DIGITS-CNN/cars/architecture-investigations/fc/4-layers/8192/caffe_output.log

I0410 01:29:54.554296 24451 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-220932-e584/solver.prototxt
I0410 01:29:54.554482 24451 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0410 01:29:54.554491 24451 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0410 01:29:54.554564 24451 caffe.cpp:218] Using GPUs 3
I0410 01:29:54.576622 24451 caffe.cpp:223] GPU 3: GeForce GTX 1080 Ti
I0410 01:29:54.873715 24451 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: 3
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0410 01:29:54.874498 24451 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0410 01:29:54.875114 24451 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0410 01:29:54.875130 24451 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0410 01:29:54.875293 24451 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.6"
type: "InnerProduct"
bottom: "fc7.5"
top: "fc7.6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.6"
type: "ReLU"
bottom: "fc7.6"
top: "fc7.6"
}
layer {
name: "drop7.6"
type: "Dropout"
bottom: "fc7.6"
top: "fc7.6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.6"
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"
}
I0410 01:29:54.875389 24451 layer_factory.hpp:77] Creating layer train-data
I0410 01:29:54.877025 24451 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0410 01:29:54.877233 24451 net.cpp:84] Creating Layer train-data
I0410 01:29:54.877243 24451 net.cpp:380] train-data -> data
I0410 01:29:54.877261 24451 net.cpp:380] train-data -> label
I0410 01:29:54.877274 24451 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 01:29:54.885766 24451 data_layer.cpp:45] output data size: 128,3,227,227
I0410 01:29:55.026679 24451 net.cpp:122] Setting up train-data
I0410 01:29:55.026701 24451 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0410 01:29:55.026707 24451 net.cpp:129] Top shape: 128 (128)
I0410 01:29:55.026711 24451 net.cpp:137] Memory required for data: 79149056
I0410 01:29:55.026741 24451 layer_factory.hpp:77] Creating layer conv1
I0410 01:29:55.026764 24451 net.cpp:84] Creating Layer conv1
I0410 01:29:55.026770 24451 net.cpp:406] conv1 <- data
I0410 01:29:55.026783 24451 net.cpp:380] conv1 -> conv1
I0410 01:29:55.620322 24451 net.cpp:122] Setting up conv1
I0410 01:29:55.620345 24451 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 01:29:55.620350 24451 net.cpp:137] Memory required for data: 227833856
I0410 01:29:55.620369 24451 layer_factory.hpp:77] Creating layer relu1
I0410 01:29:55.620381 24451 net.cpp:84] Creating Layer relu1
I0410 01:29:55.620386 24451 net.cpp:406] relu1 <- conv1
I0410 01:29:55.620393 24451 net.cpp:367] relu1 -> conv1 (in-place)
I0410 01:29:55.620684 24451 net.cpp:122] Setting up relu1
I0410 01:29:55.620693 24451 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 01:29:55.620697 24451 net.cpp:137] Memory required for data: 376518656
I0410 01:29:55.620702 24451 layer_factory.hpp:77] Creating layer norm1
I0410 01:29:55.620710 24451 net.cpp:84] Creating Layer norm1
I0410 01:29:55.620714 24451 net.cpp:406] norm1 <- conv1
I0410 01:29:55.620720 24451 net.cpp:380] norm1 -> norm1
I0410 01:29:55.621166 24451 net.cpp:122] Setting up norm1
I0410 01:29:55.621176 24451 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 01:29:55.621181 24451 net.cpp:137] Memory required for data: 525203456
I0410 01:29:55.621184 24451 layer_factory.hpp:77] Creating layer pool1
I0410 01:29:55.621192 24451 net.cpp:84] Creating Layer pool1
I0410 01:29:55.621196 24451 net.cpp:406] pool1 <- norm1
I0410 01:29:55.621201 24451 net.cpp:380] pool1 -> pool1
I0410 01:29:55.621237 24451 net.cpp:122] Setting up pool1
I0410 01:29:55.621243 24451 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0410 01:29:55.621248 24451 net.cpp:137] Memory required for data: 561035264
I0410 01:29:55.621250 24451 layer_factory.hpp:77] Creating layer conv2
I0410 01:29:55.621261 24451 net.cpp:84] Creating Layer conv2
I0410 01:29:55.621264 24451 net.cpp:406] conv2 <- pool1
I0410 01:29:55.621270 24451 net.cpp:380] conv2 -> conv2
I0410 01:29:55.631134 24451 net.cpp:122] Setting up conv2
I0410 01:29:55.631153 24451 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 01:29:55.631156 24451 net.cpp:137] Memory required for data: 656586752
I0410 01:29:55.631168 24451 layer_factory.hpp:77] Creating layer relu2
I0410 01:29:55.631177 24451 net.cpp:84] Creating Layer relu2
I0410 01:29:55.631181 24451 net.cpp:406] relu2 <- conv2
I0410 01:29:55.631187 24451 net.cpp:367] relu2 -> conv2 (in-place)
I0410 01:29:55.631613 24451 net.cpp:122] Setting up relu2
I0410 01:29:55.631623 24451 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 01:29:55.631626 24451 net.cpp:137] Memory required for data: 752138240
I0410 01:29:55.631630 24451 layer_factory.hpp:77] Creating layer norm2
I0410 01:29:55.631639 24451 net.cpp:84] Creating Layer norm2
I0410 01:29:55.631641 24451 net.cpp:406] norm2 <- conv2
I0410 01:29:55.631647 24451 net.cpp:380] norm2 -> norm2
I0410 01:29:55.631934 24451 net.cpp:122] Setting up norm2
I0410 01:29:55.631942 24451 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 01:29:55.631947 24451 net.cpp:137] Memory required for data: 847689728
I0410 01:29:55.631950 24451 layer_factory.hpp:77] Creating layer pool2
I0410 01:29:55.631958 24451 net.cpp:84] Creating Layer pool2
I0410 01:29:55.631963 24451 net.cpp:406] pool2 <- norm2
I0410 01:29:55.631968 24451 net.cpp:380] pool2 -> pool2
I0410 01:29:55.631994 24451 net.cpp:122] Setting up pool2
I0410 01:29:55.631999 24451 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 01:29:55.632002 24451 net.cpp:137] Memory required for data: 869840896
I0410 01:29:55.632006 24451 layer_factory.hpp:77] Creating layer conv3
I0410 01:29:55.632015 24451 net.cpp:84] Creating Layer conv3
I0410 01:29:55.632019 24451 net.cpp:406] conv3 <- pool2
I0410 01:29:55.632025 24451 net.cpp:380] conv3 -> conv3
I0410 01:29:55.641849 24451 net.cpp:122] Setting up conv3
I0410 01:29:55.641865 24451 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 01:29:55.641868 24451 net.cpp:137] Memory required for data: 903067648
I0410 01:29:55.641896 24451 layer_factory.hpp:77] Creating layer relu3
I0410 01:29:55.641904 24451 net.cpp:84] Creating Layer relu3
I0410 01:29:55.641908 24451 net.cpp:406] relu3 <- conv3
I0410 01:29:55.641914 24451 net.cpp:367] relu3 -> conv3 (in-place)
I0410 01:29:55.642356 24451 net.cpp:122] Setting up relu3
I0410 01:29:55.642365 24451 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 01:29:55.642369 24451 net.cpp:137] Memory required for data: 936294400
I0410 01:29:55.642374 24451 layer_factory.hpp:77] Creating layer conv4
I0410 01:29:55.642383 24451 net.cpp:84] Creating Layer conv4
I0410 01:29:55.642387 24451 net.cpp:406] conv4 <- conv3
I0410 01:29:55.642393 24451 net.cpp:380] conv4 -> conv4
I0410 01:29:55.652381 24451 net.cpp:122] Setting up conv4
I0410 01:29:55.652398 24451 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 01:29:55.652402 24451 net.cpp:137] Memory required for data: 969521152
I0410 01:29:55.652412 24451 layer_factory.hpp:77] Creating layer relu4
I0410 01:29:55.652421 24451 net.cpp:84] Creating Layer relu4
I0410 01:29:55.652426 24451 net.cpp:406] relu4 <- conv4
I0410 01:29:55.652431 24451 net.cpp:367] relu4 -> conv4 (in-place)
I0410 01:29:55.652709 24451 net.cpp:122] Setting up relu4
I0410 01:29:55.652716 24451 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 01:29:55.652720 24451 net.cpp:137] Memory required for data: 1002747904
I0410 01:29:55.652724 24451 layer_factory.hpp:77] Creating layer conv5
I0410 01:29:55.652734 24451 net.cpp:84] Creating Layer conv5
I0410 01:29:55.652737 24451 net.cpp:406] conv5 <- conv4
I0410 01:29:55.652743 24451 net.cpp:380] conv5 -> conv5
I0410 01:29:55.660715 24451 net.cpp:122] Setting up conv5
I0410 01:29:55.660732 24451 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 01:29:55.660735 24451 net.cpp:137] Memory required for data: 1024899072
I0410 01:29:55.660748 24451 layer_factory.hpp:77] Creating layer relu5
I0410 01:29:55.660758 24451 net.cpp:84] Creating Layer relu5
I0410 01:29:55.660761 24451 net.cpp:406] relu5 <- conv5
I0410 01:29:55.660768 24451 net.cpp:367] relu5 -> conv5 (in-place)
I0410 01:29:55.661183 24451 net.cpp:122] Setting up relu5
I0410 01:29:55.661192 24451 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 01:29:55.661196 24451 net.cpp:137] Memory required for data: 1047050240
I0410 01:29:55.661201 24451 layer_factory.hpp:77] Creating layer pool5
I0410 01:29:55.661207 24451 net.cpp:84] Creating Layer pool5
I0410 01:29:55.661211 24451 net.cpp:406] pool5 <- conv5
I0410 01:29:55.661217 24451 net.cpp:380] pool5 -> pool5
I0410 01:29:55.661252 24451 net.cpp:122] Setting up pool5
I0410 01:29:55.661257 24451 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0410 01:29:55.661262 24451 net.cpp:137] Memory required for data: 1051768832
I0410 01:29:55.661264 24451 layer_factory.hpp:77] Creating layer fc6
I0410 01:29:55.661273 24451 net.cpp:84] Creating Layer fc6
I0410 01:29:55.661276 24451 net.cpp:406] fc6 <- pool5
I0410 01:29:55.661283 24451 net.cpp:380] fc6 -> fc6
I0410 01:29:56.379945 24451 net.cpp:122] Setting up fc6
I0410 01:29:56.379966 24451 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 01:29:56.379971 24451 net.cpp:137] Memory required for data: 1055963136
I0410 01:29:56.379981 24451 layer_factory.hpp:77] Creating layer relu6
I0410 01:29:56.379990 24451 net.cpp:84] Creating Layer relu6
I0410 01:29:56.379994 24451 net.cpp:406] relu6 <- fc6
I0410 01:29:56.380002 24451 net.cpp:367] relu6 -> fc6 (in-place)
I0410 01:29:56.380556 24451 net.cpp:122] Setting up relu6
I0410 01:29:56.380568 24451 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 01:29:56.380570 24451 net.cpp:137] Memory required for data: 1060157440
I0410 01:29:56.380574 24451 layer_factory.hpp:77] Creating layer drop6
I0410 01:29:56.380581 24451 net.cpp:84] Creating Layer drop6
I0410 01:29:56.380585 24451 net.cpp:406] drop6 <- fc6
I0410 01:29:56.380591 24451 net.cpp:367] drop6 -> fc6 (in-place)
I0410 01:29:56.380617 24451 net.cpp:122] Setting up drop6
I0410 01:29:56.380623 24451 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 01:29:56.380651 24451 net.cpp:137] Memory required for data: 1064351744
I0410 01:29:56.380656 24451 layer_factory.hpp:77] Creating layer fc7
I0410 01:29:56.380662 24451 net.cpp:84] Creating Layer fc7
I0410 01:29:56.380666 24451 net.cpp:406] fc7 <- fc6
I0410 01:29:56.380672 24451 net.cpp:380] fc7 -> fc7
I0410 01:29:57.008792 24451 net.cpp:122] Setting up fc7
I0410 01:29:57.008814 24451 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 01:29:57.008819 24451 net.cpp:137] Memory required for data: 1068546048
I0410 01:29:57.008828 24451 layer_factory.hpp:77] Creating layer relu7
I0410 01:29:57.008838 24451 net.cpp:84] Creating Layer relu7
I0410 01:29:57.008843 24451 net.cpp:406] relu7 <- fc7
I0410 01:29:57.008850 24451 net.cpp:367] relu7 -> fc7 (in-place)
I0410 01:29:57.009395 24451 net.cpp:122] Setting up relu7
I0410 01:29:57.009405 24451 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 01:29:57.009409 24451 net.cpp:137] Memory required for data: 1072740352
I0410 01:29:57.009414 24451 layer_factory.hpp:77] Creating layer drop7
I0410 01:29:57.009421 24451 net.cpp:84] Creating Layer drop7
I0410 01:29:57.009426 24451 net.cpp:406] drop7 <- fc7
I0410 01:29:57.009431 24451 net.cpp:367] drop7 -> fc7 (in-place)
I0410 01:29:57.009455 24451 net.cpp:122] Setting up drop7
I0410 01:29:57.009460 24451 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 01:29:57.009465 24451 net.cpp:137] Memory required for data: 1076934656
I0410 01:29:57.009469 24451 layer_factory.hpp:77] Creating layer fc7.5
I0410 01:29:57.009476 24451 net.cpp:84] Creating Layer fc7.5
I0410 01:29:57.009480 24451 net.cpp:406] fc7.5 <- fc7
I0410 01:29:57.009487 24451 net.cpp:380] fc7.5 -> fc7.5
I0410 01:29:57.635810 24451 net.cpp:122] Setting up fc7.5
I0410 01:29:57.635833 24451 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 01:29:57.635836 24451 net.cpp:137] Memory required for data: 1081128960
I0410 01:29:57.635845 24451 layer_factory.hpp:77] Creating layer relu7.5
I0410 01:29:57.635855 24451 net.cpp:84] Creating Layer relu7.5
I0410 01:29:57.635859 24451 net.cpp:406] relu7.5 <- fc7.5
I0410 01:29:57.635866 24451 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0410 01:29:57.637253 24451 net.cpp:122] Setting up relu7.5
I0410 01:29:57.637264 24451 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 01:29:57.637267 24451 net.cpp:137] Memory required for data: 1085323264
I0410 01:29:57.637271 24451 layer_factory.hpp:77] Creating layer drop7.5
I0410 01:29:57.637279 24451 net.cpp:84] Creating Layer drop7.5
I0410 01:29:57.637282 24451 net.cpp:406] drop7.5 <- fc7.5
I0410 01:29:57.637288 24451 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0410 01:29:57.637311 24451 net.cpp:122] Setting up drop7.5
I0410 01:29:57.637316 24451 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 01:29:57.637320 24451 net.cpp:137] Memory required for data: 1089517568
I0410 01:29:57.637323 24451 layer_factory.hpp:77] Creating layer fc7.6
I0410 01:29:57.637331 24451 net.cpp:84] Creating Layer fc7.6
I0410 01:29:57.637334 24451 net.cpp:406] fc7.6 <- fc7.5
I0410 01:29:57.637339 24451 net.cpp:380] fc7.6 -> fc7.6
I0410 01:29:58.264122 24451 net.cpp:122] Setting up fc7.6
I0410 01:29:58.264147 24451 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 01:29:58.264153 24451 net.cpp:137] Memory required for data: 1093711872
I0410 01:29:58.264168 24451 layer_factory.hpp:77] Creating layer relu7.6
I0410 01:29:58.264178 24451 net.cpp:84] Creating Layer relu7.6
I0410 01:29:58.264184 24451 net.cpp:406] relu7.6 <- fc7.6
I0410 01:29:58.264191 24451 net.cpp:367] relu7.6 -> fc7.6 (in-place)
I0410 01:29:58.264753 24451 net.cpp:122] Setting up relu7.6
I0410 01:29:58.264765 24451 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 01:29:58.264768 24451 net.cpp:137] Memory required for data: 1097906176
I0410 01:29:58.264773 24451 layer_factory.hpp:77] Creating layer drop7.6
I0410 01:29:58.264781 24451 net.cpp:84] Creating Layer drop7.6
I0410 01:29:58.264786 24451 net.cpp:406] drop7.6 <- fc7.6
I0410 01:29:58.264793 24451 net.cpp:367] drop7.6 -> fc7.6 (in-place)
I0410 01:29:58.264817 24451 net.cpp:122] Setting up drop7.6
I0410 01:29:58.264822 24451 net.cpp:129] Top shape: 128 8192 (1048576)
I0410 01:29:58.264847 24451 net.cpp:137] Memory required for data: 1102100480
I0410 01:29:58.264853 24451 layer_factory.hpp:77] Creating layer fc8
I0410 01:29:58.264859 24451 net.cpp:84] Creating Layer fc8
I0410 01:29:58.264864 24451 net.cpp:406] fc8 <- fc7.6
I0410 01:29:58.264870 24451 net.cpp:380] fc8 -> fc8
I0410 01:29:58.281172 24451 net.cpp:122] Setting up fc8
I0410 01:29:58.281186 24451 net.cpp:129] Top shape: 128 196 (25088)
I0410 01:29:58.281190 24451 net.cpp:137] Memory required for data: 1102200832
I0410 01:29:58.281200 24451 layer_factory.hpp:77] Creating layer loss
I0410 01:29:58.281208 24451 net.cpp:84] Creating Layer loss
I0410 01:29:58.281213 24451 net.cpp:406] loss <- fc8
I0410 01:29:58.281219 24451 net.cpp:406] loss <- label
I0410 01:29:58.281225 24451 net.cpp:380] loss -> loss
I0410 01:29:58.281235 24451 layer_factory.hpp:77] Creating layer loss
I0410 01:29:58.281811 24451 net.cpp:122] Setting up loss
I0410 01:29:58.281821 24451 net.cpp:129] Top shape: (1)
I0410 01:29:58.281824 24451 net.cpp:132]     with loss weight 1
I0410 01:29:58.281842 24451 net.cpp:137] Memory required for data: 1102200836
I0410 01:29:58.281847 24451 net.cpp:198] loss needs backward computation.
I0410 01:29:58.281853 24451 net.cpp:198] fc8 needs backward computation.
I0410 01:29:58.281858 24451 net.cpp:198] drop7.6 needs backward computation.
I0410 01:29:58.281862 24451 net.cpp:198] relu7.6 needs backward computation.
I0410 01:29:58.281865 24451 net.cpp:198] fc7.6 needs backward computation.
I0410 01:29:58.281869 24451 net.cpp:198] drop7.5 needs backward computation.
I0410 01:29:58.281872 24451 net.cpp:198] relu7.5 needs backward computation.
I0410 01:29:58.281877 24451 net.cpp:198] fc7.5 needs backward computation.
I0410 01:29:58.281880 24451 net.cpp:198] drop7 needs backward computation.
I0410 01:29:58.281884 24451 net.cpp:198] relu7 needs backward computation.
I0410 01:29:58.281888 24451 net.cpp:198] fc7 needs backward computation.
I0410 01:29:58.281893 24451 net.cpp:198] drop6 needs backward computation.
I0410 01:29:58.281895 24451 net.cpp:198] relu6 needs backward computation.
I0410 01:29:58.281899 24451 net.cpp:198] fc6 needs backward computation.
I0410 01:29:58.281903 24451 net.cpp:198] pool5 needs backward computation.
I0410 01:29:58.281906 24451 net.cpp:198] relu5 needs backward computation.
I0410 01:29:58.281910 24451 net.cpp:198] conv5 needs backward computation.
I0410 01:29:58.281914 24451 net.cpp:198] relu4 needs backward computation.
I0410 01:29:58.281919 24451 net.cpp:198] conv4 needs backward computation.
I0410 01:29:58.281922 24451 net.cpp:198] relu3 needs backward computation.
I0410 01:29:58.281926 24451 net.cpp:198] conv3 needs backward computation.
I0410 01:29:58.281930 24451 net.cpp:198] pool2 needs backward computation.
I0410 01:29:58.281935 24451 net.cpp:198] norm2 needs backward computation.
I0410 01:29:58.281941 24451 net.cpp:198] relu2 needs backward computation.
I0410 01:29:58.281945 24451 net.cpp:198] conv2 needs backward computation.
I0410 01:29:58.281949 24451 net.cpp:198] pool1 needs backward computation.
I0410 01:29:58.281973 24451 net.cpp:198] norm1 needs backward computation.
I0410 01:29:58.281980 24451 net.cpp:198] relu1 needs backward computation.
I0410 01:29:58.281985 24451 net.cpp:198] conv1 needs backward computation.
I0410 01:29:58.281989 24451 net.cpp:200] train-data does not need backward computation.
I0410 01:29:58.281993 24451 net.cpp:242] This network produces output loss
I0410 01:29:58.282008 24451 net.cpp:255] Network initialization done.
I0410 01:29:58.282536 24451 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0410 01:29:58.282569 24451 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0410 01:29:58.282729 24451 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.5"
type: "InnerProduct"
bottom: "fc7"
top: "fc7.5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.5"
type: "ReLU"
bottom: "fc7.5"
top: "fc7.5"
}
layer {
name: "drop7.5"
type: "Dropout"
bottom: "fc7.5"
top: "fc7.5"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7.6"
type: "InnerProduct"
bottom: "fc7.5"
top: "fc7.6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7.6"
type: "ReLU"
bottom: "fc7.6"
top: "fc7.6"
}
layer {
name: "drop7.6"
type: "Dropout"
bottom: "fc7.6"
top: "fc7.6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7.6"
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"
}
I0410 01:29:58.282836 24451 layer_factory.hpp:77] Creating layer val-data
I0410 01:29:58.284412 24451 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0410 01:29:58.284617 24451 net.cpp:84] Creating Layer val-data
I0410 01:29:58.284627 24451 net.cpp:380] val-data -> data
I0410 01:29:58.284636 24451 net.cpp:380] val-data -> label
I0410 01:29:58.284643 24451 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 01:29:58.288043 24451 data_layer.cpp:45] output data size: 32,3,227,227
I0410 01:29:58.318419 24451 net.cpp:122] Setting up val-data
I0410 01:29:58.318439 24451 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0410 01:29:58.318444 24451 net.cpp:129] Top shape: 32 (32)
I0410 01:29:58.318447 24451 net.cpp:137] Memory required for data: 19787264
I0410 01:29:58.318454 24451 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0410 01:29:58.318466 24451 net.cpp:84] Creating Layer label_val-data_1_split
I0410 01:29:58.318471 24451 net.cpp:406] label_val-data_1_split <- label
I0410 01:29:58.318478 24451 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0410 01:29:58.318488 24451 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0410 01:29:58.318538 24451 net.cpp:122] Setting up label_val-data_1_split
I0410 01:29:58.318543 24451 net.cpp:129] Top shape: 32 (32)
I0410 01:29:58.318547 24451 net.cpp:129] Top shape: 32 (32)
I0410 01:29:58.318552 24451 net.cpp:137] Memory required for data: 19787520
I0410 01:29:58.318554 24451 layer_factory.hpp:77] Creating layer conv1
I0410 01:29:58.318567 24451 net.cpp:84] Creating Layer conv1
I0410 01:29:58.318570 24451 net.cpp:406] conv1 <- data
I0410 01:29:58.318576 24451 net.cpp:380] conv1 -> conv1
I0410 01:29:58.320500 24451 net.cpp:122] Setting up conv1
I0410 01:29:58.320511 24451 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 01:29:58.320514 24451 net.cpp:137] Memory required for data: 56958720
I0410 01:29:58.320525 24451 layer_factory.hpp:77] Creating layer relu1
I0410 01:29:58.320531 24451 net.cpp:84] Creating Layer relu1
I0410 01:29:58.320535 24451 net.cpp:406] relu1 <- conv1
I0410 01:29:58.320559 24451 net.cpp:367] relu1 -> conv1 (in-place)
I0410 01:29:58.322075 24451 net.cpp:122] Setting up relu1
I0410 01:29:58.322088 24451 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 01:29:58.322090 24451 net.cpp:137] Memory required for data: 94129920
I0410 01:29:58.322094 24451 layer_factory.hpp:77] Creating layer norm1
I0410 01:29:58.322103 24451 net.cpp:84] Creating Layer norm1
I0410 01:29:58.322108 24451 net.cpp:406] norm1 <- conv1
I0410 01:29:58.322113 24451 net.cpp:380] norm1 -> norm1
I0410 01:29:58.322571 24451 net.cpp:122] Setting up norm1
I0410 01:29:58.322582 24451 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 01:29:58.322584 24451 net.cpp:137] Memory required for data: 131301120
I0410 01:29:58.322588 24451 layer_factory.hpp:77] Creating layer pool1
I0410 01:29:58.322595 24451 net.cpp:84] Creating Layer pool1
I0410 01:29:58.322599 24451 net.cpp:406] pool1 <- norm1
I0410 01:29:58.322604 24451 net.cpp:380] pool1 -> pool1
I0410 01:29:58.322633 24451 net.cpp:122] Setting up pool1
I0410 01:29:58.322638 24451 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0410 01:29:58.322641 24451 net.cpp:137] Memory required for data: 140259072
I0410 01:29:58.322645 24451 layer_factory.hpp:77] Creating layer conv2
I0410 01:29:58.322654 24451 net.cpp:84] Creating Layer conv2
I0410 01:29:58.322657 24451 net.cpp:406] conv2 <- pool1
I0410 01:29:58.322662 24451 net.cpp:380] conv2 -> conv2
I0410 01:29:58.331749 24451 net.cpp:122] Setting up conv2
I0410 01:29:58.331763 24451 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 01:29:58.331766 24451 net.cpp:137] Memory required for data: 164146944
I0410 01:29:58.331775 24451 layer_factory.hpp:77] Creating layer relu2
I0410 01:29:58.331784 24451 net.cpp:84] Creating Layer relu2
I0410 01:29:58.331789 24451 net.cpp:406] relu2 <- conv2
I0410 01:29:58.331794 24451 net.cpp:367] relu2 -> conv2 (in-place)
I0410 01:29:58.332144 24451 net.cpp:122] Setting up relu2
I0410 01:29:58.332152 24451 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 01:29:58.332155 24451 net.cpp:137] Memory required for data: 188034816
I0410 01:29:58.332159 24451 layer_factory.hpp:77] Creating layer norm2
I0410 01:29:58.332168 24451 net.cpp:84] Creating Layer norm2
I0410 01:29:58.332172 24451 net.cpp:406] norm2 <- conv2
I0410 01:29:58.332180 24451 net.cpp:380] norm2 -> norm2
I0410 01:29:58.332696 24451 net.cpp:122] Setting up norm2
I0410 01:29:58.332705 24451 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 01:29:58.332710 24451 net.cpp:137] Memory required for data: 211922688
I0410 01:29:58.332713 24451 layer_factory.hpp:77] Creating layer pool2
I0410 01:29:58.332720 24451 net.cpp:84] Creating Layer pool2
I0410 01:29:58.332724 24451 net.cpp:406] pool2 <- norm2
I0410 01:29:58.332729 24451 net.cpp:380] pool2 -> pool2
I0410 01:29:58.332760 24451 net.cpp:122] Setting up pool2
I0410 01:29:58.332767 24451 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 01:29:58.332769 24451 net.cpp:137] Memory required for data: 217460480
I0410 01:29:58.332773 24451 layer_factory.hpp:77] Creating layer conv3
I0410 01:29:58.332782 24451 net.cpp:84] Creating Layer conv3
I0410 01:29:58.332785 24451 net.cpp:406] conv3 <- pool2
I0410 01:29:58.332792 24451 net.cpp:380] conv3 -> conv3
I0410 01:29:58.344218 24451 net.cpp:122] Setting up conv3
I0410 01:29:58.344235 24451 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 01:29:58.344239 24451 net.cpp:137] Memory required for data: 225767168
I0410 01:29:58.344252 24451 layer_factory.hpp:77] Creating layer relu3
I0410 01:29:58.344261 24451 net.cpp:84] Creating Layer relu3
I0410 01:29:58.344266 24451 net.cpp:406] relu3 <- conv3
I0410 01:29:58.344272 24451 net.cpp:367] relu3 -> conv3 (in-place)
I0410 01:29:58.344790 24451 net.cpp:122] Setting up relu3
I0410 01:29:58.344799 24451 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 01:29:58.344802 24451 net.cpp:137] Memory required for data: 234073856
I0410 01:29:58.344806 24451 layer_factory.hpp:77] Creating layer conv4
I0410 01:29:58.344817 24451 net.cpp:84] Creating Layer conv4
I0410 01:29:58.344821 24451 net.cpp:406] conv4 <- conv3
I0410 01:29:58.344846 24451 net.cpp:380] conv4 -> conv4
I0410 01:29:58.356678 24451 net.cpp:122] Setting up conv4
I0410 01:29:58.356695 24451 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 01:29:58.356699 24451 net.cpp:137] Memory required for data: 242380544
I0410 01:29:58.356709 24451 layer_factory.hpp:77] Creating layer relu4
I0410 01:29:58.356716 24451 net.cpp:84] Creating Layer relu4
I0410 01:29:58.356720 24451 net.cpp:406] relu4 <- conv4
I0410 01:29:58.356727 24451 net.cpp:367] relu4 -> conv4 (in-place)
I0410 01:29:58.358340 24451 net.cpp:122] Setting up relu4
I0410 01:29:58.358352 24451 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 01:29:58.358356 24451 net.cpp:137] Memory required for data: 250687232
I0410 01:29:58.358361 24451 layer_factory.hpp:77] Creating layer conv5
I0410 01:29:58.358372 24451 net.cpp:84] Creating Layer conv5
I0410 01:29:58.358377 24451 net.cpp:406] conv5 <- conv4
I0410 01:29:58.358386 24451 net.cpp:380] conv5 -> conv5
I0410 01:29:58.366039 24451 net.cpp:122] Setting up conv5
I0410 01:29:58.366055 24451 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 01:29:58.366058 24451 net.cpp:137] Memory required for data: 256225024
I0410 01:29:58.366070 24451 layer_factory.hpp:77] Creating layer relu5
I0410 01:29:58.366077 24451 net.cpp:84] Creating Layer relu5
I0410 01:29:58.366082 24451 net.cpp:406] relu5 <- conv5
I0410 01:29:58.366088 24451 net.cpp:367] relu5 -> conv5 (in-place)
I0410 01:29:58.366449 24451 net.cpp:122] Setting up relu5
I0410 01:29:58.366457 24451 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 01:29:58.366461 24451 net.cpp:137] Memory required for data: 261762816
I0410 01:29:58.366464 24451 layer_factory.hpp:77] Creating layer pool5
I0410 01:29:58.366474 24451 net.cpp:84] Creating Layer pool5
I0410 01:29:58.366478 24451 net.cpp:406] pool5 <- conv5
I0410 01:29:58.366484 24451 net.cpp:380] pool5 -> pool5
I0410 01:29:58.366523 24451 net.cpp:122] Setting up pool5
I0410 01:29:58.366529 24451 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0410 01:29:58.366533 24451 net.cpp:137] Memory required for data: 262942464
I0410 01:29:58.366537 24451 layer_factory.hpp:77] Creating layer fc6
I0410 01:29:58.366544 24451 net.cpp:84] Creating Layer fc6
I0410 01:29:58.366547 24451 net.cpp:406] fc6 <- pool5
I0410 01:29:58.366555 24451 net.cpp:380] fc6 -> fc6
I0410 01:29:59.077092 24451 net.cpp:122] Setting up fc6
I0410 01:29:59.077114 24451 net.cpp:129] Top shape: 32 8192 (262144)
I0410 01:29:59.077118 24451 net.cpp:137] Memory required for data: 263991040
I0410 01:29:59.077127 24451 layer_factory.hpp:77] Creating layer relu6
I0410 01:29:59.077137 24451 net.cpp:84] Creating Layer relu6
I0410 01:29:59.077142 24451 net.cpp:406] relu6 <- fc6
I0410 01:29:59.077148 24451 net.cpp:367] relu6 -> fc6 (in-place)
I0410 01:29:59.080541 24451 net.cpp:122] Setting up relu6
I0410 01:29:59.080554 24451 net.cpp:129] Top shape: 32 8192 (262144)
I0410 01:29:59.080556 24451 net.cpp:137] Memory required for data: 265039616
I0410 01:29:59.080560 24451 layer_factory.hpp:77] Creating layer drop6
I0410 01:29:59.080567 24451 net.cpp:84] Creating Layer drop6
I0410 01:29:59.080572 24451 net.cpp:406] drop6 <- fc6
I0410 01:29:59.080579 24451 net.cpp:367] drop6 -> fc6 (in-place)
I0410 01:29:59.080602 24451 net.cpp:122] Setting up drop6
I0410 01:29:59.080608 24451 net.cpp:129] Top shape: 32 8192 (262144)
I0410 01:29:59.080611 24451 net.cpp:137] Memory required for data: 266088192
I0410 01:29:59.080615 24451 layer_factory.hpp:77] Creating layer fc7
I0410 01:29:59.080623 24451 net.cpp:84] Creating Layer fc7
I0410 01:29:59.080626 24451 net.cpp:406] fc7 <- fc6
I0410 01:29:59.080634 24451 net.cpp:380] fc7 -> fc7
I0410 01:29:59.713482 24451 net.cpp:122] Setting up fc7
I0410 01:29:59.713505 24451 net.cpp:129] Top shape: 32 8192 (262144)
I0410 01:29:59.713508 24451 net.cpp:137] Memory required for data: 267136768
I0410 01:29:59.713516 24451 layer_factory.hpp:77] Creating layer relu7
I0410 01:29:59.713526 24451 net.cpp:84] Creating Layer relu7
I0410 01:29:59.713531 24451 net.cpp:406] relu7 <- fc7
I0410 01:29:59.713557 24451 net.cpp:367] relu7 -> fc7 (in-place)
I0410 01:29:59.714200 24451 net.cpp:122] Setting up relu7
I0410 01:29:59.714210 24451 net.cpp:129] Top shape: 32 8192 (262144)
I0410 01:29:59.714215 24451 net.cpp:137] Memory required for data: 268185344
I0410 01:29:59.714218 24451 layer_factory.hpp:77] Creating layer drop7
I0410 01:29:59.714226 24451 net.cpp:84] Creating Layer drop7
I0410 01:29:59.714229 24451 net.cpp:406] drop7 <- fc7
I0410 01:29:59.714234 24451 net.cpp:367] drop7 -> fc7 (in-place)
I0410 01:29:59.714262 24451 net.cpp:122] Setting up drop7
I0410 01:29:59.714267 24451 net.cpp:129] Top shape: 32 8192 (262144)
I0410 01:29:59.714270 24451 net.cpp:137] Memory required for data: 269233920
I0410 01:29:59.714273 24451 layer_factory.hpp:77] Creating layer fc7.5
I0410 01:29:59.714283 24451 net.cpp:84] Creating Layer fc7.5
I0410 01:29:59.714287 24451 net.cpp:406] fc7.5 <- fc7
I0410 01:29:59.714291 24451 net.cpp:380] fc7.5 -> fc7.5
I0410 01:30:00.345986 24451 net.cpp:122] Setting up fc7.5
I0410 01:30:00.346009 24451 net.cpp:129] Top shape: 32 8192 (262144)
I0410 01:30:00.346012 24451 net.cpp:137] Memory required for data: 270282496
I0410 01:30:00.346022 24451 layer_factory.hpp:77] Creating layer relu7.5
I0410 01:30:00.346032 24451 net.cpp:84] Creating Layer relu7.5
I0410 01:30:00.346037 24451 net.cpp:406] relu7.5 <- fc7.5
I0410 01:30:00.346045 24451 net.cpp:367] relu7.5 -> fc7.5 (in-place)
I0410 01:30:00.350298 24451 net.cpp:122] Setting up relu7.5
I0410 01:30:00.350309 24451 net.cpp:129] Top shape: 32 8192 (262144)
I0410 01:30:00.350312 24451 net.cpp:137] Memory required for data: 271331072
I0410 01:30:00.350317 24451 layer_factory.hpp:77] Creating layer drop7.5
I0410 01:30:00.350323 24451 net.cpp:84] Creating Layer drop7.5
I0410 01:30:00.350327 24451 net.cpp:406] drop7.5 <- fc7.5
I0410 01:30:00.350334 24451 net.cpp:367] drop7.5 -> fc7.5 (in-place)
I0410 01:30:00.350355 24451 net.cpp:122] Setting up drop7.5
I0410 01:30:00.350361 24451 net.cpp:129] Top shape: 32 8192 (262144)
I0410 01:30:00.350364 24451 net.cpp:137] Memory required for data: 272379648
I0410 01:30:00.350368 24451 layer_factory.hpp:77] Creating layer fc7.6
I0410 01:30:00.350376 24451 net.cpp:84] Creating Layer fc7.6
I0410 01:30:00.350380 24451 net.cpp:406] fc7.6 <- fc7.5
I0410 01:30:00.350386 24451 net.cpp:380] fc7.6 -> fc7.6
I0410 01:30:00.982791 24451 net.cpp:122] Setting up fc7.6
I0410 01:30:00.982810 24451 net.cpp:129] Top shape: 32 8192 (262144)
I0410 01:30:00.982815 24451 net.cpp:137] Memory required for data: 273428224
I0410 01:30:00.982827 24451 layer_factory.hpp:77] Creating layer relu7.6
I0410 01:30:00.982837 24451 net.cpp:84] Creating Layer relu7.6
I0410 01:30:00.982842 24451 net.cpp:406] relu7.6 <- fc7.6
I0410 01:30:00.982849 24451 net.cpp:367] relu7.6 -> fc7.6 (in-place)
I0410 01:30:00.983263 24451 net.cpp:122] Setting up relu7.6
I0410 01:30:00.983271 24451 net.cpp:129] Top shape: 32 8192 (262144)
I0410 01:30:00.983274 24451 net.cpp:137] Memory required for data: 274476800
I0410 01:30:00.983278 24451 layer_factory.hpp:77] Creating layer drop7.6
I0410 01:30:00.983284 24451 net.cpp:84] Creating Layer drop7.6
I0410 01:30:00.983289 24451 net.cpp:406] drop7.6 <- fc7.6
I0410 01:30:00.983295 24451 net.cpp:367] drop7.6 -> fc7.6 (in-place)
I0410 01:30:00.983317 24451 net.cpp:122] Setting up drop7.6
I0410 01:30:00.983323 24451 net.cpp:129] Top shape: 32 8192 (262144)
I0410 01:30:00.983327 24451 net.cpp:137] Memory required for data: 275525376
I0410 01:30:00.983331 24451 layer_factory.hpp:77] Creating layer fc8
I0410 01:30:00.983340 24451 net.cpp:84] Creating Layer fc8
I0410 01:30:00.983343 24451 net.cpp:406] fc8 <- fc7.6
I0410 01:30:00.983350 24451 net.cpp:380] fc8 -> fc8
I0410 01:30:01.000533 24451 net.cpp:122] Setting up fc8
I0410 01:30:01.000550 24451 net.cpp:129] Top shape: 32 196 (6272)
I0410 01:30:01.000553 24451 net.cpp:137] Memory required for data: 275550464
I0410 01:30:01.000562 24451 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0410 01:30:01.000572 24451 net.cpp:84] Creating Layer fc8_fc8_0_split
I0410 01:30:01.000597 24451 net.cpp:406] fc8_fc8_0_split <- fc8
I0410 01:30:01.000602 24451 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0410 01:30:01.000612 24451 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0410 01:30:01.000648 24451 net.cpp:122] Setting up fc8_fc8_0_split
I0410 01:30:01.000653 24451 net.cpp:129] Top shape: 32 196 (6272)
I0410 01:30:01.000658 24451 net.cpp:129] Top shape: 32 196 (6272)
I0410 01:30:01.000660 24451 net.cpp:137] Memory required for data: 275600640
I0410 01:30:01.000663 24451 layer_factory.hpp:77] Creating layer accuracy
I0410 01:30:01.000671 24451 net.cpp:84] Creating Layer accuracy
I0410 01:30:01.000675 24451 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0410 01:30:01.000680 24451 net.cpp:406] accuracy <- label_val-data_1_split_0
I0410 01:30:01.000685 24451 net.cpp:380] accuracy -> accuracy
I0410 01:30:01.000692 24451 net.cpp:122] Setting up accuracy
I0410 01:30:01.000697 24451 net.cpp:129] Top shape: (1)
I0410 01:30:01.000700 24451 net.cpp:137] Memory required for data: 275600644
I0410 01:30:01.000703 24451 layer_factory.hpp:77] Creating layer loss
I0410 01:30:01.000718 24451 net.cpp:84] Creating Layer loss
I0410 01:30:01.000722 24451 net.cpp:406] loss <- fc8_fc8_0_split_1
I0410 01:30:01.000726 24451 net.cpp:406] loss <- label_val-data_1_split_1
I0410 01:30:01.000732 24451 net.cpp:380] loss -> loss
I0410 01:30:01.000741 24451 layer_factory.hpp:77] Creating layer loss
I0410 01:30:01.001406 24451 net.cpp:122] Setting up loss
I0410 01:30:01.001416 24451 net.cpp:129] Top shape: (1)
I0410 01:30:01.001420 24451 net.cpp:132]     with loss weight 1
I0410 01:30:01.001430 24451 net.cpp:137] Memory required for data: 275600648
I0410 01:30:01.001433 24451 net.cpp:198] loss needs backward computation.
I0410 01:30:01.001438 24451 net.cpp:200] accuracy does not need backward computation.
I0410 01:30:01.001442 24451 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0410 01:30:01.001446 24451 net.cpp:198] fc8 needs backward computation.
I0410 01:30:01.001449 24451 net.cpp:198] drop7.6 needs backward computation.
I0410 01:30:01.001453 24451 net.cpp:198] relu7.6 needs backward computation.
I0410 01:30:01.001456 24451 net.cpp:198] fc7.6 needs backward computation.
I0410 01:30:01.001461 24451 net.cpp:198] drop7.5 needs backward computation.
I0410 01:30:01.001463 24451 net.cpp:198] relu7.5 needs backward computation.
I0410 01:30:01.001467 24451 net.cpp:198] fc7.5 needs backward computation.
I0410 01:30:01.001471 24451 net.cpp:198] drop7 needs backward computation.
I0410 01:30:01.001474 24451 net.cpp:198] relu7 needs backward computation.
I0410 01:30:01.001478 24451 net.cpp:198] fc7 needs backward computation.
I0410 01:30:01.001482 24451 net.cpp:198] drop6 needs backward computation.
I0410 01:30:01.001485 24451 net.cpp:198] relu6 needs backward computation.
I0410 01:30:01.001488 24451 net.cpp:198] fc6 needs backward computation.
I0410 01:30:01.001492 24451 net.cpp:198] pool5 needs backward computation.
I0410 01:30:01.001497 24451 net.cpp:198] relu5 needs backward computation.
I0410 01:30:01.001499 24451 net.cpp:198] conv5 needs backward computation.
I0410 01:30:01.001503 24451 net.cpp:198] relu4 needs backward computation.
I0410 01:30:01.001507 24451 net.cpp:198] conv4 needs backward computation.
I0410 01:30:01.001510 24451 net.cpp:198] relu3 needs backward computation.
I0410 01:30:01.001514 24451 net.cpp:198] conv3 needs backward computation.
I0410 01:30:01.001518 24451 net.cpp:198] pool2 needs backward computation.
I0410 01:30:01.001521 24451 net.cpp:198] norm2 needs backward computation.
I0410 01:30:01.001525 24451 net.cpp:198] relu2 needs backward computation.
I0410 01:30:01.001528 24451 net.cpp:198] conv2 needs backward computation.
I0410 01:30:01.001533 24451 net.cpp:198] pool1 needs backward computation.
I0410 01:30:01.001538 24451 net.cpp:198] norm1 needs backward computation.
I0410 01:30:01.001540 24451 net.cpp:198] relu1 needs backward computation.
I0410 01:30:01.001544 24451 net.cpp:198] conv1 needs backward computation.
I0410 01:30:01.001549 24451 net.cpp:200] label_val-data_1_split does not need backward computation.
I0410 01:30:01.001561 24451 net.cpp:200] val-data does not need backward computation.
I0410 01:30:01.001564 24451 net.cpp:242] This network produces output accuracy
I0410 01:30:01.001569 24451 net.cpp:242] This network produces output loss
I0410 01:30:01.001586 24451 net.cpp:255] Network initialization done.
I0410 01:30:01.001670 24451 solver.cpp:56] Solver scaffolding done.
I0410 01:30:01.002208 24451 caffe.cpp:248] Starting Optimization
I0410 01:30:01.002218 24451 solver.cpp:272] Solving
I0410 01:30:01.002223 24451 solver.cpp:273] Learning Rate Policy: exp
I0410 01:30:01.009513 24451 solver.cpp:330] Iteration 0, Testing net (#0)
I0410 01:30:01.009524 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:30:01.302436 24451 blocking_queue.cpp:49] Waiting for data
I0410 01:30:05.663153 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:30:05.711540 24451 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0410 01:30:05.711568 24451 solver.cpp:397]     Test net output #1: loss = 5.28057 (* 1 = 5.28057 loss)
I0410 01:30:05.847517 24451 solver.cpp:218] Iteration 0 (-6.93735e-39 iter/s, 4.84505s/12 iters), loss = 5.28564
I0410 01:30:05.849030 24451 solver.cpp:237]     Train net output #0: loss = 5.28564 (* 1 = 5.28564 loss)
I0410 01:30:05.849052 24451 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0410 01:30:09.793599 24451 solver.cpp:218] Iteration 12 (3.04229 iter/s, 3.94439s/12 iters), loss = 5.30199
I0410 01:30:09.793644 24451 solver.cpp:237]     Train net output #0: loss = 5.30199 (* 1 = 5.30199 loss)
I0410 01:30:09.793654 24451 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0410 01:30:14.636351 24451 solver.cpp:218] Iteration 24 (2.47806 iter/s, 4.8425s/12 iters), loss = 5.30572
I0410 01:30:14.636390 24451 solver.cpp:237]     Train net output #0: loss = 5.30572 (* 1 = 5.30572 loss)
I0410 01:30:14.636399 24451 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0410 01:30:19.269012 24451 solver.cpp:218] Iteration 36 (2.59044 iter/s, 4.63242s/12 iters), loss = 5.31869
I0410 01:30:19.269057 24451 solver.cpp:237]     Train net output #0: loss = 5.31869 (* 1 = 5.31869 loss)
I0410 01:30:19.269068 24451 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0410 01:30:24.089723 24451 solver.cpp:218] Iteration 48 (2.48939 iter/s, 4.82046s/12 iters), loss = 5.32272
I0410 01:30:24.089766 24451 solver.cpp:237]     Train net output #0: loss = 5.32272 (* 1 = 5.32272 loss)
I0410 01:30:24.089776 24451 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0410 01:30:28.937148 24451 solver.cpp:218] Iteration 60 (2.47567 iter/s, 4.84718s/12 iters), loss = 5.32407
I0410 01:30:28.937244 24451 solver.cpp:237]     Train net output #0: loss = 5.32407 (* 1 = 5.32407 loss)
I0410 01:30:28.937258 24451 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0410 01:30:33.622113 24451 solver.cpp:218] Iteration 72 (2.56155 iter/s, 4.68467s/12 iters), loss = 5.33815
I0410 01:30:33.622155 24451 solver.cpp:237]     Train net output #0: loss = 5.33815 (* 1 = 5.33815 loss)
I0410 01:30:33.622165 24451 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0410 01:30:38.414580 24451 solver.cpp:218] Iteration 84 (2.50406 iter/s, 4.79222s/12 iters), loss = 5.31961
I0410 01:30:38.414624 24451 solver.cpp:237]     Train net output #0: loss = 5.31961 (* 1 = 5.31961 loss)
I0410 01:30:38.414634 24451 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0410 01:30:43.364588 24451 solver.cpp:218] Iteration 96 (2.42437 iter/s, 4.94975s/12 iters), loss = 5.33198
I0410 01:30:43.364635 24451 solver.cpp:237]     Train net output #0: loss = 5.33198 (* 1 = 5.33198 loss)
I0410 01:30:43.364647 24451 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0410 01:30:44.882030 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:30:45.230916 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0410 01:31:14.046320 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0410 01:31:32.961001 24451 solver.cpp:330] Iteration 102, Testing net (#0)
I0410 01:31:32.961022 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:31:37.416687 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:31:37.497887 24451 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:31:37.497917 24451 solver.cpp:397]     Test net output #1: loss = 5.30453 (* 1 = 5.30453 loss)
I0410 01:31:39.295730 24451 solver.cpp:218] Iteration 108 (0.214558 iter/s, 55.9288s/12 iters), loss = 5.34806
I0410 01:31:39.295778 24451 solver.cpp:237]     Train net output #0: loss = 5.34806 (* 1 = 5.34806 loss)
I0410 01:31:39.295787 24451 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0410 01:31:44.015468 24451 solver.cpp:218] Iteration 120 (2.54265 iter/s, 4.71948s/12 iters), loss = 5.28543
I0410 01:31:44.015512 24451 solver.cpp:237]     Train net output #0: loss = 5.28543 (* 1 = 5.28543 loss)
I0410 01:31:44.015522 24451 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0410 01:31:48.667351 24451 solver.cpp:218] Iteration 132 (2.57974 iter/s, 4.65164s/12 iters), loss = 5.25516
I0410 01:31:48.667452 24451 solver.cpp:237]     Train net output #0: loss = 5.25516 (* 1 = 5.25516 loss)
I0410 01:31:48.667465 24451 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0410 01:31:53.273619 24451 solver.cpp:218] Iteration 144 (2.60532 iter/s, 4.60597s/12 iters), loss = 5.32349
I0410 01:31:53.273663 24451 solver.cpp:237]     Train net output #0: loss = 5.32349 (* 1 = 5.32349 loss)
I0410 01:31:53.273674 24451 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0410 01:31:57.838177 24451 solver.cpp:218] Iteration 156 (2.6291 iter/s, 4.56431s/12 iters), loss = 5.26925
I0410 01:31:57.838234 24451 solver.cpp:237]     Train net output #0: loss = 5.26925 (* 1 = 5.26925 loss)
I0410 01:31:57.838246 24451 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0410 01:32:02.574885 24451 solver.cpp:218] Iteration 168 (2.53354 iter/s, 4.73645s/12 iters), loss = 5.265
I0410 01:32:02.574929 24451 solver.cpp:237]     Train net output #0: loss = 5.265 (* 1 = 5.265 loss)
I0410 01:32:02.574941 24451 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0410 01:32:07.536087 24451 solver.cpp:218] Iteration 180 (2.4189 iter/s, 4.96094s/12 iters), loss = 5.28225
I0410 01:32:07.536131 24451 solver.cpp:237]     Train net output #0: loss = 5.28225 (* 1 = 5.28225 loss)
I0410 01:32:07.536140 24451 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0410 01:32:12.469281 24451 solver.cpp:218] Iteration 192 (2.43263 iter/s, 4.93294s/12 iters), loss = 5.29365
I0410 01:32:12.469326 24451 solver.cpp:237]     Train net output #0: loss = 5.29365 (* 1 = 5.29365 loss)
I0410 01:32:12.469337 24451 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0410 01:32:16.408213 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:32:17.153820 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0410 01:32:35.699012 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0410 01:32:48.963266 24451 solver.cpp:330] Iteration 204, Testing net (#0)
I0410 01:32:48.963287 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:32:53.596127 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:32:53.789497 24451 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:32:53.789546 24451 solver.cpp:397]     Test net output #1: loss = 5.29695 (* 1 = 5.29695 loss)
I0410 01:32:53.920472 24451 solver.cpp:218] Iteration 204 (0.289509 iter/s, 41.4494s/12 iters), loss = 5.26262
I0410 01:32:53.922019 24451 solver.cpp:237]     Train net output #0: loss = 5.26262 (* 1 = 5.26262 loss)
I0410 01:32:53.922032 24451 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0410 01:32:58.173895 24451 solver.cpp:218] Iteration 216 (2.82241 iter/s, 4.25169s/12 iters), loss = 5.28534
I0410 01:32:58.173940 24451 solver.cpp:237]     Train net output #0: loss = 5.28534 (* 1 = 5.28534 loss)
I0410 01:32:58.173949 24451 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0410 01:33:03.225286 24451 solver.cpp:218] Iteration 228 (2.37571 iter/s, 5.05113s/12 iters), loss = 5.28721
I0410 01:33:03.225325 24451 solver.cpp:237]     Train net output #0: loss = 5.28721 (* 1 = 5.28721 loss)
I0410 01:33:03.225333 24451 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0410 01:33:08.237473 24451 solver.cpp:218] Iteration 240 (2.39429 iter/s, 5.01193s/12 iters), loss = 5.31527
I0410 01:33:08.237609 24451 solver.cpp:237]     Train net output #0: loss = 5.31527 (* 1 = 5.31527 loss)
I0410 01:33:08.237620 24451 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0410 01:33:13.344096 24451 solver.cpp:218] Iteration 252 (2.35006 iter/s, 5.10626s/12 iters), loss = 5.28725
I0410 01:33:13.344152 24451 solver.cpp:237]     Train net output #0: loss = 5.28725 (* 1 = 5.28725 loss)
I0410 01:33:13.344164 24451 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0410 01:33:18.474211 24451 solver.cpp:218] Iteration 264 (2.33926 iter/s, 5.12984s/12 iters), loss = 5.28951
I0410 01:33:18.474258 24451 solver.cpp:237]     Train net output #0: loss = 5.28951 (* 1 = 5.28951 loss)
I0410 01:33:18.474269 24451 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0410 01:33:23.314558 24451 solver.cpp:218] Iteration 276 (2.47929 iter/s, 4.84009s/12 iters), loss = 5.30532
I0410 01:33:23.314601 24451 solver.cpp:237]     Train net output #0: loss = 5.30532 (* 1 = 5.30532 loss)
I0410 01:33:23.314610 24451 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0410 01:33:28.047307 24451 solver.cpp:218] Iteration 288 (2.53566 iter/s, 4.7325s/12 iters), loss = 5.28073
I0410 01:33:28.047351 24451 solver.cpp:237]     Train net output #0: loss = 5.28073 (* 1 = 5.28073 loss)
I0410 01:33:28.047361 24451 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0410 01:33:32.915347 24451 solver.cpp:218] Iteration 300 (2.46519 iter/s, 4.86778s/12 iters), loss = 5.31727
I0410 01:33:32.915397 24451 solver.cpp:237]     Train net output #0: loss = 5.31727 (* 1 = 5.31727 loss)
I0410 01:33:32.915410 24451 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0410 01:33:33.849061 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:33:34.964692 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0410 01:33:48.890849 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0410 01:34:29.551947 24451 solver.cpp:330] Iteration 306, Testing net (#0)
I0410 01:34:29.552013 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:34:34.336544 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:34:34.499722 24451 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:34:34.499753 24451 solver.cpp:397]     Test net output #1: loss = 5.29203 (* 1 = 5.29203 loss)
I0410 01:34:36.441097 24451 solver.cpp:218] Iteration 312 (0.188908 iter/s, 63.5231s/12 iters), loss = 5.29349
I0410 01:34:36.441159 24451 solver.cpp:237]     Train net output #0: loss = 5.29349 (* 1 = 5.29349 loss)
I0410 01:34:36.441171 24451 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0410 01:34:41.497660 24451 solver.cpp:218] Iteration 324 (2.37329 iter/s, 5.05628s/12 iters), loss = 5.24077
I0410 01:34:41.497710 24451 solver.cpp:237]     Train net output #0: loss = 5.24077 (* 1 = 5.24077 loss)
I0410 01:34:41.497721 24451 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0410 01:34:46.236861 24451 solver.cpp:218] Iteration 336 (2.53221 iter/s, 4.73894s/12 iters), loss = 5.26812
I0410 01:34:46.236910 24451 solver.cpp:237]     Train net output #0: loss = 5.26812 (* 1 = 5.26812 loss)
I0410 01:34:46.236922 24451 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0410 01:34:50.885922 24451 solver.cpp:218] Iteration 348 (2.58131 iter/s, 4.64881s/12 iters), loss = 5.27701
I0410 01:34:50.885985 24451 solver.cpp:237]     Train net output #0: loss = 5.27701 (* 1 = 5.27701 loss)
I0410 01:34:50.885998 24451 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0410 01:34:55.580025 24451 solver.cpp:218] Iteration 360 (2.55655 iter/s, 4.69383s/12 iters), loss = 5.29042
I0410 01:34:55.580070 24451 solver.cpp:237]     Train net output #0: loss = 5.29042 (* 1 = 5.29042 loss)
I0410 01:34:55.580080 24451 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0410 01:35:00.275909 24451 solver.cpp:218] Iteration 372 (2.55557 iter/s, 4.69563s/12 iters), loss = 5.29307
I0410 01:35:00.276062 24451 solver.cpp:237]     Train net output #0: loss = 5.29307 (* 1 = 5.29307 loss)
I0410 01:35:00.276074 24451 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0410 01:35:04.982551 24451 solver.cpp:218] Iteration 384 (2.54978 iter/s, 4.70629s/12 iters), loss = 5.29251
I0410 01:35:04.982599 24451 solver.cpp:237]     Train net output #0: loss = 5.29251 (* 1 = 5.29251 loss)
I0410 01:35:04.982609 24451 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0410 01:35:09.823870 24451 solver.cpp:218] Iteration 396 (2.47879 iter/s, 4.84106s/12 iters), loss = 5.28873
I0410 01:35:09.823911 24451 solver.cpp:237]     Train net output #0: loss = 5.28873 (* 1 = 5.28873 loss)
I0410 01:35:09.823922 24451 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0410 01:35:12.781738 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:35:14.232724 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0410 01:35:45.449735 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0410 01:36:01.003288 24451 solver.cpp:330] Iteration 408, Testing net (#0)
I0410 01:36:01.003307 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:36:05.465257 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:36:05.718856 24451 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 01:36:05.718900 24451 solver.cpp:397]     Test net output #1: loss = 5.28951 (* 1 = 5.28951 loss)
I0410 01:36:05.854889 24451 solver.cpp:218] Iteration 408 (0.214176 iter/s, 56.0287s/12 iters), loss = 5.29936
I0410 01:36:05.856411 24451 solver.cpp:237]     Train net output #0: loss = 5.29936 (* 1 = 5.29936 loss)
I0410 01:36:05.856423 24451 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0410 01:36:09.976859 24451 solver.cpp:218] Iteration 420 (2.91243 iter/s, 4.12027s/12 iters), loss = 5.28549
I0410 01:36:09.976903 24451 solver.cpp:237]     Train net output #0: loss = 5.28549 (* 1 = 5.28549 loss)
I0410 01:36:09.976914 24451 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0410 01:36:14.873245 24451 solver.cpp:218] Iteration 432 (2.45092 iter/s, 4.89613s/12 iters), loss = 5.27997
I0410 01:36:14.873289 24451 solver.cpp:237]     Train net output #0: loss = 5.27997 (* 1 = 5.27997 loss)
I0410 01:36:14.873298 24451 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0410 01:36:19.915067 24451 solver.cpp:218] Iteration 444 (2.38022 iter/s, 5.04155s/12 iters), loss = 5.29828
I0410 01:36:19.915177 24451 solver.cpp:237]     Train net output #0: loss = 5.29828 (* 1 = 5.29828 loss)
I0410 01:36:19.915190 24451 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0410 01:36:25.058755 24451 solver.cpp:218] Iteration 456 (2.33311 iter/s, 5.14335s/12 iters), loss = 5.29563
I0410 01:36:25.058800 24451 solver.cpp:237]     Train net output #0: loss = 5.29563 (* 1 = 5.29563 loss)
I0410 01:36:25.058810 24451 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0410 01:36:30.207301 24451 solver.cpp:218] Iteration 468 (2.33088 iter/s, 5.14828s/12 iters), loss = 5.30059
I0410 01:36:30.207343 24451 solver.cpp:237]     Train net output #0: loss = 5.30059 (* 1 = 5.30059 loss)
I0410 01:36:30.207353 24451 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0410 01:36:35.105865 24451 solver.cpp:218] Iteration 480 (2.44983 iter/s, 4.8983s/12 iters), loss = 5.28289
I0410 01:36:35.105902 24451 solver.cpp:237]     Train net output #0: loss = 5.28289 (* 1 = 5.28289 loss)
I0410 01:36:35.105911 24451 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0410 01:36:39.766880 24451 solver.cpp:218] Iteration 492 (2.57468 iter/s, 4.66077s/12 iters), loss = 5.29891
I0410 01:36:39.766932 24451 solver.cpp:237]     Train net output #0: loss = 5.29891 (* 1 = 5.29891 loss)
I0410 01:36:39.766943 24451 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0410 01:36:44.820397 24451 solver.cpp:218] Iteration 504 (2.37471 iter/s, 5.05324s/12 iters), loss = 5.2743
I0410 01:36:44.820442 24451 solver.cpp:237]     Train net output #0: loss = 5.2743 (* 1 = 5.2743 loss)
I0410 01:36:44.820454 24451 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0410 01:36:45.053284 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:36:46.927320 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0410 01:37:03.333662 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0410 01:37:16.190479 24451 solver.cpp:330] Iteration 510, Testing net (#0)
I0410 01:37:16.190501 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:37:20.459991 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:37:20.711166 24451 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 01:37:20.711207 24451 solver.cpp:397]     Test net output #1: loss = 5.28343 (* 1 = 5.28343 loss)
I0410 01:37:22.521107 24451 solver.cpp:218] Iteration 516 (0.31831 iter/s, 37.6991s/12 iters), loss = 5.29193
I0410 01:37:22.521153 24451 solver.cpp:237]     Train net output #0: loss = 5.29193 (* 1 = 5.29193 loss)
I0410 01:37:22.521165 24451 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0410 01:37:27.651226 24451 solver.cpp:218] Iteration 528 (2.33925 iter/s, 5.12985s/12 iters), loss = 5.29049
I0410 01:37:27.651266 24451 solver.cpp:237]     Train net output #0: loss = 5.29049 (* 1 = 5.29049 loss)
I0410 01:37:27.651274 24451 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0410 01:37:32.690109 24451 solver.cpp:218] Iteration 540 (2.38161 iter/s, 5.03862s/12 iters), loss = 5.27684
I0410 01:37:32.690165 24451 solver.cpp:237]     Train net output #0: loss = 5.27684 (* 1 = 5.27684 loss)
I0410 01:37:32.690178 24451 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0410 01:37:37.780894 24451 solver.cpp:218] Iteration 552 (2.35733 iter/s, 5.09051s/12 iters), loss = 5.26608
I0410 01:37:37.780977 24451 solver.cpp:237]     Train net output #0: loss = 5.26608 (* 1 = 5.26608 loss)
I0410 01:37:37.780987 24451 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0410 01:37:42.853287 24451 solver.cpp:218] Iteration 564 (2.36589 iter/s, 5.07208s/12 iters), loss = 5.25368
I0410 01:37:42.853348 24451 solver.cpp:237]     Train net output #0: loss = 5.25368 (* 1 = 5.25368 loss)
I0410 01:37:42.853361 24451 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0410 01:37:47.569406 24451 solver.cpp:218] Iteration 576 (2.54461 iter/s, 4.71585s/12 iters), loss = 5.25137
I0410 01:37:47.569458 24451 solver.cpp:237]     Train net output #0: loss = 5.25137 (* 1 = 5.25137 loss)
I0410 01:37:47.569470 24451 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0410 01:37:52.232167 24451 solver.cpp:218] Iteration 588 (2.57372 iter/s, 4.6625s/12 iters), loss = 5.2026
I0410 01:37:52.232218 24451 solver.cpp:237]     Train net output #0: loss = 5.2026 (* 1 = 5.2026 loss)
I0410 01:37:52.232228 24451 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0410 01:37:57.013424 24451 solver.cpp:218] Iteration 600 (2.50994 iter/s, 4.78099s/12 iters), loss = 5.2319
I0410 01:37:57.013476 24451 solver.cpp:237]     Train net output #0: loss = 5.2319 (* 1 = 5.2319 loss)
I0410 01:37:57.013489 24451 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0410 01:37:59.461668 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:38:01.714893 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0410 01:38:17.995157 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0410 01:38:47.423303 24451 solver.cpp:330] Iteration 612, Testing net (#0)
I0410 01:38:47.423319 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:38:51.653841 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:38:51.948019 24451 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0410 01:38:51.948068 24451 solver.cpp:397]     Test net output #1: loss = 5.18437 (* 1 = 5.18437 loss)
I0410 01:38:52.082613 24451 solver.cpp:218] Iteration 612 (0.217917 iter/s, 55.0669s/12 iters), loss = 5.26921
I0410 01:38:52.084137 24451 solver.cpp:237]     Train net output #0: loss = 5.26921 (* 1 = 5.26921 loss)
I0410 01:38:52.084149 24451 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0410 01:38:56.340179 24451 solver.cpp:218] Iteration 624 (2.81965 iter/s, 4.25585s/12 iters), loss = 5.23842
I0410 01:38:56.340232 24451 solver.cpp:237]     Train net output #0: loss = 5.23842 (* 1 = 5.23842 loss)
I0410 01:38:56.340245 24451 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0410 01:39:01.507601 24451 solver.cpp:218] Iteration 636 (2.32237 iter/s, 5.16715s/12 iters), loss = 5.1132
I0410 01:39:01.507644 24451 solver.cpp:237]     Train net output #0: loss = 5.1132 (* 1 = 5.1132 loss)
I0410 01:39:01.507654 24451 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0410 01:39:06.230865 24451 solver.cpp:218] Iteration 648 (2.54075 iter/s, 4.72301s/12 iters), loss = 5.27827
I0410 01:39:06.230914 24451 solver.cpp:237]     Train net output #0: loss = 5.27827 (* 1 = 5.27827 loss)
I0410 01:39:06.230926 24451 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0410 01:39:10.909778 24451 solver.cpp:218] Iteration 660 (2.56484 iter/s, 4.67866s/12 iters), loss = 5.18392
I0410 01:39:10.909828 24451 solver.cpp:237]     Train net output #0: loss = 5.18392 (* 1 = 5.18392 loss)
I0410 01:39:10.909840 24451 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0410 01:39:15.588462 24451 solver.cpp:218] Iteration 672 (2.56496 iter/s, 4.67843s/12 iters), loss = 5.18728
I0410 01:39:15.588515 24451 solver.cpp:237]     Train net output #0: loss = 5.18728 (* 1 = 5.18728 loss)
I0410 01:39:15.588526 24451 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0410 01:39:20.423161 24451 solver.cpp:218] Iteration 684 (2.48219 iter/s, 4.83443s/12 iters), loss = 5.08091
I0410 01:39:20.423219 24451 solver.cpp:237]     Train net output #0: loss = 5.08091 (* 1 = 5.08091 loss)
I0410 01:39:20.423231 24451 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0410 01:39:21.528820 24451 blocking_queue.cpp:49] Waiting for data
I0410 01:39:25.275048 24451 solver.cpp:218] Iteration 696 (2.4734 iter/s, 4.85162s/12 iters), loss = 5.1844
I0410 01:39:25.275151 24451 solver.cpp:237]     Train net output #0: loss = 5.1844 (* 1 = 5.1844 loss)
I0410 01:39:25.275162 24451 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0410 01:39:29.508359 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:39:29.945983 24451 solver.cpp:218] Iteration 708 (2.56925 iter/s, 4.67063s/12 iters), loss = 5.17094
I0410 01:39:29.946029 24451 solver.cpp:237]     Train net output #0: loss = 5.17094 (* 1 = 5.17094 loss)
I0410 01:39:29.946041 24451 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0410 01:39:31.822741 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0410 01:39:52.959256 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0410 01:40:09.447357 24451 solver.cpp:330] Iteration 714, Testing net (#0)
I0410 01:40:09.447423 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:40:13.641557 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:40:13.998607 24451 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0410 01:40:13.998656 24451 solver.cpp:397]     Test net output #1: loss = 5.16518 (* 1 = 5.16518 loss)
I0410 01:40:15.912221 24451 solver.cpp:218] Iteration 720 (0.261072 iter/s, 45.9643s/12 iters), loss = 5.18154
I0410 01:40:15.912268 24451 solver.cpp:237]     Train net output #0: loss = 5.18154 (* 1 = 5.18154 loss)
I0410 01:40:15.912281 24451 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0410 01:40:20.697408 24451 solver.cpp:218] Iteration 732 (2.50787 iter/s, 4.78493s/12 iters), loss = 5.14475
I0410 01:40:20.697456 24451 solver.cpp:237]     Train net output #0: loss = 5.14475 (* 1 = 5.14475 loss)
I0410 01:40:20.697468 24451 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0410 01:40:25.312881 24451 solver.cpp:218] Iteration 744 (2.60009 iter/s, 4.61522s/12 iters), loss = 5.15995
I0410 01:40:25.312932 24451 solver.cpp:237]     Train net output #0: loss = 5.15995 (* 1 = 5.15995 loss)
I0410 01:40:25.312945 24451 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0410 01:40:30.403291 24451 solver.cpp:218] Iteration 756 (2.3575 iter/s, 5.09014s/12 iters), loss = 5.16377
I0410 01:40:30.403342 24451 solver.cpp:237]     Train net output #0: loss = 5.16377 (* 1 = 5.16377 loss)
I0410 01:40:30.403352 24451 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0410 01:40:35.121423 24451 solver.cpp:218] Iteration 768 (2.54352 iter/s, 4.71788s/12 iters), loss = 5.18275
I0410 01:40:35.121466 24451 solver.cpp:237]     Train net output #0: loss = 5.18275 (* 1 = 5.18275 loss)
I0410 01:40:35.121476 24451 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0410 01:40:39.867094 24451 solver.cpp:218] Iteration 780 (2.52875 iter/s, 4.74543s/12 iters), loss = 5.21357
I0410 01:40:39.870784 24451 solver.cpp:237]     Train net output #0: loss = 5.21357 (* 1 = 5.21357 loss)
I0410 01:40:39.870793 24451 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0410 01:40:44.620574 24451 solver.cpp:218] Iteration 792 (2.52654 iter/s, 4.74958s/12 iters), loss = 5.0988
I0410 01:40:44.620626 24451 solver.cpp:237]     Train net output #0: loss = 5.0988 (* 1 = 5.0988 loss)
I0410 01:40:44.620637 24451 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0410 01:40:49.611009 24451 solver.cpp:218] Iteration 804 (2.40473 iter/s, 4.99017s/12 iters), loss = 5.19108
I0410 01:40:49.611049 24451 solver.cpp:237]     Train net output #0: loss = 5.19108 (* 1 = 5.19108 loss)
I0410 01:40:49.611058 24451 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0410 01:40:51.222162 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:40:53.979882 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0410 01:41:15.990571 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0410 01:41:41.220605 24451 solver.cpp:330] Iteration 816, Testing net (#0)
I0410 01:41:41.220626 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:41:45.368765 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:41:45.733076 24451 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0410 01:41:45.733124 24451 solver.cpp:397]     Test net output #1: loss = 5.15525 (* 1 = 5.15525 loss)
I0410 01:41:45.868283 24451 solver.cpp:218] Iteration 816 (0.213315 iter/s, 56.2549s/12 iters), loss = 5.17065
I0410 01:41:45.869863 24451 solver.cpp:237]     Train net output #0: loss = 5.17065 (* 1 = 5.17065 loss)
I0410 01:41:45.869874 24451 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0410 01:41:49.975950 24451 solver.cpp:218] Iteration 828 (2.92262 iter/s, 4.1059s/12 iters), loss = 5.19518
I0410 01:41:49.976058 24451 solver.cpp:237]     Train net output #0: loss = 5.19518 (* 1 = 5.19518 loss)
I0410 01:41:49.976070 24451 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0410 01:41:55.139377 24451 solver.cpp:218] Iteration 840 (2.32419 iter/s, 5.16309s/12 iters), loss = 5.13418
I0410 01:41:55.139425 24451 solver.cpp:237]     Train net output #0: loss = 5.13418 (* 1 = 5.13418 loss)
I0410 01:41:55.139436 24451 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0410 01:42:00.167114 24451 solver.cpp:218] Iteration 852 (2.38689 iter/s, 5.02747s/12 iters), loss = 5.13816
I0410 01:42:00.167162 24451 solver.cpp:237]     Train net output #0: loss = 5.13816 (* 1 = 5.13816 loss)
I0410 01:42:00.167174 24451 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0410 01:42:05.242959 24451 solver.cpp:218] Iteration 864 (2.36427 iter/s, 5.07557s/12 iters), loss = 5.12802
I0410 01:42:05.243014 24451 solver.cpp:237]     Train net output #0: loss = 5.12802 (* 1 = 5.12802 loss)
I0410 01:42:05.243028 24451 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0410 01:42:10.254742 24451 solver.cpp:218] Iteration 876 (2.39449 iter/s, 5.01151s/12 iters), loss = 5.16212
I0410 01:42:10.254782 24451 solver.cpp:237]     Train net output #0: loss = 5.16212 (* 1 = 5.16212 loss)
I0410 01:42:10.254792 24451 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0410 01:42:15.134722 24451 solver.cpp:218] Iteration 888 (2.45916 iter/s, 4.87972s/12 iters), loss = 5.06967
I0410 01:42:15.134769 24451 solver.cpp:237]     Train net output #0: loss = 5.06967 (* 1 = 5.06967 loss)
I0410 01:42:15.134781 24451 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0410 01:42:20.262953 24451 solver.cpp:218] Iteration 900 (2.34011 iter/s, 5.12796s/12 iters), loss = 5.20286
I0410 01:42:20.263064 24451 solver.cpp:237]     Train net output #0: loss = 5.20286 (* 1 = 5.20286 loss)
I0410 01:42:20.263075 24451 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0410 01:42:24.181638 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:42:25.390563 24451 solver.cpp:218] Iteration 912 (2.34043 iter/s, 5.12727s/12 iters), loss = 5.04191
I0410 01:42:25.390612 24451 solver.cpp:237]     Train net output #0: loss = 5.04191 (* 1 = 5.04191 loss)
I0410 01:42:25.390623 24451 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0410 01:42:27.460618 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0410 01:42:48.285634 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0410 01:43:02.242738 24451 solver.cpp:330] Iteration 918, Testing net (#0)
I0410 01:43:02.242782 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:43:06.290712 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:43:06.700340 24451 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0410 01:43:06.700387 24451 solver.cpp:397]     Test net output #1: loss = 5.15028 (* 1 = 5.15028 loss)
I0410 01:43:08.468582 24451 solver.cpp:218] Iteration 924 (0.278576 iter/s, 43.0762s/12 iters), loss = 5.18315
I0410 01:43:08.468627 24451 solver.cpp:237]     Train net output #0: loss = 5.18315 (* 1 = 5.18315 loss)
I0410 01:43:08.468636 24451 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0410 01:43:13.246614 24451 solver.cpp:218] Iteration 936 (2.51163 iter/s, 4.77778s/12 iters), loss = 5.21668
I0410 01:43:13.246662 24451 solver.cpp:237]     Train net output #0: loss = 5.21668 (* 1 = 5.21668 loss)
I0410 01:43:13.246673 24451 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0410 01:43:18.077637 24451 solver.cpp:218] Iteration 948 (2.48408 iter/s, 4.83076s/12 iters), loss = 5.15264
I0410 01:43:18.077687 24451 solver.cpp:237]     Train net output #0: loss = 5.15264 (* 1 = 5.15264 loss)
I0410 01:43:18.077699 24451 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0410 01:43:22.918495 24451 solver.cpp:218] Iteration 960 (2.47903 iter/s, 4.8406s/12 iters), loss = 5.08966
I0410 01:43:22.918538 24451 solver.cpp:237]     Train net output #0: loss = 5.08966 (* 1 = 5.08966 loss)
I0410 01:43:22.918550 24451 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0410 01:43:27.945883 24451 solver.cpp:218] Iteration 972 (2.38705 iter/s, 5.02712s/12 iters), loss = 5.17091
I0410 01:43:27.945941 24451 solver.cpp:237]     Train net output #0: loss = 5.17091 (* 1 = 5.17091 loss)
I0410 01:43:27.945978 24451 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0410 01:43:32.523823 24451 solver.cpp:218] Iteration 984 (2.62141 iter/s, 4.57769s/12 iters), loss = 5.15159
I0410 01:43:32.523921 24451 solver.cpp:237]     Train net output #0: loss = 5.15159 (* 1 = 5.15159 loss)
I0410 01:43:32.523931 24451 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0410 01:43:37.234014 24451 solver.cpp:218] Iteration 996 (2.54783 iter/s, 4.70989s/12 iters), loss = 5.03792
I0410 01:43:37.234057 24451 solver.cpp:237]     Train net output #0: loss = 5.03792 (* 1 = 5.03792 loss)
I0410 01:43:37.234068 24451 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0410 01:43:41.963456 24451 solver.cpp:218] Iteration 1008 (2.53743 iter/s, 4.7292s/12 iters), loss = 5.23247
I0410 01:43:41.963491 24451 solver.cpp:237]     Train net output #0: loss = 5.23247 (* 1 = 5.23247 loss)
I0410 01:43:41.963501 24451 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0410 01:43:42.940099 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:43:46.162474 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0410 01:44:02.516800 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0410 01:44:41.020453 24451 solver.cpp:330] Iteration 1020, Testing net (#0)
I0410 01:44:41.021901 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:44:45.234395 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:44:45.683620 24451 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0410 01:44:45.683651 24451 solver.cpp:397]     Test net output #1: loss = 5.14132 (* 1 = 5.14132 loss)
I0410 01:44:45.817925 24451 solver.cpp:218] Iteration 1020 (0.187935 iter/s, 63.8518s/12 iters), loss = 5.10617
I0410 01:44:45.819449 24451 solver.cpp:237]     Train net output #0: loss = 5.10617 (* 1 = 5.10617 loss)
I0410 01:44:45.819463 24451 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0410 01:44:50.179005 24451 solver.cpp:218] Iteration 1032 (2.75269 iter/s, 4.35937s/12 iters), loss = 5.13938
I0410 01:44:50.179052 24451 solver.cpp:237]     Train net output #0: loss = 5.13938 (* 1 = 5.13938 loss)
I0410 01:44:50.179064 24451 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0410 01:44:55.181931 24451 solver.cpp:218] Iteration 1044 (2.39872 iter/s, 5.00266s/12 iters), loss = 5.15126
I0410 01:44:55.181998 24451 solver.cpp:237]     Train net output #0: loss = 5.15126 (* 1 = 5.15126 loss)
I0410 01:44:55.182008 24451 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0410 01:45:00.073719 24451 solver.cpp:218] Iteration 1056 (2.45323 iter/s, 4.8915s/12 iters), loss = 5.157
I0410 01:45:00.073767 24451 solver.cpp:237]     Train net output #0: loss = 5.157 (* 1 = 5.157 loss)
I0410 01:45:00.073778 24451 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0410 01:45:04.786880 24451 solver.cpp:218] Iteration 1068 (2.5462 iter/s, 4.7129s/12 iters), loss = 5.19642
I0410 01:45:04.786919 24451 solver.cpp:237]     Train net output #0: loss = 5.19642 (* 1 = 5.19642 loss)
I0410 01:45:04.786928 24451 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0410 01:45:09.939826 24451 solver.cpp:218] Iteration 1080 (2.32888 iter/s, 5.15268s/12 iters), loss = 5.07049
I0410 01:45:09.939877 24451 solver.cpp:237]     Train net output #0: loss = 5.07049 (* 1 = 5.07049 loss)
I0410 01:45:09.939888 24451 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0410 01:45:15.003706 24451 solver.cpp:218] Iteration 1092 (2.36985 iter/s, 5.06361s/12 iters), loss = 5.11898
I0410 01:45:15.003805 24451 solver.cpp:237]     Train net output #0: loss = 5.11898 (* 1 = 5.11898 loss)
I0410 01:45:15.003816 24451 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0410 01:45:20.213434 24451 solver.cpp:218] Iteration 1104 (2.30353 iter/s, 5.2094s/12 iters), loss = 5.02183
I0410 01:45:20.213485 24451 solver.cpp:237]     Train net output #0: loss = 5.02183 (* 1 = 5.02183 loss)
I0410 01:45:20.213497 24451 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0410 01:45:23.474836 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:45:25.439472 24451 solver.cpp:218] Iteration 1116 (2.29632 iter/s, 5.22576s/12 iters), loss = 5.11342
I0410 01:45:25.439517 24451 solver.cpp:237]     Train net output #0: loss = 5.11342 (* 1 = 5.11342 loss)
I0410 01:45:25.439528 24451 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0410 01:45:27.543658 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0410 01:46:01.759847 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0410 01:46:12.914496 24451 solver.cpp:330] Iteration 1122, Testing net (#0)
I0410 01:46:12.914520 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:46:16.866917 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:46:17.356380 24451 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0410 01:46:17.356431 24451 solver.cpp:397]     Test net output #1: loss = 5.11081 (* 1 = 5.11081 loss)
I0410 01:46:19.198796 24451 solver.cpp:218] Iteration 1128 (0.223226 iter/s, 53.7571s/12 iters), loss = 5.17934
I0410 01:46:19.198841 24451 solver.cpp:237]     Train net output #0: loss = 5.17934 (* 1 = 5.17934 loss)
I0410 01:46:19.198851 24451 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0410 01:46:23.844373 24451 solver.cpp:218] Iteration 1140 (2.58324 iter/s, 4.64533s/12 iters), loss = 5.14063
I0410 01:46:23.844419 24451 solver.cpp:237]     Train net output #0: loss = 5.14063 (* 1 = 5.14063 loss)
I0410 01:46:23.844431 24451 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0410 01:46:28.487946 24451 solver.cpp:218] Iteration 1152 (2.58436 iter/s, 4.64332s/12 iters), loss = 5.03346
I0410 01:46:28.487993 24451 solver.cpp:237]     Train net output #0: loss = 5.03346 (* 1 = 5.03346 loss)
I0410 01:46:28.488006 24451 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0410 01:46:33.474779 24451 solver.cpp:218] Iteration 1164 (2.40646 iter/s, 4.98657s/12 iters), loss = 5.07688
I0410 01:46:33.474839 24451 solver.cpp:237]     Train net output #0: loss = 5.07688 (* 1 = 5.07688 loss)
I0410 01:46:33.474848 24451 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0410 01:46:38.594404 24451 solver.cpp:218] Iteration 1176 (2.34405 iter/s, 5.11934s/12 iters), loss = 5.15361
I0410 01:46:38.594451 24451 solver.cpp:237]     Train net output #0: loss = 5.15361 (* 1 = 5.15361 loss)
I0410 01:46:38.594462 24451 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0410 01:46:43.536615 24451 solver.cpp:218] Iteration 1188 (2.42819 iter/s, 4.94195s/12 iters), loss = 5.03917
I0410 01:46:43.536669 24451 solver.cpp:237]     Train net output #0: loss = 5.03917 (* 1 = 5.03917 loss)
I0410 01:46:43.536680 24451 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0410 01:46:48.492693 24451 solver.cpp:218] Iteration 1200 (2.4214 iter/s, 4.95581s/12 iters), loss = 5.10639
I0410 01:46:48.492743 24451 solver.cpp:237]     Train net output #0: loss = 5.10639 (* 1 = 5.10639 loss)
I0410 01:46:48.492753 24451 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0410 01:46:53.541509 24451 solver.cpp:218] Iteration 1212 (2.37692 iter/s, 5.04855s/12 iters), loss = 5.15894
I0410 01:46:53.541559 24451 solver.cpp:237]     Train net output #0: loss = 5.15894 (* 1 = 5.15894 loss)
I0410 01:46:53.541571 24451 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0410 01:46:53.802381 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:46:58.258339 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0410 01:47:12.570526 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0410 01:47:27.921794 24451 solver.cpp:330] Iteration 1224, Testing net (#0)
I0410 01:47:27.921816 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:47:32.022436 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:47:32.599104 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0410 01:47:32.599151 24451 solver.cpp:397]     Test net output #1: loss = 5.08893 (* 1 = 5.08893 loss)
I0410 01:47:32.728487 24451 solver.cpp:218] Iteration 1224 (0.306237 iter/s, 39.1853s/12 iters), loss = 5.05816
I0410 01:47:32.730012 24451 solver.cpp:237]     Train net output #0: loss = 5.05816 (* 1 = 5.05816 loss)
I0410 01:47:32.730029 24451 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0410 01:47:37.139775 24451 solver.cpp:218] Iteration 1236 (2.72135 iter/s, 4.40958s/12 iters), loss = 5.17705
I0410 01:47:37.139818 24451 solver.cpp:237]     Train net output #0: loss = 5.17705 (* 1 = 5.17705 loss)
I0410 01:47:37.139827 24451 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0410 01:47:42.202512 24451 solver.cpp:218] Iteration 1248 (2.37038 iter/s, 5.06247s/12 iters), loss = 5.01664
I0410 01:47:42.202558 24451 solver.cpp:237]     Train net output #0: loss = 5.01664 (* 1 = 5.01664 loss)
I0410 01:47:42.202569 24451 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0410 01:47:47.236526 24451 solver.cpp:218] Iteration 1260 (2.38391 iter/s, 5.03375s/12 iters), loss = 5.04521
I0410 01:47:47.236639 24451 solver.cpp:237]     Train net output #0: loss = 5.04521 (* 1 = 5.04521 loss)
I0410 01:47:47.236649 24451 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0410 01:47:52.441836 24451 solver.cpp:218] Iteration 1272 (2.30549 iter/s, 5.20497s/12 iters), loss = 5.06707
I0410 01:47:52.441876 24451 solver.cpp:237]     Train net output #0: loss = 5.06707 (* 1 = 5.06707 loss)
I0410 01:47:52.441884 24451 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0410 01:47:57.605003 24451 solver.cpp:218] Iteration 1284 (2.32428 iter/s, 5.1629s/12 iters), loss = 5.08246
I0410 01:47:57.605058 24451 solver.cpp:237]     Train net output #0: loss = 5.08246 (* 1 = 5.08246 loss)
I0410 01:47:57.605069 24451 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0410 01:48:02.674597 24451 solver.cpp:218] Iteration 1296 (2.36718 iter/s, 5.06932s/12 iters), loss = 4.99977
I0410 01:48:02.674639 24451 solver.cpp:237]     Train net output #0: loss = 4.99977 (* 1 = 4.99977 loss)
I0410 01:48:02.674649 24451 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0410 01:48:07.569455 24451 solver.cpp:218] Iteration 1308 (2.45168 iter/s, 4.8946s/12 iters), loss = 5.09898
I0410 01:48:07.569500 24451 solver.cpp:237]     Train net output #0: loss = 5.09898 (* 1 = 5.09898 loss)
I0410 01:48:07.569511 24451 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0410 01:48:10.104387 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:48:12.653210 24451 solver.cpp:218] Iteration 1320 (2.36058 iter/s, 5.08349s/12 iters), loss = 5.07978
I0410 01:48:12.653259 24451 solver.cpp:237]     Train net output #0: loss = 5.07978 (* 1 = 5.07978 loss)
I0410 01:48:12.653268 24451 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0410 01:48:14.465987 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0410 01:48:32.626010 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0410 01:49:03.592685 24451 solver.cpp:330] Iteration 1326, Testing net (#0)
I0410 01:49:03.592756 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:49:07.571074 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:49:08.141741 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0134804
I0410 01:49:08.141777 24451 solver.cpp:397]     Test net output #1: loss = 5.06191 (* 1 = 5.06191 loss)
I0410 01:49:10.084250 24451 solver.cpp:218] Iteration 1332 (0.208955 iter/s, 57.4286s/12 iters), loss = 5.01778
I0410 01:49:10.084293 24451 solver.cpp:237]     Train net output #0: loss = 5.01778 (* 1 = 5.01778 loss)
I0410 01:49:10.084302 24451 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0410 01:49:15.053236 24451 solver.cpp:218] Iteration 1344 (2.41511 iter/s, 4.96872s/12 iters), loss = 4.93386
I0410 01:49:15.053287 24451 solver.cpp:237]     Train net output #0: loss = 4.93386 (* 1 = 4.93386 loss)
I0410 01:49:15.053297 24451 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0410 01:49:19.630120 24451 solver.cpp:218] Iteration 1356 (2.62201 iter/s, 4.57664s/12 iters), loss = 4.99202
I0410 01:49:19.630162 24451 solver.cpp:237]     Train net output #0: loss = 4.99202 (* 1 = 4.99202 loss)
I0410 01:49:19.630172 24451 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0410 01:49:24.347164 24451 solver.cpp:218] Iteration 1368 (2.5441 iter/s, 4.71679s/12 iters), loss = 5.09908
I0410 01:49:24.347218 24451 solver.cpp:237]     Train net output #0: loss = 5.09908 (* 1 = 5.09908 loss)
I0410 01:49:24.347230 24451 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0410 01:49:26.020483 24451 blocking_queue.cpp:49] Waiting for data
I0410 01:49:29.538386 24451 solver.cpp:218] Iteration 1380 (2.31172 iter/s, 5.19094s/12 iters), loss = 5.0225
I0410 01:49:29.538432 24451 solver.cpp:237]     Train net output #0: loss = 5.0225 (* 1 = 5.0225 loss)
I0410 01:49:29.538442 24451 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0410 01:49:34.420910 24451 solver.cpp:218] Iteration 1392 (2.45788 iter/s, 4.88226s/12 iters), loss = 4.92164
I0410 01:49:34.421061 24451 solver.cpp:237]     Train net output #0: loss = 4.92164 (* 1 = 4.92164 loss)
I0410 01:49:34.421073 24451 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0410 01:49:39.577869 24451 solver.cpp:218] Iteration 1404 (2.32712 iter/s, 5.15659s/12 iters), loss = 5.01168
I0410 01:49:39.577909 24451 solver.cpp:237]     Train net output #0: loss = 5.01168 (* 1 = 5.01168 loss)
I0410 01:49:39.577917 24451 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0410 01:49:44.358911 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:49:44.780081 24451 solver.cpp:218] Iteration 1416 (2.30683 iter/s, 5.20194s/12 iters), loss = 4.99744
I0410 01:49:44.780133 24451 solver.cpp:237]     Train net output #0: loss = 4.99744 (* 1 = 4.99744 loss)
I0410 01:49:44.780145 24451 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0410 01:49:49.321368 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0410 01:50:23.729631 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0410 01:50:35.151486 24451 solver.cpp:330] Iteration 1428, Testing net (#0)
I0410 01:50:35.151506 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:50:39.148321 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:50:39.775404 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0410 01:50:39.775454 24451 solver.cpp:397]     Test net output #1: loss = 5.07804 (* 1 = 5.07804 loss)
I0410 01:50:39.906632 24451 solver.cpp:218] Iteration 1428 (0.21769 iter/s, 55.1242s/12 iters), loss = 5.12979
I0410 01:50:39.908157 24451 solver.cpp:237]     Train net output #0: loss = 5.12979 (* 1 = 5.12979 loss)
I0410 01:50:39.908169 24451 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0410 01:50:44.136410 24451 solver.cpp:218] Iteration 1440 (2.83818 iter/s, 4.22807s/12 iters), loss = 4.99124
I0410 01:50:44.136459 24451 solver.cpp:237]     Train net output #0: loss = 4.99124 (* 1 = 4.99124 loss)
I0410 01:50:44.136471 24451 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0410 01:50:48.831701 24451 solver.cpp:218] Iteration 1452 (2.55589 iter/s, 4.69503s/12 iters), loss = 4.92258
I0410 01:50:48.831746 24451 solver.cpp:237]     Train net output #0: loss = 4.92258 (* 1 = 4.92258 loss)
I0410 01:50:48.831756 24451 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0410 01:50:53.861415 24451 solver.cpp:218] Iteration 1464 (2.38595 iter/s, 5.02945s/12 iters), loss = 4.99239
I0410 01:50:53.861523 24451 solver.cpp:237]     Train net output #0: loss = 4.99239 (* 1 = 4.99239 loss)
I0410 01:50:53.861536 24451 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0410 01:50:58.802182 24451 solver.cpp:218] Iteration 1476 (2.42893 iter/s, 4.94044s/12 iters), loss = 4.98245
I0410 01:50:58.802242 24451 solver.cpp:237]     Train net output #0: loss = 4.98245 (* 1 = 4.98245 loss)
I0410 01:50:58.802254 24451 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0410 01:51:03.308787 24451 solver.cpp:218] Iteration 1488 (2.66291 iter/s, 4.50634s/12 iters), loss = 5.04326
I0410 01:51:03.308842 24451 solver.cpp:237]     Train net output #0: loss = 5.04326 (* 1 = 5.04326 loss)
I0410 01:51:03.308857 24451 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0410 01:51:08.245767 24451 solver.cpp:218] Iteration 1500 (2.43077 iter/s, 4.93671s/12 iters), loss = 4.91044
I0410 01:51:08.245813 24451 solver.cpp:237]     Train net output #0: loss = 4.91044 (* 1 = 4.91044 loss)
I0410 01:51:08.245823 24451 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0410 01:51:13.330590 24451 solver.cpp:218] Iteration 1512 (2.36009 iter/s, 5.08455s/12 iters), loss = 5.02932
I0410 01:51:13.330641 24451 solver.cpp:237]     Train net output #0: loss = 5.02932 (* 1 = 5.02932 loss)
I0410 01:51:13.330652 24451 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0410 01:51:15.087258 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:51:18.458523 24451 solver.cpp:218] Iteration 1524 (2.34025 iter/s, 5.12766s/12 iters), loss = 5.04446
I0410 01:51:18.458564 24451 solver.cpp:237]     Train net output #0: loss = 5.04446 (* 1 = 5.04446 loss)
I0410 01:51:18.458573 24451 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0410 01:51:20.550717 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0410 01:51:40.833864 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0410 01:52:03.469686 24451 solver.cpp:330] Iteration 1530, Testing net (#0)
I0410 01:52:03.469707 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:52:07.365515 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:52:08.084331 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0196078
I0410 01:52:08.084369 24451 solver.cpp:397]     Test net output #1: loss = 4.95853 (* 1 = 4.95853 loss)
I0410 01:52:09.824198 24451 solver.cpp:218] Iteration 1536 (0.233629 iter/s, 51.3635s/12 iters), loss = 4.96917
I0410 01:52:09.824247 24451 solver.cpp:237]     Train net output #0: loss = 4.96917 (* 1 = 4.96917 loss)
I0410 01:52:09.824259 24451 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0410 01:52:14.435665 24451 solver.cpp:218] Iteration 1548 (2.60235 iter/s, 4.61122s/12 iters), loss = 4.83931
I0410 01:52:14.435781 24451 solver.cpp:237]     Train net output #0: loss = 4.83931 (* 1 = 4.83931 loss)
I0410 01:52:14.435792 24451 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0410 01:52:19.052800 24451 solver.cpp:218] Iteration 1560 (2.59919 iter/s, 4.61682s/12 iters), loss = 4.93155
I0410 01:52:19.052852 24451 solver.cpp:237]     Train net output #0: loss = 4.93155 (* 1 = 4.93155 loss)
I0410 01:52:19.052866 24451 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0410 01:52:23.750850 24451 solver.cpp:218] Iteration 1572 (2.55439 iter/s, 4.69779s/12 iters), loss = 4.95301
I0410 01:52:23.750902 24451 solver.cpp:237]     Train net output #0: loss = 4.95301 (* 1 = 4.95301 loss)
I0410 01:52:23.750914 24451 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0410 01:52:28.682762 24451 solver.cpp:218] Iteration 1584 (2.43326 iter/s, 4.93165s/12 iters), loss = 4.99275
I0410 01:52:28.682806 24451 solver.cpp:237]     Train net output #0: loss = 4.99275 (* 1 = 4.99275 loss)
I0410 01:52:28.682817 24451 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0410 01:52:33.693861 24451 solver.cpp:218] Iteration 1596 (2.39481 iter/s, 5.01083s/12 iters), loss = 4.85403
I0410 01:52:33.693914 24451 solver.cpp:237]     Train net output #0: loss = 4.85403 (* 1 = 4.85403 loss)
I0410 01:52:33.693926 24451 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0410 01:52:38.725121 24451 solver.cpp:218] Iteration 1608 (2.38522 iter/s, 5.03099s/12 iters), loss = 4.92491
I0410 01:52:38.725167 24451 solver.cpp:237]     Train net output #0: loss = 4.92491 (* 1 = 4.92491 loss)
I0410 01:52:38.725178 24451 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0410 01:52:42.428733 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:52:43.549757 24451 solver.cpp:218] Iteration 1620 (2.48737 iter/s, 4.82438s/12 iters), loss = 4.8089
I0410 01:52:43.549801 24451 solver.cpp:237]     Train net output #0: loss = 4.8089 (* 1 = 4.8089 loss)
I0410 01:52:43.549811 24451 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0410 01:52:48.412477 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0410 01:53:14.099718 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0410 01:53:33.922242 24451 solver.cpp:330] Iteration 1632, Testing net (#0)
I0410 01:53:33.922338 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:53:37.778137 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:53:38.462424 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0257353
I0410 01:53:38.462457 24451 solver.cpp:397]     Test net output #1: loss = 4.93101 (* 1 = 4.93101 loss)
I0410 01:53:38.592175 24451 solver.cpp:218] Iteration 1632 (0.218023 iter/s, 55.0401s/12 iters), loss = 4.97276
I0410 01:53:38.593775 24451 solver.cpp:237]     Train net output #0: loss = 4.97276 (* 1 = 4.97276 loss)
I0410 01:53:38.593789 24451 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0410 01:53:42.983438 24451 solver.cpp:218] Iteration 1644 (2.73381 iter/s, 4.38948s/12 iters), loss = 4.94196
I0410 01:53:42.983482 24451 solver.cpp:237]     Train net output #0: loss = 4.94196 (* 1 = 4.94196 loss)
I0410 01:53:42.983492 24451 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0410 01:53:47.994678 24451 solver.cpp:218] Iteration 1656 (2.39474 iter/s, 5.01097s/12 iters), loss = 5.06834
I0410 01:53:47.994719 24451 solver.cpp:237]     Train net output #0: loss = 5.06834 (* 1 = 5.06834 loss)
I0410 01:53:47.994727 24451 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0410 01:53:53.159858 24451 solver.cpp:218] Iteration 1668 (2.32337 iter/s, 5.16491s/12 iters), loss = 4.78086
I0410 01:53:53.159902 24451 solver.cpp:237]     Train net output #0: loss = 4.78086 (* 1 = 4.78086 loss)
I0410 01:53:53.159912 24451 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0410 01:53:58.214371 24451 solver.cpp:218] Iteration 1680 (2.37424 iter/s, 5.05424s/12 iters), loss = 4.89932
I0410 01:53:58.214433 24451 solver.cpp:237]     Train net output #0: loss = 4.89932 (* 1 = 4.89932 loss)
I0410 01:53:58.214445 24451 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0410 01:54:03.329188 24451 solver.cpp:218] Iteration 1692 (2.34625 iter/s, 5.11453s/12 iters), loss = 4.89528
I0410 01:54:03.329231 24451 solver.cpp:237]     Train net output #0: loss = 4.89528 (* 1 = 4.89528 loss)
I0410 01:54:03.329241 24451 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0410 01:54:08.492312 24451 solver.cpp:218] Iteration 1704 (2.3243 iter/s, 5.16285s/12 iters), loss = 4.63362
I0410 01:54:08.492434 24451 solver.cpp:237]     Train net output #0: loss = 4.63362 (* 1 = 4.63362 loss)
I0410 01:54:08.492449 24451 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0410 01:54:13.727766 24451 solver.cpp:218] Iteration 1716 (2.29222 iter/s, 5.23511s/12 iters), loss = 4.83305
I0410 01:54:13.727807 24451 solver.cpp:237]     Train net output #0: loss = 4.83305 (* 1 = 4.83305 loss)
I0410 01:54:13.727815 24451 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0410 01:54:14.751127 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:54:18.886643 24451 solver.cpp:218] Iteration 1728 (2.32621 iter/s, 5.15861s/12 iters), loss = 4.80725
I0410 01:54:18.886684 24451 solver.cpp:237]     Train net output #0: loss = 4.80725 (* 1 = 4.80725 loss)
I0410 01:54:18.886694 24451 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0410 01:54:20.956610 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0410 01:54:35.059461 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0410 01:55:07.953907 24451 solver.cpp:330] Iteration 1734, Testing net (#0)
I0410 01:55:07.954023 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:55:11.810197 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:55:12.528934 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0251225
I0410 01:55:12.528988 24451 solver.cpp:397]     Test net output #1: loss = 4.80619 (* 1 = 4.80619 loss)
I0410 01:55:14.401237 24451 solver.cpp:218] Iteration 1740 (0.216168 iter/s, 55.5123s/12 iters), loss = 4.91525
I0410 01:55:14.401285 24451 solver.cpp:237]     Train net output #0: loss = 4.91525 (* 1 = 4.91525 loss)
I0410 01:55:14.401294 24451 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0410 01:55:19.019338 24451 solver.cpp:218] Iteration 1752 (2.59861 iter/s, 4.61785s/12 iters), loss = 4.79882
I0410 01:55:19.019388 24451 solver.cpp:237]     Train net output #0: loss = 4.79882 (* 1 = 4.79882 loss)
I0410 01:55:19.019400 24451 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0410 01:55:23.674039 24451 solver.cpp:218] Iteration 1764 (2.57818 iter/s, 4.65445s/12 iters), loss = 4.77077
I0410 01:55:23.674088 24451 solver.cpp:237]     Train net output #0: loss = 4.77077 (* 1 = 4.77077 loss)
I0410 01:55:23.674098 24451 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0410 01:55:28.277882 24451 solver.cpp:218] Iteration 1776 (2.60666 iter/s, 4.60359s/12 iters), loss = 4.89277
I0410 01:55:28.277926 24451 solver.cpp:237]     Train net output #0: loss = 4.89277 (* 1 = 4.89277 loss)
I0410 01:55:28.277936 24451 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0410 01:55:33.386601 24451 solver.cpp:218] Iteration 1788 (2.34905 iter/s, 5.10845s/12 iters), loss = 4.77312
I0410 01:55:33.386646 24451 solver.cpp:237]     Train net output #0: loss = 4.77312 (* 1 = 4.77312 loss)
I0410 01:55:33.386654 24451 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0410 01:55:38.667778 24451 solver.cpp:218] Iteration 1800 (2.27234 iter/s, 5.2809s/12 iters), loss = 4.72652
I0410 01:55:38.667906 24451 solver.cpp:237]     Train net output #0: loss = 4.72652 (* 1 = 4.72652 loss)
I0410 01:55:38.667917 24451 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0410 01:55:43.667793 24451 solver.cpp:218] Iteration 1812 (2.40016 iter/s, 4.99967s/12 iters), loss = 4.69864
I0410 01:55:43.667836 24451 solver.cpp:237]     Train net output #0: loss = 4.69864 (* 1 = 4.69864 loss)
I0410 01:55:43.667846 24451 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0410 01:55:46.595854 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:55:48.574237 24451 solver.cpp:218] Iteration 1824 (2.44589 iter/s, 4.90618s/12 iters), loss = 4.704
I0410 01:55:48.574283 24451 solver.cpp:237]     Train net output #0: loss = 4.704 (* 1 = 4.704 loss)
I0410 01:55:48.574292 24451 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0410 01:55:53.286505 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0410 01:56:32.619184 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0410 01:56:48.839790 24451 solver.cpp:330] Iteration 1836, Testing net (#0)
I0410 01:56:48.839812 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:56:52.485965 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:56:53.253813 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0324755
I0410 01:56:53.253877 24451 solver.cpp:397]     Test net output #1: loss = 4.65184 (* 1 = 4.65184 loss)
I0410 01:56:53.388528 24451 solver.cpp:218] Iteration 1836 (0.185152 iter/s, 64.8116s/12 iters), loss = 4.69409
I0410 01:56:53.390051 24451 solver.cpp:237]     Train net output #0: loss = 4.69409 (* 1 = 4.69409 loss)
I0410 01:56:53.390064 24451 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0410 01:56:57.333283 24451 solver.cpp:218] Iteration 1848 (3.04332 iter/s, 3.94306s/12 iters), loss = 4.83891
I0410 01:56:57.333323 24451 solver.cpp:237]     Train net output #0: loss = 4.83891 (* 1 = 4.83891 loss)
I0410 01:56:57.333331 24451 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0410 01:57:01.905851 24451 solver.cpp:218] Iteration 1860 (2.62449 iter/s, 4.57232s/12 iters), loss = 4.70986
I0410 01:57:01.905903 24451 solver.cpp:237]     Train net output #0: loss = 4.70986 (* 1 = 4.70986 loss)
I0410 01:57:01.905915 24451 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0410 01:57:06.964354 24451 solver.cpp:218] Iteration 1872 (2.37237 iter/s, 5.05823s/12 iters), loss = 4.73989
I0410 01:57:06.964452 24451 solver.cpp:237]     Train net output #0: loss = 4.73989 (* 1 = 4.73989 loss)
I0410 01:57:06.964462 24451 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0410 01:57:11.973918 24451 solver.cpp:218] Iteration 1884 (2.39557 iter/s, 5.00925s/12 iters), loss = 4.74557
I0410 01:57:11.973975 24451 solver.cpp:237]     Train net output #0: loss = 4.74557 (* 1 = 4.74557 loss)
I0410 01:57:11.973984 24451 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0410 01:57:17.073627 24451 solver.cpp:218] Iteration 1896 (2.3532 iter/s, 5.09945s/12 iters), loss = 4.60436
I0410 01:57:17.073671 24451 solver.cpp:237]     Train net output #0: loss = 4.60436 (* 1 = 4.60436 loss)
I0410 01:57:17.073683 24451 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0410 01:57:22.215641 24451 solver.cpp:218] Iteration 1908 (2.33384 iter/s, 5.14174s/12 iters), loss = 4.63944
I0410 01:57:22.215688 24451 solver.cpp:237]     Train net output #0: loss = 4.63944 (* 1 = 4.63944 loss)
I0410 01:57:22.215699 24451 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0410 01:57:27.298511 24451 solver.cpp:218] Iteration 1920 (2.361 iter/s, 5.0826s/12 iters), loss = 4.65875
I0410 01:57:27.298549 24451 solver.cpp:237]     Train net output #0: loss = 4.65875 (* 1 = 4.65875 loss)
I0410 01:57:27.298557 24451 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0410 01:57:27.596443 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:57:32.454507 24451 solver.cpp:218] Iteration 1932 (2.32751 iter/s, 5.15573s/12 iters), loss = 4.66855
I0410 01:57:32.454548 24451 solver.cpp:237]     Train net output #0: loss = 4.66855 (* 1 = 4.66855 loss)
I0410 01:57:32.454557 24451 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0410 01:57:34.583609 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0410 01:57:52.860736 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0410 01:58:07.300756 24451 solver.cpp:330] Iteration 1938, Testing net (#0)
I0410 01:58:07.300779 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:58:11.009846 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:58:11.810714 24451 solver.cpp:397]     Test net output #0: accuracy = 0.033701
I0410 01:58:11.810765 24451 solver.cpp:397]     Test net output #1: loss = 4.64837 (* 1 = 4.64837 loss)
I0410 01:58:13.628465 24451 solver.cpp:218] Iteration 1944 (0.291459 iter/s, 41.1722s/12 iters), loss = 4.67599
I0410 01:58:13.628517 24451 solver.cpp:237]     Train net output #0: loss = 4.67599 (* 1 = 4.67599 loss)
I0410 01:58:13.628527 24451 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0410 01:58:18.331563 24451 solver.cpp:218] Iteration 1956 (2.55165 iter/s, 4.70284s/12 iters), loss = 4.43748
I0410 01:58:18.331602 24451 solver.cpp:237]     Train net output #0: loss = 4.43748 (* 1 = 4.43748 loss)
I0410 01:58:18.331612 24451 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0410 01:58:23.217183 24451 solver.cpp:218] Iteration 1968 (2.45632 iter/s, 4.88537s/12 iters), loss = 4.51847
I0410 01:58:23.217281 24451 solver.cpp:237]     Train net output #0: loss = 4.51847 (* 1 = 4.51847 loss)
I0410 01:58:23.217291 24451 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0410 01:58:28.427606 24451 solver.cpp:218] Iteration 1980 (2.30322 iter/s, 5.2101s/12 iters), loss = 4.50349
I0410 01:58:28.427654 24451 solver.cpp:237]     Train net output #0: loss = 4.50349 (* 1 = 4.50349 loss)
I0410 01:58:28.427667 24451 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0410 01:58:33.347330 24451 solver.cpp:218] Iteration 1992 (2.43929 iter/s, 4.91946s/12 iters), loss = 4.51468
I0410 01:58:33.347378 24451 solver.cpp:237]     Train net output #0: loss = 4.51468 (* 1 = 4.51468 loss)
I0410 01:58:33.347386 24451 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0410 01:58:38.468838 24451 solver.cpp:218] Iteration 2004 (2.34318 iter/s, 5.12124s/12 iters), loss = 4.51526
I0410 01:58:38.468883 24451 solver.cpp:237]     Train net output #0: loss = 4.51526 (* 1 = 4.51526 loss)
I0410 01:58:38.468891 24451 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0410 01:58:43.244766 24451 solver.cpp:218] Iteration 2016 (2.51273 iter/s, 4.77568s/12 iters), loss = 4.44435
I0410 01:58:43.244809 24451 solver.cpp:237]     Train net output #0: loss = 4.44435 (* 1 = 4.44435 loss)
I0410 01:58:43.244819 24451 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0410 01:58:45.529052 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:58:47.951018 24451 solver.cpp:218] Iteration 2028 (2.54994 iter/s, 4.706s/12 iters), loss = 4.48834
I0410 01:58:47.951059 24451 solver.cpp:237]     Train net output #0: loss = 4.48834 (* 1 = 4.48834 loss)
I0410 01:58:47.951068 24451 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0410 01:58:52.659695 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0410 01:59:15.320118 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0410 01:59:36.848464 24451 solver.cpp:330] Iteration 2040, Testing net (#0)
I0410 01:59:36.848484 24451 net.cpp:676] Ignoring source layer train-data
I0410 01:59:40.501911 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:59:41.401186 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0379902
I0410 01:59:41.401237 24451 solver.cpp:397]     Test net output #1: loss = 4.58711 (* 1 = 4.58711 loss)
I0410 01:59:41.535862 24451 solver.cpp:218] Iteration 2040 (0.223953 iter/s, 53.5826s/12 iters), loss = 4.4581
I0410 01:59:41.537386 24451 solver.cpp:237]     Train net output #0: loss = 4.4581 (* 1 = 4.4581 loss)
I0410 01:59:41.537400 24451 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0410 01:59:45.819752 24451 solver.cpp:218] Iteration 2052 (2.80231 iter/s, 4.28218s/12 iters), loss = 4.27285
I0410 01:59:45.820255 24451 solver.cpp:237]     Train net output #0: loss = 4.27285 (* 1 = 4.27285 loss)
I0410 01:59:45.820266 24451 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0410 01:59:47.870983 24451 blocking_queue.cpp:49] Waiting for data
I0410 01:59:50.965256 24451 solver.cpp:218] Iteration 2064 (2.33246 iter/s, 5.14478s/12 iters), loss = 4.39897
I0410 01:59:50.965304 24451 solver.cpp:237]     Train net output #0: loss = 4.39897 (* 1 = 4.39897 loss)
I0410 01:59:50.965317 24451 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0410 01:59:55.833318 24451 solver.cpp:218] Iteration 2076 (2.46518 iter/s, 4.8678s/12 iters), loss = 4.54602
I0410 01:59:55.833369 24451 solver.cpp:237]     Train net output #0: loss = 4.54602 (* 1 = 4.54602 loss)
I0410 01:59:55.833380 24451 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0410 02:00:00.506745 24451 solver.cpp:218] Iteration 2088 (2.56785 iter/s, 4.67317s/12 iters), loss = 4.29145
I0410 02:00:00.506796 24451 solver.cpp:237]     Train net output #0: loss = 4.29145 (* 1 = 4.29145 loss)
I0410 02:00:00.506808 24451 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0410 02:00:05.040721 24451 solver.cpp:218] Iteration 2100 (2.64683 iter/s, 4.53373s/12 iters), loss = 4.35107
I0410 02:00:05.040763 24451 solver.cpp:237]     Train net output #0: loss = 4.35107 (* 1 = 4.35107 loss)
I0410 02:00:05.040773 24451 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0410 02:00:10.033917 24451 solver.cpp:218] Iteration 2112 (2.4034 iter/s, 4.99294s/12 iters), loss = 4.39026
I0410 02:00:10.033991 24451 solver.cpp:237]     Train net output #0: loss = 4.39026 (* 1 = 4.39026 loss)
I0410 02:00:10.034003 24451 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0410 02:00:14.403265 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:00:14.762658 24451 solver.cpp:218] Iteration 2124 (2.53782 iter/s, 4.72846s/12 iters), loss = 4.23745
I0410 02:00:14.762712 24451 solver.cpp:237]     Train net output #0: loss = 4.23745 (* 1 = 4.23745 loss)
I0410 02:00:14.762725 24451 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0410 02:00:19.259289 24451 solver.cpp:218] Iteration 2136 (2.66881 iter/s, 4.49638s/12 iters), loss = 4.35352
I0410 02:00:19.259390 24451 solver.cpp:237]     Train net output #0: loss = 4.35352 (* 1 = 4.35352 loss)
I0410 02:00:19.259402 24451 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0410 02:00:21.251001 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0410 02:00:47.152690 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0410 02:01:06.839190 24451 solver.cpp:330] Iteration 2142, Testing net (#0)
I0410 02:01:06.839258 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:01:10.500447 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:01:11.577212 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0441176
I0410 02:01:11.577262 24451 solver.cpp:397]     Test net output #1: loss = 4.4488 (* 1 = 4.4488 loss)
I0410 02:01:13.320107 24451 solver.cpp:218] Iteration 2148 (0.221982 iter/s, 54.0585s/12 iters), loss = 4.3425
I0410 02:01:13.320149 24451 solver.cpp:237]     Train net output #0: loss = 4.3425 (* 1 = 4.3425 loss)
I0410 02:01:13.320159 24451 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0410 02:01:18.111434 24451 solver.cpp:218] Iteration 2160 (2.50466 iter/s, 4.79108s/12 iters), loss = 4.53575
I0410 02:01:18.111476 24451 solver.cpp:237]     Train net output #0: loss = 4.53575 (* 1 = 4.53575 loss)
I0410 02:01:18.111485 24451 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0410 02:01:23.305759 24451 solver.cpp:218] Iteration 2172 (2.31033 iter/s, 5.19405s/12 iters), loss = 4.52219
I0410 02:01:23.305800 24451 solver.cpp:237]     Train net output #0: loss = 4.52219 (* 1 = 4.52219 loss)
I0410 02:01:23.305809 24451 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0410 02:01:28.435412 24451 solver.cpp:218] Iteration 2184 (2.33946 iter/s, 5.12938s/12 iters), loss = 4.36329
I0410 02:01:28.435461 24451 solver.cpp:237]     Train net output #0: loss = 4.36329 (* 1 = 4.36329 loss)
I0410 02:01:28.435472 24451 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0410 02:01:33.551229 24451 solver.cpp:218] Iteration 2196 (2.34579 iter/s, 5.11554s/12 iters), loss = 4.40096
I0410 02:01:33.551271 24451 solver.cpp:237]     Train net output #0: loss = 4.40096 (* 1 = 4.40096 loss)
I0410 02:01:33.551281 24451 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0410 02:01:38.699901 24451 solver.cpp:218] Iteration 2208 (2.33082 iter/s, 5.1484s/12 iters), loss = 4.2426
I0410 02:01:38.699999 24451 solver.cpp:237]     Train net output #0: loss = 4.2426 (* 1 = 4.2426 loss)
I0410 02:01:38.700011 24451 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0410 02:01:43.923478 24451 solver.cpp:218] Iteration 2220 (2.29742 iter/s, 5.22326s/12 iters), loss = 4.2453
I0410 02:01:43.923516 24451 solver.cpp:237]     Train net output #0: loss = 4.2453 (* 1 = 4.2453 loss)
I0410 02:01:43.923524 24451 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0410 02:01:45.777177 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:01:48.830574 24451 solver.cpp:218] Iteration 2232 (2.44557 iter/s, 4.90684s/12 iters), loss = 4.35374
I0410 02:01:48.830624 24451 solver.cpp:237]     Train net output #0: loss = 4.35374 (* 1 = 4.35374 loss)
I0410 02:01:48.830636 24451 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0410 02:01:53.204026 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0410 02:02:10.514737 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0410 02:02:21.694535 24451 solver.cpp:330] Iteration 2244, Testing net (#0)
I0410 02:02:21.694557 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:02:25.392501 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:02:26.447227 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0545343
I0410 02:02:26.447263 24451 solver.cpp:397]     Test net output #1: loss = 4.38039 (* 1 = 4.38039 loss)
I0410 02:02:26.581722 24451 solver.cpp:218] Iteration 2244 (0.317885 iter/s, 37.7495s/12 iters), loss = 4.428
I0410 02:02:26.583238 24451 solver.cpp:237]     Train net output #0: loss = 4.428 (* 1 = 4.428 loss)
I0410 02:02:26.583248 24451 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0410 02:02:30.632208 24451 solver.cpp:218] Iteration 2256 (2.96385 iter/s, 4.04879s/12 iters), loss = 4.1191
I0410 02:02:30.632256 24451 solver.cpp:237]     Train net output #0: loss = 4.1191 (* 1 = 4.1191 loss)
I0410 02:02:30.632266 24451 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0410 02:02:35.670521 24451 solver.cpp:218] Iteration 2268 (2.38188 iter/s, 5.03804s/12 iters), loss = 4.36008
I0410 02:02:35.670569 24451 solver.cpp:237]     Train net output #0: loss = 4.36008 (* 1 = 4.36008 loss)
I0410 02:02:35.670580 24451 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0410 02:02:40.904619 24451 solver.cpp:218] Iteration 2280 (2.29278 iter/s, 5.23382s/12 iters), loss = 4.06855
I0410 02:02:40.904755 24451 solver.cpp:237]     Train net output #0: loss = 4.06855 (* 1 = 4.06855 loss)
I0410 02:02:40.904767 24451 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0410 02:02:46.105317 24451 solver.cpp:218] Iteration 2292 (2.30754 iter/s, 5.20034s/12 iters), loss = 4.19686
I0410 02:02:46.105374 24451 solver.cpp:237]     Train net output #0: loss = 4.19686 (* 1 = 4.19686 loss)
I0410 02:02:46.105387 24451 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0410 02:02:51.289867 24451 solver.cpp:218] Iteration 2304 (2.31469 iter/s, 5.18427s/12 iters), loss = 4.50298
I0410 02:02:51.289908 24451 solver.cpp:237]     Train net output #0: loss = 4.50298 (* 1 = 4.50298 loss)
I0410 02:02:51.289917 24451 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0410 02:02:56.194082 24451 solver.cpp:218] Iteration 2316 (2.447 iter/s, 4.90396s/12 iters), loss = 4.30951
I0410 02:02:56.194124 24451 solver.cpp:237]     Train net output #0: loss = 4.30951 (* 1 = 4.30951 loss)
I0410 02:02:56.194134 24451 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0410 02:03:00.222154 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:03:01.332562 24451 solver.cpp:218] Iteration 2328 (2.33544 iter/s, 5.13821s/12 iters), loss = 4.12794
I0410 02:03:01.332607 24451 solver.cpp:237]     Train net output #0: loss = 4.12794 (* 1 = 4.12794 loss)
I0410 02:03:01.332617 24451 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0410 02:03:06.190116 24451 solver.cpp:218] Iteration 2340 (2.47051 iter/s, 4.8573s/12 iters), loss = 4.33923
I0410 02:03:06.190166 24451 solver.cpp:237]     Train net output #0: loss = 4.33923 (* 1 = 4.33923 loss)
I0410 02:03:06.190178 24451 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0410 02:03:08.265926 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0410 02:03:22.260089 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0410 02:03:38.741065 24451 solver.cpp:330] Iteration 2346, Testing net (#0)
I0410 02:03:38.741083 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:03:42.602916 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:03:43.618711 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0557598
I0410 02:03:43.618746 24451 solver.cpp:397]     Test net output #1: loss = 4.24141 (* 1 = 4.24141 loss)
I0410 02:03:45.489137 24451 solver.cpp:218] Iteration 2352 (0.305364 iter/s, 39.2974s/12 iters), loss = 4.14958
I0410 02:03:45.489185 24451 solver.cpp:237]     Train net output #0: loss = 4.14958 (* 1 = 4.14958 loss)
I0410 02:03:45.489197 24451 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0410 02:03:50.437650 24451 solver.cpp:218] Iteration 2364 (2.4251 iter/s, 4.94826s/12 iters), loss = 4.08099
I0410 02:03:50.437687 24451 solver.cpp:237]     Train net output #0: loss = 4.08099 (* 1 = 4.08099 loss)
I0410 02:03:50.437697 24451 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0410 02:03:55.412034 24451 solver.cpp:218] Iteration 2376 (2.41248 iter/s, 4.97413s/12 iters), loss = 4.04265
I0410 02:03:55.412125 24451 solver.cpp:237]     Train net output #0: loss = 4.04265 (* 1 = 4.04265 loss)
I0410 02:03:55.412135 24451 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0410 02:04:00.081450 24451 solver.cpp:218] Iteration 2388 (2.57008 iter/s, 4.66912s/12 iters), loss = 4.13592
I0410 02:04:00.081496 24451 solver.cpp:237]     Train net output #0: loss = 4.13592 (* 1 = 4.13592 loss)
I0410 02:04:00.081506 24451 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0410 02:04:04.749326 24451 solver.cpp:218] Iteration 2400 (2.5709 iter/s, 4.66762s/12 iters), loss = 4.02051
I0410 02:04:04.749370 24451 solver.cpp:237]     Train net output #0: loss = 4.02051 (* 1 = 4.02051 loss)
I0410 02:04:04.749380 24451 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0410 02:04:09.446977 24451 solver.cpp:218] Iteration 2412 (2.55461 iter/s, 4.6974s/12 iters), loss = 3.91055
I0410 02:04:09.447027 24451 solver.cpp:237]     Train net output #0: loss = 3.91055 (* 1 = 3.91055 loss)
I0410 02:04:09.447039 24451 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0410 02:04:14.198575 24451 solver.cpp:218] Iteration 2424 (2.5256 iter/s, 4.75134s/12 iters), loss = 4.15739
I0410 02:04:14.198623 24451 solver.cpp:237]     Train net output #0: loss = 4.15739 (* 1 = 4.15739 loss)
I0410 02:04:14.198634 24451 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0410 02:04:15.175164 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:04:18.858741 24451 solver.cpp:218] Iteration 2436 (2.57516 iter/s, 4.65991s/12 iters), loss = 4.05109
I0410 02:04:18.858783 24451 solver.cpp:237]     Train net output #0: loss = 4.05109 (* 1 = 4.05109 loss)
I0410 02:04:18.858794 24451 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0410 02:04:23.346539 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0410 02:04:41.435642 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0410 02:05:09.460286 24451 solver.cpp:330] Iteration 2448, Testing net (#0)
I0410 02:05:09.460306 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:05:12.994069 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:05:14.091964 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0680147
I0410 02:05:14.092007 24451 solver.cpp:397]     Test net output #1: loss = 4.16875 (* 1 = 4.16875 loss)
I0410 02:05:14.226352 24451 solver.cpp:218] Iteration 2448 (0.216742 iter/s, 55.3653s/12 iters), loss = 4.02849
I0410 02:05:14.227871 24451 solver.cpp:237]     Train net output #0: loss = 4.02849 (* 1 = 4.02849 loss)
I0410 02:05:14.227886 24451 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0410 02:05:18.155324 24451 solver.cpp:218] Iteration 2460 (3.05555 iter/s, 3.92728s/12 iters), loss = 4.02832
I0410 02:05:18.155365 24451 solver.cpp:237]     Train net output #0: loss = 4.02832 (* 1 = 4.02832 loss)
I0410 02:05:18.155375 24451 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0410 02:05:22.685727 24451 solver.cpp:218] Iteration 2472 (2.64891 iter/s, 4.53016s/12 iters), loss = 3.97137
I0410 02:05:22.685768 24451 solver.cpp:237]     Train net output #0: loss = 3.97137 (* 1 = 3.97137 loss)
I0410 02:05:22.685779 24451 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0410 02:05:27.465715 24451 solver.cpp:218] Iteration 2484 (2.5106 iter/s, 4.77974s/12 iters), loss = 4.22093
I0410 02:05:27.465761 24451 solver.cpp:237]     Train net output #0: loss = 4.22093 (* 1 = 4.22093 loss)
I0410 02:05:27.465771 24451 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0410 02:05:32.181227 24451 solver.cpp:218] Iteration 2496 (2.54493 iter/s, 4.71526s/12 iters), loss = 4.09456
I0410 02:05:32.181267 24451 solver.cpp:237]     Train net output #0: loss = 4.09456 (* 1 = 4.09456 loss)
I0410 02:05:32.181275 24451 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0410 02:05:37.400889 24451 solver.cpp:218] Iteration 2508 (2.29912 iter/s, 5.21939s/12 iters), loss = 3.92424
I0410 02:05:37.400936 24451 solver.cpp:237]     Train net output #0: loss = 3.92424 (* 1 = 3.92424 loss)
I0410 02:05:37.400947 24451 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0410 02:05:42.042894 24451 solver.cpp:218] Iteration 2520 (2.58523 iter/s, 4.64175s/12 iters), loss = 4.18084
I0410 02:05:42.042940 24451 solver.cpp:237]     Train net output #0: loss = 4.18084 (* 1 = 4.18084 loss)
I0410 02:05:42.042949 24451 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0410 02:05:44.968145 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:05:46.638885 24451 solver.cpp:218] Iteration 2532 (2.61111 iter/s, 4.59574s/12 iters), loss = 4.13841
I0410 02:05:46.638924 24451 solver.cpp:237]     Train net output #0: loss = 4.13841 (* 1 = 4.13841 loss)
I0410 02:05:46.638934 24451 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0410 02:05:51.798542 24451 solver.cpp:218] Iteration 2544 (2.32586 iter/s, 5.15939s/12 iters), loss = 3.90579
I0410 02:05:51.798586 24451 solver.cpp:237]     Train net output #0: loss = 3.90579 (* 1 = 3.90579 loss)
I0410 02:05:51.798596 24451 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0410 02:05:53.765362 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0410 02:06:15.719090 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0410 02:06:26.884025 24451 solver.cpp:330] Iteration 2550, Testing net (#0)
I0410 02:06:26.884048 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:06:30.371606 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:06:31.416169 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0802696
I0410 02:06:31.416203 24451 solver.cpp:397]     Test net output #1: loss = 4.10118 (* 1 = 4.10118 loss)
I0410 02:06:33.219545 24451 solver.cpp:218] Iteration 2556 (0.28972 iter/s, 41.4193s/12 iters), loss = 4.14333
I0410 02:06:33.219588 24451 solver.cpp:237]     Train net output #0: loss = 4.14333 (* 1 = 4.14333 loss)
I0410 02:06:33.219599 24451 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0410 02:06:38.419705 24451 solver.cpp:218] Iteration 2568 (2.30774 iter/s, 5.19988s/12 iters), loss = 4.00518
I0410 02:06:38.419759 24451 solver.cpp:237]     Train net output #0: loss = 4.00518 (* 1 = 4.00518 loss)
I0410 02:06:38.419771 24451 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0410 02:06:43.307010 24451 solver.cpp:218] Iteration 2580 (2.45547 iter/s, 4.88704s/12 iters), loss = 4.05101
I0410 02:06:43.307054 24451 solver.cpp:237]     Train net output #0: loss = 4.05101 (* 1 = 4.05101 loss)
I0410 02:06:43.307065 24451 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0410 02:06:48.341873 24451 solver.cpp:218] Iteration 2592 (2.38351 iter/s, 5.0346s/12 iters), loss = 4.12994
I0410 02:06:48.341995 24451 solver.cpp:237]     Train net output #0: loss = 4.12994 (* 1 = 4.12994 loss)
I0410 02:06:48.342005 24451 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0410 02:06:53.027832 24451 solver.cpp:218] Iteration 2604 (2.56102 iter/s, 4.68564s/12 iters), loss = 3.90059
I0410 02:06:53.027873 24451 solver.cpp:237]     Train net output #0: loss = 3.90059 (* 1 = 3.90059 loss)
I0410 02:06:53.027884 24451 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0410 02:06:57.699653 24451 solver.cpp:218] Iteration 2616 (2.56873 iter/s, 4.67157s/12 iters), loss = 3.90521
I0410 02:06:57.699704 24451 solver.cpp:237]     Train net output #0: loss = 3.90521 (* 1 = 3.90521 loss)
I0410 02:06:57.699715 24451 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0410 02:07:02.748440 24451 solver.cpp:218] Iteration 2628 (2.37694 iter/s, 5.04851s/12 iters), loss = 3.98764
I0410 02:07:02.748493 24451 solver.cpp:237]     Train net output #0: loss = 3.98764 (* 1 = 3.98764 loss)
I0410 02:07:02.748505 24451 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0410 02:07:03.139294 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:07:07.930680 24451 solver.cpp:218] Iteration 2640 (2.31572 iter/s, 5.18196s/12 iters), loss = 3.81212
I0410 02:07:07.930723 24451 solver.cpp:237]     Train net output #0: loss = 3.81212 (* 1 = 3.81212 loss)
I0410 02:07:07.930733 24451 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0410 02:07:12.571799 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0410 02:07:26.688974 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0410 02:07:43.209892 24451 solver.cpp:330] Iteration 2652, Testing net (#0)
I0410 02:07:43.209916 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:07:46.687427 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:07:47.800017 24451 solver.cpp:397]     Test net output #0: accuracy = 0.0845588
I0410 02:07:47.800067 24451 solver.cpp:397]     Test net output #1: loss = 3.92327 (* 1 = 3.92327 loss)
I0410 02:07:47.929481 24451 solver.cpp:218] Iteration 2652 (0.300022 iter/s, 39.9971s/12 iters), loss = 3.74435
I0410 02:07:47.930999 24451 solver.cpp:237]     Train net output #0: loss = 3.74435 (* 1 = 3.74435 loss)
I0410 02:07:47.931012 24451 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0410 02:07:51.905630 24451 solver.cpp:218] Iteration 2664 (3.01928 iter/s, 3.97445s/12 iters), loss = 3.80172
I0410 02:07:51.905697 24451 solver.cpp:237]     Train net output #0: loss = 3.80172 (* 1 = 3.80172 loss)
I0410 02:07:51.905714 24451 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0410 02:07:56.749408 24451 solver.cpp:218] Iteration 2676 (2.47755 iter/s, 4.8435s/12 iters), loss = 3.93776
I0410 02:07:56.749538 24451 solver.cpp:237]     Train net output #0: loss = 3.93776 (* 1 = 3.93776 loss)
I0410 02:07:56.749550 24451 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0410 02:08:01.837903 24451 solver.cpp:218] Iteration 2688 (2.35842 iter/s, 5.08814s/12 iters), loss = 3.80342
I0410 02:08:01.837946 24451 solver.cpp:237]     Train net output #0: loss = 3.80342 (* 1 = 3.80342 loss)
I0410 02:08:01.837978 24451 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0410 02:08:06.974025 24451 solver.cpp:218] Iteration 2700 (2.33651 iter/s, 5.13586s/12 iters), loss = 3.76947
I0410 02:08:06.974057 24451 solver.cpp:237]     Train net output #0: loss = 3.76947 (* 1 = 3.76947 loss)
I0410 02:08:06.974066 24451 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0410 02:08:12.128937 24451 solver.cpp:218] Iteration 2712 (2.32799 iter/s, 5.15466s/12 iters), loss = 3.72616
I0410 02:08:12.128976 24451 solver.cpp:237]     Train net output #0: loss = 3.72616 (* 1 = 3.72616 loss)
I0410 02:08:12.128985 24451 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0410 02:08:16.932344 24451 solver.cpp:218] Iteration 2724 (2.49836 iter/s, 4.80315s/12 iters), loss = 3.9094
I0410 02:08:16.932386 24451 solver.cpp:237]     Train net output #0: loss = 3.9094 (* 1 = 3.9094 loss)
I0410 02:08:16.932395 24451 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0410 02:08:19.174926 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:08:21.504961 24451 solver.cpp:218] Iteration 2736 (2.62446 iter/s, 4.57237s/12 iters), loss = 3.71623
I0410 02:08:21.505012 24451 solver.cpp:237]     Train net output #0: loss = 3.71623 (* 1 = 3.71623 loss)
I0410 02:08:21.505026 24451 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0410 02:08:26.128679 24451 solver.cpp:218] Iteration 2748 (2.59546 iter/s, 4.62346s/12 iters), loss = 3.79329
I0410 02:08:26.128733 24451 solver.cpp:237]     Train net output #0: loss = 3.79329 (* 1 = 3.79329 loss)
I0410 02:08:26.128744 24451 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0410 02:08:28.018461 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0410 02:08:56.087865 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0410 02:09:14.022451 24451 solver.cpp:330] Iteration 2754, Testing net (#0)
I0410 02:09:14.022493 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:09:17.250461 24451 blocking_queue.cpp:49] Waiting for data
I0410 02:09:17.397951 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:09:18.528538 24451 solver.cpp:397]     Test net output #0: accuracy = 0.10049
I0410 02:09:18.528573 24451 solver.cpp:397]     Test net output #1: loss = 3.83263 (* 1 = 3.83263 loss)
I0410 02:09:20.402712 24451 solver.cpp:218] Iteration 2760 (0.221109 iter/s, 54.2718s/12 iters), loss = 3.55644
I0410 02:09:20.402765 24451 solver.cpp:237]     Train net output #0: loss = 3.55644 (* 1 = 3.55644 loss)
I0410 02:09:20.402777 24451 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0410 02:09:25.466046 24451 solver.cpp:218] Iteration 2772 (2.37011 iter/s, 5.06306s/12 iters), loss = 3.86067
I0410 02:09:25.466089 24451 solver.cpp:237]     Train net output #0: loss = 3.86067 (* 1 = 3.86067 loss)
I0410 02:09:25.466099 24451 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0410 02:09:30.322326 24451 solver.cpp:218] Iteration 2784 (2.47116 iter/s, 4.85602s/12 iters), loss = 3.89009
I0410 02:09:30.322379 24451 solver.cpp:237]     Train net output #0: loss = 3.89009 (* 1 = 3.89009 loss)
I0410 02:09:30.322391 24451 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0410 02:09:34.881480 24451 solver.cpp:218] Iteration 2796 (2.63222 iter/s, 4.5589s/12 iters), loss = 3.50216
I0410 02:09:34.881525 24451 solver.cpp:237]     Train net output #0: loss = 3.50216 (* 1 = 3.50216 loss)
I0410 02:09:34.881536 24451 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0410 02:09:39.583046 24451 solver.cpp:218] Iteration 2808 (2.55248 iter/s, 4.70131s/12 iters), loss = 3.81327
I0410 02:09:39.583092 24451 solver.cpp:237]     Train net output #0: loss = 3.81327 (* 1 = 3.81327 loss)
I0410 02:09:39.583101 24451 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0410 02:09:44.745661 24451 solver.cpp:218] Iteration 2820 (2.32453 iter/s, 5.16234s/12 iters), loss = 3.73413
I0410 02:09:44.745754 24451 solver.cpp:237]     Train net output #0: loss = 3.73413 (* 1 = 3.73413 loss)
I0410 02:09:44.745764 24451 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0410 02:09:49.228686 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:09:49.556963 24451 solver.cpp:218] Iteration 2832 (2.49428 iter/s, 4.811s/12 iters), loss = 3.46954
I0410 02:09:49.556999 24451 solver.cpp:237]     Train net output #0: loss = 3.46954 (* 1 = 3.46954 loss)
I0410 02:09:49.557008 24451 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0410 02:09:54.397810 24451 solver.cpp:218] Iteration 2844 (2.47903 iter/s, 4.8406s/12 iters), loss = 3.66044
I0410 02:09:54.397851 24451 solver.cpp:237]     Train net output #0: loss = 3.66044 (* 1 = 3.66044 loss)
I0410 02:09:54.397861 24451 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0410 02:09:58.935313 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0410 02:10:17.008823 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0410 02:10:30.555786 24451 solver.cpp:330] Iteration 2856, Testing net (#0)
I0410 02:10:30.555809 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:10:33.847586 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:10:35.011930 24451 solver.cpp:397]     Test net output #0: accuracy = 0.105392
I0410 02:10:35.011970 24451 solver.cpp:397]     Test net output #1: loss = 3.88662 (* 1 = 3.88662 loss)
I0410 02:10:35.146924 24451 solver.cpp:218] Iteration 2856 (0.294497 iter/s, 40.7474s/12 iters), loss = 3.62413
I0410 02:10:35.148486 24451 solver.cpp:237]     Train net output #0: loss = 3.62413 (* 1 = 3.62413 loss)
I0410 02:10:35.148497 24451 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0410 02:10:39.107458 24451 solver.cpp:218] Iteration 2868 (3.03122 iter/s, 3.9588s/12 iters), loss = 4.02472
I0410 02:10:39.107501 24451 solver.cpp:237]     Train net output #0: loss = 4.02472 (* 1 = 4.02472 loss)
I0410 02:10:39.107511 24451 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0410 02:10:43.822479 24451 solver.cpp:218] Iteration 2880 (2.54519 iter/s, 4.71477s/12 iters), loss = 3.61645
I0410 02:10:43.822528 24451 solver.cpp:237]     Train net output #0: loss = 3.61645 (* 1 = 3.61645 loss)
I0410 02:10:43.822540 24451 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0410 02:10:48.441489 24451 solver.cpp:218] Iteration 2892 (2.5981 iter/s, 4.61876s/12 iters), loss = 3.58048
I0410 02:10:48.441571 24451 solver.cpp:237]     Train net output #0: loss = 3.58048 (* 1 = 3.58048 loss)
I0410 02:10:48.441581 24451 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0410 02:10:53.062350 24451 solver.cpp:218] Iteration 2904 (2.59708 iter/s, 4.62057s/12 iters), loss = 3.73061
I0410 02:10:53.062400 24451 solver.cpp:237]     Train net output #0: loss = 3.73061 (* 1 = 3.73061 loss)
I0410 02:10:53.062412 24451 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0410 02:10:58.057547 24451 solver.cpp:218] Iteration 2916 (2.40244 iter/s, 4.99493s/12 iters), loss = 3.51112
I0410 02:10:58.057596 24451 solver.cpp:237]     Train net output #0: loss = 3.51112 (* 1 = 3.51112 loss)
I0410 02:10:58.057606 24451 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0410 02:11:02.772027 24451 solver.cpp:218] Iteration 2928 (2.54549 iter/s, 4.71423s/12 iters), loss = 3.85889
I0410 02:11:02.772068 24451 solver.cpp:237]     Train net output #0: loss = 3.85889 (* 1 = 3.85889 loss)
I0410 02:11:02.772078 24451 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0410 02:11:04.379413 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:11:07.332175 24451 solver.cpp:218] Iteration 2940 (2.63163 iter/s, 4.55991s/12 iters), loss = 3.61361
I0410 02:11:07.332226 24451 solver.cpp:237]     Train net output #0: loss = 3.61361 (* 1 = 3.61361 loss)
I0410 02:11:07.332237 24451 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0410 02:11:12.221180 24451 solver.cpp:218] Iteration 2952 (2.45462 iter/s, 4.88874s/12 iters), loss = 3.66838
I0410 02:11:12.221228 24451 solver.cpp:237]     Train net output #0: loss = 3.66838 (* 1 = 3.66838 loss)
I0410 02:11:12.221237 24451 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0410 02:11:14.101692 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0410 02:11:27.966439 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0410 02:11:52.010329 24451 solver.cpp:330] Iteration 2958, Testing net (#0)
I0410 02:11:52.010349 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:11:55.310568 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:11:56.515231 24451 solver.cpp:397]     Test net output #0: accuracy = 0.104779
I0410 02:11:56.515280 24451 solver.cpp:397]     Test net output #1: loss = 3.81904 (* 1 = 3.81904 loss)
I0410 02:11:58.350277 24451 solver.cpp:218] Iteration 2964 (0.26015 iter/s, 46.1272s/12 iters), loss = 3.39993
I0410 02:11:58.350368 24451 solver.cpp:237]     Train net output #0: loss = 3.39993 (* 1 = 3.39993 loss)
I0410 02:11:58.350378 24451 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0410 02:12:02.933553 24451 solver.cpp:218] Iteration 2976 (2.61838 iter/s, 4.58299s/12 iters), loss = 3.75256
I0410 02:12:02.933598 24451 solver.cpp:237]     Train net output #0: loss = 3.75256 (* 1 = 3.75256 loss)
I0410 02:12:02.933609 24451 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0410 02:12:08.052289 24451 solver.cpp:218] Iteration 2988 (2.34445 iter/s, 5.11847s/12 iters), loss = 3.43035
I0410 02:12:08.052338 24451 solver.cpp:237]     Train net output #0: loss = 3.43035 (* 1 = 3.43035 loss)
I0410 02:12:08.052350 24451 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0410 02:12:12.999481 24451 solver.cpp:218] Iteration 3000 (2.42575 iter/s, 4.94693s/12 iters), loss = 3.56313
I0410 02:12:12.999527 24451 solver.cpp:237]     Train net output #0: loss = 3.56313 (* 1 = 3.56313 loss)
I0410 02:12:12.999537 24451 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0410 02:12:17.682572 24451 solver.cpp:218] Iteration 3012 (2.56255 iter/s, 4.68284s/12 iters), loss = 3.52916
I0410 02:12:17.682610 24451 solver.cpp:237]     Train net output #0: loss = 3.52916 (* 1 = 3.52916 loss)
I0410 02:12:17.682619 24451 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0410 02:12:22.397044 24451 solver.cpp:218] Iteration 3024 (2.54549 iter/s, 4.71423s/12 iters), loss = 3.45327
I0410 02:12:22.397095 24451 solver.cpp:237]     Train net output #0: loss = 3.45327 (* 1 = 3.45327 loss)
I0410 02:12:22.397106 24451 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0410 02:12:26.113276 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:12:27.098778 24451 solver.cpp:218] Iteration 3036 (2.55239 iter/s, 4.70148s/12 iters), loss = 3.34572
I0410 02:12:27.098824 24451 solver.cpp:237]     Train net output #0: loss = 3.34572 (* 1 = 3.34572 loss)
I0410 02:12:27.098836 24451 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0410 02:12:31.761142 24451 solver.cpp:218] Iteration 3048 (2.57394 iter/s, 4.66212s/12 iters), loss = 3.55988
I0410 02:12:31.761245 24451 solver.cpp:237]     Train net output #0: loss = 3.55988 (* 1 = 3.55988 loss)
I0410 02:12:31.761255 24451 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0410 02:12:36.322333 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0410 02:13:03.399883 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0410 02:13:19.739136 24451 solver.cpp:330] Iteration 3060, Testing net (#0)
I0410 02:13:19.739159 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:13:23.111030 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:13:24.384057 24451 solver.cpp:397]     Test net output #0: accuracy = 0.134804
I0410 02:13:24.384085 24451 solver.cpp:397]     Test net output #1: loss = 3.56756 (* 1 = 3.56756 loss)
I0410 02:13:24.514580 24451 solver.cpp:218] Iteration 3060 (0.227483 iter/s, 52.7512s/12 iters), loss = 3.43281
I0410 02:13:24.516101 24451 solver.cpp:237]     Train net output #0: loss = 3.43281 (* 1 = 3.43281 loss)
I0410 02:13:24.516111 24451 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0410 02:13:28.906191 24451 solver.cpp:218] Iteration 3072 (2.73356 iter/s, 4.38988s/12 iters), loss = 3.24682
I0410 02:13:28.906253 24451 solver.cpp:237]     Train net output #0: loss = 3.24682 (* 1 = 3.24682 loss)
I0410 02:13:28.906267 24451 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0410 02:13:33.867657 24451 solver.cpp:218] Iteration 3084 (2.41878 iter/s, 4.96119s/12 iters), loss = 3.41898
I0410 02:13:33.867760 24451 solver.cpp:237]     Train net output #0: loss = 3.41898 (* 1 = 3.41898 loss)
I0410 02:13:33.867771 24451 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0410 02:13:39.058887 24451 solver.cpp:218] Iteration 3096 (2.31174 iter/s, 5.1909s/12 iters), loss = 3.26839
I0410 02:13:39.058933 24451 solver.cpp:237]     Train net output #0: loss = 3.26839 (* 1 = 3.26839 loss)
I0410 02:13:39.058941 24451 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0410 02:13:43.978422 24451 solver.cpp:218] Iteration 3108 (2.43938 iter/s, 4.91928s/12 iters), loss = 3.27922
I0410 02:13:43.978473 24451 solver.cpp:237]     Train net output #0: loss = 3.27922 (* 1 = 3.27922 loss)
I0410 02:13:43.978484 24451 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0410 02:13:49.178465 24451 solver.cpp:218] Iteration 3120 (2.3078 iter/s, 5.19977s/12 iters), loss = 3.24569
I0410 02:13:49.178510 24451 solver.cpp:237]     Train net output #0: loss = 3.24569 (* 1 = 3.24569 loss)
I0410 02:13:49.178524 24451 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0410 02:13:54.097745 24451 solver.cpp:218] Iteration 3132 (2.43951 iter/s, 4.91902s/12 iters), loss = 3.5876
I0410 02:13:54.097790 24451 solver.cpp:237]     Train net output #0: loss = 3.5876 (* 1 = 3.5876 loss)
I0410 02:13:54.097801 24451 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0410 02:13:55.092936 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:13:59.162089 24451 solver.cpp:218] Iteration 3144 (2.36963 iter/s, 5.06407s/12 iters), loss = 3.19986
I0410 02:13:59.162144 24451 solver.cpp:237]     Train net output #0: loss = 3.19986 (* 1 = 3.19986 loss)
I0410 02:13:59.162155 24451 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0410 02:14:04.287878 24451 solver.cpp:218] Iteration 3156 (2.34123 iter/s, 5.12551s/12 iters), loss = 3.3048
I0410 02:14:04.287972 24451 solver.cpp:237]     Train net output #0: loss = 3.3048 (* 1 = 3.3048 loss)
I0410 02:14:04.288004 24451 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0410 02:14:06.131523 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0410 02:14:24.266309 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0410 02:14:38.149317 24451 solver.cpp:330] Iteration 3162, Testing net (#0)
I0410 02:14:38.149410 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:14:41.639230 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:14:42.927410 24451 solver.cpp:397]     Test net output #0: accuracy = 0.151348
I0410 02:14:42.927457 24451 solver.cpp:397]     Test net output #1: loss = 3.55338 (* 1 = 3.55338 loss)
I0410 02:14:44.681023 24451 solver.cpp:218] Iteration 3168 (0.297093 iter/s, 40.3914s/12 iters), loss = 3.26192
I0410 02:14:44.681066 24451 solver.cpp:237]     Train net output #0: loss = 3.26192 (* 1 = 3.26192 loss)
I0410 02:14:44.681073 24451 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0410 02:14:49.668179 24451 solver.cpp:218] Iteration 3180 (2.40631 iter/s, 4.9869s/12 iters), loss = 3.48354
I0410 02:14:49.668224 24451 solver.cpp:237]     Train net output #0: loss = 3.48354 (* 1 = 3.48354 loss)
I0410 02:14:49.668236 24451 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0410 02:14:54.469615 24451 solver.cpp:218] Iteration 3192 (2.49938 iter/s, 4.80118s/12 iters), loss = 3.30472
I0410 02:14:54.469657 24451 solver.cpp:237]     Train net output #0: loss = 3.30472 (* 1 = 3.30472 loss)
I0410 02:14:54.469667 24451 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0410 02:14:59.649435 24451 solver.cpp:218] Iteration 3204 (2.3168 iter/s, 5.17955s/12 iters), loss = 3.23107
I0410 02:14:59.649480 24451 solver.cpp:237]     Train net output #0: loss = 3.23107 (* 1 = 3.23107 loss)
I0410 02:14:59.649489 24451 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0410 02:15:04.861224 24451 solver.cpp:218] Iteration 3216 (2.30259 iter/s, 5.21151s/12 iters), loss = 3.3673
I0410 02:15:04.861272 24451 solver.cpp:237]     Train net output #0: loss = 3.3673 (* 1 = 3.3673 loss)
I0410 02:15:04.861284 24451 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0410 02:15:10.009526 24451 solver.cpp:218] Iteration 3228 (2.33099 iter/s, 5.14803s/12 iters), loss = 3.50815
I0410 02:15:10.009629 24451 solver.cpp:237]     Train net output #0: loss = 3.50815 (* 1 = 3.50815 loss)
I0410 02:15:10.009639 24451 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0410 02:15:13.662679 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:15:15.859551 24451 solver.cpp:218] Iteration 3240 (2.0514 iter/s, 5.84967s/12 iters), loss = 3.33343
I0410 02:15:15.859596 24451 solver.cpp:237]     Train net output #0: loss = 3.33343 (* 1 = 3.33343 loss)
I0410 02:15:15.859606 24451 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0410 02:15:20.597532 24451 solver.cpp:218] Iteration 3252 (2.53286 iter/s, 4.73773s/12 iters), loss = 3.31183
I0410 02:15:20.597584 24451 solver.cpp:237]     Train net output #0: loss = 3.31183 (* 1 = 3.31183 loss)
I0410 02:15:20.597597 24451 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0410 02:15:25.114545 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0410 02:15:52.248718 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0410 02:16:05.835532 24451 solver.cpp:330] Iteration 3264, Testing net (#0)
I0410 02:16:05.835553 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:16:09.038923 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:16:10.404222 24451 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0410 02:16:10.404258 24451 solver.cpp:397]     Test net output #1: loss = 3.51241 (* 1 = 3.51241 loss)
I0410 02:16:10.538559 24451 solver.cpp:218] Iteration 3264 (0.240293 iter/s, 49.9389s/12 iters), loss = 3.37664
I0410 02:16:10.540086 24451 solver.cpp:237]     Train net output #0: loss = 3.37664 (* 1 = 3.37664 loss)
I0410 02:16:10.540096 24451 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0410 02:16:14.708858 24451 solver.cpp:218] Iteration 3276 (2.87867 iter/s, 4.16859s/12 iters), loss = 3.25441
I0410 02:16:14.708902 24451 solver.cpp:237]     Train net output #0: loss = 3.25441 (* 1 = 3.25441 loss)
I0410 02:16:14.708912 24451 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0410 02:16:19.880367 24451 solver.cpp:218] Iteration 3288 (2.32053 iter/s, 5.17124s/12 iters), loss = 3.15472
I0410 02:16:19.880411 24451 solver.cpp:237]     Train net output #0: loss = 3.15472 (* 1 = 3.15472 loss)
I0410 02:16:19.880422 24451 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0410 02:16:24.656400 24451 solver.cpp:218] Iteration 3300 (2.51268 iter/s, 4.77578s/12 iters), loss = 3.41096
I0410 02:16:24.656500 24451 solver.cpp:237]     Train net output #0: loss = 3.41096 (* 1 = 3.41096 loss)
I0410 02:16:24.656510 24451 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0410 02:16:29.374332 24451 solver.cpp:218] Iteration 3312 (2.54365 iter/s, 4.71763s/12 iters), loss = 3.23041
I0410 02:16:29.374374 24451 solver.cpp:237]     Train net output #0: loss = 3.23041 (* 1 = 3.23041 loss)
I0410 02:16:29.374384 24451 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0410 02:16:34.083614 24451 solver.cpp:218] Iteration 3324 (2.5483 iter/s, 4.70903s/12 iters), loss = 3.12747
I0410 02:16:34.083664 24451 solver.cpp:237]     Train net output #0: loss = 3.12747 (* 1 = 3.12747 loss)
I0410 02:16:34.083675 24451 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0410 02:16:38.771701 24451 solver.cpp:218] Iteration 3336 (2.55982 iter/s, 4.68783s/12 iters), loss = 3.09566
I0410 02:16:38.771744 24451 solver.cpp:237]     Train net output #0: loss = 3.09566 (* 1 = 3.09566 loss)
I0410 02:16:38.771754 24451 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0410 02:16:39.165064 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:16:43.459672 24451 solver.cpp:218] Iteration 3348 (2.55988 iter/s, 4.68773s/12 iters), loss = 3.14469
I0410 02:16:43.459709 24451 solver.cpp:237]     Train net output #0: loss = 3.14469 (* 1 = 3.14469 loss)
I0410 02:16:43.459717 24451 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0410 02:16:48.082309 24451 solver.cpp:218] Iteration 3360 (2.59606 iter/s, 4.6224s/12 iters), loss = 3.05896
I0410 02:16:48.082357 24451 solver.cpp:237]     Train net output #0: loss = 3.05896 (* 1 = 3.05896 loss)
I0410 02:16:48.082370 24451 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0410 02:16:49.985239 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0410 02:17:05.245610 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0410 02:17:24.090999 24451 solver.cpp:330] Iteration 3366, Testing net (#0)
I0410 02:17:24.091017 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:17:27.321468 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:17:28.817862 24451 solver.cpp:397]     Test net output #0: accuracy = 0.167279
I0410 02:17:28.817919 24451 solver.cpp:397]     Test net output #1: loss = 3.41404 (* 1 = 3.41404 loss)
I0410 02:17:30.573071 24451 solver.cpp:218] Iteration 3372 (0.282426 iter/s, 42.489s/12 iters), loss = 3.14431
I0410 02:17:30.573115 24451 solver.cpp:237]     Train net output #0: loss = 3.14431 (* 1 = 3.14431 loss)
I0410 02:17:30.573125 24451 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0410 02:17:35.222574 24451 solver.cpp:218] Iteration 3384 (2.58106 iter/s, 4.64926s/12 iters), loss = 3.19337
I0410 02:17:35.222615 24451 solver.cpp:237]     Train net output #0: loss = 3.19337 (* 1 = 3.19337 loss)
I0410 02:17:35.222623 24451 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0410 02:17:39.929216 24451 solver.cpp:218] Iteration 3396 (2.54972 iter/s, 4.70639s/12 iters), loss = 3.1573
I0410 02:17:39.929327 24451 solver.cpp:237]     Train net output #0: loss = 3.1573 (* 1 = 3.1573 loss)
I0410 02:17:39.929339 24451 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0410 02:17:44.697718 24451 solver.cpp:218] Iteration 3408 (2.51668 iter/s, 4.76819s/12 iters), loss = 3.13633
I0410 02:17:44.697764 24451 solver.cpp:237]     Train net output #0: loss = 3.13633 (* 1 = 3.13633 loss)
I0410 02:17:44.697774 24451 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0410 02:17:49.662045 24451 solver.cpp:218] Iteration 3420 (2.41737 iter/s, 4.96407s/12 iters), loss = 3.08376
I0410 02:17:49.662088 24451 solver.cpp:237]     Train net output #0: loss = 3.08376 (* 1 = 3.08376 loss)
I0410 02:17:49.662098 24451 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0410 02:17:54.538053 24451 solver.cpp:218] Iteration 3432 (2.46116 iter/s, 4.87575s/12 iters), loss = 3.14316
I0410 02:17:54.538092 24451 solver.cpp:237]     Train net output #0: loss = 3.14316 (* 1 = 3.14316 loss)
I0410 02:17:54.538101 24451 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0410 02:17:56.957940 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:17:59.259146 24451 solver.cpp:218] Iteration 3444 (2.54192 iter/s, 4.72084s/12 iters), loss = 2.79747
I0410 02:17:59.259191 24451 solver.cpp:237]     Train net output #0: loss = 2.79747 (* 1 = 2.79747 loss)
I0410 02:17:59.259200 24451 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0410 02:18:04.415711 24451 solver.cpp:218] Iteration 3456 (2.32725 iter/s, 5.1563s/12 iters), loss = 3.05924
I0410 02:18:04.415750 24451 solver.cpp:237]     Train net output #0: loss = 3.05924 (* 1 = 3.05924 loss)
I0410 02:18:04.415757 24451 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0410 02:18:09.071807 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0410 02:18:27.223433 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0410 02:18:44.344619 24451 solver.cpp:330] Iteration 3468, Testing net (#0)
I0410 02:18:44.344636 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:18:44.921535 24451 blocking_queue.cpp:49] Waiting for data
I0410 02:18:47.478848 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:18:48.895174 24451 solver.cpp:397]     Test net output #0: accuracy = 0.183211
I0410 02:18:48.895224 24451 solver.cpp:397]     Test net output #1: loss = 3.32047 (* 1 = 3.32047 loss)
I0410 02:18:49.024861 24451 solver.cpp:218] Iteration 3468 (0.269014 iter/s, 44.6073s/12 iters), loss = 3.03999
I0410 02:18:49.026391 24451 solver.cpp:237]     Train net output #0: loss = 3.03999 (* 1 = 3.03999 loss)
I0410 02:18:49.026402 24451 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0410 02:18:53.250685 24451 solver.cpp:218] Iteration 3480 (2.84083 iter/s, 4.22411s/12 iters), loss = 3.02798
I0410 02:18:53.250731 24451 solver.cpp:237]     Train net output #0: loss = 3.02798 (* 1 = 3.02798 loss)
I0410 02:18:53.250741 24451 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0410 02:18:57.977794 24451 solver.cpp:218] Iteration 3492 (2.53869 iter/s, 4.72685s/12 iters), loss = 2.93017
I0410 02:18:57.977869 24451 solver.cpp:237]     Train net output #0: loss = 2.93017 (* 1 = 2.93017 loss)
I0410 02:18:57.977877 24451 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0410 02:19:02.696141 24451 solver.cpp:218] Iteration 3504 (2.54341 iter/s, 4.71807s/12 iters), loss = 2.8206
I0410 02:19:02.696187 24451 solver.cpp:237]     Train net output #0: loss = 2.8206 (* 1 = 2.8206 loss)
I0410 02:19:02.696197 24451 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0410 02:19:07.380767 24451 solver.cpp:218] Iteration 3516 (2.56171 iter/s, 4.68438s/12 iters), loss = 2.92883
I0410 02:19:07.380810 24451 solver.cpp:237]     Train net output #0: loss = 2.92883 (* 1 = 2.92883 loss)
I0410 02:19:07.380820 24451 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0410 02:19:12.026623 24451 solver.cpp:218] Iteration 3528 (2.58308 iter/s, 4.64561s/12 iters), loss = 2.77462
I0410 02:19:12.026661 24451 solver.cpp:237]     Train net output #0: loss = 2.77462 (* 1 = 2.77462 loss)
I0410 02:19:12.026670 24451 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0410 02:19:16.817260 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:19:17.137447 24451 solver.cpp:218] Iteration 3540 (2.34808 iter/s, 5.11057s/12 iters), loss = 2.97529
I0410 02:19:17.137483 24451 solver.cpp:237]     Train net output #0: loss = 2.97529 (* 1 = 2.97529 loss)
I0410 02:19:17.137492 24451 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0410 02:19:22.374260 24451 solver.cpp:218] Iteration 3552 (2.29159 iter/s, 5.23655s/12 iters), loss = 3.01173
I0410 02:19:22.374310 24451 solver.cpp:237]     Train net output #0: loss = 3.01173 (* 1 = 3.01173 loss)
I0410 02:19:22.374321 24451 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0410 02:19:27.232368 24451 solver.cpp:218] Iteration 3564 (2.47023 iter/s, 4.85785s/12 iters), loss = 2.73818
I0410 02:19:27.232409 24451 solver.cpp:237]     Train net output #0: loss = 2.73818 (* 1 = 2.73818 loss)
I0410 02:19:27.232419 24451 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0410 02:19:29.250332 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0410 02:19:53.663018 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0410 02:20:04.942631 24451 solver.cpp:330] Iteration 3570, Testing net (#0)
I0410 02:20:04.942692 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:20:08.003391 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:20:09.462297 24451 solver.cpp:397]     Test net output #0: accuracy = 0.188725
I0410 02:20:09.462347 24451 solver.cpp:397]     Test net output #1: loss = 3.2528 (* 1 = 3.2528 loss)
I0410 02:20:11.329236 24451 solver.cpp:218] Iteration 3576 (0.27214 iter/s, 44.095s/12 iters), loss = 3.11441
I0410 02:20:11.329288 24451 solver.cpp:237]     Train net output #0: loss = 3.11441 (* 1 = 3.11441 loss)
I0410 02:20:11.329299 24451 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0410 02:20:16.113991 24451 solver.cpp:218] Iteration 3588 (2.5081 iter/s, 4.78449s/12 iters), loss = 2.98786
I0410 02:20:16.114040 24451 solver.cpp:237]     Train net output #0: loss = 2.98786 (* 1 = 2.98786 loss)
I0410 02:20:16.114053 24451 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0410 02:20:20.870817 24451 solver.cpp:218] Iteration 3600 (2.52283 iter/s, 4.75657s/12 iters), loss = 2.82392
I0410 02:20:20.870863 24451 solver.cpp:237]     Train net output #0: loss = 2.82392 (* 1 = 2.82392 loss)
I0410 02:20:20.870874 24451 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0410 02:20:26.014358 24451 solver.cpp:218] Iteration 3612 (2.33315 iter/s, 5.14327s/12 iters), loss = 2.96303
I0410 02:20:26.014407 24451 solver.cpp:237]     Train net output #0: loss = 2.96303 (* 1 = 2.96303 loss)
I0410 02:20:26.014420 24451 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0410 02:20:30.982753 24451 solver.cpp:218] Iteration 3624 (2.4154 iter/s, 4.96813s/12 iters), loss = 2.91931
I0410 02:20:30.982796 24451 solver.cpp:237]     Train net output #0: loss = 2.91931 (* 1 = 2.91931 loss)
I0410 02:20:30.982806 24451 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0410 02:20:35.934275 24451 solver.cpp:218] Iteration 3636 (2.42363 iter/s, 4.95126s/12 iters), loss = 2.83083
I0410 02:20:35.934394 24451 solver.cpp:237]     Train net output #0: loss = 2.83083 (* 1 = 2.83083 loss)
I0410 02:20:35.934406 24451 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0410 02:20:37.624176 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:20:40.580870 24451 solver.cpp:218] Iteration 3648 (2.58271 iter/s, 4.64627s/12 iters), loss = 2.86307
I0410 02:20:40.580921 24451 solver.cpp:237]     Train net output #0: loss = 2.86307 (* 1 = 2.86307 loss)
I0410 02:20:40.580932 24451 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0410 02:20:45.389987 24451 solver.cpp:218] Iteration 3660 (2.49539 iter/s, 4.80886s/12 iters), loss = 2.97276
I0410 02:20:45.390024 24451 solver.cpp:237]     Train net output #0: loss = 2.97276 (* 1 = 2.97276 loss)
I0410 02:20:45.390033 24451 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0410 02:20:49.904340 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0410 02:21:04.259368 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0410 02:21:15.512136 24451 solver.cpp:330] Iteration 3672, Testing net (#0)
I0410 02:21:15.512183 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:21:18.537568 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:21:20.029179 24451 solver.cpp:397]     Test net output #0: accuracy = 0.193015
I0410 02:21:20.029228 24451 solver.cpp:397]     Test net output #1: loss = 3.21894 (* 1 = 3.21894 loss)
I0410 02:21:20.164206 24451 solver.cpp:218] Iteration 3672 (0.345098 iter/s, 34.7728s/12 iters), loss = 2.89923
I0410 02:21:20.165766 24451 solver.cpp:237]     Train net output #0: loss = 2.89923 (* 1 = 2.89923 loss)
I0410 02:21:20.165779 24451 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0410 02:21:24.152048 24451 solver.cpp:218] Iteration 3684 (3.01045 iter/s, 3.98611s/12 iters), loss = 3.0372
I0410 02:21:24.152096 24451 solver.cpp:237]     Train net output #0: loss = 3.0372 (* 1 = 3.0372 loss)
I0410 02:21:24.152107 24451 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0410 02:21:28.872805 24451 solver.cpp:218] Iteration 3696 (2.5421 iter/s, 4.7205s/12 iters), loss = 2.66073
I0410 02:21:28.872856 24451 solver.cpp:237]     Train net output #0: loss = 2.66073 (* 1 = 2.66073 loss)
I0410 02:21:28.872869 24451 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0410 02:21:33.798651 24451 solver.cpp:218] Iteration 3708 (2.43626 iter/s, 4.92559s/12 iters), loss = 2.87972
I0410 02:21:33.798688 24451 solver.cpp:237]     Train net output #0: loss = 2.87972 (* 1 = 2.87972 loss)
I0410 02:21:33.798697 24451 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0410 02:21:39.007134 24451 solver.cpp:218] Iteration 3720 (2.30405 iter/s, 5.20822s/12 iters), loss = 2.83295
I0410 02:21:39.007172 24451 solver.cpp:237]     Train net output #0: loss = 2.83295 (* 1 = 2.83295 loss)
I0410 02:21:39.007180 24451 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0410 02:21:43.815654 24451 solver.cpp:218] Iteration 3732 (2.4957 iter/s, 4.80827s/12 iters), loss = 2.73081
I0410 02:21:43.815701 24451 solver.cpp:237]     Train net output #0: loss = 2.73081 (* 1 = 2.73081 loss)
I0410 02:21:43.815712 24451 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0410 02:21:47.355473 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:21:48.323151 24451 solver.cpp:218] Iteration 3744 (2.66238 iter/s, 4.50725s/12 iters), loss = 2.55327
I0410 02:21:48.323201 24451 solver.cpp:237]     Train net output #0: loss = 2.55327 (* 1 = 2.55327 loss)
I0410 02:21:48.323212 24451 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0410 02:21:53.094828 24451 solver.cpp:218] Iteration 3756 (2.51497 iter/s, 4.77142s/12 iters), loss = 2.81409
I0410 02:21:53.094873 24451 solver.cpp:237]     Train net output #0: loss = 2.81409 (* 1 = 2.81409 loss)
I0410 02:21:53.094882 24451 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0410 02:21:57.837505 24451 solver.cpp:218] Iteration 3768 (2.53035 iter/s, 4.74243s/12 iters), loss = 2.61712
I0410 02:21:57.837551 24451 solver.cpp:237]     Train net output #0: loss = 2.61712 (* 1 = 2.61712 loss)
I0410 02:21:57.837563 24451 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0410 02:21:59.676275 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0410 02:22:14.589890 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0410 02:22:32.905016 24451 solver.cpp:330] Iteration 3774, Testing net (#0)
I0410 02:22:32.905082 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:22:35.961738 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:22:37.561657 24451 solver.cpp:397]     Test net output #0: accuracy = 0.216299
I0410 02:22:37.561697 24451 solver.cpp:397]     Test net output #1: loss = 3.1279 (* 1 = 3.1279 loss)
I0410 02:22:39.335574 24451 solver.cpp:218] Iteration 3780 (0.289182 iter/s, 41.4963s/12 iters), loss = 2.70534
I0410 02:22:39.335623 24451 solver.cpp:237]     Train net output #0: loss = 2.70534 (* 1 = 2.70534 loss)
I0410 02:22:39.335634 24451 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0410 02:22:43.947775 24451 solver.cpp:218] Iteration 3792 (2.60194 iter/s, 4.61195s/12 iters), loss = 2.78672
I0410 02:22:43.947822 24451 solver.cpp:237]     Train net output #0: loss = 2.78672 (* 1 = 2.78672 loss)
I0410 02:22:43.947832 24451 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0410 02:22:48.465270 24451 solver.cpp:218] Iteration 3804 (2.65649 iter/s, 4.51725s/12 iters), loss = 2.55803
I0410 02:22:48.465315 24451 solver.cpp:237]     Train net output #0: loss = 2.55803 (* 1 = 2.55803 loss)
I0410 02:22:48.465325 24451 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0410 02:22:53.162475 24451 solver.cpp:218] Iteration 3816 (2.55485 iter/s, 4.69695s/12 iters), loss = 2.80402
I0410 02:22:53.162509 24451 solver.cpp:237]     Train net output #0: loss = 2.80402 (* 1 = 2.80402 loss)
I0410 02:22:53.162518 24451 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0410 02:22:57.706115 24451 solver.cpp:218] Iteration 3828 (2.64119 iter/s, 4.5434s/12 iters), loss = 2.28517
I0410 02:22:57.706164 24451 solver.cpp:237]     Train net output #0: loss = 2.28517 (* 1 = 2.28517 loss)
I0410 02:22:57.706176 24451 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0410 02:23:02.311736 24451 solver.cpp:218] Iteration 3840 (2.60565 iter/s, 4.60537s/12 iters), loss = 2.62654
I0410 02:23:02.311787 24451 solver.cpp:237]     Train net output #0: loss = 2.62654 (* 1 = 2.62654 loss)
I0410 02:23:02.311800 24451 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0410 02:23:03.294050 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:23:06.816808 24451 solver.cpp:218] Iteration 3852 (2.66381 iter/s, 4.50483s/12 iters), loss = 2.41356
I0410 02:23:06.816849 24451 solver.cpp:237]     Train net output #0: loss = 2.41356 (* 1 = 2.41356 loss)
I0410 02:23:06.816859 24451 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0410 02:23:11.417467 24451 solver.cpp:218] Iteration 3864 (2.60846 iter/s, 4.60042s/12 iters), loss = 2.65557
I0410 02:23:11.417517 24451 solver.cpp:237]     Train net output #0: loss = 2.65557 (* 1 = 2.65557 loss)
I0410 02:23:11.417529 24451 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0410 02:23:15.662849 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0410 02:23:35.174501 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0410 02:23:54.349300 24451 solver.cpp:330] Iteration 3876, Testing net (#0)
I0410 02:23:54.349325 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:23:57.387763 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:23:59.011420 24451 solver.cpp:397]     Test net output #0: accuracy = 0.213848
I0410 02:23:59.011454 24451 solver.cpp:397]     Test net output #1: loss = 3.22682 (* 1 = 3.22682 loss)
I0410 02:23:59.146075 24451 solver.cpp:218] Iteration 3876 (0.251432 iter/s, 47.7266s/12 iters), loss = 2.6979
I0410 02:23:59.147598 24451 solver.cpp:237]     Train net output #0: loss = 2.6979 (* 1 = 2.6979 loss)
I0410 02:23:59.147612 24451 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0410 02:24:03.550832 24451 solver.cpp:218] Iteration 3888 (2.72538 iter/s, 4.40305s/12 iters), loss = 2.64023
I0410 02:24:03.550871 24451 solver.cpp:237]     Train net output #0: loss = 2.64023 (* 1 = 2.64023 loss)
I0410 02:24:03.550880 24451 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0410 02:24:08.577258 24451 solver.cpp:218] Iteration 3900 (2.38751 iter/s, 5.02617s/12 iters), loss = 2.69758
I0410 02:24:08.577365 24451 solver.cpp:237]     Train net output #0: loss = 2.69758 (* 1 = 2.69758 loss)
I0410 02:24:08.577378 24451 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0410 02:24:13.142127 24451 solver.cpp:218] Iteration 3912 (2.62895 iter/s, 4.56457s/12 iters), loss = 2.44053
I0410 02:24:13.142163 24451 solver.cpp:237]     Train net output #0: loss = 2.44053 (* 1 = 2.44053 loss)
I0410 02:24:13.142172 24451 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0410 02:24:17.883250 24451 solver.cpp:218] Iteration 3924 (2.53118 iter/s, 4.74088s/12 iters), loss = 2.45818
I0410 02:24:17.883297 24451 solver.cpp:237]     Train net output #0: loss = 2.45818 (* 1 = 2.45818 loss)
I0410 02:24:17.883306 24451 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0410 02:24:22.872138 24451 solver.cpp:218] Iteration 3936 (2.40547 iter/s, 4.98862s/12 iters), loss = 2.60536
I0410 02:24:22.872184 24451 solver.cpp:237]     Train net output #0: loss = 2.60536 (* 1 = 2.60536 loss)
I0410 02:24:22.872192 24451 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0410 02:24:25.996747 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:24:27.628466 24451 solver.cpp:218] Iteration 3948 (2.52309 iter/s, 4.75608s/12 iters), loss = 2.61796
I0410 02:24:27.628505 24451 solver.cpp:237]     Train net output #0: loss = 2.61796 (* 1 = 2.61796 loss)
I0410 02:24:27.628513 24451 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0410 02:24:32.317876 24451 solver.cpp:218] Iteration 3960 (2.55909 iter/s, 4.68916s/12 iters), loss = 2.40091
I0410 02:24:32.317922 24451 solver.cpp:237]     Train net output #0: loss = 2.40091 (* 1 = 2.40091 loss)
I0410 02:24:32.317932 24451 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0410 02:24:37.040601 24451 solver.cpp:218] Iteration 3972 (2.54104 iter/s, 4.72247s/12 iters), loss = 2.60308
I0410 02:24:37.040650 24451 solver.cpp:237]     Train net output #0: loss = 2.60308 (* 1 = 2.60308 loss)
I0410 02:24:37.040660 24451 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0410 02:24:39.136019 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0410 02:25:04.123481 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0410 02:25:15.286646 24451 solver.cpp:330] Iteration 3978, Testing net (#0)
I0410 02:25:15.286690 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:25:18.222671 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:25:19.835196 24451 solver.cpp:397]     Test net output #0: accuracy = 0.216912
I0410 02:25:19.835242 24451 solver.cpp:397]     Test net output #1: loss = 3.2379 (* 1 = 3.2379 loss)
I0410 02:25:21.726402 24451 solver.cpp:218] Iteration 3984 (0.268553 iter/s, 44.6839s/12 iters), loss = 2.69415
I0410 02:25:21.726454 24451 solver.cpp:237]     Train net output #0: loss = 2.69415 (* 1 = 2.69415 loss)
I0410 02:25:21.726464 24451 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0410 02:25:26.293874 24451 solver.cpp:218] Iteration 3996 (2.62742 iter/s, 4.56722s/12 iters), loss = 2.83053
I0410 02:25:26.293921 24451 solver.cpp:237]     Train net output #0: loss = 2.83053 (* 1 = 2.83053 loss)
I0410 02:25:26.293933 24451 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0410 02:25:31.153601 24451 solver.cpp:218] Iteration 4008 (2.46941 iter/s, 4.85947s/12 iters), loss = 2.74081
I0410 02:25:31.153642 24451 solver.cpp:237]     Train net output #0: loss = 2.74081 (* 1 = 2.74081 loss)
I0410 02:25:31.153651 24451 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0410 02:25:36.039952 24451 solver.cpp:218] Iteration 4020 (2.45595 iter/s, 4.88609s/12 iters), loss = 2.53732
I0410 02:25:36.040000 24451 solver.cpp:237]     Train net output #0: loss = 2.53732 (* 1 = 2.53732 loss)
I0410 02:25:36.040010 24451 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0410 02:25:41.242267 24451 solver.cpp:218] Iteration 4032 (2.30679 iter/s, 5.20204s/12 iters), loss = 2.51796
I0410 02:25:41.242306 24451 solver.cpp:237]     Train net output #0: loss = 2.51796 (* 1 = 2.51796 loss)
I0410 02:25:41.242316 24451 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0410 02:25:46.381413 24451 solver.cpp:218] Iteration 4044 (2.33514 iter/s, 5.13888s/12 iters), loss = 2.29239
I0410 02:25:46.382153 24451 solver.cpp:237]     Train net output #0: loss = 2.29239 (* 1 = 2.29239 loss)
I0410 02:25:46.382164 24451 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0410 02:25:46.818984 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:25:51.375247 24451 solver.cpp:218] Iteration 4056 (2.40342 iter/s, 4.99288s/12 iters), loss = 2.6002
I0410 02:25:51.375300 24451 solver.cpp:237]     Train net output #0: loss = 2.6002 (* 1 = 2.6002 loss)
I0410 02:25:51.375313 24451 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0410 02:25:56.347716 24451 solver.cpp:218] Iteration 4068 (2.41342 iter/s, 4.9722s/12 iters), loss = 2.61051
I0410 02:25:56.347759 24451 solver.cpp:237]     Train net output #0: loss = 2.61051 (* 1 = 2.61051 loss)
I0410 02:25:56.347769 24451 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0410 02:26:01.055491 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0410 02:26:16.101621 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0410 02:26:28.251526 24451 solver.cpp:330] Iteration 4080, Testing net (#0)
I0410 02:26:28.251621 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:26:31.084831 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:26:32.766243 24451 solver.cpp:397]     Test net output #0: accuracy = 0.229779
I0410 02:26:32.766290 24451 solver.cpp:397]     Test net output #1: loss = 3.1779 (* 1 = 3.1779 loss)
I0410 02:26:32.898844 24451 solver.cpp:218] Iteration 4080 (0.328321 iter/s, 36.5496s/12 iters), loss = 2.57404
I0410 02:26:32.900369 24451 solver.cpp:237]     Train net output #0: loss = 2.57404 (* 1 = 2.57404 loss)
I0410 02:26:32.900382 24451 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0410 02:26:36.853929 24451 solver.cpp:218] Iteration 4092 (3.03537 iter/s, 3.95338s/12 iters), loss = 2.48687
I0410 02:26:36.854001 24451 solver.cpp:237]     Train net output #0: loss = 2.48687 (* 1 = 2.48687 loss)
I0410 02:26:36.854012 24451 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0410 02:26:41.588789 24451 solver.cpp:218] Iteration 4104 (2.53454 iter/s, 4.73458s/12 iters), loss = 2.30744
I0410 02:26:41.588838 24451 solver.cpp:237]     Train net output #0: loss = 2.30744 (* 1 = 2.30744 loss)
I0410 02:26:41.588850 24451 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0410 02:26:46.730121 24451 solver.cpp:218] Iteration 4116 (2.33415 iter/s, 5.14106s/12 iters), loss = 2.5314
I0410 02:26:46.730162 24451 solver.cpp:237]     Train net output #0: loss = 2.5314 (* 1 = 2.5314 loss)
I0410 02:26:46.730171 24451 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0410 02:26:51.700096 24451 solver.cpp:218] Iteration 4128 (2.41463 iter/s, 4.96971s/12 iters), loss = 2.34903
I0410 02:26:51.700142 24451 solver.cpp:237]     Train net output #0: loss = 2.34903 (* 1 = 2.34903 loss)
I0410 02:26:51.700153 24451 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0410 02:26:56.334991 24451 solver.cpp:218] Iteration 4140 (2.58919 iter/s, 4.63465s/12 iters), loss = 2.57685
I0410 02:26:56.335036 24451 solver.cpp:237]     Train net output #0: loss = 2.57685 (* 1 = 2.57685 loss)
I0410 02:26:56.335048 24451 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0410 02:26:58.846026 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:27:01.216035 24451 solver.cpp:218] Iteration 4152 (2.45862 iter/s, 4.88079s/12 iters), loss = 2.19481
I0410 02:27:01.216080 24451 solver.cpp:237]     Train net output #0: loss = 2.19481 (* 1 = 2.19481 loss)
I0410 02:27:01.216090 24451 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0410 02:27:03.044754 24451 blocking_queue.cpp:49] Waiting for data
I0410 02:27:05.850461 24451 solver.cpp:218] Iteration 4164 (2.58945 iter/s, 4.63418s/12 iters), loss = 2.19381
I0410 02:27:05.850503 24451 solver.cpp:237]     Train net output #0: loss = 2.19381 (* 1 = 2.19381 loss)
I0410 02:27:05.850514 24451 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0410 02:27:10.534972 24451 solver.cpp:218] Iteration 4176 (2.56177 iter/s, 4.68426s/12 iters), loss = 2.33491
I0410 02:27:10.535014 24451 solver.cpp:237]     Train net output #0: loss = 2.33491 (* 1 = 2.33491 loss)
I0410 02:27:10.535023 24451 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0410 02:27:12.441819 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0410 02:27:26.654740 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0410 02:27:49.485663 24451 solver.cpp:330] Iteration 4182, Testing net (#0)
I0410 02:27:49.485755 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:27:52.336961 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:27:54.028589 24451 solver.cpp:397]     Test net output #0: accuracy = 0.288603
I0410 02:27:54.028641 24451 solver.cpp:397]     Test net output #1: loss = 2.79129 (* 1 = 2.79129 loss)
I0410 02:27:55.799224 24451 solver.cpp:218] Iteration 4188 (0.265121 iter/s, 45.2624s/12 iters), loss = 2.24033
I0410 02:27:55.799266 24451 solver.cpp:237]     Train net output #0: loss = 2.24033 (* 1 = 2.24033 loss)
I0410 02:27:55.799275 24451 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0410 02:28:00.707653 24451 solver.cpp:218] Iteration 4200 (2.4449 iter/s, 4.90817s/12 iters), loss = 2.53134
I0410 02:28:00.707700 24451 solver.cpp:237]     Train net output #0: loss = 2.53134 (* 1 = 2.53134 loss)
I0410 02:28:00.707712 24451 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0410 02:28:05.918254 24451 solver.cpp:218] Iteration 4212 (2.30312 iter/s, 5.21033s/12 iters), loss = 2.29705
I0410 02:28:05.918294 24451 solver.cpp:237]     Train net output #0: loss = 2.29705 (* 1 = 2.29705 loss)
I0410 02:28:05.918303 24451 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0410 02:28:11.120831 24451 solver.cpp:218] Iteration 4224 (2.30667 iter/s, 5.20231s/12 iters), loss = 2.22409
I0410 02:28:11.120872 24451 solver.cpp:237]     Train net output #0: loss = 2.22409 (* 1 = 2.22409 loss)
I0410 02:28:11.120880 24451 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0410 02:28:16.026474 24451 solver.cpp:218] Iteration 4236 (2.44629 iter/s, 4.90539s/12 iters), loss = 2.14701
I0410 02:28:16.026522 24451 solver.cpp:237]     Train net output #0: loss = 2.14701 (* 1 = 2.14701 loss)
I0410 02:28:16.026531 24451 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0410 02:28:20.647222 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:28:20.929219 24451 solver.cpp:218] Iteration 4248 (2.44774 iter/s, 4.90248s/12 iters), loss = 2.30509
I0410 02:28:20.929268 24451 solver.cpp:237]     Train net output #0: loss = 2.30509 (* 1 = 2.30509 loss)
I0410 02:28:20.929280 24451 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0410 02:28:26.139735 24451 solver.cpp:218] Iteration 4260 (2.30316 iter/s, 5.21024s/12 iters), loss = 2.14197
I0410 02:28:26.139776 24451 solver.cpp:237]     Train net output #0: loss = 2.14197 (* 1 = 2.14197 loss)
I0410 02:28:26.139786 24451 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0410 02:28:31.056090 24451 solver.cpp:218] Iteration 4272 (2.44096 iter/s, 4.9161s/12 iters), loss = 2.03049
I0410 02:28:31.056138 24451 solver.cpp:237]     Train net output #0: loss = 2.03049 (* 1 = 2.03049 loss)
I0410 02:28:31.056149 24451 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0410 02:28:35.601567 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0410 02:28:54.673916 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0410 02:29:10.440192 24451 solver.cpp:330] Iteration 4284, Testing net (#0)
I0410 02:29:10.440212 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:29:13.268378 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:29:14.996656 24451 solver.cpp:397]     Test net output #0: accuracy = 0.291667
I0410 02:29:14.996696 24451 solver.cpp:397]     Test net output #1: loss = 2.74797 (* 1 = 2.74797 loss)
I0410 02:29:15.131206 24451 solver.cpp:218] Iteration 4284 (0.272274 iter/s, 44.0732s/12 iters), loss = 2.42744
I0410 02:29:15.132776 24451 solver.cpp:237]     Train net output #0: loss = 2.42744 (* 1 = 2.42744 loss)
I0410 02:29:15.132787 24451 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0410 02:29:19.494557 24451 solver.cpp:218] Iteration 4296 (2.75129 iter/s, 4.36159s/12 iters), loss = 2.33048
I0410 02:29:19.494609 24451 solver.cpp:237]     Train net output #0: loss = 2.33048 (* 1 = 2.33048 loss)
I0410 02:29:19.494621 24451 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0410 02:29:24.628367 24451 solver.cpp:218] Iteration 4308 (2.33757 iter/s, 5.13354s/12 iters), loss = 2.07311
I0410 02:29:24.628417 24451 solver.cpp:237]     Train net output #0: loss = 2.07311 (* 1 = 2.07311 loss)
I0410 02:29:24.628429 24451 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0410 02:29:29.340399 24451 solver.cpp:218] Iteration 4320 (2.54681 iter/s, 4.71178s/12 iters), loss = 2.11178
I0410 02:29:29.342077 24451 solver.cpp:237]     Train net output #0: loss = 2.11178 (* 1 = 2.11178 loss)
I0410 02:29:29.342088 24451 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0410 02:29:34.222429 24451 solver.cpp:218] Iteration 4332 (2.45894 iter/s, 4.88014s/12 iters), loss = 2.18007
I0410 02:29:34.222476 24451 solver.cpp:237]     Train net output #0: loss = 2.18007 (* 1 = 2.18007 loss)
I0410 02:29:34.222486 24451 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0410 02:29:39.094429 24451 solver.cpp:218] Iteration 4344 (2.46318 iter/s, 4.87174s/12 iters), loss = 2.31265
I0410 02:29:39.094475 24451 solver.cpp:237]     Train net output #0: loss = 2.31265 (* 1 = 2.31265 loss)
I0410 02:29:39.094486 24451 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0410 02:29:40.998437 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:29:44.217315 24451 solver.cpp:218] Iteration 4356 (2.34255 iter/s, 5.12262s/12 iters), loss = 1.93898
I0410 02:29:44.217356 24451 solver.cpp:237]     Train net output #0: loss = 1.93898 (* 1 = 1.93898 loss)
I0410 02:29:44.217366 24451 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0410 02:29:49.197225 24451 solver.cpp:218] Iteration 4368 (2.40981 iter/s, 4.97965s/12 iters), loss = 2.23562
I0410 02:29:49.197275 24451 solver.cpp:237]     Train net output #0: loss = 2.23562 (* 1 = 2.23562 loss)
I0410 02:29:49.197285 24451 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0410 02:29:54.320358 24451 solver.cpp:218] Iteration 4380 (2.34244 iter/s, 5.12286s/12 iters), loss = 1.98128
I0410 02:29:54.320397 24451 solver.cpp:237]     Train net output #0: loss = 1.98128 (* 1 = 1.98128 loss)
I0410 02:29:54.320407 24451 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0410 02:29:56.204804 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0410 02:30:17.666327 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0410 02:30:28.852614 24451 solver.cpp:330] Iteration 4386, Testing net (#0)
I0410 02:30:28.852638 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:30:31.635200 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:30:33.514613 24451 solver.cpp:397]     Test net output #0: accuracy = 0.300858
I0410 02:30:33.514664 24451 solver.cpp:397]     Test net output #1: loss = 2.73972 (* 1 = 2.73972 loss)
I0410 02:30:35.386130 24451 solver.cpp:218] Iteration 4392 (0.292226 iter/s, 41.064s/12 iters), loss = 2.21656
I0410 02:30:35.386178 24451 solver.cpp:237]     Train net output #0: loss = 2.21656 (* 1 = 2.21656 loss)
I0410 02:30:35.386189 24451 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0410 02:30:40.513535 24451 solver.cpp:218] Iteration 4404 (2.34049 iter/s, 5.12713s/12 iters), loss = 2.00063
I0410 02:30:40.513579 24451 solver.cpp:237]     Train net output #0: loss = 2.00063 (* 1 = 2.00063 loss)
I0410 02:30:40.513588 24451 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0410 02:30:45.665884 24451 solver.cpp:218] Iteration 4416 (2.32916 iter/s, 5.15208s/12 iters), loss = 2.15362
I0410 02:30:45.665933 24451 solver.cpp:237]     Train net output #0: loss = 2.15362 (* 1 = 2.15362 loss)
I0410 02:30:45.665944 24451 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0410 02:30:50.562486 24451 solver.cpp:218] Iteration 4428 (2.45081 iter/s, 4.89633s/12 iters), loss = 2.0733
I0410 02:30:50.562568 24451 solver.cpp:237]     Train net output #0: loss = 2.0733 (* 1 = 2.0733 loss)
I0410 02:30:50.562577 24451 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0410 02:30:55.686986 24451 solver.cpp:218] Iteration 4440 (2.34183 iter/s, 5.12419s/12 iters), loss = 2.03589
I0410 02:30:55.687031 24451 solver.cpp:237]     Train net output #0: loss = 2.03589 (* 1 = 2.03589 loss)
I0410 02:30:55.687041 24451 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0410 02:30:59.561269 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:31:00.620267 24451 solver.cpp:218] Iteration 4452 (2.43259 iter/s, 4.93302s/12 iters), loss = 1.88277
I0410 02:31:00.620319 24451 solver.cpp:237]     Train net output #0: loss = 1.88277 (* 1 = 1.88277 loss)
I0410 02:31:00.620332 24451 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0410 02:31:05.404883 24451 solver.cpp:218] Iteration 4464 (2.50817 iter/s, 4.78436s/12 iters), loss = 2.06585
I0410 02:31:05.404932 24451 solver.cpp:237]     Train net output #0: loss = 2.06585 (* 1 = 2.06585 loss)
I0410 02:31:05.404943 24451 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0410 02:31:10.069011 24451 solver.cpp:218] Iteration 4476 (2.57297 iter/s, 4.66387s/12 iters), loss = 2.16969
I0410 02:31:10.069061 24451 solver.cpp:237]     Train net output #0: loss = 2.16969 (* 1 = 2.16969 loss)
I0410 02:31:10.069072 24451 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0410 02:31:14.286414 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0410 02:31:28.188738 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0410 02:31:48.050395 24451 solver.cpp:330] Iteration 4488, Testing net (#0)
I0410 02:31:48.050415 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:31:50.771937 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:31:52.576587 24451 solver.cpp:397]     Test net output #0: accuracy = 0.290441
I0410 02:31:52.576633 24451 solver.cpp:397]     Test net output #1: loss = 2.79607 (* 1 = 2.79607 loss)
I0410 02:31:52.711172 24451 solver.cpp:218] Iteration 4488 (0.281424 iter/s, 42.6404s/12 iters), loss = 1.78186
I0410 02:31:52.712693 24451 solver.cpp:237]     Train net output #0: loss = 1.78186 (* 1 = 1.78186 loss)
I0410 02:31:52.712708 24451 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0410 02:31:56.764336 24451 solver.cpp:218] Iteration 4500 (2.96189 iter/s, 4.05147s/12 iters), loss = 2.03729
I0410 02:31:56.764376 24451 solver.cpp:237]     Train net output #0: loss = 2.03729 (* 1 = 2.03729 loss)
I0410 02:31:56.764387 24451 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0410 02:32:01.361850 24451 solver.cpp:218] Iteration 4512 (2.61024 iter/s, 4.59727s/12 iters), loss = 2.15199
I0410 02:32:01.361984 24451 solver.cpp:237]     Train net output #0: loss = 2.15199 (* 1 = 2.15199 loss)
I0410 02:32:01.361996 24451 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0410 02:32:06.040416 24451 solver.cpp:218] Iteration 4524 (2.56508 iter/s, 4.67823s/12 iters), loss = 2.1121
I0410 02:32:06.040468 24451 solver.cpp:237]     Train net output #0: loss = 2.1121 (* 1 = 2.1121 loss)
I0410 02:32:06.040480 24451 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0410 02:32:10.761073 24451 solver.cpp:218] Iteration 4536 (2.54216 iter/s, 4.7204s/12 iters), loss = 2.04333
I0410 02:32:10.761126 24451 solver.cpp:237]     Train net output #0: loss = 2.04333 (* 1 = 2.04333 loss)
I0410 02:32:10.761137 24451 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0410 02:32:15.426293 24451 solver.cpp:218] Iteration 4548 (2.57237 iter/s, 4.66495s/12 iters), loss = 2.05939
I0410 02:32:15.426340 24451 solver.cpp:237]     Train net output #0: loss = 2.05939 (* 1 = 2.05939 loss)
I0410 02:32:15.426352 24451 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0410 02:32:16.539387 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:32:20.105748 24451 solver.cpp:218] Iteration 4560 (2.56454 iter/s, 4.6792s/12 iters), loss = 1.71673
I0410 02:32:20.105788 24451 solver.cpp:237]     Train net output #0: loss = 1.71673 (* 1 = 1.71673 loss)
I0410 02:32:20.105798 24451 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0410 02:32:24.740183 24451 solver.cpp:218] Iteration 4572 (2.58945 iter/s, 4.63419s/12 iters), loss = 1.97624
I0410 02:32:24.740226 24451 solver.cpp:237]     Train net output #0: loss = 1.97624 (* 1 = 1.97624 loss)
I0410 02:32:24.740234 24451 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0410 02:32:29.259702 24451 solver.cpp:218] Iteration 4584 (2.65529 iter/s, 4.51927s/12 iters), loss = 2.06135
I0410 02:32:29.259752 24451 solver.cpp:237]     Train net output #0: loss = 2.06135 (* 1 = 2.06135 loss)
I0410 02:32:29.259764 24451 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0410 02:32:31.089365 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0410 02:32:57.470204 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0410 02:33:12.962699 24451 solver.cpp:330] Iteration 4590, Testing net (#0)
I0410 02:33:12.962720 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:33:15.801795 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:33:17.710278 24451 solver.cpp:397]     Test net output #0: accuracy = 0.316176
I0410 02:33:17.710325 24451 solver.cpp:397]     Test net output #1: loss = 2.69138 (* 1 = 2.69138 loss)
I0410 02:33:19.483620 24451 solver.cpp:218] Iteration 4596 (0.23894 iter/s, 50.2218s/12 iters), loss = 2.25518
I0410 02:33:19.483664 24451 solver.cpp:237]     Train net output #0: loss = 2.25518 (* 1 = 2.25518 loss)
I0410 02:33:19.483673 24451 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0410 02:33:24.613227 24451 solver.cpp:218] Iteration 4608 (2.33948 iter/s, 5.12934s/12 iters), loss = 1.9469
I0410 02:33:24.613271 24451 solver.cpp:237]     Train net output #0: loss = 1.9469 (* 1 = 1.9469 loss)
I0410 02:33:24.613281 24451 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0410 02:33:29.556859 24451 solver.cpp:218] Iteration 4620 (2.4275 iter/s, 4.94337s/12 iters), loss = 1.82077
I0410 02:33:29.556965 24451 solver.cpp:237]     Train net output #0: loss = 1.82077 (* 1 = 1.82077 loss)
I0410 02:33:29.556975 24451 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0410 02:33:34.721875 24451 solver.cpp:218] Iteration 4632 (2.32347 iter/s, 5.16468s/12 iters), loss = 1.97176
I0410 02:33:34.721920 24451 solver.cpp:237]     Train net output #0: loss = 1.97176 (* 1 = 1.97176 loss)
I0410 02:33:34.721930 24451 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0410 02:33:40.073451 24451 solver.cpp:218] Iteration 4644 (2.24245 iter/s, 5.3513s/12 iters), loss = 1.92465
I0410 02:33:40.073499 24451 solver.cpp:237]     Train net output #0: loss = 1.92465 (* 1 = 1.92465 loss)
I0410 02:33:40.073510 24451 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0410 02:33:43.499497 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:33:45.225168 24451 solver.cpp:218] Iteration 4656 (2.32944 iter/s, 5.15144s/12 iters), loss = 1.92541
I0410 02:33:45.225209 24451 solver.cpp:237]     Train net output #0: loss = 1.92541 (* 1 = 1.92541 loss)
I0410 02:33:45.225219 24451 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0410 02:33:50.197082 24451 solver.cpp:218] Iteration 4668 (2.41369 iter/s, 4.97165s/12 iters), loss = 1.91128
I0410 02:33:50.197135 24451 solver.cpp:237]     Train net output #0: loss = 1.91128 (* 1 = 1.91128 loss)
I0410 02:33:50.197147 24451 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0410 02:33:55.337042 24451 solver.cpp:218] Iteration 4680 (2.33477 iter/s, 5.13969s/12 iters), loss = 2.18914
I0410 02:33:55.337083 24451 solver.cpp:237]     Train net output #0: loss = 2.18914 (* 1 = 2.18914 loss)
I0410 02:33:55.337092 24451 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0410 02:33:59.787525 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0410 02:34:19.549882 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0410 02:34:33.390895 24451 solver.cpp:330] Iteration 4692, Testing net (#0)
I0410 02:34:33.390938 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:34:36.038020 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:34:37.932452 24451 solver.cpp:397]     Test net output #0: accuracy = 0.31924
I0410 02:34:37.932497 24451 solver.cpp:397]     Test net output #1: loss = 2.6904 (* 1 = 2.6904 loss)
I0410 02:34:38.067550 24451 solver.cpp:218] Iteration 4692 (0.280842 iter/s, 42.7287s/12 iters), loss = 2.01279
I0410 02:34:38.069067 24451 solver.cpp:237]     Train net output #0: loss = 2.01279 (* 1 = 2.01279 loss)
I0410 02:34:38.069078 24451 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0410 02:34:42.421567 24451 solver.cpp:218] Iteration 4704 (2.75716 iter/s, 4.35231s/12 iters), loss = 1.94549
I0410 02:34:42.421613 24451 solver.cpp:237]     Train net output #0: loss = 1.94549 (* 1 = 1.94549 loss)
I0410 02:34:42.421625 24451 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0410 02:34:47.614470 24451 solver.cpp:218] Iteration 4716 (2.31097 iter/s, 5.19263s/12 iters), loss = 1.88007
I0410 02:34:47.614519 24451 solver.cpp:237]     Train net output #0: loss = 1.88007 (* 1 = 1.88007 loss)
I0410 02:34:47.614531 24451 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0410 02:34:52.536455 24451 solver.cpp:218] Iteration 4728 (2.43817 iter/s, 4.92172s/12 iters), loss = 2.03703
I0410 02:34:52.536507 24451 solver.cpp:237]     Train net output #0: loss = 2.03703 (* 1 = 2.03703 loss)
I0410 02:34:52.536518 24451 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0410 02:34:57.087620 24451 solver.cpp:218] Iteration 4740 (2.63683 iter/s, 4.55091s/12 iters), loss = 1.90423
I0410 02:34:57.087667 24451 solver.cpp:237]     Train net output #0: loss = 1.90423 (* 1 = 1.90423 loss)
I0410 02:34:57.087679 24451 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0410 02:35:01.935197 24451 solver.cpp:218] Iteration 4752 (2.4756 iter/s, 4.84731s/12 iters), loss = 1.80489
I0410 02:35:01.935256 24451 solver.cpp:237]     Train net output #0: loss = 1.80489 (* 1 = 1.80489 loss)
I0410 02:35:01.935268 24451 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0410 02:35:02.406692 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:35:06.805039 24451 solver.cpp:218] Iteration 4764 (2.46428 iter/s, 4.86957s/12 iters), loss = 1.99682
I0410 02:35:06.805187 24451 solver.cpp:237]     Train net output #0: loss = 1.99682 (* 1 = 1.99682 loss)
I0410 02:35:06.805200 24451 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0410 02:35:11.555280 24451 solver.cpp:218] Iteration 4776 (2.52638 iter/s, 4.74989s/12 iters), loss = 1.93587
I0410 02:35:11.555335 24451 solver.cpp:237]     Train net output #0: loss = 1.93587 (* 1 = 1.93587 loss)
I0410 02:35:11.555346 24451 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0410 02:35:16.061472 24451 solver.cpp:218] Iteration 4788 (2.66315 iter/s, 4.50594s/12 iters), loss = 1.84075
I0410 02:35:16.061523 24451 solver.cpp:237]     Train net output #0: loss = 1.84075 (* 1 = 1.84075 loss)
I0410 02:35:16.061535 24451 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0410 02:35:17.872638 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0410 02:35:32.210398 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0410 02:35:44.093008 24451 solver.cpp:330] Iteration 4794, Testing net (#0)
I0410 02:35:44.093051 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:35:46.985023 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:35:48.975652 24451 solver.cpp:397]     Test net output #0: accuracy = 0.324755
I0410 02:35:48.975692 24451 solver.cpp:397]     Test net output #1: loss = 2.67059 (* 1 = 2.67059 loss)
I0410 02:35:50.893903 24451 solver.cpp:218] Iteration 4800 (0.344521 iter/s, 34.8309s/12 iters), loss = 1.70237
I0410 02:35:50.893947 24451 solver.cpp:237]     Train net output #0: loss = 1.70237 (* 1 = 1.70237 loss)
I0410 02:35:50.893980 24451 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0410 02:35:56.088627 24451 solver.cpp:218] Iteration 4812 (2.31016 iter/s, 5.19446s/12 iters), loss = 1.75156
I0410 02:35:56.088671 24451 solver.cpp:237]     Train net output #0: loss = 1.75156 (* 1 = 1.75156 loss)
I0410 02:35:56.088680 24451 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0410 02:36:01.161258 24451 solver.cpp:218] Iteration 4824 (2.36576 iter/s, 5.07236s/12 iters), loss = 1.99399
I0410 02:36:01.161306 24451 solver.cpp:237]     Train net output #0: loss = 1.99399 (* 1 = 1.99399 loss)
I0410 02:36:01.161317 24451 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0410 02:36:05.868863 24451 solver.cpp:218] Iteration 4836 (2.54921 iter/s, 4.70735s/12 iters), loss = 1.82314
I0410 02:36:05.868911 24451 solver.cpp:237]     Train net output #0: loss = 1.82314 (* 1 = 1.82314 loss)
I0410 02:36:05.868923 24451 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0410 02:36:08.359324 24451 blocking_queue.cpp:49] Waiting for data
I0410 02:36:10.782095 24451 solver.cpp:218] Iteration 4848 (2.44251 iter/s, 4.91297s/12 iters), loss = 1.87789
I0410 02:36:10.782128 24451 solver.cpp:237]     Train net output #0: loss = 1.87789 (* 1 = 1.87789 loss)
I0410 02:36:10.782135 24451 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0410 02:36:13.181725 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:36:15.389922 24451 solver.cpp:218] Iteration 4860 (2.6044 iter/s, 4.60759s/12 iters), loss = 1.72107
I0410 02:36:15.390092 24451 solver.cpp:237]     Train net output #0: loss = 1.72107 (* 1 = 1.72107 loss)
I0410 02:36:15.390105 24451 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0410 02:36:19.978255 24451 solver.cpp:218] Iteration 4872 (2.61554 iter/s, 4.58797s/12 iters), loss = 1.81579
I0410 02:36:19.978300 24451 solver.cpp:237]     Train net output #0: loss = 1.81579 (* 1 = 1.81579 loss)
I0410 02:36:19.978309 24451 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0410 02:36:24.580229 24451 solver.cpp:218] Iteration 4884 (2.60772 iter/s, 4.60173s/12 iters), loss = 1.92627
I0410 02:36:24.580267 24451 solver.cpp:237]     Train net output #0: loss = 1.92627 (* 1 = 1.92627 loss)
I0410 02:36:24.580276 24451 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0410 02:36:28.969367 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0410 02:36:44.067857 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0410 02:37:05.566689 24451 solver.cpp:330] Iteration 4896, Testing net (#0)
I0410 02:37:05.566732 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:37:08.109992 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:37:10.156141 24451 solver.cpp:397]     Test net output #0: accuracy = 0.341299
I0410 02:37:10.156188 24451 solver.cpp:397]     Test net output #1: loss = 2.72137 (* 1 = 2.72137 loss)
I0410 02:37:10.291155 24451 solver.cpp:218] Iteration 4896 (0.26253 iter/s, 45.709s/12 iters), loss = 1.69141
I0410 02:37:10.292675 24451 solver.cpp:237]     Train net output #0: loss = 1.69141 (* 1 = 1.69141 loss)
I0410 02:37:10.292687 24451 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0410 02:37:14.426647 24451 solver.cpp:218] Iteration 4908 (2.90291 iter/s, 4.13378s/12 iters), loss = 1.78224
I0410 02:37:14.426712 24451 solver.cpp:237]     Train net output #0: loss = 1.78224 (* 1 = 1.78224 loss)
I0410 02:37:14.426728 24451 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0410 02:37:19.480965 24451 solver.cpp:218] Iteration 4920 (2.37434 iter/s, 5.05403s/12 iters), loss = 1.78912
I0410 02:37:19.481020 24451 solver.cpp:237]     Train net output #0: loss = 1.78912 (* 1 = 1.78912 loss)
I0410 02:37:19.481034 24451 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0410 02:37:24.583361 24451 solver.cpp:218] Iteration 4932 (2.35196 iter/s, 5.10212s/12 iters), loss = 1.76036
I0410 02:37:24.583403 24451 solver.cpp:237]     Train net output #0: loss = 1.76036 (* 1 = 1.76036 loss)
I0410 02:37:24.583413 24451 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0410 02:37:29.210398 24451 solver.cpp:218] Iteration 4944 (2.59359 iter/s, 4.62679s/12 iters), loss = 1.64821
I0410 02:37:29.210443 24451 solver.cpp:237]     Train net output #0: loss = 1.64821 (* 1 = 1.64821 loss)
I0410 02:37:29.210453 24451 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0410 02:37:33.563294 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:37:33.800580 24451 solver.cpp:218] Iteration 4956 (2.61442 iter/s, 4.58993s/12 iters), loss = 1.85272
I0410 02:37:33.800629 24451 solver.cpp:237]     Train net output #0: loss = 1.85272 (* 1 = 1.85272 loss)
I0410 02:37:33.800642 24451 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0410 02:37:38.308326 24451 solver.cpp:218] Iteration 4968 (2.66223 iter/s, 4.5075s/12 iters), loss = 1.69018
I0410 02:37:38.308449 24451 solver.cpp:237]     Train net output #0: loss = 1.69018 (* 1 = 1.69018 loss)
I0410 02:37:38.308462 24451 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0410 02:37:42.903165 24451 solver.cpp:218] Iteration 4980 (2.61181 iter/s, 4.59452s/12 iters), loss = 1.62999
I0410 02:37:42.903208 24451 solver.cpp:237]     Train net output #0: loss = 1.62999 (* 1 = 1.62999 loss)
I0410 02:37:42.903218 24451 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0410 02:37:47.670931 24451 solver.cpp:218] Iteration 4992 (2.51704 iter/s, 4.76751s/12 iters), loss = 1.69994
I0410 02:37:47.670981 24451 solver.cpp:237]     Train net output #0: loss = 1.69994 (* 1 = 1.69994 loss)
I0410 02:37:47.670994 24451 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0410 02:37:49.786818 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0410 02:38:14.553609 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0410 02:38:26.680913 24451 solver.cpp:330] Iteration 4998, Testing net (#0)
I0410 02:38:26.680934 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:38:29.212486 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:38:31.222892 24451 solver.cpp:397]     Test net output #0: accuracy = 0.352328
I0410 02:38:31.222931 24451 solver.cpp:397]     Test net output #1: loss = 2.63451 (* 1 = 2.63451 loss)
I0410 02:38:33.115593 24451 solver.cpp:218] Iteration 5004 (0.264069 iter/s, 45.4427s/12 iters), loss = 1.65983
I0410 02:38:33.115648 24451 solver.cpp:237]     Train net output #0: loss = 1.65983 (* 1 = 1.65983 loss)
I0410 02:38:33.115659 24451 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0410 02:38:38.177078 24451 solver.cpp:218] Iteration 5016 (2.37098 iter/s, 5.06121s/12 iters), loss = 1.46739
I0410 02:38:38.177124 24451 solver.cpp:237]     Train net output #0: loss = 1.46739 (* 1 = 1.46739 loss)
I0410 02:38:38.177134 24451 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0410 02:38:43.191089 24451 solver.cpp:218] Iteration 5028 (2.39342 iter/s, 5.01375s/12 iters), loss = 1.62705
I0410 02:38:43.191134 24451 solver.cpp:237]     Train net output #0: loss = 1.62705 (* 1 = 1.62705 loss)
I0410 02:38:43.191145 24451 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0410 02:38:48.324780 24451 solver.cpp:218] Iteration 5040 (2.33762 iter/s, 5.13342s/12 iters), loss = 1.97631
I0410 02:38:48.324882 24451 solver.cpp:237]     Train net output #0: loss = 1.97631 (* 1 = 1.97631 loss)
I0410 02:38:48.324894 24451 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0410 02:38:53.334139 24451 solver.cpp:218] Iteration 5052 (2.39567 iter/s, 5.00904s/12 iters), loss = 1.6411
I0410 02:38:53.334188 24451 solver.cpp:237]     Train net output #0: loss = 1.6411 (* 1 = 1.6411 loss)
I0410 02:38:53.334200 24451 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0410 02:38:55.285365 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:38:58.490651 24451 solver.cpp:218] Iteration 5064 (2.32728 iter/s, 5.15624s/12 iters), loss = 1.72425
I0410 02:38:58.490692 24451 solver.cpp:237]     Train net output #0: loss = 1.72425 (* 1 = 1.72425 loss)
I0410 02:38:58.490702 24451 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0410 02:39:03.411584 24451 solver.cpp:218] Iteration 5076 (2.43869 iter/s, 4.92068s/12 iters), loss = 1.72038
I0410 02:39:03.411631 24451 solver.cpp:237]     Train net output #0: loss = 1.72038 (* 1 = 1.72038 loss)
I0410 02:39:03.411641 24451 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0410 02:39:08.561568 24451 solver.cpp:218] Iteration 5088 (2.33023 iter/s, 5.14971s/12 iters), loss = 1.72207
I0410 02:39:08.561612 24451 solver.cpp:237]     Train net output #0: loss = 1.72207 (* 1 = 1.72207 loss)
I0410 02:39:08.561623 24451 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0410 02:39:13.224320 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0410 02:39:27.133898 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0410 02:39:38.915325 24451 solver.cpp:330] Iteration 5100, Testing net (#0)
I0410 02:39:38.915347 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:39:41.323253 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:39:43.422806 24451 solver.cpp:397]     Test net output #0: accuracy = 0.369485
I0410 02:39:43.422852 24451 solver.cpp:397]     Test net output #1: loss = 2.59614 (* 1 = 2.59614 loss)
I0410 02:39:43.548992 24451 solver.cpp:218] Iteration 5100 (0.342995 iter/s, 34.9859s/12 iters), loss = 1.5712
I0410 02:39:43.550514 24451 solver.cpp:237]     Train net output #0: loss = 1.5712 (* 1 = 1.5712 loss)
I0410 02:39:43.550529 24451 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0410 02:39:47.600710 24451 solver.cpp:218] Iteration 5112 (2.96295 iter/s, 4.05002s/12 iters), loss = 1.74387
I0410 02:39:47.600754 24451 solver.cpp:237]     Train net output #0: loss = 1.74387 (* 1 = 1.74387 loss)
I0410 02:39:47.600762 24451 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0410 02:39:52.161738 24451 solver.cpp:218] Iteration 5124 (2.63113 iter/s, 4.56078s/12 iters), loss = 1.84266
I0410 02:39:52.161798 24451 solver.cpp:237]     Train net output #0: loss = 1.84266 (* 1 = 1.84266 loss)
I0410 02:39:52.161813 24451 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0410 02:39:57.207145 24451 solver.cpp:218] Iteration 5136 (2.37853 iter/s, 5.04513s/12 iters), loss = 1.66352
I0410 02:39:57.207218 24451 solver.cpp:237]     Train net output #0: loss = 1.66352 (* 1 = 1.66352 loss)
I0410 02:39:57.207231 24451 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0410 02:40:02.189205 24451 solver.cpp:218] Iteration 5148 (2.40878 iter/s, 4.98177s/12 iters), loss = 1.51904
I0410 02:40:02.189249 24451 solver.cpp:237]     Train net output #0: loss = 1.51904 (* 1 = 1.51904 loss)
I0410 02:40:02.189258 24451 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0410 02:40:06.101609 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:40:06.981241 24451 solver.cpp:218] Iteration 5160 (2.50429 iter/s, 4.79178s/12 iters), loss = 1.35737
I0410 02:40:06.981303 24451 solver.cpp:237]     Train net output #0: loss = 1.35737 (* 1 = 1.35737 loss)
I0410 02:40:06.981319 24451 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0410 02:40:11.914618 24451 solver.cpp:218] Iteration 5172 (2.43254 iter/s, 4.93311s/12 iters), loss = 1.54348
I0410 02:40:11.914656 24451 solver.cpp:237]     Train net output #0: loss = 1.54348 (* 1 = 1.54348 loss)
I0410 02:40:11.914664 24451 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0410 02:40:16.627377 24451 solver.cpp:218] Iteration 5184 (2.54641 iter/s, 4.71251s/12 iters), loss = 1.75818
I0410 02:40:16.627425 24451 solver.cpp:237]     Train net output #0: loss = 1.75818 (* 1 = 1.75818 loss)
I0410 02:40:16.627436 24451 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0410 02:40:21.711700 24451 solver.cpp:218] Iteration 5196 (2.36032 iter/s, 5.08406s/12 iters), loss = 1.3459
I0410 02:40:21.711746 24451 solver.cpp:237]     Train net output #0: loss = 1.3459 (* 1 = 1.3459 loss)
I0410 02:40:21.711757 24451 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0410 02:40:23.836360 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0410 02:40:41.444545 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0410 02:40:56.958052 24451 solver.cpp:330] Iteration 5202, Testing net (#0)
I0410 02:40:56.958073 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:40:59.369210 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:41:01.491678 24451 solver.cpp:397]     Test net output #0: accuracy = 0.375613
I0410 02:41:01.491717 24451 solver.cpp:397]     Test net output #1: loss = 2.59443 (* 1 = 2.59443 loss)
I0410 02:41:03.220325 24451 solver.cpp:218] Iteration 5208 (0.289109 iter/s, 41.5069s/12 iters), loss = 1.35286
I0410 02:41:03.220373 24451 solver.cpp:237]     Train net output #0: loss = 1.35286 (* 1 = 1.35286 loss)
I0410 02:41:03.220384 24451 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0410 02:41:07.907433 24451 solver.cpp:218] Iteration 5220 (2.56035 iter/s, 4.68685s/12 iters), loss = 1.51272
I0410 02:41:07.907480 24451 solver.cpp:237]     Train net output #0: loss = 1.51272 (* 1 = 1.51272 loss)
I0410 02:41:07.907490 24451 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0410 02:41:12.560446 24451 solver.cpp:218] Iteration 5232 (2.57911 iter/s, 4.65276s/12 iters), loss = 1.61682
I0410 02:41:12.560582 24451 solver.cpp:237]     Train net output #0: loss = 1.61682 (* 1 = 1.61682 loss)
I0410 02:41:12.560595 24451 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0410 02:41:17.460402 24451 solver.cpp:218] Iteration 5244 (2.44918 iter/s, 4.89961s/12 iters), loss = 1.625
I0410 02:41:17.460450 24451 solver.cpp:237]     Train net output #0: loss = 1.625 (* 1 = 1.625 loss)
I0410 02:41:17.460461 24451 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0410 02:41:22.105654 24451 solver.cpp:218] Iteration 5256 (2.58342 iter/s, 4.645s/12 iters), loss = 1.32566
I0410 02:41:22.105696 24451 solver.cpp:237]     Train net output #0: loss = 1.32566 (* 1 = 1.32566 loss)
I0410 02:41:22.105708 24451 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0410 02:41:23.290756 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:41:27.166817 24451 solver.cpp:218] Iteration 5268 (2.37112 iter/s, 5.0609s/12 iters), loss = 1.18637
I0410 02:41:27.166863 24451 solver.cpp:237]     Train net output #0: loss = 1.18637 (* 1 = 1.18637 loss)
I0410 02:41:27.166873 24451 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0410 02:41:32.238413 24451 solver.cpp:218] Iteration 5280 (2.36624 iter/s, 5.07133s/12 iters), loss = 1.55922
I0410 02:41:32.238464 24451 solver.cpp:237]     Train net output #0: loss = 1.55922 (* 1 = 1.55922 loss)
I0410 02:41:32.238488 24451 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0410 02:41:37.018752 24451 solver.cpp:218] Iteration 5292 (2.51042 iter/s, 4.78008s/12 iters), loss = 1.5142
I0410 02:41:37.018798 24451 solver.cpp:237]     Train net output #0: loss = 1.5142 (* 1 = 1.5142 loss)
I0410 02:41:37.018807 24451 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0410 02:41:41.449239 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0410 02:42:00.123939 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0410 02:42:23.380951 24451 solver.cpp:330] Iteration 5304, Testing net (#0)
I0410 02:42:23.380975 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:42:25.801090 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:42:28.004997 24451 solver.cpp:397]     Test net output #0: accuracy = 0.387868
I0410 02:42:28.005034 24451 solver.cpp:397]     Test net output #1: loss = 2.63472 (* 1 = 2.63472 loss)
I0410 02:42:28.139715 24451 solver.cpp:218] Iteration 5304 (0.234747 iter/s, 51.1188s/12 iters), loss = 1.30023
I0410 02:42:28.141307 24451 solver.cpp:237]     Train net output #0: loss = 1.30023 (* 1 = 1.30023 loss)
I0410 02:42:28.141319 24451 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0410 02:42:32.304710 24451 solver.cpp:218] Iteration 5316 (2.88239 iter/s, 4.16322s/12 iters), loss = 1.29567
I0410 02:42:32.304812 24451 solver.cpp:237]     Train net output #0: loss = 1.29567 (* 1 = 1.29567 loss)
I0410 02:42:32.304822 24451 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0410 02:42:37.515779 24451 solver.cpp:218] Iteration 5328 (2.30294 iter/s, 5.21074s/12 iters), loss = 1.3974
I0410 02:42:37.515816 24451 solver.cpp:237]     Train net output #0: loss = 1.3974 (* 1 = 1.3974 loss)
I0410 02:42:37.515825 24451 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0410 02:42:42.197472 24451 solver.cpp:218] Iteration 5340 (2.56331 iter/s, 4.68145s/12 iters), loss = 1.65574
I0410 02:42:42.197515 24451 solver.cpp:237]     Train net output #0: loss = 1.65574 (* 1 = 1.65574 loss)
I0410 02:42:42.197527 24451 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0410 02:42:47.306093 24451 solver.cpp:218] Iteration 5352 (2.34909 iter/s, 5.10835s/12 iters), loss = 1.50205
I0410 02:42:47.306140 24451 solver.cpp:237]     Train net output #0: loss = 1.50205 (* 1 = 1.50205 loss)
I0410 02:42:47.306149 24451 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0410 02:42:50.754259 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:42:52.435992 24451 solver.cpp:218] Iteration 5364 (2.33935 iter/s, 5.12963s/12 iters), loss = 1.18679
I0410 02:42:52.436038 24451 solver.cpp:237]     Train net output #0: loss = 1.18679 (* 1 = 1.18679 loss)
I0410 02:42:52.436046 24451 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0410 02:42:57.280884 24451 solver.cpp:218] Iteration 5376 (2.47697 iter/s, 4.84463s/12 iters), loss = 1.50061
I0410 02:42:57.280936 24451 solver.cpp:237]     Train net output #0: loss = 1.50061 (* 1 = 1.50061 loss)
I0410 02:42:57.280948 24451 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0410 02:43:02.351711 24451 solver.cpp:218] Iteration 5388 (2.36661 iter/s, 5.07055s/12 iters), loss = 1.56331
I0410 02:43:02.351819 24451 solver.cpp:237]     Train net output #0: loss = 1.56331 (* 1 = 1.56331 loss)
I0410 02:43:02.351832 24451 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0410 02:43:07.549219 24451 solver.cpp:218] Iteration 5400 (2.30895 iter/s, 5.19717s/12 iters), loss = 1.60276
I0410 02:43:07.549269 24451 solver.cpp:237]     Train net output #0: loss = 1.60276 (* 1 = 1.60276 loss)
I0410 02:43:07.549280 24451 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0410 02:43:09.569943 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0410 02:43:28.363759 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0410 02:43:39.512714 24451 solver.cpp:330] Iteration 5406, Testing net (#0)
I0410 02:43:39.512785 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:43:41.914741 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:43:44.087523 24451 solver.cpp:397]     Test net output #0: accuracy = 0.396446
I0410 02:43:44.087571 24451 solver.cpp:397]     Test net output #1: loss = 2.66006 (* 1 = 2.66006 loss)
I0410 02:43:46.021855 24451 solver.cpp:218] Iteration 5412 (0.311923 iter/s, 38.471s/12 iters), loss = 1.30986
I0410 02:43:46.021909 24451 solver.cpp:237]     Train net output #0: loss = 1.30986 (* 1 = 1.30986 loss)
I0410 02:43:46.021921 24451 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0410 02:43:51.225126 24451 solver.cpp:218] Iteration 5424 (2.30637 iter/s, 5.20299s/12 iters), loss = 1.37687
I0410 02:43:51.225178 24451 solver.cpp:237]     Train net output #0: loss = 1.37687 (* 1 = 1.37687 loss)
I0410 02:43:51.225190 24451 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0410 02:43:56.173900 24451 solver.cpp:218] Iteration 5436 (2.42497 iter/s, 4.94851s/12 iters), loss = 1.52803
I0410 02:43:56.173945 24451 solver.cpp:237]     Train net output #0: loss = 1.52803 (* 1 = 1.52803 loss)
I0410 02:43:56.173970 24451 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0410 02:44:01.384904 24451 solver.cpp:218] Iteration 5448 (2.30294 iter/s, 5.21073s/12 iters), loss = 1.37224
I0410 02:44:01.384948 24451 solver.cpp:237]     Train net output #0: loss = 1.37224 (* 1 = 1.37224 loss)
I0410 02:44:01.384958 24451 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0410 02:44:06.484359 24451 solver.cpp:218] Iteration 5460 (2.35332 iter/s, 5.09919s/12 iters), loss = 1.42649
I0410 02:44:06.484400 24451 solver.cpp:237]     Train net output #0: loss = 1.42649 (* 1 = 1.42649 loss)
I0410 02:44:06.484408 24451 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0410 02:44:07.025367 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:44:11.692430 24451 solver.cpp:218] Iteration 5472 (2.30423 iter/s, 5.2078s/12 iters), loss = 1.19808
I0410 02:44:11.692548 24451 solver.cpp:237]     Train net output #0: loss = 1.19808 (* 1 = 1.19808 loss)
I0410 02:44:11.692559 24451 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0410 02:44:16.395210 24451 solver.cpp:218] Iteration 5484 (2.55186 iter/s, 4.70246s/12 iters), loss = 1.2826
I0410 02:44:16.395253 24451 solver.cpp:237]     Train net output #0: loss = 1.2826 (* 1 = 1.2826 loss)
I0410 02:44:16.395264 24451 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0410 02:44:21.186456 24451 solver.cpp:218] Iteration 5496 (2.5047 iter/s, 4.79099s/12 iters), loss = 1.21283
I0410 02:44:21.186514 24451 solver.cpp:237]     Train net output #0: loss = 1.21283 (* 1 = 1.21283 loss)
I0410 02:44:21.186529 24451 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0410 02:44:25.598488 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0410 02:44:39.867848 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0410 02:44:56.415086 24451 solver.cpp:330] Iteration 5508, Testing net (#0)
I0410 02:44:56.415171 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:44:58.764719 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:45:01.046530 24451 solver.cpp:397]     Test net output #0: accuracy = 0.390931
I0410 02:45:01.046571 24451 solver.cpp:397]     Test net output #1: loss = 2.55258 (* 1 = 2.55258 loss)
I0410 02:45:01.174840 24451 solver.cpp:218] Iteration 5508 (0.3001 iter/s, 39.9867s/12 iters), loss = 1.43358
I0410 02:45:01.176360 24451 solver.cpp:237]     Train net output #0: loss = 1.43358 (* 1 = 1.43358 loss)
I0410 02:45:01.176371 24451 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0410 02:45:05.064646 24451 solver.cpp:218] Iteration 5520 (3.08633 iter/s, 3.88812s/12 iters), loss = 1.34818
I0410 02:45:05.064692 24451 solver.cpp:237]     Train net output #0: loss = 1.34818 (* 1 = 1.34818 loss)
I0410 02:45:05.064702 24451 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0410 02:45:07.701756 24451 blocking_queue.cpp:49] Waiting for data
I0410 02:45:09.889484 24451 solver.cpp:218] Iteration 5532 (2.48726 iter/s, 4.82458s/12 iters), loss = 1.47625
I0410 02:45:09.889528 24451 solver.cpp:237]     Train net output #0: loss = 1.47625 (* 1 = 1.47625 loss)
I0410 02:45:09.889539 24451 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0410 02:45:15.025382 24451 solver.cpp:218] Iteration 5544 (2.33662 iter/s, 5.13563s/12 iters), loss = 1.12673
I0410 02:45:15.025429 24451 solver.cpp:237]     Train net output #0: loss = 1.12673 (* 1 = 1.12673 loss)
I0410 02:45:15.025439 24451 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0410 02:45:20.208261 24451 solver.cpp:218] Iteration 5556 (2.31544 iter/s, 5.18261s/12 iters), loss = 1.31253
I0410 02:45:20.208307 24451 solver.cpp:237]     Train net output #0: loss = 1.31253 (* 1 = 1.31253 loss)
I0410 02:45:20.208317 24451 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0410 02:45:22.953557 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:45:25.277922 24451 solver.cpp:218] Iteration 5568 (2.36715 iter/s, 5.06939s/12 iters), loss = 1.13876
I0410 02:45:25.277977 24451 solver.cpp:237]     Train net output #0: loss = 1.13876 (* 1 = 1.13876 loss)
I0410 02:45:25.277985 24451 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0410 02:45:30.054214 24451 solver.cpp:218] Iteration 5580 (2.51255 iter/s, 4.77603s/12 iters), loss = 1.30899
I0410 02:45:30.054306 24451 solver.cpp:237]     Train net output #0: loss = 1.30899 (* 1 = 1.30899 loss)
I0410 02:45:30.054316 24451 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0410 02:45:34.908558 24451 solver.cpp:218] Iteration 5592 (2.47216 iter/s, 4.85405s/12 iters), loss = 1.43523
I0410 02:45:34.908597 24451 solver.cpp:237]     Train net output #0: loss = 1.43523 (* 1 = 1.43523 loss)
I0410 02:45:34.908607 24451 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0410 02:45:39.502851 24451 solver.cpp:218] Iteration 5604 (2.61208 iter/s, 4.59405s/12 iters), loss = 1.22785
I0410 02:45:39.502900 24451 solver.cpp:237]     Train net output #0: loss = 1.22785 (* 1 = 1.22785 loss)
I0410 02:45:39.502912 24451 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0410 02:45:41.365151 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0410 02:45:58.264714 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0410 02:46:11.677203 24451 solver.cpp:330] Iteration 5610, Testing net (#0)
I0410 02:46:11.677300 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:46:13.946398 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:46:16.203759 24451 solver.cpp:397]     Test net output #0: accuracy = 0.414828
I0410 02:46:16.203795 24451 solver.cpp:397]     Test net output #1: loss = 2.56693 (* 1 = 2.56693 loss)
I0410 02:46:17.994640 24451 solver.cpp:218] Iteration 5616 (0.311768 iter/s, 38.4902s/12 iters), loss = 1.38685
I0410 02:46:17.994689 24451 solver.cpp:237]     Train net output #0: loss = 1.38685 (* 1 = 1.38685 loss)
I0410 02:46:17.994700 24451 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0410 02:46:22.787657 24451 solver.cpp:218] Iteration 5628 (2.50378 iter/s, 4.79276s/12 iters), loss = 1.17938
I0410 02:46:22.787703 24451 solver.cpp:237]     Train net output #0: loss = 1.17938 (* 1 = 1.17938 loss)
I0410 02:46:22.787712 24451 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0410 02:46:27.512691 24451 solver.cpp:218] Iteration 5640 (2.5398 iter/s, 4.72478s/12 iters), loss = 1.31659
I0410 02:46:27.512737 24451 solver.cpp:237]     Train net output #0: loss = 1.31659 (* 1 = 1.31659 loss)
I0410 02:46:27.512746 24451 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0410 02:46:32.414726 24451 solver.cpp:218] Iteration 5652 (2.44809 iter/s, 4.90177s/12 iters), loss = 1.24383
I0410 02:46:32.414770 24451 solver.cpp:237]     Train net output #0: loss = 1.24383 (* 1 = 1.24383 loss)
I0410 02:46:32.414779 24451 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0410 02:46:37.356302 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:46:37.571225 24451 solver.cpp:218] Iteration 5664 (2.32728 iter/s, 5.15623s/12 iters), loss = 1.41079
I0410 02:46:37.571280 24451 solver.cpp:237]     Train net output #0: loss = 1.41079 (* 1 = 1.41079 loss)
I0410 02:46:37.571290 24451 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0410 02:46:42.690492 24451 solver.cpp:218] Iteration 5676 (2.34421 iter/s, 5.11899s/12 iters), loss = 1.31324
I0410 02:46:42.691494 24451 solver.cpp:237]     Train net output #0: loss = 1.31324 (* 1 = 1.31324 loss)
I0410 02:46:42.691507 24451 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0410 02:46:47.525420 24451 solver.cpp:218] Iteration 5688 (2.48256 iter/s, 4.83372s/12 iters), loss = 1.23943
I0410 02:46:47.525471 24451 solver.cpp:237]     Train net output #0: loss = 1.23943 (* 1 = 1.23943 loss)
I0410 02:46:47.525483 24451 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0410 02:46:52.602854 24451 solver.cpp:218] Iteration 5700 (2.36353 iter/s, 5.07716s/12 iters), loss = 1.37422
I0410 02:46:52.602903 24451 solver.cpp:237]     Train net output #0: loss = 1.37422 (* 1 = 1.37422 loss)
I0410 02:46:52.602914 24451 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0410 02:46:57.104281 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0410 02:47:13.429430 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0410 02:47:32.731454 24451 solver.cpp:330] Iteration 5712, Testing net (#0)
I0410 02:47:32.731475 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:47:35.066648 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:47:37.477663 24451 solver.cpp:397]     Test net output #0: accuracy = 0.394608
I0410 02:47:37.477705 24451 solver.cpp:397]     Test net output #1: loss = 2.60507 (* 1 = 2.60507 loss)
I0410 02:47:37.612022 24451 solver.cpp:218] Iteration 5712 (0.266624 iter/s, 45.0073s/12 iters), loss = 1.14119
I0410 02:47:37.613605 24451 solver.cpp:237]     Train net output #0: loss = 1.14119 (* 1 = 1.14119 loss)
I0410 02:47:37.613617 24451 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0410 02:47:41.697232 24451 solver.cpp:218] Iteration 5724 (2.93869 iter/s, 4.08345s/12 iters), loss = 1.16453
I0410 02:47:41.697283 24451 solver.cpp:237]     Train net output #0: loss = 1.16453 (* 1 = 1.16453 loss)
I0410 02:47:41.697294 24451 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0410 02:47:46.896654 24451 solver.cpp:218] Iteration 5736 (2.30807 iter/s, 5.19915s/12 iters), loss = 1.32117
I0410 02:47:46.897657 24451 solver.cpp:237]     Train net output #0: loss = 1.32117 (* 1 = 1.32117 loss)
I0410 02:47:46.897670 24451 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0410 02:47:51.586077 24451 solver.cpp:218] Iteration 5748 (2.55961 iter/s, 4.68822s/12 iters), loss = 1.50064
I0410 02:47:51.586117 24451 solver.cpp:237]     Train net output #0: loss = 1.50064 (* 1 = 1.50064 loss)
I0410 02:47:51.586125 24451 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0410 02:47:56.408241 24451 solver.cpp:218] Iteration 5760 (2.48864 iter/s, 4.82191s/12 iters), loss = 1.12841
I0410 02:47:56.408284 24451 solver.cpp:237]     Train net output #0: loss = 1.12841 (* 1 = 1.12841 loss)
I0410 02:47:56.408296 24451 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0410 02:47:58.215970 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:48:01.207113 24451 solver.cpp:218] Iteration 5772 (2.50072 iter/s, 4.79862s/12 iters), loss = 1.20814
I0410 02:48:01.207156 24451 solver.cpp:237]     Train net output #0: loss = 1.20814 (* 1 = 1.20814 loss)
I0410 02:48:01.207165 24451 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0410 02:48:06.024967 24451 solver.cpp:218] Iteration 5784 (2.49087 iter/s, 4.8176s/12 iters), loss = 1.2228
I0410 02:48:06.025009 24451 solver.cpp:237]     Train net output #0: loss = 1.2228 (* 1 = 1.2228 loss)
I0410 02:48:06.025018 24451 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0410 02:48:10.835649 24451 solver.cpp:218] Iteration 5796 (2.49458 iter/s, 4.81042s/12 iters), loss = 0.961171
I0410 02:48:10.835701 24451 solver.cpp:237]     Train net output #0: loss = 0.961171 (* 1 = 0.961171 loss)
I0410 02:48:10.835712 24451 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0410 02:48:15.493083 24451 solver.cpp:218] Iteration 5808 (2.57667 iter/s, 4.65718s/12 iters), loss = 1.21048
I0410 02:48:15.493129 24451 solver.cpp:237]     Train net output #0: loss = 1.21048 (* 1 = 1.21048 loss)
I0410 02:48:15.493139 24451 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0410 02:48:17.479007 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0410 02:48:34.466017 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0410 02:48:50.326329 24451 solver.cpp:330] Iteration 5814, Testing net (#0)
I0410 02:48:50.326400 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:48:52.504308 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:48:54.894989 24451 solver.cpp:397]     Test net output #0: accuracy = 0.39277
I0410 02:48:54.895036 24451 solver.cpp:397]     Test net output #1: loss = 2.64249 (* 1 = 2.64249 loss)
I0410 02:48:56.803540 24451 solver.cpp:218] Iteration 5820 (0.290496 iter/s, 41.3087s/12 iters), loss = 1.14942
I0410 02:48:56.803594 24451 solver.cpp:237]     Train net output #0: loss = 1.14942 (* 1 = 1.14942 loss)
I0410 02:48:56.803606 24451 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0410 02:49:01.535143 24451 solver.cpp:218] Iteration 5832 (2.53628 iter/s, 4.73134s/12 iters), loss = 1.39878
I0410 02:49:01.535182 24451 solver.cpp:237]     Train net output #0: loss = 1.39878 (* 1 = 1.39878 loss)
I0410 02:49:01.535192 24451 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0410 02:49:06.693395 24451 solver.cpp:218] Iteration 5844 (2.32649 iter/s, 5.15799s/12 iters), loss = 1.4317
I0410 02:49:06.693449 24451 solver.cpp:237]     Train net output #0: loss = 1.4317 (* 1 = 1.4317 loss)
I0410 02:49:06.693459 24451 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0410 02:49:11.486956 24451 solver.cpp:218] Iteration 5856 (2.5035 iter/s, 4.7933s/12 iters), loss = 1.12307
I0410 02:49:11.487008 24451 solver.cpp:237]     Train net output #0: loss = 1.12307 (* 1 = 1.12307 loss)
I0410 02:49:11.487020 24451 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0410 02:49:15.537802 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:49:16.462549 24451 solver.cpp:218] Iteration 5868 (2.4119 iter/s, 4.97533s/12 iters), loss = 1.06039
I0410 02:49:16.462599 24451 solver.cpp:237]     Train net output #0: loss = 1.06039 (* 1 = 1.06039 loss)
I0410 02:49:16.462610 24451 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0410 02:49:21.535228 24451 solver.cpp:218] Iteration 5880 (2.36574 iter/s, 5.07241s/12 iters), loss = 1.17843
I0410 02:49:21.535375 24451 solver.cpp:237]     Train net output #0: loss = 1.17843 (* 1 = 1.17843 loss)
I0410 02:49:21.535388 24451 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0410 02:49:26.544184 24451 solver.cpp:218] Iteration 5892 (2.39588 iter/s, 5.00859s/12 iters), loss = 1.24466
I0410 02:49:26.544229 24451 solver.cpp:237]     Train net output #0: loss = 1.24466 (* 1 = 1.24466 loss)
I0410 02:49:26.544237 24451 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0410 02:49:31.642254 24451 solver.cpp:218] Iteration 5904 (2.35396 iter/s, 5.0978s/12 iters), loss = 0.806615
I0410 02:49:31.642298 24451 solver.cpp:237]     Train net output #0: loss = 0.806615 (* 1 = 0.806615 loss)
I0410 02:49:31.642307 24451 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0410 02:49:35.986625 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0410 02:49:52.534283 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0410 02:50:06.242652 24451 solver.cpp:330] Iteration 5916, Testing net (#0)
I0410 02:50:06.242676 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:50:08.476920 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:50:10.975309 24451 solver.cpp:397]     Test net output #0: accuracy = 0.403186
I0410 02:50:10.975345 24451 solver.cpp:397]     Test net output #1: loss = 2.59981 (* 1 = 2.59981 loss)
I0410 02:50:11.106372 24451 solver.cpp:218] Iteration 5916 (0.304087 iter/s, 39.4625s/12 iters), loss = 1.26588
I0410 02:50:11.107903 24451 solver.cpp:237]     Train net output #0: loss = 1.26588 (* 1 = 1.26588 loss)
I0410 02:50:11.107918 24451 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0410 02:50:15.341051 24451 solver.cpp:218] Iteration 5928 (2.83489 iter/s, 4.23297s/12 iters), loss = 1.1147
I0410 02:50:15.341095 24451 solver.cpp:237]     Train net output #0: loss = 1.1147 (* 1 = 1.1147 loss)
I0410 02:50:15.341106 24451 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0410 02:50:20.459686 24451 solver.cpp:218] Iteration 5940 (2.3445 iter/s, 5.11837s/12 iters), loss = 1.14281
I0410 02:50:20.459729 24451 solver.cpp:237]     Train net output #0: loss = 1.14281 (* 1 = 1.14281 loss)
I0410 02:50:20.459738 24451 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0410 02:50:25.152276 24451 solver.cpp:218] Iteration 5952 (2.55736 iter/s, 4.69234s/12 iters), loss = 1.1394
I0410 02:50:25.152384 24451 solver.cpp:237]     Train net output #0: loss = 1.1394 (* 1 = 1.1394 loss)
I0410 02:50:25.152395 24451 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0410 02:50:29.950489 24451 solver.cpp:218] Iteration 5964 (2.5011 iter/s, 4.7979s/12 iters), loss = 1.02693
I0410 02:50:29.950529 24451 solver.cpp:237]     Train net output #0: loss = 1.02693 (* 1 = 1.02693 loss)
I0410 02:50:29.950539 24451 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0410 02:50:31.279258 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:50:34.901726 24451 solver.cpp:218] Iteration 5976 (2.42376 iter/s, 4.95098s/12 iters), loss = 0.910735
I0410 02:50:34.901764 24451 solver.cpp:237]     Train net output #0: loss = 0.910735 (* 1 = 0.910735 loss)
I0410 02:50:34.901773 24451 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0410 02:50:39.552462 24451 solver.cpp:218] Iteration 5988 (2.58037 iter/s, 4.65049s/12 iters), loss = 1.20076
I0410 02:50:39.552515 24451 solver.cpp:237]     Train net output #0: loss = 1.20076 (* 1 = 1.20076 loss)
I0410 02:50:39.552527 24451 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0410 02:50:44.279098 24451 solver.cpp:218] Iteration 6000 (2.53894 iter/s, 4.72638s/12 iters), loss = 0.946329
I0410 02:50:44.279150 24451 solver.cpp:237]     Train net output #0: loss = 0.946329 (* 1 = 0.946329 loss)
I0410 02:50:44.279161 24451 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0410 02:50:48.946637 24451 solver.cpp:218] Iteration 6012 (2.57109 iter/s, 4.66728s/12 iters), loss = 0.850729
I0410 02:50:48.946684 24451 solver.cpp:237]     Train net output #0: loss = 0.850729 (* 1 = 0.850729 loss)
I0410 02:50:48.946694 24451 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0410 02:50:50.988274 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0410 02:51:05.972540 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0410 02:51:24.043133 24451 solver.cpp:330] Iteration 6018, Testing net (#0)
I0410 02:51:24.043156 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:51:26.224253 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:51:28.709288 24451 solver.cpp:397]     Test net output #0: accuracy = 0.425858
I0410 02:51:28.709337 24451 solver.cpp:397]     Test net output #1: loss = 2.60734 (* 1 = 2.60734 loss)
I0410 02:51:30.515739 24451 solver.cpp:218] Iteration 6024 (0.288688 iter/s, 41.5674s/12 iters), loss = 0.793945
I0410 02:51:30.515794 24451 solver.cpp:237]     Train net output #0: loss = 0.793945 (* 1 = 0.793945 loss)
I0410 02:51:30.515805 24451 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0410 02:51:35.479868 24451 solver.cpp:218] Iteration 6036 (2.41747 iter/s, 4.96386s/12 iters), loss = 1.08542
I0410 02:51:35.479912 24451 solver.cpp:237]     Train net output #0: loss = 1.08542 (* 1 = 1.08542 loss)
I0410 02:51:35.479920 24451 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0410 02:51:40.714234 24451 solver.cpp:218] Iteration 6048 (2.29266 iter/s, 5.2341s/12 iters), loss = 1.2138
I0410 02:51:40.714339 24451 solver.cpp:237]     Train net output #0: loss = 1.2138 (* 1 = 1.2138 loss)
I0410 02:51:40.714351 24451 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0410 02:51:45.728768 24451 solver.cpp:218] Iteration 6060 (2.3932 iter/s, 5.01421s/12 iters), loss = 0.865537
I0410 02:51:45.728817 24451 solver.cpp:237]     Train net output #0: loss = 0.865537 (* 1 = 0.865537 loss)
I0410 02:51:45.728828 24451 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0410 02:51:49.288693 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:51:50.849908 24451 solver.cpp:218] Iteration 6072 (2.34335 iter/s, 5.12087s/12 iters), loss = 0.997283
I0410 02:51:50.849980 24451 solver.cpp:237]     Train net output #0: loss = 0.997283 (* 1 = 0.997283 loss)
I0410 02:51:50.849990 24451 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0410 02:51:55.919852 24451 solver.cpp:218] Iteration 6084 (2.36702 iter/s, 5.06967s/12 iters), loss = 1.22708
I0410 02:51:55.919900 24451 solver.cpp:237]     Train net output #0: loss = 1.22708 (* 1 = 1.22708 loss)
I0410 02:51:55.919911 24451 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0410 02:52:00.685761 24451 solver.cpp:218] Iteration 6096 (2.51802 iter/s, 4.76565s/12 iters), loss = 1.17466
I0410 02:52:00.685812 24451 solver.cpp:237]     Train net output #0: loss = 1.17466 (* 1 = 1.17466 loss)
I0410 02:52:00.685824 24451 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0410 02:52:05.307945 24451 solver.cpp:218] Iteration 6108 (2.59632 iter/s, 4.62193s/12 iters), loss = 1.16736
I0410 02:52:05.307994 24451 solver.cpp:237]     Train net output #0: loss = 1.16736 (* 1 = 1.16736 loss)
I0410 02:52:05.308005 24451 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0410 02:52:10.001073 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0410 02:52:36.036316 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0410 02:52:48.665571 24451 solver.cpp:330] Iteration 6120, Testing net (#0)
I0410 02:52:48.665596 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:52:50.785975 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:52:53.239627 24451 solver.cpp:397]     Test net output #0: accuracy = 0.415441
I0410 02:52:53.239666 24451 solver.cpp:397]     Test net output #1: loss = 2.64262 (* 1 = 2.64262 loss)
I0410 02:52:53.373814 24451 solver.cpp:218] Iteration 6120 (0.249668 iter/s, 48.0639s/12 iters), loss = 1.17018
I0410 02:52:53.375347 24451 solver.cpp:237]     Train net output #0: loss = 1.17018 (* 1 = 1.17018 loss)
I0410 02:52:53.375360 24451 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0410 02:52:57.224535 24451 solver.cpp:218] Iteration 6132 (3.11768 iter/s, 3.84901s/12 iters), loss = 0.971438
I0410 02:52:57.224596 24451 solver.cpp:237]     Train net output #0: loss = 0.971438 (* 1 = 0.971438 loss)
I0410 02:52:57.224606 24451 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0410 02:53:02.337672 24451 solver.cpp:218] Iteration 6144 (2.34702 iter/s, 5.11286s/12 iters), loss = 1.0151
I0410 02:53:02.337710 24451 solver.cpp:237]     Train net output #0: loss = 1.0151 (* 1 = 1.0151 loss)
I0410 02:53:02.337720 24451 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0410 02:53:07.074666 24451 solver.cpp:218] Iteration 6156 (2.53339 iter/s, 4.73674s/12 iters), loss = 0.886367
I0410 02:53:07.074779 24451 solver.cpp:237]     Train net output #0: loss = 0.886367 (* 1 = 0.886367 loss)
I0410 02:53:07.074791 24451 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0410 02:53:11.760365 24451 solver.cpp:218] Iteration 6168 (2.56116 iter/s, 4.68538s/12 iters), loss = 0.886504
I0410 02:53:11.760422 24451 solver.cpp:237]     Train net output #0: loss = 0.886504 (* 1 = 0.886504 loss)
I0410 02:53:11.760434 24451 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0410 02:53:12.262498 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:53:16.434634 24451 solver.cpp:218] Iteration 6180 (2.56739 iter/s, 4.67401s/12 iters), loss = 1.02649
I0410 02:53:16.434680 24451 solver.cpp:237]     Train net output #0: loss = 1.02649 (* 1 = 1.02649 loss)
I0410 02:53:16.434690 24451 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0410 02:53:21.066098 24451 solver.cpp:218] Iteration 6192 (2.59111 iter/s, 4.63122s/12 iters), loss = 0.804049
I0410 02:53:21.066141 24451 solver.cpp:237]     Train net output #0: loss = 0.804049 (* 1 = 0.804049 loss)
I0410 02:53:21.066151 24451 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0410 02:53:25.670431 24451 solver.cpp:218] Iteration 6204 (2.60638 iter/s, 4.60409s/12 iters), loss = 0.885536
I0410 02:53:25.670487 24451 solver.cpp:237]     Train net output #0: loss = 0.885536 (* 1 = 0.885536 loss)
I0410 02:53:25.670500 24451 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0410 02:53:30.644374 24451 solver.cpp:218] Iteration 6216 (2.41271 iter/s, 4.97367s/12 iters), loss = 1.03563
I0410 02:53:30.644428 24451 solver.cpp:237]     Train net output #0: loss = 1.03563 (* 1 = 1.03563 loss)
I0410 02:53:30.644438 24451 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0410 02:53:32.666007 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0410 02:53:48.579170 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0410 02:53:59.768744 24451 solver.cpp:330] Iteration 6222, Testing net (#0)
I0410 02:53:59.768764 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:54:01.834236 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:54:03.315910 24451 blocking_queue.cpp:49] Waiting for data
I0410 02:54:04.473857 24451 solver.cpp:397]     Test net output #0: accuracy = 0.441789
I0410 02:54:04.473912 24451 solver.cpp:397]     Test net output #1: loss = 2.53821 (* 1 = 2.53821 loss)
I0410 02:54:06.245420 24451 solver.cpp:218] Iteration 6228 (0.337083 iter/s, 35.5995s/12 iters), loss = 0.952301
I0410 02:54:06.245465 24451 solver.cpp:237]     Train net output #0: loss = 0.952301 (* 1 = 0.952301 loss)
I0410 02:54:06.245474 24451 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0410 02:54:10.909979 24451 solver.cpp:218] Iteration 6240 (2.57274 iter/s, 4.66429s/12 iters), loss = 1.01364
I0410 02:54:10.910018 24451 solver.cpp:237]     Train net output #0: loss = 1.01364 (* 1 = 1.01364 loss)
I0410 02:54:10.910027 24451 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0410 02:54:15.539655 24451 solver.cpp:218] Iteration 6252 (2.59211 iter/s, 4.62943s/12 iters), loss = 0.855689
I0410 02:54:15.539705 24451 solver.cpp:237]     Train net output #0: loss = 0.855689 (* 1 = 0.855689 loss)
I0410 02:54:15.539719 24451 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0410 02:54:20.426755 24451 solver.cpp:218] Iteration 6264 (2.45558 iter/s, 4.88684s/12 iters), loss = 0.986651
I0410 02:54:20.426898 24451 solver.cpp:237]     Train net output #0: loss = 0.986651 (* 1 = 0.986651 loss)
I0410 02:54:20.426911 24451 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0410 02:54:22.936376 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:54:25.061908 24451 solver.cpp:218] Iteration 6276 (2.5891 iter/s, 4.63481s/12 iters), loss = 0.767514
I0410 02:54:25.061949 24451 solver.cpp:237]     Train net output #0: loss = 0.767514 (* 1 = 0.767514 loss)
I0410 02:54:25.061973 24451 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0410 02:54:29.679320 24451 solver.cpp:218] Iteration 6288 (2.59899 iter/s, 4.61717s/12 iters), loss = 0.994743
I0410 02:54:29.679363 24451 solver.cpp:237]     Train net output #0: loss = 0.994743 (* 1 = 0.994743 loss)
I0410 02:54:29.679373 24451 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0410 02:54:34.352583 24451 solver.cpp:218] Iteration 6300 (2.56794 iter/s, 4.67301s/12 iters), loss = 1.12484
I0410 02:54:34.352636 24451 solver.cpp:237]     Train net output #0: loss = 1.12484 (* 1 = 1.12484 loss)
I0410 02:54:34.352648 24451 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0410 02:54:38.969826 24451 solver.cpp:218] Iteration 6312 (2.59909 iter/s, 4.61699s/12 iters), loss = 0.790157
I0410 02:54:38.969868 24451 solver.cpp:237]     Train net output #0: loss = 0.790157 (* 1 = 0.790157 loss)
I0410 02:54:38.969879 24451 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0410 02:54:43.222477 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0410 02:54:57.170171 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0410 02:55:12.449770 24451 solver.cpp:330] Iteration 6324, Testing net (#0)
I0410 02:55:12.449795 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:55:14.468847 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:55:17.090536 24451 solver.cpp:397]     Test net output #0: accuracy = 0.440564
I0410 02:55:17.090582 24451 solver.cpp:397]     Test net output #1: loss = 2.57545 (* 1 = 2.57545 loss)
I0410 02:55:17.220186 24451 solver.cpp:218] Iteration 6324 (0.313736 iter/s, 38.2487s/12 iters), loss = 0.906715
I0410 02:55:17.221705 24451 solver.cpp:237]     Train net output #0: loss = 0.906715 (* 1 = 0.906715 loss)
I0410 02:55:17.221717 24451 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0410 02:55:21.230829 24451 solver.cpp:218] Iteration 6336 (2.9933 iter/s, 4.00895s/12 iters), loss = 0.85596
I0410 02:55:21.230878 24451 solver.cpp:237]     Train net output #0: loss = 0.85596 (* 1 = 0.85596 loss)
I0410 02:55:21.230890 24451 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0410 02:55:25.873062 24451 solver.cpp:218] Iteration 6348 (2.5851 iter/s, 4.64198s/12 iters), loss = 1.1217
I0410 02:55:25.873116 24451 solver.cpp:237]     Train net output #0: loss = 1.1217 (* 1 = 1.1217 loss)
I0410 02:55:25.873126 24451 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0410 02:55:30.421757 24451 solver.cpp:218] Iteration 6360 (2.63827 iter/s, 4.54844s/12 iters), loss = 0.810325
I0410 02:55:30.421895 24451 solver.cpp:237]     Train net output #0: loss = 0.810325 (* 1 = 0.810325 loss)
I0410 02:55:30.421907 24451 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0410 02:55:35.098702 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:55:35.276911 24451 solver.cpp:218] Iteration 6372 (2.47178 iter/s, 4.85481s/12 iters), loss = 0.866243
I0410 02:55:35.276957 24451 solver.cpp:237]     Train net output #0: loss = 0.866243 (* 1 = 0.866243 loss)
I0410 02:55:35.276966 24451 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0410 02:55:40.109897 24451 solver.cpp:218] Iteration 6384 (2.48307 iter/s, 4.83273s/12 iters), loss = 0.949997
I0410 02:55:40.109943 24451 solver.cpp:237]     Train net output #0: loss = 0.949997 (* 1 = 0.949997 loss)
I0410 02:55:40.109979 24451 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0410 02:55:45.170171 24451 solver.cpp:218] Iteration 6396 (2.37154 iter/s, 5.06s/12 iters), loss = 0.78396
I0410 02:55:45.170223 24451 solver.cpp:237]     Train net output #0: loss = 0.78396 (* 1 = 0.78396 loss)
I0410 02:55:45.170234 24451 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0410 02:55:50.178599 24451 solver.cpp:218] Iteration 6408 (2.39609 iter/s, 5.00816s/12 iters), loss = 0.891786
I0410 02:55:50.178643 24451 solver.cpp:237]     Train net output #0: loss = 0.891786 (* 1 = 0.891786 loss)
I0410 02:55:50.178654 24451 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0410 02:55:55.039929 24451 solver.cpp:218] Iteration 6420 (2.46859 iter/s, 4.86107s/12 iters), loss = 0.990971
I0410 02:55:55.039976 24451 solver.cpp:237]     Train net output #0: loss = 0.990971 (* 1 = 0.990971 loss)
I0410 02:55:55.039986 24451 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0410 02:55:57.173890 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0410 02:56:11.974061 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0410 02:56:23.613514 24451 solver.cpp:330] Iteration 6426, Testing net (#0)
I0410 02:56:23.613536 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:56:25.557272 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:56:28.139556 24451 solver.cpp:397]     Test net output #0: accuracy = 0.420956
I0410 02:56:28.139602 24451 solver.cpp:397]     Test net output #1: loss = 2.63624 (* 1 = 2.63624 loss)
I0410 02:56:29.833263 24451 solver.cpp:218] Iteration 6432 (0.344908 iter/s, 34.7919s/12 iters), loss = 0.64374
I0410 02:56:29.833308 24451 solver.cpp:237]     Train net output #0: loss = 0.64374 (* 1 = 0.64374 loss)
I0410 02:56:29.833318 24451 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0410 02:56:34.784770 24451 solver.cpp:218] Iteration 6444 (2.42364 iter/s, 4.95124s/12 iters), loss = 0.762704
I0410 02:56:34.784823 24451 solver.cpp:237]     Train net output #0: loss = 0.762704 (* 1 = 0.762704 loss)
I0410 02:56:34.784837 24451 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0410 02:56:39.715978 24451 solver.cpp:218] Iteration 6456 (2.43361 iter/s, 4.93094s/12 iters), loss = 0.916402
I0410 02:56:39.716020 24451 solver.cpp:237]     Train net output #0: loss = 0.916402 (* 1 = 0.916402 loss)
I0410 02:56:39.716029 24451 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0410 02:56:44.487697 24451 solver.cpp:218] Iteration 6468 (2.51495 iter/s, 4.77147s/12 iters), loss = 0.833015
I0410 02:56:44.487794 24451 solver.cpp:237]     Train net output #0: loss = 0.833015 (* 1 = 0.833015 loss)
I0410 02:56:44.487807 24451 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0410 02:56:46.413106 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:56:49.513952 24451 solver.cpp:218] Iteration 6480 (2.38761 iter/s, 5.02594s/12 iters), loss = 0.780351
I0410 02:56:49.514008 24451 solver.cpp:237]     Train net output #0: loss = 0.780351 (* 1 = 0.780351 loss)
I0410 02:56:49.514017 24451 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0410 02:56:54.193887 24451 solver.cpp:218] Iteration 6492 (2.56428 iter/s, 4.67967s/12 iters), loss = 0.768406
I0410 02:56:54.193933 24451 solver.cpp:237]     Train net output #0: loss = 0.768406 (* 1 = 0.768406 loss)
I0410 02:56:54.193941 24451 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0410 02:56:58.820436 24451 solver.cpp:218] Iteration 6504 (2.59386 iter/s, 4.6263s/12 iters), loss = 0.671331
I0410 02:56:58.820477 24451 solver.cpp:237]     Train net output #0: loss = 0.671331 (* 1 = 0.671331 loss)
I0410 02:56:58.820487 24451 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0410 02:57:03.619086 24451 solver.cpp:218] Iteration 6516 (2.50083 iter/s, 4.7984s/12 iters), loss = 0.617758
I0410 02:57:03.619141 24451 solver.cpp:237]     Train net output #0: loss = 0.617758 (* 1 = 0.617758 loss)
I0410 02:57:03.619153 24451 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0410 02:57:07.852828 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0410 02:57:21.986567 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0410 02:57:49.876758 24451 solver.cpp:330] Iteration 6528, Testing net (#0)
I0410 02:57:49.876780 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:57:51.895176 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:57:54.537887 24451 solver.cpp:397]     Test net output #0: accuracy = 0.431985
I0410 02:57:54.537976 24451 solver.cpp:397]     Test net output #1: loss = 2.66036 (* 1 = 2.66036 loss)
I0410 02:57:54.671371 24451 solver.cpp:218] Iteration 6528 (0.235063 iter/s, 51.0501s/12 iters), loss = 0.647214
I0410 02:57:54.672890 24451 solver.cpp:237]     Train net output #0: loss = 0.647214 (* 1 = 0.647214 loss)
I0410 02:57:54.672901 24451 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0410 02:57:58.606220 24451 solver.cpp:218] Iteration 6540 (3.05098 iter/s, 3.93316s/12 iters), loss = 0.903202
I0410 02:57:58.606268 24451 solver.cpp:237]     Train net output #0: loss = 0.903202 (* 1 = 0.903202 loss)
I0410 02:57:58.606281 24451 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0410 02:58:03.302831 24451 solver.cpp:218] Iteration 6552 (2.55518 iter/s, 4.69635s/12 iters), loss = 0.739219
I0410 02:58:03.302878 24451 solver.cpp:237]     Train net output #0: loss = 0.739219 (* 1 = 0.739219 loss)
I0410 02:58:03.302889 24451 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0410 02:58:07.928623 24451 solver.cpp:218] Iteration 6564 (2.59429 iter/s, 4.62554s/12 iters), loss = 0.868633
I0410 02:58:07.928668 24451 solver.cpp:237]     Train net output #0: loss = 0.868633 (* 1 = 0.868633 loss)
I0410 02:58:07.928676 24451 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0410 02:58:11.864406 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:58:12.686136 24451 solver.cpp:218] Iteration 6576 (2.52246 iter/s, 4.75726s/12 iters), loss = 1.02694
I0410 02:58:12.686187 24451 solver.cpp:237]     Train net output #0: loss = 1.02694 (* 1 = 1.02694 loss)
I0410 02:58:12.686198 24451 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0410 02:58:17.378877 24451 solver.cpp:218] Iteration 6588 (2.55728 iter/s, 4.69249s/12 iters), loss = 0.662671
I0410 02:58:17.378924 24451 solver.cpp:237]     Train net output #0: loss = 0.662671 (* 1 = 0.662671 loss)
I0410 02:58:17.378933 24451 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0410 02:58:22.163800 24451 solver.cpp:218] Iteration 6600 (2.50801 iter/s, 4.78467s/12 iters), loss = 0.917694
I0410 02:58:22.163851 24451 solver.cpp:237]     Train net output #0: loss = 0.917694 (* 1 = 0.917694 loss)
I0410 02:58:22.163862 24451 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0410 02:58:26.846292 24451 solver.cpp:218] Iteration 6612 (2.56288 iter/s, 4.68224s/12 iters), loss = 0.797938
I0410 02:58:26.846415 24451 solver.cpp:237]     Train net output #0: loss = 0.797938 (* 1 = 0.797938 loss)
I0410 02:58:26.846426 24451 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0410 02:58:31.621079 24451 solver.cpp:218] Iteration 6624 (2.51338 iter/s, 4.77446s/12 iters), loss = 0.794067
I0410 02:58:31.621129 24451 solver.cpp:237]     Train net output #0: loss = 0.794067 (* 1 = 0.794067 loss)
I0410 02:58:31.621141 24451 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0410 02:58:33.537941 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0410 02:58:56.193925 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0410 02:59:08.674223 24451 solver.cpp:330] Iteration 6630, Testing net (#0)
I0410 02:59:08.674299 24451 net.cpp:676] Ignoring source layer train-data
I0410 02:59:10.454123 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:59:13.134292 24451 solver.cpp:397]     Test net output #0: accuracy = 0.4375
I0410 02:59:13.134341 24451 solver.cpp:397]     Test net output #1: loss = 2.49444 (* 1 = 2.49444 loss)
I0410 02:59:14.962513 24451 solver.cpp:218] Iteration 6636 (0.276883 iter/s, 43.3396s/12 iters), loss = 0.580683
I0410 02:59:14.962558 24451 solver.cpp:237]     Train net output #0: loss = 0.580683 (* 1 = 0.580683 loss)
I0410 02:59:14.962565 24451 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0410 02:59:19.571297 24451 solver.cpp:218] Iteration 6648 (2.60386 iter/s, 4.60854s/12 iters), loss = 0.864689
I0410 02:59:19.571346 24451 solver.cpp:237]     Train net output #0: loss = 0.864689 (* 1 = 0.864689 loss)
I0410 02:59:19.571359 24451 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0410 02:59:24.215860 24451 solver.cpp:218] Iteration 6660 (2.58381 iter/s, 4.64431s/12 iters), loss = 0.832049
I0410 02:59:24.215912 24451 solver.cpp:237]     Train net output #0: loss = 0.832049 (* 1 = 0.832049 loss)
I0410 02:59:24.215924 24451 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0410 02:59:28.999572 24451 solver.cpp:218] Iteration 6672 (2.50865 iter/s, 4.78345s/12 iters), loss = 0.814709
I0410 02:59:28.999619 24451 solver.cpp:237]     Train net output #0: loss = 0.814709 (* 1 = 0.814709 loss)
I0410 02:59:28.999630 24451 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0410 02:59:30.259021 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 02:59:34.019215 24451 solver.cpp:218] Iteration 6684 (2.39073 iter/s, 5.01938s/12 iters), loss = 0.779512
I0410 02:59:34.019258 24451 solver.cpp:237]     Train net output #0: loss = 0.779512 (* 1 = 0.779512 loss)
I0410 02:59:34.019270 24451 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0410 02:59:38.907582 24451 solver.cpp:218] Iteration 6696 (2.45493 iter/s, 4.88811s/12 iters), loss = 0.803139
I0410 02:59:38.907810 24451 solver.cpp:237]     Train net output #0: loss = 0.803139 (* 1 = 0.803139 loss)
I0410 02:59:38.907821 24451 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0410 02:59:43.649530 24451 solver.cpp:218] Iteration 6708 (2.53084 iter/s, 4.74151s/12 iters), loss = 0.597809
I0410 02:59:43.649585 24451 solver.cpp:237]     Train net output #0: loss = 0.597809 (* 1 = 0.597809 loss)
I0410 02:59:43.649596 24451 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0410 02:59:48.799187 24451 solver.cpp:218] Iteration 6720 (2.33038 iter/s, 5.14938s/12 iters), loss = 0.631085
I0410 02:59:48.799235 24451 solver.cpp:237]     Train net output #0: loss = 0.631085 (* 1 = 0.631085 loss)
I0410 02:59:48.799247 24451 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0410 02:59:53.252737 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0410 03:00:07.265623 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0410 03:00:18.543792 24451 solver.cpp:330] Iteration 6732, Testing net (#0)
I0410 03:00:18.543839 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:00:20.453570 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:00:23.201509 24451 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0410 03:00:23.201560 24451 solver.cpp:397]     Test net output #1: loss = 2.59852 (* 1 = 2.59852 loss)
I0410 03:00:23.336464 24451 solver.cpp:218] Iteration 6732 (0.347465 iter/s, 34.5358s/12 iters), loss = 0.754333
I0410 03:00:23.336531 24451 solver.cpp:237]     Train net output #0: loss = 0.754333 (* 1 = 0.754333 loss)
I0410 03:00:23.336544 24451 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0410 03:00:27.484344 24451 solver.cpp:218] Iteration 6744 (2.89321 iter/s, 4.14764s/12 iters), loss = 0.758671
I0410 03:00:27.484390 24451 solver.cpp:237]     Train net output #0: loss = 0.758671 (* 1 = 0.758671 loss)
I0410 03:00:27.484398 24451 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0410 03:00:32.498831 24451 solver.cpp:218] Iteration 6756 (2.39319 iter/s, 5.01422s/12 iters), loss = 0.66277
I0410 03:00:32.498878 24451 solver.cpp:237]     Train net output #0: loss = 0.66277 (* 1 = 0.66277 loss)
I0410 03:00:32.498888 24451 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0410 03:00:37.509994 24451 solver.cpp:218] Iteration 6768 (2.39478 iter/s, 5.0109s/12 iters), loss = 0.633679
I0410 03:00:37.510030 24451 solver.cpp:237]     Train net output #0: loss = 0.633679 (* 1 = 0.633679 loss)
I0410 03:00:37.510038 24451 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0410 03:00:41.080991 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:00:42.551662 24451 solver.cpp:218] Iteration 6780 (2.38029 iter/s, 5.04141s/12 iters), loss = 0.683397
I0410 03:00:42.551699 24451 solver.cpp:237]     Train net output #0: loss = 0.683397 (* 1 = 0.683397 loss)
I0410 03:00:42.551707 24451 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0410 03:00:47.401582 24451 solver.cpp:218] Iteration 6792 (2.4744 iter/s, 4.84967s/12 iters), loss = 0.752261
I0410 03:00:47.401628 24451 solver.cpp:237]     Train net output #0: loss = 0.752261 (* 1 = 0.752261 loss)
I0410 03:00:47.401640 24451 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0410 03:00:52.415071 24451 solver.cpp:218] Iteration 6804 (2.39367 iter/s, 5.01322s/12 iters), loss = 0.775395
I0410 03:00:52.415212 24451 solver.cpp:237]     Train net output #0: loss = 0.775395 (* 1 = 0.775395 loss)
I0410 03:00:52.415225 24451 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0410 03:00:57.576866 24451 solver.cpp:218] Iteration 6816 (2.32493 iter/s, 5.16144s/12 iters), loss = 0.705234
I0410 03:00:57.576913 24451 solver.cpp:237]     Train net output #0: loss = 0.705234 (* 1 = 0.705234 loss)
I0410 03:00:57.576925 24451 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0410 03:01:02.443250 24451 solver.cpp:218] Iteration 6828 (2.46603 iter/s, 4.86612s/12 iters), loss = 0.56461
I0410 03:01:02.443303 24451 solver.cpp:237]     Train net output #0: loss = 0.56461 (* 1 = 0.56461 loss)
I0410 03:01:02.443316 24451 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0410 03:01:04.548789 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0410 03:01:20.785290 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0410 03:01:39.379349 24451 solver.cpp:330] Iteration 6834, Testing net (#0)
I0410 03:01:39.379420 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:01:41.058151 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:01:43.783043 24451 solver.cpp:397]     Test net output #0: accuracy = 0.449142
I0410 03:01:43.783077 24451 solver.cpp:397]     Test net output #1: loss = 2.5396 (* 1 = 2.5396 loss)
I0410 03:01:45.637482 24451 solver.cpp:218] Iteration 6840 (0.277827 iter/s, 43.1924s/12 iters), loss = 0.531021
I0410 03:01:45.637531 24451 solver.cpp:237]     Train net output #0: loss = 0.531021 (* 1 = 0.531021 loss)
I0410 03:01:45.637542 24451 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0410 03:01:50.656652 24451 solver.cpp:218] Iteration 6852 (2.39096 iter/s, 5.0189s/12 iters), loss = 0.946395
I0410 03:01:50.656697 24451 solver.cpp:237]     Train net output #0: loss = 0.946395 (* 1 = 0.946395 loss)
I0410 03:01:50.656706 24451 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0410 03:01:55.812000 24451 solver.cpp:218] Iteration 6864 (2.3278 iter/s, 5.15507s/12 iters), loss = 0.793817
I0410 03:01:55.812047 24451 solver.cpp:237]     Train net output #0: loss = 0.793817 (* 1 = 0.793817 loss)
I0410 03:01:55.812058 24451 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0410 03:02:00.775707 24451 solver.cpp:218] Iteration 6876 (2.41768 iter/s, 4.96344s/12 iters), loss = 0.655376
I0410 03:02:00.775753 24451 solver.cpp:237]     Train net output #0: loss = 0.655376 (* 1 = 0.655376 loss)
I0410 03:02:00.775763 24451 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0410 03:02:01.383728 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:02:05.932484 24451 solver.cpp:218] Iteration 6888 (2.32716 iter/s, 5.1565s/12 iters), loss = 0.61423
I0410 03:02:05.932543 24451 solver.cpp:237]     Train net output #0: loss = 0.61423 (* 1 = 0.61423 loss)
I0410 03:02:05.932556 24451 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0410 03:02:10.867440 24451 solver.cpp:218] Iteration 6900 (2.43176 iter/s, 4.93469s/12 iters), loss = 0.826502
I0410 03:02:10.867565 24451 solver.cpp:237]     Train net output #0: loss = 0.826502 (* 1 = 0.826502 loss)
I0410 03:02:10.867575 24451 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0410 03:02:15.653789 24451 solver.cpp:218] Iteration 6912 (2.50731 iter/s, 4.78602s/12 iters), loss = 0.704714
I0410 03:02:15.653841 24451 solver.cpp:237]     Train net output #0: loss = 0.704714 (* 1 = 0.704714 loss)
I0410 03:02:15.653851 24451 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0410 03:02:20.679925 24451 solver.cpp:218] Iteration 6924 (2.38765 iter/s, 5.02587s/12 iters), loss = 0.823924
I0410 03:02:20.679971 24451 solver.cpp:237]     Train net output #0: loss = 0.823924 (* 1 = 0.823924 loss)
I0410 03:02:20.679982 24451 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0410 03:02:25.215706 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0410 03:02:45.344075 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0410 03:03:03.866138 24451 solver.cpp:330] Iteration 6936, Testing net (#0)
I0410 03:03:03.866159 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:03:04.734388 24451 blocking_queue.cpp:49] Waiting for data
I0410 03:03:05.631315 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:03:08.415904 24451 solver.cpp:397]     Test net output #0: accuracy = 0.447304
I0410 03:03:08.415953 24451 solver.cpp:397]     Test net output #1: loss = 2.69511 (* 1 = 2.69511 loss)
I0410 03:03:08.548756 24451 solver.cpp:218] Iteration 6936 (0.250695 iter/s, 47.8668s/12 iters), loss = 0.725428
I0410 03:03:08.550276 24451 solver.cpp:237]     Train net output #0: loss = 0.725428 (* 1 = 0.725428 loss)
I0410 03:03:08.550288 24451 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0410 03:03:12.360985 24451 solver.cpp:218] Iteration 6948 (3.14916 iter/s, 3.81054s/12 iters), loss = 0.875529
I0410 03:03:12.361035 24451 solver.cpp:237]     Train net output #0: loss = 0.875529 (* 1 = 0.875529 loss)
I0410 03:03:12.361047 24451 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0410 03:03:17.022871 24451 solver.cpp:218] Iteration 6960 (2.5742 iter/s, 4.66164s/12 iters), loss = 0.705851
I0410 03:03:17.024101 24451 solver.cpp:237]     Train net output #0: loss = 0.705851 (* 1 = 0.705851 loss)
I0410 03:03:17.024111 24451 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0410 03:03:21.580885 24451 solver.cpp:218] Iteration 6972 (2.63355 iter/s, 4.55658s/12 iters), loss = 0.635573
I0410 03:03:21.580929 24451 solver.cpp:237]     Train net output #0: loss = 0.635573 (* 1 = 0.635573 loss)
I0410 03:03:21.580940 24451 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0410 03:03:24.079703 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:03:26.306350 24451 solver.cpp:218] Iteration 6984 (2.53957 iter/s, 4.72521s/12 iters), loss = 0.600528
I0410 03:03:26.306403 24451 solver.cpp:237]     Train net output #0: loss = 0.600528 (* 1 = 0.600528 loss)
I0410 03:03:26.306416 24451 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0410 03:03:31.039366 24451 solver.cpp:218] Iteration 6996 (2.53552 iter/s, 4.73276s/12 iters), loss = 0.625142
I0410 03:03:31.039409 24451 solver.cpp:237]     Train net output #0: loss = 0.625142 (* 1 = 0.625142 loss)
I0410 03:03:31.039418 24451 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0410 03:03:35.833022 24451 solver.cpp:218] Iteration 7008 (2.50344 iter/s, 4.7934s/12 iters), loss = 0.462296
I0410 03:03:35.833066 24451 solver.cpp:237]     Train net output #0: loss = 0.462296 (* 1 = 0.462296 loss)
I0410 03:03:35.833076 24451 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0410 03:03:40.508069 24451 solver.cpp:218] Iteration 7020 (2.56696 iter/s, 4.6748s/12 iters), loss = 0.654889
I0410 03:03:40.508124 24451 solver.cpp:237]     Train net output #0: loss = 0.654889 (* 1 = 0.654889 loss)
I0410 03:03:40.508136 24451 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0410 03:03:45.423322 24451 solver.cpp:218] Iteration 7032 (2.44151 iter/s, 4.91498s/12 iters), loss = 0.733166
I0410 03:03:45.423385 24451 solver.cpp:237]     Train net output #0: loss = 0.733166 (* 1 = 0.733166 loss)
I0410 03:03:45.423400 24451 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0410 03:03:47.557668 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0410 03:04:07.292866 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0410 03:04:18.590205 24451 solver.cpp:330] Iteration 7038, Testing net (#0)
I0410 03:04:18.590263 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:04:20.302058 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:04:23.244983 24451 solver.cpp:397]     Test net output #0: accuracy = 0.460172
I0410 03:04:23.245043 24451 solver.cpp:397]     Test net output #1: loss = 2.52497 (* 1 = 2.52497 loss)
I0410 03:04:25.014065 24451 solver.cpp:218] Iteration 7044 (0.303114 iter/s, 39.5891s/12 iters), loss = 0.569203
I0410 03:04:25.014122 24451 solver.cpp:237]     Train net output #0: loss = 0.569203 (* 1 = 0.569203 loss)
I0410 03:04:25.014133 24451 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0410 03:04:30.019243 24451 solver.cpp:218] Iteration 7056 (2.39765 iter/s, 5.00491s/12 iters), loss = 0.695343
I0410 03:04:30.019290 24451 solver.cpp:237]     Train net output #0: loss = 0.695343 (* 1 = 0.695343 loss)
I0410 03:04:30.019300 24451 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0410 03:04:35.190496 24451 solver.cpp:218] Iteration 7068 (2.32064 iter/s, 5.17099s/12 iters), loss = 0.666071
I0410 03:04:35.190541 24451 solver.cpp:237]     Train net output #0: loss = 0.666071 (* 1 = 0.666071 loss)
I0410 03:04:35.190551 24451 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0410 03:04:40.142594 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:04:40.299599 24451 solver.cpp:218] Iteration 7080 (2.34887 iter/s, 5.10884s/12 iters), loss = 0.545007
I0410 03:04:40.299644 24451 solver.cpp:237]     Train net output #0: loss = 0.545007 (* 1 = 0.545007 loss)
I0410 03:04:40.299654 24451 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0410 03:04:45.510949 24451 solver.cpp:218] Iteration 7092 (2.30278 iter/s, 5.21109s/12 iters), loss = 0.582542
I0410 03:04:45.510987 24451 solver.cpp:237]     Train net output #0: loss = 0.582542 (* 1 = 0.582542 loss)
I0410 03:04:45.510998 24451 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0410 03:04:50.596362 24451 solver.cpp:218] Iteration 7104 (2.35981 iter/s, 5.08515s/12 iters), loss = 0.545256
I0410 03:04:50.596467 24451 solver.cpp:237]     Train net output #0: loss = 0.545256 (* 1 = 0.545256 loss)
I0410 03:04:50.596480 24451 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0410 03:04:55.649706 24451 solver.cpp:218] Iteration 7116 (2.37482 iter/s, 5.05302s/12 iters), loss = 0.801914
I0410 03:04:55.649750 24451 solver.cpp:237]     Train net output #0: loss = 0.801914 (* 1 = 0.801914 loss)
I0410 03:04:55.649758 24451 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0410 03:05:00.265452 24451 solver.cpp:218] Iteration 7128 (2.59993 iter/s, 4.6155s/12 iters), loss = 0.579962
I0410 03:05:00.265502 24451 solver.cpp:237]     Train net output #0: loss = 0.579962 (* 1 = 0.579962 loss)
I0410 03:05:00.265514 24451 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0410 03:05:04.969010 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0410 03:05:18.858034 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0410 03:05:30.704746 24451 solver.cpp:330] Iteration 7140, Testing net (#0)
I0410 03:05:30.704843 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:05:32.391140 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:05:35.246102 24451 solver.cpp:397]     Test net output #0: accuracy = 0.473652
I0410 03:05:35.246131 24451 solver.cpp:397]     Test net output #1: loss = 2.66177 (* 1 = 2.66177 loss)
I0410 03:05:35.380923 24451 solver.cpp:218] Iteration 7140 (0.341744 iter/s, 35.114s/12 iters), loss = 0.53209
I0410 03:05:35.382447 24451 solver.cpp:237]     Train net output #0: loss = 0.53209 (* 1 = 0.53209 loss)
I0410 03:05:35.382458 24451 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0410 03:05:39.606918 24451 solver.cpp:218] Iteration 7152 (2.84072 iter/s, 4.22429s/12 iters), loss = 0.576911
I0410 03:05:39.606961 24451 solver.cpp:237]     Train net output #0: loss = 0.576911 (* 1 = 0.576911 loss)
I0410 03:05:39.606971 24451 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0410 03:05:44.761307 24451 solver.cpp:218] Iteration 7164 (2.32824 iter/s, 5.15412s/12 iters), loss = 0.704531
I0410 03:05:44.761351 24451 solver.cpp:237]     Train net output #0: loss = 0.704531 (* 1 = 0.704531 loss)
I0410 03:05:44.761361 24451 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0410 03:05:49.534643 24451 solver.cpp:218] Iteration 7176 (2.5141 iter/s, 4.77308s/12 iters), loss = 0.556596
I0410 03:05:49.534690 24451 solver.cpp:237]     Train net output #0: loss = 0.556596 (* 1 = 0.556596 loss)
I0410 03:05:49.534701 24451 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0410 03:05:51.687655 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:05:54.764817 24451 solver.cpp:218] Iteration 7188 (2.2945 iter/s, 5.2299s/12 iters), loss = 0.720745
I0410 03:05:54.764861 24451 solver.cpp:237]     Train net output #0: loss = 0.720745 (* 1 = 0.720745 loss)
I0410 03:05:54.764870 24451 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 03:05:59.887488 24451 solver.cpp:218] Iteration 7200 (2.34265 iter/s, 5.12241s/12 iters), loss = 0.636805
I0410 03:05:59.887531 24451 solver.cpp:237]     Train net output #0: loss = 0.636805 (* 1 = 0.636805 loss)
I0410 03:05:59.887540 24451 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 03:06:05.025856 24451 solver.cpp:218] Iteration 7212 (2.33549 iter/s, 5.13811s/12 iters), loss = 0.529908
I0410 03:06:05.025976 24451 solver.cpp:237]     Train net output #0: loss = 0.529908 (* 1 = 0.529908 loss)
I0410 03:06:05.025987 24451 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 03:06:10.082383 24451 solver.cpp:218] Iteration 7224 (2.37332 iter/s, 5.05621s/12 iters), loss = 0.578389
I0410 03:06:10.082428 24451 solver.cpp:237]     Train net output #0: loss = 0.578389 (* 1 = 0.578389 loss)
I0410 03:06:10.082437 24451 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 03:06:14.934242 24451 solver.cpp:218] Iteration 7236 (2.47341 iter/s, 4.85161s/12 iters), loss = 0.57468
I0410 03:06:14.934283 24451 solver.cpp:237]     Train net output #0: loss = 0.57468 (* 1 = 0.57468 loss)
I0410 03:06:14.934291 24451 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 03:06:17.012992 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 03:06:34.501255 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 03:06:50.838606 24451 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 03:06:50.838697 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:06:52.482710 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:06:55.498242 24451 solver.cpp:397]     Test net output #0: accuracy = 0.470588
I0410 03:06:55.498289 24451 solver.cpp:397]     Test net output #1: loss = 2.77503 (* 1 = 2.77503 loss)
I0410 03:06:57.290235 24451 solver.cpp:218] Iteration 7248 (0.283325 iter/s, 42.3542s/12 iters), loss = 0.606228
I0410 03:06:57.290275 24451 solver.cpp:237]     Train net output #0: loss = 0.606228 (* 1 = 0.606228 loss)
I0410 03:06:57.290284 24451 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 03:07:01.960907 24451 solver.cpp:218] Iteration 7260 (2.56936 iter/s, 4.67043s/12 iters), loss = 0.61683
I0410 03:07:01.960948 24451 solver.cpp:237]     Train net output #0: loss = 0.61683 (* 1 = 0.61683 loss)
I0410 03:07:01.960958 24451 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 03:07:06.658144 24451 solver.cpp:218] Iteration 7272 (2.55483 iter/s, 4.69699s/12 iters), loss = 0.646739
I0410 03:07:06.658191 24451 solver.cpp:237]     Train net output #0: loss = 0.646739 (* 1 = 0.646739 loss)
I0410 03:07:06.658203 24451 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 03:07:10.440610 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:07:11.181217 24451 solver.cpp:218] Iteration 7284 (2.65321 iter/s, 4.52282s/12 iters), loss = 0.697192
I0410 03:07:11.181262 24451 solver.cpp:237]     Train net output #0: loss = 0.697192 (* 1 = 0.697192 loss)
I0410 03:07:11.181272 24451 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 03:07:15.939188 24451 solver.cpp:218] Iteration 7296 (2.52222 iter/s, 4.75771s/12 iters), loss = 0.601232
I0410 03:07:15.939245 24451 solver.cpp:237]     Train net output #0: loss = 0.601232 (* 1 = 0.601232 loss)
I0410 03:07:15.939258 24451 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 03:07:20.889428 24451 solver.cpp:218] Iteration 7308 (2.42426 iter/s, 4.94997s/12 iters), loss = 0.813706
I0410 03:07:20.891220 24451 solver.cpp:237]     Train net output #0: loss = 0.813706 (* 1 = 0.813706 loss)
I0410 03:07:20.891233 24451 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 03:07:25.674743 24451 solver.cpp:218] Iteration 7320 (2.50872 iter/s, 4.78332s/12 iters), loss = 0.427496
I0410 03:07:25.674785 24451 solver.cpp:237]     Train net output #0: loss = 0.427496 (* 1 = 0.427496 loss)
I0410 03:07:25.674794 24451 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 03:07:30.627712 24451 solver.cpp:218] Iteration 7332 (2.42292 iter/s, 4.95271s/12 iters), loss = 0.554948
I0410 03:07:30.627766 24451 solver.cpp:237]     Train net output #0: loss = 0.554948 (* 1 = 0.554948 loss)
I0410 03:07:30.627779 24451 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 03:07:34.917369 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 03:07:49.673595 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 03:08:10.321483 24451 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 03:08:10.321543 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:08:11.872720 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:08:14.872504 24451 solver.cpp:397]     Test net output #0: accuracy = 0.471814
I0410 03:08:14.872545 24451 solver.cpp:397]     Test net output #1: loss = 2.71503 (* 1 = 2.71503 loss)
I0410 03:08:15.007023 24451 solver.cpp:218] Iteration 7344 (0.270408 iter/s, 44.3775s/12 iters), loss = 0.339801
I0410 03:08:15.008543 24451 solver.cpp:237]     Train net output #0: loss = 0.339801 (* 1 = 0.339801 loss)
I0410 03:08:15.008553 24451 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 03:08:19.384528 24451 solver.cpp:218] Iteration 7356 (2.74236 iter/s, 4.3758s/12 iters), loss = 0.645049
I0410 03:08:19.384572 24451 solver.cpp:237]     Train net output #0: loss = 0.645049 (* 1 = 0.645049 loss)
I0410 03:08:19.384582 24451 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 03:08:24.281489 24451 solver.cpp:218] Iteration 7368 (2.45063 iter/s, 4.89671s/12 iters), loss = 0.541912
I0410 03:08:24.281529 24451 solver.cpp:237]     Train net output #0: loss = 0.541912 (* 1 = 0.541912 loss)
I0410 03:08:24.281539 24451 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 03:08:29.528445 24451 solver.cpp:218] Iteration 7380 (2.28716 iter/s, 5.24668s/12 iters), loss = 0.649741
I0410 03:08:29.528496 24451 solver.cpp:237]     Train net output #0: loss = 0.649741 (* 1 = 0.649741 loss)
I0410 03:08:29.528507 24451 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 03:08:30.834666 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:08:34.275660 24451 solver.cpp:218] Iteration 7392 (2.52793 iter/s, 4.74696s/12 iters), loss = 0.60874
I0410 03:08:34.275710 24451 solver.cpp:237]     Train net output #0: loss = 0.60874 (* 1 = 0.60874 loss)
I0410 03:08:34.275722 24451 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 03:08:38.886307 24451 solver.cpp:218] Iteration 7404 (2.60281 iter/s, 4.6104s/12 iters), loss = 0.459381
I0410 03:08:38.886354 24451 solver.cpp:237]     Train net output #0: loss = 0.459381 (* 1 = 0.459381 loss)
I0410 03:08:38.886365 24451 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 03:08:43.490451 24451 solver.cpp:218] Iteration 7416 (2.60649 iter/s, 4.6039s/12 iters), loss = 0.683551
I0410 03:08:43.490597 24451 solver.cpp:237]     Train net output #0: loss = 0.683551 (* 1 = 0.683551 loss)
I0410 03:08:43.490609 24451 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 03:08:48.174294 24451 solver.cpp:218] Iteration 7428 (2.56219 iter/s, 4.6835s/12 iters), loss = 0.567935
I0410 03:08:48.174338 24451 solver.cpp:237]     Train net output #0: loss = 0.567935 (* 1 = 0.567935 loss)
I0410 03:08:48.174350 24451 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 03:08:53.351692 24451 solver.cpp:218] Iteration 7440 (2.31789 iter/s, 5.17713s/12 iters), loss = 0.429502
I0410 03:08:53.351739 24451 solver.cpp:237]     Train net output #0: loss = 0.429502 (* 1 = 0.429502 loss)
I0410 03:08:53.351749 24451 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 03:08:55.361428 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 03:09:11.694301 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 03:09:22.942291 24451 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 03:09:22.942338 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:09:24.478988 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:09:27.534597 24451 solver.cpp:397]     Test net output #0: accuracy = 0.465074
I0410 03:09:27.534644 24451 solver.cpp:397]     Test net output #1: loss = 2.67231 (* 1 = 2.67231 loss)
I0410 03:09:29.409499 24451 solver.cpp:218] Iteration 7452 (0.332813 iter/s, 36.0563s/12 iters), loss = 0.381353
I0410 03:09:29.409552 24451 solver.cpp:237]     Train net output #0: loss = 0.381353 (* 1 = 0.381353 loss)
I0410 03:09:29.409564 24451 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 03:09:34.662034 24451 solver.cpp:218] Iteration 7464 (2.28473 iter/s, 5.25226s/12 iters), loss = 0.635958
I0410 03:09:34.662075 24451 solver.cpp:237]     Train net output #0: loss = 0.635958 (* 1 = 0.635958 loss)
I0410 03:09:34.662088 24451 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 03:09:39.871616 24451 solver.cpp:218] Iteration 7476 (2.30357 iter/s, 5.20931s/12 iters), loss = 0.507364
I0410 03:09:39.871662 24451 solver.cpp:237]     Train net output #0: loss = 0.507364 (* 1 = 0.507364 loss)
I0410 03:09:39.871671 24451 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 03:09:43.392375 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:09:44.984014 24451 solver.cpp:218] Iteration 7488 (2.34736 iter/s, 5.11213s/12 iters), loss = 0.353645
I0410 03:09:44.984057 24451 solver.cpp:237]     Train net output #0: loss = 0.353645 (* 1 = 0.353645 loss)
I0410 03:09:44.984067 24451 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 03:09:50.201599 24451 solver.cpp:218] Iteration 7500 (2.30003 iter/s, 5.21732s/12 iters), loss = 0.529788
I0410 03:09:50.201640 24451 solver.cpp:237]     Train net output #0: loss = 0.529788 (* 1 = 0.529788 loss)
I0410 03:09:50.201651 24451 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 03:09:55.313406 24451 solver.cpp:218] Iteration 7512 (2.34763 iter/s, 5.11154s/12 iters), loss = 0.549838
I0410 03:09:55.313542 24451 solver.cpp:237]     Train net output #0: loss = 0.549838 (* 1 = 0.549838 loss)
I0410 03:09:55.313553 24451 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 03:09:59.981905 24451 solver.cpp:218] Iteration 7524 (2.5706 iter/s, 4.66816s/12 iters), loss = 0.649691
I0410 03:09:59.981951 24451 solver.cpp:237]     Train net output #0: loss = 0.649691 (* 1 = 0.649691 loss)
I0410 03:09:59.981978 24451 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 03:10:04.737469 24451 solver.cpp:218] Iteration 7536 (2.52349 iter/s, 4.75531s/12 iters), loss = 0.35412
I0410 03:10:04.737519 24451 solver.cpp:237]     Train net output #0: loss = 0.35412 (* 1 = 0.35412 loss)
I0410 03:10:04.737530 24451 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 03:10:09.167225 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 03:10:24.305496 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 03:10:39.114106 24451 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 03:10:39.114176 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:10:40.569695 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:10:43.645555 24451 solver.cpp:397]     Test net output #0: accuracy = 0.482843
I0410 03:10:43.645606 24451 solver.cpp:397]     Test net output #1: loss = 2.65119 (* 1 = 2.65119 loss)
I0410 03:10:43.775645 24451 solver.cpp:218] Iteration 7548 (0.307404 iter/s, 39.0365s/12 iters), loss = 0.460893
I0410 03:10:43.777170 24451 solver.cpp:237]     Train net output #0: loss = 0.460893 (* 1 = 0.460893 loss)
I0410 03:10:43.777184 24451 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 03:10:48.112076 24451 solver.cpp:218] Iteration 7560 (2.76834 iter/s, 4.33472s/12 iters), loss = 0.410856
I0410 03:10:48.112123 24451 solver.cpp:237]     Train net output #0: loss = 0.410856 (* 1 = 0.410856 loss)
I0410 03:10:48.112134 24451 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 03:10:53.294467 24451 solver.cpp:218] Iteration 7572 (2.31565 iter/s, 5.18212s/12 iters), loss = 0.452859
I0410 03:10:53.294512 24451 solver.cpp:237]     Train net output #0: loss = 0.452859 (* 1 = 0.452859 loss)
I0410 03:10:53.294523 24451 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 03:10:58.121428 24451 solver.cpp:218] Iteration 7584 (2.48617 iter/s, 4.82671s/12 iters), loss = 0.483874
I0410 03:10:58.121479 24451 solver.cpp:237]     Train net output #0: loss = 0.483874 (* 1 = 0.483874 loss)
I0410 03:10:58.121490 24451 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 03:10:58.758292 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:11:03.341940 24451 solver.cpp:218] Iteration 7596 (2.29875 iter/s, 5.22024s/12 iters), loss = 0.491
I0410 03:11:03.341997 24451 solver.cpp:237]     Train net output #0: loss = 0.491 (* 1 = 0.491 loss)
I0410 03:11:03.342007 24451 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 03:11:08.543339 24451 solver.cpp:218] Iteration 7608 (2.3072 iter/s, 5.20112s/12 iters), loss = 0.436697
I0410 03:11:08.543387 24451 solver.cpp:237]     Train net output #0: loss = 0.436697 (* 1 = 0.436697 loss)
I0410 03:11:08.543399 24451 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 03:11:13.770409 24451 solver.cpp:218] Iteration 7620 (2.29586 iter/s, 5.2268s/12 iters), loss = 0.542648
I0410 03:11:13.772063 24451 solver.cpp:237]     Train net output #0: loss = 0.542648 (* 1 = 0.542648 loss)
I0410 03:11:13.772074 24451 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 03:11:16.876157 24451 blocking_queue.cpp:49] Waiting for data
I0410 03:11:18.588950 24451 solver.cpp:218] Iteration 7632 (2.49134 iter/s, 4.81668s/12 iters), loss = 0.462451
I0410 03:11:18.588994 24451 solver.cpp:237]     Train net output #0: loss = 0.462451 (* 1 = 0.462451 loss)
I0410 03:11:18.589002 24451 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 03:11:23.649454 24451 solver.cpp:218] Iteration 7644 (2.37143 iter/s, 5.06024s/12 iters), loss = 0.405257
I0410 03:11:23.649507 24451 solver.cpp:237]     Train net output #0: loss = 0.405257 (* 1 = 0.405257 loss)
I0410 03:11:23.649518 24451 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 03:11:25.458765 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 03:11:40.158733 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 03:11:54.628839 24451 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 03:11:54.628938 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:11:56.364157 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:11:59.418615 24451 solver.cpp:397]     Test net output #0: accuracy = 0.487745
I0410 03:11:59.418663 24451 solver.cpp:397]     Test net output #1: loss = 2.70982 (* 1 = 2.70982 loss)
I0410 03:12:01.333593 24451 solver.cpp:218] Iteration 7656 (0.31845 iter/s, 37.6826s/12 iters), loss = 0.46851
I0410 03:12:01.333636 24451 solver.cpp:237]     Train net output #0: loss = 0.46851 (* 1 = 0.46851 loss)
I0410 03:12:01.333647 24451 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 03:12:06.529637 24451 solver.cpp:218] Iteration 7668 (2.30957 iter/s, 5.19578s/12 iters), loss = 0.479091
I0410 03:12:06.529682 24451 solver.cpp:237]     Train net output #0: loss = 0.479091 (* 1 = 0.479091 loss)
I0410 03:12:06.529693 24451 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 03:12:11.741313 24451 solver.cpp:218] Iteration 7680 (2.30264 iter/s, 5.21141s/12 iters), loss = 0.510291
I0410 03:12:11.741355 24451 solver.cpp:237]     Train net output #0: loss = 0.510291 (* 1 = 0.510291 loss)
I0410 03:12:11.741365 24451 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 03:12:14.596431 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:12:16.871107 24451 solver.cpp:218] Iteration 7692 (2.3394 iter/s, 5.12953s/12 iters), loss = 0.569638
I0410 03:12:16.871153 24451 solver.cpp:237]     Train net output #0: loss = 0.569638 (* 1 = 0.569638 loss)
I0410 03:12:16.871165 24451 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 03:12:22.104431 24451 solver.cpp:218] Iteration 7704 (2.29312 iter/s, 5.23305s/12 iters), loss = 0.711386
I0410 03:12:22.104476 24451 solver.cpp:237]     Train net output #0: loss = 0.711386 (* 1 = 0.711386 loss)
I0410 03:12:22.104487 24451 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 03:12:27.256830 24451 solver.cpp:218] Iteration 7716 (2.32913 iter/s, 5.15214s/12 iters), loss = 0.473086
I0410 03:12:27.256909 24451 solver.cpp:237]     Train net output #0: loss = 0.473086 (* 1 = 0.473086 loss)
I0410 03:12:27.256922 24451 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 03:12:32.450825 24451 solver.cpp:218] Iteration 7728 (2.31049 iter/s, 5.1937s/12 iters), loss = 0.39766
I0410 03:12:32.450871 24451 solver.cpp:237]     Train net output #0: loss = 0.39766 (* 1 = 0.39766 loss)
I0410 03:12:32.450881 24451 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 03:12:37.065007 24451 solver.cpp:218] Iteration 7740 (2.60081 iter/s, 4.61394s/12 iters), loss = 0.642269
I0410 03:12:37.065050 24451 solver.cpp:237]     Train net output #0: loss = 0.642269 (* 1 = 0.642269 loss)
I0410 03:12:37.065059 24451 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 03:12:41.479969 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 03:12:57.559677 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 03:13:12.782591 24451 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 03:13:12.782613 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:13:14.222718 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:13:17.313922 24451 solver.cpp:397]     Test net output #0: accuracy = 0.48652
I0410 03:13:17.314004 24451 solver.cpp:397]     Test net output #1: loss = 2.78978 (* 1 = 2.78978 loss)
I0410 03:13:17.448004 24451 solver.cpp:218] Iteration 7752 (0.297167 iter/s, 40.3813s/12 iters), loss = 0.415436
I0410 03:13:17.449530 24451 solver.cpp:237]     Train net output #0: loss = 0.415436 (* 1 = 0.415436 loss)
I0410 03:13:17.449546 24451 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 03:13:21.579147 24451 solver.cpp:218] Iteration 7764 (2.90596 iter/s, 4.12945s/12 iters), loss = 0.406763
I0410 03:13:21.579186 24451 solver.cpp:237]     Train net output #0: loss = 0.406763 (* 1 = 0.406763 loss)
I0410 03:13:21.579195 24451 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 03:13:26.805392 24451 solver.cpp:218] Iteration 7776 (2.29622 iter/s, 5.22598s/12 iters), loss = 0.649905
I0410 03:13:26.805436 24451 solver.cpp:237]     Train net output #0: loss = 0.649905 (* 1 = 0.649905 loss)
I0410 03:13:26.805446 24451 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 03:13:31.938223 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:13:32.004999 24451 solver.cpp:218] Iteration 7788 (2.30799 iter/s, 5.19934s/12 iters), loss = 0.448396
I0410 03:13:32.005053 24451 solver.cpp:237]     Train net output #0: loss = 0.448396 (* 1 = 0.448396 loss)
I0410 03:13:32.005064 24451 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 03:13:37.155867 24451 solver.cpp:218] Iteration 7800 (2.32983 iter/s, 5.1506s/12 iters), loss = 0.402298
I0410 03:13:37.155912 24451 solver.cpp:237]     Train net output #0: loss = 0.402298 (* 1 = 0.402298 loss)
I0410 03:13:37.155921 24451 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 03:13:41.750339 24451 solver.cpp:218] Iteration 7812 (2.61197 iter/s, 4.59423s/12 iters), loss = 0.420904
I0410 03:13:41.750378 24451 solver.cpp:237]     Train net output #0: loss = 0.420904 (* 1 = 0.420904 loss)
I0410 03:13:41.750386 24451 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 03:13:46.375355 24451 solver.cpp:218] Iteration 7824 (2.59472 iter/s, 4.62477s/12 iters), loss = 0.422863
I0410 03:13:46.375413 24451 solver.cpp:237]     Train net output #0: loss = 0.422863 (* 1 = 0.422863 loss)
I0410 03:13:46.375427 24451 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 03:13:51.047250 24451 solver.cpp:218] Iteration 7836 (2.56869 iter/s, 4.67165s/12 iters), loss = 0.443822
I0410 03:13:51.047292 24451 solver.cpp:237]     Train net output #0: loss = 0.443822 (* 1 = 0.443822 loss)
I0410 03:13:51.047302 24451 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 03:13:55.765892 24451 solver.cpp:218] Iteration 7848 (2.54324 iter/s, 4.7184s/12 iters), loss = 0.356758
I0410 03:13:55.765935 24451 solver.cpp:237]     Train net output #0: loss = 0.356758 (* 1 = 0.356758 loss)
I0410 03:13:55.765944 24451 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 03:13:57.647598 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 03:14:15.852936 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 03:14:30.587540 24451 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 03:14:30.587561 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:14:32.045361 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:14:35.324087 24451 solver.cpp:397]     Test net output #0: accuracy = 0.49326
I0410 03:14:35.324134 24451 solver.cpp:397]     Test net output #1: loss = 2.78936 (* 1 = 2.78936 loss)
I0410 03:14:37.240093 24451 solver.cpp:218] Iteration 7860 (0.289348 iter/s, 41.4725s/12 iters), loss = 0.485862
I0410 03:14:37.240140 24451 solver.cpp:237]     Train net output #0: loss = 0.485862 (* 1 = 0.485862 loss)
I0410 03:14:37.240152 24451 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 03:14:42.081854 24451 solver.cpp:218] Iteration 7872 (2.47857 iter/s, 4.84151s/12 iters), loss = 0.47637
I0410 03:14:42.081897 24451 solver.cpp:237]     Train net output #0: loss = 0.47637 (* 1 = 0.47637 loss)
I0410 03:14:42.081907 24451 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 03:14:46.786453 24451 solver.cpp:218] Iteration 7884 (2.55083 iter/s, 4.70435s/12 iters), loss = 0.523344
I0410 03:14:46.786600 24451 solver.cpp:237]     Train net output #0: loss = 0.523344 (* 1 = 0.523344 loss)
I0410 03:14:46.786612 24451 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 03:14:48.700533 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:14:51.443694 24451 solver.cpp:218] Iteration 7896 (2.57682 iter/s, 4.6569s/12 iters), loss = 0.391292
I0410 03:14:51.443727 24451 solver.cpp:237]     Train net output #0: loss = 0.391292 (* 1 = 0.391292 loss)
I0410 03:14:51.443737 24451 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 03:14:56.115038 24451 solver.cpp:218] Iteration 7908 (2.56898 iter/s, 4.67111s/12 iters), loss = 0.306322
I0410 03:14:56.115082 24451 solver.cpp:237]     Train net output #0: loss = 0.306322 (* 1 = 0.306322 loss)
I0410 03:14:56.115092 24451 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 03:15:00.738536 24451 solver.cpp:218] Iteration 7920 (2.59557 iter/s, 4.62326s/12 iters), loss = 0.303638
I0410 03:15:00.738574 24451 solver.cpp:237]     Train net output #0: loss = 0.303638 (* 1 = 0.303638 loss)
I0410 03:15:00.738582 24451 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 03:15:05.661845 24451 solver.cpp:218] Iteration 7932 (2.43751 iter/s, 4.92306s/12 iters), loss = 0.373009
I0410 03:15:05.661890 24451 solver.cpp:237]     Train net output #0: loss = 0.373009 (* 1 = 0.373009 loss)
I0410 03:15:05.661900 24451 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 03:15:10.893712 24451 solver.cpp:218] Iteration 7944 (2.29375 iter/s, 5.2316s/12 iters), loss = 0.297446
I0410 03:15:10.893752 24451 solver.cpp:237]     Train net output #0: loss = 0.297446 (* 1 = 0.297446 loss)
I0410 03:15:10.893762 24451 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 03:15:15.602543 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 03:15:31.582486 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 03:15:43.083753 24451 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 03:15:43.083775 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:15:44.325129 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:15:47.494907 24451 solver.cpp:397]     Test net output #0: accuracy = 0.496324
I0410 03:15:47.494952 24451 solver.cpp:397]     Test net output #1: loss = 2.83064 (* 1 = 2.83064 loss)
I0410 03:15:47.629283 24451 solver.cpp:218] Iteration 7956 (0.326672 iter/s, 36.734s/12 iters), loss = 0.368411
I0410 03:15:47.630803 24451 solver.cpp:237]     Train net output #0: loss = 0.368411 (* 1 = 0.368411 loss)
I0410 03:15:47.630817 24451 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 03:15:51.861951 24451 solver.cpp:218] Iteration 7968 (2.83623 iter/s, 4.23097s/12 iters), loss = 0.479295
I0410 03:15:51.862013 24451 solver.cpp:237]     Train net output #0: loss = 0.479295 (* 1 = 0.479295 loss)
I0410 03:15:51.862025 24451 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 03:15:56.523767 24451 solver.cpp:218] Iteration 7980 (2.57425 iter/s, 4.66156s/12 iters), loss = 0.469878
I0410 03:15:56.523816 24451 solver.cpp:237]     Train net output #0: loss = 0.469878 (* 1 = 0.469878 loss)
I0410 03:15:56.523828 24451 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 03:16:00.486052 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:16:01.232795 24451 solver.cpp:218] Iteration 7992 (2.54843 iter/s, 4.70877s/12 iters), loss = 0.485349
I0410 03:16:01.232846 24451 solver.cpp:237]     Train net output #0: loss = 0.485349 (* 1 = 0.485349 loss)
I0410 03:16:01.232857 24451 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 03:16:05.909824 24451 solver.cpp:218] Iteration 8004 (2.56587 iter/s, 4.67678s/12 iters), loss = 0.497098
I0410 03:16:05.909991 24451 solver.cpp:237]     Train net output #0: loss = 0.497098 (* 1 = 0.497098 loss)
I0410 03:16:05.910004 24451 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 03:16:11.053951 24451 solver.cpp:218] Iteration 8016 (2.33293 iter/s, 5.14374s/12 iters), loss = 0.345275
I0410 03:16:11.054011 24451 solver.cpp:237]     Train net output #0: loss = 0.345275 (* 1 = 0.345275 loss)
I0410 03:16:11.054020 24451 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 03:16:15.820793 24451 solver.cpp:218] Iteration 8028 (2.51753 iter/s, 4.76657s/12 iters), loss = 0.330674
I0410 03:16:15.820842 24451 solver.cpp:237]     Train net output #0: loss = 0.330674 (* 1 = 0.330674 loss)
I0410 03:16:15.820850 24451 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 03:16:20.941354 24451 solver.cpp:218] Iteration 8040 (2.34362 iter/s, 5.12029s/12 iters), loss = 0.497592
I0410 03:16:20.941395 24451 solver.cpp:237]     Train net output #0: loss = 0.497592 (* 1 = 0.497592 loss)
I0410 03:16:20.941404 24451 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 03:16:25.990608 24451 solver.cpp:218] Iteration 8052 (2.37671 iter/s, 5.049s/12 iters), loss = 0.347066
I0410 03:16:25.990654 24451 solver.cpp:237]     Train net output #0: loss = 0.347066 (* 1 = 0.347066 loss)
I0410 03:16:25.990664 24451 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 03:16:28.013804 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 03:16:43.015983 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 03:16:54.711761 24451 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 03:16:54.711783 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:16:56.129201 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:16:59.530175 24451 solver.cpp:397]     Test net output #0: accuracy = 0.501838
I0410 03:16:59.530222 24451 solver.cpp:397]     Test net output #1: loss = 2.71954 (* 1 = 2.71954 loss)
I0410 03:17:01.447748 24451 solver.cpp:218] Iteration 8064 (0.338451 iter/s, 35.4557s/12 iters), loss = 0.392027
I0410 03:17:01.447793 24451 solver.cpp:237]     Train net output #0: loss = 0.392027 (* 1 = 0.392027 loss)
I0410 03:17:01.447803 24451 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 03:17:06.586712 24451 solver.cpp:218] Iteration 8076 (2.33522 iter/s, 5.1387s/12 iters), loss = 0.362283
I0410 03:17:06.586757 24451 solver.cpp:237]     Train net output #0: loss = 0.362283 (* 1 = 0.362283 loss)
I0410 03:17:06.586766 24451 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 03:17:11.751487 24451 solver.cpp:218] Iteration 8088 (2.32355 iter/s, 5.16451s/12 iters), loss = 0.274616
I0410 03:17:11.751534 24451 solver.cpp:237]     Train net output #0: loss = 0.274616 (* 1 = 0.274616 loss)
I0410 03:17:11.751546 24451 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 03:17:13.101830 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:17:16.627593 24451 solver.cpp:218] Iteration 8100 (2.46111 iter/s, 4.87585s/12 iters), loss = 0.399957
I0410 03:17:16.627641 24451 solver.cpp:237]     Train net output #0: loss = 0.399957 (* 1 = 0.399957 loss)
I0410 03:17:16.627652 24451 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 03:17:21.863763 24451 solver.cpp:218] Iteration 8112 (2.29187 iter/s, 5.2359s/12 iters), loss = 0.341379
I0410 03:17:21.863819 24451 solver.cpp:237]     Train net output #0: loss = 0.341379 (* 1 = 0.341379 loss)
I0410 03:17:21.863832 24451 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 03:17:27.025130 24451 solver.cpp:218] Iteration 8124 (2.32509 iter/s, 5.16109s/12 iters), loss = 0.40939
I0410 03:17:27.025182 24451 solver.cpp:237]     Train net output #0: loss = 0.40939 (* 1 = 0.40939 loss)
I0410 03:17:27.025194 24451 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 03:17:32.117278 24451 solver.cpp:218] Iteration 8136 (2.35669 iter/s, 5.09188s/12 iters), loss = 0.420318
I0410 03:17:32.117328 24451 solver.cpp:237]     Train net output #0: loss = 0.420318 (* 1 = 0.420318 loss)
I0410 03:17:32.117339 24451 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 03:17:37.292151 24451 solver.cpp:218] Iteration 8148 (2.31902 iter/s, 5.17461s/12 iters), loss = 0.359301
I0410 03:17:37.292194 24451 solver.cpp:237]     Train net output #0: loss = 0.359301 (* 1 = 0.359301 loss)
I0410 03:17:37.292204 24451 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 03:17:41.630584 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 03:17:55.523893 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 03:18:19.999562 24451 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 03:18:19.999583 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:18:21.292143 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:18:24.544021 24451 solver.cpp:397]     Test net output #0: accuracy = 0.489583
I0410 03:18:24.544066 24451 solver.cpp:397]     Test net output #1: loss = 2.88671 (* 1 = 2.88671 loss)
I0410 03:18:24.678350 24451 solver.cpp:218] Iteration 8160 (0.253249 iter/s, 47.3843s/12 iters), loss = 0.287245
I0410 03:18:24.679879 24451 solver.cpp:237]     Train net output #0: loss = 0.287245 (* 1 = 0.287245 loss)
I0410 03:18:24.679890 24451 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 03:18:29.047497 24451 solver.cpp:218] Iteration 8172 (2.74761 iter/s, 4.36744s/12 iters), loss = 0.374931
I0410 03:18:29.047600 24451 solver.cpp:237]     Train net output #0: loss = 0.374931 (* 1 = 0.374931 loss)
I0410 03:18:29.047610 24451 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 03:18:34.190384 24451 solver.cpp:218] Iteration 8184 (2.33346 iter/s, 5.14257s/12 iters), loss = 0.420924
I0410 03:18:34.190428 24451 solver.cpp:237]     Train net output #0: loss = 0.420924 (* 1 = 0.420924 loss)
I0410 03:18:34.190438 24451 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 03:18:37.684202 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:18:39.236191 24451 solver.cpp:218] Iteration 8196 (2.37833 iter/s, 5.04555s/12 iters), loss = 0.37067
I0410 03:18:39.236238 24451 solver.cpp:237]     Train net output #0: loss = 0.37067 (* 1 = 0.37067 loss)
I0410 03:18:39.236249 24451 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 03:18:44.366874 24451 solver.cpp:218] Iteration 8208 (2.33899 iter/s, 5.13042s/12 iters), loss = 0.275162
I0410 03:18:44.366915 24451 solver.cpp:237]     Train net output #0: loss = 0.275162 (* 1 = 0.275162 loss)
I0410 03:18:44.366925 24451 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 03:18:49.559082 24451 solver.cpp:218] Iteration 8220 (2.31127 iter/s, 5.19195s/12 iters), loss = 0.249449
I0410 03:18:49.559126 24451 solver.cpp:237]     Train net output #0: loss = 0.249449 (* 1 = 0.249449 loss)
I0410 03:18:49.559139 24451 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 03:18:54.555341 24451 solver.cpp:218] Iteration 8232 (2.40192 iter/s, 4.996s/12 iters), loss = 0.450847
I0410 03:18:54.555389 24451 solver.cpp:237]     Train net output #0: loss = 0.450847 (* 1 = 0.450847 loss)
I0410 03:18:54.555402 24451 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 03:18:59.522630 24451 solver.cpp:218] Iteration 8244 (2.41593 iter/s, 4.96703s/12 iters), loss = 0.382092
I0410 03:18:59.522734 24451 solver.cpp:237]     Train net output #0: loss = 0.382092 (* 1 = 0.382092 loss)
I0410 03:18:59.522747 24451 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 03:19:04.420222 24451 solver.cpp:218] Iteration 8256 (2.45034 iter/s, 4.89728s/12 iters), loss = 0.368954
I0410 03:19:04.420270 24451 solver.cpp:237]     Train net output #0: loss = 0.368954 (* 1 = 0.368954 loss)
I0410 03:19:04.420281 24451 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 03:19:06.271355 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 03:19:26.044417 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 03:19:37.393368 24451 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 03:19:37.393462 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:19:38.652056 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:19:41.933648 24451 solver.cpp:397]     Test net output #0: accuracy = 0.484069
I0410 03:19:41.933696 24451 solver.cpp:397]     Test net output #1: loss = 2.89278 (* 1 = 2.89278 loss)
I0410 03:19:43.791108 24451 solver.cpp:218] Iteration 8268 (0.304806 iter/s, 39.3693s/12 iters), loss = 0.439425
I0410 03:19:43.791155 24451 solver.cpp:237]     Train net output #0: loss = 0.439425 (* 1 = 0.439425 loss)
I0410 03:19:43.791164 24451 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 03:19:48.762760 24451 solver.cpp:218] Iteration 8280 (2.41381 iter/s, 4.97139s/12 iters), loss = 0.376813
I0410 03:19:48.762804 24451 solver.cpp:237]     Train net output #0: loss = 0.376813 (* 1 = 0.376813 loss)
I0410 03:19:48.762814 24451 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 03:19:53.938706 24451 solver.cpp:218] Iteration 8292 (2.31853 iter/s, 5.17568s/12 iters), loss = 0.510894
I0410 03:19:53.938748 24451 solver.cpp:237]     Train net output #0: loss = 0.510894 (* 1 = 0.510894 loss)
I0410 03:19:53.938758 24451 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 03:19:54.624550 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:19:59.109588 24451 solver.cpp:218] Iteration 8304 (2.32081 iter/s, 5.17062s/12 iters), loss = 0.377003
I0410 03:19:59.109642 24451 solver.cpp:237]     Train net output #0: loss = 0.377003 (* 1 = 0.377003 loss)
I0410 03:19:59.109654 24451 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 03:20:02.940666 24451 blocking_queue.cpp:49] Waiting for data
I0410 03:20:04.301157 24451 solver.cpp:218] Iteration 8316 (2.31156 iter/s, 5.19129s/12 iters), loss = 0.280844
I0410 03:20:04.301200 24451 solver.cpp:237]     Train net output #0: loss = 0.280844 (* 1 = 0.280844 loss)
I0410 03:20:04.301210 24451 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 03:20:09.534032 24451 solver.cpp:218] Iteration 8328 (2.29331 iter/s, 5.23261s/12 iters), loss = 0.228512
I0410 03:20:09.534137 24451 solver.cpp:237]     Train net output #0: loss = 0.228512 (* 1 = 0.228512 loss)
I0410 03:20:09.534147 24451 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 03:20:14.644747 24451 solver.cpp:218] Iteration 8340 (2.34815 iter/s, 5.1104s/12 iters), loss = 0.397078
I0410 03:20:14.644793 24451 solver.cpp:237]     Train net output #0: loss = 0.397078 (* 1 = 0.397078 loss)
I0410 03:20:14.644802 24451 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 03:20:19.834919 24451 solver.cpp:218] Iteration 8352 (2.31218 iter/s, 5.1899s/12 iters), loss = 0.274717
I0410 03:20:19.834969 24451 solver.cpp:237]     Train net output #0: loss = 0.274717 (* 1 = 0.274717 loss)
I0410 03:20:19.834980 24451 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 03:20:24.579015 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 03:20:40.392184 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 03:20:51.724243 24451 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 03:20:51.724265 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:20:52.909991 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:20:56.418669 24451 solver.cpp:397]     Test net output #0: accuracy = 0.5
I0410 03:20:56.418715 24451 solver.cpp:397]     Test net output #1: loss = 2.8716 (* 1 = 2.8716 loss)
I0410 03:20:56.552255 24451 solver.cpp:218] Iteration 8364 (0.326835 iter/s, 36.7158s/12 iters), loss = 0.468689
I0410 03:20:56.553776 24451 solver.cpp:237]     Train net output #0: loss = 0.468689 (* 1 = 0.468689 loss)
I0410 03:20:56.553787 24451 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 03:21:00.925534 24451 solver.cpp:218] Iteration 8376 (2.74501 iter/s, 4.37157s/12 iters), loss = 0.385396
I0410 03:21:00.925578 24451 solver.cpp:237]     Train net output #0: loss = 0.385396 (* 1 = 0.385396 loss)
I0410 03:21:00.925590 24451 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 03:21:05.868719 24451 solver.cpp:218] Iteration 8388 (2.42771 iter/s, 4.94293s/12 iters), loss = 0.504519
I0410 03:21:05.868762 24451 solver.cpp:237]     Train net output #0: loss = 0.504519 (* 1 = 0.504519 loss)
I0410 03:21:05.868772 24451 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 03:21:08.535719 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:21:10.799906 24451 solver.cpp:218] Iteration 8400 (2.43361 iter/s, 4.93094s/12 iters), loss = 0.318722
I0410 03:21:10.800026 24451 solver.cpp:237]     Train net output #0: loss = 0.318722 (* 1 = 0.318722 loss)
I0410 03:21:10.800038 24451 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 03:21:15.952615 24451 solver.cpp:218] Iteration 8412 (2.32902 iter/s, 5.15237s/12 iters), loss = 0.399694
I0410 03:21:15.952654 24451 solver.cpp:237]     Train net output #0: loss = 0.399694 (* 1 = 0.399694 loss)
I0410 03:21:15.952663 24451 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 03:21:21.013998 24451 solver.cpp:218] Iteration 8424 (2.37101 iter/s, 5.06113s/12 iters), loss = 0.35261
I0410 03:21:21.014042 24451 solver.cpp:237]     Train net output #0: loss = 0.35261 (* 1 = 0.35261 loss)
I0410 03:21:21.014052 24451 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 03:21:26.034482 24451 solver.cpp:218] Iteration 8436 (2.39033 iter/s, 5.02022s/12 iters), loss = 0.307721
I0410 03:21:26.034525 24451 solver.cpp:237]     Train net output #0: loss = 0.307721 (* 1 = 0.307721 loss)
I0410 03:21:26.034535 24451 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 03:21:30.993686 24451 solver.cpp:218] Iteration 8448 (2.41987 iter/s, 4.95895s/12 iters), loss = 0.383264
I0410 03:21:30.993728 24451 solver.cpp:237]     Train net output #0: loss = 0.383264 (* 1 = 0.383264 loss)
I0410 03:21:30.993739 24451 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 03:21:36.143843 24451 solver.cpp:218] Iteration 8460 (2.33014 iter/s, 5.1499s/12 iters), loss = 0.249182
I0410 03:21:36.143889 24451 solver.cpp:237]     Train net output #0: loss = 0.249182 (* 1 = 0.249182 loss)
I0410 03:21:36.143900 24451 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 03:21:37.953516 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 03:21:52.071442 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 03:22:03.662079 24451 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 03:22:03.662101 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:22:04.819643 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:22:08.262359 24451 solver.cpp:397]     Test net output #0: accuracy = 0.502451
I0410 03:22:08.262405 24451 solver.cpp:397]     Test net output #1: loss = 2.92621 (* 1 = 2.92621 loss)
I0410 03:22:10.094805 24451 solver.cpp:218] Iteration 8472 (0.353466 iter/s, 33.9496s/12 iters), loss = 0.326168
I0410 03:22:10.094853 24451 solver.cpp:237]     Train net output #0: loss = 0.326168 (* 1 = 0.326168 loss)
I0410 03:22:10.094866 24451 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 03:22:14.880185 24451 solver.cpp:218] Iteration 8484 (2.50777 iter/s, 4.78513s/12 iters), loss = 0.324032
I0410 03:22:14.880228 24451 solver.cpp:237]     Train net output #0: loss = 0.324032 (* 1 = 0.324032 loss)
I0410 03:22:14.880236 24451 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 03:22:19.427892 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:22:19.463299 24451 solver.cpp:218] Iteration 8496 (2.61844 iter/s, 4.58287s/12 iters), loss = 0.307607
I0410 03:22:19.463342 24451 solver.cpp:237]     Train net output #0: loss = 0.307607 (* 1 = 0.307607 loss)
I0410 03:22:19.463353 24451 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 03:22:24.044415 24451 solver.cpp:218] Iteration 8508 (2.61959 iter/s, 4.58088s/12 iters), loss = 0.236306
I0410 03:22:24.044569 24451 solver.cpp:237]     Train net output #0: loss = 0.236306 (* 1 = 0.236306 loss)
I0410 03:22:24.044582 24451 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 03:22:28.699919 24451 solver.cpp:218] Iteration 8520 (2.57779 iter/s, 4.65516s/12 iters), loss = 0.369294
I0410 03:22:28.699967 24451 solver.cpp:237]     Train net output #0: loss = 0.369294 (* 1 = 0.369294 loss)
I0410 03:22:28.699980 24451 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 03:22:33.300387 24451 solver.cpp:218] Iteration 8532 (2.60857 iter/s, 4.60022s/12 iters), loss = 0.253746
I0410 03:22:33.300444 24451 solver.cpp:237]     Train net output #0: loss = 0.253746 (* 1 = 0.253746 loss)
I0410 03:22:33.300457 24451 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 03:22:37.858369 24451 solver.cpp:218] Iteration 8544 (2.63289 iter/s, 4.55773s/12 iters), loss = 0.232254
I0410 03:22:37.858422 24451 solver.cpp:237]     Train net output #0: loss = 0.232254 (* 1 = 0.232254 loss)
I0410 03:22:37.858433 24451 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 03:22:42.568063 24451 solver.cpp:218] Iteration 8556 (2.54807 iter/s, 4.70944s/12 iters), loss = 0.237762
I0410 03:22:42.568112 24451 solver.cpp:237]     Train net output #0: loss = 0.237762 (* 1 = 0.237762 loss)
I0410 03:22:42.568123 24451 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 03:22:46.706694 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 03:23:01.609856 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 03:23:17.808655 24451 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 03:23:17.808676 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:23:18.880795 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:23:22.294230 24451 solver.cpp:397]     Test net output #0: accuracy = 0.514093
I0410 03:23:22.294279 24451 solver.cpp:397]     Test net output #1: loss = 2.84423 (* 1 = 2.84423 loss)
I0410 03:23:22.429652 24451 solver.cpp:218] Iteration 8568 (0.301054 iter/s, 39.86s/12 iters), loss = 0.370621
I0410 03:23:22.431174 24451 solver.cpp:237]     Train net output #0: loss = 0.370621 (* 1 = 0.370621 loss)
I0410 03:23:22.431186 24451 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 03:23:26.356235 24451 solver.cpp:218] Iteration 8580 (3.0574 iter/s, 3.9249s/12 iters), loss = 0.326258
I0410 03:23:26.356268 24451 solver.cpp:237]     Train net output #0: loss = 0.326258 (* 1 = 0.326258 loss)
I0410 03:23:26.356276 24451 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 03:23:31.317003 24451 solver.cpp:218] Iteration 8592 (2.4191 iter/s, 4.96052s/12 iters), loss = 0.204516
I0410 03:23:31.317049 24451 solver.cpp:237]     Train net output #0: loss = 0.204516 (* 1 = 0.204516 loss)
I0410 03:23:31.317059 24451 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 03:23:33.537487 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:23:36.527678 24451 solver.cpp:218] Iteration 8604 (2.30308 iter/s, 5.21041s/12 iters), loss = 0.368854
I0410 03:23:36.527724 24451 solver.cpp:237]     Train net output #0: loss = 0.368854 (* 1 = 0.368854 loss)
I0410 03:23:36.527732 24451 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 03:23:41.681886 24451 solver.cpp:218] Iteration 8616 (2.32831 iter/s, 5.15395s/12 iters), loss = 0.226495
I0410 03:23:41.681928 24451 solver.cpp:237]     Train net output #0: loss = 0.226495 (* 1 = 0.226495 loss)
I0410 03:23:41.681938 24451 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 03:23:46.636387 24451 solver.cpp:218] Iteration 8628 (2.42216 iter/s, 4.95425s/12 iters), loss = 0.245444
I0410 03:23:46.636435 24451 solver.cpp:237]     Train net output #0: loss = 0.245444 (* 1 = 0.245444 loss)
I0410 03:23:46.636445 24451 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 03:23:51.247263 24451 solver.cpp:218] Iteration 8640 (2.60268 iter/s, 4.61064s/12 iters), loss = 0.23981
I0410 03:23:51.247310 24451 solver.cpp:237]     Train net output #0: loss = 0.23981 (* 1 = 0.23981 loss)
I0410 03:23:51.247321 24451 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 03:23:55.938215 24451 solver.cpp:218] Iteration 8652 (2.55825 iter/s, 4.69071s/12 iters), loss = 0.301732
I0410 03:23:55.938261 24451 solver.cpp:237]     Train net output #0: loss = 0.301732 (* 1 = 0.301732 loss)
I0410 03:23:55.938271 24451 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 03:24:00.850234 24451 solver.cpp:218] Iteration 8664 (2.44311 iter/s, 4.91177s/12 iters), loss = 0.265931
I0410 03:24:00.850284 24451 solver.cpp:237]     Train net output #0: loss = 0.265931 (* 1 = 0.265931 loss)
I0410 03:24:00.850296 24451 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 03:24:02.896147 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 03:24:23.298292 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 03:24:39.051640 24451 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 03:24:39.051659 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:24:40.105516 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:24:43.556357 24451 solver.cpp:397]     Test net output #0: accuracy = 0.495711
I0410 03:24:43.556406 24451 solver.cpp:397]     Test net output #1: loss = 2.94066 (* 1 = 2.94066 loss)
I0410 03:24:45.426910 24451 solver.cpp:218] Iteration 8676 (0.26921 iter/s, 44.5749s/12 iters), loss = 0.206373
I0410 03:24:45.426955 24451 solver.cpp:237]     Train net output #0: loss = 0.206373 (* 1 = 0.206373 loss)
I0410 03:24:45.426964 24451 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 03:24:50.633065 24451 solver.cpp:218] Iteration 8688 (2.30508 iter/s, 5.20589s/12 iters), loss = 0.350325
I0410 03:24:50.633116 24451 solver.cpp:237]     Train net output #0: loss = 0.350325 (* 1 = 0.350325 loss)
I0410 03:24:50.633128 24451 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 03:24:55.033092 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:24:55.766516 24451 solver.cpp:218] Iteration 8700 (2.33773 iter/s, 5.13319s/12 iters), loss = 0.367039
I0410 03:24:55.766562 24451 solver.cpp:237]     Train net output #0: loss = 0.367039 (* 1 = 0.367039 loss)
I0410 03:24:55.766573 24451 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 03:25:00.763254 24451 solver.cpp:218] Iteration 8712 (2.40169 iter/s, 4.99648s/12 iters), loss = 0.302992
I0410 03:25:00.763300 24451 solver.cpp:237]     Train net output #0: loss = 0.302992 (* 1 = 0.302992 loss)
I0410 03:25:00.763311 24451 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 03:25:05.926321 24451 solver.cpp:218] Iteration 8724 (2.32432 iter/s, 5.1628s/12 iters), loss = 0.145702
I0410 03:25:05.926370 24451 solver.cpp:237]     Train net output #0: loss = 0.145702 (* 1 = 0.145702 loss)
I0410 03:25:05.926383 24451 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 03:25:11.047700 24451 solver.cpp:218] Iteration 8736 (2.34324 iter/s, 5.12112s/12 iters), loss = 0.129263
I0410 03:25:11.047742 24451 solver.cpp:237]     Train net output #0: loss = 0.129263 (* 1 = 0.129263 loss)
I0410 03:25:11.047752 24451 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 03:25:15.956246 24451 solver.cpp:218] Iteration 8748 (2.44484 iter/s, 4.9083s/12 iters), loss = 0.279051
I0410 03:25:15.956290 24451 solver.cpp:237]     Train net output #0: loss = 0.279051 (* 1 = 0.279051 loss)
I0410 03:25:15.956298 24451 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 03:25:20.682317 24451 solver.cpp:218] Iteration 8760 (2.53924 iter/s, 4.72583s/12 iters), loss = 0.317899
I0410 03:25:20.682358 24451 solver.cpp:237]     Train net output #0: loss = 0.317899 (* 1 = 0.317899 loss)
I0410 03:25:20.682368 24451 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 03:25:24.986444 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 03:25:43.258193 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 03:25:54.641482 24451 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 03:25:54.641503 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:25:55.571269 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:25:59.128405 24451 solver.cpp:397]     Test net output #0: accuracy = 0.503064
I0410 03:25:59.128454 24451 solver.cpp:397]     Test net output #1: loss = 3.06399 (* 1 = 3.06399 loss)
I0410 03:25:59.262820 24451 solver.cpp:218] Iteration 8772 (0.311051 iter/s, 38.5789s/12 iters), loss = 0.266547
I0410 03:25:59.264341 24451 solver.cpp:237]     Train net output #0: loss = 0.266547 (* 1 = 0.266547 loss)
I0410 03:25:59.264353 24451 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 03:26:03.259655 24451 solver.cpp:218] Iteration 8784 (3.00364 iter/s, 3.99515s/12 iters), loss = 0.291839
I0410 03:26:03.259696 24451 solver.cpp:237]     Train net output #0: loss = 0.291839 (* 1 = 0.291839 loss)
I0410 03:26:03.259707 24451 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 03:26:08.488112 24451 solver.cpp:218] Iteration 8796 (2.29525 iter/s, 5.2282s/12 iters), loss = 0.234163
I0410 03:26:08.488158 24451 solver.cpp:237]     Train net output #0: loss = 0.234163 (* 1 = 0.234163 loss)
I0410 03:26:08.488169 24451 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 03:26:09.960492 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:26:13.700083 24451 solver.cpp:218] Iteration 8808 (2.30251 iter/s, 5.2117s/12 iters), loss = 0.171891
I0410 03:26:13.700198 24451 solver.cpp:237]     Train net output #0: loss = 0.171891 (* 1 = 0.171891 loss)
I0410 03:26:13.700210 24451 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 03:26:18.708652 24451 solver.cpp:218] Iteration 8820 (2.39605 iter/s, 5.00825s/12 iters), loss = 0.307574
I0410 03:26:18.708695 24451 solver.cpp:237]     Train net output #0: loss = 0.307574 (* 1 = 0.307574 loss)
I0410 03:26:18.708705 24451 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 03:26:23.926671 24451 solver.cpp:218] Iteration 8832 (2.29984 iter/s, 5.21776s/12 iters), loss = 0.271926
I0410 03:26:23.926705 24451 solver.cpp:237]     Train net output #0: loss = 0.271926 (* 1 = 0.271926 loss)
I0410 03:26:23.926713 24451 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 03:26:29.075191 24451 solver.cpp:218] Iteration 8844 (2.33088 iter/s, 5.14827s/12 iters), loss = 0.235371
I0410 03:26:29.075237 24451 solver.cpp:237]     Train net output #0: loss = 0.235371 (* 1 = 0.235371 loss)
I0410 03:26:29.075248 24451 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 03:26:34.102075 24451 solver.cpp:218] Iteration 8856 (2.38729 iter/s, 5.02663s/12 iters), loss = 0.18307
I0410 03:26:34.102123 24451 solver.cpp:237]     Train net output #0: loss = 0.18307 (* 1 = 0.18307 loss)
I0410 03:26:34.102134 24451 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 03:26:39.270068 24451 solver.cpp:218] Iteration 8868 (2.3221 iter/s, 5.16773s/12 iters), loss = 0.204554
I0410 03:26:39.270112 24451 solver.cpp:237]     Train net output #0: loss = 0.204554 (* 1 = 0.204554 loss)
I0410 03:26:39.270121 24451 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 03:26:41.391435 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 03:26:56.052965 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 03:27:07.365237 24451 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 03:27:07.365259 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:27:08.372918 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:27:11.903267 24451 solver.cpp:397]     Test net output #0: accuracy = 0.520833
I0410 03:27:11.903314 24451 solver.cpp:397]     Test net output #1: loss = 2.84215 (* 1 = 2.84215 loss)
I0410 03:27:13.775565 24451 solver.cpp:218] Iteration 8880 (0.347785 iter/s, 34.5041s/12 iters), loss = 0.195731
I0410 03:27:13.775615 24451 solver.cpp:237]     Train net output #0: loss = 0.195731 (* 1 = 0.195731 loss)
I0410 03:27:13.775625 24451 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 03:27:18.589051 24451 solver.cpp:218] Iteration 8892 (2.49313 iter/s, 4.81323s/12 iters), loss = 0.243553
I0410 03:27:18.589093 24451 solver.cpp:237]     Train net output #0: loss = 0.243553 (* 1 = 0.243553 loss)
I0410 03:27:18.589103 24451 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 03:27:22.189653 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:27:23.713013 24451 solver.cpp:218] Iteration 8904 (2.34206 iter/s, 5.1237s/12 iters), loss = 0.152462
I0410 03:27:23.713063 24451 solver.cpp:237]     Train net output #0: loss = 0.152462 (* 1 = 0.152462 loss)
I0410 03:27:23.713074 24451 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 03:27:28.926707 24451 solver.cpp:218] Iteration 8916 (2.30175 iter/s, 5.21343s/12 iters), loss = 0.181477
I0410 03:27:28.926863 24451 solver.cpp:237]     Train net output #0: loss = 0.181477 (* 1 = 0.181477 loss)
I0410 03:27:28.926877 24451 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 03:27:33.842711 24451 solver.cpp:218] Iteration 8928 (2.44118 iter/s, 4.91565s/12 iters), loss = 0.187493
I0410 03:27:33.842753 24451 solver.cpp:237]     Train net output #0: loss = 0.187493 (* 1 = 0.187493 loss)
I0410 03:27:33.842764 24451 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 03:27:39.027916 24451 solver.cpp:218] Iteration 8940 (2.31439 iter/s, 5.18495s/12 iters), loss = 0.206909
I0410 03:27:39.027951 24451 solver.cpp:237]     Train net output #0: loss = 0.206909 (* 1 = 0.206909 loss)
I0410 03:27:39.027959 24451 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 03:27:44.254768 24451 solver.cpp:218] Iteration 8952 (2.29595 iter/s, 5.2266s/12 iters), loss = 0.203114
I0410 03:27:44.254806 24451 solver.cpp:237]     Train net output #0: loss = 0.203114 (* 1 = 0.203114 loss)
I0410 03:27:44.254814 24451 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 03:27:49.171833 24451 solver.cpp:218] Iteration 8964 (2.4406 iter/s, 4.91682s/12 iters), loss = 0.205814
I0410 03:27:49.171886 24451 solver.cpp:237]     Train net output #0: loss = 0.205814 (* 1 = 0.205814 loss)
I0410 03:27:49.171898 24451 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 03:27:53.492528 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 03:28:07.403342 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 03:28:21.328797 24451 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 03:28:21.328822 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:28:22.309473 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:28:25.876250 24451 solver.cpp:397]     Test net output #0: accuracy = 0.525123
I0410 03:28:25.876298 24451 solver.cpp:397]     Test net output #1: loss = 3.00559 (* 1 = 3.00559 loss)
I0410 03:28:26.008895 24451 solver.cpp:218] Iteration 8976 (0.325772 iter/s, 36.8356s/12 iters), loss = 0.260529
I0410 03:28:26.010416 24451 solver.cpp:237]     Train net output #0: loss = 0.260529 (* 1 = 0.260529 loss)
I0410 03:28:26.010429 24451 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 03:28:30.393203 24451 solver.cpp:218] Iteration 8988 (2.7381 iter/s, 4.3826s/12 iters), loss = 0.12034
I0410 03:28:30.393251 24451 solver.cpp:237]     Train net output #0: loss = 0.12034 (* 1 = 0.12034 loss)
I0410 03:28:30.393261 24451 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 03:28:34.684305 24451 blocking_queue.cpp:49] Waiting for data
I0410 03:28:35.621239 24451 solver.cpp:218] Iteration 9000 (2.29543 iter/s, 5.22777s/12 iters), loss = 0.299332
I0410 03:28:35.621282 24451 solver.cpp:237]     Train net output #0: loss = 0.299332 (* 1 = 0.299332 loss)
I0410 03:28:35.621292 24451 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 03:28:36.337863 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:28:40.668764 24451 solver.cpp:218] Iteration 9012 (2.37752 iter/s, 5.04727s/12 iters), loss = 0.305807
I0410 03:28:40.668891 24451 solver.cpp:237]     Train net output #0: loss = 0.305807 (* 1 = 0.305807 loss)
I0410 03:28:40.668901 24451 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 03:28:45.292958 24451 solver.cpp:218] Iteration 9024 (2.59523 iter/s, 4.62388s/12 iters), loss = 0.199592
I0410 03:28:45.293002 24451 solver.cpp:237]     Train net output #0: loss = 0.199592 (* 1 = 0.199592 loss)
I0410 03:28:45.293013 24451 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 03:28:50.240561 24451 solver.cpp:218] Iteration 9036 (2.42554 iter/s, 4.94735s/12 iters), loss = 0.231789
I0410 03:28:50.240605 24451 solver.cpp:237]     Train net output #0: loss = 0.231789 (* 1 = 0.231789 loss)
I0410 03:28:50.240615 24451 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 03:28:55.346808 24451 solver.cpp:218] Iteration 9048 (2.35018 iter/s, 5.10598s/12 iters), loss = 0.190642
I0410 03:28:55.346865 24451 solver.cpp:237]     Train net output #0: loss = 0.190642 (* 1 = 0.190642 loss)
I0410 03:28:55.346880 24451 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 03:29:00.553711 24451 solver.cpp:218] Iteration 9060 (2.30475 iter/s, 5.20663s/12 iters), loss = 0.151577
I0410 03:29:00.553764 24451 solver.cpp:237]     Train net output #0: loss = 0.151577 (* 1 = 0.151577 loss)
I0410 03:29:00.553776 24451 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 03:29:05.757860 24451 solver.cpp:218] Iteration 9072 (2.30597 iter/s, 5.20388s/12 iters), loss = 0.194502
I0410 03:29:05.757907 24451 solver.cpp:237]     Train net output #0: loss = 0.194502 (* 1 = 0.194502 loss)
I0410 03:29:05.757917 24451 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 03:29:07.653273 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 03:29:23.364225 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 03:29:37.913475 24451 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 03:29:37.913496 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:29:38.797662 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:29:42.408610 24451 solver.cpp:397]     Test net output #0: accuracy = 0.509804
I0410 03:29:42.408658 24451 solver.cpp:397]     Test net output #1: loss = 2.96003 (* 1 = 2.96003 loss)
I0410 03:29:44.296727 24451 solver.cpp:218] Iteration 9084 (0.311387 iter/s, 38.5373s/12 iters), loss = 0.277561
I0410 03:29:44.296777 24451 solver.cpp:237]     Train net output #0: loss = 0.277561 (* 1 = 0.277561 loss)
I0410 03:29:44.296787 24451 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 03:29:49.488325 24451 solver.cpp:218] Iteration 9096 (2.31155 iter/s, 5.19133s/12 iters), loss = 0.308101
I0410 03:29:49.488373 24451 solver.cpp:237]     Train net output #0: loss = 0.308101 (* 1 = 0.308101 loss)
I0410 03:29:49.488385 24451 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 03:29:52.475090 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:29:54.691624 24451 solver.cpp:218] Iteration 9108 (2.30635 iter/s, 5.20304s/12 iters), loss = 0.239566
I0410 03:29:54.691732 24451 solver.cpp:237]     Train net output #0: loss = 0.239566 (* 1 = 0.239566 loss)
I0410 03:29:54.691745 24451 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 03:29:59.914585 24451 solver.cpp:218] Iteration 9120 (2.29769 iter/s, 5.22264s/12 iters), loss = 0.226715
I0410 03:29:59.914634 24451 solver.cpp:237]     Train net output #0: loss = 0.226715 (* 1 = 0.226715 loss)
I0410 03:29:59.914645 24451 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 03:30:04.709772 24451 solver.cpp:218] Iteration 9132 (2.50264 iter/s, 4.79494s/12 iters), loss = 0.210759
I0410 03:30:04.709821 24451 solver.cpp:237]     Train net output #0: loss = 0.210759 (* 1 = 0.210759 loss)
I0410 03:30:04.709834 24451 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 03:30:09.832389 24451 solver.cpp:218] Iteration 9144 (2.34267 iter/s, 5.12235s/12 iters), loss = 0.167876
I0410 03:30:09.832439 24451 solver.cpp:237]     Train net output #0: loss = 0.167876 (* 1 = 0.167876 loss)
I0410 03:30:09.832449 24451 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 03:30:15.015730 24451 solver.cpp:218] Iteration 9156 (2.31523 iter/s, 5.18308s/12 iters), loss = 0.185249
I0410 03:30:15.015771 24451 solver.cpp:237]     Train net output #0: loss = 0.185249 (* 1 = 0.185249 loss)
I0410 03:30:15.015784 24451 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 03:30:20.235977 24451 solver.cpp:218] Iteration 9168 (2.29886 iter/s, 5.21999s/12 iters), loss = 0.226314
I0410 03:30:20.236023 24451 solver.cpp:237]     Train net output #0: loss = 0.226314 (* 1 = 0.226314 loss)
I0410 03:30:20.236035 24451 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 03:30:24.961199 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 03:30:41.229900 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 03:30:52.796375 24451 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 03:30:52.796396 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:30:53.685261 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:30:57.336333 24451 solver.cpp:397]     Test net output #0: accuracy = 0.509191
I0410 03:30:57.336449 24451 solver.cpp:397]     Test net output #1: loss = 3.09819 (* 1 = 3.09819 loss)
I0410 03:30:57.466496 24451 solver.cpp:218] Iteration 9180 (0.322329 iter/s, 37.229s/12 iters), loss = 0.145973
I0410 03:30:57.468020 24451 solver.cpp:237]     Train net output #0: loss = 0.145973 (* 1 = 0.145973 loss)
I0410 03:30:57.468032 24451 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 03:31:01.794128 24451 solver.cpp:218] Iteration 9192 (2.77397 iter/s, 4.32593s/12 iters), loss = 0.242784
I0410 03:31:01.794180 24451 solver.cpp:237]     Train net output #0: loss = 0.242784 (* 1 = 0.242784 loss)
I0410 03:31:01.794191 24451 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 03:31:06.816068 24451 solver.cpp:218] Iteration 9204 (2.38964 iter/s, 5.02168s/12 iters), loss = 0.214929
I0410 03:31:06.816109 24451 solver.cpp:237]     Train net output #0: loss = 0.214929 (* 1 = 0.214929 loss)
I0410 03:31:06.816118 24451 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 03:31:06.846768 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:31:11.959003 24451 solver.cpp:218] Iteration 9216 (2.33341 iter/s, 5.14268s/12 iters), loss = 0.174355
I0410 03:31:11.959049 24451 solver.cpp:237]     Train net output #0: loss = 0.174355 (* 1 = 0.174355 loss)
I0410 03:31:11.959061 24451 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 03:31:17.057688 24451 solver.cpp:218] Iteration 9228 (2.35367 iter/s, 5.09843s/12 iters), loss = 0.186381
I0410 03:31:17.057737 24451 solver.cpp:237]     Train net output #0: loss = 0.186381 (* 1 = 0.186381 loss)
I0410 03:31:17.057749 24451 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 03:31:22.098668 24451 solver.cpp:218] Iteration 9240 (2.38061 iter/s, 5.04072s/12 iters), loss = 0.150129
I0410 03:31:22.098706 24451 solver.cpp:237]     Train net output #0: loss = 0.150129 (* 1 = 0.150129 loss)
I0410 03:31:22.098716 24451 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 03:31:27.209748 24451 solver.cpp:218] Iteration 9252 (2.34796 iter/s, 5.11083s/12 iters), loss = 0.247704
I0410 03:31:27.209795 24451 solver.cpp:237]     Train net output #0: loss = 0.247704 (* 1 = 0.247704 loss)
I0410 03:31:27.209806 24451 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 03:31:32.441563 24451 solver.cpp:218] Iteration 9264 (2.29377 iter/s, 5.23155s/12 iters), loss = 0.221
I0410 03:31:32.441664 24451 solver.cpp:237]     Train net output #0: loss = 0.221 (* 1 = 0.221 loss)
I0410 03:31:32.441675 24451 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 03:31:37.616451 24451 solver.cpp:218] Iteration 9276 (2.31903 iter/s, 5.17457s/12 iters), loss = 0.214247
I0410 03:31:37.616497 24451 solver.cpp:237]     Train net output #0: loss = 0.214247 (* 1 = 0.214247 loss)
I0410 03:31:37.616506 24451 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 03:31:39.692780 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 03:31:54.786660 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 03:32:05.988489 24451 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 03:32:05.988590 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:32:06.821925 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:32:10.521879 24451 solver.cpp:397]     Test net output #0: accuracy = 0.514093
I0410 03:32:10.521929 24451 solver.cpp:397]     Test net output #1: loss = 3.10746 (* 1 = 3.10746 loss)
I0410 03:32:12.446700 24451 solver.cpp:218] Iteration 9288 (0.344542 iter/s, 34.8288s/12 iters), loss = 0.204806
I0410 03:32:12.446745 24451 solver.cpp:237]     Train net output #0: loss = 0.204806 (* 1 = 0.204806 loss)
I0410 03:32:12.446756 24451 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 03:32:17.607432 24451 solver.cpp:218] Iteration 9300 (2.32537 iter/s, 5.16047s/12 iters), loss = 0.229752
I0410 03:32:17.607487 24451 solver.cpp:237]     Train net output #0: loss = 0.229752 (* 1 = 0.229752 loss)
I0410 03:32:17.607498 24451 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 03:32:19.813879 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:32:22.726987 24451 solver.cpp:218] Iteration 9312 (2.34408 iter/s, 5.11929s/12 iters), loss = 0.160812
I0410 03:32:22.727026 24451 solver.cpp:237]     Train net output #0: loss = 0.160812 (* 1 = 0.160812 loss)
I0410 03:32:22.727035 24451 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 03:32:27.955683 24451 solver.cpp:218] Iteration 9324 (2.29514 iter/s, 5.22844s/12 iters), loss = 0.162806
I0410 03:32:27.955729 24451 solver.cpp:237]     Train net output #0: loss = 0.162806 (* 1 = 0.162806 loss)
I0410 03:32:27.955740 24451 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 03:32:33.154423 24451 solver.cpp:218] Iteration 9336 (2.30837 iter/s, 5.19847s/12 iters), loss = 0.179687
I0410 03:32:33.154484 24451 solver.cpp:237]     Train net output #0: loss = 0.179687 (* 1 = 0.179687 loss)
I0410 03:32:33.154495 24451 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 03:32:37.890213 24451 solver.cpp:218] Iteration 9348 (2.53403 iter/s, 4.73554s/12 iters), loss = 0.193618
I0410 03:32:37.890303 24451 solver.cpp:237]     Train net output #0: loss = 0.193618 (* 1 = 0.193618 loss)
I0410 03:32:37.890313 24451 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 03:32:43.062650 24451 solver.cpp:218] Iteration 9360 (2.32013 iter/s, 5.17213s/12 iters), loss = 0.152122
I0410 03:32:43.062698 24451 solver.cpp:237]     Train net output #0: loss = 0.152122 (* 1 = 0.152122 loss)
I0410 03:32:43.062709 24451 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 03:32:48.295859 24451 solver.cpp:218] Iteration 9372 (2.29316 iter/s, 5.23295s/12 iters), loss = 0.169329
I0410 03:32:48.295902 24451 solver.cpp:237]     Train net output #0: loss = 0.169329 (* 1 = 0.169329 loss)
I0410 03:32:48.295912 24451 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 03:32:53.000669 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 03:33:07.519716 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 03:33:18.864892 24451 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 03:33:18.864964 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:33:19.567137 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:33:23.292006 24451 solver.cpp:397]     Test net output #0: accuracy = 0.525123
I0410 03:33:23.292057 24451 solver.cpp:397]     Test net output #1: loss = 3.02794 (* 1 = 3.02794 loss)
I0410 03:33:23.426748 24451 solver.cpp:218] Iteration 9384 (0.341594 iter/s, 35.1295s/12 iters), loss = 0.176775
I0410 03:33:23.428318 24451 solver.cpp:237]     Train net output #0: loss = 0.176775 (* 1 = 0.176775 loss)
I0410 03:33:23.428331 24451 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 03:33:27.781220 24451 solver.cpp:218] Iteration 9396 (2.75689 iter/s, 4.35272s/12 iters), loss = 0.232954
I0410 03:33:27.781270 24451 solver.cpp:237]     Train net output #0: loss = 0.232954 (* 1 = 0.232954 loss)
I0410 03:33:27.781280 24451 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 03:33:32.257385 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:33:32.981950 24451 solver.cpp:218] Iteration 9408 (2.30749 iter/s, 5.20046s/12 iters), loss = 0.244168
I0410 03:33:32.982003 24451 solver.cpp:237]     Train net output #0: loss = 0.244168 (* 1 = 0.244168 loss)
I0410 03:33:32.982015 24451 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 03:33:38.193449 24451 solver.cpp:218] Iteration 9420 (2.30272 iter/s, 5.21122s/12 iters), loss = 0.181055
I0410 03:33:38.193493 24451 solver.cpp:237]     Train net output #0: loss = 0.181055 (* 1 = 0.181055 loss)
I0410 03:33:38.193502 24451 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 03:33:43.408622 24451 solver.cpp:218] Iteration 9432 (2.30109 iter/s, 5.21491s/12 iters), loss = 0.131011
I0410 03:33:43.408668 24451 solver.cpp:237]     Train net output #0: loss = 0.131011 (* 1 = 0.131011 loss)
I0410 03:33:43.408679 24451 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 03:33:48.588479 24451 solver.cpp:218] Iteration 9444 (2.31678 iter/s, 5.1796s/12 iters), loss = 0.147817
I0410 03:33:48.588521 24451 solver.cpp:237]     Train net output #0: loss = 0.147817 (* 1 = 0.147817 loss)
I0410 03:33:48.588531 24451 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 03:33:53.350208 24451 solver.cpp:218] Iteration 9456 (2.52022 iter/s, 4.76149s/12 iters), loss = 0.126698
I0410 03:33:53.351538 24451 solver.cpp:237]     Train net output #0: loss = 0.126698 (* 1 = 0.126698 loss)
I0410 03:33:53.351549 24451 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 03:33:58.550428 24451 solver.cpp:218] Iteration 9468 (2.30828 iter/s, 5.19867s/12 iters), loss = 0.151999
I0410 03:33:58.550479 24451 solver.cpp:237]     Train net output #0: loss = 0.151999 (* 1 = 0.151999 loss)
I0410 03:33:58.550490 24451 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 03:34:03.730434 24451 solver.cpp:218] Iteration 9480 (2.31672 iter/s, 5.17974s/12 iters), loss = 0.256138
I0410 03:34:03.730490 24451 solver.cpp:237]     Train net output #0: loss = 0.256138 (* 1 = 0.256138 loss)
I0410 03:34:03.730501 24451 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 03:34:05.808259 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 03:34:19.730295 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 03:34:35.705291 24451 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 03:34:35.705359 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:34:36.451676 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:34:40.233544 24451 solver.cpp:397]     Test net output #0: accuracy = 0.521446
I0410 03:34:40.233592 24451 solver.cpp:397]     Test net output #1: loss = 2.97338 (* 1 = 2.97338 loss)
I0410 03:34:42.147825 24451 solver.cpp:218] Iteration 9492 (0.312371 iter/s, 38.4158s/12 iters), loss = 0.151403
I0410 03:34:42.147871 24451 solver.cpp:237]     Train net output #0: loss = 0.151403 (* 1 = 0.151403 loss)
I0410 03:34:42.147881 24451 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 03:34:47.318689 24451 solver.cpp:218] Iteration 9504 (2.32081 iter/s, 5.17061s/12 iters), loss = 0.211794
I0410 03:34:47.318727 24451 solver.cpp:237]     Train net output #0: loss = 0.211794 (* 1 = 0.211794 loss)
I0410 03:34:47.318737 24451 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 03:34:48.824443 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:34:52.540141 24451 solver.cpp:218] Iteration 9516 (2.29833 iter/s, 5.22119s/12 iters), loss = 0.211162
I0410 03:34:52.540194 24451 solver.cpp:237]     Train net output #0: loss = 0.211162 (* 1 = 0.211162 loss)
I0410 03:34:52.540206 24451 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 03:34:57.765735 24451 solver.cpp:218] Iteration 9528 (2.29651 iter/s, 5.22532s/12 iters), loss = 0.184212
I0410 03:34:57.765785 24451 solver.cpp:237]     Train net output #0: loss = 0.184212 (* 1 = 0.184212 loss)
I0410 03:34:57.765797 24451 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 03:35:02.894691 24451 solver.cpp:218] Iteration 9540 (2.33978 iter/s, 5.12869s/12 iters), loss = 0.12988
I0410 03:35:02.894733 24451 solver.cpp:237]     Train net output #0: loss = 0.12988 (* 1 = 0.12988 loss)
I0410 03:35:02.894743 24451 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 03:35:08.033078 24451 solver.cpp:218] Iteration 9552 (2.33548 iter/s, 5.13812s/12 iters), loss = 0.156202
I0410 03:35:08.033246 24451 solver.cpp:237]     Train net output #0: loss = 0.156202 (* 1 = 0.156202 loss)
I0410 03:35:08.033260 24451 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 03:35:13.170747 24451 solver.cpp:218] Iteration 9564 (2.33586 iter/s, 5.13729s/12 iters), loss = 0.122109
I0410 03:35:13.170785 24451 solver.cpp:237]     Train net output #0: loss = 0.122109 (* 1 = 0.122109 loss)
I0410 03:35:13.170795 24451 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 03:35:18.153703 24451 solver.cpp:218] Iteration 9576 (2.40833 iter/s, 4.9827s/12 iters), loss = 0.222318
I0410 03:35:18.153750 24451 solver.cpp:237]     Train net output #0: loss = 0.222318 (* 1 = 0.222318 loss)
I0410 03:35:18.153762 24451 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 03:35:22.624197 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 03:35:41.025924 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 03:35:56.021139 24451 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 03:35:56.021163 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:35:56.731777 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:36:00.550428 24451 solver.cpp:397]     Test net output #0: accuracy = 0.515319
I0410 03:36:00.550474 24451 solver.cpp:397]     Test net output #1: loss = 2.94399 (* 1 = 2.94399 loss)
I0410 03:36:00.685133 24451 solver.cpp:218] Iteration 9588 (0.282156 iter/s, 42.5297s/12 iters), loss = 0.178387
I0410 03:36:00.686655 24451 solver.cpp:237]     Train net output #0: loss = 0.178387 (* 1 = 0.178387 loss)
I0410 03:36:00.686667 24451 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 03:36:05.082549 24451 solver.cpp:218] Iteration 9600 (2.72994 iter/s, 4.39571s/12 iters), loss = 0.186679
I0410 03:36:05.082598 24451 solver.cpp:237]     Train net output #0: loss = 0.186679 (* 1 = 0.186679 loss)
I0410 03:36:05.082612 24451 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 03:36:08.750259 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:36:10.253402 24451 solver.cpp:218] Iteration 9612 (2.32082 iter/s, 5.17058s/12 iters), loss = 0.15022
I0410 03:36:10.253451 24451 solver.cpp:237]     Train net output #0: loss = 0.15022 (* 1 = 0.15022 loss)
I0410 03:36:10.253463 24451 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 03:36:15.137977 24451 solver.cpp:218] Iteration 9624 (2.45685 iter/s, 4.88431s/12 iters), loss = 0.134314
I0410 03:36:15.138088 24451 solver.cpp:237]     Train net output #0: loss = 0.134314 (* 1 = 0.134314 loss)
I0410 03:36:15.138103 24451 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 03:36:20.311086 24451 solver.cpp:218] Iteration 9636 (2.31983 iter/s, 5.17278s/12 iters), loss = 0.16933
I0410 03:36:20.311129 24451 solver.cpp:237]     Train net output #0: loss = 0.16933 (* 1 = 0.16933 loss)
I0410 03:36:20.311139 24451 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 03:36:25.173331 24451 solver.cpp:218] Iteration 9648 (2.46813 iter/s, 4.86199s/12 iters), loss = 0.194283
I0410 03:36:25.173386 24451 solver.cpp:237]     Train net output #0: loss = 0.194283 (* 1 = 0.194283 loss)
I0410 03:36:25.173401 24451 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 03:36:30.327937 24451 solver.cpp:218] Iteration 9660 (2.32814 iter/s, 5.15434s/12 iters), loss = 0.107669
I0410 03:36:30.327986 24451 solver.cpp:237]     Train net output #0: loss = 0.107669 (* 1 = 0.107669 loss)
I0410 03:36:30.327996 24451 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 03:36:35.211277 24451 solver.cpp:218] Iteration 9672 (2.45746 iter/s, 4.88309s/12 iters), loss = 0.230712
I0410 03:36:35.211321 24451 solver.cpp:237]     Train net output #0: loss = 0.230712 (* 1 = 0.230712 loss)
I0410 03:36:35.211333 24451 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 03:36:40.381271 24451 solver.cpp:218] Iteration 9684 (2.3212 iter/s, 5.16973s/12 iters), loss = 0.0995588
I0410 03:36:40.381319 24451 solver.cpp:237]     Train net output #0: loss = 0.0995588 (* 1 = 0.0995588 loss)
I0410 03:36:40.381331 24451 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 03:36:42.512035 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 03:36:59.742081 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 03:37:11.085661 24451 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 03:37:11.085688 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:37:11.745980 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:37:14.798974 24451 blocking_queue.cpp:49] Waiting for data
I0410 03:37:15.615128 24451 solver.cpp:397]     Test net output #0: accuracy = 0.525735
I0410 03:37:15.615173 24451 solver.cpp:397]     Test net output #1: loss = 2.93642 (* 1 = 2.93642 loss)
I0410 03:37:17.518222 24451 solver.cpp:218] Iteration 9696 (0.323142 iter/s, 37.1354s/12 iters), loss = 0.131421
I0410 03:37:17.518267 24451 solver.cpp:237]     Train net output #0: loss = 0.131421 (* 1 = 0.131421 loss)
I0410 03:37:17.518278 24451 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 03:37:22.738656 24451 solver.cpp:218] Iteration 9708 (2.29878 iter/s, 5.22017s/12 iters), loss = 0.239824
I0410 03:37:22.738703 24451 solver.cpp:237]     Train net output #0: loss = 0.239824 (* 1 = 0.239824 loss)
I0410 03:37:22.738714 24451 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 03:37:23.471576 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:37:27.939755 24451 solver.cpp:218] Iteration 9720 (2.30732 iter/s, 5.20084s/12 iters), loss = 0.21096
I0410 03:37:27.939803 24451 solver.cpp:237]     Train net output #0: loss = 0.21096 (* 1 = 0.21096 loss)
I0410 03:37:27.939815 24451 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 03:37:33.130121 24451 solver.cpp:218] Iteration 9732 (2.31209 iter/s, 5.1901s/12 iters), loss = 0.16702
I0410 03:37:33.130250 24451 solver.cpp:237]     Train net output #0: loss = 0.16702 (* 1 = 0.16702 loss)
I0410 03:37:33.130262 24451 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 03:37:38.344096 24451 solver.cpp:218] Iteration 9744 (2.30166 iter/s, 5.21363s/12 iters), loss = 0.250521
I0410 03:37:38.344146 24451 solver.cpp:237]     Train net output #0: loss = 0.250521 (* 1 = 0.250521 loss)
I0410 03:37:38.344157 24451 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 03:37:43.544621 24451 solver.cpp:218] Iteration 9756 (2.30758 iter/s, 5.20026s/12 iters), loss = 0.192038
I0410 03:37:43.544665 24451 solver.cpp:237]     Train net output #0: loss = 0.192038 (* 1 = 0.192038 loss)
I0410 03:37:43.544677 24451 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 03:37:48.757371 24451 solver.cpp:218] Iteration 9768 (2.30217 iter/s, 5.21248s/12 iters), loss = 0.12792
I0410 03:37:48.757421 24451 solver.cpp:237]     Train net output #0: loss = 0.12792 (* 1 = 0.12792 loss)
I0410 03:37:48.757432 24451 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 03:37:53.938607 24451 solver.cpp:218] Iteration 9780 (2.31617 iter/s, 5.18097s/12 iters), loss = 0.249361
I0410 03:37:53.938657 24451 solver.cpp:237]     Train net output #0: loss = 0.249361 (* 1 = 0.249361 loss)
I0410 03:37:53.938669 24451 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 03:37:58.650599 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 03:38:12.817247 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 03:38:24.142527 24451 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 03:38:24.142552 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:38:24.764247 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:38:28.675189 24451 solver.cpp:397]     Test net output #0: accuracy = 0.518382
I0410 03:38:28.675233 24451 solver.cpp:397]     Test net output #1: loss = 3.00705 (* 1 = 3.00705 loss)
I0410 03:38:28.809621 24451 solver.cpp:218] Iteration 9792 (0.34414 iter/s, 34.8696s/12 iters), loss = 0.185021
I0410 03:38:28.811151 24451 solver.cpp:237]     Train net output #0: loss = 0.18502 (* 1 = 0.18502 loss)
I0410 03:38:28.811161 24451 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 03:38:33.193588 24451 solver.cpp:218] Iteration 9804 (2.73832 iter/s, 4.38225s/12 iters), loss = 0.208407
I0410 03:38:33.193634 24451 solver.cpp:237]     Train net output #0: loss = 0.208407 (* 1 = 0.208407 loss)
I0410 03:38:33.193646 24451 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 03:38:36.209018 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:38:38.392066 24451 solver.cpp:218] Iteration 9816 (2.30849 iter/s, 5.19821s/12 iters), loss = 0.21236
I0410 03:38:38.392114 24451 solver.cpp:237]     Train net output #0: loss = 0.21236 (* 1 = 0.21236 loss)
I0410 03:38:38.392127 24451 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 03:38:43.575868 24451 solver.cpp:218] Iteration 9828 (2.31502 iter/s, 5.18353s/12 iters), loss = 0.0675929
I0410 03:38:43.575999 24451 solver.cpp:237]     Train net output #0: loss = 0.0675929 (* 1 = 0.0675929 loss)
I0410 03:38:43.576014 24451 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 03:38:48.776235 24451 solver.cpp:218] Iteration 9840 (2.30769 iter/s, 5.20001s/12 iters), loss = 0.209407
I0410 03:38:48.776288 24451 solver.cpp:237]     Train net output #0: loss = 0.209407 (* 1 = 0.209407 loss)
I0410 03:38:48.776298 24451 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 03:38:53.822542 24451 solver.cpp:218] Iteration 9852 (2.3781 iter/s, 5.04604s/12 iters), loss = 0.372441
I0410 03:38:53.822592 24451 solver.cpp:237]     Train net output #0: loss = 0.372441 (* 1 = 0.372441 loss)
I0410 03:38:53.822605 24451 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 03:38:59.031517 24451 solver.cpp:218] Iteration 9864 (2.30384 iter/s, 5.2087s/12 iters), loss = 0.128317
I0410 03:38:59.031570 24451 solver.cpp:237]     Train net output #0: loss = 0.128317 (* 1 = 0.128317 loss)
I0410 03:38:59.031584 24451 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 03:39:04.125250 24451 solver.cpp:218] Iteration 9876 (2.35596 iter/s, 5.09347s/12 iters), loss = 0.14841
I0410 03:39:04.125295 24451 solver.cpp:237]     Train net output #0: loss = 0.14841 (* 1 = 0.14841 loss)
I0410 03:39:04.125306 24451 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 03:39:09.274976 24451 solver.cpp:218] Iteration 9888 (2.33034 iter/s, 5.14946s/12 iters), loss = 0.198272
I0410 03:39:09.275023 24451 solver.cpp:237]     Train net output #0: loss = 0.198272 (* 1 = 0.198272 loss)
I0410 03:39:09.275034 24451 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 03:39:11.386343 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 03:39:25.471792 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 03:39:38.874918 24451 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 03:39:38.874948 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:39:39.431414 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:39:43.376190 24451 solver.cpp:397]     Test net output #0: accuracy = 0.522672
I0410 03:39:43.376236 24451 solver.cpp:397]     Test net output #1: loss = 3.1005 (* 1 = 3.1005 loss)
I0410 03:39:45.220621 24451 solver.cpp:218] Iteration 9900 (0.333851 iter/s, 35.9442s/12 iters), loss = 0.247895
I0410 03:39:45.220669 24451 solver.cpp:237]     Train net output #0: loss = 0.247895 (* 1 = 0.247895 loss)
I0410 03:39:45.220679 24451 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 03:39:50.323143 24451 solver.cpp:218] Iteration 9912 (2.3519 iter/s, 5.10226s/12 iters), loss = 0.194263
I0410 03:39:50.323192 24451 solver.cpp:237]     Train net output #0: loss = 0.194263 (* 1 = 0.194263 loss)
I0410 03:39:50.323204 24451 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 03:39:50.381340 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:39:55.414824 24451 solver.cpp:218] Iteration 9924 (2.35691 iter/s, 5.09142s/12 iters), loss = 0.131014
I0410 03:39:55.414862 24451 solver.cpp:237]     Train net output #0: loss = 0.131014 (* 1 = 0.131014 loss)
I0410 03:39:55.414871 24451 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 03:40:00.138093 24451 solver.cpp:218] Iteration 9936 (2.54074 iter/s, 4.72303s/12 iters), loss = 0.137741
I0410 03:40:00.138242 24451 solver.cpp:237]     Train net output #0: loss = 0.137741 (* 1 = 0.137741 loss)
I0410 03:40:00.138254 24451 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 03:40:05.202482 24451 solver.cpp:218] Iteration 9948 (2.36966 iter/s, 5.06403s/12 iters), loss = 0.175381
I0410 03:40:05.202529 24451 solver.cpp:237]     Train net output #0: loss = 0.17538 (* 1 = 0.17538 loss)
I0410 03:40:05.202541 24451 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 03:40:10.424767 24451 solver.cpp:218] Iteration 9960 (2.29796 iter/s, 5.22201s/12 iters), loss = 0.139877
I0410 03:40:10.424816 24451 solver.cpp:237]     Train net output #0: loss = 0.139877 (* 1 = 0.139877 loss)
I0410 03:40:10.424829 24451 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 03:40:15.555433 24451 solver.cpp:218] Iteration 9972 (2.339 iter/s, 5.1304s/12 iters), loss = 0.135517
I0410 03:40:15.555482 24451 solver.cpp:237]     Train net output #0: loss = 0.135517 (* 1 = 0.135517 loss)
I0410 03:40:15.555493 24451 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 03:40:20.754411 24451 solver.cpp:218] Iteration 9984 (2.30827 iter/s, 5.19871s/12 iters), loss = 0.156113
I0410 03:40:20.754459 24451 solver.cpp:237]     Train net output #0: loss = 0.156113 (* 1 = 0.156113 loss)
I0410 03:40:20.754472 24451 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 03:40:25.407716 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 03:40:39.866142 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 03:40:56.979327 24451 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 03:40:56.979351 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:40:57.503598 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:41:01.505084 24451 solver.cpp:397]     Test net output #0: accuracy = 0.523284
I0410 03:41:01.505131 24451 solver.cpp:397]     Test net output #1: loss = 2.96291 (* 1 = 2.96291 loss)
I0410 03:41:01.637383 24451 solver.cpp:218] Iteration 9996 (0.293533 iter/s, 40.8813s/12 iters), loss = 0.0812913
I0410 03:41:01.638911 24451 solver.cpp:237]     Train net output #0: loss = 0.0812913 (* 1 = 0.0812913 loss)
I0410 03:41:01.638928 24451 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 03:41:05.545688 24451 solver.cpp:218] Iteration 10008 (3.07171 iter/s, 3.90662s/12 iters), loss = 0.0791747
I0410 03:41:05.545734 24451 solver.cpp:237]     Train net output #0: loss = 0.0791746 (* 1 = 0.0791746 loss)
I0410 03:41:05.545745 24451 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 03:41:07.501781 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:41:10.042083 24451 solver.cpp:218] Iteration 10020 (2.66895 iter/s, 4.49616s/12 iters), loss = 0.135807
I0410 03:41:10.042223 24451 solver.cpp:237]     Train net output #0: loss = 0.135807 (* 1 = 0.135807 loss)
I0410 03:41:10.042237 24451 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 03:41:15.230399 24451 solver.cpp:218] Iteration 10032 (2.31305 iter/s, 5.18796s/12 iters), loss = 0.120771
I0410 03:41:15.230437 24451 solver.cpp:237]     Train net output #0: loss = 0.120771 (* 1 = 0.120771 loss)
I0410 03:41:15.230446 24451 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 03:41:20.448164 24451 solver.cpp:218] Iteration 10044 (2.29995 iter/s, 5.2175s/12 iters), loss = 0.14424
I0410 03:41:20.448211 24451 solver.cpp:237]     Train net output #0: loss = 0.14424 (* 1 = 0.14424 loss)
I0410 03:41:20.448223 24451 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 03:41:25.657678 24451 solver.cpp:218] Iteration 10056 (2.3036 iter/s, 5.20924s/12 iters), loss = 0.0462113
I0410 03:41:25.657732 24451 solver.cpp:237]     Train net output #0: loss = 0.0462112 (* 1 = 0.0462112 loss)
I0410 03:41:25.657743 24451 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 03:41:30.845259 24451 solver.cpp:218] Iteration 10068 (2.31334 iter/s, 5.18731s/12 iters), loss = 0.116494
I0410 03:41:30.845309 24451 solver.cpp:237]     Train net output #0: loss = 0.116494 (* 1 = 0.116494 loss)
I0410 03:41:30.845321 24451 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 03:41:35.982822 24451 solver.cpp:218] Iteration 10080 (2.33586 iter/s, 5.13729s/12 iters), loss = 0.171632
I0410 03:41:35.982872 24451 solver.cpp:237]     Train net output #0: loss = 0.171632 (* 1 = 0.171632 loss)
I0410 03:41:35.982884 24451 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 03:41:41.039486 24451 solver.cpp:218] Iteration 10092 (2.37323 iter/s, 5.0564s/12 iters), loss = 0.169249
I0410 03:41:41.039603 24451 solver.cpp:237]     Train net output #0: loss = 0.169248 (* 1 = 0.169248 loss)
I0410 03:41:41.039616 24451 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 03:41:42.969259 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 03:42:01.198299 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 03:42:12.452116 24451 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 03:42:12.452189 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:42:12.901818 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:42:16.916555 24451 solver.cpp:397]     Test net output #0: accuracy = 0.530637
I0410 03:42:16.916604 24451 solver.cpp:397]     Test net output #1: loss = 2.92815 (* 1 = 2.92815 loss)
I0410 03:42:18.817732 24451 solver.cpp:218] Iteration 10104 (0.317657 iter/s, 37.7766s/12 iters), loss = 0.117321
I0410 03:42:18.817783 24451 solver.cpp:237]     Train net output #0: loss = 0.117321 (* 1 = 0.117321 loss)
I0410 03:42:18.817795 24451 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 03:42:23.304607 24462 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:42:24.007464 24451 solver.cpp:218] Iteration 10116 (2.31238 iter/s, 5.18946s/12 iters), loss = 0.0771413
I0410 03:42:24.007512 24451 solver.cpp:237]     Train net output #0: loss = 0.0771412 (* 1 = 0.0771412 loss)
I0410 03:42:24.007525 24451 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 03:42:28.894656 24451 solver.cpp:218] Iteration 10128 (2.45553 iter/s, 4.88693s/12 iters), loss = 0.117001
I0410 03:42:28.894703 24451 solver.cpp:237]     Train net output #0: loss = 0.117001 (* 1 = 0.117001 loss)
I0410 03:42:28.894714 24451 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 03:42:34.099109 24451 solver.cpp:218] Iteration 10140 (2.30584 iter/s, 5.20419s/12 iters), loss = 0.187631
I0410 03:42:34.099159 24451 solver.cpp:237]     Train net output #0: loss = 0.187631 (* 1 = 0.187631 loss)
I0410 03:42:34.099169 24451 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 03:42:39.264962 24451 solver.cpp:218] Iteration 10152 (2.32307 iter/s, 5.16558s/12 iters), loss = 0.132714
I0410 03:42:39.265004 24451 solver.cpp:237]     Train net output #0: loss = 0.132714 (* 1 = 0.132714 loss)
I0410 03:42:39.265015 24451 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 03:42:44.311054 24451 solver.cpp:218] Iteration 10164 (2.3782 iter/s, 5.04584s/12 iters), loss = 0.0918499
I0410 03:42:44.314711 24451 solver.cpp:237]     Train net output #0: loss = 0.0918498 (* 1 = 0.0918498 loss)
I0410 03:42:44.314723 24451 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 03:42:49.451128 24451 solver.cpp:218] Iteration 10176 (2.33636 iter/s, 5.1362s/12 iters), loss = 0.0907027
I0410 03:42:49.451179 24451 solver.cpp:237]     Train net output #0: loss = 0.0907026 (* 1 = 0.0907026 loss)
I0410 03:42:49.451189 24451 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 03:42:54.406643 24451 solver.cpp:218] Iteration 10188 (2.42167 iter/s, 4.95526s/12 iters), loss = 0.185908
I0410 03:42:54.406692 24451 solver.cpp:237]     Train net output #0: loss = 0.185908 (* 1 = 0.185908 loss)
I0410 03:42:54.406703 24451 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 03:42:58.796948 24451 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 03:43:12.884552 24451 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 03:43:24.297858 24451 solver.cpp:310] Iteration 10200, loss = 0.107263
I0410 03:43:24.297972 24451 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 03:43:24.297981 24451 net.cpp:676] Ignoring source layer train-data
I0410 03:43:24.737201 24463 data_layer.cpp:73] Restarting data prefetching from start.
I0410 03:43:28.801237 24451 solver.cpp:397]     Test net output #0: accuracy = 0.539216
I0410 03:43:28.801285 24451 solver.cpp:397]     Test net output #1: loss = 2.97383 (* 1 = 2.97383 loss)
I0410 03:43:28.801297 24451 solver.cpp:315] Optimization Done.
I0410 03:43:28.801306 24451 caffe.cpp:259] Optimization Done.