DIGITS-CNN/cars/architecture-investigations/fc/0-layer/caffe_output.log

I0410 00:23:15.007177 16216 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-220554-d871/solver.prototxt
I0410 00:23:15.007323 16216 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0410 00:23:15.007329 16216 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0410 00:23:15.007390 16216 caffe.cpp:218] Using GPUs 1
I0410 00:23:15.019804 16216 caffe.cpp:223] GPU 1: GeForce GTX 1080 Ti
I0410 00:23:15.296703 16216 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: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0410 00:23:15.297412 16216 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0410 00:23:15.297926 16216 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0410 00:23:15.297940 16216 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0410 00:23:15.298079 16216 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: "fc8"
type: "InnerProduct"
bottom: "pool5"
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 00:23:15.298159 16216 layer_factory.hpp:77] Creating layer train-data
I0410 00:23:15.299744 16216 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0410 00:23:15.299952 16216 net.cpp:84] Creating Layer train-data
I0410 00:23:15.299962 16216 net.cpp:380] train-data -> data
I0410 00:23:15.299981 16216 net.cpp:380] train-data -> label
I0410 00:23:15.299993 16216 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 00:23:15.304729 16216 data_layer.cpp:45] output data size: 128,3,227,227
I0410 00:23:15.437470 16216 net.cpp:122] Setting up train-data
I0410 00:23:15.437498 16216 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0410 00:23:15.437505 16216 net.cpp:129] Top shape: 128 (128)
I0410 00:23:15.437510 16216 net.cpp:137] Memory required for data: 79149056
I0410 00:23:15.437523 16216 layer_factory.hpp:77] Creating layer conv1
I0410 00:23:15.437548 16216 net.cpp:84] Creating Layer conv1
I0410 00:23:15.437556 16216 net.cpp:406] conv1 <- data
I0410 00:23:15.437570 16216 net.cpp:380] conv1 -> conv1
I0410 00:23:16.008185 16216 net.cpp:122] Setting up conv1
I0410 00:23:16.008208 16216 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 00:23:16.008213 16216 net.cpp:137] Memory required for data: 227833856
I0410 00:23:16.008231 16216 layer_factory.hpp:77] Creating layer relu1
I0410 00:23:16.008242 16216 net.cpp:84] Creating Layer relu1
I0410 00:23:16.008247 16216 net.cpp:406] relu1 <- conv1
I0410 00:23:16.008253 16216 net.cpp:367] relu1 -> conv1 (in-place)
I0410 00:23:16.008540 16216 net.cpp:122] Setting up relu1
I0410 00:23:16.008549 16216 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 00:23:16.008553 16216 net.cpp:137] Memory required for data: 376518656
I0410 00:23:16.008558 16216 layer_factory.hpp:77] Creating layer norm1
I0410 00:23:16.008566 16216 net.cpp:84] Creating Layer norm1
I0410 00:23:16.008570 16216 net.cpp:406] norm1 <- conv1
I0410 00:23:16.008575 16216 net.cpp:380] norm1 -> norm1
I0410 00:23:16.009016 16216 net.cpp:122] Setting up norm1
I0410 00:23:16.009027 16216 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 00:23:16.009032 16216 net.cpp:137] Memory required for data: 525203456
I0410 00:23:16.009035 16216 layer_factory.hpp:77] Creating layer pool1
I0410 00:23:16.009043 16216 net.cpp:84] Creating Layer pool1
I0410 00:23:16.009047 16216 net.cpp:406] pool1 <- norm1
I0410 00:23:16.009052 16216 net.cpp:380] pool1 -> pool1
I0410 00:23:16.009088 16216 net.cpp:122] Setting up pool1
I0410 00:23:16.009095 16216 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0410 00:23:16.009099 16216 net.cpp:137] Memory required for data: 561035264
I0410 00:23:16.009102 16216 layer_factory.hpp:77] Creating layer conv2
I0410 00:23:16.009111 16216 net.cpp:84] Creating Layer conv2
I0410 00:23:16.009115 16216 net.cpp:406] conv2 <- pool1
I0410 00:23:16.009121 16216 net.cpp:380] conv2 -> conv2
I0410 00:23:16.023756 16216 net.cpp:122] Setting up conv2
I0410 00:23:16.023773 16216 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 00:23:16.023798 16216 net.cpp:137] Memory required for data: 656586752
I0410 00:23:16.023810 16216 layer_factory.hpp:77] Creating layer relu2
I0410 00:23:16.023820 16216 net.cpp:84] Creating Layer relu2
I0410 00:23:16.023824 16216 net.cpp:406] relu2 <- conv2
I0410 00:23:16.023830 16216 net.cpp:367] relu2 -> conv2 (in-place)
I0410 00:23:16.024255 16216 net.cpp:122] Setting up relu2
I0410 00:23:16.024264 16216 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 00:23:16.024268 16216 net.cpp:137] Memory required for data: 752138240
I0410 00:23:16.024272 16216 layer_factory.hpp:77] Creating layer norm2
I0410 00:23:16.024279 16216 net.cpp:84] Creating Layer norm2
I0410 00:23:16.024283 16216 net.cpp:406] norm2 <- conv2
I0410 00:23:16.024289 16216 net.cpp:380] norm2 -> norm2
I0410 00:23:16.024644 16216 net.cpp:122] Setting up norm2
I0410 00:23:16.024654 16216 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 00:23:16.024657 16216 net.cpp:137] Memory required for data: 847689728
I0410 00:23:16.024662 16216 layer_factory.hpp:77] Creating layer pool2
I0410 00:23:16.024669 16216 net.cpp:84] Creating Layer pool2
I0410 00:23:16.024673 16216 net.cpp:406] pool2 <- norm2
I0410 00:23:16.024679 16216 net.cpp:380] pool2 -> pool2
I0410 00:23:16.024706 16216 net.cpp:122] Setting up pool2
I0410 00:23:16.024713 16216 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 00:23:16.024715 16216 net.cpp:137] Memory required for data: 869840896
I0410 00:23:16.024719 16216 layer_factory.hpp:77] Creating layer conv3
I0410 00:23:16.024729 16216 net.cpp:84] Creating Layer conv3
I0410 00:23:16.024734 16216 net.cpp:406] conv3 <- pool2
I0410 00:23:16.024739 16216 net.cpp:380] conv3 -> conv3
I0410 00:23:16.035297 16216 net.cpp:122] Setting up conv3
I0410 00:23:16.035315 16216 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:23:16.035317 16216 net.cpp:137] Memory required for data: 903067648
I0410 00:23:16.035327 16216 layer_factory.hpp:77] Creating layer relu3
I0410 00:23:16.035336 16216 net.cpp:84] Creating Layer relu3
I0410 00:23:16.035339 16216 net.cpp:406] relu3 <- conv3
I0410 00:23:16.035344 16216 net.cpp:367] relu3 -> conv3 (in-place)
I0410 00:23:16.035760 16216 net.cpp:122] Setting up relu3
I0410 00:23:16.035770 16216 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:23:16.035773 16216 net.cpp:137] Memory required for data: 936294400
I0410 00:23:16.035778 16216 layer_factory.hpp:77] Creating layer conv4
I0410 00:23:16.035786 16216 net.cpp:84] Creating Layer conv4
I0410 00:23:16.035790 16216 net.cpp:406] conv4 <- conv3
I0410 00:23:16.035796 16216 net.cpp:380] conv4 -> conv4
I0410 00:23:16.053678 16216 net.cpp:122] Setting up conv4
I0410 00:23:16.053697 16216 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:23:16.053701 16216 net.cpp:137] Memory required for data: 969521152
I0410 00:23:16.053710 16216 layer_factory.hpp:77] Creating layer relu4
I0410 00:23:16.053719 16216 net.cpp:84] Creating Layer relu4
I0410 00:23:16.053723 16216 net.cpp:406] relu4 <- conv4
I0410 00:23:16.053730 16216 net.cpp:367] relu4 -> conv4 (in-place)
I0410 00:23:16.054028 16216 net.cpp:122] Setting up relu4
I0410 00:23:16.054036 16216 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 00:23:16.054040 16216 net.cpp:137] Memory required for data: 1002747904
I0410 00:23:16.054044 16216 layer_factory.hpp:77] Creating layer conv5
I0410 00:23:16.054054 16216 net.cpp:84] Creating Layer conv5
I0410 00:23:16.054059 16216 net.cpp:406] conv5 <- conv4
I0410 00:23:16.054064 16216 net.cpp:380] conv5 -> conv5
I0410 00:23:16.062054 16216 net.cpp:122] Setting up conv5
I0410 00:23:16.062072 16216 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 00:23:16.062077 16216 net.cpp:137] Memory required for data: 1024899072
I0410 00:23:16.062088 16216 layer_factory.hpp:77] Creating layer relu5
I0410 00:23:16.062098 16216 net.cpp:84] Creating Layer relu5
I0410 00:23:16.062101 16216 net.cpp:406] relu5 <- conv5
I0410 00:23:16.062106 16216 net.cpp:367] relu5 -> conv5 (in-place)
I0410 00:23:16.069792 16216 net.cpp:122] Setting up relu5
I0410 00:23:16.069823 16216 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 00:23:16.069826 16216 net.cpp:137] Memory required for data: 1047050240
I0410 00:23:16.069830 16216 layer_factory.hpp:77] Creating layer pool5
I0410 00:23:16.069839 16216 net.cpp:84] Creating Layer pool5
I0410 00:23:16.069842 16216 net.cpp:406] pool5 <- conv5
I0410 00:23:16.069847 16216 net.cpp:380] pool5 -> pool5
I0410 00:23:16.069885 16216 net.cpp:122] Setting up pool5
I0410 00:23:16.069891 16216 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0410 00:23:16.069895 16216 net.cpp:137] Memory required for data: 1051768832
I0410 00:23:16.069897 16216 layer_factory.hpp:77] Creating layer fc8
I0410 00:23:16.069906 16216 net.cpp:84] Creating Layer fc8
I0410 00:23:16.069909 16216 net.cpp:406] fc8 <- pool5
I0410 00:23:16.069916 16216 net.cpp:380] fc8 -> fc8
I0410 00:23:16.089295 16216 net.cpp:122] Setting up fc8
I0410 00:23:16.089318 16216 net.cpp:129] Top shape: 128 196 (25088)
I0410 00:23:16.089320 16216 net.cpp:137] Memory required for data: 1051869184
I0410 00:23:16.089330 16216 layer_factory.hpp:77] Creating layer loss
I0410 00:23:16.089339 16216 net.cpp:84] Creating Layer loss
I0410 00:23:16.089344 16216 net.cpp:406] loss <- fc8
I0410 00:23:16.089349 16216 net.cpp:406] loss <- label
I0410 00:23:16.089356 16216 net.cpp:380] loss -> loss
I0410 00:23:16.089366 16216 layer_factory.hpp:77] Creating layer loss
I0410 00:23:16.090013 16216 net.cpp:122] Setting up loss
I0410 00:23:16.090023 16216 net.cpp:129] Top shape: (1)
I0410 00:23:16.090025 16216 net.cpp:132]     with loss weight 1
I0410 00:23:16.090044 16216 net.cpp:137] Memory required for data: 1051869188
I0410 00:23:16.090047 16216 net.cpp:198] loss needs backward computation.
I0410 00:23:16.090054 16216 net.cpp:198] fc8 needs backward computation.
I0410 00:23:16.090059 16216 net.cpp:198] pool5 needs backward computation.
I0410 00:23:16.090061 16216 net.cpp:198] relu5 needs backward computation.
I0410 00:23:16.090065 16216 net.cpp:198] conv5 needs backward computation.
I0410 00:23:16.090070 16216 net.cpp:198] relu4 needs backward computation.
I0410 00:23:16.090072 16216 net.cpp:198] conv4 needs backward computation.
I0410 00:23:16.090076 16216 net.cpp:198] relu3 needs backward computation.
I0410 00:23:16.090080 16216 net.cpp:198] conv3 needs backward computation.
I0410 00:23:16.090083 16216 net.cpp:198] pool2 needs backward computation.
I0410 00:23:16.090088 16216 net.cpp:198] norm2 needs backward computation.
I0410 00:23:16.090091 16216 net.cpp:198] relu2 needs backward computation.
I0410 00:23:16.090095 16216 net.cpp:198] conv2 needs backward computation.
I0410 00:23:16.090099 16216 net.cpp:198] pool1 needs backward computation.
I0410 00:23:16.090103 16216 net.cpp:198] norm1 needs backward computation.
I0410 00:23:16.090107 16216 net.cpp:198] relu1 needs backward computation.
I0410 00:23:16.090111 16216 net.cpp:198] conv1 needs backward computation.
I0410 00:23:16.090114 16216 net.cpp:200] train-data does not need backward computation.
I0410 00:23:16.090118 16216 net.cpp:242] This network produces output loss
I0410 00:23:16.090129 16216 net.cpp:255] Network initialization done.
I0410 00:23:16.090541 16216 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0410 00:23:16.090569 16216 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0410 00:23:16.090684 16216 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: "fc8"
type: "InnerProduct"
bottom: "pool5"
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 00:23:16.090772 16216 layer_factory.hpp:77] Creating layer val-data
I0410 00:23:16.092363 16216 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0410 00:23:16.092569 16216 net.cpp:84] Creating Layer val-data
I0410 00:23:16.092578 16216 net.cpp:380] val-data -> data
I0410 00:23:16.092586 16216 net.cpp:380] val-data -> label
I0410 00:23:16.092594 16216 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 00:23:16.096035 16216 data_layer.cpp:45] output data size: 32,3,227,227
I0410 00:23:16.130146 16216 net.cpp:122] Setting up val-data
I0410 00:23:16.130187 16216 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0410 00:23:16.130192 16216 net.cpp:129] Top shape: 32 (32)
I0410 00:23:16.130195 16216 net.cpp:137] Memory required for data: 19787264
I0410 00:23:16.130201 16216 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0410 00:23:16.130213 16216 net.cpp:84] Creating Layer label_val-data_1_split
I0410 00:23:16.130218 16216 net.cpp:406] label_val-data_1_split <- label
I0410 00:23:16.130224 16216 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0410 00:23:16.130234 16216 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0410 00:23:16.130281 16216 net.cpp:122] Setting up label_val-data_1_split
I0410 00:23:16.130286 16216 net.cpp:129] Top shape: 32 (32)
I0410 00:23:16.130291 16216 net.cpp:129] Top shape: 32 (32)
I0410 00:23:16.130295 16216 net.cpp:137] Memory required for data: 19787520
I0410 00:23:16.130297 16216 layer_factory.hpp:77] Creating layer conv1
I0410 00:23:16.130309 16216 net.cpp:84] Creating Layer conv1
I0410 00:23:16.130313 16216 net.cpp:406] conv1 <- data
I0410 00:23:16.130318 16216 net.cpp:380] conv1 -> conv1
I0410 00:23:16.133680 16216 net.cpp:122] Setting up conv1
I0410 00:23:16.133692 16216 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 00:23:16.133695 16216 net.cpp:137] Memory required for data: 56958720
I0410 00:23:16.133705 16216 layer_factory.hpp:77] Creating layer relu1
I0410 00:23:16.133711 16216 net.cpp:84] Creating Layer relu1
I0410 00:23:16.133715 16216 net.cpp:406] relu1 <- conv1
I0410 00:23:16.133720 16216 net.cpp:367] relu1 -> conv1 (in-place)
I0410 00:23:16.134223 16216 net.cpp:122] Setting up relu1
I0410 00:23:16.134233 16216 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 00:23:16.134236 16216 net.cpp:137] Memory required for data: 94129920
I0410 00:23:16.134240 16216 layer_factory.hpp:77] Creating layer norm1
I0410 00:23:16.134249 16216 net.cpp:84] Creating Layer norm1
I0410 00:23:16.134253 16216 net.cpp:406] norm1 <- conv1
I0410 00:23:16.134258 16216 net.cpp:380] norm1 -> norm1
I0410 00:23:16.134711 16216 net.cpp:122] Setting up norm1
I0410 00:23:16.134721 16216 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 00:23:16.134724 16216 net.cpp:137] Memory required for data: 131301120
I0410 00:23:16.134728 16216 layer_factory.hpp:77] Creating layer pool1
I0410 00:23:16.134735 16216 net.cpp:84] Creating Layer pool1
I0410 00:23:16.134738 16216 net.cpp:406] pool1 <- norm1
I0410 00:23:16.134744 16216 net.cpp:380] pool1 -> pool1
I0410 00:23:16.134771 16216 net.cpp:122] Setting up pool1
I0410 00:23:16.134778 16216 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0410 00:23:16.134780 16216 net.cpp:137] Memory required for data: 140259072
I0410 00:23:16.134783 16216 layer_factory.hpp:77] Creating layer conv2
I0410 00:23:16.134791 16216 net.cpp:84] Creating Layer conv2
I0410 00:23:16.134794 16216 net.cpp:406] conv2 <- pool1
I0410 00:23:16.134799 16216 net.cpp:380] conv2 -> conv2
I0410 00:23:16.145325 16216 net.cpp:122] Setting up conv2
I0410 00:23:16.145344 16216 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 00:23:16.145349 16216 net.cpp:137] Memory required for data: 164146944
I0410 00:23:16.145359 16216 layer_factory.hpp:77] Creating layer relu2
I0410 00:23:16.145368 16216 net.cpp:84] Creating Layer relu2
I0410 00:23:16.145372 16216 net.cpp:406] relu2 <- conv2
I0410 00:23:16.145380 16216 net.cpp:367] relu2 -> conv2 (in-place)
I0410 00:23:16.145767 16216 net.cpp:122] Setting up relu2
I0410 00:23:16.145776 16216 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 00:23:16.145779 16216 net.cpp:137] Memory required for data: 188034816
I0410 00:23:16.145783 16216 layer_factory.hpp:77] Creating layer norm2
I0410 00:23:16.145793 16216 net.cpp:84] Creating Layer norm2
I0410 00:23:16.145797 16216 net.cpp:406] norm2 <- conv2
I0410 00:23:16.145803 16216 net.cpp:380] norm2 -> norm2
I0410 00:23:16.146355 16216 net.cpp:122] Setting up norm2
I0410 00:23:16.146365 16216 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 00:23:16.146368 16216 net.cpp:137] Memory required for data: 211922688
I0410 00:23:16.146390 16216 layer_factory.hpp:77] Creating layer pool2
I0410 00:23:16.146399 16216 net.cpp:84] Creating Layer pool2
I0410 00:23:16.146402 16216 net.cpp:406] pool2 <- norm2
I0410 00:23:16.146409 16216 net.cpp:380] pool2 -> pool2
I0410 00:23:16.146440 16216 net.cpp:122] Setting up pool2
I0410 00:23:16.146446 16216 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 00:23:16.146450 16216 net.cpp:137] Memory required for data: 217460480
I0410 00:23:16.146453 16216 layer_factory.hpp:77] Creating layer conv3
I0410 00:23:16.146469 16216 net.cpp:84] Creating Layer conv3
I0410 00:23:16.146472 16216 net.cpp:406] conv3 <- pool2
I0410 00:23:16.146478 16216 net.cpp:380] conv3 -> conv3
I0410 00:23:16.157383 16216 net.cpp:122] Setting up conv3
I0410 00:23:16.157402 16216 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:23:16.157404 16216 net.cpp:137] Memory required for data: 225767168
I0410 00:23:16.157415 16216 layer_factory.hpp:77] Creating layer relu3
I0410 00:23:16.157424 16216 net.cpp:84] Creating Layer relu3
I0410 00:23:16.157436 16216 net.cpp:406] relu3 <- conv3
I0410 00:23:16.157444 16216 net.cpp:367] relu3 -> conv3 (in-place)
I0410 00:23:16.157948 16216 net.cpp:122] Setting up relu3
I0410 00:23:16.157982 16216 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:23:16.157986 16216 net.cpp:137] Memory required for data: 234073856
I0410 00:23:16.157990 16216 layer_factory.hpp:77] Creating layer conv4
I0410 00:23:16.158000 16216 net.cpp:84] Creating Layer conv4
I0410 00:23:16.158004 16216 net.cpp:406] conv4 <- conv3
I0410 00:23:16.158010 16216 net.cpp:380] conv4 -> conv4
I0410 00:23:16.169486 16216 net.cpp:122] Setting up conv4
I0410 00:23:16.169503 16216 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:23:16.169507 16216 net.cpp:137] Memory required for data: 242380544
I0410 00:23:16.169517 16216 layer_factory.hpp:77] Creating layer relu4
I0410 00:23:16.169525 16216 net.cpp:84] Creating Layer relu4
I0410 00:23:16.169530 16216 net.cpp:406] relu4 <- conv4
I0410 00:23:16.169536 16216 net.cpp:367] relu4 -> conv4 (in-place)
I0410 00:23:16.170047 16216 net.cpp:122] Setting up relu4
I0410 00:23:16.170056 16216 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 00:23:16.170060 16216 net.cpp:137] Memory required for data: 250687232
I0410 00:23:16.170063 16216 layer_factory.hpp:77] Creating layer conv5
I0410 00:23:16.170074 16216 net.cpp:84] Creating Layer conv5
I0410 00:23:16.170078 16216 net.cpp:406] conv5 <- conv4
I0410 00:23:16.170085 16216 net.cpp:380] conv5 -> conv5
I0410 00:23:16.178671 16216 net.cpp:122] Setting up conv5
I0410 00:23:16.178687 16216 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 00:23:16.178692 16216 net.cpp:137] Memory required for data: 256225024
I0410 00:23:16.178704 16216 layer_factory.hpp:77] Creating layer relu5
I0410 00:23:16.178712 16216 net.cpp:84] Creating Layer relu5
I0410 00:23:16.178716 16216 net.cpp:406] relu5 <- conv5
I0410 00:23:16.178725 16216 net.cpp:367] relu5 -> conv5 (in-place)
I0410 00:23:16.179076 16216 net.cpp:122] Setting up relu5
I0410 00:23:16.179085 16216 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 00:23:16.179087 16216 net.cpp:137] Memory required for data: 261762816
I0410 00:23:16.179091 16216 layer_factory.hpp:77] Creating layer pool5
I0410 00:23:16.179101 16216 net.cpp:84] Creating Layer pool5
I0410 00:23:16.179105 16216 net.cpp:406] pool5 <- conv5
I0410 00:23:16.179111 16216 net.cpp:380] pool5 -> pool5
I0410 00:23:16.179149 16216 net.cpp:122] Setting up pool5
I0410 00:23:16.179155 16216 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0410 00:23:16.179159 16216 net.cpp:137] Memory required for data: 262942464
I0410 00:23:16.179162 16216 layer_factory.hpp:77] Creating layer fc8
I0410 00:23:16.179169 16216 net.cpp:84] Creating Layer fc8
I0410 00:23:16.179173 16216 net.cpp:406] fc8 <- pool5
I0410 00:23:16.179179 16216 net.cpp:380] fc8 -> fc8
I0410 00:23:16.196489 16216 net.cpp:122] Setting up fc8
I0410 00:23:16.196511 16216 net.cpp:129] Top shape: 32 196 (6272)
I0410 00:23:16.196514 16216 net.cpp:137] Memory required for data: 262967552
I0410 00:23:16.196544 16216 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0410 00:23:16.196552 16216 net.cpp:84] Creating Layer fc8_fc8_0_split
I0410 00:23:16.196557 16216 net.cpp:406] fc8_fc8_0_split <- fc8
I0410 00:23:16.196565 16216 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0410 00:23:16.196574 16216 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0410 00:23:16.196614 16216 net.cpp:122] Setting up fc8_fc8_0_split
I0410 00:23:16.196619 16216 net.cpp:129] Top shape: 32 196 (6272)
I0410 00:23:16.196624 16216 net.cpp:129] Top shape: 32 196 (6272)
I0410 00:23:16.196626 16216 net.cpp:137] Memory required for data: 263017728
I0410 00:23:16.196630 16216 layer_factory.hpp:77] Creating layer accuracy
I0410 00:23:16.196636 16216 net.cpp:84] Creating Layer accuracy
I0410 00:23:16.196640 16216 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0410 00:23:16.196645 16216 net.cpp:406] accuracy <- label_val-data_1_split_0
I0410 00:23:16.196650 16216 net.cpp:380] accuracy -> accuracy
I0410 00:23:16.196657 16216 net.cpp:122] Setting up accuracy
I0410 00:23:16.196661 16216 net.cpp:129] Top shape: (1)
I0410 00:23:16.196664 16216 net.cpp:137] Memory required for data: 263017732
I0410 00:23:16.196667 16216 layer_factory.hpp:77] Creating layer loss
I0410 00:23:16.196673 16216 net.cpp:84] Creating Layer loss
I0410 00:23:16.196676 16216 net.cpp:406] loss <- fc8_fc8_0_split_1
I0410 00:23:16.196681 16216 net.cpp:406] loss <- label_val-data_1_split_1
I0410 00:23:16.196686 16216 net.cpp:380] loss -> loss
I0410 00:23:16.196693 16216 layer_factory.hpp:77] Creating layer loss
I0410 00:23:16.197412 16216 net.cpp:122] Setting up loss
I0410 00:23:16.197420 16216 net.cpp:129] Top shape: (1)
I0410 00:23:16.197424 16216 net.cpp:132]     with loss weight 1
I0410 00:23:16.197434 16216 net.cpp:137] Memory required for data: 263017736
I0410 00:23:16.197438 16216 net.cpp:198] loss needs backward computation.
I0410 00:23:16.197443 16216 net.cpp:200] accuracy does not need backward computation.
I0410 00:23:16.197448 16216 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0410 00:23:16.197450 16216 net.cpp:198] fc8 needs backward computation.
I0410 00:23:16.197454 16216 net.cpp:198] pool5 needs backward computation.
I0410 00:23:16.197458 16216 net.cpp:198] relu5 needs backward computation.
I0410 00:23:16.197461 16216 net.cpp:198] conv5 needs backward computation.
I0410 00:23:16.197465 16216 net.cpp:198] relu4 needs backward computation.
I0410 00:23:16.197468 16216 net.cpp:198] conv4 needs backward computation.
I0410 00:23:16.197471 16216 net.cpp:198] relu3 needs backward computation.
I0410 00:23:16.197475 16216 net.cpp:198] conv3 needs backward computation.
I0410 00:23:16.197479 16216 net.cpp:198] pool2 needs backward computation.
I0410 00:23:16.197482 16216 net.cpp:198] norm2 needs backward computation.
I0410 00:23:16.197486 16216 net.cpp:198] relu2 needs backward computation.
I0410 00:23:16.197489 16216 net.cpp:198] conv2 needs backward computation.
I0410 00:23:16.197494 16216 net.cpp:198] pool1 needs backward computation.
I0410 00:23:16.197496 16216 net.cpp:198] norm1 needs backward computation.
I0410 00:23:16.197500 16216 net.cpp:198] relu1 needs backward computation.
I0410 00:23:16.197504 16216 net.cpp:198] conv1 needs backward computation.
I0410 00:23:16.197508 16216 net.cpp:200] label_val-data_1_split does not need backward computation.
I0410 00:23:16.197512 16216 net.cpp:200] val-data does not need backward computation.
I0410 00:23:16.197515 16216 net.cpp:242] This network produces output accuracy
I0410 00:23:16.197520 16216 net.cpp:242] This network produces output loss
I0410 00:23:16.197535 16216 net.cpp:255] Network initialization done.
I0410 00:23:16.197589 16216 solver.cpp:56] Solver scaffolding done.
I0410 00:23:16.197940 16216 caffe.cpp:248] Starting Optimization
I0410 00:23:16.197949 16216 solver.cpp:272] Solving
I0410 00:23:16.197952 16216 solver.cpp:273] Learning Rate Policy: exp
I0410 00:23:16.198835 16216 solver.cpp:330] Iteration 0, Testing net (#0)
I0410 00:23:16.198845 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:23:16.201169 16216 blocking_queue.cpp:49] Waiting for data
I0410 00:23:20.836145 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:23:20.880231 16216 solver.cpp:397]     Test net output #0: accuracy = 0.00490196
I0410 00:23:20.880278 16216 solver.cpp:397]     Test net output #1: loss = 5.30323 (* 1 = 5.30323 loss)
I0410 00:23:20.970185 16216 solver.cpp:218] Iteration 0 (-nan iter/s, 4.77204s/12 iters), loss = 5.31115
I0410 00:23:20.971706 16216 solver.cpp:237]     Train net output #0: loss = 5.31115 (* 1 = 5.31115 loss)
I0410 00:23:20.971735 16216 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0410 00:23:24.922763 16216 solver.cpp:218] Iteration 12 (3.03727 iter/s, 3.95092s/12 iters), loss = 5.30869
I0410 00:23:24.922821 16216 solver.cpp:237]     Train net output #0: loss = 5.30869 (* 1 = 5.30869 loss)
I0410 00:23:24.922834 16216 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0410 00:23:29.858935 16216 solver.cpp:218] Iteration 24 (2.43114 iter/s, 4.93596s/12 iters), loss = 5.28766
I0410 00:23:29.858986 16216 solver.cpp:237]     Train net output #0: loss = 5.28766 (* 1 = 5.28766 loss)
I0410 00:23:29.858996 16216 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0410 00:23:34.812211 16216 solver.cpp:218] Iteration 36 (2.42274 iter/s, 4.95307s/12 iters), loss = 5.27067
I0410 00:23:34.812254 16216 solver.cpp:237]     Train net output #0: loss = 5.27067 (* 1 = 5.27067 loss)
I0410 00:23:34.812263 16216 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0410 00:23:39.752113 16216 solver.cpp:218] Iteration 48 (2.4293 iter/s, 4.9397s/12 iters), loss = 5.27355
I0410 00:23:39.752159 16216 solver.cpp:237]     Train net output #0: loss = 5.27355 (* 1 = 5.27355 loss)
I0410 00:23:39.752168 16216 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0410 00:23:44.663834 16216 solver.cpp:218] Iteration 60 (2.44323 iter/s, 4.91152s/12 iters), loss = 5.23598
I0410 00:23:44.663877 16216 solver.cpp:237]     Train net output #0: loss = 5.23598 (* 1 = 5.23598 loss)
I0410 00:23:44.663887 16216 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0410 00:23:49.618162 16216 solver.cpp:218] Iteration 72 (2.42222 iter/s, 4.95413s/12 iters), loss = 5.25785
I0410 00:23:49.618249 16216 solver.cpp:237]     Train net output #0: loss = 5.25785 (* 1 = 5.25785 loss)
I0410 00:23:49.618259 16216 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0410 00:23:54.516039 16216 solver.cpp:218] Iteration 84 (2.45016 iter/s, 4.89764s/12 iters), loss = 5.17902
I0410 00:23:54.516081 16216 solver.cpp:237]     Train net output #0: loss = 5.17902 (* 1 = 5.17902 loss)
I0410 00:23:54.516089 16216 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0410 00:23:59.394500 16216 solver.cpp:218] Iteration 96 (2.45989 iter/s, 4.87826s/12 iters), loss = 5.1584
I0410 00:23:59.394544 16216 solver.cpp:237]     Train net output #0: loss = 5.1584 (* 1 = 5.1584 loss)
I0410 00:23:59.394556 16216 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0410 00:24:01.101338 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:24:01.407632 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0410 00:24:01.691790 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0410 00:24:01.877168 16216 solver.cpp:330] Iteration 102, Testing net (#0)
I0410 00:24:01.877199 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:24:06.234767 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:24:06.310832 16216 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0410 00:24:06.310869 16216 solver.cpp:397]     Test net output #1: loss = 5.18823 (* 1 = 5.18823 loss)
I0410 00:24:08.264231 16216 solver.cpp:218] Iteration 108 (1.35296 iter/s, 8.86941s/12 iters), loss = 5.19627
I0410 00:24:08.264300 16216 solver.cpp:237]     Train net output #0: loss = 5.19627 (* 1 = 5.19627 loss)
I0410 00:24:08.264317 16216 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0410 00:24:13.711859 16216 solver.cpp:218] Iteration 120 (2.20289 iter/s, 5.44739s/12 iters), loss = 5.17934
I0410 00:24:13.711910 16216 solver.cpp:237]     Train net output #0: loss = 5.17934 (* 1 = 5.17934 loss)
I0410 00:24:13.711923 16216 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0410 00:24:18.681993 16216 solver.cpp:218] Iteration 132 (2.41452 iter/s, 4.96992s/12 iters), loss = 5.11039
I0410 00:24:18.682035 16216 solver.cpp:237]     Train net output #0: loss = 5.11039 (* 1 = 5.11039 loss)
I0410 00:24:18.682044 16216 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0410 00:24:23.602067 16216 solver.cpp:218] Iteration 144 (2.43908 iter/s, 4.91988s/12 iters), loss = 5.13984
I0410 00:24:23.604665 16216 solver.cpp:237]     Train net output #0: loss = 5.13984 (* 1 = 5.13984 loss)
I0410 00:24:23.604676 16216 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0410 00:24:28.513062 16216 solver.cpp:218] Iteration 156 (2.44487 iter/s, 4.90825s/12 iters), loss = 5.15936
I0410 00:24:28.513104 16216 solver.cpp:237]     Train net output #0: loss = 5.15936 (* 1 = 5.15936 loss)
I0410 00:24:28.513114 16216 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0410 00:24:33.407614 16216 solver.cpp:218] Iteration 168 (2.45181 iter/s, 4.89435s/12 iters), loss = 5.07743
I0410 00:24:33.407658 16216 solver.cpp:237]     Train net output #0: loss = 5.07743 (* 1 = 5.07743 loss)
I0410 00:24:33.407667 16216 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0410 00:24:38.425562 16216 solver.cpp:218] Iteration 180 (2.39151 iter/s, 5.01775s/12 iters), loss = 5.03518
I0410 00:24:38.425609 16216 solver.cpp:237]     Train net output #0: loss = 5.03518 (* 1 = 5.03518 loss)
I0410 00:24:38.425619 16216 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0410 00:24:43.425702 16216 solver.cpp:218] Iteration 192 (2.40003 iter/s, 4.99994s/12 iters), loss = 5.09693
I0410 00:24:43.425748 16216 solver.cpp:237]     Train net output #0: loss = 5.09693 (* 1 = 5.09693 loss)
I0410 00:24:43.425758 16216 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0410 00:24:47.206363 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:24:47.888165 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0410 00:24:48.142547 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0410 00:24:48.319821 16216 solver.cpp:330] Iteration 204, Testing net (#0)
I0410 00:24:48.319842 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:24:52.652956 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:24:52.775426 16216 solver.cpp:397]     Test net output #0: accuracy = 0.0232843
I0410 00:24:52.775475 16216 solver.cpp:397]     Test net output #1: loss = 5.09893 (* 1 = 5.09893 loss)
I0410 00:24:52.858248 16216 solver.cpp:218] Iteration 204 (1.27224 iter/s, 9.43221s/12 iters), loss = 4.90306
I0410 00:24:52.858302 16216 solver.cpp:237]     Train net output #0: loss = 4.90306 (* 1 = 4.90306 loss)
I0410 00:24:52.858314 16216 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0410 00:24:57.018260 16216 solver.cpp:218] Iteration 216 (2.88474 iter/s, 4.15982s/12 iters), loss = 5.05411
I0410 00:24:57.018364 16216 solver.cpp:237]     Train net output #0: loss = 5.05411 (* 1 = 5.05411 loss)
I0410 00:24:57.018375 16216 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0410 00:25:01.977952 16216 solver.cpp:218] Iteration 228 (2.41963 iter/s, 4.95943s/12 iters), loss = 5.04152
I0410 00:25:01.978024 16216 solver.cpp:237]     Train net output #0: loss = 5.04152 (* 1 = 5.04152 loss)
I0410 00:25:01.978034 16216 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0410 00:25:06.925626 16216 solver.cpp:218] Iteration 240 (2.42549 iter/s, 4.94745s/12 iters), loss = 5.02752
I0410 00:25:06.925670 16216 solver.cpp:237]     Train net output #0: loss = 5.02752 (* 1 = 5.02752 loss)
I0410 00:25:06.925679 16216 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0410 00:25:11.878326 16216 solver.cpp:218] Iteration 252 (2.42302 iter/s, 4.9525s/12 iters), loss = 5.0857
I0410 00:25:11.878371 16216 solver.cpp:237]     Train net output #0: loss = 5.0857 (* 1 = 5.0857 loss)
I0410 00:25:11.878381 16216 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0410 00:25:16.786223 16216 solver.cpp:218] Iteration 264 (2.44514 iter/s, 4.9077s/12 iters), loss = 5.03986
I0410 00:25:16.786273 16216 solver.cpp:237]     Train net output #0: loss = 5.03986 (* 1 = 5.03986 loss)
I0410 00:25:16.786280 16216 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0410 00:25:22.040346 16216 solver.cpp:218] Iteration 276 (2.28401 iter/s, 5.25391s/12 iters), loss = 4.96676
I0410 00:25:22.040397 16216 solver.cpp:237]     Train net output #0: loss = 4.96676 (* 1 = 4.96676 loss)
I0410 00:25:22.040410 16216 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0410 00:25:27.017046 16216 solver.cpp:218] Iteration 288 (2.41134 iter/s, 4.97649s/12 iters), loss = 5.00806
I0410 00:25:27.017103 16216 solver.cpp:237]     Train net output #0: loss = 5.00806 (* 1 = 5.00806 loss)
I0410 00:25:27.017117 16216 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0410 00:25:32.002836 16216 solver.cpp:218] Iteration 300 (2.40694 iter/s, 4.98558s/12 iters), loss = 4.98779
I0410 00:25:32.003000 16216 solver.cpp:237]     Train net output #0: loss = 4.98779 (* 1 = 4.98779 loss)
I0410 00:25:32.003015 16216 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0410 00:25:32.970225 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:25:33.997931 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0410 00:25:34.254942 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0410 00:25:34.421422 16216 solver.cpp:330] Iteration 306, Testing net (#0)
I0410 00:25:34.421442 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:25:38.778280 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:25:38.934826 16216 solver.cpp:397]     Test net output #0: accuracy = 0.0257353
I0410 00:25:38.934865 16216 solver.cpp:397]     Test net output #1: loss = 5.04954 (* 1 = 5.04954 loss)
I0410 00:25:40.677500 16216 solver.cpp:218] Iteration 312 (1.38341 iter/s, 8.67425s/12 iters), loss = 4.88429
I0410 00:25:40.677541 16216 solver.cpp:237]     Train net output #0: loss = 4.88429 (* 1 = 4.88429 loss)
I0410 00:25:40.677551 16216 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0410 00:25:45.507231 16216 solver.cpp:218] Iteration 324 (2.48471 iter/s, 4.82954s/12 iters), loss = 4.97537
I0410 00:25:45.507274 16216 solver.cpp:237]     Train net output #0: loss = 4.97537 (* 1 = 4.97537 loss)
I0410 00:25:45.507284 16216 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0410 00:25:50.447979 16216 solver.cpp:218] Iteration 336 (2.42888 iter/s, 4.94055s/12 iters), loss = 5.04912
I0410 00:25:50.448032 16216 solver.cpp:237]     Train net output #0: loss = 5.04912 (* 1 = 5.04912 loss)
I0410 00:25:50.448043 16216 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0410 00:25:55.399037 16216 solver.cpp:218] Iteration 348 (2.42382 iter/s, 4.95085s/12 iters), loss = 4.88033
I0410 00:25:55.399080 16216 solver.cpp:237]     Train net output #0: loss = 4.88033 (* 1 = 4.88033 loss)
I0410 00:25:55.399089 16216 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0410 00:26:00.378098 16216 solver.cpp:218] Iteration 360 (2.41019 iter/s, 4.97885s/12 iters), loss = 5.00991
I0410 00:26:00.378221 16216 solver.cpp:237]     Train net output #0: loss = 5.00991 (* 1 = 5.00991 loss)
I0410 00:26:00.378234 16216 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0410 00:26:05.328722 16216 solver.cpp:218] Iteration 372 (2.42407 iter/s, 4.95035s/12 iters), loss = 4.81455
I0410 00:26:05.328797 16216 solver.cpp:237]     Train net output #0: loss = 4.81455 (* 1 = 4.81455 loss)
I0410 00:26:05.328809 16216 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0410 00:26:10.228951 16216 solver.cpp:218] Iteration 384 (2.44898 iter/s, 4.90001s/12 iters), loss = 4.93256
I0410 00:26:10.228991 16216 solver.cpp:237]     Train net output #0: loss = 4.93256 (* 1 = 4.93256 loss)
I0410 00:26:10.229001 16216 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0410 00:26:15.173354 16216 solver.cpp:218] Iteration 396 (2.42708 iter/s, 4.94421s/12 iters), loss = 5.01422
I0410 00:26:15.173399 16216 solver.cpp:237]     Train net output #0: loss = 5.01422 (* 1 = 5.01422 loss)
I0410 00:26:15.173408 16216 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0410 00:26:18.217242 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:26:19.621434 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0410 00:26:20.365690 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0410 00:26:21.819056 16216 solver.cpp:330] Iteration 408, Testing net (#0)
I0410 00:26:21.819075 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:26:26.127624 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:26:26.334836 16216 solver.cpp:397]     Test net output #0: accuracy = 0.0294118
I0410 00:26:26.334887 16216 solver.cpp:397]     Test net output #1: loss = 4.99515 (* 1 = 4.99515 loss)
I0410 00:26:26.417533 16216 solver.cpp:218] Iteration 408 (1.06725 iter/s, 11.2438s/12 iters), loss = 4.93493
I0410 00:26:26.417582 16216 solver.cpp:237]     Train net output #0: loss = 4.93493 (* 1 = 4.93493 loss)
I0410 00:26:26.417594 16216 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0410 00:26:30.871028 16216 solver.cpp:218] Iteration 420 (2.69463 iter/s, 4.4533s/12 iters), loss = 4.90843
I0410 00:26:30.871074 16216 solver.cpp:237]     Train net output #0: loss = 4.90843 (* 1 = 4.90843 loss)
I0410 00:26:30.871083 16216 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0410 00:26:35.841293 16216 solver.cpp:218] Iteration 432 (2.41446 iter/s, 4.97006s/12 iters), loss = 4.88396
I0410 00:26:35.841451 16216 solver.cpp:237]     Train net output #0: loss = 4.88396 (* 1 = 4.88396 loss)
I0410 00:26:35.841464 16216 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0410 00:26:40.810945 16216 solver.cpp:218] Iteration 444 (2.41481 iter/s, 4.96934s/12 iters), loss = 4.72151
I0410 00:26:40.810992 16216 solver.cpp:237]     Train net output #0: loss = 4.72151 (* 1 = 4.72151 loss)
I0410 00:26:40.811000 16216 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0410 00:26:45.815091 16216 solver.cpp:218] Iteration 456 (2.39811 iter/s, 5.00394s/12 iters), loss = 4.76954
I0410 00:26:45.815142 16216 solver.cpp:237]     Train net output #0: loss = 4.76954 (* 1 = 4.76954 loss)
I0410 00:26:45.815155 16216 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0410 00:26:50.748950 16216 solver.cpp:218] Iteration 468 (2.43227 iter/s, 4.93365s/12 iters), loss = 4.79211
I0410 00:26:50.748997 16216 solver.cpp:237]     Train net output #0: loss = 4.79211 (* 1 = 4.79211 loss)
I0410 00:26:50.749006 16216 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0410 00:26:55.683568 16216 solver.cpp:218] Iteration 480 (2.4319 iter/s, 4.93442s/12 iters), loss = 4.8106
I0410 00:26:55.683617 16216 solver.cpp:237]     Train net output #0: loss = 4.8106 (* 1 = 4.8106 loss)
I0410 00:26:55.683629 16216 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0410 00:27:00.663762 16216 solver.cpp:218] Iteration 492 (2.40964 iter/s, 4.97999s/12 iters), loss = 4.9117
I0410 00:27:00.663810 16216 solver.cpp:237]     Train net output #0: loss = 4.9117 (* 1 = 4.9117 loss)
I0410 00:27:00.663821 16216 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0410 00:27:05.555169 16216 solver.cpp:218] Iteration 504 (2.45338 iter/s, 4.89121s/12 iters), loss = 4.87257
I0410 00:27:05.555217 16216 solver.cpp:237]     Train net output #0: loss = 4.87257 (* 1 = 4.87257 loss)
I0410 00:27:05.555227 16216 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0410 00:27:05.814721 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:27:07.614992 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0410 00:27:08.319391 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0410 00:27:09.155261 16216 solver.cpp:330] Iteration 510, Testing net (#0)
I0410 00:27:09.155285 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:27:13.385849 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:27:13.628160 16216 solver.cpp:397]     Test net output #0: accuracy = 0.0355392
I0410 00:27:13.628198 16216 solver.cpp:397]     Test net output #1: loss = 4.95303 (* 1 = 4.95303 loss)
I0410 00:27:15.369683 16216 solver.cpp:218] Iteration 516 (1.22272 iter/s, 9.81417s/12 iters), loss = 4.78071
I0410 00:27:15.369740 16216 solver.cpp:237]     Train net output #0: loss = 4.78071 (* 1 = 4.78071 loss)
I0410 00:27:15.369752 16216 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0410 00:27:20.230872 16216 solver.cpp:218] Iteration 528 (2.46864 iter/s, 4.86098s/12 iters), loss = 4.92147
I0410 00:27:20.230937 16216 solver.cpp:237]     Train net output #0: loss = 4.92147 (* 1 = 4.92147 loss)
I0410 00:27:20.230949 16216 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0410 00:27:25.090993 16216 solver.cpp:218] Iteration 540 (2.46918 iter/s, 4.8599s/12 iters), loss = 4.72436
I0410 00:27:25.091053 16216 solver.cpp:237]     Train net output #0: loss = 4.72436 (* 1 = 4.72436 loss)
I0410 00:27:25.091065 16216 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0410 00:27:29.904779 16216 solver.cpp:218] Iteration 552 (2.49295 iter/s, 4.81357s/12 iters), loss = 4.86064
I0410 00:27:29.904841 16216 solver.cpp:237]     Train net output #0: loss = 4.86064 (* 1 = 4.86064 loss)
I0410 00:27:29.904855 16216 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0410 00:27:34.955323 16216 solver.cpp:218] Iteration 564 (2.37609 iter/s, 5.05032s/12 iters), loss = 4.71785
I0410 00:27:34.955365 16216 solver.cpp:237]     Train net output #0: loss = 4.71785 (* 1 = 4.71785 loss)
I0410 00:27:34.955374 16216 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0410 00:27:39.943961 16216 solver.cpp:218] Iteration 576 (2.40556 iter/s, 4.98843s/12 iters), loss = 4.81805
I0410 00:27:39.944052 16216 solver.cpp:237]     Train net output #0: loss = 4.81805 (* 1 = 4.81805 loss)
I0410 00:27:39.944063 16216 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0410 00:27:44.844525 16216 solver.cpp:218] Iteration 588 (2.44882 iter/s, 4.90032s/12 iters), loss = 4.607
I0410 00:27:44.844573 16216 solver.cpp:237]     Train net output #0: loss = 4.607 (* 1 = 4.607 loss)
I0410 00:27:44.844583 16216 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0410 00:27:49.803573 16216 solver.cpp:218] Iteration 600 (2.41992 iter/s, 4.95884s/12 iters), loss = 4.68884
I0410 00:27:49.803628 16216 solver.cpp:237]     Train net output #0: loss = 4.68884 (* 1 = 4.68884 loss)
I0410 00:27:49.803640 16216 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0410 00:27:52.183158 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:27:54.460999 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0410 00:27:54.729574 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0410 00:27:54.916903 16216 solver.cpp:330] Iteration 612, Testing net (#0)
I0410 00:27:54.916921 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:27:59.139555 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:27:59.433249 16216 solver.cpp:397]     Test net output #0: accuracy = 0.0349265
I0410 00:27:59.433296 16216 solver.cpp:397]     Test net output #1: loss = 4.91152 (* 1 = 4.91152 loss)
I0410 00:27:59.515359 16216 solver.cpp:218] Iteration 612 (1.23566 iter/s, 9.71144s/12 iters), loss = 4.76135
I0410 00:27:59.515404 16216 solver.cpp:237]     Train net output #0: loss = 4.76135 (* 1 = 4.76135 loss)
I0410 00:27:59.515415 16216 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0410 00:28:03.960093 16216 solver.cpp:218] Iteration 624 (2.69994 iter/s, 4.44454s/12 iters), loss = 4.75816
I0410 00:28:03.960137 16216 solver.cpp:237]     Train net output #0: loss = 4.75816 (* 1 = 4.75816 loss)
I0410 00:28:03.960147 16216 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0410 00:28:08.862759 16216 solver.cpp:218] Iteration 636 (2.44775 iter/s, 4.90246s/12 iters), loss = 4.57586
I0410 00:28:08.862812 16216 solver.cpp:237]     Train net output #0: loss = 4.57586 (* 1 = 4.57586 loss)
I0410 00:28:08.862821 16216 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0410 00:28:13.795754 16216 solver.cpp:218] Iteration 648 (2.4327 iter/s, 4.93279s/12 iters), loss = 4.66993
I0410 00:28:13.795876 16216 solver.cpp:237]     Train net output #0: loss = 4.66993 (* 1 = 4.66993 loss)
I0410 00:28:13.795886 16216 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0410 00:28:18.681349 16216 solver.cpp:218] Iteration 660 (2.45634 iter/s, 4.88532s/12 iters), loss = 4.66714
I0410 00:28:18.681396 16216 solver.cpp:237]     Train net output #0: loss = 4.66714 (* 1 = 4.66714 loss)
I0410 00:28:18.681407 16216 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0410 00:28:23.600435 16216 solver.cpp:218] Iteration 672 (2.43958 iter/s, 4.91888s/12 iters), loss = 4.51941
I0410 00:28:23.600492 16216 solver.cpp:237]     Train net output #0: loss = 4.51941 (* 1 = 4.51941 loss)
I0410 00:28:23.600504 16216 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0410 00:28:27.639636 16216 blocking_queue.cpp:49] Waiting for data
I0410 00:28:28.492321 16216 solver.cpp:218] Iteration 684 (2.45315 iter/s, 4.89167s/12 iters), loss = 4.70714
I0410 00:28:28.492374 16216 solver.cpp:237]     Train net output #0: loss = 4.70714 (* 1 = 4.70714 loss)
I0410 00:28:28.492386 16216 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0410 00:28:33.438100 16216 solver.cpp:218] Iteration 696 (2.42641 iter/s, 4.94557s/12 iters), loss = 4.68792
I0410 00:28:33.438153 16216 solver.cpp:237]     Train net output #0: loss = 4.68792 (* 1 = 4.68792 loss)
I0410 00:28:33.438165 16216 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0410 00:28:38.198495 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:28:38.604422 16216 solver.cpp:218] Iteration 708 (2.32283 iter/s, 5.16611s/12 iters), loss = 4.46587
I0410 00:28:38.604483 16216 solver.cpp:237]     Train net output #0: loss = 4.46587 (* 1 = 4.46587 loss)
I0410 00:28:38.604496 16216 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0410 00:28:40.733989 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0410 00:28:42.306548 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0410 00:28:42.778481 16216 solver.cpp:330] Iteration 714, Testing net (#0)
I0410 00:28:42.778501 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:28:46.933028 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:28:47.254849 16216 solver.cpp:397]     Test net output #0: accuracy = 0.0514706
I0410 00:28:47.254895 16216 solver.cpp:397]     Test net output #1: loss = 4.89363 (* 1 = 4.89363 loss)
I0410 00:28:49.203387 16216 solver.cpp:218] Iteration 720 (1.13223 iter/s, 10.5986s/12 iters), loss = 4.74911
I0410 00:28:49.203447 16216 solver.cpp:237]     Train net output #0: loss = 4.74911 (* 1 = 4.74911 loss)
I0410 00:28:49.203459 16216 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0410 00:28:54.236235 16216 solver.cpp:218] Iteration 732 (2.38444 iter/s, 5.03263s/12 iters), loss = 4.44995
I0410 00:28:54.236289 16216 solver.cpp:237]     Train net output #0: loss = 4.44995 (* 1 = 4.44995 loss)
I0410 00:28:54.236299 16216 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0410 00:28:59.141284 16216 solver.cpp:218] Iteration 744 (2.44656 iter/s, 4.90484s/12 iters), loss = 4.50685
I0410 00:28:59.141340 16216 solver.cpp:237]     Train net output #0: loss = 4.50685 (* 1 = 4.50685 loss)
I0410 00:28:59.141352 16216 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0410 00:29:04.102528 16216 solver.cpp:218] Iteration 756 (2.41885 iter/s, 4.96104s/12 iters), loss = 4.62998
I0410 00:29:04.102574 16216 solver.cpp:237]     Train net output #0: loss = 4.62998 (* 1 = 4.62998 loss)
I0410 00:29:04.102583 16216 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0410 00:29:09.161425 16216 solver.cpp:218] Iteration 768 (2.37216 iter/s, 5.05868s/12 iters), loss = 4.60467
I0410 00:29:09.161480 16216 solver.cpp:237]     Train net output #0: loss = 4.60467 (* 1 = 4.60467 loss)
I0410 00:29:09.161492 16216 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0410 00:29:14.098129 16216 solver.cpp:218] Iteration 780 (2.43087 iter/s, 4.93649s/12 iters), loss = 4.51586
I0410 00:29:14.098178 16216 solver.cpp:237]     Train net output #0: loss = 4.51586 (* 1 = 4.51586 loss)
I0410 00:29:14.098189 16216 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0410 00:29:19.071259 16216 solver.cpp:218] Iteration 792 (2.41307 iter/s, 4.97293s/12 iters), loss = 4.20241
I0410 00:29:19.071377 16216 solver.cpp:237]     Train net output #0: loss = 4.20241 (* 1 = 4.20241 loss)
I0410 00:29:19.071385 16216 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0410 00:29:24.023394 16216 solver.cpp:218] Iteration 804 (2.42333 iter/s, 4.95186s/12 iters), loss = 4.61453
I0410 00:29:24.023442 16216 solver.cpp:237]     Train net output #0: loss = 4.61453 (* 1 = 4.61453 loss)
I0410 00:29:24.023454 16216 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0410 00:29:25.715679 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:29:28.488483 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0410 00:29:29.089218 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0410 00:29:29.704144 16216 solver.cpp:330] Iteration 816, Testing net (#0)
I0410 00:29:29.704172 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:29:33.744742 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:29:34.098134 16216 solver.cpp:397]     Test net output #0: accuracy = 0.0582108
I0410 00:29:34.098184 16216 solver.cpp:397]     Test net output #1: loss = 4.79008 (* 1 = 4.79008 loss)
I0410 00:29:34.180768 16216 solver.cpp:218] Iteration 816 (1.18145 iter/s, 10.157s/12 iters), loss = 4.42042
I0410 00:29:34.180814 16216 solver.cpp:237]     Train net output #0: loss = 4.42042 (* 1 = 4.42042 loss)
I0410 00:29:34.180825 16216 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0410 00:29:38.367082 16216 solver.cpp:218] Iteration 828 (2.86661 iter/s, 4.18614s/12 iters), loss = 4.44094
I0410 00:29:38.367132 16216 solver.cpp:237]     Train net output #0: loss = 4.44094 (* 1 = 4.44094 loss)
I0410 00:29:38.367143 16216 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0410 00:29:43.263527 16216 solver.cpp:218] Iteration 840 (2.45086 iter/s, 4.89624s/12 iters), loss = 4.1094
I0410 00:29:43.263574 16216 solver.cpp:237]     Train net output #0: loss = 4.1094 (* 1 = 4.1094 loss)
I0410 00:29:43.263586 16216 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0410 00:29:48.178900 16216 solver.cpp:218] Iteration 852 (2.44142 iter/s, 4.91517s/12 iters), loss = 4.34633
I0410 00:29:48.178949 16216 solver.cpp:237]     Train net output #0: loss = 4.34633 (* 1 = 4.34633 loss)
I0410 00:29:48.178961 16216 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0410 00:29:53.138687 16216 solver.cpp:218] Iteration 864 (2.41956 iter/s, 4.95958s/12 iters), loss = 4.40994
I0410 00:29:53.138801 16216 solver.cpp:237]     Train net output #0: loss = 4.40994 (* 1 = 4.40994 loss)
I0410 00:29:53.138813 16216 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0410 00:29:58.015823 16216 solver.cpp:218] Iteration 876 (2.4606 iter/s, 4.87687s/12 iters), loss = 4.19579
I0410 00:29:58.015868 16216 solver.cpp:237]     Train net output #0: loss = 4.19579 (* 1 = 4.19579 loss)
I0410 00:29:58.015877 16216 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0410 00:30:02.980304 16216 solver.cpp:218] Iteration 888 (2.41727 iter/s, 4.96428s/12 iters), loss = 4.27817
I0410 00:30:02.980347 16216 solver.cpp:237]     Train net output #0: loss = 4.27817 (* 1 = 4.27817 loss)
I0410 00:30:02.980356 16216 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0410 00:30:07.956231 16216 solver.cpp:218] Iteration 900 (2.41171 iter/s, 4.97573s/12 iters), loss = 4.49884
I0410 00:30:07.956276 16216 solver.cpp:237]     Train net output #0: loss = 4.49884 (* 1 = 4.49884 loss)
I0410 00:30:07.956286 16216 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0410 00:30:11.865381 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:30:13.114414 16216 solver.cpp:218] Iteration 912 (2.32649 iter/s, 5.15798s/12 iters), loss = 4.19156
I0410 00:30:13.114459 16216 solver.cpp:237]     Train net output #0: loss = 4.19156 (* 1 = 4.19156 loss)
I0410 00:30:13.114468 16216 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0410 00:30:15.222895 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0410 00:30:15.465709 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0410 00:30:15.643491 16216 solver.cpp:330] Iteration 918, Testing net (#0)
I0410 00:30:15.643509 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:30:19.974943 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:30:20.376855 16216 solver.cpp:397]     Test net output #0: accuracy = 0.0667892
I0410 00:30:20.376902 16216 solver.cpp:397]     Test net output #1: loss = 4.70033 (* 1 = 4.70033 loss)
I0410 00:30:22.297158 16216 solver.cpp:218] Iteration 924 (1.30684 iter/s, 9.18243s/12 iters), loss = 4.1392
I0410 00:30:22.297201 16216 solver.cpp:237]     Train net output #0: loss = 4.1392 (* 1 = 4.1392 loss)
I0410 00:30:22.297209 16216 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0410 00:30:27.212558 16216 solver.cpp:218] Iteration 936 (2.4414 iter/s, 4.9152s/12 iters), loss = 4.2915
I0410 00:30:27.212707 16216 solver.cpp:237]     Train net output #0: loss = 4.2915 (* 1 = 4.2915 loss)
I0410 00:30:27.212719 16216 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0410 00:30:32.107368 16216 solver.cpp:218] Iteration 948 (2.45172 iter/s, 4.89452s/12 iters), loss = 4.18828
I0410 00:30:32.107405 16216 solver.cpp:237]     Train net output #0: loss = 4.18828 (* 1 = 4.18828 loss)
I0410 00:30:32.107414 16216 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0410 00:30:36.987355 16216 solver.cpp:218] Iteration 960 (2.45912 iter/s, 4.8798s/12 iters), loss = 3.97087
I0410 00:30:36.987398 16216 solver.cpp:237]     Train net output #0: loss = 3.97087 (* 1 = 3.97087 loss)
I0410 00:30:36.987409 16216 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0410 00:30:41.932965 16216 solver.cpp:218] Iteration 972 (2.42649 iter/s, 4.94542s/12 iters), loss = 4.09403
I0410 00:30:41.933005 16216 solver.cpp:237]     Train net output #0: loss = 4.09403 (* 1 = 4.09403 loss)
I0410 00:30:41.933013 16216 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0410 00:30:47.067288 16216 solver.cpp:218] Iteration 984 (2.3373 iter/s, 5.13413s/12 iters), loss = 4.21393
I0410 00:30:47.067328 16216 solver.cpp:237]     Train net output #0: loss = 4.21393 (* 1 = 4.21393 loss)
I0410 00:30:47.067337 16216 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0410 00:30:52.072212 16216 solver.cpp:218] Iteration 996 (2.39774 iter/s, 5.00472s/12 iters), loss = 4.18969
I0410 00:30:52.072273 16216 solver.cpp:237]     Train net output #0: loss = 4.18969 (* 1 = 4.18969 loss)
I0410 00:30:52.072285 16216 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0410 00:30:56.951929 16216 solver.cpp:218] Iteration 1008 (2.45927 iter/s, 4.87951s/12 iters), loss = 4.56675
I0410 00:30:56.951977 16216 solver.cpp:237]     Train net output #0: loss = 4.56675 (* 1 = 4.56675 loss)
I0410 00:30:56.951987 16216 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0410 00:30:57.958729 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:31:01.406148 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0410 00:31:02.119205 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0410 00:31:02.285917 16216 solver.cpp:330] Iteration 1020, Testing net (#0)
I0410 00:31:02.285936 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:31:06.287602 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:31:06.735216 16216 solver.cpp:397]     Test net output #0: accuracy = 0.0821078
I0410 00:31:06.735260 16216 solver.cpp:397]     Test net output #1: loss = 4.57809 (* 1 = 4.57809 loss)
I0410 00:31:06.818307 16216 solver.cpp:218] Iteration 1020 (1.21629 iter/s, 9.86604s/12 iters), loss = 3.87833
I0410 00:31:06.818356 16216 solver.cpp:237]     Train net output #0: loss = 3.87833 (* 1 = 3.87833 loss)
I0410 00:31:06.818367 16216 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0410 00:31:11.291138 16216 solver.cpp:218] Iteration 1032 (2.68298 iter/s, 4.47263s/12 iters), loss = 4.14379
I0410 00:31:11.291193 16216 solver.cpp:237]     Train net output #0: loss = 4.14379 (* 1 = 4.14379 loss)
I0410 00:31:11.291204 16216 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0410 00:31:16.248921 16216 solver.cpp:218] Iteration 1044 (2.42054 iter/s, 4.95757s/12 iters), loss = 4.01499
I0410 00:31:16.248975 16216 solver.cpp:237]     Train net output #0: loss = 4.01499 (* 1 = 4.01499 loss)
I0410 00:31:16.248986 16216 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0410 00:31:21.149757 16216 solver.cpp:218] Iteration 1056 (2.44867 iter/s, 4.90063s/12 iters), loss = 4.21454
I0410 00:31:21.149809 16216 solver.cpp:237]     Train net output #0: loss = 4.21454 (* 1 = 4.21454 loss)
I0410 00:31:21.149819 16216 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0410 00:31:26.117462 16216 solver.cpp:218] Iteration 1068 (2.4157 iter/s, 4.9675s/12 iters), loss = 4.19487
I0410 00:31:26.117512 16216 solver.cpp:237]     Train net output #0: loss = 4.19487 (* 1 = 4.19487 loss)
I0410 00:31:26.117524 16216 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0410 00:31:31.026136 16216 solver.cpp:218] Iteration 1080 (2.44475 iter/s, 4.90847s/12 iters), loss = 3.89391
I0410 00:31:31.026245 16216 solver.cpp:237]     Train net output #0: loss = 3.89391 (* 1 = 3.89391 loss)
I0410 00:31:31.026257 16216 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0410 00:31:35.928490 16216 solver.cpp:218] Iteration 1092 (2.44793 iter/s, 4.9021s/12 iters), loss = 4.10213
I0410 00:31:35.928529 16216 solver.cpp:237]     Train net output #0: loss = 4.10213 (* 1 = 4.10213 loss)
I0410 00:31:35.928539 16216 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0410 00:31:40.854908 16216 solver.cpp:218] Iteration 1104 (2.43595 iter/s, 4.92622s/12 iters), loss = 3.97653
I0410 00:31:40.854965 16216 solver.cpp:237]     Train net output #0: loss = 3.97653 (* 1 = 3.97653 loss)
I0410 00:31:40.854979 16216 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0410 00:31:43.934039 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:31:45.849071 16216 solver.cpp:218] Iteration 1116 (2.40291 iter/s, 4.99395s/12 iters), loss = 4.05687
I0410 00:31:45.849110 16216 solver.cpp:237]     Train net output #0: loss = 4.05687 (* 1 = 4.05687 loss)
I0410 00:31:45.849120 16216 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0410 00:31:47.863055 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0410 00:31:48.266758 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0410 00:31:49.086869 16216 solver.cpp:330] Iteration 1122, Testing net (#0)
I0410 00:31:49.086887 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:31:53.054852 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:31:53.536999 16216 solver.cpp:397]     Test net output #0: accuracy = 0.0851716
I0410 00:31:53.537039 16216 solver.cpp:397]     Test net output #1: loss = 4.61317 (* 1 = 4.61317 loss)
I0410 00:31:55.343161 16216 solver.cpp:218] Iteration 1128 (1.26399 iter/s, 9.49377s/12 iters), loss = 4.09111
I0410 00:31:55.343211 16216 solver.cpp:237]     Train net output #0: loss = 4.09111 (* 1 = 4.09111 loss)
I0410 00:31:55.343220 16216 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0410 00:32:00.355684 16216 solver.cpp:218] Iteration 1140 (2.39411 iter/s, 5.01231s/12 iters), loss = 3.94489
I0410 00:32:00.355762 16216 solver.cpp:237]     Train net output #0: loss = 3.94489 (* 1 = 3.94489 loss)
I0410 00:32:00.355777 16216 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0410 00:32:05.241917 16216 solver.cpp:218] Iteration 1152 (2.45599 iter/s, 4.886s/12 iters), loss = 3.82842
I0410 00:32:05.242060 16216 solver.cpp:237]     Train net output #0: loss = 3.82842 (* 1 = 3.82842 loss)
I0410 00:32:05.242074 16216 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0410 00:32:10.141055 16216 solver.cpp:218] Iteration 1164 (2.44956 iter/s, 4.89884s/12 iters), loss = 3.7797
I0410 00:32:10.141109 16216 solver.cpp:237]     Train net output #0: loss = 3.7797 (* 1 = 3.7797 loss)
I0410 00:32:10.141122 16216 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0410 00:32:15.082774 16216 solver.cpp:218] Iteration 1176 (2.42841 iter/s, 4.94151s/12 iters), loss = 3.84286
I0410 00:32:15.082806 16216 solver.cpp:237]     Train net output #0: loss = 3.84286 (* 1 = 3.84286 loss)
I0410 00:32:15.082813 16216 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0410 00:32:20.064594 16216 solver.cpp:218] Iteration 1188 (2.40885 iter/s, 4.98162s/12 iters), loss = 4.10534
I0410 00:32:20.064651 16216 solver.cpp:237]     Train net output #0: loss = 4.10534 (* 1 = 4.10534 loss)
I0410 00:32:20.064662 16216 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0410 00:32:25.011229 16216 solver.cpp:218] Iteration 1200 (2.426 iter/s, 4.94641s/12 iters), loss = 3.891
I0410 00:32:25.011289 16216 solver.cpp:237]     Train net output #0: loss = 3.891 (* 1 = 3.891 loss)
I0410 00:32:25.011301 16216 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0410 00:32:29.973625 16216 solver.cpp:218] Iteration 1212 (2.41829 iter/s, 4.96218s/12 iters), loss = 4.10968
I0410 00:32:29.973675 16216 solver.cpp:237]     Train net output #0: loss = 4.10968 (* 1 = 4.10968 loss)
I0410 00:32:29.973686 16216 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0410 00:32:30.253854 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:32:34.427973 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0410 00:32:35.181756 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0410 00:32:35.879315 16216 solver.cpp:330] Iteration 1224, Testing net (#0)
I0410 00:32:35.879386 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:32:39.846195 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:32:40.370918 16216 solver.cpp:397]     Test net output #0: accuracy = 0.0894608
I0410 00:32:40.370967 16216 solver.cpp:397]     Test net output #1: loss = 4.4786 (* 1 = 4.4786 loss)
I0410 00:32:40.453611 16216 solver.cpp:218] Iteration 1224 (1.14508 iter/s, 10.4796s/12 iters), loss = 3.69613
I0410 00:32:40.453670 16216 solver.cpp:237]     Train net output #0: loss = 3.69613 (* 1 = 3.69613 loss)
I0410 00:32:40.453681 16216 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0410 00:32:44.642614 16216 solver.cpp:218] Iteration 1236 (2.86478 iter/s, 4.18881s/12 iters), loss = 4.11877
I0410 00:32:44.642671 16216 solver.cpp:237]     Train net output #0: loss = 4.11877 (* 1 = 4.11877 loss)
I0410 00:32:44.642684 16216 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0410 00:32:49.588600 16216 solver.cpp:218] Iteration 1248 (2.42631 iter/s, 4.94578s/12 iters), loss = 3.67451
I0410 00:32:49.588639 16216 solver.cpp:237]     Train net output #0: loss = 3.67451 (* 1 = 3.67451 loss)
I0410 00:32:49.588649 16216 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0410 00:32:54.512769 16216 solver.cpp:218] Iteration 1260 (2.43706 iter/s, 4.92397s/12 iters), loss = 3.85552
I0410 00:32:54.512823 16216 solver.cpp:237]     Train net output #0: loss = 3.85552 (* 1 = 3.85552 loss)
I0410 00:32:54.512836 16216 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0410 00:32:59.451700 16216 solver.cpp:218] Iteration 1272 (2.42977 iter/s, 4.93873s/12 iters), loss = 3.97094
I0410 00:32:59.451743 16216 solver.cpp:237]     Train net output #0: loss = 3.97094 (* 1 = 3.97094 loss)
I0410 00:32:59.451752 16216 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0410 00:33:04.448153 16216 solver.cpp:218] Iteration 1284 (2.4018 iter/s, 4.99625s/12 iters), loss = 3.79519
I0410 00:33:04.448205 16216 solver.cpp:237]     Train net output #0: loss = 3.79519 (* 1 = 3.79519 loss)
I0410 00:33:04.448216 16216 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0410 00:33:09.456598 16216 solver.cpp:218] Iteration 1296 (2.39605 iter/s, 5.00823s/12 iters), loss = 3.65804
I0410 00:33:09.456707 16216 solver.cpp:237]     Train net output #0: loss = 3.65804 (* 1 = 3.65804 loss)
I0410 00:33:09.456717 16216 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0410 00:33:14.425412 16216 solver.cpp:218] Iteration 1308 (2.41519 iter/s, 4.96855s/12 iters), loss = 4.01671
I0410 00:33:14.425458 16216 solver.cpp:237]     Train net output #0: loss = 4.01671 (* 1 = 4.01671 loss)
I0410 00:33:14.425467 16216 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0410 00:33:16.874032 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:33:19.309453 16216 solver.cpp:218] Iteration 1320 (2.45708 iter/s, 4.88384s/12 iters), loss = 3.51104
I0410 00:33:19.309504 16216 solver.cpp:237]     Train net output #0: loss = 3.51104 (* 1 = 3.51104 loss)
I0410 00:33:19.309515 16216 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0410 00:33:21.302738 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0410 00:33:21.747869 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0410 00:33:22.933574 16216 solver.cpp:330] Iteration 1326, Testing net (#0)
I0410 00:33:22.933604 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:33:26.840276 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:33:27.501381 16216 solver.cpp:397]     Test net output #0: accuracy = 0.106618
I0410 00:33:27.501437 16216 solver.cpp:397]     Test net output #1: loss = 4.44452 (* 1 = 4.44452 loss)
I0410 00:33:29.430215 16216 solver.cpp:218] Iteration 1332 (1.18572 iter/s, 10.1204s/12 iters), loss = 3.43519
I0410 00:33:29.430264 16216 solver.cpp:237]     Train net output #0: loss = 3.43519 (* 1 = 3.43519 loss)
I0410 00:33:29.430274 16216 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0410 00:33:34.479907 16216 solver.cpp:218] Iteration 1344 (2.37648 iter/s, 5.04948s/12 iters), loss = 3.88337
I0410 00:33:34.479951 16216 solver.cpp:237]     Train net output #0: loss = 3.88337 (* 1 = 3.88337 loss)
I0410 00:33:34.479962 16216 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0410 00:33:39.570242 16216 solver.cpp:218] Iteration 1356 (2.35751 iter/s, 5.09013s/12 iters), loss = 3.68746
I0410 00:33:39.570375 16216 solver.cpp:237]     Train net output #0: loss = 3.68746 (* 1 = 3.68746 loss)
I0410 00:33:39.570387 16216 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0410 00:33:44.170044 16216 blocking_queue.cpp:49] Waiting for data
I0410 00:33:44.626154 16216 solver.cpp:218] Iteration 1368 (2.37359 iter/s, 5.05562s/12 iters), loss = 3.79244
I0410 00:33:44.626209 16216 solver.cpp:237]     Train net output #0: loss = 3.79244 (* 1 = 3.79244 loss)
I0410 00:33:44.626220 16216 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0410 00:33:49.666838 16216 solver.cpp:218] Iteration 1380 (2.38073 iter/s, 5.04047s/12 iters), loss = 3.80908
I0410 00:33:49.666895 16216 solver.cpp:237]     Train net output #0: loss = 3.80908 (* 1 = 3.80908 loss)
I0410 00:33:49.666909 16216 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0410 00:33:54.615396 16216 solver.cpp:218] Iteration 1392 (2.42505 iter/s, 4.94835s/12 iters), loss = 3.81308
I0410 00:33:54.615447 16216 solver.cpp:237]     Train net output #0: loss = 3.81308 (* 1 = 3.81308 loss)
I0410 00:33:54.615458 16216 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0410 00:33:59.632874 16216 solver.cpp:218] Iteration 1404 (2.39174 iter/s, 5.01727s/12 iters), loss = 3.5825
I0410 00:33:59.632926 16216 solver.cpp:237]     Train net output #0: loss = 3.5825 (* 1 = 3.5825 loss)
I0410 00:33:59.632938 16216 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0410 00:34:04.219316 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:34:04.568282 16216 solver.cpp:218] Iteration 1416 (2.43151 iter/s, 4.9352s/12 iters), loss = 3.5079
I0410 00:34:04.568348 16216 solver.cpp:237]     Train net output #0: loss = 3.5079 (* 1 = 3.5079 loss)
I0410 00:34:04.568367 16216 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0410 00:34:09.054600 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0410 00:34:09.304893 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0410 00:34:09.481299 16216 solver.cpp:330] Iteration 1428, Testing net (#0)
I0410 00:34:09.481318 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:34:13.473932 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:34:14.062883 16216 solver.cpp:397]     Test net output #0: accuracy = 0.101716
I0410 00:34:14.062932 16216 solver.cpp:397]     Test net output #1: loss = 4.53318 (* 1 = 4.53318 loss)
I0410 00:34:14.145622 16216 solver.cpp:218] Iteration 1428 (1.253 iter/s, 9.57699s/12 iters), loss = 3.92271
I0410 00:34:14.145682 16216 solver.cpp:237]     Train net output #0: loss = 3.92271 (* 1 = 3.92271 loss)
I0410 00:34:14.145694 16216 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0410 00:34:18.307018 16216 solver.cpp:218] Iteration 1440 (2.88379 iter/s, 4.1612s/12 iters), loss = 3.55598
I0410 00:34:18.307075 16216 solver.cpp:237]     Train net output #0: loss = 3.55598 (* 1 = 3.55598 loss)
I0410 00:34:18.307087 16216 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0410 00:34:23.193034 16216 solver.cpp:218] Iteration 1452 (2.45609 iter/s, 4.88581s/12 iters), loss = 3.92375
I0410 00:34:23.193084 16216 solver.cpp:237]     Train net output #0: loss = 3.92375 (* 1 = 3.92375 loss)
I0410 00:34:23.193095 16216 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0410 00:34:28.144451 16216 solver.cpp:218] Iteration 1464 (2.42365 iter/s, 4.95121s/12 iters), loss = 3.63901
I0410 00:34:28.144495 16216 solver.cpp:237]     Train net output #0: loss = 3.63901 (* 1 = 3.63901 loss)
I0410 00:34:28.144502 16216 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0410 00:34:33.042464 16216 solver.cpp:218] Iteration 1476 (2.45007 iter/s, 4.89781s/12 iters), loss = 3.62949
I0410 00:34:33.042510 16216 solver.cpp:237]     Train net output #0: loss = 3.62949 (* 1 = 3.62949 loss)
I0410 00:34:33.042520 16216 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0410 00:34:38.192615 16216 solver.cpp:218] Iteration 1488 (2.33012 iter/s, 5.14994s/12 iters), loss = 3.20818
I0410 00:34:38.192657 16216 solver.cpp:237]     Train net output #0: loss = 3.20818 (* 1 = 3.20818 loss)
I0410 00:34:38.192667 16216 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0410 00:34:43.067662 16216 solver.cpp:218] Iteration 1500 (2.46162 iter/s, 4.87485s/12 iters), loss = 3.10667
I0410 00:34:43.067718 16216 solver.cpp:237]     Train net output #0: loss = 3.10667 (* 1 = 3.10667 loss)
I0410 00:34:43.067729 16216 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0410 00:34:48.162482 16216 solver.cpp:218] Iteration 1512 (2.35544 iter/s, 5.0946s/12 iters), loss = 3.30973
I0410 00:34:48.162590 16216 solver.cpp:237]     Train net output #0: loss = 3.30973 (* 1 = 3.30973 loss)
I0410 00:34:48.162600 16216 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0410 00:34:49.947005 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:34:53.117952 16216 solver.cpp:218] Iteration 1524 (2.42169 iter/s, 4.95521s/12 iters), loss = 3.63026
I0410 00:34:53.118023 16216 solver.cpp:237]     Train net output #0: loss = 3.63026 (* 1 = 3.63026 loss)
I0410 00:34:53.118036 16216 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0410 00:34:55.120909 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0410 00:34:56.070220 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0410 00:34:56.371419 16216 solver.cpp:330] Iteration 1530, Testing net (#0)
I0410 00:34:56.371445 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:35:00.353377 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:35:00.993278 16216 solver.cpp:397]     Test net output #0: accuracy = 0.104779
I0410 00:35:00.993325 16216 solver.cpp:397]     Test net output #1: loss = 4.4071 (* 1 = 4.4071 loss)
I0410 00:35:02.873662 16216 solver.cpp:218] Iteration 1536 (1.23009 iter/s, 9.75535s/12 iters), loss = 3.37975
I0410 00:35:02.873715 16216 solver.cpp:237]     Train net output #0: loss = 3.37975 (* 1 = 3.37975 loss)
I0410 00:35:02.873729 16216 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0410 00:35:07.828614 16216 solver.cpp:218] Iteration 1548 (2.42192 iter/s, 4.95475s/12 iters), loss = 3.26545
I0410 00:35:07.828658 16216 solver.cpp:237]     Train net output #0: loss = 3.26545 (* 1 = 3.26545 loss)
I0410 00:35:07.828668 16216 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0410 00:35:12.786542 16216 solver.cpp:218] Iteration 1560 (2.42046 iter/s, 4.95773s/12 iters), loss = 3.34853
I0410 00:35:12.786598 16216 solver.cpp:237]     Train net output #0: loss = 3.34853 (* 1 = 3.34853 loss)
I0410 00:35:12.786609 16216 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0410 00:35:17.786347 16216 solver.cpp:218] Iteration 1572 (2.4002 iter/s, 4.99959s/12 iters), loss = 3.37484
I0410 00:35:17.786393 16216 solver.cpp:237]     Train net output #0: loss = 3.37484 (* 1 = 3.37484 loss)
I0410 00:35:17.786403 16216 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0410 00:35:23.727423 16216 solver.cpp:218] Iteration 1584 (2.01991 iter/s, 5.94084s/12 iters), loss = 3.63955
I0410 00:35:23.727571 16216 solver.cpp:237]     Train net output #0: loss = 3.63955 (* 1 = 3.63955 loss)
I0410 00:35:23.727586 16216 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0410 00:35:28.659889 16216 solver.cpp:218] Iteration 1596 (2.43301 iter/s, 4.93216s/12 iters), loss = 3.12925
I0410 00:35:28.659945 16216 solver.cpp:237]     Train net output #0: loss = 3.12925 (* 1 = 3.12925 loss)
I0410 00:35:28.659956 16216 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0410 00:35:33.755049 16216 solver.cpp:218] Iteration 1608 (2.35528 iter/s, 5.09495s/12 iters), loss = 3.41388
I0410 00:35:33.755097 16216 solver.cpp:237]     Train net output #0: loss = 3.41388 (* 1 = 3.41388 loss)
I0410 00:35:33.755108 16216 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0410 00:35:37.538450 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:35:38.588057 16216 solver.cpp:218] Iteration 1620 (2.48303 iter/s, 4.83281s/12 iters), loss = 3.38025
I0410 00:35:38.588106 16216 solver.cpp:237]     Train net output #0: loss = 3.38025 (* 1 = 3.38025 loss)
I0410 00:35:38.588115 16216 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0410 00:35:43.055068 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0410 00:35:43.804226 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0410 00:35:44.837788 16216 solver.cpp:330] Iteration 1632, Testing net (#0)
I0410 00:35:44.837819 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:35:48.555186 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:35:49.226415 16216 solver.cpp:397]     Test net output #0: accuracy = 0.110294
I0410 00:35:49.226459 16216 solver.cpp:397]     Test net output #1: loss = 4.40902 (* 1 = 4.40902 loss)
I0410 00:35:49.309195 16216 solver.cpp:218] Iteration 1632 (1.11932 iter/s, 10.7208s/12 iters), loss = 3.34504
I0410 00:35:49.309245 16216 solver.cpp:237]     Train net output #0: loss = 3.34504 (* 1 = 3.34504 loss)
I0410 00:35:49.309257 16216 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0410 00:35:53.530053 16216 solver.cpp:218] Iteration 1644 (2.84315 iter/s, 4.22067s/12 iters), loss = 3.52428
I0410 00:35:53.530103 16216 solver.cpp:237]     Train net output #0: loss = 3.52428 (* 1 = 3.52428 loss)
I0410 00:35:53.530114 16216 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0410 00:35:58.461236 16216 solver.cpp:218] Iteration 1656 (2.43359 iter/s, 4.93098s/12 iters), loss = 3.34688
I0410 00:35:58.461335 16216 solver.cpp:237]     Train net output #0: loss = 3.34688 (* 1 = 3.34688 loss)
I0410 00:35:58.461345 16216 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0410 00:36:03.389816 16216 solver.cpp:218] Iteration 1668 (2.43491 iter/s, 4.92832s/12 iters), loss = 3.16836
I0410 00:36:03.389868 16216 solver.cpp:237]     Train net output #0: loss = 3.16836 (* 1 = 3.16836 loss)
I0410 00:36:03.389879 16216 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0410 00:36:08.511629 16216 solver.cpp:218] Iteration 1680 (2.34302 iter/s, 5.1216s/12 iters), loss = 3.20391
I0410 00:36:08.511688 16216 solver.cpp:237]     Train net output #0: loss = 3.20391 (* 1 = 3.20391 loss)
I0410 00:36:08.511701 16216 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0410 00:36:13.667462 16216 solver.cpp:218] Iteration 1692 (2.32756 iter/s, 5.15561s/12 iters), loss = 3.29402
I0410 00:36:13.667521 16216 solver.cpp:237]     Train net output #0: loss = 3.29402 (* 1 = 3.29402 loss)
I0410 00:36:13.667533 16216 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0410 00:36:18.500380 16216 solver.cpp:218] Iteration 1704 (2.48308 iter/s, 4.83271s/12 iters), loss = 2.97218
I0410 00:36:18.500439 16216 solver.cpp:237]     Train net output #0: loss = 2.97218 (* 1 = 2.97218 loss)
I0410 00:36:18.500452 16216 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0410 00:36:23.382109 16216 solver.cpp:218] Iteration 1716 (2.45826 iter/s, 4.88151s/12 iters), loss = 3.63019
I0410 00:36:23.382174 16216 solver.cpp:237]     Train net output #0: loss = 3.63019 (* 1 = 3.63019 loss)
I0410 00:36:23.382185 16216 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0410 00:36:24.401150 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:36:28.258203 16216 solver.cpp:218] Iteration 1728 (2.4611 iter/s, 4.87587s/12 iters), loss = 3.17255
I0410 00:36:28.258263 16216 solver.cpp:237]     Train net output #0: loss = 3.17255 (* 1 = 3.17255 loss)
I0410 00:36:28.258275 16216 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0410 00:36:30.230935 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0410 00:36:30.488421 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0410 00:36:30.675642 16216 solver.cpp:330] Iteration 1734, Testing net (#0)
I0410 00:36:30.675671 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:36:34.379774 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:36:35.098202 16216 solver.cpp:397]     Test net output #0: accuracy = 0.121936
I0410 00:36:35.098237 16216 solver.cpp:397]     Test net output #1: loss = 4.24043 (* 1 = 4.24043 loss)
I0410 00:36:36.900117 16216 solver.cpp:218] Iteration 1740 (1.38863 iter/s, 8.64159s/12 iters), loss = 3.09194
I0410 00:36:36.900174 16216 solver.cpp:237]     Train net output #0: loss = 3.09194 (* 1 = 3.09194 loss)
I0410 00:36:36.900185 16216 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0410 00:36:42.010643 16216 solver.cpp:218] Iteration 1752 (2.3482 iter/s, 5.1103s/12 iters), loss = 3.04687
I0410 00:36:42.010697 16216 solver.cpp:237]     Train net output #0: loss = 3.04687 (* 1 = 3.04687 loss)
I0410 00:36:42.010710 16216 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0410 00:36:46.954100 16216 solver.cpp:218] Iteration 1764 (2.42756 iter/s, 4.94324s/12 iters), loss = 2.76593
I0410 00:36:46.954156 16216 solver.cpp:237]     Train net output #0: loss = 2.76593 (* 1 = 2.76593 loss)
I0410 00:36:46.954166 16216 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0410 00:36:51.958693 16216 solver.cpp:218] Iteration 1776 (2.3979 iter/s, 5.00438s/12 iters), loss = 3.24826
I0410 00:36:51.958736 16216 solver.cpp:237]     Train net output #0: loss = 3.24826 (* 1 = 3.24826 loss)
I0410 00:36:51.958745 16216 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0410 00:36:56.863476 16216 solver.cpp:218] Iteration 1788 (2.44669 iter/s, 4.90459s/12 iters), loss = 3.3386
I0410 00:36:56.863520 16216 solver.cpp:237]     Train net output #0: loss = 3.3386 (* 1 = 3.3386 loss)
I0410 00:36:56.863531 16216 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0410 00:37:01.765136 16216 solver.cpp:218] Iteration 1800 (2.44825 iter/s, 4.90146s/12 iters), loss = 3.35864
I0410 00:37:01.765285 16216 solver.cpp:237]     Train net output #0: loss = 3.35864 (* 1 = 3.35864 loss)
I0410 00:37:01.765298 16216 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0410 00:37:06.784451 16216 solver.cpp:218] Iteration 1812 (2.39091 iter/s, 5.01901s/12 iters), loss = 3.1337
I0410 00:37:06.784503 16216 solver.cpp:237]     Train net output #0: loss = 3.1337 (* 1 = 3.1337 loss)
I0410 00:37:06.784515 16216 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0410 00:37:10.202154 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:37:11.993489 16216 solver.cpp:218] Iteration 1824 (2.30378 iter/s, 5.20882s/12 iters), loss = 3.31101
I0410 00:37:11.993532 16216 solver.cpp:237]     Train net output #0: loss = 3.31101 (* 1 = 3.31101 loss)
I0410 00:37:11.993542 16216 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0410 00:37:16.435637 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0410 00:37:16.929323 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0410 00:37:17.107445 16216 solver.cpp:330] Iteration 1836, Testing net (#0)
I0410 00:37:17.107465 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:37:20.818790 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:37:21.565726 16216 solver.cpp:397]     Test net output #0: accuracy = 0.128676
I0410 00:37:21.565778 16216 solver.cpp:397]     Test net output #1: loss = 4.24661 (* 1 = 4.24661 loss)
I0410 00:37:21.648659 16216 solver.cpp:218] Iteration 1836 (1.2429 iter/s, 9.65483s/12 iters), loss = 3.27337
I0410 00:37:21.648715 16216 solver.cpp:237]     Train net output #0: loss = 3.27337 (* 1 = 3.27337 loss)
I0410 00:37:21.648726 16216 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0410 00:37:25.937589 16216 solver.cpp:218] Iteration 1848 (2.79803 iter/s, 4.28873s/12 iters), loss = 3.01584
I0410 00:37:25.937638 16216 solver.cpp:237]     Train net output #0: loss = 3.01584 (* 1 = 3.01584 loss)
I0410 00:37:25.937647 16216 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0410 00:37:30.817914 16216 solver.cpp:218] Iteration 1860 (2.45896 iter/s, 4.88012s/12 iters), loss = 2.79813
I0410 00:37:30.817975 16216 solver.cpp:237]     Train net output #0: loss = 2.79813 (* 1 = 2.79813 loss)
I0410 00:37:30.817986 16216 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0410 00:37:35.752326 16216 solver.cpp:218] Iteration 1872 (2.43201 iter/s, 4.93419s/12 iters), loss = 2.75399
I0410 00:37:35.752461 16216 solver.cpp:237]     Train net output #0: loss = 2.75399 (* 1 = 2.75399 loss)
I0410 00:37:35.752476 16216 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0410 00:37:40.668154 16216 solver.cpp:218] Iteration 1884 (2.44123 iter/s, 4.91555s/12 iters), loss = 3.09686
I0410 00:37:40.668200 16216 solver.cpp:237]     Train net output #0: loss = 3.09686 (* 1 = 3.09686 loss)
I0410 00:37:40.668210 16216 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0410 00:37:45.709973 16216 solver.cpp:218] Iteration 1896 (2.3802 iter/s, 5.04159s/12 iters), loss = 2.96156
I0410 00:37:45.710036 16216 solver.cpp:237]     Train net output #0: loss = 2.96156 (* 1 = 2.96156 loss)
I0410 00:37:45.710050 16216 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0410 00:37:50.667600 16216 solver.cpp:218] Iteration 1908 (2.42062 iter/s, 4.9574s/12 iters), loss = 3.02131
I0410 00:37:50.667657 16216 solver.cpp:237]     Train net output #0: loss = 3.02131 (* 1 = 3.02131 loss)
I0410 00:37:50.667670 16216 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0410 00:37:55.578649 16216 solver.cpp:218] Iteration 1920 (2.44357 iter/s, 4.91084s/12 iters), loss = 3.16493
I0410 00:37:55.578698 16216 solver.cpp:237]     Train net output #0: loss = 3.16493 (* 1 = 3.16493 loss)
I0410 00:37:55.578711 16216 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0410 00:37:55.897281 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:38:00.523525 16216 solver.cpp:218] Iteration 1932 (2.42685 iter/s, 4.94467s/12 iters), loss = 3.19251
I0410 00:38:00.523572 16216 solver.cpp:237]     Train net output #0: loss = 3.19251 (* 1 = 3.19251 loss)
I0410 00:38:00.523584 16216 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0410 00:38:02.510596 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0410 00:38:03.015254 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0410 00:38:03.272150 16216 solver.cpp:330] Iteration 1938, Testing net (#0)
I0410 00:38:03.272173 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:38:06.979236 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:38:07.762297 16216 solver.cpp:397]     Test net output #0: accuracy = 0.146446
I0410 00:38:07.762357 16216 solver.cpp:397]     Test net output #1: loss = 4.21215 (* 1 = 4.21215 loss)
I0410 00:38:09.722959 16216 solver.cpp:218] Iteration 1944 (1.30447 iter/s, 9.19911s/12 iters), loss = 2.7597
I0410 00:38:09.723002 16216 solver.cpp:237]     Train net output #0: loss = 2.7597 (* 1 = 2.7597 loss)
I0410 00:38:09.723011 16216 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0410 00:38:14.651746 16216 solver.cpp:218] Iteration 1956 (2.43477 iter/s, 4.92859s/12 iters), loss = 2.78895
I0410 00:38:14.651780 16216 solver.cpp:237]     Train net output #0: loss = 2.78895 (* 1 = 2.78895 loss)
I0410 00:38:14.651789 16216 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0410 00:38:19.659602 16216 solver.cpp:218] Iteration 1968 (2.39633 iter/s, 5.00766s/12 iters), loss = 2.85901
I0410 00:38:19.659658 16216 solver.cpp:237]     Train net output #0: loss = 2.85901 (* 1 = 2.85901 loss)
I0410 00:38:19.659670 16216 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0410 00:38:24.500977 16216 solver.cpp:218] Iteration 1980 (2.47874 iter/s, 4.84116s/12 iters), loss = 3.21636
I0410 00:38:24.501039 16216 solver.cpp:237]     Train net output #0: loss = 3.21636 (* 1 = 3.21636 loss)
I0410 00:38:24.501050 16216 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0410 00:38:29.371701 16216 solver.cpp:218] Iteration 1992 (2.46381 iter/s, 4.87051s/12 iters), loss = 2.75187
I0410 00:38:29.371757 16216 solver.cpp:237]     Train net output #0: loss = 2.75187 (* 1 = 2.75187 loss)
I0410 00:38:29.371768 16216 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0410 00:38:34.312995 16216 solver.cpp:218] Iteration 2004 (2.42862 iter/s, 4.94108s/12 iters), loss = 2.48782
I0410 00:38:34.313048 16216 solver.cpp:237]     Train net output #0: loss = 2.48782 (* 1 = 2.48782 loss)
I0410 00:38:34.313060 16216 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0410 00:38:39.390959 16216 solver.cpp:218] Iteration 2016 (2.36325 iter/s, 5.07775s/12 iters), loss = 2.8666
I0410 00:38:39.391072 16216 solver.cpp:237]     Train net output #0: loss = 2.8666 (* 1 = 2.8666 loss)
I0410 00:38:39.391084 16216 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0410 00:38:41.938136 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:38:44.366789 16216 solver.cpp:218] Iteration 2028 (2.41179 iter/s, 4.97556s/12 iters), loss = 2.54867
I0410 00:38:44.366840 16216 solver.cpp:237]     Train net output #0: loss = 2.54867 (* 1 = 2.54867 loss)
I0410 00:38:44.366852 16216 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0410 00:38:48.852897 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0410 00:38:49.090133 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0410 00:38:49.267719 16216 solver.cpp:330] Iteration 2040, Testing net (#0)
I0410 00:38:49.267745 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:38:53.038368 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:38:53.863237 16216 solver.cpp:397]     Test net output #0: accuracy = 0.134804
I0410 00:38:53.863291 16216 solver.cpp:397]     Test net output #1: loss = 4.28016 (* 1 = 4.28016 loss)
I0410 00:38:53.944159 16216 solver.cpp:218] Iteration 2040 (1.253 iter/s, 9.57703s/12 iters), loss = 2.95378
I0410 00:38:53.944209 16216 solver.cpp:237]     Train net output #0: loss = 2.95378 (* 1 = 2.95378 loss)
I0410 00:38:53.944222 16216 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0410 00:38:58.366420 16216 solver.cpp:218] Iteration 2052 (2.71367 iter/s, 4.42206s/12 iters), loss = 2.66882
I0410 00:38:58.366468 16216 solver.cpp:237]     Train net output #0: loss = 2.66882 (* 1 = 2.66882 loss)
I0410 00:38:58.366479 16216 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0410 00:38:58.366693 16216 blocking_queue.cpp:49] Waiting for data
I0410 00:39:03.470268 16216 solver.cpp:218] Iteration 2064 (2.35126 iter/s, 5.10364s/12 iters), loss = 2.82268
I0410 00:39:03.470314 16216 solver.cpp:237]     Train net output #0: loss = 2.82268 (* 1 = 2.82268 loss)
I0410 00:39:03.470324 16216 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0410 00:39:08.457383 16216 solver.cpp:218] Iteration 2076 (2.4063 iter/s, 4.9869s/12 iters), loss = 2.61794
I0410 00:39:08.457439 16216 solver.cpp:237]     Train net output #0: loss = 2.61794 (* 1 = 2.61794 loss)
I0410 00:39:08.457451 16216 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0410 00:39:13.440492 16216 solver.cpp:218] Iteration 2088 (2.40824 iter/s, 4.9829s/12 iters), loss = 2.72435
I0410 00:39:13.440639 16216 solver.cpp:237]     Train net output #0: loss = 2.72435 (* 1 = 2.72435 loss)
I0410 00:39:13.440651 16216 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0410 00:39:18.367205 16216 solver.cpp:218] Iteration 2100 (2.43585 iter/s, 4.92642s/12 iters), loss = 2.41434
I0410 00:39:18.367254 16216 solver.cpp:237]     Train net output #0: loss = 2.41434 (* 1 = 2.41434 loss)
I0410 00:39:18.367264 16216 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0410 00:39:23.277263 16216 solver.cpp:218] Iteration 2112 (2.44406 iter/s, 4.90985s/12 iters), loss = 2.84604
I0410 00:39:23.277312 16216 solver.cpp:237]     Train net output #0: loss = 2.84604 (* 1 = 2.84604 loss)
I0410 00:39:23.277324 16216 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0410 00:39:27.875617 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:39:28.192687 16216 solver.cpp:218] Iteration 2124 (2.4414 iter/s, 4.91521s/12 iters), loss = 2.62535
I0410 00:39:28.192744 16216 solver.cpp:237]     Train net output #0: loss = 2.62535 (* 1 = 2.62535 loss)
I0410 00:39:28.192755 16216 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0410 00:39:33.112087 16216 solver.cpp:218] Iteration 2136 (2.43943 iter/s, 4.91918s/12 iters), loss = 3.02727
I0410 00:39:33.112143 16216 solver.cpp:237]     Train net output #0: loss = 3.02727 (* 1 = 3.02727 loss)
I0410 00:39:33.112154 16216 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0410 00:39:35.112960 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0410 00:39:36.526298 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0410 00:39:37.042243 16216 solver.cpp:330] Iteration 2142, Testing net (#0)
I0410 00:39:37.042273 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:39:40.781467 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:39:41.741053 16216 solver.cpp:397]     Test net output #0: accuracy = 0.136029
I0410 00:39:41.741096 16216 solver.cpp:397]     Test net output #1: loss = 4.32917 (* 1 = 4.32917 loss)
I0410 00:39:43.503495 16216 solver.cpp:218] Iteration 2148 (1.15484 iter/s, 10.391s/12 iters), loss = 2.35752
I0410 00:39:43.503613 16216 solver.cpp:237]     Train net output #0: loss = 2.35752 (* 1 = 2.35752 loss)
I0410 00:39:43.503628 16216 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0410 00:39:48.368237 16216 solver.cpp:218] Iteration 2160 (2.46686 iter/s, 4.86448s/12 iters), loss = 2.55996
I0410 00:39:48.368281 16216 solver.cpp:237]     Train net output #0: loss = 2.55996 (* 1 = 2.55996 loss)
I0410 00:39:48.368290 16216 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0410 00:39:53.339612 16216 solver.cpp:218] Iteration 2172 (2.41392 iter/s, 4.97118s/12 iters), loss = 2.52505
I0410 00:39:53.339651 16216 solver.cpp:237]     Train net output #0: loss = 2.52505 (* 1 = 2.52505 loss)
I0410 00:39:53.339660 16216 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0410 00:39:58.279047 16216 solver.cpp:218] Iteration 2184 (2.42952 iter/s, 4.93924s/12 iters), loss = 2.72682
I0410 00:39:58.279096 16216 solver.cpp:237]     Train net output #0: loss = 2.72682 (* 1 = 2.72682 loss)
I0410 00:39:58.279106 16216 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0410 00:40:03.122934 16216 solver.cpp:218] Iteration 2196 (2.47745 iter/s, 4.84369s/12 iters), loss = 2.33589
I0410 00:40:03.122982 16216 solver.cpp:237]     Train net output #0: loss = 2.33589 (* 1 = 2.33589 loss)
I0410 00:40:03.122994 16216 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0410 00:40:08.067176 16216 solver.cpp:218] Iteration 2208 (2.42717 iter/s, 4.94403s/12 iters), loss = 2.0843
I0410 00:40:08.067227 16216 solver.cpp:237]     Train net output #0: loss = 2.0843 (* 1 = 2.0843 loss)
I0410 00:40:08.067238 16216 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0410 00:40:13.050319 16216 solver.cpp:218] Iteration 2220 (2.40822 iter/s, 4.98294s/12 iters), loss = 2.29555
I0410 00:40:13.050357 16216 solver.cpp:237]     Train net output #0: loss = 2.29555 (* 1 = 2.29555 loss)
I0410 00:40:13.050366 16216 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0410 00:40:14.814985 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:40:17.958137 16216 solver.cpp:218] Iteration 2232 (2.44518 iter/s, 4.90762s/12 iters), loss = 2.67161
I0410 00:40:17.958187 16216 solver.cpp:237]     Train net output #0: loss = 2.67161 (* 1 = 2.67161 loss)
I0410 00:40:17.958199 16216 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0410 00:40:22.405227 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0410 00:40:22.645299 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0410 00:40:22.816424 16216 solver.cpp:330] Iteration 2244, Testing net (#0)
I0410 00:40:22.816453 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:40:26.382484 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:40:27.287557 16216 solver.cpp:397]     Test net output #0: accuracy = 0.161152
I0410 00:40:27.287586 16216 solver.cpp:397]     Test net output #1: loss = 4.19617 (* 1 = 4.19617 loss)
I0410 00:40:27.370287 16216 solver.cpp:218] Iteration 2244 (1.27499 iter/s, 9.41182s/12 iters), loss = 2.53741
I0410 00:40:27.370354 16216 solver.cpp:237]     Train net output #0: loss = 2.53741 (* 1 = 2.53741 loss)
I0410 00:40:27.370368 16216 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0410 00:40:31.535310 16216 solver.cpp:218] Iteration 2256 (2.88127 iter/s, 4.16482s/12 iters), loss = 2.29591
I0410 00:40:31.535368 16216 solver.cpp:237]     Train net output #0: loss = 2.29591 (* 1 = 2.29591 loss)
I0410 00:40:31.535382 16216 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0410 00:40:36.538846 16216 solver.cpp:218] Iteration 2268 (2.39841 iter/s, 5.00332s/12 iters), loss = 2.3652
I0410 00:40:36.538898 16216 solver.cpp:237]     Train net output #0: loss = 2.3652 (* 1 = 2.3652 loss)
I0410 00:40:36.538910 16216 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0410 00:40:41.433861 16216 solver.cpp:218] Iteration 2280 (2.45158 iter/s, 4.89481s/12 iters), loss = 2.61908
I0410 00:40:41.433908 16216 solver.cpp:237]     Train net output #0: loss = 2.61908 (* 1 = 2.61908 loss)
I0410 00:40:41.433920 16216 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0410 00:40:46.409801 16216 solver.cpp:218] Iteration 2292 (2.4117 iter/s, 4.97574s/12 iters), loss = 2.49412
I0410 00:40:46.409943 16216 solver.cpp:237]     Train net output #0: loss = 2.49412 (* 1 = 2.49412 loss)
I0410 00:40:46.409976 16216 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0410 00:40:51.297854 16216 solver.cpp:218] Iteration 2304 (2.45511 iter/s, 4.88776s/12 iters), loss = 2.49307
I0410 00:40:51.297906 16216 solver.cpp:237]     Train net output #0: loss = 2.49307 (* 1 = 2.49307 loss)
I0410 00:40:51.297919 16216 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0410 00:40:56.259860 16216 solver.cpp:218] Iteration 2316 (2.41848 iter/s, 4.9618s/12 iters), loss = 2.00708
I0410 00:40:56.259910 16216 solver.cpp:237]     Train net output #0: loss = 2.00708 (* 1 = 2.00708 loss)
I0410 00:40:56.259922 16216 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0410 00:41:00.143563 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:41:01.181183 16216 solver.cpp:218] Iteration 2328 (2.43847 iter/s, 4.92112s/12 iters), loss = 2.6784
I0410 00:41:01.181236 16216 solver.cpp:237]     Train net output #0: loss = 2.6784 (* 1 = 2.6784 loss)
I0410 00:41:01.181248 16216 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0410 00:41:06.152858 16216 solver.cpp:218] Iteration 2340 (2.41378 iter/s, 4.97147s/12 iters), loss = 2.24116
I0410 00:41:06.152911 16216 solver.cpp:237]     Train net output #0: loss = 2.24116 (* 1 = 2.24116 loss)
I0410 00:41:06.152923 16216 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0410 00:41:08.267275 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0410 00:41:11.075965 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0410 00:41:11.520136 16216 solver.cpp:330] Iteration 2346, Testing net (#0)
I0410 00:41:11.520169 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:41:15.012814 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:41:15.958264 16216 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0410 00:41:15.958313 16216 solver.cpp:397]     Test net output #1: loss = 4.39197 (* 1 = 4.39197 loss)
I0410 00:41:17.728204 16216 solver.cpp:218] Iteration 2352 (1.03672 iter/s, 11.575s/12 iters), loss = 2.46881
I0410 00:41:17.728333 16216 solver.cpp:237]     Train net output #0: loss = 2.46881 (* 1 = 2.46881 loss)
I0410 00:41:17.728348 16216 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0410 00:41:22.790879 16216 solver.cpp:218] Iteration 2364 (2.37042 iter/s, 5.06239s/12 iters), loss = 2.6069
I0410 00:41:22.790922 16216 solver.cpp:237]     Train net output #0: loss = 2.6069 (* 1 = 2.6069 loss)
I0410 00:41:22.790932 16216 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0410 00:41:27.969877 16216 solver.cpp:218] Iteration 2376 (2.31714 iter/s, 5.17879s/12 iters), loss = 2.25876
I0410 00:41:27.969929 16216 solver.cpp:237]     Train net output #0: loss = 2.25876 (* 1 = 2.25876 loss)
I0410 00:41:27.969944 16216 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0410 00:41:33.310813 16216 solver.cpp:218] Iteration 2388 (2.24689 iter/s, 5.34072s/12 iters), loss = 2.26281
I0410 00:41:33.310858 16216 solver.cpp:237]     Train net output #0: loss = 2.26281 (* 1 = 2.26281 loss)
I0410 00:41:33.310868 16216 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0410 00:41:38.202664 16216 solver.cpp:218] Iteration 2400 (2.45316 iter/s, 4.89165s/12 iters), loss = 2.15637
I0410 00:41:38.202723 16216 solver.cpp:237]     Train net output #0: loss = 2.15637 (* 1 = 2.15637 loss)
I0410 00:41:38.202735 16216 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0410 00:41:43.141487 16216 solver.cpp:218] Iteration 2412 (2.42983 iter/s, 4.93861s/12 iters), loss = 2.14478
I0410 00:41:43.141546 16216 solver.cpp:237]     Train net output #0: loss = 2.14478 (* 1 = 2.14478 loss)
I0410 00:41:43.141557 16216 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0410 00:41:48.075520 16216 solver.cpp:218] Iteration 2424 (2.4322 iter/s, 4.93381s/12 iters), loss = 2.54861
I0410 00:41:48.075651 16216 solver.cpp:237]     Train net output #0: loss = 2.54861 (* 1 = 2.54861 loss)
I0410 00:41:48.075665 16216 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0410 00:41:49.120081 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:41:53.033540 16216 solver.cpp:218] Iteration 2436 (2.42046 iter/s, 4.95773s/12 iters), loss = 2.31381
I0410 00:41:53.033583 16216 solver.cpp:237]     Train net output #0: loss = 2.31381 (* 1 = 2.31381 loss)
I0410 00:41:53.033593 16216 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0410 00:41:57.496430 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0410 00:41:58.034479 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0410 00:41:58.516250 16216 solver.cpp:330] Iteration 2448, Testing net (#0)
I0410 00:41:58.516275 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:42:02.117354 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:42:03.090090 16216 solver.cpp:397]     Test net output #0: accuracy = 0.160539
I0410 00:42:03.090135 16216 solver.cpp:397]     Test net output #1: loss = 4.45442 (* 1 = 4.45442 loss)
I0410 00:42:03.172727 16216 solver.cpp:218] Iteration 2448 (1.18357 iter/s, 10.1388s/12 iters), loss = 2.46387
I0410 00:42:03.172772 16216 solver.cpp:237]     Train net output #0: loss = 2.46387 (* 1 = 2.46387 loss)
I0410 00:42:03.172783 16216 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0410 00:42:07.337838 16216 solver.cpp:218] Iteration 2460 (2.8812 iter/s, 4.16493s/12 iters), loss = 2.39024
I0410 00:42:07.337884 16216 solver.cpp:237]     Train net output #0: loss = 2.39024 (* 1 = 2.39024 loss)
I0410 00:42:07.337895 16216 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0410 00:42:12.231024 16216 solver.cpp:218] Iteration 2472 (2.45249 iter/s, 4.89298s/12 iters), loss = 2.10241
I0410 00:42:12.231084 16216 solver.cpp:237]     Train net output #0: loss = 2.10241 (* 1 = 2.10241 loss)
I0410 00:42:12.231096 16216 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0410 00:42:17.162644 16216 solver.cpp:218] Iteration 2484 (2.43338 iter/s, 4.93141s/12 iters), loss = 1.89377
I0410 00:42:17.162688 16216 solver.cpp:237]     Train net output #0: loss = 1.89377 (* 1 = 1.89377 loss)
I0410 00:42:17.162700 16216 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0410 00:42:22.043215 16216 solver.cpp:218] Iteration 2496 (2.45883 iter/s, 4.88037s/12 iters), loss = 2.07571
I0410 00:42:22.043375 16216 solver.cpp:237]     Train net output #0: loss = 2.07571 (* 1 = 2.07571 loss)
I0410 00:42:22.043388 16216 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0410 00:42:26.927237 16216 solver.cpp:218] Iteration 2508 (2.45715 iter/s, 4.88371s/12 iters), loss = 2.148
I0410 00:42:26.927299 16216 solver.cpp:237]     Train net output #0: loss = 2.148 (* 1 = 2.148 loss)
I0410 00:42:26.927310 16216 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0410 00:42:31.876173 16216 solver.cpp:218] Iteration 2520 (2.42487 iter/s, 4.94872s/12 iters), loss = 1.66681
I0410 00:42:31.876219 16216 solver.cpp:237]     Train net output #0: loss = 1.66681 (* 1 = 1.66681 loss)
I0410 00:42:31.876228 16216 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0410 00:42:35.025640 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:42:36.766803 16216 solver.cpp:218] Iteration 2532 (2.45378 iter/s, 4.89042s/12 iters), loss = 2.18712
I0410 00:42:36.766857 16216 solver.cpp:237]     Train net output #0: loss = 2.18712 (* 1 = 2.18712 loss)
I0410 00:42:36.766868 16216 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0410 00:42:41.696710 16216 solver.cpp:218] Iteration 2544 (2.43423 iter/s, 4.9297s/12 iters), loss = 1.83822
I0410 00:42:41.696764 16216 solver.cpp:237]     Train net output #0: loss = 1.83822 (* 1 = 1.83822 loss)
I0410 00:42:41.696777 16216 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0410 00:42:43.691831 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0410 00:42:43.950497 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0410 00:42:44.136744 16216 solver.cpp:330] Iteration 2550, Testing net (#0)
I0410 00:42:44.136765 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:42:47.573482 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:42:48.595410 16216 solver.cpp:397]     Test net output #0: accuracy = 0.181373
I0410 00:42:48.595466 16216 solver.cpp:397]     Test net output #1: loss = 4.47151 (* 1 = 4.47151 loss)
I0410 00:42:50.367178 16216 solver.cpp:218] Iteration 2556 (1.38406 iter/s, 8.67015s/12 iters), loss = 2.32644
I0410 00:42:50.367231 16216 solver.cpp:237]     Train net output #0: loss = 2.32644 (* 1 = 2.32644 loss)
I0410 00:42:50.367247 16216 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0410 00:42:55.289629 16216 solver.cpp:218] Iteration 2568 (2.43791 iter/s, 4.92224s/12 iters), loss = 2.27025
I0410 00:42:55.289810 16216 solver.cpp:237]     Train net output #0: loss = 2.27025 (* 1 = 2.27025 loss)
I0410 00:42:55.289825 16216 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0410 00:43:00.316880 16216 solver.cpp:218] Iteration 2580 (2.38715 iter/s, 5.02692s/12 iters), loss = 2.1136
I0410 00:43:00.316931 16216 solver.cpp:237]     Train net output #0: loss = 2.1136 (* 1 = 2.1136 loss)
I0410 00:43:00.316942 16216 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0410 00:43:05.290092 16216 solver.cpp:218] Iteration 2592 (2.41303 iter/s, 4.973s/12 iters), loss = 1.99034
I0410 00:43:05.290139 16216 solver.cpp:237]     Train net output #0: loss = 1.99034 (* 1 = 1.99034 loss)
I0410 00:43:05.290149 16216 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0410 00:43:10.186147 16216 solver.cpp:218] Iteration 2604 (2.45106 iter/s, 4.89585s/12 iters), loss = 2.35223
I0410 00:43:10.186195 16216 solver.cpp:237]     Train net output #0: loss = 2.35223 (* 1 = 2.35223 loss)
I0410 00:43:10.186204 16216 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0410 00:43:15.118036 16216 solver.cpp:218] Iteration 2616 (2.43325 iter/s, 4.93168s/12 iters), loss = 2.16696
I0410 00:43:15.118088 16216 solver.cpp:237]     Train net output #0: loss = 2.16696 (* 1 = 2.16696 loss)
I0410 00:43:15.118098 16216 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0410 00:43:20.015923 16216 solver.cpp:218] Iteration 2628 (2.45014 iter/s, 4.89768s/12 iters), loss = 2.18163
I0410 00:43:20.015967 16216 solver.cpp:237]     Train net output #0: loss = 2.18163 (* 1 = 2.18163 loss)
I0410 00:43:20.015976 16216 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0410 00:43:20.450901 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:43:24.946250 16216 solver.cpp:218] Iteration 2640 (2.43401 iter/s, 4.93013s/12 iters), loss = 2.25671
I0410 00:43:24.946306 16216 solver.cpp:237]     Train net output #0: loss = 2.25671 (* 1 = 2.25671 loss)
I0410 00:43:24.946319 16216 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0410 00:43:29.389101 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0410 00:43:30.803575 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0410 00:43:31.472009 16216 solver.cpp:330] Iteration 2652, Testing net (#0)
I0410 00:43:31.472033 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:43:35.159444 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:43:36.218787 16216 solver.cpp:397]     Test net output #0: accuracy = 0.1875
I0410 00:43:36.218833 16216 solver.cpp:397]     Test net output #1: loss = 4.35857 (* 1 = 4.35857 loss)
I0410 00:43:36.301694 16216 solver.cpp:218] Iteration 2652 (1.0568 iter/s, 11.3551s/12 iters), loss = 2.13115
I0410 00:43:36.301748 16216 solver.cpp:237]     Train net output #0: loss = 2.13115 (* 1 = 2.13115 loss)
I0410 00:43:36.301760 16216 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0410 00:43:40.459110 16216 solver.cpp:218] Iteration 2664 (2.88654 iter/s, 4.15723s/12 iters), loss = 1.80951
I0410 00:43:40.459152 16216 solver.cpp:237]     Train net output #0: loss = 1.80951 (* 1 = 1.80951 loss)
I0410 00:43:40.459161 16216 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0410 00:43:45.335064 16216 solver.cpp:218] Iteration 2676 (2.46116 iter/s, 4.87576s/12 iters), loss = 1.78891
I0410 00:43:45.335114 16216 solver.cpp:237]     Train net output #0: loss = 1.78891 (* 1 = 1.78891 loss)
I0410 00:43:45.335125 16216 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0410 00:43:50.191269 16216 solver.cpp:218] Iteration 2688 (2.47117 iter/s, 4.85601s/12 iters), loss = 2.09119
I0410 00:43:50.191309 16216 solver.cpp:237]     Train net output #0: loss = 2.09119 (* 1 = 2.09119 loss)
I0410 00:43:50.191318 16216 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0410 00:43:55.062080 16216 solver.cpp:218] Iteration 2700 (2.46375 iter/s, 4.87062s/12 iters), loss = 1.80538
I0410 00:43:55.062126 16216 solver.cpp:237]     Train net output #0: loss = 1.80538 (* 1 = 1.80538 loss)
I0410 00:43:55.062137 16216 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0410 00:44:00.016108 16216 solver.cpp:218] Iteration 2712 (2.42237 iter/s, 4.95382s/12 iters), loss = 1.88032
I0410 00:44:00.016247 16216 solver.cpp:237]     Train net output #0: loss = 1.88032 (* 1 = 1.88032 loss)
I0410 00:44:00.016258 16216 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0410 00:44:04.977681 16216 solver.cpp:218] Iteration 2724 (2.41873 iter/s, 4.96129s/12 iters), loss = 2.07368
I0410 00:44:04.977725 16216 solver.cpp:237]     Train net output #0: loss = 2.07368 (* 1 = 2.07368 loss)
I0410 00:44:04.977733 16216 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0410 00:44:07.614756 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:44:10.260010 16216 solver.cpp:218] Iteration 2736 (2.27181 iter/s, 5.28212s/12 iters), loss = 1.60109
I0410 00:44:10.260054 16216 solver.cpp:237]     Train net output #0: loss = 1.60109 (* 1 = 1.60109 loss)
I0410 00:44:10.260062 16216 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0410 00:44:15.340828 16216 solver.cpp:218] Iteration 2748 (2.36192 iter/s, 5.08061s/12 iters), loss = 1.83628
I0410 00:44:15.340878 16216 solver.cpp:237]     Train net output #0: loss = 1.83628 (* 1 = 1.83628 loss)
I0410 00:44:15.340889 16216 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0410 00:44:17.299522 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0410 00:44:17.924374 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0410 00:44:19.203857 16216 solver.cpp:330] Iteration 2754, Testing net (#0)
I0410 00:44:19.203881 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:44:21.908963 16216 blocking_queue.cpp:49] Waiting for data
I0410 00:44:22.505951 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:44:23.608520 16216 solver.cpp:397]     Test net output #0: accuracy = 0.196078
I0410 00:44:23.608556 16216 solver.cpp:397]     Test net output #1: loss = 4.36257 (* 1 = 4.36257 loss)
I0410 00:44:25.576128 16216 solver.cpp:218] Iteration 2760 (1.17245 iter/s, 10.235s/12 iters), loss = 2.16604
I0410 00:44:25.576169 16216 solver.cpp:237]     Train net output #0: loss = 2.16604 (* 1 = 2.16604 loss)
I0410 00:44:25.576179 16216 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0410 00:44:30.509018 16216 solver.cpp:218] Iteration 2772 (2.43275 iter/s, 4.93269s/12 iters), loss = 2.17979
I0410 00:44:30.509104 16216 solver.cpp:237]     Train net output #0: loss = 2.17979 (* 1 = 2.17979 loss)
I0410 00:44:30.509117 16216 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0410 00:44:35.343430 16216 solver.cpp:218] Iteration 2784 (2.48233 iter/s, 4.83417s/12 iters), loss = 1.90535
I0410 00:44:35.343483 16216 solver.cpp:237]     Train net output #0: loss = 1.90535 (* 1 = 1.90535 loss)
I0410 00:44:35.343497 16216 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0410 00:44:40.271800 16216 solver.cpp:218] Iteration 2796 (2.43499 iter/s, 4.92816s/12 iters), loss = 1.76393
I0410 00:44:40.271853 16216 solver.cpp:237]     Train net output #0: loss = 1.76393 (* 1 = 1.76393 loss)
I0410 00:44:40.271864 16216 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0410 00:44:45.218731 16216 solver.cpp:218] Iteration 2808 (2.42585 iter/s, 4.94672s/12 iters), loss = 1.79844
I0410 00:44:45.218786 16216 solver.cpp:237]     Train net output #0: loss = 1.79844 (* 1 = 1.79844 loss)
I0410 00:44:45.218797 16216 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0410 00:44:50.159277 16216 solver.cpp:218] Iteration 2820 (2.42898 iter/s, 4.94034s/12 iters), loss = 1.523
I0410 00:44:50.159318 16216 solver.cpp:237]     Train net output #0: loss = 1.523 (* 1 = 1.523 loss)
I0410 00:44:50.159327 16216 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0410 00:44:54.739485 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:44:55.027144 16216 solver.cpp:218] Iteration 2832 (2.46524 iter/s, 4.86767s/12 iters), loss = 1.67202
I0410 00:44:55.027187 16216 solver.cpp:237]     Train net output #0: loss = 1.67202 (* 1 = 1.67202 loss)
I0410 00:44:55.027197 16216 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0410 00:44:59.971207 16216 solver.cpp:218] Iteration 2844 (2.42725 iter/s, 4.94386s/12 iters), loss = 1.76797
I0410 00:44:59.971258 16216 solver.cpp:237]     Train net output #0: loss = 1.76797 (* 1 = 1.76797 loss)
I0410 00:44:59.971271 16216 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0410 00:45:04.406549 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0410 00:45:04.941519 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0410 00:45:05.371275 16216 solver.cpp:330] Iteration 2856, Testing net (#0)
I0410 00:45:05.371305 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:45:08.682663 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:45:09.815840 16216 solver.cpp:397]     Test net output #0: accuracy = 0.192402
I0410 00:45:09.815892 16216 solver.cpp:397]     Test net output #1: loss = 4.5144 (* 1 = 4.5144 loss)
I0410 00:45:09.898491 16216 solver.cpp:218] Iteration 2856 (1.20883 iter/s, 9.92693s/12 iters), loss = 1.83533
I0410 00:45:09.898553 16216 solver.cpp:237]     Train net output #0: loss = 1.83533 (* 1 = 1.83533 loss)
I0410 00:45:09.898568 16216 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0410 00:45:14.061986 16216 solver.cpp:218] Iteration 2868 (2.88233 iter/s, 4.1633s/12 iters), loss = 1.61557
I0410 00:45:14.062033 16216 solver.cpp:237]     Train net output #0: loss = 1.61557 (* 1 = 1.61557 loss)
I0410 00:45:14.062044 16216 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0410 00:45:18.967442 16216 solver.cpp:218] Iteration 2880 (2.44636 iter/s, 4.90526s/12 iters), loss = 1.76563
I0410 00:45:18.967492 16216 solver.cpp:237]     Train net output #0: loss = 1.76563 (* 1 = 1.76563 loss)
I0410 00:45:18.967504 16216 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0410 00:45:23.904841 16216 solver.cpp:218] Iteration 2892 (2.43053 iter/s, 4.93719s/12 iters), loss = 1.58235
I0410 00:45:23.904894 16216 solver.cpp:237]     Train net output #0: loss = 1.58235 (* 1 = 1.58235 loss)
I0410 00:45:23.904906 16216 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0410 00:45:28.846837 16216 solver.cpp:218] Iteration 2904 (2.42827 iter/s, 4.94178s/12 iters), loss = 1.55708
I0410 00:45:28.846894 16216 solver.cpp:237]     Train net output #0: loss = 1.55708 (* 1 = 1.55708 loss)
I0410 00:45:28.846905 16216 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0410 00:45:33.769606 16216 solver.cpp:218] Iteration 2916 (2.43776 iter/s, 4.92255s/12 iters), loss = 1.5026
I0410 00:45:33.769659 16216 solver.cpp:237]     Train net output #0: loss = 1.5026 (* 1 = 1.5026 loss)
I0410 00:45:33.769672 16216 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0410 00:45:38.656183 16216 solver.cpp:218] Iteration 2928 (2.45581 iter/s, 4.88637s/12 iters), loss = 1.25101
I0410 00:45:38.656287 16216 solver.cpp:237]     Train net output #0: loss = 1.25101 (* 1 = 1.25101 loss)
I0410 00:45:38.656299 16216 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0410 00:45:40.446354 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:45:43.589200 16216 solver.cpp:218] Iteration 2940 (2.43271 iter/s, 4.93276s/12 iters), loss = 1.29575
I0410 00:45:43.589252 16216 solver.cpp:237]     Train net output #0: loss = 1.29575 (* 1 = 1.29575 loss)
I0410 00:45:43.589264 16216 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0410 00:45:48.492308 16216 solver.cpp:218] Iteration 2952 (2.44753 iter/s, 4.9029s/12 iters), loss = 1.59657
I0410 00:45:48.492354 16216 solver.cpp:237]     Train net output #0: loss = 1.59657 (* 1 = 1.59657 loss)
I0410 00:45:48.492364 16216 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0410 00:45:50.475026 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0410 00:45:50.715245 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0410 00:45:50.884037 16216 solver.cpp:330] Iteration 2958, Testing net (#0)
I0410 00:45:50.884065 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:45:54.083703 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:45:55.269909 16216 solver.cpp:397]     Test net output #0: accuracy = 0.188725
I0410 00:45:55.269942 16216 solver.cpp:397]     Test net output #1: loss = 4.75296 (* 1 = 4.75296 loss)
I0410 00:45:57.120414 16216 solver.cpp:218] Iteration 2964 (1.39085 iter/s, 8.6278s/12 iters), loss = 1.37209
I0410 00:45:57.120463 16216 solver.cpp:237]     Train net output #0: loss = 1.37209 (* 1 = 1.37209 loss)
I0410 00:45:57.120476 16216 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0410 00:46:02.012075 16216 solver.cpp:218] Iteration 2976 (2.45326 iter/s, 4.89146s/12 iters), loss = 1.5348
I0410 00:46:02.012127 16216 solver.cpp:237]     Train net output #0: loss = 1.5348 (* 1 = 1.5348 loss)
I0410 00:46:02.012138 16216 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0410 00:46:06.923290 16216 solver.cpp:218] Iteration 2988 (2.44349 iter/s, 4.91101s/12 iters), loss = 1.45424
I0410 00:46:06.923341 16216 solver.cpp:237]     Train net output #0: loss = 1.45424 (* 1 = 1.45424 loss)
I0410 00:46:06.923352 16216 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0410 00:46:11.870352 16216 solver.cpp:218] Iteration 3000 (2.42578 iter/s, 4.94686s/12 iters), loss = 1.73129
I0410 00:46:11.870469 16216 solver.cpp:237]     Train net output #0: loss = 1.73129 (* 1 = 1.73129 loss)
I0410 00:46:11.870483 16216 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0410 00:46:17.051757 16216 solver.cpp:218] Iteration 3012 (2.31609 iter/s, 5.18114s/12 iters), loss = 1.6678
I0410 00:46:17.051793 16216 solver.cpp:237]     Train net output #0: loss = 1.6678 (* 1 = 1.6678 loss)
I0410 00:46:17.051801 16216 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0410 00:46:22.069180 16216 solver.cpp:218] Iteration 3024 (2.39176 iter/s, 5.01722s/12 iters), loss = 1.48142
I0410 00:46:22.069237 16216 solver.cpp:237]     Train net output #0: loss = 1.48142 (* 1 = 1.48142 loss)
I0410 00:46:22.069250 16216 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0410 00:46:26.005033 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:46:27.028519 16216 solver.cpp:218] Iteration 3036 (2.41978 iter/s, 4.95913s/12 iters), loss = 1.59456
I0410 00:46:27.028571 16216 solver.cpp:237]     Train net output #0: loss = 1.59456 (* 1 = 1.59456 loss)
I0410 00:46:27.028582 16216 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0410 00:46:32.085160 16216 solver.cpp:218] Iteration 3048 (2.37322 iter/s, 5.05643s/12 iters), loss = 1.28575
I0410 00:46:32.085211 16216 solver.cpp:237]     Train net output #0: loss = 1.28575 (* 1 = 1.28575 loss)
I0410 00:46:32.085224 16216 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0410 00:46:36.577710 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0410 00:46:37.602176 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0410 00:46:38.047106 16216 solver.cpp:330] Iteration 3060, Testing net (#0)
I0410 00:46:38.047133 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:46:41.310443 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:46:42.540076 16216 solver.cpp:397]     Test net output #0: accuracy = 0.202819
I0410 00:46:42.540236 16216 solver.cpp:397]     Test net output #1: loss = 4.7066 (* 1 = 4.7066 loss)
I0410 00:46:42.622901 16216 solver.cpp:218] Iteration 3060 (1.1388 iter/s, 10.5374s/12 iters), loss = 1.42803
I0410 00:46:42.622977 16216 solver.cpp:237]     Train net output #0: loss = 1.42803 (* 1 = 1.42803 loss)
I0410 00:46:42.622992 16216 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0410 00:46:46.772018 16216 solver.cpp:218] Iteration 3072 (2.89232 iter/s, 4.14891s/12 iters), loss = 1.51236
I0410 00:46:46.772074 16216 solver.cpp:237]     Train net output #0: loss = 1.51236 (* 1 = 1.51236 loss)
I0410 00:46:46.772085 16216 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0410 00:46:51.771059 16216 solver.cpp:218] Iteration 3084 (2.40056 iter/s, 4.99883s/12 iters), loss = 1.21935
I0410 00:46:51.771103 16216 solver.cpp:237]     Train net output #0: loss = 1.21935 (* 1 = 1.21935 loss)
I0410 00:46:51.771111 16216 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0410 00:46:56.794862 16216 solver.cpp:218] Iteration 3096 (2.38872 iter/s, 5.02361s/12 iters), loss = 1.16983
I0410 00:46:56.794903 16216 solver.cpp:237]     Train net output #0: loss = 1.16983 (* 1 = 1.16983 loss)
I0410 00:46:56.794912 16216 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0410 00:47:01.774595 16216 solver.cpp:218] Iteration 3108 (2.40987 iter/s, 4.97953s/12 iters), loss = 1.28403
I0410 00:47:01.774648 16216 solver.cpp:237]     Train net output #0: loss = 1.28403 (* 1 = 1.28403 loss)
I0410 00:47:01.774660 16216 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0410 00:47:06.719630 16216 solver.cpp:218] Iteration 3120 (2.42678 iter/s, 4.94483s/12 iters), loss = 1.36408
I0410 00:47:06.719674 16216 solver.cpp:237]     Train net output #0: loss = 1.36408 (* 1 = 1.36408 loss)
I0410 00:47:06.719686 16216 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0410 00:47:11.655817 16216 solver.cpp:218] Iteration 3132 (2.43112 iter/s, 4.93599s/12 iters), loss = 1.52813
I0410 00:47:11.655858 16216 solver.cpp:237]     Train net output #0: loss = 1.52813 (* 1 = 1.52813 loss)
I0410 00:47:11.655865 16216 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0410 00:47:12.725054 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:47:16.520908 16216 solver.cpp:218] Iteration 3144 (2.46665 iter/s, 4.8649s/12 iters), loss = 1.16285
I0410 00:47:16.520956 16216 solver.cpp:237]     Train net output #0: loss = 1.16285 (* 1 = 1.16285 loss)
I0410 00:47:16.520967 16216 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0410 00:47:21.478720 16216 solver.cpp:218] Iteration 3156 (2.42052 iter/s, 4.95761s/12 iters), loss = 1.50207
I0410 00:47:21.478770 16216 solver.cpp:237]     Train net output #0: loss = 1.50207 (* 1 = 1.50207 loss)
I0410 00:47:21.478780 16216 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0410 00:47:23.505785 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0410 00:47:23.753633 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0410 00:47:24.245695 16216 solver.cpp:330] Iteration 3162, Testing net (#0)
I0410 00:47:24.245724 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:47:27.528170 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:47:28.787777 16216 solver.cpp:397]     Test net output #0: accuracy = 0.192402
I0410 00:47:28.787819 16216 solver.cpp:397]     Test net output #1: loss = 4.7026 (* 1 = 4.7026 loss)
I0410 00:47:30.640436 16216 solver.cpp:218] Iteration 3168 (1.30984 iter/s, 9.16139s/12 iters), loss = 1.25175
I0410 00:47:30.640486 16216 solver.cpp:237]     Train net output #0: loss = 1.25175 (* 1 = 1.25175 loss)
I0410 00:47:30.640498 16216 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0410 00:47:35.638685 16216 solver.cpp:218] Iteration 3180 (2.40094 iter/s, 4.99804s/12 iters), loss = 1.4453
I0410 00:47:35.638737 16216 solver.cpp:237]     Train net output #0: loss = 1.4453 (* 1 = 1.4453 loss)
I0410 00:47:35.638751 16216 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0410 00:47:40.844211 16216 solver.cpp:218] Iteration 3192 (2.30534 iter/s, 5.20531s/12 iters), loss = 1.43222
I0410 00:47:40.844261 16216 solver.cpp:237]     Train net output #0: loss = 1.43222 (* 1 = 1.43222 loss)
I0410 00:47:40.844272 16216 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0410 00:47:45.780587 16216 solver.cpp:218] Iteration 3204 (2.43104 iter/s, 4.93617s/12 iters), loss = 1.17467
I0410 00:47:45.780717 16216 solver.cpp:237]     Train net output #0: loss = 1.17467 (* 1 = 1.17467 loss)
I0410 00:47:45.780731 16216 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0410 00:47:50.768002 16216 solver.cpp:218] Iteration 3216 (2.40619 iter/s, 4.98713s/12 iters), loss = 1.31714
I0410 00:47:50.768049 16216 solver.cpp:237]     Train net output #0: loss = 1.31714 (* 1 = 1.31714 loss)
I0410 00:47:50.768059 16216 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0410 00:47:55.914778 16216 solver.cpp:218] Iteration 3228 (2.33165 iter/s, 5.14657s/12 iters), loss = 1.17638
I0410 00:47:55.914830 16216 solver.cpp:237]     Train net output #0: loss = 1.17638 (* 1 = 1.17638 loss)
I0410 00:47:55.914844 16216 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0410 00:47:59.317212 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:48:01.030397 16216 solver.cpp:218] Iteration 3240 (2.34585 iter/s, 5.11541s/12 iters), loss = 1.09848
I0410 00:48:01.030452 16216 solver.cpp:237]     Train net output #0: loss = 1.09848 (* 1 = 1.09848 loss)
I0410 00:48:01.030467 16216 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0410 00:48:05.922194 16216 solver.cpp:218] Iteration 3252 (2.45319 iter/s, 4.89159s/12 iters), loss = 1.22051
I0410 00:48:05.922243 16216 solver.cpp:237]     Train net output #0: loss = 1.22051 (* 1 = 1.22051 loss)
I0410 00:48:05.922255 16216 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0410 00:48:10.381721 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0410 00:48:10.890993 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0410 00:48:11.485810 16216 solver.cpp:330] Iteration 3264, Testing net (#0)
I0410 00:48:11.485839 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:48:14.634883 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:48:16.071831 16216 solver.cpp:397]     Test net output #0: accuracy = 0.197304
I0410 00:48:16.072948 16216 solver.cpp:397]     Test net output #1: loss = 4.90085 (* 1 = 4.90085 loss)
I0410 00:48:16.155710 16216 solver.cpp:218] Iteration 3264 (1.17266 iter/s, 10.2332s/12 iters), loss = 1.39285
I0410 00:48:16.155773 16216 solver.cpp:237]     Train net output #0: loss = 1.39285 (* 1 = 1.39285 loss)
I0410 00:48:16.155787 16216 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0410 00:48:20.217134 16216 solver.cpp:218] Iteration 3276 (2.95477 iter/s, 4.06122s/12 iters), loss = 1.21756
I0410 00:48:20.217206 16216 solver.cpp:237]     Train net output #0: loss = 1.21756 (* 1 = 1.21756 loss)
I0410 00:48:20.217226 16216 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0410 00:48:25.194213 16216 solver.cpp:218] Iteration 3288 (2.41116 iter/s, 4.97686s/12 iters), loss = 1.23655
I0410 00:48:25.194260 16216 solver.cpp:237]     Train net output #0: loss = 1.23655 (* 1 = 1.23655 loss)
I0410 00:48:25.194270 16216 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0410 00:48:30.100659 16216 solver.cpp:218] Iteration 3300 (2.44586 iter/s, 4.90624s/12 iters), loss = 1.28637
I0410 00:48:30.100705 16216 solver.cpp:237]     Train net output #0: loss = 1.28637 (* 1 = 1.28637 loss)
I0410 00:48:30.100714 16216 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0410 00:48:35.026072 16216 solver.cpp:218] Iteration 3312 (2.43644 iter/s, 4.92521s/12 iters), loss = 1.27577
I0410 00:48:35.026130 16216 solver.cpp:237]     Train net output #0: loss = 1.27577 (* 1 = 1.27577 loss)
I0410 00:48:35.026142 16216 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0410 00:48:39.994642 16216 solver.cpp:218] Iteration 3324 (2.41528 iter/s, 4.96836s/12 iters), loss = 1.21475
I0410 00:48:39.994681 16216 solver.cpp:237]     Train net output #0: loss = 1.21475 (* 1 = 1.21475 loss)
I0410 00:48:39.994690 16216 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0410 00:48:45.044025 16216 solver.cpp:218] Iteration 3336 (2.37662 iter/s, 5.04918s/12 iters), loss = 0.952658
I0410 00:48:45.044071 16216 solver.cpp:237]     Train net output #0: loss = 0.952658 (* 1 = 0.952658 loss)
I0410 00:48:45.044080 16216 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0410 00:48:45.508877 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:48:50.046133 16216 solver.cpp:218] Iteration 3348 (2.39909 iter/s, 5.00191s/12 iters), loss = 1.27
I0410 00:48:50.046233 16216 solver.cpp:237]     Train net output #0: loss = 1.27 (* 1 = 1.27 loss)
I0410 00:48:50.046244 16216 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0410 00:48:55.082715 16216 solver.cpp:218] Iteration 3360 (2.38269 iter/s, 5.03632s/12 iters), loss = 1.14461
I0410 00:48:55.082767 16216 solver.cpp:237]     Train net output #0: loss = 1.14461 (* 1 = 1.14461 loss)
I0410 00:48:55.082778 16216 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0410 00:48:57.135116 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0410 00:48:57.375150 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0410 00:48:57.562820 16216 solver.cpp:330] Iteration 3366, Testing net (#0)
I0410 00:48:57.562846 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:49:00.699429 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:49:02.152995 16216 solver.cpp:397]     Test net output #0: accuracy = 0.206495
I0410 00:49:02.153043 16216 solver.cpp:397]     Test net output #1: loss = 5.08379 (* 1 = 5.08379 loss)
I0410 00:49:04.199748 16216 solver.cpp:218] Iteration 3372 (1.31626 iter/s, 9.11671s/12 iters), loss = 1.22727
I0410 00:49:04.199795 16216 solver.cpp:237]     Train net output #0: loss = 1.22727 (* 1 = 1.22727 loss)
I0410 00:49:04.199805 16216 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0410 00:49:09.193424 16216 solver.cpp:218] Iteration 3384 (2.40314 iter/s, 4.99347s/12 iters), loss = 0.87077
I0410 00:49:09.193470 16216 solver.cpp:237]     Train net output #0: loss = 0.87077 (* 1 = 0.87077 loss)
I0410 00:49:09.193482 16216 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0410 00:49:14.400894 16216 solver.cpp:218] Iteration 3396 (2.30448 iter/s, 5.20726s/12 iters), loss = 1.26928
I0410 00:49:14.400949 16216 solver.cpp:237]     Train net output #0: loss = 1.26928 (* 1 = 1.26928 loss)
I0410 00:49:14.400959 16216 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0410 00:49:19.262943 16216 solver.cpp:218] Iteration 3408 (2.4682 iter/s, 4.86184s/12 iters), loss = 1.28245
I0410 00:49:19.262993 16216 solver.cpp:237]     Train net output #0: loss = 1.28245 (* 1 = 1.28245 loss)
I0410 00:49:19.263005 16216 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0410 00:49:24.196461 16216 solver.cpp:218] Iteration 3420 (2.43244 iter/s, 4.93331s/12 iters), loss = 1.38713
I0410 00:49:24.196574 16216 solver.cpp:237]     Train net output #0: loss = 1.38713 (* 1 = 1.38713 loss)
I0410 00:49:24.196588 16216 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0410 00:49:29.201181 16216 solver.cpp:218] Iteration 3432 (2.39786 iter/s, 5.00446s/12 iters), loss = 1.18237
I0410 00:49:29.201225 16216 solver.cpp:237]     Train net output #0: loss = 1.18237 (* 1 = 1.18237 loss)
I0410 00:49:29.201234 16216 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0410 00:49:31.794642 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:49:34.200515 16216 solver.cpp:218] Iteration 3444 (2.40042 iter/s, 4.99913s/12 iters), loss = 1.03068
I0410 00:49:34.200554 16216 solver.cpp:237]     Train net output #0: loss = 1.03068 (* 1 = 1.03068 loss)
I0410 00:49:34.200563 16216 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0410 00:49:39.167232 16216 solver.cpp:218] Iteration 3456 (2.41618 iter/s, 4.96652s/12 iters), loss = 1.20597
I0410 00:49:39.167276 16216 solver.cpp:237]     Train net output #0: loss = 1.20597 (* 1 = 1.20597 loss)
I0410 00:49:39.167285 16216 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0410 00:49:43.669299 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0410 00:49:43.914685 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0410 00:49:44.198022 16216 solver.cpp:330] Iteration 3468, Testing net (#0)
I0410 00:49:44.198046 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:49:44.221427 16216 blocking_queue.cpp:49] Waiting for data
I0410 00:49:47.276727 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:49:48.654466 16216 solver.cpp:397]     Test net output #0: accuracy = 0.204044
I0410 00:49:48.654512 16216 solver.cpp:397]     Test net output #1: loss = 5.23042 (* 1 = 5.23042 loss)
I0410 00:49:48.737159 16216 solver.cpp:218] Iteration 3468 (1.25397 iter/s, 9.56959s/12 iters), loss = 1.18094
I0410 00:49:48.737207 16216 solver.cpp:237]     Train net output #0: loss = 1.18094 (* 1 = 1.18094 loss)
I0410 00:49:48.737219 16216 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0410 00:49:52.793576 16216 solver.cpp:218] Iteration 3480 (2.9584 iter/s, 4.05624s/12 iters), loss = 1.12818
I0410 00:49:52.793622 16216 solver.cpp:237]     Train net output #0: loss = 1.12818 (* 1 = 1.12818 loss)
I0410 00:49:52.793632 16216 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0410 00:49:57.677237 16216 solver.cpp:218] Iteration 3492 (2.45728 iter/s, 4.88346s/12 iters), loss = 1.19796
I0410 00:49:57.677394 16216 solver.cpp:237]     Train net output #0: loss = 1.19796 (* 1 = 1.19796 loss)
I0410 00:49:57.677409 16216 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0410 00:50:02.868332 16216 solver.cpp:218] Iteration 3504 (2.31179 iter/s, 5.19079s/12 iters), loss = 1.14782
I0410 00:50:02.868374 16216 solver.cpp:237]     Train net output #0: loss = 1.14782 (* 1 = 1.14782 loss)
I0410 00:50:02.868383 16216 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0410 00:50:07.961611 16216 solver.cpp:218] Iteration 3516 (2.35614 iter/s, 5.09308s/12 iters), loss = 0.790123
I0410 00:50:07.961673 16216 solver.cpp:237]     Train net output #0: loss = 0.790123 (* 1 = 0.790123 loss)
I0410 00:50:07.961685 16216 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0410 00:50:13.124518 16216 solver.cpp:218] Iteration 3528 (2.32437 iter/s, 5.16269s/12 iters), loss = 1.03393
I0410 00:50:13.124575 16216 solver.cpp:237]     Train net output #0: loss = 1.03393 (* 1 = 1.03393 loss)
I0410 00:50:13.124588 16216 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0410 00:50:17.774132 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:50:18.038568 16216 solver.cpp:218] Iteration 3540 (2.44208 iter/s, 4.91384s/12 iters), loss = 0.917629
I0410 00:50:18.038625 16216 solver.cpp:237]     Train net output #0: loss = 0.917629 (* 1 = 0.917629 loss)
I0410 00:50:18.038636 16216 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0410 00:50:23.036370 16216 solver.cpp:218] Iteration 3552 (2.40116 iter/s, 4.99759s/12 iters), loss = 1.07321
I0410 00:50:23.036420 16216 solver.cpp:237]     Train net output #0: loss = 1.07321 (* 1 = 1.07321 loss)
I0410 00:50:23.036432 16216 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0410 00:50:27.980693 16216 solver.cpp:218] Iteration 3564 (2.42713 iter/s, 4.94412s/12 iters), loss = 0.989176
I0410 00:50:27.980783 16216 solver.cpp:237]     Train net output #0: loss = 0.989176 (* 1 = 0.989176 loss)
I0410 00:50:27.980794 16216 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0410 00:50:30.101522 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0410 00:50:32.717118 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0410 00:50:34.849411 16216 solver.cpp:330] Iteration 3570, Testing net (#0)
I0410 00:50:34.849437 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:50:37.890023 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:50:39.298837 16216 solver.cpp:397]     Test net output #0: accuracy = 0.214461
I0410 00:50:39.298871 16216 solver.cpp:397]     Test net output #1: loss = 5.25568 (* 1 = 5.25568 loss)
I0410 00:50:41.144886 16216 solver.cpp:218] Iteration 3576 (0.911597 iter/s, 13.1637s/12 iters), loss = 1.02952
I0410 00:50:41.144939 16216 solver.cpp:237]     Train net output #0: loss = 1.02952 (* 1 = 1.02952 loss)
I0410 00:50:41.144949 16216 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0410 00:50:46.105070 16216 solver.cpp:218] Iteration 3588 (2.41937 iter/s, 4.95998s/12 iters), loss = 0.880477
I0410 00:50:46.105118 16216 solver.cpp:237]     Train net output #0: loss = 0.880477 (* 1 = 0.880477 loss)
I0410 00:50:46.105131 16216 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0410 00:50:51.128415 16216 solver.cpp:218] Iteration 3600 (2.38895 iter/s, 5.02314s/12 iters), loss = 1.11399
I0410 00:50:51.128466 16216 solver.cpp:237]     Train net output #0: loss = 1.11399 (* 1 = 1.11399 loss)
I0410 00:50:51.128476 16216 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0410 00:50:56.110008 16216 solver.cpp:218] Iteration 3612 (2.40897 iter/s, 4.98139s/12 iters), loss = 1.01726
I0410 00:50:56.110057 16216 solver.cpp:237]     Train net output #0: loss = 1.01726 (* 1 = 1.01726 loss)
I0410 00:50:56.110069 16216 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0410 00:51:00.995748 16216 solver.cpp:218] Iteration 3624 (2.45623 iter/s, 4.88554s/12 iters), loss = 1.09877
I0410 00:51:00.995878 16216 solver.cpp:237]     Train net output #0: loss = 1.09877 (* 1 = 1.09877 loss)
I0410 00:51:00.995890 16216 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0410 00:51:05.946249 16216 solver.cpp:218] Iteration 3636 (2.42414 iter/s, 4.95022s/12 iters), loss = 1.02339
I0410 00:51:05.946298 16216 solver.cpp:237]     Train net output #0: loss = 1.02339 (* 1 = 1.02339 loss)
I0410 00:51:05.946310 16216 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0410 00:51:07.805258 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:51:10.967965 16216 solver.cpp:218] Iteration 3648 (2.38972 iter/s, 5.02151s/12 iters), loss = 1.0073
I0410 00:51:10.968017 16216 solver.cpp:237]     Train net output #0: loss = 1.0073 (* 1 = 1.0073 loss)
I0410 00:51:10.968029 16216 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0410 00:51:15.909329 16216 solver.cpp:218] Iteration 3660 (2.42858 iter/s, 4.94115s/12 iters), loss = 0.762507
I0410 00:51:15.909381 16216 solver.cpp:237]     Train net output #0: loss = 0.762507 (* 1 = 0.762507 loss)
I0410 00:51:15.909392 16216 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0410 00:51:20.365360 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0410 00:51:20.607771 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0410 00:51:20.778360 16216 solver.cpp:330] Iteration 3672, Testing net (#0)
I0410 00:51:20.778391 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:51:23.712879 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:51:25.166419 16216 solver.cpp:397]     Test net output #0: accuracy = 0.220588
I0410 00:51:25.166447 16216 solver.cpp:397]     Test net output #1: loss = 5.28898 (* 1 = 5.28898 loss)
I0410 00:51:25.249032 16216 solver.cpp:218] Iteration 3672 (1.28488 iter/s, 9.33936s/12 iters), loss = 0.904657
I0410 00:51:25.249078 16216 solver.cpp:237]     Train net output #0: loss = 0.904657 (* 1 = 0.904657 loss)
I0410 00:51:25.249087 16216 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0410 00:51:29.526798 16216 solver.cpp:218] Iteration 3684 (2.80532 iter/s, 4.27759s/12 iters), loss = 1.00363
I0410 00:51:29.526844 16216 solver.cpp:237]     Train net output #0: loss = 1.00363 (* 1 = 1.00363 loss)
I0410 00:51:29.526855 16216 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0410 00:51:34.443619 16216 solver.cpp:218] Iteration 3696 (2.4407 iter/s, 4.91663s/12 iters), loss = 0.757633
I0410 00:51:34.443735 16216 solver.cpp:237]     Train net output #0: loss = 0.757633 (* 1 = 0.757633 loss)
I0410 00:51:34.443744 16216 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0410 00:51:39.334785 16216 solver.cpp:218] Iteration 3708 (2.45354 iter/s, 4.8909s/12 iters), loss = 1.06564
I0410 00:51:39.334831 16216 solver.cpp:237]     Train net output #0: loss = 1.06564 (* 1 = 1.06564 loss)
I0410 00:51:39.334841 16216 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0410 00:51:44.258002 16216 solver.cpp:218] Iteration 3720 (2.43755 iter/s, 4.92299s/12 iters), loss = 1.04552
I0410 00:51:44.258054 16216 solver.cpp:237]     Train net output #0: loss = 1.04552 (* 1 = 1.04552 loss)
I0410 00:51:44.258064 16216 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0410 00:51:49.159868 16216 solver.cpp:218] Iteration 3732 (2.44815 iter/s, 4.90166s/12 iters), loss = 1.082
I0410 00:51:49.159914 16216 solver.cpp:237]     Train net output #0: loss = 1.082 (* 1 = 1.082 loss)
I0410 00:51:49.159926 16216 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0410 00:51:53.127074 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:51:54.096267 16216 solver.cpp:218] Iteration 3744 (2.43102 iter/s, 4.9362s/12 iters), loss = 0.82267
I0410 00:51:54.096320 16216 solver.cpp:237]     Train net output #0: loss = 0.82267 (* 1 = 0.82267 loss)
I0410 00:51:54.096333 16216 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0410 00:51:59.002171 16216 solver.cpp:218] Iteration 3756 (2.44614 iter/s, 4.90569s/12 iters), loss = 0.626955
I0410 00:51:59.002225 16216 solver.cpp:237]     Train net output #0: loss = 0.626955 (* 1 = 0.626955 loss)
I0410 00:51:59.002238 16216 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0410 00:52:03.896327 16216 solver.cpp:218] Iteration 3768 (2.45201 iter/s, 4.89394s/12 iters), loss = 1.15477
I0410 00:52:03.896394 16216 solver.cpp:237]     Train net output #0: loss = 1.15477 (* 1 = 1.15477 loss)
I0410 00:52:03.896407 16216 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0410 00:52:05.917167 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0410 00:52:06.174376 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0410 00:52:06.358145 16216 solver.cpp:330] Iteration 3774, Testing net (#0)
I0410 00:52:06.358175 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:52:09.217968 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:52:10.727296 16216 solver.cpp:397]     Test net output #0: accuracy = 0.234681
I0410 00:52:10.727344 16216 solver.cpp:397]     Test net output #1: loss = 5.09131 (* 1 = 5.09131 loss)
I0410 00:52:12.625924 16216 solver.cpp:218] Iteration 3780 (1.37468 iter/s, 8.72927s/12 iters), loss = 0.686193
I0410 00:52:12.625984 16216 solver.cpp:237]     Train net output #0: loss = 0.686193 (* 1 = 0.686193 loss)
I0410 00:52:12.625994 16216 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0410 00:52:17.611567 16216 solver.cpp:218] Iteration 3792 (2.40702 iter/s, 4.98542s/12 iters), loss = 0.719786
I0410 00:52:17.611624 16216 solver.cpp:237]     Train net output #0: loss = 0.719786 (* 1 = 0.719786 loss)
I0410 00:52:17.611637 16216 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0410 00:52:22.543881 16216 solver.cpp:218] Iteration 3804 (2.43304 iter/s, 4.9321s/12 iters), loss = 0.605888
I0410 00:52:22.543936 16216 solver.cpp:237]     Train net output #0: loss = 0.605888 (* 1 = 0.605888 loss)
I0410 00:52:22.543947 16216 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0410 00:52:27.466188 16216 solver.cpp:218] Iteration 3816 (2.43799 iter/s, 4.92209s/12 iters), loss = 0.799369
I0410 00:52:27.466269 16216 solver.cpp:237]     Train net output #0: loss = 0.799369 (* 1 = 0.799369 loss)
I0410 00:52:27.466286 16216 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0410 00:52:32.349089 16216 solver.cpp:218] Iteration 3828 (2.45767 iter/s, 4.88267s/12 iters), loss = 0.872882
I0410 00:52:32.349144 16216 solver.cpp:237]     Train net output #0: loss = 0.872882 (* 1 = 0.872882 loss)
I0410 00:52:32.349156 16216 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0410 00:52:37.275477 16216 solver.cpp:218] Iteration 3840 (2.43597 iter/s, 4.92617s/12 iters), loss = 0.884574
I0410 00:52:37.275631 16216 solver.cpp:237]     Train net output #0: loss = 0.884574 (* 1 = 0.884574 loss)
I0410 00:52:37.275645 16216 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0410 00:52:38.395964 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:52:42.209079 16216 solver.cpp:218] Iteration 3852 (2.43245 iter/s, 4.9333s/12 iters), loss = 0.819168
I0410 00:52:42.209129 16216 solver.cpp:237]     Train net output #0: loss = 0.819168 (* 1 = 0.819168 loss)
I0410 00:52:42.209139 16216 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0410 00:52:47.135478 16216 solver.cpp:218] Iteration 3864 (2.43596 iter/s, 4.9262s/12 iters), loss = 1.1627
I0410 00:52:47.135521 16216 solver.cpp:237]     Train net output #0: loss = 1.1627 (* 1 = 1.1627 loss)
I0410 00:52:47.135533 16216 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0410 00:52:51.583482 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0410 00:52:52.245285 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0410 00:52:52.877544 16216 solver.cpp:330] Iteration 3876, Testing net (#0)
I0410 00:52:52.877564 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:52:55.736557 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:52:57.275180 16216 solver.cpp:397]     Test net output #0: accuracy = 0.223039
I0410 00:52:57.275216 16216 solver.cpp:397]     Test net output #1: loss = 5.38096 (* 1 = 5.38096 loss)
I0410 00:52:57.358052 16216 solver.cpp:218] Iteration 3876 (1.17391 iter/s, 10.2222s/12 iters), loss = 0.988511
I0410 00:52:57.358106 16216 solver.cpp:237]     Train net output #0: loss = 0.988511 (* 1 = 0.988511 loss)
I0410 00:52:57.358117 16216 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0410 00:53:01.715467 16216 solver.cpp:218] Iteration 3888 (2.75405 iter/s, 4.35722s/12 iters), loss = 1.04603
I0410 00:53:01.715518 16216 solver.cpp:237]     Train net output #0: loss = 1.04603 (* 1 = 1.04603 loss)
I0410 00:53:01.715530 16216 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0410 00:53:06.604480 16216 solver.cpp:218] Iteration 3900 (2.45459 iter/s, 4.88881s/12 iters), loss = 1.46647
I0410 00:53:06.604528 16216 solver.cpp:237]     Train net output #0: loss = 1.46647 (* 1 = 1.46647 loss)
I0410 00:53:06.604539 16216 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0410 00:53:11.489676 16216 solver.cpp:218] Iteration 3912 (2.4565 iter/s, 4.88499s/12 iters), loss = 0.872636
I0410 00:53:11.489758 16216 solver.cpp:237]     Train net output #0: loss = 0.872636 (* 1 = 0.872636 loss)
I0410 00:53:11.489769 16216 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0410 00:53:16.437057 16216 solver.cpp:218] Iteration 3924 (2.42564 iter/s, 4.94714s/12 iters), loss = 0.828796
I0410 00:53:16.437112 16216 solver.cpp:237]     Train net output #0: loss = 0.828796 (* 1 = 0.828796 loss)
I0410 00:53:16.437125 16216 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0410 00:53:21.519127 16216 solver.cpp:218] Iteration 3936 (2.36134 iter/s, 5.08185s/12 iters), loss = 1.04064
I0410 00:53:21.519178 16216 solver.cpp:237]     Train net output #0: loss = 1.04064 (* 1 = 1.04064 loss)
I0410 00:53:21.519189 16216 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0410 00:53:24.821866 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:53:26.550734 16216 solver.cpp:218] Iteration 3948 (2.38502 iter/s, 5.0314s/12 iters), loss = 0.778841
I0410 00:53:26.550781 16216 solver.cpp:237]     Train net output #0: loss = 0.778841 (* 1 = 0.778841 loss)
I0410 00:53:26.550792 16216 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0410 00:53:31.825557 16216 solver.cpp:218] Iteration 3960 (2.27505 iter/s, 5.27461s/12 iters), loss = 0.744215
I0410 00:53:31.825615 16216 solver.cpp:237]     Train net output #0: loss = 0.744215 (* 1 = 0.744215 loss)
I0410 00:53:31.825628 16216 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0410 00:53:36.833473 16216 solver.cpp:218] Iteration 3972 (2.39631 iter/s, 5.0077s/12 iters), loss = 0.686624
I0410 00:53:36.833525 16216 solver.cpp:237]     Train net output #0: loss = 0.686624 (* 1 = 0.686624 loss)
I0410 00:53:36.833536 16216 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0410 00:53:38.854051 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0410 00:53:39.583007 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0410 00:53:39.768855 16216 solver.cpp:330] Iteration 3978, Testing net (#0)
I0410 00:53:39.768879 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:53:42.618467 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:53:44.194568 16216 solver.cpp:397]     Test net output #0: accuracy = 0.227328
I0410 00:53:44.194612 16216 solver.cpp:397]     Test net output #1: loss = 5.24878 (* 1 = 5.24878 loss)
I0410 00:53:46.057401 16216 solver.cpp:218] Iteration 3984 (1.30101 iter/s, 9.22359s/12 iters), loss = 0.888719
I0410 00:53:46.057462 16216 solver.cpp:237]     Train net output #0: loss = 0.888719 (* 1 = 0.888719 loss)
I0410 00:53:46.057476 16216 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0410 00:53:51.261297 16216 solver.cpp:218] Iteration 3996 (2.30606 iter/s, 5.20368s/12 iters), loss = 0.918666
I0410 00:53:51.261349 16216 solver.cpp:237]     Train net output #0: loss = 0.918666 (* 1 = 0.918666 loss)
I0410 00:53:51.261361 16216 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0410 00:53:56.102068 16216 solver.cpp:218] Iteration 4008 (2.47905 iter/s, 4.84057s/12 iters), loss = 0.591539
I0410 00:53:56.102120 16216 solver.cpp:237]     Train net output #0: loss = 0.591539 (* 1 = 0.591539 loss)
I0410 00:53:56.102133 16216 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0410 00:54:01.299238 16216 solver.cpp:218] Iteration 4020 (2.30905 iter/s, 5.19695s/12 iters), loss = 0.941433
I0410 00:54:01.299294 16216 solver.cpp:237]     Train net output #0: loss = 0.941433 (* 1 = 0.941433 loss)
I0410 00:54:01.299309 16216 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0410 00:54:06.183470 16216 solver.cpp:218] Iteration 4032 (2.45699 iter/s, 4.88402s/12 iters), loss = 0.706959
I0410 00:54:06.183521 16216 solver.cpp:237]     Train net output #0: loss = 0.706959 (* 1 = 0.706959 loss)
I0410 00:54:06.183531 16216 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0410 00:54:11.100152 16216 solver.cpp:218] Iteration 4044 (2.44077 iter/s, 4.91648s/12 iters), loss = 0.636118
I0410 00:54:11.100205 16216 solver.cpp:237]     Train net output #0: loss = 0.636118 (* 1 = 0.636118 loss)
I0410 00:54:11.100217 16216 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0410 00:54:11.587496 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:54:16.283972 16216 solver.cpp:218] Iteration 4056 (2.31499 iter/s, 5.1836s/12 iters), loss = 0.824416
I0410 00:54:16.284080 16216 solver.cpp:237]     Train net output #0: loss = 0.824416 (* 1 = 0.824416 loss)
I0410 00:54:16.284091 16216 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0410 00:54:21.122869 16216 solver.cpp:218] Iteration 4068 (2.48004 iter/s, 4.83863s/12 iters), loss = 0.729259
I0410 00:54:21.122926 16216 solver.cpp:237]     Train net output #0: loss = 0.729259 (* 1 = 0.729259 loss)
I0410 00:54:21.122939 16216 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0410 00:54:25.624155 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0410 00:54:25.872232 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0410 00:54:26.044127 16216 solver.cpp:330] Iteration 4080, Testing net (#0)
I0410 00:54:26.044154 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:54:29.015329 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:54:30.681581 16216 solver.cpp:397]     Test net output #0: accuracy = 0.253064
I0410 00:54:30.681630 16216 solver.cpp:397]     Test net output #1: loss = 5.1543 (* 1 = 5.1543 loss)
I0410 00:54:30.764439 16216 solver.cpp:218] Iteration 4080 (1.24465 iter/s, 9.64123s/12 iters), loss = 0.649489
I0410 00:54:30.764492 16216 solver.cpp:237]     Train net output #0: loss = 0.649489 (* 1 = 0.649489 loss)
I0410 00:54:30.764505 16216 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0410 00:54:34.997917 16216 solver.cpp:218] Iteration 4092 (2.83467 iter/s, 4.23329s/12 iters), loss = 0.74865
I0410 00:54:34.997975 16216 solver.cpp:237]     Train net output #0: loss = 0.74865 (* 1 = 0.74865 loss)
I0410 00:54:34.997987 16216 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0410 00:54:40.025709 16216 solver.cpp:218] Iteration 4104 (2.38683 iter/s, 5.02759s/12 iters), loss = 0.700639
I0410 00:54:40.025758 16216 solver.cpp:237]     Train net output #0: loss = 0.700639 (* 1 = 0.700639 loss)
I0410 00:54:40.025768 16216 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0410 00:54:44.880231 16216 solver.cpp:218] Iteration 4116 (2.47203 iter/s, 4.85432s/12 iters), loss = 0.433363
I0410 00:54:44.880293 16216 solver.cpp:237]     Train net output #0: loss = 0.433363 (* 1 = 0.433363 loss)
I0410 00:54:44.880306 16216 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0410 00:54:49.790309 16216 solver.cpp:218] Iteration 4128 (2.44406 iter/s, 4.90987s/12 iters), loss = 0.686574
I0410 00:54:49.790446 16216 solver.cpp:237]     Train net output #0: loss = 0.686574 (* 1 = 0.686574 loss)
I0410 00:54:49.790457 16216 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0410 00:54:54.754220 16216 solver.cpp:218] Iteration 4140 (2.41759 iter/s, 4.96362s/12 iters), loss = 0.779574
I0410 00:54:54.754266 16216 solver.cpp:237]     Train net output #0: loss = 0.779574 (* 1 = 0.779574 loss)
I0410 00:54:54.754274 16216 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0410 00:54:57.343214 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:54:59.696282 16216 solver.cpp:218] Iteration 4152 (2.42824 iter/s, 4.94185s/12 iters), loss = 0.490031
I0410 00:54:59.696343 16216 solver.cpp:237]     Train net output #0: loss = 0.490031 (* 1 = 0.490031 loss)
I0410 00:54:59.696357 16216 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0410 00:54:59.696614 16216 blocking_queue.cpp:49] Waiting for data
I0410 00:55:04.647025 16216 solver.cpp:218] Iteration 4164 (2.42398 iter/s, 4.95053s/12 iters), loss = 0.892884
I0410 00:55:04.647078 16216 solver.cpp:237]     Train net output #0: loss = 0.892884 (* 1 = 0.892884 loss)
I0410 00:55:04.647091 16216 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0410 00:55:09.516827 16216 solver.cpp:218] Iteration 4176 (2.46427 iter/s, 4.8696s/12 iters), loss = 0.71968
I0410 00:55:09.516880 16216 solver.cpp:237]     Train net output #0: loss = 0.71968 (* 1 = 0.71968 loss)
I0410 00:55:09.516892 16216 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0410 00:55:11.525485 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0410 00:55:12.381567 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0410 00:55:12.894340 16216 solver.cpp:330] Iteration 4182, Testing net (#0)
I0410 00:55:12.894361 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:55:15.565840 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:55:17.236007 16216 solver.cpp:397]     Test net output #0: accuracy = 0.237745
I0410 00:55:17.236052 16216 solver.cpp:397]     Test net output #1: loss = 5.7861 (* 1 = 5.7861 loss)
I0410 00:55:19.215394 16216 solver.cpp:218] Iteration 4188 (1.23734 iter/s, 9.69822s/12 iters), loss = 0.828086
I0410 00:55:19.215443 16216 solver.cpp:237]     Train net output #0: loss = 0.828086 (* 1 = 0.828086 loss)
I0410 00:55:19.215454 16216 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0410 00:55:24.230695 16216 solver.cpp:218] Iteration 4200 (2.39278 iter/s, 5.01509s/12 iters), loss = 0.595809
I0410 00:55:24.230829 16216 solver.cpp:237]     Train net output #0: loss = 0.595809 (* 1 = 0.595809 loss)
I0410 00:55:24.230839 16216 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0410 00:55:29.183058 16216 solver.cpp:218] Iteration 4212 (2.42323 iter/s, 4.95208s/12 iters), loss = 0.713648
I0410 00:55:29.183115 16216 solver.cpp:237]     Train net output #0: loss = 0.713647 (* 1 = 0.713647 loss)
I0410 00:55:29.183126 16216 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0410 00:55:34.073840 16216 solver.cpp:218] Iteration 4224 (2.4537 iter/s, 4.89057s/12 iters), loss = 0.604282
I0410 00:55:34.073894 16216 solver.cpp:237]     Train net output #0: loss = 0.604282 (* 1 = 0.604282 loss)
I0410 00:55:34.073907 16216 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0410 00:55:39.020943 16216 solver.cpp:218] Iteration 4236 (2.42576 iter/s, 4.94689s/12 iters), loss = 0.567443
I0410 00:55:39.020999 16216 solver.cpp:237]     Train net output #0: loss = 0.567443 (* 1 = 0.567443 loss)
I0410 00:55:39.021013 16216 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0410 00:55:43.665120 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:55:43.888696 16216 solver.cpp:218] Iteration 4248 (2.46531 iter/s, 4.86755s/12 iters), loss = 0.763191
I0410 00:55:43.888746 16216 solver.cpp:237]     Train net output #0: loss = 0.763191 (* 1 = 0.763191 loss)
I0410 00:55:43.888757 16216 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0410 00:55:48.790446 16216 solver.cpp:218] Iteration 4260 (2.44821 iter/s, 4.90155s/12 iters), loss = 0.644879
I0410 00:55:48.790499 16216 solver.cpp:237]     Train net output #0: loss = 0.644879 (* 1 = 0.644879 loss)
I0410 00:55:48.790511 16216 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0410 00:55:53.707870 16216 solver.cpp:218] Iteration 4272 (2.44041 iter/s, 4.91721s/12 iters), loss = 0.711094
I0410 00:55:53.707916 16216 solver.cpp:237]     Train net output #0: loss = 0.711094 (* 1 = 0.711094 loss)
I0410 00:55:53.707924 16216 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0410 00:55:58.192152 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0410 00:55:58.440232 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0410 00:55:58.684515 16216 solver.cpp:330] Iteration 4284, Testing net (#0)
I0410 00:55:58.684545 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:56:01.453161 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:56:03.140280 16216 solver.cpp:397]     Test net output #0: accuracy = 0.242034
I0410 00:56:03.140324 16216 solver.cpp:397]     Test net output #1: loss = 5.74542 (* 1 = 5.74542 loss)
I0410 00:56:03.222837 16216 solver.cpp:218] Iteration 4284 (1.26121 iter/s, 9.51464s/12 iters), loss = 0.750117
I0410 00:56:03.222888 16216 solver.cpp:237]     Train net output #0: loss = 0.750116 (* 1 = 0.750116 loss)
I0410 00:56:03.222899 16216 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0410 00:56:07.439478 16216 solver.cpp:218] Iteration 4296 (2.846 iter/s, 4.21645s/12 iters), loss = 0.753711
I0410 00:56:07.439535 16216 solver.cpp:237]     Train net output #0: loss = 0.753711 (* 1 = 0.753711 loss)
I0410 00:56:07.439548 16216 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0410 00:56:12.352592 16216 solver.cpp:218] Iteration 4308 (2.44255 iter/s, 4.9129s/12 iters), loss = 0.609571
I0410 00:56:12.352645 16216 solver.cpp:237]     Train net output #0: loss = 0.609571 (* 1 = 0.609571 loss)
I0410 00:56:12.352656 16216 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0410 00:56:17.225097 16216 solver.cpp:218] Iteration 4320 (2.46291 iter/s, 4.87229s/12 iters), loss = 0.506561
I0410 00:56:17.225157 16216 solver.cpp:237]     Train net output #0: loss = 0.506561 (* 1 = 0.506561 loss)
I0410 00:56:17.225170 16216 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0410 00:56:22.141229 16216 solver.cpp:218] Iteration 4332 (2.44105 iter/s, 4.91591s/12 iters), loss = 0.798655
I0410 00:56:22.141283 16216 solver.cpp:237]     Train net output #0: loss = 0.798655 (* 1 = 0.798655 loss)
I0410 00:56:22.141294 16216 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0410 00:56:27.073249 16216 solver.cpp:218] Iteration 4344 (2.43318 iter/s, 4.93181s/12 iters), loss = 0.708335
I0410 00:56:27.073308 16216 solver.cpp:237]     Train net output #0: loss = 0.708335 (* 1 = 0.708335 loss)
I0410 00:56:27.073321 16216 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0410 00:56:28.970444 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:56:32.071861 16216 solver.cpp:218] Iteration 4356 (2.40077 iter/s, 4.9984s/12 iters), loss = 0.599973
I0410 00:56:32.071904 16216 solver.cpp:237]     Train net output #0: loss = 0.599973 (* 1 = 0.599973 loss)
I0410 00:56:32.071913 16216 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0410 00:56:37.368961 16216 solver.cpp:218] Iteration 4368 (2.26548 iter/s, 5.29689s/12 iters), loss = 0.710188
I0410 00:56:37.369020 16216 solver.cpp:237]     Train net output #0: loss = 0.710188 (* 1 = 0.710188 loss)
I0410 00:56:37.369032 16216 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0410 00:56:42.269508 16216 solver.cpp:218] Iteration 4380 (2.44881 iter/s, 4.90034s/12 iters), loss = 0.558943
I0410 00:56:42.269556 16216 solver.cpp:237]     Train net output #0: loss = 0.558943 (* 1 = 0.558943 loss)
I0410 00:56:42.269564 16216 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0410 00:56:44.300029 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0410 00:56:44.563671 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0410 00:56:44.758062 16216 solver.cpp:330] Iteration 4386, Testing net (#0)
I0410 00:56:44.758105 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:56:47.584107 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:56:49.319020 16216 solver.cpp:397]     Test net output #0: accuracy = 0.241422
I0410 00:56:49.319056 16216 solver.cpp:397]     Test net output #1: loss = 5.74396 (* 1 = 5.74396 loss)
I0410 00:56:51.185765 16216 solver.cpp:218] Iteration 4392 (1.3459 iter/s, 8.91594s/12 iters), loss = 0.703966
I0410 00:56:51.185818 16216 solver.cpp:237]     Train net output #0: loss = 0.703966 (* 1 = 0.703966 loss)
I0410 00:56:51.185828 16216 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0410 00:56:56.310318 16216 solver.cpp:218] Iteration 4404 (2.34176 iter/s, 5.12434s/12 iters), loss = 0.571532
I0410 00:56:56.310364 16216 solver.cpp:237]     Train net output #0: loss = 0.571532 (* 1 = 0.571532 loss)
I0410 00:56:56.310374 16216 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0410 00:57:01.231807 16216 solver.cpp:218] Iteration 4416 (2.43839 iter/s, 4.92128s/12 iters), loss = 0.855852
I0410 00:57:01.231941 16216 solver.cpp:237]     Train net output #0: loss = 0.855852 (* 1 = 0.855852 loss)
I0410 00:57:01.231959 16216 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0410 00:57:06.234733 16216 solver.cpp:218] Iteration 4428 (2.39873 iter/s, 5.00264s/12 iters), loss = 0.475337
I0410 00:57:06.234779 16216 solver.cpp:237]     Train net output #0: loss = 0.475337 (* 1 = 0.475337 loss)
I0410 00:57:06.234788 16216 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0410 00:57:11.126646 16216 solver.cpp:218] Iteration 4440 (2.45313 iter/s, 4.89172s/12 iters), loss = 0.469769
I0410 00:57:11.126691 16216 solver.cpp:237]     Train net output #0: loss = 0.469769 (* 1 = 0.469769 loss)
I0410 00:57:11.126700 16216 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0410 00:57:15.063933 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:57:16.006347 16216 solver.cpp:218] Iteration 4452 (2.45927 iter/s, 4.8795s/12 iters), loss = 0.594372
I0410 00:57:16.006392 16216 solver.cpp:237]     Train net output #0: loss = 0.594372 (* 1 = 0.594372 loss)
I0410 00:57:16.006402 16216 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0410 00:57:20.954049 16216 solver.cpp:218] Iteration 4464 (2.42547 iter/s, 4.9475s/12 iters), loss = 0.573276
I0410 00:57:20.954106 16216 solver.cpp:237]     Train net output #0: loss = 0.573276 (* 1 = 0.573276 loss)
I0410 00:57:20.954118 16216 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0410 00:57:25.829474 16216 solver.cpp:218] Iteration 4476 (2.46143 iter/s, 4.87521s/12 iters), loss = 0.542394
I0410 00:57:25.829535 16216 solver.cpp:237]     Train net output #0: loss = 0.542393 (* 1 = 0.542393 loss)
I0410 00:57:25.829548 16216 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0410 00:57:30.367120 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0410 00:57:30.683851 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0410 00:57:31.133316 16216 solver.cpp:330] Iteration 4488, Testing net (#0)
I0410 00:57:31.133340 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:57:33.965533 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:57:35.739374 16216 solver.cpp:397]     Test net output #0: accuracy = 0.256127
I0410 00:57:35.739409 16216 solver.cpp:397]     Test net output #1: loss = 5.64915 (* 1 = 5.64915 loss)
I0410 00:57:35.822053 16216 solver.cpp:218] Iteration 4488 (1.20093 iter/s, 9.99223s/12 iters), loss = 0.711898
I0410 00:57:35.822096 16216 solver.cpp:237]     Train net output #0: loss = 0.711898 (* 1 = 0.711898 loss)
I0410 00:57:35.822106 16216 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0410 00:57:40.031534 16216 solver.cpp:218] Iteration 4500 (2.85083 iter/s, 4.2093s/12 iters), loss = 0.336331
I0410 00:57:40.031589 16216 solver.cpp:237]     Train net output #0: loss = 0.336331 (* 1 = 0.336331 loss)
I0410 00:57:40.031602 16216 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0410 00:57:44.968875 16216 solver.cpp:218] Iteration 4512 (2.43056 iter/s, 4.93713s/12 iters), loss = 0.50505
I0410 00:57:44.968926 16216 solver.cpp:237]     Train net output #0: loss = 0.50505 (* 1 = 0.50505 loss)
I0410 00:57:44.968938 16216 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0410 00:57:49.996971 16216 solver.cpp:218] Iteration 4524 (2.38669 iter/s, 5.02789s/12 iters), loss = 0.483943
I0410 00:57:49.997028 16216 solver.cpp:237]     Train net output #0: loss = 0.483943 (* 1 = 0.483943 loss)
I0410 00:57:49.997040 16216 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0410 00:57:55.138459 16216 solver.cpp:218] Iteration 4536 (2.33405 iter/s, 5.14127s/12 iters), loss = 0.491944
I0410 00:57:55.138511 16216 solver.cpp:237]     Train net output #0: loss = 0.491944 (* 1 = 0.491944 loss)
I0410 00:57:55.138523 16216 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0410 00:58:00.122391 16216 solver.cpp:218] Iteration 4548 (2.40784 iter/s, 4.98372s/12 iters), loss = 0.614384
I0410 00:58:00.122449 16216 solver.cpp:237]     Train net output #0: loss = 0.614383 (* 1 = 0.614383 loss)
I0410 00:58:00.122463 16216 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0410 00:58:01.372189 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:58:05.072764 16216 solver.cpp:218] Iteration 4560 (2.42416 iter/s, 4.95017s/12 iters), loss = 0.475679
I0410 00:58:05.072896 16216 solver.cpp:237]     Train net output #0: loss = 0.475679 (* 1 = 0.475679 loss)
I0410 00:58:05.072912 16216 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0410 00:58:10.042958 16216 solver.cpp:218] Iteration 4572 (2.41453 iter/s, 4.96991s/12 iters), loss = 0.599515
I0410 00:58:10.043015 16216 solver.cpp:237]     Train net output #0: loss = 0.599515 (* 1 = 0.599515 loss)
I0410 00:58:10.043025 16216 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0410 00:58:15.044868 16216 solver.cpp:218] Iteration 4584 (2.39919 iter/s, 5.0017s/12 iters), loss = 0.466488
I0410 00:58:15.044915 16216 solver.cpp:237]     Train net output #0: loss = 0.466488 (* 1 = 0.466488 loss)
I0410 00:58:15.044925 16216 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0410 00:58:17.104552 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0410 00:58:18.184262 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0410 00:58:18.543401 16216 solver.cpp:330] Iteration 4590, Testing net (#0)
I0410 00:58:18.543431 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:58:21.440656 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:58:23.254772 16216 solver.cpp:397]     Test net output #0: accuracy = 0.253064
I0410 00:58:23.254807 16216 solver.cpp:397]     Test net output #1: loss = 5.77317 (* 1 = 5.77317 loss)
I0410 00:58:25.157500 16216 solver.cpp:218] Iteration 4596 (1.18668 iter/s, 10.1123s/12 iters), loss = 0.589123
I0410 00:58:25.157553 16216 solver.cpp:237]     Train net output #0: loss = 0.589123 (* 1 = 0.589123 loss)
I0410 00:58:25.157563 16216 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0410 00:58:30.110141 16216 solver.cpp:218] Iteration 4608 (2.42305 iter/s, 4.95243s/12 iters), loss = 0.489436
I0410 00:58:30.110196 16216 solver.cpp:237]     Train net output #0: loss = 0.489436 (* 1 = 0.489436 loss)
I0410 00:58:30.110209 16216 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0410 00:58:35.091603 16216 solver.cpp:218] Iteration 4620 (2.40903 iter/s, 4.98125s/12 iters), loss = 0.267289
I0410 00:58:35.091737 16216 solver.cpp:237]     Train net output #0: loss = 0.267289 (* 1 = 0.267289 loss)
I0410 00:58:35.091747 16216 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0410 00:58:40.048612 16216 solver.cpp:218] Iteration 4632 (2.42096 iter/s, 4.95672s/12 iters), loss = 0.373075
I0410 00:58:40.048668 16216 solver.cpp:237]     Train net output #0: loss = 0.373075 (* 1 = 0.373075 loss)
I0410 00:58:40.048681 16216 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0410 00:58:44.980716 16216 solver.cpp:218] Iteration 4644 (2.43314 iter/s, 4.9319s/12 iters), loss = 0.346457
I0410 00:58:44.980772 16216 solver.cpp:237]     Train net output #0: loss = 0.346457 (* 1 = 0.346457 loss)
I0410 00:58:44.980787 16216 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0410 00:58:48.320205 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:58:49.891680 16216 solver.cpp:218] Iteration 4656 (2.44362 iter/s, 4.91075s/12 iters), loss = 0.687079
I0410 00:58:49.891736 16216 solver.cpp:237]     Train net output #0: loss = 0.687079 (* 1 = 0.687079 loss)
I0410 00:58:49.891748 16216 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0410 00:58:54.836151 16216 solver.cpp:218] Iteration 4668 (2.42706 iter/s, 4.94426s/12 iters), loss = 0.444505
I0410 00:58:54.836205 16216 solver.cpp:237]     Train net output #0: loss = 0.444505 (* 1 = 0.444505 loss)
I0410 00:58:54.836215 16216 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0410 00:58:59.857064 16216 solver.cpp:218] Iteration 4680 (2.3901 iter/s, 5.0207s/12 iters), loss = 0.379917
I0410 00:58:59.857118 16216 solver.cpp:237]     Train net output #0: loss = 0.379917 (* 1 = 0.379917 loss)
I0410 00:58:59.857131 16216 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0410 00:59:04.357674 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0410 00:59:05.113310 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0410 00:59:05.289355 16216 solver.cpp:330] Iteration 4692, Testing net (#0)
I0410 00:59:05.289373 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:59:07.886195 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:59:09.733832 16216 solver.cpp:397]     Test net output #0: accuracy = 0.26348
I0410 00:59:09.733903 16216 solver.cpp:397]     Test net output #1: loss = 6.08968 (* 1 = 6.08968 loss)
I0410 00:59:09.816607 16216 solver.cpp:218] Iteration 4692 (1.20492 iter/s, 9.95919s/12 iters), loss = 0.542384
I0410 00:59:09.816656 16216 solver.cpp:237]     Train net output #0: loss = 0.542384 (* 1 = 0.542384 loss)
I0410 00:59:09.816666 16216 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0410 00:59:14.045020 16216 solver.cpp:218] Iteration 4704 (2.83807 iter/s, 4.22823s/12 iters), loss = 0.434521
I0410 00:59:14.045075 16216 solver.cpp:237]     Train net output #0: loss = 0.434521 (* 1 = 0.434521 loss)
I0410 00:59:14.045087 16216 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0410 00:59:18.937255 16216 solver.cpp:218] Iteration 4716 (2.45297 iter/s, 4.89203s/12 iters), loss = 0.312255
I0410 00:59:18.937304 16216 solver.cpp:237]     Train net output #0: loss = 0.312255 (* 1 = 0.312255 loss)
I0410 00:59:18.937317 16216 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0410 00:59:24.024932 16216 solver.cpp:218] Iteration 4728 (2.35873 iter/s, 5.08747s/12 iters), loss = 0.572095
I0410 00:59:24.024979 16216 solver.cpp:237]     Train net output #0: loss = 0.572095 (* 1 = 0.572095 loss)
I0410 00:59:24.024991 16216 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0410 00:59:29.069110 16216 solver.cpp:218] Iteration 4740 (2.37908 iter/s, 5.04397s/12 iters), loss = 0.228332
I0410 00:59:29.069156 16216 solver.cpp:237]     Train net output #0: loss = 0.228332 (* 1 = 0.228332 loss)
I0410 00:59:29.069167 16216 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0410 00:59:34.201262 16216 solver.cpp:218] Iteration 4752 (2.3383 iter/s, 5.13194s/12 iters), loss = 0.351077
I0410 00:59:34.201313 16216 solver.cpp:237]     Train net output #0: loss = 0.351077 (* 1 = 0.351077 loss)
I0410 00:59:34.201326 16216 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0410 00:59:34.760816 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:59:39.169421 16216 solver.cpp:218] Iteration 4764 (2.41548 iter/s, 4.96795s/12 iters), loss = 0.273798
I0410 00:59:39.169889 16216 solver.cpp:237]     Train net output #0: loss = 0.273798 (* 1 = 0.273798 loss)
I0410 00:59:39.169903 16216 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0410 00:59:44.043951 16216 solver.cpp:218] Iteration 4776 (2.46209 iter/s, 4.87391s/12 iters), loss = 0.448568
I0410 00:59:44.043996 16216 solver.cpp:237]     Train net output #0: loss = 0.448568 (* 1 = 0.448568 loss)
I0410 00:59:44.044006 16216 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0410 00:59:49.148190 16216 solver.cpp:218] Iteration 4788 (2.35108 iter/s, 5.10404s/12 iters), loss = 0.377987
I0410 00:59:49.148231 16216 solver.cpp:237]     Train net output #0: loss = 0.377987 (* 1 = 0.377987 loss)
I0410 00:59:49.148241 16216 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0410 00:59:51.179266 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0410 00:59:51.436110 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0410 00:59:51.620095 16216 solver.cpp:330] Iteration 4794, Testing net (#0)
I0410 00:59:51.620115 16216 net.cpp:676] Ignoring source layer train-data
I0410 00:59:54.171942 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:59:56.236116 16216 solver.cpp:397]     Test net output #0: accuracy = 0.261642
I0410 00:59:56.236166 16216 solver.cpp:397]     Test net output #1: loss = 5.8711 (* 1 = 5.8711 loss)
I0410 00:59:58.270205 16216 solver.cpp:218] Iteration 4800 (1.31554 iter/s, 9.1217s/12 iters), loss = 0.354221
I0410 00:59:58.270246 16216 solver.cpp:237]     Train net output #0: loss = 0.354221 (* 1 = 0.354221 loss)
I0410 00:59:58.270256 16216 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0410 01:00:03.291477 16216 solver.cpp:218] Iteration 4812 (2.38993 iter/s, 5.02107s/12 iters), loss = 0.428586
I0410 01:00:03.291529 16216 solver.cpp:237]     Train net output #0: loss = 0.428586 (* 1 = 0.428586 loss)
I0410 01:00:03.291540 16216 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0410 01:00:08.289813 16216 solver.cpp:218] Iteration 4824 (2.4009 iter/s, 4.99813s/12 iters), loss = 0.554895
I0410 01:00:08.289865 16216 solver.cpp:237]     Train net output #0: loss = 0.554895 (* 1 = 0.554895 loss)
I0410 01:00:08.289876 16216 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0410 01:00:13.169239 16216 solver.cpp:218] Iteration 4836 (2.45941 iter/s, 4.87922s/12 iters), loss = 0.225086
I0410 01:00:13.169328 16216 solver.cpp:237]     Train net output #0: loss = 0.225086 (* 1 = 0.225086 loss)
I0410 01:00:13.169342 16216 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0410 01:00:13.531738 16216 blocking_queue.cpp:49] Waiting for data
I0410 01:00:18.170197 16216 solver.cpp:218] Iteration 4848 (2.39966 iter/s, 5.00072s/12 iters), loss = 0.401934
I0410 01:00:18.170248 16216 solver.cpp:237]     Train net output #0: loss = 0.401934 (* 1 = 0.401934 loss)
I0410 01:00:18.170260 16216 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0410 01:00:20.724225 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:00:23.047705 16216 solver.cpp:218] Iteration 4860 (2.46038 iter/s, 4.87731s/12 iters), loss = 0.321669
I0410 01:00:23.047749 16216 solver.cpp:237]     Train net output #0: loss = 0.321669 (* 1 = 0.321669 loss)
I0410 01:00:23.047761 16216 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0410 01:00:27.946523 16216 solver.cpp:218] Iteration 4872 (2.44967 iter/s, 4.89862s/12 iters), loss = 0.384392
I0410 01:00:27.946573 16216 solver.cpp:237]     Train net output #0: loss = 0.384392 (* 1 = 0.384392 loss)
I0410 01:00:27.946583 16216 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0410 01:00:32.845358 16216 solver.cpp:218] Iteration 4884 (2.44966 iter/s, 4.89864s/12 iters), loss = 0.380577
I0410 01:00:32.845405 16216 solver.cpp:237]     Train net output #0: loss = 0.380577 (* 1 = 0.380577 loss)
I0410 01:00:32.845417 16216 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0410 01:00:37.292080 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0410 01:00:37.669045 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0410 01:00:37.860880 16216 solver.cpp:330] Iteration 4896, Testing net (#0)
I0410 01:00:37.860909 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:00:40.347627 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:00:42.278328 16216 solver.cpp:397]     Test net output #0: accuracy = 0.264093
I0410 01:00:42.278378 16216 solver.cpp:397]     Test net output #1: loss = 6.26031 (* 1 = 6.26031 loss)
I0410 01:00:42.360975 16216 solver.cpp:218] Iteration 4896 (1.26113 iter/s, 9.51528s/12 iters), loss = 0.569382
I0410 01:00:42.361028 16216 solver.cpp:237]     Train net output #0: loss = 0.569382 (* 1 = 0.569382 loss)
I0410 01:00:42.361042 16216 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0410 01:00:46.582204 16216 solver.cpp:218] Iteration 4908 (2.8429 iter/s, 4.22104s/12 iters), loss = 0.351552
I0410 01:00:46.582348 16216 solver.cpp:237]     Train net output #0: loss = 0.351552 (* 1 = 0.351552 loss)
I0410 01:00:46.582360 16216 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0410 01:00:51.467057 16216 solver.cpp:218] Iteration 4920 (2.45672 iter/s, 4.88455s/12 iters), loss = 0.453471
I0410 01:00:51.467118 16216 solver.cpp:237]     Train net output #0: loss = 0.453471 (* 1 = 0.453471 loss)
I0410 01:00:51.467130 16216 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0410 01:00:56.399911 16216 solver.cpp:218] Iteration 4932 (2.43278 iter/s, 4.93264s/12 iters), loss = 0.293328
I0410 01:00:56.399968 16216 solver.cpp:237]     Train net output #0: loss = 0.293328 (* 1 = 0.293328 loss)
I0410 01:00:56.399981 16216 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0410 01:01:01.446235 16216 solver.cpp:218] Iteration 4944 (2.37807 iter/s, 5.04611s/12 iters), loss = 0.357084
I0410 01:01:01.446288 16216 solver.cpp:237]     Train net output #0: loss = 0.357084 (* 1 = 0.357084 loss)
I0410 01:01:01.446300 16216 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0410 01:01:06.198602 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:01:06.395610 16216 solver.cpp:218] Iteration 4956 (2.42465 iter/s, 4.94917s/12 iters), loss = 0.352649
I0410 01:01:06.395654 16216 solver.cpp:237]     Train net output #0: loss = 0.352649 (* 1 = 0.352649 loss)
I0410 01:01:06.395663 16216 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0410 01:01:11.391502 16216 solver.cpp:218] Iteration 4968 (2.40207 iter/s, 4.99568s/12 iters), loss = 0.35302
I0410 01:01:11.391561 16216 solver.cpp:237]     Train net output #0: loss = 0.35302 (* 1 = 0.35302 loss)
I0410 01:01:11.391571 16216 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0410 01:01:16.292747 16216 solver.cpp:218] Iteration 4980 (2.44847 iter/s, 4.90103s/12 iters), loss = 0.334716
I0410 01:01:16.292794 16216 solver.cpp:237]     Train net output #0: loss = 0.334716 (* 1 = 0.334716 loss)
I0410 01:01:16.292804 16216 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0410 01:01:21.161597 16216 solver.cpp:218] Iteration 4992 (2.46475 iter/s, 4.86864s/12 iters), loss = 0.404438
I0410 01:01:21.161772 16216 solver.cpp:237]     Train net output #0: loss = 0.404438 (* 1 = 0.404438 loss)
I0410 01:01:21.161785 16216 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0410 01:01:23.148344 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0410 01:01:23.827694 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0410 01:01:24.440793 16216 solver.cpp:330] Iteration 4998, Testing net (#0)
I0410 01:01:24.440821 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:01:26.972004 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:01:28.966712 16216 solver.cpp:397]     Test net output #0: accuracy = 0.257966
I0410 01:01:28.966760 16216 solver.cpp:397]     Test net output #1: loss = 6.39322 (* 1 = 6.39322 loss)
I0410 01:01:30.823031 16216 solver.cpp:218] Iteration 5004 (1.24211 iter/s, 9.66097s/12 iters), loss = 0.308353
I0410 01:01:30.823094 16216 solver.cpp:237]     Train net output #0: loss = 0.308353 (* 1 = 0.308353 loss)
I0410 01:01:30.823107 16216 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0410 01:01:35.714520 16216 solver.cpp:218] Iteration 5016 (2.45334 iter/s, 4.89128s/12 iters), loss = 0.354002
I0410 01:01:35.714560 16216 solver.cpp:237]     Train net output #0: loss = 0.354002 (* 1 = 0.354002 loss)
I0410 01:01:35.714567 16216 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0410 01:01:40.975118 16216 solver.cpp:218] Iteration 5028 (2.2812 iter/s, 5.2604s/12 iters), loss = 0.368498
I0410 01:01:40.975153 16216 solver.cpp:237]     Train net output #0: loss = 0.368498 (* 1 = 0.368498 loss)
I0410 01:01:40.975162 16216 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0410 01:01:45.928925 16216 solver.cpp:218] Iteration 5040 (2.42247 iter/s, 4.95361s/12 iters), loss = 0.369703
I0410 01:01:45.928977 16216 solver.cpp:237]     Train net output #0: loss = 0.369703 (* 1 = 0.369703 loss)
I0410 01:01:45.928987 16216 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0410 01:01:50.901715 16216 solver.cpp:218] Iteration 5052 (2.41323 iter/s, 4.97258s/12 iters), loss = 0.390182
I0410 01:01:50.901775 16216 solver.cpp:237]     Train net output #0: loss = 0.390182 (* 1 = 0.390182 loss)
I0410 01:01:50.901789 16216 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0410 01:01:52.829977 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:01:55.827209 16216 solver.cpp:218] Iteration 5064 (2.43641 iter/s, 4.92528s/12 iters), loss = 0.558126
I0410 01:01:55.827265 16216 solver.cpp:237]     Train net output #0: loss = 0.558126 (* 1 = 0.558126 loss)
I0410 01:01:55.827278 16216 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0410 01:02:00.943486 16216 solver.cpp:218] Iteration 5076 (2.34555 iter/s, 5.11607s/12 iters), loss = 0.360902
I0410 01:02:00.943545 16216 solver.cpp:237]     Train net output #0: loss = 0.360902 (* 1 = 0.360902 loss)
I0410 01:02:00.943560 16216 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0410 01:02:05.791590 16216 solver.cpp:218] Iteration 5088 (2.4753 iter/s, 4.84789s/12 iters), loss = 0.485925
I0410 01:02:05.791654 16216 solver.cpp:237]     Train net output #0: loss = 0.485925 (* 1 = 0.485925 loss)
I0410 01:02:05.791667 16216 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0410 01:02:10.206058 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0410 01:02:10.673732 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0410 01:02:10.873340 16216 solver.cpp:330] Iteration 5100, Testing net (#0)
I0410 01:02:10.873360 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:02:13.273435 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:02:15.282613 16216 solver.cpp:397]     Test net output #0: accuracy = 0.26777
I0410 01:02:15.282649 16216 solver.cpp:397]     Test net output #1: loss = 6.3945 (* 1 = 6.3945 loss)
I0410 01:02:15.365383 16216 solver.cpp:218] Iteration 5100 (1.25347 iter/s, 9.57345s/12 iters), loss = 0.489946
I0410 01:02:15.365425 16216 solver.cpp:237]     Train net output #0: loss = 0.489946 (* 1 = 0.489946 loss)
I0410 01:02:15.365435 16216 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0410 01:02:19.677577 16216 solver.cpp:218] Iteration 5112 (2.78292 iter/s, 4.31201s/12 iters), loss = 0.390807
I0410 01:02:19.677634 16216 solver.cpp:237]     Train net output #0: loss = 0.390807 (* 1 = 0.390807 loss)
I0410 01:02:19.677646 16216 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0410 01:02:25.316879 16216 solver.cpp:218] Iteration 5124 (2.12801 iter/s, 5.63907s/12 iters), loss = 0.450071
I0410 01:02:25.317001 16216 solver.cpp:237]     Train net output #0: loss = 0.450071 (* 1 = 0.450071 loss)
I0410 01:02:25.317015 16216 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0410 01:02:30.336678 16216 solver.cpp:218] Iteration 5136 (2.39066 iter/s, 5.01953s/12 iters), loss = 0.266043
I0410 01:02:30.336728 16216 solver.cpp:237]     Train net output #0: loss = 0.266043 (* 1 = 0.266043 loss)
I0410 01:02:30.336740 16216 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0410 01:02:35.264333 16216 solver.cpp:218] Iteration 5148 (2.43533 iter/s, 4.92745s/12 iters), loss = 0.329665
I0410 01:02:35.264385 16216 solver.cpp:237]     Train net output #0: loss = 0.329665 (* 1 = 0.329665 loss)
I0410 01:02:35.264396 16216 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0410 01:02:39.387586 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:02:40.283031 16216 solver.cpp:218] Iteration 5160 (2.39116 iter/s, 5.01849s/12 iters), loss = 0.378739
I0410 01:02:40.283080 16216 solver.cpp:237]     Train net output #0: loss = 0.378739 (* 1 = 0.378739 loss)
I0410 01:02:40.283089 16216 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0410 01:02:45.207423 16216 solver.cpp:218] Iteration 5172 (2.43695 iter/s, 4.92419s/12 iters), loss = 0.314079
I0410 01:02:45.207474 16216 solver.cpp:237]     Train net output #0: loss = 0.314079 (* 1 = 0.314079 loss)
I0410 01:02:45.207485 16216 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0410 01:02:50.098778 16216 solver.cpp:218] Iteration 5184 (2.45341 iter/s, 4.89115s/12 iters), loss = 0.369524
I0410 01:02:50.098831 16216 solver.cpp:237]     Train net output #0: loss = 0.369524 (* 1 = 0.369524 loss)
I0410 01:02:50.098843 16216 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0410 01:02:54.986583 16216 solver.cpp:218] Iteration 5196 (2.45519 iter/s, 4.8876s/12 iters), loss = 0.376211
I0410 01:02:54.986629 16216 solver.cpp:237]     Train net output #0: loss = 0.376211 (* 1 = 0.376211 loss)
I0410 01:02:54.986637 16216 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0410 01:02:56.984616 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0410 01:02:57.232270 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0410 01:02:57.409773 16216 solver.cpp:330] Iteration 5202, Testing net (#0)
I0410 01:02:57.409802 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:03:00.099591 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:03:02.172334 16216 solver.cpp:397]     Test net output #0: accuracy = 0.261029
I0410 01:03:02.172371 16216 solver.cpp:397]     Test net output #1: loss = 6.47588 (* 1 = 6.47588 loss)
I0410 01:03:04.117913 16216 solver.cpp:218] Iteration 5208 (1.3142 iter/s, 9.131s/12 iters), loss = 0.320895
I0410 01:03:04.117980 16216 solver.cpp:237]     Train net output #0: loss = 0.320895 (* 1 = 0.320895 loss)
I0410 01:03:04.117993 16216 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0410 01:03:09.105579 16216 solver.cpp:218] Iteration 5220 (2.40604 iter/s, 4.98746s/12 iters), loss = 0.463247
I0410 01:03:09.105643 16216 solver.cpp:237]     Train net output #0: loss = 0.463247 (* 1 = 0.463247 loss)
I0410 01:03:09.105656 16216 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0410 01:03:13.978893 16216 solver.cpp:218] Iteration 5232 (2.4625 iter/s, 4.8731s/12 iters), loss = 0.229117
I0410 01:03:13.978955 16216 solver.cpp:237]     Train net output #0: loss = 0.229117 (* 1 = 0.229117 loss)
I0410 01:03:13.978967 16216 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0410 01:03:18.860122 16216 solver.cpp:218] Iteration 5244 (2.45851 iter/s, 4.88101s/12 iters), loss = 0.341519
I0410 01:03:18.860180 16216 solver.cpp:237]     Train net output #0: loss = 0.341519 (* 1 = 0.341519 loss)
I0410 01:03:18.860191 16216 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0410 01:03:23.738587 16216 solver.cpp:218] Iteration 5256 (2.4599 iter/s, 4.87826s/12 iters), loss = 0.261185
I0410 01:03:23.738637 16216 solver.cpp:237]     Train net output #0: loss = 0.261185 (* 1 = 0.261185 loss)
I0410 01:03:23.738649 16216 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0410 01:03:24.979383 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:03:28.563500 16216 solver.cpp:218] Iteration 5268 (2.4872 iter/s, 4.82471s/12 iters), loss = 0.31297
I0410 01:03:28.563658 16216 solver.cpp:237]     Train net output #0: loss = 0.31297 (* 1 = 0.31297 loss)
I0410 01:03:28.563671 16216 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0410 01:03:33.583134 16216 solver.cpp:218] Iteration 5280 (2.39076 iter/s, 5.01932s/12 iters), loss = 0.453648
I0410 01:03:33.583179 16216 solver.cpp:237]     Train net output #0: loss = 0.453648 (* 1 = 0.453648 loss)
I0410 01:03:33.583189 16216 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0410 01:03:38.714910 16216 solver.cpp:218] Iteration 5292 (2.33847 iter/s, 5.13157s/12 iters), loss = 0.318843
I0410 01:03:38.714962 16216 solver.cpp:237]     Train net output #0: loss = 0.318843 (* 1 = 0.318843 loss)
I0410 01:03:38.714972 16216 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0410 01:03:43.204355 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0410 01:03:44.231009 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0410 01:03:45.617413 16216 solver.cpp:330] Iteration 5304, Testing net (#0)
I0410 01:03:45.617442 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:03:47.940441 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:03:50.033218 16216 solver.cpp:397]     Test net output #0: accuracy = 0.285539
I0410 01:03:50.033260 16216 solver.cpp:397]     Test net output #1: loss = 6.20274 (* 1 = 6.20274 loss)
I0410 01:03:50.115912 16216 solver.cpp:218] Iteration 5304 (1.05257 iter/s, 11.4006s/12 iters), loss = 0.30959
I0410 01:03:50.115959 16216 solver.cpp:237]     Train net output #0: loss = 0.30959 (* 1 = 0.30959 loss)
I0410 01:03:50.115969 16216 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0410 01:03:54.458853 16216 solver.cpp:218] Iteration 5316 (2.76322 iter/s, 4.34276s/12 iters), loss = 0.336927
I0410 01:03:54.458895 16216 solver.cpp:237]     Train net output #0: loss = 0.336927 (* 1 = 0.336927 loss)
I0410 01:03:54.458905 16216 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0410 01:03:59.289363 16216 solver.cpp:218] Iteration 5328 (2.48431 iter/s, 4.83031s/12 iters), loss = 0.161065
I0410 01:03:59.289475 16216 solver.cpp:237]     Train net output #0: loss = 0.161065 (* 1 = 0.161065 loss)
I0410 01:03:59.289490 16216 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0410 01:04:04.217424 16216 solver.cpp:218] Iteration 5340 (2.43517 iter/s, 4.9278s/12 iters), loss = 0.398794
I0410 01:04:04.217468 16216 solver.cpp:237]     Train net output #0: loss = 0.398794 (* 1 = 0.398794 loss)
I0410 01:04:04.217476 16216 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0410 01:04:09.164532 16216 solver.cpp:218] Iteration 5352 (2.42576 iter/s, 4.94691s/12 iters), loss = 0.253894
I0410 01:04:09.164575 16216 solver.cpp:237]     Train net output #0: loss = 0.253894 (* 1 = 0.253894 loss)
I0410 01:04:09.164583 16216 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0410 01:04:12.483408 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:04:14.038499 16216 solver.cpp:218] Iteration 5364 (2.46216 iter/s, 4.87377s/12 iters), loss = 0.322297
I0410 01:04:14.038563 16216 solver.cpp:237]     Train net output #0: loss = 0.322297 (* 1 = 0.322297 loss)
I0410 01:04:14.038578 16216 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0410 01:04:18.980083 16216 solver.cpp:218] Iteration 5376 (2.42848 iter/s, 4.94136s/12 iters), loss = 0.250864
I0410 01:04:18.980137 16216 solver.cpp:237]     Train net output #0: loss = 0.250864 (* 1 = 0.250864 loss)
I0410 01:04:18.980147 16216 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0410 01:04:24.110792 16216 solver.cpp:218] Iteration 5388 (2.33896 iter/s, 5.13049s/12 iters), loss = 0.293357
I0410 01:04:24.110849 16216 solver.cpp:237]     Train net output #0: loss = 0.293357 (* 1 = 0.293357 loss)
I0410 01:04:24.110862 16216 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0410 01:04:29.009630 16216 solver.cpp:218] Iteration 5400 (2.44967 iter/s, 4.89862s/12 iters), loss = 0.374002
I0410 01:04:29.009685 16216 solver.cpp:237]     Train net output #0: loss = 0.374002 (* 1 = 0.374002 loss)
I0410 01:04:29.009696 16216 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0410 01:04:31.007973 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0410 01:04:31.970999 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0410 01:04:32.834926 16216 solver.cpp:330] Iteration 5406, Testing net (#0)
I0410 01:04:32.834947 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:04:35.184428 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:04:37.309412 16216 solver.cpp:397]     Test net output #0: accuracy = 0.255515
I0410 01:04:37.309460 16216 solver.cpp:397]     Test net output #1: loss = 6.67589 (* 1 = 6.67589 loss)
I0410 01:04:39.158082 16216 solver.cpp:218] Iteration 5412 (1.18249 iter/s, 10.1481s/12 iters), loss = 0.239861
I0410 01:04:39.158129 16216 solver.cpp:237]     Train net output #0: loss = 0.239861 (* 1 = 0.239861 loss)
I0410 01:04:39.158139 16216 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0410 01:04:44.093084 16216 solver.cpp:218] Iteration 5424 (2.43171 iter/s, 4.9348s/12 iters), loss = 0.244945
I0410 01:04:44.093129 16216 solver.cpp:237]     Train net output #0: loss = 0.244945 (* 1 = 0.244945 loss)
I0410 01:04:44.093142 16216 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0410 01:04:49.036631 16216 solver.cpp:218] Iteration 5436 (2.42751 iter/s, 4.94334s/12 iters), loss = 0.380585
I0410 01:04:49.036689 16216 solver.cpp:237]     Train net output #0: loss = 0.380585 (* 1 = 0.380585 loss)
I0410 01:04:49.036701 16216 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0410 01:04:53.942023 16216 solver.cpp:218] Iteration 5448 (2.44639 iter/s, 4.90518s/12 iters), loss = 0.306721
I0410 01:04:53.942077 16216 solver.cpp:237]     Train net output #0: loss = 0.306721 (* 1 = 0.306721 loss)
I0410 01:04:53.942090 16216 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0410 01:04:58.884580 16216 solver.cpp:218] Iteration 5460 (2.428 iter/s, 4.94235s/12 iters), loss = 0.244481
I0410 01:04:58.884635 16216 solver.cpp:237]     Train net output #0: loss = 0.244481 (* 1 = 0.244481 loss)
I0410 01:04:58.884647 16216 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0410 01:04:59.457746 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:05:03.803061 16216 solver.cpp:218] Iteration 5472 (2.43988 iter/s, 4.91827s/12 iters), loss = 0.181943
I0410 01:05:03.803182 16216 solver.cpp:237]     Train net output #0: loss = 0.181943 (* 1 = 0.181943 loss)
I0410 01:05:03.803195 16216 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0410 01:05:08.744402 16216 solver.cpp:218] Iteration 5484 (2.42862 iter/s, 4.94107s/12 iters), loss = 0.371848
I0410 01:05:08.744457 16216 solver.cpp:237]     Train net output #0: loss = 0.371848 (* 1 = 0.371848 loss)
I0410 01:05:08.744470 16216 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0410 01:05:13.625972 16216 solver.cpp:218] Iteration 5496 (2.45833 iter/s, 4.88136s/12 iters), loss = 0.213055
I0410 01:05:13.626017 16216 solver.cpp:237]     Train net output #0: loss = 0.213055 (* 1 = 0.213055 loss)
I0410 01:05:13.626027 16216 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0410 01:05:18.078732 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0410 01:05:18.333868 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0410 01:05:18.518167 16216 solver.cpp:330] Iteration 5508, Testing net (#0)
I0410 01:05:18.518194 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:05:20.760244 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:05:22.967583 16216 solver.cpp:397]     Test net output #0: accuracy = 0.273284
I0410 01:05:22.967615 16216 solver.cpp:397]     Test net output #1: loss = 6.67126 (* 1 = 6.67126 loss)
I0410 01:05:23.050371 16216 solver.cpp:218] Iteration 5508 (1.27334 iter/s, 9.42407s/12 iters), loss = 0.380391
I0410 01:05:23.050426 16216 solver.cpp:237]     Train net output #0: loss = 0.380391 (* 1 = 0.380391 loss)
I0410 01:05:23.050436 16216 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0410 01:05:27.159799 16216 solver.cpp:218] Iteration 5520 (2.92025 iter/s, 4.10924s/12 iters), loss = 0.292621
I0410 01:05:27.159852 16216 solver.cpp:237]     Train net output #0: loss = 0.292621 (* 1 = 0.292621 loss)
I0410 01:05:27.159864 16216 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0410 01:05:27.907219 16216 blocking_queue.cpp:49] Waiting for data
I0410 01:05:32.089743 16216 solver.cpp:218] Iteration 5532 (2.43421 iter/s, 4.92974s/12 iters), loss = 0.29687
I0410 01:05:32.089795 16216 solver.cpp:237]     Train net output #0: loss = 0.29687 (* 1 = 0.29687 loss)
I0410 01:05:32.089805 16216 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0410 01:05:37.022363 16216 solver.cpp:218] Iteration 5544 (2.43289 iter/s, 4.93241s/12 iters), loss = 0.438008
I0410 01:05:37.022485 16216 solver.cpp:237]     Train net output #0: loss = 0.438008 (* 1 = 0.438008 loss)
I0410 01:05:37.022498 16216 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0410 01:05:42.066792 16216 solver.cpp:218] Iteration 5556 (2.37899 iter/s, 5.04415s/12 iters), loss = 0.232671
I0410 01:05:42.066845 16216 solver.cpp:237]     Train net output #0: loss = 0.232671 (* 1 = 0.232671 loss)
I0410 01:05:42.066856 16216 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0410 01:05:44.764767 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:05:47.020623 16216 solver.cpp:218] Iteration 5568 (2.42247 iter/s, 4.95362s/12 iters), loss = 0.210113
I0410 01:05:47.020681 16216 solver.cpp:237]     Train net output #0: loss = 0.210113 (* 1 = 0.210113 loss)
I0410 01:05:47.020695 16216 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0410 01:05:51.905093 16216 solver.cpp:218] Iteration 5580 (2.45687 iter/s, 4.88426s/12 iters), loss = 0.273654
I0410 01:05:51.905150 16216 solver.cpp:237]     Train net output #0: loss = 0.273654 (* 1 = 0.273654 loss)
I0410 01:05:51.905162 16216 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0410 01:05:56.806171 16216 solver.cpp:218] Iteration 5592 (2.44855 iter/s, 4.90086s/12 iters), loss = 0.164708
I0410 01:05:56.806228 16216 solver.cpp:237]     Train net output #0: loss = 0.164708 (* 1 = 0.164708 loss)
I0410 01:05:56.806241 16216 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0410 01:06:01.709643 16216 solver.cpp:218] Iteration 5604 (2.44735 iter/s, 4.90326s/12 iters), loss = 0.270912
I0410 01:06:01.709693 16216 solver.cpp:237]     Train net output #0: loss = 0.270912 (* 1 = 0.270912 loss)
I0410 01:06:01.709704 16216 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0410 01:06:03.775744 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0410 01:06:04.748495 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0410 01:06:05.762087 16216 solver.cpp:330] Iteration 5610, Testing net (#0)
I0410 01:06:05.762117 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:06:08.176293 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:06:10.414227 16216 solver.cpp:397]     Test net output #0: accuracy = 0.288603
I0410 01:06:10.414276 16216 solver.cpp:397]     Test net output #1: loss = 6.43733 (* 1 = 6.43733 loss)
I0410 01:06:12.367511 16216 solver.cpp:218] Iteration 5616 (1.12597 iter/s, 10.6575s/12 iters), loss = 0.200241
I0410 01:06:12.367568 16216 solver.cpp:237]     Train net output #0: loss = 0.200241 (* 1 = 0.200241 loss)
I0410 01:06:12.367580 16216 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0410 01:06:17.453763 16216 solver.cpp:218] Iteration 5628 (2.3594 iter/s, 5.08604s/12 iters), loss = 0.380712
I0410 01:06:17.453815 16216 solver.cpp:237]     Train net output #0: loss = 0.380712 (* 1 = 0.380712 loss)
I0410 01:06:17.453825 16216 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0410 01:06:22.435979 16216 solver.cpp:218] Iteration 5640 (2.40867 iter/s, 4.98201s/12 iters), loss = 0.403606
I0410 01:06:22.436033 16216 solver.cpp:237]     Train net output #0: loss = 0.403606 (* 1 = 0.403606 loss)
I0410 01:06:22.436043 16216 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0410 01:06:27.393714 16216 solver.cpp:218] Iteration 5652 (2.42056 iter/s, 4.95752s/12 iters), loss = 0.330106
I0410 01:06:27.393771 16216 solver.cpp:237]     Train net output #0: loss = 0.330106 (* 1 = 0.330106 loss)
I0410 01:06:27.393783 16216 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0410 01:06:32.299607 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:06:32.472146 16216 solver.cpp:218] Iteration 5664 (2.36304 iter/s, 5.07821s/12 iters), loss = 0.18015
I0410 01:06:32.472198 16216 solver.cpp:237]     Train net output #0: loss = 0.18015 (* 1 = 0.18015 loss)
I0410 01:06:32.472208 16216 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0410 01:06:37.451021 16216 solver.cpp:218] Iteration 5676 (2.41028 iter/s, 4.97867s/12 iters), loss = 0.197727
I0410 01:06:37.451064 16216 solver.cpp:237]     Train net output #0: loss = 0.197727 (* 1 = 0.197727 loss)
I0410 01:06:37.451073 16216 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0410 01:06:42.425591 16216 solver.cpp:218] Iteration 5688 (2.41237 iter/s, 4.97437s/12 iters), loss = 0.24105
I0410 01:06:42.425685 16216 solver.cpp:237]     Train net output #0: loss = 0.24105 (* 1 = 0.24105 loss)
I0410 01:06:42.425694 16216 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0410 01:06:47.428048 16216 solver.cpp:218] Iteration 5700 (2.39895 iter/s, 5.0022s/12 iters), loss = 0.18462
I0410 01:06:47.428109 16216 solver.cpp:237]     Train net output #0: loss = 0.18462 (* 1 = 0.18462 loss)
I0410 01:06:47.428122 16216 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0410 01:06:51.943825 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0410 01:06:52.481742 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0410 01:06:53.333923 16216 solver.cpp:330] Iteration 5712, Testing net (#0)
I0410 01:06:53.333942 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:06:55.587111 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:06:57.852845 16216 solver.cpp:397]     Test net output #0: accuracy = 0.273897
I0410 01:06:57.852874 16216 solver.cpp:397]     Test net output #1: loss = 6.69064 (* 1 = 6.69064 loss)
I0410 01:06:57.934870 16216 solver.cpp:218] Iteration 5712 (1.14216 iter/s, 10.5065s/12 iters), loss = 0.277002
I0410 01:06:57.934914 16216 solver.cpp:237]     Train net output #0: loss = 0.277002 (* 1 = 0.277002 loss)
I0410 01:06:57.934923 16216 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0410 01:07:02.207617 16216 solver.cpp:218] Iteration 5724 (2.80862 iter/s, 4.27257s/12 iters), loss = 0.209621
I0410 01:07:02.207665 16216 solver.cpp:237]     Train net output #0: loss = 0.209621 (* 1 = 0.209621 loss)
I0410 01:07:02.207677 16216 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0410 01:07:07.223495 16216 solver.cpp:218] Iteration 5736 (2.3925 iter/s, 5.01568s/12 iters), loss = 0.171347
I0410 01:07:07.223538 16216 solver.cpp:237]     Train net output #0: loss = 0.171347 (* 1 = 0.171347 loss)
I0410 01:07:07.223547 16216 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0410 01:07:12.217520 16216 solver.cpp:218] Iteration 5748 (2.40297 iter/s, 4.99382s/12 iters), loss = 0.488791
I0410 01:07:12.217573 16216 solver.cpp:237]     Train net output #0: loss = 0.488791 (* 1 = 0.488791 loss)
I0410 01:07:12.217586 16216 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0410 01:07:17.243014 16216 solver.cpp:218] Iteration 5760 (2.38792 iter/s, 5.02529s/12 iters), loss = 0.214225
I0410 01:07:17.243144 16216 solver.cpp:237]     Train net output #0: loss = 0.214225 (* 1 = 0.214225 loss)
I0410 01:07:17.243155 16216 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0410 01:07:19.206761 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:07:22.346992 16216 solver.cpp:218] Iteration 5772 (2.35124 iter/s, 5.10369s/12 iters), loss = 0.340774
I0410 01:07:22.347051 16216 solver.cpp:237]     Train net output #0: loss = 0.340774 (* 1 = 0.340774 loss)
I0410 01:07:22.347064 16216 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0410 01:07:27.380719 16216 solver.cpp:218] Iteration 5784 (2.38402 iter/s, 5.03351s/12 iters), loss = 0.117149
I0410 01:07:27.380764 16216 solver.cpp:237]     Train net output #0: loss = 0.117149 (* 1 = 0.117149 loss)
I0410 01:07:27.380774 16216 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0410 01:07:32.328948 16216 solver.cpp:218] Iteration 5796 (2.42521 iter/s, 4.94803s/12 iters), loss = 0.258687
I0410 01:07:32.329001 16216 solver.cpp:237]     Train net output #0: loss = 0.258687 (* 1 = 0.258687 loss)
I0410 01:07:32.329015 16216 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0410 01:07:37.177251 16216 solver.cpp:218] Iteration 5808 (2.4752 iter/s, 4.8481s/12 iters), loss = 0.299866
I0410 01:07:37.177301 16216 solver.cpp:237]     Train net output #0: loss = 0.299866 (* 1 = 0.299866 loss)
I0410 01:07:37.177311 16216 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0410 01:07:39.162132 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0410 01:07:39.413234 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0410 01:07:39.601449 16216 solver.cpp:330] Iteration 5814, Testing net (#0)
I0410 01:07:39.601473 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:07:41.794713 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:07:44.077440 16216 solver.cpp:397]     Test net output #0: accuracy = 0.270221
I0410 01:07:44.077481 16216 solver.cpp:397]     Test net output #1: loss = 6.73837 (* 1 = 6.73837 loss)
I0410 01:07:45.801578 16216 solver.cpp:218] Iteration 5820 (1.39146 iter/s, 8.62401s/12 iters), loss = 0.177484
I0410 01:07:45.801638 16216 solver.cpp:237]     Train net output #0: loss = 0.177484 (* 1 = 0.177484 loss)
I0410 01:07:45.801651 16216 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0410 01:07:50.689992 16216 solver.cpp:218] Iteration 5832 (2.4549 iter/s, 4.88818s/12 iters), loss = 0.186179
I0410 01:07:50.690070 16216 solver.cpp:237]     Train net output #0: loss = 0.186179 (* 1 = 0.186179 loss)
I0410 01:07:50.690080 16216 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0410 01:07:55.628969 16216 solver.cpp:218] Iteration 5844 (2.42977 iter/s, 4.93873s/12 iters), loss = 0.2248
I0410 01:07:55.629036 16216 solver.cpp:237]     Train net output #0: loss = 0.2248 (* 1 = 0.2248 loss)
I0410 01:07:55.629050 16216 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0410 01:08:00.536187 16216 solver.cpp:218] Iteration 5856 (2.44549 iter/s, 4.907s/12 iters), loss = 0.266852
I0410 01:08:00.536231 16216 solver.cpp:237]     Train net output #0: loss = 0.266852 (* 1 = 0.266852 loss)
I0410 01:08:00.536242 16216 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0410 01:08:04.657878 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:08:05.550756 16216 solver.cpp:218] Iteration 5868 (2.39312 iter/s, 5.01437s/12 iters), loss = 0.246414
I0410 01:08:05.550801 16216 solver.cpp:237]     Train net output #0: loss = 0.246414 (* 1 = 0.246414 loss)
I0410 01:08:05.550813 16216 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0410 01:08:10.479925 16216 solver.cpp:218] Iteration 5880 (2.43459 iter/s, 4.92897s/12 iters), loss = 0.159779
I0410 01:08:10.479975 16216 solver.cpp:237]     Train net output #0: loss = 0.159779 (* 1 = 0.159779 loss)
I0410 01:08:10.479987 16216 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0410 01:08:15.473004 16216 solver.cpp:218] Iteration 5892 (2.40343 iter/s, 4.99287s/12 iters), loss = 0.23657
I0410 01:08:15.473059 16216 solver.cpp:237]     Train net output #0: loss = 0.23657 (* 1 = 0.23657 loss)
I0410 01:08:15.473070 16216 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0410 01:08:20.321900 16216 solver.cpp:218] Iteration 5904 (2.4749 iter/s, 4.84868s/12 iters), loss = 0.157514
I0410 01:08:20.321947 16216 solver.cpp:237]     Train net output #0: loss = 0.157514 (* 1 = 0.157514 loss)
I0410 01:08:20.321982 16216 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0410 01:08:24.734715 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0410 01:08:25.006243 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0410 01:08:25.175423 16216 solver.cpp:330] Iteration 5916, Testing net (#0)
I0410 01:08:25.175449 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:08:27.601063 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:08:29.922160 16216 solver.cpp:397]     Test net output #0: accuracy = 0.278799
I0410 01:08:29.922201 16216 solver.cpp:397]     Test net output #1: loss = 6.7625 (* 1 = 6.7625 loss)
I0410 01:08:30.004806 16216 solver.cpp:218] Iteration 5916 (1.23934 iter/s, 9.68257s/12 iters), loss = 0.189802
I0410 01:08:30.004849 16216 solver.cpp:237]     Train net output #0: loss = 0.189802 (* 1 = 0.189802 loss)
I0410 01:08:30.004858 16216 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0410 01:08:34.342481 16216 solver.cpp:218] Iteration 5928 (2.76657 iter/s, 4.3375s/12 iters), loss = 0.1081
I0410 01:08:34.342530 16216 solver.cpp:237]     Train net output #0: loss = 0.1081 (* 1 = 0.1081 loss)
I0410 01:08:34.342540 16216 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0410 01:08:39.284782 16216 solver.cpp:218] Iteration 5940 (2.42812 iter/s, 4.94209s/12 iters), loss = 0.284933
I0410 01:08:39.284830 16216 solver.cpp:237]     Train net output #0: loss = 0.284933 (* 1 = 0.284933 loss)
I0410 01:08:39.284839 16216 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0410 01:08:44.155958 16216 solver.cpp:218] Iteration 5952 (2.46358 iter/s, 4.87097s/12 iters), loss = 0.218437
I0410 01:08:44.156008 16216 solver.cpp:237]     Train net output #0: loss = 0.218437 (* 1 = 0.218437 loss)
I0410 01:08:44.156018 16216 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0410 01:08:49.130115 16216 solver.cpp:218] Iteration 5964 (2.41257 iter/s, 4.97395s/12 iters), loss = 0.2118
I0410 01:08:49.130160 16216 solver.cpp:237]     Train net output #0: loss = 0.2118 (* 1 = 0.2118 loss)
I0410 01:08:49.130169 16216 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0410 01:08:50.402148 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:08:54.026839 16216 solver.cpp:218] Iteration 5976 (2.45072 iter/s, 4.89652s/12 iters), loss = 0.246456
I0410 01:08:54.026898 16216 solver.cpp:237]     Train net output #0: loss = 0.246456 (* 1 = 0.246456 loss)
I0410 01:08:54.026911 16216 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0410 01:08:58.944682 16216 solver.cpp:218] Iteration 5988 (2.4402 iter/s, 4.91763s/12 iters), loss = 0.241256
I0410 01:08:58.944815 16216 solver.cpp:237]     Train net output #0: loss = 0.241256 (* 1 = 0.241256 loss)
I0410 01:08:58.944825 16216 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0410 01:09:03.829301 16216 solver.cpp:218] Iteration 6000 (2.45683 iter/s, 4.88434s/12 iters), loss = 0.141852
I0410 01:09:03.829339 16216 solver.cpp:237]     Train net output #0: loss = 0.141852 (* 1 = 0.141852 loss)
I0410 01:09:03.829349 16216 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0410 01:09:08.837474 16216 solver.cpp:218] Iteration 6012 (2.39618 iter/s, 5.00798s/12 iters), loss = 0.108728
I0410 01:09:08.837523 16216 solver.cpp:237]     Train net output #0: loss = 0.108728 (* 1 = 0.108728 loss)
I0410 01:09:08.837532 16216 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0410 01:09:10.927963 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0410 01:09:11.623306 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0410 01:09:12.202898 16216 solver.cpp:330] Iteration 6018, Testing net (#0)
I0410 01:09:12.202917 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:09:14.289376 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:09:16.650925 16216 solver.cpp:397]     Test net output #0: accuracy = 0.275735
I0410 01:09:16.650970 16216 solver.cpp:397]     Test net output #1: loss = 7.06787 (* 1 = 7.06787 loss)
I0410 01:09:18.622129 16216 solver.cpp:218] Iteration 6024 (1.22646 iter/s, 9.78429s/12 iters), loss = 0.166557
I0410 01:09:18.622200 16216 solver.cpp:237]     Train net output #0: loss = 0.166557 (* 1 = 0.166557 loss)
I0410 01:09:18.622220 16216 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0410 01:09:24.688649 16216 solver.cpp:218] Iteration 6036 (1.97815 iter/s, 6.06627s/12 iters), loss = 0.159671
I0410 01:09:24.688696 16216 solver.cpp:237]     Train net output #0: loss = 0.159671 (* 1 = 0.159671 loss)
I0410 01:09:24.688706 16216 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0410 01:09:29.630616 16216 solver.cpp:218] Iteration 6048 (2.42828 iter/s, 4.94176s/12 iters), loss = 0.191842
I0410 01:09:29.630718 16216 solver.cpp:237]     Train net output #0: loss = 0.191842 (* 1 = 0.191842 loss)
I0410 01:09:29.630729 16216 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0410 01:09:34.587900 16216 solver.cpp:218] Iteration 6060 (2.42081 iter/s, 4.95702s/12 iters), loss = 0.281453
I0410 01:09:34.587955 16216 solver.cpp:237]     Train net output #0: loss = 0.281453 (* 1 = 0.281453 loss)
I0410 01:09:34.587968 16216 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0410 01:09:37.982650 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:09:39.494030 16216 solver.cpp:218] Iteration 6072 (2.44603 iter/s, 4.90591s/12 iters), loss = 0.144284
I0410 01:09:39.494102 16216 solver.cpp:237]     Train net output #0: loss = 0.144283 (* 1 = 0.144283 loss)
I0410 01:09:39.494119 16216 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0410 01:09:44.565552 16216 solver.cpp:218] Iteration 6084 (2.36626 iter/s, 5.0713s/12 iters), loss = 0.132226
I0410 01:09:44.565606 16216 solver.cpp:237]     Train net output #0: loss = 0.132226 (* 1 = 0.132226 loss)
I0410 01:09:44.565620 16216 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0410 01:09:49.564982 16216 solver.cpp:218] Iteration 6096 (2.40038 iter/s, 4.99922s/12 iters), loss = 0.175378
I0410 01:09:49.565040 16216 solver.cpp:237]     Train net output #0: loss = 0.175378 (* 1 = 0.175378 loss)
I0410 01:09:49.565054 16216 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0410 01:09:54.525434 16216 solver.cpp:218] Iteration 6108 (2.41924 iter/s, 4.96024s/12 iters), loss = 0.170669
I0410 01:09:54.525485 16216 solver.cpp:237]     Train net output #0: loss = 0.170669 (* 1 = 0.170669 loss)
I0410 01:09:54.525496 16216 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0410 01:09:59.025444 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0410 01:09:59.265704 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0410 01:09:59.438643 16216 solver.cpp:330] Iteration 6120, Testing net (#0)
I0410 01:09:59.438666 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:10:01.386955 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:10:03.781255 16216 solver.cpp:397]     Test net output #0: accuracy = 0.273284
I0410 01:10:03.781303 16216 solver.cpp:397]     Test net output #1: loss = 6.94808 (* 1 = 6.94808 loss)
I0410 01:10:03.863997 16216 solver.cpp:218] Iteration 6120 (1.28504 iter/s, 9.33823s/12 iters), loss = 0.222561
I0410 01:10:03.864053 16216 solver.cpp:237]     Train net output #0: loss = 0.222561 (* 1 = 0.222561 loss)
I0410 01:10:03.864064 16216 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0410 01:10:08.134063 16216 solver.cpp:218] Iteration 6132 (2.81038 iter/s, 4.26988s/12 iters), loss = 0.143341
I0410 01:10:08.134112 16216 solver.cpp:237]     Train net output #0: loss = 0.143341 (* 1 = 0.143341 loss)
I0410 01:10:08.134125 16216 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0410 01:10:13.068104 16216 solver.cpp:218] Iteration 6144 (2.43218 iter/s, 4.93384s/12 iters), loss = 0.264553
I0410 01:10:13.068154 16216 solver.cpp:237]     Train net output #0: loss = 0.264553 (* 1 = 0.264553 loss)
I0410 01:10:13.068166 16216 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0410 01:10:17.989466 16216 solver.cpp:218] Iteration 6156 (2.43845 iter/s, 4.92116s/12 iters), loss = 0.123925
I0410 01:10:17.989516 16216 solver.cpp:237]     Train net output #0: loss = 0.123925 (* 1 = 0.123925 loss)
I0410 01:10:17.989528 16216 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0410 01:10:22.902451 16216 solver.cpp:218] Iteration 6168 (2.44261 iter/s, 4.91278s/12 iters), loss = 0.152653
I0410 01:10:22.902499 16216 solver.cpp:237]     Train net output #0: loss = 0.152653 (* 1 = 0.152653 loss)
I0410 01:10:22.902511 16216 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0410 01:10:23.488238 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:10:27.811108 16216 solver.cpp:218] Iteration 6180 (2.44476 iter/s, 4.90845s/12 iters), loss = 0.212816
I0410 01:10:27.811165 16216 solver.cpp:237]     Train net output #0: loss = 0.212816 (* 1 = 0.212816 loss)
I0410 01:10:27.811177 16216 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0410 01:10:32.743897 16216 solver.cpp:218] Iteration 6192 (2.4328 iter/s, 4.93258s/12 iters), loss = 0.195087
I0410 01:10:32.743978 16216 solver.cpp:237]     Train net output #0: loss = 0.195087 (* 1 = 0.195087 loss)
I0410 01:10:32.743991 16216 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0410 01:10:37.811368 16216 solver.cpp:218] Iteration 6204 (2.36816 iter/s, 5.06723s/12 iters), loss = 0.176726
I0410 01:10:37.811422 16216 solver.cpp:237]     Train net output #0: loss = 0.176726 (* 1 = 0.176726 loss)
I0410 01:10:37.811435 16216 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0410 01:10:42.756500 16216 solver.cpp:218] Iteration 6216 (2.42673 iter/s, 4.94492s/12 iters), loss = 0.189923
I0410 01:10:42.756551 16216 solver.cpp:237]     Train net output #0: loss = 0.189923 (* 1 = 0.189923 loss)
I0410 01:10:42.756563 16216 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0410 01:10:44.751157 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0410 01:10:45.008005 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0410 01:10:45.194837 16216 solver.cpp:330] Iteration 6222, Testing net (#0)
I0410 01:10:45.194866 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:10:47.093320 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:10:48.003096 16216 blocking_queue.cpp:49] Waiting for data
I0410 01:10:49.535190 16216 solver.cpp:397]     Test net output #0: accuracy = 0.297794
I0410 01:10:49.535236 16216 solver.cpp:397]     Test net output #1: loss = 6.85517 (* 1 = 6.85517 loss)
I0410 01:10:51.382827 16216 solver.cpp:218] Iteration 6228 (1.39114 iter/s, 8.62601s/12 iters), loss = 0.158296
I0410 01:10:51.382887 16216 solver.cpp:237]     Train net output #0: loss = 0.158296 (* 1 = 0.158296 loss)
I0410 01:10:51.382899 16216 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0410 01:10:56.241274 16216 solver.cpp:218] Iteration 6240 (2.47003 iter/s, 4.85823s/12 iters), loss = 0.165238
I0410 01:10:56.241333 16216 solver.cpp:237]     Train net output #0: loss = 0.165238 (* 1 = 0.165238 loss)
I0410 01:10:56.241345 16216 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0410 01:11:01.180416 16216 solver.cpp:218] Iteration 6252 (2.42967 iter/s, 4.93893s/12 iters), loss = 0.165575
I0410 01:11:01.180471 16216 solver.cpp:237]     Train net output #0: loss = 0.165575 (* 1 = 0.165575 loss)
I0410 01:11:01.180483 16216 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0410 01:11:06.110291 16216 solver.cpp:218] Iteration 6264 (2.43424 iter/s, 4.92967s/12 iters), loss = 0.246618
I0410 01:11:06.110427 16216 solver.cpp:237]     Train net output #0: loss = 0.246618 (* 1 = 0.246618 loss)
I0410 01:11:06.110438 16216 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0410 01:11:08.787530 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:11:11.068418 16216 solver.cpp:218] Iteration 6276 (2.42041 iter/s, 4.95784s/12 iters), loss = 0.38364
I0410 01:11:11.068468 16216 solver.cpp:237]     Train net output #0: loss = 0.38364 (* 1 = 0.38364 loss)
I0410 01:11:11.068480 16216 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0410 01:11:15.949225 16216 solver.cpp:218] Iteration 6288 (2.45871 iter/s, 4.88061s/12 iters), loss = 0.222791
I0410 01:11:15.949275 16216 solver.cpp:237]     Train net output #0: loss = 0.222791 (* 1 = 0.222791 loss)
I0410 01:11:15.949285 16216 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0410 01:11:20.863497 16216 solver.cpp:218] Iteration 6300 (2.44197 iter/s, 4.91407s/12 iters), loss = 0.138233
I0410 01:11:20.863555 16216 solver.cpp:237]     Train net output #0: loss = 0.138233 (* 1 = 0.138233 loss)
I0410 01:11:20.863569 16216 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0410 01:11:25.814209 16216 solver.cpp:218] Iteration 6312 (2.424 iter/s, 4.9505s/12 iters), loss = 0.208116
I0410 01:11:25.814254 16216 solver.cpp:237]     Train net output #0: loss = 0.208116 (* 1 = 0.208116 loss)
I0410 01:11:25.814266 16216 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0410 01:11:30.250034 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0410 01:11:32.175259 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0410 01:11:32.906679 16216 solver.cpp:330] Iteration 6324, Testing net (#0)
I0410 01:11:32.906702 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:11:34.872095 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:11:37.345180 16216 solver.cpp:397]     Test net output #0: accuracy = 0.293505
I0410 01:11:37.345244 16216 solver.cpp:397]     Test net output #1: loss = 6.74206 (* 1 = 6.74206 loss)
I0410 01:11:37.427943 16216 solver.cpp:218] Iteration 6324 (1.03329 iter/s, 11.6133s/12 iters), loss = 0.100359
I0410 01:11:37.427996 16216 solver.cpp:237]     Train net output #0: loss = 0.100359 (* 1 = 0.100359 loss)
I0410 01:11:37.428009 16216 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0410 01:11:41.577819 16216 solver.cpp:218] Iteration 6336 (2.89179 iter/s, 4.14969s/12 iters), loss = 0.134085
I0410 01:11:41.577881 16216 solver.cpp:237]     Train net output #0: loss = 0.134085 (* 1 = 0.134085 loss)
I0410 01:11:41.577893 16216 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0410 01:11:46.655841 16216 solver.cpp:218] Iteration 6348 (2.36323 iter/s, 5.0778s/12 iters), loss = 0.133157
I0410 01:11:46.655890 16216 solver.cpp:237]     Train net output #0: loss = 0.133157 (* 1 = 0.133157 loss)
I0410 01:11:46.655900 16216 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0410 01:11:51.611263 16216 solver.cpp:218] Iteration 6360 (2.42169 iter/s, 4.95521s/12 iters), loss = 0.182709
I0410 01:11:51.611306 16216 solver.cpp:237]     Train net output #0: loss = 0.182709 (* 1 = 0.182709 loss)
I0410 01:11:51.611316 16216 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0410 01:11:56.381860 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:11:56.516129 16216 solver.cpp:218] Iteration 6372 (2.44665 iter/s, 4.90466s/12 iters), loss = 0.0967252
I0410 01:11:56.516185 16216 solver.cpp:237]     Train net output #0: loss = 0.0967252 (* 1 = 0.0967252 loss)
I0410 01:11:56.516197 16216 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0410 01:12:01.400045 16216 solver.cpp:218] Iteration 6384 (2.45715 iter/s, 4.88371s/12 iters), loss = 0.199353
I0410 01:12:01.400099 16216 solver.cpp:237]     Train net output #0: loss = 0.199353 (* 1 = 0.199353 loss)
I0410 01:12:01.400110 16216 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0410 01:12:06.368870 16216 solver.cpp:218] Iteration 6396 (2.41516 iter/s, 4.96861s/12 iters), loss = 0.182604
I0410 01:12:06.368928 16216 solver.cpp:237]     Train net output #0: loss = 0.182604 (* 1 = 0.182604 loss)
I0410 01:12:06.368942 16216 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0410 01:12:11.546939 16216 solver.cpp:218] Iteration 6408 (2.31757 iter/s, 5.17784s/12 iters), loss = 0.138634
I0410 01:12:11.547080 16216 solver.cpp:237]     Train net output #0: loss = 0.138634 (* 1 = 0.138634 loss)
I0410 01:12:11.547092 16216 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0410 01:12:16.588660 16216 solver.cpp:218] Iteration 6420 (2.38028 iter/s, 5.04142s/12 iters), loss = 0.279216
I0410 01:12:16.588716 16216 solver.cpp:237]     Train net output #0: loss = 0.279216 (* 1 = 0.279216 loss)
I0410 01:12:16.588728 16216 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0410 01:12:18.566561 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0410 01:12:19.289680 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0410 01:12:19.997476 16216 solver.cpp:330] Iteration 6426, Testing net (#0)
I0410 01:12:19.997501 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:12:21.995517 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:12:24.600236 16216 solver.cpp:397]     Test net output #0: accuracy = 0.283088
I0410 01:12:24.600282 16216 solver.cpp:397]     Test net output #1: loss = 7.15014 (* 1 = 7.15014 loss)
I0410 01:12:26.531571 16216 solver.cpp:218] Iteration 6432 (1.20693 iter/s, 9.94256s/12 iters), loss = 0.356011
I0410 01:12:26.531625 16216 solver.cpp:237]     Train net output #0: loss = 0.356011 (* 1 = 0.356011 loss)
I0410 01:12:26.531637 16216 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0410 01:12:31.502040 16216 solver.cpp:218] Iteration 6444 (2.41436 iter/s, 4.97025s/12 iters), loss = 0.249322
I0410 01:12:31.502091 16216 solver.cpp:237]     Train net output #0: loss = 0.249322 (* 1 = 0.249322 loss)
I0410 01:12:31.502104 16216 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0410 01:12:36.414734 16216 solver.cpp:218] Iteration 6456 (2.44275 iter/s, 4.91249s/12 iters), loss = 0.110374
I0410 01:12:36.414785 16216 solver.cpp:237]     Train net output #0: loss = 0.110374 (* 1 = 0.110374 loss)
I0410 01:12:36.414798 16216 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0410 01:12:41.421983 16216 solver.cpp:218] Iteration 6468 (2.39663 iter/s, 5.00704s/12 iters), loss = 0.181541
I0410 01:12:41.422039 16216 solver.cpp:237]     Train net output #0: loss = 0.181541 (* 1 = 0.181541 loss)
I0410 01:12:41.422051 16216 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0410 01:12:43.359098 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:12:46.266191 16216 solver.cpp:218] Iteration 6480 (2.47729 iter/s, 4.844s/12 iters), loss = 0.151357
I0410 01:12:46.266247 16216 solver.cpp:237]     Train net output #0: loss = 0.151357 (* 1 = 0.151357 loss)
I0410 01:12:46.266258 16216 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0410 01:12:51.170529 16216 solver.cpp:218] Iteration 6492 (2.44691 iter/s, 4.90414s/12 iters), loss = 0.0961224
I0410 01:12:51.170572 16216 solver.cpp:237]     Train net output #0: loss = 0.0961224 (* 1 = 0.0961224 loss)
I0410 01:12:51.170581 16216 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0410 01:12:56.104552 16216 solver.cpp:218] Iteration 6504 (2.43219 iter/s, 4.93383s/12 iters), loss = 0.0931682
I0410 01:12:56.104594 16216 solver.cpp:237]     Train net output #0: loss = 0.0931681 (* 1 = 0.0931681 loss)
I0410 01:12:56.104604 16216 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0410 01:13:00.994581 16216 solver.cpp:218] Iteration 6516 (2.45407 iter/s, 4.88983s/12 iters), loss = 0.417497
I0410 01:13:00.994628 16216 solver.cpp:237]     Train net output #0: loss = 0.417497 (* 1 = 0.417497 loss)
I0410 01:13:00.994637 16216 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0410 01:13:05.496961 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0410 01:13:05.739387 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0410 01:13:05.927968 16216 solver.cpp:330] Iteration 6528, Testing net (#0)
I0410 01:13:05.927996 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:13:07.879428 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:13:10.437284 16216 solver.cpp:397]     Test net output #0: accuracy = 0.282476
I0410 01:13:10.437333 16216 solver.cpp:397]     Test net output #1: loss = 6.91712 (* 1 = 6.91712 loss)
I0410 01:13:10.520267 16216 solver.cpp:218] Iteration 6528 (1.2598 iter/s, 9.52534s/12 iters), loss = 0.0530898
I0410 01:13:10.520346 16216 solver.cpp:237]     Train net output #0: loss = 0.0530898 (* 1 = 0.0530898 loss)
I0410 01:13:10.520363 16216 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0410 01:13:15.058665 16216 solver.cpp:218] Iteration 6540 (2.64423 iter/s, 4.53818s/12 iters), loss = 0.251419
I0410 01:13:15.058791 16216 solver.cpp:237]     Train net output #0: loss = 0.251419 (* 1 = 0.251419 loss)
I0410 01:13:15.058805 16216 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0410 01:13:20.030522 16216 solver.cpp:218] Iteration 6552 (2.41372 iter/s, 4.97158s/12 iters), loss = 0.187579
I0410 01:13:20.030568 16216 solver.cpp:237]     Train net output #0: loss = 0.187579 (* 1 = 0.187579 loss)
I0410 01:13:20.030580 16216 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0410 01:13:24.973474 16216 solver.cpp:218] Iteration 6564 (2.4278 iter/s, 4.94275s/12 iters), loss = 0.133523
I0410 01:13:24.973520 16216 solver.cpp:237]     Train net output #0: loss = 0.133523 (* 1 = 0.133523 loss)
I0410 01:13:24.973528 16216 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0410 01:13:29.138450 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:13:29.904831 16216 solver.cpp:218] Iteration 6576 (2.43351 iter/s, 4.93115s/12 iters), loss = 0.0822704
I0410 01:13:29.904891 16216 solver.cpp:237]     Train net output #0: loss = 0.0822704 (* 1 = 0.0822704 loss)
I0410 01:13:29.904906 16216 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0410 01:13:34.808590 16216 solver.cpp:218] Iteration 6588 (2.44721 iter/s, 4.90355s/12 iters), loss = 0.182969
I0410 01:13:34.808645 16216 solver.cpp:237]     Train net output #0: loss = 0.182969 (* 1 = 0.182969 loss)
I0410 01:13:34.808657 16216 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0410 01:13:40.075086 16216 solver.cpp:218] Iteration 6600 (2.27865 iter/s, 5.26628s/12 iters), loss = 0.152681
I0410 01:13:40.075134 16216 solver.cpp:237]     Train net output #0: loss = 0.152681 (* 1 = 0.152681 loss)
I0410 01:13:40.075145 16216 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0410 01:13:45.407138 16216 solver.cpp:218] Iteration 6612 (2.25063 iter/s, 5.33184s/12 iters), loss = 0.127525
I0410 01:13:45.407238 16216 solver.cpp:237]     Train net output #0: loss = 0.127525 (* 1 = 0.127525 loss)
I0410 01:13:45.407248 16216 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0410 01:13:50.374948 16216 solver.cpp:218] Iteration 6624 (2.41568 iter/s, 4.96755s/12 iters), loss = 0.164473
I0410 01:13:50.375000 16216 solver.cpp:237]     Train net output #0: loss = 0.164473 (* 1 = 0.164473 loss)
I0410 01:13:50.375012 16216 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0410 01:13:52.379771 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0410 01:13:53.747884 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0410 01:13:54.193626 16216 solver.cpp:330] Iteration 6630, Testing net (#0)
I0410 01:13:54.193651 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:13:56.072697 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:13:58.725303 16216 solver.cpp:397]     Test net output #0: accuracy = 0.282476
I0410 01:13:58.725350 16216 solver.cpp:397]     Test net output #1: loss = 6.82585 (* 1 = 6.82585 loss)
I0410 01:14:00.560416 16216 solver.cpp:218] Iteration 6636 (1.17819 iter/s, 10.1851s/12 iters), loss = 0.237528
I0410 01:14:00.560467 16216 solver.cpp:237]     Train net output #0: loss = 0.237528 (* 1 = 0.237528 loss)
I0410 01:14:00.560478 16216 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0410 01:14:05.525650 16216 solver.cpp:218] Iteration 6648 (2.4169 iter/s, 4.96503s/12 iters), loss = 0.152024
I0410 01:14:05.525694 16216 solver.cpp:237]     Train net output #0: loss = 0.152024 (* 1 = 0.152024 loss)
I0410 01:14:05.525704 16216 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0410 01:14:10.449909 16216 solver.cpp:218] Iteration 6660 (2.43702 iter/s, 4.92405s/12 iters), loss = 0.254382
I0410 01:14:10.449996 16216 solver.cpp:237]     Train net output #0: loss = 0.254382 (* 1 = 0.254382 loss)
I0410 01:14:10.450011 16216 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0410 01:14:15.343169 16216 solver.cpp:218] Iteration 6672 (2.45247 iter/s, 4.89303s/12 iters), loss = 0.162295
I0410 01:14:15.343214 16216 solver.cpp:237]     Train net output #0: loss = 0.162295 (* 1 = 0.162295 loss)
I0410 01:14:15.343223 16216 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0410 01:14:16.685395 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:14:20.273216 16216 solver.cpp:218] Iteration 6684 (2.43415 iter/s, 4.92984s/12 iters), loss = 0.0932296
I0410 01:14:20.273263 16216 solver.cpp:237]     Train net output #0: loss = 0.0932296 (* 1 = 0.0932296 loss)
I0410 01:14:20.273273 16216 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0410 01:14:25.186187 16216 solver.cpp:218] Iteration 6696 (2.44261 iter/s, 4.91277s/12 iters), loss = 0.173548
I0410 01:14:25.186234 16216 solver.cpp:237]     Train net output #0: loss = 0.173548 (* 1 = 0.173548 loss)
I0410 01:14:25.186246 16216 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0410 01:14:30.086226 16216 solver.cpp:218] Iteration 6708 (2.44906 iter/s, 4.89984s/12 iters), loss = 0.166963
I0410 01:14:30.086283 16216 solver.cpp:237]     Train net output #0: loss = 0.166963 (* 1 = 0.166963 loss)
I0410 01:14:30.086297 16216 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0410 01:14:35.018242 16216 solver.cpp:218] Iteration 6720 (2.43318 iter/s, 4.93181s/12 iters), loss = 0.0605338
I0410 01:14:35.018282 16216 solver.cpp:237]     Train net output #0: loss = 0.0605338 (* 1 = 0.0605338 loss)
I0410 01:14:35.018292 16216 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0410 01:14:39.621374 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0410 01:14:39.871793 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0410 01:14:40.060333 16216 solver.cpp:330] Iteration 6732, Testing net (#0)
I0410 01:14:40.060361 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:14:41.912752 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:14:44.571954 16216 solver.cpp:397]     Test net output #0: accuracy = 0.283701
I0410 01:14:44.572002 16216 solver.cpp:397]     Test net output #1: loss = 6.9854 (* 1 = 6.9854 loss)
I0410 01:14:44.654599 16216 solver.cpp:218] Iteration 6732 (1.24533 iter/s, 9.63602s/12 iters), loss = 0.187135
I0410 01:14:44.654654 16216 solver.cpp:237]     Train net output #0: loss = 0.187135 (* 1 = 0.187135 loss)
I0410 01:14:44.654666 16216 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0410 01:14:48.696679 16216 solver.cpp:218] Iteration 6744 (2.96891 iter/s, 4.04189s/12 iters), loss = 0.10086
I0410 01:14:48.698843 16216 solver.cpp:237]     Train net output #0: loss = 0.10086 (* 1 = 0.10086 loss)
I0410 01:14:48.698858 16216 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0410 01:14:53.677829 16216 solver.cpp:218] Iteration 6756 (2.4102 iter/s, 4.97884s/12 iters), loss = 0.145314
I0410 01:14:53.677877 16216 solver.cpp:237]     Train net output #0: loss = 0.145314 (* 1 = 0.145314 loss)
I0410 01:14:53.677891 16216 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0410 01:14:58.627450 16216 solver.cpp:218] Iteration 6768 (2.42453 iter/s, 4.94942s/12 iters), loss = 0.23041
I0410 01:14:58.627498 16216 solver.cpp:237]     Train net output #0: loss = 0.23041 (* 1 = 0.23041 loss)
I0410 01:14:58.627508 16216 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0410 01:15:02.033941 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:15:03.491608 16216 solver.cpp:218] Iteration 6780 (2.46713 iter/s, 4.86395s/12 iters), loss = 0.0871784
I0410 01:15:03.491667 16216 solver.cpp:237]     Train net output #0: loss = 0.0871784 (* 1 = 0.0871784 loss)
I0410 01:15:03.491677 16216 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0410 01:15:08.430281 16216 solver.cpp:218] Iteration 6792 (2.42991 iter/s, 4.93846s/12 iters), loss = 0.12072
I0410 01:15:08.430326 16216 solver.cpp:237]     Train net output #0: loss = 0.12072 (* 1 = 0.12072 loss)
I0410 01:15:08.430336 16216 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0410 01:15:13.358227 16216 solver.cpp:218] Iteration 6804 (2.43519 iter/s, 4.92774s/12 iters), loss = 0.162267
I0410 01:15:13.358283 16216 solver.cpp:237]     Train net output #0: loss = 0.162267 (* 1 = 0.162267 loss)
I0410 01:15:13.358294 16216 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0410 01:15:18.217999 16216 solver.cpp:218] Iteration 6816 (2.46935 iter/s, 4.85957s/12 iters), loss = 0.128403
I0410 01:15:18.218044 16216 solver.cpp:237]     Train net output #0: loss = 0.128403 (* 1 = 0.128403 loss)
I0410 01:15:18.218055 16216 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0410 01:15:23.389817 16216 solver.cpp:218] Iteration 6828 (2.32036 iter/s, 5.17161s/12 iters), loss = 0.206058
I0410 01:15:23.389894 16216 solver.cpp:237]     Train net output #0: loss = 0.206058 (* 1 = 0.206058 loss)
I0410 01:15:23.389905 16216 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0410 01:15:25.413100 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0410 01:15:26.213779 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0410 01:15:26.545596 16216 solver.cpp:330] Iteration 6834, Testing net (#0)
I0410 01:15:26.545620 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:15:28.327443 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:15:31.046427 16216 solver.cpp:397]     Test net output #0: accuracy = 0.283701
I0410 01:15:31.046458 16216 solver.cpp:397]     Test net output #1: loss = 6.80828 (* 1 = 6.80828 loss)
I0410 01:15:32.954425 16216 solver.cpp:218] Iteration 6840 (1.25467 iter/s, 9.56425s/12 iters), loss = 0.218761
I0410 01:15:32.954469 16216 solver.cpp:237]     Train net output #0: loss = 0.218761 (* 1 = 0.218761 loss)
I0410 01:15:32.954478 16216 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0410 01:15:37.921707 16216 solver.cpp:218] Iteration 6852 (2.41591 iter/s, 4.96708s/12 iters), loss = 0.0308475
I0410 01:15:37.921759 16216 solver.cpp:237]     Train net output #0: loss = 0.0308474 (* 1 = 0.0308474 loss)
I0410 01:15:37.921770 16216 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0410 01:15:43.130201 16216 solver.cpp:218] Iteration 6864 (2.30402 iter/s, 5.20828s/12 iters), loss = 0.151494
I0410 01:15:43.130254 16216 solver.cpp:237]     Train net output #0: loss = 0.151494 (* 1 = 0.151494 loss)
I0410 01:15:43.130265 16216 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0410 01:15:48.116560 16216 solver.cpp:218] Iteration 6876 (2.40667 iter/s, 4.98615s/12 iters), loss = 0.153316
I0410 01:15:48.116616 16216 solver.cpp:237]     Train net output #0: loss = 0.153316 (* 1 = 0.153316 loss)
I0410 01:15:48.116626 16216 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0410 01:15:48.736531 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:15:53.053786 16216 solver.cpp:218] Iteration 6888 (2.43062 iter/s, 4.93701s/12 iters), loss = 0.13068
I0410 01:15:53.053843 16216 solver.cpp:237]     Train net output #0: loss = 0.13068 (* 1 = 0.13068 loss)
I0410 01:15:53.053854 16216 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0410 01:15:58.003315 16216 solver.cpp:218] Iteration 6900 (2.42457 iter/s, 4.94932s/12 iters), loss = 0.251942
I0410 01:15:58.003414 16216 solver.cpp:237]     Train net output #0: loss = 0.251942 (* 1 = 0.251942 loss)
I0410 01:15:58.003424 16216 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0410 01:16:03.016376 16216 solver.cpp:218] Iteration 6912 (2.39387 iter/s, 5.0128s/12 iters), loss = 0.1694
I0410 01:16:03.016433 16216 solver.cpp:237]     Train net output #0: loss = 0.1694 (* 1 = 0.1694 loss)
I0410 01:16:03.016446 16216 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0410 01:16:07.981896 16216 solver.cpp:218] Iteration 6924 (2.41677 iter/s, 4.96531s/12 iters), loss = 0.0736617
I0410 01:16:07.981946 16216 solver.cpp:237]     Train net output #0: loss = 0.0736617 (* 1 = 0.0736617 loss)
I0410 01:16:07.981971 16216 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0410 01:16:12.508769 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0410 01:16:13.059723 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0410 01:16:13.820173 16216 solver.cpp:330] Iteration 6936, Testing net (#0)
I0410 01:16:13.820210 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:16:14.028319 16216 blocking_queue.cpp:49] Waiting for data
I0410 01:16:15.450461 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:16:18.194584 16216 solver.cpp:397]     Test net output #0: accuracy = 0.302696
I0410 01:16:18.194628 16216 solver.cpp:397]     Test net output #1: loss = 6.72633 (* 1 = 6.72633 loss)
I0410 01:16:18.277256 16216 solver.cpp:218] Iteration 6936 (1.16562 iter/s, 10.295s/12 iters), loss = 0.0937336
I0410 01:16:18.277309 16216 solver.cpp:237]     Train net output #0: loss = 0.0937336 (* 1 = 0.0937336 loss)
I0410 01:16:18.277321 16216 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0410 01:16:22.478000 16216 solver.cpp:218] Iteration 6948 (2.85677 iter/s, 4.20055s/12 iters), loss = 0.225013
I0410 01:16:22.478060 16216 solver.cpp:237]     Train net output #0: loss = 0.225013 (* 1 = 0.225013 loss)
I0410 01:16:22.478073 16216 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0410 01:16:27.422160 16216 solver.cpp:218] Iteration 6960 (2.42721 iter/s, 4.94395s/12 iters), loss = 0.104754
I0410 01:16:27.422216 16216 solver.cpp:237]     Train net output #0: loss = 0.104754 (* 1 = 0.104754 loss)
I0410 01:16:27.422230 16216 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0410 01:16:32.331449 16216 solver.cpp:218] Iteration 6972 (2.44445 iter/s, 4.90908s/12 iters), loss = 0.0215973
I0410 01:16:32.331578 16216 solver.cpp:237]     Train net output #0: loss = 0.0215973 (* 1 = 0.0215973 loss)
I0410 01:16:32.331588 16216 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0410 01:16:35.089236 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:16:37.358155 16216 solver.cpp:218] Iteration 6984 (2.38739 iter/s, 5.02641s/12 iters), loss = 0.139684
I0410 01:16:37.358209 16216 solver.cpp:237]     Train net output #0: loss = 0.139684 (* 1 = 0.139684 loss)
I0410 01:16:37.358222 16216 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0410 01:16:42.186224 16216 solver.cpp:218] Iteration 6996 (2.48557 iter/s, 4.82787s/12 iters), loss = 0.270907
I0410 01:16:42.186278 16216 solver.cpp:237]     Train net output #0: loss = 0.270907 (* 1 = 0.270907 loss)
I0410 01:16:42.186290 16216 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0410 01:16:47.049580 16216 solver.cpp:218] Iteration 7008 (2.46754 iter/s, 4.86315s/12 iters), loss = 0.197511
I0410 01:16:47.049645 16216 solver.cpp:237]     Train net output #0: loss = 0.197511 (* 1 = 0.197511 loss)
I0410 01:16:47.049659 16216 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0410 01:16:51.903121 16216 solver.cpp:218] Iteration 7020 (2.47253 iter/s, 4.85333s/12 iters), loss = 0.0610196
I0410 01:16:51.903178 16216 solver.cpp:237]     Train net output #0: loss = 0.0610196 (* 1 = 0.0610196 loss)
I0410 01:16:51.903190 16216 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0410 01:16:56.914376 16216 solver.cpp:218] Iteration 7032 (2.39471 iter/s, 5.01104s/12 iters), loss = 0.0974044
I0410 01:16:56.914415 16216 solver.cpp:237]     Train net output #0: loss = 0.0974044 (* 1 = 0.0974044 loss)
I0410 01:16:56.914425 16216 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0410 01:16:58.945276 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0410 01:16:59.196499 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0410 01:16:59.383330 16216 solver.cpp:330] Iteration 7038, Testing net (#0)
I0410 01:16:59.383353 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:17:01.006414 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:17:03.764942 16216 solver.cpp:397]     Test net output #0: accuracy = 0.291054
I0410 01:17:03.765079 16216 solver.cpp:397]     Test net output #1: loss = 6.92944 (* 1 = 6.92944 loss)
I0410 01:17:05.712419 16216 solver.cpp:218] Iteration 7044 (1.36399 iter/s, 8.79774s/12 iters), loss = 0.22135
I0410 01:17:05.712462 16216 solver.cpp:237]     Train net output #0: loss = 0.22135 (* 1 = 0.22135 loss)
I0410 01:17:05.712472 16216 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0410 01:17:10.828686 16216 solver.cpp:218] Iteration 7056 (2.34555 iter/s, 5.11606s/12 iters), loss = 0.164873
I0410 01:17:10.828735 16216 solver.cpp:237]     Train net output #0: loss = 0.164873 (* 1 = 0.164873 loss)
I0410 01:17:10.828745 16216 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0410 01:17:15.779233 16216 solver.cpp:218] Iteration 7068 (2.42408 iter/s, 4.95034s/12 iters), loss = 0.111779
I0410 01:17:15.779294 16216 solver.cpp:237]     Train net output #0: loss = 0.111779 (* 1 = 0.111779 loss)
I0410 01:17:15.779306 16216 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0410 01:17:20.582109 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:17:20.691540 16216 solver.cpp:218] Iteration 7080 (2.44295 iter/s, 4.91209s/12 iters), loss = 0.156546
I0410 01:17:20.691598 16216 solver.cpp:237]     Train net output #0: loss = 0.156546 (* 1 = 0.156546 loss)
I0410 01:17:20.691610 16216 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0410 01:17:25.631614 16216 solver.cpp:218] Iteration 7092 (2.42922 iter/s, 4.93986s/12 iters), loss = 0.108032
I0410 01:17:25.631670 16216 solver.cpp:237]     Train net output #0: loss = 0.108032 (* 1 = 0.108032 loss)
I0410 01:17:25.631683 16216 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0410 01:17:30.593813 16216 solver.cpp:218] Iteration 7104 (2.41838 iter/s, 4.96199s/12 iters), loss = 0.0998286
I0410 01:17:30.593856 16216 solver.cpp:237]     Train net output #0: loss = 0.0998286 (* 1 = 0.0998286 loss)
I0410 01:17:30.593865 16216 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0410 01:17:35.540122 16216 solver.cpp:218] Iteration 7116 (2.42615 iter/s, 4.94611s/12 iters), loss = 0.0605408
I0410 01:17:35.540638 16216 solver.cpp:237]     Train net output #0: loss = 0.0605408 (* 1 = 0.0605408 loss)
I0410 01:17:35.540648 16216 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0410 01:17:40.507215 16216 solver.cpp:218] Iteration 7128 (2.41623 iter/s, 4.96642s/12 iters), loss = 0.0603146
I0410 01:17:40.507268 16216 solver.cpp:237]     Train net output #0: loss = 0.0603146 (* 1 = 0.0603146 loss)
I0410 01:17:40.507280 16216 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0410 01:17:44.979586 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0410 01:17:45.390158 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0410 01:17:45.836551 16216 solver.cpp:330] Iteration 7140, Testing net (#0)
I0410 01:17:45.836580 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:17:47.474443 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:17:50.263617 16216 solver.cpp:397]     Test net output #0: accuracy = 0.307598
I0410 01:17:50.263653 16216 solver.cpp:397]     Test net output #1: loss = 6.91245 (* 1 = 6.91245 loss)
I0410 01:17:50.346262 16216 solver.cpp:218] Iteration 7140 (1.21967 iter/s, 9.8387s/12 iters), loss = 0.058719
I0410 01:17:50.346320 16216 solver.cpp:237]     Train net output #0: loss = 0.058719 (* 1 = 0.058719 loss)
I0410 01:17:50.346331 16216 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0410 01:17:54.694526 16216 solver.cpp:218] Iteration 7152 (2.75985 iter/s, 4.34807s/12 iters), loss = 0.144643
I0410 01:17:54.694578 16216 solver.cpp:237]     Train net output #0: loss = 0.144643 (* 1 = 0.144643 loss)
I0410 01:17:54.694591 16216 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0410 01:17:59.603260 16216 solver.cpp:218] Iteration 7164 (2.44472 iter/s, 4.90853s/12 iters), loss = 0.100083
I0410 01:17:59.603305 16216 solver.cpp:237]     Train net output #0: loss = 0.100083 (* 1 = 0.100083 loss)
I0410 01:17:59.603317 16216 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0410 01:18:04.555685 16216 solver.cpp:218] Iteration 7176 (2.42315 iter/s, 4.95223s/12 iters), loss = 0.067152
I0410 01:18:04.555728 16216 solver.cpp:237]     Train net output #0: loss = 0.067152 (* 1 = 0.067152 loss)
I0410 01:18:04.555740 16216 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0410 01:18:06.645565 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:18:09.468847 16216 solver.cpp:218] Iteration 7188 (2.44252 iter/s, 4.91297s/12 iters), loss = 0.0859119
I0410 01:18:09.468894 16216 solver.cpp:237]     Train net output #0: loss = 0.085912 (* 1 = 0.085912 loss)
I0410 01:18:09.468906 16216 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 01:18:14.482059 16216 solver.cpp:218] Iteration 7200 (2.39378 iter/s, 5.013s/12 iters), loss = 0.137181
I0410 01:18:14.482103 16216 solver.cpp:237]     Train net output #0: loss = 0.137181 (* 1 = 0.137181 loss)
I0410 01:18:14.482112 16216 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 01:18:19.686007 16216 solver.cpp:218] Iteration 7212 (2.30603 iter/s, 5.20374s/12 iters), loss = 0.14234
I0410 01:18:19.686064 16216 solver.cpp:237]     Train net output #0: loss = 0.14234 (* 1 = 0.14234 loss)
I0410 01:18:19.686076 16216 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 01:18:24.584830 16216 solver.cpp:218] Iteration 7224 (2.44967 iter/s, 4.89861s/12 iters), loss = 0.215652
I0410 01:18:24.584879 16216 solver.cpp:237]     Train net output #0: loss = 0.215652 (* 1 = 0.215652 loss)
I0410 01:18:24.584890 16216 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 01:18:29.483059 16216 solver.cpp:218] Iteration 7236 (2.44997 iter/s, 4.89803s/12 iters), loss = 0.0895703
I0410 01:18:29.483106 16216 solver.cpp:237]     Train net output #0: loss = 0.0895703 (* 1 = 0.0895703 loss)
I0410 01:18:29.483117 16216 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 01:18:31.461879 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 01:18:31.967590 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 01:18:32.137420 16216 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 01:18:32.137445 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:18:33.721762 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:18:36.705657 16216 solver.cpp:397]     Test net output #0: accuracy = 0.315564
I0410 01:18:36.705824 16216 solver.cpp:397]     Test net output #1: loss = 6.79892 (* 1 = 6.79892 loss)
I0410 01:18:38.587091 16216 solver.cpp:218] Iteration 7248 (1.31814 iter/s, 9.10371s/12 iters), loss = 0.0673056
I0410 01:18:38.587136 16216 solver.cpp:237]     Train net output #0: loss = 0.0673056 (* 1 = 0.0673056 loss)
I0410 01:18:38.587146 16216 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 01:18:43.703441 16216 solver.cpp:218] Iteration 7260 (2.34551 iter/s, 5.11615s/12 iters), loss = 0.015424
I0410 01:18:43.703480 16216 solver.cpp:237]     Train net output #0: loss = 0.015424 (* 1 = 0.015424 loss)
I0410 01:18:43.703488 16216 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 01:18:48.743851 16216 solver.cpp:218] Iteration 7272 (2.38085 iter/s, 5.04021s/12 iters), loss = 0.0678212
I0410 01:18:48.743896 16216 solver.cpp:237]     Train net output #0: loss = 0.0678212 (* 1 = 0.0678212 loss)
I0410 01:18:48.743906 16216 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 01:18:52.955626 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:18:53.704368 16216 solver.cpp:218] Iteration 7284 (2.4192 iter/s, 4.96032s/12 iters), loss = 0.126393
I0410 01:18:53.704412 16216 solver.cpp:237]     Train net output #0: loss = 0.126393 (* 1 = 0.126393 loss)
I0410 01:18:53.704422 16216 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 01:18:58.667326 16216 solver.cpp:218] Iteration 7296 (2.41801 iter/s, 4.96276s/12 iters), loss = 0.0268146
I0410 01:18:58.667373 16216 solver.cpp:237]     Train net output #0: loss = 0.0268146 (* 1 = 0.0268146 loss)
I0410 01:18:58.667382 16216 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 01:19:03.657413 16216 solver.cpp:218] Iteration 7308 (2.40487 iter/s, 4.98988s/12 iters), loss = 0.0824211
I0410 01:19:03.657462 16216 solver.cpp:237]     Train net output #0: loss = 0.0824211 (* 1 = 0.0824211 loss)
I0410 01:19:03.657471 16216 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 01:19:08.676723 16216 solver.cpp:218] Iteration 7320 (2.39087 iter/s, 5.0191s/12 iters), loss = 0.046299
I0410 01:19:08.676838 16216 solver.cpp:237]     Train net output #0: loss = 0.046299 (* 1 = 0.046299 loss)
I0410 01:19:08.676851 16216 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 01:19:13.700675 16216 solver.cpp:218] Iteration 7332 (2.38869 iter/s, 5.02368s/12 iters), loss = 0.0475168
I0410 01:19:13.700729 16216 solver.cpp:237]     Train net output #0: loss = 0.0475168 (* 1 = 0.0475168 loss)
I0410 01:19:13.700742 16216 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 01:19:18.207221 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 01:19:18.462960 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 01:19:18.630858 16216 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 01:19:18.630877 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:19:20.227550 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:19:23.094394 16216 solver.cpp:397]     Test net output #0: accuracy = 0.31924
I0410 01:19:23.094439 16216 solver.cpp:397]     Test net output #1: loss = 6.92028 (* 1 = 6.92028 loss)
I0410 01:19:23.177297 16216 solver.cpp:218] Iteration 7344 (1.26632 iter/s, 9.47628s/12 iters), loss = 0.0935901
I0410 01:19:23.177350 16216 solver.cpp:237]     Train net output #0: loss = 0.0935901 (* 1 = 0.0935901 loss)
I0410 01:19:23.177361 16216 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 01:19:27.457249 16216 solver.cpp:218] Iteration 7356 (2.80389 iter/s, 4.27976s/12 iters), loss = 0.101274
I0410 01:19:27.457307 16216 solver.cpp:237]     Train net output #0: loss = 0.101274 (* 1 = 0.101274 loss)
I0410 01:19:27.457319 16216 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 01:19:32.422854 16216 solver.cpp:218] Iteration 7368 (2.41673 iter/s, 4.96539s/12 iters), loss = 0.178405
I0410 01:19:32.422904 16216 solver.cpp:237]     Train net output #0: loss = 0.178405 (* 1 = 0.178405 loss)
I0410 01:19:32.422912 16216 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 01:19:37.353091 16216 solver.cpp:218] Iteration 7380 (2.43406 iter/s, 4.93003s/12 iters), loss = 0.142036
I0410 01:19:37.353142 16216 solver.cpp:237]     Train net output #0: loss = 0.142036 (* 1 = 0.142036 loss)
I0410 01:19:37.353155 16216 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 01:19:38.727597 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:19:42.477932 16216 solver.cpp:218] Iteration 7392 (2.34163 iter/s, 5.12463s/12 iters), loss = 0.257605
I0410 01:19:42.477993 16216 solver.cpp:237]     Train net output #0: loss = 0.257605 (* 1 = 0.257605 loss)
I0410 01:19:42.478004 16216 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 01:19:47.354521 16216 solver.cpp:218] Iteration 7404 (2.46084 iter/s, 4.87638s/12 iters), loss = 0.109546
I0410 01:19:47.354565 16216 solver.cpp:237]     Train net output #0: loss = 0.109546 (* 1 = 0.109546 loss)
I0410 01:19:47.354576 16216 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 01:19:52.475404 16216 solver.cpp:218] Iteration 7416 (2.34344 iter/s, 5.12068s/12 iters), loss = 0.184219
I0410 01:19:52.475451 16216 solver.cpp:237]     Train net output #0: loss = 0.184219 (* 1 = 0.184219 loss)
I0410 01:19:52.475461 16216 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 01:19:57.407933 16216 solver.cpp:218] Iteration 7428 (2.43293 iter/s, 4.93233s/12 iters), loss = 0.163908
I0410 01:19:57.407987 16216 solver.cpp:237]     Train net output #0: loss = 0.163908 (* 1 = 0.163908 loss)
I0410 01:19:57.407999 16216 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 01:20:02.304414 16216 solver.cpp:218] Iteration 7440 (2.45085 iter/s, 4.89627s/12 iters), loss = 0.150856
I0410 01:20:02.304457 16216 solver.cpp:237]     Train net output #0: loss = 0.150856 (* 1 = 0.150856 loss)
I0410 01:20:02.304466 16216 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 01:20:04.265496 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 01:20:04.541990 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 01:20:04.724931 16216 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 01:20:04.724951 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:20:06.245102 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:20:09.333019 16216 solver.cpp:397]     Test net output #0: accuracy = 0.302696
I0410 01:20:09.333159 16216 solver.cpp:397]     Test net output #1: loss = 6.71185 (* 1 = 6.71185 loss)
I0410 01:20:11.286880 16216 solver.cpp:218] Iteration 7452 (1.33598 iter/s, 8.98215s/12 iters), loss = 0.0152498
I0410 01:20:11.286931 16216 solver.cpp:237]     Train net output #0: loss = 0.0152498 (* 1 = 0.0152498 loss)
I0410 01:20:11.286942 16216 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 01:20:16.400414 16216 solver.cpp:218] Iteration 7464 (2.34681 iter/s, 5.11332s/12 iters), loss = 0.100426
I0410 01:20:16.400473 16216 solver.cpp:237]     Train net output #0: loss = 0.100426 (* 1 = 0.100426 loss)
I0410 01:20:16.400487 16216 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 01:20:21.426810 16216 solver.cpp:218] Iteration 7476 (2.3875 iter/s, 5.02619s/12 iters), loss = 0.0536704
I0410 01:20:21.426849 16216 solver.cpp:237]     Train net output #0: loss = 0.0536704 (* 1 = 0.0536704 loss)
I0410 01:20:21.426857 16216 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 01:20:24.900543 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:20:26.366183 16216 solver.cpp:218] Iteration 7488 (2.42956 iter/s, 4.93917s/12 iters), loss = 0.098602
I0410 01:20:26.366238 16216 solver.cpp:237]     Train net output #0: loss = 0.098602 (* 1 = 0.098602 loss)
I0410 01:20:26.366252 16216 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 01:20:31.351933 16216 solver.cpp:218] Iteration 7500 (2.40696 iter/s, 4.98554s/12 iters), loss = 0.0879413
I0410 01:20:31.351980 16216 solver.cpp:237]     Train net output #0: loss = 0.0879413 (* 1 = 0.0879413 loss)
I0410 01:20:31.351991 16216 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 01:20:36.270165 16216 solver.cpp:218] Iteration 7512 (2.44 iter/s, 4.91803s/12 iters), loss = 0.0218187
I0410 01:20:36.270211 16216 solver.cpp:237]     Train net output #0: loss = 0.0218187 (* 1 = 0.0218187 loss)
I0410 01:20:36.270223 16216 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 01:20:41.144881 16216 solver.cpp:218] Iteration 7524 (2.46178 iter/s, 4.87452s/12 iters), loss = 0.0446461
I0410 01:20:41.144995 16216 solver.cpp:237]     Train net output #0: loss = 0.0446461 (* 1 = 0.0446461 loss)
I0410 01:20:41.145006 16216 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 01:20:46.102398 16216 solver.cpp:218] Iteration 7536 (2.4207 iter/s, 4.95725s/12 iters), loss = 0.117304
I0410 01:20:46.102453 16216 solver.cpp:237]     Train net output #0: loss = 0.117304 (* 1 = 0.117304 loss)
I0410 01:20:46.102465 16216 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 01:20:50.948731 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 01:20:51.618649 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 01:20:52.251782 16216 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 01:20:52.251806 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:20:53.753037 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:20:56.692663 16216 solver.cpp:397]     Test net output #0: accuracy = 0.311274
I0410 01:20:56.692713 16216 solver.cpp:397]     Test net output #1: loss = 6.93291 (* 1 = 6.93291 loss)
I0410 01:20:56.775701 16216 solver.cpp:218] Iteration 7548 (1.12434 iter/s, 10.6729s/12 iters), loss = 0.108917
I0410 01:20:56.775741 16216 solver.cpp:237]     Train net output #0: loss = 0.108917 (* 1 = 0.108917 loss)
I0410 01:20:56.775750 16216 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 01:21:00.902853 16216 solver.cpp:218] Iteration 7560 (2.9077 iter/s, 4.12698s/12 iters), loss = 0.123785
I0410 01:21:00.902899 16216 solver.cpp:237]     Train net output #0: loss = 0.123785 (* 1 = 0.123785 loss)
I0410 01:21:00.902907 16216 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 01:21:05.928107 16216 solver.cpp:218] Iteration 7572 (2.38804 iter/s, 5.02505s/12 iters), loss = 0.101289
I0410 01:21:05.928166 16216 solver.cpp:237]     Train net output #0: loss = 0.101289 (* 1 = 0.101289 loss)
I0410 01:21:05.928179 16216 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 01:21:10.934109 16216 solver.cpp:218] Iteration 7584 (2.39723 iter/s, 5.00578s/12 iters), loss = 0.124039
I0410 01:21:10.934170 16216 solver.cpp:237]     Train net output #0: loss = 0.124039 (* 1 = 0.124039 loss)
I0410 01:21:10.934182 16216 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 01:21:11.568265 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:21:15.854038 16216 solver.cpp:218] Iteration 7596 (2.43917 iter/s, 4.91972s/12 iters), loss = 0.0678028
I0410 01:21:15.854085 16216 solver.cpp:237]     Train net output #0: loss = 0.0678028 (* 1 = 0.0678028 loss)
I0410 01:21:15.854095 16216 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 01:21:20.757339 16216 solver.cpp:218] Iteration 7608 (2.44743 iter/s, 4.9031s/12 iters), loss = 0.0963292
I0410 01:21:20.757381 16216 solver.cpp:237]     Train net output #0: loss = 0.0963292 (* 1 = 0.0963292 loss)
I0410 01:21:20.757391 16216 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 01:21:25.633051 16216 solver.cpp:218] Iteration 7620 (2.46128 iter/s, 4.87551s/12 iters), loss = 0.0755724
I0410 01:21:25.633109 16216 solver.cpp:237]     Train net output #0: loss = 0.0755724 (* 1 = 0.0755724 loss)
I0410 01:21:25.633121 16216 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 01:21:26.382086 16216 blocking_queue.cpp:49] Waiting for data
I0410 01:21:30.553061 16216 solver.cpp:218] Iteration 7632 (2.43913 iter/s, 4.91979s/12 iters), loss = 0.183038
I0410 01:21:30.553117 16216 solver.cpp:237]     Train net output #0: loss = 0.183038 (* 1 = 0.183038 loss)
I0410 01:21:30.553126 16216 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 01:21:35.484345 16216 solver.cpp:218] Iteration 7644 (2.43355 iter/s, 4.93108s/12 iters), loss = 0.0448697
I0410 01:21:35.484388 16216 solver.cpp:237]     Train net output #0: loss = 0.0448697 (* 1 = 0.0448697 loss)
I0410 01:21:35.484398 16216 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 01:21:37.505242 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 01:21:37.754923 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 01:21:37.932381 16216 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 01:21:37.932405 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:21:39.272668 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:21:42.264518 16216 solver.cpp:397]     Test net output #0: accuracy = 0.316789
I0410 01:21:42.264694 16216 solver.cpp:397]     Test net output #1: loss = 6.85283 (* 1 = 6.85283 loss)
I0410 01:21:44.143777 16216 solver.cpp:218] Iteration 7656 (1.38582 iter/s, 8.65913s/12 iters), loss = 0.0545904
I0410 01:21:44.143831 16216 solver.cpp:237]     Train net output #0: loss = 0.0545904 (* 1 = 0.0545904 loss)
I0410 01:21:44.143839 16216 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 01:21:49.096846 16216 solver.cpp:218] Iteration 7668 (2.42284 iter/s, 4.95286s/12 iters), loss = 0.0593151
I0410 01:21:49.096890 16216 solver.cpp:237]     Train net output #0: loss = 0.0593151 (* 1 = 0.0593151 loss)
I0410 01:21:49.096899 16216 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 01:21:54.195672 16216 solver.cpp:218] Iteration 7680 (2.35358 iter/s, 5.09862s/12 iters), loss = 0.0421506
I0410 01:21:54.195722 16216 solver.cpp:237]     Train net output #0: loss = 0.0421506 (* 1 = 0.0421506 loss)
I0410 01:21:54.195734 16216 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 01:21:56.862361 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:21:59.018579 16216 solver.cpp:218] Iteration 7692 (2.48823 iter/s, 4.8227s/12 iters), loss = 0.350349
I0410 01:21:59.018625 16216 solver.cpp:237]     Train net output #0: loss = 0.350349 (* 1 = 0.350349 loss)
I0410 01:21:59.018635 16216 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 01:22:03.973440 16216 solver.cpp:218] Iteration 7704 (2.42196 iter/s, 4.95466s/12 iters), loss = 0.0452273
I0410 01:22:03.973489 16216 solver.cpp:237]     Train net output #0: loss = 0.0452273 (* 1 = 0.0452273 loss)
I0410 01:22:03.973498 16216 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 01:22:08.890707 16216 solver.cpp:218] Iteration 7716 (2.44048 iter/s, 4.91706s/12 iters), loss = 0.00819069
I0410 01:22:08.890756 16216 solver.cpp:237]     Train net output #0: loss = 0.00819069 (* 1 = 0.00819069 loss)
I0410 01:22:08.890766 16216 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 01:22:14.081329 16216 solver.cpp:218] Iteration 7728 (2.31196 iter/s, 5.19041s/12 iters), loss = 0.106618
I0410 01:22:14.081450 16216 solver.cpp:237]     Train net output #0: loss = 0.106618 (* 1 = 0.106618 loss)
I0410 01:22:14.081465 16216 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 01:22:19.331894 16216 solver.cpp:218] Iteration 7740 (2.28559 iter/s, 5.25028s/12 iters), loss = 0.158858
I0410 01:22:19.331950 16216 solver.cpp:237]     Train net output #0: loss = 0.158858 (* 1 = 0.158858 loss)
I0410 01:22:19.331962 16216 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 01:22:23.783638 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 01:22:24.366164 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 01:22:24.829099 16216 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 01:22:24.829131 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:22:26.403795 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:22:29.617872 16216 solver.cpp:397]     Test net output #0: accuracy = 0.310662
I0410 01:22:29.617923 16216 solver.cpp:397]     Test net output #1: loss = 6.70273 (* 1 = 6.70273 loss)
I0410 01:22:29.699960 16216 solver.cpp:218] Iteration 7752 (1.15744 iter/s, 10.3677s/12 iters), loss = 0.0890955
I0410 01:22:29.700017 16216 solver.cpp:237]     Train net output #0: loss = 0.0890955 (* 1 = 0.0890955 loss)
I0410 01:22:29.700029 16216 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 01:22:34.023746 16216 solver.cpp:218] Iteration 7764 (2.77547 iter/s, 4.32359s/12 iters), loss = 0.142893
I0410 01:22:34.023795 16216 solver.cpp:237]     Train net output #0: loss = 0.142893 (* 1 = 0.142893 loss)
I0410 01:22:34.023805 16216 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 01:22:38.984714 16216 solver.cpp:218] Iteration 7776 (2.41898 iter/s, 4.96077s/12 iters), loss = 0.00923327
I0410 01:22:38.984755 16216 solver.cpp:237]     Train net output #0: loss = 0.00923326 (* 1 = 0.00923326 loss)
I0410 01:22:38.984764 16216 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 01:22:43.873494 16216 solver.cpp:218] Iteration 7788 (2.4547 iter/s, 4.88858s/12 iters), loss = 0.0855056
I0410 01:22:43.873555 16216 solver.cpp:237]     Train net output #0: loss = 0.0855056 (* 1 = 0.0855056 loss)
I0410 01:22:43.873569 16216 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 01:22:43.883961 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:22:48.803133 16216 solver.cpp:218] Iteration 7800 (2.43436 iter/s, 4.92942s/12 iters), loss = 0.0450004
I0410 01:22:48.803275 16216 solver.cpp:237]     Train net output #0: loss = 0.0450004 (* 1 = 0.0450004 loss)
I0410 01:22:48.803288 16216 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 01:22:53.676918 16216 solver.cpp:218] Iteration 7812 (2.4623 iter/s, 4.87349s/12 iters), loss = 0.0452684
I0410 01:22:53.676973 16216 solver.cpp:237]     Train net output #0: loss = 0.0452684 (* 1 = 0.0452684 loss)
I0410 01:22:53.676985 16216 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 01:22:58.615955 16216 solver.cpp:218] Iteration 7824 (2.42973 iter/s, 4.93882s/12 iters), loss = 0.036125
I0410 01:22:58.616005 16216 solver.cpp:237]     Train net output #0: loss = 0.0361251 (* 1 = 0.0361251 loss)
I0410 01:22:58.616016 16216 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 01:23:03.594449 16216 solver.cpp:218] Iteration 7836 (2.41047 iter/s, 4.97829s/12 iters), loss = 0.11363
I0410 01:23:03.594498 16216 solver.cpp:237]     Train net output #0: loss = 0.11363 (* 1 = 0.11363 loss)
I0410 01:23:03.594507 16216 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 01:23:08.580778 16216 solver.cpp:218] Iteration 7848 (2.40668 iter/s, 4.98612s/12 iters), loss = 0.0308078
I0410 01:23:08.580828 16216 solver.cpp:237]     Train net output #0: loss = 0.0308078 (* 1 = 0.0308078 loss)
I0410 01:23:08.580839 16216 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 01:23:10.764463 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 01:23:11.221467 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 01:23:11.411358 16216 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 01:23:11.411386 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:23:12.861447 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:23:16.160487 16216 solver.cpp:397]     Test net output #0: accuracy = 0.319853
I0410 01:23:16.160532 16216 solver.cpp:397]     Test net output #1: loss = 6.8087 (* 1 = 6.8087 loss)
I0410 01:23:18.005244 16216 solver.cpp:218] Iteration 7860 (1.27333 iter/s, 9.42413s/12 iters), loss = 0.0353567
I0410 01:23:18.005303 16216 solver.cpp:237]     Train net output #0: loss = 0.0353567 (* 1 = 0.0353567 loss)
I0410 01:23:18.005316 16216 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 01:23:22.919713 16216 solver.cpp:218] Iteration 7872 (2.44187 iter/s, 4.91426s/12 iters), loss = 0.087034
I0410 01:23:22.920266 16216 solver.cpp:237]     Train net output #0: loss = 0.087034 (* 1 = 0.087034 loss)
I0410 01:23:22.920276 16216 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 01:23:27.886723 16216 solver.cpp:218] Iteration 7884 (2.41629 iter/s, 4.9663s/12 iters), loss = 0.0308093
I0410 01:23:27.886775 16216 solver.cpp:237]     Train net output #0: loss = 0.0308093 (* 1 = 0.0308093 loss)
I0410 01:23:27.886786 16216 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 01:23:30.049757 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:23:32.841027 16216 solver.cpp:218] Iteration 7896 (2.42224 iter/s, 4.9541s/12 iters), loss = 0.0269502
I0410 01:23:32.841078 16216 solver.cpp:237]     Train net output #0: loss = 0.0269502 (* 1 = 0.0269502 loss)
I0410 01:23:32.841090 16216 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 01:23:37.750025 16216 solver.cpp:218] Iteration 7908 (2.44459 iter/s, 4.90879s/12 iters), loss = 0.00675478
I0410 01:23:37.750083 16216 solver.cpp:237]     Train net output #0: loss = 0.0067548 (* 1 = 0.0067548 loss)
I0410 01:23:37.750095 16216 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 01:23:42.645350 16216 solver.cpp:218] Iteration 7920 (2.45143 iter/s, 4.89511s/12 iters), loss = 0.0806815
I0410 01:23:42.645411 16216 solver.cpp:237]     Train net output #0: loss = 0.0806815 (* 1 = 0.0806815 loss)
I0410 01:23:42.645424 16216 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 01:23:47.524991 16216 solver.cpp:218] Iteration 7932 (2.4593 iter/s, 4.87943s/12 iters), loss = 0.07312
I0410 01:23:47.525035 16216 solver.cpp:237]     Train net output #0: loss = 0.07312 (* 1 = 0.07312 loss)
I0410 01:23:47.525046 16216 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 01:23:52.468606 16216 solver.cpp:218] Iteration 7944 (2.42747 iter/s, 4.94341s/12 iters), loss = 0.0547694
I0410 01:23:52.468662 16216 solver.cpp:237]     Train net output #0: loss = 0.0547694 (* 1 = 0.0547694 loss)
I0410 01:23:52.468675 16216 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 01:23:56.914134 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 01:23:57.420655 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 01:23:57.606678 16216 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 01:23:57.606699 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:23:58.870287 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:24:01.979311 16216 solver.cpp:397]     Test net output #0: accuracy = 0.3125
I0410 01:24:01.979346 16216 solver.cpp:397]     Test net output #1: loss = 6.89238 (* 1 = 6.89238 loss)
I0410 01:24:02.062045 16216 solver.cpp:218] Iteration 7956 (1.2509 iter/s, 9.59309s/12 iters), loss = 0.113963
I0410 01:24:02.062106 16216 solver.cpp:237]     Train net output #0: loss = 0.113963 (* 1 = 0.113963 loss)
I0410 01:24:02.062119 16216 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 01:24:06.196075 16216 solver.cpp:218] Iteration 7968 (2.90287 iter/s, 4.13384s/12 iters), loss = 0.0912032
I0410 01:24:06.196113 16216 solver.cpp:237]     Train net output #0: loss = 0.0912032 (* 1 = 0.0912032 loss)
I0410 01:24:06.196123 16216 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 01:24:11.103117 16216 solver.cpp:218] Iteration 7980 (2.44556 iter/s, 4.90685s/12 iters), loss = 0.024798
I0410 01:24:11.103163 16216 solver.cpp:237]     Train net output #0: loss = 0.024798 (* 1 = 0.024798 loss)
I0410 01:24:11.103171 16216 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 01:24:15.334131 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:24:16.054844 16216 solver.cpp:218] Iteration 7992 (2.4235 iter/s, 4.95152s/12 iters), loss = 0.0849235
I0410 01:24:16.054893 16216 solver.cpp:237]     Train net output #0: loss = 0.0849235 (* 1 = 0.0849235 loss)
I0410 01:24:16.054903 16216 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 01:24:20.969888 16216 solver.cpp:218] Iteration 8004 (2.44159 iter/s, 4.91484s/12 iters), loss = 0.0785823
I0410 01:24:20.969939 16216 solver.cpp:237]     Train net output #0: loss = 0.0785823 (* 1 = 0.0785823 loss)
I0410 01:24:20.969950 16216 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 01:24:25.846506 16216 solver.cpp:218] Iteration 8016 (2.46082 iter/s, 4.87641s/12 iters), loss = 0.0297591
I0410 01:24:25.846556 16216 solver.cpp:237]     Train net output #0: loss = 0.0297591 (* 1 = 0.0297591 loss)
I0410 01:24:25.846567 16216 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 01:24:30.788421 16216 solver.cpp:218] Iteration 8028 (2.42831 iter/s, 4.9417s/12 iters), loss = 0.163318
I0410 01:24:30.788573 16216 solver.cpp:237]     Train net output #0: loss = 0.163318 (* 1 = 0.163318 loss)
I0410 01:24:30.788587 16216 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 01:24:35.749837 16216 solver.cpp:218] Iteration 8040 (2.41882 iter/s, 4.9611s/12 iters), loss = 0.0403807
I0410 01:24:35.749897 16216 solver.cpp:237]     Train net output #0: loss = 0.0403808 (* 1 = 0.0403808 loss)
I0410 01:24:35.749909 16216 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 01:24:40.601562 16216 solver.cpp:218] Iteration 8052 (2.47346 iter/s, 4.85151s/12 iters), loss = 0.0728805
I0410 01:24:40.601615 16216 solver.cpp:237]     Train net output #0: loss = 0.0728806 (* 1 = 0.0728806 loss)
I0410 01:24:40.601626 16216 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 01:24:42.600746 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 01:24:42.848726 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 01:24:43.017396 16216 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 01:24:43.017421 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:24:44.320307 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:24:47.471127 16216 solver.cpp:397]     Test net output #0: accuracy = 0.326593
I0410 01:24:47.471163 16216 solver.cpp:397]     Test net output #1: loss = 6.72028 (* 1 = 6.72028 loss)
I0410 01:24:49.417749 16216 solver.cpp:218] Iteration 8064 (1.36118 iter/s, 8.81587s/12 iters), loss = 0.0255889
I0410 01:24:49.417801 16216 solver.cpp:237]     Train net output #0: loss = 0.0255889 (* 1 = 0.0255889 loss)
I0410 01:24:49.417814 16216 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 01:24:54.544553 16216 solver.cpp:218] Iteration 8076 (2.34074 iter/s, 5.12659s/12 iters), loss = 0.123228
I0410 01:24:54.544605 16216 solver.cpp:237]     Train net output #0: loss = 0.123228 (* 1 = 0.123228 loss)
I0410 01:24:54.544615 16216 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 01:24:59.482193 16216 solver.cpp:218] Iteration 8088 (2.43041 iter/s, 4.93743s/12 iters), loss = 0.211713
I0410 01:24:59.482251 16216 solver.cpp:237]     Train net output #0: loss = 0.211713 (* 1 = 0.211713 loss)
I0410 01:24:59.482264 16216 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 01:25:00.872279 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:25:04.371613 16216 solver.cpp:218] Iteration 8100 (2.45439 iter/s, 4.8892s/12 iters), loss = 0.195753
I0410 01:25:04.371676 16216 solver.cpp:237]     Train net output #0: loss = 0.195753 (* 1 = 0.195753 loss)
I0410 01:25:04.371691 16216 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 01:25:09.292924 16216 solver.cpp:218] Iteration 8112 (2.43848 iter/s, 4.9211s/12 iters), loss = 0.056831
I0410 01:25:09.292971 16216 solver.cpp:237]     Train net output #0: loss = 0.056831 (* 1 = 0.056831 loss)
I0410 01:25:09.292981 16216 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 01:25:14.442484 16216 solver.cpp:218] Iteration 8124 (2.33039 iter/s, 5.14935s/12 iters), loss = 0.0260838
I0410 01:25:14.442530 16216 solver.cpp:237]     Train net output #0: loss = 0.0260838 (* 1 = 0.0260838 loss)
I0410 01:25:14.442541 16216 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 01:25:19.485985 16216 solver.cpp:218] Iteration 8136 (2.37941 iter/s, 5.04328s/12 iters), loss = 0.142465
I0410 01:25:19.486040 16216 solver.cpp:237]     Train net output #0: loss = 0.142465 (* 1 = 0.142465 loss)
I0410 01:25:19.486052 16216 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 01:25:24.434907 16216 solver.cpp:218] Iteration 8148 (2.42487 iter/s, 4.94872s/12 iters), loss = 0.0254901
I0410 01:25:24.434957 16216 solver.cpp:237]     Train net output #0: loss = 0.0254901 (* 1 = 0.0254901 loss)
I0410 01:25:24.434969 16216 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 01:25:28.964857 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 01:25:29.206374 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 01:25:29.378227 16216 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 01:25:29.378247 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:25:30.648813 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:25:33.936136 16216 solver.cpp:397]     Test net output #0: accuracy = 0.324142
I0410 01:25:33.936266 16216 solver.cpp:397]     Test net output #1: loss = 6.83085 (* 1 = 6.83085 loss)
I0410 01:25:34.019212 16216 solver.cpp:218] Iteration 8160 (1.25209 iter/s, 9.58397s/12 iters), loss = 0.01975
I0410 01:25:34.019259 16216 solver.cpp:237]     Train net output #0: loss = 0.01975 (* 1 = 0.01975 loss)
I0410 01:25:34.019269 16216 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 01:25:38.264974 16216 solver.cpp:218] Iteration 8172 (2.82647 iter/s, 4.24558s/12 iters), loss = 0.0545132
I0410 01:25:38.265034 16216 solver.cpp:237]     Train net output #0: loss = 0.0545132 (* 1 = 0.0545132 loss)
I0410 01:25:38.265046 16216 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 01:25:43.193039 16216 solver.cpp:218] Iteration 8184 (2.43514 iter/s, 4.92785s/12 iters), loss = 0.0376418
I0410 01:25:43.193089 16216 solver.cpp:237]     Train net output #0: loss = 0.0376418 (* 1 = 0.0376418 loss)
I0410 01:25:43.193099 16216 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 01:25:46.964835 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:25:48.478250 16216 solver.cpp:218] Iteration 8196 (2.27058 iter/s, 5.28499s/12 iters), loss = 0.0534274
I0410 01:25:48.478309 16216 solver.cpp:237]     Train net output #0: loss = 0.0534274 (* 1 = 0.0534274 loss)
I0410 01:25:48.478320 16216 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 01:25:53.633579 16216 solver.cpp:218] Iteration 8208 (2.32779 iter/s, 5.15511s/12 iters), loss = 0.0593538
I0410 01:25:53.633618 16216 solver.cpp:237]     Train net output #0: loss = 0.0593537 (* 1 = 0.0593537 loss)
I0410 01:25:53.633627 16216 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 01:25:58.684691 16216 solver.cpp:218] Iteration 8220 (2.37581 iter/s, 5.05091s/12 iters), loss = 0.0347578
I0410 01:25:58.684747 16216 solver.cpp:237]     Train net output #0: loss = 0.0347578 (* 1 = 0.0347578 loss)
I0410 01:25:58.684760 16216 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 01:26:03.594357 16216 solver.cpp:218] Iteration 8232 (2.44426 iter/s, 4.90946s/12 iters), loss = 0.0261127
I0410 01:26:03.594401 16216 solver.cpp:237]     Train net output #0: loss = 0.0261127 (* 1 = 0.0261127 loss)
I0410 01:26:03.594411 16216 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 01:26:08.583410 16216 solver.cpp:218] Iteration 8244 (2.40536 iter/s, 4.98885s/12 iters), loss = 0.255092
I0410 01:26:08.584012 16216 solver.cpp:237]     Train net output #0: loss = 0.255092 (* 1 = 0.255092 loss)
I0410 01:26:08.584022 16216 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 01:26:13.593339 16216 solver.cpp:218] Iteration 8256 (2.39561 iter/s, 5.00917s/12 iters), loss = 0.0625647
I0410 01:26:13.593384 16216 solver.cpp:237]     Train net output #0: loss = 0.0625647 (* 1 = 0.0625647 loss)
I0410 01:26:13.593394 16216 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 01:26:15.603845 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 01:26:17.271787 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 01:26:17.924746 16216 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 01:26:17.924770 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:26:19.148866 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:26:22.364369 16216 solver.cpp:397]     Test net output #0: accuracy = 0.314951
I0410 01:26:22.364418 16216 solver.cpp:397]     Test net output #1: loss = 6.79377 (* 1 = 6.79377 loss)
I0410 01:26:24.231812 16216 solver.cpp:218] Iteration 8268 (1.12802 iter/s, 10.6381s/12 iters), loss = 0.0640423
I0410 01:26:24.231864 16216 solver.cpp:237]     Train net output #0: loss = 0.0640423 (* 1 = 0.0640423 loss)
I0410 01:26:24.231876 16216 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 01:26:29.167562 16216 solver.cpp:218] Iteration 8280 (2.43134 iter/s, 4.93555s/12 iters), loss = 0.0237344
I0410 01:26:29.167598 16216 solver.cpp:237]     Train net output #0: loss = 0.0237344 (* 1 = 0.0237344 loss)
I0410 01:26:29.167606 16216 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 01:26:34.288519 16216 solver.cpp:218] Iteration 8292 (2.34341 iter/s, 5.12075s/12 iters), loss = 0.0166808
I0410 01:26:34.288573 16216 solver.cpp:237]     Train net output #0: loss = 0.0166807 (* 1 = 0.0166807 loss)
I0410 01:26:34.288586 16216 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 01:26:34.967330 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:26:39.235383 16216 solver.cpp:218] Iteration 8304 (2.42588 iter/s, 4.94666s/12 iters), loss = 0.0282376
I0410 01:26:39.235524 16216 solver.cpp:237]     Train net output #0: loss = 0.0282376 (* 1 = 0.0282376 loss)
I0410 01:26:39.235535 16216 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 01:26:40.393576 16216 blocking_queue.cpp:49] Waiting for data
I0410 01:26:44.074470 16216 solver.cpp:218] Iteration 8316 (2.47996 iter/s, 4.83879s/12 iters), loss = 0.111096
I0410 01:26:44.074532 16216 solver.cpp:237]     Train net output #0: loss = 0.111096 (* 1 = 0.111096 loss)
I0410 01:26:44.074544 16216 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 01:26:49.012877 16216 solver.cpp:218] Iteration 8328 (2.43004 iter/s, 4.93819s/12 iters), loss = 0.0105931
I0410 01:26:49.012921 16216 solver.cpp:237]     Train net output #0: loss = 0.0105931 (* 1 = 0.0105931 loss)
I0410 01:26:49.012930 16216 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 01:26:53.943310 16216 solver.cpp:218] Iteration 8340 (2.43396 iter/s, 4.93023s/12 iters), loss = 0.108698
I0410 01:26:53.943361 16216 solver.cpp:237]     Train net output #0: loss = 0.108698 (* 1 = 0.108698 loss)
I0410 01:26:53.943374 16216 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 01:26:58.885368 16216 solver.cpp:218] Iteration 8352 (2.42824 iter/s, 4.94186s/12 iters), loss = 0.0166125
I0410 01:26:58.885414 16216 solver.cpp:237]     Train net output #0: loss = 0.0166125 (* 1 = 0.0166125 loss)
I0410 01:26:58.885426 16216 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 01:27:03.393697 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 01:27:03.645870 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 01:27:03.834350 16216 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 01:27:03.834385 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:27:05.030483 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:27:08.323438 16216 solver.cpp:397]     Test net output #0: accuracy = 0.314338
I0410 01:27:08.323485 16216 solver.cpp:397]     Test net output #1: loss = 6.69044 (* 1 = 6.69044 loss)
I0410 01:27:08.406271 16216 solver.cpp:218] Iteration 8364 (1.26043 iter/s, 9.52057s/12 iters), loss = 0.102724
I0410 01:27:08.406316 16216 solver.cpp:237]     Train net output #0: loss = 0.102724 (* 1 = 0.102724 loss)
I0410 01:27:08.406328 16216 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 01:27:12.601943 16216 solver.cpp:218] Iteration 8376 (2.86021 iter/s, 4.19549s/12 iters), loss = 0.0486534
I0410 01:27:12.602140 16216 solver.cpp:237]     Train net output #0: loss = 0.0486534 (* 1 = 0.0486534 loss)
I0410 01:27:12.602156 16216 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 01:27:17.548048 16216 solver.cpp:218] Iteration 8388 (2.42632 iter/s, 4.94576s/12 iters), loss = 0.0294388
I0410 01:27:17.548099 16216 solver.cpp:237]     Train net output #0: loss = 0.0294388 (* 1 = 0.0294388 loss)
I0410 01:27:17.548112 16216 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 01:27:20.330400 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:27:22.473256 16216 solver.cpp:218] Iteration 8400 (2.43655 iter/s, 4.925s/12 iters), loss = 0.0174066
I0410 01:27:22.473317 16216 solver.cpp:237]     Train net output #0: loss = 0.0174066 (* 1 = 0.0174066 loss)
I0410 01:27:22.473330 16216 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 01:27:27.362244 16216 solver.cpp:218] Iteration 8412 (2.4546 iter/s, 4.88878s/12 iters), loss = 0.08157
I0410 01:27:27.362293 16216 solver.cpp:237]     Train net output #0: loss = 0.0815699 (* 1 = 0.0815699 loss)
I0410 01:27:27.362303 16216 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 01:27:32.281560 16216 solver.cpp:218] Iteration 8424 (2.43947 iter/s, 4.91911s/12 iters), loss = 0.00901486
I0410 01:27:32.281607 16216 solver.cpp:237]     Train net output #0: loss = 0.00901483 (* 1 = 0.00901483 loss)
I0410 01:27:32.281616 16216 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 01:27:37.202827 16216 solver.cpp:218] Iteration 8436 (2.4385 iter/s, 4.92107s/12 iters), loss = 0.0732448
I0410 01:27:37.202872 16216 solver.cpp:237]     Train net output #0: loss = 0.0732448 (* 1 = 0.0732448 loss)
I0410 01:27:37.202880 16216 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 01:27:42.102481 16216 solver.cpp:218] Iteration 8448 (2.44925 iter/s, 4.89945s/12 iters), loss = 0.0303277
I0410 01:27:42.102541 16216 solver.cpp:237]     Train net output #0: loss = 0.0303277 (* 1 = 0.0303277 loss)
I0410 01:27:42.102556 16216 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 01:27:47.022644 16216 solver.cpp:218] Iteration 8460 (2.43905 iter/s, 4.91995s/12 iters), loss = 0.0498939
I0410 01:27:47.022737 16216 solver.cpp:237]     Train net output #0: loss = 0.0498938 (* 1 = 0.0498938 loss)
I0410 01:27:47.022747 16216 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 01:27:48.993872 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 01:27:49.520694 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 01:27:49.964979 16216 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 01:27:49.965004 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:27:51.085135 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:27:54.495383 16216 solver.cpp:397]     Test net output #0: accuracy = 0.320466
I0410 01:27:54.495445 16216 solver.cpp:397]     Test net output #1: loss = 6.69801 (* 1 = 6.69801 loss)
I0410 01:27:56.239164 16216 solver.cpp:218] Iteration 8472 (1.30206 iter/s, 9.21615s/12 iters), loss = 0.0766463
I0410 01:27:56.239210 16216 solver.cpp:237]     Train net output #0: loss = 0.0766463 (* 1 = 0.0766463 loss)
I0410 01:27:56.239220 16216 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 01:28:01.241914 16216 solver.cpp:218] Iteration 8484 (2.39878 iter/s, 5.00255s/12 iters), loss = 0.054636
I0410 01:28:01.241986 16216 solver.cpp:237]     Train net output #0: loss = 0.054636 (* 1 = 0.054636 loss)
I0410 01:28:01.241997 16216 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 01:28:06.191911 16216 solver.cpp:218] Iteration 8496 (2.42435 iter/s, 4.94977s/12 iters), loss = 0.0423535
I0410 01:28:06.191964 16216 solver.cpp:237]     Train net output #0: loss = 0.0423535 (* 1 = 0.0423535 loss)
I0410 01:28:06.191974 16216 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 01:28:06.230811 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:28:11.150575 16216 solver.cpp:218] Iteration 8508 (2.42011 iter/s, 4.95846s/12 iters), loss = 0.0943549
I0410 01:28:11.150619 16216 solver.cpp:237]     Train net output #0: loss = 0.0943549 (* 1 = 0.0943549 loss)
I0410 01:28:11.150627 16216 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 01:28:16.086176 16216 solver.cpp:218] Iteration 8520 (2.43141 iter/s, 4.9354s/12 iters), loss = 0.0483398
I0410 01:28:16.086236 16216 solver.cpp:237]     Train net output #0: loss = 0.0483398 (* 1 = 0.0483398 loss)
I0410 01:28:16.086247 16216 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 01:28:21.006323 16216 solver.cpp:218] Iteration 8532 (2.43906 iter/s, 4.91993s/12 iters), loss = 0.0319474
I0410 01:28:21.006469 16216 solver.cpp:237]     Train net output #0: loss = 0.0319474 (* 1 = 0.0319474 loss)
I0410 01:28:21.006482 16216 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 01:28:26.121811 16216 solver.cpp:218] Iteration 8544 (2.34595 iter/s, 5.11519s/12 iters), loss = 0.106596
I0410 01:28:26.121862 16216 solver.cpp:237]     Train net output #0: loss = 0.106596 (* 1 = 0.106596 loss)
I0410 01:28:26.121873 16216 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 01:28:31.156543 16216 solver.cpp:218] Iteration 8556 (2.38354 iter/s, 5.03453s/12 iters), loss = 0.0882446
I0410 01:28:31.156592 16216 solver.cpp:237]     Train net output #0: loss = 0.0882446 (* 1 = 0.0882446 loss)
I0410 01:28:31.156603 16216 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 01:28:35.644361 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 01:28:35.881800 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 01:28:36.050918 16216 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 01:28:36.050948 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:28:37.144230 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:28:40.664824 16216 solver.cpp:397]     Test net output #0: accuracy = 0.319853
I0410 01:28:40.664875 16216 solver.cpp:397]     Test net output #1: loss = 6.646 (* 1 = 6.646 loss)
I0410 01:28:40.747793 16216 solver.cpp:218] Iteration 8568 (1.25118 iter/s, 9.59091s/12 iters), loss = 0.0440773
I0410 01:28:40.747849 16216 solver.cpp:237]     Train net output #0: loss = 0.0440774 (* 1 = 0.0440774 loss)
I0410 01:28:40.747861 16216 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 01:28:44.961055 16216 solver.cpp:218] Iteration 8580 (2.84828 iter/s, 4.21307s/12 iters), loss = 0.00501015
I0410 01:28:44.961109 16216 solver.cpp:237]     Train net output #0: loss = 0.00501017 (* 1 = 0.00501017 loss)
I0410 01:28:44.961122 16216 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 01:28:49.887437 16216 solver.cpp:218] Iteration 8592 (2.43597 iter/s, 4.92617s/12 iters), loss = 0.0199836
I0410 01:28:49.887495 16216 solver.cpp:237]     Train net output #0: loss = 0.0199836 (* 1 = 0.0199836 loss)
I0410 01:28:49.887506 16216 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 01:28:52.025913 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:28:54.800700 16216 solver.cpp:218] Iteration 8604 (2.44247 iter/s, 4.91306s/12 iters), loss = 0.0588739
I0410 01:28:54.800740 16216 solver.cpp:237]     Train net output #0: loss = 0.058874 (* 1 = 0.058874 loss)
I0410 01:28:54.800748 16216 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 01:28:59.714318 16216 solver.cpp:218] Iteration 8616 (2.44229 iter/s, 4.91342s/12 iters), loss = 0.0263872
I0410 01:28:59.714371 16216 solver.cpp:237]     Train net output #0: loss = 0.0263872 (* 1 = 0.0263872 loss)
I0410 01:28:59.714383 16216 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 01:29:04.567732 16216 solver.cpp:218] Iteration 8628 (2.47259 iter/s, 4.8532s/12 iters), loss = 0.0773134
I0410 01:29:04.567787 16216 solver.cpp:237]     Train net output #0: loss = 0.0773135 (* 1 = 0.0773135 loss)
I0410 01:29:04.567800 16216 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 01:29:09.564884 16216 solver.cpp:218] Iteration 8640 (2.40147 iter/s, 4.99694s/12 iters), loss = 0.0368238
I0410 01:29:09.564926 16216 solver.cpp:237]     Train net output #0: loss = 0.0368238 (* 1 = 0.0368238 loss)
I0410 01:29:09.564935 16216 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 01:29:14.619114 16216 solver.cpp:218] Iteration 8652 (2.37434 iter/s, 5.05403s/12 iters), loss = 0.0739934
I0410 01:29:14.619163 16216 solver.cpp:237]     Train net output #0: loss = 0.0739934 (* 1 = 0.0739934 loss)
I0410 01:29:14.619170 16216 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 01:29:19.917166 16216 solver.cpp:218] Iteration 8664 (2.26508 iter/s, 5.29783s/12 iters), loss = 0.197648
I0410 01:29:19.917227 16216 solver.cpp:237]     Train net output #0: loss = 0.197648 (* 1 = 0.197648 loss)
I0410 01:29:19.917240 16216 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 01:29:21.948978 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 01:29:22.190178 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 01:29:22.356745 16216 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 01:29:22.356768 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:29:23.298739 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:29:26.695976 16216 solver.cpp:397]     Test net output #0: accuracy = 0.326593
I0410 01:29:26.696024 16216 solver.cpp:397]     Test net output #1: loss = 6.61181 (* 1 = 6.61181 loss)
I0410 01:29:28.582394 16216 solver.cpp:218] Iteration 8676 (1.3849 iter/s, 8.66491s/12 iters), loss = 0.0319147
I0410 01:29:28.582444 16216 solver.cpp:237]     Train net output #0: loss = 0.0319147 (* 1 = 0.0319147 loss)
I0410 01:29:28.582456 16216 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 01:29:33.514068 16216 solver.cpp:218] Iteration 8688 (2.43335 iter/s, 4.93147s/12 iters), loss = 0.00744564
I0410 01:29:33.514122 16216 solver.cpp:237]     Train net output #0: loss = 0.00744568 (* 1 = 0.00744568 loss)
I0410 01:29:33.514132 16216 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 01:29:37.769454 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:29:38.454421 16216 solver.cpp:218] Iteration 8700 (2.42908 iter/s, 4.94014s/12 iters), loss = 0.0396649
I0410 01:29:38.454474 16216 solver.cpp:237]     Train net output #0: loss = 0.0396649 (* 1 = 0.0396649 loss)
I0410 01:29:38.454493 16216 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 01:29:43.459146 16216 solver.cpp:218] Iteration 8712 (2.39783 iter/s, 5.00452s/12 iters), loss = 0.0159696
I0410 01:29:43.459199 16216 solver.cpp:237]     Train net output #0: loss = 0.0159697 (* 1 = 0.0159697 loss)
I0410 01:29:43.459211 16216 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 01:29:48.416821 16216 solver.cpp:218] Iteration 8724 (2.42059 iter/s, 4.95746s/12 iters), loss = 0.00806836
I0410 01:29:48.416882 16216 solver.cpp:237]     Train net output #0: loss = 0.00806842 (* 1 = 0.00806842 loss)
I0410 01:29:48.416895 16216 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 01:29:53.326503 16216 solver.cpp:218] Iteration 8736 (2.44426 iter/s, 4.90947s/12 iters), loss = 0.0490487
I0410 01:29:53.326622 16216 solver.cpp:237]     Train net output #0: loss = 0.0490487 (* 1 = 0.0490487 loss)
I0410 01:29:53.326638 16216 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 01:29:58.289810 16216 solver.cpp:218] Iteration 8748 (2.41787 iter/s, 4.96304s/12 iters), loss = 0.00884048
I0410 01:29:58.289863 16216 solver.cpp:237]     Train net output #0: loss = 0.00884054 (* 1 = 0.00884054 loss)
I0410 01:29:58.289875 16216 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 01:30:03.234822 16216 solver.cpp:218] Iteration 8760 (2.42679 iter/s, 4.9448s/12 iters), loss = 0.0590148
I0410 01:30:03.234869 16216 solver.cpp:237]     Train net output #0: loss = 0.0590148 (* 1 = 0.0590148 loss)
I0410 01:30:03.234879 16216 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 01:30:07.733937 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 01:30:08.298653 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 01:30:08.734428 16216 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 01:30:08.734457 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:30:09.783118 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:30:13.236270 16216 solver.cpp:397]     Test net output #0: accuracy = 0.331495
I0410 01:30:13.236313 16216 solver.cpp:397]     Test net output #1: loss = 6.64806 (* 1 = 6.64806 loss)
I0410 01:30:13.319294 16216 solver.cpp:218] Iteration 8772 (1.18999 iter/s, 10.0841s/12 iters), loss = 0.117259
I0410 01:30:13.319345 16216 solver.cpp:237]     Train net output #0: loss = 0.117259 (* 1 = 0.117259 loss)
I0410 01:30:13.319355 16216 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 01:30:17.522719 16216 solver.cpp:218] Iteration 8784 (2.85494 iter/s, 4.20323s/12 iters), loss = 0.00317782
I0410 01:30:17.522769 16216 solver.cpp:237]     Train net output #0: loss = 0.00317787 (* 1 = 0.00317787 loss)
I0410 01:30:17.522778 16216 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 01:30:22.444012 16216 solver.cpp:218] Iteration 8796 (2.43849 iter/s, 4.92108s/12 iters), loss = 0.0210446
I0410 01:30:22.444069 16216 solver.cpp:237]     Train net output #0: loss = 0.0210447 (* 1 = 0.0210447 loss)
I0410 01:30:22.444082 16216 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 01:30:23.858502 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:30:27.373462 16216 solver.cpp:218] Iteration 8808 (2.43446 iter/s, 4.92922s/12 iters), loss = 0.0725238
I0410 01:30:27.373530 16216 solver.cpp:237]     Train net output #0: loss = 0.0725238 (* 1 = 0.0725238 loss)
I0410 01:30:27.373548 16216 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 01:30:32.307422 16216 solver.cpp:218] Iteration 8820 (2.43223 iter/s, 4.93374s/12 iters), loss = 0.0445582
I0410 01:30:32.307476 16216 solver.cpp:237]     Train net output #0: loss = 0.0445582 (* 1 = 0.0445582 loss)
I0410 01:30:32.307488 16216 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 01:30:37.228758 16216 solver.cpp:218] Iteration 8832 (2.43847 iter/s, 4.92113s/12 iters), loss = 0.14862
I0410 01:30:37.228814 16216 solver.cpp:237]     Train net output #0: loss = 0.14862 (* 1 = 0.14862 loss)
I0410 01:30:37.228826 16216 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 01:30:42.103142 16216 solver.cpp:218] Iteration 8844 (2.46195 iter/s, 4.87418s/12 iters), loss = 0.0124632
I0410 01:30:42.103188 16216 solver.cpp:237]     Train net output #0: loss = 0.0124633 (* 1 = 0.0124633 loss)
I0410 01:30:42.103199 16216 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 01:30:47.014132 16216 solver.cpp:218] Iteration 8856 (2.4436 iter/s, 4.91078s/12 iters), loss = 0.0111803
I0410 01:30:47.014183 16216 solver.cpp:237]     Train net output #0: loss = 0.0111803 (* 1 = 0.0111803 loss)
I0410 01:30:47.014192 16216 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 01:30:51.930299 16216 solver.cpp:218] Iteration 8868 (2.44103 iter/s, 4.91596s/12 iters), loss = 0.0109672
I0410 01:30:51.930346 16216 solver.cpp:237]     Train net output #0: loss = 0.0109672 (* 1 = 0.0109672 loss)
I0410 01:30:51.930356 16216 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 01:30:53.922363 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 01:30:54.597787 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 01:30:55.208689 16216 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 01:30:55.208719 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:30:56.105916 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:30:59.559841 16216 solver.cpp:397]     Test net output #0: accuracy = 0.330882
I0410 01:30:59.559890 16216 solver.cpp:397]     Test net output #1: loss = 6.68246 (* 1 = 6.68246 loss)
I0410 01:31:01.576694 16216 solver.cpp:218] Iteration 8880 (1.24403 iter/s, 9.64605s/12 iters), loss = 0.0250263
I0410 01:31:01.576751 16216 solver.cpp:237]     Train net output #0: loss = 0.0250263 (* 1 = 0.0250263 loss)
I0410 01:31:01.576763 16216 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 01:31:06.689378 16216 solver.cpp:218] Iteration 8892 (2.3472 iter/s, 5.11247s/12 iters), loss = 0.0127672
I0410 01:31:06.689424 16216 solver.cpp:237]     Train net output #0: loss = 0.0127673 (* 1 = 0.0127673 loss)
I0410 01:31:06.689435 16216 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 01:31:10.245529 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:31:11.620021 16216 solver.cpp:218] Iteration 8904 (2.43386 iter/s, 4.93044s/12 iters), loss = 0.0414321
I0410 01:31:11.620083 16216 solver.cpp:237]     Train net output #0: loss = 0.0414321 (* 1 = 0.0414321 loss)
I0410 01:31:11.620095 16216 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 01:31:16.546854 16216 solver.cpp:218] Iteration 8916 (2.43575 iter/s, 4.92662s/12 iters), loss = 0.0415514
I0410 01:31:16.546898 16216 solver.cpp:237]     Train net output #0: loss = 0.0415514 (* 1 = 0.0415514 loss)
I0410 01:31:16.546911 16216 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 01:31:21.460024 16216 solver.cpp:218] Iteration 8928 (2.44251 iter/s, 4.91298s/12 iters), loss = 0.00517937
I0410 01:31:21.460064 16216 solver.cpp:237]     Train net output #0: loss = 0.0051794 (* 1 = 0.0051794 loss)
I0410 01:31:21.460073 16216 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 01:31:26.379348 16216 solver.cpp:218] Iteration 8940 (2.43946 iter/s, 4.91913s/12 iters), loss = 0.0110758
I0410 01:31:26.379494 16216 solver.cpp:237]     Train net output #0: loss = 0.0110758 (* 1 = 0.0110758 loss)
I0410 01:31:26.379508 16216 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 01:31:31.310724 16216 solver.cpp:218] Iteration 8952 (2.43355 iter/s, 4.93108s/12 iters), loss = 0.0452501
I0410 01:31:31.310767 16216 solver.cpp:237]     Train net output #0: loss = 0.0452502 (* 1 = 0.0452502 loss)
I0410 01:31:31.310776 16216 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 01:31:36.238250 16216 solver.cpp:218] Iteration 8964 (2.4354 iter/s, 4.92733s/12 iters), loss = 0.0836812
I0410 01:31:36.238291 16216 solver.cpp:237]     Train net output #0: loss = 0.0836812 (* 1 = 0.0836812 loss)
I0410 01:31:36.238301 16216 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 01:31:40.793392 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 01:31:41.049531 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 01:31:41.236507 16216 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 01:31:41.236537 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:31:42.196344 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:31:45.700021 16216 solver.cpp:397]     Test net output #0: accuracy = 0.326593
I0410 01:31:45.700062 16216 solver.cpp:397]     Test net output #1: loss = 6.68032 (* 1 = 6.68032 loss)
I0410 01:31:45.782768 16216 solver.cpp:218] Iteration 8976 (1.25731 iter/s, 9.54419s/12 iters), loss = 0.030287
I0410 01:31:45.782812 16216 solver.cpp:237]     Train net output #0: loss = 0.030287 (* 1 = 0.030287 loss)
I0410 01:31:45.782822 16216 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 01:31:49.864643 16216 solver.cpp:218] Iteration 8988 (2.93995 iter/s, 4.0817s/12 iters), loss = 0.0375476
I0410 01:31:49.864701 16216 solver.cpp:237]     Train net output #0: loss = 0.0375477 (* 1 = 0.0375477 loss)
I0410 01:31:49.864712 16216 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 01:31:51.440141 16216 blocking_queue.cpp:49] Waiting for data
I0410 01:31:54.733669 16216 solver.cpp:218] Iteration 9000 (2.46466 iter/s, 4.86882s/12 iters), loss = 0.0945725
I0410 01:31:54.733724 16216 solver.cpp:237]     Train net output #0: loss = 0.0945726 (* 1 = 0.0945726 loss)
I0410 01:31:54.733736 16216 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 01:31:55.426297 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:31:59.596195 16216 solver.cpp:218] Iteration 9012 (2.46796 iter/s, 4.86232s/12 iters), loss = 0.0584348
I0410 01:31:59.596340 16216 solver.cpp:237]     Train net output #0: loss = 0.0584349 (* 1 = 0.0584349 loss)
I0410 01:31:59.596352 16216 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 01:32:04.575050 16216 solver.cpp:218] Iteration 9024 (2.41034 iter/s, 4.97856s/12 iters), loss = 0.112922
I0410 01:32:04.575093 16216 solver.cpp:237]     Train net output #0: loss = 0.112922 (* 1 = 0.112922 loss)
I0410 01:32:04.575101 16216 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 01:32:09.843997 16216 solver.cpp:218] Iteration 9036 (2.27759 iter/s, 5.26874s/12 iters), loss = 0.013442
I0410 01:32:09.844045 16216 solver.cpp:237]     Train net output #0: loss = 0.013442 (* 1 = 0.013442 loss)
I0410 01:32:09.844054 16216 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 01:32:14.706188 16216 solver.cpp:218] Iteration 9048 (2.46813 iter/s, 4.86199s/12 iters), loss = 0.0919951
I0410 01:32:14.706236 16216 solver.cpp:237]     Train net output #0: loss = 0.0919951 (* 1 = 0.0919951 loss)
I0410 01:32:14.706245 16216 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 01:32:19.660171 16216 solver.cpp:218] Iteration 9060 (2.4224 iter/s, 4.95377s/12 iters), loss = 0.0553731
I0410 01:32:19.660233 16216 solver.cpp:237]     Train net output #0: loss = 0.0553731 (* 1 = 0.0553731 loss)
I0410 01:32:19.660248 16216 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 01:32:24.526120 16216 solver.cpp:218] Iteration 9072 (2.46623 iter/s, 4.86574s/12 iters), loss = 0.129585
I0410 01:32:24.526170 16216 solver.cpp:237]     Train net output #0: loss = 0.129585 (* 1 = 0.129585 loss)
I0410 01:32:24.526181 16216 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 01:32:26.616863 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 01:32:28.774154 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 01:32:29.233747 16216 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 01:32:29.233773 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:32:30.127585 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:32:33.668829 16216 solver.cpp:397]     Test net output #0: accuracy = 0.326593
I0410 01:32:33.668867 16216 solver.cpp:397]     Test net output #1: loss = 6.68269 (* 1 = 6.68269 loss)
I0410 01:32:35.551406 16216 solver.cpp:218] Iteration 9084 (1.08844 iter/s, 11.0249s/12 iters), loss = 0.0379978
I0410 01:32:35.551456 16216 solver.cpp:237]     Train net output #0: loss = 0.0379978 (* 1 = 0.0379978 loss)
I0410 01:32:35.551467 16216 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 01:32:40.641938 16216 solver.cpp:218] Iteration 9096 (2.35742 iter/s, 5.09032s/12 iters), loss = 0.008804
I0410 01:32:40.642014 16216 solver.cpp:237]     Train net output #0: loss = 0.00880404 (* 1 = 0.00880404 loss)
I0410 01:32:40.642027 16216 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 01:32:43.564545 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:32:45.606377 16216 solver.cpp:218] Iteration 9108 (2.4173 iter/s, 4.96421s/12 iters), loss = 0.0563817
I0410 01:32:45.606428 16216 solver.cpp:237]     Train net output #0: loss = 0.0563818 (* 1 = 0.0563818 loss)
I0410 01:32:45.606439 16216 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 01:32:50.503468 16216 solver.cpp:218] Iteration 9120 (2.45054 iter/s, 4.89688s/12 iters), loss = 0.0113566
I0410 01:32:50.503526 16216 solver.cpp:237]     Train net output #0: loss = 0.0113566 (* 1 = 0.0113566 loss)
I0410 01:32:50.503538 16216 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 01:32:55.407522 16216 solver.cpp:218] Iteration 9132 (2.44706 iter/s, 4.90384s/12 iters), loss = 0.077167
I0410 01:32:55.407582 16216 solver.cpp:237]     Train net output #0: loss = 0.0771671 (* 1 = 0.0771671 loss)
I0410 01:32:55.407594 16216 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 01:33:00.315363 16216 solver.cpp:218] Iteration 9144 (2.44517 iter/s, 4.90763s/12 iters), loss = 0.00549591
I0410 01:33:00.315492 16216 solver.cpp:237]     Train net output #0: loss = 0.00549595 (* 1 = 0.00549595 loss)
I0410 01:33:00.315506 16216 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 01:33:05.450420 16216 solver.cpp:218] Iteration 9156 (2.33701 iter/s, 5.13477s/12 iters), loss = 0.00725421
I0410 01:33:05.450475 16216 solver.cpp:237]     Train net output #0: loss = 0.00725424 (* 1 = 0.00725424 loss)
I0410 01:33:05.450486 16216 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 01:33:10.558423 16216 solver.cpp:218] Iteration 9168 (2.34935 iter/s, 5.10779s/12 iters), loss = 0.028546
I0410 01:33:10.558480 16216 solver.cpp:237]     Train net output #0: loss = 0.028546 (* 1 = 0.028546 loss)
I0410 01:33:10.558490 16216 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 01:33:15.111833 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 01:33:15.425997 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 01:33:15.616572 16216 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 01:33:15.616591 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:33:16.633322 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:33:20.474665 16216 solver.cpp:397]     Test net output #0: accuracy = 0.321691
I0410 01:33:20.474714 16216 solver.cpp:397]     Test net output #1: loss = 6.73511 (* 1 = 6.73511 loss)
I0410 01:33:20.557948 16216 solver.cpp:218] Iteration 9180 (1.2001 iter/s, 9.99918s/12 iters), loss = 0.167055
I0410 01:33:20.558023 16216 solver.cpp:237]     Train net output #0: loss = 0.167055 (* 1 = 0.167055 loss)
I0410 01:33:20.558037 16216 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 01:33:24.770117 16216 solver.cpp:218] Iteration 9192 (2.84903 iter/s, 4.21196s/12 iters), loss = 0.0316133
I0410 01:33:24.770169 16216 solver.cpp:237]     Train net output #0: loss = 0.0316133 (* 1 = 0.0316133 loss)
I0410 01:33:24.770179 16216 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 01:33:29.639307 16216 solver.cpp:218] Iteration 9204 (2.46458 iter/s, 4.86899s/12 iters), loss = 0.0213364
I0410 01:33:29.639355 16216 solver.cpp:237]     Train net output #0: loss = 0.0213364 (* 1 = 0.0213364 loss)
I0410 01:33:29.639365 16216 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 01:33:29.719296 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:33:34.561833 16216 solver.cpp:218] Iteration 9216 (2.43787 iter/s, 4.92232s/12 iters), loss = 0.0610078
I0410 01:33:34.561980 16216 solver.cpp:237]     Train net output #0: loss = 0.0610078 (* 1 = 0.0610078 loss)
I0410 01:33:34.561995 16216 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 01:33:39.484457 16216 solver.cpp:218] Iteration 9228 (2.43786 iter/s, 4.92234s/12 iters), loss = 0.0428988
I0410 01:33:39.484505 16216 solver.cpp:237]     Train net output #0: loss = 0.0428988 (* 1 = 0.0428988 loss)
I0410 01:33:39.484519 16216 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 01:33:44.586442 16216 solver.cpp:218] Iteration 9240 (2.35212 iter/s, 5.10178s/12 iters), loss = 0.0588523
I0410 01:33:44.586493 16216 solver.cpp:237]     Train net output #0: loss = 0.0588523 (* 1 = 0.0588523 loss)
I0410 01:33:44.586503 16216 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 01:33:49.514823 16216 solver.cpp:218] Iteration 9252 (2.43498 iter/s, 4.92817s/12 iters), loss = 0.018989
I0410 01:33:49.514886 16216 solver.cpp:237]     Train net output #0: loss = 0.018989 (* 1 = 0.018989 loss)
I0410 01:33:49.514897 16216 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 01:33:54.505867 16216 solver.cpp:218] Iteration 9264 (2.40441 iter/s, 4.99083s/12 iters), loss = 0.053431
I0410 01:33:54.505909 16216 solver.cpp:237]     Train net output #0: loss = 0.053431 (* 1 = 0.053431 loss)
I0410 01:33:54.505920 16216 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 01:33:59.357411 16216 solver.cpp:218] Iteration 9276 (2.47354 iter/s, 4.85135s/12 iters), loss = 0.00315896
I0410 01:33:59.357460 16216 solver.cpp:237]     Train net output #0: loss = 0.00315899 (* 1 = 0.00315899 loss)
I0410 01:33:59.357472 16216 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 01:34:01.337476 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 01:34:02.157459 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 01:34:02.961210 16216 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 01:34:02.961243 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:34:03.742861 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:34:07.387856 16216 solver.cpp:397]     Test net output #0: accuracy = 0.331495
I0410 01:34:07.387959 16216 solver.cpp:397]     Test net output #1: loss = 6.5897 (* 1 = 6.5897 loss)
I0410 01:34:09.133342 16216 solver.cpp:218] Iteration 9288 (1.22755 iter/s, 9.77558s/12 iters), loss = 0.00859439
I0410 01:34:09.133401 16216 solver.cpp:237]     Train net output #0: loss = 0.00859441 (* 1 = 0.00859441 loss)
I0410 01:34:09.133415 16216 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 01:34:14.068545 16216 solver.cpp:218] Iteration 9300 (2.43161 iter/s, 4.93499s/12 iters), loss = 0.039609
I0410 01:34:14.068595 16216 solver.cpp:237]     Train net output #0: loss = 0.0396091 (* 1 = 0.0396091 loss)
I0410 01:34:14.068608 16216 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 01:34:16.251868 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:34:19.003463 16216 solver.cpp:218] Iteration 9312 (2.43175 iter/s, 4.93472s/12 iters), loss = 0.0132364
I0410 01:34:19.003510 16216 solver.cpp:237]     Train net output #0: loss = 0.0132364 (* 1 = 0.0132364 loss)
I0410 01:34:19.003522 16216 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 01:34:23.931696 16216 solver.cpp:218] Iteration 9324 (2.43505 iter/s, 4.92804s/12 iters), loss = 0.00628079
I0410 01:34:23.931741 16216 solver.cpp:237]     Train net output #0: loss = 0.0062808 (* 1 = 0.0062808 loss)
I0410 01:34:23.931752 16216 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 01:34:28.837245 16216 solver.cpp:218] Iteration 9336 (2.44631 iter/s, 4.90535s/12 iters), loss = 0.009761
I0410 01:34:28.837292 16216 solver.cpp:237]     Train net output #0: loss = 0.00976101 (* 1 = 0.00976101 loss)
I0410 01:34:28.837303 16216 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 01:34:33.893641 16216 solver.cpp:218] Iteration 9348 (2.37333 iter/s, 5.05619s/12 iters), loss = 0.0180122
I0410 01:34:33.893685 16216 solver.cpp:237]     Train net output #0: loss = 0.0180122 (* 1 = 0.0180122 loss)
I0410 01:34:33.893695 16216 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 01:34:38.859073 16216 solver.cpp:218] Iteration 9360 (2.41681 iter/s, 4.96523s/12 iters), loss = 0.00582101
I0410 01:34:38.859177 16216 solver.cpp:237]     Train net output #0: loss = 0.00582104 (* 1 = 0.00582104 loss)
I0410 01:34:38.859187 16216 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 01:34:43.779686 16216 solver.cpp:218] Iteration 9372 (2.43885 iter/s, 4.92036s/12 iters), loss = 0.0164475
I0410 01:34:43.779740 16216 solver.cpp:237]     Train net output #0: loss = 0.0164476 (* 1 = 0.0164476 loss)
I0410 01:34:43.779755 16216 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 01:34:48.259402 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 01:34:48.491799 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 01:34:48.661504 16216 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 01:34:48.661530 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:34:49.439579 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:34:53.503927 16216 solver.cpp:397]     Test net output #0: accuracy = 0.33701
I0410 01:34:53.503964 16216 solver.cpp:397]     Test net output #1: loss = 6.43164 (* 1 = 6.43164 loss)
I0410 01:34:53.586517 16216 solver.cpp:218] Iteration 9384 (1.22368 iter/s, 9.80648s/12 iters), loss = 0.00732478
I0410 01:34:53.586572 16216 solver.cpp:237]     Train net output #0: loss = 0.00732481 (* 1 = 0.00732481 loss)
I0410 01:34:53.586585 16216 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 01:34:57.716951 16216 solver.cpp:218] Iteration 9396 (2.90539 iter/s, 4.13025s/12 iters), loss = 0.016072
I0410 01:34:57.717002 16216 solver.cpp:237]     Train net output #0: loss = 0.016072 (* 1 = 0.016072 loss)
I0410 01:34:57.717015 16216 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 01:35:01.982089 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:35:02.685382 16216 solver.cpp:218] Iteration 9408 (2.41535 iter/s, 4.96822s/12 iters), loss = 0.00540609
I0410 01:35:02.685441 16216 solver.cpp:237]     Train net output #0: loss = 0.00540612 (* 1 = 0.00540612 loss)
I0410 01:35:02.685454 16216 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 01:35:07.635546 16216 solver.cpp:218] Iteration 9420 (2.42426 iter/s, 4.94995s/12 iters), loss = 0.021593
I0410 01:35:07.635591 16216 solver.cpp:237]     Train net output #0: loss = 0.021593 (* 1 = 0.021593 loss)
I0410 01:35:07.635602 16216 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 01:35:12.919476 16216 solver.cpp:218] Iteration 9432 (2.27113 iter/s, 5.28372s/12 iters), loss = 0.0113149
I0410 01:35:12.919613 16216 solver.cpp:237]     Train net output #0: loss = 0.0113149 (* 1 = 0.0113149 loss)
I0410 01:35:12.919626 16216 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 01:35:17.777741 16216 solver.cpp:218] Iteration 9444 (2.47017 iter/s, 4.85797s/12 iters), loss = 0.00662409
I0410 01:35:17.777801 16216 solver.cpp:237]     Train net output #0: loss = 0.0066241 (* 1 = 0.0066241 loss)
I0410 01:35:17.777813 16216 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 01:35:22.707595 16216 solver.cpp:218] Iteration 9456 (2.43425 iter/s, 4.92965s/12 iters), loss = 0.0278584
I0410 01:35:22.707640 16216 solver.cpp:237]     Train net output #0: loss = 0.0278584 (* 1 = 0.0278584 loss)
I0410 01:35:22.707653 16216 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 01:35:27.632907 16216 solver.cpp:218] Iteration 9468 (2.43649 iter/s, 4.92511s/12 iters), loss = 0.00619135
I0410 01:35:27.632954 16216 solver.cpp:237]     Train net output #0: loss = 0.00619135 (* 1 = 0.00619135 loss)
I0410 01:35:27.632966 16216 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 01:35:32.519068 16216 solver.cpp:218] Iteration 9480 (2.45602 iter/s, 4.88595s/12 iters), loss = 0.0082088
I0410 01:35:32.519145 16216 solver.cpp:237]     Train net output #0: loss = 0.0082088 (* 1 = 0.0082088 loss)
I0410 01:35:32.519163 16216 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 01:35:34.551359 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 01:35:35.199663 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 01:35:35.835470 16216 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 01:35:35.835501 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:35:36.571938 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:35:40.291687 16216 solver.cpp:397]     Test net output #0: accuracy = 0.329044
I0410 01:35:40.291723 16216 solver.cpp:397]     Test net output #1: loss = 6.59279 (* 1 = 6.59279 loss)
I0410 01:35:42.212261 16216 solver.cpp:218] Iteration 9492 (1.23803 iter/s, 9.69283s/12 iters), loss = 0.02378
I0410 01:35:42.212314 16216 solver.cpp:237]     Train net output #0: loss = 0.02378 (* 1 = 0.02378 loss)
I0410 01:35:42.212327 16216 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 01:35:47.197882 16216 solver.cpp:218] Iteration 9504 (2.40703 iter/s, 4.98539s/12 iters), loss = 0.142777
I0410 01:35:47.198047 16216 solver.cpp:237]     Train net output #0: loss = 0.142777 (* 1 = 0.142777 loss)
I0410 01:35:47.198065 16216 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 01:35:48.653290 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:35:52.221714 16216 solver.cpp:218] Iteration 9516 (2.38877 iter/s, 5.02351s/12 iters), loss = 0.00476591
I0410 01:35:52.221781 16216 solver.cpp:237]     Train net output #0: loss = 0.00476591 (* 1 = 0.00476591 loss)
I0410 01:35:52.221794 16216 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 01:35:57.324946 16216 solver.cpp:218] Iteration 9528 (2.35156 iter/s, 5.103s/12 iters), loss = 0.00450174
I0410 01:35:57.325001 16216 solver.cpp:237]     Train net output #0: loss = 0.00450174 (* 1 = 0.00450174 loss)
I0410 01:35:57.325012 16216 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 01:36:02.223228 16216 solver.cpp:218] Iteration 9540 (2.44994 iter/s, 4.89807s/12 iters), loss = 0.0171885
I0410 01:36:02.223278 16216 solver.cpp:237]     Train net output #0: loss = 0.0171885 (* 1 = 0.0171885 loss)
I0410 01:36:02.223289 16216 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 01:36:07.254483 16216 solver.cpp:218] Iteration 9552 (2.38519 iter/s, 5.03104s/12 iters), loss = 0.171453
I0410 01:36:07.254539 16216 solver.cpp:237]     Train net output #0: loss = 0.171453 (* 1 = 0.171453 loss)
I0410 01:36:07.254551 16216 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 01:36:12.298933 16216 solver.cpp:218] Iteration 9564 (2.37895 iter/s, 5.04424s/12 iters), loss = 0.00702819
I0410 01:36:12.298975 16216 solver.cpp:237]     Train net output #0: loss = 0.00702818 (* 1 = 0.00702818 loss)
I0410 01:36:12.298987 16216 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 01:36:17.438428 16216 solver.cpp:218] Iteration 9576 (2.33495 iter/s, 5.13929s/12 iters), loss = 0.0505967
I0410 01:36:17.438515 16216 solver.cpp:237]     Train net output #0: loss = 0.0505967 (* 1 = 0.0505967 loss)
I0410 01:36:17.438529 16216 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 01:36:22.029947 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 01:36:24.215572 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 01:36:24.990216 16216 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 01:36:24.990247 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:36:25.694762 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:36:29.578181 16216 solver.cpp:397]     Test net output #0: accuracy = 0.331495
I0410 01:36:29.578231 16216 solver.cpp:397]     Test net output #1: loss = 6.58313 (* 1 = 6.58313 loss)
I0410 01:36:29.661100 16216 solver.cpp:218] Iteration 9588 (0.981819 iter/s, 12.2222s/12 iters), loss = 0.0360775
I0410 01:36:29.661170 16216 solver.cpp:237]     Train net output #0: loss = 0.0360775 (* 1 = 0.0360775 loss)
I0410 01:36:29.661187 16216 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 01:36:33.914527 16216 solver.cpp:218] Iteration 9600 (2.82139 iter/s, 4.25322s/12 iters), loss = 0.0493745
I0410 01:36:33.914590 16216 solver.cpp:237]     Train net output #0: loss = 0.0493745 (* 1 = 0.0493745 loss)
I0410 01:36:33.914606 16216 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 01:36:37.496049 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:36:38.861126 16216 solver.cpp:218] Iteration 9612 (2.42601 iter/s, 4.94639s/12 iters), loss = 0.00745922
I0410 01:36:38.861172 16216 solver.cpp:237]     Train net output #0: loss = 0.00745921 (* 1 = 0.00745921 loss)
I0410 01:36:38.861181 16216 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 01:36:43.774575 16216 solver.cpp:218] Iteration 9624 (2.44238 iter/s, 4.91325s/12 iters), loss = 0.0680858
I0410 01:36:43.774632 16216 solver.cpp:237]     Train net output #0: loss = 0.0680858 (* 1 = 0.0680858 loss)
I0410 01:36:43.774644 16216 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 01:36:48.772125 16216 solver.cpp:218] Iteration 9636 (2.40128 iter/s, 4.99733s/12 iters), loss = 0.0242336
I0410 01:36:48.772248 16216 solver.cpp:237]     Train net output #0: loss = 0.0242336 (* 1 = 0.0242336 loss)
I0410 01:36:48.772258 16216 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 01:36:53.963398 16216 solver.cpp:218] Iteration 9648 (2.3117 iter/s, 5.19098s/12 iters), loss = 0.00907534
I0410 01:36:53.963452 16216 solver.cpp:237]     Train net output #0: loss = 0.00907534 (* 1 = 0.00907534 loss)
I0410 01:36:53.963464 16216 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 01:36:58.926587 16216 solver.cpp:218] Iteration 9660 (2.4179 iter/s, 4.96298s/12 iters), loss = 0.0126056
I0410 01:36:58.926638 16216 solver.cpp:237]     Train net output #0: loss = 0.0126056 (* 1 = 0.0126056 loss)
I0410 01:36:58.926648 16216 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 01:37:03.891589 16216 solver.cpp:218] Iteration 9672 (2.41702 iter/s, 4.96479s/12 iters), loss = 0.0331983
I0410 01:37:03.891640 16216 solver.cpp:237]     Train net output #0: loss = 0.0331983 (* 1 = 0.0331983 loss)
I0410 01:37:03.891650 16216 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 01:37:08.802130 16216 solver.cpp:218] Iteration 9684 (2.44383 iter/s, 4.91034s/12 iters), loss = 0.00990473
I0410 01:37:08.802188 16216 solver.cpp:237]     Train net output #0: loss = 0.00990473 (* 1 = 0.00990473 loss)
I0410 01:37:08.802198 16216 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 01:37:10.805480 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 01:37:11.349072 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 01:37:11.792822 16216 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 01:37:11.792853 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:37:12.458654 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:37:14.929828 16216 blocking_queue.cpp:49] Waiting for data
I0410 01:37:16.268607 16216 solver.cpp:397]     Test net output #0: accuracy = 0.334559
I0410 01:37:16.268644 16216 solver.cpp:397]     Test net output #1: loss = 6.51046 (* 1 = 6.51046 loss)
I0410 01:37:18.299211 16216 solver.cpp:218] Iteration 9696 (1.26359 iter/s, 9.49674s/12 iters), loss = 0.00826129
I0410 01:37:18.299258 16216 solver.cpp:237]     Train net output #0: loss = 0.00826129 (* 1 = 0.00826129 loss)
I0410 01:37:18.299270 16216 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 01:37:23.391834 16216 solver.cpp:218] Iteration 9708 (2.35645 iter/s, 5.09242s/12 iters), loss = 0.0191613
I0410 01:37:23.391917 16216 solver.cpp:237]     Train net output #0: loss = 0.0191613 (* 1 = 0.0191613 loss)
I0410 01:37:23.391930 16216 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 01:37:24.184897 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:37:28.507161 16216 solver.cpp:218] Iteration 9720 (2.346 iter/s, 5.11509s/12 iters), loss = 0.00746569
I0410 01:37:28.507217 16216 solver.cpp:237]     Train net output #0: loss = 0.00746568 (* 1 = 0.00746568 loss)
I0410 01:37:28.507230 16216 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 01:37:33.619658 16216 solver.cpp:218] Iteration 9732 (2.34729 iter/s, 5.11228s/12 iters), loss = 0.155288
I0410 01:37:33.619715 16216 solver.cpp:237]     Train net output #0: loss = 0.155288 (* 1 = 0.155288 loss)
I0410 01:37:33.619729 16216 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 01:37:38.819320 16216 solver.cpp:218] Iteration 9744 (2.30794 iter/s, 5.19944s/12 iters), loss = 0.0140453
I0410 01:37:38.819373 16216 solver.cpp:237]     Train net output #0: loss = 0.0140453 (* 1 = 0.0140453 loss)
I0410 01:37:38.819386 16216 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 01:37:43.739697 16216 solver.cpp:218] Iteration 9756 (2.43894 iter/s, 4.92017s/12 iters), loss = 0.147159
I0410 01:37:43.739739 16216 solver.cpp:237]     Train net output #0: loss = 0.147159 (* 1 = 0.147159 loss)
I0410 01:37:43.739748 16216 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 01:37:48.683540 16216 solver.cpp:218] Iteration 9768 (2.42736 iter/s, 4.94364s/12 iters), loss = 0.0143496
I0410 01:37:48.683596 16216 solver.cpp:237]     Train net output #0: loss = 0.0143496 (* 1 = 0.0143496 loss)
I0410 01:37:48.683607 16216 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 01:37:53.626139 16216 solver.cpp:218] Iteration 9780 (2.42798 iter/s, 4.94238s/12 iters), loss = 0.0186277
I0410 01:37:53.626288 16216 solver.cpp:237]     Train net output #0: loss = 0.0186277 (* 1 = 0.0186277 loss)
I0410 01:37:53.626302 16216 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 01:37:58.067041 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 01:37:58.310235 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 01:37:58.482385 16216 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 01:37:58.482416 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:37:59.041661 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:38:03.023422 16216 solver.cpp:397]     Test net output #0: accuracy = 0.337623
I0410 01:38:03.023468 16216 solver.cpp:397]     Test net output #1: loss = 6.6224 (* 1 = 6.6224 loss)
I0410 01:38:03.106020 16216 solver.cpp:218] Iteration 9792 (1.2659 iter/s, 9.47945s/12 iters), loss = 0.00606067
I0410 01:38:03.106076 16216 solver.cpp:237]     Train net output #0: loss = 0.00606067 (* 1 = 0.00606067 loss)
I0410 01:38:03.106087 16216 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 01:38:07.390923 16216 solver.cpp:218] Iteration 9804 (2.80066 iter/s, 4.28471s/12 iters), loss = 0.00181369
I0410 01:38:07.390982 16216 solver.cpp:237]     Train net output #0: loss = 0.0018137 (* 1 = 0.0018137 loss)
I0410 01:38:07.390995 16216 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 01:38:10.257012 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:38:12.273355 16216 solver.cpp:218] Iteration 9816 (2.4579 iter/s, 4.88222s/12 iters), loss = 0.021106
I0410 01:38:12.273401 16216 solver.cpp:237]     Train net output #0: loss = 0.021106 (* 1 = 0.021106 loss)
I0410 01:38:12.273412 16216 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 01:38:17.196452 16216 solver.cpp:218] Iteration 9828 (2.43759 iter/s, 4.92289s/12 iters), loss = 0.00307883
I0410 01:38:17.196509 16216 solver.cpp:237]     Train net output #0: loss = 0.00307884 (* 1 = 0.00307884 loss)
I0410 01:38:17.196521 16216 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 01:38:22.101461 16216 solver.cpp:218] Iteration 9840 (2.44658 iter/s, 4.9048s/12 iters), loss = 0.0384718
I0410 01:38:22.101516 16216 solver.cpp:237]     Train net output #0: loss = 0.0384718 (* 1 = 0.0384718 loss)
I0410 01:38:22.101527 16216 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 01:38:27.248545 16216 solver.cpp:218] Iteration 9852 (2.33151 iter/s, 5.14688s/12 iters), loss = 0.00875842
I0410 01:38:27.248620 16216 solver.cpp:237]     Train net output #0: loss = 0.00875846 (* 1 = 0.00875846 loss)
I0410 01:38:27.248627 16216 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 01:38:32.368496 16216 solver.cpp:218] Iteration 9864 (2.34388 iter/s, 5.11972s/12 iters), loss = 0.00380138
I0410 01:38:32.368548 16216 solver.cpp:237]     Train net output #0: loss = 0.00380141 (* 1 = 0.00380141 loss)
I0410 01:38:32.368561 16216 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 01:38:37.266778 16216 solver.cpp:218] Iteration 9876 (2.44994 iter/s, 4.89808s/12 iters), loss = 0.0149728
I0410 01:38:37.266829 16216 solver.cpp:237]     Train net output #0: loss = 0.0149728 (* 1 = 0.0149728 loss)
I0410 01:38:37.266841 16216 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 01:38:42.149986 16216 solver.cpp:218] Iteration 9888 (2.4575 iter/s, 4.88301s/12 iters), loss = 0.0448053
I0410 01:38:42.150029 16216 solver.cpp:237]     Train net output #0: loss = 0.0448053 (* 1 = 0.0448053 loss)
I0410 01:38:42.150039 16216 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 01:38:44.165606 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 01:38:44.718782 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 01:38:46.758957 16216 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 01:38:46.758987 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:38:47.332173 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:38:51.174417 16216 solver.cpp:397]     Test net output #0: accuracy = 0.341299
I0410 01:38:51.174465 16216 solver.cpp:397]     Test net output #1: loss = 6.54296 (* 1 = 6.54296 loss)
I0410 01:38:52.986974 16216 solver.cpp:218] Iteration 9900 (1.10736 iter/s, 10.8366s/12 iters), loss = 0.08158
I0410 01:38:52.987025 16216 solver.cpp:237]     Train net output #0: loss = 0.08158 (* 1 = 0.08158 loss)
I0410 01:38:52.987036 16216 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 01:38:58.145179 16216 solver.cpp:218] Iteration 9912 (2.32649 iter/s, 5.15799s/12 iters), loss = 0.00342171
I0410 01:38:58.145328 16216 solver.cpp:237]     Train net output #0: loss = 0.00342173 (* 1 = 0.00342173 loss)
I0410 01:38:58.145342 16216 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 01:38:58.245788 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:39:03.225042 16216 solver.cpp:218] Iteration 9924 (2.36241 iter/s, 5.07956s/12 iters), loss = 0.0104758
I0410 01:39:03.225102 16216 solver.cpp:237]     Train net output #0: loss = 0.0104758 (* 1 = 0.0104758 loss)
I0410 01:39:03.225116 16216 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 01:39:08.150985 16216 solver.cpp:218] Iteration 9936 (2.43619 iter/s, 4.92573s/12 iters), loss = 0.00446182
I0410 01:39:08.151034 16216 solver.cpp:237]     Train net output #0: loss = 0.00446183 (* 1 = 0.00446183 loss)
I0410 01:39:08.151044 16216 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 01:39:13.119441 16216 solver.cpp:218] Iteration 9948 (2.41534 iter/s, 4.96825s/12 iters), loss = 0.00994803
I0410 01:39:13.119504 16216 solver.cpp:237]     Train net output #0: loss = 0.00994805 (* 1 = 0.00994805 loss)
I0410 01:39:13.119520 16216 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 01:39:18.027815 16216 solver.cpp:218] Iteration 9960 (2.44491 iter/s, 4.90816s/12 iters), loss = 0.0561042
I0410 01:39:18.027866 16216 solver.cpp:237]     Train net output #0: loss = 0.0561042 (* 1 = 0.0561042 loss)
I0410 01:39:18.027876 16216 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 01:39:23.202384 16216 solver.cpp:218] Iteration 9972 (2.31913 iter/s, 5.17436s/12 iters), loss = 0.0817738
I0410 01:39:23.202440 16216 solver.cpp:237]     Train net output #0: loss = 0.0817739 (* 1 = 0.0817739 loss)
I0410 01:39:23.202455 16216 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 01:39:28.114487 16216 solver.cpp:218] Iteration 9984 (2.44305 iter/s, 4.91189s/12 iters), loss = 0.0465138
I0410 01:39:28.114535 16216 solver.cpp:237]     Train net output #0: loss = 0.0465138 (* 1 = 0.0465138 loss)
I0410 01:39:28.114547 16216 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 01:39:32.872790 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 01:39:33.205186 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 01:39:33.390766 16216 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 01:39:33.390785 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:39:33.857421 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:39:37.782233 16216 solver.cpp:397]     Test net output #0: accuracy = 0.341299
I0410 01:39:37.782279 16216 solver.cpp:397]     Test net output #1: loss = 6.51216 (* 1 = 6.51216 loss)
I0410 01:39:37.864972 16216 solver.cpp:218] Iteration 9996 (1.23075 iter/s, 9.75014s/12 iters), loss = 0.00559697
I0410 01:39:37.865025 16216 solver.cpp:237]     Train net output #0: loss = 0.00559697 (* 1 = 0.00559697 loss)
I0410 01:39:37.865036 16216 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 01:39:42.088081 16216 solver.cpp:218] Iteration 10008 (2.84163 iter/s, 4.22292s/12 iters), loss = 0.00442777
I0410 01:39:42.088135 16216 solver.cpp:237]     Train net output #0: loss = 0.00442776 (* 1 = 0.00442776 loss)
I0410 01:39:42.088145 16216 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 01:39:44.310737 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:39:47.072620 16216 solver.cpp:218] Iteration 10020 (2.40754 iter/s, 4.98433s/12 iters), loss = 0.00929732
I0410 01:39:47.072664 16216 solver.cpp:237]     Train net output #0: loss = 0.00929732 (* 1 = 0.00929732 loss)
I0410 01:39:47.072674 16216 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 01:39:52.052445 16216 solver.cpp:218] Iteration 10032 (2.40982 iter/s, 4.97962s/12 iters), loss = 0.00393289
I0410 01:39:52.052503 16216 solver.cpp:237]     Train net output #0: loss = 0.00393289 (* 1 = 0.00393289 loss)
I0410 01:39:52.052516 16216 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 01:39:56.904433 16216 solver.cpp:218] Iteration 10044 (2.47332 iter/s, 4.85178s/12 iters), loss = 0.00556033
I0410 01:39:56.904485 16216 solver.cpp:237]     Train net output #0: loss = 0.00556033 (* 1 = 0.00556033 loss)
I0410 01:39:56.904498 16216 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 01:40:01.831486 16216 solver.cpp:218] Iteration 10056 (2.43563 iter/s, 4.92685s/12 iters), loss = 0.050408
I0410 01:40:01.831532 16216 solver.cpp:237]     Train net output #0: loss = 0.050408 (* 1 = 0.050408 loss)
I0410 01:40:01.831543 16216 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 01:40:06.789487 16216 solver.cpp:218] Iteration 10068 (2.42043 iter/s, 4.95779s/12 iters), loss = 0.00835523
I0410 01:40:06.789635 16216 solver.cpp:237]     Train net output #0: loss = 0.00835523 (* 1 = 0.00835523 loss)
I0410 01:40:06.789651 16216 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 01:40:12.016436 16216 solver.cpp:218] Iteration 10080 (2.29593 iter/s, 5.22664s/12 iters), loss = 0.0918315
I0410 01:40:12.016491 16216 solver.cpp:237]     Train net output #0: loss = 0.0918315 (* 1 = 0.0918315 loss)
I0410 01:40:12.016502 16216 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 01:40:17.391453 16216 solver.cpp:218] Iteration 10092 (2.23264 iter/s, 5.37479s/12 iters), loss = 0.0191171
I0410 01:40:17.391503 16216 solver.cpp:237]     Train net output #0: loss = 0.0191171 (* 1 = 0.0191171 loss)
I0410 01:40:17.391512 16216 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 01:40:19.403174 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 01:40:20.551031 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 01:40:21.055264 16216 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 01:40:21.055296 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:40:21.511924 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:40:25.611135 16216 solver.cpp:397]     Test net output #0: accuracy = 0.34375
I0410 01:40:25.611183 16216 solver.cpp:397]     Test net output #1: loss = 6.51806 (* 1 = 6.51806 loss)
I0410 01:40:27.394165 16216 solver.cpp:218] Iteration 10104 (1.19972 iter/s, 10.0024s/12 iters), loss = 0.0021925
I0410 01:40:27.394220 16216 solver.cpp:237]     Train net output #0: loss = 0.0021925 (* 1 = 0.0021925 loss)
I0410 01:40:27.394232 16216 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 01:40:31.733896 16220 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:40:32.355540 16216 solver.cpp:218] Iteration 10116 (2.41879 iter/s, 4.96117s/12 iters), loss = 0.0344419
I0410 01:40:32.355592 16216 solver.cpp:237]     Train net output #0: loss = 0.0344419 (* 1 = 0.0344419 loss)
I0410 01:40:32.355603 16216 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 01:40:37.319100 16216 solver.cpp:218] Iteration 10128 (2.41772 iter/s, 4.96335s/12 iters), loss = 0.00770245
I0410 01:40:37.319218 16216 solver.cpp:237]     Train net output #0: loss = 0.00770245 (* 1 = 0.00770245 loss)
I0410 01:40:37.319233 16216 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 01:40:42.360129 16216 solver.cpp:218] Iteration 10140 (2.38059 iter/s, 5.04076s/12 iters), loss = 0.0160141
I0410 01:40:42.360167 16216 solver.cpp:237]     Train net output #0: loss = 0.0160141 (* 1 = 0.0160141 loss)
I0410 01:40:42.360177 16216 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 01:40:47.277091 16216 solver.cpp:218] Iteration 10152 (2.44063 iter/s, 4.91677s/12 iters), loss = 0.082121
I0410 01:40:47.277135 16216 solver.cpp:237]     Train net output #0: loss = 0.082121 (* 1 = 0.082121 loss)
I0410 01:40:47.277146 16216 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 01:40:52.348776 16216 solver.cpp:218] Iteration 10164 (2.36618 iter/s, 5.07147s/12 iters), loss = 0.00886085
I0410 01:40:52.348834 16216 solver.cpp:237]     Train net output #0: loss = 0.00886086 (* 1 = 0.00886086 loss)
I0410 01:40:52.348847 16216 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 01:40:57.281364 16216 solver.cpp:218] Iteration 10176 (2.4329 iter/s, 4.93237s/12 iters), loss = 0.0105053
I0410 01:40:57.281415 16216 solver.cpp:237]     Train net output #0: loss = 0.0105053 (* 1 = 0.0105053 loss)
I0410 01:40:57.281426 16216 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 01:41:02.243947 16216 solver.cpp:218] Iteration 10188 (2.4182 iter/s, 4.96238s/12 iters), loss = 0.0243392
I0410 01:41:02.243993 16216 solver.cpp:237]     Train net output #0: loss = 0.0243392 (* 1 = 0.0243392 loss)
I0410 01:41:02.244004 16216 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 01:41:06.699189 16216 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 01:41:07.375787 16216 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 01:41:07.999713 16216 solver.cpp:310] Iteration 10200, loss = 0.0878055
I0410 01:41:07.999747 16216 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 01:41:07.999754 16216 net.cpp:676] Ignoring source layer train-data
I0410 01:41:08.484833 16221 data_layer.cpp:73] Restarting data prefetching from start.
I0410 01:41:12.623195 16216 solver.cpp:397]     Test net output #0: accuracy = 0.332108
I0410 01:41:12.623240 16216 solver.cpp:397]     Test net output #1: loss = 6.52492 (* 1 = 6.52492 loss)
I0410 01:41:12.623251 16216 solver.cpp:315] Optimization Done.
I0410 01:41:12.623257 16216 caffe.cpp:259] Optimization Done.