DIGITS-CNN/cars/architecture-investigations/fc/2-layers/2048/caffe_output.log

I0410 00:06:20.584287 14080 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-211133-e29b/solver.prototxt
I0410 00:06:20.584439 14080 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0410 00:06:20.584445 14080 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0410 00:06:20.584504 14080 caffe.cpp:218] Using GPUs 3
I0410 00:06:20.618716 14080 caffe.cpp:223] GPU 3: GeForce GTX 1080 Ti
I0410 00:06:20.894178 14080 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 00:06:20.894933 14080 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0410 00:06:20.895526 14080 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0410 00:06:20.895543 14080 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0410 00:06:20.895691 14080 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: 2048
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: 2048
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0410 00:06:20.895784 14080 layer_factory.hpp:77] Creating layer train-data
I0410 00:06:20.897408 14080 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0410 00:06:20.897615 14080 net.cpp:84] Creating Layer train-data
I0410 00:06:20.897626 14080 net.cpp:380] train-data -> data
I0410 00:06:20.897645 14080 net.cpp:380] train-data -> label
I0410 00:06:20.897657 14080 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 00:06:20.903841 14080 data_layer.cpp:45] output data size: 128,3,227,227
I0410 00:06:21.032671 14080 net.cpp:122] Setting up train-data
I0410 00:06:21.032696 14080 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0410 00:06:21.032701 14080 net.cpp:129] Top shape: 128 (128)
I0410 00:06:21.032706 14080 net.cpp:137] Memory required for data: 79149056
I0410 00:06:21.032716 14080 layer_factory.hpp:77] Creating layer conv1
I0410 00:06:21.032737 14080 net.cpp:84] Creating Layer conv1
I0410 00:06:21.032742 14080 net.cpp:406] conv1 <- data
I0410 00:06:21.032753 14080 net.cpp:380] conv1 -> conv1
I0410 00:06:21.600085 14080 net.cpp:122] Setting up conv1
I0410 00:06:21.600107 14080 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 00:06:21.600111 14080 net.cpp:137] Memory required for data: 227833856
I0410 00:06:21.600131 14080 layer_factory.hpp:77] Creating layer relu1
I0410 00:06:21.600142 14080 net.cpp:84] Creating Layer relu1
I0410 00:06:21.600145 14080 net.cpp:406] relu1 <- conv1
I0410 00:06:21.600152 14080 net.cpp:367] relu1 -> conv1 (in-place)
I0410 00:06:21.600504 14080 net.cpp:122] Setting up relu1
I0410 00:06:21.600513 14080 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 00:06:21.600517 14080 net.cpp:137] Memory required for data: 376518656
I0410 00:06:21.600520 14080 layer_factory.hpp:77] Creating layer norm1
I0410 00:06:21.600530 14080 net.cpp:84] Creating Layer norm1
I0410 00:06:21.600534 14080 net.cpp:406] norm1 <- conv1
I0410 00:06:21.600558 14080 net.cpp:380] norm1 -> norm1
I0410 00:06:21.601066 14080 net.cpp:122] Setting up norm1
I0410 00:06:21.601076 14080 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 00:06:21.601079 14080 net.cpp:137] Memory required for data: 525203456
I0410 00:06:21.601083 14080 layer_factory.hpp:77] Creating layer pool1
I0410 00:06:21.601092 14080 net.cpp:84] Creating Layer pool1
I0410 00:06:21.601096 14080 net.cpp:406] pool1 <- norm1
I0410 00:06:21.601104 14080 net.cpp:380] pool1 -> pool1
I0410 00:06:21.601140 14080 net.cpp:122] Setting up pool1
I0410 00:06:21.601147 14080 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0410 00:06:21.601150 14080 net.cpp:137] Memory required for data: 561035264
I0410 00:06:21.601155 14080 layer_factory.hpp:77] Creating layer conv2
I0410 00:06:21.601164 14080 net.cpp:84] Creating Layer conv2
I0410 00:06:21.601168 14080 net.cpp:406] conv2 <- pool1
I0410 00:06:21.601173 14080 net.cpp:380] conv2 -> conv2
I0410 00:06:21.608233 14080 net.cpp:122] Setting up conv2
I0410 00:06:21.608250 14080 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 00:06:21.608254 14080 net.cpp:137] Memory required for data: 656586752
I0410 00:06:21.608265 14080 layer_factory.hpp:77] Creating layer relu2
I0410 00:06:21.608274 14080 net.cpp:84] Creating Layer relu2
I0410 00:06:21.608278 14080 net.cpp:406] relu2 <- conv2
I0410 00:06:21.608284 14080 net.cpp:367] relu2 -> conv2 (in-place)
I0410 00:06:21.608772 14080 net.cpp:122] Setting up relu2
I0410 00:06:21.608783 14080 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 00:06:21.608785 14080 net.cpp:137] Memory required for data: 752138240
I0410 00:06:21.608789 14080 layer_factory.hpp:77] Creating layer norm2
I0410 00:06:21.608798 14080 net.cpp:84] Creating Layer norm2
I0410 00:06:21.608803 14080 net.cpp:406] norm2 <- conv2
I0410 00:06:21.608808 14080 net.cpp:380] norm2 -> norm2
I0410 00:06:21.609160 14080 net.cpp:122] Setting up norm2
I0410 00:06:21.609169 14080 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 00:06:21.609172 14080 net.cpp:137] Memory required for data: 847689728
I0410 00:06:21.609175 14080 layer_factory.hpp:77] Creating layer pool2
I0410 00:06:21.609184 14080 net.cpp:84] Creating Layer pool2
I0410 00:06:21.609189 14080 net.cpp:406] pool2 <- norm2
I0410 00:06:21.609194 14080 net.cpp:380] pool2 -> pool2
I0410 00:06:21.609221 14080 net.cpp:122] Setting up pool2
I0410 00:06:21.609226 14080 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 00:06:21.609230 14080 net.cpp:137] Memory required for data: 869840896
I0410 00:06:21.609232 14080 layer_factory.hpp:77] Creating layer conv3
I0410 00:06:21.609244 14080 net.cpp:84] Creating Layer conv3
I0410 00:06:21.609247 14080 net.cpp:406] conv3 <- pool2
I0410 00:06:21.609252 14080 net.cpp:380] conv3 -> conv3
I0410 00:06:21.626212 14080 net.cpp:122] Setting up conv3
I0410 00:06:21.626232 14080 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:06:21.626235 14080 net.cpp:137] Memory required for data: 903067648
I0410 00:06:21.626247 14080 layer_factory.hpp:77] Creating layer relu3
I0410 00:06:21.626258 14080 net.cpp:84] Creating Layer relu3
I0410 00:06:21.626263 14080 net.cpp:406] relu3 <- conv3
I0410 00:06:21.626271 14080 net.cpp:367] relu3 -> conv3 (in-place)
I0410 00:06:21.626763 14080 net.cpp:122] Setting up relu3
I0410 00:06:21.626773 14080 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:06:21.626776 14080 net.cpp:137] Memory required for data: 936294400
I0410 00:06:21.626780 14080 layer_factory.hpp:77] Creating layer conv4
I0410 00:06:21.626791 14080 net.cpp:84] Creating Layer conv4
I0410 00:06:21.626794 14080 net.cpp:406] conv4 <- conv3
I0410 00:06:21.626801 14080 net.cpp:380] conv4 -> conv4
I0410 00:06:21.639935 14080 net.cpp:122] Setting up conv4
I0410 00:06:21.639953 14080 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:06:21.639957 14080 net.cpp:137] Memory required for data: 969521152
I0410 00:06:21.639967 14080 layer_factory.hpp:77] Creating layer relu4
I0410 00:06:21.639976 14080 net.cpp:84] Creating Layer relu4
I0410 00:06:21.639997 14080 net.cpp:406] relu4 <- conv4
I0410 00:06:21.640003 14080 net.cpp:367] relu4 -> conv4 (in-place)
I0410 00:06:21.640345 14080 net.cpp:122] Setting up relu4
I0410 00:06:21.640353 14080 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:06:21.640357 14080 net.cpp:137] Memory required for data: 1002747904
I0410 00:06:21.640362 14080 layer_factory.hpp:77] Creating layer conv5
I0410 00:06:21.640372 14080 net.cpp:84] Creating Layer conv5
I0410 00:06:21.640375 14080 net.cpp:406] conv5 <- conv4
I0410 00:06:21.640383 14080 net.cpp:380] conv5 -> conv5
I0410 00:06:21.648993 14080 net.cpp:122] Setting up conv5
I0410 00:06:21.649011 14080 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 00:06:21.649015 14080 net.cpp:137] Memory required for data: 1024899072
I0410 00:06:21.649029 14080 layer_factory.hpp:77] Creating layer relu5
I0410 00:06:21.649037 14080 net.cpp:84] Creating Layer relu5
I0410 00:06:21.649041 14080 net.cpp:406] relu5 <- conv5
I0410 00:06:21.649047 14080 net.cpp:367] relu5 -> conv5 (in-place)
I0410 00:06:21.649530 14080 net.cpp:122] Setting up relu5
I0410 00:06:21.649539 14080 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 00:06:21.649542 14080 net.cpp:137] Memory required for data: 1047050240
I0410 00:06:21.649546 14080 layer_factory.hpp:77] Creating layer pool5
I0410 00:06:21.649554 14080 net.cpp:84] Creating Layer pool5
I0410 00:06:21.649557 14080 net.cpp:406] pool5 <- conv5
I0410 00:06:21.649564 14080 net.cpp:380] pool5 -> pool5
I0410 00:06:21.649600 14080 net.cpp:122] Setting up pool5
I0410 00:06:21.649606 14080 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0410 00:06:21.649610 14080 net.cpp:137] Memory required for data: 1051768832
I0410 00:06:21.649613 14080 layer_factory.hpp:77] Creating layer fc6
I0410 00:06:21.649623 14080 net.cpp:84] Creating Layer fc6
I0410 00:06:21.649626 14080 net.cpp:406] fc6 <- pool5
I0410 00:06:21.649632 14080 net.cpp:380] fc6 -> fc6
I0410 00:06:21.826719 14080 net.cpp:122] Setting up fc6
I0410 00:06:21.826740 14080 net.cpp:129] Top shape: 128 2048 (262144)
I0410 00:06:21.826745 14080 net.cpp:137] Memory required for data: 1052817408
I0410 00:06:21.826753 14080 layer_factory.hpp:77] Creating layer relu6
I0410 00:06:21.826763 14080 net.cpp:84] Creating Layer relu6
I0410 00:06:21.826767 14080 net.cpp:406] relu6 <- fc6
I0410 00:06:21.826773 14080 net.cpp:367] relu6 -> fc6 (in-place)
I0410 00:06:21.827397 14080 net.cpp:122] Setting up relu6
I0410 00:06:21.827409 14080 net.cpp:129] Top shape: 128 2048 (262144)
I0410 00:06:21.827414 14080 net.cpp:137] Memory required for data: 1053865984
I0410 00:06:21.827417 14080 layer_factory.hpp:77] Creating layer drop6
I0410 00:06:21.827425 14080 net.cpp:84] Creating Layer drop6
I0410 00:06:21.827430 14080 net.cpp:406] drop6 <- fc6
I0410 00:06:21.827433 14080 net.cpp:367] drop6 -> fc6 (in-place)
I0410 00:06:21.827462 14080 net.cpp:122] Setting up drop6
I0410 00:06:21.827467 14080 net.cpp:129] Top shape: 128 2048 (262144)
I0410 00:06:21.827472 14080 net.cpp:137] Memory required for data: 1054914560
I0410 00:06:21.827476 14080 layer_factory.hpp:77] Creating layer fc7
I0410 00:06:21.827484 14080 net.cpp:84] Creating Layer fc7
I0410 00:06:21.827488 14080 net.cpp:406] fc7 <- fc6
I0410 00:06:21.827493 14080 net.cpp:380] fc7 -> fc7
I0410 00:06:21.866945 14080 net.cpp:122] Setting up fc7
I0410 00:06:21.866966 14080 net.cpp:129] Top shape: 128 2048 (262144)
I0410 00:06:21.866969 14080 net.cpp:137] Memory required for data: 1055963136
I0410 00:06:21.866978 14080 layer_factory.hpp:77] Creating layer relu7
I0410 00:06:21.866988 14080 net.cpp:84] Creating Layer relu7
I0410 00:06:21.866993 14080 net.cpp:406] relu7 <- fc7
I0410 00:06:21.866999 14080 net.cpp:367] relu7 -> fc7 (in-place)
I0410 00:06:21.867803 14080 net.cpp:122] Setting up relu7
I0410 00:06:21.867812 14080 net.cpp:129] Top shape: 128 2048 (262144)
I0410 00:06:21.867816 14080 net.cpp:137] Memory required for data: 1057011712
I0410 00:06:21.867820 14080 layer_factory.hpp:77] Creating layer drop7
I0410 00:06:21.867826 14080 net.cpp:84] Creating Layer drop7
I0410 00:06:21.867849 14080 net.cpp:406] drop7 <- fc7
I0410 00:06:21.867856 14080 net.cpp:367] drop7 -> fc7 (in-place)
I0410 00:06:21.867880 14080 net.cpp:122] Setting up drop7
I0410 00:06:21.867887 14080 net.cpp:129] Top shape: 128 2048 (262144)
I0410 00:06:21.867889 14080 net.cpp:137] Memory required for data: 1058060288
I0410 00:06:21.867892 14080 layer_factory.hpp:77] Creating layer fc8
I0410 00:06:21.867902 14080 net.cpp:84] Creating Layer fc8
I0410 00:06:21.867905 14080 net.cpp:406] fc8 <- fc7
I0410 00:06:21.867910 14080 net.cpp:380] fc8 -> fc8
I0410 00:06:21.871971 14080 net.cpp:122] Setting up fc8
I0410 00:06:21.871981 14080 net.cpp:129] Top shape: 128 196 (25088)
I0410 00:06:21.871985 14080 net.cpp:137] Memory required for data: 1058160640
I0410 00:06:21.871992 14080 layer_factory.hpp:77] Creating layer loss
I0410 00:06:21.871999 14080 net.cpp:84] Creating Layer loss
I0410 00:06:21.872004 14080 net.cpp:406] loss <- fc8
I0410 00:06:21.872009 14080 net.cpp:406] loss <- label
I0410 00:06:21.872014 14080 net.cpp:380] loss -> loss
I0410 00:06:21.872023 14080 layer_factory.hpp:77] Creating layer loss
I0410 00:06:21.872635 14080 net.cpp:122] Setting up loss
I0410 00:06:21.872644 14080 net.cpp:129] Top shape: (1)
I0410 00:06:21.872648 14080 net.cpp:132]     with loss weight 1
I0410 00:06:21.872664 14080 net.cpp:137] Memory required for data: 1058160644
I0410 00:06:21.872668 14080 net.cpp:198] loss needs backward computation.
I0410 00:06:21.872675 14080 net.cpp:198] fc8 needs backward computation.
I0410 00:06:21.872679 14080 net.cpp:198] drop7 needs backward computation.
I0410 00:06:21.872682 14080 net.cpp:198] relu7 needs backward computation.
I0410 00:06:21.872685 14080 net.cpp:198] fc7 needs backward computation.
I0410 00:06:21.872689 14080 net.cpp:198] drop6 needs backward computation.
I0410 00:06:21.872692 14080 net.cpp:198] relu6 needs backward computation.
I0410 00:06:21.872695 14080 net.cpp:198] fc6 needs backward computation.
I0410 00:06:21.872699 14080 net.cpp:198] pool5 needs backward computation.
I0410 00:06:21.872702 14080 net.cpp:198] relu5 needs backward computation.
I0410 00:06:21.872706 14080 net.cpp:198] conv5 needs backward computation.
I0410 00:06:21.872709 14080 net.cpp:198] relu4 needs backward computation.
I0410 00:06:21.872712 14080 net.cpp:198] conv4 needs backward computation.
I0410 00:06:21.872716 14080 net.cpp:198] relu3 needs backward computation.
I0410 00:06:21.872720 14080 net.cpp:198] conv3 needs backward computation.
I0410 00:06:21.872725 14080 net.cpp:198] pool2 needs backward computation.
I0410 00:06:21.872727 14080 net.cpp:198] norm2 needs backward computation.
I0410 00:06:21.872730 14080 net.cpp:198] relu2 needs backward computation.
I0410 00:06:21.872735 14080 net.cpp:198] conv2 needs backward computation.
I0410 00:06:21.872738 14080 net.cpp:198] pool1 needs backward computation.
I0410 00:06:21.872741 14080 net.cpp:198] norm1 needs backward computation.
I0410 00:06:21.872745 14080 net.cpp:198] relu1 needs backward computation.
I0410 00:06:21.872748 14080 net.cpp:198] conv1 needs backward computation.
I0410 00:06:21.872752 14080 net.cpp:200] train-data does not need backward computation.
I0410 00:06:21.872756 14080 net.cpp:242] This network produces output loss
I0410 00:06:21.872771 14080 net.cpp:255] Network initialization done.
I0410 00:06:21.873278 14080 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0410 00:06:21.873308 14080 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0410 00:06:21.873446 14080 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: 2048
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: 2048
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0410 00:06:21.873545 14080 layer_factory.hpp:77] Creating layer val-data
I0410 00:06:21.876121 14080 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0410 00:06:21.876529 14080 net.cpp:84] Creating Layer val-data
I0410 00:06:21.876539 14080 net.cpp:380] val-data -> data
I0410 00:06:21.876549 14080 net.cpp:380] val-data -> label
I0410 00:06:21.876555 14080 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 00:06:21.880457 14080 data_layer.cpp:45] output data size: 32,3,227,227
I0410 00:06:21.923034 14080 net.cpp:122] Setting up val-data
I0410 00:06:21.923056 14080 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0410 00:06:21.923061 14080 net.cpp:129] Top shape: 32 (32)
I0410 00:06:21.923065 14080 net.cpp:137] Memory required for data: 19787264
I0410 00:06:21.923070 14080 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0410 00:06:21.923084 14080 net.cpp:84] Creating Layer label_val-data_1_split
I0410 00:06:21.923087 14080 net.cpp:406] label_val-data_1_split <- label
I0410 00:06:21.923094 14080 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0410 00:06:21.923103 14080 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0410 00:06:21.923156 14080 net.cpp:122] Setting up label_val-data_1_split
I0410 00:06:21.923162 14080 net.cpp:129] Top shape: 32 (32)
I0410 00:06:21.923166 14080 net.cpp:129] Top shape: 32 (32)
I0410 00:06:21.923169 14080 net.cpp:137] Memory required for data: 19787520
I0410 00:06:21.923173 14080 layer_factory.hpp:77] Creating layer conv1
I0410 00:06:21.923183 14080 net.cpp:84] Creating Layer conv1
I0410 00:06:21.923187 14080 net.cpp:406] conv1 <- data
I0410 00:06:21.923192 14080 net.cpp:380] conv1 -> conv1
I0410 00:06:21.928262 14080 net.cpp:122] Setting up conv1
I0410 00:06:21.928274 14080 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 00:06:21.928277 14080 net.cpp:137] Memory required for data: 56958720
I0410 00:06:21.928287 14080 layer_factory.hpp:77] Creating layer relu1
I0410 00:06:21.928295 14080 net.cpp:84] Creating Layer relu1
I0410 00:06:21.928299 14080 net.cpp:406] relu1 <- conv1
I0410 00:06:21.928304 14080 net.cpp:367] relu1 -> conv1 (in-place)
I0410 00:06:21.928594 14080 net.cpp:122] Setting up relu1
I0410 00:06:21.928603 14080 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 00:06:21.928606 14080 net.cpp:137] Memory required for data: 94129920
I0410 00:06:21.928611 14080 layer_factory.hpp:77] Creating layer norm1
I0410 00:06:21.928619 14080 net.cpp:84] Creating Layer norm1
I0410 00:06:21.928623 14080 net.cpp:406] norm1 <- conv1
I0410 00:06:21.928629 14080 net.cpp:380] norm1 -> norm1
I0410 00:06:21.929085 14080 net.cpp:122] Setting up norm1
I0410 00:06:21.929093 14080 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 00:06:21.929097 14080 net.cpp:137] Memory required for data: 131301120
I0410 00:06:21.929100 14080 layer_factory.hpp:77] Creating layer pool1
I0410 00:06:21.929107 14080 net.cpp:84] Creating Layer pool1
I0410 00:06:21.929111 14080 net.cpp:406] pool1 <- norm1
I0410 00:06:21.929116 14080 net.cpp:380] pool1 -> pool1
I0410 00:06:21.929144 14080 net.cpp:122] Setting up pool1
I0410 00:06:21.929149 14080 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0410 00:06:21.929152 14080 net.cpp:137] Memory required for data: 140259072
I0410 00:06:21.929155 14080 layer_factory.hpp:77] Creating layer conv2
I0410 00:06:21.929163 14080 net.cpp:84] Creating Layer conv2
I0410 00:06:21.929167 14080 net.cpp:406] conv2 <- pool1
I0410 00:06:21.929191 14080 net.cpp:380] conv2 -> conv2
I0410 00:06:21.942306 14080 net.cpp:122] Setting up conv2
I0410 00:06:21.942324 14080 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 00:06:21.942328 14080 net.cpp:137] Memory required for data: 164146944
I0410 00:06:21.942338 14080 layer_factory.hpp:77] Creating layer relu2
I0410 00:06:21.942348 14080 net.cpp:84] Creating Layer relu2
I0410 00:06:21.942351 14080 net.cpp:406] relu2 <- conv2
I0410 00:06:21.942358 14080 net.cpp:367] relu2 -> conv2 (in-place)
I0410 00:06:21.942857 14080 net.cpp:122] Setting up relu2
I0410 00:06:21.942868 14080 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 00:06:21.942871 14080 net.cpp:137] Memory required for data: 188034816
I0410 00:06:21.942875 14080 layer_factory.hpp:77] Creating layer norm2
I0410 00:06:21.942885 14080 net.cpp:84] Creating Layer norm2
I0410 00:06:21.942888 14080 net.cpp:406] norm2 <- conv2
I0410 00:06:21.942894 14080 net.cpp:380] norm2 -> norm2
I0410 00:06:21.943409 14080 net.cpp:122] Setting up norm2
I0410 00:06:21.943419 14080 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 00:06:21.943423 14080 net.cpp:137] Memory required for data: 211922688
I0410 00:06:21.943428 14080 layer_factory.hpp:77] Creating layer pool2
I0410 00:06:21.943435 14080 net.cpp:84] Creating Layer pool2
I0410 00:06:21.943439 14080 net.cpp:406] pool2 <- norm2
I0410 00:06:21.943444 14080 net.cpp:380] pool2 -> pool2
I0410 00:06:21.943475 14080 net.cpp:122] Setting up pool2
I0410 00:06:21.943480 14080 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 00:06:21.943485 14080 net.cpp:137] Memory required for data: 217460480
I0410 00:06:21.943488 14080 layer_factory.hpp:77] Creating layer conv3
I0410 00:06:21.943498 14080 net.cpp:84] Creating Layer conv3
I0410 00:06:21.943502 14080 net.cpp:406] conv3 <- pool2
I0410 00:06:21.943509 14080 net.cpp:380] conv3 -> conv3
I0410 00:06:21.960819 14080 net.cpp:122] Setting up conv3
I0410 00:06:21.960836 14080 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:06:21.960841 14080 net.cpp:137] Memory required for data: 225767168
I0410 00:06:21.960853 14080 layer_factory.hpp:77] Creating layer relu3
I0410 00:06:21.960861 14080 net.cpp:84] Creating Layer relu3
I0410 00:06:21.960865 14080 net.cpp:406] relu3 <- conv3
I0410 00:06:21.960875 14080 net.cpp:367] relu3 -> conv3 (in-place)
I0410 00:06:21.961382 14080 net.cpp:122] Setting up relu3
I0410 00:06:21.961395 14080 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:06:21.961398 14080 net.cpp:137] Memory required for data: 234073856
I0410 00:06:21.961402 14080 layer_factory.hpp:77] Creating layer conv4
I0410 00:06:21.961413 14080 net.cpp:84] Creating Layer conv4
I0410 00:06:21.961418 14080 net.cpp:406] conv4 <- conv3
I0410 00:06:21.961424 14080 net.cpp:380] conv4 -> conv4
I0410 00:06:21.982378 14080 net.cpp:122] Setting up conv4
I0410 00:06:21.982398 14080 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:06:21.982401 14080 net.cpp:137] Memory required for data: 242380544
I0410 00:06:21.982411 14080 layer_factory.hpp:77] Creating layer relu4
I0410 00:06:21.982420 14080 net.cpp:84] Creating Layer relu4
I0410 00:06:21.982425 14080 net.cpp:406] relu4 <- conv4
I0410 00:06:21.982434 14080 net.cpp:367] relu4 -> conv4 (in-place)
I0410 00:06:21.982780 14080 net.cpp:122] Setting up relu4
I0410 00:06:21.982789 14080 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:06:21.982795 14080 net.cpp:137] Memory required for data: 250687232
I0410 00:06:21.982797 14080 layer_factory.hpp:77] Creating layer conv5
I0410 00:06:21.982808 14080 net.cpp:84] Creating Layer conv5
I0410 00:06:21.982813 14080 net.cpp:406] conv5 <- conv4
I0410 00:06:21.982820 14080 net.cpp:380] conv5 -> conv5
I0410 00:06:21.991370 14080 net.cpp:122] Setting up conv5
I0410 00:06:21.991389 14080 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 00:06:21.991394 14080 net.cpp:137] Memory required for data: 256225024
I0410 00:06:21.991406 14080 layer_factory.hpp:77] Creating layer relu5
I0410 00:06:21.991415 14080 net.cpp:84] Creating Layer relu5
I0410 00:06:21.991420 14080 net.cpp:406] relu5 <- conv5
I0410 00:06:21.991444 14080 net.cpp:367] relu5 -> conv5 (in-place)
I0410 00:06:21.991935 14080 net.cpp:122] Setting up relu5
I0410 00:06:21.991945 14080 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 00:06:21.991948 14080 net.cpp:137] Memory required for data: 261762816
I0410 00:06:21.991952 14080 layer_factory.hpp:77] Creating layer pool5
I0410 00:06:21.991962 14080 net.cpp:84] Creating Layer pool5
I0410 00:06:21.991966 14080 net.cpp:406] pool5 <- conv5
I0410 00:06:21.991972 14080 net.cpp:380] pool5 -> pool5
I0410 00:06:21.992012 14080 net.cpp:122] Setting up pool5
I0410 00:06:21.992018 14080 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0410 00:06:21.992022 14080 net.cpp:137] Memory required for data: 262942464
I0410 00:06:21.992024 14080 layer_factory.hpp:77] Creating layer fc6
I0410 00:06:21.992031 14080 net.cpp:84] Creating Layer fc6
I0410 00:06:21.992034 14080 net.cpp:406] fc6 <- pool5
I0410 00:06:21.992040 14080 net.cpp:380] fc6 -> fc6
I0410 00:06:22.169602 14080 net.cpp:122] Setting up fc6
I0410 00:06:22.169623 14080 net.cpp:129] Top shape: 32 2048 (65536)
I0410 00:06:22.169628 14080 net.cpp:137] Memory required for data: 263204608
I0410 00:06:22.169637 14080 layer_factory.hpp:77] Creating layer relu6
I0410 00:06:22.169647 14080 net.cpp:84] Creating Layer relu6
I0410 00:06:22.169653 14080 net.cpp:406] relu6 <- fc6
I0410 00:06:22.169659 14080 net.cpp:367] relu6 -> fc6 (in-place)
I0410 00:06:22.170495 14080 net.cpp:122] Setting up relu6
I0410 00:06:22.170504 14080 net.cpp:129] Top shape: 32 2048 (65536)
I0410 00:06:22.170508 14080 net.cpp:137] Memory required for data: 263466752
I0410 00:06:22.170513 14080 layer_factory.hpp:77] Creating layer drop6
I0410 00:06:22.170521 14080 net.cpp:84] Creating Layer drop6
I0410 00:06:22.170523 14080 net.cpp:406] drop6 <- fc6
I0410 00:06:22.170531 14080 net.cpp:367] drop6 -> fc6 (in-place)
I0410 00:06:22.170555 14080 net.cpp:122] Setting up drop6
I0410 00:06:22.170562 14080 net.cpp:129] Top shape: 32 2048 (65536)
I0410 00:06:22.170565 14080 net.cpp:137] Memory required for data: 263728896
I0410 00:06:22.170569 14080 layer_factory.hpp:77] Creating layer fc7
I0410 00:06:22.170580 14080 net.cpp:84] Creating Layer fc7
I0410 00:06:22.170584 14080 net.cpp:406] fc7 <- fc6
I0410 00:06:22.170593 14080 net.cpp:380] fc7 -> fc7
I0410 00:06:22.212087 14080 net.cpp:122] Setting up fc7
I0410 00:06:22.212105 14080 net.cpp:129] Top shape: 32 2048 (65536)
I0410 00:06:22.212110 14080 net.cpp:137] Memory required for data: 263991040
I0410 00:06:22.212119 14080 layer_factory.hpp:77] Creating layer relu7
I0410 00:06:22.212128 14080 net.cpp:84] Creating Layer relu7
I0410 00:06:22.212132 14080 net.cpp:406] relu7 <- fc7
I0410 00:06:22.212139 14080 net.cpp:367] relu7 -> fc7 (in-place)
I0410 00:06:22.212563 14080 net.cpp:122] Setting up relu7
I0410 00:06:22.212570 14080 net.cpp:129] Top shape: 32 2048 (65536)
I0410 00:06:22.212574 14080 net.cpp:137] Memory required for data: 264253184
I0410 00:06:22.212577 14080 layer_factory.hpp:77] Creating layer drop7
I0410 00:06:22.212584 14080 net.cpp:84] Creating Layer drop7
I0410 00:06:22.212589 14080 net.cpp:406] drop7 <- fc7
I0410 00:06:22.212594 14080 net.cpp:367] drop7 -> fc7 (in-place)
I0410 00:06:22.212618 14080 net.cpp:122] Setting up drop7
I0410 00:06:22.212623 14080 net.cpp:129] Top shape: 32 2048 (65536)
I0410 00:06:22.212626 14080 net.cpp:137] Memory required for data: 264515328
I0410 00:06:22.212630 14080 layer_factory.hpp:77] Creating layer fc8
I0410 00:06:22.212638 14080 net.cpp:84] Creating Layer fc8
I0410 00:06:22.212642 14080 net.cpp:406] fc8 <- fc7
I0410 00:06:22.212647 14080 net.cpp:380] fc8 -> fc8
I0410 00:06:22.216800 14080 net.cpp:122] Setting up fc8
I0410 00:06:22.216811 14080 net.cpp:129] Top shape: 32 196 (6272)
I0410 00:06:22.216815 14080 net.cpp:137] Memory required for data: 264540416
I0410 00:06:22.216822 14080 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0410 00:06:22.216831 14080 net.cpp:84] Creating Layer fc8_fc8_0_split
I0410 00:06:22.216835 14080 net.cpp:406] fc8_fc8_0_split <- fc8
I0410 00:06:22.216858 14080 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0410 00:06:22.216866 14080 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0410 00:06:22.216902 14080 net.cpp:122] Setting up fc8_fc8_0_split
I0410 00:06:22.216908 14080 net.cpp:129] Top shape: 32 196 (6272)
I0410 00:06:22.216912 14080 net.cpp:129] Top shape: 32 196 (6272)
I0410 00:06:22.216914 14080 net.cpp:137] Memory required for data: 264590592
I0410 00:06:22.216918 14080 layer_factory.hpp:77] Creating layer accuracy
I0410 00:06:22.216925 14080 net.cpp:84] Creating Layer accuracy
I0410 00:06:22.216928 14080 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0410 00:06:22.216933 14080 net.cpp:406] accuracy <- label_val-data_1_split_0
I0410 00:06:22.216938 14080 net.cpp:380] accuracy -> accuracy
I0410 00:06:22.216944 14080 net.cpp:122] Setting up accuracy
I0410 00:06:22.216949 14080 net.cpp:129] Top shape: (1)
I0410 00:06:22.216953 14080 net.cpp:137] Memory required for data: 264590596
I0410 00:06:22.216956 14080 layer_factory.hpp:77] Creating layer loss
I0410 00:06:22.216962 14080 net.cpp:84] Creating Layer loss
I0410 00:06:22.216965 14080 net.cpp:406] loss <- fc8_fc8_0_split_1
I0410 00:06:22.216969 14080 net.cpp:406] loss <- label_val-data_1_split_1
I0410 00:06:22.216975 14080 net.cpp:380] loss -> loss
I0410 00:06:22.216982 14080 layer_factory.hpp:77] Creating layer loss
I0410 00:06:22.217599 14080 net.cpp:122] Setting up loss
I0410 00:06:22.217608 14080 net.cpp:129] Top shape: (1)
I0410 00:06:22.217612 14080 net.cpp:132]     with loss weight 1
I0410 00:06:22.217622 14080 net.cpp:137] Memory required for data: 264590600
I0410 00:06:22.217626 14080 net.cpp:198] loss needs backward computation.
I0410 00:06:22.217631 14080 net.cpp:200] accuracy does not need backward computation.
I0410 00:06:22.217636 14080 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0410 00:06:22.217640 14080 net.cpp:198] fc8 needs backward computation.
I0410 00:06:22.217643 14080 net.cpp:198] drop7 needs backward computation.
I0410 00:06:22.217648 14080 net.cpp:198] relu7 needs backward computation.
I0410 00:06:22.217650 14080 net.cpp:198] fc7 needs backward computation.
I0410 00:06:22.217654 14080 net.cpp:198] drop6 needs backward computation.
I0410 00:06:22.217658 14080 net.cpp:198] relu6 needs backward computation.
I0410 00:06:22.217661 14080 net.cpp:198] fc6 needs backward computation.
I0410 00:06:22.217665 14080 net.cpp:198] pool5 needs backward computation.
I0410 00:06:22.217669 14080 net.cpp:198] relu5 needs backward computation.
I0410 00:06:22.217672 14080 net.cpp:198] conv5 needs backward computation.
I0410 00:06:22.217676 14080 net.cpp:198] relu4 needs backward computation.
I0410 00:06:22.217679 14080 net.cpp:198] conv4 needs backward computation.
I0410 00:06:22.217684 14080 net.cpp:198] relu3 needs backward computation.
I0410 00:06:22.217687 14080 net.cpp:198] conv3 needs backward computation.
I0410 00:06:22.217690 14080 net.cpp:198] pool2 needs backward computation.
I0410 00:06:22.217694 14080 net.cpp:198] norm2 needs backward computation.
I0410 00:06:22.217698 14080 net.cpp:198] relu2 needs backward computation.
I0410 00:06:22.217701 14080 net.cpp:198] conv2 needs backward computation.
I0410 00:06:22.217705 14080 net.cpp:198] pool1 needs backward computation.
I0410 00:06:22.217708 14080 net.cpp:198] norm1 needs backward computation.
I0410 00:06:22.217712 14080 net.cpp:198] relu1 needs backward computation.
I0410 00:06:22.217716 14080 net.cpp:198] conv1 needs backward computation.
I0410 00:06:22.217720 14080 net.cpp:200] label_val-data_1_split does not need backward computation.
I0410 00:06:22.217725 14080 net.cpp:200] val-data does not need backward computation.
I0410 00:06:22.217727 14080 net.cpp:242] This network produces output accuracy
I0410 00:06:22.217730 14080 net.cpp:242] This network produces output loss
I0410 00:06:22.217746 14080 net.cpp:255] Network initialization done.
I0410 00:06:22.217816 14080 solver.cpp:56] Solver scaffolding done.
I0410 00:06:22.218271 14080 caffe.cpp:248] Starting Optimization
I0410 00:06:22.218279 14080 solver.cpp:272] Solving
I0410 00:06:22.218292 14080 solver.cpp:273] Learning Rate Policy: exp
I0410 00:06:22.219432 14080 solver.cpp:330] Iteration 0, Testing net (#0)
I0410 00:06:22.219442 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:06:22.270411 14080 blocking_queue.cpp:49] Waiting for data
I0410 00:06:26.695472 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:06:26.739893 14080 solver.cpp:397]     Test net output #0: accuracy = 0.0067402
I0410 00:06:26.739940 14080 solver.cpp:397]     Test net output #1: loss = 5.27995 (* 1 = 5.27995 loss)
I0410 00:06:26.839867 14080 solver.cpp:218] Iteration 0 (0 iter/s, 4.62134s/12 iters), loss = 5.28058
I0410 00:06:26.841461 14080 solver.cpp:237]     Train net output #0: loss = 5.28058 (* 1 = 5.28058 loss)
I0410 00:06:26.841482 14080 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0410 00:06:30.835690 14080 solver.cpp:218] Iteration 12 (3.00447 iter/s, 3.99404s/12 iters), loss = 5.27019
I0410 00:06:30.835747 14080 solver.cpp:237]     Train net output #0: loss = 5.27019 (* 1 = 5.27019 loss)
I0410 00:06:30.835758 14080 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0410 00:06:35.829478 14080 solver.cpp:218] Iteration 24 (2.40312 iter/s, 4.99352s/12 iters), loss = 5.28692
I0410 00:06:35.829528 14080 solver.cpp:237]     Train net output #0: loss = 5.28692 (* 1 = 5.28692 loss)
I0410 00:06:35.829540 14080 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0410 00:06:40.743723 14080 solver.cpp:218] Iteration 36 (2.44201 iter/s, 4.91398s/12 iters), loss = 5.28643
I0410 00:06:40.743772 14080 solver.cpp:237]     Train net output #0: loss = 5.28643 (* 1 = 5.28643 loss)
I0410 00:06:40.743782 14080 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0410 00:06:45.624789 14080 solver.cpp:218] Iteration 48 (2.45861 iter/s, 4.88081s/12 iters), loss = 5.29573
I0410 00:06:45.624838 14080 solver.cpp:237]     Train net output #0: loss = 5.29573 (* 1 = 5.29573 loss)
I0410 00:06:45.624850 14080 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0410 00:06:50.528260 14080 solver.cpp:218] Iteration 60 (2.44738 iter/s, 4.90321s/12 iters), loss = 5.2894
I0410 00:06:50.528304 14080 solver.cpp:237]     Train net output #0: loss = 5.2894 (* 1 = 5.2894 loss)
I0410 00:06:50.528313 14080 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0410 00:06:55.424527 14080 solver.cpp:218] Iteration 72 (2.45097 iter/s, 4.89601s/12 iters), loss = 5.28431
I0410 00:06:55.424631 14080 solver.cpp:237]     Train net output #0: loss = 5.28431 (* 1 = 5.28431 loss)
I0410 00:06:55.424644 14080 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0410 00:07:00.383697 14080 solver.cpp:218] Iteration 84 (2.41991 iter/s, 4.95885s/12 iters), loss = 5.28835
I0410 00:07:00.383747 14080 solver.cpp:237]     Train net output #0: loss = 5.28835 (* 1 = 5.28835 loss)
I0410 00:07:00.383760 14080 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0410 00:07:05.286757 14080 solver.cpp:218] Iteration 96 (2.44758 iter/s, 4.90279s/12 iters), loss = 5.30308
I0410 00:07:05.286811 14080 solver.cpp:237]     Train net output #0: loss = 5.30308 (* 1 = 5.30308 loss)
I0410 00:07:05.286823 14080 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0410 00:07:06.979595 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:07:07.286417 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0410 00:07:08.689055 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0410 00:07:09.729207 14080 solver.cpp:330] Iteration 102, Testing net (#0)
I0410 00:07:09.729234 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:07:14.078332 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:07:14.155066 14080 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 00:07:14.155110 14080 solver.cpp:397]     Test net output #1: loss = 5.28386 (* 1 = 5.28386 loss)
I0410 00:07:16.050732 14080 solver.cpp:218] Iteration 108 (1.11488 iter/s, 10.7635s/12 iters), loss = 5.28893
I0410 00:07:16.050779 14080 solver.cpp:237]     Train net output #0: loss = 5.28893 (* 1 = 5.28893 loss)
I0410 00:07:16.050788 14080 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0410 00:07:20.965400 14080 solver.cpp:218] Iteration 120 (2.4418 iter/s, 4.91441s/12 iters), loss = 5.27419
I0410 00:07:20.965445 14080 solver.cpp:237]     Train net output #0: loss = 5.27419 (* 1 = 5.27419 loss)
I0410 00:07:20.965453 14080 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0410 00:07:25.889838 14080 solver.cpp:218] Iteration 132 (2.43696 iter/s, 4.92417s/12 iters), loss = 5.23935
I0410 00:07:25.890007 14080 solver.cpp:237]     Train net output #0: loss = 5.23935 (* 1 = 5.23935 loss)
I0410 00:07:25.890022 14080 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0410 00:07:30.830863 14080 solver.cpp:218] Iteration 144 (2.42883 iter/s, 4.94064s/12 iters), loss = 5.299
I0410 00:07:30.830919 14080 solver.cpp:237]     Train net output #0: loss = 5.299 (* 1 = 5.299 loss)
I0410 00:07:30.830932 14080 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0410 00:07:35.800446 14080 solver.cpp:218] Iteration 156 (2.41482 iter/s, 4.96931s/12 iters), loss = 5.2568
I0410 00:07:35.800498 14080 solver.cpp:237]     Train net output #0: loss = 5.2568 (* 1 = 5.2568 loss)
I0410 00:07:35.800510 14080 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0410 00:07:40.696857 14080 solver.cpp:218] Iteration 168 (2.45091 iter/s, 4.89615s/12 iters), loss = 5.26192
I0410 00:07:40.696913 14080 solver.cpp:237]     Train net output #0: loss = 5.26192 (* 1 = 5.26192 loss)
I0410 00:07:40.696924 14080 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0410 00:07:45.587162 14080 solver.cpp:218] Iteration 180 (2.45397 iter/s, 4.89004s/12 iters), loss = 5.26689
I0410 00:07:45.587217 14080 solver.cpp:237]     Train net output #0: loss = 5.26689 (* 1 = 5.26689 loss)
I0410 00:07:45.587230 14080 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0410 00:07:50.731848 14080 solver.cpp:218] Iteration 192 (2.33263 iter/s, 5.14441s/12 iters), loss = 5.27702
I0410 00:07:50.731899 14080 solver.cpp:237]     Train net output #0: loss = 5.27702 (* 1 = 5.27702 loss)
I0410 00:07:50.731911 14080 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0410 00:07:54.542587 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:07:55.215302 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0410 00:07:57.592507 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0410 00:07:58.636288 14080 solver.cpp:330] Iteration 204, Testing net (#0)
I0410 00:07:58.636319 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:08:02.975337 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:08:03.097191 14080 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 00:08:03.097229 14080 solver.cpp:397]     Test net output #1: loss = 5.28379 (* 1 = 5.28379 loss)
I0410 00:08:03.182907 14080 solver.cpp:218] Iteration 204 (0.963817 iter/s, 12.4505s/12 iters), loss = 5.27436
I0410 00:08:03.182951 14080 solver.cpp:237]     Train net output #0: loss = 5.27436 (* 1 = 5.27436 loss)
I0410 00:08:03.182960 14080 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0410 00:08:07.423209 14080 solver.cpp:218] Iteration 216 (2.83014 iter/s, 4.24007s/12 iters), loss = 5.2801
I0410 00:08:07.423252 14080 solver.cpp:237]     Train net output #0: loss = 5.2801 (* 1 = 5.2801 loss)
I0410 00:08:07.423260 14080 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0410 00:08:12.405263 14080 solver.cpp:218] Iteration 228 (2.40877 iter/s, 4.98179s/12 iters), loss = 5.25395
I0410 00:08:12.405323 14080 solver.cpp:237]     Train net output #0: loss = 5.25395 (* 1 = 5.25395 loss)
I0410 00:08:12.405339 14080 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0410 00:08:17.310997 14080 solver.cpp:218] Iteration 240 (2.44625 iter/s, 4.90546s/12 iters), loss = 5.29032
I0410 00:08:17.311049 14080 solver.cpp:237]     Train net output #0: loss = 5.29032 (* 1 = 5.29032 loss)
I0410 00:08:17.311062 14080 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0410 00:08:22.321645 14080 solver.cpp:218] Iteration 252 (2.39503 iter/s, 5.01038s/12 iters), loss = 5.26911
I0410 00:08:22.321700 14080 solver.cpp:237]     Train net output #0: loss = 5.26911 (* 1 = 5.26911 loss)
I0410 00:08:22.321714 14080 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0410 00:08:27.285327 14080 solver.cpp:218] Iteration 264 (2.41769 iter/s, 4.96342s/12 iters), loss = 5.25497
I0410 00:08:27.285374 14080 solver.cpp:237]     Train net output #0: loss = 5.25497 (* 1 = 5.25497 loss)
I0410 00:08:27.285385 14080 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0410 00:08:32.213464 14080 solver.cpp:218] Iteration 276 (2.43513 iter/s, 4.92787s/12 iters), loss = 5.27536
I0410 00:08:32.215059 14080 solver.cpp:237]     Train net output #0: loss = 5.27536 (* 1 = 5.27536 loss)
I0410 00:08:32.215072 14080 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0410 00:08:37.092999 14080 solver.cpp:218] Iteration 288 (2.46016 iter/s, 4.87773s/12 iters), loss = 5.15376
I0410 00:08:37.093052 14080 solver.cpp:237]     Train net output #0: loss = 5.15376 (* 1 = 5.15376 loss)
I0410 00:08:37.093063 14080 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0410 00:08:41.947446 14080 solver.cpp:218] Iteration 300 (2.4721 iter/s, 4.85418s/12 iters), loss = 5.22692
I0410 00:08:41.947502 14080 solver.cpp:237]     Train net output #0: loss = 5.22692 (* 1 = 5.22692 loss)
I0410 00:08:41.947513 14080 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0410 00:08:42.903630 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:08:43.916832 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0410 00:08:45.296016 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0410 00:08:46.327868 14080 solver.cpp:330] Iteration 306, Testing net (#0)
I0410 00:08:46.327895 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:08:50.634714 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:08:50.791983 14080 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0410 00:08:50.792034 14080 solver.cpp:397]     Test net output #1: loss = 5.19038 (* 1 = 5.19038 loss)
I0410 00:08:52.606326 14080 solver.cpp:218] Iteration 312 (1.12588 iter/s, 10.6584s/12 iters), loss = 5.17837
I0410 00:08:52.606385 14080 solver.cpp:237]     Train net output #0: loss = 5.17837 (* 1 = 5.17837 loss)
I0410 00:08:52.606396 14080 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0410 00:08:57.531697 14080 solver.cpp:218] Iteration 324 (2.4365 iter/s, 4.9251s/12 iters), loss = 5.19732
I0410 00:08:57.531738 14080 solver.cpp:237]     Train net output #0: loss = 5.19732 (* 1 = 5.19732 loss)
I0410 00:08:57.531747 14080 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0410 00:09:02.471212 14080 solver.cpp:218] Iteration 336 (2.42952 iter/s, 4.93926s/12 iters), loss = 5.20428
I0410 00:09:02.471304 14080 solver.cpp:237]     Train net output #0: loss = 5.20428 (* 1 = 5.20428 loss)
I0410 00:09:02.471314 14080 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0410 00:09:07.377912 14080 solver.cpp:218] Iteration 348 (2.44579 iter/s, 4.90639s/12 iters), loss = 5.16294
I0410 00:09:07.378000 14080 solver.cpp:237]     Train net output #0: loss = 5.16294 (* 1 = 5.16294 loss)
I0410 00:09:07.378013 14080 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0410 00:09:12.202350 14080 solver.cpp:218] Iteration 360 (2.48747 iter/s, 4.82417s/12 iters), loss = 5.22366
I0410 00:09:12.202407 14080 solver.cpp:237]     Train net output #0: loss = 5.22366 (* 1 = 5.22366 loss)
I0410 00:09:12.202420 14080 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0410 00:09:17.062136 14080 solver.cpp:218] Iteration 372 (2.46938 iter/s, 4.85952s/12 iters), loss = 5.14323
I0410 00:09:17.062192 14080 solver.cpp:237]     Train net output #0: loss = 5.14323 (* 1 = 5.14323 loss)
I0410 00:09:17.062204 14080 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0410 00:09:22.126299 14080 solver.cpp:218] Iteration 384 (2.36972 iter/s, 5.06389s/12 iters), loss = 5.16239
I0410 00:09:22.126340 14080 solver.cpp:237]     Train net output #0: loss = 5.16239 (* 1 = 5.16239 loss)
I0410 00:09:22.126348 14080 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0410 00:09:27.052034 14080 solver.cpp:218] Iteration 396 (2.43631 iter/s, 4.92548s/12 iters), loss = 5.0939
I0410 00:09:27.052076 14080 solver.cpp:237]     Train net output #0: loss = 5.0939 (* 1 = 5.0939 loss)
I0410 00:09:27.052086 14080 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0410 00:09:30.135027 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:09:31.535535 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0410 00:09:32.961220 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0410 00:09:34.084764 14080 solver.cpp:330] Iteration 408, Testing net (#0)
I0410 00:09:34.084792 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:09:38.519670 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:09:38.728288 14080 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0410 00:09:38.728322 14080 solver.cpp:397]     Test net output #1: loss = 5.13924 (* 1 = 5.13924 loss)
I0410 00:09:38.814075 14080 solver.cpp:218] Iteration 408 (1.02028 iter/s, 11.7615s/12 iters), loss = 5.21771
I0410 00:09:38.814121 14080 solver.cpp:237]     Train net output #0: loss = 5.21771 (* 1 = 5.21771 loss)
I0410 00:09:38.814129 14080 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0410 00:09:42.887681 14080 solver.cpp:218] Iteration 420 (2.94596 iter/s, 4.07338s/12 iters), loss = 5.19749
I0410 00:09:42.887730 14080 solver.cpp:237]     Train net output #0: loss = 5.19749 (* 1 = 5.19749 loss)
I0410 00:09:42.887740 14080 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0410 00:09:47.766044 14080 solver.cpp:218] Iteration 432 (2.45997 iter/s, 4.8781s/12 iters), loss = 5.15738
I0410 00:09:47.766094 14080 solver.cpp:237]     Train net output #0: loss = 5.15738 (* 1 = 5.15738 loss)
I0410 00:09:47.766106 14080 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0410 00:09:52.649565 14080 solver.cpp:218] Iteration 444 (2.45738 iter/s, 4.88326s/12 iters), loss = 5.08318
I0410 00:09:52.649612 14080 solver.cpp:237]     Train net output #0: loss = 5.08318 (* 1 = 5.08318 loss)
I0410 00:09:52.649622 14080 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0410 00:09:57.714540 14080 solver.cpp:218] Iteration 456 (2.36934 iter/s, 5.0647s/12 iters), loss = 5.15567
I0410 00:09:57.714593 14080 solver.cpp:237]     Train net output #0: loss = 5.15567 (* 1 = 5.15567 loss)
I0410 00:09:57.714603 14080 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0410 00:10:02.741911 14080 solver.cpp:218] Iteration 468 (2.38706 iter/s, 5.0271s/12 iters), loss = 5.13445
I0410 00:10:02.741976 14080 solver.cpp:237]     Train net output #0: loss = 5.13445 (* 1 = 5.13445 loss)
I0410 00:10:02.741986 14080 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0410 00:10:07.639742 14080 solver.cpp:218] Iteration 480 (2.45019 iter/s, 4.89757s/12 iters), loss = 5.0815
I0410 00:10:07.639837 14080 solver.cpp:237]     Train net output #0: loss = 5.0815 (* 1 = 5.0815 loss)
I0410 00:10:07.639847 14080 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0410 00:10:12.575548 14080 solver.cpp:218] Iteration 492 (2.43137 iter/s, 4.9355s/12 iters), loss = 5.13884
I0410 00:10:12.575593 14080 solver.cpp:237]     Train net output #0: loss = 5.13884 (* 1 = 5.13884 loss)
I0410 00:10:12.575601 14080 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0410 00:10:17.502912 14080 solver.cpp:218] Iteration 504 (2.43551 iter/s, 4.9271s/12 iters), loss = 5.1322
I0410 00:10:17.502972 14080 solver.cpp:237]     Train net output #0: loss = 5.1322 (* 1 = 5.1322 loss)
I0410 00:10:17.502985 14080 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0410 00:10:17.766672 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:10:19.509805 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0410 00:10:26.304342 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0410 00:10:28.826159 14080 solver.cpp:330] Iteration 510, Testing net (#0)
I0410 00:10:28.826189 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:10:33.055088 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:10:33.291911 14080 solver.cpp:397]     Test net output #0: accuracy = 0.0134804
I0410 00:10:33.291947 14080 solver.cpp:397]     Test net output #1: loss = 5.08776 (* 1 = 5.08776 loss)
I0410 00:10:35.106040 14080 solver.cpp:218] Iteration 516 (0.681727 iter/s, 17.6023s/12 iters), loss = 5.01745
I0410 00:10:35.106087 14080 solver.cpp:237]     Train net output #0: loss = 5.01745 (* 1 = 5.01745 loss)
I0410 00:10:35.106097 14080 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0410 00:10:40.229269 14080 solver.cpp:218] Iteration 528 (2.34239 iter/s, 5.12296s/12 iters), loss = 5.08559
I0410 00:10:40.229379 14080 solver.cpp:237]     Train net output #0: loss = 5.08559 (* 1 = 5.08559 loss)
I0410 00:10:40.229389 14080 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0410 00:10:45.091385 14080 solver.cpp:218] Iteration 540 (2.46822 iter/s, 4.86179s/12 iters), loss = 5.05059
I0410 00:10:45.091436 14080 solver.cpp:237]     Train net output #0: loss = 5.05059 (* 1 = 5.05059 loss)
I0410 00:10:45.091449 14080 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0410 00:10:50.035869 14080 solver.cpp:218] Iteration 552 (2.42708 iter/s, 4.94422s/12 iters), loss = 5.08027
I0410 00:10:50.035914 14080 solver.cpp:237]     Train net output #0: loss = 5.08027 (* 1 = 5.08027 loss)
I0410 00:10:50.035923 14080 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0410 00:10:55.016026 14080 solver.cpp:218] Iteration 564 (2.40969 iter/s, 4.9799s/12 iters), loss = 5.0658
I0410 00:10:55.016074 14080 solver.cpp:237]     Train net output #0: loss = 5.0658 (* 1 = 5.0658 loss)
I0410 00:10:55.016083 14080 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0410 00:10:59.933487 14080 solver.cpp:218] Iteration 576 (2.44041 iter/s, 4.9172s/12 iters), loss = 5.06932
I0410 00:10:59.933537 14080 solver.cpp:237]     Train net output #0: loss = 5.06932 (* 1 = 5.06932 loss)
I0410 00:10:59.933547 14080 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0410 00:11:05.081434 14080 solver.cpp:218] Iteration 588 (2.33115 iter/s, 5.14767s/12 iters), loss = 4.98106
I0410 00:11:05.081496 14080 solver.cpp:237]     Train net output #0: loss = 4.98106 (* 1 = 4.98106 loss)
I0410 00:11:05.081509 14080 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0410 00:11:10.018779 14080 solver.cpp:218] Iteration 600 (2.43059 iter/s, 4.93707s/12 iters), loss = 5.08368
I0410 00:11:10.018831 14080 solver.cpp:237]     Train net output #0: loss = 5.08368 (* 1 = 5.08368 loss)
I0410 00:11:10.018843 14080 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0410 00:11:12.371968 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:11:14.487352 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0410 00:11:15.945365 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0410 00:11:19.113474 14080 solver.cpp:330] Iteration 612, Testing net (#0)
I0410 00:11:19.113504 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:11:23.287246 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:11:23.571242 14080 solver.cpp:397]     Test net output #0: accuracy = 0.0165441
I0410 00:11:23.571295 14080 solver.cpp:397]     Test net output #1: loss = 5.03641 (* 1 = 5.03641 loss)
I0410 00:11:23.657321 14080 solver.cpp:218] Iteration 612 (0.879899 iter/s, 13.6379s/12 iters), loss = 5.04896
I0410 00:11:23.657371 14080 solver.cpp:237]     Train net output #0: loss = 5.04896 (* 1 = 5.04896 loss)
I0410 00:11:23.657382 14080 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0410 00:11:28.420310 14080 solver.cpp:218] Iteration 624 (2.51956 iter/s, 4.76273s/12 iters), loss = 5.05977
I0410 00:11:28.420361 14080 solver.cpp:237]     Train net output #0: loss = 5.05977 (* 1 = 5.05977 loss)
I0410 00:11:28.420374 14080 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0410 00:11:33.328699 14080 solver.cpp:218] Iteration 636 (2.44493 iter/s, 4.90812s/12 iters), loss = 4.90978
I0410 00:11:33.328760 14080 solver.cpp:237]     Train net output #0: loss = 4.90978 (* 1 = 4.90978 loss)
I0410 00:11:33.328774 14080 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0410 00:11:38.378540 14080 solver.cpp:218] Iteration 648 (2.37644 iter/s, 5.04956s/12 iters), loss = 5.06369
I0410 00:11:38.378584 14080 solver.cpp:237]     Train net output #0: loss = 5.06369 (* 1 = 5.06369 loss)
I0410 00:11:38.378593 14080 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0410 00:11:43.271880 14080 solver.cpp:218] Iteration 660 (2.45244 iter/s, 4.89309s/12 iters), loss = 4.99884
I0410 00:11:43.272011 14080 solver.cpp:237]     Train net output #0: loss = 4.99884 (* 1 = 4.99884 loss)
I0410 00:11:43.272022 14080 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0410 00:11:48.216558 14080 solver.cpp:218] Iteration 672 (2.42702 iter/s, 4.94433s/12 iters), loss = 4.9198
I0410 00:11:48.216620 14080 solver.cpp:237]     Train net output #0: loss = 4.9198 (* 1 = 4.9198 loss)
I0410 00:11:48.216632 14080 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0410 00:11:53.099517 14080 solver.cpp:218] Iteration 684 (2.45766 iter/s, 4.88269s/12 iters), loss = 4.81688
I0410 00:11:53.099566 14080 solver.cpp:237]     Train net output #0: loss = 4.81688 (* 1 = 4.81688 loss)
I0410 00:11:53.099577 14080 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0410 00:11:53.474261 14080 blocking_queue.cpp:49] Waiting for data
I0410 00:11:58.072729 14080 solver.cpp:218] Iteration 696 (2.41305 iter/s, 4.97295s/12 iters), loss = 4.96642
I0410 00:11:58.072773 14080 solver.cpp:237]     Train net output #0: loss = 4.96642 (* 1 = 4.96642 loss)
I0410 00:11:58.072782 14080 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0410 00:12:02.941229 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:12:03.312479 14080 solver.cpp:218] Iteration 708 (2.2903 iter/s, 5.23948s/12 iters), loss = 5.07169
I0410 00:12:03.312531 14080 solver.cpp:237]     Train net output #0: loss = 5.07169 (* 1 = 5.07169 loss)
I0410 00:12:03.312543 14080 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0410 00:12:05.329135 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0410 00:12:06.755223 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0410 00:12:08.058058 14080 solver.cpp:330] Iteration 714, Testing net (#0)
I0410 00:12:08.058084 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:12:12.174540 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:12:12.495831 14080 solver.cpp:397]     Test net output #0: accuracy = 0.0214461
I0410 00:12:12.495880 14080 solver.cpp:397]     Test net output #1: loss = 4.98875 (* 1 = 4.98875 loss)
I0410 00:12:14.417634 14080 solver.cpp:218] Iteration 720 (1.08063 iter/s, 11.1046s/12 iters), loss = 5.12212
I0410 00:12:14.417783 14080 solver.cpp:237]     Train net output #0: loss = 5.12212 (* 1 = 5.12212 loss)
I0410 00:12:14.417799 14080 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0410 00:12:19.277465 14080 solver.cpp:218] Iteration 732 (2.4694 iter/s, 4.85948s/12 iters), loss = 4.85819
I0410 00:12:19.277510 14080 solver.cpp:237]     Train net output #0: loss = 4.85819 (* 1 = 4.85819 loss)
I0410 00:12:19.277519 14080 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0410 00:12:24.153899 14080 solver.cpp:218] Iteration 744 (2.46095 iter/s, 4.87617s/12 iters), loss = 5.00346
I0410 00:12:24.153972 14080 solver.cpp:237]     Train net output #0: loss = 5.00346 (* 1 = 5.00346 loss)
I0410 00:12:24.153985 14080 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0410 00:12:29.095521 14080 solver.cpp:218] Iteration 756 (2.42849 iter/s, 4.94135s/12 iters), loss = 4.99818
I0410 00:12:29.095575 14080 solver.cpp:237]     Train net output #0: loss = 4.99818 (* 1 = 4.99818 loss)
I0410 00:12:29.095587 14080 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0410 00:12:34.059587 14080 solver.cpp:218] Iteration 768 (2.4175 iter/s, 4.9638s/12 iters), loss = 4.94896
I0410 00:12:34.059628 14080 solver.cpp:237]     Train net output #0: loss = 4.94896 (* 1 = 4.94896 loss)
I0410 00:12:34.059636 14080 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0410 00:12:38.983098 14080 solver.cpp:218] Iteration 780 (2.43741 iter/s, 4.92326s/12 iters), loss = 4.99788
I0410 00:12:38.983141 14080 solver.cpp:237]     Train net output #0: loss = 4.99788 (* 1 = 4.99788 loss)
I0410 00:12:38.983150 14080 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0410 00:12:43.895167 14080 solver.cpp:218] Iteration 792 (2.44309 iter/s, 4.91181s/12 iters), loss = 4.82782
I0410 00:12:43.895220 14080 solver.cpp:237]     Train net output #0: loss = 4.82782 (* 1 = 4.82782 loss)
I0410 00:12:43.895232 14080 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0410 00:12:48.840080 14080 solver.cpp:218] Iteration 804 (2.42687 iter/s, 4.94465s/12 iters), loss = 4.92303
I0410 00:12:48.840220 14080 solver.cpp:237]     Train net output #0: loss = 4.92303 (* 1 = 4.92303 loss)
I0410 00:12:48.840231 14080 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0410 00:12:50.557520 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:12:53.295686 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0410 00:12:55.830307 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0410 00:12:56.946830 14080 solver.cpp:330] Iteration 816, Testing net (#0)
I0410 00:12:56.946861 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:13:01.098186 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:13:01.452219 14080 solver.cpp:397]     Test net output #0: accuracy = 0.0300245
I0410 00:13:01.452252 14080 solver.cpp:397]     Test net output #1: loss = 4.88122 (* 1 = 4.88122 loss)
I0410 00:13:01.538431 14080 solver.cpp:218] Iteration 816 (0.945054 iter/s, 12.6977s/12 iters), loss = 4.9519
I0410 00:13:01.538466 14080 solver.cpp:237]     Train net output #0: loss = 4.9519 (* 1 = 4.9519 loss)
I0410 00:13:01.538475 14080 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0410 00:13:05.761271 14080 solver.cpp:218] Iteration 828 (2.84184 iter/s, 4.22262s/12 iters), loss = 4.94602
I0410 00:13:05.761315 14080 solver.cpp:237]     Train net output #0: loss = 4.94602 (* 1 = 4.94602 loss)
I0410 00:13:05.761324 14080 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0410 00:13:10.653903 14080 solver.cpp:218] Iteration 840 (2.4528 iter/s, 4.89237s/12 iters), loss = 4.78236
I0410 00:13:10.653978 14080 solver.cpp:237]     Train net output #0: loss = 4.78236 (* 1 = 4.78236 loss)
I0410 00:13:10.653992 14080 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0410 00:13:15.532119 14080 solver.cpp:218] Iteration 852 (2.46005 iter/s, 4.87795s/12 iters), loss = 4.80707
I0410 00:13:15.532171 14080 solver.cpp:237]     Train net output #0: loss = 4.80707 (* 1 = 4.80707 loss)
I0410 00:13:15.532181 14080 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0410 00:13:20.380666 14080 solver.cpp:218] Iteration 864 (2.4751 iter/s, 4.84828s/12 iters), loss = 4.79261
I0410 00:13:20.380779 14080 solver.cpp:237]     Train net output #0: loss = 4.79261 (* 1 = 4.79261 loss)
I0410 00:13:20.380791 14080 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0410 00:13:25.288192 14080 solver.cpp:218] Iteration 876 (2.44538 iter/s, 4.9072s/12 iters), loss = 4.85142
I0410 00:13:25.288233 14080 solver.cpp:237]     Train net output #0: loss = 4.85142 (* 1 = 4.85142 loss)
I0410 00:13:25.288242 14080 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0410 00:13:30.186095 14080 solver.cpp:218] Iteration 888 (2.45016 iter/s, 4.89765s/12 iters), loss = 4.83094
I0410 00:13:30.186156 14080 solver.cpp:237]     Train net output #0: loss = 4.83094 (* 1 = 4.83094 loss)
I0410 00:13:30.186167 14080 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0410 00:13:35.084662 14080 solver.cpp:218] Iteration 900 (2.44983 iter/s, 4.89829s/12 iters), loss = 4.85621
I0410 00:13:35.084722 14080 solver.cpp:237]     Train net output #0: loss = 4.85621 (* 1 = 4.85621 loss)
I0410 00:13:35.084733 14080 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0410 00:13:38.887187 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:13:39.961511 14080 solver.cpp:218] Iteration 912 (2.46074 iter/s, 4.87658s/12 iters), loss = 4.61554
I0410 00:13:39.961563 14080 solver.cpp:237]     Train net output #0: loss = 4.61554 (* 1 = 4.61554 loss)
I0410 00:13:39.961575 14080 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0410 00:13:41.945745 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0410 00:13:44.894114 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0410 00:13:46.823344 14080 solver.cpp:330] Iteration 918, Testing net (#0)
I0410 00:13:46.823374 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:13:50.885535 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:13:51.296288 14080 solver.cpp:397]     Test net output #0: accuracy = 0.0318627
I0410 00:13:51.296334 14080 solver.cpp:397]     Test net output #1: loss = 4.83757 (* 1 = 4.83757 loss)
I0410 00:13:53.141270 14080 solver.cpp:218] Iteration 924 (0.910528 iter/s, 13.1792s/12 iters), loss = 4.93265
I0410 00:13:53.141320 14080 solver.cpp:237]     Train net output #0: loss = 4.93265 (* 1 = 4.93265 loss)
I0410 00:13:53.141331 14080 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0410 00:13:58.028369 14080 solver.cpp:218] Iteration 936 (2.45558 iter/s, 4.88684s/12 iters), loss = 4.90775
I0410 00:13:58.028424 14080 solver.cpp:237]     Train net output #0: loss = 4.90775 (* 1 = 4.90775 loss)
I0410 00:13:58.028436 14080 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0410 00:14:02.861428 14080 solver.cpp:218] Iteration 948 (2.48303 iter/s, 4.8328s/12 iters), loss = 4.69204
I0410 00:14:02.861479 14080 solver.cpp:237]     Train net output #0: loss = 4.69204 (* 1 = 4.69204 loss)
I0410 00:14:02.861490 14080 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0410 00:14:07.809104 14080 solver.cpp:218] Iteration 960 (2.42551 iter/s, 4.94741s/12 iters), loss = 4.59718
I0410 00:14:07.809155 14080 solver.cpp:237]     Train net output #0: loss = 4.59718 (* 1 = 4.59718 loss)
I0410 00:14:07.809165 14080 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0410 00:14:12.743777 14080 solver.cpp:218] Iteration 972 (2.4319 iter/s, 4.93441s/12 iters), loss = 4.71422
I0410 00:14:12.743820 14080 solver.cpp:237]     Train net output #0: loss = 4.71422 (* 1 = 4.71422 loss)
I0410 00:14:12.743829 14080 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0410 00:14:17.755746 14080 solver.cpp:218] Iteration 984 (2.39439 iter/s, 5.01171s/12 iters), loss = 4.70578
I0410 00:14:17.755790 14080 solver.cpp:237]     Train net output #0: loss = 4.70578 (* 1 = 4.70578 loss)
I0410 00:14:17.755800 14080 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0410 00:14:22.592998 14080 solver.cpp:218] Iteration 996 (2.48088 iter/s, 4.837s/12 iters), loss = 4.5662
I0410 00:14:22.593072 14080 solver.cpp:237]     Train net output #0: loss = 4.5662 (* 1 = 4.5662 loss)
I0410 00:14:22.593081 14080 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0410 00:14:27.488116 14080 solver.cpp:218] Iteration 1008 (2.45157 iter/s, 4.89483s/12 iters), loss = 4.86373
I0410 00:14:27.488157 14080 solver.cpp:237]     Train net output #0: loss = 4.86373 (* 1 = 4.86373 loss)
I0410 00:14:27.488166 14080 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0410 00:14:28.470779 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:14:31.923349 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0410 00:14:36.127526 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0410 00:14:38.645644 14080 solver.cpp:330] Iteration 1020, Testing net (#0)
I0410 00:14:38.645674 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:14:42.689150 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:14:43.120090 14080 solver.cpp:397]     Test net output #0: accuracy = 0.0410539
I0410 00:14:43.120131 14080 solver.cpp:397]     Test net output #1: loss = 4.70952 (* 1 = 4.70952 loss)
I0410 00:14:43.206190 14080 solver.cpp:218] Iteration 1020 (0.763486 iter/s, 15.7174s/12 iters), loss = 4.65212
I0410 00:14:43.206241 14080 solver.cpp:237]     Train net output #0: loss = 4.65212 (* 1 = 4.65212 loss)
I0410 00:14:43.206250 14080 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0410 00:14:47.353824 14080 solver.cpp:218] Iteration 1032 (2.89338 iter/s, 4.1474s/12 iters), loss = 4.76647
I0410 00:14:47.353881 14080 solver.cpp:237]     Train net output #0: loss = 4.76647 (* 1 = 4.76647 loss)
I0410 00:14:47.353894 14080 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0410 00:14:52.256667 14080 solver.cpp:218] Iteration 1044 (2.4477 iter/s, 4.90257s/12 iters), loss = 4.66474
I0410 00:14:52.256718 14080 solver.cpp:237]     Train net output #0: loss = 4.66474 (* 1 = 4.66474 loss)
I0410 00:14:52.256731 14080 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0410 00:14:57.148095 14080 solver.cpp:218] Iteration 1056 (2.45341 iter/s, 4.89116s/12 iters), loss = 4.7178
I0410 00:14:57.148270 14080 solver.cpp:237]     Train net output #0: loss = 4.7178 (* 1 = 4.7178 loss)
I0410 00:14:57.148286 14080 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0410 00:15:02.436167 14080 solver.cpp:218] Iteration 1068 (2.26943 iter/s, 5.28767s/12 iters), loss = 4.59898
I0410 00:15:02.436223 14080 solver.cpp:237]     Train net output #0: loss = 4.59898 (* 1 = 4.59898 loss)
I0410 00:15:02.436234 14080 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0410 00:15:07.312086 14080 solver.cpp:218] Iteration 1080 (2.46121 iter/s, 4.87565s/12 iters), loss = 4.59695
I0410 00:15:07.312139 14080 solver.cpp:237]     Train net output #0: loss = 4.59695 (* 1 = 4.59695 loss)
I0410 00:15:07.312151 14080 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0410 00:15:12.262946 14080 solver.cpp:218] Iteration 1092 (2.42395 iter/s, 4.95059s/12 iters), loss = 4.621
I0410 00:15:12.262993 14080 solver.cpp:237]     Train net output #0: loss = 4.621 (* 1 = 4.621 loss)
I0410 00:15:12.263005 14080 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0410 00:15:17.154717 14080 solver.cpp:218] Iteration 1104 (2.45323 iter/s, 4.89151s/12 iters), loss = 4.53827
I0410 00:15:17.154757 14080 solver.cpp:237]     Train net output #0: loss = 4.53827 (* 1 = 4.53827 loss)
I0410 00:15:17.154767 14080 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0410 00:15:20.432226 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:15:22.228518 14080 solver.cpp:218] Iteration 1116 (2.36521 iter/s, 5.07354s/12 iters), loss = 4.58343
I0410 00:15:22.228565 14080 solver.cpp:237]     Train net output #0: loss = 4.58343 (* 1 = 4.58343 loss)
I0410 00:15:22.228576 14080 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0410 00:15:24.210737 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0410 00:15:25.589641 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0410 00:15:27.297505 14080 solver.cpp:330] Iteration 1122, Testing net (#0)
I0410 00:15:27.297547 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:15:31.345173 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:15:31.832775 14080 solver.cpp:397]     Test net output #0: accuracy = 0.0606618
I0410 00:15:31.832824 14080 solver.cpp:397]     Test net output #1: loss = 4.5345 (* 1 = 4.5345 loss)
I0410 00:15:33.669171 14080 solver.cpp:218] Iteration 1128 (1.04894 iter/s, 11.4401s/12 iters), loss = 4.51635
I0410 00:15:33.669224 14080 solver.cpp:237]     Train net output #0: loss = 4.51635 (* 1 = 4.51635 loss)
I0410 00:15:33.669236 14080 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0410 00:15:38.642127 14080 solver.cpp:218] Iteration 1140 (2.41318 iter/s, 4.97268s/12 iters), loss = 4.49166
I0410 00:15:38.642185 14080 solver.cpp:237]     Train net output #0: loss = 4.49166 (* 1 = 4.49166 loss)
I0410 00:15:38.642199 14080 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0410 00:15:43.586139 14080 solver.cpp:218] Iteration 1152 (2.42731 iter/s, 4.94373s/12 iters), loss = 4.36702
I0410 00:15:43.586206 14080 solver.cpp:237]     Train net output #0: loss = 4.36702 (* 1 = 4.36702 loss)
I0410 00:15:43.586220 14080 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0410 00:15:48.429316 14080 solver.cpp:218] Iteration 1164 (2.47785 iter/s, 4.8429s/12 iters), loss = 4.43832
I0410 00:15:48.429368 14080 solver.cpp:237]     Train net output #0: loss = 4.43832 (* 1 = 4.43832 loss)
I0410 00:15:48.429380 14080 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0410 00:15:53.300508 14080 solver.cpp:218] Iteration 1176 (2.4636 iter/s, 4.87093s/12 iters), loss = 4.54658
I0410 00:15:53.300556 14080 solver.cpp:237]     Train net output #0: loss = 4.54658 (* 1 = 4.54658 loss)
I0410 00:15:53.300568 14080 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0410 00:15:58.178506 14080 solver.cpp:218] Iteration 1188 (2.46016 iter/s, 4.87773s/12 iters), loss = 4.45988
I0410 00:15:58.178639 14080 solver.cpp:237]     Train net output #0: loss = 4.45988 (* 1 = 4.45988 loss)
I0410 00:15:58.178651 14080 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0410 00:16:03.090576 14080 solver.cpp:218] Iteration 1200 (2.44313 iter/s, 4.91172s/12 iters), loss = 4.6338
I0410 00:16:03.090627 14080 solver.cpp:237]     Train net output #0: loss = 4.6338 (* 1 = 4.6338 loss)
I0410 00:16:03.090639 14080 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0410 00:16:08.005889 14080 solver.cpp:218] Iteration 1212 (2.44148 iter/s, 4.91505s/12 iters), loss = 4.51163
I0410 00:16:08.005935 14080 solver.cpp:237]     Train net output #0: loss = 4.51163 (* 1 = 4.51163 loss)
I0410 00:16:08.005944 14080 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0410 00:16:08.296762 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:16:12.451129 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0410 00:16:13.809077 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0410 00:16:17.262745 14080 solver.cpp:330] Iteration 1224, Testing net (#0)
I0410 00:16:17.262771 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:16:22.156706 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:16:22.667269 14080 solver.cpp:397]     Test net output #0: accuracy = 0.064951
I0410 00:16:22.667320 14080 solver.cpp:397]     Test net output #1: loss = 4.40247 (* 1 = 4.40247 loss)
I0410 00:16:22.753198 14080 solver.cpp:218] Iteration 1224 (0.813744 iter/s, 14.7467s/12 iters), loss = 4.44033
I0410 00:16:22.753247 14080 solver.cpp:237]     Train net output #0: loss = 4.44033 (* 1 = 4.44033 loss)
I0410 00:16:22.753259 14080 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0410 00:16:27.382088 14080 solver.cpp:218] Iteration 1236 (2.59255 iter/s, 4.62864s/12 iters), loss = 4.47998
I0410 00:16:27.382146 14080 solver.cpp:237]     Train net output #0: loss = 4.47998 (* 1 = 4.47998 loss)
I0410 00:16:27.382158 14080 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0410 00:16:32.334336 14080 solver.cpp:218] Iteration 1248 (2.42328 iter/s, 4.95197s/12 iters), loss = 4.34167
I0410 00:16:32.334425 14080 solver.cpp:237]     Train net output #0: loss = 4.34167 (* 1 = 4.34167 loss)
I0410 00:16:32.334437 14080 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0410 00:16:37.278990 14080 solver.cpp:218] Iteration 1260 (2.42701 iter/s, 4.94435s/12 iters), loss = 4.39817
I0410 00:16:37.279042 14080 solver.cpp:237]     Train net output #0: loss = 4.39817 (* 1 = 4.39817 loss)
I0410 00:16:37.279054 14080 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0410 00:16:42.195590 14080 solver.cpp:218] Iteration 1272 (2.44084 iter/s, 4.91634s/12 iters), loss = 4.27556
I0410 00:16:42.195647 14080 solver.cpp:237]     Train net output #0: loss = 4.27556 (* 1 = 4.27556 loss)
I0410 00:16:42.195659 14080 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0410 00:16:47.273294 14080 solver.cpp:218] Iteration 1284 (2.3634 iter/s, 5.07743s/12 iters), loss = 4.43421
I0410 00:16:47.273339 14080 solver.cpp:237]     Train net output #0: loss = 4.43421 (* 1 = 4.43421 loss)
I0410 00:16:47.273348 14080 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0410 00:16:52.748145 14080 solver.cpp:218] Iteration 1296 (2.19195 iter/s, 5.47457s/12 iters), loss = 4.21383
I0410 00:16:52.748199 14080 solver.cpp:237]     Train net output #0: loss = 4.21383 (* 1 = 4.21383 loss)
I0410 00:16:52.748210 14080 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0410 00:16:57.727690 14080 solver.cpp:218] Iteration 1308 (2.40999 iter/s, 4.97927s/12 iters), loss = 4.33464
I0410 00:16:57.727736 14080 solver.cpp:237]     Train net output #0: loss = 4.33464 (* 1 = 4.33464 loss)
I0410 00:16:57.727744 14080 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0410 00:17:00.199652 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:17:02.650588 14080 solver.cpp:218] Iteration 1320 (2.43772 iter/s, 4.92263s/12 iters), loss = 4.21414
I0410 00:17:02.650750 14080 solver.cpp:237]     Train net output #0: loss = 4.21414 (* 1 = 4.21414 loss)
I0410 00:17:02.650763 14080 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0410 00:17:04.701853 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0410 00:17:06.104494 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0410 00:17:07.152878 14080 solver.cpp:330] Iteration 1326, Testing net (#0)
I0410 00:17:07.152907 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:17:11.107672 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:17:11.662858 14080 solver.cpp:397]     Test net output #0: accuracy = 0.0778186
I0410 00:17:11.662887 14080 solver.cpp:397]     Test net output #1: loss = 4.31054 (* 1 = 4.31054 loss)
I0410 00:17:13.483526 14080 solver.cpp:218] Iteration 1332 (1.10779 iter/s, 10.8323s/12 iters), loss = 4.0448
I0410 00:17:13.483580 14080 solver.cpp:237]     Train net output #0: loss = 4.0448 (* 1 = 4.0448 loss)
I0410 00:17:13.483592 14080 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0410 00:17:18.510928 14080 solver.cpp:218] Iteration 1344 (2.38705 iter/s, 5.02713s/12 iters), loss = 4.05941
I0410 00:17:18.510977 14080 solver.cpp:237]     Train net output #0: loss = 4.05941 (* 1 = 4.05941 loss)
I0410 00:17:18.510987 14080 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0410 00:17:23.499466 14080 solver.cpp:218] Iteration 1356 (2.40565 iter/s, 4.98827s/12 iters), loss = 4.23974
I0410 00:17:23.499526 14080 solver.cpp:237]     Train net output #0: loss = 4.23974 (* 1 = 4.23974 loss)
I0410 00:17:23.499538 14080 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0410 00:17:28.405529 14080 solver.cpp:218] Iteration 1368 (2.44609 iter/s, 4.90579s/12 iters), loss = 4.16397
I0410 00:17:28.405580 14080 solver.cpp:237]     Train net output #0: loss = 4.16397 (* 1 = 4.16397 loss)
I0410 00:17:28.405592 14080 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0410 00:17:29.178457 14080 blocking_queue.cpp:49] Waiting for data
I0410 00:17:33.281950 14080 solver.cpp:218] Iteration 1380 (2.46095 iter/s, 4.87616s/12 iters), loss = 4.1467
I0410 00:17:33.282049 14080 solver.cpp:237]     Train net output #0: loss = 4.1467 (* 1 = 4.1467 loss)
I0410 00:17:33.282063 14080 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0410 00:17:38.407699 14080 solver.cpp:218] Iteration 1392 (2.34126 iter/s, 5.12543s/12 iters), loss = 4.16702
I0410 00:17:38.407739 14080 solver.cpp:237]     Train net output #0: loss = 4.16702 (* 1 = 4.16702 loss)
I0410 00:17:38.407748 14080 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0410 00:17:43.357424 14080 solver.cpp:218] Iteration 1404 (2.4245 iter/s, 4.94947s/12 iters), loss = 4.21324
I0410 00:17:43.357467 14080 solver.cpp:237]     Train net output #0: loss = 4.21324 (* 1 = 4.21324 loss)
I0410 00:17:43.357477 14080 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0410 00:17:47.907786 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:17:48.253466 14080 solver.cpp:218] Iteration 1416 (2.45109 iter/s, 4.89579s/12 iters), loss = 4.07408
I0410 00:17:48.253512 14080 solver.cpp:237]     Train net output #0: loss = 4.07408 (* 1 = 4.07408 loss)
I0410 00:17:48.253523 14080 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0410 00:17:52.690399 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0410 00:17:54.106645 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0410 00:17:55.169574 14080 solver.cpp:330] Iteration 1428, Testing net (#0)
I0410 00:17:55.169605 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:17:59.048209 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:17:59.647065 14080 solver.cpp:397]     Test net output #0: accuracy = 0.102941
I0410 00:17:59.647114 14080 solver.cpp:397]     Test net output #1: loss = 4.15418 (* 1 = 4.15418 loss)
I0410 00:17:59.733317 14080 solver.cpp:218] Iteration 1428 (1.04536 iter/s, 11.4793s/12 iters), loss = 4.11601
I0410 00:17:59.733372 14080 solver.cpp:237]     Train net output #0: loss = 4.11601 (* 1 = 4.11601 loss)
I0410 00:17:59.733383 14080 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0410 00:18:03.923378 14080 solver.cpp:218] Iteration 1440 (2.86408 iter/s, 4.18982s/12 iters), loss = 4.02598
I0410 00:18:03.923501 14080 solver.cpp:237]     Train net output #0: loss = 4.02598 (* 1 = 4.02598 loss)
I0410 00:18:03.923509 14080 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0410 00:18:08.821050 14080 solver.cpp:218] Iteration 1452 (2.45031 iter/s, 4.89733s/12 iters), loss = 4.25592
I0410 00:18:08.821106 14080 solver.cpp:237]     Train net output #0: loss = 4.25592 (* 1 = 4.25592 loss)
I0410 00:18:08.821120 14080 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0410 00:18:13.692770 14080 solver.cpp:218] Iteration 1464 (2.46333 iter/s, 4.87145s/12 iters), loss = 4.01255
I0410 00:18:13.692827 14080 solver.cpp:237]     Train net output #0: loss = 4.01255 (* 1 = 4.01255 loss)
I0410 00:18:13.692839 14080 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0410 00:18:18.529429 14080 solver.cpp:218] Iteration 1476 (2.48119 iter/s, 4.83639s/12 iters), loss = 4.04651
I0410 00:18:18.529480 14080 solver.cpp:237]     Train net output #0: loss = 4.04651 (* 1 = 4.04651 loss)
I0410 00:18:18.529490 14080 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0410 00:18:23.372349 14080 solver.cpp:218] Iteration 1488 (2.47798 iter/s, 4.84266s/12 iters), loss = 4.04675
I0410 00:18:23.372404 14080 solver.cpp:237]     Train net output #0: loss = 4.04675 (* 1 = 4.04675 loss)
I0410 00:18:23.372416 14080 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0410 00:18:28.178642 14080 solver.cpp:218] Iteration 1500 (2.49686 iter/s, 4.80603s/12 iters), loss = 3.85899
I0410 00:18:28.178687 14080 solver.cpp:237]     Train net output #0: loss = 3.85899 (* 1 = 3.85899 loss)
I0410 00:18:28.178697 14080 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0410 00:18:33.222376 14080 solver.cpp:218] Iteration 1512 (2.37931 iter/s, 5.04347s/12 iters), loss = 3.96762
I0410 00:18:33.222432 14080 solver.cpp:237]     Train net output #0: loss = 3.96762 (* 1 = 3.96762 loss)
I0410 00:18:33.222443 14080 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0410 00:18:34.955165 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:18:38.100123 14080 solver.cpp:218] Iteration 1524 (2.46029 iter/s, 4.87748s/12 iters), loss = 3.86667
I0410 00:18:38.100169 14080 solver.cpp:237]     Train net output #0: loss = 3.86667 (* 1 = 3.86667 loss)
I0410 00:18:38.100178 14080 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0410 00:18:40.093894 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0410 00:18:42.523748 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0410 00:18:48.635385 14080 solver.cpp:330] Iteration 1530, Testing net (#0)
I0410 00:18:48.635414 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:18:52.535303 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:18:53.169812 14080 solver.cpp:397]     Test net output #0: accuracy = 0.123775
I0410 00:18:53.169867 14080 solver.cpp:397]     Test net output #1: loss = 3.98306 (* 1 = 3.98306 loss)
I0410 00:18:55.083680 14080 solver.cpp:218] Iteration 1536 (0.706597 iter/s, 16.9828s/12 iters), loss = 4.07495
I0410 00:18:55.083726 14080 solver.cpp:237]     Train net output #0: loss = 4.07495 (* 1 = 4.07495 loss)
I0410 00:18:55.083736 14080 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0410 00:19:00.154086 14080 solver.cpp:218] Iteration 1548 (2.3668 iter/s, 5.07014s/12 iters), loss = 3.6288
I0410 00:19:00.154145 14080 solver.cpp:237]     Train net output #0: loss = 3.6288 (* 1 = 3.6288 loss)
I0410 00:19:00.154156 14080 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0410 00:19:05.156879 14080 solver.cpp:218] Iteration 1560 (2.39879 iter/s, 5.00252s/12 iters), loss = 3.98695
I0410 00:19:05.159258 14080 solver.cpp:237]     Train net output #0: loss = 3.98695 (* 1 = 3.98695 loss)
I0410 00:19:05.159271 14080 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0410 00:19:10.117707 14080 solver.cpp:218] Iteration 1572 (2.42022 iter/s, 4.95824s/12 iters), loss = 3.91243
I0410 00:19:10.117763 14080 solver.cpp:237]     Train net output #0: loss = 3.91243 (* 1 = 3.91243 loss)
I0410 00:19:10.117774 14080 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0410 00:19:15.020812 14080 solver.cpp:218] Iteration 1584 (2.44756 iter/s, 4.90284s/12 iters), loss = 3.9148
I0410 00:19:15.020859 14080 solver.cpp:237]     Train net output #0: loss = 3.9148 (* 1 = 3.9148 loss)
I0410 00:19:15.020869 14080 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0410 00:19:19.968669 14080 solver.cpp:218] Iteration 1596 (2.42542 iter/s, 4.94759s/12 iters), loss = 3.8939
I0410 00:19:19.968724 14080 solver.cpp:237]     Train net output #0: loss = 3.8939 (* 1 = 3.8939 loss)
I0410 00:19:19.968736 14080 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0410 00:19:24.906177 14080 solver.cpp:218] Iteration 1608 (2.43051 iter/s, 4.93724s/12 iters), loss = 3.79007
I0410 00:19:24.906229 14080 solver.cpp:237]     Train net output #0: loss = 3.79007 (* 1 = 3.79007 loss)
I0410 00:19:24.906241 14080 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0410 00:19:28.753082 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:19:29.821230 14080 solver.cpp:218] Iteration 1620 (2.44161 iter/s, 4.91479s/12 iters), loss = 3.67939
I0410 00:19:29.821285 14080 solver.cpp:237]     Train net output #0: loss = 3.67939 (* 1 = 3.67939 loss)
I0410 00:19:29.821297 14080 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0410 00:19:34.266319 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0410 00:19:35.957773 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0410 00:19:37.496371 14080 solver.cpp:330] Iteration 1632, Testing net (#0)
I0410 00:19:37.496397 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:19:41.294998 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:19:41.966766 14080 solver.cpp:397]     Test net output #0: accuracy = 0.140319
I0410 00:19:41.966797 14080 solver.cpp:397]     Test net output #1: loss = 3.84863 (* 1 = 3.84863 loss)
I0410 00:19:42.052578 14080 solver.cpp:218] Iteration 1632 (0.98113 iter/s, 12.2308s/12 iters), loss = 3.66107
I0410 00:19:42.052625 14080 solver.cpp:237]     Train net output #0: loss = 3.66107 (* 1 = 3.66107 loss)
I0410 00:19:42.052634 14080 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0410 00:19:46.239125 14080 solver.cpp:218] Iteration 1644 (2.86648 iter/s, 4.18631s/12 iters), loss = 3.83163
I0410 00:19:46.239178 14080 solver.cpp:237]     Train net output #0: loss = 3.83163 (* 1 = 3.83163 loss)
I0410 00:19:46.239190 14080 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0410 00:19:51.114547 14080 solver.cpp:218] Iteration 1656 (2.46146 iter/s, 4.87516s/12 iters), loss = 3.86964
I0410 00:19:51.114589 14080 solver.cpp:237]     Train net output #0: loss = 3.86964 (* 1 = 3.86964 loss)
I0410 00:19:51.114599 14080 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0410 00:19:56.026937 14080 solver.cpp:218] Iteration 1668 (2.44293 iter/s, 4.91213s/12 iters), loss = 3.5441
I0410 00:19:56.026983 14080 solver.cpp:237]     Train net output #0: loss = 3.5441 (* 1 = 3.5441 loss)
I0410 00:19:56.026994 14080 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0410 00:20:00.972992 14080 solver.cpp:218] Iteration 1680 (2.4263 iter/s, 4.94579s/12 iters), loss = 3.73322
I0410 00:20:00.973045 14080 solver.cpp:237]     Train net output #0: loss = 3.73322 (* 1 = 3.73322 loss)
I0410 00:20:00.973058 14080 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0410 00:20:05.940464 14080 solver.cpp:218] Iteration 1692 (2.41585 iter/s, 4.96721s/12 iters), loss = 3.82427
I0410 00:20:05.940505 14080 solver.cpp:237]     Train net output #0: loss = 3.82427 (* 1 = 3.82427 loss)
I0410 00:20:05.940513 14080 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0410 00:20:10.797698 14080 solver.cpp:218] Iteration 1704 (2.47067 iter/s, 4.85698s/12 iters), loss = 3.40512
I0410 00:20:10.797833 14080 solver.cpp:237]     Train net output #0: loss = 3.40512 (* 1 = 3.40512 loss)
I0410 00:20:10.797847 14080 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0410 00:20:15.644757 14080 solver.cpp:218] Iteration 1716 (2.4759 iter/s, 4.84672s/12 iters), loss = 3.73206
I0410 00:20:15.644802 14080 solver.cpp:237]     Train net output #0: loss = 3.73206 (* 1 = 3.73206 loss)
I0410 00:20:15.644811 14080 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0410 00:20:16.668393 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:20:20.531123 14080 solver.cpp:218] Iteration 1728 (2.45595 iter/s, 4.8861s/12 iters), loss = 3.52509
I0410 00:20:20.531179 14080 solver.cpp:237]     Train net output #0: loss = 3.52509 (* 1 = 3.52509 loss)
I0410 00:20:20.531190 14080 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0410 00:20:22.517372 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0410 00:20:26.849200 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0410 00:20:28.863938 14080 solver.cpp:330] Iteration 1734, Testing net (#0)
I0410 00:20:28.863968 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:20:32.635370 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:20:33.448230 14080 solver.cpp:397]     Test net output #0: accuracy = 0.15625
I0410 00:20:33.448277 14080 solver.cpp:397]     Test net output #1: loss = 3.70144 (* 1 = 3.70144 loss)
I0410 00:20:35.279407 14080 solver.cpp:218] Iteration 1740 (0.813691 iter/s, 14.7476s/12 iters), loss = 3.68505
I0410 00:20:35.279459 14080 solver.cpp:237]     Train net output #0: loss = 3.68505 (* 1 = 3.68505 loss)
I0410 00:20:35.279470 14080 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0410 00:20:40.310096 14080 solver.cpp:218] Iteration 1752 (2.38549 iter/s, 5.03042s/12 iters), loss = 3.52253
I0410 00:20:40.310142 14080 solver.cpp:237]     Train net output #0: loss = 3.52253 (* 1 = 3.52253 loss)
I0410 00:20:40.310153 14080 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0410 00:20:45.171031 14080 solver.cpp:218] Iteration 1764 (2.46879 iter/s, 4.86068s/12 iters), loss = 3.79239
I0410 00:20:45.171146 14080 solver.cpp:237]     Train net output #0: loss = 3.79239 (* 1 = 3.79239 loss)
I0410 00:20:45.171159 14080 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0410 00:20:50.035245 14080 solver.cpp:218] Iteration 1776 (2.46716 iter/s, 4.86389s/12 iters), loss = 3.75109
I0410 00:20:50.035302 14080 solver.cpp:237]     Train net output #0: loss = 3.75109 (* 1 = 3.75109 loss)
I0410 00:20:50.035315 14080 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0410 00:20:54.921494 14080 solver.cpp:218] Iteration 1788 (2.45601 iter/s, 4.88598s/12 iters), loss = 3.60862
I0410 00:20:54.921540 14080 solver.cpp:237]     Train net output #0: loss = 3.60862 (* 1 = 3.60862 loss)
I0410 00:20:54.921548 14080 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0410 00:20:59.932777 14080 solver.cpp:218] Iteration 1800 (2.39472 iter/s, 5.01102s/12 iters), loss = 3.64636
I0410 00:20:59.932826 14080 solver.cpp:237]     Train net output #0: loss = 3.64636 (* 1 = 3.64636 loss)
I0410 00:20:59.932837 14080 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0410 00:21:04.902071 14080 solver.cpp:218] Iteration 1812 (2.41496 iter/s, 4.96903s/12 iters), loss = 3.49947
I0410 00:21:04.902125 14080 solver.cpp:237]     Train net output #0: loss = 3.49947 (* 1 = 3.49947 loss)
I0410 00:21:04.902137 14080 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0410 00:21:08.055160 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:21:09.846076 14080 solver.cpp:218] Iteration 1824 (2.42731 iter/s, 4.94374s/12 iters), loss = 3.79818
I0410 00:21:09.846117 14080 solver.cpp:237]     Train net output #0: loss = 3.79818 (* 1 = 3.79818 loss)
I0410 00:21:09.846124 14080 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0410 00:21:14.561718 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0410 00:21:15.958983 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0410 00:21:16.994112 14080 solver.cpp:330] Iteration 1836, Testing net (#0)
I0410 00:21:16.994135 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:21:20.684206 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:21:21.434412 14080 solver.cpp:397]     Test net output #0: accuracy = 0.176471
I0410 00:21:21.434449 14080 solver.cpp:397]     Test net output #1: loss = 3.55515 (* 1 = 3.55515 loss)
I0410 00:21:21.520085 14080 solver.cpp:218] Iteration 1836 (1.02797 iter/s, 11.6735s/12 iters), loss = 3.5018
I0410 00:21:21.520136 14080 solver.cpp:237]     Train net output #0: loss = 3.5018 (* 1 = 3.5018 loss)
I0410 00:21:21.520146 14080 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0410 00:21:25.770933 14080 solver.cpp:218] Iteration 1848 (2.82312 iter/s, 4.25061s/12 iters), loss = 3.57908
I0410 00:21:25.770983 14080 solver.cpp:237]     Train net output #0: loss = 3.57908 (* 1 = 3.57908 loss)
I0410 00:21:25.770994 14080 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0410 00:21:30.683022 14080 solver.cpp:218] Iteration 1860 (2.44308 iter/s, 4.91183s/12 iters), loss = 3.41737
I0410 00:21:30.683060 14080 solver.cpp:237]     Train net output #0: loss = 3.41737 (* 1 = 3.41737 loss)
I0410 00:21:30.683069 14080 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0410 00:21:35.881254 14080 solver.cpp:218] Iteration 1872 (2.30859 iter/s, 5.19797s/12 iters), loss = 3.45506
I0410 00:21:35.881300 14080 solver.cpp:237]     Train net output #0: loss = 3.45506 (* 1 = 3.45506 loss)
I0410 00:21:35.881309 14080 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0410 00:21:40.725126 14080 solver.cpp:218] Iteration 1884 (2.47749 iter/s, 4.84361s/12 iters), loss = 3.61878
I0410 00:21:40.725174 14080 solver.cpp:237]     Train net output #0: loss = 3.61878 (* 1 = 3.61878 loss)
I0410 00:21:40.725183 14080 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0410 00:21:45.710137 14080 solver.cpp:218] Iteration 1896 (2.40734 iter/s, 4.98475s/12 iters), loss = 3.48279
I0410 00:21:45.710183 14080 solver.cpp:237]     Train net output #0: loss = 3.48279 (* 1 = 3.48279 loss)
I0410 00:21:45.710193 14080 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0410 00:21:50.626691 14080 solver.cpp:218] Iteration 1908 (2.44086 iter/s, 4.91629s/12 iters), loss = 3.37029
I0410 00:21:50.626852 14080 solver.cpp:237]     Train net output #0: loss = 3.37029 (* 1 = 3.37029 loss)
I0410 00:21:50.626865 14080 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0410 00:21:55.659250 14080 solver.cpp:218] Iteration 1920 (2.38465 iter/s, 5.03218s/12 iters), loss = 3.2553
I0410 00:21:55.659301 14080 solver.cpp:237]     Train net output #0: loss = 3.2553 (* 1 = 3.2553 loss)
I0410 00:21:55.659310 14080 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0410 00:21:55.978933 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:22:00.566656 14080 solver.cpp:218] Iteration 1932 (2.44541 iter/s, 4.90715s/12 iters), loss = 3.24647
I0410 00:22:00.566696 14080 solver.cpp:237]     Train net output #0: loss = 3.24647 (* 1 = 3.24647 loss)
I0410 00:22:00.566706 14080 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0410 00:22:02.567883 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0410 00:22:04.029330 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0410 00:22:05.075569 14080 solver.cpp:330] Iteration 1938, Testing net (#0)
I0410 00:22:05.075598 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:22:08.682827 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:22:09.470297 14080 solver.cpp:397]     Test net output #0: accuracy = 0.208333
I0410 00:22:09.470336 14080 solver.cpp:397]     Test net output #1: loss = 3.36659 (* 1 = 3.36659 loss)
I0410 00:22:11.475834 14080 solver.cpp:218] Iteration 1944 (1.10004 iter/s, 10.9087s/12 iters), loss = 3.19061
I0410 00:22:11.475891 14080 solver.cpp:237]     Train net output #0: loss = 3.19061 (* 1 = 3.19061 loss)
I0410 00:22:11.475903 14080 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0410 00:22:16.395223 14080 solver.cpp:218] Iteration 1956 (2.43946 iter/s, 4.91912s/12 iters), loss = 3.19681
I0410 00:22:16.395283 14080 solver.cpp:237]     Train net output #0: loss = 3.19681 (* 1 = 3.19681 loss)
I0410 00:22:16.395298 14080 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0410 00:22:21.370726 14080 solver.cpp:218] Iteration 1968 (2.41195 iter/s, 4.97523s/12 iters), loss = 3.42258
I0410 00:22:21.370815 14080 solver.cpp:237]     Train net output #0: loss = 3.42258 (* 1 = 3.42258 loss)
I0410 00:22:21.370828 14080 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0410 00:22:26.305234 14080 solver.cpp:218] Iteration 1980 (2.432 iter/s, 4.9342s/12 iters), loss = 3.33888
I0410 00:22:26.305294 14080 solver.cpp:237]     Train net output #0: loss = 3.33888 (* 1 = 3.33888 loss)
I0410 00:22:26.305306 14080 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0410 00:22:31.360066 14080 solver.cpp:218] Iteration 1992 (2.3741 iter/s, 5.05455s/12 iters), loss = 3.48986
I0410 00:22:31.360121 14080 solver.cpp:237]     Train net output #0: loss = 3.48986 (* 1 = 3.48986 loss)
I0410 00:22:31.360133 14080 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0410 00:22:36.411923 14080 solver.cpp:218] Iteration 2004 (2.37549 iter/s, 5.05159s/12 iters), loss = 3.08165
I0410 00:22:36.411967 14080 solver.cpp:237]     Train net output #0: loss = 3.08165 (* 1 = 3.08165 loss)
I0410 00:22:36.411976 14080 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0410 00:22:41.406666 14080 solver.cpp:218] Iteration 2016 (2.40265 iter/s, 4.99448s/12 iters), loss = 3.08689
I0410 00:22:41.406704 14080 solver.cpp:237]     Train net output #0: loss = 3.08689 (* 1 = 3.08689 loss)
I0410 00:22:41.406713 14080 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0410 00:22:43.939203 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:22:46.353878 14080 solver.cpp:218] Iteration 2028 (2.42573 iter/s, 4.94696s/12 iters), loss = 3.06791
I0410 00:22:46.353924 14080 solver.cpp:237]     Train net output #0: loss = 3.06791 (* 1 = 3.06791 loss)
I0410 00:22:46.353933 14080 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0410 00:22:50.823900 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0410 00:22:53.300119 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0410 00:22:56.796809 14080 solver.cpp:330] Iteration 2040, Testing net (#0)
I0410 00:22:56.796839 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:23:00.522656 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:23:01.372736 14080 solver.cpp:397]     Test net output #0: accuracy = 0.213848
I0410 00:23:01.372786 14080 solver.cpp:397]     Test net output #1: loss = 3.28747 (* 1 = 3.28747 loss)
I0410 00:23:01.458086 14080 solver.cpp:218] Iteration 2040 (0.794516 iter/s, 15.1035s/12 iters), loss = 3.25745
I0410 00:23:01.458138 14080 solver.cpp:237]     Train net output #0: loss = 3.25745 (* 1 = 3.25745 loss)
I0410 00:23:01.458149 14080 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0410 00:23:05.704079 14080 solver.cpp:218] Iteration 2052 (2.82635 iter/s, 4.24576s/12 iters), loss = 3.20104
I0410 00:23:05.704123 14080 solver.cpp:237]     Train net output #0: loss = 3.20104 (* 1 = 3.20104 loss)
I0410 00:23:05.704130 14080 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0410 00:23:06.946482 14080 blocking_queue.cpp:49] Waiting for data
I0410 00:23:10.642151 14080 solver.cpp:218] Iteration 2064 (2.43023 iter/s, 4.93781s/12 iters), loss = 3.17656
I0410 00:23:10.642210 14080 solver.cpp:237]     Train net output #0: loss = 3.17656 (* 1 = 3.17656 loss)
I0410 00:23:10.642221 14080 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0410 00:23:15.613073 14080 solver.cpp:218] Iteration 2076 (2.41417 iter/s, 4.97065s/12 iters), loss = 3.12765
I0410 00:23:15.613131 14080 solver.cpp:237]     Train net output #0: loss = 3.12765 (* 1 = 3.12765 loss)
I0410 00:23:15.613143 14080 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0410 00:23:20.515882 14080 solver.cpp:218] Iteration 2088 (2.44772 iter/s, 4.90253s/12 iters), loss = 3.29416
I0410 00:23:20.515939 14080 solver.cpp:237]     Train net output #0: loss = 3.29416 (* 1 = 3.29416 loss)
I0410 00:23:20.515954 14080 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0410 00:23:25.324658 14080 solver.cpp:218] Iteration 2100 (2.49558 iter/s, 4.80851s/12 iters), loss = 3.18379
I0410 00:23:25.324772 14080 solver.cpp:237]     Train net output #0: loss = 3.18379 (* 1 = 3.18379 loss)
I0410 00:23:25.324782 14080 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0410 00:23:30.258064 14080 solver.cpp:218] Iteration 2112 (2.43256 iter/s, 4.93307s/12 iters), loss = 3.21905
I0410 00:23:30.258126 14080 solver.cpp:237]     Train net output #0: loss = 3.21905 (* 1 = 3.21905 loss)
I0410 00:23:30.258141 14080 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0410 00:23:34.844588 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:23:35.156522 14080 solver.cpp:218] Iteration 2124 (2.44989 iter/s, 4.89818s/12 iters), loss = 2.95056
I0410 00:23:35.156574 14080 solver.cpp:237]     Train net output #0: loss = 2.95056 (* 1 = 2.95056 loss)
I0410 00:23:35.156584 14080 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0410 00:23:40.111390 14080 solver.cpp:218] Iteration 2136 (2.42199 iter/s, 4.9546s/12 iters), loss = 3.03535
I0410 00:23:40.111431 14080 solver.cpp:237]     Train net output #0: loss = 3.03535 (* 1 = 3.03535 loss)
I0410 00:23:40.111439 14080 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0410 00:23:42.110416 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0410 00:23:43.509179 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0410 00:23:44.542524 14080 solver.cpp:330] Iteration 2142, Testing net (#0)
I0410 00:23:44.542546 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:23:48.220014 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:23:49.110356 14080 solver.cpp:397]     Test net output #0: accuracy = 0.231618
I0410 00:23:49.110406 14080 solver.cpp:397]     Test net output #1: loss = 3.16372 (* 1 = 3.16372 loss)
I0410 00:23:50.967800 14080 solver.cpp:218] Iteration 2148 (1.10539 iter/s, 10.8559s/12 iters), loss = 3.10783
I0410 00:23:50.967850 14080 solver.cpp:237]     Train net output #0: loss = 3.10783 (* 1 = 3.10783 loss)
I0410 00:23:50.967861 14080 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0410 00:23:55.860289 14080 solver.cpp:218] Iteration 2160 (2.45287 iter/s, 4.89223s/12 iters), loss = 3.07675
I0410 00:23:55.860409 14080 solver.cpp:237]     Train net output #0: loss = 3.07675 (* 1 = 3.07675 loss)
I0410 00:23:55.860417 14080 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0410 00:24:00.752281 14080 solver.cpp:218] Iteration 2172 (2.45316 iter/s, 4.89166s/12 iters), loss = 2.78875
I0410 00:24:00.752331 14080 solver.cpp:237]     Train net output #0: loss = 2.78875 (* 1 = 2.78875 loss)
I0410 00:24:00.752343 14080 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0410 00:24:05.638304 14080 solver.cpp:218] Iteration 2184 (2.45612 iter/s, 4.88575s/12 iters), loss = 3.06739
I0410 00:24:05.638358 14080 solver.cpp:237]     Train net output #0: loss = 3.06739 (* 1 = 3.06739 loss)
I0410 00:24:05.638371 14080 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0410 00:24:10.620858 14080 solver.cpp:218] Iteration 2196 (2.40853 iter/s, 4.98229s/12 iters), loss = 2.99632
I0410 00:24:10.620910 14080 solver.cpp:237]     Train net output #0: loss = 2.99632 (* 1 = 2.99632 loss)
I0410 00:24:10.620923 14080 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0410 00:24:15.596441 14080 solver.cpp:218] Iteration 2208 (2.41191 iter/s, 4.97531s/12 iters), loss = 2.62736
I0410 00:24:15.596488 14080 solver.cpp:237]     Train net output #0: loss = 2.62736 (* 1 = 2.62736 loss)
I0410 00:24:15.596496 14080 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0410 00:24:20.434334 14080 solver.cpp:218] Iteration 2220 (2.48055 iter/s, 4.83763s/12 iters), loss = 2.81635
I0410 00:24:20.434386 14080 solver.cpp:237]     Train net output #0: loss = 2.81635 (* 1 = 2.81635 loss)
I0410 00:24:20.434398 14080 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0410 00:24:22.225066 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:24:25.566915 14080 solver.cpp:218] Iteration 2232 (2.33813 iter/s, 5.13231s/12 iters), loss = 2.94579
I0410 00:24:25.566962 14080 solver.cpp:237]     Train net output #0: loss = 2.94579 (* 1 = 2.94579 loss)
I0410 00:24:25.566972 14080 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0410 00:24:30.073390 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0410 00:24:31.494550 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0410 00:24:32.531742 14080 solver.cpp:330] Iteration 2244, Testing net (#0)
I0410 00:24:32.531772 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:24:36.161922 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:24:37.094357 14080 solver.cpp:397]     Test net output #0: accuracy = 0.252451
I0410 00:24:37.094405 14080 solver.cpp:397]     Test net output #1: loss = 3.0697 (* 1 = 3.0697 loss)
I0410 00:24:37.180224 14080 solver.cpp:218] Iteration 2244 (1.03334 iter/s, 11.6128s/12 iters), loss = 2.81169
I0410 00:24:37.180276 14080 solver.cpp:237]     Train net output #0: loss = 2.81169 (* 1 = 2.81169 loss)
I0410 00:24:37.180286 14080 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0410 00:24:41.533758 14080 solver.cpp:218] Iteration 2256 (2.75653 iter/s, 4.35329s/12 iters), loss = 2.81333
I0410 00:24:41.533804 14080 solver.cpp:237]     Train net output #0: loss = 2.81333 (* 1 = 2.81333 loss)
I0410 00:24:41.533813 14080 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0410 00:24:46.400430 14080 solver.cpp:218] Iteration 2268 (2.46588 iter/s, 4.86641s/12 iters), loss = 3.07259
I0410 00:24:46.400485 14080 solver.cpp:237]     Train net output #0: loss = 3.07259 (* 1 = 3.07259 loss)
I0410 00:24:46.400498 14080 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0410 00:24:51.261072 14080 solver.cpp:218] Iteration 2280 (2.46895 iter/s, 4.86037s/12 iters), loss = 2.7247
I0410 00:24:51.261123 14080 solver.cpp:237]     Train net output #0: loss = 2.7247 (* 1 = 2.7247 loss)
I0410 00:24:51.261134 14080 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0410 00:24:56.151474 14080 solver.cpp:218] Iteration 2292 (2.45392 iter/s, 4.89013s/12 iters), loss = 2.87221
I0410 00:24:56.151535 14080 solver.cpp:237]     Train net output #0: loss = 2.87221 (* 1 = 2.87221 loss)
I0410 00:24:56.151548 14080 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0410 00:25:01.150426 14080 solver.cpp:218] Iteration 2304 (2.40063 iter/s, 4.99868s/12 iters), loss = 2.8401
I0410 00:25:01.150543 14080 solver.cpp:237]     Train net output #0: loss = 2.8401 (* 1 = 2.8401 loss)
I0410 00:25:01.150557 14080 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0410 00:25:06.083755 14080 solver.cpp:218] Iteration 2316 (2.43259 iter/s, 4.93301s/12 iters), loss = 2.54266
I0410 00:25:06.083792 14080 solver.cpp:237]     Train net output #0: loss = 2.54266 (* 1 = 2.54266 loss)
I0410 00:25:06.083799 14080 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0410 00:25:09.986181 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:25:11.013645 14080 solver.cpp:218] Iteration 2328 (2.43426 iter/s, 4.92963s/12 iters), loss = 2.6617
I0410 00:25:11.013693 14080 solver.cpp:237]     Train net output #0: loss = 2.6617 (* 1 = 2.6617 loss)
I0410 00:25:11.013703 14080 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0410 00:25:15.899855 14080 solver.cpp:218] Iteration 2340 (2.45602 iter/s, 4.88594s/12 iters), loss = 2.57872
I0410 00:25:15.899914 14080 solver.cpp:237]     Train net output #0: loss = 2.57872 (* 1 = 2.57872 loss)
I0410 00:25:15.899930 14080 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0410 00:25:17.892659 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0410 00:25:19.426039 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0410 00:25:20.468238 14080 solver.cpp:330] Iteration 2346, Testing net (#0)
I0410 00:25:20.468259 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:25:24.388592 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:25:25.331265 14080 solver.cpp:397]     Test net output #0: accuracy = 0.273897
I0410 00:25:25.331315 14080 solver.cpp:397]     Test net output #1: loss = 2.97995 (* 1 = 2.97995 loss)
I0410 00:25:27.291684 14080 solver.cpp:218] Iteration 2352 (1.05344 iter/s, 11.3913s/12 iters), loss = 2.81798
I0410 00:25:27.291741 14080 solver.cpp:237]     Train net output #0: loss = 2.81798 (* 1 = 2.81798 loss)
I0410 00:25:27.291754 14080 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0410 00:25:32.166546 14080 solver.cpp:218] Iteration 2364 (2.46175 iter/s, 4.87459s/12 iters), loss = 2.57913
I0410 00:25:32.166654 14080 solver.cpp:237]     Train net output #0: loss = 2.57913 (* 1 = 2.57913 loss)
I0410 00:25:32.166666 14080 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0410 00:25:37.030350 14080 solver.cpp:218] Iteration 2376 (2.46737 iter/s, 4.86349s/12 iters), loss = 2.54041
I0410 00:25:37.030400 14080 solver.cpp:237]     Train net output #0: loss = 2.54041 (* 1 = 2.54041 loss)
I0410 00:25:37.030411 14080 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0410 00:25:42.200130 14080 solver.cpp:218] Iteration 2388 (2.32131 iter/s, 5.1695s/12 iters), loss = 2.7347
I0410 00:25:42.200187 14080 solver.cpp:237]     Train net output #0: loss = 2.7347 (* 1 = 2.7347 loss)
I0410 00:25:42.200201 14080 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0410 00:25:47.374115 14080 solver.cpp:218] Iteration 2400 (2.31942 iter/s, 5.17371s/12 iters), loss = 2.30988
I0410 00:25:47.374162 14080 solver.cpp:237]     Train net output #0: loss = 2.30988 (* 1 = 2.30988 loss)
I0410 00:25:47.374172 14080 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0410 00:25:52.312713 14080 solver.cpp:218] Iteration 2412 (2.42997 iter/s, 4.93833s/12 iters), loss = 2.44952
I0410 00:25:52.312763 14080 solver.cpp:237]     Train net output #0: loss = 2.44952 (* 1 = 2.44952 loss)
I0410 00:25:52.312774 14080 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0410 00:25:57.194707 14080 solver.cpp:218] Iteration 2424 (2.45814 iter/s, 4.88174s/12 iters), loss = 2.64171
I0410 00:25:57.194746 14080 solver.cpp:237]     Train net output #0: loss = 2.64171 (* 1 = 2.64171 loss)
I0410 00:25:57.194753 14080 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0410 00:25:58.245443 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:26:02.119082 14080 solver.cpp:218] Iteration 2436 (2.43698 iter/s, 4.92412s/12 iters), loss = 2.43437
I0410 00:26:02.119133 14080 solver.cpp:237]     Train net output #0: loss = 2.43437 (* 1 = 2.43437 loss)
I0410 00:26:02.119145 14080 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0410 00:26:06.508514 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0410 00:26:12.703158 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0410 00:26:14.676221 14080 solver.cpp:330] Iteration 2448, Testing net (#0)
I0410 00:26:14.676247 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:26:18.060878 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:26:19.036444 14080 solver.cpp:397]     Test net output #0: accuracy = 0.266544
I0410 00:26:19.036484 14080 solver.cpp:397]     Test net output #1: loss = 2.93546 (* 1 = 2.93546 loss)
I0410 00:26:19.120703 14080 solver.cpp:218] Iteration 2448 (0.705846 iter/s, 17.0009s/12 iters), loss = 2.59631
I0410 00:26:19.120759 14080 solver.cpp:237]     Train net output #0: loss = 2.59631 (* 1 = 2.59631 loss)
I0410 00:26:19.120769 14080 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0410 00:26:23.279762 14080 solver.cpp:218] Iteration 2460 (2.88543 iter/s, 4.15882s/12 iters), loss = 2.13235
I0410 00:26:23.279811 14080 solver.cpp:237]     Train net output #0: loss = 2.13235 (* 1 = 2.13235 loss)
I0410 00:26:23.279822 14080 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0410 00:26:28.203474 14080 solver.cpp:218] Iteration 2472 (2.43732 iter/s, 4.92344s/12 iters), loss = 2.51765
I0410 00:26:28.203533 14080 solver.cpp:237]     Train net output #0: loss = 2.51765 (* 1 = 2.51765 loss)
I0410 00:26:28.203545 14080 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0410 00:26:33.144129 14080 solver.cpp:218] Iteration 2484 (2.42896 iter/s, 4.94038s/12 iters), loss = 2.50078
I0410 00:26:33.144176 14080 solver.cpp:237]     Train net output #0: loss = 2.50078 (* 1 = 2.50078 loss)
I0410 00:26:33.144186 14080 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0410 00:26:38.118633 14080 solver.cpp:218] Iteration 2496 (2.41243 iter/s, 4.97424s/12 iters), loss = 2.69641
I0410 00:26:38.118741 14080 solver.cpp:237]     Train net output #0: loss = 2.69641 (* 1 = 2.69641 loss)
I0410 00:26:38.118754 14080 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0410 00:26:42.990594 14080 solver.cpp:218] Iteration 2508 (2.46324 iter/s, 4.87164s/12 iters), loss = 2.77936
I0410 00:26:42.990646 14080 solver.cpp:237]     Train net output #0: loss = 2.77936 (* 1 = 2.77936 loss)
I0410 00:26:42.990658 14080 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0410 00:26:48.111805 14080 solver.cpp:218] Iteration 2520 (2.34332 iter/s, 5.12094s/12 iters), loss = 2.62917
I0410 00:26:48.111860 14080 solver.cpp:237]     Train net output #0: loss = 2.62917 (* 1 = 2.62917 loss)
I0410 00:26:48.111872 14080 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0410 00:26:51.258090 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:26:52.976595 14080 solver.cpp:218] Iteration 2532 (2.46684 iter/s, 4.86452s/12 iters), loss = 2.40557
I0410 00:26:52.976651 14080 solver.cpp:237]     Train net output #0: loss = 2.40557 (* 1 = 2.40557 loss)
I0410 00:26:52.976663 14080 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0410 00:26:58.015661 14080 solver.cpp:218] Iteration 2544 (2.38152 iter/s, 5.03879s/12 iters), loss = 2.14584
I0410 00:26:58.015714 14080 solver.cpp:237]     Train net output #0: loss = 2.14584 (* 1 = 2.14584 loss)
I0410 00:26:58.015727 14080 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0410 00:27:00.002569 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0410 00:27:01.666766 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0410 00:27:03.036310 14080 solver.cpp:330] Iteration 2550, Testing net (#0)
I0410 00:27:03.036339 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:27:06.827325 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:27:07.851792 14080 solver.cpp:397]     Test net output #0: accuracy = 0.250613
I0410 00:27:07.851831 14080 solver.cpp:397]     Test net output #1: loss = 3.07003 (* 1 = 3.07003 loss)
I0410 00:27:09.816705 14080 solver.cpp:218] Iteration 2556 (1.01691 iter/s, 11.8005s/12 iters), loss = 2.85863
I0410 00:27:09.816854 14080 solver.cpp:237]     Train net output #0: loss = 2.85863 (* 1 = 2.85863 loss)
I0410 00:27:09.816867 14080 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0410 00:27:14.796871 14080 solver.cpp:218] Iteration 2568 (2.40973 iter/s, 4.97981s/12 iters), loss = 2.73494
I0410 00:27:14.796912 14080 solver.cpp:237]     Train net output #0: loss = 2.73494 (* 1 = 2.73494 loss)
I0410 00:27:14.796921 14080 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0410 00:27:19.724179 14080 solver.cpp:218] Iteration 2580 (2.43553 iter/s, 4.92705s/12 iters), loss = 2.49516
I0410 00:27:19.724231 14080 solver.cpp:237]     Train net output #0: loss = 2.49516 (* 1 = 2.49516 loss)
I0410 00:27:19.724242 14080 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0410 00:27:24.649735 14080 solver.cpp:218] Iteration 2592 (2.4364 iter/s, 4.92529s/12 iters), loss = 2.68121
I0410 00:27:24.649782 14080 solver.cpp:237]     Train net output #0: loss = 2.68121 (* 1 = 2.68121 loss)
I0410 00:27:24.649794 14080 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0410 00:27:29.731573 14080 solver.cpp:218] Iteration 2604 (2.36148 iter/s, 5.08157s/12 iters), loss = 2.60923
I0410 00:27:29.731627 14080 solver.cpp:237]     Train net output #0: loss = 2.60923 (* 1 = 2.60923 loss)
I0410 00:27:29.731638 14080 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0410 00:27:34.816730 14080 solver.cpp:218] Iteration 2616 (2.35994 iter/s, 5.08488s/12 iters), loss = 2.63494
I0410 00:27:34.816779 14080 solver.cpp:237]     Train net output #0: loss = 2.63494 (* 1 = 2.63494 loss)
I0410 00:27:34.816789 14080 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0410 00:27:39.783486 14080 solver.cpp:218] Iteration 2628 (2.41619 iter/s, 4.96649s/12 iters), loss = 2.45045
I0410 00:27:39.783538 14080 solver.cpp:237]     Train net output #0: loss = 2.45045 (* 1 = 2.45045 loss)
I0410 00:27:39.783550 14080 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0410 00:27:40.211937 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:27:44.705838 14080 solver.cpp:218] Iteration 2640 (2.43799 iter/s, 4.92209s/12 iters), loss = 2.31278
I0410 00:27:44.705885 14080 solver.cpp:237]     Train net output #0: loss = 2.31278 (* 1 = 2.31278 loss)
I0410 00:27:44.705894 14080 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0410 00:27:49.200461 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0410 00:27:50.565057 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0410 00:27:51.602454 14080 solver.cpp:330] Iteration 2652, Testing net (#0)
I0410 00:27:51.602483 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:27:54.974932 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:27:56.038165 14080 solver.cpp:397]     Test net output #0: accuracy = 0.278186
I0410 00:27:56.038200 14080 solver.cpp:397]     Test net output #1: loss = 2.91641 (* 1 = 2.91641 loss)
I0410 00:27:56.121454 14080 solver.cpp:218] Iteration 2652 (1.05124 iter/s, 11.4151s/12 iters), loss = 2.50651
I0410 00:27:56.121506 14080 solver.cpp:237]     Train net output #0: loss = 2.50651 (* 1 = 2.50651 loss)
I0410 00:27:56.121517 14080 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0410 00:28:00.285894 14080 solver.cpp:218] Iteration 2664 (2.88171 iter/s, 4.1642s/12 iters), loss = 2.24326
I0410 00:28:00.285975 14080 solver.cpp:237]     Train net output #0: loss = 2.24326 (* 1 = 2.24326 loss)
I0410 00:28:00.285988 14080 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0410 00:28:05.119894 14080 solver.cpp:218] Iteration 2676 (2.48256 iter/s, 4.83373s/12 iters), loss = 2.33438
I0410 00:28:05.119948 14080 solver.cpp:237]     Train net output #0: loss = 2.33438 (* 1 = 2.33438 loss)
I0410 00:28:05.119959 14080 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0410 00:28:09.958449 14080 solver.cpp:218] Iteration 2688 (2.48022 iter/s, 4.83828s/12 iters), loss = 2.34095
I0410 00:28:09.958511 14080 solver.cpp:237]     Train net output #0: loss = 2.34095 (* 1 = 2.34095 loss)
I0410 00:28:09.958523 14080 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0410 00:28:14.802908 14080 solver.cpp:218] Iteration 2700 (2.4772 iter/s, 4.84419s/12 iters), loss = 2.53062
I0410 00:28:14.803056 14080 solver.cpp:237]     Train net output #0: loss = 2.53062 (* 1 = 2.53062 loss)
I0410 00:28:14.803067 14080 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0410 00:28:19.822434 14080 solver.cpp:218] Iteration 2712 (2.39084 iter/s, 5.01916s/12 iters), loss = 2.26503
I0410 00:28:19.822485 14080 solver.cpp:237]     Train net output #0: loss = 2.26503 (* 1 = 2.26503 loss)
I0410 00:28:19.822497 14080 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0410 00:28:24.728854 14080 solver.cpp:218] Iteration 2724 (2.44591 iter/s, 4.90616s/12 iters), loss = 2.20691
I0410 00:28:24.728906 14080 solver.cpp:237]     Train net output #0: loss = 2.20691 (* 1 = 2.20691 loss)
I0410 00:28:24.728919 14080 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0410 00:28:27.276660 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:28:29.613695 14080 solver.cpp:218] Iteration 2736 (2.45671 iter/s, 4.88458s/12 iters), loss = 2.01306
I0410 00:28:29.613749 14080 solver.cpp:237]     Train net output #0: loss = 2.01306 (* 1 = 2.01306 loss)
I0410 00:28:29.613759 14080 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0410 00:28:34.527380 14080 solver.cpp:218] Iteration 2748 (2.44229 iter/s, 4.91342s/12 iters), loss = 2.42675
I0410 00:28:34.527427 14080 solver.cpp:237]     Train net output #0: loss = 2.42675 (* 1 = 2.42675 loss)
I0410 00:28:34.527437 14080 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0410 00:28:36.493232 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0410 00:28:39.689831 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0410 00:28:42.893766 14080 solver.cpp:330] Iteration 2754, Testing net (#0)
I0410 00:28:42.893796 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:28:46.145901 14080 blocking_queue.cpp:49] Waiting for data
I0410 00:28:46.553890 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:28:47.833343 14080 solver.cpp:397]     Test net output #0: accuracy = 0.295956
I0410 00:28:47.833391 14080 solver.cpp:397]     Test net output #1: loss = 2.86591 (* 1 = 2.86591 loss)
I0410 00:28:49.563524 14080 solver.cpp:218] Iteration 2760 (0.798113 iter/s, 15.0355s/12 iters), loss = 2.2248
I0410 00:28:49.563585 14080 solver.cpp:237]     Train net output #0: loss = 2.2248 (* 1 = 2.2248 loss)
I0410 00:28:49.563598 14080 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0410 00:28:54.434566 14080 solver.cpp:218] Iteration 2772 (2.46368 iter/s, 4.87077s/12 iters), loss = 1.89901
I0410 00:28:54.434609 14080 solver.cpp:237]     Train net output #0: loss = 1.89901 (* 1 = 1.89901 loss)
I0410 00:28:54.434619 14080 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0410 00:28:59.375718 14080 solver.cpp:218] Iteration 2784 (2.42871 iter/s, 4.94089s/12 iters), loss = 2.28903
I0410 00:28:59.375773 14080 solver.cpp:237]     Train net output #0: loss = 2.28903 (* 1 = 2.28903 loss)
I0410 00:28:59.375787 14080 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0410 00:29:04.338496 14080 solver.cpp:218] Iteration 2796 (2.41813 iter/s, 4.9625s/12 iters), loss = 2.326
I0410 00:29:04.338557 14080 solver.cpp:237]     Train net output #0: loss = 2.326 (* 1 = 2.326 loss)
I0410 00:29:04.338568 14080 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0410 00:29:09.332903 14080 solver.cpp:218] Iteration 2808 (2.40282 iter/s, 4.99413s/12 iters), loss = 2.10919
I0410 00:29:09.332959 14080 solver.cpp:237]     Train net output #0: loss = 2.10919 (* 1 = 2.10919 loss)
I0410 00:29:09.332971 14080 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0410 00:29:14.313832 14080 solver.cpp:218] Iteration 2820 (2.40932 iter/s, 4.98065s/12 iters), loss = 2.35942
I0410 00:29:14.313880 14080 solver.cpp:237]     Train net output #0: loss = 2.35942 (* 1 = 2.35942 loss)
I0410 00:29:14.313891 14080 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0410 00:29:18.962404 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:29:19.254716 14080 solver.cpp:218] Iteration 2832 (2.42884 iter/s, 4.94062s/12 iters), loss = 2.21765
I0410 00:29:19.254758 14080 solver.cpp:237]     Train net output #0: loss = 2.21765 (* 1 = 2.21765 loss)
I0410 00:29:19.254766 14080 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0410 00:29:24.212050 14080 solver.cpp:218] Iteration 2844 (2.42078 iter/s, 4.95707s/12 iters), loss = 2.24667
I0410 00:29:24.212105 14080 solver.cpp:237]     Train net output #0: loss = 2.24667 (* 1 = 2.24667 loss)
I0410 00:29:24.212117 14080 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0410 00:29:28.697696 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0410 00:29:30.646960 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0410 00:29:31.991875 14080 solver.cpp:330] Iteration 2856, Testing net (#0)
I0410 00:29:31.991897 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:29:35.176192 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:29:36.312250 14080 solver.cpp:397]     Test net output #0: accuracy = 0.303309
I0410 00:29:36.312292 14080 solver.cpp:397]     Test net output #1: loss = 2.90159 (* 1 = 2.90159 loss)
I0410 00:29:36.398054 14080 solver.cpp:218] Iteration 2856 (0.984782 iter/s, 12.1854s/12 iters), loss = 2.25753
I0410 00:29:36.398108 14080 solver.cpp:237]     Train net output #0: loss = 2.25753 (* 1 = 2.25753 loss)
I0410 00:29:36.398118 14080 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0410 00:29:40.468436 14080 solver.cpp:218] Iteration 2868 (2.9483 iter/s, 4.07015s/12 iters), loss = 2.09716
I0410 00:29:40.468494 14080 solver.cpp:237]     Train net output #0: loss = 2.09716 (* 1 = 2.09716 loss)
I0410 00:29:40.468506 14080 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0410 00:29:45.359666 14080 solver.cpp:218] Iteration 2880 (2.45351 iter/s, 4.89095s/12 iters), loss = 1.98258
I0410 00:29:45.359730 14080 solver.cpp:237]     Train net output #0: loss = 1.98258 (* 1 = 1.98258 loss)
I0410 00:29:45.359743 14080 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0410 00:29:50.419957 14080 solver.cpp:218] Iteration 2892 (2.37154 iter/s, 5.06001s/12 iters), loss = 2.03634
I0410 00:29:50.422747 14080 solver.cpp:237]     Train net output #0: loss = 2.03634 (* 1 = 2.03634 loss)
I0410 00:29:50.422760 14080 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0410 00:29:55.441743 14080 solver.cpp:218] Iteration 2904 (2.39102 iter/s, 5.01877s/12 iters), loss = 1.87729
I0410 00:29:55.441802 14080 solver.cpp:237]     Train net output #0: loss = 1.87729 (* 1 = 1.87729 loss)
I0410 00:29:55.441814 14080 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0410 00:30:00.325994 14080 solver.cpp:218] Iteration 2916 (2.45701 iter/s, 4.88398s/12 iters), loss = 2.07164
I0410 00:30:00.326040 14080 solver.cpp:237]     Train net output #0: loss = 2.07164 (* 1 = 2.07164 loss)
I0410 00:30:00.326050 14080 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0410 00:30:05.213865 14080 solver.cpp:218] Iteration 2928 (2.45519 iter/s, 4.88761s/12 iters), loss = 1.86251
I0410 00:30:05.213918 14080 solver.cpp:237]     Train net output #0: loss = 1.86251 (* 1 = 1.86251 loss)
I0410 00:30:05.213928 14080 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0410 00:30:07.030915 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:30:10.153014 14080 solver.cpp:218] Iteration 2940 (2.4297 iter/s, 4.93888s/12 iters), loss = 2.10252
I0410 00:30:10.153061 14080 solver.cpp:237]     Train net output #0: loss = 2.10252 (* 1 = 2.10252 loss)
I0410 00:30:10.153070 14080 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0410 00:30:15.093883 14080 solver.cpp:218] Iteration 2952 (2.42885 iter/s, 4.94061s/12 iters), loss = 1.99205
I0410 00:30:15.093925 14080 solver.cpp:237]     Train net output #0: loss = 1.99205 (* 1 = 1.99205 loss)
I0410 00:30:15.093935 14080 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0410 00:30:17.225303 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0410 00:30:18.662421 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0410 00:30:19.717495 14080 solver.cpp:330] Iteration 2958, Testing net (#0)
I0410 00:30:19.717525 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:30:22.931100 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:30:24.112424 14080 solver.cpp:397]     Test net output #0: accuracy = 0.340686
I0410 00:30:24.112474 14080 solver.cpp:397]     Test net output #1: loss = 2.71923 (* 1 = 2.71923 loss)
I0410 00:30:26.008803 14080 solver.cpp:218] Iteration 2964 (1.09946 iter/s, 10.9144s/12 iters), loss = 1.95001
I0410 00:30:26.008860 14080 solver.cpp:237]     Train net output #0: loss = 1.95001 (* 1 = 1.95001 loss)
I0410 00:30:26.008872 14080 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0410 00:30:30.872787 14080 solver.cpp:218] Iteration 2976 (2.46725 iter/s, 4.86372s/12 iters), loss = 2.20621
I0410 00:30:30.872824 14080 solver.cpp:237]     Train net output #0: loss = 2.20621 (* 1 = 2.20621 loss)
I0410 00:30:30.872831 14080 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0410 00:30:35.892731 14080 solver.cpp:218] Iteration 2988 (2.39059 iter/s, 5.01969s/12 iters), loss = 2.02451
I0410 00:30:35.892781 14080 solver.cpp:237]     Train net output #0: loss = 2.02451 (* 1 = 2.02451 loss)
I0410 00:30:35.892792 14080 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0410 00:30:40.772759 14080 solver.cpp:218] Iteration 3000 (2.45913 iter/s, 4.87977s/12 iters), loss = 1.82422
I0410 00:30:40.772796 14080 solver.cpp:237]     Train net output #0: loss = 1.82422 (* 1 = 1.82422 loss)
I0410 00:30:40.772804 14080 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0410 00:30:45.633934 14080 solver.cpp:218] Iteration 3012 (2.46867 iter/s, 4.86092s/12 iters), loss = 1.76305
I0410 00:30:45.633998 14080 solver.cpp:237]     Train net output #0: loss = 1.76305 (* 1 = 1.76305 loss)
I0410 00:30:45.634012 14080 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0410 00:30:50.547617 14080 solver.cpp:218] Iteration 3024 (2.4423 iter/s, 4.91341s/12 iters), loss = 1.93173
I0410 00:30:50.547664 14080 solver.cpp:237]     Train net output #0: loss = 1.93173 (* 1 = 1.93173 loss)
I0410 00:30:50.547674 14080 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0410 00:30:54.474016 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:30:55.482924 14080 solver.cpp:218] Iteration 3036 (2.43159 iter/s, 4.93504s/12 iters), loss = 1.94706
I0410 00:30:55.482978 14080 solver.cpp:237]     Train net output #0: loss = 1.94706 (* 1 = 1.94706 loss)
I0410 00:30:55.482991 14080 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0410 00:31:00.559440 14080 solver.cpp:218] Iteration 3048 (2.36395 iter/s, 5.07624s/12 iters), loss = 1.97677
I0410 00:31:00.559489 14080 solver.cpp:237]     Train net output #0: loss = 1.97677 (* 1 = 1.97677 loss)
I0410 00:31:00.559499 14080 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0410 00:31:04.991318 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0410 00:31:06.827270 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0410 00:31:08.151087 14080 solver.cpp:330] Iteration 3060, Testing net (#0)
I0410 00:31:08.151113 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:31:11.448390 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:31:12.702260 14080 solver.cpp:397]     Test net output #0: accuracy = 0.328431
I0410 00:31:12.702308 14080 solver.cpp:397]     Test net output #1: loss = 2.66446 (* 1 = 2.66446 loss)
I0410 00:31:12.787540 14080 solver.cpp:218] Iteration 3060 (0.981391 iter/s, 12.2275s/12 iters), loss = 2.19885
I0410 00:31:12.787593 14080 solver.cpp:237]     Train net output #0: loss = 2.19885 (* 1 = 2.19885 loss)
I0410 00:31:12.787606 14080 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0410 00:31:17.013399 14080 solver.cpp:218] Iteration 3072 (2.83982 iter/s, 4.22562s/12 iters), loss = 1.71649
I0410 00:31:17.013446 14080 solver.cpp:237]     Train net output #0: loss = 1.71649 (* 1 = 1.71649 loss)
I0410 00:31:17.013455 14080 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0410 00:31:21.923768 14080 solver.cpp:218] Iteration 3084 (2.44394 iter/s, 4.91011s/12 iters), loss = 1.83753
I0410 00:31:21.923812 14080 solver.cpp:237]     Train net output #0: loss = 1.83753 (* 1 = 1.83753 loss)
I0410 00:31:21.923822 14080 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0410 00:31:27.003172 14080 solver.cpp:218] Iteration 3096 (2.36261 iter/s, 5.07914s/12 iters), loss = 1.89546
I0410 00:31:27.003320 14080 solver.cpp:237]     Train net output #0: loss = 1.89546 (* 1 = 1.89546 loss)
I0410 00:31:27.003334 14080 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0410 00:31:31.945081 14080 solver.cpp:218] Iteration 3108 (2.42839 iter/s, 4.94155s/12 iters), loss = 1.6583
I0410 00:31:31.945132 14080 solver.cpp:237]     Train net output #0: loss = 1.6583 (* 1 = 1.6583 loss)
I0410 00:31:31.945142 14080 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0410 00:31:36.881585 14080 solver.cpp:218] Iteration 3120 (2.431 iter/s, 4.93624s/12 iters), loss = 1.77479
I0410 00:31:36.881640 14080 solver.cpp:237]     Train net output #0: loss = 1.77479 (* 1 = 1.77479 loss)
I0410 00:31:36.881651 14080 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0410 00:31:41.780030 14080 solver.cpp:218] Iteration 3132 (2.44989 iter/s, 4.89817s/12 iters), loss = 2.20147
I0410 00:31:41.780086 14080 solver.cpp:237]     Train net output #0: loss = 2.20147 (* 1 = 2.20147 loss)
I0410 00:31:41.780097 14080 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0410 00:31:42.864153 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:31:46.695506 14080 solver.cpp:218] Iteration 3144 (2.4414 iter/s, 4.91521s/12 iters), loss = 2.04778
I0410 00:31:46.695559 14080 solver.cpp:237]     Train net output #0: loss = 2.04778 (* 1 = 2.04778 loss)
I0410 00:31:46.695569 14080 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0410 00:31:51.638183 14080 solver.cpp:218] Iteration 3156 (2.42796 iter/s, 4.94242s/12 iters), loss = 2.06102
I0410 00:31:51.638222 14080 solver.cpp:237]     Train net output #0: loss = 2.06102 (* 1 = 2.06102 loss)
I0410 00:31:51.638231 14080 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0410 00:31:53.721513 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0410 00:31:56.865586 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0410 00:31:58.993609 14080 solver.cpp:330] Iteration 3162, Testing net (#0)
I0410 00:31:58.993671 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:32:02.077337 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:32:03.343291 14080 solver.cpp:397]     Test net output #0: accuracy = 0.34375
I0410 00:32:03.343339 14080 solver.cpp:397]     Test net output #1: loss = 2.66985 (* 1 = 2.66985 loss)
I0410 00:32:05.263365 14080 solver.cpp:218] Iteration 3168 (0.880761 iter/s, 13.6246s/12 iters), loss = 1.65549
I0410 00:32:05.263407 14080 solver.cpp:237]     Train net output #0: loss = 1.65549 (* 1 = 1.65549 loss)
I0410 00:32:05.263417 14080 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0410 00:32:10.214509 14080 solver.cpp:218] Iteration 3180 (2.42381 iter/s, 4.95088s/12 iters), loss = 1.83446
I0410 00:32:10.214565 14080 solver.cpp:237]     Train net output #0: loss = 1.83446 (* 1 = 1.83446 loss)
I0410 00:32:10.214581 14080 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0410 00:32:15.073897 14080 solver.cpp:218] Iteration 3192 (2.46959 iter/s, 4.85911s/12 iters), loss = 1.62562
I0410 00:32:15.073948 14080 solver.cpp:237]     Train net output #0: loss = 1.62562 (* 1 = 1.62562 loss)
I0410 00:32:15.073978 14080 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0410 00:32:19.978035 14080 solver.cpp:218] Iteration 3204 (2.44705 iter/s, 4.90387s/12 iters), loss = 1.55108
I0410 00:32:19.978091 14080 solver.cpp:237]     Train net output #0: loss = 1.55108 (* 1 = 1.55108 loss)
I0410 00:32:19.978102 14080 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0410 00:32:24.901597 14080 solver.cpp:218] Iteration 3216 (2.43739 iter/s, 4.9233s/12 iters), loss = 1.95353
I0410 00:32:24.901638 14080 solver.cpp:237]     Train net output #0: loss = 1.95353 (* 1 = 1.95353 loss)
I0410 00:32:24.901648 14080 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0410 00:32:29.860535 14080 solver.cpp:218] Iteration 3228 (2.42 iter/s, 4.95868s/12 iters), loss = 1.74237
I0410 00:32:29.860682 14080 solver.cpp:237]     Train net output #0: loss = 1.74237 (* 1 = 1.74237 loss)
I0410 00:32:29.860695 14080 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0410 00:32:33.067334 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:32:34.823946 14080 solver.cpp:218] Iteration 3240 (2.41787 iter/s, 4.96304s/12 iters), loss = 1.56918
I0410 00:32:34.824000 14080 solver.cpp:237]     Train net output #0: loss = 1.56918 (* 1 = 1.56918 loss)
I0410 00:32:34.824013 14080 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0410 00:32:39.781986 14080 solver.cpp:218] Iteration 3252 (2.42045 iter/s, 4.95775s/12 iters), loss = 1.72768
I0410 00:32:39.782042 14080 solver.cpp:237]     Train net output #0: loss = 1.72768 (* 1 = 1.72768 loss)
I0410 00:32:39.782053 14080 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0410 00:32:44.198304 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0410 00:32:48.979404 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0410 00:32:51.227147 14080 solver.cpp:330] Iteration 3264, Testing net (#0)
I0410 00:32:51.227169 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:32:54.510681 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:32:55.837447 14080 solver.cpp:397]     Test net output #0: accuracy = 0.373162
I0410 00:32:55.837496 14080 solver.cpp:397]     Test net output #1: loss = 2.54731 (* 1 = 2.54731 loss)
I0410 00:32:55.923149 14080 solver.cpp:218] Iteration 3264 (0.743474 iter/s, 16.1404s/12 iters), loss = 1.86429
I0410 00:32:55.923204 14080 solver.cpp:237]     Train net output #0: loss = 1.86429 (* 1 = 1.86429 loss)
I0410 00:32:55.923215 14080 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0410 00:33:00.110306 14080 solver.cpp:218] Iteration 3276 (2.86607 iter/s, 4.18692s/12 iters), loss = 1.54298
I0410 00:33:00.110428 14080 solver.cpp:237]     Train net output #0: loss = 1.54298 (* 1 = 1.54298 loss)
I0410 00:33:00.110441 14080 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0410 00:33:04.973577 14080 solver.cpp:218] Iteration 3288 (2.46764 iter/s, 4.86294s/12 iters), loss = 1.54181
I0410 00:33:04.973628 14080 solver.cpp:237]     Train net output #0: loss = 1.54181 (* 1 = 1.54181 loss)
I0410 00:33:04.973637 14080 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0410 00:33:09.835994 14080 solver.cpp:218] Iteration 3300 (2.46804 iter/s, 4.86216s/12 iters), loss = 1.7065
I0410 00:33:09.836043 14080 solver.cpp:237]     Train net output #0: loss = 1.7065 (* 1 = 1.7065 loss)
I0410 00:33:09.836055 14080 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0410 00:33:14.953297 14080 solver.cpp:218] Iteration 3312 (2.34511 iter/s, 5.11703s/12 iters), loss = 1.7366
I0410 00:33:14.953351 14080 solver.cpp:237]     Train net output #0: loss = 1.7366 (* 1 = 1.7366 loss)
I0410 00:33:14.953362 14080 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0410 00:33:19.821560 14080 solver.cpp:218] Iteration 3324 (2.46508 iter/s, 4.868s/12 iters), loss = 1.64076
I0410 00:33:19.821610 14080 solver.cpp:237]     Train net output #0: loss = 1.64076 (* 1 = 1.64076 loss)
I0410 00:33:19.821621 14080 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0410 00:33:24.692564 14080 solver.cpp:218] Iteration 3336 (2.46369 iter/s, 4.87074s/12 iters), loss = 1.64567
I0410 00:33:24.692616 14080 solver.cpp:237]     Train net output #0: loss = 1.64567 (* 1 = 1.64567 loss)
I0410 00:33:24.692627 14080 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0410 00:33:25.142971 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:33:29.543768 14080 solver.cpp:218] Iteration 3348 (2.47375 iter/s, 4.85094s/12 iters), loss = 1.71872
I0410 00:33:29.543817 14080 solver.cpp:237]     Train net output #0: loss = 1.71872 (* 1 = 1.71872 loss)
I0410 00:33:29.543825 14080 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0410 00:33:34.465801 14080 solver.cpp:218] Iteration 3360 (2.43815 iter/s, 4.92177s/12 iters), loss = 1.63831
I0410 00:33:34.465950 14080 solver.cpp:237]     Train net output #0: loss = 1.63831 (* 1 = 1.63831 loss)
I0410 00:33:34.465991 14080 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0410 00:33:36.461230 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0410 00:33:38.058784 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0410 00:33:40.884208 14080 solver.cpp:330] Iteration 3366, Testing net (#0)
I0410 00:33:40.884233 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:33:43.954109 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:33:45.291018 14080 solver.cpp:397]     Test net output #0: accuracy = 0.371324
I0410 00:33:45.291047 14080 solver.cpp:397]     Test net output #1: loss = 2.55736 (* 1 = 2.55736 loss)
I0410 00:33:47.005890 14080 solver.cpp:218] Iteration 3372 (0.956982 iter/s, 12.5394s/12 iters), loss = 1.34503
I0410 00:33:47.005946 14080 solver.cpp:237]     Train net output #0: loss = 1.34503 (* 1 = 1.34503 loss)
I0410 00:33:47.005975 14080 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0410 00:33:51.966794 14080 solver.cpp:218] Iteration 3384 (2.41905 iter/s, 4.96063s/12 iters), loss = 1.54232
I0410 00:33:51.966843 14080 solver.cpp:237]     Train net output #0: loss = 1.54232 (* 1 = 1.54232 loss)
I0410 00:33:51.966856 14080 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0410 00:33:56.912279 14080 solver.cpp:218] Iteration 3396 (2.42658 iter/s, 4.94522s/12 iters), loss = 1.69112
I0410 00:33:56.912334 14080 solver.cpp:237]     Train net output #0: loss = 1.69112 (* 1 = 1.69112 loss)
I0410 00:33:56.912346 14080 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0410 00:34:01.871486 14080 solver.cpp:218] Iteration 3408 (2.41987 iter/s, 4.95894s/12 iters), loss = 1.49195
I0410 00:34:01.871536 14080 solver.cpp:237]     Train net output #0: loss = 1.49195 (* 1 = 1.49195 loss)
I0410 00:34:01.871546 14080 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0410 00:34:06.812288 14080 solver.cpp:218] Iteration 3420 (2.42888 iter/s, 4.94054s/12 iters), loss = 1.36617
I0410 00:34:06.812384 14080 solver.cpp:237]     Train net output #0: loss = 1.36617 (* 1 = 1.36617 loss)
I0410 00:34:06.812394 14080 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0410 00:34:11.855629 14080 solver.cpp:218] Iteration 3432 (2.37952 iter/s, 5.04303s/12 iters), loss = 1.6066
I0410 00:34:11.855675 14080 solver.cpp:237]     Train net output #0: loss = 1.6066 (* 1 = 1.6066 loss)
I0410 00:34:11.855686 14080 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0410 00:34:14.423979 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:34:16.804965 14080 solver.cpp:218] Iteration 3444 (2.4247 iter/s, 4.94908s/12 iters), loss = 1.21272
I0410 00:34:16.805018 14080 solver.cpp:237]     Train net output #0: loss = 1.21272 (* 1 = 1.21272 loss)
I0410 00:34:16.805028 14080 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0410 00:34:21.796907 14080 solver.cpp:218] Iteration 3456 (2.404 iter/s, 4.99167s/12 iters), loss = 1.73555
I0410 00:34:21.796963 14080 solver.cpp:237]     Train net output #0: loss = 1.73555 (* 1 = 1.73555 loss)
I0410 00:34:21.796977 14080 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0410 00:34:26.407974 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0410 00:34:28.440323 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0410 00:34:31.364781 14080 solver.cpp:330] Iteration 3468, Testing net (#0)
I0410 00:34:31.364810 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:34:31.792008 14080 blocking_queue.cpp:49] Waiting for data
I0410 00:34:34.721042 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:34:36.127612 14080 solver.cpp:397]     Test net output #0: accuracy = 0.36826
I0410 00:34:36.127645 14080 solver.cpp:397]     Test net output #1: loss = 2.53038 (* 1 = 2.53038 loss)
I0410 00:34:36.213419 14080 solver.cpp:218] Iteration 3468 (0.832417 iter/s, 14.4159s/12 iters), loss = 1.55852
I0410 00:34:36.213466 14080 solver.cpp:237]     Train net output #0: loss = 1.55852 (* 1 = 1.55852 loss)
I0410 00:34:36.213475 14080 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0410 00:34:40.314553 14080 solver.cpp:218] Iteration 3480 (2.92618 iter/s, 4.10091s/12 iters), loss = 1.41028
I0410 00:34:40.314662 14080 solver.cpp:237]     Train net output #0: loss = 1.41028 (* 1 = 1.41028 loss)
I0410 00:34:40.314674 14080 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0410 00:34:45.205888 14080 solver.cpp:218] Iteration 3492 (2.45348 iter/s, 4.89101s/12 iters), loss = 1.61873
I0410 00:34:45.205940 14080 solver.cpp:237]     Train net output #0: loss = 1.61873 (* 1 = 1.61873 loss)
I0410 00:34:45.205952 14080 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0410 00:34:50.127547 14080 solver.cpp:218] Iteration 3504 (2.43833 iter/s, 4.92139s/12 iters), loss = 1.38858
I0410 00:34:50.127604 14080 solver.cpp:237]     Train net output #0: loss = 1.38858 (* 1 = 1.38858 loss)
I0410 00:34:50.127615 14080 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0410 00:34:55.036031 14080 solver.cpp:218] Iteration 3516 (2.44488 iter/s, 4.90821s/12 iters), loss = 1.34667
I0410 00:34:55.036083 14080 solver.cpp:237]     Train net output #0: loss = 1.34667 (* 1 = 1.34667 loss)
I0410 00:34:55.036095 14080 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0410 00:35:00.446756 14080 solver.cpp:218] Iteration 3528 (2.21793 iter/s, 5.41044s/12 iters), loss = 1.47264
I0410 00:35:00.446808 14080 solver.cpp:237]     Train net output #0: loss = 1.47264 (* 1 = 1.47264 loss)
I0410 00:35:00.446820 14080 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0410 00:35:05.145102 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:35:05.403658 14080 solver.cpp:218] Iteration 3540 (2.421 iter/s, 4.95663s/12 iters), loss = 1.31473
I0410 00:35:05.403712 14080 solver.cpp:237]     Train net output #0: loss = 1.31473 (* 1 = 1.31473 loss)
I0410 00:35:05.403723 14080 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0410 00:35:10.295048 14080 solver.cpp:218] Iteration 3552 (2.45342 iter/s, 4.89113s/12 iters), loss = 1.41208
I0410 00:35:10.295100 14080 solver.cpp:237]     Train net output #0: loss = 1.41208 (* 1 = 1.41208 loss)
I0410 00:35:10.295112 14080 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0410 00:35:15.256561 14080 solver.cpp:218] Iteration 3564 (2.41875 iter/s, 4.96125s/12 iters), loss = 1.56696
I0410 00:35:15.256645 14080 solver.cpp:237]     Train net output #0: loss = 1.56696 (* 1 = 1.56696 loss)
I0410 00:35:15.256654 14080 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0410 00:35:17.236393 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0410 00:35:19.182752 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0410 00:35:20.228067 14080 solver.cpp:330] Iteration 3570, Testing net (#0)
I0410 00:35:20.228096 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:35:24.299186 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:35:25.772009 14080 solver.cpp:397]     Test net output #0: accuracy = 0.409314
I0410 00:35:25.772058 14080 solver.cpp:397]     Test net output #1: loss = 2.45097 (* 1 = 2.45097 loss)
I0410 00:35:27.667762 14080 solver.cpp:218] Iteration 3576 (0.966916 iter/s, 12.4106s/12 iters), loss = 1.39911
I0410 00:35:27.667821 14080 solver.cpp:237]     Train net output #0: loss = 1.39911 (* 1 = 1.39911 loss)
I0410 00:35:27.667834 14080 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0410 00:35:32.570446 14080 solver.cpp:218] Iteration 3588 (2.44777 iter/s, 4.90242s/12 iters), loss = 1.22772
I0410 00:35:32.570503 14080 solver.cpp:237]     Train net output #0: loss = 1.22772 (* 1 = 1.22772 loss)
I0410 00:35:32.570514 14080 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0410 00:35:37.566118 14080 solver.cpp:218] Iteration 3600 (2.40221 iter/s, 4.9954s/12 iters), loss = 1.23348
I0410 00:35:37.566171 14080 solver.cpp:237]     Train net output #0: loss = 1.23348 (* 1 = 1.23348 loss)
I0410 00:35:37.566184 14080 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0410 00:35:42.549022 14080 solver.cpp:218] Iteration 3612 (2.40836 iter/s, 4.98264s/12 iters), loss = 1.18067
I0410 00:35:42.549074 14080 solver.cpp:237]     Train net output #0: loss = 1.18067 (* 1 = 1.18067 loss)
I0410 00:35:42.549086 14080 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0410 00:35:47.480159 14080 solver.cpp:218] Iteration 3624 (2.43365 iter/s, 4.93086s/12 iters), loss = 1.30156
I0410 00:35:47.480355 14080 solver.cpp:237]     Train net output #0: loss = 1.30156 (* 1 = 1.30156 loss)
I0410 00:35:47.480372 14080 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0410 00:35:52.433234 14080 solver.cpp:218] Iteration 3636 (2.42293 iter/s, 4.95267s/12 iters), loss = 1.2164
I0410 00:35:52.433290 14080 solver.cpp:237]     Train net output #0: loss = 1.2164 (* 1 = 1.2164 loss)
I0410 00:35:52.433302 14080 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0410 00:35:54.294108 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:35:57.389699 14080 solver.cpp:218] Iteration 3648 (2.42121 iter/s, 4.9562s/12 iters), loss = 1.34371
I0410 00:35:57.389750 14080 solver.cpp:237]     Train net output #0: loss = 1.34371 (* 1 = 1.34371 loss)
I0410 00:35:57.389761 14080 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0410 00:36:02.370580 14080 solver.cpp:218] Iteration 3660 (2.40934 iter/s, 4.98061s/12 iters), loss = 1.37381
I0410 00:36:02.370626 14080 solver.cpp:237]     Train net output #0: loss = 1.37381 (* 1 = 1.37381 loss)
I0410 00:36:02.370635 14080 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0410 00:36:06.833052 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0410 00:36:08.254086 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0410 00:36:09.298718 14080 solver.cpp:330] Iteration 3672, Testing net (#0)
I0410 00:36:09.298746 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:36:12.497687 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:36:13.956375 14080 solver.cpp:397]     Test net output #0: accuracy = 0.414828
I0410 00:36:13.956426 14080 solver.cpp:397]     Test net output #1: loss = 2.41146 (* 1 = 2.41146 loss)
I0410 00:36:14.042686 14080 solver.cpp:218] Iteration 3672 (1.02814 iter/s, 11.6716s/12 iters), loss = 1.15057
I0410 00:36:14.042754 14080 solver.cpp:237]     Train net output #0: loss = 1.15057 (* 1 = 1.15057 loss)
I0410 00:36:14.042770 14080 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0410 00:36:18.156194 14080 solver.cpp:218] Iteration 3684 (2.91739 iter/s, 4.11327s/12 iters), loss = 1.45075
I0410 00:36:18.156327 14080 solver.cpp:237]     Train net output #0: loss = 1.45075 (* 1 = 1.45075 loss)
I0410 00:36:18.156337 14080 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0410 00:36:23.237496 14080 solver.cpp:218] Iteration 3696 (2.36176 iter/s, 5.08095s/12 iters), loss = 1.13216
I0410 00:36:23.237542 14080 solver.cpp:237]     Train net output #0: loss = 1.13216 (* 1 = 1.13216 loss)
I0410 00:36:23.237553 14080 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0410 00:36:28.212262 14080 solver.cpp:218] Iteration 3708 (2.4123 iter/s, 4.9745s/12 iters), loss = 1.36492
I0410 00:36:28.212314 14080 solver.cpp:237]     Train net output #0: loss = 1.36492 (* 1 = 1.36492 loss)
I0410 00:36:28.212327 14080 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0410 00:36:33.151903 14080 solver.cpp:218] Iteration 3720 (2.42946 iter/s, 4.93938s/12 iters), loss = 1.5326
I0410 00:36:33.151954 14080 solver.cpp:237]     Train net output #0: loss = 1.5326 (* 1 = 1.5326 loss)
I0410 00:36:33.151968 14080 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0410 00:36:38.244899 14080 solver.cpp:218] Iteration 3732 (2.3563 iter/s, 5.09273s/12 iters), loss = 1.24975
I0410 00:36:38.244937 14080 solver.cpp:237]     Train net output #0: loss = 1.24975 (* 1 = 1.24975 loss)
I0410 00:36:38.244946 14080 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0410 00:36:42.173815 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:36:43.149085 14080 solver.cpp:218] Iteration 3744 (2.44702 iter/s, 4.90393s/12 iters), loss = 0.95139
I0410 00:36:43.149135 14080 solver.cpp:237]     Train net output #0: loss = 0.95139 (* 1 = 0.95139 loss)
I0410 00:36:43.149149 14080 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0410 00:36:48.104892 14080 solver.cpp:218] Iteration 3756 (2.42153 iter/s, 4.95554s/12 iters), loss = 1.3552
I0410 00:36:48.104941 14080 solver.cpp:237]     Train net output #0: loss = 1.3552 (* 1 = 1.3552 loss)
I0410 00:36:48.104954 14080 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0410 00:36:53.005525 14080 solver.cpp:218] Iteration 3768 (2.44879 iter/s, 4.90037s/12 iters), loss = 1.34236
I0410 00:36:53.005633 14080 solver.cpp:237]     Train net output #0: loss = 1.34236 (* 1 = 1.34236 loss)
I0410 00:36:53.005645 14080 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0410 00:36:55.001327 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0410 00:36:57.429435 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0410 00:37:00.451063 14080 solver.cpp:330] Iteration 3774, Testing net (#0)
I0410 00:37:00.451094 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:37:03.375566 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:37:04.873127 14080 solver.cpp:397]     Test net output #0: accuracy = 0.403799
I0410 00:37:04.873176 14080 solver.cpp:397]     Test net output #1: loss = 2.41838 (* 1 = 2.41838 loss)
I0410 00:37:06.744488 14080 solver.cpp:218] Iteration 3780 (0.873472 iter/s, 13.7383s/12 iters), loss = 1.02429
I0410 00:37:06.744541 14080 solver.cpp:237]     Train net output #0: loss = 1.02429 (* 1 = 1.02429 loss)
I0410 00:37:06.744554 14080 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0410 00:37:11.629788 14080 solver.cpp:218] Iteration 3792 (2.45648 iter/s, 4.88503s/12 iters), loss = 1.2629
I0410 00:37:11.629839 14080 solver.cpp:237]     Train net output #0: loss = 1.2629 (* 1 = 1.2629 loss)
I0410 00:37:11.629853 14080 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0410 00:37:16.630674 14080 solver.cpp:218] Iteration 3804 (2.3997 iter/s, 5.00062s/12 iters), loss = 1.34138
I0410 00:37:16.630720 14080 solver.cpp:237]     Train net output #0: loss = 1.34138 (* 1 = 1.34138 loss)
I0410 00:37:16.630731 14080 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0410 00:37:21.561019 14080 solver.cpp:218] Iteration 3816 (2.43403 iter/s, 4.93009s/12 iters), loss = 1.23648
I0410 00:37:21.561059 14080 solver.cpp:237]     Train net output #0: loss = 1.23648 (* 1 = 1.23648 loss)
I0410 00:37:21.561067 14080 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0410 00:37:26.497498 14080 solver.cpp:218] Iteration 3828 (2.43101 iter/s, 4.93622s/12 iters), loss = 1.19649
I0410 00:37:26.497629 14080 solver.cpp:237]     Train net output #0: loss = 1.19649 (* 1 = 1.19649 loss)
I0410 00:37:26.497638 14080 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0410 00:37:31.416112 14080 solver.cpp:218] Iteration 3840 (2.43988 iter/s, 4.91827s/12 iters), loss = 1.34575
I0410 00:37:31.416162 14080 solver.cpp:237]     Train net output #0: loss = 1.34575 (* 1 = 1.34575 loss)
I0410 00:37:31.416172 14080 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0410 00:37:32.519620 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:37:36.303401 14080 solver.cpp:218] Iteration 3852 (2.45548 iter/s, 4.88703s/12 iters), loss = 1.24798
I0410 00:37:36.303452 14080 solver.cpp:237]     Train net output #0: loss = 1.24798 (* 1 = 1.24798 loss)
I0410 00:37:36.303463 14080 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0410 00:37:41.171895 14080 solver.cpp:218] Iteration 3864 (2.46496 iter/s, 4.86823s/12 iters), loss = 1.09219
I0410 00:37:41.171944 14080 solver.cpp:237]     Train net output #0: loss = 1.09219 (* 1 = 1.09219 loss)
I0410 00:37:41.171954 14080 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0410 00:37:45.608305 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0410 00:37:47.447438 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0410 00:37:48.766182 14080 solver.cpp:330] Iteration 3876, Testing net (#0)
I0410 00:37:48.766211 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:37:51.627758 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:37:53.170346 14080 solver.cpp:397]     Test net output #0: accuracy = 0.393382
I0410 00:37:53.170390 14080 solver.cpp:397]     Test net output #1: loss = 2.55623 (* 1 = 2.55623 loss)
I0410 00:37:53.256081 14080 solver.cpp:218] Iteration 3876 (0.993079 iter/s, 12.0836s/12 iters), loss = 1.01343
I0410 00:37:53.256136 14080 solver.cpp:237]     Train net output #0: loss = 1.01343 (* 1 = 1.01343 loss)
I0410 00:37:53.256147 14080 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0410 00:37:57.331091 14080 solver.cpp:218] Iteration 3888 (2.94495 iter/s, 4.07478s/12 iters), loss = 1.0558
I0410 00:37:57.332154 14080 solver.cpp:237]     Train net output #0: loss = 1.0558 (* 1 = 1.0558 loss)
I0410 00:37:57.332166 14080 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0410 00:38:02.248322 14080 solver.cpp:218] Iteration 3900 (2.44103 iter/s, 4.91596s/12 iters), loss = 1.20535
I0410 00:38:02.248371 14080 solver.cpp:237]     Train net output #0: loss = 1.20535 (* 1 = 1.20535 loss)
I0410 00:38:02.248383 14080 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0410 00:38:07.161396 14080 solver.cpp:218] Iteration 3912 (2.44259 iter/s, 4.91281s/12 iters), loss = 1.08056
I0410 00:38:07.161446 14080 solver.cpp:237]     Train net output #0: loss = 1.08056 (* 1 = 1.08056 loss)
I0410 00:38:07.161458 14080 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0410 00:38:12.121515 14080 solver.cpp:218] Iteration 3924 (2.41943 iter/s, 4.95985s/12 iters), loss = 0.935749
I0410 00:38:12.121572 14080 solver.cpp:237]     Train net output #0: loss = 0.935749 (* 1 = 0.935749 loss)
I0410 00:38:12.121585 14080 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0410 00:38:17.104202 14080 solver.cpp:218] Iteration 3936 (2.40847 iter/s, 4.98242s/12 iters), loss = 0.877678
I0410 00:38:17.104252 14080 solver.cpp:237]     Train net output #0: loss = 0.877678 (* 1 = 0.877678 loss)
I0410 00:38:17.104264 14080 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0410 00:38:20.452354 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:38:22.091852 14080 solver.cpp:218] Iteration 3948 (2.40607 iter/s, 4.98738s/12 iters), loss = 0.88547
I0410 00:38:22.091902 14080 solver.cpp:237]     Train net output #0: loss = 0.88547 (* 1 = 0.88547 loss)
I0410 00:38:22.091913 14080 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0410 00:38:27.031791 14080 solver.cpp:218] Iteration 3960 (2.42931 iter/s, 4.93967s/12 iters), loss = 0.918202
I0410 00:38:27.031849 14080 solver.cpp:237]     Train net output #0: loss = 0.918202 (* 1 = 0.918202 loss)
I0410 00:38:27.031862 14080 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0410 00:38:31.935009 14080 solver.cpp:218] Iteration 3972 (2.44751 iter/s, 4.90294s/12 iters), loss = 1.23065
I0410 00:38:31.935127 14080 solver.cpp:237]     Train net output #0: loss = 1.23065 (* 1 = 1.23065 loss)
I0410 00:38:31.935139 14080 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0410 00:38:33.960664 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0410 00:38:36.120923 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0410 00:38:38.863790 14080 solver.cpp:330] Iteration 3978, Testing net (#0)
I0410 00:38:38.863817 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:38:42.043932 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:38:43.623143 14080 solver.cpp:397]     Test net output #0: accuracy = 0.42402
I0410 00:38:43.623185 14080 solver.cpp:397]     Test net output #1: loss = 2.37797 (* 1 = 2.37797 loss)
I0410 00:38:45.357883 14080 solver.cpp:218] Iteration 3984 (0.894042 iter/s, 13.4222s/12 iters), loss = 1.0341
I0410 00:38:45.357942 14080 solver.cpp:237]     Train net output #0: loss = 1.0341 (* 1 = 1.0341 loss)
I0410 00:38:45.357969 14080 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0410 00:38:50.294049 14080 solver.cpp:218] Iteration 3996 (2.43117 iter/s, 4.93589s/12 iters), loss = 0.935167
I0410 00:38:50.294095 14080 solver.cpp:237]     Train net output #0: loss = 0.935167 (* 1 = 0.935167 loss)
I0410 00:38:50.294104 14080 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0410 00:38:55.137727 14080 solver.cpp:218] Iteration 4008 (2.47759 iter/s, 4.84342s/12 iters), loss = 1.04963
I0410 00:38:55.137770 14080 solver.cpp:237]     Train net output #0: loss = 1.04963 (* 1 = 1.04963 loss)
I0410 00:38:55.137779 14080 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0410 00:39:00.023442 14080 solver.cpp:218] Iteration 4020 (2.45627 iter/s, 4.88546s/12 iters), loss = 1.33593
I0410 00:39:00.023490 14080 solver.cpp:237]     Train net output #0: loss = 1.33593 (* 1 = 1.33593 loss)
I0410 00:39:00.023501 14080 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0410 00:39:04.925869 14080 solver.cpp:218] Iteration 4032 (2.4479 iter/s, 4.90216s/12 iters), loss = 1.01308
I0410 00:39:04.926002 14080 solver.cpp:237]     Train net output #0: loss = 1.01308 (* 1 = 1.01308 loss)
I0410 00:39:04.926017 14080 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0410 00:39:09.821921 14080 solver.cpp:218] Iteration 4044 (2.45113 iter/s, 4.89571s/12 iters), loss = 1.00537
I0410 00:39:09.821996 14080 solver.cpp:237]     Train net output #0: loss = 1.00537 (* 1 = 1.00537 loss)
I0410 00:39:09.822010 14080 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0410 00:39:10.300693 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:39:14.728937 14080 solver.cpp:218] Iteration 4056 (2.44562 iter/s, 4.90673s/12 iters), loss = 1.10731
I0410 00:39:14.728977 14080 solver.cpp:237]     Train net output #0: loss = 1.10731 (* 1 = 1.10731 loss)
I0410 00:39:14.728986 14080 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0410 00:39:19.651018 14080 solver.cpp:218] Iteration 4068 (2.43812 iter/s, 4.92182s/12 iters), loss = 0.965116
I0410 00:39:19.651072 14080 solver.cpp:237]     Train net output #0: loss = 0.965116 (* 1 = 0.965116 loss)
I0410 00:39:19.651084 14080 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0410 00:39:24.139439 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0410 00:39:25.565225 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0410 00:39:26.620586 14080 solver.cpp:330] Iteration 4080, Testing net (#0)
I0410 00:39:26.620615 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:39:29.502053 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:39:31.163240 14080 solver.cpp:397]     Test net output #0: accuracy = 0.435662
I0410 00:39:31.163285 14080 solver.cpp:397]     Test net output #1: loss = 2.38541 (* 1 = 2.38541 loss)
I0410 00:39:31.249332 14080 solver.cpp:218] Iteration 4080 (1.03468 iter/s, 11.5978s/12 iters), loss = 0.951203
I0410 00:39:31.249384 14080 solver.cpp:237]     Train net output #0: loss = 0.951203 (* 1 = 0.951203 loss)
I0410 00:39:31.249395 14080 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0410 00:39:35.373262 14080 solver.cpp:218] Iteration 4092 (2.91001 iter/s, 4.12369s/12 iters), loss = 0.895476
I0410 00:39:35.373414 14080 solver.cpp:237]     Train net output #0: loss = 0.895476 (* 1 = 0.895476 loss)
I0410 00:39:35.373428 14080 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0410 00:39:40.222329 14080 solver.cpp:218] Iteration 4104 (2.47488 iter/s, 4.84871s/12 iters), loss = 0.920924
I0410 00:39:40.222378 14080 solver.cpp:237]     Train net output #0: loss = 0.920924 (* 1 = 0.920924 loss)
I0410 00:39:40.222389 14080 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0410 00:39:45.108934 14080 solver.cpp:218] Iteration 4116 (2.45583 iter/s, 4.88634s/12 iters), loss = 0.982471
I0410 00:39:45.108995 14080 solver.cpp:237]     Train net output #0: loss = 0.982471 (* 1 = 0.982471 loss)
I0410 00:39:45.109009 14080 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0410 00:39:50.092667 14080 solver.cpp:218] Iteration 4128 (2.40797 iter/s, 4.98346s/12 iters), loss = 0.906814
I0410 00:39:50.092725 14080 solver.cpp:237]     Train net output #0: loss = 0.906814 (* 1 = 0.906814 loss)
I0410 00:39:50.092736 14080 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0410 00:39:54.990087 14080 solver.cpp:218] Iteration 4140 (2.4504 iter/s, 4.89715s/12 iters), loss = 0.94127
I0410 00:39:54.990141 14080 solver.cpp:237]     Train net output #0: loss = 0.94127 (* 1 = 0.94127 loss)
I0410 00:39:54.990154 14080 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0410 00:39:57.557543 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:39:59.928792 14080 solver.cpp:218] Iteration 4152 (2.42992 iter/s, 4.93844s/12 iters), loss = 0.886646
I0410 00:39:59.928849 14080 solver.cpp:237]     Train net output #0: loss = 0.886646 (* 1 = 0.886646 loss)
I0410 00:39:59.928860 14080 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0410 00:40:01.105185 14080 blocking_queue.cpp:49] Waiting for data
I0410 00:40:04.822654 14080 solver.cpp:218] Iteration 4164 (2.45219 iter/s, 4.89359s/12 iters), loss = 1.03486
I0410 00:40:04.822712 14080 solver.cpp:237]     Train net output #0: loss = 1.03486 (* 1 = 1.03486 loss)
I0410 00:40:04.822724 14080 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0410 00:40:09.720013 14080 solver.cpp:218] Iteration 4176 (2.45044 iter/s, 4.89709s/12 iters), loss = 1.02951
I0410 00:40:09.720095 14080 solver.cpp:237]     Train net output #0: loss = 1.02951 (* 1 = 1.02951 loss)
I0410 00:40:09.720106 14080 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0410 00:40:11.698799 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0410 00:40:16.763975 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0410 00:40:19.271026 14080 solver.cpp:330] Iteration 4182, Testing net (#0)
I0410 00:40:19.271054 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:40:22.016916 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:40:23.719646 14080 solver.cpp:397]     Test net output #0: accuracy = 0.449755
I0410 00:40:23.719691 14080 solver.cpp:397]     Test net output #1: loss = 2.26792 (* 1 = 2.26792 loss)
I0410 00:40:25.628559 14080 solver.cpp:218] Iteration 4188 (0.754347 iter/s, 15.9078s/12 iters), loss = 0.937302
I0410 00:40:25.628614 14080 solver.cpp:237]     Train net output #0: loss = 0.937302 (* 1 = 0.937302 loss)
I0410 00:40:25.628626 14080 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0410 00:40:30.590312 14080 solver.cpp:218] Iteration 4200 (2.41863 iter/s, 4.96148s/12 iters), loss = 0.895433
I0410 00:40:30.590370 14080 solver.cpp:237]     Train net output #0: loss = 0.895433 (* 1 = 0.895433 loss)
I0410 00:40:30.590382 14080 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0410 00:40:35.413863 14080 solver.cpp:218] Iteration 4212 (2.48793 iter/s, 4.82329s/12 iters), loss = 0.778009
I0410 00:40:35.413916 14080 solver.cpp:237]     Train net output #0: loss = 0.778009 (* 1 = 0.778009 loss)
I0410 00:40:35.413928 14080 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0410 00:40:40.271924 14080 solver.cpp:218] Iteration 4224 (2.47025 iter/s, 4.8578s/12 iters), loss = 0.956891
I0410 00:40:40.272047 14080 solver.cpp:237]     Train net output #0: loss = 0.956891 (* 1 = 0.956891 loss)
I0410 00:40:40.272056 14080 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0410 00:40:45.235978 14080 solver.cpp:218] Iteration 4236 (2.41754 iter/s, 4.96371s/12 iters), loss = 0.871602
I0410 00:40:45.236016 14080 solver.cpp:237]     Train net output #0: loss = 0.871602 (* 1 = 0.871602 loss)
I0410 00:40:45.236024 14080 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0410 00:40:49.919770 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:40:50.143136 14080 solver.cpp:218] Iteration 4248 (2.44553 iter/s, 4.9069s/12 iters), loss = 0.859314
I0410 00:40:50.143178 14080 solver.cpp:237]     Train net output #0: loss = 0.859314 (* 1 = 0.859314 loss)
I0410 00:40:50.143186 14080 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0410 00:40:55.034483 14080 solver.cpp:218] Iteration 4260 (2.45344 iter/s, 4.89109s/12 iters), loss = 0.958918
I0410 00:40:55.034529 14080 solver.cpp:237]     Train net output #0: loss = 0.958918 (* 1 = 0.958918 loss)
I0410 00:40:55.034539 14080 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0410 00:40:59.933400 14080 solver.cpp:218] Iteration 4272 (2.44965 iter/s, 4.89866s/12 iters), loss = 0.945942
I0410 00:40:59.933440 14080 solver.cpp:237]     Train net output #0: loss = 0.945942 (* 1 = 0.945942 loss)
I0410 00:40:59.933449 14080 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0410 00:41:04.355720 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0410 00:41:06.414181 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0410 00:41:11.385293 14080 solver.cpp:330] Iteration 4284, Testing net (#0)
I0410 00:41:11.385380 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:41:14.137948 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:41:15.834803 14080 solver.cpp:397]     Test net output #0: accuracy = 0.440564
I0410 00:41:15.834839 14080 solver.cpp:397]     Test net output #1: loss = 2.33877 (* 1 = 2.33877 loss)
I0410 00:41:15.920433 14080 solver.cpp:218] Iteration 4284 (0.750642 iter/s, 15.9863s/12 iters), loss = 1.11129
I0410 00:41:15.920486 14080 solver.cpp:237]     Train net output #0: loss = 1.11129 (* 1 = 1.11129 loss)
I0410 00:41:15.920496 14080 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0410 00:41:20.256650 14080 solver.cpp:218] Iteration 4296 (2.76754 iter/s, 4.33598s/12 iters), loss = 0.991826
I0410 00:41:20.256690 14080 solver.cpp:237]     Train net output #0: loss = 0.991826 (* 1 = 0.991826 loss)
I0410 00:41:20.256700 14080 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0410 00:41:25.536942 14080 solver.cpp:218] Iteration 4308 (2.27272 iter/s, 5.28002s/12 iters), loss = 0.963989
I0410 00:41:25.536991 14080 solver.cpp:237]     Train net output #0: loss = 0.963989 (* 1 = 0.963989 loss)
I0410 00:41:25.537003 14080 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0410 00:41:30.479341 14080 solver.cpp:218] Iteration 4320 (2.4281 iter/s, 4.94214s/12 iters), loss = 1.10732
I0410 00:41:30.479387 14080 solver.cpp:237]     Train net output #0: loss = 1.10732 (* 1 = 1.10732 loss)
I0410 00:41:30.479395 14080 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0410 00:41:35.569782 14080 solver.cpp:218] Iteration 4332 (2.35748 iter/s, 5.09017s/12 iters), loss = 0.762603
I0410 00:41:35.569831 14080 solver.cpp:237]     Train net output #0: loss = 0.762603 (* 1 = 0.762603 loss)
I0410 00:41:35.569840 14080 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0410 00:41:40.540745 14080 solver.cpp:218] Iteration 4344 (2.41415 iter/s, 4.9707s/12 iters), loss = 0.831097
I0410 00:41:40.540798 14080 solver.cpp:237]     Train net output #0: loss = 0.831097 (* 1 = 0.831097 loss)
I0410 00:41:40.540812 14080 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0410 00:41:42.434720 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:41:45.495772 14080 solver.cpp:218] Iteration 4356 (2.42191 iter/s, 4.95476s/12 iters), loss = 0.799295
I0410 00:41:45.495828 14080 solver.cpp:237]     Train net output #0: loss = 0.799295 (* 1 = 0.799295 loss)
I0410 00:41:45.495839 14080 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0410 00:41:50.597093 14080 solver.cpp:218] Iteration 4368 (2.35246 iter/s, 5.10104s/12 iters), loss = 0.78288
I0410 00:41:50.597148 14080 solver.cpp:237]     Train net output #0: loss = 0.78288 (* 1 = 0.78288 loss)
I0410 00:41:50.597162 14080 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0410 00:41:55.877977 14080 solver.cpp:218] Iteration 4380 (2.27248 iter/s, 5.28059s/12 iters), loss = 0.894117
I0410 00:41:55.878033 14080 solver.cpp:237]     Train net output #0: loss = 0.894117 (* 1 = 0.894117 loss)
I0410 00:41:55.878046 14080 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0410 00:41:57.892210 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0410 00:42:00.404247 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0410 00:42:02.485211 14080 solver.cpp:330] Iteration 4386, Testing net (#0)
I0410 00:42:02.485240 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:42:05.115788 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:42:06.869812 14080 solver.cpp:397]     Test net output #0: accuracy = 0.438113
I0410 00:42:06.869866 14080 solver.cpp:397]     Test net output #1: loss = 2.31781 (* 1 = 2.31781 loss)
I0410 00:42:08.708159 14080 solver.cpp:218] Iteration 4392 (0.935338 iter/s, 12.8296s/12 iters), loss = 0.976703
I0410 00:42:08.708206 14080 solver.cpp:237]     Train net output #0: loss = 0.976703 (* 1 = 0.976703 loss)
I0410 00:42:08.708215 14080 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0410 00:42:13.619069 14080 solver.cpp:218] Iteration 4404 (2.44367 iter/s, 4.91064s/12 iters), loss = 0.926879
I0410 00:42:13.619192 14080 solver.cpp:237]     Train net output #0: loss = 0.926879 (* 1 = 0.926879 loss)
I0410 00:42:13.619205 14080 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0410 00:42:18.541611 14080 solver.cpp:218] Iteration 4416 (2.43793 iter/s, 4.9222s/12 iters), loss = 1.04865
I0410 00:42:18.541667 14080 solver.cpp:237]     Train net output #0: loss = 1.04865 (* 1 = 1.04865 loss)
I0410 00:42:18.541678 14080 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0410 00:42:23.466329 14080 solver.cpp:218] Iteration 4428 (2.43683 iter/s, 4.92444s/12 iters), loss = 0.665375
I0410 00:42:23.466398 14080 solver.cpp:237]     Train net output #0: loss = 0.665375 (* 1 = 0.665375 loss)
I0410 00:42:23.466415 14080 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0410 00:42:28.389111 14080 solver.cpp:218] Iteration 4440 (2.43778 iter/s, 4.9225s/12 iters), loss = 0.676935
I0410 00:42:28.389169 14080 solver.cpp:237]     Train net output #0: loss = 0.676935 (* 1 = 0.676935 loss)
I0410 00:42:28.389183 14080 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0410 00:42:32.345623 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:42:33.302233 14080 solver.cpp:218] Iteration 4452 (2.44257 iter/s, 4.91285s/12 iters), loss = 0.821521
I0410 00:42:33.302284 14080 solver.cpp:237]     Train net output #0: loss = 0.821521 (* 1 = 0.821521 loss)
I0410 00:42:33.302295 14080 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0410 00:42:38.221680 14080 solver.cpp:218] Iteration 4464 (2.43943 iter/s, 4.91918s/12 iters), loss = 1.1571
I0410 00:42:38.221729 14080 solver.cpp:237]     Train net output #0: loss = 1.1571 (* 1 = 1.1571 loss)
I0410 00:42:38.221737 14080 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0410 00:42:43.143740 14080 solver.cpp:218] Iteration 4476 (2.43814 iter/s, 4.92179s/12 iters), loss = 0.934926
I0410 00:42:43.143796 14080 solver.cpp:237]     Train net output #0: loss = 0.934926 (* 1 = 0.934926 loss)
I0410 00:42:43.143808 14080 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0410 00:42:47.597694 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0410 00:42:49.074081 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0410 00:42:50.266595 14080 solver.cpp:330] Iteration 4488, Testing net (#0)
I0410 00:42:50.266625 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:42:52.955546 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:42:54.731768 14080 solver.cpp:397]     Test net output #0: accuracy = 0.458946
I0410 00:42:54.731808 14080 solver.cpp:397]     Test net output #1: loss = 2.31295 (* 1 = 2.31295 loss)
I0410 00:42:54.817545 14080 solver.cpp:218] Iteration 4488 (1.02799 iter/s, 11.6733s/12 iters), loss = 0.937975
I0410 00:42:54.817598 14080 solver.cpp:237]     Train net output #0: loss = 0.937975 (* 1 = 0.937975 loss)
I0410 00:42:54.817610 14080 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0410 00:42:58.867100 14080 solver.cpp:218] Iteration 4500 (2.96346 iter/s, 4.04932s/12 iters), loss = 0.668573
I0410 00:42:58.867146 14080 solver.cpp:237]     Train net output #0: loss = 0.668573 (* 1 = 0.668573 loss)
I0410 00:42:58.867153 14080 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0410 00:43:03.769907 14080 solver.cpp:218] Iteration 4512 (2.44771 iter/s, 4.90255s/12 iters), loss = 0.853731
I0410 00:43:03.769985 14080 solver.cpp:237]     Train net output #0: loss = 0.853731 (* 1 = 0.853731 loss)
I0410 00:43:03.769999 14080 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0410 00:43:08.682514 14080 solver.cpp:218] Iteration 4524 (2.44283 iter/s, 4.91234s/12 iters), loss = 0.825012
I0410 00:43:08.682562 14080 solver.cpp:237]     Train net output #0: loss = 0.825012 (* 1 = 0.825012 loss)
I0410 00:43:08.682571 14080 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0410 00:43:13.635001 14080 solver.cpp:218] Iteration 4536 (2.42316 iter/s, 4.95222s/12 iters), loss = 0.989581
I0410 00:43:13.635058 14080 solver.cpp:237]     Train net output #0: loss = 0.989581 (* 1 = 0.989581 loss)
I0410 00:43:13.635071 14080 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0410 00:43:18.622953 14080 solver.cpp:218] Iteration 4548 (2.40593 iter/s, 4.98767s/12 iters), loss = 0.918357
I0410 00:43:18.623076 14080 solver.cpp:237]     Train net output #0: loss = 0.918357 (* 1 = 0.918357 loss)
I0410 00:43:18.623090 14080 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0410 00:43:19.870426 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:43:23.533859 14080 solver.cpp:218] Iteration 4560 (2.44371 iter/s, 4.91057s/12 iters), loss = 0.742334
I0410 00:43:23.533907 14080 solver.cpp:237]     Train net output #0: loss = 0.742334 (* 1 = 0.742334 loss)
I0410 00:43:23.533917 14080 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0410 00:43:28.386633 14080 solver.cpp:218] Iteration 4572 (2.47295 iter/s, 4.85251s/12 iters), loss = 0.835044
I0410 00:43:28.386688 14080 solver.cpp:237]     Train net output #0: loss = 0.835044 (* 1 = 0.835044 loss)
I0410 00:43:28.386700 14080 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0410 00:43:33.210729 14080 solver.cpp:218] Iteration 4584 (2.48765 iter/s, 4.82383s/12 iters), loss = 0.668947
I0410 00:43:33.210783 14080 solver.cpp:237]     Train net output #0: loss = 0.668947 (* 1 = 0.668947 loss)
I0410 00:43:33.210795 14080 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0410 00:43:35.167424 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0410 00:43:36.581287 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0410 00:43:37.660115 14080 solver.cpp:330] Iteration 4590, Testing net (#0)
I0410 00:43:37.660145 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:43:40.338452 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:43:42.256649 14080 solver.cpp:397]     Test net output #0: accuracy = 0.466912
I0410 00:43:42.256695 14080 solver.cpp:397]     Test net output #1: loss = 2.27568 (* 1 = 2.27568 loss)
I0410 00:43:44.062379 14080 solver.cpp:218] Iteration 4596 (1.10587 iter/s, 10.8511s/12 iters), loss = 0.882961
I0410 00:43:44.062425 14080 solver.cpp:237]     Train net output #0: loss = 0.882961 (* 1 = 0.882961 loss)
I0410 00:43:44.062436 14080 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0410 00:43:49.154875 14080 solver.cpp:218] Iteration 4608 (2.35653 iter/s, 5.09223s/12 iters), loss = 0.737532
I0410 00:43:49.154995 14080 solver.cpp:237]     Train net output #0: loss = 0.737532 (* 1 = 0.737532 loss)
I0410 00:43:49.155005 14080 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0410 00:43:54.093329 14080 solver.cpp:218] Iteration 4620 (2.43008 iter/s, 4.93811s/12 iters), loss = 0.821858
I0410 00:43:54.093389 14080 solver.cpp:237]     Train net output #0: loss = 0.821858 (* 1 = 0.821858 loss)
I0410 00:43:54.093400 14080 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0410 00:43:59.039974 14080 solver.cpp:218] Iteration 4632 (2.42602 iter/s, 4.94638s/12 iters), loss = 0.664217
I0410 00:43:59.040017 14080 solver.cpp:237]     Train net output #0: loss = 0.664217 (* 1 = 0.664217 loss)
I0410 00:43:59.040025 14080 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0410 00:44:03.973922 14080 solver.cpp:218] Iteration 4644 (2.43226 iter/s, 4.93369s/12 iters), loss = 0.62765
I0410 00:44:03.973981 14080 solver.cpp:237]     Train net output #0: loss = 0.62765 (* 1 = 0.62765 loss)
I0410 00:44:03.973991 14080 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0410 00:44:07.312685 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:44:08.888907 14080 solver.cpp:218] Iteration 4656 (2.44165 iter/s, 4.91471s/12 iters), loss = 0.7835
I0410 00:44:08.888955 14080 solver.cpp:237]     Train net output #0: loss = 0.7835 (* 1 = 0.7835 loss)
I0410 00:44:08.888965 14080 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0410 00:44:13.742972 14080 solver.cpp:218] Iteration 4668 (2.47229 iter/s, 4.85381s/12 iters), loss = 0.625013
I0410 00:44:13.743016 14080 solver.cpp:237]     Train net output #0: loss = 0.625013 (* 1 = 0.625013 loss)
I0410 00:44:13.743024 14080 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0410 00:44:18.643193 14080 solver.cpp:218] Iteration 4680 (2.449 iter/s, 4.89996s/12 iters), loss = 0.866444
I0410 00:44:18.643244 14080 solver.cpp:237]     Train net output #0: loss = 0.866444 (* 1 = 0.866444 loss)
I0410 00:44:18.643255 14080 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0410 00:44:23.161201 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0410 00:44:26.466085 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0410 00:44:30.736613 14080 solver.cpp:330] Iteration 4692, Testing net (#0)
I0410 00:44:30.736642 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:44:33.329514 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:44:35.187356 14080 solver.cpp:397]     Test net output #0: accuracy = 0.471814
I0410 00:44:35.187407 14080 solver.cpp:397]     Test net output #1: loss = 2.35153 (* 1 = 2.35153 loss)
I0410 00:44:35.273176 14080 solver.cpp:218] Iteration 4692 (0.72162 iter/s, 16.6292s/12 iters), loss = 0.615544
I0410 00:44:35.273231 14080 solver.cpp:237]     Train net output #0: loss = 0.615544 (* 1 = 0.615544 loss)
I0410 00:44:35.273242 14080 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0410 00:44:39.392160 14080 solver.cpp:218] Iteration 4704 (2.91351 iter/s, 4.11875s/12 iters), loss = 0.697411
I0410 00:44:39.392202 14080 solver.cpp:237]     Train net output #0: loss = 0.697411 (* 1 = 0.697411 loss)
I0410 00:44:39.392212 14080 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0410 00:44:44.283401 14080 solver.cpp:218] Iteration 4716 (2.45349 iter/s, 4.89098s/12 iters), loss = 0.487074
I0410 00:44:44.283450 14080 solver.cpp:237]     Train net output #0: loss = 0.487074 (* 1 = 0.487074 loss)
I0410 00:44:44.283460 14080 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0410 00:44:49.216832 14080 solver.cpp:218] Iteration 4728 (2.43252 iter/s, 4.93316s/12 iters), loss = 0.802931
I0410 00:44:49.216894 14080 solver.cpp:237]     Train net output #0: loss = 0.802931 (* 1 = 0.802931 loss)
I0410 00:44:49.216907 14080 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0410 00:44:54.159862 14080 solver.cpp:218] Iteration 4740 (2.4278 iter/s, 4.94275s/12 iters), loss = 0.724051
I0410 00:44:54.160002 14080 solver.cpp:237]     Train net output #0: loss = 0.724051 (* 1 = 0.724051 loss)
I0410 00:44:54.160015 14080 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0410 00:44:59.278596 14080 solver.cpp:218] Iteration 4752 (2.34449 iter/s, 5.11838s/12 iters), loss = 0.669339
I0410 00:44:59.278640 14080 solver.cpp:237]     Train net output #0: loss = 0.669339 (* 1 = 0.669339 loss)
I0410 00:44:59.278650 14080 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0410 00:44:59.785966 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:45:04.175792 14080 solver.cpp:218] Iteration 4764 (2.45052 iter/s, 4.89693s/12 iters), loss = 0.756775
I0410 00:45:04.175844 14080 solver.cpp:237]     Train net output #0: loss = 0.756775 (* 1 = 0.756775 loss)
I0410 00:45:04.175855 14080 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0410 00:45:09.065992 14080 solver.cpp:218] Iteration 4776 (2.45403 iter/s, 4.88991s/12 iters), loss = 0.682934
I0410 00:45:09.066038 14080 solver.cpp:237]     Train net output #0: loss = 0.682934 (* 1 = 0.682934 loss)
I0410 00:45:09.066047 14080 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0410 00:45:13.957084 14080 solver.cpp:218] Iteration 4788 (2.45357 iter/s, 4.89083s/12 iters), loss = 0.750034
I0410 00:45:13.957139 14080 solver.cpp:237]     Train net output #0: loss = 0.750034 (* 1 = 0.750034 loss)
I0410 00:45:13.957150 14080 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0410 00:45:15.945463 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0410 00:45:17.322577 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0410 00:45:19.452368 14080 solver.cpp:330] Iteration 4794, Testing net (#0)
I0410 00:45:19.452395 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:45:21.915171 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:45:23.812158 14080 solver.cpp:397]     Test net output #0: accuracy = 0.47549
I0410 00:45:23.812208 14080 solver.cpp:397]     Test net output #1: loss = 2.23548 (* 1 = 2.23548 loss)
I0410 00:45:25.759249 14080 solver.cpp:218] Iteration 4800 (1.01681 iter/s, 11.8016s/12 iters), loss = 0.78821
I0410 00:45:25.759372 14080 solver.cpp:237]     Train net output #0: loss = 0.78821 (* 1 = 0.78821 loss)
I0410 00:45:25.759387 14080 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0410 00:45:30.764384 14080 solver.cpp:218] Iteration 4812 (2.3977 iter/s, 5.0048s/12 iters), loss = 0.626187
I0410 00:45:30.764434 14080 solver.cpp:237]     Train net output #0: loss = 0.626187 (* 1 = 0.626187 loss)
I0410 00:45:30.764444 14080 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0410 00:45:35.662212 14080 solver.cpp:218] Iteration 4824 (2.4502 iter/s, 4.89756s/12 iters), loss = 0.756821
I0410 00:45:35.662259 14080 solver.cpp:237]     Train net output #0: loss = 0.756821 (* 1 = 0.756821 loss)
I0410 00:45:35.662269 14080 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0410 00:45:40.512044 14080 solver.cpp:218] Iteration 4836 (2.47444 iter/s, 4.84958s/12 iters), loss = 0.546879
I0410 00:45:40.512085 14080 solver.cpp:237]     Train net output #0: loss = 0.546879 (* 1 = 0.546879 loss)
I0410 00:45:40.512094 14080 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0410 00:45:42.099114 14080 blocking_queue.cpp:49] Waiting for data
I0410 00:45:45.420388 14080 solver.cpp:218] Iteration 4848 (2.44495 iter/s, 4.90808s/12 iters), loss = 0.713229
I0410 00:45:45.420441 14080 solver.cpp:237]     Train net output #0: loss = 0.713229 (* 1 = 0.713229 loss)
I0410 00:45:45.420452 14080 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0410 00:45:48.061375 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:45:50.365520 14080 solver.cpp:218] Iteration 4860 (2.42676 iter/s, 4.94486s/12 iters), loss = 0.756533
I0410 00:45:50.365564 14080 solver.cpp:237]     Train net output #0: loss = 0.756533 (* 1 = 0.756533 loss)
I0410 00:45:50.365573 14080 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0410 00:45:55.313514 14080 solver.cpp:218] Iteration 4872 (2.42535 iter/s, 4.94774s/12 iters), loss = 0.585658
I0410 00:45:55.313549 14080 solver.cpp:237]     Train net output #0: loss = 0.585658 (* 1 = 0.585658 loss)
I0410 00:45:55.313556 14080 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0410 00:46:00.209726 14080 solver.cpp:218] Iteration 4884 (2.451 iter/s, 4.89596s/12 iters), loss = 0.897758
I0410 00:46:00.209856 14080 solver.cpp:237]     Train net output #0: loss = 0.897758 (* 1 = 0.897758 loss)
I0410 00:46:00.209869 14080 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0410 00:46:04.871351 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0410 00:46:07.295608 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0410 00:46:09.410641 14080 solver.cpp:330] Iteration 4896, Testing net (#0)
I0410 00:46:09.410670 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:46:12.066835 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:46:13.995553 14080 solver.cpp:397]     Test net output #0: accuracy = 0.481618
I0410 00:46:13.995607 14080 solver.cpp:397]     Test net output #1: loss = 2.27168 (* 1 = 2.27168 loss)
I0410 00:46:14.081578 14080 solver.cpp:218] Iteration 4896 (0.865105 iter/s, 13.8712s/12 iters), loss = 0.537627
I0410 00:46:14.081629 14080 solver.cpp:237]     Train net output #0: loss = 0.537627 (* 1 = 0.537627 loss)
I0410 00:46:14.081641 14080 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0410 00:46:18.303514 14080 solver.cpp:218] Iteration 4908 (2.84245 iter/s, 4.2217s/12 iters), loss = 0.681342
I0410 00:46:18.303562 14080 solver.cpp:237]     Train net output #0: loss = 0.681342 (* 1 = 0.681342 loss)
I0410 00:46:18.303572 14080 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0410 00:46:23.200044 14080 solver.cpp:218] Iteration 4920 (2.45085 iter/s, 4.89627s/12 iters), loss = 0.681719
I0410 00:46:23.200105 14080 solver.cpp:237]     Train net output #0: loss = 0.681719 (* 1 = 0.681719 loss)
I0410 00:46:23.200119 14080 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0410 00:46:28.134369 14080 solver.cpp:218] Iteration 4932 (2.43208 iter/s, 4.93405s/12 iters), loss = 0.557284
I0410 00:46:28.134415 14080 solver.cpp:237]     Train net output #0: loss = 0.557284 (* 1 = 0.557284 loss)
I0410 00:46:28.134425 14080 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0410 00:46:33.065083 14080 solver.cpp:218] Iteration 4944 (2.43385 iter/s, 4.93045s/12 iters), loss = 0.641834
I0410 00:46:33.065176 14080 solver.cpp:237]     Train net output #0: loss = 0.641834 (* 1 = 0.641834 loss)
I0410 00:46:33.065187 14080 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0410 00:46:37.832634 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:46:38.027403 14080 solver.cpp:218] Iteration 4956 (2.41838 iter/s, 4.96201s/12 iters), loss = 0.448417
I0410 00:46:38.027454 14080 solver.cpp:237]     Train net output #0: loss = 0.448417 (* 1 = 0.448417 loss)
I0410 00:46:38.027467 14080 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0410 00:46:42.979794 14080 solver.cpp:218] Iteration 4968 (2.4232 iter/s, 4.95213s/12 iters), loss = 0.769697
I0410 00:46:42.979835 14080 solver.cpp:237]     Train net output #0: loss = 0.769697 (* 1 = 0.769697 loss)
I0410 00:46:42.979846 14080 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0410 00:46:48.023566 14080 solver.cpp:218] Iteration 4980 (2.37929 iter/s, 5.04351s/12 iters), loss = 0.656532
I0410 00:46:48.023607 14080 solver.cpp:237]     Train net output #0: loss = 0.656532 (* 1 = 0.656532 loss)
I0410 00:46:48.023617 14080 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0410 00:46:52.910938 14080 solver.cpp:218] Iteration 4992 (2.45544 iter/s, 4.88712s/12 iters), loss = 0.764221
I0410 00:46:52.910996 14080 solver.cpp:237]     Train net output #0: loss = 0.764221 (* 1 = 0.764221 loss)
I0410 00:46:52.911010 14080 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0410 00:46:54.914129 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0410 00:46:56.274322 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0410 00:46:58.124485 14080 solver.cpp:330] Iteration 4998, Testing net (#0)
I0410 00:46:58.124505 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:47:00.564597 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:47:02.552145 14080 solver.cpp:397]     Test net output #0: accuracy = 0.480392
I0410 00:47:02.552196 14080 solver.cpp:397]     Test net output #1: loss = 2.21143 (* 1 = 2.21143 loss)
I0410 00:47:04.480688 14080 solver.cpp:218] Iteration 5004 (1.03724 iter/s, 11.5692s/12 iters), loss = 0.721811
I0410 00:47:04.480787 14080 solver.cpp:237]     Train net output #0: loss = 0.721811 (* 1 = 0.721811 loss)
I0410 00:47:04.480798 14080 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0410 00:47:09.412672 14080 solver.cpp:218] Iteration 5016 (2.43325 iter/s, 4.93167s/12 iters), loss = 0.860348
I0410 00:47:09.412716 14080 solver.cpp:237]     Train net output #0: loss = 0.860348 (* 1 = 0.860348 loss)
I0410 00:47:09.412725 14080 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0410 00:47:14.315434 14080 solver.cpp:218] Iteration 5028 (2.44773 iter/s, 4.9025s/12 iters), loss = 0.43099
I0410 00:47:14.315488 14080 solver.cpp:237]     Train net output #0: loss = 0.43099 (* 1 = 0.43099 loss)
I0410 00:47:14.315500 14080 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0410 00:47:19.209043 14080 solver.cpp:218] Iteration 5040 (2.45231 iter/s, 4.89335s/12 iters), loss = 0.707196
I0410 00:47:19.209091 14080 solver.cpp:237]     Train net output #0: loss = 0.707196 (* 1 = 0.707196 loss)
I0410 00:47:19.209102 14080 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0410 00:47:24.232266 14080 solver.cpp:218] Iteration 5052 (2.38903 iter/s, 5.02295s/12 iters), loss = 0.55667
I0410 00:47:24.232332 14080 solver.cpp:237]     Train net output #0: loss = 0.55667 (* 1 = 0.55667 loss)
I0410 00:47:24.232347 14080 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0410 00:47:26.146896 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:47:29.157063 14080 solver.cpp:218] Iteration 5064 (2.43679 iter/s, 4.92451s/12 iters), loss = 0.762247
I0410 00:47:29.157126 14080 solver.cpp:237]     Train net output #0: loss = 0.762247 (* 1 = 0.762247 loss)
I0410 00:47:29.157140 14080 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0410 00:47:34.114500 14080 solver.cpp:218] Iteration 5076 (2.42074 iter/s, 4.95716s/12 iters), loss = 0.851278
I0410 00:47:34.114552 14080 solver.cpp:237]     Train net output #0: loss = 0.851278 (* 1 = 0.851278 loss)
I0410 00:47:34.114562 14080 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0410 00:47:39.038909 14080 solver.cpp:218] Iteration 5088 (2.43697 iter/s, 4.92415s/12 iters), loss = 0.360069
I0410 00:47:39.039032 14080 solver.cpp:237]     Train net output #0: loss = 0.360069 (* 1 = 0.360069 loss)
I0410 00:47:39.039047 14080 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0410 00:47:43.521392 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0410 00:47:46.507422 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0410 00:47:48.069993 14080 solver.cpp:330] Iteration 5100, Testing net (#0)
I0410 00:47:48.070017 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:47:50.493149 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:47:52.516207 14080 solver.cpp:397]     Test net output #0: accuracy = 0.503676
I0410 00:47:52.516256 14080 solver.cpp:397]     Test net output #1: loss = 2.23424 (* 1 = 2.23424 loss)
I0410 00:47:52.602125 14080 solver.cpp:218] Iteration 5100 (0.88479 iter/s, 13.5625s/12 iters), loss = 0.506852
I0410 00:47:52.602185 14080 solver.cpp:237]     Train net output #0: loss = 0.506852 (* 1 = 0.506852 loss)
I0410 00:47:52.602197 14080 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0410 00:47:56.846323 14080 solver.cpp:218] Iteration 5112 (2.82755 iter/s, 4.24396s/12 iters), loss = 0.713528
I0410 00:47:56.846361 14080 solver.cpp:237]     Train net output #0: loss = 0.713528 (* 1 = 0.713528 loss)
I0410 00:47:56.846369 14080 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0410 00:48:01.904917 14080 solver.cpp:218] Iteration 5124 (2.37232 iter/s, 5.05833s/12 iters), loss = 0.621355
I0410 00:48:01.904974 14080 solver.cpp:237]     Train net output #0: loss = 0.621355 (* 1 = 0.621355 loss)
I0410 00:48:01.904986 14080 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0410 00:48:06.774291 14080 solver.cpp:218] Iteration 5136 (2.46452 iter/s, 4.86911s/12 iters), loss = 0.417374
I0410 00:48:06.774339 14080 solver.cpp:237]     Train net output #0: loss = 0.417374 (* 1 = 0.417374 loss)
I0410 00:48:06.774350 14080 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0410 00:48:11.630595 14080 solver.cpp:218] Iteration 5148 (2.47115 iter/s, 4.85605s/12 iters), loss = 0.638483
I0410 00:48:11.630708 14080 solver.cpp:237]     Train net output #0: loss = 0.638483 (* 1 = 0.638483 loss)
I0410 00:48:11.630720 14080 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0410 00:48:15.581035 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:48:16.492818 14080 solver.cpp:218] Iteration 5160 (2.46817 iter/s, 4.86191s/12 iters), loss = 0.517392
I0410 00:48:16.492870 14080 solver.cpp:237]     Train net output #0: loss = 0.517392 (* 1 = 0.517392 loss)
I0410 00:48:16.492882 14080 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0410 00:48:21.432202 14080 solver.cpp:218] Iteration 5172 (2.42958 iter/s, 4.93912s/12 iters), loss = 0.648184
I0410 00:48:21.432245 14080 solver.cpp:237]     Train net output #0: loss = 0.648184 (* 1 = 0.648184 loss)
I0410 00:48:21.432255 14080 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0410 00:48:26.349009 14080 solver.cpp:218] Iteration 5184 (2.44074 iter/s, 4.91655s/12 iters), loss = 0.507743
I0410 00:48:26.349062 14080 solver.cpp:237]     Train net output #0: loss = 0.507743 (* 1 = 0.507743 loss)
I0410 00:48:26.349073 14080 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0410 00:48:31.247104 14080 solver.cpp:218] Iteration 5196 (2.45007 iter/s, 4.89783s/12 iters), loss = 0.625026
I0410 00:48:31.247159 14080 solver.cpp:237]     Train net output #0: loss = 0.625026 (* 1 = 0.625026 loss)
I0410 00:48:31.247171 14080 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0410 00:48:33.267069 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0410 00:48:35.968833 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0410 00:48:38.288975 14080 solver.cpp:330] Iteration 5202, Testing net (#0)
I0410 00:48:38.289002 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:48:40.671100 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:48:42.722470 14080 solver.cpp:397]     Test net output #0: accuracy = 0.509191
I0410 00:48:42.722550 14080 solver.cpp:397]     Test net output #1: loss = 2.27652 (* 1 = 2.27652 loss)
I0410 00:48:44.464639 14080 solver.cpp:218] Iteration 5208 (0.907926 iter/s, 13.2169s/12 iters), loss = 0.531164
I0410 00:48:44.464694 14080 solver.cpp:237]     Train net output #0: loss = 0.531164 (* 1 = 0.531164 loss)
I0410 00:48:44.464706 14080 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0410 00:48:49.373821 14080 solver.cpp:218] Iteration 5220 (2.44453 iter/s, 4.90891s/12 iters), loss = 0.51849
I0410 00:48:49.373870 14080 solver.cpp:237]     Train net output #0: loss = 0.51849 (* 1 = 0.51849 loss)
I0410 00:48:49.373881 14080 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0410 00:48:54.257863 14080 solver.cpp:218] Iteration 5232 (2.45711 iter/s, 4.88378s/12 iters), loss = 0.511665
I0410 00:48:54.257913 14080 solver.cpp:237]     Train net output #0: loss = 0.511665 (* 1 = 0.511665 loss)
I0410 00:48:54.257925 14080 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0410 00:48:59.147378 14080 solver.cpp:218] Iteration 5244 (2.45436 iter/s, 4.88925s/12 iters), loss = 0.403046
I0410 00:48:59.147430 14080 solver.cpp:237]     Train net output #0: loss = 0.403046 (* 1 = 0.403046 loss)
I0410 00:48:59.147441 14080 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0410 00:49:04.256814 14080 solver.cpp:218] Iteration 5256 (2.34872 iter/s, 5.10917s/12 iters), loss = 0.585052
I0410 00:49:04.256856 14080 solver.cpp:237]     Train net output #0: loss = 0.585052 (* 1 = 0.585052 loss)
I0410 00:49:04.256865 14080 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0410 00:49:05.616076 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:49:09.224493 14080 solver.cpp:218] Iteration 5268 (2.41574 iter/s, 4.96742s/12 iters), loss = 0.57948
I0410 00:49:09.224531 14080 solver.cpp:237]     Train net output #0: loss = 0.57948 (* 1 = 0.57948 loss)
I0410 00:49:09.224541 14080 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0410 00:49:14.133072 14080 solver.cpp:218] Iteration 5280 (2.44483 iter/s, 4.90832s/12 iters), loss = 0.447498
I0410 00:49:14.142058 14080 solver.cpp:237]     Train net output #0: loss = 0.447498 (* 1 = 0.447498 loss)
I0410 00:49:14.142073 14080 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0410 00:49:19.058605 14080 solver.cpp:218] Iteration 5292 (2.44084 iter/s, 4.91634s/12 iters), loss = 0.497777
I0410 00:49:19.058655 14080 solver.cpp:237]     Train net output #0: loss = 0.497777 (* 1 = 0.497777 loss)
I0410 00:49:19.058666 14080 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0410 00:49:23.466120 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0410 00:49:25.564934 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0410 00:49:27.772960 14080 solver.cpp:330] Iteration 5304, Testing net (#0)
I0410 00:49:27.772989 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:49:30.932441 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:49:33.191588 14080 solver.cpp:397]     Test net output #0: accuracy = 0.506127
I0410 00:49:33.191637 14080 solver.cpp:397]     Test net output #1: loss = 2.24377 (* 1 = 2.24377 loss)
I0410 00:49:33.276582 14080 solver.cpp:218] Iteration 5304 (0.84404 iter/s, 14.2173s/12 iters), loss = 0.614939
I0410 00:49:33.276644 14080 solver.cpp:237]     Train net output #0: loss = 0.614939 (* 1 = 0.614939 loss)
I0410 00:49:33.276657 14080 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0410 00:49:37.469869 14080 solver.cpp:218] Iteration 5316 (2.86189 iter/s, 4.19304s/12 iters), loss = 0.446066
I0410 00:49:37.469920 14080 solver.cpp:237]     Train net output #0: loss = 0.446066 (* 1 = 0.446066 loss)
I0410 00:49:37.469930 14080 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0410 00:49:42.343700 14080 solver.cpp:218] Iteration 5328 (2.46226 iter/s, 4.87357s/12 iters), loss = 0.323892
I0410 00:49:42.343755 14080 solver.cpp:237]     Train net output #0: loss = 0.323892 (* 1 = 0.323892 loss)
I0410 00:49:42.343766 14080 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0410 00:49:47.235949 14080 solver.cpp:218] Iteration 5340 (2.45299 iter/s, 4.89198s/12 iters), loss = 0.516289
I0410 00:49:47.236091 14080 solver.cpp:237]     Train net output #0: loss = 0.516289 (* 1 = 0.516289 loss)
I0410 00:49:47.236104 14080 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0410 00:49:52.126263 14080 solver.cpp:218] Iteration 5352 (2.454 iter/s, 4.88997s/12 iters), loss = 0.336327
I0410 00:49:52.126312 14080 solver.cpp:237]     Train net output #0: loss = 0.336327 (* 1 = 0.336327 loss)
I0410 00:49:52.126322 14080 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0410 00:49:55.494810 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:49:57.029455 14080 solver.cpp:218] Iteration 5364 (2.44752 iter/s, 4.90293s/12 iters), loss = 0.552987
I0410 00:49:57.029512 14080 solver.cpp:237]     Train net output #0: loss = 0.552987 (* 1 = 0.552987 loss)
I0410 00:49:57.029525 14080 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0410 00:50:01.937842 14080 solver.cpp:218] Iteration 5376 (2.44493 iter/s, 4.90812s/12 iters), loss = 0.635798
I0410 00:50:01.937899 14080 solver.cpp:237]     Train net output #0: loss = 0.635798 (* 1 = 0.635798 loss)
I0410 00:50:01.937912 14080 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0410 00:50:06.983120 14080 solver.cpp:218] Iteration 5388 (2.37859 iter/s, 5.04501s/12 iters), loss = 0.455055
I0410 00:50:06.983175 14080 solver.cpp:237]     Train net output #0: loss = 0.455055 (* 1 = 0.455055 loss)
I0410 00:50:06.983186 14080 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0410 00:50:11.956846 14080 solver.cpp:218] Iteration 5400 (2.41281 iter/s, 4.97346s/12 iters), loss = 0.501165
I0410 00:50:11.956885 14080 solver.cpp:237]     Train net output #0: loss = 0.501165 (* 1 = 0.501165 loss)
I0410 00:50:11.956895 14080 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0410 00:50:14.140944 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0410 00:50:16.574177 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0410 00:50:18.340679 14080 solver.cpp:330] Iteration 5406, Testing net (#0)
I0410 00:50:18.340770 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:50:20.672914 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:50:22.804426 14080 solver.cpp:397]     Test net output #0: accuracy = 0.496324
I0410 00:50:22.804458 14080 solver.cpp:397]     Test net output #1: loss = 2.26079 (* 1 = 2.26079 loss)
I0410 00:50:24.767074 14080 solver.cpp:218] Iteration 5412 (0.936793 iter/s, 12.8097s/12 iters), loss = 0.465361
I0410 00:50:24.767128 14080 solver.cpp:237]     Train net output #0: loss = 0.465361 (* 1 = 0.465361 loss)
I0410 00:50:24.767140 14080 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0410 00:50:29.817891 14080 solver.cpp:218] Iteration 5424 (2.37598 iter/s, 5.05055s/12 iters), loss = 0.450384
I0410 00:50:29.817941 14080 solver.cpp:237]     Train net output #0: loss = 0.450384 (* 1 = 0.450384 loss)
I0410 00:50:29.817952 14080 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0410 00:50:34.693596 14080 solver.cpp:218] Iteration 5436 (2.46131 iter/s, 4.87545s/12 iters), loss = 0.516402
I0410 00:50:34.693643 14080 solver.cpp:237]     Train net output #0: loss = 0.516402 (* 1 = 0.516402 loss)
I0410 00:50:34.693655 14080 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0410 00:50:39.572698 14080 solver.cpp:218] Iteration 5448 (2.4596 iter/s, 4.87884s/12 iters), loss = 0.324289
I0410 00:50:39.572741 14080 solver.cpp:237]     Train net output #0: loss = 0.324289 (* 1 = 0.324289 loss)
I0410 00:50:39.572751 14080 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0410 00:50:44.487921 14080 solver.cpp:218] Iteration 5460 (2.44152 iter/s, 4.91496s/12 iters), loss = 0.311606
I0410 00:50:44.487977 14080 solver.cpp:237]     Train net output #0: loss = 0.311606 (* 1 = 0.311606 loss)
I0410 00:50:44.487989 14080 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0410 00:50:45.040643 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:50:49.406975 14080 solver.cpp:218] Iteration 5472 (2.43963 iter/s, 4.91878s/12 iters), loss = 0.361928
I0410 00:50:49.407140 14080 solver.cpp:237]     Train net output #0: loss = 0.361928 (* 1 = 0.361928 loss)
I0410 00:50:49.407155 14080 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0410 00:50:54.330667 14080 solver.cpp:218] Iteration 5484 (2.43738 iter/s, 4.92331s/12 iters), loss = 0.595429
I0410 00:50:54.330725 14080 solver.cpp:237]     Train net output #0: loss = 0.595429 (* 1 = 0.595429 loss)
I0410 00:50:54.330739 14080 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0410 00:50:59.554428 14080 solver.cpp:218] Iteration 5496 (2.29732 iter/s, 5.22348s/12 iters), loss = 0.566673
I0410 00:50:59.554474 14080 solver.cpp:237]     Train net output #0: loss = 0.566673 (* 1 = 0.566673 loss)
I0410 00:50:59.554483 14080 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0410 00:51:04.150898 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0410 00:51:05.911725 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0410 00:51:07.265035 14080 solver.cpp:330] Iteration 5508, Testing net (#0)
I0410 00:51:07.265064 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:51:09.561655 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:51:11.848948 14080 solver.cpp:397]     Test net output #0: accuracy = 0.487132
I0410 00:51:11.848984 14080 solver.cpp:397]     Test net output #1: loss = 2.3098 (* 1 = 2.3098 loss)
I0410 00:51:11.934835 14080 solver.cpp:218] Iteration 5508 (0.969317 iter/s, 12.3798s/12 iters), loss = 0.238094
I0410 00:51:11.934880 14080 solver.cpp:237]     Train net output #0: loss = 0.238094 (* 1 = 0.238094 loss)
I0410 00:51:11.934888 14080 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0410 00:51:16.111490 14080 solver.cpp:218] Iteration 5520 (2.87327 iter/s, 4.17642s/12 iters), loss = 0.467651
I0410 00:51:16.111549 14080 solver.cpp:237]     Train net output #0: loss = 0.467651 (* 1 = 0.467651 loss)
I0410 00:51:16.111562 14080 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0410 00:51:18.087867 14080 blocking_queue.cpp:49] Waiting for data
I0410 00:51:20.993124 14080 solver.cpp:218] Iteration 5532 (2.45833 iter/s, 4.88137s/12 iters), loss = 0.593112
I0410 00:51:20.993229 14080 solver.cpp:237]     Train net output #0: loss = 0.593112 (* 1 = 0.593112 loss)
I0410 00:51:20.993239 14080 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0410 00:51:25.884341 14080 solver.cpp:218] Iteration 5544 (2.45353 iter/s, 4.8909s/12 iters), loss = 0.214459
I0410 00:51:25.884378 14080 solver.cpp:237]     Train net output #0: loss = 0.214459 (* 1 = 0.214459 loss)
I0410 00:51:25.884387 14080 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0410 00:51:30.790594 14080 solver.cpp:218] Iteration 5556 (2.44599 iter/s, 4.906s/12 iters), loss = 0.379822
I0410 00:51:30.790654 14080 solver.cpp:237]     Train net output #0: loss = 0.379822 (* 1 = 0.379822 loss)
I0410 00:51:30.790668 14080 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0410 00:51:33.432756 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:51:35.693091 14080 solver.cpp:218] Iteration 5568 (2.44787 iter/s, 4.90222s/12 iters), loss = 0.3653
I0410 00:51:35.693150 14080 solver.cpp:237]     Train net output #0: loss = 0.3653 (* 1 = 0.3653 loss)
I0410 00:51:35.693162 14080 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0410 00:51:40.623514 14080 solver.cpp:218] Iteration 5580 (2.434 iter/s, 4.93015s/12 iters), loss = 0.403715
I0410 00:51:40.623566 14080 solver.cpp:237]     Train net output #0: loss = 0.403715 (* 1 = 0.403715 loss)
I0410 00:51:40.623577 14080 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0410 00:51:45.725477 14080 solver.cpp:218] Iteration 5592 (2.35216 iter/s, 5.10169s/12 iters), loss = 0.367963
I0410 00:51:45.725530 14080 solver.cpp:237]     Train net output #0: loss = 0.367963 (* 1 = 0.367963 loss)
I0410 00:51:45.725543 14080 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0410 00:51:50.586745 14080 solver.cpp:218] Iteration 5604 (2.46862 iter/s, 4.86101s/12 iters), loss = 0.571317
I0410 00:51:50.586793 14080 solver.cpp:237]     Train net output #0: loss = 0.571317 (* 1 = 0.571317 loss)
I0410 00:51:50.586807 14080 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0410 00:51:52.598143 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0410 00:51:54.806088 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0410 00:51:56.907627 14080 solver.cpp:330] Iteration 5610, Testing net (#0)
I0410 00:51:56.907650 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:51:59.162971 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:52:01.390607 14080 solver.cpp:397]     Test net output #0: accuracy = 0.498774
I0410 00:52:01.390653 14080 solver.cpp:397]     Test net output #1: loss = 2.30339 (* 1 = 2.30339 loss)
I0410 00:52:03.264406 14080 solver.cpp:218] Iteration 5616 (0.94659 iter/s, 12.6771s/12 iters), loss = 0.409591
I0410 00:52:03.264463 14080 solver.cpp:237]     Train net output #0: loss = 0.409591 (* 1 = 0.409591 loss)
I0410 00:52:03.264477 14080 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0410 00:52:08.152115 14080 solver.cpp:218] Iteration 5628 (2.45527 iter/s, 4.88744s/12 iters), loss = 0.396964
I0410 00:52:08.152161 14080 solver.cpp:237]     Train net output #0: loss = 0.396964 (* 1 = 0.396964 loss)
I0410 00:52:08.152171 14080 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0410 00:52:13.060842 14080 solver.cpp:218] Iteration 5640 (2.44475 iter/s, 4.90847s/12 iters), loss = 0.47699
I0410 00:52:13.060886 14080 solver.cpp:237]     Train net output #0: loss = 0.47699 (* 1 = 0.47699 loss)
I0410 00:52:13.060895 14080 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0410 00:52:17.963366 14080 solver.cpp:218] Iteration 5652 (2.44785 iter/s, 4.90227s/12 iters), loss = 0.46063
I0410 00:52:17.963410 14080 solver.cpp:237]     Train net output #0: loss = 0.46063 (* 1 = 0.46063 loss)
I0410 00:52:17.963420 14080 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0410 00:52:22.795080 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:52:22.953897 14080 solver.cpp:218] Iteration 5664 (2.40468 iter/s, 4.99027s/12 iters), loss = 0.402478
I0410 00:52:22.953953 14080 solver.cpp:237]     Train net output #0: loss = 0.402478 (* 1 = 0.402478 loss)
I0410 00:52:22.953976 14080 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0410 00:52:27.928290 14080 solver.cpp:218] Iteration 5676 (2.41249 iter/s, 4.97412s/12 iters), loss = 0.563148
I0410 00:52:27.928341 14080 solver.cpp:237]     Train net output #0: loss = 0.563148 (* 1 = 0.563148 loss)
I0410 00:52:27.928352 14080 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0410 00:52:32.868494 14080 solver.cpp:218] Iteration 5688 (2.42918 iter/s, 4.93994s/12 iters), loss = 0.356448
I0410 00:52:32.868546 14080 solver.cpp:237]     Train net output #0: loss = 0.356448 (* 1 = 0.356448 loss)
I0410 00:52:32.868557 14080 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0410 00:52:37.820338 14080 solver.cpp:218] Iteration 5700 (2.42347 iter/s, 4.95158s/12 iters), loss = 0.314106
I0410 00:52:37.820394 14080 solver.cpp:237]     Train net output #0: loss = 0.314106 (* 1 = 0.314106 loss)
I0410 00:52:37.820405 14080 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0410 00:52:42.301949 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0410 00:52:48.668241 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0410 00:52:51.659314 14080 solver.cpp:330] Iteration 5712, Testing net (#0)
I0410 00:52:51.659343 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:52:53.775707 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:52:56.018769 14080 solver.cpp:397]     Test net output #0: accuracy = 0.497549
I0410 00:52:56.018816 14080 solver.cpp:397]     Test net output #1: loss = 2.34199 (* 1 = 2.34199 loss)
I0410 00:52:56.104422 14080 solver.cpp:218] Iteration 5712 (0.656337 iter/s, 18.2833s/12 iters), loss = 0.389495
I0410 00:52:56.104466 14080 solver.cpp:237]     Train net output #0: loss = 0.389495 (* 1 = 0.389495 loss)
I0410 00:52:56.104477 14080 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0410 00:53:00.576615 14080 solver.cpp:218] Iteration 5724 (2.68339 iter/s, 4.47195s/12 iters), loss = 0.39262
I0410 00:53:00.576668 14080 solver.cpp:237]     Train net output #0: loss = 0.39262 (* 1 = 0.39262 loss)
I0410 00:53:00.576678 14080 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0410 00:53:05.509732 14080 solver.cpp:218] Iteration 5736 (2.43267 iter/s, 4.93285s/12 iters), loss = 0.261647
I0410 00:53:05.509783 14080 solver.cpp:237]     Train net output #0: loss = 0.261647 (* 1 = 0.261647 loss)
I0410 00:53:05.509793 14080 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0410 00:53:10.395099 14080 solver.cpp:218] Iteration 5748 (2.45645 iter/s, 4.8851s/12 iters), loss = 0.253399
I0410 00:53:10.395149 14080 solver.cpp:237]     Train net output #0: loss = 0.253399 (* 1 = 0.253399 loss)
I0410 00:53:10.395159 14080 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0410 00:53:15.326303 14080 solver.cpp:218] Iteration 5760 (2.43361 iter/s, 4.93094s/12 iters), loss = 0.343962
I0410 00:53:15.326350 14080 solver.cpp:237]     Train net output #0: loss = 0.343962 (* 1 = 0.343962 loss)
I0410 00:53:15.326360 14080 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0410 00:53:17.224787 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:53:20.200886 14080 solver.cpp:218] Iteration 5772 (2.46188 iter/s, 4.87432s/12 iters), loss = 0.472952
I0410 00:53:20.200937 14080 solver.cpp:237]     Train net output #0: loss = 0.472952 (* 1 = 0.472952 loss)
I0410 00:53:20.200949 14080 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0410 00:53:25.314965 14080 solver.cpp:218] Iteration 5784 (2.34659 iter/s, 5.11381s/12 iters), loss = 0.346801
I0410 00:53:25.318397 14080 solver.cpp:237]     Train net output #0: loss = 0.346801 (* 1 = 0.346801 loss)
I0410 00:53:25.318408 14080 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0410 00:53:30.262631 14080 solver.cpp:218] Iteration 5796 (2.42717 iter/s, 4.94402s/12 iters), loss = 0.387292
I0410 00:53:30.262681 14080 solver.cpp:237]     Train net output #0: loss = 0.387292 (* 1 = 0.387292 loss)
I0410 00:53:30.262692 14080 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0410 00:53:35.125226 14080 solver.cpp:218] Iteration 5808 (2.46795 iter/s, 4.86233s/12 iters), loss = 0.363975
I0410 00:53:35.125277 14080 solver.cpp:237]     Train net output #0: loss = 0.363975 (* 1 = 0.363975 loss)
I0410 00:53:35.125288 14080 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0410 00:53:37.112001 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0410 00:53:39.767458 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0410 00:53:40.815343 14080 solver.cpp:330] Iteration 5814, Testing net (#0)
I0410 00:53:40.815371 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:53:42.896747 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:53:45.234314 14080 solver.cpp:397]     Test net output #0: accuracy = 0.514706
I0410 00:53:45.234354 14080 solver.cpp:397]     Test net output #1: loss = 2.32728 (* 1 = 2.32728 loss)
I0410 00:53:47.136088 14080 solver.cpp:218] Iteration 5820 (0.999141 iter/s, 12.0103s/12 iters), loss = 0.291488
I0410 00:53:47.136140 14080 solver.cpp:237]     Train net output #0: loss = 0.291488 (* 1 = 0.291488 loss)
I0410 00:53:47.136152 14080 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0410 00:53:52.388800 14080 solver.cpp:218] Iteration 5832 (2.28465 iter/s, 5.25243s/12 iters), loss = 0.507053
I0410 00:53:52.388850 14080 solver.cpp:237]     Train net output #0: loss = 0.507053 (* 1 = 0.507053 loss)
I0410 00:53:52.388861 14080 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0410 00:53:57.567647 14080 solver.cpp:218] Iteration 5844 (2.31724 iter/s, 5.17858s/12 iters), loss = 0.391678
I0410 00:53:57.567798 14080 solver.cpp:237]     Train net output #0: loss = 0.391678 (* 1 = 0.391678 loss)
I0410 00:53:57.567811 14080 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0410 00:54:02.457986 14080 solver.cpp:218] Iteration 5856 (2.454 iter/s, 4.88997s/12 iters), loss = 0.286862
I0410 00:54:02.458035 14080 solver.cpp:237]     Train net output #0: loss = 0.286862 (* 1 = 0.286862 loss)
I0410 00:54:02.458046 14080 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0410 00:54:06.766714 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:54:07.576277 14080 solver.cpp:218] Iteration 5868 (2.34466 iter/s, 5.11802s/12 iters), loss = 0.468337
I0410 00:54:07.576333 14080 solver.cpp:237]     Train net output #0: loss = 0.468337 (* 1 = 0.468337 loss)
I0410 00:54:07.576345 14080 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0410 00:54:12.527566 14080 solver.cpp:218] Iteration 5880 (2.42374 iter/s, 4.95102s/12 iters), loss = 0.476458
I0410 00:54:12.527624 14080 solver.cpp:237]     Train net output #0: loss = 0.476458 (* 1 = 0.476458 loss)
I0410 00:54:12.527637 14080 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0410 00:54:17.506356 14080 solver.cpp:218] Iteration 5892 (2.41036 iter/s, 4.97852s/12 iters), loss = 0.498526
I0410 00:54:17.506414 14080 solver.cpp:237]     Train net output #0: loss = 0.498526 (* 1 = 0.498526 loss)
I0410 00:54:17.506428 14080 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0410 00:54:22.451460 14080 solver.cpp:218] Iteration 5904 (2.42678 iter/s, 4.94483s/12 iters), loss = 0.36508
I0410 00:54:22.451517 14080 solver.cpp:237]     Train net output #0: loss = 0.36508 (* 1 = 0.36508 loss)
I0410 00:54:22.451529 14080 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0410 00:54:26.956992 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0410 00:54:28.359340 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0410 00:54:29.399684 14080 solver.cpp:330] Iteration 5916, Testing net (#0)
I0410 00:54:29.399704 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:54:31.528565 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:54:33.851799 14080 solver.cpp:397]     Test net output #0: accuracy = 0.504289
I0410 00:54:33.851848 14080 solver.cpp:397]     Test net output #1: loss = 2.3293 (* 1 = 2.3293 loss)
I0410 00:54:33.937780 14080 solver.cpp:218] Iteration 5916 (1.04477 iter/s, 11.4858s/12 iters), loss = 0.450934
I0410 00:54:33.937831 14080 solver.cpp:237]     Train net output #0: loss = 0.450934 (* 1 = 0.450934 loss)
I0410 00:54:33.937842 14080 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0410 00:54:38.164009 14080 solver.cpp:218] Iteration 5928 (2.83957 iter/s, 4.22599s/12 iters), loss = 0.316451
I0410 00:54:38.164060 14080 solver.cpp:237]     Train net output #0: loss = 0.316451 (* 1 = 0.316451 loss)
I0410 00:54:38.164072 14080 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0410 00:54:43.472244 14080 solver.cpp:218] Iteration 5940 (2.26076 iter/s, 5.30795s/12 iters), loss = 0.342154
I0410 00:54:43.472299 14080 solver.cpp:237]     Train net output #0: loss = 0.342154 (* 1 = 0.342154 loss)
I0410 00:54:43.472311 14080 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0410 00:54:48.334539 14080 solver.cpp:218] Iteration 5952 (2.46811 iter/s, 4.86203s/12 iters), loss = 0.3627
I0410 00:54:48.334585 14080 solver.cpp:237]     Train net output #0: loss = 0.3627 (* 1 = 0.3627 loss)
I0410 00:54:48.334594 14080 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0410 00:54:53.236148 14080 solver.cpp:218] Iteration 5964 (2.44831 iter/s, 4.90134s/12 iters), loss = 0.396182
I0410 00:54:53.236202 14080 solver.cpp:237]     Train net output #0: loss = 0.396182 (* 1 = 0.396182 loss)
I0410 00:54:53.236213 14080 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0410 00:54:54.548354 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:54:58.153862 14080 solver.cpp:218] Iteration 5976 (2.44029 iter/s, 4.91744s/12 iters), loss = 0.320534
I0410 00:54:58.153928 14080 solver.cpp:237]     Train net output #0: loss = 0.320534 (* 1 = 0.320534 loss)
I0410 00:54:58.153944 14080 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0410 00:55:03.139811 14080 solver.cpp:218] Iteration 5988 (2.4069 iter/s, 4.98567s/12 iters), loss = 0.265568
I0410 00:55:03.139930 14080 solver.cpp:237]     Train net output #0: loss = 0.265568 (* 1 = 0.265568 loss)
I0410 00:55:03.139941 14080 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0410 00:55:08.040915 14080 solver.cpp:218] Iteration 6000 (2.4486 iter/s, 4.90076s/12 iters), loss = 0.391876
I0410 00:55:08.040969 14080 solver.cpp:237]     Train net output #0: loss = 0.391876 (* 1 = 0.391876 loss)
I0410 00:55:08.040982 14080 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0410 00:55:12.922536 14080 solver.cpp:218] Iteration 6012 (2.45833 iter/s, 4.88136s/12 iters), loss = 0.407402
I0410 00:55:12.922576 14080 solver.cpp:237]     Train net output #0: loss = 0.407402 (* 1 = 0.407402 loss)
I0410 00:55:12.922585 14080 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0410 00:55:14.906013 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0410 00:55:17.130371 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0410 00:55:18.163084 14080 solver.cpp:330] Iteration 6018, Testing net (#0)
I0410 00:55:18.163103 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:55:20.291183 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:55:23.472518 14080 solver.cpp:397]     Test net output #0: accuracy = 0.508578
I0410 00:55:23.472566 14080 solver.cpp:397]     Test net output #1: loss = 2.28005 (* 1 = 2.28005 loss)
I0410 00:55:25.225056 14080 solver.cpp:218] Iteration 6024 (0.975454 iter/s, 12.302s/12 iters), loss = 0.350752
I0410 00:55:25.225107 14080 solver.cpp:237]     Train net output #0: loss = 0.350752 (* 1 = 0.350752 loss)
I0410 00:55:25.225118 14080 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0410 00:55:30.116609 14080 solver.cpp:218] Iteration 6036 (2.45334 iter/s, 4.89128s/12 iters), loss = 0.35871
I0410 00:55:30.116655 14080 solver.cpp:237]     Train net output #0: loss = 0.35871 (* 1 = 0.35871 loss)
I0410 00:55:30.116665 14080 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0410 00:55:35.005175 14080 solver.cpp:218] Iteration 6048 (2.45484 iter/s, 4.88831s/12 iters), loss = 0.290512
I0410 00:55:35.005272 14080 solver.cpp:237]     Train net output #0: loss = 0.290512 (* 1 = 0.290512 loss)
I0410 00:55:35.005282 14080 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0410 00:55:40.050144 14080 solver.cpp:218] Iteration 6060 (2.37876 iter/s, 5.04464s/12 iters), loss = 0.305484
I0410 00:55:40.050210 14080 solver.cpp:237]     Train net output #0: loss = 0.305484 (* 1 = 0.305484 loss)
I0410 00:55:40.050226 14080 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0410 00:55:43.468755 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:55:44.971479 14080 solver.cpp:218] Iteration 6072 (2.4385 iter/s, 4.92106s/12 iters), loss = 0.488584
I0410 00:55:44.971540 14080 solver.cpp:237]     Train net output #0: loss = 0.488584 (* 1 = 0.488584 loss)
I0410 00:55:44.971552 14080 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0410 00:55:49.870677 14080 solver.cpp:218] Iteration 6084 (2.44952 iter/s, 4.89892s/12 iters), loss = 0.479574
I0410 00:55:49.870733 14080 solver.cpp:237]     Train net output #0: loss = 0.479574 (* 1 = 0.479574 loss)
I0410 00:55:49.870744 14080 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0410 00:55:54.905287 14080 solver.cpp:218] Iteration 6096 (2.38363 iter/s, 5.03434s/12 iters), loss = 0.249305
I0410 00:55:54.905335 14080 solver.cpp:237]     Train net output #0: loss = 0.249305 (* 1 = 0.249305 loss)
I0410 00:55:54.905346 14080 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0410 00:55:59.811964 14080 solver.cpp:218] Iteration 6108 (2.44578 iter/s, 4.90641s/12 iters), loss = 0.306502
I0410 00:55:59.812007 14080 solver.cpp:237]     Train net output #0: loss = 0.306502 (* 1 = 0.306502 loss)
I0410 00:55:59.812017 14080 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0410 00:56:04.294858 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0410 00:56:06.034152 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0410 00:56:09.295644 14080 solver.cpp:330] Iteration 6120, Testing net (#0)
I0410 00:56:09.295670 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:56:11.426975 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:56:13.828395 14080 solver.cpp:397]     Test net output #0: accuracy = 0.51777
I0410 00:56:13.828446 14080 solver.cpp:397]     Test net output #1: loss = 2.29952 (* 1 = 2.29952 loss)
I0410 00:56:13.914372 14080 solver.cpp:218] Iteration 6120 (0.850957 iter/s, 14.1018s/12 iters), loss = 0.29989
I0410 00:56:13.914429 14080 solver.cpp:237]     Train net output #0: loss = 0.29989 (* 1 = 0.29989 loss)
I0410 00:56:13.914443 14080 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0410 00:56:18.013779 14080 solver.cpp:218] Iteration 6132 (2.92742 iter/s, 4.09917s/12 iters), loss = 0.282747
I0410 00:56:18.013833 14080 solver.cpp:237]     Train net output #0: loss = 0.282747 (* 1 = 0.282747 loss)
I0410 00:56:18.013845 14080 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0410 00:56:22.907424 14080 solver.cpp:218] Iteration 6144 (2.45229 iter/s, 4.89338s/12 iters), loss = 0.348995
I0410 00:56:22.907470 14080 solver.cpp:237]     Train net output #0: loss = 0.348995 (* 1 = 0.348995 loss)
I0410 00:56:22.907480 14080 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0410 00:56:27.822142 14080 solver.cpp:218] Iteration 6156 (2.44178 iter/s, 4.91446s/12 iters), loss = 0.365009
I0410 00:56:27.822193 14080 solver.cpp:237]     Train net output #0: loss = 0.365009 (* 1 = 0.365009 loss)
I0410 00:56:27.822207 14080 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0410 00:56:32.702517 14080 solver.cpp:218] Iteration 6168 (2.45896 iter/s, 4.8801s/12 iters), loss = 0.272587
I0410 00:56:32.702589 14080 solver.cpp:237]     Train net output #0: loss = 0.272587 (* 1 = 0.272587 loss)
I0410 00:56:32.702605 14080 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0410 00:56:33.302790 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:56:37.625767 14080 solver.cpp:218] Iteration 6180 (2.43755 iter/s, 4.92298s/12 iters), loss = 0.374735
I0410 00:56:37.625840 14080 solver.cpp:237]     Train net output #0: loss = 0.374735 (* 1 = 0.374735 loss)
I0410 00:56:37.625850 14080 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0410 00:56:42.521028 14080 solver.cpp:218] Iteration 6192 (2.45149 iter/s, 4.89498s/12 iters), loss = 0.332801
I0410 00:56:42.521068 14080 solver.cpp:237]     Train net output #0: loss = 0.332801 (* 1 = 0.332801 loss)
I0410 00:56:42.521076 14080 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0410 00:56:47.413259 14080 solver.cpp:218] Iteration 6204 (2.453 iter/s, 4.89197s/12 iters), loss = 0.251767
I0410 00:56:47.413313 14080 solver.cpp:237]     Train net output #0: loss = 0.251767 (* 1 = 0.251767 loss)
I0410 00:56:47.413327 14080 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0410 00:56:52.372910 14080 solver.cpp:218] Iteration 6216 (2.41965 iter/s, 4.95939s/12 iters), loss = 0.336583
I0410 00:56:52.372953 14080 solver.cpp:237]     Train net output #0: loss = 0.336583 (* 1 = 0.336583 loss)
I0410 00:56:52.372963 14080 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0410 00:56:54.373529 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0410 00:56:58.048405 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0410 00:56:59.813557 14080 solver.cpp:330] Iteration 6222, Testing net (#0)
I0410 00:56:59.813587 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:57:01.784564 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:57:02.965222 14080 blocking_queue.cpp:49] Waiting for data
I0410 00:57:04.229806 14080 solver.cpp:397]     Test net output #0: accuracy = 0.50674
I0410 00:57:04.229851 14080 solver.cpp:397]     Test net output #1: loss = 2.35702 (* 1 = 2.35702 loss)
I0410 00:57:06.099402 14080 solver.cpp:218] Iteration 6228 (0.874261 iter/s, 13.7259s/12 iters), loss = 0.349845
I0410 00:57:06.099450 14080 solver.cpp:237]     Train net output #0: loss = 0.349845 (* 1 = 0.349845 loss)
I0410 00:57:06.099460 14080 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0410 00:57:11.011361 14080 solver.cpp:218] Iteration 6240 (2.44315 iter/s, 4.9117s/12 iters), loss = 0.22285
I0410 00:57:11.011476 14080 solver.cpp:237]     Train net output #0: loss = 0.22285 (* 1 = 0.22285 loss)
I0410 00:57:11.011485 14080 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0410 00:57:15.936751 14080 solver.cpp:218] Iteration 6252 (2.43652 iter/s, 4.92506s/12 iters), loss = 0.401861
I0410 00:57:15.936800 14080 solver.cpp:237]     Train net output #0: loss = 0.401861 (* 1 = 0.401861 loss)
I0410 00:57:15.936810 14080 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0410 00:57:21.069118 14080 solver.cpp:218] Iteration 6264 (2.33823 iter/s, 5.1321s/12 iters), loss = 0.283153
I0410 00:57:21.069165 14080 solver.cpp:237]     Train net output #0: loss = 0.283153 (* 1 = 0.283153 loss)
I0410 00:57:21.069175 14080 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0410 00:57:23.902406 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:57:26.151453 14080 solver.cpp:218] Iteration 6276 (2.36125 iter/s, 5.08206s/12 iters), loss = 0.319923
I0410 00:57:26.151516 14080 solver.cpp:237]     Train net output #0: loss = 0.319923 (* 1 = 0.319923 loss)
I0410 00:57:26.151530 14080 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0410 00:57:31.138550 14080 solver.cpp:218] Iteration 6288 (2.40634 iter/s, 4.98682s/12 iters), loss = 0.213125
I0410 00:57:31.138593 14080 solver.cpp:237]     Train net output #0: loss = 0.213125 (* 1 = 0.213125 loss)
I0410 00:57:31.138604 14080 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0410 00:57:36.036011 14080 solver.cpp:218] Iteration 6300 (2.45038 iter/s, 4.89721s/12 iters), loss = 0.387055
I0410 00:57:36.036054 14080 solver.cpp:237]     Train net output #0: loss = 0.387055 (* 1 = 0.387055 loss)
I0410 00:57:36.036064 14080 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0410 00:57:41.003913 14080 solver.cpp:218] Iteration 6312 (2.41564 iter/s, 4.96764s/12 iters), loss = 0.358962
I0410 00:57:41.003973 14080 solver.cpp:237]     Train net output #0: loss = 0.358962 (* 1 = 0.358962 loss)
I0410 00:57:41.003985 14080 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0410 00:57:45.535205 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0410 00:57:47.197062 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0410 00:57:48.244029 14080 solver.cpp:330] Iteration 6324, Testing net (#0)
I0410 00:57:48.244058 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:57:50.237851 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:57:52.716630 14080 solver.cpp:397]     Test net output #0: accuracy = 0.508578
I0410 00:57:52.716668 14080 solver.cpp:397]     Test net output #1: loss = 2.36933 (* 1 = 2.36933 loss)
I0410 00:57:52.802534 14080 solver.cpp:218] Iteration 6324 (1.01712 iter/s, 11.7981s/12 iters), loss = 0.238067
I0410 00:57:52.802594 14080 solver.cpp:237]     Train net output #0: loss = 0.238067 (* 1 = 0.238067 loss)
I0410 00:57:52.802606 14080 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0410 00:57:56.922904 14080 solver.cpp:218] Iteration 6336 (2.91253 iter/s, 4.12013s/12 iters), loss = 0.269764
I0410 00:57:56.922961 14080 solver.cpp:237]     Train net output #0: loss = 0.269764 (* 1 = 0.269764 loss)
I0410 00:57:56.922974 14080 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0410 00:58:02.177978 14080 solver.cpp:218] Iteration 6348 (2.28363 iter/s, 5.25478s/12 iters), loss = 0.305625
I0410 00:58:02.178031 14080 solver.cpp:237]     Train net output #0: loss = 0.305625 (* 1 = 0.305625 loss)
I0410 00:58:02.178043 14080 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0410 00:58:07.152206 14080 solver.cpp:218] Iteration 6360 (2.41256 iter/s, 4.97396s/12 iters), loss = 0.343308
I0410 00:58:07.152261 14080 solver.cpp:237]     Train net output #0: loss = 0.343308 (* 1 = 0.343308 loss)
I0410 00:58:07.152271 14080 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0410 00:58:11.912516 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:58:12.046909 14080 solver.cpp:218] Iteration 6372 (2.45178 iter/s, 4.89439s/12 iters), loss = 0.332526
I0410 00:58:12.046988 14080 solver.cpp:237]     Train net output #0: loss = 0.332526 (* 1 = 0.332526 loss)
I0410 00:58:12.047006 14080 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0410 00:58:16.902921 14080 solver.cpp:218] Iteration 6384 (2.47131 iter/s, 4.85573s/12 iters), loss = 0.316157
I0410 00:58:16.903024 14080 solver.cpp:237]     Train net output #0: loss = 0.316157 (* 1 = 0.316157 loss)
I0410 00:58:16.903035 14080 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0410 00:58:21.931406 14080 solver.cpp:218] Iteration 6396 (2.38656 iter/s, 5.02817s/12 iters), loss = 0.125828
I0410 00:58:21.931459 14080 solver.cpp:237]     Train net output #0: loss = 0.125828 (* 1 = 0.125828 loss)
I0410 00:58:21.931471 14080 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0410 00:58:26.866964 14080 solver.cpp:218] Iteration 6408 (2.43147 iter/s, 4.93529s/12 iters), loss = 0.221473
I0410 00:58:26.867019 14080 solver.cpp:237]     Train net output #0: loss = 0.221473 (* 1 = 0.221473 loss)
I0410 00:58:26.867031 14080 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0410 00:58:31.875763 14080 solver.cpp:218] Iteration 6420 (2.39591 iter/s, 5.00853s/12 iters), loss = 0.325185
I0410 00:58:31.875815 14080 solver.cpp:237]     Train net output #0: loss = 0.325185 (* 1 = 0.325185 loss)
I0410 00:58:31.875828 14080 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0410 00:58:33.876240 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0410 00:58:37.927083 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0410 00:58:41.114385 14080 solver.cpp:330] Iteration 6426, Testing net (#0)
I0410 00:58:41.114413 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:58:42.979128 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:58:45.651727 14080 solver.cpp:397]     Test net output #0: accuracy = 0.523284
I0410 00:58:45.651767 14080 solver.cpp:397]     Test net output #1: loss = 2.31434 (* 1 = 2.31434 loss)
I0410 00:58:47.429582 14080 solver.cpp:218] Iteration 6432 (0.771549 iter/s, 15.5531s/12 iters), loss = 0.27947
I0410 00:58:47.429702 14080 solver.cpp:237]     Train net output #0: loss = 0.27947 (* 1 = 0.27947 loss)
I0410 00:58:47.429713 14080 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0410 00:58:52.509188 14080 solver.cpp:218] Iteration 6444 (2.36255 iter/s, 5.07927s/12 iters), loss = 0.235532
I0410 00:58:52.509236 14080 solver.cpp:237]     Train net output #0: loss = 0.235532 (* 1 = 0.235532 loss)
I0410 00:58:52.509245 14080 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0410 00:58:57.536336 14080 solver.cpp:218] Iteration 6456 (2.38717 iter/s, 5.02688s/12 iters), loss = 0.223286
I0410 00:58:57.536384 14080 solver.cpp:237]     Train net output #0: loss = 0.223286 (* 1 = 0.223286 loss)
I0410 00:58:57.536393 14080 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0410 00:59:02.463459 14080 solver.cpp:218] Iteration 6468 (2.43563 iter/s, 4.92686s/12 iters), loss = 0.176296
I0410 00:59:02.463515 14080 solver.cpp:237]     Train net output #0: loss = 0.176296 (* 1 = 0.176296 loss)
I0410 00:59:02.463526 14080 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0410 00:59:04.416584 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:59:07.384316 14080 solver.cpp:218] Iteration 6480 (2.43873 iter/s, 4.92059s/12 iters), loss = 0.192317
I0410 00:59:07.384371 14080 solver.cpp:237]     Train net output #0: loss = 0.192317 (* 1 = 0.192317 loss)
I0410 00:59:07.384382 14080 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0410 00:59:12.298766 14080 solver.cpp:218] Iteration 6492 (2.44191 iter/s, 4.91419s/12 iters), loss = 0.175939
I0410 00:59:12.298817 14080 solver.cpp:237]     Train net output #0: loss = 0.175939 (* 1 = 0.175939 loss)
I0410 00:59:12.298830 14080 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0410 00:59:17.223393 14080 solver.cpp:218] Iteration 6504 (2.43686 iter/s, 4.92436s/12 iters), loss = 0.231118
I0410 00:59:17.223440 14080 solver.cpp:237]     Train net output #0: loss = 0.231118 (* 1 = 0.231118 loss)
I0410 00:59:17.223451 14080 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0410 00:59:22.056183 14080 solver.cpp:218] Iteration 6516 (2.48317 iter/s, 4.83253s/12 iters), loss = 0.297108
I0410 00:59:22.056330 14080 solver.cpp:237]     Train net output #0: loss = 0.297108 (* 1 = 0.297108 loss)
I0410 00:59:22.056344 14080 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0410 00:59:26.463570 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0410 00:59:29.916208 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0410 00:59:31.632488 14080 solver.cpp:330] Iteration 6528, Testing net (#0)
I0410 00:59:31.632519 14080 net.cpp:676] Ignoring source layer train-data
I0410 00:59:33.443048 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:59:36.010994 14080 solver.cpp:397]     Test net output #0: accuracy = 0.508578
I0410 00:59:36.011044 14080 solver.cpp:397]     Test net output #1: loss = 2.37911 (* 1 = 2.37911 loss)
I0410 00:59:36.097187 14080 solver.cpp:218] Iteration 6528 (0.854684 iter/s, 14.0403s/12 iters), loss = 0.27609
I0410 00:59:36.097235 14080 solver.cpp:237]     Train net output #0: loss = 0.27609 (* 1 = 0.27609 loss)
I0410 00:59:36.097246 14080 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0410 00:59:40.271297 14080 solver.cpp:218] Iteration 6540 (2.87503 iter/s, 4.17388s/12 iters), loss = 0.279514
I0410 00:59:40.271355 14080 solver.cpp:237]     Train net output #0: loss = 0.279514 (* 1 = 0.279514 loss)
I0410 00:59:40.271368 14080 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0410 00:59:45.147084 14080 solver.cpp:218] Iteration 6552 (2.46128 iter/s, 4.87552s/12 iters), loss = 0.286684
I0410 00:59:45.147143 14080 solver.cpp:237]     Train net output #0: loss = 0.286684 (* 1 = 0.286684 loss)
I0410 00:59:45.147156 14080 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0410 00:59:50.035141 14080 solver.cpp:218] Iteration 6564 (2.4551 iter/s, 4.88779s/12 iters), loss = 0.245862
I0410 00:59:50.035189 14080 solver.cpp:237]     Train net output #0: loss = 0.245862 (* 1 = 0.245862 loss)
I0410 00:59:50.035198 14080 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0410 00:59:54.236636 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:59:55.044927 14080 solver.cpp:218] Iteration 6576 (2.39544 iter/s, 5.00952s/12 iters), loss = 0.260523
I0410 00:59:55.044986 14080 solver.cpp:237]     Train net output #0: loss = 0.260523 (* 1 = 0.260523 loss)
I0410 00:59:55.044998 14080 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0410 00:59:59.973554 14080 solver.cpp:218] Iteration 6588 (2.43489 iter/s, 4.92835s/12 iters), loss = 0.245037
I0410 00:59:59.973611 14080 solver.cpp:237]     Train net output #0: loss = 0.245037 (* 1 = 0.245037 loss)
I0410 00:59:59.973623 14080 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0410 01:00:04.895675 14080 solver.cpp:218] Iteration 6600 (2.4381 iter/s, 4.92186s/12 iters), loss = 0.244672
I0410 01:00:04.895714 14080 solver.cpp:237]     Train net output #0: loss = 0.244672 (* 1 = 0.244672 loss)
I0410 01:00:04.895721 14080 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0410 01:00:10.039842 14080 solver.cpp:218] Iteration 6612 (2.33286 iter/s, 5.14391s/12 iters), loss = 0.299842
I0410 01:00:10.039888 14080 solver.cpp:237]     Train net output #0: loss = 0.299842 (* 1 = 0.299842 loss)
I0410 01:00:10.039897 14080 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0410 01:00:15.123993 14080 solver.cpp:218] Iteration 6624 (2.3604 iter/s, 5.08389s/12 iters), loss = 0.26558
I0410 01:00:15.124039 14080 solver.cpp:237]     Train net output #0: loss = 0.26558 (* 1 = 0.26558 loss)
I0410 01:00:15.124049 14080 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0410 01:00:17.139555 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0410 01:00:24.550426 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0410 01:00:27.561827 14080 solver.cpp:330] Iteration 6630, Testing net (#0)
I0410 01:00:27.561858 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:00:29.394969 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:00:31.986899 14080 solver.cpp:397]     Test net output #0: accuracy = 0.517157
I0410 01:00:31.986948 14080 solver.cpp:397]     Test net output #1: loss = 2.38108 (* 1 = 2.38108 loss)
I0410 01:00:33.799826 14080 solver.cpp:218] Iteration 6636 (0.64257 iter/s, 18.675s/12 iters), loss = 0.288904
I0410 01:00:33.799875 14080 solver.cpp:237]     Train net output #0: loss = 0.288904 (* 1 = 0.288904 loss)
I0410 01:00:33.799886 14080 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0410 01:00:38.659801 14080 solver.cpp:218] Iteration 6648 (2.46928 iter/s, 4.85971s/12 iters), loss = 0.159183
I0410 01:00:38.659854 14080 solver.cpp:237]     Train net output #0: loss = 0.159183 (* 1 = 0.159183 loss)
I0410 01:00:38.659866 14080 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0410 01:00:43.551365 14080 solver.cpp:218] Iteration 6660 (2.45333 iter/s, 4.8913s/12 iters), loss = 0.158956
I0410 01:00:43.551404 14080 solver.cpp:237]     Train net output #0: loss = 0.158956 (* 1 = 0.158956 loss)
I0410 01:00:43.551414 14080 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0410 01:00:48.528702 14080 solver.cpp:218] Iteration 6672 (2.41105 iter/s, 4.97707s/12 iters), loss = 0.195558
I0410 01:00:48.528765 14080 solver.cpp:237]     Train net output #0: loss = 0.195558 (* 1 = 0.195558 loss)
I0410 01:00:48.528777 14080 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0410 01:00:49.833993 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:00:53.365178 14080 solver.cpp:218] Iteration 6684 (2.48129 iter/s, 4.8362s/12 iters), loss = 0.250181
I0410 01:00:53.365239 14080 solver.cpp:237]     Train net output #0: loss = 0.250181 (* 1 = 0.250181 loss)
I0410 01:00:53.365252 14080 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0410 01:00:58.361544 14080 solver.cpp:218] Iteration 6696 (2.40188 iter/s, 4.99609s/12 iters), loss = 0.15265
I0410 01:00:58.361647 14080 solver.cpp:237]     Train net output #0: loss = 0.15265 (* 1 = 0.15265 loss)
I0410 01:00:58.361660 14080 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0410 01:01:03.476413 14080 solver.cpp:218] Iteration 6708 (2.34625 iter/s, 5.11454s/12 iters), loss = 0.22018
I0410 01:01:03.476469 14080 solver.cpp:237]     Train net output #0: loss = 0.22018 (* 1 = 0.22018 loss)
I0410 01:01:03.476482 14080 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0410 01:01:08.310890 14080 solver.cpp:218] Iteration 6720 (2.48231 iter/s, 4.83421s/12 iters), loss = 0.158319
I0410 01:01:08.310933 14080 solver.cpp:237]     Train net output #0: loss = 0.158319 (* 1 = 0.158319 loss)
I0410 01:01:08.310943 14080 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0410 01:01:12.775537 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0410 01:01:18.333978 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0410 01:01:22.848218 14080 solver.cpp:330] Iteration 6732, Testing net (#0)
I0410 01:01:22.848249 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:01:24.665261 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:01:27.301082 14080 solver.cpp:397]     Test net output #0: accuracy = 0.528186
I0410 01:01:27.301131 14080 solver.cpp:397]     Test net output #1: loss = 2.35802 (* 1 = 2.35802 loss)
I0410 01:01:27.386094 14080 solver.cpp:218] Iteration 6732 (0.629116 iter/s, 19.0744s/12 iters), loss = 0.121657
I0410 01:01:27.386152 14080 solver.cpp:237]     Train net output #0: loss = 0.121657 (* 1 = 0.121657 loss)
I0410 01:01:27.386163 14080 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0410 01:01:31.571916 14080 solver.cpp:218] Iteration 6744 (2.86699 iter/s, 4.18558s/12 iters), loss = 0.221791
I0410 01:01:31.572085 14080 solver.cpp:237]     Train net output #0: loss = 0.221791 (* 1 = 0.221791 loss)
I0410 01:01:31.572104 14080 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0410 01:01:36.488078 14080 solver.cpp:218] Iteration 6756 (2.44111 iter/s, 4.91579s/12 iters), loss = 0.311261
I0410 01:01:36.488116 14080 solver.cpp:237]     Train net output #0: loss = 0.311261 (* 1 = 0.311261 loss)
I0410 01:01:36.488126 14080 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0410 01:01:41.433333 14080 solver.cpp:218] Iteration 6768 (2.42669 iter/s, 4.945s/12 iters), loss = 0.288916
I0410 01:01:41.433388 14080 solver.cpp:237]     Train net output #0: loss = 0.288916 (* 1 = 0.288916 loss)
I0410 01:01:41.433400 14080 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0410 01:01:44.835860 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:01:46.309114 14080 solver.cpp:218] Iteration 6780 (2.46128 iter/s, 4.87552s/12 iters), loss = 0.330099
I0410 01:01:46.309160 14080 solver.cpp:237]     Train net output #0: loss = 0.330099 (* 1 = 0.330099 loss)
I0410 01:01:46.309170 14080 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0410 01:01:51.189908 14080 solver.cpp:218] Iteration 6792 (2.45875 iter/s, 4.88053s/12 iters), loss = 0.211683
I0410 01:01:51.189986 14080 solver.cpp:237]     Train net output #0: loss = 0.211683 (* 1 = 0.211683 loss)
I0410 01:01:51.189998 14080 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0410 01:01:56.042393 14080 solver.cpp:218] Iteration 6804 (2.4731 iter/s, 4.8522s/12 iters), loss = 0.275976
I0410 01:01:56.042443 14080 solver.cpp:237]     Train net output #0: loss = 0.275976 (* 1 = 0.275976 loss)
I0410 01:01:56.042452 14080 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0410 01:02:00.963649 14080 solver.cpp:218] Iteration 6816 (2.43853 iter/s, 4.92099s/12 iters), loss = 0.263594
I0410 01:02:00.963696 14080 solver.cpp:237]     Train net output #0: loss = 0.263594 (* 1 = 0.263594 loss)
I0410 01:02:00.963707 14080 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0410 01:02:05.940258 14080 solver.cpp:218] Iteration 6828 (2.41141 iter/s, 4.97635s/12 iters), loss = 0.348369
I0410 01:02:05.940403 14080 solver.cpp:237]     Train net output #0: loss = 0.348369 (* 1 = 0.348369 loss)
I0410 01:02:05.940418 14080 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0410 01:02:07.954545 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0410 01:02:10.842984 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0410 01:02:15.271056 14080 solver.cpp:330] Iteration 6834, Testing net (#0)
I0410 01:02:15.271086 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:02:17.057363 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:02:19.733384 14080 solver.cpp:397]     Test net output #0: accuracy = 0.516544
I0410 01:02:19.733422 14080 solver.cpp:397]     Test net output #1: loss = 2.43937 (* 1 = 2.43937 loss)
I0410 01:02:22.058631 14080 solver.cpp:218] Iteration 6840 (0.74453 iter/s, 16.1176s/12 iters), loss = 0.145945
I0410 01:02:22.058681 14080 solver.cpp:237]     Train net output #0: loss = 0.145945 (* 1 = 0.145945 loss)
I0410 01:02:22.058689 14080 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0410 01:02:27.373184 14080 solver.cpp:218] Iteration 6852 (2.25807 iter/s, 5.31427s/12 iters), loss = 0.197066
I0410 01:02:27.373250 14080 solver.cpp:237]     Train net output #0: loss = 0.197066 (* 1 = 0.197066 loss)
I0410 01:02:27.373261 14080 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0410 01:02:32.599728 14080 solver.cpp:218] Iteration 6864 (2.2961 iter/s, 5.22625s/12 iters), loss = 0.160181
I0410 01:02:32.599786 14080 solver.cpp:237]     Train net output #0: loss = 0.160181 (* 1 = 0.160181 loss)
I0410 01:02:32.599798 14080 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0410 01:02:37.617347 14080 solver.cpp:218] Iteration 6876 (2.3917 iter/s, 5.01734s/12 iters), loss = 0.192973
I0410 01:02:37.617667 14080 solver.cpp:237]     Train net output #0: loss = 0.192973 (* 1 = 0.192973 loss)
I0410 01:02:37.617679 14080 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0410 01:02:38.269317 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:02:42.561280 14080 solver.cpp:218] Iteration 6888 (2.42748 iter/s, 4.9434s/12 iters), loss = 0.16248
I0410 01:02:42.561328 14080 solver.cpp:237]     Train net output #0: loss = 0.16248 (* 1 = 0.16248 loss)
I0410 01:02:42.561339 14080 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0410 01:02:47.476673 14080 solver.cpp:218] Iteration 6900 (2.44144 iter/s, 4.91512s/12 iters), loss = 0.332497
I0410 01:02:47.476745 14080 solver.cpp:237]     Train net output #0: loss = 0.332497 (* 1 = 0.332497 loss)
I0410 01:02:47.476763 14080 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0410 01:02:52.395646 14080 solver.cpp:218] Iteration 6912 (2.43967 iter/s, 4.9187s/12 iters), loss = 0.242791
I0410 01:02:52.395685 14080 solver.cpp:237]     Train net output #0: loss = 0.242791 (* 1 = 0.242791 loss)
I0410 01:02:52.395694 14080 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0410 01:02:57.231106 14080 solver.cpp:218] Iteration 6924 (2.4818 iter/s, 4.8352s/12 iters), loss = 0.361403
I0410 01:02:57.231164 14080 solver.cpp:237]     Train net output #0: loss = 0.361403 (* 1 = 0.361403 loss)
I0410 01:02:57.231178 14080 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0410 01:03:01.710095 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0410 01:03:03.058818 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0410 01:03:04.108122 14080 solver.cpp:330] Iteration 6936, Testing net (#0)
I0410 01:03:04.108151 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:03:04.612498 14080 blocking_queue.cpp:49] Waiting for data
I0410 01:03:05.773680 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:03:08.511029 14080 solver.cpp:397]     Test net output #0: accuracy = 0.540441
I0410 01:03:08.511123 14080 solver.cpp:397]     Test net output #1: loss = 2.28786 (* 1 = 2.28786 loss)
I0410 01:03:08.596964 14080 solver.cpp:218] Iteration 6936 (1.05584 iter/s, 11.3653s/12 iters), loss = 0.146225
I0410 01:03:08.597018 14080 solver.cpp:237]     Train net output #0: loss = 0.146225 (* 1 = 0.146225 loss)
I0410 01:03:08.597028 14080 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0410 01:03:12.738754 14080 solver.cpp:218] Iteration 6948 (2.89746 iter/s, 4.14155s/12 iters), loss = 0.167349
I0410 01:03:12.738806 14080 solver.cpp:237]     Train net output #0: loss = 0.167349 (* 1 = 0.167349 loss)
I0410 01:03:12.738818 14080 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0410 01:03:17.591800 14080 solver.cpp:218] Iteration 6960 (2.47281 iter/s, 4.85279s/12 iters), loss = 0.204895
I0410 01:03:17.591850 14080 solver.cpp:237]     Train net output #0: loss = 0.204895 (* 1 = 0.204895 loss)
I0410 01:03:17.591861 14080 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0410 01:03:22.514848 14080 solver.cpp:218] Iteration 6972 (2.43764 iter/s, 4.92279s/12 iters), loss = 0.142137
I0410 01:03:22.514899 14080 solver.cpp:237]     Train net output #0: loss = 0.142137 (* 1 = 0.142137 loss)
I0410 01:03:22.514909 14080 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0410 01:03:25.223577 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:03:27.428896 14080 solver.cpp:218] Iteration 6984 (2.44211 iter/s, 4.91378s/12 iters), loss = 0.0792559
I0410 01:03:27.428948 14080 solver.cpp:237]     Train net output #0: loss = 0.0792559 (* 1 = 0.0792559 loss)
I0410 01:03:27.428961 14080 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0410 01:03:32.335094 14080 solver.cpp:218] Iteration 6996 (2.44602 iter/s, 4.90593s/12 iters), loss = 0.307393
I0410 01:03:32.335147 14080 solver.cpp:237]     Train net output #0: loss = 0.307393 (* 1 = 0.307393 loss)
I0410 01:03:32.335160 14080 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0410 01:03:37.241365 14080 solver.cpp:218] Iteration 7008 (2.44598 iter/s, 4.906s/12 iters), loss = 0.242819
I0410 01:03:37.241422 14080 solver.cpp:237]     Train net output #0: loss = 0.242819 (* 1 = 0.242819 loss)
I0410 01:03:37.241436 14080 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0410 01:03:42.143597 14080 solver.cpp:218] Iteration 7020 (2.448 iter/s, 4.90196s/12 iters), loss = 0.159826
I0410 01:03:42.143728 14080 solver.cpp:237]     Train net output #0: loss = 0.159826 (* 1 = 0.159826 loss)
I0410 01:03:42.143740 14080 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0410 01:03:47.028663 14080 solver.cpp:218] Iteration 7032 (2.45664 iter/s, 4.88473s/12 iters), loss = 0.378996
I0410 01:03:47.028713 14080 solver.cpp:237]     Train net output #0: loss = 0.378996 (* 1 = 0.378996 loss)
I0410 01:03:47.028725 14080 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0410 01:03:48.989464 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0410 01:03:50.671797 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0410 01:03:52.742218 14080 solver.cpp:330] Iteration 7038, Testing net (#0)
I0410 01:03:52.742242 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:03:54.375907 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:03:57.140095 14080 solver.cpp:397]     Test net output #0: accuracy = 0.528799
I0410 01:03:57.140125 14080 solver.cpp:397]     Test net output #1: loss = 2.36575 (* 1 = 2.36575 loss)
I0410 01:03:58.967234 14080 solver.cpp:218] Iteration 7044 (1.00519 iter/s, 11.938s/12 iters), loss = 0.181791
I0410 01:03:58.967301 14080 solver.cpp:237]     Train net output #0: loss = 0.181791 (* 1 = 0.181791 loss)
I0410 01:03:58.967320 14080 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0410 01:04:03.897878 14080 solver.cpp:218] Iteration 7056 (2.4339 iter/s, 4.93037s/12 iters), loss = 0.176772
I0410 01:04:03.897933 14080 solver.cpp:237]     Train net output #0: loss = 0.176772 (* 1 = 0.176772 loss)
I0410 01:04:03.897946 14080 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0410 01:04:08.793447 14080 solver.cpp:218] Iteration 7068 (2.45133 iter/s, 4.8953s/12 iters), loss = 0.323485
I0410 01:04:08.793495 14080 solver.cpp:237]     Train net output #0: loss = 0.323485 (* 1 = 0.323485 loss)
I0410 01:04:08.793504 14080 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0410 01:04:13.518973 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:04:13.622666 14080 solver.cpp:218] Iteration 7080 (2.485 iter/s, 4.82896s/12 iters), loss = 0.134006
I0410 01:04:13.622714 14080 solver.cpp:237]     Train net output #0: loss = 0.134006 (* 1 = 0.134006 loss)
I0410 01:04:13.622725 14080 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0410 01:04:18.525029 14080 solver.cpp:218] Iteration 7092 (2.44793 iter/s, 4.9021s/12 iters), loss = 0.217325
I0410 01:04:18.525079 14080 solver.cpp:237]     Train net output #0: loss = 0.217325 (* 1 = 0.217325 loss)
I0410 01:04:18.525089 14080 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0410 01:04:23.436203 14080 solver.cpp:218] Iteration 7104 (2.44354 iter/s, 4.91091s/12 iters), loss = 0.167423
I0410 01:04:23.436251 14080 solver.cpp:237]     Train net output #0: loss = 0.167423 (* 1 = 0.167423 loss)
I0410 01:04:23.436264 14080 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0410 01:04:28.388695 14080 solver.cpp:218] Iteration 7116 (2.42316 iter/s, 4.95222s/12 iters), loss = 0.204774
I0410 01:04:28.388751 14080 solver.cpp:237]     Train net output #0: loss = 0.204774 (* 1 = 0.204774 loss)
I0410 01:04:28.388762 14080 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0410 01:04:33.329159 14080 solver.cpp:218] Iteration 7128 (2.42905 iter/s, 4.9402s/12 iters), loss = 0.212335
I0410 01:04:33.329210 14080 solver.cpp:237]     Train net output #0: loss = 0.212335 (* 1 = 0.212335 loss)
I0410 01:04:33.329222 14080 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0410 01:04:37.826828 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0410 01:04:41.189946 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0410 01:04:44.964627 14080 solver.cpp:330] Iteration 7140, Testing net (#0)
I0410 01:04:44.964752 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:04:46.618113 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:04:49.415407 14080 solver.cpp:397]     Test net output #0: accuracy = 0.532475
I0410 01:04:49.415457 14080 solver.cpp:397]     Test net output #1: loss = 2.29877 (* 1 = 2.29877 loss)
I0410 01:04:49.499728 14080 solver.cpp:218] Iteration 7140 (0.742122 iter/s, 16.1698s/12 iters), loss = 0.161135
I0410 01:04:49.499786 14080 solver.cpp:237]     Train net output #0: loss = 0.161135 (* 1 = 0.161135 loss)
I0410 01:04:49.499797 14080 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0410 01:04:53.888847 14080 solver.cpp:218] Iteration 7152 (2.73419 iter/s, 4.38887s/12 iters), loss = 0.213112
I0410 01:04:53.888901 14080 solver.cpp:237]     Train net output #0: loss = 0.213112 (* 1 = 0.213112 loss)
I0410 01:04:53.888916 14080 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0410 01:04:58.957659 14080 solver.cpp:218] Iteration 7164 (2.36755 iter/s, 5.06854s/12 iters), loss = 0.0909159
I0410 01:04:58.957715 14080 solver.cpp:237]     Train net output #0: loss = 0.0909159 (* 1 = 0.0909159 loss)
I0410 01:04:58.957728 14080 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0410 01:05:03.918718 14080 solver.cpp:218] Iteration 7176 (2.41897 iter/s, 4.96079s/12 iters), loss = 0.144816
I0410 01:05:03.918772 14080 solver.cpp:237]     Train net output #0: loss = 0.144816 (* 1 = 0.144816 loss)
I0410 01:05:03.918785 14080 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0410 01:05:06.053480 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:05:08.954739 14080 solver.cpp:218] Iteration 7188 (2.38296 iter/s, 5.03575s/12 iters), loss = 0.24018
I0410 01:05:08.954793 14080 solver.cpp:237]     Train net output #0: loss = 0.24018 (* 1 = 0.24018 loss)
I0410 01:05:08.954805 14080 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 01:05:14.153746 14080 solver.cpp:218] Iteration 7200 (2.30826 iter/s, 5.19873s/12 iters), loss = 0.265023
I0410 01:05:14.153800 14080 solver.cpp:237]     Train net output #0: loss = 0.265023 (* 1 = 0.265023 loss)
I0410 01:05:14.153811 14080 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 01:05:19.267593 14080 solver.cpp:218] Iteration 7212 (2.3467 iter/s, 5.11357s/12 iters), loss = 0.200459
I0410 01:05:19.267699 14080 solver.cpp:237]     Train net output #0: loss = 0.200459 (* 1 = 0.200459 loss)
I0410 01:05:19.267709 14080 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 01:05:24.204638 14080 solver.cpp:218] Iteration 7224 (2.43076 iter/s, 4.93673s/12 iters), loss = 0.317578
I0410 01:05:24.204691 14080 solver.cpp:237]     Train net output #0: loss = 0.317578 (* 1 = 0.317578 loss)
I0410 01:05:24.204704 14080 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 01:05:29.198756 14080 solver.cpp:218] Iteration 7236 (2.40295 iter/s, 4.99385s/12 iters), loss = 0.170354
I0410 01:05:29.198807 14080 solver.cpp:237]     Train net output #0: loss = 0.170354 (* 1 = 0.170354 loss)
I0410 01:05:29.198819 14080 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 01:05:31.188665 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 01:05:33.787864 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 01:05:36.588238 14080 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 01:05:36.588263 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:05:38.252857 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:05:41.147825 14080 solver.cpp:397]     Test net output #0: accuracy = 0.534926
I0410 01:05:41.147874 14080 solver.cpp:397]     Test net output #1: loss = 2.31789 (* 1 = 2.31789 loss)
I0410 01:05:42.930680 14080 solver.cpp:218] Iteration 7248 (0.873916 iter/s, 13.7313s/12 iters), loss = 0.278805
I0410 01:05:42.930737 14080 solver.cpp:237]     Train net output #0: loss = 0.278805 (* 1 = 0.278805 loss)
I0410 01:05:42.930749 14080 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 01:05:47.928622 14080 solver.cpp:218] Iteration 7260 (2.40112 iter/s, 4.99767s/12 iters), loss = 0.441587
I0410 01:05:47.928665 14080 solver.cpp:237]     Train net output #0: loss = 0.441587 (* 1 = 0.441587 loss)
I0410 01:05:47.928674 14080 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 01:05:52.998019 14080 solver.cpp:218] Iteration 7272 (2.36727 iter/s, 5.06913s/12 iters), loss = 0.310829
I0410 01:05:52.998152 14080 solver.cpp:237]     Train net output #0: loss = 0.310829 (* 1 = 0.310829 loss)
I0410 01:05:52.998164 14080 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 01:05:57.124195 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:05:57.862366 14080 solver.cpp:218] Iteration 7284 (2.4671 iter/s, 4.86401s/12 iters), loss = 0.155435
I0410 01:05:57.862411 14080 solver.cpp:237]     Train net output #0: loss = 0.155435 (* 1 = 0.155435 loss)
I0410 01:05:57.862419 14080 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 01:06:02.789337 14080 solver.cpp:218] Iteration 7296 (2.4357 iter/s, 4.92671s/12 iters), loss = 0.175801
I0410 01:06:02.789382 14080 solver.cpp:237]     Train net output #0: loss = 0.175801 (* 1 = 0.175801 loss)
I0410 01:06:02.789391 14080 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 01:06:08.044966 14080 solver.cpp:218] Iteration 7308 (2.28339 iter/s, 5.25535s/12 iters), loss = 0.186626
I0410 01:06:08.045009 14080 solver.cpp:237]     Train net output #0: loss = 0.186626 (* 1 = 0.186626 loss)
I0410 01:06:08.045017 14080 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 01:06:12.929343 14080 solver.cpp:218] Iteration 7320 (2.45694 iter/s, 4.88412s/12 iters), loss = 0.118963
I0410 01:06:12.929392 14080 solver.cpp:237]     Train net output #0: loss = 0.118963 (* 1 = 0.118963 loss)
I0410 01:06:12.929404 14080 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 01:06:18.196031 14080 solver.cpp:218] Iteration 7332 (2.27859 iter/s, 5.26641s/12 iters), loss = 0.280723
I0410 01:06:18.196090 14080 solver.cpp:237]     Train net output #0: loss = 0.280723 (* 1 = 0.280723 loss)
I0410 01:06:18.196102 14080 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 01:06:22.638476 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 01:06:24.045567 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 01:06:25.105499 14080 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 01:06:25.105530 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:06:26.658134 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:06:29.729394 14080 solver.cpp:397]     Test net output #0: accuracy = 0.528799
I0410 01:06:29.729444 14080 solver.cpp:397]     Test net output #1: loss = 2.37827 (* 1 = 2.37827 loss)
I0410 01:06:29.817131 14080 solver.cpp:218] Iteration 7344 (1.03265 iter/s, 11.6206s/12 iters), loss = 0.231388
I0410 01:06:29.817184 14080 solver.cpp:237]     Train net output #0: loss = 0.231388 (* 1 = 0.231388 loss)
I0410 01:06:29.817198 14080 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 01:06:33.949064 14080 solver.cpp:218] Iteration 7356 (2.90437 iter/s, 4.1317s/12 iters), loss = 0.102181
I0410 01:06:33.949120 14080 solver.cpp:237]     Train net output #0: loss = 0.102181 (* 1 = 0.102181 loss)
I0410 01:06:33.949131 14080 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 01:06:38.857519 14080 solver.cpp:218] Iteration 7368 (2.44489 iter/s, 4.90819s/12 iters), loss = 0.18745
I0410 01:06:38.857563 14080 solver.cpp:237]     Train net output #0: loss = 0.18745 (* 1 = 0.18745 loss)
I0410 01:06:38.857571 14080 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 01:06:43.793244 14080 solver.cpp:218] Iteration 7380 (2.43138 iter/s, 4.93547s/12 iters), loss = 0.112865
I0410 01:06:43.793293 14080 solver.cpp:237]     Train net output #0: loss = 0.112865 (* 1 = 0.112865 loss)
I0410 01:06:43.793303 14080 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 01:06:45.193584 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:06:48.754813 14080 solver.cpp:218] Iteration 7392 (2.41872 iter/s, 4.9613s/12 iters), loss = 0.212068
I0410 01:06:48.754869 14080 solver.cpp:237]     Train net output #0: loss = 0.212068 (* 1 = 0.212068 loss)
I0410 01:06:48.754881 14080 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 01:06:53.663784 14080 solver.cpp:218] Iteration 7404 (2.44464 iter/s, 4.9087s/12 iters), loss = 0.0872819
I0410 01:06:53.663839 14080 solver.cpp:237]     Train net output #0: loss = 0.0872819 (* 1 = 0.0872819 loss)
I0410 01:06:53.663851 14080 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 01:06:58.526968 14080 solver.cpp:218] Iteration 7416 (2.46765 iter/s, 4.86292s/12 iters), loss = 0.0714162
I0410 01:06:58.527117 14080 solver.cpp:237]     Train net output #0: loss = 0.0714162 (* 1 = 0.0714162 loss)
I0410 01:06:58.527128 14080 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 01:07:03.406672 14080 solver.cpp:218] Iteration 7428 (2.45934 iter/s, 4.87935s/12 iters), loss = 0.37096
I0410 01:07:03.406721 14080 solver.cpp:237]     Train net output #0: loss = 0.37096 (* 1 = 0.37096 loss)
I0410 01:07:03.406733 14080 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 01:07:08.324424 14080 solver.cpp:218] Iteration 7440 (2.44027 iter/s, 4.91748s/12 iters), loss = 0.108723
I0410 01:07:08.324476 14080 solver.cpp:237]     Train net output #0: loss = 0.108723 (* 1 = 0.108723 loss)
I0410 01:07:08.324487 14080 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 01:07:10.301362 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 01:07:11.674998 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 01:07:12.714603 14080 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 01:07:12.714629 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:07:14.117295 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:07:17.178026 14080 solver.cpp:397]     Test net output #0: accuracy = 0.519608
I0410 01:07:17.178076 14080 solver.cpp:397]     Test net output #1: loss = 2.3913 (* 1 = 2.3913 loss)
I0410 01:07:18.958770 14080 solver.cpp:218] Iteration 7452 (1.12847 iter/s, 10.6339s/12 iters), loss = 0.242509
I0410 01:07:18.958824 14080 solver.cpp:237]     Train net output #0: loss = 0.242509 (* 1 = 0.242509 loss)
I0410 01:07:18.958835 14080 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 01:07:23.868607 14080 solver.cpp:218] Iteration 7464 (2.4442 iter/s, 4.90957s/12 iters), loss = 0.121114
I0410 01:07:23.868654 14080 solver.cpp:237]     Train net output #0: loss = 0.121114 (* 1 = 0.121114 loss)
I0410 01:07:23.868664 14080 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 01:07:28.804433 14080 solver.cpp:218] Iteration 7476 (2.43133 iter/s, 4.93557s/12 iters), loss = 0.216644
I0410 01:07:28.804502 14080 solver.cpp:237]     Train net output #0: loss = 0.216644 (* 1 = 0.216644 loss)
I0410 01:07:28.804510 14080 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 01:07:32.289186 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:07:33.757134 14080 solver.cpp:218] Iteration 7488 (2.42306 iter/s, 4.95241s/12 iters), loss = 0.225959
I0410 01:07:33.757197 14080 solver.cpp:237]     Train net output #0: loss = 0.225959 (* 1 = 0.225959 loss)
I0410 01:07:33.757212 14080 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 01:07:38.723119 14080 solver.cpp:218] Iteration 7500 (2.41657 iter/s, 4.96571s/12 iters), loss = 0.187008
I0410 01:07:38.723165 14080 solver.cpp:237]     Train net output #0: loss = 0.187008 (* 1 = 0.187008 loss)
I0410 01:07:38.723178 14080 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 01:07:43.644755 14080 solver.cpp:218] Iteration 7512 (2.43834 iter/s, 4.92138s/12 iters), loss = 0.158228
I0410 01:07:43.644794 14080 solver.cpp:237]     Train net output #0: loss = 0.158228 (* 1 = 0.158228 loss)
I0410 01:07:43.644804 14080 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 01:07:48.589813 14080 solver.cpp:218] Iteration 7524 (2.42679 iter/s, 4.9448s/12 iters), loss = 0.155591
I0410 01:07:48.589864 14080 solver.cpp:237]     Train net output #0: loss = 0.155591 (* 1 = 0.155591 loss)
I0410 01:07:48.589876 14080 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 01:07:53.556321 14080 solver.cpp:218] Iteration 7536 (2.41631 iter/s, 4.96624s/12 iters), loss = 0.17621
I0410 01:07:53.556367 14080 solver.cpp:237]     Train net output #0: loss = 0.17621 (* 1 = 0.17621 loss)
I0410 01:07:53.556380 14080 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 01:07:58.050524 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 01:08:02.385856 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 01:08:06.394735 14080 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 01:08:06.394764 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:08:07.881208 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:08:10.823679 14080 solver.cpp:397]     Test net output #0: accuracy = 0.533088
I0410 01:08:10.823719 14080 solver.cpp:397]     Test net output #1: loss = 2.31064 (* 1 = 2.31064 loss)
I0410 01:08:10.909739 14080 solver.cpp:218] Iteration 7548 (0.691537 iter/s, 17.3527s/12 iters), loss = 0.163544
I0410 01:08:10.909792 14080 solver.cpp:237]     Train net output #0: loss = 0.163544 (* 1 = 0.163544 loss)
I0410 01:08:10.909803 14080 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 01:08:15.105365 14080 solver.cpp:218] Iteration 7560 (2.86028 iter/s, 4.19539s/12 iters), loss = 0.11844
I0410 01:08:15.105412 14080 solver.cpp:237]     Train net output #0: loss = 0.11844 (* 1 = 0.11844 loss)
I0410 01:08:15.105420 14080 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 01:08:20.264427 14080 solver.cpp:218] Iteration 7572 (2.32613 iter/s, 5.15879s/12 iters), loss = 0.124919
I0410 01:08:20.264475 14080 solver.cpp:237]     Train net output #0: loss = 0.124919 (* 1 = 0.124919 loss)
I0410 01:08:20.264485 14080 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 01:08:25.149403 14080 solver.cpp:218] Iteration 7584 (2.45664 iter/s, 4.88471s/12 iters), loss = 0.164465
I0410 01:08:25.149461 14080 solver.cpp:237]     Train net output #0: loss = 0.164465 (* 1 = 0.164465 loss)
I0410 01:08:25.149473 14080 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 01:08:25.779896 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:08:30.038004 14080 solver.cpp:218] Iteration 7596 (2.45482 iter/s, 4.88833s/12 iters), loss = 0.176542
I0410 01:08:30.038056 14080 solver.cpp:237]     Train net output #0: loss = 0.176542 (* 1 = 0.176542 loss)
I0410 01:08:30.038067 14080 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 01:08:34.940820 14080 solver.cpp:218] Iteration 7608 (2.4477 iter/s, 4.90255s/12 iters), loss = 0.187294
I0410 01:08:34.941838 14080 solver.cpp:237]     Train net output #0: loss = 0.187294 (* 1 = 0.187294 loss)
I0410 01:08:34.941848 14080 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 01:08:39.856683 14080 solver.cpp:218] Iteration 7620 (2.44169 iter/s, 4.91463s/12 iters), loss = 0.165876
I0410 01:08:39.856732 14080 solver.cpp:237]     Train net output #0: loss = 0.165876 (* 1 = 0.165876 loss)
I0410 01:08:39.856745 14080 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 01:08:41.824836 14080 blocking_queue.cpp:49] Waiting for data
I0410 01:08:44.712831 14080 solver.cpp:218] Iteration 7632 (2.47123 iter/s, 4.85589s/12 iters), loss = 0.155952
I0410 01:08:44.712888 14080 solver.cpp:237]     Train net output #0: loss = 0.155952 (* 1 = 0.155952 loss)
I0410 01:08:44.712900 14080 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 01:08:49.645402 14080 solver.cpp:218] Iteration 7644 (2.43294 iter/s, 4.9323s/12 iters), loss = 0.22761
I0410 01:08:49.645444 14080 solver.cpp:237]     Train net output #0: loss = 0.22761 (* 1 = 0.22761 loss)
I0410 01:08:49.645453 14080 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 01:08:51.597877 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 01:08:56.096796 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 01:09:01.672636 14080 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 01:09:01.672657 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:09:03.096014 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:09:06.087067 14080 solver.cpp:397]     Test net output #0: accuracy = 0.520833
I0410 01:09:06.087222 14080 solver.cpp:397]     Test net output #1: loss = 2.38743 (* 1 = 2.38743 loss)
I0410 01:09:07.964300 14080 solver.cpp:218] Iteration 7656 (0.65509 iter/s, 18.3181s/12 iters), loss = 0.175277
I0410 01:09:07.964359 14080 solver.cpp:237]     Train net output #0: loss = 0.175277 (* 1 = 0.175277 loss)
I0410 01:09:07.964370 14080 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 01:09:12.879750 14080 solver.cpp:218] Iteration 7668 (2.44141 iter/s, 4.91518s/12 iters), loss = 0.0792379
I0410 01:09:12.879796 14080 solver.cpp:237]     Train net output #0: loss = 0.0792379 (* 1 = 0.0792379 loss)
I0410 01:09:12.879807 14080 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 01:09:17.802394 14080 solver.cpp:218] Iteration 7680 (2.43784 iter/s, 4.92239s/12 iters), loss = 0.145659
I0410 01:09:17.802443 14080 solver.cpp:237]     Train net output #0: loss = 0.145659 (* 1 = 0.145659 loss)
I0410 01:09:17.802455 14080 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 01:09:20.499696 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:09:22.650507 14080 solver.cpp:218] Iteration 7692 (2.47532 iter/s, 4.84785s/12 iters), loss = 0.137527
I0410 01:09:22.650554 14080 solver.cpp:237]     Train net output #0: loss = 0.137527 (* 1 = 0.137527 loss)
I0410 01:09:22.650563 14080 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 01:09:27.555231 14080 solver.cpp:218] Iteration 7704 (2.44675 iter/s, 4.90447s/12 iters), loss = 0.164817
I0410 01:09:27.555274 14080 solver.cpp:237]     Train net output #0: loss = 0.164817 (* 1 = 0.164817 loss)
I0410 01:09:27.555281 14080 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 01:09:32.401343 14080 solver.cpp:218] Iteration 7716 (2.47634 iter/s, 4.84585s/12 iters), loss = 0.204288
I0410 01:09:32.401403 14080 solver.cpp:237]     Train net output #0: loss = 0.204288 (* 1 = 0.204288 loss)
I0410 01:09:32.401417 14080 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 01:09:37.263821 14080 solver.cpp:218] Iteration 7728 (2.46802 iter/s, 4.8622s/12 iters), loss = 0.129599
I0410 01:09:37.263948 14080 solver.cpp:237]     Train net output #0: loss = 0.129599 (* 1 = 0.129599 loss)
I0410 01:09:37.263962 14080 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 01:09:42.145506 14080 solver.cpp:218] Iteration 7740 (2.45834 iter/s, 4.88135s/12 iters), loss = 0.142198
I0410 01:09:42.145552 14080 solver.cpp:237]     Train net output #0: loss = 0.142198 (* 1 = 0.142198 loss)
I0410 01:09:42.145561 14080 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 01:09:46.575536 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 01:09:47.949493 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 01:09:48.988540 14080 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 01:09:48.988567 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:09:50.517976 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:09:53.554118 14080 solver.cpp:397]     Test net output #0: accuracy = 0.532475
I0410 01:09:53.554149 14080 solver.cpp:397]     Test net output #1: loss = 2.39622 (* 1 = 2.39622 loss)
I0410 01:09:53.637600 14080 solver.cpp:218] Iteration 7752 (1.04424 iter/s, 11.4916s/12 iters), loss = 0.106058
I0410 01:09:53.637655 14080 solver.cpp:237]     Train net output #0: loss = 0.106058 (* 1 = 0.106058 loss)
I0410 01:09:53.637665 14080 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 01:09:57.857928 14080 solver.cpp:218] Iteration 7764 (2.84354 iter/s, 4.22009s/12 iters), loss = 0.158082
I0410 01:09:57.857985 14080 solver.cpp:237]     Train net output #0: loss = 0.158082 (* 1 = 0.158082 loss)
I0410 01:09:57.857995 14080 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 01:10:02.756076 14080 solver.cpp:218] Iteration 7776 (2.45004 iter/s, 4.89788s/12 iters), loss = 0.309891
I0410 01:10:02.756120 14080 solver.cpp:237]     Train net output #0: loss = 0.309891 (* 1 = 0.309891 loss)
I0410 01:10:02.756129 14080 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 01:10:07.635228 14080 solver.cpp:218] Iteration 7788 (2.45957 iter/s, 4.87889s/12 iters), loss = 0.0839029
I0410 01:10:07.635370 14080 solver.cpp:237]     Train net output #0: loss = 0.0839029 (* 1 = 0.0839029 loss)
I0410 01:10:07.635383 14080 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 01:10:07.643303 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:10:12.478597 14080 solver.cpp:218] Iteration 7800 (2.47779 iter/s, 4.84302s/12 iters), loss = 0.166498
I0410 01:10:12.478647 14080 solver.cpp:237]     Train net output #0: loss = 0.166498 (* 1 = 0.166498 loss)
I0410 01:10:12.478659 14080 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 01:10:17.372015 14080 solver.cpp:218] Iteration 7812 (2.4524 iter/s, 4.89316s/12 iters), loss = 0.20218
I0410 01:10:17.372063 14080 solver.cpp:237]     Train net output #0: loss = 0.20218 (* 1 = 0.20218 loss)
I0410 01:10:17.372073 14080 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 01:10:22.296768 14080 solver.cpp:218] Iteration 7824 (2.4368 iter/s, 4.92449s/12 iters), loss = 0.102503
I0410 01:10:22.296818 14080 solver.cpp:237]     Train net output #0: loss = 0.102503 (* 1 = 0.102503 loss)
I0410 01:10:22.296829 14080 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 01:10:27.233748 14080 solver.cpp:218] Iteration 7836 (2.43077 iter/s, 4.93671s/12 iters), loss = 0.0565112
I0410 01:10:27.233804 14080 solver.cpp:237]     Train net output #0: loss = 0.0565113 (* 1 = 0.0565113 loss)
I0410 01:10:27.233817 14080 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 01:10:32.189986 14080 solver.cpp:218] Iteration 7848 (2.42132 iter/s, 4.95597s/12 iters), loss = 0.187586
I0410 01:10:32.190033 14080 solver.cpp:237]     Train net output #0: loss = 0.187586 (* 1 = 0.187586 loss)
I0410 01:10:32.190043 14080 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 01:10:34.216033 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 01:10:35.563674 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 01:10:36.604979 14080 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 01:10:36.605002 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:10:37.849942 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:10:41.018020 14080 solver.cpp:397]     Test net output #0: accuracy = 0.535539
I0410 01:10:41.018070 14080 solver.cpp:397]     Test net output #1: loss = 2.45185 (* 1 = 2.45185 loss)
I0410 01:10:42.941792 14080 solver.cpp:218] Iteration 7860 (1.11614 iter/s, 10.7513s/12 iters), loss = 0.124245
I0410 01:10:42.941848 14080 solver.cpp:237]     Train net output #0: loss = 0.124245 (* 1 = 0.124245 loss)
I0410 01:10:42.941861 14080 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 01:10:47.919303 14080 solver.cpp:218] Iteration 7872 (2.41098 iter/s, 4.97724s/12 iters), loss = 0.0760527
I0410 01:10:47.919363 14080 solver.cpp:237]     Train net output #0: loss = 0.0760528 (* 1 = 0.0760528 loss)
I0410 01:10:47.919378 14080 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 01:10:52.918728 14080 solver.cpp:218] Iteration 7884 (2.40041 iter/s, 4.99915s/12 iters), loss = 0.145666
I0410 01:10:52.918782 14080 solver.cpp:237]     Train net output #0: loss = 0.145666 (* 1 = 0.145666 loss)
I0410 01:10:52.918795 14080 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 01:10:55.035192 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:10:57.861443 14080 solver.cpp:218] Iteration 7896 (2.42795 iter/s, 4.94244s/12 iters), loss = 0.136852
I0410 01:10:57.861495 14080 solver.cpp:237]     Train net output #0: loss = 0.136852 (* 1 = 0.136852 loss)
I0410 01:10:57.861507 14080 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 01:11:02.834144 14080 solver.cpp:218] Iteration 7908 (2.41331 iter/s, 4.97243s/12 iters), loss = 0.0670132
I0410 01:11:02.834197 14080 solver.cpp:237]     Train net output #0: loss = 0.0670133 (* 1 = 0.0670133 loss)
I0410 01:11:02.834209 14080 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 01:11:07.787879 14080 solver.cpp:218] Iteration 7920 (2.42255 iter/s, 4.95347s/12 iters), loss = 0.142395
I0410 01:11:07.787940 14080 solver.cpp:237]     Train net output #0: loss = 0.142395 (* 1 = 0.142395 loss)
I0410 01:11:07.787951 14080 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 01:11:12.885905 14080 solver.cpp:218] Iteration 7932 (2.35398 iter/s, 5.09775s/12 iters), loss = 0.220988
I0410 01:11:12.886026 14080 solver.cpp:237]     Train net output #0: loss = 0.220988 (* 1 = 0.220988 loss)
I0410 01:11:12.886039 14080 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 01:11:17.796777 14080 solver.cpp:218] Iteration 7944 (2.44372 iter/s, 4.91054s/12 iters), loss = 0.0669005
I0410 01:11:17.796823 14080 solver.cpp:237]     Train net output #0: loss = 0.0669006 (* 1 = 0.0669006 loss)
I0410 01:11:17.796833 14080 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 01:11:22.310235 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 01:11:27.788647 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 01:11:33.411361 14080 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 01:11:33.411392 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:11:34.756865 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:11:37.900139 14080 solver.cpp:397]     Test net output #0: accuracy = 0.529412
I0410 01:11:37.900185 14080 solver.cpp:397]     Test net output #1: loss = 2.41035 (* 1 = 2.41035 loss)
I0410 01:11:37.986021 14080 solver.cpp:218] Iteration 7956 (0.594402 iter/s, 20.1884s/12 iters), loss = 0.147273
I0410 01:11:37.986093 14080 solver.cpp:237]     Train net output #0: loss = 0.147273 (* 1 = 0.147273 loss)
I0410 01:11:37.986106 14080 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 01:11:42.167285 14080 solver.cpp:218] Iteration 7968 (2.87011 iter/s, 4.18102s/12 iters), loss = 0.147565
I0410 01:11:42.167333 14080 solver.cpp:237]     Train net output #0: loss = 0.147565 (* 1 = 0.147565 loss)
I0410 01:11:42.167343 14080 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 01:11:47.192553 14080 solver.cpp:218] Iteration 7980 (2.38806 iter/s, 5.025s/12 iters), loss = 0.0767159
I0410 01:11:47.192642 14080 solver.cpp:237]     Train net output #0: loss = 0.076716 (* 1 = 0.076716 loss)
I0410 01:11:47.192651 14080 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 01:11:51.416692 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:11:52.114317 14080 solver.cpp:218] Iteration 7992 (2.4383 iter/s, 4.92146s/12 iters), loss = 0.112319
I0410 01:11:52.114367 14080 solver.cpp:237]     Train net output #0: loss = 0.112319 (* 1 = 0.112319 loss)
I0410 01:11:52.114378 14080 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 01:11:56.997732 14080 solver.cpp:218] Iteration 8004 (2.45743 iter/s, 4.88315s/12 iters), loss = 0.0738543
I0410 01:11:56.997782 14080 solver.cpp:237]     Train net output #0: loss = 0.0738543 (* 1 = 0.0738543 loss)
I0410 01:11:56.997792 14080 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 01:12:01.845209 14080 solver.cpp:218] Iteration 8016 (2.47565 iter/s, 4.84722s/12 iters), loss = 0.204883
I0410 01:12:01.845255 14080 solver.cpp:237]     Train net output #0: loss = 0.204883 (* 1 = 0.204883 loss)
I0410 01:12:01.845263 14080 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 01:12:06.767937 14080 solver.cpp:218] Iteration 8028 (2.4378 iter/s, 4.92247s/12 iters), loss = 0.0897222
I0410 01:12:06.767976 14080 solver.cpp:237]     Train net output #0: loss = 0.0897222 (* 1 = 0.0897222 loss)
I0410 01:12:06.767985 14080 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 01:12:11.831230 14080 solver.cpp:218] Iteration 8040 (2.37012 iter/s, 5.06303s/12 iters), loss = 0.118858
I0410 01:12:11.831285 14080 solver.cpp:237]     Train net output #0: loss = 0.118858 (* 1 = 0.118858 loss)
I0410 01:12:11.831296 14080 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 01:12:16.734213 14080 solver.cpp:218] Iteration 8052 (2.44762 iter/s, 4.90272s/12 iters), loss = 0.144225
I0410 01:12:16.734270 14080 solver.cpp:237]     Train net output #0: loss = 0.144225 (* 1 = 0.144225 loss)
I0410 01:12:16.734282 14080 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 01:12:18.731068 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 01:12:21.838886 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 01:12:24.254200 14080 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 01:12:24.254225 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:12:25.610754 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:12:28.767071 14080 solver.cpp:397]     Test net output #0: accuracy = 0.547181
I0410 01:12:28.767112 14080 solver.cpp:397]     Test net output #1: loss = 2.36352 (* 1 = 2.36352 loss)
I0410 01:12:30.623386 14080 solver.cpp:218] Iteration 8064 (0.864021 iter/s, 13.8885s/12 iters), loss = 0.0764173
I0410 01:12:30.623435 14080 solver.cpp:237]     Train net output #0: loss = 0.0764173 (* 1 = 0.0764173 loss)
I0410 01:12:30.623445 14080 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 01:12:35.498977 14080 solver.cpp:218] Iteration 8076 (2.46137 iter/s, 4.87533s/12 iters), loss = 0.164345
I0410 01:12:35.499028 14080 solver.cpp:237]     Train net output #0: loss = 0.164345 (* 1 = 0.164345 loss)
I0410 01:12:35.499040 14080 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 01:12:40.387940 14080 solver.cpp:218] Iteration 8088 (2.45464 iter/s, 4.8887s/12 iters), loss = 0.16637
I0410 01:12:40.387993 14080 solver.cpp:237]     Train net output #0: loss = 0.166371 (* 1 = 0.166371 loss)
I0410 01:12:40.388005 14080 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 01:12:41.794553 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:12:45.313504 14080 solver.cpp:218] Iteration 8100 (2.4364 iter/s, 4.9253s/12 iters), loss = 0.106028
I0410 01:12:45.313558 14080 solver.cpp:237]     Train net output #0: loss = 0.106028 (* 1 = 0.106028 loss)
I0410 01:12:45.313570 14080 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 01:12:50.294939 14080 solver.cpp:218] Iteration 8112 (2.40907 iter/s, 4.98117s/12 iters), loss = 0.0843948
I0410 01:12:50.295013 14080 solver.cpp:237]     Train net output #0: loss = 0.0843948 (* 1 = 0.0843948 loss)
I0410 01:12:50.295024 14080 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 01:12:55.262557 14080 solver.cpp:218] Iteration 8124 (2.41579 iter/s, 4.96732s/12 iters), loss = 0.103783
I0410 01:12:55.262614 14080 solver.cpp:237]     Train net output #0: loss = 0.103783 (* 1 = 0.103783 loss)
I0410 01:12:55.262627 14080 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 01:13:00.180385 14080 solver.cpp:218] Iteration 8136 (2.44023 iter/s, 4.91756s/12 iters), loss = 0.19482
I0410 01:13:00.180434 14080 solver.cpp:237]     Train net output #0: loss = 0.19482 (* 1 = 0.19482 loss)
I0410 01:13:00.180444 14080 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 01:13:05.279780 14080 solver.cpp:218] Iteration 8148 (2.35334 iter/s, 5.09913s/12 iters), loss = 0.177725
I0410 01:13:05.279824 14080 solver.cpp:237]     Train net output #0: loss = 0.177725 (* 1 = 0.177725 loss)
I0410 01:13:05.279834 14080 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 01:13:09.760283 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 01:13:11.266131 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 01:13:12.314039 14080 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 01:13:12.314067 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:13:13.581802 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:13:16.886684 14080 solver.cpp:397]     Test net output #0: accuracy = 0.543505
I0410 01:13:16.886734 14080 solver.cpp:397]     Test net output #1: loss = 2.42102 (* 1 = 2.42102 loss)
I0410 01:13:16.972445 14080 solver.cpp:218] Iteration 8160 (1.02633 iter/s, 11.6921s/12 iters), loss = 0.174769
I0410 01:13:16.972499 14080 solver.cpp:237]     Train net output #0: loss = 0.174769 (* 1 = 0.174769 loss)
I0410 01:13:16.972512 14080 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 01:13:21.080029 14080 solver.cpp:218] Iteration 8172 (2.92159 iter/s, 4.10735s/12 iters), loss = 0.0848242
I0410 01:13:21.080166 14080 solver.cpp:237]     Train net output #0: loss = 0.0848242 (* 1 = 0.0848242 loss)
I0410 01:13:21.080178 14080 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 01:13:25.998747 14080 solver.cpp:218] Iteration 8184 (2.43983 iter/s, 4.91837s/12 iters), loss = 0.22464
I0410 01:13:25.998797 14080 solver.cpp:237]     Train net output #0: loss = 0.22464 (* 1 = 0.22464 loss)
I0410 01:13:25.998808 14080 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 01:13:29.547147 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:13:30.985025 14080 solver.cpp:218] Iteration 8196 (2.40673 iter/s, 4.98601s/12 iters), loss = 0.13592
I0410 01:13:30.985069 14080 solver.cpp:237]     Train net output #0: loss = 0.13592 (* 1 = 0.13592 loss)
I0410 01:13:30.985077 14080 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 01:13:35.964996 14080 solver.cpp:218] Iteration 8208 (2.40978 iter/s, 4.97971s/12 iters), loss = 0.110668
I0410 01:13:35.965055 14080 solver.cpp:237]     Train net output #0: loss = 0.110668 (* 1 = 0.110668 loss)
I0410 01:13:35.965068 14080 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 01:13:41.244015 14080 solver.cpp:218] Iteration 8220 (2.27327 iter/s, 5.27873s/12 iters), loss = 0.114432
I0410 01:13:41.244071 14080 solver.cpp:237]     Train net output #0: loss = 0.114432 (* 1 = 0.114432 loss)
I0410 01:13:41.244081 14080 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 01:13:46.102649 14080 solver.cpp:218] Iteration 8232 (2.46997 iter/s, 4.85837s/12 iters), loss = 0.122173
I0410 01:13:46.102706 14080 solver.cpp:237]     Train net output #0: loss = 0.122173 (* 1 = 0.122173 loss)
I0410 01:13:46.102718 14080 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 01:13:51.063151 14080 solver.cpp:218] Iteration 8244 (2.41924 iter/s, 4.96023s/12 iters), loss = 0.14914
I0410 01:13:51.063201 14080 solver.cpp:237]     Train net output #0: loss = 0.14914 (* 1 = 0.14914 loss)
I0410 01:13:51.063212 14080 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 01:13:56.028312 14080 solver.cpp:218] Iteration 8256 (2.41697 iter/s, 4.96489s/12 iters), loss = 0.116487
I0410 01:13:56.028424 14080 solver.cpp:237]     Train net output #0: loss = 0.116487 (* 1 = 0.116487 loss)
I0410 01:13:56.028437 14080 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 01:13:58.048774 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 01:13:59.514477 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 01:14:00.542657 14080 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 01:14:00.542678 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:14:01.751277 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:14:05.097944 14080 solver.cpp:397]     Test net output #0: accuracy = 0.532475
I0410 01:14:05.098012 14080 solver.cpp:397]     Test net output #1: loss = 2.42365 (* 1 = 2.42365 loss)
I0410 01:14:07.047329 14080 solver.cpp:218] Iteration 8268 (1.08908 iter/s, 11.0184s/12 iters), loss = 0.0822411
I0410 01:14:07.047394 14080 solver.cpp:237]     Train net output #0: loss = 0.0822412 (* 1 = 0.0822412 loss)
I0410 01:14:07.047407 14080 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 01:14:11.969990 14080 solver.cpp:218] Iteration 8280 (2.43785 iter/s, 4.92237s/12 iters), loss = 0.162059
I0410 01:14:11.970042 14080 solver.cpp:237]     Train net output #0: loss = 0.16206 (* 1 = 0.16206 loss)
I0410 01:14:11.970052 14080 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 01:14:16.883352 14080 solver.cpp:218] Iteration 8292 (2.44245 iter/s, 4.91309s/12 iters), loss = 0.0963233
I0410 01:14:16.883399 14080 solver.cpp:237]     Train net output #0: loss = 0.0963234 (* 1 = 0.0963234 loss)
I0410 01:14:16.883409 14080 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 01:14:17.571740 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:14:21.882421 14080 solver.cpp:218] Iteration 8304 (2.40058 iter/s, 4.9988s/12 iters), loss = 0.0980065
I0410 01:14:21.882474 14080 solver.cpp:237]     Train net output #0: loss = 0.0980066 (* 1 = 0.0980066 loss)
I0410 01:14:21.882488 14080 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 01:14:24.400460 14080 blocking_queue.cpp:49] Waiting for data
I0410 01:14:26.880668 14080 solver.cpp:218] Iteration 8316 (2.40097 iter/s, 4.99798s/12 iters), loss = 0.121353
I0410 01:14:26.880816 14080 solver.cpp:237]     Train net output #0: loss = 0.121353 (* 1 = 0.121353 loss)
I0410 01:14:26.880833 14080 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 01:14:31.775720 14080 solver.cpp:218] Iteration 8328 (2.45163 iter/s, 4.8947s/12 iters), loss = 0.0868595
I0410 01:14:31.775768 14080 solver.cpp:237]     Train net output #0: loss = 0.0868595 (* 1 = 0.0868595 loss)
I0410 01:14:31.775776 14080 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 01:14:36.648057 14080 solver.cpp:218] Iteration 8340 (2.46301 iter/s, 4.87208s/12 iters), loss = 0.15492
I0410 01:14:36.648102 14080 solver.cpp:237]     Train net output #0: loss = 0.15492 (* 1 = 0.15492 loss)
I0410 01:14:36.648109 14080 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 01:14:41.547070 14080 solver.cpp:218] Iteration 8352 (2.44961 iter/s, 4.89874s/12 iters), loss = 0.142518
I0410 01:14:41.547134 14080 solver.cpp:237]     Train net output #0: loss = 0.142518 (* 1 = 0.142518 loss)
I0410 01:14:41.547148 14080 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 01:14:45.999094 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 01:14:47.909680 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 01:14:49.052819 14080 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 01:14:49.052847 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:14:50.203285 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:14:53.622620 14080 solver.cpp:397]     Test net output #0: accuracy = 0.53799
I0410 01:14:53.622659 14080 solver.cpp:397]     Test net output #1: loss = 2.43708 (* 1 = 2.43708 loss)
I0410 01:14:53.708357 14080 solver.cpp:218] Iteration 8364 (0.986784 iter/s, 12.1607s/12 iters), loss = 0.277402
I0410 01:14:53.708413 14080 solver.cpp:237]     Train net output #0: loss = 0.277402 (* 1 = 0.277402 loss)
I0410 01:14:53.708425 14080 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 01:14:57.936519 14080 solver.cpp:218] Iteration 8376 (2.83827 iter/s, 4.22792s/12 iters), loss = 0.0618771
I0410 01:14:57.936638 14080 solver.cpp:237]     Train net output #0: loss = 0.0618772 (* 1 = 0.0618772 loss)
I0410 01:14:57.936648 14080 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 01:15:02.824697 14080 solver.cpp:218] Iteration 8388 (2.45507 iter/s, 4.88784s/12 iters), loss = 0.084694
I0410 01:15:02.824746 14080 solver.cpp:237]     Train net output #0: loss = 0.084694 (* 1 = 0.084694 loss)
I0410 01:15:02.824755 14080 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 01:15:05.584762 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:15:07.714985 14080 solver.cpp:218] Iteration 8400 (2.45397 iter/s, 4.89003s/12 iters), loss = 0.0460932
I0410 01:15:07.715041 14080 solver.cpp:237]     Train net output #0: loss = 0.0460932 (* 1 = 0.0460932 loss)
I0410 01:15:07.715054 14080 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 01:15:12.637502 14080 solver.cpp:218] Iteration 8412 (2.43791 iter/s, 4.92225s/12 iters), loss = 0.247509
I0410 01:15:12.637557 14080 solver.cpp:237]     Train net output #0: loss = 0.247509 (* 1 = 0.247509 loss)
I0410 01:15:12.637567 14080 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 01:15:17.538714 14080 solver.cpp:218] Iteration 8424 (2.44851 iter/s, 4.90094s/12 iters), loss = 0.123459
I0410 01:15:17.538771 14080 solver.cpp:237]     Train net output #0: loss = 0.123459 (* 1 = 0.123459 loss)
I0410 01:15:17.538782 14080 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 01:15:22.448514 14080 solver.cpp:218] Iteration 8436 (2.44423 iter/s, 4.90953s/12 iters), loss = 0.0854848
I0410 01:15:22.448559 14080 solver.cpp:237]     Train net output #0: loss = 0.0854848 (* 1 = 0.0854848 loss)
I0410 01:15:22.448570 14080 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 01:15:27.312959 14080 solver.cpp:218] Iteration 8448 (2.46701 iter/s, 4.86419s/12 iters), loss = 0.100963
I0410 01:15:27.313000 14080 solver.cpp:237]     Train net output #0: loss = 0.100963 (* 1 = 0.100963 loss)
I0410 01:15:27.313009 14080 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 01:15:32.237859 14080 solver.cpp:218] Iteration 8460 (2.43673 iter/s, 4.92463s/12 iters), loss = 0.0976618
I0410 01:15:32.238018 14080 solver.cpp:237]     Train net output #0: loss = 0.0976619 (* 1 = 0.0976619 loss)
I0410 01:15:32.238031 14080 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 01:15:34.232947 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 01:15:36.202565 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 01:15:37.240775 14080 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 01:15:37.240804 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:15:38.370045 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:15:41.678315 14080 solver.cpp:397]     Test net output #0: accuracy = 0.547181
I0410 01:15:41.678364 14080 solver.cpp:397]     Test net output #1: loss = 2.47557 (* 1 = 2.47557 loss)
I0410 01:15:43.472460 14080 solver.cpp:218] Iteration 8472 (1.06819 iter/s, 11.234s/12 iters), loss = 0.0803118
I0410 01:15:43.472509 14080 solver.cpp:237]     Train net output #0: loss = 0.0803119 (* 1 = 0.0803119 loss)
I0410 01:15:43.472518 14080 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 01:15:48.406615 14080 solver.cpp:218] Iteration 8484 (2.43216 iter/s, 4.93389s/12 iters), loss = 0.209462
I0410 01:15:48.406661 14080 solver.cpp:237]     Train net output #0: loss = 0.209462 (* 1 = 0.209462 loss)
I0410 01:15:48.406672 14080 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 01:15:53.341612 14080 solver.cpp:218] Iteration 8496 (2.43174 iter/s, 4.93474s/12 iters), loss = 0.0598451
I0410 01:15:53.341668 14080 solver.cpp:237]     Train net output #0: loss = 0.0598452 (* 1 = 0.0598452 loss)
I0410 01:15:53.341681 14080 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 01:15:53.383203 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:15:58.274785 14080 solver.cpp:218] Iteration 8508 (2.43265 iter/s, 4.9329s/12 iters), loss = 0.281551
I0410 01:15:58.274829 14080 solver.cpp:237]     Train net output #0: loss = 0.281551 (* 1 = 0.281551 loss)
I0410 01:15:58.274837 14080 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 01:16:03.198307 14080 solver.cpp:218] Iteration 8520 (2.43741 iter/s, 4.92326s/12 iters), loss = 0.0986215
I0410 01:16:03.198473 14080 solver.cpp:237]     Train net output #0: loss = 0.0986215 (* 1 = 0.0986215 loss)
I0410 01:16:03.198488 14080 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 01:16:08.128260 14080 solver.cpp:218] Iteration 8532 (2.43429 iter/s, 4.92958s/12 iters), loss = 0.0862573
I0410 01:16:08.128320 14080 solver.cpp:237]     Train net output #0: loss = 0.0862574 (* 1 = 0.0862574 loss)
I0410 01:16:08.128332 14080 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 01:16:13.029704 14080 solver.cpp:218] Iteration 8544 (2.44839 iter/s, 4.90117s/12 iters), loss = 0.0482555
I0410 01:16:13.029763 14080 solver.cpp:237]     Train net output #0: loss = 0.0482556 (* 1 = 0.0482556 loss)
I0410 01:16:13.029775 14080 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 01:16:18.001278 14080 solver.cpp:218] Iteration 8556 (2.41385 iter/s, 4.97131s/12 iters), loss = 0.128854
I0410 01:16:18.001317 14080 solver.cpp:237]     Train net output #0: loss = 0.128854 (* 1 = 0.128854 loss)
I0410 01:16:18.001324 14080 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 01:16:22.510831 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 01:16:24.218950 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 01:16:27.209544 14080 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 01:16:27.209568 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:16:28.274075 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:16:31.691203 14080 solver.cpp:397]     Test net output #0: accuracy = 0.538603
I0410 01:16:31.691233 14080 solver.cpp:397]     Test net output #1: loss = 2.42495 (* 1 = 2.42495 loss)
I0410 01:16:31.775074 14080 solver.cpp:218] Iteration 8568 (0.871258 iter/s, 13.7732s/12 iters), loss = 0.0954624
I0410 01:16:31.775117 14080 solver.cpp:237]     Train net output #0: loss = 0.0954624 (* 1 = 0.0954624 loss)
I0410 01:16:31.775126 14080 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 01:16:35.942417 14080 solver.cpp:218] Iteration 8580 (2.87969 iter/s, 4.16712s/12 iters), loss = 0.0336742
I0410 01:16:35.942530 14080 solver.cpp:237]     Train net output #0: loss = 0.0336742 (* 1 = 0.0336742 loss)
I0410 01:16:35.942545 14080 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 01:16:41.044138 14080 solver.cpp:218] Iteration 8592 (2.3523 iter/s, 5.10139s/12 iters), loss = 0.063565
I0410 01:16:41.044194 14080 solver.cpp:237]     Train net output #0: loss = 0.063565 (* 1 = 0.063565 loss)
I0410 01:16:41.044206 14080 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 01:16:43.302354 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:16:46.025806 14080 solver.cpp:218] Iteration 8604 (2.40896 iter/s, 4.9814s/12 iters), loss = 0.139336
I0410 01:16:46.025863 14080 solver.cpp:237]     Train net output #0: loss = 0.139336 (* 1 = 0.139336 loss)
I0410 01:16:46.025876 14080 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 01:16:50.848829 14080 solver.cpp:218] Iteration 8616 (2.4882 iter/s, 4.82276s/12 iters), loss = 0.0478182
I0410 01:16:50.848888 14080 solver.cpp:237]     Train net output #0: loss = 0.0478182 (* 1 = 0.0478182 loss)
I0410 01:16:50.848901 14080 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 01:16:55.749143 14080 solver.cpp:218] Iteration 8628 (2.44896 iter/s, 4.90005s/12 iters), loss = 0.0904927
I0410 01:16:55.749189 14080 solver.cpp:237]     Train net output #0: loss = 0.0904927 (* 1 = 0.0904927 loss)
I0410 01:16:55.749200 14080 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 01:17:00.639344 14080 solver.cpp:218] Iteration 8640 (2.45402 iter/s, 4.88994s/12 iters), loss = 0.102114
I0410 01:17:00.639400 14080 solver.cpp:237]     Train net output #0: loss = 0.102114 (* 1 = 0.102114 loss)
I0410 01:17:00.639412 14080 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 01:17:05.513883 14080 solver.cpp:218] Iteration 8652 (2.46191 iter/s, 4.87427s/12 iters), loss = 0.0892973
I0410 01:17:05.513945 14080 solver.cpp:237]     Train net output #0: loss = 0.0892973 (* 1 = 0.0892973 loss)
I0410 01:17:05.513986 14080 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 01:17:10.427047 14080 solver.cpp:218] Iteration 8664 (2.44256 iter/s, 4.91289s/12 iters), loss = 0.089245
I0410 01:17:10.427218 14080 solver.cpp:237]     Train net output #0: loss = 0.0892451 (* 1 = 0.0892451 loss)
I0410 01:17:10.427233 14080 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 01:17:12.407737 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 01:17:14.873662 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 01:17:15.934335 14080 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 01:17:15.934365 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:17:16.969405 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:17:20.439049 14080 solver.cpp:397]     Test net output #0: accuracy = 0.547181
I0410 01:17:20.439103 14080 solver.cpp:397]     Test net output #1: loss = 2.48744 (* 1 = 2.48744 loss)
I0410 01:17:22.330303 14080 solver.cpp:218] Iteration 8676 (1.00818 iter/s, 11.9026s/12 iters), loss = 0.0624553
I0410 01:17:22.330363 14080 solver.cpp:237]     Train net output #0: loss = 0.0624554 (* 1 = 0.0624554 loss)
I0410 01:17:22.330376 14080 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 01:17:27.248973 14080 solver.cpp:218] Iteration 8688 (2.43982 iter/s, 4.9184s/12 iters), loss = 0.111119
I0410 01:17:27.249022 14080 solver.cpp:237]     Train net output #0: loss = 0.111119 (* 1 = 0.111119 loss)
I0410 01:17:27.249032 14080 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 01:17:31.453197 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:17:32.131378 14080 solver.cpp:218] Iteration 8700 (2.45794 iter/s, 4.88214s/12 iters), loss = 0.120551
I0410 01:17:32.131429 14080 solver.cpp:237]     Train net output #0: loss = 0.120551 (* 1 = 0.120551 loss)
I0410 01:17:32.131441 14080 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 01:17:37.039059 14080 solver.cpp:218] Iteration 8712 (2.44528 iter/s, 4.90741s/12 iters), loss = 0.0790433
I0410 01:17:37.039119 14080 solver.cpp:237]     Train net output #0: loss = 0.0790434 (* 1 = 0.0790434 loss)
I0410 01:17:37.039130 14080 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 01:17:41.980819 14080 solver.cpp:218] Iteration 8724 (2.42842 iter/s, 4.94149s/12 iters), loss = 0.117861
I0410 01:17:41.980902 14080 solver.cpp:237]     Train net output #0: loss = 0.117861 (* 1 = 0.117861 loss)
I0410 01:17:41.980914 14080 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 01:17:47.065866 14080 solver.cpp:218] Iteration 8736 (2.36 iter/s, 5.08475s/12 iters), loss = 0.144116
I0410 01:17:47.065913 14080 solver.cpp:237]     Train net output #0: loss = 0.144116 (* 1 = 0.144116 loss)
I0410 01:17:47.065924 14080 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 01:17:51.943269 14080 solver.cpp:218] Iteration 8748 (2.46046 iter/s, 4.87714s/12 iters), loss = 0.182784
I0410 01:17:51.943315 14080 solver.cpp:237]     Train net output #0: loss = 0.182784 (* 1 = 0.182784 loss)
I0410 01:17:51.943325 14080 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 01:17:56.993595 14080 solver.cpp:218] Iteration 8760 (2.37621 iter/s, 5.05006s/12 iters), loss = 0.0544637
I0410 01:17:56.993643 14080 solver.cpp:237]     Train net output #0: loss = 0.0544638 (* 1 = 0.0544638 loss)
I0410 01:17:56.993652 14080 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 01:18:01.546830 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 01:18:03.948843 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 01:18:05.743160 14080 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 01:18:05.743188 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:18:06.743487 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:18:10.450932 14080 solver.cpp:397]     Test net output #0: accuracy = 0.536152
I0410 01:18:10.450978 14080 solver.cpp:397]     Test net output #1: loss = 2.47271 (* 1 = 2.47271 loss)
I0410 01:18:10.536614 14080 solver.cpp:218] Iteration 8772 (0.886106 iter/s, 13.5424s/12 iters), loss = 0.126758
I0410 01:18:10.536661 14080 solver.cpp:237]     Train net output #0: loss = 0.126758 (* 1 = 0.126758 loss)
I0410 01:18:10.536671 14080 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 01:18:14.724769 14080 solver.cpp:218] Iteration 8784 (2.86539 iter/s, 4.18792s/12 iters), loss = 0.105646
I0410 01:18:14.724920 14080 solver.cpp:237]     Train net output #0: loss = 0.105646 (* 1 = 0.105646 loss)
I0410 01:18:14.724932 14080 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 01:18:19.629254 14080 solver.cpp:218] Iteration 8796 (2.44692 iter/s, 4.90413s/12 iters), loss = 0.0423224
I0410 01:18:19.629304 14080 solver.cpp:237]     Train net output #0: loss = 0.0423224 (* 1 = 0.0423224 loss)
I0410 01:18:19.629315 14080 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 01:18:21.042893 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:18:24.595032 14080 solver.cpp:218] Iteration 8808 (2.41667 iter/s, 4.96552s/12 iters), loss = 0.0650759
I0410 01:18:24.595073 14080 solver.cpp:237]     Train net output #0: loss = 0.0650759 (* 1 = 0.0650759 loss)
I0410 01:18:24.595084 14080 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 01:18:29.533457 14080 solver.cpp:218] Iteration 8820 (2.43005 iter/s, 4.93817s/12 iters), loss = 0.092004
I0410 01:18:29.533517 14080 solver.cpp:237]     Train net output #0: loss = 0.092004 (* 1 = 0.092004 loss)
I0410 01:18:29.533529 14080 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 01:18:34.428220 14080 solver.cpp:218] Iteration 8832 (2.45174 iter/s, 4.89449s/12 iters), loss = 0.0499699
I0410 01:18:34.428275 14080 solver.cpp:237]     Train net output #0: loss = 0.04997 (* 1 = 0.04997 loss)
I0410 01:18:34.428285 14080 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 01:18:39.291946 14080 solver.cpp:218] Iteration 8844 (2.46738 iter/s, 4.86347s/12 iters), loss = 0.156363
I0410 01:18:39.291987 14080 solver.cpp:237]     Train net output #0: loss = 0.156363 (* 1 = 0.156363 loss)
I0410 01:18:39.291996 14080 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 01:18:44.172017 14080 solver.cpp:218] Iteration 8856 (2.45911 iter/s, 4.87981s/12 iters), loss = 0.0868962
I0410 01:18:44.172073 14080 solver.cpp:237]     Train net output #0: loss = 0.0868962 (* 1 = 0.0868962 loss)
I0410 01:18:44.172086 14080 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 01:18:49.029516 14080 solver.cpp:218] Iteration 8868 (2.47054 iter/s, 4.85724s/12 iters), loss = 0.119826
I0410 01:18:49.029610 14080 solver.cpp:237]     Train net output #0: loss = 0.119826 (* 1 = 0.119826 loss)
I0410 01:18:49.029620 14080 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 01:18:51.051553 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 01:18:52.505887 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 01:18:53.530723 14080 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 01:18:53.530747 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:18:54.450440 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:18:58.303431 14080 solver.cpp:397]     Test net output #0: accuracy = 0.552083
I0410 01:18:58.303481 14080 solver.cpp:397]     Test net output #1: loss = 2.42672 (* 1 = 2.42672 loss)
I0410 01:19:00.185075 14080 solver.cpp:218] Iteration 8880 (1.07575 iter/s, 11.155s/12 iters), loss = 0.135479
I0410 01:19:00.185119 14080 solver.cpp:237]     Train net output #0: loss = 0.135479 (* 1 = 0.135479 loss)
I0410 01:19:00.185127 14080 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 01:19:05.212817 14080 solver.cpp:218] Iteration 8892 (2.38688 iter/s, 5.02748s/12 iters), loss = 0.087949
I0410 01:19:05.212870 14080 solver.cpp:237]     Train net output #0: loss = 0.0879491 (* 1 = 0.0879491 loss)
I0410 01:19:05.212883 14080 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 01:19:08.828347 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:19:10.210608 14080 solver.cpp:218] Iteration 8904 (2.40119 iter/s, 4.99752s/12 iters), loss = 0.0894486
I0410 01:19:10.210654 14080 solver.cpp:237]     Train net output #0: loss = 0.0894486 (* 1 = 0.0894486 loss)
I0410 01:19:10.210662 14080 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 01:19:15.114909 14080 solver.cpp:218] Iteration 8916 (2.44696 iter/s, 4.90404s/12 iters), loss = 0.0743793
I0410 01:19:15.114976 14080 solver.cpp:237]     Train net output #0: loss = 0.0743794 (* 1 = 0.0743794 loss)
I0410 01:19:15.114989 14080 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 01:19:19.930946 14080 solver.cpp:218] Iteration 8928 (2.49182 iter/s, 4.81576s/12 iters), loss = 0.0746814
I0410 01:19:19.931105 14080 solver.cpp:237]     Train net output #0: loss = 0.0746815 (* 1 = 0.0746815 loss)
I0410 01:19:19.931118 14080 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 01:19:24.748916 14080 solver.cpp:218] Iteration 8940 (2.49087 iter/s, 4.8176s/12 iters), loss = 0.0658545
I0410 01:19:24.748980 14080 solver.cpp:237]     Train net output #0: loss = 0.0658546 (* 1 = 0.0658546 loss)
I0410 01:19:24.748993 14080 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 01:19:29.713224 14080 solver.cpp:218] Iteration 8952 (2.41739 iter/s, 4.96403s/12 iters), loss = 0.0998877
I0410 01:19:29.713275 14080 solver.cpp:237]     Train net output #0: loss = 0.0998878 (* 1 = 0.0998878 loss)
I0410 01:19:29.713286 14080 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 01:19:34.598057 14080 solver.cpp:218] Iteration 8964 (2.45672 iter/s, 4.88457s/12 iters), loss = 0.148962
I0410 01:19:34.598109 14080 solver.cpp:237]     Train net output #0: loss = 0.148962 (* 1 = 0.148962 loss)
I0410 01:19:34.598121 14080 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 01:19:38.999737 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 01:19:41.456660 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 01:19:43.032032 14080 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 01:19:43.032058 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:19:43.968374 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:19:47.688333 14080 solver.cpp:397]     Test net output #0: accuracy = 0.558211
I0410 01:19:47.688377 14080 solver.cpp:397]     Test net output #1: loss = 2.35878 (* 1 = 2.35878 loss)
I0410 01:19:47.771909 14080 solver.cpp:218] Iteration 8976 (0.910936 iter/s, 13.1733s/12 iters), loss = 0.110338
I0410 01:19:47.771950 14080 solver.cpp:237]     Train net output #0: loss = 0.110338 (* 1 = 0.110338 loss)
I0410 01:19:47.771960 14080 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 01:19:51.841488 14080 solver.cpp:218] Iteration 8988 (2.94887 iter/s, 4.06935s/12 iters), loss = 0.177139
I0410 01:19:51.841614 14080 solver.cpp:237]     Train net output #0: loss = 0.177139 (* 1 = 0.177139 loss)
I0410 01:19:51.841629 14080 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 01:19:54.683830 14080 blocking_queue.cpp:49] Waiting for data
I0410 01:19:56.932904 14080 solver.cpp:218] Iteration 9000 (2.35707 iter/s, 5.09107s/12 iters), loss = 0.138597
I0410 01:19:56.932955 14080 solver.cpp:237]     Train net output #0: loss = 0.138597 (* 1 = 0.138597 loss)
I0410 01:19:56.932965 14080 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 01:19:57.652081 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:20:02.051322 14080 solver.cpp:218] Iteration 9012 (2.3446 iter/s, 5.11814s/12 iters), loss = 0.192252
I0410 01:20:02.051383 14080 solver.cpp:237]     Train net output #0: loss = 0.192252 (* 1 = 0.192252 loss)
I0410 01:20:02.051398 14080 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 01:20:06.958003 14080 solver.cpp:218] Iteration 9024 (2.44579 iter/s, 4.9064s/12 iters), loss = 0.107983
I0410 01:20:06.958065 14080 solver.cpp:237]     Train net output #0: loss = 0.107983 (* 1 = 0.107983 loss)
I0410 01:20:06.958077 14080 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 01:20:11.885994 14080 solver.cpp:218] Iteration 9036 (2.4352 iter/s, 4.92772s/12 iters), loss = 0.0834251
I0410 01:20:11.886041 14080 solver.cpp:237]     Train net output #0: loss = 0.0834251 (* 1 = 0.0834251 loss)
I0410 01:20:11.886050 14080 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 01:20:16.900498 14080 solver.cpp:218] Iteration 9048 (2.39318 iter/s, 5.01424s/12 iters), loss = 0.0597204
I0410 01:20:16.900539 14080 solver.cpp:237]     Train net output #0: loss = 0.0597204 (* 1 = 0.0597204 loss)
I0410 01:20:16.900547 14080 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 01:20:21.904322 14080 solver.cpp:218] Iteration 9060 (2.39829 iter/s, 5.00356s/12 iters), loss = 0.0533536
I0410 01:20:21.904492 14080 solver.cpp:237]     Train net output #0: loss = 0.0533537 (* 1 = 0.0533537 loss)
I0410 01:20:21.904507 14080 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 01:20:26.930903 14080 solver.cpp:218] Iteration 9072 (2.38749 iter/s, 5.0262s/12 iters), loss = 0.112347
I0410 01:20:26.930963 14080 solver.cpp:237]     Train net output #0: loss = 0.112347 (* 1 = 0.112347 loss)
I0410 01:20:26.930976 14080 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 01:20:28.940116 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 01:20:30.341794 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 01:20:31.379361 14080 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 01:20:31.379388 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:20:32.285444 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:20:36.012775 14080 solver.cpp:397]     Test net output #0: accuracy = 0.553309
I0410 01:20:36.012820 14080 solver.cpp:397]     Test net output #1: loss = 2.39915 (* 1 = 2.39915 loss)
I0410 01:20:37.871084 14080 solver.cpp:218] Iteration 9084 (1.09693 iter/s, 10.9397s/12 iters), loss = 0.0262528
I0410 01:20:37.871147 14080 solver.cpp:237]     Train net output #0: loss = 0.0262529 (* 1 = 0.0262529 loss)
I0410 01:20:37.871160 14080 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 01:20:42.779979 14080 solver.cpp:218] Iteration 9096 (2.44468 iter/s, 4.90862s/12 iters), loss = 0.167748
I0410 01:20:42.780028 14080 solver.cpp:237]     Train net output #0: loss = 0.167748 (* 1 = 0.167748 loss)
I0410 01:20:42.780040 14080 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 01:20:45.665491 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:20:47.670580 14080 solver.cpp:218] Iteration 9108 (2.45382 iter/s, 4.89034s/12 iters), loss = 0.150451
I0410 01:20:47.670631 14080 solver.cpp:237]     Train net output #0: loss = 0.150452 (* 1 = 0.150452 loss)
I0410 01:20:47.670644 14080 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 01:20:52.891230 14080 solver.cpp:218] Iteration 9120 (2.29869 iter/s, 5.22038s/12 iters), loss = 0.11216
I0410 01:20:52.891309 14080 solver.cpp:237]     Train net output #0: loss = 0.11216 (* 1 = 0.11216 loss)
I0410 01:20:52.891320 14080 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 01:20:57.768957 14080 solver.cpp:218] Iteration 9132 (2.46031 iter/s, 4.87743s/12 iters), loss = 0.096714
I0410 01:20:57.769011 14080 solver.cpp:237]     Train net output #0: loss = 0.0967141 (* 1 = 0.0967141 loss)
I0410 01:20:57.769022 14080 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 01:21:02.843144 14080 solver.cpp:218] Iteration 9144 (2.36504 iter/s, 5.07391s/12 iters), loss = 0.0694132
I0410 01:21:02.843215 14080 solver.cpp:237]     Train net output #0: loss = 0.0694132 (* 1 = 0.0694132 loss)
I0410 01:21:02.843230 14080 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 01:21:07.770582 14080 solver.cpp:218] Iteration 9156 (2.43549 iter/s, 4.92715s/12 iters), loss = 0.0961121
I0410 01:21:07.770643 14080 solver.cpp:237]     Train net output #0: loss = 0.0961122 (* 1 = 0.0961122 loss)
I0410 01:21:07.770654 14080 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 01:21:12.653322 14080 solver.cpp:218] Iteration 9168 (2.45777 iter/s, 4.88247s/12 iters), loss = 0.052443
I0410 01:21:12.653383 14080 solver.cpp:237]     Train net output #0: loss = 0.0524431 (* 1 = 0.0524431 loss)
I0410 01:21:12.653395 14080 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 01:21:17.083220 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 01:21:19.944046 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 01:21:20.993629 14080 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 01:21:20.993656 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:21:21.868369 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:21:25.558053 14080 solver.cpp:397]     Test net output #0: accuracy = 0.548407
I0410 01:21:25.558188 14080 solver.cpp:397]     Test net output #1: loss = 2.45894 (* 1 = 2.45894 loss)
I0410 01:21:25.644227 14080 solver.cpp:218] Iteration 9180 (0.923766 iter/s, 12.9903s/12 iters), loss = 0.131837
I0410 01:21:25.644271 14080 solver.cpp:237]     Train net output #0: loss = 0.131837 (* 1 = 0.131837 loss)
I0410 01:21:25.644284 14080 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 01:21:29.844712 14080 solver.cpp:218] Iteration 9192 (2.85697 iter/s, 4.20026s/12 iters), loss = 0.0898979
I0410 01:21:29.844767 14080 solver.cpp:237]     Train net output #0: loss = 0.089898 (* 1 = 0.089898 loss)
I0410 01:21:29.844779 14080 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 01:21:34.738656 14080 solver.cpp:218] Iteration 9204 (2.45214 iter/s, 4.89368s/12 iters), loss = 0.0346712
I0410 01:21:34.738703 14080 solver.cpp:237]     Train net output #0: loss = 0.0346713 (* 1 = 0.0346713 loss)
I0410 01:21:34.738711 14080 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 01:21:34.806346 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:21:39.609948 14080 solver.cpp:218] Iteration 9216 (2.46355 iter/s, 4.87102s/12 iters), loss = 0.0236587
I0410 01:21:39.610033 14080 solver.cpp:237]     Train net output #0: loss = 0.0236588 (* 1 = 0.0236588 loss)
I0410 01:21:39.610047 14080 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 01:21:44.511137 14080 solver.cpp:218] Iteration 9228 (2.44853 iter/s, 4.9009s/12 iters), loss = 0.12493
I0410 01:21:44.511176 14080 solver.cpp:237]     Train net output #0: loss = 0.124931 (* 1 = 0.124931 loss)
I0410 01:21:44.511185 14080 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 01:21:49.401006 14080 solver.cpp:218] Iteration 9240 (2.45418 iter/s, 4.88961s/12 iters), loss = 0.0794383
I0410 01:21:49.401064 14080 solver.cpp:237]     Train net output #0: loss = 0.0794384 (* 1 = 0.0794384 loss)
I0410 01:21:49.401077 14080 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 01:21:54.268157 14080 solver.cpp:218] Iteration 9252 (2.46564 iter/s, 4.86689s/12 iters), loss = 0.0887286
I0410 01:21:54.268198 14080 solver.cpp:237]     Train net output #0: loss = 0.0887287 (* 1 = 0.0887287 loss)
I0410 01:21:54.268208 14080 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 01:21:59.127983 14080 solver.cpp:218] Iteration 9264 (2.46935 iter/s, 4.85957s/12 iters), loss = 0.154515
I0410 01:21:59.128147 14080 solver.cpp:237]     Train net output #0: loss = 0.154516 (* 1 = 0.154516 loss)
I0410 01:21:59.128157 14080 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 01:22:04.376858 14080 solver.cpp:218] Iteration 9276 (2.28638 iter/s, 5.24848s/12 iters), loss = 0.0744317
I0410 01:22:04.376914 14080 solver.cpp:237]     Train net output #0: loss = 0.0744318 (* 1 = 0.0744318 loss)
I0410 01:22:04.376927 14080 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 01:22:06.361703 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 01:22:08.428093 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 01:22:10.597219 14080 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 01:22:10.597249 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:22:11.592797 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:22:15.394353 14080 solver.cpp:397]     Test net output #0: accuracy = 0.555147
I0410 01:22:15.394402 14080 solver.cpp:397]     Test net output #1: loss = 2.50204 (* 1 = 2.50204 loss)
I0410 01:22:17.318677 14080 solver.cpp:218] Iteration 9288 (0.92727 iter/s, 12.9412s/12 iters), loss = 0.106055
I0410 01:22:17.318742 14080 solver.cpp:237]     Train net output #0: loss = 0.106055 (* 1 = 0.106055 loss)
I0410 01:22:17.318755 14080 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 01:22:22.221422 14080 solver.cpp:218] Iteration 9300 (2.44774 iter/s, 4.90247s/12 iters), loss = 0.0242883
I0410 01:22:22.221462 14080 solver.cpp:237]     Train net output #0: loss = 0.0242884 (* 1 = 0.0242884 loss)
I0410 01:22:22.221472 14080 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 01:22:24.378641 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:22:27.098872 14080 solver.cpp:218] Iteration 9312 (2.46043 iter/s, 4.87719s/12 iters), loss = 0.0944247
I0410 01:22:27.098925 14080 solver.cpp:237]     Train net output #0: loss = 0.0944248 (* 1 = 0.0944248 loss)
I0410 01:22:27.098937 14080 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 01:22:32.197095 14080 solver.cpp:218] Iteration 9324 (2.35389 iter/s, 5.09795s/12 iters), loss = 0.0780026
I0410 01:22:32.197254 14080 solver.cpp:237]     Train net output #0: loss = 0.0780026 (* 1 = 0.0780026 loss)
I0410 01:22:32.197268 14080 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 01:22:37.051077 14080 solver.cpp:218] Iteration 9336 (2.47238 iter/s, 4.85362s/12 iters), loss = 0.0378472
I0410 01:22:37.051132 14080 solver.cpp:237]     Train net output #0: loss = 0.0378473 (* 1 = 0.0378473 loss)
I0410 01:22:37.051144 14080 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 01:22:41.880947 14080 solver.cpp:218] Iteration 9348 (2.48468 iter/s, 4.8296s/12 iters), loss = 0.147786
I0410 01:22:41.881007 14080 solver.cpp:237]     Train net output #0: loss = 0.147786 (* 1 = 0.147786 loss)
I0410 01:22:41.881021 14080 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 01:22:46.865473 14080 solver.cpp:218] Iteration 9360 (2.40759 iter/s, 4.98424s/12 iters), loss = 0.0155777
I0410 01:22:46.865532 14080 solver.cpp:237]     Train net output #0: loss = 0.0155778 (* 1 = 0.0155778 loss)
I0410 01:22:46.865546 14080 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 01:22:51.792250 14080 solver.cpp:218] Iteration 9372 (2.43581 iter/s, 4.9265s/12 iters), loss = 0.125312
I0410 01:22:51.792307 14080 solver.cpp:237]     Train net output #0: loss = 0.125312 (* 1 = 0.125312 loss)
I0410 01:22:51.792320 14080 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 01:22:56.299836 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 01:22:57.654490 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 01:22:58.682453 14080 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 01:22:58.682473 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:22:59.520156 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:23:03.352490 14080 solver.cpp:397]     Test net output #0: accuracy = 0.555147
I0410 01:23:03.352625 14080 solver.cpp:397]     Test net output #1: loss = 2.44148 (* 1 = 2.44148 loss)
I0410 01:23:03.441977 14080 solver.cpp:218] Iteration 9384 (1.03012 iter/s, 11.6492s/12 iters), loss = 0.11655
I0410 01:23:03.442045 14080 solver.cpp:237]     Train net output #0: loss = 0.11655 (* 1 = 0.11655 loss)
I0410 01:23:03.442059 14080 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 01:23:07.511085 14080 solver.cpp:218] Iteration 9396 (2.94923 iter/s, 4.06886s/12 iters), loss = 0.0736363
I0410 01:23:07.511148 14080 solver.cpp:237]     Train net output #0: loss = 0.0736364 (* 1 = 0.0736364 loss)
I0410 01:23:07.511162 14080 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 01:23:11.802904 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:23:12.452598 14080 solver.cpp:218] Iteration 9408 (2.42854 iter/s, 4.94123s/12 iters), loss = 0.0742946
I0410 01:23:12.452657 14080 solver.cpp:237]     Train net output #0: loss = 0.0742947 (* 1 = 0.0742947 loss)
I0410 01:23:12.452669 14080 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 01:23:17.420459 14080 solver.cpp:218] Iteration 9420 (2.41566 iter/s, 4.96759s/12 iters), loss = 0.0812428
I0410 01:23:17.420501 14080 solver.cpp:237]     Train net output #0: loss = 0.0812429 (* 1 = 0.0812429 loss)
I0410 01:23:17.420511 14080 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 01:23:22.373486 14080 solver.cpp:218] Iteration 9432 (2.42289 iter/s, 4.95276s/12 iters), loss = 0.0549466
I0410 01:23:22.373543 14080 solver.cpp:237]     Train net output #0: loss = 0.0549467 (* 1 = 0.0549467 loss)
I0410 01:23:22.373555 14080 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 01:23:27.369115 14080 solver.cpp:218] Iteration 9444 (2.40223 iter/s, 4.99536s/12 iters), loss = 0.0485814
I0410 01:23:27.369163 14080 solver.cpp:237]     Train net output #0: loss = 0.0485815 (* 1 = 0.0485815 loss)
I0410 01:23:27.369174 14080 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 01:23:32.237834 14080 solver.cpp:218] Iteration 9456 (2.46484 iter/s, 4.86846s/12 iters), loss = 0.0739622
I0410 01:23:32.237882 14080 solver.cpp:237]     Train net output #0: loss = 0.0739623 (* 1 = 0.0739623 loss)
I0410 01:23:32.237891 14080 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 01:23:37.088709 14080 solver.cpp:218] Iteration 9468 (2.47392 iter/s, 4.85061s/12 iters), loss = 0.0353306
I0410 01:23:37.089406 14080 solver.cpp:237]     Train net output #0: loss = 0.0353307 (* 1 = 0.0353307 loss)
I0410 01:23:37.089418 14080 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 01:23:42.015211 14080 solver.cpp:218] Iteration 9480 (2.43625 iter/s, 4.92559s/12 iters), loss = 0.0524814
I0410 01:23:42.015262 14080 solver.cpp:237]     Train net output #0: loss = 0.0524815 (* 1 = 0.0524815 loss)
I0410 01:23:42.015272 14080 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 01:23:44.022153 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 01:23:46.488945 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 01:23:48.646092 14080 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 01:23:48.646128 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:23:49.378947 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:23:53.101438 14080 solver.cpp:397]     Test net output #0: accuracy = 0.560662
I0410 01:23:53.101495 14080 solver.cpp:397]     Test net output #1: loss = 2.48581 (* 1 = 2.48581 loss)
I0410 01:23:55.032085 14080 solver.cpp:218] Iteration 9492 (0.921923 iter/s, 13.0163s/12 iters), loss = 0.184857
I0410 01:23:55.032135 14080 solver.cpp:237]     Train net output #0: loss = 0.184857 (* 1 = 0.184857 loss)
I0410 01:23:55.032145 14080 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 01:23:59.936866 14080 solver.cpp:218] Iteration 9504 (2.44673 iter/s, 4.90451s/12 iters), loss = 0.0332462
I0410 01:23:59.936919 14080 solver.cpp:237]     Train net output #0: loss = 0.0332463 (* 1 = 0.0332463 loss)
I0410 01:23:59.936930 14080 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 01:24:01.367400 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:24:04.881181 14080 solver.cpp:218] Iteration 9516 (2.42716 iter/s, 4.94405s/12 iters), loss = 0.0553003
I0410 01:24:04.881237 14080 solver.cpp:237]     Train net output #0: loss = 0.0553004 (* 1 = 0.0553004 loss)
I0410 01:24:04.881250 14080 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 01:24:09.709677 14080 solver.cpp:218] Iteration 9528 (2.48539 iter/s, 4.82823s/12 iters), loss = 0.129524
I0410 01:24:09.709812 14080 solver.cpp:237]     Train net output #0: loss = 0.129524 (* 1 = 0.129524 loss)
I0410 01:24:09.709825 14080 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 01:24:14.643630 14080 solver.cpp:218] Iteration 9540 (2.4323 iter/s, 4.93361s/12 iters), loss = 0.0366372
I0410 01:24:14.643671 14080 solver.cpp:237]     Train net output #0: loss = 0.0366373 (* 1 = 0.0366373 loss)
I0410 01:24:14.643678 14080 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 01:24:19.829185 14080 solver.cpp:218] Iteration 9552 (2.31424 iter/s, 5.18528s/12 iters), loss = 0.243852
I0410 01:24:19.829243 14080 solver.cpp:237]     Train net output #0: loss = 0.243852 (* 1 = 0.243852 loss)
I0410 01:24:19.829255 14080 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 01:24:24.712551 14080 solver.cpp:218] Iteration 9564 (2.45746 iter/s, 4.88309s/12 iters), loss = 0.0573821
I0410 01:24:24.712612 14080 solver.cpp:237]     Train net output #0: loss = 0.0573822 (* 1 = 0.0573822 loss)
I0410 01:24:24.712625 14080 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 01:24:29.813398 14080 solver.cpp:218] Iteration 9576 (2.35268 iter/s, 5.10056s/12 iters), loss = 0.075705
I0410 01:24:29.813447 14080 solver.cpp:237]     Train net output #0: loss = 0.0757051 (* 1 = 0.0757051 loss)
I0410 01:24:29.813457 14080 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 01:24:34.259450 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 01:24:35.676442 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 01:24:36.715270 14080 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 01:24:36.715296 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:24:37.403368 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:24:41.159966 14080 solver.cpp:397]     Test net output #0: accuracy = 0.556373
I0410 01:24:41.160054 14080 solver.cpp:397]     Test net output #1: loss = 2.4335 (* 1 = 2.4335 loss)
I0410 01:24:41.246065 14080 solver.cpp:218] Iteration 9588 (1.04967 iter/s, 11.4321s/12 iters), loss = 0.0953732
I0410 01:24:41.246117 14080 solver.cpp:237]     Train net output #0: loss = 0.0953734 (* 1 = 0.0953734 loss)
I0410 01:24:41.246129 14080 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 01:24:45.398972 14080 solver.cpp:218] Iteration 9600 (2.88971 iter/s, 4.15267s/12 iters), loss = 0.0984672
I0410 01:24:45.399024 14080 solver.cpp:237]     Train net output #0: loss = 0.0984674 (* 1 = 0.0984674 loss)
I0410 01:24:45.399037 14080 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 01:24:48.908890 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:24:50.262316 14080 solver.cpp:218] Iteration 9612 (2.46757 iter/s, 4.86308s/12 iters), loss = 0.0629295
I0410 01:24:50.262378 14080 solver.cpp:237]     Train net output #0: loss = 0.0629296 (* 1 = 0.0629296 loss)
I0410 01:24:50.262392 14080 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 01:24:55.187238 14080 solver.cpp:218] Iteration 9624 (2.43672 iter/s, 4.92464s/12 iters), loss = 0.155358
I0410 01:24:55.187294 14080 solver.cpp:237]     Train net output #0: loss = 0.155358 (* 1 = 0.155358 loss)
I0410 01:24:55.187307 14080 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 01:25:00.024796 14080 solver.cpp:218] Iteration 9636 (2.48073 iter/s, 4.83729s/12 iters), loss = 0.0781322
I0410 01:25:00.024855 14080 solver.cpp:237]     Train net output #0: loss = 0.0781323 (* 1 = 0.0781323 loss)
I0410 01:25:00.024868 14080 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 01:25:05.035043 14080 solver.cpp:218] Iteration 9648 (2.39522 iter/s, 5.00997s/12 iters), loss = 0.0976754
I0410 01:25:05.035096 14080 solver.cpp:237]     Train net output #0: loss = 0.0976755 (* 1 = 0.0976755 loss)
I0410 01:25:05.035109 14080 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 01:25:09.911947 14080 solver.cpp:218] Iteration 9660 (2.46071 iter/s, 4.87664s/12 iters), loss = 0.0858325
I0410 01:25:09.912004 14080 solver.cpp:237]     Train net output #0: loss = 0.0858325 (* 1 = 0.0858325 loss)
I0410 01:25:09.912019 14080 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 01:25:14.867671 14080 solver.cpp:218] Iteration 9672 (2.42157 iter/s, 4.95546s/12 iters), loss = 0.101093
I0410 01:25:14.867782 14080 solver.cpp:237]     Train net output #0: loss = 0.101093 (* 1 = 0.101093 loss)
I0410 01:25:14.867792 14080 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 01:25:19.769361 14080 solver.cpp:218] Iteration 9684 (2.4483 iter/s, 4.90136s/12 iters), loss = 0.079644
I0410 01:25:19.769418 14080 solver.cpp:237]     Train net output #0: loss = 0.0796441 (* 1 = 0.0796441 loss)
I0410 01:25:19.769430 14080 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 01:25:21.767832 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 01:25:26.161594 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 01:25:28.281286 14080 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 01:25:28.281312 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:25:28.926782 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:25:31.665807 14080 blocking_queue.cpp:49] Waiting for data
I0410 01:25:32.930836 14080 solver.cpp:397]     Test net output #0: accuracy = 0.549632
I0410 01:25:32.930886 14080 solver.cpp:397]     Test net output #1: loss = 2.50396 (* 1 = 2.50396 loss)
I0410 01:25:34.751076 14080 solver.cpp:218] Iteration 9696 (0.801013 iter/s, 14.981s/12 iters), loss = 0.0453226
I0410 01:25:34.751124 14080 solver.cpp:237]     Train net output #0: loss = 0.0453227 (* 1 = 0.0453227 loss)
I0410 01:25:34.751133 14080 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 01:25:39.895450 14080 solver.cpp:218] Iteration 9708 (2.33277 iter/s, 5.1441s/12 iters), loss = 0.0286475
I0410 01:25:39.895498 14080 solver.cpp:237]     Train net output #0: loss = 0.0286476 (* 1 = 0.0286476 loss)
I0410 01:25:39.895509 14080 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 01:25:40.695861 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:25:45.045236 14080 solver.cpp:218] Iteration 9720 (2.33032 iter/s, 5.14952s/12 iters), loss = 0.0756126
I0410 01:25:45.045308 14080 solver.cpp:237]     Train net output #0: loss = 0.0756126 (* 1 = 0.0756126 loss)
I0410 01:25:45.045317 14080 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 01:25:49.966919 14080 solver.cpp:218] Iteration 9732 (2.43833 iter/s, 4.9214s/12 iters), loss = 0.0353675
I0410 01:25:49.966966 14080 solver.cpp:237]     Train net output #0: loss = 0.0353676 (* 1 = 0.0353676 loss)
I0410 01:25:49.966977 14080 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 01:25:54.849486 14080 solver.cpp:218] Iteration 9744 (2.45786 iter/s, 4.8823s/12 iters), loss = 0.0263113
I0410 01:25:54.849541 14080 solver.cpp:237]     Train net output #0: loss = 0.0263114 (* 1 = 0.0263114 loss)
I0410 01:25:54.849553 14080 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 01:25:59.669698 14080 solver.cpp:218] Iteration 9756 (2.48966 iter/s, 4.81994s/12 iters), loss = 0.0503464
I0410 01:25:59.669761 14080 solver.cpp:237]     Train net output #0: loss = 0.0503464 (* 1 = 0.0503464 loss)
I0410 01:25:59.669775 14080 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 01:26:04.741075 14080 solver.cpp:218] Iteration 9768 (2.36635 iter/s, 5.07109s/12 iters), loss = 0.108481
I0410 01:26:04.741122 14080 solver.cpp:237]     Train net output #0: loss = 0.108481 (* 1 = 0.108481 loss)
I0410 01:26:04.741132 14080 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 01:26:09.659446 14080 solver.cpp:218] Iteration 9780 (2.43996 iter/s, 4.9181s/12 iters), loss = 0.113593
I0410 01:26:09.659490 14080 solver.cpp:237]     Train net output #0: loss = 0.113593 (* 1 = 0.113593 loss)
I0410 01:26:09.659499 14080 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 01:26:14.245996 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 01:26:20.303912 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 01:26:24.520714 14080 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 01:26:24.520735 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:26:25.128803 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:26:28.977768 14080 solver.cpp:397]     Test net output #0: accuracy = 0.553922
I0410 01:26:28.977816 14080 solver.cpp:397]     Test net output #1: loss = 2.47972 (* 1 = 2.47972 loss)
I0410 01:26:29.063601 14080 solver.cpp:218] Iteration 9792 (0.618451 iter/s, 19.4033s/12 iters), loss = 0.0655506
I0410 01:26:29.063657 14080 solver.cpp:237]     Train net output #0: loss = 0.0655506 (* 1 = 0.0655506 loss)
I0410 01:26:29.063669 14080 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 01:26:33.206466 14080 solver.cpp:218] Iteration 9804 (2.89671 iter/s, 4.14262s/12 iters), loss = 0.0190533
I0410 01:26:33.206526 14080 solver.cpp:237]     Train net output #0: loss = 0.0190534 (* 1 = 0.0190534 loss)
I0410 01:26:33.206538 14080 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 01:26:36.124805 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:26:38.112419 14080 solver.cpp:218] Iteration 9816 (2.44614 iter/s, 4.90568s/12 iters), loss = 0.0330355
I0410 01:26:38.112473 14080 solver.cpp:237]     Train net output #0: loss = 0.0330355 (* 1 = 0.0330355 loss)
I0410 01:26:38.112485 14080 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 01:26:42.997484 14080 solver.cpp:218] Iteration 9828 (2.4566 iter/s, 4.8848s/12 iters), loss = 0.0382707
I0410 01:26:42.997542 14080 solver.cpp:237]     Train net output #0: loss = 0.0382708 (* 1 = 0.0382708 loss)
I0410 01:26:42.997555 14080 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 01:26:47.835507 14080 solver.cpp:218] Iteration 9840 (2.48049 iter/s, 4.83775s/12 iters), loss = 0.0452951
I0410 01:26:47.835567 14080 solver.cpp:237]     Train net output #0: loss = 0.0452952 (* 1 = 0.0452952 loss)
I0410 01:26:47.835579 14080 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 01:26:52.726855 14080 solver.cpp:218] Iteration 9852 (2.45345 iter/s, 4.89108s/12 iters), loss = 0.0321178
I0410 01:26:52.726920 14080 solver.cpp:237]     Train net output #0: loss = 0.0321179 (* 1 = 0.0321179 loss)
I0410 01:26:52.726930 14080 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 01:26:57.737123 14080 solver.cpp:218] Iteration 9864 (2.39522 iter/s, 5.00998s/12 iters), loss = 0.06177
I0410 01:26:57.737195 14080 solver.cpp:237]     Train net output #0: loss = 0.0617701 (* 1 = 0.0617701 loss)
I0410 01:26:57.737210 14080 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 01:27:02.595810 14080 solver.cpp:218] Iteration 9876 (2.46994 iter/s, 4.85841s/12 iters), loss = 0.0220065
I0410 01:27:02.595858 14080 solver.cpp:237]     Train net output #0: loss = 0.0220065 (* 1 = 0.0220065 loss)
I0410 01:27:02.595870 14080 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 01:27:07.578327 14080 solver.cpp:218] Iteration 9888 (2.40855 iter/s, 4.98225s/12 iters), loss = 0.0708661
I0410 01:27:07.578378 14080 solver.cpp:237]     Train net output #0: loss = 0.0708662 (* 1 = 0.0708662 loss)
I0410 01:27:07.578388 14080 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 01:27:09.649596 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 01:27:13.516449 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 01:27:17.746409 14080 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 01:27:17.746438 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:27:18.365850 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:27:22.330117 14080 solver.cpp:397]     Test net output #0: accuracy = 0.556373
I0410 01:27:22.330161 14080 solver.cpp:397]     Test net output #1: loss = 2.50271 (* 1 = 2.50271 loss)
I0410 01:27:24.203938 14080 solver.cpp:218] Iteration 9900 (0.72181 iter/s, 16.6249s/12 iters), loss = 0.0739422
I0410 01:27:24.204048 14080 solver.cpp:237]     Train net output #0: loss = 0.0739423 (* 1 = 0.0739423 loss)
I0410 01:27:24.204061 14080 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 01:27:29.171046 14080 solver.cpp:218] Iteration 9912 (2.41605 iter/s, 4.96678s/12 iters), loss = 0.0568576
I0410 01:27:29.171103 14080 solver.cpp:237]     Train net output #0: loss = 0.0568577 (* 1 = 0.0568577 loss)
I0410 01:27:29.171114 14080 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 01:27:29.271471 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:27:34.151898 14080 solver.cpp:218] Iteration 9924 (2.40936 iter/s, 4.98058s/12 iters), loss = 0.0359792
I0410 01:27:34.151943 14080 solver.cpp:237]     Train net output #0: loss = 0.0359793 (* 1 = 0.0359793 loss)
I0410 01:27:34.151952 14080 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 01:27:39.043134 14080 solver.cpp:218] Iteration 9936 (2.4535 iter/s, 4.89097s/12 iters), loss = 0.099314
I0410 01:27:39.043193 14080 solver.cpp:237]     Train net output #0: loss = 0.0993141 (* 1 = 0.0993141 loss)
I0410 01:27:39.043205 14080 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 01:27:44.039469 14080 solver.cpp:218] Iteration 9948 (2.40189 iter/s, 4.99606s/12 iters), loss = 0.0267046
I0410 01:27:44.039535 14080 solver.cpp:237]     Train net output #0: loss = 0.0267047 (* 1 = 0.0267047 loss)
I0410 01:27:44.039549 14080 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 01:27:49.173379 14080 solver.cpp:218] Iteration 9960 (2.33753 iter/s, 5.13363s/12 iters), loss = 0.121596
I0410 01:27:49.173424 14080 solver.cpp:237]     Train net output #0: loss = 0.121596 (* 1 = 0.121596 loss)
I0410 01:27:49.173431 14080 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 01:27:54.084502 14080 solver.cpp:218] Iteration 9972 (2.44356 iter/s, 4.91087s/12 iters), loss = 0.0366563
I0410 01:27:54.084540 14080 solver.cpp:237]     Train net output #0: loss = 0.0366564 (* 1 = 0.0366564 loss)
I0410 01:27:54.084548 14080 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 01:27:59.046432 14080 solver.cpp:218] Iteration 9984 (2.41854 iter/s, 4.96167s/12 iters), loss = 0.0626421
I0410 01:27:59.046528 14080 solver.cpp:237]     Train net output #0: loss = 0.0626421 (* 1 = 0.0626421 loss)
I0410 01:27:59.046538 14080 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 01:28:03.532725 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 01:28:06.727882 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 01:28:08.066282 14080 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 01:28:08.066303 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:28:08.558313 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:28:12.525950 14080 solver.cpp:397]     Test net output #0: accuracy = 0.561275
I0410 01:28:12.526013 14080 solver.cpp:397]     Test net output #1: loss = 2.45033 (* 1 = 2.45033 loss)
I0410 01:28:12.611877 14080 solver.cpp:218] Iteration 9996 (0.884644 iter/s, 13.5648s/12 iters), loss = 0.0669966
I0410 01:28:12.611922 14080 solver.cpp:237]     Train net output #0: loss = 0.0669967 (* 1 = 0.0669967 loss)
I0410 01:28:12.611929 14080 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 01:28:16.811985 14080 solver.cpp:218] Iteration 10008 (2.85723 iter/s, 4.19987s/12 iters), loss = 0.0464792
I0410 01:28:16.812038 14080 solver.cpp:237]     Train net output #0: loss = 0.0464793 (* 1 = 0.0464793 loss)
I0410 01:28:16.812050 14080 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 01:28:19.156090 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:28:22.032296 14080 solver.cpp:218] Iteration 10020 (2.29884 iter/s, 5.22003s/12 iters), loss = 0.0745108
I0410 01:28:22.032353 14080 solver.cpp:237]     Train net output #0: loss = 0.0745109 (* 1 = 0.0745109 loss)
I0410 01:28:22.032366 14080 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 01:28:26.977455 14080 solver.cpp:218] Iteration 10032 (2.42675 iter/s, 4.94489s/12 iters), loss = 0.0635061
I0410 01:28:26.977511 14080 solver.cpp:237]     Train net output #0: loss = 0.0635062 (* 1 = 0.0635062 loss)
I0410 01:28:26.977524 14080 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 01:28:31.963471 14080 solver.cpp:218] Iteration 10044 (2.40686 iter/s, 4.98574s/12 iters), loss = 0.202103
I0410 01:28:31.965788 14080 solver.cpp:237]     Train net output #0: loss = 0.202103 (* 1 = 0.202103 loss)
I0410 01:28:31.965798 14080 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 01:28:36.856719 14080 solver.cpp:218] Iteration 10056 (2.45363 iter/s, 4.89072s/12 iters), loss = 0.0353245
I0410 01:28:36.856775 14080 solver.cpp:237]     Train net output #0: loss = 0.0353246 (* 1 = 0.0353246 loss)
I0410 01:28:36.856787 14080 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 01:28:41.782606 14080 solver.cpp:218] Iteration 10068 (2.43624 iter/s, 4.92562s/12 iters), loss = 0.0527206
I0410 01:28:41.782665 14080 solver.cpp:237]     Train net output #0: loss = 0.0527207 (* 1 = 0.0527207 loss)
I0410 01:28:41.782677 14080 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 01:28:46.701485 14080 solver.cpp:218] Iteration 10080 (2.43972 iter/s, 4.9186s/12 iters), loss = 0.0556034
I0410 01:28:46.701545 14080 solver.cpp:237]     Train net output #0: loss = 0.0556035 (* 1 = 0.0556035 loss)
I0410 01:28:46.701557 14080 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 01:28:51.685670 14080 solver.cpp:218] Iteration 10092 (2.40775 iter/s, 4.9839s/12 iters), loss = 0.0972886
I0410 01:28:51.685745 14080 solver.cpp:237]     Train net output #0: loss = 0.0972887 (* 1 = 0.0972887 loss)
I0410 01:28:51.685761 14080 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 01:28:53.702842 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 01:28:55.089440 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 01:28:56.167587 14080 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 01:28:56.167611 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:28:56.617535 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:29:00.684221 14080 solver.cpp:397]     Test net output #0: accuracy = 0.568015
I0410 01:29:00.684270 14080 solver.cpp:397]     Test net output #1: loss = 2.39347 (* 1 = 2.39347 loss)
I0410 01:29:02.600447 14080 solver.cpp:218] Iteration 10104 (1.09948 iter/s, 10.9142s/12 iters), loss = 0.0609491
I0410 01:29:02.600562 14080 solver.cpp:237]     Train net output #0: loss = 0.0609492 (* 1 = 0.0609492 loss)
I0410 01:29:02.600575 14080 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 01:29:06.880654 14084 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:29:07.506808 14080 solver.cpp:218] Iteration 10116 (2.44597 iter/s, 4.90603s/12 iters), loss = 0.0685291
I0410 01:29:07.506865 14080 solver.cpp:237]     Train net output #0: loss = 0.0685292 (* 1 = 0.0685292 loss)
I0410 01:29:07.506877 14080 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 01:29:12.412540 14080 solver.cpp:218] Iteration 10128 (2.44625 iter/s, 4.90546s/12 iters), loss = 0.0283969
I0410 01:29:12.412596 14080 solver.cpp:237]     Train net output #0: loss = 0.028397 (* 1 = 0.028397 loss)
I0410 01:29:12.412608 14080 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 01:29:17.307344 14080 solver.cpp:218] Iteration 10140 (2.45171 iter/s, 4.89454s/12 iters), loss = 0.0611065
I0410 01:29:17.307385 14080 solver.cpp:237]     Train net output #0: loss = 0.0611066 (* 1 = 0.0611066 loss)
I0410 01:29:17.307394 14080 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 01:29:22.153795 14080 solver.cpp:218] Iteration 10152 (2.47617 iter/s, 4.84619s/12 iters), loss = 0.0569566
I0410 01:29:22.153868 14080 solver.cpp:237]     Train net output #0: loss = 0.0569566 (* 1 = 0.0569566 loss)
I0410 01:29:22.153887 14080 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 01:29:27.035660 14080 solver.cpp:218] Iteration 10164 (2.45822 iter/s, 4.88158s/12 iters), loss = 0.0673052
I0410 01:29:27.035727 14080 solver.cpp:237]     Train net output #0: loss = 0.0673053 (* 1 = 0.0673053 loss)
I0410 01:29:27.035742 14080 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 01:29:31.915607 14080 solver.cpp:218] Iteration 10176 (2.45918 iter/s, 4.87967s/12 iters), loss = 0.034073
I0410 01:29:31.915658 14080 solver.cpp:237]     Train net output #0: loss = 0.0340731 (* 1 = 0.0340731 loss)
I0410 01:29:31.915669 14080 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 01:29:36.810885 14080 solver.cpp:218] Iteration 10188 (2.45147 iter/s, 4.89502s/12 iters), loss = 0.0325832
I0410 01:29:36.811024 14080 solver.cpp:237]     Train net output #0: loss = 0.0325832 (* 1 = 0.0325832 loss)
I0410 01:29:36.811033 14080 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 01:29:41.193112 14080 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 01:29:43.666874 14080 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 01:29:45.835183 14080 solver.cpp:310] Iteration 10200, loss = 0.0747256
I0410 01:29:45.835219 14080 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 01:29:45.835227 14080 net.cpp:676] Ignoring source layer train-data
I0410 01:29:46.261745 14085 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:29:50.375548 14080 solver.cpp:397]     Test net output #0: accuracy = 0.567402
I0410 01:29:50.375583 14080 solver.cpp:397]     Test net output #1: loss = 2.38007 (* 1 = 2.38007 loss)
I0410 01:29:50.375592 14080 solver.cpp:315] Optimization Done.
I0410 01:29:50.375597 14080 caffe.cpp:259] Optimization Done.