s, 5.35021s/12 iters), loss = 1.9477
I0428 14:07:45.319077 27120 solver.cpp:237]     Train net output #0: loss = 1.9477 (* 1 = 1.9477 loss)
I0428 14:07:45.319085 27120 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0428 14:07:50.700305 27120 solver.cpp:218] Iteration 2808 (2.22996 iter/s, 5.38126s/12 iters), loss = 1.92625
I0428 14:07:50.700348 27120 solver.cpp:237]     Train net output #0: loss = 1.92625 (* 1 = 1.92625 loss)
I0428 14:07:50.700357 27120 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0428 14:07:56.063244 27120 solver.cpp:218] Iteration 2820 (2.23758 iter/s, 5.36293s/12 iters), loss = 1.9279
I0428 14:07:56.063289 27120 solver.cpp:237]     Train net output #0: loss = 1.9279 (* 1 = 1.9279 loss)
I0428 14:07:56.063298 27120 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0428 14:08:01.139436 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:08:01.464951 27120 solver.cpp:218] Iteration 2832 (2.22152 iter/s, 5.4017s/12 iters), loss = 1.90719
I0428 14:08:01.464994 27120 solver.cpp:237]     Train net output #0: loss = 1.90719 (* 1 = 1.90719 loss)
I0428 14:08:01.465003 27120 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0428 14:08:06.873474 27120 solver.cpp:218] Iteration 2844 (2.21872 iter/s, 5.40851s/12 iters), loss = 1.82992
I0428 14:08:06.873520 27120 solver.cpp:237]     Train net output #0: loss = 1.82992 (* 1 = 1.82992 loss)
I0428 14:08:06.873529 27120 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0428 14:08:11.694224 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0428 14:08:14.532806 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0428 14:08:18.008363 27120 solver.cpp:330] Iteration 2856, Testing net (#0)
I0428 14:08:18.008472 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:08:21.750481 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:08:23.090235 27120 solver.cpp:397]     Test net output #0: accuracy = 0.340074
I0428 14:08:23.090271 27120 solver.cpp:397]     Test net output #1: loss = 2.78277 (* 1 = 2.78277 loss)
I0428 14:08:23.227262 27120 solver.cpp:218] Iteration 2856 (0.733771 iter/s, 16.3539s/12 iters), loss = 2.12138
I0428 14:08:23.227313 27120 solver.cpp:237]     Train net output #0: loss = 2.12138 (* 1 = 2.12138 loss)
I0428 14:08:23.227322 27120 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0428 14:08:27.634343 27120 solver.cpp:218] Iteration 2868 (2.72291 iter/s, 4.40706s/12 iters), loss = 1.85671
I0428 14:08:27.634390 27120 solver.cpp:237]     Train net output #0: loss = 1.85671 (* 1 = 1.85671 loss)
I0428 14:08:27.634399 27120 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0428 14:08:33.114900 27120 solver.cpp:218] Iteration 2880 (2.18957 iter/s, 5.48054s/12 iters), loss = 1.76148
I0428 14:08:33.114945 27120 solver.cpp:237]     Train net output #0: loss = 1.76148 (* 1 = 1.76148 loss)
I0428 14:08:33.114954 27120 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0428 14:08:38.398600 27120 solver.cpp:218] Iteration 2892 (2.27114 iter/s, 5.28368s/12 iters), loss = 1.85907
I0428 14:08:38.398643 27120 solver.cpp:237]     Train net output #0: loss = 1.85907 (* 1 = 1.85907 loss)
I0428 14:08:38.398650 27120 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0428 14:08:43.836391 27120 solver.cpp:218] Iteration 2904 (2.20678 iter/s, 5.43778s/12 iters), loss = 2.00298
I0428 14:08:43.836438 27120 solver.cpp:237]     Train net output #0: loss = 2.00298 (* 1 = 2.00298 loss)
I0428 14:08:43.836447 27120 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0428 14:08:49.289078 27120 solver.cpp:218] Iteration 2916 (2.20076 iter/s, 5.45266s/12 iters), loss = 1.70981
I0428 14:08:49.289188 27120 solver.cpp:237]     Train net output #0: loss = 1.70981 (* 1 = 1.70981 loss)
I0428 14:08:49.289201 27120 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0428 14:08:54.663802 27120 solver.cpp:218] Iteration 2928 (2.2327 iter/s, 5.37466s/12 iters), loss = 1.9384
I0428 14:08:54.663849 27120 solver.cpp:237]     Train net output #0: loss = 1.9384 (* 1 = 1.9384 loss)
I0428 14:08:54.663858 27120 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0428 14:08:56.630667 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:09:00.076476 27120 solver.cpp:218] Iteration 2940 (2.21703 iter/s, 5.41266s/12 iters), loss = 1.97657
I0428 14:09:00.076519 27120 solver.cpp:237]     Train net output #0: loss = 1.97657 (* 1 = 1.97657 loss)
I0428 14:09:00.076527 27120 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0428 14:09:05.402503 27120 solver.cpp:218] Iteration 2952 (2.25309 iter/s, 5.32602s/12 iters), loss = 1.76595
I0428 14:09:05.402544 27120 solver.cpp:237]     Train net output #0: loss = 1.76595 (* 1 = 1.76595 loss)
I0428 14:09:05.402552 27120 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0428 14:09:07.559372 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0428 14:09:10.138509 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0428 14:09:12.214745 27120 solver.cpp:330] Iteration 2958, Testing net (#0)
I0428 14:09:12.214777 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:09:15.964100 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:09:17.372709 27120 solver.cpp:397]     Test net output #0: accuracy = 0.334559
I0428 14:09:17.372746 27120 solver.cpp:397]     Test net output #1: loss = 2.71591 (* 1 = 2.71591 loss)
I0428 14:09:19.352241 27120 solver.cpp:218] Iteration 2964 (0.860227 iter/s, 13.9498s/12 iters), loss = 1.80685
I0428 14:09:19.352778 27120 solver.cpp:237]     Train net output #0: loss = 1.80685 (* 1 = 1.80685 loss)
I0428 14:09:19.352789 27120 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0428 14:09:24.750417 27120 solver.cpp:218] Iteration 2976 (2.22318 iter/s, 5.39767s/12 iters), loss = 1.88503
I0428 14:09:24.750465 27120 solver.cpp:237]     Train net output #0: loss = 1.88503 (* 1 = 1.88503 loss)
I0428 14:09:24.750473 27120 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0428 14:09:30.111667 27120 solver.cpp:218] Iteration 2988 (2.23829 iter/s, 5.36123s/12 iters), loss = 2.11226
I0428 14:09:30.111713 27120 solver.cpp:237]     Train net output #0: loss = 2.11226 (* 1 = 2.11226 loss)
I0428 14:09:30.111721 27120 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0428 14:09:35.586020 27120 solver.cpp:218] Iteration 3000 (2.19205 iter/s, 5.47434s/12 iters), loss = 1.9588
I0428 14:09:35.586067 27120 solver.cpp:237]     Train net output #0: loss = 1.9588 (* 1 = 1.9588 loss)
I0428 14:09:35.586076 27120 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0428 14:09:40.905022 27120 solver.cpp:218] Iteration 3012 (2.25607 iter/s, 5.31899s/12 iters), loss = 1.90965
I0428 14:09:40.905066 27120 solver.cpp:237]     Train net output #0: loss = 1.90965 (* 1 = 1.90965 loss)
I0428 14:09:40.905074 27120 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0428 14:09:46.291282 27120 solver.cpp:218] Iteration 3024 (2.2279 iter/s, 5.38625s/12 iters), loss = 1.44884
I0428 14:09:46.291329 27120 solver.cpp:237]     Train net output #0: loss = 1.44884 (* 1 = 1.44884 loss)
I0428 14:09:46.291338 27120 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0428 14:09:50.550457 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:09:51.672904 27120 solver.cpp:218] Iteration 3036 (2.22981 iter/s, 5.38161s/12 iters), loss = 1.56563
I0428 14:09:51.672941 27120 solver.cpp:237]     Train net output #0: loss = 1.56563 (* 1 = 1.56563 loss)
I0428 14:09:51.672950 27120 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0428 14:09:57.055999 27120 solver.cpp:218] Iteration 3048 (2.2292 iter/s, 5.38309s/12 iters), loss = 1.50446
I0428 14:09:57.056038 27120 solver.cpp:237]     Train net output #0: loss = 1.50446 (* 1 = 1.50446 loss)
I0428 14:09:57.056046 27120 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0428 14:10:01.919513 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0428 14:10:04.504388 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0428 14:10:06.563886 27120 solver.cpp:330] Iteration 3060, Testing net (#0)
I0428 14:10:06.563905 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:10:10.210919 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:10:11.643319 27120 solver.cpp:397]     Test net output #0: accuracy = 0.349877
I0428 14:10:11.643347 27120 solver.cpp:397]     Test net output #1: loss = 2.74841 (* 1 = 2.74841 loss)
I0428 14:10:11.780427 27120 solver.cpp:218] Iteration 3060 (0.814968 iter/s, 14.7245s/12 iters), loss = 1.59865
I0428 14:10:11.780496 27120 solver.cpp:237]     Train net output #0: loss = 1.59865 (* 1 = 1.59865 loss)
I0428 14:10:11.780511 27120 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0428 14:10:16.276621 27120 solver.cpp:218] Iteration 3072 (2.66895 iter/s, 4.49615s/12 iters), loss = 1.6529
I0428 14:10:16.276667 27120 solver.cpp:237]     Train net output #0: loss = 1.6529 (* 1 = 1.6529 loss)
I0428 14:10:16.276676 27120 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0428 14:10:21.586813 27120 solver.cpp:218] Iteration 3084 (2.25981 iter/s, 5.31017s/12 iters), loss = 1.79909
I0428 14:10:21.586941 27120 solver.cpp:237]     Train net output #0: loss = 1.79909 (* 1 = 1.79909 loss)
I0428 14:10:21.586952 27120 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0428 14:10:26.969134 27120 solver.cpp:218] Iteration 3096 (2.22956 iter/s, 5.38223s/12 iters), loss = 1.78036
I0428 14:10:26.969183 27120 solver.cpp:237]     Train net output #0: loss = 1.78036 (* 1 = 1.78036 loss)
I0428 14:10:26.969192 27120 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0428 14:10:32.253262 27120 solver.cpp:218] Iteration 3108 (2.27096 iter/s, 5.28411s/12 iters), loss = 1.42113
I0428 14:10:32.253299 27120 solver.cpp:237]     Train net output #0: loss = 1.42113 (* 1 = 1.42113 loss)
I0428 14:10:32.253306 27120 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0428 14:10:37.630897 27120 solver.cpp:218] Iteration 3120 (2.23147 iter/s, 5.37763s/12 iters), loss = 1.71156
I0428 14:10:37.630939 27120 solver.cpp:237]     Train net output #0: loss = 1.71156 (* 1 = 1.71156 loss)
I0428 14:10:37.630949 27120 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0428 14:10:43.012823 27120 solver.cpp:218] Iteration 3132 (2.22969 iter/s, 5.38191s/12 iters), loss = 1.59652
I0428 14:10:43.012863 27120 solver.cpp:237]     Train net output #0: loss = 1.59652 (* 1 = 1.59652 loss)
I0428 14:10:43.012871 27120 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0428 14:10:44.189641 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:10:48.457123 27120 solver.cpp:218] Iteration 3144 (2.20414 iter/s, 5.4443s/12 iters), loss = 1.75813
I0428 14:10:48.457166 27120 solver.cpp:237]     Train net output #0: loss = 1.75813 (* 1 = 1.75813 loss)
I0428 14:10:48.457175 27120 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0428 14:10:53.831504 27120 solver.cpp:218] Iteration 3156 (2.23282 iter/s, 5.37436s/12 iters), loss = 1.53396
I0428 14:10:53.831594 27120 solver.cpp:237]     Train net output #0: loss = 1.53396 (* 1 = 1.53396 loss)
I0428 14:10:53.831604 27120 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0428 14:10:56.003211 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0428 14:10:59.748710 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0428 14:11:02.838498 27120 solver.cpp:330] Iteration 3162, Testing net (#0)
I0428 14:11:02.838526 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:11:06.423743 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:11:07.920248 27120 solver.cpp:397]     Test net output #0: accuracy = 0.364583
I0428 14:11:07.920279 27120 solver.cpp:397]     Test net output #1: loss = 2.69917 (* 1 = 2.69917 loss)
I0428 14:11:09.905752 27120 solver.cpp:218] Iteration 3168 (0.746533 iter/s, 16.0743s/12 iters), loss = 1.63556
I0428 14:11:09.905797 27120 solver.cpp:237]     Train net output #0: loss = 1.63556 (* 1 = 1.63556 loss)
I0428 14:11:09.905804 27120 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0428 14:11:15.288414 27120 solver.cpp:218] Iteration 3180 (2.22939 iter/s, 5.38265s/12 iters), loss = 1.37277
I0428 14:11:15.288457 27120 solver.cpp:237]     Train net output #0: loss = 1.37277 (* 1 = 1.37277 loss)
I0428 14:11:15.288466 27120 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0428 14:11:20.695147 27120 solver.cpp:218] Iteration 3192 (2.21946 iter/s, 5.40672s/12 iters), loss = 1.47734
I0428 14:11:20.695188 27120 solver.cpp:237]     Train net output #0: loss = 1.47734 (* 1 = 1.47734 loss)
I0428 14:11:20.695197 27120 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0428 14:11:26.149523 27120 solver.cpp:218] Iteration 3204 (2.20007 iter/s, 5.45436s/12 iters), loss = 1.27247
I0428 14:11:26.149643 27120 solver.cpp:237]     Train net output #0: loss = 1.27247 (* 1 = 1.27247 loss)
I0428 14:11:26.149653 27120 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0428 14:11:31.519786 27120 solver.cpp:218] Iteration 3216 (2.23456 iter/s, 5.37018s/12 iters), loss = 1.57992
I0428 14:11:31.519834 27120 solver.cpp:237]     Train net output #0: loss = 1.57992 (* 1 = 1.57992 loss)
I0428 14:11:31.519841 27120 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0428 14:11:36.881753 27120 solver.cpp:218] Iteration 3228 (2.23799 iter/s, 5.36195s/12 iters), loss = 1.92695
I0428 14:11:36.881801 27120 solver.cpp:237]     Train net output #0: loss = 1.92695 (* 1 = 1.92695 loss)
I0428 14:11:36.881811 27120 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0428 14:11:40.342733 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:11:42.272305 27120 solver.cpp:218] Iteration 3240 (2.22612 iter/s, 5.39054s/12 iters), loss = 1.41155
I0428 14:11:42.272341 27120 solver.cpp:237]     Train net output #0: loss = 1.41155 (* 1 = 1.41155 loss)
I0428 14:11:42.272351 27120 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0428 14:11:47.549573 27120 solver.cpp:218] Iteration 3252 (2.27391 iter/s, 5.27726s/12 iters), loss = 1.31722
I0428 14:11:47.549621 27120 solver.cpp:237]     Train net output #0: loss = 1.31722 (* 1 = 1.31722 loss)
I0428 14:11:47.549630 27120 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0428 14:11:52.232228 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0428 14:11:55.814692 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0428 14:11:59.112591 27120 solver.cpp:330] Iteration 3264, Testing net (#0)
I0428 14:11:59.112728 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:12:02.677912 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:12:04.204123 27120 solver.cpp:397]     Test net output #0: accuracy = 0.378064
I0428 14:12:04.204154 27120 solver.cpp:397]     Test net output #1: loss = 2.61455 (* 1 = 2.61455 loss)
I0428 14:12:04.340946 27120 solver.cpp:218] Iteration 3264 (0.714649 iter/s, 16.7915s/12 iters), loss = 1.49124
I0428 14:12:04.340989 27120 solver.cpp:237]     Train net output #0: loss = 1.49124 (* 1 = 1.49124 loss)
I0428 14:12:04.340997 27120 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0428 14:12:08.809512 27120 solver.cpp:218] Iteration 3276 (2.68544 iter/s, 4.46855s/12 iters), loss = 1.32038
I0428 14:12:08.809558 27120 solver.cpp:237]     Train net output #0: loss = 1.32038 (* 1 = 1.32038 loss)
I0428 14:12:08.809566 27120 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0428 14:12:14.234423 27120 solver.cpp:218] Iteration 3288 (2.21202 iter/s, 5.4249s/12 iters), loss = 1.34282
I0428 14:12:14.234467 27120 solver.cpp:237]     Train net output #0: loss = 1.34282 (* 1 = 1.34282 loss)
I0428 14:12:14.234475 27120 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0428 14:12:19.589812 27120 solver.cpp:218] Iteration 3300 (2.24074 iter/s, 5.35538s/12 iters), loss = 1.34155
I0428 14:12:19.589857 27120 solver.cpp:237]     Train net output #0: loss = 1.34155 (* 1 = 1.34155 loss)
I0428 14:12:19.589866 27120 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0428 14:12:24.941208 27120 solver.cpp:218] Iteration 3312 (2.24241 iter/s, 5.35138s/12 iters), loss = 1.30324
I0428 14:12:24.941256 27120 solver.cpp:237]     Train net output #0: loss = 1.30324 (* 1 = 1.30324 loss)
I0428 14:12:24.941265 27120 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0428 14:12:30.324857 27120 solver.cpp:218] Iteration 3324 (2.22898 iter/s, 5.38364s/12 iters), loss = 1.3109
I0428 14:12:30.324934 27120 solver.cpp:237]     Train net output #0: loss = 1.3109 (* 1 = 1.3109 loss)
I0428 14:12:30.324942 27120 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0428 14:12:35.719000 27120 solver.cpp:218] Iteration 3336 (2.22465 iter/s, 5.39409s/12 iters), loss = 1.4547
I0428 14:12:35.719046 27120 solver.cpp:237]     Train net output #0: loss = 1.4547 (* 1 = 1.4547 loss)
I0428 14:12:35.719055 27120 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0428 14:12:36.218189 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:12:40.937434 27120 solver.cpp:218] Iteration 3348 (2.29955 iter/s, 5.21842s/12 iters), loss = 1.41158
I0428 14:12:40.937479 27120 solver.cpp:237]     Train net output #0: loss = 1.41158 (* 1 = 1.41158 loss)
I0428 14:12:40.937487 27120 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0428 14:12:46.367436 27120 solver.cpp:218] Iteration 3360 (2.20995 iter/s, 5.42999s/12 iters), loss = 1.30405
I0428 14:12:46.367481 27120 solver.cpp:237]     Train net output #0: loss = 1.30405 (* 1 = 1.30405 loss)
I0428 14:12:46.367491 27120 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0428 14:12:48.548722 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0428 14:12:52.759263 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0428 14:12:54.804950 27120 solver.cpp:330] Iteration 3366, Testing net (#0)
I0428 14:12:54.804970 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:12:58.324774 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:12:59.897532 27120 solver.cpp:397]     Test net output #0: accuracy = 0.387255
I0428 14:12:59.897572 27120 solver.cpp:397]     Test net output #1: loss = 2.65455 (* 1 = 2.65455 loss)
I0428 14:13:01.900823 27120 solver.cpp:218] Iteration 3372 (0.772525 iter/s, 15.5335s/12 iters), loss = 1.30757
I0428 14:13:01.900969 27120 solver.cpp:237]     Train net output #0: loss = 1.30757 (* 1 = 1.30757 loss)
I0428 14:13:01.900979 27120 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0428 14:13:07.294664 27120 solver.cpp:218] Iteration 3384 (2.22481 iter/s, 5.39373s/12 iters), loss = 1.11569
I0428 14:13:07.294706 27120 solver.cpp:237]     Train net output #0: loss = 1.11569 (* 1 = 1.11569 loss)
I0428 14:13:07.294715 27120 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0428 14:13:12.590214 27120 solver.cpp:218] Iteration 3396 (2.26606 iter/s, 5.29553s/12 iters), loss = 1.32807
I0428 14:13:12.590261 27120 solver.cpp:237]     Train net output #0: loss = 1.32807 (* 1 = 1.32807 loss)
I0428 14:13:12.590270 27120 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0428 14:13:17.865698 27120 solver.cpp:218] Iteration 3408 (2.27468 iter/s, 5.27547s/12 iters), loss = 1.4402
I0428 14:13:17.865739 27120 solver.cpp:237]     Train net output #0: loss = 1.4402 (* 1 = 1.4402 loss)
I0428 14:13:17.865747 27120 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0428 14:13:23.301502 27120 solver.cpp:218] Iteration 3420 (2.20759 iter/s, 5.43579s/12 iters), loss = 1.32362
I0428 14:13:23.301545 27120 solver.cpp:237]     Train net output #0: loss = 1.32362 (* 1 = 1.32362 loss)
I0428 14:13:23.301554 27120 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0428 14:13:28.690394 27120 solver.cpp:218] Iteration 3432 (2.22681 iter/s, 5.38888s/12 iters), loss = 1.09296
I0428 14:13:28.690436 27120 solver.cpp:237]     Train net output #0: loss = 1.09296 (* 1 = 1.09296 loss)
I0428 14:13:28.690445 27120 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0428 14:13:31.490093 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:13:34.087769 27120 solver.cpp:218] Iteration 3444 (2.22331 iter/s, 5.39737s/12 iters), loss = 1.14068
I0428 14:13:34.087883 27120 solver.cpp:237]     Train net output #0: loss = 1.14068 (* 1 = 1.14068 loss)
I0428 14:13:34.087893 27120 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0428 14:13:39.377615 27120 solver.cpp:218] Iteration 3456 (2.26853 iter/s, 5.28976s/12 iters), loss = 1.23031
I0428 14:13:39.377661 27120 solver.cpp:237]     Train net output #0: loss = 1.23031 (* 1 = 1.23031 loss)
I0428 14:13:39.377669 27120 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0428 14:13:44.161684 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0428 14:13:46.818632 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0428 14:13:48.858273 27120 solver.cpp:330] Iteration 3468, Testing net (#0)
I0428 14:13:48.858304 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:13:49.321028 27120 blocking_queue.cpp:49] Waiting for data
I0428 14:13:52.435417 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:13:54.105257 27120 solver.cpp:397]     Test net output #0: accuracy = 0.411765
I0428 14:13:54.105283 27120 solver.cpp:397]     Test net output #1: loss = 2.56489 (* 1 = 2.56489 loss)
I0428 14:13:54.243775 27120 solver.cpp:218] Iteration 3468 (0.807198 iter/s, 14.8662s/12 iters), loss = 0.966571
I0428 14:13:54.243844 27120 solver.cpp:237]     Train net output #0: loss = 0.966571 (* 1 = 0.966571 loss)
I0428 14:13:54.243854 27120 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0428 14:13:58.753374 27120 solver.cpp:218] Iteration 3480 (2.66101 iter/s, 4.50956s/12 iters), loss = 1.08693
I0428 14:13:58.753420 27120 solver.cpp:237]     Train net output #0: loss = 1.08693 (* 1 = 1.08693 loss)
I0428 14:13:58.753429 27120 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0428 14:14:04.121992 27120 solver.cpp:218] Iteration 3492 (2.23522 iter/s, 5.3686s/12 iters), loss = 1.33704
I0428 14:14:04.122123 27120 solver.cpp:237]     Train net output #0: loss = 1.33704 (* 1 = 1.33704 loss)
I0428 14:14:04.122133 27120 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0428 14:14:09.534857 27120 solver.cpp:218] Iteration 3504 (2.21698 iter/s, 5.41277s/12 iters), loss = 1.03047
I0428 14:14:09.534901 27120 solver.cpp:237]     Train net output #0: loss = 1.03047 (* 1 = 1.03047 loss)
I0428 14:14:09.534910 27120 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0428 14:14:14.826771 27120 solver.cpp:218] Iteration 3516 (2.26762 iter/s, 5.2919s/12 iters), loss = 1.39705
I0428 14:14:14.826810 27120 solver.cpp:237]     Train net output #0: loss = 1.39705 (* 1 = 1.39705 loss)
I0428 14:14:14.826818 27120 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0428 14:14:20.203243 27120 solver.cpp:218] Iteration 3528 (2.23195 iter/s, 5.37646s/12 iters), loss = 1.21008
I0428 14:14:20.203284 27120 solver.cpp:237]     Train net output #0: loss = 1.21008 (* 1 = 1.21008 loss)
I0428 14:14:20.203292 27120 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0428 14:14:25.209115 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:14:25.506186 27120 solver.cpp:218] Iteration 3540 (2.2629 iter/s, 5.30294s/12 iters), loss = 1.3097
I0428 14:14:25.506229 27120 solver.cpp:237]     Train net output #0: loss = 1.3097 (* 1 = 1.3097 loss)
I0428 14:14:25.506237 27120 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0428 14:14:30.885500 27120 solver.cpp:218] Iteration 3552 (2.23077 iter/s, 5.3793s/12 iters), loss = 0.985482
I0428 14:14:30.885550 27120 solver.cpp:237]     Train net output #0: loss = 0.985482 (* 1 = 0.985482 loss)
I0428 14:14:30.885558 27120 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0428 14:14:36.243666 27120 solver.cpp:218] Iteration 3564 (2.23958 iter/s, 5.35815s/12 iters), loss = 1.35818
I0428 14:14:36.243749 27120 solver.cpp:237]     Train net output #0: loss = 1.35818 (* 1 = 1.35818 loss)
I0428 14:14:36.243758 27120 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0428 14:14:38.410279 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0428 14:14:42.171330 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0428 14:14:44.220556 27120 solver.cpp:330] Iteration 3570, Testing net (#0)
I0428 14:14:44.220575 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:14:47.484439 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:14:49.142637 27120 solver.cpp:397]     Test net output #0: accuracy = 0.376838
I0428 14:14:49.142681 27120 solver.cpp:397]     Test net output #1: loss = 2.73089 (* 1 = 2.73089 loss)
I0428 14:14:51.053153 27120 solver.cpp:218] Iteration 3576 (0.810289 iter/s, 14.8095s/12 iters), loss = 1.15513
I0428 14:14:51.053192 27120 solver.cpp:237]     Train net output #0: loss = 1.15513 (* 1 = 1.15513 loss)
I0428 14:14:51.053201 27120 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0428 14:14:56.444344 27120 solver.cpp:218] Iteration 3588 (2.22586 iter/s, 5.39118s/12 iters), loss = 1.01252
I0428 14:14:56.444387 27120 solver.cpp:237]     Train net output #0: loss = 1.01252 (* 1 = 1.01252 loss)
I0428 14:14:56.444396 27120 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0428 14:15:01.816465 27120 solver.cpp:218] Iteration 3600 (2.23376 iter/s, 5.37211s/12 iters), loss = 1.00748
I0428 14:15:01.816514 27120 solver.cpp:237]     Train net output #0: loss = 1.00748 (* 1 = 1.00748 loss)
I0428 14:15:01.816521 27120 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0428 14:15:07.171140 27120 solver.cpp:218] Iteration 3612 (2.24104 iter/s, 5.35466s/12 iters), loss = 1.07631
I0428 14:15:07.171284 27120 solver.cpp:237]     Train net output #0: loss = 1.07631 (* 1 = 1.07631 loss)
I0428 14:15:07.171293 27120 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0428 14:15:12.465750 27120 solver.cpp:218] Iteration 3624 (2.26651 iter/s, 5.29449s/12 iters), loss = 0.995863
I0428 14:15:12.465802 27120 solver.cpp:237]     Train net output #0: loss = 0.995863 (* 1 = 0.995863 loss)
I0428 14:15:12.465812 27120 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0428 14:15:17.748728 27120 solver.cpp:218] Iteration 3636 (2.27146 iter/s, 5.28296s/12 iters), loss = 1.06487
I0428 14:15:17.748771 27120 solver.cpp:237]     Train net output #0: loss = 1.06487 (* 1 = 1.06487 loss)
I0428 14:15:17.748780 27120 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0428 14:15:19.747921 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:15:23.150120 27120 solver.cpp:218] Iteration 3648 (2.22165 iter/s, 5.40138s/12 iters), loss = 1.14101
I0428 14:15:23.150161 27120 solver.cpp:237]     Train net output #0: loss = 1.14101 (* 1 = 1.14101 loss)
I0428 14:15:23.150169 27120 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0428 14:15:28.501638 27120 solver.cpp:218] Iteration 3660 (2.24236 iter/s, 5.35151s/12 iters), loss = 1.20433
I0428 14:15:28.501684 27120 solver.cpp:237]     Train net output #0: loss = 1.20433 (* 1 = 1.20433 loss)
I0428 14:15:28.501694 27120 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0428 14:15:33.331060 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0428 14:15:37.192792 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0428 14:15:40.110931 27120 solver.cpp:330] Iteration 3672, Testing net (#0)
I0428 14:15:40.110953 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:15:43.469063 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:15:45.203125 27120 solver.cpp:397]     Test net output #0: accuracy = 0.390931
I0428 14:15:45.203161 27120 solver.cpp:397]     Test net output #1: loss = 2.62435 (* 1 = 2.62435 loss)
I0428 14:15:45.335047 27120 solver.cpp:218] Iteration 3672 (0.712864 iter/s, 16.8335s/12 iters), loss = 1.18214
I0428 14:15:45.335091 27120 solver.cpp:237]     Train net output #0: loss = 1.18214 (* 1 = 1.18214 loss)
I0428 14:15:45.335099 27120 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0428 14:15:49.824030 27120 solver.cpp:218] Iteration 3684 (2.67322 iter/s, 4.48896s/12 iters), loss = 1.15195
I0428 14:15:49.824079 27120 solver.cpp:237]     Train net output #0: loss = 1.15195 (* 1 = 1.15195 loss)
I0428 14:15:49.824087 27120 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0428 14:15:55.181497 27120 solver.cpp:218] Iteration 3696 (2.23987 iter/s, 5.35745s/12 iters), loss = 1.02895
I0428 14:15:55.181540 27120 solver.cpp:237]     Train net output #0: loss = 1.02895 (* 1 = 1.02895 loss)
I0428 14:15:55.181548 27120 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0428 14:16:00.450520 27120 solver.cpp:218] Iteration 3708 (2.27747 iter/s, 5.26901s/12 iters), loss = 1.48302
I0428 14:16:00.450562 27120 solver.cpp:237]     Train net output #0: loss = 1.48302 (* 1 = 1.48302 loss)
I0428 14:16:00.450570 27120 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0428 14:16:05.825700 27120 solver.cpp:218] Iteration 3720 (2.23249 iter/s, 5.37517s/12 iters), loss = 1.18797
I0428 14:16:05.825745 27120 solver.cpp:237]     Train net output #0: loss = 1.18797 (* 1 = 1.18797 loss)
I0428 14:16:05.825754 27120 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0428 14:16:11.100262 27120 solver.cpp:218] Iteration 3732 (2.27508 iter/s, 5.27455s/12 iters), loss = 0.934738
I0428 14:16:11.100402 27120 solver.cpp:237]     Train net output #0: loss = 0.934738 (* 1 = 0.934738 loss)
I0428 14:16:11.100412 27120 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0428 14:16:15.341356 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:16:16.436009 27120 solver.cpp:218] Iteration 3744 (2.24903 iter/s, 5.33564s/12 iters), loss = 1.05938
I0428 14:16:16.436051 27120 solver.cpp:237]     Train net output #0: loss = 1.05938 (* 1 = 1.05938 loss)
I0428 14:16:16.436059 27120 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0428 14:16:21.816606 27120 solver.cpp:218] Iteration 3756 (2.23024 iter/s, 5.38058s/12 iters), loss = 0.921127
I0428 14:16:21.816648 27120 solver.cpp:237]     Train net output #0: loss = 0.921127 (* 1 = 0.921127 loss)
I0428 14:16:21.816658 27120 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0428 14:16:27.184253 27120 solver.cpp:218] Iteration 3768 (2.23562 iter/s, 5.36764s/12 iters), loss = 0.859071
I0428 14:16:27.184298 27120 solver.cpp:237]     Train net output #0: loss = 0.859071 (* 1 = 0.859071 loss)
I0428 14:16:27.184307 27120 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0428 14:16:29.331714 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0428 14:16:34.580766 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0428 14:16:38.867727 27120 solver.cpp:330] Iteration 3774, Testing net (#0)
I0428 14:16:38.867748 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:16:42.241683 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:16:44.022575 27120 solver.cpp:397]     Test net output #0: accuracy = 0.415441
I0428 14:16:44.022639 27120 solver.cpp:397]     Test net output #1: loss = 2.51291 (* 1 = 2.51291 loss)
I0428 14:16:46.041357 27120 solver.cpp:218] Iteration 3780 (0.636361 iter/s, 18.8572s/12 iters), loss = 1.1429
I0428 14:16:46.041404 27120 solver.cpp:237]     Train net output #0: loss = 1.1429 (* 1 = 1.1429 loss)
I0428 14:16:46.041412 27120 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0428 14:16:51.403532 27120 solver.cpp:218] Iteration 3792 (2.2379 iter/s, 5.36216s/12 iters), loss = 0.921158
I0428 14:16:51.403570 27120 solver.cpp:237]     Train net output #0: loss = 0.921158 (* 1 = 0.921158 loss)
I0428 14:16:51.403579 27120 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0428 14:16:56.624131 27120 solver.cpp:218] Iteration 3804 (2.29859 iter/s, 5.22058s/12 iters), loss = 0.788768
I0428 14:16:56.624177 27120 solver.cpp:237]     Train net output #0: loss = 0.788768 (* 1 = 0.788768 loss)
I0428 14:16:56.624186 27120 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0428 14:17:02.006647 27120 solver.cpp:218] Iteration 3816 (2.22945 iter/s, 5.3825s/12 iters), loss = 1.02332
I0428 14:17:02.006687 27120 solver.cpp:237]     Train net output #0: loss = 1.02332 (* 1 = 1.02332 loss)
I0428 14:17:02.006696 27120 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0428 14:17:07.371606 27120 solver.cpp:218] Iteration 3828 (2.23674 iter/s, 5.36495s/12 iters), loss = 1.04852
I0428 14:17:07.371642 27120 solver.cpp:237]     Train net output #0: loss = 1.04852 (* 1 = 1.04852 loss)
I0428 14:17:07.371650 27120 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0428 14:17:12.736205 27120 solver.cpp:218] Iteration 3840 (2.23689 iter/s, 5.36459s/12 iters), loss = 1.11794
I0428 14:17:12.736346 27120 solver.cpp:237]     Train net output #0: loss = 1.11794 (* 1 = 1.11794 loss)
I0428 14:17:12.736354 27120 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0428 14:17:13.934813 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:17:18.143597 27120 solver.cpp:218] Iteration 3852 (2.21923 iter/s, 5.40728s/12 iters), loss = 1.08365
I0428 14:17:18.143640 27120 solver.cpp:237]     Train net output #0: loss = 1.08365 (* 1 = 1.08365 loss)
I0428 14:17:18.143647 27120 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0428 14:17:23.500703 27120 solver.cpp:218] Iteration 3864 (2.24002 iter/s, 5.35709s/12 iters), loss = 0.738701
I0428 14:17:23.500746 27120 solver.cpp:237]     Train net output #0: loss = 0.738701 (* 1 = 0.738701 loss)
I0428 14:17:23.500756 27120 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0428 14:17:28.338567 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0428 14:17:31.010051 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0428 14:17:37.367282 27120 solver.cpp:330] Iteration 3876, Testing net (#0)
I0428 14:17:37.367306 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:17:40.658732 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:17:42.434767 27120 solver.cpp:397]     Test net output #0: accuracy = 0.443627
I0428 14:17:42.434811 27120 solver.cpp:397]     Test net output #1: loss = 2.52465 (* 1 = 2.52465 loss)
I0428 14:17:42.571326 27120 solver.cpp:218] Iteration 3876 (0.629236 iter/s, 19.0707s/12 iters), loss = 0.952419
I0428 14:17:42.571373 27120 solver.cpp:237]     Train net output #0: loss = 0.952419 (* 1 = 0.952419 loss)
I0428 14:17:42.571383 27120 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0428 14:17:47.078002 27120 solver.cpp:218] Iteration 3888 (2.66273 iter/s, 4.50665s/12 iters), loss = 0.652072
I0428 14:17:47.078181 27120 solver.cpp:237]     Train net output #0: loss = 0.652072 (* 1 = 0.652072 loss)
I0428 14:17:47.078192 27120 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0428 14:17:52.488972 27120 solver.cpp:218] Iteration 3900 (2.21778 iter/s, 5.41082s/12 iters), loss = 0.885833
I0428 14:17:52.489022 27120 solver.cpp:237]     Train net output #0: loss = 0.885833 (* 1 = 0.885833 loss)
I0428 14:17:52.489030 27120 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0428 14:17:57.921332 27120 solver.cpp:218] Iteration 3912 (2.20899 iter/s, 5.43234s/12 iters), loss = 0.752153
I0428 14:17:57.921376 27120 solver.cpp:237]     Train net output #0: loss = 0.752153 (* 1 = 0.752153 loss)
I0428 14:17:57.921384 27120 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0428 14:18:03.333586 27120 solver.cpp:218] Iteration 3924 (2.2172 iter/s, 5.41223s/12 iters), loss = 0.953882
I0428 14:18:03.333633 27120 solver.cpp:237]     Train net output #0: loss = 0.953882 (* 1 = 0.953882 loss)
I0428 14:18:03.333642 27120 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0428 14:18:08.750882 27120 solver.cpp:218] Iteration 3936 (2.21513 iter/s, 5.41728s/12 iters), loss = 0.878267
I0428 14:18:08.750931 27120 solver.cpp:237]     Train net output #0: loss = 0.878267 (* 1 = 0.878267 loss)
I0428 14:18:08.750941 27120 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0428 14:18:12.464926 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:18:14.233727 27120 solver.cpp:218] Iteration 3948 (2.18865 iter/s, 5.48282s/12 iters), loss = 0.645084
I0428 14:18:14.233769 27120 solver.cpp:237]     Train net output #0: loss = 0.645084 (* 1 = 0.645084 loss)
I0428 14:18:14.233778 27120 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0428 14:18:19.604383 27120 solver.cpp:218] Iteration 3960 (2.23437 iter/s, 5.37064s/12 iters), loss = 0.954277
I0428 14:18:19.604519 27120 solver.cpp:237]     Train net output #0: loss = 0.954277 (* 1 = 0.954277 loss)
I0428 14:18:19.604529 27120 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0428 14:18:24.978292 27120 solver.cpp:218] Iteration 3972 (2.23306 iter/s, 5.3738s/12 iters), loss = 0.944867
I0428 14:18:24.978338 27120 solver.cpp:237]     Train net output #0: loss = 0.944867 (* 1 = 0.944867 loss)
I0428 14:18:24.978348 27120 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0428 14:18:27.129405 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0428 14:18:31.829596 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0428 14:18:37.874764 27120 solver.cpp:330] Iteration 3978, Testing net (#0)
I0428 14:18:37.874783 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:18:41.122932 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:18:42.978039 27120 solver.cpp:397]     Test net output #0: accuracy = 0.436887
I0428 14:18:42.978083 27120 solver.cpp:397]     Test net output #1: loss = 2.62474 (* 1 = 2.62474 loss)
I0428 14:18:44.966337 27120 solver.cpp:218] Iteration 3984 (0.600355 iter/s, 19.9882s/12 iters), loss = 0.85291
I0428 14:18:44.966385 27120 solver.cpp:237]     Train net output #0: loss = 0.85291 (* 1 = 0.85291 loss)
I0428 14:18:44.966394 27120 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0428 14:18:50.349845 27120 solver.cpp:218] Iteration 3996 (2.22904 iter/s, 5.38349s/12 iters), loss = 1.10571
I0428 14:18:50.349978 27120 solver.cpp:237]     Train net output #0: loss = 1.10571 (* 1 = 1.10571 loss)
I0428 14:18:50.349988 27120 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0428 14:18:55.710018 27120 solver.cpp:218] Iteration 4008 (2.23878 iter/s, 5.36006s/12 iters), loss = 0.628966
I0428 14:18:55.710067 27120 solver.cpp:237]     Train net output #0: loss = 0.628966 (* 1 = 0.628966 loss)
I0428 14:18:55.710075 27120 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0428 14:19:01.115852 27120 solver.cpp:218] Iteration 4020 (2.21983 iter/s, 5.40582s/12 iters), loss = 0.643868
I0428 14:19:01.115893 27120 solver.cpp:237]     Train net output #0: loss = 0.643868 (* 1 = 0.643868 loss)
I0428 14:19:01.115902 27120 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0428 14:19:06.531433 27120 solver.cpp:218] Iteration 4032 (2.21584 iter/s, 5.41557s/12 iters), loss = 0.911045
I0428 14:19:06.531473 27120 solver.cpp:237]     Train net output #0: loss = 0.911045 (* 1 = 0.911045 loss)
I0428 14:19:06.531481 27120 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0428 14:19:11.926481 27120 solver.cpp:218] Iteration 4044 (2.22427 iter/s, 5.39503s/12 iters), loss = 0.732501
I0428 14:19:11.926532 27120 solver.cpp:237]     Train net output #0: loss = 0.732501 (* 1 = 0.732501 loss)
I0428 14:19:11.926540 27120 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0428 14:19:12.455849 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:19:17.308200 27120 solver.cpp:218] Iteration 4056 (2.22978 iter/s, 5.3817s/12 iters), loss = 0.759935
I0428 14:19:17.308240 27120 solver.cpp:237]     Train net output #0: loss = 0.759935 (* 1 = 0.759935 loss)
I0428 14:19:17.308248 27120 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0428 14:19:22.761049 27120 solver.cpp:218] Iteration 4068 (2.20069 iter/s, 5.45283s/12 iters), loss = 0.773604
I0428 14:19:22.761168 27120 solver.cpp:237]     Train net output #0: loss = 0.773604 (* 1 = 0.773604 loss)
I0428 14:19:22.761178 27120 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0428 14:19:27.519711 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0428 14:19:33.273262 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0428 14:19:38.652858 27120 solver.cpp:330] Iteration 4080, Testing net (#0)
I0428 14:19:38.652878 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:19:41.918628 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:19:43.835559 27120 solver.cpp:397]     Test net output #0: accuracy = 0.454657
I0428 14:19:43.835587 27120 solver.cpp:397]     Test net output #1: loss = 2.52943 (* 1 = 2.52943 loss)
I0428 14:19:43.972275 27120 solver.cpp:218] Iteration 4080 (0.565737 iter/s, 21.2113s/12 iters), loss = 0.760566
I0428 14:19:43.972326 27120 solver.cpp:237]     Train net output #0: loss = 0.760566 (* 1 = 0.760566 loss)
I0428 14:19:43.972334 27120 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0428 14:19:48.485846 27120 solver.cpp:218] Iteration 4092 (2.65867 iter/s, 4.51354s/12 iters), loss = 0.742624
I0428 14:19:48.485894 27120 solver.cpp:237]     Train net output #0: loss = 0.742624 (* 1 = 0.742624 loss)
I0428 14:19:48.485903 27120 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0428 14:19:54.081457 27120 solver.cpp:218] Iteration 4104 (2.14455 iter/s, 5.59559s/12 iters), loss = 0.694444
I0428 14:19:54.081586 27120 solver.cpp:237]     Train net output #0: loss = 0.694444 (* 1 = 0.694444 loss)
I0428 14:19:54.081596 27120 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0428 14:19:59.479753 27120 solver.cpp:218] Iteration 4116 (2.22297 iter/s, 5.39819s/12 iters), loss = 0.867848
I0428 14:19:59.479800 27120 solver.cpp:237]     Train net output #0: loss = 0.867848 (* 1 = 0.867848 loss)
I0428 14:19:59.479810 27120 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0428 14:20:04.860173 27120 solver.cpp:218] Iteration 4128 (2.23032 iter/s, 5.3804s/12 iters), loss = 0.809691
I0428 14:20:04.860219 27120 solver.cpp:237]     Train net output #0: loss = 0.809691 (* 1 = 0.809691 loss)
I0428 14:20:04.860229 27120 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0428 14:20:10.250775 27120 solver.cpp:218] Iteration 4140 (2.22611 iter/s, 5.39058s/12 iters), loss = 0.768646
I0428 14:20:10.250821 27120 solver.cpp:237]     Train net output #0: loss = 0.768646 (* 1 = 0.768646 loss)
I0428 14:20:10.250830 27120 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0428 14:20:13.071048 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:20:15.644837 27120 solver.cpp:218] Iteration 4152 (2.22468 iter/s, 5.39405s/12 iters), loss = 0.771119
I0428 14:20:15.644884 27120 solver.cpp:237]     Train net output #0: loss = 0.771119 (* 1 = 0.771119 loss)
I0428 14:20:15.644893 27120 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0428 14:20:17.363911 27120 blocking_queue.cpp:49] Waiting for data
I0428 14:20:20.994199 27120 solver.cpp:218] Iteration 4164 (2.24327 iter/s, 5.34934s/12 iters), loss = 0.871789
I0428 14:20:20.994240 27120 solver.cpp:237]     Train net output #0: loss = 0.871789 (* 1 = 0.871789 loss)
I0428 14:20:20.994249 27120 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0428 14:20:26.370849 27120 solver.cpp:218] Iteration 4176 (2.23188 iter/s, 5.37664s/12 iters), loss = 0.614978
I0428 14:20:26.370978 27120 solver.cpp:237]     Train net output #0: loss = 0.614978 (* 1 = 0.614978 loss)
I0428 14:20:26.370987 27120 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0428 14:20:28.642973 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0428 14:20:35.641669 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0428 14:20:40.045397 27120 solver.cpp:330] Iteration 4182, Testing net (#0)
I0428 14:20:40.045414 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:20:43.197474 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:20:45.109201 27120 solver.cpp:397]     Test net output #0: accuracy = 0.454657
I0428 14:20:45.109230 27120 solver.cpp:397]     Test net output #1: loss = 2.43074 (* 1 = 2.43074 loss)
I0428 14:20:47.209280 27120 solver.cpp:218] Iteration 4188 (0.575858 iter/s, 20.8385s/12 iters), loss = 0.674996
I0428 14:20:47.209321 27120 solver.cpp:237]     Train net output #0: loss = 0.674996 (* 1 = 0.674996 loss)
I0428 14:20:47.209329 27120 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0428 14:20:52.551564 27120 solver.cpp:218] Iteration 4200 (2.24624 iter/s, 5.34227s/12 iters), loss = 0.828246
I0428 14:20:52.551610 27120 solver.cpp:237]     Train net output #0: loss = 0.828246 (* 1 = 0.828246 loss)
I0428 14:20:52.551620 27120 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0428 14:20:57.888288 27120 solver.cpp:218] Iteration 4212 (2.24858 iter/s, 5.3367s/12 iters), loss = 0.621012
I0428 14:20:57.888391 27120 solver.cpp:237]     Train net output #0: loss = 0.621012 (* 1 = 0.621012 loss)
I0428 14:20:57.888401 27120 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0428 14:21:03.088490 27120 solver.cpp:218] Iteration 4224 (2.30764 iter/s, 5.20013s/12 iters), loss = 0.698389
I0428 14:21:03.088531 27120 solver.cpp:237]     Train net output #0: loss = 0.698389 (* 1 = 0.698389 loss)
I0428 14:21:03.088541 27120 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0428 14:21:08.409500 27120 solver.cpp:218] Iteration 4236 (2.25522 iter/s, 5.32099s/12 iters), loss = 0.824163
I0428 14:21:08.409546 27120 solver.cpp:237]     Train net output #0: loss = 0.824163 (* 1 = 0.824163 loss)
I0428 14:21:08.409555 27120 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0428 14:21:13.519760 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:21:13.785327 27120 solver.cpp:218] Iteration 4248 (2.23222 iter/s, 5.37581s/12 iters), loss = 0.665722
I0428 14:21:13.785375 27120 solver.cpp:237]     Train net output #0: loss = 0.665722 (* 1 = 0.665722 loss)
I0428 14:21:13.785384 27120 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0428 14:21:18.993454 27120 solver.cpp:218] Iteration 4260 (2.3041 iter/s, 5.2081s/12 iters), loss = 0.611119
I0428 14:21:18.993486 27120 solver.cpp:237]     Train net output #0: loss = 0.611119 (* 1 = 0.611119 loss)
I0428 14:21:18.993494 27120 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0428 14:21:24.176748 27120 solver.cpp:218] Iteration 4272 (2.31514 iter/s, 5.18328s/12 iters), loss = 0.703756
I0428 14:21:24.176791 27120 solver.cpp:237]     Train net output #0: loss = 0.703756 (* 1 = 0.703756 loss)
I0428 14:21:24.176800 27120 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0428 14:21:29.156740 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0428 14:21:34.051620 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0428 14:21:40.590904 27120 solver.cpp:330] Iteration 4284, Testing net (#0)
I0428 14:21:40.590934 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:21:43.857430 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:21:45.905855 27120 solver.cpp:397]     Test net output #0: accuracy = 0.478554
I0428 14:21:45.905905 27120 solver.cpp:397]     Test net output #1: loss = 2.4117 (* 1 = 2.4117 loss)
I0428 14:21:46.043013 27120 solver.cpp:218] Iteration 4284 (0.548787 iter/s, 21.8664s/12 iters), loss = 0.713301
I0428 14:21:46.043079 27120 solver.cpp:237]     Train net output #0: loss = 0.713301 (* 1 = 0.713301 loss)
I0428 14:21:46.043089 27120 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0428 14:21:50.565322 27120 solver.cpp:218] Iteration 4296 (2.65354 iter/s, 4.52226s/12 iters), loss = 0.491089
I0428 14:21:50.565367 27120 solver.cpp:237]     Train net output #0: loss = 0.491089 (* 1 = 0.491089 loss)
I0428 14:21:50.565376 27120 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0428 14:21:55.916110 27120 solver.cpp:218] Iteration 4308 (2.24267 iter/s, 5.35076s/12 iters), loss = 0.770578
I0428 14:21:55.916158 27120 solver.cpp:237]     Train net output #0: loss = 0.770578 (* 1 = 0.770578 loss)
I0428 14:21:55.916167 27120 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0428 14:22:01.193313 27120 solver.cpp:218] Iteration 4320 (2.27394 iter/s, 5.27718s/12 iters), loss = 0.823683
I0428 14:22:01.193439 27120 solver.cpp:237]     Train net output #0: loss = 0.823683 (* 1 = 0.823683 loss)
I0428 14:22:01.193449 27120 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0428 14:22:06.552553 27120 solver.cpp:218] Iteration 4332 (2.23917 iter/s, 5.35914s/12 iters), loss = 0.636051
I0428 14:22:06.552599 27120 solver.cpp:237]     Train net output #0: loss = 0.636051 (* 1 = 0.636051 loss)
I0428 14:22:06.552608 27120 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0428 14:22:11.921166 27120 solver.cpp:218] Iteration 4344 (2.23522 iter/s, 5.36859s/12 iters), loss = 0.775394
I0428 14:22:11.921211 27120 solver.cpp:237]     Train net output #0: loss = 0.775394 (* 1 = 0.775394 loss)
I0428 14:22:11.921219 27120 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0428 14:22:13.965894 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:22:17.323451 27120 solver.cpp:218] Iteration 4356 (2.22129 iter/s, 5.40227s/12 iters), loss = 0.614159
I0428 14:22:17.323493 27120 solver.cpp:237]     Train net output #0: loss = 0.614159 (* 1 = 0.614159 loss)
I0428 14:22:17.323503 27120 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0428 14:22:22.693333 27120 solver.cpp:218] Iteration 4368 (2.23469 iter/s, 5.36987s/12 iters), loss = 0.583878
I0428 14:22:22.693373 27120 solver.cpp:237]     Train net output #0: loss = 0.583878 (* 1 = 0.583878 loss)
I0428 14:22:22.693382 27120 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0428 14:22:28.065060 27120 solver.cpp:218] Iteration 4380 (2.23393 iter/s, 5.37171s/12 iters), loss = 0.47992
I0428 14:22:28.065099 27120 solver.cpp:237]     Train net output #0: loss = 0.47992 (* 1 = 0.47992 loss)
I0428 14:22:28.065107 27120 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0428 14:22:30.229580 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0428 14:22:32.884569 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0428 14:22:40.019748 27120 solver.cpp:330] Iteration 4386, Testing net (#0)
I0428 14:22:40.019769 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:22:43.024982 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:22:44.947021 27120 solver.cpp:397]     Test net output #0: accuracy = 0.46875
I0428 14:22:44.947057 27120 solver.cpp:397]     Test net output #1: loss = 2.5085 (* 1 = 2.5085 loss)
I0428 14:22:46.968120 27120 solver.cpp:218] Iteration 4392 (0.634814 iter/s, 18.9032s/12 iters), loss = 0.640885
I0428 14:22:46.968161 27120 solver.cpp:237]     Train net output #0: loss = 0.640885 (* 1 = 0.640885 loss)
I0428 14:22:46.968169 27120 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0428 14:22:52.352131 27120 solver.cpp:218] Iteration 4404 (2.22883 iter/s, 5.38399s/12 iters), loss = 0.549332
I0428 14:22:52.352171 27120 solver.cpp:237]     Train net output #0: loss = 0.549332 (* 1 = 0.549332 loss)
I0428 14:22:52.352180 27120 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0428 14:22:57.687543 27120 solver.cpp:218] Iteration 4416 (2.24913 iter/s, 5.3354s/12 iters), loss = 0.659681
I0428 14:22:57.687587 27120 solver.cpp:237]     Train net output #0: loss = 0.659681 (* 1 = 0.659681 loss)
I0428 14:22:57.687595 27120 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0428 14:23:03.055806 27120 solver.cpp:218] Iteration 4428 (2.23537 iter/s, 5.36825s/12 iters), loss = 0.657159
I0428 14:23:03.055925 27120 solver.cpp:237]     Train net output #0: loss = 0.657159 (* 1 = 0.657159 loss)
I0428 14:23:03.055934 27120 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0428 14:23:08.331244 27120 solver.cpp:218] Iteration 4440 (2.27473 iter/s, 5.27534s/12 iters), loss = 0.725524
I0428 14:23:08.331290 27120 solver.cpp:237]     Train net output #0: loss = 0.725524 (* 1 = 0.725524 loss)
I0428 14:23:08.331300 27120 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0428 14:23:12.649338 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:23:13.718655 27120 solver.cpp:218] Iteration 4452 (2.22742 iter/s, 5.38739s/12 iters), loss = 0.865081
I0428 14:23:13.718705 27120 solver.cpp:237]     Train net output #0: loss = 0.865081 (* 1 = 0.865081 loss)
I0428 14:23:13.718714 27120 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0428 14:23:19.074776 27120 solver.cpp:218] Iteration 4464 (2.24044 iter/s, 5.3561s/12 iters), loss = 0.414274
I0428 14:23:19.074817 27120 solver.cpp:237]     Train net output #0: loss = 0.414274 (* 1 = 0.414274 loss)
I0428 14:23:19.074826 27120 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0428 14:23:24.420238 27120 solver.cpp:218] Iteration 4476 (2.2449 iter/s, 5.34545s/12 iters), loss = 0.491959
I0428 14:23:24.420279 27120 solver.cpp:237]     Train net output #0: loss = 0.491959 (* 1 = 0.491959 loss)
I0428 14:23:24.420286 27120 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0428 14:23:29.280483 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0428 14:23:31.896972 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0428 14:23:34.893514 27120 solver.cpp:330] Iteration 4488, Testing net (#0)
I0428 14:23:34.893608 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:23:37.940091 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:23:40.083305 27120 solver.cpp:397]     Test net output #0: accuracy = 0.465686
I0428 14:23:40.083334 27120 solver.cpp:397]     Test net output #1: loss = 2.4166 (* 1 = 2.4166 loss)
I0428 14:23:40.219652 27120 solver.cpp:218] Iteration 4488 (0.759518 iter/s, 15.7995s/12 iters), loss = 0.517099
I0428 14:23:40.219710 27120 solver.cpp:237]     Train net output #0: loss = 0.517099 (* 1 = 0.517099 loss)
I0428 14:23:40.219719 27120 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0428 14:23:44.808499 27120 solver.cpp:218] Iteration 4500 (2.61505 iter/s, 4.58882s/12 iters), loss = 0.495161
I0428 14:23:44.808542 27120 solver.cpp:237]     Train net output #0: loss = 0.495161 (* 1 = 0.495161 loss)
I0428 14:23:44.808550 27120 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0428 14:23:50.204774 27120 solver.cpp:218] Iteration 4512 (2.22376 iter/s, 5.39626s/12 iters), loss = 0.469953
I0428 14:23:50.204821 27120 solver.cpp:237]     Train net output #0: loss = 0.469953 (* 1 = 0.469953 loss)
I0428 14:23:50.204830 27120 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0428 14:23:55.570775 27120 solver.cpp:218] Iteration 4524 (2.23631 iter/s, 5.36598s/12 iters), loss = 0.452657
I0428 14:23:55.570818 27120 solver.cpp:237]     Train net output #0: loss = 0.452657 (* 1 = 0.452657 loss)
I0428 14:23:55.570827 27120 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0428 14:24:00.850350 27120 solver.cpp:218] Iteration 4536 (2.27292 iter/s, 5.27956s/12 iters), loss = 0.480058
I0428 14:24:00.850399 27120 solver.cpp:237]     Train net output #0: loss = 0.480058 (* 1 = 0.480058 loss)
I0428 14:24:00.850409 27120 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0428 14:24:06.145879 27120 solver.cpp:218] Iteration 4548 (2.26607 iter/s, 5.29551s/12 iters), loss = 0.698822
I0428 14:24:06.146019 27120 solver.cpp:237]     Train net output #0: loss = 0.698822 (* 1 = 0.698822 loss)
I0428 14:24:06.146029 27120 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0428 14:24:07.547374 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:24:11.615164 27120 solver.cpp:218] Iteration 4560 (2.19411 iter/s, 5.46918s/12 iters), loss = 0.599359
I0428 14:24:11.615202 27120 solver.cpp:237]     Train net output #0: loss = 0.599359 (* 1 = 0.599359 loss)
I0428 14:24:11.615211 27120 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0428 14:24:16.986847 27120 solver.cpp:218] Iteration 4572 (2.23394 iter/s, 5.37167s/12 iters), loss = 0.62142
I0428 14:24:16.986892 27120 solver.cpp:237]     Train net output #0: loss = 0.62142 (* 1 = 0.62142 loss)
I0428 14:24:16.986901 27120 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0428 14:24:22.356997 27120 solver.cpp:218] Iteration 4584 (2.23458 iter/s, 5.37013s/12 iters), loss = 0.413226
I0428 14:24:22.357044 27120 solver.cpp:237]     Train net output #0: loss = 0.413226 (* 1 = 0.413226 loss)
I0428 14:24:22.357054 27120 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0428 14:24:24.508867 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0428 14:24:27.940032 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0428 14:24:31.065284 27120 solver.cpp:330] Iteration 4590, Testing net (#0)
I0428 14:24:31.065304 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:24:33.947146 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:24:36.123080 27120 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0428 14:24:36.123118 27120 solver.cpp:397]     Test net output #1: loss = 2.5343 (* 1 = 2.5343 loss)
I0428 14:24:38.040310 27120 solver.cpp:218] Iteration 4596 (0.765141 iter/s, 15.6834s/12 iters), loss = 0.550471
I0428 14:24:38.040432 27120 solver.cpp:237]     Train net output #0: loss = 0.550471 (* 1 = 0.550471 loss)
I0428 14:24:38.040443 27120 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0428 14:24:43.345552 27120 solver.cpp:218] Iteration 4608 (2.26195 iter/s, 5.30515s/12 iters), loss = 0.400402
I0428 14:24:43.345600 27120 solver.cpp:237]     Train net output #0: loss = 0.400402 (* 1 = 0.400402 loss)
I0428 14:24:43.345608 27120 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0428 14:24:48.695907 27120 solver.cpp:218] Iteration 4620 (2.24285 iter/s, 5.35033s/12 iters), loss = 0.511398
I0428 14:24:48.695951 27120 solver.cpp:237]     Train net output #0: loss = 0.511398 (* 1 = 0.511398 loss)
I0428 14:24:48.695960 27120 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0428 14:24:53.909368 27120 solver.cpp:218] Iteration 4632 (2.30174 iter/s, 5.21344s/12 iters), loss = 0.624858
I0428 14:24:53.909408 27120 solver.cpp:237]     Train net output #0: loss = 0.624858 (* 1 = 0.624858 loss)
I0428 14:24:53.909417 27120 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0428 14:24:59.266453 27120 solver.cpp:218] Iteration 4644 (2.24003 iter/s, 5.35707s/12 iters), loss = 0.589634
I0428 14:24:59.266497 27120 solver.cpp:237]     Train net output #0: loss = 0.589634 (* 1 = 0.589634 loss)
I0428 14:24:59.266507 27120 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0428 14:25:02.939859 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:25:04.754096 27120 solver.cpp:218] Iteration 4656 (2.18674 iter/s, 5.48762s/12 iters), loss = 0.694281
I0428 14:25:04.754143 27120 solver.cpp:237]     Train net output #0: loss = 0.694281 (* 1 = 0.694281 loss)
I0428 14:25:04.754151 27120 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0428 14:25:10.201572 27120 solver.cpp:218] Iteration 4668 (2.20286 iter/s, 5.44746s/12 iters), loss = 0.420728
I0428 14:25:10.201686 27120 solver.cpp:237]     Train net output #0: loss = 0.420728 (* 1 = 0.420728 loss)
I0428 14:25:10.201695 27120 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0428 14:25:15.497619 27120 solver.cpp:218] Iteration 4680 (2.26588 iter/s, 5.29596s/12 iters), loss = 0.650152
I0428 14:25:15.497665 27120 solver.cpp:237]     Train net output #0: loss = 0.650152 (* 1 = 0.650152 loss)
I0428 14:25:15.497674 27120 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0428 14:25:20.323761 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0428 14:25:22.981318 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0428 14:25:25.800779 27120 solver.cpp:330] Iteration 4692, Testing net (#0)
I0428 14:25:25.800798 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:25:28.573112 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:25:30.769423 27120 solver.cpp:397]     Test net output #0: accuracy = 0.476716
I0428 14:25:30.769470 27120 solver.cpp:397]     Test net output #1: loss = 2.47007 (* 1 = 2.47007 loss)
I0428 14:25:30.906136 27120 solver.cpp:218] Iteration 4692 (0.778787 iter/s, 15.4086s/12 iters), loss = 0.477019
I0428 14:25:30.906183 27120 solver.cpp:237]     Train net output #0: loss = 0.477019 (* 1 = 0.477019 loss)
I0428 14:25:30.906193 27120 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0428 14:25:35.418850 27120 solver.cpp:218] Iteration 4704 (2.65917 iter/s, 4.51269s/12 iters), loss = 0.566338
I0428 14:25:35.418898 27120 solver.cpp:237]     Train net output #0: loss = 0.566338 (* 1 = 0.566338 loss)
I0428 14:25:35.418908 27120 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0428 14:25:40.716573 27120 solver.cpp:218] Iteration 4716 (2.26513 iter/s, 5.2977s/12 iters), loss = 0.422313
I0428 14:25:40.716711 27120 solver.cpp:237]     Train net output #0: loss = 0.422313 (* 1 = 0.422313 loss)
I0428 14:25:40.716720 27120 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0428 14:25:46.079764 27120 solver.cpp:218] Iteration 4728 (2.23752 iter/s, 5.36309s/12 iters), loss = 0.424852
I0428 14:25:46.079807 27120 solver.cpp:237]     Train net output #0: loss = 0.424852 (* 1 = 0.424852 loss)
I0428 14:25:46.079814 27120 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0428 14:25:51.458741 27120 solver.cpp:218] Iteration 4740 (2.23092 iter/s, 5.37896s/12 iters), loss = 0.365349
I0428 14:25:51.458789 27120 solver.cpp:237]     Train net output #0: loss = 0.365349 (* 1 = 0.365349 loss)
I0428 14:25:51.458798 27120 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0428 14:25:56.835741 27120 solver.cpp:218] Iteration 4752 (2.23174 iter/s, 5.37697s/12 iters), loss = 0.517702
I0428 14:25:56.835785 27120 solver.cpp:237]     Train net output #0: loss = 0.517702 (* 1 = 0.517702 loss)
I0428 14:25:56.835794 27120 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0428 14:25:57.396505 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:26:02.220929 27120 solver.cpp:218] Iteration 4764 (2.22834 iter/s, 5.38517s/12 iters), loss = 0.492444
I0428 14:26:02.220976 27120 solver.cpp:237]     Train net output #0: loss = 0.492444 (* 1 = 0.492444 loss)
I0428 14:26:02.220985 27120 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0428 14:26:07.566498 27120 solver.cpp:218] Iteration 4776 (2.24486 iter/s, 5.34555s/12 iters), loss = 0.490132
I0428 14:26:07.566545 27120 solver.cpp:237]     Train net output #0: loss = 0.490132 (* 1 = 0.490132 loss)
I0428 14:26:07.566553 27120 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0428 14:26:12.928103 27120 solver.cpp:218] Iteration 4788 (2.23814 iter/s, 5.36159s/12 iters), loss = 0.622608
I0428 14:26:12.928233 27120 solver.cpp:237]     Train net output #0: loss = 0.622608 (* 1 = 0.622608 loss)
I0428 14:26:12.928242 27120 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0428 14:26:15.078039 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0428 14:26:19.357120 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0428 14:26:21.949738 27120 solver.cpp:330] Iteration 4794, Testing net (#0)
I0428 14:26:21.949757 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:26:24.818610 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:26:27.075943 27120 solver.cpp:397]     Test net output #0: accuracy = 0.471201
I0428 14:26:27.075971 27120 solver.cpp:397]     Test net output #1: loss = 2.59258 (* 1 = 2.59258 loss)
I0428 14:26:29.065263 27120 solver.cpp:218] Iteration 4800 (0.743626 iter/s, 16.1372s/12 iters), loss = 0.455053
I0428 14:26:29.065310 27120 solver.cpp:237]     Train net output #0: loss = 0.455053 (* 1 = 0.455053 loss)
I0428 14:26:29.065317 27120 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0428 14:26:34.485476 27120 solver.cpp:218] Iteration 4812 (2.21394 iter/s, 5.42019s/12 iters), loss = 0.715286
I0428 14:26:34.485515 27120 solver.cpp:237]     Train net output #0: loss = 0.715286 (* 1 = 0.715286 loss)
I0428 14:26:34.485523 27120 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0428 14:26:39.897205 27120 solver.cpp:218] Iteration 4824 (2.21741 iter/s, 5.41171s/12 iters), loss = 0.590022
I0428 14:26:39.897250 27120 solver.cpp:237]     Train net output #0: loss = 0.590022 (* 1 = 0.590022 loss)
I0428 14:26:39.897259 27120 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0428 14:26:45.256151 27120 solver.cpp:218] Iteration 4836 (2.23925 iter/s, 5.35893s/12 iters), loss = 0.490475
I0428 14:26:45.256264 27120 solver.cpp:237]     Train net output #0: loss = 0.490475 (* 1 = 0.490475 loss)
I0428 14:26:45.256273 27120 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0428 14:26:47.418457 27120 blocking_queue.cpp:49] Waiting for data
I0428 14:26:50.632113 27120 solver.cpp:218] Iteration 4848 (2.2322 iter/s, 5.37587s/12 iters), loss = 0.421836
I0428 14:26:50.632166 27120 solver.cpp:237]     Train net output #0: loss = 0.421836 (* 1 = 0.421836 loss)
I0428 14:26:50.632181 27120 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0428 14:26:53.486764 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:26:56.011289 27120 solver.cpp:218] Iteration 4860 (2.23084 iter/s, 5.37915s/12 iters), loss = 0.406093
I0428 14:26:56.011335 27120 solver.cpp:237]     Train net output #0: loss = 0.406093 (* 1 = 0.406093 loss)
I0428 14:26:56.011344 27120 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0428 14:27:01.366000 27120 solver.cpp:218] Iteration 4872 (2.24103 iter/s, 5.35469s/12 iters), loss = 0.342154
I0428 14:27:01.366044 27120 solver.cpp:237]     Train net output #0: loss = 0.342154 (* 1 = 0.342154 loss)
I0428 14:27:01.366051 27120 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0428 14:27:06.645123 27120 solver.cpp:218] Iteration 4884 (2.27311 iter/s, 5.27911s/12 iters), loss = 0.364605
I0428 14:27:06.645164 27120 solver.cpp:237]     Train net output #0: loss = 0.364605 (* 1 = 0.364605 loss)
I0428 14:27:06.645172 27120 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0428 14:27:11.387079 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0428 14:27:14.950070 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0428 14:27:20.871291 27120 solver.cpp:330] Iteration 4896, Testing net (#0)
I0428 14:27:20.871428 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:27:23.555559 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:27:25.833166 27120 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0428 14:27:25.833195 27120 solver.cpp:397]     Test net output #1: loss = 2.44731 (* 1 = 2.44731 loss)
I0428 14:27:25.970093 27120 solver.cpp:218] Iteration 4896 (0.620955 iter/s, 19.3251s/12 iters), loss = 0.527489
I0428 14:27:25.970144 27120 solver.cpp:237]     Train net output #0: loss = 0.527489 (* 1 = 0.527489 loss)
I0428 14:27:25.970153 27120 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0428 14:27:30.413836 27120 solver.cpp:218] Iteration 4908 (2.70044 iter/s, 4.44372s/12 iters), loss = 0.463397
I0428 14:27:30.413869 27120 solver.cpp:237]     Train net output #0: loss = 0.463397 (* 1 = 0.463397 loss)
I0428 14:27:30.413877 27120 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0428 14:27:35.804360 27120 solver.cpp:218] Iteration 4920 (2.22613 iter/s, 5.39052s/12 iters), loss = 0.500921
I0428 14:27:35.804402 27120 solver.cpp:237]     Train net output #0: loss = 0.500921 (* 1 = 0.500921 loss)
I0428 14:27:35.804410 27120 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0428 14:27:41.207712 27120 solver.cpp:218] Iteration 4932 (2.22085 iter/s, 5.40334s/12 iters), loss = 0.368936
I0428 14:27:41.207756 27120 solver.cpp:237]     Train net output #0: loss = 0.368936 (* 1 = 0.368936 loss)
I0428 14:27:41.207765 27120 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0428 14:27:46.578572 27120 solver.cpp:218] Iteration 4944 (2.23429 iter/s, 5.37084s/12 iters), loss = 0.49078
I0428 14:27:46.578622 27120 solver.cpp:237]     Train net output #0: loss = 0.49078 (* 1 = 0.49078 loss)
I0428 14:27:46.578631 27120 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0428 14:27:51.707960 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:27:51.944483 27120 solver.cpp:218] Iteration 4956 (2.23635 iter/s, 5.36589s/12 iters), loss = 0.388628
I0428 14:27:51.944530 27120 solver.cpp:237]     Train net output #0: loss = 0.388628 (* 1 = 0.388628 loss)
I0428 14:27:51.944540 27120 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0428 14:27:57.217365 27120 solver.cpp:218] Iteration 4968 (2.27581 iter/s, 5.27286s/12 iters), loss = 0.42146
I0428 14:27:57.217408 27120 solver.cpp:237]     Train net output #0: loss = 0.42146 (* 1 = 0.42146 loss)
I0428 14:27:57.217417 27120 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0428 14:28:02.564746 27120 solver.cpp:218] Iteration 4980 (2.24409 iter/s, 5.34737s/12 iters), loss = 0.510852
I0428 14:28:02.564783 27120 solver.cpp:237]     Train net output #0: loss = 0.510852 (* 1 = 0.510852 loss)
I0428 14:28:02.564790 27120 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0428 14:28:07.917548 27120 solver.cpp:218] Iteration 4992 (2.24182 iter/s, 5.35279s/12 iters), loss = 0.443431
I0428 14:28:07.917598 27120 solver.cpp:237]     Train net output #0: loss = 0.443431 (* 1 = 0.443431 loss)
I0428 14:28:07.917606 27120 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0428 14:28:10.074771 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0428 14:28:13.855123 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0428 14:28:17.419107 27120 solver.cpp:330] Iteration 4998, Testing net (#0)
I0428 14:28:17.419126 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:28:20.192678 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:28:22.492622 27120 solver.cpp:397]     Test net output #0: accuracy = 0.487132
I0428 14:28:22.492789 27120 solver.cpp:397]     Test net output #1: loss = 2.47339 (* 1 = 2.47339 loss)
I0428 14:28:24.487684 27120 solver.cpp:218] Iteration 5004 (0.724191 iter/s, 16.5702s/12 iters), loss = 0.457068
I0428 14:28:24.487728 27120 solver.cpp:237]     Train net output #0: loss = 0.457068 (* 1 = 0.457068 loss)
I0428 14:28:24.487736 27120 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0428 14:28:29.785610 27120 solver.cpp:218] Iteration 5016 (2.26505 iter/s, 5.2979s/12 iters), loss = 0.278754
I0428 14:28:29.785656 27120 solver.cpp:237]     Train net output #0: loss = 0.278754 (* 1 = 0.278754 loss)
I0428 14:28:29.785665 27120 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0428 14:28:35.219041 27120 solver.cpp:218] Iteration 5028 (2.20856 iter/s, 5.43341s/12 iters), loss = 0.448999
I0428 14:28:35.219081 27120 solver.cpp:237]     Train net output #0: loss = 0.448999 (* 1 = 0.448999 loss)
I0428 14:28:35.219089 27120 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0428 14:28:40.600937 27120 solver.cpp:218] Iteration 5040 (2.2297 iter/s, 5.38188s/12 iters), loss = 0.362411
I0428 14:28:40.600986 27120 solver.cpp:237]     Train net output #0: loss = 0.362411 (* 1 = 0.362411 loss)
I0428 14:28:40.600994 27120 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0428 14:28:45.957329 27120 solver.cpp:218] Iteration 5052 (2.24032 iter/s, 5.35637s/12 iters), loss = 0.436257
I0428 14:28:45.957370 27120 solver.cpp:237]     Train net output #0: loss = 0.436257 (* 1 = 0.436257 loss)
I0428 14:28:45.957377 27120 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0428 14:28:48.022120 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:28:51.258731 27120 solver.cpp:218] Iteration 5064 (2.26356 iter/s, 5.30139s/12 iters), loss = 0.316964
I0428 14:28:51.258772 27120 solver.cpp:237]     Train net output #0: loss = 0.316964 (* 1 = 0.316964 loss)
I0428 14:28:51.258781 27120 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0428 14:28:56.599589 27120 solver.cpp:218] Iteration 5076 (2.24684 iter/s, 5.34084s/12 iters), loss = 0.311138
I0428 14:28:56.599727 27120 solver.cpp:237]     Train net output #0: loss = 0.311138 (* 1 = 0.311138 loss)
I0428 14:28:56.599737 27120 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0428 14:29:01.941468 27120 solver.cpp:218] Iteration 5088 (2.24645 iter/s, 5.34177s/12 iters), loss = 0.38289
I0428 14:29:01.941500 27120 solver.cpp:237]     Train net output #0: loss = 0.38289 (* 1 = 0.38289 loss)
I0428 14:29:01.941509 27120 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0428 14:29:06.704280 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0428 14:29:09.282912 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0428 14:29:12.093453 27120 solver.cpp:330] Iteration 5100, Testing net (#0)
I0428 14:29:12.093474 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:29:14.858276 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:29:17.236786 27120 solver.cpp:397]     Test net output #0: accuracy = 0.507353
I0428 14:29:17.236815 27120 solver.cpp:397]     Test net output #1: loss = 2.51627 (* 1 = 2.51627 loss)
I0428 14:29:17.367003 27120 solver.cpp:218] Iteration 5100 (0.777927 iter/s, 15.4256s/12 iters), loss = 0.440118
I0428 14:29:17.367045 27120 solver.cpp:237]     Train net output #0: loss = 0.440118 (* 1 = 0.440118 loss)
I0428 14:29:17.367054 27120 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0428 14:29:21.877562 27120 solver.cpp:218] Iteration 5112 (2.66044 iter/s, 4.51053s/12 iters), loss = 0.345147
I0428 14:29:21.877609 27120 solver.cpp:237]     Train net output #0: loss = 0.345147 (* 1 = 0.345147 loss)
I0428 14:29:21.877619 27120 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0428 14:29:27.172015 27120 solver.cpp:218] Iteration 5124 (2.26654 iter/s, 5.29442s/12 iters), loss = 0.48692
I0428 14:29:27.172184 27120 solver.cpp:237]     Train net output #0: loss = 0.48692 (* 1 = 0.48692 loss)
I0428 14:29:27.172200 27120 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0428 14:29:32.542351 27120 solver.cpp:218] Iteration 5136 (2.23455 iter/s, 5.3702s/12 iters), loss = 0.333315
I0428 14:29:32.542398 27120 solver.cpp:237]     Train net output #0: loss = 0.333315 (* 1 = 0.333315 loss)
I0428 14:29:32.542407 27120 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0428 14:29:37.725456 27120 solver.cpp:218] Iteration 5148 (2.31523 iter/s, 5.18308s/12 iters), loss = 0.291754
I0428 14:29:37.725503 27120 solver.cpp:237]     Train net output #0: loss = 0.291754 (* 1 = 0.291754 loss)
I0428 14:29:37.725512 27120 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0428 14:29:41.986227 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:29:43.019265 27120 solver.cpp:218] Iteration 5160 (2.26681 iter/s, 5.29379s/12 iters), loss = 0.397111
I0428 14:29:43.019310 27120 solver.cpp:237]     Train net output #0: loss = 0.397111 (* 1 = 0.397111 loss)
I0428 14:29:43.019320 27120 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0428 14:29:48.370041 27120 solver.cpp:218] Iteration 5172 (2.24268 iter/s, 5.35075s/12 iters), loss = 0.388447
I0428 14:29:48.370087 27120 solver.cpp:237]     Train net output #0: loss = 0.388447 (* 1 = 0.388447 loss)
I0428 14:29:48.370096 27120 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0428 14:29:53.712076 27120 solver.cpp:218] Iteration 5184 (2.24634 iter/s, 5.34201s/12 iters), loss = 0.390689
I0428 14:29:53.712117 27120 solver.cpp:237]     Train net output #0: loss = 0.390689 (* 1 = 0.390689 loss)
I0428 14:29:53.712126 27120 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0428 14:29:58.972339 27120 solver.cpp:218] Iteration 5196 (2.28126 iter/s, 5.26025s/12 iters), loss = 0.331227
I0428 14:29:58.972432 27120 solver.cpp:237]     Train net output #0: loss = 0.331227 (* 1 = 0.331227 loss)
I0428 14:29:58.972442 27120 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0428 14:30:01.142807 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0428 14:30:03.771728 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0428 14:30:05.976920 27120 solver.cpp:330] Iteration 5202, Testing net (#0)
I0428 14:30:05.976939 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:30:08.652024 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:30:11.059542 27120 solver.cpp:397]     Test net output #0: accuracy = 0.503676
I0428 14:30:11.059572 27120 solver.cpp:397]     Test net output #1: loss = 2.58643 (* 1 = 2.58643 loss)
I0428 14:30:13.041713 27120 solver.cpp:218] Iteration 5208 (0.852917 iter/s, 14.0694s/12 iters), loss = 0.412833
I0428 14:30:13.041760 27120 solver.cpp:237]     Train net output #0: loss = 0.412833 (* 1 = 0.412833 loss)
I0428 14:30:13.041769 27120 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0428 14:30:18.394001 27120 solver.cpp:218] Iteration 5220 (2.24204 iter/s, 5.35226s/12 iters), loss = 0.572243
I0428 14:30:18.394048 27120 solver.cpp:237]     Train net output #0: loss = 0.572243 (* 1 = 0.572243 loss)
I0428 14:30:18.394057 27120 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0428 14:30:23.746698 27120 solver.cpp:218] Iteration 5232 (2.24187 iter/s, 5.35267s/12 iters), loss = 0.283406
I0428 14:30:23.746735 27120 solver.cpp:237]     Train net output #0: loss = 0.283406 (* 1 = 0.283406 loss)
I0428 14:30:23.746743 27120 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0428 14:30:28.994458 27120 solver.cpp:218] Iteration 5244 (2.2867 iter/s, 5.24774s/12 iters), loss = 0.236799
I0428 14:30:28.994623 27120 solver.cpp:237]     Train net output #0: loss = 0.236799 (* 1 = 0.236799 loss)
I0428 14:30:28.994634 27120 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0428 14:30:34.359635 27120 solver.cpp:218] Iteration 5256 (2.2367 iter/s, 5.36504s/12 iters), loss = 0.268781
I0428 14:30:34.359678 27120 solver.cpp:237]     Train net output #0: loss = 0.268781 (* 1 = 0.268781 loss)
I0428 14:30:34.359688 27120 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0428 14:30:35.740547 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:30:39.732856 27120 solver.cpp:218] Iteration 5268 (2.23331 iter/s, 5.3732s/12 iters), loss = 0.331909
I0428 14:30:39.732900 27120 solver.cpp:237]     Train net output #0: loss = 0.331909 (* 1 = 0.331909 loss)
I0428 14:30:39.732909 27120 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0428 14:30:45.049465 27120 solver.cpp:218] Iteration 5280 (2.25708 iter/s, 5.31659s/12 iters), loss = 0.224694
I0428 14:30:45.049499 27120 solver.cpp:237]     Train net output #0: loss = 0.224694 (* 1 = 0.224694 loss)
I0428 14:30:45.049507 27120 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0428 14:30:50.403044 27120 solver.cpp:218] Iteration 5292 (2.2415 iter/s, 5.35357s/12 iters), loss = 0.355655
I0428 14:30:50.403084 27120 solver.cpp:237]     Train net output #0: loss = 0.355655 (* 1 = 0.355655 loss)
I0428 14:30:50.403093 27120 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0428 14:30:55.237182 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0428 14:30:57.857995 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0428 14:31:00.673681 27120 solver.cpp:330] Iteration 5304, Testing net (#0)
I0428 14:31:00.673779 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:31:03.296375 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:31:05.749706 27120 solver.cpp:397]     Test net output #0: accuracy = 0.493873
I0428 14:31:05.749742 27120 solver.cpp:397]     Test net output #1: loss = 2.57439 (* 1 = 2.57439 loss)
I0428 14:31:05.886291 27120 solver.cpp:218] Iteration 5304 (0.775029 iter/s, 15.4833s/12 iters), loss = 0.27593
I0428 14:31:05.886358 27120 solver.cpp:237]     Train net output #0: loss = 0.27593 (* 1 = 0.27593 loss)
I0428 14:31:05.886368 27120 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0428 14:31:10.350333 27120 solver.cpp:218] Iteration 5316 (2.68818 iter/s, 4.46399s/12 iters), loss = 0.431051
I0428 14:31:10.350397 27120 solver.cpp:237]     Train net output #0: loss = 0.431051 (* 1 = 0.431051 loss)
I0428 14:31:10.350409 27120 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0428 14:31:15.687016 27120 solver.cpp:218] Iteration 5328 (2.2486 iter/s, 5.33664s/12 iters), loss = 0.182102
I0428 14:31:15.687062 27120 solver.cpp:237]     Train net output #0: loss = 0.182102 (* 1 = 0.182102 loss)
I0428 14:31:15.687070 27120 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0428 14:31:20.955381 27120 solver.cpp:218] Iteration 5340 (2.27779 iter/s, 5.26827s/12 iters), loss = 0.215163
I0428 14:31:20.955579 27120 solver.cpp:237]     Train net output #0: loss = 0.215163 (* 1 = 0.215163 loss)
I0428 14:31:20.955612 27120 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0428 14:31:26.352929 27120 solver.cpp:218] Iteration 5352 (2.22329 iter/s, 5.39741s/12 iters), loss = 0.395428
I0428 14:31:26.352963 27120 solver.cpp:237]     Train net output #0: loss = 0.395428 (* 1 = 0.395428 loss)
I0428 14:31:26.352972 27120 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0428 14:31:30.026656 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:31:31.755196 27120 solver.cpp:218] Iteration 5364 (2.22129 iter/s, 5.40226s/12 iters), loss = 0.270395
I0428 14:31:31.755298 27120 solver.cpp:237]     Train net output #0: loss = 0.270395 (* 1 = 0.270395 loss)
I0428 14:31:31.755308 27120 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0428 14:31:37.115805 27120 solver.cpp:218] Iteration 5376 (2.23858 iter/s, 5.36053s/12 iters), loss = 0.414502
I0428 14:31:37.115845 27120 solver.cpp:237]     Train net output #0: loss = 0.414502 (* 1 = 0.414502 loss)
I0428 14:31:37.115854 27120 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0428 14:31:42.461141 27120 solver.cpp:218] Iteration 5388 (2.24496 iter/s, 5.34532s/12 iters), loss = 0.221183
I0428 14:31:42.461187 27120 solver.cpp:237]     Train net output #0: loss = 0.221183 (* 1 = 0.221183 loss)
I0428 14:31:42.461196 27120 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0428 14:31:47.788344 27120 solver.cpp:218] Iteration 5400 (2.2526 iter/s, 5.32718s/12 iters), loss = 0.322099
I0428 14:31:47.788385 27120 solver.cpp:237]     Train net output #0: loss = 0.322099 (* 1 = 0.322099 loss)
I0428 14:31:47.788393 27120 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0428 14:31:49.948750 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0428 14:31:53.345183 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0428 14:31:55.396641 27120 solver.cpp:330] Iteration 5406, Testing net (#0)
I0428 14:31:55.396662 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:31:57.978499 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:32:00.475504 27120 solver.cpp:397]     Test net output #0: accuracy = 0.5
I0428 14:32:00.475534 27120 solver.cpp:397]     Test net output #1: loss = 2.63773 (* 1 = 2.63773 loss)
I0428 14:32:02.468619 27120 solver.cpp:218] Iteration 5412 (0.817421 iter/s, 14.6803s/12 iters), loss = 0.321229
I0428 14:32:02.468775 27120 solver.cpp:237]     Train net output #0: loss = 0.321229 (* 1 = 0.321229 loss)
I0428 14:32:02.468786 27120 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0428 14:32:07.885892 27120 solver.cpp:218] Iteration 5424 (2.21519 iter/s, 5.41714s/12 iters), loss = 0.374514
I0428 14:32:07.885932 27120 solver.cpp:237]     Train net output #0: loss = 0.374514 (* 1 = 0.374514 loss)
I0428 14:32:07.885941 27120 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0428 14:32:13.222446 27120 solver.cpp:218] Iteration 5436 (2.24865 iter/s, 5.33653s/12 iters), loss = 0.289993
I0428 14:32:13.222491 27120 solver.cpp:237]     Train net output #0: loss = 0.289993 (* 1 = 0.289993 loss)
I0428 14:32:13.222501 27120 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0428 14:32:18.597196 27120 solver.cpp:218] Iteration 5448 (2.23267 iter/s, 5.37472s/12 iters), loss = 0.35664
I0428 14:32:18.597237 27120 solver.cpp:237]     Train net output #0: loss = 0.35664 (* 1 = 0.35664 loss)
I0428 14:32:18.597246 27120 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0428 14:32:23.995029 27120 solver.cpp:218] Iteration 5460 (2.22312 iter/s, 5.39781s/12 iters), loss = 0.42838
I0428 14:32:23.995082 27120 solver.cpp:237]     Train net output #0: loss = 0.42838 (* 1 = 0.42838 loss)
I0428 14:32:23.995095 27120 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0428 14:32:24.646358 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:32:29.458500 27120 solver.cpp:218] Iteration 5472 (2.19642 iter/s, 5.46344s/12 iters), loss = 0.260118
I0428 14:32:29.458545 27120 solver.cpp:237]     Train net output #0: loss = 0.260118 (* 1 = 0.260118 loss)
I0428 14:32:29.458554 27120 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0428 14:32:35.086700 27120 solver.cpp:218] Iteration 5484 (2.13213 iter/s, 5.62817s/12 iters), loss = 0.41083
I0428 14:32:35.086824 27120 solver.cpp:237]     Train net output #0: loss = 0.41083 (* 1 = 0.41083 loss)
I0428 14:32:35.086834 27120 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0428 14:32:40.769227 27120 solver.cpp:218] Iteration 5496 (2.11177 iter/s, 5.68243s/12 iters), loss = 0.302184
I0428 14:32:40.769271 27120 solver.cpp:237]     Train net output #0: loss = 0.302184 (* 1 = 0.302184 loss)
I0428 14:32:40.769280 27120 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0428 14:32:45.661769 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0428 14:32:48.276217 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0428 14:32:50.330399 27120 solver.cpp:330] Iteration 5508, Testing net (#0)
I0428 14:32:50.330420 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:32:52.880188 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:32:55.442706 27120 solver.cpp:397]     Test net output #0: accuracy = 0.5
I0428 14:32:55.442744 27120 solver.cpp:397]     Test net output #1: loss = 2.46021 (* 1 = 2.46021 loss)
I0428 14:32:55.579407 27120 solver.cpp:218] Iteration 5508 (0.810251 iter/s, 14.8102s/12 iters), loss = 0.17036
I0428 14:32:55.579454 27120 solver.cpp:237]     Train net output #0: loss = 0.17036 (* 1 = 0.17036 loss)
I0428 14:32:55.579463 27120 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0428 14:33:00.165067 27120 solver.cpp:218] Iteration 5520 (2.61688 iter/s, 4.58562s/12 iters), loss = 0.250351
I0428 14:33:00.165114 27120 solver.cpp:237]     Train net output #0: loss = 0.250351 (* 1 = 0.250351 loss)
I0428 14:33:00.165124 27120 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0428 14:33:02.748189 27120 blocking_queue.cpp:49] Waiting for data
I0428 14:33:05.427839 27120 solver.cpp:218] Iteration 5532 (2.28018 iter/s, 5.26274s/12 iters), loss = 0.204608
I0428 14:33:05.428007 27120 solver.cpp:237]     Train net output #0: loss = 0.204608 (* 1 = 0.204608 loss)
I0428 14:33:05.428020 27120 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0428 14:33:10.772748 27120 solver.cpp:218] Iteration 5544 (2.24519 iter/s, 5.34477s/12 iters), loss = 0.384984
I0428 14:33:10.772780 27120 solver.cpp:237]     Train net output #0: loss = 0.384984 (* 1 = 0.384984 loss)
I0428 14:33:10.772789 27120 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0428 14:33:16.193678 27120 solver.cpp:218] Iteration 5556 (2.21365 iter/s, 5.42092s/12 iters), loss = 0.19347
I0428 14:33:16.193715 27120 solver.cpp:237]     Train net output #0: loss = 0.19347 (* 1 = 0.19347 loss)
I0428 14:33:16.193724 27120 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0428 14:33:19.085670 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:33:21.584326 27120 solver.cpp:218] Iteration 5568 (2.22608 iter/s, 5.39063s/12 iters), loss = 0.333094
I0428 14:33:21.584367 27120 solver.cpp:237]     Train net output #0: loss = 0.333094 (* 1 = 0.333094 loss)
I0428 14:33:21.584374 27120 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0428 14:33:26.915874 27120 solver.cpp:218] Iteration 5580 (2.25076 iter/s, 5.33153s/12 iters), loss = 0.385409
I0428 14:33:26.915916 27120 solver.cpp:237]     Train net output #0: loss = 0.385409 (* 1 = 0.385409 loss)
I0428 14:33:26.915925 27120 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0428 14:33:32.326473 27120 solver.cpp:218] Iteration 5592 (2.21788 iter/s, 5.41056s/12 iters), loss = 0.320514
I0428 14:33:32.326550 27120 solver.cpp:237]     Train net output #0: loss = 0.320514 (* 1 = 0.320514 loss)
I0428 14:33:32.326565 27120 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0428 14:33:37.730437 27120 solver.cpp:218] Iteration 5604 (2.22062 iter/s, 5.4039s/12 iters), loss = 0.23051
I0428 14:33:37.730631 27120 solver.cpp:237]     Train net output #0: loss = 0.23051 (* 1 = 0.23051 loss)
I0428 14:33:37.730650 27120 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0428 14:33:39.880302 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0428 14:33:45.563091 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0428 14:33:47.954870 27120 solver.cpp:330] Iteration 5610, Testing net (#0)
I0428 14:33:47.954887 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:33:50.490418 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:33:53.083676 27120 solver.cpp:397]     Test net output #0: accuracy = 0.511029
I0428 14:33:53.083704 27120 solver.cpp:397]     Test net output #1: loss = 2.58545 (* 1 = 2.58545 loss)
I0428 14:33:55.004798 27120 solver.cpp:218] Iteration 5616 (0.694674 iter/s, 17.2743s/12 iters), loss = 0.321805
I0428 14:33:55.004845 27120 solver.cpp:237]     Train net output #0: loss = 0.321805 (* 1 = 0.321805 loss)
I0428 14:33:55.004854 27120 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0428 14:34:00.333701 27120 solver.cpp:218] Iteration 5628 (2.25188 iter/s, 5.32888s/12 iters), loss = 0.289819
I0428 14:34:00.333739 27120 solver.cpp:237]     Train net output #0: loss = 0.289819 (* 1 = 0.289819 loss)
I0428 14:34:00.333746 27120 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0428 14:34:05.604352 27120 solver.cpp:218] Iteration 5640 (2.27677 iter/s, 5.27063s/12 iters), loss = 0.21602
I0428 14:34:05.604398 27120 solver.cpp:237]     Train net output #0: loss = 0.21602 (* 1 = 0.21602 loss)
I0428 14:34:05.604406 27120 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0428 14:34:10.977294 27120 solver.cpp:218] Iteration 5652 (2.23342 iter/s, 5.37292s/12 iters), loss = 0.364579
I0428 14:34:10.977444 27120 solver.cpp:237]     Train net output #0: loss = 0.364579 (* 1 = 0.364579 loss)
I0428 14:34:10.977453 27120 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0428 14:34:16.159883 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:34:16.365093 27120 solver.cpp:218] Iteration 5664 (2.22731 iter/s, 5.38767s/12 iters), loss = 0.34248
I0428 14:34:16.365154 27120 solver.cpp:237]     Train net output #0: loss = 0.34248 (* 1 = 0.34248 loss)
I0428 14:34:16.365164 27120 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0428 14:34:21.757668 27120 solver.cpp:218] Iteration 5676 (2.2253 iter/s, 5.39254s/12 iters), loss = 0.238281
I0428 14:34:21.757709 27120 solver.cpp:237]     Train net output #0: loss = 0.238281 (* 1 = 0.238281 loss)
I0428 14:34:21.757717 27120 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0428 14:34:27.090888 27120 solver.cpp:218] Iteration 5688 (2.25006 iter/s, 5.3332s/12 iters), loss = 0.205188
I0428 14:34:27.090931 27120 solver.cpp:237]     Train net output #0: loss = 0.205188 (* 1 = 0.205188 loss)
I0428 14:34:27.090940 27120 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0428 14:34:32.427124 27120 solver.cpp:218] Iteration 5700 (2.24879 iter/s, 5.33621s/12 iters), loss = 0.270997
I0428 14:34:32.427166 27120 solver.cpp:237]     Train net output #0: loss = 0.270997 (* 1 = 0.270997 loss)
I0428 14:34:32.427175 27120 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0428 14:34:37.261948 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0428 14:34:41.561841 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0428 14:34:45.176024 27120 solver.cpp:330] Iteration 5712, Testing net (#0)
I0428 14:34:45.176043 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:34:47.623930 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:34:50.309365 27120 solver.cpp:397]     Test net output #0: accuracy = 0.508578
I0428 14:34:50.309413 27120 solver.cpp:397]     Test net output #1: loss = 2.52653 (* 1 = 2.52653 loss)
I0428 14:34:50.445483 27120 solver.cpp:218] Iteration 5712 (0.665985 iter/s, 18.0184s/12 iters), loss = 0.222713
I0428 14:34:50.445550 27120 solver.cpp:237]     Train net output #0: loss = 0.222713 (* 1 = 0.222713 loss)
I0428 14:34:50.445561 27120 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0428 14:34:54.849880 27120 solver.cpp:218] Iteration 5724 (2.72458 iter/s, 4.40435s/12 iters), loss = 0.227487
I0428 14:34:54.849927 27120 solver.cpp:237]     Train net output #0: loss = 0.227487 (* 1 = 0.227487 loss)
I0428 14:34:54.849936 27120 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0428 14:35:00.277048 27120 solver.cpp:218] Iteration 5736 (2.21111 iter/s, 5.42714s/12 iters), loss = 0.390846
I0428 14:35:00.277096 27120 solver.cpp:237]     Train net output #0: loss = 0.390846 (* 1 = 0.390846 loss)
I0428 14:35:00.277109 27120 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0428 14:35:05.744412 27120 solver.cpp:218] Iteration 5748 (2.19485 iter/s, 5.46734s/12 iters), loss = 0.250909
I0428 14:35:05.744462 27120 solver.cpp:237]     Train net output #0: loss = 0.250909 (* 1 = 0.250909 loss)
I0428 14:35:05.744470 27120 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0428 14:35:11.352907 27120 solver.cpp:218] Iteration 5760 (2.13962 iter/s, 5.60847s/12 iters), loss = 0.196451
I0428 14:35:11.352953 27120 solver.cpp:237]     Train net output #0: loss = 0.196451 (* 1 = 0.196451 loss)
I0428 14:35:11.352962 27120 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0428 14:35:13.440253 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:35:16.854543 27120 solver.cpp:218] Iteration 5772 (2.18118 iter/s, 5.50161s/12 iters), loss = 0.241991
I0428 14:35:16.854588 27120 solver.cpp:237]     Train net output #0: loss = 0.241991 (* 1 = 0.241991 loss)
I0428 14:35:16.854604 27120 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0428 14:35:22.135212 27120 solver.cpp:218] Iteration 5784 (2.27245 iter/s, 5.28064s/12 iters), loss = 0.166498
I0428 14:35:22.135262 27120 solver.cpp:237]     Train net output #0: loss = 0.166498 (* 1 = 0.166498 loss)
I0428 14:35:22.135272 27120 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0428 14:35:27.548549 27120 solver.cpp:218] Iteration 5796 (2.21676 iter/s, 5.41331s/12 iters), loss = 0.248495
I0428 14:35:27.548597 27120 solver.cpp:237]     Train net output #0: loss = 0.248495 (* 1 = 0.248495 loss)
I0428 14:35:27.548606 27120 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0428 14:35:32.915022 27120 solver.cpp:218] Iteration 5808 (2.23612 iter/s, 5.36644s/12 iters), loss = 0.318035
I0428 14:35:32.915073 27120 solver.cpp:237]     Train net output #0: loss = 0.318035 (* 1 = 0.318035 loss)
I0428 14:35:32.915081 27120 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0428 14:35:35.113157 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0428 14:35:37.704071 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0428 14:35:41.256186 27120 solver.cpp:330] Iteration 5814, Testing net (#0)
I0428 14:35:41.256204 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:35:43.678429 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:35:46.373438 27120 solver.cpp:397]     Test net output #0: accuracy = 0.511642
I0428 14:35:46.373481 27120 solver.cpp:397]     Test net output #1: loss = 2.58108 (* 1 = 2.58108 loss)
I0428 14:35:48.458349 27120 solver.cpp:218] Iteration 5820 (0.772033 iter/s, 15.5434s/12 iters), loss = 0.20417
I0428 14:35:48.458389 27120 solver.cpp:237]     Train net output #0: loss = 0.20417 (* 1 = 0.20417 loss)
I0428 14:35:48.458397 27120 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0428 14:35:53.972744 27120 solver.cpp:218] Iteration 5832 (2.17613 iter/s, 5.51438s/12 iters), loss = 0.240968
I0428 14:35:53.972788 27120 solver.cpp:237]     Train net output #0: loss = 0.240968 (* 1 = 0.240968 loss)
I0428 14:35:53.972797 27120 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0428 14:35:59.374568 27120 solver.cpp:218] Iteration 5844 (2.22148 iter/s, 5.4018s/12 iters), loss = 0.264084
I0428 14:35:59.374611 27120 solver.cpp:237]     Train net output #0: loss = 0.264084 (* 1 = 0.264084 loss)
I0428 14:35:59.374620 27120 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0428 14:36:04.714237 27120 solver.cpp:218] Iteration 5856 (2.24734 iter/s, 5.33964s/12 iters), loss = 0.12427
I0428 14:36:04.714284 27120 solver.cpp:237]     Train net output #0: loss = 0.12427 (* 1 = 0.12427 loss)
I0428 14:36:04.714293 27120 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0428 14:36:09.201692 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:36:10.074086 27120 solver.cpp:218] Iteration 5868 (2.23888 iter/s, 5.35982s/12 iters), loss = 0.318175
I0428 14:36:10.074131 27120 solver.cpp:237]     Train net output #0: loss = 0.318175 (* 1 = 0.318175 loss)
I0428 14:36:10.074141 27120 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0428 14:36:15.377507 27120 solver.cpp:218] Iteration 5880 (2.2627 iter/s, 5.3034s/12 iters), loss = 0.232056
I0428 14:36:15.377651 27120 solver.cpp:237]     Train net output #0: loss = 0.232056 (* 1 = 0.232056 loss)
I0428 14:36:15.377662 27120 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0428 14:36:20.710192 27120 solver.cpp:218] Iteration 5892 (2.25032 iter/s, 5.33257s/12 iters), loss = 0.134458
I0428 14:36:20.710233 27120 solver.cpp:237]     Train net output #0: loss = 0.134458 (* 1 = 0.134458 loss)
I0428 14:36:20.710242 27120 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0428 14:36:26.043938 27120 solver.cpp:218] Iteration 5904 (2.24983 iter/s, 5.33372s/12 iters), loss = 0.271005
I0428 14:36:26.043982 27120 solver.cpp:237]     Train net output #0: loss = 0.271005 (* 1 = 0.271005 loss)
I0428 14:36:26.043990 27120 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0428 14:36:30.886791 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0428 14:36:34.474165 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0428 14:36:36.519371 27120 solver.cpp:330] Iteration 5916, Testing net (#0)
I0428 14:36:36.519392 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:36:38.883426 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:36:41.692093 27120 solver.cpp:397]     Test net output #0: accuracy = 0.523897
I0428 14:36:41.692133 27120 solver.cpp:397]     Test net output #1: loss = 2.54663 (* 1 = 2.54663 loss)
I0428 14:36:41.827132 27120 solver.cpp:218] Iteration 5916 (0.760301 iter/s, 15.7832s/12 iters), loss = 0.150627
I0428 14:36:41.827183 27120 solver.cpp:237]     Train net output #0: loss = 0.150627 (* 1 = 0.150627 loss)
I0428 14:36:41.827191 27120 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0428 14:36:46.569195 27120 solver.cpp:218] Iteration 5928 (2.53056 iter/s, 4.74203s/12 iters), loss = 0.0758217
I0428 14:36:46.569334 27120 solver.cpp:237]     Train net output #0: loss = 0.0758218 (* 1 = 0.0758218 loss)
I0428 14:36:46.569345 27120 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0428 14:36:52.118367 27120 solver.cpp:218] Iteration 5940 (2.16253 iter/s, 5.54906s/12 iters), loss = 0.122082
I0428 14:36:52.118410 27120 solver.cpp:237]     Train net output #0: loss = 0.122082 (* 1 = 0.122082 loss)
I0428 14:36:52.118419 27120 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0428 14:36:57.454622 27120 solver.cpp:218] Iteration 5952 (2.24878 iter/s, 5.33623s/12 iters), loss = 0.171202
I0428 14:36:57.454669 27120 solver.cpp:237]     Train net output #0: loss = 0.171202 (* 1 = 0.171202 loss)
I0428 14:36:57.454677 27120 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0428 14:37:02.718945 27120 solver.cpp:218] Iteration 5964 (2.27951 iter/s, 5.2643s/12 iters), loss = 0.22075
I0428 14:37:02.718993 27120 solver.cpp:237]     Train net output #0: loss = 0.22075 (* 1 = 0.22075 loss)
I0428 14:37:02.719002 27120 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0428 14:37:04.127053 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:37:08.093948 27120 solver.cpp:218] Iteration 5976 (2.23257 iter/s, 5.37497s/12 iters), loss = 0.434106
I0428 14:37:08.093993 27120 solver.cpp:237]     Train net output #0: loss = 0.434106 (* 1 = 0.434106 loss)
I0428 14:37:08.094002 27120 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0428 14:37:13.436712 27120 solver.cpp:218] Iteration 5988 (2.24604 iter/s, 5.34274s/12 iters), loss = 0.161145
I0428 14:37:13.436756 27120 solver.cpp:237]     Train net output #0: loss = 0.161145 (* 1 = 0.161145 loss)
I0428 14:37:13.436766 27120 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0428 14:37:18.787633 27120 solver.cpp:218] Iteration 6000 (2.24261 iter/s, 5.3509s/12 iters), loss = 0.304933
I0428 14:37:18.787760 27120 solver.cpp:237]     Train net output #0: loss = 0.304933 (* 1 = 0.304933 loss)
I0428 14:37:18.787770 27120 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0428 14:37:24.132087 27120 solver.cpp:218] Iteration 6012 (2.24536 iter/s, 5.34435s/12 iters), loss = 0.214789
I0428 14:37:24.132131 27120 solver.cpp:237]     Train net output #0: loss = 0.214789 (* 1 = 0.214789 loss)
I0428 14:37:24.132140 27120 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0428 14:37:26.275283 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0428 14:37:29.336328 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0428 14:37:32.623698 27120 solver.cpp:330] Iteration 6018, Testing net (#0)
I0428 14:37:32.623719 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:37:34.932135 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:37:37.732769 27120 solver.cpp:397]     Test net output #0: accuracy = 0.517157
I0428 14:37:37.732798 27120 solver.cpp:397]     Test net output #1: loss = 2.47493 (* 1 = 2.47493 loss)
I0428 14:37:39.782685 27120 solver.cpp:218] Iteration 6024 (0.766742 iter/s, 15.6506s/12 iters), loss = 0.211083
I0428 14:37:39.782742 27120 solver.cpp:237]     Train net output #0: loss = 0.211083 (* 1 = 0.211083 loss)
I0428 14:37:39.782752 27120 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0428 14:37:45.174023 27120 solver.cpp:218] Iteration 6036 (2.22581 iter/s, 5.3913s/12 iters), loss = 0.190631
I0428 14:37:45.174067 27120 solver.cpp:237]     Train net output #0: loss = 0.190631 (* 1 = 0.190631 loss)
I0428 14:37:45.174073 27120 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0428 14:37:50.500516 27120 solver.cpp:218] Iteration 6048 (2.2529 iter/s, 5.32647s/12 iters), loss = 0.27776
I0428 14:37:50.500670 27120 solver.cpp:237]     Train net output #0: loss = 0.27776 (* 1 = 0.27776 loss)
I0428 14:37:50.500679 27120 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0428 14:37:55.936101 27120 solver.cpp:218] Iteration 6060 (2.20773 iter/s, 5.43545s/12 iters), loss = 0.286955
I0428 14:37:55.936147 27120 solver.cpp:237]     Train net output #0: loss = 0.286955 (* 1 = 0.286955 loss)
I0428 14:37:55.936156 27120 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0428 14:37:59.653007 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:38:01.319571 27120 solver.cpp:218] Iteration 6072 (2.22906 iter/s, 5.38343s/12 iters), loss = 0.166247
I0428 14:38:01.319635 27120 solver.cpp:237]     Train net output #0: loss = 0.166247 (* 1 = 0.166247 loss)
I0428 14:38:01.319648 27120 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0428 14:38:06.608569 27120 solver.cpp:218] Iteration 6084 (2.26888 iter/s, 5.28896s/12 iters), loss = 0.223155
I0428 14:38:06.608613 27120 solver.cpp:237]     Train net output #0: loss = 0.223155 (* 1 = 0.223155 loss)
I0428 14:38:06.608620 27120 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0428 14:38:11.949988 27120 solver.cpp:218] Iteration 6096 (2.2466 iter/s, 5.34139s/12 iters), loss = 0.150007
I0428 14:38:11.950031 27120 solver.cpp:237]     Train net output #0: loss = 0.150007 (* 1 = 0.150007 loss)
I0428 14:38:11.950040 27120 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0428 14:38:17.366219 27120 solver.cpp:218] Iteration 6108 (2.21557 iter/s, 5.41621s/12 iters), loss = 0.17202
I0428 14:38:17.366267 27120 solver.cpp:237]     Train net output #0: loss = 0.17202 (* 1 = 0.17202 loss)
I0428 14:38:17.366276 27120 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0428 14:38:22.198160 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0428 14:38:24.792088 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0428 14:38:26.838093 27120 solver.cpp:330] Iteration 6120, Testing net (#0)
I0428 14:38:26.838119 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:38:28.975669 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:38:31.678033 27120 solver.cpp:397]     Test net output #0: accuracy = 0.53125
I0428 14:38:31.678081 27120 solver.cpp:397]     Test net output #1: loss = 2.53157 (* 1 = 2.53157 loss)
I0428 14:38:31.809621 27120 solver.cpp:218] Iteration 6120 (0.830827 iter/s, 14.4434s/12 iters), loss = 0.220884
I0428 14:38:31.809664 27120 solver.cpp:237]     Train net output #0: loss = 0.220884 (* 1 = 0.220884 loss)
I0428 14:38:31.809671 27120 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0428 14:38:36.285497 27120 solver.cpp:218] Iteration 6132 (2.68106 iter/s, 4.47584s/12 iters), loss = 0.194372
I0428 14:38:36.285542 27120 solver.cpp:237]     Train net output #0: loss = 0.194372 (* 1 = 0.194372 loss)
I0428 14:38:36.285550 27120 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0428 14:38:41.633325 27120 solver.cpp:218] Iteration 6144 (2.24391 iter/s, 5.34781s/12 iters), loss = 0.176536
I0428 14:38:41.633365 27120 solver.cpp:237]     Train net output #0: loss = 0.176536 (* 1 = 0.176536 loss)
I0428 14:38:41.633374 27120 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0428 14:38:46.992134 27120 solver.cpp:218] Iteration 6156 (2.23931 iter/s, 5.35879s/12 iters), loss = 0.131095
I0428 14:38:46.992178 27120 solver.cpp:237]     Train net output #0: loss = 0.131095 (* 1 = 0.131095 loss)
I0428 14:38:46.992187 27120 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0428 14:38:52.347390 27120 solver.cpp:218] Iteration 6168 (2.2408 iter/s, 5.35523s/12 iters), loss = 0.169636
I0428 14:38:52.347553 27120 solver.cpp:237]     Train net output #0: loss = 0.169636 (* 1 = 0.169636 loss)
I0428 14:38:52.347563 27120 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0428 14:38:52.966544 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:38:57.799423 27120 solver.cpp:218] Iteration 6180 (2.20108 iter/s, 5.45188s/12 iters), loss = 0.209952
I0428 14:38:57.799492 27120 solver.cpp:237]     Train net output #0: loss = 0.209952 (* 1 = 0.209952 loss)
I0428 14:38:57.799508 27120 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0428 14:39:03.170706 27120 solver.cpp:218] Iteration 6192 (2.23412 iter/s, 5.37123s/12 iters), loss = 0.187365
I0428 14:39:03.170753 27120 solver.cpp:237]     Train net output #0: loss = 0.187365 (* 1 = 0.187365 loss)
I0428 14:39:03.170763 27120 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0428 14:39:08.510741 27120 solver.cpp:218] Iteration 6204 (2.24719 iter/s, 5.34001s/12 iters), loss = 0.2505
I0428 14:39:08.510777 27120 solver.cpp:237]     Train net output #0: loss = 0.2505 (* 1 = 0.2505 loss)
I0428 14:39:08.510785 27120 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0428 14:39:13.775446 27120 solver.cpp:218] Iteration 6216 (2.27934 iter/s, 5.26469s/12 iters), loss = 0.229091
I0428 14:39:13.775487 27120 solver.cpp:237]     Train net output #0: loss = 0.229091 (* 1 = 0.229091 loss)
I0428 14:39:13.775496 27120 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0428 14:39:15.911249 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0428 14:39:20.189693 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0428 14:39:24.228739 27120 solver.cpp:330] Iteration 6222, Testing net (#0)
I0428 14:39:24.228790 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:39:26.441953 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:39:27.956693 27120 blocking_queue.cpp:49] Waiting for data
I0428 14:39:29.324400 27120 solver.cpp:397]     Test net output #0: accuracy = 0.51348
I0428 14:39:29.324429 27120 solver.cpp:397]     Test net output #1: loss = 2.62805 (* 1 = 2.62805 loss)
I0428 14:39:31.240746 27120 solver.cpp:218] Iteration 6228 (0.687074 iter/s, 17.4654s/12 iters), loss = 0.148691
I0428 14:39:31.240808 27120 solver.cpp:237]     Train net output #0: loss = 0.148691 (* 1 = 0.148691 loss)
I0428 14:39:31.240823 27120 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0428 14:39:36.564922 27120 solver.cpp:218] Iteration 6240 (2.25388 iter/s, 5.32414s/12 iters), loss = 0.172955
I0428 14:39:36.564961 27120 solver.cpp:237]     Train net output #0: loss = 0.172955 (* 1 = 0.172955 loss)
I0428 14:39:36.564970 27120 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0428 14:39:41.902235 27120 solver.cpp:218] Iteration 6252 (2.24833 iter/s, 5.33729s/12 iters), loss = 0.164414
I0428 14:39:41.902278 27120 solver.cpp:237]     Train net output #0: loss = 0.164414 (* 1 = 0.164414 loss)
I0428 14:39:41.902287 27120 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0428 14:39:47.246258 27120 solver.cpp:218] Iteration 6264 (2.24551 iter/s, 5.344s/12 iters), loss = 0.180492
I0428 14:39:47.246299 27120 solver.cpp:237]     Train net output #0: loss = 0.180492 (* 1 = 0.180492 loss)
I0428 14:39:47.246306 27120 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0428 14:39:50.163118 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:39:52.628542 27120 solver.cpp:218] Iteration 6276 (2.22954 iter/s, 5.38227s/12 iters), loss = 0.115522
I0428 14:39:52.628582 27120 solver.cpp:237]     Train net output #0: loss = 0.115522 (* 1 = 0.115522 loss)
I0428 14:39:52.628590 27120 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0428 14:39:57.995766 27120 solver.cpp:218] Iteration 6288 (2.2358 iter/s, 5.36721s/12 iters), loss = 0.34184
I0428 14:39:57.995889 27120 solver.cpp:237]     Train net output #0: loss = 0.34184 (* 1 = 0.34184 loss)
I0428 14:39:57.995899 27120 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0428 14:40:03.439815 27120 solver.cpp:218] Iteration 6300 (2.20428 iter/s, 5.44394s/12 iters), loss = 0.165517
I0428 14:40:03.439860 27120 solver.cpp:237]     Train net output #0: loss = 0.165517 (* 1 = 0.165517 loss)
I0428 14:40:03.439869 27120 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0428 14:40:08.887899 27120 solver.cpp:218] Iteration 6312 (2.20262 iter/s, 5.44806s/12 iters), loss = 0.0974824
I0428 14:40:08.887944 27120 solver.cpp:237]     Train net output #0: loss = 0.0974824 (* 1 = 0.0974824 loss)
I0428 14:40:08.887953 27120 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0428 14:40:13.638749 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0428 14:40:17.415434 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0428 14:40:21.109066 27120 solver.cpp:330] Iteration 6324, Testing net (#0)
I0428 14:40:21.109086 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:40:23.280421 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:40:26.197535 27120 solver.cpp:397]     Test net output #0: accuracy = 0.51777
I0428 14:40:26.197569 27120 solver.cpp:397]     Test net output #1: loss = 2.53339 (* 1 = 2.53339 loss)
I0428 14:40:26.335911 27120 solver.cpp:218] Iteration 6324 (0.687755 iter/s, 17.4481s/12 iters), loss = 0.0810863
I0428 14:40:26.335980 27120 solver.cpp:237]     Train net output #0: loss = 0.0810863 (* 1 = 0.0810863 loss)
I0428 14:40:26.335989 27120 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0428 14:40:30.779325 27120 solver.cpp:218] Iteration 6336 (2.70066 iter/s, 4.44336s/12 iters), loss = 0.0782516
I0428 14:40:30.779436 27120 solver.cpp:237]     Train net output #0: loss = 0.0782516 (* 1 = 0.0782516 loss)
I0428 14:40:30.779445 27120 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0428 14:40:36.116951 27120 solver.cpp:218] Iteration 6348 (2.24823 iter/s, 5.33754s/12 iters), loss = 0.13692
I0428 14:40:36.116991 27120 solver.cpp:237]     Train net output #0: loss = 0.13692 (* 1 = 0.13692 loss)
I0428 14:40:36.116999 27120 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0428 14:40:41.470469 27120 solver.cpp:218] Iteration 6360 (2.24153 iter/s, 5.3535s/12 iters), loss = 0.262368
I0428 14:40:41.470505 27120 solver.cpp:237]     Train net output #0: loss = 0.262368 (* 1 = 0.262368 loss)
I0428 14:40:41.470513 27120 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0428 14:40:46.644876 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:40:46.819669 27120 solver.cpp:218] Iteration 6372 (2.24334 iter/s, 5.34918s/12 iters), loss = 0.21526
I0428 14:40:46.819717 27120 solver.cpp:237]     Train net output #0: loss = 0.21526 (* 1 = 0.21526 loss)
I0428 14:40:46.819726 27120 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0428 14:40:52.166941 27120 solver.cpp:218] Iteration 6384 (2.24415 iter/s, 5.34724s/12 iters), loss = 0.192341
I0428 14:40:52.166985 27120 solver.cpp:237]     Train net output #0: loss = 0.192341 (* 1 = 0.192341 loss)
I0428 14:40:52.166994 27120 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0428 14:40:57.499992 27120 solver.cpp:218] Iteration 6396 (2.25013 iter/s, 5.33302s/12 iters), loss = 0.154494
I0428 14:40:57.500041 27120 solver.cpp:237]     Train net output #0: loss = 0.154494 (* 1 = 0.154494 loss)
I0428 14:40:57.500051 27120 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0428 14:41:02.838431 27120 solver.cpp:218] Iteration 6408 (2.24786 iter/s, 5.33841s/12 iters), loss = 0.114671
I0428 14:41:02.838601 27120 solver.cpp:237]     Train net output #0: loss = 0.114671 (* 1 = 0.114671 loss)
I0428 14:41:02.838611 27120 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0428 14:41:08.231004 27120 solver.cpp:218] Iteration 6420 (2.22534 iter/s, 5.39242s/12 iters), loss = 0.154434
I0428 14:41:08.231045 27120 solver.cpp:237]     Train net output #0: loss = 0.154434 (* 1 = 0.154434 loss)
I0428 14:41:08.231055 27120 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0428 14:41:10.434442 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0428 14:41:16.001965 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0428 14:41:19.347079 27120 solver.cpp:330] Iteration 6426, Testing net (#0)
I0428 14:41:19.347100 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:41:21.488162 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:41:24.502169 27120 solver.cpp:397]     Test net output #0: accuracy = 0.530637
I0428 14:41:24.502197 27120 solver.cpp:397]     Test net output #1: loss = 2.49917 (* 1 = 2.49917 loss)
I0428 14:41:26.504513 27120 solver.cpp:218] Iteration 6432 (0.656686 iter/s, 18.2736s/12 iters), loss = 0.13704
I0428 14:41:26.504560 27120 solver.cpp:237]     Train net output #0: loss = 0.13704 (* 1 = 0.13704 loss)
I0428 14:41:26.504570 27120 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0428 14:41:31.826503 27120 solver.cpp:218] Iteration 6444 (2.25481 iter/s, 5.32195s/12 iters), loss = 0.156031
I0428 14:41:31.826551 27120 solver.cpp:237]     Train net output #0: loss = 0.156031 (* 1 = 0.156031 loss)
I0428 14:41:31.826560 27120 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0428 14:41:37.163486 27120 solver.cpp:218] Iteration 6456 (2.24848 iter/s, 5.33695s/12 iters), loss = 0.349405
I0428 14:41:37.163632 27120 solver.cpp:237]     Train net output #0: loss = 0.349405 (* 1 = 0.349405 loss)
I0428 14:41:37.163642 27120 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0428 14:41:42.584951 27120 solver.cpp:218] Iteration 6468 (2.21348 iter/s, 5.42133s/12 iters), loss = 0.217509
I0428 14:41:42.584997 27120 solver.cpp:237]     Train net output #0: loss = 0.217509 (* 1 = 0.217509 loss)
I0428 14:41:42.585006 27120 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0428 14:41:44.707079 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:41:47.963407 27120 solver.cpp:218] Iteration 6480 (2.23113 iter/s, 5.37843s/12 iters), loss = 0.230252
I0428 14:41:47.963447 27120 solver.cpp:237]     Train net output #0: loss = 0.230252 (* 1 = 0.230252 loss)
I0428 14:41:47.963456 27120 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0428 14:41:53.295939 27120 solver.cpp:218] Iteration 6492 (2.25035 iter/s, 5.3325s/12 iters), loss = 0.13841
I0428 14:41:53.295981 27120 solver.cpp:237]     Train net output #0: loss = 0.13841 (* 1 = 0.13841 loss)
I0428 14:41:53.295990 27120 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0428 14:41:58.554793 27120 solver.cpp:218] Iteration 6504 (2.28188 iter/s, 5.25883s/12 iters), loss = 0.139531
I0428 14:41:58.554836 27120 solver.cpp:237]     Train net output #0: loss = 0.139531 (* 1 = 0.139531 loss)
I0428 14:41:58.554843 27120 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0428 14:42:03.908324 27120 solver.cpp:218] Iteration 6516 (2.24152 iter/s, 5.3535s/12 iters), loss = 0.209277
I0428 14:42:03.908371 27120 solver.cpp:237]     Train net output #0: loss = 0.209277 (* 1 = 0.209277 loss)
I0428 14:42:03.908380 27120 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0428 14:42:08.707273 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0428 14:42:16.949822 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0428 14:42:19.554705 27120 solver.cpp:330] Iteration 6528, Testing net (#0)
I0428 14:42:19.554733 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:42:21.622350 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:42:24.630941 27120 solver.cpp:397]     Test net output #0: accuracy = 0.534926
I0428 14:42:24.630971 27120 solver.cpp:397]     Test net output #1: loss = 2.50634 (* 1 = 2.50634 loss)
I0428 14:42:24.763638 27120 solver.cpp:218] Iteration 6528 (0.575391 iter/s, 20.8554s/12 iters), loss = 0.102654
I0428 14:42:24.763689 27120 solver.cpp:237]     Train net output #0: loss = 0.102654 (* 1 = 0.102654 loss)
I0428 14:42:24.763698 27120 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0428 14:42:29.214210 27120 solver.cpp:218] Iteration 6540 (2.6963 iter/s, 4.45054s/12 iters), loss = 0.262452
I0428 14:42:29.214249 27120 solver.cpp:237]     Train net output #0: loss = 0.262452 (* 1 = 0.262452 loss)
I0428 14:42:29.214257 27120 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0428 14:42:34.548713 27120 solver.cpp:218] Iteration 6552 (2.24952 iter/s, 5.33448s/12 iters), loss = 0.074806
I0428 14:42:34.548760 27120 solver.cpp:237]     Train net output #0: loss = 0.074806 (* 1 = 0.074806 loss)
I0428 14:42:34.548769 27120 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0428 14:42:39.811266 27120 solver.cpp:218] Iteration 6564 (2.28028 iter/s, 5.26252s/12 iters), loss = 0.113411
I0428 14:42:39.811406 27120 solver.cpp:237]     Train net output #0: loss = 0.113411 (* 1 = 0.113411 loss)
I0428 14:42:39.811416 27120 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0428 14:42:44.337903 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:42:45.182739 27120 solver.cpp:218] Iteration 6576 (2.23408 iter/s, 5.37135s/12 iters), loss = 0.145067
I0428 14:42:45.182782 27120 solver.cpp:237]     Train net output #0: loss = 0.145067 (* 1 = 0.145067 loss)
I0428 14:42:45.182791 27120 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0428 14:42:50.369846 27120 solver.cpp:218] Iteration 6588 (2.31344 iter/s, 5.18708s/12 iters), loss = 0.161861
I0428 14:42:50.369894 27120 solver.cpp:237]     Train net output #0: loss = 0.161861 (* 1 = 0.161861 loss)
I0428 14:42:50.369902 27120 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0428 14:42:55.705443 27120 solver.cpp:218] Iteration 6600 (2.24906 iter/s, 5.33557s/12 iters), loss = 0.12048
I0428 14:42:55.705484 27120 solver.cpp:237]     Train net output #0: loss = 0.12048 (* 1 = 0.12048 loss)
I0428 14:42:55.705492 27120 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0428 14:43:00.967033 27120 solver.cpp:218] Iteration 6612 (2.28069 iter/s, 5.26156s/12 iters), loss = 0.112365
I0428 14:43:00.967072 27120 solver.cpp:237]     Train net output #0: loss = 0.112365 (* 1 = 0.112365 loss)
I0428 14:43:00.967080 27120 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0428 14:43:06.239056 27120 solver.cpp:218] Iteration 6624 (2.27618 iter/s, 5.272s/12 iters), loss = 0.112599
I0428 14:43:06.239097 27120 solver.cpp:237]     Train net output #0: loss = 0.112599 (* 1 = 0.112599 loss)
I0428 14:43:06.239106 27120 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0428 14:43:08.389304 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0428 14:43:13.325229 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0428 14:43:16.360440 27120 solver.cpp:330] Iteration 6630, Testing net (#0)
I0428 14:43:16.360457 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:43:18.411648 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:43:21.463194 27120 solver.cpp:397]     Test net output #0: accuracy = 0.536765
I0428 14:43:21.463222 27120 solver.cpp:397]     Test net output #1: loss = 2.52856 (* 1 = 2.52856 loss)
I0428 14:43:23.456800 27120 solver.cpp:218] Iteration 6636 (0.696953 iter/s, 17.2178s/12 iters), loss = 0.0980245
I0428 14:43:23.456845 27120 solver.cpp:237]     Train net output #0: loss = 0.0980245 (* 1 = 0.0980245 loss)
I0428 14:43:23.456853 27120 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0428 14:43:28.787358 27120 solver.cpp:218] Iteration 6648 (2.25118 iter/s, 5.33053s/12 iters), loss = 0.115428
I0428 14:43:28.787403 27120 solver.cpp:237]     Train net output #0: loss = 0.115428 (* 1 = 0.115428 loss)
I0428 14:43:28.787411 27120 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0428 14:43:34.126572 27120 solver.cpp:218] Iteration 6660 (2.24753 iter/s, 5.33919s/12 iters), loss = 0.0739782
I0428 14:43:34.126617 27120 solver.cpp:237]     Train net output #0: loss = 0.0739782 (* 1 = 0.0739782 loss)
I0428 14:43:34.126626 27120 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0428 14:43:39.496552 27120 solver.cpp:218] Iteration 6672 (2.23466 iter/s, 5.36995s/12 iters), loss = 0.0857159
I0428 14:43:39.496598 27120 solver.cpp:237]     Train net output #0: loss = 0.0857159 (* 1 = 0.0857159 loss)
I0428 14:43:39.496606 27120 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0428 14:43:40.936487 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:43:45.033823 27120 solver.cpp:218] Iteration 6684 (2.16715 iter/s, 5.53724s/12 iters), loss = 0.133982
I0428 14:43:45.034001 27120 solver.cpp:237]     Train net output #0: loss = 0.133982 (* 1 = 0.133982 loss)
I0428 14:43:45.034014 27120 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0428 14:43:50.375316 27120 solver.cpp:218] Iteration 6696 (2.24663 iter/s, 5.34133s/12 iters), loss = 0.205559
I0428 14:43:50.375365 27120 solver.cpp:237]     Train net output #0: loss = 0.205559 (* 1 = 0.205559 loss)
I0428 14:43:50.375373 27120 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0428 14:43:55.720388 27120 solver.cpp:218] Iteration 6708 (2.24507 iter/s, 5.34504s/12 iters), loss = 0.118313
I0428 14:43:55.720433 27120 solver.cpp:237]     Train net output #0: loss = 0.118313 (* 1 = 0.118313 loss)
I0428 14:43:55.720441 27120 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0428 14:44:01.156854 27120 solver.cpp:218] Iteration 6720 (2.20733 iter/s, 5.43643s/12 iters), loss = 0.112162
I0428 14:44:01.156903 27120 solver.cpp:237]     Train net output #0: loss = 0.112162 (* 1 = 0.112162 loss)
I0428 14:44:01.156913 27120 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0428 14:44:05.995692 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0428 14:44:09.295574 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0428 14:44:14.042383 27120 solver.cpp:330] Iteration 6732, Testing net (#0)
I0428 14:44:14.042407 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:44:16.078534 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:44:19.184219 27120 solver.cpp:397]     Test net output #0: accuracy = 0.525123
I0428 14:44:19.184249 27120 solver.cpp:397]     Test net output #1: loss = 2.57073 (* 1 = 2.57073 loss)
I0428 14:44:19.317883 27120 solver.cpp:218] Iteration 6732 (0.660754 iter/s, 18.1611s/12 iters), loss = 0.294421
I0428 14:44:19.317947 27120 solver.cpp:237]     Train net output #0: loss = 0.294421 (* 1 = 0.294421 loss)
I0428 14:44:19.317957 27120 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0428 14:44:23.774353 27120 solver.cpp:218] Iteration 6744 (2.69274 iter/s, 4.45642s/12 iters), loss = 0.151834
I0428 14:44:23.774395 27120 solver.cpp:237]     Train net output #0: loss = 0.151834 (* 1 = 0.151834 loss)
I0428 14:44:23.774403 27120 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0428 14:44:29.065069 27120 solver.cpp:218] Iteration 6756 (2.26814 iter/s, 5.29069s/12 iters), loss = 0.0821355
I0428 14:44:29.065116 27120 solver.cpp:237]     Train net output #0: loss = 0.0821355 (* 1 = 0.0821355 loss)
I0428 14:44:29.065125 27120 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0428 14:44:34.439517 27120 solver.cpp:218] Iteration 6768 (2.2328 iter/s, 5.37441s/12 iters), loss = 0.081158
I0428 14:44:34.439566 27120 solver.cpp:237]     Train net output #0: loss = 0.081158 (* 1 = 0.081158 loss)
I0428 14:44:34.439576 27120 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0428 14:44:38.089565 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:44:39.721681 27120 solver.cpp:218] Iteration 6780 (2.27181 iter/s, 5.28213s/12 iters), loss = 0.261622
I0428 14:44:39.721721 27120 solver.cpp:237]     Train net output #0: loss = 0.261622 (* 1 = 0.261622 loss)
I0428 14:44:39.721729 27120 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0428 14:44:45.078768 27120 solver.cpp:218] Iteration 6792 (2.24004 iter/s, 5.35705s/12 iters), loss = 0.0744966
I0428 14:44:45.078833 27120 solver.cpp:237]     Train net output #0: loss = 0.0744966 (* 1 = 0.0744966 loss)
I0428 14:44:45.078847 27120 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0428 14:44:50.435775 27120 solver.cpp:218] Iteration 6804 (2.24008 iter/s, 5.35696s/12 iters), loss = 0.0971546
I0428 14:44:50.435945 27120 solver.cpp:237]     Train net output #0: loss = 0.0971546 (* 1 = 0.0971546 loss)
I0428 14:44:50.435954 27120 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0428 14:44:55.798811 27120 solver.cpp:218] Iteration 6816 (2.2376 iter/s, 5.36289s/12 iters), loss = 0.107148
I0428 14:44:55.798853 27120 solver.cpp:237]     Train net output #0: loss = 0.107148 (* 1 = 0.107148 loss)
I0428 14:44:55.798862 27120 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0428 14:45:01.151221 27120 solver.cpp:218] Iteration 6828 (2.24199 iter/s, 5.35239s/12 iters), loss = 0.0790228
I0428 14:45:01.151259 27120 solver.cpp:237]     Train net output #0: loss = 0.0790228 (* 1 = 0.0790228 loss)
I0428 14:45:01.151268 27120 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0428 14:45:03.324664 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0428 14:45:09.739068 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0428 14:45:12.537961 27120 solver.cpp:330] Iteration 6834, Testing net (#0)
I0428 14:45:12.537992 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:45:14.494086 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:45:17.648137 27120 solver.cpp:397]     Test net output #0: accuracy = 0.526348
I0428 14:45:17.648172 27120 solver.cpp:397]     Test net output #1: loss = 2.61853 (* 1 = 2.61853 loss)
I0428 14:45:19.645956 27120 solver.cpp:218] Iteration 6840 (0.648831 iter/s, 18.4948s/12 iters), loss = 0.090798
I0428 14:45:19.646003 27120 solver.cpp:237]     Train net output #0: loss = 0.090798 (* 1 = 0.090798 loss)
I0428 14:45:19.646011 27120 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0428 14:45:25.001582 27120 solver.cpp:218] Iteration 6852 (2.24065 iter/s, 5.3556s/12 iters), loss = 0.064706
I0428 14:45:25.001683 27120 solver.cpp:237]     Train net output #0: loss = 0.064706 (* 1 = 0.064706 loss)
I0428 14:45:25.001693 27120 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0428 14:45:30.361550 27120 solver.cpp:218] Iteration 6864 (2.23886 iter/s, 5.35988s/12 iters), loss = 0.061986
I0428 14:45:30.361598 27120 solver.cpp:237]     Train net output #0: loss = 0.061986 (* 1 = 0.061986 loss)
I0428 14:45:30.361606 27120 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0428 14:45:35.729950 27120 solver.cpp:218] Iteration 6876 (2.23532 iter/s, 5.36837s/12 iters), loss = 0.100131
I0428 14:45:35.729995 27120 solver.cpp:237]     Train net output #0: loss = 0.100131 (* 1 = 0.100131 loss)
I0428 14:45:35.730005 27120 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0428 14:45:36.381176 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:45:41.115501 27120 solver.cpp:218] Iteration 6888 (2.2282 iter/s, 5.38552s/12 iters), loss = 0.0636473
I0428 14:45:41.115546 27120 solver.cpp:237]     Train net output #0: loss = 0.0636474 (* 1 = 0.0636474 loss)
I0428 14:45:41.115556 27120 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0428 14:45:46.456666 27120 solver.cpp:218] Iteration 6900 (2.24671 iter/s, 5.34114s/12 iters), loss = 0.0853447
I0428 14:45:46.456708 27120 solver.cpp:237]     Train net output #0: loss = 0.0853447 (* 1 = 0.0853447 loss)
I0428 14:45:46.456718 27120 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0428 14:45:51.870463 27120 solver.cpp:218] Iteration 6912 (2.21657 iter/s, 5.41377s/12 iters), loss = 0.10181
I0428 14:45:51.870507 27120 solver.cpp:237]     Train net output #0: loss = 0.10181 (* 1 = 0.10181 loss)
I0428 14:45:51.870515 27120 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0428 14:45:57.155791 27120 solver.cpp:218] Iteration 6924 (2.27045 iter/s, 5.2853s/12 iters), loss = 0.0863651
I0428 14:45:57.155958 27120 solver.cpp:237]     Train net output #0: loss = 0.0863652 (* 1 = 0.0863652 loss)
I0428 14:45:57.155968 27120 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0428 14:46:01.921391 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0428 14:46:05.006122 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0428 14:46:09.102759 27120 solver.cpp:330] Iteration 6936, Testing net (#0)
I0428 14:46:09.102779 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:46:09.716382 27120 blocking_queue.cpp:49] Waiting for data
I0428 14:46:10.885601 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:46:13.901787 27120 solver.cpp:397]     Test net output #0: accuracy = 0.53799
I0428 14:46:13.901829 27120 solver.cpp:397]     Test net output #1: loss = 2.64943 (* 1 = 2.64943 loss)
I0428 14:46:14.038679 27120 solver.cpp:218] Iteration 6936 (0.710781 iter/s, 16.8828s/12 iters), loss = 0.101737
I0428 14:46:14.038724 27120 solver.cpp:237]     Train net output #0: loss = 0.101737 (* 1 = 0.101737 loss)
I0428 14:46:14.038733 27120 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0428 14:46:18.515401 27120 solver.cpp:218] Iteration 6948 (2.68055 iter/s, 4.47669s/12 iters), loss = 0.0857419
I0428 14:46:18.515442 27120 solver.cpp:237]     Train net output #0: loss = 0.0857419 (* 1 = 0.0857419 loss)
I0428 14:46:18.515451 27120 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0428 14:46:23.790179 27120 solver.cpp:218] Iteration 6960 (2.27499 iter/s, 5.27476s/12 iters), loss = 0.127744
I0428 14:46:23.790225 27120 solver.cpp:237]     Train net output #0: loss = 0.127744 (* 1 = 0.127744 loss)
I0428 14:46:23.790232 27120 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0428 14:46:29.199733 27120 solver.cpp:218] Iteration 6972 (2.21831 iter/s, 5.40953s/12 iters), loss = 0.144314
I0428 14:46:29.199865 27120 solver.cpp:237]     Train net output #0: loss = 0.144315 (* 1 = 0.144315 loss)
I0428 14:46:29.199875 27120 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0428 14:46:32.166726 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:46:34.627336 27120 solver.cpp:218] Iteration 6984 (2.21097 iter/s, 5.42749s/12 iters), loss = 0.0981044
I0428 14:46:34.627384 27120 solver.cpp:237]     Train net output #0: loss = 0.0981044 (* 1 = 0.0981044 loss)
I0428 14:46:34.627393 27120 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0428 14:46:39.998899 27120 solver.cpp:218] Iteration 6996 (2.234 iter/s, 5.37153s/12 iters), loss = 0.222059
I0428 14:46:39.998945 27120 solver.cpp:237]     Train net output #0: loss = 0.222059 (* 1 = 0.222059 loss)
I0428 14:46:39.998955 27120 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0428 14:46:45.434475 27120 solver.cpp:218] Iteration 7008 (2.20769 iter/s, 5.43555s/12 iters), loss = 0.112772
I0428 14:46:45.434514 27120 solver.cpp:237]     Train net output #0: loss = 0.112772 (* 1 = 0.112772 loss)
I0428 14:46:45.434523 27120 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0428 14:46:50.873533 27120 solver.cpp:218] Iteration 7020 (2.20627 iter/s, 5.43903s/12 iters), loss = 0.200047
I0428 14:46:50.873594 27120 solver.cpp:237]     Train net output #0: loss = 0.200047 (* 1 = 0.200047 loss)
I0428 14:46:50.873607 27120 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0428 14:46:56.436414 27120 solver.cpp:218] Iteration 7032 (2.15717 iter/s, 5.56285s/12 iters), loss = 0.125818
I0428 14:46:56.436460 27120 solver.cpp:237]     Train net output #0: loss = 0.125818 (* 1 = 0.125818 loss)
I0428 14:46:56.436470 27120 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0428 14:46:58.653584 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0428 14:47:02.610407 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0428 14:47:06.299989 27120 solver.cpp:330] Iteration 7038, Testing net (#0)
I0428 14:47:06.300009 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:47:08.140291 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:47:11.369343 27120 solver.cpp:397]     Test net output #0: accuracy = 0.535539
I0428 14:47:11.369393 27120 solver.cpp:397]     Test net output #1: loss = 2.53191 (* 1 = 2.53191 loss)
I0428 14:47:13.257553 27120 solver.cpp:218] Iteration 7044 (0.713385 iter/s, 16.8212s/12 iters), loss = 0.163618
I0428 14:47:13.257597 27120 solver.cpp:237]     Train net output #0: loss = 0.163618 (* 1 = 0.163618 loss)
I0428 14:47:13.257606 27120 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0428 14:47:18.607156 27120 solver.cpp:218] Iteration 7056 (2.24317 iter/s, 5.34958s/12 iters), loss = 0.0862882
I0428 14:47:18.607200 27120 solver.cpp:237]     Train net output #0: loss = 0.0862882 (* 1 = 0.0862882 loss)
I0428 14:47:18.607209 27120 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0428 14:47:23.977471 27120 solver.cpp:218] Iteration 7068 (2.23452 iter/s, 5.37029s/12 iters), loss = 0.107581
I0428 14:47:23.977515 27120 solver.cpp:237]     Train net output #0: loss = 0.107581 (* 1 = 0.107581 loss)
I0428 14:47:23.977524 27120 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0428 14:47:29.201741 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:47:29.349504 27120 solver.cpp:218] Iteration 7080 (2.2338 iter/s, 5.37201s/12 iters), loss = 0.0693961
I0428 14:47:29.349552 27120 solver.cpp:237]     Train net output #0: loss = 0.0693962 (* 1 = 0.0693962 loss)
I0428 14:47:29.349561 27120 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0428 14:47:34.568974 27120 solver.cpp:218] Iteration 7092 (2.2991 iter/s, 5.21944s/12 iters), loss = 0.156598
I0428 14:47:34.569069 27120 solver.cpp:237]     Train net output #0: loss = 0.156598 (* 1 = 0.156598 loss)
I0428 14:47:34.569079 27120 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0428 14:47:39.929680 27120 solver.cpp:218] Iteration 7104 (2.23854 iter/s, 5.36064s/12 iters), loss = 0.0990199
I0428 14:47:39.929720 27120 solver.cpp:237]     Train net output #0: loss = 0.09902 (* 1 = 0.09902 loss)
I0428 14:47:39.929728 27120 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0428 14:47:45.288308 27120 solver.cpp:218] Iteration 7116 (2.23939 iter/s, 5.35861s/12 iters), loss = 0.0933719
I0428 14:47:45.288357 27120 solver.cpp:237]     Train net output #0: loss = 0.0933719 (* 1 = 0.0933719 loss)
I0428 14:47:45.288365 27120 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0428 14:47:50.668576 27120 solver.cpp:218] Iteration 7128 (2.23038 iter/s, 5.38024s/12 iters), loss = 0.0472165
I0428 14:47:50.668623 27120 solver.cpp:237]     Train net output #0: loss = 0.0472165 (* 1 = 0.0472165 loss)
I0428 14:47:50.668632 27120 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0428 14:47:55.518805 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0428 14:47:58.117137 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0428 14:48:01.036866 27120 solver.cpp:330] Iteration 7140, Testing net (#0)
I0428 14:48:01.036885 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:48:02.871052 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:48:06.217926 27120 solver.cpp:397]     Test net output #0: accuracy = 0.530637
I0428 14:48:06.218119 27120 solver.cpp:397]     Test net output #1: loss = 2.51173 (* 1 = 2.51173 loss)
I0428 14:48:06.354694 27120 solver.cpp:218] Iteration 7140 (0.765005 iter/s, 15.6862s/12 iters), loss = 0.127343
I0428 14:48:06.354755 27120 solver.cpp:237]     Train net output #0: loss = 0.127343 (* 1 = 0.127343 loss)
I0428 14:48:06.354768 27120 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0428 14:48:10.755710 27120 solver.cpp:218] Iteration 7152 (2.72667 iter/s, 4.40097s/12 iters), loss = 0.103306
I0428 14:48:10.755757 27120 solver.cpp:237]     Train net output #0: loss = 0.103306 (* 1 = 0.103306 loss)
I0428 14:48:10.755766 27120 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0428 14:48:16.280069 27120 solver.cpp:218] Iteration 7164 (2.17221 iter/s, 5.52434s/12 iters), loss = 0.147066
I0428 14:48:16.280114 27120 solver.cpp:237]     Train net output #0: loss = 0.147066 (* 1 = 0.147066 loss)
I0428 14:48:16.280123 27120 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0428 14:48:21.652338 27120 solver.cpp:218] Iteration 7176 (2.2337 iter/s, 5.37224s/12 iters), loss = 0.0908789
I0428 14:48:21.652385 27120 solver.cpp:237]     Train net output #0: loss = 0.090879 (* 1 = 0.090879 loss)
I0428 14:48:21.652395 27120 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0428 14:48:23.934850 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:48:27.160156 27120 solver.cpp:218] Iteration 7188 (2.17873 iter/s, 5.50779s/12 iters), loss = 0.10822
I0428 14:48:27.160202 27120 solver.cpp:237]     Train net output #0: loss = 0.10822 (* 1 = 0.10822 loss)
I0428 14:48:27.160210 27120 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0428 14:48:32.598491 27120 solver.cpp:218] Iteration 7200 (2.20657 iter/s, 5.43831s/12 iters), loss = 0.083048
I0428 14:48:32.598539 27120 solver.cpp:237]     Train net output #0: loss = 0.083048 (* 1 = 0.083048 loss)
I0428 14:48:32.598548 27120 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0428 14:48:37.971014 27120 solver.cpp:218] Iteration 7212 (2.2336 iter/s, 5.3725s/12 iters), loss = 0.0554329
I0428 14:48:37.971124 27120 solver.cpp:237]     Train net output #0: loss = 0.0554329 (* 1 = 0.0554329 loss)
I0428 14:48:37.971134 27120 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0428 14:48:43.263044 27120 solver.cpp:218] Iteration 7224 (2.2676 iter/s, 5.29194s/12 iters), loss = 0.0843367
I0428 14:48:43.263094 27120 solver.cpp:237]     Train net output #0: loss = 0.0843367 (* 1 = 0.0843367 loss)
I0428 14:48:43.263103 27120 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0428 14:48:48.614379 27120 solver.cpp:218] Iteration 7236 (2.24244 iter/s, 5.35131s/12 iters), loss = 0.0799545
I0428 14:48:48.614428 27120 solver.cpp:237]     Train net output #0: loss = 0.0799546 (* 1 = 0.0799546 loss)
I0428 14:48:48.614436 27120 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0428 14:48:50.771522 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0428 14:48:56.276384 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0428 14:48:59.417834 27120 solver.cpp:330] Iteration 7242, Testing net (#0)
I0428 14:48:59.417857 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:49:01.168397 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:49:04.506145 27120 solver.cpp:397]     Test net output #0: accuracy = 0.541667
I0428 14:49:04.506191 27120 solver.cpp:397]     Test net output #1: loss = 2.60751 (* 1 = 2.60751 loss)
I0428 14:49:06.495503 27120 solver.cpp:218] Iteration 7248 (0.671096 iter/s, 17.8812s/12 iters), loss = 0.0823993
I0428 14:49:06.495549 27120 solver.cpp:237]     Train net output #0: loss = 0.0823993 (* 1 = 0.0823993 loss)
I0428 14:49:06.495558 27120 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0428 14:49:11.904726 27120 solver.cpp:218] Iteration 7260 (2.21844 iter/s, 5.4092s/12 iters), loss = 0.120644
I0428 14:49:11.904894 27120 solver.cpp:237]     Train net output #0: loss = 0.120644 (* 1 = 0.120644 loss)
I0428 14:49:11.904903 27120 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0428 14:49:17.339042 27120 solver.cpp:218] Iteration 7272 (2.20825 iter/s, 5.43417s/12 iters), loss = 0.074866
I0428 14:49:17.339087 27120 solver.cpp:237]     Train net output #0: loss = 0.0748661 (* 1 = 0.0748661 loss)
I0428 14:49:17.339097 27120 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0428 14:49:21.876758 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:49:22.705360 27120 solver.cpp:218] Iteration 7284 (2.23618 iter/s, 5.3663s/12 iters), loss = 0.15818
I0428 14:49:22.705400 27120 solver.cpp:237]     Train net output #0: loss = 0.15818 (* 1 = 0.15818 loss)
I0428 14:49:22.705408 27120 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0428 14:49:28.138849 27120 solver.cpp:218] Iteration 7296 (2.20853 iter/s, 5.43347s/12 iters), loss = 0.07155
I0428 14:49:28.138890 27120 solver.cpp:237]     Train net output #0: loss = 0.07155 (* 1 = 0.07155 loss)
I0428 14:49:28.138898 27120 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0428 14:49:33.448135 27120 solver.cpp:218] Iteration 7308 (2.2602 iter/s, 5.30927s/12 iters), loss = 0.117929
I0428 14:49:33.448180 27120 solver.cpp:237]     Train net output #0: loss = 0.117929 (* 1 = 0.117929 loss)
I0428 14:49:33.448189 27120 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0428 14:49:38.714579 27120 solver.cpp:218] Iteration 7320 (2.27859 iter/s, 5.26642s/12 iters), loss = 0.0820412
I0428 14:49:38.714646 27120 solver.cpp:237]     Train net output #0: loss = 0.0820412 (* 1 = 0.0820412 loss)
I0428 14:49:38.714658 27120 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0428 14:49:44.086714 27120 solver.cpp:218] Iteration 7332 (2.23376 iter/s, 5.3721s/12 iters), loss = 0.0354875
I0428 14:49:44.086833 27120 solver.cpp:237]     Train net output #0: loss = 0.0354875 (* 1 = 0.0354875 loss)
I0428 14:49:44.086841 27120 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0428 14:49:48.842829 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0428 14:49:51.470731 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0428 14:49:54.582875 27120 solver.cpp:330] Iteration 7344, Testing net (#0)
I0428 14:49:54.582895 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:49:56.270938 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:49:59.580859 27120 solver.cpp:397]     Test net output #0: accuracy = 0.539828
I0428 14:49:59.580896 27120 solver.cpp:397]     Test net output #1: loss = 2.66436 (* 1 = 2.66436 loss)
I0428 14:49:59.717185 27120 solver.cpp:218] Iteration 7344 (0.767732 iter/s, 15.6305s/12 iters), loss = 0.0404448
I0428 14:49:59.717232 27120 solver.cpp:237]     Train net output #0: loss = 0.0404448 (* 1 = 0.0404448 loss)
I0428 14:49:59.717240 27120 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0428 14:50:04.269554 27120 solver.cpp:218] Iteration 7356 (2.63601 iter/s, 4.55234s/12 iters), loss = 0.0723925
I0428 14:50:04.269604 27120 solver.cpp:237]     Train net output #0: loss = 0.0723925 (* 1 = 0.0723925 loss)
I0428 14:50:04.269613 27120 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0428 14:50:09.614974 27120 solver.cpp:218] Iteration 7368 (2.24492 iter/s, 5.3454s/12 iters), loss = 0.0988609
I0428 14:50:09.615016 27120 solver.cpp:237]     Train net output #0: loss = 0.0988609 (* 1 = 0.0988609 loss)
I0428 14:50:09.615025 27120 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0428 14:50:14.976440 27120 solver.cpp:218] Iteration 7380 (2.2382 iter/s, 5.36145s/12 iters), loss = 0.118724
I0428 14:50:14.976524 27120 solver.cpp:237]     Train net output #0: loss = 0.118724 (* 1 = 0.118724 loss)
I0428 14:50:14.976533 27120 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0428 14:50:16.453073 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:50:20.351349 27120 solver.cpp:218] Iteration 7392 (2.23262 iter/s, 5.37485s/12 iters), loss = 0.109422
I0428 14:50:20.351392 27120 solver.cpp:237]     Train net output #0: loss = 0.109422 (* 1 = 0.109422 loss)
I0428 14:50:20.351400 27120 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0428 14:50:25.617769 27120 solver.cpp:218] Iteration 7404 (2.2786 iter/s, 5.26639s/12 iters), loss = 0.0814519
I0428 14:50:25.617828 27120 solver.cpp:237]     Train net output #0: loss = 0.081452 (* 1 = 0.081452 loss)
I0428 14:50:25.617839 27120 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0428 14:50:31.018180 27120 solver.cpp:218] Iteration 7416 (2.22207 iter/s, 5.40038s/12 iters), loss = 0.0554098
I0428 14:50:31.018225 27120 solver.cpp:237]     Train net output #0: loss = 0.0554099 (* 1 = 0.0554099 loss)
I0428 14:50:31.018234 27120 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0428 14:50:36.400020 27120 solver.cpp:218] Iteration 7428 (2.22973 iter/s, 5.38182s/12 iters), loss = 0.0912039
I0428 14:50:36.400066 27120 solver.cpp:237]     Train net output #0: loss = 0.0912039 (* 1 = 0.0912039 loss)
I0428 14:50:36.400075 27120 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0428 14:50:41.750686 27120 solver.cpp:218] Iteration 7440 (2.24272 iter/s, 5.35064s/12 iters), loss = 0.0938661
I0428 14:50:41.750727 27120 solver.cpp:237]     Train net output #0: loss = 0.0938661 (* 1 = 0.0938661 loss)
I0428 14:50:41.750736 27120 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0428 14:50:43.911237 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0428 14:50:48.062719 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0428 14:50:51.386670 27120 solver.cpp:330] Iteration 7446, Testing net (#0)
I0428 14:50:51.386693 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:50:53.049479 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:50:56.531391 27120 solver.cpp:397]     Test net output #0: accuracy = 0.526961
I0428 14:50:56.531421 27120 solver.cpp:397]     Test net output #1: loss = 2.65955 (* 1 = 2.65955 loss)
I0428 14:50:58.624894 27120 solver.cpp:218] Iteration 7452 (0.711141 iter/s, 16.8743s/12 iters), loss = 0.0560229
I0428 14:50:58.624940 27120 solver.cpp:237]     Train net output #0: loss = 0.0560229 (* 1 = 0.0560229 loss)
I0428 14:50:58.624948 27120 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0428 14:51:03.896757 27120 solver.cpp:218] Iteration 7464 (2.27625 iter/s, 5.27183s/12 iters), loss = 0.0928773
I0428 14:51:03.896823 27120 solver.cpp:237]     Train net output #0: loss = 0.0928773 (* 1 = 0.0928773 loss)
I0428 14:51:03.896836 27120 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0428 14:51:09.287289 27120 solver.cpp:218] Iteration 7476 (2.22614 iter/s, 5.3905s/12 iters), loss = 0.095545
I0428 14:51:09.287334 27120 solver.cpp:237]     Train net output #0: loss = 0.0955451 (* 1 = 0.0955451 loss)
I0428 14:51:09.287343 27120 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0428 14:51:13.068774 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:51:14.680451 27120 solver.cpp:218] Iteration 7488 (2.22505 iter/s, 5.39314s/12 iters), loss = 0.0754047
I0428 14:51:14.680495 27120 solver.cpp:237]     Train net output #0: loss = 0.0754047 (* 1 = 0.0754047 loss)
I0428 14:51:14.680503 27120 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0428 14:51:19.964408 27120 solver.cpp:218] Iteration 7500 (2.27103 iter/s, 5.28394s/12 iters), loss = 0.0179058
I0428 14:51:19.964534 27120 solver.cpp:237]     Train net output #0: loss = 0.0179058 (* 1 = 0.0179058 loss)
I0428 14:51:19.964543 27120 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0428 14:51:25.343040 27120 solver.cpp:218] Iteration 7512 (2.23109 iter/s, 5.37853s/12 iters), loss = 0.125716
I0428 14:51:25.343081 27120 solver.cpp:237]     Train net output #0: loss = 0.125716 (* 1 = 0.125716 loss)
I0428 14:51:25.343089 27120 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0428 14:51:30.707561 27120 solver.cpp:218] Iteration 7524 (2.23693 iter/s, 5.3645s/12 iters), loss = 0.0258386
I0428 14:51:30.707605 27120 solver.cpp:237]     Train net output #0: loss = 0.0258387 (* 1 = 0.0258387 loss)
I0428 14:51:30.707614 27120 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0428 14:51:36.078137 27120 solver.cpp:218] Iteration 7536 (2.2344 iter/s, 5.37056s/12 iters), loss = 0.137389
I0428 14:51:36.078177 27120 solver.cpp:237]     Train net output #0: loss = 0.137389 (* 1 = 0.137389 loss)
I0428 14:51:36.078186 27120 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0428 14:51:40.978055 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0428 14:51:43.795711 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0428 14:51:46.613262 27120 solver.cpp:330] Iteration 7548, Testing net (#0)
I0428 14:51:46.613289 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:51:48.211910 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:51:51.651479 27120 solver.cpp:397]     Test net output #0: accuracy = 0.53799
I0428 14:51:51.651674 27120 solver.cpp:397]     Test net output #1: loss = 2.66326 (* 1 = 2.66326 loss)
I0428 14:51:51.784677 27120 solver.cpp:218] Iteration 7548 (0.76401 iter/s, 15.7066s/12 iters), loss = 0.0789542
I0428 14:51:51.784723 27120 solver.cpp:237]     Train net output #0: loss = 0.0789542 (* 1 = 0.0789542 loss)
I0428 14:51:51.784734 27120 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0428 14:51:56.364785 27120 solver.cpp:218] Iteration 7560 (2.62004 iter/s, 4.58008s/12 iters), loss = 0.0648218
I0428 14:51:56.364835 27120 solver.cpp:237]     Train net output #0: loss = 0.0648218 (* 1 = 0.0648218 loss)
I0428 14:51:56.364845 27120 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0428 14:52:01.795719 27120 solver.cpp:218] Iteration 7572 (2.20957 iter/s, 5.43091s/12 iters), loss = 0.0324698
I0428 14:52:01.795769 27120 solver.cpp:237]     Train net output #0: loss = 0.0324699 (* 1 = 0.0324699 loss)
I0428 14:52:01.795778 27120 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0428 14:52:07.107131 27120 solver.cpp:218] Iteration 7584 (2.2593 iter/s, 5.31138s/12 iters), loss = 0.0232139
I0428 14:52:07.107174 27120 solver.cpp:237]     Train net output #0: loss = 0.0232139 (* 1 = 0.0232139 loss)
I0428 14:52:07.107183 27120 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0428 14:52:07.785246 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:52:12.497587 27120 solver.cpp:218] Iteration 7596 (2.22616 iter/s, 5.39044s/12 iters), loss = 0.0586481
I0428 14:52:12.497635 27120 solver.cpp:237]     Train net output #0: loss = 0.0586481 (* 1 = 0.0586481 loss)
I0428 14:52:12.497644 27120 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0428 14:52:17.846248 27120 solver.cpp:218] Iteration 7608 (2.24357 iter/s, 5.34863s/12 iters), loss = 0.0476654
I0428 14:52:17.846309 27120 solver.cpp:237]     Train net output #0: loss = 0.0476654 (* 1 = 0.0476654 loss)
I0428 14:52:17.846320 27120 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0428 14:52:23.271623 27120 solver.cpp:218] Iteration 7620 (2.21184 iter/s, 5.42534s/12 iters), loss = 0.0606761
I0428 14:52:23.271746 27120 solver.cpp:237]     Train net output #0: loss = 0.0606762 (* 1 = 0.0606762 loss)
I0428 14:52:23.271755 27120 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0428 14:52:25.891228 27120 blocking_queue.cpp:49] Waiting for data
I0428 14:52:28.685037 27120 solver.cpp:218] Iteration 7632 (2.21676 iter/s, 5.41332s/12 iters), loss = 0.0180372
I0428 14:52:28.685086 27120 solver.cpp:237]     Train net output #0: loss = 0.0180373 (* 1 = 0.0180373 loss)
I0428 14:52:28.685096 27120 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0428 14:52:34.205377 27120 solver.cpp:218] Iteration 7644 (2.17379 iter/s, 5.52031s/12 iters), loss = 0.15018
I0428 14:52:34.205422 27120 solver.cpp:237]     Train net output #0: loss = 0.15018 (* 1 = 0.15018 loss)
I0428 14:52:34.205431 27120 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0428 14:52:36.374290 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0428 14:52:40.147467 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0428 14:52:42.535455 27120 solver.cpp:330] Iteration 7650, Testing net (#0)
I0428 14:52:42.535475 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:52:44.007990 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:52:47.307469 27120 solver.cpp:397]     Test net output #0: accuracy = 0.542279
I0428 14:52:47.307504 27120 solver.cpp:397]     Test net output #1: loss = 2.68115 (* 1 = 2.68115 loss)
I0428 14:52:49.332870 27120 solver.cpp:218] Iteration 7656 (0.793255 iter/s, 15.1275s/12 iters), loss = 0.0427264
I0428 14:52:49.332913 27120 solver.cpp:237]     Train net output #0: loss = 0.0427265 (* 1 = 0.0427265 loss)
I0428 14:52:49.332922 27120 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0428 14:52:54.767547 27120 solver.cpp:218] Iteration 7668 (2.20805 iter/s, 5.43466s/12 iters), loss = 0.0198291
I0428 14:52:54.767679 27120 solver.cpp:237]     Train net output #0: loss = 0.0198292 (* 1 = 0.0198292 loss)
I0428 14:52:54.767690 27120 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0428 14:53:00.176548 27120 solver.cpp:218] Iteration 7680 (2.21857 iter/s, 5.4089s/12 iters), loss = 0.0942371
I0428 14:53:00.176592 27120 solver.cpp:237]     Train net output #0: loss = 0.0942372 (* 1 = 0.0942372 loss)
I0428 14:53:00.176601 27120 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0428 14:53:03.269042 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:53:05.700978 27120 solver.cpp:218] Iteration 7692 (2.17218 iter/s, 5.52441s/12 iters), loss = 0.0140719
I0428 14:53:05.701023 27120 solver.cpp:237]     Train net output #0: loss = 0.0140719 (* 1 = 0.0140719 loss)
I0428 14:53:05.701032 27120 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0428 14:53:11.147450 27120 solver.cpp:218] Iteration 7704 (2.20327 iter/s, 5.44644s/12 iters), loss = 0.0175942
I0428 14:53:11.147522 27120 solver.cpp:237]     Train net output #0: loss = 0.0175942 (* 1 = 0.0175942 loss)
I0428 14:53:11.147536 27120 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0428 14:53:16.626147 27120 solver.cpp:218] Iteration 7716 (2.19032 iter/s, 5.47866s/12 iters), loss = 0.0458772
I0428 14:53:16.626181 27120 solver.cpp:237]     Train net output #0: loss = 0.0458772 (* 1 = 0.0458772 loss)
I0428 14:53:16.626189 27120 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0428 14:53:22.058929 27120 solver.cpp:218] Iteration 7728 (2.20882 iter/s, 5.43277s/12 iters), loss = 0.0836676
I0428 14:53:22.058976 27120 solver.cpp:237]     Train net output #0: loss = 0.0836676 (* 1 = 0.0836676 loss)
I0428 14:53:22.058985 27120 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0428 14:53:27.550164 27120 solver.cpp:218] Iteration 7740 (2.18531 iter/s, 5.49122s/12 iters), loss = 0.104336
I0428 14:53:27.550293 27120 solver.cpp:237]     Train net output #0: loss = 0.104336 (* 1 = 0.104336 loss)
I0428 14:53:27.550304 27120 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0428 14:53:32.403667 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0428 14:53:35.111940 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0428 14:53:37.467293 27120 solver.cpp:330] Iteration 7752, Testing net (#0)
I0428 14:53:37.467327 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:53:39.064595 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:53:42.801468 27120 solver.cpp:397]     Test net output #0: accuracy = 0.552083
I0428 14:53:42.801506 27120 solver.cpp:397]     Test net output #1: loss = 2.61565 (* 1 = 2.61565 loss)
I0428 14:53:42.934949 27120 solver.cpp:218] Iteration 7752 (0.779993 iter/s, 15.3848s/12 iters), loss = 0.0773194
I0428 14:53:42.935021 27120 solver.cpp:237]     Train net output #0: loss = 0.0773194 (* 1 = 0.0773194 loss)
I0428 14:53:42.935034 27120 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0428 14:53:47.592667 27120 solver.cpp:218] Iteration 7764 (2.57641 iter/s, 4.65765s/12 iters), loss = 0.0535908
I0428 14:53:47.592731 27120 solver.cpp:237]     Train net output #0: loss = 0.0535908 (* 1 = 0.0535908 loss)
I0428 14:53:47.592743 27120 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0428 14:53:53.175755 27120 solver.cpp:218] Iteration 7776 (2.14936 iter/s, 5.58305s/12 iters), loss = 0.12028
I0428 14:53:53.175815 27120 solver.cpp:237]     Train net output #0: loss = 0.120279 (* 1 = 0.120279 loss)
I0428 14:53:53.175825 27120 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0428 14:53:58.634599 27120 solver.cpp:218] Iteration 7788 (2.19829 iter/s, 5.4588s/12 iters), loss = 0.037521
I0428 14:53:58.634780 27120 solver.cpp:237]     Train net output #0: loss = 0.037521 (* 1 = 0.037521 loss)
I0428 14:53:58.634791 27120 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0428 14:53:58.642414 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:54:04.019170 27120 solver.cpp:218] Iteration 7800 (2.22865 iter/s, 5.38441s/12 iters), loss = 0.0632713
I0428 14:54:04.019233 27120 solver.cpp:237]     Train net output #0: loss = 0.0632713 (* 1 = 0.0632713 loss)
I0428 14:54:04.019245 27120 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0428 14:54:09.388564 27120 solver.cpp:218] Iteration 7812 (2.23491 iter/s, 5.36935s/12 iters), loss = 0.0513567
I0428 14:54:09.388624 27120 solver.cpp:237]     Train net output #0: loss = 0.0513567 (* 1 = 0.0513567 loss)
I0428 14:54:09.388635 27120 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0428 14:54:14.742529 27120 solver.cpp:218] Iteration 7824 (2.24134 iter/s, 5.35393s/12 iters), loss = 0.066296
I0428 14:54:14.742601 27120 solver.cpp:237]     Train net output #0: loss = 0.066296 (* 1 = 0.066296 loss)
I0428 14:54:14.742614 27120 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0428 14:54:20.156286 27120 solver.cpp:218] Iteration 7836 (2.21659 iter/s, 5.41371s/12 iters), loss = 0.0903671
I0428 14:54:20.156340 27120 solver.cpp:237]     Train net output #0: loss = 0.0903671 (* 1 = 0.0903671 loss)
I0428 14:54:20.156350 27120 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0428 14:54:25.784103 27120 solver.cpp:218] Iteration 7848 (2.13228 iter/s, 5.62779s/12 iters), loss = 0.0888604
I0428 14:54:25.784152 27120 solver.cpp:237]     Train net output #0: loss = 0.0888604 (* 1 = 0.0888604 loss)
I0428 14:54:25.784160 27120 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0428 14:54:28.022531 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0428 14:54:30.712805 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0428 14:54:33.954775 27120 solver.cpp:330] Iteration 7854, Testing net (#0)
I0428 14:54:33.954803 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:54:35.401154 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:54:39.080788 27120 solver.cpp:397]     Test net output #0: accuracy = 0.552083
I0428 14:54:39.080830 27120 solver.cpp:397]     Test net output #1: loss = 2.62765 (* 1 = 2.62765 loss)
I0428 14:54:41.144914 27120 solver.cpp:218] Iteration 7860 (0.781206 iter/s, 15.3609s/12 iters), loss = 0.0431108
I0428 14:54:41.144961 27120 solver.cpp:237]     Train net output #0: loss = 0.0431108 (* 1 = 0.0431108 loss)
I0428 14:54:41.144969 27120 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0428 14:54:46.597007 27120 solver.cpp:218] Iteration 7872 (2.201 iter/s, 5.45207s/12 iters), loss = 0.0740572
I0428 14:54:46.597055 27120 solver.cpp:237]     Train net output #0: loss = 0.0740572 (* 1 = 0.0740572 loss)
I0428 14:54:46.597064 27120 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0428 14:54:52.031942 27120 solver.cpp:218] Iteration 7884 (2.20795 iter/s, 5.4349s/12 iters), loss = 0.0569456
I0428 14:54:52.032011 27120 solver.cpp:237]     Train net output #0: loss = 0.0569457 (* 1 = 0.0569457 loss)
I0428 14:54:52.032024 27120 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0428 14:54:54.362267 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:54:57.483516 27120 solver.cpp:218] Iteration 7896 (2.20122 iter/s, 5.45152s/12 iters), loss = 0.118172
I0428 14:54:57.483595 27120 solver.cpp:237]     Train net output #0: loss = 0.118172 (* 1 = 0.118172 loss)
I0428 14:54:57.483609 27120 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0428 14:55:02.916154 27120 solver.cpp:218] Iteration 7908 (2.20889 iter/s, 5.43259s/12 iters), loss = 0.0466572
I0428 14:55:02.916288 27120 solver.cpp:237]     Train net output #0: loss = 0.0466572 (* 1 = 0.0466572 loss)
I0428 14:55:02.916298 27120 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0428 14:55:08.311280 27120 solver.cpp:218] Iteration 7920 (2.22427 iter/s, 5.39502s/12 iters), loss = 0.030228
I0428 14:55:08.311326 27120 solver.cpp:237]     Train net output #0: loss = 0.030228 (* 1 = 0.030228 loss)
I0428 14:55:08.311334 27120 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0428 14:55:13.802059 27120 solver.cpp:218] Iteration 7932 (2.18549 iter/s, 5.49076s/12 iters), loss = 0.086218
I0428 14:55:13.802100 27120 solver.cpp:237]     Train net output #0: loss = 0.086218 (* 1 = 0.086218 loss)
I0428 14:55:13.802109 27120 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0428 14:55:19.143961 27120 solver.cpp:218] Iteration 7944 (2.2464 iter/s, 5.34189s/12 iters), loss = 0.0649088
I0428 14:55:19.144006 27120 solver.cpp:237]     Train net output #0: loss = 0.0649088 (* 1 = 0.0649088 loss)
I0428 14:55:19.144014 27120 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0428 14:55:24.005081 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0428 14:55:27.542377 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0428 14:55:30.608148 27120 solver.cpp:330] Iteration 7956, Testing net (#0)
I0428 14:55:30.608173 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:55:32.134758 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:55:35.783649 27120 solver.cpp:397]     Test net output #0: accuracy = 0.544118
I0428 14:55:35.783756 27120 solver.cpp:397]     Test net output #1: loss = 2.66895 (* 1 = 2.66895 loss)
I0428 14:55:35.915707 27120 solver.cpp:218] Iteration 7956 (0.715486 iter/s, 16.7718s/12 iters), loss = 0.0452688
I0428 14:55:35.915760 27120 solver.cpp:237]     Train net output #0: loss = 0.0452688 (* 1 = 0.0452688 loss)
I0428 14:55:35.915768 27120 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0428 14:55:40.505946 27120 solver.cpp:218] Iteration 7968 (2.61426 iter/s, 4.59021s/12 iters), loss = 0.0240587
I0428 14:55:40.505993 27120 solver.cpp:237]     Train net output #0: loss = 0.0240587 (* 1 = 0.0240587 loss)
I0428 14:55:40.506001 27120 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0428 14:55:45.848413 27120 solver.cpp:218] Iteration 7980 (2.24616 iter/s, 5.34245s/12 iters), loss = 0.0360654
I0428 14:55:45.848464 27120 solver.cpp:237]     Train net output #0: loss = 0.0360654 (* 1 = 0.0360654 loss)
I0428 14:55:45.848472 27120 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0428 14:55:50.504720 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:55:51.291591 27120 solver.cpp:218] Iteration 7992 (2.20461 iter/s, 5.44315s/12 iters), loss = 0.0993955
I0428 14:55:51.291637 27120 solver.cpp:237]     Train net output #0: loss = 0.0993955 (* 1 = 0.0993955 loss)
I0428 14:55:51.291646 27120 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0428 14:55:56.772773 27120 solver.cpp:218] Iteration 8004 (2.18932 iter/s, 5.48116s/12 iters), loss = 0.0479765
I0428 14:55:56.772819 27120 solver.cpp:237]     Train net output #0: loss = 0.0479765 (* 1 = 0.0479765 loss)
I0428 14:55:56.772826 27120 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0428 14:56:02.076829 27120 solver.cpp:218] Iteration 8016 (2.26243 iter/s, 5.30403s/12 iters), loss = 0.0311518
I0428 14:56:02.076879 27120 solver.cpp:237]     Train net output #0: loss = 0.0311518 (* 1 = 0.0311518 loss)
I0428 14:56:02.076887 27120 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0428 14:56:07.538132 27120 solver.cpp:218] Iteration 8028 (2.19729 iter/s, 5.46127s/12 iters), loss = 0.0805572
I0428 14:56:07.538327 27120 solver.cpp:237]     Train net output #0: loss = 0.0805572 (* 1 = 0.0805572 loss)
I0428 14:56:07.538342 27120 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0428 14:56:13.029563 27120 solver.cpp:218] Iteration 8040 (2.18529 iter/s, 5.49127s/12 iters), loss = 0.0714653
I0428 14:56:13.029604 27120 solver.cpp:237]     Train net output #0: loss = 0.0714653 (* 1 = 0.0714653 loss)
I0428 14:56:13.029613 27120 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0428 14:56:18.437465 27120 solver.cpp:218] Iteration 8052 (2.21898 iter/s, 5.40789s/12 iters), loss = 0.0485822
I0428 14:56:18.437512 27120 solver.cpp:237]     Train net output #0: loss = 0.0485822 (* 1 = 0.0485822 loss)
I0428 14:56:18.437520 27120 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0428 14:56:20.585844 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0428 14:56:29.589816 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0428 14:56:33.740023 27120 solver.cpp:330] Iteration 8058, Testing net (#0)
I0428 14:56:33.740046 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:56:35.147238 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:56:38.845543 27120 solver.cpp:397]     Test net output #0: accuracy = 0.558824
I0428 14:56:38.845679 27120 solver.cpp:397]     Test net output #1: loss = 2.62606 (* 1 = 2.62606 loss)
I0428 14:56:40.881537 27120 solver.cpp:218] Iteration 8064 (0.53466 iter/s, 22.4442s/12 iters), loss = 0.0597869
I0428 14:56:40.881582 27120 solver.cpp:237]     Train net output #0: loss = 0.0597869 (* 1 = 0.0597869 loss)
I0428 14:56:40.881590 27120 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0428 14:56:46.240097 27120 solver.cpp:218] Iteration 8076 (2.23942 iter/s, 5.35853s/12 iters), loss = 0.0237986
I0428 14:56:46.240144 27120 solver.cpp:237]     Train net output #0: loss = 0.0237986 (* 1 = 0.0237986 loss)
I0428 14:56:46.240152 27120 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0428 14:56:51.609274 27120 solver.cpp:218] Iteration 8088 (2.23499 iter/s, 5.36916s/12 iters), loss = 0.115382
I0428 14:56:51.609324 27120 solver.cpp:237]     Train net output #0: loss = 0.115382 (* 1 = 0.115382 loss)
I0428 14:56:51.609333 27120 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0428 14:56:53.112547 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:56:57.001729 27120 solver.cpp:218] Iteration 8100 (2.22534 iter/s, 5.39242s/12 iters), loss = 0.0399831
I0428 14:56:57.001775 27120 solver.cpp:237]     Train net output #0: loss = 0.0399831 (* 1 = 0.0399831 loss)
I0428 14:56:57.001782 27120 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0428 14:57:02.385004 27120 solver.cpp:218] Iteration 8112 (2.22914 iter/s, 5.38324s/12 iters), loss = 0.0532652
I0428 14:57:02.385080 27120 solver.cpp:237]     Train net output #0: loss = 0.0532652 (* 1 = 0.0532652 loss)
I0428 14:57:02.385097 27120 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0428 14:57:07.669694 27120 solver.cpp:218] Iteration 8124 (2.27073 iter/s, 5.28463s/12 iters), loss = 0.0582866
I0428 14:57:07.669744 27120 solver.cpp:237]     Train net output #0: loss = 0.0582866 (* 1 = 0.0582866 loss)
I0428 14:57:07.669754 27120 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0428 14:57:13.074009 27120 solver.cpp:218] Iteration 8136 (2.22046 iter/s, 5.40428s/12 iters), loss = 0.0638277
I0428 14:57:13.074115 27120 solver.cpp:237]     Train net output #0: loss = 0.0638277 (* 1 = 0.0638277 loss)
I0428 14:57:13.074126 27120 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0428 14:57:18.475582 27120 solver.cpp:218] Iteration 8148 (2.22161 iter/s, 5.40149s/12 iters), loss = 0.0574038
I0428 14:57:18.475621 27120 solver.cpp:237]     Train net output #0: loss = 0.0574038 (* 1 = 0.0574038 loss)
I0428 14:57:18.475630 27120 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0428 14:57:23.364785 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0428 14:57:28.225888 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0428 14:57:33.285472 27120 solver.cpp:330] Iteration 8160, Testing net (#0)
I0428 14:57:33.285495 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:57:34.547783 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:57:38.051851 27120 solver.cpp:397]     Test net output #0: accuracy = 0.556985
I0428 14:57:38.051883 27120 solver.cpp:397]     Test net output #1: loss = 2.73842 (* 1 = 2.73842 loss)
I0428 14:57:38.193465 27120 solver.cpp:218] Iteration 8160 (0.608582 iter/s, 19.718s/12 iters), loss = 0.0264619
I0428 14:57:38.193527 27120 solver.cpp:237]     Train net output #0: loss = 0.0264619 (* 1 = 0.0264619 loss)
I0428 14:57:38.193539 27120 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0428 14:57:42.805562 27120 solver.cpp:218] Iteration 8172 (2.60188 iter/s, 4.61206s/12 iters), loss = 0.0374382
I0428 14:57:42.805609 27120 solver.cpp:237]     Train net output #0: loss = 0.0374382 (* 1 = 0.0374382 loss)
I0428 14:57:42.805619 27120 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0428 14:57:48.213321 27120 solver.cpp:218] Iteration 8184 (2.21905 iter/s, 5.40773s/12 iters), loss = 0.0560641
I0428 14:57:48.213495 27120 solver.cpp:237]     Train net output #0: loss = 0.0560641 (* 1 = 0.0560641 loss)
I0428 14:57:48.213503 27120 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0428 14:57:52.011865 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:57:53.633000 27120 solver.cpp:218] Iteration 8196 (2.21421 iter/s, 5.41953s/12 iters), loss = 0.062872
I0428 14:57:53.633049 27120 solver.cpp:237]     Train net output #0: loss = 0.062872 (* 1 = 0.062872 loss)
I0428 14:57:53.633059 27120 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0428 14:57:59.026471 27120 solver.cpp:218] Iteration 8208 (2.22492 iter/s, 5.39345s/12 iters), loss = 0.0755762
I0428 14:57:59.026520 27120 solver.cpp:237]     Train net output #0: loss = 0.0755762 (* 1 = 0.0755762 loss)
I0428 14:57:59.026527 27120 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0428 14:58:04.458959 27120 solver.cpp:218] Iteration 8220 (2.20894 iter/s, 5.43246s/12 iters), loss = 0.0250603
I0428 14:58:04.459007 27120 solver.cpp:237]     Train net output #0: loss = 0.0250603 (* 1 = 0.0250603 loss)
I0428 14:58:04.459014 27120 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0428 14:58:09.745157 27120 solver.cpp:218] Iteration 8232 (2.27007 iter/s, 5.28617s/12 iters), loss = 0.0603891
I0428 14:58:09.745206 27120 solver.cpp:237]     Train net output #0: loss = 0.060389 (* 1 = 0.060389 loss)
I0428 14:58:09.745216 27120 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0428 14:58:15.136368 27120 solver.cpp:218] Iteration 8244 (2.22586 iter/s, 5.39118s/12 iters), loss = 0.0373529
I0428 14:58:15.136417 27120 solver.cpp:237]     Train net output #0: loss = 0.0373529 (* 1 = 0.0373529 loss)
I0428 14:58:15.136427 27120 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0428 14:58:20.574499 27120 solver.cpp:218] Iteration 8256 (2.20665 iter/s, 5.4381s/12 iters), loss = 0.031027
I0428 14:58:20.574687 27120 solver.cpp:237]     Train net output #0: loss = 0.0310269 (* 1 = 0.0310269 loss)
I0428 14:58:20.574703 27120 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0428 14:58:22.732905 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0428 14:58:28.171563 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0428 14:58:30.598359 27120 solver.cpp:330] Iteration 8262, Testing net (#0)
I0428 14:58:30.598381 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:58:31.954030 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:58:35.677564 27120 solver.cpp:397]     Test net output #0: accuracy = 0.556373
I0428 14:58:35.677610 27120 solver.cpp:397]     Test net output #1: loss = 2.57725 (* 1 = 2.57725 loss)
I0428 14:58:37.691578 27120 solver.cpp:218] Iteration 8268 (0.701057 iter/s, 17.117s/12 iters), loss = 0.0319705
I0428 14:58:37.691628 27120 solver.cpp:237]     Train net output #0: loss = 0.0319705 (* 1 = 0.0319705 loss)
I0428 14:58:37.691637 27120 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0428 14:58:43.057878 27120 solver.cpp:218] Iteration 8280 (2.23619 iter/s, 5.36627s/12 iters), loss = 0.111647
I0428 14:58:43.057919 27120 solver.cpp:237]     Train net output #0: loss = 0.111646 (* 1 = 0.111646 loss)
I0428 14:58:43.057927 27120 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0428 14:58:48.441243 27120 solver.cpp:218] Iteration 8292 (2.2291 iter/s, 5.38335s/12 iters), loss = 0.0198015
I0428 14:58:48.441303 27120 solver.cpp:237]     Train net output #0: loss = 0.0198015 (* 1 = 0.0198015 loss)
I0428 14:58:48.441315 27120 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0428 14:58:49.152977 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:58:53.839843 27120 solver.cpp:218] Iteration 8304 (2.22282 iter/s, 5.39856s/12 iters), loss = 0.0490814
I0428 14:58:53.840055 27120 solver.cpp:237]     Train net output #0: loss = 0.0490814 (* 1 = 0.0490814 loss)
I0428 14:58:53.840071 27120 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0428 14:58:56.876091 27120 blocking_queue.cpp:49] Waiting for data
I0428 14:58:59.236538 27120 solver.cpp:218] Iteration 8316 (2.22366 iter/s, 5.39652s/12 iters), loss = 0.0665084
I0428 14:58:59.236588 27120 solver.cpp:237]     Train net output #0: loss = 0.0665084 (* 1 = 0.0665084 loss)
I0428 14:58:59.236596 27120 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0428 14:59:04.662633 27120 solver.cpp:218] Iteration 8328 (2.21155 iter/s, 5.42607s/12 iters), loss = 0.036902
I0428 14:59:04.662683 27120 solver.cpp:237]     Train net output #0: loss = 0.036902 (* 1 = 0.036902 loss)
I0428 14:59:04.662691 27120 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0428 14:59:10.093606 27120 solver.cpp:218] Iteration 8340 (2.20956 iter/s, 5.43094s/12 iters), loss = 0.0563387
I0428 14:59:10.093672 27120 solver.cpp:237]     Train net output #0: loss = 0.0563386 (* 1 = 0.0563386 loss)
I0428 14:59:10.093684 27120 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0428 14:59:15.462970 27120 solver.cpp:218] Iteration 8352 (2.23492 iter/s, 5.36933s/12 iters), loss = 0.0770192
I0428 14:59:15.463013 27120 solver.cpp:237]     Train net output #0: loss = 0.0770192 (* 1 = 0.0770192 loss)
I0428 14:59:15.463023 27120 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0428 14:59:20.329574 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0428 14:59:24.568688 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0428 14:59:28.169483 27120 solver.cpp:330] Iteration 8364, Testing net (#0)
I0428 14:59:28.169503 27120 net.cpp:676] Ignoring source layer train-data
I0428 14:59:29.437661 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:59:33.334394 27120 solver.cpp:397]     Test net output #0: accuracy = 0.556373
I0428 14:59:33.334430 27120 solver.cpp:397]     Test net output #1: loss = 2.61712 (* 1 = 2.61712 loss)
I0428 14:59:33.463356 27120 solver.cpp:218] Iteration 8364 (0.666649 iter/s, 18.0005s/12 iters), loss = 0.0603004
I0428 14:59:33.463397 27120 solver.cpp:237]     Train net output #0: loss = 0.0603004 (* 1 = 0.0603004 loss)
I0428 14:59:33.463404 27120 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0428 14:59:38.063083 27120 solver.cpp:218] Iteration 8376 (2.60887 iter/s, 4.59969s/12 iters), loss = 0.0336506
I0428 14:59:38.063153 27120 solver.cpp:237]     Train net output #0: loss = 0.0336506 (* 1 = 0.0336506 loss)
I0428 14:59:38.063165 27120 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0428 14:59:43.477911 27120 solver.cpp:218] Iteration 8388 (2.21615 iter/s, 5.41479s/12 iters), loss = 0.0492745
I0428 14:59:43.477959 27120 solver.cpp:237]     Train net output #0: loss = 0.0492745 (* 1 = 0.0492745 loss)
I0428 14:59:43.477967 27120 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0428 14:59:46.543273 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 14:59:48.926389 27120 solver.cpp:218] Iteration 8400 (2.20246 iter/s, 5.44845s/12 iters), loss = 0.0183306
I0428 14:59:48.926437 27120 solver.cpp:237]     Train net output #0: loss = 0.0183306 (* 1 = 0.0183306 loss)
I0428 14:59:48.926446 27120 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0428 14:59:54.355581 27120 solver.cpp:218] Iteration 8412 (2.21029 iter/s, 5.42916s/12 iters), loss = 0.026419
I0428 14:59:54.355630 27120 solver.cpp:237]     Train net output #0: loss = 0.026419 (* 1 = 0.026419 loss)
I0428 14:59:54.355639 27120 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0428 14:59:59.760392 27120 solver.cpp:218] Iteration 8424 (2.22026 iter/s, 5.40478s/12 iters), loss = 0.102786
I0428 14:59:59.760545 27120 solver.cpp:237]     Train net output #0: loss = 0.102786 (* 1 = 0.102786 loss)
I0428 14:59:59.760555 27120 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0428 15:00:05.056974 27120 solver.cpp:218] Iteration 8436 (2.26567 iter/s, 5.29645s/12 iters), loss = 0.0306872
I0428 15:00:05.057021 27120 solver.cpp:237]     Train net output #0: loss = 0.0306872 (* 1 = 0.0306872 loss)
I0428 15:00:05.057031 27120 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0428 15:00:10.694329 27120 solver.cpp:218] Iteration 8448 (2.12866 iter/s, 5.63734s/12 iters), loss = 0.0619925
I0428 15:00:10.694375 27120 solver.cpp:237]     Train net output #0: loss = 0.0619925 (* 1 = 0.0619925 loss)
I0428 15:00:10.694383 27120 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0428 15:00:16.263517 27120 solver.cpp:218] Iteration 8460 (2.15472 iter/s, 5.56917s/12 iters), loss = 0.111746
I0428 15:00:16.263557 27120 solver.cpp:237]     Train net output #0: loss = 0.111746 (* 1 = 0.111746 loss)
I0428 15:00:16.263566 27120 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0428 15:00:18.498489 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0428 15:00:32.187440 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0428 15:00:35.038425 27120 solver.cpp:330] Iteration 8466, Testing net (#0)
I0428 15:00:35.038448 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:00:36.329149 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:00:40.298004 27120 solver.cpp:397]     Test net output #0: accuracy = 0.553922
I0428 15:00:40.298043 27120 solver.cpp:397]     Test net output #1: loss = 2.65234 (* 1 = 2.65234 loss)
I0428 15:00:42.273336 27120 solver.cpp:218] Iteration 8472 (0.461362 iter/s, 26.01s/12 iters), loss = 0.0319058
I0428 15:00:42.273381 27120 solver.cpp:237]     Train net output #0: loss = 0.0319058 (* 1 = 0.0319058 loss)
I0428 15:00:42.273389 27120 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0428 15:00:47.681071 27120 solver.cpp:218] Iteration 8484 (2.21905 iter/s, 5.40771s/12 iters), loss = 0.052178
I0428 15:00:47.681111 27120 solver.cpp:237]     Train net output #0: loss = 0.052178 (* 1 = 0.052178 loss)
I0428 15:00:47.681119 27120 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0428 15:00:53.085772 27120 solver.cpp:218] Iteration 8496 (2.2203 iter/s, 5.40468s/12 iters), loss = 0.075996
I0428 15:00:53.085815 27120 solver.cpp:237]     Train net output #0: loss = 0.075996 (* 1 = 0.075996 loss)
I0428 15:00:53.085824 27120 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0428 15:00:53.122303 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:00:58.424372 27120 solver.cpp:218] Iteration 8508 (2.24779 iter/s, 5.33857s/12 iters), loss = 0.0812362
I0428 15:00:58.424418 27120 solver.cpp:237]     Train net output #0: loss = 0.0812362 (* 1 = 0.0812362 loss)
I0428 15:00:58.424427 27120 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0428 15:01:03.695183 27120 solver.cpp:218] Iteration 8520 (2.2767 iter/s, 5.27078s/12 iters), loss = 0.0703593
I0428 15:01:03.695286 27120 solver.cpp:237]     Train net output #0: loss = 0.0703593 (* 1 = 0.0703593 loss)
I0428 15:01:03.695294 27120 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0428 15:01:09.124971 27120 solver.cpp:218] Iteration 8532 (2.21006 iter/s, 5.42971s/12 iters), loss = 0.0134085
I0428 15:01:09.125008 27120 solver.cpp:237]     Train net output #0: loss = 0.0134085 (* 1 = 0.0134085 loss)
I0428 15:01:09.125016 27120 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0428 15:01:14.525629 27120 solver.cpp:218] Iteration 8544 (2.22196 iter/s, 5.40064s/12 iters), loss = 0.0220532
I0428 15:01:14.525691 27120 solver.cpp:237]     Train net output #0: loss = 0.0220532 (* 1 = 0.0220532 loss)
I0428 15:01:14.525702 27120 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0428 15:01:19.893514 27120 solver.cpp:218] Iteration 8556 (2.23553 iter/s, 5.36785s/12 iters), loss = 0.0481589
I0428 15:01:19.893553 27120 solver.cpp:237]     Train net output #0: loss = 0.0481589 (* 1 = 0.0481589 loss)
I0428 15:01:19.893560 27120 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0428 15:01:24.647944 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0428 15:01:33.082975 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0428 15:01:35.925781 27120 solver.cpp:330] Iteration 8568, Testing net (#0)
I0428 15:01:35.925928 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:01:37.146353 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:01:41.164145 27120 solver.cpp:397]     Test net output #0: accuracy = 0.555147
I0428 15:01:41.164175 27120 solver.cpp:397]     Test net output #1: loss = 2.66669 (* 1 = 2.66669 loss)
I0428 15:01:41.301023 27120 solver.cpp:218] Iteration 8568 (0.560548 iter/s, 21.4076s/12 iters), loss = 0.103417
I0428 15:01:41.301064 27120 solver.cpp:237]     Train net output #0: loss = 0.103417 (* 1 = 0.103417 loss)
I0428 15:01:41.301075 27120 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0428 15:01:45.807235 27120 solver.cpp:218] Iteration 8580 (2.66301 iter/s, 4.50618s/12 iters), loss = 0.0296362
I0428 15:01:45.807281 27120 solver.cpp:237]     Train net output #0: loss = 0.0296362 (* 1 = 0.0296362 loss)
I0428 15:01:45.807291 27120 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0428 15:01:51.076388 27120 solver.cpp:218] Iteration 8592 (2.27742 iter/s, 5.26912s/12 iters), loss = 0.0296177
I0428 15:01:51.076431 27120 solver.cpp:237]     Train net output #0: loss = 0.0296177 (* 1 = 0.0296177 loss)
I0428 15:01:51.076440 27120 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0428 15:01:53.434242 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:01:56.521999 27120 solver.cpp:218] Iteration 8604 (2.20363 iter/s, 5.44557s/12 iters), loss = 0.0760088
I0428 15:01:56.522065 27120 solver.cpp:237]     Train net output #0: loss = 0.0760088 (* 1 = 0.0760088 loss)
I0428 15:01:56.522080 27120 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0428 15:02:01.901906 27120 solver.cpp:218] Iteration 8616 (2.23054 iter/s, 5.37987s/12 iters), loss = 0.0361814
I0428 15:02:01.901947 27120 solver.cpp:237]     Train net output #0: loss = 0.0361814 (* 1 = 0.0361814 loss)
I0428 15:02:01.901957 27120 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0428 15:02:07.281883 27120 solver.cpp:218] Iteration 8628 (2.2305 iter/s, 5.37996s/12 iters), loss = 0.029405
I0428 15:02:07.281967 27120 solver.cpp:237]     Train net output #0: loss = 0.029405 (* 1 = 0.029405 loss)
I0428 15:02:07.281976 27120 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0428 15:02:12.658833 27120 solver.cpp:218] Iteration 8640 (2.23177 iter/s, 5.37689s/12 iters), loss = 0.101837
I0428 15:02:12.658881 27120 solver.cpp:237]     Train net output #0: loss = 0.101837 (* 1 = 0.101837 loss)
I0428 15:02:12.658890 27120 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0428 15:02:18.018858 27120 solver.cpp:218] Iteration 8652 (2.23881 iter/s, 5.36s/12 iters), loss = 0.0294191
I0428 15:02:18.018906 27120 solver.cpp:237]     Train net output #0: loss = 0.0294191 (* 1 = 0.0294191 loss)
I0428 15:02:18.018915 27120 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0428 15:02:23.310179 27120 solver.cpp:218] Iteration 8664 (2.26787 iter/s, 5.2913s/12 iters), loss = 0.0152709
I0428 15:02:23.310226 27120 solver.cpp:237]     Train net output #0: loss = 0.0152709 (* 1 = 0.0152709 loss)
I0428 15:02:23.310236 27120 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0428 15:02:25.470212 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0428 15:02:30.845706 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0428 15:02:35.561599 27120 solver.cpp:330] Iteration 8670, Testing net (#0)
I0428 15:02:35.561625 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:02:36.694615 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:02:40.670639 27120 solver.cpp:397]     Test net output #0: accuracy = 0.553922
I0428 15:02:40.670759 27120 solver.cpp:397]     Test net output #1: loss = 2.75219 (* 1 = 2.75219 loss)
I0428 15:02:42.661595 27120 solver.cpp:218] Iteration 8676 (0.620107 iter/s, 19.3515s/12 iters), loss = 0.0236578
I0428 15:02:42.661641 27120 solver.cpp:237]     Train net output #0: loss = 0.0236577 (* 1 = 0.0236577 loss)
I0428 15:02:42.661650 27120 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0428 15:02:48.017690 27120 solver.cpp:218] Iteration 8688 (2.24045 iter/s, 5.35607s/12 iters), loss = 0.0642317
I0428 15:02:48.017737 27120 solver.cpp:237]     Train net output #0: loss = 0.0642317 (* 1 = 0.0642317 loss)
I0428 15:02:48.017745 27120 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0428 15:02:52.685039 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:02:53.443480 27120 solver.cpp:218] Iteration 8700 (2.21167 iter/s, 5.42576s/12 iters), loss = 0.0374317
I0428 15:02:53.443527 27120 solver.cpp:237]     Train net output #0: loss = 0.0374316 (* 1 = 0.0374316 loss)
I0428 15:02:53.443537 27120 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0428 15:02:58.817332 27120 solver.cpp:218] Iteration 8712 (2.23305 iter/s, 5.37383s/12 iters), loss = 0.0634997
I0428 15:02:58.817373 27120 solver.cpp:237]     Train net output #0: loss = 0.0634997 (* 1 = 0.0634997 loss)
I0428 15:02:58.817381 27120 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0428 15:03:04.173586 27120 solver.cpp:218] Iteration 8724 (2.24038 iter/s, 5.35624s/12 iters), loss = 0.0461755
I0428 15:03:04.173626 27120 solver.cpp:237]     Train net output #0: loss = 0.0461755 (* 1 = 0.0461755 loss)
I0428 15:03:04.173635 27120 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0428 15:03:09.529876 27120 solver.cpp:218] Iteration 8736 (2.24036 iter/s, 5.35627s/12 iters), loss = 0.0940839
I0428 15:03:09.529925 27120 solver.cpp:237]     Train net output #0: loss = 0.0940839 (* 1 = 0.0940839 loss)
I0428 15:03:09.529934 27120 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0428 15:03:14.907411 27120 solver.cpp:218] Iteration 8748 (2.23152 iter/s, 5.37751s/12 iters), loss = 0.0506286
I0428 15:03:14.907539 27120 solver.cpp:237]     Train net output #0: loss = 0.0506286 (* 1 = 0.0506286 loss)
I0428 15:03:14.907549 27120 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0428 15:03:20.277177 27120 solver.cpp:218] Iteration 8760 (2.23478 iter/s, 5.36966s/12 iters), loss = 0.0131493
I0428 15:03:20.277235 27120 solver.cpp:237]     Train net output #0: loss = 0.0131493 (* 1 = 0.0131493 loss)
I0428 15:03:20.277246 27120 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0428 15:03:25.100119 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0428 15:03:27.949404 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0428 15:03:33.904465 27120 solver.cpp:330] Iteration 8772, Testing net (#0)
I0428 15:03:33.904486 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:03:35.040215 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:03:39.138622 27120 solver.cpp:397]     Test net output #0: accuracy = 0.550245
I0428 15:03:39.138662 27120 solver.cpp:397]     Test net output #1: loss = 2.72638 (* 1 = 2.72638 loss)
I0428 15:03:39.279425 27120 solver.cpp:218] Iteration 8772 (0.631501 iter/s, 19.0023s/12 iters), loss = 0.0216512
I0428 15:03:39.279470 27120 solver.cpp:237]     Train net output #0: loss = 0.0216512 (* 1 = 0.0216512 loss)
I0428 15:03:39.279480 27120 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0428 15:03:43.767851 27120 solver.cpp:218] Iteration 8784 (2.67356 iter/s, 4.4884s/12 iters), loss = 0.0308156
I0428 15:03:43.767894 27120 solver.cpp:237]     Train net output #0: loss = 0.0308156 (* 1 = 0.0308156 loss)
I0428 15:03:43.767902 27120 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0428 15:03:49.142940 27120 solver.cpp:218] Iteration 8796 (2.23253 iter/s, 5.37507s/12 iters), loss = 0.0780396
I0428 15:03:49.143065 27120 solver.cpp:237]     Train net output #0: loss = 0.0780395 (* 1 = 0.0780395 loss)
I0428 15:03:49.143075 27120 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0428 15:03:50.597278 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:03:54.465582 27120 solver.cpp:218] Iteration 8808 (2.25456 iter/s, 5.32255s/12 iters), loss = 0.0590931
I0428 15:03:54.465631 27120 solver.cpp:237]     Train net output #0: loss = 0.0590931 (* 1 = 0.0590931 loss)
I0428 15:03:54.465638 27120 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0428 15:03:59.874320 27120 solver.cpp:218] Iteration 8820 (2.21864 iter/s, 5.40871s/12 iters), loss = 0.109237
I0428 15:03:59.874369 27120 solver.cpp:237]     Train net output #0: loss = 0.109237 (* 1 = 0.109237 loss)
I0428 15:03:59.874378 27120 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0428 15:04:05.270004 27120 solver.cpp:218] Iteration 8832 (2.22401 iter/s, 5.39565s/12 iters), loss = 0.00715292
I0428 15:04:05.270056 27120 solver.cpp:237]     Train net output #0: loss = 0.00715289 (* 1 = 0.00715289 loss)
I0428 15:04:05.270066 27120 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0428 15:04:10.872020 27120 solver.cpp:218] Iteration 8844 (2.14209 iter/s, 5.60199s/12 iters), loss = 0.0205882
I0428 15:04:10.872066 27120 solver.cpp:237]     Train net output #0: loss = 0.0205882 (* 1 = 0.0205882 loss)
I0428 15:04:10.872076 27120 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0428 15:04:16.311964 27120 solver.cpp:218] Iteration 8856 (2.20591 iter/s, 5.43992s/12 iters), loss = 0.0200424
I0428 15:04:16.312011 27120 solver.cpp:237]     Train net output #0: loss = 0.0200424 (* 1 = 0.0200424 loss)
I0428 15:04:16.312021 27120 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0428 15:04:21.662864 27120 solver.cpp:218] Iteration 8868 (2.24262 iter/s, 5.35088s/12 iters), loss = 0.0902263
I0428 15:04:21.662984 27120 solver.cpp:237]     Train net output #0: loss = 0.0902263 (* 1 = 0.0902263 loss)
I0428 15:04:21.662994 27120 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0428 15:04:23.741477 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0428 15:04:29.369832 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0428 15:04:34.071739 27120 solver.cpp:330] Iteration 8874, Testing net (#0)
I0428 15:04:34.071763 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:04:35.125790 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:04:39.193615 27120 solver.cpp:397]     Test net output #0: accuracy = 0.566789
I0428 15:04:39.193655 27120 solver.cpp:397]     Test net output #1: loss = 2.70179 (* 1 = 2.70179 loss)
I0428 15:04:41.184890 27120 solver.cpp:218] Iteration 8880 (0.61469 iter/s, 19.522s/12 iters), loss = 0.0365522
I0428 15:04:41.184937 27120 solver.cpp:237]     Train net output #0: loss = 0.0365522 (* 1 = 0.0365522 loss)
I0428 15:04:41.184944 27120 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0428 15:04:46.562142 27120 solver.cpp:218] Iteration 8892 (2.23163 iter/s, 5.37723s/12 iters), loss = 0.0473757
I0428 15:04:46.562191 27120 solver.cpp:237]     Train net output #0: loss = 0.0473756 (* 1 = 0.0473756 loss)
I0428 15:04:46.562201 27120 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0428 15:04:50.427263 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:04:51.991945 27120 solver.cpp:218] Iteration 8904 (2.21004 iter/s, 5.42978s/12 iters), loss = 0.037782
I0428 15:04:51.992077 27120 solver.cpp:237]     Train net output #0: loss = 0.037782 (* 1 = 0.037782 loss)
I0428 15:04:51.992089 27120 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0428 15:04:57.398947 27120 solver.cpp:218] Iteration 8916 (2.21939 iter/s, 5.4069s/12 iters), loss = 0.0537056
I0428 15:04:57.398991 27120 solver.cpp:237]     Train net output #0: loss = 0.0537056 (* 1 = 0.0537056 loss)
I0428 15:04:57.398999 27120 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0428 15:05:02.812217 27120 solver.cpp:218] Iteration 8928 (2.21678 iter/s, 5.41325s/12 iters), loss = 0.0132831
I0428 15:05:02.812263 27120 solver.cpp:237]     Train net output #0: loss = 0.0132831 (* 1 = 0.0132831 loss)
I0428 15:05:02.812271 27120 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0428 15:05:08.096061 27120 solver.cpp:218] Iteration 8940 (2.27108 iter/s, 5.28382s/12 iters), loss = 0.0495958
I0428 15:05:08.096108 27120 solver.cpp:237]     Train net output #0: loss = 0.0495958 (* 1 = 0.0495958 loss)
I0428 15:05:08.096118 27120 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0428 15:05:13.485997 27120 solver.cpp:218] Iteration 8952 (2.22638 iter/s, 5.38991s/12 iters), loss = 0.0240573
I0428 15:05:13.486058 27120 solver.cpp:237]     Train net output #0: loss = 0.0240572 (* 1 = 0.0240572 loss)
I0428 15:05:13.486073 27120 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0428 15:05:18.850286 27120 solver.cpp:218] Iteration 8964 (2.23703 iter/s, 5.36426s/12 iters), loss = 0.0173339
I0428 15:05:18.850335 27120 solver.cpp:237]     Train net output #0: loss = 0.0173339 (* 1 = 0.0173339 loss)
I0428 15:05:18.850345 27120 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0428 15:05:23.699863 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0428 15:05:29.274580 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0428 15:05:33.338809 27120 solver.cpp:330] Iteration 8976, Testing net (#0)
I0428 15:05:33.338829 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:05:34.397786 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:05:38.422019 27120 solver.cpp:397]     Test net output #0: accuracy = 0.555147
I0428 15:05:38.422062 27120 solver.cpp:397]     Test net output #1: loss = 2.63687 (* 1 = 2.63687 loss)
I0428 15:05:38.558739 27120 solver.cpp:218] Iteration 8976 (0.608873 iter/s, 19.7086s/12 iters), loss = 0.047989
I0428 15:05:38.558796 27120 solver.cpp:237]     Train net output #0: loss = 0.047989 (* 1 = 0.047989 loss)
I0428 15:05:38.558807 27120 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0428 15:05:43.135164 27120 solver.cpp:218] Iteration 8988 (2.62215 iter/s, 4.57639s/12 iters), loss = 0.028691
I0428 15:05:43.135205 27120 solver.cpp:237]     Train net output #0: loss = 0.0286909 (* 1 = 0.0286909 loss)
I0428 15:05:43.135215 27120 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0428 15:05:46.700412 27120 blocking_queue.cpp:49] Waiting for data
I0428 15:05:48.596565 27120 solver.cpp:218] Iteration 9000 (2.19725 iter/s, 5.46138s/12 iters), loss = 0.0516929
I0428 15:05:48.596611 27120 solver.cpp:237]     Train net output #0: loss = 0.0516929 (* 1 = 0.0516929 loss)
I0428 15:05:48.596621 27120 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0428 15:05:49.335784 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:05:54.008359 27120 solver.cpp:218] Iteration 9012 (2.21739 iter/s, 5.41177s/12 iters), loss = 0.0640737
I0428 15:05:54.008463 27120 solver.cpp:237]     Train net output #0: loss = 0.0640737 (* 1 = 0.0640737 loss)
I0428 15:05:54.008472 27120 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0428 15:05:59.426399 27120 solver.cpp:218] Iteration 9024 (2.21485 iter/s, 5.41797s/12 iters), loss = 0.0344473
I0428 15:05:59.426445 27120 solver.cpp:237]     Train net output #0: loss = 0.0344473 (* 1 = 0.0344473 loss)
I0428 15:05:59.426453 27120 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0428 15:06:04.836901 27120 solver.cpp:218] Iteration 9036 (2.21791 iter/s, 5.41049s/12 iters), loss = 0.0657275
I0428 15:06:04.836942 27120 solver.cpp:237]     Train net output #0: loss = 0.0657275 (* 1 = 0.0657275 loss)
I0428 15:06:04.836951 27120 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0428 15:06:10.208782 27120 solver.cpp:218] Iteration 9048 (2.23386 iter/s, 5.37186s/12 iters), loss = 0.0221227
I0428 15:06:10.208827 27120 solver.cpp:237]     Train net output #0: loss = 0.0221227 (* 1 = 0.0221227 loss)
I0428 15:06:10.208834 27120 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0428 15:06:15.567510 27120 solver.cpp:218] Iteration 9060 (2.23935 iter/s, 5.35871s/12 iters), loss = 0.0529863
I0428 15:06:15.567562 27120 solver.cpp:237]     Train net output #0: loss = 0.0529862 (* 1 = 0.0529862 loss)
I0428 15:06:15.567574 27120 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0428 15:06:20.928288 27120 solver.cpp:218] Iteration 9072 (2.23849 iter/s, 5.36075s/12 iters), loss = 0.0302983
I0428 15:06:20.928334 27120 solver.cpp:237]     Train net output #0: loss = 0.0302983 (* 1 = 0.0302983 loss)
I0428 15:06:20.928344 27120 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0428 15:06:23.081264 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0428 15:06:28.529172 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0428 15:06:33.147544 27120 solver.cpp:330] Iteration 9078, Testing net (#0)
I0428 15:06:33.147562 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:06:34.101912 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:06:38.332736 27120 solver.cpp:397]     Test net output #0: accuracy = 0.560662
I0428 15:06:38.332767 27120 solver.cpp:397]     Test net output #1: loss = 2.71411 (* 1 = 2.71411 loss)
I0428 15:06:40.341275 27120 solver.cpp:218] Iteration 9084 (0.61814 iter/s, 19.4131s/12 iters), loss = 0.0646318
I0428 15:06:40.341315 27120 solver.cpp:237]     Train net output #0: loss = 0.0646318 (* 1 = 0.0646318 loss)
I0428 15:06:40.341323 27120 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0428 15:06:45.732116 27120 solver.cpp:218] Iteration 9096 (2.22601 iter/s, 5.39082s/12 iters), loss = 0.049959
I0428 15:06:45.732165 27120 solver.cpp:237]     Train net output #0: loss = 0.049959 (* 1 = 0.049959 loss)
I0428 15:06:45.732174 27120 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0428 15:06:48.813066 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:06:51.032387 27120 solver.cpp:218] Iteration 9108 (2.26405 iter/s, 5.30025s/12 iters), loss = 0.0127044
I0428 15:06:51.032434 27120 solver.cpp:237]     Train net output #0: loss = 0.0127044 (* 1 = 0.0127044 loss)
I0428 15:06:51.032444 27120 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0428 15:06:56.264183 27120 solver.cpp:218] Iteration 9120 (2.29368 iter/s, 5.23177s/12 iters), loss = 0.0511084
I0428 15:06:56.264232 27120 solver.cpp:237]     Train net output #0: loss = 0.0511084 (* 1 = 0.0511084 loss)
I0428 15:06:56.264241 27120 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0428 15:07:01.641286 27120 solver.cpp:218] Iteration 9132 (2.2317 iter/s, 5.37707s/12 iters), loss = 0.079295
I0428 15:07:01.641422 27120 solver.cpp:237]     Train net output #0: loss = 0.079295 (* 1 = 0.079295 loss)
I0428 15:07:01.641433 27120 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0428 15:07:07.008690 27120 solver.cpp:218] Iteration 9144 (2.23576 iter/s, 5.36729s/12 iters), loss = 0.0705079
I0428 15:07:07.008739 27120 solver.cpp:237]     Train net output #0: loss = 0.0705079 (* 1 = 0.0705079 loss)
I0428 15:07:07.008749 27120 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0428 15:07:12.304232 27120 solver.cpp:218] Iteration 9156 (2.26607 iter/s, 5.29552s/12 iters), loss = 0.0523603
I0428 15:07:12.304271 27120 solver.cpp:237]     Train net output #0: loss = 0.0523603 (* 1 = 0.0523603 loss)
I0428 15:07:12.304280 27120 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0428 15:07:17.673274 27120 solver.cpp:218] Iteration 9168 (2.23504 iter/s, 5.36903s/12 iters), loss = 0.0542607
I0428 15:07:17.673307 27120 solver.cpp:237]     Train net output #0: loss = 0.0542607 (* 1 = 0.0542607 loss)
I0428 15:07:17.673314 27120 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0428 15:07:22.548765 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0428 15:07:27.132787 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0428 15:07:32.752241 27120 solver.cpp:330] Iteration 9180, Testing net (#0)
I0428 15:07:32.752388 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:07:33.670536 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:07:37.782068 27120 solver.cpp:397]     Test net output #0: accuracy = 0.563113
I0428 15:07:37.782117 27120 solver.cpp:397]     Test net output #1: loss = 2.708 (* 1 = 2.708 loss)
I0428 15:07:37.918929 27120 solver.cpp:218] Iteration 9180 (0.592716 iter/s, 20.2458s/12 iters), loss = 0.0118752
I0428 15:07:37.920449 27120 solver.cpp:237]     Train net output #0: loss = 0.0118752 (* 1 = 0.0118752 loss)
I0428 15:07:37.920462 27120 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0428 15:07:42.338886 27120 solver.cpp:218] Iteration 9192 (2.71588 iter/s, 4.41846s/12 iters), loss = 0.0398255
I0428 15:07:42.338933 27120 solver.cpp:237]     Train net output #0: loss = 0.0398255 (* 1 = 0.0398255 loss)
I0428 15:07:42.338941 27120 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0428 15:07:47.715353 27120 solver.cpp:218] Iteration 9204 (2.23196 iter/s, 5.37645s/12 iters), loss = 0.0921972
I0428 15:07:47.715399 27120 solver.cpp:237]     Train net output #0: loss = 0.0921972 (* 1 = 0.0921972 loss)
I0428 15:07:47.715409 27120 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0428 15:07:47.781823 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:07:53.225927 27120 solver.cpp:218] Iteration 9216 (2.17764 iter/s, 5.51055s/12 iters), loss = 0.0693783
I0428 15:07:53.225972 27120 solver.cpp:237]     Train net output #0: loss = 0.0693783 (* 1 = 0.0693783 loss)
I0428 15:07:53.225982 27120 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0428 15:07:58.592782 27120 solver.cpp:218] Iteration 9228 (2.23595 iter/s, 5.36683s/12 iters), loss = 0.0279533
I0428 15:07:58.592828 27120 solver.cpp:237]     Train net output #0: loss = 0.0279533 (* 1 = 0.0279533 loss)
I0428 15:07:58.592837 27120 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0428 15:08:03.956398 27120 solver.cpp:218] Iteration 9240 (2.23731 iter/s, 5.36359s/12 iters), loss = 0.0205567
I0428 15:08:03.956527 27120 solver.cpp:237]     Train net output #0: loss = 0.0205567 (* 1 = 0.0205567 loss)
I0428 15:08:03.956537 27120 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0428 15:08:09.337929 27120 solver.cpp:218] Iteration 9252 (2.22989 iter/s, 5.38143s/12 iters), loss = 0.0312006
I0428 15:08:09.337972 27120 solver.cpp:237]     Train net output #0: loss = 0.0312006 (* 1 = 0.0312006 loss)
I0428 15:08:09.337980 27120 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0428 15:08:14.703168 27120 solver.cpp:218] Iteration 9264 (2.23663 iter/s, 5.36522s/12 iters), loss = 0.0381681
I0428 15:08:14.703214 27120 solver.cpp:237]     Train net output #0: loss = 0.0381681 (* 1 = 0.0381681 loss)
I0428 15:08:14.703223 27120 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0428 15:08:20.058043 27120 solver.cpp:218] Iteration 9276 (2.24096 iter/s, 5.35485s/12 iters), loss = 0.00590109
I0428 15:08:20.058090 27120 solver.cpp:237]     Train net output #0: loss = 0.00590108 (* 1 = 0.00590108 loss)
I0428 15:08:20.058100 27120 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0428 15:08:22.204535 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0428 15:08:24.626382 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0428 15:08:26.688828 27120 solver.cpp:330] Iteration 9282, Testing net (#0)
I0428 15:08:26.688848 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:08:27.528738 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:08:31.622886 27120 solver.cpp:397]     Test net output #0: accuracy = 0.557598
I0428 15:08:31.622920 27120 solver.cpp:397]     Test net output #1: loss = 2.77036 (* 1 = 2.77036 loss)
I0428 15:08:33.594735 27120 solver.cpp:218] Iteration 9288 (0.886476 iter/s, 13.5367s/12 iters), loss = 0.0172811
I0428 15:08:33.594774 27120 solver.cpp:237]     Train net output #0: loss = 0.017281 (* 1 = 0.017281 loss)
I0428 15:08:33.594784 27120 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0428 15:08:38.986654 27120 solver.cpp:218] Iteration 9300 (2.22556 iter/s, 5.3919s/12 iters), loss = 0.0277243
I0428 15:08:38.986819 27120 solver.cpp:237]     Train net output #0: loss = 0.0277243 (* 1 = 0.0277243 loss)
I0428 15:08:38.986829 27120 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0428 15:08:41.359117 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:08:44.375856 27120 solver.cpp:218] Iteration 9312 (2.22673 iter/s, 5.38907s/12 iters), loss = 0.0331642
I0428 15:08:44.375898 27120 solver.cpp:237]     Train net output #0: loss = 0.0331642 (* 1 = 0.0331642 loss)
I0428 15:08:44.375907 27120 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0428 15:08:49.698973 27120 solver.cpp:218] Iteration 9324 (2.25433 iter/s, 5.32309s/12 iters), loss = 0.0469051
I0428 15:08:49.699021 27120 solver.cpp:237]     Train net output #0: loss = 0.0469051 (* 1 = 0.0469051 loss)
I0428 15:08:49.699031 27120 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0428 15:08:54.973547 27120 solver.cpp:218] Iteration 9336 (2.27507 iter/s, 5.27456s/12 iters), loss = 0.0488943
I0428 15:08:54.973590 27120 solver.cpp:237]     Train net output #0: loss = 0.0488943 (* 1 = 0.0488943 loss)
I0428 15:08:54.973599 27120 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0428 15:09:00.264043 27120 solver.cpp:218] Iteration 9348 (2.26822 iter/s, 5.29048s/12 iters), loss = 0.0379497
I0428 15:09:00.264091 27120 solver.cpp:237]     Train net output #0: loss = 0.0379497 (* 1 = 0.0379497 loss)
I0428 15:09:00.264099 27120 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0428 15:09:05.608664 27120 solver.cpp:218] Iteration 9360 (2.24526 iter/s, 5.3446s/12 iters), loss = 0.0681163
I0428 15:09:05.608726 27120 solver.cpp:237]     Train net output #0: loss = 0.0681163 (* 1 = 0.0681163 loss)
I0428 15:09:05.608739 27120 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0428 15:09:10.965004 27120 solver.cpp:218] Iteration 9372 (2.24035 iter/s, 5.35631s/12 iters), loss = 0.0113192
I0428 15:09:10.965124 27120 solver.cpp:237]     Train net output #0: loss = 0.0113192 (* 1 = 0.0113192 loss)
I0428 15:09:10.965134 27120 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0428 15:09:15.791002 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0428 15:09:18.400377 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0428 15:09:20.473269 27120 solver.cpp:330] Iteration 9384, Testing net (#0)
I0428 15:09:20.473294 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:09:21.266229 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:09:25.570729 27120 solver.cpp:397]     Test net output #0: accuracy = 0.568015
I0428 15:09:25.570755 27120 solver.cpp:397]     Test net output #1: loss = 2.68909 (* 1 = 2.68909 loss)
I0428 15:09:25.706584 27120 solver.cpp:218] Iteration 9384 (0.814024 iter/s, 14.7416s/12 iters), loss = 0.0164104
I0428 15:09:25.706656 27120 solver.cpp:237]     Train net output #0: loss = 0.0164104 (* 1 = 0.0164104 loss)
I0428 15:09:25.706666 27120 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0428 15:09:30.024551 27120 solver.cpp:218] Iteration 9396 (2.77912 iter/s, 4.31792s/12 iters), loss = 0.0185705
I0428 15:09:30.024600 27120 solver.cpp:237]     Train net output #0: loss = 0.0185705 (* 1 = 0.0185705 loss)
I0428 15:09:30.024608 27120 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0428 15:09:34.685703 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:09:35.411366 27120 solver.cpp:218] Iteration 9408 (2.22767 iter/s, 5.3868s/12 iters), loss = 0.0442407
I0428 15:09:35.411407 27120 solver.cpp:237]     Train net output #0: loss = 0.0442407 (* 1 = 0.0442407 loss)
I0428 15:09:35.411417 27120 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0428 15:09:40.783246 27120 solver.cpp:218] Iteration 9420 (2.23386 iter/s, 5.37186s/12 iters), loss = 0.0448377
I0428 15:09:40.783303 27120 solver.cpp:237]     Train net output #0: loss = 0.0448377 (* 1 = 0.0448377 loss)
I0428 15:09:40.783313 27120 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0428 15:09:46.146961 27120 solver.cpp:218] Iteration 9432 (2.23727 iter/s, 5.36369s/12 iters), loss = 0.0445999
I0428 15:09:46.147099 27120 solver.cpp:237]     Train net output #0: loss = 0.0445998 (* 1 = 0.0445998 loss)
I0428 15:09:46.147109 27120 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0428 15:09:51.505530 27120 solver.cpp:218] Iteration 9444 (2.23945 iter/s, 5.35846s/12 iters), loss = 0.0107291
I0428 15:09:51.505573 27120 solver.cpp:237]     Train net output #0: loss = 0.010729 (* 1 = 0.010729 loss)
I0428 15:09:51.505581 27120 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0428 15:09:56.793941 27120 solver.cpp:218] Iteration 9456 (2.26912 iter/s, 5.28839s/12 iters), loss = 0.0215415
I0428 15:09:56.793988 27120 solver.cpp:237]     Train net output #0: loss = 0.0215414 (* 1 = 0.0215414 loss)
I0428 15:09:56.793998 27120 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0428 15:10:02.191368 27120 solver.cpp:218] Iteration 9468 (2.22329 iter/s, 5.39741s/12 iters), loss = 0.0098884
I0428 15:10:02.191406 27120 solver.cpp:237]     Train net output #0: loss = 0.00988835 (* 1 = 0.00988835 loss)
I0428 15:10:02.191414 27120 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0428 15:10:07.555928 27120 solver.cpp:218] Iteration 9480 (2.23691 iter/s, 5.36455s/12 iters), loss = 0.00689483
I0428 15:10:07.555979 27120 solver.cpp:237]     Train net output #0: loss = 0.00689478 (* 1 = 0.00689478 loss)
I0428 15:10:07.555986 27120 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0428 15:10:09.698089 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0428 15:10:12.304910 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0428 15:10:14.346678 27120 solver.cpp:330] Iteration 9486, Testing net (#0)
I0428 15:10:14.346696 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:10:15.107223 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:10:19.534301 27120 solver.cpp:397]     Test net output #0: accuracy = 0.558824
I0428 15:10:19.534443 27120 solver.cpp:397]     Test net output #1: loss = 2.7067 (* 1 = 2.7067 loss)
I0428 15:10:21.520367 27120 solver.cpp:218] Iteration 9492 (0.859323 iter/s, 13.9645s/12 iters), loss = 0.0278142
I0428 15:10:21.520416 27120 solver.cpp:237]     Train net output #0: loss = 0.0278142 (* 1 = 0.0278142 loss)
I0428 15:10:21.520424 27120 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0428 15:10:26.893059 27120 solver.cpp:218] Iteration 9504 (2.23353 iter/s, 5.37267s/12 iters), loss = 0.0355444
I0428 15:10:26.893107 27120 solver.cpp:237]     Train net output #0: loss = 0.0355443 (* 1 = 0.0355443 loss)
I0428 15:10:26.893117 27120 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0428 15:10:28.456661 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:10:32.272076 27120 solver.cpp:218] Iteration 9516 (2.2309 iter/s, 5.37899s/12 iters), loss = 0.0147167
I0428 15:10:32.272125 27120 solver.cpp:237]     Train net output #0: loss = 0.0147166 (* 1 = 0.0147166 loss)
I0428 15:10:32.272135 27120 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0428 15:10:37.617691 27120 solver.cpp:218] Iteration 9528 (2.24484 iter/s, 5.34559s/12 iters), loss = 0.0222891
I0428 15:10:37.617731 27120 solver.cpp:237]     Train net output #0: loss = 0.0222891 (* 1 = 0.0222891 loss)
I0428 15:10:37.617739 27120 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0428 15:10:42.975203 27120 solver.cpp:218] Iteration 9540 (2.23986 iter/s, 5.35749s/12 iters), loss = 0.0344445
I0428 15:10:42.975267 27120 solver.cpp:237]     Train net output #0: loss = 0.0344444 (* 1 = 0.0344444 loss)
I0428 15:10:42.975281 27120 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0428 15:10:48.260345 27120 solver.cpp:218] Iteration 9552 (2.27053 iter/s, 5.28511s/12 iters), loss = 0.0475661
I0428 15:10:48.260387 27120 solver.cpp:237]     Train net output #0: loss = 0.0475661 (* 1 = 0.0475661 loss)
I0428 15:10:48.260396 27120 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0428 15:10:53.534663 27120 solver.cpp:218] Iteration 9564 (2.27518 iter/s, 5.2743s/12 iters), loss = 0.0495614
I0428 15:10:53.534811 27120 solver.cpp:237]     Train net output #0: loss = 0.0495614 (* 1 = 0.0495614 loss)
I0428 15:10:53.534821 27120 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0428 15:10:58.884917 27120 solver.cpp:218] Iteration 9576 (2.24294 iter/s, 5.35012s/12 iters), loss = 0.039433
I0428 15:10:58.884981 27120 solver.cpp:237]     Train net output #0: loss = 0.0394329 (* 1 = 0.0394329 loss)
I0428 15:10:58.884995 27120 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0428 15:11:03.646209 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0428 15:11:06.253731 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0428 15:11:08.299504 27120 solver.cpp:330] Iteration 9588, Testing net (#0)
I0428 15:11:08.299525 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:11:09.031147 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:11:13.464673 27120 solver.cpp:397]     Test net output #0: accuracy = 0.563113
I0428 15:11:13.464704 27120 solver.cpp:397]     Test net output #1: loss = 2.74364 (* 1 = 2.74364 loss)
I0428 15:11:13.602999 27120 solver.cpp:218] Iteration 9588 (0.815321 iter/s, 14.7181s/12 iters), loss = 0.0318141
I0428 15:11:13.603068 27120 solver.cpp:237]     Train net output #0: loss = 0.0318141 (* 1 = 0.0318141 loss)
I0428 15:11:13.603078 27120 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0428 15:11:18.090744 27120 solver.cpp:218] Iteration 9600 (2.67398 iter/s, 4.48769s/12 iters), loss = 0.0185339
I0428 15:11:18.090796 27120 solver.cpp:237]     Train net output #0: loss = 0.0185338 (* 1 = 0.0185338 loss)
I0428 15:11:18.090806 27120 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0428 15:11:21.965337 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:11:23.487686 27120 solver.cpp:218] Iteration 9612 (2.22349 iter/s, 5.39692s/12 iters), loss = 0.059263
I0428 15:11:23.487730 27120 solver.cpp:237]     Train net output #0: loss = 0.059263 (* 1 = 0.059263 loss)
I0428 15:11:23.487738 27120 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0428 15:11:28.844228 27120 solver.cpp:218] Iteration 9624 (2.24026 iter/s, 5.35653s/12 iters), loss = 0.0582217
I0428 15:11:28.844353 27120 solver.cpp:237]     Train net output #0: loss = 0.0582217 (* 1 = 0.0582217 loss)
I0428 15:11:28.844364 27120 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0428 15:11:34.224356 27120 solver.cpp:218] Iteration 9636 (2.23047 iter/s, 5.38003s/12 iters), loss = 0.017754
I0428 15:11:34.224401 27120 solver.cpp:237]     Train net output #0: loss = 0.0177539 (* 1 = 0.0177539 loss)
I0428 15:11:34.224411 27120 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0428 15:11:39.460199 27120 solver.cpp:218] Iteration 9648 (2.2919 iter/s, 5.23582s/12 iters), loss = 0.054741
I0428 15:11:39.460245 27120 solver.cpp:237]     Train net output #0: loss = 0.0547409 (* 1 = 0.0547409 loss)
I0428 15:11:39.460254 27120 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0428 15:11:44.849740 27120 solver.cpp:218] Iteration 9660 (2.22654 iter/s, 5.38952s/12 iters), loss = 0.0391036
I0428 15:11:44.849781 27120 solver.cpp:237]     Train net output #0: loss = 0.0391035 (* 1 = 0.0391035 loss)
I0428 15:11:44.849788 27120 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0428 15:11:50.254124 27120 solver.cpp:218] Iteration 9672 (2.22042 iter/s, 5.40437s/12 iters), loss = 0.00939723
I0428 15:11:50.254164 27120 solver.cpp:237]     Train net output #0: loss = 0.00939719 (* 1 = 0.00939719 loss)
I0428 15:11:50.254173 27120 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0428 15:11:55.823801 27120 solver.cpp:218] Iteration 9684 (2.15453 iter/s, 5.56967s/12 iters), loss = 0.0220444
I0428 15:11:55.823848 27120 solver.cpp:237]     Train net output #0: loss = 0.0220443 (* 1 = 0.0220443 loss)
I0428 15:11:55.823856 27120 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0428 15:11:58.014308 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0428 15:12:01.047189 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0428 15:12:03.097507 27120 solver.cpp:330] Iteration 9690, Testing net (#0)
I0428 15:12:03.097532 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:12:03.771898 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:12:07.079705 27120 blocking_queue.cpp:49] Waiting for data
I0428 15:12:08.171541 27120 solver.cpp:397]     Test net output #0: accuracy = 0.560662
I0428 15:12:08.171567 27120 solver.cpp:397]     Test net output #1: loss = 2.77211 (* 1 = 2.77211 loss)
I0428 15:12:10.280210 27120 solver.cpp:218] Iteration 9696 (0.830078 iter/s, 14.4565s/12 iters), loss = 0.0170391
I0428 15:12:10.280261 27120 solver.cpp:237]     Train net output #0: loss = 0.0170391 (* 1 = 0.0170391 loss)
I0428 15:12:10.280269 27120 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0428 15:12:15.726245 27120 solver.cpp:218] Iteration 9708 (2.20345 iter/s, 5.446s/12 iters), loss = 0.00504002
I0428 15:12:15.726306 27120 solver.cpp:237]     Train net output #0: loss = 0.00503999 (* 1 = 0.00503999 loss)
I0428 15:12:15.726318 27120 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0428 15:12:16.492911 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:12:21.136045 27120 solver.cpp:218] Iteration 9720 (2.21821 iter/s, 5.40977s/12 iters), loss = 0.015481
I0428 15:12:21.136108 27120 solver.cpp:237]     Train net output #0: loss = 0.015481 (* 1 = 0.015481 loss)
I0428 15:12:21.136121 27120 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0428 15:12:26.436725 27120 solver.cpp:218] Iteration 9732 (2.26388 iter/s, 5.30064s/12 iters), loss = 0.054602
I0428 15:12:26.436771 27120 solver.cpp:237]     Train net output #0: loss = 0.054602 (* 1 = 0.054602 loss)
I0428 15:12:26.436781 27120 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0428 15:12:31.716658 27120 solver.cpp:218] Iteration 9744 (2.27277 iter/s, 5.27991s/12 iters), loss = 0.0298523
I0428 15:12:31.716743 27120 solver.cpp:237]     Train net output #0: loss = 0.0298523 (* 1 = 0.0298523 loss)
I0428 15:12:31.716753 27120 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0428 15:12:37.088696 27120 solver.cpp:218] Iteration 9756 (2.23381 iter/s, 5.37198s/12 iters), loss = 0.0396429
I0428 15:12:37.088739 27120 solver.cpp:237]     Train net output #0: loss = 0.0396428 (* 1 = 0.0396428 loss)
I0428 15:12:37.088748 27120 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0428 15:12:42.449890 27120 solver.cpp:218] Iteration 9768 (2.23831 iter/s, 5.36118s/12 iters), loss = 0.0392177
I0428 15:12:42.449934 27120 solver.cpp:237]     Train net output #0: loss = 0.0392177 (* 1 = 0.0392177 loss)
I0428 15:12:42.449942 27120 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0428 15:12:47.808089 27120 solver.cpp:218] Iteration 9780 (2.23957 iter/s, 5.35818s/12 iters), loss = 0.0272312
I0428 15:12:47.808133 27120 solver.cpp:237]     Train net output #0: loss = 0.0272311 (* 1 = 0.0272311 loss)
I0428 15:12:47.808142 27120 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0428 15:12:52.648103 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0428 15:12:55.803016 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0428 15:12:58.753705 27120 solver.cpp:330] Iteration 9792, Testing net (#0)
I0428 15:12:58.753723 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:12:59.385015 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:13:03.894080 27120 solver.cpp:397]     Test net output #0: accuracy = 0.564951
I0428 15:13:03.894263 27120 solver.cpp:397]     Test net output #1: loss = 2.72328 (* 1 = 2.72328 loss)
I0428 15:13:04.027472 27120 solver.cpp:218] Iteration 9792 (0.739852 iter/s, 16.2195s/12 iters), loss = 0.00938185
I0428 15:13:04.027519 27120 solver.cpp:237]     Train net output #0: loss = 0.00938181 (* 1 = 0.00938181 loss)
I0428 15:13:04.027529 27120 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0428 15:13:08.516264 27120 solver.cpp:218] Iteration 9804 (2.67334 iter/s, 4.48877s/12 iters), loss = 0.0462613
I0428 15:13:08.516310 27120 solver.cpp:237]     Train net output #0: loss = 0.0462613 (* 1 = 0.0462613 loss)
I0428 15:13:08.516320 27120 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0428 15:13:11.637212 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:13:13.836407 27120 solver.cpp:218] Iteration 9816 (2.25559 iter/s, 5.32012s/12 iters), loss = 0.0345716
I0428 15:13:13.836455 27120 solver.cpp:237]     Train net output #0: loss = 0.0345715 (* 1 = 0.0345715 loss)
I0428 15:13:13.836464 27120 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0428 15:13:19.109575 27120 solver.cpp:218] Iteration 9828 (2.27568 iter/s, 5.27314s/12 iters), loss = 0.00931963
I0428 15:13:19.109614 27120 solver.cpp:237]     Train net output #0: loss = 0.0093196 (* 1 = 0.0093196 loss)
I0428 15:13:19.109622 27120 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0428 15:13:24.518522 27120 solver.cpp:218] Iteration 9840 (2.21856 iter/s, 5.40893s/12 iters), loss = 0.0360915
I0428 15:13:24.518570 27120 solver.cpp:237]     Train net output #0: loss = 0.0360915 (* 1 = 0.0360915 loss)
I0428 15:13:24.518580 27120 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0428 15:13:29.882791 27120 solver.cpp:218] Iteration 9852 (2.23703 iter/s, 5.36425s/12 iters), loss = 0.0086102
I0428 15:13:29.882839 27120 solver.cpp:237]     Train net output #0: loss = 0.00861016 (* 1 = 0.00861016 loss)
I0428 15:13:29.882848 27120 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0428 15:13:35.069195 27120 solver.cpp:218] Iteration 9864 (2.31375 iter/s, 5.18639s/12 iters), loss = 0.0397443
I0428 15:13:35.069327 27120 solver.cpp:237]     Train net output #0: loss = 0.0397443 (* 1 = 0.0397443 loss)
I0428 15:13:35.069336 27120 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0428 15:13:40.339171 27120 solver.cpp:218] Iteration 9876 (2.2771 iter/s, 5.26987s/12 iters), loss = 0.0298787
I0428 15:13:40.339221 27120 solver.cpp:237]     Train net output #0: loss = 0.0298787 (* 1 = 0.0298787 loss)
I0428 15:13:40.339229 27120 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0428 15:13:45.687669 27120 solver.cpp:218] Iteration 9888 (2.24363 iter/s, 5.34847s/12 iters), loss = 0.0608755
I0428 15:13:45.687716 27120 solver.cpp:237]     Train net output #0: loss = 0.0608754 (* 1 = 0.0608754 loss)
I0428 15:13:45.687726 27120 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0428 15:13:47.853890 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0428 15:13:50.454277 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0428 15:13:52.490136 27120 solver.cpp:330] Iteration 9894, Testing net (#0)
I0428 15:13:52.490162 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:13:53.075651 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:13:57.629330 27120 solver.cpp:397]     Test net output #0: accuracy = 0.561275
I0428 15:13:57.629374 27120 solver.cpp:397]     Test net output #1: loss = 2.76046 (* 1 = 2.76046 loss)
I0428 15:13:59.644415 27120 solver.cpp:218] Iteration 9900 (0.859796 iter/s, 13.9568s/12 iters), loss = 0.0777455
I0428 15:13:59.644464 27120 solver.cpp:237]     Train net output #0: loss = 0.0777455 (* 1 = 0.0777455 loss)
I0428 15:13:59.644472 27120 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0428 15:14:05.032166 27120 solver.cpp:218] Iteration 9912 (2.22728 iter/s, 5.38773s/12 iters), loss = 0.042004
I0428 15:14:05.032212 27120 solver.cpp:237]     Train net output #0: loss = 0.042004 (* 1 = 0.042004 loss)
I0428 15:14:05.032220 27120 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0428 15:14:05.129081 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:14:10.410233 27120 solver.cpp:218] Iteration 9924 (2.23129 iter/s, 5.37805s/12 iters), loss = 0.0559353
I0428 15:14:10.410275 27120 solver.cpp:237]     Train net output #0: loss = 0.0559353 (* 1 = 0.0559353 loss)
I0428 15:14:10.410284 27120 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0428 15:14:15.615002 27120 solver.cpp:218] Iteration 9936 (2.30559 iter/s, 5.20475s/12 iters), loss = 0.0214442
I0428 15:14:15.615051 27120 solver.cpp:237]     Train net output #0: loss = 0.0214442 (* 1 = 0.0214442 loss)
I0428 15:14:15.615059 27120 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0428 15:14:21.036514 27120 solver.cpp:218] Iteration 9948 (2.21341 iter/s, 5.4215s/12 iters), loss = 0.0108265
I0428 15:14:21.036553 27120 solver.cpp:237]     Train net output #0: loss = 0.0108265 (* 1 = 0.0108265 loss)
I0428 15:14:21.036562 27120 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0428 15:14:26.429317 27120 solver.cpp:218] Iteration 9960 (2.22519 iter/s, 5.39279s/12 iters), loss = 0.0121216
I0428 15:14:26.429364 27120 solver.cpp:237]     Train net output #0: loss = 0.0121216 (* 1 = 0.0121216 loss)
I0428 15:14:26.429373 27120 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0428 15:14:31.796663 27120 solver.cpp:218] Iteration 9972 (2.23575 iter/s, 5.36733s/12 iters), loss = 0.00880744
I0428 15:14:31.796708 27120 solver.cpp:237]     Train net output #0: loss = 0.00880741 (* 1 = 0.00880741 loss)
I0428 15:14:31.796717 27120 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0428 15:14:37.151156 27120 solver.cpp:218] Iteration 9984 (2.24112 iter/s, 5.35448s/12 iters), loss = 0.0182177
I0428 15:14:37.151322 27120 solver.cpp:237]     Train net output #0: loss = 0.0182176 (* 1 = 0.0182176 loss)
I0428 15:14:37.151331 27120 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0428 15:14:41.897014 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0428 15:14:45.168272 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0428 15:14:48.287516 27120 solver.cpp:330] Iteration 9996, Testing net (#0)
I0428 15:14:48.287539 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:14:48.808827 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:14:53.467262 27120 solver.cpp:397]     Test net output #0: accuracy = 0.575368
I0428 15:14:53.467298 27120 solver.cpp:397]     Test net output #1: loss = 2.74093 (* 1 = 2.74093 loss)
I0428 15:14:53.596462 27120 solver.cpp:218] Iteration 9996 (0.729693 iter/s, 16.4453s/12 iters), loss = 0.00712935
I0428 15:14:53.596504 27120 solver.cpp:237]     Train net output #0: loss = 0.00712932 (* 1 = 0.00712932 loss)
I0428 15:14:53.596513 27120 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0428 15:14:58.091256 27120 solver.cpp:218] Iteration 10008 (2.66977 iter/s, 4.49477s/12 iters), loss = 0.0336075
I0428 15:14:58.091302 27120 solver.cpp:237]     Train net output #0: loss = 0.0336075 (* 1 = 0.0336075 loss)
I0428 15:14:58.091311 27120 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0428 15:15:00.403074 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:15:03.396260 27120 solver.cpp:218] Iteration 10020 (2.26202 iter/s, 5.30498s/12 iters), loss = 0.0355848
I0428 15:15:03.396307 27120 solver.cpp:237]     Train net output #0: loss = 0.0355848 (* 1 = 0.0355848 loss)
I0428 15:15:03.396317 27120 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0428 15:15:08.758179 27120 solver.cpp:218] Iteration 10032 (2.23801 iter/s, 5.3619s/12 iters), loss = 0.0195318
I0428 15:15:08.758337 27120 solver.cpp:237]     Train net output #0: loss = 0.0195318 (* 1 = 0.0195318 loss)
I0428 15:15:08.758347 27120 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0428 15:15:14.267252 27120 solver.cpp:218] Iteration 10044 (2.17828 iter/s, 5.50894s/12 iters), loss = 0.0527647
I0428 15:15:14.267300 27120 solver.cpp:237]     Train net output #0: loss = 0.0527647 (* 1 = 0.0527647 loss)
I0428 15:15:14.267309 27120 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0428 15:15:19.856000 27120 solver.cpp:218] Iteration 10056 (2.14718 iter/s, 5.58873s/12 iters), loss = 0.0152132
I0428 15:15:19.856040 27120 solver.cpp:237]     Train net output #0: loss = 0.0152132 (* 1 = 0.0152132 loss)
I0428 15:15:19.856051 27120 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0428 15:15:25.293574 27120 solver.cpp:218] Iteration 10068 (2.20688 iter/s, 5.43754s/12 iters), loss = 0.0308832
I0428 15:15:25.293648 27120 solver.cpp:237]     Train net output #0: loss = 0.0308832 (* 1 = 0.0308832 loss)
I0428 15:15:25.293669 27120 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0428 15:15:30.695701 27120 solver.cpp:218] Iteration 10080 (2.22136 iter/s, 5.40208s/12 iters), loss = 0.0251709
I0428 15:15:30.695741 27120 solver.cpp:237]     Train net output #0: loss = 0.0251708 (* 1 = 0.0251708 loss)
I0428 15:15:30.695749 27120 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0428 15:15:35.983716 27120 solver.cpp:218] Iteration 10092 (2.26929 iter/s, 5.28799s/12 iters), loss = 0.00940303
I0428 15:15:35.983765 27120 solver.cpp:237]     Train net output #0: loss = 0.00940301 (* 1 = 0.00940301 loss)
I0428 15:15:35.983774 27120 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0428 15:15:38.141369 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0428 15:15:41.483089 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0428 15:15:43.533047 27120 solver.cpp:330] Iteration 10098, Testing net (#0)
I0428 15:15:43.533067 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:15:44.028189 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:15:48.751479 27120 solver.cpp:397]     Test net output #0: accuracy = 0.568627
I0428 15:15:48.751509 27120 solver.cpp:397]     Test net output #1: loss = 2.7016 (* 1 = 2.7016 loss)
I0428 15:15:50.751286 27120 solver.cpp:218] Iteration 10104 (0.812589 iter/s, 14.7676s/12 iters), loss = 0.00948434
I0428 15:15:50.751335 27120 solver.cpp:237]     Train net output #0: loss = 0.00948433 (* 1 = 0.00948433 loss)
I0428 15:15:50.751344 27120 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0428 15:15:55.368304 27147 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:15:56.059687 27120 solver.cpp:218] Iteration 10116 (2.26058 iter/s, 5.30838s/12 iters), loss = 0.0300712
I0428 15:15:56.059724 27120 solver.cpp:237]     Train net output #0: loss = 0.0300712 (* 1 = 0.0300712 loss)
I0428 15:15:56.059731 27120 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0428 15:16:01.434119 27120 solver.cpp:218] Iteration 10128 (2.2328 iter/s, 5.37442s/12 iters), loss = 0.0538103
I0428 15:16:01.434165 27120 solver.cpp:237]     Train net output #0: loss = 0.0538103 (* 1 = 0.0538103 loss)
I0428 15:16:01.434173 27120 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0428 15:16:06.784631 27120 solver.cpp:218] Iteration 10140 (2.24278 iter/s, 5.35049s/12 iters), loss = 0.0448166
I0428 15:16:06.784667 27120 solver.cpp:237]     Train net output #0: loss = 0.0448166 (* 1 = 0.0448166 loss)
I0428 15:16:06.784675 27120 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0428 15:16:12.206560 27120 solver.cpp:218] Iteration 10152 (2.21325 iter/s, 5.4219s/12 iters), loss = 0.0454775
I0428 15:16:12.206673 27120 solver.cpp:237]     Train net output #0: loss = 0.0454775 (* 1 = 0.0454775 loss)
I0428 15:16:12.206683 27120 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0428 15:16:17.492746 27120 solver.cpp:218] Iteration 10164 (2.2701 iter/s, 5.2861s/12 iters), loss = 0.0308024
I0428 15:16:17.492785 27120 solver.cpp:237]     Train net output #0: loss = 0.0308024 (* 1 = 0.0308024 loss)
I0428 15:16:17.492794 27120 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0428 15:16:22.861258 27120 solver.cpp:218] Iteration 10176 (2.23526 iter/s, 5.36849s/12 iters), loss = 0.00952581
I0428 15:16:22.861301 27120 solver.cpp:237]     Train net output #0: loss = 0.0095258 (* 1 = 0.0095258 loss)
I0428 15:16:22.861310 27120 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0428 15:16:28.217396 27120 solver.cpp:218] Iteration 10188 (2.24043 iter/s, 5.35612s/12 iters), loss = 0.0123749
I0428 15:16:28.217439 27120 solver.cpp:237]     Train net output #0: loss = 0.0123749 (* 1 = 0.0123749 loss)
I0428 15:16:28.217449 27120 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0428 15:16:32.982407 27120 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0428 15:16:36.722889 27120 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0428 15:16:38.813506 27120 solver.cpp:310] Iteration 10200, loss = 0.0216321
I0428 15:16:38.813529 27120 solver.cpp:330] Iteration 10200, Testing net (#0)
I0428 15:16:38.813534 27120 net.cpp:676] Ignoring source layer train-data
I0428 15:16:39.237754 27157 data_layer.cpp:73] Restarting data prefetching from start.
I0428 15:16:43.891575 27120 solver.cpp:397]     Test net output #0: accuracy = 0.570466
I0428 15:16:43.891681 27120 solver.cpp:397]     Test net output #1: loss = 2.7403 (* 1 = 2.7403 loss)
I0428 15:16:43.891687 27120 solver.cpp:315] Optimization Done.
I0428 15:16:43.891691 27120 caffe.cpp:259] Optimization Done.