DIGITS-CNN/cars/architecture-investigations/fc/2-layers/1024/caffe_output.log

I0409 23:19:45.908432  8290 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-211041-f3f5/solver.prototxt
I0409 23:19:45.908712  8290 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0409 23:19:45.908726  8290 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0409 23:19:45.908850  8290 caffe.cpp:218] Using GPUs 2
I0409 23:19:45.933754  8290 caffe.cpp:223] GPU 2: GeForce GTX 1080 Ti
I0409 23:19:46.224370  8290 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99980193
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0409 23:19:46.225265  8290 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0409 23:19:46.225858  8290 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0409 23:19:46.225874  8290 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0409 23:19:46.226034  8290 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1024
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1024
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0409 23:19:46.226128  8290 layer_factory.hpp:77] Creating layer train-data
I0409 23:19:46.228001  8290 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0409 23:19:46.228206  8290 net.cpp:84] Creating Layer train-data
I0409 23:19:46.228216  8290 net.cpp:380] train-data -> data
I0409 23:19:46.228236  8290 net.cpp:380] train-data -> label
I0409 23:19:46.228247  8290 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 23:19:46.239208  8290 data_layer.cpp:45] output data size: 128,3,227,227
I0409 23:19:46.374977  8290 net.cpp:122] Setting up train-data
I0409 23:19:46.375000  8290 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0409 23:19:46.375005  8290 net.cpp:129] Top shape: 128 (128)
I0409 23:19:46.375008  8290 net.cpp:137] Memory required for data: 79149056
I0409 23:19:46.375018  8290 layer_factory.hpp:77] Creating layer conv1
I0409 23:19:46.375039  8290 net.cpp:84] Creating Layer conv1
I0409 23:19:46.375044  8290 net.cpp:406] conv1 <- data
I0409 23:19:46.375057  8290 net.cpp:380] conv1 -> conv1
I0409 23:19:46.988574  8290 net.cpp:122] Setting up conv1
I0409 23:19:46.988597  8290 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 23:19:46.988601  8290 net.cpp:137] Memory required for data: 227833856
I0409 23:19:46.988621  8290 layer_factory.hpp:77] Creating layer relu1
I0409 23:19:46.988632  8290 net.cpp:84] Creating Layer relu1
I0409 23:19:46.988638  8290 net.cpp:406] relu1 <- conv1
I0409 23:19:46.988646  8290 net.cpp:367] relu1 -> conv1 (in-place)
I0409 23:19:46.988961  8290 net.cpp:122] Setting up relu1
I0409 23:19:46.988970  8290 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 23:19:46.988974  8290 net.cpp:137] Memory required for data: 376518656
I0409 23:19:46.988978  8290 layer_factory.hpp:77] Creating layer norm1
I0409 23:19:46.988987  8290 net.cpp:84] Creating Layer norm1
I0409 23:19:46.988991  8290 net.cpp:406] norm1 <- conv1
I0409 23:19:46.989017  8290 net.cpp:380] norm1 -> norm1
I0409 23:19:46.989492  8290 net.cpp:122] Setting up norm1
I0409 23:19:46.989502  8290 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 23:19:46.989506  8290 net.cpp:137] Memory required for data: 525203456
I0409 23:19:46.989511  8290 layer_factory.hpp:77] Creating layer pool1
I0409 23:19:46.989519  8290 net.cpp:84] Creating Layer pool1
I0409 23:19:46.989523  8290 net.cpp:406] pool1 <- norm1
I0409 23:19:46.989529  8290 net.cpp:380] pool1 -> pool1
I0409 23:19:46.989567  8290 net.cpp:122] Setting up pool1
I0409 23:19:46.989573  8290 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0409 23:19:46.989576  8290 net.cpp:137] Memory required for data: 561035264
I0409 23:19:46.989580  8290 layer_factory.hpp:77] Creating layer conv2
I0409 23:19:46.989591  8290 net.cpp:84] Creating Layer conv2
I0409 23:19:46.989595  8290 net.cpp:406] conv2 <- pool1
I0409 23:19:46.989600  8290 net.cpp:380] conv2 -> conv2
I0409 23:19:46.998409  8290 net.cpp:122] Setting up conv2
I0409 23:19:46.998428  8290 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 23:19:46.998432  8290 net.cpp:137] Memory required for data: 656586752
I0409 23:19:46.998445  8290 layer_factory.hpp:77] Creating layer relu2
I0409 23:19:46.998452  8290 net.cpp:84] Creating Layer relu2
I0409 23:19:46.998456  8290 net.cpp:406] relu2 <- conv2
I0409 23:19:46.998461  8290 net.cpp:367] relu2 -> conv2 (in-place)
I0409 23:19:46.998900  8290 net.cpp:122] Setting up relu2
I0409 23:19:46.998910  8290 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 23:19:46.998914  8290 net.cpp:137] Memory required for data: 752138240
I0409 23:19:46.998917  8290 layer_factory.hpp:77] Creating layer norm2
I0409 23:19:46.998925  8290 net.cpp:84] Creating Layer norm2
I0409 23:19:46.998929  8290 net.cpp:406] norm2 <- conv2
I0409 23:19:46.998934  8290 net.cpp:380] norm2 -> norm2
I0409 23:19:46.999236  8290 net.cpp:122] Setting up norm2
I0409 23:19:46.999245  8290 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 23:19:46.999249  8290 net.cpp:137] Memory required for data: 847689728
I0409 23:19:46.999253  8290 layer_factory.hpp:77] Creating layer pool2
I0409 23:19:46.999262  8290 net.cpp:84] Creating Layer pool2
I0409 23:19:46.999266  8290 net.cpp:406] pool2 <- norm2
I0409 23:19:46.999271  8290 net.cpp:380] pool2 -> pool2
I0409 23:19:46.999298  8290 net.cpp:122] Setting up pool2
I0409 23:19:46.999303  8290 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 23:19:46.999306  8290 net.cpp:137] Memory required for data: 869840896
I0409 23:19:46.999310  8290 layer_factory.hpp:77] Creating layer conv3
I0409 23:19:46.999320  8290 net.cpp:84] Creating Layer conv3
I0409 23:19:46.999322  8290 net.cpp:406] conv3 <- pool2
I0409 23:19:46.999327  8290 net.cpp:380] conv3 -> conv3
I0409 23:19:47.009213  8290 net.cpp:122] Setting up conv3
I0409 23:19:47.009232  8290 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 23:19:47.009236  8290 net.cpp:137] Memory required for data: 903067648
I0409 23:19:47.009248  8290 layer_factory.hpp:77] Creating layer relu3
I0409 23:19:47.009256  8290 net.cpp:84] Creating Layer relu3
I0409 23:19:47.009260  8290 net.cpp:406] relu3 <- conv3
I0409 23:19:47.009265  8290 net.cpp:367] relu3 -> conv3 (in-place)
I0409 23:19:47.009692  8290 net.cpp:122] Setting up relu3
I0409 23:19:47.009703  8290 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 23:19:47.009706  8290 net.cpp:137] Memory required for data: 936294400
I0409 23:19:47.009711  8290 layer_factory.hpp:77] Creating layer conv4
I0409 23:19:47.009721  8290 net.cpp:84] Creating Layer conv4
I0409 23:19:47.009725  8290 net.cpp:406] conv4 <- conv3
I0409 23:19:47.009732  8290 net.cpp:380] conv4 -> conv4
I0409 23:19:47.019748  8290 net.cpp:122] Setting up conv4
I0409 23:19:47.019769  8290 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 23:19:47.019773  8290 net.cpp:137] Memory required for data: 969521152
I0409 23:19:47.019783  8290 layer_factory.hpp:77] Creating layer relu4
I0409 23:19:47.019791  8290 net.cpp:84] Creating Layer relu4
I0409 23:19:47.019814  8290 net.cpp:406] relu4 <- conv4
I0409 23:19:47.019821  8290 net.cpp:367] relu4 -> conv4 (in-place)
I0409 23:19:47.020102  8290 net.cpp:122] Setting up relu4
I0409 23:19:47.020112  8290 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 23:19:47.020115  8290 net.cpp:137] Memory required for data: 1002747904
I0409 23:19:47.020119  8290 layer_factory.hpp:77] Creating layer conv5
I0409 23:19:47.020129  8290 net.cpp:84] Creating Layer conv5
I0409 23:19:47.020133  8290 net.cpp:406] conv5 <- conv4
I0409 23:19:47.020139  8290 net.cpp:380] conv5 -> conv5
I0409 23:19:47.032969  8290 net.cpp:122] Setting up conv5
I0409 23:19:47.032989  8290 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 23:19:47.032991  8290 net.cpp:137] Memory required for data: 1024899072
I0409 23:19:47.033005  8290 layer_factory.hpp:77] Creating layer relu5
I0409 23:19:47.033015  8290 net.cpp:84] Creating Layer relu5
I0409 23:19:47.033020  8290 net.cpp:406] relu5 <- conv5
I0409 23:19:47.033025  8290 net.cpp:367] relu5 -> conv5 (in-place)
I0409 23:19:47.033468  8290 net.cpp:122] Setting up relu5
I0409 23:19:47.033478  8290 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 23:19:47.033481  8290 net.cpp:137] Memory required for data: 1047050240
I0409 23:19:47.033485  8290 layer_factory.hpp:77] Creating layer pool5
I0409 23:19:47.033493  8290 net.cpp:84] Creating Layer pool5
I0409 23:19:47.033496  8290 net.cpp:406] pool5 <- conv5
I0409 23:19:47.033501  8290 net.cpp:380] pool5 -> pool5
I0409 23:19:47.033542  8290 net.cpp:122] Setting up pool5
I0409 23:19:47.033547  8290 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0409 23:19:47.033550  8290 net.cpp:137] Memory required for data: 1051768832
I0409 23:19:47.033555  8290 layer_factory.hpp:77] Creating layer fc6
I0409 23:19:47.033563  8290 net.cpp:84] Creating Layer fc6
I0409 23:19:47.033566  8290 net.cpp:406] fc6 <- pool5
I0409 23:19:47.033571  8290 net.cpp:380] fc6 -> fc6
I0409 23:19:47.122498  8290 net.cpp:122] Setting up fc6
I0409 23:19:47.122520  8290 net.cpp:129] Top shape: 128 1024 (131072)
I0409 23:19:47.122524  8290 net.cpp:137] Memory required for data: 1052293120
I0409 23:19:47.122534  8290 layer_factory.hpp:77] Creating layer relu6
I0409 23:19:47.122542  8290 net.cpp:84] Creating Layer relu6
I0409 23:19:47.122547  8290 net.cpp:406] relu6 <- fc6
I0409 23:19:47.122553  8290 net.cpp:367] relu6 -> fc6 (in-place)
I0409 23:19:47.123090  8290 net.cpp:122] Setting up relu6
I0409 23:19:47.123100  8290 net.cpp:129] Top shape: 128 1024 (131072)
I0409 23:19:47.123102  8290 net.cpp:137] Memory required for data: 1052817408
I0409 23:19:47.123106  8290 layer_factory.hpp:77] Creating layer drop6
I0409 23:19:47.123113  8290 net.cpp:84] Creating Layer drop6
I0409 23:19:47.123117  8290 net.cpp:406] drop6 <- fc6
I0409 23:19:47.123122  8290 net.cpp:367] drop6 -> fc6 (in-place)
I0409 23:19:47.123149  8290 net.cpp:122] Setting up drop6
I0409 23:19:47.123154  8290 net.cpp:129] Top shape: 128 1024 (131072)
I0409 23:19:47.123157  8290 net.cpp:137] Memory required for data: 1053341696
I0409 23:19:47.123162  8290 layer_factory.hpp:77] Creating layer fc7
I0409 23:19:47.123168  8290 net.cpp:84] Creating Layer fc7
I0409 23:19:47.123173  8290 net.cpp:406] fc7 <- fc6
I0409 23:19:47.123178  8290 net.cpp:380] fc7 -> fc7
I0409 23:19:47.133177  8290 net.cpp:122] Setting up fc7
I0409 23:19:47.133196  8290 net.cpp:129] Top shape: 128 1024 (131072)
I0409 23:19:47.133199  8290 net.cpp:137] Memory required for data: 1053865984
I0409 23:19:47.133209  8290 layer_factory.hpp:77] Creating layer relu7
I0409 23:19:47.133217  8290 net.cpp:84] Creating Layer relu7
I0409 23:19:47.133221  8290 net.cpp:406] relu7 <- fc7
I0409 23:19:47.133229  8290 net.cpp:367] relu7 -> fc7 (in-place)
I0409 23:19:47.133780  8290 net.cpp:122] Setting up relu7
I0409 23:19:47.133790  8290 net.cpp:129] Top shape: 128 1024 (131072)
I0409 23:19:47.133793  8290 net.cpp:137] Memory required for data: 1054390272
I0409 23:19:47.133798  8290 layer_factory.hpp:77] Creating layer drop7
I0409 23:19:47.133806  8290 net.cpp:84] Creating Layer drop7
I0409 23:19:47.133828  8290 net.cpp:406] drop7 <- fc7
I0409 23:19:47.133836  8290 net.cpp:367] drop7 -> fc7 (in-place)
I0409 23:19:47.133862  8290 net.cpp:122] Setting up drop7
I0409 23:19:47.133868  8290 net.cpp:129] Top shape: 128 1024 (131072)
I0409 23:19:47.133872  8290 net.cpp:137] Memory required for data: 1054914560
I0409 23:19:47.133875  8290 layer_factory.hpp:77] Creating layer fc8
I0409 23:19:47.133883  8290 net.cpp:84] Creating Layer fc8
I0409 23:19:47.133888  8290 net.cpp:406] fc8 <- fc7
I0409 23:19:47.133893  8290 net.cpp:380] fc8 -> fc8
I0409 23:19:47.135690  8290 net.cpp:122] Setting up fc8
I0409 23:19:47.135699  8290 net.cpp:129] Top shape: 128 196 (25088)
I0409 23:19:47.135701  8290 net.cpp:137] Memory required for data: 1055014912
I0409 23:19:47.135707  8290 layer_factory.hpp:77] Creating layer loss
I0409 23:19:47.135713  8290 net.cpp:84] Creating Layer loss
I0409 23:19:47.135717  8290 net.cpp:406] loss <- fc8
I0409 23:19:47.135721  8290 net.cpp:406] loss <- label
I0409 23:19:47.135728  8290 net.cpp:380] loss -> loss
I0409 23:19:47.135738  8290 layer_factory.hpp:77] Creating layer loss
I0409 23:19:47.136328  8290 net.cpp:122] Setting up loss
I0409 23:19:47.136338  8290 net.cpp:129] Top shape: (1)
I0409 23:19:47.136343  8290 net.cpp:132]     with loss weight 1
I0409 23:19:47.136359  8290 net.cpp:137] Memory required for data: 1055014916
I0409 23:19:47.136364  8290 net.cpp:198] loss needs backward computation.
I0409 23:19:47.136371  8290 net.cpp:198] fc8 needs backward computation.
I0409 23:19:47.136375  8290 net.cpp:198] drop7 needs backward computation.
I0409 23:19:47.136379  8290 net.cpp:198] relu7 needs backward computation.
I0409 23:19:47.136381  8290 net.cpp:198] fc7 needs backward computation.
I0409 23:19:47.136386  8290 net.cpp:198] drop6 needs backward computation.
I0409 23:19:47.136389  8290 net.cpp:198] relu6 needs backward computation.
I0409 23:19:47.136394  8290 net.cpp:198] fc6 needs backward computation.
I0409 23:19:47.136397  8290 net.cpp:198] pool5 needs backward computation.
I0409 23:19:47.136402  8290 net.cpp:198] relu5 needs backward computation.
I0409 23:19:47.136406  8290 net.cpp:198] conv5 needs backward computation.
I0409 23:19:47.136411  8290 net.cpp:198] relu4 needs backward computation.
I0409 23:19:47.136415  8290 net.cpp:198] conv4 needs backward computation.
I0409 23:19:47.136418  8290 net.cpp:198] relu3 needs backward computation.
I0409 23:19:47.136422  8290 net.cpp:198] conv3 needs backward computation.
I0409 23:19:47.136428  8290 net.cpp:198] pool2 needs backward computation.
I0409 23:19:47.136432  8290 net.cpp:198] norm2 needs backward computation.
I0409 23:19:47.136436  8290 net.cpp:198] relu2 needs backward computation.
I0409 23:19:47.136441  8290 net.cpp:198] conv2 needs backward computation.
I0409 23:19:47.136445  8290 net.cpp:198] pool1 needs backward computation.
I0409 23:19:47.136449  8290 net.cpp:198] norm1 needs backward computation.
I0409 23:19:47.136452  8290 net.cpp:198] relu1 needs backward computation.
I0409 23:19:47.136456  8290 net.cpp:198] conv1 needs backward computation.
I0409 23:19:47.136461  8290 net.cpp:200] train-data does not need backward computation.
I0409 23:19:47.136464  8290 net.cpp:242] This network produces output loss
I0409 23:19:47.136477  8290 net.cpp:255] Network initialization done.
I0409 23:19:47.136993  8290 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0409 23:19:47.137025  8290 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0409 23:19:47.137171  8290 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1024
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 1024
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0409 23:19:47.137265  8290 layer_factory.hpp:77] Creating layer val-data
I0409 23:19:47.138947  8290 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0409 23:19:47.139151  8290 net.cpp:84] Creating Layer val-data
I0409 23:19:47.139160  8290 net.cpp:380] val-data -> data
I0409 23:19:47.139171  8290 net.cpp:380] val-data -> label
I0409 23:19:47.139178  8290 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 23:19:47.143074  8290 data_layer.cpp:45] output data size: 32,3,227,227
I0409 23:19:47.177220  8290 net.cpp:122] Setting up val-data
I0409 23:19:47.177242  8290 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0409 23:19:47.177248  8290 net.cpp:129] Top shape: 32 (32)
I0409 23:19:47.177251  8290 net.cpp:137] Memory required for data: 19787264
I0409 23:19:47.177258  8290 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0409 23:19:47.177270  8290 net.cpp:84] Creating Layer label_val-data_1_split
I0409 23:19:47.177275  8290 net.cpp:406] label_val-data_1_split <- label
I0409 23:19:47.177282  8290 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0409 23:19:47.177291  8290 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0409 23:19:47.177345  8290 net.cpp:122] Setting up label_val-data_1_split
I0409 23:19:47.177350  8290 net.cpp:129] Top shape: 32 (32)
I0409 23:19:47.177353  8290 net.cpp:129] Top shape: 32 (32)
I0409 23:19:47.177356  8290 net.cpp:137] Memory required for data: 19787520
I0409 23:19:47.177361  8290 layer_factory.hpp:77] Creating layer conv1
I0409 23:19:47.177373  8290 net.cpp:84] Creating Layer conv1
I0409 23:19:47.177376  8290 net.cpp:406] conv1 <- data
I0409 23:19:47.177382  8290 net.cpp:380] conv1 -> conv1
I0409 23:19:47.179538  8290 net.cpp:122] Setting up conv1
I0409 23:19:47.179550  8290 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 23:19:47.179554  8290 net.cpp:137] Memory required for data: 56958720
I0409 23:19:47.179565  8290 layer_factory.hpp:77] Creating layer relu1
I0409 23:19:47.179572  8290 net.cpp:84] Creating Layer relu1
I0409 23:19:47.179576  8290 net.cpp:406] relu1 <- conv1
I0409 23:19:47.179581  8290 net.cpp:367] relu1 -> conv1 (in-place)
I0409 23:19:47.179872  8290 net.cpp:122] Setting up relu1
I0409 23:19:47.179880  8290 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 23:19:47.179883  8290 net.cpp:137] Memory required for data: 94129920
I0409 23:19:47.179888  8290 layer_factory.hpp:77] Creating layer norm1
I0409 23:19:47.179895  8290 net.cpp:84] Creating Layer norm1
I0409 23:19:47.179900  8290 net.cpp:406] norm1 <- conv1
I0409 23:19:47.179905  8290 net.cpp:380] norm1 -> norm1
I0409 23:19:47.180354  8290 net.cpp:122] Setting up norm1
I0409 23:19:47.180363  8290 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 23:19:47.180367  8290 net.cpp:137] Memory required for data: 131301120
I0409 23:19:47.180371  8290 layer_factory.hpp:77] Creating layer pool1
I0409 23:19:47.180378  8290 net.cpp:84] Creating Layer pool1
I0409 23:19:47.180382  8290 net.cpp:406] pool1 <- norm1
I0409 23:19:47.180387  8290 net.cpp:380] pool1 -> pool1
I0409 23:19:47.180415  8290 net.cpp:122] Setting up pool1
I0409 23:19:47.180421  8290 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0409 23:19:47.180424  8290 net.cpp:137] Memory required for data: 140259072
I0409 23:19:47.180428  8290 layer_factory.hpp:77] Creating layer conv2
I0409 23:19:47.180435  8290 net.cpp:84] Creating Layer conv2
I0409 23:19:47.180439  8290 net.cpp:406] conv2 <- pool1
I0409 23:19:47.180464  8290 net.cpp:380] conv2 -> conv2
I0409 23:19:47.189268  8290 net.cpp:122] Setting up conv2
I0409 23:19:47.189288  8290 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 23:19:47.189292  8290 net.cpp:137] Memory required for data: 164146944
I0409 23:19:47.189303  8290 layer_factory.hpp:77] Creating layer relu2
I0409 23:19:47.189312  8290 net.cpp:84] Creating Layer relu2
I0409 23:19:47.189316  8290 net.cpp:406] relu2 <- conv2
I0409 23:19:47.189323  8290 net.cpp:367] relu2 -> conv2 (in-place)
I0409 23:19:47.190894  8290 net.cpp:122] Setting up relu2
I0409 23:19:47.190907  8290 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 23:19:47.190910  8290 net.cpp:137] Memory required for data: 188034816
I0409 23:19:47.190914  8290 layer_factory.hpp:77] Creating layer norm2
I0409 23:19:47.190925  8290 net.cpp:84] Creating Layer norm2
I0409 23:19:47.190929  8290 net.cpp:406] norm2 <- conv2
I0409 23:19:47.190937  8290 net.cpp:380] norm2 -> norm2
I0409 23:19:47.191463  8290 net.cpp:122] Setting up norm2
I0409 23:19:47.191473  8290 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 23:19:47.191478  8290 net.cpp:137] Memory required for data: 211922688
I0409 23:19:47.191481  8290 layer_factory.hpp:77] Creating layer pool2
I0409 23:19:47.191489  8290 net.cpp:84] Creating Layer pool2
I0409 23:19:47.191493  8290 net.cpp:406] pool2 <- norm2
I0409 23:19:47.191498  8290 net.cpp:380] pool2 -> pool2
I0409 23:19:47.191529  8290 net.cpp:122] Setting up pool2
I0409 23:19:47.191536  8290 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 23:19:47.191540  8290 net.cpp:137] Memory required for data: 217460480
I0409 23:19:47.191543  8290 layer_factory.hpp:77] Creating layer conv3
I0409 23:19:47.191552  8290 net.cpp:84] Creating Layer conv3
I0409 23:19:47.191556  8290 net.cpp:406] conv3 <- pool2
I0409 23:19:47.191563  8290 net.cpp:380] conv3 -> conv3
I0409 23:19:47.202658  8290 net.cpp:122] Setting up conv3
I0409 23:19:47.202677  8290 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 23:19:47.202680  8290 net.cpp:137] Memory required for data: 225767168
I0409 23:19:47.202693  8290 layer_factory.hpp:77] Creating layer relu3
I0409 23:19:47.202703  8290 net.cpp:84] Creating Layer relu3
I0409 23:19:47.202708  8290 net.cpp:406] relu3 <- conv3
I0409 23:19:47.202713  8290 net.cpp:367] relu3 -> conv3 (in-place)
I0409 23:19:47.203219  8290 net.cpp:122] Setting up relu3
I0409 23:19:47.203230  8290 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 23:19:47.203235  8290 net.cpp:137] Memory required for data: 234073856
I0409 23:19:47.203238  8290 layer_factory.hpp:77] Creating layer conv4
I0409 23:19:47.203250  8290 net.cpp:84] Creating Layer conv4
I0409 23:19:47.203254  8290 net.cpp:406] conv4 <- conv3
I0409 23:19:47.203260  8290 net.cpp:380] conv4 -> conv4
I0409 23:19:47.213644  8290 net.cpp:122] Setting up conv4
I0409 23:19:47.213663  8290 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 23:19:47.213666  8290 net.cpp:137] Memory required for data: 242380544
I0409 23:19:47.213675  8290 layer_factory.hpp:77] Creating layer relu4
I0409 23:19:47.213687  8290 net.cpp:84] Creating Layer relu4
I0409 23:19:47.213691  8290 net.cpp:406] relu4 <- conv4
I0409 23:19:47.213698  8290 net.cpp:367] relu4 -> conv4 (in-place)
I0409 23:19:47.214066  8290 net.cpp:122] Setting up relu4
I0409 23:19:47.214074  8290 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 23:19:47.214078  8290 net.cpp:137] Memory required for data: 250687232
I0409 23:19:47.214082  8290 layer_factory.hpp:77] Creating layer conv5
I0409 23:19:47.214093  8290 net.cpp:84] Creating Layer conv5
I0409 23:19:47.214097  8290 net.cpp:406] conv5 <- conv4
I0409 23:19:47.214103  8290 net.cpp:380] conv5 -> conv5
I0409 23:19:47.229457  8290 net.cpp:122] Setting up conv5
I0409 23:19:47.229476  8290 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 23:19:47.229480  8290 net.cpp:137] Memory required for data: 256225024
I0409 23:19:47.229493  8290 layer_factory.hpp:77] Creating layer relu5
I0409 23:19:47.229504  8290 net.cpp:84] Creating Layer relu5
I0409 23:19:47.229508  8290 net.cpp:406] relu5 <- conv5
I0409 23:19:47.229533  8290 net.cpp:367] relu5 -> conv5 (in-place)
I0409 23:19:47.230054  8290 net.cpp:122] Setting up relu5
I0409 23:19:47.230063  8290 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 23:19:47.230067  8290 net.cpp:137] Memory required for data: 261762816
I0409 23:19:47.230072  8290 layer_factory.hpp:77] Creating layer pool5
I0409 23:19:47.230082  8290 net.cpp:84] Creating Layer pool5
I0409 23:19:47.230084  8290 net.cpp:406] pool5 <- conv5
I0409 23:19:47.230090  8290 net.cpp:380] pool5 -> pool5
I0409 23:19:47.230130  8290 net.cpp:122] Setting up pool5
I0409 23:19:47.230136  8290 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0409 23:19:47.230139  8290 net.cpp:137] Memory required for data: 262942464
I0409 23:19:47.230142  8290 layer_factory.hpp:77] Creating layer fc6
I0409 23:19:47.230149  8290 net.cpp:84] Creating Layer fc6
I0409 23:19:47.230152  8290 net.cpp:406] fc6 <- pool5
I0409 23:19:47.230159  8290 net.cpp:380] fc6 -> fc6
I0409 23:19:47.319193  8290 net.cpp:122] Setting up fc6
I0409 23:19:47.319213  8290 net.cpp:129] Top shape: 32 1024 (32768)
I0409 23:19:47.319216  8290 net.cpp:137] Memory required for data: 263073536
I0409 23:19:47.319226  8290 layer_factory.hpp:77] Creating layer relu6
I0409 23:19:47.319236  8290 net.cpp:84] Creating Layer relu6
I0409 23:19:47.319239  8290 net.cpp:406] relu6 <- fc6
I0409 23:19:47.319245  8290 net.cpp:367] relu6 -> fc6 (in-place)
I0409 23:19:47.320091  8290 net.cpp:122] Setting up relu6
I0409 23:19:47.320101  8290 net.cpp:129] Top shape: 32 1024 (32768)
I0409 23:19:47.320103  8290 net.cpp:137] Memory required for data: 263204608
I0409 23:19:47.320108  8290 layer_factory.hpp:77] Creating layer drop6
I0409 23:19:47.320114  8290 net.cpp:84] Creating Layer drop6
I0409 23:19:47.320118  8290 net.cpp:406] drop6 <- fc6
I0409 23:19:47.320127  8290 net.cpp:367] drop6 -> fc6 (in-place)
I0409 23:19:47.320152  8290 net.cpp:122] Setting up drop6
I0409 23:19:47.320158  8290 net.cpp:129] Top shape: 32 1024 (32768)
I0409 23:19:47.320161  8290 net.cpp:137] Memory required for data: 263335680
I0409 23:19:47.320164  8290 layer_factory.hpp:77] Creating layer fc7
I0409 23:19:47.320171  8290 net.cpp:84] Creating Layer fc7
I0409 23:19:47.320174  8290 net.cpp:406] fc7 <- fc6
I0409 23:19:47.320180  8290 net.cpp:380] fc7 -> fc7
I0409 23:19:47.330269  8290 net.cpp:122] Setting up fc7
I0409 23:19:47.330286  8290 net.cpp:129] Top shape: 32 1024 (32768)
I0409 23:19:47.330289  8290 net.cpp:137] Memory required for data: 263466752
I0409 23:19:47.330298  8290 layer_factory.hpp:77] Creating layer relu7
I0409 23:19:47.330307  8290 net.cpp:84] Creating Layer relu7
I0409 23:19:47.330312  8290 net.cpp:406] relu7 <- fc7
I0409 23:19:47.330319  8290 net.cpp:367] relu7 -> fc7 (in-place)
I0409 23:19:47.330749  8290 net.cpp:122] Setting up relu7
I0409 23:19:47.330757  8290 net.cpp:129] Top shape: 32 1024 (32768)
I0409 23:19:47.330760  8290 net.cpp:137] Memory required for data: 263597824
I0409 23:19:47.330765  8290 layer_factory.hpp:77] Creating layer drop7
I0409 23:19:47.330770  8290 net.cpp:84] Creating Layer drop7
I0409 23:19:47.330773  8290 net.cpp:406] drop7 <- fc7
I0409 23:19:47.330778  8290 net.cpp:367] drop7 -> fc7 (in-place)
I0409 23:19:47.330803  8290 net.cpp:122] Setting up drop7
I0409 23:19:47.330807  8290 net.cpp:129] Top shape: 32 1024 (32768)
I0409 23:19:47.330811  8290 net.cpp:137] Memory required for data: 263728896
I0409 23:19:47.330813  8290 layer_factory.hpp:77] Creating layer fc8
I0409 23:19:47.330821  8290 net.cpp:84] Creating Layer fc8
I0409 23:19:47.330824  8290 net.cpp:406] fc8 <- fc7
I0409 23:19:47.330829  8290 net.cpp:380] fc8 -> fc8
I0409 23:19:47.332617  8290 net.cpp:122] Setting up fc8
I0409 23:19:47.332623  8290 net.cpp:129] Top shape: 32 196 (6272)
I0409 23:19:47.332626  8290 net.cpp:137] Memory required for data: 263753984
I0409 23:19:47.332633  8290 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0409 23:19:47.332638  8290 net.cpp:84] Creating Layer fc8_fc8_0_split
I0409 23:19:47.332641  8290 net.cpp:406] fc8_fc8_0_split <- fc8
I0409 23:19:47.332665  8290 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0409 23:19:47.332672  8290 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0409 23:19:47.332706  8290 net.cpp:122] Setting up fc8_fc8_0_split
I0409 23:19:47.332710  8290 net.cpp:129] Top shape: 32 196 (6272)
I0409 23:19:47.332715  8290 net.cpp:129] Top shape: 32 196 (6272)
I0409 23:19:47.332717  8290 net.cpp:137] Memory required for data: 263804160
I0409 23:19:47.332720  8290 layer_factory.hpp:77] Creating layer accuracy
I0409 23:19:47.332726  8290 net.cpp:84] Creating Layer accuracy
I0409 23:19:47.332729  8290 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0409 23:19:47.332734  8290 net.cpp:406] accuracy <- label_val-data_1_split_0
I0409 23:19:47.332741  8290 net.cpp:380] accuracy -> accuracy
I0409 23:19:47.332746  8290 net.cpp:122] Setting up accuracy
I0409 23:19:47.332751  8290 net.cpp:129] Top shape: (1)
I0409 23:19:47.332753  8290 net.cpp:137] Memory required for data: 263804164
I0409 23:19:47.332756  8290 layer_factory.hpp:77] Creating layer loss
I0409 23:19:47.332762  8290 net.cpp:84] Creating Layer loss
I0409 23:19:47.332765  8290 net.cpp:406] loss <- fc8_fc8_0_split_1
I0409 23:19:47.332769  8290 net.cpp:406] loss <- label_val-data_1_split_1
I0409 23:19:47.332777  8290 net.cpp:380] loss -> loss
I0409 23:19:47.332783  8290 layer_factory.hpp:77] Creating layer loss
I0409 23:19:47.338613  8290 net.cpp:122] Setting up loss
I0409 23:19:47.338626  8290 net.cpp:129] Top shape: (1)
I0409 23:19:47.338630  8290 net.cpp:132]     with loss weight 1
I0409 23:19:47.338641  8290 net.cpp:137] Memory required for data: 263804168
I0409 23:19:47.338646  8290 net.cpp:198] loss needs backward computation.
I0409 23:19:47.338651  8290 net.cpp:200] accuracy does not need backward computation.
I0409 23:19:47.338656  8290 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0409 23:19:47.338660  8290 net.cpp:198] fc8 needs backward computation.
I0409 23:19:47.338663  8290 net.cpp:198] drop7 needs backward computation.
I0409 23:19:47.338667  8290 net.cpp:198] relu7 needs backward computation.
I0409 23:19:47.338670  8290 net.cpp:198] fc7 needs backward computation.
I0409 23:19:47.338675  8290 net.cpp:198] drop6 needs backward computation.
I0409 23:19:47.338678  8290 net.cpp:198] relu6 needs backward computation.
I0409 23:19:47.338682  8290 net.cpp:198] fc6 needs backward computation.
I0409 23:19:47.338686  8290 net.cpp:198] pool5 needs backward computation.
I0409 23:19:47.338690  8290 net.cpp:198] relu5 needs backward computation.
I0409 23:19:47.338693  8290 net.cpp:198] conv5 needs backward computation.
I0409 23:19:47.338697  8290 net.cpp:198] relu4 needs backward computation.
I0409 23:19:47.338701  8290 net.cpp:198] conv4 needs backward computation.
I0409 23:19:47.338704  8290 net.cpp:198] relu3 needs backward computation.
I0409 23:19:47.338708  8290 net.cpp:198] conv3 needs backward computation.
I0409 23:19:47.338711  8290 net.cpp:198] pool2 needs backward computation.
I0409 23:19:47.338716  8290 net.cpp:198] norm2 needs backward computation.
I0409 23:19:47.338719  8290 net.cpp:198] relu2 needs backward computation.
I0409 23:19:47.338722  8290 net.cpp:198] conv2 needs backward computation.
I0409 23:19:47.338726  8290 net.cpp:198] pool1 needs backward computation.
I0409 23:19:47.338730  8290 net.cpp:198] norm1 needs backward computation.
I0409 23:19:47.338733  8290 net.cpp:198] relu1 needs backward computation.
I0409 23:19:47.338737  8290 net.cpp:198] conv1 needs backward computation.
I0409 23:19:47.338742  8290 net.cpp:200] label_val-data_1_split does not need backward computation.
I0409 23:19:47.338745  8290 net.cpp:200] val-data does not need backward computation.
I0409 23:19:47.338748  8290 net.cpp:242] This network produces output accuracy
I0409 23:19:47.338752  8290 net.cpp:242] This network produces output loss
I0409 23:19:47.338770  8290 net.cpp:255] Network initialization done.
I0409 23:19:47.338860  8290 solver.cpp:56] Solver scaffolding done.
I0409 23:19:47.339293  8290 caffe.cpp:248] Starting Optimization
I0409 23:19:47.339300  8290 solver.cpp:272] Solving
I0409 23:19:47.339318  8290 solver.cpp:273] Learning Rate Policy: exp
I0409 23:19:47.340405  8290 solver.cpp:330] Iteration 0, Testing net (#0)
I0409 23:19:47.340415  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:19:47.365118  8290 blocking_queue.cpp:49] Waiting for data
I0409 23:19:51.924713  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:19:51.968183  8290 solver.cpp:397]     Test net output #0: accuracy = 0.00306373
I0409 23:19:51.968235  8290 solver.cpp:397]     Test net output #1: loss = 5.27914 (* 1 = 5.27914 loss)
I0409 23:19:52.049546  8290 solver.cpp:218] Iteration 0 (5.64535e+36 iter/s, 4.71002s/12 iters), loss = 5.27669
I0409 23:19:52.049598  8290 solver.cpp:237]     Train net output #0: loss = 5.27669 (* 1 = 5.27669 loss)
I0409 23:19:52.049618  8290 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0409 23:19:55.973853  8290 solver.cpp:218] Iteration 12 (3.05803 iter/s, 3.9241s/12 iters), loss = 5.2816
I0409 23:19:55.973908  8290 solver.cpp:237]     Train net output #0: loss = 5.2816 (* 1 = 5.2816 loss)
I0409 23:19:55.973920  8290 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0409 23:20:00.940675  8290 solver.cpp:218] Iteration 24 (2.41615 iter/s, 4.96658s/12 iters), loss = 5.27762
I0409 23:20:00.940727  8290 solver.cpp:237]     Train net output #0: loss = 5.27762 (* 1 = 5.27762 loss)
I0409 23:20:00.940739  8290 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0409 23:20:05.773468  8290 solver.cpp:218] Iteration 36 (2.48316 iter/s, 4.83256s/12 iters), loss = 5.28161
I0409 23:20:05.773511  8290 solver.cpp:237]     Train net output #0: loss = 5.28161 (* 1 = 5.28161 loss)
I0409 23:20:05.773519  8290 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0409 23:20:10.706547  8290 solver.cpp:218] Iteration 48 (2.43267 iter/s, 4.93284s/12 iters), loss = 5.28536
I0409 23:20:10.706599  8290 solver.cpp:237]     Train net output #0: loss = 5.28536 (* 1 = 5.28536 loss)
I0409 23:20:10.706610  8290 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0409 23:20:15.732256  8290 solver.cpp:218] Iteration 60 (2.38784 iter/s, 5.02547s/12 iters), loss = 5.27684
I0409 23:20:15.732304  8290 solver.cpp:237]     Train net output #0: loss = 5.27684 (* 1 = 5.27684 loss)
I0409 23:20:15.732314  8290 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0409 23:20:20.782860  8290 solver.cpp:218] Iteration 72 (2.37607 iter/s, 5.05037s/12 iters), loss = 5.28087
I0409 23:20:20.782936  8290 solver.cpp:237]     Train net output #0: loss = 5.28087 (* 1 = 5.28087 loss)
I0409 23:20:20.782944  8290 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0409 23:20:25.646050  8290 solver.cpp:218] Iteration 84 (2.46765 iter/s, 4.86293s/12 iters), loss = 5.28276
I0409 23:20:25.646096  8290 solver.cpp:237]     Train net output #0: loss = 5.28276 (* 1 = 5.28276 loss)
I0409 23:20:25.646104  8290 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0409 23:20:30.591168  8290 solver.cpp:218] Iteration 96 (2.42675 iter/s, 4.94488s/12 iters), loss = 5.2916
I0409 23:20:30.591224  8290 solver.cpp:237]     Train net output #0: loss = 5.2916 (* 1 = 5.2916 loss)
I0409 23:20:30.591235  8290 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0409 23:20:32.276285  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:20:32.580329  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0409 23:20:33.298744  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0409 23:20:33.819922  8290 solver.cpp:330] Iteration 102, Testing net (#0)
I0409 23:20:33.819943  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:20:38.314425  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:20:38.392498  8290 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 23:20:38.392532  8290 solver.cpp:397]     Test net output #1: loss = 5.28154 (* 1 = 5.28154 loss)
I0409 23:20:40.231236  8290 solver.cpp:218] Iteration 108 (1.24486 iter/s, 9.63967s/12 iters), loss = 5.2848
I0409 23:20:40.231276  8290 solver.cpp:237]     Train net output #0: loss = 5.2848 (* 1 = 5.2848 loss)
I0409 23:20:40.231284  8290 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0409 23:20:45.138468  8290 solver.cpp:218] Iteration 120 (2.44549 iter/s, 4.907s/12 iters), loss = 5.27712
I0409 23:20:45.138515  8290 solver.cpp:237]     Train net output #0: loss = 5.27712 (* 1 = 5.27712 loss)
I0409 23:20:45.138523  8290 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0409 23:20:49.984782  8290 solver.cpp:218] Iteration 132 (2.47623 iter/s, 4.84608s/12 iters), loss = 5.25285
I0409 23:20:49.984834  8290 solver.cpp:237]     Train net output #0: loss = 5.25285 (* 1 = 5.25285 loss)
I0409 23:20:49.984844  8290 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0409 23:20:54.844215  8290 solver.cpp:218] Iteration 144 (2.46954 iter/s, 4.85919s/12 iters), loss = 5.29752
I0409 23:20:54.844352  8290 solver.cpp:237]     Train net output #0: loss = 5.29752 (* 1 = 5.29752 loss)
I0409 23:20:54.844362  8290 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0409 23:20:59.759099  8290 solver.cpp:218] Iteration 156 (2.44173 iter/s, 4.91456s/12 iters), loss = 5.26319
I0409 23:20:59.759140  8290 solver.cpp:237]     Train net output #0: loss = 5.26319 (* 1 = 5.26319 loss)
I0409 23:20:59.759150  8290 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0409 23:21:04.966065  8290 solver.cpp:218] Iteration 168 (2.30471 iter/s, 5.20673s/12 iters), loss = 5.27089
I0409 23:21:04.966125  8290 solver.cpp:237]     Train net output #0: loss = 5.27089 (* 1 = 5.27089 loss)
I0409 23:21:04.966136  8290 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0409 23:21:09.825464  8290 solver.cpp:218] Iteration 180 (2.46957 iter/s, 4.85915s/12 iters), loss = 5.26295
I0409 23:21:09.825523  8290 solver.cpp:237]     Train net output #0: loss = 5.26295 (* 1 = 5.26295 loss)
I0409 23:21:09.825536  8290 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0409 23:21:14.754928  8290 solver.cpp:218] Iteration 192 (2.43446 iter/s, 4.92922s/12 iters), loss = 5.27637
I0409 23:21:14.754985  8290 solver.cpp:237]     Train net output #0: loss = 5.27637 (* 1 = 5.27637 loss)
I0409 23:21:14.754997  8290 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0409 23:21:18.518112  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:21:19.177662  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0409 23:21:19.981514  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0409 23:21:20.597496  8290 solver.cpp:330] Iteration 204, Testing net (#0)
I0409 23:21:20.597517  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:21:25.162750  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:21:25.337328  8290 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 23:21:25.337381  8290 solver.cpp:397]     Test net output #1: loss = 5.28376 (* 1 = 5.28376 loss)
I0409 23:21:25.419421  8290 solver.cpp:218] Iteration 204 (1.12528 iter/s, 10.6641s/12 iters), loss = 5.27461
I0409 23:21:25.419473  8290 solver.cpp:237]     Train net output #0: loss = 5.27461 (* 1 = 5.27461 loss)
I0409 23:21:25.419484  8290 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0409 23:21:29.560321  8290 solver.cpp:218] Iteration 216 (2.89807 iter/s, 4.14069s/12 iters), loss = 5.28212
I0409 23:21:29.560365  8290 solver.cpp:237]     Train net output #0: loss = 5.28212 (* 1 = 5.28212 loss)
I0409 23:21:29.560374  8290 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0409 23:21:34.442471  8290 solver.cpp:218] Iteration 228 (2.45805 iter/s, 4.88192s/12 iters), loss = 5.25867
I0409 23:21:34.442519  8290 solver.cpp:237]     Train net output #0: loss = 5.25867 (* 1 = 5.25867 loss)
I0409 23:21:34.442528  8290 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0409 23:21:39.386453  8290 solver.cpp:218] Iteration 240 (2.42731 iter/s, 4.94375s/12 iters), loss = 5.28863
I0409 23:21:39.386507  8290 solver.cpp:237]     Train net output #0: loss = 5.28863 (* 1 = 5.28863 loss)
I0409 23:21:39.386515  8290 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0409 23:21:44.323591  8290 solver.cpp:218] Iteration 252 (2.43068 iter/s, 4.93689s/12 iters), loss = 5.26954
I0409 23:21:44.323652  8290 solver.cpp:237]     Train net output #0: loss = 5.26954 (* 1 = 5.26954 loss)
I0409 23:21:44.323663  8290 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0409 23:21:49.872251  8290 solver.cpp:218] Iteration 264 (2.16279 iter/s, 5.54839s/12 iters), loss = 5.2717
I0409 23:21:49.872305  8290 solver.cpp:237]     Train net output #0: loss = 5.2717 (* 1 = 5.2717 loss)
I0409 23:21:49.872316  8290 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0409 23:21:55.425114  8290 solver.cpp:218] Iteration 276 (2.16115 iter/s, 5.5526s/12 iters), loss = 5.29481
I0409 23:21:55.425262  8290 solver.cpp:237]     Train net output #0: loss = 5.29481 (* 1 = 5.29481 loss)
I0409 23:21:55.425274  8290 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0409 23:22:00.365600  8290 solver.cpp:218] Iteration 288 (2.42908 iter/s, 4.94015s/12 iters), loss = 5.28299
I0409 23:22:00.365649  8290 solver.cpp:237]     Train net output #0: loss = 5.28299 (* 1 = 5.28299 loss)
I0409 23:22:00.365658  8290 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0409 23:22:05.361449  8290 solver.cpp:218] Iteration 300 (2.40211 iter/s, 4.99561s/12 iters), loss = 5.28051
I0409 23:22:05.361495  8290 solver.cpp:237]     Train net output #0: loss = 5.28051 (* 1 = 5.28051 loss)
I0409 23:22:05.361501  8290 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0409 23:22:06.352334  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:22:07.388548  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0409 23:22:08.087924  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0409 23:22:08.604457  8290 solver.cpp:330] Iteration 306, Testing net (#0)
I0409 23:22:08.604477  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:22:13.147574  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:22:13.305665  8290 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 23:22:13.305701  8290 solver.cpp:397]     Test net output #1: loss = 5.28283 (* 1 = 5.28283 loss)
I0409 23:22:15.167047  8290 solver.cpp:218] Iteration 312 (1.22384 iter/s, 9.8052s/12 iters), loss = 5.28122
I0409 23:22:15.167098  8290 solver.cpp:237]     Train net output #0: loss = 5.28122 (* 1 = 5.28122 loss)
I0409 23:22:15.167110  8290 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0409 23:22:20.446130  8290 solver.cpp:218] Iteration 324 (2.27323 iter/s, 5.27882s/12 iters), loss = 5.25074
I0409 23:22:20.446189  8290 solver.cpp:237]     Train net output #0: loss = 5.25074 (* 1 = 5.25074 loss)
I0409 23:22:20.446199  8290 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0409 23:22:25.345999  8290 solver.cpp:218] Iteration 336 (2.44917 iter/s, 4.89961s/12 iters), loss = 5.25652
I0409 23:22:25.346053  8290 solver.cpp:237]     Train net output #0: loss = 5.25652 (* 1 = 5.25652 loss)
I0409 23:22:25.346063  8290 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0409 23:22:30.169062  8290 solver.cpp:218] Iteration 348 (2.48817 iter/s, 4.82282s/12 iters), loss = 5.26414
I0409 23:22:30.169179  8290 solver.cpp:237]     Train net output #0: loss = 5.26414 (* 1 = 5.26414 loss)
I0409 23:22:30.169191  8290 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0409 23:22:35.111898  8290 solver.cpp:218] Iteration 360 (2.42791 iter/s, 4.94253s/12 iters), loss = 5.27855
I0409 23:22:35.111958  8290 solver.cpp:237]     Train net output #0: loss = 5.27855 (* 1 = 5.27855 loss)
I0409 23:22:35.111969  8290 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0409 23:22:40.025184  8290 solver.cpp:218] Iteration 372 (2.44248 iter/s, 4.91304s/12 iters), loss = 5.24325
I0409 23:22:40.025246  8290 solver.cpp:237]     Train net output #0: loss = 5.24325 (* 1 = 5.24325 loss)
I0409 23:22:40.025259  8290 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0409 23:22:45.013183  8290 solver.cpp:218] Iteration 384 (2.4059 iter/s, 4.98775s/12 iters), loss = 5.24091
I0409 23:22:45.013247  8290 solver.cpp:237]     Train net output #0: loss = 5.24091 (* 1 = 5.24091 loss)
I0409 23:22:45.013260  8290 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0409 23:22:49.951985  8290 solver.cpp:218] Iteration 396 (2.42987 iter/s, 4.93854s/12 iters), loss = 5.12189
I0409 23:22:49.952057  8290 solver.cpp:237]     Train net output #0: loss = 5.12189 (* 1 = 5.12189 loss)
I0409 23:22:49.952071  8290 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0409 23:22:53.038700  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:22:54.449772  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0409 23:22:55.171120  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0409 23:22:55.723948  8290 solver.cpp:330] Iteration 408, Testing net (#0)
I0409 23:22:55.723968  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:23:00.043753  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:23:00.256889  8290 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0409 23:23:00.257015  8290 solver.cpp:397]     Test net output #1: loss = 5.1932 (* 1 = 5.1932 loss)
I0409 23:23:00.339095  8290 solver.cpp:218] Iteration 408 (1.15533 iter/s, 10.3867s/12 iters), loss = 5.27157
I0409 23:23:00.339144  8290 solver.cpp:237]     Train net output #0: loss = 5.27157 (* 1 = 5.27157 loss)
I0409 23:23:00.339152  8290 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0409 23:23:04.824095  8290 solver.cpp:218] Iteration 420 (2.67572 iter/s, 4.48478s/12 iters), loss = 5.24984
I0409 23:23:04.824137  8290 solver.cpp:237]     Train net output #0: loss = 5.24984 (* 1 = 5.24984 loss)
I0409 23:23:04.824146  8290 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0409 23:23:09.768404  8290 solver.cpp:218] Iteration 432 (2.42715 iter/s, 4.94408s/12 iters), loss = 5.16829
I0409 23:23:09.768451  8290 solver.cpp:237]     Train net output #0: loss = 5.16829 (* 1 = 5.16829 loss)
I0409 23:23:09.768460  8290 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0409 23:23:14.580699  8290 solver.cpp:218] Iteration 444 (2.49373 iter/s, 4.81207s/12 iters), loss = 5.1603
I0409 23:23:14.580744  8290 solver.cpp:237]     Train net output #0: loss = 5.1603 (* 1 = 5.1603 loss)
I0409 23:23:14.580754  8290 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0409 23:23:19.507822  8290 solver.cpp:218] Iteration 456 (2.43561 iter/s, 4.92689s/12 iters), loss = 5.1878
I0409 23:23:19.507865  8290 solver.cpp:237]     Train net output #0: loss = 5.1878 (* 1 = 5.1878 loss)
I0409 23:23:19.507874  8290 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0409 23:23:24.476239  8290 solver.cpp:218] Iteration 468 (2.41537 iter/s, 4.96818s/12 iters), loss = 5.20535
I0409 23:23:24.476295  8290 solver.cpp:237]     Train net output #0: loss = 5.20535 (* 1 = 5.20535 loss)
I0409 23:23:24.476306  8290 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0409 23:23:29.411095  8290 solver.cpp:218] Iteration 480 (2.4318 iter/s, 4.93461s/12 iters), loss = 5.10144
I0409 23:23:29.411159  8290 solver.cpp:237]     Train net output #0: loss = 5.10144 (* 1 = 5.10144 loss)
I0409 23:23:29.411170  8290 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0409 23:23:34.370455  8290 solver.cpp:218] Iteration 492 (2.41979 iter/s, 4.95911s/12 iters), loss = 5.15032
I0409 23:23:34.370551  8290 solver.cpp:237]     Train net output #0: loss = 5.15032 (* 1 = 5.15032 loss)
I0409 23:23:34.370561  8290 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0409 23:23:39.406342  8290 solver.cpp:218] Iteration 504 (2.38303 iter/s, 5.0356s/12 iters), loss = 5.15177
I0409 23:23:39.406406  8290 solver.cpp:237]     Train net output #0: loss = 5.15177 (* 1 = 5.15177 loss)
I0409 23:23:39.406420  8290 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0409 23:23:39.654234  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:23:41.406615  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0409 23:23:42.987236  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0409 23:23:44.890985  8290 solver.cpp:330] Iteration 510, Testing net (#0)
I0409 23:23:44.891008  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:23:49.106227  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:23:49.351322  8290 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0409 23:23:49.351366  8290 solver.cpp:397]     Test net output #1: loss = 5.12622 (* 1 = 5.12622 loss)
I0409 23:23:51.396620  8290 solver.cpp:218] Iteration 516 (1.00085 iter/s, 11.9898s/12 iters), loss = 5.09345
I0409 23:23:51.396672  8290 solver.cpp:237]     Train net output #0: loss = 5.09345 (* 1 = 5.09345 loss)
I0409 23:23:51.396683  8290 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0409 23:23:56.422710  8290 solver.cpp:218] Iteration 528 (2.38766 iter/s, 5.02584s/12 iters), loss = 5.15411
I0409 23:23:56.422767  8290 solver.cpp:237]     Train net output #0: loss = 5.15411 (* 1 = 5.15411 loss)
I0409 23:23:56.422778  8290 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0409 23:24:01.359074  8290 solver.cpp:218] Iteration 540 (2.43106 iter/s, 4.93612s/12 iters), loss = 5.08132
I0409 23:24:01.359131  8290 solver.cpp:237]     Train net output #0: loss = 5.08132 (* 1 = 5.08132 loss)
I0409 23:24:01.359146  8290 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0409 23:24:06.385541  8290 solver.cpp:218] Iteration 552 (2.38748 iter/s, 5.02622s/12 iters), loss = 5.12524
I0409 23:24:06.385675  8290 solver.cpp:237]     Train net output #0: loss = 5.12524 (* 1 = 5.12524 loss)
I0409 23:24:06.385685  8290 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0409 23:24:11.314667  8290 solver.cpp:218] Iteration 564 (2.43467 iter/s, 4.92881s/12 iters), loss = 5.1088
I0409 23:24:11.314721  8290 solver.cpp:237]     Train net output #0: loss = 5.1088 (* 1 = 5.1088 loss)
I0409 23:24:11.314733  8290 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0409 23:24:16.341600  8290 solver.cpp:218] Iteration 576 (2.38726 iter/s, 5.02668s/12 iters), loss = 5.0716
I0409 23:24:16.341668  8290 solver.cpp:237]     Train net output #0: loss = 5.0716 (* 1 = 5.0716 loss)
I0409 23:24:16.341679  8290 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0409 23:24:21.174381  8290 solver.cpp:218] Iteration 588 (2.48317 iter/s, 4.83253s/12 iters), loss = 5.05626
I0409 23:24:21.174435  8290 solver.cpp:237]     Train net output #0: loss = 5.05626 (* 1 = 5.05626 loss)
I0409 23:24:21.174448  8290 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0409 23:24:26.040066  8290 solver.cpp:218] Iteration 600 (2.46637 iter/s, 4.86544s/12 iters), loss = 5.12093
I0409 23:24:26.040127  8290 solver.cpp:237]     Train net output #0: loss = 5.12093 (* 1 = 5.12093 loss)
I0409 23:24:26.040138  8290 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0409 23:24:28.350975  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:24:30.426426  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0409 23:24:31.191913  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0409 23:24:31.744830  8290 solver.cpp:330] Iteration 612, Testing net (#0)
I0409 23:24:31.744853  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:24:36.018237  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:24:36.306394  8290 solver.cpp:397]     Test net output #0: accuracy = 0.0128676
I0409 23:24:36.306439  8290 solver.cpp:397]     Test net output #1: loss = 5.07849 (* 1 = 5.07849 loss)
I0409 23:24:36.388813  8290 solver.cpp:218] Iteration 612 (1.15961 iter/s, 10.3483s/12 iters), loss = 5.12088
I0409 23:24:36.388979  8290 solver.cpp:237]     Train net output #0: loss = 5.12088 (* 1 = 5.12088 loss)
I0409 23:24:36.388990  8290 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0409 23:24:40.538466  8290 solver.cpp:218] Iteration 624 (2.89203 iter/s, 4.14933s/12 iters), loss = 5.09892
I0409 23:24:40.538513  8290 solver.cpp:237]     Train net output #0: loss = 5.09892 (* 1 = 5.09892 loss)
I0409 23:24:40.538523  8290 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0409 23:24:45.376135  8290 solver.cpp:218] Iteration 636 (2.48065 iter/s, 4.83744s/12 iters), loss = 4.96814
I0409 23:24:45.376188  8290 solver.cpp:237]     Train net output #0: loss = 4.96814 (* 1 = 4.96814 loss)
I0409 23:24:45.376199  8290 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0409 23:24:50.291000  8290 solver.cpp:218] Iteration 648 (2.44169 iter/s, 4.91463s/12 iters), loss = 5.13027
I0409 23:24:50.291062  8290 solver.cpp:237]     Train net output #0: loss = 5.13027 (* 1 = 5.13027 loss)
I0409 23:24:50.291074  8290 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0409 23:24:55.148797  8290 solver.cpp:218] Iteration 660 (2.47038 iter/s, 4.85755s/12 iters), loss = 5.05457
I0409 23:24:55.148857  8290 solver.cpp:237]     Train net output #0: loss = 5.05457 (* 1 = 5.05457 loss)
I0409 23:24:55.148869  8290 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0409 23:25:00.245666  8290 solver.cpp:218] Iteration 672 (2.3545 iter/s, 5.09662s/12 iters), loss = 5.01062
I0409 23:25:00.245713  8290 solver.cpp:237]     Train net output #0: loss = 5.01062 (* 1 = 5.01062 loss)
I0409 23:25:00.245723  8290 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0409 23:25:04.780802  8290 blocking_queue.cpp:49] Waiting for data
I0409 23:25:05.238978  8290 solver.cpp:218] Iteration 684 (2.40333 iter/s, 4.99307s/12 iters), loss = 4.87769
I0409 23:25:05.239025  8290 solver.cpp:237]     Train net output #0: loss = 4.87769 (* 1 = 4.87769 loss)
I0409 23:25:05.239034  8290 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0409 23:25:10.179200  8290 solver.cpp:218] Iteration 696 (2.42916 iter/s, 4.93998s/12 iters), loss = 4.98702
I0409 23:25:10.179293  8290 solver.cpp:237]     Train net output #0: loss = 4.98702 (* 1 = 4.98702 loss)
I0409 23:25:10.179302  8290 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0409 23:25:14.721011  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:25:15.098070  8290 solver.cpp:218] Iteration 708 (2.43972 iter/s, 4.91859s/12 iters), loss = 5.14826
I0409 23:25:15.098115  8290 solver.cpp:237]     Train net output #0: loss = 5.14826 (* 1 = 5.14826 loss)
I0409 23:25:15.098124  8290 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0409 23:25:17.101343  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0409 23:25:18.233521  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0409 23:25:18.874177  8290 solver.cpp:330] Iteration 714, Testing net (#0)
I0409 23:25:18.874202  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:25:23.040326  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:25:23.392906  8290 solver.cpp:397]     Test net output #0: accuracy = 0.0134804
I0409 23:25:23.392940  8290 solver.cpp:397]     Test net output #1: loss = 5.04969 (* 1 = 5.04969 loss)
I0409 23:25:25.204324  8290 solver.cpp:218] Iteration 720 (1.18743 iter/s, 10.1058s/12 iters), loss = 5.14904
I0409 23:25:25.204372  8290 solver.cpp:237]     Train net output #0: loss = 5.14904 (* 1 = 5.14904 loss)
I0409 23:25:25.204382  8290 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0409 23:25:30.313688  8290 solver.cpp:218] Iteration 732 (2.34874 iter/s, 5.10912s/12 iters), loss = 4.90405
I0409 23:25:30.313742  8290 solver.cpp:237]     Train net output #0: loss = 4.90405 (* 1 = 4.90405 loss)
I0409 23:25:30.313753  8290 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0409 23:25:35.197059  8290 solver.cpp:218] Iteration 744 (2.45744 iter/s, 4.88313s/12 iters), loss = 5.02938
I0409 23:25:35.197105  8290 solver.cpp:237]     Train net output #0: loss = 5.02938 (* 1 = 5.02938 loss)
I0409 23:25:35.197113  8290 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0409 23:25:40.039484  8290 solver.cpp:218] Iteration 756 (2.47822 iter/s, 4.84219s/12 iters), loss = 5.06139
I0409 23:25:40.039542  8290 solver.cpp:237]     Train net output #0: loss = 5.06139 (* 1 = 5.06139 loss)
I0409 23:25:40.039554  8290 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0409 23:25:45.002554  8290 solver.cpp:218] Iteration 768 (2.41798 iter/s, 4.96282s/12 iters), loss = 5.05668
I0409 23:25:45.002713  8290 solver.cpp:237]     Train net output #0: loss = 5.05668 (* 1 = 5.05668 loss)
I0409 23:25:45.002727  8290 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0409 23:25:49.857067  8290 solver.cpp:218] Iteration 780 (2.4721 iter/s, 4.85418s/12 iters), loss = 5.10074
I0409 23:25:49.857112  8290 solver.cpp:237]     Train net output #0: loss = 5.10074 (* 1 = 5.10074 loss)
I0409 23:25:49.857120  8290 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0409 23:25:54.788230  8290 solver.cpp:218] Iteration 792 (2.43362 iter/s, 4.93093s/12 iters), loss = 4.91015
I0409 23:25:54.788278  8290 solver.cpp:237]     Train net output #0: loss = 4.91015 (* 1 = 4.91015 loss)
I0409 23:25:54.788290  8290 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0409 23:25:59.677273  8290 solver.cpp:218] Iteration 804 (2.45459 iter/s, 4.88881s/12 iters), loss = 4.98586
I0409 23:25:59.677320  8290 solver.cpp:237]     Train net output #0: loss = 4.98586 (* 1 = 4.98586 loss)
I0409 23:25:59.677330  8290 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0409 23:26:01.409255  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:26:04.242090  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0409 23:26:04.992431  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0409 23:26:05.534564  8290 solver.cpp:330] Iteration 816, Testing net (#0)
I0409 23:26:05.534593  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:26:09.633242  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:26:09.993831  8290 solver.cpp:397]     Test net output #0: accuracy = 0.0196078
I0409 23:26:09.993880  8290 solver.cpp:397]     Test net output #1: loss = 4.99197 (* 1 = 4.99197 loss)
I0409 23:26:10.075881  8290 solver.cpp:218] Iteration 816 (1.15405 iter/s, 10.3982s/12 iters), loss = 5.0223
I0409 23:26:10.075949  8290 solver.cpp:237]     Train net output #0: loss = 5.0223 (* 1 = 5.0223 loss)
I0409 23:26:10.075963  8290 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0409 23:26:14.249557  8290 solver.cpp:218] Iteration 828 (2.87532 iter/s, 4.17345s/12 iters), loss = 5.11764
I0409 23:26:14.249612  8290 solver.cpp:237]     Train net output #0: loss = 5.11764 (* 1 = 5.11764 loss)
I0409 23:26:14.249624  8290 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0409 23:26:19.198470  8290 solver.cpp:218] Iteration 840 (2.42489 iter/s, 4.94867s/12 iters), loss = 4.90733
I0409 23:26:19.198571  8290 solver.cpp:237]     Train net output #0: loss = 4.90733 (* 1 = 4.90733 loss)
I0409 23:26:19.198580  8290 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0409 23:26:24.232456  8290 solver.cpp:218] Iteration 852 (2.38394 iter/s, 5.03369s/12 iters), loss = 4.94363
I0409 23:26:24.232506  8290 solver.cpp:237]     Train net output #0: loss = 4.94363 (* 1 = 4.94363 loss)
I0409 23:26:24.232515  8290 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0409 23:26:29.170835  8290 solver.cpp:218] Iteration 864 (2.43007 iter/s, 4.93814s/12 iters), loss = 4.96307
I0409 23:26:29.170892  8290 solver.cpp:237]     Train net output #0: loss = 4.96307 (* 1 = 4.96307 loss)
I0409 23:26:29.170903  8290 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0409 23:26:34.254690  8290 solver.cpp:218] Iteration 876 (2.36053 iter/s, 5.0836s/12 iters), loss = 4.99974
I0409 23:26:34.254743  8290 solver.cpp:237]     Train net output #0: loss = 4.99974 (* 1 = 4.99974 loss)
I0409 23:26:34.254755  8290 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0409 23:26:39.224880  8290 solver.cpp:218] Iteration 888 (2.41451 iter/s, 4.96995s/12 iters), loss = 4.87933
I0409 23:26:39.224932  8290 solver.cpp:237]     Train net output #0: loss = 4.87933 (* 1 = 4.87933 loss)
I0409 23:26:39.224942  8290 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0409 23:26:44.143494  8290 solver.cpp:218] Iteration 900 (2.43983 iter/s, 4.91838s/12 iters), loss = 4.98942
I0409 23:26:44.143546  8290 solver.cpp:237]     Train net output #0: loss = 4.98942 (* 1 = 4.98942 loss)
I0409 23:26:44.143558  8290 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0409 23:26:47.993121  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:26:49.090915  8290 solver.cpp:218] Iteration 912 (2.42562 iter/s, 4.94718s/12 iters), loss = 4.76947
I0409 23:26:49.090968  8290 solver.cpp:237]     Train net output #0: loss = 4.76947 (* 1 = 4.76947 loss)
I0409 23:26:49.090978  8290 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0409 23:26:51.091018  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0409 23:26:51.823252  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0409 23:26:52.378254  8290 solver.cpp:330] Iteration 918, Testing net (#0)
I0409 23:26:52.378278  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:26:56.350680  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:26:56.749975  8290 solver.cpp:397]     Test net output #0: accuracy = 0.0245098
I0409 23:26:56.750025  8290 solver.cpp:397]     Test net output #1: loss = 4.91046 (* 1 = 4.91046 loss)
I0409 23:26:58.726446  8290 solver.cpp:218] Iteration 924 (1.24544 iter/s, 9.63513s/12 iters), loss = 4.97043
I0409 23:26:58.726495  8290 solver.cpp:237]     Train net output #0: loss = 4.97043 (* 1 = 4.97043 loss)
I0409 23:26:58.726505  8290 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0409 23:27:03.876677  8290 solver.cpp:218] Iteration 936 (2.3301 iter/s, 5.14999s/12 iters), loss = 4.93574
I0409 23:27:03.876726  8290 solver.cpp:237]     Train net output #0: loss = 4.93574 (* 1 = 4.93574 loss)
I0409 23:27:03.876737  8290 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0409 23:27:08.731297  8290 solver.cpp:218] Iteration 948 (2.47199 iter/s, 4.85439s/12 iters), loss = 4.8595
I0409 23:27:08.731343  8290 solver.cpp:237]     Train net output #0: loss = 4.8595 (* 1 = 4.8595 loss)
I0409 23:27:08.731353  8290 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0409 23:27:13.796998  8290 solver.cpp:218] Iteration 960 (2.36899 iter/s, 5.06546s/12 iters), loss = 4.72768
I0409 23:27:13.797056  8290 solver.cpp:237]     Train net output #0: loss = 4.72768 (* 1 = 4.72768 loss)
I0409 23:27:13.797067  8290 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0409 23:27:18.768213  8290 solver.cpp:218] Iteration 972 (2.41401 iter/s, 4.97097s/12 iters), loss = 4.9028
I0409 23:27:18.768262  8290 solver.cpp:237]     Train net output #0: loss = 4.9028 (* 1 = 4.9028 loss)
I0409 23:27:18.768273  8290 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0409 23:27:23.718634  8290 solver.cpp:218] Iteration 984 (2.42415 iter/s, 4.95018s/12 iters), loss = 4.95181
I0409 23:27:23.718768  8290 solver.cpp:237]     Train net output #0: loss = 4.95181 (* 1 = 4.95181 loss)
I0409 23:27:23.718781  8290 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0409 23:27:28.555603  8290 solver.cpp:218] Iteration 996 (2.48105 iter/s, 4.83665s/12 iters), loss = 4.70357
I0409 23:27:28.555660  8290 solver.cpp:237]     Train net output #0: loss = 4.70357 (* 1 = 4.70357 loss)
I0409 23:27:28.555671  8290 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0409 23:27:33.686547  8290 solver.cpp:218] Iteration 1008 (2.33887 iter/s, 5.13069s/12 iters), loss = 4.91445
I0409 23:27:33.686599  8290 solver.cpp:237]     Train net output #0: loss = 4.91445 (* 1 = 4.91445 loss)
I0409 23:27:33.686610  8290 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0409 23:27:34.694994  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:27:38.194854  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0409 23:27:39.462452  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0409 23:27:39.978704  8290 solver.cpp:330] Iteration 1020, Testing net (#0)
I0409 23:27:39.978724  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:27:44.040923  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:27:44.474414  8290 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0409 23:27:44.474444  8290 solver.cpp:397]     Test net output #1: loss = 4.84341 (* 1 = 4.84341 loss)
I0409 23:27:44.556373  8290 solver.cpp:218] Iteration 1020 (1.10402 iter/s, 10.8694s/12 iters), loss = 4.75265
I0409 23:27:44.556438  8290 solver.cpp:237]     Train net output #0: loss = 4.75265 (* 1 = 4.75265 loss)
I0409 23:27:44.556449  8290 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0409 23:27:48.648869  8290 solver.cpp:218] Iteration 1032 (2.93236 iter/s, 4.09227s/12 iters), loss = 4.73618
I0409 23:27:48.648917  8290 solver.cpp:237]     Train net output #0: loss = 4.73618 (* 1 = 4.73618 loss)
I0409 23:27:48.648926  8290 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0409 23:27:53.536084  8290 solver.cpp:218] Iteration 1044 (2.4555 iter/s, 4.88698s/12 iters), loss = 4.77176
I0409 23:27:53.536131  8290 solver.cpp:237]     Train net output #0: loss = 4.77176 (* 1 = 4.77176 loss)
I0409 23:27:53.536140  8290 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0409 23:27:58.400857  8290 solver.cpp:218] Iteration 1056 (2.46683 iter/s, 4.86454s/12 iters), loss = 4.84565
I0409 23:27:58.401021  8290 solver.cpp:237]     Train net output #0: loss = 4.84565 (* 1 = 4.84565 loss)
I0409 23:27:58.401034  8290 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0409 23:28:03.348965  8290 solver.cpp:218] Iteration 1068 (2.42534 iter/s, 4.94776s/12 iters), loss = 4.84731
I0409 23:28:03.349025  8290 solver.cpp:237]     Train net output #0: loss = 4.84731 (* 1 = 4.84731 loss)
I0409 23:28:03.349040  8290 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0409 23:28:08.245748  8290 solver.cpp:218] Iteration 1080 (2.45071 iter/s, 4.89654s/12 iters), loss = 4.73938
I0409 23:28:08.245802  8290 solver.cpp:237]     Train net output #0: loss = 4.73938 (* 1 = 4.73938 loss)
I0409 23:28:08.245810  8290 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0409 23:28:13.249998  8290 solver.cpp:218] Iteration 1092 (2.39809 iter/s, 5.00399s/12 iters), loss = 4.67332
I0409 23:28:13.250043  8290 solver.cpp:237]     Train net output #0: loss = 4.67332 (* 1 = 4.67332 loss)
I0409 23:28:13.250052  8290 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0409 23:28:18.150962  8290 solver.cpp:218] Iteration 1104 (2.44861 iter/s, 4.90074s/12 iters), loss = 4.67712
I0409 23:28:18.151010  8290 solver.cpp:237]     Train net output #0: loss = 4.67712 (* 1 = 4.67712 loss)
I0409 23:28:18.151021  8290 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0409 23:28:21.255975  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:28:23.057354  8290 solver.cpp:218] Iteration 1116 (2.44591 iter/s, 4.90615s/12 iters), loss = 4.65016
I0409 23:28:23.057404  8290 solver.cpp:237]     Train net output #0: loss = 4.65016 (* 1 = 4.65016 loss)
I0409 23:28:23.057413  8290 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0409 23:28:25.083551  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0409 23:28:25.834357  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0409 23:28:26.360682  8290 solver.cpp:330] Iteration 1122, Testing net (#0)
I0409 23:28:26.360702  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:28:30.308708  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:28:30.789525  8290 solver.cpp:397]     Test net output #0: accuracy = 0.0410539
I0409 23:28:30.789566  8290 solver.cpp:397]     Test net output #1: loss = 4.69028 (* 1 = 4.69028 loss)
I0409 23:28:32.640273  8290 solver.cpp:218] Iteration 1128 (1.25228 iter/s, 9.58252s/12 iters), loss = 4.85477
I0409 23:28:32.640321  8290 solver.cpp:237]     Train net output #0: loss = 4.85477 (* 1 = 4.85477 loss)
I0409 23:28:32.640331  8290 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0409 23:28:37.577080  8290 solver.cpp:218] Iteration 1140 (2.43084 iter/s, 4.93657s/12 iters), loss = 4.75351
I0409 23:28:37.577138  8290 solver.cpp:237]     Train net output #0: loss = 4.75351 (* 1 = 4.75351 loss)
I0409 23:28:37.577150  8290 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0409 23:28:42.475792  8290 solver.cpp:218] Iteration 1152 (2.44974 iter/s, 4.89847s/12 iters), loss = 4.56573
I0409 23:28:42.475845  8290 solver.cpp:237]     Train net output #0: loss = 4.56573 (* 1 = 4.56573 loss)
I0409 23:28:42.475857  8290 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0409 23:28:47.341917  8290 solver.cpp:218] Iteration 1164 (2.46615 iter/s, 4.86588s/12 iters), loss = 4.62684
I0409 23:28:47.341997  8290 solver.cpp:237]     Train net output #0: loss = 4.62684 (* 1 = 4.62684 loss)
I0409 23:28:47.342010  8290 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0409 23:28:52.166054  8290 solver.cpp:218] Iteration 1176 (2.48763 iter/s, 4.82387s/12 iters), loss = 4.65726
I0409 23:28:52.166110  8290 solver.cpp:237]     Train net output #0: loss = 4.65726 (* 1 = 4.65726 loss)
I0409 23:28:52.166121  8290 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0409 23:28:57.043906  8290 solver.cpp:218] Iteration 1188 (2.46022 iter/s, 4.87761s/12 iters), loss = 4.5568
I0409 23:28:57.043947  8290 solver.cpp:237]     Train net output #0: loss = 4.5568 (* 1 = 4.5568 loss)
I0409 23:28:57.043956  8290 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0409 23:29:01.944789  8290 solver.cpp:218] Iteration 1200 (2.44865 iter/s, 4.90066s/12 iters), loss = 4.67804
I0409 23:29:01.944903  8290 solver.cpp:237]     Train net output #0: loss = 4.67804 (* 1 = 4.67804 loss)
I0409 23:29:01.944912  8290 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0409 23:29:06.866992  8290 solver.cpp:218] Iteration 1212 (2.43808 iter/s, 4.9219s/12 iters), loss = 4.61582
I0409 23:29:06.867040  8290 solver.cpp:237]     Train net output #0: loss = 4.61582 (* 1 = 4.61582 loss)
I0409 23:29:06.867050  8290 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0409 23:29:07.151301  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:29:11.299476  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0409 23:29:13.169529  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0409 23:29:14.635448  8290 solver.cpp:330] Iteration 1224, Testing net (#0)
I0409 23:29:14.635475  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:29:18.435395  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:29:18.945602  8290 solver.cpp:397]     Test net output #0: accuracy = 0.0520833
I0409 23:29:18.945665  8290 solver.cpp:397]     Test net output #1: loss = 4.56958 (* 1 = 4.56958 loss)
I0409 23:29:19.027751  8290 solver.cpp:218] Iteration 1224 (0.98682 iter/s, 12.1603s/12 iters), loss = 4.59611
I0409 23:29:19.027813  8290 solver.cpp:237]     Train net output #0: loss = 4.59611 (* 1 = 4.59611 loss)
I0409 23:29:19.027824  8290 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0409 23:29:23.140089  8290 solver.cpp:218] Iteration 1236 (2.9182 iter/s, 4.11212s/12 iters), loss = 4.82215
I0409 23:29:23.140134  8290 solver.cpp:237]     Train net output #0: loss = 4.82215 (* 1 = 4.82215 loss)
I0409 23:29:23.140146  8290 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0409 23:29:27.951359  8290 solver.cpp:218] Iteration 1248 (2.49426 iter/s, 4.81105s/12 iters), loss = 4.49676
I0409 23:29:27.951401  8290 solver.cpp:237]     Train net output #0: loss = 4.49676 (* 1 = 4.49676 loss)
I0409 23:29:27.951409  8290 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0409 23:29:32.786814  8290 solver.cpp:218] Iteration 1260 (2.48179 iter/s, 4.83523s/12 iters), loss = 4.56647
I0409 23:29:32.786942  8290 solver.cpp:237]     Train net output #0: loss = 4.56647 (* 1 = 4.56647 loss)
I0409 23:29:32.786953  8290 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0409 23:29:37.641471  8290 solver.cpp:218] Iteration 1272 (2.47201 iter/s, 4.85435s/12 iters), loss = 4.44183
I0409 23:29:37.641521  8290 solver.cpp:237]     Train net output #0: loss = 4.44183 (* 1 = 4.44183 loss)
I0409 23:29:37.641533  8290 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0409 23:29:42.544235  8290 solver.cpp:218] Iteration 1284 (2.44771 iter/s, 4.90253s/12 iters), loss = 4.46105
I0409 23:29:42.544282  8290 solver.cpp:237]     Train net output #0: loss = 4.46105 (* 1 = 4.46105 loss)
I0409 23:29:42.544292  8290 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0409 23:29:47.376801  8290 solver.cpp:218] Iteration 1296 (2.48327 iter/s, 4.83233s/12 iters), loss = 4.36804
I0409 23:29:47.376866  8290 solver.cpp:237]     Train net output #0: loss = 4.36804 (* 1 = 4.36804 loss)
I0409 23:29:47.376878  8290 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0409 23:29:52.216853  8290 solver.cpp:218] Iteration 1308 (2.47944 iter/s, 4.83981s/12 iters), loss = 4.4353
I0409 23:29:52.216907  8290 solver.cpp:237]     Train net output #0: loss = 4.4353 (* 1 = 4.4353 loss)
I0409 23:29:52.216918  8290 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0409 23:29:54.624832  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:29:57.030966  8290 solver.cpp:218] Iteration 1320 (2.49279 iter/s, 4.81388s/12 iters), loss = 4.4104
I0409 23:29:57.031023  8290 solver.cpp:237]     Train net output #0: loss = 4.4104 (* 1 = 4.4104 loss)
I0409 23:29:57.031034  8290 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0409 23:29:59.004508  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0409 23:29:59.760129  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0409 23:30:00.284618  8290 solver.cpp:330] Iteration 1326, Testing net (#0)
I0409 23:30:00.284644  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:30:04.222672  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:30:04.779300  8290 solver.cpp:397]     Test net output #0: accuracy = 0.0594363
I0409 23:30:04.779350  8290 solver.cpp:397]     Test net output #1: loss = 4.39001 (* 1 = 4.39001 loss)
I0409 23:30:06.697471  8290 solver.cpp:218] Iteration 1332 (1.24145 iter/s, 9.6661s/12 iters), loss = 4.28586
I0409 23:30:06.697527  8290 solver.cpp:237]     Train net output #0: loss = 4.28586 (* 1 = 4.28586 loss)
I0409 23:30:06.697540  8290 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0409 23:30:11.579267  8290 solver.cpp:218] Iteration 1344 (2.45824 iter/s, 4.88155s/12 iters), loss = 4.2958
I0409 23:30:11.579325  8290 solver.cpp:237]     Train net output #0: loss = 4.2958 (* 1 = 4.2958 loss)
I0409 23:30:11.579337  8290 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0409 23:30:16.455214  8290 solver.cpp:218] Iteration 1356 (2.46118 iter/s, 4.87571s/12 iters), loss = 4.55699
I0409 23:30:16.455258  8290 solver.cpp:237]     Train net output #0: loss = 4.55699 (* 1 = 4.55699 loss)
I0409 23:30:16.455268  8290 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0409 23:30:21.256958  8290 solver.cpp:218] Iteration 1368 (2.49922 iter/s, 4.80151s/12 iters), loss = 4.5433
I0409 23:30:21.257016  8290 solver.cpp:237]     Train net output #0: loss = 4.5433 (* 1 = 4.5433 loss)
I0409 23:30:21.257028  8290 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0409 23:30:21.257306  8290 blocking_queue.cpp:49] Waiting for data
I0409 23:30:26.094386  8290 solver.cpp:218] Iteration 1380 (2.48078 iter/s, 4.83719s/12 iters), loss = 4.29493
I0409 23:30:26.094434  8290 solver.cpp:237]     Train net output #0: loss = 4.29493 (* 1 = 4.29493 loss)
I0409 23:30:26.094442  8290 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0409 23:30:30.939294  8290 solver.cpp:218] Iteration 1392 (2.47695 iter/s, 4.84468s/12 iters), loss = 4.31019
I0409 23:30:30.939340  8290 solver.cpp:237]     Train net output #0: loss = 4.31019 (* 1 = 4.31019 loss)
I0409 23:30:30.939349  8290 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0409 23:30:35.783933  8290 solver.cpp:218] Iteration 1404 (2.47708 iter/s, 4.84441s/12 iters), loss = 4.40704
I0409 23:30:35.784130  8290 solver.cpp:237]     Train net output #0: loss = 4.40704 (* 1 = 4.40704 loss)
I0409 23:30:35.784145  8290 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0409 23:30:40.363777  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:30:40.716806  8290 solver.cpp:218] Iteration 1416 (2.43284 iter/s, 4.9325s/12 iters), loss = 4.29338
I0409 23:30:40.716856  8290 solver.cpp:237]     Train net output #0: loss = 4.29338 (* 1 = 4.29338 loss)
I0409 23:30:40.716868  8290 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0409 23:30:45.132454  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0409 23:30:45.857223  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0409 23:30:46.394294  8290 solver.cpp:330] Iteration 1428, Testing net (#0)
I0409 23:30:46.394320  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:30:50.234884  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:30:50.823966  8290 solver.cpp:397]     Test net output #0: accuracy = 0.0833333
I0409 23:30:50.824012  8290 solver.cpp:397]     Test net output #1: loss = 4.2649 (* 1 = 4.2649 loss)
I0409 23:30:50.906538  8290 solver.cpp:218] Iteration 1428 (1.1777 iter/s, 10.1893s/12 iters), loss = 4.30635
I0409 23:30:50.906613  8290 solver.cpp:237]     Train net output #0: loss = 4.30635 (* 1 = 4.30635 loss)
I0409 23:30:50.906628  8290 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0409 23:30:54.988200  8290 solver.cpp:218] Iteration 1440 (2.94014 iter/s, 4.08143s/12 iters), loss = 4.27452
I0409 23:30:54.988266  8290 solver.cpp:237]     Train net output #0: loss = 4.27452 (* 1 = 4.27452 loss)
I0409 23:30:54.988277  8290 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0409 23:30:59.786989  8290 solver.cpp:218] Iteration 1452 (2.50076 iter/s, 4.79854s/12 iters), loss = 4.3055
I0409 23:30:59.787046  8290 solver.cpp:237]     Train net output #0: loss = 4.3055 (* 1 = 4.3055 loss)
I0409 23:30:59.787060  8290 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0409 23:31:04.615373  8290 solver.cpp:218] Iteration 1464 (2.48543 iter/s, 4.82814s/12 iters), loss = 4.35901
I0409 23:31:04.615437  8290 solver.cpp:237]     Train net output #0: loss = 4.35901 (* 1 = 4.35901 loss)
I0409 23:31:04.615449  8290 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0409 23:31:09.453071  8290 solver.cpp:218] Iteration 1476 (2.48064 iter/s, 4.83746s/12 iters), loss = 4.32005
I0409 23:31:09.453153  8290 solver.cpp:237]     Train net output #0: loss = 4.32005 (* 1 = 4.32005 loss)
I0409 23:31:09.453164  8290 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0409 23:31:14.246240  8290 solver.cpp:218] Iteration 1488 (2.5037 iter/s, 4.7929s/12 iters), loss = 4.21909
I0409 23:31:14.246296  8290 solver.cpp:237]     Train net output #0: loss = 4.21909 (* 1 = 4.21909 loss)
I0409 23:31:14.246309  8290 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0409 23:31:19.067498  8290 solver.cpp:218] Iteration 1500 (2.4891 iter/s, 4.82102s/12 iters), loss = 4.19458
I0409 23:31:19.067554  8290 solver.cpp:237]     Train net output #0: loss = 4.19458 (* 1 = 4.19458 loss)
I0409 23:31:19.067565  8290 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0409 23:31:23.871696  8290 solver.cpp:218] Iteration 1512 (2.49794 iter/s, 4.80396s/12 iters), loss = 4.38337
I0409 23:31:23.871757  8290 solver.cpp:237]     Train net output #0: loss = 4.38337 (* 1 = 4.38337 loss)
I0409 23:31:23.871767  8290 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0409 23:31:25.642429  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:31:28.821491  8290 solver.cpp:218] Iteration 1524 (2.42446 iter/s, 4.94955s/12 iters), loss = 4.24504
I0409 23:31:28.821539  8290 solver.cpp:237]     Train net output #0: loss = 4.24504 (* 1 = 4.24504 loss)
I0409 23:31:28.821550  8290 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0409 23:31:30.830524  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0409 23:31:31.611694  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0409 23:31:32.154698  8290 solver.cpp:330] Iteration 1530, Testing net (#0)
I0409 23:31:32.154726  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:31:36.103706  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:31:36.789288  8290 solver.cpp:397]     Test net output #0: accuracy = 0.0925245
I0409 23:31:36.789340  8290 solver.cpp:397]     Test net output #1: loss = 4.14525 (* 1 = 4.14525 loss)
I0409 23:31:38.512797  8290 solver.cpp:218] Iteration 1536 (1.23827 iter/s, 9.69091s/12 iters), loss = 4.31062
I0409 23:31:38.512840  8290 solver.cpp:237]     Train net output #0: loss = 4.31062 (* 1 = 4.31062 loss)
I0409 23:31:38.512848  8290 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0409 23:31:43.371649  8290 solver.cpp:218] Iteration 1548 (2.46983 iter/s, 4.85862s/12 iters), loss = 3.86418
I0409 23:31:43.371767  8290 solver.cpp:237]     Train net output #0: loss = 3.86418 (* 1 = 3.86418 loss)
I0409 23:31:43.371775  8290 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0409 23:31:48.266250  8290 solver.cpp:218] Iteration 1560 (2.45183 iter/s, 4.8943s/12 iters), loss = 4.20813
I0409 23:31:48.266299  8290 solver.cpp:237]     Train net output #0: loss = 4.20813 (* 1 = 4.20813 loss)
I0409 23:31:48.266307  8290 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0409 23:31:53.236055  8290 solver.cpp:218] Iteration 1572 (2.4147 iter/s, 4.96956s/12 iters), loss = 4.05597
I0409 23:31:53.236115  8290 solver.cpp:237]     Train net output #0: loss = 4.05597 (* 1 = 4.05597 loss)
I0409 23:31:53.236129  8290 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0409 23:31:58.109220  8290 solver.cpp:218] Iteration 1584 (2.46259 iter/s, 4.87292s/12 iters), loss = 4.16828
I0409 23:31:58.109277  8290 solver.cpp:237]     Train net output #0: loss = 4.16828 (* 1 = 4.16828 loss)
I0409 23:31:58.109288  8290 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0409 23:32:03.032187  8290 solver.cpp:218] Iteration 1596 (2.43768 iter/s, 4.92272s/12 iters), loss = 4.0268
I0409 23:32:03.032241  8290 solver.cpp:237]     Train net output #0: loss = 4.0268 (* 1 = 4.0268 loss)
I0409 23:32:03.032253  8290 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0409 23:32:07.969082  8290 solver.cpp:218] Iteration 1608 (2.43079 iter/s, 4.93666s/12 iters), loss = 4.32273
I0409 23:32:07.969131  8290 solver.cpp:237]     Train net output #0: loss = 4.32273 (* 1 = 4.32273 loss)
I0409 23:32:07.969142  8290 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0409 23:32:11.770505  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:32:12.848798  8290 solver.cpp:218] Iteration 1620 (2.45928 iter/s, 4.87948s/12 iters), loss = 4.14153
I0409 23:32:12.848845  8290 solver.cpp:237]     Train net output #0: loss = 4.14153 (* 1 = 4.14153 loss)
I0409 23:32:12.848853  8290 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0409 23:32:17.349336  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0409 23:32:18.056066  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0409 23:32:18.592262  8290 solver.cpp:330] Iteration 1632, Testing net (#0)
I0409 23:32:18.592289  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:32:22.471453  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:32:23.162001  8290 solver.cpp:397]     Test net output #0: accuracy = 0.106618
I0409 23:32:23.162042  8290 solver.cpp:397]     Test net output #1: loss = 4.07655 (* 1 = 4.07655 loss)
I0409 23:32:23.244151  8290 solver.cpp:218] Iteration 1632 (1.15441 iter/s, 10.3949s/12 iters), loss = 3.96122
I0409 23:32:23.244202  8290 solver.cpp:237]     Train net output #0: loss = 3.96122 (* 1 = 3.96122 loss)
I0409 23:32:23.244212  8290 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0409 23:32:27.439003  8290 solver.cpp:218] Iteration 1644 (2.86079 iter/s, 4.19464s/12 iters), loss = 4.08151
I0409 23:32:27.439043  8290 solver.cpp:237]     Train net output #0: loss = 4.08151 (* 1 = 4.08151 loss)
I0409 23:32:27.439051  8290 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0409 23:32:32.327906  8290 solver.cpp:218] Iteration 1656 (2.45465 iter/s, 4.88868s/12 iters), loss = 4.11488
I0409 23:32:32.327960  8290 solver.cpp:237]     Train net output #0: loss = 4.11488 (* 1 = 4.11488 loss)
I0409 23:32:32.327972  8290 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0409 23:32:37.232858  8290 solver.cpp:218] Iteration 1668 (2.44662 iter/s, 4.90472s/12 iters), loss = 3.87917
I0409 23:32:37.232899  8290 solver.cpp:237]     Train net output #0: loss = 3.87917 (* 1 = 3.87917 loss)
I0409 23:32:37.232908  8290 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0409 23:32:42.140982  8290 solver.cpp:218] Iteration 1680 (2.44504 iter/s, 4.9079s/12 iters), loss = 3.99036
I0409 23:32:42.141029  8290 solver.cpp:237]     Train net output #0: loss = 3.99036 (* 1 = 3.99036 loss)
I0409 23:32:42.141039  8290 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0409 23:32:47.341327  8290 solver.cpp:218] Iteration 1692 (2.30764 iter/s, 5.20011s/12 iters), loss = 3.95914
I0409 23:32:47.341372  8290 solver.cpp:237]     Train net output #0: loss = 3.95914 (* 1 = 3.95914 loss)
I0409 23:32:47.341382  8290 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0409 23:32:52.276499  8290 solver.cpp:218] Iteration 1704 (2.43164 iter/s, 4.93494s/12 iters), loss = 3.90232
I0409 23:32:52.276624  8290 solver.cpp:237]     Train net output #0: loss = 3.90232 (* 1 = 3.90232 loss)
I0409 23:32:52.276633  8290 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0409 23:32:57.151278  8290 solver.cpp:218] Iteration 1716 (2.46181 iter/s, 4.87447s/12 iters), loss = 4.00772
I0409 23:32:57.151327  8290 solver.cpp:237]     Train net output #0: loss = 4.00772 (* 1 = 4.00772 loss)
I0409 23:32:57.151340  8290 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0409 23:32:58.185544  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:02.037331  8290 solver.cpp:218] Iteration 1728 (2.45609 iter/s, 4.88582s/12 iters), loss = 4.00857
I0409 23:33:02.037385  8290 solver.cpp:237]     Train net output #0: loss = 4.00857 (* 1 = 4.00857 loss)
I0409 23:33:02.037397  8290 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0409 23:33:04.058495  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0409 23:33:04.982288  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0409 23:33:05.679397  8290 solver.cpp:330] Iteration 1734, Testing net (#0)
I0409 23:33:05.679423  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:33:09.271613  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:09.973120  8290 solver.cpp:397]     Test net output #0: accuracy = 0.0931373
I0409 23:33:09.973170  8290 solver.cpp:397]     Test net output #1: loss = 4.06416 (* 1 = 4.06416 loss)
I0409 23:33:11.824493  8290 solver.cpp:218] Iteration 1740 (1.22615 iter/s, 9.78675s/12 iters), loss = 3.93492
I0409 23:33:11.824537  8290 solver.cpp:237]     Train net output #0: loss = 3.93492 (* 1 = 3.93492 loss)
I0409 23:33:11.824545  8290 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0409 23:33:16.750566  8290 solver.cpp:218] Iteration 1752 (2.43613 iter/s, 4.92584s/12 iters), loss = 3.90515
I0409 23:33:16.750628  8290 solver.cpp:237]     Train net output #0: loss = 3.90515 (* 1 = 3.90515 loss)
I0409 23:33:16.750641  8290 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0409 23:33:21.627084  8290 solver.cpp:218] Iteration 1764 (2.4609 iter/s, 4.87627s/12 iters), loss = 3.87551
I0409 23:33:21.627133  8290 solver.cpp:237]     Train net output #0: loss = 3.87551 (* 1 = 3.87551 loss)
I0409 23:33:21.627144  8290 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0409 23:33:26.517915  8290 solver.cpp:218] Iteration 1776 (2.45369 iter/s, 4.8906s/12 iters), loss = 3.86141
I0409 23:33:26.519124  8290 solver.cpp:237]     Train net output #0: loss = 3.86141 (* 1 = 3.86141 loss)
I0409 23:33:26.519137  8290 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0409 23:33:31.504731  8290 solver.cpp:218] Iteration 1788 (2.40702 iter/s, 4.98543s/12 iters), loss = 3.8601
I0409 23:33:31.504779  8290 solver.cpp:237]     Train net output #0: loss = 3.8601 (* 1 = 3.8601 loss)
I0409 23:33:31.504788  8290 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0409 23:33:36.384677  8290 solver.cpp:218] Iteration 1800 (2.45916 iter/s, 4.87971s/12 iters), loss = 3.94373
I0409 23:33:36.384729  8290 solver.cpp:237]     Train net output #0: loss = 3.94373 (* 1 = 3.94373 loss)
I0409 23:33:36.384742  8290 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0409 23:33:41.466390  8290 solver.cpp:218] Iteration 1812 (2.36152 iter/s, 5.08147s/12 iters), loss = 3.81715
I0409 23:33:41.466444  8290 solver.cpp:237]     Train net output #0: loss = 3.81715 (* 1 = 3.81715 loss)
I0409 23:33:41.466455  8290 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0409 23:33:44.657694  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:46.438589  8290 solver.cpp:218] Iteration 1824 (2.41354 iter/s, 4.97196s/12 iters), loss = 3.9224
I0409 23:33:46.438645  8290 solver.cpp:237]     Train net output #0: loss = 3.9224 (* 1 = 3.9224 loss)
I0409 23:33:46.438657  8290 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0409 23:33:50.937685  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0409 23:33:51.701740  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0409 23:33:52.240957  8290 solver.cpp:330] Iteration 1836, Testing net (#0)
I0409 23:33:52.240983  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:33:55.938870  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:33:56.685804  8290 solver.cpp:397]     Test net output #0: accuracy = 0.112745
I0409 23:33:56.685938  8290 solver.cpp:397]     Test net output #1: loss = 3.91485 (* 1 = 3.91485 loss)
I0409 23:33:56.767382  8290 solver.cpp:218] Iteration 1836 (1.16185 iter/s, 10.3284s/12 iters), loss = 3.89828
I0409 23:33:56.767436  8290 solver.cpp:237]     Train net output #0: loss = 3.89828 (* 1 = 3.89828 loss)
I0409 23:33:56.767448  8290 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0409 23:34:01.054229  8290 solver.cpp:218] Iteration 1848 (2.7994 iter/s, 4.28663s/12 iters), loss = 3.87536
I0409 23:34:01.054270  8290 solver.cpp:237]     Train net output #0: loss = 3.87536 (* 1 = 3.87536 loss)
I0409 23:34:01.054280  8290 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0409 23:34:05.899570  8290 solver.cpp:218] Iteration 1860 (2.47672 iter/s, 4.84512s/12 iters), loss = 3.61103
I0409 23:34:05.899622  8290 solver.cpp:237]     Train net output #0: loss = 3.61103 (* 1 = 3.61103 loss)
I0409 23:34:05.899633  8290 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0409 23:34:10.819231  8290 solver.cpp:218] Iteration 1872 (2.43931 iter/s, 4.91943s/12 iters), loss = 3.8362
I0409 23:34:10.819286  8290 solver.cpp:237]     Train net output #0: loss = 3.8362 (* 1 = 3.8362 loss)
I0409 23:34:10.819301  8290 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0409 23:34:15.827095  8290 solver.cpp:218] Iteration 1884 (2.39635 iter/s, 5.00761s/12 iters), loss = 3.8556
I0409 23:34:15.827165  8290 solver.cpp:237]     Train net output #0: loss = 3.8556 (* 1 = 3.8556 loss)
I0409 23:34:15.827181  8290 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0409 23:34:20.797189  8290 solver.cpp:218] Iteration 1896 (2.41456 iter/s, 4.96985s/12 iters), loss = 3.81883
I0409 23:34:20.797235  8290 solver.cpp:237]     Train net output #0: loss = 3.81883 (* 1 = 3.81883 loss)
I0409 23:34:20.797245  8290 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0409 23:34:25.700306  8290 solver.cpp:218] Iteration 1908 (2.44754 iter/s, 4.90289s/12 iters), loss = 3.99652
I0409 23:34:25.700356  8290 solver.cpp:237]     Train net output #0: loss = 3.99652 (* 1 = 3.99652 loss)
I0409 23:34:25.700364  8290 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0409 23:34:30.577050  8290 solver.cpp:218] Iteration 1920 (2.46078 iter/s, 4.87651s/12 iters), loss = 3.8157
I0409 23:34:30.580497  8290 solver.cpp:237]     Train net output #0: loss = 3.8157 (* 1 = 3.8157 loss)
I0409 23:34:30.580508  8290 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0409 23:34:30.896201  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:34:35.493530  8290 solver.cpp:218] Iteration 1932 (2.44257 iter/s, 4.91285s/12 iters), loss = 3.77454
I0409 23:34:35.493588  8290 solver.cpp:237]     Train net output #0: loss = 3.77454 (* 1 = 3.77454 loss)
I0409 23:34:35.493602  8290 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0409 23:34:37.464057  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0409 23:34:38.196755  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0409 23:34:38.725883  8290 solver.cpp:330] Iteration 1938, Testing net (#0)
I0409 23:34:38.725912  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:34:42.463874  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:34:43.244628  8290 solver.cpp:397]     Test net output #0: accuracy = 0.145221
I0409 23:34:43.244668  8290 solver.cpp:397]     Test net output #1: loss = 3.69411 (* 1 = 3.69411 loss)
I0409 23:34:45.064960  8290 solver.cpp:218] Iteration 1944 (1.25378 iter/s, 9.57102s/12 iters), loss = 3.7683
I0409 23:34:45.065006  8290 solver.cpp:237]     Train net output #0: loss = 3.7683 (* 1 = 3.7683 loss)
I0409 23:34:45.065014  8290 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0409 23:34:50.044875  8290 solver.cpp:218] Iteration 1956 (2.40979 iter/s, 4.97968s/12 iters), loss = 3.57099
I0409 23:34:50.044926  8290 solver.cpp:237]     Train net output #0: loss = 3.57099 (* 1 = 3.57099 loss)
I0409 23:34:50.044935  8290 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0409 23:34:54.915809  8290 solver.cpp:218] Iteration 1968 (2.46371 iter/s, 4.8707s/12 iters), loss = 3.66782
I0409 23:34:54.915853  8290 solver.cpp:237]     Train net output #0: loss = 3.66782 (* 1 = 3.66782 loss)
I0409 23:34:54.915863  8290 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0409 23:34:59.751766  8290 solver.cpp:218] Iteration 1980 (2.48153 iter/s, 4.83573s/12 iters), loss = 3.53628
I0409 23:34:59.751807  8290 solver.cpp:237]     Train net output #0: loss = 3.53628 (* 1 = 3.53628 loss)
I0409 23:34:59.751816  8290 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0409 23:35:04.552168  8290 solver.cpp:218] Iteration 1992 (2.49991 iter/s, 4.80018s/12 iters), loss = 3.71887
I0409 23:35:04.552253  8290 solver.cpp:237]     Train net output #0: loss = 3.71887 (* 1 = 3.71887 loss)
I0409 23:35:04.552266  8290 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0409 23:35:09.386368  8290 solver.cpp:218] Iteration 2004 (2.48245 iter/s, 4.83393s/12 iters), loss = 3.52654
I0409 23:35:09.386422  8290 solver.cpp:237]     Train net output #0: loss = 3.52654 (* 1 = 3.52654 loss)
I0409 23:35:09.386435  8290 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0409 23:35:14.290952  8290 solver.cpp:218] Iteration 2016 (2.44681 iter/s, 4.90435s/12 iters), loss = 3.61567
I0409 23:35:14.290997  8290 solver.cpp:237]     Train net output #0: loss = 3.61567 (* 1 = 3.61567 loss)
I0409 23:35:14.291009  8290 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0409 23:35:16.788079  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:35:19.172032  8290 solver.cpp:218] Iteration 2028 (2.45859 iter/s, 4.88085s/12 iters), loss = 3.44332
I0409 23:35:19.172078  8290 solver.cpp:237]     Train net output #0: loss = 3.44332 (* 1 = 3.44332 loss)
I0409 23:35:19.172087  8290 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0409 23:35:23.688318  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0409 23:35:24.434070  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0409 23:35:24.975539  8290 solver.cpp:330] Iteration 2040, Testing net (#0)
I0409 23:35:24.975564  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:35:28.579335  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:35:29.403887  8290 solver.cpp:397]     Test net output #0: accuracy = 0.170343
I0409 23:35:29.403923  8290 solver.cpp:397]     Test net output #1: loss = 3.51427 (* 1 = 3.51427 loss)
I0409 23:35:29.486008  8290 solver.cpp:218] Iteration 2040 (1.16352 iter/s, 10.3136s/12 iters), loss = 3.4685
I0409 23:35:29.486068  8290 solver.cpp:237]     Train net output #0: loss = 3.4685 (* 1 = 3.4685 loss)
I0409 23:35:29.486080  8290 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0409 23:35:33.750592  8290 solver.cpp:218] Iteration 2052 (2.81402 iter/s, 4.26436s/12 iters), loss = 3.53668
I0409 23:35:33.750639  8290 solver.cpp:237]     Train net output #0: loss = 3.53668 (* 1 = 3.53668 loss)
I0409 23:35:33.750648  8290 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0409 23:35:34.116518  8290 blocking_queue.cpp:49] Waiting for data
I0409 23:35:38.606665  8290 solver.cpp:218] Iteration 2064 (2.47125 iter/s, 4.85584s/12 iters), loss = 3.45614
I0409 23:35:38.606786  8290 solver.cpp:237]     Train net output #0: loss = 3.45614 (* 1 = 3.45614 loss)
I0409 23:35:38.606796  8290 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0409 23:35:43.415724  8290 solver.cpp:218] Iteration 2076 (2.49545 iter/s, 4.80876s/12 iters), loss = 3.82129
I0409 23:35:43.415768  8290 solver.cpp:237]     Train net output #0: loss = 3.82129 (* 1 = 3.82129 loss)
I0409 23:35:43.415778  8290 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0409 23:35:48.314791  8290 solver.cpp:218] Iteration 2088 (2.44956 iter/s, 4.89883s/12 iters), loss = 3.36986
I0409 23:35:48.314846  8290 solver.cpp:237]     Train net output #0: loss = 3.36986 (* 1 = 3.36986 loss)
I0409 23:35:48.314860  8290 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0409 23:35:53.226948  8290 solver.cpp:218] Iteration 2100 (2.44304 iter/s, 4.91192s/12 iters), loss = 3.33483
I0409 23:35:53.226992  8290 solver.cpp:237]     Train net output #0: loss = 3.33483 (* 1 = 3.33483 loss)
I0409 23:35:53.227001  8290 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0409 23:35:58.132997  8290 solver.cpp:218] Iteration 2112 (2.44608 iter/s, 4.90582s/12 iters), loss = 3.40783
I0409 23:35:58.133051  8290 solver.cpp:237]     Train net output #0: loss = 3.40783 (* 1 = 3.40783 loss)
I0409 23:35:58.133064  8290 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0409 23:36:02.723071  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:36:03.030594  8290 solver.cpp:218] Iteration 2124 (2.4503 iter/s, 4.89736s/12 iters), loss = 3.14603
I0409 23:36:03.030643  8290 solver.cpp:237]     Train net output #0: loss = 3.14603 (* 1 = 3.14603 loss)
I0409 23:36:03.030653  8290 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0409 23:36:07.913058  8290 solver.cpp:218] Iteration 2136 (2.45789 iter/s, 4.88223s/12 iters), loss = 3.38008
I0409 23:36:07.913103  8290 solver.cpp:237]     Train net output #0: loss = 3.38008 (* 1 = 3.38008 loss)
I0409 23:36:07.913112  8290 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0409 23:36:09.885239  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0409 23:36:12.569259  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0409 23:36:13.488327  8290 solver.cpp:330] Iteration 2142, Testing net (#0)
I0409 23:36:13.488346  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:36:17.069357  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:36:17.928733  8290 solver.cpp:397]     Test net output #0: accuracy = 0.196078
I0409 23:36:17.928783  8290 solver.cpp:397]     Test net output #1: loss = 3.36436 (* 1 = 3.36436 loss)
I0409 23:36:19.583251  8290 solver.cpp:218] Iteration 2148 (1.0283 iter/s, 11.6697s/12 iters), loss = 3.25187
I0409 23:36:19.583303  8290 solver.cpp:237]     Train net output #0: loss = 3.25187 (* 1 = 3.25187 loss)
I0409 23:36:19.583315  8290 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0409 23:36:24.426235  8290 solver.cpp:218] Iteration 2160 (2.47793 iter/s, 4.84275s/12 iters), loss = 3.54573
I0409 23:36:24.426276  8290 solver.cpp:237]     Train net output #0: loss = 3.54573 (* 1 = 3.54573 loss)
I0409 23:36:24.426285  8290 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0409 23:36:29.550704  8290 solver.cpp:218] Iteration 2172 (2.34181 iter/s, 5.12423s/12 iters), loss = 3.52319
I0409 23:36:29.550760  8290 solver.cpp:237]     Train net output #0: loss = 3.52319 (* 1 = 3.52319 loss)
I0409 23:36:29.550770  8290 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0409 23:36:34.622613  8290 solver.cpp:218] Iteration 2184 (2.36609 iter/s, 5.07166s/12 iters), loss = 3.44352
I0409 23:36:34.622673  8290 solver.cpp:237]     Train net output #0: loss = 3.44352 (* 1 = 3.44352 loss)
I0409 23:36:34.622686  8290 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0409 23:36:39.666606  8290 solver.cpp:218] Iteration 2196 (2.37918 iter/s, 5.04375s/12 iters), loss = 3.32733
I0409 23:36:39.666661  8290 solver.cpp:237]     Train net output #0: loss = 3.32733 (* 1 = 3.32733 loss)
I0409 23:36:39.666672  8290 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0409 23:36:45.516759  8290 solver.cpp:218] Iteration 2208 (2.05132 iter/s, 5.84988s/12 iters), loss = 3.28936
I0409 23:36:45.517407  8290 solver.cpp:237]     Train net output #0: loss = 3.28936 (* 1 = 3.28936 loss)
I0409 23:36:45.517416  8290 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0409 23:36:50.383365  8290 solver.cpp:218] Iteration 2220 (2.46621 iter/s, 4.86577s/12 iters), loss = 3.26467
I0409 23:36:50.383424  8290 solver.cpp:237]     Train net output #0: loss = 3.26467 (* 1 = 3.26467 loss)
I0409 23:36:50.383435  8290 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0409 23:36:52.141044  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:36:55.294721  8290 solver.cpp:218] Iteration 2232 (2.44344 iter/s, 4.91111s/12 iters), loss = 3.11914
I0409 23:36:55.294778  8290 solver.cpp:237]     Train net output #0: loss = 3.11914 (* 1 = 3.11914 loss)
I0409 23:36:55.294790  8290 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0409 23:36:59.747815  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0409 23:37:00.915973  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0409 23:37:01.447135  8290 solver.cpp:330] Iteration 2244, Testing net (#0)
I0409 23:37:01.447163  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:37:05.061018  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:37:05.964597  8290 solver.cpp:397]     Test net output #0: accuracy = 0.199755
I0409 23:37:05.964627  8290 solver.cpp:397]     Test net output #1: loss = 3.30525 (* 1 = 3.30525 loss)
I0409 23:37:06.046761  8290 solver.cpp:218] Iteration 2244 (1.11611 iter/s, 10.7516s/12 iters), loss = 3.16469
I0409 23:37:06.046810  8290 solver.cpp:237]     Train net output #0: loss = 3.16469 (* 1 = 3.16469 loss)
I0409 23:37:06.046818  8290 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0409 23:37:10.174962  8290 solver.cpp:218] Iteration 2256 (2.90698 iter/s, 4.12799s/12 iters), loss = 2.92105
I0409 23:37:10.175022  8290 solver.cpp:237]     Train net output #0: loss = 2.92105 (* 1 = 2.92105 loss)
I0409 23:37:10.175033  8290 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0409 23:37:15.012398  8290 solver.cpp:218] Iteration 2268 (2.48078 iter/s, 4.8372s/12 iters), loss = 3.24201
I0409 23:37:15.012454  8290 solver.cpp:237]     Train net output #0: loss = 3.24201 (* 1 = 3.24201 loss)
I0409 23:37:15.012465  8290 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0409 23:37:19.974560  8290 solver.cpp:218] Iteration 2280 (2.41842 iter/s, 4.96193s/12 iters), loss = 3.37218
I0409 23:37:19.974663  8290 solver.cpp:237]     Train net output #0: loss = 3.37218 (* 1 = 3.37218 loss)
I0409 23:37:19.974678  8290 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0409 23:37:24.773232  8290 solver.cpp:218] Iteration 2292 (2.50084 iter/s, 4.79839s/12 iters), loss = 3.2766
I0409 23:37:24.773286  8290 solver.cpp:237]     Train net output #0: loss = 3.2766 (* 1 = 3.2766 loss)
I0409 23:37:24.773298  8290 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0409 23:37:29.670367  8290 solver.cpp:218] Iteration 2304 (2.45053 iter/s, 4.8969s/12 iters), loss = 3.29726
I0409 23:37:29.670418  8290 solver.cpp:237]     Train net output #0: loss = 3.29726 (* 1 = 3.29726 loss)
I0409 23:37:29.670429  8290 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0409 23:37:34.594386  8290 solver.cpp:218] Iteration 2316 (2.43715 iter/s, 4.92378s/12 iters), loss = 3.20252
I0409 23:37:34.594434  8290 solver.cpp:237]     Train net output #0: loss = 3.20252 (* 1 = 3.20252 loss)
I0409 23:37:34.594444  8290 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0409 23:37:38.478787  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:37:39.522573  8290 solver.cpp:218] Iteration 2328 (2.43509 iter/s, 4.92795s/12 iters), loss = 3.22207
I0409 23:37:39.522626  8290 solver.cpp:237]     Train net output #0: loss = 3.22207 (* 1 = 3.22207 loss)
I0409 23:37:39.522637  8290 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0409 23:37:44.426620  8290 solver.cpp:218] Iteration 2340 (2.44707 iter/s, 4.90381s/12 iters), loss = 3.06521
I0409 23:37:44.426674  8290 solver.cpp:237]     Train net output #0: loss = 3.06521 (* 1 = 3.06521 loss)
I0409 23:37:44.426685  8290 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0409 23:37:46.409425  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0409 23:37:47.517639  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0409 23:37:48.071400  8290 solver.cpp:330] Iteration 2346, Testing net (#0)
I0409 23:37:48.071424  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:37:51.463989  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:37:52.414507  8290 solver.cpp:397]     Test net output #0: accuracy = 0.244485
I0409 23:37:52.414561  8290 solver.cpp:397]     Test net output #1: loss = 3.15593 (* 1 = 3.15593 loss)
I0409 23:37:54.311903  8290 solver.cpp:218] Iteration 2352 (1.21398 iter/s, 9.88487s/12 iters), loss = 3.05673
I0409 23:37:54.311960  8290 solver.cpp:237]     Train net output #0: loss = 3.05673 (* 1 = 3.05673 loss)
I0409 23:37:54.311972  8290 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0409 23:37:59.197875  8290 solver.cpp:218] Iteration 2364 (2.45613 iter/s, 4.88574s/12 iters), loss = 2.90498
I0409 23:37:59.197921  8290 solver.cpp:237]     Train net output #0: loss = 2.90498 (* 1 = 2.90498 loss)
I0409 23:37:59.197932  8290 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0409 23:38:04.089411  8290 solver.cpp:218] Iteration 2376 (2.45333 iter/s, 4.89131s/12 iters), loss = 3.00697
I0409 23:38:04.089462  8290 solver.cpp:237]     Train net output #0: loss = 3.00697 (* 1 = 3.00697 loss)
I0409 23:38:04.089474  8290 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0409 23:38:09.198693  8290 solver.cpp:218] Iteration 2388 (2.34878 iter/s, 5.10904s/12 iters), loss = 2.97851
I0409 23:38:09.198745  8290 solver.cpp:237]     Train net output #0: loss = 2.97851 (* 1 = 2.97851 loss)
I0409 23:38:09.198756  8290 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0409 23:38:14.146504  8290 solver.cpp:218] Iteration 2400 (2.42543 iter/s, 4.94758s/12 iters), loss = 3.03367
I0409 23:38:14.146550  8290 solver.cpp:237]     Train net output #0: loss = 3.03367 (* 1 = 3.03367 loss)
I0409 23:38:14.146560  8290 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0409 23:38:19.066754  8290 solver.cpp:218] Iteration 2412 (2.43901 iter/s, 4.92002s/12 iters), loss = 2.80439
I0409 23:38:19.066805  8290 solver.cpp:237]     Train net output #0: loss = 2.80439 (* 1 = 2.80439 loss)
I0409 23:38:19.066817  8290 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0409 23:38:23.976961  8290 solver.cpp:218] Iteration 2424 (2.44401 iter/s, 4.90997s/12 iters), loss = 3.06577
I0409 23:38:23.977120  8290 solver.cpp:237]     Train net output #0: loss = 3.06577 (* 1 = 3.06577 loss)
I0409 23:38:23.977134  8290 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0409 23:38:25.035281  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:38:28.900079  8290 solver.cpp:218] Iteration 2436 (2.43765 iter/s, 4.92277s/12 iters), loss = 2.97528
I0409 23:38:28.900138  8290 solver.cpp:237]     Train net output #0: loss = 2.97528 (* 1 = 2.97528 loss)
I0409 23:38:28.900151  8290 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0409 23:38:33.325450  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0409 23:38:34.467243  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0409 23:38:35.003835  8290 solver.cpp:330] Iteration 2448, Testing net (#0)
I0409 23:38:35.003865  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:38:38.381402  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:38:39.355664  8290 solver.cpp:397]     Test net output #0: accuracy = 0.259804
I0409 23:38:39.355707  8290 solver.cpp:397]     Test net output #1: loss = 3.03298 (* 1 = 3.03298 loss)
I0409 23:38:39.437536  8290 solver.cpp:218] Iteration 2448 (1.13884 iter/s, 10.537s/12 iters), loss = 2.83385
I0409 23:38:39.437585  8290 solver.cpp:237]     Train net output #0: loss = 2.83385 (* 1 = 2.83385 loss)
I0409 23:38:39.437595  8290 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0409 23:38:43.530462  8290 solver.cpp:218] Iteration 2460 (2.93204 iter/s, 4.09272s/12 iters), loss = 2.83407
I0409 23:38:43.530516  8290 solver.cpp:237]     Train net output #0: loss = 2.83407 (* 1 = 2.83407 loss)
I0409 23:38:43.530529  8290 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0409 23:38:48.388763  8290 solver.cpp:218] Iteration 2472 (2.47012 iter/s, 4.85807s/12 iters), loss = 3.03771
I0409 23:38:48.388819  8290 solver.cpp:237]     Train net output #0: loss = 3.03771 (* 1 = 3.03771 loss)
I0409 23:38:48.388830  8290 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0409 23:38:53.310086  8290 solver.cpp:218] Iteration 2484 (2.43849 iter/s, 4.92109s/12 iters), loss = 3.06575
I0409 23:38:53.310137  8290 solver.cpp:237]     Train net output #0: loss = 3.06575 (* 1 = 3.06575 loss)
I0409 23:38:53.310148  8290 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0409 23:38:58.213066  8290 solver.cpp:218] Iteration 2496 (2.44761 iter/s, 4.90275s/12 iters), loss = 2.80665
I0409 23:38:58.213201  8290 solver.cpp:237]     Train net output #0: loss = 2.80665 (* 1 = 2.80665 loss)
I0409 23:38:58.213213  8290 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0409 23:39:03.100324  8290 solver.cpp:218] Iteration 2508 (2.45552 iter/s, 4.88695s/12 iters), loss = 3.09614
I0409 23:39:03.100375  8290 solver.cpp:237]     Train net output #0: loss = 3.09614 (* 1 = 3.09614 loss)
I0409 23:39:03.100386  8290 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0409 23:39:08.007918  8290 solver.cpp:218] Iteration 2520 (2.4453 iter/s, 4.90737s/12 iters), loss = 2.89344
I0409 23:39:08.007961  8290 solver.cpp:237]     Train net output #0: loss = 2.89344 (* 1 = 2.89344 loss)
I0409 23:39:08.007970  8290 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0409 23:39:11.174528  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:39:12.925168  8290 solver.cpp:218] Iteration 2532 (2.4405 iter/s, 4.91703s/12 iters), loss = 2.89829
I0409 23:39:12.925205  8290 solver.cpp:237]     Train net output #0: loss = 2.89829 (* 1 = 2.89829 loss)
I0409 23:39:12.925215  8290 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0409 23:39:17.843164  8290 solver.cpp:218] Iteration 2544 (2.44013 iter/s, 4.91777s/12 iters), loss = 2.9613
I0409 23:39:17.843209  8290 solver.cpp:237]     Train net output #0: loss = 2.9613 (* 1 = 2.9613 loss)
I0409 23:39:17.843219  8290 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0409 23:39:19.828464  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0409 23:39:20.809114  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0409 23:39:21.592602  8290 solver.cpp:330] Iteration 2550, Testing net (#0)
I0409 23:39:21.592631  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:39:24.930742  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:39:25.950460  8290 solver.cpp:397]     Test net output #0: accuracy = 0.264706
I0409 23:39:25.950501  8290 solver.cpp:397]     Test net output #1: loss = 3.01086 (* 1 = 3.01086 loss)
I0409 23:39:27.688946  8290 solver.cpp:218] Iteration 2556 (1.21885 iter/s, 9.84539s/12 iters), loss = 3.0912
I0409 23:39:27.688997  8290 solver.cpp:237]     Train net output #0: loss = 3.0912 (* 1 = 3.0912 loss)
I0409 23:39:27.689007  8290 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0409 23:39:32.567674  8290 solver.cpp:218] Iteration 2568 (2.45978 iter/s, 4.87849s/12 iters), loss = 2.6834
I0409 23:39:32.567823  8290 solver.cpp:237]     Train net output #0: loss = 2.6834 (* 1 = 2.6834 loss)
I0409 23:39:32.567837  8290 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0409 23:39:37.479401  8290 solver.cpp:218] Iteration 2580 (2.4433 iter/s, 4.9114s/12 iters), loss = 2.69897
I0409 23:39:37.479457  8290 solver.cpp:237]     Train net output #0: loss = 2.69897 (* 1 = 2.69897 loss)
I0409 23:39:37.479470  8290 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0409 23:39:42.415720  8290 solver.cpp:218] Iteration 2592 (2.43108 iter/s, 4.93608s/12 iters), loss = 3.21887
I0409 23:39:42.415773  8290 solver.cpp:237]     Train net output #0: loss = 3.21887 (* 1 = 3.21887 loss)
I0409 23:39:42.415786  8290 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0409 23:39:47.332022  8290 solver.cpp:218] Iteration 2604 (2.44097 iter/s, 4.91607s/12 iters), loss = 2.8906
I0409 23:39:47.332072  8290 solver.cpp:237]     Train net output #0: loss = 2.8906 (* 1 = 2.8906 loss)
I0409 23:39:47.332080  8290 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0409 23:39:52.266383  8290 solver.cpp:218] Iteration 2616 (2.43204 iter/s, 4.93413s/12 iters), loss = 2.84578
I0409 23:39:52.266427  8290 solver.cpp:237]     Train net output #0: loss = 2.84578 (* 1 = 2.84578 loss)
I0409 23:39:52.266434  8290 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0409 23:39:57.155295  8290 solver.cpp:218] Iteration 2628 (2.45465 iter/s, 4.88868s/12 iters), loss = 2.54841
I0409 23:39:57.155340  8290 solver.cpp:237]     Train net output #0: loss = 2.54841 (* 1 = 2.54841 loss)
I0409 23:39:57.155349  8290 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0409 23:39:57.583505  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:40:02.086506  8290 solver.cpp:218] Iteration 2640 (2.43359 iter/s, 4.93098s/12 iters), loss = 2.74976
I0409 23:40:02.086565  8290 solver.cpp:237]     Train net output #0: loss = 2.74976 (* 1 = 2.74976 loss)
I0409 23:40:02.086577  8290 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0409 23:40:06.535418  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0409 23:40:09.203194  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0409 23:40:12.563077  8290 solver.cpp:330] Iteration 2652, Testing net (#0)
I0409 23:40:12.563107  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:40:15.945986  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:40:16.999275  8290 solver.cpp:397]     Test net output #0: accuracy = 0.294118
I0409 23:40:16.999321  8290 solver.cpp:397]     Test net output #1: loss = 2.89701 (* 1 = 2.89701 loss)
I0409 23:40:17.081398  8290 solver.cpp:218] Iteration 2652 (0.800304 iter/s, 14.9943s/12 iters), loss = 2.80156
I0409 23:40:17.081452  8290 solver.cpp:237]     Train net output #0: loss = 2.80156 (* 1 = 2.80156 loss)
I0409 23:40:17.081465  8290 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0409 23:40:21.174474  8290 solver.cpp:218] Iteration 2664 (2.93193 iter/s, 4.09287s/12 iters), loss = 3.05952
I0409 23:40:21.174518  8290 solver.cpp:237]     Train net output #0: loss = 3.05952 (* 1 = 3.05952 loss)
I0409 23:40:21.174527  8290 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0409 23:40:26.085662  8290 solver.cpp:218] Iteration 2676 (2.44351 iter/s, 4.91096s/12 iters), loss = 2.79877
I0409 23:40:26.085706  8290 solver.cpp:237]     Train net output #0: loss = 2.79877 (* 1 = 2.79877 loss)
I0409 23:40:26.085717  8290 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0409 23:40:30.907835  8290 solver.cpp:218] Iteration 2688 (2.48862 iter/s, 4.82195s/12 iters), loss = 2.80286
I0409 23:40:30.907900  8290 solver.cpp:237]     Train net output #0: loss = 2.80286 (* 1 = 2.80286 loss)
I0409 23:40:30.907912  8290 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0409 23:40:35.931373  8290 solver.cpp:218] Iteration 2700 (2.38887 iter/s, 5.02329s/12 iters), loss = 2.5956
I0409 23:40:35.931423  8290 solver.cpp:237]     Train net output #0: loss = 2.5956 (* 1 = 2.5956 loss)
I0409 23:40:35.931433  8290 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0409 23:40:40.794185  8290 solver.cpp:218] Iteration 2712 (2.46782 iter/s, 4.86259s/12 iters), loss = 2.68849
I0409 23:40:40.794301  8290 solver.cpp:237]     Train net output #0: loss = 2.68849 (* 1 = 2.68849 loss)
I0409 23:40:40.794310  8290 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0409 23:40:45.712975  8290 solver.cpp:218] Iteration 2724 (2.43977 iter/s, 4.91849s/12 iters), loss = 2.71414
I0409 23:40:45.713030  8290 solver.cpp:237]     Train net output #0: loss = 2.71414 (* 1 = 2.71414 loss)
I0409 23:40:45.713042  8290 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0409 23:40:48.197527  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:40:50.614279  8290 solver.cpp:218] Iteration 2736 (2.44845 iter/s, 4.90107s/12 iters), loss = 2.23938
I0409 23:40:50.614334  8290 solver.cpp:237]     Train net output #0: loss = 2.23938 (* 1 = 2.23938 loss)
I0409 23:40:50.614346  8290 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0409 23:40:55.509424  8290 solver.cpp:218] Iteration 2748 (2.45153 iter/s, 4.89491s/12 iters), loss = 2.60263
I0409 23:40:55.509471  8290 solver.cpp:237]     Train net output #0: loss = 2.60263 (* 1 = 2.60263 loss)
I0409 23:40:55.509480  8290 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0409 23:40:57.467082  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0409 23:40:58.158877  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0409 23:40:58.688045  8290 solver.cpp:330] Iteration 2754, Testing net (#0)
I0409 23:40:58.688076  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:41:01.598464  8290 blocking_queue.cpp:49] Waiting for data
I0409 23:41:02.155438  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:41:03.280329  8290 solver.cpp:397]     Test net output #0: accuracy = 0.299632
I0409 23:41:03.280359  8290 solver.cpp:397]     Test net output #1: loss = 2.81262 (* 1 = 2.81262 loss)
I0409 23:41:05.119145  8290 solver.cpp:218] Iteration 2760 (1.24879 iter/s, 9.60933s/12 iters), loss = 2.53244
I0409 23:41:05.119189  8290 solver.cpp:237]     Train net output #0: loss = 2.53244 (* 1 = 2.53244 loss)
I0409 23:41:05.119196  8290 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0409 23:41:10.132064  8290 solver.cpp:218] Iteration 2772 (2.39393 iter/s, 5.01268s/12 iters), loss = 2.48954
I0409 23:41:10.132118  8290 solver.cpp:237]     Train net output #0: loss = 2.48954 (* 1 = 2.48954 loss)
I0409 23:41:10.132130  8290 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0409 23:41:15.007477  8290 solver.cpp:218] Iteration 2784 (2.46145 iter/s, 4.87517s/12 iters), loss = 2.66821
I0409 23:41:15.007606  8290 solver.cpp:237]     Train net output #0: loss = 2.66821 (* 1 = 2.66821 loss)
I0409 23:41:15.007620  8290 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0409 23:41:19.903470  8290 solver.cpp:218] Iteration 2796 (2.45114 iter/s, 4.89569s/12 iters), loss = 2.59045
I0409 23:41:19.903513  8290 solver.cpp:237]     Train net output #0: loss = 2.59045 (* 1 = 2.59045 loss)
I0409 23:41:19.903523  8290 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0409 23:41:24.817687  8290 solver.cpp:218] Iteration 2808 (2.44201 iter/s, 4.91399s/12 iters), loss = 2.52482
I0409 23:41:24.817736  8290 solver.cpp:237]     Train net output #0: loss = 2.52482 (* 1 = 2.52482 loss)
I0409 23:41:24.817749  8290 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0409 23:41:29.740837  8290 solver.cpp:218] Iteration 2820 (2.43758 iter/s, 4.92291s/12 iters), loss = 2.81171
I0409 23:41:29.740891  8290 solver.cpp:237]     Train net output #0: loss = 2.81171 (* 1 = 2.81171 loss)
I0409 23:41:29.740904  8290 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0409 23:41:34.350088  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:41:34.634891  8290 solver.cpp:218] Iteration 2832 (2.45207 iter/s, 4.89382s/12 iters), loss = 2.4636
I0409 23:41:34.634932  8290 solver.cpp:237]     Train net output #0: loss = 2.4636 (* 1 = 2.4636 loss)
I0409 23:41:34.634940  8290 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0409 23:41:39.512459  8290 solver.cpp:218] Iteration 2844 (2.46036 iter/s, 4.87734s/12 iters), loss = 2.47835
I0409 23:41:39.512514  8290 solver.cpp:237]     Train net output #0: loss = 2.47835 (* 1 = 2.47835 loss)
I0409 23:41:39.512526  8290 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0409 23:41:43.959719  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0409 23:41:44.690907  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0409 23:41:45.263929  8290 solver.cpp:330] Iteration 2856, Testing net (#0)
I0409 23:41:45.264050  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:41:48.619391  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:41:49.804621  8290 solver.cpp:397]     Test net output #0: accuracy = 0.327819
I0409 23:41:49.804663  8290 solver.cpp:397]     Test net output #1: loss = 2.68503 (* 1 = 2.68503 loss)
I0409 23:41:49.886793  8290 solver.cpp:218] Iteration 2856 (1.15675 iter/s, 10.3739s/12 iters), loss = 2.38392
I0409 23:41:49.886845  8290 solver.cpp:237]     Train net output #0: loss = 2.38392 (* 1 = 2.38392 loss)
I0409 23:41:49.886855  8290 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0409 23:41:54.026728  8290 solver.cpp:218] Iteration 2868 (2.89874 iter/s, 4.13973s/12 iters), loss = 2.49541
I0409 23:41:54.026772  8290 solver.cpp:237]     Train net output #0: loss = 2.49541 (* 1 = 2.49541 loss)
I0409 23:41:54.026782  8290 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0409 23:41:58.934428  8290 solver.cpp:218] Iteration 2880 (2.44525 iter/s, 4.90747s/12 iters), loss = 2.78279
I0409 23:41:58.934485  8290 solver.cpp:237]     Train net output #0: loss = 2.78279 (* 1 = 2.78279 loss)
I0409 23:41:58.934499  8290 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0409 23:42:04.012657  8290 solver.cpp:218] Iteration 2892 (2.36314 iter/s, 5.07798s/12 iters), loss = 2.44381
I0409 23:42:04.012709  8290 solver.cpp:237]     Train net output #0: loss = 2.44381 (* 1 = 2.44381 loss)
I0409 23:42:04.012722  8290 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0409 23:42:09.032096  8290 solver.cpp:218] Iteration 2904 (2.39082 iter/s, 5.0192s/12 iters), loss = 2.58685
I0409 23:42:09.032150  8290 solver.cpp:237]     Train net output #0: loss = 2.58685 (* 1 = 2.58685 loss)
I0409 23:42:09.032162  8290 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0409 23:42:13.934837  8290 solver.cpp:218] Iteration 2916 (2.44773 iter/s, 4.90251s/12 iters), loss = 2.54589
I0409 23:42:13.934887  8290 solver.cpp:237]     Train net output #0: loss = 2.54589 (* 1 = 2.54589 loss)
I0409 23:42:13.934898  8290 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0409 23:42:18.891132  8290 solver.cpp:218] Iteration 2928 (2.42128 iter/s, 4.95606s/12 iters), loss = 2.49586
I0409 23:42:18.891278  8290 solver.cpp:237]     Train net output #0: loss = 2.49586 (* 1 = 2.49586 loss)
I0409 23:42:18.891289  8290 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0409 23:42:20.704308  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:42:23.803261  8290 solver.cpp:218] Iteration 2940 (2.4431 iter/s, 4.9118s/12 iters), loss = 2.40454
I0409 23:42:23.803320  8290 solver.cpp:237]     Train net output #0: loss = 2.40454 (* 1 = 2.40454 loss)
I0409 23:42:23.803333  8290 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0409 23:42:28.768942  8290 solver.cpp:218] Iteration 2952 (2.4167 iter/s, 4.96544s/12 iters), loss = 2.54542
I0409 23:42:28.768988  8290 solver.cpp:237]     Train net output #0: loss = 2.54542 (* 1 = 2.54542 loss)
I0409 23:42:28.768998  8290 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0409 23:42:30.744809  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0409 23:42:32.435125  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0409 23:42:33.889734  8290 solver.cpp:330] Iteration 2958, Testing net (#0)
I0409 23:42:33.889755  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:42:37.360661  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:42:38.538379  8290 solver.cpp:397]     Test net output #0: accuracy = 0.331495
I0409 23:42:38.538427  8290 solver.cpp:397]     Test net output #1: loss = 2.69524 (* 1 = 2.69524 loss)
I0409 23:42:40.386364  8290 solver.cpp:218] Iteration 2964 (1.03297 iter/s, 11.617s/12 iters), loss = 2.10124
I0409 23:42:40.386422  8290 solver.cpp:237]     Train net output #0: loss = 2.10124 (* 1 = 2.10124 loss)
I0409 23:42:40.386435  8290 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0409 23:42:45.367318  8290 solver.cpp:218] Iteration 2976 (2.4093 iter/s, 4.98071s/12 iters), loss = 2.45287
I0409 23:42:45.367377  8290 solver.cpp:237]     Train net output #0: loss = 2.45287 (* 1 = 2.45287 loss)
I0409 23:42:45.367389  8290 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0409 23:42:50.286463  8290 solver.cpp:218] Iteration 2988 (2.43957 iter/s, 4.9189s/12 iters), loss = 2.41698
I0409 23:42:50.286562  8290 solver.cpp:237]     Train net output #0: loss = 2.41698 (* 1 = 2.41698 loss)
I0409 23:42:50.286572  8290 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0409 23:42:55.236131  8290 solver.cpp:218] Iteration 3000 (2.42455 iter/s, 4.94938s/12 iters), loss = 2.55435
I0409 23:42:55.236186  8290 solver.cpp:237]     Train net output #0: loss = 2.55435 (* 1 = 2.55435 loss)
I0409 23:42:55.236199  8290 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0409 23:43:00.215443  8290 solver.cpp:218] Iteration 3012 (2.41009 iter/s, 4.97907s/12 iters), loss = 2.27664
I0409 23:43:00.215497  8290 solver.cpp:237]     Train net output #0: loss = 2.27664 (* 1 = 2.27664 loss)
I0409 23:43:00.215507  8290 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0409 23:43:05.203102  8290 solver.cpp:218] Iteration 3024 (2.40606 iter/s, 4.98742s/12 iters), loss = 2.19187
I0409 23:43:05.203163  8290 solver.cpp:237]     Train net output #0: loss = 2.19187 (* 1 = 2.19187 loss)
I0409 23:43:05.203176  8290 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0409 23:43:09.133152  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:43:10.149665  8290 solver.cpp:218] Iteration 3036 (2.42605 iter/s, 4.94632s/12 iters), loss = 2.43849
I0409 23:43:10.149724  8290 solver.cpp:237]     Train net output #0: loss = 2.43849 (* 1 = 2.43849 loss)
I0409 23:43:10.149736  8290 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0409 23:43:15.081331  8290 solver.cpp:218] Iteration 3048 (2.43337 iter/s, 4.93143s/12 iters), loss = 2.42832
I0409 23:43:15.081372  8290 solver.cpp:237]     Train net output #0: loss = 2.42832 (* 1 = 2.42832 loss)
I0409 23:43:15.081379  8290 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0409 23:43:19.466102  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0409 23:43:21.203698  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0409 23:43:22.524596  8290 solver.cpp:330] Iteration 3060, Testing net (#0)
I0409 23:43:22.524619  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:43:25.754217  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:43:26.964608  8290 solver.cpp:397]     Test net output #0: accuracy = 0.349265
I0409 23:43:26.964658  8290 solver.cpp:397]     Test net output #1: loss = 2.58876 (* 1 = 2.58876 loss)
I0409 23:43:27.046912  8290 solver.cpp:218] Iteration 3060 (1.00292 iter/s, 11.9651s/12 iters), loss = 2.17913
I0409 23:43:27.046970  8290 solver.cpp:237]     Train net output #0: loss = 2.17913 (* 1 = 2.17913 loss)
I0409 23:43:27.046983  8290 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0409 23:43:31.273551  8290 solver.cpp:218] Iteration 3072 (2.83928 iter/s, 4.22643s/12 iters), loss = 2.10995
I0409 23:43:31.273597  8290 solver.cpp:237]     Train net output #0: loss = 2.10995 (* 1 = 2.10995 loss)
I0409 23:43:31.273607  8290 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0409 23:43:36.204576  8290 solver.cpp:218] Iteration 3084 (2.43369 iter/s, 4.93079s/12 iters), loss = 2.24212
I0409 23:43:36.204636  8290 solver.cpp:237]     Train net output #0: loss = 2.24212 (* 1 = 2.24212 loss)
I0409 23:43:36.204649  8290 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0409 23:43:41.600889  8290 solver.cpp:218] Iteration 3096 (2.22385 iter/s, 5.39605s/12 iters), loss = 2.26911
I0409 23:43:41.600941  8290 solver.cpp:237]     Train net output #0: loss = 2.26911 (* 1 = 2.26911 loss)
I0409 23:43:41.600953  8290 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0409 23:43:46.522462  8290 solver.cpp:218] Iteration 3108 (2.43836 iter/s, 4.92134s/12 iters), loss = 2.19154
I0409 23:43:46.522514  8290 solver.cpp:237]     Train net output #0: loss = 2.19154 (* 1 = 2.19154 loss)
I0409 23:43:46.522527  8290 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0409 23:43:51.441433  8290 solver.cpp:218] Iteration 3120 (2.43965 iter/s, 4.91874s/12 iters), loss = 1.95425
I0409 23:43:51.441540  8290 solver.cpp:237]     Train net output #0: loss = 1.95425 (* 1 = 1.95425 loss)
I0409 23:43:51.441550  8290 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0409 23:43:56.364061  8290 solver.cpp:218] Iteration 3132 (2.43787 iter/s, 4.92234s/12 iters), loss = 2.20819
I0409 23:43:56.364104  8290 solver.cpp:237]     Train net output #0: loss = 2.20819 (* 1 = 2.20819 loss)
I0409 23:43:56.364113  8290 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0409 23:43:57.479707  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:44:01.295049  8290 solver.cpp:218] Iteration 3144 (2.4337 iter/s, 4.93077s/12 iters), loss = 1.97696
I0409 23:44:01.295091  8290 solver.cpp:237]     Train net output #0: loss = 1.97696 (* 1 = 1.97696 loss)
I0409 23:44:01.295099  8290 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0409 23:44:06.289306  8290 solver.cpp:218] Iteration 3156 (2.40287 iter/s, 4.99402s/12 iters), loss = 2.174
I0409 23:44:06.289363  8290 solver.cpp:237]     Train net output #0: loss = 2.174 (* 1 = 2.174 loss)
I0409 23:44:06.289376  8290 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0409 23:44:08.240392  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0409 23:44:08.969074  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0409 23:44:09.497838  8290 solver.cpp:330] Iteration 3162, Testing net (#0)
I0409 23:44:09.497866  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:44:12.629631  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:44:13.889091  8290 solver.cpp:397]     Test net output #0: accuracy = 0.368873
I0409 23:44:13.889137  8290 solver.cpp:397]     Test net output #1: loss = 2.57876 (* 1 = 2.57876 loss)
I0409 23:44:15.651801  8290 solver.cpp:218] Iteration 3168 (1.28176 iter/s, 9.3621s/12 iters), loss = 2.00537
I0409 23:44:15.651849  8290 solver.cpp:237]     Train net output #0: loss = 2.00537 (* 1 = 2.00537 loss)
I0409 23:44:15.651856  8290 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0409 23:44:20.568625  8290 solver.cpp:218] Iteration 3180 (2.44071 iter/s, 4.9166s/12 iters), loss = 2.22928
I0409 23:44:20.568670  8290 solver.cpp:237]     Train net output #0: loss = 2.22928 (* 1 = 2.22928 loss)
I0409 23:44:20.568679  8290 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0409 23:44:25.484344  8290 solver.cpp:218] Iteration 3192 (2.44126 iter/s, 4.91549s/12 iters), loss = 2.18067
I0409 23:44:25.484496  8290 solver.cpp:237]     Train net output #0: loss = 2.18067 (* 1 = 2.18067 loss)
I0409 23:44:25.484510  8290 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0409 23:44:30.363672  8290 solver.cpp:218] Iteration 3204 (2.45952 iter/s, 4.879s/12 iters), loss = 1.97483
I0409 23:44:30.363723  8290 solver.cpp:237]     Train net output #0: loss = 1.97483 (* 1 = 1.97483 loss)
I0409 23:44:30.363734  8290 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0409 23:44:35.297948  8290 solver.cpp:218] Iteration 3216 (2.43208 iter/s, 4.93404s/12 iters), loss = 2.32981
I0409 23:44:35.298022  8290 solver.cpp:237]     Train net output #0: loss = 2.32981 (* 1 = 2.32981 loss)
I0409 23:44:35.298033  8290 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0409 23:44:40.253381  8290 solver.cpp:218] Iteration 3228 (2.42171 iter/s, 4.95517s/12 iters), loss = 2.16532
I0409 23:44:40.253430  8290 solver.cpp:237]     Train net output #0: loss = 2.16532 (* 1 = 2.16532 loss)
I0409 23:44:40.253443  8290 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0409 23:44:43.422979  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:44:45.141145  8290 solver.cpp:218] Iteration 3240 (2.45523 iter/s, 4.88753s/12 iters), loss = 2.07136
I0409 23:44:45.141201  8290 solver.cpp:237]     Train net output #0: loss = 2.07136 (* 1 = 2.07136 loss)
I0409 23:44:45.141211  8290 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0409 23:44:50.085705  8290 solver.cpp:218] Iteration 3252 (2.42703 iter/s, 4.94432s/12 iters), loss = 2.1272
I0409 23:44:50.085749  8290 solver.cpp:237]     Train net output #0: loss = 2.1272 (* 1 = 2.1272 loss)
I0409 23:44:50.085758  8290 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0409 23:44:54.501513  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0409 23:44:55.200434  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0409 23:44:55.729660  8290 solver.cpp:330] Iteration 3264, Testing net (#0)
I0409 23:44:55.729741  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:44:58.870083  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:45:00.166416  8290 solver.cpp:397]     Test net output #0: accuracy = 0.386029
I0409 23:45:00.166468  8290 solver.cpp:397]     Test net output #1: loss = 2.42004 (* 1 = 2.42004 loss)
I0409 23:45:00.248785  8290 solver.cpp:218] Iteration 3264 (1.18079 iter/s, 10.1627s/12 iters), loss = 2.17537
I0409 23:45:00.248843  8290 solver.cpp:237]     Train net output #0: loss = 2.17537 (* 1 = 2.17537 loss)
I0409 23:45:00.248855  8290 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0409 23:45:04.344063  8290 solver.cpp:218] Iteration 3276 (2.93036 iter/s, 4.09506s/12 iters), loss = 2.05759
I0409 23:45:04.344123  8290 solver.cpp:237]     Train net output #0: loss = 2.05759 (* 1 = 2.05759 loss)
I0409 23:45:04.344136  8290 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0409 23:45:09.306692  8290 solver.cpp:218] Iteration 3288 (2.41819 iter/s, 4.96239s/12 iters), loss = 2.05552
I0409 23:45:09.306740  8290 solver.cpp:237]     Train net output #0: loss = 2.05552 (* 1 = 2.05552 loss)
I0409 23:45:09.306751  8290 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0409 23:45:14.400722  8290 solver.cpp:218] Iteration 3300 (2.35581 iter/s, 5.09379s/12 iters), loss = 2.26359
I0409 23:45:14.400763  8290 solver.cpp:237]     Train net output #0: loss = 2.26359 (* 1 = 2.26359 loss)
I0409 23:45:14.400772  8290 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0409 23:45:19.610350  8290 solver.cpp:218] Iteration 3312 (2.30353 iter/s, 5.20939s/12 iters), loss = 2.15509
I0409 23:45:19.610406  8290 solver.cpp:237]     Train net output #0: loss = 2.15509 (* 1 = 2.15509 loss)
I0409 23:45:19.610419  8290 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0409 23:45:24.448082  8290 solver.cpp:218] Iteration 3324 (2.48062 iter/s, 4.8375s/12 iters), loss = 1.88312
I0409 23:45:24.448135  8290 solver.cpp:237]     Train net output #0: loss = 1.88312 (* 1 = 1.88312 loss)
I0409 23:45:24.448146  8290 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0409 23:45:29.342063  8290 solver.cpp:218] Iteration 3336 (2.45211 iter/s, 4.89374s/12 iters), loss = 1.8928
I0409 23:45:29.342202  8290 solver.cpp:237]     Train net output #0: loss = 1.8928 (* 1 = 1.8928 loss)
I0409 23:45:29.342211  8290 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0409 23:45:29.802016  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:45:34.286425  8290 solver.cpp:218] Iteration 3348 (2.42716 iter/s, 4.94404s/12 iters), loss = 2.09763
I0409 23:45:34.286466  8290 solver.cpp:237]     Train net output #0: loss = 2.09763 (* 1 = 2.09763 loss)
I0409 23:45:34.286475  8290 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0409 23:45:39.156406  8290 solver.cpp:218] Iteration 3360 (2.46419 iter/s, 4.86976s/12 iters), loss = 1.90158
I0409 23:45:39.156450  8290 solver.cpp:237]     Train net output #0: loss = 1.90158 (* 1 = 1.90158 loss)
I0409 23:45:39.156459  8290 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0409 23:45:41.160094  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0409 23:45:41.908550  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0409 23:45:42.433007  8290 solver.cpp:330] Iteration 3366, Testing net (#0)
I0409 23:45:42.433027  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:45:45.699524  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:45:47.374178  8290 solver.cpp:397]     Test net output #0: accuracy = 0.398284
I0409 23:45:47.374217  8290 solver.cpp:397]     Test net output #1: loss = 2.39045 (* 1 = 2.39045 loss)
I0409 23:45:49.142846  8290 solver.cpp:218] Iteration 3372 (1.20168 iter/s, 9.98604s/12 iters), loss = 1.86262
I0409 23:45:49.142902  8290 solver.cpp:237]     Train net output #0: loss = 1.86262 (* 1 = 1.86262 loss)
I0409 23:45:49.142915  8290 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0409 23:45:54.049203  8290 solver.cpp:218] Iteration 3384 (2.44593 iter/s, 4.90611s/12 iters), loss = 2.04228
I0409 23:45:54.049261  8290 solver.cpp:237]     Train net output #0: loss = 2.04228 (* 1 = 2.04228 loss)
I0409 23:45:54.049273  8290 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0409 23:45:58.906373  8290 solver.cpp:218] Iteration 3396 (2.4707 iter/s, 4.85693s/12 iters), loss = 1.8072
I0409 23:45:58.906421  8290 solver.cpp:237]     Train net output #0: loss = 1.8072 (* 1 = 1.8072 loss)
I0409 23:45:58.906430  8290 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0409 23:46:03.773345  8290 solver.cpp:218] Iteration 3408 (2.46572 iter/s, 4.86674s/12 iters), loss = 2.16614
I0409 23:46:03.773447  8290 solver.cpp:237]     Train net output #0: loss = 2.16614 (* 1 = 2.16614 loss)
I0409 23:46:03.773458  8290 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0409 23:46:08.657644  8290 solver.cpp:218] Iteration 3420 (2.45699 iter/s, 4.88402s/12 iters), loss = 1.97075
I0409 23:46:08.657686  8290 solver.cpp:237]     Train net output #0: loss = 1.97075 (* 1 = 1.97075 loss)
I0409 23:46:08.657696  8290 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0409 23:46:13.581550  8290 solver.cpp:218] Iteration 3432 (2.4372 iter/s, 4.92368s/12 iters), loss = 2.1129
I0409 23:46:13.581601  8290 solver.cpp:237]     Train net output #0: loss = 2.1129 (* 1 = 2.1129 loss)
I0409 23:46:13.581612  8290 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0409 23:46:16.189293  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:46:18.609823  8290 solver.cpp:218] Iteration 3444 (2.38662 iter/s, 5.02803s/12 iters), loss = 1.72469
I0409 23:46:18.609884  8290 solver.cpp:237]     Train net output #0: loss = 1.72469 (* 1 = 1.72469 loss)
I0409 23:46:18.609895  8290 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0409 23:46:23.533217  8290 solver.cpp:218] Iteration 3456 (2.43746 iter/s, 4.92315s/12 iters), loss = 1.89476
I0409 23:46:23.533264  8290 solver.cpp:237]     Train net output #0: loss = 1.89476 (* 1 = 1.89476 loss)
I0409 23:46:23.533273  8290 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0409 23:46:27.952025  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0409 23:46:28.722370  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0409 23:46:29.278995  8290 solver.cpp:330] Iteration 3468, Testing net (#0)
I0409 23:46:29.279024  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:46:29.437665  8290 blocking_queue.cpp:49] Waiting for data
I0409 23:46:32.440157  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:46:33.815349  8290 solver.cpp:397]     Test net output #0: accuracy = 0.421569
I0409 23:46:33.815460  8290 solver.cpp:397]     Test net output #1: loss = 2.2615 (* 1 = 2.2615 loss)
I0409 23:46:33.897562  8290 solver.cpp:218] Iteration 3468 (1.15786 iter/s, 10.3639s/12 iters), loss = 1.78716
I0409 23:46:33.897599  8290 solver.cpp:237]     Train net output #0: loss = 1.78716 (* 1 = 1.78716 loss)
I0409 23:46:33.897608  8290 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0409 23:46:37.945580  8290 solver.cpp:218] Iteration 3480 (2.96456 iter/s, 4.04782s/12 iters), loss = 1.73026
I0409 23:46:37.945633  8290 solver.cpp:237]     Train net output #0: loss = 1.73026 (* 1 = 1.73026 loss)
I0409 23:46:37.945647  8290 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0409 23:46:42.825986  8290 solver.cpp:218] Iteration 3492 (2.45894 iter/s, 4.88016s/12 iters), loss = 1.87035
I0409 23:46:42.826037  8290 solver.cpp:237]     Train net output #0: loss = 1.87035 (* 1 = 1.87035 loss)
I0409 23:46:42.826047  8290 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0409 23:46:47.653174  8290 solver.cpp:218] Iteration 3504 (2.48604 iter/s, 4.82695s/12 iters), loss = 1.94196
I0409 23:46:47.653234  8290 solver.cpp:237]     Train net output #0: loss = 1.94196 (* 1 = 1.94196 loss)
I0409 23:46:47.653246  8290 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0409 23:46:52.564391  8290 solver.cpp:218] Iteration 3516 (2.44351 iter/s, 4.91097s/12 iters), loss = 1.73156
I0409 23:46:52.564447  8290 solver.cpp:237]     Train net output #0: loss = 1.73156 (* 1 = 1.73156 loss)
I0409 23:46:52.564458  8290 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0409 23:46:57.508980  8290 solver.cpp:218] Iteration 3528 (2.42701 iter/s, 4.94435s/12 iters), loss = 1.75489
I0409 23:46:57.509038  8290 solver.cpp:237]     Train net output #0: loss = 1.75489 (* 1 = 1.75489 loss)
I0409 23:46:57.509052  8290 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0409 23:47:02.195500  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:47:02.455960  8290 solver.cpp:218] Iteration 3540 (2.42584 iter/s, 4.94674s/12 iters), loss = 1.74277
I0409 23:47:02.456017  8290 solver.cpp:237]     Train net output #0: loss = 1.74277 (* 1 = 1.74277 loss)
I0409 23:47:02.456029  8290 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0409 23:47:07.469157  8290 solver.cpp:218] Iteration 3552 (2.3938 iter/s, 5.01296s/12 iters), loss = 1.57062
I0409 23:47:07.469270  8290 solver.cpp:237]     Train net output #0: loss = 1.57062 (* 1 = 1.57062 loss)
I0409 23:47:07.469280  8290 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0409 23:47:12.381736  8290 solver.cpp:218] Iteration 3564 (2.44286 iter/s, 4.91228s/12 iters), loss = 1.7567
I0409 23:47:12.381796  8290 solver.cpp:237]     Train net output #0: loss = 1.7567 (* 1 = 1.7567 loss)
I0409 23:47:12.381809  8290 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0409 23:47:14.389802  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0409 23:47:15.685762  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0409 23:47:17.318701  8290 solver.cpp:330] Iteration 3570, Testing net (#0)
I0409 23:47:17.318730  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:47:20.371796  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:47:21.794375  8290 solver.cpp:397]     Test net output #0: accuracy = 0.424632
I0409 23:47:21.794420  8290 solver.cpp:397]     Test net output #1: loss = 2.30742 (* 1 = 2.30742 loss)
I0409 23:47:23.808280  8290 solver.cpp:218] Iteration 3576 (1.05023 iter/s, 11.4261s/12 iters), loss = 1.81491
I0409 23:47:23.808331  8290 solver.cpp:237]     Train net output #0: loss = 1.81491 (* 1 = 1.81491 loss)
I0409 23:47:23.808342  8290 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0409 23:47:28.749543  8290 solver.cpp:218] Iteration 3588 (2.42865 iter/s, 4.94102s/12 iters), loss = 1.63647
I0409 23:47:28.749593  8290 solver.cpp:237]     Train net output #0: loss = 1.63647 (* 1 = 1.63647 loss)
I0409 23:47:28.749605  8290 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0409 23:47:33.615478  8290 solver.cpp:218] Iteration 3600 (2.46624 iter/s, 4.8657s/12 iters), loss = 1.89781
I0409 23:47:33.615535  8290 solver.cpp:237]     Train net output #0: loss = 1.89781 (* 1 = 1.89781 loss)
I0409 23:47:33.615545  8290 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0409 23:47:38.536669  8290 solver.cpp:218] Iteration 3612 (2.43855 iter/s, 4.92095s/12 iters), loss = 1.58957
I0409 23:47:38.536798  8290 solver.cpp:237]     Train net output #0: loss = 1.58957 (* 1 = 1.58957 loss)
I0409 23:47:38.536808  8290 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0409 23:47:43.396014  8290 solver.cpp:218] Iteration 3624 (2.46962 iter/s, 4.85904s/12 iters), loss = 1.77983
I0409 23:47:43.396059  8290 solver.cpp:237]     Train net output #0: loss = 1.77983 (* 1 = 1.77983 loss)
I0409 23:47:43.396071  8290 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0409 23:47:48.320739  8290 solver.cpp:218] Iteration 3636 (2.4368 iter/s, 4.92449s/12 iters), loss = 1.87013
I0409 23:47:48.320792  8290 solver.cpp:237]     Train net output #0: loss = 1.87013 (* 1 = 1.87013 loss)
I0409 23:47:48.320803  8290 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0409 23:47:50.204185  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:47:53.279423  8290 solver.cpp:218] Iteration 3648 (2.42011 iter/s, 4.95844s/12 iters), loss = 1.71929
I0409 23:47:53.279479  8290 solver.cpp:237]     Train net output #0: loss = 1.71929 (* 1 = 1.71929 loss)
I0409 23:47:53.279492  8290 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0409 23:47:58.237931  8290 solver.cpp:218] Iteration 3660 (2.4202 iter/s, 4.95827s/12 iters), loss = 1.64435
I0409 23:47:58.237998  8290 solver.cpp:237]     Train net output #0: loss = 1.64435 (* 1 = 1.64435 loss)
I0409 23:47:58.238009  8290 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0409 23:48:02.689723  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0409 23:48:03.439018  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0409 23:48:03.961390  8290 solver.cpp:330] Iteration 3672, Testing net (#0)
I0409 23:48:03.961419  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:48:06.987442  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:48:08.466853  8290 solver.cpp:397]     Test net output #0: accuracy = 0.422181
I0409 23:48:08.466902  8290 solver.cpp:397]     Test net output #1: loss = 2.28559 (* 1 = 2.28559 loss)
I0409 23:48:08.548833  8290 solver.cpp:218] Iteration 3672 (1.16387 iter/s, 10.3105s/12 iters), loss = 1.53923
I0409 23:48:08.548945  8290 solver.cpp:237]     Train net output #0: loss = 1.53923 (* 1 = 1.53923 loss)
I0409 23:48:08.548959  8290 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0409 23:48:12.621711  8290 solver.cpp:218] Iteration 3684 (2.94651 iter/s, 4.07262s/12 iters), loss = 1.715
I0409 23:48:12.621749  8290 solver.cpp:237]     Train net output #0: loss = 1.715 (* 1 = 1.715 loss)
I0409 23:48:12.621757  8290 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0409 23:48:17.500367  8290 solver.cpp:218] Iteration 3696 (2.45981 iter/s, 4.87843s/12 iters), loss = 1.4571
I0409 23:48:17.500420  8290 solver.cpp:237]     Train net output #0: loss = 1.4571 (* 1 = 1.4571 loss)
I0409 23:48:17.500433  8290 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0409 23:48:22.345739  8290 solver.cpp:218] Iteration 3708 (2.47671 iter/s, 4.84513s/12 iters), loss = 1.78765
I0409 23:48:22.345803  8290 solver.cpp:237]     Train net output #0: loss = 1.78765 (* 1 = 1.78765 loss)
I0409 23:48:22.345815  8290 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0409 23:48:27.176802  8290 solver.cpp:218] Iteration 3720 (2.48405 iter/s, 4.83082s/12 iters), loss = 1.75992
I0409 23:48:27.176851  8290 solver.cpp:237]     Train net output #0: loss = 1.75992 (* 1 = 1.75992 loss)
I0409 23:48:27.176862  8290 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0409 23:48:32.039196  8290 solver.cpp:218] Iteration 3732 (2.46804 iter/s, 4.86216s/12 iters), loss = 1.54473
I0409 23:48:32.039247  8290 solver.cpp:237]     Train net output #0: loss = 1.54473 (* 1 = 1.54473 loss)
I0409 23:48:32.039259  8290 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0409 23:48:35.975546  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:48:36.949851  8290 solver.cpp:218] Iteration 3744 (2.44378 iter/s, 4.91042s/12 iters), loss = 1.36617
I0409 23:48:36.949906  8290 solver.cpp:237]     Train net output #0: loss = 1.36617 (* 1 = 1.36617 loss)
I0409 23:48:36.949918  8290 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0409 23:48:41.853332  8290 solver.cpp:218] Iteration 3756 (2.44736 iter/s, 4.90324s/12 iters), loss = 1.72069
I0409 23:48:41.853480  8290 solver.cpp:237]     Train net output #0: loss = 1.72069 (* 1 = 1.72069 loss)
I0409 23:48:41.853494  8290 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0409 23:48:46.738883  8290 solver.cpp:218] Iteration 3768 (2.45639 iter/s, 4.88522s/12 iters), loss = 1.55933
I0409 23:48:46.738936  8290 solver.cpp:237]     Train net output #0: loss = 1.55933 (* 1 = 1.55933 loss)
I0409 23:48:46.738947  8290 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0409 23:48:48.709553  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0409 23:48:49.630421  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0409 23:48:51.620402  8290 solver.cpp:330] Iteration 3774, Testing net (#0)
I0409 23:48:51.620431  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:48:54.565026  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:48:56.054229  8290 solver.cpp:397]     Test net output #0: accuracy = 0.444853
I0409 23:48:56.054275  8290 solver.cpp:397]     Test net output #1: loss = 2.20636 (* 1 = 2.20636 loss)
I0409 23:48:57.856783  8290 solver.cpp:218] Iteration 3780 (1.07938 iter/s, 11.1175s/12 iters), loss = 1.61166
I0409 23:48:57.856827  8290 solver.cpp:237]     Train net output #0: loss = 1.61166 (* 1 = 1.61166 loss)
I0409 23:48:57.856835  8290 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0409 23:49:02.756069  8290 solver.cpp:218] Iteration 3792 (2.44945 iter/s, 4.89906s/12 iters), loss = 1.75684
I0409 23:49:02.756129  8290 solver.cpp:237]     Train net output #0: loss = 1.75684 (* 1 = 1.75684 loss)
I0409 23:49:02.756141  8290 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0409 23:49:07.678290  8290 solver.cpp:218] Iteration 3804 (2.43804 iter/s, 4.92198s/12 iters), loss = 1.59822
I0409 23:49:07.678347  8290 solver.cpp:237]     Train net output #0: loss = 1.59822 (* 1 = 1.59822 loss)
I0409 23:49:07.678360  8290 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0409 23:49:12.589466  8290 solver.cpp:218] Iteration 3816 (2.44353 iter/s, 4.91093s/12 iters), loss = 1.68191
I0409 23:49:12.589637  8290 solver.cpp:237]     Train net output #0: loss = 1.68191 (* 1 = 1.68191 loss)
I0409 23:49:12.589650  8290 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0409 23:49:17.516793  8290 solver.cpp:218] Iteration 3828 (2.43557 iter/s, 4.92698s/12 iters), loss = 1.4884
I0409 23:49:17.516846  8290 solver.cpp:237]     Train net output #0: loss = 1.4884 (* 1 = 1.4884 loss)
I0409 23:49:17.516860  8290 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0409 23:49:22.450219  8290 solver.cpp:218] Iteration 3840 (2.4325 iter/s, 4.93319s/12 iters), loss = 1.65363
I0409 23:49:22.450274  8290 solver.cpp:237]     Train net output #0: loss = 1.65363 (* 1 = 1.65363 loss)
I0409 23:49:22.450287  8290 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0409 23:49:23.569676  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:49:27.373251  8290 solver.cpp:218] Iteration 3852 (2.43764 iter/s, 4.92279s/12 iters), loss = 1.39563
I0409 23:49:27.373302  8290 solver.cpp:237]     Train net output #0: loss = 1.39563 (* 1 = 1.39563 loss)
I0409 23:49:27.373313  8290 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0409 23:49:32.224956  8290 solver.cpp:218] Iteration 3864 (2.47347 iter/s, 4.85148s/12 iters), loss = 1.64109
I0409 23:49:32.224999  8290 solver.cpp:237]     Train net output #0: loss = 1.64109 (* 1 = 1.64109 loss)
I0409 23:49:32.225008  8290 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0409 23:49:36.727711  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0409 23:49:38.102047  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0409 23:49:38.698487  8290 solver.cpp:330] Iteration 3876, Testing net (#0)
I0409 23:49:38.698514  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:49:41.749194  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:49:43.285117  8290 solver.cpp:397]     Test net output #0: accuracy = 0.4375
I0409 23:49:43.285212  8290 solver.cpp:397]     Test net output #1: loss = 2.23677 (* 1 = 2.23677 loss)
I0409 23:49:43.367342  8290 solver.cpp:218] Iteration 3876 (1.07701 iter/s, 11.1419s/12 iters), loss = 1.28539
I0409 23:49:43.367391  8290 solver.cpp:237]     Train net output #0: loss = 1.28539 (* 1 = 1.28539 loss)
I0409 23:49:43.367401  8290 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0409 23:49:47.519464  8290 solver.cpp:218] Iteration 3888 (2.89024 iter/s, 4.15191s/12 iters), loss = 1.31226
I0409 23:49:47.519523  8290 solver.cpp:237]     Train net output #0: loss = 1.31226 (* 1 = 1.31226 loss)
I0409 23:49:47.519536  8290 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0409 23:49:52.439903  8290 solver.cpp:218] Iteration 3900 (2.43893 iter/s, 4.92019s/12 iters), loss = 1.64808
I0409 23:49:52.439958  8290 solver.cpp:237]     Train net output #0: loss = 1.64808 (* 1 = 1.64808 loss)
I0409 23:49:52.439972  8290 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0409 23:49:57.465807  8290 solver.cpp:218] Iteration 3912 (2.38774 iter/s, 5.02566s/12 iters), loss = 1.5681
I0409 23:49:57.465848  8290 solver.cpp:237]     Train net output #0: loss = 1.5681 (* 1 = 1.5681 loss)
I0409 23:49:57.465858  8290 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0409 23:50:02.369686  8290 solver.cpp:218] Iteration 3924 (2.44716 iter/s, 4.90365s/12 iters), loss = 1.87503
I0409 23:50:02.369740  8290 solver.cpp:237]     Train net output #0: loss = 1.87503 (* 1 = 1.87503 loss)
I0409 23:50:02.369752  8290 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0409 23:50:07.314265  8290 solver.cpp:218] Iteration 3936 (2.42702 iter/s, 4.94434s/12 iters), loss = 1.37226
I0409 23:50:07.314311  8290 solver.cpp:237]     Train net output #0: loss = 1.37226 (* 1 = 1.37226 loss)
I0409 23:50:07.314321  8290 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0409 23:50:10.603907  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:50:12.207752  8290 solver.cpp:218] Iteration 3948 (2.45235 iter/s, 4.89326s/12 iters), loss = 1.3524
I0409 23:50:12.207805  8290 solver.cpp:237]     Train net output #0: loss = 1.3524 (* 1 = 1.3524 loss)
I0409 23:50:12.207818  8290 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0409 23:50:17.125541  8290 solver.cpp:218] Iteration 3960 (2.44024 iter/s, 4.91755s/12 iters), loss = 1.39645
I0409 23:50:17.125635  8290 solver.cpp:237]     Train net output #0: loss = 1.39645 (* 1 = 1.39645 loss)
I0409 23:50:17.125644  8290 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0409 23:50:22.082315  8290 solver.cpp:218] Iteration 3972 (2.42106 iter/s, 4.9565s/12 iters), loss = 1.52334
I0409 23:50:22.082357  8290 solver.cpp:237]     Train net output #0: loss = 1.52334 (* 1 = 1.52334 loss)
I0409 23:50:22.082365  8290 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0409 23:50:24.089246  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0409 23:50:24.861644  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0409 23:50:25.386670  8290 solver.cpp:330] Iteration 3978, Testing net (#0)
I0409 23:50:25.386698  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:50:28.246001  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:50:29.820086  8290 solver.cpp:397]     Test net output #0: accuracy = 0.435049
I0409 23:50:29.820124  8290 solver.cpp:397]     Test net output #1: loss = 2.33587 (* 1 = 2.33587 loss)
I0409 23:50:31.715505  8290 solver.cpp:218] Iteration 3984 (1.24574 iter/s, 9.63279s/12 iters), loss = 1.36613
I0409 23:50:31.715555  8290 solver.cpp:237]     Train net output #0: loss = 1.36613 (* 1 = 1.36613 loss)
I0409 23:50:31.715564  8290 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0409 23:50:36.614022  8290 solver.cpp:218] Iteration 3996 (2.44984 iter/s, 4.89828s/12 iters), loss = 1.33713
I0409 23:50:36.614075  8290 solver.cpp:237]     Train net output #0: loss = 1.33713 (* 1 = 1.33713 loss)
I0409 23:50:36.614086  8290 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0409 23:50:41.469730  8290 solver.cpp:218] Iteration 4008 (2.47144 iter/s, 4.85547s/12 iters), loss = 1.47918
I0409 23:50:41.469781  8290 solver.cpp:237]     Train net output #0: loss = 1.47918 (* 1 = 1.47918 loss)
I0409 23:50:41.469792  8290 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0409 23:50:46.377209  8290 solver.cpp:218] Iteration 4020 (2.44536 iter/s, 4.90725s/12 iters), loss = 1.88098
I0409 23:50:46.377251  8290 solver.cpp:237]     Train net output #0: loss = 1.88098 (* 1 = 1.88098 loss)
I0409 23:50:46.377260  8290 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0409 23:50:51.308956  8290 solver.cpp:218] Iteration 4032 (2.43333 iter/s, 4.93151s/12 iters), loss = 1.36177
I0409 23:50:51.309039  8290 solver.cpp:237]     Train net output #0: loss = 1.36177 (* 1 = 1.36177 loss)
I0409 23:50:51.309048  8290 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0409 23:50:56.302120  8290 solver.cpp:218] Iteration 4044 (2.40342 iter/s, 4.99289s/12 iters), loss = 1.50081
I0409 23:50:56.302168  8290 solver.cpp:237]     Train net output #0: loss = 1.50081 (* 1 = 1.50081 loss)
I0409 23:50:56.302177  8290 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0409 23:50:56.809135  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:51:01.183562  8290 solver.cpp:218] Iteration 4056 (2.45841 iter/s, 4.88121s/12 iters), loss = 1.35092
I0409 23:51:01.183607  8290 solver.cpp:237]     Train net output #0: loss = 1.35092 (* 1 = 1.35092 loss)
I0409 23:51:01.183616  8290 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0409 23:51:06.041770  8290 solver.cpp:218] Iteration 4068 (2.47016 iter/s, 4.85798s/12 iters), loss = 1.32497
I0409 23:51:06.041813  8290 solver.cpp:237]     Train net output #0: loss = 1.32497 (* 1 = 1.32497 loss)
I0409 23:51:06.041822  8290 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0409 23:51:10.506534  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0409 23:51:11.854439  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0409 23:51:14.214437  8290 solver.cpp:330] Iteration 4080, Testing net (#0)
I0409 23:51:14.214462  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:51:17.104187  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:51:18.756247  8290 solver.cpp:397]     Test net output #0: accuracy = 0.455882
I0409 23:51:18.756276  8290 solver.cpp:397]     Test net output #1: loss = 2.23724 (* 1 = 2.23724 loss)
I0409 23:51:18.838342  8290 solver.cpp:218] Iteration 4080 (0.937788 iter/s, 12.7961s/12 iters), loss = 1.48484
I0409 23:51:18.838392  8290 solver.cpp:237]     Train net output #0: loss = 1.48484 (* 1 = 1.48484 loss)
I0409 23:51:18.838402  8290 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0409 23:51:23.031556  8290 solver.cpp:218] Iteration 4092 (2.86191 iter/s, 4.193s/12 iters), loss = 1.38393
I0409 23:51:23.031710  8290 solver.cpp:237]     Train net output #0: loss = 1.38393 (* 1 = 1.38393 loss)
I0409 23:51:23.031723  8290 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0409 23:51:27.912693  8290 solver.cpp:218] Iteration 4104 (2.45861 iter/s, 4.88081s/12 iters), loss = 1.53786
I0409 23:51:27.912744  8290 solver.cpp:237]     Train net output #0: loss = 1.53786 (* 1 = 1.53786 loss)
I0409 23:51:27.912756  8290 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0409 23:51:32.788465  8290 solver.cpp:218] Iteration 4116 (2.46127 iter/s, 4.87554s/12 iters), loss = 1.23407
I0409 23:51:32.788520  8290 solver.cpp:237]     Train net output #0: loss = 1.23407 (* 1 = 1.23407 loss)
I0409 23:51:32.788532  8290 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0409 23:51:37.741430  8290 solver.cpp:218] Iteration 4128 (2.42291 iter/s, 4.95272s/12 iters), loss = 1.38823
I0409 23:51:37.741475  8290 solver.cpp:237]     Train net output #0: loss = 1.38823 (* 1 = 1.38823 loss)
I0409 23:51:37.741484  8290 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0409 23:51:42.782907  8290 solver.cpp:218] Iteration 4140 (2.38037 iter/s, 5.04124s/12 iters), loss = 1.31468
I0409 23:51:42.782968  8290 solver.cpp:237]     Train net output #0: loss = 1.31468 (* 1 = 1.31468 loss)
I0409 23:51:42.782984  8290 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0409 23:51:45.485509  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:51:47.811229  8290 solver.cpp:218] Iteration 4152 (2.3866 iter/s, 5.02807s/12 iters), loss = 1.29549
I0409 23:51:47.811285  8290 solver.cpp:237]     Train net output #0: loss = 1.29549 (* 1 = 1.29549 loss)
I0409 23:51:47.811297  8290 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0409 23:51:48.166707  8290 blocking_queue.cpp:49] Waiting for data
I0409 23:51:52.727006  8290 solver.cpp:218] Iteration 4164 (2.44123 iter/s, 4.91554s/12 iters), loss = 1.56223
I0409 23:51:52.727056  8290 solver.cpp:237]     Train net output #0: loss = 1.56223 (* 1 = 1.56223 loss)
I0409 23:51:52.727066  8290 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0409 23:51:57.822247  8290 solver.cpp:218] Iteration 4176 (2.35525 iter/s, 5.09501s/12 iters), loss = 1.42918
I0409 23:51:57.822362  8290 solver.cpp:237]     Train net output #0: loss = 1.42918 (* 1 = 1.42918 loss)
I0409 23:51:57.822374  8290 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0409 23:51:59.852980  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0409 23:52:00.544700  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0409 23:52:01.078088  8290 solver.cpp:330] Iteration 4182, Testing net (#0)
I0409 23:52:01.078116  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:52:03.958343  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:52:05.611275  8290 solver.cpp:397]     Test net output #0: accuracy = 0.454044
I0409 23:52:05.611338  8290 solver.cpp:397]     Test net output #1: loss = 2.19504 (* 1 = 2.19504 loss)
I0409 23:52:07.512776  8290 solver.cpp:218] Iteration 4188 (1.23838 iter/s, 9.69007s/12 iters), loss = 1.57497
I0409 23:52:07.512830  8290 solver.cpp:237]     Train net output #0: loss = 1.57497 (* 1 = 1.57497 loss)
I0409 23:52:07.512842  8290 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0409 23:52:12.411929  8290 solver.cpp:218] Iteration 4200 (2.44952 iter/s, 4.89891s/12 iters), loss = 1.38988
I0409 23:52:12.411981  8290 solver.cpp:237]     Train net output #0: loss = 1.38988 (* 1 = 1.38988 loss)
I0409 23:52:12.411993  8290 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0409 23:52:17.352622  8290 solver.cpp:218] Iteration 4212 (2.42893 iter/s, 4.94045s/12 iters), loss = 1.452
I0409 23:52:17.352689  8290 solver.cpp:237]     Train net output #0: loss = 1.452 (* 1 = 1.452 loss)
I0409 23:52:17.352705  8290 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0409 23:52:22.261436  8290 solver.cpp:218] Iteration 4224 (2.44471 iter/s, 4.90857s/12 iters), loss = 1.57436
I0409 23:52:22.261478  8290 solver.cpp:237]     Train net output #0: loss = 1.57436 (* 1 = 1.57436 loss)
I0409 23:52:22.261487  8290 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0409 23:52:27.184332  8290 solver.cpp:218] Iteration 4236 (2.4377 iter/s, 4.92266s/12 iters), loss = 1.35118
I0409 23:52:27.184389  8290 solver.cpp:237]     Train net output #0: loss = 1.35118 (* 1 = 1.35118 loss)
I0409 23:52:27.184401  8290 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0409 23:52:31.882993  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:52:32.101380  8290 solver.cpp:218] Iteration 4248 (2.44061 iter/s, 4.9168s/12 iters), loss = 1.04025
I0409 23:52:32.101461  8290 solver.cpp:237]     Train net output #0: loss = 1.04025 (* 1 = 1.04025 loss)
I0409 23:52:32.101480  8290 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0409 23:52:36.959612  8290 solver.cpp:218] Iteration 4260 (2.47016 iter/s, 4.85798s/12 iters), loss = 1.24168
I0409 23:52:36.959672  8290 solver.cpp:237]     Train net output #0: loss = 1.24168 (* 1 = 1.24168 loss)
I0409 23:52:36.959686  8290 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0409 23:52:41.877373  8290 solver.cpp:218] Iteration 4272 (2.44025 iter/s, 4.91752s/12 iters), loss = 1.09712
I0409 23:52:41.877416  8290 solver.cpp:237]     Train net output #0: loss = 1.09712 (* 1 = 1.09712 loss)
I0409 23:52:41.877425  8290 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0409 23:52:46.335994  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0409 23:52:47.084933  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0409 23:52:47.607565  8290 solver.cpp:330] Iteration 4284, Testing net (#0)
I0409 23:52:47.607591  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:52:50.399416  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:52:52.084784  8290 solver.cpp:397]     Test net output #0: accuracy = 0.446691
I0409 23:52:52.084830  8290 solver.cpp:397]     Test net output #1: loss = 2.23426 (* 1 = 2.23426 loss)
I0409 23:52:52.166374  8290 solver.cpp:218] Iteration 4284 (1.16634 iter/s, 10.2886s/12 iters), loss = 1.60854
I0409 23:52:52.166429  8290 solver.cpp:237]     Train net output #0: loss = 1.60854 (* 1 = 1.60854 loss)
I0409 23:52:52.166440  8290 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0409 23:52:56.320402  8290 solver.cpp:218] Iteration 4296 (2.88891 iter/s, 4.15381s/12 iters), loss = 1.36127
I0409 23:52:56.320449  8290 solver.cpp:237]     Train net output #0: loss = 1.36127 (* 1 = 1.36127 loss)
I0409 23:52:56.320458  8290 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0409 23:53:01.159010  8290 solver.cpp:218] Iteration 4308 (2.48017 iter/s, 4.83838s/12 iters), loss = 1.21943
I0409 23:53:01.159061  8290 solver.cpp:237]     Train net output #0: loss = 1.21943 (* 1 = 1.21943 loss)
I0409 23:53:01.159073  8290 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0409 23:53:06.086122  8290 solver.cpp:218] Iteration 4320 (2.43562 iter/s, 4.92687s/12 iters), loss = 1.26629
I0409 23:53:06.086241  8290 solver.cpp:237]     Train net output #0: loss = 1.26629 (* 1 = 1.26629 loss)
I0409 23:53:06.086253  8290 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0409 23:53:10.970409  8290 solver.cpp:218] Iteration 4332 (2.45701 iter/s, 4.88399s/12 iters), loss = 1.36497
I0409 23:53:10.970464  8290 solver.cpp:237]     Train net output #0: loss = 1.36497 (* 1 = 1.36497 loss)
I0409 23:53:10.970476  8290 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0409 23:53:15.907630  8290 solver.cpp:218] Iteration 4344 (2.43063 iter/s, 4.93698s/12 iters), loss = 1.30082
I0409 23:53:15.907678  8290 solver.cpp:237]     Train net output #0: loss = 1.30082 (* 1 = 1.30082 loss)
I0409 23:53:15.907688  8290 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0409 23:53:17.769068  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:53:20.813935  8290 solver.cpp:218] Iteration 4356 (2.44595 iter/s, 4.90607s/12 iters), loss = 1.37851
I0409 23:53:20.814004  8290 solver.cpp:237]     Train net output #0: loss = 1.37851 (* 1 = 1.37851 loss)
I0409 23:53:20.814014  8290 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0409 23:53:25.728251  8290 solver.cpp:218] Iteration 4368 (2.44197 iter/s, 4.91407s/12 iters), loss = 1.26958
I0409 23:53:25.728304  8290 solver.cpp:237]     Train net output #0: loss = 1.26958 (* 1 = 1.26958 loss)
I0409 23:53:25.728315  8290 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0409 23:53:31.244678  8290 solver.cpp:218] Iteration 4380 (2.17542 iter/s, 5.51617s/12 iters), loss = 1.21446
I0409 23:53:31.244719  8290 solver.cpp:237]     Train net output #0: loss = 1.21446 (* 1 = 1.21446 loss)
I0409 23:53:31.244729  8290 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0409 23:53:33.251343  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0409 23:53:34.027514  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0409 23:53:34.561821  8290 solver.cpp:330] Iteration 4386, Testing net (#0)
I0409 23:53:34.561838  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:53:37.367461  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:53:39.100157  8290 solver.cpp:397]     Test net output #0: accuracy = 0.468137
I0409 23:53:39.100205  8290 solver.cpp:397]     Test net output #1: loss = 2.21393 (* 1 = 2.21393 loss)
I0409 23:53:40.958820  8290 solver.cpp:218] Iteration 4392 (1.23536 iter/s, 9.71375s/12 iters), loss = 1.26002
I0409 23:53:40.958873  8290 solver.cpp:237]     Train net output #0: loss = 1.26002 (* 1 = 1.26002 loss)
I0409 23:53:40.958884  8290 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0409 23:53:45.941700  8290 solver.cpp:218] Iteration 4404 (2.40836 iter/s, 4.98264s/12 iters), loss = 1.16198
I0409 23:53:45.941746  8290 solver.cpp:237]     Train net output #0: loss = 1.16198 (* 1 = 1.16198 loss)
I0409 23:53:45.941756  8290 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0409 23:53:50.793996  8290 solver.cpp:218] Iteration 4416 (2.47318 iter/s, 4.85205s/12 iters), loss = 1.42229
I0409 23:53:50.794050  8290 solver.cpp:237]     Train net output #0: loss = 1.42229 (* 1 = 1.42229 loss)
I0409 23:53:50.794062  8290 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0409 23:53:55.657975  8290 solver.cpp:218] Iteration 4428 (2.46724 iter/s, 4.86373s/12 iters), loss = 1.14573
I0409 23:53:55.658033  8290 solver.cpp:237]     Train net output #0: loss = 1.14573 (* 1 = 1.14573 loss)
I0409 23:53:55.658046  8290 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0409 23:54:00.557265  8290 solver.cpp:218] Iteration 4440 (2.44945 iter/s, 4.89905s/12 iters), loss = 1.21155
I0409 23:54:00.557315  8290 solver.cpp:237]     Train net output #0: loss = 1.21155 (* 1 = 1.21155 loss)
I0409 23:54:00.557325  8290 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0409 23:54:04.517450  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:54:05.468076  8290 solver.cpp:218] Iteration 4452 (2.44371 iter/s, 4.91057s/12 iters), loss = 1.13745
I0409 23:54:05.468133  8290 solver.cpp:237]     Train net output #0: loss = 1.13745 (* 1 = 1.13745 loss)
I0409 23:54:05.468145  8290 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0409 23:54:10.392105  8290 solver.cpp:218] Iteration 4464 (2.43715 iter/s, 4.92378s/12 iters), loss = 1.36519
I0409 23:54:10.392246  8290 solver.cpp:237]     Train net output #0: loss = 1.36519 (* 1 = 1.36519 loss)
I0409 23:54:10.392259  8290 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0409 23:54:15.251965  8290 solver.cpp:218] Iteration 4476 (2.46937 iter/s, 4.85954s/12 iters), loss = 1.52804
I0409 23:54:15.252023  8290 solver.cpp:237]     Train net output #0: loss = 1.52804 (* 1 = 1.52804 loss)
I0409 23:54:15.252036  8290 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0409 23:54:19.695441  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0409 23:54:23.417356  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0409 23:54:25.413084  8290 solver.cpp:330] Iteration 4488, Testing net (#0)
I0409 23:54:25.413110  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:54:28.062319  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:54:29.857825  8290 solver.cpp:397]     Test net output #0: accuracy = 0.468137
I0409 23:54:29.857877  8290 solver.cpp:397]     Test net output #1: loss = 2.24018 (* 1 = 2.24018 loss)
I0409 23:54:29.940197  8290 solver.cpp:218] Iteration 4488 (0.817013 iter/s, 14.6877s/12 iters), loss = 1.10547
I0409 23:54:29.940254  8290 solver.cpp:237]     Train net output #0: loss = 1.10547 (* 1 = 1.10547 loss)
I0409 23:54:29.940268  8290 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0409 23:54:34.076458  8290 solver.cpp:218] Iteration 4500 (2.90132 iter/s, 4.13605s/12 iters), loss = 1.09814
I0409 23:54:34.076508  8290 solver.cpp:237]     Train net output #0: loss = 1.09814 (* 1 = 1.09814 loss)
I0409 23:54:34.076520  8290 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0409 23:54:39.110210  8290 solver.cpp:218] Iteration 4512 (2.38402 iter/s, 5.03351s/12 iters), loss = 1.02717
I0409 23:54:39.110265  8290 solver.cpp:237]     Train net output #0: loss = 1.02717 (* 1 = 1.02717 loss)
I0409 23:54:39.110275  8290 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0409 23:54:43.955699  8290 solver.cpp:218] Iteration 4524 (2.47665 iter/s, 4.84525s/12 iters), loss = 1.11081
I0409 23:54:43.955785  8290 solver.cpp:237]     Train net output #0: loss = 1.11081 (* 1 = 1.11081 loss)
I0409 23:54:43.955796  8290 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0409 23:54:48.821203  8290 solver.cpp:218] Iteration 4536 (2.46648 iter/s, 4.86523s/12 iters), loss = 1.36165
I0409 23:54:48.821262  8290 solver.cpp:237]     Train net output #0: loss = 1.36165 (* 1 = 1.36165 loss)
I0409 23:54:48.821274  8290 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0409 23:54:53.878635  8290 solver.cpp:218] Iteration 4548 (2.37286 iter/s, 5.05718s/12 iters), loss = 1.22499
I0409 23:54:53.878692  8290 solver.cpp:237]     Train net output #0: loss = 1.22499 (* 1 = 1.22499 loss)
I0409 23:54:53.878703  8290 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0409 23:54:55.085114  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:54:58.710942  8290 solver.cpp:218] Iteration 4560 (2.48341 iter/s, 4.83206s/12 iters), loss = 1.02395
I0409 23:54:58.710999  8290 solver.cpp:237]     Train net output #0: loss = 1.02395 (* 1 = 1.02395 loss)
I0409 23:54:58.711010  8290 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0409 23:55:03.567121  8290 solver.cpp:218] Iteration 4572 (2.4712 iter/s, 4.85594s/12 iters), loss = 1.26879
I0409 23:55:03.567173  8290 solver.cpp:237]     Train net output #0: loss = 1.26879 (* 1 = 1.26879 loss)
I0409 23:55:03.567183  8290 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0409 23:55:08.544296  8290 solver.cpp:218] Iteration 4584 (2.41112 iter/s, 4.97693s/12 iters), loss = 0.887832
I0409 23:55:08.544355  8290 solver.cpp:237]     Train net output #0: loss = 0.887832 (* 1 = 0.887832 loss)
I0409 23:55:08.544366  8290 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0409 23:55:10.566144  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0409 23:55:11.369118  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0409 23:55:11.908617  8290 solver.cpp:330] Iteration 4590, Testing net (#0)
I0409 23:55:11.908648  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:55:14.781257  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:55:16.592936  8290 solver.cpp:397]     Test net output #0: accuracy = 0.483456
I0409 23:55:16.592983  8290 solver.cpp:397]     Test net output #1: loss = 2.17275 (* 1 = 2.17275 loss)
I0409 23:55:18.391978  8290 solver.cpp:218] Iteration 4596 (1.21861 iter/s, 9.84726s/12 iters), loss = 0.970405
I0409 23:55:18.392042  8290 solver.cpp:237]     Train net output #0: loss = 0.970405 (* 1 = 0.970405 loss)
I0409 23:55:18.392055  8290 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0409 23:55:23.222213  8290 solver.cpp:218] Iteration 4608 (2.48448 iter/s, 4.82999s/12 iters), loss = 1.03565
I0409 23:55:23.222268  8290 solver.cpp:237]     Train net output #0: loss = 1.03565 (* 1 = 1.03565 loss)
I0409 23:55:23.222277  8290 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0409 23:55:28.126272  8290 solver.cpp:218] Iteration 4620 (2.44707 iter/s, 4.90382s/12 iters), loss = 1.16419
I0409 23:55:28.126314  8290 solver.cpp:237]     Train net output #0: loss = 1.16419 (* 1 = 1.16419 loss)
I0409 23:55:28.126323  8290 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0409 23:55:33.185132  8290 solver.cpp:218] Iteration 4632 (2.37219 iter/s, 5.05862s/12 iters), loss = 1.13456
I0409 23:55:33.185181  8290 solver.cpp:237]     Train net output #0: loss = 1.13456 (* 1 = 1.13456 loss)
I0409 23:55:33.185190  8290 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0409 23:55:38.274279  8290 solver.cpp:218] Iteration 4644 (2.35807 iter/s, 5.08891s/12 iters), loss = 1.25694
I0409 23:55:38.274331  8290 solver.cpp:237]     Train net output #0: loss = 1.25694 (* 1 = 1.25694 loss)
I0409 23:55:38.274343  8290 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0409 23:55:41.643256  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:55:43.192955  8290 solver.cpp:218] Iteration 4656 (2.4398 iter/s, 4.91844s/12 iters), loss = 1.23005
I0409 23:55:43.192999  8290 solver.cpp:237]     Train net output #0: loss = 1.23005 (* 1 = 1.23005 loss)
I0409 23:55:43.193008  8290 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0409 23:55:48.064340  8290 solver.cpp:218] Iteration 4668 (2.46348 iter/s, 4.87115s/12 iters), loss = 1.20181
I0409 23:55:48.064463  8290 solver.cpp:237]     Train net output #0: loss = 1.20181 (* 1 = 1.20181 loss)
I0409 23:55:48.064477  8290 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0409 23:55:52.949193  8290 solver.cpp:218] Iteration 4680 (2.45673 iter/s, 4.88455s/12 iters), loss = 1.20693
I0409 23:55:52.949249  8290 solver.cpp:237]     Train net output #0: loss = 1.20693 (* 1 = 1.20693 loss)
I0409 23:55:52.949261  8290 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0409 23:55:57.420397  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0409 23:55:58.178609  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0409 23:55:58.719936  8290 solver.cpp:330] Iteration 4692, Testing net (#0)
I0409 23:55:58.719962  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:56:01.282091  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:56:03.127385  8290 solver.cpp:397]     Test net output #0: accuracy = 0.468137
I0409 23:56:03.127415  8290 solver.cpp:397]     Test net output #1: loss = 2.16981 (* 1 = 2.16981 loss)
I0409 23:56:03.209278  8290 solver.cpp:218] Iteration 4692 (1.16963 iter/s, 10.2597s/12 iters), loss = 1.0826
I0409 23:56:03.209321  8290 solver.cpp:237]     Train net output #0: loss = 1.0826 (* 1 = 1.0826 loss)
I0409 23:56:03.209331  8290 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0409 23:56:07.343286  8290 solver.cpp:218] Iteration 4704 (2.9029 iter/s, 4.1338s/12 iters), loss = 1.01998
I0409 23:56:07.343348  8290 solver.cpp:237]     Train net output #0: loss = 1.01998 (* 1 = 1.01998 loss)
I0409 23:56:07.343361  8290 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0409 23:56:12.263005  8290 solver.cpp:218] Iteration 4716 (2.43929 iter/s, 4.91947s/12 iters), loss = 0.993161
I0409 23:56:12.263058  8290 solver.cpp:237]     Train net output #0: loss = 0.993161 (* 1 = 0.993161 loss)
I0409 23:56:12.263069  8290 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0409 23:56:17.374104  8290 solver.cpp:218] Iteration 4728 (2.34794 iter/s, 5.11085s/12 iters), loss = 1.09407
I0409 23:56:17.374152  8290 solver.cpp:237]     Train net output #0: loss = 1.09407 (* 1 = 1.09407 loss)
I0409 23:56:17.374164  8290 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0409 23:56:22.278601  8290 solver.cpp:218] Iteration 4740 (2.44685 iter/s, 4.90426s/12 iters), loss = 0.878493
I0409 23:56:22.278759  8290 solver.cpp:237]     Train net output #0: loss = 0.878493 (* 1 = 0.878493 loss)
I0409 23:56:22.278774  8290 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0409 23:56:27.192579  8290 solver.cpp:218] Iteration 4752 (2.44218 iter/s, 4.91364s/12 iters), loss = 1.03671
I0409 23:56:27.192625  8290 solver.cpp:237]     Train net output #0: loss = 1.03671 (* 1 = 1.03671 loss)
I0409 23:56:27.192634  8290 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0409 23:56:27.713654  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:56:32.122736  8290 solver.cpp:218] Iteration 4764 (2.43411 iter/s, 4.92993s/12 iters), loss = 0.920601
I0409 23:56:32.122788  8290 solver.cpp:237]     Train net output #0: loss = 0.920601 (* 1 = 0.920601 loss)
I0409 23:56:32.122799  8290 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0409 23:56:37.028599  8290 solver.cpp:218] Iteration 4776 (2.44617 iter/s, 4.90562s/12 iters), loss = 1.10183
I0409 23:56:37.028656  8290 solver.cpp:237]     Train net output #0: loss = 1.10183 (* 1 = 1.10183 loss)
I0409 23:56:37.028668  8290 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0409 23:56:41.937158  8290 solver.cpp:218] Iteration 4788 (2.44483 iter/s, 4.90832s/12 iters), loss = 1.11958
I0409 23:56:41.937203  8290 solver.cpp:237]     Train net output #0: loss = 1.11958 (* 1 = 1.11958 loss)
I0409 23:56:41.937212  8290 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0409 23:56:43.918778  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0409 23:56:44.693334  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0409 23:56:45.569761  8290 solver.cpp:330] Iteration 4794, Testing net (#0)
I0409 23:56:45.569790  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:56:48.150605  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:56:50.042027  8290 solver.cpp:397]     Test net output #0: accuracy = 0.501838
I0409 23:56:50.042062  8290 solver.cpp:397]     Test net output #1: loss = 2.08856 (* 1 = 2.08856 loss)
I0409 23:56:51.847458  8290 solver.cpp:218] Iteration 4800 (1.21091 iter/s, 9.90989s/12 iters), loss = 1.10591
I0409 23:56:51.847515  8290 solver.cpp:237]     Train net output #0: loss = 1.10591 (* 1 = 1.10591 loss)
I0409 23:56:51.847527  8290 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0409 23:56:56.692636  8290 solver.cpp:218] Iteration 4812 (2.47681 iter/s, 4.84494s/12 iters), loss = 1.15976
I0409 23:56:56.692744  8290 solver.cpp:237]     Train net output #0: loss = 1.15976 (* 1 = 1.15976 loss)
I0409 23:56:56.692756  8290 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0409 23:57:01.552069  8290 solver.cpp:218] Iteration 4824 (2.46957 iter/s, 4.85915s/12 iters), loss = 1.18468
I0409 23:57:01.552116  8290 solver.cpp:237]     Train net output #0: loss = 1.18468 (* 1 = 1.18468 loss)
I0409 23:57:01.552125  8290 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0409 23:57:06.441391  8290 solver.cpp:218] Iteration 4836 (2.45445 iter/s, 4.88909s/12 iters), loss = 0.981182
I0409 23:57:06.441442  8290 solver.cpp:237]     Train net output #0: loss = 0.981182 (* 1 = 0.981182 loss)
I0409 23:57:06.441452  8290 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0409 23:57:07.221670  8290 blocking_queue.cpp:49] Waiting for data
I0409 23:57:11.393298  8290 solver.cpp:218] Iteration 4848 (2.42343 iter/s, 4.95166s/12 iters), loss = 1.55507
I0409 23:57:11.393354  8290 solver.cpp:237]     Train net output #0: loss = 1.55507 (* 1 = 1.55507 loss)
I0409 23:57:11.393368  8290 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0409 23:57:14.035589  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:57:16.362376  8290 solver.cpp:218] Iteration 4860 (2.41506 iter/s, 4.96883s/12 iters), loss = 0.749391
I0409 23:57:16.362443  8290 solver.cpp:237]     Train net output #0: loss = 0.749391 (* 1 = 0.749391 loss)
I0409 23:57:16.362457  8290 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0409 23:57:21.294312  8290 solver.cpp:218] Iteration 4872 (2.43325 iter/s, 4.93168s/12 iters), loss = 1.01604
I0409 23:57:21.294369  8290 solver.cpp:237]     Train net output #0: loss = 1.01604 (* 1 = 1.01604 loss)
I0409 23:57:21.294381  8290 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0409 23:57:26.175042  8290 solver.cpp:218] Iteration 4884 (2.45877 iter/s, 4.88049s/12 iters), loss = 1.03738
I0409 23:57:26.175102  8290 solver.cpp:237]     Train net output #0: loss = 1.03738 (* 1 = 1.03738 loss)
I0409 23:57:26.175115  8290 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0409 23:57:30.611259  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0409 23:57:31.708482  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0409 23:57:32.883111  8290 solver.cpp:330] Iteration 4896, Testing net (#0)
I0409 23:57:32.883139  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:57:35.437299  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:57:37.441174  8290 solver.cpp:397]     Test net output #0: accuracy = 0.495098
I0409 23:57:37.441222  8290 solver.cpp:397]     Test net output #1: loss = 2.13307 (* 1 = 2.13307 loss)
I0409 23:57:37.523397  8290 solver.cpp:218] Iteration 4896 (1.05746 iter/s, 11.3479s/12 iters), loss = 1.0057
I0409 23:57:37.523440  8290 solver.cpp:237]     Train net output #0: loss = 1.0057 (* 1 = 1.0057 loss)
I0409 23:57:37.523449  8290 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0409 23:57:41.700645  8290 solver.cpp:218] Iteration 4908 (2.87285 iter/s, 4.17704s/12 iters), loss = 0.875682
I0409 23:57:41.700707  8290 solver.cpp:237]     Train net output #0: loss = 0.875682 (* 1 = 0.875682 loss)
I0409 23:57:41.700721  8290 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0409 23:57:46.509399  8290 solver.cpp:218] Iteration 4920 (2.49558 iter/s, 4.80851s/12 iters), loss = 0.883767
I0409 23:57:46.509461  8290 solver.cpp:237]     Train net output #0: loss = 0.883767 (* 1 = 0.883767 loss)
I0409 23:57:46.509474  8290 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0409 23:57:51.313369  8290 solver.cpp:218] Iteration 4932 (2.49806 iter/s, 4.80373s/12 iters), loss = 0.791983
I0409 23:57:51.313429  8290 solver.cpp:237]     Train net output #0: loss = 0.791983 (* 1 = 0.791983 loss)
I0409 23:57:51.313442  8290 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0409 23:57:56.142195  8290 solver.cpp:218] Iteration 4944 (2.4852 iter/s, 4.82858s/12 iters), loss = 1.15994
I0409 23:57:56.142256  8290 solver.cpp:237]     Train net output #0: loss = 1.15994 (* 1 = 1.15994 loss)
I0409 23:57:56.142268  8290 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0409 23:58:00.765650  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:58:00.958300  8290 solver.cpp:218] Iteration 4956 (2.49177 iter/s, 4.81586s/12 iters), loss = 0.879959
I0409 23:58:00.958359  8290 solver.cpp:237]     Train net output #0: loss = 0.879959 (* 1 = 0.879959 loss)
I0409 23:58:00.958376  8290 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0409 23:58:05.914618  8290 solver.cpp:218] Iteration 4968 (2.42127 iter/s, 4.95607s/12 iters), loss = 1.14483
I0409 23:58:05.914667  8290 solver.cpp:237]     Train net output #0: loss = 1.14483 (* 1 = 1.14483 loss)
I0409 23:58:05.914680  8290 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0409 23:58:10.811332  8290 solver.cpp:218] Iteration 4980 (2.45074 iter/s, 4.89648s/12 iters), loss = 0.814194
I0409 23:58:10.811388  8290 solver.cpp:237]     Train net output #0: loss = 0.814194 (* 1 = 0.814194 loss)
I0409 23:58:10.811400  8290 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0409 23:58:15.709762  8290 solver.cpp:218] Iteration 4992 (2.44988 iter/s, 4.89819s/12 iters), loss = 1.07069
I0409 23:58:15.709816  8290 solver.cpp:237]     Train net output #0: loss = 1.07069 (* 1 = 1.07069 loss)
I0409 23:58:15.709830  8290 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0409 23:58:17.676952  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0409 23:58:19.287154  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0409 23:58:20.037914  8290 solver.cpp:330] Iteration 4998, Testing net (#0)
I0409 23:58:20.037933  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:58:22.525213  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:58:24.526917  8290 solver.cpp:397]     Test net output #0: accuracy = 0.496324
I0409 23:58:24.526968  8290 solver.cpp:397]     Test net output #1: loss = 2.10013 (* 1 = 2.10013 loss)
I0409 23:58:26.380173  8290 solver.cpp:218] Iteration 5004 (1.12465 iter/s, 10.67s/12 iters), loss = 0.864698
I0409 23:58:26.380234  8290 solver.cpp:237]     Train net output #0: loss = 0.864698 (* 1 = 0.864698 loss)
I0409 23:58:26.380246  8290 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0409 23:58:31.335803  8290 solver.cpp:218] Iteration 5016 (2.42161 iter/s, 4.95538s/12 iters), loss = 0.9993
I0409 23:58:31.338155  8290 solver.cpp:237]     Train net output #0: loss = 0.9993 (* 1 = 0.9993 loss)
I0409 23:58:31.338171  8290 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0409 23:58:36.262377  8290 solver.cpp:218] Iteration 5028 (2.43702 iter/s, 4.92404s/12 iters), loss = 1.14541
I0409 23:58:36.262428  8290 solver.cpp:237]     Train net output #0: loss = 1.14541 (* 1 = 1.14541 loss)
I0409 23:58:36.262440  8290 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0409 23:58:41.235661  8290 solver.cpp:218] Iteration 5040 (2.41301 iter/s, 4.97305s/12 iters), loss = 0.948971
I0409 23:58:41.235721  8290 solver.cpp:237]     Train net output #0: loss = 0.948971 (* 1 = 0.948971 loss)
I0409 23:58:41.235734  8290 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0409 23:58:46.132496  8290 solver.cpp:218] Iteration 5052 (2.45069 iter/s, 4.89659s/12 iters), loss = 1.04798
I0409 23:58:46.132551  8290 solver.cpp:237]     Train net output #0: loss = 1.04798 (* 1 = 1.04798 loss)
I0409 23:58:46.132563  8290 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0409 23:58:48.032507  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:58:51.033787  8290 solver.cpp:218] Iteration 5064 (2.44846 iter/s, 4.90105s/12 iters), loss = 1.03006
I0409 23:58:51.033833  8290 solver.cpp:237]     Train net output #0: loss = 1.03006 (* 1 = 1.03006 loss)
I0409 23:58:51.033841  8290 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0409 23:58:55.949652  8290 solver.cpp:218] Iteration 5076 (2.44119 iter/s, 4.91563s/12 iters), loss = 1.01222
I0409 23:58:55.949715  8290 solver.cpp:237]     Train net output #0: loss = 1.01222 (* 1 = 1.01222 loss)
I0409 23:58:55.949728  8290 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0409 23:59:00.905095  8290 solver.cpp:218] Iteration 5088 (2.4217 iter/s, 4.9552s/12 iters), loss = 0.738719
I0409 23:59:00.905144  8290 solver.cpp:237]     Train net output #0: loss = 0.738719 (* 1 = 0.738719 loss)
I0409 23:59:00.905153  8290 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0409 23:59:05.470147  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0409 23:59:06.466974  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0409 23:59:08.034795  8290 solver.cpp:330] Iteration 5100, Testing net (#0)
I0409 23:59:08.034819  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:59:10.357369  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:59:12.363190  8290 solver.cpp:397]     Test net output #0: accuracy = 0.508578
I0409 23:59:12.363241  8290 solver.cpp:397]     Test net output #1: loss = 2.07228 (* 1 = 2.07228 loss)
I0409 23:59:12.443960  8290 solver.cpp:218] Iteration 5100 (1.04001 iter/s, 11.5384s/12 iters), loss = 1.01295
I0409 23:59:12.444016  8290 solver.cpp:237]     Train net output #0: loss = 1.01295 (* 1 = 1.01295 loss)
I0409 23:59:12.444028  8290 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0409 23:59:16.713414  8290 solver.cpp:218] Iteration 5112 (2.81081 iter/s, 4.26923s/12 iters), loss = 0.899726
I0409 23:59:16.713471  8290 solver.cpp:237]     Train net output #0: loss = 0.899726 (* 1 = 0.899726 loss)
I0409 23:59:16.713482  8290 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0409 23:59:21.698756  8290 solver.cpp:218] Iteration 5124 (2.40718 iter/s, 4.98509s/12 iters), loss = 0.969474
I0409 23:59:21.698818  8290 solver.cpp:237]     Train net output #0: loss = 0.969474 (* 1 = 0.969474 loss)
I0409 23:59:21.698832  8290 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0409 23:59:26.606659  8290 solver.cpp:218] Iteration 5136 (2.44516 iter/s, 4.90765s/12 iters), loss = 0.890349
I0409 23:59:26.606717  8290 solver.cpp:237]     Train net output #0: loss = 0.890349 (* 1 = 0.890349 loss)
I0409 23:59:26.606729  8290 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0409 23:59:31.526281  8290 solver.cpp:218] Iteration 5148 (2.43933 iter/s, 4.91938s/12 iters), loss = 0.794216
I0409 23:59:31.526330  8290 solver.cpp:237]     Train net output #0: loss = 0.794216 (* 1 = 0.794216 loss)
I0409 23:59:31.526343  8290 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0409 23:59:35.497975  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:59:36.407846  8290 solver.cpp:218] Iteration 5160 (2.45835 iter/s, 4.88133s/12 iters), loss = 0.65668
I0409 23:59:36.407900  8290 solver.cpp:237]     Train net output #0: loss = 0.65668 (* 1 = 0.65668 loss)
I0409 23:59:36.407914  8290 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0409 23:59:41.324265  8290 solver.cpp:218] Iteration 5172 (2.44092 iter/s, 4.91619s/12 iters), loss = 0.955993
I0409 23:59:41.324303  8290 solver.cpp:237]     Train net output #0: loss = 0.955993 (* 1 = 0.955993 loss)
I0409 23:59:41.324311  8290 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0409 23:59:46.271700  8290 solver.cpp:218] Iteration 5184 (2.42561 iter/s, 4.94722s/12 iters), loss = 1.09478
I0409 23:59:46.271739  8290 solver.cpp:237]     Train net output #0: loss = 1.09478 (* 1 = 1.09478 loss)
I0409 23:59:46.271746  8290 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0409 23:59:51.109177  8290 solver.cpp:218] Iteration 5196 (2.48075 iter/s, 4.83725s/12 iters), loss = 0.875414
I0409 23:59:51.109231  8290 solver.cpp:237]     Train net output #0: loss = 0.875414 (* 1 = 0.875414 loss)
I0409 23:59:51.109243  8290 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0409 23:59:53.103684  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0409 23:59:53.836236  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0409 23:59:54.365973  8290 solver.cpp:330] Iteration 5202, Testing net (#0)
I0409 23:59:54.365994  8290 net.cpp:676] Ignoring source layer train-data
I0409 23:59:56.775156  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:59:58.911933  8290 solver.cpp:397]     Test net output #0: accuracy = 0.515319
I0409 23:59:58.911986  8290 solver.cpp:397]     Test net output #1: loss = 2.04432 (* 1 = 2.04432 loss)
I0410 00:00:00.876673  8290 solver.cpp:218] Iteration 5208 (1.22862 iter/s, 9.76709s/12 iters), loss = 0.709369
I0410 00:00:00.876730  8290 solver.cpp:237]     Train net output #0: loss = 0.709369 (* 1 = 0.709369 loss)
I0410 00:00:00.876744  8290 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0410 00:00:05.909173  8290 solver.cpp:218] Iteration 5220 (2.38462 iter/s, 5.03226s/12 iters), loss = 0.925779
I0410 00:00:05.910245  8290 solver.cpp:237]     Train net output #0: loss = 0.925779 (* 1 = 0.925779 loss)
I0410 00:00:05.910256  8290 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0410 00:00:10.843024  8290 solver.cpp:218] Iteration 5232 (2.4328 iter/s, 4.93259s/12 iters), loss = 0.840775
I0410 00:00:10.843087  8290 solver.cpp:237]     Train net output #0: loss = 0.840775 (* 1 = 0.840775 loss)
I0410 00:00:10.843101  8290 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0410 00:00:15.736297  8290 solver.cpp:218] Iteration 5244 (2.45247 iter/s, 4.89303s/12 iters), loss = 0.836296
I0410 00:00:15.736344  8290 solver.cpp:237]     Train net output #0: loss = 0.836296 (* 1 = 0.836296 loss)
I0410 00:00:15.736354  8290 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0410 00:00:20.616495  8290 solver.cpp:218] Iteration 5256 (2.45904 iter/s, 4.87996s/12 iters), loss = 0.809875
I0410 00:00:20.616567  8290 solver.cpp:237]     Train net output #0: loss = 0.809875 (* 1 = 0.809875 loss)
I0410 00:00:20.616583  8290 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0410 00:00:21.914547  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:25.549396  8290 solver.cpp:218] Iteration 5268 (2.43277 iter/s, 4.93265s/12 iters), loss = 0.984964
I0410 00:00:25.549438  8290 solver.cpp:237]     Train net output #0: loss = 0.984964 (* 1 = 0.984964 loss)
I0410 00:00:25.549449  8290 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0410 00:00:30.337952  8290 solver.cpp:218] Iteration 5280 (2.50609 iter/s, 4.78833s/12 iters), loss = 1.08922
I0410 00:00:30.338029  8290 solver.cpp:237]     Train net output #0: loss = 1.08922 (* 1 = 1.08922 loss)
I0410 00:00:30.338043  8290 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0410 00:00:35.200825  8290 solver.cpp:218] Iteration 5292 (2.46781 iter/s, 4.86261s/12 iters), loss = 0.910194
I0410 00:00:35.200891  8290 solver.cpp:237]     Train net output #0: loss = 0.910194 (* 1 = 0.910194 loss)
I0410 00:00:35.200904  8290 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0410 00:00:39.579545  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0410 00:00:40.297986  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0410 00:00:40.846813  8290 solver.cpp:330] Iteration 5304, Testing net (#0)
I0410 00:00:40.846837  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:00:43.295428  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:00:45.383836  8290 solver.cpp:397]     Test net output #0: accuracy = 0.525123
I0410 00:00:45.383886  8290 solver.cpp:397]     Test net output #1: loss = 2.07691 (* 1 = 2.07691 loss)
I0410 00:00:45.465824  8290 solver.cpp:218] Iteration 5304 (1.16907 iter/s, 10.2646s/12 iters), loss = 0.674758
I0410 00:00:45.465893  8290 solver.cpp:237]     Train net output #0: loss = 0.674758 (* 1 = 0.674758 loss)
I0410 00:00:45.465911  8290 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0410 00:00:49.690873  8290 solver.cpp:218] Iteration 5316 (2.84036 iter/s, 4.22482s/12 iters), loss = 0.721853
I0410 00:00:49.690924  8290 solver.cpp:237]     Train net output #0: loss = 0.721853 (* 1 = 0.721853 loss)
I0410 00:00:49.690934  8290 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0410 00:00:54.610493  8290 solver.cpp:218] Iteration 5328 (2.43933 iter/s, 4.91939s/12 iters), loss = 0.829293
I0410 00:00:54.610533  8290 solver.cpp:237]     Train net output #0: loss = 0.829293 (* 1 = 0.829293 loss)
I0410 00:00:54.610543  8290 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0410 00:00:59.488955  8290 solver.cpp:218] Iteration 5340 (2.4599 iter/s, 4.87824s/12 iters), loss = 0.759039
I0410 00:00:59.489006  8290 solver.cpp:237]     Train net output #0: loss = 0.759039 (* 1 = 0.759039 loss)
I0410 00:00:59.489017  8290 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0410 00:01:04.322804  8290 solver.cpp:218] Iteration 5352 (2.48261 iter/s, 4.83361s/12 iters), loss = 0.768784
I0410 00:01:04.322860  8290 solver.cpp:237]     Train net output #0: loss = 0.768784 (* 1 = 0.768784 loss)
I0410 00:01:04.322871  8290 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0410 00:01:07.663305  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:01:09.212760  8290 solver.cpp:218] Iteration 5364 (2.45413 iter/s, 4.88972s/12 iters), loss = 0.796877
I0410 00:01:09.212807  8290 solver.cpp:237]     Train net output #0: loss = 0.796877 (* 1 = 0.796877 loss)
I0410 00:01:09.212816  8290 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0410 00:01:14.127781  8290 solver.cpp:218] Iteration 5376 (2.44161 iter/s, 4.91479s/12 iters), loss = 0.904363
I0410 00:01:14.127936  8290 solver.cpp:237]     Train net output #0: loss = 0.904363 (* 1 = 0.904363 loss)
I0410 00:01:14.127949  8290 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0410 00:01:19.029549  8290 solver.cpp:218] Iteration 5388 (2.44827 iter/s, 4.90143s/12 iters), loss = 0.794196
I0410 00:01:19.029603  8290 solver.cpp:237]     Train net output #0: loss = 0.794196 (* 1 = 0.794196 loss)
I0410 00:01:19.029613  8290 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0410 00:01:23.898237  8290 solver.cpp:218] Iteration 5400 (2.46485 iter/s, 4.86845s/12 iters), loss = 0.743833
I0410 00:01:23.898288  8290 solver.cpp:237]     Train net output #0: loss = 0.743833 (* 1 = 0.743833 loss)
I0410 00:01:23.898299  8290 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0410 00:01:25.890710  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0410 00:01:27.031088  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0410 00:01:29.059310  8290 solver.cpp:330] Iteration 5406, Testing net (#0)
I0410 00:01:29.059340  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:01:31.334719  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:01:33.456110  8290 solver.cpp:397]     Test net output #0: accuracy = 0.518995
I0410 00:01:33.456161  8290 solver.cpp:397]     Test net output #1: loss = 2.0315 (* 1 = 2.0315 loss)
I0410 00:01:35.368889  8290 solver.cpp:218] Iteration 5412 (1.04619 iter/s, 11.4702s/12 iters), loss = 0.588786
I0410 00:01:35.368949  8290 solver.cpp:237]     Train net output #0: loss = 0.588786 (* 1 = 0.588786 loss)
I0410 00:01:35.368963  8290 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0410 00:01:40.358317  8290 solver.cpp:218] Iteration 5424 (2.40521 iter/s, 4.98918s/12 iters), loss = 0.666277
I0410 00:01:40.358372  8290 solver.cpp:237]     Train net output #0: loss = 0.666277 (* 1 = 0.666277 loss)
I0410 00:01:40.358387  8290 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0410 00:01:45.230075  8290 solver.cpp:218] Iteration 5436 (2.4633 iter/s, 4.87152s/12 iters), loss = 0.842297
I0410 00:01:45.230180  8290 solver.cpp:237]     Train net output #0: loss = 0.842297 (* 1 = 0.842297 loss)
I0410 00:01:45.230190  8290 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0410 00:01:50.101874  8290 solver.cpp:218] Iteration 5448 (2.4633 iter/s, 4.87151s/12 iters), loss = 0.70531
I0410 00:01:50.101938  8290 solver.cpp:237]     Train net output #0: loss = 0.70531 (* 1 = 0.70531 loss)
I0410 00:01:50.101951  8290 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0410 00:01:54.996147  8290 solver.cpp:218] Iteration 5460 (2.45197 iter/s, 4.89403s/12 iters), loss = 0.949301
I0410 00:01:54.996199  8290 solver.cpp:237]     Train net output #0: loss = 0.949301 (* 1 = 0.949301 loss)
I0410 00:01:54.996210  8290 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0410 00:01:55.552121  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:01:59.948421  8290 solver.cpp:218] Iteration 5472 (2.42325 iter/s, 4.95204s/12 iters), loss = 0.589799
I0410 00:01:59.948475  8290 solver.cpp:237]     Train net output #0: loss = 0.589799 (* 1 = 0.589799 loss)
I0410 00:01:59.948490  8290 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0410 00:02:04.899266  8290 solver.cpp:218] Iteration 5484 (2.42395 iter/s, 4.9506s/12 iters), loss = 0.634544
I0410 00:02:04.899318  8290 solver.cpp:237]     Train net output #0: loss = 0.634544 (* 1 = 0.634544 loss)
I0410 00:02:04.899329  8290 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0410 00:02:09.867267  8290 solver.cpp:218] Iteration 5496 (2.41557 iter/s, 4.96776s/12 iters), loss = 0.740435
I0410 00:02:09.867309  8290 solver.cpp:237]     Train net output #0: loss = 0.740435 (* 1 = 0.740435 loss)
I0410 00:02:09.867318  8290 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0410 00:02:14.281339  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0410 00:02:16.389374  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0410 00:02:17.577189  8290 solver.cpp:330] Iteration 5508, Testing net (#0)
I0410 00:02:17.577212  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:02:19.859067  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:02:22.023528  8290 solver.cpp:397]     Test net output #0: accuracy = 0.51348
I0410 00:02:22.023567  8290 solver.cpp:397]     Test net output #1: loss = 2.10371 (* 1 = 2.10371 loss)
I0410 00:02:22.105638  8290 solver.cpp:218] Iteration 5508 (0.980562 iter/s, 12.2379s/12 iters), loss = 0.91706
I0410 00:02:22.105695  8290 solver.cpp:237]     Train net output #0: loss = 0.91706 (* 1 = 0.91706 loss)
I0410 00:02:22.105708  8290 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0410 00:02:26.332438  8290 solver.cpp:218] Iteration 5520 (2.83917 iter/s, 4.22658s/12 iters), loss = 0.655388
I0410 00:02:26.332481  8290 solver.cpp:237]     Train net output #0: loss = 0.655388 (* 1 = 0.655388 loss)
I0410 00:02:26.332490  8290 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0410 00:02:27.489584  8290 blocking_queue.cpp:49] Waiting for data
I0410 00:02:31.182519  8290 solver.cpp:218] Iteration 5532 (2.4743 iter/s, 4.84986s/12 iters), loss = 0.7302
I0410 00:02:31.182564  8290 solver.cpp:237]     Train net output #0: loss = 0.7302 (* 1 = 0.7302 loss)
I0410 00:02:31.182576  8290 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0410 00:02:36.040218  8290 solver.cpp:218] Iteration 5544 (2.47042 iter/s, 4.85747s/12 iters), loss = 0.735182
I0410 00:02:36.040277  8290 solver.cpp:237]     Train net output #0: loss = 0.735182 (* 1 = 0.735182 loss)
I0410 00:02:36.040289  8290 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0410 00:02:40.974306  8290 solver.cpp:218] Iteration 5556 (2.43218 iter/s, 4.93385s/12 iters), loss = 0.690315
I0410 00:02:40.974346  8290 solver.cpp:237]     Train net output #0: loss = 0.690315 (* 1 = 0.690315 loss)
I0410 00:02:40.974355  8290 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0410 00:02:43.631821  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:02:45.917627  8290 solver.cpp:218] Iteration 5568 (2.42763 iter/s, 4.94309s/12 iters), loss = 0.605914
I0410 00:02:45.917686  8290 solver.cpp:237]     Train net output #0: loss = 0.605914 (* 1 = 0.605914 loss)
I0410 00:02:45.917701  8290 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0410 00:02:50.832407  8290 solver.cpp:218] Iteration 5580 (2.44173 iter/s, 4.91454s/12 iters), loss = 0.73981
I0410 00:02:50.832502  8290 solver.cpp:237]     Train net output #0: loss = 0.73981 (* 1 = 0.73981 loss)
I0410 00:02:50.832512  8290 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0410 00:02:55.877570  8290 solver.cpp:218] Iteration 5592 (2.37865 iter/s, 5.04488s/12 iters), loss = 0.817539
I0410 00:02:55.877631  8290 solver.cpp:237]     Train net output #0: loss = 0.817539 (* 1 = 0.817539 loss)
I0410 00:02:55.877643  8290 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0410 00:03:00.822158  8290 solver.cpp:218] Iteration 5604 (2.42702 iter/s, 4.94434s/12 iters), loss = 0.754433
I0410 00:03:00.822213  8290 solver.cpp:237]     Train net output #0: loss = 0.754433 (* 1 = 0.754433 loss)
I0410 00:03:00.822227  8290 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0410 00:03:02.840323  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0410 00:03:05.227392  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0410 00:03:05.989826  8290 solver.cpp:330] Iteration 5610, Testing net (#0)
I0410 00:03:05.989861  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:03:08.231685  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:03:10.431178  8290 solver.cpp:397]     Test net output #0: accuracy = 0.516544
I0410 00:03:10.431226  8290 solver.cpp:397]     Test net output #1: loss = 2.05436 (* 1 = 2.05436 loss)
I0410 00:03:12.412919  8290 solver.cpp:218] Iteration 5616 (1.03535 iter/s, 11.5903s/12 iters), loss = 0.474643
I0410 00:03:12.412971  8290 solver.cpp:237]     Train net output #0: loss = 0.474643 (* 1 = 0.474643 loss)
I0410 00:03:12.412981  8290 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0410 00:03:17.265465  8290 solver.cpp:218] Iteration 5628 (2.47305 iter/s, 4.85231s/12 iters), loss = 0.929968
I0410 00:03:17.265523  8290 solver.cpp:237]     Train net output #0: loss = 0.929968 (* 1 = 0.929968 loss)
I0410 00:03:17.265535  8290 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0410 00:03:22.131011  8290 solver.cpp:218] Iteration 5640 (2.46644 iter/s, 4.86531s/12 iters), loss = 0.70494
I0410 00:03:22.131171  8290 solver.cpp:237]     Train net output #0: loss = 0.70494 (* 1 = 0.70494 loss)
I0410 00:03:22.131186  8290 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0410 00:03:27.049278  8290 solver.cpp:218] Iteration 5652 (2.44005 iter/s, 4.91793s/12 iters), loss = 0.595452
I0410 00:03:27.049326  8290 solver.cpp:237]     Train net output #0: loss = 0.595452 (* 1 = 0.595452 loss)
I0410 00:03:27.049340  8290 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0410 00:03:31.908968  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:03:32.074568  8290 solver.cpp:218] Iteration 5664 (2.38804 iter/s, 5.02505s/12 iters), loss = 0.548925
I0410 00:03:32.074623  8290 solver.cpp:237]     Train net output #0: loss = 0.548925 (* 1 = 0.548925 loss)
I0410 00:03:32.074636  8290 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0410 00:03:36.882417  8290 solver.cpp:218] Iteration 5676 (2.49604 iter/s, 4.80762s/12 iters), loss = 0.712179
I0410 00:03:36.882468  8290 solver.cpp:237]     Train net output #0: loss = 0.712179 (* 1 = 0.712179 loss)
I0410 00:03:36.882479  8290 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0410 00:03:41.769889  8290 solver.cpp:218] Iteration 5688 (2.45538 iter/s, 4.88724s/12 iters), loss = 0.59573
I0410 00:03:41.769946  8290 solver.cpp:237]     Train net output #0: loss = 0.59573 (* 1 = 0.59573 loss)
I0410 00:03:41.769977  8290 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0410 00:03:46.698716  8290 solver.cpp:218] Iteration 5700 (2.43478 iter/s, 4.92859s/12 iters), loss = 0.913676
I0410 00:03:46.698773  8290 solver.cpp:237]     Train net output #0: loss = 0.913676 (* 1 = 0.913676 loss)
I0410 00:03:46.698786  8290 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0410 00:03:51.143872  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0410 00:03:52.272445  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0410 00:03:53.265769  8290 solver.cpp:330] Iteration 5712, Testing net (#0)
I0410 00:03:53.265799  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:03:55.475025  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:03:57.866689  8290 solver.cpp:397]     Test net output #0: accuracy = 0.512255
I0410 00:03:57.866729  8290 solver.cpp:397]     Test net output #1: loss = 2.10088 (* 1 = 2.10088 loss)
I0410 00:03:57.948678  8290 solver.cpp:218] Iteration 5712 (1.06671 iter/s, 11.2495s/12 iters), loss = 0.788975
I0410 00:03:57.948731  8290 solver.cpp:237]     Train net output #0: loss = 0.788975 (* 1 = 0.788975 loss)
I0410 00:03:57.948742  8290 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0410 00:04:02.030028  8290 solver.cpp:218] Iteration 5724 (2.94035 iter/s, 4.08115s/12 iters), loss = 0.626184
I0410 00:04:02.030069  8290 solver.cpp:237]     Train net output #0: loss = 0.626184 (* 1 = 0.626184 loss)
I0410 00:04:02.030078  8290 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0410 00:04:06.940503  8290 solver.cpp:218] Iteration 5736 (2.44387 iter/s, 4.91025s/12 iters), loss = 0.946896
I0410 00:04:06.940546  8290 solver.cpp:237]     Train net output #0: loss = 0.946896 (* 1 = 0.946896 loss)
I0410 00:04:06.940554  8290 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0410 00:04:11.798530  8290 solver.cpp:218] Iteration 5748 (2.47026 iter/s, 4.8578s/12 iters), loss = 0.715861
I0410 00:04:11.798591  8290 solver.cpp:237]     Train net output #0: loss = 0.715861 (* 1 = 0.715861 loss)
I0410 00:04:11.798604  8290 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0410 00:04:16.605235  8290 solver.cpp:218] Iteration 5760 (2.49664 iter/s, 4.80645s/12 iters), loss = 0.77179
I0410 00:04:16.605309  8290 solver.cpp:237]     Train net output #0: loss = 0.77179 (* 1 = 0.77179 loss)
I0410 00:04:16.605327  8290 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0410 00:04:18.416002  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:04:21.394546  8290 solver.cpp:218] Iteration 5772 (2.50571 iter/s, 4.78906s/12 iters), loss = 0.801741
I0410 00:04:21.394605  8290 solver.cpp:237]     Train net output #0: loss = 0.801741 (* 1 = 0.801741 loss)
I0410 00:04:21.394619  8290 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0410 00:04:26.241533  8290 solver.cpp:218] Iteration 5784 (2.47589 iter/s, 4.84675s/12 iters), loss = 0.789568
I0410 00:04:26.241684  8290 solver.cpp:237]     Train net output #0: loss = 0.789568 (* 1 = 0.789568 loss)
I0410 00:04:26.241698  8290 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0410 00:04:31.154621  8290 solver.cpp:218] Iteration 5796 (2.44262 iter/s, 4.91276s/12 iters), loss = 0.603219
I0410 00:04:31.154665  8290 solver.cpp:237]     Train net output #0: loss = 0.603219 (* 1 = 0.603219 loss)
I0410 00:04:31.154675  8290 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0410 00:04:36.021534  8290 solver.cpp:218] Iteration 5808 (2.46574 iter/s, 4.86669s/12 iters), loss = 0.699942
I0410 00:04:36.021579  8290 solver.cpp:237]     Train net output #0: loss = 0.699942 (* 1 = 0.699942 loss)
I0410 00:04:36.021586  8290 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0410 00:04:38.006338  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0410 00:04:38.775503  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0410 00:04:39.789558  8290 solver.cpp:330] Iteration 5814, Testing net (#0)
I0410 00:04:39.789589  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:04:41.983492  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:04:44.263751  8290 solver.cpp:397]     Test net output #0: accuracy = 0.549632
I0410 00:04:44.263789  8290 solver.cpp:397]     Test net output #1: loss = 1.94675 (* 1 = 1.94675 loss)
I0410 00:04:46.106922  8290 solver.cpp:218] Iteration 5820 (1.18989 iter/s, 10.085s/12 iters), loss = 0.620817
I0410 00:04:46.106969  8290 solver.cpp:237]     Train net output #0: loss = 0.620817 (* 1 = 0.620817 loss)
I0410 00:04:46.106979  8290 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0410 00:04:50.966054  8290 solver.cpp:218] Iteration 5832 (2.4697 iter/s, 4.85889s/12 iters), loss = 0.652825
I0410 00:04:50.966116  8290 solver.cpp:237]     Train net output #0: loss = 0.652825 (* 1 = 0.652825 loss)
I0410 00:04:50.966130  8290 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0410 00:04:55.790983  8290 solver.cpp:218] Iteration 5844 (2.48721 iter/s, 4.82469s/12 iters), loss = 0.573823
I0410 00:04:55.791039  8290 solver.cpp:237]     Train net output #0: loss = 0.573823 (* 1 = 0.573823 loss)
I0410 00:04:55.791051  8290 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0410 00:05:00.628418  8290 solver.cpp:218] Iteration 5856 (2.48078 iter/s, 4.8372s/12 iters), loss = 0.576656
I0410 00:05:00.628571  8290 solver.cpp:237]     Train net output #0: loss = 0.576656 (* 1 = 0.576656 loss)
I0410 00:05:00.628584  8290 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0410 00:05:04.682401  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:05:05.527017  8290 solver.cpp:218] Iteration 5868 (2.44985 iter/s, 4.89827s/12 iters), loss = 0.521781
I0410 00:05:05.527058  8290 solver.cpp:237]     Train net output #0: loss = 0.521781 (* 1 = 0.521781 loss)
I0410 00:05:05.527068  8290 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0410 00:05:10.583498  8290 solver.cpp:218] Iteration 5880 (2.3733 iter/s, 5.05625s/12 iters), loss = 0.722767
I0410 00:05:10.583544  8290 solver.cpp:237]     Train net output #0: loss = 0.722767 (* 1 = 0.722767 loss)
I0410 00:05:10.583554  8290 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0410 00:05:15.512598  8290 solver.cpp:218] Iteration 5892 (2.43464 iter/s, 4.92886s/12 iters), loss = 0.578501
I0410 00:05:15.512660  8290 solver.cpp:237]     Train net output #0: loss = 0.578501 (* 1 = 0.578501 loss)
I0410 00:05:15.512674  8290 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0410 00:05:20.471253  8290 solver.cpp:218] Iteration 5904 (2.42013 iter/s, 4.95841s/12 iters), loss = 0.580986
I0410 00:05:20.471311  8290 solver.cpp:237]     Train net output #0: loss = 0.580986 (* 1 = 0.580986 loss)
I0410 00:05:20.471325  8290 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0410 00:05:24.979913  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0410 00:05:25.836776  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0410 00:05:27.184896  8290 solver.cpp:330] Iteration 5916, Testing net (#0)
I0410 00:05:27.184931  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:05:29.309374  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:05:31.629616  8290 solver.cpp:397]     Test net output #0: accuracy = 0.536765
I0410 00:05:31.629726  8290 solver.cpp:397]     Test net output #1: loss = 2.00299 (* 1 = 2.00299 loss)
I0410 00:05:31.711707  8290 solver.cpp:218] Iteration 5916 (1.06762 iter/s, 11.24s/12 iters), loss = 0.483691
I0410 00:05:31.711760  8290 solver.cpp:237]     Train net output #0: loss = 0.483691 (* 1 = 0.483691 loss)
I0410 00:05:31.711771  8290 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0410 00:05:35.892721  8290 solver.cpp:218] Iteration 5928 (2.87026 iter/s, 4.18081s/12 iters), loss = 0.690266
I0410 00:05:35.892760  8290 solver.cpp:237]     Train net output #0: loss = 0.690266 (* 1 = 0.690266 loss)
I0410 00:05:35.892768  8290 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0410 00:05:40.839993  8290 solver.cpp:218] Iteration 5940 (2.42569 iter/s, 4.94704s/12 iters), loss = 0.587467
I0410 00:05:40.840051  8290 solver.cpp:237]     Train net output #0: loss = 0.587467 (* 1 = 0.587467 loss)
I0410 00:05:40.840063  8290 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0410 00:05:45.659807  8290 solver.cpp:218] Iteration 5952 (2.48985 iter/s, 4.81958s/12 iters), loss = 0.569671
I0410 00:05:45.659862  8290 solver.cpp:237]     Train net output #0: loss = 0.569671 (* 1 = 0.569671 loss)
I0410 00:05:45.659873  8290 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0410 00:05:50.610430  8290 solver.cpp:218] Iteration 5964 (2.42406 iter/s, 4.95038s/12 iters), loss = 0.709331
I0410 00:05:50.610476  8290 solver.cpp:237]     Train net output #0: loss = 0.709331 (* 1 = 0.709331 loss)
I0410 00:05:50.610484  8290 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0410 00:05:51.925163  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:05:55.520113  8290 solver.cpp:218] Iteration 5976 (2.44427 iter/s, 4.90944s/12 iters), loss = 0.60588
I0410 00:05:55.520184  8290 solver.cpp:237]     Train net output #0: loss = 0.60588 (* 1 = 0.60588 loss)
I0410 00:05:55.520201  8290 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0410 00:06:00.440176  8290 solver.cpp:218] Iteration 5988 (2.43912 iter/s, 4.91982s/12 iters), loss = 0.62738
I0410 00:06:00.440224  8290 solver.cpp:237]     Train net output #0: loss = 0.62738 (* 1 = 0.62738 loss)
I0410 00:06:00.440235  8290 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0410 00:06:05.333123  8290 solver.cpp:218] Iteration 6000 (2.45263 iter/s, 4.89271s/12 iters), loss = 0.463792
I0410 00:06:05.333276  8290 solver.cpp:237]     Train net output #0: loss = 0.463792 (* 1 = 0.463792 loss)
I0410 00:06:05.333288  8290 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0410 00:06:10.169826  8290 solver.cpp:218] Iteration 6012 (2.4812 iter/s, 4.83637s/12 iters), loss = 0.58877
I0410 00:06:10.169879  8290 solver.cpp:237]     Train net output #0: loss = 0.58877 (* 1 = 0.58877 loss)
I0410 00:06:10.169890  8290 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0410 00:06:12.130745  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0410 00:06:13.078645  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0410 00:06:14.175768  8290 solver.cpp:330] Iteration 6018, Testing net (#0)
I0410 00:06:14.175797  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:06:16.160061  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:06:18.540982  8290 solver.cpp:397]     Test net output #0: accuracy = 0.530637
I0410 00:06:18.541030  8290 solver.cpp:397]     Test net output #1: loss = 2.01316 (* 1 = 2.01316 loss)
I0410 00:06:20.380129  8290 solver.cpp:218] Iteration 6024 (1.17533 iter/s, 10.2099s/12 iters), loss = 0.60988
I0410 00:06:20.380187  8290 solver.cpp:237]     Train net output #0: loss = 0.60988 (* 1 = 0.60988 loss)
I0410 00:06:20.380199  8290 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0410 00:06:25.199316  8290 solver.cpp:218] Iteration 6036 (2.49017 iter/s, 4.81895s/12 iters), loss = 0.572384
I0410 00:06:25.199368  8290 solver.cpp:237]     Train net output #0: loss = 0.572384 (* 1 = 0.572384 loss)
I0410 00:06:25.199379  8290 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0410 00:06:30.128013  8290 solver.cpp:218] Iteration 6048 (2.43484 iter/s, 4.92846s/12 iters), loss = 0.539664
I0410 00:06:30.128065  8290 solver.cpp:237]     Train net output #0: loss = 0.539664 (* 1 = 0.539664 loss)
I0410 00:06:30.128077  8290 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0410 00:06:35.060801  8290 solver.cpp:218] Iteration 6060 (2.43282 iter/s, 4.93255s/12 iters), loss = 0.452945
I0410 00:06:35.060856  8290 solver.cpp:237]     Train net output #0: loss = 0.452945 (* 1 = 0.452945 loss)
I0410 00:06:35.060868  8290 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0410 00:06:38.417596  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:06:39.945755  8290 solver.cpp:218] Iteration 6072 (2.45664 iter/s, 4.88471s/12 iters), loss = 0.730669
I0410 00:06:39.945814  8290 solver.cpp:237]     Train net output #0: loss = 0.730669 (* 1 = 0.730669 loss)
I0410 00:06:39.945827  8290 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0410 00:06:44.894287  8290 solver.cpp:218] Iteration 6084 (2.42508 iter/s, 4.94829s/12 iters), loss = 0.48598
I0410 00:06:44.894336  8290 solver.cpp:237]     Train net output #0: loss = 0.48598 (* 1 = 0.48598 loss)
I0410 00:06:44.894347  8290 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0410 00:06:49.787099  8290 solver.cpp:218] Iteration 6096 (2.45269 iter/s, 4.89258s/12 iters), loss = 0.504175
I0410 00:06:49.787147  8290 solver.cpp:237]     Train net output #0: loss = 0.504175 (* 1 = 0.504175 loss)
I0410 00:06:49.787158  8290 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0410 00:06:54.639062  8290 solver.cpp:218] Iteration 6108 (2.47334 iter/s, 4.85173s/12 iters), loss = 0.609597
I0410 00:06:54.639112  8290 solver.cpp:237]     Train net output #0: loss = 0.609597 (* 1 = 0.609597 loss)
I0410 00:06:54.639122  8290 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0410 00:06:59.127777  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0410 00:07:00.735793  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0410 00:07:01.452494  8290 solver.cpp:330] Iteration 6120, Testing net (#0)
I0410 00:07:01.452525  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:07:03.488490  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:07:05.887006  8290 solver.cpp:397]     Test net output #0: accuracy = 0.540441
I0410 00:07:05.887044  8290 solver.cpp:397]     Test net output #1: loss = 2.07781 (* 1 = 2.07781 loss)
I0410 00:07:05.968050  8290 solver.cpp:218] Iteration 6120 (1.05927 iter/s, 11.3285s/12 iters), loss = 0.561702
I0410 00:07:05.968112  8290 solver.cpp:237]     Train net output #0: loss = 0.561702 (* 1 = 0.561702 loss)
I0410 00:07:05.968123  8290 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0410 00:07:10.081701  8290 solver.cpp:218] Iteration 6132 (2.91727 iter/s, 4.11343s/12 iters), loss = 0.505237
I0410 00:07:10.081826  8290 solver.cpp:237]     Train net output #0: loss = 0.505237 (* 1 = 0.505237 loss)
I0410 00:07:10.081840  8290 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0410 00:07:14.959841  8290 solver.cpp:218] Iteration 6144 (2.46011 iter/s, 4.87784s/12 iters), loss = 0.579004
I0410 00:07:14.959887  8290 solver.cpp:237]     Train net output #0: loss = 0.579004 (* 1 = 0.579004 loss)
I0410 00:07:14.959897  8290 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0410 00:07:19.913527  8290 solver.cpp:218] Iteration 6156 (2.42255 iter/s, 4.95345s/12 iters), loss = 0.486451
I0410 00:07:19.913581  8290 solver.cpp:237]     Train net output #0: loss = 0.486451 (* 1 = 0.486451 loss)
I0410 00:07:19.913592  8290 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0410 00:07:25.060628  8290 solver.cpp:218] Iteration 6168 (2.33152 iter/s, 5.14685s/12 iters), loss = 0.586908
I0410 00:07:25.060693  8290 solver.cpp:237]     Train net output #0: loss = 0.586908 (* 1 = 0.586908 loss)
I0410 00:07:25.060708  8290 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0410 00:07:25.643815  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:07:29.969993  8290 solver.cpp:218] Iteration 6180 (2.44444 iter/s, 4.90911s/12 iters), loss = 0.449448
I0410 00:07:29.970053  8290 solver.cpp:237]     Train net output #0: loss = 0.449448 (* 1 = 0.449448 loss)
I0410 00:07:29.970067  8290 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0410 00:07:34.908761  8290 solver.cpp:218] Iteration 6192 (2.42988 iter/s, 4.93852s/12 iters), loss = 0.480697
I0410 00:07:34.908802  8290 solver.cpp:237]     Train net output #0: loss = 0.480697 (* 1 = 0.480697 loss)
I0410 00:07:34.908811  8290 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0410 00:07:39.865079  8290 solver.cpp:218] Iteration 6204 (2.42126 iter/s, 4.95609s/12 iters), loss = 0.577426
I0410 00:07:39.865123  8290 solver.cpp:237]     Train net output #0: loss = 0.577426 (* 1 = 0.577426 loss)
I0410 00:07:39.865132  8290 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0410 00:07:44.858558  8290 solver.cpp:218] Iteration 6216 (2.40325 iter/s, 4.99325s/12 iters), loss = 0.584556
I0410 00:07:44.858665  8290 solver.cpp:237]     Train net output #0: loss = 0.584556 (* 1 = 0.584556 loss)
I0410 00:07:44.858675  8290 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0410 00:07:46.877518  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0410 00:07:48.155856  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0410 00:07:48.692081  8290 solver.cpp:330] Iteration 6222, Testing net (#0)
I0410 00:07:48.692107  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:07:50.690016  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:07:51.809947  8290 blocking_queue.cpp:49] Waiting for data
I0410 00:07:53.250202  8290 solver.cpp:397]     Test net output #0: accuracy = 0.515931
I0410 00:07:53.250239  8290 solver.cpp:397]     Test net output #1: loss = 2.25744 (* 1 = 2.25744 loss)
I0410 00:07:55.194708  8290 solver.cpp:218] Iteration 6228 (1.16103 iter/s, 10.3357s/12 iters), loss = 0.63461
I0410 00:07:55.194758  8290 solver.cpp:237]     Train net output #0: loss = 0.63461 (* 1 = 0.63461 loss)
I0410 00:07:55.194769  8290 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0410 00:08:00.038091  8290 solver.cpp:218] Iteration 6240 (2.47773 iter/s, 4.84315s/12 iters), loss = 0.580539
I0410 00:08:00.038143  8290 solver.cpp:237]     Train net output #0: loss = 0.580539 (* 1 = 0.580539 loss)
I0410 00:08:00.038156  8290 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0410 00:08:04.888216  8290 solver.cpp:218] Iteration 6252 (2.47428 iter/s, 4.84989s/12 iters), loss = 0.406778
I0410 00:08:04.888276  8290 solver.cpp:237]     Train net output #0: loss = 0.406778 (* 1 = 0.406778 loss)
I0410 00:08:04.888289  8290 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0410 00:08:09.780768  8290 solver.cpp:218] Iteration 6264 (2.45283 iter/s, 4.89231s/12 iters), loss = 0.574151
I0410 00:08:09.780822  8290 solver.cpp:237]     Train net output #0: loss = 0.574151 (* 1 = 0.574151 loss)
I0410 00:08:09.780835  8290 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0410 00:08:12.412778  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:08:14.610579  8290 solver.cpp:218] Iteration 6276 (2.48469 iter/s, 4.82958s/12 iters), loss = 0.614238
I0410 00:08:14.610635  8290 solver.cpp:237]     Train net output #0: loss = 0.614238 (* 1 = 0.614238 loss)
I0410 00:08:14.610646  8290 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0410 00:08:19.505864  8290 solver.cpp:218] Iteration 6288 (2.45146 iter/s, 4.89504s/12 iters), loss = 0.575991
I0410 00:08:19.506048  8290 solver.cpp:237]     Train net output #0: loss = 0.575991 (* 1 = 0.575991 loss)
I0410 00:08:19.506063  8290 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0410 00:08:24.394554  8290 solver.cpp:218] Iteration 6300 (2.45483 iter/s, 4.88833s/12 iters), loss = 0.473831
I0410 00:08:24.394608  8290 solver.cpp:237]     Train net output #0: loss = 0.473831 (* 1 = 0.473831 loss)
I0410 00:08:24.394621  8290 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0410 00:08:29.317675  8290 solver.cpp:218] Iteration 6312 (2.4376 iter/s, 4.92288s/12 iters), loss = 0.503258
I0410 00:08:29.317720  8290 solver.cpp:237]     Train net output #0: loss = 0.503258 (* 1 = 0.503258 loss)
I0410 00:08:29.317730  8290 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0410 00:08:33.773023  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0410 00:08:34.519413  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0410 00:08:35.091976  8290 solver.cpp:330] Iteration 6324, Testing net (#0)
I0410 00:08:35.091995  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:08:36.966316  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:08:39.480388  8290 solver.cpp:397]     Test net output #0: accuracy = 0.538603
I0410 00:08:39.480440  8290 solver.cpp:397]     Test net output #1: loss = 2.11558 (* 1 = 2.11558 loss)
I0410 00:08:39.562875  8290 solver.cpp:218] Iteration 6324 (1.17133 iter/s, 10.2448s/12 iters), loss = 0.371492
I0410 00:08:39.562927  8290 solver.cpp:237]     Train net output #0: loss = 0.371492 (* 1 = 0.371492 loss)
I0410 00:08:39.562937  8290 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0410 00:08:43.633905  8290 solver.cpp:218] Iteration 6336 (2.94781 iter/s, 4.07082s/12 iters), loss = 0.535114
I0410 00:08:43.633975  8290 solver.cpp:237]     Train net output #0: loss = 0.535114 (* 1 = 0.535114 loss)
I0410 00:08:43.633989  8290 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0410 00:08:48.705104  8290 solver.cpp:218] Iteration 6348 (2.36642 iter/s, 5.07095s/12 iters), loss = 0.488435
I0410 00:08:48.705159  8290 solver.cpp:237]     Train net output #0: loss = 0.488435 (* 1 = 0.488435 loss)
I0410 00:08:48.705171  8290 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0410 00:08:53.529034  8290 solver.cpp:218] Iteration 6360 (2.48772 iter/s, 4.8237s/12 iters), loss = 0.798207
I0410 00:08:53.529165  8290 solver.cpp:237]     Train net output #0: loss = 0.798207 (* 1 = 0.798207 loss)
I0410 00:08:53.529175  8290 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0410 00:08:58.279906  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:08:58.424419  8290 solver.cpp:218] Iteration 6372 (2.45144 iter/s, 4.89507s/12 iters), loss = 0.405343
I0410 00:08:58.424464  8290 solver.cpp:237]     Train net output #0: loss = 0.405343 (* 1 = 0.405343 loss)
I0410 00:08:58.424474  8290 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0410 00:09:03.331527  8290 solver.cpp:218] Iteration 6384 (2.44555 iter/s, 4.90688s/12 iters), loss = 0.534917
I0410 00:09:03.331583  8290 solver.cpp:237]     Train net output #0: loss = 0.534917 (* 1 = 0.534917 loss)
I0410 00:09:03.331594  8290 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0410 00:09:08.244582  8290 solver.cpp:218] Iteration 6396 (2.44259 iter/s, 4.91282s/12 iters), loss = 0.520506
I0410 00:09:08.244639  8290 solver.cpp:237]     Train net output #0: loss = 0.520506 (* 1 = 0.520506 loss)
I0410 00:09:08.244652  8290 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0410 00:09:13.203960  8290 solver.cpp:218] Iteration 6408 (2.41978 iter/s, 4.95913s/12 iters), loss = 0.4304
I0410 00:09:13.204020  8290 solver.cpp:237]     Train net output #0: loss = 0.4304 (* 1 = 0.4304 loss)
I0410 00:09:13.204032  8290 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0410 00:09:18.103283  8290 solver.cpp:218] Iteration 6420 (2.44944 iter/s, 4.89908s/12 iters), loss = 0.560743
I0410 00:09:18.103340  8290 solver.cpp:237]     Train net output #0: loss = 0.560743 (* 1 = 0.560743 loss)
I0410 00:09:18.103353  8290 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0410 00:09:20.211769  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0410 00:09:21.220504  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0410 00:09:21.765604  8290 solver.cpp:330] Iteration 6426, Testing net (#0)
I0410 00:09:21.765632  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:09:23.659780  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:09:26.183552  8290 solver.cpp:397]     Test net output #0: accuracy = 0.552696
I0410 00:09:26.183610  8290 solver.cpp:397]     Test net output #1: loss = 2.06168 (* 1 = 2.06168 loss)
I0410 00:09:28.070467  8290 solver.cpp:218] Iteration 6432 (1.204 iter/s, 9.96677s/12 iters), loss = 0.447168
I0410 00:09:28.070520  8290 solver.cpp:237]     Train net output #0: loss = 0.447168 (* 1 = 0.447168 loss)
I0410 00:09:28.070529  8290 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0410 00:09:32.945250  8290 solver.cpp:218] Iteration 6444 (2.46177 iter/s, 4.87455s/12 iters), loss = 0.437946
I0410 00:09:32.945302  8290 solver.cpp:237]     Train net output #0: loss = 0.437946 (* 1 = 0.437946 loss)
I0410 00:09:32.945314  8290 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0410 00:09:37.841274  8290 solver.cpp:218] Iteration 6456 (2.45109 iter/s, 4.89579s/12 iters), loss = 0.458514
I0410 00:09:37.841329  8290 solver.cpp:237]     Train net output #0: loss = 0.458514 (* 1 = 0.458514 loss)
I0410 00:09:37.841341  8290 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0410 00:09:42.664781  8290 solver.cpp:218] Iteration 6468 (2.48794 iter/s, 4.82327s/12 iters), loss = 0.607888
I0410 00:09:42.664849  8290 solver.cpp:237]     Train net output #0: loss = 0.607888 (* 1 = 0.607888 loss)
I0410 00:09:42.664861  8290 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0410 00:09:44.592664  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:09:47.508502  8290 solver.cpp:218] Iteration 6480 (2.47756 iter/s, 4.84347s/12 iters), loss = 0.606123
I0410 00:09:47.508558  8290 solver.cpp:237]     Train net output #0: loss = 0.606123 (* 1 = 0.606123 loss)
I0410 00:09:47.508571  8290 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0410 00:09:52.310124  8290 solver.cpp:218] Iteration 6492 (2.49928 iter/s, 4.80138s/12 iters), loss = 0.534001
I0410 00:09:52.310181  8290 solver.cpp:237]     Train net output #0: loss = 0.534001 (* 1 = 0.534001 loss)
I0410 00:09:52.310194  8290 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0410 00:09:57.146239  8290 solver.cpp:218] Iteration 6504 (2.48145 iter/s, 4.83587s/12 iters), loss = 0.47894
I0410 00:09:57.146394  8290 solver.cpp:237]     Train net output #0: loss = 0.47894 (* 1 = 0.47894 loss)
I0410 00:09:57.146407  8290 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0410 00:10:01.946913  8290 solver.cpp:218] Iteration 6516 (2.49982 iter/s, 4.80034s/12 iters), loss = 0.483504
I0410 00:10:01.946966  8290 solver.cpp:237]     Train net output #0: loss = 0.483504 (* 1 = 0.483504 loss)
I0410 00:10:01.946979  8290 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0410 00:10:06.335464  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0410 00:10:07.100059  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0410 00:10:07.656551  8290 solver.cpp:330] Iteration 6528, Testing net (#0)
I0410 00:10:07.656574  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:10:09.437356  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:10:12.134176  8290 solver.cpp:397]     Test net output #0: accuracy = 0.54473
I0410 00:10:12.134225  8290 solver.cpp:397]     Test net output #1: loss = 2.10635 (* 1 = 2.10635 loss)
I0410 00:10:12.216429  8290 solver.cpp:218] Iteration 6528 (1.16856 iter/s, 10.2691s/12 iters), loss = 0.660729
I0410 00:10:12.216482  8290 solver.cpp:237]     Train net output #0: loss = 0.660729 (* 1 = 0.660729 loss)
I0410 00:10:12.216495  8290 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0410 00:10:16.382246  8290 solver.cpp:218] Iteration 6540 (2.88073 iter/s, 4.16561s/12 iters), loss = 0.644949
I0410 00:10:16.382293  8290 solver.cpp:237]     Train net output #0: loss = 0.644949 (* 1 = 0.644949 loss)
I0410 00:10:16.382302  8290 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0410 00:10:21.283988  8290 solver.cpp:218] Iteration 6552 (2.44822 iter/s, 4.90151s/12 iters), loss = 0.482584
I0410 00:10:21.284040  8290 solver.cpp:237]     Train net output #0: loss = 0.482584 (* 1 = 0.482584 loss)
I0410 00:10:21.284054  8290 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0410 00:10:26.139734  8290 solver.cpp:218] Iteration 6564 (2.47142 iter/s, 4.85551s/12 iters), loss = 0.613881
I0410 00:10:26.139786  8290 solver.cpp:237]     Train net output #0: loss = 0.613881 (* 1 = 0.613881 loss)
I0410 00:10:26.139797  8290 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0410 00:10:30.297065  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:10:31.053843  8290 solver.cpp:218] Iteration 6576 (2.44206 iter/s, 4.91388s/12 iters), loss = 0.465202
I0410 00:10:31.053895  8290 solver.cpp:237]     Train net output #0: loss = 0.465202 (* 1 = 0.465202 loss)
I0410 00:10:31.053906  8290 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0410 00:10:35.918792  8290 solver.cpp:218] Iteration 6588 (2.46674 iter/s, 4.86472s/12 iters), loss = 0.331781
I0410 00:10:35.918833  8290 solver.cpp:237]     Train net output #0: loss = 0.331781 (* 1 = 0.331781 loss)
I0410 00:10:35.918843  8290 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0410 00:10:40.812351  8290 solver.cpp:218] Iteration 6600 (2.45232 iter/s, 4.89333s/12 iters), loss = 0.44854
I0410 00:10:40.812412  8290 solver.cpp:237]     Train net output #0: loss = 0.44854 (* 1 = 0.44854 loss)
I0410 00:10:40.812425  8290 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0410 00:10:45.692826  8290 solver.cpp:218] Iteration 6612 (2.4589 iter/s, 4.88023s/12 iters), loss = 0.310617
I0410 00:10:45.692881  8290 solver.cpp:237]     Train net output #0: loss = 0.310617 (* 1 = 0.310617 loss)
I0410 00:10:45.692893  8290 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0410 00:10:50.575812  8290 solver.cpp:218] Iteration 6624 (2.45763 iter/s, 4.88275s/12 iters), loss = 0.59819
I0410 00:10:50.575865  8290 solver.cpp:237]     Train net output #0: loss = 0.59819 (* 1 = 0.59819 loss)
I0410 00:10:50.575878  8290 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0410 00:10:52.580266  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0410 00:10:53.322365  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0410 00:10:53.856657  8290 solver.cpp:330] Iteration 6630, Testing net (#0)
I0410 00:10:53.856688  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:10:55.612336  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:10:58.355756  8290 solver.cpp:397]     Test net output #0: accuracy = 0.544118
I0410 00:10:58.355808  8290 solver.cpp:397]     Test net output #1: loss = 2.09781 (* 1 = 2.09781 loss)
I0410 00:11:00.240206  8290 solver.cpp:218] Iteration 6636 (1.24172 iter/s, 9.66399s/12 iters), loss = 0.315081
I0410 00:11:00.240262  8290 solver.cpp:237]     Train net output #0: loss = 0.315081 (* 1 = 0.315081 loss)
I0410 00:11:00.240274  8290 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0410 00:11:05.217854  8290 solver.cpp:218] Iteration 6648 (2.4109 iter/s, 4.9774s/12 iters), loss = 0.513665
I0410 00:11:05.218032  8290 solver.cpp:237]     Train net output #0: loss = 0.513665 (* 1 = 0.513665 loss)
I0410 00:11:05.218046  8290 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0410 00:11:10.061324  8290 solver.cpp:218] Iteration 6660 (2.47774 iter/s, 4.84311s/12 iters), loss = 0.36853
I0410 00:11:10.061378  8290 solver.cpp:237]     Train net output #0: loss = 0.36853 (* 1 = 0.36853 loss)
I0410 00:11:10.061393  8290 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0410 00:11:15.067926  8290 solver.cpp:218] Iteration 6672 (2.39695 iter/s, 5.00636s/12 iters), loss = 0.358161
I0410 00:11:15.067986  8290 solver.cpp:237]     Train net output #0: loss = 0.358161 (* 1 = 0.358161 loss)
I0410 00:11:15.067999  8290 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0410 00:11:16.388207  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:11:19.979506  8290 solver.cpp:218] Iteration 6684 (2.44333 iter/s, 4.91133s/12 iters), loss = 0.449289
I0410 00:11:19.979563  8290 solver.cpp:237]     Train net output #0: loss = 0.449289 (* 1 = 0.449289 loss)
I0410 00:11:19.979578  8290 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0410 00:11:24.903851  8290 solver.cpp:218] Iteration 6696 (2.43699 iter/s, 4.9241s/12 iters), loss = 0.581136
I0410 00:11:24.903906  8290 solver.cpp:237]     Train net output #0: loss = 0.581136 (* 1 = 0.581136 loss)
I0410 00:11:24.903916  8290 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0410 00:11:29.780802  8290 solver.cpp:218] Iteration 6708 (2.46067 iter/s, 4.87672s/12 iters), loss = 0.492784
I0410 00:11:29.780856  8290 solver.cpp:237]     Train net output #0: loss = 0.492784 (* 1 = 0.492784 loss)
I0410 00:11:29.780869  8290 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0410 00:11:34.647174  8290 solver.cpp:218] Iteration 6720 (2.46602 iter/s, 4.86613s/12 iters), loss = 0.488919
I0410 00:11:34.647230  8290 solver.cpp:237]     Train net output #0: loss = 0.488919 (* 1 = 0.488919 loss)
I0410 00:11:34.647243  8290 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0410 00:11:39.116151  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0410 00:11:40.570005  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0410 00:11:43.522338  8290 solver.cpp:330] Iteration 6732, Testing net (#0)
I0410 00:11:43.522369  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:11:45.327801  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:11:47.955147  8290 solver.cpp:397]     Test net output #0: accuracy = 0.550858
I0410 00:11:47.955193  8290 solver.cpp:397]     Test net output #1: loss = 2.05955 (* 1 = 2.05955 loss)
I0410 00:11:48.036324  8290 solver.cpp:218] Iteration 6732 (0.896284 iter/s, 13.3886s/12 iters), loss = 0.335625
I0410 00:11:48.036381  8290 solver.cpp:237]     Train net output #0: loss = 0.335625 (* 1 = 0.335625 loss)
I0410 00:11:48.036393  8290 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0410 00:11:52.268337  8290 solver.cpp:218] Iteration 6744 (2.83567 iter/s, 4.2318s/12 iters), loss = 0.356864
I0410 00:11:52.268383  8290 solver.cpp:237]     Train net output #0: loss = 0.356864 (* 1 = 0.356864 loss)
I0410 00:11:52.268394  8290 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0410 00:11:57.153434  8290 solver.cpp:218] Iteration 6756 (2.45657 iter/s, 4.88487s/12 iters), loss = 0.411146
I0410 00:11:57.153475  8290 solver.cpp:237]     Train net output #0: loss = 0.411146 (* 1 = 0.411146 loss)
I0410 00:11:57.153486  8290 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0410 00:12:02.010110  8290 solver.cpp:218] Iteration 6768 (2.47094 iter/s, 4.85645s/12 iters), loss = 0.440689
I0410 00:12:02.010152  8290 solver.cpp:237]     Train net output #0: loss = 0.440689 (* 1 = 0.440689 loss)
I0410 00:12:02.010160  8290 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0410 00:12:05.354015  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:12:06.821746  8290 solver.cpp:218] Iteration 6780 (2.49407 iter/s, 4.8114s/12 iters), loss = 0.53905
I0410 00:12:06.821807  8290 solver.cpp:237]     Train net output #0: loss = 0.53905 (* 1 = 0.53905 loss)
I0410 00:12:06.821820  8290 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0410 00:12:11.740805  8290 solver.cpp:218] Iteration 6792 (2.43961 iter/s, 4.91882s/12 iters), loss = 0.522296
I0410 00:12:11.740962  8290 solver.cpp:237]     Train net output #0: loss = 0.522296 (* 1 = 0.522296 loss)
I0410 00:12:11.740978  8290 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0410 00:12:16.718518  8290 solver.cpp:218] Iteration 6804 (2.41091 iter/s, 4.97738s/12 iters), loss = 0.400508
I0410 00:12:16.718576  8290 solver.cpp:237]     Train net output #0: loss = 0.400508 (* 1 = 0.400508 loss)
I0410 00:12:16.718590  8290 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0410 00:12:21.595844  8290 solver.cpp:218] Iteration 6816 (2.46049 iter/s, 4.87708s/12 iters), loss = 0.393078
I0410 00:12:21.595901  8290 solver.cpp:237]     Train net output #0: loss = 0.393078 (* 1 = 0.393078 loss)
I0410 00:12:21.595913  8290 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0410 00:12:26.512301  8290 solver.cpp:218] Iteration 6828 (2.4409 iter/s, 4.91622s/12 iters), loss = 0.344447
I0410 00:12:26.512357  8290 solver.cpp:237]     Train net output #0: loss = 0.344447 (* 1 = 0.344447 loss)
I0410 00:12:26.512369  8290 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0410 00:12:28.528702  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0410 00:12:30.118472  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0410 00:12:30.648898  8290 solver.cpp:330] Iteration 6834, Testing net (#0)
I0410 00:12:30.648926  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:12:32.513252  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:12:35.177022  8290 solver.cpp:397]     Test net output #0: accuracy = 0.554534
I0410 00:12:35.177054  8290 solver.cpp:397]     Test net output #1: loss = 2.13941 (* 1 = 2.13941 loss)
I0410 00:12:36.897424  8290 solver.cpp:218] Iteration 6840 (1.15555 iter/s, 10.3847s/12 iters), loss = 0.384576
I0410 00:12:36.897473  8290 solver.cpp:237]     Train net output #0: loss = 0.384576 (* 1 = 0.384576 loss)
I0410 00:12:36.897485  8290 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0410 00:12:41.809419  8290 solver.cpp:218] Iteration 6852 (2.44312 iter/s, 4.91176s/12 iters), loss = 0.415139
I0410 00:12:41.809506  8290 solver.cpp:237]     Train net output #0: loss = 0.415139 (* 1 = 0.415139 loss)
I0410 00:12:41.809520  8290 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0410 00:12:46.713253  8290 solver.cpp:218] Iteration 6864 (2.4472 iter/s, 4.90357s/12 iters), loss = 0.444716
I0410 00:12:46.713308  8290 solver.cpp:237]     Train net output #0: loss = 0.444716 (* 1 = 0.444716 loss)
I0410 00:12:46.713322  8290 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0410 00:12:51.882786  8290 solver.cpp:218] Iteration 6876 (2.3214 iter/s, 5.16929s/12 iters), loss = 0.378115
I0410 00:12:51.882830  8290 solver.cpp:237]     Train net output #0: loss = 0.378115 (* 1 = 0.378115 loss)
I0410 00:12:51.882838  8290 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0410 00:12:52.495540  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:12:56.720636  8290 solver.cpp:218] Iteration 6888 (2.48055 iter/s, 4.83763s/12 iters), loss = 0.45459
I0410 00:12:56.720679  8290 solver.cpp:237]     Train net output #0: loss = 0.45459 (* 1 = 0.45459 loss)
I0410 00:12:56.720688  8290 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0410 00:13:01.530130  8290 solver.cpp:218] Iteration 6900 (2.49518 iter/s, 4.80926s/12 iters), loss = 0.319153
I0410 00:13:01.530191  8290 solver.cpp:237]     Train net output #0: loss = 0.319153 (* 1 = 0.319153 loss)
I0410 00:13:01.530202  8290 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0410 00:13:06.415678  8290 solver.cpp:218] Iteration 6912 (2.45635 iter/s, 4.88531s/12 iters), loss = 0.291447
I0410 00:13:06.415725  8290 solver.cpp:237]     Train net output #0: loss = 0.291447 (* 1 = 0.291447 loss)
I0410 00:13:06.415735  8290 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0410 00:13:11.321732  8290 solver.cpp:218] Iteration 6924 (2.44607 iter/s, 4.90582s/12 iters), loss = 0.5254
I0410 00:13:11.321789  8290 solver.cpp:237]     Train net output #0: loss = 0.5254 (* 1 = 0.5254 loss)
I0410 00:13:11.321802  8290 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0410 00:13:15.798259  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0410 00:13:17.803653  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0410 00:13:18.414649  8290 solver.cpp:330] Iteration 6936, Testing net (#0)
I0410 00:13:18.414677  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:13:18.771971  8290 blocking_queue.cpp:49] Waiting for data
I0410 00:13:20.253600  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:13:22.963068  8290 solver.cpp:397]     Test net output #0: accuracy = 0.558824
I0410 00:13:22.963117  8290 solver.cpp:397]     Test net output #1: loss = 2.12237 (* 1 = 2.12237 loss)
I0410 00:13:23.045222  8290 solver.cpp:218] Iteration 6936 (1.02363 iter/s, 11.723s/12 iters), loss = 0.33607
I0410 00:13:23.045284  8290 solver.cpp:237]     Train net output #0: loss = 0.33607 (* 1 = 0.33607 loss)
I0410 00:13:23.045298  8290 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0410 00:13:27.181895  8290 solver.cpp:218] Iteration 6948 (2.90103 iter/s, 4.13646s/12 iters), loss = 0.267889
I0410 00:13:27.181943  8290 solver.cpp:237]     Train net output #0: loss = 0.267889 (* 1 = 0.267889 loss)
I0410 00:13:27.181952  8290 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0410 00:13:32.019124  8290 solver.cpp:218] Iteration 6960 (2.48087 iter/s, 4.837s/12 iters), loss = 0.375408
I0410 00:13:32.019166  8290 solver.cpp:237]     Train net output #0: loss = 0.375408 (* 1 = 0.375408 loss)
I0410 00:13:32.019174  8290 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0410 00:13:36.896560  8290 solver.cpp:218] Iteration 6972 (2.46042 iter/s, 4.87721s/12 iters), loss = 0.37758
I0410 00:13:36.896620  8290 solver.cpp:237]     Train net output #0: loss = 0.37758 (* 1 = 0.37758 loss)
I0410 00:13:36.896631  8290 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0410 00:13:39.548703  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:13:41.697029  8290 solver.cpp:218] Iteration 6984 (2.49988 iter/s, 4.80023s/12 iters), loss = 0.396917
I0410 00:13:41.697089  8290 solver.cpp:237]     Train net output #0: loss = 0.396917 (* 1 = 0.396917 loss)
I0410 00:13:41.697101  8290 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0410 00:13:46.521322  8290 solver.cpp:218] Iteration 6996 (2.48753 iter/s, 4.82406s/12 iters), loss = 0.370897
I0410 00:13:46.521481  8290 solver.cpp:237]     Train net output #0: loss = 0.370897 (* 1 = 0.370897 loss)
I0410 00:13:46.521493  8290 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0410 00:13:51.304394  8290 solver.cpp:218] Iteration 7008 (2.50902 iter/s, 4.78274s/12 iters), loss = 0.39137
I0410 00:13:51.304438  8290 solver.cpp:237]     Train net output #0: loss = 0.39137 (* 1 = 0.39137 loss)
I0410 00:13:51.304450  8290 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0410 00:13:56.113278  8290 solver.cpp:218] Iteration 7020 (2.4955 iter/s, 4.80865s/12 iters), loss = 0.360271
I0410 00:13:56.113343  8290 solver.cpp:237]     Train net output #0: loss = 0.360271 (* 1 = 0.360271 loss)
I0410 00:13:56.113354  8290 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0410 00:14:00.920302  8290 solver.cpp:218] Iteration 7032 (2.49647 iter/s, 4.80678s/12 iters), loss = 0.215464
I0410 00:14:00.920361  8290 solver.cpp:237]     Train net output #0: loss = 0.215464 (* 1 = 0.215464 loss)
I0410 00:14:00.920372  8290 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0410 00:14:02.898269  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0410 00:14:03.715867  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0410 00:14:04.247495  8290 solver.cpp:330] Iteration 7038, Testing net (#0)
I0410 00:14:04.247515  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:14:05.965710  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:14:08.881884  8290 solver.cpp:397]     Test net output #0: accuracy = 0.548407
I0410 00:14:08.881923  8290 solver.cpp:397]     Test net output #1: loss = 2.1936 (* 1 = 2.1936 loss)
I0410 00:14:10.711333  8290 solver.cpp:218] Iteration 7044 (1.22566 iter/s, 9.79062s/12 iters), loss = 0.395046
I0410 00:14:10.711392  8290 solver.cpp:237]     Train net output #0: loss = 0.395046 (* 1 = 0.395046 loss)
I0410 00:14:10.711405  8290 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0410 00:14:15.656577  8290 solver.cpp:218] Iteration 7056 (2.42669 iter/s, 4.945s/12 iters), loss = 0.213187
I0410 00:14:15.656632  8290 solver.cpp:237]     Train net output #0: loss = 0.213187 (* 1 = 0.213187 loss)
I0410 00:14:15.656646  8290 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0410 00:14:20.613742  8290 solver.cpp:218] Iteration 7068 (2.42086 iter/s, 4.95692s/12 iters), loss = 0.441787
I0410 00:14:20.613878  8290 solver.cpp:237]     Train net output #0: loss = 0.441787 (* 1 = 0.441787 loss)
I0410 00:14:20.613893  8290 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0410 00:14:25.413007  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:14:25.513123  8290 solver.cpp:218] Iteration 7080 (2.44945 iter/s, 4.89907s/12 iters), loss = 0.436123
I0410 00:14:25.513171  8290 solver.cpp:237]     Train net output #0: loss = 0.436123 (* 1 = 0.436123 loss)
I0410 00:14:25.513181  8290 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0410 00:14:30.408571  8290 solver.cpp:218] Iteration 7092 (2.45137 iter/s, 4.89522s/12 iters), loss = 0.251691
I0410 00:14:30.408624  8290 solver.cpp:237]     Train net output #0: loss = 0.251691 (* 1 = 0.251691 loss)
I0410 00:14:30.408638  8290 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0410 00:14:35.335680  8290 solver.cpp:218] Iteration 7104 (2.43562 iter/s, 4.92688s/12 iters), loss = 0.409799
I0410 00:14:35.335716  8290 solver.cpp:237]     Train net output #0: loss = 0.409799 (* 1 = 0.409799 loss)
I0410 00:14:35.335726  8290 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0410 00:14:40.254062  8290 solver.cpp:218] Iteration 7116 (2.43993 iter/s, 4.91817s/12 iters), loss = 0.434978
I0410 00:14:40.254107  8290 solver.cpp:237]     Train net output #0: loss = 0.434978 (* 1 = 0.434978 loss)
I0410 00:14:40.254117  8290 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0410 00:14:45.205792  8290 solver.cpp:218] Iteration 7128 (2.42351 iter/s, 4.9515s/12 iters), loss = 0.212282
I0410 00:14:45.205847  8290 solver.cpp:237]     Train net output #0: loss = 0.212282 (* 1 = 0.212282 loss)
I0410 00:14:45.205858  8290 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0410 00:14:49.663784  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0410 00:14:51.019032  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0410 00:14:51.626639  8290 solver.cpp:330] Iteration 7140, Testing net (#0)
I0410 00:14:51.626662  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:14:53.261564  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:14:56.073537  8290 solver.cpp:397]     Test net output #0: accuracy = 0.559436
I0410 00:14:56.073581  8290 solver.cpp:397]     Test net output #1: loss = 2.08959 (* 1 = 2.08959 loss)
I0410 00:14:56.155583  8290 solver.cpp:218] Iteration 7140 (1.09596 iter/s, 10.9494s/12 iters), loss = 0.377493
I0410 00:14:56.155629  8290 solver.cpp:237]     Train net output #0: loss = 0.377493 (* 1 = 0.377493 loss)
I0410 00:14:56.155640  8290 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0410 00:15:00.215585  8290 solver.cpp:218] Iteration 7152 (2.95581 iter/s, 4.0598s/12 iters), loss = 0.45525
I0410 00:15:00.215641  8290 solver.cpp:237]     Train net output #0: loss = 0.45525 (* 1 = 0.45525 loss)
I0410 00:15:00.215654  8290 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0410 00:15:05.112862  8290 solver.cpp:218] Iteration 7164 (2.45046 iter/s, 4.89704s/12 iters), loss = 0.403663
I0410 00:15:05.112916  8290 solver.cpp:237]     Train net output #0: loss = 0.403663 (* 1 = 0.403663 loss)
I0410 00:15:05.112928  8290 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0410 00:15:10.089776  8290 solver.cpp:218] Iteration 7176 (2.41125 iter/s, 4.97668s/12 iters), loss = 0.320715
I0410 00:15:10.089818  8290 solver.cpp:237]     Train net output #0: loss = 0.320715 (* 1 = 0.320715 loss)
I0410 00:15:10.089828  8290 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0410 00:15:12.182691  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:15:15.030546  8290 solver.cpp:218] Iteration 7188 (2.42888 iter/s, 4.94054s/12 iters), loss = 0.40741
I0410 00:15:15.030593  8290 solver.cpp:237]     Train net output #0: loss = 0.40741 (* 1 = 0.40741 loss)
I0410 00:15:15.030602  8290 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 00:15:20.007007  8290 solver.cpp:218] Iteration 7200 (2.41147 iter/s, 4.97622s/12 iters), loss = 0.292301
I0410 00:15:20.007068  8290 solver.cpp:237]     Train net output #0: loss = 0.292301 (* 1 = 0.292301 loss)
I0410 00:15:20.007082  8290 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 00:15:24.914036  8290 solver.cpp:218] Iteration 7212 (2.44559 iter/s, 4.90679s/12 iters), loss = 0.307401
I0410 00:15:24.914155  8290 solver.cpp:237]     Train net output #0: loss = 0.307401 (* 1 = 0.307401 loss)
I0410 00:15:24.914170  8290 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 00:15:29.815397  8290 solver.cpp:218] Iteration 7224 (2.44845 iter/s, 4.90106s/12 iters), loss = 0.334204
I0410 00:15:29.815454  8290 solver.cpp:237]     Train net output #0: loss = 0.334204 (* 1 = 0.334204 loss)
I0410 00:15:29.815465  8290 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 00:15:34.719729  8290 solver.cpp:218] Iteration 7236 (2.44693 iter/s, 4.9041s/12 iters), loss = 0.284888
I0410 00:15:34.719784  8290 solver.cpp:237]     Train net output #0: loss = 0.284888 (* 1 = 0.284888 loss)
I0410 00:15:34.719796  8290 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 00:15:36.717363  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 00:15:37.445669  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 00:15:37.967248  8290 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 00:15:37.967275  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:15:39.588606  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:15:42.718309  8290 solver.cpp:397]     Test net output #0: accuracy = 0.553309
I0410 00:15:42.718358  8290 solver.cpp:397]     Test net output #1: loss = 2.05818 (* 1 = 2.05818 loss)
I0410 00:15:44.495118  8290 solver.cpp:218] Iteration 7248 (1.22762 iter/s, 9.77498s/12 iters), loss = 0.35937
I0410 00:15:44.495187  8290 solver.cpp:237]     Train net output #0: loss = 0.35937 (* 1 = 0.35937 loss)
I0410 00:15:44.495198  8290 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 00:15:49.294003  8290 solver.cpp:218] Iteration 7260 (2.50071 iter/s, 4.79864s/12 iters), loss = 0.444432
I0410 00:15:49.294064  8290 solver.cpp:237]     Train net output #0: loss = 0.444432 (* 1 = 0.444432 loss)
I0410 00:15:49.294077  8290 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 00:15:54.189496  8290 solver.cpp:218] Iteration 7272 (2.45136 iter/s, 4.89525s/12 iters), loss = 0.32969
I0410 00:15:54.189541  8290 solver.cpp:237]     Train net output #0: loss = 0.32969 (* 1 = 0.32969 loss)
I0410 00:15:54.189549  8290 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 00:15:58.357163  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:15:59.095175  8290 solver.cpp:218] Iteration 7284 (2.44626 iter/s, 4.90544s/12 iters), loss = 0.301174
I0410 00:15:59.095233  8290 solver.cpp:237]     Train net output #0: loss = 0.301174 (* 1 = 0.301174 loss)
I0410 00:15:59.095245  8290 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 00:16:04.019037  8290 solver.cpp:218] Iteration 7296 (2.43723 iter/s, 4.92362s/12 iters), loss = 0.359445
I0410 00:16:04.019083  8290 solver.cpp:237]     Train net output #0: loss = 0.359445 (* 1 = 0.359445 loss)
I0410 00:16:04.019093  8290 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 00:16:09.046658  8290 solver.cpp:218] Iteration 7308 (2.38693 iter/s, 5.02739s/12 iters), loss = 0.257474
I0410 00:16:09.046706  8290 solver.cpp:237]     Train net output #0: loss = 0.257474 (* 1 = 0.257474 loss)
I0410 00:16:09.046716  8290 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 00:16:14.087198  8290 solver.cpp:218] Iteration 7320 (2.38081 iter/s, 5.0403s/12 iters), loss = 0.23265
I0410 00:16:14.087251  8290 solver.cpp:237]     Train net output #0: loss = 0.23265 (* 1 = 0.23265 loss)
I0410 00:16:14.087263  8290 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 00:16:18.978297  8290 solver.cpp:218] Iteration 7332 (2.45356 iter/s, 4.89086s/12 iters), loss = 0.318418
I0410 00:16:18.978363  8290 solver.cpp:237]     Train net output #0: loss = 0.318418 (* 1 = 0.318418 loss)
I0410 00:16:18.978376  8290 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 00:16:23.890508  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 00:16:24.656288  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 00:16:25.192394  8290 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 00:16:25.192423  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:16:26.699359  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:16:29.600721  8290 solver.cpp:397]     Test net output #0: accuracy = 0.565564
I0410 00:16:29.600843  8290 solver.cpp:397]     Test net output #1: loss = 2.11968 (* 1 = 2.11968 loss)
I0410 00:16:29.682945  8290 solver.cpp:218] Iteration 7344 (1.12106 iter/s, 10.7042s/12 iters), loss = 0.302619
I0410 00:16:29.682996  8290 solver.cpp:237]     Train net output #0: loss = 0.302619 (* 1 = 0.302619 loss)
I0410 00:16:29.683009  8290 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 00:16:33.847020  8290 solver.cpp:218] Iteration 7356 (2.88194 iter/s, 4.16387s/12 iters), loss = 0.435911
I0410 00:16:33.847064  8290 solver.cpp:237]     Train net output #0: loss = 0.435911 (* 1 = 0.435911 loss)
I0410 00:16:33.847074  8290 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 00:16:38.706588  8290 solver.cpp:218] Iteration 7368 (2.46947 iter/s, 4.85934s/12 iters), loss = 0.244273
I0410 00:16:38.706645  8290 solver.cpp:237]     Train net output #0: loss = 0.244273 (* 1 = 0.244273 loss)
I0410 00:16:38.706656  8290 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 00:16:43.727018  8290 solver.cpp:218] Iteration 7380 (2.39035 iter/s, 5.02018s/12 iters), loss = 0.262518
I0410 00:16:43.727077  8290 solver.cpp:237]     Train net output #0: loss = 0.262518 (* 1 = 0.262518 loss)
I0410 00:16:43.727090  8290 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 00:16:45.098707  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:16:48.627827  8290 solver.cpp:218] Iteration 7392 (2.44869 iter/s, 4.90058s/12 iters), loss = 0.442333
I0410 00:16:48.627866  8290 solver.cpp:237]     Train net output #0: loss = 0.442333 (* 1 = 0.442333 loss)
I0410 00:16:48.627876  8290 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 00:16:53.562304  8290 solver.cpp:218] Iteration 7404 (2.43198 iter/s, 4.93424s/12 iters), loss = 0.429372
I0410 00:16:53.562352  8290 solver.cpp:237]     Train net output #0: loss = 0.429372 (* 1 = 0.429372 loss)
I0410 00:16:53.562362  8290 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 00:16:58.468744  8290 solver.cpp:218] Iteration 7416 (2.44588 iter/s, 4.90621s/12 iters), loss = 0.282718
I0410 00:16:58.468792  8290 solver.cpp:237]     Train net output #0: loss = 0.282718 (* 1 = 0.282718 loss)
I0410 00:16:58.468801  8290 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 00:17:03.343253  8290 solver.cpp:218] Iteration 7428 (2.46191 iter/s, 4.87427s/12 iters), loss = 0.257598
I0410 00:17:03.343406  8290 solver.cpp:237]     Train net output #0: loss = 0.257598 (* 1 = 0.257598 loss)
I0410 00:17:03.343420  8290 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 00:17:08.261379  8290 solver.cpp:218] Iteration 7440 (2.44012 iter/s, 4.9178s/12 iters), loss = 0.242623
I0410 00:17:08.261425  8290 solver.cpp:237]     Train net output #0: loss = 0.242623 (* 1 = 0.242623 loss)
I0410 00:17:08.261435  8290 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 00:17:10.230624  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 00:17:10.943652  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 00:17:11.469255  8290 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 00:17:11.469274  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:17:12.894330  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:17:15.785437  8290 solver.cpp:397]     Test net output #0: accuracy = 0.558824
I0410 00:17:15.785488  8290 solver.cpp:397]     Test net output #1: loss = 2.10302 (* 1 = 2.10302 loss)
I0410 00:17:17.558974  8290 solver.cpp:218] Iteration 7452 (1.29071 iter/s, 9.29721s/12 iters), loss = 0.24377
I0410 00:17:17.559031  8290 solver.cpp:237]     Train net output #0: loss = 0.24377 (* 1 = 0.24377 loss)
I0410 00:17:17.559044  8290 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 00:17:22.378674  8290 solver.cpp:218] Iteration 7464 (2.48991 iter/s, 4.81946s/12 iters), loss = 0.259899
I0410 00:17:22.378737  8290 solver.cpp:237]     Train net output #0: loss = 0.259899 (* 1 = 0.259899 loss)
I0410 00:17:22.378751  8290 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 00:17:27.204377  8290 solver.cpp:218] Iteration 7476 (2.48681 iter/s, 4.82546s/12 iters), loss = 0.375804
I0410 00:17:27.204438  8290 solver.cpp:237]     Train net output #0: loss = 0.375804 (* 1 = 0.375804 loss)
I0410 00:17:27.204449  8290 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 00:17:30.584125  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:17:32.120705  8290 solver.cpp:218] Iteration 7488 (2.44096 iter/s, 4.91609s/12 iters), loss = 0.358901
I0410 00:17:32.120749  8290 solver.cpp:237]     Train net output #0: loss = 0.358901 (* 1 = 0.358901 loss)
I0410 00:17:32.120757  8290 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 00:17:37.006624  8290 solver.cpp:218] Iteration 7500 (2.45615 iter/s, 4.88569s/12 iters), loss = 0.244359
I0410 00:17:37.006748  8290 solver.cpp:237]     Train net output #0: loss = 0.244359 (* 1 = 0.244359 loss)
I0410 00:17:37.006762  8290 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 00:17:41.884639  8290 solver.cpp:218] Iteration 7512 (2.46017 iter/s, 4.87771s/12 iters), loss = 0.330834
I0410 00:17:41.884699  8290 solver.cpp:237]     Train net output #0: loss = 0.330834 (* 1 = 0.330834 loss)
I0410 00:17:41.884711  8290 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 00:17:46.810220  8290 solver.cpp:218] Iteration 7524 (2.43638 iter/s, 4.92533s/12 iters), loss = 0.354861
I0410 00:17:46.810283  8290 solver.cpp:237]     Train net output #0: loss = 0.354861 (* 1 = 0.354861 loss)
I0410 00:17:46.810297  8290 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 00:17:51.727787  8290 solver.cpp:218] Iteration 7536 (2.44035 iter/s, 4.91732s/12 iters), loss = 0.157747
I0410 00:17:51.727838  8290 solver.cpp:237]     Train net output #0: loss = 0.157747 (* 1 = 0.157747 loss)
I0410 00:17:51.727849  8290 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 00:17:56.122534  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 00:17:56.937644  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 00:17:57.481019  8290 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 00:17:57.481047  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:17:58.868945  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:18:02.006114  8290 solver.cpp:397]     Test net output #0: accuracy = 0.563725
I0410 00:18:02.006166  8290 solver.cpp:397]     Test net output #1: loss = 2.13985 (* 1 = 2.13985 loss)
I0410 00:18:02.088582  8290 solver.cpp:218] Iteration 7548 (1.15826 iter/s, 10.3604s/12 iters), loss = 0.370903
I0410 00:18:02.088639  8290 solver.cpp:237]     Train net output #0: loss = 0.370903 (* 1 = 0.370903 loss)
I0410 00:18:02.088651  8290 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 00:18:06.331981  8290 solver.cpp:218] Iteration 7560 (2.82806 iter/s, 4.24318s/12 iters), loss = 0.267183
I0410 00:18:06.332024  8290 solver.cpp:237]     Train net output #0: loss = 0.267183 (* 1 = 0.267183 loss)
I0410 00:18:06.332033  8290 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 00:18:11.183434  8290 solver.cpp:218] Iteration 7572 (2.4736 iter/s, 4.85123s/12 iters), loss = 0.306771
I0410 00:18:11.183571  8290 solver.cpp:237]     Train net output #0: loss = 0.306771 (* 1 = 0.306771 loss)
I0410 00:18:11.183581  8290 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 00:18:16.073840  8290 solver.cpp:218] Iteration 7584 (2.45394 iter/s, 4.89009s/12 iters), loss = 0.320192
I0410 00:18:16.073892  8290 solver.cpp:237]     Train net output #0: loss = 0.320192 (* 1 = 0.320192 loss)
I0410 00:18:16.073904  8290 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 00:18:16.702793  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:18:20.929947  8290 solver.cpp:218] Iteration 7596 (2.47124 iter/s, 4.85587s/12 iters), loss = 0.356399
I0410 00:18:20.930024  8290 solver.cpp:237]     Train net output #0: loss = 0.356399 (* 1 = 0.356399 loss)
I0410 00:18:20.930037  8290 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 00:18:25.769974  8290 solver.cpp:218] Iteration 7608 (2.47947 iter/s, 4.83975s/12 iters), loss = 0.307699
I0410 00:18:25.770032  8290 solver.cpp:237]     Train net output #0: loss = 0.307699 (* 1 = 0.307699 loss)
I0410 00:18:25.770043  8290 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 00:18:30.695399  8290 solver.cpp:218] Iteration 7620 (2.43646 iter/s, 4.92519s/12 iters), loss = 0.292419
I0410 00:18:30.695444  8290 solver.cpp:237]     Train net output #0: loss = 0.292419 (* 1 = 0.292419 loss)
I0410 00:18:30.695453  8290 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 00:18:31.835811  8290 blocking_queue.cpp:49] Waiting for data
I0410 00:18:35.561364  8290 solver.cpp:218] Iteration 7632 (2.46623 iter/s, 4.86574s/12 iters), loss = 0.373504
I0410 00:18:35.561414  8290 solver.cpp:237]     Train net output #0: loss = 0.373504 (* 1 = 0.373504 loss)
I0410 00:18:35.561425  8290 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 00:18:40.506567  8290 solver.cpp:218] Iteration 7644 (2.42671 iter/s, 4.94496s/12 iters), loss = 0.305735
I0410 00:18:40.506623  8290 solver.cpp:237]     Train net output #0: loss = 0.305735 (* 1 = 0.305735 loss)
I0410 00:18:40.506635  8290 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 00:18:42.472445  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 00:18:46.394789  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 00:18:50.092567  8290 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 00:18:50.092599  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:18:51.550907  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:18:54.536432  8290 solver.cpp:397]     Test net output #0: accuracy = 0.566176
I0410 00:18:54.536497  8290 solver.cpp:397]     Test net output #1: loss = 2.11352 (* 1 = 2.11352 loss)
I0410 00:18:56.308341  8290 solver.cpp:218] Iteration 7656 (0.759438 iter/s, 15.8012s/12 iters), loss = 0.386883
I0410 00:18:56.308391  8290 solver.cpp:237]     Train net output #0: loss = 0.386883 (* 1 = 0.386883 loss)
I0410 00:18:56.308403  8290 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 00:19:01.144536  8290 solver.cpp:218] Iteration 7668 (2.48141 iter/s, 4.83596s/12 iters), loss = 0.313453
I0410 00:19:01.144593  8290 solver.cpp:237]     Train net output #0: loss = 0.313453 (* 1 = 0.313453 loss)
I0410 00:19:01.144606  8290 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 00:19:06.223179  8290 solver.cpp:218] Iteration 7680 (2.36295 iter/s, 5.0784s/12 iters), loss = 0.178307
I0410 00:19:06.223224  8290 solver.cpp:237]     Train net output #0: loss = 0.178307 (* 1 = 0.178307 loss)
I0410 00:19:06.223234  8290 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 00:19:08.907179  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:19:11.017411  8290 solver.cpp:218] Iteration 7692 (2.50313 iter/s, 4.79401s/12 iters), loss = 0.392266
I0410 00:19:11.017460  8290 solver.cpp:237]     Train net output #0: loss = 0.392266 (* 1 = 0.392266 loss)
I0410 00:19:11.017470  8290 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 00:19:15.868811  8290 solver.cpp:218] Iteration 7704 (2.47363 iter/s, 4.85117s/12 iters), loss = 0.312185
I0410 00:19:15.868932  8290 solver.cpp:237]     Train net output #0: loss = 0.312185 (* 1 = 0.312185 loss)
I0410 00:19:15.868947  8290 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 00:19:20.688429  8290 solver.cpp:218] Iteration 7716 (2.48998 iter/s, 4.81932s/12 iters), loss = 0.202147
I0410 00:19:20.688482  8290 solver.cpp:237]     Train net output #0: loss = 0.202147 (* 1 = 0.202147 loss)
I0410 00:19:20.688495  8290 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 00:19:25.565441  8290 solver.cpp:218] Iteration 7728 (2.46064 iter/s, 4.87678s/12 iters), loss = 0.158035
I0410 00:19:25.565501  8290 solver.cpp:237]     Train net output #0: loss = 0.158035 (* 1 = 0.158035 loss)
I0410 00:19:25.565515  8290 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 00:19:30.440733  8290 solver.cpp:218] Iteration 7740 (2.46151 iter/s, 4.87505s/12 iters), loss = 0.341519
I0410 00:19:30.440790  8290 solver.cpp:237]     Train net output #0: loss = 0.341519 (* 1 = 0.341519 loss)
I0410 00:19:30.440804  8290 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 00:19:34.900364  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 00:19:36.022127  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 00:19:36.999070  8290 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 00:19:36.999089  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:19:38.405129  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:19:41.485286  8290 solver.cpp:397]     Test net output #0: accuracy = 0.57598
I0410 00:19:41.485334  8290 solver.cpp:397]     Test net output #1: loss = 2.12999 (* 1 = 2.12999 loss)
I0410 00:19:41.568823  8290 solver.cpp:218] Iteration 7752 (1.0784 iter/s, 11.1276s/12 iters), loss = 0.237162
I0410 00:19:41.568878  8290 solver.cpp:237]     Train net output #0: loss = 0.237162 (* 1 = 0.237162 loss)
I0410 00:19:41.568890  8290 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 00:19:45.660111  8290 solver.cpp:218] Iteration 7764 (2.93322 iter/s, 4.09107s/12 iters), loss = 0.339464
I0410 00:19:45.660163  8290 solver.cpp:237]     Train net output #0: loss = 0.339464 (* 1 = 0.339464 loss)
I0410 00:19:45.660173  8290 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 00:19:50.758641  8290 solver.cpp:218] Iteration 7776 (2.35373 iter/s, 5.09829s/12 iters), loss = 0.155812
I0410 00:19:50.758786  8290 solver.cpp:237]     Train net output #0: loss = 0.155812 (* 1 = 0.155812 loss)
I0410 00:19:50.758800  8290 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 00:19:55.640420  8290 solver.cpp:218] Iteration 7788 (2.45828 iter/s, 4.88146s/12 iters), loss = 0.2173
I0410 00:19:55.640470  8290 solver.cpp:237]     Train net output #0: loss = 0.2173 (* 1 = 0.2173 loss)
I0410 00:19:55.640480  8290 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 00:19:55.648499  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:20:00.465906  8290 solver.cpp:218] Iteration 7800 (2.48692 iter/s, 4.82525s/12 iters), loss = 0.237393
I0410 00:20:00.465982  8290 solver.cpp:237]     Train net output #0: loss = 0.237393 (* 1 = 0.237393 loss)
I0410 00:20:00.465996  8290 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 00:20:05.263590  8290 solver.cpp:218] Iteration 7812 (2.50134 iter/s, 4.79743s/12 iters), loss = 0.211319
I0410 00:20:05.263638  8290 solver.cpp:237]     Train net output #0: loss = 0.211319 (* 1 = 0.211319 loss)
I0410 00:20:05.263648  8290 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 00:20:10.148105  8290 solver.cpp:218] Iteration 7824 (2.45686 iter/s, 4.88428s/12 iters), loss = 0.284689
I0410 00:20:10.148151  8290 solver.cpp:237]     Train net output #0: loss = 0.284689 (* 1 = 0.284689 loss)
I0410 00:20:10.148159  8290 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 00:20:15.119771  8290 solver.cpp:218] Iteration 7836 (2.41379 iter/s, 4.97143s/12 iters), loss = 0.306284
I0410 00:20:15.119827  8290 solver.cpp:237]     Train net output #0: loss = 0.306284 (* 1 = 0.306284 loss)
I0410 00:20:15.119840  8290 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 00:20:20.033447  8290 solver.cpp:218] Iteration 7848 (2.44228 iter/s, 4.91343s/12 iters), loss = 0.238862
I0410 00:20:20.033501  8290 solver.cpp:237]     Train net output #0: loss = 0.238862 (* 1 = 0.238862 loss)
I0410 00:20:20.033514  8290 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 00:20:22.027971  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 00:20:23.723453  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 00:20:26.684101  8290 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 00:20:26.684130  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:20:28.059895  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:20:31.121490  8290 solver.cpp:397]     Test net output #0: accuracy = 0.571078
I0410 00:20:31.121536  8290 solver.cpp:397]     Test net output #1: loss = 2.13229 (* 1 = 2.13229 loss)
I0410 00:20:32.955960  8290 solver.cpp:218] Iteration 7860 (0.928649 iter/s, 12.922s/12 iters), loss = 0.421062
I0410 00:20:32.956015  8290 solver.cpp:237]     Train net output #0: loss = 0.421062 (* 1 = 0.421062 loss)
I0410 00:20:32.956027  8290 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 00:20:37.876070  8290 solver.cpp:218] Iteration 7872 (2.43909 iter/s, 4.91987s/12 iters), loss = 0.222452
I0410 00:20:37.876117  8290 solver.cpp:237]     Train net output #0: loss = 0.222452 (* 1 = 0.222452 loss)
I0410 00:20:37.876129  8290 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 00:20:42.784296  8290 solver.cpp:218] Iteration 7884 (2.44499 iter/s, 4.908s/12 iters), loss = 0.132319
I0410 00:20:42.784339  8290 solver.cpp:237]     Train net output #0: loss = 0.132319 (* 1 = 0.132319 loss)
I0410 00:20:42.784348  8290 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 00:20:44.874143  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:20:47.670291  8290 solver.cpp:218] Iteration 7896 (2.45611 iter/s, 4.88577s/12 iters), loss = 0.390463
I0410 00:20:47.670356  8290 solver.cpp:237]     Train net output #0: loss = 0.390463 (* 1 = 0.390463 loss)
I0410 00:20:47.670370  8290 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 00:20:52.582386  8290 solver.cpp:218] Iteration 7908 (2.44307 iter/s, 4.91185s/12 iters), loss = 0.280547
I0410 00:20:52.582501  8290 solver.cpp:237]     Train net output #0: loss = 0.280547 (* 1 = 0.280547 loss)
I0410 00:20:52.582513  8290 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 00:20:57.531527  8290 solver.cpp:218] Iteration 7920 (2.42481 iter/s, 4.94884s/12 iters), loss = 0.431512
I0410 00:20:57.531575  8290 solver.cpp:237]     Train net output #0: loss = 0.431512 (* 1 = 0.431512 loss)
I0410 00:20:57.531586  8290 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 00:21:02.562114  8290 solver.cpp:218] Iteration 7932 (2.38552 iter/s, 5.03034s/12 iters), loss = 0.265816
I0410 00:21:02.562167  8290 solver.cpp:237]     Train net output #0: loss = 0.265816 (* 1 = 0.265816 loss)
I0410 00:21:02.562180  8290 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 00:21:07.747567  8290 solver.cpp:218] Iteration 7944 (2.31428 iter/s, 5.18521s/12 iters), loss = 0.246522
I0410 00:21:07.747622  8290 solver.cpp:237]     Train net output #0: loss = 0.246522 (* 1 = 0.246522 loss)
I0410 00:21:07.747635  8290 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 00:21:12.144661  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 00:21:12.870913  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 00:21:13.401293  8290 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 00:21:13.401319  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:21:14.756047  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:21:17.852914  8290 solver.cpp:397]     Test net output #0: accuracy = 0.564951
I0410 00:21:17.852964  8290 solver.cpp:397]     Test net output #1: loss = 2.19199 (* 1 = 2.19199 loss)
I0410 00:21:17.934376  8290 solver.cpp:218] Iteration 7956 (1.17804 iter/s, 10.1864s/12 iters), loss = 0.187908
I0410 00:21:17.934425  8290 solver.cpp:237]     Train net output #0: loss = 0.187908 (* 1 = 0.187908 loss)
I0410 00:21:17.934438  8290 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 00:21:22.093056  8290 solver.cpp:218] Iteration 7968 (2.88567 iter/s, 4.15847s/12 iters), loss = 0.222756
I0410 00:21:22.093106  8290 solver.cpp:237]     Train net output #0: loss = 0.222756 (* 1 = 0.222756 loss)
I0410 00:21:22.093117  8290 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 00:21:26.966959  8290 solver.cpp:218] Iteration 7980 (2.46221 iter/s, 4.87367s/12 iters), loss = 0.368423
I0410 00:21:26.972957  8290 solver.cpp:237]     Train net output #0: loss = 0.368423 (* 1 = 0.368423 loss)
I0410 00:21:26.972971  8290 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 00:21:31.198251  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:21:31.908491  8290 solver.cpp:218] Iteration 7992 (2.43144 iter/s, 4.93536s/12 iters), loss = 0.202405
I0410 00:21:31.908548  8290 solver.cpp:237]     Train net output #0: loss = 0.202405 (* 1 = 0.202405 loss)
I0410 00:21:31.908561  8290 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 00:21:36.791064  8290 solver.cpp:218] Iteration 8004 (2.45784 iter/s, 4.88233s/12 iters), loss = 0.315508
I0410 00:21:36.791115  8290 solver.cpp:237]     Train net output #0: loss = 0.315508 (* 1 = 0.315508 loss)
I0410 00:21:36.791126  8290 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 00:21:41.999848  8290 solver.cpp:218] Iteration 8016 (2.30391 iter/s, 5.20854s/12 iters), loss = 0.262591
I0410 00:21:41.999893  8290 solver.cpp:237]     Train net output #0: loss = 0.262591 (* 1 = 0.262591 loss)
I0410 00:21:41.999903  8290 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 00:21:46.901751  8290 solver.cpp:218] Iteration 8028 (2.44815 iter/s, 4.90167s/12 iters), loss = 0.213203
I0410 00:21:46.901808  8290 solver.cpp:237]     Train net output #0: loss = 0.213203 (* 1 = 0.213203 loss)
I0410 00:21:46.901821  8290 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 00:21:51.784634  8290 solver.cpp:218] Iteration 8040 (2.45768 iter/s, 4.88265s/12 iters), loss = 0.302689
I0410 00:21:51.784682  8290 solver.cpp:237]     Train net output #0: loss = 0.302689 (* 1 = 0.302689 loss)
I0410 00:21:51.784693  8290 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 00:21:56.659981  8290 solver.cpp:218] Iteration 8052 (2.46148 iter/s, 4.87511s/12 iters), loss = 0.272849
I0410 00:21:56.660037  8290 solver.cpp:237]     Train net output #0: loss = 0.272849 (* 1 = 0.272849 loss)
I0410 00:21:56.660049  8290 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 00:21:58.672499  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 00:22:01.041780  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 00:22:01.803067  8290 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 00:22:01.803090  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:22:03.233884  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:22:06.460572  8290 solver.cpp:397]     Test net output #0: accuracy = 0.571691
I0410 00:22:06.460613  8290 solver.cpp:397]     Test net output #1: loss = 2.17252 (* 1 = 2.17252 loss)
I0410 00:22:08.211179  8290 solver.cpp:218] Iteration 8064 (1.0389 iter/s, 11.5507s/12 iters), loss = 0.264658
I0410 00:22:08.211241  8290 solver.cpp:237]     Train net output #0: loss = 0.264658 (* 1 = 0.264658 loss)
I0410 00:22:08.211252  8290 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 00:22:13.012990  8290 solver.cpp:218] Iteration 8076 (2.49918 iter/s, 4.80157s/12 iters), loss = 0.143362
I0410 00:22:13.013052  8290 solver.cpp:237]     Train net output #0: loss = 0.143362 (* 1 = 0.143362 loss)
I0410 00:22:13.013064  8290 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 00:22:17.828493  8290 solver.cpp:218] Iteration 8088 (2.49208 iter/s, 4.81526s/12 iters), loss = 0.115795
I0410 00:22:17.828537  8290 solver.cpp:237]     Train net output #0: loss = 0.115795 (* 1 = 0.115795 loss)
I0410 00:22:17.828547  8290 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 00:22:19.265069  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:22:22.720925  8290 solver.cpp:218] Iteration 8100 (2.45289 iter/s, 4.8922s/12 iters), loss = 0.150658
I0410 00:22:22.720983  8290 solver.cpp:237]     Train net output #0: loss = 0.150658 (* 1 = 0.150658 loss)
I0410 00:22:22.720996  8290 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 00:22:27.602357  8290 solver.cpp:218] Iteration 8112 (2.45842 iter/s, 4.88119s/12 iters), loss = 0.159067
I0410 00:22:27.602416  8290 solver.cpp:237]     Train net output #0: loss = 0.159067 (* 1 = 0.159067 loss)
I0410 00:22:27.602428  8290 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 00:22:32.587342  8290 solver.cpp:218] Iteration 8124 (2.40735 iter/s, 4.98474s/12 iters), loss = 0.193695
I0410 00:22:32.587440  8290 solver.cpp:237]     Train net output #0: loss = 0.193695 (* 1 = 0.193695 loss)
I0410 00:22:32.587451  8290 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 00:22:37.461678  8290 solver.cpp:218] Iteration 8136 (2.46202 iter/s, 4.87405s/12 iters), loss = 0.168223
I0410 00:22:37.461730  8290 solver.cpp:237]     Train net output #0: loss = 0.168223 (* 1 = 0.168223 loss)
I0410 00:22:37.461740  8290 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 00:22:42.396459  8290 solver.cpp:218] Iteration 8148 (2.43183 iter/s, 4.93455s/12 iters), loss = 0.2309
I0410 00:22:42.396507  8290 solver.cpp:237]     Train net output #0: loss = 0.2309 (* 1 = 0.2309 loss)
I0410 00:22:42.396514  8290 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 00:22:46.853055  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 00:22:48.134265  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 00:22:49.223165  8290 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 00:22:49.223191  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:22:50.479188  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:22:53.694967  8290 solver.cpp:397]     Test net output #0: accuracy = 0.596814
I0410 00:22:53.694996  8290 solver.cpp:397]     Test net output #1: loss = 2.13111 (* 1 = 2.13111 loss)
I0410 00:22:53.777209  8290 solver.cpp:218] Iteration 8160 (1.05445 iter/s, 11.3803s/12 iters), loss = 0.254937
I0410 00:22:53.777276  8290 solver.cpp:237]     Train net output #0: loss = 0.254937 (* 1 = 0.254937 loss)
I0410 00:22:53.777288  8290 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 00:22:57.938599  8290 solver.cpp:218] Iteration 8172 (2.88381 iter/s, 4.16116s/12 iters), loss = 0.274137
I0410 00:22:57.938655  8290 solver.cpp:237]     Train net output #0: loss = 0.274137 (* 1 = 0.274137 loss)
I0410 00:22:57.938666  8290 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 00:23:02.835796  8290 solver.cpp:218] Iteration 8184 (2.4505 iter/s, 4.89696s/12 iters), loss = 0.316743
I0410 00:23:02.835903  8290 solver.cpp:237]     Train net output #0: loss = 0.316743 (* 1 = 0.316743 loss)
I0410 00:23:02.835912  8290 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 00:23:06.350543  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:23:07.781509  8290 solver.cpp:218] Iteration 8196 (2.42649 iter/s, 4.94542s/12 iters), loss = 0.218738
I0410 00:23:07.781558  8290 solver.cpp:237]     Train net output #0: loss = 0.218738 (* 1 = 0.218738 loss)
I0410 00:23:07.781569  8290 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 00:23:12.667675  8290 solver.cpp:218] Iteration 8208 (2.45603 iter/s, 4.88593s/12 iters), loss = 0.309058
I0410 00:23:12.667723  8290 solver.cpp:237]     Train net output #0: loss = 0.309058 (* 1 = 0.309058 loss)
I0410 00:23:12.667733  8290 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 00:23:17.597995  8290 solver.cpp:218] Iteration 8220 (2.43404 iter/s, 4.93008s/12 iters), loss = 0.286609
I0410 00:23:17.598053  8290 solver.cpp:237]     Train net output #0: loss = 0.286609 (* 1 = 0.286609 loss)
I0410 00:23:17.598067  8290 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 00:23:22.435389  8290 solver.cpp:218] Iteration 8232 (2.4808 iter/s, 4.83715s/12 iters), loss = 0.157205
I0410 00:23:22.435442  8290 solver.cpp:237]     Train net output #0: loss = 0.157205 (* 1 = 0.157205 loss)
I0410 00:23:22.435454  8290 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 00:23:27.275046  8290 solver.cpp:218] Iteration 8244 (2.47964 iter/s, 4.83942s/12 iters), loss = 0.348536
I0410 00:23:27.275104  8290 solver.cpp:237]     Train net output #0: loss = 0.348536 (* 1 = 0.348536 loss)
I0410 00:23:27.275116  8290 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 00:23:32.108201  8290 solver.cpp:218] Iteration 8256 (2.48297 iter/s, 4.83292s/12 iters), loss = 0.184274
I0410 00:23:32.108244  8290 solver.cpp:237]     Train net output #0: loss = 0.184274 (* 1 = 0.184274 loss)
I0410 00:23:32.108254  8290 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 00:23:34.116806  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 00:23:34.846777  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 00:23:35.364845  8290 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 00:23:35.364868  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:23:36.578696  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:23:39.872459  8290 solver.cpp:397]     Test net output #0: accuracy = 0.581495
I0410 00:23:39.872510  8290 solver.cpp:397]     Test net output #1: loss = 2.10737 (* 1 = 2.10737 loss)
I0410 00:23:41.796787  8290 solver.cpp:218] Iteration 8268 (1.23862 iter/s, 9.68819s/12 iters), loss = 0.259163
I0410 00:23:41.796829  8290 solver.cpp:237]     Train net output #0: loss = 0.259163 (* 1 = 0.259163 loss)
I0410 00:23:41.796838  8290 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 00:23:46.663822  8290 solver.cpp:218] Iteration 8280 (2.46568 iter/s, 4.86681s/12 iters), loss = 0.163577
I0410 00:23:46.663877  8290 solver.cpp:237]     Train net output #0: loss = 0.163577 (* 1 = 0.163577 loss)
I0410 00:23:46.663888  8290 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 00:23:51.531723  8290 solver.cpp:218] Iteration 8292 (2.46525 iter/s, 4.86766s/12 iters), loss = 0.152952
I0410 00:23:51.531776  8290 solver.cpp:237]     Train net output #0: loss = 0.152952 (* 1 = 0.152952 loss)
I0410 00:23:51.531788  8290 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 00:23:52.218973  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:23:56.427489  8290 solver.cpp:218] Iteration 8304 (2.45122 iter/s, 4.89552s/12 iters), loss = 0.194439
I0410 00:23:56.427542  8290 solver.cpp:237]     Train net output #0: loss = 0.194439 (* 1 = 0.194439 loss)
I0410 00:23:56.427552  8290 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 00:23:57.997330  8290 blocking_queue.cpp:49] Waiting for data
I0410 00:24:01.274127  8290 solver.cpp:218] Iteration 8316 (2.47606 iter/s, 4.8464s/12 iters), loss = 0.314609
I0410 00:24:01.274173  8290 solver.cpp:237]     Train net output #0: loss = 0.314609 (* 1 = 0.314609 loss)
I0410 00:24:01.274183  8290 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 00:24:06.123714  8290 solver.cpp:218] Iteration 8328 (2.47456 iter/s, 4.84935s/12 iters), loss = 0.16997
I0410 00:24:06.123859  8290 solver.cpp:237]     Train net output #0: loss = 0.16997 (* 1 = 0.16997 loss)
I0410 00:24:06.123872  8290 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 00:24:11.015341  8290 solver.cpp:218] Iteration 8340 (2.45333 iter/s, 4.89131s/12 iters), loss = 0.300923
I0410 00:24:11.015380  8290 solver.cpp:237]     Train net output #0: loss = 0.300923 (* 1 = 0.300923 loss)
I0410 00:24:11.015388  8290 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 00:24:15.902874  8290 solver.cpp:218] Iteration 8352 (2.45534 iter/s, 4.8873s/12 iters), loss = 0.266017
I0410 00:24:15.902936  8290 solver.cpp:237]     Train net output #0: loss = 0.266017 (* 1 = 0.266017 loss)
I0410 00:24:15.902948  8290 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 00:24:20.435041  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 00:24:23.751924  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 00:24:24.307135  8290 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 00:24:24.307161  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:24:25.471869  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:24:28.807790  8290 solver.cpp:397]     Test net output #0: accuracy = 0.583946
I0410 00:24:28.807840  8290 solver.cpp:397]     Test net output #1: loss = 2.11828 (* 1 = 2.11828 loss)
I0410 00:24:28.890017  8290 solver.cpp:218] Iteration 8364 (0.924028 iter/s, 12.9866s/12 iters), loss = 0.100368
I0410 00:24:28.890064  8290 solver.cpp:237]     Train net output #0: loss = 0.100368 (* 1 = 0.100368 loss)
I0410 00:24:28.890076  8290 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 00:24:32.929672  8290 solver.cpp:218] Iteration 8376 (2.9707 iter/s, 4.03945s/12 iters), loss = 0.175626
I0410 00:24:32.929731  8290 solver.cpp:237]     Train net output #0: loss = 0.175626 (* 1 = 0.175626 loss)
I0410 00:24:32.929744  8290 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 00:24:37.843675  8290 solver.cpp:218] Iteration 8388 (2.44212 iter/s, 4.91376s/12 iters), loss = 0.185903
I0410 00:24:37.843838  8290 solver.cpp:237]     Train net output #0: loss = 0.185903 (* 1 = 0.185903 loss)
I0410 00:24:37.843853  8290 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 00:24:40.596304  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:24:42.718523  8290 solver.cpp:218] Iteration 8400 (2.46179 iter/s, 4.8745s/12 iters), loss = 0.20911
I0410 00:24:42.718571  8290 solver.cpp:237]     Train net output #0: loss = 0.20911 (* 1 = 0.20911 loss)
I0410 00:24:42.718580  8290 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 00:24:47.679744  8290 solver.cpp:218] Iteration 8412 (2.41887 iter/s, 4.96099s/12 iters), loss = 0.245319
I0410 00:24:47.679792  8290 solver.cpp:237]     Train net output #0: loss = 0.245319 (* 1 = 0.245319 loss)
I0410 00:24:47.679802  8290 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 00:24:52.579453  8290 solver.cpp:218] Iteration 8424 (2.44925 iter/s, 4.89947s/12 iters), loss = 0.130997
I0410 00:24:52.579515  8290 solver.cpp:237]     Train net output #0: loss = 0.130997 (* 1 = 0.130997 loss)
I0410 00:24:52.579528  8290 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 00:24:57.426086  8290 solver.cpp:218] Iteration 8436 (2.47607 iter/s, 4.84639s/12 iters), loss = 0.179863
I0410 00:24:57.426128  8290 solver.cpp:237]     Train net output #0: loss = 0.179863 (* 1 = 0.179863 loss)
I0410 00:24:57.426136  8290 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 00:25:02.242312  8290 solver.cpp:218] Iteration 8448 (2.49169 iter/s, 4.816s/12 iters), loss = 0.230896
I0410 00:25:02.242367  8290 solver.cpp:237]     Train net output #0: loss = 0.230896 (* 1 = 0.230896 loss)
I0410 00:25:02.242380  8290 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 00:25:07.144034  8290 solver.cpp:218] Iteration 8460 (2.44824 iter/s, 4.90148s/12 iters), loss = 0.304391
I0410 00:25:07.144093  8290 solver.cpp:237]     Train net output #0: loss = 0.304391 (* 1 = 0.304391 loss)
I0410 00:25:07.144105  8290 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 00:25:09.138064  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 00:25:11.049363  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 00:25:12.191504  8290 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 00:25:12.191536  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:25:13.339028  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:25:16.856205  8290 solver.cpp:397]     Test net output #0: accuracy = 0.573529
I0410 00:25:16.856256  8290 solver.cpp:397]     Test net output #1: loss = 2.11242 (* 1 = 2.11242 loss)
I0410 00:25:18.618288  8290 solver.cpp:218] Iteration 8472 (1.04586 iter/s, 11.4738s/12 iters), loss = 0.294388
I0410 00:25:18.618345  8290 solver.cpp:237]     Train net output #0: loss = 0.294388 (* 1 = 0.294388 loss)
I0410 00:25:18.618355  8290 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 00:25:23.516219  8290 solver.cpp:218] Iteration 8484 (2.45013 iter/s, 4.89769s/12 iters), loss = 0.292131
I0410 00:25:23.516264  8290 solver.cpp:237]     Train net output #0: loss = 0.292131 (* 1 = 0.292131 loss)
I0410 00:25:23.516273  8290 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 00:25:28.409649  8290 solver.cpp:218] Iteration 8496 (2.45238 iter/s, 4.8932s/12 iters), loss = 0.219757
I0410 00:25:28.409698  8290 solver.cpp:237]     Train net output #0: loss = 0.219757 (* 1 = 0.219757 loss)
I0410 00:25:28.409708  8290 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 00:25:28.456806  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:25:33.285233  8290 solver.cpp:218] Iteration 8508 (2.46137 iter/s, 4.87534s/12 iters), loss = 0.221145
I0410 00:25:33.285290  8290 solver.cpp:237]     Train net output #0: loss = 0.221145 (* 1 = 0.221145 loss)
I0410 00:25:33.285300  8290 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 00:25:38.076993  8290 solver.cpp:218] Iteration 8520 (2.50442 iter/s, 4.79152s/12 iters), loss = 0.184015
I0410 00:25:38.077042  8290 solver.cpp:237]     Train net output #0: loss = 0.184015 (* 1 = 0.184015 loss)
I0410 00:25:38.077051  8290 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 00:25:42.970315  8290 solver.cpp:218] Iteration 8532 (2.45244 iter/s, 4.89308s/12 iters), loss = 0.156644
I0410 00:25:42.970486  8290 solver.cpp:237]     Train net output #0: loss = 0.156644 (* 1 = 0.156644 loss)
I0410 00:25:42.970499  8290 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 00:25:47.900785  8290 solver.cpp:218] Iteration 8544 (2.43402 iter/s, 4.93012s/12 iters), loss = 0.238158
I0410 00:25:47.900837  8290 solver.cpp:237]     Train net output #0: loss = 0.238158 (* 1 = 0.238158 loss)
I0410 00:25:47.900849  8290 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 00:25:52.803990  8290 solver.cpp:218] Iteration 8556 (2.4475 iter/s, 4.90297s/12 iters), loss = 0.194764
I0410 00:25:52.804040  8290 solver.cpp:237]     Train net output #0: loss = 0.194764 (* 1 = 0.194764 loss)
I0410 00:25:52.804051  8290 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 00:25:57.242966  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 00:25:58.003305  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 00:25:58.544335  8290 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 00:25:58.544363  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:25:59.649576  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:26:03.136148  8290 solver.cpp:397]     Test net output #0: accuracy = 0.582721
I0410 00:26:03.136198  8290 solver.cpp:397]     Test net output #1: loss = 2.1201 (* 1 = 2.1201 loss)
I0410 00:26:03.218354  8290 solver.cpp:218] Iteration 8568 (1.1523 iter/s, 10.4139s/12 iters), loss = 0.167068
I0410 00:26:03.218402  8290 solver.cpp:237]     Train net output #0: loss = 0.167068 (* 1 = 0.167068 loss)
I0410 00:26:03.218413  8290 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 00:26:07.367878  8290 solver.cpp:218] Iteration 8580 (2.89204 iter/s, 4.14932s/12 iters), loss = 0.178444
I0410 00:26:07.367930  8290 solver.cpp:237]     Train net output #0: loss = 0.178444 (* 1 = 0.178444 loss)
I0410 00:26:07.367942  8290 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 00:26:12.257040  8290 solver.cpp:218] Iteration 8592 (2.45452 iter/s, 4.88893s/12 iters), loss = 0.14112
I0410 00:26:12.257086  8290 solver.cpp:237]     Train net output #0: loss = 0.14112 (* 1 = 0.14112 loss)
I0410 00:26:12.257098  8290 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 00:26:14.381743  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:26:17.164995  8290 solver.cpp:218] Iteration 8604 (2.44512 iter/s, 4.90773s/12 iters), loss = 0.184819
I0410 00:26:17.165048  8290 solver.cpp:237]     Train net output #0: loss = 0.184819 (* 1 = 0.184819 loss)
I0410 00:26:17.165060  8290 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 00:26:22.057543  8290 solver.cpp:218] Iteration 8616 (2.45283 iter/s, 4.89231s/12 iters), loss = 0.103497
I0410 00:26:22.057597  8290 solver.cpp:237]     Train net output #0: loss = 0.103497 (* 1 = 0.103497 loss)
I0410 00:26:22.057610  8290 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 00:26:26.935436  8290 solver.cpp:218] Iteration 8628 (2.4602 iter/s, 4.87766s/12 iters), loss = 0.223018
I0410 00:26:26.935489  8290 solver.cpp:237]     Train net output #0: loss = 0.223018 (* 1 = 0.223018 loss)
I0410 00:26:26.935503  8290 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 00:26:31.848541  8290 solver.cpp:218] Iteration 8640 (2.44257 iter/s, 4.91287s/12 iters), loss = 0.189599
I0410 00:26:31.848592  8290 solver.cpp:237]     Train net output #0: loss = 0.189599 (* 1 = 0.189599 loss)
I0410 00:26:31.848603  8290 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 00:26:36.673072  8290 solver.cpp:218] Iteration 8652 (2.48741 iter/s, 4.8243s/12 iters), loss = 0.232721
I0410 00:26:36.673128  8290 solver.cpp:237]     Train net output #0: loss = 0.232721 (* 1 = 0.232721 loss)
I0410 00:26:36.673139  8290 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 00:26:41.556663  8290 solver.cpp:218] Iteration 8664 (2.45733 iter/s, 4.88336s/12 iters), loss = 0.230148
I0410 00:26:41.556717  8290 solver.cpp:237]     Train net output #0: loss = 0.230148 (* 1 = 0.230148 loss)
I0410 00:26:41.556728  8290 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 00:26:43.550441  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 00:26:44.318194  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 00:26:44.968897  8290 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 00:26:44.969041  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:26:45.935660  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:26:49.428129  8290 solver.cpp:397]     Test net output #0: accuracy = 0.58701
I0410 00:26:49.428179  8290 solver.cpp:397]     Test net output #1: loss = 2.10335 (* 1 = 2.10335 loss)
I0410 00:26:51.254488  8290 solver.cpp:218] Iteration 8676 (1.23744 iter/s, 9.69742s/12 iters), loss = 0.201568
I0410 00:26:51.254545  8290 solver.cpp:237]     Train net output #0: loss = 0.201568 (* 1 = 0.201568 loss)
I0410 00:26:51.254559  8290 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 00:26:56.131996  8290 solver.cpp:218] Iteration 8688 (2.46039 iter/s, 4.87727s/12 iters), loss = 0.135361
I0410 00:26:56.132051  8290 solver.cpp:237]     Train net output #0: loss = 0.135361 (* 1 = 0.135361 loss)
I0410 00:26:56.132063  8290 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 00:27:00.360118  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:27:01.039868  8290 solver.cpp:218] Iteration 8700 (2.44517 iter/s, 4.90764s/12 iters), loss = 0.143948
I0410 00:27:01.039916  8290 solver.cpp:237]     Train net output #0: loss = 0.143948 (* 1 = 0.143948 loss)
I0410 00:27:01.039927  8290 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 00:27:06.219358  8290 solver.cpp:218] Iteration 8712 (2.31694 iter/s, 5.17925s/12 iters), loss = 0.197376
I0410 00:27:06.219406  8290 solver.cpp:237]     Train net output #0: loss = 0.197376 (* 1 = 0.197376 loss)
I0410 00:27:06.219416  8290 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 00:27:11.084776  8290 solver.cpp:218] Iteration 8724 (2.46651 iter/s, 4.86518s/12 iters), loss = 0.199831
I0410 00:27:11.084841  8290 solver.cpp:237]     Train net output #0: loss = 0.199831 (* 1 = 0.199831 loss)
I0410 00:27:11.084853  8290 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 00:27:16.008332  8290 solver.cpp:218] Iteration 8736 (2.43738 iter/s, 4.92331s/12 iters), loss = 0.213477
I0410 00:27:16.008425  8290 solver.cpp:237]     Train net output #0: loss = 0.213477 (* 1 = 0.213477 loss)
I0410 00:27:16.008437  8290 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 00:27:20.955869  8290 solver.cpp:218] Iteration 8748 (2.42559 iter/s, 4.94726s/12 iters), loss = 0.274857
I0410 00:27:20.955925  8290 solver.cpp:237]     Train net output #0: loss = 0.274857 (* 1 = 0.274857 loss)
I0410 00:27:20.955938  8290 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 00:27:25.833679  8290 solver.cpp:218] Iteration 8760 (2.46024 iter/s, 4.87757s/12 iters), loss = 0.116077
I0410 00:27:25.833730  8290 solver.cpp:237]     Train net output #0: loss = 0.116077 (* 1 = 0.116077 loss)
I0410 00:27:25.833743  8290 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 00:27:30.341877  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 00:27:31.100474  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 00:27:31.637431  8290 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 00:27:31.637454  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:27:32.616006  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:27:36.036389  8290 solver.cpp:397]     Test net output #0: accuracy = 0.582108
I0410 00:27:36.036440  8290 solver.cpp:397]     Test net output #1: loss = 2.10336 (* 1 = 2.10336 loss)
I0410 00:27:36.118360  8290 solver.cpp:218] Iteration 8772 (1.16683 iter/s, 10.2842s/12 iters), loss = 0.261093
I0410 00:27:36.118418  8290 solver.cpp:237]     Train net output #0: loss = 0.261093 (* 1 = 0.261093 loss)
I0410 00:27:36.118432  8290 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 00:27:40.341454  8290 solver.cpp:218] Iteration 8784 (2.84166 iter/s, 4.22288s/12 iters), loss = 0.107797
I0410 00:27:40.341504  8290 solver.cpp:237]     Train net output #0: loss = 0.107797 (* 1 = 0.107797 loss)
I0410 00:27:40.341514  8290 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 00:27:45.263326  8290 solver.cpp:218] Iteration 8796 (2.43821 iter/s, 4.92164s/12 iters), loss = 0.307385
I0410 00:27:45.263373  8290 solver.cpp:237]     Train net output #0: loss = 0.307385 (* 1 = 0.307385 loss)
I0410 00:27:45.263382  8290 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 00:27:46.690704  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:27:50.307494  8290 solver.cpp:218] Iteration 8808 (2.3791 iter/s, 5.04393s/12 iters), loss = 0.265095
I0410 00:27:50.307545  8290 solver.cpp:237]     Train net output #0: loss = 0.265095 (* 1 = 0.265095 loss)
I0410 00:27:50.307557  8290 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 00:27:55.187048  8290 solver.cpp:218] Iteration 8820 (2.45936 iter/s, 4.87931s/12 iters), loss = 0.280195
I0410 00:27:55.187108  8290 solver.cpp:237]     Train net output #0: loss = 0.280195 (* 1 = 0.280195 loss)
I0410 00:27:55.187119  8290 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 00:28:00.010776  8290 solver.cpp:218] Iteration 8832 (2.48783 iter/s, 4.82349s/12 iters), loss = 0.213436
I0410 00:28:00.010829  8290 solver.cpp:237]     Train net output #0: loss = 0.213436 (* 1 = 0.213436 loss)
I0410 00:28:00.010840  8290 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 00:28:04.929276  8290 solver.cpp:218] Iteration 8844 (2.43989 iter/s, 4.91826s/12 iters), loss = 0.2066
I0410 00:28:04.929335  8290 solver.cpp:237]     Train net output #0: loss = 0.2066 (* 1 = 0.2066 loss)
I0410 00:28:04.929348  8290 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 00:28:09.858299  8290 solver.cpp:218] Iteration 8856 (2.43468 iter/s, 4.92877s/12 iters), loss = 0.0969012
I0410 00:28:09.858355  8290 solver.cpp:237]     Train net output #0: loss = 0.0969012 (* 1 = 0.0969012 loss)
I0410 00:28:09.858368  8290 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 00:28:14.789523  8290 solver.cpp:218] Iteration 8868 (2.43359 iter/s, 4.93099s/12 iters), loss = 0.155928
I0410 00:28:14.789567  8290 solver.cpp:237]     Train net output #0: loss = 0.155928 (* 1 = 0.155928 loss)
I0410 00:28:14.789577  8290 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 00:28:16.801411  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 00:28:18.113499  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 00:28:19.305073  8290 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 00:28:19.305104  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:28:20.297330  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:28:23.801442  8290 solver.cpp:397]     Test net output #0: accuracy = 0.596814
I0410 00:28:23.801488  8290 solver.cpp:397]     Test net output #1: loss = 2.10785 (* 1 = 2.10785 loss)
I0410 00:28:25.646703  8290 solver.cpp:218] Iteration 8880 (1.1053 iter/s, 10.8567s/12 iters), loss = 0.265009
I0410 00:28:25.646760  8290 solver.cpp:237]     Train net output #0: loss = 0.265009 (* 1 = 0.265009 loss)
I0410 00:28:25.646771  8290 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 00:28:30.789779  8290 solver.cpp:218] Iteration 8892 (2.33335 iter/s, 5.14282s/12 iters), loss = 0.23544
I0410 00:28:30.789836  8290 solver.cpp:237]     Train net output #0: loss = 0.23544 (* 1 = 0.23544 loss)
I0410 00:28:30.789850  8290 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 00:28:34.225215  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:28:35.622805  8290 solver.cpp:218] Iteration 8904 (2.48304 iter/s, 4.83279s/12 iters), loss = 0.190199
I0410 00:28:35.622859  8290 solver.cpp:237]     Train net output #0: loss = 0.190199 (* 1 = 0.190199 loss)
I0410 00:28:35.622871  8290 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 00:28:40.723546  8290 solver.cpp:218] Iteration 8916 (2.35271 iter/s, 5.1005s/12 iters), loss = 0.228277
I0410 00:28:40.723594  8290 solver.cpp:237]     Train net output #0: loss = 0.228277 (* 1 = 0.228277 loss)
I0410 00:28:40.723605  8290 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 00:28:45.712471  8290 solver.cpp:218] Iteration 8928 (2.40544 iter/s, 4.98869s/12 iters), loss = 0.170864
I0410 00:28:45.712522  8290 solver.cpp:237]     Train net output #0: loss = 0.170864 (* 1 = 0.170864 loss)
I0410 00:28:45.712535  8290 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 00:28:50.581276  8290 solver.cpp:218] Iteration 8940 (2.46479 iter/s, 4.86857s/12 iters), loss = 0.194494
I0410 00:28:50.581403  8290 solver.cpp:237]     Train net output #0: loss = 0.194494 (* 1 = 0.194494 loss)
I0410 00:28:50.581418  8290 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 00:28:55.509181  8290 solver.cpp:218] Iteration 8952 (2.43527 iter/s, 4.92759s/12 iters), loss = 0.248457
I0410 00:28:55.509243  8290 solver.cpp:237]     Train net output #0: loss = 0.248457 (* 1 = 0.248457 loss)
I0410 00:28:55.509258  8290 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 00:29:00.404501  8290 solver.cpp:218] Iteration 8964 (2.45144 iter/s, 4.89508s/12 iters), loss = 0.191258
I0410 00:29:00.404551  8290 solver.cpp:237]     Train net output #0: loss = 0.191258 (* 1 = 0.191258 loss)
I0410 00:29:00.404561  8290 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 00:29:04.857890  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 00:29:07.009693  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 00:29:07.985846  8290 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 00:29:07.985875  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:29:08.988958  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:29:12.496011  8290 solver.cpp:397]     Test net output #0: accuracy = 0.590074
I0410 00:29:12.496064  8290 solver.cpp:397]     Test net output #1: loss = 2.04515 (* 1 = 2.04515 loss)
I0410 00:29:12.578445  8290 solver.cpp:218] Iteration 8976 (0.985751 iter/s, 12.1735s/12 iters), loss = 0.11501
I0410 00:29:12.578529  8290 solver.cpp:237]     Train net output #0: loss = 0.11501 (* 1 = 0.11501 loss)
I0410 00:29:12.578545  8290 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 00:29:16.723217  8290 solver.cpp:218] Iteration 8988 (2.89538 iter/s, 4.14454s/12 iters), loss = 0.131813
I0410 00:29:16.723256  8290 solver.cpp:237]     Train net output #0: loss = 0.131813 (* 1 = 0.131813 loss)
I0410 00:29:16.723265  8290 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 00:29:18.756350  8290 blocking_queue.cpp:49] Waiting for data
I0410 00:29:21.646869  8290 solver.cpp:218] Iteration 9000 (2.43733 iter/s, 4.92343s/12 iters), loss = 0.159584
I0410 00:29:21.646952  8290 solver.cpp:237]     Train net output #0: loss = 0.159584 (* 1 = 0.159584 loss)
I0410 00:29:21.646963  8290 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 00:29:22.329551  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:29:26.399751  8290 solver.cpp:218] Iteration 9012 (2.52492 iter/s, 4.75262s/12 iters), loss = 0.251617
I0410 00:29:26.399803  8290 solver.cpp:237]     Train net output #0: loss = 0.251617 (* 1 = 0.251617 loss)
I0410 00:29:26.399816  8290 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 00:29:31.362857  8290 solver.cpp:218] Iteration 9024 (2.41796 iter/s, 4.96286s/12 iters), loss = 0.259443
I0410 00:29:31.362916  8290 solver.cpp:237]     Train net output #0: loss = 0.259443 (* 1 = 0.259443 loss)
I0410 00:29:31.362931  8290 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 00:29:36.295392  8290 solver.cpp:218] Iteration 9036 (2.43295 iter/s, 4.93229s/12 iters), loss = 0.18201
I0410 00:29:36.295442  8290 solver.cpp:237]     Train net output #0: loss = 0.18201 (* 1 = 0.18201 loss)
I0410 00:29:36.295455  8290 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 00:29:41.230516  8290 solver.cpp:218] Iteration 9048 (2.43167 iter/s, 4.93489s/12 iters), loss = 0.215586
I0410 00:29:41.230567  8290 solver.cpp:237]     Train net output #0: loss = 0.215586 (* 1 = 0.215586 loss)
I0410 00:29:41.230578  8290 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 00:29:46.139770  8290 solver.cpp:218] Iteration 9060 (2.44448 iter/s, 4.90902s/12 iters), loss = 0.266254
I0410 00:29:46.139822  8290 solver.cpp:237]     Train net output #0: loss = 0.266254 (* 1 = 0.266254 loss)
I0410 00:29:46.139833  8290 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 00:29:51.066745  8290 solver.cpp:218] Iteration 9072 (2.43569 iter/s, 4.92674s/12 iters), loss = 0.170245
I0410 00:29:51.066790  8290 solver.cpp:237]     Train net output #0: loss = 0.170245 (* 1 = 0.170245 loss)
I0410 00:29:51.066802  8290 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 00:29:53.074246  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 00:29:53.817332  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 00:29:54.349016  8290 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 00:29:54.349045  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:29:55.240712  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:29:58.780174  8290 solver.cpp:397]     Test net output #0: accuracy = 0.604167
I0410 00:29:58.780222  8290 solver.cpp:397]     Test net output #1: loss = 2.09165 (* 1 = 2.09165 loss)
I0410 00:30:00.547986  8290 solver.cpp:218] Iteration 9084 (1.26571 iter/s, 9.48085s/12 iters), loss = 0.125276
I0410 00:30:00.548051  8290 solver.cpp:237]     Train net output #0: loss = 0.125276 (* 1 = 0.125276 loss)
I0410 00:30:00.548063  8290 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 00:30:05.348831  8290 solver.cpp:218] Iteration 9096 (2.49969 iter/s, 4.8006s/12 iters), loss = 0.0673945
I0410 00:30:05.348892  8290 solver.cpp:237]     Train net output #0: loss = 0.0673945 (* 1 = 0.0673945 loss)
I0410 00:30:05.348903  8290 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 00:30:08.194993  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:30:10.203805  8290 solver.cpp:218] Iteration 9108 (2.47181 iter/s, 4.85473s/12 iters), loss = 0.215655
I0410 00:30:10.203847  8290 solver.cpp:237]     Train net output #0: loss = 0.215655 (* 1 = 0.215655 loss)
I0410 00:30:10.203856  8290 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 00:30:15.119817  8290 solver.cpp:218] Iteration 9120 (2.44112 iter/s, 4.91578s/12 iters), loss = 0.124072
I0410 00:30:15.119865  8290 solver.cpp:237]     Train net output #0: loss = 0.124072 (* 1 = 0.124072 loss)
I0410 00:30:15.119874  8290 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 00:30:19.951079  8290 solver.cpp:218] Iteration 9132 (2.48394 iter/s, 4.83103s/12 iters), loss = 0.1231
I0410 00:30:19.951122  8290 solver.cpp:237]     Train net output #0: loss = 0.1231 (* 1 = 0.1231 loss)
I0410 00:30:19.951131  8290 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 00:30:24.819855  8290 solver.cpp:218] Iteration 9144 (2.4648 iter/s, 4.86854s/12 iters), loss = 0.16798
I0410 00:30:24.819983  8290 solver.cpp:237]     Train net output #0: loss = 0.16798 (* 1 = 0.16798 loss)
I0410 00:30:24.819994  8290 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 00:30:29.725283  8290 solver.cpp:218] Iteration 9156 (2.44642 iter/s, 4.90512s/12 iters), loss = 0.159549
I0410 00:30:29.725342  8290 solver.cpp:237]     Train net output #0: loss = 0.159549 (* 1 = 0.159549 loss)
I0410 00:30:29.725353  8290 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 00:30:34.648039  8290 solver.cpp:218] Iteration 9168 (2.43778 iter/s, 4.92252s/12 iters), loss = 0.189383
I0410 00:30:34.648147  8290 solver.cpp:237]     Train net output #0: loss = 0.189383 (* 1 = 0.189383 loss)
I0410 00:30:34.648160  8290 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 00:30:39.260759  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 00:30:40.958412  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 00:30:42.071524  8290 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 00:30:42.071552  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:30:42.964655  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:30:46.805498  8290 solver.cpp:397]     Test net output #0: accuracy = 0.602941
I0410 00:30:46.805541  8290 solver.cpp:397]     Test net output #1: loss = 2.15935 (* 1 = 2.15935 loss)
I0410 00:30:46.887812  8290 solver.cpp:218] Iteration 9180 (0.980451 iter/s, 12.2393s/12 iters), loss = 0.115548
I0410 00:30:46.887876  8290 solver.cpp:237]     Train net output #0: loss = 0.115548 (* 1 = 0.115548 loss)
I0410 00:30:46.887889  8290 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 00:30:51.091737  8290 solver.cpp:218] Iteration 9192 (2.85463 iter/s, 4.2037s/12 iters), loss = 0.149557
I0410 00:30:51.091796  8290 solver.cpp:237]     Train net output #0: loss = 0.149557 (* 1 = 0.149557 loss)
I0410 00:30:51.091809  8290 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 00:30:55.942015  8290 solver.cpp:218] Iteration 9204 (2.47421 iter/s, 4.85004s/12 iters), loss = 0.164562
I0410 00:30:55.942124  8290 solver.cpp:237]     Train net output #0: loss = 0.164562 (* 1 = 0.164562 loss)
I0410 00:30:55.942135  8290 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 00:30:56.021745  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:31:00.862694  8290 solver.cpp:218] Iteration 9216 (2.43883 iter/s, 4.92039s/12 iters), loss = 0.195578
I0410 00:31:00.862744  8290 solver.cpp:237]     Train net output #0: loss = 0.195578 (* 1 = 0.195578 loss)
I0410 00:31:00.862754  8290 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 00:31:05.691622  8290 solver.cpp:218] Iteration 9228 (2.48514 iter/s, 4.82869s/12 iters), loss = 0.234862
I0410 00:31:05.691679  8290 solver.cpp:237]     Train net output #0: loss = 0.234862 (* 1 = 0.234862 loss)
I0410 00:31:05.691692  8290 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 00:31:10.593757  8290 solver.cpp:218] Iteration 9240 (2.44803 iter/s, 4.90189s/12 iters), loss = 0.189641
I0410 00:31:10.593812  8290 solver.cpp:237]     Train net output #0: loss = 0.189641 (* 1 = 0.189641 loss)
I0410 00:31:10.593824  8290 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 00:31:15.413465  8290 solver.cpp:218] Iteration 9252 (2.4899 iter/s, 4.81948s/12 iters), loss = 0.115376
I0410 00:31:15.413504  8290 solver.cpp:237]     Train net output #0: loss = 0.115376 (* 1 = 0.115376 loss)
I0410 00:31:15.413512  8290 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 00:31:20.346664  8290 solver.cpp:218] Iteration 9264 (2.43261 iter/s, 4.93297s/12 iters), loss = 0.118274
I0410 00:31:20.346721  8290 solver.cpp:237]     Train net output #0: loss = 0.118274 (* 1 = 0.118274 loss)
I0410 00:31:20.346732  8290 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 00:31:25.461244  8290 solver.cpp:218] Iteration 9276 (2.34635 iter/s, 5.11433s/12 iters), loss = 0.0920208
I0410 00:31:25.461305  8290 solver.cpp:237]     Train net output #0: loss = 0.0920208 (* 1 = 0.0920208 loss)
I0410 00:31:25.461318  8290 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 00:31:27.604758  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 00:31:28.379467  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 00:31:28.921085  8290 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 00:31:28.921114  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:31:29.645447  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:31:33.256567  8290 solver.cpp:397]     Test net output #0: accuracy = 0.594975
I0410 00:31:33.256613  8290 solver.cpp:397]     Test net output #1: loss = 2.15205 (* 1 = 2.15205 loss)
I0410 00:31:35.147245  8290 solver.cpp:218] Iteration 9288 (1.23895 iter/s, 9.68559s/12 iters), loss = 0.126785
I0410 00:31:35.147290  8290 solver.cpp:237]     Train net output #0: loss = 0.126785 (* 1 = 0.126785 loss)
I0410 00:31:35.147300  8290 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 00:31:39.938809  8290 solver.cpp:218] Iteration 9300 (2.50452 iter/s, 4.79133s/12 iters), loss = 0.132317
I0410 00:31:39.938868  8290 solver.cpp:237]     Train net output #0: loss = 0.132317 (* 1 = 0.132317 loss)
I0410 00:31:39.938879  8290 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 00:31:42.115159  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:31:44.856489  8290 solver.cpp:218] Iteration 9312 (2.4403 iter/s, 4.91744s/12 iters), loss = 0.142326
I0410 00:31:44.856534  8290 solver.cpp:237]     Train net output #0: loss = 0.142326 (* 1 = 0.142326 loss)
I0410 00:31:44.856544  8290 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 00:31:50.038736  8290 solver.cpp:218] Iteration 9324 (2.31571 iter/s, 5.18201s/12 iters), loss = 0.184327
I0410 00:31:50.038792  8290 solver.cpp:237]     Train net output #0: loss = 0.184327 (* 1 = 0.184327 loss)
I0410 00:31:50.038805  8290 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 00:31:54.916597  8290 solver.cpp:218] Iteration 9336 (2.46021 iter/s, 4.87763s/12 iters), loss = 0.139177
I0410 00:31:54.916633  8290 solver.cpp:237]     Train net output #0: loss = 0.139177 (* 1 = 0.139177 loss)
I0410 00:31:54.916640  8290 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 00:31:59.907281  8290 solver.cpp:218] Iteration 9348 (2.40459 iter/s, 4.99046s/12 iters), loss = 0.142394
I0410 00:31:59.907366  8290 solver.cpp:237]     Train net output #0: loss = 0.142394 (* 1 = 0.142394 loss)
I0410 00:31:59.907377  8290 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 00:32:04.762895  8290 solver.cpp:218] Iteration 9360 (2.4715 iter/s, 4.85535s/12 iters), loss = 0.0649428
I0410 00:32:04.762941  8290 solver.cpp:237]     Train net output #0: loss = 0.0649428 (* 1 = 0.0649428 loss)
I0410 00:32:04.762951  8290 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 00:32:09.692785  8290 solver.cpp:218] Iteration 9372 (2.43425 iter/s, 4.92966s/12 iters), loss = 0.212618
I0410 00:32:09.692842  8290 solver.cpp:237]     Train net output #0: loss = 0.212618 (* 1 = 0.212618 loss)
I0410 00:32:09.692854  8290 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 00:32:14.104759  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 00:32:14.827345  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 00:32:15.363853  8290 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 00:32:15.363874  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:32:16.108899  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:32:19.764768  8290 solver.cpp:397]     Test net output #0: accuracy = 0.598652
I0410 00:32:19.764819  8290 solver.cpp:397]     Test net output #1: loss = 2.13416 (* 1 = 2.13416 loss)
I0410 00:32:19.846912  8290 solver.cpp:218] Iteration 9384 (1.18183 iter/s, 10.1537s/12 iters), loss = 0.134881
I0410 00:32:19.846971  8290 solver.cpp:237]     Train net output #0: loss = 0.134881 (* 1 = 0.134881 loss)
I0410 00:32:19.846983  8290 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 00:32:23.930310  8290 solver.cpp:218] Iteration 9396 (2.93888 iter/s, 4.08318s/12 iters), loss = 0.136849
I0410 00:32:23.930369  8290 solver.cpp:237]     Train net output #0: loss = 0.136849 (* 1 = 0.136849 loss)
I0410 00:32:23.930382  8290 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 00:32:28.117674  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:32:28.757885  8290 solver.cpp:218] Iteration 9408 (2.48584 iter/s, 4.82734s/12 iters), loss = 0.206749
I0410 00:32:28.757941  8290 solver.cpp:237]     Train net output #0: loss = 0.206749 (* 1 = 0.206749 loss)
I0410 00:32:28.757951  8290 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 00:32:33.762195  8290 solver.cpp:218] Iteration 9420 (2.39805 iter/s, 5.00407s/12 iters), loss = 0.100561
I0410 00:32:33.762358  8290 solver.cpp:237]     Train net output #0: loss = 0.100561 (* 1 = 0.100561 loss)
I0410 00:32:33.762372  8290 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 00:32:38.728732  8290 solver.cpp:218] Iteration 9432 (2.41634 iter/s, 4.96619s/12 iters), loss = 0.167086
I0410 00:32:38.728782  8290 solver.cpp:237]     Train net output #0: loss = 0.167086 (* 1 = 0.167086 loss)
I0410 00:32:38.728793  8290 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 00:32:43.641361  8290 solver.cpp:218] Iteration 9444 (2.4428 iter/s, 4.9124s/12 iters), loss = 0.17093
I0410 00:32:43.641419  8290 solver.cpp:237]     Train net output #0: loss = 0.17093 (* 1 = 0.17093 loss)
I0410 00:32:43.641433  8290 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 00:32:48.571614  8290 solver.cpp:218] Iteration 9456 (2.43407 iter/s, 4.93001s/12 iters), loss = 0.114931
I0410 00:32:48.571663  8290 solver.cpp:237]     Train net output #0: loss = 0.114931 (* 1 = 0.114931 loss)
I0410 00:32:48.571676  8290 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 00:32:53.421175  8290 solver.cpp:218] Iteration 9468 (2.47457 iter/s, 4.84932s/12 iters), loss = 0.118632
I0410 00:32:53.421233  8290 solver.cpp:237]     Train net output #0: loss = 0.118632 (* 1 = 0.118632 loss)
I0410 00:32:53.421244  8290 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 00:32:58.256907  8290 solver.cpp:218] Iteration 9480 (2.48165 iter/s, 4.83549s/12 iters), loss = 0.179933
I0410 00:32:58.256951  8290 solver.cpp:237]     Train net output #0: loss = 0.179933 (* 1 = 0.179933 loss)
I0410 00:32:58.256959  8290 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 00:33:00.203931  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 00:33:00.908666  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 00:33:01.437599  8290 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 00:33:01.437623  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:33:02.109122  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:33:05.802000  8290 solver.cpp:397]     Test net output #0: accuracy = 0.608456
I0410 00:33:05.802088  8290 solver.cpp:397]     Test net output #1: loss = 2.14265 (* 1 = 2.14265 loss)
I0410 00:33:07.607544  8290 solver.cpp:218] Iteration 9492 (1.28339 iter/s, 9.35025s/12 iters), loss = 0.215604
I0410 00:33:07.607595  8290 solver.cpp:237]     Train net output #0: loss = 0.215604 (* 1 = 0.215604 loss)
I0410 00:33:07.607605  8290 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 00:33:12.517712  8290 solver.cpp:218] Iteration 9504 (2.44403 iter/s, 4.90993s/12 iters), loss = 0.147493
I0410 00:33:12.517760  8290 solver.cpp:237]     Train net output #0: loss = 0.147493 (* 1 = 0.147493 loss)
I0410 00:33:12.517768  8290 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 00:33:13.948803  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:33:17.429267  8290 solver.cpp:218] Iteration 9516 (2.44334 iter/s, 4.91132s/12 iters), loss = 0.171664
I0410 00:33:17.429329  8290 solver.cpp:237]     Train net output #0: loss = 0.171664 (* 1 = 0.171664 loss)
I0410 00:33:17.429342  8290 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 00:33:22.360376  8290 solver.cpp:218] Iteration 9528 (2.43365 iter/s, 4.93086s/12 iters), loss = 0.0868357
I0410 00:33:22.360435  8290 solver.cpp:237]     Train net output #0: loss = 0.0868357 (* 1 = 0.0868357 loss)
I0410 00:33:22.360448  8290 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 00:33:27.311655  8290 solver.cpp:218] Iteration 9540 (2.42374 iter/s, 4.95103s/12 iters), loss = 0.226056
I0410 00:33:27.311708  8290 solver.cpp:237]     Train net output #0: loss = 0.226055 (* 1 = 0.226055 loss)
I0410 00:33:27.311719  8290 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 00:33:32.231431  8290 solver.cpp:218] Iteration 9552 (2.43925 iter/s, 4.91954s/12 iters), loss = 0.140518
I0410 00:33:32.231482  8290 solver.cpp:237]     Train net output #0: loss = 0.140518 (* 1 = 0.140518 loss)
I0410 00:33:32.231493  8290 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 00:33:37.162298  8290 solver.cpp:218] Iteration 9564 (2.43376 iter/s, 4.93063s/12 iters), loss = 0.103133
I0410 00:33:37.162418  8290 solver.cpp:237]     Train net output #0: loss = 0.103133 (* 1 = 0.103133 loss)
I0410 00:33:37.162428  8290 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 00:33:42.063607  8290 solver.cpp:218] Iteration 9576 (2.44847 iter/s, 4.90101s/12 iters), loss = 0.0799665
I0410 00:33:42.063647  8290 solver.cpp:237]     Train net output #0: loss = 0.0799664 (* 1 = 0.0799664 loss)
I0410 00:33:42.063657  8290 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 00:33:46.545234  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 00:33:47.939637  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 00:33:48.871769  8290 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 00:33:48.871799  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:33:49.557360  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:33:53.557411  8290 solver.cpp:397]     Test net output #0: accuracy = 0.598652
I0410 00:33:53.557461  8290 solver.cpp:397]     Test net output #1: loss = 2.16206 (* 1 = 2.16206 loss)
I0410 00:33:53.639933  8290 solver.cpp:218] Iteration 9588 (1.03664 iter/s, 11.5759s/12 iters), loss = 0.11849
I0410 00:33:53.639981  8290 solver.cpp:237]     Train net output #0: loss = 0.11849 (* 1 = 0.11849 loss)
I0410 00:33:53.639994  8290 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 00:33:57.837108  8290 solver.cpp:218] Iteration 9600 (2.85921 iter/s, 4.19697s/12 iters), loss = 0.179366
I0410 00:33:57.837157  8290 solver.cpp:237]     Train net output #0: loss = 0.179365 (* 1 = 0.179365 loss)
I0410 00:33:57.837167  8290 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 00:34:01.385905  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:34:02.748914  8290 solver.cpp:218] Iteration 9612 (2.44321 iter/s, 4.91157s/12 iters), loss = 0.191174
I0410 00:34:02.748966  8290 solver.cpp:237]     Train net output #0: loss = 0.191174 (* 1 = 0.191174 loss)
I0410 00:34:02.748977  8290 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 00:34:07.719799  8290 solver.cpp:218] Iteration 9624 (2.41417 iter/s, 4.97065s/12 iters), loss = 0.222863
I0410 00:34:07.719877  8290 solver.cpp:237]     Train net output #0: loss = 0.222863 (* 1 = 0.222863 loss)
I0410 00:34:07.719887  8290 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 00:34:12.702438  8290 solver.cpp:218] Iteration 9636 (2.40849 iter/s, 4.98238s/12 iters), loss = 0.152528
I0410 00:34:12.702488  8290 solver.cpp:237]     Train net output #0: loss = 0.152528 (* 1 = 0.152528 loss)
I0410 00:34:12.702497  8290 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 00:34:17.631826  8290 solver.cpp:218] Iteration 9648 (2.43449 iter/s, 4.92916s/12 iters), loss = 0.118202
I0410 00:34:17.631878  8290 solver.cpp:237]     Train net output #0: loss = 0.118202 (* 1 = 0.118202 loss)
I0410 00:34:17.631888  8290 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 00:34:22.616988  8290 solver.cpp:218] Iteration 9660 (2.40726 iter/s, 4.98492s/12 iters), loss = 0.185248
I0410 00:34:22.617031  8290 solver.cpp:237]     Train net output #0: loss = 0.185248 (* 1 = 0.185248 loss)
I0410 00:34:22.617039  8290 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 00:34:27.522323  8290 solver.cpp:218] Iteration 9672 (2.44643 iter/s, 4.9051s/12 iters), loss = 0.17702
I0410 00:34:27.522385  8290 solver.cpp:237]     Train net output #0: loss = 0.17702 (* 1 = 0.17702 loss)
I0410 00:34:27.522397  8290 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 00:34:32.530542  8290 solver.cpp:218] Iteration 9684 (2.39618 iter/s, 5.00797s/12 iters), loss = 0.155898
I0410 00:34:32.530596  8290 solver.cpp:237]     Train net output #0: loss = 0.155898 (* 1 = 0.155898 loss)
I0410 00:34:32.530608  8290 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 00:34:34.530082  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 00:34:36.131292  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 00:34:38.692986  8290 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 00:34:38.693109  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:34:39.316804  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:34:41.926131  8290 blocking_queue.cpp:49] Waiting for data
I0410 00:34:43.221021  8290 solver.cpp:397]     Test net output #0: accuracy = 0.597426
I0410 00:34:43.221065  8290 solver.cpp:397]     Test net output #1: loss = 2.23705 (* 1 = 2.23705 loss)
I0410 00:34:45.085345  8290 solver.cpp:218] Iteration 9696 (0.955848 iter/s, 12.5543s/12 iters), loss = 0.126858
I0410 00:34:45.085393  8290 solver.cpp:237]     Train net output #0: loss = 0.126858 (* 1 = 0.126858 loss)
I0410 00:34:45.085403  8290 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 00:34:49.933279  8290 solver.cpp:218] Iteration 9708 (2.4754 iter/s, 4.84769s/12 iters), loss = 0.123293
I0410 00:34:49.933341  8290 solver.cpp:237]     Train net output #0: loss = 0.123293 (* 1 = 0.123293 loss)
I0410 00:34:49.933353  8290 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 00:34:50.667102  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:34:54.817234  8290 solver.cpp:218] Iteration 9720 (2.45715 iter/s, 4.88371s/12 iters), loss = 0.210054
I0410 00:34:54.817284  8290 solver.cpp:237]     Train net output #0: loss = 0.210054 (* 1 = 0.210054 loss)
I0410 00:34:54.817296  8290 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 00:34:59.644315  8290 solver.cpp:218] Iteration 9732 (2.48609 iter/s, 4.82685s/12 iters), loss = 0.163827
I0410 00:34:59.644372  8290 solver.cpp:237]     Train net output #0: loss = 0.163827 (* 1 = 0.163827 loss)
I0410 00:34:59.644385  8290 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 00:35:04.554832  8290 solver.cpp:218] Iteration 9744 (2.44385 iter/s, 4.91028s/12 iters), loss = 0.0851454
I0410 00:35:04.554874  8290 solver.cpp:237]     Train net output #0: loss = 0.0851453 (* 1 = 0.0851453 loss)
I0410 00:35:04.554883  8290 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 00:35:09.408694  8290 solver.cpp:218] Iteration 9756 (2.47238 iter/s, 4.85363s/12 iters), loss = 0.190312
I0410 00:35:09.408792  8290 solver.cpp:237]     Train net output #0: loss = 0.190312 (* 1 = 0.190312 loss)
I0410 00:35:09.408805  8290 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 00:35:14.348487  8290 solver.cpp:218] Iteration 9768 (2.42939 iter/s, 4.93951s/12 iters), loss = 0.216784
I0410 00:35:14.348536  8290 solver.cpp:237]     Train net output #0: loss = 0.216784 (* 1 = 0.216784 loss)
I0410 00:35:14.348546  8290 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 00:35:19.372787  8290 solver.cpp:218] Iteration 9780 (2.38851 iter/s, 5.02406s/12 iters), loss = 0.157838
I0410 00:35:19.372839  8290 solver.cpp:237]     Train net output #0: loss = 0.157838 (* 1 = 0.157838 loss)
I0410 00:35:19.372849  8290 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 00:35:23.760664  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 00:35:24.574888  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 00:35:25.939250  8290 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 00:35:25.939282  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:35:26.503574  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:35:30.317384  8290 solver.cpp:397]     Test net output #0: accuracy = 0.599877
I0410 00:35:30.317432  8290 solver.cpp:397]     Test net output #1: loss = 2.1071 (* 1 = 2.1071 loss)
I0410 00:35:30.399585  8290 solver.cpp:218] Iteration 9792 (1.0883 iter/s, 11.0263s/12 iters), loss = 0.0671187
I0410 00:35:30.399643  8290 solver.cpp:237]     Train net output #0: loss = 0.0671187 (* 1 = 0.0671187 loss)
I0410 00:35:30.399655  8290 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 00:35:34.721369  8290 solver.cpp:218] Iteration 9804 (2.77677 iter/s, 4.32156s/12 iters), loss = 0.225088
I0410 00:35:34.721418  8290 solver.cpp:237]     Train net output #0: loss = 0.225088 (* 1 = 0.225088 loss)
I0410 00:35:34.721427  8290 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 00:35:37.590451  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:35:39.568425  8290 solver.cpp:218] Iteration 9816 (2.47585 iter/s, 4.84682s/12 iters), loss = 0.169179
I0410 00:35:39.568533  8290 solver.cpp:237]     Train net output #0: loss = 0.169179 (* 1 = 0.169179 loss)
I0410 00:35:39.568544  8290 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 00:35:44.491994  8290 solver.cpp:218] Iteration 9828 (2.4374 iter/s, 4.92328s/12 iters), loss = 0.249713
I0410 00:35:44.492048  8290 solver.cpp:237]     Train net output #0: loss = 0.249713 (* 1 = 0.249713 loss)
I0410 00:35:44.492061  8290 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 00:35:49.356606  8290 solver.cpp:218] Iteration 9840 (2.46691 iter/s, 4.86438s/12 iters), loss = 0.106891
I0410 00:35:49.356655  8290 solver.cpp:237]     Train net output #0: loss = 0.106891 (* 1 = 0.106891 loss)
I0410 00:35:49.356665  8290 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 00:35:54.209165  8290 solver.cpp:218] Iteration 9852 (2.47304 iter/s, 4.85233s/12 iters), loss = 0.147359
I0410 00:35:54.209224  8290 solver.cpp:237]     Train net output #0: loss = 0.147359 (* 1 = 0.147359 loss)
I0410 00:35:54.209236  8290 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 00:35:59.078310  8290 solver.cpp:218] Iteration 9864 (2.46462 iter/s, 4.86891s/12 iters), loss = 0.142445
I0410 00:35:59.078364  8290 solver.cpp:237]     Train net output #0: loss = 0.142445 (* 1 = 0.142445 loss)
I0410 00:35:59.078377  8290 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 00:36:03.945679  8290 solver.cpp:218] Iteration 9876 (2.46552 iter/s, 4.86713s/12 iters), loss = 0.219695
I0410 00:36:03.945734  8290 solver.cpp:237]     Train net output #0: loss = 0.219695 (* 1 = 0.219695 loss)
I0410 00:36:03.945747  8290 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 00:36:08.859158  8290 solver.cpp:218] Iteration 9888 (2.44238 iter/s, 4.91324s/12 iters), loss = 0.151694
I0410 00:36:08.859211  8290 solver.cpp:237]     Train net output #0: loss = 0.151694 (* 1 = 0.151694 loss)
I0410 00:36:08.859225  8290 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 00:36:10.863267  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 00:36:11.615602  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 00:36:12.153570  8290 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 00:36:12.153597  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:36:12.701270  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:36:16.681671  8290 solver.cpp:397]     Test net output #0: accuracy = 0.609069
I0410 00:36:16.681721  8290 solver.cpp:397]     Test net output #1: loss = 2.08726 (* 1 = 2.08726 loss)
I0410 00:36:18.430235  8290 solver.cpp:218] Iteration 9900 (1.25383 iter/s, 9.57068s/12 iters), loss = 0.132175
I0410 00:36:18.430290  8290 solver.cpp:237]     Train net output #0: loss = 0.132175 (* 1 = 0.132175 loss)
I0410 00:36:18.430302  8290 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 00:36:23.257194  8290 solver.cpp:218] Iteration 9912 (2.48616 iter/s, 4.82673s/12 iters), loss = 0.145986
I0410 00:36:23.257230  8290 solver.cpp:237]     Train net output #0: loss = 0.145986 (* 1 = 0.145986 loss)
I0410 00:36:23.257238  8290 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 00:36:23.355392  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:36:28.105561  8290 solver.cpp:218] Iteration 9924 (2.47517 iter/s, 4.84815s/12 iters), loss = 0.218555
I0410 00:36:28.105604  8290 solver.cpp:237]     Train net output #0: loss = 0.218555 (* 1 = 0.218555 loss)
I0410 00:36:28.105613  8290 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 00:36:33.091598  8290 solver.cpp:218] Iteration 9936 (2.40683 iter/s, 4.98581s/12 iters), loss = 0.130835
I0410 00:36:33.091640  8290 solver.cpp:237]     Train net output #0: loss = 0.130835 (* 1 = 0.130835 loss)
I0410 00:36:33.091648  8290 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 00:36:37.965807  8290 solver.cpp:218] Iteration 9948 (2.46205 iter/s, 4.87398s/12 iters), loss = 0.145394
I0410 00:36:37.965864  8290 solver.cpp:237]     Train net output #0: loss = 0.145394 (* 1 = 0.145394 loss)
I0410 00:36:37.965879  8290 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 00:36:42.804741  8290 solver.cpp:218] Iteration 9960 (2.48001 iter/s, 4.8387s/12 iters), loss = 0.124511
I0410 00:36:42.804877  8290 solver.cpp:237]     Train net output #0: loss = 0.124511 (* 1 = 0.124511 loss)
I0410 00:36:42.804889  8290 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 00:36:47.691112  8290 solver.cpp:218] Iteration 9972 (2.45597 iter/s, 4.88606s/12 iters), loss = 0.133182
I0410 00:36:47.691161  8290 solver.cpp:237]     Train net output #0: loss = 0.133182 (* 1 = 0.133182 loss)
I0410 00:36:47.691170  8290 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 00:36:52.588011  8290 solver.cpp:218] Iteration 9984 (2.45065 iter/s, 4.89667s/12 iters), loss = 0.111334
I0410 00:36:52.588057  8290 solver.cpp:237]     Train net output #0: loss = 0.111334 (* 1 = 0.111334 loss)
I0410 00:36:52.588068  8290 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 00:36:57.070251  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 00:36:58.823534  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 00:37:00.408417  8290 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 00:37:00.408447  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:37:00.941998  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:37:05.043601  8290 solver.cpp:397]     Test net output #0: accuracy = 0.615196
I0410 00:37:05.043649  8290 solver.cpp:397]     Test net output #1: loss = 2.08788 (* 1 = 2.08788 loss)
I0410 00:37:05.126503  8290 solver.cpp:218] Iteration 9996 (0.957091 iter/s, 12.538s/12 iters), loss = 0.0860792
I0410 00:37:05.126585  8290 solver.cpp:237]     Train net output #0: loss = 0.0860792 (* 1 = 0.0860792 loss)
I0410 00:37:05.126605  8290 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 00:37:09.444545  8290 solver.cpp:218] Iteration 10008 (2.77919 iter/s, 4.3178s/12 iters), loss = 0.142106
I0410 00:37:09.444594  8290 solver.cpp:237]     Train net output #0: loss = 0.142106 (* 1 = 0.142106 loss)
I0410 00:37:09.444604  8290 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 00:37:11.613652  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:37:14.267819  8290 solver.cpp:218] Iteration 10020 (2.48806 iter/s, 4.82304s/12 iters), loss = 0.208689
I0410 00:37:14.267904  8290 solver.cpp:237]     Train net output #0: loss = 0.208689 (* 1 = 0.208689 loss)
I0410 00:37:14.267915  8290 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 00:37:19.170140  8290 solver.cpp:218] Iteration 10032 (2.44795 iter/s, 4.90205s/12 iters), loss = 0.0948505
I0410 00:37:19.170183  8290 solver.cpp:237]     Train net output #0: loss = 0.0948505 (* 1 = 0.0948505 loss)
I0410 00:37:19.170192  8290 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 00:37:24.075242  8290 solver.cpp:218] Iteration 10044 (2.44655 iter/s, 4.90487s/12 iters), loss = 0.0727096
I0410 00:37:24.075301  8290 solver.cpp:237]     Train net output #0: loss = 0.0727096 (* 1 = 0.0727096 loss)
I0410 00:37:24.075314  8290 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 00:37:28.957461  8290 solver.cpp:218] Iteration 10056 (2.45802 iter/s, 4.88198s/12 iters), loss = 0.165142
I0410 00:37:28.957509  8290 solver.cpp:237]     Train net output #0: loss = 0.165142 (* 1 = 0.165142 loss)
I0410 00:37:28.957517  8290 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 00:37:33.900094  8290 solver.cpp:218] Iteration 10068 (2.42797 iter/s, 4.9424s/12 iters), loss = 0.166083
I0410 00:37:33.900143  8290 solver.cpp:237]     Train net output #0: loss = 0.166083 (* 1 = 0.166083 loss)
I0410 00:37:33.900154  8290 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 00:37:38.965620  8290 solver.cpp:218] Iteration 10080 (2.36907 iter/s, 5.06529s/12 iters), loss = 0.212723
I0410 00:37:38.965674  8290 solver.cpp:237]     Train net output #0: loss = 0.212723 (* 1 = 0.212723 loss)
I0410 00:37:38.965685  8290 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 00:37:43.933156  8290 solver.cpp:218] Iteration 10092 (2.4158 iter/s, 4.9673s/12 iters), loss = 0.16935
I0410 00:37:43.933202  8290 solver.cpp:237]     Train net output #0: loss = 0.16935 (* 1 = 0.16935 loss)
I0410 00:37:43.933213  8290 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 00:37:46.055474  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 00:37:47.328294  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 00:37:48.062430  8290 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 00:37:48.062450  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:37:48.538372  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:37:52.501708  8290 solver.cpp:397]     Test net output #0: accuracy = 0.609069
I0410 00:37:52.501739  8290 solver.cpp:397]     Test net output #1: loss = 2.11572 (* 1 = 2.11572 loss)
I0410 00:37:54.352721  8290 solver.cpp:218] Iteration 10104 (1.15173 iter/s, 10.4191s/12 iters), loss = 0.0977715
I0410 00:37:54.352777  8290 solver.cpp:237]     Train net output #0: loss = 0.0977715 (* 1 = 0.0977715 loss)
I0410 00:37:54.352787  8290 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 00:37:58.552284  8294 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:37:59.147158  8290 solver.cpp:218] Iteration 10116 (2.50302 iter/s, 4.7942s/12 iters), loss = 0.0882249
I0410 00:37:59.147215  8290 solver.cpp:237]     Train net output #0: loss = 0.0882249 (* 1 = 0.0882249 loss)
I0410 00:37:59.147228  8290 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 00:38:03.966773  8290 solver.cpp:218] Iteration 10128 (2.48995 iter/s, 4.81938s/12 iters), loss = 0.249112
I0410 00:38:03.966827  8290 solver.cpp:237]     Train net output #0: loss = 0.249112 (* 1 = 0.249112 loss)
I0410 00:38:03.966840  8290 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 00:38:08.920841  8290 solver.cpp:218] Iteration 10140 (2.42237 iter/s, 4.95383s/12 iters), loss = 0.0939745
I0410 00:38:08.920890  8290 solver.cpp:237]     Train net output #0: loss = 0.0939745 (* 1 = 0.0939745 loss)
I0410 00:38:08.920899  8290 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 00:38:13.870360  8290 solver.cpp:218] Iteration 10152 (2.42459 iter/s, 4.94929s/12 iters), loss = 0.0703192
I0410 00:38:13.870406  8290 solver.cpp:237]     Train net output #0: loss = 0.0703191 (* 1 = 0.0703191 loss)
I0410 00:38:13.870416  8290 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 00:38:18.714112  8290 solver.cpp:218] Iteration 10164 (2.47754 iter/s, 4.84352s/12 iters), loss = 0.124716
I0410 00:38:18.714257  8290 solver.cpp:237]     Train net output #0: loss = 0.124716 (* 1 = 0.124716 loss)
I0410 00:38:18.714267  8290 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 00:38:23.588946  8290 solver.cpp:218] Iteration 10176 (2.46179 iter/s, 4.87451s/12 iters), loss = 0.107524
I0410 00:38:23.589005  8290 solver.cpp:237]     Train net output #0: loss = 0.107524 (* 1 = 0.107524 loss)
I0410 00:38:23.589017  8290 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 00:38:28.485512  8290 solver.cpp:218] Iteration 10188 (2.45082 iter/s, 4.89632s/12 iters), loss = 0.0592905
I0410 00:38:28.485558  8290 solver.cpp:237]     Train net output #0: loss = 0.0592904 (* 1 = 0.0592904 loss)
I0410 00:38:28.485567  8290 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 00:38:32.955538  8290 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 00:38:34.173661  8290 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 00:38:35.317695  8290 solver.cpp:310] Iteration 10200, loss = 0.115279
I0410 00:38:35.317731  8290 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 00:38:35.317740  8290 net.cpp:676] Ignoring source layer train-data
I0410 00:38:35.728943  8295 data_layer.cpp:73] Restarting data prefetching from start.
I0410 00:38:39.791018  8290 solver.cpp:397]     Test net output #0: accuracy = 0.609069
I0410 00:38:39.791056  8290 solver.cpp:397]     Test net output #1: loss = 2.08302 (* 1 = 2.08302 loss)
I0410 00:38:39.791065  8290 solver.cpp:315] Optimization Done.
I0410 00:38:39.791071  8290 caffe.cpp:259] Optimization Done.