DIGITS-CNN/cars/lr-investigations/exponential/1e-1/0.85/caffe_output.log

I0408 07:38:25.138231 31616 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210408-073823-fd70/solver.prototxt
I0408 07:38:25.138411 31616 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0408 07:38:25.138418 31616 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0408 07:38:25.138491 31616 caffe.cpp:218] Using GPUs 1
I0408 07:38:25.158753 31616 caffe.cpp:223] GPU 1: GeForce GTX 1080 Ti
I0408 07:38:25.416832 31616 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.1
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99840796
momentum: 0.9
weight_decay: 0.001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0408 07:38:25.417500 31616 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0408 07:38:25.418243 31616 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0408 07:38:25.418259 31616 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0408 07:38:25.418402 31616 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: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0408 07:38:25.418490 31616 layer_factory.hpp:77] Creating layer train-data
I0408 07:38:25.420650 31616 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0408 07:38:25.420856 31616 net.cpp:84] Creating Layer train-data
I0408 07:38:25.420866 31616 net.cpp:380] train-data -> data
I0408 07:38:25.420886 31616 net.cpp:380] train-data -> label
I0408 07:38:25.420897 31616 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0408 07:38:25.425640 31616 data_layer.cpp:45] output data size: 128,3,227,227
I0408 07:38:25.556176 31616 net.cpp:122] Setting up train-data
I0408 07:38:25.556200 31616 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0408 07:38:25.556205 31616 net.cpp:129] Top shape: 128 (128)
I0408 07:38:25.556208 31616 net.cpp:137] Memory required for data: 79149056
I0408 07:38:25.556218 31616 layer_factory.hpp:77] Creating layer conv1
I0408 07:38:25.556238 31616 net.cpp:84] Creating Layer conv1
I0408 07:38:25.556243 31616 net.cpp:406] conv1 <- data
I0408 07:38:25.556255 31616 net.cpp:380] conv1 -> conv1
I0408 07:38:26.110599 31616 net.cpp:122] Setting up conv1
I0408 07:38:26.110620 31616 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 07:38:26.110625 31616 net.cpp:137] Memory required for data: 227833856
I0408 07:38:26.110643 31616 layer_factory.hpp:77] Creating layer relu1
I0408 07:38:26.110654 31616 net.cpp:84] Creating Layer relu1
I0408 07:38:26.110658 31616 net.cpp:406] relu1 <- conv1
I0408 07:38:26.110664 31616 net.cpp:367] relu1 -> conv1 (in-place)
I0408 07:38:26.110949 31616 net.cpp:122] Setting up relu1
I0408 07:38:26.110958 31616 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 07:38:26.110961 31616 net.cpp:137] Memory required for data: 376518656
I0408 07:38:26.110965 31616 layer_factory.hpp:77] Creating layer norm1
I0408 07:38:26.110975 31616 net.cpp:84] Creating Layer norm1
I0408 07:38:26.110977 31616 net.cpp:406] norm1 <- conv1
I0408 07:38:26.111002 31616 net.cpp:380] norm1 -> norm1
I0408 07:38:26.111438 31616 net.cpp:122] Setting up norm1
I0408 07:38:26.111449 31616 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 07:38:26.111452 31616 net.cpp:137] Memory required for data: 525203456
I0408 07:38:26.111456 31616 layer_factory.hpp:77] Creating layer pool1
I0408 07:38:26.111464 31616 net.cpp:84] Creating Layer pool1
I0408 07:38:26.111467 31616 net.cpp:406] pool1 <- norm1
I0408 07:38:26.111472 31616 net.cpp:380] pool1 -> pool1
I0408 07:38:26.111508 31616 net.cpp:122] Setting up pool1
I0408 07:38:26.111515 31616 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0408 07:38:26.111517 31616 net.cpp:137] Memory required for data: 561035264
I0408 07:38:26.111521 31616 layer_factory.hpp:77] Creating layer conv2
I0408 07:38:26.111531 31616 net.cpp:84] Creating Layer conv2
I0408 07:38:26.111534 31616 net.cpp:406] conv2 <- pool1
I0408 07:38:26.111539 31616 net.cpp:380] conv2 -> conv2
I0408 07:38:26.118985 31616 net.cpp:122] Setting up conv2
I0408 07:38:26.118999 31616 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 07:38:26.119004 31616 net.cpp:137] Memory required for data: 656586752
I0408 07:38:26.119012 31616 layer_factory.hpp:77] Creating layer relu2
I0408 07:38:26.119020 31616 net.cpp:84] Creating Layer relu2
I0408 07:38:26.119024 31616 net.cpp:406] relu2 <- conv2
I0408 07:38:26.119029 31616 net.cpp:367] relu2 -> conv2 (in-place)
I0408 07:38:26.119446 31616 net.cpp:122] Setting up relu2
I0408 07:38:26.119457 31616 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 07:38:26.119460 31616 net.cpp:137] Memory required for data: 752138240
I0408 07:38:26.119463 31616 layer_factory.hpp:77] Creating layer norm2
I0408 07:38:26.119470 31616 net.cpp:84] Creating Layer norm2
I0408 07:38:26.119474 31616 net.cpp:406] norm2 <- conv2
I0408 07:38:26.119479 31616 net.cpp:380] norm2 -> norm2
I0408 07:38:26.119772 31616 net.cpp:122] Setting up norm2
I0408 07:38:26.119781 31616 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 07:38:26.119784 31616 net.cpp:137] Memory required for data: 847689728
I0408 07:38:26.119788 31616 layer_factory.hpp:77] Creating layer pool2
I0408 07:38:26.119796 31616 net.cpp:84] Creating Layer pool2
I0408 07:38:26.119798 31616 net.cpp:406] pool2 <- norm2
I0408 07:38:26.119803 31616 net.cpp:380] pool2 -> pool2
I0408 07:38:26.119830 31616 net.cpp:122] Setting up pool2
I0408 07:38:26.119835 31616 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 07:38:26.119838 31616 net.cpp:137] Memory required for data: 869840896
I0408 07:38:26.119841 31616 layer_factory.hpp:77] Creating layer conv3
I0408 07:38:26.119850 31616 net.cpp:84] Creating Layer conv3
I0408 07:38:26.119853 31616 net.cpp:406] conv3 <- pool2
I0408 07:38:26.119858 31616 net.cpp:380] conv3 -> conv3
I0408 07:38:26.129544 31616 net.cpp:122] Setting up conv3
I0408 07:38:26.129555 31616 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 07:38:26.129559 31616 net.cpp:137] Memory required for data: 903067648
I0408 07:38:26.129568 31616 layer_factory.hpp:77] Creating layer relu3
I0408 07:38:26.129575 31616 net.cpp:84] Creating Layer relu3
I0408 07:38:26.129578 31616 net.cpp:406] relu3 <- conv3
I0408 07:38:26.129583 31616 net.cpp:367] relu3 -> conv3 (in-place)
I0408 07:38:26.130009 31616 net.cpp:122] Setting up relu3
I0408 07:38:26.130019 31616 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 07:38:26.130023 31616 net.cpp:137] Memory required for data: 936294400
I0408 07:38:26.130026 31616 layer_factory.hpp:77] Creating layer conv4
I0408 07:38:26.130036 31616 net.cpp:84] Creating Layer conv4
I0408 07:38:26.130040 31616 net.cpp:406] conv4 <- conv3
I0408 07:38:26.130046 31616 net.cpp:380] conv4 -> conv4
I0408 07:38:26.140185 31616 net.cpp:122] Setting up conv4
I0408 07:38:26.140199 31616 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 07:38:26.140203 31616 net.cpp:137] Memory required for data: 969521152
I0408 07:38:26.140210 31616 layer_factory.hpp:77] Creating layer relu4
I0408 07:38:26.140218 31616 net.cpp:84] Creating Layer relu4
I0408 07:38:26.140237 31616 net.cpp:406] relu4 <- conv4
I0408 07:38:26.140242 31616 net.cpp:367] relu4 -> conv4 (in-place)
I0408 07:38:26.140579 31616 net.cpp:122] Setting up relu4
I0408 07:38:26.140588 31616 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 07:38:26.140592 31616 net.cpp:137] Memory required for data: 1002747904
I0408 07:38:26.140596 31616 layer_factory.hpp:77] Creating layer conv5
I0408 07:38:26.140606 31616 net.cpp:84] Creating Layer conv5
I0408 07:38:26.140610 31616 net.cpp:406] conv5 <- conv4
I0408 07:38:26.140616 31616 net.cpp:380] conv5 -> conv5
I0408 07:38:26.148886 31616 net.cpp:122] Setting up conv5
I0408 07:38:26.148898 31616 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 07:38:26.148901 31616 net.cpp:137] Memory required for data: 1024899072
I0408 07:38:26.148913 31616 layer_factory.hpp:77] Creating layer relu5
I0408 07:38:26.148921 31616 net.cpp:84] Creating Layer relu5
I0408 07:38:26.148926 31616 net.cpp:406] relu5 <- conv5
I0408 07:38:26.148931 31616 net.cpp:367] relu5 -> conv5 (in-place)
I0408 07:38:26.149410 31616 net.cpp:122] Setting up relu5
I0408 07:38:26.149418 31616 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 07:38:26.149421 31616 net.cpp:137] Memory required for data: 1047050240
I0408 07:38:26.149425 31616 layer_factory.hpp:77] Creating layer pool5
I0408 07:38:26.149432 31616 net.cpp:84] Creating Layer pool5
I0408 07:38:26.149435 31616 net.cpp:406] pool5 <- conv5
I0408 07:38:26.149442 31616 net.cpp:380] pool5 -> pool5
I0408 07:38:26.149478 31616 net.cpp:122] Setting up pool5
I0408 07:38:26.149484 31616 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0408 07:38:26.149487 31616 net.cpp:137] Memory required for data: 1051768832
I0408 07:38:26.149490 31616 layer_factory.hpp:77] Creating layer fc6
I0408 07:38:26.149499 31616 net.cpp:84] Creating Layer fc6
I0408 07:38:26.149503 31616 net.cpp:406] fc6 <- pool5
I0408 07:38:26.149508 31616 net.cpp:380] fc6 -> fc6
I0408 07:38:26.597638 31616 net.cpp:122] Setting up fc6
I0408 07:38:26.597661 31616 net.cpp:129] Top shape: 128 4096 (524288)
I0408 07:38:26.597666 31616 net.cpp:137] Memory required for data: 1053865984
I0408 07:38:26.597676 31616 layer_factory.hpp:77] Creating layer relu6
I0408 07:38:26.597684 31616 net.cpp:84] Creating Layer relu6
I0408 07:38:26.597688 31616 net.cpp:406] relu6 <- fc6
I0408 07:38:26.597697 31616 net.cpp:367] relu6 -> fc6 (in-place)
I0408 07:38:26.598387 31616 net.cpp:122] Setting up relu6
I0408 07:38:26.598397 31616 net.cpp:129] Top shape: 128 4096 (524288)
I0408 07:38:26.598402 31616 net.cpp:137] Memory required for data: 1055963136
I0408 07:38:26.598405 31616 layer_factory.hpp:77] Creating layer drop6
I0408 07:38:26.598413 31616 net.cpp:84] Creating Layer drop6
I0408 07:38:26.598417 31616 net.cpp:406] drop6 <- fc6
I0408 07:38:26.598423 31616 net.cpp:367] drop6 -> fc6 (in-place)
I0408 07:38:26.598451 31616 net.cpp:122] Setting up drop6
I0408 07:38:26.598456 31616 net.cpp:129] Top shape: 128 4096 (524288)
I0408 07:38:26.598460 31616 net.cpp:137] Memory required for data: 1058060288
I0408 07:38:26.598464 31616 layer_factory.hpp:77] Creating layer fc7
I0408 07:38:26.598472 31616 net.cpp:84] Creating Layer fc7
I0408 07:38:26.598476 31616 net.cpp:406] fc7 <- fc6
I0408 07:38:26.598484 31616 net.cpp:380] fc7 -> fc7
I0408 07:38:26.770041 31616 net.cpp:122] Setting up fc7
I0408 07:38:26.770061 31616 net.cpp:129] Top shape: 128 4096 (524288)
I0408 07:38:26.770066 31616 net.cpp:137] Memory required for data: 1060157440
I0408 07:38:26.770074 31616 layer_factory.hpp:77] Creating layer relu7
I0408 07:38:26.770084 31616 net.cpp:84] Creating Layer relu7
I0408 07:38:26.770088 31616 net.cpp:406] relu7 <- fc7
I0408 07:38:26.770097 31616 net.cpp:367] relu7 -> fc7 (in-place)
I0408 07:38:26.770745 31616 net.cpp:122] Setting up relu7
I0408 07:38:26.770756 31616 net.cpp:129] Top shape: 128 4096 (524288)
I0408 07:38:26.770759 31616 net.cpp:137] Memory required for data: 1062254592
I0408 07:38:26.770762 31616 layer_factory.hpp:77] Creating layer drop7
I0408 07:38:26.770769 31616 net.cpp:84] Creating Layer drop7
I0408 07:38:26.770792 31616 net.cpp:406] drop7 <- fc7
I0408 07:38:26.770797 31616 net.cpp:367] drop7 -> fc7 (in-place)
I0408 07:38:26.770824 31616 net.cpp:122] Setting up drop7
I0408 07:38:26.770829 31616 net.cpp:129] Top shape: 128 4096 (524288)
I0408 07:38:26.770833 31616 net.cpp:137] Memory required for data: 1064351744
I0408 07:38:26.770836 31616 layer_factory.hpp:77] Creating layer fc8
I0408 07:38:26.770844 31616 net.cpp:84] Creating Layer fc8
I0408 07:38:26.770848 31616 net.cpp:406] fc8 <- fc7
I0408 07:38:26.770853 31616 net.cpp:380] fc8 -> fc8
I0408 07:38:26.779201 31616 net.cpp:122] Setting up fc8
I0408 07:38:26.779211 31616 net.cpp:129] Top shape: 128 196 (25088)
I0408 07:38:26.779215 31616 net.cpp:137] Memory required for data: 1064452096
I0408 07:38:26.779222 31616 layer_factory.hpp:77] Creating layer loss
I0408 07:38:26.779228 31616 net.cpp:84] Creating Layer loss
I0408 07:38:26.779232 31616 net.cpp:406] loss <- fc8
I0408 07:38:26.779237 31616 net.cpp:406] loss <- label
I0408 07:38:26.779244 31616 net.cpp:380] loss -> loss
I0408 07:38:26.779254 31616 layer_factory.hpp:77] Creating layer loss
I0408 07:38:26.779892 31616 net.cpp:122] Setting up loss
I0408 07:38:26.779901 31616 net.cpp:129] Top shape: (1)
I0408 07:38:26.779906 31616 net.cpp:132]     with loss weight 1
I0408 07:38:26.779924 31616 net.cpp:137] Memory required for data: 1064452100
I0408 07:38:26.779928 31616 net.cpp:198] loss needs backward computation.
I0408 07:38:26.779935 31616 net.cpp:198] fc8 needs backward computation.
I0408 07:38:26.779939 31616 net.cpp:198] drop7 needs backward computation.
I0408 07:38:26.779942 31616 net.cpp:198] relu7 needs backward computation.
I0408 07:38:26.779947 31616 net.cpp:198] fc7 needs backward computation.
I0408 07:38:26.779949 31616 net.cpp:198] drop6 needs backward computation.
I0408 07:38:26.779953 31616 net.cpp:198] relu6 needs backward computation.
I0408 07:38:26.779956 31616 net.cpp:198] fc6 needs backward computation.
I0408 07:38:26.779960 31616 net.cpp:198] pool5 needs backward computation.
I0408 07:38:26.779964 31616 net.cpp:198] relu5 needs backward computation.
I0408 07:38:26.779968 31616 net.cpp:198] conv5 needs backward computation.
I0408 07:38:26.779973 31616 net.cpp:198] relu4 needs backward computation.
I0408 07:38:26.779976 31616 net.cpp:198] conv4 needs backward computation.
I0408 07:38:26.779980 31616 net.cpp:198] relu3 needs backward computation.
I0408 07:38:26.779983 31616 net.cpp:198] conv3 needs backward computation.
I0408 07:38:26.779989 31616 net.cpp:198] pool2 needs backward computation.
I0408 07:38:26.779994 31616 net.cpp:198] norm2 needs backward computation.
I0408 07:38:26.779997 31616 net.cpp:198] relu2 needs backward computation.
I0408 07:38:26.780000 31616 net.cpp:198] conv2 needs backward computation.
I0408 07:38:26.780004 31616 net.cpp:198] pool1 needs backward computation.
I0408 07:38:26.780009 31616 net.cpp:198] norm1 needs backward computation.
I0408 07:38:26.780012 31616 net.cpp:198] relu1 needs backward computation.
I0408 07:38:26.780015 31616 net.cpp:198] conv1 needs backward computation.
I0408 07:38:26.780019 31616 net.cpp:200] train-data does not need backward computation.
I0408 07:38:26.780023 31616 net.cpp:242] This network produces output loss
I0408 07:38:26.780037 31616 net.cpp:255] Network initialization done.
I0408 07:38:26.780544 31616 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0408 07:38:26.780575 31616 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0408 07:38:26.780725 31616 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: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0408 07:38:26.780827 31616 layer_factory.hpp:77] Creating layer val-data
I0408 07:38:26.782496 31616 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0408 07:38:26.782703 31616 net.cpp:84] Creating Layer val-data
I0408 07:38:26.782712 31616 net.cpp:380] val-data -> data
I0408 07:38:26.782722 31616 net.cpp:380] val-data -> label
I0408 07:38:26.782729 31616 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0408 07:38:26.786876 31616 data_layer.cpp:45] output data size: 32,3,227,227
I0408 07:38:26.818327 31616 net.cpp:122] Setting up val-data
I0408 07:38:26.818347 31616 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0408 07:38:26.818352 31616 net.cpp:129] Top shape: 32 (32)
I0408 07:38:26.818356 31616 net.cpp:137] Memory required for data: 19787264
I0408 07:38:26.818363 31616 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0408 07:38:26.818377 31616 net.cpp:84] Creating Layer label_val-data_1_split
I0408 07:38:26.818380 31616 net.cpp:406] label_val-data_1_split <- label
I0408 07:38:26.818387 31616 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0408 07:38:26.818397 31616 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0408 07:38:26.818439 31616 net.cpp:122] Setting up label_val-data_1_split
I0408 07:38:26.818444 31616 net.cpp:129] Top shape: 32 (32)
I0408 07:38:26.818449 31616 net.cpp:129] Top shape: 32 (32)
I0408 07:38:26.818451 31616 net.cpp:137] Memory required for data: 19787520
I0408 07:38:26.818455 31616 layer_factory.hpp:77] Creating layer conv1
I0408 07:38:26.818466 31616 net.cpp:84] Creating Layer conv1
I0408 07:38:26.818470 31616 net.cpp:406] conv1 <- data
I0408 07:38:26.818476 31616 net.cpp:380] conv1 -> conv1
I0408 07:38:26.820694 31616 net.cpp:122] Setting up conv1
I0408 07:38:26.820705 31616 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 07:38:26.820709 31616 net.cpp:137] Memory required for data: 56958720
I0408 07:38:26.820719 31616 layer_factory.hpp:77] Creating layer relu1
I0408 07:38:26.820727 31616 net.cpp:84] Creating Layer relu1
I0408 07:38:26.820730 31616 net.cpp:406] relu1 <- conv1
I0408 07:38:26.820736 31616 net.cpp:367] relu1 -> conv1 (in-place)
I0408 07:38:26.821051 31616 net.cpp:122] Setting up relu1
I0408 07:38:26.821060 31616 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 07:38:26.821064 31616 net.cpp:137] Memory required for data: 94129920
I0408 07:38:26.821067 31616 layer_factory.hpp:77] Creating layer norm1
I0408 07:38:26.821076 31616 net.cpp:84] Creating Layer norm1
I0408 07:38:26.821079 31616 net.cpp:406] norm1 <- conv1
I0408 07:38:26.821085 31616 net.cpp:380] norm1 -> norm1
I0408 07:38:26.821571 31616 net.cpp:122] Setting up norm1
I0408 07:38:26.821581 31616 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 07:38:26.821584 31616 net.cpp:137] Memory required for data: 131301120
I0408 07:38:26.821588 31616 layer_factory.hpp:77] Creating layer pool1
I0408 07:38:26.821596 31616 net.cpp:84] Creating Layer pool1
I0408 07:38:26.821599 31616 net.cpp:406] pool1 <- norm1
I0408 07:38:26.821605 31616 net.cpp:380] pool1 -> pool1
I0408 07:38:26.821636 31616 net.cpp:122] Setting up pool1
I0408 07:38:26.821641 31616 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0408 07:38:26.821645 31616 net.cpp:137] Memory required for data: 140259072
I0408 07:38:26.821648 31616 layer_factory.hpp:77] Creating layer conv2
I0408 07:38:26.821655 31616 net.cpp:84] Creating Layer conv2
I0408 07:38:26.821660 31616 net.cpp:406] conv2 <- pool1
I0408 07:38:26.821682 31616 net.cpp:380] conv2 -> conv2
I0408 07:38:26.830883 31616 net.cpp:122] Setting up conv2
I0408 07:38:26.830897 31616 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 07:38:26.830901 31616 net.cpp:137] Memory required for data: 164146944
I0408 07:38:26.830911 31616 layer_factory.hpp:77] Creating layer relu2
I0408 07:38:26.830919 31616 net.cpp:84] Creating Layer relu2
I0408 07:38:26.830924 31616 net.cpp:406] relu2 <- conv2
I0408 07:38:26.830929 31616 net.cpp:367] relu2 -> conv2 (in-place)
I0408 07:38:26.831462 31616 net.cpp:122] Setting up relu2
I0408 07:38:26.831471 31616 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 07:38:26.831475 31616 net.cpp:137] Memory required for data: 188034816
I0408 07:38:26.831480 31616 layer_factory.hpp:77] Creating layer norm2
I0408 07:38:26.831490 31616 net.cpp:84] Creating Layer norm2
I0408 07:38:26.831493 31616 net.cpp:406] norm2 <- conv2
I0408 07:38:26.831499 31616 net.cpp:380] norm2 -> norm2
I0408 07:38:26.832056 31616 net.cpp:122] Setting up norm2
I0408 07:38:26.832065 31616 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 07:38:26.832069 31616 net.cpp:137] Memory required for data: 211922688
I0408 07:38:26.832073 31616 layer_factory.hpp:77] Creating layer pool2
I0408 07:38:26.832079 31616 net.cpp:84] Creating Layer pool2
I0408 07:38:26.832083 31616 net.cpp:406] pool2 <- norm2
I0408 07:38:26.832090 31616 net.cpp:380] pool2 -> pool2
I0408 07:38:26.832121 31616 net.cpp:122] Setting up pool2
I0408 07:38:26.832127 31616 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 07:38:26.832130 31616 net.cpp:137] Memory required for data: 217460480
I0408 07:38:26.832134 31616 layer_factory.hpp:77] Creating layer conv3
I0408 07:38:26.832145 31616 net.cpp:84] Creating Layer conv3
I0408 07:38:26.832149 31616 net.cpp:406] conv3 <- pool2
I0408 07:38:26.832154 31616 net.cpp:380] conv3 -> conv3
I0408 07:38:26.843953 31616 net.cpp:122] Setting up conv3
I0408 07:38:26.843971 31616 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 07:38:26.843974 31616 net.cpp:137] Memory required for data: 225767168
I0408 07:38:26.843986 31616 layer_factory.hpp:77] Creating layer relu3
I0408 07:38:26.843994 31616 net.cpp:84] Creating Layer relu3
I0408 07:38:26.843998 31616 net.cpp:406] relu3 <- conv3
I0408 07:38:26.844004 31616 net.cpp:367] relu3 -> conv3 (in-place)
I0408 07:38:26.844552 31616 net.cpp:122] Setting up relu3
I0408 07:38:26.844561 31616 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 07:38:26.844565 31616 net.cpp:137] Memory required for data: 234073856
I0408 07:38:26.844568 31616 layer_factory.hpp:77] Creating layer conv4
I0408 07:38:26.844581 31616 net.cpp:84] Creating Layer conv4
I0408 07:38:26.844585 31616 net.cpp:406] conv4 <- conv3
I0408 07:38:26.844592 31616 net.cpp:380] conv4 -> conv4
I0408 07:38:26.854796 31616 net.cpp:122] Setting up conv4
I0408 07:38:26.854810 31616 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 07:38:26.854813 31616 net.cpp:137] Memory required for data: 242380544
I0408 07:38:26.854821 31616 layer_factory.hpp:77] Creating layer relu4
I0408 07:38:26.854828 31616 net.cpp:84] Creating Layer relu4
I0408 07:38:26.854832 31616 net.cpp:406] relu4 <- conv4
I0408 07:38:26.854840 31616 net.cpp:367] relu4 -> conv4 (in-place)
I0408 07:38:26.855211 31616 net.cpp:122] Setting up relu4
I0408 07:38:26.855219 31616 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 07:38:26.855223 31616 net.cpp:137] Memory required for data: 250687232
I0408 07:38:26.855227 31616 layer_factory.hpp:77] Creating layer conv5
I0408 07:38:26.855237 31616 net.cpp:84] Creating Layer conv5
I0408 07:38:26.855242 31616 net.cpp:406] conv5 <- conv4
I0408 07:38:26.855248 31616 net.cpp:380] conv5 -> conv5
I0408 07:38:26.864305 31616 net.cpp:122] Setting up conv5
I0408 07:38:26.864317 31616 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 07:38:26.864321 31616 net.cpp:137] Memory required for data: 256225024
I0408 07:38:26.864333 31616 layer_factory.hpp:77] Creating layer relu5
I0408 07:38:26.864341 31616 net.cpp:84] Creating Layer relu5
I0408 07:38:26.864346 31616 net.cpp:406] relu5 <- conv5
I0408 07:38:26.864368 31616 net.cpp:367] relu5 -> conv5 (in-place)
I0408 07:38:26.864893 31616 net.cpp:122] Setting up relu5
I0408 07:38:26.864903 31616 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 07:38:26.864907 31616 net.cpp:137] Memory required for data: 261762816
I0408 07:38:26.864912 31616 layer_factory.hpp:77] Creating layer pool5
I0408 07:38:26.864923 31616 net.cpp:84] Creating Layer pool5
I0408 07:38:26.864926 31616 net.cpp:406] pool5 <- conv5
I0408 07:38:26.864933 31616 net.cpp:380] pool5 -> pool5
I0408 07:38:26.864974 31616 net.cpp:122] Setting up pool5
I0408 07:38:26.864979 31616 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0408 07:38:26.864984 31616 net.cpp:137] Memory required for data: 262942464
I0408 07:38:26.864986 31616 layer_factory.hpp:77] Creating layer fc6
I0408 07:38:26.864993 31616 net.cpp:84] Creating Layer fc6
I0408 07:38:26.864997 31616 net.cpp:406] fc6 <- pool5
I0408 07:38:26.865003 31616 net.cpp:380] fc6 -> fc6
I0408 07:38:27.241144 31616 net.cpp:122] Setting up fc6
I0408 07:38:27.241164 31616 net.cpp:129] Top shape: 32 4096 (131072)
I0408 07:38:27.241168 31616 net.cpp:137] Memory required for data: 263466752
I0408 07:38:27.241178 31616 layer_factory.hpp:77] Creating layer relu6
I0408 07:38:27.241187 31616 net.cpp:84] Creating Layer relu6
I0408 07:38:27.241192 31616 net.cpp:406] relu6 <- fc6
I0408 07:38:27.241199 31616 net.cpp:367] relu6 -> fc6 (in-place)
I0408 07:38:27.242046 31616 net.cpp:122] Setting up relu6
I0408 07:38:27.242056 31616 net.cpp:129] Top shape: 32 4096 (131072)
I0408 07:38:27.242059 31616 net.cpp:137] Memory required for data: 263991040
I0408 07:38:27.242063 31616 layer_factory.hpp:77] Creating layer drop6
I0408 07:38:27.242070 31616 net.cpp:84] Creating Layer drop6
I0408 07:38:27.242074 31616 net.cpp:406] drop6 <- fc6
I0408 07:38:27.242081 31616 net.cpp:367] drop6 -> fc6 (in-place)
I0408 07:38:27.242108 31616 net.cpp:122] Setting up drop6
I0408 07:38:27.242115 31616 net.cpp:129] Top shape: 32 4096 (131072)
I0408 07:38:27.242117 31616 net.cpp:137] Memory required for data: 264515328
I0408 07:38:27.242121 31616 layer_factory.hpp:77] Creating layer fc7
I0408 07:38:27.242128 31616 net.cpp:84] Creating Layer fc7
I0408 07:38:27.242132 31616 net.cpp:406] fc7 <- fc6
I0408 07:38:27.242138 31616 net.cpp:380] fc7 -> fc7
I0408 07:38:27.398536 31616 net.cpp:122] Setting up fc7
I0408 07:38:27.398552 31616 net.cpp:129] Top shape: 32 4096 (131072)
I0408 07:38:27.398556 31616 net.cpp:137] Memory required for data: 265039616
I0408 07:38:27.398566 31616 layer_factory.hpp:77] Creating layer relu7
I0408 07:38:27.398574 31616 net.cpp:84] Creating Layer relu7
I0408 07:38:27.398579 31616 net.cpp:406] relu7 <- fc7
I0408 07:38:27.398586 31616 net.cpp:367] relu7 -> fc7 (in-place)
I0408 07:38:27.399019 31616 net.cpp:122] Setting up relu7
I0408 07:38:27.399027 31616 net.cpp:129] Top shape: 32 4096 (131072)
I0408 07:38:27.399030 31616 net.cpp:137] Memory required for data: 265563904
I0408 07:38:27.399034 31616 layer_factory.hpp:77] Creating layer drop7
I0408 07:38:27.399041 31616 net.cpp:84] Creating Layer drop7
I0408 07:38:27.399044 31616 net.cpp:406] drop7 <- fc7
I0408 07:38:27.399049 31616 net.cpp:367] drop7 -> fc7 (in-place)
I0408 07:38:27.399073 31616 net.cpp:122] Setting up drop7
I0408 07:38:27.399078 31616 net.cpp:129] Top shape: 32 4096 (131072)
I0408 07:38:27.399081 31616 net.cpp:137] Memory required for data: 266088192
I0408 07:38:27.399085 31616 layer_factory.hpp:77] Creating layer fc8
I0408 07:38:27.399091 31616 net.cpp:84] Creating Layer fc8
I0408 07:38:27.399096 31616 net.cpp:406] fc8 <- fc7
I0408 07:38:27.399101 31616 net.cpp:380] fc8 -> fc8
I0408 07:38:27.406790 31616 net.cpp:122] Setting up fc8
I0408 07:38:27.406801 31616 net.cpp:129] Top shape: 32 196 (6272)
I0408 07:38:27.406805 31616 net.cpp:137] Memory required for data: 266113280
I0408 07:38:27.406810 31616 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0408 07:38:27.406817 31616 net.cpp:84] Creating Layer fc8_fc8_0_split
I0408 07:38:27.406821 31616 net.cpp:406] fc8_fc8_0_split <- fc8
I0408 07:38:27.406842 31616 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0408 07:38:27.406850 31616 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0408 07:38:27.406881 31616 net.cpp:122] Setting up fc8_fc8_0_split
I0408 07:38:27.406886 31616 net.cpp:129] Top shape: 32 196 (6272)
I0408 07:38:27.406890 31616 net.cpp:129] Top shape: 32 196 (6272)
I0408 07:38:27.406893 31616 net.cpp:137] Memory required for data: 266163456
I0408 07:38:27.406896 31616 layer_factory.hpp:77] Creating layer accuracy
I0408 07:38:27.406903 31616 net.cpp:84] Creating Layer accuracy
I0408 07:38:27.406908 31616 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0408 07:38:27.406911 31616 net.cpp:406] accuracy <- label_val-data_1_split_0
I0408 07:38:27.406916 31616 net.cpp:380] accuracy -> accuracy
I0408 07:38:27.406924 31616 net.cpp:122] Setting up accuracy
I0408 07:38:27.406927 31616 net.cpp:129] Top shape: (1)
I0408 07:38:27.406929 31616 net.cpp:137] Memory required for data: 266163460
I0408 07:38:27.406934 31616 layer_factory.hpp:77] Creating layer loss
I0408 07:38:27.406939 31616 net.cpp:84] Creating Layer loss
I0408 07:38:27.406942 31616 net.cpp:406] loss <- fc8_fc8_0_split_1
I0408 07:38:27.406946 31616 net.cpp:406] loss <- label_val-data_1_split_1
I0408 07:38:27.406950 31616 net.cpp:380] loss -> loss
I0408 07:38:27.406957 31616 layer_factory.hpp:77] Creating layer loss
I0408 07:38:27.407544 31616 net.cpp:122] Setting up loss
I0408 07:38:27.407555 31616 net.cpp:129] Top shape: (1)
I0408 07:38:27.407558 31616 net.cpp:132]     with loss weight 1
I0408 07:38:27.407568 31616 net.cpp:137] Memory required for data: 266163464
I0408 07:38:27.407572 31616 net.cpp:198] loss needs backward computation.
I0408 07:38:27.407577 31616 net.cpp:200] accuracy does not need backward computation.
I0408 07:38:27.407582 31616 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0408 07:38:27.407584 31616 net.cpp:198] fc8 needs backward computation.
I0408 07:38:27.407588 31616 net.cpp:198] drop7 needs backward computation.
I0408 07:38:27.407591 31616 net.cpp:198] relu7 needs backward computation.
I0408 07:38:27.407594 31616 net.cpp:198] fc7 needs backward computation.
I0408 07:38:27.407598 31616 net.cpp:198] drop6 needs backward computation.
I0408 07:38:27.407600 31616 net.cpp:198] relu6 needs backward computation.
I0408 07:38:27.407603 31616 net.cpp:198] fc6 needs backward computation.
I0408 07:38:27.407608 31616 net.cpp:198] pool5 needs backward computation.
I0408 07:38:27.407611 31616 net.cpp:198] relu5 needs backward computation.
I0408 07:38:27.407614 31616 net.cpp:198] conv5 needs backward computation.
I0408 07:38:27.407618 31616 net.cpp:198] relu4 needs backward computation.
I0408 07:38:27.407621 31616 net.cpp:198] conv4 needs backward computation.
I0408 07:38:27.407625 31616 net.cpp:198] relu3 needs backward computation.
I0408 07:38:27.407629 31616 net.cpp:198] conv3 needs backward computation.
I0408 07:38:27.407631 31616 net.cpp:198] pool2 needs backward computation.
I0408 07:38:27.407635 31616 net.cpp:198] norm2 needs backward computation.
I0408 07:38:27.407639 31616 net.cpp:198] relu2 needs backward computation.
I0408 07:38:27.407642 31616 net.cpp:198] conv2 needs backward computation.
I0408 07:38:27.407645 31616 net.cpp:198] pool1 needs backward computation.
I0408 07:38:27.407649 31616 net.cpp:198] norm1 needs backward computation.
I0408 07:38:27.407652 31616 net.cpp:198] relu1 needs backward computation.
I0408 07:38:27.407655 31616 net.cpp:198] conv1 needs backward computation.
I0408 07:38:27.407660 31616 net.cpp:200] label_val-data_1_split does not need backward computation.
I0408 07:38:27.407663 31616 net.cpp:200] val-data does not need backward computation.
I0408 07:38:27.407666 31616 net.cpp:242] This network produces output accuracy
I0408 07:38:27.407670 31616 net.cpp:242] This network produces output loss
I0408 07:38:27.407688 31616 net.cpp:255] Network initialization done.
I0408 07:38:27.407758 31616 solver.cpp:56] Solver scaffolding done.
I0408 07:38:27.408179 31616 caffe.cpp:248] Starting Optimization
I0408 07:38:27.408186 31616 solver.cpp:272] Solving
I0408 07:38:27.408197 31616 solver.cpp:273] Learning Rate Policy: exp
I0408 07:38:27.409468 31616 solver.cpp:330] Iteration 0, Testing net (#0)
I0408 07:38:27.409478 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:38:27.488181 31616 blocking_queue.cpp:49] Waiting for data
I0408 07:38:31.677695 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:38:31.722256 31616 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0408 07:38:31.722285 31616 solver.cpp:397]     Test net output #1: loss = 5.28337 (* 1 = 5.28337 loss)
I0408 07:38:31.817409 31616 solver.cpp:218] Iteration 0 (-6.82655e-36 iter/s, 4.40903s/12 iters), loss = 5.30827
I0408 07:38:31.819274 31616 solver.cpp:237]     Train net output #0: loss = 5.30827 (* 1 = 5.30827 loss)
I0408 07:38:31.819308 31616 sgd_solver.cpp:105] Iteration 0, lr = 0.1
I0408 07:38:35.750808 31616 solver.cpp:218] Iteration 12 (3.05235 iter/s, 3.93139s/12 iters), loss = 5.35019
I0408 07:38:35.750854 31616 solver.cpp:237]     Train net output #0: loss = 5.35019 (* 1 = 5.35019 loss)
I0408 07:38:35.750866 31616 sgd_solver.cpp:105] Iteration 12, lr = 0.0981062
I0408 07:38:40.634084 31616 solver.cpp:218] Iteration 24 (2.45747 iter/s, 4.88306s/12 iters), loss = 5.3197
I0408 07:38:40.634131 31616 solver.cpp:237]     Train net output #0: loss = 5.3197 (* 1 = 5.3197 loss)
I0408 07:38:40.634143 31616 sgd_solver.cpp:105] Iteration 24, lr = 0.0962482
I0408 07:38:45.653894 31616 solver.cpp:218] Iteration 36 (2.39063 iter/s, 5.01959s/12 iters), loss = 5.29957
I0408 07:38:45.653937 31616 solver.cpp:237]     Train net output #0: loss = 5.29957 (* 1 = 5.29957 loss)
I0408 07:38:45.653949 31616 sgd_solver.cpp:105] Iteration 36, lr = 0.0944255
I0408 07:38:50.638828 31616 solver.cpp:218] Iteration 48 (2.40736 iter/s, 4.98472s/12 iters), loss = 5.28263
I0408 07:38:50.638872 31616 solver.cpp:237]     Train net output #0: loss = 5.28263 (* 1 = 5.28263 loss)
I0408 07:38:50.638883 31616 sgd_solver.cpp:105] Iteration 48, lr = 0.0926373
I0408 07:38:55.629715 31616 solver.cpp:218] Iteration 60 (2.40448 iter/s, 4.99068s/12 iters), loss = 5.28315
I0408 07:38:55.629863 31616 solver.cpp:237]     Train net output #0: loss = 5.28315 (* 1 = 5.28315 loss)
I0408 07:38:55.629873 31616 sgd_solver.cpp:105] Iteration 60, lr = 0.0908829
I0408 07:39:00.611550 31616 solver.cpp:218] Iteration 72 (2.4089 iter/s, 4.98152s/12 iters), loss = 5.30475
I0408 07:39:00.611589 31616 solver.cpp:237]     Train net output #0: loss = 5.30475 (* 1 = 5.30475 loss)
I0408 07:39:00.611598 31616 sgd_solver.cpp:105] Iteration 72, lr = 0.0891617
I0408 07:39:05.616482 31616 solver.cpp:218] Iteration 84 (2.39774 iter/s, 5.00472s/12 iters), loss = 5.28231
I0408 07:39:05.616528 31616 solver.cpp:237]     Train net output #0: loss = 5.28231 (* 1 = 5.28231 loss)
I0408 07:39:05.616540 31616 sgd_solver.cpp:105] Iteration 84, lr = 0.0874732
I0408 07:39:10.618319 31616 solver.cpp:218] Iteration 96 (2.39922 iter/s, 5.00162s/12 iters), loss = 5.2954
I0408 07:39:10.618366 31616 solver.cpp:237]     Train net output #0: loss = 5.2954 (* 1 = 5.2954 loss)
I0408 07:39:10.618376 31616 sgd_solver.cpp:105] Iteration 96, lr = 0.0858166
I0408 07:39:12.341301 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:39:12.698467 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0408 07:39:15.777462 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0408 07:39:18.098502 31616 solver.cpp:330] Iteration 102, Testing net (#0)
I0408 07:39:18.098533 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:39:22.492331 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:39:22.569065 31616 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:39:22.569108 31616 solver.cpp:397]     Test net output #1: loss = 5.28388 (* 1 = 5.28388 loss)
I0408 07:39:24.557962 31616 solver.cpp:218] Iteration 108 (0.860886 iter/s, 13.9391s/12 iters), loss = 5.2996
I0408 07:39:24.558012 31616 solver.cpp:237]     Train net output #0: loss = 5.2996 (* 1 = 5.2996 loss)
I0408 07:39:24.558024 31616 sgd_solver.cpp:105] Iteration 108, lr = 0.0841914
I0408 07:39:29.859488 31616 solver.cpp:218] Iteration 120 (2.2636 iter/s, 5.30129s/12 iters), loss = 5.27317
I0408 07:39:29.859647 31616 solver.cpp:237]     Train net output #0: loss = 5.27317 (* 1 = 5.27317 loss)
I0408 07:39:29.859659 31616 sgd_solver.cpp:105] Iteration 120, lr = 0.082597
I0408 07:39:34.821799 31616 solver.cpp:218] Iteration 132 (2.41839 iter/s, 4.96199s/12 iters), loss = 5.24011
I0408 07:39:34.821838 31616 solver.cpp:237]     Train net output #0: loss = 5.24011 (* 1 = 5.24011 loss)
I0408 07:39:34.821848 31616 sgd_solver.cpp:105] Iteration 132, lr = 0.0810328
I0408 07:39:39.851938 31616 solver.cpp:218] Iteration 144 (2.38572 iter/s, 5.02993s/12 iters), loss = 5.29906
I0408 07:39:39.851975 31616 solver.cpp:237]     Train net output #0: loss = 5.29906 (* 1 = 5.29906 loss)
I0408 07:39:39.851985 31616 sgd_solver.cpp:105] Iteration 144, lr = 0.0794981
I0408 07:39:44.865990 31616 solver.cpp:218] Iteration 156 (2.39339 iter/s, 5.01382s/12 iters), loss = 5.26736
I0408 07:39:44.866037 31616 solver.cpp:237]     Train net output #0: loss = 5.26736 (* 1 = 5.26736 loss)
I0408 07:39:44.866050 31616 sgd_solver.cpp:105] Iteration 156, lr = 0.0779926
I0408 07:39:49.950726 31616 solver.cpp:218] Iteration 168 (2.36011 iter/s, 5.08451s/12 iters), loss = 5.25363
I0408 07:39:49.950770 31616 solver.cpp:237]     Train net output #0: loss = 5.25363 (* 1 = 5.25363 loss)
I0408 07:39:49.950781 31616 sgd_solver.cpp:105] Iteration 168, lr = 0.0765156
I0408 07:39:54.984666 31616 solver.cpp:218] Iteration 180 (2.38392 iter/s, 5.03372s/12 iters), loss = 5.27786
I0408 07:39:54.984712 31616 solver.cpp:237]     Train net output #0: loss = 5.27786 (* 1 = 5.27786 loss)
I0408 07:39:54.984724 31616 sgd_solver.cpp:105] Iteration 180, lr = 0.0750665
I0408 07:39:59.976857 31616 solver.cpp:218] Iteration 192 (2.40386 iter/s, 4.99197s/12 iters), loss = 5.28667
I0408 07:39:59.976987 31616 solver.cpp:237]     Train net output #0: loss = 5.28667 (* 1 = 5.28667 loss)
I0408 07:39:59.977001 31616 sgd_solver.cpp:105] Iteration 192, lr = 0.0736449
I0408 07:40:03.830072 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:40:04.513257 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0408 07:40:07.516614 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0408 07:40:09.848512 31616 solver.cpp:330] Iteration 204, Testing net (#0)
I0408 07:40:09.848538 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:40:14.204177 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:40:14.327591 31616 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0408 07:40:14.327634 31616 solver.cpp:397]     Test net output #1: loss = 5.27992 (* 1 = 5.27992 loss)
I0408 07:40:14.417979 31616 solver.cpp:218] Iteration 204 (0.830996 iter/s, 14.4405s/12 iters), loss = 5.24821
I0408 07:40:14.418030 31616 solver.cpp:237]     Train net output #0: loss = 5.24821 (* 1 = 5.24821 loss)
I0408 07:40:14.418040 31616 sgd_solver.cpp:105] Iteration 204, lr = 0.0722502
I0408 07:40:18.669932 31616 solver.cpp:218] Iteration 216 (2.82236 iter/s, 4.25176s/12 iters), loss = 5.28302
I0408 07:40:18.669984 31616 solver.cpp:237]     Train net output #0: loss = 5.28302 (* 1 = 5.28302 loss)
I0408 07:40:18.669996 31616 sgd_solver.cpp:105] Iteration 216, lr = 0.0708819
I0408 07:40:23.689206 31616 solver.cpp:218] Iteration 228 (2.39089 iter/s, 5.01904s/12 iters), loss = 5.21986
I0408 07:40:23.689256 31616 solver.cpp:237]     Train net output #0: loss = 5.21986 (* 1 = 5.21986 loss)
I0408 07:40:23.689268 31616 sgd_solver.cpp:105] Iteration 228, lr = 0.0695396
I0408 07:40:28.710597 31616 solver.cpp:218] Iteration 240 (2.38988 iter/s, 5.02117s/12 iters), loss = 5.28175
I0408 07:40:28.710633 31616 solver.cpp:237]     Train net output #0: loss = 5.28175 (* 1 = 5.28175 loss)
I0408 07:40:28.710644 31616 sgd_solver.cpp:105] Iteration 240, lr = 0.0682226
I0408 07:40:33.813529 31616 solver.cpp:218] Iteration 252 (2.35169 iter/s, 5.10272s/12 iters), loss = 5.27541
I0408 07:40:33.813670 31616 solver.cpp:237]     Train net output #0: loss = 5.27541 (* 1 = 5.27541 loss)
I0408 07:40:33.813683 31616 sgd_solver.cpp:105] Iteration 252, lr = 0.0669306
I0408 07:40:38.756767 31616 solver.cpp:218] Iteration 264 (2.42771 iter/s, 4.94293s/12 iters), loss = 5.26822
I0408 07:40:38.756814 31616 solver.cpp:237]     Train net output #0: loss = 5.26822 (* 1 = 5.26822 loss)
I0408 07:40:38.756827 31616 sgd_solver.cpp:105] Iteration 264, lr = 0.0656631
I0408 07:40:43.712366 31616 solver.cpp:218] Iteration 276 (2.42161 iter/s, 4.95538s/12 iters), loss = 5.28654
I0408 07:40:43.712409 31616 solver.cpp:237]     Train net output #0: loss = 5.28654 (* 1 = 5.28654 loss)
I0408 07:40:43.712419 31616 sgd_solver.cpp:105] Iteration 276, lr = 0.0644195
I0408 07:40:48.746851 31616 solver.cpp:218] Iteration 288 (2.38366 iter/s, 5.03427s/12 iters), loss = 5.28431
I0408 07:40:48.746893 31616 solver.cpp:237]     Train net output #0: loss = 5.28431 (* 1 = 5.28431 loss)
I0408 07:40:48.746904 31616 sgd_solver.cpp:105] Iteration 288, lr = 0.0631996
I0408 07:40:53.683094 31616 solver.cpp:218] Iteration 300 (2.4311 iter/s, 4.93603s/12 iters), loss = 5.28415
I0408 07:40:53.683140 31616 solver.cpp:237]     Train net output #0: loss = 5.28415 (* 1 = 5.28415 loss)
I0408 07:40:53.683152 31616 sgd_solver.cpp:105] Iteration 300, lr = 0.0620027
I0408 07:40:54.674050 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:40:55.703016 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0408 07:40:58.741807 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0408 07:41:01.068857 31616 solver.cpp:330] Iteration 306, Testing net (#0)
I0408 07:41:01.068882 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:41:05.245556 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:41:05.403631 31616 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 07:41:05.403678 31616 solver.cpp:397]     Test net output #1: loss = 5.29373 (* 1 = 5.29373 loss)
I0408 07:41:07.418812 31616 solver.cpp:218] Iteration 312 (0.873667 iter/s, 13.7352s/12 iters), loss = 5.29357
I0408 07:41:07.418856 31616 solver.cpp:237]     Train net output #0: loss = 5.29357 (* 1 = 5.29357 loss)
I0408 07:41:07.418869 31616 sgd_solver.cpp:105] Iteration 312, lr = 0.0608285
I0408 07:41:12.847282 31616 solver.cpp:218] Iteration 324 (2.21066 iter/s, 5.42824s/12 iters), loss = 5.25252
I0408 07:41:12.847326 31616 solver.cpp:237]     Train net output #0: loss = 5.25252 (* 1 = 5.25252 loss)
I0408 07:41:12.847337 31616 sgd_solver.cpp:105] Iteration 324, lr = 0.0596765
I0408 07:41:18.121636 31616 solver.cpp:218] Iteration 336 (2.27526 iter/s, 5.27412s/12 iters), loss = 5.26068
I0408 07:41:18.121690 31616 solver.cpp:237]     Train net output #0: loss = 5.26068 (* 1 = 5.26068 loss)
I0408 07:41:18.121701 31616 sgd_solver.cpp:105] Iteration 336, lr = 0.0585463
I0408 07:41:23.188395 31616 solver.cpp:218] Iteration 348 (2.36848 iter/s, 5.06653s/12 iters), loss = 5.26836
I0408 07:41:23.188441 31616 solver.cpp:237]     Train net output #0: loss = 5.26836 (* 1 = 5.26836 loss)
I0408 07:41:23.188452 31616 sgd_solver.cpp:105] Iteration 348, lr = 0.0574376
I0408 07:41:28.266566 31616 solver.cpp:218] Iteration 360 (2.36316 iter/s, 5.07795s/12 iters), loss = 5.28463
I0408 07:41:28.266613 31616 solver.cpp:237]     Train net output #0: loss = 5.28463 (* 1 = 5.28463 loss)
I0408 07:41:28.266624 31616 sgd_solver.cpp:105] Iteration 360, lr = 0.0563498
I0408 07:41:33.362016 31616 solver.cpp:218] Iteration 372 (2.35515 iter/s, 5.09523s/12 iters), loss = 5.24212
I0408 07:41:33.362063 31616 solver.cpp:237]     Train net output #0: loss = 5.24212 (* 1 = 5.24212 loss)
I0408 07:41:33.362076 31616 sgd_solver.cpp:105] Iteration 372, lr = 0.0552827
I0408 07:41:38.421098 31616 solver.cpp:218] Iteration 384 (2.37208 iter/s, 5.05886s/12 iters), loss = 5.2156
I0408 07:41:38.421244 31616 solver.cpp:237]     Train net output #0: loss = 5.2156 (* 1 = 5.2156 loss)
I0408 07:41:38.421257 31616 sgd_solver.cpp:105] Iteration 384, lr = 0.0542357
I0408 07:41:43.432109 31616 solver.cpp:218] Iteration 396 (2.39488 iter/s, 5.01069s/12 iters), loss = 5.21519
I0408 07:41:43.432154 31616 solver.cpp:237]     Train net output #0: loss = 5.21519 (* 1 = 5.21519 loss)
I0408 07:41:43.432166 31616 sgd_solver.cpp:105] Iteration 396, lr = 0.0532086
I0408 07:41:46.574033 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:41:48.003901 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0408 07:41:51.026553 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0408 07:41:53.337975 31616 solver.cpp:330] Iteration 408, Testing net (#0)
I0408 07:41:53.337998 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:41:57.613430 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:41:57.817296 31616 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0408 07:41:57.817343 31616 solver.cpp:397]     Test net output #1: loss = 5.22255 (* 1 = 5.22255 loss)
I0408 07:41:57.908268 31616 solver.cpp:218] Iteration 408 (0.82898 iter/s, 14.4756s/12 iters), loss = 5.26391
I0408 07:41:57.908316 31616 solver.cpp:237]     Train net output #0: loss = 5.26391 (* 1 = 5.26391 loss)
I0408 07:41:57.908329 31616 sgd_solver.cpp:105] Iteration 408, lr = 0.0522009
I0408 07:42:02.107146 31616 solver.cpp:218] Iteration 420 (2.85804 iter/s, 4.19868s/12 iters), loss = 5.25125
I0408 07:42:02.107192 31616 solver.cpp:237]     Train net output #0: loss = 5.25125 (* 1 = 5.25125 loss)
I0408 07:42:02.107203 31616 sgd_solver.cpp:105] Iteration 420, lr = 0.0512123
I0408 07:42:07.070392 31616 solver.cpp:218] Iteration 432 (2.41788 iter/s, 4.96303s/12 iters), loss = 5.2037
I0408 07:42:07.070442 31616 solver.cpp:237]     Train net output #0: loss = 5.2037 (* 1 = 5.2037 loss)
I0408 07:42:07.070454 31616 sgd_solver.cpp:105] Iteration 432, lr = 0.0502425
I0408 07:42:12.103612 31616 solver.cpp:218] Iteration 444 (2.38427 iter/s, 5.033s/12 iters), loss = 5.19325
I0408 07:42:12.103706 31616 solver.cpp:237]     Train net output #0: loss = 5.19325 (* 1 = 5.19325 loss)
I0408 07:42:12.103716 31616 sgd_solver.cpp:105] Iteration 444, lr = 0.049291
I0408 07:42:17.094890 31616 solver.cpp:218] Iteration 456 (2.40432 iter/s, 4.99101s/12 iters), loss = 5.20942
I0408 07:42:17.094935 31616 solver.cpp:237]     Train net output #0: loss = 5.20942 (* 1 = 5.20942 loss)
I0408 07:42:17.094947 31616 sgd_solver.cpp:105] Iteration 456, lr = 0.0483575
I0408 07:42:22.080705 31616 solver.cpp:218] Iteration 468 (2.40693 iter/s, 4.98559s/12 iters), loss = 5.23055
I0408 07:42:22.080744 31616 solver.cpp:237]     Train net output #0: loss = 5.23055 (* 1 = 5.23055 loss)
I0408 07:42:22.080751 31616 sgd_solver.cpp:105] Iteration 468, lr = 0.0474417
I0408 07:42:27.046478 31616 solver.cpp:218] Iteration 480 (2.41665 iter/s, 4.96556s/12 iters), loss = 5.14033
I0408 07:42:27.046521 31616 solver.cpp:237]     Train net output #0: loss = 5.14033 (* 1 = 5.14033 loss)
I0408 07:42:27.046532 31616 sgd_solver.cpp:105] Iteration 480, lr = 0.0465433
I0408 07:42:32.016582 31616 solver.cpp:218] Iteration 492 (2.41454 iter/s, 4.96989s/12 iters), loss = 5.19471
I0408 07:42:32.016618 31616 solver.cpp:237]     Train net output #0: loss = 5.19471 (* 1 = 5.19471 loss)
I0408 07:42:32.016624 31616 sgd_solver.cpp:105] Iteration 492, lr = 0.0456618
I0408 07:42:37.110918 31616 solver.cpp:218] Iteration 504 (2.35566 iter/s, 5.09412s/12 iters), loss = 5.22792
I0408 07:42:37.110973 31616 solver.cpp:237]     Train net output #0: loss = 5.22792 (* 1 = 5.22792 loss)
I0408 07:42:37.110989 31616 sgd_solver.cpp:105] Iteration 504, lr = 0.0447971
I0408 07:42:37.352072 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:42:39.058738 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0408 07:42:42.144682 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0408 07:42:44.474432 31616 solver.cpp:330] Iteration 510, Testing net (#0)
I0408 07:42:44.474459 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:42:48.764165 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:42:49.018999 31616 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0408 07:42:49.019044 31616 solver.cpp:397]     Test net output #1: loss = 5.1883 (* 1 = 5.1883 loss)
I0408 07:42:50.983103 31616 solver.cpp:218] Iteration 516 (0.865072 iter/s, 13.8717s/12 iters), loss = 5.16051
I0408 07:42:50.983139 31616 solver.cpp:237]     Train net output #0: loss = 5.16051 (* 1 = 5.16051 loss)
I0408 07:42:50.983147 31616 sgd_solver.cpp:105] Iteration 516, lr = 0.0439487
I0408 07:42:55.995774 31616 solver.cpp:218] Iteration 528 (2.39403 iter/s, 5.01246s/12 iters), loss = 5.22895
I0408 07:42:55.995810 31616 solver.cpp:237]     Train net output #0: loss = 5.22895 (* 1 = 5.22895 loss)
I0408 07:42:55.995818 31616 sgd_solver.cpp:105] Iteration 528, lr = 0.0431164
I0408 07:43:00.980262 31616 solver.cpp:218] Iteration 540 (2.40757 iter/s, 4.98428s/12 iters), loss = 5.16887
I0408 07:43:00.980296 31616 solver.cpp:237]     Train net output #0: loss = 5.16887 (* 1 = 5.16887 loss)
I0408 07:43:00.980304 31616 sgd_solver.cpp:105] Iteration 540, lr = 0.0422998
I0408 07:43:05.903517 31616 solver.cpp:218] Iteration 552 (2.43752 iter/s, 4.92305s/12 iters), loss = 5.13655
I0408 07:43:05.903555 31616 solver.cpp:237]     Train net output #0: loss = 5.13655 (* 1 = 5.13655 loss)
I0408 07:43:05.903568 31616 sgd_solver.cpp:105] Iteration 552, lr = 0.0414988
I0408 07:43:10.854038 31616 solver.cpp:218] Iteration 564 (2.42409 iter/s, 4.95031s/12 iters), loss = 5.16353
I0408 07:43:10.854080 31616 solver.cpp:237]     Train net output #0: loss = 5.16353 (* 1 = 5.16353 loss)
I0408 07:43:10.854092 31616 sgd_solver.cpp:105] Iteration 564, lr = 0.0407129
I0408 07:43:15.865064 31616 solver.cpp:218] Iteration 576 (2.39482 iter/s, 5.01081s/12 iters), loss = 5.14719
I0408 07:43:15.865525 31616 solver.cpp:237]     Train net output #0: loss = 5.14719 (* 1 = 5.14719 loss)
I0408 07:43:15.865536 31616 sgd_solver.cpp:105] Iteration 576, lr = 0.0399418
I0408 07:43:20.819622 31616 solver.cpp:218] Iteration 588 (2.42232 iter/s, 4.95392s/12 iters), loss = 5.14864
I0408 07:43:20.819674 31616 solver.cpp:237]     Train net output #0: loss = 5.14864 (* 1 = 5.14864 loss)
I0408 07:43:20.819684 31616 sgd_solver.cpp:105] Iteration 588, lr = 0.0391854
I0408 07:43:25.807504 31616 solver.cpp:218] Iteration 600 (2.40594 iter/s, 4.98765s/12 iters), loss = 5.16798
I0408 07:43:25.807550 31616 solver.cpp:237]     Train net output #0: loss = 5.16798 (* 1 = 5.16798 loss)
I0408 07:43:25.807561 31616 sgd_solver.cpp:105] Iteration 600, lr = 0.0384433
I0408 07:43:28.197990 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:43:30.344760 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0408 07:43:33.319943 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0408 07:43:35.627280 31616 solver.cpp:330] Iteration 612, Testing net (#0)
I0408 07:43:35.627307 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:43:39.779489 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:43:40.065376 31616 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0408 07:43:40.065423 31616 solver.cpp:397]     Test net output #1: loss = 5.17748 (* 1 = 5.17748 loss)
I0408 07:43:40.154954 31616 solver.cpp:218] Iteration 612 (0.836416 iter/s, 14.3469s/12 iters), loss = 5.19623
I0408 07:43:40.155004 31616 solver.cpp:237]     Train net output #0: loss = 5.19623 (* 1 = 5.19623 loss)
I0408 07:43:40.155015 31616 sgd_solver.cpp:105] Iteration 612, lr = 0.0377153
I0408 07:43:44.445817 31616 solver.cpp:218] Iteration 624 (2.79677 iter/s, 4.29066s/12 iters), loss = 5.22341
I0408 07:43:44.445871 31616 solver.cpp:237]     Train net output #0: loss = 5.22341 (* 1 = 5.22341 loss)
I0408 07:43:44.445883 31616 sgd_solver.cpp:105] Iteration 624, lr = 0.037001
I0408 07:43:49.603087 31616 solver.cpp:218] Iteration 636 (2.32692 iter/s, 5.15704s/12 iters), loss = 5.10505
I0408 07:43:49.603235 31616 solver.cpp:237]     Train net output #0: loss = 5.10505 (* 1 = 5.10505 loss)
I0408 07:43:49.603247 31616 sgd_solver.cpp:105] Iteration 636, lr = 0.0363003
I0408 07:43:54.541349 31616 solver.cpp:218] Iteration 648 (2.43016 iter/s, 4.93795s/12 iters), loss = 5.16046
I0408 07:43:54.541389 31616 solver.cpp:237]     Train net output #0: loss = 5.16046 (* 1 = 5.16046 loss)
I0408 07:43:54.541400 31616 sgd_solver.cpp:105] Iteration 648, lr = 0.0356128
I0408 07:43:59.502673 31616 solver.cpp:218] Iteration 660 (2.41881 iter/s, 4.96111s/12 iters), loss = 5.15856
I0408 07:43:59.502717 31616 solver.cpp:237]     Train net output #0: loss = 5.15856 (* 1 = 5.15856 loss)
I0408 07:43:59.502728 31616 sgd_solver.cpp:105] Iteration 660, lr = 0.0349384
I0408 07:44:04.528733 31616 solver.cpp:218] Iteration 672 (2.38766 iter/s, 5.02585s/12 iters), loss = 5.16786
I0408 07:44:04.528769 31616 solver.cpp:237]     Train net output #0: loss = 5.16786 (* 1 = 5.16786 loss)
I0408 07:44:04.528776 31616 sgd_solver.cpp:105] Iteration 672, lr = 0.0342767
I0408 07:44:09.541687 31616 solver.cpp:218] Iteration 684 (2.3939 iter/s, 5.01274s/12 iters), loss = 5.03777
I0408 07:44:09.541733 31616 solver.cpp:237]     Train net output #0: loss = 5.03777 (* 1 = 5.03777 loss)
I0408 07:44:09.541743 31616 sgd_solver.cpp:105] Iteration 684, lr = 0.0336276
I0408 07:44:10.329670 31616 blocking_queue.cpp:49] Waiting for data
I0408 07:44:14.572522 31616 solver.cpp:218] Iteration 696 (2.38539 iter/s, 5.03062s/12 iters), loss = 5.12815
I0408 07:44:14.572567 31616 solver.cpp:237]     Train net output #0: loss = 5.12815 (* 1 = 5.12815 loss)
I0408 07:44:14.572579 31616 sgd_solver.cpp:105] Iteration 696, lr = 0.0329908
I0408 07:44:19.194945 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:44:19.569211 31616 solver.cpp:218] Iteration 708 (2.4017 iter/s, 4.99647s/12 iters), loss = 5.15877
I0408 07:44:19.569254 31616 solver.cpp:237]     Train net output #0: loss = 5.15877 (* 1 = 5.15877 loss)
I0408 07:44:19.569267 31616 sgd_solver.cpp:105] Iteration 708, lr = 0.032366
I0408 07:44:21.589589 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0408 07:44:24.637953 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0408 07:44:26.962600 31616 solver.cpp:330] Iteration 714, Testing net (#0)
I0408 07:44:26.962626 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:44:31.111527 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:44:31.432562 31616 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0408 07:44:31.432610 31616 solver.cpp:397]     Test net output #1: loss = 5.16469 (* 1 = 5.16469 loss)
I0408 07:44:33.295684 31616 solver.cpp:218] Iteration 720 (0.874255 iter/s, 13.726s/12 iters), loss = 5.15832
I0408 07:44:33.295733 31616 solver.cpp:237]     Train net output #0: loss = 5.15832 (* 1 = 5.15832 loss)
I0408 07:44:33.295745 31616 sgd_solver.cpp:105] Iteration 720, lr = 0.031753
I0408 07:44:38.330070 31616 solver.cpp:218] Iteration 732 (2.38371 iter/s, 5.03416s/12 iters), loss = 5.11874
I0408 07:44:38.330116 31616 solver.cpp:237]     Train net output #0: loss = 5.11874 (* 1 = 5.11874 loss)
I0408 07:44:38.330127 31616 sgd_solver.cpp:105] Iteration 732, lr = 0.0311517
I0408 07:44:43.358129 31616 solver.cpp:218] Iteration 744 (2.38671 iter/s, 5.02784s/12 iters), loss = 5.09504
I0408 07:44:43.358166 31616 solver.cpp:237]     Train net output #0: loss = 5.09504 (* 1 = 5.09504 loss)
I0408 07:44:43.358175 31616 sgd_solver.cpp:105] Iteration 744, lr = 0.0305617
I0408 07:44:48.379001 31616 solver.cpp:218] Iteration 756 (2.39012 iter/s, 5.02066s/12 iters), loss = 5.12618
I0408 07:44:48.379045 31616 solver.cpp:237]     Train net output #0: loss = 5.12618 (* 1 = 5.12618 loss)
I0408 07:44:48.379057 31616 sgd_solver.cpp:105] Iteration 756, lr = 0.0299829
I0408 07:44:53.404371 31616 solver.cpp:218] Iteration 768 (2.38799 iter/s, 5.02515s/12 iters), loss = 5.13358
I0408 07:44:53.404464 31616 solver.cpp:237]     Train net output #0: loss = 5.13358 (* 1 = 5.13358 loss)
I0408 07:44:53.404474 31616 sgd_solver.cpp:105] Iteration 768, lr = 0.0294151
I0408 07:44:58.528870 31616 solver.cpp:218] Iteration 780 (2.34181 iter/s, 5.12423s/12 iters), loss = 5.17351
I0408 07:44:58.528908 31616 solver.cpp:237]     Train net output #0: loss = 5.17351 (* 1 = 5.17351 loss)
I0408 07:44:58.528914 31616 sgd_solver.cpp:105] Iteration 780, lr = 0.0288581
I0408 07:45:03.536484 31616 solver.cpp:218] Iteration 792 (2.39645 iter/s, 5.0074s/12 iters), loss = 5.07982
I0408 07:45:03.536522 31616 solver.cpp:237]     Train net output #0: loss = 5.07982 (* 1 = 5.07982 loss)
I0408 07:45:03.536530 31616 sgd_solver.cpp:105] Iteration 792, lr = 0.0283115
I0408 07:45:08.582595 31616 solver.cpp:218] Iteration 804 (2.37817 iter/s, 5.0459s/12 iters), loss = 5.10905
I0408 07:45:08.582630 31616 solver.cpp:237]     Train net output #0: loss = 5.10905 (* 1 = 5.10905 loss)
I0408 07:45:08.582638 31616 sgd_solver.cpp:105] Iteration 804, lr = 0.0277754
I0408 07:45:10.366106 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:45:13.193408 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0408 07:45:16.189122 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0408 07:45:18.512367 31616 solver.cpp:330] Iteration 816, Testing net (#0)
I0408 07:45:18.512393 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:45:22.639384 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:45:22.994988 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0122549
I0408 07:45:22.995026 31616 solver.cpp:397]     Test net output #1: loss = 5.14045 (* 1 = 5.14045 loss)
I0408 07:45:23.086086 31616 solver.cpp:218] Iteration 816 (0.827416 iter/s, 14.503s/12 iters), loss = 5.1391
I0408 07:45:23.086143 31616 solver.cpp:237]     Train net output #0: loss = 5.1391 (* 1 = 5.1391 loss)
I0408 07:45:23.086153 31616 sgd_solver.cpp:105] Iteration 816, lr = 0.0272494
I0408 07:45:27.627353 31616 solver.cpp:218] Iteration 828 (2.64256 iter/s, 4.54105s/12 iters), loss = 5.18486
I0408 07:45:27.627456 31616 solver.cpp:237]     Train net output #0: loss = 5.18486 (* 1 = 5.18486 loss)
I0408 07:45:27.627470 31616 sgd_solver.cpp:105] Iteration 828, lr = 0.0267333
I0408 07:45:32.655043 31616 solver.cpp:218] Iteration 840 (2.38691 iter/s, 5.02742s/12 iters), loss = 5.05967
I0408 07:45:32.655088 31616 solver.cpp:237]     Train net output #0: loss = 5.05967 (* 1 = 5.05967 loss)
I0408 07:45:32.655100 31616 sgd_solver.cpp:105] Iteration 840, lr = 0.026227
I0408 07:45:37.696506 31616 solver.cpp:218] Iteration 852 (2.38036 iter/s, 5.04125s/12 iters), loss = 5.07769
I0408 07:45:37.696553 31616 solver.cpp:237]     Train net output #0: loss = 5.07769 (* 1 = 5.07769 loss)
I0408 07:45:37.696565 31616 sgd_solver.cpp:105] Iteration 852, lr = 0.0257303
I0408 07:45:42.712090 31616 solver.cpp:218] Iteration 864 (2.39265 iter/s, 5.01536s/12 iters), loss = 5.10282
I0408 07:45:42.712152 31616 solver.cpp:237]     Train net output #0: loss = 5.10282 (* 1 = 5.10282 loss)
I0408 07:45:42.712165 31616 sgd_solver.cpp:105] Iteration 864, lr = 0.0252431
I0408 07:45:47.793377 31616 solver.cpp:218] Iteration 876 (2.36172 iter/s, 5.08105s/12 iters), loss = 5.11746
I0408 07:45:47.793426 31616 solver.cpp:237]     Train net output #0: loss = 5.11746 (* 1 = 5.11746 loss)
I0408 07:45:47.793438 31616 sgd_solver.cpp:105] Iteration 876, lr = 0.024765
I0408 07:45:53.259599 31616 solver.cpp:218] Iteration 888 (2.19539 iter/s, 5.46599s/12 iters), loss = 5.049
I0408 07:45:53.259644 31616 solver.cpp:237]     Train net output #0: loss = 5.049 (* 1 = 5.049 loss)
I0408 07:45:53.259656 31616 sgd_solver.cpp:105] Iteration 888, lr = 0.024296
I0408 07:45:58.729102 31616 solver.cpp:218] Iteration 900 (2.19408 iter/s, 5.46927s/12 iters), loss = 5.14732
I0408 07:45:58.729255 31616 solver.cpp:237]     Train net output #0: loss = 5.14732 (* 1 = 5.14732 loss)
I0408 07:45:58.729269 31616 sgd_solver.cpp:105] Iteration 900, lr = 0.0238359
I0408 07:46:02.860586 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:46:03.980706 31616 solver.cpp:218] Iteration 912 (2.28516 iter/s, 5.25128s/12 iters), loss = 4.93611
I0408 07:46:03.980753 31616 solver.cpp:237]     Train net output #0: loss = 4.93611 (* 1 = 4.93611 loss)
I0408 07:46:03.980764 31616 sgd_solver.cpp:105] Iteration 912, lr = 0.0233845
I0408 07:46:06.030757 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0408 07:46:09.067178 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0408 07:46:11.364539 31616 solver.cpp:330] Iteration 918, Testing net (#0)
I0408 07:46:11.364558 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:46:15.508631 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:46:15.915931 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0408 07:46:15.915978 31616 solver.cpp:397]     Test net output #1: loss = 5.12334 (* 1 = 5.12334 loss)
I0408 07:46:17.888979 31616 solver.cpp:218] Iteration 924 (0.862827 iter/s, 13.9078s/12 iters), loss = 5.12286
I0408 07:46:17.889024 31616 solver.cpp:237]     Train net output #0: loss = 5.12286 (* 1 = 5.12286 loss)
I0408 07:46:17.889034 31616 sgd_solver.cpp:105] Iteration 924, lr = 0.0229416
I0408 07:46:22.877449 31616 solver.cpp:218] Iteration 936 (2.40565 iter/s, 4.98826s/12 iters), loss = 5.16666
I0408 07:46:22.877493 31616 solver.cpp:237]     Train net output #0: loss = 5.16666 (* 1 = 5.16666 loss)
I0408 07:46:22.877504 31616 sgd_solver.cpp:105] Iteration 936, lr = 0.0225072
I0408 07:46:27.861094 31616 solver.cpp:218] Iteration 948 (2.40798 iter/s, 4.98343s/12 iters), loss = 5.05375
I0408 07:46:27.861140 31616 solver.cpp:237]     Train net output #0: loss = 5.05375 (* 1 = 5.05375 loss)
I0408 07:46:27.861150 31616 sgd_solver.cpp:105] Iteration 948, lr = 0.0220809
I0408 07:46:32.888554 31616 solver.cpp:218] Iteration 960 (2.38699 iter/s, 5.02725s/12 iters), loss = 5.03113
I0408 07:46:32.888629 31616 solver.cpp:237]     Train net output #0: loss = 5.03113 (* 1 = 5.03113 loss)
I0408 07:46:32.888641 31616 sgd_solver.cpp:105] Iteration 960, lr = 0.0216627
I0408 07:46:37.884999 31616 solver.cpp:218] Iteration 972 (2.40183 iter/s, 4.9962s/12 iters), loss = 5.15696
I0408 07:46:37.885042 31616 solver.cpp:237]     Train net output #0: loss = 5.15696 (* 1 = 5.15696 loss)
I0408 07:46:37.885054 31616 sgd_solver.cpp:105] Iteration 972, lr = 0.0212525
I0408 07:46:42.896351 31616 solver.cpp:218] Iteration 984 (2.39467 iter/s, 5.01114s/12 iters), loss = 5.06481
I0408 07:46:42.896409 31616 solver.cpp:237]     Train net output #0: loss = 5.06481 (* 1 = 5.06481 loss)
I0408 07:46:42.896425 31616 sgd_solver.cpp:105] Iteration 984, lr = 0.02085
I0408 07:46:47.871466 31616 solver.cpp:218] Iteration 996 (2.41211 iter/s, 4.97489s/12 iters), loss = 5.00329
I0408 07:46:47.871511 31616 solver.cpp:237]     Train net output #0: loss = 5.00329 (* 1 = 5.00329 loss)
I0408 07:46:47.871524 31616 sgd_solver.cpp:105] Iteration 996, lr = 0.0204552
I0408 07:46:52.920747 31616 solver.cpp:218] Iteration 1008 (2.37668 iter/s, 5.04906s/12 iters), loss = 5.16084
I0408 07:46:52.920792 31616 solver.cpp:237]     Train net output #0: loss = 5.16084 (* 1 = 5.16084 loss)
I0408 07:46:52.920804 31616 sgd_solver.cpp:105] Iteration 1008, lr = 0.0200678
I0408 07:46:53.955449 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:46:57.669365 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0408 07:47:00.682564 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0408 07:47:02.988415 31616 solver.cpp:330] Iteration 1020, Testing net (#0)
I0408 07:47:02.988494 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:47:07.036924 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:47:07.470001 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0128676
I0408 07:47:07.470048 31616 solver.cpp:397]     Test net output #1: loss = 5.09197 (* 1 = 5.09197 loss)
I0408 07:47:07.557474 31616 solver.cpp:218] Iteration 1020 (0.819885 iter/s, 14.6362s/12 iters), loss = 5.0054
I0408 07:47:07.557520 31616 solver.cpp:237]     Train net output #0: loss = 5.0054 (* 1 = 5.0054 loss)
I0408 07:47:07.557533 31616 sgd_solver.cpp:105] Iteration 1020, lr = 0.0196877
I0408 07:47:11.896536 31616 solver.cpp:218] Iteration 1032 (2.7657 iter/s, 4.33886s/12 iters), loss = 5.06774
I0408 07:47:11.896589 31616 solver.cpp:237]     Train net output #0: loss = 5.06774 (* 1 = 5.06774 loss)
I0408 07:47:11.896600 31616 sgd_solver.cpp:105] Iteration 1032, lr = 0.0193149
I0408 07:47:16.890416 31616 solver.cpp:218] Iteration 1044 (2.40305 iter/s, 4.99365s/12 iters), loss = 5.08926
I0408 07:47:16.890465 31616 solver.cpp:237]     Train net output #0: loss = 5.08926 (* 1 = 5.08926 loss)
I0408 07:47:16.890475 31616 sgd_solver.cpp:105] Iteration 1044, lr = 0.0189491
I0408 07:47:21.849994 31616 solver.cpp:218] Iteration 1056 (2.41967 iter/s, 4.95936s/12 iters), loss = 5.05789
I0408 07:47:21.850037 31616 solver.cpp:237]     Train net output #0: loss = 5.05789 (* 1 = 5.05789 loss)
I0408 07:47:21.850049 31616 sgd_solver.cpp:105] Iteration 1056, lr = 0.0185902
I0408 07:47:26.884702 31616 solver.cpp:218] Iteration 1068 (2.38356 iter/s, 5.03448s/12 iters), loss = 5.0882
I0408 07:47:26.884749 31616 solver.cpp:237]     Train net output #0: loss = 5.0882 (* 1 = 5.0882 loss)
I0408 07:47:26.884760 31616 sgd_solver.cpp:105] Iteration 1068, lr = 0.0182382
I0408 07:47:31.874933 31616 solver.cpp:218] Iteration 1080 (2.40481 iter/s, 4.99001s/12 iters), loss = 5.03647
I0408 07:47:31.874980 31616 solver.cpp:237]     Train net output #0: loss = 5.03647 (* 1 = 5.03647 loss)
I0408 07:47:31.874991 31616 sgd_solver.cpp:105] Iteration 1080, lr = 0.0178928
I0408 07:47:36.921846 31616 solver.cpp:218] Iteration 1092 (2.3778 iter/s, 5.04669s/12 iters), loss = 5.05334
I0408 07:47:36.921973 31616 solver.cpp:237]     Train net output #0: loss = 5.05334 (* 1 = 5.05334 loss)
I0408 07:47:36.921986 31616 sgd_solver.cpp:105] Iteration 1092, lr = 0.0175539
I0408 07:47:41.957986 31616 solver.cpp:218] Iteration 1104 (2.38291 iter/s, 5.03585s/12 iters), loss = 4.99918
I0408 07:47:41.958034 31616 solver.cpp:237]     Train net output #0: loss = 4.99918 (* 1 = 4.99918 loss)
I0408 07:47:41.958045 31616 sgd_solver.cpp:105] Iteration 1104, lr = 0.0172215
I0408 07:47:45.076318 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:47:46.923022 31616 solver.cpp:218] Iteration 1116 (2.41701 iter/s, 4.96481s/12 iters), loss = 5.09505
I0408 07:47:46.923065 31616 solver.cpp:237]     Train net output #0: loss = 5.09505 (* 1 = 5.09505 loss)
I0408 07:47:46.923077 31616 sgd_solver.cpp:105] Iteration 1116, lr = 0.0168953
I0408 07:47:48.969234 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0408 07:47:51.799646 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0408 07:47:54.124765 31616 solver.cpp:330] Iteration 1122, Testing net (#0)
I0408 07:47:54.124792 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:47:58.117023 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:47:58.595216 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0408 07:47:58.595264 31616 solver.cpp:397]     Test net output #1: loss = 5.07408 (* 1 = 5.07408 loss)
I0408 07:48:00.585912 31616 solver.cpp:218] Iteration 1128 (0.878324 iter/s, 13.6624s/12 iters), loss = 5.09519
I0408 07:48:00.585971 31616 solver.cpp:237]     Train net output #0: loss = 5.09519 (* 1 = 5.09519 loss)
I0408 07:48:00.585983 31616 sgd_solver.cpp:105] Iteration 1128, lr = 0.0165754
I0408 07:48:05.931356 31616 solver.cpp:218] Iteration 1140 (2.245 iter/s, 5.34521s/12 iters), loss = 5.06787
I0408 07:48:05.931402 31616 solver.cpp:237]     Train net output #0: loss = 5.06787 (* 1 = 5.06787 loss)
I0408 07:48:05.931413 31616 sgd_solver.cpp:105] Iteration 1140, lr = 0.0162615
I0408 07:48:10.828953 31616 solver.cpp:218] Iteration 1152 (2.45029 iter/s, 4.89737s/12 iters), loss = 4.98532
I0408 07:48:10.829103 31616 solver.cpp:237]     Train net output #0: loss = 4.98532 (* 1 = 4.98532 loss)
I0408 07:48:10.829116 31616 sgd_solver.cpp:105] Iteration 1152, lr = 0.0159535
I0408 07:48:15.763157 31616 solver.cpp:218] Iteration 1164 (2.43216 iter/s, 4.93388s/12 iters), loss = 5.03088
I0408 07:48:15.763203 31616 solver.cpp:237]     Train net output #0: loss = 5.03088 (* 1 = 5.03088 loss)
I0408 07:48:15.763214 31616 sgd_solver.cpp:105] Iteration 1164, lr = 0.0156514
I0408 07:48:20.752701 31616 solver.cpp:218] Iteration 1176 (2.40514 iter/s, 4.98932s/12 iters), loss = 5.08253
I0408 07:48:20.752737 31616 solver.cpp:237]     Train net output #0: loss = 5.08253 (* 1 = 5.08253 loss)
I0408 07:48:20.752744 31616 sgd_solver.cpp:105] Iteration 1176, lr = 0.015355
I0408 07:48:25.766324 31616 solver.cpp:218] Iteration 1188 (2.39358 iter/s, 5.01341s/12 iters), loss = 4.98988
I0408 07:48:25.766361 31616 solver.cpp:237]     Train net output #0: loss = 4.98988 (* 1 = 4.98988 loss)
I0408 07:48:25.766371 31616 sgd_solver.cpp:105] Iteration 1188, lr = 0.0150642
I0408 07:48:31.202939 31616 solver.cpp:218] Iteration 1200 (2.20735 iter/s, 5.43638s/12 iters), loss = 5.12648
I0408 07:48:31.202981 31616 solver.cpp:237]     Train net output #0: loss = 5.12648 (* 1 = 5.12648 loss)
I0408 07:48:31.202993 31616 sgd_solver.cpp:105] Iteration 1200, lr = 0.0147789
I0408 07:48:36.207854 31616 solver.cpp:218] Iteration 1212 (2.39775 iter/s, 5.0047s/12 iters), loss = 5.08486
I0408 07:48:36.207899 31616 solver.cpp:237]     Train net output #0: loss = 5.08486 (* 1 = 5.08486 loss)
I0408 07:48:36.207911 31616 sgd_solver.cpp:105] Iteration 1212, lr = 0.014499
I0408 07:48:36.485121 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:48:40.788803 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0408 07:48:43.791978 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0408 07:48:46.093151 31616 solver.cpp:330] Iteration 1224, Testing net (#0)
I0408 07:48:46.093178 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:48:50.061739 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:48:50.575479 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0177696
I0408 07:48:50.575526 31616 solver.cpp:397]     Test net output #1: loss = 5.05504 (* 1 = 5.05504 loss)
I0408 07:48:50.666594 31616 solver.cpp:218] Iteration 1224 (0.829978 iter/s, 14.4582s/12 iters), loss = 5.06283
I0408 07:48:50.666642 31616 solver.cpp:237]     Train net output #0: loss = 5.06283 (* 1 = 5.06283 loss)
I0408 07:48:50.666653 31616 sgd_solver.cpp:105] Iteration 1224, lr = 0.0142244
I0408 07:48:55.211163 31616 solver.cpp:218] Iteration 1236 (2.64064 iter/s, 4.54436s/12 iters), loss = 5.14757
I0408 07:48:55.211201 31616 solver.cpp:237]     Train net output #0: loss = 5.14757 (* 1 = 5.14757 loss)
I0408 07:48:55.211210 31616 sgd_solver.cpp:105] Iteration 1236, lr = 0.013955
I0408 07:49:00.269984 31616 solver.cpp:218] Iteration 1248 (2.3722 iter/s, 5.05861s/12 iters), loss = 4.96403
I0408 07:49:00.270026 31616 solver.cpp:237]     Train net output #0: loss = 4.96403 (* 1 = 4.96403 loss)
I0408 07:49:00.270036 31616 sgd_solver.cpp:105] Iteration 1248, lr = 0.0136908
I0408 07:49:05.294869 31616 solver.cpp:218] Iteration 1260 (2.38822 iter/s, 5.02466s/12 iters), loss = 4.9498
I0408 07:49:05.294924 31616 solver.cpp:237]     Train net output #0: loss = 4.9498 (* 1 = 4.9498 loss)
I0408 07:49:05.294936 31616 sgd_solver.cpp:105] Iteration 1260, lr = 0.0134315
I0408 07:49:10.271879 31616 solver.cpp:218] Iteration 1272 (2.4112 iter/s, 4.97678s/12 iters), loss = 5.02342
I0408 07:49:10.271924 31616 solver.cpp:237]     Train net output #0: loss = 5.02342 (* 1 = 5.02342 loss)
I0408 07:49:10.271935 31616 sgd_solver.cpp:105] Iteration 1272, lr = 0.0131771
I0408 07:49:15.245976 31616 solver.cpp:218] Iteration 1284 (2.41261 iter/s, 4.97387s/12 iters), loss = 5.01801
I0408 07:49:15.246506 31616 solver.cpp:237]     Train net output #0: loss = 5.01801 (* 1 = 5.01801 loss)
I0408 07:49:15.246520 31616 sgd_solver.cpp:105] Iteration 1284, lr = 0.0129276
I0408 07:49:20.361241 31616 solver.cpp:218] Iteration 1296 (2.34624 iter/s, 5.11456s/12 iters), loss = 4.9425
I0408 07:49:20.361287 31616 solver.cpp:237]     Train net output #0: loss = 4.9425 (* 1 = 4.9425 loss)
I0408 07:49:20.361299 31616 sgd_solver.cpp:105] Iteration 1296, lr = 0.0126827
I0408 07:49:25.487433 31616 solver.cpp:218] Iteration 1308 (2.34102 iter/s, 5.12597s/12 iters), loss = 4.96702
I0408 07:49:25.487478 31616 solver.cpp:237]     Train net output #0: loss = 4.96702 (* 1 = 4.96702 loss)
I0408 07:49:25.487488 31616 sgd_solver.cpp:105] Iteration 1308, lr = 0.0124426
I0408 07:49:27.983836 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:49:30.483196 31616 solver.cpp:218] Iteration 1320 (2.40214 iter/s, 4.99554s/12 iters), loss = 4.97239
I0408 07:49:30.483242 31616 solver.cpp:237]     Train net output #0: loss = 4.97239 (* 1 = 4.97239 loss)
I0408 07:49:30.483253 31616 sgd_solver.cpp:105] Iteration 1320, lr = 0.0122069
I0408 07:49:32.531602 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0408 07:49:35.596608 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0408 07:49:37.914716 31616 solver.cpp:330] Iteration 1326, Testing net (#0)
I0408 07:49:37.914742 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:49:41.779742 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:49:42.340714 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0189951
I0408 07:49:42.340750 31616 solver.cpp:397]     Test net output #1: loss = 5.01444 (* 1 = 5.01444 loss)
I0408 07:49:44.329427 31616 solver.cpp:218] Iteration 1332 (0.866694 iter/s, 13.8457s/12 iters), loss = 4.93946
I0408 07:49:44.329474 31616 solver.cpp:237]     Train net output #0: loss = 4.93946 (* 1 = 4.93946 loss)
I0408 07:49:44.329486 31616 sgd_solver.cpp:105] Iteration 1332, lr = 0.0119757
I0408 07:49:49.318250 31616 solver.cpp:218] Iteration 1344 (2.40548 iter/s, 4.9886s/12 iters), loss = 4.84857
I0408 07:49:49.318351 31616 solver.cpp:237]     Train net output #0: loss = 4.84857 (* 1 = 4.84857 loss)
I0408 07:49:49.318362 31616 sgd_solver.cpp:105] Iteration 1344, lr = 0.0117489
I0408 07:49:54.341426 31616 solver.cpp:218] Iteration 1356 (2.38906 iter/s, 5.0229s/12 iters), loss = 4.94899
I0408 07:49:54.341476 31616 solver.cpp:237]     Train net output #0: loss = 4.94899 (* 1 = 4.94899 loss)
I0408 07:49:54.341487 31616 sgd_solver.cpp:105] Iteration 1356, lr = 0.0115264
I0408 07:49:59.335431 31616 solver.cpp:218] Iteration 1368 (2.40299 iter/s, 4.99378s/12 iters), loss = 4.94715
I0408 07:49:59.335476 31616 solver.cpp:237]     Train net output #0: loss = 4.94715 (* 1 = 4.94715 loss)
I0408 07:49:59.335489 31616 sgd_solver.cpp:105] Iteration 1368, lr = 0.0113082
I0408 07:50:00.533535 31616 blocking_queue.cpp:49] Waiting for data
I0408 07:50:04.356199 31616 solver.cpp:218] Iteration 1380 (2.39018 iter/s, 5.02055s/12 iters), loss = 4.856
I0408 07:50:04.356240 31616 solver.cpp:237]     Train net output #0: loss = 4.856 (* 1 = 4.856 loss)
I0408 07:50:04.356251 31616 sgd_solver.cpp:105] Iteration 1380, lr = 0.011094
I0408 07:50:09.721052 31616 solver.cpp:218] Iteration 1392 (2.23688 iter/s, 5.36462s/12 iters), loss = 4.84415
I0408 07:50:09.721098 31616 solver.cpp:237]     Train net output #0: loss = 4.84415 (* 1 = 4.84415 loss)
I0408 07:50:09.721109 31616 sgd_solver.cpp:105] Iteration 1392, lr = 0.0108839
I0408 07:50:14.938874 31616 solver.cpp:218] Iteration 1404 (2.29991 iter/s, 5.2176s/12 iters), loss = 4.95866
I0408 07:50:14.938920 31616 solver.cpp:237]     Train net output #0: loss = 4.95866 (* 1 = 4.95866 loss)
I0408 07:50:14.938930 31616 sgd_solver.cpp:105] Iteration 1404, lr = 0.0106778
I0408 07:50:19.567827 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:50:19.918016 31616 solver.cpp:218] Iteration 1416 (2.41016 iter/s, 4.97892s/12 iters), loss = 4.99573
I0408 07:50:19.918061 31616 solver.cpp:237]     Train net output #0: loss = 4.99573 (* 1 = 4.99573 loss)
I0408 07:50:19.918072 31616 sgd_solver.cpp:105] Iteration 1416, lr = 0.0104756
I0408 07:50:24.500674 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0408 07:50:27.593374 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0408 07:50:29.916231 31616 solver.cpp:330] Iteration 1428, Testing net (#0)
I0408 07:50:29.916258 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:50:33.888236 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:50:34.496140 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0220588
I0408 07:50:34.496186 31616 solver.cpp:397]     Test net output #1: loss = 4.95566 (* 1 = 4.95566 loss)
I0408 07:50:34.587548 31616 solver.cpp:218] Iteration 1428 (0.818052 iter/s, 14.669s/12 iters), loss = 5.00591
I0408 07:50:34.587620 31616 solver.cpp:237]     Train net output #0: loss = 5.00591 (* 1 = 5.00591 loss)
I0408 07:50:34.587635 31616 sgd_solver.cpp:105] Iteration 1428, lr = 0.0102772
I0408 07:50:38.797297 31616 solver.cpp:218] Iteration 1440 (2.85068 iter/s, 4.20952s/12 iters), loss = 4.90005
I0408 07:50:38.797345 31616 solver.cpp:237]     Train net output #0: loss = 4.90005 (* 1 = 4.90005 loss)
I0408 07:50:38.797356 31616 sgd_solver.cpp:105] Iteration 1440, lr = 0.0100825
I0408 07:50:43.726888 31616 solver.cpp:218] Iteration 1452 (2.43439 iter/s, 4.92936s/12 iters), loss = 4.94611
I0408 07:50:43.726949 31616 solver.cpp:237]     Train net output #0: loss = 4.94611 (* 1 = 4.94611 loss)
I0408 07:50:43.726961 31616 sgd_solver.cpp:105] Iteration 1452, lr = 0.0098916
I0408 07:50:48.666391 31616 solver.cpp:218] Iteration 1464 (2.42951 iter/s, 4.93927s/12 iters), loss = 4.97895
I0408 07:50:48.666437 31616 solver.cpp:237]     Train net output #0: loss = 4.97895 (* 1 = 4.97895 loss)
I0408 07:50:48.666448 31616 sgd_solver.cpp:105] Iteration 1464, lr = 0.00970427
I0408 07:50:53.811532 31616 solver.cpp:218] Iteration 1476 (2.3324 iter/s, 5.14492s/12 iters), loss = 4.89645
I0408 07:50:53.811619 31616 solver.cpp:237]     Train net output #0: loss = 4.89645 (* 1 = 4.89645 loss)
I0408 07:50:53.811628 31616 sgd_solver.cpp:105] Iteration 1476, lr = 0.00952049
I0408 07:50:58.810092 31616 solver.cpp:218] Iteration 1488 (2.40082 iter/s, 4.9983s/12 iters), loss = 4.90534
I0408 07:50:58.810135 31616 solver.cpp:237]     Train net output #0: loss = 4.90534 (* 1 = 4.90534 loss)
I0408 07:50:58.810145 31616 sgd_solver.cpp:105] Iteration 1488, lr = 0.00934019
I0408 07:51:03.837568 31616 solver.cpp:218] Iteration 1500 (2.38699 iter/s, 5.02726s/12 iters), loss = 4.80827
I0408 07:51:03.837613 31616 solver.cpp:237]     Train net output #0: loss = 4.80827 (* 1 = 4.80827 loss)
I0408 07:51:03.837625 31616 sgd_solver.cpp:105] Iteration 1500, lr = 0.00916331
I0408 07:51:08.836627 31616 solver.cpp:218] Iteration 1512 (2.40056 iter/s, 4.99884s/12 iters), loss = 4.92935
I0408 07:51:08.836676 31616 solver.cpp:237]     Train net output #0: loss = 4.92935 (* 1 = 4.92935 loss)
I0408 07:51:08.836688 31616 sgd_solver.cpp:105] Iteration 1512, lr = 0.00898977
I0408 07:51:10.704996 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:51:13.927680 31616 solver.cpp:218] Iteration 1524 (2.35718 iter/s, 5.09083s/12 iters), loss = 4.93544
I0408 07:51:13.927728 31616 solver.cpp:237]     Train net output #0: loss = 4.93544 (* 1 = 4.93544 loss)
I0408 07:51:13.927739 31616 sgd_solver.cpp:105] Iteration 1524, lr = 0.00881952
I0408 07:51:15.967728 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0408 07:51:18.935122 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0408 07:51:21.256014 31616 solver.cpp:330] Iteration 1530, Testing net (#0)
I0408 07:51:21.256039 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:51:25.089570 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:51:25.728775 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0269608
I0408 07:51:25.728823 31616 solver.cpp:397]     Test net output #1: loss = 4.92514 (* 1 = 4.92514 loss)
I0408 07:51:27.717633 31616 solver.cpp:218] Iteration 1536 (0.870231 iter/s, 13.7894s/12 iters), loss = 4.91204
I0408 07:51:27.717684 31616 solver.cpp:237]     Train net output #0: loss = 4.91204 (* 1 = 4.91204 loss)
I0408 07:51:27.717694 31616 sgd_solver.cpp:105] Iteration 1536, lr = 0.0086525
I0408 07:51:33.150182 31616 solver.cpp:218] Iteration 1548 (2.209 iter/s, 5.43231s/12 iters), loss = 4.80586
I0408 07:51:33.150226 31616 solver.cpp:237]     Train net output #0: loss = 4.80586 (* 1 = 4.80586 loss)
I0408 07:51:33.150238 31616 sgd_solver.cpp:105] Iteration 1548, lr = 0.00848864
I0408 07:51:38.249778 31616 solver.cpp:218] Iteration 1560 (2.35323 iter/s, 5.09937s/12 iters), loss = 4.73796
I0408 07:51:38.249830 31616 solver.cpp:237]     Train net output #0: loss = 4.73796 (* 1 = 4.73796 loss)
I0408 07:51:38.249841 31616 sgd_solver.cpp:105] Iteration 1560, lr = 0.00832788
I0408 07:51:43.284282 31616 solver.cpp:218] Iteration 1572 (2.38366 iter/s, 5.03428s/12 iters), loss = 4.91681
I0408 07:51:43.284327 31616 solver.cpp:237]     Train net output #0: loss = 4.91681 (* 1 = 4.91681 loss)
I0408 07:51:43.284337 31616 sgd_solver.cpp:105] Iteration 1572, lr = 0.00817016
I0408 07:51:48.263532 31616 solver.cpp:218] Iteration 1584 (2.41011 iter/s, 4.97903s/12 iters), loss = 4.87915
I0408 07:51:48.263574 31616 solver.cpp:237]     Train net output #0: loss = 4.87915 (* 1 = 4.87915 loss)
I0408 07:51:48.263584 31616 sgd_solver.cpp:105] Iteration 1584, lr = 0.00801544
I0408 07:51:53.412134 31616 solver.cpp:218] Iteration 1596 (2.33083 iter/s, 5.14838s/12 iters), loss = 4.76891
I0408 07:51:53.412180 31616 solver.cpp:237]     Train net output #0: loss = 4.76891 (* 1 = 4.76891 loss)
I0408 07:51:53.412191 31616 sgd_solver.cpp:105] Iteration 1596, lr = 0.00786364
I0408 07:51:58.360231 31616 solver.cpp:218] Iteration 1608 (2.42528 iter/s, 4.94788s/12 iters), loss = 4.8375
I0408 07:51:58.360330 31616 solver.cpp:237]     Train net output #0: loss = 4.8375 (* 1 = 4.8375 loss)
I0408 07:51:58.360342 31616 sgd_solver.cpp:105] Iteration 1608, lr = 0.00771472
I0408 07:52:02.279415 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:52:03.370769 31616 solver.cpp:218] Iteration 1620 (2.39508 iter/s, 5.01027s/12 iters), loss = 4.78704
I0408 07:52:03.370817 31616 solver.cpp:237]     Train net output #0: loss = 4.78704 (* 1 = 4.78704 loss)
I0408 07:52:03.370829 31616 sgd_solver.cpp:105] Iteration 1620, lr = 0.00756862
I0408 07:52:07.830539 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0408 07:52:10.831476 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0408 07:52:13.155333 31616 solver.cpp:330] Iteration 1632, Testing net (#0)
I0408 07:52:13.155361 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:52:16.964057 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:52:17.638135 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0300245
I0408 07:52:17.638183 31616 solver.cpp:397]     Test net output #1: loss = 4.88031 (* 1 = 4.88031 loss)
I0408 07:52:17.729100 31616 solver.cpp:218] Iteration 1632 (0.835783 iter/s, 14.3578s/12 iters), loss = 4.92894
I0408 07:52:17.729151 31616 solver.cpp:237]     Train net output #0: loss = 4.92894 (* 1 = 4.92894 loss)
I0408 07:52:17.729162 31616 sgd_solver.cpp:105] Iteration 1632, lr = 0.00742528
I0408 07:52:21.968442 31616 solver.cpp:218] Iteration 1644 (2.83076 iter/s, 4.23914s/12 iters), loss = 4.9666
I0408 07:52:21.968487 31616 solver.cpp:237]     Train net output #0: loss = 4.9666 (* 1 = 4.9666 loss)
I0408 07:52:21.968497 31616 sgd_solver.cpp:105] Iteration 1644, lr = 0.00728466
I0408 07:52:26.955826 31616 solver.cpp:218] Iteration 1656 (2.40618 iter/s, 4.98716s/12 iters), loss = 4.72623
I0408 07:52:26.955881 31616 solver.cpp:237]     Train net output #0: loss = 4.72623 (* 1 = 4.72623 loss)
I0408 07:52:26.955893 31616 sgd_solver.cpp:105] Iteration 1656, lr = 0.0071467
I0408 07:52:31.892500 31616 solver.cpp:218] Iteration 1668 (2.4309 iter/s, 4.93644s/12 iters), loss = 4.64714
I0408 07:52:31.892618 31616 solver.cpp:237]     Train net output #0: loss = 4.64714 (* 1 = 4.64714 loss)
I0408 07:52:31.892632 31616 sgd_solver.cpp:105] Iteration 1668, lr = 0.00701136
I0408 07:52:36.934027 31616 solver.cpp:218] Iteration 1680 (2.38037 iter/s, 5.04124s/12 iters), loss = 4.77155
I0408 07:52:36.934062 31616 solver.cpp:237]     Train net output #0: loss = 4.77155 (* 1 = 4.77155 loss)
I0408 07:52:36.934070 31616 sgd_solver.cpp:105] Iteration 1680, lr = 0.00687858
I0408 07:52:41.934906 31616 solver.cpp:218] Iteration 1692 (2.39968 iter/s, 5.00067s/12 iters), loss = 4.70856
I0408 07:52:41.934952 31616 solver.cpp:237]     Train net output #0: loss = 4.70856 (* 1 = 4.70856 loss)
I0408 07:52:41.934963 31616 sgd_solver.cpp:105] Iteration 1692, lr = 0.00674831
I0408 07:52:47.058099 31616 solver.cpp:218] Iteration 1704 (2.34239 iter/s, 5.12297s/12 iters), loss = 4.51524
I0408 07:52:47.058145 31616 solver.cpp:237]     Train net output #0: loss = 4.51524 (* 1 = 4.51524 loss)
I0408 07:52:47.058156 31616 sgd_solver.cpp:105] Iteration 1704, lr = 0.00662051
I0408 07:52:52.042564 31616 solver.cpp:218] Iteration 1716 (2.40759 iter/s, 4.98424s/12 iters), loss = 4.80264
I0408 07:52:52.042613 31616 solver.cpp:237]     Train net output #0: loss = 4.80264 (* 1 = 4.80264 loss)
I0408 07:52:52.042623 31616 sgd_solver.cpp:105] Iteration 1716, lr = 0.00649513
I0408 07:52:53.088043 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:52:56.994876 31616 solver.cpp:218] Iteration 1728 (2.42322 iter/s, 4.95209s/12 iters), loss = 4.87298
I0408 07:52:56.994925 31616 solver.cpp:237]     Train net output #0: loss = 4.87298 (* 1 = 4.87298 loss)
I0408 07:52:56.994936 31616 sgd_solver.cpp:105] Iteration 1728, lr = 0.00637212
I0408 07:52:59.025677 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0408 07:53:01.995255 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0408 07:53:04.352141 31616 solver.cpp:330] Iteration 1734, Testing net (#0)
I0408 07:53:04.352166 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:53:08.119674 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:53:08.827344 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0379902
I0408 07:53:08.827394 31616 solver.cpp:397]     Test net output #1: loss = 4.81556 (* 1 = 4.81556 loss)
I0408 07:53:10.799504 31616 solver.cpp:218] Iteration 1740 (0.869305 iter/s, 13.8041s/12 iters), loss = 4.68471
I0408 07:53:10.799544 31616 solver.cpp:237]     Train net output #0: loss = 4.68471 (* 1 = 4.68471 loss)
I0408 07:53:10.799552 31616 sgd_solver.cpp:105] Iteration 1740, lr = 0.00625145
I0408 07:53:15.827571 31616 solver.cpp:218] Iteration 1752 (2.38671 iter/s, 5.02784s/12 iters), loss = 4.68621
I0408 07:53:15.827622 31616 solver.cpp:237]     Train net output #0: loss = 4.68621 (* 1 = 4.68621 loss)
I0408 07:53:15.827634 31616 sgd_solver.cpp:105] Iteration 1752, lr = 0.00613306
I0408 07:53:20.847589 31616 solver.cpp:218] Iteration 1764 (2.39054 iter/s, 5.01978s/12 iters), loss = 4.69132
I0408 07:53:20.847637 31616 solver.cpp:237]     Train net output #0: loss = 4.69132 (* 1 = 4.69132 loss)
I0408 07:53:20.847646 31616 sgd_solver.cpp:105] Iteration 1764, lr = 0.00601691
I0408 07:53:25.860276 31616 solver.cpp:218] Iteration 1776 (2.39403 iter/s, 5.01246s/12 iters), loss = 4.80645
I0408 07:53:25.860323 31616 solver.cpp:237]     Train net output #0: loss = 4.80645 (* 1 = 4.80645 loss)
I0408 07:53:25.860334 31616 sgd_solver.cpp:105] Iteration 1776, lr = 0.00590296
I0408 07:53:30.828291 31616 solver.cpp:218] Iteration 1788 (2.41556 iter/s, 4.96779s/12 iters), loss = 4.81064
I0408 07:53:30.828341 31616 solver.cpp:237]     Train net output #0: loss = 4.81064 (* 1 = 4.81064 loss)
I0408 07:53:30.828352 31616 sgd_solver.cpp:105] Iteration 1788, lr = 0.00579117
I0408 07:53:35.753311 31616 solver.cpp:218] Iteration 1800 (2.43665 iter/s, 4.9248s/12 iters), loss = 4.66656
I0408 07:53:35.753455 31616 solver.cpp:237]     Train net output #0: loss = 4.66656 (* 1 = 4.66656 loss)
I0408 07:53:35.753468 31616 sgd_solver.cpp:105] Iteration 1800, lr = 0.0056815
I0408 07:53:40.791122 31616 solver.cpp:218] Iteration 1812 (2.38213 iter/s, 5.0375s/12 iters), loss = 4.7261
I0408 07:53:40.791168 31616 solver.cpp:237]     Train net output #0: loss = 4.7261 (* 1 = 4.7261 loss)
I0408 07:53:40.791180 31616 sgd_solver.cpp:105] Iteration 1812, lr = 0.0055739
I0408 07:53:43.967396 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:53:45.869632 31616 solver.cpp:218] Iteration 1824 (2.363 iter/s, 5.07829s/12 iters), loss = 4.68351
I0408 07:53:45.869681 31616 solver.cpp:237]     Train net output #0: loss = 4.68351 (* 1 = 4.68351 loss)
I0408 07:53:45.869693 31616 sgd_solver.cpp:105] Iteration 1824, lr = 0.00546834
I0408 07:53:50.442407 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0408 07:53:53.467550 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0408 07:53:55.789494 31616 solver.cpp:330] Iteration 1836, Testing net (#0)
I0408 07:53:55.789520 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:53:59.385141 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:54:00.137202 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0465686
I0408 07:54:00.137249 31616 solver.cpp:397]     Test net output #1: loss = 4.71595 (* 1 = 4.71595 loss)
I0408 07:54:00.228615 31616 solver.cpp:218] Iteration 1836 (0.835744 iter/s, 14.3585s/12 iters), loss = 4.69996
I0408 07:54:00.228660 31616 solver.cpp:237]     Train net output #0: loss = 4.69996 (* 1 = 4.69996 loss)
I0408 07:54:00.228670 31616 sgd_solver.cpp:105] Iteration 1836, lr = 0.00536478
I0408 07:54:04.782361 31616 solver.cpp:218] Iteration 1848 (2.63531 iter/s, 4.55354s/12 iters), loss = 4.64176
I0408 07:54:04.782403 31616 solver.cpp:237]     Train net output #0: loss = 4.64176 (* 1 = 4.64176 loss)
I0408 07:54:04.782413 31616 sgd_solver.cpp:105] Iteration 1848, lr = 0.00526318
I0408 07:54:09.843951 31616 solver.cpp:218] Iteration 1860 (2.3709 iter/s, 5.06138s/12 iters), loss = 4.57581
I0408 07:54:09.844060 31616 solver.cpp:237]     Train net output #0: loss = 4.57581 (* 1 = 4.57581 loss)
I0408 07:54:09.844072 31616 sgd_solver.cpp:105] Iteration 1860, lr = 0.00516351
I0408 07:54:14.845785 31616 solver.cpp:218] Iteration 1872 (2.39925 iter/s, 5.00155s/12 iters), loss = 4.63306
I0408 07:54:14.845829 31616 solver.cpp:237]     Train net output #0: loss = 4.63306 (* 1 = 4.63306 loss)
I0408 07:54:14.845840 31616 sgd_solver.cpp:105] Iteration 1872, lr = 0.00506572
I0408 07:54:19.840087 31616 solver.cpp:218] Iteration 1884 (2.40284 iter/s, 4.99408s/12 iters), loss = 4.53756
I0408 07:54:19.840142 31616 solver.cpp:237]     Train net output #0: loss = 4.53756 (* 1 = 4.53756 loss)
I0408 07:54:19.840157 31616 sgd_solver.cpp:105] Iteration 1884, lr = 0.00496978
I0408 07:54:24.813102 31616 solver.cpp:218] Iteration 1896 (2.41313 iter/s, 4.97279s/12 iters), loss = 4.74505
I0408 07:54:24.813148 31616 solver.cpp:237]     Train net output #0: loss = 4.74505 (* 1 = 4.74505 loss)
I0408 07:54:24.813159 31616 sgd_solver.cpp:105] Iteration 1896, lr = 0.00487567
I0408 07:54:29.838616 31616 solver.cpp:218] Iteration 1908 (2.38792 iter/s, 5.02529s/12 iters), loss = 4.73452
I0408 07:54:29.838662 31616 solver.cpp:237]     Train net output #0: loss = 4.73452 (* 1 = 4.73452 loss)
I0408 07:54:29.838673 31616 sgd_solver.cpp:105] Iteration 1908, lr = 0.00478333
I0408 07:54:34.869614 31616 solver.cpp:218] Iteration 1920 (2.38532 iter/s, 5.03078s/12 iters), loss = 4.7208
I0408 07:54:34.869661 31616 solver.cpp:237]     Train net output #0: loss = 4.7208 (* 1 = 4.7208 loss)
I0408 07:54:34.869673 31616 sgd_solver.cpp:105] Iteration 1920, lr = 0.00469274
I0408 07:54:35.175947 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:54:39.913192 31616 solver.cpp:218] Iteration 1932 (2.37937 iter/s, 5.04336s/12 iters), loss = 4.66148
I0408 07:54:39.913345 31616 solver.cpp:237]     Train net output #0: loss = 4.66148 (* 1 = 4.66148 loss)
I0408 07:54:39.913359 31616 sgd_solver.cpp:105] Iteration 1932, lr = 0.00460387
I0408 07:54:41.978394 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0408 07:54:44.989630 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0408 07:54:47.308470 31616 solver.cpp:330] Iteration 1938, Testing net (#0)
I0408 07:54:47.308495 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:54:50.993592 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:54:51.782778 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0514706
I0408 07:54:51.782820 31616 solver.cpp:397]     Test net output #1: loss = 4.65284 (* 1 = 4.65284 loss)
I0408 07:54:53.751893 31616 solver.cpp:218] Iteration 1944 (0.867171 iter/s, 13.8381s/12 iters), loss = 4.70182
I0408 07:54:53.751930 31616 solver.cpp:237]     Train net output #0: loss = 4.70182 (* 1 = 4.70182 loss)
I0408 07:54:53.751938 31616 sgd_solver.cpp:105] Iteration 1944, lr = 0.00451668
I0408 07:54:58.798759 31616 solver.cpp:218] Iteration 1956 (2.37781 iter/s, 5.04666s/12 iters), loss = 4.57858
I0408 07:54:58.798796 31616 solver.cpp:237]     Train net output #0: loss = 4.57858 (* 1 = 4.57858 loss)
I0408 07:54:58.798805 31616 sgd_solver.cpp:105] Iteration 1956, lr = 0.00443115
I0408 07:55:03.844610 31616 solver.cpp:218] Iteration 1968 (2.37829 iter/s, 5.04564s/12 iters), loss = 4.61035
I0408 07:55:03.844650 31616 solver.cpp:237]     Train net output #0: loss = 4.61035 (* 1 = 4.61035 loss)
I0408 07:55:03.844660 31616 sgd_solver.cpp:105] Iteration 1968, lr = 0.00434723
I0408 07:55:08.803964 31616 solver.cpp:218] Iteration 1980 (2.41977 iter/s, 4.95914s/12 iters), loss = 4.53098
I0408 07:55:08.804010 31616 solver.cpp:237]     Train net output #0: loss = 4.53098 (* 1 = 4.53098 loss)
I0408 07:55:08.804023 31616 sgd_solver.cpp:105] Iteration 1980, lr = 0.0042649
I0408 07:55:13.911834 31616 solver.cpp:218] Iteration 1992 (2.34942 iter/s, 5.10765s/12 iters), loss = 4.60955
I0408 07:55:13.911960 31616 solver.cpp:237]     Train net output #0: loss = 4.60955 (* 1 = 4.60955 loss)
I0408 07:55:13.911972 31616 sgd_solver.cpp:105] Iteration 1992, lr = 0.00418413
I0408 07:55:18.950891 31616 solver.cpp:218] Iteration 2004 (2.38154 iter/s, 5.03876s/12 iters), loss = 4.40763
I0408 07:55:18.950937 31616 solver.cpp:237]     Train net output #0: loss = 4.40763 (* 1 = 4.40763 loss)
I0408 07:55:18.950947 31616 sgd_solver.cpp:105] Iteration 2004, lr = 0.00410489
I0408 07:55:23.978474 31616 solver.cpp:218] Iteration 2016 (2.38694 iter/s, 5.02736s/12 iters), loss = 4.5066
I0408 07:55:23.978521 31616 solver.cpp:237]     Train net output #0: loss = 4.5066 (* 1 = 4.5066 loss)
I0408 07:55:23.978533 31616 sgd_solver.cpp:105] Iteration 2016, lr = 0.00402715
I0408 07:55:26.538842 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:55:28.993343 31616 solver.cpp:218] Iteration 2028 (2.39299 iter/s, 5.01465s/12 iters), loss = 4.37514
I0408 07:55:28.993386 31616 solver.cpp:237]     Train net output #0: loss = 4.37514 (* 1 = 4.37514 loss)
I0408 07:55:28.993398 31616 sgd_solver.cpp:105] Iteration 2028, lr = 0.00395089
I0408 07:55:33.558377 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0408 07:55:36.597468 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0408 07:55:38.932289 31616 solver.cpp:330] Iteration 2040, Testing net (#0)
I0408 07:55:38.932315 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:55:42.526522 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:55:43.362041 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0551471
I0408 07:55:43.362089 31616 solver.cpp:397]     Test net output #1: loss = 4.58979 (* 1 = 4.58979 loss)
I0408 07:55:43.453364 31616 solver.cpp:218] Iteration 2040 (0.829904 iter/s, 14.4595s/12 iters), loss = 4.58173
I0408 07:55:43.453413 31616 solver.cpp:237]     Train net output #0: loss = 4.58173 (* 1 = 4.58173 loss)
I0408 07:55:43.453424 31616 sgd_solver.cpp:105] Iteration 2040, lr = 0.00387606
I0408 07:55:47.680399 31616 solver.cpp:218] Iteration 2052 (2.839 iter/s, 4.22684s/12 iters), loss = 4.47134
I0408 07:55:47.680567 31616 solver.cpp:237]     Train net output #0: loss = 4.47134 (* 1 = 4.47134 loss)
I0408 07:55:47.680583 31616 sgd_solver.cpp:105] Iteration 2052, lr = 0.00380266
I0408 07:55:49.263283 31616 blocking_queue.cpp:49] Waiting for data
I0408 07:55:52.696161 31616 solver.cpp:218] Iteration 2064 (2.39262 iter/s, 5.01543s/12 iters), loss = 4.5001
I0408 07:55:52.696204 31616 solver.cpp:237]     Train net output #0: loss = 4.5001 (* 1 = 4.5001 loss)
I0408 07:55:52.696216 31616 sgd_solver.cpp:105] Iteration 2064, lr = 0.00373064
I0408 07:55:57.780520 31616 solver.cpp:218] Iteration 2076 (2.36028 iter/s, 5.08414s/12 iters), loss = 4.4973
I0408 07:55:57.780565 31616 solver.cpp:237]     Train net output #0: loss = 4.4973 (* 1 = 4.4973 loss)
I0408 07:55:57.780576 31616 sgd_solver.cpp:105] Iteration 2076, lr = 0.00365999
I0408 07:56:02.693392 31616 solver.cpp:218] Iteration 2088 (2.44267 iter/s, 4.91266s/12 iters), loss = 4.27186
I0408 07:56:02.693439 31616 solver.cpp:237]     Train net output #0: loss = 4.27186 (* 1 = 4.27186 loss)
I0408 07:56:02.693449 31616 sgd_solver.cpp:105] Iteration 2088, lr = 0.00359068
I0408 07:56:07.684542 31616 solver.cpp:218] Iteration 2100 (2.40436 iter/s, 4.99093s/12 iters), loss = 4.43748
I0408 07:56:07.684587 31616 solver.cpp:237]     Train net output #0: loss = 4.43748 (* 1 = 4.43748 loss)
I0408 07:56:07.684598 31616 sgd_solver.cpp:105] Iteration 2100, lr = 0.00352268
I0408 07:56:12.685272 31616 solver.cpp:218] Iteration 2112 (2.39975 iter/s, 5.00051s/12 iters), loss = 4.38264
I0408 07:56:12.685318 31616 solver.cpp:237]     Train net output #0: loss = 4.38264 (* 1 = 4.38264 loss)
I0408 07:56:12.685329 31616 sgd_solver.cpp:105] Iteration 2112, lr = 0.00345597
I0408 07:56:17.387578 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:56:17.709911 31616 solver.cpp:218] Iteration 2124 (2.38833 iter/s, 5.02442s/12 iters), loss = 4.36394
I0408 07:56:17.710041 31616 solver.cpp:237]     Train net output #0: loss = 4.36394 (* 1 = 4.36394 loss)
I0408 07:56:17.710052 31616 sgd_solver.cpp:105] Iteration 2124, lr = 0.00339052
I0408 07:56:22.711308 31616 solver.cpp:218] Iteration 2136 (2.39947 iter/s, 5.0011s/12 iters), loss = 4.44366
I0408 07:56:22.711351 31616 solver.cpp:237]     Train net output #0: loss = 4.44366 (* 1 = 4.44366 loss)
I0408 07:56:22.711362 31616 sgd_solver.cpp:105] Iteration 2136, lr = 0.00332631
I0408 07:56:24.781121 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0408 07:56:27.804852 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0408 07:56:30.127763 31616 solver.cpp:330] Iteration 2142, Testing net (#0)
I0408 07:56:30.127789 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:56:33.737108 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:56:34.603698 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0661765
I0408 07:56:34.603741 31616 solver.cpp:397]     Test net output #1: loss = 4.53215 (* 1 = 4.53215 loss)
I0408 07:56:36.437136 31616 solver.cpp:218] Iteration 2148 (0.874296 iter/s, 13.7253s/12 iters), loss = 4.43989
I0408 07:56:36.437181 31616 solver.cpp:237]     Train net output #0: loss = 4.43989 (* 1 = 4.43989 loss)
I0408 07:56:36.437192 31616 sgd_solver.cpp:105] Iteration 2148, lr = 0.00326331
I0408 07:56:41.408836 31616 solver.cpp:218] Iteration 2160 (2.41377 iter/s, 4.97148s/12 iters), loss = 4.38554
I0408 07:56:41.408881 31616 solver.cpp:237]     Train net output #0: loss = 4.38554 (* 1 = 4.38554 loss)
I0408 07:56:41.408892 31616 sgd_solver.cpp:105] Iteration 2160, lr = 0.00320151
I0408 07:56:46.424782 31616 solver.cpp:218] Iteration 2172 (2.39247 iter/s, 5.01573s/12 iters), loss = 4.47429
I0408 07:56:46.424837 31616 solver.cpp:237]     Train net output #0: loss = 4.47429 (* 1 = 4.47429 loss)
I0408 07:56:46.424854 31616 sgd_solver.cpp:105] Iteration 2172, lr = 0.00314088
I0408 07:56:51.458348 31616 solver.cpp:218] Iteration 2184 (2.3841 iter/s, 5.03334s/12 iters), loss = 4.38139
I0408 07:56:51.458504 31616 solver.cpp:237]     Train net output #0: loss = 4.38139 (* 1 = 4.38139 loss)
I0408 07:56:51.458519 31616 sgd_solver.cpp:105] Iteration 2184, lr = 0.0030814
I0408 07:56:56.488905 31616 solver.cpp:218] Iteration 2196 (2.38558 iter/s, 5.03023s/12 iters), loss = 4.39074
I0408 07:56:56.488953 31616 solver.cpp:237]     Train net output #0: loss = 4.39074 (* 1 = 4.39074 loss)
I0408 07:56:56.488966 31616 sgd_solver.cpp:105] Iteration 2196, lr = 0.00302304
I0408 07:57:01.465364 31616 solver.cpp:218] Iteration 2208 (2.41146 iter/s, 4.97624s/12 iters), loss = 4.26672
I0408 07:57:01.465411 31616 solver.cpp:237]     Train net output #0: loss = 4.26672 (* 1 = 4.26672 loss)
I0408 07:57:01.465423 31616 sgd_solver.cpp:105] Iteration 2208, lr = 0.00296579
I0408 07:57:06.555429 31616 solver.cpp:218] Iteration 2220 (2.35764 iter/s, 5.08984s/12 iters), loss = 4.37398
I0408 07:57:06.555472 31616 solver.cpp:237]     Train net output #0: loss = 4.37398 (* 1 = 4.37398 loss)
I0408 07:57:06.555483 31616 sgd_solver.cpp:105] Iteration 2220, lr = 0.00290963
I0408 07:57:08.387689 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:57:11.575706 31616 solver.cpp:218] Iteration 2232 (2.39041 iter/s, 5.02006s/12 iters), loss = 4.46262
I0408 07:57:11.575757 31616 solver.cpp:237]     Train net output #0: loss = 4.46262 (* 1 = 4.46262 loss)
I0408 07:57:11.575771 31616 sgd_solver.cpp:105] Iteration 2232, lr = 0.00285452
I0408 07:57:16.124629 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0408 07:57:19.197443 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0408 07:57:21.520879 31616 solver.cpp:330] Iteration 2244, Testing net (#0)
I0408 07:57:21.520963 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:57:25.078173 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:57:25.990725 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0618873
I0408 07:57:25.990773 31616 solver.cpp:397]     Test net output #1: loss = 4.49577 (* 1 = 4.49577 loss)
I0408 07:57:26.080353 31616 solver.cpp:218] Iteration 2244 (0.827351 iter/s, 14.5041s/12 iters), loss = 4.42477
I0408 07:57:26.080401 31616 solver.cpp:237]     Train net output #0: loss = 4.42477 (* 1 = 4.42477 loss)
I0408 07:57:26.080412 31616 sgd_solver.cpp:105] Iteration 2244, lr = 0.00280047
I0408 07:57:30.462342 31616 solver.cpp:218] Iteration 2256 (2.73861 iter/s, 4.38179s/12 iters), loss = 4.10495
I0408 07:57:30.462391 31616 solver.cpp:237]     Train net output #0: loss = 4.10495 (* 1 = 4.10495 loss)
I0408 07:57:30.462404 31616 sgd_solver.cpp:105] Iteration 2256, lr = 0.00274743
I0408 07:57:35.498373 31616 solver.cpp:218] Iteration 2268 (2.38293 iter/s, 5.03581s/12 iters), loss = 4.0761
I0408 07:57:35.498417 31616 solver.cpp:237]     Train net output #0: loss = 4.0761 (* 1 = 4.0761 loss)
I0408 07:57:35.498428 31616 sgd_solver.cpp:105] Iteration 2268, lr = 0.0026954
I0408 07:57:40.537715 31616 solver.cpp:218] Iteration 2280 (2.38137 iter/s, 5.03912s/12 iters), loss = 4.29065
I0408 07:57:40.537762 31616 solver.cpp:237]     Train net output #0: loss = 4.29065 (* 1 = 4.29065 loss)
I0408 07:57:40.537775 31616 sgd_solver.cpp:105] Iteration 2280, lr = 0.00264435
I0408 07:57:45.616601 31616 solver.cpp:218] Iteration 2292 (2.36282 iter/s, 5.07867s/12 iters), loss = 4.34931
I0408 07:57:45.616636 31616 solver.cpp:237]     Train net output #0: loss = 4.34931 (* 1 = 4.34931 loss)
I0408 07:57:45.616645 31616 sgd_solver.cpp:105] Iteration 2292, lr = 0.00259427
I0408 07:57:50.693698 31616 solver.cpp:218] Iteration 2304 (2.36365 iter/s, 5.07689s/12 iters), loss = 4.2572
I0408 07:57:50.693744 31616 solver.cpp:237]     Train net output #0: loss = 4.2572 (* 1 = 4.2572 loss)
I0408 07:57:50.693756 31616 sgd_solver.cpp:105] Iteration 2304, lr = 0.00254514
I0408 07:57:55.730337 31616 solver.cpp:218] Iteration 2316 (2.38264 iter/s, 5.03642s/12 iters), loss = 4.26902
I0408 07:57:55.730479 31616 solver.cpp:237]     Train net output #0: loss = 4.26902 (* 1 = 4.26902 loss)
I0408 07:57:55.730489 31616 sgd_solver.cpp:105] Iteration 2316, lr = 0.00249694
I0408 07:57:59.667167 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:58:00.722102 31616 solver.cpp:218] Iteration 2328 (2.40411 iter/s, 4.99146s/12 iters), loss = 4.27466
I0408 07:58:00.722146 31616 solver.cpp:237]     Train net output #0: loss = 4.27466 (* 1 = 4.27466 loss)
I0408 07:58:00.722157 31616 sgd_solver.cpp:105] Iteration 2328, lr = 0.00244966
I0408 07:58:05.771020 31616 solver.cpp:218] Iteration 2340 (2.37685 iter/s, 5.0487s/12 iters), loss = 4.43374
I0408 07:58:05.771067 31616 solver.cpp:237]     Train net output #0: loss = 4.43374 (* 1 = 4.43374 loss)
I0408 07:58:05.771080 31616 sgd_solver.cpp:105] Iteration 2340, lr = 0.00240326
I0408 07:58:07.763186 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0408 07:58:10.840998 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0408 07:58:13.167079 31616 solver.cpp:330] Iteration 2346, Testing net (#0)
I0408 07:58:13.167101 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:58:16.833422 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:58:17.777416 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0741422
I0408 07:58:17.777464 31616 solver.cpp:397]     Test net output #1: loss = 4.42475 (* 1 = 4.42475 loss)
I0408 07:58:19.597235 31616 solver.cpp:218] Iteration 2352 (0.867948 iter/s, 13.8257s/12 iters), loss = 4.28756
I0408 07:58:19.597285 31616 solver.cpp:237]     Train net output #0: loss = 4.28756 (* 1 = 4.28756 loss)
I0408 07:58:19.597296 31616 sgd_solver.cpp:105] Iteration 2352, lr = 0.00235775
I0408 07:58:24.513005 31616 solver.cpp:218] Iteration 2364 (2.44123 iter/s, 4.91555s/12 iters), loss = 4.04836
I0408 07:58:24.513059 31616 solver.cpp:237]     Train net output #0: loss = 4.04836 (* 1 = 4.04836 loss)
I0408 07:58:24.513072 31616 sgd_solver.cpp:105] Iteration 2364, lr = 0.0023131
I0408 07:58:29.459911 31616 solver.cpp:218] Iteration 2376 (2.42587 iter/s, 4.94668s/12 iters), loss = 4.20321
I0408 07:58:29.460000 31616 solver.cpp:237]     Train net output #0: loss = 4.20321 (* 1 = 4.20321 loss)
I0408 07:58:29.460011 31616 sgd_solver.cpp:105] Iteration 2376, lr = 0.00226929
I0408 07:58:34.418308 31616 solver.cpp:218] Iteration 2388 (2.42026 iter/s, 4.95814s/12 iters), loss = 4.12323
I0408 07:58:34.418354 31616 solver.cpp:237]     Train net output #0: loss = 4.12323 (* 1 = 4.12323 loss)
I0408 07:58:34.418365 31616 sgd_solver.cpp:105] Iteration 2388, lr = 0.00222632
I0408 07:58:39.366501 31616 solver.cpp:218] Iteration 2400 (2.42523 iter/s, 4.94798s/12 iters), loss = 4.09436
I0408 07:58:39.366550 31616 solver.cpp:237]     Train net output #0: loss = 4.09436 (* 1 = 4.09436 loss)
I0408 07:58:39.366561 31616 sgd_solver.cpp:105] Iteration 2400, lr = 0.00218416
I0408 07:58:44.291842 31616 solver.cpp:218] Iteration 2412 (2.43649 iter/s, 4.92512s/12 iters), loss = 3.94153
I0408 07:58:44.291889 31616 solver.cpp:237]     Train net output #0: loss = 3.94153 (* 1 = 3.94153 loss)
I0408 07:58:44.291900 31616 sgd_solver.cpp:105] Iteration 2412, lr = 0.00214279
I0408 07:58:49.234539 31616 solver.cpp:218] Iteration 2424 (2.42793 iter/s, 4.94248s/12 iters), loss = 4.21517
I0408 07:58:49.234582 31616 solver.cpp:237]     Train net output #0: loss = 4.21517 (* 1 = 4.21517 loss)
I0408 07:58:49.234592 31616 sgd_solver.cpp:105] Iteration 2424, lr = 0.00210221
I0408 07:58:50.294668 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:58:54.193106 31616 solver.cpp:218] Iteration 2436 (2.42016 iter/s, 4.95836s/12 iters), loss = 4.18478
I0408 07:58:54.193153 31616 solver.cpp:237]     Train net output #0: loss = 4.18478 (* 1 = 4.18478 loss)
I0408 07:58:54.193163 31616 sgd_solver.cpp:105] Iteration 2436, lr = 0.0020624
I0408 07:58:58.696079 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0408 07:59:01.819816 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0408 07:59:04.151643 31616 solver.cpp:330] Iteration 2448, Testing net (#0)
I0408 07:59:04.151670 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:59:07.641461 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:59:08.621606 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0870098
I0408 07:59:08.621654 31616 solver.cpp:397]     Test net output #1: loss = 4.34987 (* 1 = 4.34987 loss)
I0408 07:59:08.712793 31616 solver.cpp:218] Iteration 2448 (0.826494 iter/s, 14.5192s/12 iters), loss = 4.09
I0408 07:59:08.712841 31616 solver.cpp:237]     Train net output #0: loss = 4.09 (* 1 = 4.09 loss)
I0408 07:59:08.712852 31616 sgd_solver.cpp:105] Iteration 2448, lr = 0.00202334
I0408 07:59:12.955442 31616 solver.cpp:218] Iteration 2460 (2.82855 iter/s, 4.24245s/12 iters), loss = 4.13402
I0408 07:59:12.955497 31616 solver.cpp:237]     Train net output #0: loss = 4.13402 (* 1 = 4.13402 loss)
I0408 07:59:12.955509 31616 sgd_solver.cpp:105] Iteration 2460, lr = 0.00198502
I0408 07:59:17.925141 31616 solver.cpp:218] Iteration 2472 (2.41474 iter/s, 4.96947s/12 iters), loss = 4.26278
I0408 07:59:17.925187 31616 solver.cpp:237]     Train net output #0: loss = 4.26278 (* 1 = 4.26278 loss)
I0408 07:59:17.925199 31616 sgd_solver.cpp:105] Iteration 2472, lr = 0.00194743
I0408 07:59:22.998451 31616 solver.cpp:218] Iteration 2484 (2.36542 iter/s, 5.07309s/12 iters), loss = 4.234
I0408 07:59:22.998495 31616 solver.cpp:237]     Train net output #0: loss = 4.234 (* 1 = 4.234 loss)
I0408 07:59:22.998505 31616 sgd_solver.cpp:105] Iteration 2484, lr = 0.00191055
I0408 07:59:28.027292 31616 solver.cpp:218] Iteration 2496 (2.38634 iter/s, 5.02863s/12 iters), loss = 4.39567
I0408 07:59:28.027335 31616 solver.cpp:237]     Train net output #0: loss = 4.39567 (* 1 = 4.39567 loss)
I0408 07:59:28.027346 31616 sgd_solver.cpp:105] Iteration 2496, lr = 0.00187437
I0408 07:59:33.083034 31616 solver.cpp:218] Iteration 2508 (2.37364 iter/s, 5.05552s/12 iters), loss = 4.15206
I0408 07:59:33.083139 31616 solver.cpp:237]     Train net output #0: loss = 4.15206 (* 1 = 4.15206 loss)
I0408 07:59:33.083151 31616 sgd_solver.cpp:105] Iteration 2508, lr = 0.00183887
I0408 07:59:38.113029 31616 solver.cpp:218] Iteration 2520 (2.38582 iter/s, 5.02972s/12 iters), loss = 4.15217
I0408 07:59:38.113073 31616 solver.cpp:237]     Train net output #0: loss = 4.15217 (* 1 = 4.15217 loss)
I0408 07:59:38.113085 31616 sgd_solver.cpp:105] Iteration 2520, lr = 0.00180405
I0408 07:59:41.319569 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:59:43.126520 31616 solver.cpp:218] Iteration 2532 (2.39364 iter/s, 5.01328s/12 iters), loss = 4.34828
I0408 07:59:43.126564 31616 solver.cpp:237]     Train net output #0: loss = 4.34828 (* 1 = 4.34828 loss)
I0408 07:59:43.126575 31616 sgd_solver.cpp:105] Iteration 2532, lr = 0.00176988
I0408 07:59:48.120995 31616 solver.cpp:218] Iteration 2544 (2.40276 iter/s, 4.99426s/12 iters), loss = 3.97785
I0408 07:59:48.121039 31616 solver.cpp:237]     Train net output #0: loss = 3.97785 (* 1 = 3.97785 loss)
I0408 07:59:48.121052 31616 sgd_solver.cpp:105] Iteration 2544, lr = 0.00173636
I0408 07:59:50.140858 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0408 07:59:53.163604 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0408 07:59:55.481950 31616 solver.cpp:330] Iteration 2550, Testing net (#0)
I0408 07:59:55.481989 31616 net.cpp:676] Ignoring source layer train-data
I0408 07:59:58.927572 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 07:59:59.956086 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0863971
I0408 07:59:59.956130 31616 solver.cpp:397]     Test net output #1: loss = 4.2964 (* 1 = 4.2964 loss)
I0408 08:00:01.859114 31616 solver.cpp:218] Iteration 2556 (0.873513 iter/s, 13.7376s/12 iters), loss = 4.12776
I0408 08:00:01.859156 31616 solver.cpp:237]     Train net output #0: loss = 4.12776 (* 1 = 4.12776 loss)
I0408 08:00:01.859167 31616 sgd_solver.cpp:105] Iteration 2556, lr = 0.00170348
I0408 08:00:06.882908 31616 solver.cpp:218] Iteration 2568 (2.38874 iter/s, 5.02358s/12 iters), loss = 4.17538
I0408 08:00:06.883049 31616 solver.cpp:237]     Train net output #0: loss = 4.17538 (* 1 = 4.17538 loss)
I0408 08:00:06.883064 31616 sgd_solver.cpp:105] Iteration 2568, lr = 0.00167122
I0408 08:00:11.875176 31616 solver.cpp:218] Iteration 2580 (2.40386 iter/s, 4.99196s/12 iters), loss = 4.17282
I0408 08:00:11.875222 31616 solver.cpp:237]     Train net output #0: loss = 4.17282 (* 1 = 4.17282 loss)
I0408 08:00:11.875236 31616 sgd_solver.cpp:105] Iteration 2580, lr = 0.00163957
I0408 08:00:16.920344 31616 solver.cpp:218] Iteration 2592 (2.37862 iter/s, 5.04495s/12 iters), loss = 4.24512
I0408 08:00:16.920392 31616 solver.cpp:237]     Train net output #0: loss = 4.24512 (* 1 = 4.24512 loss)
I0408 08:00:16.920403 31616 sgd_solver.cpp:105] Iteration 2592, lr = 0.00160852
I0408 08:00:21.979598 31616 solver.cpp:218] Iteration 2604 (2.372 iter/s, 5.05902s/12 iters), loss = 4.1916
I0408 08:00:21.979645 31616 solver.cpp:237]     Train net output #0: loss = 4.1916 (* 1 = 4.1916 loss)
I0408 08:00:21.979657 31616 sgd_solver.cpp:105] Iteration 2604, lr = 0.00157806
I0408 08:00:26.986119 31616 solver.cpp:218] Iteration 2616 (2.39698 iter/s, 5.0063s/12 iters), loss = 4.20111
I0408 08:00:26.986163 31616 solver.cpp:237]     Train net output #0: loss = 4.20111 (* 1 = 4.20111 loss)
I0408 08:00:26.986174 31616 sgd_solver.cpp:105] Iteration 2616, lr = 0.00154817
I0408 08:00:32.038040 31616 solver.cpp:218] Iteration 2628 (2.37543 iter/s, 5.05171s/12 iters), loss = 4.09596
I0408 08:00:32.038086 31616 solver.cpp:237]     Train net output #0: loss = 4.09596 (* 1 = 4.09596 loss)
I0408 08:00:32.038098 31616 sgd_solver.cpp:105] Iteration 2628, lr = 0.00151885
I0408 08:00:32.487084 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:00:37.094305 31616 solver.cpp:218] Iteration 2640 (2.3734 iter/s, 5.05605s/12 iters), loss = 3.97887
I0408 08:00:37.094403 31616 solver.cpp:237]     Train net output #0: loss = 3.97887 (* 1 = 3.97887 loss)
I0408 08:00:37.094414 31616 sgd_solver.cpp:105] Iteration 2640, lr = 0.00149009
I0408 08:00:41.699570 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0408 08:00:44.724618 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0408 08:00:47.047075 31616 solver.cpp:330] Iteration 2652, Testing net (#0)
I0408 08:00:47.047102 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:00:50.458787 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:00:51.520221 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0900735
I0408 08:00:51.520263 31616 solver.cpp:397]     Test net output #1: loss = 4.2503 (* 1 = 4.2503 loss)
I0408 08:00:51.611395 31616 solver.cpp:218] Iteration 2652 (0.826644 iter/s, 14.5165s/12 iters), loss = 4.16303
I0408 08:00:51.611443 31616 solver.cpp:237]     Train net output #0: loss = 4.16303 (* 1 = 4.16303 loss)
I0408 08:00:51.611452 31616 sgd_solver.cpp:105] Iteration 2652, lr = 0.00146187
I0408 08:00:55.896250 31616 solver.cpp:218] Iteration 2664 (2.80069 iter/s, 4.28466s/12 iters), loss = 3.85902
I0408 08:00:55.896293 31616 solver.cpp:237]     Train net output #0: loss = 3.85902 (* 1 = 3.85902 loss)
I0408 08:00:55.896303 31616 sgd_solver.cpp:105] Iteration 2664, lr = 0.00143418
I0408 08:01:00.829555 31616 solver.cpp:218] Iteration 2676 (2.43255 iter/s, 4.93309s/12 iters), loss = 3.79704
I0408 08:01:00.829589 31616 solver.cpp:237]     Train net output #0: loss = 3.79704 (* 1 = 3.79704 loss)
I0408 08:01:00.829598 31616 sgd_solver.cpp:105] Iteration 2676, lr = 0.00140702
I0408 08:01:05.928750 31616 solver.cpp:218] Iteration 2688 (2.35341 iter/s, 5.09899s/12 iters), loss = 4.15059
I0408 08:01:05.928788 31616 solver.cpp:237]     Train net output #0: loss = 4.15059 (* 1 = 4.15059 loss)
I0408 08:01:05.928797 31616 sgd_solver.cpp:105] Iteration 2688, lr = 0.00138038
I0408 08:01:10.928269 31616 solver.cpp:218] Iteration 2700 (2.40033 iter/s, 4.99931s/12 iters), loss = 4.08304
I0408 08:01:10.928397 31616 solver.cpp:237]     Train net output #0: loss = 4.08304 (* 1 = 4.08304 loss)
I0408 08:01:10.928407 31616 sgd_solver.cpp:105] Iteration 2700, lr = 0.00135423
I0408 08:01:15.957285 31616 solver.cpp:218] Iteration 2712 (2.3863 iter/s, 5.02871s/12 iters), loss = 3.83676
I0408 08:01:15.957335 31616 solver.cpp:237]     Train net output #0: loss = 3.83676 (* 1 = 3.83676 loss)
I0408 08:01:15.957347 31616 sgd_solver.cpp:105] Iteration 2712, lr = 0.00132859
I0408 08:01:20.916154 31616 solver.cpp:218] Iteration 2724 (2.42001 iter/s, 4.95865s/12 iters), loss = 4.2246
I0408 08:01:20.916200 31616 solver.cpp:237]     Train net output #0: loss = 4.2246 (* 1 = 4.2246 loss)
I0408 08:01:20.916211 31616 sgd_solver.cpp:105] Iteration 2724, lr = 0.00130343
I0408 08:01:23.529944 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:01:25.997117 31616 solver.cpp:218] Iteration 2736 (2.36186 iter/s, 5.08074s/12 iters), loss = 3.81658
I0408 08:01:25.997160 31616 solver.cpp:237]     Train net output #0: loss = 3.81658 (* 1 = 3.81658 loss)
I0408 08:01:25.997170 31616 sgd_solver.cpp:105] Iteration 2736, lr = 0.00127874
I0408 08:01:30.892971 31616 solver.cpp:218] Iteration 2748 (2.45116 iter/s, 4.89564s/12 iters), loss = 4.04079
I0408 08:01:30.893019 31616 solver.cpp:237]     Train net output #0: loss = 4.04079 (* 1 = 4.04079 loss)
I0408 08:01:30.893031 31616 sgd_solver.cpp:105] Iteration 2748, lr = 0.00125453
I0408 08:01:32.926486 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0408 08:01:35.949060 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0408 08:01:38.273721 31616 solver.cpp:330] Iteration 2754, Testing net (#0)
I0408 08:01:38.273746 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:01:41.405122 31616 blocking_queue.cpp:49] Waiting for data
I0408 08:01:41.641459 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:01:42.752270 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0931373
I0408 08:01:42.752317 31616 solver.cpp:397]     Test net output #1: loss = 4.23731 (* 1 = 4.23731 loss)
I0408 08:01:44.756315 31616 solver.cpp:218] Iteration 2760 (0.865623 iter/s, 13.8628s/12 iters), loss = 4.01234
I0408 08:01:44.756361 31616 solver.cpp:237]     Train net output #0: loss = 4.01234 (* 1 = 4.01234 loss)
I0408 08:01:44.756373 31616 sgd_solver.cpp:105] Iteration 2760, lr = 0.00123077
I0408 08:01:49.922722 31616 solver.cpp:218] Iteration 2772 (2.3228 iter/s, 5.16619s/12 iters), loss = 4.06585
I0408 08:01:49.922767 31616 solver.cpp:237]     Train net output #0: loss = 4.06585 (* 1 = 4.06585 loss)
I0408 08:01:49.922780 31616 sgd_solver.cpp:105] Iteration 2772, lr = 0.00120746
I0408 08:01:54.892756 31616 solver.cpp:218] Iteration 2784 (2.41457 iter/s, 4.96982s/12 iters), loss = 4.03872
I0408 08:01:54.892794 31616 solver.cpp:237]     Train net output #0: loss = 4.03872 (* 1 = 4.03872 loss)
I0408 08:01:54.892804 31616 sgd_solver.cpp:105] Iteration 2784, lr = 0.00118459
I0408 08:01:59.943940 31616 solver.cpp:218] Iteration 2796 (2.37578 iter/s, 5.05097s/12 iters), loss = 3.84238
I0408 08:01:59.943984 31616 solver.cpp:237]     Train net output #0: loss = 3.84238 (* 1 = 3.84238 loss)
I0408 08:01:59.943994 31616 sgd_solver.cpp:105] Iteration 2796, lr = 0.00116216
I0408 08:02:04.954099 31616 solver.cpp:218] Iteration 2808 (2.39523 iter/s, 5.00995s/12 iters), loss = 3.96735
I0408 08:02:04.954133 31616 solver.cpp:237]     Train net output #0: loss = 3.96735 (* 1 = 3.96735 loss)
I0408 08:02:04.954140 31616 sgd_solver.cpp:105] Iteration 2808, lr = 0.00114015
I0408 08:02:10.094741 31616 solver.cpp:218] Iteration 2820 (2.33444 iter/s, 5.14042s/12 iters), loss = 3.92424
I0408 08:02:10.094784 31616 solver.cpp:237]     Train net output #0: loss = 3.92424 (* 1 = 3.92424 loss)
I0408 08:02:10.094791 31616 sgd_solver.cpp:105] Iteration 2820, lr = 0.00111856
I0408 08:02:14.794780 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:02:15.081473 31616 solver.cpp:218] Iteration 2832 (2.40649 iter/s, 4.98652s/12 iters), loss = 3.95135
I0408 08:02:15.081521 31616 solver.cpp:237]     Train net output #0: loss = 3.95135 (* 1 = 3.95135 loss)
I0408 08:02:15.081533 31616 sgd_solver.cpp:105] Iteration 2832, lr = 0.00109737
I0408 08:02:20.086261 31616 solver.cpp:218] Iteration 2844 (2.39781 iter/s, 5.00457s/12 iters), loss = 3.93281
I0408 08:02:20.086298 31616 solver.cpp:237]     Train net output #0: loss = 3.93281 (* 1 = 3.93281 loss)
I0408 08:02:20.086306 31616 sgd_solver.cpp:105] Iteration 2844, lr = 0.00107659
I0408 08:02:24.645660 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0408 08:02:27.666152 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0408 08:02:29.992784 31616 solver.cpp:330] Iteration 2856, Testing net (#0)
I0408 08:02:29.992810 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:02:33.324308 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:02:34.472738 31616 solver.cpp:397]     Test net output #0: accuracy = 0.0980392
I0408 08:02:34.472784 31616 solver.cpp:397]     Test net output #1: loss = 4.18163 (* 1 = 4.18163 loss)
I0408 08:02:34.565032 31616 solver.cpp:218] Iteration 2856 (0.828829 iter/s, 14.4783s/12 iters), loss = 3.85297
I0408 08:02:34.565076 31616 solver.cpp:237]     Train net output #0: loss = 3.85297 (* 1 = 3.85297 loss)
I0408 08:02:34.565088 31616 sgd_solver.cpp:105] Iteration 2856, lr = 0.0010562
I0408 08:02:39.120304 31616 solver.cpp:218] Iteration 2868 (2.63443 iter/s, 4.55507s/12 iters), loss = 4.00851
I0408 08:02:39.120352 31616 solver.cpp:237]     Train net output #0: loss = 4.00851 (* 1 = 4.00851 loss)
I0408 08:02:39.120362 31616 sgd_solver.cpp:105] Iteration 2868, lr = 0.0010362
I0408 08:02:44.161023 31616 solver.cpp:218] Iteration 2880 (2.38072 iter/s, 5.0405s/12 iters), loss = 3.71859
I0408 08:02:44.161084 31616 solver.cpp:237]     Train net output #0: loss = 3.71859 (* 1 = 3.71859 loss)
I0408 08:02:44.161099 31616 sgd_solver.cpp:105] Iteration 2880, lr = 0.00101658
I0408 08:02:49.581073 31616 solver.cpp:218] Iteration 2892 (2.2141 iter/s, 5.41981s/12 iters), loss = 4.13988
I0408 08:02:49.581176 31616 solver.cpp:237]     Train net output #0: loss = 4.13988 (* 1 = 4.13988 loss)
I0408 08:02:49.581187 31616 sgd_solver.cpp:105] Iteration 2892, lr = 0.000997325
I0408 08:02:54.809819 31616 solver.cpp:218] Iteration 2904 (2.29513 iter/s, 5.22847s/12 iters), loss = 4.005
I0408 08:02:54.809859 31616 solver.cpp:237]     Train net output #0: loss = 4.005 (* 1 = 4.005 loss)
I0408 08:02:54.809866 31616 sgd_solver.cpp:105] Iteration 2904, lr = 0.000978438
I0408 08:02:59.813423 31616 solver.cpp:218] Iteration 2916 (2.39837 iter/s, 5.0034s/12 iters), loss = 3.9892
I0408 08:02:59.813460 31616 solver.cpp:237]     Train net output #0: loss = 3.9892 (* 1 = 3.9892 loss)
I0408 08:02:59.813468 31616 sgd_solver.cpp:105] Iteration 2916, lr = 0.000959908
I0408 08:03:04.786926 31616 solver.cpp:218] Iteration 2928 (2.41289 iter/s, 4.97329s/12 iters), loss = 3.85898
I0408 08:03:04.786962 31616 solver.cpp:237]     Train net output #0: loss = 3.85898 (* 1 = 3.85898 loss)
I0408 08:03:04.786970 31616 sgd_solver.cpp:105] Iteration 2928, lr = 0.000941729
I0408 08:03:06.601087 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:03:09.695272 31616 solver.cpp:218] Iteration 2940 (2.44492 iter/s, 4.90814s/12 iters), loss = 3.89527
I0408 08:03:09.695307 31616 solver.cpp:237]     Train net output #0: loss = 3.89527 (* 1 = 3.89527 loss)
I0408 08:03:09.695315 31616 sgd_solver.cpp:105] Iteration 2940, lr = 0.000923895
I0408 08:03:14.677645 31616 solver.cpp:218] Iteration 2952 (2.40859 iter/s, 4.98216s/12 iters), loss = 3.91243
I0408 08:03:14.677682 31616 solver.cpp:237]     Train net output #0: loss = 3.91243 (* 1 = 3.91243 loss)
I0408 08:03:14.677691 31616 sgd_solver.cpp:105] Iteration 2952, lr = 0.000906398
I0408 08:03:16.718639 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0408 08:03:19.737541 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0408 08:03:22.060817 31616 solver.cpp:330] Iteration 2958, Testing net (#0)
I0408 08:03:22.060843 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:03:25.340231 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:03:26.524425 31616 solver.cpp:397]     Test net output #0: accuracy = 0.103554
I0408 08:03:26.524473 31616 solver.cpp:397]     Test net output #1: loss = 4.13897 (* 1 = 4.13897 loss)
I0408 08:03:28.359957 31616 solver.cpp:218] Iteration 2964 (0.877076 iter/s, 13.6818s/12 iters), loss = 3.53113
I0408 08:03:28.360008 31616 solver.cpp:237]     Train net output #0: loss = 3.53113 (* 1 = 3.53113 loss)
I0408 08:03:28.360018 31616 sgd_solver.cpp:105] Iteration 2964, lr = 0.000889232
I0408 08:03:33.392971 31616 solver.cpp:218] Iteration 2976 (2.38436 iter/s, 5.03279s/12 iters), loss = 3.76324
I0408 08:03:33.393013 31616 solver.cpp:237]     Train net output #0: loss = 3.76324 (* 1 = 3.76324 loss)
I0408 08:03:33.393023 31616 sgd_solver.cpp:105] Iteration 2976, lr = 0.000872392
I0408 08:03:38.408035 31616 solver.cpp:218] Iteration 2988 (2.39289 iter/s, 5.01485s/12 iters), loss = 4.02781
I0408 08:03:38.408075 31616 solver.cpp:237]     Train net output #0: loss = 4.02781 (* 1 = 4.02781 loss)
I0408 08:03:38.408084 31616 sgd_solver.cpp:105] Iteration 2988, lr = 0.00085587
I0408 08:03:43.418810 31616 solver.cpp:218] Iteration 3000 (2.39494 iter/s, 5.01057s/12 iters), loss = 3.90833
I0408 08:03:43.418848 31616 solver.cpp:237]     Train net output #0: loss = 3.90833 (* 1 = 3.90833 loss)
I0408 08:03:43.418856 31616 sgd_solver.cpp:105] Iteration 3000, lr = 0.000839662
I0408 08:03:48.453606 31616 solver.cpp:218] Iteration 3012 (2.38351 iter/s, 5.03458s/12 iters), loss = 3.85333
I0408 08:03:48.453646 31616 solver.cpp:237]     Train net output #0: loss = 3.85333 (* 1 = 3.85333 loss)
I0408 08:03:48.453656 31616 sgd_solver.cpp:105] Iteration 3012, lr = 0.00082376
I0408 08:03:53.490175 31616 solver.cpp:218] Iteration 3024 (2.38268 iter/s, 5.03636s/12 iters), loss = 3.82716
I0408 08:03:53.490281 31616 solver.cpp:237]     Train net output #0: loss = 3.82716 (* 1 = 3.82716 loss)
I0408 08:03:53.490295 31616 sgd_solver.cpp:105] Iteration 3024, lr = 0.00080816
I0408 08:03:57.507783 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:03:58.546654 31616 solver.cpp:218] Iteration 3036 (2.37332 iter/s, 5.0562s/12 iters), loss = 3.74511
I0408 08:03:58.546700 31616 solver.cpp:237]     Train net output #0: loss = 3.74511 (* 1 = 3.74511 loss)
I0408 08:03:58.546712 31616 sgd_solver.cpp:105] Iteration 3036, lr = 0.000792855
I0408 08:04:03.599668 31616 solver.cpp:218] Iteration 3048 (2.37492 iter/s, 5.05279s/12 iters), loss = 3.92943
I0408 08:04:03.599720 31616 solver.cpp:237]     Train net output #0: loss = 3.92943 (* 1 = 3.92943 loss)
I0408 08:04:03.599732 31616 sgd_solver.cpp:105] Iteration 3048, lr = 0.00077784
I0408 08:04:08.161221 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0408 08:04:11.149196 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0408 08:04:13.463227 31616 solver.cpp:330] Iteration 3060, Testing net (#0)
I0408 08:04:13.463253 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:04:16.711464 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:04:17.931354 31616 solver.cpp:397]     Test net output #0: accuracy = 0.112745
I0408 08:04:17.931385 31616 solver.cpp:397]     Test net output #1: loss = 4.09621 (* 1 = 4.09621 loss)
I0408 08:04:18.022279 31616 solver.cpp:218] Iteration 3060 (0.832057 iter/s, 14.4221s/12 iters), loss = 3.81445
I0408 08:04:18.022317 31616 solver.cpp:237]     Train net output #0: loss = 3.81445 (* 1 = 3.81445 loss)
I0408 08:04:18.022326 31616 sgd_solver.cpp:105] Iteration 3060, lr = 0.000763109
I0408 08:04:22.395367 31616 solver.cpp:218] Iteration 3072 (2.74417 iter/s, 4.3729s/12 iters), loss = 3.88502
I0408 08:04:22.395402 31616 solver.cpp:237]     Train net output #0: loss = 3.88502 (* 1 = 3.88502 loss)
I0408 08:04:22.395411 31616 sgd_solver.cpp:105] Iteration 3072, lr = 0.000748657
I0408 08:04:27.426096 31616 solver.cpp:218] Iteration 3084 (2.38544 iter/s, 5.03051s/12 iters), loss = 3.80124
I0408 08:04:27.426249 31616 solver.cpp:237]     Train net output #0: loss = 3.80124 (* 1 = 3.80124 loss)
I0408 08:04:27.426261 31616 sgd_solver.cpp:105] Iteration 3084, lr = 0.000734479
I0408 08:04:32.438549 31616 solver.cpp:218] Iteration 3096 (2.39419 iter/s, 5.01213s/12 iters), loss = 3.75061
I0408 08:04:32.438589 31616 solver.cpp:237]     Train net output #0: loss = 3.75061 (* 1 = 3.75061 loss)
I0408 08:04:32.438601 31616 sgd_solver.cpp:105] Iteration 3096, lr = 0.000720569
I0408 08:04:37.414170 31616 solver.cpp:218] Iteration 3108 (2.41186 iter/s, 4.97541s/12 iters), loss = 3.80979
I0408 08:04:37.414206 31616 solver.cpp:237]     Train net output #0: loss = 3.80979 (* 1 = 3.80979 loss)
I0408 08:04:37.414213 31616 sgd_solver.cpp:105] Iteration 3108, lr = 0.000706923
I0408 08:04:42.555474 31616 solver.cpp:218] Iteration 3120 (2.33414 iter/s, 5.14109s/12 iters), loss = 3.57515
I0408 08:04:42.555521 31616 solver.cpp:237]     Train net output #0: loss = 3.57515 (* 1 = 3.57515 loss)
I0408 08:04:42.555532 31616 sgd_solver.cpp:105] Iteration 3120, lr = 0.000693535
I0408 08:04:47.602990 31616 solver.cpp:218] Iteration 3132 (2.37751 iter/s, 5.0473s/12 iters), loss = 3.94757
I0408 08:04:47.603039 31616 solver.cpp:237]     Train net output #0: loss = 3.94757 (* 1 = 3.94757 loss)
I0408 08:04:47.603049 31616 sgd_solver.cpp:105] Iteration 3132, lr = 0.000680401
I0408 08:04:48.734793 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:04:52.649073 31616 solver.cpp:218] Iteration 3144 (2.37819 iter/s, 5.04586s/12 iters), loss = 3.57563
I0408 08:04:52.649119 31616 solver.cpp:237]     Train net output #0: loss = 3.57563 (* 1 = 3.57563 loss)
I0408 08:04:52.649129 31616 sgd_solver.cpp:105] Iteration 3144, lr = 0.000667516
I0408 08:04:57.668869 31616 solver.cpp:218] Iteration 3156 (2.39064 iter/s, 5.01958s/12 iters), loss = 3.69919
I0408 08:04:57.668998 31616 solver.cpp:237]     Train net output #0: loss = 3.69919 (* 1 = 3.69919 loss)
I0408 08:04:57.669010 31616 sgd_solver.cpp:105] Iteration 3156, lr = 0.000654874
I0408 08:04:59.649837 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0408 08:05:02.776257 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0408 08:05:05.103051 31616 solver.cpp:330] Iteration 3162, Testing net (#0)
I0408 08:05:05.103077 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:05:08.300787 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:05:09.567469 31616 solver.cpp:397]     Test net output #0: accuracy = 0.107843
I0408 08:05:09.567514 31616 solver.cpp:397]     Test net output #1: loss = 4.07963 (* 1 = 4.07963 loss)
I0408 08:05:11.570698 31616 solver.cpp:218] Iteration 3168 (0.863231 iter/s, 13.9013s/12 iters), loss = 3.69168
I0408 08:05:11.570736 31616 solver.cpp:237]     Train net output #0: loss = 3.69168 (* 1 = 3.69168 loss)
I0408 08:05:11.570745 31616 sgd_solver.cpp:105] Iteration 3168, lr = 0.000642472
I0408 08:05:16.589541 31616 solver.cpp:218] Iteration 3180 (2.39109 iter/s, 5.01863s/12 iters), loss = 3.90691
I0408 08:05:16.589588 31616 solver.cpp:237]     Train net output #0: loss = 3.90691 (* 1 = 3.90691 loss)
I0408 08:05:16.589601 31616 sgd_solver.cpp:105] Iteration 3180, lr = 0.000630305
I0408 08:05:21.656119 31616 solver.cpp:218] Iteration 3192 (2.36856 iter/s, 5.06636s/12 iters), loss = 3.74242
I0408 08:05:21.656158 31616 solver.cpp:237]     Train net output #0: loss = 3.74242 (* 1 = 3.74242 loss)
I0408 08:05:21.656168 31616 sgd_solver.cpp:105] Iteration 3192, lr = 0.000618368
I0408 08:05:27.145923 31616 solver.cpp:218] Iteration 3204 (2.18596 iter/s, 5.48958s/12 iters), loss = 3.87437
I0408 08:05:27.145965 31616 solver.cpp:237]     Train net output #0: loss = 3.87437 (* 1 = 3.87437 loss)
I0408 08:05:27.145973 31616 sgd_solver.cpp:105] Iteration 3204, lr = 0.000606658
I0408 08:05:32.153581 31616 solver.cpp:218] Iteration 3216 (2.39643 iter/s, 5.00745s/12 iters), loss = 3.71989
I0408 08:05:32.153699 31616 solver.cpp:237]     Train net output #0: loss = 3.71989 (* 1 = 3.71989 loss)
I0408 08:05:32.153709 31616 sgd_solver.cpp:105] Iteration 3216, lr = 0.000595169
I0408 08:05:37.123462 31616 solver.cpp:218] Iteration 3228 (2.41468 iter/s, 4.96959s/12 iters), loss = 3.8041
I0408 08:05:37.123502 31616 solver.cpp:237]     Train net output #0: loss = 3.8041 (* 1 = 3.8041 loss)
I0408 08:05:37.123509 31616 sgd_solver.cpp:105] Iteration 3228, lr = 0.000583897
I0408 08:05:40.320792 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:05:42.059391 31616 solver.cpp:218] Iteration 3240 (2.43126 iter/s, 4.93572s/12 iters), loss = 4.00916
I0408 08:05:42.059427 31616 solver.cpp:237]     Train net output #0: loss = 4.00916 (* 1 = 4.00916 loss)
I0408 08:05:42.059433 31616 sgd_solver.cpp:105] Iteration 3240, lr = 0.000572839
I0408 08:05:47.082572 31616 solver.cpp:218] Iteration 3252 (2.38902 iter/s, 5.02297s/12 iters), loss = 3.58321
I0408 08:05:47.082612 31616 solver.cpp:237]     Train net output #0: loss = 3.58321 (* 1 = 3.58321 loss)
I0408 08:05:47.082619 31616 sgd_solver.cpp:105] Iteration 3252, lr = 0.000561991
I0408 08:05:51.624866 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0408 08:05:54.563872 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0408 08:05:56.852632 31616 solver.cpp:330] Iteration 3264, Testing net (#0)
I0408 08:05:56.852653 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:06:00.022775 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:06:01.325923 31616 solver.cpp:397]     Test net output #0: accuracy = 0.109681
I0408 08:06:01.325984 31616 solver.cpp:397]     Test net output #1: loss = 4.06159 (* 1 = 4.06159 loss)
I0408 08:06:01.417243 31616 solver.cpp:218] Iteration 3264 (0.837161 iter/s, 14.3342s/12 iters), loss = 3.69041
I0408 08:06:01.417291 31616 solver.cpp:237]     Train net output #0: loss = 3.69041 (* 1 = 3.69041 loss)
I0408 08:06:01.417302 31616 sgd_solver.cpp:105] Iteration 3264, lr = 0.000551348
I0408 08:06:05.969991 31616 solver.cpp:218] Iteration 3276 (2.63589 iter/s, 4.55254s/12 iters), loss = 3.56373
I0408 08:06:05.970103 31616 solver.cpp:237]     Train net output #0: loss = 3.56373 (* 1 = 3.56373 loss)
I0408 08:06:05.970115 31616 sgd_solver.cpp:105] Iteration 3276, lr = 0.000540906
I0408 08:06:11.129606 31616 solver.cpp:218] Iteration 3288 (2.32589 iter/s, 5.15932s/12 iters), loss = 3.62456
I0408 08:06:11.129664 31616 solver.cpp:237]     Train net output #0: loss = 3.62456 (* 1 = 3.62456 loss)
I0408 08:06:11.129676 31616 sgd_solver.cpp:105] Iteration 3288, lr = 0.000530663
I0408 08:06:16.129978 31616 solver.cpp:218] Iteration 3300 (2.39993 iter/s, 5.00015s/12 iters), loss = 3.85881
I0408 08:06:16.130017 31616 solver.cpp:237]     Train net output #0: loss = 3.85881 (* 1 = 3.85881 loss)
I0408 08:06:16.130026 31616 sgd_solver.cpp:105] Iteration 3300, lr = 0.000520613
I0408 08:06:21.182337 31616 solver.cpp:218] Iteration 3312 (2.37523 iter/s, 5.05215s/12 iters), loss = 3.90797
I0408 08:06:21.182381 31616 solver.cpp:237]     Train net output #0: loss = 3.90797 (* 1 = 3.90797 loss)
I0408 08:06:21.182394 31616 sgd_solver.cpp:105] Iteration 3312, lr = 0.000510753
I0408 08:06:26.250433 31616 solver.cpp:218] Iteration 3324 (2.36785 iter/s, 5.06788s/12 iters), loss = 3.68278
I0408 08:06:26.250474 31616 solver.cpp:237]     Train net output #0: loss = 3.68278 (* 1 = 3.68278 loss)
I0408 08:06:26.250484 31616 sgd_solver.cpp:105] Iteration 3324, lr = 0.000501081
I0408 08:06:31.235785 31616 solver.cpp:218] Iteration 3336 (2.40716 iter/s, 4.98514s/12 iters), loss = 3.73479
I0408 08:06:31.235823 31616 solver.cpp:237]     Train net output #0: loss = 3.73479 (* 1 = 3.73479 loss)
I0408 08:06:31.235833 31616 sgd_solver.cpp:105] Iteration 3336, lr = 0.000491591
I0408 08:06:31.715585 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:06:36.277113 31616 solver.cpp:218] Iteration 3348 (2.38043 iter/s, 5.04112s/12 iters), loss = 3.64489
I0408 08:06:36.277251 31616 solver.cpp:237]     Train net output #0: loss = 3.64489 (* 1 = 3.64489 loss)
I0408 08:06:36.277266 31616 sgd_solver.cpp:105] Iteration 3348, lr = 0.000482281
I0408 08:06:41.344807 31616 solver.cpp:218] Iteration 3360 (2.36809 iter/s, 5.06738s/12 iters), loss = 3.57084
I0408 08:06:41.344859 31616 solver.cpp:237]     Train net output #0: loss = 3.57084 (* 1 = 3.57084 loss)
I0408 08:06:41.344871 31616 sgd_solver.cpp:105] Iteration 3360, lr = 0.000473148
I0408 08:06:43.348273 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0408 08:06:46.372313 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0408 08:06:48.752211 31616 solver.cpp:330] Iteration 3366, Testing net (#0)
I0408 08:06:48.752238 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:06:51.878132 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:06:53.218048 31616 solver.cpp:397]     Test net output #0: accuracy = 0.117034
I0408 08:06:53.218096 31616 solver.cpp:397]     Test net output #1: loss = 4.04044 (* 1 = 4.04044 loss)
I0408 08:06:55.121011 31616 solver.cpp:218] Iteration 3372 (0.871099 iter/s, 13.7757s/12 iters), loss = 3.56951
I0408 08:06:55.121059 31616 solver.cpp:237]     Train net output #0: loss = 3.56951 (* 1 = 3.56951 loss)
I0408 08:06:55.121071 31616 sgd_solver.cpp:105] Iteration 3372, lr = 0.000464187
I0408 08:07:00.104526 31616 solver.cpp:218] Iteration 3384 (2.40805 iter/s, 4.98329s/12 iters), loss = 3.61781
I0408 08:07:00.104573 31616 solver.cpp:237]     Train net output #0: loss = 3.61781 (* 1 = 3.61781 loss)
I0408 08:07:00.104583 31616 sgd_solver.cpp:105] Iteration 3384, lr = 0.000455397
I0408 08:07:05.131309 31616 solver.cpp:218] Iteration 3396 (2.38732 iter/s, 5.02656s/12 iters), loss = 3.63093
I0408 08:07:05.131356 31616 solver.cpp:237]     Train net output #0: loss = 3.63093 (* 1 = 3.63093 loss)
I0408 08:07:05.131368 31616 sgd_solver.cpp:105] Iteration 3396, lr = 0.000446772
I0408 08:07:10.136662 31616 solver.cpp:218] Iteration 3408 (2.39754 iter/s, 5.00513s/12 iters), loss = 3.75059
I0408 08:07:10.136770 31616 solver.cpp:237]     Train net output #0: loss = 3.75059 (* 1 = 3.75059 loss)
I0408 08:07:10.136783 31616 sgd_solver.cpp:105] Iteration 3408, lr = 0.000438311
I0408 08:07:15.181547 31616 solver.cpp:218] Iteration 3420 (2.37878 iter/s, 5.04461s/12 iters), loss = 3.36372
I0408 08:07:15.181591 31616 solver.cpp:237]     Train net output #0: loss = 3.36372 (* 1 = 3.36372 loss)
I0408 08:07:15.181602 31616 sgd_solver.cpp:105] Iteration 3420, lr = 0.000430011
I0408 08:07:20.208076 31616 solver.cpp:218] Iteration 3432 (2.38743 iter/s, 5.02632s/12 iters), loss = 3.72446
I0408 08:07:20.208110 31616 solver.cpp:237]     Train net output #0: loss = 3.72446 (* 1 = 3.72446 loss)
I0408 08:07:20.208118 31616 sgd_solver.cpp:105] Iteration 3432, lr = 0.000421867
I0408 08:07:22.805893 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:07:25.236590 31616 solver.cpp:218] Iteration 3444 (2.38649 iter/s, 5.02831s/12 iters), loss = 3.4019
I0408 08:07:25.236624 31616 solver.cpp:237]     Train net output #0: loss = 3.4019 (* 1 = 3.4019 loss)
I0408 08:07:25.236631 31616 sgd_solver.cpp:105] Iteration 3444, lr = 0.000413878
I0408 08:07:30.377765 31616 solver.cpp:218] Iteration 3456 (2.33419 iter/s, 5.14096s/12 iters), loss = 3.52959
I0408 08:07:30.377805 31616 solver.cpp:237]     Train net output #0: loss = 3.52959 (* 1 = 3.52959 loss)
I0408 08:07:30.377815 31616 sgd_solver.cpp:105] Iteration 3456, lr = 0.00040604
I0408 08:07:34.952911 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0408 08:07:37.970567 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0408 08:07:40.299139 31616 solver.cpp:330] Iteration 3468, Testing net (#0)
I0408 08:07:40.299254 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:07:40.738548 31616 blocking_queue.cpp:49] Waiting for data
I0408 08:07:43.346483 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:07:44.733256 31616 solver.cpp:397]     Test net output #0: accuracy = 0.124387
I0408 08:07:44.733299 31616 solver.cpp:397]     Test net output #1: loss = 4.0212 (* 1 = 4.0212 loss)
I0408 08:07:44.824702 31616 solver.cpp:218] Iteration 3468 (0.830656 iter/s, 14.4464s/12 iters), loss = 3.60182
I0408 08:07:44.824748 31616 solver.cpp:237]     Train net output #0: loss = 3.60182 (* 1 = 3.60182 loss)
I0408 08:07:44.824756 31616 sgd_solver.cpp:105] Iteration 3468, lr = 0.00039835
I0408 08:07:49.066082 31616 solver.cpp:218] Iteration 3480 (2.8294 iter/s, 4.24119s/12 iters), loss = 3.64588
I0408 08:07:49.066124 31616 solver.cpp:237]     Train net output #0: loss = 3.64588 (* 1 = 3.64588 loss)
I0408 08:07:49.066133 31616 sgd_solver.cpp:105] Iteration 3480, lr = 0.000390806
I0408 08:07:54.070433 31616 solver.cpp:218] Iteration 3492 (2.39802 iter/s, 5.00414s/12 iters), loss = 3.83344
I0408 08:07:54.070477 31616 solver.cpp:237]     Train net output #0: loss = 3.83344 (* 1 = 3.83344 loss)
I0408 08:07:54.070487 31616 sgd_solver.cpp:105] Iteration 3492, lr = 0.000383405
I0408 08:07:59.080163 31616 solver.cpp:218] Iteration 3504 (2.39544 iter/s, 5.00951s/12 iters), loss = 3.51601
I0408 08:07:59.080209 31616 solver.cpp:237]     Train net output #0: loss = 3.51601 (* 1 = 3.51601 loss)
I0408 08:07:59.080219 31616 sgd_solver.cpp:105] Iteration 3504, lr = 0.000376144
I0408 08:08:04.106724 31616 solver.cpp:218] Iteration 3516 (2.38742 iter/s, 5.02635s/12 iters), loss = 3.47697
I0408 08:08:04.106760 31616 solver.cpp:237]     Train net output #0: loss = 3.47697 (* 1 = 3.47697 loss)
I0408 08:08:04.106768 31616 sgd_solver.cpp:105] Iteration 3516, lr = 0.00036902
I0408 08:08:09.189931 31616 solver.cpp:218] Iteration 3528 (2.36081 iter/s, 5.083s/12 iters), loss = 3.6246
I0408 08:08:09.189972 31616 solver.cpp:237]     Train net output #0: loss = 3.6246 (* 1 = 3.6246 loss)
I0408 08:08:09.189981 31616 sgd_solver.cpp:105] Iteration 3528, lr = 0.000362032
I0408 08:08:13.965139 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:08:14.225734 31616 solver.cpp:218] Iteration 3540 (2.38304 iter/s, 5.03559s/12 iters), loss = 3.44248
I0408 08:08:14.225776 31616 solver.cpp:237]     Train net output #0: loss = 3.44248 (* 1 = 3.44248 loss)
I0408 08:08:14.225786 31616 sgd_solver.cpp:105] Iteration 3540, lr = 0.000355176
I0408 08:08:19.266021 31616 solver.cpp:218] Iteration 3552 (2.38092 iter/s, 5.04007s/12 iters), loss = 3.63079
I0408 08:08:19.266067 31616 solver.cpp:237]     Train net output #0: loss = 3.63079 (* 1 = 3.63079 loss)
I0408 08:08:19.266078 31616 sgd_solver.cpp:105] Iteration 3552, lr = 0.000348449
I0408 08:08:24.236243 31616 solver.cpp:218] Iteration 3564 (2.41448 iter/s, 4.97001s/12 iters), loss = 3.49816
I0408 08:08:24.236279 31616 solver.cpp:237]     Train net output #0: loss = 3.49816 (* 1 = 3.49816 loss)
I0408 08:08:24.236287 31616 sgd_solver.cpp:105] Iteration 3564, lr = 0.00034185
I0408 08:08:26.286280 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0408 08:08:29.305879 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0408 08:08:31.672600 31616 solver.cpp:330] Iteration 3570, Testing net (#0)
I0408 08:08:31.672626 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:08:34.798038 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:08:36.213928 31616 solver.cpp:397]     Test net output #0: accuracy = 0.127451
I0408 08:08:36.213990 31616 solver.cpp:397]     Test net output #1: loss = 4.00105 (* 1 = 4.00105 loss)
I0408 08:08:38.091349 31616 solver.cpp:218] Iteration 3576 (0.866137 iter/s, 13.8546s/12 iters), loss = 3.52731
I0408 08:08:38.091398 31616 solver.cpp:237]     Train net output #0: loss = 3.52731 (* 1 = 3.52731 loss)
I0408 08:08:38.091408 31616 sgd_solver.cpp:105] Iteration 3576, lr = 0.000335376
I0408 08:08:43.124406 31616 solver.cpp:218] Iteration 3588 (2.38434 iter/s, 5.03284s/12 iters), loss = 3.36639
I0408 08:08:43.124441 31616 solver.cpp:237]     Train net output #0: loss = 3.36639 (* 1 = 3.36639 loss)
I0408 08:08:43.124449 31616 sgd_solver.cpp:105] Iteration 3588, lr = 0.000329025
I0408 08:08:48.160398 31616 solver.cpp:218] Iteration 3600 (2.38294 iter/s, 5.03579s/12 iters), loss = 3.57174
I0408 08:08:48.160516 31616 solver.cpp:237]     Train net output #0: loss = 3.57174 (* 1 = 3.57174 loss)
I0408 08:08:48.160526 31616 sgd_solver.cpp:105] Iteration 3600, lr = 0.000322794
I0408 08:08:53.159919 31616 solver.cpp:218] Iteration 3612 (2.40037 iter/s, 4.99924s/12 iters), loss = 3.57897
I0408 08:08:53.159948 31616 solver.cpp:237]     Train net output #0: loss = 3.57897 (* 1 = 3.57897 loss)
I0408 08:08:53.159955 31616 sgd_solver.cpp:105] Iteration 3612, lr = 0.000316681
I0408 08:08:58.168148 31616 solver.cpp:218] Iteration 3624 (2.39615 iter/s, 5.00803s/12 iters), loss = 3.56843
I0408 08:08:58.168191 31616 solver.cpp:237]     Train net output #0: loss = 3.56843 (* 1 = 3.56843 loss)
I0408 08:08:58.168201 31616 sgd_solver.cpp:105] Iteration 3624, lr = 0.000310684
I0408 08:09:03.122742 31616 solver.cpp:218] Iteration 3636 (2.4221 iter/s, 4.95438s/12 iters), loss = 3.58727
I0408 08:09:03.122786 31616 solver.cpp:237]     Train net output #0: loss = 3.58727 (* 1 = 3.58727 loss)
I0408 08:09:03.122795 31616 sgd_solver.cpp:105] Iteration 3636, lr = 0.0003048
I0408 08:09:05.023947 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:09:08.176728 31616 solver.cpp:218] Iteration 3648 (2.37447 iter/s, 5.05377s/12 iters), loss = 3.46391
I0408 08:09:08.176774 31616 solver.cpp:237]     Train net output #0: loss = 3.46391 (* 1 = 3.46391 loss)
I0408 08:09:08.176786 31616 sgd_solver.cpp:105] Iteration 3648, lr = 0.000299027
I0408 08:09:13.183192 31616 solver.cpp:218] Iteration 3660 (2.397 iter/s, 5.00625s/12 iters), loss = 3.51722
I0408 08:09:13.183229 31616 solver.cpp:237]     Train net output #0: loss = 3.51722 (* 1 = 3.51722 loss)
I0408 08:09:13.183238 31616 sgd_solver.cpp:105] Iteration 3660, lr = 0.000293364
I0408 08:09:17.697124 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0408 08:09:20.861346 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0408 08:09:23.280462 31616 solver.cpp:330] Iteration 3672, Testing net (#0)
I0408 08:09:23.280489 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:09:26.281801 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:09:27.743927 31616 solver.cpp:397]     Test net output #0: accuracy = 0.13174
I0408 08:09:27.743974 31616 solver.cpp:397]     Test net output #1: loss = 3.98292 (* 1 = 3.98292 loss)
I0408 08:09:27.834777 31616 solver.cpp:218] Iteration 3672 (0.819053 iter/s, 14.6511s/12 iters), loss = 3.20263
I0408 08:09:27.834820 31616 solver.cpp:237]     Train net output #0: loss = 3.20263 (* 1 = 3.20263 loss)
I0408 08:09:27.834830 31616 sgd_solver.cpp:105] Iteration 3672, lr = 0.000287809
I0408 08:09:32.059854 31616 solver.cpp:218] Iteration 3684 (2.84031 iter/s, 4.22489s/12 iters), loss = 3.7122
I0408 08:09:32.059902 31616 solver.cpp:237]     Train net output #0: loss = 3.7122 (* 1 = 3.7122 loss)
I0408 08:09:32.059916 31616 sgd_solver.cpp:105] Iteration 3684, lr = 0.000282358
I0408 08:09:37.084518 31616 solver.cpp:218] Iteration 3696 (2.38832 iter/s, 5.02445s/12 iters), loss = 3.51259
I0408 08:09:37.084561 31616 solver.cpp:237]     Train net output #0: loss = 3.51259 (* 1 = 3.51259 loss)
I0408 08:09:37.084573 31616 sgd_solver.cpp:105] Iteration 3696, lr = 0.000277011
I0408 08:09:42.089241 31616 solver.cpp:218] Iteration 3708 (2.39784 iter/s, 5.00451s/12 iters), loss = 3.49912
I0408 08:09:42.089288 31616 solver.cpp:237]     Train net output #0: loss = 3.49912 (* 1 = 3.49912 loss)
I0408 08:09:42.089299 31616 sgd_solver.cpp:105] Iteration 3708, lr = 0.000271765
I0408 08:09:47.067802 31616 solver.cpp:218] Iteration 3720 (2.41044 iter/s, 4.97835s/12 iters), loss = 3.53722
I0408 08:09:47.067836 31616 solver.cpp:237]     Train net output #0: loss = 3.53722 (* 1 = 3.53722 loss)
I0408 08:09:47.067843 31616 sgd_solver.cpp:105] Iteration 3720, lr = 0.000266618
I0408 08:09:52.018519 31616 solver.cpp:218] Iteration 3732 (2.42399 iter/s, 4.95051s/12 iters), loss = 3.31853
I0408 08:09:52.018649 31616 solver.cpp:237]     Train net output #0: loss = 3.31853 (* 1 = 3.31853 loss)
I0408 08:09:52.018658 31616 sgd_solver.cpp:105] Iteration 3732, lr = 0.000261569
I0408 08:09:56.045289 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:09:57.055292 31616 solver.cpp:218] Iteration 3744 (2.38262 iter/s, 5.03647s/12 iters), loss = 3.48082
I0408 08:09:57.055337 31616 solver.cpp:237]     Train net output #0: loss = 3.48082 (* 1 = 3.48082 loss)
I0408 08:09:57.055349 31616 sgd_solver.cpp:105] Iteration 3744, lr = 0.000256615
I0408 08:10:02.146940 31616 solver.cpp:218] Iteration 3756 (2.3569 iter/s, 5.09143s/12 iters), loss = 3.63181
I0408 08:10:02.146982 31616 solver.cpp:237]     Train net output #0: loss = 3.63181 (* 1 = 3.63181 loss)
I0408 08:10:02.146993 31616 sgd_solver.cpp:105] Iteration 3756, lr = 0.000251755
I0408 08:10:07.239800 31616 solver.cpp:218] Iteration 3768 (2.35634 iter/s, 5.09264s/12 iters), loss = 3.37794
I0408 08:10:07.239845 31616 solver.cpp:237]     Train net output #0: loss = 3.37794 (* 1 = 3.37794 loss)
I0408 08:10:07.239856 31616 sgd_solver.cpp:105] Iteration 3768, lr = 0.000246988
I0408 08:10:09.256520 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0408 08:10:14.248198 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0408 08:10:16.552078 31616 solver.cpp:330] Iteration 3774, Testing net (#0)
I0408 08:10:16.552105 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:10:19.495391 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:10:20.993294 31616 solver.cpp:397]     Test net output #0: accuracy = 0.134191
I0408 08:10:20.993332 31616 solver.cpp:397]     Test net output #1: loss = 3.96484 (* 1 = 3.96484 loss)
I0408 08:10:22.815011 31616 solver.cpp:218] Iteration 3780 (0.770482 iter/s, 15.5747s/12 iters), loss = 3.58319
I0408 08:10:22.815107 31616 solver.cpp:237]     Train net output #0: loss = 3.58319 (* 1 = 3.58319 loss)
I0408 08:10:22.815117 31616 sgd_solver.cpp:105] Iteration 3780, lr = 0.00024231
I0408 08:10:27.822332 31616 solver.cpp:218] Iteration 3792 (2.39662 iter/s, 5.00705s/12 iters), loss = 3.45694
I0408 08:10:27.822376 31616 solver.cpp:237]     Train net output #0: loss = 3.45694 (* 1 = 3.45694 loss)
I0408 08:10:27.822386 31616 sgd_solver.cpp:105] Iteration 3792, lr = 0.000237721
I0408 08:10:32.830026 31616 solver.cpp:218] Iteration 3804 (2.39642 iter/s, 5.00748s/12 iters), loss = 3.58517
I0408 08:10:32.830072 31616 solver.cpp:237]     Train net output #0: loss = 3.58517 (* 1 = 3.58517 loss)
I0408 08:10:32.830085 31616 sgd_solver.cpp:105] Iteration 3804, lr = 0.000233219
I0408 08:10:37.861935 31616 solver.cpp:218] Iteration 3816 (2.38489 iter/s, 5.03169s/12 iters), loss = 3.39571
I0408 08:10:37.861994 31616 solver.cpp:237]     Train net output #0: loss = 3.39571 (* 1 = 3.39571 loss)
I0408 08:10:37.862006 31616 sgd_solver.cpp:105] Iteration 3816, lr = 0.000228803
I0408 08:10:42.853152 31616 solver.cpp:218] Iteration 3828 (2.40434 iter/s, 4.99098s/12 iters), loss = 3.33241
I0408 08:10:42.853200 31616 solver.cpp:237]     Train net output #0: loss = 3.33241 (* 1 = 3.33241 loss)
I0408 08:10:42.853212 31616 sgd_solver.cpp:105] Iteration 3828, lr = 0.000224469
I0408 08:10:47.838223 31616 solver.cpp:218] Iteration 3840 (2.40729 iter/s, 4.98485s/12 iters), loss = 3.57385
I0408 08:10:47.838276 31616 solver.cpp:237]     Train net output #0: loss = 3.57385 (* 1 = 3.57385 loss)
I0408 08:10:47.838289 31616 sgd_solver.cpp:105] Iteration 3840, lr = 0.000220218
I0408 08:10:48.949867 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:10:52.839192 31616 solver.cpp:218] Iteration 3852 (2.39964 iter/s, 5.00074s/12 iters), loss = 3.29988
I0408 08:10:52.839339 31616 solver.cpp:237]     Train net output #0: loss = 3.29988 (* 1 = 3.29988 loss)
I0408 08:10:52.839356 31616 sgd_solver.cpp:105] Iteration 3852, lr = 0.000216048
I0408 08:10:57.798527 31616 solver.cpp:218] Iteration 3864 (2.41983 iter/s, 4.95902s/12 iters), loss = 3.53863
I0408 08:10:57.798568 31616 solver.cpp:237]     Train net output #0: loss = 3.53863 (* 1 = 3.53863 loss)
I0408 08:10:57.798579 31616 sgd_solver.cpp:105] Iteration 3864, lr = 0.000211956
I0408 08:11:02.525147 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0408 08:11:10.031441 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0408 08:11:13.953583 31616 solver.cpp:330] Iteration 3876, Testing net (#0)
I0408 08:11:13.953603 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:11:16.752446 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:11:18.296878 31616 solver.cpp:397]     Test net output #0: accuracy = 0.139093
I0408 08:11:18.296923 31616 solver.cpp:397]     Test net output #1: loss = 3.95439 (* 1 = 3.95439 loss)
I0408 08:11:18.388177 31616 solver.cpp:218] Iteration 3876 (0.582837 iter/s, 20.5889s/12 iters), loss = 3.4522
I0408 08:11:18.388226 31616 solver.cpp:237]     Train net output #0: loss = 3.4522 (* 1 = 3.4522 loss)
I0408 08:11:18.388236 31616 sgd_solver.cpp:105] Iteration 3876, lr = 0.000207942
I0408 08:11:22.661990 31616 solver.cpp:218] Iteration 3888 (2.80793 iter/s, 4.27362s/12 iters), loss = 3.5474
I0408 08:11:22.662034 31616 solver.cpp:237]     Train net output #0: loss = 3.5474 (* 1 = 3.5474 loss)
I0408 08:11:22.662045 31616 sgd_solver.cpp:105] Iteration 3888, lr = 0.000204004
I0408 08:11:27.666864 31616 solver.cpp:218] Iteration 3900 (2.39777 iter/s, 5.00466s/12 iters), loss = 3.54762
I0408 08:11:27.666949 31616 solver.cpp:237]     Train net output #0: loss = 3.54762 (* 1 = 3.54762 loss)
I0408 08:11:27.666960 31616 sgd_solver.cpp:105] Iteration 3900, lr = 0.000200141
I0408 08:11:32.660637 31616 solver.cpp:218] Iteration 3912 (2.40311 iter/s, 4.99352s/12 iters), loss = 3.54788
I0408 08:11:32.660677 31616 solver.cpp:237]     Train net output #0: loss = 3.54788 (* 1 = 3.54788 loss)
I0408 08:11:32.660687 31616 sgd_solver.cpp:105] Iteration 3912, lr = 0.000196351
I0408 08:11:37.686533 31616 solver.cpp:218] Iteration 3924 (2.38773 iter/s, 5.02568s/12 iters), loss = 3.17756
I0408 08:11:37.686581 31616 solver.cpp:237]     Train net output #0: loss = 3.17756 (* 1 = 3.17756 loss)
I0408 08:11:37.686594 31616 sgd_solver.cpp:105] Iteration 3924, lr = 0.000192632
I0408 08:11:42.739367 31616 solver.cpp:218] Iteration 3936 (2.37501 iter/s, 5.05261s/12 iters), loss = 3.49678
I0408 08:11:42.739418 31616 solver.cpp:237]     Train net output #0: loss = 3.49678 (* 1 = 3.49678 loss)
I0408 08:11:42.739429 31616 sgd_solver.cpp:105] Iteration 3936, lr = 0.000188984
I0408 08:11:46.108940 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:11:47.752247 31616 solver.cpp:218] Iteration 3948 (2.39394 iter/s, 5.01266s/12 iters), loss = 3.77014
I0408 08:11:47.752295 31616 solver.cpp:237]     Train net output #0: loss = 3.77014 (* 1 = 3.77014 loss)
I0408 08:11:47.752308 31616 sgd_solver.cpp:105] Iteration 3948, lr = 0.000185405
I0408 08:11:52.775391 31616 solver.cpp:218] Iteration 3960 (2.38905 iter/s, 5.02293s/12 iters), loss = 3.39268
I0408 08:11:52.775439 31616 solver.cpp:237]     Train net output #0: loss = 3.39268 (* 1 = 3.39268 loss)
I0408 08:11:52.775450 31616 sgd_solver.cpp:105] Iteration 3960, lr = 0.000181894
I0408 08:11:57.786329 31616 solver.cpp:218] Iteration 3972 (2.39487 iter/s, 5.01072s/12 iters), loss = 3.45778
I0408 08:11:57.786486 31616 solver.cpp:237]     Train net output #0: loss = 3.45778 (* 1 = 3.45778 loss)
I0408 08:11:57.786500 31616 sgd_solver.cpp:105] Iteration 3972, lr = 0.000178449
I0408 08:11:59.842278 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0408 08:12:04.939494 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0408 08:12:07.261677 31616 solver.cpp:330] Iteration 3978, Testing net (#0)
I0408 08:12:07.261703 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:12:10.289059 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:12:11.877338 31616 solver.cpp:397]     Test net output #0: accuracy = 0.136642
I0408 08:12:11.877384 31616 solver.cpp:397]     Test net output #1: loss = 3.93338 (* 1 = 3.93338 loss)
I0408 08:12:13.786293 31616 solver.cpp:218] Iteration 3984 (0.750033 iter/s, 15.9993s/12 iters), loss = 3.3434
I0408 08:12:13.786348 31616 solver.cpp:237]     Train net output #0: loss = 3.3434 (* 1 = 3.3434 loss)
I0408 08:12:13.786362 31616 sgd_solver.cpp:105] Iteration 3984, lr = 0.00017507
I0408 08:12:18.887877 31616 solver.cpp:218] Iteration 3996 (2.35232 iter/s, 5.10136s/12 iters), loss = 3.46246
I0408 08:12:18.887923 31616 solver.cpp:237]     Train net output #0: loss = 3.46246 (* 1 = 3.46246 loss)
I0408 08:12:18.887935 31616 sgd_solver.cpp:105] Iteration 3996, lr = 0.000171754
I0408 08:12:24.114204 31616 solver.cpp:218] Iteration 4008 (2.29617 iter/s, 5.2261s/12 iters), loss = 3.4958
I0408 08:12:24.114248 31616 solver.cpp:237]     Train net output #0: loss = 3.4958 (* 1 = 3.4958 loss)
I0408 08:12:24.114259 31616 sgd_solver.cpp:105] Iteration 4008, lr = 0.000168501
I0408 08:12:29.313391 31616 solver.cpp:218] Iteration 4020 (2.30815 iter/s, 5.19896s/12 iters), loss = 3.60612
I0408 08:12:29.313469 31616 solver.cpp:237]     Train net output #0: loss = 3.60612 (* 1 = 3.60612 loss)
I0408 08:12:29.313478 31616 sgd_solver.cpp:105] Iteration 4020, lr = 0.00016531
I0408 08:12:34.327014 31616 solver.cpp:218] Iteration 4032 (2.3936 iter/s, 5.01337s/12 iters), loss = 3.63843
I0408 08:12:34.327056 31616 solver.cpp:237]     Train net output #0: loss = 3.63843 (* 1 = 3.63843 loss)
I0408 08:12:34.327065 31616 sgd_solver.cpp:105] Iteration 4032, lr = 0.00016218
I0408 08:12:39.368516 31616 solver.cpp:218] Iteration 4044 (2.38034 iter/s, 5.04129s/12 iters), loss = 3.45458
I0408 08:12:39.368564 31616 solver.cpp:237]     Train net output #0: loss = 3.45458 (* 1 = 3.45458 loss)
I0408 08:12:39.368577 31616 sgd_solver.cpp:105] Iteration 4044, lr = 0.000159108
I0408 08:12:39.879206 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:12:44.416186 31616 solver.cpp:218] Iteration 4056 (2.37744 iter/s, 5.04745s/12 iters), loss = 3.3973
I0408 08:12:44.416222 31616 solver.cpp:237]     Train net output #0: loss = 3.3973 (* 1 = 3.3973 loss)
I0408 08:12:44.416230 31616 sgd_solver.cpp:105] Iteration 4056, lr = 0.000156095
I0408 08:12:49.452875 31616 solver.cpp:218] Iteration 4068 (2.38262 iter/s, 5.03648s/12 iters), loss = 3.28184
I0408 08:12:49.452922 31616 solver.cpp:237]     Train net output #0: loss = 3.28184 (* 1 = 3.28184 loss)
I0408 08:12:49.452934 31616 sgd_solver.cpp:105] Iteration 4068, lr = 0.000153139
I0408 08:12:53.991739 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0408 08:12:57.121907 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0408 08:12:59.443238 31616 solver.cpp:330] Iteration 4080, Testing net (#0)
I0408 08:12:59.443344 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:13:02.425042 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:13:04.040304 31616 solver.cpp:397]     Test net output #0: accuracy = 0.137255
I0408 08:13:04.040350 31616 solver.cpp:397]     Test net output #1: loss = 3.94385 (* 1 = 3.94385 loss)
I0408 08:13:04.131348 31616 solver.cpp:218] Iteration 4080 (0.817553 iter/s, 14.6779s/12 iters), loss = 3.17375
I0408 08:13:04.131397 31616 solver.cpp:237]     Train net output #0: loss = 3.17375 (* 1 = 3.17375 loss)
I0408 08:13:04.131409 31616 sgd_solver.cpp:105] Iteration 4080, lr = 0.000150239
I0408 08:13:08.699779 31616 solver.cpp:218] Iteration 4092 (2.62684 iter/s, 4.56823s/12 iters), loss = 3.42679
I0408 08:13:08.699826 31616 solver.cpp:237]     Train net output #0: loss = 3.42679 (* 1 = 3.42679 loss)
I0408 08:13:08.699837 31616 sgd_solver.cpp:105] Iteration 4092, lr = 0.000147394
I0408 08:13:13.673195 31616 solver.cpp:218] Iteration 4104 (2.41293 iter/s, 4.9732s/12 iters), loss = 3.51938
I0408 08:13:13.673240 31616 solver.cpp:237]     Train net output #0: loss = 3.51938 (* 1 = 3.51938 loss)
I0408 08:13:13.673251 31616 sgd_solver.cpp:105] Iteration 4104, lr = 0.000144602
I0408 08:13:18.647600 31616 solver.cpp:218] Iteration 4116 (2.41245 iter/s, 4.97419s/12 iters), loss = 3.45164
I0408 08:13:18.647648 31616 solver.cpp:237]     Train net output #0: loss = 3.45164 (* 1 = 3.45164 loss)
I0408 08:13:18.647660 31616 sgd_solver.cpp:105] Iteration 4116, lr = 0.000141864
I0408 08:13:23.643328 31616 solver.cpp:218] Iteration 4128 (2.40216 iter/s, 4.99551s/12 iters), loss = 3.12911
I0408 08:13:23.643374 31616 solver.cpp:237]     Train net output #0: loss = 3.12911 (* 1 = 3.12911 loss)
I0408 08:13:23.643386 31616 sgd_solver.cpp:105] Iteration 4128, lr = 0.000139177
I0408 08:13:28.625419 31616 solver.cpp:218] Iteration 4140 (2.40873 iter/s, 4.98187s/12 iters), loss = 3.42797
I0408 08:13:28.625483 31616 solver.cpp:237]     Train net output #0: loss = 3.42797 (* 1 = 3.42797 loss)
I0408 08:13:28.625499 31616 sgd_solver.cpp:105] Iteration 4140, lr = 0.000136541
I0408 08:13:31.481263 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:13:34.023475 31616 solver.cpp:218] Iteration 4152 (2.22312 iter/s, 5.39781s/12 iters), loss = 3.27412
I0408 08:13:34.023522 31616 solver.cpp:237]     Train net output #0: loss = 3.27412 (* 1 = 3.27412 loss)
I0408 08:13:34.023535 31616 sgd_solver.cpp:105] Iteration 4152, lr = 0.000133956
I0408 08:13:35.681429 31616 blocking_queue.cpp:49] Waiting for data
I0408 08:13:39.014807 31616 solver.cpp:218] Iteration 4164 (2.40427 iter/s, 4.99111s/12 iters), loss = 3.45628
I0408 08:13:39.014853 31616 solver.cpp:237]     Train net output #0: loss = 3.45628 (* 1 = 3.45628 loss)
I0408 08:13:39.014865 31616 sgd_solver.cpp:105] Iteration 4164, lr = 0.000131419
I0408 08:13:44.057144 31616 solver.cpp:218] Iteration 4176 (2.37995 iter/s, 5.04212s/12 iters), loss = 3.3423
I0408 08:13:44.057194 31616 solver.cpp:237]     Train net output #0: loss = 3.3423 (* 1 = 3.3423 loss)
I0408 08:13:44.057206 31616 sgd_solver.cpp:105] Iteration 4176, lr = 0.00012893
I0408 08:13:46.082165 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0408 08:13:49.085947 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0408 08:13:51.388973 31616 solver.cpp:330] Iteration 4182, Testing net (#0)
I0408 08:13:51.388998 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:13:54.206394 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:13:55.866106 31616 solver.cpp:397]     Test net output #0: accuracy = 0.145833
I0408 08:13:55.866145 31616 solver.cpp:397]     Test net output #1: loss = 3.92976 (* 1 = 3.92976 loss)
I0408 08:13:57.690603 31616 solver.cpp:218] Iteration 4188 (0.880219 iter/s, 13.633s/12 iters), loss = 3.54444
I0408 08:13:57.690649 31616 solver.cpp:237]     Train net output #0: loss = 3.54444 (* 1 = 3.54444 loss)
I0408 08:13:57.690660 31616 sgd_solver.cpp:105] Iteration 4188, lr = 0.000126488
I0408 08:14:02.622189 31616 solver.cpp:218] Iteration 4200 (2.4334 iter/s, 4.93137s/12 iters), loss = 3.48685
I0408 08:14:02.622303 31616 solver.cpp:237]     Train net output #0: loss = 3.48685 (* 1 = 3.48685 loss)
I0408 08:14:02.622314 31616 sgd_solver.cpp:105] Iteration 4200, lr = 0.000124093
I0408 08:14:07.778848 31616 solver.cpp:218] Iteration 4212 (2.32722 iter/s, 5.15637s/12 iters), loss = 3.30198
I0408 08:14:07.778892 31616 solver.cpp:237]     Train net output #0: loss = 3.30198 (* 1 = 3.30198 loss)
I0408 08:14:07.778904 31616 sgd_solver.cpp:105] Iteration 4212, lr = 0.000121743
I0408 08:14:12.644686 31616 solver.cpp:218] Iteration 4224 (2.46628 iter/s, 4.86563s/12 iters), loss = 3.24664
I0408 08:14:12.644739 31616 solver.cpp:237]     Train net output #0: loss = 3.24664 (* 1 = 3.24664 loss)
I0408 08:14:12.644752 31616 sgd_solver.cpp:105] Iteration 4224, lr = 0.000119437
I0408 08:14:17.662500 31616 solver.cpp:218] Iteration 4236 (2.39159 iter/s, 5.01759s/12 iters), loss = 3.44757
I0408 08:14:17.662549 31616 solver.cpp:237]     Train net output #0: loss = 3.44757 (* 1 = 3.44757 loss)
I0408 08:14:17.662559 31616 sgd_solver.cpp:105] Iteration 4236, lr = 0.000117175
I0408 08:14:22.415172 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:14:22.645406 31616 solver.cpp:218] Iteration 4248 (2.40834 iter/s, 4.98269s/12 iters), loss = 3.28811
I0408 08:14:22.645452 31616 solver.cpp:237]     Train net output #0: loss = 3.28811 (* 1 = 3.28811 loss)
I0408 08:14:22.645464 31616 sgd_solver.cpp:105] Iteration 4248, lr = 0.000114956
I0408 08:14:27.681033 31616 solver.cpp:218] Iteration 4260 (2.38312 iter/s, 5.03541s/12 iters), loss = 3.49976
I0408 08:14:27.681082 31616 solver.cpp:237]     Train net output #0: loss = 3.49976 (* 1 = 3.49976 loss)
I0408 08:14:27.681093 31616 sgd_solver.cpp:105] Iteration 4260, lr = 0.000112779
I0408 08:14:32.667853 31616 solver.cpp:218] Iteration 4272 (2.40645 iter/s, 4.9866s/12 iters), loss = 3.27963
I0408 08:14:32.667958 31616 solver.cpp:237]     Train net output #0: loss = 3.27963 (* 1 = 3.27963 loss)
I0408 08:14:32.667970 31616 sgd_solver.cpp:105] Iteration 4272, lr = 0.000110643
I0408 08:14:37.238435 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0408 08:14:40.329362 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0408 08:14:42.657132 31616 solver.cpp:330] Iteration 4284, Testing net (#0)
I0408 08:14:42.657158 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:14:45.441901 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:14:47.137472 31616 solver.cpp:397]     Test net output #0: accuracy = 0.139093
I0408 08:14:47.137521 31616 solver.cpp:397]     Test net output #1: loss = 3.91481 (* 1 = 3.91481 loss)
I0408 08:14:47.228806 31616 solver.cpp:218] Iteration 4284 (0.824154 iter/s, 14.5604s/12 iters), loss = 3.49614
I0408 08:14:47.228853 31616 solver.cpp:237]     Train net output #0: loss = 3.49614 (* 1 = 3.49614 loss)
I0408 08:14:47.228864 31616 sgd_solver.cpp:105] Iteration 4284, lr = 0.000108548
I0408 08:14:51.491111 31616 solver.cpp:218] Iteration 4296 (2.81552 iter/s, 4.26209s/12 iters), loss = 3.22578
I0408 08:14:51.491204 31616 solver.cpp:237]     Train net output #0: loss = 3.22578 (* 1 = 3.22578 loss)
I0408 08:14:51.491219 31616 sgd_solver.cpp:105] Iteration 4296, lr = 0.000106492
I0408 08:14:56.665611 31616 solver.cpp:218] Iteration 4308 (2.31917 iter/s, 5.17426s/12 iters), loss = 3.42893
I0408 08:14:56.665652 31616 solver.cpp:237]     Train net output #0: loss = 3.42893 (* 1 = 3.42893 loss)
I0408 08:14:56.665661 31616 sgd_solver.cpp:105] Iteration 4308, lr = 0.000104475
I0408 08:15:01.765425 31616 solver.cpp:218] Iteration 4320 (2.35312 iter/s, 5.0996s/12 iters), loss = 3.52168
I0408 08:15:01.765462 31616 solver.cpp:237]     Train net output #0: loss = 3.52168 (* 1 = 3.52168 loss)
I0408 08:15:01.765471 31616 sgd_solver.cpp:105] Iteration 4320, lr = 0.000102497
I0408 08:15:06.717475 31616 solver.cpp:218] Iteration 4332 (2.42334 iter/s, 4.95185s/12 iters), loss = 3.34948
I0408 08:15:06.717610 31616 solver.cpp:237]     Train net output #0: loss = 3.34948 (* 1 = 3.34948 loss)
I0408 08:15:06.717620 31616 sgd_solver.cpp:105] Iteration 4332, lr = 0.000100556
I0408 08:15:11.726895 31616 solver.cpp:218] Iteration 4344 (2.39563 iter/s, 5.00912s/12 iters), loss = 3.35869
I0408 08:15:11.726928 31616 solver.cpp:237]     Train net output #0: loss = 3.35869 (* 1 = 3.35869 loss)
I0408 08:15:11.726935 31616 sgd_solver.cpp:105] Iteration 4344, lr = 9.86514e-05
I0408 08:15:13.648878 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:15:16.778534 31616 solver.cpp:218] Iteration 4356 (2.37556 iter/s, 5.05143s/12 iters), loss = 3.35964
I0408 08:15:16.778579 31616 solver.cpp:237]     Train net output #0: loss = 3.35964 (* 1 = 3.35964 loss)
I0408 08:15:16.778590 31616 sgd_solver.cpp:105] Iteration 4356, lr = 9.67831e-05
I0408 08:15:22.031456 31616 solver.cpp:218] Iteration 4368 (2.28454 iter/s, 5.2527s/12 iters), loss = 3.51172
I0408 08:15:22.031504 31616 solver.cpp:237]     Train net output #0: loss = 3.51172 (* 1 = 3.51172 loss)
I0408 08:15:22.031517 31616 sgd_solver.cpp:105] Iteration 4368, lr = 9.49503e-05
I0408 08:15:27.089555 31616 solver.cpp:218] Iteration 4380 (2.37254 iter/s, 5.05788s/12 iters), loss = 3.22259
I0408 08:15:27.089601 31616 solver.cpp:237]     Train net output #0: loss = 3.22259 (* 1 = 3.22259 loss)
I0408 08:15:27.089612 31616 sgd_solver.cpp:105] Iteration 4380, lr = 9.31521e-05
I0408 08:15:29.124517 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0408 08:15:32.152354 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0408 08:15:34.532014 31616 solver.cpp:330] Iteration 4386, Testing net (#0)
I0408 08:15:34.532038 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:15:37.243600 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:15:38.992259 31616 solver.cpp:397]     Test net output #0: accuracy = 0.13848
I0408 08:15:38.992305 31616 solver.cpp:397]     Test net output #1: loss = 3.90618 (* 1 = 3.90618 loss)
I0408 08:15:40.991557 31616 solver.cpp:218] Iteration 4392 (0.863216 iter/s, 13.9015s/12 iters), loss = 3.33143
I0408 08:15:40.991617 31616 solver.cpp:237]     Train net output #0: loss = 3.33143 (* 1 = 3.33143 loss)
I0408 08:15:40.991631 31616 sgd_solver.cpp:105] Iteration 4392, lr = 9.13879e-05
I0408 08:15:46.294520 31616 solver.cpp:218] Iteration 4404 (2.26299 iter/s, 5.30273s/12 iters), loss = 3.59832
I0408 08:15:46.294562 31616 solver.cpp:237]     Train net output #0: loss = 3.59832 (* 1 = 3.59832 loss)
I0408 08:15:46.294574 31616 sgd_solver.cpp:105] Iteration 4404, lr = 8.96572e-05
I0408 08:15:51.236129 31616 solver.cpp:218] Iteration 4416 (2.42846 iter/s, 4.9414s/12 iters), loss = 3.24514
I0408 08:15:51.236164 31616 solver.cpp:237]     Train net output #0: loss = 3.24514 (* 1 = 3.24514 loss)
I0408 08:15:51.236172 31616 sgd_solver.cpp:105] Iteration 4416, lr = 8.79593e-05
I0408 08:15:56.286785 31616 solver.cpp:218] Iteration 4428 (2.37603 iter/s, 5.05044s/12 iters), loss = 3.15138
I0408 08:15:56.286834 31616 solver.cpp:237]     Train net output #0: loss = 3.15138 (* 1 = 3.15138 loss)
I0408 08:15:56.286845 31616 sgd_solver.cpp:105] Iteration 4428, lr = 8.62935e-05
I0408 08:16:01.340132 31616 solver.cpp:218] Iteration 4440 (2.37477 iter/s, 5.05313s/12 iters), loss = 3.31588
I0408 08:16:01.340173 31616 solver.cpp:237]     Train net output #0: loss = 3.31588 (* 1 = 3.31588 loss)
I0408 08:16:01.340183 31616 sgd_solver.cpp:105] Iteration 4440, lr = 8.46593e-05
I0408 08:16:05.374133 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:16:06.354945 31616 solver.cpp:218] Iteration 4452 (2.39301 iter/s, 5.0146s/12 iters), loss = 3.35244
I0408 08:16:06.354995 31616 solver.cpp:237]     Train net output #0: loss = 3.35244 (* 1 = 3.35244 loss)
I0408 08:16:06.355005 31616 sgd_solver.cpp:105] Iteration 4452, lr = 8.3056e-05
I0408 08:16:11.397087 31616 solver.cpp:218] Iteration 4464 (2.38005 iter/s, 5.04192s/12 iters), loss = 3.47392
I0408 08:16:11.397217 31616 solver.cpp:237]     Train net output #0: loss = 3.47392 (* 1 = 3.47392 loss)
I0408 08:16:11.397229 31616 sgd_solver.cpp:105] Iteration 4464, lr = 8.14831e-05
I0408 08:16:16.364267 31616 solver.cpp:218] Iteration 4476 (2.416 iter/s, 4.96689s/12 iters), loss = 3.34414
I0408 08:16:16.364307 31616 solver.cpp:237]     Train net output #0: loss = 3.34414 (* 1 = 3.34414 loss)
I0408 08:16:16.364317 31616 sgd_solver.cpp:105] Iteration 4476, lr = 7.99399e-05
I0408 08:16:20.919070 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0408 08:16:23.897980 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0408 08:16:26.224347 31616 solver.cpp:330] Iteration 4488, Testing net (#0)
I0408 08:16:26.224371 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:16:28.918891 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:16:30.697877 31616 solver.cpp:397]     Test net output #0: accuracy = 0.144608
I0408 08:16:30.697924 31616 solver.cpp:397]     Test net output #1: loss = 3.90053 (* 1 = 3.90053 loss)
I0408 08:16:30.789155 31616 solver.cpp:218] Iteration 4488 (0.831925 iter/s, 14.4244s/12 iters), loss = 3.3816
I0408 08:16:30.789202 31616 solver.cpp:237]     Train net output #0: loss = 3.3816 (* 1 = 3.3816 loss)
I0408 08:16:30.789213 31616 sgd_solver.cpp:105] Iteration 4488, lr = 7.8426e-05
I0408 08:16:35.388197 31616 solver.cpp:218] Iteration 4500 (2.60936 iter/s, 4.59884s/12 iters), loss = 3.16625
I0408 08:16:35.388240 31616 solver.cpp:237]     Train net output #0: loss = 3.16625 (* 1 = 3.16625 loss)
I0408 08:16:35.388252 31616 sgd_solver.cpp:105] Iteration 4500, lr = 7.69408e-05
I0408 08:16:40.418081 31616 solver.cpp:218] Iteration 4512 (2.38584 iter/s, 5.02967s/12 iters), loss = 3.40715
I0408 08:16:40.418129 31616 solver.cpp:237]     Train net output #0: loss = 3.40715 (* 1 = 3.40715 loss)
I0408 08:16:40.418141 31616 sgd_solver.cpp:105] Iteration 4512, lr = 7.54837e-05
I0408 08:16:45.442375 31616 solver.cpp:218] Iteration 4524 (2.3885 iter/s, 5.02406s/12 iters), loss = 3.30406
I0408 08:16:45.442490 31616 solver.cpp:237]     Train net output #0: loss = 3.30406 (* 1 = 3.30406 loss)
I0408 08:16:45.442502 31616 sgd_solver.cpp:105] Iteration 4524, lr = 7.40542e-05
I0408 08:16:50.431447 31616 solver.cpp:218] Iteration 4536 (2.40539 iter/s, 4.98879s/12 iters), loss = 3.2431
I0408 08:16:50.431486 31616 solver.cpp:237]     Train net output #0: loss = 3.2431 (* 1 = 3.2431 loss)
I0408 08:16:50.431494 31616 sgd_solver.cpp:105] Iteration 4536, lr = 7.26517e-05
I0408 08:16:55.459590 31616 solver.cpp:218] Iteration 4548 (2.38667 iter/s, 5.02793s/12 iters), loss = 3.46962
I0408 08:16:55.459636 31616 solver.cpp:237]     Train net output #0: loss = 3.46962 (* 1 = 3.46962 loss)
I0408 08:16:55.459648 31616 sgd_solver.cpp:105] Iteration 4548, lr = 7.12758e-05
I0408 08:16:56.768453 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:17:00.840152 31616 solver.cpp:218] Iteration 4560 (2.23035 iter/s, 5.38033s/12 iters), loss = 3.09842
I0408 08:17:00.840204 31616 solver.cpp:237]     Train net output #0: loss = 3.09842 (* 1 = 3.09842 loss)
I0408 08:17:00.840216 31616 sgd_solver.cpp:105] Iteration 4560, lr = 6.9926e-05
I0408 08:17:06.202210 31616 solver.cpp:218] Iteration 4572 (2.23804 iter/s, 5.36183s/12 iters), loss = 3.38445
I0408 08:17:06.202253 31616 solver.cpp:237]     Train net output #0: loss = 3.38445 (* 1 = 3.38445 loss)
I0408 08:17:06.202263 31616 sgd_solver.cpp:105] Iteration 4572, lr = 6.86018e-05
I0408 08:17:11.248553 31616 solver.cpp:218] Iteration 4584 (2.37806 iter/s, 5.04613s/12 iters), loss = 3.37198
I0408 08:17:11.248598 31616 solver.cpp:237]     Train net output #0: loss = 3.37198 (* 1 = 3.37198 loss)
I0408 08:17:11.248610 31616 sgd_solver.cpp:105] Iteration 4584, lr = 6.73026e-05
I0408 08:17:13.290439 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0408 08:17:16.323405 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0408 08:17:18.652626 31616 solver.cpp:330] Iteration 4590, Testing net (#0)
I0408 08:17:18.652655 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:17:21.262461 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:17:23.090238 31616 solver.cpp:397]     Test net output #0: accuracy = 0.144608
I0408 08:17:23.090272 31616 solver.cpp:397]     Test net output #1: loss = 3.88988 (* 1 = 3.88988 loss)
I0408 08:17:25.030089 31616 solver.cpp:218] Iteration 4596 (0.870761 iter/s, 13.781s/12 iters), loss = 3.23796
I0408 08:17:25.030143 31616 solver.cpp:237]     Train net output #0: loss = 3.23796 (* 1 = 3.23796 loss)
I0408 08:17:25.030153 31616 sgd_solver.cpp:105] Iteration 4596, lr = 6.6028e-05
I0408 08:17:30.101382 31616 solver.cpp:218] Iteration 4608 (2.36637 iter/s, 5.07107s/12 iters), loss = 3.39027
I0408 08:17:30.101424 31616 solver.cpp:237]     Train net output #0: loss = 3.39027 (* 1 = 3.39027 loss)
I0408 08:17:30.101434 31616 sgd_solver.cpp:105] Iteration 4608, lr = 6.47775e-05
I0408 08:17:35.116541 31616 solver.cpp:218] Iteration 4620 (2.39285 iter/s, 5.01495s/12 iters), loss = 3.57876
I0408 08:17:35.116580 31616 solver.cpp:237]     Train net output #0: loss = 3.57876 (* 1 = 3.57876 loss)
I0408 08:17:35.116587 31616 sgd_solver.cpp:105] Iteration 4620, lr = 6.35508e-05
I0408 08:17:40.164918 31616 solver.cpp:218] Iteration 4632 (2.3771 iter/s, 5.04817s/12 iters), loss = 3.10703
I0408 08:17:40.164959 31616 solver.cpp:237]     Train net output #0: loss = 3.10703 (* 1 = 3.10703 loss)
I0408 08:17:40.164968 31616 sgd_solver.cpp:105] Iteration 4632, lr = 6.23473e-05
I0408 08:17:45.220134 31616 solver.cpp:218] Iteration 4644 (2.37389 iter/s, 5.055s/12 iters), loss = 3.28388
I0408 08:17:45.220170 31616 solver.cpp:237]     Train net output #0: loss = 3.28388 (* 1 = 3.28388 loss)
I0408 08:17:45.220178 31616 sgd_solver.cpp:105] Iteration 4644, lr = 6.11665e-05
I0408 08:17:48.608186 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:17:50.218197 31616 solver.cpp:218] Iteration 4656 (2.40103 iter/s, 4.99786s/12 iters), loss = 3.62192
I0408 08:17:50.218235 31616 solver.cpp:237]     Train net output #0: loss = 3.62192 (* 1 = 3.62192 loss)
I0408 08:17:50.218242 31616 sgd_solver.cpp:105] Iteration 4656, lr = 6.00081e-05
I0408 08:17:55.231053 31616 solver.cpp:218] Iteration 4668 (2.39394 iter/s, 5.01265s/12 iters), loss = 3.29419
I0408 08:17:55.231087 31616 solver.cpp:237]     Train net output #0: loss = 3.29419 (* 1 = 3.29419 loss)
I0408 08:17:55.231096 31616 sgd_solver.cpp:105] Iteration 4668, lr = 5.88717e-05
I0408 08:18:00.215637 31616 solver.cpp:218] Iteration 4680 (2.40752 iter/s, 4.98438s/12 iters), loss = 3.34508
I0408 08:18:00.215672 31616 solver.cpp:237]     Train net output #0: loss = 3.34508 (* 1 = 3.34508 loss)
I0408 08:18:00.215678 31616 sgd_solver.cpp:105] Iteration 4680, lr = 5.77568e-05
I0408 08:18:04.721927 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0408 08:18:07.778697 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0408 08:18:10.224488 31616 solver.cpp:330] Iteration 4692, Testing net (#0)
I0408 08:18:10.224516 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:18:12.820256 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:18:14.680388 31616 solver.cpp:397]     Test net output #0: accuracy = 0.139706
I0408 08:18:14.680435 31616 solver.cpp:397]     Test net output #1: loss = 3.90413 (* 1 = 3.90413 loss)
I0408 08:18:14.771497 31616 solver.cpp:218] Iteration 4692 (0.824439 iter/s, 14.5554s/12 iters), loss = 3.25086
I0408 08:18:14.771546 31616 solver.cpp:237]     Train net output #0: loss = 3.25086 (* 1 = 3.25086 loss)
I0408 08:18:14.771559 31616 sgd_solver.cpp:105] Iteration 4692, lr = 5.6663e-05
I0408 08:18:19.086534 31616 solver.cpp:218] Iteration 4704 (2.7811 iter/s, 4.31484s/12 iters), loss = 3.40187
I0408 08:18:19.086663 31616 solver.cpp:237]     Train net output #0: loss = 3.40187 (* 1 = 3.40187 loss)
I0408 08:18:19.086675 31616 sgd_solver.cpp:105] Iteration 4704, lr = 5.55899e-05
I0408 08:18:24.061975 31616 solver.cpp:218] Iteration 4716 (2.41199 iter/s, 4.97514s/12 iters), loss = 3.46747
I0408 08:18:24.062019 31616 solver.cpp:237]     Train net output #0: loss = 3.46747 (* 1 = 3.46747 loss)
I0408 08:18:24.062031 31616 sgd_solver.cpp:105] Iteration 4716, lr = 5.45371e-05
I0408 08:18:29.095806 31616 solver.cpp:218] Iteration 4728 (2.38397 iter/s, 5.03362s/12 iters), loss = 3.42128
I0408 08:18:29.095850 31616 solver.cpp:237]     Train net output #0: loss = 3.42128 (* 1 = 3.42128 loss)
I0408 08:18:29.095862 31616 sgd_solver.cpp:105] Iteration 4728, lr = 5.35043e-05
I0408 08:18:34.136124 31616 solver.cpp:218] Iteration 4740 (2.3809 iter/s, 5.0401s/12 iters), loss = 3.40982
I0408 08:18:34.136170 31616 solver.cpp:237]     Train net output #0: loss = 3.40982 (* 1 = 3.40982 loss)
I0408 08:18:34.136183 31616 sgd_solver.cpp:105] Iteration 4740, lr = 5.2491e-05
I0408 08:18:39.180758 31616 solver.cpp:218] Iteration 4752 (2.37887 iter/s, 5.04442s/12 iters), loss = 3.48585
I0408 08:18:39.180815 31616 solver.cpp:237]     Train net output #0: loss = 3.48585 (* 1 = 3.48585 loss)
I0408 08:18:39.180830 31616 sgd_solver.cpp:105] Iteration 4752, lr = 5.14969e-05
I0408 08:18:39.709244 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:18:44.191637 31616 solver.cpp:218] Iteration 4764 (2.3949 iter/s, 5.01066s/12 iters), loss = 3.19278
I0408 08:18:44.191682 31616 solver.cpp:237]     Train net output #0: loss = 3.19278 (* 1 = 3.19278 loss)
I0408 08:18:44.191694 31616 sgd_solver.cpp:105] Iteration 4764, lr = 5.05217e-05
I0408 08:18:49.198812 31616 solver.cpp:218] Iteration 4776 (2.39666 iter/s, 5.00696s/12 iters), loss = 3.21319
I0408 08:18:49.198922 31616 solver.cpp:237]     Train net output #0: loss = 3.21319 (* 1 = 3.21319 loss)
I0408 08:18:49.198935 31616 sgd_solver.cpp:105] Iteration 4776, lr = 4.95649e-05
I0408 08:18:54.258569 31616 solver.cpp:218] Iteration 4788 (2.37179 iter/s, 5.05947s/12 iters), loss = 3.20628
I0408 08:18:54.258620 31616 solver.cpp:237]     Train net output #0: loss = 3.20628 (* 1 = 3.20628 loss)
I0408 08:18:54.258632 31616 sgd_solver.cpp:105] Iteration 4788, lr = 4.86262e-05
I0408 08:18:56.259759 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0408 08:18:59.281116 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0408 08:19:01.609931 31616 solver.cpp:330] Iteration 4794, Testing net (#0)
I0408 08:19:01.609969 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:19:04.174432 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:19:06.078675 31616 solver.cpp:397]     Test net output #0: accuracy = 0.142157
I0408 08:19:06.078722 31616 solver.cpp:397]     Test net output #1: loss = 3.87755 (* 1 = 3.87755 loss)
I0408 08:19:08.028357 31616 solver.cpp:218] Iteration 4800 (0.871505 iter/s, 13.7693s/12 iters), loss = 3.1199
I0408 08:19:08.028409 31616 solver.cpp:237]     Train net output #0: loss = 3.1199 (* 1 = 3.1199 loss)
I0408 08:19:08.028420 31616 sgd_solver.cpp:105] Iteration 4800, lr = 4.77054e-05
I0408 08:19:13.053424 31616 solver.cpp:218] Iteration 4812 (2.38813 iter/s, 5.02485s/12 iters), loss = 3.37574
I0408 08:19:13.053468 31616 solver.cpp:237]     Train net output #0: loss = 3.37574 (* 1 = 3.37574 loss)
I0408 08:19:13.053480 31616 sgd_solver.cpp:105] Iteration 4812, lr = 4.68019e-05
I0408 08:19:18.171428 31616 solver.cpp:218] Iteration 4824 (2.34476 iter/s, 5.11779s/12 iters), loss = 3.47489
I0408 08:19:18.171465 31616 solver.cpp:237]     Train net output #0: loss = 3.47489 (* 1 = 3.47489 loss)
I0408 08:19:18.171473 31616 sgd_solver.cpp:105] Iteration 4824, lr = 4.59156e-05
I0408 08:19:23.214457 31616 solver.cpp:218] Iteration 4836 (2.37962 iter/s, 5.04282s/12 iters), loss = 3.251
I0408 08:19:23.214591 31616 solver.cpp:237]     Train net output #0: loss = 3.251 (* 1 = 3.251 loss)
I0408 08:19:23.214607 31616 sgd_solver.cpp:105] Iteration 4836, lr = 4.5046e-05
I0408 08:19:25.221735 31616 blocking_queue.cpp:49] Waiting for data
I0408 08:19:28.199570 31616 solver.cpp:218] Iteration 4848 (2.40731 iter/s, 4.98481s/12 iters), loss = 3.41041
I0408 08:19:28.199613 31616 solver.cpp:237]     Train net output #0: loss = 3.41041 (* 1 = 3.41041 loss)
I0408 08:19:28.199625 31616 sgd_solver.cpp:105] Iteration 4848, lr = 4.41929e-05
I0408 08:19:30.859331 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:19:33.183300 31616 solver.cpp:218] Iteration 4860 (2.40794 iter/s, 4.98352s/12 iters), loss = 3.00141
I0408 08:19:33.183342 31616 solver.cpp:237]     Train net output #0: loss = 3.00141 (* 1 = 3.00141 loss)
I0408 08:19:33.183354 31616 sgd_solver.cpp:105] Iteration 4860, lr = 4.3356e-05
I0408 08:19:38.202513 31616 solver.cpp:218] Iteration 4872 (2.39091 iter/s, 5.019s/12 iters), loss = 3.27604
I0408 08:19:38.202558 31616 solver.cpp:237]     Train net output #0: loss = 3.27604 (* 1 = 3.27604 loss)
I0408 08:19:38.202569 31616 sgd_solver.cpp:105] Iteration 4872, lr = 4.25349e-05
I0408 08:19:43.199080 31616 solver.cpp:218] Iteration 4884 (2.40175 iter/s, 4.99635s/12 iters), loss = 3.29388
I0408 08:19:43.199123 31616 solver.cpp:237]     Train net output #0: loss = 3.29388 (* 1 = 3.29388 loss)
I0408 08:19:43.199133 31616 sgd_solver.cpp:105] Iteration 4884, lr = 4.17294e-05
I0408 08:19:47.748093 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0408 08:19:53.071386 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0408 08:19:55.389565 31616 solver.cpp:330] Iteration 4896, Testing net (#0)
I0408 08:19:55.389616 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:19:57.951390 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:20:00.137908 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 08:20:00.137969 31616 solver.cpp:397]     Test net output #1: loss = 3.86419 (* 1 = 3.86419 loss)
I0408 08:20:00.229172 31616 solver.cpp:218] Iteration 4896 (0.70466 iter/s, 17.0295s/12 iters), loss = 3.30328
I0408 08:20:00.229215 31616 solver.cpp:237]     Train net output #0: loss = 3.30328 (* 1 = 3.30328 loss)
I0408 08:20:00.229225 31616 sgd_solver.cpp:105] Iteration 4896, lr = 4.09391e-05
I0408 08:20:04.586544 31616 solver.cpp:218] Iteration 4908 (2.75407 iter/s, 4.35718s/12 iters), loss = 3.52969
I0408 08:20:04.586586 31616 solver.cpp:237]     Train net output #0: loss = 3.52969 (* 1 = 3.52969 loss)
I0408 08:20:04.586597 31616 sgd_solver.cpp:105] Iteration 4908, lr = 4.01638e-05
I0408 08:20:09.655419 31616 solver.cpp:218] Iteration 4920 (2.36749 iter/s, 5.06867s/12 iters), loss = 3.3
I0408 08:20:09.655457 31616 solver.cpp:237]     Train net output #0: loss = 3.3 (* 1 = 3.3 loss)
I0408 08:20:09.655465 31616 sgd_solver.cpp:105] Iteration 4920, lr = 3.94032e-05
I0408 08:20:14.686408 31616 solver.cpp:218] Iteration 4932 (2.38532 iter/s, 5.03078s/12 iters), loss = 3.37912
I0408 08:20:14.686448 31616 solver.cpp:237]     Train net output #0: loss = 3.37912 (* 1 = 3.37912 loss)
I0408 08:20:14.686458 31616 sgd_solver.cpp:105] Iteration 4932, lr = 3.8657e-05
I0408 08:20:19.579344 31616 solver.cpp:218] Iteration 4944 (2.45262 iter/s, 4.89272s/12 iters), loss = 3.359
I0408 08:20:19.579391 31616 solver.cpp:237]     Train net output #0: loss = 3.359 (* 1 = 3.359 loss)
I0408 08:20:19.579403 31616 sgd_solver.cpp:105] Iteration 4944, lr = 3.79249e-05
I0408 08:20:24.413832 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:20:24.611301 31616 solver.cpp:218] Iteration 4956 (2.38486 iter/s, 5.03174s/12 iters), loss = 3.24102
I0408 08:20:24.611349 31616 solver.cpp:237]     Train net output #0: loss = 3.24102 (* 1 = 3.24102 loss)
I0408 08:20:24.611361 31616 sgd_solver.cpp:105] Iteration 4956, lr = 3.72066e-05
I0408 08:20:29.564504 31616 solver.cpp:218] Iteration 4968 (2.42278 iter/s, 4.95299s/12 iters), loss = 3.39631
I0408 08:20:29.564615 31616 solver.cpp:237]     Train net output #0: loss = 3.39631 (* 1 = 3.39631 loss)
I0408 08:20:29.564628 31616 sgd_solver.cpp:105] Iteration 4968, lr = 3.6502e-05
I0408 08:20:34.587162 31616 solver.cpp:218] Iteration 4980 (2.38931 iter/s, 5.02238s/12 iters), loss = 3.02468
I0408 08:20:34.587209 31616 solver.cpp:237]     Train net output #0: loss = 3.02468 (* 1 = 3.02468 loss)
I0408 08:20:34.587220 31616 sgd_solver.cpp:105] Iteration 4980, lr = 3.58107e-05
I0408 08:20:39.574256 31616 solver.cpp:218] Iteration 4992 (2.40631 iter/s, 4.98688s/12 iters), loss = 3.35754
I0408 08:20:39.574292 31616 solver.cpp:237]     Train net output #0: loss = 3.35754 (* 1 = 3.35754 loss)
I0408 08:20:39.574301 31616 sgd_solver.cpp:105] Iteration 4992, lr = 3.51326e-05
I0408 08:20:41.619143 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0408 08:20:47.811890 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0408 08:20:50.781114 31616 solver.cpp:330] Iteration 4998, Testing net (#0)
I0408 08:20:50.781138 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:20:53.283807 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:20:55.256986 31616 solver.cpp:397]     Test net output #0: accuracy = 0.147059
I0408 08:20:55.257031 31616 solver.cpp:397]     Test net output #1: loss = 3.87101 (* 1 = 3.87101 loss)
I0408 08:20:57.204054 31616 solver.cpp:218] Iteration 5004 (0.680689 iter/s, 17.6292s/12 iters), loss = 3.06516
I0408 08:20:57.204097 31616 solver.cpp:237]     Train net output #0: loss = 3.06516 (* 1 = 3.06516 loss)
I0408 08:20:57.204106 31616 sgd_solver.cpp:105] Iteration 5004, lr = 3.44672e-05
I0408 08:21:02.212747 31616 solver.cpp:218] Iteration 5016 (2.39594 iter/s, 5.00848s/12 iters), loss = 3.34036
I0408 08:21:02.212828 31616 solver.cpp:237]     Train net output #0: loss = 3.34036 (* 1 = 3.34036 loss)
I0408 08:21:02.212837 31616 sgd_solver.cpp:105] Iteration 5016, lr = 3.38145e-05
I0408 08:21:07.224170 31616 solver.cpp:218] Iteration 5028 (2.39465 iter/s, 5.01117s/12 iters), loss = 3.45308
I0408 08:21:07.224207 31616 solver.cpp:237]     Train net output #0: loss = 3.45308 (* 1 = 3.45308 loss)
I0408 08:21:07.224215 31616 sgd_solver.cpp:105] Iteration 5028, lr = 3.31741e-05
I0408 08:21:12.179831 31616 solver.cpp:218] Iteration 5040 (2.42158 iter/s, 4.95545s/12 iters), loss = 3.21041
I0408 08:21:12.179873 31616 solver.cpp:237]     Train net output #0: loss = 3.21041 (* 1 = 3.21041 loss)
I0408 08:21:12.179883 31616 sgd_solver.cpp:105] Iteration 5040, lr = 3.25458e-05
I0408 08:21:17.167641 31616 solver.cpp:218] Iteration 5052 (2.40597 iter/s, 4.98759s/12 iters), loss = 3.41201
I0408 08:21:17.167688 31616 solver.cpp:237]     Train net output #0: loss = 3.41201 (* 1 = 3.41201 loss)
I0408 08:21:17.167699 31616 sgd_solver.cpp:105] Iteration 5052, lr = 3.19295e-05
I0408 08:21:19.073235 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:21:22.120553 31616 solver.cpp:218] Iteration 5064 (2.42292 iter/s, 4.9527s/12 iters), loss = 3.37441
I0408 08:21:22.120591 31616 solver.cpp:237]     Train net output #0: loss = 3.37441 (* 1 = 3.37441 loss)
I0408 08:21:22.120600 31616 sgd_solver.cpp:105] Iteration 5064, lr = 3.13248e-05
I0408 08:21:27.229178 31616 solver.cpp:218] Iteration 5076 (2.34907 iter/s, 5.10841s/12 iters), loss = 3.3098
I0408 08:21:27.229226 31616 solver.cpp:237]     Train net output #0: loss = 3.3098 (* 1 = 3.3098 loss)
I0408 08:21:27.229239 31616 sgd_solver.cpp:105] Iteration 5076, lr = 3.07316e-05
I0408 08:21:32.255306 31616 solver.cpp:218] Iteration 5088 (2.38763 iter/s, 5.02591s/12 iters), loss = 2.99942
I0408 08:21:32.255360 31616 solver.cpp:237]     Train net output #0: loss = 2.99942 (* 1 = 2.99942 loss)
I0408 08:21:32.255368 31616 sgd_solver.cpp:105] Iteration 5088, lr = 3.01496e-05
I0408 08:21:36.809877 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0408 08:21:41.441824 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0408 08:21:47.001075 31616 solver.cpp:330] Iteration 5100, Testing net (#0)
I0408 08:21:47.001107 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:21:49.451608 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:21:51.469614 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148284
I0408 08:21:51.469660 31616 solver.cpp:397]     Test net output #1: loss = 3.8627 (* 1 = 3.8627 loss)
I0408 08:21:51.560561 31616 solver.cpp:218] Iteration 5100 (0.621614 iter/s, 19.3046s/12 iters), loss = 3.34935
I0408 08:21:51.560608 31616 solver.cpp:237]     Train net output #0: loss = 3.34935 (* 1 = 3.34935 loss)
I0408 08:21:51.560619 31616 sgd_solver.cpp:105] Iteration 5100, lr = 2.95786e-05
I0408 08:21:56.070734 31616 solver.cpp:218] Iteration 5112 (2.66077 iter/s, 4.50997s/12 iters), loss = 3.43997
I0408 08:21:56.070771 31616 solver.cpp:237]     Train net output #0: loss = 3.43997 (* 1 = 3.43997 loss)
I0408 08:21:56.070781 31616 sgd_solver.cpp:105] Iteration 5112, lr = 2.90184e-05
I0408 08:22:01.163908 31616 solver.cpp:218] Iteration 5124 (2.35619 iter/s, 5.09296s/12 iters), loss = 3.27208
I0408 08:22:01.163954 31616 solver.cpp:237]     Train net output #0: loss = 3.27208 (* 1 = 3.27208 loss)
I0408 08:22:01.163964 31616 sgd_solver.cpp:105] Iteration 5124, lr = 2.84689e-05
I0408 08:22:06.192411 31616 solver.cpp:218] Iteration 5136 (2.3865 iter/s, 5.02829s/12 iters), loss = 3.30623
I0408 08:22:06.192549 31616 solver.cpp:237]     Train net output #0: loss = 3.30623 (* 1 = 3.30623 loss)
I0408 08:22:06.192562 31616 sgd_solver.cpp:105] Iteration 5136, lr = 2.79297e-05
I0408 08:22:11.172379 31616 solver.cpp:218] Iteration 5148 (2.4098 iter/s, 4.97966s/12 iters), loss = 3.19492
I0408 08:22:11.172427 31616 solver.cpp:237]     Train net output #0: loss = 3.19492 (* 1 = 3.19492 loss)
I0408 08:22:11.172438 31616 sgd_solver.cpp:105] Iteration 5148, lr = 2.74008e-05
I0408 08:22:15.292285 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:22:16.236593 31616 solver.cpp:218] Iteration 5160 (2.36967 iter/s, 5.06399s/12 iters), loss = 3.38631
I0408 08:22:16.236639 31616 solver.cpp:237]     Train net output #0: loss = 3.38631 (* 1 = 3.38631 loss)
I0408 08:22:16.236649 31616 sgd_solver.cpp:105] Iteration 5160, lr = 2.68819e-05
I0408 08:22:21.270475 31616 solver.cpp:218] Iteration 5172 (2.38395 iter/s, 5.03367s/12 iters), loss = 3.3672
I0408 08:22:21.270521 31616 solver.cpp:237]     Train net output #0: loss = 3.3672 (* 1 = 3.3672 loss)
I0408 08:22:21.270534 31616 sgd_solver.cpp:105] Iteration 5172, lr = 2.63728e-05
I0408 08:22:26.324944 31616 solver.cpp:218] Iteration 5184 (2.37424 iter/s, 5.05425s/12 iters), loss = 3.25895
I0408 08:22:26.324988 31616 solver.cpp:237]     Train net output #0: loss = 3.25895 (* 1 = 3.25895 loss)
I0408 08:22:26.325001 31616 sgd_solver.cpp:105] Iteration 5184, lr = 2.58733e-05
I0408 08:22:31.374752 31616 solver.cpp:218] Iteration 5196 (2.37643 iter/s, 5.04959s/12 iters), loss = 3.20738
I0408 08:22:31.374799 31616 solver.cpp:237]     Train net output #0: loss = 3.20738 (* 1 = 3.20738 loss)
I0408 08:22:31.374810 31616 sgd_solver.cpp:105] Iteration 5196, lr = 2.53833e-05
I0408 08:22:33.445405 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0408 08:22:39.109964 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0408 08:22:44.614540 31616 solver.cpp:330] Iteration 5202, Testing net (#0)
I0408 08:22:44.614575 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:22:47.019302 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:22:49.075390 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148284
I0408 08:22:49.075425 31616 solver.cpp:397]     Test net output #1: loss = 3.86579 (* 1 = 3.86579 loss)
I0408 08:22:51.045543 31616 solver.cpp:218] Iteration 5208 (0.610063 iter/s, 19.6701s/12 iters), loss = 3.10444
I0408 08:22:51.045588 31616 solver.cpp:237]     Train net output #0: loss = 3.10444 (* 1 = 3.10444 loss)
I0408 08:22:51.045598 31616 sgd_solver.cpp:105] Iteration 5208, lr = 2.49026e-05
I0408 08:22:56.086436 31616 solver.cpp:218] Iteration 5220 (2.38063 iter/s, 5.04068s/12 iters), loss = 3.39579
I0408 08:22:56.086475 31616 solver.cpp:237]     Train net output #0: loss = 3.39579 (* 1 = 3.39579 loss)
I0408 08:22:56.086485 31616 sgd_solver.cpp:105] Iteration 5220, lr = 2.4431e-05
I0408 08:23:01.142966 31616 solver.cpp:218] Iteration 5232 (2.37327 iter/s, 5.05632s/12 iters), loss = 3.39371
I0408 08:23:01.143013 31616 solver.cpp:237]     Train net output #0: loss = 3.39371 (* 1 = 3.39371 loss)
I0408 08:23:01.143025 31616 sgd_solver.cpp:105] Iteration 5232, lr = 2.39684e-05
I0408 08:23:06.276264 31616 solver.cpp:218] Iteration 5244 (2.33778 iter/s, 5.13307s/12 iters), loss = 3.20834
I0408 08:23:06.276309 31616 solver.cpp:237]     Train net output #0: loss = 3.20834 (* 1 = 3.20834 loss)
I0408 08:23:06.276321 31616 sgd_solver.cpp:105] Iteration 5244, lr = 2.35144e-05
I0408 08:23:11.260799 31616 solver.cpp:218] Iteration 5256 (2.40755 iter/s, 4.98432s/12 iters), loss = 3.32495
I0408 08:23:11.260936 31616 solver.cpp:237]     Train net output #0: loss = 3.32495 (* 1 = 3.32495 loss)
I0408 08:23:11.260949 31616 sgd_solver.cpp:105] Iteration 5256, lr = 2.30691e-05
I0408 08:23:12.556793 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:23:16.299890 31616 solver.cpp:218] Iteration 5268 (2.38153 iter/s, 5.03878s/12 iters), loss = 3.08814
I0408 08:23:16.299935 31616 solver.cpp:237]     Train net output #0: loss = 3.08814 (* 1 = 3.08814 loss)
I0408 08:23:16.299947 31616 sgd_solver.cpp:105] Iteration 5268, lr = 2.26322e-05
I0408 08:23:21.353619 31616 solver.cpp:218] Iteration 5280 (2.37459 iter/s, 5.05351s/12 iters), loss = 3.39819
I0408 08:23:21.353664 31616 solver.cpp:237]     Train net output #0: loss = 3.39819 (* 1 = 3.39819 loss)
I0408 08:23:21.353674 31616 sgd_solver.cpp:105] Iteration 5280, lr = 2.22036e-05
I0408 08:23:26.291182 31616 solver.cpp:218] Iteration 5292 (2.43045 iter/s, 4.93735s/12 iters), loss = 3.34026
I0408 08:23:26.291230 31616 solver.cpp:237]     Train net output #0: loss = 3.34026 (* 1 = 3.34026 loss)
I0408 08:23:26.291242 31616 sgd_solver.cpp:105] Iteration 5292, lr = 2.17831e-05
I0408 08:23:30.883316 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0408 08:23:35.971262 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0408 08:23:41.989156 31616 solver.cpp:330] Iteration 5304, Testing net (#0)
I0408 08:23:41.989260 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:23:44.363548 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:23:46.461647 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150123
I0408 08:23:46.461696 31616 solver.cpp:397]     Test net output #1: loss = 3.85979 (* 1 = 3.85979 loss)
I0408 08:23:46.553030 31616 solver.cpp:218] Iteration 5304 (0.592267 iter/s, 20.2611s/12 iters), loss = 3.3093
I0408 08:23:46.553078 31616 solver.cpp:237]     Train net output #0: loss = 3.3093 (* 1 = 3.3093 loss)
I0408 08:23:46.553089 31616 sgd_solver.cpp:105] Iteration 5304, lr = 2.13706e-05
I0408 08:23:51.034096 31616 solver.cpp:218] Iteration 5316 (2.67806 iter/s, 4.48086s/12 iters), loss = 3.20575
I0408 08:23:51.034143 31616 solver.cpp:237]     Train net output #0: loss = 3.20575 (* 1 = 3.20575 loss)
I0408 08:23:51.034154 31616 sgd_solver.cpp:105] Iteration 5316, lr = 2.09659e-05
I0408 08:23:56.309662 31616 solver.cpp:218] Iteration 5328 (2.27474 iter/s, 5.27534s/12 iters), loss = 3.34202
I0408 08:23:56.309705 31616 solver.cpp:237]     Train net output #0: loss = 3.34202 (* 1 = 3.34202 loss)
I0408 08:23:56.309715 31616 sgd_solver.cpp:105] Iteration 5328, lr = 2.05688e-05
I0408 08:24:01.348052 31616 solver.cpp:218] Iteration 5340 (2.38182 iter/s, 5.03817s/12 iters), loss = 3.27148
I0408 08:24:01.348104 31616 solver.cpp:237]     Train net output #0: loss = 3.27148 (* 1 = 3.27148 loss)
I0408 08:24:01.348115 31616 sgd_solver.cpp:105] Iteration 5340, lr = 2.01793e-05
I0408 08:24:06.348954 31616 solver.cpp:218] Iteration 5352 (2.39967 iter/s, 5.00068s/12 iters), loss = 3.30796
I0408 08:24:06.348999 31616 solver.cpp:237]     Train net output #0: loss = 3.30796 (* 1 = 3.30796 loss)
I0408 08:24:06.349009 31616 sgd_solver.cpp:105] Iteration 5352, lr = 1.97971e-05
I0408 08:24:09.797652 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:24:11.375365 31616 solver.cpp:218] Iteration 5364 (2.38749 iter/s, 5.02619s/12 iters), loss = 3.43398
I0408 08:24:11.375404 31616 solver.cpp:237]     Train net output #0: loss = 3.43398 (* 1 = 3.43398 loss)
I0408 08:24:11.375416 31616 sgd_solver.cpp:105] Iteration 5364, lr = 1.94222e-05
I0408 08:24:16.672168 31616 solver.cpp:218] Iteration 5376 (2.26561 iter/s, 5.29659s/12 iters), loss = 3.20355
I0408 08:24:16.672313 31616 solver.cpp:237]     Train net output #0: loss = 3.20355 (* 1 = 3.20355 loss)
I0408 08:24:16.672327 31616 sgd_solver.cpp:105] Iteration 5376, lr = 1.90544e-05
I0408 08:24:21.777130 31616 solver.cpp:218] Iteration 5388 (2.3508 iter/s, 5.10465s/12 iters), loss = 3.29897
I0408 08:24:21.777175 31616 solver.cpp:237]     Train net output #0: loss = 3.29897 (* 1 = 3.29897 loss)
I0408 08:24:21.777186 31616 sgd_solver.cpp:105] Iteration 5388, lr = 1.86935e-05
I0408 08:24:26.766538 31616 solver.cpp:218] Iteration 5400 (2.4052 iter/s, 4.98919s/12 iters), loss = 3.35498
I0408 08:24:26.766580 31616 solver.cpp:237]     Train net output #0: loss = 3.35498 (* 1 = 3.35498 loss)
I0408 08:24:26.766590 31616 sgd_solver.cpp:105] Iteration 5400, lr = 1.83395e-05
I0408 08:24:28.833333 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0408 08:24:32.990829 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0408 08:24:36.685386 31616 solver.cpp:330] Iteration 5406, Testing net (#0)
I0408 08:24:36.685420 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:24:39.031677 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:24:41.166698 31616 solver.cpp:397]     Test net output #0: accuracy = 0.153186
I0408 08:24:41.166736 31616 solver.cpp:397]     Test net output #1: loss = 3.85291 (* 1 = 3.85291 loss)
I0408 08:24:43.008674 31616 solver.cpp:218] Iteration 5412 (0.738845 iter/s, 16.2416s/12 iters), loss = 3.19893
I0408 08:24:43.008724 31616 solver.cpp:237]     Train net output #0: loss = 3.19893 (* 1 = 3.19893 loss)
I0408 08:24:43.008735 31616 sgd_solver.cpp:105] Iteration 5412, lr = 1.79922e-05
I0408 08:24:48.191529 31616 solver.cpp:218] Iteration 5424 (2.31543 iter/s, 5.18263s/12 iters), loss = 3.31384
I0408 08:24:48.191615 31616 solver.cpp:237]     Train net output #0: loss = 3.31384 (* 1 = 3.31384 loss)
I0408 08:24:48.191628 31616 sgd_solver.cpp:105] Iteration 5424, lr = 1.76515e-05
I0408 08:24:53.227658 31616 solver.cpp:218] Iteration 5436 (2.3829 iter/s, 5.03587s/12 iters), loss = 3.34584
I0408 08:24:53.227708 31616 solver.cpp:237]     Train net output #0: loss = 3.34584 (* 1 = 3.34584 loss)
I0408 08:24:53.227720 31616 sgd_solver.cpp:105] Iteration 5436, lr = 1.73172e-05
I0408 08:24:58.264688 31616 solver.cpp:218] Iteration 5448 (2.38246 iter/s, 5.03681s/12 iters), loss = 3.28734
I0408 08:24:58.264736 31616 solver.cpp:237]     Train net output #0: loss = 3.28734 (* 1 = 3.28734 loss)
I0408 08:24:58.264748 31616 sgd_solver.cpp:105] Iteration 5448, lr = 1.69892e-05
I0408 08:25:03.403403 31616 solver.cpp:218] Iteration 5460 (2.33531 iter/s, 5.13849s/12 iters), loss = 3.33196
I0408 08:25:03.403445 31616 solver.cpp:237]     Train net output #0: loss = 3.33196 (* 1 = 3.33196 loss)
I0408 08:25:03.403455 31616 sgd_solver.cpp:105] Iteration 5460, lr = 1.66675e-05
I0408 08:25:04.026001 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:25:08.617460 31616 solver.cpp:218] Iteration 5472 (2.30157 iter/s, 5.21384s/12 iters), loss = 3.2516
I0408 08:25:08.617499 31616 solver.cpp:237]     Train net output #0: loss = 3.2516 (* 1 = 3.2516 loss)
I0408 08:25:08.617508 31616 sgd_solver.cpp:105] Iteration 5472, lr = 1.63518e-05
I0408 08:25:13.590584 31616 solver.cpp:218] Iteration 5484 (2.41307 iter/s, 4.97291s/12 iters), loss = 2.97083
I0408 08:25:13.590621 31616 solver.cpp:237]     Train net output #0: loss = 2.97083 (* 1 = 2.97083 loss)
I0408 08:25:13.590629 31616 sgd_solver.cpp:105] Iteration 5484, lr = 1.60422e-05
I0408 08:25:18.926779 31616 solver.cpp:218] Iteration 5496 (2.24889 iter/s, 5.33598s/12 iters), loss = 3.16755
I0408 08:25:18.926932 31616 solver.cpp:237]     Train net output #0: loss = 3.16755 (* 1 = 3.16755 loss)
I0408 08:25:18.926946 31616 sgd_solver.cpp:105] Iteration 5496, lr = 1.57384e-05
I0408 08:25:23.514488 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0408 08:25:27.774662 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0408 08:25:31.466892 31616 solver.cpp:330] Iteration 5508, Testing net (#0)
I0408 08:25:31.466923 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:25:33.746644 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:25:35.924170 31616 solver.cpp:397]     Test net output #0: accuracy = 0.147059
I0408 08:25:35.924216 31616 solver.cpp:397]     Test net output #1: loss = 3.86179 (* 1 = 3.86179 loss)
I0408 08:25:36.015611 31616 solver.cpp:218] Iteration 5508 (0.702242 iter/s, 17.0881s/12 iters), loss = 3.16899
I0408 08:25:36.015683 31616 solver.cpp:237]     Train net output #0: loss = 3.16899 (* 1 = 3.16899 loss)
I0408 08:25:36.015700 31616 sgd_solver.cpp:105] Iteration 5508, lr = 1.54403e-05
I0408 08:25:40.216073 31616 solver.cpp:218] Iteration 5520 (2.85697 iter/s, 4.20025s/12 iters), loss = 3.23552
I0408 08:25:40.216120 31616 solver.cpp:237]     Train net output #0: loss = 3.23552 (* 1 = 3.23552 loss)
I0408 08:25:40.216132 31616 sgd_solver.cpp:105] Iteration 5520, lr = 1.51479e-05
I0408 08:25:42.626154 31616 blocking_queue.cpp:49] Waiting for data
I0408 08:25:45.180694 31616 solver.cpp:218] Iteration 5532 (2.41721 iter/s, 4.96441s/12 iters), loss = 3.20728
I0408 08:25:45.180739 31616 solver.cpp:237]     Train net output #0: loss = 3.20728 (* 1 = 3.20728 loss)
I0408 08:25:45.180752 31616 sgd_solver.cpp:105] Iteration 5532, lr = 1.4861e-05
I0408 08:25:50.206820 31616 solver.cpp:218] Iteration 5544 (2.38763 iter/s, 5.02591s/12 iters), loss = 3.14503
I0408 08:25:50.206900 31616 solver.cpp:237]     Train net output #0: loss = 3.14503 (* 1 = 3.14503 loss)
I0408 08:25:50.206913 31616 sgd_solver.cpp:105] Iteration 5544, lr = 1.45796e-05
I0408 08:25:55.220432 31616 solver.cpp:218] Iteration 5556 (2.3936 iter/s, 5.01336s/12 iters), loss = 3.39797
I0408 08:25:55.220476 31616 solver.cpp:237]     Train net output #0: loss = 3.39797 (* 1 = 3.39797 loss)
I0408 08:25:55.220489 31616 sgd_solver.cpp:105] Iteration 5556, lr = 1.43035e-05
I0408 08:25:57.918640 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:26:00.235287 31616 solver.cpp:218] Iteration 5568 (2.39299 iter/s, 5.01464s/12 iters), loss = 3.04199
I0408 08:26:00.235337 31616 solver.cpp:237]     Train net output #0: loss = 3.04199 (* 1 = 3.04199 loss)
I0408 08:26:00.235348 31616 sgd_solver.cpp:105] Iteration 5568, lr = 1.40326e-05
I0408 08:26:05.222270 31616 solver.cpp:218] Iteration 5580 (2.40637 iter/s, 4.98676s/12 iters), loss = 3.33206
I0408 08:26:05.222316 31616 solver.cpp:237]     Train net output #0: loss = 3.33206 (* 1 = 3.33206 loss)
I0408 08:26:05.222327 31616 sgd_solver.cpp:105] Iteration 5580, lr = 1.37668e-05
I0408 08:26:10.516597 31616 solver.cpp:218] Iteration 5592 (2.26667 iter/s, 5.2941s/12 iters), loss = 3.41479
I0408 08:26:10.516644 31616 solver.cpp:237]     Train net output #0: loss = 3.41479 (* 1 = 3.41479 loss)
I0408 08:26:10.516654 31616 sgd_solver.cpp:105] Iteration 5592, lr = 1.35061e-05
I0408 08:26:15.817221 31616 solver.cpp:218] Iteration 5604 (2.26398 iter/s, 5.3004s/12 iters), loss = 3.31891
I0408 08:26:15.817255 31616 solver.cpp:237]     Train net output #0: loss = 3.31891 (* 1 = 3.31891 loss)
I0408 08:26:15.817262 31616 sgd_solver.cpp:105] Iteration 5604, lr = 1.32503e-05
I0408 08:26:17.978848 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0408 08:26:22.239348 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0408 08:26:26.092208 31616 solver.cpp:330] Iteration 5610, Testing net (#0)
I0408 08:26:26.092243 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:26:28.307824 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:26:30.654902 31616 solver.cpp:397]     Test net output #0: accuracy = 0.151348
I0408 08:26:30.654951 31616 solver.cpp:397]     Test net output #1: loss = 3.84758 (* 1 = 3.84758 loss)
I0408 08:26:32.652766 31616 solver.cpp:218] Iteration 5616 (0.712802 iter/s, 16.835s/12 iters), loss = 3.33916
I0408 08:26:32.652817 31616 solver.cpp:237]     Train net output #0: loss = 3.33916 (* 1 = 3.33916 loss)
I0408 08:26:32.652827 31616 sgd_solver.cpp:105] Iteration 5616, lr = 1.29994e-05
I0408 08:26:38.062642 31616 solver.cpp:218] Iteration 5628 (2.21826 iter/s, 5.40965s/12 iters), loss = 3.21
I0408 08:26:38.062687 31616 solver.cpp:237]     Train net output #0: loss = 3.21 (* 1 = 3.21 loss)
I0408 08:26:38.062696 31616 sgd_solver.cpp:105] Iteration 5628, lr = 1.27532e-05
I0408 08:26:43.031296 31616 solver.cpp:218] Iteration 5640 (2.41525 iter/s, 4.96844s/12 iters), loss = 3.445
I0408 08:26:43.031334 31616 solver.cpp:237]     Train net output #0: loss = 3.445 (* 1 = 3.445 loss)
I0408 08:26:43.031343 31616 sgd_solver.cpp:105] Iteration 5640, lr = 1.25117e-05
I0408 08:26:48.025720 31616 solver.cpp:218] Iteration 5652 (2.40278 iter/s, 4.99421s/12 iters), loss = 3.46959
I0408 08:26:48.025768 31616 solver.cpp:237]     Train net output #0: loss = 3.46959 (* 1 = 3.46959 loss)
I0408 08:26:48.025780 31616 sgd_solver.cpp:105] Iteration 5652, lr = 1.22748e-05
I0408 08:26:52.883069 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:26:53.052280 31616 solver.cpp:218] Iteration 5664 (2.38742 iter/s, 5.02634s/12 iters), loss = 3.0836
I0408 08:26:53.052322 31616 solver.cpp:237]     Train net output #0: loss = 3.0836 (* 1 = 3.0836 loss)
I0408 08:26:53.052332 31616 sgd_solver.cpp:105] Iteration 5664, lr = 1.20423e-05
I0408 08:26:58.387032 31616 solver.cpp:218] Iteration 5676 (2.2495 iter/s, 5.33453s/12 iters), loss = 3.45828
I0408 08:26:58.387070 31616 solver.cpp:237]     Train net output #0: loss = 3.45828 (* 1 = 3.45828 loss)
I0408 08:26:58.387079 31616 sgd_solver.cpp:105] Iteration 5676, lr = 1.18142e-05
I0408 08:27:03.522488 31616 solver.cpp:218] Iteration 5688 (2.33679 iter/s, 5.13524s/12 iters), loss = 3.01447
I0408 08:27:03.522527 31616 solver.cpp:237]     Train net output #0: loss = 3.01447 (* 1 = 3.01447 loss)
I0408 08:27:03.522536 31616 sgd_solver.cpp:105] Iteration 5688, lr = 1.15905e-05
I0408 08:27:08.545727 31616 solver.cpp:218] Iteration 5700 (2.389 iter/s, 5.02302s/12 iters), loss = 3.25092
I0408 08:27:08.545766 31616 solver.cpp:237]     Train net output #0: loss = 3.25092 (* 1 = 3.25092 loss)
I0408 08:27:08.545776 31616 sgd_solver.cpp:105] Iteration 5700, lr = 1.1371e-05
I0408 08:27:13.289435 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0408 08:27:20.070325 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0408 08:27:25.769842 31616 solver.cpp:330] Iteration 5712, Testing net (#0)
I0408 08:27:25.769919 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:27:27.984532 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:27:30.232493 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150123
I0408 08:27:30.232542 31616 solver.cpp:397]     Test net output #1: loss = 3.85668 (* 1 = 3.85668 loss)
I0408 08:27:30.323716 31616 solver.cpp:218] Iteration 5712 (0.551034 iter/s, 21.7772s/12 iters), loss = 3.04481
I0408 08:27:30.323766 31616 solver.cpp:237]     Train net output #0: loss = 3.04481 (* 1 = 3.04481 loss)
I0408 08:27:30.323777 31616 sgd_solver.cpp:105] Iteration 5712, lr = 1.11557e-05
I0408 08:27:34.627547 31616 solver.cpp:218] Iteration 5724 (2.78834 iter/s, 4.30364s/12 iters), loss = 3.40646
I0408 08:27:34.627580 31616 solver.cpp:237]     Train net output #0: loss = 3.40646 (* 1 = 3.40646 loss)
I0408 08:27:34.627589 31616 sgd_solver.cpp:105] Iteration 5724, lr = 1.09444e-05
I0408 08:27:39.584904 31616 solver.cpp:218] Iteration 5736 (2.42075 iter/s, 4.95715s/12 iters), loss = 3.35659
I0408 08:27:39.584955 31616 solver.cpp:237]     Train net output #0: loss = 3.35659 (* 1 = 3.35659 loss)
I0408 08:27:39.584967 31616 sgd_solver.cpp:105] Iteration 5736, lr = 1.07371e-05
I0408 08:27:44.587258 31616 solver.cpp:218] Iteration 5748 (2.39898 iter/s, 5.00213s/12 iters), loss = 3.29693
I0408 08:27:44.587307 31616 solver.cpp:237]     Train net output #0: loss = 3.29693 (* 1 = 3.29693 loss)
I0408 08:27:44.587319 31616 sgd_solver.cpp:105] Iteration 5748, lr = 1.05338e-05
I0408 08:27:49.602880 31616 solver.cpp:218] Iteration 5760 (2.39263 iter/s, 5.0154s/12 iters), loss = 3.38212
I0408 08:27:49.602931 31616 solver.cpp:237]     Train net output #0: loss = 3.38212 (* 1 = 3.38212 loss)
I0408 08:27:49.602941 31616 sgd_solver.cpp:105] Iteration 5760, lr = 1.03343e-05
I0408 08:27:51.528442 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:27:54.562569 31616 solver.cpp:218] Iteration 5772 (2.41961 iter/s, 4.95947s/12 iters), loss = 3.3882
I0408 08:27:54.562614 31616 solver.cpp:237]     Train net output #0: loss = 3.3882 (* 1 = 3.3882 loss)
I0408 08:27:54.562623 31616 sgd_solver.cpp:105] Iteration 5772, lr = 1.01386e-05
I0408 08:27:59.561740 31616 solver.cpp:218] Iteration 5784 (2.4005 iter/s, 4.99895s/12 iters), loss = 3.36207
I0408 08:27:59.561852 31616 solver.cpp:237]     Train net output #0: loss = 3.36207 (* 1 = 3.36207 loss)
I0408 08:27:59.561864 31616 sgd_solver.cpp:105] Iteration 5784, lr = 9.94657e-06
I0408 08:28:04.573181 31616 solver.cpp:218] Iteration 5796 (2.39466 iter/s, 5.01116s/12 iters), loss = 3.12302
I0408 08:28:04.573231 31616 solver.cpp:237]     Train net output #0: loss = 3.12302 (* 1 = 3.12302 loss)
I0408 08:28:04.573243 31616 sgd_solver.cpp:105] Iteration 5796, lr = 9.7582e-06
I0408 08:28:09.584087 31616 solver.cpp:218] Iteration 5808 (2.39488 iter/s, 5.01068s/12 iters), loss = 3.41599
I0408 08:28:09.584126 31616 solver.cpp:237]     Train net output #0: loss = 3.41599 (* 1 = 3.41599 loss)
I0408 08:28:09.584134 31616 sgd_solver.cpp:105] Iteration 5808, lr = 9.5734e-06
I0408 08:28:11.613010 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0408 08:28:15.982117 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0408 08:28:19.726797 31616 solver.cpp:330] Iteration 5814, Testing net (#0)
I0408 08:28:19.726819 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:28:21.823704 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:28:24.117630 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150123
I0408 08:28:24.117678 31616 solver.cpp:397]     Test net output #1: loss = 3.85089 (* 1 = 3.85089 loss)
I0408 08:28:26.102618 31616 solver.cpp:218] Iteration 5820 (0.726482 iter/s, 16.518s/12 iters), loss = 3.31013
I0408 08:28:26.102667 31616 solver.cpp:237]     Train net output #0: loss = 3.31013 (* 1 = 3.31013 loss)
I0408 08:28:26.102679 31616 sgd_solver.cpp:105] Iteration 5820, lr = 9.3921e-06
I0408 08:28:31.190199 31616 solver.cpp:218] Iteration 5832 (2.35879 iter/s, 5.08736s/12 iters), loss = 3.20774
I0408 08:28:31.190295 31616 solver.cpp:237]     Train net output #0: loss = 3.20774 (* 1 = 3.20774 loss)
I0408 08:28:31.190308 31616 sgd_solver.cpp:105] Iteration 5832, lr = 9.21423e-06
I0408 08:28:36.260591 31616 solver.cpp:218] Iteration 5844 (2.36681 iter/s, 5.07013s/12 iters), loss = 3.35769
I0408 08:28:36.260637 31616 solver.cpp:237]     Train net output #0: loss = 3.35769 (* 1 = 3.35769 loss)
I0408 08:28:36.260648 31616 sgd_solver.cpp:105] Iteration 5844, lr = 9.03973e-06
I0408 08:28:41.175375 31616 solver.cpp:218] Iteration 5856 (2.44172 iter/s, 4.91457s/12 iters), loss = 3.16509
I0408 08:28:41.175422 31616 solver.cpp:237]     Train net output #0: loss = 3.16509 (* 1 = 3.16509 loss)
I0408 08:28:41.175436 31616 sgd_solver.cpp:105] Iteration 5856, lr = 8.86854e-06
I0408 08:28:45.399540 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:28:46.216305 31616 solver.cpp:218] Iteration 5868 (2.38062 iter/s, 5.04071s/12 iters), loss = 3.11306
I0408 08:28:46.216352 31616 solver.cpp:237]     Train net output #0: loss = 3.11306 (* 1 = 3.11306 loss)
I0408 08:28:46.216364 31616 sgd_solver.cpp:105] Iteration 5868, lr = 8.70058e-06
I0408 08:28:51.191845 31616 solver.cpp:218] Iteration 5880 (2.41191 iter/s, 4.97532s/12 iters), loss = 3.52969
I0408 08:28:51.191891 31616 solver.cpp:237]     Train net output #0: loss = 3.52969 (* 1 = 3.52969 loss)
I0408 08:28:51.191902 31616 sgd_solver.cpp:105] Iteration 5880, lr = 8.53581e-06
I0408 08:28:56.225104 31616 solver.cpp:218] Iteration 5892 (2.38424 iter/s, 5.03304s/12 iters), loss = 3.35271
I0408 08:28:56.225150 31616 solver.cpp:237]     Train net output #0: loss = 3.35271 (* 1 = 3.35271 loss)
I0408 08:28:56.225160 31616 sgd_solver.cpp:105] Iteration 5892, lr = 8.37416e-06
I0408 08:29:01.262979 31616 solver.cpp:218] Iteration 5904 (2.38206 iter/s, 5.03766s/12 iters), loss = 3.18375
I0408 08:29:01.263121 31616 solver.cpp:237]     Train net output #0: loss = 3.18375 (* 1 = 3.18375 loss)
I0408 08:29:01.263134 31616 sgd_solver.cpp:105] Iteration 5904, lr = 8.21557e-06
I0408 08:29:05.754638 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0408 08:29:10.095127 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0408 08:29:13.320051 31616 solver.cpp:330] Iteration 5916, Testing net (#0)
I0408 08:29:13.320076 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:29:15.518864 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:29:17.852165 31616 solver.cpp:397]     Test net output #0: accuracy = 0.151961
I0408 08:29:17.852200 31616 solver.cpp:397]     Test net output #1: loss = 3.84827 (* 1 = 3.84827 loss)
I0408 08:29:17.943213 31616 solver.cpp:218] Iteration 5916 (0.719444 iter/s, 16.6796s/12 iters), loss = 3.23404
I0408 08:29:17.943253 31616 solver.cpp:237]     Train net output #0: loss = 3.23404 (* 1 = 3.23404 loss)
I0408 08:29:17.943262 31616 sgd_solver.cpp:105] Iteration 5916, lr = 8.05998e-06
I0408 08:29:22.468539 31616 solver.cpp:218] Iteration 5928 (2.65186 iter/s, 4.52513s/12 iters), loss = 3.29655
I0408 08:29:22.468580 31616 solver.cpp:237]     Train net output #0: loss = 3.29655 (* 1 = 3.29655 loss)
I0408 08:29:22.468590 31616 sgd_solver.cpp:105] Iteration 5928, lr = 7.90734e-06
I0408 08:29:27.917929 31616 solver.cpp:218] Iteration 5940 (2.20217 iter/s, 5.44916s/12 iters), loss = 3.3757
I0408 08:29:27.917986 31616 solver.cpp:237]     Train net output #0: loss = 3.3757 (* 1 = 3.3757 loss)
I0408 08:29:27.917999 31616 sgd_solver.cpp:105] Iteration 5940, lr = 7.75759e-06
I0408 08:29:32.963774 31616 solver.cpp:218] Iteration 5952 (2.3783 iter/s, 5.04562s/12 iters), loss = 3.09153
I0408 08:29:32.963867 31616 solver.cpp:237]     Train net output #0: loss = 3.09153 (* 1 = 3.09153 loss)
I0408 08:29:32.963876 31616 sgd_solver.cpp:105] Iteration 5952, lr = 7.61068e-06
I0408 08:29:38.035154 31616 solver.cpp:218] Iteration 5964 (2.36635 iter/s, 5.07111s/12 iters), loss = 3.18533
I0408 08:29:38.035212 31616 solver.cpp:237]     Train net output #0: loss = 3.18533 (* 1 = 3.18533 loss)
I0408 08:29:38.035228 31616 sgd_solver.cpp:105] Iteration 5964, lr = 7.46654e-06
I0408 08:29:39.355314 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:29:43.025128 31616 solver.cpp:218] Iteration 5976 (2.40493 iter/s, 4.98974s/12 iters), loss = 3.0543
I0408 08:29:43.025177 31616 solver.cpp:237]     Train net output #0: loss = 3.0543 (* 1 = 3.0543 loss)
I0408 08:29:43.025187 31616 sgd_solver.cpp:105] Iteration 5976, lr = 7.32514e-06
I0408 08:29:48.198459 31616 solver.cpp:218] Iteration 5988 (2.31969 iter/s, 5.17311s/12 iters), loss = 3.38941
I0408 08:29:48.198495 31616 solver.cpp:237]     Train net output #0: loss = 3.38941 (* 1 = 3.38941 loss)
I0408 08:29:48.198504 31616 sgd_solver.cpp:105] Iteration 5988, lr = 7.18642e-06
I0408 08:29:53.219660 31616 solver.cpp:218] Iteration 6000 (2.38997 iter/s, 5.02099s/12 iters), loss = 3.22189
I0408 08:29:53.219702 31616 solver.cpp:237]     Train net output #0: loss = 3.22189 (* 1 = 3.22189 loss)
I0408 08:29:53.219712 31616 sgd_solver.cpp:105] Iteration 6000, lr = 7.05032e-06
I0408 08:29:58.509155 31616 solver.cpp:218] Iteration 6012 (2.26875 iter/s, 5.28927s/12 iters), loss = 3.18168
I0408 08:29:58.509212 31616 solver.cpp:237]     Train net output #0: loss = 3.18168 (* 1 = 3.18168 loss)
I0408 08:29:58.509223 31616 sgd_solver.cpp:105] Iteration 6012, lr = 6.9168e-06
I0408 08:30:00.533124 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0408 08:30:05.318994 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0408 08:30:07.650614 31616 solver.cpp:330] Iteration 6018, Testing net (#0)
I0408 08:30:07.650641 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:30:09.814193 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:30:12.190716 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 08:30:12.190765 31616 solver.cpp:397]     Test net output #1: loss = 3.85352 (* 1 = 3.85352 loss)
I0408 08:30:14.097056 31616 solver.cpp:218] Iteration 6024 (0.769855 iter/s, 15.5873s/12 iters), loss = 3.27557
I0408 08:30:14.097095 31616 solver.cpp:237]     Train net output #0: loss = 3.27557 (* 1 = 3.27557 loss)
I0408 08:30:14.097103 31616 sgd_solver.cpp:105] Iteration 6024, lr = 6.78581e-06
I0408 08:30:19.044330 31616 solver.cpp:218] Iteration 6036 (2.42568 iter/s, 4.94706s/12 iters), loss = 3.31956
I0408 08:30:19.044376 31616 solver.cpp:237]     Train net output #0: loss = 3.31956 (* 1 = 3.31956 loss)
I0408 08:30:19.044386 31616 sgd_solver.cpp:105] Iteration 6036, lr = 6.6573e-06
I0408 08:30:24.028353 31616 solver.cpp:218] Iteration 6048 (2.4078 iter/s, 4.9838s/12 iters), loss = 3.26941
I0408 08:30:24.028401 31616 solver.cpp:237]     Train net output #0: loss = 3.26941 (* 1 = 3.26941 loss)
I0408 08:30:24.028414 31616 sgd_solver.cpp:105] Iteration 6048, lr = 6.53122e-06
I0408 08:30:29.051499 31616 solver.cpp:218] Iteration 6060 (2.38905 iter/s, 5.02293s/12 iters), loss = 3.21508
I0408 08:30:29.051548 31616 solver.cpp:237]     Train net output #0: loss = 3.21508 (* 1 = 3.21508 loss)
I0408 08:30:29.051560 31616 sgd_solver.cpp:105] Iteration 6060, lr = 6.40754e-06
I0408 08:30:32.470175 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:30:33.995986 31616 solver.cpp:218] Iteration 6072 (2.42705 iter/s, 4.94427s/12 iters), loss = 3.34123
I0408 08:30:33.996031 31616 solver.cpp:237]     Train net output #0: loss = 3.34123 (* 1 = 3.34123 loss)
I0408 08:30:33.996042 31616 sgd_solver.cpp:105] Iteration 6072, lr = 6.28619e-06
I0408 08:30:39.045348 31616 solver.cpp:218] Iteration 6084 (2.37664 iter/s, 5.04914s/12 iters), loss = 3.33526
I0408 08:30:39.045430 31616 solver.cpp:237]     Train net output #0: loss = 3.33526 (* 1 = 3.33526 loss)
I0408 08:30:39.045444 31616 sgd_solver.cpp:105] Iteration 6084, lr = 6.16714e-06
I0408 08:30:44.013922 31616 solver.cpp:218] Iteration 6096 (2.4153 iter/s, 4.96833s/12 iters), loss = 3.35746
I0408 08:30:44.013965 31616 solver.cpp:237]     Train net output #0: loss = 3.35746 (* 1 = 3.35746 loss)
I0408 08:30:44.013973 31616 sgd_solver.cpp:105] Iteration 6096, lr = 6.05035e-06
I0408 08:30:48.987985 31616 solver.cpp:218] Iteration 6108 (2.41261 iter/s, 4.97386s/12 iters), loss = 3.31829
I0408 08:30:48.988020 31616 solver.cpp:237]     Train net output #0: loss = 3.31829 (* 1 = 3.31829 loss)
I0408 08:30:48.988027 31616 sgd_solver.cpp:105] Iteration 6108, lr = 5.93576e-06
I0408 08:30:53.521718 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0408 08:30:58.551581 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0408 08:31:02.861202 31616 solver.cpp:330] Iteration 6120, Testing net (#0)
I0408 08:31:02.861230 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:31:04.884259 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:31:07.293810 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0408 08:31:07.293859 31616 solver.cpp:397]     Test net output #1: loss = 3.84923 (* 1 = 3.84923 loss)
I0408 08:31:07.385263 31616 solver.cpp:218] Iteration 6120 (0.652293 iter/s, 18.3966s/12 iters), loss = 3.3215
I0408 08:31:07.385313 31616 solver.cpp:237]     Train net output #0: loss = 3.3215 (* 1 = 3.3215 loss)
I0408 08:31:07.385324 31616 sgd_solver.cpp:105] Iteration 6120, lr = 5.82335e-06
I0408 08:31:11.628904 31616 solver.cpp:218] Iteration 6132 (2.82789 iter/s, 4.24345s/12 iters), loss = 3.34011
I0408 08:31:11.629011 31616 solver.cpp:237]     Train net output #0: loss = 3.34011 (* 1 = 3.34011 loss)
I0408 08:31:11.629024 31616 sgd_solver.cpp:105] Iteration 6132, lr = 5.71307e-06
I0408 08:31:16.613298 31616 solver.cpp:218] Iteration 6144 (2.40765 iter/s, 4.98412s/12 iters), loss = 3.41327
I0408 08:31:16.613343 31616 solver.cpp:237]     Train net output #0: loss = 3.41327 (* 1 = 3.41327 loss)
I0408 08:31:16.613354 31616 sgd_solver.cpp:105] Iteration 6144, lr = 5.60488e-06
I0408 08:31:21.652040 31616 solver.cpp:218] Iteration 6156 (2.38165 iter/s, 5.03852s/12 iters), loss = 3.43229
I0408 08:31:21.652091 31616 solver.cpp:237]     Train net output #0: loss = 3.43229 (* 1 = 3.43229 loss)
I0408 08:31:21.652103 31616 sgd_solver.cpp:105] Iteration 6156, lr = 5.49873e-06
I0408 08:31:26.665408 31616 solver.cpp:218] Iteration 6168 (2.39371 iter/s, 5.01314s/12 iters), loss = 3.16028
I0408 08:31:26.665458 31616 solver.cpp:237]     Train net output #0: loss = 3.16028 (* 1 = 3.16028 loss)
I0408 08:31:26.665470 31616 sgd_solver.cpp:105] Iteration 6168, lr = 5.39459e-06
I0408 08:31:27.271740 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:31:31.705729 31616 solver.cpp:218] Iteration 6180 (2.3809 iter/s, 5.0401s/12 iters), loss = 3.25973
I0408 08:31:31.705775 31616 solver.cpp:237]     Train net output #0: loss = 3.25973 (* 1 = 3.25973 loss)
I0408 08:31:31.705787 31616 sgd_solver.cpp:105] Iteration 6180, lr = 5.29243e-06
I0408 08:31:36.699165 31616 solver.cpp:218] Iteration 6192 (2.40326 iter/s, 4.99322s/12 iters), loss = 2.99059
I0408 08:31:36.699221 31616 solver.cpp:237]     Train net output #0: loss = 2.99059 (* 1 = 2.99059 loss)
I0408 08:31:36.699236 31616 sgd_solver.cpp:105] Iteration 6192, lr = 5.1922e-06
I0408 08:31:41.654119 31616 solver.cpp:218] Iteration 6204 (2.42193 iter/s, 4.95473s/12 iters), loss = 3.09322
I0408 08:31:41.654184 31616 solver.cpp:237]     Train net output #0: loss = 3.09322 (* 1 = 3.09322 loss)
I0408 08:31:41.654192 31616 sgd_solver.cpp:105] Iteration 6204, lr = 5.09387e-06
I0408 08:31:46.788313 31616 solver.cpp:218] Iteration 6216 (2.33738 iter/s, 5.13396s/12 iters), loss = 3.11826
I0408 08:31:46.788354 31616 solver.cpp:237]     Train net output #0: loss = 3.11826 (* 1 = 3.11826 loss)
I0408 08:31:46.788364 31616 sgd_solver.cpp:105] Iteration 6216, lr = 4.9974e-06
I0408 08:31:48.998090 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0408 08:31:54.067435 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0408 08:32:00.316241 31616 solver.cpp:330] Iteration 6222, Testing net (#0)
I0408 08:32:00.316267 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:32:02.344130 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:32:03.618767 31616 blocking_queue.cpp:49] Waiting for data
I0408 08:32:04.791937 31616 solver.cpp:397]     Test net output #0: accuracy = 0.151961
I0408 08:32:04.791980 31616 solver.cpp:397]     Test net output #1: loss = 3.84732 (* 1 = 3.84732 loss)
I0408 08:32:06.705859 31616 solver.cpp:218] Iteration 6228 (0.602505 iter/s, 19.9169s/12 iters), loss = 3.2202
I0408 08:32:06.705904 31616 solver.cpp:237]     Train net output #0: loss = 3.2202 (* 1 = 3.2202 loss)
I0408 08:32:06.705915 31616 sgd_solver.cpp:105] Iteration 6228, lr = 4.90276e-06
I0408 08:32:11.705181 31616 solver.cpp:218] Iteration 6240 (2.40043 iter/s, 4.99911s/12 iters), loss = 3.37969
I0408 08:32:11.705328 31616 solver.cpp:237]     Train net output #0: loss = 3.37969 (* 1 = 3.37969 loss)
I0408 08:32:11.705341 31616 sgd_solver.cpp:105] Iteration 6240, lr = 4.80991e-06
I0408 08:32:16.729626 31616 solver.cpp:218] Iteration 6252 (2.38847 iter/s, 5.02413s/12 iters), loss = 3.21293
I0408 08:32:16.729672 31616 solver.cpp:237]     Train net output #0: loss = 3.21293 (* 1 = 3.21293 loss)
I0408 08:32:16.729684 31616 sgd_solver.cpp:105] Iteration 6252, lr = 4.71882e-06
I0408 08:32:21.735725 31616 solver.cpp:218] Iteration 6264 (2.39718 iter/s, 5.00588s/12 iters), loss = 3.3938
I0408 08:32:21.735769 31616 solver.cpp:237]     Train net output #0: loss = 3.3938 (* 1 = 3.3938 loss)
I0408 08:32:21.735780 31616 sgd_solver.cpp:105] Iteration 6264, lr = 4.62946e-06
I0408 08:32:24.464148 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:32:26.762490 31616 solver.cpp:218] Iteration 6276 (2.38732 iter/s, 5.02655s/12 iters), loss = 3.08628
I0408 08:32:26.762537 31616 solver.cpp:237]     Train net output #0: loss = 3.08628 (* 1 = 3.08628 loss)
I0408 08:32:26.762548 31616 sgd_solver.cpp:105] Iteration 6276, lr = 4.54178e-06
I0408 08:32:31.791658 31616 solver.cpp:218] Iteration 6288 (2.38618 iter/s, 5.02895s/12 iters), loss = 3.46769
I0408 08:32:31.791705 31616 solver.cpp:237]     Train net output #0: loss = 3.46769 (* 1 = 3.46769 loss)
I0408 08:32:31.791718 31616 sgd_solver.cpp:105] Iteration 6288, lr = 4.45577e-06
I0408 08:32:36.781514 31616 solver.cpp:218] Iteration 6300 (2.40498 iter/s, 4.98964s/12 iters), loss = 3.35253
I0408 08:32:36.781559 31616 solver.cpp:237]     Train net output #0: loss = 3.35253 (* 1 = 3.35253 loss)
I0408 08:32:36.781570 31616 sgd_solver.cpp:105] Iteration 6300, lr = 4.37139e-06
I0408 08:32:41.788278 31616 solver.cpp:218] Iteration 6312 (2.39686 iter/s, 5.00654s/12 iters), loss = 3.37527
I0408 08:32:41.788415 31616 solver.cpp:237]     Train net output #0: loss = 3.37527 (* 1 = 3.37527 loss)
I0408 08:32:41.788429 31616 sgd_solver.cpp:105] Iteration 6312, lr = 4.2886e-06
I0408 08:32:46.302944 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0408 08:32:51.161813 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0408 08:33:02.396637 31616 solver.cpp:330] Iteration 6324, Testing net (#0)
I0408 08:33:02.396670 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:33:04.389991 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:33:06.872965 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0408 08:33:06.873008 31616 solver.cpp:397]     Test net output #1: loss = 3.84472 (* 1 = 3.84472 loss)
I0408 08:33:06.964154 31616 solver.cpp:218] Iteration 6324 (0.476665 iter/s, 25.1749s/12 iters), loss = 3.45266
I0408 08:33:06.964195 31616 solver.cpp:237]     Train net output #0: loss = 3.45266 (* 1 = 3.45266 loss)
I0408 08:33:06.964205 31616 sgd_solver.cpp:105] Iteration 6324, lr = 4.20738e-06
I0408 08:33:11.570526 31616 solver.cpp:218] Iteration 6336 (2.6052 iter/s, 4.60617s/12 iters), loss = 3.18407
I0408 08:33:11.570571 31616 solver.cpp:237]     Train net output #0: loss = 3.18407 (* 1 = 3.18407 loss)
I0408 08:33:11.570583 31616 sgd_solver.cpp:105] Iteration 6336, lr = 4.1277e-06
I0408 08:33:16.547881 31616 solver.cpp:218] Iteration 6348 (2.41103 iter/s, 4.97713s/12 iters), loss = 3.48238
I0408 08:33:16.547983 31616 solver.cpp:237]     Train net output #0: loss = 3.48238 (* 1 = 3.48238 loss)
I0408 08:33:16.547997 31616 sgd_solver.cpp:105] Iteration 6348, lr = 4.04953e-06
I0408 08:33:21.556514 31616 solver.cpp:218] Iteration 6360 (2.396 iter/s, 5.00836s/12 iters), loss = 3.3559
I0408 08:33:21.556568 31616 solver.cpp:237]     Train net output #0: loss = 3.3559 (* 1 = 3.3559 loss)
I0408 08:33:21.556581 31616 sgd_solver.cpp:105] Iteration 6360, lr = 3.97284e-06
I0408 08:33:26.454479 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:33:26.594074 31616 solver.cpp:218] Iteration 6372 (2.38221 iter/s, 5.03733s/12 iters), loss = 3.31555
I0408 08:33:26.594120 31616 solver.cpp:237]     Train net output #0: loss = 3.31555 (* 1 = 3.31555 loss)
I0408 08:33:26.594130 31616 sgd_solver.cpp:105] Iteration 6372, lr = 3.89761e-06
I0408 08:33:31.629608 31616 solver.cpp:218] Iteration 6384 (2.38317 iter/s, 5.03532s/12 iters), loss = 3.32486
I0408 08:33:31.629654 31616 solver.cpp:237]     Train net output #0: loss = 3.32486 (* 1 = 3.32486 loss)
I0408 08:33:31.629665 31616 sgd_solver.cpp:105] Iteration 6384, lr = 3.82379e-06
I0408 08:33:36.577531 31616 solver.cpp:218] Iteration 6396 (2.42537 iter/s, 4.94771s/12 iters), loss = 3.14113
I0408 08:33:36.577580 31616 solver.cpp:237]     Train net output #0: loss = 3.14113 (* 1 = 3.14113 loss)
I0408 08:33:36.577591 31616 sgd_solver.cpp:105] Iteration 6396, lr = 3.75138e-06
I0408 08:33:41.592520 31616 solver.cpp:218] Iteration 6408 (2.39293 iter/s, 5.01477s/12 iters), loss = 3.16522
I0408 08:33:41.592567 31616 solver.cpp:237]     Train net output #0: loss = 3.16522 (* 1 = 3.16522 loss)
I0408 08:33:41.592578 31616 sgd_solver.cpp:105] Iteration 6408, lr = 3.68033e-06
I0408 08:33:46.642355 31616 solver.cpp:218] Iteration 6420 (2.37642 iter/s, 5.04961s/12 iters), loss = 3.10344
I0408 08:33:46.642498 31616 solver.cpp:237]     Train net output #0: loss = 3.10344 (* 1 = 3.10344 loss)
I0408 08:33:46.642510 31616 sgd_solver.cpp:105] Iteration 6420, lr = 3.61063e-06
I0408 08:33:48.682621 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0408 08:34:00.515266 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0408 08:34:09.719628 31616 solver.cpp:330] Iteration 6426, Testing net (#0)
I0408 08:34:09.719661 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:34:11.650585 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:34:14.190075 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150123
I0408 08:34:14.190124 31616 solver.cpp:397]     Test net output #1: loss = 3.84823 (* 1 = 3.84823 loss)
I0408 08:34:16.419162 31616 solver.cpp:218] Iteration 6432 (0.403013 iter/s, 29.7757s/12 iters), loss = 3.38891
I0408 08:34:16.419206 31616 solver.cpp:237]     Train net output #0: loss = 3.38891 (* 1 = 3.38891 loss)
I0408 08:34:16.419216 31616 sgd_solver.cpp:105] Iteration 6432, lr = 3.54226e-06
I0408 08:34:21.862668 31616 solver.cpp:218] Iteration 6444 (2.20456 iter/s, 5.44328s/12 iters), loss = 3.46007
I0408 08:34:21.862746 31616 solver.cpp:237]     Train net output #0: loss = 3.46007 (* 1 = 3.46007 loss)
I0408 08:34:21.862757 31616 sgd_solver.cpp:105] Iteration 6444, lr = 3.47517e-06
I0408 08:34:27.207454 31616 solver.cpp:218] Iteration 6456 (2.24529 iter/s, 5.34452s/12 iters), loss = 3.24758
I0408 08:34:27.207501 31616 solver.cpp:237]     Train net output #0: loss = 3.24758 (* 1 = 3.24758 loss)
I0408 08:34:27.207512 31616 sgd_solver.cpp:105] Iteration 6456, lr = 3.40936e-06
I0408 08:34:32.229290 31616 solver.cpp:218] Iteration 6468 (2.38967 iter/s, 5.02161s/12 iters), loss = 3.32057
I0408 08:34:32.229336 31616 solver.cpp:237]     Train net output #0: loss = 3.32057 (* 1 = 3.32057 loss)
I0408 08:34:32.229347 31616 sgd_solver.cpp:105] Iteration 6468, lr = 3.34479e-06
I0408 08:34:34.251771 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:34:37.214887 31616 solver.cpp:218] Iteration 6480 (2.40704 iter/s, 4.98538s/12 iters), loss = 3.16656
I0408 08:34:37.214934 31616 solver.cpp:237]     Train net output #0: loss = 3.16656 (* 1 = 3.16656 loss)
I0408 08:34:37.214944 31616 sgd_solver.cpp:105] Iteration 6480, lr = 3.28145e-06
I0408 08:34:42.141856 31616 solver.cpp:218] Iteration 6492 (2.43568 iter/s, 4.92675s/12 iters), loss = 3.09726
I0408 08:34:42.141901 31616 solver.cpp:237]     Train net output #0: loss = 3.09726 (* 1 = 3.09726 loss)
I0408 08:34:42.141913 31616 sgd_solver.cpp:105] Iteration 6492, lr = 3.2193e-06
I0408 08:34:47.168617 31616 solver.cpp:218] Iteration 6504 (2.38733 iter/s, 5.02654s/12 iters), loss = 3.04079
I0408 08:34:47.168661 31616 solver.cpp:237]     Train net output #0: loss = 3.04079 (* 1 = 3.04079 loss)
I0408 08:34:47.168673 31616 sgd_solver.cpp:105] Iteration 6504, lr = 3.15834e-06
I0408 08:34:52.067868 31616 solver.cpp:218] Iteration 6516 (2.44946 iter/s, 4.89904s/12 iters), loss = 3.39861
I0408 08:34:52.067983 31616 solver.cpp:237]     Train net output #0: loss = 3.39861 (* 1 = 3.39861 loss)
I0408 08:34:52.067996 31616 sgd_solver.cpp:105] Iteration 6516, lr = 3.09852e-06
I0408 08:34:56.624363 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0408 08:35:05.280706 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0408 08:35:10.634513 31616 solver.cpp:330] Iteration 6528, Testing net (#0)
I0408 08:35:10.634543 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:35:12.536142 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:35:15.103744 31616 solver.cpp:397]     Test net output #0: accuracy = 0.14951
I0408 08:35:15.103787 31616 solver.cpp:397]     Test net output #1: loss = 3.85664 (* 1 = 3.85664 loss)
I0408 08:35:15.192075 31616 solver.cpp:218] Iteration 6528 (0.518956 iter/s, 23.1233s/12 iters), loss = 3.41343
I0408 08:35:15.192119 31616 solver.cpp:237]     Train net output #0: loss = 3.41343 (* 1 = 3.41343 loss)
I0408 08:35:15.192129 31616 sgd_solver.cpp:105] Iteration 6528, lr = 3.03984e-06
I0408 08:35:19.446523 31616 solver.cpp:218] Iteration 6540 (2.8207 iter/s, 4.25426s/12 iters), loss = 3.25876
I0408 08:35:19.446568 31616 solver.cpp:237]     Train net output #0: loss = 3.25876 (* 1 = 3.25876 loss)
I0408 08:35:19.446578 31616 sgd_solver.cpp:105] Iteration 6540, lr = 2.98228e-06
I0408 08:35:24.519874 31616 solver.cpp:218] Iteration 6552 (2.3654 iter/s, 5.07313s/12 iters), loss = 3.35174
I0408 08:35:24.519980 31616 solver.cpp:237]     Train net output #0: loss = 3.35174 (* 1 = 3.35174 loss)
I0408 08:35:24.519992 31616 sgd_solver.cpp:105] Iteration 6552, lr = 2.9258e-06
I0408 08:35:29.494491 31616 solver.cpp:218] Iteration 6564 (2.41238 iter/s, 4.97434s/12 iters), loss = 3.2256
I0408 08:35:29.494536 31616 solver.cpp:237]     Train net output #0: loss = 3.2256 (* 1 = 3.2256 loss)
I0408 08:35:29.494549 31616 sgd_solver.cpp:105] Iteration 6564, lr = 2.87039e-06
I0408 08:35:33.770682 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:35:34.554764 31616 solver.cpp:218] Iteration 6576 (2.37152 iter/s, 5.06006s/12 iters), loss = 3.15813
I0408 08:35:34.554811 31616 solver.cpp:237]     Train net output #0: loss = 3.15813 (* 1 = 3.15813 loss)
I0408 08:35:34.554821 31616 sgd_solver.cpp:105] Iteration 6576, lr = 2.81603e-06
I0408 08:35:39.569979 31616 solver.cpp:218] Iteration 6588 (2.39283 iter/s, 5.01499s/12 iters), loss = 3.35301
I0408 08:35:39.570024 31616 solver.cpp:237]     Train net output #0: loss = 3.35301 (* 1 = 3.35301 loss)
I0408 08:35:39.570034 31616 sgd_solver.cpp:105] Iteration 6588, lr = 2.7627e-06
I0408 08:35:44.783238 31616 solver.cpp:218] Iteration 6600 (2.30192 iter/s, 5.21304s/12 iters), loss = 3.25094
I0408 08:35:44.783283 31616 solver.cpp:237]     Train net output #0: loss = 3.25094 (* 1 = 3.25094 loss)
I0408 08:35:44.783294 31616 sgd_solver.cpp:105] Iteration 6600, lr = 2.71038e-06
I0408 08:35:49.819561 31616 solver.cpp:218] Iteration 6612 (2.3828 iter/s, 5.0361s/12 iters), loss = 3.11496
I0408 08:35:49.819605 31616 solver.cpp:237]     Train net output #0: loss = 3.11496 (* 1 = 3.11496 loss)
I0408 08:35:49.819617 31616 sgd_solver.cpp:105] Iteration 6612, lr = 2.65905e-06
I0408 08:35:54.859656 31616 solver.cpp:218] Iteration 6624 (2.38101 iter/s, 5.03988s/12 iters), loss = 3.26604
I0408 08:35:54.859747 31616 solver.cpp:237]     Train net output #0: loss = 3.26604 (* 1 = 3.26604 loss)
I0408 08:35:54.859758 31616 sgd_solver.cpp:105] Iteration 6624, lr = 2.60869e-06
I0408 08:35:56.884045 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0408 08:36:01.232319 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0408 08:36:04.892117 31616 solver.cpp:330] Iteration 6630, Testing net (#0)
I0408 08:36:04.892144 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:36:06.749200 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:36:09.350641 31616 solver.cpp:397]     Test net output #0: accuracy = 0.151961
I0408 08:36:09.350687 31616 solver.cpp:397]     Test net output #1: loss = 3.84654 (* 1 = 3.84654 loss)
I0408 08:36:11.161470 31616 solver.cpp:218] Iteration 6636 (0.736142 iter/s, 16.3012s/12 iters), loss = 3.15032
I0408 08:36:11.161523 31616 solver.cpp:237]     Train net output #0: loss = 3.15032 (* 1 = 3.15032 loss)
I0408 08:36:11.161535 31616 sgd_solver.cpp:105] Iteration 6636, lr = 2.55929e-06
I0408 08:36:16.185300 31616 solver.cpp:218] Iteration 6648 (2.38872 iter/s, 5.02361s/12 iters), loss = 3.42589
I0408 08:36:16.185333 31616 solver.cpp:237]     Train net output #0: loss = 3.42589 (* 1 = 3.42589 loss)
I0408 08:36:16.185343 31616 sgd_solver.cpp:105] Iteration 6648, lr = 2.51082e-06
I0408 08:36:21.198978 31616 solver.cpp:218] Iteration 6660 (2.39355 iter/s, 5.01347s/12 iters), loss = 3.22801
I0408 08:36:21.199020 31616 solver.cpp:237]     Train net output #0: loss = 3.22801 (* 1 = 3.22801 loss)
I0408 08:36:21.199031 31616 sgd_solver.cpp:105] Iteration 6660, lr = 2.46327e-06
I0408 08:36:26.159595 31616 solver.cpp:218] Iteration 6672 (2.41916 iter/s, 4.9604s/12 iters), loss = 3.28648
I0408 08:36:26.159708 31616 solver.cpp:237]     Train net output #0: loss = 3.28648 (* 1 = 3.28648 loss)
I0408 08:36:26.159720 31616 sgd_solver.cpp:105] Iteration 6672, lr = 2.41662e-06
I0408 08:36:27.496767 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:36:31.056077 31616 solver.cpp:218] Iteration 6684 (2.45088 iter/s, 4.8962s/12 iters), loss = 3.16661
I0408 08:36:31.056123 31616 solver.cpp:237]     Train net output #0: loss = 3.16661 (* 1 = 3.16661 loss)
I0408 08:36:31.056134 31616 sgd_solver.cpp:105] Iteration 6684, lr = 2.37085e-06
I0408 08:36:35.999694 31616 solver.cpp:218] Iteration 6696 (2.42748 iter/s, 4.9434s/12 iters), loss = 3.58602
I0408 08:36:35.999737 31616 solver.cpp:237]     Train net output #0: loss = 3.58602 (* 1 = 3.58602 loss)
I0408 08:36:35.999747 31616 sgd_solver.cpp:105] Iteration 6696, lr = 2.32595e-06
I0408 08:36:41.064468 31616 solver.cpp:218] Iteration 6708 (2.36941 iter/s, 5.06456s/12 iters), loss = 3.32763
I0408 08:36:41.064509 31616 solver.cpp:237]     Train net output #0: loss = 3.32763 (* 1 = 3.32763 loss)
I0408 08:36:41.064520 31616 sgd_solver.cpp:105] Iteration 6708, lr = 2.28191e-06
I0408 08:36:46.318017 31616 solver.cpp:218] Iteration 6720 (2.28427 iter/s, 5.25333s/12 iters), loss = 3.23526
I0408 08:36:46.318063 31616 solver.cpp:237]     Train net output #0: loss = 3.23526 (* 1 = 3.23526 loss)
I0408 08:36:46.318074 31616 sgd_solver.cpp:105] Iteration 6720, lr = 2.23869e-06
I0408 08:36:50.902586 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0408 08:36:55.542481 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0408 08:36:59.601461 31616 solver.cpp:330] Iteration 6732, Testing net (#0)
I0408 08:36:59.601577 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:37:01.398221 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:37:04.040047 31616 solver.cpp:397]     Test net output #0: accuracy = 0.151961
I0408 08:37:04.040096 31616 solver.cpp:397]     Test net output #1: loss = 3.84953 (* 1 = 3.84953 loss)
I0408 08:37:04.131484 31616 solver.cpp:218] Iteration 6732 (0.673672 iter/s, 17.8128s/12 iters), loss = 3.48208
I0408 08:37:04.131561 31616 solver.cpp:237]     Train net output #0: loss = 3.48208 (* 1 = 3.48208 loss)
I0408 08:37:04.131578 31616 sgd_solver.cpp:105] Iteration 6732, lr = 2.19629e-06
I0408 08:37:08.678115 31616 solver.cpp:218] Iteration 6744 (2.63945 iter/s, 4.5464s/12 iters), loss = 3.42722
I0408 08:37:08.678162 31616 solver.cpp:237]     Train net output #0: loss = 3.42722 (* 1 = 3.42722 loss)
I0408 08:37:08.678174 31616 sgd_solver.cpp:105] Iteration 6744, lr = 2.1547e-06
I0408 08:37:14.086100 31616 solver.cpp:218] Iteration 6756 (2.21904 iter/s, 5.40775s/12 iters), loss = 3.0908
I0408 08:37:14.086148 31616 solver.cpp:237]     Train net output #0: loss = 3.0908 (* 1 = 3.0908 loss)
I0408 08:37:14.086160 31616 sgd_solver.cpp:105] Iteration 6756, lr = 2.11389e-06
I0408 08:37:19.083971 31616 solver.cpp:218] Iteration 6768 (2.40113 iter/s, 4.99765s/12 iters), loss = 3.25232
I0408 08:37:19.084017 31616 solver.cpp:237]     Train net output #0: loss = 3.25232 (* 1 = 3.25232 loss)
I0408 08:37:19.084028 31616 sgd_solver.cpp:105] Iteration 6768, lr = 2.07386e-06
I0408 08:37:22.609416 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:37:24.112087 31616 solver.cpp:218] Iteration 6780 (2.38668 iter/s, 5.0279s/12 iters), loss = 3.46846
I0408 08:37:24.112123 31616 solver.cpp:237]     Train net output #0: loss = 3.46846 (* 1 = 3.46846 loss)
I0408 08:37:24.112131 31616 sgd_solver.cpp:105] Iteration 6780, lr = 2.03459e-06
I0408 08:37:29.141736 31616 solver.cpp:218] Iteration 6792 (2.38595 iter/s, 5.02943s/12 iters), loss = 3.27656
I0408 08:37:29.141784 31616 solver.cpp:237]     Train net output #0: loss = 3.27656 (* 1 = 3.27656 loss)
I0408 08:37:29.141795 31616 sgd_solver.cpp:105] Iteration 6792, lr = 1.99606e-06
I0408 08:37:34.136795 31616 solver.cpp:218] Iteration 6804 (2.40248 iter/s, 4.99484s/12 iters), loss = 3.36162
I0408 08:37:34.136917 31616 solver.cpp:237]     Train net output #0: loss = 3.36162 (* 1 = 3.36162 loss)
I0408 08:37:34.136929 31616 sgd_solver.cpp:105] Iteration 6804, lr = 1.95825e-06
I0408 08:37:39.145059 31616 solver.cpp:218] Iteration 6816 (2.39618 iter/s, 5.00797s/12 iters), loss = 3.21296
I0408 08:37:39.145107 31616 solver.cpp:237]     Train net output #0: loss = 3.21296 (* 1 = 3.21296 loss)
I0408 08:37:39.145118 31616 sgd_solver.cpp:105] Iteration 6816, lr = 1.92117e-06
I0408 08:37:44.176746 31616 solver.cpp:218] Iteration 6828 (2.38499 iter/s, 5.03147s/12 iters), loss = 3.26163
I0408 08:37:44.176790 31616 solver.cpp:237]     Train net output #0: loss = 3.26163 (* 1 = 3.26163 loss)
I0408 08:37:44.176801 31616 sgd_solver.cpp:105] Iteration 6828, lr = 1.88478e-06
I0408 08:37:46.234119 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0408 08:37:51.133172 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0408 08:37:55.301250 31616 solver.cpp:330] Iteration 6834, Testing net (#0)
I0408 08:37:55.301278 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:37:57.083932 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:37:59.760707 31616 solver.cpp:397]     Test net output #0: accuracy = 0.14951
I0408 08:37:59.760756 31616 solver.cpp:397]     Test net output #1: loss = 3.85167 (* 1 = 3.85167 loss)
I0408 08:38:01.631209 31616 solver.cpp:218] Iteration 6840 (0.687527 iter/s, 17.4539s/12 iters), loss = 3.34364
I0408 08:38:01.631253 31616 solver.cpp:237]     Train net output #0: loss = 3.34364 (* 1 = 3.34364 loss)
I0408 08:38:01.631263 31616 sgd_solver.cpp:105] Iteration 6840, lr = 1.84909e-06
I0408 08:38:06.592804 31616 solver.cpp:218] Iteration 6852 (2.41868 iter/s, 4.96138s/12 iters), loss = 3.48458
I0408 08:38:06.592883 31616 solver.cpp:237]     Train net output #0: loss = 3.48458 (* 1 = 3.48458 loss)
I0408 08:38:06.592895 31616 sgd_solver.cpp:105] Iteration 6852, lr = 1.81407e-06
I0408 08:38:11.617285 31616 solver.cpp:218] Iteration 6864 (2.38843 iter/s, 5.02423s/12 iters), loss = 3.27987
I0408 08:38:11.617333 31616 solver.cpp:237]     Train net output #0: loss = 3.27987 (* 1 = 3.27987 loss)
I0408 08:38:11.617345 31616 sgd_solver.cpp:105] Iteration 6864, lr = 1.77972e-06
I0408 08:38:16.588536 31616 solver.cpp:218] Iteration 6876 (2.41399 iter/s, 4.97103s/12 iters), loss = 3.37403
I0408 08:38:16.588588 31616 solver.cpp:237]     Train net output #0: loss = 3.37403 (* 1 = 3.37403 loss)
I0408 08:38:16.588600 31616 sgd_solver.cpp:105] Iteration 6876, lr = 1.74601e-06
I0408 08:38:17.213762 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:38:21.618140 31616 solver.cpp:218] Iteration 6888 (2.38598 iter/s, 5.02938s/12 iters), loss = 3.23426
I0408 08:38:21.618186 31616 solver.cpp:237]     Train net output #0: loss = 3.23426 (* 1 = 3.23426 loss)
I0408 08:38:21.618197 31616 sgd_solver.cpp:105] Iteration 6888, lr = 1.71295e-06
I0408 08:38:26.635972 31616 solver.cpp:218] Iteration 6900 (2.39157 iter/s, 5.01762s/12 iters), loss = 3.12523
I0408 08:38:26.636019 31616 solver.cpp:237]     Train net output #0: loss = 3.12523 (* 1 = 3.12523 loss)
I0408 08:38:26.636030 31616 sgd_solver.cpp:105] Iteration 6900, lr = 1.68051e-06
I0408 08:38:32.018950 31616 solver.cpp:218] Iteration 6912 (2.22935 iter/s, 5.38275s/12 iters), loss = 3.05228
I0408 08:38:32.018999 31616 solver.cpp:237]     Train net output #0: loss = 3.05228 (* 1 = 3.05228 loss)
I0408 08:38:32.019011 31616 sgd_solver.cpp:105] Iteration 6912, lr = 1.64868e-06
I0408 08:38:37.249126 31616 solver.cpp:218] Iteration 6924 (2.29448 iter/s, 5.22995s/12 iters), loss = 3.11503
I0408 08:38:37.249279 31616 solver.cpp:237]     Train net output #0: loss = 3.11503 (* 1 = 3.11503 loss)
I0408 08:38:37.249292 31616 sgd_solver.cpp:105] Iteration 6924, lr = 1.61746e-06
I0408 08:38:41.842813 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0408 08:38:46.184185 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0408 08:38:54.534974 31616 solver.cpp:330] Iteration 6936, Testing net (#0)
I0408 08:38:54.534997 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:38:55.197566 31616 blocking_queue.cpp:49] Waiting for data
I0408 08:38:56.276058 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:38:59.000392 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150123
I0408 08:38:59.000438 31616 solver.cpp:397]     Test net output #1: loss = 3.85177 (* 1 = 3.85177 loss)
I0408 08:38:59.091662 31616 solver.cpp:218] Iteration 6936 (0.549408 iter/s, 21.8417s/12 iters), loss = 3.15667
I0408 08:38:59.091709 31616 solver.cpp:237]     Train net output #0: loss = 3.15667 (* 1 = 3.15667 loss)
I0408 08:38:59.091722 31616 sgd_solver.cpp:105] Iteration 6936, lr = 1.58683e-06
I0408 08:39:03.655408 31616 solver.cpp:218] Iteration 6948 (2.62954 iter/s, 4.56354s/12 iters), loss = 3.2697
I0408 08:39:03.655457 31616 solver.cpp:237]     Train net output #0: loss = 3.2697 (* 1 = 3.2697 loss)
I0408 08:39:03.655469 31616 sgd_solver.cpp:105] Iteration 6948, lr = 1.55678e-06
I0408 08:39:09.123601 31616 solver.cpp:218] Iteration 6960 (2.1946 iter/s, 5.46796s/12 iters), loss = 3.37752
I0408 08:39:09.123703 31616 solver.cpp:237]     Train net output #0: loss = 3.37752 (* 1 = 3.37752 loss)
I0408 08:39:09.123715 31616 sgd_solver.cpp:105] Iteration 6960, lr = 1.52729e-06
I0408 08:39:14.569298 31616 solver.cpp:218] Iteration 6972 (2.20369 iter/s, 5.44541s/12 iters), loss = 3.3119
I0408 08:39:14.569345 31616 solver.cpp:237]     Train net output #0: loss = 3.3119 (* 1 = 3.3119 loss)
I0408 08:39:14.569356 31616 sgd_solver.cpp:105] Iteration 6972, lr = 1.49837e-06
I0408 08:39:17.405339 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:39:19.659626 31616 solver.cpp:218] Iteration 6984 (2.35751 iter/s, 5.09011s/12 iters), loss = 3.15513
I0408 08:39:19.659667 31616 solver.cpp:237]     Train net output #0: loss = 3.15513 (* 1 = 3.15513 loss)
I0408 08:39:19.659677 31616 sgd_solver.cpp:105] Iteration 6984, lr = 1.46999e-06
I0408 08:39:24.638233 31616 solver.cpp:218] Iteration 6996 (2.41042 iter/s, 4.9784s/12 iters), loss = 3.39642
I0408 08:39:24.638281 31616 solver.cpp:237]     Train net output #0: loss = 3.39642 (* 1 = 3.39642 loss)
I0408 08:39:24.638293 31616 sgd_solver.cpp:105] Iteration 6996, lr = 1.44215e-06
I0408 08:39:29.631716 31616 solver.cpp:218] Iteration 7008 (2.40324 iter/s, 4.99326s/12 iters), loss = 3.43895
I0408 08:39:29.631762 31616 solver.cpp:237]     Train net output #0: loss = 3.43895 (* 1 = 3.43895 loss)
I0408 08:39:29.631772 31616 sgd_solver.cpp:105] Iteration 7008, lr = 1.41484e-06
I0408 08:39:34.603225 31616 solver.cpp:218] Iteration 7020 (2.41386 iter/s, 4.97129s/12 iters), loss = 3.32431
I0408 08:39:34.603266 31616 solver.cpp:237]     Train net output #0: loss = 3.32431 (* 1 = 3.32431 loss)
I0408 08:39:34.603276 31616 sgd_solver.cpp:105] Iteration 7020, lr = 1.38805e-06
I0408 08:39:39.537739 31616 solver.cpp:218] Iteration 7032 (2.43195 iter/s, 4.9343s/12 iters), loss = 3.52857
I0408 08:39:39.537875 31616 solver.cpp:237]     Train net output #0: loss = 3.52857 (* 1 = 3.52857 loss)
I0408 08:39:39.537887 31616 sgd_solver.cpp:105] Iteration 7032, lr = 1.36176e-06
I0408 08:39:41.540555 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0408 08:39:49.478011 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0408 08:39:51.797149 31616 solver.cpp:330] Iteration 7038, Testing net (#0)
I0408 08:39:51.797171 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:39:53.496562 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:39:56.260334 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150123
I0408 08:39:56.260375 31616 solver.cpp:397]     Test net output #1: loss = 3.84431 (* 1 = 3.84431 loss)
I0408 08:39:58.271492 31616 solver.cpp:218] Iteration 7044 (0.64058 iter/s, 18.733s/12 iters), loss = 3.38262
I0408 08:39:58.271538 31616 solver.cpp:237]     Train net output #0: loss = 3.38262 (* 1 = 3.38262 loss)
I0408 08:39:58.271548 31616 sgd_solver.cpp:105] Iteration 7044, lr = 1.33597e-06
I0408 08:40:03.246739 31616 solver.cpp:218] Iteration 7056 (2.41204 iter/s, 4.97503s/12 iters), loss = 3.40234
I0408 08:40:03.246786 31616 solver.cpp:237]     Train net output #0: loss = 3.40234 (* 1 = 3.40234 loss)
I0408 08:40:03.246798 31616 sgd_solver.cpp:105] Iteration 7056, lr = 1.31067e-06
I0408 08:40:08.296222 31616 solver.cpp:218] Iteration 7068 (2.37658 iter/s, 5.04927s/12 iters), loss = 3.38444
I0408 08:40:08.296259 31616 solver.cpp:237]     Train net output #0: loss = 3.38444 (* 1 = 3.38444 loss)
I0408 08:40:08.296267 31616 sgd_solver.cpp:105] Iteration 7068, lr = 1.28585e-06
I0408 08:40:13.240579 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:40:13.351163 31616 solver.cpp:218] Iteration 7080 (2.37401 iter/s, 5.05473s/12 iters), loss = 3.20701
I0408 08:40:13.351202 31616 solver.cpp:237]     Train net output #0: loss = 3.20701 (* 1 = 3.20701 loss)
I0408 08:40:13.351209 31616 sgd_solver.cpp:105] Iteration 7080, lr = 1.2615e-06
I0408 08:40:18.375558 31616 solver.cpp:218] Iteration 7092 (2.38845 iter/s, 5.02418s/12 iters), loss = 3.26842
I0408 08:40:18.375603 31616 solver.cpp:237]     Train net output #0: loss = 3.26842 (* 1 = 3.26842 loss)
I0408 08:40:18.375613 31616 sgd_solver.cpp:105] Iteration 7092, lr = 1.23761e-06
I0408 08:40:23.604005 31616 solver.cpp:218] Iteration 7104 (2.29523 iter/s, 5.22822s/12 iters), loss = 3.20397
I0408 08:40:23.604049 31616 solver.cpp:237]     Train net output #0: loss = 3.20397 (* 1 = 3.20397 loss)
I0408 08:40:23.604059 31616 sgd_solver.cpp:105] Iteration 7104, lr = 1.21417e-06
I0408 08:40:28.566360 31616 solver.cpp:218] Iteration 7116 (2.41831 iter/s, 4.96214s/12 iters), loss = 3.16155
I0408 08:40:28.566402 31616 solver.cpp:237]     Train net output #0: loss = 3.16155 (* 1 = 3.16155 loss)
I0408 08:40:28.566412 31616 sgd_solver.cpp:105] Iteration 7116, lr = 1.19118e-06
I0408 08:40:33.560753 31616 solver.cpp:218] Iteration 7128 (2.4028 iter/s, 4.99418s/12 iters), loss = 3.21304
I0408 08:40:33.560789 31616 solver.cpp:237]     Train net output #0: loss = 3.21304 (* 1 = 3.21304 loss)
I0408 08:40:33.560797 31616 sgd_solver.cpp:105] Iteration 7128, lr = 1.16862e-06
I0408 08:40:38.102464 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0408 08:40:43.724007 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0408 08:40:46.135792 31616 solver.cpp:330] Iteration 7140, Testing net (#0)
I0408 08:40:46.135816 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:40:47.792302 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:40:50.598158 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0408 08:40:50.598206 31616 solver.cpp:397]     Test net output #1: loss = 3.84978 (* 1 = 3.84978 loss)
I0408 08:40:50.689314 31616 solver.cpp:218] Iteration 7140 (0.700608 iter/s, 17.128s/12 iters), loss = 3.24644
I0408 08:40:50.689363 31616 solver.cpp:237]     Train net output #0: loss = 3.24644 (* 1 = 3.24644 loss)
I0408 08:40:50.689374 31616 sgd_solver.cpp:105] Iteration 7140, lr = 1.14649e-06
I0408 08:40:55.066745 31616 solver.cpp:218] Iteration 7152 (2.74146 iter/s, 4.37723s/12 iters), loss = 3.40412
I0408 08:40:55.066787 31616 solver.cpp:237]     Train net output #0: loss = 3.40412 (* 1 = 3.40412 loss)
I0408 08:40:55.066797 31616 sgd_solver.cpp:105] Iteration 7152, lr = 1.12477e-06
I0408 08:41:00.116654 31616 solver.cpp:218] Iteration 7164 (2.37638 iter/s, 5.0497s/12 iters), loss = 3.36793
I0408 08:41:00.116690 31616 solver.cpp:237]     Train net output #0: loss = 3.36793 (* 1 = 3.36793 loss)
I0408 08:41:00.116698 31616 sgd_solver.cpp:105] Iteration 7164, lr = 1.10347e-06
I0408 08:41:05.153084 31616 solver.cpp:218] Iteration 7176 (2.38274 iter/s, 5.03622s/12 iters), loss = 3.24129
I0408 08:41:05.153120 31616 solver.cpp:237]     Train net output #0: loss = 3.24129 (* 1 = 3.24129 loss)
I0408 08:41:05.153127 31616 sgd_solver.cpp:105] Iteration 7176, lr = 1.08257e-06
I0408 08:41:07.258922 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:41:10.151870 31616 solver.cpp:218] Iteration 7188 (2.40069 iter/s, 4.99857s/12 iters), loss = 3.32909
I0408 08:41:10.151906 31616 solver.cpp:237]     Train net output #0: loss = 3.32909 (* 1 = 3.32909 loss)
I0408 08:41:10.151914 31616 sgd_solver.cpp:105] Iteration 7188, lr = 1.06207e-06
I0408 08:41:15.470827 31616 solver.cpp:218] Iteration 7200 (2.25617 iter/s, 5.31874s/12 iters), loss = 3.15893
I0408 08:41:15.470959 31616 solver.cpp:237]     Train net output #0: loss = 3.15893 (* 1 = 3.15893 loss)
I0408 08:41:15.470973 31616 sgd_solver.cpp:105] Iteration 7200, lr = 1.04196e-06
I0408 08:41:20.562640 31616 solver.cpp:218] Iteration 7212 (2.35686 iter/s, 5.09151s/12 iters), loss = 3.04585
I0408 08:41:20.562686 31616 solver.cpp:237]     Train net output #0: loss = 3.04585 (* 1 = 3.04585 loss)
I0408 08:41:20.562698 31616 sgd_solver.cpp:105] Iteration 7212, lr = 1.02223e-06
I0408 08:41:25.599895 31616 solver.cpp:218] Iteration 7224 (2.38235 iter/s, 5.03704s/12 iters), loss = 3.52475
I0408 08:41:25.599943 31616 solver.cpp:237]     Train net output #0: loss = 3.52475 (* 1 = 3.52475 loss)
I0408 08:41:25.599956 31616 sgd_solver.cpp:105] Iteration 7224, lr = 1.00287e-06
I0408 08:41:30.631778 31616 solver.cpp:218] Iteration 7236 (2.3849 iter/s, 5.03167s/12 iters), loss = 3.23374
I0408 08:41:30.631826 31616 solver.cpp:237]     Train net output #0: loss = 3.23374 (* 1 = 3.23374 loss)
I0408 08:41:30.631839 31616 sgd_solver.cpp:105] Iteration 7236, lr = 9.83875e-07
I0408 08:41:32.641150 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0408 08:41:37.444378 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0408 08:41:39.788539 31616 solver.cpp:330] Iteration 7242, Testing net (#0)
I0408 08:41:39.788565 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:41:41.416011 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:41:44.260751 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 08:41:44.260800 31616 solver.cpp:397]     Test net output #1: loss = 3.84617 (* 1 = 3.84617 loss)
I0408 08:41:46.231606 31616 solver.cpp:218] Iteration 7248 (0.769267 iter/s, 15.5993s/12 iters), loss = 3.14036
I0408 08:41:46.231776 31616 solver.cpp:237]     Train net output #0: loss = 3.14036 (* 1 = 3.14036 loss)
I0408 08:41:46.231789 31616 sgd_solver.cpp:105] Iteration 7248, lr = 9.65242e-07
I0408 08:41:51.277240 31616 solver.cpp:218] Iteration 7260 (2.37845 iter/s, 5.04529s/12 iters), loss = 3.16399
I0408 08:41:51.277287 31616 solver.cpp:237]     Train net output #0: loss = 3.16399 (* 1 = 3.16399 loss)
I0408 08:41:51.277298 31616 sgd_solver.cpp:105] Iteration 7260, lr = 9.46963e-07
I0408 08:41:56.349925 31616 solver.cpp:218] Iteration 7272 (2.36571 iter/s, 5.07247s/12 iters), loss = 3.18634
I0408 08:41:56.349982 31616 solver.cpp:237]     Train net output #0: loss = 3.18634 (* 1 = 3.18634 loss)
I0408 08:41:56.349995 31616 sgd_solver.cpp:105] Iteration 7272, lr = 9.29029e-07
I0408 08:42:00.715783 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:42:01.470664 31616 solver.cpp:218] Iteration 7284 (2.34352 iter/s, 5.12051s/12 iters), loss = 3.2807
I0408 08:42:01.470706 31616 solver.cpp:237]     Train net output #0: loss = 3.2807 (* 1 = 3.2807 loss)
I0408 08:42:01.470717 31616 sgd_solver.cpp:105] Iteration 7284, lr = 9.11435e-07
I0408 08:42:06.478385 31616 solver.cpp:218] Iteration 7296 (2.3964 iter/s, 5.00751s/12 iters), loss = 3.44417
I0408 08:42:06.478428 31616 solver.cpp:237]     Train net output #0: loss = 3.44417 (* 1 = 3.44417 loss)
I0408 08:42:06.478440 31616 sgd_solver.cpp:105] Iteration 7296, lr = 8.94174e-07
I0408 08:42:11.532456 31616 solver.cpp:218] Iteration 7308 (2.37443 iter/s, 5.05385s/12 iters), loss = 3.47438
I0408 08:42:11.532505 31616 solver.cpp:237]     Train net output #0: loss = 3.47438 (* 1 = 3.47438 loss)
I0408 08:42:11.532516 31616 sgd_solver.cpp:105] Iteration 7308, lr = 8.7724e-07
I0408 08:42:16.632993 31616 solver.cpp:218] Iteration 7320 (2.3528 iter/s, 5.10032s/12 iters), loss = 3.30783
I0408 08:42:16.633092 31616 solver.cpp:237]     Train net output #0: loss = 3.30783 (* 1 = 3.30783 loss)
I0408 08:42:16.633105 31616 sgd_solver.cpp:105] Iteration 7320, lr = 8.60627e-07
I0408 08:42:21.689209 31616 solver.cpp:218] Iteration 7332 (2.37344 iter/s, 5.05595s/12 iters), loss = 3.31716
I0408 08:42:21.689252 31616 solver.cpp:237]     Train net output #0: loss = 3.31716 (* 1 = 3.31716 loss)
I0408 08:42:21.689263 31616 sgd_solver.cpp:105] Iteration 7332, lr = 8.44328e-07
I0408 08:42:26.157680 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0408 08:42:30.281188 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0408 08:42:32.625182 31616 solver.cpp:330] Iteration 7344, Testing net (#0)
I0408 08:42:32.625208 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:42:34.204988 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:42:37.081573 31616 solver.cpp:397]     Test net output #0: accuracy = 0.152574
I0408 08:42:37.081620 31616 solver.cpp:397]     Test net output #1: loss = 3.84214 (* 1 = 3.84214 loss)
I0408 08:42:37.172614 31616 solver.cpp:218] Iteration 7344 (0.775051 iter/s, 15.4829s/12 iters), loss = 3.21229
I0408 08:42:37.172677 31616 solver.cpp:237]     Train net output #0: loss = 3.21229 (* 1 = 3.21229 loss)
I0408 08:42:37.172691 31616 sgd_solver.cpp:105] Iteration 7344, lr = 8.28338e-07
I0408 08:42:41.539029 31616 solver.cpp:218] Iteration 7356 (2.74838 iter/s, 4.3662s/12 iters), loss = 3.2522
I0408 08:42:41.539078 31616 solver.cpp:237]     Train net output #0: loss = 3.2522 (* 1 = 3.2522 loss)
I0408 08:42:41.539089 31616 sgd_solver.cpp:105] Iteration 7356, lr = 8.12651e-07
I0408 08:42:46.476300 31616 solver.cpp:218] Iteration 7368 (2.4306 iter/s, 4.93705s/12 iters), loss = 3.25882
I0408 08:42:46.476351 31616 solver.cpp:237]     Train net output #0: loss = 3.25882 (* 1 = 3.25882 loss)
I0408 08:42:46.476362 31616 sgd_solver.cpp:105] Iteration 7368, lr = 7.97261e-07
I0408 08:42:51.497938 31616 solver.cpp:218] Iteration 7380 (2.38976 iter/s, 5.02142s/12 iters), loss = 3.10027
I0408 08:42:51.498046 31616 solver.cpp:237]     Train net output #0: loss = 3.10027 (* 1 = 3.10027 loss)
I0408 08:42:51.498059 31616 sgd_solver.cpp:105] Iteration 7380, lr = 7.82162e-07
I0408 08:42:52.897222 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:42:56.518260 31616 solver.cpp:218] Iteration 7392 (2.39042 iter/s, 5.02004s/12 iters), loss = 3.37777
I0408 08:42:56.518308 31616 solver.cpp:237]     Train net output #0: loss = 3.37777 (* 1 = 3.37777 loss)
I0408 08:42:56.518321 31616 sgd_solver.cpp:105] Iteration 7392, lr = 7.6735e-07
I0408 08:43:01.524708 31616 solver.cpp:218] Iteration 7404 (2.39701 iter/s, 5.00623s/12 iters), loss = 3.27868
I0408 08:43:01.524750 31616 solver.cpp:237]     Train net output #0: loss = 3.27868 (* 1 = 3.27868 loss)
I0408 08:43:01.524762 31616 sgd_solver.cpp:105] Iteration 7404, lr = 7.52818e-07
I0408 08:43:06.586478 31616 solver.cpp:218] Iteration 7416 (2.37081 iter/s, 5.06155s/12 iters), loss = 3.15637
I0408 08:43:06.586525 31616 solver.cpp:237]     Train net output #0: loss = 3.15637 (* 1 = 3.15637 loss)
I0408 08:43:06.586536 31616 sgd_solver.cpp:105] Iteration 7416, lr = 7.38561e-07
I0408 08:43:11.585489 31616 solver.cpp:218] Iteration 7428 (2.40058 iter/s, 4.9988s/12 iters), loss = 3.24341
I0408 08:43:11.585536 31616 solver.cpp:237]     Train net output #0: loss = 3.24341 (* 1 = 3.24341 loss)
I0408 08:43:11.585546 31616 sgd_solver.cpp:105] Iteration 7428, lr = 7.24574e-07
I0408 08:43:16.672502 31616 solver.cpp:218] Iteration 7440 (2.35905 iter/s, 5.08679s/12 iters), loss = 3.40129
I0408 08:43:16.672551 31616 solver.cpp:237]     Train net output #0: loss = 3.40129 (* 1 = 3.40129 loss)
I0408 08:43:16.672562 31616 sgd_solver.cpp:105] Iteration 7440, lr = 7.10852e-07
I0408 08:43:18.683008 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0408 08:43:24.135306 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0408 08:43:26.547438 31616 solver.cpp:330] Iteration 7446, Testing net (#0)
I0408 08:43:26.547461 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:43:28.090369 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:43:31.012917 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0408 08:43:31.012966 31616 solver.cpp:397]     Test net output #1: loss = 3.8491 (* 1 = 3.8491 loss)
I0408 08:43:32.980933 31616 solver.cpp:218] Iteration 7452 (0.735842 iter/s, 16.3079s/12 iters), loss = 3.32773
I0408 08:43:32.980986 31616 solver.cpp:237]     Train net output #0: loss = 3.32773 (* 1 = 3.32773 loss)
I0408 08:43:32.980998 31616 sgd_solver.cpp:105] Iteration 7452, lr = 6.9739e-07
I0408 08:43:38.190380 31616 solver.cpp:218] Iteration 7464 (2.30361 iter/s, 5.20921s/12 iters), loss = 3.04053
I0408 08:43:38.190420 31616 solver.cpp:237]     Train net output #0: loss = 3.04053 (* 1 = 3.04053 loss)
I0408 08:43:38.190429 31616 sgd_solver.cpp:105] Iteration 7464, lr = 6.84182e-07
I0408 08:43:43.223577 31616 solver.cpp:218] Iteration 7476 (2.38427 iter/s, 5.03298s/12 iters), loss = 3.19402
I0408 08:43:43.223623 31616 solver.cpp:237]     Train net output #0: loss = 3.19402 (* 1 = 3.19402 loss)
I0408 08:43:43.223634 31616 sgd_solver.cpp:105] Iteration 7476, lr = 6.71225e-07
I0408 08:43:46.801288 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:43:48.301174 31616 solver.cpp:218] Iteration 7488 (2.36342 iter/s, 5.07738s/12 iters), loss = 3.37615
I0408 08:43:48.301216 31616 solver.cpp:237]     Train net output #0: loss = 3.37615 (* 1 = 3.37615 loss)
I0408 08:43:48.301225 31616 sgd_solver.cpp:105] Iteration 7488, lr = 6.58514e-07
I0408 08:43:53.374778 31616 solver.cpp:218] Iteration 7500 (2.36528 iter/s, 5.07339s/12 iters), loss = 3.18723
I0408 08:43:53.374825 31616 solver.cpp:237]     Train net output #0: loss = 3.18723 (* 1 = 3.18723 loss)
I0408 08:43:53.374836 31616 sgd_solver.cpp:105] Iteration 7500, lr = 6.46043e-07
I0408 08:43:58.450742 31616 solver.cpp:218] Iteration 7512 (2.36418 iter/s, 5.07575s/12 iters), loss = 3.33819
I0408 08:43:58.450870 31616 solver.cpp:237]     Train net output #0: loss = 3.33819 (* 1 = 3.33819 loss)
I0408 08:43:58.450881 31616 sgd_solver.cpp:105] Iteration 7512, lr = 6.33808e-07
I0408 08:44:03.512430 31616 solver.cpp:218] Iteration 7524 (2.37089 iter/s, 5.06139s/12 iters), loss = 3.24795
I0408 08:44:03.512477 31616 solver.cpp:237]     Train net output #0: loss = 3.24795 (* 1 = 3.24795 loss)
I0408 08:44:03.512488 31616 sgd_solver.cpp:105] Iteration 7524, lr = 6.21805e-07
I0408 08:44:08.529624 31616 solver.cpp:218] Iteration 7536 (2.39188 iter/s, 5.01697s/12 iters), loss = 3.2163
I0408 08:44:08.529671 31616 solver.cpp:237]     Train net output #0: loss = 3.2163 (* 1 = 3.2163 loss)
I0408 08:44:08.529683 31616 sgd_solver.cpp:105] Iteration 7536, lr = 6.10029e-07
I0408 08:44:13.083104 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0408 08:44:17.703831 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0408 08:44:20.225931 31616 solver.cpp:330] Iteration 7548, Testing net (#0)
I0408 08:44:20.225970 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:44:21.735590 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:44:24.836527 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150123
I0408 08:44:24.836575 31616 solver.cpp:397]     Test net output #1: loss = 3.8445 (* 1 = 3.8445 loss)
I0408 08:44:24.927867 31616 solver.cpp:218] Iteration 7548 (0.731811 iter/s, 16.3977s/12 iters), loss = 3.40394
I0408 08:44:24.927914 31616 solver.cpp:237]     Train net output #0: loss = 3.40394 (* 1 = 3.40394 loss)
I0408 08:44:24.927927 31616 sgd_solver.cpp:105] Iteration 7548, lr = 5.98476e-07
I0408 08:44:29.359437 31616 solver.cpp:218] Iteration 7560 (2.70797 iter/s, 4.43137s/12 iters), loss = 3.26854
I0408 08:44:29.359573 31616 solver.cpp:237]     Train net output #0: loss = 3.26854 (* 1 = 3.26854 loss)
I0408 08:44:29.359586 31616 sgd_solver.cpp:105] Iteration 7560, lr = 5.87142e-07
I0408 08:44:34.376281 31616 solver.cpp:218] Iteration 7572 (2.39209 iter/s, 5.01654s/12 iters), loss = 3.32863
I0408 08:44:34.376322 31616 solver.cpp:237]     Train net output #0: loss = 3.32863 (* 1 = 3.32863 loss)
I0408 08:44:34.376332 31616 sgd_solver.cpp:105] Iteration 7572, lr = 5.76023e-07
I0408 08:44:39.381788 31616 solver.cpp:218] Iteration 7584 (2.39746 iter/s, 5.00529s/12 iters), loss = 3.11061
I0408 08:44:39.381831 31616 solver.cpp:237]     Train net output #0: loss = 3.11061 (* 1 = 3.11061 loss)
I0408 08:44:39.381841 31616 sgd_solver.cpp:105] Iteration 7584, lr = 5.65114e-07
I0408 08:44:40.034859 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:44:44.447484 31616 solver.cpp:218] Iteration 7596 (2.36898 iter/s, 5.06548s/12 iters), loss = 3.19353
I0408 08:44:44.447528 31616 solver.cpp:237]     Train net output #0: loss = 3.19353 (* 1 = 3.19353 loss)
I0408 08:44:44.447540 31616 sgd_solver.cpp:105] Iteration 7596, lr = 5.54412e-07
I0408 08:44:49.460306 31616 solver.cpp:218] Iteration 7608 (2.39396 iter/s, 5.01261s/12 iters), loss = 3.20609
I0408 08:44:49.460350 31616 solver.cpp:237]     Train net output #0: loss = 3.20609 (* 1 = 3.20609 loss)
I0408 08:44:49.460361 31616 sgd_solver.cpp:105] Iteration 7608, lr = 5.43912e-07
I0408 08:44:54.469110 31616 solver.cpp:218] Iteration 7620 (2.39588 iter/s, 5.00859s/12 iters), loss = 3.00052
I0408 08:44:54.469154 31616 solver.cpp:237]     Train net output #0: loss = 3.00052 (* 1 = 3.00052 loss)
I0408 08:44:54.469166 31616 sgd_solver.cpp:105] Iteration 7620, lr = 5.33612e-07
I0408 08:44:56.851730 31616 blocking_queue.cpp:49] Waiting for data
I0408 08:44:59.429221 31616 solver.cpp:218] Iteration 7632 (2.41941 iter/s, 4.9599s/12 iters), loss = 2.97571
I0408 08:44:59.429325 31616 solver.cpp:237]     Train net output #0: loss = 2.97571 (* 1 = 2.97571 loss)
I0408 08:44:59.429337 31616 sgd_solver.cpp:105] Iteration 7632, lr = 5.23506e-07
I0408 08:45:04.508356 31616 solver.cpp:218] Iteration 7644 (2.36274 iter/s, 5.07886s/12 iters), loss = 3.24411
I0408 08:45:04.508407 31616 solver.cpp:237]     Train net output #0: loss = 3.24411 (* 1 = 3.24411 loss)
I0408 08:45:04.508419 31616 sgd_solver.cpp:105] Iteration 7644, lr = 5.13592e-07
I0408 08:45:06.466889 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0408 08:45:11.339368 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0408 08:45:18.063351 31616 solver.cpp:330] Iteration 7650, Testing net (#0)
I0408 08:45:18.063375 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:45:19.519598 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:45:22.523649 31616 solver.cpp:397]     Test net output #0: accuracy = 0.151961
I0408 08:45:22.523699 31616 solver.cpp:397]     Test net output #1: loss = 3.84128 (* 1 = 3.84128 loss)
I0408 08:45:24.443061 31616 solver.cpp:218] Iteration 7656 (0.601986 iter/s, 19.934s/12 iters), loss = 3.32539
I0408 08:45:24.443116 31616 solver.cpp:237]     Train net output #0: loss = 3.32539 (* 1 = 3.32539 loss)
I0408 08:45:24.443128 31616 sgd_solver.cpp:105] Iteration 7656, lr = 5.03865e-07
I0408 08:45:29.421690 31616 solver.cpp:218] Iteration 7668 (2.41041 iter/s, 4.9784s/12 iters), loss = 3.28115
I0408 08:45:29.421739 31616 solver.cpp:237]     Train net output #0: loss = 3.28115 (* 1 = 3.28115 loss)
I0408 08:45:29.421752 31616 sgd_solver.cpp:105] Iteration 7668, lr = 4.94323e-07
I0408 08:45:34.491230 31616 solver.cpp:218] Iteration 7680 (2.36718 iter/s, 5.06932s/12 iters), loss = 3.20764
I0408 08:45:34.491328 31616 solver.cpp:237]     Train net output #0: loss = 3.20764 (* 1 = 3.20764 loss)
I0408 08:45:34.491338 31616 sgd_solver.cpp:105] Iteration 7680, lr = 4.84962e-07
I0408 08:45:37.335021 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:45:39.562650 31616 solver.cpp:218] Iteration 7692 (2.36633 iter/s, 5.07115s/12 iters), loss = 3.17892
I0408 08:45:39.562696 31616 solver.cpp:237]     Train net output #0: loss = 3.17892 (* 1 = 3.17892 loss)
I0408 08:45:39.562707 31616 sgd_solver.cpp:105] Iteration 7692, lr = 4.75777e-07
I0408 08:45:44.620442 31616 solver.cpp:218] Iteration 7704 (2.37268 iter/s, 5.05757s/12 iters), loss = 3.32653
I0408 08:45:44.620488 31616 solver.cpp:237]     Train net output #0: loss = 3.32653 (* 1 = 3.32653 loss)
I0408 08:45:44.620499 31616 sgd_solver.cpp:105] Iteration 7704, lr = 4.66767e-07
I0408 08:45:49.683656 31616 solver.cpp:218] Iteration 7716 (2.37014 iter/s, 5.063s/12 iters), loss = 3.48341
I0408 08:45:49.683692 31616 solver.cpp:237]     Train net output #0: loss = 3.48341 (* 1 = 3.48341 loss)
I0408 08:45:49.683702 31616 sgd_solver.cpp:105] Iteration 7716, lr = 4.57927e-07
I0408 08:45:54.665110 31616 solver.cpp:218] Iteration 7728 (2.40903 iter/s, 4.98125s/12 iters), loss = 3.36426
I0408 08:45:54.665136 31616 solver.cpp:237]     Train net output #0: loss = 3.36426 (* 1 = 3.36426 loss)
I0408 08:45:54.665144 31616 sgd_solver.cpp:105] Iteration 7728, lr = 4.49255e-07
I0408 08:45:59.703034 31616 solver.cpp:218] Iteration 7740 (2.38204 iter/s, 5.0377s/12 iters), loss = 3.39145
I0408 08:45:59.703092 31616 solver.cpp:237]     Train net output #0: loss = 3.39145 (* 1 = 3.39145 loss)
I0408 08:45:59.703104 31616 sgd_solver.cpp:105] Iteration 7740, lr = 4.40747e-07
I0408 08:46:04.306344 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0408 08:46:07.686282 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0408 08:46:12.072862 31616 solver.cpp:330] Iteration 7752, Testing net (#0)
I0408 08:46:12.072888 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:46:13.494439 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:46:16.532758 31616 solver.cpp:397]     Test net output #0: accuracy = 0.14951
I0408 08:46:16.532804 31616 solver.cpp:397]     Test net output #1: loss = 3.85264 (* 1 = 3.85264 loss)
I0408 08:46:16.620908 31616 solver.cpp:218] Iteration 7752 (0.709334 iter/s, 16.9173s/12 iters), loss = 3.24051
I0408 08:46:16.620959 31616 solver.cpp:237]     Train net output #0: loss = 3.24051 (* 1 = 3.24051 loss)
I0408 08:46:16.620970 31616 sgd_solver.cpp:105] Iteration 7752, lr = 4.324e-07
I0408 08:46:20.935550 31616 solver.cpp:218] Iteration 7764 (2.78135 iter/s, 4.31445s/12 iters), loss = 3.33043
I0408 08:46:20.935593 31616 solver.cpp:237]     Train net output #0: loss = 3.33043 (* 1 = 3.33043 loss)
I0408 08:46:20.935604 31616 sgd_solver.cpp:105] Iteration 7764, lr = 4.24211e-07
I0408 08:46:25.954469 31616 solver.cpp:218] Iteration 7776 (2.39106 iter/s, 5.0187s/12 iters), loss = 3.25297
I0408 08:46:25.954515 31616 solver.cpp:237]     Train net output #0: loss = 3.25297 (* 1 = 3.25297 loss)
I0408 08:46:25.954525 31616 sgd_solver.cpp:105] Iteration 7776, lr = 4.16178e-07
I0408 08:46:31.009716 31616 solver.cpp:218] Iteration 7788 (2.37387 iter/s, 5.05503s/12 iters), loss = 3.17705
I0408 08:46:31.009760 31616 solver.cpp:237]     Train net output #0: loss = 3.17705 (* 1 = 3.17705 loss)
I0408 08:46:31.009771 31616 sgd_solver.cpp:105] Iteration 7788, lr = 4.08296e-07
I0408 08:46:31.017782 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:46:36.157208 31616 solver.cpp:218] Iteration 7800 (2.33133 iter/s, 5.14727s/12 iters), loss = 3.17921
I0408 08:46:36.157253 31616 solver.cpp:237]     Train net output #0: loss = 3.17921 (* 1 = 3.17921 loss)
I0408 08:46:36.157264 31616 sgd_solver.cpp:105] Iteration 7800, lr = 4.00564e-07
I0408 08:46:41.209062 31616 solver.cpp:218] Iteration 7812 (2.37547 iter/s, 5.05164s/12 iters), loss = 3.09345
I0408 08:46:41.211571 31616 solver.cpp:237]     Train net output #0: loss = 3.09345 (* 1 = 3.09345 loss)
I0408 08:46:41.211585 31616 sgd_solver.cpp:105] Iteration 7812, lr = 3.92978e-07
I0408 08:46:46.212301 31616 solver.cpp:218] Iteration 7824 (2.39973 iter/s, 5.00056s/12 iters), loss = 3.18622
I0408 08:46:46.212345 31616 solver.cpp:237]     Train net output #0: loss = 3.18622 (* 1 = 3.18622 loss)
I0408 08:46:46.212357 31616 sgd_solver.cpp:105] Iteration 7824, lr = 3.85536e-07
I0408 08:46:51.261559 31616 solver.cpp:218] Iteration 7836 (2.37669 iter/s, 5.04904s/12 iters), loss = 3.35923
I0408 08:46:51.261605 31616 solver.cpp:237]     Train net output #0: loss = 3.35923 (* 1 = 3.35923 loss)
I0408 08:46:51.261615 31616 sgd_solver.cpp:105] Iteration 7836, lr = 3.78234e-07
I0408 08:46:56.338025 31616 solver.cpp:218] Iteration 7848 (2.36395 iter/s, 5.07625s/12 iters), loss = 3.23236
I0408 08:46:56.338073 31616 solver.cpp:237]     Train net output #0: loss = 3.23236 (* 1 = 3.23236 loss)
I0408 08:46:56.338084 31616 sgd_solver.cpp:105] Iteration 7848, lr = 3.71071e-07
I0408 08:46:58.363358 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0408 08:47:03.575925 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0408 08:47:08.080215 31616 solver.cpp:330] Iteration 7854, Testing net (#0)
I0408 08:47:08.080241 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:47:09.474618 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:47:12.630698 31616 solver.cpp:397]     Test net output #0: accuracy = 0.14951
I0408 08:47:12.630813 31616 solver.cpp:397]     Test net output #1: loss = 3.85012 (* 1 = 3.85012 loss)
I0408 08:47:14.631373 31616 solver.cpp:218] Iteration 7860 (0.655999 iter/s, 18.2927s/12 iters), loss = 3.44929
I0408 08:47:14.631419 31616 solver.cpp:237]     Train net output #0: loss = 3.44929 (* 1 = 3.44929 loss)
I0408 08:47:14.631428 31616 sgd_solver.cpp:105] Iteration 7860, lr = 3.64044e-07
I0408 08:47:19.710130 31616 solver.cpp:218] Iteration 7872 (2.36288 iter/s, 5.07854s/12 iters), loss = 3.44129
I0408 08:47:19.710176 31616 solver.cpp:237]     Train net output #0: loss = 3.44129 (* 1 = 3.44129 loss)
I0408 08:47:19.710188 31616 sgd_solver.cpp:105] Iteration 7872, lr = 3.5715e-07
I0408 08:47:24.740576 31616 solver.cpp:218] Iteration 7884 (2.38558 iter/s, 5.03023s/12 iters), loss = 3.34756
I0408 08:47:24.740622 31616 solver.cpp:237]     Train net output #0: loss = 3.34756 (* 1 = 3.34756 loss)
I0408 08:47:24.740633 31616 sgd_solver.cpp:105] Iteration 7884, lr = 3.50386e-07
I0408 08:47:26.923909 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:47:29.855511 31616 solver.cpp:218] Iteration 7896 (2.34617 iter/s, 5.11472s/12 iters), loss = 3.16348
I0408 08:47:29.855559 31616 solver.cpp:237]     Train net output #0: loss = 3.16348 (* 1 = 3.16348 loss)
I0408 08:47:29.855572 31616 sgd_solver.cpp:105] Iteration 7896, lr = 3.4375e-07
I0408 08:47:34.863971 31616 solver.cpp:218] Iteration 7908 (2.39605 iter/s, 5.00824s/12 iters), loss = 3.2515
I0408 08:47:34.864017 31616 solver.cpp:237]     Train net output #0: loss = 3.2515 (* 1 = 3.2515 loss)
I0408 08:47:34.864027 31616 sgd_solver.cpp:105] Iteration 7908, lr = 3.3724e-07
I0408 08:47:40.209312 31616 solver.cpp:218] Iteration 7920 (2.24504 iter/s, 5.34512s/12 iters), loss = 3.00759
I0408 08:47:40.209349 31616 solver.cpp:237]     Train net output #0: loss = 3.00759 (* 1 = 3.00759 loss)
I0408 08:47:40.209357 31616 sgd_solver.cpp:105] Iteration 7920, lr = 3.30854e-07
I0408 08:47:45.327819 31616 solver.cpp:218] Iteration 7932 (2.34453 iter/s, 5.1183s/12 iters), loss = 3.40795
I0408 08:47:45.327960 31616 solver.cpp:237]     Train net output #0: loss = 3.40795 (* 1 = 3.40795 loss)
I0408 08:47:45.327972 31616 sgd_solver.cpp:105] Iteration 7932, lr = 3.24588e-07
I0408 08:47:50.341717 31616 solver.cpp:218] Iteration 7944 (2.3935 iter/s, 5.01359s/12 iters), loss = 3.35484
I0408 08:47:50.341759 31616 solver.cpp:237]     Train net output #0: loss = 3.35484 (* 1 = 3.35484 loss)
I0408 08:47:50.341769 31616 sgd_solver.cpp:105] Iteration 7944, lr = 3.18441e-07
I0408 08:47:54.861229 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0408 08:47:59.668320 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0408 08:48:07.934800 31616 solver.cpp:330] Iteration 7956, Testing net (#0)
I0408 08:48:07.934826 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:48:09.646250 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:48:12.818608 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150123
I0408 08:48:12.818657 31616 solver.cpp:397]     Test net output #1: loss = 3.84558 (* 1 = 3.84558 loss)
I0408 08:48:12.909783 31616 solver.cpp:218] Iteration 7956 (0.531743 iter/s, 22.5673s/12 iters), loss = 3.16186
I0408 08:48:12.909835 31616 solver.cpp:237]     Train net output #0: loss = 3.16186 (* 1 = 3.16186 loss)
I0408 08:48:12.909847 31616 sgd_solver.cpp:105] Iteration 7956, lr = 3.1241e-07
I0408 08:48:17.299005 31616 solver.cpp:218] Iteration 7968 (2.7341 iter/s, 4.38902s/12 iters), loss = 3.29111
I0408 08:48:17.299110 31616 solver.cpp:237]     Train net output #0: loss = 3.29111 (* 1 = 3.29111 loss)
I0408 08:48:17.299121 31616 sgd_solver.cpp:105] Iteration 7968, lr = 3.06494e-07
I0408 08:48:22.310195 31616 solver.cpp:218] Iteration 7980 (2.39477 iter/s, 5.01092s/12 iters), loss = 3.28093
I0408 08:48:22.310238 31616 solver.cpp:237]     Train net output #0: loss = 3.28093 (* 1 = 3.28093 loss)
I0408 08:48:22.310248 31616 sgd_solver.cpp:105] Iteration 7980, lr = 3.00689e-07
I0408 08:48:26.454586 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:48:27.157308 31616 solver.cpp:218] Iteration 7992 (2.47581 iter/s, 4.8469s/12 iters), loss = 3.17611
I0408 08:48:27.157357 31616 solver.cpp:237]     Train net output #0: loss = 3.17611 (* 1 = 3.17611 loss)
I0408 08:48:27.157369 31616 sgd_solver.cpp:105] Iteration 7992, lr = 2.94995e-07
I0408 08:48:32.050458 31616 solver.cpp:218] Iteration 8004 (2.45252 iter/s, 4.89292s/12 iters), loss = 3.47977
I0408 08:48:32.050520 31616 solver.cpp:237]     Train net output #0: loss = 3.47977 (* 1 = 3.47977 loss)
I0408 08:48:32.050534 31616 sgd_solver.cpp:105] Iteration 8004, lr = 2.89408e-07
I0408 08:48:36.975358 31616 solver.cpp:218] Iteration 8016 (2.43671 iter/s, 4.92467s/12 iters), loss = 3.46105
I0408 08:48:36.975406 31616 solver.cpp:237]     Train net output #0: loss = 3.46105 (* 1 = 3.46105 loss)
I0408 08:48:36.975419 31616 sgd_solver.cpp:105] Iteration 8016, lr = 2.83927e-07
I0408 08:48:42.115424 31616 solver.cpp:218] Iteration 8028 (2.3347 iter/s, 5.13984s/12 iters), loss = 3.21203
I0408 08:48:42.115470 31616 solver.cpp:237]     Train net output #0: loss = 3.21203 (* 1 = 3.21203 loss)
I0408 08:48:42.115483 31616 sgd_solver.cpp:105] Iteration 8028, lr = 2.7855e-07
I0408 08:48:47.163744 31616 solver.cpp:218] Iteration 8040 (2.37713 iter/s, 5.0481s/12 iters), loss = 3.21919
I0408 08:48:47.163789 31616 solver.cpp:237]     Train net output #0: loss = 3.21919 (* 1 = 3.21919 loss)
I0408 08:48:47.163800 31616 sgd_solver.cpp:105] Iteration 8040, lr = 2.73275e-07
I0408 08:48:52.216050 31616 solver.cpp:218] Iteration 8052 (2.37526 iter/s, 5.05209s/12 iters), loss = 3.22624
I0408 08:48:52.216164 31616 solver.cpp:237]     Train net output #0: loss = 3.22624 (* 1 = 3.22624 loss)
I0408 08:48:52.216177 31616 sgd_solver.cpp:105] Iteration 8052, lr = 2.681e-07
I0408 08:48:54.261777 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0408 08:49:00.517650 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0408 08:49:04.591143 31616 solver.cpp:330] Iteration 8058, Testing net (#0)
I0408 08:49:04.591169 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:49:05.889778 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:49:09.054152 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 08:49:09.054199 31616 solver.cpp:397]     Test net output #1: loss = 3.85395 (* 1 = 3.85395 loss)
I0408 08:49:11.035975 31616 solver.cpp:218] Iteration 8064 (0.637647 iter/s, 18.8192s/12 iters), loss = 3.52379
I0408 08:49:11.036029 31616 solver.cpp:237]     Train net output #0: loss = 3.52379 (* 1 = 3.52379 loss)
I0408 08:49:11.036041 31616 sgd_solver.cpp:105] Iteration 8064, lr = 2.63022e-07
I0408 08:49:16.169587 31616 solver.cpp:218] Iteration 8076 (2.33764 iter/s, 5.13339s/12 iters), loss = 3.24793
I0408 08:49:16.169636 31616 solver.cpp:237]     Train net output #0: loss = 3.24793 (* 1 = 3.24793 loss)
I0408 08:49:16.169648 31616 sgd_solver.cpp:105] Iteration 8076, lr = 2.58041e-07
I0408 08:49:21.231037 31616 solver.cpp:218] Iteration 8088 (2.37097 iter/s, 5.06122s/12 iters), loss = 3.08764
I0408 08:49:21.231086 31616 solver.cpp:237]     Train net output #0: loss = 3.08764 (* 1 = 3.08764 loss)
I0408 08:49:21.231097 31616 sgd_solver.cpp:105] Iteration 8088, lr = 2.53154e-07
I0408 08:49:22.616535 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:49:26.226953 31616 solver.cpp:218] Iteration 8100 (2.40206 iter/s, 4.9957s/12 iters), loss = 3.26381
I0408 08:49:26.226989 31616 solver.cpp:237]     Train net output #0: loss = 3.26381 (* 1 = 3.26381 loss)
I0408 08:49:26.226997 31616 sgd_solver.cpp:105] Iteration 8100, lr = 2.4836e-07
I0408 08:49:31.249174 31616 solver.cpp:218] Iteration 8112 (2.38948 iter/s, 5.02201s/12 iters), loss = 3.3854
I0408 08:49:31.249212 31616 solver.cpp:237]     Train net output #0: loss = 3.3854 (* 1 = 3.3854 loss)
I0408 08:49:31.249222 31616 sgd_solver.cpp:105] Iteration 8112, lr = 2.43657e-07
I0408 08:49:36.253662 31616 solver.cpp:218] Iteration 8124 (2.39795 iter/s, 5.00428s/12 iters), loss = 3.28079
I0408 08:49:36.253708 31616 solver.cpp:237]     Train net output #0: loss = 3.28079 (* 1 = 3.28079 loss)
I0408 08:49:36.253720 31616 sgd_solver.cpp:105] Iteration 8124, lr = 2.39042e-07
I0408 08:49:41.282732 31616 solver.cpp:218] Iteration 8136 (2.38623 iter/s, 5.02885s/12 iters), loss = 3.20986
I0408 08:49:41.282775 31616 solver.cpp:237]     Train net output #0: loss = 3.20986 (* 1 = 3.20986 loss)
I0408 08:49:41.282788 31616 sgd_solver.cpp:105] Iteration 8136, lr = 2.34515e-07
I0408 08:49:46.278654 31616 solver.cpp:218] Iteration 8148 (2.40206 iter/s, 4.99571s/12 iters), loss = 3.2761
I0408 08:49:46.278702 31616 solver.cpp:237]     Train net output #0: loss = 3.2761 (* 1 = 3.2761 loss)
I0408 08:49:46.278714 31616 sgd_solver.cpp:105] Iteration 8148, lr = 2.30074e-07
I0408 08:49:50.843502 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0408 08:49:57.355304 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0408 08:50:00.776304 31616 solver.cpp:330] Iteration 8160, Testing net (#0)
I0408 08:50:00.776331 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:50:02.011605 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:50:05.353273 31616 solver.cpp:397]     Test net output #0: accuracy = 0.14951
I0408 08:50:05.353322 31616 solver.cpp:397]     Test net output #1: loss = 3.84525 (* 1 = 3.84525 loss)
I0408 08:50:05.444547 31616 solver.cpp:218] Iteration 8160 (0.626134 iter/s, 19.1652s/12 iters), loss = 3.45749
I0408 08:50:05.444597 31616 solver.cpp:237]     Train net output #0: loss = 3.45749 (* 1 = 3.45749 loss)
I0408 08:50:05.444609 31616 sgd_solver.cpp:105] Iteration 8160, lr = 2.25717e-07
I0408 08:50:09.606139 31616 solver.cpp:218] Iteration 8172 (2.88364 iter/s, 4.1614s/12 iters), loss = 3.09415
I0408 08:50:09.606181 31616 solver.cpp:237]     Train net output #0: loss = 3.09415 (* 1 = 3.09415 loss)
I0408 08:50:09.606192 31616 sgd_solver.cpp:105] Iteration 8172, lr = 2.21442e-07
I0408 08:50:14.507963 31616 solver.cpp:218] Iteration 8184 (2.44817 iter/s, 4.90162s/12 iters), loss = 3.30611
I0408 08:50:14.508004 31616 solver.cpp:237]     Train net output #0: loss = 3.30611 (* 1 = 3.30611 loss)
I0408 08:50:14.508013 31616 sgd_solver.cpp:105] Iteration 8184, lr = 2.17249e-07
I0408 08:50:18.053645 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:50:19.521919 31616 solver.cpp:218] Iteration 8196 (2.39342 iter/s, 5.01374s/12 iters), loss = 3.4018
I0408 08:50:19.521973 31616 solver.cpp:237]     Train net output #0: loss = 3.4018 (* 1 = 3.4018 loss)
I0408 08:50:19.521984 31616 sgd_solver.cpp:105] Iteration 8196, lr = 2.13134e-07
I0408 08:50:24.520720 31616 solver.cpp:218] Iteration 8208 (2.40068 iter/s, 4.99858s/12 iters), loss = 3.15247
I0408 08:50:24.520771 31616 solver.cpp:237]     Train net output #0: loss = 3.15247 (* 1 = 3.15247 loss)
I0408 08:50:24.520787 31616 sgd_solver.cpp:105] Iteration 8208, lr = 2.09098e-07
I0408 08:50:29.648880 31616 solver.cpp:218] Iteration 8220 (2.34012 iter/s, 5.12794s/12 iters), loss = 3.41602
I0408 08:50:29.648993 31616 solver.cpp:237]     Train net output #0: loss = 3.41602 (* 1 = 3.41602 loss)
I0408 08:50:29.649003 31616 sgd_solver.cpp:105] Iteration 8220, lr = 2.05138e-07
I0408 08:50:34.668123 31616 solver.cpp:218] Iteration 8232 (2.39093 iter/s, 5.01896s/12 iters), loss = 3.31703
I0408 08:50:34.668157 31616 solver.cpp:237]     Train net output #0: loss = 3.31703 (* 1 = 3.31703 loss)
I0408 08:50:34.668166 31616 sgd_solver.cpp:105] Iteration 8232, lr = 2.01253e-07
I0408 08:50:39.727342 31616 solver.cpp:218] Iteration 8244 (2.37201 iter/s, 5.05901s/12 iters), loss = 3.21334
I0408 08:50:39.727389 31616 solver.cpp:237]     Train net output #0: loss = 3.21334 (* 1 = 3.21334 loss)
I0408 08:50:39.727401 31616 sgd_solver.cpp:105] Iteration 8244, lr = 1.97442e-07
I0408 08:50:44.726773 31616 solver.cpp:218] Iteration 8256 (2.40038 iter/s, 4.99921s/12 iters), loss = 3.26209
I0408 08:50:44.726822 31616 solver.cpp:237]     Train net output #0: loss = 3.26209 (* 1 = 3.26209 loss)
I0408 08:50:44.726833 31616 sgd_solver.cpp:105] Iteration 8256, lr = 1.93703e-07
I0408 08:50:46.758710 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0408 08:50:52.209462 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0408 08:50:54.541615 31616 solver.cpp:330] Iteration 8262, Testing net (#0)
I0408 08:50:54.541643 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:50:55.732800 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:50:58.965786 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148284
I0408 08:50:58.965816 31616 solver.cpp:397]     Test net output #1: loss = 3.85348 (* 1 = 3.85348 loss)
I0408 08:51:00.962814 31616 solver.cpp:218] Iteration 8268 (0.739123 iter/s, 16.2355s/12 iters), loss = 3.34844
I0408 08:51:00.962972 31616 solver.cpp:237]     Train net output #0: loss = 3.34844 (* 1 = 3.34844 loss)
I0408 08:51:00.962987 31616 sgd_solver.cpp:105] Iteration 8268, lr = 1.90034e-07
I0408 08:51:06.332113 31616 solver.cpp:218] Iteration 8280 (2.23507 iter/s, 5.36897s/12 iters), loss = 3.41362
I0408 08:51:06.332154 31616 solver.cpp:237]     Train net output #0: loss = 3.41362 (* 1 = 3.41362 loss)
I0408 08:51:06.332165 31616 sgd_solver.cpp:105] Iteration 8280, lr = 1.86435e-07
I0408 08:51:11.383628 31616 solver.cpp:218] Iteration 8292 (2.37563 iter/s, 5.0513s/12 iters), loss = 3.2897
I0408 08:51:11.383671 31616 solver.cpp:237]     Train net output #0: loss = 3.2897 (* 1 = 3.2897 loss)
I0408 08:51:11.383682 31616 sgd_solver.cpp:105] Iteration 8292, lr = 1.82905e-07
I0408 08:51:12.071570 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:51:16.403214 31616 solver.cpp:218] Iteration 8304 (2.39074 iter/s, 5.01937s/12 iters), loss = 3.22313
I0408 08:51:16.403273 31616 solver.cpp:237]     Train net output #0: loss = 3.22313 (* 1 = 3.22313 loss)
I0408 08:51:16.403286 31616 sgd_solver.cpp:105] Iteration 8304, lr = 1.79441e-07
I0408 08:51:19.288038 31616 blocking_queue.cpp:49] Waiting for data
I0408 08:51:21.408510 31616 solver.cpp:218] Iteration 8316 (2.39757 iter/s, 5.00507s/12 iters), loss = 3.30727
I0408 08:51:21.408555 31616 solver.cpp:237]     Train net output #0: loss = 3.30727 (* 1 = 3.30727 loss)
I0408 08:51:21.408565 31616 sgd_solver.cpp:105] Iteration 8316, lr = 1.76043e-07
I0408 08:51:26.471325 31616 solver.cpp:218] Iteration 8328 (2.37032 iter/s, 5.0626s/12 iters), loss = 3.10312
I0408 08:51:26.471364 31616 solver.cpp:237]     Train net output #0: loss = 3.10312 (* 1 = 3.10312 loss)
I0408 08:51:26.471374 31616 sgd_solver.cpp:105] Iteration 8328, lr = 1.72709e-07
I0408 08:51:31.456053 31616 solver.cpp:218] Iteration 8340 (2.40746 iter/s, 4.98451s/12 iters), loss = 3.14338
I0408 08:51:31.456158 31616 solver.cpp:237]     Train net output #0: loss = 3.14338 (* 1 = 3.14338 loss)
I0408 08:51:31.456171 31616 sgd_solver.cpp:105] Iteration 8340, lr = 1.69438e-07
I0408 08:51:36.479296 31616 solver.cpp:218] Iteration 8352 (2.38903 iter/s, 5.02297s/12 iters), loss = 3.29666
I0408 08:51:36.479341 31616 solver.cpp:237]     Train net output #0: loss = 3.29666 (* 1 = 3.29666 loss)
I0408 08:51:36.479352 31616 sgd_solver.cpp:105] Iteration 8352, lr = 1.66229e-07
I0408 08:51:40.990399 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0408 08:51:45.160194 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0408 08:51:47.489504 31616 solver.cpp:330] Iteration 8364, Testing net (#0)
I0408 08:51:47.489531 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:51:48.692531 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:51:51.971599 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 08:51:51.971647 31616 solver.cpp:397]     Test net output #1: loss = 3.8579 (* 1 = 3.8579 loss)
I0408 08:51:52.062985 31616 solver.cpp:218] Iteration 8364 (0.770063 iter/s, 15.5831s/12 iters), loss = 3.36696
I0408 08:51:52.063036 31616 solver.cpp:237]     Train net output #0: loss = 3.36696 (* 1 = 3.36696 loss)
I0408 08:51:52.063047 31616 sgd_solver.cpp:105] Iteration 8364, lr = 1.63081e-07
I0408 08:51:56.282610 31616 solver.cpp:218] Iteration 8376 (2.84399 iter/s, 4.21943s/12 iters), loss = 3.27798
I0408 08:51:56.282660 31616 solver.cpp:237]     Train net output #0: loss = 3.27798 (* 1 = 3.27798 loss)
I0408 08:51:56.282672 31616 sgd_solver.cpp:105] Iteration 8376, lr = 1.59993e-07
I0408 08:52:01.248806 31616 solver.cpp:218] Iteration 8388 (2.41644 iter/s, 4.96598s/12 iters), loss = 3.30127
I0408 08:52:01.248855 31616 solver.cpp:237]     Train net output #0: loss = 3.30127 (* 1 = 3.30127 loss)
I0408 08:52:01.248867 31616 sgd_solver.cpp:105] Iteration 8388, lr = 1.56963e-07
I0408 08:52:04.052263 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:52:06.242750 31616 solver.cpp:218] Iteration 8400 (2.40302 iter/s, 4.99373s/12 iters), loss = 3.32152
I0408 08:52:06.242796 31616 solver.cpp:237]     Train net output #0: loss = 3.32152 (* 1 = 3.32152 loss)
I0408 08:52:06.242807 31616 sgd_solver.cpp:105] Iteration 8400, lr = 1.5399e-07
I0408 08:52:11.300813 31616 solver.cpp:218] Iteration 8412 (2.37255 iter/s, 5.05785s/12 iters), loss = 3.42809
I0408 08:52:11.300863 31616 solver.cpp:237]     Train net output #0: loss = 3.42809 (* 1 = 3.42809 loss)
I0408 08:52:11.300876 31616 sgd_solver.cpp:105] Iteration 8412, lr = 1.51074e-07
I0408 08:52:16.276366 31616 solver.cpp:218] Iteration 8424 (2.4119 iter/s, 4.97533s/12 iters), loss = 3.47266
I0408 08:52:16.276413 31616 solver.cpp:237]     Train net output #0: loss = 3.47266 (* 1 = 3.47266 loss)
I0408 08:52:16.276424 31616 sgd_solver.cpp:105] Iteration 8424, lr = 1.48213e-07
I0408 08:52:21.294600 31616 solver.cpp:218] Iteration 8436 (2.39139 iter/s, 5.01801s/12 iters), loss = 3.36055
I0408 08:52:21.294648 31616 solver.cpp:237]     Train net output #0: loss = 3.36055 (* 1 = 3.36055 loss)
I0408 08:52:21.294659 31616 sgd_solver.cpp:105] Iteration 8436, lr = 1.45406e-07
I0408 08:52:26.219028 31616 solver.cpp:218] Iteration 8448 (2.43694 iter/s, 4.92421s/12 iters), loss = 3.51933
I0408 08:52:26.219074 31616 solver.cpp:237]     Train net output #0: loss = 3.51933 (* 1 = 3.51933 loss)
I0408 08:52:26.219084 31616 sgd_solver.cpp:105] Iteration 8448, lr = 1.42652e-07
I0408 08:52:31.236317 31616 solver.cpp:218] Iteration 8460 (2.39183 iter/s, 5.01707s/12 iters), loss = 3.18621
I0408 08:52:31.236362 31616 solver.cpp:237]     Train net output #0: loss = 3.18621 (* 1 = 3.18621 loss)
I0408 08:52:31.236372 31616 sgd_solver.cpp:105] Iteration 8460, lr = 1.39951e-07
I0408 08:52:33.282629 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0408 08:52:37.874752 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0408 08:52:40.188285 31616 solver.cpp:330] Iteration 8466, Testing net (#0)
I0408 08:52:40.188310 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:52:41.358067 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:52:44.814496 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150123
I0408 08:52:44.814544 31616 solver.cpp:397]     Test net output #1: loss = 3.85264 (* 1 = 3.85264 loss)
I0408 08:52:46.718385 31616 solver.cpp:218] Iteration 8472 (0.775118 iter/s, 15.4815s/12 iters), loss = 3.42241
I0408 08:52:46.718425 31616 solver.cpp:237]     Train net output #0: loss = 3.42241 (* 1 = 3.42241 loss)
I0408 08:52:46.718433 31616 sgd_solver.cpp:105] Iteration 8472, lr = 1.373e-07
I0408 08:52:51.753904 31616 solver.cpp:218] Iteration 8484 (2.38317 iter/s, 5.03531s/12 iters), loss = 3.41469
I0408 08:52:51.753943 31616 solver.cpp:237]     Train net output #0: loss = 3.41469 (* 1 = 3.41469 loss)
I0408 08:52:51.753952 31616 sgd_solver.cpp:105] Iteration 8484, lr = 1.347e-07
I0408 08:52:56.793215 31616 solver.cpp:218] Iteration 8496 (2.38138 iter/s, 5.0391s/12 iters), loss = 3.19651
I0408 08:52:56.793258 31616 solver.cpp:237]     Train net output #0: loss = 3.19651 (* 1 = 3.19651 loss)
I0408 08:52:56.793269 31616 sgd_solver.cpp:105] Iteration 8496, lr = 1.32149e-07
I0408 08:52:56.844379 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:53:02.023025 31616 solver.cpp:218] Iteration 8508 (2.29463 iter/s, 5.22959s/12 iters), loss = 3.29825
I0408 08:53:02.023061 31616 solver.cpp:237]     Train net output #0: loss = 3.29825 (* 1 = 3.29825 loss)
I0408 08:53:02.023069 31616 sgd_solver.cpp:105] Iteration 8508, lr = 1.29646e-07
I0408 08:53:07.017347 31616 solver.cpp:218] Iteration 8520 (2.40283 iter/s, 4.99411s/12 iters), loss = 2.99169
I0408 08:53:07.017400 31616 solver.cpp:237]     Train net output #0: loss = 2.99169 (* 1 = 2.99169 loss)
I0408 08:53:07.017412 31616 sgd_solver.cpp:105] Iteration 8520, lr = 1.27191e-07
I0408 08:53:11.965776 31616 solver.cpp:218] Iteration 8532 (2.42512 iter/s, 4.94821s/12 iters), loss = 3.21257
I0408 08:53:11.965914 31616 solver.cpp:237]     Train net output #0: loss = 3.21257 (* 1 = 3.21257 loss)
I0408 08:53:11.965926 31616 sgd_solver.cpp:105] Iteration 8532, lr = 1.24782e-07
I0408 08:53:17.007462 31616 solver.cpp:218] Iteration 8544 (2.3803 iter/s, 5.04138s/12 iters), loss = 3.46537
I0408 08:53:17.007513 31616 solver.cpp:237]     Train net output #0: loss = 3.46537 (* 1 = 3.46537 loss)
I0408 08:53:17.007525 31616 sgd_solver.cpp:105] Iteration 8544, lr = 1.22419e-07
I0408 08:53:21.972048 31616 solver.cpp:218] Iteration 8556 (2.41722 iter/s, 4.96437s/12 iters), loss = 3.26871
I0408 08:53:21.972084 31616 solver.cpp:237]     Train net output #0: loss = 3.26871 (* 1 = 3.26871 loss)
I0408 08:53:21.972095 31616 sgd_solver.cpp:105] Iteration 8556, lr = 1.20101e-07
I0408 08:53:26.456158 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0408 08:53:30.023264 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0408 08:53:32.654738 31616 solver.cpp:330] Iteration 8568, Testing net (#0)
I0408 08:53:32.654760 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:53:33.716866 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:53:37.082185 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 08:53:37.082234 31616 solver.cpp:397]     Test net output #1: loss = 3.85481 (* 1 = 3.85481 loss)
I0408 08:53:37.173856 31616 solver.cpp:218] Iteration 8568 (0.789408 iter/s, 15.2013s/12 iters), loss = 3.40171
I0408 08:53:37.173928 31616 solver.cpp:237]     Train net output #0: loss = 3.40171 (* 1 = 3.40171 loss)
I0408 08:53:37.173945 31616 sgd_solver.cpp:105] Iteration 8568, lr = 1.17826e-07
I0408 08:53:41.405432 31616 solver.cpp:218] Iteration 8580 (2.83596 iter/s, 4.23137s/12 iters), loss = 3.2886
I0408 08:53:41.405465 31616 solver.cpp:237]     Train net output #0: loss = 3.2886 (* 1 = 3.2886 loss)
I0408 08:53:41.405473 31616 sgd_solver.cpp:105] Iteration 8580, lr = 1.15595e-07
I0408 08:53:46.549742 31616 solver.cpp:218] Iteration 8592 (2.33277 iter/s, 5.1441s/12 iters), loss = 3.18502
I0408 08:53:46.549815 31616 solver.cpp:237]     Train net output #0: loss = 3.18502 (* 1 = 3.18502 loss)
I0408 08:53:46.549827 31616 sgd_solver.cpp:105] Iteration 8592, lr = 1.13406e-07
I0408 08:53:48.697456 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:53:51.518874 31616 solver.cpp:218] Iteration 8604 (2.41502 iter/s, 4.96889s/12 iters), loss = 3.18916
I0408 08:53:51.518920 31616 solver.cpp:237]     Train net output #0: loss = 3.18916 (* 1 = 3.18916 loss)
I0408 08:53:51.518932 31616 sgd_solver.cpp:105] Iteration 8604, lr = 1.11258e-07
I0408 08:53:56.528088 31616 solver.cpp:218] Iteration 8616 (2.39569 iter/s, 5.009s/12 iters), loss = 3.12639
I0408 08:53:56.528133 31616 solver.cpp:237]     Train net output #0: loss = 3.12639 (* 1 = 3.12639 loss)
I0408 08:53:56.528146 31616 sgd_solver.cpp:105] Iteration 8616, lr = 1.09151e-07
I0408 08:54:01.565106 31616 solver.cpp:218] Iteration 8628 (2.38246 iter/s, 5.0368s/12 iters), loss = 3.18652
I0408 08:54:01.565147 31616 solver.cpp:237]     Train net output #0: loss = 3.18652 (* 1 = 3.18652 loss)
I0408 08:54:01.565158 31616 sgd_solver.cpp:105] Iteration 8628, lr = 1.07084e-07
I0408 08:54:06.583931 31616 solver.cpp:218] Iteration 8640 (2.3911 iter/s, 5.0186s/12 iters), loss = 3.3878
I0408 08:54:06.583981 31616 solver.cpp:237]     Train net output #0: loss = 3.3878 (* 1 = 3.3878 loss)
I0408 08:54:06.583992 31616 sgd_solver.cpp:105] Iteration 8640, lr = 1.05056e-07
I0408 08:54:11.590838 31616 solver.cpp:218] Iteration 8652 (2.39679 iter/s, 5.00669s/12 iters), loss = 3.27056
I0408 08:54:11.590883 31616 solver.cpp:237]     Train net output #0: loss = 3.27056 (* 1 = 3.27056 loss)
I0408 08:54:11.590895 31616 sgd_solver.cpp:105] Iteration 8652, lr = 1.03066e-07
I0408 08:54:16.858358 31616 solver.cpp:218] Iteration 8664 (2.27821 iter/s, 5.2673s/12 iters), loss = 3.19816
I0408 08:54:16.858462 31616 solver.cpp:237]     Train net output #0: loss = 3.19816 (* 1 = 3.19816 loss)
I0408 08:54:16.858474 31616 sgd_solver.cpp:105] Iteration 8664, lr = 1.01115e-07
I0408 08:54:18.975677 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0408 08:54:24.055073 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0408 08:54:26.386166 31616 solver.cpp:330] Iteration 8670, Testing net (#0)
I0408 08:54:26.386193 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:54:27.468109 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:54:30.864930 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148284
I0408 08:54:30.864959 31616 solver.cpp:397]     Test net output #1: loss = 3.84972 (* 1 = 3.84972 loss)
I0408 08:54:32.832590 31616 solver.cpp:218] Iteration 8676 (0.751239 iter/s, 15.9736s/12 iters), loss = 3.34385
I0408 08:54:32.832638 31616 solver.cpp:237]     Train net output #0: loss = 3.34385 (* 1 = 3.34385 loss)
I0408 08:54:32.832649 31616 sgd_solver.cpp:105] Iteration 8676, lr = 9.91996e-08
I0408 08:54:37.853142 31616 solver.cpp:218] Iteration 8688 (2.39028 iter/s, 5.02033s/12 iters), loss = 3.35923
I0408 08:54:37.853188 31616 solver.cpp:237]     Train net output #0: loss = 3.35923 (* 1 = 3.35923 loss)
I0408 08:54:37.853199 31616 sgd_solver.cpp:105] Iteration 8688, lr = 9.7321e-08
I0408 08:54:42.202483 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:54:42.890873 31616 solver.cpp:218] Iteration 8700 (2.38213 iter/s, 5.03752s/12 iters), loss = 3.28517
I0408 08:54:42.890918 31616 solver.cpp:237]     Train net output #0: loss = 3.28517 (* 1 = 3.28517 loss)
I0408 08:54:42.890928 31616 sgd_solver.cpp:105] Iteration 8700, lr = 9.54779e-08
I0408 08:54:48.039932 31616 solver.cpp:218] Iteration 8712 (2.33062 iter/s, 5.14884s/12 iters), loss = 3.48903
I0408 08:54:48.040050 31616 solver.cpp:237]     Train net output #0: loss = 3.48903 (* 1 = 3.48903 loss)
I0408 08:54:48.040062 31616 sgd_solver.cpp:105] Iteration 8712, lr = 9.36698e-08
I0408 08:54:53.258318 31616 solver.cpp:218] Iteration 8724 (2.29975 iter/s, 5.21796s/12 iters), loss = 3.17866
I0408 08:54:53.258368 31616 solver.cpp:237]     Train net output #0: loss = 3.17866 (* 1 = 3.17866 loss)
I0408 08:54:53.258379 31616 sgd_solver.cpp:105] Iteration 8724, lr = 9.18958e-08
I0408 08:54:58.485476 31616 solver.cpp:218] Iteration 8736 (2.2958 iter/s, 5.22693s/12 iters), loss = 3.36795
I0408 08:54:58.485522 31616 solver.cpp:237]     Train net output #0: loss = 3.36795 (* 1 = 3.36795 loss)
I0408 08:54:58.485534 31616 sgd_solver.cpp:105] Iteration 8736, lr = 9.01555e-08
I0408 08:55:03.533367 31616 solver.cpp:218] Iteration 8748 (2.37733 iter/s, 5.04767s/12 iters), loss = 3.32611
I0408 08:55:03.533414 31616 solver.cpp:237]     Train net output #0: loss = 3.32611 (* 1 = 3.32611 loss)
I0408 08:55:03.533425 31616 sgd_solver.cpp:105] Iteration 8748, lr = 8.84481e-08
I0408 08:55:08.582563 31616 solver.cpp:218] Iteration 8760 (2.37672 iter/s, 5.04898s/12 iters), loss = 3.29454
I0408 08:55:08.582607 31616 solver.cpp:237]     Train net output #0: loss = 3.29454 (* 1 = 3.29454 loss)
I0408 08:55:08.582618 31616 sgd_solver.cpp:105] Iteration 8760, lr = 8.67731e-08
I0408 08:55:13.167217 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0408 08:55:18.623742 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0408 08:55:21.250299 31616 solver.cpp:330] Iteration 8772, Testing net (#0)
I0408 08:55:21.250324 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:55:22.282254 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:55:25.723796 31616 solver.cpp:397]     Test net output #0: accuracy = 0.14951
I0408 08:55:25.723843 31616 solver.cpp:397]     Test net output #1: loss = 3.85113 (* 1 = 3.85113 loss)
I0408 08:55:25.813987 31616 solver.cpp:218] Iteration 8772 (0.696424 iter/s, 17.2309s/12 iters), loss = 3.46359
I0408 08:55:25.814038 31616 solver.cpp:237]     Train net output #0: loss = 3.46359 (* 1 = 3.46359 loss)
I0408 08:55:25.814049 31616 sgd_solver.cpp:105] Iteration 8772, lr = 8.51298e-08
I0408 08:55:30.100670 31616 solver.cpp:218] Iteration 8784 (2.79947 iter/s, 4.28652s/12 iters), loss = 3.16449
I0408 08:55:30.100710 31616 solver.cpp:237]     Train net output #0: loss = 3.16449 (* 1 = 3.16449 loss)
I0408 08:55:30.100719 31616 sgd_solver.cpp:105] Iteration 8784, lr = 8.35176e-08
I0408 08:55:35.105060 31616 solver.cpp:218] Iteration 8796 (2.39798 iter/s, 5.00422s/12 iters), loss = 2.97787
I0408 08:55:35.105108 31616 solver.cpp:237]     Train net output #0: loss = 2.97787 (* 1 = 2.97787 loss)
I0408 08:55:35.105118 31616 sgd_solver.cpp:105] Iteration 8796, lr = 8.19359e-08
I0408 08:55:36.529672 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:55:40.015961 31616 solver.cpp:218] Iteration 8808 (2.44363 iter/s, 4.91073s/12 iters), loss = 3.26268
I0408 08:55:40.016008 31616 solver.cpp:237]     Train net output #0: loss = 3.26268 (* 1 = 3.26268 loss)
I0408 08:55:40.016019 31616 sgd_solver.cpp:105] Iteration 8808, lr = 8.03842e-08
I0408 08:55:45.035480 31616 solver.cpp:218] Iteration 8820 (2.39075 iter/s, 5.01934s/12 iters), loss = 3.44941
I0408 08:55:45.035527 31616 solver.cpp:237]     Train net output #0: loss = 3.44941 (* 1 = 3.44941 loss)
I0408 08:55:45.035539 31616 sgd_solver.cpp:105] Iteration 8820, lr = 7.88619e-08
I0408 08:55:50.074230 31616 solver.cpp:218] Iteration 8832 (2.38163 iter/s, 5.03857s/12 iters), loss = 3.11327
I0408 08:55:50.074344 31616 solver.cpp:237]     Train net output #0: loss = 3.11327 (* 1 = 3.11327 loss)
I0408 08:55:50.074357 31616 sgd_solver.cpp:105] Iteration 8832, lr = 7.73684e-08
I0408 08:55:55.016728 31616 solver.cpp:218] Iteration 8844 (2.42804 iter/s, 4.94226s/12 iters), loss = 3.20047
I0408 08:55:55.016767 31616 solver.cpp:237]     Train net output #0: loss = 3.20047 (* 1 = 3.20047 loss)
I0408 08:55:55.016777 31616 sgd_solver.cpp:105] Iteration 8844, lr = 7.59032e-08
I0408 08:56:00.081465 31616 solver.cpp:218] Iteration 8856 (2.3694 iter/s, 5.06457s/12 iters), loss = 3.24832
I0408 08:56:00.081497 31616 solver.cpp:237]     Train net output #0: loss = 3.24832 (* 1 = 3.24832 loss)
I0408 08:56:00.081506 31616 sgd_solver.cpp:105] Iteration 8856, lr = 7.44657e-08
I0408 08:56:05.059160 31616 solver.cpp:218] Iteration 8868 (2.41084 iter/s, 4.97752s/12 iters), loss = 3.38869
I0408 08:56:05.059209 31616 solver.cpp:237]     Train net output #0: loss = 3.38869 (* 1 = 3.38869 loss)
I0408 08:56:05.059221 31616 sgd_solver.cpp:105] Iteration 8868, lr = 7.30555e-08
I0408 08:56:07.106380 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0408 08:56:10.082861 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0408 08:56:16.293116 31616 solver.cpp:330] Iteration 8874, Testing net (#0)
I0408 08:56:16.293135 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:56:17.290057 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:56:20.765416 31616 solver.cpp:397]     Test net output #0: accuracy = 0.151348
I0408 08:56:20.765529 31616 solver.cpp:397]     Test net output #1: loss = 3.84052 (* 1 = 3.84052 loss)
I0408 08:56:22.741881 31616 solver.cpp:218] Iteration 8880 (0.678647 iter/s, 17.6822s/12 iters), loss = 3.10205
I0408 08:56:22.741927 31616 solver.cpp:237]     Train net output #0: loss = 3.10205 (* 1 = 3.10205 loss)
I0408 08:56:22.741938 31616 sgd_solver.cpp:105] Iteration 8880, lr = 7.16719e-08
I0408 08:56:28.031030 31616 solver.cpp:218] Iteration 8892 (2.26888 iter/s, 5.28896s/12 iters), loss = 3.27031
I0408 08:56:28.031073 31616 solver.cpp:237]     Train net output #0: loss = 3.27031 (* 1 = 3.27031 loss)
I0408 08:56:28.031083 31616 sgd_solver.cpp:105] Iteration 8892, lr = 7.03146e-08
I0408 08:56:31.599436 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:56:33.001186 31616 solver.cpp:218] Iteration 8904 (2.4145 iter/s, 4.96998s/12 iters), loss = 3.34856
I0408 08:56:33.001240 31616 solver.cpp:237]     Train net output #0: loss = 3.34856 (* 1 = 3.34856 loss)
I0408 08:56:33.001251 31616 sgd_solver.cpp:105] Iteration 8904, lr = 6.8983e-08
I0408 08:56:37.939527 31616 solver.cpp:218] Iteration 8916 (2.43006 iter/s, 4.93816s/12 iters), loss = 3.05685
I0408 08:56:37.939565 31616 solver.cpp:237]     Train net output #0: loss = 3.05685 (* 1 = 3.05685 loss)
I0408 08:56:37.939575 31616 sgd_solver.cpp:105] Iteration 8916, lr = 6.76766e-08
I0408 08:56:42.891376 31616 solver.cpp:218] Iteration 8928 (2.42342 iter/s, 4.95168s/12 iters), loss = 3.54844
I0408 08:56:42.891422 31616 solver.cpp:237]     Train net output #0: loss = 3.54844 (* 1 = 3.54844 loss)
I0408 08:56:42.891433 31616 sgd_solver.cpp:105] Iteration 8928, lr = 6.63949e-08
I0408 08:56:47.750669 31616 solver.cpp:218] Iteration 8940 (2.46958 iter/s, 4.85912s/12 iters), loss = 3.25627
I0408 08:56:47.750725 31616 solver.cpp:237]     Train net output #0: loss = 3.25627 (* 1 = 3.25627 loss)
I0408 08:56:47.750739 31616 sgd_solver.cpp:105] Iteration 8940, lr = 6.51375e-08
I0408 08:56:52.745864 31616 solver.cpp:218] Iteration 8952 (2.4024 iter/s, 4.99501s/12 iters), loss = 3.26586
I0408 08:56:52.746019 31616 solver.cpp:237]     Train net output #0: loss = 3.26586 (* 1 = 3.26586 loss)
I0408 08:56:52.746033 31616 sgd_solver.cpp:105] Iteration 8952, lr = 6.3904e-08
I0408 08:56:57.775959 31616 solver.cpp:218] Iteration 8964 (2.38578 iter/s, 5.02981s/12 iters), loss = 3.3668
I0408 08:56:57.776005 31616 solver.cpp:237]     Train net output #0: loss = 3.3668 (* 1 = 3.3668 loss)
I0408 08:56:57.776017 31616 sgd_solver.cpp:105] Iteration 8964, lr = 6.26937e-08
I0408 08:57:02.414443 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0408 08:57:05.452863 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0408 08:57:09.369490 31616 solver.cpp:330] Iteration 8976, Testing net (#0)
I0408 08:57:09.369518 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:57:10.324012 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:57:13.832839 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 08:57:13.832887 31616 solver.cpp:397]     Test net output #1: loss = 3.85124 (* 1 = 3.85124 loss)
I0408 08:57:13.924185 31616 solver.cpp:218] Iteration 8976 (0.743137 iter/s, 16.1478s/12 iters), loss = 3.37783
I0408 08:57:13.924230 31616 solver.cpp:237]     Train net output #0: loss = 3.37783 (* 1 = 3.37783 loss)
I0408 08:57:13.924242 31616 sgd_solver.cpp:105] Iteration 8976, lr = 6.15064e-08
I0408 08:57:18.152873 31616 solver.cpp:218] Iteration 8988 (2.83787 iter/s, 4.22852s/12 iters), loss = 3.24417
I0408 08:57:18.152917 31616 solver.cpp:237]     Train net output #0: loss = 3.24417 (* 1 = 3.24417 loss)
I0408 08:57:18.152928 31616 sgd_solver.cpp:105] Iteration 8988, lr = 6.03416e-08
I0408 08:57:21.465160 31616 blocking_queue.cpp:49] Waiting for data
I0408 08:57:23.204404 31616 solver.cpp:218] Iteration 9000 (2.3756 iter/s, 5.05135s/12 iters), loss = 3.32745
I0408 08:57:23.204511 31616 solver.cpp:237]     Train net output #0: loss = 3.32745 (* 1 = 3.32745 loss)
I0408 08:57:23.204524 31616 sgd_solver.cpp:105] Iteration 9000, lr = 5.91989e-08
I0408 08:57:23.985669 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:57:28.619493 31616 solver.cpp:218] Iteration 9012 (2.21613 iter/s, 5.41484s/12 iters), loss = 3.13931
I0408 08:57:28.619537 31616 solver.cpp:237]     Train net output #0: loss = 3.13931 (* 1 = 3.13931 loss)
I0408 08:57:28.619549 31616 sgd_solver.cpp:105] Iteration 9012, lr = 5.80778e-08
I0408 08:57:33.709981 31616 solver.cpp:218] Iteration 9024 (2.35743 iter/s, 5.09029s/12 iters), loss = 3.13523
I0408 08:57:33.710028 31616 solver.cpp:237]     Train net output #0: loss = 3.13523 (* 1 = 3.13523 loss)
I0408 08:57:33.710039 31616 sgd_solver.cpp:105] Iteration 9024, lr = 5.69779e-08
I0408 08:57:38.731060 31616 solver.cpp:218] Iteration 9036 (2.39001 iter/s, 5.0209s/12 iters), loss = 3.20841
I0408 08:57:38.731096 31616 solver.cpp:237]     Train net output #0: loss = 3.20841 (* 1 = 3.20841 loss)
I0408 08:57:38.731103 31616 sgd_solver.cpp:105] Iteration 9036, lr = 5.58988e-08
I0408 08:57:43.756893 31616 solver.cpp:218] Iteration 9048 (2.38775 iter/s, 5.02566s/12 iters), loss = 3.10969
I0408 08:57:43.756932 31616 solver.cpp:237]     Train net output #0: loss = 3.10969 (* 1 = 3.10969 loss)
I0408 08:57:43.756942 31616 sgd_solver.cpp:105] Iteration 9048, lr = 5.48402e-08
I0408 08:57:48.830852 31616 solver.cpp:218] Iteration 9060 (2.3651 iter/s, 5.07378s/12 iters), loss = 3.27115
I0408 08:57:48.830889 31616 solver.cpp:237]     Train net output #0: loss = 3.27115 (* 1 = 3.27115 loss)
I0408 08:57:48.830899 31616 sgd_solver.cpp:105] Iteration 9060, lr = 5.38016e-08
I0408 08:57:53.876416 31616 solver.cpp:218] Iteration 9072 (2.37841 iter/s, 5.04539s/12 iters), loss = 3.28007
I0408 08:57:53.876566 31616 solver.cpp:237]     Train net output #0: loss = 3.28007 (* 1 = 3.28007 loss)
I0408 08:57:53.876579 31616 sgd_solver.cpp:105] Iteration 9072, lr = 5.27827e-08
I0408 08:57:55.924410 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0408 08:57:59.059675 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0408 08:58:04.627741 31616 solver.cpp:330] Iteration 9078, Testing net (#0)
I0408 08:58:04.627768 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:58:05.529867 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:58:09.085148 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150123
I0408 08:58:09.085194 31616 solver.cpp:397]     Test net output #1: loss = 3.8513 (* 1 = 3.8513 loss)
I0408 08:58:11.089083 31616 solver.cpp:218] Iteration 9084 (0.697185 iter/s, 17.2121s/12 iters), loss = 3.20463
I0408 08:58:11.089136 31616 solver.cpp:237]     Train net output #0: loss = 3.20463 (* 1 = 3.20463 loss)
I0408 08:58:11.089148 31616 sgd_solver.cpp:105] Iteration 9084, lr = 5.17831e-08
I0408 08:58:16.537907 31616 solver.cpp:218] Iteration 9096 (2.20239 iter/s, 5.44862s/12 iters), loss = 3.17293
I0408 08:58:16.537981 31616 solver.cpp:237]     Train net output #0: loss = 3.17293 (* 1 = 3.17293 loss)
I0408 08:58:16.537992 31616 sgd_solver.cpp:105] Iteration 9096, lr = 5.08025e-08
I0408 08:58:19.511804 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:58:21.578965 31616 solver.cpp:218] Iteration 9108 (2.38054 iter/s, 5.04087s/12 iters), loss = 3.43145
I0408 08:58:21.579000 31616 solver.cpp:237]     Train net output #0: loss = 3.43145 (* 1 = 3.43145 loss)
I0408 08:58:21.579010 31616 sgd_solver.cpp:105] Iteration 9108, lr = 4.98404e-08
I0408 08:58:26.628692 31616 solver.cpp:218] Iteration 9120 (2.37645 iter/s, 5.04955s/12 iters), loss = 3.26593
I0408 08:58:26.628755 31616 solver.cpp:237]     Train net output #0: loss = 3.26593 (* 1 = 3.26593 loss)
I0408 08:58:26.628764 31616 sgd_solver.cpp:105] Iteration 9120, lr = 4.88965e-08
I0408 08:58:31.723814 31616 solver.cpp:218] Iteration 9132 (2.35529 iter/s, 5.09491s/12 iters), loss = 3.47597
I0408 08:58:31.723856 31616 solver.cpp:237]     Train net output #0: loss = 3.47597 (* 1 = 3.47597 loss)
I0408 08:58:31.723866 31616 sgd_solver.cpp:105] Iteration 9132, lr = 4.79705e-08
I0408 08:58:36.756630 31616 solver.cpp:218] Iteration 9144 (2.38444 iter/s, 5.03263s/12 iters), loss = 3.47513
I0408 08:58:36.756677 31616 solver.cpp:237]     Train net output #0: loss = 3.47513 (* 1 = 3.47513 loss)
I0408 08:58:36.756688 31616 sgd_solver.cpp:105] Iteration 9144, lr = 4.7062e-08
I0408 08:58:41.812826 31616 solver.cpp:218] Iteration 9156 (2.37341 iter/s, 5.05601s/12 iters), loss = 3.36465
I0408 08:58:41.812860 31616 solver.cpp:237]     Train net output #0: loss = 3.36465 (* 1 = 3.36465 loss)
I0408 08:58:41.812867 31616 sgd_solver.cpp:105] Iteration 9156, lr = 4.61707e-08
I0408 08:58:46.953722 31616 solver.cpp:218] Iteration 9168 (2.33431 iter/s, 5.14071s/12 iters), loss = 3.17304
I0408 08:58:46.953763 31616 solver.cpp:237]     Train net output #0: loss = 3.17304 (* 1 = 3.17304 loss)
I0408 08:58:46.953771 31616 sgd_solver.cpp:105] Iteration 9168, lr = 4.52964e-08
I0408 08:58:51.679314 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0408 08:58:56.068938 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0408 08:59:00.194442 31616 solver.cpp:330] Iteration 9180, Testing net (#0)
I0408 08:59:00.194557 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:59:01.139602 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:59:04.750202 31616 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0408 08:59:04.750252 31616 solver.cpp:397]     Test net output #1: loss = 3.84761 (* 1 = 3.84761 loss)
I0408 08:59:04.841753 31616 solver.cpp:218] Iteration 9180 (0.670859 iter/s, 17.8875s/12 iters), loss = 3.48267
I0408 08:59:04.841809 31616 solver.cpp:237]     Train net output #0: loss = 3.48267 (* 1 = 3.48267 loss)
I0408 08:59:04.841823 31616 sgd_solver.cpp:105] Iteration 9180, lr = 4.44385e-08
I0408 08:59:09.335726 31616 solver.cpp:218] Iteration 9192 (2.67035 iter/s, 4.49379s/12 iters), loss = 3.26772
I0408 08:59:09.335772 31616 solver.cpp:237]     Train net output #0: loss = 3.26772 (* 1 = 3.26772 loss)
I0408 08:59:09.335783 31616 sgd_solver.cpp:105] Iteration 9192, lr = 4.35969e-08
I0408 08:59:14.546533 31616 solver.cpp:218] Iteration 9204 (2.30299 iter/s, 5.21062s/12 iters), loss = 3.16648
I0408 08:59:14.546579 31616 solver.cpp:237]     Train net output #0: loss = 3.16648 (* 1 = 3.16648 loss)
I0408 08:59:14.546591 31616 sgd_solver.cpp:105] Iteration 9204, lr = 4.27713e-08
I0408 08:59:14.625761 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:59:19.487088 31616 solver.cpp:218] Iteration 9216 (2.42897 iter/s, 4.94037s/12 iters), loss = 3.23117
I0408 08:59:19.487133 31616 solver.cpp:237]     Train net output #0: loss = 3.23117 (* 1 = 3.23117 loss)
I0408 08:59:19.487145 31616 sgd_solver.cpp:105] Iteration 9216, lr = 4.19613e-08
I0408 08:59:24.498469 31616 solver.cpp:218] Iteration 9228 (2.39464 iter/s, 5.01119s/12 iters), loss = 3.22624
I0408 08:59:24.498524 31616 solver.cpp:237]     Train net output #0: loss = 3.22624 (* 1 = 3.22624 loss)
I0408 08:59:24.498539 31616 sgd_solver.cpp:105] Iteration 9228, lr = 4.11666e-08
I0408 08:59:29.496433 31616 solver.cpp:218] Iteration 9240 (2.40107 iter/s, 4.99777s/12 iters), loss = 3.07697
I0408 08:59:29.496477 31616 solver.cpp:237]     Train net output #0: loss = 3.07697 (* 1 = 3.07697 loss)
I0408 08:59:29.496487 31616 sgd_solver.cpp:105] Iteration 9240, lr = 4.0387e-08
I0408 08:59:34.531509 31616 solver.cpp:218] Iteration 9252 (2.38337 iter/s, 5.03488s/12 iters), loss = 3.36628
I0408 08:59:34.532009 31616 solver.cpp:237]     Train net output #0: loss = 3.36628 (* 1 = 3.36628 loss)
I0408 08:59:34.532022 31616 sgd_solver.cpp:105] Iteration 9252, lr = 3.96222e-08
I0408 08:59:39.502065 31616 solver.cpp:218] Iteration 9264 (2.41453 iter/s, 4.96992s/12 iters), loss = 3.48428
I0408 08:59:39.502111 31616 solver.cpp:237]     Train net output #0: loss = 3.48428 (* 1 = 3.48428 loss)
I0408 08:59:39.502123 31616 sgd_solver.cpp:105] Iteration 9264, lr = 3.88718e-08
I0408 08:59:44.547765 31616 solver.cpp:218] Iteration 9276 (2.37835 iter/s, 5.04551s/12 iters), loss = 3.28046
I0408 08:59:44.547814 31616 solver.cpp:237]     Train net output #0: loss = 3.28046 (* 1 = 3.28046 loss)
I0408 08:59:44.547825 31616 sgd_solver.cpp:105] Iteration 9276, lr = 3.81356e-08
I0408 08:59:46.573705 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0408 08:59:51.065172 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0408 08:59:53.400940 31616 solver.cpp:330] Iteration 9282, Testing net (#0)
I0408 08:59:53.400964 31616 net.cpp:676] Ignoring source layer train-data
I0408 08:59:54.223748 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 08:59:57.872272 31616 solver.cpp:397]     Test net output #0: accuracy = 0.14951
I0408 08:59:57.872311 31616 solver.cpp:397]     Test net output #1: loss = 3.86038 (* 1 = 3.86038 loss)
I0408 08:59:59.643466 31616 solver.cpp:218] Iteration 9288 (0.794953 iter/s, 15.0952s/12 iters), loss = 3.37266
I0408 08:59:59.643507 31616 solver.cpp:237]     Train net output #0: loss = 3.37266 (* 1 = 3.37266 loss)
I0408 08:59:59.643515 31616 sgd_solver.cpp:105] Iteration 9288, lr = 3.74134e-08
I0408 09:00:04.764024 31616 solver.cpp:218] Iteration 9300 (2.34358 iter/s, 5.12037s/12 iters), loss = 3.31146
I0408 09:00:04.764128 31616 solver.cpp:237]     Train net output #0: loss = 3.31146 (* 1 = 3.31146 loss)
I0408 09:00:04.764139 31616 sgd_solver.cpp:105] Iteration 9300, lr = 3.67049e-08
I0408 09:00:07.181633 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:00:10.218631 31616 solver.cpp:218] Iteration 9312 (2.20008 iter/s, 5.45435s/12 iters), loss = 3.28025
I0408 09:00:10.218672 31616 solver.cpp:237]     Train net output #0: loss = 3.28025 (* 1 = 3.28025 loss)
I0408 09:00:10.218683 31616 sgd_solver.cpp:105] Iteration 9312, lr = 3.60098e-08
I0408 09:00:15.360975 31616 solver.cpp:218] Iteration 9324 (2.33365 iter/s, 5.14216s/12 iters), loss = 3.24066
I0408 09:00:15.361012 31616 solver.cpp:237]     Train net output #0: loss = 3.24066 (* 1 = 3.24066 loss)
I0408 09:00:15.361021 31616 sgd_solver.cpp:105] Iteration 9324, lr = 3.53278e-08
I0408 09:00:20.384305 31616 solver.cpp:218] Iteration 9336 (2.38894 iter/s, 5.02315s/12 iters), loss = 3.03865
I0408 09:00:20.384349 31616 solver.cpp:237]     Train net output #0: loss = 3.03865 (* 1 = 3.03865 loss)
I0408 09:00:20.384361 31616 sgd_solver.cpp:105] Iteration 9336, lr = 3.46588e-08
I0408 09:00:25.476902 31616 solver.cpp:218] Iteration 9348 (2.35645 iter/s, 5.09241s/12 iters), loss = 3.60713
I0408 09:00:25.476948 31616 solver.cpp:237]     Train net output #0: loss = 3.60713 (* 1 = 3.60713 loss)
I0408 09:00:25.476959 31616 sgd_solver.cpp:105] Iteration 9348, lr = 3.40024e-08
I0408 09:00:30.606976 31616 solver.cpp:218] Iteration 9360 (2.33924 iter/s, 5.12988s/12 iters), loss = 3.42638
I0408 09:00:30.607012 31616 solver.cpp:237]     Train net output #0: loss = 3.42638 (* 1 = 3.42638 loss)
I0408 09:00:30.607018 31616 sgd_solver.cpp:105] Iteration 9360, lr = 3.33585e-08
I0408 09:00:35.592646 31616 solver.cpp:218] Iteration 9372 (2.40699 iter/s, 4.98549s/12 iters), loss = 3.22668
I0408 09:00:35.592778 31616 solver.cpp:237]     Train net output #0: loss = 3.22668 (* 1 = 3.22668 loss)
I0408 09:00:35.592792 31616 sgd_solver.cpp:105] Iteration 9372, lr = 3.27267e-08
I0408 09:00:40.147673 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0408 09:00:43.121167 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0408 09:00:45.429734 31616 solver.cpp:330] Iteration 9384, Testing net (#0)
I0408 09:00:45.429762 31616 net.cpp:676] Ignoring source layer train-data
I0408 09:00:46.207883 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:00:49.885890 31616 solver.cpp:397]     Test net output #0: accuracy = 0.14951
I0408 09:00:49.885937 31616 solver.cpp:397]     Test net output #1: loss = 3.84613 (* 1 = 3.84613 loss)
I0408 09:00:49.976919 31616 solver.cpp:218] Iteration 9384 (0.834275 iter/s, 14.3837s/12 iters), loss = 3.28615
I0408 09:00:49.976964 31616 solver.cpp:237]     Train net output #0: loss = 3.28615 (* 1 = 3.28615 loss)
I0408 09:00:49.976975 31616 sgd_solver.cpp:105] Iteration 9384, lr = 3.21069e-08
I0408 09:00:54.275784 31616 solver.cpp:218] Iteration 9396 (2.79155 iter/s, 4.29869s/12 iters), loss = 3.18538
I0408 09:00:54.275828 31616 solver.cpp:237]     Train net output #0: loss = 3.18538 (* 1 = 3.18538 loss)
I0408 09:00:54.275840 31616 sgd_solver.cpp:105] Iteration 9396, lr = 3.14989e-08
I0408 09:00:58.652781 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:00:59.302536 31616 solver.cpp:218] Iteration 9408 (2.38732 iter/s, 5.02656s/12 iters), loss = 3.42495
I0408 09:00:59.302582 31616 solver.cpp:237]     Train net output #0: loss = 3.42495 (* 1 = 3.42495 loss)
I0408 09:00:59.302594 31616 sgd_solver.cpp:105] Iteration 9408, lr = 3.09024e-08
I0408 09:01:04.310513 31616 solver.cpp:218] Iteration 9420 (2.39627 iter/s, 5.00779s/12 iters), loss = 3.29648
I0408 09:01:04.310557 31616 solver.cpp:237]     Train net output #0: loss = 3.29648 (* 1 = 3.29648 loss)
I0408 09:01:04.310568 31616 sgd_solver.cpp:105] Iteration 9420, lr = 3.03171e-08
I0408 09:01:09.301673 31616 solver.cpp:218] Iteration 9432 (2.40434 iter/s, 4.99097s/12 iters), loss = 3.09224
I0408 09:01:09.304023 31616 solver.cpp:237]     Train net output #0: loss = 3.09224 (* 1 = 3.09224 loss)
I0408 09:01:09.304036 31616 sgd_solver.cpp:105] Iteration 9432, lr = 2.9743e-08
I0408 09:01:14.330292 31616 solver.cpp:218] Iteration 9444 (2.38753 iter/s, 5.02612s/12 iters), loss = 3.2777
I0408 09:01:14.330340 31616 solver.cpp:237]     Train net output #0: loss = 3.2777 (* 1 = 3.2777 loss)
I0408 09:01:14.330353 31616 sgd_solver.cpp:105] Iteration 9444, lr = 2.91797e-08
I0408 09:01:19.395890 31616 solver.cpp:218] Iteration 9456 (2.36901 iter/s, 5.0654s/12 iters), loss = 3.37954
I0408 09:01:19.395938 31616 solver.cpp:237]     Train net output #0: loss = 3.37954 (* 1 = 3.37954 loss)
I0408 09:01:19.395951 31616 sgd_solver.cpp:105] Iteration 9456, lr = 2.86271e-08
I0408 09:01:24.328517 31616 solver.cpp:218] Iteration 9468 (2.43288 iter/s, 4.93244s/12 iters), loss = 3.23857
I0408 09:01:24.328562 31616 solver.cpp:237]     Train net output #0: loss = 3.23857 (* 1 = 3.23857 loss)
I0408 09:01:24.328575 31616 sgd_solver.cpp:105] Iteration 9468, lr = 2.8085e-08
I0408 09:01:29.331001 31616 solver.cpp:218] Iteration 9480 (2.3989 iter/s, 5.00229s/12 iters), loss = 3.31861
I0408 09:01:29.331044 31616 solver.cpp:237]     Train net output #0: loss = 3.31861 (* 1 = 3.31861 loss)
I0408 09:01:29.331055 31616 sgd_solver.cpp:105] Iteration 9480, lr = 2.75531e-08
I0408 09:01:31.360803 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0408 09:01:34.416666 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0408 09:01:36.800496 31616 solver.cpp:330] Iteration 9486, Testing net (#0)
I0408 09:01:36.800519 31616 net.cpp:676] Ignoring source layer train-data
I0408 09:01:37.525534 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:01:41.254321 31616 solver.cpp:397]     Test net output #0: accuracy = 0.14951
I0408 09:01:41.254429 31616 solver.cpp:397]     Test net output #1: loss = 3.84415 (* 1 = 3.84415 loss)
I0408 09:01:43.065886 31616 solver.cpp:218] Iteration 9492 (0.873715 iter/s, 13.7345s/12 iters), loss = 3.27745
I0408 09:01:43.065938 31616 solver.cpp:237]     Train net output #0: loss = 3.27745 (* 1 = 3.27745 loss)
I0408 09:01:43.065953 31616 sgd_solver.cpp:105] Iteration 9492, lr = 2.70313e-08
I0408 09:01:47.977442 31616 solver.cpp:218] Iteration 9504 (2.44332 iter/s, 4.91136s/12 iters), loss = 3.22407
I0408 09:01:47.977483 31616 solver.cpp:237]     Train net output #0: loss = 3.22407 (* 1 = 3.22407 loss)
I0408 09:01:47.977495 31616 sgd_solver.cpp:105] Iteration 9504, lr = 2.65194e-08
I0408 09:01:49.386102 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:01:52.875916 31616 solver.cpp:218] Iteration 9516 (2.44983 iter/s, 4.89829s/12 iters), loss = 3.1979
I0408 09:01:52.875978 31616 solver.cpp:237]     Train net output #0: loss = 3.1979 (* 1 = 3.1979 loss)
I0408 09:01:52.875990 31616 sgd_solver.cpp:105] Iteration 9516, lr = 2.60171e-08
I0408 09:01:57.893620 31616 solver.cpp:218] Iteration 9528 (2.39163 iter/s, 5.01749s/12 iters), loss = 3.53372
I0408 09:01:57.893667 31616 solver.cpp:237]     Train net output #0: loss = 3.53372 (* 1 = 3.53372 loss)
I0408 09:01:57.893679 31616 sgd_solver.cpp:105] Iteration 9528, lr = 2.55244e-08
I0408 09:02:02.860472 31616 solver.cpp:218] Iteration 9540 (2.41611 iter/s, 4.96666s/12 iters), loss = 3.23711
I0408 09:02:02.860513 31616 solver.cpp:237]     Train net output #0: loss = 3.23711 (* 1 = 3.23711 loss)
I0408 09:02:02.860522 31616 sgd_solver.cpp:105] Iteration 9540, lr = 2.5041e-08
I0408 09:02:07.888458 31616 solver.cpp:218] Iteration 9552 (2.38673 iter/s, 5.0278s/12 iters), loss = 3.22474
I0408 09:02:07.888504 31616 solver.cpp:237]     Train net output #0: loss = 3.22474 (* 1 = 3.22474 loss)
I0408 09:02:07.888516 31616 sgd_solver.cpp:105] Iteration 9552, lr = 2.45668e-08
I0408 09:02:12.806488 31616 solver.cpp:218] Iteration 9564 (2.4401 iter/s, 4.91784s/12 iters), loss = 3.26748
I0408 09:02:12.806641 31616 solver.cpp:237]     Train net output #0: loss = 3.26748 (* 1 = 3.26748 loss)
I0408 09:02:12.806654 31616 sgd_solver.cpp:105] Iteration 9564, lr = 2.41016e-08
I0408 09:02:17.801404 31616 solver.cpp:218] Iteration 9576 (2.40259 iter/s, 4.99462s/12 iters), loss = 3.44657
I0408 09:02:17.801455 31616 solver.cpp:237]     Train net output #0: loss = 3.44657 (* 1 = 3.44657 loss)
I0408 09:02:17.801465 31616 sgd_solver.cpp:105] Iteration 9576, lr = 2.36451e-08
I0408 09:02:22.357878 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0408 09:02:25.382966 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0408 09:02:27.813663 31616 solver.cpp:330] Iteration 9588, Testing net (#0)
I0408 09:02:27.813690 31616 net.cpp:676] Ignoring source layer train-data
I0408 09:02:28.501844 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:02:32.414116 31616 solver.cpp:397]     Test net output #0: accuracy = 0.14951
I0408 09:02:32.414147 31616 solver.cpp:397]     Test net output #1: loss = 3.84436 (* 1 = 3.84436 loss)
I0408 09:02:32.505314 31616 solver.cpp:218] Iteration 9588 (0.816135 iter/s, 14.7034s/12 iters), loss = 3.08752
I0408 09:02:32.505352 31616 solver.cpp:237]     Train net output #0: loss = 3.08752 (* 1 = 3.08752 loss)
I0408 09:02:32.505360 31616 sgd_solver.cpp:105] Iteration 9588, lr = 2.31973e-08
I0408 09:02:36.625244 31616 solver.cpp:218] Iteration 9600 (2.91279 iter/s, 4.11977s/12 iters), loss = 3.31424
I0408 09:02:36.625288 31616 solver.cpp:237]     Train net output #0: loss = 3.31424 (* 1 = 3.31424 loss)
I0408 09:02:36.625299 31616 sgd_solver.cpp:105] Iteration 9600, lr = 2.2758e-08
I0408 09:02:40.241709 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:02:41.624579 31616 solver.cpp:218] Iteration 9612 (2.40041 iter/s, 4.99914s/12 iters), loss = 3.33998
I0408 09:02:41.624614 31616 solver.cpp:237]     Train net output #0: loss = 3.33998 (* 1 = 3.33998 loss)
I0408 09:02:41.624624 31616 sgd_solver.cpp:105] Iteration 9612, lr = 2.2327e-08
I0408 09:02:46.569931 31616 solver.cpp:218] Iteration 9624 (2.42661 iter/s, 4.94517s/12 iters), loss = 2.99107
I0408 09:02:46.570034 31616 solver.cpp:237]     Train net output #0: loss = 2.99107 (* 1 = 2.99107 loss)
I0408 09:02:46.570044 31616 sgd_solver.cpp:105] Iteration 9624, lr = 2.19042e-08
I0408 09:02:51.562515 31616 solver.cpp:218] Iteration 9636 (2.40368 iter/s, 4.99234s/12 iters), loss = 3.39393
I0408 09:02:51.562551 31616 solver.cpp:237]     Train net output #0: loss = 3.39393 (* 1 = 3.39393 loss)
I0408 09:02:51.562558 31616 sgd_solver.cpp:105] Iteration 9636, lr = 2.14894e-08
I0408 09:02:56.576004 31616 solver.cpp:218] Iteration 9648 (2.39364 iter/s, 5.01329s/12 iters), loss = 3.40208
I0408 09:02:56.576056 31616 solver.cpp:237]     Train net output #0: loss = 3.40208 (* 1 = 3.40208 loss)
I0408 09:02:56.576071 31616 sgd_solver.cpp:105] Iteration 9648, lr = 2.10824e-08
I0408 09:03:01.649034 31616 solver.cpp:218] Iteration 9660 (2.36554 iter/s, 5.07283s/12 iters), loss = 3.34748
I0408 09:03:01.649070 31616 solver.cpp:237]     Train net output #0: loss = 3.34748 (* 1 = 3.34748 loss)
I0408 09:03:01.649077 31616 sgd_solver.cpp:105] Iteration 9660, lr = 2.06831e-08
I0408 09:03:06.583515 31616 solver.cpp:218] Iteration 9672 (2.43196 iter/s, 4.93429s/12 iters), loss = 3.34765
I0408 09:03:06.583564 31616 solver.cpp:237]     Train net output #0: loss = 3.34765 (* 1 = 3.34765 loss)
I0408 09:03:06.583575 31616 sgd_solver.cpp:105] Iteration 9672, lr = 2.02914e-08
I0408 09:03:11.629783 31616 solver.cpp:218] Iteration 9684 (2.37809 iter/s, 5.04607s/12 iters), loss = 3.29272
I0408 09:03:11.629829 31616 solver.cpp:237]     Train net output #0: loss = 3.29272 (* 1 = 3.29272 loss)
I0408 09:03:11.629840 31616 sgd_solver.cpp:105] Iteration 9684, lr = 1.99072e-08
I0408 09:03:13.674434 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0408 09:03:16.687908 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0408 09:03:19.026182 31616 solver.cpp:330] Iteration 9690, Testing net (#0)
I0408 09:03:19.026211 31616 net.cpp:676] Ignoring source layer train-data
I0408 09:03:19.676735 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:03:22.503330 31616 blocking_queue.cpp:49] Waiting for data
I0408 09:03:23.491811 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 09:03:23.491855 31616 solver.cpp:397]     Test net output #1: loss = 3.85837 (* 1 = 3.85837 loss)
I0408 09:03:25.472277 31616 solver.cpp:218] Iteration 9696 (0.866923 iter/s, 13.8421s/12 iters), loss = 3.1917
I0408 09:03:25.472317 31616 solver.cpp:237]     Train net output #0: loss = 3.1917 (* 1 = 3.1917 loss)
I0408 09:03:25.472326 31616 sgd_solver.cpp:105] Iteration 9696, lr = 1.95302e-08
I0408 09:03:30.525286 31616 solver.cpp:218] Iteration 9708 (2.37491 iter/s, 5.05282s/12 iters), loss = 3.44879
I0408 09:03:30.525329 31616 solver.cpp:237]     Train net output #0: loss = 3.44879 (* 1 = 3.44879 loss)
I0408 09:03:30.525341 31616 sgd_solver.cpp:105] Iteration 9708, lr = 1.91603e-08
I0408 09:03:31.264282 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:03:35.571991 31616 solver.cpp:218] Iteration 9720 (2.37788 iter/s, 5.04651s/12 iters), loss = 3.16624
I0408 09:03:35.572036 31616 solver.cpp:237]     Train net output #0: loss = 3.16624 (* 1 = 3.16624 loss)
I0408 09:03:35.572048 31616 sgd_solver.cpp:105] Iteration 9720, lr = 1.87974e-08
I0408 09:03:40.628262 31616 solver.cpp:218] Iteration 9732 (2.37338 iter/s, 5.05607s/12 iters), loss = 3.32528
I0408 09:03:40.628309 31616 solver.cpp:237]     Train net output #0: loss = 3.32528 (* 1 = 3.32528 loss)
I0408 09:03:40.628321 31616 sgd_solver.cpp:105] Iteration 9732, lr = 1.84414e-08
I0408 09:03:45.573480 31616 solver.cpp:218] Iteration 9744 (2.42668 iter/s, 4.94502s/12 iters), loss = 3.19877
I0408 09:03:45.573523 31616 solver.cpp:237]     Train net output #0: loss = 3.19877 (* 1 = 3.19877 loss)
I0408 09:03:45.573534 31616 sgd_solver.cpp:105] Iteration 9744, lr = 1.80922e-08
I0408 09:03:50.568976 31616 solver.cpp:218] Iteration 9756 (2.40226 iter/s, 4.9953s/12 iters), loss = 3.1347
I0408 09:03:50.569093 31616 solver.cpp:237]     Train net output #0: loss = 3.1347 (* 1 = 3.1347 loss)
I0408 09:03:50.569106 31616 sgd_solver.cpp:105] Iteration 9756, lr = 1.77496e-08
I0408 09:03:55.583469 31616 solver.cpp:218] Iteration 9768 (2.39319 iter/s, 5.01423s/12 iters), loss = 3.30993
I0408 09:03:55.583513 31616 solver.cpp:237]     Train net output #0: loss = 3.30993 (* 1 = 3.30993 loss)
I0408 09:03:55.583524 31616 sgd_solver.cpp:105] Iteration 9768, lr = 1.74134e-08
I0408 09:04:00.598063 31616 solver.cpp:218] Iteration 9780 (2.39311 iter/s, 5.0144s/12 iters), loss = 3.35885
I0408 09:04:00.598109 31616 solver.cpp:237]     Train net output #0: loss = 3.35885 (* 1 = 3.35885 loss)
I0408 09:04:00.598121 31616 sgd_solver.cpp:105] Iteration 9780, lr = 1.70836e-08
I0408 09:04:05.248132 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0408 09:04:08.239058 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0408 09:04:10.581401 31616 solver.cpp:330] Iteration 9792, Testing net (#0)
I0408 09:04:10.581429 31616 net.cpp:676] Ignoring source layer train-data
I0408 09:04:11.190697 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:04:15.042297 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 09:04:15.042346 31616 solver.cpp:397]     Test net output #1: loss = 3.845 (* 1 = 3.845 loss)
I0408 09:04:15.133391 31616 solver.cpp:218] Iteration 9792 (0.825601 iter/s, 14.5349s/12 iters), loss = 3.3387
I0408 09:04:15.133440 31616 solver.cpp:237]     Train net output #0: loss = 3.3387 (* 1 = 3.3387 loss)
I0408 09:04:15.133450 31616 sgd_solver.cpp:105] Iteration 9792, lr = 1.67601e-08
I0408 09:04:19.303144 31616 solver.cpp:218] Iteration 9804 (2.87799 iter/s, 4.16958s/12 iters), loss = 3.33874
I0408 09:04:19.303196 31616 solver.cpp:237]     Train net output #0: loss = 3.33874 (* 1 = 3.33874 loss)
I0408 09:04:19.303207 31616 sgd_solver.cpp:105] Iteration 9804, lr = 1.64427e-08
I0408 09:04:22.297327 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:04:24.303475 31616 solver.cpp:218] Iteration 9816 (2.39994 iter/s, 5.00013s/12 iters), loss = 3.33079
I0408 09:04:24.303519 31616 solver.cpp:237]     Train net output #0: loss = 3.33079 (* 1 = 3.33079 loss)
I0408 09:04:24.303534 31616 sgd_solver.cpp:105] Iteration 9816, lr = 1.61313e-08
I0408 09:04:29.302361 31616 solver.cpp:218] Iteration 9828 (2.40063 iter/s, 4.99869s/12 iters), loss = 3.25041
I0408 09:04:29.302403 31616 solver.cpp:237]     Train net output #0: loss = 3.25041 (* 1 = 3.25041 loss)
I0408 09:04:29.302414 31616 sgd_solver.cpp:105] Iteration 9828, lr = 1.58258e-08
I0408 09:04:34.295169 31616 solver.cpp:218] Iteration 9840 (2.40355 iter/s, 4.99261s/12 iters), loss = 3.33721
I0408 09:04:34.295217 31616 solver.cpp:237]     Train net output #0: loss = 3.33721 (* 1 = 3.33721 loss)
I0408 09:04:34.295228 31616 sgd_solver.cpp:105] Iteration 9840, lr = 1.55261e-08
I0408 09:04:39.309623 31616 solver.cpp:218] Iteration 9852 (2.39318 iter/s, 5.01425s/12 iters), loss = 3.40838
I0408 09:04:39.309671 31616 solver.cpp:237]     Train net output #0: loss = 3.40838 (* 1 = 3.40838 loss)
I0408 09:04:39.309684 31616 sgd_solver.cpp:105] Iteration 9852, lr = 1.52321e-08
I0408 09:04:44.349931 31616 solver.cpp:218] Iteration 9864 (2.3809 iter/s, 5.04011s/12 iters), loss = 3.27902
I0408 09:04:44.349987 31616 solver.cpp:237]     Train net output #0: loss = 3.27902 (* 1 = 3.27902 loss)
I0408 09:04:44.349998 31616 sgd_solver.cpp:105] Iteration 9864, lr = 1.49436e-08
I0408 09:04:49.327867 31616 solver.cpp:218] Iteration 9876 (2.41074 iter/s, 4.97773s/12 iters), loss = 3.2725
I0408 09:04:49.327915 31616 solver.cpp:237]     Train net output #0: loss = 3.2725 (* 1 = 3.2725 loss)
I0408 09:04:49.327927 31616 sgd_solver.cpp:105] Iteration 9876, lr = 1.46606e-08
I0408 09:04:54.252853 31616 solver.cpp:218] Iteration 9888 (2.43665 iter/s, 4.92479s/12 iters), loss = 3.61321
I0408 09:04:54.252959 31616 solver.cpp:237]     Train net output #0: loss = 3.61321 (* 1 = 3.61321 loss)
I0408 09:04:54.252971 31616 sgd_solver.cpp:105] Iteration 9888, lr = 1.4383e-08
I0408 09:04:56.298525 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0408 09:04:59.270483 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0408 09:05:02.025544 31616 solver.cpp:330] Iteration 9894, Testing net (#0)
I0408 09:05:02.025569 31616 net.cpp:676] Ignoring source layer train-data
I0408 09:05:02.594628 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:05:06.467025 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148284
I0408 09:05:06.467067 31616 solver.cpp:397]     Test net output #1: loss = 3.85053 (* 1 = 3.85053 loss)
I0408 09:05:08.485982 31616 solver.cpp:218] Iteration 9900 (0.843134 iter/s, 14.2326s/12 iters), loss = 3.39405
I0408 09:05:08.486025 31616 solver.cpp:237]     Train net output #0: loss = 3.39405 (* 1 = 3.39405 loss)
I0408 09:05:08.486033 31616 sgd_solver.cpp:105] Iteration 9900, lr = 1.41106e-08
I0408 09:05:13.486522 31616 solver.cpp:218] Iteration 9912 (2.39983 iter/s, 5.00035s/12 iters), loss = 3.28365
I0408 09:05:13.486562 31616 solver.cpp:237]     Train net output #0: loss = 3.28365 (* 1 = 3.28365 loss)
I0408 09:05:13.486572 31616 sgd_solver.cpp:105] Iteration 9912, lr = 1.38433e-08
I0408 09:05:13.603199 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:05:18.480712 31616 solver.cpp:218] Iteration 9924 (2.40288 iter/s, 4.994s/12 iters), loss = 3.33666
I0408 09:05:18.480754 31616 solver.cpp:237]     Train net output #0: loss = 3.33666 (* 1 = 3.33666 loss)
I0408 09:05:18.480764 31616 sgd_solver.cpp:105] Iteration 9924, lr = 1.35812e-08
I0408 09:05:23.448964 31616 solver.cpp:218] Iteration 9936 (2.41543 iter/s, 4.96805s/12 iters), loss = 3.02995
I0408 09:05:23.449010 31616 solver.cpp:237]     Train net output #0: loss = 3.02995 (* 1 = 3.02995 loss)
I0408 09:05:23.449023 31616 sgd_solver.cpp:105] Iteration 9936, lr = 1.3324e-08
I0408 09:05:28.470093 31616 solver.cpp:218] Iteration 9948 (2.39 iter/s, 5.02093s/12 iters), loss = 3.15942
I0408 09:05:28.470213 31616 solver.cpp:237]     Train net output #0: loss = 3.15942 (* 1 = 3.15942 loss)
I0408 09:05:28.470227 31616 sgd_solver.cpp:105] Iteration 9948, lr = 1.30717e-08
I0408 09:05:33.520088 31616 solver.cpp:218] Iteration 9960 (2.37637 iter/s, 5.04973s/12 iters), loss = 3.37996
I0408 09:05:33.520121 31616 solver.cpp:237]     Train net output #0: loss = 3.37996 (* 1 = 3.37996 loss)
I0408 09:05:33.520129 31616 sgd_solver.cpp:105] Iteration 9960, lr = 1.28241e-08
I0408 09:05:38.607285 31616 solver.cpp:218] Iteration 9972 (2.35895 iter/s, 5.087s/12 iters), loss = 3.33365
I0408 09:05:38.607333 31616 solver.cpp:237]     Train net output #0: loss = 3.33365 (* 1 = 3.33365 loss)
I0408 09:05:38.607344 31616 sgd_solver.cpp:105] Iteration 9972, lr = 1.25812e-08
I0408 09:05:43.664227 31616 solver.cpp:218] Iteration 9984 (2.37307 iter/s, 5.05673s/12 iters), loss = 3.39842
I0408 09:05:43.664276 31616 solver.cpp:237]     Train net output #0: loss = 3.39842 (* 1 = 3.39842 loss)
I0408 09:05:43.664288 31616 sgd_solver.cpp:105] Iteration 9984, lr = 1.2343e-08
I0408 09:05:48.185866 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0408 09:05:51.199342 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0408 09:05:53.516358 31616 solver.cpp:330] Iteration 9996, Testing net (#0)
I0408 09:05:53.516381 31616 net.cpp:676] Ignoring source layer train-data
I0408 09:05:54.037163 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:05:57.988113 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 09:05:57.988162 31616 solver.cpp:397]     Test net output #1: loss = 3.84843 (* 1 = 3.84843 loss)
I0408 09:05:58.078088 31616 solver.cpp:218] Iteration 9996 (0.832559 iter/s, 14.4134s/12 iters), loss = 3.32454
I0408 09:05:58.078135 31616 solver.cpp:237]     Train net output #0: loss = 3.32454 (* 1 = 3.32454 loss)
I0408 09:05:58.078147 31616 sgd_solver.cpp:105] Iteration 9996, lr = 1.21092e-08
I0408 09:06:02.289891 31616 solver.cpp:218] Iteration 10008 (2.84926 iter/s, 4.21162s/12 iters), loss = 3.23418
I0408 09:06:02.290014 31616 solver.cpp:237]     Train net output #0: loss = 3.23418 (* 1 = 3.23418 loss)
I0408 09:06:02.290030 31616 sgd_solver.cpp:105] Iteration 10008, lr = 1.18799e-08
I0408 09:06:04.553897 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:06:07.344219 31616 solver.cpp:218] Iteration 10020 (2.37433 iter/s, 5.05405s/12 iters), loss = 3.09193
I0408 09:06:07.344264 31616 solver.cpp:237]     Train net output #0: loss = 3.09193 (* 1 = 3.09193 loss)
I0408 09:06:07.344276 31616 sgd_solver.cpp:105] Iteration 10020, lr = 1.16549e-08
I0408 09:06:12.385731 31616 solver.cpp:218] Iteration 10032 (2.38033 iter/s, 5.04131s/12 iters), loss = 3.13656
I0408 09:06:12.385776 31616 solver.cpp:237]     Train net output #0: loss = 3.13656 (* 1 = 3.13656 loss)
I0408 09:06:12.385788 31616 sgd_solver.cpp:105] Iteration 10032, lr = 1.14342e-08
I0408 09:06:17.422922 31616 solver.cpp:218] Iteration 10044 (2.38238 iter/s, 5.03699s/12 iters), loss = 3.33215
I0408 09:06:17.422977 31616 solver.cpp:237]     Train net output #0: loss = 3.33215 (* 1 = 3.33215 loss)
I0408 09:06:17.422991 31616 sgd_solver.cpp:105] Iteration 10044, lr = 1.12176e-08
I0408 09:06:22.489442 31616 solver.cpp:218] Iteration 10056 (2.36859 iter/s, 5.06631s/12 iters), loss = 3.51147
I0408 09:06:22.489488 31616 solver.cpp:237]     Train net output #0: loss = 3.51147 (* 1 = 3.51147 loss)
I0408 09:06:22.489500 31616 sgd_solver.cpp:105] Iteration 10056, lr = 1.10052e-08
I0408 09:06:27.568796 31616 solver.cpp:218] Iteration 10068 (2.3626 iter/s, 5.07915s/12 iters), loss = 3.35305
I0408 09:06:27.568835 31616 solver.cpp:237]     Train net output #0: loss = 3.35305 (* 1 = 3.35305 loss)
I0408 09:06:27.568845 31616 sgd_solver.cpp:105] Iteration 10068, lr = 1.07968e-08
I0408 09:06:32.553828 31616 solver.cpp:218] Iteration 10080 (2.4073 iter/s, 4.98484s/12 iters), loss = 3.14856
I0408 09:06:32.553992 31616 solver.cpp:237]     Train net output #0: loss = 3.14856 (* 1 = 3.14856 loss)
I0408 09:06:32.554005 31616 sgd_solver.cpp:105] Iteration 10080, lr = 1.05923e-08
I0408 09:06:37.538856 31616 solver.cpp:218] Iteration 10092 (2.40736 iter/s, 4.98472s/12 iters), loss = 3.17362
I0408 09:06:37.538902 31616 solver.cpp:237]     Train net output #0: loss = 3.17362 (* 1 = 3.17362 loss)
I0408 09:06:37.538913 31616 sgd_solver.cpp:105] Iteration 10092, lr = 1.03917e-08
I0408 09:06:39.545188 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0408 09:06:42.561805 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0408 09:06:44.936506 31616 solver.cpp:330] Iteration 10098, Testing net (#0)
I0408 09:06:44.936527 31616 net.cpp:676] Ignoring source layer train-data
I0408 09:06:45.423218 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:06:49.421339 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 09:06:49.421375 31616 solver.cpp:397]     Test net output #1: loss = 3.85336 (* 1 = 3.85336 loss)
I0408 09:06:51.426683 31616 solver.cpp:218] Iteration 10104 (0.864094 iter/s, 13.8874s/12 iters), loss = 3.16381
I0408 09:06:51.426720 31616 solver.cpp:237]     Train net output #0: loss = 3.16381 (* 1 = 3.16381 loss)
I0408 09:06:51.426728 31616 sgd_solver.cpp:105] Iteration 10104, lr = 1.01949e-08
I0408 09:06:56.214447 31620 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:06:56.898712 31616 solver.cpp:218] Iteration 10116 (2.19305 iter/s, 5.47182s/12 iters), loss = 3.22939
I0408 09:06:56.898748 31616 solver.cpp:237]     Train net output #0: loss = 3.22939 (* 1 = 3.22939 loss)
I0408 09:06:56.898757 31616 sgd_solver.cpp:105] Iteration 10116, lr = 1.00018e-08
I0408 09:07:02.114977 31616 solver.cpp:218] Iteration 10128 (2.30059 iter/s, 5.21606s/12 iters), loss = 3.44386
I0408 09:07:02.115027 31616 solver.cpp:237]     Train net output #0: loss = 3.44386 (* 1 = 3.44386 loss)
I0408 09:07:02.115039 31616 sgd_solver.cpp:105] Iteration 10128, lr = 9.81243e-09
I0408 09:07:07.150169 31616 solver.cpp:218] Iteration 10140 (2.38332 iter/s, 5.03499s/12 iters), loss = 3.39375
I0408 09:07:07.150269 31616 solver.cpp:237]     Train net output #0: loss = 3.39375 (* 1 = 3.39375 loss)
I0408 09:07:07.150291 31616 sgd_solver.cpp:105] Iteration 10140, lr = 9.6266e-09
I0408 09:07:12.170254 31616 solver.cpp:218] Iteration 10152 (2.39052 iter/s, 5.01983s/12 iters), loss = 3.36659
I0408 09:07:12.170297 31616 solver.cpp:237]     Train net output #0: loss = 3.36659 (* 1 = 3.36659 loss)
I0408 09:07:12.170307 31616 sgd_solver.cpp:105] Iteration 10152, lr = 9.44429e-09
I0408 09:07:17.227344 31616 solver.cpp:218] Iteration 10164 (2.373 iter/s, 5.05689s/12 iters), loss = 3.20429
I0408 09:07:17.227388 31616 solver.cpp:237]     Train net output #0: loss = 3.20429 (* 1 = 3.20429 loss)
I0408 09:07:17.227399 31616 sgd_solver.cpp:105] Iteration 10164, lr = 9.26544e-09
I0408 09:07:22.578837 31616 solver.cpp:218] Iteration 10176 (2.24245 iter/s, 5.35128s/12 iters), loss = 3.27397
I0408 09:07:22.578884 31616 solver.cpp:237]     Train net output #0: loss = 3.27397 (* 1 = 3.27397 loss)
I0408 09:07:22.578896 31616 sgd_solver.cpp:105] Iteration 10176, lr = 9.08997e-09
I0408 09:07:27.588835 31616 solver.cpp:218] Iteration 10188 (2.39531 iter/s, 5.00979s/12 iters), loss = 3.57061
I0408 09:07:27.588873 31616 solver.cpp:237]     Train net output #0: loss = 3.57061 (* 1 = 3.57061 loss)
I0408 09:07:27.588882 31616 sgd_solver.cpp:105] Iteration 10188, lr = 8.91782e-09
I0408 09:07:32.105206 31616 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0408 09:07:35.164217 31616 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0408 09:07:37.533205 31616 solver.cpp:310] Iteration 10200, loss = 3.33724
I0408 09:07:37.533324 31616 solver.cpp:330] Iteration 10200, Testing net (#0)
I0408 09:07:37.533332 31616 net.cpp:676] Ignoring source layer train-data
I0408 09:07:37.919234 31621 data_layer.cpp:73] Restarting data prefetching from start.
I0408 09:07:41.935091 31616 solver.cpp:397]     Test net output #0: accuracy = 0.148897
I0408 09:07:41.935127 31616 solver.cpp:397]     Test net output #1: loss = 3.85234 (* 1 = 3.85234 loss)
I0408 09:07:41.935134 31616 solver.cpp:315] Optimization Done.
I0408 09:07:41.935139 31616 caffe.cpp:259] Optimization Done.