DIGITS-CNN/cars/architecture-investigations/fc/2-layers/256/caffe_output.log

I0410 13:29:11.132328 18414 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210410-132909-6e23/solver.prototxt
I0410 13:29:11.132555 18414 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0410 13:29:11.132565 18414 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0410 13:29:11.132664 18414 caffe.cpp:218] Using GPUs 1
I0410 13:29:11.160727 18414 caffe.cpp:223] GPU 1: GeForce GTX 1080 Ti
I0410 13:29:11.455583 18414 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99980193
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0410 13:29:11.456357 18414 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0410 13:29:11.456928 18414 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0410 13:29:11.456944 18414 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0410 13:29:11.457085 18414 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: 256
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: 256
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0410 13:29:11.457176 18414 layer_factory.hpp:77] Creating layer train-data
I0410 13:29:11.458528 18414 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0410 13:29:11.458734 18414 net.cpp:84] Creating Layer train-data
I0410 13:29:11.458745 18414 net.cpp:380] train-data -> data
I0410 13:29:11.458765 18414 net.cpp:380] train-data -> label
I0410 13:29:11.458776 18414 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 13:29:11.463439 18414 data_layer.cpp:45] output data size: 128,3,227,227
I0410 13:29:11.602185 18414 net.cpp:122] Setting up train-data
I0410 13:29:11.602208 18414 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0410 13:29:11.602214 18414 net.cpp:129] Top shape: 128 (128)
I0410 13:29:11.602217 18414 net.cpp:137] Memory required for data: 79149056
I0410 13:29:11.602226 18414 layer_factory.hpp:77] Creating layer conv1
I0410 13:29:11.602248 18414 net.cpp:84] Creating Layer conv1
I0410 13:29:11.602254 18414 net.cpp:406] conv1 <- data
I0410 13:29:11.602267 18414 net.cpp:380] conv1 -> conv1
I0410 13:29:12.171809 18414 net.cpp:122] Setting up conv1
I0410 13:29:12.171830 18414 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 13:29:12.171834 18414 net.cpp:137] Memory required for data: 227833856
I0410 13:29:12.171854 18414 layer_factory.hpp:77] Creating layer relu1
I0410 13:29:12.171864 18414 net.cpp:84] Creating Layer relu1
I0410 13:29:12.171869 18414 net.cpp:406] relu1 <- conv1
I0410 13:29:12.171875 18414 net.cpp:367] relu1 -> conv1 (in-place)
I0410 13:29:12.172163 18414 net.cpp:122] Setting up relu1
I0410 13:29:12.172171 18414 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 13:29:12.172175 18414 net.cpp:137] Memory required for data: 376518656
I0410 13:29:12.172179 18414 layer_factory.hpp:77] Creating layer norm1
I0410 13:29:12.172188 18414 net.cpp:84] Creating Layer norm1
I0410 13:29:12.172191 18414 net.cpp:406] norm1 <- conv1
I0410 13:29:12.172216 18414 net.cpp:380] norm1 -> norm1
I0410 13:29:12.172655 18414 net.cpp:122] Setting up norm1
I0410 13:29:12.172665 18414 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0410 13:29:12.172669 18414 net.cpp:137] Memory required for data: 525203456
I0410 13:29:12.172673 18414 layer_factory.hpp:77] Creating layer pool1
I0410 13:29:12.172681 18414 net.cpp:84] Creating Layer pool1
I0410 13:29:12.172684 18414 net.cpp:406] pool1 <- norm1
I0410 13:29:12.172690 18414 net.cpp:380] pool1 -> pool1
I0410 13:29:12.172725 18414 net.cpp:122] Setting up pool1
I0410 13:29:12.172732 18414 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0410 13:29:12.172735 18414 net.cpp:137] Memory required for data: 561035264
I0410 13:29:12.172739 18414 layer_factory.hpp:77] Creating layer conv2
I0410 13:29:12.172749 18414 net.cpp:84] Creating Layer conv2
I0410 13:29:12.172752 18414 net.cpp:406] conv2 <- pool1
I0410 13:29:12.172758 18414 net.cpp:380] conv2 -> conv2
I0410 13:29:12.180650 18414 net.cpp:122] Setting up conv2
I0410 13:29:12.180666 18414 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 13:29:12.180670 18414 net.cpp:137] Memory required for data: 656586752
I0410 13:29:12.180680 18414 layer_factory.hpp:77] Creating layer relu2
I0410 13:29:12.180688 18414 net.cpp:84] Creating Layer relu2
I0410 13:29:12.180691 18414 net.cpp:406] relu2 <- conv2
I0410 13:29:12.180697 18414 net.cpp:367] relu2 -> conv2 (in-place)
I0410 13:29:12.181121 18414 net.cpp:122] Setting up relu2
I0410 13:29:12.181130 18414 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 13:29:12.181134 18414 net.cpp:137] Memory required for data: 752138240
I0410 13:29:12.181138 18414 layer_factory.hpp:77] Creating layer norm2
I0410 13:29:12.181145 18414 net.cpp:84] Creating Layer norm2
I0410 13:29:12.181149 18414 net.cpp:406] norm2 <- conv2
I0410 13:29:12.181154 18414 net.cpp:380] norm2 -> norm2
I0410 13:29:12.181437 18414 net.cpp:122] Setting up norm2
I0410 13:29:12.181445 18414 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0410 13:29:12.181448 18414 net.cpp:137] Memory required for data: 847689728
I0410 13:29:12.181452 18414 layer_factory.hpp:77] Creating layer pool2
I0410 13:29:12.181459 18414 net.cpp:84] Creating Layer pool2
I0410 13:29:12.181463 18414 net.cpp:406] pool2 <- norm2
I0410 13:29:12.181468 18414 net.cpp:380] pool2 -> pool2
I0410 13:29:12.181494 18414 net.cpp:122] Setting up pool2
I0410 13:29:12.181499 18414 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 13:29:12.181502 18414 net.cpp:137] Memory required for data: 869840896
I0410 13:29:12.181505 18414 layer_factory.hpp:77] Creating layer conv3
I0410 13:29:12.181514 18414 net.cpp:84] Creating Layer conv3
I0410 13:29:12.181517 18414 net.cpp:406] conv3 <- pool2
I0410 13:29:12.181522 18414 net.cpp:380] conv3 -> conv3
I0410 13:29:12.191294 18414 net.cpp:122] Setting up conv3
I0410 13:29:12.191306 18414 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:29:12.191309 18414 net.cpp:137] Memory required for data: 903067648
I0410 13:29:12.191319 18414 layer_factory.hpp:77] Creating layer relu3
I0410 13:29:12.191326 18414 net.cpp:84] Creating Layer relu3
I0410 13:29:12.191330 18414 net.cpp:406] relu3 <- conv3
I0410 13:29:12.191335 18414 net.cpp:367] relu3 -> conv3 (in-place)
I0410 13:29:12.191756 18414 net.cpp:122] Setting up relu3
I0410 13:29:12.191766 18414 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:29:12.191769 18414 net.cpp:137] Memory required for data: 936294400
I0410 13:29:12.191772 18414 layer_factory.hpp:77] Creating layer conv4
I0410 13:29:12.191782 18414 net.cpp:84] Creating Layer conv4
I0410 13:29:12.191787 18414 net.cpp:406] conv4 <- conv3
I0410 13:29:12.191792 18414 net.cpp:380] conv4 -> conv4
I0410 13:29:12.202033 18414 net.cpp:122] Setting up conv4
I0410 13:29:12.202045 18414 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:29:12.202049 18414 net.cpp:137] Memory required for data: 969521152
I0410 13:29:12.202056 18414 layer_factory.hpp:77] Creating layer relu4
I0410 13:29:12.202064 18414 net.cpp:84] Creating Layer relu4
I0410 13:29:12.202086 18414 net.cpp:406] relu4 <- conv4
I0410 13:29:12.202092 18414 net.cpp:367] relu4 -> conv4 (in-place)
I0410 13:29:12.202430 18414 net.cpp:122] Setting up relu4
I0410 13:29:12.202437 18414 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0410 13:29:12.202440 18414 net.cpp:137] Memory required for data: 1002747904
I0410 13:29:12.202445 18414 layer_factory.hpp:77] Creating layer conv5
I0410 13:29:12.202455 18414 net.cpp:84] Creating Layer conv5
I0410 13:29:12.202458 18414 net.cpp:406] conv5 <- conv4
I0410 13:29:12.202466 18414 net.cpp:380] conv5 -> conv5
I0410 13:29:12.210791 18414 net.cpp:122] Setting up conv5
I0410 13:29:12.210804 18414 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 13:29:12.210808 18414 net.cpp:137] Memory required for data: 1024899072
I0410 13:29:12.210819 18414 layer_factory.hpp:77] Creating layer relu5
I0410 13:29:12.210827 18414 net.cpp:84] Creating Layer relu5
I0410 13:29:12.210830 18414 net.cpp:406] relu5 <- conv5
I0410 13:29:12.210836 18414 net.cpp:367] relu5 -> conv5 (in-place)
I0410 13:29:12.211318 18414 net.cpp:122] Setting up relu5
I0410 13:29:12.211329 18414 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0410 13:29:12.211333 18414 net.cpp:137] Memory required for data: 1047050240
I0410 13:29:12.211336 18414 layer_factory.hpp:77] Creating layer pool5
I0410 13:29:12.211344 18414 net.cpp:84] Creating Layer pool5
I0410 13:29:12.211346 18414 net.cpp:406] pool5 <- conv5
I0410 13:29:12.211352 18414 net.cpp:380] pool5 -> pool5
I0410 13:29:12.211390 18414 net.cpp:122] Setting up pool5
I0410 13:29:12.211396 18414 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0410 13:29:12.211400 18414 net.cpp:137] Memory required for data: 1051768832
I0410 13:29:12.211402 18414 layer_factory.hpp:77] Creating layer fc6
I0410 13:29:12.211411 18414 net.cpp:84] Creating Layer fc6
I0410 13:29:12.211416 18414 net.cpp:406] fc6 <- pool5
I0410 13:29:12.211421 18414 net.cpp:380] fc6 -> fc6
I0410 13:29:12.233635 18414 net.cpp:122] Setting up fc6
I0410 13:29:12.233650 18414 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:12.233654 18414 net.cpp:137] Memory required for data: 1051899904
I0410 13:29:12.233662 18414 layer_factory.hpp:77] Creating layer relu6
I0410 13:29:12.233670 18414 net.cpp:84] Creating Layer relu6
I0410 13:29:12.233675 18414 net.cpp:406] relu6 <- fc6
I0410 13:29:12.233680 18414 net.cpp:367] relu6 -> fc6 (in-place)
I0410 13:29:12.234284 18414 net.cpp:122] Setting up relu6
I0410 13:29:12.234294 18414 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:12.234297 18414 net.cpp:137] Memory required for data: 1052030976
I0410 13:29:12.234302 18414 layer_factory.hpp:77] Creating layer drop6
I0410 13:29:12.234308 18414 net.cpp:84] Creating Layer drop6
I0410 13:29:12.234313 18414 net.cpp:406] drop6 <- fc6
I0410 13:29:12.234318 18414 net.cpp:367] drop6 -> fc6 (in-place)
I0410 13:29:12.234347 18414 net.cpp:122] Setting up drop6
I0410 13:29:12.234352 18414 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:12.234356 18414 net.cpp:137] Memory required for data: 1052162048
I0410 13:29:12.234360 18414 layer_factory.hpp:77] Creating layer fc7
I0410 13:29:12.234369 18414 net.cpp:84] Creating Layer fc7
I0410 13:29:12.234371 18414 net.cpp:406] fc7 <- fc6
I0410 13:29:12.234377 18414 net.cpp:380] fc7 -> fc7
I0410 13:29:12.235016 18414 net.cpp:122] Setting up fc7
I0410 13:29:12.235023 18414 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:12.235026 18414 net.cpp:137] Memory required for data: 1052293120
I0410 13:29:12.235033 18414 layer_factory.hpp:77] Creating layer relu7
I0410 13:29:12.235038 18414 net.cpp:84] Creating Layer relu7
I0410 13:29:12.235041 18414 net.cpp:406] relu7 <- fc7
I0410 13:29:12.235046 18414 net.cpp:367] relu7 -> fc7 (in-place)
I0410 13:29:12.235522 18414 net.cpp:122] Setting up relu7
I0410 13:29:12.235532 18414 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:12.235534 18414 net.cpp:137] Memory required for data: 1052424192
I0410 13:29:12.235539 18414 layer_factory.hpp:77] Creating layer drop7
I0410 13:29:12.235545 18414 net.cpp:84] Creating Layer drop7
I0410 13:29:12.235549 18414 net.cpp:406] drop7 <- fc7
I0410 13:29:12.235571 18414 net.cpp:367] drop7 -> fc7 (in-place)
I0410 13:29:12.235597 18414 net.cpp:122] Setting up drop7
I0410 13:29:12.235602 18414 net.cpp:129] Top shape: 128 256 (32768)
I0410 13:29:12.235606 18414 net.cpp:137] Memory required for data: 1052555264
I0410 13:29:12.235610 18414 layer_factory.hpp:77] Creating layer fc8
I0410 13:29:12.235616 18414 net.cpp:84] Creating Layer fc8
I0410 13:29:12.235620 18414 net.cpp:406] fc8 <- fc7
I0410 13:29:12.235626 18414 net.cpp:380] fc8 -> fc8
I0410 13:29:12.236143 18414 net.cpp:122] Setting up fc8
I0410 13:29:12.236150 18414 net.cpp:129] Top shape: 128 196 (25088)
I0410 13:29:12.236152 18414 net.cpp:137] Memory required for data: 1052655616
I0410 13:29:12.236158 18414 layer_factory.hpp:77] Creating layer loss
I0410 13:29:12.236166 18414 net.cpp:84] Creating Layer loss
I0410 13:29:12.236168 18414 net.cpp:406] loss <- fc8
I0410 13:29:12.236172 18414 net.cpp:406] loss <- label
I0410 13:29:12.236179 18414 net.cpp:380] loss -> loss
I0410 13:29:12.236188 18414 layer_factory.hpp:77] Creating layer loss
I0410 13:29:12.236773 18414 net.cpp:122] Setting up loss
I0410 13:29:12.236781 18414 net.cpp:129] Top shape: (1)
I0410 13:29:12.236785 18414 net.cpp:132]     with loss weight 1
I0410 13:29:12.236802 18414 net.cpp:137] Memory required for data: 1052655620
I0410 13:29:12.236806 18414 net.cpp:198] loss needs backward computation.
I0410 13:29:12.236814 18414 net.cpp:198] fc8 needs backward computation.
I0410 13:29:12.236817 18414 net.cpp:198] drop7 needs backward computation.
I0410 13:29:12.236821 18414 net.cpp:198] relu7 needs backward computation.
I0410 13:29:12.236825 18414 net.cpp:198] fc7 needs backward computation.
I0410 13:29:12.236829 18414 net.cpp:198] drop6 needs backward computation.
I0410 13:29:12.236832 18414 net.cpp:198] relu6 needs backward computation.
I0410 13:29:12.236836 18414 net.cpp:198] fc6 needs backward computation.
I0410 13:29:12.236841 18414 net.cpp:198] pool5 needs backward computation.
I0410 13:29:12.236845 18414 net.cpp:198] relu5 needs backward computation.
I0410 13:29:12.236848 18414 net.cpp:198] conv5 needs backward computation.
I0410 13:29:12.236851 18414 net.cpp:198] relu4 needs backward computation.
I0410 13:29:12.236855 18414 net.cpp:198] conv4 needs backward computation.
I0410 13:29:12.236860 18414 net.cpp:198] relu3 needs backward computation.
I0410 13:29:12.236863 18414 net.cpp:198] conv3 needs backward computation.
I0410 13:29:12.236867 18414 net.cpp:198] pool2 needs backward computation.
I0410 13:29:12.236871 18414 net.cpp:198] norm2 needs backward computation.
I0410 13:29:12.236874 18414 net.cpp:198] relu2 needs backward computation.
I0410 13:29:12.236878 18414 net.cpp:198] conv2 needs backward computation.
I0410 13:29:12.236882 18414 net.cpp:198] pool1 needs backward computation.
I0410 13:29:12.236886 18414 net.cpp:198] norm1 needs backward computation.
I0410 13:29:12.236891 18414 net.cpp:198] relu1 needs backward computation.
I0410 13:29:12.236893 18414 net.cpp:198] conv1 needs backward computation.
I0410 13:29:12.236898 18414 net.cpp:200] train-data does not need backward computation.
I0410 13:29:12.236901 18414 net.cpp:242] This network produces output loss
I0410 13:29:12.236915 18414 net.cpp:255] Network initialization done.
I0410 13:29:12.237438 18414 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0410 13:29:12.237470 18414 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0410 13:29:12.237617 18414 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: 256
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: 256
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0410 13:29:12.237707 18414 layer_factory.hpp:77] Creating layer val-data
I0410 13:29:12.239034 18414 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0410 13:29:12.239235 18414 net.cpp:84] Creating Layer val-data
I0410 13:29:12.239246 18414 net.cpp:380] val-data -> data
I0410 13:29:12.239254 18414 net.cpp:380] val-data -> label
I0410 13:29:12.239262 18414 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0410 13:29:12.243901 18414 data_layer.cpp:45] output data size: 32,3,227,227
I0410 13:29:12.276731 18414 net.cpp:122] Setting up val-data
I0410 13:29:12.276749 18414 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0410 13:29:12.276754 18414 net.cpp:129] Top shape: 32 (32)
I0410 13:29:12.276758 18414 net.cpp:137] Memory required for data: 19787264
I0410 13:29:12.276764 18414 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0410 13:29:12.276777 18414 net.cpp:84] Creating Layer label_val-data_1_split
I0410 13:29:12.276782 18414 net.cpp:406] label_val-data_1_split <- label
I0410 13:29:12.276789 18414 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0410 13:29:12.276798 18414 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0410 13:29:12.276841 18414 net.cpp:122] Setting up label_val-data_1_split
I0410 13:29:12.276847 18414 net.cpp:129] Top shape: 32 (32)
I0410 13:29:12.276851 18414 net.cpp:129] Top shape: 32 (32)
I0410 13:29:12.276854 18414 net.cpp:137] Memory required for data: 19787520
I0410 13:29:12.276857 18414 layer_factory.hpp:77] Creating layer conv1
I0410 13:29:12.276868 18414 net.cpp:84] Creating Layer conv1
I0410 13:29:12.276872 18414 net.cpp:406] conv1 <- data
I0410 13:29:12.276878 18414 net.cpp:380] conv1 -> conv1
I0410 13:29:12.279009 18414 net.cpp:122] Setting up conv1
I0410 13:29:12.279021 18414 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 13:29:12.279024 18414 net.cpp:137] Memory required for data: 56958720
I0410 13:29:12.279034 18414 layer_factory.hpp:77] Creating layer relu1
I0410 13:29:12.279042 18414 net.cpp:84] Creating Layer relu1
I0410 13:29:12.279047 18414 net.cpp:406] relu1 <- conv1
I0410 13:29:12.279052 18414 net.cpp:367] relu1 -> conv1 (in-place)
I0410 13:29:12.279341 18414 net.cpp:122] Setting up relu1
I0410 13:29:12.279350 18414 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 13:29:12.279354 18414 net.cpp:137] Memory required for data: 94129920
I0410 13:29:12.279358 18414 layer_factory.hpp:77] Creating layer norm1
I0410 13:29:12.279366 18414 net.cpp:84] Creating Layer norm1
I0410 13:29:12.279371 18414 net.cpp:406] norm1 <- conv1
I0410 13:29:12.279376 18414 net.cpp:380] norm1 -> norm1
I0410 13:29:12.279829 18414 net.cpp:122] Setting up norm1
I0410 13:29:12.279839 18414 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0410 13:29:12.279844 18414 net.cpp:137] Memory required for data: 131301120
I0410 13:29:12.279846 18414 layer_factory.hpp:77] Creating layer pool1
I0410 13:29:12.279853 18414 net.cpp:84] Creating Layer pool1
I0410 13:29:12.279857 18414 net.cpp:406] pool1 <- norm1
I0410 13:29:12.279862 18414 net.cpp:380] pool1 -> pool1
I0410 13:29:12.279891 18414 net.cpp:122] Setting up pool1
I0410 13:29:12.279896 18414 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0410 13:29:12.279899 18414 net.cpp:137] Memory required for data: 140259072
I0410 13:29:12.279903 18414 layer_factory.hpp:77] Creating layer conv2
I0410 13:29:12.279911 18414 net.cpp:84] Creating Layer conv2
I0410 13:29:12.279914 18414 net.cpp:406] conv2 <- pool1
I0410 13:29:12.279937 18414 net.cpp:380] conv2 -> conv2
I0410 13:29:12.291481 18414 net.cpp:122] Setting up conv2
I0410 13:29:12.291496 18414 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 13:29:12.291499 18414 net.cpp:137] Memory required for data: 164146944
I0410 13:29:12.291509 18414 layer_factory.hpp:77] Creating layer relu2
I0410 13:29:12.291517 18414 net.cpp:84] Creating Layer relu2
I0410 13:29:12.291522 18414 net.cpp:406] relu2 <- conv2
I0410 13:29:12.291528 18414 net.cpp:367] relu2 -> conv2 (in-place)
I0410 13:29:12.292035 18414 net.cpp:122] Setting up relu2
I0410 13:29:12.292047 18414 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 13:29:12.292050 18414 net.cpp:137] Memory required for data: 188034816
I0410 13:29:12.292054 18414 layer_factory.hpp:77] Creating layer norm2
I0410 13:29:12.292064 18414 net.cpp:84] Creating Layer norm2
I0410 13:29:12.292068 18414 net.cpp:406] norm2 <- conv2
I0410 13:29:12.292074 18414 net.cpp:380] norm2 -> norm2
I0410 13:29:12.292591 18414 net.cpp:122] Setting up norm2
I0410 13:29:12.292601 18414 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0410 13:29:12.292606 18414 net.cpp:137] Memory required for data: 211922688
I0410 13:29:12.292610 18414 layer_factory.hpp:77] Creating layer pool2
I0410 13:29:12.292618 18414 net.cpp:84] Creating Layer pool2
I0410 13:29:12.292620 18414 net.cpp:406] pool2 <- norm2
I0410 13:29:12.292626 18414 net.cpp:380] pool2 -> pool2
I0410 13:29:12.292659 18414 net.cpp:122] Setting up pool2
I0410 13:29:12.292665 18414 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 13:29:12.292668 18414 net.cpp:137] Memory required for data: 217460480
I0410 13:29:12.292672 18414 layer_factory.hpp:77] Creating layer conv3
I0410 13:29:12.292681 18414 net.cpp:84] Creating Layer conv3
I0410 13:29:12.292685 18414 net.cpp:406] conv3 <- pool2
I0410 13:29:12.292692 18414 net.cpp:380] conv3 -> conv3
I0410 13:29:12.303673 18414 net.cpp:122] Setting up conv3
I0410 13:29:12.303689 18414 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:29:12.303692 18414 net.cpp:137] Memory required for data: 225767168
I0410 13:29:12.303704 18414 layer_factory.hpp:77] Creating layer relu3
I0410 13:29:12.303712 18414 net.cpp:84] Creating Layer relu3
I0410 13:29:12.303717 18414 net.cpp:406] relu3 <- conv3
I0410 13:29:12.303723 18414 net.cpp:367] relu3 -> conv3 (in-place)
I0410 13:29:12.304247 18414 net.cpp:122] Setting up relu3
I0410 13:29:12.304256 18414 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:29:12.304260 18414 net.cpp:137] Memory required for data: 234073856
I0410 13:29:12.304263 18414 layer_factory.hpp:77] Creating layer conv4
I0410 13:29:12.304275 18414 net.cpp:84] Creating Layer conv4
I0410 13:29:12.304278 18414 net.cpp:406] conv4 <- conv3
I0410 13:29:12.304286 18414 net.cpp:380] conv4 -> conv4
I0410 13:29:12.313686 18414 net.cpp:122] Setting up conv4
I0410 13:29:12.313700 18414 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:29:12.313704 18414 net.cpp:137] Memory required for data: 242380544
I0410 13:29:12.313711 18414 layer_factory.hpp:77] Creating layer relu4
I0410 13:29:12.313717 18414 net.cpp:84] Creating Layer relu4
I0410 13:29:12.313721 18414 net.cpp:406] relu4 <- conv4
I0410 13:29:12.313727 18414 net.cpp:367] relu4 -> conv4 (in-place)
I0410 13:29:12.314086 18414 net.cpp:122] Setting up relu4
I0410 13:29:12.314095 18414 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0410 13:29:12.314098 18414 net.cpp:137] Memory required for data: 250687232
I0410 13:29:12.314101 18414 layer_factory.hpp:77] Creating layer conv5
I0410 13:29:12.314112 18414 net.cpp:84] Creating Layer conv5
I0410 13:29:12.314116 18414 net.cpp:406] conv5 <- conv4
I0410 13:29:12.314122 18414 net.cpp:380] conv5 -> conv5
I0410 13:29:12.324683 18414 net.cpp:122] Setting up conv5
I0410 13:29:12.324699 18414 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 13:29:12.324703 18414 net.cpp:137] Memory required for data: 256225024
I0410 13:29:12.324715 18414 layer_factory.hpp:77] Creating layer relu5
I0410 13:29:12.324723 18414 net.cpp:84] Creating Layer relu5
I0410 13:29:12.324728 18414 net.cpp:406] relu5 <- conv5
I0410 13:29:12.324751 18414 net.cpp:367] relu5 -> conv5 (in-place)
I0410 13:29:12.325245 18414 net.cpp:122] Setting up relu5
I0410 13:29:12.325256 18414 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0410 13:29:12.325259 18414 net.cpp:137] Memory required for data: 261762816
I0410 13:29:12.325263 18414 layer_factory.hpp:77] Creating layer pool5
I0410 13:29:12.325273 18414 net.cpp:84] Creating Layer pool5
I0410 13:29:12.325278 18414 net.cpp:406] pool5 <- conv5
I0410 13:29:12.325284 18414 net.cpp:380] pool5 -> pool5
I0410 13:29:12.325321 18414 net.cpp:122] Setting up pool5
I0410 13:29:12.325328 18414 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0410 13:29:12.325331 18414 net.cpp:137] Memory required for data: 262942464
I0410 13:29:12.325335 18414 layer_factory.hpp:77] Creating layer fc6
I0410 13:29:12.325341 18414 net.cpp:84] Creating Layer fc6
I0410 13:29:12.325345 18414 net.cpp:406] fc6 <- pool5
I0410 13:29:12.325350 18414 net.cpp:380] fc6 -> fc6
I0410 13:29:12.348320 18414 net.cpp:122] Setting up fc6
I0410 13:29:12.348340 18414 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:12.348342 18414 net.cpp:137] Memory required for data: 262975232
I0410 13:29:12.348352 18414 layer_factory.hpp:77] Creating layer relu6
I0410 13:29:12.348361 18414 net.cpp:84] Creating Layer relu6
I0410 13:29:12.348366 18414 net.cpp:406] relu6 <- fc6
I0410 13:29:12.348371 18414 net.cpp:367] relu6 -> fc6 (in-place)
I0410 13:29:12.349212 18414 net.cpp:122] Setting up relu6
I0410 13:29:12.349221 18414 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:12.349225 18414 net.cpp:137] Memory required for data: 263008000
I0410 13:29:12.349228 18414 layer_factory.hpp:77] Creating layer drop6
I0410 13:29:12.349234 18414 net.cpp:84] Creating Layer drop6
I0410 13:29:12.349238 18414 net.cpp:406] drop6 <- fc6
I0410 13:29:12.349246 18414 net.cpp:367] drop6 -> fc6 (in-place)
I0410 13:29:12.349272 18414 net.cpp:122] Setting up drop6
I0410 13:29:12.349277 18414 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:12.349282 18414 net.cpp:137] Memory required for data: 263040768
I0410 13:29:12.349284 18414 layer_factory.hpp:77] Creating layer fc7
I0410 13:29:12.349292 18414 net.cpp:84] Creating Layer fc7
I0410 13:29:12.349294 18414 net.cpp:406] fc7 <- fc6
I0410 13:29:12.349300 18414 net.cpp:380] fc7 -> fc7
I0410 13:29:12.349946 18414 net.cpp:122] Setting up fc7
I0410 13:29:12.349952 18414 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:12.349968 18414 net.cpp:137] Memory required for data: 263073536
I0410 13:29:12.349974 18414 layer_factory.hpp:77] Creating layer relu7
I0410 13:29:12.349980 18414 net.cpp:84] Creating Layer relu7
I0410 13:29:12.349983 18414 net.cpp:406] relu7 <- fc7
I0410 13:29:12.349988 18414 net.cpp:367] relu7 -> fc7 (in-place)
I0410 13:29:12.350343 18414 net.cpp:122] Setting up relu7
I0410 13:29:12.350351 18414 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:12.350354 18414 net.cpp:137] Memory required for data: 263106304
I0410 13:29:12.350358 18414 layer_factory.hpp:77] Creating layer drop7
I0410 13:29:12.350364 18414 net.cpp:84] Creating Layer drop7
I0410 13:29:12.350368 18414 net.cpp:406] drop7 <- fc7
I0410 13:29:12.350373 18414 net.cpp:367] drop7 -> fc7 (in-place)
I0410 13:29:12.350395 18414 net.cpp:122] Setting up drop7
I0410 13:29:12.350400 18414 net.cpp:129] Top shape: 32 256 (8192)
I0410 13:29:12.350404 18414 net.cpp:137] Memory required for data: 263139072
I0410 13:29:12.350406 18414 layer_factory.hpp:77] Creating layer fc8
I0410 13:29:12.350412 18414 net.cpp:84] Creating Layer fc8
I0410 13:29:12.350416 18414 net.cpp:406] fc8 <- fc7
I0410 13:29:12.350421 18414 net.cpp:380] fc8 -> fc8
I0410 13:29:12.350946 18414 net.cpp:122] Setting up fc8
I0410 13:29:12.350953 18414 net.cpp:129] Top shape: 32 196 (6272)
I0410 13:29:12.350956 18414 net.cpp:137] Memory required for data: 263164160
I0410 13:29:12.350962 18414 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0410 13:29:12.350967 18414 net.cpp:84] Creating Layer fc8_fc8_0_split
I0410 13:29:12.350971 18414 net.cpp:406] fc8_fc8_0_split <- fc8
I0410 13:29:12.350977 18414 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0410 13:29:12.351001 18414 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0410 13:29:12.351033 18414 net.cpp:122] Setting up fc8_fc8_0_split
I0410 13:29:12.351039 18414 net.cpp:129] Top shape: 32 196 (6272)
I0410 13:29:12.351042 18414 net.cpp:129] Top shape: 32 196 (6272)
I0410 13:29:12.351045 18414 net.cpp:137] Memory required for data: 263214336
I0410 13:29:12.351048 18414 layer_factory.hpp:77] Creating layer accuracy
I0410 13:29:12.351055 18414 net.cpp:84] Creating Layer accuracy
I0410 13:29:12.351058 18414 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0410 13:29:12.351063 18414 net.cpp:406] accuracy <- label_val-data_1_split_0
I0410 13:29:12.351069 18414 net.cpp:380] accuracy -> accuracy
I0410 13:29:12.351076 18414 net.cpp:122] Setting up accuracy
I0410 13:29:12.351080 18414 net.cpp:129] Top shape: (1)
I0410 13:29:12.351083 18414 net.cpp:137] Memory required for data: 263214340
I0410 13:29:12.351086 18414 layer_factory.hpp:77] Creating layer loss
I0410 13:29:12.351092 18414 net.cpp:84] Creating Layer loss
I0410 13:29:12.351095 18414 net.cpp:406] loss <- fc8_fc8_0_split_1
I0410 13:29:12.351099 18414 net.cpp:406] loss <- label_val-data_1_split_1
I0410 13:29:12.351104 18414 net.cpp:380] loss -> loss
I0410 13:29:12.351111 18414 layer_factory.hpp:77] Creating layer loss
I0410 13:29:12.351688 18414 net.cpp:122] Setting up loss
I0410 13:29:12.351697 18414 net.cpp:129] Top shape: (1)
I0410 13:29:12.351701 18414 net.cpp:132]     with loss weight 1
I0410 13:29:12.351711 18414 net.cpp:137] Memory required for data: 263214344
I0410 13:29:12.351714 18414 net.cpp:198] loss needs backward computation.
I0410 13:29:12.351719 18414 net.cpp:200] accuracy does not need backward computation.
I0410 13:29:12.351723 18414 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0410 13:29:12.351727 18414 net.cpp:198] fc8 needs backward computation.
I0410 13:29:12.351729 18414 net.cpp:198] drop7 needs backward computation.
I0410 13:29:12.351732 18414 net.cpp:198] relu7 needs backward computation.
I0410 13:29:12.351737 18414 net.cpp:198] fc7 needs backward computation.
I0410 13:29:12.351739 18414 net.cpp:198] drop6 needs backward computation.
I0410 13:29:12.351742 18414 net.cpp:198] relu6 needs backward computation.
I0410 13:29:12.351745 18414 net.cpp:198] fc6 needs backward computation.
I0410 13:29:12.351748 18414 net.cpp:198] pool5 needs backward computation.
I0410 13:29:12.351752 18414 net.cpp:198] relu5 needs backward computation.
I0410 13:29:12.351755 18414 net.cpp:198] conv5 needs backward computation.
I0410 13:29:12.351759 18414 net.cpp:198] relu4 needs backward computation.
I0410 13:29:12.351763 18414 net.cpp:198] conv4 needs backward computation.
I0410 13:29:12.351765 18414 net.cpp:198] relu3 needs backward computation.
I0410 13:29:12.351768 18414 net.cpp:198] conv3 needs backward computation.
I0410 13:29:12.351773 18414 net.cpp:198] pool2 needs backward computation.
I0410 13:29:12.351775 18414 net.cpp:198] norm2 needs backward computation.
I0410 13:29:12.351779 18414 net.cpp:198] relu2 needs backward computation.
I0410 13:29:12.351783 18414 net.cpp:198] conv2 needs backward computation.
I0410 13:29:12.351785 18414 net.cpp:198] pool1 needs backward computation.
I0410 13:29:12.351789 18414 net.cpp:198] norm1 needs backward computation.
I0410 13:29:12.351792 18414 net.cpp:198] relu1 needs backward computation.
I0410 13:29:12.351795 18414 net.cpp:198] conv1 needs backward computation.
I0410 13:29:12.351799 18414 net.cpp:200] label_val-data_1_split does not need backward computation.
I0410 13:29:12.351804 18414 net.cpp:200] val-data does not need backward computation.
I0410 13:29:12.351809 18414 net.cpp:242] This network produces output accuracy
I0410 13:29:12.351811 18414 net.cpp:242] This network produces output loss
I0410 13:29:12.351828 18414 net.cpp:255] Network initialization done.
I0410 13:29:12.351910 18414 solver.cpp:56] Solver scaffolding done.
I0410 13:29:12.352341 18414 caffe.cpp:248] Starting Optimization
I0410 13:29:12.352350 18414 solver.cpp:272] Solving
I0410 13:29:12.352355 18414 solver.cpp:273] Learning Rate Policy: exp
I0410 13:29:12.353152 18414 solver.cpp:330] Iteration 0, Testing net (#0)
I0410 13:29:12.353163 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:29:12.355518 18414 blocking_queue.cpp:49] Waiting for data
I0410 13:29:16.928656 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:29:16.972996 18414 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0410 13:29:16.973042 18414 solver.cpp:397]     Test net output #1: loss = 5.27825 (* 1 = 5.27825 loss)
I0410 13:29:17.060310 18414 solver.cpp:218] Iteration 0 (9.03957e+36 iter/s, 4.70775s/12 iters), loss = 5.27781
I0410 13:29:17.061883 18414 solver.cpp:237]     Train net output #0: loss = 5.27781 (* 1 = 5.27781 loss)
I0410 13:29:17.061901 18414 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0410 13:29:20.970299 18414 solver.cpp:218] Iteration 12 (3.07041 iter/s, 3.90828s/12 iters), loss = 5.27916
I0410 13:29:20.970337 18414 solver.cpp:237]     Train net output #0: loss = 5.27916 (* 1 = 5.27916 loss)
I0410 13:29:20.970346 18414 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0410 13:29:25.779376 18414 solver.cpp:218] Iteration 24 (2.49539 iter/s, 4.80887s/12 iters), loss = 5.28016
I0410 13:29:25.779422 18414 solver.cpp:237]     Train net output #0: loss = 5.28016 (* 1 = 5.28016 loss)
I0410 13:29:25.779433 18414 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0410 13:29:30.566710 18414 solver.cpp:218] Iteration 36 (2.50673 iter/s, 4.78712s/12 iters), loss = 5.27703
I0410 13:29:30.566757 18414 solver.cpp:237]     Train net output #0: loss = 5.27703 (* 1 = 5.27703 loss)
I0410 13:29:30.566769 18414 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0410 13:29:35.390864 18414 solver.cpp:218] Iteration 48 (2.48759 iter/s, 4.82394s/12 iters), loss = 5.27829
I0410 13:29:35.390905 18414 solver.cpp:237]     Train net output #0: loss = 5.27829 (* 1 = 5.27829 loss)
I0410 13:29:35.390916 18414 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0410 13:29:40.202005 18414 solver.cpp:218] Iteration 60 (2.49432 iter/s, 4.81093s/12 iters), loss = 5.2779
I0410 13:29:40.202051 18414 solver.cpp:237]     Train net output #0: loss = 5.2779 (* 1 = 5.2779 loss)
I0410 13:29:40.202064 18414 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0410 13:29:45.040838 18414 solver.cpp:218] Iteration 72 (2.48005 iter/s, 4.83861s/12 iters), loss = 5.27761
I0410 13:29:45.040966 18414 solver.cpp:237]     Train net output #0: loss = 5.27761 (* 1 = 5.27761 loss)
I0410 13:29:45.040983 18414 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0410 13:29:49.860772 18414 solver.cpp:218] Iteration 84 (2.48981 iter/s, 4.81965s/12 iters), loss = 5.27931
I0410 13:29:49.860810 18414 solver.cpp:237]     Train net output #0: loss = 5.27931 (* 1 = 5.27931 loss)
I0410 13:29:49.860818 18414 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0410 13:29:54.713986 18414 solver.cpp:218] Iteration 96 (2.4727 iter/s, 4.85299s/12 iters), loss = 5.28099
I0410 13:29:54.714032 18414 solver.cpp:237]     Train net output #0: loss = 5.28099 (* 1 = 5.28099 loss)
I0410 13:29:54.714046 18414 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0410 13:29:56.364625 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:29:56.668895 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0410 13:29:57.002784 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0410 13:29:57.210002 18414 solver.cpp:330] Iteration 102, Testing net (#0)
I0410 13:29:57.210033 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:30:01.706233 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:30:01.782299 18414 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:30:01.782347 18414 solver.cpp:397]     Test net output #1: loss = 5.27891 (* 1 = 5.27891 loss)
I0410 13:30:03.687377 18414 solver.cpp:218] Iteration 108 (1.33734 iter/s, 8.97303s/12 iters), loss = 5.27768
I0410 13:30:03.687427 18414 solver.cpp:237]     Train net output #0: loss = 5.27768 (* 1 = 5.27768 loss)
I0410 13:30:03.687438 18414 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0410 13:30:08.481166 18414 solver.cpp:218] Iteration 120 (2.50336 iter/s, 4.79356s/12 iters), loss = 5.27537
I0410 13:30:08.481222 18414 solver.cpp:237]     Train net output #0: loss = 5.27537 (* 1 = 5.27537 loss)
I0410 13:30:08.481236 18414 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0410 13:30:13.265877 18414 solver.cpp:218] Iteration 132 (2.50811 iter/s, 4.78448s/12 iters), loss = 5.26061
I0410 13:30:13.265926 18414 solver.cpp:237]     Train net output #0: loss = 5.26061 (* 1 = 5.26061 loss)
I0410 13:30:13.265939 18414 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0410 13:30:18.070266 18414 solver.cpp:218] Iteration 144 (2.49783 iter/s, 4.80417s/12 iters), loss = 5.28169
I0410 13:30:18.070437 18414 solver.cpp:237]     Train net output #0: loss = 5.28169 (* 1 = 5.28169 loss)
I0410 13:30:18.070451 18414 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0410 13:30:22.921808 18414 solver.cpp:218] Iteration 156 (2.47362 iter/s, 4.8512s/12 iters), loss = 5.26972
I0410 13:30:22.921851 18414 solver.cpp:237]     Train net output #0: loss = 5.26972 (* 1 = 5.26972 loss)
I0410 13:30:22.921860 18414 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0410 13:30:27.726739 18414 solver.cpp:218] Iteration 168 (2.49755 iter/s, 4.80471s/12 iters), loss = 5.27281
I0410 13:30:27.726794 18414 solver.cpp:237]     Train net output #0: loss = 5.27281 (* 1 = 5.27281 loss)
I0410 13:30:27.726805 18414 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0410 13:30:32.571811 18414 solver.cpp:218] Iteration 180 (2.47686 iter/s, 4.84484s/12 iters), loss = 5.27197
I0410 13:30:32.571871 18414 solver.cpp:237]     Train net output #0: loss = 5.27197 (* 1 = 5.27197 loss)
I0410 13:30:32.571883 18414 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0410 13:30:37.399507 18414 solver.cpp:218] Iteration 192 (2.48578 iter/s, 4.82747s/12 iters), loss = 5.27435
I0410 13:30:37.399555 18414 solver.cpp:237]     Train net output #0: loss = 5.27435 (* 1 = 5.27435 loss)
I0410 13:30:37.399564 18414 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0410 13:30:41.140643 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:30:41.845474 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0410 13:30:42.155871 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0410 13:30:42.379150 18414 solver.cpp:330] Iteration 204, Testing net (#0)
I0410 13:30:42.379171 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:30:46.826395 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:30:46.950076 18414 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:30:46.950122 18414 solver.cpp:397]     Test net output #1: loss = 5.28007 (* 1 = 5.28007 loss)
I0410 13:30:47.032701 18414 solver.cpp:218] Iteration 204 (1.24574 iter/s, 9.63281s/12 iters), loss = 5.27689
I0410 13:30:47.032761 18414 solver.cpp:237]     Train net output #0: loss = 5.27689 (* 1 = 5.27689 loss)
I0410 13:30:47.032773 18414 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0410 13:30:51.115669 18414 solver.cpp:218] Iteration 216 (2.93919 iter/s, 4.08276s/12 iters), loss = 5.27862
I0410 13:30:51.115793 18414 solver.cpp:237]     Train net output #0: loss = 5.27862 (* 1 = 5.27862 loss)
I0410 13:30:51.115806 18414 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0410 13:30:55.959874 18414 solver.cpp:218] Iteration 228 (2.47734 iter/s, 4.84391s/12 iters), loss = 5.26686
I0410 13:30:55.959928 18414 solver.cpp:237]     Train net output #0: loss = 5.26686 (* 1 = 5.26686 loss)
I0410 13:30:55.959939 18414 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0410 13:31:00.761535 18414 solver.cpp:218] Iteration 240 (2.49925 iter/s, 4.80144s/12 iters), loss = 5.28253
I0410 13:31:00.761586 18414 solver.cpp:237]     Train net output #0: loss = 5.28253 (* 1 = 5.28253 loss)
I0410 13:31:00.761600 18414 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0410 13:31:05.583853 18414 solver.cpp:218] Iteration 252 (2.48855 iter/s, 4.82209s/12 iters), loss = 5.27331
I0410 13:31:05.583895 18414 solver.cpp:237]     Train net output #0: loss = 5.27331 (* 1 = 5.27331 loss)
I0410 13:31:05.583906 18414 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0410 13:31:10.379379 18414 solver.cpp:218] Iteration 264 (2.50245 iter/s, 4.79531s/12 iters), loss = 5.27661
I0410 13:31:10.379431 18414 solver.cpp:237]     Train net output #0: loss = 5.27661 (* 1 = 5.27661 loss)
I0410 13:31:10.379443 18414 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0410 13:31:15.191030 18414 solver.cpp:218] Iteration 276 (2.49406 iter/s, 4.81143s/12 iters), loss = 5.28686
I0410 13:31:15.191078 18414 solver.cpp:237]     Train net output #0: loss = 5.28686 (* 1 = 5.28686 loss)
I0410 13:31:15.191088 18414 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0410 13:31:19.979941 18414 solver.cpp:218] Iteration 288 (2.50591 iter/s, 4.78868s/12 iters), loss = 5.27789
I0410 13:31:19.980008 18414 solver.cpp:237]     Train net output #0: loss = 5.27789 (* 1 = 5.27789 loss)
I0410 13:31:19.980020 18414 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0410 13:31:24.865764 18414 solver.cpp:218] Iteration 300 (2.45621 iter/s, 4.88558s/12 iters), loss = 5.2833
I0410 13:31:24.865900 18414 solver.cpp:237]     Train net output #0: loss = 5.2833 (* 1 = 5.2833 loss)
I0410 13:31:24.865911 18414 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0410 13:31:25.813176 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:31:26.827906 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0410 13:31:27.108428 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0410 13:31:27.302245 18414 solver.cpp:330] Iteration 306, Testing net (#0)
I0410 13:31:27.302265 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:31:31.440220 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:31:31.596529 18414 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:31:31.596571 18414 solver.cpp:397]     Test net output #1: loss = 5.28129 (* 1 = 5.28129 loss)
I0410 13:31:33.351917 18414 solver.cpp:218] Iteration 312 (1.41414 iter/s, 8.48572s/12 iters), loss = 5.28109
I0410 13:31:33.351974 18414 solver.cpp:237]     Train net output #0: loss = 5.28109 (* 1 = 5.28109 loss)
I0410 13:31:33.351984 18414 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0410 13:31:38.130451 18414 solver.cpp:218] Iteration 324 (2.51135 iter/s, 4.77831s/12 iters), loss = 5.25853
I0410 13:31:38.130508 18414 solver.cpp:237]     Train net output #0: loss = 5.25853 (* 1 = 5.25853 loss)
I0410 13:31:38.130522 18414 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0410 13:31:42.913368 18414 solver.cpp:218] Iteration 336 (2.50905 iter/s, 4.78269s/12 iters), loss = 5.26553
I0410 13:31:42.913429 18414 solver.cpp:237]     Train net output #0: loss = 5.26553 (* 1 = 5.26553 loss)
I0410 13:31:42.913444 18414 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0410 13:31:47.690304 18414 solver.cpp:218] Iteration 348 (2.51219 iter/s, 4.7767s/12 iters), loss = 5.26952
I0410 13:31:47.690361 18414 solver.cpp:237]     Train net output #0: loss = 5.26952 (* 1 = 5.26952 loss)
I0410 13:31:47.690380 18414 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0410 13:31:52.485208 18414 solver.cpp:218] Iteration 360 (2.50278 iter/s, 4.79467s/12 iters), loss = 5.28552
I0410 13:31:52.485265 18414 solver.cpp:237]     Train net output #0: loss = 5.28552 (* 1 = 5.28552 loss)
I0410 13:31:52.485275 18414 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0410 13:31:57.272385 18414 solver.cpp:218] Iteration 372 (2.50682 iter/s, 4.78695s/12 iters), loss = 5.27111
I0410 13:31:57.272539 18414 solver.cpp:237]     Train net output #0: loss = 5.27111 (* 1 = 5.27111 loss)
I0410 13:31:57.272552 18414 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0410 13:32:02.067373 18414 solver.cpp:218] Iteration 384 (2.50278 iter/s, 4.79466s/12 iters), loss = 5.27791
I0410 13:32:02.067437 18414 solver.cpp:237]     Train net output #0: loss = 5.27791 (* 1 = 5.27791 loss)
I0410 13:32:02.067451 18414 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0410 13:32:06.874275 18414 solver.cpp:218] Iteration 396 (2.49653 iter/s, 4.80667s/12 iters), loss = 5.27045
I0410 13:32:06.874331 18414 solver.cpp:237]     Train net output #0: loss = 5.27045 (* 1 = 5.27045 loss)
I0410 13:32:06.874343 18414 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0410 13:32:09.868899 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:32:11.244029 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0410 13:32:11.567994 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0410 13:32:11.779083 18414 solver.cpp:330] Iteration 408, Testing net (#0)
I0410 13:32:11.779109 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:32:15.929858 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:32:16.130236 18414 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:32:16.130270 18414 solver.cpp:397]     Test net output #1: loss = 5.28308 (* 1 = 5.28308 loss)
I0410 13:32:16.204085 18414 solver.cpp:218] Iteration 408 (1.28625 iter/s, 9.32943s/12 iters), loss = 5.27665
I0410 13:32:16.204131 18414 solver.cpp:237]     Train net output #0: loss = 5.27665 (* 1 = 5.27665 loss)
I0410 13:32:16.204139 18414 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0410 13:32:20.363898 18414 solver.cpp:218] Iteration 420 (2.88489 iter/s, 4.15961s/12 iters), loss = 5.27662
I0410 13:32:20.363955 18414 solver.cpp:237]     Train net output #0: loss = 5.27662 (* 1 = 5.27662 loss)
I0410 13:32:20.363968 18414 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0410 13:32:25.366330 18414 solver.cpp:218] Iteration 432 (2.39894 iter/s, 5.00221s/12 iters), loss = 5.27123
I0410 13:32:25.366360 18414 solver.cpp:237]     Train net output #0: loss = 5.27123 (* 1 = 5.27123 loss)
I0410 13:32:25.366369 18414 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0410 13:32:30.235595 18414 solver.cpp:218] Iteration 444 (2.46454 iter/s, 4.86906s/12 iters), loss = 5.2826
I0410 13:32:30.235695 18414 solver.cpp:237]     Train net output #0: loss = 5.2826 (* 1 = 5.2826 loss)
I0410 13:32:30.235705 18414 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0410 13:32:35.206437 18414 solver.cpp:218] Iteration 456 (2.41421 iter/s, 4.97056s/12 iters), loss = 5.27899
I0410 13:32:35.206487 18414 solver.cpp:237]     Train net output #0: loss = 5.27899 (* 1 = 5.27899 loss)
I0410 13:32:35.206498 18414 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0410 13:32:40.016019 18414 solver.cpp:218] Iteration 468 (2.49513 iter/s, 4.80936s/12 iters), loss = 5.2865
I0410 13:32:40.016073 18414 solver.cpp:237]     Train net output #0: loss = 5.2865 (* 1 = 5.2865 loss)
I0410 13:32:40.016085 18414 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0410 13:32:44.960484 18414 solver.cpp:218] Iteration 480 (2.42707 iter/s, 4.94424s/12 iters), loss = 5.26674
I0410 13:32:44.960530 18414 solver.cpp:237]     Train net output #0: loss = 5.26674 (* 1 = 5.26674 loss)
I0410 13:32:44.960539 18414 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0410 13:32:49.800788 18414 solver.cpp:218] Iteration 492 (2.4793 iter/s, 4.84008s/12 iters), loss = 5.28935
I0410 13:32:49.800844 18414 solver.cpp:237]     Train net output #0: loss = 5.28935 (* 1 = 5.28935 loss)
I0410 13:32:49.800858 18414 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0410 13:32:54.639428 18414 solver.cpp:218] Iteration 504 (2.48015 iter/s, 4.83841s/12 iters), loss = 5.26999
I0410 13:32:54.639482 18414 solver.cpp:237]     Train net output #0: loss = 5.26999 (* 1 = 5.26999 loss)
I0410 13:32:54.639492 18414 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0410 13:32:54.899322 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:32:56.627377 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0410 13:32:56.943753 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0410 13:32:57.157532 18414 solver.cpp:330] Iteration 510, Testing net (#0)
I0410 13:32:57.157560 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:33:01.405076 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:33:01.642314 18414 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0410 13:33:01.642357 18414 solver.cpp:397]     Test net output #1: loss = 5.28317 (* 1 = 5.28317 loss)
I0410 13:33:03.380848 18414 solver.cpp:218] Iteration 516 (1.37283 iter/s, 8.74107s/12 iters), loss = 5.27861
I0410 13:33:03.380897 18414 solver.cpp:237]     Train net output #0: loss = 5.27861 (* 1 = 5.27861 loss)
I0410 13:33:03.380908 18414 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0410 13:33:08.243428 18414 solver.cpp:218] Iteration 528 (2.46794 iter/s, 4.86235s/12 iters), loss = 5.27168
I0410 13:33:08.243489 18414 solver.cpp:237]     Train net output #0: loss = 5.27168 (* 1 = 5.27168 loss)
I0410 13:33:08.243502 18414 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0410 13:33:13.099728 18414 solver.cpp:218] Iteration 540 (2.47113 iter/s, 4.85607s/12 iters), loss = 5.27509
I0410 13:33:13.099784 18414 solver.cpp:237]     Train net output #0: loss = 5.27509 (* 1 = 5.27509 loss)
I0410 13:33:13.099798 18414 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0410 13:33:18.011384 18414 solver.cpp:218] Iteration 552 (2.44328 iter/s, 4.91142s/12 iters), loss = 5.27305
I0410 13:33:18.011445 18414 solver.cpp:237]     Train net output #0: loss = 5.27305 (* 1 = 5.27305 loss)
I0410 13:33:18.011458 18414 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0410 13:33:22.850450 18414 solver.cpp:218] Iteration 564 (2.47994 iter/s, 4.83884s/12 iters), loss = 5.26045
I0410 13:33:22.850507 18414 solver.cpp:237]     Train net output #0: loss = 5.26045 (* 1 = 5.26045 loss)
I0410 13:33:22.850520 18414 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0410 13:33:27.735179 18414 solver.cpp:218] Iteration 576 (2.45675 iter/s, 4.8845s/12 iters), loss = 5.2767
I0410 13:33:27.735231 18414 solver.cpp:237]     Train net output #0: loss = 5.2767 (* 1 = 5.2767 loss)
I0410 13:33:27.735244 18414 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0410 13:33:32.560617 18414 solver.cpp:218] Iteration 588 (2.48694 iter/s, 4.82522s/12 iters), loss = 5.26867
I0410 13:33:32.560745 18414 solver.cpp:237]     Train net output #0: loss = 5.26867 (* 1 = 5.26867 loss)
I0410 13:33:32.560760 18414 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0410 13:33:37.397773 18414 solver.cpp:218] Iteration 600 (2.48095 iter/s, 4.83686s/12 iters), loss = 5.25873
I0410 13:33:37.397823 18414 solver.cpp:237]     Train net output #0: loss = 5.25873 (* 1 = 5.25873 loss)
I0410 13:33:37.397835 18414 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0410 13:33:39.845280 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:33:41.906404 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0410 13:33:42.673825 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0410 13:33:42.869518 18414 solver.cpp:330] Iteration 612, Testing net (#0)
I0410 13:33:42.869535 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:33:47.100347 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:33:47.384822 18414 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:33:47.384873 18414 solver.cpp:397]     Test net output #1: loss = 5.2833 (* 1 = 5.2833 loss)
I0410 13:33:47.467782 18414 solver.cpp:218] Iteration 612 (1.1917 iter/s, 10.0696s/12 iters), loss = 5.27269
I0410 13:33:47.467840 18414 solver.cpp:237]     Train net output #0: loss = 5.27269 (* 1 = 5.27269 loss)
I0410 13:33:47.467852 18414 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0410 13:33:51.631808 18414 solver.cpp:218] Iteration 624 (2.88197 iter/s, 4.16382s/12 iters), loss = 5.2868
I0410 13:33:51.631853 18414 solver.cpp:237]     Train net output #0: loss = 5.2868 (* 1 = 5.2868 loss)
I0410 13:33:51.631861 18414 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0410 13:33:56.509260 18414 solver.cpp:218] Iteration 636 (2.46041 iter/s, 4.87723s/12 iters), loss = 5.28431
I0410 13:33:56.509326 18414 solver.cpp:237]     Train net output #0: loss = 5.28431 (* 1 = 5.28431 loss)
I0410 13:33:56.509344 18414 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0410 13:34:01.268994 18414 solver.cpp:218] Iteration 648 (2.52127 iter/s, 4.7595s/12 iters), loss = 5.27278
I0410 13:34:01.269047 18414 solver.cpp:237]     Train net output #0: loss = 5.27278 (* 1 = 5.27278 loss)
I0410 13:34:01.269060 18414 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0410 13:34:06.096830 18414 solver.cpp:218] Iteration 660 (2.4857 iter/s, 4.82761s/12 iters), loss = 5.26942
I0410 13:34:06.097004 18414 solver.cpp:237]     Train net output #0: loss = 5.26942 (* 1 = 5.26942 loss)
I0410 13:34:06.097018 18414 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0410 13:34:10.931980 18414 solver.cpp:218] Iteration 672 (2.482 iter/s, 4.83481s/12 iters), loss = 5.27223
I0410 13:34:10.932036 18414 solver.cpp:237]     Train net output #0: loss = 5.27223 (* 1 = 5.27223 loss)
I0410 13:34:10.932049 18414 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0410 13:34:14.973541 18414 blocking_queue.cpp:49] Waiting for data
I0410 13:34:15.823873 18414 solver.cpp:218] Iteration 684 (2.45315 iter/s, 4.89167s/12 iters), loss = 5.2743
I0410 13:34:15.823920 18414 solver.cpp:237]     Train net output #0: loss = 5.2743 (* 1 = 5.2743 loss)
I0410 13:34:15.823931 18414 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0410 13:34:20.701836 18414 solver.cpp:218] Iteration 696 (2.46016 iter/s, 4.87774s/12 iters), loss = 5.2648
I0410 13:34:20.701894 18414 solver.cpp:237]     Train net output #0: loss = 5.2648 (* 1 = 5.2648 loss)
I0410 13:34:20.701907 18414 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0410 13:34:25.183463 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:34:25.556548 18414 solver.cpp:218] Iteration 708 (2.47194 iter/s, 4.85448s/12 iters), loss = 5.25816
I0410 13:34:25.556610 18414 solver.cpp:237]     Train net output #0: loss = 5.25816 (* 1 = 5.25816 loss)
I0410 13:34:25.556624 18414 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0410 13:34:27.573058 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0410 13:34:27.882889 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0410 13:34:28.095609 18414 solver.cpp:330] Iteration 714, Testing net (#0)
I0410 13:34:28.095638 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:34:32.205555 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:34:32.529147 18414 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0410 13:34:32.529198 18414 solver.cpp:397]     Test net output #1: loss = 5.28343 (* 1 = 5.28343 loss)
I0410 13:34:34.409166 18414 solver.cpp:218] Iteration 720 (1.35559 iter/s, 8.85226s/12 iters), loss = 5.27277
I0410 13:34:34.409209 18414 solver.cpp:237]     Train net output #0: loss = 5.27277 (* 1 = 5.27277 loss)
I0410 13:34:34.409219 18414 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0410 13:34:39.207520 18414 solver.cpp:218] Iteration 732 (2.50097 iter/s, 4.79813s/12 iters), loss = 5.277
I0410 13:34:39.207655 18414 solver.cpp:237]     Train net output #0: loss = 5.277 (* 1 = 5.277 loss)
I0410 13:34:39.207670 18414 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0410 13:34:44.035487 18414 solver.cpp:218] Iteration 744 (2.48567 iter/s, 4.82766s/12 iters), loss = 5.27333
I0410 13:34:44.035545 18414 solver.cpp:237]     Train net output #0: loss = 5.27333 (* 1 = 5.27333 loss)
I0410 13:34:44.035559 18414 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0410 13:34:48.964545 18414 solver.cpp:218] Iteration 756 (2.43466 iter/s, 4.92883s/12 iters), loss = 5.27173
I0410 13:34:48.964592 18414 solver.cpp:237]     Train net output #0: loss = 5.27173 (* 1 = 5.27173 loss)
I0410 13:34:48.964602 18414 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0410 13:34:53.827921 18414 solver.cpp:218] Iteration 768 (2.46753 iter/s, 4.86316s/12 iters), loss = 5.27053
I0410 13:34:53.827966 18414 solver.cpp:237]     Train net output #0: loss = 5.27053 (* 1 = 5.27053 loss)
I0410 13:34:53.827978 18414 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0410 13:34:58.755915 18414 solver.cpp:218] Iteration 780 (2.43518 iter/s, 4.92778s/12 iters), loss = 5.25003
I0410 13:34:58.755970 18414 solver.cpp:237]     Train net output #0: loss = 5.25003 (* 1 = 5.25003 loss)
I0410 13:34:58.755983 18414 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0410 13:35:03.647716 18414 solver.cpp:218] Iteration 792 (2.4532 iter/s, 4.89158s/12 iters), loss = 5.23546
I0410 13:35:03.647768 18414 solver.cpp:237]     Train net output #0: loss = 5.23546 (* 1 = 5.23546 loss)
I0410 13:35:03.647778 18414 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0410 13:35:08.542412 18414 solver.cpp:218] Iteration 804 (2.45174 iter/s, 4.89447s/12 iters), loss = 5.24207
I0410 13:35:08.542464 18414 solver.cpp:237]     Train net output #0: loss = 5.24207 (* 1 = 5.24207 loss)
I0410 13:35:08.542475 18414 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0410 13:35:10.254098 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:35:13.000504 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0410 13:35:13.297406 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0410 13:35:13.501272 18414 solver.cpp:330] Iteration 816, Testing net (#0)
I0410 13:35:13.501307 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:35:17.812043 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:35:18.188352 18414 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0410 13:35:18.188401 18414 solver.cpp:397]     Test net output #1: loss = 5.23191 (* 1 = 5.23191 loss)
I0410 13:35:18.271304 18414 solver.cpp:218] Iteration 816 (1.23349 iter/s, 9.72852s/12 iters), loss = 5.24341
I0410 13:35:18.271358 18414 solver.cpp:237]     Train net output #0: loss = 5.24341 (* 1 = 5.24341 loss)
I0410 13:35:18.271369 18414 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0410 13:35:22.440999 18414 solver.cpp:218] Iteration 828 (2.87805 iter/s, 4.16949s/12 iters), loss = 5.21934
I0410 13:35:22.441053 18414 solver.cpp:237]     Train net output #0: loss = 5.21934 (* 1 = 5.21934 loss)
I0410 13:35:22.441066 18414 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0410 13:35:27.316193 18414 solver.cpp:218] Iteration 840 (2.46155 iter/s, 4.87497s/12 iters), loss = 5.16046
I0410 13:35:27.316237 18414 solver.cpp:237]     Train net output #0: loss = 5.16046 (* 1 = 5.16046 loss)
I0410 13:35:27.316246 18414 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0410 13:35:32.136536 18414 solver.cpp:218] Iteration 852 (2.48956 iter/s, 4.82012s/12 iters), loss = 5.20175
I0410 13:35:32.136592 18414 solver.cpp:237]     Train net output #0: loss = 5.20175 (* 1 = 5.20175 loss)
I0410 13:35:32.136605 18414 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0410 13:35:37.038434 18414 solver.cpp:218] Iteration 864 (2.44815 iter/s, 4.90167s/12 iters), loss = 5.13432
I0410 13:35:37.038484 18414 solver.cpp:237]     Train net output #0: loss = 5.13432 (* 1 = 5.13432 loss)
I0410 13:35:37.038497 18414 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0410 13:35:41.933192 18414 solver.cpp:218] Iteration 876 (2.45171 iter/s, 4.89453s/12 iters), loss = 5.1922
I0410 13:35:41.933305 18414 solver.cpp:237]     Train net output #0: loss = 5.1922 (* 1 = 5.1922 loss)
I0410 13:35:41.933315 18414 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0410 13:35:46.860473 18414 solver.cpp:218] Iteration 888 (2.43556 iter/s, 4.927s/12 iters), loss = 5.07627
I0410 13:35:46.860527 18414 solver.cpp:237]     Train net output #0: loss = 5.07627 (* 1 = 5.07627 loss)
I0410 13:35:46.860538 18414 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0410 13:35:51.720036 18414 solver.cpp:218] Iteration 900 (2.46947 iter/s, 4.85934s/12 iters), loss = 5.25271
I0410 13:35:51.720093 18414 solver.cpp:237]     Train net output #0: loss = 5.25271 (* 1 = 5.25271 loss)
I0410 13:35:51.720104 18414 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0410 13:35:55.543215 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:35:56.646065 18414 solver.cpp:218] Iteration 912 (2.43615 iter/s, 4.9258s/12 iters), loss = 5.07518
I0410 13:35:56.646113 18414 solver.cpp:237]     Train net output #0: loss = 5.07518 (* 1 = 5.07518 loss)
I0410 13:35:56.646122 18414 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0410 13:35:58.675138 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0410 13:35:59.000520 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0410 13:35:59.214232 18414 solver.cpp:330] Iteration 918, Testing net (#0)
I0410 13:35:59.214260 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:36:03.193490 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:36:03.592269 18414 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0410 13:36:03.592301 18414 solver.cpp:397]     Test net output #1: loss = 5.14877 (* 1 = 5.14877 loss)
I0410 13:36:05.423198 18414 solver.cpp:218] Iteration 924 (1.36724 iter/s, 8.77678s/12 iters), loss = 5.15584
I0410 13:36:05.423259 18414 solver.cpp:237]     Train net output #0: loss = 5.15584 (* 1 = 5.15584 loss)
I0410 13:36:05.423270 18414 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0410 13:36:10.415086 18414 solver.cpp:218] Iteration 936 (2.40401 iter/s, 4.99165s/12 iters), loss = 5.20976
I0410 13:36:10.415135 18414 solver.cpp:237]     Train net output #0: loss = 5.20976 (* 1 = 5.20976 loss)
I0410 13:36:10.415148 18414 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0410 13:36:15.361109 18414 solver.cpp:218] Iteration 948 (2.4263 iter/s, 4.9458s/12 iters), loss = 5.15877
I0410 13:36:15.361255 18414 solver.cpp:237]     Train net output #0: loss = 5.15877 (* 1 = 5.15877 loss)
I0410 13:36:15.361268 18414 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0410 13:36:20.320794 18414 solver.cpp:218] Iteration 960 (2.41966 iter/s, 4.95937s/12 iters), loss = 5.11153
I0410 13:36:20.320848 18414 solver.cpp:237]     Train net output #0: loss = 5.11153 (* 1 = 5.11153 loss)
I0410 13:36:20.320860 18414 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0410 13:36:25.214808 18414 solver.cpp:218] Iteration 972 (2.45209 iter/s, 4.89379s/12 iters), loss = 5.16652
I0410 13:36:25.214856 18414 solver.cpp:237]     Train net output #0: loss = 5.16652 (* 1 = 5.16652 loss)
I0410 13:36:25.214865 18414 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0410 13:36:30.167196 18414 solver.cpp:218] Iteration 984 (2.42318 iter/s, 4.95216s/12 iters), loss = 5.15878
I0410 13:36:30.167248 18414 solver.cpp:237]     Train net output #0: loss = 5.15878 (* 1 = 5.15878 loss)
I0410 13:36:30.167260 18414 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0410 13:36:34.984027 18414 solver.cpp:218] Iteration 996 (2.49138 iter/s, 4.81662s/12 iters), loss = 5.04231
I0410 13:36:34.984073 18414 solver.cpp:237]     Train net output #0: loss = 5.04231 (* 1 = 5.04231 loss)
I0410 13:36:34.984083 18414 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0410 13:36:39.974649 18414 solver.cpp:218] Iteration 1008 (2.40462 iter/s, 4.9904s/12 iters), loss = 5.15956
I0410 13:36:39.974704 18414 solver.cpp:237]     Train net output #0: loss = 5.15956 (* 1 = 5.15956 loss)
I0410 13:36:39.974717 18414 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0410 13:36:40.963227 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:36:44.353693 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0410 13:36:45.472123 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0410 13:36:46.419459 18414 solver.cpp:330] Iteration 1020, Testing net (#0)
I0410 13:36:46.419487 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:36:50.435389 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:36:50.865402 18414 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0410 13:36:50.865458 18414 solver.cpp:397]     Test net output #1: loss = 5.11962 (* 1 = 5.11962 loss)
I0410 13:36:50.948312 18414 solver.cpp:218] Iteration 1020 (1.09357 iter/s, 10.9732s/12 iters), loss = 5.09065
I0410 13:36:50.948369 18414 solver.cpp:237]     Train net output #0: loss = 5.09065 (* 1 = 5.09065 loss)
I0410 13:36:50.948381 18414 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0410 13:36:55.331732 18414 solver.cpp:218] Iteration 1032 (2.73772 iter/s, 4.38321s/12 iters), loss = 5.12848
I0410 13:36:55.331776 18414 solver.cpp:237]     Train net output #0: loss = 5.12848 (* 1 = 5.12848 loss)
I0410 13:36:55.331786 18414 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0410 13:37:00.161909 18414 solver.cpp:218] Iteration 1044 (2.48449 iter/s, 4.82996s/12 iters), loss = 5.14631
I0410 13:37:00.161986 18414 solver.cpp:237]     Train net output #0: loss = 5.14631 (* 1 = 5.14631 loss)
I0410 13:37:00.162000 18414 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0410 13:37:05.099752 18414 solver.cpp:218] Iteration 1056 (2.43033 iter/s, 4.93759s/12 iters), loss = 5.11856
I0410 13:37:05.099804 18414 solver.cpp:237]     Train net output #0: loss = 5.11856 (* 1 = 5.11856 loss)
I0410 13:37:05.099815 18414 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0410 13:37:10.067718 18414 solver.cpp:218] Iteration 1068 (2.41559 iter/s, 4.96774s/12 iters), loss = 5.15348
I0410 13:37:10.067771 18414 solver.cpp:237]     Train net output #0: loss = 5.15348 (* 1 = 5.15348 loss)
I0410 13:37:10.067783 18414 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0410 13:37:14.947788 18414 solver.cpp:218] Iteration 1080 (2.4591 iter/s, 4.87984s/12 iters), loss = 5.11424
I0410 13:37:14.947849 18414 solver.cpp:237]     Train net output #0: loss = 5.11424 (* 1 = 5.11424 loss)
I0410 13:37:14.947863 18414 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0410 13:37:19.874876 18414 solver.cpp:218] Iteration 1092 (2.43563 iter/s, 4.92686s/12 iters), loss = 5.05678
I0410 13:37:19.875000 18414 solver.cpp:237]     Train net output #0: loss = 5.05678 (* 1 = 5.05678 loss)
I0410 13:37:19.875013 18414 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0410 13:37:24.777276 18414 solver.cpp:218] Iteration 1104 (2.44793 iter/s, 4.90211s/12 iters), loss = 5.06221
I0410 13:37:24.777318 18414 solver.cpp:237]     Train net output #0: loss = 5.06221 (* 1 = 5.06221 loss)
I0410 13:37:24.777328 18414 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0410 13:37:27.871193 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:37:29.698330 18414 solver.cpp:218] Iteration 1116 (2.43861 iter/s, 4.92084s/12 iters), loss = 5.13468
I0410 13:37:29.698390 18414 solver.cpp:237]     Train net output #0: loss = 5.13468 (* 1 = 5.13468 loss)
I0410 13:37:29.698400 18414 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0410 13:37:31.720798 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0410 13:37:32.041496 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0410 13:37:32.252424 18414 solver.cpp:330] Iteration 1122, Testing net (#0)
I0410 13:37:32.252454 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:37:36.189384 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:37:36.663794 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0116422
I0410 13:37:36.663844 18414 solver.cpp:397]     Test net output #1: loss = 5.07439 (* 1 = 5.07439 loss)
I0410 13:37:38.437510 18414 solver.cpp:218] Iteration 1128 (1.37318 iter/s, 8.73882s/12 iters), loss = 5.19451
I0410 13:37:38.437568 18414 solver.cpp:237]     Train net output #0: loss = 5.19451 (* 1 = 5.19451 loss)
I0410 13:37:38.437582 18414 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0410 13:37:43.374339 18414 solver.cpp:218] Iteration 1140 (2.43082 iter/s, 4.9366s/12 iters), loss = 5.14523
I0410 13:37:43.374399 18414 solver.cpp:237]     Train net output #0: loss = 5.14523 (* 1 = 5.14523 loss)
I0410 13:37:43.374414 18414 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0410 13:37:48.282910 18414 solver.cpp:218] Iteration 1152 (2.44482 iter/s, 4.90834s/12 iters), loss = 5.07681
I0410 13:37:48.282960 18414 solver.cpp:237]     Train net output #0: loss = 5.07681 (* 1 = 5.07681 loss)
I0410 13:37:48.282974 18414 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0410 13:37:53.226547 18414 solver.cpp:218] Iteration 1164 (2.42747 iter/s, 4.94342s/12 iters), loss = 5.09931
I0410 13:37:53.226671 18414 solver.cpp:237]     Train net output #0: loss = 5.09931 (* 1 = 5.09931 loss)
I0410 13:37:53.226683 18414 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0410 13:37:58.188537 18414 solver.cpp:218] Iteration 1176 (2.41853 iter/s, 4.9617s/12 iters), loss = 5.06658
I0410 13:37:58.188585 18414 solver.cpp:237]     Train net output #0: loss = 5.06658 (* 1 = 5.06658 loss)
I0410 13:37:58.188593 18414 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0410 13:38:03.237505 18414 solver.cpp:218] Iteration 1188 (2.37683 iter/s, 5.04875s/12 iters), loss = 5.09439
I0410 13:38:03.237546 18414 solver.cpp:237]     Train net output #0: loss = 5.09439 (* 1 = 5.09439 loss)
I0410 13:38:03.237555 18414 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0410 13:38:08.278012 18414 solver.cpp:218] Iteration 1200 (2.38082 iter/s, 5.04029s/12 iters), loss = 5.13578
I0410 13:38:08.278060 18414 solver.cpp:237]     Train net output #0: loss = 5.13578 (* 1 = 5.13578 loss)
I0410 13:38:08.278071 18414 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0410 13:38:13.282841 18414 solver.cpp:218] Iteration 1212 (2.39779 iter/s, 5.0046s/12 iters), loss = 5.10662
I0410 13:38:13.282899 18414 solver.cpp:237]     Train net output #0: loss = 5.10662 (* 1 = 5.10662 loss)
I0410 13:38:13.282913 18414 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0410 13:38:13.570884 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:38:17.793365 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0410 13:38:18.113535 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0410 13:38:18.325714 18414 solver.cpp:330] Iteration 1224, Testing net (#0)
I0410 13:38:18.325747 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:38:22.268731 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:38:22.777717 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0410 13:38:22.777770 18414 solver.cpp:397]     Test net output #1: loss = 5.07459 (* 1 = 5.07459 loss)
I0410 13:38:22.861155 18414 solver.cpp:218] Iteration 1224 (1.25288 iter/s, 9.57793s/12 iters), loss = 5.05978
I0410 13:38:22.861232 18414 solver.cpp:237]     Train net output #0: loss = 5.05978 (* 1 = 5.05978 loss)
I0410 13:38:22.861249 18414 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0410 13:38:27.044953 18414 solver.cpp:218] Iteration 1236 (2.86836 iter/s, 4.18357s/12 iters), loss = 5.16312
I0410 13:38:27.045087 18414 solver.cpp:237]     Train net output #0: loss = 5.16312 (* 1 = 5.16312 loss)
I0410 13:38:27.045099 18414 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0410 13:38:31.934890 18414 solver.cpp:218] Iteration 1248 (2.45417 iter/s, 4.88963s/12 iters), loss = 4.99644
I0410 13:38:31.934948 18414 solver.cpp:237]     Train net output #0: loss = 4.99644 (* 1 = 4.99644 loss)
I0410 13:38:31.934962 18414 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0410 13:38:36.853236 18414 solver.cpp:218] Iteration 1260 (2.43996 iter/s, 4.91811s/12 iters), loss = 5.08416
I0410 13:38:36.853299 18414 solver.cpp:237]     Train net output #0: loss = 5.08416 (* 1 = 5.08416 loss)
I0410 13:38:36.853312 18414 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0410 13:38:41.794574 18414 solver.cpp:218] Iteration 1272 (2.42861 iter/s, 4.9411s/12 iters), loss = 5.01848
I0410 13:38:41.794629 18414 solver.cpp:237]     Train net output #0: loss = 5.01848 (* 1 = 5.01848 loss)
I0410 13:38:41.794643 18414 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0410 13:38:46.779919 18414 solver.cpp:218] Iteration 1284 (2.40717 iter/s, 4.98511s/12 iters), loss = 5.00842
I0410 13:38:46.779971 18414 solver.cpp:237]     Train net output #0: loss = 5.00842 (* 1 = 5.00842 loss)
I0410 13:38:46.779984 18414 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0410 13:38:51.867154 18414 solver.cpp:218] Iteration 1296 (2.35895 iter/s, 5.087s/12 iters), loss = 4.97769
I0410 13:38:51.867210 18414 solver.cpp:237]     Train net output #0: loss = 4.97769 (* 1 = 4.97769 loss)
I0410 13:38:51.867224 18414 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0410 13:38:56.720449 18414 solver.cpp:218] Iteration 1308 (2.47266 iter/s, 4.85307s/12 iters), loss = 5.00322
I0410 13:38:56.720499 18414 solver.cpp:237]     Train net output #0: loss = 5.00322 (* 1 = 5.00322 loss)
I0410 13:38:56.720510 18414 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0410 13:38:59.174463 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:39:01.612107 18414 solver.cpp:218] Iteration 1320 (2.45327 iter/s, 4.89143s/12 iters), loss = 5.0406
I0410 13:39:01.612154 18414 solver.cpp:237]     Train net output #0: loss = 5.0406 (* 1 = 5.0406 loss)
I0410 13:39:01.612165 18414 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0410 13:39:03.617300 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0410 13:39:03.939131 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0410 13:39:04.152679 18414 solver.cpp:330] Iteration 1326, Testing net (#0)
I0410 13:39:04.152712 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:39:07.982985 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:39:08.537451 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0171569
I0410 13:39:08.537501 18414 solver.cpp:397]     Test net output #1: loss = 5.03808 (* 1 = 5.03808 loss)
I0410 13:39:10.763094 18414 solver.cpp:218] Iteration 1332 (1.31138 iter/s, 9.15064s/12 iters), loss = 5.0067
I0410 13:39:10.763135 18414 solver.cpp:237]     Train net output #0: loss = 5.0067 (* 1 = 5.0067 loss)
I0410 13:39:10.763144 18414 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0410 13:39:15.817493 18414 solver.cpp:218] Iteration 1344 (2.37427 iter/s, 5.05418s/12 iters), loss = 4.96974
I0410 13:39:15.817546 18414 solver.cpp:237]     Train net output #0: loss = 4.96974 (* 1 = 4.96974 loss)
I0410 13:39:15.817560 18414 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0410 13:39:20.690896 18414 solver.cpp:218] Iteration 1356 (2.46246 iter/s, 4.87318s/12 iters), loss = 5.05981
I0410 13:39:20.690945 18414 solver.cpp:237]     Train net output #0: loss = 5.05981 (* 1 = 5.05981 loss)
I0410 13:39:20.690956 18414 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0410 13:39:25.272742 18414 blocking_queue.cpp:49] Waiting for data
I0410 13:39:25.745815 18414 solver.cpp:218] Iteration 1368 (2.37403 iter/s, 5.05469s/12 iters), loss = 5.06717
I0410 13:39:25.745859 18414 solver.cpp:237]     Train net output #0: loss = 5.06717 (* 1 = 5.06717 loss)
I0410 13:39:25.745868 18414 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0410 13:39:30.724884 18414 solver.cpp:218] Iteration 1380 (2.4102 iter/s, 4.97884s/12 iters), loss = 4.94453
I0410 13:39:30.725064 18414 solver.cpp:237]     Train net output #0: loss = 4.94453 (* 1 = 4.94453 loss)
I0410 13:39:30.725082 18414 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0410 13:39:35.693132 18414 solver.cpp:218] Iteration 1392 (2.41551 iter/s, 4.9679s/12 iters), loss = 4.87116
I0410 13:39:35.693188 18414 solver.cpp:237]     Train net output #0: loss = 4.87116 (* 1 = 4.87116 loss)
I0410 13:39:35.693203 18414 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0410 13:39:40.557808 18414 solver.cpp:218] Iteration 1404 (2.46688 iter/s, 4.86445s/12 iters), loss = 4.99481
I0410 13:39:40.557865 18414 solver.cpp:237]     Train net output #0: loss = 4.99481 (* 1 = 4.99481 loss)
I0410 13:39:40.557878 18414 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0410 13:39:45.183990 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:39:45.536074 18414 solver.cpp:218] Iteration 1416 (2.41059 iter/s, 4.97804s/12 iters), loss = 5.06835
I0410 13:39:45.536124 18414 solver.cpp:237]     Train net output #0: loss = 5.06835 (* 1 = 5.06835 loss)
I0410 13:39:45.536135 18414 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0410 13:39:50.096536 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0410 13:39:50.984773 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0410 13:39:51.799734 18414 solver.cpp:330] Iteration 1428, Testing net (#0)
I0410 13:39:51.799754 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:39:55.665325 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:39:56.253597 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0147059
I0410 13:39:56.253643 18414 solver.cpp:397]     Test net output #1: loss = 5.00002 (* 1 = 5.00002 loss)
I0410 13:39:56.336794 18414 solver.cpp:218] Iteration 1428 (1.11108 iter/s, 10.8003s/12 iters), loss = 5.11335
I0410 13:39:56.336840 18414 solver.cpp:237]     Train net output #0: loss = 5.11335 (* 1 = 5.11335 loss)
I0410 13:39:56.336851 18414 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0410 13:40:00.626067 18414 solver.cpp:218] Iteration 1440 (2.79781 iter/s, 4.28907s/12 iters), loss = 4.94122
I0410 13:40:00.626127 18414 solver.cpp:237]     Train net output #0: loss = 4.94122 (* 1 = 4.94122 loss)
I0410 13:40:00.626138 18414 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0410 13:40:05.680697 18414 solver.cpp:218] Iteration 1452 (2.37417 iter/s, 5.0544s/12 iters), loss = 4.97424
I0410 13:40:05.680856 18414 solver.cpp:237]     Train net output #0: loss = 4.97424 (* 1 = 4.97424 loss)
I0410 13:40:05.680871 18414 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0410 13:40:10.697561 18414 solver.cpp:218] Iteration 1464 (2.39209 iter/s, 5.01653s/12 iters), loss = 4.9814
I0410 13:40:10.697619 18414 solver.cpp:237]     Train net output #0: loss = 4.9814 (* 1 = 4.9814 loss)
I0410 13:40:10.697633 18414 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0410 13:40:15.780824 18414 solver.cpp:218] Iteration 1476 (2.3608 iter/s, 5.08303s/12 iters), loss = 4.97256
I0410 13:40:15.780871 18414 solver.cpp:237]     Train net output #0: loss = 4.97256 (* 1 = 4.97256 loss)
I0410 13:40:15.780881 18414 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0410 13:40:20.852403 18414 solver.cpp:218] Iteration 1488 (2.36623 iter/s, 5.07135s/12 iters), loss = 4.93309
I0410 13:40:20.852456 18414 solver.cpp:237]     Train net output #0: loss = 4.93309 (* 1 = 4.93309 loss)
I0410 13:40:20.852469 18414 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0410 13:40:25.847476 18414 solver.cpp:218] Iteration 1500 (2.40248 iter/s, 4.99485s/12 iters), loss = 4.8382
I0410 13:40:25.847519 18414 solver.cpp:237]     Train net output #0: loss = 4.8382 (* 1 = 4.8382 loss)
I0410 13:40:25.847529 18414 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0410 13:40:30.720330 18414 solver.cpp:218] Iteration 1512 (2.46273 iter/s, 4.87264s/12 iters), loss = 4.98873
I0410 13:40:30.720383 18414 solver.cpp:237]     Train net output #0: loss = 4.98873 (* 1 = 4.98873 loss)
I0410 13:40:30.720396 18414 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0410 13:40:32.499296 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:40:35.648749 18414 solver.cpp:218] Iteration 1524 (2.43497 iter/s, 4.92819s/12 iters), loss = 5.00206
I0410 13:40:35.648792 18414 solver.cpp:237]     Train net output #0: loss = 5.00206 (* 1 = 5.00206 loss)
I0410 13:40:35.648800 18414 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0410 13:40:37.619890 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0410 13:40:37.938519 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0410 13:40:38.154229 18414 solver.cpp:330] Iteration 1530, Testing net (#0)
I0410 13:40:38.154251 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:40:41.955472 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:40:42.589799 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0269608
I0410 13:40:42.589843 18414 solver.cpp:397]     Test net output #1: loss = 4.88975 (* 1 = 4.88975 loss)
I0410 13:40:44.476305 18414 solver.cpp:218] Iteration 1536 (1.35943 iter/s, 8.82721s/12 iters), loss = 4.92441
I0410 13:40:44.476359 18414 solver.cpp:237]     Train net output #0: loss = 4.92441 (* 1 = 4.92441 loss)
I0410 13:40:44.476370 18414 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0410 13:40:49.364060 18414 solver.cpp:218] Iteration 1548 (2.45523 iter/s, 4.88753s/12 iters), loss = 4.86293
I0410 13:40:49.364116 18414 solver.cpp:237]     Train net output #0: loss = 4.86293 (* 1 = 4.86293 loss)
I0410 13:40:49.364130 18414 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0410 13:40:54.475062 18414 solver.cpp:218] Iteration 1560 (2.34798 iter/s, 5.11077s/12 iters), loss = 4.96922
I0410 13:40:54.475109 18414 solver.cpp:237]     Train net output #0: loss = 4.96922 (* 1 = 4.96922 loss)
I0410 13:40:54.475122 18414 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0410 13:40:59.455812 18414 solver.cpp:218] Iteration 1572 (2.40938 iter/s, 4.98053s/12 iters), loss = 4.85737
I0410 13:40:59.455866 18414 solver.cpp:237]     Train net output #0: loss = 4.85737 (* 1 = 4.85737 loss)
I0410 13:40:59.455878 18414 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0410 13:41:04.354627 18414 solver.cpp:218] Iteration 1584 (2.44968 iter/s, 4.89859s/12 iters), loss = 4.89667
I0410 13:41:04.354681 18414 solver.cpp:237]     Train net output #0: loss = 4.89667 (* 1 = 4.89667 loss)
I0410 13:41:04.354692 18414 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0410 13:41:09.247287 18414 solver.cpp:218] Iteration 1596 (2.45277 iter/s, 4.89244s/12 iters), loss = 4.76169
I0410 13:41:09.247396 18414 solver.cpp:237]     Train net output #0: loss = 4.76169 (* 1 = 4.76169 loss)
I0410 13:41:09.247409 18414 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0410 13:41:14.152238 18414 solver.cpp:218] Iteration 1608 (2.44665 iter/s, 4.90467s/12 iters), loss = 4.84343
I0410 13:41:14.152297 18414 solver.cpp:237]     Train net output #0: loss = 4.84343 (* 1 = 4.84343 loss)
I0410 13:41:14.152309 18414 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0410 13:41:18.008484 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:41:19.085561 18414 solver.cpp:218] Iteration 1620 (2.43255 iter/s, 4.9331s/12 iters), loss = 4.79993
I0410 13:41:19.085604 18414 solver.cpp:237]     Train net output #0: loss = 4.79993 (* 1 = 4.79993 loss)
I0410 13:41:19.085613 18414 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0410 13:41:23.625901 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0410 13:41:23.963229 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0410 13:41:24.178838 18414 solver.cpp:330] Iteration 1632, Testing net (#0)
I0410 13:41:24.178867 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:41:28.003849 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:41:28.672793 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0226716
I0410 13:41:28.672839 18414 solver.cpp:397]     Test net output #1: loss = 4.87081 (* 1 = 4.87081 loss)
I0410 13:41:28.755868 18414 solver.cpp:218] Iteration 1632 (1.24096 iter/s, 9.66993s/12 iters), loss = 4.91069
I0410 13:41:28.755926 18414 solver.cpp:237]     Train net output #0: loss = 4.91069 (* 1 = 4.91069 loss)
I0410 13:41:28.755941 18414 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0410 13:41:32.987231 18414 solver.cpp:218] Iteration 1644 (2.8361 iter/s, 4.23116s/12 iters), loss = 4.92342
I0410 13:41:32.987277 18414 solver.cpp:237]     Train net output #0: loss = 4.92342 (* 1 = 4.92342 loss)
I0410 13:41:32.987290 18414 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0410 13:41:37.923141 18414 solver.cpp:218] Iteration 1656 (2.43127 iter/s, 4.93569s/12 iters), loss = 4.83072
I0410 13:41:37.923198 18414 solver.cpp:237]     Train net output #0: loss = 4.83072 (* 1 = 4.83072 loss)
I0410 13:41:37.923211 18414 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0410 13:41:42.810792 18414 solver.cpp:218] Iteration 1668 (2.45528 iter/s, 4.88742s/12 iters), loss = 4.66138
I0410 13:41:42.810951 18414 solver.cpp:237]     Train net output #0: loss = 4.66138 (* 1 = 4.66138 loss)
I0410 13:41:42.810966 18414 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0410 13:41:47.710206 18414 solver.cpp:218] Iteration 1680 (2.44943 iter/s, 4.89909s/12 iters), loss = 4.82562
I0410 13:41:47.710247 18414 solver.cpp:237]     Train net output #0: loss = 4.82562 (* 1 = 4.82562 loss)
I0410 13:41:47.710258 18414 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0410 13:41:52.622938 18414 solver.cpp:218] Iteration 1692 (2.44274 iter/s, 4.91251s/12 iters), loss = 4.83826
I0410 13:41:52.623004 18414 solver.cpp:237]     Train net output #0: loss = 4.83826 (* 1 = 4.83826 loss)
I0410 13:41:52.623023 18414 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0410 13:41:57.545172 18414 solver.cpp:218] Iteration 1704 (2.43803 iter/s, 4.922s/12 iters), loss = 4.61118
I0410 13:41:57.545220 18414 solver.cpp:237]     Train net output #0: loss = 4.61118 (* 1 = 4.61118 loss)
I0410 13:41:57.545230 18414 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0410 13:42:02.465366 18414 solver.cpp:218] Iteration 1716 (2.43904 iter/s, 4.91998s/12 iters), loss = 4.77231
I0410 13:42:02.465415 18414 solver.cpp:237]     Train net output #0: loss = 4.77231 (* 1 = 4.77231 loss)
I0410 13:42:02.465427 18414 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0410 13:42:03.495221 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:42:07.408921 18414 solver.cpp:218] Iteration 1728 (2.42751 iter/s, 4.94333s/12 iters), loss = 4.78694
I0410 13:42:07.408982 18414 solver.cpp:237]     Train net output #0: loss = 4.78694 (* 1 = 4.78694 loss)
I0410 13:42:07.408995 18414 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0410 13:42:09.394531 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0410 13:42:10.254133 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0410 13:42:10.553369 18414 solver.cpp:330] Iteration 1734, Testing net (#0)
I0410 13:42:10.553390 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:42:14.299103 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:42:15.003398 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0324755
I0410 13:42:15.003449 18414 solver.cpp:397]     Test net output #1: loss = 4.78457 (* 1 = 4.78457 loss)
I0410 13:42:16.882753 18414 solver.cpp:218] Iteration 1740 (1.2667 iter/s, 9.47345s/12 iters), loss = 4.7619
I0410 13:42:16.882812 18414 solver.cpp:237]     Train net output #0: loss = 4.7619 (* 1 = 4.7619 loss)
I0410 13:42:16.882824 18414 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0410 13:42:21.768186 18414 solver.cpp:218] Iteration 1752 (2.4564 iter/s, 4.88521s/12 iters), loss = 4.78071
I0410 13:42:21.768234 18414 solver.cpp:237]     Train net output #0: loss = 4.78071 (* 1 = 4.78071 loss)
I0410 13:42:21.768244 18414 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0410 13:42:26.859372 18414 solver.cpp:218] Iteration 1764 (2.35712 iter/s, 5.09096s/12 iters), loss = 4.71936
I0410 13:42:26.859416 18414 solver.cpp:237]     Train net output #0: loss = 4.71936 (* 1 = 4.71936 loss)
I0410 13:42:26.859426 18414 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0410 13:42:31.782546 18414 solver.cpp:218] Iteration 1776 (2.43756 iter/s, 4.92295s/12 iters), loss = 4.80977
I0410 13:42:31.782603 18414 solver.cpp:237]     Train net output #0: loss = 4.80977 (* 1 = 4.80977 loss)
I0410 13:42:31.782614 18414 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0410 13:42:36.699331 18414 solver.cpp:218] Iteration 1788 (2.44073 iter/s, 4.91656s/12 iters), loss = 4.74778
I0410 13:42:36.699379 18414 solver.cpp:237]     Train net output #0: loss = 4.74778 (* 1 = 4.74778 loss)
I0410 13:42:36.699391 18414 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0410 13:42:41.605751 18414 solver.cpp:218] Iteration 1800 (2.44589 iter/s, 4.9062s/12 iters), loss = 4.5991
I0410 13:42:41.605798 18414 solver.cpp:237]     Train net output #0: loss = 4.5991 (* 1 = 4.5991 loss)
I0410 13:42:41.605809 18414 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0410 13:42:46.550184 18414 solver.cpp:218] Iteration 1812 (2.42708 iter/s, 4.94421s/12 iters), loss = 4.78978
I0410 13:42:46.550308 18414 solver.cpp:237]     Train net output #0: loss = 4.78978 (* 1 = 4.78978 loss)
I0410 13:42:46.550318 18414 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0410 13:42:49.668670 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:42:51.453701 18414 solver.cpp:218] Iteration 1824 (2.44737 iter/s, 4.90322s/12 iters), loss = 4.60533
I0410 13:42:51.453760 18414 solver.cpp:237]     Train net output #0: loss = 4.60533 (* 1 = 4.60533 loss)
I0410 13:42:51.453773 18414 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0410 13:42:55.932952 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0410 13:42:56.244904 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0410 13:42:56.464938 18414 solver.cpp:330] Iteration 1836, Testing net (#0)
I0410 13:42:56.464956 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:43:00.234799 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:43:00.982280 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0373775
I0410 13:43:00.982324 18414 solver.cpp:397]     Test net output #1: loss = 4.63891 (* 1 = 4.63891 loss)
I0410 13:43:01.065310 18414 solver.cpp:218] Iteration 1836 (1.24854 iter/s, 9.61123s/12 iters), loss = 4.79966
I0410 13:43:01.065361 18414 solver.cpp:237]     Train net output #0: loss = 4.79966 (* 1 = 4.79966 loss)
I0410 13:43:01.065372 18414 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0410 13:43:05.138000 18414 solver.cpp:218] Iteration 1848 (2.9466 iter/s, 4.07249s/12 iters), loss = 4.77414
I0410 13:43:05.138052 18414 solver.cpp:237]     Train net output #0: loss = 4.77414 (* 1 = 4.77414 loss)
I0410 13:43:05.138065 18414 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0410 13:43:10.057704 18414 solver.cpp:218] Iteration 1860 (2.43928 iter/s, 4.91948s/12 iters), loss = 4.66096
I0410 13:43:10.057754 18414 solver.cpp:237]     Train net output #0: loss = 4.66096 (* 1 = 4.66096 loss)
I0410 13:43:10.057765 18414 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0410 13:43:15.008285 18414 solver.cpp:218] Iteration 1872 (2.42407 iter/s, 4.95036s/12 iters), loss = 4.64144
I0410 13:43:15.008330 18414 solver.cpp:237]     Train net output #0: loss = 4.64144 (* 1 = 4.64144 loss)
I0410 13:43:15.008342 18414 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0410 13:43:20.224874 18414 solver.cpp:218] Iteration 1884 (2.30046 iter/s, 5.21636s/12 iters), loss = 4.68019
I0410 13:43:20.224961 18414 solver.cpp:237]     Train net output #0: loss = 4.68019 (* 1 = 4.68019 loss)
I0410 13:43:20.224973 18414 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0410 13:43:25.203523 18414 solver.cpp:218] Iteration 1896 (2.41042 iter/s, 4.97839s/12 iters), loss = 4.67905
I0410 13:43:25.203579 18414 solver.cpp:237]     Train net output #0: loss = 4.67905 (* 1 = 4.67905 loss)
I0410 13:43:25.203591 18414 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0410 13:43:30.132406 18414 solver.cpp:218] Iteration 1908 (2.43474 iter/s, 4.92866s/12 iters), loss = 4.62178
I0410 13:43:30.132450 18414 solver.cpp:237]     Train net output #0: loss = 4.62178 (* 1 = 4.62178 loss)
I0410 13:43:30.132459 18414 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0410 13:43:35.060539 18414 solver.cpp:218] Iteration 1920 (2.43511 iter/s, 4.92791s/12 iters), loss = 4.70867
I0410 13:43:35.060591 18414 solver.cpp:237]     Train net output #0: loss = 4.70867 (* 1 = 4.70867 loss)
I0410 13:43:35.060601 18414 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0410 13:43:35.486928 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:43:40.111781 18414 solver.cpp:218] Iteration 1932 (2.37576 iter/s, 5.05101s/12 iters), loss = 4.61917
I0410 13:43:40.111833 18414 solver.cpp:237]     Train net output #0: loss = 4.61917 (* 1 = 4.61917 loss)
I0410 13:43:40.111845 18414 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0410 13:43:42.113250 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0410 13:43:42.438810 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0410 13:43:42.651198 18414 solver.cpp:330] Iteration 1938, Testing net (#0)
I0410 13:43:42.651217 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:43:46.248142 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:43:47.031141 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0496324
I0410 13:43:47.031177 18414 solver.cpp:397]     Test net output #1: loss = 4.52498 (* 1 = 4.52498 loss)
I0410 13:43:48.798861 18414 solver.cpp:218] Iteration 1944 (1.38142 iter/s, 8.68673s/12 iters), loss = 4.64515
I0410 13:43:48.798908 18414 solver.cpp:237]     Train net output #0: loss = 4.64515 (* 1 = 4.64515 loss)
I0410 13:43:48.798918 18414 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0410 13:43:53.707008 18414 solver.cpp:218] Iteration 1956 (2.44503 iter/s, 4.90792s/12 iters), loss = 4.5065
I0410 13:43:53.707159 18414 solver.cpp:237]     Train net output #0: loss = 4.5065 (* 1 = 4.5065 loss)
I0410 13:43:53.707171 18414 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0410 13:43:58.673869 18414 solver.cpp:218] Iteration 1968 (2.41617 iter/s, 4.96654s/12 iters), loss = 4.45062
I0410 13:43:58.673918 18414 solver.cpp:237]     Train net output #0: loss = 4.45062 (* 1 = 4.45062 loss)
I0410 13:43:58.673930 18414 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0410 13:44:03.538074 18414 solver.cpp:218] Iteration 1980 (2.46711 iter/s, 4.86399s/12 iters), loss = 4.5621
I0410 13:44:03.538127 18414 solver.cpp:237]     Train net output #0: loss = 4.5621 (* 1 = 4.5621 loss)
I0410 13:44:03.538139 18414 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0410 13:44:08.412128 18414 solver.cpp:218] Iteration 1992 (2.46213 iter/s, 4.87383s/12 iters), loss = 4.62087
I0410 13:44:08.412189 18414 solver.cpp:237]     Train net output #0: loss = 4.62087 (* 1 = 4.62087 loss)
I0410 13:44:08.412202 18414 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0410 13:44:13.508309 18414 solver.cpp:218] Iteration 2004 (2.35482 iter/s, 5.09594s/12 iters), loss = 4.47741
I0410 13:44:13.508363 18414 solver.cpp:237]     Train net output #0: loss = 4.47741 (* 1 = 4.47741 loss)
I0410 13:44:13.508375 18414 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0410 13:44:18.480022 18414 solver.cpp:218] Iteration 2016 (2.41377 iter/s, 4.97148s/12 iters), loss = 4.54577
I0410 13:44:18.480077 18414 solver.cpp:237]     Train net output #0: loss = 4.54577 (* 1 = 4.54577 loss)
I0410 13:44:18.480088 18414 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0410 13:44:21.103185 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:44:23.597563 18414 solver.cpp:218] Iteration 2028 (2.34498 iter/s, 5.1173s/12 iters), loss = 4.32547
I0410 13:44:23.597617 18414 solver.cpp:237]     Train net output #0: loss = 4.32547 (* 1 = 4.32547 loss)
I0410 13:44:23.597631 18414 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0410 13:44:28.030637 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0410 13:44:28.349472 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0410 13:44:28.561725 18414 solver.cpp:330] Iteration 2040, Testing net (#0)
I0410 13:44:28.561751 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:44:32.157577 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:44:32.983654 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0545343
I0410 13:44:32.983707 18414 solver.cpp:397]     Test net output #1: loss = 4.42935 (* 1 = 4.42935 loss)
I0410 13:44:33.066874 18414 solver.cpp:218] Iteration 2040 (1.2673 iter/s, 9.46894s/12 iters), loss = 4.37663
I0410 13:44:33.066928 18414 solver.cpp:237]     Train net output #0: loss = 4.37663 (* 1 = 4.37663 loss)
I0410 13:44:33.066941 18414 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0410 13:44:37.381688 18414 solver.cpp:218] Iteration 2052 (2.78125 iter/s, 4.31461s/12 iters), loss = 4.38097
I0410 13:44:37.381744 18414 solver.cpp:237]     Train net output #0: loss = 4.38097 (* 1 = 4.38097 loss)
I0410 13:44:37.381757 18414 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0410 13:44:37.382052 18414 blocking_queue.cpp:49] Waiting for data
I0410 13:44:42.272420 18414 solver.cpp:218] Iteration 2064 (2.45373 iter/s, 4.89051s/12 iters), loss = 4.52869
I0410 13:44:42.272464 18414 solver.cpp:237]     Train net output #0: loss = 4.52869 (* 1 = 4.52869 loss)
I0410 13:44:42.272475 18414 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0410 13:44:47.217397 18414 solver.cpp:218] Iteration 2076 (2.42681 iter/s, 4.94475s/12 iters), loss = 4.67386
I0410 13:44:47.217453 18414 solver.cpp:237]     Train net output #0: loss = 4.67386 (* 1 = 4.67386 loss)
I0410 13:44:47.217464 18414 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0410 13:44:52.225270 18414 solver.cpp:218] Iteration 2088 (2.39634 iter/s, 5.00764s/12 iters), loss = 4.42582
I0410 13:44:52.225320 18414 solver.cpp:237]     Train net output #0: loss = 4.42582 (* 1 = 4.42582 loss)
I0410 13:44:52.225332 18414 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0410 13:44:57.231570 18414 solver.cpp:218] Iteration 2100 (2.39709 iter/s, 5.00607s/12 iters), loss = 4.38782
I0410 13:44:57.231616 18414 solver.cpp:237]     Train net output #0: loss = 4.38782 (* 1 = 4.38782 loss)
I0410 13:44:57.231626 18414 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0410 13:45:02.159150 18414 solver.cpp:218] Iteration 2112 (2.43538 iter/s, 4.92736s/12 iters), loss = 4.41396
I0410 13:45:02.159265 18414 solver.cpp:237]     Train net output #0: loss = 4.41396 (* 1 = 4.41396 loss)
I0410 13:45:02.159279 18414 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0410 13:45:07.005097 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:45:07.434115 18414 solver.cpp:218] Iteration 2124 (2.27502 iter/s, 5.27467s/12 iters), loss = 4.2531
I0410 13:45:07.434168 18414 solver.cpp:237]     Train net output #0: loss = 4.2531 (* 1 = 4.2531 loss)
I0410 13:45:07.434180 18414 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0410 13:45:12.429741 18414 solver.cpp:218] Iteration 2136 (2.40221 iter/s, 4.9954s/12 iters), loss = 4.32118
I0410 13:45:12.429788 18414 solver.cpp:237]     Train net output #0: loss = 4.32118 (* 1 = 4.32118 loss)
I0410 13:45:12.429800 18414 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0410 13:45:14.480376 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0410 13:45:15.462234 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0410 13:45:16.503474 18414 solver.cpp:330] Iteration 2142, Testing net (#0)
I0410 13:45:16.503506 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:45:20.162389 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:45:21.036149 18414 solver.cpp:397]     Test net output #0: accuracy = 0.057598
I0410 13:45:21.036196 18414 solver.cpp:397]     Test net output #1: loss = 4.42432 (* 1 = 4.42432 loss)
I0410 13:45:22.951026 18414 solver.cpp:218] Iteration 2148 (1.14059 iter/s, 10.5209s/12 iters), loss = 4.3511
I0410 13:45:22.951078 18414 solver.cpp:237]     Train net output #0: loss = 4.3511 (* 1 = 4.3511 loss)
I0410 13:45:22.951089 18414 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0410 13:45:27.896766 18414 solver.cpp:218] Iteration 2160 (2.42644 iter/s, 4.94552s/12 iters), loss = 4.47949
I0410 13:45:27.896814 18414 solver.cpp:237]     Train net output #0: loss = 4.47949 (* 1 = 4.47949 loss)
I0410 13:45:27.896824 18414 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0410 13:45:32.867326 18414 solver.cpp:218] Iteration 2172 (2.41432 iter/s, 4.97034s/12 iters), loss = 4.32458
I0410 13:45:32.867513 18414 solver.cpp:237]     Train net output #0: loss = 4.32458 (* 1 = 4.32458 loss)
I0410 13:45:32.867528 18414 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0410 13:45:37.889499 18414 solver.cpp:218] Iteration 2184 (2.38957 iter/s, 5.02182s/12 iters), loss = 4.43832
I0410 13:45:37.889554 18414 solver.cpp:237]     Train net output #0: loss = 4.43832 (* 1 = 4.43832 loss)
I0410 13:45:37.889565 18414 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0410 13:45:42.897665 18414 solver.cpp:218] Iteration 2196 (2.3962 iter/s, 5.00794s/12 iters), loss = 4.51654
I0410 13:45:42.897722 18414 solver.cpp:237]     Train net output #0: loss = 4.51654 (* 1 = 4.51654 loss)
I0410 13:45:42.897735 18414 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0410 13:45:47.812731 18414 solver.cpp:218] Iteration 2208 (2.44159 iter/s, 4.91484s/12 iters), loss = 4.27511
I0410 13:45:47.812789 18414 solver.cpp:237]     Train net output #0: loss = 4.27511 (* 1 = 4.27511 loss)
I0410 13:45:47.812803 18414 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0410 13:45:52.777060 18414 solver.cpp:218] Iteration 2220 (2.41736 iter/s, 4.96409s/12 iters), loss = 4.43864
I0410 13:45:52.777110 18414 solver.cpp:237]     Train net output #0: loss = 4.43864 (* 1 = 4.43864 loss)
I0410 13:45:52.777120 18414 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0410 13:45:54.535348 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:45:57.663246 18414 solver.cpp:218] Iteration 2232 (2.45601 iter/s, 4.88596s/12 iters), loss = 4.37391
I0410 13:45:57.663293 18414 solver.cpp:237]     Train net output #0: loss = 4.37391 (* 1 = 4.37391 loss)
I0410 13:45:57.663302 18414 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0410 13:46:02.173698 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0410 13:46:02.473256 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0410 13:46:02.670337 18414 solver.cpp:330] Iteration 2244, Testing net (#0)
I0410 13:46:02.670357 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:46:06.203639 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:46:07.109249 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0643382
I0410 13:46:07.109300 18414 solver.cpp:397]     Test net output #1: loss = 4.26176 (* 1 = 4.26176 loss)
I0410 13:46:07.192387 18414 solver.cpp:218] Iteration 2244 (1.25934 iter/s, 9.52877s/12 iters), loss = 4.40179
I0410 13:46:07.192440 18414 solver.cpp:237]     Train net output #0: loss = 4.40179 (* 1 = 4.40179 loss)
I0410 13:46:07.192451 18414 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0410 13:46:11.318854 18414 solver.cpp:218] Iteration 2256 (2.9082 iter/s, 4.12627s/12 iters), loss = 4.16425
I0410 13:46:11.318900 18414 solver.cpp:237]     Train net output #0: loss = 4.16425 (* 1 = 4.16425 loss)
I0410 13:46:11.318909 18414 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0410 13:46:16.196516 18414 solver.cpp:218] Iteration 2268 (2.46031 iter/s, 4.87744s/12 iters), loss = 4.33489
I0410 13:46:16.196561 18414 solver.cpp:237]     Train net output #0: loss = 4.33489 (* 1 = 4.33489 loss)
I0410 13:46:16.196570 18414 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0410 13:46:21.154881 18414 solver.cpp:218] Iteration 2280 (2.42026 iter/s, 4.95814s/12 iters), loss = 4.36555
I0410 13:46:21.154925 18414 solver.cpp:237]     Train net output #0: loss = 4.36555 (* 1 = 4.36555 loss)
I0410 13:46:21.154934 18414 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0410 13:46:26.164386 18414 solver.cpp:218] Iteration 2292 (2.39555 iter/s, 5.00928s/12 iters), loss = 4.24164
I0410 13:46:26.164438 18414 solver.cpp:237]     Train net output #0: loss = 4.24164 (* 1 = 4.24164 loss)
I0410 13:46:26.164448 18414 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0410 13:46:31.249780 18414 solver.cpp:218] Iteration 2304 (2.35981 iter/s, 5.08517s/12 iters), loss = 4.40367
I0410 13:46:31.249828 18414 solver.cpp:237]     Train net output #0: loss = 4.40367 (* 1 = 4.40367 loss)
I0410 13:46:31.249838 18414 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0410 13:46:36.202759 18414 solver.cpp:218] Iteration 2316 (2.42289 iter/s, 4.95276s/12 iters), loss = 4.33381
I0410 13:46:36.202816 18414 solver.cpp:237]     Train net output #0: loss = 4.33381 (* 1 = 4.33381 loss)
I0410 13:46:36.202829 18414 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0410 13:46:40.108769 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:46:41.153715 18414 solver.cpp:218] Iteration 2328 (2.42389 iter/s, 4.95073s/12 iters), loss = 4.20526
I0410 13:46:41.153775 18414 solver.cpp:237]     Train net output #0: loss = 4.20526 (* 1 = 4.20526 loss)
I0410 13:46:41.153789 18414 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0410 13:46:46.125656 18414 solver.cpp:218] Iteration 2340 (2.41366 iter/s, 4.97171s/12 iters), loss = 4.09605
I0410 13:46:46.125708 18414 solver.cpp:237]     Train net output #0: loss = 4.09605 (* 1 = 4.09605 loss)
I0410 13:46:46.125720 18414 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0410 13:46:48.164641 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0410 13:46:48.456393 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0410 13:46:48.662020 18414 solver.cpp:330] Iteration 2346, Testing net (#0)
I0410 13:46:48.662043 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:46:52.159699 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:46:53.099689 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0827206
I0410 13:46:53.099740 18414 solver.cpp:397]     Test net output #1: loss = 4.12775 (* 1 = 4.12775 loss)
I0410 13:46:55.068559 18414 solver.cpp:218] Iteration 2352 (1.3419 iter/s, 8.94255s/12 iters), loss = 4.17091
I0410 13:46:55.068608 18414 solver.cpp:237]     Train net output #0: loss = 4.17091 (* 1 = 4.17091 loss)
I0410 13:46:55.068619 18414 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0410 13:47:00.101277 18414 solver.cpp:218] Iteration 2364 (2.38451 iter/s, 5.03249s/12 iters), loss = 4.17065
I0410 13:47:00.101330 18414 solver.cpp:237]     Train net output #0: loss = 4.17065 (* 1 = 4.17065 loss)
I0410 13:47:00.101341 18414 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0410 13:47:05.114555 18414 solver.cpp:218] Iteration 2376 (2.39375 iter/s, 5.01305s/12 iters), loss = 4.12378
I0410 13:47:05.114606 18414 solver.cpp:237]     Train net output #0: loss = 4.12378 (* 1 = 4.12378 loss)
I0410 13:47:05.114620 18414 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0410 13:47:10.107283 18414 solver.cpp:218] Iteration 2388 (2.40361 iter/s, 4.9925s/12 iters), loss = 4.17782
I0410 13:47:10.107331 18414 solver.cpp:237]     Train net output #0: loss = 4.17782 (* 1 = 4.17782 loss)
I0410 13:47:10.107340 18414 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0410 13:47:15.085644 18414 solver.cpp:218] Iteration 2400 (2.41054 iter/s, 4.97813s/12 iters), loss = 4.14265
I0410 13:47:15.085774 18414 solver.cpp:237]     Train net output #0: loss = 4.14265 (* 1 = 4.14265 loss)
I0410 13:47:15.085789 18414 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0410 13:47:19.941720 18414 solver.cpp:218] Iteration 2412 (2.47128 iter/s, 4.85578s/12 iters), loss = 3.9827
I0410 13:47:19.941761 18414 solver.cpp:237]     Train net output #0: loss = 3.9827 (* 1 = 3.9827 loss)
I0410 13:47:19.941771 18414 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0410 13:47:24.905184 18414 solver.cpp:218] Iteration 2424 (2.41777 iter/s, 4.96325s/12 iters), loss = 4.23445
I0410 13:47:24.905233 18414 solver.cpp:237]     Train net output #0: loss = 4.23445 (* 1 = 4.23445 loss)
I0410 13:47:24.905246 18414 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0410 13:47:25.940922 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:47:29.914173 18414 solver.cpp:218] Iteration 2436 (2.3958 iter/s, 5.00876s/12 iters), loss = 3.963
I0410 13:47:29.914232 18414 solver.cpp:237]     Train net output #0: loss = 3.963 (* 1 = 3.963 loss)
I0410 13:47:29.914244 18414 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0410 13:47:34.603446 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0410 13:47:35.163326 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0410 13:47:35.377991 18414 solver.cpp:330] Iteration 2448, Testing net (#0)
I0410 13:47:35.378028 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:47:38.784369 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:47:39.809201 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0863971
I0410 13:47:39.809253 18414 solver.cpp:397]     Test net output #1: loss = 4.02927 (* 1 = 4.02927 loss)
I0410 13:47:39.892274 18414 solver.cpp:218] Iteration 2448 (1.20268 iter/s, 9.97771s/12 iters), loss = 3.98828
I0410 13:47:39.892323 18414 solver.cpp:237]     Train net output #0: loss = 3.98828 (* 1 = 3.98828 loss)
I0410 13:47:39.892333 18414 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0410 13:47:44.013083 18414 solver.cpp:218] Iteration 2460 (2.91219 iter/s, 4.12061s/12 iters), loss = 4.13796
I0410 13:47:44.013131 18414 solver.cpp:237]     Train net output #0: loss = 4.13796 (* 1 = 4.13796 loss)
I0410 13:47:44.013144 18414 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0410 13:47:48.871409 18414 solver.cpp:218] Iteration 2472 (2.4701 iter/s, 4.8581s/12 iters), loss = 4.05721
I0410 13:47:48.871553 18414 solver.cpp:237]     Train net output #0: loss = 4.05721 (* 1 = 4.05721 loss)
I0410 13:47:48.871565 18414 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0410 13:47:53.833326 18414 solver.cpp:218] Iteration 2484 (2.41857 iter/s, 4.9616s/12 iters), loss = 4.14055
I0410 13:47:53.833366 18414 solver.cpp:237]     Train net output #0: loss = 4.14055 (* 1 = 4.14055 loss)
I0410 13:47:53.833375 18414 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0410 13:47:58.803802 18414 solver.cpp:218] Iteration 2496 (2.41436 iter/s, 4.97026s/12 iters), loss = 4.23468
I0410 13:47:58.803855 18414 solver.cpp:237]     Train net output #0: loss = 4.23468 (* 1 = 4.23468 loss)
I0410 13:47:58.803866 18414 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0410 13:48:03.754518 18414 solver.cpp:218] Iteration 2508 (2.424 iter/s, 4.95049s/12 iters), loss = 4.15612
I0410 13:48:03.754575 18414 solver.cpp:237]     Train net output #0: loss = 4.15612 (* 1 = 4.15612 loss)
I0410 13:48:03.754590 18414 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0410 13:48:08.726279 18414 solver.cpp:218] Iteration 2520 (2.41374 iter/s, 4.97153s/12 iters), loss = 4.20219
I0410 13:48:08.726326 18414 solver.cpp:237]     Train net output #0: loss = 4.20219 (* 1 = 4.20219 loss)
I0410 13:48:08.726336 18414 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0410 13:48:11.860965 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:48:13.585193 18414 solver.cpp:218] Iteration 2532 (2.4698 iter/s, 4.85869s/12 iters), loss = 4.10859
I0410 13:48:13.585249 18414 solver.cpp:237]     Train net output #0: loss = 4.10859 (* 1 = 4.10859 loss)
I0410 13:48:13.585263 18414 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0410 13:48:18.490806 18414 solver.cpp:218] Iteration 2544 (2.44629 iter/s, 4.90539s/12 iters), loss = 3.9133
I0410 13:48:18.490847 18414 solver.cpp:237]     Train net output #0: loss = 3.9133 (* 1 = 3.9133 loss)
I0410 13:48:18.490856 18414 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0410 13:48:20.506240 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0410 13:48:21.324074 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0410 13:48:21.526445 18414 solver.cpp:330] Iteration 2550, Testing net (#0)
I0410 13:48:21.526464 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:48:24.938338 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:48:25.968605 18414 solver.cpp:397]     Test net output #0: accuracy = 0.0949755
I0410 13:48:25.968664 18414 solver.cpp:397]     Test net output #1: loss = 3.97405 (* 1 = 3.97405 loss)
I0410 13:48:27.928553 18414 solver.cpp:218] Iteration 2556 (1.27154 iter/s, 9.43739s/12 iters), loss = 4.10569
I0410 13:48:27.928601 18414 solver.cpp:237]     Train net output #0: loss = 4.10569 (* 1 = 4.10569 loss)
I0410 13:48:27.928611 18414 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0410 13:48:32.883217 18414 solver.cpp:218] Iteration 2568 (2.42207 iter/s, 4.95444s/12 iters), loss = 3.96756
I0410 13:48:32.883267 18414 solver.cpp:237]     Train net output #0: loss = 3.96756 (* 1 = 3.96756 loss)
I0410 13:48:32.883276 18414 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0410 13:48:37.810613 18414 solver.cpp:218] Iteration 2580 (2.43548 iter/s, 4.92717s/12 iters), loss = 3.9357
I0410 13:48:37.810664 18414 solver.cpp:237]     Train net output #0: loss = 3.9357 (* 1 = 3.9357 loss)
I0410 13:48:37.810678 18414 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0410 13:48:42.691910 18414 solver.cpp:218] Iteration 2592 (2.45848 iter/s, 4.88107s/12 iters), loss = 4.15716
I0410 13:48:42.691970 18414 solver.cpp:237]     Train net output #0: loss = 4.15716 (* 1 = 4.15716 loss)
I0410 13:48:42.691982 18414 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0410 13:48:47.614104 18414 solver.cpp:218] Iteration 2604 (2.43805 iter/s, 4.92196s/12 iters), loss = 4.04234
I0410 13:48:47.614161 18414 solver.cpp:237]     Train net output #0: loss = 4.04234 (* 1 = 4.04234 loss)
I0410 13:48:47.614173 18414 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0410 13:48:52.855720 18414 solver.cpp:218] Iteration 2616 (2.28948 iter/s, 5.24137s/12 iters), loss = 4.04193
I0410 13:48:52.855826 18414 solver.cpp:237]     Train net output #0: loss = 4.04193 (* 1 = 4.04193 loss)
I0410 13:48:52.855837 18414 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0410 13:48:57.791118 18414 solver.cpp:218] Iteration 2628 (2.43155 iter/s, 4.93512s/12 iters), loss = 4.17375
I0410 13:48:57.791172 18414 solver.cpp:237]     Train net output #0: loss = 4.17375 (* 1 = 4.17375 loss)
I0410 13:48:57.791184 18414 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0410 13:48:58.217216 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:49:02.785001 18414 solver.cpp:218] Iteration 2640 (2.40305 iter/s, 4.99366s/12 iters), loss = 3.93077
I0410 13:49:02.785053 18414 solver.cpp:237]     Train net output #0: loss = 3.93077 (* 1 = 3.93077 loss)
I0410 13:49:02.785064 18414 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0410 13:49:07.333087 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0410 13:49:07.663471 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0410 13:49:07.875030 18414 solver.cpp:330] Iteration 2652, Testing net (#0)
I0410 13:49:07.875052 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:49:11.378588 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:49:12.440490 18414 solver.cpp:397]     Test net output #0: accuracy = 0.11152
I0410 13:49:12.440537 18414 solver.cpp:397]     Test net output #1: loss = 3.81984 (* 1 = 3.81984 loss)
I0410 13:49:12.523479 18414 solver.cpp:218] Iteration 2652 (1.23227 iter/s, 9.7381s/12 iters), loss = 3.79404
I0410 13:49:12.523531 18414 solver.cpp:237]     Train net output #0: loss = 3.79404 (* 1 = 3.79404 loss)
I0410 13:49:12.523542 18414 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0410 13:49:16.828434 18414 solver.cpp:218] Iteration 2664 (2.78762 iter/s, 4.30475s/12 iters), loss = 3.87898
I0410 13:49:16.828483 18414 solver.cpp:237]     Train net output #0: loss = 3.87898 (* 1 = 3.87898 loss)
I0410 13:49:16.828495 18414 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0410 13:49:21.773969 18414 solver.cpp:218] Iteration 2676 (2.42655 iter/s, 4.9453s/12 iters), loss = 3.94354
I0410 13:49:21.774016 18414 solver.cpp:237]     Train net output #0: loss = 3.94354 (* 1 = 3.94354 loss)
I0410 13:49:21.774026 18414 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0410 13:49:27.082185 18414 solver.cpp:218] Iteration 2688 (2.26075 iter/s, 5.30798s/12 iters), loss = 3.9881
I0410 13:49:27.082345 18414 solver.cpp:237]     Train net output #0: loss = 3.9881 (* 1 = 3.9881 loss)
I0410 13:49:27.082358 18414 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0410 13:49:32.049407 18414 solver.cpp:218] Iteration 2700 (2.416 iter/s, 4.96689s/12 iters), loss = 3.8019
I0410 13:49:32.049463 18414 solver.cpp:237]     Train net output #0: loss = 3.8019 (* 1 = 3.8019 loss)
I0410 13:49:32.049475 18414 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0410 13:49:37.004009 18414 solver.cpp:218] Iteration 2712 (2.4221 iter/s, 4.95437s/12 iters), loss = 3.86684
I0410 13:49:37.004050 18414 solver.cpp:237]     Train net output #0: loss = 3.86684 (* 1 = 3.86684 loss)
I0410 13:49:37.004058 18414 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0410 13:49:41.874984 18414 solver.cpp:218] Iteration 2724 (2.46368 iter/s, 4.87076s/12 iters), loss = 4.03465
I0410 13:49:41.875032 18414 solver.cpp:237]     Train net output #0: loss = 4.03465 (* 1 = 4.03465 loss)
I0410 13:49:41.875041 18414 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0410 13:49:44.416754 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:49:46.799170 18414 solver.cpp:218] Iteration 2736 (2.43706 iter/s, 4.92396s/12 iters), loss = 3.73403
I0410 13:49:46.799219 18414 solver.cpp:237]     Train net output #0: loss = 3.73403 (* 1 = 3.73403 loss)
I0410 13:49:46.799232 18414 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0410 13:49:51.909889 18414 solver.cpp:218] Iteration 2748 (2.34811 iter/s, 5.11049s/12 iters), loss = 3.88427
I0410 13:49:51.909943 18414 solver.cpp:237]     Train net output #0: loss = 3.88427 (* 1 = 3.88427 loss)
I0410 13:49:51.909971 18414 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0410 13:49:54.110026 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0410 13:49:54.401068 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0410 13:49:54.597044 18414 solver.cpp:330] Iteration 2754, Testing net (#0)
I0410 13:49:54.597070 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:49:57.503176 18414 blocking_queue.cpp:49] Waiting for data
I0410 13:49:58.144167 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:49:59.321875 18414 solver.cpp:397]     Test net output #0: accuracy = 0.115196
I0410 13:49:59.321904 18414 solver.cpp:397]     Test net output #1: loss = 3.78614 (* 1 = 3.78614 loss)
I0410 13:50:01.644927 18414 solver.cpp:218] Iteration 2760 (1.23271 iter/s, 9.73466s/12 iters), loss = 3.77909
I0410 13:50:01.644975 18414 solver.cpp:237]     Train net output #0: loss = 3.77909 (* 1 = 3.77909 loss)
I0410 13:50:01.644984 18414 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0410 13:50:07.340348 18414 solver.cpp:218] Iteration 2772 (2.10705 iter/s, 5.69517s/12 iters), loss = 3.70015
I0410 13:50:07.340404 18414 solver.cpp:237]     Train net output #0: loss = 3.70015 (* 1 = 3.70015 loss)
I0410 13:50:07.340416 18414 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0410 13:50:12.330922 18414 solver.cpp:218] Iteration 2784 (2.40464 iter/s, 4.99034s/12 iters), loss = 3.91902
I0410 13:50:12.330983 18414 solver.cpp:237]     Train net output #0: loss = 3.91902 (* 1 = 3.91902 loss)
I0410 13:50:12.330996 18414 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0410 13:50:17.314252 18414 solver.cpp:218] Iteration 2796 (2.40814 iter/s, 4.98309s/12 iters), loss = 3.64917
I0410 13:50:17.314314 18414 solver.cpp:237]     Train net output #0: loss = 3.64917 (* 1 = 3.64917 loss)
I0410 13:50:17.314327 18414 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0410 13:50:22.293895 18414 solver.cpp:218] Iteration 2808 (2.40992 iter/s, 4.97941s/12 iters), loss = 3.63892
I0410 13:50:22.293946 18414 solver.cpp:237]     Train net output #0: loss = 3.63892 (* 1 = 3.63892 loss)
I0410 13:50:22.293974 18414 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0410 13:50:27.337122 18414 solver.cpp:218] Iteration 2820 (2.37954 iter/s, 5.04299s/12 iters), loss = 3.73945
I0410 13:50:27.337183 18414 solver.cpp:237]     Train net output #0: loss = 3.73945 (* 1 = 3.73945 loss)
I0410 13:50:27.337196 18414 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0410 13:50:32.026021 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:50:32.307150 18414 solver.cpp:218] Iteration 2832 (2.41459 iter/s, 4.96979s/12 iters), loss = 3.47928
I0410 13:50:32.307206 18414 solver.cpp:237]     Train net output #0: loss = 3.47928 (* 1 = 3.47928 loss)
I0410 13:50:32.307227 18414 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0410 13:50:37.189402 18414 solver.cpp:218] Iteration 2844 (2.458 iter/s, 4.88202s/12 iters), loss = 3.81042
I0410 13:50:37.189472 18414 solver.cpp:237]     Train net output #0: loss = 3.81042 (* 1 = 3.81042 loss)
I0410 13:50:37.189487 18414 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0410 13:50:41.593636 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0410 13:50:41.888159 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0410 13:50:42.082072 18414 solver.cpp:330] Iteration 2856, Testing net (#0)
I0410 13:50:42.082091 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:50:45.402696 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:50:46.688247 18414 solver.cpp:397]     Test net output #0: accuracy = 0.110907
I0410 13:50:46.688298 18414 solver.cpp:397]     Test net output #1: loss = 3.71302 (* 1 = 3.71302 loss)
I0410 13:50:46.774282 18414 solver.cpp:218] Iteration 2856 (1.25201 iter/s, 9.5846s/12 iters), loss = 3.59756
I0410 13:50:46.774322 18414 solver.cpp:237]     Train net output #0: loss = 3.59756 (* 1 = 3.59756 loss)
I0410 13:50:46.774333 18414 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0410 13:50:50.906733 18414 solver.cpp:218] Iteration 2868 (2.90395 iter/s, 4.1323s/12 iters), loss = 3.96012
I0410 13:50:50.906790 18414 solver.cpp:237]     Train net output #0: loss = 3.96012 (* 1 = 3.96012 loss)
I0410 13:50:50.906803 18414 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0410 13:50:55.825377 18414 solver.cpp:218] Iteration 2880 (2.43979 iter/s, 4.91846s/12 iters), loss = 3.78693
I0410 13:50:55.825443 18414 solver.cpp:237]     Train net output #0: loss = 3.78693 (* 1 = 3.78693 loss)
I0410 13:50:55.825459 18414 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0410 13:51:00.775544 18414 solver.cpp:218] Iteration 2892 (2.42425 iter/s, 4.94998s/12 iters), loss = 3.67015
I0410 13:51:00.775604 18414 solver.cpp:237]     Train net output #0: loss = 3.67015 (* 1 = 3.67015 loss)
I0410 13:51:00.775617 18414 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0410 13:51:05.678243 18414 solver.cpp:218] Iteration 2904 (2.44772 iter/s, 4.90252s/12 iters), loss = 3.69001
I0410 13:51:05.678319 18414 solver.cpp:237]     Train net output #0: loss = 3.69001 (* 1 = 3.69001 loss)
I0410 13:51:05.678329 18414 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0410 13:51:10.566437 18414 solver.cpp:218] Iteration 2916 (2.455 iter/s, 4.88799s/12 iters), loss = 3.71568
I0410 13:51:10.566494 18414 solver.cpp:237]     Train net output #0: loss = 3.71568 (* 1 = 3.71568 loss)
I0410 13:51:10.566506 18414 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0410 13:51:15.436333 18414 solver.cpp:218] Iteration 2928 (2.46421 iter/s, 4.86971s/12 iters), loss = 3.76573
I0410 13:51:15.436383 18414 solver.cpp:237]     Train net output #0: loss = 3.76573 (* 1 = 3.76573 loss)
I0410 13:51:15.436394 18414 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0410 13:51:17.263586 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:51:20.352831 18414 solver.cpp:218] Iteration 2940 (2.44085 iter/s, 4.91632s/12 iters), loss = 3.54438
I0410 13:51:20.352888 18414 solver.cpp:237]     Train net output #0: loss = 3.54438 (* 1 = 3.54438 loss)
I0410 13:51:20.352900 18414 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0410 13:51:25.314632 18414 solver.cpp:218] Iteration 2952 (2.41857 iter/s, 4.96161s/12 iters), loss = 3.69539
I0410 13:51:25.314679 18414 solver.cpp:237]     Train net output #0: loss = 3.69539 (* 1 = 3.69539 loss)
I0410 13:51:25.314688 18414 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0410 13:51:27.299798 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0410 13:51:27.596752 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0410 13:51:27.807029 18414 solver.cpp:330] Iteration 2958, Testing net (#0)
I0410 13:51:27.807050 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:51:31.070546 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:51:32.249120 18414 solver.cpp:397]     Test net output #0: accuracy = 0.171569
I0410 13:51:32.249174 18414 solver.cpp:397]     Test net output #1: loss = 3.47051 (* 1 = 3.47051 loss)
I0410 13:51:33.950291 18414 solver.cpp:218] Iteration 2964 (1.38963 iter/s, 8.6354s/12 iters), loss = 3.32671
I0410 13:51:33.950342 18414 solver.cpp:237]     Train net output #0: loss = 3.32671 (* 1 = 3.32671 loss)
I0410 13:51:33.950354 18414 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0410 13:51:38.821691 18414 solver.cpp:218] Iteration 2976 (2.46345 iter/s, 4.87122s/12 iters), loss = 3.63552
I0410 13:51:38.821848 18414 solver.cpp:237]     Train net output #0: loss = 3.63552 (* 1 = 3.63552 loss)
I0410 13:51:38.821861 18414 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0410 13:51:43.743206 18414 solver.cpp:218] Iteration 2988 (2.43841 iter/s, 4.92124s/12 iters), loss = 3.52068
I0410 13:51:43.743257 18414 solver.cpp:237]     Train net output #0: loss = 3.52068 (* 1 = 3.52068 loss)
I0410 13:51:43.743268 18414 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0410 13:51:48.692811 18414 solver.cpp:218] Iteration 3000 (2.42452 iter/s, 4.94942s/12 iters), loss = 3.77422
I0410 13:51:48.692867 18414 solver.cpp:237]     Train net output #0: loss = 3.77422 (* 1 = 3.77422 loss)
I0410 13:51:48.692880 18414 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0410 13:51:53.586128 18414 solver.cpp:218] Iteration 3012 (2.45242 iter/s, 4.89314s/12 iters), loss = 3.70725
I0410 13:51:53.586176 18414 solver.cpp:237]     Train net output #0: loss = 3.70725 (* 1 = 3.70725 loss)
I0410 13:51:53.586185 18414 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0410 13:51:58.669782 18414 solver.cpp:218] Iteration 3024 (2.36059 iter/s, 5.08347s/12 iters), loss = 3.48861
I0410 13:51:58.669842 18414 solver.cpp:237]     Train net output #0: loss = 3.48861 (* 1 = 3.48861 loss)
I0410 13:51:58.669855 18414 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0410 13:52:02.708534 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:52:03.712018 18414 solver.cpp:218] Iteration 3036 (2.37999 iter/s, 5.04204s/12 iters), loss = 3.56953
I0410 13:52:03.712074 18414 solver.cpp:237]     Train net output #0: loss = 3.56953 (* 1 = 3.56953 loss)
I0410 13:52:03.712088 18414 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0410 13:52:08.602952 18414 solver.cpp:218] Iteration 3048 (2.45361 iter/s, 4.89075s/12 iters), loss = 3.65539
I0410 13:52:08.603008 18414 solver.cpp:237]     Train net output #0: loss = 3.65539 (* 1 = 3.65539 loss)
I0410 13:52:08.603020 18414 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0410 13:52:13.026718 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0410 13:52:13.335188 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0410 13:52:13.545740 18414 solver.cpp:330] Iteration 3060, Testing net (#0)
I0410 13:52:13.545763 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:52:16.786015 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:52:17.998577 18414 solver.cpp:397]     Test net output #0: accuracy = 0.146446
I0410 13:52:17.998615 18414 solver.cpp:397]     Test net output #1: loss = 3.51531 (* 1 = 3.51531 loss)
I0410 13:52:18.081508 18414 solver.cpp:218] Iteration 3060 (1.26605 iter/s, 9.47826s/12 iters), loss = 3.64994
I0410 13:52:18.081547 18414 solver.cpp:237]     Train net output #0: loss = 3.64994 (* 1 = 3.64994 loss)
I0410 13:52:18.081555 18414 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0410 13:52:22.235957 18414 solver.cpp:218] Iteration 3072 (2.88857 iter/s, 4.1543s/12 iters), loss = 3.33306
I0410 13:52:22.235998 18414 solver.cpp:237]     Train net output #0: loss = 3.33306 (* 1 = 3.33306 loss)
I0410 13:52:22.236006 18414 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0410 13:52:27.193996 18414 solver.cpp:218] Iteration 3084 (2.4204 iter/s, 4.95786s/12 iters), loss = 3.60152
I0410 13:52:27.194058 18414 solver.cpp:237]     Train net output #0: loss = 3.60152 (* 1 = 3.60152 loss)
I0410 13:52:27.194072 18414 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0410 13:52:32.099275 18414 solver.cpp:218] Iteration 3096 (2.44644 iter/s, 4.90509s/12 iters), loss = 3.59683
I0410 13:52:32.099330 18414 solver.cpp:237]     Train net output #0: loss = 3.59683 (* 1 = 3.59683 loss)
I0410 13:52:32.099342 18414 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0410 13:52:37.028882 18414 solver.cpp:218] Iteration 3108 (2.43437 iter/s, 4.92942s/12 iters), loss = 3.38059
I0410 13:52:37.028942 18414 solver.cpp:237]     Train net output #0: loss = 3.38059 (* 1 = 3.38059 loss)
I0410 13:52:37.028954 18414 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0410 13:52:41.871585 18414 solver.cpp:218] Iteration 3120 (2.47805 iter/s, 4.84251s/12 iters), loss = 3.21714
I0410 13:52:41.871644 18414 solver.cpp:237]     Train net output #0: loss = 3.21714 (* 1 = 3.21714 loss)
I0410 13:52:41.871655 18414 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0410 13:52:46.746475 18414 solver.cpp:218] Iteration 3132 (2.46169 iter/s, 4.8747s/12 iters), loss = 3.69685
I0410 13:52:46.746605 18414 solver.cpp:237]     Train net output #0: loss = 3.69685 (* 1 = 3.69685 loss)
I0410 13:52:46.746618 18414 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0410 13:52:47.827234 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:52:51.636044 18414 solver.cpp:218] Iteration 3144 (2.45434 iter/s, 4.8893s/12 iters), loss = 3.42414
I0410 13:52:51.636099 18414 solver.cpp:237]     Train net output #0: loss = 3.42414 (* 1 = 3.42414 loss)
I0410 13:52:51.636111 18414 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0410 13:52:56.492864 18414 solver.cpp:218] Iteration 3156 (2.47085 iter/s, 4.85663s/12 iters), loss = 3.30308
I0410 13:52:56.492929 18414 solver.cpp:237]     Train net output #0: loss = 3.30308 (* 1 = 3.30308 loss)
I0410 13:52:56.492942 18414 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0410 13:52:58.449638 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0410 13:52:58.771698 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0410 13:52:58.966497 18414 solver.cpp:330] Iteration 3162, Testing net (#0)
I0410 13:52:58.966521 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:53:02.217779 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:53:03.478456 18414 solver.cpp:397]     Test net output #0: accuracy = 0.17402
I0410 13:53:03.478507 18414 solver.cpp:397]     Test net output #1: loss = 3.37063 (* 1 = 3.37063 loss)
I0410 13:53:05.266052 18414 solver.cpp:218] Iteration 3168 (1.36785 iter/s, 8.77289s/12 iters), loss = 3.27858
I0410 13:53:05.266117 18414 solver.cpp:237]     Train net output #0: loss = 3.27858 (* 1 = 3.27858 loss)
I0410 13:53:05.266129 18414 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0410 13:53:10.185015 18414 solver.cpp:218] Iteration 3180 (2.43964 iter/s, 4.91876s/12 iters), loss = 3.37539
I0410 13:53:10.185072 18414 solver.cpp:237]     Train net output #0: loss = 3.37539 (* 1 = 3.37539 loss)
I0410 13:53:10.185086 18414 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0410 13:53:15.113373 18414 solver.cpp:218] Iteration 3192 (2.43499 iter/s, 4.92816s/12 iters), loss = 3.44837
I0410 13:53:15.113431 18414 solver.cpp:237]     Train net output #0: loss = 3.44837 (* 1 = 3.44837 loss)
I0410 13:53:15.113446 18414 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0410 13:53:20.022343 18414 solver.cpp:218] Iteration 3204 (2.4446 iter/s, 4.90878s/12 iters), loss = 3.67105
I0410 13:53:20.022505 18414 solver.cpp:237]     Train net output #0: loss = 3.67105 (* 1 = 3.67105 loss)
I0410 13:53:20.022518 18414 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0410 13:53:24.906257 18414 solver.cpp:218] Iteration 3216 (2.45719 iter/s, 4.88362s/12 iters), loss = 3.55829
I0410 13:53:24.906312 18414 solver.cpp:237]     Train net output #0: loss = 3.55829 (* 1 = 3.55829 loss)
I0410 13:53:24.906324 18414 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0410 13:53:29.871451 18414 solver.cpp:218] Iteration 3228 (2.41692 iter/s, 4.965s/12 iters), loss = 3.40999
I0410 13:53:29.871505 18414 solver.cpp:237]     Train net output #0: loss = 3.40999 (* 1 = 3.40999 loss)
I0410 13:53:29.871518 18414 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0410 13:53:33.086696 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:53:34.783105 18414 solver.cpp:218] Iteration 3240 (2.44326 iter/s, 4.91147s/12 iters), loss = 3.70686
I0410 13:53:34.783149 18414 solver.cpp:237]     Train net output #0: loss = 3.70686 (* 1 = 3.70686 loss)
I0410 13:53:34.783159 18414 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0410 13:53:39.706470 18414 solver.cpp:218] Iteration 3252 (2.43745 iter/s, 4.92318s/12 iters), loss = 3.43318
I0410 13:53:39.706524 18414 solver.cpp:237]     Train net output #0: loss = 3.43318 (* 1 = 3.43318 loss)
I0410 13:53:39.706535 18414 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0410 13:53:44.154359 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0410 13:53:44.475097 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0410 13:53:44.899706 18414 solver.cpp:330] Iteration 3264, Testing net (#0)
I0410 13:53:44.899739 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:53:47.982921 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:53:49.280207 18414 solver.cpp:397]     Test net output #0: accuracy = 0.20098
I0410 13:53:49.280249 18414 solver.cpp:397]     Test net output #1: loss = 3.23878 (* 1 = 3.23878 loss)
I0410 13:53:49.363109 18414 solver.cpp:218] Iteration 3264 (1.24271 iter/s, 9.65633s/12 iters), loss = 3.41443
I0410 13:53:49.363159 18414 solver.cpp:237]     Train net output #0: loss = 3.41443 (* 1 = 3.41443 loss)
I0410 13:53:49.363171 18414 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0410 13:53:53.587370 18414 solver.cpp:218] Iteration 3276 (2.84085 iter/s, 4.22409s/12 iters), loss = 3.29945
I0410 13:53:53.587460 18414 solver.cpp:237]     Train net output #0: loss = 3.29945 (* 1 = 3.29945 loss)
I0410 13:53:53.587473 18414 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0410 13:53:58.598001 18414 solver.cpp:218] Iteration 3288 (2.39502 iter/s, 5.01041s/12 iters), loss = 3.46563
I0410 13:53:58.598063 18414 solver.cpp:237]     Train net output #0: loss = 3.46563 (* 1 = 3.46563 loss)
I0410 13:53:58.598076 18414 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0410 13:54:03.555496 18414 solver.cpp:218] Iteration 3300 (2.42067 iter/s, 4.95731s/12 iters), loss = 3.60111
I0410 13:54:03.555555 18414 solver.cpp:237]     Train net output #0: loss = 3.60111 (* 1 = 3.60111 loss)
I0410 13:54:03.555568 18414 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0410 13:54:08.515661 18414 solver.cpp:218] Iteration 3312 (2.41937 iter/s, 4.95997s/12 iters), loss = 3.40017
I0410 13:54:08.515707 18414 solver.cpp:237]     Train net output #0: loss = 3.40017 (* 1 = 3.40017 loss)
I0410 13:54:08.515718 18414 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0410 13:54:13.529778 18414 solver.cpp:218] Iteration 3324 (2.39333 iter/s, 5.01393s/12 iters), loss = 3.21156
I0410 13:54:13.529829 18414 solver.cpp:237]     Train net output #0: loss = 3.21156 (* 1 = 3.21156 loss)
I0410 13:54:13.529840 18414 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0410 13:54:18.490012 18414 solver.cpp:218] Iteration 3336 (2.41933 iter/s, 4.96004s/12 iters), loss = 3.28743
I0410 13:54:18.490065 18414 solver.cpp:237]     Train net output #0: loss = 3.28743 (* 1 = 3.28743 loss)
I0410 13:54:18.490080 18414 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0410 13:54:19.000916 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:54:23.835016 18414 solver.cpp:218] Iteration 3348 (2.24517 iter/s, 5.3448s/12 iters), loss = 3.04482
I0410 13:54:23.835178 18414 solver.cpp:237]     Train net output #0: loss = 3.04482 (* 1 = 3.04482 loss)
I0410 13:54:23.835193 18414 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0410 13:54:28.814024 18414 solver.cpp:218] Iteration 3360 (2.41026 iter/s, 4.97871s/12 iters), loss = 3.2114
I0410 13:54:28.814071 18414 solver.cpp:237]     Train net output #0: loss = 3.2114 (* 1 = 3.2114 loss)
I0410 13:54:28.814080 18414 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0410 13:54:30.819401 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0410 13:54:31.098374 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0410 13:54:31.292048 18414 solver.cpp:330] Iteration 3366, Testing net (#0)
I0410 13:54:31.292069 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:54:34.370232 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:54:35.701139 18414 solver.cpp:397]     Test net output #0: accuracy = 0.220588
I0410 13:54:35.701190 18414 solver.cpp:397]     Test net output #1: loss = 3.13798 (* 1 = 3.13798 loss)
I0410 13:54:37.602406 18414 solver.cpp:218] Iteration 3372 (1.36548 iter/s, 8.7881s/12 iters), loss = 3.17512
I0410 13:54:37.602452 18414 solver.cpp:237]     Train net output #0: loss = 3.17512 (* 1 = 3.17512 loss)
I0410 13:54:37.602460 18414 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0410 13:54:42.732477 18414 solver.cpp:218] Iteration 3384 (2.33924 iter/s, 5.12987s/12 iters), loss = 3.08541
I0410 13:54:42.732532 18414 solver.cpp:237]     Train net output #0: loss = 3.08541 (* 1 = 3.08541 loss)
I0410 13:54:42.732542 18414 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0410 13:54:47.719519 18414 solver.cpp:218] Iteration 3396 (2.40633 iter/s, 4.98684s/12 iters), loss = 3.36047
I0410 13:54:47.719578 18414 solver.cpp:237]     Train net output #0: loss = 3.36047 (* 1 = 3.36047 loss)
I0410 13:54:47.719590 18414 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0410 13:54:52.788141 18414 solver.cpp:218] Iteration 3408 (2.3676 iter/s, 5.06842s/12 iters), loss = 3.32197
I0410 13:54:52.788187 18414 solver.cpp:237]     Train net output #0: loss = 3.32197 (* 1 = 3.32197 loss)
I0410 13:54:52.788197 18414 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0410 13:54:57.708122 18414 solver.cpp:218] Iteration 3420 (2.43913 iter/s, 4.91979s/12 iters), loss = 3.03265
I0410 13:54:57.708232 18414 solver.cpp:237]     Train net output #0: loss = 3.03265 (* 1 = 3.03265 loss)
I0410 13:54:57.708246 18414 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0410 13:55:02.617564 18414 solver.cpp:218] Iteration 3432 (2.44439 iter/s, 4.90919s/12 iters), loss = 3.3957
I0410 13:55:02.617616 18414 solver.cpp:237]     Train net output #0: loss = 3.3957 (* 1 = 3.3957 loss)
I0410 13:55:02.617630 18414 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0410 13:55:05.189458 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:55:07.544013 18414 solver.cpp:218] Iteration 3444 (2.43593 iter/s, 4.92625s/12 iters), loss = 2.92423
I0410 13:55:07.544067 18414 solver.cpp:237]     Train net output #0: loss = 2.92423 (* 1 = 2.92423 loss)
I0410 13:55:07.544081 18414 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0410 13:55:12.448598 18414 solver.cpp:218] Iteration 3456 (2.44679 iter/s, 4.90439s/12 iters), loss = 2.98268
I0410 13:55:12.448649 18414 solver.cpp:237]     Train net output #0: loss = 2.98268 (* 1 = 2.98268 loss)
I0410 13:55:12.448662 18414 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0410 13:55:16.908411 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0410 13:55:17.215858 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0410 13:55:17.428023 18414 solver.cpp:330] Iteration 3468, Testing net (#0)
I0410 13:55:17.428057 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:55:17.450659 18414 blocking_queue.cpp:49] Waiting for data
I0410 13:55:20.530405 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:55:21.943506 18414 solver.cpp:397]     Test net output #0: accuracy = 0.210172
I0410 13:55:21.943557 18414 solver.cpp:397]     Test net output #1: loss = 3.10701 (* 1 = 3.10701 loss)
I0410 13:55:22.025673 18414 solver.cpp:218] Iteration 3468 (1.25303 iter/s, 9.57676s/12 iters), loss = 3.0695
I0410 13:55:22.025727 18414 solver.cpp:237]     Train net output #0: loss = 3.0695 (* 1 = 3.0695 loss)
I0410 13:55:22.025740 18414 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0410 13:55:26.315555 18414 solver.cpp:218] Iteration 3480 (2.7974 iter/s, 4.2897s/12 iters), loss = 3.27457
I0410 13:55:26.315606 18414 solver.cpp:237]     Train net output #0: loss = 3.27457 (* 1 = 3.27457 loss)
I0410 13:55:26.315618 18414 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0410 13:55:31.414228 18414 solver.cpp:218] Iteration 3492 (2.35364 iter/s, 5.09848s/12 iters), loss = 3.32446
I0410 13:55:31.414352 18414 solver.cpp:237]     Train net output #0: loss = 3.32446 (* 1 = 3.32446 loss)
I0410 13:55:31.414362 18414 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0410 13:55:36.376430 18414 solver.cpp:218] Iteration 3504 (2.41841 iter/s, 4.96194s/12 iters), loss = 3.05821
I0410 13:55:36.376472 18414 solver.cpp:237]     Train net output #0: loss = 3.05821 (* 1 = 3.05821 loss)
I0410 13:55:36.376482 18414 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0410 13:55:41.322083 18414 solver.cpp:218] Iteration 3516 (2.42647 iter/s, 4.94546s/12 iters), loss = 2.98277
I0410 13:55:41.322144 18414 solver.cpp:237]     Train net output #0: loss = 2.98277 (* 1 = 2.98277 loss)
I0410 13:55:41.322155 18414 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0410 13:55:46.300992 18414 solver.cpp:218] Iteration 3528 (2.41027 iter/s, 4.9787s/12 iters), loss = 3.16743
I0410 13:55:46.301049 18414 solver.cpp:237]     Train net output #0: loss = 3.16743 (* 1 = 3.16743 loss)
I0410 13:55:46.301064 18414 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0410 13:55:50.967958 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:55:51.234496 18414 solver.cpp:218] Iteration 3540 (2.43245 iter/s, 4.9333s/12 iters), loss = 2.97683
I0410 13:55:51.234549 18414 solver.cpp:237]     Train net output #0: loss = 2.97683 (* 1 = 2.97683 loss)
I0410 13:55:51.234561 18414 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0410 13:55:56.257071 18414 solver.cpp:218] Iteration 3552 (2.38931 iter/s, 5.02238s/12 iters), loss = 3.00463
I0410 13:55:56.257115 18414 solver.cpp:237]     Train net output #0: loss = 3.00463 (* 1 = 3.00463 loss)
I0410 13:55:56.257126 18414 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0410 13:56:01.165144 18414 solver.cpp:218] Iteration 3564 (2.44504 iter/s, 4.90789s/12 iters), loss = 2.86452
I0410 13:56:01.165189 18414 solver.cpp:237]     Train net output #0: loss = 2.86452 (* 1 = 2.86452 loss)
I0410 13:56:01.165197 18414 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0410 13:56:03.212183 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0410 13:56:03.513849 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0410 13:56:03.711982 18414 solver.cpp:330] Iteration 3570, Testing net (#0)
I0410 13:56:03.712011 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:56:06.879987 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:56:08.442607 18414 solver.cpp:397]     Test net output #0: accuracy = 0.217525
I0410 13:56:08.442652 18414 solver.cpp:397]     Test net output #1: loss = 3.01251 (* 1 = 3.01251 loss)
I0410 13:56:10.326153 18414 solver.cpp:218] Iteration 3576 (1.30994 iter/s, 9.16071s/12 iters), loss = 3.16498
I0410 13:56:10.326206 18414 solver.cpp:237]     Train net output #0: loss = 3.16498 (* 1 = 3.16498 loss)
I0410 13:56:10.326215 18414 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0410 13:56:15.275367 18414 solver.cpp:218] Iteration 3588 (2.42473 iter/s, 4.94901s/12 iters), loss = 2.93402
I0410 13:56:15.275424 18414 solver.cpp:237]     Train net output #0: loss = 2.93402 (* 1 = 2.93402 loss)
I0410 13:56:15.275439 18414 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0410 13:56:20.207276 18414 solver.cpp:218] Iteration 3600 (2.43323 iter/s, 4.93171s/12 iters), loss = 3.03669
I0410 13:56:20.207334 18414 solver.cpp:237]     Train net output #0: loss = 3.03669 (* 1 = 3.03669 loss)
I0410 13:56:20.207348 18414 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0410 13:56:25.160887 18414 solver.cpp:218] Iteration 3612 (2.42257 iter/s, 4.95341s/12 iters), loss = 3.07548
I0410 13:56:25.160933 18414 solver.cpp:237]     Train net output #0: loss = 3.07548 (* 1 = 3.07548 loss)
I0410 13:56:25.160943 18414 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0410 13:56:30.068615 18414 solver.cpp:218] Iteration 3624 (2.44522 iter/s, 4.90753s/12 iters), loss = 3.20084
I0410 13:56:30.068662 18414 solver.cpp:237]     Train net output #0: loss = 3.20084 (* 1 = 3.20084 loss)
I0410 13:56:30.068671 18414 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0410 13:56:34.967572 18414 solver.cpp:218] Iteration 3636 (2.4496 iter/s, 4.89877s/12 iters), loss = 3.22739
I0410 13:56:34.967679 18414 solver.cpp:237]     Train net output #0: loss = 3.22739 (* 1 = 3.22739 loss)
I0410 13:56:34.967689 18414 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0410 13:56:36.822014 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:56:39.894299 18414 solver.cpp:218] Iteration 3648 (2.43582 iter/s, 4.92648s/12 iters), loss = 3.01096
I0410 13:56:39.894345 18414 solver.cpp:237]     Train net output #0: loss = 3.01096 (* 1 = 3.01096 loss)
I0410 13:56:39.894354 18414 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0410 13:56:44.830453 18414 solver.cpp:218] Iteration 3660 (2.43114 iter/s, 4.93596s/12 iters), loss = 2.79562
I0410 13:56:44.830497 18414 solver.cpp:237]     Train net output #0: loss = 2.79562 (* 1 = 2.79562 loss)
I0410 13:56:44.830505 18414 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0410 13:56:49.248656 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0410 13:56:49.891188 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0410 13:56:50.175750 18414 solver.cpp:330] Iteration 3672, Testing net (#0)
I0410 13:56:50.175781 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:56:53.095834 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:56:54.552925 18414 solver.cpp:397]     Test net output #0: accuracy = 0.26348
I0410 13:56:54.552956 18414 solver.cpp:397]     Test net output #1: loss = 2.85146 (* 1 = 2.85146 loss)
I0410 13:56:54.635833 18414 solver.cpp:218] Iteration 3672 (1.22386 iter/s, 9.80506s/12 iters), loss = 2.91754
I0410 13:56:54.635874 18414 solver.cpp:237]     Train net output #0: loss = 2.91754 (* 1 = 2.91754 loss)
I0410 13:56:54.635882 18414 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0410 13:56:58.704449 18414 solver.cpp:218] Iteration 3684 (2.94953 iter/s, 4.06845s/12 iters), loss = 3.03404
I0410 13:56:58.704507 18414 solver.cpp:237]     Train net output #0: loss = 3.03404 (* 1 = 3.03404 loss)
I0410 13:56:58.704520 18414 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0410 13:57:03.900980 18414 solver.cpp:218] Iteration 3696 (2.30933 iter/s, 5.19632s/12 iters), loss = 2.84856
I0410 13:57:03.901048 18414 solver.cpp:237]     Train net output #0: loss = 2.84856 (* 1 = 2.84856 loss)
I0410 13:57:03.901062 18414 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0410 13:57:08.770165 18414 solver.cpp:218] Iteration 3708 (2.46458 iter/s, 4.86898s/12 iters), loss = 3.04494
I0410 13:57:08.770310 18414 solver.cpp:237]     Train net output #0: loss = 3.04494 (* 1 = 3.04494 loss)
I0410 13:57:08.770320 18414 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0410 13:57:13.795089 18414 solver.cpp:218] Iteration 3720 (2.38824 iter/s, 5.02463s/12 iters), loss = 2.99097
I0410 13:57:13.795131 18414 solver.cpp:237]     Train net output #0: loss = 2.99097 (* 1 = 2.99097 loss)
I0410 13:57:13.795141 18414 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0410 13:57:18.770179 18414 solver.cpp:218] Iteration 3732 (2.41211 iter/s, 4.9749s/12 iters), loss = 2.94415
I0410 13:57:18.770220 18414 solver.cpp:237]     Train net output #0: loss = 2.94415 (* 1 = 2.94415 loss)
I0410 13:57:18.770228 18414 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0410 13:57:22.732805 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:57:23.735224 18414 solver.cpp:218] Iteration 3744 (2.41699 iter/s, 4.96485s/12 iters), loss = 2.93997
I0410 13:57:23.735281 18414 solver.cpp:237]     Train net output #0: loss = 2.93997 (* 1 = 2.93997 loss)
I0410 13:57:23.735293 18414 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0410 13:57:28.650617 18414 solver.cpp:218] Iteration 3756 (2.44141 iter/s, 4.91519s/12 iters), loss = 3.03087
I0410 13:57:28.650667 18414 solver.cpp:237]     Train net output #0: loss = 3.03087 (* 1 = 3.03087 loss)
I0410 13:57:28.650679 18414 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0410 13:57:33.538763 18414 solver.cpp:218] Iteration 3768 (2.45502 iter/s, 4.88795s/12 iters), loss = 2.94635
I0410 13:57:33.538806 18414 solver.cpp:237]     Train net output #0: loss = 2.94635 (* 1 = 2.94635 loss)
I0410 13:57:33.538816 18414 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0410 13:57:35.540868 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0410 13:57:35.880388 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0410 13:57:36.092072 18414 solver.cpp:330] Iteration 3774, Testing net (#0)
I0410 13:57:36.092103 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:57:38.973953 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:57:40.477241 18414 solver.cpp:397]     Test net output #0: accuracy = 0.265319
I0410 13:57:40.477290 18414 solver.cpp:397]     Test net output #1: loss = 2.78307 (* 1 = 2.78307 loss)
I0410 13:57:42.341066 18414 solver.cpp:218] Iteration 3780 (1.36333 iter/s, 8.802s/12 iters), loss = 2.95994
I0410 13:57:42.341121 18414 solver.cpp:237]     Train net output #0: loss = 2.95994 (* 1 = 2.95994 loss)
I0410 13:57:42.341133 18414 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0410 13:57:47.283696 18414 solver.cpp:218] Iteration 3792 (2.42795 iter/s, 4.94243s/12 iters), loss = 2.77478
I0410 13:57:47.283743 18414 solver.cpp:237]     Train net output #0: loss = 2.77478 (* 1 = 2.77478 loss)
I0410 13:57:47.283756 18414 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0410 13:57:52.229271 18414 solver.cpp:218] Iteration 3804 (2.42651 iter/s, 4.94538s/12 iters), loss = 2.90176
I0410 13:57:52.229317 18414 solver.cpp:237]     Train net output #0: loss = 2.90176 (* 1 = 2.90176 loss)
I0410 13:57:52.229329 18414 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0410 13:57:57.294710 18414 solver.cpp:218] Iteration 3816 (2.36909 iter/s, 5.06524s/12 iters), loss = 2.85068
I0410 13:57:57.294770 18414 solver.cpp:237]     Train net output #0: loss = 2.85068 (* 1 = 2.85068 loss)
I0410 13:57:57.294781 18414 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0410 13:58:02.221580 18414 solver.cpp:218] Iteration 3828 (2.43573 iter/s, 4.92666s/12 iters), loss = 3.07162
I0410 13:58:02.221632 18414 solver.cpp:237]     Train net output #0: loss = 3.07162 (* 1 = 3.07162 loss)
I0410 13:58:02.221643 18414 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0410 13:58:07.127607 18414 solver.cpp:218] Iteration 3840 (2.44607 iter/s, 4.90583s/12 iters), loss = 2.9526
I0410 13:58:07.127661 18414 solver.cpp:237]     Train net output #0: loss = 2.9526 (* 1 = 2.9526 loss)
I0410 13:58:07.127673 18414 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0410 13:58:08.269623 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:58:12.080744 18414 solver.cpp:218] Iteration 3852 (2.42281 iter/s, 4.95293s/12 iters), loss = 2.69912
I0410 13:58:12.080926 18414 solver.cpp:237]     Train net output #0: loss = 2.69912 (* 1 = 2.69912 loss)
I0410 13:58:12.080948 18414 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0410 13:58:16.985276 18414 solver.cpp:218] Iteration 3864 (2.44687 iter/s, 4.90422s/12 iters), loss = 2.95602
I0410 13:58:16.985321 18414 solver.cpp:237]     Train net output #0: loss = 2.95602 (* 1 = 2.95602 loss)
I0410 13:58:16.985330 18414 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0410 13:58:21.461838 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0410 13:58:21.771426 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0410 13:58:21.982115 18414 solver.cpp:330] Iteration 3876, Testing net (#0)
I0410 13:58:21.982139 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:58:24.906162 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:58:26.492285 18414 solver.cpp:397]     Test net output #0: accuracy = 0.261642
I0410 13:58:26.492331 18414 solver.cpp:397]     Test net output #1: loss = 2.8455 (* 1 = 2.8455 loss)
I0410 13:58:26.574719 18414 solver.cpp:218] Iteration 3876 (1.25142 iter/s, 9.58912s/12 iters), loss = 2.81109
I0410 13:58:26.574776 18414 solver.cpp:237]     Train net output #0: loss = 2.81109 (* 1 = 2.81109 loss)
I0410 13:58:26.574787 18414 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0410 13:58:30.712575 18414 solver.cpp:218] Iteration 3888 (2.90018 iter/s, 4.13767s/12 iters), loss = 2.6814
I0410 13:58:30.712620 18414 solver.cpp:237]     Train net output #0: loss = 2.6814 (* 1 = 2.6814 loss)
I0410 13:58:30.712630 18414 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0410 13:58:35.570740 18414 solver.cpp:218] Iteration 3900 (2.47017 iter/s, 4.85797s/12 iters), loss = 2.67094
I0410 13:58:35.570789 18414 solver.cpp:237]     Train net output #0: loss = 2.67094 (* 1 = 2.67094 loss)
I0410 13:58:35.570801 18414 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0410 13:58:40.521500 18414 solver.cpp:218] Iteration 3912 (2.42397 iter/s, 4.95056s/12 iters), loss = 2.93082
I0410 13:58:40.521556 18414 solver.cpp:237]     Train net output #0: loss = 2.93082 (* 1 = 2.93082 loss)
I0410 13:58:40.521569 18414 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0410 13:58:45.613479 18414 solver.cpp:218] Iteration 3924 (2.35674 iter/s, 5.09177s/12 iters), loss = 2.93666
I0410 13:58:45.613598 18414 solver.cpp:237]     Train net output #0: loss = 2.93666 (* 1 = 2.93666 loss)
I0410 13:58:45.613615 18414 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0410 13:58:50.543184 18414 solver.cpp:218] Iteration 3936 (2.43436 iter/s, 4.92944s/12 iters), loss = 2.54878
I0410 13:58:50.543242 18414 solver.cpp:237]     Train net output #0: loss = 2.54878 (* 1 = 2.54878 loss)
I0410 13:58:50.543256 18414 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0410 13:58:53.886200 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:58:55.478505 18414 solver.cpp:218] Iteration 3948 (2.43155 iter/s, 4.93512s/12 iters), loss = 2.63647
I0410 13:58:55.478552 18414 solver.cpp:237]     Train net output #0: loss = 2.63647 (* 1 = 2.63647 loss)
I0410 13:58:55.478562 18414 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0410 13:59:00.376485 18414 solver.cpp:218] Iteration 3960 (2.45009 iter/s, 4.89778s/12 iters), loss = 2.62746
I0410 13:59:00.376546 18414 solver.cpp:237]     Train net output #0: loss = 2.62746 (* 1 = 2.62746 loss)
I0410 13:59:00.376560 18414 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0410 13:59:05.298736 18414 solver.cpp:218] Iteration 3972 (2.43801 iter/s, 4.92204s/12 iters), loss = 2.89322
I0410 13:59:05.298790 18414 solver.cpp:237]     Train net output #0: loss = 2.89322 (* 1 = 2.89322 loss)
I0410 13:59:05.298801 18414 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0410 13:59:07.284513 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0410 13:59:08.108355 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0410 13:59:08.343611 18414 solver.cpp:330] Iteration 3978, Testing net (#0)
I0410 13:59:08.343637 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:59:11.151216 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:59:12.728888 18414 solver.cpp:397]     Test net output #0: accuracy = 0.287377
I0410 13:59:12.728935 18414 solver.cpp:397]     Test net output #1: loss = 2.70535 (* 1 = 2.70535 loss)
I0410 13:59:14.525759 18414 solver.cpp:218] Iteration 3984 (1.30057 iter/s, 9.2267s/12 iters), loss = 2.92262
I0410 13:59:14.525804 18414 solver.cpp:237]     Train net output #0: loss = 2.92262 (* 1 = 2.92262 loss)
I0410 13:59:14.525812 18414 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0410 13:59:19.572402 18414 solver.cpp:218] Iteration 3996 (2.37791 iter/s, 5.04644s/12 iters), loss = 2.58188
I0410 13:59:19.572527 18414 solver.cpp:237]     Train net output #0: loss = 2.58188 (* 1 = 2.58188 loss)
I0410 13:59:19.572543 18414 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0410 13:59:24.598122 18414 solver.cpp:218] Iteration 4008 (2.38785 iter/s, 5.02544s/12 iters), loss = 2.74006
I0410 13:59:24.598186 18414 solver.cpp:237]     Train net output #0: loss = 2.74006 (* 1 = 2.74006 loss)
I0410 13:59:24.598197 18414 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0410 13:59:29.607030 18414 solver.cpp:218] Iteration 4020 (2.39583 iter/s, 5.0087s/12 iters), loss = 2.95298
I0410 13:59:29.607086 18414 solver.cpp:237]     Train net output #0: loss = 2.95298 (* 1 = 2.95298 loss)
I0410 13:59:29.607098 18414 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0410 13:59:34.565754 18414 solver.cpp:218] Iteration 4032 (2.42008 iter/s, 4.95851s/12 iters), loss = 2.69841
I0410 13:59:34.565809 18414 solver.cpp:237]     Train net output #0: loss = 2.69841 (* 1 = 2.69841 loss)
I0410 13:59:34.565819 18414 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0410 13:59:39.496455 18414 solver.cpp:218] Iteration 4044 (2.43383 iter/s, 4.9305s/12 iters), loss = 2.78749
I0410 13:59:39.496503 18414 solver.cpp:237]     Train net output #0: loss = 2.78749 (* 1 = 2.78749 loss)
I0410 13:59:39.496513 18414 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0410 13:59:39.992655 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:59:44.445119 18414 solver.cpp:218] Iteration 4056 (2.425 iter/s, 4.94846s/12 iters), loss = 2.67322
I0410 13:59:44.445171 18414 solver.cpp:237]     Train net output #0: loss = 2.67322 (* 1 = 2.67322 loss)
I0410 13:59:44.445184 18414 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0410 13:59:49.350200 18414 solver.cpp:218] Iteration 4068 (2.44655 iter/s, 4.90487s/12 iters), loss = 2.66436
I0410 13:59:49.350260 18414 solver.cpp:237]     Train net output #0: loss = 2.66436 (* 1 = 2.66436 loss)
I0410 13:59:49.350273 18414 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0410 13:59:53.831138 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0410 13:59:54.123172 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0410 13:59:54.324872 18414 solver.cpp:330] Iteration 4080, Testing net (#0)
I0410 13:59:54.324899 18414 net.cpp:676] Ignoring source layer train-data
I0410 13:59:57.130178 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 13:59:58.742940 18414 solver.cpp:397]     Test net output #0: accuracy = 0.303309
I0410 13:59:58.742988 18414 solver.cpp:397]     Test net output #1: loss = 2.70645 (* 1 = 2.70645 loss)
I0410 13:59:58.826071 18414 solver.cpp:218] Iteration 4080 (1.26642 iter/s, 9.47553s/12 iters), loss = 2.72842
I0410 13:59:58.826128 18414 solver.cpp:237]     Train net output #0: loss = 2.72842 (* 1 = 2.72842 loss)
I0410 13:59:58.826138 18414 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0410 14:00:03.000277 18414 solver.cpp:218] Iteration 4092 (2.87493 iter/s, 4.17401s/12 iters), loss = 2.64337
I0410 14:00:03.000336 18414 solver.cpp:237]     Train net output #0: loss = 2.64337 (* 1 = 2.64337 loss)
I0410 14:00:03.000349 18414 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0410 14:00:07.951275 18414 solver.cpp:218] Iteration 4104 (2.42386 iter/s, 4.95078s/12 iters), loss = 2.91753
I0410 14:00:07.951328 18414 solver.cpp:237]     Train net output #0: loss = 2.91753 (* 1 = 2.91753 loss)
I0410 14:00:07.951340 18414 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0410 14:00:12.857589 18414 solver.cpp:218] Iteration 4116 (2.44593 iter/s, 4.90611s/12 iters), loss = 2.51763
I0410 14:00:12.857636 18414 solver.cpp:237]     Train net output #0: loss = 2.51763 (* 1 = 2.51763 loss)
I0410 14:00:12.857645 18414 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0410 14:00:17.831074 18414 solver.cpp:218] Iteration 4128 (2.41289 iter/s, 4.97328s/12 iters), loss = 2.45217
I0410 14:00:17.831126 18414 solver.cpp:237]     Train net output #0: loss = 2.45217 (* 1 = 2.45217 loss)
I0410 14:00:17.831138 18414 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0410 14:00:22.720276 18414 solver.cpp:218] Iteration 4140 (2.45449 iter/s, 4.889s/12 iters), loss = 2.788
I0410 14:00:22.720330 18414 solver.cpp:237]     Train net output #0: loss = 2.788 (* 1 = 2.788 loss)
I0410 14:00:22.720341 18414 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0410 14:00:25.333513 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:00:27.672665 18414 solver.cpp:218] Iteration 4152 (2.42317 iter/s, 4.95218s/12 iters), loss = 2.49024
I0410 14:00:27.672715 18414 solver.cpp:237]     Train net output #0: loss = 2.49024 (* 1 = 2.49024 loss)
I0410 14:00:27.672725 18414 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0410 14:00:27.673000 18414 blocking_queue.cpp:49] Waiting for data
I0410 14:00:32.558754 18414 solver.cpp:218] Iteration 4164 (2.45605 iter/s, 4.88589s/12 iters), loss = 2.32443
I0410 14:00:32.558801 18414 solver.cpp:237]     Train net output #0: loss = 2.32443 (* 1 = 2.32443 loss)
I0410 14:00:32.558811 18414 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0410 14:00:37.454324 18414 solver.cpp:218] Iteration 4176 (2.45129 iter/s, 4.89537s/12 iters), loss = 2.44923
I0410 14:00:37.454375 18414 solver.cpp:237]     Train net output #0: loss = 2.44923 (* 1 = 2.44923 loss)
I0410 14:00:37.454387 18414 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0410 14:00:39.494097 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0410 14:00:39.819110 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0410 14:00:40.028364 18414 solver.cpp:330] Iteration 4182, Testing net (#0)
I0410 14:00:40.028388 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:00:42.903447 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:00:44.561077 18414 solver.cpp:397]     Test net output #0: accuracy = 0.337623
I0410 14:00:44.561138 18414 solver.cpp:397]     Test net output #1: loss = 2.53815 (* 1 = 2.53815 loss)
I0410 14:00:46.490641 18414 solver.cpp:218] Iteration 4188 (1.32802 iter/s, 9.036s/12 iters), loss = 2.35225
I0410 14:00:46.490690 18414 solver.cpp:237]     Train net output #0: loss = 2.35225 (* 1 = 2.35225 loss)
I0410 14:00:46.490700 18414 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0410 14:00:51.417002 18414 solver.cpp:218] Iteration 4200 (2.43598 iter/s, 4.92616s/12 iters), loss = 2.58267
I0410 14:00:51.417057 18414 solver.cpp:237]     Train net output #0: loss = 2.58267 (* 1 = 2.58267 loss)
I0410 14:00:51.417070 18414 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0410 14:00:56.316273 18414 solver.cpp:218] Iteration 4212 (2.44945 iter/s, 4.89907s/12 iters), loss = 2.34055
I0410 14:00:56.316447 18414 solver.cpp:237]     Train net output #0: loss = 2.34055 (* 1 = 2.34055 loss)
I0410 14:00:56.316464 18414 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0410 14:01:01.307729 18414 solver.cpp:218] Iteration 4224 (2.40426 iter/s, 4.99113s/12 iters), loss = 2.617
I0410 14:01:01.307780 18414 solver.cpp:237]     Train net output #0: loss = 2.617 (* 1 = 2.617 loss)
I0410 14:01:01.307793 18414 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0410 14:01:06.290788 18414 solver.cpp:218] Iteration 4236 (2.40826 iter/s, 4.98285s/12 iters), loss = 2.48291
I0410 14:01:06.290840 18414 solver.cpp:237]     Train net output #0: loss = 2.48291 (* 1 = 2.48291 loss)
I0410 14:01:06.290853 18414 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0410 14:01:11.057847 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:01:11.288161 18414 solver.cpp:218] Iteration 4248 (2.40136 iter/s, 4.99717s/12 iters), loss = 2.83814
I0410 14:01:11.288209 18414 solver.cpp:237]     Train net output #0: loss = 2.83814 (* 1 = 2.83814 loss)
I0410 14:01:11.288219 18414 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0410 14:01:16.241717 18414 solver.cpp:218] Iteration 4260 (2.4226 iter/s, 4.95335s/12 iters), loss = 2.49977
I0410 14:01:16.241777 18414 solver.cpp:237]     Train net output #0: loss = 2.49977 (* 1 = 2.49977 loss)
I0410 14:01:16.241791 18414 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0410 14:01:21.263298 18414 solver.cpp:218] Iteration 4272 (2.38979 iter/s, 5.02137s/12 iters), loss = 2.56613
I0410 14:01:21.263351 18414 solver.cpp:237]     Train net output #0: loss = 2.56613 (* 1 = 2.56613 loss)
I0410 14:01:21.263365 18414 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0410 14:01:25.847436 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0410 14:01:26.170532 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0410 14:01:26.424517 18414 solver.cpp:330] Iteration 4284, Testing net (#0)
I0410 14:01:26.424603 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:01:29.363044 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:01:31.100821 18414 solver.cpp:397]     Test net output #0: accuracy = 0.348652
I0410 14:01:31.100872 18414 solver.cpp:397]     Test net output #1: loss = 2.47666 (* 1 = 2.47666 loss)
I0410 14:01:31.183770 18414 solver.cpp:218] Iteration 4284 (1.20966 iter/s, 9.92013s/12 iters), loss = 2.54673
I0410 14:01:31.183820 18414 solver.cpp:237]     Train net output #0: loss = 2.54673 (* 1 = 2.54673 loss)
I0410 14:01:31.183832 18414 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0410 14:01:35.257284 18414 solver.cpp:218] Iteration 4296 (2.94599 iter/s, 4.07333s/12 iters), loss = 2.55562
I0410 14:01:35.257339 18414 solver.cpp:237]     Train net output #0: loss = 2.55562 (* 1 = 2.55562 loss)
I0410 14:01:35.257350 18414 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0410 14:01:40.168298 18414 solver.cpp:218] Iteration 4308 (2.44359 iter/s, 4.9108s/12 iters), loss = 2.4171
I0410 14:01:40.168354 18414 solver.cpp:237]     Train net output #0: loss = 2.4171 (* 1 = 2.4171 loss)
I0410 14:01:40.168368 18414 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0410 14:01:45.034011 18414 solver.cpp:218] Iteration 4320 (2.46634 iter/s, 4.8655s/12 iters), loss = 2.72478
I0410 14:01:45.034075 18414 solver.cpp:237]     Train net output #0: loss = 2.72478 (* 1 = 2.72478 loss)
I0410 14:01:45.034088 18414 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0410 14:01:49.907608 18414 solver.cpp:218] Iteration 4332 (2.46236 iter/s, 4.87338s/12 iters), loss = 2.4895
I0410 14:01:49.907667 18414 solver.cpp:237]     Train net output #0: loss = 2.4895 (* 1 = 2.4895 loss)
I0410 14:01:49.907680 18414 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0410 14:01:54.900946 18414 solver.cpp:218] Iteration 4344 (2.4033 iter/s, 4.99313s/12 iters), loss = 2.484
I0410 14:01:54.900981 18414 solver.cpp:237]     Train net output #0: loss = 2.484 (* 1 = 2.484 loss)
I0410 14:01:54.900990 18414 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0410 14:01:56.775885 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:02:00.231731 18414 solver.cpp:218] Iteration 4356 (2.25116 iter/s, 5.33058s/12 iters), loss = 2.42662
I0410 14:02:00.231779 18414 solver.cpp:237]     Train net output #0: loss = 2.42662 (* 1 = 2.42662 loss)
I0410 14:02:00.231789 18414 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0410 14:02:05.266218 18414 solver.cpp:218] Iteration 4368 (2.38366 iter/s, 5.03428s/12 iters), loss = 2.57138
I0410 14:02:05.266283 18414 solver.cpp:237]     Train net output #0: loss = 2.57138 (* 1 = 2.57138 loss)
I0410 14:02:05.266297 18414 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0410 14:02:10.218008 18414 solver.cpp:218] Iteration 4380 (2.42348 iter/s, 4.95157s/12 iters), loss = 2.28366
I0410 14:02:10.218073 18414 solver.cpp:237]     Train net output #0: loss = 2.28366 (* 1 = 2.28366 loss)
I0410 14:02:10.218088 18414 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0410 14:02:12.233098 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0410 14:02:12.544100 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0410 14:02:12.764894 18414 solver.cpp:330] Iteration 4386, Testing net (#0)
I0410 14:02:12.764926 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:02:15.682173 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:02:17.417742 18414 solver.cpp:397]     Test net output #0: accuracy = 0.347426
I0410 14:02:17.417804 18414 solver.cpp:397]     Test net output #1: loss = 2.52323 (* 1 = 2.52323 loss)
I0410 14:02:19.333441 18414 solver.cpp:218] Iteration 4392 (1.3165 iter/s, 9.11508s/12 iters), loss = 2.45253
I0410 14:02:19.333518 18414 solver.cpp:237]     Train net output #0: loss = 2.45253 (* 1 = 2.45253 loss)
I0410 14:02:19.333536 18414 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0410 14:02:24.390537 18414 solver.cpp:218] Iteration 4404 (2.37301 iter/s, 5.05687s/12 iters), loss = 2.16032
I0410 14:02:24.390578 18414 solver.cpp:237]     Train net output #0: loss = 2.16032 (* 1 = 2.16032 loss)
I0410 14:02:24.390585 18414 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0410 14:02:29.276520 18414 solver.cpp:218] Iteration 4416 (2.45611 iter/s, 4.88578s/12 iters), loss = 2.45917
I0410 14:02:29.276608 18414 solver.cpp:237]     Train net output #0: loss = 2.45917 (* 1 = 2.45917 loss)
I0410 14:02:29.276619 18414 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0410 14:02:34.453112 18414 solver.cpp:218] Iteration 4428 (2.31824 iter/s, 5.17634s/12 iters), loss = 2.40949
I0410 14:02:34.453155 18414 solver.cpp:237]     Train net output #0: loss = 2.40949 (* 1 = 2.40949 loss)
I0410 14:02:34.453164 18414 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0410 14:02:39.342815 18414 solver.cpp:218] Iteration 4440 (2.45424 iter/s, 4.8895s/12 iters), loss = 2.50414
I0410 14:02:39.342866 18414 solver.cpp:237]     Train net output #0: loss = 2.50414 (* 1 = 2.50414 loss)
I0410 14:02:39.342880 18414 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0410 14:02:43.344833 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:02:44.286103 18414 solver.cpp:218] Iteration 4452 (2.42764 iter/s, 4.94307s/12 iters), loss = 2.30117
I0410 14:02:44.286159 18414 solver.cpp:237]     Train net output #0: loss = 2.30117 (* 1 = 2.30117 loss)
I0410 14:02:44.286170 18414 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0410 14:02:49.474768 18414 solver.cpp:218] Iteration 4464 (2.31283 iter/s, 5.18844s/12 iters), loss = 2.34118
I0410 14:02:49.474822 18414 solver.cpp:237]     Train net output #0: loss = 2.34118 (* 1 = 2.34118 loss)
I0410 14:02:49.474833 18414 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0410 14:02:54.447268 18414 solver.cpp:218] Iteration 4476 (2.41338 iter/s, 4.97229s/12 iters), loss = 2.31643
I0410 14:02:54.447319 18414 solver.cpp:237]     Train net output #0: loss = 2.31643 (* 1 = 2.31643 loss)
I0410 14:02:54.447329 18414 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0410 14:02:58.949576 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0410 14:02:59.263324 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0410 14:02:59.626415 18414 solver.cpp:330] Iteration 4488, Testing net (#0)
I0410 14:02:59.626549 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:03:02.332475 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:03:04.142942 18414 solver.cpp:397]     Test net output #0: accuracy = 0.373162
I0410 14:03:04.142976 18414 solver.cpp:397]     Test net output #1: loss = 2.39807 (* 1 = 2.39807 loss)
I0410 14:03:04.225935 18414 solver.cpp:218] Iteration 4488 (1.22721 iter/s, 9.77832s/12 iters), loss = 2.34088
I0410 14:03:04.226014 18414 solver.cpp:237]     Train net output #0: loss = 2.34088 (* 1 = 2.34088 loss)
I0410 14:03:04.226027 18414 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0410 14:03:08.375540 18414 solver.cpp:218] Iteration 4500 (2.89199 iter/s, 4.14939s/12 iters), loss = 2.3684
I0410 14:03:08.375583 18414 solver.cpp:237]     Train net output #0: loss = 2.3684 (* 1 = 2.3684 loss)
I0410 14:03:08.375592 18414 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0410 14:03:13.301244 18414 solver.cpp:218] Iteration 4512 (2.4363 iter/s, 4.9255s/12 iters), loss = 2.37274
I0410 14:03:13.301303 18414 solver.cpp:237]     Train net output #0: loss = 2.37274 (* 1 = 2.37274 loss)
I0410 14:03:13.301317 18414 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0410 14:03:18.211583 18414 solver.cpp:218] Iteration 4524 (2.44393 iter/s, 4.91012s/12 iters), loss = 2.1132
I0410 14:03:18.211645 18414 solver.cpp:237]     Train net output #0: loss = 2.1132 (* 1 = 2.1132 loss)
I0410 14:03:18.211658 18414 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0410 14:03:23.154806 18414 solver.cpp:218] Iteration 4536 (2.42767 iter/s, 4.943s/12 iters), loss = 2.23513
I0410 14:03:23.154868 18414 solver.cpp:237]     Train net output #0: loss = 2.23513 (* 1 = 2.23513 loss)
I0410 14:03:23.154881 18414 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0410 14:03:28.087304 18414 solver.cpp:218] Iteration 4548 (2.43295 iter/s, 4.93228s/12 iters), loss = 2.41148
I0410 14:03:28.087350 18414 solver.cpp:237]     Train net output #0: loss = 2.41148 (* 1 = 2.41148 loss)
I0410 14:03:28.087359 18414 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0410 14:03:29.306751 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:03:33.003424 18414 solver.cpp:218] Iteration 4560 (2.44105 iter/s, 4.91592s/12 iters), loss = 2.14062
I0410 14:03:33.003527 18414 solver.cpp:237]     Train net output #0: loss = 2.14062 (* 1 = 2.14062 loss)
I0410 14:03:33.003540 18414 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0410 14:03:37.927881 18414 solver.cpp:218] Iteration 4572 (2.43695 iter/s, 4.9242s/12 iters), loss = 2.30613
I0410 14:03:37.927937 18414 solver.cpp:237]     Train net output #0: loss = 2.30613 (* 1 = 2.30613 loss)
I0410 14:03:37.927951 18414 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0410 14:03:42.882622 18414 solver.cpp:218] Iteration 4584 (2.42203 iter/s, 4.95453s/12 iters), loss = 2.2986
I0410 14:03:42.882680 18414 solver.cpp:237]     Train net output #0: loss = 2.2986 (* 1 = 2.2986 loss)
I0410 14:03:42.882694 18414 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0410 14:03:44.829561 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0410 14:03:45.145336 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0410 14:03:45.354544 18414 solver.cpp:330] Iteration 4590, Testing net (#0)
I0410 14:03:45.354568 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:03:47.914117 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:03:49.775494 18414 solver.cpp:397]     Test net output #0: accuracy = 0.340074
I0410 14:03:49.775528 18414 solver.cpp:397]     Test net output #1: loss = 2.50666 (* 1 = 2.50666 loss)
I0410 14:03:51.588933 18414 solver.cpp:218] Iteration 4596 (1.37836 iter/s, 8.70599s/12 iters), loss = 2.37106
I0410 14:03:51.588987 18414 solver.cpp:237]     Train net output #0: loss = 2.37106 (* 1 = 2.37106 loss)
I0410 14:03:51.589000 18414 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0410 14:03:56.565462 18414 solver.cpp:218] Iteration 4608 (2.41142 iter/s, 4.97632s/12 iters), loss = 2.18917
I0410 14:03:56.565517 18414 solver.cpp:237]     Train net output #0: loss = 2.18917 (* 1 = 2.18917 loss)
I0410 14:03:56.565531 18414 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0410 14:04:01.507630 18414 solver.cpp:218] Iteration 4620 (2.42819 iter/s, 4.94195s/12 iters), loss = 2.21332
I0410 14:04:01.507683 18414 solver.cpp:237]     Train net output #0: loss = 2.21332 (* 1 = 2.21332 loss)
I0410 14:04:01.507696 18414 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0410 14:04:06.480552 18414 solver.cpp:218] Iteration 4632 (2.41317 iter/s, 4.97271s/12 iters), loss = 2.29576
I0410 14:04:06.480706 18414 solver.cpp:237]     Train net output #0: loss = 2.29576 (* 1 = 2.29576 loss)
I0410 14:04:06.480718 18414 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0410 14:04:11.422371 18414 solver.cpp:218] Iteration 4644 (2.42841 iter/s, 4.94151s/12 iters), loss = 2.04189
I0410 14:04:11.422422 18414 solver.cpp:237]     Train net output #0: loss = 2.04189 (* 1 = 2.04189 loss)
I0410 14:04:11.422435 18414 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0410 14:04:14.808686 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:04:16.410308 18414 solver.cpp:218] Iteration 4656 (2.40591 iter/s, 4.98772s/12 iters), loss = 2.24202
I0410 14:04:16.410365 18414 solver.cpp:237]     Train net output #0: loss = 2.24202 (* 1 = 2.24202 loss)
I0410 14:04:16.410378 18414 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0410 14:04:21.351442 18414 solver.cpp:218] Iteration 4668 (2.4287 iter/s, 4.94092s/12 iters), loss = 2.30526
I0410 14:04:21.351501 18414 solver.cpp:237]     Train net output #0: loss = 2.30526 (* 1 = 2.30526 loss)
I0410 14:04:21.351514 18414 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0410 14:04:26.247016 18414 solver.cpp:218] Iteration 4680 (2.4513 iter/s, 4.89536s/12 iters), loss = 2.43121
I0410 14:04:26.247068 18414 solver.cpp:237]     Train net output #0: loss = 2.43121 (* 1 = 2.43121 loss)
I0410 14:04:26.247079 18414 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0410 14:04:30.717337 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0410 14:04:31.043011 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0410 14:04:31.252470 18414 solver.cpp:330] Iteration 4692, Testing net (#0)
I0410 14:04:31.252499 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:04:33.840610 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:04:35.692467 18414 solver.cpp:397]     Test net output #0: accuracy = 0.368873
I0410 14:04:35.692517 18414 solver.cpp:397]     Test net output #1: loss = 2.36165 (* 1 = 2.36165 loss)
I0410 14:04:35.775476 18414 solver.cpp:218] Iteration 4692 (1.25943 iter/s, 9.52812s/12 iters), loss = 2.21889
I0410 14:04:35.775528 18414 solver.cpp:237]     Train net output #0: loss = 2.21889 (* 1 = 2.21889 loss)
I0410 14:04:35.775540 18414 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0410 14:04:40.016005 18414 solver.cpp:218] Iteration 4704 (2.82996 iter/s, 4.24034s/12 iters), loss = 2.0089
I0410 14:04:40.016081 18414 solver.cpp:237]     Train net output #0: loss = 2.0089 (* 1 = 2.0089 loss)
I0410 14:04:40.016094 18414 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0410 14:04:44.864478 18414 solver.cpp:218] Iteration 4716 (2.47513 iter/s, 4.84824s/12 iters), loss = 2.07954
I0410 14:04:44.864521 18414 solver.cpp:237]     Train net output #0: loss = 2.07954 (* 1 = 2.07954 loss)
I0410 14:04:44.864531 18414 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0410 14:04:49.742425 18414 solver.cpp:218] Iteration 4728 (2.46015 iter/s, 4.87774s/12 iters), loss = 2.20523
I0410 14:04:49.742483 18414 solver.cpp:237]     Train net output #0: loss = 2.20523 (* 1 = 2.20523 loss)
I0410 14:04:49.742497 18414 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0410 14:04:54.893491 18414 solver.cpp:218] Iteration 4740 (2.32972 iter/s, 5.15084s/12 iters), loss = 2.09447
I0410 14:04:54.893532 18414 solver.cpp:237]     Train net output #0: loss = 2.09447 (* 1 = 2.09447 loss)
I0410 14:04:54.893540 18414 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0410 14:04:59.855967 18414 solver.cpp:218] Iteration 4752 (2.41825 iter/s, 4.96227s/12 iters), loss = 2.2461
I0410 14:04:59.856031 18414 solver.cpp:237]     Train net output #0: loss = 2.2461 (* 1 = 2.2461 loss)
I0410 14:04:59.856045 18414 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0410 14:05:00.383807 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:05:04.790400 18414 solver.cpp:218] Iteration 4764 (2.432 iter/s, 4.93422s/12 iters), loss = 2.21551
I0410 14:05:04.790447 18414 solver.cpp:237]     Train net output #0: loss = 2.21551 (* 1 = 2.21551 loss)
I0410 14:05:04.790458 18414 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0410 14:05:09.776535 18414 solver.cpp:218] Iteration 4776 (2.40677 iter/s, 4.98593s/12 iters), loss = 2.27317
I0410 14:05:09.776589 18414 solver.cpp:237]     Train net output #0: loss = 2.27317 (* 1 = 2.27317 loss)
I0410 14:05:09.776603 18414 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0410 14:05:14.828886 18414 solver.cpp:218] Iteration 4788 (2.37524 iter/s, 5.05213s/12 iters), loss = 2.11977
I0410 14:05:14.829006 18414 solver.cpp:237]     Train net output #0: loss = 2.11977 (* 1 = 2.11977 loss)
I0410 14:05:14.829016 18414 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0410 14:05:16.854892 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0410 14:05:17.165350 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0410 14:05:17.373826 18414 solver.cpp:330] Iteration 4794, Testing net (#0)
I0410 14:05:17.373853 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:05:19.931380 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:05:21.831094 18414 solver.cpp:397]     Test net output #0: accuracy = 0.386642
I0410 14:05:21.831143 18414 solver.cpp:397]     Test net output #1: loss = 2.32682 (* 1 = 2.32682 loss)
I0410 14:05:23.668174 18414 solver.cpp:218] Iteration 4800 (1.35764 iter/s, 8.83889s/12 iters), loss = 2.18756
I0410 14:05:23.668231 18414 solver.cpp:237]     Train net output #0: loss = 2.18756 (* 1 = 2.18756 loss)
I0410 14:05:23.668244 18414 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0410 14:05:28.702368 18414 solver.cpp:218] Iteration 4812 (2.3838 iter/s, 5.03398s/12 iters), loss = 2.40763
I0410 14:05:28.702425 18414 solver.cpp:237]     Train net output #0: loss = 2.40763 (* 1 = 2.40763 loss)
I0410 14:05:28.702436 18414 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0410 14:05:33.634449 18414 solver.cpp:218] Iteration 4824 (2.43316 iter/s, 4.93187s/12 iters), loss = 2.12281
I0410 14:05:33.634496 18414 solver.cpp:237]     Train net output #0: loss = 2.12281 (* 1 = 2.12281 loss)
I0410 14:05:33.634505 18414 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0410 14:05:38.592432 18414 solver.cpp:218] Iteration 4836 (2.42044 iter/s, 4.95777s/12 iters), loss = 1.9435
I0410 14:05:38.592483 18414 solver.cpp:237]     Train net output #0: loss = 1.9435 (* 1 = 1.9435 loss)
I0410 14:05:38.592494 18414 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0410 14:05:38.954097 18414 blocking_queue.cpp:49] Waiting for data
I0410 14:05:43.614082 18414 solver.cpp:218] Iteration 4848 (2.38975 iter/s, 5.02144s/12 iters), loss = 2.25356
I0410 14:05:43.614130 18414 solver.cpp:237]     Train net output #0: loss = 2.25356 (* 1 = 2.25356 loss)
I0410 14:05:43.614142 18414 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0410 14:05:46.271085 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:05:48.643687 18414 solver.cpp:218] Iteration 4860 (2.38597 iter/s, 5.02939s/12 iters), loss = 1.6811
I0410 14:05:48.643739 18414 solver.cpp:237]     Train net output #0: loss = 1.6811 (* 1 = 1.6811 loss)
I0410 14:05:48.643754 18414 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0410 14:05:53.681126 18414 solver.cpp:218] Iteration 4872 (2.38227 iter/s, 5.03722s/12 iters), loss = 1.94442
I0410 14:05:53.681185 18414 solver.cpp:237]     Train net output #0: loss = 1.94442 (* 1 = 1.94442 loss)
I0410 14:05:53.681198 18414 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0410 14:05:58.627323 18414 solver.cpp:218] Iteration 4884 (2.42622 iter/s, 4.94597s/12 iters), loss = 2.15385
I0410 14:05:58.627379 18414 solver.cpp:237]     Train net output #0: loss = 2.15385 (* 1 = 2.15385 loss)
I0410 14:05:58.627393 18414 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0410 14:06:03.070109 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0410 14:06:03.950569 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0410 14:06:04.164127 18414 solver.cpp:330] Iteration 4896, Testing net (#0)
I0410 14:06:04.164157 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:06:06.675734 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:06:08.604876 18414 solver.cpp:397]     Test net output #0: accuracy = 0.393382
I0410 14:06:08.604913 18414 solver.cpp:397]     Test net output #1: loss = 2.31131 (* 1 = 2.31131 loss)
I0410 14:06:08.687649 18414 solver.cpp:218] Iteration 4896 (1.19285 iter/s, 10.06s/12 iters), loss = 2.08474
I0410 14:06:08.687695 18414 solver.cpp:237]     Train net output #0: loss = 2.08474 (* 1 = 2.08474 loss)
I0410 14:06:08.687705 18414 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0410 14:06:13.148077 18414 solver.cpp:218] Iteration 4908 (2.69044 iter/s, 4.46024s/12 iters), loss = 2.12181
I0410 14:06:13.148129 18414 solver.cpp:237]     Train net output #0: loss = 2.12181 (* 1 = 2.12181 loss)
I0410 14:06:13.148142 18414 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0410 14:06:18.037402 18414 solver.cpp:218] Iteration 4920 (2.45443 iter/s, 4.88911s/12 iters), loss = 2.13644
I0410 14:06:18.037546 18414 solver.cpp:237]     Train net output #0: loss = 2.13644 (* 1 = 2.13644 loss)
I0410 14:06:18.037560 18414 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0410 14:06:22.922582 18414 solver.cpp:218] Iteration 4932 (2.45656 iter/s, 4.88488s/12 iters), loss = 2.06838
I0410 14:06:22.922628 18414 solver.cpp:237]     Train net output #0: loss = 2.06838 (* 1 = 2.06838 loss)
I0410 14:06:22.922641 18414 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0410 14:06:27.899281 18414 solver.cpp:218] Iteration 4944 (2.41134 iter/s, 4.97648s/12 iters), loss = 1.93448
I0410 14:06:27.899340 18414 solver.cpp:237]     Train net output #0: loss = 1.93448 (* 1 = 1.93448 loss)
I0410 14:06:27.899354 18414 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0410 14:06:32.616598 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:06:32.808260 18414 solver.cpp:218] Iteration 4956 (2.44461 iter/s, 4.90877s/12 iters), loss = 1.92429
I0410 14:06:32.808305 18414 solver.cpp:237]     Train net output #0: loss = 1.92429 (* 1 = 1.92429 loss)
I0410 14:06:32.808313 18414 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0410 14:06:37.707438 18414 solver.cpp:218] Iteration 4968 (2.44949 iter/s, 4.89897s/12 iters), loss = 1.81271
I0410 14:06:37.707494 18414 solver.cpp:237]     Train net output #0: loss = 1.81271 (* 1 = 1.81271 loss)
I0410 14:06:37.707509 18414 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0410 14:06:42.748921 18414 solver.cpp:218] Iteration 4980 (2.38035 iter/s, 5.04127s/12 iters), loss = 1.76764
I0410 14:06:42.748961 18414 solver.cpp:237]     Train net output #0: loss = 1.76764 (* 1 = 1.76764 loss)
I0410 14:06:42.748970 18414 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0410 14:06:48.008517 18414 solver.cpp:218] Iteration 4992 (2.28164 iter/s, 5.25938s/12 iters), loss = 1.9683
I0410 14:06:48.008574 18414 solver.cpp:237]     Train net output #0: loss = 1.9683 (* 1 = 1.9683 loss)
I0410 14:06:48.008589 18414 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0410 14:06:50.085268 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0410 14:06:50.388633 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0410 14:06:50.584743 18414 solver.cpp:330] Iteration 4998, Testing net (#0)
I0410 14:06:50.584764 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:06:53.050520 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:06:55.016012 18414 solver.cpp:397]     Test net output #0: accuracy = 0.398897
I0410 14:06:55.016063 18414 solver.cpp:397]     Test net output #1: loss = 2.28344 (* 1 = 2.28344 loss)
I0410 14:06:56.759085 18414 solver.cpp:218] Iteration 5004 (1.37139 iter/s, 8.75024s/12 iters), loss = 1.92985
I0410 14:06:56.759143 18414 solver.cpp:237]     Train net output #0: loss = 1.92985 (* 1 = 1.92985 loss)
I0410 14:06:56.759157 18414 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0410 14:07:01.805079 18414 solver.cpp:218] Iteration 5016 (2.37823 iter/s, 5.04578s/12 iters), loss = 2.03553
I0410 14:07:01.805122 18414 solver.cpp:237]     Train net output #0: loss = 2.03553 (* 1 = 2.03553 loss)
I0410 14:07:01.805130 18414 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0410 14:07:06.742432 18414 solver.cpp:218] Iteration 5028 (2.43055 iter/s, 4.93715s/12 iters), loss = 2.19006
I0410 14:07:06.742484 18414 solver.cpp:237]     Train net output #0: loss = 2.19006 (* 1 = 2.19006 loss)
I0410 14:07:06.742497 18414 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0410 14:07:11.684944 18414 solver.cpp:218] Iteration 5040 (2.42802 iter/s, 4.9423s/12 iters), loss = 2.27043
I0410 14:07:11.684998 18414 solver.cpp:237]     Train net output #0: loss = 2.27043 (* 1 = 2.27043 loss)
I0410 14:07:11.685010 18414 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0410 14:07:16.624229 18414 solver.cpp:218] Iteration 5052 (2.42961 iter/s, 4.93907s/12 iters), loss = 1.96993
I0410 14:07:16.624276 18414 solver.cpp:237]     Train net output #0: loss = 1.96993 (* 1 = 1.96993 loss)
I0410 14:07:16.624285 18414 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0410 14:07:18.520078 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:07:21.534570 18414 solver.cpp:218] Iteration 5064 (2.44393 iter/s, 4.91013s/12 iters), loss = 2.09901
I0410 14:07:21.534687 18414 solver.cpp:237]     Train net output #0: loss = 2.09901 (* 1 = 2.09901 loss)
I0410 14:07:21.534699 18414 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0410 14:07:26.448649 18414 solver.cpp:218] Iteration 5076 (2.4421 iter/s, 4.91381s/12 iters), loss = 2.31146
I0410 14:07:26.448702 18414 solver.cpp:237]     Train net output #0: loss = 2.31146 (* 1 = 2.31146 loss)
I0410 14:07:26.448714 18414 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0410 14:07:31.383373 18414 solver.cpp:218] Iteration 5088 (2.43185 iter/s, 4.93451s/12 iters), loss = 1.76715
I0410 14:07:31.383427 18414 solver.cpp:237]     Train net output #0: loss = 1.76715 (* 1 = 1.76715 loss)
I0410 14:07:31.383440 18414 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0410 14:07:35.836490 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0410 14:07:36.159860 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0410 14:07:36.372545 18414 solver.cpp:330] Iteration 5100, Testing net (#0)
I0410 14:07:36.372575 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:07:38.831662 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:07:40.884613 18414 solver.cpp:397]     Test net output #0: accuracy = 0.420343
I0410 14:07:40.884665 18414 solver.cpp:397]     Test net output #1: loss = 2.18801 (* 1 = 2.18801 loss)
I0410 14:07:40.967559 18414 solver.cpp:218] Iteration 5100 (1.25211 iter/s, 9.58383s/12 iters), loss = 1.89703
I0410 14:07:40.967612 18414 solver.cpp:237]     Train net output #0: loss = 1.89703 (* 1 = 1.89703 loss)
I0410 14:07:40.967625 18414 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0410 14:07:45.308614 18414 solver.cpp:218] Iteration 5112 (2.76443 iter/s, 4.34085s/12 iters), loss = 2.05624
I0410 14:07:45.308673 18414 solver.cpp:237]     Train net output #0: loss = 2.05624 (* 1 = 2.05624 loss)
I0410 14:07:45.308686 18414 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0410 14:07:50.226284 18414 solver.cpp:218] Iteration 5124 (2.44029 iter/s, 4.91745s/12 iters), loss = 2.06316
I0410 14:07:50.226336 18414 solver.cpp:237]     Train net output #0: loss = 2.06316 (* 1 = 2.06316 loss)
I0410 14:07:50.226347 18414 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0410 14:07:55.164845 18414 solver.cpp:218] Iteration 5136 (2.42996 iter/s, 4.93834s/12 iters), loss = 2.0304
I0410 14:07:55.164999 18414 solver.cpp:237]     Train net output #0: loss = 2.0304 (* 1 = 2.0304 loss)
I0410 14:07:55.165011 18414 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0410 14:08:00.091740 18414 solver.cpp:218] Iteration 5148 (2.43577 iter/s, 4.92658s/12 iters), loss = 1.86117
I0410 14:08:00.091801 18414 solver.cpp:237]     Train net output #0: loss = 1.86117 (* 1 = 1.86117 loss)
I0410 14:08:00.091815 18414 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0410 14:08:04.047418 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:08:04.976651 18414 solver.cpp:218] Iteration 5160 (2.45666 iter/s, 4.88469s/12 iters), loss = 1.99456
I0410 14:08:04.976697 18414 solver.cpp:237]     Train net output #0: loss = 1.99456 (* 1 = 1.99456 loss)
I0410 14:08:04.976707 18414 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0410 14:08:09.872403 18414 solver.cpp:218] Iteration 5172 (2.45121 iter/s, 4.89555s/12 iters), loss = 2.18147
I0410 14:08:09.872447 18414 solver.cpp:237]     Train net output #0: loss = 2.18147 (* 1 = 2.18147 loss)
I0410 14:08:09.872455 18414 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0410 14:08:14.810307 18414 solver.cpp:218] Iteration 5184 (2.43028 iter/s, 4.93769s/12 iters), loss = 2.01357
I0410 14:08:14.810355 18414 solver.cpp:237]     Train net output #0: loss = 2.01357 (* 1 = 2.01357 loss)
I0410 14:08:14.810365 18414 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0410 14:08:19.710100 18414 solver.cpp:218] Iteration 5196 (2.44919 iter/s, 4.89958s/12 iters), loss = 1.80509
I0410 14:08:19.710152 18414 solver.cpp:237]     Train net output #0: loss = 1.80509 (* 1 = 1.80509 loss)
I0410 14:08:19.710163 18414 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0410 14:08:21.689673 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0410 14:08:22.005820 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0410 14:08:22.218195 18414 solver.cpp:330] Iteration 5202, Testing net (#0)
I0410 14:08:22.218215 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:08:24.632750 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:08:26.679730 18414 solver.cpp:397]     Test net output #0: accuracy = 0.420343
I0410 14:08:26.679837 18414 solver.cpp:397]     Test net output #1: loss = 2.23219 (* 1 = 2.23219 loss)
I0410 14:08:28.673014 18414 solver.cpp:218] Iteration 5208 (1.3389 iter/s, 8.96258s/12 iters), loss = 1.97189
I0410 14:08:28.673063 18414 solver.cpp:237]     Train net output #0: loss = 1.97189 (* 1 = 1.97189 loss)
I0410 14:08:28.673074 18414 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0410 14:08:33.798080 18414 solver.cpp:218] Iteration 5220 (2.34153 iter/s, 5.12485s/12 iters), loss = 1.82681
I0410 14:08:33.798132 18414 solver.cpp:237]     Train net output #0: loss = 1.82681 (* 1 = 1.82681 loss)
I0410 14:08:33.798146 18414 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0410 14:08:38.718647 18414 solver.cpp:218] Iteration 5232 (2.43885 iter/s, 4.92035s/12 iters), loss = 1.98373
I0410 14:08:38.718705 18414 solver.cpp:237]     Train net output #0: loss = 1.98373 (* 1 = 1.98373 loss)
I0410 14:08:38.718719 18414 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0410 14:08:43.635874 18414 solver.cpp:218] Iteration 5244 (2.44051 iter/s, 4.91701s/12 iters), loss = 1.90931
I0410 14:08:43.635929 18414 solver.cpp:237]     Train net output #0: loss = 1.90931 (* 1 = 1.90931 loss)
I0410 14:08:43.635941 18414 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0410 14:08:48.531787 18414 solver.cpp:218] Iteration 5256 (2.45113 iter/s, 4.8957s/12 iters), loss = 2.16591
I0410 14:08:48.531842 18414 solver.cpp:237]     Train net output #0: loss = 2.16591 (* 1 = 2.16591 loss)
I0410 14:08:48.531857 18414 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0410 14:08:49.806668 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:08:53.442453 18414 solver.cpp:218] Iteration 5268 (2.44377 iter/s, 4.91045s/12 iters), loss = 1.91117
I0410 14:08:53.442503 18414 solver.cpp:237]     Train net output #0: loss = 1.91117 (* 1 = 1.91117 loss)
I0410 14:08:53.442513 18414 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0410 14:08:58.410555 18414 solver.cpp:218] Iteration 5280 (2.41552 iter/s, 4.96788s/12 iters), loss = 1.7906
I0410 14:08:58.410686 18414 solver.cpp:237]     Train net output #0: loss = 1.7906 (* 1 = 1.7906 loss)
I0410 14:08:58.410698 18414 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0410 14:09:03.522944 18414 solver.cpp:218] Iteration 5292 (2.34737 iter/s, 5.11209s/12 iters), loss = 2.04153
I0410 14:09:03.522998 18414 solver.cpp:237]     Train net output #0: loss = 2.04153 (* 1 = 2.04153 loss)
I0410 14:09:03.523010 18414 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0410 14:09:08.005401 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0410 14:09:08.341248 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0410 14:09:08.558202 18414 solver.cpp:330] Iteration 5304, Testing net (#0)
I0410 14:09:08.558231 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:09:11.015028 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:09:13.108561 18414 solver.cpp:397]     Test net output #0: accuracy = 0.42402
I0410 14:09:13.108613 18414 solver.cpp:397]     Test net output #1: loss = 2.18543 (* 1 = 2.18543 loss)
I0410 14:09:13.190917 18414 solver.cpp:218] Iteration 5304 (1.24126 iter/s, 9.66761s/12 iters), loss = 1.8809
I0410 14:09:13.190969 18414 solver.cpp:237]     Train net output #0: loss = 1.8809 (* 1 = 1.8809 loss)
I0410 14:09:13.190981 18414 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0410 14:09:17.455634 18414 solver.cpp:218] Iteration 5316 (2.81392 iter/s, 4.26451s/12 iters), loss = 1.79894
I0410 14:09:17.455691 18414 solver.cpp:237]     Train net output #0: loss = 1.79894 (* 1 = 1.79894 loss)
I0410 14:09:17.455703 18414 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0410 14:09:22.367319 18414 solver.cpp:218] Iteration 5328 (2.44326 iter/s, 4.91146s/12 iters), loss = 2.00959
I0410 14:09:22.367365 18414 solver.cpp:237]     Train net output #0: loss = 2.00959 (* 1 = 2.00959 loss)
I0410 14:09:22.367374 18414 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0410 14:09:27.293129 18414 solver.cpp:218] Iteration 5340 (2.43625 iter/s, 4.9256s/12 iters), loss = 1.89632
I0410 14:09:27.293184 18414 solver.cpp:237]     Train net output #0: loss = 1.89632 (* 1 = 1.89632 loss)
I0410 14:09:27.293196 18414 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0410 14:09:32.219147 18414 solver.cpp:218] Iteration 5352 (2.43616 iter/s, 4.92579s/12 iters), loss = 1.77822
I0410 14:09:32.219300 18414 solver.cpp:237]     Train net output #0: loss = 1.77822 (* 1 = 1.77822 loss)
I0410 14:09:32.219314 18414 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0410 14:09:35.566848 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:09:37.114965 18414 solver.cpp:218] Iteration 5364 (2.45123 iter/s, 4.89551s/12 iters), loss = 1.88802
I0410 14:09:37.115010 18414 solver.cpp:237]     Train net output #0: loss = 1.88802 (* 1 = 1.88802 loss)
I0410 14:09:37.115020 18414 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0410 14:09:42.187924 18414 solver.cpp:218] Iteration 5376 (2.36558 iter/s, 5.07274s/12 iters), loss = 1.73644
I0410 14:09:42.187976 18414 solver.cpp:237]     Train net output #0: loss = 1.73644 (* 1 = 1.73644 loss)
I0410 14:09:42.187989 18414 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0410 14:09:47.278920 18414 solver.cpp:218] Iteration 5388 (2.3572 iter/s, 5.09078s/12 iters), loss = 1.89767
I0410 14:09:47.278978 18414 solver.cpp:237]     Train net output #0: loss = 1.89767 (* 1 = 1.89767 loss)
I0410 14:09:47.278990 18414 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0410 14:09:52.189736 18414 solver.cpp:218] Iteration 5400 (2.4437 iter/s, 4.91059s/12 iters), loss = 1.90474
I0410 14:09:52.189796 18414 solver.cpp:237]     Train net output #0: loss = 1.90474 (* 1 = 1.90474 loss)
I0410 14:09:52.189807 18414 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0410 14:09:54.214978 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0410 14:09:54.534536 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0410 14:09:54.745272 18414 solver.cpp:330] Iteration 5406, Testing net (#0)
I0410 14:09:54.745301 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:09:57.083885 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:09:59.210799 18414 solver.cpp:397]     Test net output #0: accuracy = 0.433824
I0410 14:09:59.210844 18414 solver.cpp:397]     Test net output #1: loss = 2.15241 (* 1 = 2.15241 loss)
I0410 14:10:01.154734 18414 solver.cpp:218] Iteration 5412 (1.33859 iter/s, 8.96465s/12 iters), loss = 1.83986
I0410 14:10:01.154778 18414 solver.cpp:237]     Train net output #0: loss = 1.83986 (* 1 = 1.83986 loss)
I0410 14:10:01.154788 18414 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0410 14:10:06.288506 18414 solver.cpp:218] Iteration 5424 (2.33756 iter/s, 5.13356s/12 iters), loss = 1.77997
I0410 14:10:06.288609 18414 solver.cpp:237]     Train net output #0: loss = 1.77997 (* 1 = 1.77997 loss)
I0410 14:10:06.288619 18414 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0410 14:10:11.370276 18414 solver.cpp:218] Iteration 5436 (2.36151 iter/s, 5.0815s/12 iters), loss = 1.97646
I0410 14:10:11.370337 18414 solver.cpp:237]     Train net output #0: loss = 1.97646 (* 1 = 1.97646 loss)
I0410 14:10:11.370350 18414 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0410 14:10:16.266507 18414 solver.cpp:218] Iteration 5448 (2.45098 iter/s, 4.89601s/12 iters), loss = 1.4659
I0410 14:10:16.266552 18414 solver.cpp:237]     Train net output #0: loss = 1.4659 (* 1 = 1.4659 loss)
I0410 14:10:16.266562 18414 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0410 14:10:21.189114 18414 solver.cpp:218] Iteration 5460 (2.43784 iter/s, 4.9224s/12 iters), loss = 1.80198
I0410 14:10:21.189167 18414 solver.cpp:237]     Train net output #0: loss = 1.80198 (* 1 = 1.80198 loss)
I0410 14:10:21.189178 18414 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0410 14:10:21.753810 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:10:26.096411 18414 solver.cpp:218] Iteration 5472 (2.44545 iter/s, 4.90708s/12 iters), loss = 1.96152
I0410 14:10:26.096463 18414 solver.cpp:237]     Train net output #0: loss = 1.96152 (* 1 = 1.96152 loss)
I0410 14:10:26.096475 18414 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0410 14:10:30.999994 18414 solver.cpp:218] Iteration 5484 (2.4473 iter/s, 4.90336s/12 iters), loss = 1.71658
I0410 14:10:31.000048 18414 solver.cpp:237]     Train net output #0: loss = 1.71658 (* 1 = 1.71658 loss)
I0410 14:10:31.000059 18414 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0410 14:10:36.022137 18414 solver.cpp:218] Iteration 5496 (2.38953 iter/s, 5.02191s/12 iters), loss = 1.51953
I0410 14:10:36.022192 18414 solver.cpp:237]     Train net output #0: loss = 1.51953 (* 1 = 1.51953 loss)
I0410 14:10:36.022203 18414 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0410 14:10:40.649127 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0410 14:10:40.945878 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0410 14:10:41.157685 18414 solver.cpp:330] Iteration 5508, Testing net (#0)
I0410 14:10:41.157707 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:10:43.417029 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:10:45.588294 18414 solver.cpp:397]     Test net output #0: accuracy = 0.452819
I0410 14:10:45.588343 18414 solver.cpp:397]     Test net output #1: loss = 2.10313 (* 1 = 2.10313 loss)
I0410 14:10:45.671418 18414 solver.cpp:218] Iteration 5508 (1.24366 iter/s, 9.64892s/12 iters), loss = 1.68995
I0410 14:10:45.671470 18414 solver.cpp:237]     Train net output #0: loss = 1.68995 (* 1 = 1.68995 loss)
I0410 14:10:45.671483 18414 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0410 14:10:50.047451 18414 solver.cpp:218] Iteration 5520 (2.74234 iter/s, 4.37583s/12 iters), loss = 1.88901
I0410 14:10:50.047508 18414 solver.cpp:237]     Train net output #0: loss = 1.88901 (* 1 = 1.88901 loss)
I0410 14:10:50.047520 18414 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0410 14:10:50.833501 18414 blocking_queue.cpp:49] Waiting for data
I0410 14:10:54.923020 18414 solver.cpp:218] Iteration 5532 (2.46136 iter/s, 4.87535s/12 iters), loss = 2.05161
I0410 14:10:54.923074 18414 solver.cpp:237]     Train net output #0: loss = 2.05161 (* 1 = 2.05161 loss)
I0410 14:10:54.923087 18414 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0410 14:10:59.798012 18414 solver.cpp:218] Iteration 5544 (2.46165 iter/s, 4.87478s/12 iters), loss = 1.69044
I0410 14:10:59.798069 18414 solver.cpp:237]     Train net output #0: loss = 1.69044 (* 1 = 1.69044 loss)
I0410 14:10:59.798084 18414 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0410 14:11:05.016223 18414 solver.cpp:218] Iteration 5556 (2.29974 iter/s, 5.21799s/12 iters), loss = 1.59317
I0410 14:11:05.016273 18414 solver.cpp:237]     Train net output #0: loss = 1.59317 (* 1 = 1.59317 loss)
I0410 14:11:05.016285 18414 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0410 14:11:07.845228 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:11:10.134531 18414 solver.cpp:218] Iteration 5568 (2.34462 iter/s, 5.11809s/12 iters), loss = 1.66163
I0410 14:11:10.134582 18414 solver.cpp:237]     Train net output #0: loss = 1.66163 (* 1 = 1.66163 loss)
I0410 14:11:10.134593 18414 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0410 14:11:15.037711 18414 solver.cpp:218] Iteration 5580 (2.4475 iter/s, 4.90297s/12 iters), loss = 1.63385
I0410 14:11:15.037838 18414 solver.cpp:237]     Train net output #0: loss = 1.63385 (* 1 = 1.63385 loss)
I0410 14:11:15.037853 18414 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0410 14:11:19.964114 18414 solver.cpp:218] Iteration 5592 (2.436 iter/s, 4.92612s/12 iters), loss = 1.90603
I0410 14:11:19.964155 18414 solver.cpp:237]     Train net output #0: loss = 1.90603 (* 1 = 1.90603 loss)
I0410 14:11:19.964164 18414 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0410 14:11:24.873448 18414 solver.cpp:218] Iteration 5604 (2.44442 iter/s, 4.90913s/12 iters), loss = 1.43032
I0410 14:11:24.873494 18414 solver.cpp:237]     Train net output #0: loss = 1.43032 (* 1 = 1.43032 loss)
I0410 14:11:24.873503 18414 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0410 14:11:26.851958 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0410 14:11:27.140264 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0410 14:11:27.340610 18414 solver.cpp:330] Iteration 5610, Testing net (#0)
I0410 14:11:27.340642 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:11:29.619860 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:11:31.859886 18414 solver.cpp:397]     Test net output #0: accuracy = 0.447917
I0410 14:11:31.859936 18414 solver.cpp:397]     Test net output #1: loss = 2.1641 (* 1 = 2.1641 loss)
I0410 14:11:34.183801 18414 solver.cpp:218] Iteration 5616 (1.28894 iter/s, 9.31001s/12 iters), loss = 1.8432
I0410 14:11:34.183853 18414 solver.cpp:237]     Train net output #0: loss = 1.8432 (* 1 = 1.8432 loss)
I0410 14:11:34.183867 18414 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0410 14:11:39.093410 18414 solver.cpp:218] Iteration 5628 (2.4443 iter/s, 4.90939s/12 iters), loss = 1.73621
I0410 14:11:39.093466 18414 solver.cpp:237]     Train net output #0: loss = 1.73621 (* 1 = 1.73621 loss)
I0410 14:11:39.093480 18414 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0410 14:11:44.073346 18414 solver.cpp:218] Iteration 5640 (2.40978 iter/s, 4.97971s/12 iters), loss = 1.68262
I0410 14:11:44.073398 18414 solver.cpp:237]     Train net output #0: loss = 1.68262 (* 1 = 1.68262 loss)
I0410 14:11:44.073410 18414 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0410 14:11:49.012058 18414 solver.cpp:218] Iteration 5652 (2.42989 iter/s, 4.9385s/12 iters), loss = 1.62812
I0410 14:11:49.012168 18414 solver.cpp:237]     Train net output #0: loss = 1.62812 (* 1 = 1.62812 loss)
I0410 14:11:49.012181 18414 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0410 14:11:53.758090 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:11:53.927008 18414 solver.cpp:218] Iteration 5664 (2.44167 iter/s, 4.91468s/12 iters), loss = 1.72085
I0410 14:11:53.927064 18414 solver.cpp:237]     Train net output #0: loss = 1.72085 (* 1 = 1.72085 loss)
I0410 14:11:53.927078 18414 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0410 14:11:58.872592 18414 solver.cpp:218] Iteration 5676 (2.42651 iter/s, 4.94537s/12 iters), loss = 1.61509
I0410 14:11:58.872645 18414 solver.cpp:237]     Train net output #0: loss = 1.61509 (* 1 = 1.61509 loss)
I0410 14:11:58.872659 18414 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0410 14:12:03.784159 18414 solver.cpp:218] Iteration 5688 (2.44332 iter/s, 4.91135s/12 iters), loss = 1.44199
I0410 14:12:03.784219 18414 solver.cpp:237]     Train net output #0: loss = 1.44199 (* 1 = 1.44199 loss)
I0410 14:12:03.784231 18414 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0410 14:12:08.705511 18414 solver.cpp:218] Iteration 5700 (2.43846 iter/s, 4.92113s/12 iters), loss = 1.826
I0410 14:12:08.705562 18414 solver.cpp:237]     Train net output #0: loss = 1.826 (* 1 = 1.826 loss)
I0410 14:12:08.705574 18414 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0410 14:12:13.146492 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0410 14:12:13.462473 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0410 14:12:13.669627 18414 solver.cpp:330] Iteration 5712, Testing net (#0)
I0410 14:12:13.669647 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:12:15.963284 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:12:18.211153 18414 solver.cpp:397]     Test net output #0: accuracy = 0.434436
I0410 14:12:18.211200 18414 solver.cpp:397]     Test net output #1: loss = 2.16868 (* 1 = 2.16868 loss)
I0410 14:12:18.294160 18414 solver.cpp:218] Iteration 5712 (1.25153 iter/s, 9.58829s/12 iters), loss = 1.74149
I0410 14:12:18.294215 18414 solver.cpp:237]     Train net output #0: loss = 1.74149 (* 1 = 1.74149 loss)
I0410 14:12:18.294227 18414 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0410 14:12:22.492503 18414 solver.cpp:218] Iteration 5724 (2.85841 iter/s, 4.19815s/12 iters), loss = 1.80591
I0410 14:12:22.492612 18414 solver.cpp:237]     Train net output #0: loss = 1.80591 (* 1 = 1.80591 loss)
I0410 14:12:22.492625 18414 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0410 14:12:27.427970 18414 solver.cpp:218] Iteration 5736 (2.43152 iter/s, 4.93519s/12 iters), loss = 1.80757
I0410 14:12:27.428017 18414 solver.cpp:237]     Train net output #0: loss = 1.80757 (* 1 = 1.80757 loss)
I0410 14:12:27.428026 18414 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0410 14:12:32.274482 18414 solver.cpp:218] Iteration 5748 (2.47612 iter/s, 4.84629s/12 iters), loss = 1.61475
I0410 14:12:32.274546 18414 solver.cpp:237]     Train net output #0: loss = 1.61475 (* 1 = 1.61475 loss)
I0410 14:12:32.274559 18414 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0410 14:12:37.174346 18414 solver.cpp:218] Iteration 5760 (2.44916 iter/s, 4.89964s/12 iters), loss = 1.72133
I0410 14:12:37.174394 18414 solver.cpp:237]     Train net output #0: loss = 1.72133 (* 1 = 1.72133 loss)
I0410 14:12:37.174404 18414 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0410 14:12:39.072938 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:12:42.061286 18414 solver.cpp:218] Iteration 5772 (2.45564 iter/s, 4.88672s/12 iters), loss = 1.44874
I0410 14:12:42.061343 18414 solver.cpp:237]     Train net output #0: loss = 1.44874 (* 1 = 1.44874 loss)
I0410 14:12:42.061355 18414 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0410 14:12:47.004791 18414 solver.cpp:218] Iteration 5784 (2.42754 iter/s, 4.94329s/12 iters), loss = 1.80078
I0410 14:12:47.004838 18414 solver.cpp:237]     Train net output #0: loss = 1.80078 (* 1 = 1.80078 loss)
I0410 14:12:47.004848 18414 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0410 14:12:52.024996 18414 solver.cpp:218] Iteration 5796 (2.39044 iter/s, 5.01999s/12 iters), loss = 1.5335
I0410 14:12:52.025036 18414 solver.cpp:237]     Train net output #0: loss = 1.5335 (* 1 = 1.5335 loss)
I0410 14:12:52.025044 18414 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0410 14:12:56.998703 18414 solver.cpp:218] Iteration 5808 (2.41279 iter/s, 4.9735s/12 iters), loss = 1.80893
I0410 14:12:56.998843 18414 solver.cpp:237]     Train net output #0: loss = 1.80893 (* 1 = 1.80893 loss)
I0410 14:12:56.998857 18414 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0410 14:12:58.991889 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0410 14:12:59.278228 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0410 14:12:59.473161 18414 solver.cpp:330] Iteration 5814, Testing net (#0)
I0410 14:12:59.473181 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:13:01.643667 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:13:03.929199 18414 solver.cpp:397]     Test net output #0: accuracy = 0.454657
I0410 14:13:03.929230 18414 solver.cpp:397]     Test net output #1: loss = 2.1196 (* 1 = 2.1196 loss)
I0410 14:13:05.774294 18414 solver.cpp:218] Iteration 5820 (1.3675 iter/s, 8.77517s/12 iters), loss = 1.43304
I0410 14:13:05.774356 18414 solver.cpp:237]     Train net output #0: loss = 1.43304 (* 1 = 1.43304 loss)
I0410 14:13:05.774370 18414 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0410 14:13:10.746611 18414 solver.cpp:218] Iteration 5832 (2.41347 iter/s, 4.97209s/12 iters), loss = 1.50841
I0410 14:13:10.746668 18414 solver.cpp:237]     Train net output #0: loss = 1.50841 (* 1 = 1.50841 loss)
I0410 14:13:10.746681 18414 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0410 14:13:15.704460 18414 solver.cpp:218] Iteration 5844 (2.42051 iter/s, 4.95763s/12 iters), loss = 1.83568
I0410 14:13:15.704502 18414 solver.cpp:237]     Train net output #0: loss = 1.83568 (* 1 = 1.83568 loss)
I0410 14:13:15.704511 18414 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0410 14:13:20.603132 18414 solver.cpp:218] Iteration 5856 (2.44974 iter/s, 4.89847s/12 iters), loss = 1.46659
I0410 14:13:20.603174 18414 solver.cpp:237]     Train net output #0: loss = 1.46659 (* 1 = 1.46659 loss)
I0410 14:13:20.603183 18414 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0410 14:13:24.796456 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:13:25.621407 18414 solver.cpp:218] Iteration 5868 (2.39136 iter/s, 5.01806s/12 iters), loss = 1.50075
I0410 14:13:25.621449 18414 solver.cpp:237]     Train net output #0: loss = 1.50075 (* 1 = 1.50075 loss)
I0410 14:13:25.621459 18414 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0410 14:13:30.623497 18414 solver.cpp:218] Iteration 5880 (2.3991 iter/s, 5.00188s/12 iters), loss = 1.57609
I0410 14:13:30.625260 18414 solver.cpp:237]     Train net output #0: loss = 1.57609 (* 1 = 1.57609 loss)
I0410 14:13:30.625273 18414 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0410 14:13:35.525029 18414 solver.cpp:218] Iteration 5892 (2.44917 iter/s, 4.89961s/12 iters), loss = 1.42474
I0410 14:13:35.525072 18414 solver.cpp:237]     Train net output #0: loss = 1.42474 (* 1 = 1.42474 loss)
I0410 14:13:35.525081 18414 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0410 14:13:40.444159 18414 solver.cpp:218] Iteration 5904 (2.43956 iter/s, 4.91891s/12 iters), loss = 1.46435
I0410 14:13:40.444221 18414 solver.cpp:237]     Train net output #0: loss = 1.46435 (* 1 = 1.46435 loss)
I0410 14:13:40.444236 18414 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0410 14:13:45.037509 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0410 14:13:45.368196 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0410 14:13:45.594982 18414 solver.cpp:330] Iteration 5916, Testing net (#0)
I0410 14:13:45.595013 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:13:47.987701 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:13:50.311076 18414 solver.cpp:397]     Test net output #0: accuracy = 0.452206
I0410 14:13:50.311125 18414 solver.cpp:397]     Test net output #1: loss = 2.12371 (* 1 = 2.12371 loss)
I0410 14:13:50.394094 18414 solver.cpp:218] Iteration 5916 (1.20608 iter/s, 9.94956s/12 iters), loss = 1.7262
I0410 14:13:50.394146 18414 solver.cpp:237]     Train net output #0: loss = 1.7262 (* 1 = 1.7262 loss)
I0410 14:13:50.394165 18414 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0410 14:13:54.725409 18414 solver.cpp:218] Iteration 5928 (2.77065 iter/s, 4.33112s/12 iters), loss = 1.70169
I0410 14:13:54.725468 18414 solver.cpp:237]     Train net output #0: loss = 1.70169 (* 1 = 1.70169 loss)
I0410 14:13:54.725481 18414 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0410 14:13:59.771517 18414 solver.cpp:218] Iteration 5940 (2.37818 iter/s, 5.04589s/12 iters), loss = 1.4387
I0410 14:13:59.771555 18414 solver.cpp:237]     Train net output #0: loss = 1.4387 (* 1 = 1.4387 loss)
I0410 14:13:59.771564 18414 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0410 14:14:04.707123 18414 solver.cpp:218] Iteration 5952 (2.43142 iter/s, 4.9354s/12 iters), loss = 1.68626
I0410 14:14:04.707267 18414 solver.cpp:237]     Train net output #0: loss = 1.68626 (* 1 = 1.68626 loss)
I0410 14:14:04.707283 18414 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0410 14:14:09.688844 18414 solver.cpp:218] Iteration 5964 (2.40895 iter/s, 4.98142s/12 iters), loss = 1.56576
I0410 14:14:09.688906 18414 solver.cpp:237]     Train net output #0: loss = 1.56576 (* 1 = 1.56576 loss)
I0410 14:14:09.688920 18414 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0410 14:14:11.008410 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:14:14.623256 18414 solver.cpp:218] Iteration 5976 (2.43201 iter/s, 4.93418s/12 iters), loss = 1.21099
I0410 14:14:14.623304 18414 solver.cpp:237]     Train net output #0: loss = 1.21099 (* 1 = 1.21099 loss)
I0410 14:14:14.623314 18414 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0410 14:14:19.561092 18414 solver.cpp:218] Iteration 5988 (2.43032 iter/s, 4.93762s/12 iters), loss = 1.42386
I0410 14:14:19.561139 18414 solver.cpp:237]     Train net output #0: loss = 1.42386 (* 1 = 1.42386 loss)
I0410 14:14:19.561151 18414 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0410 14:14:24.509713 18414 solver.cpp:218] Iteration 6000 (2.42502 iter/s, 4.94841s/12 iters), loss = 1.5428
I0410 14:14:24.509764 18414 solver.cpp:237]     Train net output #0: loss = 1.5428 (* 1 = 1.5428 loss)
I0410 14:14:24.509778 18414 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0410 14:14:29.402601 18414 solver.cpp:218] Iteration 6012 (2.45264 iter/s, 4.89268s/12 iters), loss = 1.32156
I0410 14:14:29.402642 18414 solver.cpp:237]     Train net output #0: loss = 1.32156 (* 1 = 1.32156 loss)
I0410 14:14:29.402652 18414 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0410 14:14:31.368404 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0410 14:14:31.694350 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0410 14:14:31.900483 18414 solver.cpp:330] Iteration 6018, Testing net (#0)
I0410 14:14:31.900503 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:14:33.937105 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:14:36.308902 18414 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0410 14:14:36.309082 18414 solver.cpp:397]     Test net output #1: loss = 1.96977 (* 1 = 1.96977 loss)
I0410 14:14:38.184367 18414 solver.cpp:218] Iteration 6024 (1.36652 iter/s, 8.78144s/12 iters), loss = 1.54766
I0410 14:14:38.184424 18414 solver.cpp:237]     Train net output #0: loss = 1.54766 (* 1 = 1.54766 loss)
I0410 14:14:38.184437 18414 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0410 14:14:43.137298 18414 solver.cpp:218] Iteration 6036 (2.42292 iter/s, 4.95271s/12 iters), loss = 1.31264
I0410 14:14:43.137356 18414 solver.cpp:237]     Train net output #0: loss = 1.31264 (* 1 = 1.31264 loss)
I0410 14:14:43.137368 18414 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0410 14:14:48.165387 18414 solver.cpp:218] Iteration 6048 (2.3867 iter/s, 5.02786s/12 iters), loss = 1.61528
I0410 14:14:48.165444 18414 solver.cpp:237]     Train net output #0: loss = 1.61528 (* 1 = 1.61528 loss)
I0410 14:14:48.165457 18414 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0410 14:14:53.166766 18414 solver.cpp:218] Iteration 6060 (2.39945 iter/s, 5.00115s/12 iters), loss = 1.45114
I0410 14:14:53.166821 18414 solver.cpp:237]     Train net output #0: loss = 1.45114 (* 1 = 1.45114 loss)
I0410 14:14:53.166834 18414 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0410 14:14:56.577446 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:14:58.104993 18414 solver.cpp:218] Iteration 6072 (2.43013 iter/s, 4.938s/12 iters), loss = 1.54954
I0410 14:14:58.105049 18414 solver.cpp:237]     Train net output #0: loss = 1.54954 (* 1 = 1.54954 loss)
I0410 14:14:58.105062 18414 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0410 14:15:03.091847 18414 solver.cpp:218] Iteration 6084 (2.40643 iter/s, 4.98664s/12 iters), loss = 1.37639
I0410 14:15:03.091900 18414 solver.cpp:237]     Train net output #0: loss = 1.37639 (* 1 = 1.37639 loss)
I0410 14:15:03.091912 18414 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0410 14:15:08.111497 18414 solver.cpp:218] Iteration 6096 (2.39071 iter/s, 5.01943s/12 iters), loss = 1.50133
I0410 14:15:08.111595 18414 solver.cpp:237]     Train net output #0: loss = 1.50133 (* 1 = 1.50133 loss)
I0410 14:15:08.111605 18414 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0410 14:15:12.969812 18414 solver.cpp:218] Iteration 6108 (2.47013 iter/s, 4.85805s/12 iters), loss = 1.40807
I0410 14:15:12.969852 18414 solver.cpp:237]     Train net output #0: loss = 1.40807 (* 1 = 1.40807 loss)
I0410 14:15:12.969861 18414 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0410 14:15:17.486935 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0410 14:15:17.782084 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0410 14:15:17.980114 18414 solver.cpp:330] Iteration 6120, Testing net (#0)
I0410 14:15:17.980139 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:15:20.018162 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:15:22.455744 18414 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0410 14:15:22.455794 18414 solver.cpp:397]     Test net output #1: loss = 2.00106 (* 1 = 2.00106 loss)
I0410 14:15:22.538682 18414 solver.cpp:218] Iteration 6120 (1.25411 iter/s, 9.56853s/12 iters), loss = 1.74612
I0410 14:15:22.538728 18414 solver.cpp:237]     Train net output #0: loss = 1.74612 (* 1 = 1.74612 loss)
I0410 14:15:22.538739 18414 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0410 14:15:26.739289 18414 solver.cpp:218] Iteration 6132 (2.85686 iter/s, 4.20042s/12 iters), loss = 1.588
I0410 14:15:26.739339 18414 solver.cpp:237]     Train net output #0: loss = 1.588 (* 1 = 1.588 loss)
I0410 14:15:26.739351 18414 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0410 14:15:31.644371 18414 solver.cpp:218] Iteration 6144 (2.44655 iter/s, 4.90487s/12 iters), loss = 1.68877
I0410 14:15:31.644420 18414 solver.cpp:237]     Train net output #0: loss = 1.68877 (* 1 = 1.68877 loss)
I0410 14:15:31.644433 18414 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0410 14:15:36.576131 18414 solver.cpp:218] Iteration 6156 (2.43331 iter/s, 4.93154s/12 iters), loss = 1.48164
I0410 14:15:36.576185 18414 solver.cpp:237]     Train net output #0: loss = 1.48164 (* 1 = 1.48164 loss)
I0410 14:15:36.576198 18414 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0410 14:15:41.464743 18414 solver.cpp:218] Iteration 6168 (2.4548 iter/s, 4.88839s/12 iters), loss = 1.7133
I0410 14:15:41.465759 18414 solver.cpp:237]     Train net output #0: loss = 1.7133 (* 1 = 1.7133 loss)
I0410 14:15:41.465773 18414 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0410 14:15:42.048782 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:15:46.397228 18414 solver.cpp:218] Iteration 6180 (2.43343 iter/s, 4.93131s/12 iters), loss = 1.56315
I0410 14:15:46.397276 18414 solver.cpp:237]     Train net output #0: loss = 1.56315 (* 1 = 1.56315 loss)
I0410 14:15:46.397289 18414 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0410 14:15:51.367218 18414 solver.cpp:218] Iteration 6192 (2.4146 iter/s, 4.96977s/12 iters), loss = 1.5137
I0410 14:15:51.367274 18414 solver.cpp:237]     Train net output #0: loss = 1.5137 (* 1 = 1.5137 loss)
I0410 14:15:51.367286 18414 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0410 14:15:56.380697 18414 solver.cpp:218] Iteration 6204 (2.39365 iter/s, 5.01326s/12 iters), loss = 1.26872
I0410 14:15:56.380753 18414 solver.cpp:237]     Train net output #0: loss = 1.26872 (* 1 = 1.26872 loss)
I0410 14:15:56.380765 18414 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0410 14:16:01.293336 18414 solver.cpp:218] Iteration 6216 (2.44279 iter/s, 4.91242s/12 iters), loss = 1.56036
I0410 14:16:01.293393 18414 solver.cpp:237]     Train net output #0: loss = 1.56036 (* 1 = 1.56036 loss)
I0410 14:16:01.293406 18414 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0410 14:16:03.304623 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0410 14:16:03.629603 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0410 14:16:03.839627 18414 solver.cpp:330] Iteration 6222, Testing net (#0)
I0410 14:16:03.839649 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:16:05.862006 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:16:06.772224 18414 blocking_queue.cpp:49] Waiting for data
I0410 14:16:08.302489 18414 solver.cpp:397]     Test net output #0: accuracy = 0.490809
I0410 14:16:08.302518 18414 solver.cpp:397]     Test net output #1: loss = 1.95989 (* 1 = 1.95989 loss)
I0410 14:16:10.185429 18414 solver.cpp:218] Iteration 6228 (1.34957 iter/s, 8.89175s/12 iters), loss = 1.49389
I0410 14:16:10.185487 18414 solver.cpp:237]     Train net output #0: loss = 1.49389 (* 1 = 1.49389 loss)
I0410 14:16:10.185499 18414 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0410 14:16:15.037626 18414 solver.cpp:218] Iteration 6240 (2.47322 iter/s, 4.85197s/12 iters), loss = 1.69272
I0410 14:16:15.037803 18414 solver.cpp:237]     Train net output #0: loss = 1.69272 (* 1 = 1.69272 loss)
I0410 14:16:15.037817 18414 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0410 14:16:19.959391 18414 solver.cpp:218] Iteration 6252 (2.43832 iter/s, 4.92143s/12 iters), loss = 1.51289
I0410 14:16:19.959439 18414 solver.cpp:237]     Train net output #0: loss = 1.51289 (* 1 = 1.51289 loss)
I0410 14:16:19.959448 18414 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0410 14:16:24.901213 18414 solver.cpp:218] Iteration 6264 (2.42836 iter/s, 4.94161s/12 iters), loss = 1.30591
I0410 14:16:24.901259 18414 solver.cpp:237]     Train net output #0: loss = 1.30591 (* 1 = 1.30591 loss)
I0410 14:16:24.901268 18414 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0410 14:16:27.552678 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:16:29.785207 18414 solver.cpp:218] Iteration 6276 (2.45711 iter/s, 4.88379s/12 iters), loss = 1.48237
I0410 14:16:29.785252 18414 solver.cpp:237]     Train net output #0: loss = 1.48237 (* 1 = 1.48237 loss)
I0410 14:16:29.785262 18414 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0410 14:16:34.971359 18414 solver.cpp:218] Iteration 6288 (2.31395 iter/s, 5.18593s/12 iters), loss = 1.40417
I0410 14:16:34.971405 18414 solver.cpp:237]     Train net output #0: loss = 1.40417 (* 1 = 1.40417 loss)
I0410 14:16:34.971413 18414 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0410 14:16:39.843961 18414 solver.cpp:218] Iteration 6300 (2.46286 iter/s, 4.87239s/12 iters), loss = 1.2956
I0410 14:16:39.844004 18414 solver.cpp:237]     Train net output #0: loss = 1.2956 (* 1 = 1.2956 loss)
I0410 14:16:39.844013 18414 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0410 14:16:44.951567 18414 solver.cpp:218] Iteration 6312 (2.34954 iter/s, 5.10738s/12 iters), loss = 1.39522
I0410 14:16:44.951623 18414 solver.cpp:237]     Train net output #0: loss = 1.39522 (* 1 = 1.39522 loss)
I0410 14:16:44.951635 18414 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0410 14:16:49.451963 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0410 14:16:49.754173 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0410 14:16:49.953662 18414 solver.cpp:330] Iteration 6324, Testing net (#0)
I0410 14:16:49.953703 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:16:51.914391 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:16:54.541337 18414 solver.cpp:397]     Test net output #0: accuracy = 0.490196
I0410 14:16:54.541378 18414 solver.cpp:397]     Test net output #1: loss = 1.9945 (* 1 = 1.9945 loss)
I0410 14:16:54.624186 18414 solver.cpp:218] Iteration 6324 (1.24066 iter/s, 9.67225s/12 iters), loss = 1.49005
I0410 14:16:54.624231 18414 solver.cpp:237]     Train net output #0: loss = 1.49005 (* 1 = 1.49005 loss)
I0410 14:16:54.624241 18414 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0410 14:16:59.012274 18414 solver.cpp:218] Iteration 6336 (2.7348 iter/s, 4.38789s/12 iters), loss = 1.60595
I0410 14:16:59.012326 18414 solver.cpp:237]     Train net output #0: loss = 1.60595 (* 1 = 1.60595 loss)
I0410 14:16:59.012338 18414 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0410 14:17:03.982146 18414 solver.cpp:218] Iteration 6348 (2.41465 iter/s, 4.96966s/12 iters), loss = 1.59995
I0410 14:17:03.982198 18414 solver.cpp:237]     Train net output #0: loss = 1.59995 (* 1 = 1.59995 loss)
I0410 14:17:03.982210 18414 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0410 14:17:08.907235 18414 solver.cpp:218] Iteration 6360 (2.43661 iter/s, 4.92487s/12 iters), loss = 1.72309
I0410 14:17:08.907285 18414 solver.cpp:237]     Train net output #0: loss = 1.72309 (* 1 = 1.72309 loss)
I0410 14:17:08.907296 18414 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0410 14:17:13.708006 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:17:13.846244 18414 solver.cpp:218] Iteration 6372 (2.42974 iter/s, 4.93879s/12 iters), loss = 1.52134
I0410 14:17:13.846294 18414 solver.cpp:237]     Train net output #0: loss = 1.52134 (* 1 = 1.52134 loss)
I0410 14:17:13.846307 18414 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0410 14:17:18.759430 18414 solver.cpp:218] Iteration 6384 (2.44251 iter/s, 4.91297s/12 iters), loss = 1.51103
I0410 14:17:18.759479 18414 solver.cpp:237]     Train net output #0: loss = 1.51103 (* 1 = 1.51103 loss)
I0410 14:17:18.759490 18414 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0410 14:17:23.661325 18414 solver.cpp:218] Iteration 6396 (2.44814 iter/s, 4.90168s/12 iters), loss = 1.22818
I0410 14:17:23.661474 18414 solver.cpp:237]     Train net output #0: loss = 1.22818 (* 1 = 1.22818 loss)
I0410 14:17:23.661486 18414 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0410 14:17:28.576238 18414 solver.cpp:218] Iteration 6408 (2.4417 iter/s, 4.9146s/12 iters), loss = 1.30707
I0410 14:17:28.576294 18414 solver.cpp:237]     Train net output #0: loss = 1.30707 (* 1 = 1.30707 loss)
I0410 14:17:28.576306 18414 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0410 14:17:33.500514 18414 solver.cpp:218] Iteration 6420 (2.43702 iter/s, 4.92405s/12 iters), loss = 1.37449
I0410 14:17:33.500572 18414 solver.cpp:237]     Train net output #0: loss = 1.37449 (* 1 = 1.37449 loss)
I0410 14:17:33.500586 18414 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0410 14:17:35.487210 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0410 14:17:36.038663 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0410 14:17:36.710187 18414 solver.cpp:330] Iteration 6426, Testing net (#0)
I0410 14:17:36.710219 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:17:38.692742 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:17:41.279533 18414 solver.cpp:397]     Test net output #0: accuracy = 0.476103
I0410 14:17:41.279585 18414 solver.cpp:397]     Test net output #1: loss = 2.00542 (* 1 = 2.00542 loss)
I0410 14:17:43.253239 18414 solver.cpp:218] Iteration 6432 (1.23047 iter/s, 9.75236s/12 iters), loss = 1.38057
I0410 14:17:43.253290 18414 solver.cpp:237]     Train net output #0: loss = 1.38057 (* 1 = 1.38057 loss)
I0410 14:17:43.253304 18414 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0410 14:17:48.288223 18414 solver.cpp:218] Iteration 6444 (2.38343 iter/s, 5.03477s/12 iters), loss = 1.35614
I0410 14:17:48.288264 18414 solver.cpp:237]     Train net output #0: loss = 1.35614 (* 1 = 1.35614 loss)
I0410 14:17:48.288273 18414 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0410 14:17:53.199151 18414 solver.cpp:218] Iteration 6456 (2.44363 iter/s, 4.91072s/12 iters), loss = 1.38394
I0410 14:17:53.199208 18414 solver.cpp:237]     Train net output #0: loss = 1.38394 (* 1 = 1.38394 loss)
I0410 14:17:53.199220 18414 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0410 14:17:58.103474 18414 solver.cpp:218] Iteration 6468 (2.44693 iter/s, 4.90411s/12 iters), loss = 1.20771
I0410 14:17:58.103577 18414 solver.cpp:237]     Train net output #0: loss = 1.20771 (* 1 = 1.20771 loss)
I0410 14:17:58.103592 18414 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0410 14:18:00.062495 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:18:03.043721 18414 solver.cpp:218] Iteration 6480 (2.42916 iter/s, 4.93998s/12 iters), loss = 1.44221
I0410 14:18:03.043772 18414 solver.cpp:237]     Train net output #0: loss = 1.44221 (* 1 = 1.44221 loss)
I0410 14:18:03.043783 18414 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0410 14:18:07.912861 18414 solver.cpp:218] Iteration 6492 (2.46461 iter/s, 4.86892s/12 iters), loss = 1.36119
I0410 14:18:07.912916 18414 solver.cpp:237]     Train net output #0: loss = 1.36119 (* 1 = 1.36119 loss)
I0410 14:18:07.912928 18414 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0410 14:18:12.817446 18414 solver.cpp:218] Iteration 6504 (2.4468 iter/s, 4.90436s/12 iters), loss = 1.18746
I0410 14:18:12.817502 18414 solver.cpp:237]     Train net output #0: loss = 1.18746 (* 1 = 1.18746 loss)
I0410 14:18:12.817515 18414 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0410 14:18:17.715210 18414 solver.cpp:218] Iteration 6516 (2.45021 iter/s, 4.89754s/12 iters), loss = 1.20958
I0410 14:18:17.715270 18414 solver.cpp:237]     Train net output #0: loss = 1.20958 (* 1 = 1.20958 loss)
I0410 14:18:17.715282 18414 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0410 14:18:22.186545 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0410 14:18:22.513540 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0410 14:18:22.724182 18414 solver.cpp:330] Iteration 6528, Testing net (#0)
I0410 14:18:22.724215 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:18:24.731848 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:18:27.446120 18414 solver.cpp:397]     Test net output #0: accuracy = 0.488971
I0410 14:18:27.446167 18414 solver.cpp:397]     Test net output #1: loss = 1.98633 (* 1 = 1.98633 loss)
I0410 14:18:27.529527 18414 solver.cpp:218] Iteration 6528 (1.22275 iter/s, 9.81394s/12 iters), loss = 1.11747
I0410 14:18:27.529579 18414 solver.cpp:237]     Train net output #0: loss = 1.11747 (* 1 = 1.11747 loss)
I0410 14:18:27.529592 18414 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0410 14:18:31.825594 18414 solver.cpp:218] Iteration 6540 (2.79338 iter/s, 4.29587s/12 iters), loss = 1.48234
I0410 14:18:31.825734 18414 solver.cpp:237]     Train net output #0: loss = 1.48234 (* 1 = 1.48234 loss)
I0410 14:18:31.825745 18414 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0410 14:18:36.808933 18414 solver.cpp:218] Iteration 6552 (2.40817 iter/s, 4.98303s/12 iters), loss = 1.47652
I0410 14:18:36.808984 18414 solver.cpp:237]     Train net output #0: loss = 1.47652 (* 1 = 1.47652 loss)
I0410 14:18:36.808993 18414 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0410 14:18:41.822680 18414 solver.cpp:218] Iteration 6564 (2.39352 iter/s, 5.01353s/12 iters), loss = 1.22976
I0410 14:18:41.822727 18414 solver.cpp:237]     Train net output #0: loss = 1.22976 (* 1 = 1.22976 loss)
I0410 14:18:41.822736 18414 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0410 14:18:46.033044 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:18:46.800019 18414 solver.cpp:218] Iteration 6576 (2.41103 iter/s, 4.97712s/12 iters), loss = 1.1478
I0410 14:18:46.800060 18414 solver.cpp:237]     Train net output #0: loss = 1.1478 (* 1 = 1.1478 loss)
I0410 14:18:46.800069 18414 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0410 14:18:51.858335 18414 solver.cpp:218] Iteration 6588 (2.37243 iter/s, 5.0581s/12 iters), loss = 1.50927
I0410 14:18:51.858377 18414 solver.cpp:237]     Train net output #0: loss = 1.50927 (* 1 = 1.50927 loss)
I0410 14:18:51.858386 18414 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0410 14:18:56.862643 18414 solver.cpp:218] Iteration 6600 (2.39804 iter/s, 5.0041s/12 iters), loss = 1.42751
I0410 14:18:56.862696 18414 solver.cpp:237]     Train net output #0: loss = 1.42751 (* 1 = 1.42751 loss)
I0410 14:18:56.862709 18414 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0410 14:19:01.783645 18414 solver.cpp:218] Iteration 6612 (2.43864 iter/s, 4.92078s/12 iters), loss = 1.19813
I0410 14:19:01.783706 18414 solver.cpp:237]     Train net output #0: loss = 1.19813 (* 1 = 1.19813 loss)
I0410 14:19:01.783720 18414 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0410 14:19:06.711395 18414 solver.cpp:218] Iteration 6624 (2.4353 iter/s, 4.92753s/12 iters), loss = 1.18895
I0410 14:19:06.711488 18414 solver.cpp:237]     Train net output #0: loss = 1.18895 (* 1 = 1.18895 loss)
I0410 14:19:06.711498 18414 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0410 14:19:08.721485 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0410 14:19:09.034420 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0410 14:19:09.237411 18414 solver.cpp:330] Iteration 6630, Testing net (#0)
I0410 14:19:09.237432 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:19:11.143424 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:19:13.740100 18414 solver.cpp:397]     Test net output #0: accuracy = 0.482843
I0410 14:19:13.740149 18414 solver.cpp:397]     Test net output #1: loss = 1.99013 (* 1 = 1.99013 loss)
I0410 14:19:15.743167 18414 solver.cpp:218] Iteration 6636 (1.3287 iter/s, 9.03138s/12 iters), loss = 1.38969
I0410 14:19:15.743221 18414 solver.cpp:237]     Train net output #0: loss = 1.38969 (* 1 = 1.38969 loss)
I0410 14:19:15.743233 18414 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0410 14:19:20.654672 18414 solver.cpp:218] Iteration 6648 (2.44335 iter/s, 4.91129s/12 iters), loss = 1.43961
I0410 14:19:20.654729 18414 solver.cpp:237]     Train net output #0: loss = 1.43961 (* 1 = 1.43961 loss)
I0410 14:19:20.654742 18414 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0410 14:19:25.510977 18414 solver.cpp:218] Iteration 6660 (2.47113 iter/s, 4.85609s/12 iters), loss = 1.32496
I0410 14:19:25.511018 18414 solver.cpp:237]     Train net output #0: loss = 1.32496 (* 1 = 1.32496 loss)
I0410 14:19:25.511029 18414 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0410 14:19:30.405907 18414 solver.cpp:218] Iteration 6672 (2.45162 iter/s, 4.89472s/12 iters), loss = 1.34539
I0410 14:19:30.405951 18414 solver.cpp:237]     Train net output #0: loss = 1.34539 (* 1 = 1.34539 loss)
I0410 14:19:30.405987 18414 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0410 14:19:31.721521 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:19:35.318152 18414 solver.cpp:218] Iteration 6684 (2.44298 iter/s, 4.91203s/12 iters), loss = 1.2975
I0410 14:19:35.318199 18414 solver.cpp:237]     Train net output #0: loss = 1.2975 (* 1 = 1.2975 loss)
I0410 14:19:35.318218 18414 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0410 14:19:40.228660 18414 solver.cpp:218] Iteration 6696 (2.44385 iter/s, 4.91029s/12 iters), loss = 1.11044
I0410 14:19:40.230110 18414 solver.cpp:237]     Train net output #0: loss = 1.11044 (* 1 = 1.11044 loss)
I0410 14:19:40.230120 18414 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0410 14:19:45.136044 18414 solver.cpp:218] Iteration 6708 (2.4461 iter/s, 4.90577s/12 iters), loss = 1.17301
I0410 14:19:45.136096 18414 solver.cpp:237]     Train net output #0: loss = 1.17301 (* 1 = 1.17301 loss)
I0410 14:19:45.136107 18414 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0410 14:19:50.081454 18414 solver.cpp:218] Iteration 6720 (2.4266 iter/s, 4.94519s/12 iters), loss = 1.2252
I0410 14:19:50.081509 18414 solver.cpp:237]     Train net output #0: loss = 1.2252 (* 1 = 1.2252 loss)
I0410 14:19:50.081522 18414 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0410 14:19:54.557574 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0410 14:19:54.869081 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0410 14:19:55.083308 18414 solver.cpp:330] Iteration 6732, Testing net (#0)
I0410 14:19:55.083340 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:19:56.916867 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:19:59.571962 18414 solver.cpp:397]     Test net output #0: accuracy = 0.496324
I0410 14:19:59.572005 18414 solver.cpp:397]     Test net output #1: loss = 1.91183 (* 1 = 1.91183 loss)
I0410 14:19:59.655021 18414 solver.cpp:218] Iteration 6732 (1.2535 iter/s, 9.5732s/12 iters), loss = 1.4546
I0410 14:19:59.655076 18414 solver.cpp:237]     Train net output #0: loss = 1.4546 (* 1 = 1.4546 loss)
I0410 14:19:59.655087 18414 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0410 14:20:04.117091 18414 solver.cpp:218] Iteration 6744 (2.68946 iter/s, 4.46186s/12 iters), loss = 1.09215
I0410 14:20:04.117161 18414 solver.cpp:237]     Train net output #0: loss = 1.09215 (* 1 = 1.09215 loss)
I0410 14:20:04.117179 18414 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0410 14:20:09.228328 18414 solver.cpp:218] Iteration 6756 (2.34788 iter/s, 5.111s/12 iters), loss = 1.16358
I0410 14:20:09.228387 18414 solver.cpp:237]     Train net output #0: loss = 1.16358 (* 1 = 1.16358 loss)
I0410 14:20:09.228400 18414 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0410 14:20:14.221498 18414 solver.cpp:218] Iteration 6768 (2.40339 iter/s, 4.99294s/12 iters), loss = 1.31375
I0410 14:20:14.221668 18414 solver.cpp:237]     Train net output #0: loss = 1.31375 (* 1 = 1.31375 loss)
I0410 14:20:14.221683 18414 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0410 14:20:17.662461 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:20:19.185633 18414 solver.cpp:218] Iteration 6780 (2.4175 iter/s, 4.9638s/12 iters), loss = 1.15005
I0410 14:20:19.185679 18414 solver.cpp:237]     Train net output #0: loss = 1.15005 (* 1 = 1.15005 loss)
I0410 14:20:19.185688 18414 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0410 14:20:24.096360 18414 solver.cpp:218] Iteration 6792 (2.44374 iter/s, 4.91051s/12 iters), loss = 1.42679
I0410 14:20:24.096410 18414 solver.cpp:237]     Train net output #0: loss = 1.42679 (* 1 = 1.42679 loss)
I0410 14:20:24.096421 18414 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0410 14:20:29.059020 18414 solver.cpp:218] Iteration 6804 (2.41817 iter/s, 4.96243s/12 iters), loss = 1.31804
I0410 14:20:29.059082 18414 solver.cpp:237]     Train net output #0: loss = 1.31804 (* 1 = 1.31804 loss)
I0410 14:20:29.059098 18414 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0410 14:20:33.925076 18414 solver.cpp:218] Iteration 6816 (2.46617 iter/s, 4.86584s/12 iters), loss = 1.17721
I0410 14:20:33.925120 18414 solver.cpp:237]     Train net output #0: loss = 1.17721 (* 1 = 1.17721 loss)
I0410 14:20:33.925130 18414 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0410 14:20:38.866174 18414 solver.cpp:218] Iteration 6828 (2.42872 iter/s, 4.94088s/12 iters), loss = 1.17337
I0410 14:20:38.866225 18414 solver.cpp:237]     Train net output #0: loss = 1.17337 (* 1 = 1.17337 loss)
I0410 14:20:38.866235 18414 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0410 14:20:40.858242 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0410 14:20:41.656901 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0410 14:20:41.917449 18414 solver.cpp:330] Iteration 6834, Testing net (#0)
I0410 14:20:41.917474 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:20:43.685629 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:20:46.412037 18414 solver.cpp:397]     Test net output #0: accuracy = 0.490196
I0410 14:20:46.412147 18414 solver.cpp:397]     Test net output #1: loss = 1.93653 (* 1 = 1.93653 loss)
I0410 14:20:48.374877 18414 solver.cpp:218] Iteration 6840 (1.26205 iter/s, 9.50834s/12 iters), loss = 1.38677
I0410 14:20:48.374930 18414 solver.cpp:237]     Train net output #0: loss = 1.38677 (* 1 = 1.38677 loss)
I0410 14:20:48.374941 18414 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0410 14:20:53.452790 18414 solver.cpp:218] Iteration 6852 (2.36328 iter/s, 5.07768s/12 iters), loss = 1.49923
I0410 14:20:53.452853 18414 solver.cpp:237]     Train net output #0: loss = 1.49923 (* 1 = 1.49923 loss)
I0410 14:20:53.452867 18414 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0410 14:20:58.314016 18414 solver.cpp:218] Iteration 6864 (2.46863 iter/s, 4.861s/12 iters), loss = 1.08734
I0410 14:20:58.314074 18414 solver.cpp:237]     Train net output #0: loss = 1.08734 (* 1 = 1.08734 loss)
I0410 14:20:58.314086 18414 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0410 14:21:03.263959 18414 solver.cpp:218] Iteration 6876 (2.42438 iter/s, 4.94972s/12 iters), loss = 1.23427
I0410 14:21:03.264008 18414 solver.cpp:237]     Train net output #0: loss = 1.23427 (* 1 = 1.23427 loss)
I0410 14:21:03.264017 18414 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0410 14:21:03.863543 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:21:08.147756 18414 solver.cpp:218] Iteration 6888 (2.45721 iter/s, 4.88358s/12 iters), loss = 1.11875
I0410 14:21:08.147801 18414 solver.cpp:237]     Train net output #0: loss = 1.11875 (* 1 = 1.11875 loss)
I0410 14:21:08.147810 18414 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0410 14:21:13.219282 18414 solver.cpp:218] Iteration 6900 (2.36625 iter/s, 5.07131s/12 iters), loss = 1.1107
I0410 14:21:13.219336 18414 solver.cpp:237]     Train net output #0: loss = 1.1107 (* 1 = 1.1107 loss)
I0410 14:21:13.219348 18414 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0410 14:21:18.170922 18414 solver.cpp:218] Iteration 6912 (2.42355 iter/s, 4.95141s/12 iters), loss = 0.918501
I0410 14:21:18.171077 18414 solver.cpp:237]     Train net output #0: loss = 0.918501 (* 1 = 0.918501 loss)
I0410 14:21:18.171092 18414 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0410 14:21:23.066586 18414 solver.cpp:218] Iteration 6924 (2.45131 iter/s, 4.89535s/12 iters), loss = 1.14167
I0410 14:21:23.066634 18414 solver.cpp:237]     Train net output #0: loss = 1.14167 (* 1 = 1.14167 loss)
I0410 14:21:23.066645 18414 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0410 14:21:27.529232 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0410 14:21:27.835186 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0410 14:21:28.045783 18414 solver.cpp:330] Iteration 6936, Testing net (#0)
I0410 14:21:28.045807 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:21:28.315590 18414 blocking_queue.cpp:49] Waiting for data
I0410 14:21:29.781038 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:21:32.495548 18414 solver.cpp:397]     Test net output #0: accuracy = 0.499387
I0410 14:21:32.495597 18414 solver.cpp:397]     Test net output #1: loss = 2.00999 (* 1 = 2.00999 loss)
I0410 14:21:32.578341 18414 solver.cpp:218] Iteration 6936 (1.26164 iter/s, 9.51139s/12 iters), loss = 1.39282
I0410 14:21:32.578385 18414 solver.cpp:237]     Train net output #0: loss = 1.39282 (* 1 = 1.39282 loss)
I0410 14:21:32.578397 18414 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0410 14:21:36.867779 18414 solver.cpp:218] Iteration 6948 (2.7977 iter/s, 4.28924s/12 iters), loss = 1.38134
I0410 14:21:36.867825 18414 solver.cpp:237]     Train net output #0: loss = 1.38134 (* 1 = 1.38134 loss)
I0410 14:21:36.867835 18414 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0410 14:21:41.811920 18414 solver.cpp:218] Iteration 6960 (2.42722 iter/s, 4.94393s/12 iters), loss = 1.03745
I0410 14:21:41.811965 18414 solver.cpp:237]     Train net output #0: loss = 1.03745 (* 1 = 1.03745 loss)
I0410 14:21:41.811975 18414 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0410 14:21:46.788663 18414 solver.cpp:218] Iteration 6972 (2.41132 iter/s, 4.97653s/12 iters), loss = 1.33636
I0410 14:21:46.788712 18414 solver.cpp:237]     Train net output #0: loss = 1.33636 (* 1 = 1.33636 loss)
I0410 14:21:46.788722 18414 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0410 14:21:49.503465 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:21:51.721760 18414 solver.cpp:218] Iteration 6984 (2.43266 iter/s, 4.93288s/12 iters), loss = 1.06884
I0410 14:21:51.721803 18414 solver.cpp:237]     Train net output #0: loss = 1.06884 (* 1 = 1.06884 loss)
I0410 14:21:51.721813 18414 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0410 14:21:56.660336 18414 solver.cpp:218] Iteration 6996 (2.42996 iter/s, 4.93836s/12 iters), loss = 1.09023
I0410 14:21:56.660392 18414 solver.cpp:237]     Train net output #0: loss = 1.09023 (* 1 = 1.09023 loss)
I0410 14:21:56.660405 18414 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0410 14:22:01.597285 18414 solver.cpp:218] Iteration 7008 (2.43076 iter/s, 4.93672s/12 iters), loss = 0.957894
I0410 14:22:01.597332 18414 solver.cpp:237]     Train net output #0: loss = 0.957894 (* 1 = 0.957894 loss)
I0410 14:22:01.597340 18414 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0410 14:22:06.515432 18414 solver.cpp:218] Iteration 7020 (2.44005 iter/s, 4.91794s/12 iters), loss = 1.00498
I0410 14:22:06.515470 18414 solver.cpp:237]     Train net output #0: loss = 1.00498 (* 1 = 1.00498 loss)
I0410 14:22:06.515480 18414 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0410 14:22:11.406440 18414 solver.cpp:218] Iteration 7032 (2.45359 iter/s, 4.89079s/12 iters), loss = 1.33821
I0410 14:22:11.406497 18414 solver.cpp:237]     Train net output #0: loss = 1.33821 (* 1 = 1.33821 loss)
I0410 14:22:11.406509 18414 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0410 14:22:13.400234 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0410 14:22:13.689800 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0410 14:22:13.885665 18414 solver.cpp:330] Iteration 7038, Testing net (#0)
I0410 14:22:13.885685 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:22:15.581475 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:22:18.331688 18414 solver.cpp:397]     Test net output #0: accuracy = 0.501225
I0410 14:22:18.331732 18414 solver.cpp:397]     Test net output #1: loss = 1.95722 (* 1 = 1.95722 loss)
I0410 14:22:20.226382 18414 solver.cpp:218] Iteration 7044 (1.36061 iter/s, 8.8196s/12 iters), loss = 1.18309
I0410 14:22:20.226485 18414 solver.cpp:237]     Train net output #0: loss = 1.18309 (* 1 = 1.18309 loss)
I0410 14:22:20.226495 18414 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0410 14:22:25.136857 18414 solver.cpp:218] Iteration 7056 (2.44389 iter/s, 4.9102s/12 iters), loss = 1.34916
I0410 14:22:25.136902 18414 solver.cpp:237]     Train net output #0: loss = 1.34916 (* 1 = 1.34916 loss)
I0410 14:22:25.136911 18414 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0410 14:22:30.089787 18414 solver.cpp:218] Iteration 7068 (2.42291 iter/s, 4.95271s/12 iters), loss = 1.09169
I0410 14:22:30.089840 18414 solver.cpp:237]     Train net output #0: loss = 1.09169 (* 1 = 1.09169 loss)
I0410 14:22:30.089854 18414 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0410 14:22:34.896253 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:22:35.003645 18414 solver.cpp:218] Iteration 7080 (2.44218 iter/s, 4.91364s/12 iters), loss = 1.25456
I0410 14:22:35.003687 18414 solver.cpp:237]     Train net output #0: loss = 1.25456 (* 1 = 1.25456 loss)
I0410 14:22:35.003696 18414 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0410 14:22:40.035516 18414 solver.cpp:218] Iteration 7092 (2.3849 iter/s, 5.03166s/12 iters), loss = 1.26742
I0410 14:22:40.035547 18414 solver.cpp:237]     Train net output #0: loss = 1.26742 (* 1 = 1.26742 loss)
I0410 14:22:40.035554 18414 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0410 14:22:44.981288 18414 solver.cpp:218] Iteration 7104 (2.42641 iter/s, 4.94557s/12 iters), loss = 1.07058
I0410 14:22:44.981341 18414 solver.cpp:237]     Train net output #0: loss = 1.07058 (* 1 = 1.07058 loss)
I0410 14:22:44.981354 18414 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0410 14:22:49.913674 18414 solver.cpp:218] Iteration 7116 (2.43301 iter/s, 4.93217s/12 iters), loss = 0.875932
I0410 14:22:49.913724 18414 solver.cpp:237]     Train net output #0: loss = 0.875932 (* 1 = 0.875932 loss)
I0410 14:22:49.913735 18414 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0410 14:22:54.843588 18414 solver.cpp:218] Iteration 7128 (2.43423 iter/s, 4.92969s/12 iters), loss = 1.22995
I0410 14:22:54.843698 18414 solver.cpp:237]     Train net output #0: loss = 1.22995 (* 1 = 1.22995 loss)
I0410 14:22:54.843711 18414 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0410 14:22:59.319268 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0410 14:22:59.630162 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0410 14:22:59.842388 18414 solver.cpp:330] Iteration 7140, Testing net (#0)
I0410 14:22:59.842429 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:23:01.618628 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:23:04.441291 18414 solver.cpp:397]     Test net output #0: accuracy = 0.522059
I0410 14:23:04.441340 18414 solver.cpp:397]     Test net output #1: loss = 1.91701 (* 1 = 1.91701 loss)
I0410 14:23:04.524240 18414 solver.cpp:218] Iteration 7140 (1.23964 iter/s, 9.68023s/12 iters), loss = 1.19068
I0410 14:23:04.524294 18414 solver.cpp:237]     Train net output #0: loss = 1.19068 (* 1 = 1.19068 loss)
I0410 14:23:04.524307 18414 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0410 14:23:08.866616 18414 solver.cpp:218] Iteration 7152 (2.76359 iter/s, 4.34218s/12 iters), loss = 1.20144
I0410 14:23:08.866658 18414 solver.cpp:237]     Train net output #0: loss = 1.20144 (* 1 = 1.20144 loss)
I0410 14:23:08.866667 18414 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0410 14:23:13.863247 18414 solver.cpp:218] Iteration 7164 (2.40172 iter/s, 4.99641s/12 iters), loss = 1.12621
I0410 14:23:13.863307 18414 solver.cpp:237]     Train net output #0: loss = 1.12621 (* 1 = 1.12621 loss)
I0410 14:23:13.863319 18414 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0410 14:23:18.814846 18414 solver.cpp:218] Iteration 7176 (2.42357 iter/s, 4.95137s/12 iters), loss = 1.05047
I0410 14:23:18.814905 18414 solver.cpp:237]     Train net output #0: loss = 1.05047 (* 1 = 1.05047 loss)
I0410 14:23:18.814918 18414 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0410 14:23:20.890049 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:23:23.948882 18414 solver.cpp:218] Iteration 7188 (2.33745 iter/s, 5.13381s/12 iters), loss = 1.06799
I0410 14:23:23.948936 18414 solver.cpp:237]     Train net output #0: loss = 1.06799 (* 1 = 1.06799 loss)
I0410 14:23:23.948948 18414 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0410 14:23:29.053592 18414 solver.cpp:218] Iteration 7200 (2.35087 iter/s, 5.10449s/12 iters), loss = 0.994151
I0410 14:23:29.053694 18414 solver.cpp:237]     Train net output #0: loss = 0.994151 (* 1 = 0.994151 loss)
I0410 14:23:29.053704 18414 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0410 14:23:33.970693 18414 solver.cpp:218] Iteration 7212 (2.4406 iter/s, 4.91683s/12 iters), loss = 1.1656
I0410 14:23:33.970748 18414 solver.cpp:237]     Train net output #0: loss = 1.1656 (* 1 = 1.1656 loss)
I0410 14:23:33.970762 18414 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0410 14:23:39.039124 18414 solver.cpp:218] Iteration 7224 (2.3677 iter/s, 5.0682s/12 iters), loss = 1.18154
I0410 14:23:39.039181 18414 solver.cpp:237]     Train net output #0: loss = 1.18154 (* 1 = 1.18154 loss)
I0410 14:23:39.039196 18414 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0410 14:23:43.986434 18414 solver.cpp:218] Iteration 7236 (2.42567 iter/s, 4.94708s/12 iters), loss = 1.14384
I0410 14:23:43.986495 18414 solver.cpp:237]     Train net output #0: loss = 1.14384 (* 1 = 1.14384 loss)
I0410 14:23:43.986507 18414 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0410 14:23:46.017462 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0410 14:23:46.544294 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0410 14:23:46.789932 18414 solver.cpp:330] Iteration 7242, Testing net (#0)
I0410 14:23:46.789974 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:23:48.408964 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:23:51.256851 18414 solver.cpp:397]     Test net output #0: accuracy = 0.529412
I0410 14:23:51.256894 18414 solver.cpp:397]     Test net output #1: loss = 1.89503 (* 1 = 1.89503 loss)
I0410 14:23:53.196017 18414 solver.cpp:218] Iteration 7248 (1.30304 iter/s, 9.20922s/12 iters), loss = 1.22897
I0410 14:23:53.196071 18414 solver.cpp:237]     Train net output #0: loss = 1.22897 (* 1 = 1.22897 loss)
I0410 14:23:53.196082 18414 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0410 14:23:58.156733 18414 solver.cpp:218] Iteration 7260 (2.41911 iter/s, 4.9605s/12 iters), loss = 1.17619
I0410 14:23:58.156775 18414 solver.cpp:237]     Train net output #0: loss = 1.17619 (* 1 = 1.17619 loss)
I0410 14:23:58.156783 18414 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0410 14:24:03.168313 18414 solver.cpp:218] Iteration 7272 (2.39456 iter/s, 5.01137s/12 iters), loss = 1.25402
I0410 14:24:03.168440 18414 solver.cpp:237]     Train net output #0: loss = 1.25402 (* 1 = 1.25402 loss)
I0410 14:24:03.168452 18414 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0410 14:24:07.382972 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:24:08.129170 18414 solver.cpp:218] Iteration 7284 (2.41908 iter/s, 4.96056s/12 iters), loss = 1.18268
I0410 14:24:08.129220 18414 solver.cpp:237]     Train net output #0: loss = 1.18268 (* 1 = 1.18268 loss)
I0410 14:24:08.129230 18414 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0410 14:24:13.103788 18414 solver.cpp:218] Iteration 7296 (2.41235 iter/s, 4.9744s/12 iters), loss = 1.10469
I0410 14:24:13.103832 18414 solver.cpp:237]     Train net output #0: loss = 1.10469 (* 1 = 1.10469 loss)
I0410 14:24:13.103842 18414 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0410 14:24:17.986011 18414 solver.cpp:218] Iteration 7308 (2.458 iter/s, 4.88201s/12 iters), loss = 1.00048
I0410 14:24:17.986055 18414 solver.cpp:237]     Train net output #0: loss = 1.00048 (* 1 = 1.00048 loss)
I0410 14:24:17.986064 18414 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0410 14:24:22.926424 18414 solver.cpp:218] Iteration 7320 (2.42905 iter/s, 4.9402s/12 iters), loss = 1.03816
I0410 14:24:22.926483 18414 solver.cpp:237]     Train net output #0: loss = 1.03816 (* 1 = 1.03816 loss)
I0410 14:24:22.926496 18414 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0410 14:24:27.947861 18414 solver.cpp:218] Iteration 7332 (2.38986 iter/s, 5.02121s/12 iters), loss = 1.16795
I0410 14:24:27.947911 18414 solver.cpp:237]     Train net output #0: loss = 1.16795 (* 1 = 1.16795 loss)
I0410 14:24:27.947921 18414 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0410 14:24:32.396659 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0410 14:24:32.702167 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0410 14:24:32.911664 18414 solver.cpp:330] Iteration 7344, Testing net (#0)
I0410 14:24:32.911695 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:24:34.479331 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:24:37.347826 18414 solver.cpp:397]     Test net output #0: accuracy = 0.518382
I0410 14:24:37.347874 18414 solver.cpp:397]     Test net output #1: loss = 1.95744 (* 1 = 1.95744 loss)
I0410 14:24:37.430824 18414 solver.cpp:218] Iteration 7344 (1.26548 iter/s, 9.48259s/12 iters), loss = 1.09488
I0410 14:24:37.430883 18414 solver.cpp:237]     Train net output #0: loss = 1.09488 (* 1 = 1.09488 loss)
I0410 14:24:37.430900 18414 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0410 14:24:41.521733 18414 solver.cpp:218] Iteration 7356 (2.93348 iter/s, 4.09071s/12 iters), loss = 1.11793
I0410 14:24:41.521790 18414 solver.cpp:237]     Train net output #0: loss = 1.11793 (* 1 = 1.11793 loss)
I0410 14:24:41.521802 18414 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0410 14:24:46.403319 18414 solver.cpp:218] Iteration 7368 (2.45832 iter/s, 4.88138s/12 iters), loss = 1.17063
I0410 14:24:46.403370 18414 solver.cpp:237]     Train net output #0: loss = 1.17063 (* 1 = 1.17063 loss)
I0410 14:24:46.403383 18414 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0410 14:24:51.352644 18414 solver.cpp:218] Iteration 7380 (2.42465 iter/s, 4.94917s/12 iters), loss = 1.12744
I0410 14:24:51.352696 18414 solver.cpp:237]     Train net output #0: loss = 1.12744 (* 1 = 1.12744 loss)
I0410 14:24:51.352708 18414 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0410 14:24:52.747848 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:24:56.334121 18414 solver.cpp:218] Iteration 7392 (2.409 iter/s, 4.98132s/12 iters), loss = 1.11898
I0410 14:24:56.334178 18414 solver.cpp:237]     Train net output #0: loss = 1.11898 (* 1 = 1.11898 loss)
I0410 14:24:56.334192 18414 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0410 14:25:01.261662 18414 solver.cpp:218] Iteration 7404 (2.43537 iter/s, 4.92738s/12 iters), loss = 1.13995
I0410 14:25:01.261713 18414 solver.cpp:237]     Train net output #0: loss = 1.13995 (* 1 = 1.13995 loss)
I0410 14:25:01.261726 18414 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0410 14:25:06.463400 18414 solver.cpp:218] Iteration 7416 (2.307 iter/s, 5.20157s/12 iters), loss = 0.988648
I0410 14:25:06.463562 18414 solver.cpp:237]     Train net output #0: loss = 0.988648 (* 1 = 0.988648 loss)
I0410 14:25:06.463577 18414 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0410 14:25:11.500814 18414 solver.cpp:218] Iteration 7428 (2.3823 iter/s, 5.03715s/12 iters), loss = 0.963934
I0410 14:25:11.500859 18414 solver.cpp:237]     Train net output #0: loss = 0.963934 (* 1 = 0.963934 loss)
I0410 14:25:11.500869 18414 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0410 14:25:16.409875 18414 solver.cpp:218] Iteration 7440 (2.44454 iter/s, 4.9089s/12 iters), loss = 1.10213
I0410 14:25:16.409926 18414 solver.cpp:237]     Train net output #0: loss = 1.10213 (* 1 = 1.10213 loss)
I0410 14:25:16.409940 18414 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0410 14:25:18.411612 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0410 14:25:18.709646 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0410 14:25:18.908303 18414 solver.cpp:330] Iteration 7446, Testing net (#0)
I0410 14:25:18.908334 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:25:20.520820 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:25:23.510672 18414 solver.cpp:397]     Test net output #0: accuracy = 0.522672
I0410 14:25:23.510704 18414 solver.cpp:397]     Test net output #1: loss = 1.90359 (* 1 = 1.90359 loss)
I0410 14:25:25.326786 18414 solver.cpp:218] Iteration 7452 (1.34579 iter/s, 8.91667s/12 iters), loss = 1.14832
I0410 14:25:25.326851 18414 solver.cpp:237]     Train net output #0: loss = 1.14832 (* 1 = 1.14832 loss)
I0410 14:25:25.326864 18414 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0410 14:25:30.243257 18414 solver.cpp:218] Iteration 7464 (2.44086 iter/s, 4.9163s/12 iters), loss = 1.13475
I0410 14:25:30.243306 18414 solver.cpp:237]     Train net output #0: loss = 1.13475 (* 1 = 1.13475 loss)
I0410 14:25:30.243316 18414 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0410 14:25:35.237241 18414 solver.cpp:218] Iteration 7476 (2.40297 iter/s, 4.99382s/12 iters), loss = 1.06648
I0410 14:25:35.237294 18414 solver.cpp:237]     Train net output #0: loss = 1.06648 (* 1 = 1.06648 loss)
I0410 14:25:35.237306 18414 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0410 14:25:38.715538 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:25:40.174268 18414 solver.cpp:218] Iteration 7488 (2.43069 iter/s, 4.93686s/12 iters), loss = 0.984849
I0410 14:25:40.174320 18414 solver.cpp:237]     Train net output #0: loss = 0.984849 (* 1 = 0.984849 loss)
I0410 14:25:40.174335 18414 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0410 14:25:45.108705 18414 solver.cpp:218] Iteration 7500 (2.43197 iter/s, 4.93427s/12 iters), loss = 1.06416
I0410 14:25:45.108758 18414 solver.cpp:237]     Train net output #0: loss = 1.06416 (* 1 = 1.06416 loss)
I0410 14:25:45.108772 18414 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0410 14:25:50.119803 18414 solver.cpp:218] Iteration 7512 (2.39477 iter/s, 5.01093s/12 iters), loss = 1.03451
I0410 14:25:50.119863 18414 solver.cpp:237]     Train net output #0: loss = 1.03451 (* 1 = 1.03451 loss)
I0410 14:25:50.119874 18414 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0410 14:25:55.091442 18414 solver.cpp:218] Iteration 7524 (2.41377 iter/s, 4.97147s/12 iters), loss = 1.12827
I0410 14:25:55.091488 18414 solver.cpp:237]     Train net output #0: loss = 1.12827 (* 1 = 1.12827 loss)
I0410 14:25:55.091500 18414 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0410 14:26:00.081185 18414 solver.cpp:218] Iteration 7536 (2.40501 iter/s, 4.98958s/12 iters), loss = 1.1441
I0410 14:26:00.081243 18414 solver.cpp:237]     Train net output #0: loss = 1.1441 (* 1 = 1.1441 loss)
I0410 14:26:00.081255 18414 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0410 14:26:04.622696 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0410 14:26:04.962957 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0410 14:26:05.175971 18414 solver.cpp:330] Iteration 7548, Testing net (#0)
I0410 14:26:05.176000 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:26:06.719589 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:26:09.818094 18414 solver.cpp:397]     Test net output #0: accuracy = 0.510417
I0410 14:26:09.818208 18414 solver.cpp:397]     Test net output #1: loss = 1.99745 (* 1 = 1.99745 loss)
I0410 14:26:09.902570 18414 solver.cpp:218] Iteration 7548 (1.22186 iter/s, 9.82111s/12 iters), loss = 1.12592
I0410 14:26:09.902632 18414 solver.cpp:237]     Train net output #0: loss = 1.12592 (* 1 = 1.12592 loss)
I0410 14:26:09.902647 18414 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0410 14:26:14.093571 18414 solver.cpp:218] Iteration 7560 (2.86339 iter/s, 4.19084s/12 iters), loss = 1.26234
I0410 14:26:14.093626 18414 solver.cpp:237]     Train net output #0: loss = 1.26234 (* 1 = 1.26234 loss)
I0410 14:26:14.093637 18414 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0410 14:26:19.262477 18414 solver.cpp:218] Iteration 7572 (2.32166 iter/s, 5.16872s/12 iters), loss = 0.789782
I0410 14:26:19.262534 18414 solver.cpp:237]     Train net output #0: loss = 0.789782 (* 1 = 0.789782 loss)
I0410 14:26:19.262547 18414 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0410 14:26:24.173043 18414 solver.cpp:218] Iteration 7584 (2.4438 iter/s, 4.91039s/12 iters), loss = 0.979684
I0410 14:26:24.173094 18414 solver.cpp:237]     Train net output #0: loss = 0.979684 (* 1 = 0.979684 loss)
I0410 14:26:24.173105 18414 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0410 14:26:24.803530 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:26:29.103727 18414 solver.cpp:218] Iteration 7596 (2.43382 iter/s, 4.93052s/12 iters), loss = 0.939267
I0410 14:26:29.103771 18414 solver.cpp:237]     Train net output #0: loss = 0.939267 (* 1 = 0.939267 loss)
I0410 14:26:29.103782 18414 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0410 14:26:33.993053 18414 solver.cpp:218] Iteration 7608 (2.45441 iter/s, 4.88916s/12 iters), loss = 0.937316
I0410 14:26:33.993113 18414 solver.cpp:237]     Train net output #0: loss = 0.937316 (* 1 = 0.937316 loss)
I0410 14:26:33.993126 18414 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0410 14:26:38.932235 18414 solver.cpp:218] Iteration 7620 (2.42964 iter/s, 4.93901s/12 iters), loss = 0.847892
I0410 14:26:38.932286 18414 solver.cpp:237]     Train net output #0: loss = 0.847892 (* 1 = 0.847892 loss)
I0410 14:26:38.932298 18414 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0410 14:26:39.688084 18414 blocking_queue.cpp:49] Waiting for data
I0410 14:26:43.831990 18414 solver.cpp:218] Iteration 7632 (2.44919 iter/s, 4.89958s/12 iters), loss = 1.15856
I0410 14:26:43.832116 18414 solver.cpp:237]     Train net output #0: loss = 1.15856 (* 1 = 1.15856 loss)
I0410 14:26:43.832129 18414 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0410 14:26:48.787998 18414 solver.cpp:218] Iteration 7644 (2.42142 iter/s, 4.95576s/12 iters), loss = 1.18854
I0410 14:26:48.788050 18414 solver.cpp:237]     Train net output #0: loss = 1.18854 (* 1 = 1.18854 loss)
I0410 14:26:48.788064 18414 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0410 14:26:50.789525 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0410 14:26:51.238842 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0410 14:26:51.458547 18414 solver.cpp:330] Iteration 7650, Testing net (#0)
I0410 14:26:51.458580 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:26:52.930001 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:26:55.920578 18414 solver.cpp:397]     Test net output #0: accuracy = 0.519608
I0410 14:26:55.920615 18414 solver.cpp:397]     Test net output #1: loss = 1.94239 (* 1 = 1.94239 loss)
I0410 14:26:57.820726 18414 solver.cpp:218] Iteration 7656 (1.32854 iter/s, 9.03247s/12 iters), loss = 1.07313
I0410 14:26:57.820771 18414 solver.cpp:237]     Train net output #0: loss = 1.07313 (* 1 = 1.07313 loss)
I0410 14:26:57.820780 18414 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0410 14:27:02.835763 18414 solver.cpp:218] Iteration 7668 (2.39288 iter/s, 5.01487s/12 iters), loss = 0.834537
I0410 14:27:02.835811 18414 solver.cpp:237]     Train net output #0: loss = 0.834537 (* 1 = 0.834537 loss)
I0410 14:27:02.835820 18414 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0410 14:27:07.815943 18414 solver.cpp:218] Iteration 7680 (2.40963 iter/s, 4.98001s/12 iters), loss = 0.821915
I0410 14:27:07.815999 18414 solver.cpp:237]     Train net output #0: loss = 0.821915 (* 1 = 0.821915 loss)
I0410 14:27:07.816012 18414 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0410 14:27:10.531867 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:27:12.723834 18414 solver.cpp:218] Iteration 7692 (2.44513 iter/s, 4.90772s/12 iters), loss = 0.856003
I0410 14:27:12.723875 18414 solver.cpp:237]     Train net output #0: loss = 0.856003 (* 1 = 0.856003 loss)
I0410 14:27:12.723886 18414 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0410 14:27:17.646198 18414 solver.cpp:218] Iteration 7704 (2.43793 iter/s, 4.92221s/12 iters), loss = 0.815722
I0410 14:27:17.646318 18414 solver.cpp:237]     Train net output #0: loss = 0.815722 (* 1 = 0.815722 loss)
I0410 14:27:17.646329 18414 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0410 14:27:22.509264 18414 solver.cpp:218] Iteration 7716 (2.4677 iter/s, 4.86282s/12 iters), loss = 0.941768
I0410 14:27:22.509325 18414 solver.cpp:237]     Train net output #0: loss = 0.941768 (* 1 = 0.941768 loss)
I0410 14:27:22.509338 18414 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0410 14:27:27.381495 18414 solver.cpp:218] Iteration 7728 (2.46303 iter/s, 4.87205s/12 iters), loss = 1.10138
I0410 14:27:27.381548 18414 solver.cpp:237]     Train net output #0: loss = 1.10138 (* 1 = 1.10138 loss)
I0410 14:27:27.381561 18414 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0410 14:27:32.354972 18414 solver.cpp:218] Iteration 7740 (2.41288 iter/s, 4.9733s/12 iters), loss = 0.86909
I0410 14:27:32.355026 18414 solver.cpp:237]     Train net output #0: loss = 0.86909 (* 1 = 0.86909 loss)
I0410 14:27:32.355039 18414 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0410 14:27:36.808471 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0410 14:27:38.013335 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0410 14:27:38.214043 18414 solver.cpp:330] Iteration 7752, Testing net (#0)
I0410 14:27:38.214071 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:27:39.623445 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:27:42.687055 18414 solver.cpp:397]     Test net output #0: accuracy = 0.526348
I0410 14:27:42.687088 18414 solver.cpp:397]     Test net output #1: loss = 1.92788 (* 1 = 1.92788 loss)
I0410 14:27:42.769855 18414 solver.cpp:218] Iteration 7752 (1.15223 iter/s, 10.4146s/12 iters), loss = 1.22444
I0410 14:27:42.769901 18414 solver.cpp:237]     Train net output #0: loss = 1.22444 (* 1 = 1.22444 loss)
I0410 14:27:42.769910 18414 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0410 14:27:47.083998 18414 solver.cpp:218] Iteration 7764 (2.78165 iter/s, 4.31398s/12 iters), loss = 1.07856
I0410 14:27:47.084060 18414 solver.cpp:237]     Train net output #0: loss = 1.07856 (* 1 = 1.07856 loss)
I0410 14:27:47.084074 18414 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0410 14:27:52.038280 18414 solver.cpp:218] Iteration 7776 (2.42224 iter/s, 4.9541s/12 iters), loss = 1.03472
I0410 14:27:52.038456 18414 solver.cpp:237]     Train net output #0: loss = 1.03472 (* 1 = 1.03472 loss)
I0410 14:27:52.038470 18414 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0410 14:27:56.984282 18414 solver.cpp:218] Iteration 7788 (2.42634 iter/s, 4.94571s/12 iters), loss = 0.945121
I0410 14:27:56.984328 18414 solver.cpp:237]     Train net output #0: loss = 0.945121 (* 1 = 0.945121 loss)
I0410 14:27:56.984339 18414 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0410 14:27:56.992394 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:28:02.097252 18414 solver.cpp:218] Iteration 7800 (2.34705 iter/s, 5.11279s/12 iters), loss = 0.810213
I0410 14:28:02.097298 18414 solver.cpp:237]     Train net output #0: loss = 0.810213 (* 1 = 0.810213 loss)
I0410 14:28:02.097307 18414 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0410 14:28:07.094988 18414 solver.cpp:218] Iteration 7812 (2.40117 iter/s, 4.99756s/12 iters), loss = 0.793972
I0410 14:28:07.095055 18414 solver.cpp:237]     Train net output #0: loss = 0.793972 (* 1 = 0.793972 loss)
I0410 14:28:07.095070 18414 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0410 14:28:12.080624 18414 solver.cpp:218] Iteration 7824 (2.40701 iter/s, 4.98545s/12 iters), loss = 0.856774
I0410 14:28:12.080677 18414 solver.cpp:237]     Train net output #0: loss = 0.856774 (* 1 = 0.856774 loss)
I0410 14:28:12.080689 18414 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0410 14:28:17.001536 18414 solver.cpp:218] Iteration 7836 (2.43866 iter/s, 4.92073s/12 iters), loss = 1.03895
I0410 14:28:17.001597 18414 solver.cpp:237]     Train net output #0: loss = 1.03895 (* 1 = 1.03895 loss)
I0410 14:28:17.001610 18414 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0410 14:28:21.909407 18414 solver.cpp:218] Iteration 7848 (2.44514 iter/s, 4.90769s/12 iters), loss = 1.00368
I0410 14:28:21.909467 18414 solver.cpp:237]     Train net output #0: loss = 1.00368 (* 1 = 1.00368 loss)
I0410 14:28:21.909480 18414 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0410 14:28:23.884295 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0410 14:28:24.211887 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0410 14:28:24.423677 18414 solver.cpp:330] Iteration 7854, Testing net (#0)
I0410 14:28:24.423700 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:28:25.722909 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:28:28.795946 18414 solver.cpp:397]     Test net output #0: accuracy = 0.519608
I0410 14:28:28.795994 18414 solver.cpp:397]     Test net output #1: loss = 1.96342 (* 1 = 1.96342 loss)
I0410 14:28:30.583029 18414 solver.cpp:218] Iteration 7860 (1.38355 iter/s, 8.67335s/12 iters), loss = 0.987862
I0410 14:28:30.583087 18414 solver.cpp:237]     Train net output #0: loss = 0.987862 (* 1 = 0.987862 loss)
I0410 14:28:30.583101 18414 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0410 14:28:35.435228 18414 solver.cpp:218] Iteration 7872 (2.4732 iter/s, 4.85202s/12 iters), loss = 0.949833
I0410 14:28:35.435278 18414 solver.cpp:237]     Train net output #0: loss = 0.949833 (* 1 = 0.949833 loss)
I0410 14:28:35.435289 18414 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0410 14:28:40.517856 18414 solver.cpp:218] Iteration 7884 (2.36107 iter/s, 5.08245s/12 iters), loss = 0.877397
I0410 14:28:40.517920 18414 solver.cpp:237]     Train net output #0: loss = 0.877397 (* 1 = 0.877397 loss)
I0410 14:28:40.517936 18414 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0410 14:28:42.656886 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:28:45.626358 18414 solver.cpp:218] Iteration 7896 (2.34912 iter/s, 5.1083s/12 iters), loss = 1.01841
I0410 14:28:45.626441 18414 solver.cpp:237]     Train net output #0: loss = 1.01841 (* 1 = 1.01841 loss)
I0410 14:28:45.626451 18414 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0410 14:28:50.582767 18414 solver.cpp:218] Iteration 7908 (2.42121 iter/s, 4.9562s/12 iters), loss = 0.841044
I0410 14:28:50.582823 18414 solver.cpp:237]     Train net output #0: loss = 0.841044 (* 1 = 0.841044 loss)
I0410 14:28:50.582835 18414 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0410 14:28:55.525161 18414 solver.cpp:218] Iteration 7920 (2.42806 iter/s, 4.94221s/12 iters), loss = 0.937692
I0410 14:28:55.525297 18414 solver.cpp:237]     Train net output #0: loss = 0.937692 (* 1 = 0.937692 loss)
I0410 14:28:55.525311 18414 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0410 14:29:00.452898 18414 solver.cpp:218] Iteration 7932 (2.43532 iter/s, 4.92748s/12 iters), loss = 0.8132
I0410 14:29:00.452951 18414 solver.cpp:237]     Train net output #0: loss = 0.8132 (* 1 = 0.8132 loss)
I0410 14:29:00.452965 18414 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0410 14:29:05.341290 18414 solver.cpp:218] Iteration 7944 (2.45488 iter/s, 4.88821s/12 iters), loss = 0.904703
I0410 14:29:05.341351 18414 solver.cpp:237]     Train net output #0: loss = 0.904703 (* 1 = 0.904703 loss)
I0410 14:29:05.341364 18414 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0410 14:29:09.760804 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0410 14:29:10.099877 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0410 14:29:10.311187 18414 solver.cpp:330] Iteration 7956, Testing net (#0)
I0410 14:29:10.311206 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:29:11.658978 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:29:14.817823 18414 solver.cpp:397]     Test net output #0: accuracy = 0.530637
I0410 14:29:14.817864 18414 solver.cpp:397]     Test net output #1: loss = 1.90331 (* 1 = 1.90331 loss)
I0410 14:29:14.901131 18414 solver.cpp:218] Iteration 7956 (1.25529 iter/s, 9.55955s/12 iters), loss = 0.963569
I0410 14:29:14.901175 18414 solver.cpp:237]     Train net output #0: loss = 0.963569 (* 1 = 0.963569 loss)
I0410 14:29:14.901185 18414 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0410 14:29:19.076848 18414 solver.cpp:218] Iteration 7968 (2.87386 iter/s, 4.17556s/12 iters), loss = 0.870006
I0410 14:29:19.076905 18414 solver.cpp:237]     Train net output #0: loss = 0.870006 (* 1 = 0.870006 loss)
I0410 14:29:19.076917 18414 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0410 14:29:23.985778 18414 solver.cpp:218] Iteration 7980 (2.44462 iter/s, 4.90874s/12 iters), loss = 0.914662
I0410 14:29:23.985838 18414 solver.cpp:237]     Train net output #0: loss = 0.914662 (* 1 = 0.914662 loss)
I0410 14:29:23.985852 18414 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0410 14:29:28.185251 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:29:28.949348 18414 solver.cpp:218] Iteration 7992 (2.41771 iter/s, 4.96337s/12 iters), loss = 0.930073
I0410 14:29:28.949424 18414 solver.cpp:237]     Train net output #0: loss = 0.930073 (* 1 = 0.930073 loss)
I0410 14:29:28.949442 18414 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0410 14:29:33.976307 18414 solver.cpp:218] Iteration 8004 (2.38722 iter/s, 5.02676s/12 iters), loss = 1.00502
I0410 14:29:33.976356 18414 solver.cpp:237]     Train net output #0: loss = 1.00502 (* 1 = 1.00502 loss)
I0410 14:29:33.976367 18414 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0410 14:29:38.875025 18414 solver.cpp:218] Iteration 8016 (2.44971 iter/s, 4.89853s/12 iters), loss = 1.04516
I0410 14:29:38.875075 18414 solver.cpp:237]     Train net output #0: loss = 1.04516 (* 1 = 1.04516 loss)
I0410 14:29:38.875087 18414 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0410 14:29:43.779429 18414 solver.cpp:218] Iteration 8028 (2.44687 iter/s, 4.90422s/12 iters), loss = 0.875851
I0410 14:29:43.779482 18414 solver.cpp:237]     Train net output #0: loss = 0.875851 (* 1 = 0.875851 loss)
I0410 14:29:43.779492 18414 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0410 14:29:48.841082 18414 solver.cpp:218] Iteration 8040 (2.37085 iter/s, 5.06147s/12 iters), loss = 1.08528
I0410 14:29:48.841125 18414 solver.cpp:237]     Train net output #0: loss = 1.08528 (* 1 = 1.08528 loss)
I0410 14:29:48.841135 18414 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0410 14:29:53.827847 18414 solver.cpp:218] Iteration 8052 (2.40645 iter/s, 4.98659s/12 iters), loss = 0.921939
I0410 14:29:53.827895 18414 solver.cpp:237]     Train net output #0: loss = 0.921939 (* 1 = 0.921939 loss)
I0410 14:29:53.827905 18414 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0410 14:29:55.861199 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0410 14:29:56.186614 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0410 14:29:56.402258 18414 solver.cpp:330] Iteration 8058, Testing net (#0)
I0410 14:29:56.402288 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:29:57.680546 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:30:00.832747 18414 solver.cpp:397]     Test net output #0: accuracy = 0.526961
I0410 14:30:00.832866 18414 solver.cpp:397]     Test net output #1: loss = 2.06348 (* 1 = 2.06348 loss)
I0410 14:30:02.803745 18414 solver.cpp:218] Iteration 8064 (1.33696 iter/s, 8.97561s/12 iters), loss = 1.04126
I0410 14:30:02.803807 18414 solver.cpp:237]     Train net output #0: loss = 1.04126 (* 1 = 1.04126 loss)
I0410 14:30:02.803820 18414 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0410 14:30:07.734998 18414 solver.cpp:218] Iteration 8076 (2.43356 iter/s, 4.93106s/12 iters), loss = 0.914768
I0410 14:30:07.735057 18414 solver.cpp:237]     Train net output #0: loss = 0.914768 (* 1 = 0.914768 loss)
I0410 14:30:07.735070 18414 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0410 14:30:12.711438 18414 solver.cpp:218] Iteration 8088 (2.41145 iter/s, 4.97625s/12 iters), loss = 0.844847
I0410 14:30:12.711490 18414 solver.cpp:237]     Train net output #0: loss = 0.844847 (* 1 = 0.844847 loss)
I0410 14:30:12.711503 18414 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0410 14:30:14.095271 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:30:17.637485 18414 solver.cpp:218] Iteration 8100 (2.43614 iter/s, 4.92582s/12 iters), loss = 1.01425
I0410 14:30:17.637580 18414 solver.cpp:237]     Train net output #0: loss = 1.01425 (* 1 = 1.01425 loss)
I0410 14:30:17.637627 18414 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0410 14:30:22.747383 18414 solver.cpp:218] Iteration 8112 (2.34848 iter/s, 5.10969s/12 iters), loss = 0.806604
I0410 14:30:22.747440 18414 solver.cpp:237]     Train net output #0: loss = 0.806604 (* 1 = 0.806604 loss)
I0410 14:30:22.747452 18414 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0410 14:30:27.735571 18414 solver.cpp:218] Iteration 8124 (2.40578 iter/s, 4.988s/12 iters), loss = 0.844355
I0410 14:30:27.735618 18414 solver.cpp:237]     Train net output #0: loss = 0.844355 (* 1 = 0.844355 loss)
I0410 14:30:27.735628 18414 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0410 14:30:32.594687 18414 solver.cpp:218] Iteration 8136 (2.46968 iter/s, 4.85893s/12 iters), loss = 1.14608
I0410 14:30:32.594817 18414 solver.cpp:237]     Train net output #0: loss = 1.14608 (* 1 = 1.14608 loss)
I0410 14:30:32.594830 18414 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0410 14:30:37.459879 18414 solver.cpp:218] Iteration 8148 (2.46663 iter/s, 4.86493s/12 iters), loss = 0.844105
I0410 14:30:37.459933 18414 solver.cpp:237]     Train net output #0: loss = 0.844105 (* 1 = 0.844105 loss)
I0410 14:30:37.459944 18414 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0410 14:30:41.911340 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0410 14:30:42.220844 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0410 14:30:42.490626 18414 solver.cpp:330] Iteration 8160, Testing net (#0)
I0410 14:30:42.490651 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:30:43.720429 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:30:46.899668 18414 solver.cpp:397]     Test net output #0: accuracy = 0.520833
I0410 14:30:46.899713 18414 solver.cpp:397]     Test net output #1: loss = 2.07355 (* 1 = 2.07355 loss)
I0410 14:30:46.982544 18414 solver.cpp:218] Iteration 8160 (1.26019 iter/s, 9.52237s/12 iters), loss = 0.92658
I0410 14:30:46.982592 18414 solver.cpp:237]     Train net output #0: loss = 0.92658 (* 1 = 0.92658 loss)
I0410 14:30:46.982604 18414 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0410 14:30:51.289670 18414 solver.cpp:218] Iteration 8172 (2.78619 iter/s, 4.30696s/12 iters), loss = 0.796292
I0410 14:30:51.289718 18414 solver.cpp:237]     Train net output #0: loss = 0.796292 (* 1 = 0.796292 loss)
I0410 14:30:51.289729 18414 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0410 14:30:56.448854 18414 solver.cpp:218] Iteration 8184 (2.32604 iter/s, 5.15899s/12 iters), loss = 0.765685
I0410 14:30:56.448916 18414 solver.cpp:237]     Train net output #0: loss = 0.765685 (* 1 = 0.765685 loss)
I0410 14:30:56.448931 18414 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0410 14:30:59.998064 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:31:01.440920 18414 solver.cpp:218] Iteration 8196 (2.40391 iter/s, 4.99187s/12 iters), loss = 0.870541
I0410 14:31:01.440979 18414 solver.cpp:237]     Train net output #0: loss = 0.870541 (* 1 = 0.870541 loss)
I0410 14:31:01.440991 18414 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0410 14:31:06.364451 18414 solver.cpp:218] Iteration 8208 (2.43737 iter/s, 4.92334s/12 iters), loss = 0.901341
I0410 14:31:06.364609 18414 solver.cpp:237]     Train net output #0: loss = 0.901341 (* 1 = 0.901341 loss)
I0410 14:31:06.364622 18414 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0410 14:31:11.284912 18414 solver.cpp:218] Iteration 8220 (2.43894 iter/s, 4.92017s/12 iters), loss = 0.927918
I0410 14:31:11.284974 18414 solver.cpp:237]     Train net output #0: loss = 0.927918 (* 1 = 0.927918 loss)
I0410 14:31:11.284987 18414 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0410 14:31:16.159790 18414 solver.cpp:218] Iteration 8232 (2.4617 iter/s, 4.87468s/12 iters), loss = 0.767512
I0410 14:31:16.159837 18414 solver.cpp:237]     Train net output #0: loss = 0.767512 (* 1 = 0.767512 loss)
I0410 14:31:16.159847 18414 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0410 14:31:21.151880 18414 solver.cpp:218] Iteration 8244 (2.40389 iter/s, 4.9919s/12 iters), loss = 1.05429
I0410 14:31:21.151937 18414 solver.cpp:237]     Train net output #0: loss = 1.05429 (* 1 = 1.05429 loss)
I0410 14:31:21.151950 18414 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0410 14:31:26.041786 18414 solver.cpp:218] Iteration 8256 (2.45413 iter/s, 4.88972s/12 iters), loss = 1.11133
I0410 14:31:26.041834 18414 solver.cpp:237]     Train net output #0: loss = 1.11133 (* 1 = 1.11133 loss)
I0410 14:31:26.041843 18414 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0410 14:31:28.039403 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0410 14:31:28.378412 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0410 14:31:28.591428 18414 solver.cpp:330] Iteration 8262, Testing net (#0)
I0410 14:31:28.591446 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:31:29.716562 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:31:33.015728 18414 solver.cpp:397]     Test net output #0: accuracy = 0.541667
I0410 14:31:33.015758 18414 solver.cpp:397]     Test net output #1: loss = 2.00154 (* 1 = 2.00154 loss)
I0410 14:31:34.810775 18414 solver.cpp:218] Iteration 8268 (1.3685 iter/s, 8.76871s/12 iters), loss = 0.96788
I0410 14:31:34.810827 18414 solver.cpp:237]     Train net output #0: loss = 0.96788 (* 1 = 0.96788 loss)
I0410 14:31:34.810839 18414 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0410 14:31:39.743060 18414 solver.cpp:218] Iteration 8280 (2.43304 iter/s, 4.93209s/12 iters), loss = 0.742964
I0410 14:31:39.743360 18414 solver.cpp:237]     Train net output #0: loss = 0.742964 (* 1 = 0.742964 loss)
I0410 14:31:39.743373 18414 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0410 14:31:44.613654 18414 solver.cpp:218] Iteration 8292 (2.46399 iter/s, 4.87016s/12 iters), loss = 1.03235
I0410 14:31:44.613713 18414 solver.cpp:237]     Train net output #0: loss = 1.03235 (* 1 = 1.03235 loss)
I0410 14:31:44.613724 18414 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0410 14:31:45.304823 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:31:49.649979 18414 solver.cpp:218] Iteration 8304 (2.38279 iter/s, 5.03612s/12 iters), loss = 0.966962
I0410 14:31:49.650030 18414 solver.cpp:237]     Train net output #0: loss = 0.966962 (* 1 = 0.966962 loss)
I0410 14:31:49.650044 18414 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0410 14:31:50.819299 18414 blocking_queue.cpp:49] Waiting for data
I0410 14:31:54.770850 18414 solver.cpp:218] Iteration 8316 (2.34344 iter/s, 5.12068s/12 iters), loss = 0.963788
I0410 14:31:54.770907 18414 solver.cpp:237]     Train net output #0: loss = 0.963788 (* 1 = 0.963788 loss)
I0410 14:31:54.770920 18414 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0410 14:31:59.751922 18414 solver.cpp:218] Iteration 8328 (2.40922 iter/s, 4.98087s/12 iters), loss = 0.781656
I0410 14:31:59.751976 18414 solver.cpp:237]     Train net output #0: loss = 0.781656 (* 1 = 0.781656 loss)
I0410 14:31:59.751989 18414 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0410 14:32:04.745609 18414 solver.cpp:218] Iteration 8340 (2.40313 iter/s, 4.99349s/12 iters), loss = 1.066
I0410 14:32:04.745671 18414 solver.cpp:237]     Train net output #0: loss = 1.066 (* 1 = 1.066 loss)
I0410 14:32:04.745683 18414 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0410 14:32:09.679508 18414 solver.cpp:218] Iteration 8352 (2.43225 iter/s, 4.9337s/12 iters), loss = 0.864438
I0410 14:32:09.679574 18414 solver.cpp:237]     Train net output #0: loss = 0.864438 (* 1 = 0.864438 loss)
I0410 14:32:09.679589 18414 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0410 14:32:14.147723 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0410 14:32:14.440086 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0410 14:32:14.639039 18414 solver.cpp:330] Iteration 8364, Testing net (#0)
I0410 14:32:14.639057 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:32:15.725975 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:32:19.127745 18414 solver.cpp:397]     Test net output #0: accuracy = 0.520833
I0410 14:32:19.127790 18414 solver.cpp:397]     Test net output #1: loss = 2.03299 (* 1 = 2.03299 loss)
I0410 14:32:19.212162 18414 solver.cpp:218] Iteration 8364 (1.25887 iter/s, 9.53234s/12 iters), loss = 0.894552
I0410 14:32:19.212204 18414 solver.cpp:237]     Train net output #0: loss = 0.894552 (* 1 = 0.894552 loss)
I0410 14:32:19.212215 18414 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0410 14:32:23.418411 18414 solver.cpp:218] Iteration 8376 (2.85302 iter/s, 4.20606s/12 iters), loss = 0.929386
I0410 14:32:23.418467 18414 solver.cpp:237]     Train net output #0: loss = 0.929386 (* 1 = 0.929386 loss)
I0410 14:32:23.418480 18414 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0410 14:32:28.368368 18414 solver.cpp:218] Iteration 8388 (2.42436 iter/s, 4.94976s/12 iters), loss = 0.819855
I0410 14:32:28.368422 18414 solver.cpp:237]     Train net output #0: loss = 0.819855 (* 1 = 0.819855 loss)
I0410 14:32:28.368432 18414 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0410 14:32:31.158839 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:32:33.293633 18414 solver.cpp:218] Iteration 8400 (2.43651 iter/s, 4.92507s/12 iters), loss = 0.792152
I0410 14:32:33.293694 18414 solver.cpp:237]     Train net output #0: loss = 0.792152 (* 1 = 0.792152 loss)
I0410 14:32:33.293709 18414 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0410 14:32:38.311033 18414 solver.cpp:218] Iteration 8412 (2.39178 iter/s, 5.01719s/12 iters), loss = 0.785181
I0410 14:32:38.311085 18414 solver.cpp:237]     Train net output #0: loss = 0.785181 (* 1 = 0.785181 loss)
I0410 14:32:38.311097 18414 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0410 14:32:43.421303 18414 solver.cpp:218] Iteration 8424 (2.34831 iter/s, 5.11007s/12 iters), loss = 0.778512
I0410 14:32:43.421360 18414 solver.cpp:237]     Train net output #0: loss = 0.778512 (* 1 = 0.778512 loss)
I0410 14:32:43.421372 18414 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0410 14:32:48.499680 18414 solver.cpp:218] Iteration 8436 (2.36305 iter/s, 5.07818s/12 iters), loss = 0.907849
I0410 14:32:48.499821 18414 solver.cpp:237]     Train net output #0: loss = 0.907849 (* 1 = 0.907849 loss)
I0410 14:32:48.499833 18414 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0410 14:32:53.499688 18414 solver.cpp:218] Iteration 8448 (2.40013 iter/s, 4.99973s/12 iters), loss = 0.981775
I0410 14:32:53.499747 18414 solver.cpp:237]     Train net output #0: loss = 0.981775 (* 1 = 0.981775 loss)
I0410 14:32:53.499759 18414 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0410 14:32:58.549496 18414 solver.cpp:218] Iteration 8460 (2.37642 iter/s, 5.04961s/12 iters), loss = 1.0584
I0410 14:32:58.549546 18414 solver.cpp:237]     Train net output #0: loss = 1.0584 (* 1 = 1.0584 loss)
I0410 14:32:58.549556 18414 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0410 14:33:00.589459 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0410 14:33:00.917985 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0410 14:33:01.132966 18414 solver.cpp:330] Iteration 8466, Testing net (#0)
I0410 14:33:01.132992 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:33:02.265769 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:33:05.798962 18414 solver.cpp:397]     Test net output #0: accuracy = 0.532475
I0410 14:33:05.799010 18414 solver.cpp:397]     Test net output #1: loss = 1.96563 (* 1 = 1.96563 loss)
I0410 14:33:07.641901 18414 solver.cpp:218] Iteration 8472 (1.31983 iter/s, 9.09211s/12 iters), loss = 0.953051
I0410 14:33:07.641968 18414 solver.cpp:237]     Train net output #0: loss = 0.953051 (* 1 = 0.953051 loss)
I0410 14:33:07.641979 18414 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0410 14:33:12.562494 18414 solver.cpp:218] Iteration 8484 (2.43883 iter/s, 4.9204s/12 iters), loss = 0.809484
I0410 14:33:12.562544 18414 solver.cpp:237]     Train net output #0: loss = 0.809484 (* 1 = 0.809484 loss)
I0410 14:33:12.562556 18414 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0410 14:33:17.417482 18414 solver.cpp:218] Iteration 8496 (2.47178 iter/s, 4.8548s/12 iters), loss = 0.696178
I0410 14:33:17.417539 18414 solver.cpp:237]     Train net output #0: loss = 0.696178 (* 1 = 0.696178 loss)
I0410 14:33:17.417553 18414 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0410 14:33:17.456312 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:33:22.403517 18414 solver.cpp:218] Iteration 8508 (2.40682 iter/s, 4.98583s/12 iters), loss = 0.909803
I0410 14:33:22.403614 18414 solver.cpp:237]     Train net output #0: loss = 0.909803 (* 1 = 0.909803 loss)
I0410 14:33:22.403625 18414 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0410 14:33:27.560356 18414 solver.cpp:218] Iteration 8520 (2.32711 iter/s, 5.1566s/12 iters), loss = 0.690709
I0410 14:33:27.560389 18414 solver.cpp:237]     Train net output #0: loss = 0.690709 (* 1 = 0.690709 loss)
I0410 14:33:27.560396 18414 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0410 14:33:32.575435 18414 solver.cpp:218] Iteration 8532 (2.39287 iter/s, 5.0149s/12 iters), loss = 0.673036
I0410 14:33:32.575477 18414 solver.cpp:237]     Train net output #0: loss = 0.673036 (* 1 = 0.673036 loss)
I0410 14:33:32.575486 18414 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0410 14:33:37.495810 18414 solver.cpp:218] Iteration 8544 (2.43893 iter/s, 4.92019s/12 iters), loss = 0.99305
I0410 14:33:37.495860 18414 solver.cpp:237]     Train net output #0: loss = 0.99305 (* 1 = 0.99305 loss)
I0410 14:33:37.495873 18414 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0410 14:33:42.409433 18414 solver.cpp:218] Iteration 8556 (2.44228 iter/s, 4.91344s/12 iters), loss = 0.802058
I0410 14:33:42.409477 18414 solver.cpp:237]     Train net output #0: loss = 0.802058 (* 1 = 0.802058 loss)
I0410 14:33:42.409487 18414 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0410 14:33:46.839553 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0410 14:33:47.575265 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0410 14:33:47.811269 18414 solver.cpp:330] Iteration 8568, Testing net (#0)
I0410 14:33:47.811292 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:33:49.048265 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:33:52.411481 18414 solver.cpp:397]     Test net output #0: accuracy = 0.530025
I0410 14:33:52.411640 18414 solver.cpp:397]     Test net output #1: loss = 2.0326 (* 1 = 2.0326 loss)
I0410 14:33:52.494531 18414 solver.cpp:218] Iteration 8568 (1.18991 iter/s, 10.0848s/12 iters), loss = 0.935903
I0410 14:33:52.494580 18414 solver.cpp:237]     Train net output #0: loss = 0.935903 (* 1 = 0.935903 loss)
I0410 14:33:52.494590 18414 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0410 14:33:56.647001 18414 solver.cpp:218] Iteration 8580 (2.88997 iter/s, 4.1523s/12 iters), loss = 0.507921
I0410 14:33:56.647066 18414 solver.cpp:237]     Train net output #0: loss = 0.507921 (* 1 = 0.507921 loss)
I0410 14:33:56.647083 18414 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0410 14:34:01.632923 18414 solver.cpp:218] Iteration 8592 (2.40687 iter/s, 4.98572s/12 iters), loss = 0.688685
I0410 14:34:01.632968 18414 solver.cpp:237]     Train net output #0: loss = 0.688685 (* 1 = 0.688685 loss)
I0410 14:34:01.632977 18414 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0410 14:34:03.824555 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:34:06.630748 18414 solver.cpp:218] Iteration 8604 (2.40114 iter/s, 4.99763s/12 iters), loss = 0.847369
I0410 14:34:06.630810 18414 solver.cpp:237]     Train net output #0: loss = 0.847369 (* 1 = 0.847369 loss)
I0410 14:34:06.630821 18414 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0410 14:34:11.804000 18414 solver.cpp:218] Iteration 8616 (2.31972 iter/s, 5.17304s/12 iters), loss = 0.939586
I0410 14:34:11.804054 18414 solver.cpp:237]     Train net output #0: loss = 0.939586 (* 1 = 0.939586 loss)
I0410 14:34:11.804067 18414 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0410 14:34:16.851480 18414 solver.cpp:218] Iteration 8628 (2.37752 iter/s, 5.04728s/12 iters), loss = 0.808348
I0410 14:34:16.851528 18414 solver.cpp:237]     Train net output #0: loss = 0.808348 (* 1 = 0.808348 loss)
I0410 14:34:16.851538 18414 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0410 14:34:21.789696 18414 solver.cpp:218] Iteration 8640 (2.43012 iter/s, 4.93802s/12 iters), loss = 0.723805
I0410 14:34:21.789745 18414 solver.cpp:237]     Train net output #0: loss = 0.723805 (* 1 = 0.723805 loss)
I0410 14:34:21.789755 18414 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0410 14:34:26.708482 18414 solver.cpp:218] Iteration 8652 (2.43972 iter/s, 4.9186s/12 iters), loss = 0.715691
I0410 14:34:26.708586 18414 solver.cpp:237]     Train net output #0: loss = 0.715691 (* 1 = 0.715691 loss)
I0410 14:34:26.708597 18414 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0410 14:34:31.629269 18414 solver.cpp:218] Iteration 8664 (2.43876 iter/s, 4.92054s/12 iters), loss = 0.802641
I0410 14:34:31.629330 18414 solver.cpp:237]     Train net output #0: loss = 0.802641 (* 1 = 0.802641 loss)
I0410 14:34:31.629344 18414 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0410 14:34:33.606207 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0410 14:34:33.905771 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0410 14:34:34.108810 18414 solver.cpp:330] Iteration 8670, Testing net (#0)
I0410 14:34:34.108842 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:34:35.080973 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:34:38.649478 18414 solver.cpp:397]     Test net output #0: accuracy = 0.528799
I0410 14:34:38.649513 18414 solver.cpp:397]     Test net output #1: loss = 1.98297 (* 1 = 1.98297 loss)
I0410 14:34:40.473474 18414 solver.cpp:218] Iteration 8676 (1.35687 iter/s, 8.8439s/12 iters), loss = 0.764304
I0410 14:34:40.473529 18414 solver.cpp:237]     Train net output #0: loss = 0.764304 (* 1 = 0.764304 loss)
I0410 14:34:40.473542 18414 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0410 14:34:45.336782 18414 solver.cpp:218] Iteration 8688 (2.46755 iter/s, 4.86311s/12 iters), loss = 0.768858
I0410 14:34:45.336830 18414 solver.cpp:237]     Train net output #0: loss = 0.768858 (* 1 = 0.768858 loss)
I0410 14:34:45.336843 18414 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0410 14:34:49.689927 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:34:50.363442 18414 solver.cpp:218] Iteration 8700 (2.38736 iter/s, 5.02647s/12 iters), loss = 0.641171
I0410 14:34:50.363499 18414 solver.cpp:237]     Train net output #0: loss = 0.641171 (* 1 = 0.641171 loss)
I0410 14:34:50.363512 18414 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0410 14:34:55.334476 18414 solver.cpp:218] Iteration 8712 (2.41408 iter/s, 4.97083s/12 iters), loss = 0.879907
I0410 14:34:55.334525 18414 solver.cpp:237]     Train net output #0: loss = 0.879907 (* 1 = 0.879907 loss)
I0410 14:34:55.334537 18414 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0410 14:35:00.283119 18414 solver.cpp:218] Iteration 8724 (2.425 iter/s, 4.94845s/12 iters), loss = 0.69893
I0410 14:35:00.283258 18414 solver.cpp:237]     Train net output #0: loss = 0.69893 (* 1 = 0.69893 loss)
I0410 14:35:00.283269 18414 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0410 14:35:05.176573 18414 solver.cpp:218] Iteration 8736 (2.4524 iter/s, 4.89317s/12 iters), loss = 0.592819
I0410 14:35:05.176625 18414 solver.cpp:237]     Train net output #0: loss = 0.592819 (* 1 = 0.592819 loss)
I0410 14:35:05.176635 18414 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0410 14:35:10.131582 18414 solver.cpp:218] Iteration 8748 (2.42189 iter/s, 4.95481s/12 iters), loss = 0.823669
I0410 14:35:10.131623 18414 solver.cpp:237]     Train net output #0: loss = 0.823669 (* 1 = 0.823669 loss)
I0410 14:35:10.131633 18414 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0410 14:35:15.023749 18414 solver.cpp:218] Iteration 8760 (2.453 iter/s, 4.89198s/12 iters), loss = 0.868858
I0410 14:35:15.023803 18414 solver.cpp:237]     Train net output #0: loss = 0.868858 (* 1 = 0.868858 loss)
I0410 14:35:15.023815 18414 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0410 14:35:19.480638 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0410 14:35:19.785866 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0410 14:35:19.980118 18414 solver.cpp:330] Iteration 8772, Testing net (#0)
I0410 14:35:19.980137 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:35:21.017768 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:35:24.444137 18414 solver.cpp:397]     Test net output #0: accuracy = 0.545956
I0410 14:35:24.444178 18414 solver.cpp:397]     Test net output #1: loss = 1.98288 (* 1 = 1.98288 loss)
I0410 14:35:24.527467 18414 solver.cpp:218] Iteration 8772 (1.26271 iter/s, 9.50339s/12 iters), loss = 0.907968
I0410 14:35:24.527529 18414 solver.cpp:237]     Train net output #0: loss = 0.907968 (* 1 = 0.907968 loss)
I0410 14:35:24.527542 18414 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0410 14:35:28.886421 18414 solver.cpp:218] Iteration 8784 (2.75307 iter/s, 4.35877s/12 iters), loss = 0.621794
I0410 14:35:28.886480 18414 solver.cpp:237]     Train net output #0: loss = 0.621794 (* 1 = 0.621794 loss)
I0410 14:35:28.886492 18414 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0410 14:35:33.876574 18414 solver.cpp:218] Iteration 8796 (2.40484 iter/s, 4.98995s/12 iters), loss = 0.816748
I0410 14:35:33.876729 18414 solver.cpp:237]     Train net output #0: loss = 0.816748 (* 1 = 0.816748 loss)
I0410 14:35:33.876744 18414 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0410 14:35:35.283294 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:35:38.792675 18414 solver.cpp:218] Iteration 8808 (2.44111 iter/s, 4.9158s/12 iters), loss = 0.686888
I0410 14:35:38.792727 18414 solver.cpp:237]     Train net output #0: loss = 0.686888 (* 1 = 0.686888 loss)
I0410 14:35:38.792737 18414 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0410 14:35:43.702554 18414 solver.cpp:218] Iteration 8820 (2.44415 iter/s, 4.90968s/12 iters), loss = 0.823811
I0410 14:35:43.702603 18414 solver.cpp:237]     Train net output #0: loss = 0.823811 (* 1 = 0.823811 loss)
I0410 14:35:43.702615 18414 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0410 14:35:48.617918 18414 solver.cpp:218] Iteration 8832 (2.44142 iter/s, 4.91517s/12 iters), loss = 0.603411
I0410 14:35:48.618001 18414 solver.cpp:237]     Train net output #0: loss = 0.603411 (* 1 = 0.603411 loss)
I0410 14:35:48.618016 18414 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0410 14:35:53.517201 18414 solver.cpp:218] Iteration 8844 (2.44945 iter/s, 4.89905s/12 iters), loss = 0.641044
I0410 14:35:53.517261 18414 solver.cpp:237]     Train net output #0: loss = 0.641044 (* 1 = 0.641044 loss)
I0410 14:35:53.517275 18414 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0410 14:35:58.427006 18414 solver.cpp:218] Iteration 8856 (2.44419 iter/s, 4.9096s/12 iters), loss = 0.807959
I0410 14:35:58.427053 18414 solver.cpp:237]     Train net output #0: loss = 0.807959 (* 1 = 0.807959 loss)
I0410 14:35:58.427064 18414 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0410 14:36:03.344431 18414 solver.cpp:218] Iteration 8868 (2.4404 iter/s, 4.91723s/12 iters), loss = 0.8094
I0410 14:36:03.344475 18414 solver.cpp:237]     Train net output #0: loss = 0.8094 (* 1 = 0.8094 loss)
I0410 14:36:03.344483 18414 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0410 14:36:05.363629 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0410 14:36:05.668165 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0410 14:36:05.865525 18414 solver.cpp:330] Iteration 8874, Testing net (#0)
I0410 14:36:05.865554 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:36:06.775990 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:36:10.288751 18414 solver.cpp:397]     Test net output #0: accuracy = 0.540441
I0410 14:36:10.288795 18414 solver.cpp:397]     Test net output #1: loss = 1.93966 (* 1 = 1.93966 loss)
I0410 14:36:12.164181 18414 solver.cpp:218] Iteration 8880 (1.36063 iter/s, 8.81945s/12 iters), loss = 0.770907
I0410 14:36:12.164238 18414 solver.cpp:237]     Train net output #0: loss = 0.770907 (* 1 = 0.770907 loss)
I0410 14:36:12.164252 18414 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0410 14:36:17.058516 18414 solver.cpp:218] Iteration 8892 (2.45191 iter/s, 4.89413s/12 iters), loss = 0.709561
I0410 14:36:17.058560 18414 solver.cpp:237]     Train net output #0: loss = 0.709561 (* 1 = 0.709561 loss)
I0410 14:36:17.058573 18414 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0410 14:36:20.615054 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:36:21.977376 18414 solver.cpp:218] Iteration 8904 (2.43969 iter/s, 4.91867s/12 iters), loss = 0.597866
I0410 14:36:21.977434 18414 solver.cpp:237]     Train net output #0: loss = 0.597866 (* 1 = 0.597866 loss)
I0410 14:36:21.977447 18414 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0410 14:36:26.932087 18414 solver.cpp:218] Iteration 8916 (2.42204 iter/s, 4.9545s/12 iters), loss = 0.651379
I0410 14:36:26.932132 18414 solver.cpp:237]     Train net output #0: loss = 0.651379 (* 1 = 0.651379 loss)
I0410 14:36:26.932142 18414 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0410 14:36:31.845855 18414 solver.cpp:218] Iteration 8928 (2.44221 iter/s, 4.91357s/12 iters), loss = 0.78054
I0410 14:36:31.845909 18414 solver.cpp:237]     Train net output #0: loss = 0.78054 (* 1 = 0.78054 loss)
I0410 14:36:31.845922 18414 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0410 14:36:36.798410 18414 solver.cpp:218] Iteration 8940 (2.42309 iter/s, 4.95235s/12 iters), loss = 0.936956
I0410 14:36:36.800815 18414 solver.cpp:237]     Train net output #0: loss = 0.936956 (* 1 = 0.936956 loss)
I0410 14:36:36.800829 18414 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0410 14:36:41.733444 18414 solver.cpp:218] Iteration 8952 (2.43285 iter/s, 4.93249s/12 iters), loss = 0.653108
I0410 14:36:41.733496 18414 solver.cpp:237]     Train net output #0: loss = 0.653108 (* 1 = 0.653108 loss)
I0410 14:36:41.733507 18414 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0410 14:36:46.672366 18414 solver.cpp:218] Iteration 8964 (2.42978 iter/s, 4.93872s/12 iters), loss = 0.87614
I0410 14:36:46.672426 18414 solver.cpp:237]     Train net output #0: loss = 0.87614 (* 1 = 0.87614 loss)
I0410 14:36:46.672439 18414 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0410 14:36:51.151377 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0410 14:36:51.561492 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0410 14:36:51.826493 18414 solver.cpp:330] Iteration 8976, Testing net (#0)
I0410 14:36:51.826522 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:36:52.702642 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:36:56.200641 18414 solver.cpp:397]     Test net output #0: accuracy = 0.536152
I0410 14:36:56.200676 18414 solver.cpp:397]     Test net output #1: loss = 1.99656 (* 1 = 1.99656 loss)
I0410 14:36:56.283663 18414 solver.cpp:218] Iteration 8976 (1.24857 iter/s, 9.61096s/12 iters), loss = 0.823134
I0410 14:36:56.283710 18414 solver.cpp:237]     Train net output #0: loss = 0.823134 (* 1 = 0.823134 loss)
I0410 14:36:56.283720 18414 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0410 14:37:00.408322 18414 solver.cpp:218] Iteration 8988 (2.90946 iter/s, 4.12448s/12 iters), loss = 0.59165
I0410 14:37:00.408382 18414 solver.cpp:237]     Train net output #0: loss = 0.59165 (* 1 = 0.59165 loss)
I0410 14:37:00.408396 18414 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0410 14:37:02.017624 18414 blocking_queue.cpp:49] Waiting for data
I0410 14:37:05.442418 18414 solver.cpp:218] Iteration 9000 (2.38384 iter/s, 5.03389s/12 iters), loss = 0.730048
I0410 14:37:05.442462 18414 solver.cpp:237]     Train net output #0: loss = 0.730048 (* 1 = 0.730048 loss)
I0410 14:37:05.442471 18414 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0410 14:37:06.156522 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:37:10.444382 18414 solver.cpp:218] Iteration 9012 (2.39915 iter/s, 5.00176s/12 iters), loss = 0.747812
I0410 14:37:10.444479 18414 solver.cpp:237]     Train net output #0: loss = 0.747812 (* 1 = 0.747812 loss)
I0410 14:37:10.444489 18414 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0410 14:37:15.412506 18414 solver.cpp:218] Iteration 9024 (2.41552 iter/s, 4.96788s/12 iters), loss = 0.688679
I0410 14:37:15.412552 18414 solver.cpp:237]     Train net output #0: loss = 0.688679 (* 1 = 0.688679 loss)
I0410 14:37:15.412562 18414 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0410 14:37:20.411808 18414 solver.cpp:218] Iteration 9036 (2.40043 iter/s, 4.9991s/12 iters), loss = 0.584457
I0410 14:37:20.411854 18414 solver.cpp:237]     Train net output #0: loss = 0.584457 (* 1 = 0.584457 loss)
I0410 14:37:20.411864 18414 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0410 14:37:25.420385 18414 solver.cpp:218] Iteration 9048 (2.39599 iter/s, 5.00837s/12 iters), loss = 0.80201
I0410 14:37:25.420444 18414 solver.cpp:237]     Train net output #0: loss = 0.80201 (* 1 = 0.80201 loss)
I0410 14:37:25.420455 18414 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0410 14:37:30.333690 18414 solver.cpp:218] Iteration 9060 (2.44245 iter/s, 4.9131s/12 iters), loss = 0.814795
I0410 14:37:30.333739 18414 solver.cpp:237]     Train net output #0: loss = 0.814795 (* 1 = 0.814795 loss)
I0410 14:37:30.333748 18414 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0410 14:37:35.251608 18414 solver.cpp:218] Iteration 9072 (2.44016 iter/s, 4.91772s/12 iters), loss = 0.610867
I0410 14:37:35.251663 18414 solver.cpp:237]     Train net output #0: loss = 0.610867 (* 1 = 0.610867 loss)
I0410 14:37:35.251677 18414 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0410 14:37:37.265720 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0410 14:37:37.572623 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0410 14:37:37.796666 18414 solver.cpp:330] Iteration 9078, Testing net (#0)
I0410 14:37:37.796690 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:37:38.896378 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:37:42.657948 18414 solver.cpp:397]     Test net output #0: accuracy = 0.550245
I0410 14:37:42.658125 18414 solver.cpp:397]     Test net output #1: loss = 1.8616 (* 1 = 1.8616 loss)
I0410 14:37:44.514706 18414 solver.cpp:218] Iteration 9084 (1.29551 iter/s, 9.26278s/12 iters), loss = 0.679617
I0410 14:37:44.514750 18414 solver.cpp:237]     Train net output #0: loss = 0.679617 (* 1 = 0.679617 loss)
I0410 14:37:44.514760 18414 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0410 14:37:49.393178 18414 solver.cpp:218] Iteration 9096 (2.45989 iter/s, 4.87828s/12 iters), loss = 0.773406
I0410 14:37:49.393224 18414 solver.cpp:237]     Train net output #0: loss = 0.773406 (* 1 = 0.773406 loss)
I0410 14:37:49.393234 18414 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0410 14:37:52.305719 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:37:54.365139 18414 solver.cpp:218] Iteration 9108 (2.41363 iter/s, 4.97176s/12 iters), loss = 0.63665
I0410 14:37:54.365191 18414 solver.cpp:237]     Train net output #0: loss = 0.63665 (* 1 = 0.63665 loss)
I0410 14:37:54.365206 18414 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0410 14:37:59.595955 18414 solver.cpp:218] Iteration 9120 (2.29419 iter/s, 5.23061s/12 iters), loss = 0.716428
I0410 14:37:59.596007 18414 solver.cpp:237]     Train net output #0: loss = 0.716428 (* 1 = 0.716428 loss)
I0410 14:37:59.596020 18414 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0410 14:38:04.513991 18414 solver.cpp:218] Iteration 9132 (2.4401 iter/s, 4.91782s/12 iters), loss = 0.625302
I0410 14:38:04.514048 18414 solver.cpp:237]     Train net output #0: loss = 0.625302 (* 1 = 0.625302 loss)
I0410 14:38:04.514060 18414 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0410 14:38:09.509104 18414 solver.cpp:218] Iteration 9144 (2.40245 iter/s, 4.99491s/12 iters), loss = 0.739622
I0410 14:38:09.509161 18414 solver.cpp:237]     Train net output #0: loss = 0.739622 (* 1 = 0.739622 loss)
I0410 14:38:09.509176 18414 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0410 14:38:14.528131 18414 solver.cpp:218] Iteration 9156 (2.391 iter/s, 5.01881s/12 iters), loss = 0.899157
I0410 14:38:14.528256 18414 solver.cpp:237]     Train net output #0: loss = 0.899157 (* 1 = 0.899157 loss)
I0410 14:38:14.528270 18414 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0410 14:38:19.505203 18414 solver.cpp:218] Iteration 9168 (2.41119 iter/s, 4.9768s/12 iters), loss = 0.768072
I0410 14:38:19.505256 18414 solver.cpp:237]     Train net output #0: loss = 0.768072 (* 1 = 0.768072 loss)
I0410 14:38:19.505267 18414 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0410 14:38:23.998102 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0410 14:38:24.324663 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0410 14:38:24.535616 18414 solver.cpp:330] Iteration 9180, Testing net (#0)
I0410 14:38:24.535641 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:38:25.409303 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:38:28.993861 18414 solver.cpp:397]     Test net output #0: accuracy = 0.54473
I0410 14:38:28.993913 18414 solver.cpp:397]     Test net output #1: loss = 1.99985 (* 1 = 1.99985 loss)
I0410 14:38:29.077008 18414 solver.cpp:218] Iteration 9180 (1.25373 iter/s, 9.57148s/12 iters), loss = 0.632207
I0410 14:38:29.077065 18414 solver.cpp:237]     Train net output #0: loss = 0.632207 (* 1 = 0.632207 loss)
I0410 14:38:29.077078 18414 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0410 14:38:33.473417 18414 solver.cpp:218] Iteration 9192 (2.72962 iter/s, 4.39622s/12 iters), loss = 0.826682
I0410 14:38:33.473464 18414 solver.cpp:237]     Train net output #0: loss = 0.826682 (* 1 = 0.826682 loss)
I0410 14:38:33.473474 18414 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0410 14:38:38.440662 18414 solver.cpp:218] Iteration 9204 (2.41592 iter/s, 4.96704s/12 iters), loss = 0.635372
I0410 14:38:38.440721 18414 solver.cpp:237]     Train net output #0: loss = 0.635372 (* 1 = 0.635372 loss)
I0410 14:38:38.440735 18414 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0410 14:38:38.513849 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:38:43.443893 18414 solver.cpp:218] Iteration 9216 (2.39855 iter/s, 5.00302s/12 iters), loss = 0.785794
I0410 14:38:43.443948 18414 solver.cpp:237]     Train net output #0: loss = 0.785794 (* 1 = 0.785794 loss)
I0410 14:38:43.443960 18414 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0410 14:38:48.450665 18414 solver.cpp:218] Iteration 9228 (2.39685 iter/s, 5.00657s/12 iters), loss = 0.525295
I0410 14:38:48.450800 18414 solver.cpp:237]     Train net output #0: loss = 0.525295 (* 1 = 0.525295 loss)
I0410 14:38:48.450810 18414 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0410 14:38:53.415324 18414 solver.cpp:218] Iteration 9240 (2.41722 iter/s, 4.96437s/12 iters), loss = 0.434166
I0410 14:38:53.415380 18414 solver.cpp:237]     Train net output #0: loss = 0.434166 (* 1 = 0.434166 loss)
I0410 14:38:53.415393 18414 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0410 14:38:58.445546 18414 solver.cpp:218] Iteration 9252 (2.38568 iter/s, 5.03001s/12 iters), loss = 0.694101
I0410 14:38:58.445598 18414 solver.cpp:237]     Train net output #0: loss = 0.694101 (* 1 = 0.694101 loss)
I0410 14:38:58.445608 18414 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0410 14:39:03.444638 18414 solver.cpp:218] Iteration 9264 (2.40053 iter/s, 4.99889s/12 iters), loss = 0.589266
I0410 14:39:03.444694 18414 solver.cpp:237]     Train net output #0: loss = 0.589266 (* 1 = 0.589266 loss)
I0410 14:39:03.444707 18414 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0410 14:39:08.389410 18414 solver.cpp:218] Iteration 9276 (2.42691 iter/s, 4.94456s/12 iters), loss = 0.786945
I0410 14:39:08.389470 18414 solver.cpp:237]     Train net output #0: loss = 0.786945 (* 1 = 0.786945 loss)
I0410 14:39:08.389483 18414 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0410 14:39:10.408373 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0410 14:39:10.737116 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0410 14:39:10.946974 18414 solver.cpp:330] Iteration 9282, Testing net (#0)
I0410 14:39:10.947001 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:39:11.753351 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:39:15.493651 18414 solver.cpp:397]     Test net output #0: accuracy = 0.571691
I0410 14:39:15.493700 18414 solver.cpp:397]     Test net output #1: loss = 1.83631 (* 1 = 1.83631 loss)
I0410 14:39:17.250289 18414 solver.cpp:218] Iteration 9288 (1.35432 iter/s, 8.86056s/12 iters), loss = 0.68914
I0410 14:39:17.250355 18414 solver.cpp:237]     Train net output #0: loss = 0.68914 (* 1 = 0.68914 loss)
I0410 14:39:17.250368 18414 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0410 14:39:22.132295 18414 solver.cpp:218] Iteration 9300 (2.45812 iter/s, 4.88179s/12 iters), loss = 0.442613
I0410 14:39:22.132797 18414 solver.cpp:237]     Train net output #0: loss = 0.442613 (* 1 = 0.442613 loss)
I0410 14:39:22.132808 18414 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0410 14:39:24.279884 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:39:26.993129 18414 solver.cpp:218] Iteration 9312 (2.46904 iter/s, 4.86018s/12 iters), loss = 0.662062
I0410 14:39:26.993188 18414 solver.cpp:237]     Train net output #0: loss = 0.662062 (* 1 = 0.662062 loss)
I0410 14:39:26.993201 18414 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0410 14:39:31.822940 18414 solver.cpp:218] Iteration 9324 (2.48468 iter/s, 4.8296s/12 iters), loss = 0.699768
I0410 14:39:31.822999 18414 solver.cpp:237]     Train net output #0: loss = 0.699768 (* 1 = 0.699768 loss)
I0410 14:39:31.823010 18414 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0410 14:39:36.682313 18414 solver.cpp:218] Iteration 9336 (2.46956 iter/s, 4.85916s/12 iters), loss = 0.67838
I0410 14:39:36.682370 18414 solver.cpp:237]     Train net output #0: loss = 0.67838 (* 1 = 0.67838 loss)
I0410 14:39:36.682384 18414 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0410 14:39:41.550693 18414 solver.cpp:218] Iteration 9348 (2.46499 iter/s, 4.86818s/12 iters), loss = 0.685725
I0410 14:39:41.550753 18414 solver.cpp:237]     Train net output #0: loss = 0.685725 (* 1 = 0.685725 loss)
I0410 14:39:41.550765 18414 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0410 14:39:46.400058 18414 solver.cpp:218] Iteration 9360 (2.47466 iter/s, 4.84916s/12 iters), loss = 0.716692
I0410 14:39:46.400117 18414 solver.cpp:237]     Train net output #0: loss = 0.716692 (* 1 = 0.716692 loss)
I0410 14:39:46.400131 18414 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0410 14:39:51.260393 18414 solver.cpp:218] Iteration 9372 (2.46907 iter/s, 4.86012s/12 iters), loss = 0.836972
I0410 14:39:51.260453 18414 solver.cpp:237]     Train net output #0: loss = 0.836972 (* 1 = 0.836972 loss)
I0410 14:39:51.260465 18414 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0410 14:39:55.701588 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0410 14:39:56.027362 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0410 14:39:56.234083 18414 solver.cpp:330] Iteration 9384, Testing net (#0)
I0410 14:39:56.234109 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:39:57.003648 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:40:00.710093 18414 solver.cpp:397]     Test net output #0: accuracy = 0.560662
I0410 14:40:00.710131 18414 solver.cpp:397]     Test net output #1: loss = 1.88304 (* 1 = 1.88304 loss)
I0410 14:40:00.793226 18414 solver.cpp:218] Iteration 9384 (1.25885 iter/s, 9.53249s/12 iters), loss = 0.624714
I0410 14:40:00.793282 18414 solver.cpp:237]     Train net output #0: loss = 0.624714 (* 1 = 0.624714 loss)
I0410 14:40:00.793294 18414 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0410 14:40:04.953572 18414 solver.cpp:218] Iteration 9396 (2.88451 iter/s, 4.16016s/12 iters), loss = 0.638775
I0410 14:40:04.953625 18414 solver.cpp:237]     Train net output #0: loss = 0.638775 (* 1 = 0.638775 loss)
I0410 14:40:04.953637 18414 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0410 14:40:09.232969 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:40:09.877050 18414 solver.cpp:218] Iteration 9408 (2.4374 iter/s, 4.92328s/12 iters), loss = 0.608639
I0410 14:40:09.877096 18414 solver.cpp:237]     Train net output #0: loss = 0.608639 (* 1 = 0.608639 loss)
I0410 14:40:09.877106 18414 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0410 14:40:14.784365 18414 solver.cpp:218] Iteration 9420 (2.44543 iter/s, 4.90712s/12 iters), loss = 0.597965
I0410 14:40:14.784420 18414 solver.cpp:237]     Train net output #0: loss = 0.597965 (* 1 = 0.597965 loss)
I0410 14:40:14.784432 18414 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0410 14:40:19.716338 18414 solver.cpp:218] Iteration 9432 (2.43321 iter/s, 4.93177s/12 iters), loss = 0.600003
I0410 14:40:19.716389 18414 solver.cpp:237]     Train net output #0: loss = 0.600003 (* 1 = 0.600003 loss)
I0410 14:40:19.716400 18414 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0410 14:40:24.617851 18414 solver.cpp:218] Iteration 9444 (2.44833 iter/s, 4.90131s/12 iters), loss = 0.57225
I0410 14:40:24.617903 18414 solver.cpp:237]     Train net output #0: loss = 0.57225 (* 1 = 0.57225 loss)
I0410 14:40:24.617914 18414 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0410 14:40:29.525718 18414 solver.cpp:218] Iteration 9456 (2.44516 iter/s, 4.90766s/12 iters), loss = 0.699787
I0410 14:40:29.525878 18414 solver.cpp:237]     Train net output #0: loss = 0.699787 (* 1 = 0.699787 loss)
I0410 14:40:29.525892 18414 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0410 14:40:34.457267 18414 solver.cpp:218] Iteration 9468 (2.43347 iter/s, 4.93124s/12 iters), loss = 0.605433
I0410 14:40:34.457312 18414 solver.cpp:237]     Train net output #0: loss = 0.605433 (* 1 = 0.605433 loss)
I0410 14:40:34.457322 18414 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0410 14:40:39.382369 18414 solver.cpp:218] Iteration 9480 (2.43659 iter/s, 4.92491s/12 iters), loss = 0.709078
I0410 14:40:39.382417 18414 solver.cpp:237]     Train net output #0: loss = 0.709078 (* 1 = 0.709078 loss)
I0410 14:40:39.382427 18414 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0410 14:40:41.640817 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0410 14:40:42.383343 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0410 14:40:42.778234 18414 solver.cpp:330] Iteration 9486, Testing net (#0)
I0410 14:40:42.778261 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:40:43.509119 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:40:47.237108 18414 solver.cpp:397]     Test net output #0: accuracy = 0.551471
I0410 14:40:47.237143 18414 solver.cpp:397]     Test net output #1: loss = 1.8796 (* 1 = 1.8796 loss)
I0410 14:40:48.963083 18414 solver.cpp:218] Iteration 9492 (1.25256 iter/s, 9.58038s/12 iters), loss = 0.60428
I0410 14:40:48.963140 18414 solver.cpp:237]     Train net output #0: loss = 0.60428 (* 1 = 0.60428 loss)
I0410 14:40:48.963150 18414 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0410 14:40:54.035302 18414 solver.cpp:218] Iteration 9504 (2.36593 iter/s, 5.07201s/12 iters), loss = 0.42748
I0410 14:40:54.035360 18414 solver.cpp:237]     Train net output #0: loss = 0.42748 (* 1 = 0.42748 loss)
I0410 14:40:54.035374 18414 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0410 14:40:55.472649 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:40:58.901587 18414 solver.cpp:218] Iteration 9516 (2.46606 iter/s, 4.86607s/12 iters), loss = 0.876114
I0410 14:40:58.901645 18414 solver.cpp:237]     Train net output #0: loss = 0.876114 (* 1 = 0.876114 loss)
I0410 14:40:58.901659 18414 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0410 14:41:03.801429 18414 solver.cpp:218] Iteration 9528 (2.44916 iter/s, 4.89963s/12 iters), loss = 0.567398
I0410 14:41:03.801554 18414 solver.cpp:237]     Train net output #0: loss = 0.567398 (* 1 = 0.567398 loss)
I0410 14:41:03.801568 18414 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0410 14:41:08.680946 18414 solver.cpp:218] Iteration 9540 (2.4594 iter/s, 4.87924s/12 iters), loss = 0.425231
I0410 14:41:08.680999 18414 solver.cpp:237]     Train net output #0: loss = 0.425231 (* 1 = 0.425231 loss)
I0410 14:41:08.681010 18414 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0410 14:41:13.644317 18414 solver.cpp:218] Iteration 9552 (2.41781 iter/s, 4.96317s/12 iters), loss = 0.767437
I0410 14:41:13.644366 18414 solver.cpp:237]     Train net output #0: loss = 0.767437 (* 1 = 0.767437 loss)
I0410 14:41:13.644376 18414 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0410 14:41:18.714700 18414 solver.cpp:218] Iteration 9564 (2.36678 iter/s, 5.07018s/12 iters), loss = 0.594944
I0410 14:41:18.714745 18414 solver.cpp:237]     Train net output #0: loss = 0.594944 (* 1 = 0.594944 loss)
I0410 14:41:18.714754 18414 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0410 14:41:23.818776 18414 solver.cpp:218] Iteration 9576 (2.35116 iter/s, 5.10387s/12 iters), loss = 0.59932
I0410 14:41:23.818830 18414 solver.cpp:237]     Train net output #0: loss = 0.59932 (* 1 = 0.59932 loss)
I0410 14:41:23.818843 18414 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0410 14:41:28.473346 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0410 14:41:28.789641 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0410 14:41:28.996212 18414 solver.cpp:330] Iteration 9588, Testing net (#0)
I0410 14:41:28.996240 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:41:29.821887 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:41:33.633633 18414 solver.cpp:397]     Test net output #0: accuracy = 0.573529
I0410 14:41:33.633671 18414 solver.cpp:397]     Test net output #1: loss = 1.79167 (* 1 = 1.79167 loss)
I0410 14:41:33.716745 18414 solver.cpp:218] Iteration 9588 (1.21241 iter/s, 9.89762s/12 iters), loss = 0.525336
I0410 14:41:33.716804 18414 solver.cpp:237]     Train net output #0: loss = 0.525336 (* 1 = 0.525336 loss)
I0410 14:41:33.716818 18414 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0410 14:41:38.136175 18414 solver.cpp:218] Iteration 9600 (2.7154 iter/s, 4.41923s/12 iters), loss = 0.701471
I0410 14:41:38.136345 18414 solver.cpp:237]     Train net output #0: loss = 0.701471 (* 1 = 0.701471 loss)
I0410 14:41:38.136360 18414 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0410 14:41:41.721050 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:41:43.091298 18414 solver.cpp:218] Iteration 9612 (2.42189 iter/s, 4.95481s/12 iters), loss = 0.56613
I0410 14:41:43.091354 18414 solver.cpp:237]     Train net output #0: loss = 0.56613 (* 1 = 0.56613 loss)
I0410 14:41:43.091365 18414 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0410 14:41:48.064365 18414 solver.cpp:218] Iteration 9624 (2.4131 iter/s, 4.97286s/12 iters), loss = 0.500358
I0410 14:41:48.064406 18414 solver.cpp:237]     Train net output #0: loss = 0.500358 (* 1 = 0.500358 loss)
I0410 14:41:48.064415 18414 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0410 14:41:52.996464 18414 solver.cpp:218] Iteration 9636 (2.43314 iter/s, 4.9319s/12 iters), loss = 0.649055
I0410 14:41:52.996518 18414 solver.cpp:237]     Train net output #0: loss = 0.649055 (* 1 = 0.649055 loss)
I0410 14:41:52.996532 18414 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0410 14:41:57.891806 18414 solver.cpp:218] Iteration 9648 (2.45141 iter/s, 4.89513s/12 iters), loss = 0.616539
I0410 14:41:57.891865 18414 solver.cpp:237]     Train net output #0: loss = 0.616539 (* 1 = 0.616539 loss)
I0410 14:41:57.891878 18414 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0410 14:42:02.824293 18414 solver.cpp:218] Iteration 9660 (2.43295 iter/s, 4.93228s/12 iters), loss = 0.496746
I0410 14:42:02.824349 18414 solver.cpp:237]     Train net output #0: loss = 0.496746 (* 1 = 0.496746 loss)
I0410 14:42:02.824364 18414 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0410 14:42:07.703495 18414 solver.cpp:218] Iteration 9672 (2.45953 iter/s, 4.87899s/12 iters), loss = 0.71815
I0410 14:42:07.703550 18414 solver.cpp:237]     Train net output #0: loss = 0.71815 (* 1 = 0.71815 loss)
I0410 14:42:07.703562 18414 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0410 14:42:12.726176 18414 solver.cpp:218] Iteration 9684 (2.38926 iter/s, 5.02247s/12 iters), loss = 0.504905
I0410 14:42:12.726274 18414 solver.cpp:237]     Train net output #0: loss = 0.504905 (* 1 = 0.504905 loss)
I0410 14:42:12.726284 18414 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0410 14:42:14.778883 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0410 14:42:15.082206 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0410 14:42:15.286859 18414 solver.cpp:330] Iteration 9690, Testing net (#0)
I0410 14:42:15.286883 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:42:15.930521 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:42:18.415040 18414 blocking_queue.cpp:49] Waiting for data
I0410 14:42:19.756588 18414 solver.cpp:397]     Test net output #0: accuracy = 0.568627
I0410 14:42:19.756633 18414 solver.cpp:397]     Test net output #1: loss = 1.86029 (* 1 = 1.86029 loss)
I0410 14:42:21.713918 18414 solver.cpp:218] Iteration 9696 (1.33521 iter/s, 8.98738s/12 iters), loss = 0.521212
I0410 14:42:21.713992 18414 solver.cpp:237]     Train net output #0: loss = 0.521212 (* 1 = 0.521212 loss)
I0410 14:42:21.714006 18414 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0410 14:42:26.858467 18414 solver.cpp:218] Iteration 9708 (2.33267 iter/s, 5.14432s/12 iters), loss = 0.62321
I0410 14:42:26.858518 18414 solver.cpp:237]     Train net output #0: loss = 0.62321 (* 1 = 0.62321 loss)
I0410 14:42:26.858530 18414 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0410 14:42:27.613322 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:42:31.847939 18414 solver.cpp:218] Iteration 9720 (2.40517 iter/s, 4.98926s/12 iters), loss = 0.498186
I0410 14:42:31.847990 18414 solver.cpp:237]     Train net output #0: loss = 0.498186 (* 1 = 0.498186 loss)
I0410 14:42:31.848001 18414 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0410 14:42:36.806617 18414 solver.cpp:218] Iteration 9732 (2.4201 iter/s, 4.95847s/12 iters), loss = 0.734831
I0410 14:42:36.806679 18414 solver.cpp:237]     Train net output #0: loss = 0.734831 (* 1 = 0.734831 loss)
I0410 14:42:36.806694 18414 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0410 14:42:41.722359 18414 solver.cpp:218] Iteration 9744 (2.44125 iter/s, 4.91552s/12 iters), loss = 0.727718
I0410 14:42:41.722411 18414 solver.cpp:237]     Train net output #0: loss = 0.727718 (* 1 = 0.727718 loss)
I0410 14:42:41.722421 18414 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0410 14:42:46.816112 18414 solver.cpp:218] Iteration 9756 (2.35592 iter/s, 5.09354s/12 iters), loss = 0.962466
I0410 14:42:46.816242 18414 solver.cpp:237]     Train net output #0: loss = 0.962466 (* 1 = 0.962466 loss)
I0410 14:42:46.816252 18414 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0410 14:42:51.760136 18414 solver.cpp:218] Iteration 9768 (2.42731 iter/s, 4.94373s/12 iters), loss = 0.749103
I0410 14:42:51.760206 18414 solver.cpp:237]     Train net output #0: loss = 0.749103 (* 1 = 0.749103 loss)
I0410 14:42:51.760224 18414 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0410 14:42:56.676993 18414 solver.cpp:218] Iteration 9780 (2.44069 iter/s, 4.91664s/12 iters), loss = 0.784109
I0410 14:42:56.677043 18414 solver.cpp:237]     Train net output #0: loss = 0.784109 (* 1 = 0.784109 loss)
I0410 14:42:56.677057 18414 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0410 14:43:01.200186 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0410 14:43:01.503175 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0410 14:43:01.704700 18414 solver.cpp:330] Iteration 9792, Testing net (#0)
I0410 14:43:01.704730 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:43:02.317991 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:43:06.171291 18414 solver.cpp:397]     Test net output #0: accuracy = 0.568015
I0410 14:43:06.171348 18414 solver.cpp:397]     Test net output #1: loss = 1.84652 (* 1 = 1.84652 loss)
I0410 14:43:06.254473 18414 solver.cpp:218] Iteration 9792 (1.25298 iter/s, 9.57714s/12 iters), loss = 0.567772
I0410 14:43:06.254528 18414 solver.cpp:237]     Train net output #0: loss = 0.567772 (* 1 = 0.567772 loss)
I0410 14:43:06.254539 18414 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0410 14:43:10.627009 18414 solver.cpp:218] Iteration 9804 (2.74453 iter/s, 4.37234s/12 iters), loss = 0.613607
I0410 14:43:10.627068 18414 solver.cpp:237]     Train net output #0: loss = 0.613607 (* 1 = 0.613607 loss)
I0410 14:43:10.627079 18414 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0410 14:43:13.606930 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:43:15.624326 18414 solver.cpp:218] Iteration 9816 (2.40139 iter/s, 4.9971s/12 iters), loss = 0.525517
I0410 14:43:15.624382 18414 solver.cpp:237]     Train net output #0: loss = 0.525517 (* 1 = 0.525517 loss)
I0410 14:43:15.624395 18414 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0410 14:43:20.582648 18414 solver.cpp:218] Iteration 9828 (2.42027 iter/s, 4.95811s/12 iters), loss = 0.396072
I0410 14:43:20.582787 18414 solver.cpp:237]     Train net output #0: loss = 0.396072 (* 1 = 0.396072 loss)
I0410 14:43:20.582798 18414 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0410 14:43:25.567440 18414 solver.cpp:218] Iteration 9840 (2.40746 iter/s, 4.9845s/12 iters), loss = 0.544813
I0410 14:43:25.567493 18414 solver.cpp:237]     Train net output #0: loss = 0.544813 (* 1 = 0.544813 loss)
I0410 14:43:25.567504 18414 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0410 14:43:30.500474 18414 solver.cpp:218] Iteration 9852 (2.43268 iter/s, 4.93282s/12 iters), loss = 0.601261
I0410 14:43:30.500535 18414 solver.cpp:237]     Train net output #0: loss = 0.601261 (* 1 = 0.601261 loss)
I0410 14:43:30.500551 18414 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0410 14:43:35.501883 18414 solver.cpp:218] Iteration 9864 (2.39943 iter/s, 5.00119s/12 iters), loss = 0.58732
I0410 14:43:35.501936 18414 solver.cpp:237]     Train net output #0: loss = 0.58732 (* 1 = 0.58732 loss)
I0410 14:43:35.501945 18414 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0410 14:43:40.534276 18414 solver.cpp:218] Iteration 9876 (2.38465 iter/s, 5.03218s/12 iters), loss = 0.682785
I0410 14:43:40.534327 18414 solver.cpp:237]     Train net output #0: loss = 0.682785 (* 1 = 0.682785 loss)
I0410 14:43:40.534337 18414 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0410 14:43:45.574187 18414 solver.cpp:218] Iteration 9888 (2.38109 iter/s, 5.0397s/12 iters), loss = 0.551942
I0410 14:43:45.574240 18414 solver.cpp:237]     Train net output #0: loss = 0.551942 (* 1 = 0.551942 loss)
I0410 14:43:45.574252 18414 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0410 14:43:47.610139 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0410 14:43:48.008137 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0410 14:43:48.213501 18414 solver.cpp:330] Iteration 9894, Testing net (#0)
I0410 14:43:48.213521 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:43:48.793740 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:43:52.706112 18414 solver.cpp:397]     Test net output #0: accuracy = 0.559436
I0410 14:43:52.706235 18414 solver.cpp:397]     Test net output #1: loss = 1.92606 (* 1 = 1.92606 loss)
I0410 14:43:54.461333 18414 solver.cpp:218] Iteration 9900 (1.35031 iter/s, 8.88683s/12 iters), loss = 0.611119
I0410 14:43:54.461380 18414 solver.cpp:237]     Train net output #0: loss = 0.611119 (* 1 = 0.611119 loss)
I0410 14:43:54.461391 18414 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0410 14:43:59.410238 18414 solver.cpp:218] Iteration 9912 (2.42488 iter/s, 4.94869s/12 iters), loss = 0.62072
I0410 14:43:59.410288 18414 solver.cpp:237]     Train net output #0: loss = 0.62072 (* 1 = 0.62072 loss)
I0410 14:43:59.410300 18414 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0410 14:43:59.509263 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:44:04.364137 18414 solver.cpp:218] Iteration 9924 (2.42243 iter/s, 4.9537s/12 iters), loss = 0.478863
I0410 14:44:04.364183 18414 solver.cpp:237]     Train net output #0: loss = 0.478863 (* 1 = 0.478863 loss)
I0410 14:44:04.364192 18414 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0410 14:44:09.343176 18414 solver.cpp:218] Iteration 9936 (2.4102 iter/s, 4.97883s/12 iters), loss = 0.546958
I0410 14:44:09.343233 18414 solver.cpp:237]     Train net output #0: loss = 0.546958 (* 1 = 0.546958 loss)
I0410 14:44:09.343245 18414 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0410 14:44:14.313755 18414 solver.cpp:218] Iteration 9948 (2.41431 iter/s, 4.97036s/12 iters), loss = 0.544818
I0410 14:44:14.313825 18414 solver.cpp:237]     Train net output #0: loss = 0.544818 (* 1 = 0.544818 loss)
I0410 14:44:14.313843 18414 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0410 14:44:19.240980 18414 solver.cpp:218] Iteration 9960 (2.43556 iter/s, 4.92701s/12 iters), loss = 0.592983
I0410 14:44:19.241040 18414 solver.cpp:237]     Train net output #0: loss = 0.592983 (* 1 = 0.592983 loss)
I0410 14:44:19.241055 18414 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0410 14:44:24.221575 18414 solver.cpp:218] Iteration 9972 (2.40945 iter/s, 4.98038s/12 iters), loss = 0.577307
I0410 14:44:24.221745 18414 solver.cpp:237]     Train net output #0: loss = 0.577307 (* 1 = 0.577307 loss)
I0410 14:44:24.221760 18414 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0410 14:44:29.275480 18414 solver.cpp:218] Iteration 9984 (2.37455 iter/s, 5.05358s/12 iters), loss = 0.61398
I0410 14:44:29.275540 18414 solver.cpp:237]     Train net output #0: loss = 0.61398 (* 1 = 0.61398 loss)
I0410 14:44:29.275552 18414 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0410 14:44:33.798441 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0410 14:44:34.099210 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0410 14:44:34.297539 18414 solver.cpp:330] Iteration 9996, Testing net (#0)
I0410 14:44:34.297569 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:44:34.722812 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:44:38.758143 18414 solver.cpp:397]     Test net output #0: accuracy = 0.571691
I0410 14:44:38.758180 18414 solver.cpp:397]     Test net output #1: loss = 1.84927 (* 1 = 1.84927 loss)
I0410 14:44:38.841049 18414 solver.cpp:218] Iteration 9996 (1.25455 iter/s, 9.56522s/12 iters), loss = 0.555609
I0410 14:44:38.841110 18414 solver.cpp:237]     Train net output #0: loss = 0.555609 (* 1 = 0.555609 loss)
I0410 14:44:38.841123 18414 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0410 14:44:42.932351 18414 solver.cpp:218] Iteration 10008 (2.93319 iter/s, 4.09111s/12 iters), loss = 0.572214
I0410 14:44:42.932410 18414 solver.cpp:237]     Train net output #0: loss = 0.572214 (* 1 = 0.572214 loss)
I0410 14:44:42.932421 18414 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0410 14:44:45.109884 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:44:47.807368 18414 solver.cpp:218] Iteration 10020 (2.46164 iter/s, 4.8748s/12 iters), loss = 0.49675
I0410 14:44:47.807411 18414 solver.cpp:237]     Train net output #0: loss = 0.49675 (* 1 = 0.49675 loss)
I0410 14:44:47.807420 18414 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0410 14:44:53.091578 18414 solver.cpp:218] Iteration 10032 (2.27101 iter/s, 5.284s/12 iters), loss = 0.604721
I0410 14:44:53.091634 18414 solver.cpp:237]     Train net output #0: loss = 0.604721 (* 1 = 0.604721 loss)
I0410 14:44:53.091650 18414 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0410 14:44:58.103410 18414 solver.cpp:218] Iteration 10044 (2.39444 iter/s, 5.01162s/12 iters), loss = 0.518714
I0410 14:44:58.103500 18414 solver.cpp:237]     Train net output #0: loss = 0.518714 (* 1 = 0.518714 loss)
I0410 14:44:58.103511 18414 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0410 14:45:03.016285 18414 solver.cpp:218] Iteration 10056 (2.44269 iter/s, 4.91263s/12 iters), loss = 0.49
I0410 14:45:03.016347 18414 solver.cpp:237]     Train net output #0: loss = 0.49 (* 1 = 0.49 loss)
I0410 14:45:03.016360 18414 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0410 14:45:07.932196 18414 solver.cpp:218] Iteration 10068 (2.44116 iter/s, 4.91569s/12 iters), loss = 0.432297
I0410 14:45:07.932256 18414 solver.cpp:237]     Train net output #0: loss = 0.432297 (* 1 = 0.432297 loss)
I0410 14:45:07.932269 18414 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0410 14:45:12.838721 18414 solver.cpp:218] Iteration 10080 (2.44583 iter/s, 4.90631s/12 iters), loss = 0.688353
I0410 14:45:12.838780 18414 solver.cpp:237]     Train net output #0: loss = 0.688353 (* 1 = 0.688353 loss)
I0410 14:45:12.838793 18414 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0410 14:45:17.776590 18414 solver.cpp:218] Iteration 10092 (2.43031 iter/s, 4.93765s/12 iters), loss = 0.485915
I0410 14:45:17.776652 18414 solver.cpp:237]     Train net output #0: loss = 0.485915 (* 1 = 0.485915 loss)
I0410 14:45:17.776666 18414 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0410 14:45:19.833076 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0410 14:45:20.160262 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0410 14:45:20.372012 18414 solver.cpp:330] Iteration 10098, Testing net (#0)
I0410 14:45:20.372035 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:45:20.822044 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:45:24.743923 18414 solver.cpp:397]     Test net output #0: accuracy = 0.5625
I0410 14:45:24.743970 18414 solver.cpp:397]     Test net output #1: loss = 1.93858 (* 1 = 1.93858 loss)
I0410 14:45:26.637703 18414 solver.cpp:218] Iteration 10104 (1.35428 iter/s, 8.86079s/12 iters), loss = 0.596084
I0410 14:45:26.637749 18414 solver.cpp:237]     Train net output #0: loss = 0.596084 (* 1 = 0.596084 loss)
I0410 14:45:26.637759 18414 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0410 14:45:30.896934 18418 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:45:31.510788 18414 solver.cpp:218] Iteration 10116 (2.46261 iter/s, 4.87288s/12 iters), loss = 0.559046
I0410 14:45:31.510848 18414 solver.cpp:237]     Train net output #0: loss = 0.559046 (* 1 = 0.559046 loss)
I0410 14:45:31.510862 18414 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0410 14:45:36.451548 18414 solver.cpp:218] Iteration 10128 (2.42888 iter/s, 4.94055s/12 iters), loss = 0.522692
I0410 14:45:36.451588 18414 solver.cpp:237]     Train net output #0: loss = 0.522692 (* 1 = 0.522692 loss)
I0410 14:45:36.451598 18414 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0410 14:45:41.439944 18414 solver.cpp:218] Iteration 10140 (2.40568 iter/s, 4.9882s/12 iters), loss = 0.562347
I0410 14:45:41.439997 18414 solver.cpp:237]     Train net output #0: loss = 0.562347 (* 1 = 0.562347 loss)
I0410 14:45:41.440011 18414 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0410 14:45:46.683318 18414 solver.cpp:218] Iteration 10152 (2.2887 iter/s, 5.24316s/12 iters), loss = 0.555397
I0410 14:45:46.683362 18414 solver.cpp:237]     Train net output #0: loss = 0.555397 (* 1 = 0.555397 loss)
I0410 14:45:46.683372 18414 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0410 14:45:51.560411 18414 solver.cpp:218] Iteration 10164 (2.46058 iter/s, 4.87689s/12 iters), loss = 0.699276
I0410 14:45:51.560456 18414 solver.cpp:237]     Train net output #0: loss = 0.699276 (* 1 = 0.699276 loss)
I0410 14:45:51.560465 18414 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0410 14:45:56.444880 18414 solver.cpp:218] Iteration 10176 (2.45687 iter/s, 4.88427s/12 iters), loss = 0.668431
I0410 14:45:56.444936 18414 solver.cpp:237]     Train net output #0: loss = 0.668431 (* 1 = 0.668431 loss)
I0410 14:45:56.444949 18414 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0410 14:46:01.356637 18414 solver.cpp:218] Iteration 10188 (2.44323 iter/s, 4.91154s/12 iters), loss = 0.551761
I0410 14:46:01.356783 18414 solver.cpp:237]     Train net output #0: loss = 0.551761 (* 1 = 0.551761 loss)
I0410 14:46:01.356798 18414 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0410 14:46:05.803455 18414 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0410 14:46:06.128473 18414 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0410 14:46:06.363831 18414 solver.cpp:310] Iteration 10200, loss = 0.566473
I0410 14:46:06.363865 18414 solver.cpp:330] Iteration 10200, Testing net (#0)
I0410 14:46:06.363874 18414 net.cpp:676] Ignoring source layer train-data
I0410 14:46:06.778292 18419 data_layer.cpp:73] Restarting data prefetching from start.
I0410 14:46:10.782084 18414 solver.cpp:397]     Test net output #0: accuracy = 0.571078
I0410 14:46:10.782135 18414 solver.cpp:397]     Test net output #1: loss = 1.85916 (* 1 = 1.85916 loss)
I0410 14:46:10.782146 18414 solver.cpp:315] Optimization Done.
I0410 14:46:10.782153 18414 caffe.cpp:259] Optimization Done.