DIGITS-CNN/cars/lr-investigations/exponential/1e-2/0.8/caffe_output.log

I0408 15:34:34.007990 27193 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210408-153432-9be4/solver.prototxt
I0408 15:34:34.008224 27193 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0408 15:34:34.008234 27193 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0408 15:34:34.008332 27193 caffe.cpp:218] Using GPUs 1
I0408 15:34:34.034111 27193 caffe.cpp:223] GPU 1: GeForce GTX 1080 Ti
I0408 15:34:34.318290 27193 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99781471
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0408 15:34:34.319008 27193 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0408 15:34:34.319583 27193 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0408 15:34:34.319598 27193 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0408 15:34:34.319737 27193 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 15:34:34.319823 27193 layer_factory.hpp:77] Creating layer train-data
I0408 15:34:34.322178 27193 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0408 15:34:34.322394 27193 net.cpp:84] Creating Layer train-data
I0408 15:34:34.322417 27193 net.cpp:380] train-data -> data
I0408 15:34:34.322448 27193 net.cpp:380] train-data -> label
I0408 15:34:34.322466 27193 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0408 15:34:34.330838 27193 data_layer.cpp:45] output data size: 128,3,227,227
I0408 15:34:34.482754 27193 net.cpp:122] Setting up train-data
I0408 15:34:34.482779 27193 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0408 15:34:34.482784 27193 net.cpp:129] Top shape: 128 (128)
I0408 15:34:34.482787 27193 net.cpp:137] Memory required for data: 79149056
I0408 15:34:34.482797 27193 layer_factory.hpp:77] Creating layer conv1
I0408 15:34:34.482820 27193 net.cpp:84] Creating Layer conv1
I0408 15:34:34.482825 27193 net.cpp:406] conv1 <- data
I0408 15:34:34.482837 27193 net.cpp:380] conv1 -> conv1
I0408 15:34:35.056412 27193 net.cpp:122] Setting up conv1
I0408 15:34:35.056434 27193 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 15:34:35.056438 27193 net.cpp:137] Memory required for data: 227833856
I0408 15:34:35.056459 27193 layer_factory.hpp:77] Creating layer relu1
I0408 15:34:35.056470 27193 net.cpp:84] Creating Layer relu1
I0408 15:34:35.056474 27193 net.cpp:406] relu1 <- conv1
I0408 15:34:35.056481 27193 net.cpp:367] relu1 -> conv1 (in-place)
I0408 15:34:35.056790 27193 net.cpp:122] Setting up relu1
I0408 15:34:35.056799 27193 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 15:34:35.056802 27193 net.cpp:137] Memory required for data: 376518656
I0408 15:34:35.056807 27193 layer_factory.hpp:77] Creating layer norm1
I0408 15:34:35.056815 27193 net.cpp:84] Creating Layer norm1
I0408 15:34:35.056819 27193 net.cpp:406] norm1 <- conv1
I0408 15:34:35.056846 27193 net.cpp:380] norm1 -> norm1
I0408 15:34:35.057324 27193 net.cpp:122] Setting up norm1
I0408 15:34:35.057335 27193 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0408 15:34:35.057339 27193 net.cpp:137] Memory required for data: 525203456
I0408 15:34:35.057343 27193 layer_factory.hpp:77] Creating layer pool1
I0408 15:34:35.057353 27193 net.cpp:84] Creating Layer pool1
I0408 15:34:35.057356 27193 net.cpp:406] pool1 <- norm1
I0408 15:34:35.057363 27193 net.cpp:380] pool1 -> pool1
I0408 15:34:35.057399 27193 net.cpp:122] Setting up pool1
I0408 15:34:35.057406 27193 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0408 15:34:35.057410 27193 net.cpp:137] Memory required for data: 561035264
I0408 15:34:35.057413 27193 layer_factory.hpp:77] Creating layer conv2
I0408 15:34:35.057423 27193 net.cpp:84] Creating Layer conv2
I0408 15:34:35.057427 27193 net.cpp:406] conv2 <- pool1
I0408 15:34:35.057432 27193 net.cpp:380] conv2 -> conv2
I0408 15:34:35.064498 27193 net.cpp:122] Setting up conv2
I0408 15:34:35.064512 27193 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 15:34:35.064517 27193 net.cpp:137] Memory required for data: 656586752
I0408 15:34:35.064525 27193 layer_factory.hpp:77] Creating layer relu2
I0408 15:34:35.064532 27193 net.cpp:84] Creating Layer relu2
I0408 15:34:35.064536 27193 net.cpp:406] relu2 <- conv2
I0408 15:34:35.064543 27193 net.cpp:367] relu2 -> conv2 (in-place)
I0408 15:34:35.064996 27193 net.cpp:122] Setting up relu2
I0408 15:34:35.065007 27193 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 15:34:35.065011 27193 net.cpp:137] Memory required for data: 752138240
I0408 15:34:35.065014 27193 layer_factory.hpp:77] Creating layer norm2
I0408 15:34:35.065022 27193 net.cpp:84] Creating Layer norm2
I0408 15:34:35.065026 27193 net.cpp:406] norm2 <- conv2
I0408 15:34:35.065032 27193 net.cpp:380] norm2 -> norm2
I0408 15:34:35.065343 27193 net.cpp:122] Setting up norm2
I0408 15:34:35.065351 27193 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0408 15:34:35.065356 27193 net.cpp:137] Memory required for data: 847689728
I0408 15:34:35.065359 27193 layer_factory.hpp:77] Creating layer pool2
I0408 15:34:35.065366 27193 net.cpp:84] Creating Layer pool2
I0408 15:34:35.065371 27193 net.cpp:406] pool2 <- norm2
I0408 15:34:35.065376 27193 net.cpp:380] pool2 -> pool2
I0408 15:34:35.065403 27193 net.cpp:122] Setting up pool2
I0408 15:34:35.065408 27193 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 15:34:35.065412 27193 net.cpp:137] Memory required for data: 869840896
I0408 15:34:35.065415 27193 layer_factory.hpp:77] Creating layer conv3
I0408 15:34:35.065423 27193 net.cpp:84] Creating Layer conv3
I0408 15:34:35.065428 27193 net.cpp:406] conv3 <- pool2
I0408 15:34:35.065433 27193 net.cpp:380] conv3 -> conv3
I0408 15:34:35.076141 27193 net.cpp:122] Setting up conv3
I0408 15:34:35.076156 27193 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 15:34:35.076160 27193 net.cpp:137] Memory required for data: 903067648
I0408 15:34:35.076170 27193 layer_factory.hpp:77] Creating layer relu3
I0408 15:34:35.076179 27193 net.cpp:84] Creating Layer relu3
I0408 15:34:35.076182 27193 net.cpp:406] relu3 <- conv3
I0408 15:34:35.076189 27193 net.cpp:367] relu3 -> conv3 (in-place)
I0408 15:34:35.076640 27193 net.cpp:122] Setting up relu3
I0408 15:34:35.076651 27193 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 15:34:35.076655 27193 net.cpp:137] Memory required for data: 936294400
I0408 15:34:35.076659 27193 layer_factory.hpp:77] Creating layer conv4
I0408 15:34:35.076668 27193 net.cpp:84] Creating Layer conv4
I0408 15:34:35.076673 27193 net.cpp:406] conv4 <- conv3
I0408 15:34:35.076678 27193 net.cpp:380] conv4 -> conv4
I0408 15:34:35.087776 27193 net.cpp:122] Setting up conv4
I0408 15:34:35.087790 27193 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 15:34:35.087795 27193 net.cpp:137] Memory required for data: 969521152
I0408 15:34:35.087802 27193 layer_factory.hpp:77] Creating layer relu4
I0408 15:34:35.087811 27193 net.cpp:84] Creating Layer relu4
I0408 15:34:35.087833 27193 net.cpp:406] relu4 <- conv4
I0408 15:34:35.087839 27193 net.cpp:367] relu4 -> conv4 (in-place)
I0408 15:34:35.088197 27193 net.cpp:122] Setting up relu4
I0408 15:34:35.088207 27193 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0408 15:34:35.088209 27193 net.cpp:137] Memory required for data: 1002747904
I0408 15:34:35.088213 27193 layer_factory.hpp:77] Creating layer conv5
I0408 15:34:35.088223 27193 net.cpp:84] Creating Layer conv5
I0408 15:34:35.088227 27193 net.cpp:406] conv5 <- conv4
I0408 15:34:35.088234 27193 net.cpp:380] conv5 -> conv5
I0408 15:34:35.097184 27193 net.cpp:122] Setting up conv5
I0408 15:34:35.097196 27193 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 15:34:35.097200 27193 net.cpp:137] Memory required for data: 1024899072
I0408 15:34:35.097211 27193 layer_factory.hpp:77] Creating layer relu5
I0408 15:34:35.097218 27193 net.cpp:84] Creating Layer relu5
I0408 15:34:35.097223 27193 net.cpp:406] relu5 <- conv5
I0408 15:34:35.097229 27193 net.cpp:367] relu5 -> conv5 (in-place)
I0408 15:34:35.097743 27193 net.cpp:122] Setting up relu5
I0408 15:34:35.097754 27193 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0408 15:34:35.097756 27193 net.cpp:137] Memory required for data: 1047050240
I0408 15:34:35.097760 27193 layer_factory.hpp:77] Creating layer pool5
I0408 15:34:35.097769 27193 net.cpp:84] Creating Layer pool5
I0408 15:34:35.097771 27193 net.cpp:406] pool5 <- conv5
I0408 15:34:35.097779 27193 net.cpp:380] pool5 -> pool5
I0408 15:34:35.097816 27193 net.cpp:122] Setting up pool5
I0408 15:34:35.097822 27193 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0408 15:34:35.097826 27193 net.cpp:137] Memory required for data: 1051768832
I0408 15:34:35.097829 27193 layer_factory.hpp:77] Creating layer fc6
I0408 15:34:35.097841 27193 net.cpp:84] Creating Layer fc6
I0408 15:34:35.097844 27193 net.cpp:406] fc6 <- pool5
I0408 15:34:35.097849 27193 net.cpp:380] fc6 -> fc6
I0408 15:34:35.461639 27193 net.cpp:122] Setting up fc6
I0408 15:34:35.461659 27193 net.cpp:129] Top shape: 128 4096 (524288)
I0408 15:34:35.461663 27193 net.cpp:137] Memory required for data: 1053865984
I0408 15:34:35.461673 27193 layer_factory.hpp:77] Creating layer relu6
I0408 15:34:35.461683 27193 net.cpp:84] Creating Layer relu6
I0408 15:34:35.461688 27193 net.cpp:406] relu6 <- fc6
I0408 15:34:35.461694 27193 net.cpp:367] relu6 -> fc6 (in-place)
I0408 15:34:35.462340 27193 net.cpp:122] Setting up relu6
I0408 15:34:35.462350 27193 net.cpp:129] Top shape: 128 4096 (524288)
I0408 15:34:35.462353 27193 net.cpp:137] Memory required for data: 1055963136
I0408 15:34:35.462357 27193 layer_factory.hpp:77] Creating layer drop6
I0408 15:34:35.462364 27193 net.cpp:84] Creating Layer drop6
I0408 15:34:35.462368 27193 net.cpp:406] drop6 <- fc6
I0408 15:34:35.462373 27193 net.cpp:367] drop6 -> fc6 (in-place)
I0408 15:34:35.462400 27193 net.cpp:122] Setting up drop6
I0408 15:34:35.462405 27193 net.cpp:129] Top shape: 128 4096 (524288)
I0408 15:34:35.462409 27193 net.cpp:137] Memory required for data: 1058060288
I0408 15:34:35.462411 27193 layer_factory.hpp:77] Creating layer fc7
I0408 15:34:35.462420 27193 net.cpp:84] Creating Layer fc7
I0408 15:34:35.462424 27193 net.cpp:406] fc7 <- fc6
I0408 15:34:35.462428 27193 net.cpp:380] fc7 -> fc7
I0408 15:34:35.694792 27193 net.cpp:122] Setting up fc7
I0408 15:34:35.694818 27193 net.cpp:129] Top shape: 128 4096 (524288)
I0408 15:34:35.694823 27193 net.cpp:137] Memory required for data: 1060157440
I0408 15:34:35.694835 27193 layer_factory.hpp:77] Creating layer relu7
I0408 15:34:35.694846 27193 net.cpp:84] Creating Layer relu7
I0408 15:34:35.694851 27193 net.cpp:406] relu7 <- fc7
I0408 15:34:35.694859 27193 net.cpp:367] relu7 -> fc7 (in-place)
I0408 15:34:35.695616 27193 net.cpp:122] Setting up relu7
I0408 15:34:35.695628 27193 net.cpp:129] Top shape: 128 4096 (524288)
I0408 15:34:35.695633 27193 net.cpp:137] Memory required for data: 1062254592
I0408 15:34:35.695637 27193 layer_factory.hpp:77] Creating layer drop7
I0408 15:34:35.695645 27193 net.cpp:84] Creating Layer drop7
I0408 15:34:35.695672 27193 net.cpp:406] drop7 <- fc7
I0408 15:34:35.695680 27193 net.cpp:367] drop7 -> fc7 (in-place)
I0408 15:34:35.695710 27193 net.cpp:122] Setting up drop7
I0408 15:34:35.695717 27193 net.cpp:129] Top shape: 128 4096 (524288)
I0408 15:34:35.695721 27193 net.cpp:137] Memory required for data: 1064351744
I0408 15:34:35.695724 27193 layer_factory.hpp:77] Creating layer fc8
I0408 15:34:35.695734 27193 net.cpp:84] Creating Layer fc8
I0408 15:34:35.695739 27193 net.cpp:406] fc8 <- fc7
I0408 15:34:35.695746 27193 net.cpp:380] fc8 -> fc8
I0408 15:34:35.706001 27193 net.cpp:122] Setting up fc8
I0408 15:34:35.706013 27193 net.cpp:129] Top shape: 128 196 (25088)
I0408 15:34:35.706017 27193 net.cpp:137] Memory required for data: 1064452096
I0408 15:34:35.706025 27193 layer_factory.hpp:77] Creating layer loss
I0408 15:34:35.706034 27193 net.cpp:84] Creating Layer loss
I0408 15:34:35.706038 27193 net.cpp:406] loss <- fc8
I0408 15:34:35.706044 27193 net.cpp:406] loss <- label
I0408 15:34:35.706053 27193 net.cpp:380] loss -> loss
I0408 15:34:35.706063 27193 layer_factory.hpp:77] Creating layer loss
I0408 15:34:35.706804 27193 net.cpp:122] Setting up loss
I0408 15:34:35.706815 27193 net.cpp:129] Top shape: (1)
I0408 15:34:35.706820 27193 net.cpp:132]     with loss weight 1
I0408 15:34:35.706840 27193 net.cpp:137] Memory required for data: 1064452100
I0408 15:34:35.706845 27193 net.cpp:198] loss needs backward computation.
I0408 15:34:35.706852 27193 net.cpp:198] fc8 needs backward computation.
I0408 15:34:35.706857 27193 net.cpp:198] drop7 needs backward computation.
I0408 15:34:35.706861 27193 net.cpp:198] relu7 needs backward computation.
I0408 15:34:35.706864 27193 net.cpp:198] fc7 needs backward computation.
I0408 15:34:35.706869 27193 net.cpp:198] drop6 needs backward computation.
I0408 15:34:35.706872 27193 net.cpp:198] relu6 needs backward computation.
I0408 15:34:35.706876 27193 net.cpp:198] fc6 needs backward computation.
I0408 15:34:35.706881 27193 net.cpp:198] pool5 needs backward computation.
I0408 15:34:35.706885 27193 net.cpp:198] relu5 needs backward computation.
I0408 15:34:35.706889 27193 net.cpp:198] conv5 needs backward computation.
I0408 15:34:35.706893 27193 net.cpp:198] relu4 needs backward computation.
I0408 15:34:35.706897 27193 net.cpp:198] conv4 needs backward computation.
I0408 15:34:35.706902 27193 net.cpp:198] relu3 needs backward computation.
I0408 15:34:35.706905 27193 net.cpp:198] conv3 needs backward computation.
I0408 15:34:35.706910 27193 net.cpp:198] pool2 needs backward computation.
I0408 15:34:35.706914 27193 net.cpp:198] norm2 needs backward computation.
I0408 15:34:35.706918 27193 net.cpp:198] relu2 needs backward computation.
I0408 15:34:35.706923 27193 net.cpp:198] conv2 needs backward computation.
I0408 15:34:35.706928 27193 net.cpp:198] pool1 needs backward computation.
I0408 15:34:35.706931 27193 net.cpp:198] norm1 needs backward computation.
I0408 15:34:35.706935 27193 net.cpp:198] relu1 needs backward computation.
I0408 15:34:35.706939 27193 net.cpp:198] conv1 needs backward computation.
I0408 15:34:35.706943 27193 net.cpp:200] train-data does not need backward computation.
I0408 15:34:35.706948 27193 net.cpp:242] This network produces output loss
I0408 15:34:35.706965 27193 net.cpp:255] Network initialization done.
I0408 15:34:35.707547 27193 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0408 15:34:35.707584 27193 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0408 15:34:35.707764 27193 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 15:34:35.707888 27193 layer_factory.hpp:77] Creating layer val-data
I0408 15:34:35.709761 27193 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0408 15:34:35.709985 27193 net.cpp:84] Creating Layer val-data
I0408 15:34:35.709998 27193 net.cpp:380] val-data -> data
I0408 15:34:35.710008 27193 net.cpp:380] val-data -> label
I0408 15:34:35.710016 27193 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0408 15:34:35.714972 27193 data_layer.cpp:45] output data size: 32,3,227,227
I0408 15:34:35.748775 27193 net.cpp:122] Setting up val-data
I0408 15:34:35.748798 27193 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0408 15:34:35.748803 27193 net.cpp:129] Top shape: 32 (32)
I0408 15:34:35.748806 27193 net.cpp:137] Memory required for data: 19787264
I0408 15:34:35.748813 27193 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0408 15:34:35.748826 27193 net.cpp:84] Creating Layer label_val-data_1_split
I0408 15:34:35.748831 27193 net.cpp:406] label_val-data_1_split <- label
I0408 15:34:35.748838 27193 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0408 15:34:35.748848 27193 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0408 15:34:35.748898 27193 net.cpp:122] Setting up label_val-data_1_split
I0408 15:34:35.748904 27193 net.cpp:129] Top shape: 32 (32)
I0408 15:34:35.748908 27193 net.cpp:129] Top shape: 32 (32)
I0408 15:34:35.748911 27193 net.cpp:137] Memory required for data: 19787520
I0408 15:34:35.748915 27193 layer_factory.hpp:77] Creating layer conv1
I0408 15:34:35.748927 27193 net.cpp:84] Creating Layer conv1
I0408 15:34:35.748931 27193 net.cpp:406] conv1 <- data
I0408 15:34:35.748937 27193 net.cpp:380] conv1 -> conv1
I0408 15:34:35.754885 27193 net.cpp:122] Setting up conv1
I0408 15:34:35.754897 27193 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 15:34:35.754901 27193 net.cpp:137] Memory required for data: 56958720
I0408 15:34:35.754912 27193 layer_factory.hpp:77] Creating layer relu1
I0408 15:34:35.754920 27193 net.cpp:84] Creating Layer relu1
I0408 15:34:35.754923 27193 net.cpp:406] relu1 <- conv1
I0408 15:34:35.754928 27193 net.cpp:367] relu1 -> conv1 (in-place)
I0408 15:34:35.755250 27193 net.cpp:122] Setting up relu1
I0408 15:34:35.755259 27193 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 15:34:35.755262 27193 net.cpp:137] Memory required for data: 94129920
I0408 15:34:35.755266 27193 layer_factory.hpp:77] Creating layer norm1
I0408 15:34:35.755275 27193 net.cpp:84] Creating Layer norm1
I0408 15:34:35.755278 27193 net.cpp:406] norm1 <- conv1
I0408 15:34:35.755285 27193 net.cpp:380] norm1 -> norm1
I0408 15:34:35.755781 27193 net.cpp:122] Setting up norm1
I0408 15:34:35.755792 27193 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0408 15:34:35.755795 27193 net.cpp:137] Memory required for data: 131301120
I0408 15:34:35.755800 27193 layer_factory.hpp:77] Creating layer pool1
I0408 15:34:35.755807 27193 net.cpp:84] Creating Layer pool1
I0408 15:34:35.755811 27193 net.cpp:406] pool1 <- norm1
I0408 15:34:35.755817 27193 net.cpp:380] pool1 -> pool1
I0408 15:34:35.755848 27193 net.cpp:122] Setting up pool1
I0408 15:34:35.755853 27193 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0408 15:34:35.755857 27193 net.cpp:137] Memory required for data: 140259072
I0408 15:34:35.755861 27193 layer_factory.hpp:77] Creating layer conv2
I0408 15:34:35.755868 27193 net.cpp:84] Creating Layer conv2
I0408 15:34:35.755872 27193 net.cpp:406] conv2 <- pool1
I0408 15:34:35.755897 27193 net.cpp:380] conv2 -> conv2
I0408 15:34:35.765105 27193 net.cpp:122] Setting up conv2
I0408 15:34:35.765120 27193 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 15:34:35.765125 27193 net.cpp:137] Memory required for data: 164146944
I0408 15:34:35.765134 27193 layer_factory.hpp:77] Creating layer relu2
I0408 15:34:35.765143 27193 net.cpp:84] Creating Layer relu2
I0408 15:34:35.765148 27193 net.cpp:406] relu2 <- conv2
I0408 15:34:35.765153 27193 net.cpp:367] relu2 -> conv2 (in-place)
I0408 15:34:35.765713 27193 net.cpp:122] Setting up relu2
I0408 15:34:35.765724 27193 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 15:34:35.765728 27193 net.cpp:137] Memory required for data: 188034816
I0408 15:34:35.765733 27193 layer_factory.hpp:77] Creating layer norm2
I0408 15:34:35.765743 27193 net.cpp:84] Creating Layer norm2
I0408 15:34:35.765748 27193 net.cpp:406] norm2 <- conv2
I0408 15:34:35.765753 27193 net.cpp:380] norm2 -> norm2
I0408 15:34:35.766348 27193 net.cpp:122] Setting up norm2
I0408 15:34:35.766358 27193 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0408 15:34:35.766362 27193 net.cpp:137] Memory required for data: 211922688
I0408 15:34:35.766366 27193 layer_factory.hpp:77] Creating layer pool2
I0408 15:34:35.766373 27193 net.cpp:84] Creating Layer pool2
I0408 15:34:35.766377 27193 net.cpp:406] pool2 <- norm2
I0408 15:34:35.766383 27193 net.cpp:380] pool2 -> pool2
I0408 15:34:35.766415 27193 net.cpp:122] Setting up pool2
I0408 15:34:35.766422 27193 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 15:34:35.766424 27193 net.cpp:137] Memory required for data: 217460480
I0408 15:34:35.766428 27193 layer_factory.hpp:77] Creating layer conv3
I0408 15:34:35.766438 27193 net.cpp:84] Creating Layer conv3
I0408 15:34:35.766443 27193 net.cpp:406] conv3 <- pool2
I0408 15:34:35.766448 27193 net.cpp:380] conv3 -> conv3
I0408 15:34:35.778532 27193 net.cpp:122] Setting up conv3
I0408 15:34:35.778548 27193 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 15:34:35.778551 27193 net.cpp:137] Memory required for data: 225767168
I0408 15:34:35.778564 27193 layer_factory.hpp:77] Creating layer relu3
I0408 15:34:35.778573 27193 net.cpp:84] Creating Layer relu3
I0408 15:34:35.778578 27193 net.cpp:406] relu3 <- conv3
I0408 15:34:35.778584 27193 net.cpp:367] relu3 -> conv3 (in-place)
I0408 15:34:35.779296 27193 net.cpp:122] Setting up relu3
I0408 15:34:35.779309 27193 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 15:34:35.779311 27193 net.cpp:137] Memory required for data: 234073856
I0408 15:34:35.779315 27193 layer_factory.hpp:77] Creating layer conv4
I0408 15:34:35.779327 27193 net.cpp:84] Creating Layer conv4
I0408 15:34:35.779331 27193 net.cpp:406] conv4 <- conv3
I0408 15:34:35.779337 27193 net.cpp:380] conv4 -> conv4
I0408 15:34:35.789572 27193 net.cpp:122] Setting up conv4
I0408 15:34:35.789584 27193 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 15:34:35.789587 27193 net.cpp:137] Memory required for data: 242380544
I0408 15:34:35.789595 27193 layer_factory.hpp:77] Creating layer relu4
I0408 15:34:35.789606 27193 net.cpp:84] Creating Layer relu4
I0408 15:34:35.789610 27193 net.cpp:406] relu4 <- conv4
I0408 15:34:35.789615 27193 net.cpp:367] relu4 -> conv4 (in-place)
I0408 15:34:35.790004 27193 net.cpp:122] Setting up relu4
I0408 15:34:35.790014 27193 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0408 15:34:35.790017 27193 net.cpp:137] Memory required for data: 250687232
I0408 15:34:35.790020 27193 layer_factory.hpp:77] Creating layer conv5
I0408 15:34:35.790030 27193 net.cpp:84] Creating Layer conv5
I0408 15:34:35.790035 27193 net.cpp:406] conv5 <- conv4
I0408 15:34:35.790042 27193 net.cpp:380] conv5 -> conv5
I0408 15:34:35.802616 27193 net.cpp:122] Setting up conv5
I0408 15:34:35.802634 27193 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 15:34:35.802639 27193 net.cpp:137] Memory required for data: 256225024
I0408 15:34:35.802651 27193 layer_factory.hpp:77] Creating layer relu5
I0408 15:34:35.802659 27193 net.cpp:84] Creating Layer relu5
I0408 15:34:35.802664 27193 net.cpp:406] relu5 <- conv5
I0408 15:34:35.802688 27193 net.cpp:367] relu5 -> conv5 (in-place)
I0408 15:34:35.803216 27193 net.cpp:122] Setting up relu5
I0408 15:34:35.803227 27193 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0408 15:34:35.803231 27193 net.cpp:137] Memory required for data: 261762816
I0408 15:34:35.803236 27193 layer_factory.hpp:77] Creating layer pool5
I0408 15:34:35.803246 27193 net.cpp:84] Creating Layer pool5
I0408 15:34:35.803251 27193 net.cpp:406] pool5 <- conv5
I0408 15:34:35.803256 27193 net.cpp:380] pool5 -> pool5
I0408 15:34:35.803297 27193 net.cpp:122] Setting up pool5
I0408 15:34:35.803303 27193 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0408 15:34:35.803308 27193 net.cpp:137] Memory required for data: 262942464
I0408 15:34:35.803310 27193 layer_factory.hpp:77] Creating layer fc6
I0408 15:34:35.803318 27193 net.cpp:84] Creating Layer fc6
I0408 15:34:35.803321 27193 net.cpp:406] fc6 <- pool5
I0408 15:34:35.803328 27193 net.cpp:380] fc6 -> fc6
I0408 15:34:36.189455 27193 net.cpp:122] Setting up fc6
I0408 15:34:36.189476 27193 net.cpp:129] Top shape: 32 4096 (131072)
I0408 15:34:36.189481 27193 net.cpp:137] Memory required for data: 263466752
I0408 15:34:36.189489 27193 layer_factory.hpp:77] Creating layer relu6
I0408 15:34:36.189498 27193 net.cpp:84] Creating Layer relu6
I0408 15:34:36.189503 27193 net.cpp:406] relu6 <- fc6
I0408 15:34:36.189509 27193 net.cpp:367] relu6 -> fc6 (in-place)
I0408 15:34:36.190402 27193 net.cpp:122] Setting up relu6
I0408 15:34:36.190413 27193 net.cpp:129] Top shape: 32 4096 (131072)
I0408 15:34:36.190418 27193 net.cpp:137] Memory required for data: 263991040
I0408 15:34:36.190421 27193 layer_factory.hpp:77] Creating layer drop6
I0408 15:34:36.190428 27193 net.cpp:84] Creating Layer drop6
I0408 15:34:36.190433 27193 net.cpp:406] drop6 <- fc6
I0408 15:34:36.190438 27193 net.cpp:367] drop6 -> fc6 (in-place)
I0408 15:34:36.190466 27193 net.cpp:122] Setting up drop6
I0408 15:34:36.190472 27193 net.cpp:129] Top shape: 32 4096 (131072)
I0408 15:34:36.190475 27193 net.cpp:137] Memory required for data: 264515328
I0408 15:34:36.190479 27193 layer_factory.hpp:77] Creating layer fc7
I0408 15:34:36.190486 27193 net.cpp:84] Creating Layer fc7
I0408 15:34:36.190490 27193 net.cpp:406] fc7 <- fc6
I0408 15:34:36.190495 27193 net.cpp:380] fc7 -> fc7
I0408 15:34:36.362629 27193 net.cpp:122] Setting up fc7
I0408 15:34:36.362650 27193 net.cpp:129] Top shape: 32 4096 (131072)
I0408 15:34:36.362654 27193 net.cpp:137] Memory required for data: 265039616
I0408 15:34:36.362664 27193 layer_factory.hpp:77] Creating layer relu7
I0408 15:34:36.362673 27193 net.cpp:84] Creating Layer relu7
I0408 15:34:36.362679 27193 net.cpp:406] relu7 <- fc7
I0408 15:34:36.362685 27193 net.cpp:367] relu7 -> fc7 (in-place)
I0408 15:34:36.363142 27193 net.cpp:122] Setting up relu7
I0408 15:34:36.363152 27193 net.cpp:129] Top shape: 32 4096 (131072)
I0408 15:34:36.363154 27193 net.cpp:137] Memory required for data: 265563904
I0408 15:34:36.363158 27193 layer_factory.hpp:77] Creating layer drop7
I0408 15:34:36.363166 27193 net.cpp:84] Creating Layer drop7
I0408 15:34:36.363170 27193 net.cpp:406] drop7 <- fc7
I0408 15:34:36.363175 27193 net.cpp:367] drop7 -> fc7 (in-place)
I0408 15:34:36.363201 27193 net.cpp:122] Setting up drop7
I0408 15:34:36.363206 27193 net.cpp:129] Top shape: 32 4096 (131072)
I0408 15:34:36.363209 27193 net.cpp:137] Memory required for data: 266088192
I0408 15:34:36.363214 27193 layer_factory.hpp:77] Creating layer fc8
I0408 15:34:36.363220 27193 net.cpp:84] Creating Layer fc8
I0408 15:34:36.363225 27193 net.cpp:406] fc8 <- fc7
I0408 15:34:36.363230 27193 net.cpp:380] fc8 -> fc8
I0408 15:34:36.371691 27193 net.cpp:122] Setting up fc8
I0408 15:34:36.371701 27193 net.cpp:129] Top shape: 32 196 (6272)
I0408 15:34:36.371706 27193 net.cpp:137] Memory required for data: 266113280
I0408 15:34:36.371711 27193 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0408 15:34:36.371721 27193 net.cpp:84] Creating Layer fc8_fc8_0_split
I0408 15:34:36.371723 27193 net.cpp:406] fc8_fc8_0_split <- fc8
I0408 15:34:36.371747 27193 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0408 15:34:36.371754 27193 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0408 15:34:36.371791 27193 net.cpp:122] Setting up fc8_fc8_0_split
I0408 15:34:36.371796 27193 net.cpp:129] Top shape: 32 196 (6272)
I0408 15:34:36.371800 27193 net.cpp:129] Top shape: 32 196 (6272)
I0408 15:34:36.371803 27193 net.cpp:137] Memory required for data: 266163456
I0408 15:34:36.371806 27193 layer_factory.hpp:77] Creating layer accuracy
I0408 15:34:36.371814 27193 net.cpp:84] Creating Layer accuracy
I0408 15:34:36.371816 27193 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0408 15:34:36.371821 27193 net.cpp:406] accuracy <- label_val-data_1_split_0
I0408 15:34:36.371827 27193 net.cpp:380] accuracy -> accuracy
I0408 15:34:36.371834 27193 net.cpp:122] Setting up accuracy
I0408 15:34:36.371838 27193 net.cpp:129] Top shape: (1)
I0408 15:34:36.371841 27193 net.cpp:137] Memory required for data: 266163460
I0408 15:34:36.371845 27193 layer_factory.hpp:77] Creating layer loss
I0408 15:34:36.371850 27193 net.cpp:84] Creating Layer loss
I0408 15:34:36.371853 27193 net.cpp:406] loss <- fc8_fc8_0_split_1
I0408 15:34:36.371858 27193 net.cpp:406] loss <- label_val-data_1_split_1
I0408 15:34:36.371863 27193 net.cpp:380] loss -> loss
I0408 15:34:36.371871 27193 layer_factory.hpp:77] Creating layer loss
I0408 15:34:36.372524 27193 net.cpp:122] Setting up loss
I0408 15:34:36.372532 27193 net.cpp:129] Top shape: (1)
I0408 15:34:36.372535 27193 net.cpp:132]     with loss weight 1
I0408 15:34:36.372546 27193 net.cpp:137] Memory required for data: 266163464
I0408 15:34:36.372550 27193 net.cpp:198] loss needs backward computation.
I0408 15:34:36.372555 27193 net.cpp:200] accuracy does not need backward computation.
I0408 15:34:36.372560 27193 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0408 15:34:36.372563 27193 net.cpp:198] fc8 needs backward computation.
I0408 15:34:36.372566 27193 net.cpp:198] drop7 needs backward computation.
I0408 15:34:36.372570 27193 net.cpp:198] relu7 needs backward computation.
I0408 15:34:36.372573 27193 net.cpp:198] fc7 needs backward computation.
I0408 15:34:36.372576 27193 net.cpp:198] drop6 needs backward computation.
I0408 15:34:36.372581 27193 net.cpp:198] relu6 needs backward computation.
I0408 15:34:36.372583 27193 net.cpp:198] fc6 needs backward computation.
I0408 15:34:36.372587 27193 net.cpp:198] pool5 needs backward computation.
I0408 15:34:36.372591 27193 net.cpp:198] relu5 needs backward computation.
I0408 15:34:36.372594 27193 net.cpp:198] conv5 needs backward computation.
I0408 15:34:36.372598 27193 net.cpp:198] relu4 needs backward computation.
I0408 15:34:36.372601 27193 net.cpp:198] conv4 needs backward computation.
I0408 15:34:36.372606 27193 net.cpp:198] relu3 needs backward computation.
I0408 15:34:36.372608 27193 net.cpp:198] conv3 needs backward computation.
I0408 15:34:36.372612 27193 net.cpp:198] pool2 needs backward computation.
I0408 15:34:36.372617 27193 net.cpp:198] norm2 needs backward computation.
I0408 15:34:36.372622 27193 net.cpp:198] relu2 needs backward computation.
I0408 15:34:36.372625 27193 net.cpp:198] conv2 needs backward computation.
I0408 15:34:36.372629 27193 net.cpp:198] pool1 needs backward computation.
I0408 15:34:36.372633 27193 net.cpp:198] norm1 needs backward computation.
I0408 15:34:36.372637 27193 net.cpp:198] relu1 needs backward computation.
I0408 15:34:36.372640 27193 net.cpp:198] conv1 needs backward computation.
I0408 15:34:36.372644 27193 net.cpp:200] label_val-data_1_split does not need backward computation.
I0408 15:34:36.372648 27193 net.cpp:200] val-data does not need backward computation.
I0408 15:34:36.372651 27193 net.cpp:242] This network produces output accuracy
I0408 15:34:36.372655 27193 net.cpp:242] This network produces output loss
I0408 15:34:36.372673 27193 net.cpp:255] Network initialization done.
I0408 15:34:36.372745 27193 solver.cpp:56] Solver scaffolding done.
I0408 15:34:36.373206 27193 caffe.cpp:248] Starting Optimization
I0408 15:34:36.373215 27193 solver.cpp:272] Solving
I0408 15:34:36.373227 27193 solver.cpp:273] Learning Rate Policy: exp
I0408 15:34:36.374596 27193 solver.cpp:330] Iteration 0, Testing net (#0)
I0408 15:34:36.374606 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:34:36.458696 27193 blocking_queue.cpp:49] Waiting for data
I0408 15:34:40.775729 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:34:40.820547 27193 solver.cpp:397]     Test net output #0: accuracy = 0.00490196
I0408 15:34:40.820586 27193 solver.cpp:397]     Test net output #1: loss = 5.2813 (* 1 = 5.2813 loss)
I0408 15:34:40.916071 27193 solver.cpp:218] Iteration 0 (-1.06017e-35 iter/s, 4.54266s/12 iters), loss = 5.27974
I0408 15:34:40.917590 27193 solver.cpp:237]     Train net output #0: loss = 5.27974 (* 1 = 5.27974 loss)
I0408 15:34:40.917613 27193 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0408 15:34:44.820722 27193 solver.cpp:218] Iteration 12 (3.07457 iter/s, 3.90299s/12 iters), loss = 5.28776
I0408 15:34:44.820768 27193 solver.cpp:237]     Train net output #0: loss = 5.28776 (* 1 = 5.28776 loss)
I0408 15:34:44.820780 27193 sgd_solver.cpp:105] Iteration 12, lr = 0.00974089
I0408 15:34:49.943226 27193 solver.cpp:218] Iteration 24 (2.34271 iter/s, 5.12228s/12 iters), loss = 5.29691
I0408 15:34:49.943279 27193 solver.cpp:237]     Train net output #0: loss = 5.29691 (* 1 = 5.29691 loss)
I0408 15:34:49.943290 27193 sgd_solver.cpp:105] Iteration 24, lr = 0.0094885
I0408 15:34:54.913389 27193 solver.cpp:218] Iteration 36 (2.41451 iter/s, 4.96995s/12 iters), loss = 5.29374
I0408 15:34:54.913425 27193 solver.cpp:237]     Train net output #0: loss = 5.29374 (* 1 = 5.29374 loss)
I0408 15:34:54.913434 27193 sgd_solver.cpp:105] Iteration 36, lr = 0.00924265
I0408 15:34:59.915939 27193 solver.cpp:218] Iteration 48 (2.39888 iter/s, 5.00234s/12 iters), loss = 5.31026
I0408 15:34:59.915984 27193 solver.cpp:237]     Train net output #0: loss = 5.31026 (* 1 = 5.31026 loss)
I0408 15:34:59.915997 27193 sgd_solver.cpp:105] Iteration 48, lr = 0.00900317
I0408 15:35:04.932297 27193 solver.cpp:218] Iteration 60 (2.39228 iter/s, 5.01614s/12 iters), loss = 5.30859
I0408 15:35:04.932387 27193 solver.cpp:237]     Train net output #0: loss = 5.30859 (* 1 = 5.30859 loss)
I0408 15:35:04.932399 27193 sgd_solver.cpp:105] Iteration 60, lr = 0.00876989
I0408 15:35:09.988570 27193 solver.cpp:218] Iteration 72 (2.37341 iter/s, 5.05601s/12 iters), loss = 5.29999
I0408 15:35:09.988617 27193 solver.cpp:237]     Train net output #0: loss = 5.29999 (* 1 = 5.29999 loss)
I0408 15:35:09.988628 27193 sgd_solver.cpp:105] Iteration 72, lr = 0.00854266
I0408 15:35:14.986745 27193 solver.cpp:218] Iteration 84 (2.40098 iter/s, 4.99795s/12 iters), loss = 5.31279
I0408 15:35:14.986788 27193 solver.cpp:237]     Train net output #0: loss = 5.31279 (* 1 = 5.31279 loss)
I0408 15:35:14.986799 27193 sgd_solver.cpp:105] Iteration 84, lr = 0.00832132
I0408 15:35:20.016067 27193 solver.cpp:218] Iteration 96 (2.38611 iter/s, 5.0291s/12 iters), loss = 5.31598
I0408 15:35:20.016113 27193 solver.cpp:237]     Train net output #0: loss = 5.31598 (* 1 = 5.31598 loss)
I0408 15:35:20.016124 27193 sgd_solver.cpp:105] Iteration 96, lr = 0.00810571
I0408 15:35:21.742343 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:35:22.053905 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0408 15:35:25.141510 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0408 15:35:27.486174 27193 solver.cpp:330] Iteration 102, Testing net (#0)
I0408 15:35:27.486202 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:35:31.925240 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:35:32.002173 27193 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0408 15:35:32.002218 27193 solver.cpp:397]     Test net output #1: loss = 5.28799 (* 1 = 5.28799 loss)
I0408 15:35:33.952040 27193 solver.cpp:218] Iteration 108 (0.861113 iter/s, 13.9355s/12 iters), loss = 5.30747
I0408 15:35:33.952096 27193 solver.cpp:237]     Train net output #0: loss = 5.30747 (* 1 = 5.30747 loss)
I0408 15:35:33.952108 27193 sgd_solver.cpp:105] Iteration 108, lr = 0.00789568
I0408 15:35:38.969336 27193 solver.cpp:218] Iteration 120 (2.39184 iter/s, 5.01706s/12 iters), loss = 5.27273
I0408 15:35:38.969460 27193 solver.cpp:237]     Train net output #0: loss = 5.27273 (* 1 = 5.27273 loss)
I0408 15:35:38.969471 27193 sgd_solver.cpp:105] Iteration 120, lr = 0.0076911
I0408 15:35:43.973073 27193 solver.cpp:218] Iteration 132 (2.39835 iter/s, 5.00344s/12 iters), loss = 5.24861
I0408 15:35:43.973121 27193 solver.cpp:237]     Train net output #0: loss = 5.24861 (* 1 = 5.24861 loss)
I0408 15:35:43.973132 27193 sgd_solver.cpp:105] Iteration 132, lr = 0.00749182
I0408 15:35:48.991621 27193 solver.cpp:218] Iteration 144 (2.39124 iter/s, 5.01832s/12 iters), loss = 5.31217
I0408 15:35:48.991674 27193 solver.cpp:237]     Train net output #0: loss = 5.31217 (* 1 = 5.31217 loss)
I0408 15:35:48.991688 27193 sgd_solver.cpp:105] Iteration 144, lr = 0.0072977
I0408 15:35:54.010113 27193 solver.cpp:218] Iteration 156 (2.39127 iter/s, 5.01826s/12 iters), loss = 5.26583
I0408 15:35:54.010164 27193 solver.cpp:237]     Train net output #0: loss = 5.26583 (* 1 = 5.26583 loss)
I0408 15:35:54.010176 27193 sgd_solver.cpp:105] Iteration 156, lr = 0.00710862
I0408 15:35:59.260608 27193 solver.cpp:218] Iteration 168 (2.2856 iter/s, 5.25026s/12 iters), loss = 5.2632
I0408 15:35:59.260661 27193 solver.cpp:237]     Train net output #0: loss = 5.2632 (* 1 = 5.2632 loss)
I0408 15:35:59.260672 27193 sgd_solver.cpp:105] Iteration 168, lr = 0.00692443
I0408 15:36:04.314533 27193 solver.cpp:218] Iteration 180 (2.3745 iter/s, 5.05369s/12 iters), loss = 5.26804
I0408 15:36:04.314584 27193 solver.cpp:237]     Train net output #0: loss = 5.26804 (* 1 = 5.26804 loss)
I0408 15:36:04.314595 27193 sgd_solver.cpp:105] Iteration 180, lr = 0.00674501
I0408 15:36:09.292935 27193 solver.cpp:218] Iteration 192 (2.41052 iter/s, 4.97818s/12 iters), loss = 5.27833
I0408 15:36:09.293038 27193 solver.cpp:237]     Train net output #0: loss = 5.27833 (* 1 = 5.27833 loss)
I0408 15:36:09.293048 27193 sgd_solver.cpp:105] Iteration 192, lr = 0.00657025
I0408 15:36:13.401823 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:36:14.102036 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0408 15:36:17.766530 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0408 15:36:20.738878 27193 solver.cpp:330] Iteration 204, Testing net (#0)
I0408 15:36:20.738903 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:36:25.224051 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:36:25.347056 27193 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0408 15:36:25.347090 27193 solver.cpp:397]     Test net output #1: loss = 5.25685 (* 1 = 5.25685 loss)
I0408 15:36:25.437870 27193 solver.cpp:218] Iteration 204 (0.743297 iter/s, 16.1443s/12 iters), loss = 5.23209
I0408 15:36:25.437913 27193 solver.cpp:237]     Train net output #0: loss = 5.23209 (* 1 = 5.23209 loss)
I0408 15:36:25.437923 27193 sgd_solver.cpp:105] Iteration 204, lr = 0.00640001
I0408 15:36:29.999847 27193 solver.cpp:218] Iteration 216 (2.63056 iter/s, 4.56177s/12 iters), loss = 5.22029
I0408 15:36:29.999888 27193 solver.cpp:237]     Train net output #0: loss = 5.22029 (* 1 = 5.22029 loss)
I0408 15:36:29.999899 27193 sgd_solver.cpp:105] Iteration 216, lr = 0.00623418
I0408 15:36:35.247198 27193 solver.cpp:218] Iteration 228 (2.28697 iter/s, 5.24712s/12 iters), loss = 5.19211
I0408 15:36:35.247248 27193 solver.cpp:237]     Train net output #0: loss = 5.19211 (* 1 = 5.19211 loss)
I0408 15:36:35.247260 27193 sgd_solver.cpp:105] Iteration 228, lr = 0.00607265
I0408 15:36:40.176069 27193 solver.cpp:218] Iteration 240 (2.43475 iter/s, 4.92864s/12 iters), loss = 5.25695
I0408 15:36:40.176216 27193 solver.cpp:237]     Train net output #0: loss = 5.25695 (* 1 = 5.25695 loss)
I0408 15:36:40.176230 27193 sgd_solver.cpp:105] Iteration 240, lr = 0.0059153
I0408 15:36:45.228608 27193 solver.cpp:218] Iteration 252 (2.3752 iter/s, 5.05222s/12 iters), loss = 5.1704
I0408 15:36:45.228658 27193 solver.cpp:237]     Train net output #0: loss = 5.1704 (* 1 = 5.1704 loss)
I0408 15:36:45.228670 27193 sgd_solver.cpp:105] Iteration 252, lr = 0.00576203
I0408 15:36:50.209460 27193 solver.cpp:218] Iteration 264 (2.40934 iter/s, 4.98062s/12 iters), loss = 5.24711
I0408 15:36:50.209514 27193 solver.cpp:237]     Train net output #0: loss = 5.24711 (* 1 = 5.24711 loss)
I0408 15:36:50.209527 27193 sgd_solver.cpp:105] Iteration 264, lr = 0.00561274
I0408 15:36:55.238564 27193 solver.cpp:218] Iteration 276 (2.38622 iter/s, 5.02887s/12 iters), loss = 5.20421
I0408 15:36:55.238620 27193 solver.cpp:237]     Train net output #0: loss = 5.20421 (* 1 = 5.20421 loss)
I0408 15:36:55.238631 27193 sgd_solver.cpp:105] Iteration 276, lr = 0.00546731
I0408 15:37:00.194461 27193 solver.cpp:218] Iteration 288 (2.42147 iter/s, 4.95567s/12 iters), loss = 5.07568
I0408 15:37:00.194514 27193 solver.cpp:237]     Train net output #0: loss = 5.07568 (* 1 = 5.07568 loss)
I0408 15:37:00.194525 27193 sgd_solver.cpp:105] Iteration 288, lr = 0.00532565
I0408 15:37:05.249792 27193 solver.cpp:218] Iteration 300 (2.37384 iter/s, 5.0551s/12 iters), loss = 5.16469
I0408 15:37:05.249843 27193 solver.cpp:237]     Train net output #0: loss = 5.16469 (* 1 = 5.16469 loss)
I0408 15:37:05.249855 27193 sgd_solver.cpp:105] Iteration 300, lr = 0.00518766
I0408 15:37:06.254882 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:37:07.317070 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0408 15:37:11.228960 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0408 15:37:14.140661 27193 solver.cpp:330] Iteration 306, Testing net (#0)
I0408 15:37:14.140683 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:37:18.443226 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:37:18.600849 27193 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0408 15:37:18.600898 27193 solver.cpp:397]     Test net output #1: loss = 5.15756 (* 1 = 5.15756 loss)
I0408 15:37:20.601107 27193 solver.cpp:218] Iteration 312 (0.781721 iter/s, 15.3507s/12 iters), loss = 5.14976
I0408 15:37:20.601166 27193 solver.cpp:237]     Train net output #0: loss = 5.14976 (* 1 = 5.14976 loss)
I0408 15:37:20.601178 27193 sgd_solver.cpp:105] Iteration 312, lr = 0.00505324
I0408 15:37:26.050503 27193 solver.cpp:218] Iteration 324 (2.20218 iter/s, 5.44915s/12 iters), loss = 5.1871
I0408 15:37:26.050549 27193 solver.cpp:237]     Train net output #0: loss = 5.1871 (* 1 = 5.1871 loss)
I0408 15:37:26.050560 27193 sgd_solver.cpp:105] Iteration 324, lr = 0.00492231
I0408 15:37:31.556349 27193 solver.cpp:218] Iteration 336 (2.17959 iter/s, 5.50561s/12 iters), loss = 5.11462
I0408 15:37:31.556396 27193 solver.cpp:237]     Train net output #0: loss = 5.11462 (* 1 = 5.11462 loss)
I0408 15:37:31.556408 27193 sgd_solver.cpp:105] Iteration 336, lr = 0.00479477
I0408 15:37:36.633641 27193 solver.cpp:218] Iteration 348 (2.36357 iter/s, 5.07707s/12 iters), loss = 5.11915
I0408 15:37:36.633688 27193 solver.cpp:237]     Train net output #0: loss = 5.11915 (* 1 = 5.11915 loss)
I0408 15:37:36.633699 27193 sgd_solver.cpp:105] Iteration 348, lr = 0.00467054
I0408 15:37:41.541110 27193 solver.cpp:218] Iteration 360 (2.44536 iter/s, 4.90725s/12 iters), loss = 5.17262
I0408 15:37:41.541214 27193 solver.cpp:237]     Train net output #0: loss = 5.17262 (* 1 = 5.17262 loss)
I0408 15:37:41.541229 27193 sgd_solver.cpp:105] Iteration 360, lr = 0.00454952
I0408 15:37:46.540627 27193 solver.cpp:218] Iteration 372 (2.40037 iter/s, 4.99924s/12 iters), loss = 5.10135
I0408 15:37:46.540679 27193 solver.cpp:237]     Train net output #0: loss = 5.10135 (* 1 = 5.10135 loss)
I0408 15:37:46.540693 27193 sgd_solver.cpp:105] Iteration 372, lr = 0.00443164
I0408 15:37:51.497189 27193 solver.cpp:218] Iteration 384 (2.42114 iter/s, 4.95634s/12 iters), loss = 5.15699
I0408 15:37:51.497231 27193 solver.cpp:237]     Train net output #0: loss = 5.15699 (* 1 = 5.15699 loss)
I0408 15:37:51.497242 27193 sgd_solver.cpp:105] Iteration 384, lr = 0.00431681
I0408 15:37:56.559881 27193 solver.cpp:218] Iteration 396 (2.37038 iter/s, 5.06247s/12 iters), loss = 5.0584
I0408 15:37:56.559924 27193 solver.cpp:237]     Train net output #0: loss = 5.0584 (* 1 = 5.0584 loss)
I0408 15:37:56.559934 27193 sgd_solver.cpp:105] Iteration 396, lr = 0.00420496
I0408 15:37:59.720813 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:38:01.146488 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0408 15:38:05.233217 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0408 15:38:08.236284 27193 solver.cpp:330] Iteration 408, Testing net (#0)
I0408 15:38:08.236311 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:38:12.539934 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:38:12.744411 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0408 15:38:12.744462 27193 solver.cpp:397]     Test net output #1: loss = 5.11109 (* 1 = 5.11109 loss)
I0408 15:38:12.835945 27193 solver.cpp:218] Iteration 408 (0.737306 iter/s, 16.2755s/12 iters), loss = 5.19118
I0408 15:38:12.835995 27193 solver.cpp:237]     Train net output #0: loss = 5.19118 (* 1 = 5.19118 loss)
I0408 15:38:12.836005 27193 sgd_solver.cpp:105] Iteration 408, lr = 0.00409601
I0408 15:38:17.104912 27193 solver.cpp:218] Iteration 420 (2.81112 iter/s, 4.26876s/12 iters), loss = 5.17186
I0408 15:38:17.104967 27193 solver.cpp:237]     Train net output #0: loss = 5.17186 (* 1 = 5.17186 loss)
I0408 15:38:17.104979 27193 sgd_solver.cpp:105] Iteration 420, lr = 0.00398988
I0408 15:38:22.128441 27193 solver.cpp:218] Iteration 432 (2.38887 iter/s, 5.02329s/12 iters), loss = 5.09877
I0408 15:38:22.128499 27193 solver.cpp:237]     Train net output #0: loss = 5.09877 (* 1 = 5.09877 loss)
I0408 15:38:22.128512 27193 sgd_solver.cpp:105] Iteration 432, lr = 0.0038865
I0408 15:38:27.220288 27193 solver.cpp:218] Iteration 444 (2.35682 iter/s, 5.09161s/12 iters), loss = 5.08502
I0408 15:38:27.220347 27193 solver.cpp:237]     Train net output #0: loss = 5.08502 (* 1 = 5.08502 loss)
I0408 15:38:27.220360 27193 sgd_solver.cpp:105] Iteration 444, lr = 0.0037858
I0408 15:38:32.303799 27193 solver.cpp:218] Iteration 456 (2.36068 iter/s, 5.08327s/12 iters), loss = 5.14396
I0408 15:38:32.303851 27193 solver.cpp:237]     Train net output #0: loss = 5.14396 (* 1 = 5.14396 loss)
I0408 15:38:32.303864 27193 sgd_solver.cpp:105] Iteration 456, lr = 0.00368771
I0408 15:38:37.281925 27193 solver.cpp:218] Iteration 468 (2.41066 iter/s, 4.9779s/12 iters), loss = 5.13936
I0408 15:38:37.281980 27193 solver.cpp:237]     Train net output #0: loss = 5.13936 (* 1 = 5.13936 loss)
I0408 15:38:37.281989 27193 sgd_solver.cpp:105] Iteration 468, lr = 0.00359216
I0408 15:38:42.324338 27193 solver.cpp:218] Iteration 480 (2.37992 iter/s, 5.04218s/12 iters), loss = 5.08699
I0408 15:38:42.324384 27193 solver.cpp:237]     Train net output #0: loss = 5.08699 (* 1 = 5.08699 loss)
I0408 15:38:42.324394 27193 sgd_solver.cpp:105] Iteration 480, lr = 0.00349908
I0408 15:38:47.329768 27193 solver.cpp:218] Iteration 492 (2.3975 iter/s, 5.00521s/12 iters), loss = 5.07265
I0408 15:38:47.329865 27193 solver.cpp:237]     Train net output #0: loss = 5.07265 (* 1 = 5.07265 loss)
I0408 15:38:47.329875 27193 sgd_solver.cpp:105] Iteration 492, lr = 0.00340842
I0408 15:38:52.304069 27193 solver.cpp:218] Iteration 504 (2.41253 iter/s, 4.97403s/12 iters), loss = 5.11378
I0408 15:38:52.304133 27193 solver.cpp:237]     Train net output #0: loss = 5.11378 (* 1 = 5.11378 loss)
I0408 15:38:52.304147 27193 sgd_solver.cpp:105] Iteration 504, lr = 0.0033201
I0408 15:38:52.550977 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:38:54.370126 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0408 15:38:57.993546 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0408 15:39:00.695324 27193 solver.cpp:330] Iteration 510, Testing net (#0)
I0408 15:39:00.695353 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:39:05.051079 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:39:05.289454 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0134804
I0408 15:39:05.289505 27193 solver.cpp:397]     Test net output #1: loss = 5.07142 (* 1 = 5.07142 loss)
I0408 15:39:07.323268 27193 solver.cpp:218] Iteration 516 (0.799007 iter/s, 15.0186s/12 iters), loss = 5.01534
I0408 15:39:07.323330 27193 solver.cpp:237]     Train net output #0: loss = 5.01534 (* 1 = 5.01534 loss)
I0408 15:39:07.323346 27193 sgd_solver.cpp:105] Iteration 516, lr = 0.00323408
I0408 15:39:12.358891 27193 solver.cpp:218] Iteration 528 (2.38313 iter/s, 5.03538s/12 iters), loss = 5.1071
I0408 15:39:12.358934 27193 solver.cpp:237]     Train net output #0: loss = 5.1071 (* 1 = 5.1071 loss)
I0408 15:39:12.358943 27193 sgd_solver.cpp:105] Iteration 528, lr = 0.00315028
I0408 15:39:17.425827 27193 solver.cpp:218] Iteration 540 (2.3684 iter/s, 5.06671s/12 iters), loss = 4.99626
I0408 15:39:17.426096 27193 solver.cpp:237]     Train net output #0: loss = 4.99626 (* 1 = 4.99626 loss)
I0408 15:39:17.426120 27193 sgd_solver.cpp:105] Iteration 540, lr = 0.00306866
I0408 15:39:22.449681 27193 solver.cpp:218] Iteration 552 (2.38881 iter/s, 5.02343s/12 iters), loss = 5.07179
I0408 15:39:22.449728 27193 solver.cpp:237]     Train net output #0: loss = 5.07179 (* 1 = 5.07179 loss)
I0408 15:39:22.449741 27193 sgd_solver.cpp:105] Iteration 552, lr = 0.00298915
I0408 15:39:27.513610 27193 solver.cpp:218] Iteration 564 (2.3698 iter/s, 5.06371s/12 iters), loss = 5.0203
I0408 15:39:27.513654 27193 solver.cpp:237]     Train net output #0: loss = 5.0203 (* 1 = 5.0203 loss)
I0408 15:39:27.513664 27193 sgd_solver.cpp:105] Iteration 564, lr = 0.0029117
I0408 15:39:32.532497 27193 solver.cpp:218] Iteration 576 (2.39108 iter/s, 5.01866s/12 iters), loss = 5.03791
I0408 15:39:32.532554 27193 solver.cpp:237]     Train net output #0: loss = 5.03791 (* 1 = 5.03791 loss)
I0408 15:39:32.532567 27193 sgd_solver.cpp:105] Iteration 576, lr = 0.00283625
I0408 15:39:37.594310 27193 solver.cpp:218] Iteration 588 (2.3708 iter/s, 5.06159s/12 iters), loss = 4.96886
I0408 15:39:37.594347 27193 solver.cpp:237]     Train net output #0: loss = 4.96886 (* 1 = 4.96886 loss)
I0408 15:39:37.594354 27193 sgd_solver.cpp:105] Iteration 588, lr = 0.00276276
I0408 15:39:42.694834 27193 solver.cpp:218] Iteration 600 (2.3528 iter/s, 5.10031s/12 iters), loss = 5.06433
I0408 15:39:42.694882 27193 solver.cpp:237]     Train net output #0: loss = 5.06433 (* 1 = 5.06433 loss)
I0408 15:39:42.694895 27193 sgd_solver.cpp:105] Iteration 600, lr = 0.00269118
I0408 15:39:45.114980 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:39:47.238818 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0408 15:39:50.927740 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0408 15:39:53.822479 27193 solver.cpp:330] Iteration 612, Testing net (#0)
I0408 15:39:53.822504 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:39:57.981182 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:39:58.266039 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0232843
I0408 15:39:58.266072 27193 solver.cpp:397]     Test net output #1: loss = 5.03256 (* 1 = 5.03256 loss)
I0408 15:39:58.358556 27193 solver.cpp:218] Iteration 612 (0.766129 iter/s, 15.6632s/12 iters), loss = 5.0318
I0408 15:39:58.358595 27193 solver.cpp:237]     Train net output #0: loss = 5.0318 (* 1 = 5.0318 loss)
I0408 15:39:58.358603 27193 sgd_solver.cpp:105] Iteration 612, lr = 0.00262145
I0408 15:40:02.663014 27193 solver.cpp:218] Iteration 624 (2.78793 iter/s, 4.30426s/12 iters), loss = 5.05798
I0408 15:40:02.663064 27193 solver.cpp:237]     Train net output #0: loss = 5.05798 (* 1 = 5.05798 loss)
I0408 15:40:02.663077 27193 sgd_solver.cpp:105] Iteration 624, lr = 0.00255353
I0408 15:40:07.680668 27193 solver.cpp:218] Iteration 636 (2.39166 iter/s, 5.01743s/12 iters), loss = 4.89059
I0408 15:40:07.680717 27193 solver.cpp:237]     Train net output #0: loss = 4.89059 (* 1 = 4.89059 loss)
I0408 15:40:07.680728 27193 sgd_solver.cpp:105] Iteration 636, lr = 0.00248736
I0408 15:40:12.757896 27193 solver.cpp:218] Iteration 648 (2.3636 iter/s, 5.07699s/12 iters), loss = 5.06875
I0408 15:40:12.757973 27193 solver.cpp:237]     Train net output #0: loss = 5.06875 (* 1 = 5.06875 loss)
I0408 15:40:12.757987 27193 sgd_solver.cpp:105] Iteration 648, lr = 0.00242291
I0408 15:40:17.804461 27193 solver.cpp:218] Iteration 660 (2.37796 iter/s, 5.04633s/12 iters), loss = 4.99525
I0408 15:40:17.804512 27193 solver.cpp:237]     Train net output #0: loss = 4.99525 (* 1 = 4.99525 loss)
I0408 15:40:17.804523 27193 sgd_solver.cpp:105] Iteration 660, lr = 0.00236013
I0408 15:40:22.892882 27193 solver.cpp:218] Iteration 672 (2.3584 iter/s, 5.0882s/12 iters), loss = 4.99085
I0408 15:40:22.893020 27193 solver.cpp:237]     Train net output #0: loss = 4.99085 (* 1 = 4.99085 loss)
I0408 15:40:22.893033 27193 sgd_solver.cpp:105] Iteration 672, lr = 0.00229898
I0408 15:40:27.909168 27193 solver.cpp:218] Iteration 684 (2.39236 iter/s, 5.01598s/12 iters), loss = 4.78951
I0408 15:40:27.909219 27193 solver.cpp:237]     Train net output #0: loss = 4.78951 (* 1 = 4.78951 loss)
I0408 15:40:27.909230 27193 sgd_solver.cpp:105] Iteration 684, lr = 0.00223941
I0408 15:40:28.687543 27193 blocking_queue.cpp:49] Waiting for data
I0408 15:40:32.945453 27193 solver.cpp:218] Iteration 696 (2.38281 iter/s, 5.03606s/12 iters), loss = 5.02446
I0408 15:40:32.945499 27193 solver.cpp:237]     Train net output #0: loss = 5.02446 (* 1 = 5.02446 loss)
I0408 15:40:32.945510 27193 sgd_solver.cpp:105] Iteration 696, lr = 0.00218139
I0408 15:40:37.607786 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:40:37.990761 27193 solver.cpp:218] Iteration 708 (2.37855 iter/s, 5.04509s/12 iters), loss = 5.03518
I0408 15:40:37.990798 27193 solver.cpp:237]     Train net output #0: loss = 5.03518 (* 1 = 5.03518 loss)
I0408 15:40:37.990808 27193 sgd_solver.cpp:105] Iteration 708, lr = 0.00212487
I0408 15:40:40.043642 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0408 15:40:44.049307 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0408 15:40:46.766535 27193 solver.cpp:330] Iteration 714, Testing net (#0)
I0408 15:40:46.766554 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:40:50.916579 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:40:51.236521 27193 solver.cpp:397]     Test net output #0: accuracy = 0.03125
I0408 15:40:51.236569 27193 solver.cpp:397]     Test net output #1: loss = 4.98769 (* 1 = 4.98769 loss)
I0408 15:40:52.997330 27193 solver.cpp:218] Iteration 720 (0.799678 iter/s, 15.006s/12 iters), loss = 5.08431
I0408 15:40:52.997453 27193 solver.cpp:237]     Train net output #0: loss = 5.08431 (* 1 = 5.08431 loss)
I0408 15:40:52.997467 27193 sgd_solver.cpp:105] Iteration 720, lr = 0.00206981
I0408 15:40:58.111779 27193 solver.cpp:218] Iteration 732 (2.34643 iter/s, 5.11415s/12 iters), loss = 4.84309
I0408 15:40:58.111830 27193 solver.cpp:237]     Train net output #0: loss = 4.84309 (* 1 = 4.84309 loss)
I0408 15:40:58.111842 27193 sgd_solver.cpp:105] Iteration 732, lr = 0.00201618
I0408 15:41:03.400434 27193 solver.cpp:218] Iteration 744 (2.26911 iter/s, 5.28842s/12 iters), loss = 4.9884
I0408 15:41:03.400487 27193 solver.cpp:237]     Train net output #0: loss = 4.9884 (* 1 = 4.9884 loss)
I0408 15:41:03.400501 27193 sgd_solver.cpp:105] Iteration 744, lr = 0.00196394
I0408 15:41:08.444270 27193 solver.cpp:218] Iteration 756 (2.37925 iter/s, 5.04361s/12 iters), loss = 5.03567
I0408 15:41:08.444316 27193 solver.cpp:237]     Train net output #0: loss = 5.03567 (* 1 = 5.03567 loss)
I0408 15:41:08.444327 27193 sgd_solver.cpp:105] Iteration 756, lr = 0.00191306
I0408 15:41:13.521687 27193 solver.cpp:218] Iteration 768 (2.36351 iter/s, 5.0772s/12 iters), loss = 5.01345
I0408 15:41:13.521730 27193 solver.cpp:237]     Train net output #0: loss = 5.01345 (* 1 = 5.01345 loss)
I0408 15:41:13.521741 27193 sgd_solver.cpp:105] Iteration 768, lr = 0.00186349
I0408 15:41:18.684661 27193 solver.cpp:218] Iteration 780 (2.32434 iter/s, 5.16275s/12 iters), loss = 4.9995
I0408 15:41:18.684711 27193 solver.cpp:237]     Train net output #0: loss = 4.9995 (* 1 = 4.9995 loss)
I0408 15:41:18.684723 27193 sgd_solver.cpp:105] Iteration 780, lr = 0.0018152
I0408 15:41:23.742996 27193 solver.cpp:218] Iteration 792 (2.37243 iter/s, 5.05811s/12 iters), loss = 4.83303
I0408 15:41:23.743167 27193 solver.cpp:237]     Train net output #0: loss = 4.83303 (* 1 = 4.83303 loss)
I0408 15:41:23.743182 27193 sgd_solver.cpp:105] Iteration 792, lr = 0.00176817
I0408 15:41:28.829126 27193 solver.cpp:218] Iteration 804 (2.35951 iter/s, 5.08579s/12 iters), loss = 4.93262
I0408 15:41:28.829172 27193 solver.cpp:237]     Train net output #0: loss = 4.93262 (* 1 = 4.93262 loss)
I0408 15:41:28.829186 27193 sgd_solver.cpp:105] Iteration 804, lr = 0.00172236
I0408 15:41:30.599849 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:41:33.377557 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0408 15:41:36.419186 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0408 15:41:38.741168 27193 solver.cpp:330] Iteration 816, Testing net (#0)
I0408 15:41:38.741192 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:41:42.860889 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:41:43.216511 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0300245
I0408 15:41:43.216553 27193 solver.cpp:397]     Test net output #1: loss = 4.94983 (* 1 = 4.94983 loss)
I0408 15:41:43.307627 27193 solver.cpp:218] Iteration 816 (0.828845 iter/s, 14.478s/12 iters), loss = 5.00052
I0408 15:41:43.307675 27193 solver.cpp:237]     Train net output #0: loss = 5.00052 (* 1 = 5.00052 loss)
I0408 15:41:43.307685 27193 sgd_solver.cpp:105] Iteration 816, lr = 0.00167773
I0408 15:41:47.719312 27193 solver.cpp:218] Iteration 828 (2.72018 iter/s, 4.41148s/12 iters), loss = 5.06232
I0408 15:41:47.719364 27193 solver.cpp:237]     Train net output #0: loss = 5.06232 (* 1 = 5.06232 loss)
I0408 15:41:47.719375 27193 sgd_solver.cpp:105] Iteration 828, lr = 0.00163426
I0408 15:41:52.714390 27193 solver.cpp:218] Iteration 840 (2.40247 iter/s, 4.99485s/12 iters), loss = 4.82518
I0408 15:41:52.714435 27193 solver.cpp:237]     Train net output #0: loss = 4.82518 (* 1 = 4.82518 loss)
I0408 15:41:52.714445 27193 sgd_solver.cpp:105] Iteration 840, lr = 0.00159191
I0408 15:41:57.727634 27193 solver.cpp:218] Iteration 852 (2.39376 iter/s, 5.01302s/12 iters), loss = 4.84099
I0408 15:41:57.727759 27193 solver.cpp:237]     Train net output #0: loss = 4.84099 (* 1 = 4.84099 loss)
I0408 15:41:57.727771 27193 sgd_solver.cpp:105] Iteration 852, lr = 0.00155067
I0408 15:42:02.891170 27193 solver.cpp:218] Iteration 864 (2.32412 iter/s, 5.16324s/12 iters), loss = 4.93514
I0408 15:42:02.891212 27193 solver.cpp:237]     Train net output #0: loss = 4.93514 (* 1 = 4.93514 loss)
I0408 15:42:02.891222 27193 sgd_solver.cpp:105] Iteration 864, lr = 0.00151049
I0408 15:42:08.253749 27193 solver.cpp:218] Iteration 876 (2.23783 iter/s, 5.36235s/12 iters), loss = 4.98477
I0408 15:42:08.253804 27193 solver.cpp:237]     Train net output #0: loss = 4.98477 (* 1 = 4.98477 loss)
I0408 15:42:08.253816 27193 sgd_solver.cpp:105] Iteration 876, lr = 0.00147135
I0408 15:42:13.291193 27193 solver.cpp:218] Iteration 888 (2.38227 iter/s, 5.03722s/12 iters), loss = 4.77783
I0408 15:42:13.291246 27193 solver.cpp:237]     Train net output #0: loss = 4.77783 (* 1 = 4.77783 loss)
I0408 15:42:13.291258 27193 sgd_solver.cpp:105] Iteration 888, lr = 0.00143323
I0408 15:42:18.361294 27193 solver.cpp:218] Iteration 900 (2.36692 iter/s, 5.06988s/12 iters), loss = 4.90973
I0408 15:42:18.361341 27193 solver.cpp:237]     Train net output #0: loss = 4.90973 (* 1 = 4.90973 loss)
I0408 15:42:18.361351 27193 sgd_solver.cpp:105] Iteration 900, lr = 0.00139609
I0408 15:42:22.206707 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:42:23.358639 27193 solver.cpp:218] Iteration 912 (2.40138 iter/s, 4.99713s/12 iters), loss = 4.75195
I0408 15:42:23.358675 27193 solver.cpp:237]     Train net output #0: loss = 4.75195 (* 1 = 4.75195 loss)
I0408 15:42:23.358683 27193 sgd_solver.cpp:105] Iteration 912, lr = 0.00135992
I0408 15:42:25.438169 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0408 15:42:28.484453 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0408 15:42:31.241998 27193 solver.cpp:330] Iteration 918, Testing net (#0)
I0408 15:42:31.242027 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:42:35.453254 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:42:35.855175 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0343137
I0408 15:42:35.855224 27193 solver.cpp:397]     Test net output #1: loss = 4.91821 (* 1 = 4.91821 loss)
I0408 15:42:37.805128 27193 solver.cpp:218] Iteration 924 (0.830681 iter/s, 14.446s/12 iters), loss = 4.95514
I0408 15:42:37.805184 27193 solver.cpp:237]     Train net output #0: loss = 4.95514 (* 1 = 4.95514 loss)
I0408 15:42:37.805197 27193 sgd_solver.cpp:105] Iteration 924, lr = 0.00132468
I0408 15:42:42.886759 27193 solver.cpp:218] Iteration 936 (2.36155 iter/s, 5.0814s/12 iters), loss = 4.92119
I0408 15:42:42.886804 27193 solver.cpp:237]     Train net output #0: loss = 4.92119 (* 1 = 4.92119 loss)
I0408 15:42:42.886816 27193 sgd_solver.cpp:105] Iteration 936, lr = 0.00129036
I0408 15:42:48.069846 27193 solver.cpp:218] Iteration 948 (2.31532 iter/s, 5.18286s/12 iters), loss = 4.79745
I0408 15:42:48.069898 27193 solver.cpp:237]     Train net output #0: loss = 4.79745 (* 1 = 4.79745 loss)
I0408 15:42:48.069911 27193 sgd_solver.cpp:105] Iteration 948, lr = 0.00125692
I0408 15:42:53.144537 27193 solver.cpp:218] Iteration 960 (2.36478 iter/s, 5.07447s/12 iters), loss = 4.75797
I0408 15:42:53.144584 27193 solver.cpp:237]     Train net output #0: loss = 4.75797 (* 1 = 4.75797 loss)
I0408 15:42:53.144595 27193 sgd_solver.cpp:105] Iteration 960, lr = 0.00122436
I0408 15:42:58.199388 27193 solver.cpp:218] Iteration 972 (2.37406 iter/s, 5.05463s/12 iters), loss = 4.92614
I0408 15:42:58.199442 27193 solver.cpp:237]     Train net output #0: loss = 4.92614 (* 1 = 4.92614 loss)
I0408 15:42:58.199455 27193 sgd_solver.cpp:105] Iteration 972, lr = 0.00119263
I0408 15:43:03.224015 27193 solver.cpp:218] Iteration 984 (2.38834 iter/s, 5.0244s/12 iters), loss = 4.83474
I0408 15:43:03.224120 27193 solver.cpp:237]     Train net output #0: loss = 4.83474 (* 1 = 4.83474 loss)
I0408 15:43:03.224133 27193 sgd_solver.cpp:105] Iteration 984, lr = 0.00116173
I0408 15:43:08.388700 27193 solver.cpp:218] Iteration 996 (2.3236 iter/s, 5.16441s/12 iters), loss = 4.75217
I0408 15:43:08.388741 27193 solver.cpp:237]     Train net output #0: loss = 4.75217 (* 1 = 4.75217 loss)
I0408 15:43:08.388749 27193 sgd_solver.cpp:105] Iteration 996, lr = 0.00113163
I0408 15:43:13.438302 27193 solver.cpp:218] Iteration 1008 (2.37653 iter/s, 5.04939s/12 iters), loss = 4.91473
I0408 15:43:13.438346 27193 solver.cpp:237]     Train net output #0: loss = 4.91473 (* 1 = 4.91473 loss)
I0408 15:43:13.438356 27193 sgd_solver.cpp:105] Iteration 1008, lr = 0.00110231
I0408 15:43:14.546811 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:43:18.062523 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0408 15:43:21.606719 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0408 15:43:23.939296 27193 solver.cpp:330] Iteration 1020, Testing net (#0)
I0408 15:43:23.939322 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:43:27.940160 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:43:28.372856 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0392157
I0408 15:43:28.372905 27193 solver.cpp:397]     Test net output #1: loss = 4.8821 (* 1 = 4.8821 loss)
I0408 15:43:28.464366 27193 solver.cpp:218] Iteration 1020 (0.798641 iter/s, 15.0255s/12 iters), loss = 4.72571
I0408 15:43:28.464423 27193 solver.cpp:237]     Train net output #0: loss = 4.72571 (* 1 = 4.72571 loss)
I0408 15:43:28.464434 27193 sgd_solver.cpp:105] Iteration 1020, lr = 0.00107375
I0408 15:43:33.069506 27193 solver.cpp:218] Iteration 1032 (2.60591 iter/s, 4.60492s/12 iters), loss = 4.82018
I0408 15:43:33.069562 27193 solver.cpp:237]     Train net output #0: loss = 4.82018 (* 1 = 4.82018 loss)
I0408 15:43:33.069574 27193 sgd_solver.cpp:105] Iteration 1032, lr = 0.00104593
I0408 15:43:38.286413 27193 solver.cpp:218] Iteration 1044 (2.30032 iter/s, 5.21667s/12 iters), loss = 4.93542
I0408 15:43:38.286558 27193 solver.cpp:237]     Train net output #0: loss = 4.93542 (* 1 = 4.93542 loss)
I0408 15:43:38.286571 27193 sgd_solver.cpp:105] Iteration 1044, lr = 0.00101883
I0408 15:43:43.379740 27193 solver.cpp:218] Iteration 1056 (2.35617 iter/s, 5.09301s/12 iters), loss = 4.77196
I0408 15:43:43.379793 27193 solver.cpp:237]     Train net output #0: loss = 4.77196 (* 1 = 4.77196 loss)
I0408 15:43:43.379804 27193 sgd_solver.cpp:105] Iteration 1056, lr = 0.000992428
I0408 15:43:48.398460 27193 solver.cpp:218] Iteration 1068 (2.39115 iter/s, 5.0185s/12 iters), loss = 4.90067
I0408 15:43:48.398504 27193 solver.cpp:237]     Train net output #0: loss = 4.90067 (* 1 = 4.90067 loss)
I0408 15:43:48.398514 27193 sgd_solver.cpp:105] Iteration 1068, lr = 0.000966713
I0408 15:43:53.481282 27193 solver.cpp:218] Iteration 1080 (2.361 iter/s, 5.0826s/12 iters), loss = 4.74138
I0408 15:43:53.481343 27193 solver.cpp:237]     Train net output #0: loss = 4.74138 (* 1 = 4.74138 loss)
I0408 15:43:53.481355 27193 sgd_solver.cpp:105] Iteration 1080, lr = 0.000941665
I0408 15:43:58.780347 27193 solver.cpp:218] Iteration 1092 (2.26465 iter/s, 5.29883s/12 iters), loss = 4.79281
I0408 15:43:58.780390 27193 solver.cpp:237]     Train net output #0: loss = 4.79281 (* 1 = 4.79281 loss)
I0408 15:43:58.780402 27193 sgd_solver.cpp:105] Iteration 1092, lr = 0.000917266
I0408 15:44:03.886440 27193 solver.cpp:218] Iteration 1104 (2.35024 iter/s, 5.10587s/12 iters), loss = 4.86858
I0408 15:44:03.886492 27193 solver.cpp:237]     Train net output #0: loss = 4.86858 (* 1 = 4.86858 loss)
I0408 15:44:03.886503 27193 sgd_solver.cpp:105] Iteration 1104, lr = 0.000893499
I0408 15:44:07.073783 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:44:08.971601 27193 solver.cpp:218] Iteration 1116 (2.35991 iter/s, 5.08493s/12 iters), loss = 4.89667
I0408 15:44:08.971688 27193 solver.cpp:237]     Train net output #0: loss = 4.89667 (* 1 = 4.89667 loss)
I0408 15:44:08.971700 27193 sgd_solver.cpp:105] Iteration 1116, lr = 0.000870348
I0408 15:44:11.097810 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0408 15:44:14.207808 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0408 15:44:16.496073 27193 solver.cpp:330] Iteration 1122, Testing net (#0)
I0408 15:44:16.496094 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:44:20.472355 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:44:20.950518 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0441176
I0408 15:44:20.950567 27193 solver.cpp:397]     Test net output #1: loss = 4.85047 (* 1 = 4.85047 loss)
I0408 15:44:22.742774 27193 solver.cpp:218] Iteration 1128 (0.871419 iter/s, 13.7706s/12 iters), loss = 4.87524
I0408 15:44:22.742816 27193 solver.cpp:237]     Train net output #0: loss = 4.87524 (* 1 = 4.87524 loss)
I0408 15:44:22.742825 27193 sgd_solver.cpp:105] Iteration 1128, lr = 0.000847797
I0408 15:44:27.793644 27193 solver.cpp:218] Iteration 1140 (2.37593 iter/s, 5.05066s/12 iters), loss = 4.85915
I0408 15:44:27.793684 27193 solver.cpp:237]     Train net output #0: loss = 4.85915 (* 1 = 4.85915 loss)
I0408 15:44:27.793694 27193 sgd_solver.cpp:105] Iteration 1140, lr = 0.00082583
I0408 15:44:32.853726 27193 solver.cpp:218] Iteration 1152 (2.3716 iter/s, 5.05987s/12 iters), loss = 4.85951
I0408 15:44:32.853768 27193 solver.cpp:237]     Train net output #0: loss = 4.85951 (* 1 = 4.85951 loss)
I0408 15:44:32.853778 27193 sgd_solver.cpp:105] Iteration 1152, lr = 0.000804433
I0408 15:44:38.194855 27193 solver.cpp:218] Iteration 1164 (2.24681 iter/s, 5.34091s/12 iters), loss = 4.75607
I0408 15:44:38.194900 27193 solver.cpp:237]     Train net output #0: loss = 4.75607 (* 1 = 4.75607 loss)
I0408 15:44:38.194909 27193 sgd_solver.cpp:105] Iteration 1164, lr = 0.000783589
I0408 15:44:43.552022 27193 solver.cpp:218] Iteration 1176 (2.24009 iter/s, 5.35694s/12 iters), loss = 4.75425
I0408 15:44:43.552175 27193 solver.cpp:237]     Train net output #0: loss = 4.75425 (* 1 = 4.75425 loss)
I0408 15:44:43.552188 27193 sgd_solver.cpp:105] Iteration 1176, lr = 0.000763286
I0408 15:44:48.992050 27193 solver.cpp:218] Iteration 1188 (2.20601 iter/s, 5.4397s/12 iters), loss = 4.78282
I0408 15:44:48.992092 27193 solver.cpp:237]     Train net output #0: loss = 4.78282 (* 1 = 4.78282 loss)
I0408 15:44:48.992101 27193 sgd_solver.cpp:105] Iteration 1188, lr = 0.000743509
I0408 15:44:54.030033 27193 solver.cpp:218] Iteration 1200 (2.38201 iter/s, 5.03777s/12 iters), loss = 4.88353
I0408 15:44:54.030082 27193 solver.cpp:237]     Train net output #0: loss = 4.88353 (* 1 = 4.88353 loss)
I0408 15:44:54.030093 27193 sgd_solver.cpp:105] Iteration 1200, lr = 0.000724244
I0408 15:44:59.055526 27193 solver.cpp:218] Iteration 1212 (2.38793 iter/s, 5.02527s/12 iters), loss = 4.76906
I0408 15:44:59.055572 27193 solver.cpp:237]     Train net output #0: loss = 4.76906 (* 1 = 4.76906 loss)
I0408 15:44:59.055583 27193 sgd_solver.cpp:105] Iteration 1212, lr = 0.000705479
I0408 15:44:59.334343 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:45:03.871029 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0408 15:45:06.856504 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0408 15:45:10.336797 27193 solver.cpp:330] Iteration 1224, Testing net (#0)
I0408 15:45:10.336825 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:45:14.301313 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:45:14.814646 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0441176
I0408 15:45:14.814687 27193 solver.cpp:397]     Test net output #1: loss = 4.82228 (* 1 = 4.82228 loss)
I0408 15:45:14.906195 27193 solver.cpp:218] Iteration 1224 (0.757093 iter/s, 15.8501s/12 iters), loss = 4.7284
I0408 15:45:14.906237 27193 solver.cpp:237]     Train net output #0: loss = 4.7284 (* 1 = 4.7284 loss)
I0408 15:45:14.906246 27193 sgd_solver.cpp:105] Iteration 1224, lr = 0.000687199
I0408 15:45:19.295542 27193 solver.cpp:218] Iteration 1236 (2.73402 iter/s, 4.38914s/12 iters), loss = 4.96666
I0408 15:45:19.295600 27193 solver.cpp:237]     Train net output #0: loss = 4.96666 (* 1 = 4.96666 loss)
I0408 15:45:19.295612 27193 sgd_solver.cpp:105] Iteration 1236, lr = 0.000669394
I0408 15:45:24.402418 27193 solver.cpp:218] Iteration 1248 (2.34988 iter/s, 5.10664s/12 iters), loss = 4.68919
I0408 15:45:24.402470 27193 solver.cpp:237]     Train net output #0: loss = 4.68919 (* 1 = 4.68919 loss)
I0408 15:45:24.402480 27193 sgd_solver.cpp:105] Iteration 1248, lr = 0.000652049
I0408 15:45:29.746449 27193 solver.cpp:218] Iteration 1260 (2.24559 iter/s, 5.3438s/12 iters), loss = 4.69055
I0408 15:45:29.746495 27193 solver.cpp:237]     Train net output #0: loss = 4.69055 (* 1 = 4.69055 loss)
I0408 15:45:29.746506 27193 sgd_solver.cpp:105] Iteration 1260, lr = 0.000635154
I0408 15:45:35.242317 27193 solver.cpp:218] Iteration 1272 (2.18355 iter/s, 5.49563s/12 iters), loss = 4.68256
I0408 15:45:35.242367 27193 solver.cpp:237]     Train net output #0: loss = 4.68256 (* 1 = 4.68256 loss)
I0408 15:45:35.242378 27193 sgd_solver.cpp:105] Iteration 1272, lr = 0.000618697
I0408 15:45:40.378989 27193 solver.cpp:218] Iteration 1284 (2.33625 iter/s, 5.13645s/12 iters), loss = 4.73783
I0408 15:45:40.379042 27193 solver.cpp:237]     Train net output #0: loss = 4.73783 (* 1 = 4.73783 loss)
I0408 15:45:40.379055 27193 sgd_solver.cpp:105] Iteration 1284, lr = 0.000602667
I0408 15:45:45.652169 27193 solver.cpp:218] Iteration 1296 (2.27577 iter/s, 5.27295s/12 iters), loss = 4.65095
I0408 15:45:45.652303 27193 solver.cpp:237]     Train net output #0: loss = 4.65095 (* 1 = 4.65095 loss)
I0408 15:45:45.652314 27193 sgd_solver.cpp:105] Iteration 1296, lr = 0.000587051
I0408 15:45:51.172502 27193 solver.cpp:218] Iteration 1308 (2.17391 iter/s, 5.52001s/12 iters), loss = 4.83614
I0408 15:45:51.172545 27193 solver.cpp:237]     Train net output #0: loss = 4.83614 (* 1 = 4.83614 loss)
I0408 15:45:51.172554 27193 sgd_solver.cpp:105] Iteration 1308, lr = 0.00057184
I0408 15:45:53.855134 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:45:56.408708 27193 solver.cpp:218] Iteration 1320 (2.29183 iter/s, 5.23599s/12 iters), loss = 4.72569
I0408 15:45:56.408748 27193 solver.cpp:237]     Train net output #0: loss = 4.72569 (* 1 = 4.72569 loss)
I0408 15:45:56.408758 27193 sgd_solver.cpp:105] Iteration 1320, lr = 0.000557024
I0408 15:45:58.552749 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0408 15:46:03.598701 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0408 15:46:06.420948 27193 solver.cpp:330] Iteration 1326, Testing net (#0)
I0408 15:46:06.420974 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:46:10.439240 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:46:10.998093 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0477941
I0408 15:46:10.998136 27193 solver.cpp:397]     Test net output #1: loss = 4.78645 (* 1 = 4.78645 loss)
I0408 15:46:13.011559 27193 solver.cpp:218] Iteration 1332 (0.722793 iter/s, 16.6023s/12 iters), loss = 4.5756
I0408 15:46:13.011602 27193 solver.cpp:237]     Train net output #0: loss = 4.5756 (* 1 = 4.5756 loss)
I0408 15:46:13.011611 27193 sgd_solver.cpp:105] Iteration 1332, lr = 0.000542591
I0408 15:46:18.413189 27193 solver.cpp:218] Iteration 1344 (2.22165 iter/s, 5.4014s/12 iters), loss = 4.64198
I0408 15:46:18.413285 27193 solver.cpp:237]     Train net output #0: loss = 4.64198 (* 1 = 4.64198 loss)
I0408 15:46:18.413295 27193 sgd_solver.cpp:105] Iteration 1344, lr = 0.000528532
I0408 15:46:23.495425 27193 solver.cpp:218] Iteration 1356 (2.36129 iter/s, 5.08196s/12 iters), loss = 4.80706
I0408 15:46:23.495472 27193 solver.cpp:237]     Train net output #0: loss = 4.80706 (* 1 = 4.80706 loss)
I0408 15:46:23.495481 27193 sgd_solver.cpp:105] Iteration 1356, lr = 0.000514838
I0408 15:46:28.590737 27193 solver.cpp:218] Iteration 1368 (2.35521 iter/s, 5.09509s/12 iters), loss = 4.75032
I0408 15:46:28.590781 27193 solver.cpp:237]     Train net output #0: loss = 4.75032 (* 1 = 4.75032 loss)
I0408 15:46:28.590791 27193 sgd_solver.cpp:105] Iteration 1368, lr = 0.000501498
I0408 15:46:29.820401 27193 blocking_queue.cpp:49] Waiting for data
I0408 15:46:33.678488 27193 solver.cpp:218] Iteration 1380 (2.35871 iter/s, 5.08753s/12 iters), loss = 4.55236
I0408 15:46:33.678537 27193 solver.cpp:237]     Train net output #0: loss = 4.55236 (* 1 = 4.55236 loss)
I0408 15:46:33.678547 27193 sgd_solver.cpp:105] Iteration 1380, lr = 0.000488504
I0408 15:46:38.819430 27193 solver.cpp:218] Iteration 1392 (2.33431 iter/s, 5.14071s/12 iters), loss = 4.52443
I0408 15:46:38.819486 27193 solver.cpp:237]     Train net output #0: loss = 4.52443 (* 1 = 4.52443 loss)
I0408 15:46:38.819500 27193 sgd_solver.cpp:105] Iteration 1392, lr = 0.000475846
I0408 15:46:43.946739 27193 solver.cpp:218] Iteration 1404 (2.34051 iter/s, 5.12708s/12 iters), loss = 4.71614
I0408 15:46:43.946779 27193 solver.cpp:237]     Train net output #0: loss = 4.71614 (* 1 = 4.71614 loss)
I0408 15:46:43.946789 27193 sgd_solver.cpp:105] Iteration 1404, lr = 0.000463517
I0408 15:46:48.670794 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:46:49.021471 27193 solver.cpp:218] Iteration 1416 (2.36476 iter/s, 5.07452s/12 iters), loss = 4.6916
I0408 15:46:49.021507 27193 solver.cpp:237]     Train net output #0: loss = 4.6916 (* 1 = 4.6916 loss)
I0408 15:46:49.021517 27193 sgd_solver.cpp:105] Iteration 1416, lr = 0.000451507
I0408 15:46:53.631373 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0408 15:46:59.282817 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0408 15:47:03.375217 27193 solver.cpp:330] Iteration 1428, Testing net (#0)
I0408 15:47:03.375243 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:47:07.317054 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:47:07.908859 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0490196
I0408 15:47:07.908908 27193 solver.cpp:397]     Test net output #1: loss = 4.74899 (* 1 = 4.74899 loss)
I0408 15:47:07.998531 27193 solver.cpp:218] Iteration 1428 (0.632364 iter/s, 18.9764s/12 iters), loss = 4.8978
I0408 15:47:07.998581 27193 solver.cpp:237]     Train net output #0: loss = 4.8978 (* 1 = 4.8978 loss)
I0408 15:47:07.998594 27193 sgd_solver.cpp:105] Iteration 1428, lr = 0.000439808
I0408 15:47:12.592700 27193 solver.cpp:218] Iteration 1440 (2.61213 iter/s, 4.59396s/12 iters), loss = 4.59927
I0408 15:47:12.592747 27193 solver.cpp:237]     Train net output #0: loss = 4.59927 (* 1 = 4.59927 loss)
I0408 15:47:12.592759 27193 sgd_solver.cpp:105] Iteration 1440, lr = 0.000428412
I0408 15:47:17.703950 27193 solver.cpp:218] Iteration 1452 (2.34786 iter/s, 5.11103s/12 iters), loss = 4.71031
I0408 15:47:17.703991 27193 solver.cpp:237]     Train net output #0: loss = 4.71031 (* 1 = 4.71031 loss)
I0408 15:47:17.704000 27193 sgd_solver.cpp:105] Iteration 1452, lr = 0.000417312
I0408 15:47:22.858747 27193 solver.cpp:218] Iteration 1464 (2.32803 iter/s, 5.15457s/12 iters), loss = 4.73418
I0408 15:47:22.858966 27193 solver.cpp:237]     Train net output #0: loss = 4.73418 (* 1 = 4.73418 loss)
I0408 15:47:22.858979 27193 sgd_solver.cpp:105] Iteration 1464, lr = 0.000406499
I0408 15:47:28.106755 27193 solver.cpp:218] Iteration 1476 (2.28675 iter/s, 5.24761s/12 iters), loss = 4.74075
I0408 15:47:28.106799 27193 solver.cpp:237]     Train net output #0: loss = 4.74075 (* 1 = 4.74075 loss)
I0408 15:47:28.106810 27193 sgd_solver.cpp:105] Iteration 1476, lr = 0.000395967
I0408 15:47:33.168926 27193 solver.cpp:218] Iteration 1488 (2.37063 iter/s, 5.06195s/12 iters), loss = 4.7345
I0408 15:47:33.168972 27193 solver.cpp:237]     Train net output #0: loss = 4.7345 (* 1 = 4.7345 loss)
I0408 15:47:33.168983 27193 sgd_solver.cpp:105] Iteration 1488, lr = 0.000385707
I0408 15:47:38.277545 27193 solver.cpp:218] Iteration 1500 (2.34908 iter/s, 5.10839s/12 iters), loss = 4.56937
I0408 15:47:38.277604 27193 solver.cpp:237]     Train net output #0: loss = 4.56937 (* 1 = 4.56937 loss)
I0408 15:47:38.277617 27193 sgd_solver.cpp:105] Iteration 1500, lr = 0.000375713
I0408 15:47:43.709810 27193 solver.cpp:218] Iteration 1512 (2.20912 iter/s, 5.43202s/12 iters), loss = 4.66302
I0408 15:47:43.709861 27193 solver.cpp:237]     Train net output #0: loss = 4.66302 (* 1 = 4.66302 loss)
I0408 15:47:43.709872 27193 sgd_solver.cpp:105] Iteration 1512, lr = 0.000365978
I0408 15:47:45.687863 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:47:49.110721 27193 solver.cpp:218] Iteration 1524 (2.22194 iter/s, 5.40068s/12 iters), loss = 4.76405
I0408 15:47:49.110771 27193 solver.cpp:237]     Train net output #0: loss = 4.76405 (* 1 = 4.76405 loss)
I0408 15:47:49.110782 27193 sgd_solver.cpp:105] Iteration 1524, lr = 0.000356496
I0408 15:47:51.171478 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0408 15:47:58.199376 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0408 15:48:02.000728 27193 solver.cpp:330] Iteration 1530, Testing net (#0)
I0408 15:48:02.000756 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:48:05.803820 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:48:06.442109 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0539216
I0408 15:48:06.442157 27193 solver.cpp:397]     Test net output #1: loss = 4.72949 (* 1 = 4.72949 loss)
I0408 15:48:08.318817 27193 solver.cpp:218] Iteration 1536 (0.624759 iter/s, 19.2074s/12 iters), loss = 4.71931
I0408 15:48:08.318876 27193 solver.cpp:237]     Train net output #0: loss = 4.71931 (* 1 = 4.71931 loss)
I0408 15:48:08.318888 27193 sgd_solver.cpp:105] Iteration 1536, lr = 0.000347259
I0408 15:48:13.347069 27193 solver.cpp:218] Iteration 1548 (2.38663 iter/s, 5.02802s/12 iters), loss = 4.42848
I0408 15:48:13.347126 27193 solver.cpp:237]     Train net output #0: loss = 4.42848 (* 1 = 4.42848 loss)
I0408 15:48:13.347139 27193 sgd_solver.cpp:105] Iteration 1548, lr = 0.000338261
I0408 15:48:18.501448 27193 solver.cpp:218] Iteration 1560 (2.32822 iter/s, 5.15415s/12 iters), loss = 4.62149
I0408 15:48:18.501495 27193 solver.cpp:237]     Train net output #0: loss = 4.62149 (* 1 = 4.62149 loss)
I0408 15:48:18.501507 27193 sgd_solver.cpp:105] Iteration 1560, lr = 0.000329496
I0408 15:48:23.660862 27193 solver.cpp:218] Iteration 1572 (2.32595 iter/s, 5.15919s/12 iters), loss = 4.69352
I0408 15:48:23.660908 27193 solver.cpp:237]     Train net output #0: loss = 4.69352 (* 1 = 4.69352 loss)
I0408 15:48:23.660920 27193 sgd_solver.cpp:105] Iteration 1572, lr = 0.000320959
I0408 15:48:28.774264 27193 solver.cpp:218] Iteration 1584 (2.34688 iter/s, 5.11318s/12 iters), loss = 4.73624
I0408 15:48:28.774400 27193 solver.cpp:237]     Train net output #0: loss = 4.73624 (* 1 = 4.73624 loss)
I0408 15:48:28.774418 27193 sgd_solver.cpp:105] Iteration 1584, lr = 0.000312643
I0408 15:48:33.988585 27193 solver.cpp:218] Iteration 1596 (2.30149 iter/s, 5.21401s/12 iters), loss = 4.64384
I0408 15:48:33.988636 27193 solver.cpp:237]     Train net output #0: loss = 4.64384 (* 1 = 4.64384 loss)
I0408 15:48:33.988648 27193 sgd_solver.cpp:105] Iteration 1596, lr = 0.000304542
I0408 15:48:39.104511 27193 solver.cpp:218] Iteration 1608 (2.34572 iter/s, 5.1157s/12 iters), loss = 4.61618
I0408 15:48:39.104554 27193 solver.cpp:237]     Train net output #0: loss = 4.61618 (* 1 = 4.61618 loss)
I0408 15:48:39.104566 27193 sgd_solver.cpp:105] Iteration 1608, lr = 0.000296651
I0408 15:48:43.349793 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:48:44.523947 27193 solver.cpp:218] Iteration 1620 (2.21435 iter/s, 5.41921s/12 iters), loss = 4.50672
I0408 15:48:44.523995 27193 solver.cpp:237]     Train net output #0: loss = 4.50672 (* 1 = 4.50672 loss)
I0408 15:48:44.524006 27193 sgd_solver.cpp:105] Iteration 1620, lr = 0.000288965
I0408 15:48:49.160468 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0408 15:48:57.237262 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0408 15:49:00.913883 27193 solver.cpp:330] Iteration 1632, Testing net (#0)
I0408 15:49:00.913939 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:49:04.724952 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:49:05.396684 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0551471
I0408 15:49:05.396726 27193 solver.cpp:397]     Test net output #1: loss = 4.71085 (* 1 = 4.71085 loss)
I0408 15:49:05.487841 27193 solver.cpp:218] Iteration 1632 (0.572433 iter/s, 20.9632s/12 iters), loss = 4.66336
I0408 15:49:05.487888 27193 solver.cpp:237]     Train net output #0: loss = 4.66336 (* 1 = 4.66336 loss)
I0408 15:49:05.487898 27193 sgd_solver.cpp:105] Iteration 1632, lr = 0.000281478
I0408 15:49:10.093133 27193 solver.cpp:218] Iteration 1644 (2.60582 iter/s, 4.60508s/12 iters), loss = 4.64399
I0408 15:49:10.093185 27193 solver.cpp:237]     Train net output #0: loss = 4.64399 (* 1 = 4.64399 loss)
I0408 15:49:10.093197 27193 sgd_solver.cpp:105] Iteration 1644, lr = 0.000274184
I0408 15:49:15.364133 27193 solver.cpp:218] Iteration 1656 (2.27671 iter/s, 5.27077s/12 iters), loss = 4.6803
I0408 15:49:15.364182 27193 solver.cpp:237]     Train net output #0: loss = 4.6803 (* 1 = 4.6803 loss)
I0408 15:49:15.364192 27193 sgd_solver.cpp:105] Iteration 1656, lr = 0.00026708
I0408 15:49:20.586068 27193 solver.cpp:218] Iteration 1668 (2.2981 iter/s, 5.2217s/12 iters), loss = 4.47175
I0408 15:49:20.586122 27193 solver.cpp:237]     Train net output #0: loss = 4.47175 (* 1 = 4.47175 loss)
I0408 15:49:20.586133 27193 sgd_solver.cpp:105] Iteration 1668, lr = 0.00026016
I0408 15:49:25.670821 27193 solver.cpp:218] Iteration 1680 (2.3601 iter/s, 5.08452s/12 iters), loss = 4.55878
I0408 15:49:25.670876 27193 solver.cpp:237]     Train net output #0: loss = 4.55878 (* 1 = 4.55878 loss)
I0408 15:49:25.670887 27193 sgd_solver.cpp:105] Iteration 1680, lr = 0.000253419
I0408 15:49:31.125108 27193 solver.cpp:218] Iteration 1692 (2.2002 iter/s, 5.45404s/12 iters), loss = 4.7345
I0408 15:49:31.125221 27193 solver.cpp:237]     Train net output #0: loss = 4.7345 (* 1 = 4.7345 loss)
I0408 15:49:31.125233 27193 sgd_solver.cpp:105] Iteration 1692, lr = 0.000246853
I0408 15:49:36.214128 27193 solver.cpp:218] Iteration 1704 (2.35815 iter/s, 5.08873s/12 iters), loss = 4.48659
I0408 15:49:36.214184 27193 solver.cpp:237]     Train net output #0: loss = 4.48659 (* 1 = 4.48659 loss)
I0408 15:49:36.214196 27193 sgd_solver.cpp:105] Iteration 1704, lr = 0.000240457
I0408 15:49:41.358309 27193 solver.cpp:218] Iteration 1716 (2.33284 iter/s, 5.14395s/12 iters), loss = 4.62753
I0408 15:49:41.358355 27193 solver.cpp:237]     Train net output #0: loss = 4.62753 (* 1 = 4.62753 loss)
I0408 15:49:41.358364 27193 sgd_solver.cpp:105] Iteration 1716, lr = 0.000234226
I0408 15:49:42.421144 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:49:46.427062 27193 solver.cpp:218] Iteration 1728 (2.36755 iter/s, 5.06853s/12 iters), loss = 4.61505
I0408 15:49:46.427111 27193 solver.cpp:237]     Train net output #0: loss = 4.61505 (* 1 = 4.61505 loss)
I0408 15:49:46.427124 27193 sgd_solver.cpp:105] Iteration 1728, lr = 0.000228157
I0408 15:49:48.503794 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0408 15:49:56.369580 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0408 15:50:01.725497 27193 solver.cpp:330] Iteration 1734, Testing net (#0)
I0408 15:50:01.725548 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:50:05.621894 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:50:06.331372 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0557598
I0408 15:50:06.331415 27193 solver.cpp:397]     Test net output #1: loss = 4.6974 (* 1 = 4.6974 loss)
I0408 15:50:08.121150 27193 solver.cpp:218] Iteration 1740 (0.553165 iter/s, 21.6933s/12 iters), loss = 4.61444
I0408 15:50:08.121217 27193 solver.cpp:237]     Train net output #0: loss = 4.61444 (* 1 = 4.61444 loss)
I0408 15:50:08.121228 27193 sgd_solver.cpp:105] Iteration 1740, lr = 0.000222246
I0408 15:50:13.179740 27193 solver.cpp:218] Iteration 1752 (2.37232 iter/s, 5.05835s/12 iters), loss = 4.62211
I0408 15:50:13.179790 27193 solver.cpp:237]     Train net output #0: loss = 4.62211 (* 1 = 4.62211 loss)
I0408 15:50:13.179802 27193 sgd_solver.cpp:105] Iteration 1752, lr = 0.000216487
I0408 15:50:18.124828 27193 solver.cpp:218] Iteration 1764 (2.42676 iter/s, 4.94486s/12 iters), loss = 4.53452
I0408 15:50:18.124882 27193 solver.cpp:237]     Train net output #0: loss = 4.53452 (* 1 = 4.53452 loss)
I0408 15:50:18.124894 27193 sgd_solver.cpp:105] Iteration 1764, lr = 0.000210878
I0408 15:50:23.132436 27193 solver.cpp:218] Iteration 1776 (2.39646 iter/s, 5.00738s/12 iters), loss = 4.66167
I0408 15:50:23.132485 27193 solver.cpp:237]     Train net output #0: loss = 4.66167 (* 1 = 4.66167 loss)
I0408 15:50:23.132498 27193 sgd_solver.cpp:105] Iteration 1776, lr = 0.000205414
I0408 15:50:28.184172 27193 solver.cpp:218] Iteration 1788 (2.37553 iter/s, 5.05151s/12 iters), loss = 4.64941
I0408 15:50:28.184214 27193 solver.cpp:237]     Train net output #0: loss = 4.64941 (* 1 = 4.64941 loss)
I0408 15:50:28.184223 27193 sgd_solver.cpp:105] Iteration 1788, lr = 0.000200092
I0408 15:50:33.178711 27193 solver.cpp:218] Iteration 1800 (2.40273 iter/s, 4.99432s/12 iters), loss = 4.55201
I0408 15:50:33.178839 27193 solver.cpp:237]     Train net output #0: loss = 4.55201 (* 1 = 4.55201 loss)
I0408 15:50:33.178853 27193 sgd_solver.cpp:105] Iteration 1800, lr = 0.000194907
I0408 15:50:38.256536 27193 solver.cpp:218] Iteration 1812 (2.36336 iter/s, 5.07752s/12 iters), loss = 4.70812
I0408 15:50:38.256589 27193 solver.cpp:237]     Train net output #0: loss = 4.70812 (* 1 = 4.70812 loss)
I0408 15:50:38.256599 27193 sgd_solver.cpp:105] Iteration 1812, lr = 0.000189857
I0408 15:50:41.510666 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:50:43.273659 27193 solver.cpp:218] Iteration 1824 (2.39192 iter/s, 5.0169s/12 iters), loss = 4.6755
I0408 15:50:43.273715 27193 solver.cpp:237]     Train net output #0: loss = 4.6755 (* 1 = 4.6755 loss)
I0408 15:50:43.273728 27193 sgd_solver.cpp:105] Iteration 1824, lr = 0.000184938
I0408 15:50:47.920228 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0408 15:50:57.468475 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0408 15:51:02.323087 27193 solver.cpp:330] Iteration 1836, Testing net (#0)
I0408 15:51:02.323115 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:51:06.045545 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:51:06.800053 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0637255
I0408 15:51:06.800098 27193 solver.cpp:397]     Test net output #1: loss = 4.6752 (* 1 = 4.6752 loss)
I0408 15:51:06.891444 27193 solver.cpp:218] Iteration 1836 (0.508109 iter/s, 23.617s/12 iters), loss = 4.66695
I0408 15:51:06.891500 27193 solver.cpp:237]     Train net output #0: loss = 4.66695 (* 1 = 4.66695 loss)
I0408 15:51:06.891511 27193 sgd_solver.cpp:105] Iteration 1836, lr = 0.000180146
I0408 15:51:11.092012 27193 solver.cpp:218] Iteration 1848 (2.8569 iter/s, 4.20036s/12 iters), loss = 4.55047
I0408 15:51:11.092064 27193 solver.cpp:237]     Train net output #0: loss = 4.55047 (* 1 = 4.55047 loss)
I0408 15:51:11.092077 27193 sgd_solver.cpp:105] Iteration 1848, lr = 0.000175478
I0408 15:51:16.142396 27193 solver.cpp:218] Iteration 1860 (2.37617 iter/s, 5.05015s/12 iters), loss = 4.59259
I0408 15:51:16.142446 27193 solver.cpp:237]     Train net output #0: loss = 4.59259 (* 1 = 4.59259 loss)
I0408 15:51:16.142457 27193 sgd_solver.cpp:105] Iteration 1860, lr = 0.000170931
I0408 15:51:21.251238 27193 solver.cpp:218] Iteration 1872 (2.34897 iter/s, 5.10862s/12 iters), loss = 4.64752
I0408 15:51:21.251276 27193 solver.cpp:237]     Train net output #0: loss = 4.64752 (* 1 = 4.64752 loss)
I0408 15:51:21.251284 27193 sgd_solver.cpp:105] Iteration 1872, lr = 0.000166502
I0408 15:51:26.356333 27193 solver.cpp:218] Iteration 1884 (2.3507 iter/s, 5.10487s/12 iters), loss = 4.61385
I0408 15:51:26.356385 27193 solver.cpp:237]     Train net output #0: loss = 4.61385 (* 1 = 4.61385 loss)
I0408 15:51:26.356397 27193 sgd_solver.cpp:105] Iteration 1884, lr = 0.000162188
I0408 15:51:31.666818 27193 solver.cpp:218] Iteration 1896 (2.25978 iter/s, 5.31025s/12 iters), loss = 4.54468
I0408 15:51:31.666872 27193 solver.cpp:237]     Train net output #0: loss = 4.54468 (* 1 = 4.54468 loss)
I0408 15:51:31.666883 27193 sgd_solver.cpp:105] Iteration 1896, lr = 0.000157986
I0408 15:51:37.048951 27193 solver.cpp:218] Iteration 1908 (2.2297 iter/s, 5.3819s/12 iters), loss = 4.70838
I0408 15:51:37.049105 27193 solver.cpp:237]     Train net output #0: loss = 4.70838 (* 1 = 4.70838 loss)
I0408 15:51:37.049118 27193 sgd_solver.cpp:105] Iteration 1908, lr = 0.000153892
I0408 15:51:42.228231 27193 solver.cpp:218] Iteration 1920 (2.31707 iter/s, 5.17895s/12 iters), loss = 4.70514
I0408 15:51:42.228286 27193 solver.cpp:237]     Train net output #0: loss = 4.70514 (* 1 = 4.70514 loss)
I0408 15:51:42.228298 27193 sgd_solver.cpp:105] Iteration 1920, lr = 0.000149905
I0408 15:51:42.545177 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:51:47.338011 27193 solver.cpp:218] Iteration 1932 (2.34854 iter/s, 5.10955s/12 iters), loss = 4.46562
I0408 15:51:47.338052 27193 solver.cpp:237]     Train net output #0: loss = 4.46562 (* 1 = 4.46562 loss)
I0408 15:51:47.338061 27193 sgd_solver.cpp:105] Iteration 1932, lr = 0.000146021
I0408 15:51:49.404646 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0408 15:51:56.942385 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0408 15:52:01.675570 27193 solver.cpp:330] Iteration 1938, Testing net (#0)
I0408 15:52:01.675595 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:52:05.255375 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:52:06.044534 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0655637
I0408 15:52:06.044570 27193 solver.cpp:397]     Test net output #1: loss = 4.66237 (* 1 = 4.66237 loss)
I0408 15:52:07.801395 27193 solver.cpp:218] Iteration 1944 (0.586434 iter/s, 20.4627s/12 iters), loss = 4.63903
I0408 15:52:07.801483 27193 solver.cpp:237]     Train net output #0: loss = 4.63903 (* 1 = 4.63903 loss)
I0408 15:52:07.801494 27193 sgd_solver.cpp:105] Iteration 1944, lr = 0.000142237
I0408 15:52:12.998930 27193 solver.cpp:218] Iteration 1956 (2.3089 iter/s, 5.19727s/12 iters), loss = 4.51481
I0408 15:52:12.998968 27193 solver.cpp:237]     Train net output #0: loss = 4.51481 (* 1 = 4.51481 loss)
I0408 15:52:12.998977 27193 sgd_solver.cpp:105] Iteration 1956, lr = 0.000138552
I0408 15:52:18.197302 27193 solver.cpp:218] Iteration 1968 (2.30851 iter/s, 5.19815s/12 iters), loss = 4.51942
I0408 15:52:18.197356 27193 solver.cpp:237]     Train net output #0: loss = 4.51942 (* 1 = 4.51942 loss)
I0408 15:52:18.197368 27193 sgd_solver.cpp:105] Iteration 1968, lr = 0.000134962
I0408 15:52:23.511373 27193 solver.cpp:218] Iteration 1980 (2.25826 iter/s, 5.31384s/12 iters), loss = 4.6137
I0408 15:52:23.511417 27193 solver.cpp:237]     Train net output #0: loss = 4.6137 (* 1 = 4.6137 loss)
I0408 15:52:23.511428 27193 sgd_solver.cpp:105] Iteration 1980, lr = 0.000131465
I0408 15:52:28.900899 27193 solver.cpp:218] Iteration 1992 (2.22664 iter/s, 5.38929s/12 iters), loss = 4.5401
I0408 15:52:28.900955 27193 solver.cpp:237]     Train net output #0: loss = 4.5401 (* 1 = 4.5401 loss)
I0408 15:52:28.900969 27193 sgd_solver.cpp:105] Iteration 1992, lr = 0.000128059
I0408 15:52:34.380602 27193 solver.cpp:218] Iteration 2004 (2.19 iter/s, 5.47946s/12 iters), loss = 4.60531
I0408 15:52:34.380647 27193 solver.cpp:237]     Train net output #0: loss = 4.60531 (* 1 = 4.60531 loss)
I0408 15:52:34.380659 27193 sgd_solver.cpp:105] Iteration 2004, lr = 0.000124741
I0408 15:52:39.504005 27193 solver.cpp:218] Iteration 2016 (2.34229 iter/s, 5.12319s/12 iters), loss = 4.54941
I0408 15:52:39.504101 27193 solver.cpp:237]     Train net output #0: loss = 4.54941 (* 1 = 4.54941 loss)
I0408 15:52:39.504109 27193 sgd_solver.cpp:105] Iteration 2016, lr = 0.000121509
I0408 15:52:42.140806 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:52:44.599658 27193 solver.cpp:218] Iteration 2028 (2.35508 iter/s, 5.09538s/12 iters), loss = 4.52115
I0408 15:52:44.599709 27193 solver.cpp:237]     Train net output #0: loss = 4.52115 (* 1 = 4.52115 loss)
I0408 15:52:44.599720 27193 sgd_solver.cpp:105] Iteration 2028, lr = 0.00011836
I0408 15:52:49.433173 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0408 15:52:58.157426 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0408 15:53:00.566745 27193 solver.cpp:330] Iteration 2040, Testing net (#0)
I0408 15:53:00.566768 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:53:04.197790 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:53:05.032539 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0716912
I0408 15:53:05.032588 27193 solver.cpp:397]     Test net output #1: loss = 4.63871 (* 1 = 4.63871 loss)
I0408 15:53:05.124089 27193 solver.cpp:218] Iteration 2040 (0.58469 iter/s, 20.5237s/12 iters), loss = 4.43931
I0408 15:53:05.124140 27193 solver.cpp:237]     Train net output #0: loss = 4.43931 (* 1 = 4.43931 loss)
I0408 15:53:05.124150 27193 sgd_solver.cpp:105] Iteration 2040, lr = 0.000115293
I0408 15:53:09.551481 27193 solver.cpp:218] Iteration 2052 (2.71052 iter/s, 4.42719s/12 iters), loss = 4.46167
I0408 15:53:09.551590 27193 solver.cpp:237]     Train net output #0: loss = 4.46167 (* 1 = 4.46167 loss)
I0408 15:53:09.551601 27193 sgd_solver.cpp:105] Iteration 2052, lr = 0.000112306
I0408 15:53:11.197654 27193 blocking_queue.cpp:49] Waiting for data
I0408 15:53:14.641623 27193 solver.cpp:218] Iteration 2064 (2.35763 iter/s, 5.08986s/12 iters), loss = 4.63528
I0408 15:53:14.641664 27193 solver.cpp:237]     Train net output #0: loss = 4.63528 (* 1 = 4.63528 loss)
I0408 15:53:14.641674 27193 sgd_solver.cpp:105] Iteration 2064, lr = 0.000109396
I0408 15:53:20.132102 27193 solver.cpp:218] Iteration 2076 (2.18569 iter/s, 5.49025s/12 iters), loss = 4.6939
I0408 15:53:20.132149 27193 solver.cpp:237]     Train net output #0: loss = 4.6939 (* 1 = 4.6939 loss)
I0408 15:53:20.132158 27193 sgd_solver.cpp:105] Iteration 2076, lr = 0.000106562
I0408 15:53:25.148581 27193 solver.cpp:218] Iteration 2088 (2.39222 iter/s, 5.01625s/12 iters), loss = 4.39115
I0408 15:53:25.148633 27193 solver.cpp:237]     Train net output #0: loss = 4.39115 (* 1 = 4.39115 loss)
I0408 15:53:25.148644 27193 sgd_solver.cpp:105] Iteration 2088, lr = 0.000103801
I0408 15:53:30.266449 27193 solver.cpp:218] Iteration 2100 (2.34483 iter/s, 5.11764s/12 iters), loss = 4.4594
I0408 15:53:30.266494 27193 solver.cpp:237]     Train net output #0: loss = 4.4594 (* 1 = 4.4594 loss)
I0408 15:53:30.266505 27193 sgd_solver.cpp:105] Iteration 2100, lr = 0.000101111
I0408 15:53:35.469539 27193 solver.cpp:218] Iteration 2112 (2.30642 iter/s, 5.20287s/12 iters), loss = 4.51787
I0408 15:53:35.469581 27193 solver.cpp:237]     Train net output #0: loss = 4.51787 (* 1 = 4.51787 loss)
I0408 15:53:35.469590 27193 sgd_solver.cpp:105] Iteration 2112, lr = 9.84913e-05
I0408 15:53:40.255571 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:53:40.585515 27193 solver.cpp:218] Iteration 2124 (2.34569 iter/s, 5.11576s/12 iters), loss = 4.44392
I0408 15:53:40.585554 27193 solver.cpp:237]     Train net output #0: loss = 4.44392 (* 1 = 4.44392 loss)
I0408 15:53:40.585562 27193 sgd_solver.cpp:105] Iteration 2124, lr = 9.59393e-05
I0408 15:53:45.609334 27193 solver.cpp:218] Iteration 2136 (2.38872 iter/s, 5.02361s/12 iters), loss = 4.78913
I0408 15:53:45.609369 27193 solver.cpp:237]     Train net output #0: loss = 4.78913 (* 1 = 4.78913 loss)
I0408 15:53:45.609375 27193 sgd_solver.cpp:105] Iteration 2136, lr = 9.34535e-05
I0408 15:53:47.634644 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0408 15:53:54.315920 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0408 15:54:00.353562 27193 solver.cpp:330] Iteration 2142, Testing net (#0)
I0408 15:54:00.353590 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:54:03.975693 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:54:04.840762 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0680147
I0408 15:54:04.840809 27193 solver.cpp:397]     Test net output #1: loss = 4.63869 (* 1 = 4.63869 loss)
I0408 15:54:06.828184 27193 solver.cpp:218] Iteration 2148 (0.565554 iter/s, 21.2181s/12 iters), loss = 4.51113
I0408 15:54:06.828238 27193 solver.cpp:237]     Train net output #0: loss = 4.51113 (* 1 = 4.51113 loss)
I0408 15:54:06.828251 27193 sgd_solver.cpp:105] Iteration 2148, lr = 9.10321e-05
I0408 15:54:11.954946 27193 solver.cpp:218] Iteration 2160 (2.34076 iter/s, 5.12653s/12 iters), loss = 4.50419
I0408 15:54:11.955075 27193 solver.cpp:237]     Train net output #0: loss = 4.50419 (* 1 = 4.50419 loss)
I0408 15:54:11.955087 27193 sgd_solver.cpp:105] Iteration 2160, lr = 8.86734e-05
I0408 15:54:17.210353 27193 solver.cpp:218] Iteration 2172 (2.2835 iter/s, 5.25509s/12 iters), loss = 4.64512
I0408 15:54:17.210412 27193 solver.cpp:237]     Train net output #0: loss = 4.64512 (* 1 = 4.64512 loss)
I0408 15:54:17.210425 27193 sgd_solver.cpp:105] Iteration 2172, lr = 8.63758e-05
I0408 15:54:22.340440 27193 solver.cpp:218] Iteration 2184 (2.33925 iter/s, 5.12985s/12 iters), loss = 4.49609
I0408 15:54:22.340487 27193 solver.cpp:237]     Train net output #0: loss = 4.49609 (* 1 = 4.49609 loss)
I0408 15:54:22.340497 27193 sgd_solver.cpp:105] Iteration 2184, lr = 8.41377e-05
I0408 15:54:27.754024 27193 solver.cpp:218] Iteration 2196 (2.21674 iter/s, 5.41334s/12 iters), loss = 4.59461
I0408 15:54:27.754078 27193 solver.cpp:237]     Train net output #0: loss = 4.59461 (* 1 = 4.59461 loss)
I0408 15:54:27.754089 27193 sgd_solver.cpp:105] Iteration 2196, lr = 8.19577e-05
I0408 15:54:33.262693 27193 solver.cpp:218] Iteration 2208 (2.17848 iter/s, 5.50842s/12 iters), loss = 4.41699
I0408 15:54:33.262738 27193 solver.cpp:237]     Train net output #0: loss = 4.41699 (* 1 = 4.41699 loss)
I0408 15:54:33.262748 27193 sgd_solver.cpp:105] Iteration 2208, lr = 7.98341e-05
I0408 15:54:38.300498 27193 solver.cpp:218] Iteration 2220 (2.38209 iter/s, 5.03758s/12 iters), loss = 4.46272
I0408 15:54:38.300544 27193 solver.cpp:237]     Train net output #0: loss = 4.46272 (* 1 = 4.46272 loss)
I0408 15:54:38.300554 27193 sgd_solver.cpp:105] Iteration 2220, lr = 7.77656e-05
I0408 15:54:40.174654 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:54:43.484160 27193 solver.cpp:218] Iteration 2232 (2.31507 iter/s, 5.18343s/12 iters), loss = 4.69859
I0408 15:54:43.484279 27193 solver.cpp:237]     Train net output #0: loss = 4.69859 (* 1 = 4.69859 loss)
I0408 15:54:43.484292 27193 sgd_solver.cpp:105] Iteration 2232, lr = 7.57506e-05
I0408 15:54:48.111991 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0408 15:54:51.668383 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0408 15:54:59.280913 27193 solver.cpp:330] Iteration 2244, Testing net (#0)
I0408 15:54:59.280942 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:55:02.875727 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:55:03.787766 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0680147
I0408 15:55:03.787817 27193 solver.cpp:397]     Test net output #1: loss = 4.63221 (* 1 = 4.63221 loss)
I0408 15:55:03.879436 27193 solver.cpp:218] Iteration 2244 (0.588394 iter/s, 20.3945s/12 iters), loss = 4.66862
I0408 15:55:03.879519 27193 solver.cpp:237]     Train net output #0: loss = 4.66862 (* 1 = 4.66862 loss)
I0408 15:55:03.879536 27193 sgd_solver.cpp:105] Iteration 2244, lr = 7.37879e-05
I0408 15:55:08.137893 27193 solver.cpp:218] Iteration 2256 (2.81808 iter/s, 4.25822s/12 iters), loss = 4.3408
I0408 15:55:08.137966 27193 solver.cpp:237]     Train net output #0: loss = 4.3408 (* 1 = 4.3408 loss)
I0408 15:55:08.137979 27193 sgd_solver.cpp:105] Iteration 2256, lr = 7.1876e-05
I0408 15:55:13.108029 27193 solver.cpp:218] Iteration 2268 (2.41454 iter/s, 4.9699s/12 iters), loss = 4.46868
I0408 15:55:13.108088 27193 solver.cpp:237]     Train net output #0: loss = 4.46868 (* 1 = 4.46868 loss)
I0408 15:55:13.108098 27193 sgd_solver.cpp:105] Iteration 2268, lr = 7.00137e-05
I0408 15:55:18.139170 27193 solver.cpp:218] Iteration 2280 (2.38525 iter/s, 5.03091s/12 iters), loss = 4.58776
I0408 15:55:18.139328 27193 solver.cpp:237]     Train net output #0: loss = 4.58776 (* 1 = 4.58776 loss)
I0408 15:55:18.139339 27193 sgd_solver.cpp:105] Iteration 2280, lr = 6.81996e-05
I0408 15:55:23.098110 27193 solver.cpp:218] Iteration 2292 (2.42003 iter/s, 4.95861s/12 iters), loss = 4.60146
I0408 15:55:23.098173 27193 solver.cpp:237]     Train net output #0: loss = 4.60146 (* 1 = 4.60146 loss)
I0408 15:55:23.098186 27193 sgd_solver.cpp:105] Iteration 2292, lr = 6.64325e-05
I0408 15:55:28.113245 27193 solver.cpp:218] Iteration 2304 (2.39287 iter/s, 5.0149s/12 iters), loss = 4.58837
I0408 15:55:28.113307 27193 solver.cpp:237]     Train net output #0: loss = 4.58837 (* 1 = 4.58837 loss)
I0408 15:55:28.113319 27193 sgd_solver.cpp:105] Iteration 2304, lr = 6.47112e-05
I0408 15:55:33.133282 27193 solver.cpp:218] Iteration 2316 (2.39053 iter/s, 5.0198s/12 iters), loss = 4.43997
I0408 15:55:33.133338 27193 solver.cpp:237]     Train net output #0: loss = 4.43997 (* 1 = 4.43997 loss)
I0408 15:55:33.133349 27193 sgd_solver.cpp:105] Iteration 2316, lr = 6.30345e-05
I0408 15:55:37.064093 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:55:38.133177 27193 solver.cpp:218] Iteration 2328 (2.40016 iter/s, 4.99967s/12 iters), loss = 4.57533
I0408 15:55:38.133219 27193 solver.cpp:237]     Train net output #0: loss = 4.57533 (* 1 = 4.57533 loss)
I0408 15:55:38.133230 27193 sgd_solver.cpp:105] Iteration 2328, lr = 6.14012e-05
I0408 15:55:43.131928 27193 solver.cpp:218] Iteration 2340 (2.4007 iter/s, 4.99853s/12 iters), loss = 4.48649
I0408 15:55:43.131985 27193 solver.cpp:237]     Train net output #0: loss = 4.48649 (* 1 = 4.48649 loss)
I0408 15:55:43.131997 27193 sgd_solver.cpp:105] Iteration 2340, lr = 5.98103e-05
I0408 15:55:45.150964 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0408 15:55:48.329316 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0408 15:55:53.548435 27193 solver.cpp:330] Iteration 2346, Testing net (#0)
I0408 15:55:53.548461 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:55:57.097895 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:55:58.054467 27193 solver.cpp:397]     Test net output #0: accuracy = 0.067402
I0408 15:55:58.054507 27193 solver.cpp:397]     Test net output #1: loss = 4.62901 (* 1 = 4.62901 loss)
I0408 15:56:00.081681 27193 solver.cpp:218] Iteration 2352 (0.708 iter/s, 16.9491s/12 iters), loss = 4.53113
I0408 15:56:00.081727 27193 solver.cpp:237]     Train net output #0: loss = 4.53113 (* 1 = 4.53113 loss)
I0408 15:56:00.081738 27193 sgd_solver.cpp:105] Iteration 2352, lr = 5.82606e-05
I0408 15:56:05.539927 27193 solver.cpp:218] Iteration 2364 (2.1986 iter/s, 5.45802s/12 iters), loss = 4.56705
I0408 15:56:05.539963 27193 solver.cpp:237]     Train net output #0: loss = 4.56705 (* 1 = 4.56705 loss)
I0408 15:56:05.539973 27193 sgd_solver.cpp:105] Iteration 2364, lr = 5.6751e-05
I0408 15:56:11.075206 27193 solver.cpp:218] Iteration 2376 (2.168 iter/s, 5.53505s/12 iters), loss = 4.36672
I0408 15:56:11.075253 27193 solver.cpp:237]     Train net output #0: loss = 4.36672 (* 1 = 4.36672 loss)
I0408 15:56:11.075263 27193 sgd_solver.cpp:105] Iteration 2376, lr = 5.52806e-05
I0408 15:56:16.183120 27193 solver.cpp:218] Iteration 2388 (2.3494 iter/s, 5.10768s/12 iters), loss = 4.45477
I0408 15:56:16.183173 27193 solver.cpp:237]     Train net output #0: loss = 4.45477 (* 1 = 4.45477 loss)
I0408 15:56:16.183184 27193 sgd_solver.cpp:105] Iteration 2388, lr = 5.38482e-05
I0408 15:56:21.656038 27193 solver.cpp:218] Iteration 2400 (2.19271 iter/s, 5.47268s/12 iters), loss = 4.64418
I0408 15:56:21.656155 27193 solver.cpp:237]     Train net output #0: loss = 4.64418 (* 1 = 4.64418 loss)
I0408 15:56:21.656169 27193 sgd_solver.cpp:105] Iteration 2400, lr = 5.2453e-05
I0408 15:56:27.169358 27193 solver.cpp:218] Iteration 2412 (2.17667 iter/s, 5.51301s/12 iters), loss = 4.47164
I0408 15:56:27.169407 27193 solver.cpp:237]     Train net output #0: loss = 4.47164 (* 1 = 4.47164 loss)
I0408 15:56:27.169418 27193 sgd_solver.cpp:105] Iteration 2412, lr = 5.10939e-05
I0408 15:56:32.729707 27193 solver.cpp:218] Iteration 2424 (2.15823 iter/s, 5.56011s/12 iters), loss = 4.49166
I0408 15:56:32.729740 27193 solver.cpp:237]     Train net output #0: loss = 4.49166 (* 1 = 4.49166 loss)
I0408 15:56:32.729748 27193 sgd_solver.cpp:105] Iteration 2424, lr = 4.977e-05
I0408 15:56:33.910737 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:56:38.246556 27193 solver.cpp:218] Iteration 2436 (2.17524 iter/s, 5.51663s/12 iters), loss = 4.39025
I0408 15:56:38.246595 27193 solver.cpp:237]     Train net output #0: loss = 4.39025 (* 1 = 4.39025 loss)
I0408 15:56:38.246604 27193 sgd_solver.cpp:105] Iteration 2436, lr = 4.84805e-05
I0408 15:56:43.266127 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0408 15:56:47.400380 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0408 15:56:51.160475 27193 solver.cpp:330] Iteration 2448, Testing net (#0)
I0408 15:56:51.160497 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:56:54.576107 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:56:55.557735 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0698529
I0408 15:56:55.557785 27193 solver.cpp:397]     Test net output #1: loss = 4.61844 (* 1 = 4.61844 loss)
I0408 15:56:55.649422 27193 solver.cpp:218] Iteration 2448 (0.689566 iter/s, 17.4023s/12 iters), loss = 4.4699
I0408 15:56:55.649473 27193 solver.cpp:237]     Train net output #0: loss = 4.4699 (* 1 = 4.4699 loss)
I0408 15:56:55.649484 27193 sgd_solver.cpp:105] Iteration 2448, lr = 4.72243e-05
I0408 15:57:00.258088 27193 solver.cpp:218] Iteration 2460 (2.60391 iter/s, 4.60846s/12 iters), loss = 4.58725
I0408 15:57:00.258134 27193 solver.cpp:237]     Train net output #0: loss = 4.58725 (* 1 = 4.58725 loss)
I0408 15:57:00.258144 27193 sgd_solver.cpp:105] Iteration 2460, lr = 4.60007e-05
I0408 15:57:05.771492 27193 solver.cpp:218] Iteration 2472 (2.17661 iter/s, 5.51317s/12 iters), loss = 4.60548
I0408 15:57:05.771538 27193 solver.cpp:237]     Train net output #0: loss = 4.60548 (* 1 = 4.60548 loss)
I0408 15:57:05.771548 27193 sgd_solver.cpp:105] Iteration 2472, lr = 4.48088e-05
I0408 15:57:11.138346 27193 solver.cpp:218] Iteration 2484 (2.23604 iter/s, 5.36662s/12 iters), loss = 4.62771
I0408 15:57:11.138401 27193 solver.cpp:237]     Train net output #0: loss = 4.62771 (* 1 = 4.62771 loss)
I0408 15:57:11.138413 27193 sgd_solver.cpp:105] Iteration 2484, lr = 4.36478e-05
I0408 15:57:16.436349 27193 solver.cpp:218] Iteration 2496 (2.26511 iter/s, 5.29777s/12 iters), loss = 4.5318
I0408 15:57:16.436393 27193 solver.cpp:237]     Train net output #0: loss = 4.5318 (* 1 = 4.5318 loss)
I0408 15:57:16.436401 27193 sgd_solver.cpp:105] Iteration 2496, lr = 4.25168e-05
I0408 15:57:21.541993 27193 solver.cpp:218] Iteration 2508 (2.35044 iter/s, 5.10542s/12 iters), loss = 4.46752
I0408 15:57:21.542044 27193 solver.cpp:237]     Train net output #0: loss = 4.46752 (* 1 = 4.46752 loss)
I0408 15:57:21.542057 27193 sgd_solver.cpp:105] Iteration 2508, lr = 4.14152e-05
I0408 15:57:26.723942 27193 solver.cpp:218] Iteration 2520 (2.31583 iter/s, 5.18172s/12 iters), loss = 4.63666
I0408 15:57:26.725217 27193 solver.cpp:237]     Train net output #0: loss = 4.63666 (* 1 = 4.63666 loss)
I0408 15:57:26.725231 27193 sgd_solver.cpp:105] Iteration 2520, lr = 4.03421e-05
I0408 15:57:30.008441 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:57:31.985327 27193 solver.cpp:218] Iteration 2532 (2.2814 iter/s, 5.25993s/12 iters), loss = 4.67806
I0408 15:57:31.985368 27193 solver.cpp:237]     Train net output #0: loss = 4.67806 (* 1 = 4.67806 loss)
I0408 15:57:31.985378 27193 sgd_solver.cpp:105] Iteration 2532, lr = 3.92968e-05
I0408 15:57:37.518605 27193 solver.cpp:218] Iteration 2544 (2.16879 iter/s, 5.53304s/12 iters), loss = 4.59243
I0408 15:57:37.518657 27193 solver.cpp:237]     Train net output #0: loss = 4.59243 (* 1 = 4.59243 loss)
I0408 15:57:37.518668 27193 sgd_solver.cpp:105] Iteration 2544, lr = 3.82786e-05
I0408 15:57:39.702343 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0408 15:57:42.707698 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0408 15:57:46.585103 27193 solver.cpp:330] Iteration 2550, Testing net (#0)
I0408 15:57:46.585129 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:57:50.058784 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:57:51.083776 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0716912
I0408 15:57:51.083827 27193 solver.cpp:397]     Test net output #1: loss = 4.61775 (* 1 = 4.61775 loss)
I0408 15:57:52.933224 27193 solver.cpp:218] Iteration 2556 (0.77851 iter/s, 15.4141s/12 iters), loss = 4.5286
I0408 15:57:52.933275 27193 solver.cpp:237]     Train net output #0: loss = 4.5286 (* 1 = 4.5286 loss)
I0408 15:57:52.933287 27193 sgd_solver.cpp:105] Iteration 2556, lr = 3.72868e-05
I0408 15:57:58.010155 27193 solver.cpp:218] Iteration 2568 (2.36374 iter/s, 5.0767s/12 iters), loss = 4.42827
I0408 15:57:58.014354 27193 solver.cpp:237]     Train net output #0: loss = 4.42827 (* 1 = 4.42827 loss)
I0408 15:57:58.014366 27193 sgd_solver.cpp:105] Iteration 2568, lr = 3.63207e-05
I0408 15:58:03.172623 27193 solver.cpp:218] Iteration 2580 (2.32644 iter/s, 5.15809s/12 iters), loss = 4.56468
I0408 15:58:03.172673 27193 solver.cpp:237]     Train net output #0: loss = 4.56468 (* 1 = 4.56468 loss)
I0408 15:58:03.172685 27193 sgd_solver.cpp:105] Iteration 2580, lr = 3.53796e-05
I0408 15:58:08.359928 27193 solver.cpp:218] Iteration 2592 (2.31344 iter/s, 5.18708s/12 iters), loss = 4.55433
I0408 15:58:08.359975 27193 solver.cpp:237]     Train net output #0: loss = 4.55433 (* 1 = 4.55433 loss)
I0408 15:58:08.359987 27193 sgd_solver.cpp:105] Iteration 2592, lr = 3.44629e-05
I0408 15:58:13.517191 27193 solver.cpp:218] Iteration 2604 (2.32692 iter/s, 5.15704s/12 iters), loss = 4.51419
I0408 15:58:13.517242 27193 solver.cpp:237]     Train net output #0: loss = 4.51419 (* 1 = 4.51419 loss)
I0408 15:58:13.517256 27193 sgd_solver.cpp:105] Iteration 2604, lr = 3.35699e-05
I0408 15:58:18.660750 27193 solver.cpp:218] Iteration 2616 (2.33312 iter/s, 5.14333s/12 iters), loss = 4.68027
I0408 15:58:18.660799 27193 solver.cpp:237]     Train net output #0: loss = 4.68027 (* 1 = 4.68027 loss)
I0408 15:58:18.660810 27193 sgd_solver.cpp:105] Iteration 2616, lr = 3.27001e-05
I0408 15:58:23.808667 27193 solver.cpp:218] Iteration 2628 (2.33114 iter/s, 5.1477s/12 iters), loss = 4.5818
I0408 15:58:23.808709 27193 solver.cpp:237]     Train net output #0: loss = 4.5818 (* 1 = 4.5818 loss)
I0408 15:58:23.808719 27193 sgd_solver.cpp:105] Iteration 2628, lr = 3.18529e-05
I0408 15:58:24.268523 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:58:29.010694 27193 solver.cpp:218] Iteration 2640 (2.30689 iter/s, 5.2018s/12 iters), loss = 4.3523
I0408 15:58:29.011826 27193 solver.cpp:237]     Train net output #0: loss = 4.3523 (* 1 = 4.3523 loss)
I0408 15:58:29.011840 27193 sgd_solver.cpp:105] Iteration 2640, lr = 3.10275e-05
I0408 15:58:33.718104 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0408 15:58:37.075052 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0408 15:58:40.686602 27193 solver.cpp:330] Iteration 2652, Testing net (#0)
I0408 15:58:40.686623 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:58:44.192899 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:58:45.274612 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0698529
I0408 15:58:45.274658 27193 solver.cpp:397]     Test net output #1: loss = 4.62028 (* 1 = 4.62028 loss)
I0408 15:58:45.363441 27193 solver.cpp:218] Iteration 2652 (0.733896 iter/s, 16.3511s/12 iters), loss = 4.49603
I0408 15:58:45.363488 27193 solver.cpp:237]     Train net output #0: loss = 4.49603 (* 1 = 4.49603 loss)
I0408 15:58:45.363500 27193 sgd_solver.cpp:105] Iteration 2652, lr = 3.02236e-05
I0408 15:58:49.930785 27193 solver.cpp:218] Iteration 2664 (2.62747 iter/s, 4.56713s/12 iters), loss = 4.47369
I0408 15:58:49.930836 27193 solver.cpp:237]     Train net output #0: loss = 4.47369 (* 1 = 4.47369 loss)
I0408 15:58:49.930850 27193 sgd_solver.cpp:105] Iteration 2664, lr = 2.94405e-05
I0408 15:58:55.044236 27193 solver.cpp:218] Iteration 2676 (2.34686 iter/s, 5.11322s/12 iters), loss = 4.48652
I0408 15:58:55.044286 27193 solver.cpp:237]     Train net output #0: loss = 4.48652 (* 1 = 4.48652 loss)
I0408 15:58:55.044297 27193 sgd_solver.cpp:105] Iteration 2676, lr = 2.86777e-05
I0408 15:59:00.192853 27193 solver.cpp:218] Iteration 2688 (2.33083 iter/s, 5.14839s/12 iters), loss = 4.45438
I0408 15:59:00.193018 27193 solver.cpp:237]     Train net output #0: loss = 4.45438 (* 1 = 4.45438 loss)
I0408 15:59:00.193032 27193 sgd_solver.cpp:105] Iteration 2688, lr = 2.79346e-05
I0408 15:59:05.347012 27193 solver.cpp:218] Iteration 2700 (2.32837 iter/s, 5.15382s/12 iters), loss = 4.39678
I0408 15:59:05.347056 27193 solver.cpp:237]     Train net output #0: loss = 4.39678 (* 1 = 4.39678 loss)
I0408 15:59:05.347066 27193 sgd_solver.cpp:105] Iteration 2700, lr = 2.72108e-05
I0408 15:59:10.453608 27193 solver.cpp:218] Iteration 2712 (2.35 iter/s, 5.10638s/12 iters), loss = 4.41835
I0408 15:59:10.453655 27193 solver.cpp:237]     Train net output #0: loss = 4.41835 (* 1 = 4.41835 loss)
I0408 15:59:10.453666 27193 sgd_solver.cpp:105] Iteration 2712, lr = 2.65058e-05
I0408 15:59:15.649228 27193 solver.cpp:218] Iteration 2724 (2.30974 iter/s, 5.19539s/12 iters), loss = 4.49914
I0408 15:59:15.649278 27193 solver.cpp:237]     Train net output #0: loss = 4.49914 (* 1 = 4.49914 loss)
I0408 15:59:15.649291 27193 sgd_solver.cpp:105] Iteration 2724, lr = 2.5819e-05
I0408 15:59:18.290072 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:59:20.798949 27193 solver.cpp:218] Iteration 2736 (2.33033 iter/s, 5.1495s/12 iters), loss = 4.47208
I0408 15:59:20.798993 27193 solver.cpp:237]     Train net output #0: loss = 4.47208 (* 1 = 4.47208 loss)
I0408 15:59:20.799003 27193 sgd_solver.cpp:105] Iteration 2736, lr = 2.515e-05
I0408 15:59:25.840209 27193 solver.cpp:218] Iteration 2748 (2.38046 iter/s, 5.04105s/12 iters), loss = 4.38839
I0408 15:59:25.840253 27193 solver.cpp:237]     Train net output #0: loss = 4.38839 (* 1 = 4.38839 loss)
I0408 15:59:25.840263 27193 sgd_solver.cpp:105] Iteration 2748, lr = 2.44984e-05
I0408 15:59:27.929316 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0408 15:59:32.628628 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0408 15:59:36.502288 27193 solver.cpp:330] Iteration 2754, Testing net (#0)
I0408 15:59:36.502315 27193 net.cpp:676] Ignoring source layer train-data
I0408 15:59:39.619326 27193 blocking_queue.cpp:49] Waiting for data
I0408 15:59:39.855134 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 15:59:40.961629 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0710784
I0408 15:59:40.961676 27193 solver.cpp:397]     Test net output #1: loss = 4.6191 (* 1 = 4.6191 loss)
I0408 15:59:42.893980 27193 solver.cpp:218] Iteration 2760 (0.703682 iter/s, 17.0532s/12 iters), loss = 4.42487
I0408 15:59:42.894024 27193 solver.cpp:237]     Train net output #0: loss = 4.42487 (* 1 = 4.42487 loss)
I0408 15:59:42.894035 27193 sgd_solver.cpp:105] Iteration 2760, lr = 2.38636e-05
I0408 15:59:48.045447 27193 solver.cpp:218] Iteration 2772 (2.32953 iter/s, 5.15125s/12 iters), loss = 4.54997
I0408 15:59:48.045485 27193 solver.cpp:237]     Train net output #0: loss = 4.54997 (* 1 = 4.54997 loss)
I0408 15:59:48.045495 27193 sgd_solver.cpp:105] Iteration 2772, lr = 2.32453e-05
I0408 15:59:53.321768 27193 solver.cpp:218] Iteration 2784 (2.27441 iter/s, 5.27609s/12 iters), loss = 4.5998
I0408 15:59:53.321828 27193 solver.cpp:237]     Train net output #0: loss = 4.5998 (* 1 = 4.5998 loss)
I0408 15:59:53.321841 27193 sgd_solver.cpp:105] Iteration 2784, lr = 2.2643e-05
I0408 15:59:58.841008 27193 solver.cpp:218] Iteration 2796 (2.17431 iter/s, 5.51899s/12 iters), loss = 4.36922
I0408 15:59:58.841056 27193 solver.cpp:237]     Train net output #0: loss = 4.36922 (* 1 = 4.36922 loss)
I0408 15:59:58.841068 27193 sgd_solver.cpp:105] Iteration 2796, lr = 2.20563e-05
I0408 16:00:04.330860 27193 solver.cpp:218] Iteration 2808 (2.18595 iter/s, 5.48962s/12 iters), loss = 4.40913
I0408 16:00:04.330984 27193 solver.cpp:237]     Train net output #0: loss = 4.40913 (* 1 = 4.40913 loss)
I0408 16:00:04.330996 27193 sgd_solver.cpp:105] Iteration 2808, lr = 2.14848e-05
I0408 16:00:09.430655 27193 solver.cpp:218] Iteration 2820 (2.35317 iter/s, 5.0995s/12 iters), loss = 4.50415
I0408 16:00:09.430699 27193 solver.cpp:237]     Train net output #0: loss = 4.50415 (* 1 = 4.50415 loss)
I0408 16:00:09.430711 27193 sgd_solver.cpp:105] Iteration 2820, lr = 2.09281e-05
I0408 16:00:14.329457 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:00:14.645288 27193 solver.cpp:218] Iteration 2832 (2.30131 iter/s, 5.21441s/12 iters), loss = 4.46083
I0408 16:00:14.645335 27193 solver.cpp:237]     Train net output #0: loss = 4.46083 (* 1 = 4.46083 loss)
I0408 16:00:14.645347 27193 sgd_solver.cpp:105] Iteration 2832, lr = 2.03859e-05
I0408 16:00:20.183917 27193 solver.cpp:218] Iteration 2844 (2.1667 iter/s, 5.53839s/12 iters), loss = 4.72791
I0408 16:00:20.183971 27193 solver.cpp:237]     Train net output #0: loss = 4.72791 (* 1 = 4.72791 loss)
I0408 16:00:20.183984 27193 sgd_solver.cpp:105] Iteration 2844, lr = 1.98576e-05
I0408 16:00:24.997403 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0408 16:00:29.390904 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0408 16:00:31.981942 27193 solver.cpp:330] Iteration 2856, Testing net (#0)
I0408 16:00:31.981984 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:00:35.225565 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:00:36.366130 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:00:36.366178 27193 solver.cpp:397]     Test net output #1: loss = 4.61721 (* 1 = 4.61721 loss)
I0408 16:00:36.457762 27193 solver.cpp:218] Iteration 2856 (0.737406 iter/s, 16.2733s/12 iters), loss = 4.35286
I0408 16:00:36.457813 27193 solver.cpp:237]     Train net output #0: loss = 4.35286 (* 1 = 4.35286 loss)
I0408 16:00:36.457826 27193 sgd_solver.cpp:105] Iteration 2856, lr = 1.93431e-05
I0408 16:00:41.033866 27193 solver.cpp:218] Iteration 2868 (2.62244 iter/s, 4.57589s/12 iters), loss = 4.46628
I0408 16:00:41.033913 27193 solver.cpp:237]     Train net output #0: loss = 4.46628 (* 1 = 4.46628 loss)
I0408 16:00:41.033926 27193 sgd_solver.cpp:105] Iteration 2868, lr = 1.88419e-05
I0408 16:00:46.558071 27193 solver.cpp:218] Iteration 2880 (2.17235 iter/s, 5.52397s/12 iters), loss = 4.55804
I0408 16:00:46.558118 27193 solver.cpp:237]     Train net output #0: loss = 4.55804 (* 1 = 4.55804 loss)
I0408 16:00:46.558131 27193 sgd_solver.cpp:105] Iteration 2880, lr = 1.83537e-05
I0408 16:00:51.728674 27193 solver.cpp:218] Iteration 2892 (2.32092 iter/s, 5.17037s/12 iters), loss = 4.45739
I0408 16:00:51.728726 27193 solver.cpp:237]     Train net output #0: loss = 4.45739 (* 1 = 4.45739 loss)
I0408 16:00:51.728739 27193 sgd_solver.cpp:105] Iteration 2892, lr = 1.78782e-05
I0408 16:00:56.867337 27193 solver.cpp:218] Iteration 2904 (2.33534 iter/s, 5.13844s/12 iters), loss = 4.57323
I0408 16:00:56.867383 27193 solver.cpp:237]     Train net output #0: loss = 4.57323 (* 1 = 4.57323 loss)
I0408 16:00:56.867393 27193 sgd_solver.cpp:105] Iteration 2904, lr = 1.74149e-05
I0408 16:01:01.826628 27193 solver.cpp:218] Iteration 2916 (2.41981 iter/s, 4.95907s/12 iters), loss = 4.41924
I0408 16:01:01.826671 27193 solver.cpp:237]     Train net output #0: loss = 4.41924 (* 1 = 4.41924 loss)
I0408 16:01:01.826680 27193 sgd_solver.cpp:105] Iteration 2916, lr = 1.69637e-05
I0408 16:01:06.943243 27193 solver.cpp:218] Iteration 2928 (2.3454 iter/s, 5.1164s/12 iters), loss = 4.52532
I0408 16:01:06.943341 27193 solver.cpp:237]     Train net output #0: loss = 4.52532 (* 1 = 4.52532 loss)
I0408 16:01:06.943349 27193 sgd_solver.cpp:105] Iteration 2928, lr = 1.65242e-05
I0408 16:01:08.821157 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:01:12.063153 27193 solver.cpp:218] Iteration 2940 (2.34392 iter/s, 5.11963s/12 iters), loss = 4.5656
I0408 16:01:12.063195 27193 solver.cpp:237]     Train net output #0: loss = 4.5656 (* 1 = 4.5656 loss)
I0408 16:01:12.063205 27193 sgd_solver.cpp:105] Iteration 2940, lr = 1.6096e-05
I0408 16:01:17.142968 27193 solver.cpp:218] Iteration 2952 (2.36239 iter/s, 5.0796s/12 iters), loss = 4.52522
I0408 16:01:17.143018 27193 solver.cpp:237]     Train net output #0: loss = 4.52522 (* 1 = 4.52522 loss)
I0408 16:01:17.143029 27193 sgd_solver.cpp:105] Iteration 2952, lr = 1.5679e-05
I0408 16:01:19.188431 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0408 16:01:23.795394 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0408 16:01:26.128083 27193 solver.cpp:330] Iteration 2958, Testing net (#0)
I0408 16:01:26.128109 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:01:29.780649 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:01:30.988524 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0710784
I0408 16:01:30.988569 27193 solver.cpp:397]     Test net output #1: loss = 4.60722 (* 1 = 4.60722 loss)
I0408 16:01:32.816083 27193 solver.cpp:218] Iteration 2964 (0.76567 iter/s, 15.6725s/12 iters), loss = 4.30217
I0408 16:01:32.816148 27193 solver.cpp:237]     Train net output #0: loss = 4.30217 (* 1 = 4.30217 loss)
I0408 16:01:32.816160 27193 sgd_solver.cpp:105] Iteration 2964, lr = 1.52727e-05
I0408 16:01:38.094417 27193 solver.cpp:218] Iteration 2976 (2.27355 iter/s, 5.27808s/12 iters), loss = 4.47194
I0408 16:01:38.094532 27193 solver.cpp:237]     Train net output #0: loss = 4.47194 (* 1 = 4.47194 loss)
I0408 16:01:38.094543 27193 sgd_solver.cpp:105] Iteration 2976, lr = 1.4877e-05
I0408 16:01:43.166838 27193 solver.cpp:218] Iteration 2988 (2.36587 iter/s, 5.07214s/12 iters), loss = 4.69512
I0408 16:01:43.166887 27193 solver.cpp:237]     Train net output #0: loss = 4.69512 (* 1 = 4.69512 loss)
I0408 16:01:43.166898 27193 sgd_solver.cpp:105] Iteration 2988, lr = 1.44915e-05
I0408 16:01:48.329596 27193 solver.cpp:218] Iteration 3000 (2.32444 iter/s, 5.16253s/12 iters), loss = 4.48092
I0408 16:01:48.329640 27193 solver.cpp:237]     Train net output #0: loss = 4.48092 (* 1 = 4.48092 loss)
I0408 16:01:48.329650 27193 sgd_solver.cpp:105] Iteration 3000, lr = 1.4116e-05
I0408 16:01:53.667162 27193 solver.cpp:218] Iteration 3012 (2.24831 iter/s, 5.33734s/12 iters), loss = 4.48532
I0408 16:01:53.667207 27193 solver.cpp:237]     Train net output #0: loss = 4.48532 (* 1 = 4.48532 loss)
I0408 16:01:53.667215 27193 sgd_solver.cpp:105] Iteration 3012, lr = 1.37503e-05
I0408 16:01:58.808161 27193 solver.cpp:218] Iteration 3024 (2.33428 iter/s, 5.14077s/12 iters), loss = 4.34497
I0408 16:01:58.808218 27193 solver.cpp:237]     Train net output #0: loss = 4.34497 (* 1 = 4.34497 loss)
I0408 16:01:58.808228 27193 sgd_solver.cpp:105] Iteration 3024, lr = 1.3394e-05
I0408 16:02:02.917728 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:02:03.935660 27193 solver.cpp:218] Iteration 3036 (2.34043 iter/s, 5.12726s/12 iters), loss = 4.51934
I0408 16:02:03.935714 27193 solver.cpp:237]     Train net output #0: loss = 4.51934 (* 1 = 4.51934 loss)
I0408 16:02:03.935725 27193 sgd_solver.cpp:105] Iteration 3036, lr = 1.3047e-05
I0408 16:02:08.866912 27193 solver.cpp:218] Iteration 3048 (2.43357 iter/s, 4.93103s/12 iters), loss = 4.56579
I0408 16:02:08.867031 27193 solver.cpp:237]     Train net output #0: loss = 4.56579 (* 1 = 4.56579 loss)
I0408 16:02:08.867044 27193 sgd_solver.cpp:105] Iteration 3048, lr = 1.27089e-05
I0408 16:02:13.479039 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0408 16:02:18.918154 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0408 16:02:21.372462 27193 solver.cpp:330] Iteration 3060, Testing net (#0)
I0408 16:02:21.372488 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:02:24.667529 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:02:25.885886 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:02:25.885931 27193 solver.cpp:397]     Test net output #1: loss = 4.60259 (* 1 = 4.60259 loss)
I0408 16:02:25.977236 27193 solver.cpp:218] Iteration 3060 (0.701359 iter/s, 17.1096s/12 iters), loss = 4.53374
I0408 16:02:25.977286 27193 solver.cpp:237]     Train net output #0: loss = 4.53374 (* 1 = 4.53374 loss)
I0408 16:02:25.977298 27193 sgd_solver.cpp:105] Iteration 3060, lr = 1.23796e-05
I0408 16:02:30.090097 27193 solver.cpp:218] Iteration 3072 (2.91781 iter/s, 4.11267s/12 iters), loss = 4.47386
I0408 16:02:30.090147 27193 solver.cpp:237]     Train net output #0: loss = 4.47386 (* 1 = 4.47386 loss)
I0408 16:02:30.090159 27193 sgd_solver.cpp:105] Iteration 3072, lr = 1.20588e-05
I0408 16:02:35.233912 27193 solver.cpp:218] Iteration 3084 (2.333 iter/s, 5.14359s/12 iters), loss = 4.34963
I0408 16:02:35.233973 27193 solver.cpp:237]     Train net output #0: loss = 4.34963 (* 1 = 4.34963 loss)
I0408 16:02:35.233985 27193 sgd_solver.cpp:105] Iteration 3084, lr = 1.17464e-05
I0408 16:02:40.095969 27193 solver.cpp:218] Iteration 3096 (2.4682 iter/s, 4.86184s/12 iters), loss = 4.51555
I0408 16:02:40.096108 27193 solver.cpp:237]     Train net output #0: loss = 4.51555 (* 1 = 4.51555 loss)
I0408 16:02:40.096122 27193 sgd_solver.cpp:105] Iteration 3096, lr = 1.1442e-05
I0408 16:02:45.110456 27193 solver.cpp:218] Iteration 3108 (2.39321 iter/s, 5.01418s/12 iters), loss = 4.58048
I0408 16:02:45.110504 27193 solver.cpp:237]     Train net output #0: loss = 4.58048 (* 1 = 4.58048 loss)
I0408 16:02:45.110517 27193 sgd_solver.cpp:105] Iteration 3108, lr = 1.11456e-05
I0408 16:02:50.537937 27193 solver.cpp:218] Iteration 3120 (2.21107 iter/s, 5.42725s/12 iters), loss = 4.44124
I0408 16:02:50.537990 27193 solver.cpp:237]     Train net output #0: loss = 4.44124 (* 1 = 4.44124 loss)
I0408 16:02:50.538005 27193 sgd_solver.cpp:105] Iteration 3120, lr = 1.08568e-05
I0408 16:02:55.693228 27193 solver.cpp:218] Iteration 3132 (2.32781 iter/s, 5.15506s/12 iters), loss = 4.54115
I0408 16:02:55.693272 27193 solver.cpp:237]     Train net output #0: loss = 4.54115 (* 1 = 4.54115 loss)
I0408 16:02:55.693281 27193 sgd_solver.cpp:105] Iteration 3132, lr = 1.05755e-05
I0408 16:02:56.823297 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:03:00.872664 27193 solver.cpp:218] Iteration 3144 (2.31695 iter/s, 5.17921s/12 iters), loss = 4.42964
I0408 16:03:00.872711 27193 solver.cpp:237]     Train net output #0: loss = 4.42964 (* 1 = 4.42964 loss)
I0408 16:03:00.872723 27193 sgd_solver.cpp:105] Iteration 3144, lr = 1.03015e-05
I0408 16:03:06.000718 27193 solver.cpp:218] Iteration 3156 (2.34018 iter/s, 5.12782s/12 iters), loss = 4.49021
I0408 16:03:06.000778 27193 solver.cpp:237]     Train net output #0: loss = 4.49021 (* 1 = 4.49021 loss)
I0408 16:03:06.000795 27193 sgd_solver.cpp:105] Iteration 3156, lr = 1.00345e-05
I0408 16:03:08.036056 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0408 16:03:13.189078 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0408 16:03:15.670692 27193 solver.cpp:330] Iteration 3162, Testing net (#0)
I0408 16:03:15.670719 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:03:18.886529 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:03:20.153424 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:03:20.153470 27193 solver.cpp:397]     Test net output #1: loss = 4.6049 (* 1 = 4.6049 loss)
I0408 16:03:22.015756 27193 solver.cpp:218] Iteration 3168 (0.749323 iter/s, 16.0145s/12 iters), loss = 4.60485
I0408 16:03:22.015806 27193 solver.cpp:237]     Train net output #0: loss = 4.60485 (* 1 = 4.60485 loss)
I0408 16:03:22.015817 27193 sgd_solver.cpp:105] Iteration 3168, lr = 9.77455e-06
I0408 16:03:27.201548 27193 solver.cpp:218] Iteration 3180 (2.31412 iter/s, 5.18556s/12 iters), loss = 4.60954
I0408 16:03:27.201607 27193 solver.cpp:237]     Train net output #0: loss = 4.60954 (* 1 = 4.60954 loss)
I0408 16:03:27.201619 27193 sgd_solver.cpp:105] Iteration 3180, lr = 9.52128e-06
I0408 16:03:32.370584 27193 solver.cpp:218] Iteration 3192 (2.32162 iter/s, 5.1688s/12 iters), loss = 4.497
I0408 16:03:32.370633 27193 solver.cpp:237]     Train net output #0: loss = 4.497 (* 1 = 4.497 loss)
I0408 16:03:32.370644 27193 sgd_solver.cpp:105] Iteration 3192, lr = 9.27458e-06
I0408 16:03:37.397109 27193 solver.cpp:218] Iteration 3204 (2.38744 iter/s, 5.0263s/12 iters), loss = 4.45239
I0408 16:03:37.397167 27193 solver.cpp:237]     Train net output #0: loss = 4.45239 (* 1 = 4.45239 loss)
I0408 16:03:37.397179 27193 sgd_solver.cpp:105] Iteration 3204, lr = 9.03427e-06
I0408 16:03:42.488557 27193 solver.cpp:218] Iteration 3216 (2.357 iter/s, 5.09122s/12 iters), loss = 4.46671
I0408 16:03:42.488605 27193 solver.cpp:237]     Train net output #0: loss = 4.46671 (* 1 = 4.46671 loss)
I0408 16:03:42.488615 27193 sgd_solver.cpp:105] Iteration 3216, lr = 8.80019e-06
I0408 16:03:47.629107 27193 solver.cpp:218] Iteration 3228 (2.33448 iter/s, 5.14032s/12 iters), loss = 4.61983
I0408 16:03:47.629226 27193 solver.cpp:237]     Train net output #0: loss = 4.61983 (* 1 = 4.61983 loss)
I0408 16:03:47.629237 27193 sgd_solver.cpp:105] Iteration 3228, lr = 8.57217e-06
I0408 16:03:50.921994 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:03:52.687582 27193 solver.cpp:218] Iteration 3240 (2.37239 iter/s, 5.05818s/12 iters), loss = 4.57707
I0408 16:03:52.687628 27193 solver.cpp:237]     Train net output #0: loss = 4.57707 (* 1 = 4.57707 loss)
I0408 16:03:52.687638 27193 sgd_solver.cpp:105] Iteration 3240, lr = 8.35006e-06
I0408 16:03:57.816572 27193 solver.cpp:218] Iteration 3252 (2.33975 iter/s, 5.12876s/12 iters), loss = 4.47189
I0408 16:03:57.816639 27193 solver.cpp:237]     Train net output #0: loss = 4.47189 (* 1 = 4.47189 loss)
I0408 16:03:57.816654 27193 sgd_solver.cpp:105] Iteration 3252, lr = 8.13371e-06
I0408 16:04:02.326437 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0408 16:04:06.846683 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0408 16:04:10.026147 27193 solver.cpp:330] Iteration 3264, Testing net (#0)
I0408 16:04:10.026171 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:04:13.248050 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:04:14.552932 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:04:14.552981 27193 solver.cpp:397]     Test net output #1: loss = 4.607 (* 1 = 4.607 loss)
I0408 16:04:14.644575 27193 solver.cpp:218] Iteration 3264 (0.713123 iter/s, 16.8274s/12 iters), loss = 4.59486
I0408 16:04:14.644629 27193 solver.cpp:237]     Train net output #0: loss = 4.59486 (* 1 = 4.59486 loss)
I0408 16:04:14.644640 27193 sgd_solver.cpp:105] Iteration 3264, lr = 7.92296e-06
I0408 16:04:18.824215 27193 solver.cpp:218] Iteration 3276 (2.8712 iter/s, 4.17944s/12 iters), loss = 4.42731
I0408 16:04:18.824322 27193 solver.cpp:237]     Train net output #0: loss = 4.42731 (* 1 = 4.42731 loss)
I0408 16:04:18.824335 27193 sgd_solver.cpp:105] Iteration 3276, lr = 7.71767e-06
I0408 16:04:23.911916 27193 solver.cpp:218] Iteration 3288 (2.35876 iter/s, 5.08742s/12 iters), loss = 4.45494
I0408 16:04:23.911953 27193 solver.cpp:237]     Train net output #0: loss = 4.45494 (* 1 = 4.45494 loss)
I0408 16:04:23.911962 27193 sgd_solver.cpp:105] Iteration 3288, lr = 7.5177e-06
I0408 16:04:28.937997 27193 solver.cpp:218] Iteration 3300 (2.38765 iter/s, 5.02587s/12 iters), loss = 4.45477
I0408 16:04:28.938045 27193 solver.cpp:237]     Train net output #0: loss = 4.45477 (* 1 = 4.45477 loss)
I0408 16:04:28.938056 27193 sgd_solver.cpp:105] Iteration 3300, lr = 7.32292e-06
I0408 16:04:34.146591 27193 solver.cpp:218] Iteration 3312 (2.30399 iter/s, 5.20836s/12 iters), loss = 4.4926
I0408 16:04:34.146651 27193 solver.cpp:237]     Train net output #0: loss = 4.4926 (* 1 = 4.4926 loss)
I0408 16:04:34.146662 27193 sgd_solver.cpp:105] Iteration 3312, lr = 7.13317e-06
I0408 16:04:39.278342 27193 solver.cpp:218] Iteration 3324 (2.33849 iter/s, 5.13151s/12 iters), loss = 4.55513
I0408 16:04:39.278396 27193 solver.cpp:237]     Train net output #0: loss = 4.55513 (* 1 = 4.55513 loss)
I0408 16:04:39.278407 27193 sgd_solver.cpp:105] Iteration 3324, lr = 6.94835e-06
I0408 16:04:44.428896 27193 solver.cpp:218] Iteration 3336 (2.32995 iter/s, 5.15032s/12 iters), loss = 4.58934
I0408 16:04:44.428941 27193 solver.cpp:237]     Train net output #0: loss = 4.58934 (* 1 = 4.58934 loss)
I0408 16:04:44.428952 27193 sgd_solver.cpp:105] Iteration 3336, lr = 6.76831e-06
I0408 16:04:44.902462 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:04:49.496199 27193 solver.cpp:218] Iteration 3348 (2.36823 iter/s, 5.06708s/12 iters), loss = 4.41275
I0408 16:04:49.496342 27193 solver.cpp:237]     Train net output #0: loss = 4.41275 (* 1 = 4.41275 loss)
I0408 16:04:49.496354 27193 sgd_solver.cpp:105] Iteration 3348, lr = 6.59294e-06
I0408 16:04:54.598798 27193 solver.cpp:218] Iteration 3360 (2.35189 iter/s, 5.10229s/12 iters), loss = 4.61212
I0408 16:04:54.598834 27193 solver.cpp:237]     Train net output #0: loss = 4.61212 (* 1 = 4.61212 loss)
I0408 16:04:54.598843 27193 sgd_solver.cpp:105] Iteration 3360, lr = 6.42212e-06
I0408 16:04:56.860031 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0408 16:05:00.166312 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0408 16:05:02.493292 27193 solver.cpp:330] Iteration 3366, Testing net (#0)
I0408 16:05:02.493319 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:05:05.571944 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:05:06.915151 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:05:06.915199 27193 solver.cpp:397]     Test net output #1: loss = 4.60741 (* 1 = 4.60741 loss)
I0408 16:05:08.821856 27193 solver.cpp:218] Iteration 3372 (0.843731 iter/s, 14.2225s/12 iters), loss = 4.45404
I0408 16:05:08.821903 27193 solver.cpp:237]     Train net output #0: loss = 4.45404 (* 1 = 4.45404 loss)
I0408 16:05:08.821913 27193 sgd_solver.cpp:105] Iteration 3372, lr = 6.25572e-06
I0408 16:05:13.883669 27193 solver.cpp:218] Iteration 3384 (2.3708 iter/s, 5.06158s/12 iters), loss = 4.38915
I0408 16:05:13.883723 27193 solver.cpp:237]     Train net output #0: loss = 4.38915 (* 1 = 4.38915 loss)
I0408 16:05:13.883734 27193 sgd_solver.cpp:105] Iteration 3384, lr = 6.09363e-06
I0408 16:05:18.898555 27193 solver.cpp:218] Iteration 3396 (2.39299 iter/s, 5.01466s/12 iters), loss = 4.48002
I0408 16:05:18.898600 27193 solver.cpp:237]     Train net output #0: loss = 4.48002 (* 1 = 4.48002 loss)
I0408 16:05:18.898610 27193 sgd_solver.cpp:105] Iteration 3396, lr = 5.93574e-06
I0408 16:05:24.017359 27193 solver.cpp:218] Iteration 3408 (2.3444 iter/s, 5.11858s/12 iters), loss = 4.52419
I0408 16:05:24.017457 27193 solver.cpp:237]     Train net output #0: loss = 4.52419 (* 1 = 4.52419 loss)
I0408 16:05:24.017467 27193 sgd_solver.cpp:105] Iteration 3408, lr = 5.78194e-06
I0408 16:05:29.212039 27193 solver.cpp:218] Iteration 3420 (2.31018 iter/s, 5.1944s/12 iters), loss = 4.35939
I0408 16:05:29.212088 27193 solver.cpp:237]     Train net output #0: loss = 4.35939 (* 1 = 4.35939 loss)
I0408 16:05:29.212101 27193 sgd_solver.cpp:105] Iteration 3420, lr = 5.63213e-06
I0408 16:05:34.657989 27193 solver.cpp:218] Iteration 3432 (2.20357 iter/s, 5.44571s/12 iters), loss = 4.47477
I0408 16:05:34.658035 27193 solver.cpp:237]     Train net output #0: loss = 4.47477 (* 1 = 4.47477 loss)
I0408 16:05:34.658043 27193 sgd_solver.cpp:105] Iteration 3432, lr = 5.4862e-06
I0408 16:05:37.413919 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:05:39.790796 27193 solver.cpp:218] Iteration 3444 (2.338 iter/s, 5.13258s/12 iters), loss = 4.38901
I0408 16:05:39.790838 27193 solver.cpp:237]     Train net output #0: loss = 4.38901 (* 1 = 4.38901 loss)
I0408 16:05:39.790848 27193 sgd_solver.cpp:105] Iteration 3444, lr = 5.34405e-06
I0408 16:05:44.931720 27193 solver.cpp:218] Iteration 3456 (2.33431 iter/s, 5.1407s/12 iters), loss = 4.33927
I0408 16:05:44.931769 27193 solver.cpp:237]     Train net output #0: loss = 4.33927 (* 1 = 4.33927 loss)
I0408 16:05:44.931779 27193 sgd_solver.cpp:105] Iteration 3456, lr = 5.20558e-06
I0408 16:05:49.607254 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0408 16:05:52.641436 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0408 16:05:54.971338 27193 solver.cpp:330] Iteration 3468, Testing net (#0)
I0408 16:05:54.975493 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:05:55.449611 27193 blocking_queue.cpp:49] Waiting for data
I0408 16:05:58.076346 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:05:59.464007 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:05:59.464056 27193 solver.cpp:397]     Test net output #1: loss = 4.60484 (* 1 = 4.60484 loss)
I0408 16:05:59.555496 27193 solver.cpp:218] Iteration 3468 (0.820611 iter/s, 14.6232s/12 iters), loss = 4.42649
I0408 16:05:59.555547 27193 solver.cpp:237]     Train net output #0: loss = 4.42649 (* 1 = 4.42649 loss)
I0408 16:05:59.555558 27193 sgd_solver.cpp:105] Iteration 3468, lr = 5.0707e-06
I0408 16:06:04.064988 27193 solver.cpp:218] Iteration 3480 (2.66118 iter/s, 4.50928s/12 iters), loss = 4.60152
I0408 16:06:04.065042 27193 solver.cpp:237]     Train net output #0: loss = 4.60152 (* 1 = 4.60152 loss)
I0408 16:06:04.065052 27193 sgd_solver.cpp:105] Iteration 3480, lr = 4.93932e-06
I0408 16:06:09.190361 27193 solver.cpp:218] Iteration 3492 (2.3414 iter/s, 5.12514s/12 iters), loss = 4.62258
I0408 16:06:09.190413 27193 solver.cpp:237]     Train net output #0: loss = 4.62258 (* 1 = 4.62258 loss)
I0408 16:06:09.190425 27193 sgd_solver.cpp:105] Iteration 3492, lr = 4.81134e-06
I0408 16:06:14.425290 27193 solver.cpp:218] Iteration 3504 (2.2924 iter/s, 5.2347s/12 iters), loss = 4.29951
I0408 16:06:14.425336 27193 solver.cpp:237]     Train net output #0: loss = 4.29951 (* 1 = 4.29951 loss)
I0408 16:06:14.425348 27193 sgd_solver.cpp:105] Iteration 3504, lr = 4.68667e-06
I0408 16:06:19.463403 27193 solver.cpp:218] Iteration 3516 (2.38195 iter/s, 5.03789s/12 iters), loss = 4.29581
I0408 16:06:19.463455 27193 solver.cpp:237]     Train net output #0: loss = 4.29581 (* 1 = 4.29581 loss)
I0408 16:06:19.463467 27193 sgd_solver.cpp:105] Iteration 3516, lr = 4.56524e-06
I0408 16:06:24.595407 27193 solver.cpp:218] Iteration 3528 (2.33837 iter/s, 5.13178s/12 iters), loss = 4.52488
I0408 16:06:24.595440 27193 solver.cpp:237]     Train net output #0: loss = 4.52488 (* 1 = 4.52488 loss)
I0408 16:06:24.595449 27193 sgd_solver.cpp:105] Iteration 3528, lr = 4.44695e-06
I0408 16:06:29.431906 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:06:29.692039 27193 solver.cpp:218] Iteration 3540 (2.35459 iter/s, 5.09642s/12 iters), loss = 4.42874
I0408 16:06:29.692085 27193 solver.cpp:237]     Train net output #0: loss = 4.42874 (* 1 = 4.42874 loss)
I0408 16:06:29.692096 27193 sgd_solver.cpp:105] Iteration 3540, lr = 4.33173e-06
I0408 16:06:34.735294 27193 solver.cpp:218] Iteration 3552 (2.37952 iter/s, 5.04304s/12 iters), loss = 4.67366
I0408 16:06:34.735339 27193 solver.cpp:237]     Train net output #0: loss = 4.67366 (* 1 = 4.67366 loss)
I0408 16:06:34.735349 27193 sgd_solver.cpp:105] Iteration 3552, lr = 4.21949e-06
I0408 16:06:39.939747 27193 solver.cpp:218] Iteration 3564 (2.30582 iter/s, 5.20423s/12 iters), loss = 4.394
I0408 16:06:39.939798 27193 solver.cpp:237]     Train net output #0: loss = 4.394 (* 1 = 4.394 loss)
I0408 16:06:39.939811 27193 sgd_solver.cpp:105] Iteration 3564, lr = 4.11016e-06
I0408 16:06:42.008625 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0408 16:06:45.038390 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0408 16:06:48.110572 27193 solver.cpp:330] Iteration 3570, Testing net (#0)
I0408 16:06:48.110594 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:06:51.137683 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:06:52.559422 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0710784
I0408 16:06:52.559471 27193 solver.cpp:397]     Test net output #1: loss = 4.60794 (* 1 = 4.60794 loss)
I0408 16:06:54.537905 27193 solver.cpp:218] Iteration 3576 (0.822051 iter/s, 14.5976s/12 iters), loss = 4.50159
I0408 16:06:54.537952 27193 solver.cpp:237]     Train net output #0: loss = 4.50159 (* 1 = 4.50159 loss)
I0408 16:06:54.537984 27193 sgd_solver.cpp:105] Iteration 3576, lr = 4.00366e-06
I0408 16:07:00.055406 27193 solver.cpp:218] Iteration 3588 (2.17499 iter/s, 5.51726s/12 iters), loss = 4.46988
I0408 16:07:00.055543 27193 solver.cpp:237]     Train net output #0: loss = 4.46988 (* 1 = 4.46988 loss)
I0408 16:07:00.055553 27193 sgd_solver.cpp:105] Iteration 3588, lr = 3.89993e-06
I0408 16:07:05.575657 27193 solver.cpp:218] Iteration 3600 (2.17394 iter/s, 5.51993s/12 iters), loss = 4.48369
I0408 16:07:05.575695 27193 solver.cpp:237]     Train net output #0: loss = 4.48369 (* 1 = 4.48369 loss)
I0408 16:07:05.575706 27193 sgd_solver.cpp:105] Iteration 3600, lr = 3.79888e-06
I0408 16:07:11.014559 27193 solver.cpp:218] Iteration 3612 (2.20642 iter/s, 5.43867s/12 iters), loss = 4.55048
I0408 16:07:11.014612 27193 solver.cpp:237]     Train net output #0: loss = 4.55048 (* 1 = 4.55048 loss)
I0408 16:07:11.014626 27193 sgd_solver.cpp:105] Iteration 3612, lr = 3.70045e-06
I0408 16:07:16.093299 27193 solver.cpp:218] Iteration 3624 (2.3629 iter/s, 5.07851s/12 iters), loss = 4.51802
I0408 16:07:16.093349 27193 solver.cpp:237]     Train net output #0: loss = 4.51802 (* 1 = 4.51802 loss)
I0408 16:07:16.093361 27193 sgd_solver.cpp:105] Iteration 3624, lr = 3.60457e-06
I0408 16:07:21.250123 27193 solver.cpp:218] Iteration 3636 (2.32712 iter/s, 5.1566s/12 iters), loss = 4.57597
I0408 16:07:21.250167 27193 solver.cpp:237]     Train net output #0: loss = 4.57597 (* 1 = 4.57597 loss)
I0408 16:07:21.250178 27193 sgd_solver.cpp:105] Iteration 3636, lr = 3.51117e-06
I0408 16:07:23.178972 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:07:26.375742 27193 solver.cpp:218] Iteration 3648 (2.34128 iter/s, 5.1254s/12 iters), loss = 4.49535
I0408 16:07:26.375792 27193 solver.cpp:237]     Train net output #0: loss = 4.49535 (* 1 = 4.49535 loss)
I0408 16:07:26.375803 27193 sgd_solver.cpp:105] Iteration 3648, lr = 3.42019e-06
I0408 16:07:31.511957 27193 solver.cpp:218] Iteration 3660 (2.33645 iter/s, 5.13599s/12 iters), loss = 4.55897
I0408 16:07:31.512063 27193 solver.cpp:237]     Train net output #0: loss = 4.55897 (* 1 = 4.55897 loss)
I0408 16:07:31.512076 27193 sgd_solver.cpp:105] Iteration 3660, lr = 3.33157e-06
I0408 16:07:36.097345 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0408 16:07:39.263444 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0408 16:07:41.594700 27193 solver.cpp:330] Iteration 3672, Testing net (#0)
I0408 16:07:41.594727 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:07:44.629817 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:07:46.124059 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:07:46.124109 27193 solver.cpp:397]     Test net output #1: loss = 4.60709 (* 1 = 4.60709 loss)
I0408 16:07:46.215842 27193 solver.cpp:218] Iteration 3672 (0.816144 iter/s, 14.7033s/12 iters), loss = 4.20843
I0408 16:07:46.215893 27193 solver.cpp:237]     Train net output #0: loss = 4.20843 (* 1 = 4.20843 loss)
I0408 16:07:46.215905 27193 sgd_solver.cpp:105] Iteration 3672, lr = 3.24525e-06
I0408 16:07:50.448549 27193 solver.cpp:218] Iteration 3684 (2.8352 iter/s, 4.23251s/12 iters), loss = 4.48568
I0408 16:07:50.448606 27193 solver.cpp:237]     Train net output #0: loss = 4.48568 (* 1 = 4.48568 loss)
I0408 16:07:50.448622 27193 sgd_solver.cpp:105] Iteration 3684, lr = 3.16117e-06
I0408 16:07:55.678848 27193 solver.cpp:218] Iteration 3696 (2.29443 iter/s, 5.23007s/12 iters), loss = 4.61028
I0408 16:07:55.678890 27193 solver.cpp:237]     Train net output #0: loss = 4.61028 (* 1 = 4.61028 loss)
I0408 16:07:55.678898 27193 sgd_solver.cpp:105] Iteration 3696, lr = 3.07926e-06
I0408 16:08:00.827729 27193 solver.cpp:218] Iteration 3708 (2.3307 iter/s, 5.14866s/12 iters), loss = 4.48122
I0408 16:08:00.827778 27193 solver.cpp:237]     Train net output #0: loss = 4.48122 (* 1 = 4.48122 loss)
I0408 16:08:00.827790 27193 sgd_solver.cpp:105] Iteration 3708, lr = 2.99947e-06
I0408 16:08:05.898069 27193 solver.cpp:218] Iteration 3720 (2.36681 iter/s, 5.07012s/12 iters), loss = 4.51982
I0408 16:08:05.898231 27193 solver.cpp:237]     Train net output #0: loss = 4.51982 (* 1 = 4.51982 loss)
I0408 16:08:05.898244 27193 sgd_solver.cpp:105] Iteration 3720, lr = 2.92175e-06
I0408 16:08:10.945111 27193 solver.cpp:218] Iteration 3732 (2.37779 iter/s, 5.04671s/12 iters), loss = 4.4614
I0408 16:08:10.945169 27193 solver.cpp:237]     Train net output #0: loss = 4.4614 (* 1 = 4.4614 loss)
I0408 16:08:10.945181 27193 sgd_solver.cpp:105] Iteration 3732, lr = 2.84605e-06
I0408 16:08:15.033921 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:08:15.975467 27193 solver.cpp:218] Iteration 3744 (2.38563 iter/s, 5.03013s/12 iters), loss = 4.43733
I0408 16:08:15.975517 27193 solver.cpp:237]     Train net output #0: loss = 4.43733 (* 1 = 4.43733 loss)
I0408 16:08:15.975529 27193 sgd_solver.cpp:105] Iteration 3744, lr = 2.77231e-06
I0408 16:08:21.126211 27193 solver.cpp:218] Iteration 3756 (2.32986 iter/s, 5.15051s/12 iters), loss = 4.58425
I0408 16:08:21.126264 27193 solver.cpp:237]     Train net output #0: loss = 4.58425 (* 1 = 4.58425 loss)
I0408 16:08:21.126276 27193 sgd_solver.cpp:105] Iteration 3756, lr = 2.70048e-06
I0408 16:08:26.683559 27193 solver.cpp:218] Iteration 3768 (2.1594 iter/s, 5.5571s/12 iters), loss = 4.49338
I0408 16:08:26.683609 27193 solver.cpp:237]     Train net output #0: loss = 4.49338 (* 1 = 4.49338 loss)
I0408 16:08:26.683619 27193 sgd_solver.cpp:105] Iteration 3768, lr = 2.63051e-06
I0408 16:08:28.775177 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0408 16:08:31.799906 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0408 16:08:34.135659 27193 solver.cpp:330] Iteration 3774, Testing net (#0)
I0408 16:08:34.135687 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:08:37.067557 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:08:38.632447 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:08:38.632496 27193 solver.cpp:397]     Test net output #1: loss = 4.60837 (* 1 = 4.60837 loss)
I0408 16:08:40.619222 27193 solver.cpp:218] Iteration 3780 (0.861131 iter/s, 13.9352s/12 iters), loss = 4.45049
I0408 16:08:40.619271 27193 solver.cpp:237]     Train net output #0: loss = 4.45049 (* 1 = 4.45049 loss)
I0408 16:08:40.619283 27193 sgd_solver.cpp:105] Iteration 3780, lr = 2.56235e-06
I0408 16:08:45.981263 27193 solver.cpp:218] Iteration 3792 (2.23805 iter/s, 5.36181s/12 iters), loss = 4.4293
I0408 16:08:45.981304 27193 solver.cpp:237]     Train net output #0: loss = 4.4293 (* 1 = 4.4293 loss)
I0408 16:08:45.981314 27193 sgd_solver.cpp:105] Iteration 3792, lr = 2.49596e-06
I0408 16:08:51.040874 27193 solver.cpp:218] Iteration 3804 (2.37183 iter/s, 5.05939s/12 iters), loss = 4.44989
I0408 16:08:51.040922 27193 solver.cpp:237]     Train net output #0: loss = 4.44989 (* 1 = 4.44989 loss)
I0408 16:08:51.040935 27193 sgd_solver.cpp:105] Iteration 3804, lr = 2.43128e-06
I0408 16:08:56.355239 27193 solver.cpp:218] Iteration 3816 (2.25813 iter/s, 5.31414s/12 iters), loss = 4.54977
I0408 16:08:56.355285 27193 solver.cpp:237]     Train net output #0: loss = 4.54977 (* 1 = 4.54977 loss)
I0408 16:08:56.355296 27193 sgd_solver.cpp:105] Iteration 3816, lr = 2.36829e-06
I0408 16:09:01.847818 27193 solver.cpp:218] Iteration 3828 (2.18486 iter/s, 5.49235s/12 iters), loss = 4.41314
I0408 16:09:01.847854 27193 solver.cpp:237]     Train net output #0: loss = 4.41314 (* 1 = 4.41314 loss)
I0408 16:09:01.847862 27193 sgd_solver.cpp:105] Iteration 3828, lr = 2.30692e-06
I0408 16:09:07.008183 27193 solver.cpp:218] Iteration 3840 (2.32551 iter/s, 5.16015s/12 iters), loss = 4.58426
I0408 16:09:07.008224 27193 solver.cpp:237]     Train net output #0: loss = 4.58426 (* 1 = 4.58426 loss)
I0408 16:09:07.008232 27193 sgd_solver.cpp:105] Iteration 3840, lr = 2.24715e-06
I0408 16:09:08.166075 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:09:12.424391 27193 solver.cpp:218] Iteration 3852 (2.21567 iter/s, 5.41598s/12 iters), loss = 4.37688
I0408 16:09:12.424433 27193 solver.cpp:237]     Train net output #0: loss = 4.37688 (* 1 = 4.37688 loss)
I0408 16:09:12.424443 27193 sgd_solver.cpp:105] Iteration 3852, lr = 2.18893e-06
I0408 16:09:17.908305 27193 solver.cpp:218] Iteration 3864 (2.18831 iter/s, 5.48368s/12 iters), loss = 4.36808
I0408 16:09:17.908352 27193 solver.cpp:237]     Train net output #0: loss = 4.36808 (* 1 = 4.36808 loss)
I0408 16:09:17.908365 27193 sgd_solver.cpp:105] Iteration 3864, lr = 2.13221e-06
I0408 16:09:22.840831 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0408 16:09:25.836222 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0408 16:09:28.156596 27193 solver.cpp:330] Iteration 3876, Testing net (#0)
I0408 16:09:28.156625 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:09:31.239039 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:09:32.896575 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:09:32.896623 27193 solver.cpp:397]     Test net output #1: loss = 4.60831 (* 1 = 4.60831 loss)
I0408 16:09:32.988493 27193 solver.cpp:218] Iteration 3876 (0.795775 iter/s, 15.0796s/12 iters), loss = 4.61924
I0408 16:09:32.988538 27193 solver.cpp:237]     Train net output #0: loss = 4.61924 (* 1 = 4.61924 loss)
I0408 16:09:32.988548 27193 sgd_solver.cpp:105] Iteration 3876, lr = 2.07696e-06
I0408 16:09:37.369724 27193 solver.cpp:218] Iteration 3888 (2.73908 iter/s, 4.38103s/12 iters), loss = 4.52569
I0408 16:09:37.369776 27193 solver.cpp:237]     Train net output #0: loss = 4.52569 (* 1 = 4.52569 loss)
I0408 16:09:37.369788 27193 sgd_solver.cpp:105] Iteration 3888, lr = 2.02315e-06
I0408 16:09:42.469527 27193 solver.cpp:218] Iteration 3900 (2.35314 iter/s, 5.09958s/12 iters), loss = 4.56308
I0408 16:09:42.469640 27193 solver.cpp:237]     Train net output #0: loss = 4.56308 (* 1 = 4.56308 loss)
I0408 16:09:42.469655 27193 sgd_solver.cpp:105] Iteration 3900, lr = 1.97073e-06
I0408 16:09:47.524556 27193 solver.cpp:218] Iteration 3912 (2.37401 iter/s, 5.05475s/12 iters), loss = 4.47488
I0408 16:09:47.524606 27193 solver.cpp:237]     Train net output #0: loss = 4.47488 (* 1 = 4.47488 loss)
I0408 16:09:47.524618 27193 sgd_solver.cpp:105] Iteration 3912, lr = 1.91966e-06
I0408 16:09:52.624481 27193 solver.cpp:218] Iteration 3924 (2.35308 iter/s, 5.0997s/12 iters), loss = 4.38995
I0408 16:09:52.624528 27193 solver.cpp:237]     Train net output #0: loss = 4.38995 (* 1 = 4.38995 loss)
I0408 16:09:52.624538 27193 sgd_solver.cpp:105] Iteration 3924, lr = 1.86993e-06
I0408 16:09:57.767606 27193 solver.cpp:218] Iteration 3936 (2.33331 iter/s, 5.1429s/12 iters), loss = 4.57141
I0408 16:09:57.767647 27193 solver.cpp:237]     Train net output #0: loss = 4.57141 (* 1 = 4.57141 loss)
I0408 16:09:57.767657 27193 sgd_solver.cpp:105] Iteration 3936, lr = 1.82147e-06
I0408 16:10:01.219203 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:10:02.884866 27193 solver.cpp:218] Iteration 3948 (2.3451 iter/s, 5.11704s/12 iters), loss = 4.56378
I0408 16:10:02.884917 27193 solver.cpp:237]     Train net output #0: loss = 4.56378 (* 1 = 4.56378 loss)
I0408 16:10:02.884927 27193 sgd_solver.cpp:105] Iteration 3948, lr = 1.77428e-06
I0408 16:10:07.909471 27193 solver.cpp:218] Iteration 3960 (2.38835 iter/s, 5.02438s/12 iters), loss = 4.59822
I0408 16:10:07.909518 27193 solver.cpp:237]     Train net output #0: loss = 4.59822 (* 1 = 4.59822 loss)
I0408 16:10:07.909528 27193 sgd_solver.cpp:105] Iteration 3960, lr = 1.72831e-06
I0408 16:10:13.080525 27193 solver.cpp:218] Iteration 3972 (2.32071 iter/s, 5.17083s/12 iters), loss = 4.54631
I0408 16:10:13.080636 27193 solver.cpp:237]     Train net output #0: loss = 4.54631 (* 1 = 4.54631 loss)
I0408 16:10:13.080649 27193 sgd_solver.cpp:105] Iteration 3972, lr = 1.68353e-06
I0408 16:10:15.173099 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0408 16:10:20.546730 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0408 16:10:23.237795 27193 solver.cpp:330] Iteration 3978, Testing net (#0)
I0408 16:10:23.237819 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:10:26.123452 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:10:27.706233 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:10:27.706277 27193 solver.cpp:397]     Test net output #1: loss = 4.6082 (* 1 = 4.6082 loss)
I0408 16:10:29.528272 27193 solver.cpp:218] Iteration 3984 (0.729612 iter/s, 16.4471s/12 iters), loss = 4.44728
I0408 16:10:29.528311 27193 solver.cpp:237]     Train net output #0: loss = 4.44728 (* 1 = 4.44728 loss)
I0408 16:10:29.528321 27193 sgd_solver.cpp:105] Iteration 3984, lr = 1.6399e-06
I0408 16:10:35.046710 27193 solver.cpp:218] Iteration 3996 (2.17462 iter/s, 5.51821s/12 iters), loss = 4.67063
I0408 16:10:35.046761 27193 solver.cpp:237]     Train net output #0: loss = 4.67063 (* 1 = 4.67063 loss)
I0408 16:10:35.046773 27193 sgd_solver.cpp:105] Iteration 3996, lr = 1.59741e-06
I0408 16:10:40.188675 27193 solver.cpp:218] Iteration 4008 (2.33384 iter/s, 5.14173s/12 iters), loss = 4.52871
I0408 16:10:40.188732 27193 solver.cpp:237]     Train net output #0: loss = 4.52871 (* 1 = 4.52871 loss)
I0408 16:10:40.188745 27193 sgd_solver.cpp:105] Iteration 4008, lr = 1.55602e-06
I0408 16:10:45.298907 27193 solver.cpp:218] Iteration 4020 (2.34834 iter/s, 5.11s/12 iters), loss = 4.64006
I0408 16:10:45.299010 27193 solver.cpp:237]     Train net output #0: loss = 4.64006 (* 1 = 4.64006 loss)
I0408 16:10:45.299023 27193 sgd_solver.cpp:105] Iteration 4020, lr = 1.51571e-06
I0408 16:10:50.485402 27193 solver.cpp:218] Iteration 4032 (2.31383 iter/s, 5.18622s/12 iters), loss = 4.65233
I0408 16:10:50.485451 27193 solver.cpp:237]     Train net output #0: loss = 4.65233 (* 1 = 4.65233 loss)
I0408 16:10:50.485463 27193 sgd_solver.cpp:105] Iteration 4032, lr = 1.47643e-06
I0408 16:10:55.948169 27193 solver.cpp:218] Iteration 4044 (2.19678 iter/s, 5.46253s/12 iters), loss = 4.6148
I0408 16:10:55.948210 27193 solver.cpp:237]     Train net output #0: loss = 4.6148 (* 1 = 4.6148 loss)
I0408 16:10:55.948220 27193 sgd_solver.cpp:105] Iteration 4044, lr = 1.43818e-06
I0408 16:10:56.480057 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:11:01.096462 27193 solver.cpp:218] Iteration 4056 (2.33097 iter/s, 5.14807s/12 iters), loss = 4.42658
I0408 16:11:01.096516 27193 solver.cpp:237]     Train net output #0: loss = 4.42658 (* 1 = 4.42658 loss)
I0408 16:11:01.096529 27193 sgd_solver.cpp:105] Iteration 4056, lr = 1.40091e-06
I0408 16:11:06.157249 27193 solver.cpp:218] Iteration 4068 (2.37128 iter/s, 5.06056s/12 iters), loss = 4.64259
I0408 16:11:06.157297 27193 solver.cpp:237]     Train net output #0: loss = 4.64259 (* 1 = 4.64259 loss)
I0408 16:11:06.157308 27193 sgd_solver.cpp:105] Iteration 4068, lr = 1.36462e-06
I0408 16:11:10.906839 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0408 16:11:14.561228 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0408 16:11:19.662303 27193 solver.cpp:330] Iteration 4080, Testing net (#0)
I0408 16:11:19.662392 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:11:22.659371 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:11:24.315048 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:11:24.315095 27193 solver.cpp:397]     Test net output #1: loss = 4.60958 (* 1 = 4.60958 loss)
I0408 16:11:24.406572 27193 solver.cpp:218] Iteration 4080 (0.657582 iter/s, 18.2487s/12 iters), loss = 4.42098
I0408 16:11:24.406627 27193 solver.cpp:237]     Train net output #0: loss = 4.42098 (* 1 = 4.42098 loss)
I0408 16:11:24.406639 27193 sgd_solver.cpp:105] Iteration 4080, lr = 1.32926e-06
I0408 16:11:29.011552 27193 solver.cpp:218] Iteration 4092 (2.606 iter/s, 4.60476s/12 iters), loss = 4.46206
I0408 16:11:29.011603 27193 solver.cpp:237]     Train net output #0: loss = 4.46206 (* 1 = 4.46206 loss)
I0408 16:11:29.011616 27193 sgd_solver.cpp:105] Iteration 4092, lr = 1.29482e-06
I0408 16:11:34.564052 27193 solver.cpp:218] Iteration 4104 (2.16128 iter/s, 5.55226s/12 iters), loss = 4.47516
I0408 16:11:34.564100 27193 solver.cpp:237]     Train net output #0: loss = 4.47516 (* 1 = 4.47516 loss)
I0408 16:11:34.564110 27193 sgd_solver.cpp:105] Iteration 4104, lr = 1.26127e-06
I0408 16:11:39.902667 27193 solver.cpp:218] Iteration 4116 (2.24787 iter/s, 5.33839s/12 iters), loss = 4.43347
I0408 16:11:39.902711 27193 solver.cpp:237]     Train net output #0: loss = 4.43347 (* 1 = 4.43347 loss)
I0408 16:11:39.902721 27193 sgd_solver.cpp:105] Iteration 4116, lr = 1.22859e-06
I0408 16:11:44.979580 27193 solver.cpp:218] Iteration 4128 (2.36374 iter/s, 5.07669s/12 iters), loss = 4.43082
I0408 16:11:44.979629 27193 solver.cpp:237]     Train net output #0: loss = 4.43082 (* 1 = 4.43082 loss)
I0408 16:11:44.979642 27193 sgd_solver.cpp:105] Iteration 4128, lr = 1.19675e-06
I0408 16:11:50.435484 27193 solver.cpp:218] Iteration 4140 (2.19955 iter/s, 5.45567s/12 iters), loss = 4.51297
I0408 16:11:50.435592 27193 solver.cpp:237]     Train net output #0: loss = 4.51297 (* 1 = 4.51297 loss)
I0408 16:11:50.435608 27193 sgd_solver.cpp:105] Iteration 4140, lr = 1.16575e-06
I0408 16:11:53.322373 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:11:55.760319 27193 solver.cpp:218] Iteration 4152 (2.25371 iter/s, 5.32455s/12 iters), loss = 4.27165
I0408 16:11:55.760367 27193 solver.cpp:237]     Train net output #0: loss = 4.27165 (* 1 = 4.27165 loss)
I0408 16:11:55.760380 27193 sgd_solver.cpp:105] Iteration 4152, lr = 1.13554e-06
I0408 16:11:57.394834 27193 blocking_queue.cpp:49] Waiting for data
I0408 16:12:00.951879 27193 solver.cpp:218] Iteration 4164 (2.31155 iter/s, 5.19133s/12 iters), loss = 4.40066
I0408 16:12:00.951927 27193 solver.cpp:237]     Train net output #0: loss = 4.40066 (* 1 = 4.40066 loss)
I0408 16:12:00.951941 27193 sgd_solver.cpp:105] Iteration 4164, lr = 1.10612e-06
I0408 16:12:06.477267 27193 solver.cpp:218] Iteration 4176 (2.17189 iter/s, 5.52515s/12 iters), loss = 4.48958
I0408 16:12:06.477316 27193 solver.cpp:237]     Train net output #0: loss = 4.48958 (* 1 = 4.48958 loss)
I0408 16:12:06.477329 27193 sgd_solver.cpp:105] Iteration 4176, lr = 1.07746e-06
I0408 16:12:08.732044 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0408 16:12:11.776319 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0408 16:12:14.110831 27193 solver.cpp:330] Iteration 4182, Testing net (#0)
I0408 16:12:14.110857 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:12:16.978355 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:12:18.679247 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:12:18.679287 27193 solver.cpp:397]     Test net output #1: loss = 4.60377 (* 1 = 4.60377 loss)
I0408 16:12:20.507710 27193 solver.cpp:218] Iteration 4188 (0.855314 iter/s, 14.0299s/12 iters), loss = 4.51965
I0408 16:12:20.507870 27193 solver.cpp:237]     Train net output #0: loss = 4.51965 (* 1 = 4.51965 loss)
I0408 16:12:20.507882 27193 sgd_solver.cpp:105] Iteration 4188, lr = 1.04954e-06
I0408 16:12:25.576478 27193 solver.cpp:218] Iteration 4200 (2.3676 iter/s, 5.06843s/12 iters), loss = 4.52737
I0408 16:12:25.576524 27193 solver.cpp:237]     Train net output #0: loss = 4.52737 (* 1 = 4.52737 loss)
I0408 16:12:25.576531 27193 sgd_solver.cpp:105] Iteration 4200, lr = 1.02235e-06
I0408 16:12:30.655052 27193 solver.cpp:218] Iteration 4212 (2.36297 iter/s, 5.07835s/12 iters), loss = 4.23531
I0408 16:12:30.655093 27193 solver.cpp:237]     Train net output #0: loss = 4.23531 (* 1 = 4.23531 loss)
I0408 16:12:30.655102 27193 sgd_solver.cpp:105] Iteration 4212, lr = 9.95856e-07
I0408 16:12:35.917265 27193 solver.cpp:218] Iteration 4224 (2.28051 iter/s, 5.26199s/12 iters), loss = 4.38731
I0408 16:12:35.917306 27193 solver.cpp:237]     Train net output #0: loss = 4.38731 (* 1 = 4.38731 loss)
I0408 16:12:35.917316 27193 sgd_solver.cpp:105] Iteration 4224, lr = 9.70053e-07
I0408 16:12:41.219671 27193 solver.cpp:218] Iteration 4236 (2.26322 iter/s, 5.30218s/12 iters), loss = 4.50328
I0408 16:12:41.219718 27193 solver.cpp:237]     Train net output #0: loss = 4.50328 (* 1 = 4.50328 loss)
I0408 16:12:41.219729 27193 sgd_solver.cpp:105] Iteration 4236, lr = 9.44919e-07
I0408 16:12:46.513723 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:12:46.770804 27193 solver.cpp:218] Iteration 4248 (2.16181 iter/s, 5.55089s/12 iters), loss = 4.48288
I0408 16:12:46.770856 27193 solver.cpp:237]     Train net output #0: loss = 4.48288 (* 1 = 4.48288 loss)
I0408 16:12:46.770867 27193 sgd_solver.cpp:105] Iteration 4248, lr = 9.20435e-07
I0408 16:12:52.124577 27193 solver.cpp:218] Iteration 4260 (2.24151 iter/s, 5.35354s/12 iters), loss = 4.64603
I0408 16:12:52.124644 27193 solver.cpp:237]     Train net output #0: loss = 4.64603 (* 1 = 4.64603 loss)
I0408 16:12:52.124653 27193 sgd_solver.cpp:105] Iteration 4260, lr = 8.96586e-07
I0408 16:12:57.222880 27193 solver.cpp:218] Iteration 4272 (2.35384 iter/s, 5.09806s/12 iters), loss = 4.32214
I0408 16:12:57.222924 27193 solver.cpp:237]     Train net output #0: loss = 4.32214 (* 1 = 4.32214 loss)
I0408 16:12:57.222934 27193 sgd_solver.cpp:105] Iteration 4272, lr = 8.73355e-07
I0408 16:13:01.793764 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0408 16:13:04.829216 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0408 16:13:07.155072 27193 solver.cpp:330] Iteration 4284, Testing net (#0)
I0408 16:13:07.155099 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:13:09.836107 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:13:11.534422 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:13:11.534467 27193 solver.cpp:397]     Test net output #1: loss = 4.60902 (* 1 = 4.60902 loss)
I0408 16:13:11.626127 27193 solver.cpp:218] Iteration 4284 (0.833175 iter/s, 14.4027s/12 iters), loss = 4.52576
I0408 16:13:11.626184 27193 solver.cpp:237]     Train net output #0: loss = 4.52576 (* 1 = 4.52576 loss)
I0408 16:13:11.626195 27193 sgd_solver.cpp:105] Iteration 4284, lr = 8.50726e-07
I0408 16:13:15.930140 27193 solver.cpp:218] Iteration 4296 (2.78823 iter/s, 4.30381s/12 iters), loss = 4.51241
I0408 16:13:15.930186 27193 solver.cpp:237]     Train net output #0: loss = 4.51241 (* 1 = 4.51241 loss)
I0408 16:13:15.930195 27193 sgd_solver.cpp:105] Iteration 4296, lr = 8.28683e-07
I0408 16:13:21.000222 27193 solver.cpp:218] Iteration 4308 (2.36693 iter/s, 5.06986s/12 iters), loss = 4.4867
I0408 16:13:21.000262 27193 solver.cpp:237]     Train net output #0: loss = 4.4867 (* 1 = 4.4867 loss)
I0408 16:13:21.000272 27193 sgd_solver.cpp:105] Iteration 4308, lr = 8.07212e-07
I0408 16:13:26.086823 27193 solver.cpp:218] Iteration 4320 (2.35924 iter/s, 5.08639s/12 iters), loss = 4.56089
I0408 16:13:26.086921 27193 solver.cpp:237]     Train net output #0: loss = 4.56089 (* 1 = 4.56089 loss)
I0408 16:13:26.086930 27193 sgd_solver.cpp:105] Iteration 4320, lr = 7.86297e-07
I0408 16:13:31.233247 27193 solver.cpp:218] Iteration 4332 (2.33184 iter/s, 5.14614s/12 iters), loss = 4.55202
I0408 16:13:31.233299 27193 solver.cpp:237]     Train net output #0: loss = 4.55202 (* 1 = 4.55202 loss)
I0408 16:13:31.233310 27193 sgd_solver.cpp:105] Iteration 4332, lr = 7.65923e-07
I0408 16:13:36.510051 27193 solver.cpp:218] Iteration 4344 (2.2742 iter/s, 5.27657s/12 iters), loss = 4.4742
I0408 16:13:36.510103 27193 solver.cpp:237]     Train net output #0: loss = 4.4742 (* 1 = 4.4742 loss)
I0408 16:13:36.510115 27193 sgd_solver.cpp:105] Iteration 4344, lr = 7.46078e-07
I0408 16:13:38.451599 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:13:41.661002 27193 solver.cpp:218] Iteration 4356 (2.32977 iter/s, 5.15072s/12 iters), loss = 4.58341
I0408 16:13:41.661049 27193 solver.cpp:237]     Train net output #0: loss = 4.58341 (* 1 = 4.58341 loss)
I0408 16:13:41.661062 27193 sgd_solver.cpp:105] Iteration 4356, lr = 7.26746e-07
I0408 16:13:46.723147 27193 solver.cpp:218] Iteration 4368 (2.37064 iter/s, 5.06192s/12 iters), loss = 4.55251
I0408 16:13:46.723197 27193 solver.cpp:237]     Train net output #0: loss = 4.55251 (* 1 = 4.55251 loss)
I0408 16:13:46.723209 27193 sgd_solver.cpp:105] Iteration 4368, lr = 7.07916e-07
I0408 16:13:51.811717 27193 solver.cpp:218] Iteration 4380 (2.35833 iter/s, 5.08834s/12 iters), loss = 4.33618
I0408 16:13:51.811764 27193 solver.cpp:237]     Train net output #0: loss = 4.33618 (* 1 = 4.33618 loss)
I0408 16:13:51.811774 27193 sgd_solver.cpp:105] Iteration 4380, lr = 6.89574e-07
I0408 16:13:53.851742 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0408 16:13:56.843470 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0408 16:14:00.277238 27193 solver.cpp:330] Iteration 4386, Testing net (#0)
I0408 16:14:00.277263 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:14:02.947734 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:14:04.691499 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:14:04.691547 27193 solver.cpp:397]     Test net output #1: loss = 4.60721 (* 1 = 4.60721 loss)
I0408 16:14:06.572959 27193 solver.cpp:218] Iteration 4392 (0.812969 iter/s, 14.7607s/12 iters), loss = 4.48205
I0408 16:14:06.573006 27193 solver.cpp:237]     Train net output #0: loss = 4.48205 (* 1 = 4.48205 loss)
I0408 16:14:06.573015 27193 sgd_solver.cpp:105] Iteration 4392, lr = 6.71706e-07
I0408 16:14:11.662407 27193 solver.cpp:218] Iteration 4404 (2.35792 iter/s, 5.08922s/12 iters), loss = 4.65905
I0408 16:14:11.662456 27193 solver.cpp:237]     Train net output #0: loss = 4.65905 (* 1 = 4.65905 loss)
I0408 16:14:11.662467 27193 sgd_solver.cpp:105] Iteration 4404, lr = 6.54302e-07
I0408 16:14:16.725106 27193 solver.cpp:218] Iteration 4416 (2.37038 iter/s, 5.06248s/12 iters), loss = 4.51465
I0408 16:14:16.725143 27193 solver.cpp:237]     Train net output #0: loss = 4.51465 (* 1 = 4.51465 loss)
I0408 16:14:16.725153 27193 sgd_solver.cpp:105] Iteration 4416, lr = 6.37349e-07
I0408 16:14:21.891317 27193 solver.cpp:218] Iteration 4428 (2.32288 iter/s, 5.166s/12 iters), loss = 4.53304
I0408 16:14:21.891355 27193 solver.cpp:237]     Train net output #0: loss = 4.53304 (* 1 = 4.53304 loss)
I0408 16:14:21.891362 27193 sgd_solver.cpp:105] Iteration 4428, lr = 6.20835e-07
I0408 16:14:27.033445 27193 solver.cpp:218] Iteration 4440 (2.33377 iter/s, 5.1419s/12 iters), loss = 4.41153
I0408 16:14:27.033550 27193 solver.cpp:237]     Train net output #0: loss = 4.41153 (* 1 = 4.41153 loss)
I0408 16:14:27.033560 27193 sgd_solver.cpp:105] Iteration 4440, lr = 6.04749e-07
I0408 16:14:31.251487 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:14:32.261108 27193 solver.cpp:218] Iteration 4452 (2.29561 iter/s, 5.22738s/12 iters), loss = 4.58107
I0408 16:14:32.261152 27193 solver.cpp:237]     Train net output #0: loss = 4.58107 (* 1 = 4.58107 loss)
I0408 16:14:32.261162 27193 sgd_solver.cpp:105] Iteration 4452, lr = 5.89079e-07
I0408 16:14:37.409896 27193 solver.cpp:218] Iteration 4464 (2.33075 iter/s, 5.14857s/12 iters), loss = 4.48237
I0408 16:14:37.409945 27193 solver.cpp:237]     Train net output #0: loss = 4.48237 (* 1 = 4.48237 loss)
I0408 16:14:37.409973 27193 sgd_solver.cpp:105] Iteration 4464, lr = 5.73816e-07
I0408 16:14:42.672848 27193 solver.cpp:218] Iteration 4476 (2.28019 iter/s, 5.26272s/12 iters), loss = 4.52124
I0408 16:14:42.672905 27193 solver.cpp:237]     Train net output #0: loss = 4.52124 (* 1 = 4.52124 loss)
I0408 16:14:42.672919 27193 sgd_solver.cpp:105] Iteration 4476, lr = 5.58948e-07
I0408 16:14:47.166754 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0408 16:14:50.930143 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0408 16:14:53.276595 27193 solver.cpp:330] Iteration 4488, Testing net (#0)
I0408 16:14:53.276618 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:14:55.979393 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:14:57.755621 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:14:57.755790 27193 solver.cpp:397]     Test net output #1: loss = 4.6091 (* 1 = 4.6091 loss)
I0408 16:14:57.847518 27193 solver.cpp:218] Iteration 4488 (0.79082 iter/s, 15.1741s/12 iters), loss = 4.40458
I0408 16:14:57.847571 27193 solver.cpp:237]     Train net output #0: loss = 4.40458 (* 1 = 4.40458 loss)
I0408 16:14:57.847582 27193 sgd_solver.cpp:105] Iteration 4488, lr = 5.44465e-07
I0408 16:15:02.129467 27193 solver.cpp:218] Iteration 4500 (2.80259 iter/s, 4.28175s/12 iters), loss = 4.33526
I0408 16:15:02.129514 27193 solver.cpp:237]     Train net output #0: loss = 4.33526 (* 1 = 4.33526 loss)
I0408 16:15:02.129523 27193 sgd_solver.cpp:105] Iteration 4500, lr = 5.30358e-07
I0408 16:15:07.230291 27193 solver.cpp:218] Iteration 4512 (2.35266 iter/s, 5.1006s/12 iters), loss = 4.29353
I0408 16:15:07.230338 27193 solver.cpp:237]     Train net output #0: loss = 4.29353 (* 1 = 4.29353 loss)
I0408 16:15:07.230350 27193 sgd_solver.cpp:105] Iteration 4512, lr = 5.16616e-07
I0408 16:15:12.397428 27193 solver.cpp:218] Iteration 4524 (2.32247 iter/s, 5.16691s/12 iters), loss = 4.61605
I0408 16:15:12.397487 27193 solver.cpp:237]     Train net output #0: loss = 4.61605 (* 1 = 4.61605 loss)
I0408 16:15:12.397501 27193 sgd_solver.cpp:105] Iteration 4524, lr = 5.0323e-07
I0408 16:15:17.487573 27193 solver.cpp:218] Iteration 4536 (2.3576 iter/s, 5.08991s/12 iters), loss = 4.44271
I0408 16:15:17.487615 27193 solver.cpp:237]     Train net output #0: loss = 4.44271 (* 1 = 4.44271 loss)
I0408 16:15:17.487625 27193 sgd_solver.cpp:105] Iteration 4536, lr = 4.90191e-07
I0408 16:15:22.602959 27193 solver.cpp:218] Iteration 4548 (2.34611 iter/s, 5.11486s/12 iters), loss = 4.55012
I0408 16:15:22.603040 27193 solver.cpp:237]     Train net output #0: loss = 4.55012 (* 1 = 4.55012 loss)
I0408 16:15:22.603051 27193 sgd_solver.cpp:105] Iteration 4548, lr = 4.7749e-07
I0408 16:15:23.863665 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:15:27.621421 27193 solver.cpp:218] Iteration 4560 (2.39129 iter/s, 5.01821s/12 iters), loss = 4.41847
I0408 16:15:27.621471 27193 solver.cpp:237]     Train net output #0: loss = 4.41847 (* 1 = 4.41847 loss)
I0408 16:15:27.621482 27193 sgd_solver.cpp:105] Iteration 4560, lr = 4.65118e-07
I0408 16:15:32.642763 27193 solver.cpp:218] Iteration 4572 (2.3899 iter/s, 5.02112s/12 iters), loss = 4.50558
I0408 16:15:32.642917 27193 solver.cpp:237]     Train net output #0: loss = 4.50558 (* 1 = 4.50558 loss)
I0408 16:15:32.642930 27193 sgd_solver.cpp:105] Iteration 4572, lr = 4.53067e-07
I0408 16:15:37.619211 27193 solver.cpp:218] Iteration 4584 (2.41152 iter/s, 4.97612s/12 iters), loss = 4.68747
I0408 16:15:37.619264 27193 solver.cpp:237]     Train net output #0: loss = 4.68747 (* 1 = 4.68747 loss)
I0408 16:15:37.619275 27193 sgd_solver.cpp:105] Iteration 4584, lr = 4.41328e-07
I0408 16:15:39.646220 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0408 16:15:42.687536 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0408 16:15:45.428493 27193 solver.cpp:330] Iteration 4590, Testing net (#0)
I0408 16:15:45.428519 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:15:48.081884 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:15:49.904302 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:15:49.904353 27193 solver.cpp:397]     Test net output #1: loss = 4.60273 (* 1 = 4.60273 loss)
I0408 16:15:51.805516 27193 solver.cpp:218] Iteration 4596 (0.845918 iter/s, 14.1858s/12 iters), loss = 4.57488
I0408 16:15:51.805572 27193 solver.cpp:237]     Train net output #0: loss = 4.57488 (* 1 = 4.57488 loss)
I0408 16:15:51.805584 27193 sgd_solver.cpp:105] Iteration 4596, lr = 4.29893e-07
I0408 16:15:56.879492 27193 solver.cpp:218] Iteration 4608 (2.36512 iter/s, 5.07374s/12 iters), loss = 4.49427
I0408 16:15:56.879547 27193 solver.cpp:237]     Train net output #0: loss = 4.49427 (* 1 = 4.49427 loss)
I0408 16:15:56.879560 27193 sgd_solver.cpp:105] Iteration 4608, lr = 4.18754e-07
I0408 16:16:01.967337 27193 solver.cpp:218] Iteration 4620 (2.35867 iter/s, 5.08761s/12 iters), loss = 4.53496
I0408 16:16:01.967391 27193 solver.cpp:237]     Train net output #0: loss = 4.53496 (* 1 = 4.53496 loss)
I0408 16:16:01.967403 27193 sgd_solver.cpp:105] Iteration 4620, lr = 4.07904e-07
I0408 16:16:07.110522 27193 solver.cpp:218] Iteration 4632 (2.33329 iter/s, 5.14296s/12 iters), loss = 4.29257
I0408 16:16:07.110680 27193 solver.cpp:237]     Train net output #0: loss = 4.29257 (* 1 = 4.29257 loss)
I0408 16:16:07.110693 27193 sgd_solver.cpp:105] Iteration 4632, lr = 3.97335e-07
I0408 16:16:12.341267 27193 solver.cpp:218] Iteration 4644 (2.29428 iter/s, 5.23041s/12 iters), loss = 4.61536
I0408 16:16:12.341321 27193 solver.cpp:237]     Train net output #0: loss = 4.61536 (* 1 = 4.61536 loss)
I0408 16:16:12.341333 27193 sgd_solver.cpp:105] Iteration 4644, lr = 3.8704e-07
I0408 16:16:15.724392 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:16:17.347769 27193 solver.cpp:218] Iteration 4656 (2.39699 iter/s, 5.00628s/12 iters), loss = 4.60793
I0408 16:16:17.347806 27193 solver.cpp:237]     Train net output #0: loss = 4.60793 (* 1 = 4.60793 loss)
I0408 16:16:17.347816 27193 sgd_solver.cpp:105] Iteration 4656, lr = 3.77011e-07
I0408 16:16:22.709872 27193 solver.cpp:218] Iteration 4668 (2.23802 iter/s, 5.36188s/12 iters), loss = 4.56698
I0408 16:16:22.709915 27193 solver.cpp:237]     Train net output #0: loss = 4.56698 (* 1 = 4.56698 loss)
I0408 16:16:22.709926 27193 sgd_solver.cpp:105] Iteration 4668, lr = 3.67243e-07
I0408 16:16:28.223353 27193 solver.cpp:218] Iteration 4680 (2.17657 iter/s, 5.51325s/12 iters), loss = 4.49668
I0408 16:16:28.223389 27193 solver.cpp:237]     Train net output #0: loss = 4.49668 (* 1 = 4.49668 loss)
I0408 16:16:28.223398 27193 sgd_solver.cpp:105] Iteration 4680, lr = 3.57727e-07
I0408 16:16:32.936291 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0408 16:16:35.999460 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0408 16:16:38.313869 27193 solver.cpp:330] Iteration 4692, Testing net (#0)
I0408 16:16:38.313952 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:16:40.936262 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:16:42.791749 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:16:42.791792 27193 solver.cpp:397]     Test net output #1: loss = 4.61077 (* 1 = 4.61077 loss)
I0408 16:16:42.883364 27193 solver.cpp:218] Iteration 4692 (0.818583 iter/s, 14.6595s/12 iters), loss = 4.44353
I0408 16:16:42.883415 27193 solver.cpp:237]     Train net output #0: loss = 4.44353 (* 1 = 4.44353 loss)
I0408 16:16:42.883425 27193 sgd_solver.cpp:105] Iteration 4692, lr = 3.48458e-07
I0408 16:16:47.152046 27193 solver.cpp:218] Iteration 4704 (2.81131 iter/s, 4.26848s/12 iters), loss = 4.57575
I0408 16:16:47.152092 27193 solver.cpp:237]     Train net output #0: loss = 4.57575 (* 1 = 4.57575 loss)
I0408 16:16:47.152102 27193 sgd_solver.cpp:105] Iteration 4704, lr = 3.3943e-07
I0408 16:16:52.275914 27193 solver.cpp:218] Iteration 4716 (2.34208 iter/s, 5.12365s/12 iters), loss = 4.54025
I0408 16:16:52.275954 27193 solver.cpp:237]     Train net output #0: loss = 4.54025 (* 1 = 4.54025 loss)
I0408 16:16:52.275962 27193 sgd_solver.cpp:105] Iteration 4716, lr = 3.30635e-07
I0408 16:16:57.423044 27193 solver.cpp:218] Iteration 4728 (2.3315 iter/s, 5.14691s/12 iters), loss = 4.59082
I0408 16:16:57.423090 27193 solver.cpp:237]     Train net output #0: loss = 4.59082 (* 1 = 4.59082 loss)
I0408 16:16:57.423101 27193 sgd_solver.cpp:105] Iteration 4728, lr = 3.22068e-07
I0408 16:17:02.439074 27193 solver.cpp:218] Iteration 4740 (2.39243 iter/s, 5.01581s/12 iters), loss = 4.5948
I0408 16:17:02.439110 27193 solver.cpp:237]     Train net output #0: loss = 4.5948 (* 1 = 4.5948 loss)
I0408 16:17:02.439119 27193 sgd_solver.cpp:105] Iteration 4740, lr = 3.13723e-07
I0408 16:17:07.899756 27193 solver.cpp:218] Iteration 4752 (2.19762 iter/s, 5.46045s/12 iters), loss = 4.60939
I0408 16:17:07.899806 27193 solver.cpp:237]     Train net output #0: loss = 4.60939 (* 1 = 4.60939 loss)
I0408 16:17:07.899817 27193 sgd_solver.cpp:105] Iteration 4752, lr = 3.05594e-07
I0408 16:17:08.492823 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:17:13.116583 27193 solver.cpp:218] Iteration 4764 (2.30035 iter/s, 5.2166s/12 iters), loss = 4.49223
I0408 16:17:13.116631 27193 solver.cpp:237]     Train net output #0: loss = 4.49223 (* 1 = 4.49223 loss)
I0408 16:17:13.116643 27193 sgd_solver.cpp:105] Iteration 4764, lr = 2.97676e-07
I0408 16:17:18.288621 27193 solver.cpp:218] Iteration 4776 (2.32027 iter/s, 5.17181s/12 iters), loss = 4.57633
I0408 16:17:18.288676 27193 solver.cpp:237]     Train net output #0: loss = 4.57633 (* 1 = 4.57633 loss)
I0408 16:17:18.288692 27193 sgd_solver.cpp:105] Iteration 4776, lr = 2.89963e-07
I0408 16:17:23.389199 27193 solver.cpp:218] Iteration 4788 (2.35278 iter/s, 5.10035s/12 iters), loss = 4.39698
I0408 16:17:23.389241 27193 solver.cpp:237]     Train net output #0: loss = 4.39698 (* 1 = 4.39698 loss)
I0408 16:17:23.389252 27193 sgd_solver.cpp:105] Iteration 4788, lr = 2.8245e-07
I0408 16:17:25.555550 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0408 16:17:28.538903 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0408 16:17:30.847435 27193 solver.cpp:330] Iteration 4794, Testing net (#0)
I0408 16:17:30.847460 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:17:33.418790 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:17:35.320524 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:17:35.320571 27193 solver.cpp:397]     Test net output #1: loss = 4.60432 (* 1 = 4.60432 loss)
I0408 16:17:37.312188 27193 solver.cpp:218] Iteration 4800 (0.861915 iter/s, 13.9225s/12 iters), loss = 4.39831
I0408 16:17:37.312234 27193 solver.cpp:237]     Train net output #0: loss = 4.39831 (* 1 = 4.39831 loss)
I0408 16:17:37.312247 27193 sgd_solver.cpp:105] Iteration 4800, lr = 2.75132e-07
I0408 16:17:42.564932 27193 solver.cpp:218] Iteration 4812 (2.28462 iter/s, 5.25251s/12 iters), loss = 4.46748
I0408 16:17:42.565048 27193 solver.cpp:237]     Train net output #0: loss = 4.46748 (* 1 = 4.46748 loss)
I0408 16:17:42.565061 27193 sgd_solver.cpp:105] Iteration 4812, lr = 2.68003e-07
I0408 16:17:48.013809 27193 solver.cpp:218] Iteration 4824 (2.20241 iter/s, 5.44858s/12 iters), loss = 4.59368
I0408 16:17:48.013854 27193 solver.cpp:237]     Train net output #0: loss = 4.59368 (* 1 = 4.59368 loss)
I0408 16:17:48.013866 27193 sgd_solver.cpp:105] Iteration 4824, lr = 2.61059e-07
I0408 16:17:53.538754 27193 solver.cpp:218] Iteration 4836 (2.17206 iter/s, 5.52471s/12 iters), loss = 4.45732
I0408 16:17:53.538787 27193 solver.cpp:237]     Train net output #0: loss = 4.45732 (* 1 = 4.45732 loss)
I0408 16:17:53.538798 27193 sgd_solver.cpp:105] Iteration 4836, lr = 2.54294e-07
I0408 16:17:55.788727 27193 blocking_queue.cpp:49] Waiting for data
I0408 16:17:59.078522 27193 solver.cpp:218] Iteration 4848 (2.16624 iter/s, 5.53954s/12 iters), loss = 4.51076
I0408 16:17:59.078562 27193 solver.cpp:237]     Train net output #0: loss = 4.51076 (* 1 = 4.51076 loss)
I0408 16:17:59.078573 27193 sgd_solver.cpp:105] Iteration 4848, lr = 2.47706e-07
I0408 16:18:01.985446 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:18:04.330504 27193 solver.cpp:218] Iteration 4860 (2.28495 iter/s, 5.25176s/12 iters), loss = 4.30741
I0408 16:18:04.330549 27193 solver.cpp:237]     Train net output #0: loss = 4.30741 (* 1 = 4.30741 loss)
I0408 16:18:04.330559 27193 sgd_solver.cpp:105] Iteration 4860, lr = 2.41287e-07
I0408 16:18:09.449430 27193 solver.cpp:218] Iteration 4872 (2.34434 iter/s, 5.11871s/12 iters), loss = 4.4124
I0408 16:18:09.449467 27193 solver.cpp:237]     Train net output #0: loss = 4.4124 (* 1 = 4.4124 loss)
I0408 16:18:09.449476 27193 sgd_solver.cpp:105] Iteration 4872, lr = 2.35036e-07
I0408 16:18:14.481683 27193 solver.cpp:218] Iteration 4884 (2.38472 iter/s, 5.03204s/12 iters), loss = 4.43735
I0408 16:18:14.481803 27193 solver.cpp:237]     Train net output #0: loss = 4.43735 (* 1 = 4.43735 loss)
I0408 16:18:14.481812 27193 sgd_solver.cpp:105] Iteration 4884, lr = 2.28946e-07
I0408 16:18:19.256783 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0408 16:18:22.726187 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0408 16:18:25.062019 27193 solver.cpp:330] Iteration 4896, Testing net (#0)
I0408 16:18:25.062045 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:18:27.600827 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:18:29.536931 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:18:29.536972 27193 solver.cpp:397]     Test net output #1: loss = 4.60283 (* 1 = 4.60283 loss)
I0408 16:18:29.628109 27193 solver.cpp:218] Iteration 4896 (0.792299 iter/s, 15.1458s/12 iters), loss = 4.62744
I0408 16:18:29.628149 27193 solver.cpp:237]     Train net output #0: loss = 4.62744 (* 1 = 4.62744 loss)
I0408 16:18:29.628159 27193 sgd_solver.cpp:105] Iteration 4896, lr = 2.23014e-07
I0408 16:18:33.915422 27193 solver.cpp:218] Iteration 4908 (2.79908 iter/s, 4.28712s/12 iters), loss = 4.51054
I0408 16:18:33.915465 27193 solver.cpp:237]     Train net output #0: loss = 4.51054 (* 1 = 4.51054 loss)
I0408 16:18:33.915477 27193 sgd_solver.cpp:105] Iteration 4908, lr = 2.17235e-07
I0408 16:18:39.407423 27193 solver.cpp:218] Iteration 4920 (2.18509 iter/s, 5.49176s/12 iters), loss = 4.39193
I0408 16:18:39.407467 27193 solver.cpp:237]     Train net output #0: loss = 4.39193 (* 1 = 4.39193 loss)
I0408 16:18:39.407480 27193 sgd_solver.cpp:105] Iteration 4920, lr = 2.11606e-07
I0408 16:18:44.717252 27193 solver.cpp:218] Iteration 4932 (2.26006 iter/s, 5.3096s/12 iters), loss = 4.50383
I0408 16:18:44.717348 27193 solver.cpp:237]     Train net output #0: loss = 4.50383 (* 1 = 4.50383 loss)
I0408 16:18:44.717357 27193 sgd_solver.cpp:105] Iteration 4932, lr = 2.06124e-07
I0408 16:18:50.204493 27193 solver.cpp:218] Iteration 4944 (2.18701 iter/s, 5.48695s/12 iters), loss = 4.52323
I0408 16:18:50.204545 27193 solver.cpp:237]     Train net output #0: loss = 4.52323 (* 1 = 4.52323 loss)
I0408 16:18:50.204555 27193 sgd_solver.cpp:105] Iteration 4944, lr = 2.00783e-07
I0408 16:18:55.498342 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:18:55.708233 27193 solver.cpp:218] Iteration 4956 (2.18043 iter/s, 5.50349s/12 iters), loss = 4.48432
I0408 16:18:55.708284 27193 solver.cpp:237]     Train net output #0: loss = 4.48432 (* 1 = 4.48432 loss)
I0408 16:18:55.708297 27193 sgd_solver.cpp:105] Iteration 4956, lr = 1.9558e-07
I0408 16:19:01.203933 27193 solver.cpp:218] Iteration 4968 (2.18362 iter/s, 5.49545s/12 iters), loss = 4.55731
I0408 16:19:01.203979 27193 solver.cpp:237]     Train net output #0: loss = 4.55731 (* 1 = 4.55731 loss)
I0408 16:19:01.203992 27193 sgd_solver.cpp:105] Iteration 4968, lr = 1.90513e-07
I0408 16:19:06.246287 27193 solver.cpp:218] Iteration 4980 (2.37995 iter/s, 5.04213s/12 iters), loss = 4.33163
I0408 16:19:06.246340 27193 solver.cpp:237]     Train net output #0: loss = 4.33163 (* 1 = 4.33163 loss)
I0408 16:19:06.246351 27193 sgd_solver.cpp:105] Iteration 4980, lr = 1.85577e-07
I0408 16:19:11.272101 27193 solver.cpp:218] Iteration 4992 (2.38778 iter/s, 5.02559s/12 iters), loss = 4.44983
I0408 16:19:11.272147 27193 solver.cpp:237]     Train net output #0: loss = 4.44983 (* 1 = 4.44983 loss)
I0408 16:19:11.272158 27193 sgd_solver.cpp:105] Iteration 4992, lr = 1.80768e-07
I0408 16:19:13.363337 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0408 16:19:16.344959 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0408 16:19:18.648552 27193 solver.cpp:330] Iteration 4998, Testing net (#0)
I0408 16:19:18.648576 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:19:21.097003 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:19:23.069778 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:19:23.069828 27193 solver.cpp:397]     Test net output #1: loss = 4.60841 (* 1 = 4.60841 loss)
I0408 16:19:25.052259 27193 solver.cpp:218] Iteration 5004 (0.870849 iter/s, 13.7797s/12 iters), loss = 4.46237
I0408 16:19:25.052299 27193 solver.cpp:237]     Train net output #0: loss = 4.46237 (* 1 = 4.46237 loss)
I0408 16:19:25.052307 27193 sgd_solver.cpp:105] Iteration 5004, lr = 1.76084e-07
I0408 16:19:30.540019 27193 solver.cpp:218] Iteration 5016 (2.18678 iter/s, 5.48753s/12 iters), loss = 4.42043
I0408 16:19:30.540068 27193 solver.cpp:237]     Train net output #0: loss = 4.42043 (* 1 = 4.42043 loss)
I0408 16:19:30.540081 27193 sgd_solver.cpp:105] Iteration 5016, lr = 1.71522e-07
I0408 16:19:35.789361 27193 solver.cpp:218] Iteration 5028 (2.2861 iter/s, 5.24911s/12 iters), loss = 4.57864
I0408 16:19:35.789403 27193 solver.cpp:237]     Train net output #0: loss = 4.57864 (* 1 = 4.57864 loss)
I0408 16:19:35.789415 27193 sgd_solver.cpp:105] Iteration 5028, lr = 1.67078e-07
I0408 16:19:40.718614 27193 solver.cpp:218] Iteration 5040 (2.43453 iter/s, 4.92908s/12 iters), loss = 4.55516
I0408 16:19:40.718658 27193 solver.cpp:237]     Train net output #0: loss = 4.55516 (* 1 = 4.55516 loss)
I0408 16:19:40.718670 27193 sgd_solver.cpp:105] Iteration 5040, lr = 1.62749e-07
I0408 16:19:46.032153 27193 solver.cpp:218] Iteration 5052 (2.25845 iter/s, 5.31337s/12 iters), loss = 4.56848
I0408 16:19:46.032207 27193 solver.cpp:237]     Train net output #0: loss = 4.56848 (* 1 = 4.56848 loss)
I0408 16:19:46.032220 27193 sgd_solver.cpp:105] Iteration 5052, lr = 1.58532e-07
I0408 16:19:47.975561 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:19:51.129182 27193 solver.cpp:218] Iteration 5064 (2.35439 iter/s, 5.09686s/12 iters), loss = 4.57628
I0408 16:19:51.129230 27193 solver.cpp:237]     Train net output #0: loss = 4.57628 (* 1 = 4.57628 loss)
I0408 16:19:51.129241 27193 sgd_solver.cpp:105] Iteration 5064, lr = 1.54424e-07
I0408 16:19:56.240782 27193 solver.cpp:218] Iteration 5076 (2.34768 iter/s, 5.11144s/12 iters), loss = 4.47215
I0408 16:19:56.240826 27193 solver.cpp:237]     Train net output #0: loss = 4.47215 (* 1 = 4.47215 loss)
I0408 16:19:56.240837 27193 sgd_solver.cpp:105] Iteration 5076, lr = 1.50423e-07
I0408 16:20:01.190982 27193 solver.cpp:218] Iteration 5088 (2.42422 iter/s, 4.95004s/12 iters), loss = 4.36396
I0408 16:20:01.191027 27193 solver.cpp:237]     Train net output #0: loss = 4.36396 (* 1 = 4.36396 loss)
I0408 16:20:01.191040 27193 sgd_solver.cpp:105] Iteration 5088, lr = 1.46525e-07
I0408 16:20:05.857035 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0408 16:20:09.847388 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0408 16:20:12.178990 27193 solver.cpp:330] Iteration 5100, Testing net (#0)
I0408 16:20:12.179015 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:20:14.626132 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:20:16.645161 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:20:16.645208 27193 solver.cpp:397]     Test net output #1: loss = 4.60722 (* 1 = 4.60722 loss)
I0408 16:20:16.736429 27193 solver.cpp:218] Iteration 5100 (0.771949 iter/s, 15.5451s/12 iters), loss = 4.52786
I0408 16:20:16.736477 27193 solver.cpp:237]     Train net output #0: loss = 4.52786 (* 1 = 4.52786 loss)
I0408 16:20:16.736487 27193 sgd_solver.cpp:105] Iteration 5100, lr = 1.42729e-07
I0408 16:20:21.198047 27193 solver.cpp:218] Iteration 5112 (2.6897 iter/s, 4.46146s/12 iters), loss = 4.55132
I0408 16:20:21.198190 27193 solver.cpp:237]     Train net output #0: loss = 4.55132 (* 1 = 4.55132 loss)
I0408 16:20:21.198204 27193 sgd_solver.cpp:105] Iteration 5112, lr = 1.39031e-07
I0408 16:20:26.225787 27193 solver.cpp:218] Iteration 5124 (2.38688 iter/s, 5.02748s/12 iters), loss = 4.41973
I0408 16:20:26.225831 27193 solver.cpp:237]     Train net output #0: loss = 4.41973 (* 1 = 4.41973 loss)
I0408 16:20:26.225841 27193 sgd_solver.cpp:105] Iteration 5124, lr = 1.35428e-07
I0408 16:20:31.321247 27193 solver.cpp:218] Iteration 5136 (2.35511 iter/s, 5.0953s/12 iters), loss = 4.44725
I0408 16:20:31.321293 27193 solver.cpp:237]     Train net output #0: loss = 4.44725 (* 1 = 4.44725 loss)
I0408 16:20:31.321305 27193 sgd_solver.cpp:105] Iteration 5136, lr = 1.31919e-07
I0408 16:20:36.431542 27193 solver.cpp:218] Iteration 5148 (2.34828 iter/s, 5.11013s/12 iters), loss = 4.48652
I0408 16:20:36.431587 27193 solver.cpp:237]     Train net output #0: loss = 4.48652 (* 1 = 4.48652 loss)
I0408 16:20:36.431599 27193 sgd_solver.cpp:105] Iteration 5148, lr = 1.28501e-07
I0408 16:20:40.600407 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:20:41.550220 27193 solver.cpp:218] Iteration 5160 (2.34443 iter/s, 5.11851s/12 iters), loss = 4.48576
I0408 16:20:41.550266 27193 solver.cpp:237]     Train net output #0: loss = 4.48576 (* 1 = 4.48576 loss)
I0408 16:20:41.550276 27193 sgd_solver.cpp:105] Iteration 5160, lr = 1.25172e-07
I0408 16:20:46.724107 27193 solver.cpp:218] Iteration 5172 (2.31941 iter/s, 5.17372s/12 iters), loss = 4.34918
I0408 16:20:46.724148 27193 solver.cpp:237]     Train net output #0: loss = 4.34918 (* 1 = 4.34918 loss)
I0408 16:20:46.724159 27193 sgd_solver.cpp:105] Iteration 5172, lr = 1.21928e-07
I0408 16:20:51.780359 27193 solver.cpp:218] Iteration 5184 (2.37337 iter/s, 5.05609s/12 iters), loss = 4.59807
I0408 16:20:51.780452 27193 solver.cpp:237]     Train net output #0: loss = 4.59807 (* 1 = 4.59807 loss)
I0408 16:20:51.780464 27193 sgd_solver.cpp:105] Iteration 5184, lr = 1.18769e-07
I0408 16:20:56.840814 27193 solver.cpp:218] Iteration 5196 (2.37143 iter/s, 5.06024s/12 iters), loss = 4.44108
I0408 16:20:56.840867 27193 solver.cpp:237]     Train net output #0: loss = 4.44108 (* 1 = 4.44108 loss)
I0408 16:20:56.840878 27193 sgd_solver.cpp:105] Iteration 5196, lr = 1.15692e-07
I0408 16:20:58.909862 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0408 16:21:01.968586 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0408 16:21:04.296209 27193 solver.cpp:330] Iteration 5202, Testing net (#0)
I0408 16:21:04.296232 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:21:06.662053 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:21:08.719789 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:21:08.719837 27193 solver.cpp:397]     Test net output #1: loss = 4.60623 (* 1 = 4.60623 loss)
I0408 16:21:10.636296 27193 solver.cpp:218] Iteration 5208 (0.869873 iter/s, 13.7951s/12 iters), loss = 4.25195
I0408 16:21:10.636345 27193 solver.cpp:237]     Train net output #0: loss = 4.25195 (* 1 = 4.25195 loss)
I0408 16:21:10.636358 27193 sgd_solver.cpp:105] Iteration 5208, lr = 1.12694e-07
I0408 16:21:15.703466 27193 solver.cpp:218] Iteration 5220 (2.36827 iter/s, 5.067s/12 iters), loss = 4.43078
I0408 16:21:15.703516 27193 solver.cpp:237]     Train net output #0: loss = 4.43078 (* 1 = 4.43078 loss)
I0408 16:21:15.703526 27193 sgd_solver.cpp:105] Iteration 5220, lr = 1.09774e-07
I0408 16:21:20.927467 27193 solver.cpp:218] Iteration 5232 (2.29717 iter/s, 5.22383s/12 iters), loss = 4.70393
I0408 16:21:20.927510 27193 solver.cpp:237]     Train net output #0: loss = 4.70393 (* 1 = 4.70393 loss)
I0408 16:21:20.927521 27193 sgd_solver.cpp:105] Iteration 5232, lr = 1.0693e-07
I0408 16:21:26.289345 27193 solver.cpp:218] Iteration 5244 (2.23809 iter/s, 5.3617s/12 iters), loss = 4.39747
I0408 16:21:26.289484 27193 solver.cpp:237]     Train net output #0: loss = 4.39747 (* 1 = 4.39747 loss)
I0408 16:21:26.289497 27193 sgd_solver.cpp:105] Iteration 5244, lr = 1.04159e-07
I0408 16:21:31.335438 27193 solver.cpp:218] Iteration 5256 (2.3782 iter/s, 5.04583s/12 iters), loss = 4.48928
I0408 16:21:31.335482 27193 solver.cpp:237]     Train net output #0: loss = 4.48928 (* 1 = 4.48928 loss)
I0408 16:21:31.335494 27193 sgd_solver.cpp:105] Iteration 5256, lr = 1.0146e-07
I0408 16:21:32.665166 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:21:36.410812 27193 solver.cpp:218] Iteration 5268 (2.36444 iter/s, 5.0752s/12 iters), loss = 4.34401
I0408 16:21:36.410857 27193 solver.cpp:237]     Train net output #0: loss = 4.34401 (* 1 = 4.34401 loss)
I0408 16:21:36.410869 27193 sgd_solver.cpp:105] Iteration 5268, lr = 9.88315e-08
I0408 16:21:41.482151 27193 solver.cpp:218] Iteration 5280 (2.36632 iter/s, 5.07117s/12 iters), loss = 4.49195
I0408 16:21:41.482195 27193 solver.cpp:237]     Train net output #0: loss = 4.49195 (* 1 = 4.49195 loss)
I0408 16:21:41.482206 27193 sgd_solver.cpp:105] Iteration 5280, lr = 9.62708e-08
I0408 16:21:46.517128 27193 solver.cpp:218] Iteration 5292 (2.38341 iter/s, 5.03481s/12 iters), loss = 4.59702
I0408 16:21:46.517174 27193 solver.cpp:237]     Train net output #0: loss = 4.59702 (* 1 = 4.59702 loss)
I0408 16:21:46.517185 27193 sgd_solver.cpp:105] Iteration 5292, lr = 9.37763e-08
I0408 16:21:51.100358 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0408 16:21:54.198627 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0408 16:21:56.537238 27193 solver.cpp:330] Iteration 5304, Testing net (#0)
I0408 16:21:56.537317 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:21:58.910456 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:22:01.022094 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:22:01.022141 27193 solver.cpp:397]     Test net output #1: loss = 4.60792 (* 1 = 4.60792 loss)
I0408 16:22:01.113423 27193 solver.cpp:218] Iteration 5304 (0.822148 iter/s, 14.5959s/12 iters), loss = 4.40913
I0408 16:22:01.113473 27193 solver.cpp:237]     Train net output #0: loss = 4.40913 (* 1 = 4.40913 loss)
I0408 16:22:01.113484 27193 sgd_solver.cpp:105] Iteration 5304, lr = 9.13465e-08
I0408 16:22:05.704254 27193 solver.cpp:218] Iteration 5316 (2.614 iter/s, 4.59067s/12 iters), loss = 4.40597
I0408 16:22:05.704298 27193 solver.cpp:237]     Train net output #0: loss = 4.40597 (* 1 = 4.40597 loss)
I0408 16:22:05.704310 27193 sgd_solver.cpp:105] Iteration 5316, lr = 8.89797e-08
I0408 16:22:11.170578 27193 solver.cpp:218] Iteration 5328 (2.19533 iter/s, 5.46614s/12 iters), loss = 4.47927
I0408 16:22:11.170625 27193 solver.cpp:237]     Train net output #0: loss = 4.47927 (* 1 = 4.47927 loss)
I0408 16:22:11.170637 27193 sgd_solver.cpp:105] Iteration 5328, lr = 8.66742e-08
I0408 16:22:16.531426 27193 solver.cpp:218] Iteration 5340 (2.23853 iter/s, 5.36066s/12 iters), loss = 4.24561
I0408 16:22:16.531478 27193 solver.cpp:237]     Train net output #0: loss = 4.24561 (* 1 = 4.24561 loss)
I0408 16:22:16.531491 27193 sgd_solver.cpp:105] Iteration 5340, lr = 8.44284e-08
I0408 16:22:21.628628 27193 solver.cpp:218] Iteration 5352 (2.35432 iter/s, 5.09702s/12 iters), loss = 4.46172
I0408 16:22:21.628676 27193 solver.cpp:237]     Train net output #0: loss = 4.46172 (* 1 = 4.46172 loss)
I0408 16:22:21.628687 27193 sgd_solver.cpp:105] Iteration 5352, lr = 8.22408e-08
I0408 16:22:25.074231 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:22:26.685914 27193 solver.cpp:218] Iteration 5364 (2.3729 iter/s, 5.05711s/12 iters), loss = 4.53122
I0408 16:22:26.686062 27193 solver.cpp:237]     Train net output #0: loss = 4.53122 (* 1 = 4.53122 loss)
I0408 16:22:26.686076 27193 sgd_solver.cpp:105] Iteration 5364, lr = 8.01099e-08
I0408 16:22:31.724510 27193 solver.cpp:218] Iteration 5376 (2.38175 iter/s, 5.03832s/12 iters), loss = 4.53624
I0408 16:22:31.724558 27193 solver.cpp:237]     Train net output #0: loss = 4.53624 (* 1 = 4.53624 loss)
I0408 16:22:31.724570 27193 sgd_solver.cpp:105] Iteration 5376, lr = 7.80342e-08
I0408 16:22:36.942257 27193 solver.cpp:218] Iteration 5388 (2.29992 iter/s, 5.21757s/12 iters), loss = 4.46804
I0408 16:22:36.942302 27193 solver.cpp:237]     Train net output #0: loss = 4.46804 (* 1 = 4.46804 loss)
I0408 16:22:36.942314 27193 sgd_solver.cpp:105] Iteration 5388, lr = 7.60123e-08
I0408 16:22:42.419517 27193 solver.cpp:218] Iteration 5400 (2.19095 iter/s, 5.47708s/12 iters), loss = 4.42272
I0408 16:22:42.419564 27193 solver.cpp:237]     Train net output #0: loss = 4.42272 (* 1 = 4.42272 loss)
I0408 16:22:42.419576 27193 sgd_solver.cpp:105] Iteration 5400, lr = 7.40428e-08
I0408 16:22:44.511622 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0408 16:22:48.428463 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0408 16:22:50.763633 27193 solver.cpp:330] Iteration 5406, Testing net (#0)
I0408 16:22:50.763657 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:22:53.101043 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:22:55.241369 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0741422
I0408 16:22:55.241416 27193 solver.cpp:397]     Test net output #1: loss = 4.60078 (* 1 = 4.60078 loss)
I0408 16:22:57.056625 27193 solver.cpp:218] Iteration 5412 (0.819857 iter/s, 14.6367s/12 iters), loss = 4.52523
I0408 16:22:57.056728 27193 solver.cpp:237]     Train net output #0: loss = 4.52523 (* 1 = 4.52523 loss)
I0408 16:22:57.056740 27193 sgd_solver.cpp:105] Iteration 5412, lr = 7.21243e-08
I0408 16:23:01.989500 27193 solver.cpp:218] Iteration 5424 (2.43277 iter/s, 4.93265s/12 iters), loss = 4.46879
I0408 16:23:01.989552 27193 solver.cpp:237]     Train net output #0: loss = 4.46879 (* 1 = 4.46879 loss)
I0408 16:23:01.989563 27193 sgd_solver.cpp:105] Iteration 5424, lr = 7.02556e-08
I0408 16:23:06.937275 27193 solver.cpp:218] Iteration 5436 (2.42542 iter/s, 4.9476s/12 iters), loss = 4.56521
I0408 16:23:06.937310 27193 solver.cpp:237]     Train net output #0: loss = 4.56521 (* 1 = 4.56521 loss)
I0408 16:23:06.937319 27193 sgd_solver.cpp:105] Iteration 5436, lr = 6.84352e-08
I0408 16:23:12.315551 27193 solver.cpp:218] Iteration 5448 (2.23127 iter/s, 5.3781s/12 iters), loss = 4.56021
I0408 16:23:12.315591 27193 solver.cpp:237]     Train net output #0: loss = 4.56021 (* 1 = 4.56021 loss)
I0408 16:23:12.315601 27193 sgd_solver.cpp:105] Iteration 5448, lr = 6.6662e-08
I0408 16:23:17.408427 27193 solver.cpp:218] Iteration 5460 (2.35631 iter/s, 5.0927s/12 iters), loss = 4.56973
I0408 16:23:17.408473 27193 solver.cpp:237]     Train net output #0: loss = 4.56973 (* 1 = 4.56973 loss)
I0408 16:23:17.408484 27193 sgd_solver.cpp:105] Iteration 5460, lr = 6.49348e-08
I0408 16:23:17.969339 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:23:22.473979 27193 solver.cpp:218] Iteration 5472 (2.36903 iter/s, 5.06537s/12 iters), loss = 4.51807
I0408 16:23:22.474022 27193 solver.cpp:237]     Train net output #0: loss = 4.51807 (* 1 = 4.51807 loss)
I0408 16:23:22.474033 27193 sgd_solver.cpp:105] Iteration 5472, lr = 6.32523e-08
I0408 16:23:27.718153 27193 solver.cpp:218] Iteration 5484 (2.28833 iter/s, 5.244s/12 iters), loss = 4.56095
I0408 16:23:27.718245 27193 solver.cpp:237]     Train net output #0: loss = 4.56095 (* 1 = 4.56095 loss)
I0408 16:23:27.718253 27193 sgd_solver.cpp:105] Iteration 5484, lr = 6.16134e-08
I0408 16:23:32.836633 27193 solver.cpp:218] Iteration 5496 (2.34455 iter/s, 5.11826s/12 iters), loss = 4.34496
I0408 16:23:32.836673 27193 solver.cpp:237]     Train net output #0: loss = 4.34496 (* 1 = 4.34496 loss)
I0408 16:23:32.836681 27193 sgd_solver.cpp:105] Iteration 5496, lr = 6.00169e-08
I0408 16:23:37.564496 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0408 16:23:40.617436 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0408 16:23:42.953701 27193 solver.cpp:330] Iteration 5508, Testing net (#0)
I0408 16:23:42.953728 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:23:45.213331 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:23:47.404983 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:23:47.405031 27193 solver.cpp:397]     Test net output #1: loss = 4.61221 (* 1 = 4.61221 loss)
I0408 16:23:47.496408 27193 solver.cpp:218] Iteration 5508 (0.818589 iter/s, 14.6594s/12 iters), loss = 4.38233
I0408 16:23:47.496459 27193 solver.cpp:237]     Train net output #0: loss = 4.38233 (* 1 = 4.38233 loss)
I0408 16:23:47.496470 27193 sgd_solver.cpp:105] Iteration 5508, lr = 5.84619e-08
I0408 16:23:52.097378 27193 solver.cpp:218] Iteration 5520 (2.60824 iter/s, 4.6008s/12 iters), loss = 4.40892
I0408 16:23:52.097419 27193 solver.cpp:237]     Train net output #0: loss = 4.40892 (* 1 = 4.40892 loss)
I0408 16:23:52.097427 27193 sgd_solver.cpp:105] Iteration 5520, lr = 5.69471e-08
I0408 16:23:54.626727 27193 blocking_queue.cpp:49] Waiting for data
I0408 16:23:57.220754 27193 solver.cpp:218] Iteration 5532 (2.34229 iter/s, 5.1232s/12 iters), loss = 4.55713
I0408 16:23:57.220798 27193 solver.cpp:237]     Train net output #0: loss = 4.55713 (* 1 = 4.55713 loss)
I0408 16:23:57.220810 27193 sgd_solver.cpp:105] Iteration 5532, lr = 5.54715e-08
I0408 16:24:02.300246 27193 solver.cpp:218] Iteration 5544 (2.36253 iter/s, 5.0793s/12 iters), loss = 4.35935
I0408 16:24:02.300390 27193 solver.cpp:237]     Train net output #0: loss = 4.35935 (* 1 = 4.35935 loss)
I0408 16:24:02.300413 27193 sgd_solver.cpp:105] Iteration 5544, lr = 5.40343e-08
I0408 16:24:07.373678 27193 solver.cpp:218] Iteration 5556 (2.36538 iter/s, 5.07317s/12 iters), loss = 4.51421
I0408 16:24:07.373723 27193 solver.cpp:237]     Train net output #0: loss = 4.51421 (* 1 = 4.51421 loss)
I0408 16:24:07.373734 27193 sgd_solver.cpp:105] Iteration 5556, lr = 5.26342e-08
I0408 16:24:10.223970 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:24:12.736359 27193 solver.cpp:218] Iteration 5568 (2.23776 iter/s, 5.36249s/12 iters), loss = 4.41587
I0408 16:24:12.736402 27193 solver.cpp:237]     Train net output #0: loss = 4.41587 (* 1 = 4.41587 loss)
I0408 16:24:12.736413 27193 sgd_solver.cpp:105] Iteration 5568, lr = 5.12704e-08
I0408 16:24:18.266356 27193 solver.cpp:218] Iteration 5580 (2.17006 iter/s, 5.52981s/12 iters), loss = 4.30533
I0408 16:24:18.266402 27193 solver.cpp:237]     Train net output #0: loss = 4.30533 (* 1 = 4.30533 loss)
I0408 16:24:18.266412 27193 sgd_solver.cpp:105] Iteration 5580, lr = 4.9942e-08
I0408 16:24:23.339452 27193 solver.cpp:218] Iteration 5592 (2.3655 iter/s, 5.07291s/12 iters), loss = 4.49513
I0408 16:24:23.339499 27193 solver.cpp:237]     Train net output #0: loss = 4.49513 (* 1 = 4.49513 loss)
I0408 16:24:23.339511 27193 sgd_solver.cpp:105] Iteration 5592, lr = 4.8648e-08
I0408 16:24:28.448187 27193 solver.cpp:218] Iteration 5604 (2.349 iter/s, 5.10855s/12 iters), loss = 4.58671
I0408 16:24:28.448227 27193 solver.cpp:237]     Train net output #0: loss = 4.58671 (* 1 = 4.58671 loss)
I0408 16:24:28.448237 27193 sgd_solver.cpp:105] Iteration 5604, lr = 4.73875e-08
I0408 16:24:30.506943 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0408 16:24:33.567351 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0408 16:24:35.903726 27193 solver.cpp:330] Iteration 5610, Testing net (#0)
I0408 16:24:35.903750 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:24:38.161576 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:24:40.384606 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:24:40.384657 27193 solver.cpp:397]     Test net output #1: loss = 4.60431 (* 1 = 4.60431 loss)
I0408 16:24:42.207139 27193 solver.cpp:218] Iteration 5616 (0.872185 iter/s, 13.7586s/12 iters), loss = 4.57536
I0408 16:24:42.207187 27193 solver.cpp:237]     Train net output #0: loss = 4.57536 (* 1 = 4.57536 loss)
I0408 16:24:42.207198 27193 sgd_solver.cpp:105] Iteration 5616, lr = 4.61596e-08
I0408 16:24:47.549568 27193 solver.cpp:218] Iteration 5628 (2.24625 iter/s, 5.34224s/12 iters), loss = 4.40186
I0408 16:24:47.549612 27193 solver.cpp:237]     Train net output #0: loss = 4.40186 (* 1 = 4.40186 loss)
I0408 16:24:47.549624 27193 sgd_solver.cpp:105] Iteration 5628, lr = 4.49636e-08
I0408 16:24:52.604576 27193 solver.cpp:218] Iteration 5640 (2.37397 iter/s, 5.05483s/12 iters), loss = 4.58526
I0408 16:24:52.604609 27193 solver.cpp:237]     Train net output #0: loss = 4.58526 (* 1 = 4.58526 loss)
I0408 16:24:52.604619 27193 sgd_solver.cpp:105] Iteration 5640, lr = 4.37986e-08
I0408 16:24:57.705153 27193 solver.cpp:218] Iteration 5652 (2.35276 iter/s, 5.1004s/12 iters), loss = 4.52253
I0408 16:24:57.705190 27193 solver.cpp:237]     Train net output #0: loss = 4.52253 (* 1 = 4.52253 loss)
I0408 16:24:57.705199 27193 sgd_solver.cpp:105] Iteration 5652, lr = 4.26637e-08
I0408 16:25:02.610522 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:25:02.782721 27193 solver.cpp:218] Iteration 5664 (2.36342 iter/s, 5.07739s/12 iters), loss = 4.3376
I0408 16:25:02.782768 27193 solver.cpp:237]     Train net output #0: loss = 4.3376 (* 1 = 4.3376 loss)
I0408 16:25:02.782778 27193 sgd_solver.cpp:105] Iteration 5664, lr = 4.15583e-08
I0408 16:25:07.856485 27193 solver.cpp:218] Iteration 5676 (2.3652 iter/s, 5.07358s/12 iters), loss = 4.54778
I0408 16:25:07.856591 27193 solver.cpp:237]     Train net output #0: loss = 4.54778 (* 1 = 4.54778 loss)
I0408 16:25:07.856603 27193 sgd_solver.cpp:105] Iteration 5676, lr = 4.04815e-08
I0408 16:25:12.960952 27193 solver.cpp:218] Iteration 5688 (2.351 iter/s, 5.10422s/12 iters), loss = 4.2793
I0408 16:25:12.960996 27193 solver.cpp:237]     Train net output #0: loss = 4.2793 (* 1 = 4.2793 loss)
I0408 16:25:12.961009 27193 sgd_solver.cpp:105] Iteration 5688, lr = 3.94326e-08
I0408 16:25:18.323892 27193 solver.cpp:218] Iteration 5700 (2.23766 iter/s, 5.36275s/12 iters), loss = 4.3818
I0408 16:25:18.323930 27193 solver.cpp:237]     Train net output #0: loss = 4.3818 (* 1 = 4.3818 loss)
I0408 16:25:18.323940 27193 sgd_solver.cpp:105] Iteration 5700, lr = 3.84109e-08
I0408 16:25:23.293401 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0408 16:25:26.345813 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0408 16:25:29.062108 27193 solver.cpp:330] Iteration 5712, Testing net (#0)
I0408 16:25:29.062134 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:25:31.348399 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:25:33.627910 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:25:33.627959 27193 solver.cpp:397]     Test net output #1: loss = 4.60959 (* 1 = 4.60959 loss)
I0408 16:25:33.719231 27193 solver.cpp:218] Iteration 5712 (0.779479 iter/s, 15.3949s/12 iters), loss = 4.59174
I0408 16:25:33.719282 27193 solver.cpp:237]     Train net output #0: loss = 4.59174 (* 1 = 4.59174 loss)
I0408 16:25:33.719295 27193 sgd_solver.cpp:105] Iteration 5712, lr = 3.74156e-08
I0408 16:25:37.948696 27193 solver.cpp:218] Iteration 5724 (2.83736 iter/s, 4.22929s/12 iters), loss = 4.40654
I0408 16:25:37.948808 27193 solver.cpp:237]     Train net output #0: loss = 4.40654 (* 1 = 4.40654 loss)
I0408 16:25:37.948822 27193 sgd_solver.cpp:105] Iteration 5724, lr = 3.64462e-08
I0408 16:25:43.044870 27193 solver.cpp:218] Iteration 5736 (2.35482 iter/s, 5.09593s/12 iters), loss = 4.62906
I0408 16:25:43.044909 27193 solver.cpp:237]     Train net output #0: loss = 4.62906 (* 1 = 4.62906 loss)
I0408 16:25:43.044919 27193 sgd_solver.cpp:105] Iteration 5736, lr = 3.55018e-08
I0408 16:25:48.117257 27193 solver.cpp:218] Iteration 5748 (2.36584 iter/s, 5.0722s/12 iters), loss = 4.49383
I0408 16:25:48.117316 27193 solver.cpp:237]     Train net output #0: loss = 4.49383 (* 1 = 4.49383 loss)
I0408 16:25:48.117331 27193 sgd_solver.cpp:105] Iteration 5748, lr = 3.4582e-08
I0408 16:25:53.192077 27193 solver.cpp:218] Iteration 5760 (2.36471 iter/s, 5.07462s/12 iters), loss = 4.5769
I0408 16:25:53.192123 27193 solver.cpp:237]     Train net output #0: loss = 4.5769 (* 1 = 4.5769 loss)
I0408 16:25:53.192135 27193 sgd_solver.cpp:105] Iteration 5760, lr = 3.36859e-08
I0408 16:25:55.152909 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:25:58.108672 27193 solver.cpp:218] Iteration 5772 (2.44081 iter/s, 4.91641s/12 iters), loss = 4.53723
I0408 16:25:58.108731 27193 solver.cpp:237]     Train net output #0: loss = 4.53723 (* 1 = 4.53723 loss)
I0408 16:25:58.108747 27193 sgd_solver.cpp:105] Iteration 5772, lr = 3.28131e-08
I0408 16:26:03.143020 27193 solver.cpp:218] Iteration 5784 (2.38372 iter/s, 5.03415s/12 iters), loss = 4.51592
I0408 16:26:03.143064 27193 solver.cpp:237]     Train net output #0: loss = 4.51592 (* 1 = 4.51592 loss)
I0408 16:26:03.143075 27193 sgd_solver.cpp:105] Iteration 5784, lr = 3.19629e-08
I0408 16:26:08.253039 27193 solver.cpp:218] Iteration 5796 (2.34842 iter/s, 5.10983s/12 iters), loss = 4.28165
I0408 16:26:08.253161 27193 solver.cpp:237]     Train net output #0: loss = 4.28165 (* 1 = 4.28165 loss)
I0408 16:26:08.253175 27193 sgd_solver.cpp:105] Iteration 5796, lr = 3.11347e-08
I0408 16:26:13.342013 27193 solver.cpp:218] Iteration 5808 (2.35816 iter/s, 5.08871s/12 iters), loss = 4.50119
I0408 16:26:13.342072 27193 solver.cpp:237]     Train net output #0: loss = 4.50119 (* 1 = 4.50119 loss)
I0408 16:26:13.342088 27193 sgd_solver.cpp:105] Iteration 5808, lr = 3.0328e-08
I0408 16:26:15.415208 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0408 16:26:18.516983 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0408 16:26:21.841890 27193 solver.cpp:330] Iteration 5814, Testing net (#0)
I0408 16:26:21.841917 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:26:24.025275 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:26:26.318379 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:26:26.318426 27193 solver.cpp:397]     Test net output #1: loss = 4.60645 (* 1 = 4.60645 loss)
I0408 16:26:28.295828 27193 solver.cpp:218] Iteration 5820 (0.802495 iter/s, 14.9534s/12 iters), loss = 4.54479
I0408 16:26:28.295881 27193 solver.cpp:237]     Train net output #0: loss = 4.54479 (* 1 = 4.54479 loss)
I0408 16:26:28.295893 27193 sgd_solver.cpp:105] Iteration 5820, lr = 2.95422e-08
I0408 16:26:33.367738 27193 solver.cpp:218] Iteration 5832 (2.36607 iter/s, 5.07171s/12 iters), loss = 4.41248
I0408 16:26:33.367789 27193 solver.cpp:237]     Train net output #0: loss = 4.41248 (* 1 = 4.41248 loss)
I0408 16:26:33.367801 27193 sgd_solver.cpp:105] Iteration 5832, lr = 2.87767e-08
I0408 16:26:38.467913 27193 solver.cpp:218] Iteration 5844 (2.35296 iter/s, 5.09997s/12 iters), loss = 4.48911
I0408 16:26:38.468050 27193 solver.cpp:237]     Train net output #0: loss = 4.48911 (* 1 = 4.48911 loss)
I0408 16:26:38.468061 27193 sgd_solver.cpp:105] Iteration 5844, lr = 2.80311e-08
I0408 16:26:43.562219 27193 solver.cpp:218] Iteration 5856 (2.3557 iter/s, 5.09403s/12 iters), loss = 4.4845
I0408 16:26:43.562266 27193 solver.cpp:237]     Train net output #0: loss = 4.4845 (* 1 = 4.4845 loss)
I0408 16:26:43.562278 27193 sgd_solver.cpp:105] Iteration 5856, lr = 2.73048e-08
I0408 16:26:47.762197 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:26:48.589120 27193 solver.cpp:218] Iteration 5868 (2.38725 iter/s, 5.02671s/12 iters), loss = 4.55619
I0408 16:26:48.589166 27193 solver.cpp:237]     Train net output #0: loss = 4.55619 (* 1 = 4.55619 loss)
I0408 16:26:48.589179 27193 sgd_solver.cpp:105] Iteration 5868, lr = 2.65973e-08
I0408 16:26:53.802035 27193 solver.cpp:218] Iteration 5880 (2.30206 iter/s, 5.21272s/12 iters), loss = 4.5046
I0408 16:26:53.802079 27193 solver.cpp:237]     Train net output #0: loss = 4.5046 (* 1 = 4.5046 loss)
I0408 16:26:53.802091 27193 sgd_solver.cpp:105] Iteration 5880, lr = 2.59082e-08
I0408 16:26:58.910082 27193 solver.cpp:218] Iteration 5892 (2.34932 iter/s, 5.10786s/12 iters), loss = 4.55107
I0408 16:26:58.910128 27193 solver.cpp:237]     Train net output #0: loss = 4.55107 (* 1 = 4.55107 loss)
I0408 16:26:58.910138 27193 sgd_solver.cpp:105] Iteration 5892, lr = 2.52369e-08
I0408 16:27:03.971207 27193 solver.cpp:218] Iteration 5904 (2.3711 iter/s, 5.06094s/12 iters), loss = 4.48669
I0408 16:27:03.971254 27193 solver.cpp:237]     Train net output #0: loss = 4.48669 (* 1 = 4.48669 loss)
I0408 16:27:03.971266 27193 sgd_solver.cpp:105] Iteration 5904, lr = 2.4583e-08
I0408 16:27:08.555034 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0408 16:27:12.260505 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0408 16:27:15.557915 27193 solver.cpp:330] Iteration 5916, Testing net (#0)
I0408 16:27:15.557943 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:27:17.683869 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:27:20.011857 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:27:20.011901 27193 solver.cpp:397]     Test net output #1: loss = 4.60365 (* 1 = 4.60365 loss)
I0408 16:27:20.103457 27193 solver.cpp:218] Iteration 5916 (0.743874 iter/s, 16.1318s/12 iters), loss = 4.29791
I0408 16:27:20.103503 27193 solver.cpp:237]     Train net output #0: loss = 4.29791 (* 1 = 4.29791 loss)
I0408 16:27:20.103513 27193 sgd_solver.cpp:105] Iteration 5916, lr = 2.3946e-08
I0408 16:27:24.633662 27193 solver.cpp:218] Iteration 5928 (2.64899 iter/s, 4.53003s/12 iters), loss = 4.37581
I0408 16:27:24.633709 27193 solver.cpp:237]     Train net output #0: loss = 4.37581 (* 1 = 4.37581 loss)
I0408 16:27:24.633720 27193 sgd_solver.cpp:105] Iteration 5928, lr = 2.33256e-08
I0408 16:27:29.730214 27193 solver.cpp:218] Iteration 5940 (2.35462 iter/s, 5.09636s/12 iters), loss = 4.68569
I0408 16:27:29.730254 27193 solver.cpp:237]     Train net output #0: loss = 4.68569 (* 1 = 4.68569 loss)
I0408 16:27:29.730264 27193 sgd_solver.cpp:105] Iteration 5940, lr = 2.27212e-08
I0408 16:27:34.996927 27193 solver.cpp:218] Iteration 5952 (2.27855 iter/s, 5.26651s/12 iters), loss = 4.33319
I0408 16:27:34.996975 27193 solver.cpp:237]     Train net output #0: loss = 4.33319 (* 1 = 4.33319 loss)
I0408 16:27:34.996986 27193 sgd_solver.cpp:105] Iteration 5952, lr = 2.21325e-08
I0408 16:27:40.323433 27193 solver.cpp:218] Iteration 5964 (2.25297 iter/s, 5.3263s/12 iters), loss = 4.38939
I0408 16:27:40.323559 27193 solver.cpp:237]     Train net output #0: loss = 4.38939 (* 1 = 4.38939 loss)
I0408 16:27:40.323577 27193 sgd_solver.cpp:105] Iteration 5964, lr = 2.1559e-08
I0408 16:27:41.665269 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:27:45.431747 27193 solver.cpp:218] Iteration 5976 (2.34924 iter/s, 5.10804s/12 iters), loss = 4.27155
I0408 16:27:45.431797 27193 solver.cpp:237]     Train net output #0: loss = 4.27155 (* 1 = 4.27155 loss)
I0408 16:27:45.431809 27193 sgd_solver.cpp:105] Iteration 5976, lr = 2.10004e-08
I0408 16:27:50.662024 27193 solver.cpp:218] Iteration 5988 (2.29442 iter/s, 5.23008s/12 iters), loss = 4.53687
I0408 16:27:50.662076 27193 solver.cpp:237]     Train net output #0: loss = 4.53687 (* 1 = 4.53687 loss)
I0408 16:27:50.662086 27193 sgd_solver.cpp:105] Iteration 5988, lr = 2.04563e-08
I0408 16:27:56.160012 27193 solver.cpp:218] Iteration 6000 (2.1827 iter/s, 5.49778s/12 iters), loss = 4.62165
I0408 16:27:56.160046 27193 solver.cpp:237]     Train net output #0: loss = 4.62165 (* 1 = 4.62165 loss)
I0408 16:27:56.160053 27193 sgd_solver.cpp:105] Iteration 6000, lr = 1.99262e-08
I0408 16:28:01.207567 27193 solver.cpp:218] Iteration 6012 (2.37748 iter/s, 5.04737s/12 iters), loss = 4.35141
I0408 16:28:01.207615 27193 solver.cpp:237]     Train net output #0: loss = 4.35141 (* 1 = 4.35141 loss)
I0408 16:28:01.207628 27193 sgd_solver.cpp:105] Iteration 6012, lr = 1.94099e-08
I0408 16:28:03.262020 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0408 16:28:06.305600 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0408 16:28:08.663228 27193 solver.cpp:330] Iteration 6018, Testing net (#0)
I0408 16:28:08.663252 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:28:10.715317 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:28:13.086661 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:28:13.086709 27193 solver.cpp:397]     Test net output #1: loss = 4.60333 (* 1 = 4.60333 loss)
I0408 16:28:14.863242 27193 solver.cpp:218] Iteration 6024 (0.878783 iter/s, 13.6553s/12 iters), loss = 4.54595
I0408 16:28:14.863283 27193 solver.cpp:237]     Train net output #0: loss = 4.54595 (* 1 = 4.54595 loss)
I0408 16:28:14.863294 27193 sgd_solver.cpp:105] Iteration 6024, lr = 1.8907e-08
I0408 16:28:20.149415 27193 solver.cpp:218] Iteration 6036 (2.27016 iter/s, 5.28597s/12 iters), loss = 4.46021
I0408 16:28:20.149471 27193 solver.cpp:237]     Train net output #0: loss = 4.46021 (* 1 = 4.46021 loss)
I0408 16:28:20.149484 27193 sgd_solver.cpp:105] Iteration 6036, lr = 1.84171e-08
I0408 16:28:25.362740 27193 solver.cpp:218] Iteration 6048 (2.30188 iter/s, 5.21312s/12 iters), loss = 4.43295
I0408 16:28:25.362783 27193 solver.cpp:237]     Train net output #0: loss = 4.43295 (* 1 = 4.43295 loss)
I0408 16:28:25.362794 27193 sgd_solver.cpp:105] Iteration 6048, lr = 1.79399e-08
I0408 16:28:30.472935 27193 solver.cpp:218] Iteration 6060 (2.34834 iter/s, 5.11s/12 iters), loss = 4.51225
I0408 16:28:30.472980 27193 solver.cpp:237]     Train net output #0: loss = 4.51225 (* 1 = 4.51225 loss)
I0408 16:28:30.472991 27193 sgd_solver.cpp:105] Iteration 6060, lr = 1.74751e-08
I0408 16:28:33.984760 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:28:35.589979 27193 solver.cpp:218] Iteration 6072 (2.34519 iter/s, 5.11685s/12 iters), loss = 4.6064
I0408 16:28:35.590019 27193 solver.cpp:237]     Train net output #0: loss = 4.6064 (* 1 = 4.6064 loss)
I0408 16:28:35.590029 27193 sgd_solver.cpp:105] Iteration 6072, lr = 1.70223e-08
I0408 16:28:40.731047 27193 solver.cpp:218] Iteration 6084 (2.33423 iter/s, 5.14087s/12 iters), loss = 4.56349
I0408 16:28:40.731164 27193 solver.cpp:237]     Train net output #0: loss = 4.56349 (* 1 = 4.56349 loss)
I0408 16:28:40.731178 27193 sgd_solver.cpp:105] Iteration 6084, lr = 1.65813e-08
I0408 16:28:45.870893 27193 solver.cpp:218] Iteration 6096 (2.33482 iter/s, 5.13958s/12 iters), loss = 4.36403
I0408 16:28:45.870939 27193 solver.cpp:237]     Train net output #0: loss = 4.36403 (* 1 = 4.36403 loss)
I0408 16:28:45.870950 27193 sgd_solver.cpp:105] Iteration 6096, lr = 1.61516e-08
I0408 16:28:50.962499 27193 solver.cpp:218] Iteration 6108 (2.35691 iter/s, 5.09141s/12 iters), loss = 4.44593
I0408 16:28:50.962544 27193 solver.cpp:237]     Train net output #0: loss = 4.44593 (* 1 = 4.44593 loss)
I0408 16:28:50.962555 27193 sgd_solver.cpp:105] Iteration 6108, lr = 1.57331e-08
I0408 16:28:55.642967 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0408 16:28:58.684162 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0408 16:29:01.021245 27193 solver.cpp:330] Iteration 6120, Testing net (#0)
I0408 16:29:01.021268 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:29:03.077419 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:29:05.482120 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:29:05.482167 27193 solver.cpp:397]     Test net output #1: loss = 4.60228 (* 1 = 4.60228 loss)
I0408 16:29:05.573463 27193 solver.cpp:218] Iteration 6120 (0.821327 iter/s, 14.6105s/12 iters), loss = 4.63966
I0408 16:29:05.573514 27193 solver.cpp:237]     Train net output #0: loss = 4.63966 (* 1 = 4.63966 loss)
I0408 16:29:05.573526 27193 sgd_solver.cpp:105] Iteration 6120, lr = 1.53255e-08
I0408 16:29:09.869110 27193 solver.cpp:218] Iteration 6132 (2.79364 iter/s, 4.29547s/12 iters), loss = 4.47251
I0408 16:29:09.869156 27193 solver.cpp:237]     Train net output #0: loss = 4.47251 (* 1 = 4.47251 loss)
I0408 16:29:09.869168 27193 sgd_solver.cpp:105] Iteration 6132, lr = 1.49284e-08
I0408 16:29:14.972100 27193 solver.cpp:218] Iteration 6144 (2.35165 iter/s, 5.1028s/12 iters), loss = 4.65239
I0408 16:29:14.972229 27193 solver.cpp:237]     Train net output #0: loss = 4.65239 (* 1 = 4.65239 loss)
I0408 16:29:14.972239 27193 sgd_solver.cpp:105] Iteration 6144, lr = 1.45416e-08
I0408 16:29:20.112799 27193 solver.cpp:218] Iteration 6156 (2.33444 iter/s, 5.14042s/12 iters), loss = 4.602
I0408 16:29:20.112844 27193 solver.cpp:237]     Train net output #0: loss = 4.602 (* 1 = 4.602 loss)
I0408 16:29:20.112855 27193 sgd_solver.cpp:105] Iteration 6156, lr = 1.41648e-08
I0408 16:29:25.240741 27193 solver.cpp:218] Iteration 6168 (2.34021 iter/s, 5.12774s/12 iters), loss = 4.66594
I0408 16:29:25.240787 27193 solver.cpp:237]     Train net output #0: loss = 4.66594 (* 1 = 4.66594 loss)
I0408 16:29:25.240798 27193 sgd_solver.cpp:105] Iteration 6168, lr = 1.37978e-08
I0408 16:29:25.862360 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:29:30.329274 27193 solver.cpp:218] Iteration 6180 (2.35833 iter/s, 5.08834s/12 iters), loss = 4.56264
I0408 16:29:30.329319 27193 solver.cpp:237]     Train net output #0: loss = 4.56264 (* 1 = 4.56264 loss)
I0408 16:29:30.329331 27193 sgd_solver.cpp:105] Iteration 6180, lr = 1.34403e-08
I0408 16:29:35.445470 27193 solver.cpp:218] Iteration 6192 (2.34558 iter/s, 5.116s/12 iters), loss = 4.6396
I0408 16:29:35.445511 27193 solver.cpp:237]     Train net output #0: loss = 4.6396 (* 1 = 4.6396 loss)
I0408 16:29:35.445521 27193 sgd_solver.cpp:105] Iteration 6192, lr = 1.3092e-08
I0408 16:29:40.590138 27193 solver.cpp:218] Iteration 6204 (2.3326 iter/s, 5.14448s/12 iters), loss = 4.38791
I0408 16:29:40.590183 27193 solver.cpp:237]     Train net output #0: loss = 4.38791 (* 1 = 4.38791 loss)
I0408 16:29:40.590193 27193 sgd_solver.cpp:105] Iteration 6204, lr = 1.27528e-08
I0408 16:29:45.724797 27193 solver.cpp:218] Iteration 6216 (2.33715 iter/s, 5.13446s/12 iters), loss = 4.30752
I0408 16:29:45.724920 27193 solver.cpp:237]     Train net output #0: loss = 4.30752 (* 1 = 4.30752 loss)
I0408 16:29:45.724932 27193 sgd_solver.cpp:105] Iteration 6216, lr = 1.24224e-08
I0408 16:29:47.823065 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0408 16:29:50.859999 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0408 16:29:53.187700 27193 solver.cpp:330] Iteration 6222, Testing net (#0)
I0408 16:29:53.187724 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:29:55.187355 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:29:56.463342 27193 blocking_queue.cpp:49] Waiting for data
I0408 16:29:57.637084 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:29:57.637130 27193 solver.cpp:397]     Test net output #1: loss = 4.60752 (* 1 = 4.60752 loss)
I0408 16:29:59.640286 27193 solver.cpp:218] Iteration 6228 (0.86238 iter/s, 13.915s/12 iters), loss = 4.47783
I0408 16:29:59.640331 27193 solver.cpp:237]     Train net output #0: loss = 4.47783 (* 1 = 4.47783 loss)
I0408 16:29:59.640343 27193 sgd_solver.cpp:105] Iteration 6228, lr = 1.21005e-08
I0408 16:30:04.764235 27193 solver.cpp:218] Iteration 6240 (2.34203 iter/s, 5.12375s/12 iters), loss = 4.59462
I0408 16:30:04.764277 27193 solver.cpp:237]     Train net output #0: loss = 4.59462 (* 1 = 4.59462 loss)
I0408 16:30:04.764288 27193 sgd_solver.cpp:105] Iteration 6240, lr = 1.1787e-08
I0408 16:30:09.845319 27193 solver.cpp:218] Iteration 6252 (2.36179 iter/s, 5.08089s/12 iters), loss = 4.37929
I0408 16:30:09.845363 27193 solver.cpp:237]     Train net output #0: loss = 4.37929 (* 1 = 4.37929 loss)
I0408 16:30:09.845374 27193 sgd_solver.cpp:105] Iteration 6252, lr = 1.14816e-08
I0408 16:30:14.916920 27193 solver.cpp:218] Iteration 6264 (2.36621 iter/s, 5.07141s/12 iters), loss = 4.53236
I0408 16:30:14.916965 27193 solver.cpp:237]     Train net output #0: loss = 4.53236 (* 1 = 4.53236 loss)
I0408 16:30:14.916975 27193 sgd_solver.cpp:105] Iteration 6264, lr = 1.11841e-08
I0408 16:30:17.753155 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:30:20.077101 27193 solver.cpp:218] Iteration 6276 (2.32559 iter/s, 5.15998s/12 iters), loss = 4.41006
I0408 16:30:20.077147 27193 solver.cpp:237]     Train net output #0: loss = 4.41006 (* 1 = 4.41006 loss)
I0408 16:30:20.077158 27193 sgd_solver.cpp:105] Iteration 6276, lr = 1.08943e-08
I0408 16:30:25.212997 27193 solver.cpp:218] Iteration 6288 (2.33659 iter/s, 5.1357s/12 iters), loss = 4.47763
I0408 16:30:25.213042 27193 solver.cpp:237]     Train net output #0: loss = 4.47763 (* 1 = 4.47763 loss)
I0408 16:30:25.213052 27193 sgd_solver.cpp:105] Iteration 6288, lr = 1.0612e-08
I0408 16:30:30.320366 27193 solver.cpp:218] Iteration 6300 (2.34963 iter/s, 5.10718s/12 iters), loss = 4.41282
I0408 16:30:30.320406 27193 solver.cpp:237]     Train net output #0: loss = 4.41282 (* 1 = 4.41282 loss)
I0408 16:30:30.320418 27193 sgd_solver.cpp:105] Iteration 6300, lr = 1.03371e-08
I0408 16:30:35.253741 27193 solver.cpp:218] Iteration 6312 (2.43251 iter/s, 4.93318s/12 iters), loss = 4.59614
I0408 16:30:35.253788 27193 solver.cpp:237]     Train net output #0: loss = 4.59614 (* 1 = 4.59614 loss)
I0408 16:30:35.253800 27193 sgd_solver.cpp:105] Iteration 6312, lr = 1.00692e-08
I0408 16:30:39.871369 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0408 16:30:42.928097 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0408 16:30:45.250576 27193 solver.cpp:330] Iteration 6324, Testing net (#0)
I0408 16:30:45.250599 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:30:47.223742 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:30:49.707835 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:30:49.707913 27193 solver.cpp:397]     Test net output #1: loss = 4.60313 (* 1 = 4.60313 loss)
I0408 16:30:49.796277 27193 solver.cpp:218] Iteration 6324 (0.825192 iter/s, 14.5421s/12 iters), loss = 4.59867
I0408 16:30:49.796334 27193 solver.cpp:237]     Train net output #0: loss = 4.59867 (* 1 = 4.59867 loss)
I0408 16:30:49.796344 27193 sgd_solver.cpp:105] Iteration 6324, lr = 9.80832e-09
I0408 16:30:54.302433 27193 solver.cpp:218] Iteration 6336 (2.66314 iter/s, 4.50596s/12 iters), loss = 4.40134
I0408 16:30:54.302479 27193 solver.cpp:237]     Train net output #0: loss = 4.40134 (* 1 = 4.40134 loss)
I0408 16:30:54.302490 27193 sgd_solver.cpp:105] Iteration 6336, lr = 9.55418e-09
I0408 16:30:59.402817 27193 solver.cpp:218] Iteration 6348 (2.35285 iter/s, 5.10019s/12 iters), loss = 4.61536
I0408 16:30:59.402860 27193 solver.cpp:237]     Train net output #0: loss = 4.61536 (* 1 = 4.61536 loss)
I0408 16:30:59.402871 27193 sgd_solver.cpp:105] Iteration 6348, lr = 9.30662e-09
I0408 16:31:04.425339 27193 solver.cpp:218] Iteration 6360 (2.38933 iter/s, 5.02233s/12 iters), loss = 4.53884
I0408 16:31:04.425377 27193 solver.cpp:237]     Train net output #0: loss = 4.53884 (* 1 = 4.53884 loss)
I0408 16:31:04.425385 27193 sgd_solver.cpp:105] Iteration 6360, lr = 9.06548e-09
I0408 16:31:09.355182 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:31:09.497336 27193 solver.cpp:218] Iteration 6372 (2.36602 iter/s, 5.07181s/12 iters), loss = 4.49093
I0408 16:31:09.497372 27193 solver.cpp:237]     Train net output #0: loss = 4.49093 (* 1 = 4.49093 loss)
I0408 16:31:09.497382 27193 sgd_solver.cpp:105] Iteration 6372, lr = 8.83059e-09
I0408 16:31:14.800593 27193 solver.cpp:218] Iteration 6384 (2.26284 iter/s, 5.30306s/12 iters), loss = 4.47754
I0408 16:31:14.800634 27193 solver.cpp:237]     Train net output #0: loss = 4.47754 (* 1 = 4.47754 loss)
I0408 16:31:14.800645 27193 sgd_solver.cpp:105] Iteration 6384, lr = 8.60179e-09
I0408 16:31:20.102483 27193 solver.cpp:218] Iteration 6396 (2.26343 iter/s, 5.30169s/12 iters), loss = 4.29965
I0408 16:31:20.102612 27193 solver.cpp:237]     Train net output #0: loss = 4.29965 (* 1 = 4.29965 loss)
I0408 16:31:20.102622 27193 sgd_solver.cpp:105] Iteration 6396, lr = 8.37891e-09
I0408 16:31:25.252049 27193 solver.cpp:218] Iteration 6408 (2.33042 iter/s, 5.14928s/12 iters), loss = 4.35047
I0408 16:31:25.252095 27193 solver.cpp:237]     Train net output #0: loss = 4.35047 (* 1 = 4.35047 loss)
I0408 16:31:25.252106 27193 sgd_solver.cpp:105] Iteration 6408, lr = 8.16181e-09
I0408 16:31:30.562574 27193 solver.cpp:218] Iteration 6420 (2.25975 iter/s, 5.31032s/12 iters), loss = 4.50813
I0408 16:31:30.562613 27193 solver.cpp:237]     Train net output #0: loss = 4.50813 (* 1 = 4.50813 loss)
I0408 16:31:30.562620 27193 sgd_solver.cpp:105] Iteration 6420, lr = 7.95033e-09
I0408 16:31:32.568548 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0408 16:31:35.561754 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0408 16:31:37.889438 27193 solver.cpp:330] Iteration 6426, Testing net (#0)
I0408 16:31:37.889462 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:31:39.756872 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:31:42.287951 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:31:42.287999 27193 solver.cpp:397]     Test net output #1: loss = 4.60686 (* 1 = 4.60686 loss)
I0408 16:31:44.308146 27193 solver.cpp:218] Iteration 6432 (0.873036 iter/s, 13.7451s/12 iters), loss = 4.40905
I0408 16:31:44.308194 27193 solver.cpp:237]     Train net output #0: loss = 4.40905 (* 1 = 4.40905 loss)
I0408 16:31:44.308207 27193 sgd_solver.cpp:105] Iteration 6432, lr = 7.74433e-09
I0408 16:31:49.794802 27193 solver.cpp:218] Iteration 6444 (2.18721 iter/s, 5.48644s/12 iters), loss = 4.51455
I0408 16:31:49.794864 27193 solver.cpp:237]     Train net output #0: loss = 4.51455 (* 1 = 4.51455 loss)
I0408 16:31:49.794878 27193 sgd_solver.cpp:105] Iteration 6444, lr = 7.54368e-09
I0408 16:31:55.284668 27193 solver.cpp:218] Iteration 6456 (2.18594 iter/s, 5.48964s/12 iters), loss = 4.61731
I0408 16:31:55.284775 27193 solver.cpp:237]     Train net output #0: loss = 4.61731 (* 1 = 4.61731 loss)
I0408 16:31:55.284788 27193 sgd_solver.cpp:105] Iteration 6456, lr = 7.34821e-09
I0408 16:32:00.479015 27193 solver.cpp:218] Iteration 6468 (2.31032 iter/s, 5.19408s/12 iters), loss = 4.63176
I0408 16:32:00.479065 27193 solver.cpp:237]     Train net output #0: loss = 4.63176 (* 1 = 4.63176 loss)
I0408 16:32:00.479077 27193 sgd_solver.cpp:105] Iteration 6468, lr = 7.15782e-09
I0408 16:32:02.558854 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:32:05.611259 27193 solver.cpp:218] Iteration 6480 (2.33825 iter/s, 5.13204s/12 iters), loss = 4.51698
I0408 16:32:05.611302 27193 solver.cpp:237]     Train net output #0: loss = 4.51698 (* 1 = 4.51698 loss)
I0408 16:32:05.611313 27193 sgd_solver.cpp:105] Iteration 6480, lr = 6.97236e-09
I0408 16:32:10.674576 27193 solver.cpp:218] Iteration 6492 (2.37008 iter/s, 5.06312s/12 iters), loss = 4.41326
I0408 16:32:10.674620 27193 solver.cpp:237]     Train net output #0: loss = 4.41326 (* 1 = 4.41326 loss)
I0408 16:32:10.674631 27193 sgd_solver.cpp:105] Iteration 6492, lr = 6.7917e-09
I0408 16:32:15.810330 27193 solver.cpp:218] Iteration 6504 (2.33665 iter/s, 5.13555s/12 iters), loss = 4.35685
I0408 16:32:15.810375 27193 solver.cpp:237]     Train net output #0: loss = 4.35685 (* 1 = 4.35685 loss)
I0408 16:32:15.810387 27193 sgd_solver.cpp:105] Iteration 6504, lr = 6.61572e-09
I0408 16:32:21.245097 27193 solver.cpp:218] Iteration 6516 (2.20809 iter/s, 5.43455s/12 iters), loss = 4.5163
I0408 16:32:21.245146 27193 solver.cpp:237]     Train net output #0: loss = 4.5163 (* 1 = 4.5163 loss)
I0408 16:32:21.245158 27193 sgd_solver.cpp:105] Iteration 6516, lr = 6.44431e-09
I0408 16:32:25.907410 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0408 16:32:28.957850 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0408 16:32:31.293684 27193 solver.cpp:330] Iteration 6528, Testing net (#0)
I0408 16:32:31.293710 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:32:33.194507 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:32:35.863139 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:32:35.863186 27193 solver.cpp:397]     Test net output #1: loss = 4.61103 (* 1 = 4.61103 loss)
I0408 16:32:35.953999 27193 solver.cpp:218] Iteration 6528 (0.815859 iter/s, 14.7084s/12 iters), loss = 4.57103
I0408 16:32:35.954056 27193 solver.cpp:237]     Train net output #0: loss = 4.57103 (* 1 = 4.57103 loss)
I0408 16:32:35.954069 27193 sgd_solver.cpp:105] Iteration 6528, lr = 6.27733e-09
I0408 16:32:40.454840 27193 solver.cpp:218] Iteration 6540 (2.66629 iter/s, 4.50064s/12 iters), loss = 4.40541
I0408 16:32:40.454890 27193 solver.cpp:237]     Train net output #0: loss = 4.40541 (* 1 = 4.40541 loss)
I0408 16:32:40.454900 27193 sgd_solver.cpp:105] Iteration 6540, lr = 6.11468e-09
I0408 16:32:45.850446 27193 solver.cpp:218] Iteration 6552 (2.22412 iter/s, 5.39539s/12 iters), loss = 4.58417
I0408 16:32:45.850492 27193 solver.cpp:237]     Train net output #0: loss = 4.58417 (* 1 = 4.58417 loss)
I0408 16:32:45.850503 27193 sgd_solver.cpp:105] Iteration 6552, lr = 5.95625e-09
I0408 16:32:50.867393 27193 solver.cpp:218] Iteration 6564 (2.39199 iter/s, 5.01674s/12 iters), loss = 4.39049
I0408 16:32:50.867440 27193 solver.cpp:237]     Train net output #0: loss = 4.39049 (* 1 = 4.39049 loss)
I0408 16:32:50.867452 27193 sgd_solver.cpp:105] Iteration 6564, lr = 5.80192e-09
I0408 16:32:55.203734 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:32:55.963305 27193 solver.cpp:218] Iteration 6576 (2.35492 iter/s, 5.09571s/12 iters), loss = 4.49177
I0408 16:32:55.963418 27193 solver.cpp:237]     Train net output #0: loss = 4.49177 (* 1 = 4.49177 loss)
I0408 16:32:55.963431 27193 sgd_solver.cpp:105] Iteration 6576, lr = 5.65159e-09
I0408 16:33:01.052239 27193 solver.cpp:218] Iteration 6588 (2.35818 iter/s, 5.08867s/12 iters), loss = 4.45976
I0408 16:33:01.052286 27193 solver.cpp:237]     Train net output #0: loss = 4.45976 (* 1 = 4.45976 loss)
I0408 16:33:01.052299 27193 sgd_solver.cpp:105] Iteration 6588, lr = 5.50515e-09
I0408 16:33:06.135293 27193 solver.cpp:218] Iteration 6600 (2.36088 iter/s, 5.08285s/12 iters), loss = 4.42979
I0408 16:33:06.135337 27193 solver.cpp:237]     Train net output #0: loss = 4.42979 (* 1 = 4.42979 loss)
I0408 16:33:06.135349 27193 sgd_solver.cpp:105] Iteration 6600, lr = 5.36251e-09
I0408 16:33:11.218256 27193 solver.cpp:218] Iteration 6612 (2.36092 iter/s, 5.08276s/12 iters), loss = 4.47477
I0408 16:33:11.218302 27193 solver.cpp:237]     Train net output #0: loss = 4.47477 (* 1 = 4.47477 loss)
I0408 16:33:11.218314 27193 sgd_solver.cpp:105] Iteration 6612, lr = 5.22356e-09
I0408 16:33:16.362416 27193 solver.cpp:218] Iteration 6624 (2.33283 iter/s, 5.14396s/12 iters), loss = 4.41984
I0408 16:33:16.362463 27193 solver.cpp:237]     Train net output #0: loss = 4.41984 (* 1 = 4.41984 loss)
I0408 16:33:16.362474 27193 sgd_solver.cpp:105] Iteration 6624, lr = 5.08822e-09
I0408 16:33:18.417848 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0408 16:33:21.475427 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0408 16:33:23.812764 27193 solver.cpp:330] Iteration 6630, Testing net (#0)
I0408 16:33:23.812790 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:33:25.675261 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:33:28.274633 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:33:28.274813 27193 solver.cpp:397]     Test net output #1: loss = 4.60657 (* 1 = 4.60657 loss)
I0408 16:33:30.291043 27193 solver.cpp:218] Iteration 6636 (0.861564 iter/s, 13.9282s/12 iters), loss = 4.41269
I0408 16:33:30.291117 27193 solver.cpp:237]     Train net output #0: loss = 4.41269 (* 1 = 4.41269 loss)
I0408 16:33:30.291132 27193 sgd_solver.cpp:105] Iteration 6636, lr = 4.95638e-09
I0408 16:33:35.832535 27193 solver.cpp:218] Iteration 6648 (2.16557 iter/s, 5.54126s/12 iters), loss = 4.6847
I0408 16:33:35.832572 27193 solver.cpp:237]     Train net output #0: loss = 4.6847 (* 1 = 4.6847 loss)
I0408 16:33:35.832581 27193 sgd_solver.cpp:105] Iteration 6648, lr = 4.82796e-09
I0408 16:33:41.038374 27193 solver.cpp:218] Iteration 6660 (2.30519 iter/s, 5.20564s/12 iters), loss = 4.47176
I0408 16:33:41.038411 27193 solver.cpp:237]     Train net output #0: loss = 4.47176 (* 1 = 4.47176 loss)
I0408 16:33:41.038420 27193 sgd_solver.cpp:105] Iteration 6660, lr = 4.70286e-09
I0408 16:33:46.144554 27193 solver.cpp:218] Iteration 6672 (2.35019 iter/s, 5.10598s/12 iters), loss = 4.57342
I0408 16:33:46.144591 27193 solver.cpp:237]     Train net output #0: loss = 4.57342 (* 1 = 4.57342 loss)
I0408 16:33:46.144600 27193 sgd_solver.cpp:105] Iteration 6672, lr = 4.58101e-09
I0408 16:33:47.473100 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:33:51.303365 27193 solver.cpp:218] Iteration 6684 (2.32621 iter/s, 5.15862s/12 iters), loss = 4.30533
I0408 16:33:51.303400 27193 solver.cpp:237]     Train net output #0: loss = 4.30533 (* 1 = 4.30533 loss)
I0408 16:33:51.303408 27193 sgd_solver.cpp:105] Iteration 6684, lr = 4.46231e-09
I0408 16:33:56.577067 27193 solver.cpp:218] Iteration 6696 (2.27553 iter/s, 5.27351s/12 iters), loss = 4.58128
I0408 16:33:56.577106 27193 solver.cpp:237]     Train net output #0: loss = 4.58128 (* 1 = 4.58128 loss)
I0408 16:33:56.577116 27193 sgd_solver.cpp:105] Iteration 6696, lr = 4.34669e-09
I0408 16:34:02.027248 27193 solver.cpp:218] Iteration 6708 (2.20185 iter/s, 5.44997s/12 iters), loss = 4.62421
I0408 16:34:02.027354 27193 solver.cpp:237]     Train net output #0: loss = 4.62421 (* 1 = 4.62421 loss)
I0408 16:34:02.027366 27193 sgd_solver.cpp:105] Iteration 6708, lr = 4.23407e-09
I0408 16:34:07.537761 27193 solver.cpp:218] Iteration 6720 (2.17776 iter/s, 5.51024s/12 iters), loss = 4.45597
I0408 16:34:07.537798 27193 solver.cpp:237]     Train net output #0: loss = 4.45597 (* 1 = 4.45597 loss)
I0408 16:34:07.537807 27193 sgd_solver.cpp:105] Iteration 6720, lr = 4.12436e-09
I0408 16:34:12.143082 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0408 16:34:15.182842 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0408 16:34:17.693209 27193 solver.cpp:330] Iteration 6732, Testing net (#0)
I0408 16:34:17.693235 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:34:19.516326 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:34:22.158798 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:34:22.158849 27193 solver.cpp:397]     Test net output #1: loss = 4.60206 (* 1 = 4.60206 loss)
I0408 16:34:22.250200 27193 solver.cpp:218] Iteration 6732 (0.815663 iter/s, 14.712s/12 iters), loss = 4.51227
I0408 16:34:22.250262 27193 solver.cpp:237]     Train net output #0: loss = 4.51227 (* 1 = 4.51227 loss)
I0408 16:34:22.250274 27193 sgd_solver.cpp:105] Iteration 6732, lr = 4.0175e-09
I0408 16:34:26.655844 27193 solver.cpp:218] Iteration 6744 (2.7239 iter/s, 4.40544s/12 iters), loss = 4.53503
I0408 16:34:26.655894 27193 solver.cpp:237]     Train net output #0: loss = 4.53503 (* 1 = 4.53503 loss)
I0408 16:34:26.655903 27193 sgd_solver.cpp:105] Iteration 6744, lr = 3.9134e-09
I0408 16:34:31.906769 27193 solver.cpp:218] Iteration 6756 (2.28541 iter/s, 5.25071s/12 iters), loss = 4.39754
I0408 16:34:31.906817 27193 solver.cpp:237]     Train net output #0: loss = 4.39754 (* 1 = 4.39754 loss)
I0408 16:34:31.906829 27193 sgd_solver.cpp:105] Iteration 6756, lr = 3.812e-09
I0408 16:34:36.960134 27193 solver.cpp:218] Iteration 6768 (2.37475 iter/s, 5.05316s/12 iters), loss = 4.52026
I0408 16:34:36.962623 27193 solver.cpp:237]     Train net output #0: loss = 4.52026 (* 1 = 4.52026 loss)
I0408 16:34:36.962635 27193 sgd_solver.cpp:105] Iteration 6768, lr = 3.71323e-09
I0408 16:34:40.550762 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:34:42.080868 27193 solver.cpp:218] Iteration 6780 (2.34463 iter/s, 5.11809s/12 iters), loss = 4.5985
I0408 16:34:42.080919 27193 solver.cpp:237]     Train net output #0: loss = 4.5985 (* 1 = 4.5985 loss)
I0408 16:34:42.080931 27193 sgd_solver.cpp:105] Iteration 6780, lr = 3.61702e-09
I0408 16:34:47.175865 27193 solver.cpp:218] Iteration 6792 (2.35535 iter/s, 5.09479s/12 iters), loss = 4.47694
I0408 16:34:47.175906 27193 solver.cpp:237]     Train net output #0: loss = 4.47694 (* 1 = 4.47694 loss)
I0408 16:34:47.175916 27193 sgd_solver.cpp:105] Iteration 6792, lr = 3.5233e-09
I0408 16:34:52.299461 27193 solver.cpp:218] Iteration 6804 (2.3422 iter/s, 5.12339s/12 iters), loss = 4.46964
I0408 16:34:52.299505 27193 solver.cpp:237]     Train net output #0: loss = 4.46964 (* 1 = 4.46964 loss)
I0408 16:34:52.299516 27193 sgd_solver.cpp:105] Iteration 6804, lr = 3.43201e-09
I0408 16:34:57.437860 27193 solver.cpp:218] Iteration 6816 (2.33545 iter/s, 5.13819s/12 iters), loss = 4.46089
I0408 16:34:57.437917 27193 solver.cpp:237]     Train net output #0: loss = 4.46089 (* 1 = 4.46089 loss)
I0408 16:34:57.437932 27193 sgd_solver.cpp:105] Iteration 6816, lr = 3.34308e-09
I0408 16:35:02.720722 27193 solver.cpp:218] Iteration 6828 (2.27159 iter/s, 5.28265s/12 iters), loss = 4.52717
I0408 16:35:02.720769 27193 solver.cpp:237]     Train net output #0: loss = 4.52717 (* 1 = 4.52717 loss)
I0408 16:35:02.720780 27193 sgd_solver.cpp:105] Iteration 6828, lr = 3.25646e-09
I0408 16:35:04.711894 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0408 16:35:07.757092 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0408 16:35:10.098258 27193 solver.cpp:330] Iteration 6834, Testing net (#0)
I0408 16:35:10.098282 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:35:11.967984 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:35:14.651461 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:35:14.651490 27193 solver.cpp:397]     Test net output #1: loss = 4.60693 (* 1 = 4.60693 loss)
I0408 16:35:16.695681 27193 solver.cpp:218] Iteration 6840 (0.858707 iter/s, 13.9745s/12 iters), loss = 4.4803
I0408 16:35:16.695734 27193 solver.cpp:237]     Train net output #0: loss = 4.4803 (* 1 = 4.4803 loss)
I0408 16:35:16.695744 27193 sgd_solver.cpp:105] Iteration 6840, lr = 3.17209e-09
I0408 16:35:22.201917 27193 solver.cpp:218] Iteration 6852 (2.17943 iter/s, 5.50602s/12 iters), loss = 4.67868
I0408 16:35:22.201974 27193 solver.cpp:237]     Train net output #0: loss = 4.67868 (* 1 = 4.67868 loss)
I0408 16:35:22.201985 27193 sgd_solver.cpp:105] Iteration 6852, lr = 3.0899e-09
I0408 16:35:27.394825 27193 solver.cpp:218] Iteration 6864 (2.31093 iter/s, 5.19271s/12 iters), loss = 4.50976
I0408 16:35:27.394868 27193 solver.cpp:237]     Train net output #0: loss = 4.50976 (* 1 = 4.50976 loss)
I0408 16:35:27.394879 27193 sgd_solver.cpp:105] Iteration 6864, lr = 3.00984e-09
I0408 16:35:32.450868 27193 solver.cpp:218] Iteration 6876 (2.37349 iter/s, 5.05584s/12 iters), loss = 4.59306
I0408 16:35:32.450911 27193 solver.cpp:237]     Train net output #0: loss = 4.59306 (* 1 = 4.59306 loss)
I0408 16:35:32.450922 27193 sgd_solver.cpp:105] Iteration 6876, lr = 2.93185e-09
I0408 16:35:33.073781 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:35:37.533618 27193 solver.cpp:218] Iteration 6888 (2.36102 iter/s, 5.08255s/12 iters), loss = 4.52473
I0408 16:35:37.533660 27193 solver.cpp:237]     Train net output #0: loss = 4.52473 (* 1 = 4.52473 loss)
I0408 16:35:37.533671 27193 sgd_solver.cpp:105] Iteration 6888, lr = 2.85588e-09
I0408 16:35:42.630381 27193 solver.cpp:218] Iteration 6900 (2.35453 iter/s, 5.09656s/12 iters), loss = 4.62427
I0408 16:35:42.630538 27193 solver.cpp:237]     Train net output #0: loss = 4.62427 (* 1 = 4.62427 loss)
I0408 16:35:42.630550 27193 sgd_solver.cpp:105] Iteration 6900, lr = 2.78189e-09
I0408 16:35:47.730756 27193 solver.cpp:218] Iteration 6912 (2.35291 iter/s, 5.10006s/12 iters), loss = 4.34161
I0408 16:35:47.730800 27193 solver.cpp:237]     Train net output #0: loss = 4.34161 (* 1 = 4.34161 loss)
I0408 16:35:47.730813 27193 sgd_solver.cpp:105] Iteration 6912, lr = 2.70981e-09
I0408 16:35:52.899680 27193 solver.cpp:218] Iteration 6924 (2.32166 iter/s, 5.16872s/12 iters), loss = 4.25648
I0408 16:35:52.899726 27193 solver.cpp:237]     Train net output #0: loss = 4.25648 (* 1 = 4.25648 loss)
I0408 16:35:52.899739 27193 sgd_solver.cpp:105] Iteration 6924, lr = 2.63959e-09
I0408 16:35:57.586853 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0408 16:36:00.629523 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0408 16:36:03.023772 27193 solver.cpp:330] Iteration 6936, Testing net (#0)
I0408 16:36:03.023798 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:36:03.685748 27193 blocking_queue.cpp:49] Waiting for data
I0408 16:36:04.763808 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:36:07.495702 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:36:07.495749 27193 solver.cpp:397]     Test net output #1: loss = 4.60902 (* 1 = 4.60902 loss)
I0408 16:36:07.587206 27193 solver.cpp:218] Iteration 6936 (0.817047 iter/s, 14.687s/12 iters), loss = 4.51131
I0408 16:36:07.587253 27193 solver.cpp:237]     Train net output #0: loss = 4.51131 (* 1 = 4.51131 loss)
I0408 16:36:07.587265 27193 sgd_solver.cpp:105] Iteration 6936, lr = 2.5712e-09
I0408 16:36:12.177042 27193 solver.cpp:218] Iteration 6948 (2.61458 iter/s, 4.58965s/12 iters), loss = 4.46246
I0408 16:36:12.177079 27193 solver.cpp:237]     Train net output #0: loss = 4.46246 (* 1 = 4.46246 loss)
I0408 16:36:12.177088 27193 sgd_solver.cpp:105] Iteration 6948, lr = 2.50458e-09
I0408 16:36:17.510946 27193 solver.cpp:218] Iteration 6960 (2.24985 iter/s, 5.33369s/12 iters), loss = 4.52807
I0408 16:36:17.511059 27193 solver.cpp:237]     Train net output #0: loss = 4.52807 (* 1 = 4.52807 loss)
I0408 16:36:17.511072 27193 sgd_solver.cpp:105] Iteration 6960, lr = 2.43968e-09
I0408 16:36:22.622961 27193 solver.cpp:218] Iteration 6972 (2.34754 iter/s, 5.11174s/12 iters), loss = 4.49648
I0408 16:36:22.623008 27193 solver.cpp:237]     Train net output #0: loss = 4.49648 (* 1 = 4.49648 loss)
I0408 16:36:22.623019 27193 sgd_solver.cpp:105] Iteration 6972, lr = 2.37647e-09
I0408 16:36:25.345872 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:36:27.758618 27193 solver.cpp:218] Iteration 6984 (2.3367 iter/s, 5.13545s/12 iters), loss = 4.51205
I0408 16:36:27.758654 27193 solver.cpp:237]     Train net output #0: loss = 4.51205 (* 1 = 4.51205 loss)
I0408 16:36:27.758662 27193 sgd_solver.cpp:105] Iteration 6984, lr = 2.31489e-09
I0408 16:36:32.870486 27193 solver.cpp:218] Iteration 6996 (2.34757 iter/s, 5.11167s/12 iters), loss = 4.48853
I0408 16:36:32.870532 27193 solver.cpp:237]     Train net output #0: loss = 4.48853 (* 1 = 4.48853 loss)
I0408 16:36:32.870543 27193 sgd_solver.cpp:105] Iteration 6996, lr = 2.25491e-09
I0408 16:36:37.926329 27193 solver.cpp:218] Iteration 7008 (2.37359 iter/s, 5.05564s/12 iters), loss = 4.51319
I0408 16:36:37.926367 27193 solver.cpp:237]     Train net output #0: loss = 4.51319 (* 1 = 4.51319 loss)
I0408 16:36:37.926376 27193 sgd_solver.cpp:105] Iteration 7008, lr = 2.19649e-09
I0408 16:36:42.963706 27193 solver.cpp:218] Iteration 7020 (2.38228 iter/s, 5.03718s/12 iters), loss = 4.59405
I0408 16:36:42.963752 27193 solver.cpp:237]     Train net output #0: loss = 4.59405 (* 1 = 4.59405 loss)
I0408 16:36:42.963764 27193 sgd_solver.cpp:105] Iteration 7020, lr = 2.13958e-09
I0408 16:36:48.053469 27193 solver.cpp:218] Iteration 7032 (2.35777 iter/s, 5.08956s/12 iters), loss = 4.53337
I0408 16:36:48.053630 27193 solver.cpp:237]     Train net output #0: loss = 4.53337 (* 1 = 4.53337 loss)
I0408 16:36:48.053644 27193 sgd_solver.cpp:105] Iteration 7032, lr = 2.08414e-09
I0408 16:36:50.093948 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0408 16:36:53.116529 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0408 16:36:55.437332 27193 solver.cpp:330] Iteration 7038, Testing net (#0)
I0408 16:36:55.437355 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:36:57.032274 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:36:59.793045 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:36:59.793078 27193 solver.cpp:397]     Test net output #1: loss = 4.60466 (* 1 = 4.60466 loss)
I0408 16:37:01.724870 27193 solver.cpp:218] Iteration 7044 (0.877781 iter/s, 13.6708s/12 iters), loss = 4.36247
I0408 16:37:01.724912 27193 solver.cpp:237]     Train net output #0: loss = 4.36247 (* 1 = 4.36247 loss)
I0408 16:37:01.724923 27193 sgd_solver.cpp:105] Iteration 7044, lr = 2.03014e-09
I0408 16:37:06.709110 27193 solver.cpp:218] Iteration 7056 (2.40768 iter/s, 4.98404s/12 iters), loss = 4.58049
I0408 16:37:06.709148 27193 solver.cpp:237]     Train net output #0: loss = 4.58049 (* 1 = 4.58049 loss)
I0408 16:37:06.709157 27193 sgd_solver.cpp:105] Iteration 7056, lr = 1.97754e-09
I0408 16:37:11.782315 27193 solver.cpp:218] Iteration 7068 (2.36546 iter/s, 5.07301s/12 iters), loss = 4.56856
I0408 16:37:11.782348 27193 solver.cpp:237]     Train net output #0: loss = 4.56856 (* 1 = 4.56856 loss)
I0408 16:37:11.782357 27193 sgd_solver.cpp:105] Iteration 7068, lr = 1.9263e-09
I0408 16:37:16.739787 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:37:16.849862 27193 solver.cpp:218] Iteration 7080 (2.3681 iter/s, 5.06735s/12 iters), loss = 4.46777
I0408 16:37:16.849902 27193 solver.cpp:237]     Train net output #0: loss = 4.46777 (* 1 = 4.46777 loss)
I0408 16:37:16.849911 27193 sgd_solver.cpp:105] Iteration 7080, lr = 1.87639e-09
I0408 16:37:21.929303 27193 solver.cpp:218] Iteration 7092 (2.36256 iter/s, 5.07924s/12 iters), loss = 4.50047
I0408 16:37:21.929410 27193 solver.cpp:237]     Train net output #0: loss = 4.50047 (* 1 = 4.50047 loss)
I0408 16:37:21.929419 27193 sgd_solver.cpp:105] Iteration 7092, lr = 1.82777e-09
I0408 16:37:27.024704 27193 solver.cpp:218] Iteration 7104 (2.35519 iter/s, 5.09513s/12 iters), loss = 4.40506
I0408 16:37:27.024749 27193 solver.cpp:237]     Train net output #0: loss = 4.40506 (* 1 = 4.40506 loss)
I0408 16:37:27.024761 27193 sgd_solver.cpp:105] Iteration 7104, lr = 1.78041e-09
I0408 16:37:32.041131 27193 solver.cpp:218] Iteration 7116 (2.39224 iter/s, 5.01623s/12 iters), loss = 4.36536
I0408 16:37:32.041167 27193 solver.cpp:237]     Train net output #0: loss = 4.36536 (* 1 = 4.36536 loss)
I0408 16:37:32.041177 27193 sgd_solver.cpp:105] Iteration 7116, lr = 1.73428e-09
I0408 16:37:37.157992 27193 solver.cpp:218] Iteration 7128 (2.34528 iter/s, 5.11666s/12 iters), loss = 4.55297
I0408 16:37:37.158030 27193 solver.cpp:237]     Train net output #0: loss = 4.55297 (* 1 = 4.55297 loss)
I0408 16:37:37.158037 27193 sgd_solver.cpp:105] Iteration 7128, lr = 1.68934e-09
I0408 16:37:41.748911 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0408 16:37:44.805881 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0408 16:37:47.151131 27193 solver.cpp:330] Iteration 7140, Testing net (#0)
I0408 16:37:47.151162 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:37:48.819269 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:37:51.621685 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:37:51.621729 27193 solver.cpp:397]     Test net output #1: loss = 4.60779 (* 1 = 4.60779 loss)
I0408 16:37:51.713181 27193 solver.cpp:218] Iteration 7140 (0.824475 iter/s, 14.5547s/12 iters), loss = 4.41445
I0408 16:37:51.713219 27193 solver.cpp:237]     Train net output #0: loss = 4.41445 (* 1 = 4.41445 loss)
I0408 16:37:51.713229 27193 sgd_solver.cpp:105] Iteration 7140, lr = 1.64557e-09
I0408 16:37:56.312098 27193 solver.cpp:218] Iteration 7152 (2.60942 iter/s, 4.59873s/12 iters), loss = 4.54572
I0408 16:37:56.312247 27193 solver.cpp:237]     Train net output #0: loss = 4.54572 (* 1 = 4.54572 loss)
I0408 16:37:56.312258 27193 sgd_solver.cpp:105] Iteration 7152, lr = 1.60293e-09
I0408 16:38:01.580277 27193 solver.cpp:218] Iteration 7164 (2.27796 iter/s, 5.26787s/12 iters), loss = 4.59793
I0408 16:38:01.580312 27193 solver.cpp:237]     Train net output #0: loss = 4.59793 (* 1 = 4.59793 loss)
I0408 16:38:01.580319 27193 sgd_solver.cpp:105] Iteration 7164, lr = 1.5614e-09
I0408 16:38:06.769603 27193 solver.cpp:218] Iteration 7176 (2.31253 iter/s, 5.18912s/12 iters), loss = 4.57815
I0408 16:38:06.769646 27193 solver.cpp:237]     Train net output #0: loss = 4.57815 (* 1 = 4.57815 loss)
I0408 16:38:06.769657 27193 sgd_solver.cpp:105] Iteration 7176, lr = 1.52094e-09
I0408 16:38:08.939502 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:38:11.898386 27193 solver.cpp:218] Iteration 7188 (2.33983 iter/s, 5.12858s/12 iters), loss = 4.48608
I0408 16:38:11.898427 27193 solver.cpp:237]     Train net output #0: loss = 4.48608 (* 1 = 4.48608 loss)
I0408 16:38:11.898439 27193 sgd_solver.cpp:105] Iteration 7188, lr = 1.48153e-09
I0408 16:38:16.976099 27193 solver.cpp:218] Iteration 7200 (2.36337 iter/s, 5.0775s/12 iters), loss = 4.41631
I0408 16:38:16.976145 27193 solver.cpp:237]     Train net output #0: loss = 4.41631 (* 1 = 4.41631 loss)
I0408 16:38:16.976155 27193 sgd_solver.cpp:105] Iteration 7200, lr = 1.44315e-09
I0408 16:38:22.123211 27193 solver.cpp:218] Iteration 7212 (2.3315 iter/s, 5.14691s/12 iters), loss = 4.37323
I0408 16:38:22.123253 27193 solver.cpp:237]     Train net output #0: loss = 4.37323 (* 1 = 4.37323 loss)
I0408 16:38:22.123265 27193 sgd_solver.cpp:105] Iteration 7212, lr = 1.40575e-09
I0408 16:38:27.261745 27193 solver.cpp:218] Iteration 7224 (2.33539 iter/s, 5.13833s/12 iters), loss = 4.53587
I0408 16:38:27.261862 27193 solver.cpp:237]     Train net output #0: loss = 4.53587 (* 1 = 4.53587 loss)
I0408 16:38:27.261874 27193 sgd_solver.cpp:105] Iteration 7224, lr = 1.36933e-09
I0408 16:38:32.260511 27193 solver.cpp:218] Iteration 7236 (2.40072 iter/s, 4.99849s/12 iters), loss = 4.62548
I0408 16:38:32.260560 27193 solver.cpp:237]     Train net output #0: loss = 4.62548 (* 1 = 4.62548 loss)
I0408 16:38:32.260571 27193 sgd_solver.cpp:105] Iteration 7236, lr = 1.33385e-09
I0408 16:38:34.266516 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0408 16:38:38.027192 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0408 16:38:44.059211 27193 solver.cpp:330] Iteration 7242, Testing net (#0)
I0408 16:38:44.059247 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:38:45.677281 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:38:48.518221 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:38:48.518260 27193 solver.cpp:397]     Test net output #1: loss = 4.6049 (* 1 = 4.6049 loss)
I0408 16:38:50.502737 27193 solver.cpp:218] Iteration 7248 (0.657836 iter/s, 18.2416s/12 iters), loss = 4.43772
I0408 16:38:50.502785 27193 solver.cpp:237]     Train net output #0: loss = 4.43772 (* 1 = 4.43772 loss)
I0408 16:38:50.502796 27193 sgd_solver.cpp:105] Iteration 7248, lr = 1.29929e-09
I0408 16:38:55.601250 27193 solver.cpp:218] Iteration 7260 (2.35372 iter/s, 5.0983s/12 iters), loss = 4.44606
I0408 16:38:55.601295 27193 solver.cpp:237]     Train net output #0: loss = 4.44606 (* 1 = 4.44606 loss)
I0408 16:38:55.601306 27193 sgd_solver.cpp:105] Iteration 7260, lr = 1.26562e-09
I0408 16:39:00.665603 27193 solver.cpp:218] Iteration 7272 (2.3696 iter/s, 5.06415s/12 iters), loss = 4.46318
I0408 16:39:00.665758 27193 solver.cpp:237]     Train net output #0: loss = 4.46318 (* 1 = 4.46318 loss)
I0408 16:39:00.665771 27193 sgd_solver.cpp:105] Iteration 7272, lr = 1.23283e-09
I0408 16:39:04.959578 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:39:05.718863 27193 solver.cpp:218] Iteration 7284 (2.37485 iter/s, 5.05295s/12 iters), loss = 4.61132
I0408 16:39:05.718922 27193 solver.cpp:237]     Train net output #0: loss = 4.61132 (* 1 = 4.61132 loss)
I0408 16:39:05.718935 27193 sgd_solver.cpp:105] Iteration 7284, lr = 1.20089e-09
I0408 16:39:10.862504 27193 solver.cpp:218] Iteration 7296 (2.33308 iter/s, 5.14342s/12 iters), loss = 4.46533
I0408 16:39:10.862546 27193 solver.cpp:237]     Train net output #0: loss = 4.46533 (* 1 = 4.46533 loss)
I0408 16:39:10.862558 27193 sgd_solver.cpp:105] Iteration 7296, lr = 1.16977e-09
I0408 16:39:15.903180 27193 solver.cpp:218] Iteration 7308 (2.38073 iter/s, 5.04047s/12 iters), loss = 4.51244
I0408 16:39:15.903218 27193 solver.cpp:237]     Train net output #0: loss = 4.51244 (* 1 = 4.51244 loss)
I0408 16:39:15.903226 27193 sgd_solver.cpp:105] Iteration 7308, lr = 1.13946e-09
I0408 16:39:20.918592 27193 solver.cpp:218] Iteration 7320 (2.39272 iter/s, 5.01521s/12 iters), loss = 4.3899
I0408 16:39:20.918638 27193 solver.cpp:237]     Train net output #0: loss = 4.3899 (* 1 = 4.3899 loss)
I0408 16:39:20.918650 27193 sgd_solver.cpp:105] Iteration 7320, lr = 1.10994e-09
I0408 16:39:25.921317 27193 solver.cpp:218] Iteration 7332 (2.39879 iter/s, 5.00252s/12 iters), loss = 4.34326
I0408 16:39:25.921365 27193 solver.cpp:237]     Train net output #0: loss = 4.34326 (* 1 = 4.34326 loss)
I0408 16:39:25.921377 27193 sgd_solver.cpp:105] Iteration 7332, lr = 1.08118e-09
I0408 16:39:30.444423 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0408 16:39:33.477550 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0408 16:39:35.816216 27193 solver.cpp:330] Iteration 7344, Testing net (#0)
I0408 16:39:35.816246 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:39:37.368619 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:39:40.245020 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:39:40.245067 27193 solver.cpp:397]     Test net output #1: loss = 4.60056 (* 1 = 4.60056 loss)
I0408 16:39:40.336259 27193 solver.cpp:218] Iteration 7344 (0.832497 iter/s, 14.4145s/12 iters), loss = 4.37105
I0408 16:39:40.336298 27193 solver.cpp:237]     Train net output #0: loss = 4.37105 (* 1 = 4.37105 loss)
I0408 16:39:40.336309 27193 sgd_solver.cpp:105] Iteration 7344, lr = 1.05317e-09
I0408 16:39:44.954036 27193 solver.cpp:218] Iteration 7356 (2.59876 iter/s, 4.61759s/12 iters), loss = 4.66849
I0408 16:39:44.954078 27193 solver.cpp:237]     Train net output #0: loss = 4.66849 (* 1 = 4.66849 loss)
I0408 16:39:44.954088 27193 sgd_solver.cpp:105] Iteration 7356, lr = 1.02588e-09
I0408 16:39:50.070178 27193 solver.cpp:218] Iteration 7368 (2.34561 iter/s, 5.11594s/12 iters), loss = 4.37907
I0408 16:39:50.070211 27193 solver.cpp:237]     Train net output #0: loss = 4.37907 (* 1 = 4.37907 loss)
I0408 16:39:50.070220 27193 sgd_solver.cpp:105] Iteration 7368, lr = 9.99297e-10
I0408 16:39:55.166134 27193 solver.cpp:218] Iteration 7380 (2.3549 iter/s, 5.09576s/12 iters), loss = 4.44801
I0408 16:39:55.166177 27193 solver.cpp:237]     Train net output #0: loss = 4.44801 (* 1 = 4.44801 loss)
I0408 16:39:55.166186 27193 sgd_solver.cpp:105] Iteration 7380, lr = 9.73404e-10
I0408 16:39:56.579186 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:40:00.304463 27193 solver.cpp:218] Iteration 7392 (2.33548 iter/s, 5.13812s/12 iters), loss = 4.4035
I0408 16:40:00.304512 27193 solver.cpp:237]     Train net output #0: loss = 4.4035 (* 1 = 4.4035 loss)
I0408 16:40:00.304523 27193 sgd_solver.cpp:105] Iteration 7392, lr = 9.48183e-10
I0408 16:40:05.415539 27193 solver.cpp:218] Iteration 7404 (2.34794 iter/s, 5.11087s/12 iters), loss = 4.46603
I0408 16:40:05.415688 27193 solver.cpp:237]     Train net output #0: loss = 4.46603 (* 1 = 4.46603 loss)
I0408 16:40:05.415702 27193 sgd_solver.cpp:105] Iteration 7404, lr = 9.23615e-10
I0408 16:40:10.494657 27193 solver.cpp:218] Iteration 7416 (2.36276 iter/s, 5.07881s/12 iters), loss = 4.61843
I0408 16:40:10.494701 27193 solver.cpp:237]     Train net output #0: loss = 4.61843 (* 1 = 4.61843 loss)
I0408 16:40:10.494712 27193 sgd_solver.cpp:105] Iteration 7416, lr = 8.99684e-10
I0408 16:40:15.487704 27193 solver.cpp:218] Iteration 7428 (2.40344 iter/s, 4.99285s/12 iters), loss = 4.44603
I0408 16:40:15.487746 27193 solver.cpp:237]     Train net output #0: loss = 4.44603 (* 1 = 4.44603 loss)
I0408 16:40:15.487756 27193 sgd_solver.cpp:105] Iteration 7428, lr = 8.76373e-10
I0408 16:40:20.539645 27193 solver.cpp:218] Iteration 7440 (2.37542 iter/s, 5.05174s/12 iters), loss = 4.64562
I0408 16:40:20.539688 27193 solver.cpp:237]     Train net output #0: loss = 4.64562 (* 1 = 4.64562 loss)
I0408 16:40:20.539700 27193 sgd_solver.cpp:105] Iteration 7440, lr = 8.53665e-10
I0408 16:40:22.653445 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0408 16:40:25.676321 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0408 16:40:28.864066 27193 solver.cpp:330] Iteration 7446, Testing net (#0)
I0408 16:40:28.864099 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:40:30.347617 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:40:33.271134 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:40:33.271168 27193 solver.cpp:397]     Test net output #1: loss = 4.60413 (* 1 = 4.60413 loss)
I0408 16:40:35.160092 27193 solver.cpp:218] Iteration 7452 (0.820796 iter/s, 14.62s/12 iters), loss = 4.47138
I0408 16:40:35.160140 27193 solver.cpp:237]     Train net output #0: loss = 4.47138 (* 1 = 4.47138 loss)
I0408 16:40:35.160151 27193 sgd_solver.cpp:105] Iteration 7452, lr = 8.31546e-10
I0408 16:40:40.228690 27193 solver.cpp:218] Iteration 7464 (2.36762 iter/s, 5.06839s/12 iters), loss = 4.37803
I0408 16:40:40.228806 27193 solver.cpp:237]     Train net output #0: loss = 4.37803 (* 1 = 4.37803 loss)
I0408 16:40:40.228819 27193 sgd_solver.cpp:105] Iteration 7464, lr = 8.10001e-10
I0408 16:40:45.173200 27193 solver.cpp:218] Iteration 7476 (2.42707 iter/s, 4.94424s/12 iters), loss = 4.60591
I0408 16:40:45.173245 27193 solver.cpp:237]     Train net output #0: loss = 4.60591 (* 1 = 4.60591 loss)
I0408 16:40:45.173257 27193 sgd_solver.cpp:105] Iteration 7476, lr = 7.89013e-10
I0408 16:40:48.710882 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:40:50.232906 27193 solver.cpp:218] Iteration 7488 (2.37178 iter/s, 5.0595s/12 iters), loss = 4.45741
I0408 16:40:50.232951 27193 solver.cpp:237]     Train net output #0: loss = 4.45741 (* 1 = 4.45741 loss)
I0408 16:40:50.232964 27193 sgd_solver.cpp:105] Iteration 7488, lr = 7.68569e-10
I0408 16:40:55.566093 27193 solver.cpp:218] Iteration 7500 (2.25015 iter/s, 5.33297s/12 iters), loss = 4.47546
I0408 16:40:55.566138 27193 solver.cpp:237]     Train net output #0: loss = 4.47546 (* 1 = 4.47546 loss)
I0408 16:40:55.566148 27193 sgd_solver.cpp:105] Iteration 7500, lr = 7.48655e-10
I0408 16:41:00.920428 27193 solver.cpp:218] Iteration 7512 (2.24126 iter/s, 5.35412s/12 iters), loss = 4.56511
I0408 16:41:00.920475 27193 solver.cpp:237]     Train net output #0: loss = 4.56511 (* 1 = 4.56511 loss)
I0408 16:41:00.920487 27193 sgd_solver.cpp:105] Iteration 7512, lr = 7.29257e-10
I0408 16:41:05.993695 27193 solver.cpp:218] Iteration 7524 (2.36544 iter/s, 5.07306s/12 iters), loss = 4.49467
I0408 16:41:05.993739 27193 solver.cpp:237]     Train net output #0: loss = 4.49467 (* 1 = 4.49467 loss)
I0408 16:41:05.993750 27193 sgd_solver.cpp:105] Iteration 7524, lr = 7.10362e-10
I0408 16:41:10.973286 27193 solver.cpp:218] Iteration 7536 (2.40993 iter/s, 4.97939s/12 iters), loss = 4.38515
I0408 16:41:10.973443 27193 solver.cpp:237]     Train net output #0: loss = 4.38515 (* 1 = 4.38515 loss)
I0408 16:41:10.973455 27193 sgd_solver.cpp:105] Iteration 7536, lr = 6.91956e-10
I0408 16:41:15.542191 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0408 16:41:18.567250 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0408 16:41:21.055131 27193 solver.cpp:330] Iteration 7548, Testing net (#0)
I0408 16:41:21.055151 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:41:22.543859 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:41:25.497066 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0741422
I0408 16:41:25.497102 27193 solver.cpp:397]     Test net output #1: loss = 4.60052 (* 1 = 4.60052 loss)
I0408 16:41:25.588551 27193 solver.cpp:218] Iteration 7548 (0.821093 iter/s, 14.6147s/12 iters), loss = 4.49431
I0408 16:41:25.588615 27193 solver.cpp:237]     Train net output #0: loss = 4.49431 (* 1 = 4.49431 loss)
I0408 16:41:25.588629 27193 sgd_solver.cpp:105] Iteration 7548, lr = 6.74027e-10
I0408 16:41:29.812855 27193 solver.cpp:218] Iteration 7560 (2.84084 iter/s, 4.22411s/12 iters), loss = 4.64177
I0408 16:41:29.812901 27193 solver.cpp:237]     Train net output #0: loss = 4.64177 (* 1 = 4.64177 loss)
I0408 16:41:29.812911 27193 sgd_solver.cpp:105] Iteration 7560, lr = 6.56563e-10
I0408 16:41:34.939630 27193 solver.cpp:218] Iteration 7572 (2.34075 iter/s, 5.12656s/12 iters), loss = 4.53953
I0408 16:41:34.939679 27193 solver.cpp:237]     Train net output #0: loss = 4.53953 (* 1 = 4.53953 loss)
I0408 16:41:34.939690 27193 sgd_solver.cpp:105] Iteration 7572, lr = 6.39551e-10
I0408 16:41:39.998574 27193 solver.cpp:218] Iteration 7584 (2.37213 iter/s, 5.05873s/12 iters), loss = 4.60782
I0408 16:41:39.998622 27193 solver.cpp:237]     Train net output #0: loss = 4.60782 (* 1 = 4.60782 loss)
I0408 16:41:39.998634 27193 sgd_solver.cpp:105] Iteration 7584, lr = 6.2298e-10
I0408 16:41:40.655349 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:41:45.122767 27193 solver.cpp:218] Iteration 7596 (2.34193 iter/s, 5.12398s/12 iters), loss = 4.50915
I0408 16:41:45.122896 27193 solver.cpp:237]     Train net output #0: loss = 4.50915 (* 1 = 4.50915 loss)
I0408 16:41:45.122910 27193 sgd_solver.cpp:105] Iteration 7596, lr = 6.06838e-10
I0408 16:41:50.327105 27193 solver.cpp:218] Iteration 7608 (2.3059 iter/s, 5.20404s/12 iters), loss = 4.58807
I0408 16:41:50.327147 27193 solver.cpp:237]     Train net output #0: loss = 4.58807 (* 1 = 4.58807 loss)
I0408 16:41:50.327158 27193 sgd_solver.cpp:105] Iteration 7608, lr = 5.91114e-10
I0408 16:41:55.730702 27193 solver.cpp:218] Iteration 7620 (2.22083 iter/s, 5.40338s/12 iters), loss = 4.44764
I0408 16:41:55.730744 27193 solver.cpp:237]     Train net output #0: loss = 4.44764 (* 1 = 4.44764 loss)
I0408 16:41:55.730754 27193 sgd_solver.cpp:105] Iteration 7620, lr = 5.75798e-10
I0408 16:41:58.195233 27193 blocking_queue.cpp:49] Waiting for data
I0408 16:42:00.770105 27193 solver.cpp:218] Iteration 7632 (2.38133 iter/s, 5.0392s/12 iters), loss = 4.40832
I0408 16:42:00.770141 27193 solver.cpp:237]     Train net output #0: loss = 4.40832 (* 1 = 4.40832 loss)
I0408 16:42:00.770150 27193 sgd_solver.cpp:105] Iteration 7632, lr = 5.60879e-10
I0408 16:42:05.904268 27193 solver.cpp:218] Iteration 7644 (2.33738 iter/s, 5.13396s/12 iters), loss = 4.46114
I0408 16:42:05.904300 27193 solver.cpp:237]     Train net output #0: loss = 4.46114 (* 1 = 4.46114 loss)
I0408 16:42:05.904309 27193 sgd_solver.cpp:105] Iteration 7644, lr = 5.46346e-10
I0408 16:42:08.195484 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0408 16:42:11.266041 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0408 16:42:14.896533 27193 solver.cpp:330] Iteration 7650, Testing net (#0)
I0408 16:42:14.896562 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:42:16.371613 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:42:19.372133 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0741422
I0408 16:42:19.372179 27193 solver.cpp:397]     Test net output #1: loss = 4.59966 (* 1 = 4.59966 loss)
I0408 16:42:21.368474 27193 solver.cpp:218] Iteration 7656 (0.776011 iter/s, 15.4637s/12 iters), loss = 4.51898
I0408 16:42:21.368521 27193 solver.cpp:237]     Train net output #0: loss = 4.51898 (* 1 = 4.51898 loss)
I0408 16:42:21.368532 27193 sgd_solver.cpp:105] Iteration 7656, lr = 5.3219e-10
I0408 16:42:26.327591 27193 solver.cpp:218] Iteration 7668 (2.41989 iter/s, 4.9589s/12 iters), loss = 4.51285
I0408 16:42:26.327636 27193 solver.cpp:237]     Train net output #0: loss = 4.51285 (* 1 = 4.51285 loss)
I0408 16:42:26.327646 27193 sgd_solver.cpp:105] Iteration 7668, lr = 5.18401e-10
I0408 16:42:31.422451 27193 solver.cpp:218] Iteration 7680 (2.35541 iter/s, 5.09465s/12 iters), loss = 4.47667
I0408 16:42:31.422492 27193 solver.cpp:237]     Train net output #0: loss = 4.47667 (* 1 = 4.47667 loss)
I0408 16:42:31.422502 27193 sgd_solver.cpp:105] Iteration 7680, lr = 5.04969e-10
I0408 16:42:34.196934 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:42:36.415339 27193 solver.cpp:218] Iteration 7692 (2.40352 iter/s, 4.99269s/12 iters), loss = 4.51678
I0408 16:42:36.415374 27193 solver.cpp:237]     Train net output #0: loss = 4.51678 (* 1 = 4.51678 loss)
I0408 16:42:36.415382 27193 sgd_solver.cpp:105] Iteration 7692, lr = 4.91885e-10
I0408 16:42:41.518543 27193 solver.cpp:218] Iteration 7704 (2.35156 iter/s, 5.103s/12 iters), loss = 4.38087
I0408 16:42:41.518584 27193 solver.cpp:237]     Train net output #0: loss = 4.38087 (* 1 = 4.38087 loss)
I0408 16:42:41.518592 27193 sgd_solver.cpp:105] Iteration 7704, lr = 4.7914e-10
I0408 16:42:46.838634 27193 solver.cpp:218] Iteration 7716 (2.25569 iter/s, 5.31988s/12 iters), loss = 4.50469
I0408 16:42:46.838748 27193 solver.cpp:237]     Train net output #0: loss = 4.50469 (* 1 = 4.50469 loss)
I0408 16:42:46.838758 27193 sgd_solver.cpp:105] Iteration 7716, lr = 4.66725e-10
I0408 16:42:51.925179 27193 solver.cpp:218] Iteration 7728 (2.35929 iter/s, 5.08627s/12 iters), loss = 4.58221
I0408 16:42:51.925225 27193 solver.cpp:237]     Train net output #0: loss = 4.58221 (* 1 = 4.58221 loss)
I0408 16:42:51.925235 27193 sgd_solver.cpp:105] Iteration 7728, lr = 4.54632e-10
I0408 16:42:56.948824 27193 solver.cpp:218] Iteration 7740 (2.3888 iter/s, 5.02344s/12 iters), loss = 4.7769
I0408 16:42:56.948869 27193 solver.cpp:237]     Train net output #0: loss = 4.7769 (* 1 = 4.7769 loss)
I0408 16:42:56.948880 27193 sgd_solver.cpp:105] Iteration 7740, lr = 4.42852e-10
I0408 16:43:01.476583 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0408 16:43:04.521544 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0408 16:43:09.341605 27193 solver.cpp:330] Iteration 7752, Testing net (#0)
I0408 16:43:09.341639 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:43:10.772943 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:43:13.807792 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:43:13.807837 27193 solver.cpp:397]     Test net output #1: loss = 4.60869 (* 1 = 4.60869 loss)
I0408 16:43:13.899469 27193 solver.cpp:218] Iteration 7752 (0.707961 iter/s, 16.9501s/12 iters), loss = 4.41058
I0408 16:43:13.899538 27193 solver.cpp:237]     Train net output #0: loss = 4.41058 (* 1 = 4.41058 loss)
I0408 16:43:13.899555 27193 sgd_solver.cpp:105] Iteration 7752, lr = 4.31378e-10
I0408 16:43:18.143169 27193 solver.cpp:218] Iteration 7764 (2.82785 iter/s, 4.2435s/12 iters), loss = 4.59921
I0408 16:43:18.143321 27193 solver.cpp:237]     Train net output #0: loss = 4.59921 (* 1 = 4.59921 loss)
I0408 16:43:18.143332 27193 sgd_solver.cpp:105] Iteration 7764, lr = 4.20201e-10
I0408 16:43:23.097044 27193 solver.cpp:218] Iteration 7776 (2.4225 iter/s, 4.95356s/12 iters), loss = 4.61045
I0408 16:43:23.097085 27193 solver.cpp:237]     Train net output #0: loss = 4.61045 (* 1 = 4.61045 loss)
I0408 16:43:23.097095 27193 sgd_solver.cpp:105] Iteration 7776, lr = 4.09313e-10
I0408 16:43:28.102160 27193 solver.cpp:218] Iteration 7788 (2.39764 iter/s, 5.00491s/12 iters), loss = 4.48153
I0408 16:43:28.102210 27193 solver.cpp:237]     Train net output #0: loss = 4.48153 (* 1 = 4.48153 loss)
I0408 16:43:28.102221 27193 sgd_solver.cpp:105] Iteration 7788, lr = 3.98707e-10
I0408 16:43:28.110339 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:43:33.094364 27193 solver.cpp:218] Iteration 7800 (2.40385 iter/s, 4.992s/12 iters), loss = 4.47946
I0408 16:43:33.094413 27193 solver.cpp:237]     Train net output #0: loss = 4.47946 (* 1 = 4.47946 loss)
I0408 16:43:33.094422 27193 sgd_solver.cpp:105] Iteration 7800, lr = 3.88377e-10
I0408 16:43:38.026414 27193 solver.cpp:218] Iteration 7812 (2.43317 iter/s, 4.93184s/12 iters), loss = 4.44266
I0408 16:43:38.026459 27193 solver.cpp:237]     Train net output #0: loss = 4.44266 (* 1 = 4.44266 loss)
I0408 16:43:38.026468 27193 sgd_solver.cpp:105] Iteration 7812, lr = 3.78314e-10
I0408 16:43:43.101284 27193 solver.cpp:218] Iteration 7824 (2.36469 iter/s, 5.07466s/12 iters), loss = 4.42084
I0408 16:43:43.101333 27193 solver.cpp:237]     Train net output #0: loss = 4.42084 (* 1 = 4.42084 loss)
I0408 16:43:43.101344 27193 sgd_solver.cpp:105] Iteration 7824, lr = 3.68511e-10
I0408 16:43:48.183269 27193 solver.cpp:218] Iteration 7836 (2.36138 iter/s, 5.08178s/12 iters), loss = 4.63802
I0408 16:43:48.183378 27193 solver.cpp:237]     Train net output #0: loss = 4.63802 (* 1 = 4.63802 loss)
I0408 16:43:48.183390 27193 sgd_solver.cpp:105] Iteration 7836, lr = 3.58963e-10
I0408 16:43:53.192937 27193 solver.cpp:218] Iteration 7848 (2.3955 iter/s, 5.0094s/12 iters), loss = 4.28259
I0408 16:43:53.192981 27193 solver.cpp:237]     Train net output #0: loss = 4.28259 (* 1 = 4.28259 loss)
I0408 16:43:53.192992 27193 sgd_solver.cpp:105] Iteration 7848, lr = 3.49662e-10
I0408 16:43:55.211064 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0408 16:43:58.278615 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0408 16:44:00.939395 27193 solver.cpp:330] Iteration 7854, Testing net (#0)
I0408 16:44:00.939421 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:44:02.321740 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:44:05.403915 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:44:05.403964 27193 solver.cpp:397]     Test net output #1: loss = 4.60518 (* 1 = 4.60518 loss)
I0408 16:44:07.377166 27193 solver.cpp:218] Iteration 7860 (0.846039 iter/s, 14.1838s/12 iters), loss = 4.52238
I0408 16:44:07.377218 27193 solver.cpp:237]     Train net output #0: loss = 4.52238 (* 1 = 4.52238 loss)
I0408 16:44:07.377229 27193 sgd_solver.cpp:105] Iteration 7860, lr = 3.40602e-10
I0408 16:44:12.431607 27193 solver.cpp:218] Iteration 7872 (2.37425 iter/s, 5.05423s/12 iters), loss = 4.64144
I0408 16:44:12.431656 27193 solver.cpp:237]     Train net output #0: loss = 4.64144 (* 1 = 4.64144 loss)
I0408 16:44:12.431668 27193 sgd_solver.cpp:105] Iteration 7872, lr = 3.31777e-10
I0408 16:44:17.526294 27193 solver.cpp:218] Iteration 7884 (2.35549 iter/s, 5.09448s/12 iters), loss = 4.53832
I0408 16:44:17.526340 27193 solver.cpp:237]     Train net output #0: loss = 4.53832 (* 1 = 4.53832 loss)
I0408 16:44:17.526350 27193 sgd_solver.cpp:105] Iteration 7884, lr = 3.2318e-10
I0408 16:44:19.635161 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:44:22.461328 27193 solver.cpp:218] Iteration 7896 (2.43169 iter/s, 4.93483s/12 iters), loss = 4.36714
I0408 16:44:22.461369 27193 solver.cpp:237]     Train net output #0: loss = 4.36714 (* 1 = 4.36714 loss)
I0408 16:44:22.461380 27193 sgd_solver.cpp:105] Iteration 7896, lr = 3.14807e-10
I0408 16:44:27.582329 27193 solver.cpp:218] Iteration 7908 (2.34339 iter/s, 5.1208s/12 iters), loss = 4.40653
I0408 16:44:27.582366 27193 solver.cpp:237]     Train net output #0: loss = 4.40653 (* 1 = 4.40653 loss)
I0408 16:44:27.582374 27193 sgd_solver.cpp:105] Iteration 7908, lr = 3.0665e-10
I0408 16:44:32.705338 27193 solver.cpp:218] Iteration 7920 (2.34247 iter/s, 5.12281s/12 iters), loss = 4.28464
I0408 16:44:32.705382 27193 solver.cpp:237]     Train net output #0: loss = 4.28464 (* 1 = 4.28464 loss)
I0408 16:44:32.705394 27193 sgd_solver.cpp:105] Iteration 7920, lr = 2.98704e-10
I0408 16:44:38.039374 27193 solver.cpp:218] Iteration 7932 (2.24979 iter/s, 5.33382s/12 iters), loss = 4.57661
I0408 16:44:38.039418 27193 solver.cpp:237]     Train net output #0: loss = 4.57661 (* 1 = 4.57661 loss)
I0408 16:44:38.039430 27193 sgd_solver.cpp:105] Iteration 7932, lr = 2.90965e-10
I0408 16:44:43.187691 27193 solver.cpp:218] Iteration 7944 (2.33095 iter/s, 5.14811s/12 iters), loss = 4.51269
I0408 16:44:43.187737 27193 solver.cpp:237]     Train net output #0: loss = 4.51269 (* 1 = 4.51269 loss)
I0408 16:44:43.187748 27193 sgd_solver.cpp:105] Iteration 7944, lr = 2.83426e-10
I0408 16:44:47.760149 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0408 16:44:51.659884 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0408 16:44:55.926062 27193 solver.cpp:330] Iteration 7956, Testing net (#0)
I0408 16:44:55.926095 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:44:57.275797 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:45:00.393457 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:45:00.393504 27193 solver.cpp:397]     Test net output #1: loss = 4.60217 (* 1 = 4.60217 loss)
I0408 16:45:00.484972 27193 solver.cpp:218] Iteration 7956 (0.693774 iter/s, 17.2967s/12 iters), loss = 4.2832
I0408 16:45:00.485019 27193 solver.cpp:237]     Train net output #0: loss = 4.2832 (* 1 = 4.2832 loss)
I0408 16:45:00.485031 27193 sgd_solver.cpp:105] Iteration 7956, lr = 2.76082e-10
I0408 16:45:04.856139 27193 solver.cpp:218] Iteration 7968 (2.74538 iter/s, 4.37098s/12 iters), loss = 4.56346
I0408 16:45:04.856174 27193 solver.cpp:237]     Train net output #0: loss = 4.56346 (* 1 = 4.56346 loss)
I0408 16:45:04.856184 27193 sgd_solver.cpp:105] Iteration 7968, lr = 2.68929e-10
I0408 16:45:09.934208 27193 solver.cpp:218] Iteration 7980 (2.36319 iter/s, 5.07787s/12 iters), loss = 4.44257
I0408 16:45:09.934245 27193 solver.cpp:237]     Train net output #0: loss = 4.44257 (* 1 = 4.44257 loss)
I0408 16:45:09.934254 27193 sgd_solver.cpp:105] Iteration 7980, lr = 2.61961e-10
I0408 16:45:14.242854 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:45:14.973943 27193 solver.cpp:218] Iteration 7992 (2.38117 iter/s, 5.03953s/12 iters), loss = 4.51652
I0408 16:45:14.973987 27193 solver.cpp:237]     Train net output #0: loss = 4.51652 (* 1 = 4.51652 loss)
I0408 16:45:14.973996 27193 sgd_solver.cpp:105] Iteration 7992, lr = 2.55173e-10
I0408 16:45:20.310420 27193 solver.cpp:218] Iteration 8004 (2.24877 iter/s, 5.33626s/12 iters), loss = 4.51517
I0408 16:45:20.310457 27193 solver.cpp:237]     Train net output #0: loss = 4.51517 (* 1 = 4.51517 loss)
I0408 16:45:20.310465 27193 sgd_solver.cpp:105] Iteration 8004, lr = 2.48561e-10
I0408 16:45:25.351531 27193 solver.cpp:218] Iteration 8016 (2.38052 iter/s, 5.04091s/12 iters), loss = 4.47427
I0408 16:45:25.351670 27193 solver.cpp:237]     Train net output #0: loss = 4.47427 (* 1 = 4.47427 loss)
I0408 16:45:25.351683 27193 sgd_solver.cpp:105] Iteration 8016, lr = 2.42121e-10
I0408 16:45:30.379937 27193 solver.cpp:218] Iteration 8028 (2.38658 iter/s, 5.02811s/12 iters), loss = 4.45774
I0408 16:45:30.379985 27193 solver.cpp:237]     Train net output #0: loss = 4.45774 (* 1 = 4.45774 loss)
I0408 16:45:30.379997 27193 sgd_solver.cpp:105] Iteration 8028, lr = 2.35848e-10
I0408 16:45:35.408221 27193 solver.cpp:218] Iteration 8040 (2.3866 iter/s, 5.02807s/12 iters), loss = 4.41199
I0408 16:45:35.408267 27193 solver.cpp:237]     Train net output #0: loss = 4.41199 (* 1 = 4.41199 loss)
I0408 16:45:35.408278 27193 sgd_solver.cpp:105] Iteration 8040, lr = 2.29737e-10
I0408 16:45:40.450712 27193 solver.cpp:218] Iteration 8052 (2.37987 iter/s, 5.04228s/12 iters), loss = 4.43991
I0408 16:45:40.450764 27193 solver.cpp:237]     Train net output #0: loss = 4.43991 (* 1 = 4.43991 loss)
I0408 16:45:40.450778 27193 sgd_solver.cpp:105] Iteration 8052, lr = 2.23784e-10
I0408 16:45:42.496062 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0408 16:45:45.995987 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0408 16:45:50.170462 27193 solver.cpp:330] Iteration 8058, Testing net (#0)
I0408 16:45:50.170495 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:45:51.473798 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:45:54.737056 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:45:54.737104 27193 solver.cpp:397]     Test net output #1: loss = 4.60745 (* 1 = 4.60745 loss)
I0408 16:45:56.702154 27193 solver.cpp:218] Iteration 8064 (0.738421 iter/s, 16.2509s/12 iters), loss = 4.6026
I0408 16:45:56.702267 27193 solver.cpp:237]     Train net output #0: loss = 4.6026 (* 1 = 4.6026 loss)
I0408 16:45:56.702280 27193 sgd_solver.cpp:105] Iteration 8064, lr = 2.17986e-10
I0408 16:46:02.129283 27193 solver.cpp:218] Iteration 8076 (2.21123 iter/s, 5.42684s/12 iters), loss = 4.36809
I0408 16:46:02.129334 27193 solver.cpp:237]     Train net output #0: loss = 4.36809 (* 1 = 4.36809 loss)
I0408 16:46:02.129346 27193 sgd_solver.cpp:105] Iteration 8076, lr = 2.12338e-10
I0408 16:46:07.653313 27193 solver.cpp:218] Iteration 8088 (2.17242 iter/s, 5.5238s/12 iters), loss = 4.417
I0408 16:46:07.653358 27193 solver.cpp:237]     Train net output #0: loss = 4.417 (* 1 = 4.417 loss)
I0408 16:46:07.653370 27193 sgd_solver.cpp:105] Iteration 8088, lr = 2.06836e-10
I0408 16:46:09.088186 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:46:12.660933 27193 solver.cpp:218] Iteration 8100 (2.39645 iter/s, 5.00741s/12 iters), loss = 4.36848
I0408 16:46:12.660972 27193 solver.cpp:237]     Train net output #0: loss = 4.36848 (* 1 = 4.36848 loss)
I0408 16:46:12.660982 27193 sgd_solver.cpp:105] Iteration 8100, lr = 2.01477e-10
I0408 16:46:17.929096 27193 solver.cpp:218] Iteration 8112 (2.27792 iter/s, 5.26795s/12 iters), loss = 4.56197
I0408 16:46:17.929138 27193 solver.cpp:237]     Train net output #0: loss = 4.56197 (* 1 = 4.56197 loss)
I0408 16:46:17.929149 27193 sgd_solver.cpp:105] Iteration 8112, lr = 1.96256e-10
I0408 16:46:23.219404 27193 solver.cpp:218] Iteration 8124 (2.26839 iter/s, 5.29009s/12 iters), loss = 4.66462
I0408 16:46:23.219449 27193 solver.cpp:237]     Train net output #0: loss = 4.66462 (* 1 = 4.66462 loss)
I0408 16:46:23.219461 27193 sgd_solver.cpp:105] Iteration 8124, lr = 1.91171e-10
I0408 16:46:28.264139 27193 solver.cpp:218] Iteration 8136 (2.37882 iter/s, 5.04452s/12 iters), loss = 4.46
I0408 16:46:28.264243 27193 solver.cpp:237]     Train net output #0: loss = 4.46 (* 1 = 4.46 loss)
I0408 16:46:28.264256 27193 sgd_solver.cpp:105] Iteration 8136, lr = 1.86218e-10
I0408 16:46:33.339464 27193 solver.cpp:218] Iteration 8148 (2.3645 iter/s, 5.07506s/12 iters), loss = 4.55439
I0408 16:46:33.339501 27193 solver.cpp:237]     Train net output #0: loss = 4.55439 (* 1 = 4.55439 loss)
I0408 16:46:33.339510 27193 sgd_solver.cpp:105] Iteration 8148, lr = 1.81393e-10
I0408 16:46:37.881597 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0408 16:46:40.930727 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0408 16:46:45.719624 27193 solver.cpp:330] Iteration 8160, Testing net (#0)
I0408 16:46:45.719658 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:46:47.120231 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:46:50.520329 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:46:50.520367 27193 solver.cpp:397]     Test net output #1: loss = 4.60204 (* 1 = 4.60204 loss)
I0408 16:46:50.611677 27193 solver.cpp:218] Iteration 8160 (0.694781 iter/s, 17.2716s/12 iters), loss = 4.48735
I0408 16:46:50.611732 27193 solver.cpp:237]     Train net output #0: loss = 4.48735 (* 1 = 4.48735 loss)
I0408 16:46:50.611743 27193 sgd_solver.cpp:105] Iteration 8160, lr = 1.76693e-10
I0408 16:46:54.781015 27193 solver.cpp:218] Iteration 8172 (2.87829 iter/s, 4.16914s/12 iters), loss = 4.34024
I0408 16:46:54.781062 27193 solver.cpp:237]     Train net output #0: loss = 4.34024 (* 1 = 4.34024 loss)
I0408 16:46:54.781075 27193 sgd_solver.cpp:105] Iteration 8172, lr = 1.72115e-10
I0408 16:46:59.836416 27193 solver.cpp:218] Iteration 8184 (2.3738 iter/s, 5.05518s/12 iters), loss = 4.55996
I0408 16:46:59.836571 27193 solver.cpp:237]     Train net output #0: loss = 4.55996 (* 1 = 4.55996 loss)
I0408 16:46:59.836585 27193 sgd_solver.cpp:105] Iteration 8184, lr = 1.67655e-10
I0408 16:47:03.438459 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:47:04.861816 27193 solver.cpp:218] Iteration 8196 (2.38802 iter/s, 5.02508s/12 iters), loss = 4.37941
I0408 16:47:04.861863 27193 solver.cpp:237]     Train net output #0: loss = 4.37941 (* 1 = 4.37941 loss)
I0408 16:47:04.861876 27193 sgd_solver.cpp:105] Iteration 8196, lr = 1.63311e-10
I0408 16:47:10.017343 27193 solver.cpp:218] Iteration 8208 (2.32769 iter/s, 5.15532s/12 iters), loss = 4.38092
I0408 16:47:10.017376 27193 solver.cpp:237]     Train net output #0: loss = 4.38092 (* 1 = 4.38092 loss)
I0408 16:47:10.017385 27193 sgd_solver.cpp:105] Iteration 8208, lr = 1.59079e-10
I0408 16:47:15.106892 27193 solver.cpp:218] Iteration 8220 (2.35787 iter/s, 5.08935s/12 iters), loss = 4.47026
I0408 16:47:15.106931 27193 solver.cpp:237]     Train net output #0: loss = 4.47026 (* 1 = 4.47026 loss)
I0408 16:47:15.106941 27193 sgd_solver.cpp:105] Iteration 8220, lr = 1.54958e-10
I0408 16:47:20.176076 27193 solver.cpp:218] Iteration 8232 (2.36734 iter/s, 5.06898s/12 iters), loss = 4.58811
I0408 16:47:20.176112 27193 solver.cpp:237]     Train net output #0: loss = 4.58811 (* 1 = 4.58811 loss)
I0408 16:47:20.176120 27193 sgd_solver.cpp:105] Iteration 8232, lr = 1.50943e-10
I0408 16:47:25.688668 27193 solver.cpp:218] Iteration 8244 (2.17692 iter/s, 5.51237s/12 iters), loss = 4.48075
I0408 16:47:25.688704 27193 solver.cpp:237]     Train net output #0: loss = 4.48075 (* 1 = 4.48075 loss)
I0408 16:47:25.688714 27193 sgd_solver.cpp:105] Iteration 8244, lr = 1.47032e-10
I0408 16:47:31.048727 27193 solver.cpp:218] Iteration 8256 (2.23887 iter/s, 5.35985s/12 iters), loss = 4.4805
I0408 16:47:31.048827 27193 solver.cpp:237]     Train net output #0: loss = 4.4805 (* 1 = 4.4805 loss)
I0408 16:47:31.048838 27193 sgd_solver.cpp:105] Iteration 8256, lr = 1.43222e-10
I0408 16:47:33.098875 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0408 16:47:38.261788 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0408 16:47:42.003681 27193 solver.cpp:330] Iteration 8262, Testing net (#0)
I0408 16:47:42.003715 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:47:43.240193 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:47:46.468693 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:47:46.468740 27193 solver.cpp:397]     Test net output #1: loss = 4.60523 (* 1 = 4.60523 loss)
I0408 16:47:48.468571 27193 solver.cpp:218] Iteration 8268 (0.688895 iter/s, 17.4192s/12 iters), loss = 4.80339
I0408 16:47:48.468617 27193 solver.cpp:237]     Train net output #0: loss = 4.80339 (* 1 = 4.80339 loss)
I0408 16:47:48.468628 27193 sgd_solver.cpp:105] Iteration 8268, lr = 1.39511e-10
I0408 16:47:53.623446 27193 solver.cpp:218] Iteration 8280 (2.32799 iter/s, 5.15466s/12 iters), loss = 4.50717
I0408 16:47:53.623490 27193 solver.cpp:237]     Train net output #0: loss = 4.50717 (* 1 = 4.50717 loss)
I0408 16:47:53.623502 27193 sgd_solver.cpp:105] Iteration 8280, lr = 1.35896e-10
I0408 16:47:58.704560 27193 solver.cpp:218] Iteration 8292 (2.36178 iter/s, 5.08091s/12 iters), loss = 4.59619
I0408 16:47:58.704605 27193 solver.cpp:237]     Train net output #0: loss = 4.59619 (* 1 = 4.59619 loss)
I0408 16:47:58.704617 27193 sgd_solver.cpp:105] Iteration 8292, lr = 1.32375e-10
I0408 16:47:59.385586 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:48:03.749516 27193 solver.cpp:218] Iteration 8304 (2.37871 iter/s, 5.04475s/12 iters), loss = 4.61357
I0408 16:48:03.749660 27193 solver.cpp:237]     Train net output #0: loss = 4.61357 (* 1 = 4.61357 loss)
I0408 16:48:03.749672 27193 sgd_solver.cpp:105] Iteration 8304, lr = 1.28945e-10
I0408 16:48:06.644294 27193 blocking_queue.cpp:49] Waiting for data
I0408 16:48:08.780822 27193 solver.cpp:218] Iteration 8316 (2.38521 iter/s, 5.031s/12 iters), loss = 4.64032
I0408 16:48:08.780871 27193 solver.cpp:237]     Train net output #0: loss = 4.64032 (* 1 = 4.64032 loss)
I0408 16:48:08.780884 27193 sgd_solver.cpp:105] Iteration 8316, lr = 1.25604e-10
I0408 16:48:13.817586 27193 solver.cpp:218] Iteration 8328 (2.38258 iter/s, 5.03655s/12 iters), loss = 4.42748
I0408 16:48:13.817631 27193 solver.cpp:237]     Train net output #0: loss = 4.42748 (* 1 = 4.42748 loss)
I0408 16:48:13.817642 27193 sgd_solver.cpp:105] Iteration 8328, lr = 1.2235e-10
I0408 16:48:18.835481 27193 solver.cpp:218] Iteration 8340 (2.39154 iter/s, 5.01769s/12 iters), loss = 4.46053
I0408 16:48:18.835523 27193 solver.cpp:237]     Train net output #0: loss = 4.46053 (* 1 = 4.46053 loss)
I0408 16:48:18.835534 27193 sgd_solver.cpp:105] Iteration 8340, lr = 1.19179e-10
I0408 16:48:24.167932 27193 solver.cpp:218] Iteration 8352 (2.25046 iter/s, 5.33224s/12 iters), loss = 4.3702
I0408 16:48:24.167980 27193 solver.cpp:237]     Train net output #0: loss = 4.3702 (* 1 = 4.3702 loss)
I0408 16:48:24.167992 27193 sgd_solver.cpp:105] Iteration 8352, lr = 1.16091e-10
I0408 16:48:29.157987 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0408 16:48:34.869258 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0408 16:48:39.010013 27193 solver.cpp:330] Iteration 8364, Testing net (#0)
I0408 16:48:39.010038 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:48:40.204023 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:48:43.479893 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:48:43.479945 27193 solver.cpp:397]     Test net output #1: loss = 4.61022 (* 1 = 4.61022 loss)
I0408 16:48:43.570968 27193 solver.cpp:218] Iteration 8364 (0.618481 iter/s, 19.4024s/12 iters), loss = 4.46314
I0408 16:48:43.571025 27193 solver.cpp:237]     Train net output #0: loss = 4.46314 (* 1 = 4.46314 loss)
I0408 16:48:43.571039 27193 sgd_solver.cpp:105] Iteration 8364, lr = 1.13083e-10
I0408 16:48:47.786523 27193 solver.cpp:218] Iteration 8376 (2.84673 iter/s, 4.21536s/12 iters), loss = 4.47075
I0408 16:48:47.786567 27193 solver.cpp:237]     Train net output #0: loss = 4.47075 (* 1 = 4.47075 loss)
I0408 16:48:47.786577 27193 sgd_solver.cpp:105] Iteration 8376, lr = 1.10153e-10
I0408 16:48:52.866752 27193 solver.cpp:218] Iteration 8388 (2.3622 iter/s, 5.08002s/12 iters), loss = 4.57159
I0408 16:48:52.866803 27193 solver.cpp:237]     Train net output #0: loss = 4.57159 (* 1 = 4.57159 loss)
I0408 16:48:52.866816 27193 sgd_solver.cpp:105] Iteration 8388, lr = 1.07299e-10
I0408 16:48:55.720675 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:48:57.923883 27193 solver.cpp:218] Iteration 8400 (2.37299 iter/s, 5.05692s/12 iters), loss = 4.51665
I0408 16:48:57.923928 27193 solver.cpp:237]     Train net output #0: loss = 4.51665 (* 1 = 4.51665 loss)
I0408 16:48:57.923938 27193 sgd_solver.cpp:105] Iteration 8400, lr = 1.04519e-10
I0408 16:49:03.133445 27193 solver.cpp:218] Iteration 8412 (2.30355 iter/s, 5.20935s/12 iters), loss = 4.53335
I0408 16:49:03.133486 27193 solver.cpp:237]     Train net output #0: loss = 4.53335 (* 1 = 4.53335 loss)
I0408 16:49:03.133493 27193 sgd_solver.cpp:105] Iteration 8412, lr = 1.01811e-10
I0408 16:49:08.355573 27193 solver.cpp:218] Iteration 8424 (2.29801 iter/s, 5.22192s/12 iters), loss = 4.47205
I0408 16:49:08.355713 27193 solver.cpp:237]     Train net output #0: loss = 4.47205 (* 1 = 4.47205 loss)
I0408 16:49:08.355726 27193 sgd_solver.cpp:105] Iteration 8424, lr = 9.9173e-11
I0408 16:49:13.788719 27193 solver.cpp:218] Iteration 8436 (2.20879 iter/s, 5.43283s/12 iters), loss = 4.57369
I0408 16:49:13.788762 27193 solver.cpp:237]     Train net output #0: loss = 4.57369 (* 1 = 4.57369 loss)
I0408 16:49:13.788772 27193 sgd_solver.cpp:105] Iteration 8436, lr = 9.66034e-11
I0408 16:49:18.843511 27193 solver.cpp:218] Iteration 8448 (2.37408 iter/s, 5.05458s/12 iters), loss = 4.63006
I0408 16:49:18.843552 27193 solver.cpp:237]     Train net output #0: loss = 4.63006 (* 1 = 4.63006 loss)
I0408 16:49:18.843562 27193 sgd_solver.cpp:105] Iteration 8448, lr = 9.41003e-11
I0408 16:49:23.887063 27193 solver.cpp:218] Iteration 8460 (2.37937 iter/s, 5.04335s/12 iters), loss = 4.44075
I0408 16:49:23.887106 27193 solver.cpp:237]     Train net output #0: loss = 4.44075 (* 1 = 4.44075 loss)
I0408 16:49:23.887117 27193 sgd_solver.cpp:105] Iteration 8460, lr = 9.16621e-11
I0408 16:49:25.928495 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0408 16:49:30.340301 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0408 16:49:33.281808 27193 solver.cpp:330] Iteration 8466, Testing net (#0)
I0408 16:49:33.281834 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:49:34.429860 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:49:37.741952 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:49:37.742017 27193 solver.cpp:397]     Test net output #1: loss = 4.60683 (* 1 = 4.60683 loss)
I0408 16:49:39.741611 27193 solver.cpp:218] Iteration 8472 (0.756906 iter/s, 15.854s/12 iters), loss = 4.6796
I0408 16:49:39.741716 27193 solver.cpp:237]     Train net output #0: loss = 4.6796 (* 1 = 4.6796 loss)
I0408 16:49:39.741729 27193 sgd_solver.cpp:105] Iteration 8472, lr = 8.92871e-11
I0408 16:49:44.889088 27193 solver.cpp:218] Iteration 8484 (2.33136 iter/s, 5.14721s/12 iters), loss = 4.65251
I0408 16:49:44.889133 27193 solver.cpp:237]     Train net output #0: loss = 4.65251 (* 1 = 4.65251 loss)
I0408 16:49:44.889145 27193 sgd_solver.cpp:105] Iteration 8484, lr = 8.69736e-11
I0408 16:49:49.983314 27193 solver.cpp:218] Iteration 8496 (2.3557 iter/s, 5.09402s/12 iters), loss = 4.47986
I0408 16:49:49.983351 27193 solver.cpp:237]     Train net output #0: loss = 4.47986 (* 1 = 4.47986 loss)
I0408 16:49:49.983361 27193 sgd_solver.cpp:105] Iteration 8496, lr = 8.47201e-11
I0408 16:49:50.034143 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:49:55.111804 27193 solver.cpp:218] Iteration 8508 (2.33996 iter/s, 5.12829s/12 iters), loss = 4.46242
I0408 16:49:55.111840 27193 solver.cpp:237]     Train net output #0: loss = 4.46242 (* 1 = 4.46242 loss)
I0408 16:49:55.111850 27193 sgd_solver.cpp:105] Iteration 8508, lr = 8.2525e-11
I0408 16:50:00.442229 27193 solver.cpp:218] Iteration 8520 (2.25132 iter/s, 5.33022s/12 iters), loss = 4.40591
I0408 16:50:00.442277 27193 solver.cpp:237]     Train net output #0: loss = 4.40591 (* 1 = 4.40591 loss)
I0408 16:50:00.442291 27193 sgd_solver.cpp:105] Iteration 8520, lr = 8.03867e-11
I0408 16:50:05.556627 27193 solver.cpp:218] Iteration 8532 (2.34641 iter/s, 5.11419s/12 iters), loss = 4.37712
I0408 16:50:05.556672 27193 solver.cpp:237]     Train net output #0: loss = 4.37712 (* 1 = 4.37712 loss)
I0408 16:50:05.556684 27193 sgd_solver.cpp:105] Iteration 8532, lr = 7.83038e-11
I0408 16:50:10.647460 27193 solver.cpp:218] Iteration 8544 (2.35727 iter/s, 5.09063s/12 iters), loss = 4.6384
I0408 16:50:10.648325 27193 solver.cpp:237]     Train net output #0: loss = 4.6384 (* 1 = 4.6384 loss)
I0408 16:50:10.648335 27193 sgd_solver.cpp:105] Iteration 8544, lr = 7.62749e-11
I0408 16:50:15.856528 27193 solver.cpp:218] Iteration 8556 (2.30414 iter/s, 5.20803s/12 iters), loss = 4.45385
I0408 16:50:15.856573 27193 solver.cpp:237]     Train net output #0: loss = 4.45385 (* 1 = 4.45385 loss)
I0408 16:50:15.856585 27193 sgd_solver.cpp:105] Iteration 8556, lr = 7.42986e-11
I0408 16:50:20.540915 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0408 16:50:26.027669 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0408 16:50:28.604454 27193 solver.cpp:330] Iteration 8568, Testing net (#0)
I0408 16:50:28.604481 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:50:29.705775 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:50:33.063639 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0408 16:50:33.063688 27193 solver.cpp:397]     Test net output #1: loss = 4.60891 (* 1 = 4.60891 loss)
I0408 16:50:33.153916 27193 solver.cpp:218] Iteration 8568 (0.69377 iter/s, 17.2968s/12 iters), loss = 4.61367
I0408 16:50:33.153981 27193 solver.cpp:237]     Train net output #0: loss = 4.61367 (* 1 = 4.61367 loss)
I0408 16:50:33.153993 27193 sgd_solver.cpp:105] Iteration 8568, lr = 7.23735e-11
I0408 16:50:37.444799 27193 solver.cpp:218] Iteration 8580 (2.79676 iter/s, 4.29067s/12 iters), loss = 4.65391
I0408 16:50:37.444847 27193 solver.cpp:237]     Train net output #0: loss = 4.65391 (* 1 = 4.65391 loss)
I0408 16:50:37.444859 27193 sgd_solver.cpp:105] Iteration 8580, lr = 7.04983e-11
I0408 16:50:42.530203 27193 solver.cpp:218] Iteration 8592 (2.35979 iter/s, 5.08519s/12 iters), loss = 4.54451
I0408 16:50:42.530324 27193 solver.cpp:237]     Train net output #0: loss = 4.54451 (* 1 = 4.54451 loss)
I0408 16:50:42.530336 27193 sgd_solver.cpp:105] Iteration 8592, lr = 6.86716e-11
I0408 16:50:44.693236 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:50:47.518816 27193 solver.cpp:218] Iteration 8604 (2.40561 iter/s, 4.98833s/12 iters), loss = 4.50324
I0408 16:50:47.518863 27193 solver.cpp:237]     Train net output #0: loss = 4.50324 (* 1 = 4.50324 loss)
I0408 16:50:47.518875 27193 sgd_solver.cpp:105] Iteration 8604, lr = 6.68923e-11
I0408 16:50:52.588151 27193 solver.cpp:218] Iteration 8616 (2.36727 iter/s, 5.06912s/12 iters), loss = 4.38528
I0408 16:50:52.588196 27193 solver.cpp:237]     Train net output #0: loss = 4.38528 (* 1 = 4.38528 loss)
I0408 16:50:52.588207 27193 sgd_solver.cpp:105] Iteration 8616, lr = 6.51591e-11
I0408 16:50:57.659199 27193 solver.cpp:218] Iteration 8628 (2.36647 iter/s, 5.07084s/12 iters), loss = 4.38791
I0408 16:50:57.659241 27193 solver.cpp:237]     Train net output #0: loss = 4.38791 (* 1 = 4.38791 loss)
I0408 16:50:57.659252 27193 sgd_solver.cpp:105] Iteration 8628, lr = 6.34708e-11
I0408 16:51:02.739727 27193 solver.cpp:218] Iteration 8640 (2.36206 iter/s, 5.08032s/12 iters), loss = 4.47424
I0408 16:51:02.739766 27193 solver.cpp:237]     Train net output #0: loss = 4.47424 (* 1 = 4.47424 loss)
I0408 16:51:02.739778 27193 sgd_solver.cpp:105] Iteration 8640, lr = 6.18262e-11
I0408 16:51:08.161458 27193 solver.cpp:218] Iteration 8652 (2.2134 iter/s, 5.42152s/12 iters), loss = 4.5829
I0408 16:51:08.161502 27193 solver.cpp:237]     Train net output #0: loss = 4.5829 (* 1 = 4.5829 loss)
I0408 16:51:08.161514 27193 sgd_solver.cpp:105] Iteration 8652, lr = 6.02243e-11
I0408 16:51:13.646031 27193 solver.cpp:218] Iteration 8664 (2.18804 iter/s, 5.48435s/12 iters), loss = 4.37804
I0408 16:51:13.646175 27193 solver.cpp:237]     Train net output #0: loss = 4.37804 (* 1 = 4.37804 loss)
I0408 16:51:13.646188 27193 sgd_solver.cpp:105] Iteration 8664, lr = 5.86638e-11
I0408 16:51:15.682231 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0408 16:51:19.800472 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0408 16:51:22.133335 27193 solver.cpp:330] Iteration 8670, Testing net (#0)
I0408 16:51:22.133363 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:51:23.210000 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:51:26.601689 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:51:26.601738 27193 solver.cpp:397]     Test net output #1: loss = 4.60556 (* 1 = 4.60556 loss)
I0408 16:51:28.638135 27193 solver.cpp:218] Iteration 8676 (0.800454 iter/s, 14.9915s/12 iters), loss = 4.48559
I0408 16:51:28.638183 27193 solver.cpp:237]     Train net output #0: loss = 4.48559 (* 1 = 4.48559 loss)
I0408 16:51:28.638195 27193 sgd_solver.cpp:105] Iteration 8676, lr = 5.71438e-11
I0408 16:51:33.751394 27193 solver.cpp:218] Iteration 8688 (2.34694 iter/s, 5.11304s/12 iters), loss = 4.42318
I0408 16:51:33.751438 27193 solver.cpp:237]     Train net output #0: loss = 4.42318 (* 1 = 4.42318 loss)
I0408 16:51:33.751449 27193 sgd_solver.cpp:105] Iteration 8688, lr = 5.56632e-11
I0408 16:51:38.152796 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:51:38.858196 27193 solver.cpp:218] Iteration 8700 (2.34991 iter/s, 5.10659s/12 iters), loss = 4.55916
I0408 16:51:38.858242 27193 solver.cpp:237]     Train net output #0: loss = 4.55916 (* 1 = 4.55916 loss)
I0408 16:51:38.858253 27193 sgd_solver.cpp:105] Iteration 8700, lr = 5.42209e-11
I0408 16:51:44.004755 27193 solver.cpp:218] Iteration 8712 (2.33175 iter/s, 5.14635s/12 iters), loss = 4.57343
I0408 16:51:44.006639 27193 solver.cpp:237]     Train net output #0: loss = 4.57343 (* 1 = 4.57343 loss)
I0408 16:51:44.006651 27193 sgd_solver.cpp:105] Iteration 8712, lr = 5.2816e-11
I0408 16:51:49.125226 27193 solver.cpp:218] Iteration 8724 (2.34447 iter/s, 5.11842s/12 iters), loss = 4.36375
I0408 16:51:49.125274 27193 solver.cpp:237]     Train net output #0: loss = 4.36375 (* 1 = 4.36375 loss)
I0408 16:51:49.125285 27193 sgd_solver.cpp:105] Iteration 8724, lr = 5.14475e-11
I0408 16:51:54.114282 27193 solver.cpp:218] Iteration 8736 (2.40537 iter/s, 4.98885s/12 iters), loss = 4.34526
I0408 16:51:54.114327 27193 solver.cpp:237]     Train net output #0: loss = 4.34526 (* 1 = 4.34526 loss)
I0408 16:51:54.114338 27193 sgd_solver.cpp:105] Iteration 8736, lr = 5.01145e-11
I0408 16:51:59.276058 27193 solver.cpp:218] Iteration 8748 (2.32488 iter/s, 5.16156s/12 iters), loss = 4.40494
I0408 16:51:59.276104 27193 solver.cpp:237]     Train net output #0: loss = 4.40494 (* 1 = 4.40494 loss)
I0408 16:51:59.276115 27193 sgd_solver.cpp:105] Iteration 8748, lr = 4.8816e-11
I0408 16:52:04.381937 27193 solver.cpp:218] Iteration 8760 (2.35033 iter/s, 5.10567s/12 iters), loss = 4.48773
I0408 16:52:04.381983 27193 solver.cpp:237]     Train net output #0: loss = 4.48773 (* 1 = 4.48773 loss)
I0408 16:52:04.381994 27193 sgd_solver.cpp:105] Iteration 8760, lr = 4.75512e-11
I0408 16:52:08.983302 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0408 16:52:13.014247 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0408 16:52:15.347986 27193 solver.cpp:330] Iteration 8772, Testing net (#0)
I0408 16:52:15.348063 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:52:16.382899 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:52:19.820739 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:52:19.820778 27193 solver.cpp:397]     Test net output #1: loss = 4.60883 (* 1 = 4.60883 loss)
I0408 16:52:19.912222 27193 solver.cpp:218] Iteration 8772 (0.77271 iter/s, 15.5298s/12 iters), loss = 4.52627
I0408 16:52:19.912261 27193 solver.cpp:237]     Train net output #0: loss = 4.52627 (* 1 = 4.52627 loss)
I0408 16:52:19.912271 27193 sgd_solver.cpp:105] Iteration 8772, lr = 4.63191e-11
I0408 16:52:24.472766 27193 solver.cpp:218] Iteration 8784 (2.63137 iter/s, 4.56036s/12 iters), loss = 4.42382
I0408 16:52:24.472810 27193 solver.cpp:237]     Train net output #0: loss = 4.42382 (* 1 = 4.42382 loss)
I0408 16:52:24.472821 27193 sgd_solver.cpp:105] Iteration 8784, lr = 4.51189e-11
I0408 16:52:29.865581 27193 solver.cpp:218] Iteration 8796 (2.22528 iter/s, 5.39259s/12 iters), loss = 4.4459
I0408 16:52:29.865630 27193 solver.cpp:237]     Train net output #0: loss = 4.4459 (* 1 = 4.4459 loss)
I0408 16:52:29.865641 27193 sgd_solver.cpp:105] Iteration 8796, lr = 4.39499e-11
I0408 16:52:31.444232 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:52:35.010602 27193 solver.cpp:218] Iteration 8808 (2.33245 iter/s, 5.14481s/12 iters), loss = 4.41138
I0408 16:52:35.010645 27193 solver.cpp:237]     Train net output #0: loss = 4.41138 (* 1 = 4.41138 loss)
I0408 16:52:35.010655 27193 sgd_solver.cpp:105] Iteration 8808, lr = 4.28111e-11
I0408 16:52:40.047235 27193 solver.cpp:218] Iteration 8820 (2.38265 iter/s, 5.03642s/12 iters), loss = 4.60961
I0408 16:52:40.047281 27193 solver.cpp:237]     Train net output #0: loss = 4.60961 (* 1 = 4.60961 loss)
I0408 16:52:40.047292 27193 sgd_solver.cpp:105] Iteration 8820, lr = 4.17019e-11
I0408 16:52:45.123827 27193 solver.cpp:218] Iteration 8832 (2.36389 iter/s, 5.07638s/12 iters), loss = 4.57143
I0408 16:52:45.123873 27193 solver.cpp:237]     Train net output #0: loss = 4.57143 (* 1 = 4.57143 loss)
I0408 16:52:45.123884 27193 sgd_solver.cpp:105] Iteration 8832, lr = 4.06213e-11
I0408 16:52:50.162060 27193 solver.cpp:218] Iteration 8844 (2.38189 iter/s, 5.03802s/12 iters), loss = 4.47819
I0408 16:52:50.162210 27193 solver.cpp:237]     Train net output #0: loss = 4.47819 (* 1 = 4.47819 loss)
I0408 16:52:50.162225 27193 sgd_solver.cpp:105] Iteration 8844, lr = 3.95688e-11
I0408 16:52:55.229676 27193 solver.cpp:218] Iteration 8856 (2.36812 iter/s, 5.0673s/12 iters), loss = 4.54805
I0408 16:52:55.229722 27193 solver.cpp:237]     Train net output #0: loss = 4.54805 (* 1 = 4.54805 loss)
I0408 16:52:55.229733 27193 sgd_solver.cpp:105] Iteration 8856, lr = 3.85436e-11
I0408 16:53:00.264580 27193 solver.cpp:218] Iteration 8868 (2.38346 iter/s, 5.03469s/12 iters), loss = 4.47282
I0408 16:53:00.264632 27193 solver.cpp:237]     Train net output #0: loss = 4.47282 (* 1 = 4.47282 loss)
I0408 16:53:00.264644 27193 sgd_solver.cpp:105] Iteration 8868, lr = 3.75449e-11
I0408 16:53:02.344261 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0408 16:53:05.400054 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0408 16:53:07.738237 27193 solver.cpp:330] Iteration 8874, Testing net (#0)
I0408 16:53:07.738263 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:53:08.705013 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:53:12.334209 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:53:12.334259 27193 solver.cpp:397]     Test net output #1: loss = 4.60111 (* 1 = 4.60111 loss)
I0408 16:53:14.121484 27193 solver.cpp:218] Iteration 8880 (0.866024 iter/s, 13.8564s/12 iters), loss = 4.34285
I0408 16:53:14.121526 27193 solver.cpp:237]     Train net output #0: loss = 4.34285 (* 1 = 4.34285 loss)
I0408 16:53:14.121536 27193 sgd_solver.cpp:105] Iteration 8880, lr = 3.65721e-11
I0408 16:53:19.186627 27193 solver.cpp:218] Iteration 8892 (2.36923 iter/s, 5.06494s/12 iters), loss = 4.6088
I0408 16:53:19.186669 27193 solver.cpp:237]     Train net output #0: loss = 4.6088 (* 1 = 4.6088 loss)
I0408 16:53:19.186681 27193 sgd_solver.cpp:105] Iteration 8892, lr = 3.56245e-11
I0408 16:53:22.889014 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:53:24.351728 27193 solver.cpp:218] Iteration 8904 (2.32338 iter/s, 5.16489s/12 iters), loss = 4.51584
I0408 16:53:24.351771 27193 solver.cpp:237]     Train net output #0: loss = 4.51584 (* 1 = 4.51584 loss)
I0408 16:53:24.351781 27193 sgd_solver.cpp:105] Iteration 8904, lr = 3.47014e-11
I0408 16:53:29.451269 27193 solver.cpp:218] Iteration 8916 (2.35325 iter/s, 5.09933s/12 iters), loss = 4.30704
I0408 16:53:29.451314 27193 solver.cpp:237]     Train net output #0: loss = 4.30704 (* 1 = 4.30704 loss)
I0408 16:53:29.451325 27193 sgd_solver.cpp:105] Iteration 8916, lr = 3.38023e-11
I0408 16:53:34.940600 27193 solver.cpp:218] Iteration 8928 (2.18615 iter/s, 5.48911s/12 iters), loss = 4.46504
I0408 16:53:34.940649 27193 solver.cpp:237]     Train net output #0: loss = 4.46504 (* 1 = 4.46504 loss)
I0408 16:53:34.940659 27193 sgd_solver.cpp:105] Iteration 8928, lr = 3.29265e-11
I0408 16:53:40.349866 27193 solver.cpp:218] Iteration 8940 (2.21851 iter/s, 5.40904s/12 iters), loss = 4.52444
I0408 16:53:40.349910 27193 solver.cpp:237]     Train net output #0: loss = 4.52444 (* 1 = 4.52444 loss)
I0408 16:53:40.349921 27193 sgd_solver.cpp:105] Iteration 8940, lr = 3.20733e-11
I0408 16:53:45.508860 27193 solver.cpp:218] Iteration 8952 (2.32613 iter/s, 5.15878s/12 iters), loss = 4.43596
I0408 16:53:45.508908 27193 solver.cpp:237]     Train net output #0: loss = 4.43596 (* 1 = 4.43596 loss)
I0408 16:53:45.508921 27193 sgd_solver.cpp:105] Iteration 8952, lr = 3.12423e-11
I0408 16:53:50.622609 27193 solver.cpp:218] Iteration 8964 (2.34671 iter/s, 5.11354s/12 iters), loss = 4.49226
I0408 16:53:50.622646 27193 solver.cpp:237]     Train net output #0: loss = 4.49226 (* 1 = 4.49226 loss)
I0408 16:53:50.622655 27193 sgd_solver.cpp:105] Iteration 8964, lr = 3.04328e-11
I0408 16:53:55.392606 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0408 16:53:58.420325 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0408 16:54:00.750833 27193 solver.cpp:330] Iteration 8976, Testing net (#0)
I0408 16:54:00.750859 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:54:01.780903 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:54:05.338644 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:54:05.338693 27193 solver.cpp:397]     Test net output #1: loss = 4.60674 (* 1 = 4.60674 loss)
I0408 16:54:05.430044 27193 solver.cpp:218] Iteration 8976 (0.810431 iter/s, 14.8069s/12 iters), loss = 4.88011
I0408 16:54:05.430096 27193 solver.cpp:237]     Train net output #0: loss = 4.88011 (* 1 = 4.88011 loss)
I0408 16:54:05.430107 27193 sgd_solver.cpp:105] Iteration 8976, lr = 2.96443e-11
I0408 16:54:09.688771 27193 solver.cpp:218] Iteration 8988 (2.81787 iter/s, 4.25854s/12 iters), loss = 4.49308
I0408 16:54:09.688812 27193 solver.cpp:237]     Train net output #0: loss = 4.49308 (* 1 = 4.49308 loss)
I0408 16:54:09.688822 27193 sgd_solver.cpp:105] Iteration 8988, lr = 2.88762e-11
I0408 16:54:13.067759 27193 blocking_queue.cpp:49] Waiting for data
I0408 16:54:14.797158 27193 solver.cpp:218] Iteration 9000 (2.34917 iter/s, 5.10818s/12 iters), loss = 4.52761
I0408 16:54:14.797204 27193 solver.cpp:237]     Train net output #0: loss = 4.52761 (* 1 = 4.52761 loss)
I0408 16:54:14.797214 27193 sgd_solver.cpp:105] Iteration 9000, lr = 2.8128e-11
I0408 16:54:15.531313 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:54:19.936095 27193 solver.cpp:218] Iteration 9012 (2.33521 iter/s, 5.13873s/12 iters), loss = 4.62596
I0408 16:54:19.936136 27193 solver.cpp:237]     Train net output #0: loss = 4.62596 (* 1 = 4.62596 loss)
I0408 16:54:19.936147 27193 sgd_solver.cpp:105] Iteration 9012, lr = 2.73992e-11
I0408 16:54:25.215159 27193 solver.cpp:218] Iteration 9024 (2.27322 iter/s, 5.27886s/12 iters), loss = 4.63488
I0408 16:54:25.215204 27193 solver.cpp:237]     Train net output #0: loss = 4.63488 (* 1 = 4.63488 loss)
I0408 16:54:25.215216 27193 sgd_solver.cpp:105] Iteration 9024, lr = 2.66892e-11
I0408 16:54:30.226260 27193 solver.cpp:218] Iteration 9036 (2.39478 iter/s, 5.0109s/12 iters), loss = 4.41475
I0408 16:54:30.226409 27193 solver.cpp:237]     Train net output #0: loss = 4.41475 (* 1 = 4.41475 loss)
I0408 16:54:30.226423 27193 sgd_solver.cpp:105] Iteration 9036, lr = 2.59977e-11
I0408 16:54:35.322327 27193 solver.cpp:218] Iteration 9048 (2.3549 iter/s, 5.09576s/12 iters), loss = 4.40349
I0408 16:54:35.322368 27193 solver.cpp:237]     Train net output #0: loss = 4.40349 (* 1 = 4.40349 loss)
I0408 16:54:35.322379 27193 sgd_solver.cpp:105] Iteration 9048, lr = 2.53241e-11
I0408 16:54:40.677500 27193 solver.cpp:218] Iteration 9060 (2.24091 iter/s, 5.35496s/12 iters), loss = 4.43619
I0408 16:54:40.677552 27193 solver.cpp:237]     Train net output #0: loss = 4.43619 (* 1 = 4.43619 loss)
I0408 16:54:40.677563 27193 sgd_solver.cpp:105] Iteration 9060, lr = 2.46679e-11
I0408 16:54:46.243484 27193 solver.cpp:218] Iteration 9072 (2.15604 iter/s, 5.56575s/12 iters), loss = 4.52332
I0408 16:54:46.243530 27193 solver.cpp:237]     Train net output #0: loss = 4.52332 (* 1 = 4.52332 loss)
I0408 16:54:46.243543 27193 sgd_solver.cpp:105] Iteration 9072, lr = 2.40288e-11
I0408 16:54:48.511067 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0408 16:54:52.528714 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0408 16:54:54.861439 27193 solver.cpp:330] Iteration 9078, Testing net (#0)
I0408 16:54:54.861466 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:54:55.772284 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:54:59.338318 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:54:59.338366 27193 solver.cpp:397]     Test net output #1: loss = 4.60905 (* 1 = 4.60905 loss)
I0408 16:55:01.209292 27193 solver.cpp:218] Iteration 9084 (0.801854 iter/s, 14.9653s/12 iters), loss = 4.37931
I0408 16:55:01.209403 27193 solver.cpp:237]     Train net output #0: loss = 4.37931 (* 1 = 4.37931 loss)
I0408 16:55:01.209417 27193 sgd_solver.cpp:105] Iteration 9084, lr = 2.34062e-11
I0408 16:55:06.222435 27193 solver.cpp:218] Iteration 9096 (2.39384 iter/s, 5.01288s/12 iters), loss = 4.56534
I0408 16:55:06.222482 27193 solver.cpp:237]     Train net output #0: loss = 4.56534 (* 1 = 4.56534 loss)
I0408 16:55:06.222493 27193 sgd_solver.cpp:105] Iteration 9096, lr = 2.27997e-11
I0408 16:55:09.195675 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:55:11.277833 27193 solver.cpp:218] Iteration 9108 (2.3738 iter/s, 5.05519s/12 iters), loss = 4.61088
I0408 16:55:11.277878 27193 solver.cpp:237]     Train net output #0: loss = 4.61088 (* 1 = 4.61088 loss)
I0408 16:55:11.277890 27193 sgd_solver.cpp:105] Iteration 9108, lr = 2.22089e-11
I0408 16:55:16.319624 27193 solver.cpp:218] Iteration 9120 (2.3802 iter/s, 5.04158s/12 iters), loss = 4.4176
I0408 16:55:16.319672 27193 solver.cpp:237]     Train net output #0: loss = 4.4176 (* 1 = 4.4176 loss)
I0408 16:55:16.319684 27193 sgd_solver.cpp:105] Iteration 9120, lr = 2.16335e-11
I0408 16:55:21.467120 27193 solver.cpp:218] Iteration 9132 (2.33133 iter/s, 5.14728s/12 iters), loss = 4.41119
I0408 16:55:21.467165 27193 solver.cpp:237]     Train net output #0: loss = 4.41119 (* 1 = 4.41119 loss)
I0408 16:55:21.467176 27193 sgd_solver.cpp:105] Iteration 9132, lr = 2.1073e-11
I0408 16:55:26.664191 27193 solver.cpp:218] Iteration 9144 (2.30909 iter/s, 5.19686s/12 iters), loss = 4.50215
I0408 16:55:26.664240 27193 solver.cpp:237]     Train net output #0: loss = 4.50215 (* 1 = 4.50215 loss)
I0408 16:55:26.664252 27193 sgd_solver.cpp:105] Iteration 9144, lr = 2.0527e-11
I0408 16:55:32.098484 27193 solver.cpp:218] Iteration 9156 (2.20829 iter/s, 5.43407s/12 iters), loss = 4.48387
I0408 16:55:32.098594 27193 solver.cpp:237]     Train net output #0: loss = 4.48387 (* 1 = 4.48387 loss)
I0408 16:55:32.098608 27193 sgd_solver.cpp:105] Iteration 9156, lr = 1.99951e-11
I0408 16:55:37.199944 27193 solver.cpp:218] Iteration 9168 (2.35239 iter/s, 5.10119s/12 iters), loss = 4.49282
I0408 16:55:37.199985 27193 solver.cpp:237]     Train net output #0: loss = 4.49282 (* 1 = 4.49282 loss)
I0408 16:55:37.199995 27193 sgd_solver.cpp:105] Iteration 9168, lr = 1.9477e-11
I0408 16:55:41.928153 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0408 16:55:44.958436 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0408 16:55:47.292855 27193 solver.cpp:330] Iteration 9180, Testing net (#0)
I0408 16:55:47.292881 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:55:48.160096 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:55:51.760929 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0741422
I0408 16:55:51.760977 27193 solver.cpp:397]     Test net output #1: loss = 4.602 (* 1 = 4.602 loss)
I0408 16:55:51.852545 27193 solver.cpp:218] Iteration 9180 (0.818994 iter/s, 14.6521s/12 iters), loss = 4.633
I0408 16:55:51.852600 27193 solver.cpp:237]     Train net output #0: loss = 4.633 (* 1 = 4.633 loss)
I0408 16:55:51.852613 27193 sgd_solver.cpp:105] Iteration 9180, lr = 1.89723e-11
I0408 16:55:56.042424 27193 solver.cpp:218] Iteration 9192 (2.86417 iter/s, 4.18969s/12 iters), loss = 4.54277
I0408 16:55:56.042467 27193 solver.cpp:237]     Train net output #0: loss = 4.54277 (* 1 = 4.54277 loss)
I0408 16:55:56.042479 27193 sgd_solver.cpp:105] Iteration 9192, lr = 1.84808e-11
I0408 16:56:01.088196 27193 solver.cpp:218] Iteration 9204 (2.37833 iter/s, 5.04557s/12 iters), loss = 4.45228
I0408 16:56:01.088239 27193 solver.cpp:237]     Train net output #0: loss = 4.45228 (* 1 = 4.45228 loss)
I0408 16:56:01.088251 27193 sgd_solver.cpp:105] Iteration 9204, lr = 1.80019e-11
I0408 16:56:01.167338 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:56:06.026691 27193 solver.cpp:218] Iteration 9216 (2.42999 iter/s, 4.93829s/12 iters), loss = 4.43534
I0408 16:56:06.026823 27193 solver.cpp:237]     Train net output #0: loss = 4.43534 (* 1 = 4.43534 loss)
I0408 16:56:06.026835 27193 sgd_solver.cpp:105] Iteration 9216, lr = 1.75355e-11
I0408 16:56:11.116782 27193 solver.cpp:218] Iteration 9228 (2.35766 iter/s, 5.0898s/12 iters), loss = 4.3691
I0408 16:56:11.116827 27193 solver.cpp:237]     Train net output #0: loss = 4.3691 (* 1 = 4.3691 loss)
I0408 16:56:11.116838 27193 sgd_solver.cpp:105] Iteration 9228, lr = 1.70811e-11
I0408 16:56:16.541268 27193 solver.cpp:218] Iteration 9240 (2.21228 iter/s, 5.42427s/12 iters), loss = 4.31208
I0408 16:56:16.541316 27193 solver.cpp:237]     Train net output #0: loss = 4.31208 (* 1 = 4.31208 loss)
I0408 16:56:16.541327 27193 sgd_solver.cpp:105] Iteration 9240, lr = 1.66385e-11
I0408 16:56:21.790753 27193 solver.cpp:218] Iteration 9252 (2.28603 iter/s, 5.24927s/12 iters), loss = 4.64808
I0408 16:56:21.790802 27193 solver.cpp:237]     Train net output #0: loss = 4.64808 (* 1 = 4.64808 loss)
I0408 16:56:21.790814 27193 sgd_solver.cpp:105] Iteration 9252, lr = 1.62074e-11
I0408 16:56:26.870342 27193 solver.cpp:218] Iteration 9264 (2.36249 iter/s, 5.07938s/12 iters), loss = 4.40606
I0408 16:56:26.870383 27193 solver.cpp:237]     Train net output #0: loss = 4.40606 (* 1 = 4.40606 loss)
I0408 16:56:26.870393 27193 sgd_solver.cpp:105] Iteration 9264, lr = 1.57875e-11
I0408 16:56:31.975381 27193 solver.cpp:218] Iteration 9276 (2.35071 iter/s, 5.10484s/12 iters), loss = 4.5628
I0408 16:56:31.975425 27193 solver.cpp:237]     Train net output #0: loss = 4.5628 (* 1 = 4.5628 loss)
I0408 16:56:31.975435 27193 sgd_solver.cpp:105] Iteration 9276, lr = 1.53784e-11
I0408 16:56:34.034907 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0408 16:56:37.609793 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0408 16:56:39.950565 27193 solver.cpp:330] Iteration 9282, Testing net (#0)
I0408 16:56:39.950589 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:56:40.774657 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:56:44.428355 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:56:44.428402 27193 solver.cpp:397]     Test net output #1: loss = 4.61293 (* 1 = 4.61293 loss)
I0408 16:56:46.433233 27193 solver.cpp:218] Iteration 9288 (0.830027 iter/s, 14.4574s/12 iters), loss = 4.52131
I0408 16:56:46.433279 27193 solver.cpp:237]     Train net output #0: loss = 4.52131 (* 1 = 4.52131 loss)
I0408 16:56:46.433290 27193 sgd_solver.cpp:105] Iteration 9288, lr = 1.498e-11
I0408 16:56:51.639375 27193 solver.cpp:218] Iteration 9300 (2.30506 iter/s, 5.20593s/12 iters), loss = 4.63992
I0408 16:56:51.639420 27193 solver.cpp:237]     Train net output #0: loss = 4.63992 (* 1 = 4.63992 loss)
I0408 16:56:51.639430 27193 sgd_solver.cpp:105] Iteration 9300, lr = 1.45918e-11
I0408 16:56:53.881580 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:56:56.735462 27193 solver.cpp:218] Iteration 9312 (2.35484 iter/s, 5.09588s/12 iters), loss = 4.56341
I0408 16:56:56.735505 27193 solver.cpp:237]     Train net output #0: loss = 4.56341 (* 1 = 4.56341 loss)
I0408 16:56:56.735517 27193 sgd_solver.cpp:105] Iteration 9312, lr = 1.42137e-11
I0408 16:57:01.828573 27193 solver.cpp:218] Iteration 9324 (2.35622 iter/s, 5.09291s/12 iters), loss = 4.45953
I0408 16:57:01.828608 27193 solver.cpp:237]     Train net output #0: loss = 4.45953 (* 1 = 4.45953 loss)
I0408 16:57:01.828617 27193 sgd_solver.cpp:105] Iteration 9324, lr = 1.38455e-11
I0408 16:57:06.842854 27193 solver.cpp:218] Iteration 9336 (2.39326 iter/s, 5.01409s/12 iters), loss = 4.31774
I0408 16:57:06.842890 27193 solver.cpp:237]     Train net output #0: loss = 4.31774 (* 1 = 4.31774 loss)
I0408 16:57:06.842900 27193 sgd_solver.cpp:105] Iteration 9336, lr = 1.34867e-11
I0408 16:57:11.943785 27193 solver.cpp:218] Iteration 9348 (2.3526 iter/s, 5.10074s/12 iters), loss = 4.61208
I0408 16:57:11.943902 27193 solver.cpp:237]     Train net output #0: loss = 4.61208 (* 1 = 4.61208 loss)
I0408 16:57:11.943910 27193 sgd_solver.cpp:105] Iteration 9348, lr = 1.31373e-11
I0408 16:57:17.472636 27193 solver.cpp:218] Iteration 9360 (2.17055 iter/s, 5.52856s/12 iters), loss = 4.45627
I0408 16:57:17.472688 27193 solver.cpp:237]     Train net output #0: loss = 4.45627 (* 1 = 4.45627 loss)
I0408 16:57:17.472702 27193 sgd_solver.cpp:105] Iteration 9360, lr = 1.27969e-11
I0408 16:57:22.590517 27193 solver.cpp:218] Iteration 9372 (2.34482 iter/s, 5.11767s/12 iters), loss = 4.3045
I0408 16:57:22.590560 27193 solver.cpp:237]     Train net output #0: loss = 4.3045 (* 1 = 4.3045 loss)
I0408 16:57:22.590572 27193 sgd_solver.cpp:105] Iteration 9372, lr = 1.24653e-11
I0408 16:57:27.188896 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0408 16:57:30.281373 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0408 16:57:32.606954 27193 solver.cpp:330] Iteration 9384, Testing net (#0)
I0408 16:57:32.606981 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:57:33.398360 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:57:37.075866 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:57:37.075915 27193 solver.cpp:397]     Test net output #1: loss = 4.6041 (* 1 = 4.6041 loss)
I0408 16:57:37.167441 27193 solver.cpp:218] Iteration 9384 (0.823246 iter/s, 14.5764s/12 iters), loss = 4.42004
I0408 16:57:37.167487 27193 solver.cpp:237]     Train net output #0: loss = 4.42004 (* 1 = 4.42004 loss)
I0408 16:57:37.167498 27193 sgd_solver.cpp:105] Iteration 9384, lr = 1.21423e-11
I0408 16:57:41.567252 27193 solver.cpp:218] Iteration 9396 (2.72751 iter/s, 4.39962s/12 iters), loss = 4.51628
I0408 16:57:41.567291 27193 solver.cpp:237]     Train net output #0: loss = 4.51628 (* 1 = 4.51628 loss)
I0408 16:57:41.567301 27193 sgd_solver.cpp:105] Iteration 9396, lr = 1.18277e-11
I0408 16:57:45.966398 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:57:46.635056 27193 solver.cpp:218] Iteration 9408 (2.36799 iter/s, 5.0676s/12 iters), loss = 4.58041
I0408 16:57:46.635100 27193 solver.cpp:237]     Train net output #0: loss = 4.58041 (* 1 = 4.58041 loss)
I0408 16:57:46.635113 27193 sgd_solver.cpp:105] Iteration 9408, lr = 1.15212e-11
I0408 16:57:51.720304 27193 solver.cpp:218] Iteration 9420 (2.35986 iter/s, 5.08504s/12 iters), loss = 4.69954
I0408 16:57:51.720348 27193 solver.cpp:237]     Train net output #0: loss = 4.69954 (* 1 = 4.69954 loss)
I0408 16:57:51.720360 27193 sgd_solver.cpp:105] Iteration 9420, lr = 1.12227e-11
I0408 16:57:57.020165 27193 solver.cpp:218] Iteration 9432 (2.2643 iter/s, 5.29965s/12 iters), loss = 4.32433
I0408 16:57:57.020212 27193 solver.cpp:237]     Train net output #0: loss = 4.32433 (* 1 = 4.32433 loss)
I0408 16:57:57.020224 27193 sgd_solver.cpp:105] Iteration 9432, lr = 1.09319e-11
I0408 16:58:02.440804 27193 solver.cpp:218] Iteration 9444 (2.21385 iter/s, 5.42041s/12 iters), loss = 4.47061
I0408 16:58:02.440850 27193 solver.cpp:237]     Train net output #0: loss = 4.47061 (* 1 = 4.47061 loss)
I0408 16:58:02.440861 27193 sgd_solver.cpp:105] Iteration 9444, lr = 1.06487e-11
I0408 16:58:07.546167 27193 solver.cpp:218] Iteration 9456 (2.35056 iter/s, 5.10516s/12 iters), loss = 4.46817
I0408 16:58:07.546211 27193 solver.cpp:237]     Train net output #0: loss = 4.46817 (* 1 = 4.46817 loss)
I0408 16:58:07.546221 27193 sgd_solver.cpp:105] Iteration 9456, lr = 1.03728e-11
I0408 16:58:12.824398 27193 solver.cpp:218] Iteration 9468 (2.27358 iter/s, 5.27802s/12 iters), loss = 4.4607
I0408 16:58:12.824441 27193 solver.cpp:237]     Train net output #0: loss = 4.4607 (* 1 = 4.4607 loss)
I0408 16:58:12.824452 27193 sgd_solver.cpp:105] Iteration 9468, lr = 1.0104e-11
I0408 16:58:17.898872 27193 solver.cpp:218] Iteration 9480 (2.36487 iter/s, 5.07427s/12 iters), loss = 4.6057
I0408 16:58:17.900964 27193 solver.cpp:237]     Train net output #0: loss = 4.6057 (* 1 = 4.6057 loss)
I0408 16:58:17.900979 27193 sgd_solver.cpp:105] Iteration 9480, lr = 9.8422e-12
I0408 16:58:19.953650 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0408 16:58:23.933185 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0408 16:58:26.314612 27193 solver.cpp:330] Iteration 9486, Testing net (#0)
I0408 16:58:26.314636 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:58:27.032685 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:58:30.836994 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 16:58:30.837040 27193 solver.cpp:397]     Test net output #1: loss = 4.60412 (* 1 = 4.60412 loss)
I0408 16:58:32.853055 27193 solver.cpp:218] Iteration 9492 (0.802588 iter/s, 14.9516s/12 iters), loss = 4.43222
I0408 16:58:32.853106 27193 solver.cpp:237]     Train net output #0: loss = 4.43222 (* 1 = 4.43222 loss)
I0408 16:58:32.853117 27193 sgd_solver.cpp:105] Iteration 9492, lr = 9.58719e-12
I0408 16:58:38.040676 27193 solver.cpp:218] Iteration 9504 (2.3133 iter/s, 5.18741s/12 iters), loss = 4.37912
I0408 16:58:38.040720 27193 solver.cpp:237]     Train net output #0: loss = 4.37912 (* 1 = 4.37912 loss)
I0408 16:58:38.040732 27193 sgd_solver.cpp:105] Iteration 9504, lr = 9.33878e-12
I0408 16:58:39.525240 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:58:43.099529 27193 solver.cpp:218] Iteration 9516 (2.37218 iter/s, 5.05865s/12 iters), loss = 4.37128
I0408 16:58:43.099573 27193 solver.cpp:237]     Train net output #0: loss = 4.37128 (* 1 = 4.37128 loss)
I0408 16:58:43.099584 27193 sgd_solver.cpp:105] Iteration 9516, lr = 9.0968e-12
I0408 16:58:48.302140 27193 solver.cpp:218] Iteration 9528 (2.30663 iter/s, 5.2024s/12 iters), loss = 4.62514
I0408 16:58:48.302268 27193 solver.cpp:237]     Train net output #0: loss = 4.62514 (* 1 = 4.62514 loss)
I0408 16:58:48.302281 27193 sgd_solver.cpp:105] Iteration 9528, lr = 8.8611e-12
I0408 16:58:53.482739 27193 solver.cpp:218] Iteration 9540 (2.31647 iter/s, 5.18031s/12 iters), loss = 4.49078
I0408 16:58:53.482786 27193 solver.cpp:237]     Train net output #0: loss = 4.49078 (* 1 = 4.49078 loss)
I0408 16:58:53.482798 27193 sgd_solver.cpp:105] Iteration 9540, lr = 8.63151e-12
I0408 16:58:58.594856 27193 solver.cpp:218] Iteration 9552 (2.34746 iter/s, 5.11191s/12 iters), loss = 4.49515
I0408 16:58:58.594897 27193 solver.cpp:237]     Train net output #0: loss = 4.49515 (* 1 = 4.49515 loss)
I0408 16:58:58.594908 27193 sgd_solver.cpp:105] Iteration 9552, lr = 8.40786e-12
I0408 16:59:03.843016 27193 solver.cpp:218] Iteration 9564 (2.28661 iter/s, 5.24795s/12 iters), loss = 4.46884
I0408 16:59:03.843063 27193 solver.cpp:237]     Train net output #0: loss = 4.46884 (* 1 = 4.46884 loss)
I0408 16:59:03.843075 27193 sgd_solver.cpp:105] Iteration 9564, lr = 8.19001e-12
I0408 16:59:09.159368 27193 solver.cpp:218] Iteration 9576 (2.25728 iter/s, 5.31613s/12 iters), loss = 4.47373
I0408 16:59:09.159399 27193 solver.cpp:237]     Train net output #0: loss = 4.47373 (* 1 = 4.47373 loss)
I0408 16:59:09.159407 27193 sgd_solver.cpp:105] Iteration 9576, lr = 7.9778e-12
I0408 16:59:13.773216 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0408 16:59:16.755321 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0408 16:59:19.086458 27193 solver.cpp:330] Iteration 9588, Testing net (#0)
I0408 16:59:19.086586 27193 net.cpp:676] Ignoring source layer train-data
I0408 16:59:19.779719 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:59:23.542052 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 16:59:23.542102 27193 solver.cpp:397]     Test net output #1: loss = 4.60252 (* 1 = 4.60252 loss)
I0408 16:59:23.630739 27193 solver.cpp:218] Iteration 9588 (0.829251 iter/s, 14.4709s/12 iters), loss = 4.33667
I0408 16:59:23.630780 27193 solver.cpp:237]     Train net output #0: loss = 4.33667 (* 1 = 4.33667 loss)
I0408 16:59:23.630790 27193 sgd_solver.cpp:105] Iteration 9588, lr = 7.77109e-12
I0408 16:59:28.066905 27193 solver.cpp:218] Iteration 9600 (2.70515 iter/s, 4.43598s/12 iters), loss = 4.55297
I0408 16:59:28.066948 27193 solver.cpp:237]     Train net output #0: loss = 4.55297 (* 1 = 4.55297 loss)
I0408 16:59:28.066960 27193 sgd_solver.cpp:105] Iteration 9600, lr = 7.56974e-12
I0408 16:59:31.726902 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 16:59:33.166635 27193 solver.cpp:218] Iteration 9612 (2.35316 iter/s, 5.09952s/12 iters), loss = 4.48176
I0408 16:59:33.166671 27193 solver.cpp:237]     Train net output #0: loss = 4.48176 (* 1 = 4.48176 loss)
I0408 16:59:33.166678 27193 sgd_solver.cpp:105] Iteration 9612, lr = 7.3736e-12
I0408 16:59:38.229138 27193 solver.cpp:218] Iteration 9624 (2.37046 iter/s, 5.0623s/12 iters), loss = 4.50039
I0408 16:59:38.229187 27193 solver.cpp:237]     Train net output #0: loss = 4.50039 (* 1 = 4.50039 loss)
I0408 16:59:38.229197 27193 sgd_solver.cpp:105] Iteration 9624, lr = 7.18255e-12
I0408 16:59:43.294006 27193 solver.cpp:218] Iteration 9636 (2.36936 iter/s, 5.06466s/12 iters), loss = 4.50853
I0408 16:59:43.294049 27193 solver.cpp:237]     Train net output #0: loss = 4.50853 (* 1 = 4.50853 loss)
I0408 16:59:43.294060 27193 sgd_solver.cpp:105] Iteration 9636, lr = 6.99644e-12
I0408 16:59:48.370785 27193 solver.cpp:218] Iteration 9648 (2.3638 iter/s, 5.07658s/12 iters), loss = 4.54178
I0408 16:59:48.370821 27193 solver.cpp:237]     Train net output #0: loss = 4.54178 (* 1 = 4.54178 loss)
I0408 16:59:48.370831 27193 sgd_solver.cpp:105] Iteration 9648, lr = 6.81516e-12
I0408 16:59:53.319612 27193 solver.cpp:218] Iteration 9660 (2.42491 iter/s, 4.94863s/12 iters), loss = 4.47618
I0408 16:59:53.319685 27193 solver.cpp:237]     Train net output #0: loss = 4.47618 (* 1 = 4.47618 loss)
I0408 16:59:53.319697 27193 sgd_solver.cpp:105] Iteration 9660, lr = 6.63858e-12
I0408 16:59:58.481665 27193 solver.cpp:218] Iteration 9672 (2.32476 iter/s, 5.16182s/12 iters), loss = 4.58055
I0408 16:59:58.481709 27193 solver.cpp:237]     Train net output #0: loss = 4.58055 (* 1 = 4.58055 loss)
I0408 16:59:58.481721 27193 sgd_solver.cpp:105] Iteration 9672, lr = 6.46657e-12
I0408 17:00:03.605809 27193 solver.cpp:218] Iteration 9684 (2.34195 iter/s, 5.12394s/12 iters), loss = 4.73246
I0408 17:00:03.605857 27193 solver.cpp:237]     Train net output #0: loss = 4.73246 (* 1 = 4.73246 loss)
I0408 17:00:03.605868 27193 sgd_solver.cpp:105] Iteration 9684, lr = 6.29902e-12
I0408 17:00:05.709286 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0408 17:00:08.811535 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0408 17:00:11.142601 27193 solver.cpp:330] Iteration 9690, Testing net (#0)
I0408 17:00:11.142628 27193 net.cpp:676] Ignoring source layer train-data
I0408 17:00:11.784657 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:00:14.607648 27193 blocking_queue.cpp:49] Waiting for data
I0408 17:00:15.594594 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 17:00:15.594640 27193 solver.cpp:397]     Test net output #1: loss = 4.61007 (* 1 = 4.61007 loss)
I0408 17:00:17.556022 27193 solver.cpp:218] Iteration 9696 (0.860231 iter/s, 13.9497s/12 iters), loss = 4.38908
I0408 17:00:17.556067 27193 solver.cpp:237]     Train net output #0: loss = 4.38908 (* 1 = 4.38908 loss)
I0408 17:00:17.556078 27193 sgd_solver.cpp:105] Iteration 9696, lr = 6.13581e-12
I0408 17:00:22.660507 27193 solver.cpp:218] Iteration 9708 (2.35097 iter/s, 5.10427s/12 iters), loss = 4.5884
I0408 17:00:22.660553 27193 solver.cpp:237]     Train net output #0: loss = 4.5884 (* 1 = 4.5884 loss)
I0408 17:00:22.660564 27193 sgd_solver.cpp:105] Iteration 9708, lr = 5.97682e-12
I0408 17:00:23.414121 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:00:27.708109 27193 solver.cpp:218] Iteration 9720 (2.37747 iter/s, 5.04739s/12 iters), loss = 4.54045
I0408 17:00:27.708158 27193 solver.cpp:237]     Train net output #0: loss = 4.54045 (* 1 = 4.54045 loss)
I0408 17:00:27.708168 27193 sgd_solver.cpp:105] Iteration 9720, lr = 5.82196e-12
I0408 17:00:32.854161 27193 solver.cpp:218] Iteration 9732 (2.33198 iter/s, 5.14584s/12 iters), loss = 4.63809
I0408 17:00:32.854208 27193 solver.cpp:237]     Train net output #0: loss = 4.63809 (* 1 = 4.63809 loss)
I0408 17:00:32.854219 27193 sgd_solver.cpp:105] Iteration 9732, lr = 5.67111e-12
I0408 17:00:37.869062 27193 solver.cpp:218] Iteration 9744 (2.39297 iter/s, 5.01469s/12 iters), loss = 4.37199
I0408 17:00:37.869112 27193 solver.cpp:237]     Train net output #0: loss = 4.37199 (* 1 = 4.37199 loss)
I0408 17:00:37.869123 27193 sgd_solver.cpp:105] Iteration 9744, lr = 5.52417e-12
I0408 17:00:42.934900 27193 solver.cpp:218] Iteration 9756 (2.36891 iter/s, 5.06563s/12 iters), loss = 4.42213
I0408 17:00:42.934943 27193 solver.cpp:237]     Train net output #0: loss = 4.42213 (* 1 = 4.42213 loss)
I0408 17:00:42.934955 27193 sgd_solver.cpp:105] Iteration 9756, lr = 5.38104e-12
I0408 17:00:47.959026 27193 solver.cpp:218] Iteration 9768 (2.38857 iter/s, 5.02392s/12 iters), loss = 4.45883
I0408 17:00:47.959071 27193 solver.cpp:237]     Train net output #0: loss = 4.45883 (* 1 = 4.45883 loss)
I0408 17:00:47.959081 27193 sgd_solver.cpp:105] Iteration 9768, lr = 5.24161e-12
I0408 17:00:53.032418 27193 solver.cpp:218] Iteration 9780 (2.36538 iter/s, 5.07318s/12 iters), loss = 4.53484
I0408 17:00:53.032464 27193 solver.cpp:237]     Train net output #0: loss = 4.53484 (* 1 = 4.53484 loss)
I0408 17:00:53.032474 27193 sgd_solver.cpp:105] Iteration 9780, lr = 5.1058e-12
I0408 17:00:57.904760 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0408 17:01:00.934283 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0408 17:01:03.340577 27193 solver.cpp:330] Iteration 9792, Testing net (#0)
I0408 17:01:03.340603 27193 net.cpp:676] Ignoring source layer train-data
I0408 17:01:03.950644 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:01:07.808048 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 17:01:07.808095 27193 solver.cpp:397]     Test net output #1: loss = 4.60267 (* 1 = 4.60267 loss)
I0408 17:01:07.899291 27193 solver.cpp:218] Iteration 9792 (0.807191 iter/s, 14.8664s/12 iters), loss = 4.4819
I0408 17:01:07.899339 27193 solver.cpp:237]     Train net output #0: loss = 4.4819 (* 1 = 4.4819 loss)
I0408 17:01:07.899351 27193 sgd_solver.cpp:105] Iteration 9792, lr = 4.9735e-12
I0408 17:01:12.156513 27193 solver.cpp:218] Iteration 9804 (2.81887 iter/s, 4.25703s/12 iters), loss = 4.68578
I0408 17:01:12.156559 27193 solver.cpp:237]     Train net output #0: loss = 4.68578 (* 1 = 4.68578 loss)
I0408 17:01:12.156571 27193 sgd_solver.cpp:105] Iteration 9804, lr = 4.84464e-12
I0408 17:01:15.156642 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:01:17.254324 27193 solver.cpp:218] Iteration 9816 (2.35405 iter/s, 5.0976s/12 iters), loss = 4.64873
I0408 17:01:17.254369 27193 solver.cpp:237]     Train net output #0: loss = 4.64873 (* 1 = 4.64873 loss)
I0408 17:01:17.254392 27193 sgd_solver.cpp:105] Iteration 9816, lr = 4.71911e-12
I0408 17:01:22.274540 27193 solver.cpp:218] Iteration 9828 (2.39043 iter/s, 5.02001s/12 iters), loss = 4.42926
I0408 17:01:22.274583 27193 solver.cpp:237]     Train net output #0: loss = 4.42926 (* 1 = 4.42926 loss)
I0408 17:01:22.274595 27193 sgd_solver.cpp:105] Iteration 9828, lr = 4.59684e-12
I0408 17:01:27.317632 27193 solver.cpp:218] Iteration 9840 (2.37959 iter/s, 5.04288s/12 iters), loss = 4.4811
I0408 17:01:27.317678 27193 solver.cpp:237]     Train net output #0: loss = 4.4811 (* 1 = 4.4811 loss)
I0408 17:01:27.317690 27193 sgd_solver.cpp:105] Iteration 9840, lr = 4.47773e-12
I0408 17:01:32.666918 27193 solver.cpp:218] Iteration 9852 (2.24338 iter/s, 5.34906s/12 iters), loss = 4.55394
I0408 17:01:32.667065 27193 solver.cpp:237]     Train net output #0: loss = 4.55394 (* 1 = 4.55394 loss)
I0408 17:01:32.667079 27193 sgd_solver.cpp:105] Iteration 9852, lr = 4.36171e-12
I0408 17:01:37.830734 27193 solver.cpp:218] Iteration 9864 (2.324 iter/s, 5.1635s/12 iters), loss = 4.42274
I0408 17:01:37.830785 27193 solver.cpp:237]     Train net output #0: loss = 4.42274 (* 1 = 4.42274 loss)
I0408 17:01:37.830797 27193 sgd_solver.cpp:105] Iteration 9864, lr = 4.24869e-12
I0408 17:01:42.924527 27193 solver.cpp:218] Iteration 9876 (2.35591 iter/s, 5.09358s/12 iters), loss = 4.5582
I0408 17:01:42.924572 27193 solver.cpp:237]     Train net output #0: loss = 4.5582 (* 1 = 4.5582 loss)
I0408 17:01:42.924583 27193 sgd_solver.cpp:105] Iteration 9876, lr = 4.13861e-12
I0408 17:01:48.062638 27193 solver.cpp:218] Iteration 9888 (2.33558 iter/s, 5.1379s/12 iters), loss = 4.62953
I0408 17:01:48.062682 27193 solver.cpp:237]     Train net output #0: loss = 4.62953 (* 1 = 4.62953 loss)
I0408 17:01:48.062693 27193 sgd_solver.cpp:105] Iteration 9888, lr = 4.03138e-12
I0408 17:01:50.168607 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0408 17:01:54.149698 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0408 17:01:56.541579 27193 solver.cpp:330] Iteration 9894, Testing net (#0)
I0408 17:01:56.541602 27193 net.cpp:676] Ignoring source layer train-data
I0408 17:01:57.071242 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:02:00.946739 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 17:02:00.946774 27193 solver.cpp:397]     Test net output #1: loss = 4.60519 (* 1 = 4.60519 loss)
I0408 17:02:02.968914 27193 solver.cpp:218] Iteration 9900 (0.805057 iter/s, 14.9058s/12 iters), loss = 4.55279
I0408 17:02:02.969010 27193 solver.cpp:237]     Train net output #0: loss = 4.55279 (* 1 = 4.55279 loss)
I0408 17:02:02.969019 27193 sgd_solver.cpp:105] Iteration 9900, lr = 3.92692e-12
I0408 17:02:08.289611 27193 solver.cpp:218] Iteration 9912 (2.25546 iter/s, 5.32043s/12 iters), loss = 4.48428
I0408 17:02:08.289664 27193 solver.cpp:237]     Train net output #0: loss = 4.48428 (* 1 = 4.48428 loss)
I0408 17:02:08.289675 27193 sgd_solver.cpp:105] Iteration 9912, lr = 3.82517e-12
I0408 17:02:08.405836 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:02:13.404232 27193 solver.cpp:218] Iteration 9924 (2.34632 iter/s, 5.1144s/12 iters), loss = 4.3918
I0408 17:02:13.404289 27193 solver.cpp:237]     Train net output #0: loss = 4.3918 (* 1 = 4.3918 loss)
I0408 17:02:13.404302 27193 sgd_solver.cpp:105] Iteration 9924, lr = 3.72606e-12
I0408 17:02:18.503793 27193 solver.cpp:218] Iteration 9936 (2.35325 iter/s, 5.09934s/12 iters), loss = 4.42968
I0408 17:02:18.503837 27193 solver.cpp:237]     Train net output #0: loss = 4.42968 (* 1 = 4.42968 loss)
I0408 17:02:18.503849 27193 sgd_solver.cpp:105] Iteration 9936, lr = 3.62952e-12
I0408 17:02:23.694406 27193 solver.cpp:218] Iteration 9948 (2.31196 iter/s, 5.1904s/12 iters), loss = 4.35759
I0408 17:02:23.694454 27193 solver.cpp:237]     Train net output #0: loss = 4.35759 (* 1 = 4.35759 loss)
I0408 17:02:23.694466 27193 sgd_solver.cpp:105] Iteration 9948, lr = 3.53547e-12
I0408 17:02:28.701360 27193 solver.cpp:218] Iteration 9960 (2.39677 iter/s, 5.00674s/12 iters), loss = 4.68561
I0408 17:02:28.701402 27193 solver.cpp:237]     Train net output #0: loss = 4.68561 (* 1 = 4.68561 loss)
I0408 17:02:28.701413 27193 sgd_solver.cpp:105] Iteration 9960, lr = 3.44387e-12
I0408 17:02:33.788434 27193 solver.cpp:218] Iteration 9972 (2.35901 iter/s, 5.08687s/12 iters), loss = 4.45694
I0408 17:02:33.788558 27193 solver.cpp:237]     Train net output #0: loss = 4.45694 (* 1 = 4.45694 loss)
I0408 17:02:33.788570 27193 sgd_solver.cpp:105] Iteration 9972, lr = 3.35463e-12
I0408 17:02:39.123100 27193 solver.cpp:218] Iteration 9984 (2.24956 iter/s, 5.33437s/12 iters), loss = 4.45898
I0408 17:02:39.123142 27193 solver.cpp:237]     Train net output #0: loss = 4.45898 (* 1 = 4.45898 loss)
I0408 17:02:39.123153 27193 sgd_solver.cpp:105] Iteration 9984, lr = 3.26771e-12
I0408 17:02:43.930959 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0408 17:02:46.956656 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0408 17:02:49.283604 27193 solver.cpp:330] Iteration 9996, Testing net (#0)
I0408 17:02:49.283630 27193 net.cpp:676] Ignoring source layer train-data
I0408 17:02:49.797945 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:02:53.735044 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 17:02:53.735091 27193 solver.cpp:397]     Test net output #1: loss = 4.60476 (* 1 = 4.60476 loss)
I0408 17:02:53.826309 27193 solver.cpp:218] Iteration 9996 (0.816176 iter/s, 14.7027s/12 iters), loss = 4.52913
I0408 17:02:53.826359 27193 solver.cpp:237]     Train net output #0: loss = 4.52913 (* 1 = 4.52913 loss)
I0408 17:02:53.826371 27193 sgd_solver.cpp:105] Iteration 9996, lr = 3.18305e-12
I0408 17:02:58.301800 27193 solver.cpp:218] Iteration 10008 (2.68139 iter/s, 4.47529s/12 iters), loss = 4.50072
I0408 17:02:58.301846 27193 solver.cpp:237]     Train net output #0: loss = 4.50072 (* 1 = 4.50072 loss)
I0408 17:02:58.301856 27193 sgd_solver.cpp:105] Iteration 10008, lr = 3.10057e-12
I0408 17:03:00.560714 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:03:03.403201 27193 solver.cpp:218] Iteration 10020 (2.35239 iter/s, 5.10119s/12 iters), loss = 4.43599
I0408 17:03:03.403244 27193 solver.cpp:237]     Train net output #0: loss = 4.43599 (* 1 = 4.43599 loss)
I0408 17:03:03.403255 27193 sgd_solver.cpp:105] Iteration 10020, lr = 3.02023e-12
I0408 17:03:08.495762 27193 solver.cpp:218] Iteration 10032 (2.35647 iter/s, 5.09235s/12 iters), loss = 4.47191
I0408 17:03:08.495867 27193 solver.cpp:237]     Train net output #0: loss = 4.47191 (* 1 = 4.47191 loss)
I0408 17:03:08.495879 27193 sgd_solver.cpp:105] Iteration 10032, lr = 2.94198e-12
I0408 17:03:13.538801 27193 solver.cpp:218] Iteration 10044 (2.37964 iter/s, 5.04277s/12 iters), loss = 4.33616
I0408 17:03:13.538849 27193 solver.cpp:237]     Train net output #0: loss = 4.33616 (* 1 = 4.33616 loss)
I0408 17:03:13.538861 27193 sgd_solver.cpp:105] Iteration 10044, lr = 2.86575e-12
I0408 17:03:18.585387 27193 solver.cpp:218] Iteration 10056 (2.37795 iter/s, 5.04636s/12 iters), loss = 4.6865
I0408 17:03:18.585445 27193 solver.cpp:237]     Train net output #0: loss = 4.6865 (* 1 = 4.6865 loss)
I0408 17:03:18.585456 27193 sgd_solver.cpp:105] Iteration 10056, lr = 2.7915e-12
I0408 17:03:24.053393 27193 solver.cpp:218] Iteration 10068 (2.19468 iter/s, 5.46778s/12 iters), loss = 4.51703
I0408 17:03:24.053429 27193 solver.cpp:237]     Train net output #0: loss = 4.51703 (* 1 = 4.51703 loss)
I0408 17:03:24.053437 27193 sgd_solver.cpp:105] Iteration 10068, lr = 2.71917e-12
I0408 17:03:29.576660 27193 solver.cpp:218] Iteration 10080 (2.17271 iter/s, 5.52305s/12 iters), loss = 4.25818
I0408 17:03:29.576704 27193 solver.cpp:237]     Train net output #0: loss = 4.25818 (* 1 = 4.25818 loss)
I0408 17:03:29.576715 27193 sgd_solver.cpp:105] Iteration 10080, lr = 2.64871e-12
I0408 17:03:34.686455 27193 solver.cpp:218] Iteration 10092 (2.34853 iter/s, 5.10959s/12 iters), loss = 4.29372
I0408 17:03:34.686496 27193 solver.cpp:237]     Train net output #0: loss = 4.29372 (* 1 = 4.29372 loss)
I0408 17:03:34.686508 27193 sgd_solver.cpp:105] Iteration 10092, lr = 2.58008e-12
I0408 17:03:36.704762 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0408 17:03:39.753409 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0408 17:03:42.081080 27193 solver.cpp:330] Iteration 10098, Testing net (#0)
I0408 17:03:42.081110 27193 net.cpp:676] Ignoring source layer train-data
I0408 17:03:42.564541 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:03:46.543206 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0729167
I0408 17:03:46.543233 27193 solver.cpp:397]     Test net output #1: loss = 4.61042 (* 1 = 4.61042 loss)
I0408 17:03:48.570607 27193 solver.cpp:218] Iteration 10104 (0.864324 iter/s, 13.8837s/12 iters), loss = 4.52847
I0408 17:03:48.570654 27193 solver.cpp:237]     Train net output #0: loss = 4.52847 (* 1 = 4.52847 loss)
I0408 17:03:48.570665 27193 sgd_solver.cpp:105] Iteration 10104, lr = 2.51323e-12
I0408 17:03:53.406669 27204 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:03:54.094753 27193 solver.cpp:218] Iteration 10116 (2.17237 iter/s, 5.52392s/12 iters), loss = 4.41373
I0408 17:03:54.094797 27193 solver.cpp:237]     Train net output #0: loss = 4.41373 (* 1 = 4.41373 loss)
I0408 17:03:54.094810 27193 sgd_solver.cpp:105] Iteration 10116, lr = 2.44811e-12
I0408 17:03:59.501355 27193 solver.cpp:218] Iteration 10128 (2.2196 iter/s, 5.40638s/12 iters), loss = 4.56794
I0408 17:03:59.501402 27193 solver.cpp:237]     Train net output #0: loss = 4.56794 (* 1 = 4.56794 loss)
I0408 17:03:59.501415 27193 sgd_solver.cpp:105] Iteration 10128, lr = 2.38468e-12
I0408 17:04:04.826126 27193 solver.cpp:218] Iteration 10140 (2.25371 iter/s, 5.32456s/12 iters), loss = 4.45736
I0408 17:04:04.826179 27193 solver.cpp:237]     Train net output #0: loss = 4.45736 (* 1 = 4.45736 loss)
I0408 17:04:04.826190 27193 sgd_solver.cpp:105] Iteration 10140, lr = 2.32289e-12
I0408 17:04:10.002795 27193 solver.cpp:218] Iteration 10152 (2.31819 iter/s, 5.17645s/12 iters), loss = 4.35751
I0408 17:04:10.002923 27193 solver.cpp:237]     Train net output #0: loss = 4.35751 (* 1 = 4.35751 loss)
I0408 17:04:10.002936 27193 sgd_solver.cpp:105] Iteration 10152, lr = 2.2627e-12
I0408 17:04:15.097347 27193 solver.cpp:218] Iteration 10164 (2.35559 iter/s, 5.09426s/12 iters), loss = 4.56427
I0408 17:04:15.097388 27193 solver.cpp:237]     Train net output #0: loss = 4.56427 (* 1 = 4.56427 loss)
I0408 17:04:15.097398 27193 sgd_solver.cpp:105] Iteration 10164, lr = 2.20408e-12
I0408 17:04:20.125466 27193 solver.cpp:218] Iteration 10176 (2.38667 iter/s, 5.02792s/12 iters), loss = 4.49964
I0408 17:04:20.125506 27193 solver.cpp:237]     Train net output #0: loss = 4.49964 (* 1 = 4.49964 loss)
I0408 17:04:20.125519 27193 sgd_solver.cpp:105] Iteration 10176, lr = 2.14697e-12
I0408 17:04:25.241578 27193 solver.cpp:218] Iteration 10188 (2.34563 iter/s, 5.1159s/12 iters), loss = 4.44243
I0408 17:04:25.241611 27193 solver.cpp:237]     Train net output #0: loss = 4.44243 (* 1 = 4.44243 loss)
I0408 17:04:25.241618 27193 sgd_solver.cpp:105] Iteration 10188, lr = 2.09134e-12
I0408 17:04:29.843309 27193 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0408 17:04:32.822891 27193 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0408 17:04:35.176980 27193 solver.cpp:310] Iteration 10200, loss = 4.40829
I0408 17:04:35.177006 27193 solver.cpp:330] Iteration 10200, Testing net (#0)
I0408 17:04:35.177011 27193 net.cpp:676] Ignoring source layer train-data
I0408 17:04:35.555248 27205 data_layer.cpp:73] Restarting data prefetching from start.
I0408 17:04:39.545745 27193 solver.cpp:397]     Test net output #0: accuracy = 0.0723039
I0408 17:04:39.545780 27193 solver.cpp:397]     Test net output #1: loss = 4.61144 (* 1 = 4.61144 loss)
I0408 17:04:39.545789 27193 solver.cpp:315] Optimization Done.
I0408 17:04:39.545794 27193 caffe.cpp:259] Optimization Done.