DIGITS-CNN/cars/80.10.10/base/caffe_output.log

I0401 15:21:41.137780 25640 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-132945-72ae/solver.prototxt
I0401 15:21:41.138008 25640 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0401 15:21:41.138015 25640 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0401 15:21:41.138109 25640 caffe.cpp:218] Using GPUs 2
I0401 15:21:41.161871 25640 caffe.cpp:223] GPU 2: GeForce GTX TITAN X
I0401 15:21:41.435719 25640 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.001
display: 12
max_iter: 10200
lr_policy: "fixed"
momentum: 0.9
weight_decay: 1.0000001e-05
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0401 15:21:41.436697 25640 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0401 15:21:41.437340 25640 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0401 15:21:41.437355 25640 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0401 15:21:41.437477 25640 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-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0401 15:21:41.437557 25640 layer_factory.hpp:77] Creating layer train-data
I0401 15:21:41.478404 25640 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db
I0401 15:21:41.491887 25640 net.cpp:84] Creating Layer train-data
I0401 15:21:41.492147 25640 net.cpp:380] train-data -> data
I0401 15:21:41.492210 25640 net.cpp:380] train-data -> label
I0401 15:21:41.492260 25640 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0401 15:21:41.540196 25640 data_layer.cpp:45] output data size: 128,3,227,227
I0401 15:21:41.698875 25640 net.cpp:122] Setting up train-data
I0401 15:21:41.698904 25640 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0401 15:21:41.698910 25640 net.cpp:129] Top shape: 128 (128)
I0401 15:21:41.698915 25640 net.cpp:137] Memory required for data: 79149056
I0401 15:21:41.698925 25640 layer_factory.hpp:77] Creating layer conv1
I0401 15:21:41.698949 25640 net.cpp:84] Creating Layer conv1
I0401 15:21:41.698956 25640 net.cpp:406] conv1 <- data
I0401 15:21:41.698971 25640 net.cpp:380] conv1 -> conv1
I0401 15:21:42.171262 25640 net.cpp:122] Setting up conv1
I0401 15:21:42.171283 25640 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 15:21:42.171286 25640 net.cpp:137] Memory required for data: 227833856
I0401 15:21:42.171305 25640 layer_factory.hpp:77] Creating layer relu1
I0401 15:21:42.171315 25640 net.cpp:84] Creating Layer relu1
I0401 15:21:42.171319 25640 net.cpp:406] relu1 <- conv1
I0401 15:21:42.171324 25640 net.cpp:367] relu1 -> conv1 (in-place)
I0401 15:21:42.171584 25640 net.cpp:122] Setting up relu1
I0401 15:21:42.171591 25640 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 15:21:42.171594 25640 net.cpp:137] Memory required for data: 376518656
I0401 15:21:42.171597 25640 layer_factory.hpp:77] Creating layer norm1
I0401 15:21:42.171604 25640 net.cpp:84] Creating Layer norm1
I0401 15:21:42.171607 25640 net.cpp:406] norm1 <- conv1
I0401 15:21:42.171630 25640 net.cpp:380] norm1 -> norm1
I0401 15:21:42.172076 25640 net.cpp:122] Setting up norm1
I0401 15:21:42.172088 25640 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 15:21:42.172091 25640 net.cpp:137] Memory required for data: 525203456
I0401 15:21:42.172096 25640 layer_factory.hpp:77] Creating layer pool1
I0401 15:21:42.172103 25640 net.cpp:84] Creating Layer pool1
I0401 15:21:42.172106 25640 net.cpp:406] pool1 <- norm1
I0401 15:21:42.172112 25640 net.cpp:380] pool1 -> pool1
I0401 15:21:42.172155 25640 net.cpp:122] Setting up pool1
I0401 15:21:42.172163 25640 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0401 15:21:42.172165 25640 net.cpp:137] Memory required for data: 561035264
I0401 15:21:42.172169 25640 layer_factory.hpp:77] Creating layer conv2
I0401 15:21:42.172180 25640 net.cpp:84] Creating Layer conv2
I0401 15:21:42.172183 25640 net.cpp:406] conv2 <- pool1
I0401 15:21:42.172189 25640 net.cpp:380] conv2 -> conv2
I0401 15:21:42.178746 25640 net.cpp:122] Setting up conv2
I0401 15:21:42.178764 25640 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 15:21:42.178767 25640 net.cpp:137] Memory required for data: 656586752
I0401 15:21:42.178777 25640 layer_factory.hpp:77] Creating layer relu2
I0401 15:21:42.178786 25640 net.cpp:84] Creating Layer relu2
I0401 15:21:42.178788 25640 net.cpp:406] relu2 <- conv2
I0401 15:21:42.178793 25640 net.cpp:367] relu2 -> conv2 (in-place)
I0401 15:21:42.179189 25640 net.cpp:122] Setting up relu2
I0401 15:21:42.179198 25640 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 15:21:42.179200 25640 net.cpp:137] Memory required for data: 752138240
I0401 15:21:42.179203 25640 layer_factory.hpp:77] Creating layer norm2
I0401 15:21:42.179209 25640 net.cpp:84] Creating Layer norm2
I0401 15:21:42.179212 25640 net.cpp:406] norm2 <- conv2
I0401 15:21:42.179216 25640 net.cpp:380] norm2 -> norm2
I0401 15:21:42.179476 25640 net.cpp:122] Setting up norm2
I0401 15:21:42.179483 25640 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 15:21:42.179486 25640 net.cpp:137] Memory required for data: 847689728
I0401 15:21:42.179488 25640 layer_factory.hpp:77] Creating layer pool2
I0401 15:21:42.179494 25640 net.cpp:84] Creating Layer pool2
I0401 15:21:42.179497 25640 net.cpp:406] pool2 <- norm2
I0401 15:21:42.179500 25640 net.cpp:380] pool2 -> pool2
I0401 15:21:42.179524 25640 net.cpp:122] Setting up pool2
I0401 15:21:42.179528 25640 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 15:21:42.179530 25640 net.cpp:137] Memory required for data: 869840896
I0401 15:21:42.179533 25640 layer_factory.hpp:77] Creating layer conv3
I0401 15:21:42.179540 25640 net.cpp:84] Creating Layer conv3
I0401 15:21:42.179543 25640 net.cpp:406] conv3 <- pool2
I0401 15:21:42.179546 25640 net.cpp:380] conv3 -> conv3
I0401 15:21:42.193727 25640 net.cpp:122] Setting up conv3
I0401 15:21:42.193747 25640 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 15:21:42.193749 25640 net.cpp:137] Memory required for data: 903067648
I0401 15:21:42.193761 25640 layer_factory.hpp:77] Creating layer relu3
I0401 15:21:42.193770 25640 net.cpp:84] Creating Layer relu3
I0401 15:21:42.193773 25640 net.cpp:406] relu3 <- conv3
I0401 15:21:42.193779 25640 net.cpp:367] relu3 -> conv3 (in-place)
I0401 15:21:42.194164 25640 net.cpp:122] Setting up relu3
I0401 15:21:42.194172 25640 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 15:21:42.194175 25640 net.cpp:137] Memory required for data: 936294400
I0401 15:21:42.194177 25640 layer_factory.hpp:77] Creating layer conv4
I0401 15:21:42.194186 25640 net.cpp:84] Creating Layer conv4
I0401 15:21:42.194190 25640 net.cpp:406] conv4 <- conv3
I0401 15:21:42.194193 25640 net.cpp:380] conv4 -> conv4
I0401 15:21:42.203013 25640 net.cpp:122] Setting up conv4
I0401 15:21:42.203033 25640 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 15:21:42.203037 25640 net.cpp:137] Memory required for data: 969521152
I0401 15:21:42.203044 25640 layer_factory.hpp:77] Creating layer relu4
I0401 15:21:42.203052 25640 net.cpp:84] Creating Layer relu4
I0401 15:21:42.203073 25640 net.cpp:406] relu4 <- conv4
I0401 15:21:42.203079 25640 net.cpp:367] relu4 -> conv4 (in-place)
I0401 15:21:42.203330 25640 net.cpp:122] Setting up relu4
I0401 15:21:42.203336 25640 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 15:21:42.203338 25640 net.cpp:137] Memory required for data: 1002747904
I0401 15:21:42.203341 25640 layer_factory.hpp:77] Creating layer conv5
I0401 15:21:42.203349 25640 net.cpp:84] Creating Layer conv5
I0401 15:21:42.203352 25640 net.cpp:406] conv5 <- conv4
I0401 15:21:42.203356 25640 net.cpp:380] conv5 -> conv5
I0401 15:21:42.210636 25640 net.cpp:122] Setting up conv5
I0401 15:21:42.210656 25640 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 15:21:42.210659 25640 net.cpp:137] Memory required for data: 1024899072
I0401 15:21:42.210671 25640 layer_factory.hpp:77] Creating layer relu5
I0401 15:21:42.210678 25640 net.cpp:84] Creating Layer relu5
I0401 15:21:42.210681 25640 net.cpp:406] relu5 <- conv5
I0401 15:21:42.210686 25640 net.cpp:367] relu5 -> conv5 (in-place)
I0401 15:21:42.211145 25640 net.cpp:122] Setting up relu5
I0401 15:21:42.211154 25640 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 15:21:42.211158 25640 net.cpp:137] Memory required for data: 1047050240
I0401 15:21:42.211159 25640 layer_factory.hpp:77] Creating layer pool5
I0401 15:21:42.211165 25640 net.cpp:84] Creating Layer pool5
I0401 15:21:42.211167 25640 net.cpp:406] pool5 <- conv5
I0401 15:21:42.211171 25640 net.cpp:380] pool5 -> pool5
I0401 15:21:42.211205 25640 net.cpp:122] Setting up pool5
I0401 15:21:42.211210 25640 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0401 15:21:42.211211 25640 net.cpp:137] Memory required for data: 1051768832
I0401 15:21:42.211213 25640 layer_factory.hpp:77] Creating layer fc6
I0401 15:21:42.211221 25640 net.cpp:84] Creating Layer fc6
I0401 15:21:42.211225 25640 net.cpp:406] fc6 <- pool5
I0401 15:21:42.211230 25640 net.cpp:380] fc6 -> fc6
I0401 15:21:42.607475 25640 net.cpp:122] Setting up fc6
I0401 15:21:42.607501 25640 net.cpp:129] Top shape: 128 4096 (524288)
I0401 15:21:42.607506 25640 net.cpp:137] Memory required for data: 1053865984
I0401 15:21:42.607517 25640 layer_factory.hpp:77] Creating layer relu6
I0401 15:21:42.607527 25640 net.cpp:84] Creating Layer relu6
I0401 15:21:42.607532 25640 net.cpp:406] relu6 <- fc6
I0401 15:21:42.607542 25640 net.cpp:367] relu6 -> fc6 (in-place)
I0401 15:21:42.608368 25640 net.cpp:122] Setting up relu6
I0401 15:21:42.608381 25640 net.cpp:129] Top shape: 128 4096 (524288)
I0401 15:21:42.608386 25640 net.cpp:137] Memory required for data: 1055963136
I0401 15:21:42.608389 25640 layer_factory.hpp:77] Creating layer drop6
I0401 15:21:42.608397 25640 net.cpp:84] Creating Layer drop6
I0401 15:21:42.608402 25640 net.cpp:406] drop6 <- fc6
I0401 15:21:42.608407 25640 net.cpp:367] drop6 -> fc6 (in-place)
I0401 15:21:42.608441 25640 net.cpp:122] Setting up drop6
I0401 15:21:42.608448 25640 net.cpp:129] Top shape: 128 4096 (524288)
I0401 15:21:42.608450 25640 net.cpp:137] Memory required for data: 1058060288
I0401 15:21:42.608454 25640 layer_factory.hpp:77] Creating layer fc7
I0401 15:21:42.608464 25640 net.cpp:84] Creating Layer fc7
I0401 15:21:42.608469 25640 net.cpp:406] fc7 <- fc6
I0401 15:21:42.608474 25640 net.cpp:380] fc7 -> fc7
I0401 15:21:42.851428 25640 net.cpp:122] Setting up fc7
I0401 15:21:42.851454 25640 net.cpp:129] Top shape: 128 4096 (524288)
I0401 15:21:42.851457 25640 net.cpp:137] Memory required for data: 1060157440
I0401 15:21:42.851469 25640 layer_factory.hpp:77] Creating layer relu7
I0401 15:21:42.851480 25640 net.cpp:84] Creating Layer relu7
I0401 15:21:42.851485 25640 net.cpp:406] relu7 <- fc7
I0401 15:21:42.851495 25640 net.cpp:367] relu7 -> fc7 (in-place)
I0401 15:21:42.852030 25640 net.cpp:122] Setting up relu7
I0401 15:21:42.852041 25640 net.cpp:129] Top shape: 128 4096 (524288)
I0401 15:21:42.852043 25640 net.cpp:137] Memory required for data: 1062254592
I0401 15:21:42.852047 25640 layer_factory.hpp:77] Creating layer drop7
I0401 15:21:42.852056 25640 net.cpp:84] Creating Layer drop7
I0401 15:21:42.852084 25640 net.cpp:406] drop7 <- fc7
I0401 15:21:42.852093 25640 net.cpp:367] drop7 -> fc7 (in-place)
I0401 15:21:42.852123 25640 net.cpp:122] Setting up drop7
I0401 15:21:42.852129 25640 net.cpp:129] Top shape: 128 4096 (524288)
I0401 15:21:42.852133 25640 net.cpp:137] Memory required for data: 1064351744
I0401 15:21:42.852135 25640 layer_factory.hpp:77] Creating layer fc8
I0401 15:21:42.852144 25640 net.cpp:84] Creating Layer fc8
I0401 15:21:42.852147 25640 net.cpp:406] fc8 <- fc7
I0401 15:21:42.852154 25640 net.cpp:380] fc8 -> fc8
I0401 15:21:42.864039 25640 net.cpp:122] Setting up fc8
I0401 15:21:42.864065 25640 net.cpp:129] Top shape: 128 196 (25088)
I0401 15:21:42.864069 25640 net.cpp:137] Memory required for data: 1064452096
I0401 15:21:42.864080 25640 layer_factory.hpp:77] Creating layer loss
I0401 15:21:42.864091 25640 net.cpp:84] Creating Layer loss
I0401 15:21:42.864096 25640 net.cpp:406] loss <- fc8
I0401 15:21:42.864104 25640 net.cpp:406] loss <- label
I0401 15:21:42.864111 25640 net.cpp:380] loss -> loss
I0401 15:21:42.864125 25640 layer_factory.hpp:77] Creating layer loss
I0401 15:21:42.865103 25640 net.cpp:122] Setting up loss
I0401 15:21:42.865116 25640 net.cpp:129] Top shape: (1)
I0401 15:21:42.865119 25640 net.cpp:132]     with loss weight 1
I0401 15:21:42.865145 25640 net.cpp:137] Memory required for data: 1064452100
I0401 15:21:42.865150 25640 net.cpp:198] loss needs backward computation.
I0401 15:21:42.865159 25640 net.cpp:198] fc8 needs backward computation.
I0401 15:21:42.865162 25640 net.cpp:198] drop7 needs backward computation.
I0401 15:21:42.865165 25640 net.cpp:198] relu7 needs backward computation.
I0401 15:21:42.865170 25640 net.cpp:198] fc7 needs backward computation.
I0401 15:21:42.865172 25640 net.cpp:198] drop6 needs backward computation.
I0401 15:21:42.865176 25640 net.cpp:198] relu6 needs backward computation.
I0401 15:21:42.865180 25640 net.cpp:198] fc6 needs backward computation.
I0401 15:21:42.865183 25640 net.cpp:198] pool5 needs backward computation.
I0401 15:21:42.865187 25640 net.cpp:198] relu5 needs backward computation.
I0401 15:21:42.865191 25640 net.cpp:198] conv5 needs backward computation.
I0401 15:21:42.865195 25640 net.cpp:198] relu4 needs backward computation.
I0401 15:21:42.865198 25640 net.cpp:198] conv4 needs backward computation.
I0401 15:21:42.865202 25640 net.cpp:198] relu3 needs backward computation.
I0401 15:21:42.865206 25640 net.cpp:198] conv3 needs backward computation.
I0401 15:21:42.865211 25640 net.cpp:198] pool2 needs backward computation.
I0401 15:21:42.865214 25640 net.cpp:198] norm2 needs backward computation.
I0401 15:21:42.865217 25640 net.cpp:198] relu2 needs backward computation.
I0401 15:21:42.865221 25640 net.cpp:198] conv2 needs backward computation.
I0401 15:21:42.865226 25640 net.cpp:198] pool1 needs backward computation.
I0401 15:21:42.865228 25640 net.cpp:198] norm1 needs backward computation.
I0401 15:21:42.865232 25640 net.cpp:198] relu1 needs backward computation.
I0401 15:21:42.865236 25640 net.cpp:198] conv1 needs backward computation.
I0401 15:21:42.865240 25640 net.cpp:200] train-data does not need backward computation.
I0401 15:21:42.865243 25640 net.cpp:242] This network produces output loss
I0401 15:21:42.865263 25640 net.cpp:255] Network initialization done.
I0401 15:21:42.865844 25640 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0401 15:21:42.865888 25640 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0401 15:21:42.866109 25640 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-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0401 15:21:42.866257 25640 layer_factory.hpp:77] Creating layer val-data
I0401 15:21:42.881776 25640 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db
I0401 15:21:42.911834 25640 net.cpp:84] Creating Layer val-data
I0401 15:21:42.911864 25640 net.cpp:380] val-data -> data
I0401 15:21:42.911880 25640 net.cpp:380] val-data -> label
I0401 15:21:42.911890 25640 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0401 15:21:42.919968 25640 data_layer.cpp:45] output data size: 32,3,227,227
I0401 15:21:42.963371 25640 net.cpp:122] Setting up val-data
I0401 15:21:42.963397 25640 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0401 15:21:42.963403 25640 net.cpp:129] Top shape: 32 (32)
I0401 15:21:42.963407 25640 net.cpp:137] Memory required for data: 19787264
I0401 15:21:42.963413 25640 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0401 15:21:42.963428 25640 net.cpp:84] Creating Layer label_val-data_1_split
I0401 15:21:42.963433 25640 net.cpp:406] label_val-data_1_split <- label
I0401 15:21:42.963441 25640 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0401 15:21:42.963452 25640 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0401 15:21:42.963587 25640 net.cpp:122] Setting up label_val-data_1_split
I0401 15:21:42.963595 25640 net.cpp:129] Top shape: 32 (32)
I0401 15:21:42.963599 25640 net.cpp:129] Top shape: 32 (32)
I0401 15:21:42.963603 25640 net.cpp:137] Memory required for data: 19787520
I0401 15:21:42.963606 25640 layer_factory.hpp:77] Creating layer conv1
I0401 15:21:42.963621 25640 net.cpp:84] Creating Layer conv1
I0401 15:21:42.963625 25640 net.cpp:406] conv1 <- data
I0401 15:21:42.963632 25640 net.cpp:380] conv1 -> conv1
I0401 15:21:42.967128 25640 net.cpp:122] Setting up conv1
I0401 15:21:42.967154 25640 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 15:21:42.967157 25640 net.cpp:137] Memory required for data: 56958720
I0401 15:21:42.967172 25640 layer_factory.hpp:77] Creating layer relu1
I0401 15:21:42.967182 25640 net.cpp:84] Creating Layer relu1
I0401 15:21:42.967187 25640 net.cpp:406] relu1 <- conv1
I0401 15:21:42.967195 25640 net.cpp:367] relu1 -> conv1 (in-place)
I0401 15:21:42.967586 25640 net.cpp:122] Setting up relu1
I0401 15:21:42.967597 25640 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 15:21:42.967599 25640 net.cpp:137] Memory required for data: 94129920
I0401 15:21:42.967603 25640 layer_factory.hpp:77] Creating layer norm1
I0401 15:21:42.967613 25640 net.cpp:84] Creating Layer norm1
I0401 15:21:42.967617 25640 net.cpp:406] norm1 <- conv1
I0401 15:21:42.967623 25640 net.cpp:380] norm1 -> norm1
I0401 15:21:42.968223 25640 net.cpp:122] Setting up norm1
I0401 15:21:42.968235 25640 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 15:21:42.968238 25640 net.cpp:137] Memory required for data: 131301120
I0401 15:21:42.968242 25640 layer_factory.hpp:77] Creating layer pool1
I0401 15:21:42.968250 25640 net.cpp:84] Creating Layer pool1
I0401 15:21:42.968255 25640 net.cpp:406] pool1 <- norm1
I0401 15:21:42.968261 25640 net.cpp:380] pool1 -> pool1
I0401 15:21:42.968300 25640 net.cpp:122] Setting up pool1
I0401 15:21:42.968308 25640 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0401 15:21:42.968310 25640 net.cpp:137] Memory required for data: 140259072
I0401 15:21:42.968313 25640 layer_factory.hpp:77] Creating layer conv2
I0401 15:21:42.968325 25640 net.cpp:84] Creating Layer conv2
I0401 15:21:42.968328 25640 net.cpp:406] conv2 <- pool1
I0401 15:21:42.968359 25640 net.cpp:380] conv2 -> conv2
I0401 15:21:42.977756 25640 net.cpp:122] Setting up conv2
I0401 15:21:42.977782 25640 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 15:21:42.977785 25640 net.cpp:137] Memory required for data: 164146944
I0401 15:21:42.977800 25640 layer_factory.hpp:77] Creating layer relu2
I0401 15:21:42.977811 25640 net.cpp:84] Creating Layer relu2
I0401 15:21:42.977816 25640 net.cpp:406] relu2 <- conv2
I0401 15:21:42.977828 25640 net.cpp:367] relu2 -> conv2 (in-place)
I0401 15:21:42.978507 25640 net.cpp:122] Setting up relu2
I0401 15:21:42.978519 25640 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 15:21:42.978523 25640 net.cpp:137] Memory required for data: 188034816
I0401 15:21:42.978528 25640 layer_factory.hpp:77] Creating layer norm2
I0401 15:21:42.978540 25640 net.cpp:84] Creating Layer norm2
I0401 15:21:42.978544 25640 net.cpp:406] norm2 <- conv2
I0401 15:21:42.978551 25640 net.cpp:380] norm2 -> norm2
I0401 15:21:42.979261 25640 net.cpp:122] Setting up norm2
I0401 15:21:42.979274 25640 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 15:21:42.979277 25640 net.cpp:137] Memory required for data: 211922688
I0401 15:21:42.979282 25640 layer_factory.hpp:77] Creating layer pool2
I0401 15:21:42.979296 25640 net.cpp:84] Creating Layer pool2
I0401 15:21:42.979300 25640 net.cpp:406] pool2 <- norm2
I0401 15:21:42.979308 25640 net.cpp:380] pool2 -> pool2
I0401 15:21:42.979358 25640 net.cpp:122] Setting up pool2
I0401 15:21:42.979367 25640 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 15:21:42.979369 25640 net.cpp:137] Memory required for data: 217460480
I0401 15:21:42.979372 25640 layer_factory.hpp:77] Creating layer conv3
I0401 15:21:42.979387 25640 net.cpp:84] Creating Layer conv3
I0401 15:21:42.979390 25640 net.cpp:406] conv3 <- pool2
I0401 15:21:42.979398 25640 net.cpp:380] conv3 -> conv3
I0401 15:21:42.995455 25640 net.cpp:122] Setting up conv3
I0401 15:21:42.995479 25640 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 15:21:42.995483 25640 net.cpp:137] Memory required for data: 225767168
I0401 15:21:42.995501 25640 layer_factory.hpp:77] Creating layer relu3
I0401 15:21:42.995513 25640 net.cpp:84] Creating Layer relu3
I0401 15:21:42.995518 25640 net.cpp:406] relu3 <- conv3
I0401 15:21:42.995527 25640 net.cpp:367] relu3 -> conv3 (in-place)
I0401 15:21:42.996250 25640 net.cpp:122] Setting up relu3
I0401 15:21:42.996263 25640 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 15:21:42.996266 25640 net.cpp:137] Memory required for data: 234073856
I0401 15:21:42.996270 25640 layer_factory.hpp:77] Creating layer conv4
I0401 15:21:42.996287 25640 net.cpp:84] Creating Layer conv4
I0401 15:21:42.996291 25640 net.cpp:406] conv4 <- conv3
I0401 15:21:42.996299 25640 net.cpp:380] conv4 -> conv4
I0401 15:21:43.021281 25640 net.cpp:122] Setting up conv4
I0401 15:21:43.021306 25640 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 15:21:43.021309 25640 net.cpp:137] Memory required for data: 242380544
I0401 15:21:43.021322 25640 layer_factory.hpp:77] Creating layer relu4
I0401 15:21:43.021335 25640 net.cpp:84] Creating Layer relu4
I0401 15:21:43.021340 25640 net.cpp:406] relu4 <- conv4
I0401 15:21:43.021348 25640 net.cpp:367] relu4 -> conv4 (in-place)
I0401 15:21:43.022313 25640 net.cpp:122] Setting up relu4
I0401 15:21:43.022330 25640 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 15:21:43.022333 25640 net.cpp:137] Memory required for data: 250687232
I0401 15:21:43.022338 25640 layer_factory.hpp:77] Creating layer conv5
I0401 15:21:43.022423 25640 net.cpp:84] Creating Layer conv5
I0401 15:21:43.022434 25640 net.cpp:406] conv5 <- conv4
I0401 15:21:43.022469 25640 net.cpp:380] conv5 -> conv5
I0401 15:21:43.036206 25640 net.cpp:122] Setting up conv5
I0401 15:21:43.036234 25640 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 15:21:43.036238 25640 net.cpp:137] Memory required for data: 256225024
I0401 15:21:43.036259 25640 layer_factory.hpp:77] Creating layer relu5
I0401 15:21:43.036271 25640 net.cpp:84] Creating Layer relu5
I0401 15:21:43.036275 25640 net.cpp:406] relu5 <- conv5
I0401 15:21:43.036312 25640 net.cpp:367] relu5 -> conv5 (in-place)
I0401 15:21:43.037029 25640 net.cpp:122] Setting up relu5
I0401 15:21:43.037048 25640 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 15:21:43.037053 25640 net.cpp:137] Memory required for data: 261762816
I0401 15:21:43.037058 25640 layer_factory.hpp:77] Creating layer pool5
I0401 15:21:43.037077 25640 net.cpp:84] Creating Layer pool5
I0401 15:21:43.037083 25640 net.cpp:406] pool5 <- conv5
I0401 15:21:43.037091 25640 net.cpp:380] pool5 -> pool5
I0401 15:21:43.037147 25640 net.cpp:122] Setting up pool5
I0401 15:21:43.037153 25640 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0401 15:21:43.037156 25640 net.cpp:137] Memory required for data: 262942464
I0401 15:21:43.037160 25640 layer_factory.hpp:77] Creating layer fc6
I0401 15:21:43.037168 25640 net.cpp:84] Creating Layer fc6
I0401 15:21:43.037173 25640 net.cpp:406] fc6 <- pool5
I0401 15:21:43.037179 25640 net.cpp:380] fc6 -> fc6
I0401 15:21:43.543203 25640 net.cpp:122] Setting up fc6
I0401 15:21:43.543224 25640 net.cpp:129] Top shape: 32 4096 (131072)
I0401 15:21:43.543227 25640 net.cpp:137] Memory required for data: 263466752
I0401 15:21:43.543234 25640 layer_factory.hpp:77] Creating layer relu6
I0401 15:21:43.543243 25640 net.cpp:84] Creating Layer relu6
I0401 15:21:43.543246 25640 net.cpp:406] relu6 <- fc6
I0401 15:21:43.543252 25640 net.cpp:367] relu6 -> fc6 (in-place)
I0401 15:21:43.543931 25640 net.cpp:122] Setting up relu6
I0401 15:21:43.543941 25640 net.cpp:129] Top shape: 32 4096 (131072)
I0401 15:21:43.543942 25640 net.cpp:137] Memory required for data: 263991040
I0401 15:21:43.543946 25640 layer_factory.hpp:77] Creating layer drop6
I0401 15:21:43.543952 25640 net.cpp:84] Creating Layer drop6
I0401 15:21:43.543954 25640 net.cpp:406] drop6 <- fc6
I0401 15:21:43.543960 25640 net.cpp:367] drop6 -> fc6 (in-place)
I0401 15:21:43.543982 25640 net.cpp:122] Setting up drop6
I0401 15:21:43.543987 25640 net.cpp:129] Top shape: 32 4096 (131072)
I0401 15:21:43.543988 25640 net.cpp:137] Memory required for data: 264515328
I0401 15:21:43.543990 25640 layer_factory.hpp:77] Creating layer fc7
I0401 15:21:43.543998 25640 net.cpp:84] Creating Layer fc7
I0401 15:21:43.544000 25640 net.cpp:406] fc7 <- fc6
I0401 15:21:43.544004 25640 net.cpp:380] fc7 -> fc7
I0401 15:21:43.691123 25640 net.cpp:122] Setting up fc7
I0401 15:21:43.691146 25640 net.cpp:129] Top shape: 32 4096 (131072)
I0401 15:21:43.691149 25640 net.cpp:137] Memory required for data: 265039616
I0401 15:21:43.691157 25640 layer_factory.hpp:77] Creating layer relu7
I0401 15:21:43.691164 25640 net.cpp:84] Creating Layer relu7
I0401 15:21:43.691169 25640 net.cpp:406] relu7 <- fc7
I0401 15:21:43.691174 25640 net.cpp:367] relu7 -> fc7 (in-place)
I0401 15:21:43.691565 25640 net.cpp:122] Setting up relu7
I0401 15:21:43.691573 25640 net.cpp:129] Top shape: 32 4096 (131072)
I0401 15:21:43.691576 25640 net.cpp:137] Memory required for data: 265563904
I0401 15:21:43.691578 25640 layer_factory.hpp:77] Creating layer drop7
I0401 15:21:43.691583 25640 net.cpp:84] Creating Layer drop7
I0401 15:21:43.691591 25640 net.cpp:406] drop7 <- fc7
I0401 15:21:43.691596 25640 net.cpp:367] drop7 -> fc7 (in-place)
I0401 15:21:43.691617 25640 net.cpp:122] Setting up drop7
I0401 15:21:43.691620 25640 net.cpp:129] Top shape: 32 4096 (131072)
I0401 15:21:43.691622 25640 net.cpp:137] Memory required for data: 266088192
I0401 15:21:43.691624 25640 layer_factory.hpp:77] Creating layer fc8
I0401 15:21:43.691632 25640 net.cpp:84] Creating Layer fc8
I0401 15:21:43.691633 25640 net.cpp:406] fc8 <- fc7
I0401 15:21:43.691639 25640 net.cpp:380] fc8 -> fc8
I0401 15:21:43.698961 25640 net.cpp:122] Setting up fc8
I0401 15:21:43.698979 25640 net.cpp:129] Top shape: 32 196 (6272)
I0401 15:21:43.698982 25640 net.cpp:137] Memory required for data: 266113280
I0401 15:21:43.698990 25640 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0401 15:21:43.698999 25640 net.cpp:84] Creating Layer fc8_fc8_0_split
I0401 15:21:43.699002 25640 net.cpp:406] fc8_fc8_0_split <- fc8
I0401 15:21:43.699028 25640 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0401 15:21:43.699036 25640 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0401 15:21:43.699069 25640 net.cpp:122] Setting up fc8_fc8_0_split
I0401 15:21:43.699074 25640 net.cpp:129] Top shape: 32 196 (6272)
I0401 15:21:43.699075 25640 net.cpp:129] Top shape: 32 196 (6272)
I0401 15:21:43.699077 25640 net.cpp:137] Memory required for data: 266163456
I0401 15:21:43.699079 25640 layer_factory.hpp:77] Creating layer accuracy
I0401 15:21:43.699086 25640 net.cpp:84] Creating Layer accuracy
I0401 15:21:43.699088 25640 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0401 15:21:43.699091 25640 net.cpp:406] accuracy <- label_val-data_1_split_0
I0401 15:21:43.699095 25640 net.cpp:380] accuracy -> accuracy
I0401 15:21:43.699100 25640 net.cpp:122] Setting up accuracy
I0401 15:21:43.699103 25640 net.cpp:129] Top shape: (1)
I0401 15:21:43.699105 25640 net.cpp:137] Memory required for data: 266163460
I0401 15:21:43.699107 25640 layer_factory.hpp:77] Creating layer loss
I0401 15:21:43.699112 25640 net.cpp:84] Creating Layer loss
I0401 15:21:43.699115 25640 net.cpp:406] loss <- fc8_fc8_0_split_1
I0401 15:21:43.699117 25640 net.cpp:406] loss <- label_val-data_1_split_1
I0401 15:21:43.699120 25640 net.cpp:380] loss -> loss
I0401 15:21:43.699126 25640 layer_factory.hpp:77] Creating layer loss
I0401 15:21:43.699854 25640 net.cpp:122] Setting up loss
I0401 15:21:43.699862 25640 net.cpp:129] Top shape: (1)
I0401 15:21:43.699864 25640 net.cpp:132]     with loss weight 1
I0401 15:21:43.699873 25640 net.cpp:137] Memory required for data: 266163464
I0401 15:21:43.699877 25640 net.cpp:198] loss needs backward computation.
I0401 15:21:43.699879 25640 net.cpp:200] accuracy does not need backward computation.
I0401 15:21:43.699882 25640 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0401 15:21:43.699884 25640 net.cpp:198] fc8 needs backward computation.
I0401 15:21:43.699887 25640 net.cpp:198] drop7 needs backward computation.
I0401 15:21:43.699889 25640 net.cpp:198] relu7 needs backward computation.
I0401 15:21:43.699892 25640 net.cpp:198] fc7 needs backward computation.
I0401 15:21:43.699893 25640 net.cpp:198] drop6 needs backward computation.
I0401 15:21:43.699895 25640 net.cpp:198] relu6 needs backward computation.
I0401 15:21:43.699898 25640 net.cpp:198] fc6 needs backward computation.
I0401 15:21:43.699899 25640 net.cpp:198] pool5 needs backward computation.
I0401 15:21:43.699903 25640 net.cpp:198] relu5 needs backward computation.
I0401 15:21:43.699904 25640 net.cpp:198] conv5 needs backward computation.
I0401 15:21:43.699908 25640 net.cpp:198] relu4 needs backward computation.
I0401 15:21:43.699909 25640 net.cpp:198] conv4 needs backward computation.
I0401 15:21:43.699911 25640 net.cpp:198] relu3 needs backward computation.
I0401 15:21:43.699913 25640 net.cpp:198] conv3 needs backward computation.
I0401 15:21:43.699916 25640 net.cpp:198] pool2 needs backward computation.
I0401 15:21:43.699918 25640 net.cpp:198] norm2 needs backward computation.
I0401 15:21:43.699920 25640 net.cpp:198] relu2 needs backward computation.
I0401 15:21:43.699923 25640 net.cpp:198] conv2 needs backward computation.
I0401 15:21:43.699924 25640 net.cpp:198] pool1 needs backward computation.
I0401 15:21:43.699928 25640 net.cpp:198] norm1 needs backward computation.
I0401 15:21:43.699930 25640 net.cpp:198] relu1 needs backward computation.
I0401 15:21:43.699932 25640 net.cpp:198] conv1 needs backward computation.
I0401 15:21:43.699935 25640 net.cpp:200] label_val-data_1_split does not need backward computation.
I0401 15:21:43.699939 25640 net.cpp:200] val-data does not need backward computation.
I0401 15:21:43.699940 25640 net.cpp:242] This network produces output accuracy
I0401 15:21:43.699944 25640 net.cpp:242] This network produces output loss
I0401 15:21:43.699957 25640 net.cpp:255] Network initialization done.
I0401 15:21:43.700023 25640 solver.cpp:56] Solver scaffolding done.
I0401 15:21:43.700412 25640 caffe.cpp:248] Starting Optimization
I0401 15:21:43.700419 25640 solver.cpp:272] Solving
I0401 15:21:43.700433 25640 solver.cpp:273] Learning Rate Policy: fixed
I0401 15:21:43.704432 25640 solver.cpp:330] Iteration 0, Testing net (#0)
I0401 15:21:43.704443 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:21:43.810921 25640 blocking_queue.cpp:49] Waiting for data
I0401 15:21:51.704602 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:21:51.752405 25640 solver.cpp:397]     Test net output #0: accuracy = 0.00735294
I0401 15:21:51.752437 25640 solver.cpp:397]     Test net output #1: loss = 5.27932 (* 1 = 5.27932 loss)
I0401 15:21:51.900130 25640 solver.cpp:218] Iteration 0 (-1.03483e-21 iter/s, 8.19964s/12 iters), loss = 5.28441
I0401 15:21:51.901762 25640 solver.cpp:237]     Train net output #0: loss = 5.28441 (* 1 = 5.28441 loss)
I0401 15:21:51.901777 25640 sgd_solver.cpp:105] Iteration 0, lr = 0.001
I0401 15:21:56.201368 25640 solver.cpp:218] Iteration 12 (2.79097 iter/s, 4.29959s/12 iters), loss = 5.28712
I0401 15:21:56.201432 25640 solver.cpp:237]     Train net output #0: loss = 5.28712 (* 1 = 5.28712 loss)
I0401 15:21:56.201442 25640 sgd_solver.cpp:105] Iteration 12, lr = 0.001
I0401 15:22:02.106297 25640 solver.cpp:218] Iteration 24 (2.03223 iter/s, 5.90485s/12 iters), loss = 5.28369
I0401 15:22:02.106349 25640 solver.cpp:237]     Train net output #0: loss = 5.28369 (* 1 = 5.28369 loss)
I0401 15:22:02.106357 25640 sgd_solver.cpp:105] Iteration 24, lr = 0.001
I0401 15:22:07.587844 25640 solver.cpp:218] Iteration 36 (2.18919 iter/s, 5.48148s/12 iters), loss = 5.28067
I0401 15:22:07.587896 25640 solver.cpp:237]     Train net output #0: loss = 5.28067 (* 1 = 5.28067 loss)
I0401 15:22:07.587904 25640 sgd_solver.cpp:105] Iteration 36, lr = 0.001
I0401 15:22:13.079090 25640 solver.cpp:218] Iteration 48 (2.18532 iter/s, 5.49117s/12 iters), loss = 5.27328
I0401 15:22:13.079203 25640 solver.cpp:237]     Train net output #0: loss = 5.27328 (* 1 = 5.27328 loss)
I0401 15:22:13.079212 25640 sgd_solver.cpp:105] Iteration 48, lr = 0.001
I0401 15:22:20.027571 25640 solver.cpp:218] Iteration 60 (1.72703 iter/s, 6.94835s/12 iters), loss = 5.28118
I0401 15:22:20.027626 25640 solver.cpp:237]     Train net output #0: loss = 5.28118 (* 1 = 5.28118 loss)
I0401 15:22:20.027635 25640 sgd_solver.cpp:105] Iteration 60, lr = 0.001
I0401 15:22:29.622539 25640 solver.cpp:218] Iteration 72 (1.25066 iter/s, 9.5949s/12 iters), loss = 5.29067
I0401 15:22:29.622596 25640 solver.cpp:237]     Train net output #0: loss = 5.29067 (* 1 = 5.29067 loss)
I0401 15:22:29.622603 25640 sgd_solver.cpp:105] Iteration 72, lr = 0.001
I0401 15:22:36.860093 25640 solver.cpp:218] Iteration 84 (1.65803 iter/s, 7.23749s/12 iters), loss = 5.28002
I0401 15:22:36.860131 25640 solver.cpp:237]     Train net output #0: loss = 5.28002 (* 1 = 5.28002 loss)
I0401 15:22:36.860136 25640 sgd_solver.cpp:105] Iteration 84, lr = 0.001
I0401 15:22:43.770079 25640 solver.cpp:218] Iteration 96 (1.73663 iter/s, 6.90994s/12 iters), loss = 5.29285
I0401 15:22:43.770188 25640 solver.cpp:237]     Train net output #0: loss = 5.29285 (* 1 = 5.29285 loss)
I0401 15:22:43.770195 25640 sgd_solver.cpp:105] Iteration 96, lr = 0.001
I0401 15:22:45.738665 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:22:46.116519 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0401 15:22:49.234345 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0401 15:22:51.541771 25640 solver.cpp:330] Iteration 102, Testing net (#0)
I0401 15:22:51.541790 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:22:55.989393 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:22:56.067502 25640 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0401 15:22:56.067539 25640 solver.cpp:397]     Test net output #1: loss = 5.27977 (* 1 = 5.27977 loss)
I0401 15:22:58.061359 25640 solver.cpp:218] Iteration 108 (0.83968 iter/s, 14.2912s/12 iters), loss = 5.29133
I0401 15:22:58.061424 25640 solver.cpp:237]     Train net output #0: loss = 5.29133 (* 1 = 5.29133 loss)
I0401 15:22:58.061432 25640 sgd_solver.cpp:105] Iteration 108, lr = 0.001
I0401 15:23:03.145752 25640 solver.cpp:218] Iteration 120 (2.3602 iter/s, 5.08432s/12 iters), loss = 5.28309
I0401 15:23:03.145795 25640 solver.cpp:237]     Train net output #0: loss = 5.28309 (* 1 = 5.28309 loss)
I0401 15:23:03.145802 25640 sgd_solver.cpp:105] Iteration 120, lr = 0.001
I0401 15:23:08.682600 25640 solver.cpp:218] Iteration 132 (2.16732 iter/s, 5.53679s/12 iters), loss = 5.27878
I0401 15:23:08.682652 25640 solver.cpp:237]     Train net output #0: loss = 5.27878 (* 1 = 5.27878 loss)
I0401 15:23:08.682660 25640 sgd_solver.cpp:105] Iteration 132, lr = 0.001
I0401 15:23:14.097225 25640 solver.cpp:218] Iteration 144 (2.21625 iter/s, 5.41456s/12 iters), loss = 5.2698
I0401 15:23:14.097368 25640 solver.cpp:237]     Train net output #0: loss = 5.2698 (* 1 = 5.2698 loss)
I0401 15:23:14.097378 25640 sgd_solver.cpp:105] Iteration 144, lr = 0.001
I0401 15:23:19.211436 25640 solver.cpp:218] Iteration 156 (2.34647 iter/s, 5.11406s/12 iters), loss = 5.2854
I0401 15:23:19.211477 25640 solver.cpp:237]     Train net output #0: loss = 5.2854 (* 1 = 5.2854 loss)
I0401 15:23:19.211483 25640 sgd_solver.cpp:105] Iteration 156, lr = 0.001
I0401 15:23:24.867434 25640 solver.cpp:218] Iteration 168 (2.12166 iter/s, 5.65594s/12 iters), loss = 5.2978
I0401 15:23:24.867477 25640 solver.cpp:237]     Train net output #0: loss = 5.2978 (* 1 = 5.2978 loss)
I0401 15:23:24.867482 25640 sgd_solver.cpp:105] Iteration 168, lr = 0.001
I0401 15:23:30.408322 25640 solver.cpp:218] Iteration 180 (2.16574 iter/s, 5.54083s/12 iters), loss = 5.27897
I0401 15:23:30.408376 25640 solver.cpp:237]     Train net output #0: loss = 5.27897 (* 1 = 5.27897 loss)
I0401 15:23:30.408385 25640 sgd_solver.cpp:105] Iteration 180, lr = 0.001
I0401 15:23:35.468927 25640 solver.cpp:218] Iteration 192 (2.37182 iter/s, 5.0594s/12 iters), loss = 5.27086
I0401 15:23:35.468982 25640 solver.cpp:237]     Train net output #0: loss = 5.27086 (* 1 = 5.27086 loss)
I0401 15:23:35.468991 25640 sgd_solver.cpp:105] Iteration 192, lr = 0.001
I0401 15:23:39.443852 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:23:40.179998 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0401 15:23:43.213058 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0401 15:23:45.507025 25640 solver.cpp:330] Iteration 204, Testing net (#0)
I0401 15:23:45.507087 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:23:49.831648 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:23:49.976001 25640 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0401 15:23:49.976039 25640 solver.cpp:397]     Test net output #1: loss = 5.2812 (* 1 = 5.2812 loss)
I0401 15:23:50.118139 25640 solver.cpp:218] Iteration 204 (0.81916 iter/s, 14.6491s/12 iters), loss = 5.26569
I0401 15:23:50.118197 25640 solver.cpp:237]     Train net output #0: loss = 5.26569 (* 1 = 5.26569 loss)
I0401 15:23:50.118206 25640 sgd_solver.cpp:105] Iteration 204, lr = 0.001
I0401 15:23:54.506636 25640 solver.cpp:218] Iteration 216 (2.73447 iter/s, 4.38843s/12 iters), loss = 5.27253
I0401 15:23:54.506678 25640 solver.cpp:237]     Train net output #0: loss = 5.27253 (* 1 = 5.27253 loss)
I0401 15:23:54.506685 25640 sgd_solver.cpp:105] Iteration 216, lr = 0.001
I0401 15:23:59.835068 25640 solver.cpp:218] Iteration 228 (2.2521 iter/s, 5.32837s/12 iters), loss = 5.2698
I0401 15:23:59.835119 25640 solver.cpp:237]     Train net output #0: loss = 5.2698 (* 1 = 5.2698 loss)
I0401 15:23:59.835129 25640 sgd_solver.cpp:105] Iteration 228, lr = 0.001
I0401 15:24:04.829102 25640 solver.cpp:218] Iteration 240 (2.4029 iter/s, 4.99397s/12 iters), loss = 5.27911
I0401 15:24:04.829147 25640 solver.cpp:237]     Train net output #0: loss = 5.27911 (* 1 = 5.27911 loss)
I0401 15:24:04.829152 25640 sgd_solver.cpp:105] Iteration 240, lr = 0.001
I0401 15:24:10.172998 25640 solver.cpp:218] Iteration 252 (2.24558 iter/s, 5.34383s/12 iters), loss = 5.28285
I0401 15:24:10.173056 25640 solver.cpp:237]     Train net output #0: loss = 5.28285 (* 1 = 5.28285 loss)
I0401 15:24:10.173065 25640 sgd_solver.cpp:105] Iteration 252, lr = 0.001
I0401 15:24:15.217304 25640 solver.cpp:218] Iteration 264 (2.37896 iter/s, 5.04423s/12 iters), loss = 5.27361
I0401 15:24:15.217367 25640 solver.cpp:237]     Train net output #0: loss = 5.27361 (* 1 = 5.27361 loss)
I0401 15:24:15.217377 25640 sgd_solver.cpp:105] Iteration 264, lr = 0.001
I0401 15:24:20.479902 25640 solver.cpp:218] Iteration 276 (2.28027 iter/s, 5.26252s/12 iters), loss = 5.27084
I0401 15:24:20.480057 25640 solver.cpp:237]     Train net output #0: loss = 5.27084 (* 1 = 5.27084 loss)
I0401 15:24:20.480067 25640 sgd_solver.cpp:105] Iteration 276, lr = 0.001
I0401 15:24:25.797511 25640 solver.cpp:218] Iteration 288 (2.25672 iter/s, 5.31744s/12 iters), loss = 5.26841
I0401 15:24:25.797569 25640 solver.cpp:237]     Train net output #0: loss = 5.26841 (* 1 = 5.26841 loss)
I0401 15:24:25.797576 25640 sgd_solver.cpp:105] Iteration 288, lr = 0.001
I0401 15:24:30.996147 25640 solver.cpp:218] Iteration 300 (2.30833 iter/s, 5.19857s/12 iters), loss = 5.277
I0401 15:24:30.996191 25640 solver.cpp:237]     Train net output #0: loss = 5.277 (* 1 = 5.277 loss)
I0401 15:24:30.996198 25640 sgd_solver.cpp:105] Iteration 300, lr = 0.001
I0401 15:24:32.040364 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:24:33.145218 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0401 15:24:36.166617 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0401 15:24:38.465353 25640 solver.cpp:330] Iteration 306, Testing net (#0)
I0401 15:24:38.465374 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:24:42.631057 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:24:42.787163 25640 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0401 15:24:42.787199 25640 solver.cpp:397]     Test net output #1: loss = 5.28191 (* 1 = 5.28191 loss)
I0401 15:24:44.689812 25640 solver.cpp:218] Iteration 312 (0.876321 iter/s, 13.6936s/12 iters), loss = 5.26315
I0401 15:24:44.689859 25640 solver.cpp:237]     Train net output #0: loss = 5.26315 (* 1 = 5.26315 loss)
I0401 15:24:44.689865 25640 sgd_solver.cpp:105] Iteration 312, lr = 0.001
I0401 15:24:49.850628 25640 solver.cpp:218] Iteration 324 (2.32524 iter/s, 5.16075s/12 iters), loss = 5.27982
I0401 15:24:49.850670 25640 solver.cpp:237]     Train net output #0: loss = 5.27982 (* 1 = 5.27982 loss)
I0401 15:24:49.850675 25640 sgd_solver.cpp:105] Iteration 324, lr = 0.001
I0401 15:24:55.133956 25640 solver.cpp:218] Iteration 336 (2.27132 iter/s, 5.28327s/12 iters), loss = 5.26119
I0401 15:24:55.134057 25640 solver.cpp:237]     Train net output #0: loss = 5.26119 (* 1 = 5.26119 loss)
I0401 15:24:55.134063 25640 sgd_solver.cpp:105] Iteration 336, lr = 0.001
I0401 15:25:00.309417 25640 solver.cpp:218] Iteration 348 (2.31869 iter/s, 5.17534s/12 iters), loss = 5.27575
I0401 15:25:00.309469 25640 solver.cpp:237]     Train net output #0: loss = 5.27575 (* 1 = 5.27575 loss)
I0401 15:25:00.309478 25640 sgd_solver.cpp:105] Iteration 348, lr = 0.001
I0401 15:25:05.606935 25640 solver.cpp:218] Iteration 360 (2.26524 iter/s, 5.29745s/12 iters), loss = 5.29243
I0401 15:25:05.606989 25640 solver.cpp:237]     Train net output #0: loss = 5.29243 (* 1 = 5.29243 loss)
I0401 15:25:05.606997 25640 sgd_solver.cpp:105] Iteration 360, lr = 0.001
I0401 15:25:10.995074 25640 solver.cpp:218] Iteration 372 (2.22714 iter/s, 5.38807s/12 iters), loss = 5.2707
I0401 15:25:10.995121 25640 solver.cpp:237]     Train net output #0: loss = 5.2707 (* 1 = 5.2707 loss)
I0401 15:25:10.995128 25640 sgd_solver.cpp:105] Iteration 372, lr = 0.001
I0401 15:25:16.310930 25640 solver.cpp:218] Iteration 384 (2.25743 iter/s, 5.31578s/12 iters), loss = 5.2662
I0401 15:25:16.311002 25640 solver.cpp:237]     Train net output #0: loss = 5.2662 (* 1 = 5.2662 loss)
I0401 15:25:16.311017 25640 sgd_solver.cpp:105] Iteration 384, lr = 0.001
I0401 15:25:21.761030 25640 solver.cpp:218] Iteration 396 (2.20183 iter/s, 5.45002s/12 iters), loss = 5.28911
I0401 15:25:21.761090 25640 solver.cpp:237]     Train net output #0: loss = 5.28911 (* 1 = 5.28911 loss)
I0401 15:25:21.761101 25640 sgd_solver.cpp:105] Iteration 396, lr = 0.001
I0401 15:25:25.073031 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:25:26.587419 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0401 15:25:29.619577 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0401 15:25:31.945444 25640 solver.cpp:330] Iteration 408, Testing net (#0)
I0401 15:25:31.945468 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:25:36.095237 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:25:36.299748 25640 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0401 15:25:36.299787 25640 solver.cpp:397]     Test net output #1: loss = 5.28127 (* 1 = 5.28127 loss)
I0401 15:25:36.437579 25640 solver.cpp:218] Iteration 408 (0.817635 iter/s, 14.6765s/12 iters), loss = 5.26611
I0401 15:25:36.437633 25640 solver.cpp:237]     Train net output #0: loss = 5.26611 (* 1 = 5.26611 loss)
I0401 15:25:36.437640 25640 sgd_solver.cpp:105] Iteration 408, lr = 0.001
I0401 15:25:40.580543 25640 solver.cpp:218] Iteration 420 (2.89652 iter/s, 4.1429s/12 iters), loss = 5.2663
I0401 15:25:40.580583 25640 solver.cpp:237]     Train net output #0: loss = 5.2663 (* 1 = 5.2663 loss)
I0401 15:25:40.580590 25640 sgd_solver.cpp:105] Iteration 420, lr = 0.001
I0401 15:25:45.585031 25640 solver.cpp:218] Iteration 432 (2.39787 iter/s, 5.00443s/12 iters), loss = 5.27751
I0401 15:25:45.585078 25640 solver.cpp:237]     Train net output #0: loss = 5.27751 (* 1 = 5.27751 loss)
I0401 15:25:45.585083 25640 sgd_solver.cpp:105] Iteration 432, lr = 0.001
I0401 15:25:51.018869 25640 solver.cpp:218] Iteration 444 (2.20841 iter/s, 5.43378s/12 iters), loss = 5.27174
I0401 15:25:51.018923 25640 solver.cpp:237]     Train net output #0: loss = 5.27174 (* 1 = 5.27174 loss)
I0401 15:25:51.018930 25640 sgd_solver.cpp:105] Iteration 444, lr = 0.001
I0401 15:25:56.361773 25640 solver.cpp:218] Iteration 456 (2.246 iter/s, 5.34284s/12 iters), loss = 5.28682
I0401 15:25:56.361833 25640 solver.cpp:237]     Train net output #0: loss = 5.28682 (* 1 = 5.28682 loss)
I0401 15:25:56.361843 25640 sgd_solver.cpp:105] Iteration 456, lr = 0.001
I0401 15:26:01.657074 25640 solver.cpp:218] Iteration 468 (2.26619 iter/s, 5.29523s/12 iters), loss = 5.26076
I0401 15:26:01.657205 25640 solver.cpp:237]     Train net output #0: loss = 5.26076 (* 1 = 5.26076 loss)
I0401 15:26:01.657224 25640 sgd_solver.cpp:105] Iteration 468, lr = 0.001
I0401 15:26:06.817020 25640 solver.cpp:218] Iteration 480 (2.32566 iter/s, 5.15982s/12 iters), loss = 5.26786
I0401 15:26:06.817075 25640 solver.cpp:237]     Train net output #0: loss = 5.26786 (* 1 = 5.26786 loss)
I0401 15:26:06.817085 25640 sgd_solver.cpp:105] Iteration 480, lr = 0.001
I0401 15:26:11.891742 25640 solver.cpp:218] Iteration 492 (2.36469 iter/s, 5.07466s/12 iters), loss = 5.27209
I0401 15:26:11.891785 25640 solver.cpp:237]     Train net output #0: loss = 5.27209 (* 1 = 5.27209 loss)
I0401 15:26:11.891793 25640 sgd_solver.cpp:105] Iteration 492, lr = 0.001
I0401 15:26:17.014894 25640 solver.cpp:218] Iteration 504 (2.34233 iter/s, 5.12309s/12 iters), loss = 5.2701
I0401 15:26:17.014940 25640 solver.cpp:237]     Train net output #0: loss = 5.2701 (* 1 = 5.2701 loss)
I0401 15:26:17.014945 25640 sgd_solver.cpp:105] Iteration 504, lr = 0.001
I0401 15:26:17.244333 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:26:19.166121 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0401 15:26:22.196403 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0401 15:26:24.503623 25640 solver.cpp:330] Iteration 510, Testing net (#0)
I0401 15:26:24.503641 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:26:28.610743 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:26:28.847798 25640 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0401 15:26:28.847847 25640 solver.cpp:397]     Test net output #1: loss = 5.27908 (* 1 = 5.27908 loss)
I0401 15:26:30.770421 25640 solver.cpp:218] Iteration 516 (0.87238 iter/s, 13.7555s/12 iters), loss = 5.2629
I0401 15:26:30.770458 25640 solver.cpp:237]     Train net output #0: loss = 5.2629 (* 1 = 5.2629 loss)
I0401 15:26:30.770463 25640 sgd_solver.cpp:105] Iteration 516, lr = 0.001
I0401 15:26:35.848953 25640 solver.cpp:218] Iteration 528 (2.36291 iter/s, 5.07848s/12 iters), loss = 5.27956
I0401 15:26:35.849071 25640 solver.cpp:237]     Train net output #0: loss = 5.27956 (* 1 = 5.27956 loss)
I0401 15:26:35.849077 25640 sgd_solver.cpp:105] Iteration 528, lr = 0.001
I0401 15:26:41.154156 25640 solver.cpp:218] Iteration 540 (2.26199 iter/s, 5.30507s/12 iters), loss = 5.27168
I0401 15:26:41.154213 25640 solver.cpp:237]     Train net output #0: loss = 5.27168 (* 1 = 5.27168 loss)
I0401 15:26:41.154222 25640 sgd_solver.cpp:105] Iteration 540, lr = 0.001
I0401 15:26:46.417734 25640 solver.cpp:218] Iteration 552 (2.27985 iter/s, 5.26351s/12 iters), loss = 5.28001
I0401 15:26:46.417780 25640 solver.cpp:237]     Train net output #0: loss = 5.28001 (* 1 = 5.28001 loss)
I0401 15:26:46.417785 25640 sgd_solver.cpp:105] Iteration 552, lr = 0.001
I0401 15:26:51.836302 25640 solver.cpp:218] Iteration 564 (2.21463 iter/s, 5.41851s/12 iters), loss = 5.26715
I0401 15:26:51.836350 25640 solver.cpp:237]     Train net output #0: loss = 5.26715 (* 1 = 5.26715 loss)
I0401 15:26:51.836356 25640 sgd_solver.cpp:105] Iteration 564, lr = 0.001
I0401 15:26:57.087153 25640 solver.cpp:218] Iteration 576 (2.28537 iter/s, 5.25079s/12 iters), loss = 5.24344
I0401 15:26:57.087199 25640 solver.cpp:237]     Train net output #0: loss = 5.24344 (* 1 = 5.24344 loss)
I0401 15:26:57.087206 25640 sgd_solver.cpp:105] Iteration 576, lr = 0.001
I0401 15:27:02.200050 25640 solver.cpp:218] Iteration 588 (2.34703 iter/s, 5.11284s/12 iters), loss = 5.26696
I0401 15:27:02.200096 25640 solver.cpp:237]     Train net output #0: loss = 5.26696 (* 1 = 5.26696 loss)
I0401 15:27:02.200103 25640 sgd_solver.cpp:105] Iteration 588, lr = 0.001
I0401 15:27:07.377040 25640 solver.cpp:218] Iteration 600 (2.31798 iter/s, 5.17693s/12 iters), loss = 5.25523
I0401 15:27:07.377171 25640 solver.cpp:237]     Train net output #0: loss = 5.25523 (* 1 = 5.25523 loss)
I0401 15:27:07.377179 25640 sgd_solver.cpp:105] Iteration 600, lr = 0.001
I0401 15:27:09.847285 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:27:12.174121 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0401 15:27:15.212982 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0401 15:27:17.541097 25640 solver.cpp:330] Iteration 612, Testing net (#0)
I0401 15:27:17.541124 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:27:21.624020 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:27:21.908604 25640 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0401 15:27:21.908643 25640 solver.cpp:397]     Test net output #1: loss = 5.26767 (* 1 = 5.26767 loss)
I0401 15:27:22.050029 25640 solver.cpp:218] Iteration 612 (0.817837 iter/s, 14.6729s/12 iters), loss = 5.25561
I0401 15:27:22.051594 25640 solver.cpp:237]     Train net output #0: loss = 5.25561 (* 1 = 5.25561 loss)
I0401 15:27:22.051609 25640 sgd_solver.cpp:105] Iteration 612, lr = 0.001
I0401 15:27:26.283735 25640 solver.cpp:218] Iteration 624 (2.83545 iter/s, 4.23213s/12 iters), loss = 5.24664
I0401 15:27:26.283788 25640 solver.cpp:237]     Train net output #0: loss = 5.24664 (* 1 = 5.24664 loss)
I0401 15:27:26.283797 25640 sgd_solver.cpp:105] Iteration 624, lr = 0.001
I0401 15:27:31.424203 25640 solver.cpp:218] Iteration 636 (2.33445 iter/s, 5.1404s/12 iters), loss = 5.26054
I0401 15:27:31.424247 25640 solver.cpp:237]     Train net output #0: loss = 5.26054 (* 1 = 5.26054 loss)
I0401 15:27:31.424252 25640 sgd_solver.cpp:105] Iteration 636, lr = 0.001
I0401 15:27:36.694964 25640 solver.cpp:218] Iteration 648 (2.27674 iter/s, 5.27071s/12 iters), loss = 5.25149
I0401 15:27:36.695003 25640 solver.cpp:237]     Train net output #0: loss = 5.25149 (* 1 = 5.25149 loss)
I0401 15:27:36.695009 25640 sgd_solver.cpp:105] Iteration 648, lr = 0.001
I0401 15:27:42.134075 25640 solver.cpp:218] Iteration 660 (2.20627 iter/s, 5.43905s/12 iters), loss = 5.23742
I0401 15:27:42.134222 25640 solver.cpp:237]     Train net output #0: loss = 5.23742 (* 1 = 5.23742 loss)
I0401 15:27:42.134232 25640 sgd_solver.cpp:105] Iteration 660, lr = 0.001
I0401 15:27:47.272307 25640 solver.cpp:218] Iteration 672 (2.33551 iter/s, 5.13807s/12 iters), loss = 5.25467
I0401 15:27:47.272351 25640 solver.cpp:237]     Train net output #0: loss = 5.25467 (* 1 = 5.25467 loss)
I0401 15:27:47.272356 25640 sgd_solver.cpp:105] Iteration 672, lr = 0.001
I0401 15:27:52.390012 25640 solver.cpp:218] Iteration 684 (2.34483 iter/s, 5.11765s/12 iters), loss = 5.22625
I0401 15:27:52.390056 25640 solver.cpp:237]     Train net output #0: loss = 5.22625 (* 1 = 5.22625 loss)
I0401 15:27:52.390061 25640 sgd_solver.cpp:105] Iteration 684, lr = 0.001
I0401 15:27:53.059296 25640 blocking_queue.cpp:49] Waiting for data
I0401 15:27:57.345175 25640 solver.cpp:218] Iteration 696 (2.42175 iter/s, 4.9551s/12 iters), loss = 5.24471
I0401 15:27:57.345227 25640 solver.cpp:237]     Train net output #0: loss = 5.24471 (* 1 = 5.24471 loss)
I0401 15:27:57.345233 25640 sgd_solver.cpp:105] Iteration 696, lr = 0.001
I0401 15:28:02.193349 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:28:02.608557 25640 solver.cpp:218] Iteration 708 (2.27993 iter/s, 5.26332s/12 iters), loss = 5.23554
I0401 15:28:02.608599 25640 solver.cpp:237]     Train net output #0: loss = 5.23554 (* 1 = 5.23554 loss)
I0401 15:28:02.608605 25640 sgd_solver.cpp:105] Iteration 708, lr = 0.001
I0401 15:28:04.774060 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0401 15:28:07.812793 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0401 15:28:10.104358 25640 solver.cpp:330] Iteration 714, Testing net (#0)
I0401 15:28:10.104378 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:28:14.057447 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:28:14.379739 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0401 15:28:14.379771 25640 solver.cpp:397]     Test net output #1: loss = 5.21411 (* 1 = 5.21411 loss)
I0401 15:28:16.314092 25640 solver.cpp:218] Iteration 720 (0.875562 iter/s, 13.7055s/12 iters), loss = 5.20117
I0401 15:28:16.314132 25640 solver.cpp:237]     Train net output #0: loss = 5.20117 (* 1 = 5.20117 loss)
I0401 15:28:16.314137 25640 sgd_solver.cpp:105] Iteration 720, lr = 0.001
I0401 15:28:21.315963 25640 solver.cpp:218] Iteration 732 (2.39913 iter/s, 5.00181s/12 iters), loss = 5.18497
I0401 15:28:21.316007 25640 solver.cpp:237]     Train net output #0: loss = 5.18497 (* 1 = 5.18497 loss)
I0401 15:28:21.316013 25640 sgd_solver.cpp:105] Iteration 732, lr = 0.001
I0401 15:28:26.699368 25640 solver.cpp:218] Iteration 744 (2.2291 iter/s, 5.38334s/12 iters), loss = 5.18175
I0401 15:28:26.699410 25640 solver.cpp:237]     Train net output #0: loss = 5.18175 (* 1 = 5.18175 loss)
I0401 15:28:26.699417 25640 sgd_solver.cpp:105] Iteration 744, lr = 0.001
I0401 15:28:31.734562 25640 solver.cpp:218] Iteration 756 (2.38325 iter/s, 5.03514s/12 iters), loss = 5.17771
I0401 15:28:31.734612 25640 solver.cpp:237]     Train net output #0: loss = 5.17771 (* 1 = 5.17771 loss)
I0401 15:28:31.734620 25640 sgd_solver.cpp:105] Iteration 756, lr = 0.001
I0401 15:28:36.879086 25640 solver.cpp:218] Iteration 768 (2.33261 iter/s, 5.14446s/12 iters), loss = 5.19324
I0401 15:28:36.879148 25640 solver.cpp:237]     Train net output #0: loss = 5.19324 (* 1 = 5.19324 loss)
I0401 15:28:36.879158 25640 sgd_solver.cpp:105] Iteration 768, lr = 0.001
I0401 15:28:42.323220 25640 solver.cpp:218] Iteration 780 (2.20424 iter/s, 5.44406s/12 iters), loss = 5.13182
I0401 15:28:42.323262 25640 solver.cpp:237]     Train net output #0: loss = 5.13182 (* 1 = 5.13182 loss)
I0401 15:28:42.323269 25640 sgd_solver.cpp:105] Iteration 780, lr = 0.001
I0401 15:28:47.535984 25640 solver.cpp:218] Iteration 792 (2.30207 iter/s, 5.21271s/12 iters), loss = 5.23385
I0401 15:28:47.536120 25640 solver.cpp:237]     Train net output #0: loss = 5.23385 (* 1 = 5.23385 loss)
I0401 15:28:47.536128 25640 sgd_solver.cpp:105] Iteration 792, lr = 0.001
I0401 15:28:52.724054 25640 solver.cpp:218] Iteration 804 (2.31306 iter/s, 5.18793s/12 iters), loss = 5.15453
I0401 15:28:52.724090 25640 solver.cpp:237]     Train net output #0: loss = 5.15453 (* 1 = 5.15453 loss)
I0401 15:28:52.724095 25640 sgd_solver.cpp:105] Iteration 804, lr = 0.001
I0401 15:28:54.539266 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:28:57.508934 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0401 15:29:00.537508 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0401 15:29:02.860164 25640 solver.cpp:330] Iteration 816, Testing net (#0)
I0401 15:29:02.860185 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:29:06.867784 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:29:07.213987 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0110294
I0401 15:29:07.214023 25640 solver.cpp:397]     Test net output #1: loss = 5.15711 (* 1 = 5.15711 loss)
I0401 15:29:07.353570 25640 solver.cpp:218] Iteration 816 (0.820262 iter/s, 14.6295s/12 iters), loss = 5.21449
I0401 15:29:07.353629 25640 solver.cpp:237]     Train net output #0: loss = 5.21449 (* 1 = 5.21449 loss)
I0401 15:29:07.353637 25640 sgd_solver.cpp:105] Iteration 816, lr = 0.001
I0401 15:29:11.626415 25640 solver.cpp:218] Iteration 828 (2.80848 iter/s, 4.27277s/12 iters), loss = 5.13053
I0401 15:29:11.626453 25640 solver.cpp:237]     Train net output #0: loss = 5.13053 (* 1 = 5.13053 loss)
I0401 15:29:11.626459 25640 sgd_solver.cpp:105] Iteration 828, lr = 0.001
I0401 15:29:16.657569 25640 solver.cpp:218] Iteration 840 (2.38516 iter/s, 5.0311s/12 iters), loss = 5.11347
I0401 15:29:16.657613 25640 solver.cpp:237]     Train net output #0: loss = 5.11347 (* 1 = 5.11347 loss)
I0401 15:29:16.657619 25640 sgd_solver.cpp:105] Iteration 840, lr = 0.001
I0401 15:29:21.870782 25640 solver.cpp:218] Iteration 852 (2.30187 iter/s, 5.21315s/12 iters), loss = 5.14132
I0401 15:29:21.870893 25640 solver.cpp:237]     Train net output #0: loss = 5.14132 (* 1 = 5.14132 loss)
I0401 15:29:21.870903 25640 sgd_solver.cpp:105] Iteration 852, lr = 0.001
I0401 15:29:27.249799 25640 solver.cpp:218] Iteration 864 (2.23094 iter/s, 5.3789s/12 iters), loss = 5.13194
I0401 15:29:27.249837 25640 solver.cpp:237]     Train net output #0: loss = 5.13194 (* 1 = 5.13194 loss)
I0401 15:29:27.249843 25640 sgd_solver.cpp:105] Iteration 864, lr = 0.001
I0401 15:29:32.578533 25640 solver.cpp:218] Iteration 876 (2.25196 iter/s, 5.32868s/12 iters), loss = 5.17327
I0401 15:29:32.578573 25640 solver.cpp:237]     Train net output #0: loss = 5.17327 (* 1 = 5.17327 loss)
I0401 15:29:32.578578 25640 sgd_solver.cpp:105] Iteration 876, lr = 0.001
I0401 15:29:37.844585 25640 solver.cpp:218] Iteration 888 (2.27877 iter/s, 5.266s/12 iters), loss = 5.16881
I0401 15:29:37.844630 25640 solver.cpp:237]     Train net output #0: loss = 5.16881 (* 1 = 5.16881 loss)
I0401 15:29:37.844635 25640 sgd_solver.cpp:105] Iteration 888, lr = 0.001
I0401 15:29:42.947664 25640 solver.cpp:218] Iteration 900 (2.35155 iter/s, 5.10303s/12 iters), loss = 5.06691
I0401 15:29:42.947700 25640 solver.cpp:237]     Train net output #0: loss = 5.06691 (* 1 = 5.06691 loss)
I0401 15:29:42.947706 25640 sgd_solver.cpp:105] Iteration 900, lr = 0.001
I0401 15:29:47.036661 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:29:48.204550 25640 solver.cpp:218] Iteration 912 (2.28274 iter/s, 5.25683s/12 iters), loss = 5.12238
I0401 15:29:48.204593 25640 solver.cpp:237]     Train net output #0: loss = 5.12238 (* 1 = 5.12238 loss)
I0401 15:29:48.204598 25640 sgd_solver.cpp:105] Iteration 912, lr = 0.001
I0401 15:29:50.172580 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0401 15:29:53.178999 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0401 15:29:55.482583 25640 solver.cpp:330] Iteration 918, Testing net (#0)
I0401 15:29:55.482605 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:29:59.460297 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:29:59.852337 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0122549
I0401 15:29:59.852373 25640 solver.cpp:397]     Test net output #1: loss = 5.13467 (* 1 = 5.13467 loss)
I0401 15:30:01.755281 25640 solver.cpp:218] Iteration 924 (0.885565 iter/s, 13.5507s/12 iters), loss = 5.1886
I0401 15:30:01.755323 25640 solver.cpp:237]     Train net output #0: loss = 5.1886 (* 1 = 5.1886 loss)
I0401 15:30:01.755328 25640 sgd_solver.cpp:105] Iteration 924, lr = 0.001
I0401 15:30:06.875835 25640 solver.cpp:218] Iteration 936 (2.34352 iter/s, 5.1205s/12 iters), loss = 5.1932
I0401 15:30:06.875877 25640 solver.cpp:237]     Train net output #0: loss = 5.1932 (* 1 = 5.1932 loss)
I0401 15:30:06.875882 25640 sgd_solver.cpp:105] Iteration 936, lr = 0.001
I0401 15:30:11.986161 25640 solver.cpp:218] Iteration 948 (2.34821 iter/s, 5.11027s/12 iters), loss = 5.13928
I0401 15:30:11.986214 25640 solver.cpp:237]     Train net output #0: loss = 5.13928 (* 1 = 5.13928 loss)
I0401 15:30:11.986223 25640 sgd_solver.cpp:105] Iteration 948, lr = 0.001
I0401 15:30:17.341090 25640 solver.cpp:218] Iteration 960 (2.24096 iter/s, 5.35486s/12 iters), loss = 5.15321
I0401 15:30:17.341142 25640 solver.cpp:237]     Train net output #0: loss = 5.15321 (* 1 = 5.15321 loss)
I0401 15:30:17.341150 25640 sgd_solver.cpp:105] Iteration 960, lr = 0.001
I0401 15:30:22.703364 25640 solver.cpp:218] Iteration 972 (2.23789 iter/s, 5.36221s/12 iters), loss = 5.08754
I0401 15:30:22.703430 25640 solver.cpp:237]     Train net output #0: loss = 5.08754 (* 1 = 5.08754 loss)
I0401 15:30:22.703440 25640 sgd_solver.cpp:105] Iteration 972, lr = 0.001
I0401 15:30:27.909819 25640 solver.cpp:218] Iteration 984 (2.30486 iter/s, 5.20638s/12 iters), loss = 5.12621
I0401 15:30:27.909931 25640 solver.cpp:237]     Train net output #0: loss = 5.12621 (* 1 = 5.12621 loss)
I0401 15:30:27.909938 25640 sgd_solver.cpp:105] Iteration 984, lr = 0.001
I0401 15:30:33.217337 25640 solver.cpp:218] Iteration 996 (2.261 iter/s, 5.3074s/12 iters), loss = 5.08452
I0401 15:30:33.217375 25640 solver.cpp:237]     Train net output #0: loss = 5.08452 (* 1 = 5.08452 loss)
I0401 15:30:33.217381 25640 sgd_solver.cpp:105] Iteration 996, lr = 0.001
I0401 15:30:38.341827 25640 solver.cpp:218] Iteration 1008 (2.34172 iter/s, 5.12444s/12 iters), loss = 5.1529
I0401 15:30:38.341871 25640 solver.cpp:237]     Train net output #0: loss = 5.1529 (* 1 = 5.1529 loss)
I0401 15:30:38.341876 25640 sgd_solver.cpp:105] Iteration 1008, lr = 0.001
I0401 15:30:39.408246 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:30:43.163694 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0401 15:30:46.255973 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0401 15:30:50.158268 25640 solver.cpp:330] Iteration 1020, Testing net (#0)
I0401 15:30:50.158289 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:30:54.029175 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:30:54.479176 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0140931
I0401 15:30:54.479203 25640 solver.cpp:397]     Test net output #1: loss = 5.1102 (* 1 = 5.1102 loss)
I0401 15:30:54.614750 25640 solver.cpp:218] Iteration 1020 (0.737424 iter/s, 16.2729s/12 iters), loss = 5.06366
I0401 15:30:54.614797 25640 solver.cpp:237]     Train net output #0: loss = 5.06366 (* 1 = 5.06366 loss)
I0401 15:30:54.614802 25640 sgd_solver.cpp:105] Iteration 1020, lr = 0.001
I0401 15:30:58.948137 25640 solver.cpp:218] Iteration 1032 (2.76923 iter/s, 4.33333s/12 iters), loss = 5.20811
I0401 15:30:58.948256 25640 solver.cpp:237]     Train net output #0: loss = 5.20811 (* 1 = 5.20811 loss)
I0401 15:30:58.948263 25640 sgd_solver.cpp:105] Iteration 1032, lr = 0.001
I0401 15:31:04.217907 25640 solver.cpp:218] Iteration 1044 (2.2772 iter/s, 5.26964s/12 iters), loss = 5.16762
I0401 15:31:04.217950 25640 solver.cpp:237]     Train net output #0: loss = 5.16762 (* 1 = 5.16762 loss)
I0401 15:31:04.217957 25640 sgd_solver.cpp:105] Iteration 1044, lr = 0.001
I0401 15:31:09.632944 25640 solver.cpp:218] Iteration 1056 (2.21607 iter/s, 5.41498s/12 iters), loss = 5.12603
I0401 15:31:09.632990 25640 solver.cpp:237]     Train net output #0: loss = 5.12603 (* 1 = 5.12603 loss)
I0401 15:31:09.632997 25640 sgd_solver.cpp:105] Iteration 1056, lr = 0.001
I0401 15:31:14.947224 25640 solver.cpp:218] Iteration 1068 (2.25809 iter/s, 5.31422s/12 iters), loss = 5.12289
I0401 15:31:14.947268 25640 solver.cpp:237]     Train net output #0: loss = 5.12289 (* 1 = 5.12289 loss)
I0401 15:31:14.947273 25640 sgd_solver.cpp:105] Iteration 1068, lr = 0.001
I0401 15:31:20.151155 25640 solver.cpp:218] Iteration 1080 (2.30597 iter/s, 5.20387s/12 iters), loss = 5.04416
I0401 15:31:20.151201 25640 solver.cpp:237]     Train net output #0: loss = 5.04416 (* 1 = 5.04416 loss)
I0401 15:31:20.151206 25640 sgd_solver.cpp:105] Iteration 1080, lr = 0.001
I0401 15:31:25.544853 25640 solver.cpp:218] Iteration 1092 (2.22484 iter/s, 5.39364s/12 iters), loss = 5.12881
I0401 15:31:25.544922 25640 solver.cpp:237]     Train net output #0: loss = 5.12881 (* 1 = 5.12881 loss)
I0401 15:31:25.544931 25640 sgd_solver.cpp:105] Iteration 1092, lr = 0.001
I0401 15:31:30.941572 25640 solver.cpp:218] Iteration 1104 (2.22361 iter/s, 5.39664s/12 iters), loss = 5.08369
I0401 15:31:30.941710 25640 solver.cpp:237]     Train net output #0: loss = 5.08369 (* 1 = 5.08369 loss)
I0401 15:31:30.941718 25640 sgd_solver.cpp:105] Iteration 1104, lr = 0.001
I0401 15:31:34.280891 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:31:36.313014 25640 solver.cpp:218] Iteration 1116 (2.2341 iter/s, 5.37129s/12 iters), loss = 5.02187
I0401 15:31:36.313056 25640 solver.cpp:237]     Train net output #0: loss = 5.02187 (* 1 = 5.02187 loss)
I0401 15:31:36.313062 25640 sgd_solver.cpp:105] Iteration 1116, lr = 0.001
I0401 15:31:38.492772 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0401 15:31:43.053328 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0401 15:31:45.351670 25640 solver.cpp:330] Iteration 1122, Testing net (#0)
I0401 15:31:45.351691 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:31:49.187685 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:31:49.667512 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0171569
I0401 15:31:49.667549 25640 solver.cpp:397]     Test net output #1: loss = 5.08035 (* 1 = 5.08035 loss)
I0401 15:31:51.549096 25640 solver.cpp:218] Iteration 1128 (0.787607 iter/s, 15.236s/12 iters), loss = 5.10293
I0401 15:31:51.549156 25640 solver.cpp:237]     Train net output #0: loss = 5.10293 (* 1 = 5.10293 loss)
I0401 15:31:51.549165 25640 sgd_solver.cpp:105] Iteration 1128, lr = 0.001
I0401 15:31:56.644186 25640 solver.cpp:218] Iteration 1140 (2.35524 iter/s, 5.09502s/12 iters), loss = 5.06909
I0401 15:31:56.644225 25640 solver.cpp:237]     Train net output #0: loss = 5.06909 (* 1 = 5.06909 loss)
I0401 15:31:56.644230 25640 sgd_solver.cpp:105] Iteration 1140, lr = 0.001
I0401 15:32:02.003983 25640 solver.cpp:218] Iteration 1152 (2.23891 iter/s, 5.35974s/12 iters), loss = 5.1176
I0401 15:32:02.004168 25640 solver.cpp:237]     Train net output #0: loss = 5.1176 (* 1 = 5.1176 loss)
I0401 15:32:02.004177 25640 sgd_solver.cpp:105] Iteration 1152, lr = 0.001
I0401 15:32:07.227517 25640 solver.cpp:218] Iteration 1164 (2.29738 iter/s, 5.22334s/12 iters), loss = 5.13112
I0401 15:32:07.227560 25640 solver.cpp:237]     Train net output #0: loss = 5.13112 (* 1 = 5.13112 loss)
I0401 15:32:07.227566 25640 sgd_solver.cpp:105] Iteration 1164, lr = 0.001
I0401 15:32:12.302708 25640 solver.cpp:218] Iteration 1176 (2.36447 iter/s, 5.07513s/12 iters), loss = 5.06948
I0401 15:32:12.302757 25640 solver.cpp:237]     Train net output #0: loss = 5.06948 (* 1 = 5.06948 loss)
I0401 15:32:12.302762 25640 sgd_solver.cpp:105] Iteration 1176, lr = 0.001
I0401 15:32:17.535738 25640 solver.cpp:218] Iteration 1188 (2.29316 iter/s, 5.23296s/12 iters), loss = 5.03301
I0401 15:32:17.535784 25640 solver.cpp:237]     Train net output #0: loss = 5.03301 (* 1 = 5.03301 loss)
I0401 15:32:17.535790 25640 sgd_solver.cpp:105] Iteration 1188, lr = 0.001
I0401 15:32:22.447144 25640 solver.cpp:218] Iteration 1200 (2.44332 iter/s, 4.91134s/12 iters), loss = 5.1291
I0401 15:32:22.447193 25640 solver.cpp:237]     Train net output #0: loss = 5.1291 (* 1 = 5.1291 loss)
I0401 15:32:22.447199 25640 sgd_solver.cpp:105] Iteration 1200, lr = 0.001
I0401 15:32:27.549731 25640 solver.cpp:218] Iteration 1212 (2.35178 iter/s, 5.10253s/12 iters), loss = 5.05449
I0401 15:32:27.549780 25640 solver.cpp:237]     Train net output #0: loss = 5.05449 (* 1 = 5.05449 loss)
I0401 15:32:27.549787 25640 sgd_solver.cpp:105] Iteration 1212, lr = 0.001
I0401 15:32:27.740867 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:32:32.037536 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0401 15:32:35.046490 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0401 15:32:37.359288 25640 solver.cpp:330] Iteration 1224, Testing net (#0)
I0401 15:32:37.359313 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:32:41.309088 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:32:41.811029 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0171569
I0401 15:32:41.811064 25640 solver.cpp:397]     Test net output #1: loss = 5.06229 (* 1 = 5.06229 loss)
I0401 15:32:41.951725 25640 solver.cpp:218] Iteration 1224 (0.833221 iter/s, 14.4019s/12 iters), loss = 5.06399
I0401 15:32:41.951781 25640 solver.cpp:237]     Train net output #0: loss = 5.06399 (* 1 = 5.06399 loss)
I0401 15:32:41.951788 25640 sgd_solver.cpp:105] Iteration 1224, lr = 0.001
I0401 15:32:46.369415 25640 solver.cpp:218] Iteration 1236 (2.71639 iter/s, 4.41762s/12 iters), loss = 5.18869
I0401 15:32:46.369465 25640 solver.cpp:237]     Train net output #0: loss = 5.18869 (* 1 = 5.18869 loss)
I0401 15:32:46.369472 25640 sgd_solver.cpp:105] Iteration 1236, lr = 0.001
I0401 15:32:51.582131 25640 solver.cpp:218] Iteration 1248 (2.30209 iter/s, 5.21265s/12 iters), loss = 5.04664
I0401 15:32:51.582180 25640 solver.cpp:237]     Train net output #0: loss = 5.04664 (* 1 = 5.04664 loss)
I0401 15:32:51.582185 25640 sgd_solver.cpp:105] Iteration 1248, lr = 0.001
I0401 15:32:56.735862 25640 solver.cpp:218] Iteration 1260 (2.32844 iter/s, 5.15367s/12 iters), loss = 5.1355
I0401 15:32:56.735908 25640 solver.cpp:237]     Train net output #0: loss = 5.1355 (* 1 = 5.1355 loss)
I0401 15:32:56.735915 25640 sgd_solver.cpp:105] Iteration 1260, lr = 0.001
I0401 15:33:02.081566 25640 solver.cpp:218] Iteration 1272 (2.24482 iter/s, 5.34564s/12 iters), loss = 5.05717
I0401 15:33:02.081650 25640 solver.cpp:237]     Train net output #0: loss = 5.05717 (* 1 = 5.05717 loss)
I0401 15:33:02.081657 25640 sgd_solver.cpp:105] Iteration 1272, lr = 0.001
I0401 15:33:07.356421 25640 solver.cpp:218] Iteration 1284 (2.27499 iter/s, 5.27475s/12 iters), loss = 4.99681
I0401 15:33:07.356472 25640 solver.cpp:237]     Train net output #0: loss = 4.99681 (* 1 = 4.99681 loss)
I0401 15:33:07.356479 25640 sgd_solver.cpp:105] Iteration 1284, lr = 0.001
I0401 15:33:12.542470 25640 solver.cpp:218] Iteration 1296 (2.31393 iter/s, 5.18598s/12 iters), loss = 5.12781
I0401 15:33:12.542517 25640 solver.cpp:237]     Train net output #0: loss = 5.12781 (* 1 = 5.12781 loss)
I0401 15:33:12.542522 25640 sgd_solver.cpp:105] Iteration 1296, lr = 0.001
I0401 15:33:17.672825 25640 solver.cpp:218] Iteration 1308 (2.33905 iter/s, 5.13029s/12 iters), loss = 5.07143
I0401 15:33:17.672868 25640 solver.cpp:237]     Train net output #0: loss = 5.07143 (* 1 = 5.07143 loss)
I0401 15:33:17.672873 25640 sgd_solver.cpp:105] Iteration 1308, lr = 0.001
I0401 15:33:20.267136 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:33:22.916283 25640 solver.cpp:218] Iteration 1320 (2.28859 iter/s, 5.2434s/12 iters), loss = 5.08808
I0401 15:33:22.916348 25640 solver.cpp:237]     Train net output #0: loss = 5.08808 (* 1 = 5.08808 loss)
I0401 15:33:22.916357 25640 sgd_solver.cpp:105] Iteration 1320, lr = 0.001
I0401 15:33:25.146283 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0401 15:33:28.143321 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0401 15:33:30.460186 25640 solver.cpp:330] Iteration 1326, Testing net (#0)
I0401 15:33:30.460206 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:33:34.270134 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:33:34.822134 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0202206
I0401 15:33:34.822173 25640 solver.cpp:397]     Test net output #1: loss = 5.04813 (* 1 = 5.04813 loss)
I0401 15:33:36.792618 25640 solver.cpp:218] Iteration 1332 (0.864786 iter/s, 13.8763s/12 iters), loss = 5.04992
I0401 15:33:36.792663 25640 solver.cpp:237]     Train net output #0: loss = 5.04992 (* 1 = 5.04992 loss)
I0401 15:33:36.792668 25640 sgd_solver.cpp:105] Iteration 1332, lr = 0.001
I0401 15:33:41.887017 25640 solver.cpp:218] Iteration 1344 (2.35556 iter/s, 5.09434s/12 iters), loss = 5.145
I0401 15:33:41.887063 25640 solver.cpp:237]     Train net output #0: loss = 5.145 (* 1 = 5.145 loss)
I0401 15:33:41.887068 25640 sgd_solver.cpp:105] Iteration 1344, lr = 0.001
I0401 15:33:47.043085 25640 solver.cpp:218] Iteration 1356 (2.32738 iter/s, 5.15601s/12 iters), loss = 5.06281
I0401 15:33:47.043128 25640 solver.cpp:237]     Train net output #0: loss = 5.06281 (* 1 = 5.06281 loss)
I0401 15:33:47.043134 25640 sgd_solver.cpp:105] Iteration 1356, lr = 0.001
I0401 15:33:52.416869 25640 solver.cpp:218] Iteration 1368 (2.23309 iter/s, 5.37372s/12 iters), loss = 5.10756
I0401 15:33:52.416939 25640 solver.cpp:237]     Train net output #0: loss = 5.10756 (* 1 = 5.10756 loss)
I0401 15:33:52.416946 25640 sgd_solver.cpp:105] Iteration 1368, lr = 0.001
I0401 15:33:53.612063 25640 blocking_queue.cpp:49] Waiting for data
I0401 15:33:57.466238 25640 solver.cpp:218] Iteration 1380 (2.37658 iter/s, 5.04928s/12 iters), loss = 5.04408
I0401 15:33:57.466295 25640 solver.cpp:237]     Train net output #0: loss = 5.04408 (* 1 = 5.04408 loss)
I0401 15:33:57.466303 25640 sgd_solver.cpp:105] Iteration 1380, lr = 0.001
I0401 15:34:02.511770 25640 solver.cpp:218] Iteration 1392 (2.37838 iter/s, 5.04546s/12 iters), loss = 5.00775
I0401 15:34:02.511827 25640 solver.cpp:237]     Train net output #0: loss = 5.00775 (* 1 = 5.00775 loss)
I0401 15:34:02.511834 25640 sgd_solver.cpp:105] Iteration 1392, lr = 0.001
I0401 15:34:07.880298 25640 solver.cpp:218] Iteration 1404 (2.23528 iter/s, 5.36846s/12 iters), loss = 5.01402
I0401 15:34:07.880403 25640 solver.cpp:237]     Train net output #0: loss = 5.01402 (* 1 = 5.01402 loss)
I0401 15:34:07.880409 25640 sgd_solver.cpp:105] Iteration 1404, lr = 0.001
I0401 15:34:12.497352 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:34:12.887667 25640 solver.cpp:218] Iteration 1416 (2.39653 iter/s, 5.00725s/12 iters), loss = 5.08357
I0401 15:34:12.887712 25640 solver.cpp:237]     Train net output #0: loss = 5.08357 (* 1 = 5.08357 loss)
I0401 15:34:12.887717 25640 sgd_solver.cpp:105] Iteration 1416, lr = 0.001
I0401 15:34:17.897193 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0401 15:34:20.940448 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0401 15:34:23.254135 25640 solver.cpp:330] Iteration 1428, Testing net (#0)
I0401 15:34:23.254155 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:34:26.933825 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:34:27.516433 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0238971
I0401 15:34:27.516469 25640 solver.cpp:397]     Test net output #1: loss = 5.0346 (* 1 = 5.0346 loss)
I0401 15:34:27.658136 25640 solver.cpp:218] Iteration 1428 (0.812435 iter/s, 14.7704s/12 iters), loss = 4.98732
I0401 15:34:27.659713 25640 solver.cpp:237]     Train net output #0: loss = 4.98732 (* 1 = 4.98732 loss)
I0401 15:34:27.659723 25640 sgd_solver.cpp:105] Iteration 1428, lr = 0.001
I0401 15:34:31.841955 25640 solver.cpp:218] Iteration 1440 (2.86928 iter/s, 4.18223s/12 iters), loss = 4.96192
I0401 15:34:31.841998 25640 solver.cpp:237]     Train net output #0: loss = 4.96192 (* 1 = 4.96192 loss)
I0401 15:34:31.842005 25640 sgd_solver.cpp:105] Iteration 1440, lr = 0.001
I0401 15:34:37.106535 25640 solver.cpp:218] Iteration 1452 (2.27941 iter/s, 5.26452s/12 iters), loss = 5.0066
I0401 15:34:37.106581 25640 solver.cpp:237]     Train net output #0: loss = 5.0066 (* 1 = 5.0066 loss)
I0401 15:34:37.106587 25640 sgd_solver.cpp:105] Iteration 1452, lr = 0.001
I0401 15:34:42.387256 25640 solver.cpp:218] Iteration 1464 (2.27245 iter/s, 5.28065s/12 iters), loss = 4.97099
I0401 15:34:42.387413 25640 solver.cpp:237]     Train net output #0: loss = 4.97099 (* 1 = 4.97099 loss)
I0401 15:34:42.387421 25640 sgd_solver.cpp:105] Iteration 1464, lr = 0.001
I0401 15:34:47.502568 25640 solver.cpp:218] Iteration 1476 (2.34597 iter/s, 5.11515s/12 iters), loss = 5.11803
I0401 15:34:47.502611 25640 solver.cpp:237]     Train net output #0: loss = 5.11803 (* 1 = 5.11803 loss)
I0401 15:34:47.502616 25640 sgd_solver.cpp:105] Iteration 1476, lr = 0.001
I0401 15:34:52.492946 25640 solver.cpp:218] Iteration 1488 (2.40465 iter/s, 4.99032s/12 iters), loss = 4.98914
I0401 15:34:52.492990 25640 solver.cpp:237]     Train net output #0: loss = 4.98914 (* 1 = 4.98914 loss)
I0401 15:34:52.492995 25640 sgd_solver.cpp:105] Iteration 1488, lr = 0.001
I0401 15:34:57.696640 25640 solver.cpp:218] Iteration 1500 (2.30608 iter/s, 5.20363s/12 iters), loss = 5.00726
I0401 15:34:57.696698 25640 solver.cpp:237]     Train net output #0: loss = 5.00726 (* 1 = 5.00726 loss)
I0401 15:34:57.696707 25640 sgd_solver.cpp:105] Iteration 1500, lr = 0.001
I0401 15:35:03.114126 25640 solver.cpp:218] Iteration 1512 (2.21508 iter/s, 5.41741s/12 iters), loss = 5.01938
I0401 15:35:03.114168 25640 solver.cpp:237]     Train net output #0: loss = 5.01938 (* 1 = 5.01938 loss)
I0401 15:35:03.114174 25640 sgd_solver.cpp:105] Iteration 1512, lr = 0.001
I0401 15:35:05.024097 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:35:08.461287 25640 solver.cpp:218] Iteration 1524 (2.24421 iter/s, 5.3471s/12 iters), loss = 5.02669
I0401 15:35:08.461328 25640 solver.cpp:237]     Train net output #0: loss = 5.02669 (* 1 = 5.02669 loss)
I0401 15:35:08.461334 25640 sgd_solver.cpp:105] Iteration 1524, lr = 0.001
I0401 15:35:10.596388 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0401 15:35:13.588168 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0401 15:35:15.899282 25640 solver.cpp:330] Iteration 1530, Testing net (#0)
I0401 15:35:15.899302 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:35:19.674724 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:35:20.309783 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0245098
I0401 15:35:20.309813 25640 solver.cpp:397]     Test net output #1: loss = 5.02779 (* 1 = 5.02779 loss)
I0401 15:35:22.153007 25640 solver.cpp:218] Iteration 1536 (0.876445 iter/s, 13.6917s/12 iters), loss = 4.96995
I0401 15:35:22.153056 25640 solver.cpp:237]     Train net output #0: loss = 4.96995 (* 1 = 4.96995 loss)
I0401 15:35:22.153061 25640 sgd_solver.cpp:105] Iteration 1536, lr = 0.001
I0401 15:35:27.305377 25640 solver.cpp:218] Iteration 1548 (2.32905 iter/s, 5.15231s/12 iters), loss = 5.05868
I0401 15:35:27.305418 25640 solver.cpp:237]     Train net output #0: loss = 5.05868 (* 1 = 5.05868 loss)
I0401 15:35:27.305423 25640 sgd_solver.cpp:105] Iteration 1548, lr = 0.001
I0401 15:35:32.380012 25640 solver.cpp:218] Iteration 1560 (2.36473 iter/s, 5.07458s/12 iters), loss = 4.92486
I0401 15:35:32.380054 25640 solver.cpp:237]     Train net output #0: loss = 4.92486 (* 1 = 4.92486 loss)
I0401 15:35:32.380059 25640 sgd_solver.cpp:105] Iteration 1560, lr = 0.001
I0401 15:35:37.970566 25640 solver.cpp:218] Iteration 1572 (2.1465 iter/s, 5.5905s/12 iters), loss = 4.97831
I0401 15:35:37.970604 25640 solver.cpp:237]     Train net output #0: loss = 4.97831 (* 1 = 4.97831 loss)
I0401 15:35:37.970610 25640 sgd_solver.cpp:105] Iteration 1572, lr = 0.001
I0401 15:35:43.370959 25640 solver.cpp:218] Iteration 1584 (2.22208 iter/s, 5.40034s/12 iters), loss = 5.01315
I0401 15:35:43.371014 25640 solver.cpp:237]     Train net output #0: loss = 5.01315 (* 1 = 5.01315 loss)
I0401 15:35:43.371023 25640 sgd_solver.cpp:105] Iteration 1584, lr = 0.001
I0401 15:35:48.602412 25640 solver.cpp:218] Iteration 1596 (2.29385 iter/s, 5.23139s/12 iters), loss = 5.07593
I0401 15:35:48.602533 25640 solver.cpp:237]     Train net output #0: loss = 5.07593 (* 1 = 5.07593 loss)
I0401 15:35:48.602540 25640 sgd_solver.cpp:105] Iteration 1596, lr = 0.001
I0401 15:35:53.942607 25640 solver.cpp:218] Iteration 1608 (2.24716 iter/s, 5.34006s/12 iters), loss = 4.92542
I0401 15:35:53.942654 25640 solver.cpp:237]     Train net output #0: loss = 4.92542 (* 1 = 4.92542 loss)
I0401 15:35:53.942659 25640 sgd_solver.cpp:105] Iteration 1608, lr = 0.001
I0401 15:35:57.988931 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:35:59.186053 25640 solver.cpp:218] Iteration 1620 (2.2886 iter/s, 5.24339s/12 iters), loss = 5.01596
I0401 15:35:59.186107 25640 solver.cpp:237]     Train net output #0: loss = 5.01596 (* 1 = 5.01596 loss)
I0401 15:35:59.186116 25640 sgd_solver.cpp:105] Iteration 1620, lr = 0.001
I0401 15:36:03.957751 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0401 15:36:06.987861 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0401 15:36:09.305543 25640 solver.cpp:330] Iteration 1632, Testing net (#0)
I0401 15:36:09.305563 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:36:12.972486 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:36:13.634464 25640 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0401 15:36:13.634501 25640 solver.cpp:397]     Test net output #1: loss = 5.01386 (* 1 = 5.01386 loss)
I0401 15:36:13.770218 25640 solver.cpp:218] Iteration 1632 (0.822814 iter/s, 14.5841s/12 iters), loss = 4.99208
I0401 15:36:13.770287 25640 solver.cpp:237]     Train net output #0: loss = 4.99208 (* 1 = 4.99208 loss)
I0401 15:36:13.770296 25640 sgd_solver.cpp:105] Iteration 1632, lr = 0.001
I0401 15:36:18.093086 25640 solver.cpp:218] Iteration 1644 (2.77599 iter/s, 4.32278s/12 iters), loss = 4.99629
I0401 15:36:18.093147 25640 solver.cpp:237]     Train net output #0: loss = 4.99629 (* 1 = 4.99629 loss)
I0401 15:36:18.093155 25640 sgd_solver.cpp:105] Iteration 1644, lr = 0.001
I0401 15:36:23.406484 25640 solver.cpp:218] Iteration 1656 (2.25847 iter/s, 5.31332s/12 iters), loss = 4.96021
I0401 15:36:23.406589 25640 solver.cpp:237]     Train net output #0: loss = 4.96021 (* 1 = 4.96021 loss)
I0401 15:36:23.406595 25640 sgd_solver.cpp:105] Iteration 1656, lr = 0.001
I0401 15:36:28.668244 25640 solver.cpp:218] Iteration 1668 (2.28066 iter/s, 5.26164s/12 iters), loss = 5.10318
I0401 15:36:28.668294 25640 solver.cpp:237]     Train net output #0: loss = 5.10318 (* 1 = 5.10318 loss)
I0401 15:36:28.668303 25640 sgd_solver.cpp:105] Iteration 1668, lr = 0.001
I0401 15:36:33.908203 25640 solver.cpp:218] Iteration 1680 (2.29012 iter/s, 5.23989s/12 iters), loss = 4.957
I0401 15:36:33.908252 25640 solver.cpp:237]     Train net output #0: loss = 4.957 (* 1 = 4.957 loss)
I0401 15:36:33.908259 25640 sgd_solver.cpp:105] Iteration 1680, lr = 0.001
I0401 15:36:39.250919 25640 solver.cpp:218] Iteration 1692 (2.24608 iter/s, 5.34265s/12 iters), loss = 4.97904
I0401 15:36:39.250964 25640 solver.cpp:237]     Train net output #0: loss = 4.97904 (* 1 = 4.97904 loss)
I0401 15:36:39.250970 25640 sgd_solver.cpp:105] Iteration 1692, lr = 0.001
I0401 15:36:44.522693 25640 solver.cpp:218] Iteration 1704 (2.2763 iter/s, 5.27171s/12 iters), loss = 4.90429
I0401 15:36:44.522739 25640 solver.cpp:237]     Train net output #0: loss = 4.90429 (* 1 = 4.90429 loss)
I0401 15:36:44.522745 25640 sgd_solver.cpp:105] Iteration 1704, lr = 0.001
I0401 15:36:49.823345 25640 solver.cpp:218] Iteration 1716 (2.2639 iter/s, 5.30059s/12 iters), loss = 5.01169
I0401 15:36:49.823392 25640 solver.cpp:237]     Train net output #0: loss = 5.01169 (* 1 = 5.01169 loss)
I0401 15:36:49.823398 25640 sgd_solver.cpp:105] Iteration 1716, lr = 0.001
I0401 15:36:50.850808 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:36:55.138536 25640 solver.cpp:218] Iteration 1728 (2.25771 iter/s, 5.31513s/12 iters), loss = 5.06436
I0401 15:36:55.138660 25640 solver.cpp:237]     Train net output #0: loss = 5.06436 (* 1 = 5.06436 loss)
I0401 15:36:55.138667 25640 sgd_solver.cpp:105] Iteration 1728, lr = 0.001
I0401 15:36:57.275646 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0401 15:37:00.304455 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0401 15:37:02.614481 25640 solver.cpp:330] Iteration 1734, Testing net (#0)
I0401 15:37:02.614506 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:37:06.279800 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:37:06.974400 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0401 15:37:06.974433 25640 solver.cpp:397]     Test net output #1: loss = 4.9926 (* 1 = 4.9926 loss)
I0401 15:37:08.864872 25640 solver.cpp:218] Iteration 1740 (0.87424 iter/s, 13.7262s/12 iters), loss = 5.06134
I0401 15:37:08.864928 25640 solver.cpp:237]     Train net output #0: loss = 5.06134 (* 1 = 5.06134 loss)
I0401 15:37:08.864933 25640 sgd_solver.cpp:105] Iteration 1740, lr = 0.001
I0401 15:37:14.184203 25640 solver.cpp:218] Iteration 1752 (2.25596 iter/s, 5.31925s/12 iters), loss = 5.02705
I0401 15:37:14.184267 25640 solver.cpp:237]     Train net output #0: loss = 5.02705 (* 1 = 5.02705 loss)
I0401 15:37:14.184276 25640 sgd_solver.cpp:105] Iteration 1752, lr = 0.001
I0401 15:37:19.512552 25640 solver.cpp:218] Iteration 1764 (2.25214 iter/s, 5.32827s/12 iters), loss = 4.98241
I0401 15:37:19.512598 25640 solver.cpp:237]     Train net output #0: loss = 4.98241 (* 1 = 4.98241 loss)
I0401 15:37:19.512605 25640 sgd_solver.cpp:105] Iteration 1764, lr = 0.001
I0401 15:37:24.779853 25640 solver.cpp:218] Iteration 1776 (2.27823 iter/s, 5.26724s/12 iters), loss = 5.00982
I0401 15:37:24.779909 25640 solver.cpp:237]     Train net output #0: loss = 5.00982 (* 1 = 5.00982 loss)
I0401 15:37:24.779918 25640 sgd_solver.cpp:105] Iteration 1776, lr = 0.001
I0401 15:37:30.141698 25640 solver.cpp:218] Iteration 1788 (2.23807 iter/s, 5.36177s/12 iters), loss = 4.906
I0401 15:37:30.141824 25640 solver.cpp:237]     Train net output #0: loss = 4.906 (* 1 = 4.906 loss)
I0401 15:37:30.141832 25640 sgd_solver.cpp:105] Iteration 1788, lr = 0.001
I0401 15:37:35.454188 25640 solver.cpp:218] Iteration 1800 (2.25889 iter/s, 5.31235s/12 iters), loss = 4.91995
I0401 15:37:35.454231 25640 solver.cpp:237]     Train net output #0: loss = 4.91995 (* 1 = 4.91995 loss)
I0401 15:37:35.454236 25640 sgd_solver.cpp:105] Iteration 1800, lr = 0.001
I0401 15:37:40.497608 25640 solver.cpp:218] Iteration 1812 (2.37937 iter/s, 5.04336s/12 iters), loss = 4.93052
I0401 15:37:40.497650 25640 solver.cpp:237]     Train net output #0: loss = 4.93052 (* 1 = 4.93052 loss)
I0401 15:37:40.497656 25640 sgd_solver.cpp:105] Iteration 1812, lr = 0.001
I0401 15:37:43.898979 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:37:45.996645 25640 solver.cpp:218] Iteration 1824 (2.18223 iter/s, 5.49897s/12 iters), loss = 4.91178
I0401 15:37:45.996702 25640 solver.cpp:237]     Train net output #0: loss = 4.91178 (* 1 = 4.91178 loss)
I0401 15:37:45.996711 25640 sgd_solver.cpp:105] Iteration 1824, lr = 0.001
I0401 15:37:50.729802 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0401 15:37:53.807215 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0401 15:37:56.119382 25640 solver.cpp:330] Iteration 1836, Testing net (#0)
I0401 15:37:56.119400 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:37:59.829370 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:38:00.579598 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0294118
I0401 15:38:00.579748 25640 solver.cpp:397]     Test net output #1: loss = 4.9752 (* 1 = 4.9752 loss)
I0401 15:38:00.721179 25640 solver.cpp:218] Iteration 1836 (0.81497 iter/s, 14.7245s/12 iters), loss = 4.95915
I0401 15:38:00.721222 25640 solver.cpp:237]     Train net output #0: loss = 4.95915 (* 1 = 4.95915 loss)
I0401 15:38:00.721230 25640 sgd_solver.cpp:105] Iteration 1836, lr = 0.001
I0401 15:38:05.270481 25640 solver.cpp:218] Iteration 1848 (2.63779 iter/s, 4.54925s/12 iters), loss = 4.93273
I0401 15:38:05.270526 25640 solver.cpp:237]     Train net output #0: loss = 4.93273 (* 1 = 4.93273 loss)
I0401 15:38:05.270535 25640 sgd_solver.cpp:105] Iteration 1848, lr = 0.001
I0401 15:38:10.497740 25640 solver.cpp:218] Iteration 1860 (2.29569 iter/s, 5.2272s/12 iters), loss = 4.95201
I0401 15:38:10.497786 25640 solver.cpp:237]     Train net output #0: loss = 4.95201 (* 1 = 4.95201 loss)
I0401 15:38:10.497792 25640 sgd_solver.cpp:105] Iteration 1860, lr = 0.001
I0401 15:38:15.729990 25640 solver.cpp:218] Iteration 1872 (2.2935 iter/s, 5.23219s/12 iters), loss = 5.05527
I0401 15:38:15.730057 25640 solver.cpp:237]     Train net output #0: loss = 5.05527 (* 1 = 5.05527 loss)
I0401 15:38:15.730065 25640 sgd_solver.cpp:105] Iteration 1872, lr = 0.001
I0401 15:38:20.894412 25640 solver.cpp:218] Iteration 1884 (2.32363 iter/s, 5.16434s/12 iters), loss = 4.89888
I0401 15:38:20.894471 25640 solver.cpp:237]     Train net output #0: loss = 4.89888 (* 1 = 4.89888 loss)
I0401 15:38:20.894480 25640 sgd_solver.cpp:105] Iteration 1884, lr = 0.001
I0401 15:38:26.028815 25640 solver.cpp:218] Iteration 1896 (2.33721 iter/s, 5.13433s/12 iters), loss = 4.91652
I0401 15:38:26.028867 25640 solver.cpp:237]     Train net output #0: loss = 4.91652 (* 1 = 4.91652 loss)
I0401 15:38:26.028875 25640 sgd_solver.cpp:105] Iteration 1896, lr = 0.001
I0401 15:38:31.206912 25640 solver.cpp:218] Iteration 1908 (2.31748 iter/s, 5.17803s/12 iters), loss = 4.97149
I0401 15:38:31.207005 25640 solver.cpp:237]     Train net output #0: loss = 4.97149 (* 1 = 4.97149 loss)
I0401 15:38:31.207011 25640 sgd_solver.cpp:105] Iteration 1908, lr = 0.001
I0401 15:38:36.193598 25640 solver.cpp:218] Iteration 1920 (2.40646 iter/s, 4.98658s/12 iters), loss = 4.93153
I0401 15:38:36.193642 25640 solver.cpp:237]     Train net output #0: loss = 4.93153 (* 1 = 4.93153 loss)
I0401 15:38:36.193648 25640 sgd_solver.cpp:105] Iteration 1920, lr = 0.001
I0401 15:38:36.505388 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:38:41.539958 25640 solver.cpp:218] Iteration 1932 (2.24454 iter/s, 5.3463s/12 iters), loss = 4.98511
I0401 15:38:41.540012 25640 solver.cpp:237]     Train net output #0: loss = 4.98511 (* 1 = 4.98511 loss)
I0401 15:38:41.540021 25640 sgd_solver.cpp:105] Iteration 1932, lr = 0.001
I0401 15:38:43.585997 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0401 15:38:46.609422 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0401 15:38:48.969038 25640 solver.cpp:330] Iteration 1938, Testing net (#0)
I0401 15:38:48.969063 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:38:52.754747 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:38:53.526149 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0373775
I0401 15:38:53.526186 25640 solver.cpp:397]     Test net output #1: loss = 4.93988 (* 1 = 4.93988 loss)
I0401 15:38:55.469985 25640 solver.cpp:218] Iteration 1944 (0.861452 iter/s, 13.93s/12 iters), loss = 5.09568
I0401 15:38:55.470026 25640 solver.cpp:237]     Train net output #0: loss = 5.09568 (* 1 = 5.09568 loss)
I0401 15:38:55.470031 25640 sgd_solver.cpp:105] Iteration 1944, lr = 0.001
I0401 15:39:00.655582 25640 solver.cpp:218] Iteration 1956 (2.31413 iter/s, 5.18554s/12 iters), loss = 4.90756
I0401 15:39:00.655628 25640 solver.cpp:237]     Train net output #0: loss = 4.90756 (* 1 = 4.90756 loss)
I0401 15:39:00.655634 25640 sgd_solver.cpp:105] Iteration 1956, lr = 0.001
I0401 15:39:05.970499 25640 solver.cpp:218] Iteration 1968 (2.25782 iter/s, 5.31485s/12 iters), loss = 5.035
I0401 15:39:05.970650 25640 solver.cpp:237]     Train net output #0: loss = 5.035 (* 1 = 5.035 loss)
I0401 15:39:05.970659 25640 sgd_solver.cpp:105] Iteration 1968, lr = 0.001
I0401 15:39:11.183255 25640 solver.cpp:218] Iteration 1980 (2.30212 iter/s, 5.21259s/12 iters), loss = 4.83243
I0401 15:39:11.183295 25640 solver.cpp:237]     Train net output #0: loss = 4.83243 (* 1 = 4.83243 loss)
I0401 15:39:11.183300 25640 sgd_solver.cpp:105] Iteration 1980, lr = 0.001
I0401 15:39:16.441320 25640 solver.cpp:218] Iteration 1992 (2.28223 iter/s, 5.25801s/12 iters), loss = 4.78008
I0401 15:39:16.441368 25640 solver.cpp:237]     Train net output #0: loss = 4.78008 (* 1 = 4.78008 loss)
I0401 15:39:16.441375 25640 sgd_solver.cpp:105] Iteration 1992, lr = 0.001
I0401 15:39:21.777262 25640 solver.cpp:218] Iteration 2004 (2.24893 iter/s, 5.33588s/12 iters), loss = 4.97256
I0401 15:39:21.777308 25640 solver.cpp:237]     Train net output #0: loss = 4.97256 (* 1 = 4.97256 loss)
I0401 15:39:21.777313 25640 sgd_solver.cpp:105] Iteration 2004, lr = 0.001
I0401 15:39:27.153507 25640 solver.cpp:218] Iteration 2016 (2.23207 iter/s, 5.37619s/12 iters), loss = 4.89234
I0401 15:39:27.153554 25640 solver.cpp:237]     Train net output #0: loss = 4.89234 (* 1 = 4.89234 loss)
I0401 15:39:27.153559 25640 sgd_solver.cpp:105] Iteration 2016, lr = 0.001
I0401 15:39:29.746948 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:39:32.451190 25640 solver.cpp:218] Iteration 2028 (2.26517 iter/s, 5.29762s/12 iters), loss = 4.90123
I0401 15:39:32.451236 25640 solver.cpp:237]     Train net output #0: loss = 4.90123 (* 1 = 4.90123 loss)
I0401 15:39:32.451241 25640 sgd_solver.cpp:105] Iteration 2028, lr = 0.001
I0401 15:39:37.387413 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0401 15:39:41.126971 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0401 15:39:44.813061 25640 solver.cpp:330] Iteration 2040, Testing net (#0)
I0401 15:39:44.813088 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:39:48.295840 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:39:49.115463 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0367647
I0401 15:39:49.115514 25640 solver.cpp:397]     Test net output #1: loss = 4.94128 (* 1 = 4.94128 loss)
I0401 15:39:49.256865 25640 solver.cpp:218] Iteration 2040 (0.714047 iter/s, 16.8056s/12 iters), loss = 4.94283
I0401 15:39:49.256925 25640 solver.cpp:237]     Train net output #0: loss = 4.94283 (* 1 = 4.94283 loss)
I0401 15:39:49.256933 25640 sgd_solver.cpp:105] Iteration 2040, lr = 0.001
I0401 15:39:53.532866 25640 solver.cpp:218] Iteration 2052 (2.80642 iter/s, 4.27592s/12 iters), loss = 4.93551
I0401 15:39:53.532938 25640 solver.cpp:237]     Train net output #0: loss = 4.93551 (* 1 = 4.93551 loss)
I0401 15:39:53.532946 25640 sgd_solver.cpp:105] Iteration 2052, lr = 0.001
I0401 15:39:55.209015 25640 blocking_queue.cpp:49] Waiting for data
I0401 15:39:58.710286 25640 solver.cpp:218] Iteration 2064 (2.31779 iter/s, 5.17734s/12 iters), loss = 4.87245
I0401 15:39:58.710343 25640 solver.cpp:237]     Train net output #0: loss = 4.87245 (* 1 = 4.87245 loss)
I0401 15:39:58.710352 25640 sgd_solver.cpp:105] Iteration 2064, lr = 0.001
I0401 15:40:04.049331 25640 solver.cpp:218] Iteration 2076 (2.24762 iter/s, 5.33898s/12 iters), loss = 4.88987
I0401 15:40:04.049381 25640 solver.cpp:237]     Train net output #0: loss = 4.88987 (* 1 = 4.88987 loss)
I0401 15:40:04.049388 25640 sgd_solver.cpp:105] Iteration 2076, lr = 0.001
I0401 15:40:09.403203 25640 solver.cpp:218] Iteration 2088 (2.2414 iter/s, 5.35381s/12 iters), loss = 4.88511
I0401 15:40:09.403349 25640 solver.cpp:237]     Train net output #0: loss = 4.88511 (* 1 = 4.88511 loss)
I0401 15:40:09.403359 25640 sgd_solver.cpp:105] Iteration 2088, lr = 0.001
I0401 15:40:14.539283 25640 solver.cpp:218] Iteration 2100 (2.33648 iter/s, 5.13592s/12 iters), loss = 4.8524
I0401 15:40:14.539326 25640 solver.cpp:237]     Train net output #0: loss = 4.8524 (* 1 = 4.8524 loss)
I0401 15:40:14.539332 25640 sgd_solver.cpp:105] Iteration 2100, lr = 0.001
I0401 15:40:19.701699 25640 solver.cpp:218] Iteration 2112 (2.32452 iter/s, 5.16236s/12 iters), loss = 4.67736
I0401 15:40:19.701747 25640 solver.cpp:237]     Train net output #0: loss = 4.67736 (* 1 = 4.67736 loss)
I0401 15:40:19.701753 25640 sgd_solver.cpp:105] Iteration 2112, lr = 0.001
I0401 15:40:24.467686 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:40:24.823639 25640 solver.cpp:218] Iteration 2124 (2.34289 iter/s, 5.12188s/12 iters), loss = 4.91478
I0401 15:40:24.823674 25640 solver.cpp:237]     Train net output #0: loss = 4.91478 (* 1 = 4.91478 loss)
I0401 15:40:24.823680 25640 sgd_solver.cpp:105] Iteration 2124, lr = 0.001
I0401 15:40:30.273146 25640 solver.cpp:218] Iteration 2136 (2.20206 iter/s, 5.44945s/12 iters), loss = 4.83834
I0401 15:40:30.273211 25640 solver.cpp:237]     Train net output #0: loss = 4.83834 (* 1 = 4.83834 loss)
I0401 15:40:30.273219 25640 sgd_solver.cpp:105] Iteration 2136, lr = 0.001
I0401 15:40:32.442673 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0401 15:40:37.059558 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0401 15:40:41.269924 25640 solver.cpp:330] Iteration 2142, Testing net (#0)
I0401 15:40:41.270010 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:40:44.686595 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:40:45.537227 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0441176
I0401 15:40:45.537271 25640 solver.cpp:397]     Test net output #1: loss = 4.89589 (* 1 = 4.89589 loss)
I0401 15:40:47.471652 25640 solver.cpp:218] Iteration 2148 (0.697738 iter/s, 17.1984s/12 iters), loss = 4.68351
I0401 15:40:47.471717 25640 solver.cpp:237]     Train net output #0: loss = 4.68351 (* 1 = 4.68351 loss)
I0401 15:40:47.471726 25640 sgd_solver.cpp:105] Iteration 2148, lr = 0.001
I0401 15:40:52.652245 25640 solver.cpp:218] Iteration 2160 (2.31637 iter/s, 5.18052s/12 iters), loss = 4.72135
I0401 15:40:52.652289 25640 solver.cpp:237]     Train net output #0: loss = 4.72135 (* 1 = 4.72135 loss)
I0401 15:40:52.652295 25640 sgd_solver.cpp:105] Iteration 2160, lr = 0.001
I0401 15:40:57.986390 25640 solver.cpp:218] Iteration 2172 (2.24968 iter/s, 5.33408s/12 iters), loss = 4.76747
I0401 15:40:57.986431 25640 solver.cpp:237]     Train net output #0: loss = 4.76747 (* 1 = 4.76747 loss)
I0401 15:40:57.986436 25640 sgd_solver.cpp:105] Iteration 2172, lr = 0.001
I0401 15:41:03.238018 25640 solver.cpp:218] Iteration 2184 (2.28503 iter/s, 5.25157s/12 iters), loss = 5.03378
I0401 15:41:03.238059 25640 solver.cpp:237]     Train net output #0: loss = 5.03378 (* 1 = 5.03378 loss)
I0401 15:41:03.238065 25640 sgd_solver.cpp:105] Iteration 2184, lr = 0.001
I0401 15:41:08.451689 25640 solver.cpp:218] Iteration 2196 (2.30167 iter/s, 5.21361s/12 iters), loss = 4.76593
I0401 15:41:08.451745 25640 solver.cpp:237]     Train net output #0: loss = 4.76593 (* 1 = 4.76593 loss)
I0401 15:41:08.451753 25640 sgd_solver.cpp:105] Iteration 2196, lr = 0.001
I0401 15:41:13.753131 25640 solver.cpp:218] Iteration 2208 (2.26357 iter/s, 5.30137s/12 iters), loss = 4.72284
I0401 15:41:13.753262 25640 solver.cpp:237]     Train net output #0: loss = 4.72284 (* 1 = 4.72284 loss)
I0401 15:41:13.753269 25640 sgd_solver.cpp:105] Iteration 2208, lr = 0.001
I0401 15:41:19.050457 25640 solver.cpp:218] Iteration 2220 (2.26536 iter/s, 5.29718s/12 iters), loss = 4.74775
I0401 15:41:19.050515 25640 solver.cpp:237]     Train net output #0: loss = 4.74775 (* 1 = 4.74775 loss)
I0401 15:41:19.050523 25640 sgd_solver.cpp:105] Iteration 2220, lr = 0.001
I0401 15:41:20.973428 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:41:24.499264 25640 solver.cpp:218] Iteration 2232 (2.20235 iter/s, 5.44873s/12 iters), loss = 4.7431
I0401 15:41:24.499320 25640 solver.cpp:237]     Train net output #0: loss = 4.7431 (* 1 = 4.7431 loss)
I0401 15:41:24.499328 25640 sgd_solver.cpp:105] Iteration 2232, lr = 0.001
I0401 15:41:29.130098 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0401 15:41:33.939914 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0401 15:41:38.535943 25640 solver.cpp:330] Iteration 2244, Testing net (#0)
I0401 15:41:38.535965 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:41:41.979223 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:41:42.885233 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0484069
I0401 15:41:42.885264 25640 solver.cpp:397]     Test net output #1: loss = 4.85126 (* 1 = 4.85126 loss)
I0401 15:41:43.026820 25640 solver.cpp:218] Iteration 2244 (0.647686 iter/s, 18.5275s/12 iters), loss = 4.75784
I0401 15:41:43.026871 25640 solver.cpp:237]     Train net output #0: loss = 4.75784 (* 1 = 4.75784 loss)
I0401 15:41:43.026877 25640 sgd_solver.cpp:105] Iteration 2244, lr = 0.001
I0401 15:41:47.266595 25640 solver.cpp:218] Iteration 2256 (2.83038 iter/s, 4.23971s/12 iters), loss = 4.75829
I0401 15:41:47.266680 25640 solver.cpp:237]     Train net output #0: loss = 4.75829 (* 1 = 4.75829 loss)
I0401 15:41:47.266686 25640 sgd_solver.cpp:105] Iteration 2256, lr = 0.001
I0401 15:41:52.618501 25640 solver.cpp:218] Iteration 2268 (2.24223 iter/s, 5.35181s/12 iters), loss = 4.82143
I0401 15:41:52.618541 25640 solver.cpp:237]     Train net output #0: loss = 4.82143 (* 1 = 4.82143 loss)
I0401 15:41:52.618546 25640 sgd_solver.cpp:105] Iteration 2268, lr = 0.001
I0401 15:41:57.819468 25640 solver.cpp:218] Iteration 2280 (2.30729 iter/s, 5.20091s/12 iters), loss = 4.7797
I0401 15:41:57.819514 25640 solver.cpp:237]     Train net output #0: loss = 4.7797 (* 1 = 4.7797 loss)
I0401 15:41:57.819519 25640 sgd_solver.cpp:105] Iteration 2280, lr = 0.001
I0401 15:42:03.217509 25640 solver.cpp:218] Iteration 2292 (2.22305 iter/s, 5.39799s/12 iters), loss = 4.78897
I0401 15:42:03.217548 25640 solver.cpp:237]     Train net output #0: loss = 4.78897 (* 1 = 4.78897 loss)
I0401 15:42:03.217555 25640 sgd_solver.cpp:105] Iteration 2292, lr = 0.001
I0401 15:42:08.440129 25640 solver.cpp:218] Iteration 2304 (2.29772 iter/s, 5.22256s/12 iters), loss = 4.87855
I0401 15:42:08.440172 25640 solver.cpp:237]     Train net output #0: loss = 4.87855 (* 1 = 4.87855 loss)
I0401 15:42:08.440178 25640 sgd_solver.cpp:105] Iteration 2304, lr = 0.001
I0401 15:42:13.688050 25640 solver.cpp:218] Iteration 2316 (2.28665 iter/s, 5.24786s/12 iters), loss = 4.80195
I0401 15:42:13.688094 25640 solver.cpp:237]     Train net output #0: loss = 4.80195 (* 1 = 4.80195 loss)
I0401 15:42:13.688100 25640 sgd_solver.cpp:105] Iteration 2316, lr = 0.001
I0401 15:42:17.805881 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:42:18.992648 25640 solver.cpp:218] Iteration 2328 (2.26221 iter/s, 5.30454s/12 iters), loss = 4.75342
I0401 15:42:18.992705 25640 solver.cpp:237]     Train net output #0: loss = 4.75342 (* 1 = 4.75342 loss)
I0401 15:42:18.992715 25640 sgd_solver.cpp:105] Iteration 2328, lr = 0.001
I0401 15:42:24.043293 25640 solver.cpp:218] Iteration 2340 (2.37597 iter/s, 5.05057s/12 iters), loss = 4.81635
I0401 15:42:24.043345 25640 solver.cpp:237]     Train net output #0: loss = 4.81635 (* 1 = 4.81635 loss)
I0401 15:42:24.043352 25640 sgd_solver.cpp:105] Iteration 2340, lr = 0.001
I0401 15:42:26.099267 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0401 15:42:30.976337 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0401 15:42:35.497366 25640 solver.cpp:330] Iteration 2346, Testing net (#0)
I0401 15:42:35.497386 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:42:38.933176 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:42:39.859014 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0496324
I0401 15:42:39.859050 25640 solver.cpp:397]     Test net output #1: loss = 4.81605 (* 1 = 4.81605 loss)
I0401 15:42:41.723861 25640 solver.cpp:218] Iteration 2352 (0.678713 iter/s, 17.6805s/12 iters), loss = 4.80184
I0401 15:42:41.723908 25640 solver.cpp:237]     Train net output #0: loss = 4.80184 (* 1 = 4.80184 loss)
I0401 15:42:41.723914 25640 sgd_solver.cpp:105] Iteration 2352, lr = 0.001
I0401 15:42:46.888389 25640 solver.cpp:218] Iteration 2364 (2.32357 iter/s, 5.16446s/12 iters), loss = 4.82168
I0401 15:42:46.888444 25640 solver.cpp:237]     Train net output #0: loss = 4.82168 (* 1 = 4.82168 loss)
I0401 15:42:46.888453 25640 sgd_solver.cpp:105] Iteration 2364, lr = 0.001
I0401 15:42:52.138618 25640 solver.cpp:218] Iteration 2376 (2.28565 iter/s, 5.25016s/12 iters), loss = 4.91769
I0401 15:42:52.138769 25640 solver.cpp:237]     Train net output #0: loss = 4.91769 (* 1 = 4.91769 loss)
I0401 15:42:52.138779 25640 sgd_solver.cpp:105] Iteration 2376, lr = 0.001
I0401 15:42:57.546063 25640 solver.cpp:218] Iteration 2388 (2.21923 iter/s, 5.40728s/12 iters), loss = 4.68194
I0401 15:42:57.546123 25640 solver.cpp:237]     Train net output #0: loss = 4.68194 (* 1 = 4.68194 loss)
I0401 15:42:57.546129 25640 sgd_solver.cpp:105] Iteration 2388, lr = 0.001
I0401 15:43:02.709230 25640 solver.cpp:218] Iteration 2400 (2.32419 iter/s, 5.16309s/12 iters), loss = 4.66144
I0401 15:43:02.709287 25640 solver.cpp:237]     Train net output #0: loss = 4.66144 (* 1 = 4.66144 loss)
I0401 15:43:02.709298 25640 sgd_solver.cpp:105] Iteration 2400, lr = 0.001
I0401 15:43:08.019655 25640 solver.cpp:218] Iteration 2412 (2.25974 iter/s, 5.31036s/12 iters), loss = 4.73632
I0401 15:43:08.019704 25640 solver.cpp:237]     Train net output #0: loss = 4.73632 (* 1 = 4.73632 loss)
I0401 15:43:08.019711 25640 sgd_solver.cpp:105] Iteration 2412, lr = 0.001
I0401 15:43:13.221483 25640 solver.cpp:218] Iteration 2424 (2.30691 iter/s, 5.20176s/12 iters), loss = 4.70512
I0401 15:43:13.221539 25640 solver.cpp:237]     Train net output #0: loss = 4.70512 (* 1 = 4.70512 loss)
I0401 15:43:13.221546 25640 sgd_solver.cpp:105] Iteration 2424, lr = 0.001
I0401 15:43:14.361939 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:43:18.569186 25640 solver.cpp:218] Iteration 2436 (2.24398 iter/s, 5.34763s/12 iters), loss = 4.78446
I0401 15:43:18.569232 25640 solver.cpp:237]     Train net output #0: loss = 4.78446 (* 1 = 4.78446 loss)
I0401 15:43:18.569237 25640 sgd_solver.cpp:105] Iteration 2436, lr = 0.001
I0401 15:43:23.265565 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0401 15:43:28.191731 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0401 15:43:31.565769 25640 solver.cpp:330] Iteration 2448, Testing net (#0)
I0401 15:43:31.565793 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:43:34.962447 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:43:35.920306 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0496324
I0401 15:43:35.920343 25640 solver.cpp:397]     Test net output #1: loss = 4.79257 (* 1 = 4.79257 loss)
I0401 15:43:36.061538 25640 solver.cpp:218] Iteration 2448 (0.686016 iter/s, 17.4923s/12 iters), loss = 4.92238
I0401 15:43:36.061594 25640 solver.cpp:237]     Train net output #0: loss = 4.92238 (* 1 = 4.92238 loss)
I0401 15:43:36.061601 25640 sgd_solver.cpp:105] Iteration 2448, lr = 0.001
I0401 15:43:40.395109 25640 solver.cpp:218] Iteration 2460 (2.76912 iter/s, 4.33351s/12 iters), loss = 4.78609
I0401 15:43:40.395153 25640 solver.cpp:237]     Train net output #0: loss = 4.78609 (* 1 = 4.78609 loss)
I0401 15:43:40.395159 25640 sgd_solver.cpp:105] Iteration 2460, lr = 0.001
I0401 15:43:45.790644 25640 solver.cpp:218] Iteration 2472 (2.22409 iter/s, 5.39547s/12 iters), loss = 4.79251
I0401 15:43:45.790700 25640 solver.cpp:237]     Train net output #0: loss = 4.79251 (* 1 = 4.79251 loss)
I0401 15:43:45.790709 25640 sgd_solver.cpp:105] Iteration 2472, lr = 0.001
I0401 15:43:51.177990 25640 solver.cpp:218] Iteration 2484 (2.22747 iter/s, 5.38728s/12 iters), loss = 4.70614
I0401 15:43:51.178033 25640 solver.cpp:237]     Train net output #0: loss = 4.70614 (* 1 = 4.70614 loss)
I0401 15:43:51.178040 25640 sgd_solver.cpp:105] Iteration 2484, lr = 0.001
I0401 15:43:56.406456 25640 solver.cpp:218] Iteration 2496 (2.29515 iter/s, 5.22841s/12 iters), loss = 4.74558
I0401 15:43:56.406608 25640 solver.cpp:237]     Train net output #0: loss = 4.74558 (* 1 = 4.74558 loss)
I0401 15:43:56.406615 25640 sgd_solver.cpp:105] Iteration 2496, lr = 0.001
I0401 15:44:01.754487 25640 solver.cpp:218] Iteration 2508 (2.24389 iter/s, 5.34787s/12 iters), loss = 4.61955
I0401 15:44:01.754525 25640 solver.cpp:237]     Train net output #0: loss = 4.61955 (* 1 = 4.61955 loss)
I0401 15:44:01.754530 25640 sgd_solver.cpp:105] Iteration 2508, lr = 0.001
I0401 15:44:06.813060 25640 solver.cpp:218] Iteration 2520 (2.37224 iter/s, 5.05852s/12 iters), loss = 4.74211
I0401 15:44:06.813102 25640 solver.cpp:237]     Train net output #0: loss = 4.74211 (* 1 = 4.74211 loss)
I0401 15:44:06.813107 25640 sgd_solver.cpp:105] Iteration 2520, lr = 0.001
I0401 15:44:10.091301 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:44:12.118471 25640 solver.cpp:218] Iteration 2532 (2.26187 iter/s, 5.30535s/12 iters), loss = 4.54241
I0401 15:44:12.118516 25640 solver.cpp:237]     Train net output #0: loss = 4.54241 (* 1 = 4.54241 loss)
I0401 15:44:12.118521 25640 sgd_solver.cpp:105] Iteration 2532, lr = 0.001
I0401 15:44:17.350924 25640 solver.cpp:218] Iteration 2544 (2.29341 iter/s, 5.23239s/12 iters), loss = 4.57845
I0401 15:44:17.350980 25640 solver.cpp:237]     Train net output #0: loss = 4.57845 (* 1 = 4.57845 loss)
I0401 15:44:17.350988 25640 sgd_solver.cpp:105] Iteration 2544, lr = 0.001
I0401 15:44:19.478986 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0401 15:44:24.118633 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0401 15:44:26.423168 25640 solver.cpp:330] Iteration 2550, Testing net (#0)
I0401 15:44:26.423230 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:44:29.749373 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:44:30.753626 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0533088
I0401 15:44:30.753664 25640 solver.cpp:397]     Test net output #1: loss = 4.73585 (* 1 = 4.73585 loss)
I0401 15:44:32.591446 25640 solver.cpp:218] Iteration 2556 (0.787378 iter/s, 15.2405s/12 iters), loss = 4.7573
I0401 15:44:32.591487 25640 solver.cpp:237]     Train net output #0: loss = 4.7573 (* 1 = 4.7573 loss)
I0401 15:44:32.591492 25640 sgd_solver.cpp:105] Iteration 2556, lr = 0.001
I0401 15:44:37.862830 25640 solver.cpp:218] Iteration 2568 (2.27647 iter/s, 5.27132s/12 iters), loss = 4.73937
I0401 15:44:37.862884 25640 solver.cpp:237]     Train net output #0: loss = 4.73937 (* 1 = 4.73937 loss)
I0401 15:44:37.862892 25640 sgd_solver.cpp:105] Iteration 2568, lr = 0.001
I0401 15:44:43.051470 25640 solver.cpp:218] Iteration 2580 (2.31277 iter/s, 5.18858s/12 iters), loss = 4.83712
I0401 15:44:43.051506 25640 solver.cpp:237]     Train net output #0: loss = 4.83712 (* 1 = 4.83712 loss)
I0401 15:44:43.051512 25640 sgd_solver.cpp:105] Iteration 2580, lr = 0.001
I0401 15:44:48.498930 25640 solver.cpp:218] Iteration 2592 (2.20288 iter/s, 5.44741s/12 iters), loss = 4.724
I0401 15:44:48.498975 25640 solver.cpp:237]     Train net output #0: loss = 4.724 (* 1 = 4.724 loss)
I0401 15:44:48.498980 25640 sgd_solver.cpp:105] Iteration 2592, lr = 0.001
I0401 15:44:53.683432 25640 solver.cpp:218] Iteration 2604 (2.31462 iter/s, 5.18444s/12 iters), loss = 4.64278
I0401 15:44:53.683501 25640 solver.cpp:237]     Train net output #0: loss = 4.64278 (* 1 = 4.64278 loss)
I0401 15:44:53.683509 25640 sgd_solver.cpp:105] Iteration 2604, lr = 0.001
I0401 15:44:58.954202 25640 solver.cpp:218] Iteration 2616 (2.27674 iter/s, 5.27069s/12 iters), loss = 4.72132
I0401 15:44:58.954339 25640 solver.cpp:237]     Train net output #0: loss = 4.72132 (* 1 = 4.72132 loss)
I0401 15:44:58.954346 25640 sgd_solver.cpp:105] Iteration 2616, lr = 0.001
I0401 15:45:04.163780 25640 solver.cpp:218] Iteration 2628 (2.30351 iter/s, 5.20943s/12 iters), loss = 4.59348
I0401 15:45:04.163822 25640 solver.cpp:237]     Train net output #0: loss = 4.59348 (* 1 = 4.59348 loss)
I0401 15:45:04.163827 25640 sgd_solver.cpp:105] Iteration 2628, lr = 0.001
I0401 15:45:04.661334 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:45:09.620257 25640 solver.cpp:218] Iteration 2640 (2.19925 iter/s, 5.45642s/12 iters), loss = 4.68801
I0401 15:45:09.620308 25640 solver.cpp:237]     Train net output #0: loss = 4.68801 (* 1 = 4.68801 loss)
I0401 15:45:09.620316 25640 sgd_solver.cpp:105] Iteration 2640, lr = 0.001
I0401 15:45:14.465349 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0401 15:45:17.892542 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0401 15:45:20.206759 25640 solver.cpp:330] Iteration 2652, Testing net (#0)
I0401 15:45:20.206780 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:45:23.622431 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:45:24.656709 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0594363
I0401 15:45:24.656745 25640 solver.cpp:397]     Test net output #1: loss = 4.69189 (* 1 = 4.69189 loss)
I0401 15:45:24.797941 25640 solver.cpp:218] Iteration 2652 (0.790638 iter/s, 15.1776s/12 iters), loss = 4.69875
I0401 15:45:24.797986 25640 solver.cpp:237]     Train net output #0: loss = 4.69875 (* 1 = 4.69875 loss)
I0401 15:45:24.797994 25640 sgd_solver.cpp:105] Iteration 2652, lr = 0.001
I0401 15:45:28.929206 25640 solver.cpp:218] Iteration 2664 (2.90472 iter/s, 4.13121s/12 iters), loss = 4.70932
I0401 15:45:28.929250 25640 solver.cpp:237]     Train net output #0: loss = 4.70932 (* 1 = 4.70932 loss)
I0401 15:45:28.929255 25640 sgd_solver.cpp:105] Iteration 2664, lr = 0.001
I0401 15:45:34.359649 25640 solver.cpp:218] Iteration 2676 (2.20979 iter/s, 5.43038s/12 iters), loss = 4.76265
I0401 15:45:34.359776 25640 solver.cpp:237]     Train net output #0: loss = 4.76265 (* 1 = 4.76265 loss)
I0401 15:45:34.359784 25640 sgd_solver.cpp:105] Iteration 2676, lr = 0.001
I0401 15:45:39.526772 25640 solver.cpp:218] Iteration 2688 (2.32244 iter/s, 5.16698s/12 iters), loss = 4.6297
I0401 15:45:39.526813 25640 solver.cpp:237]     Train net output #0: loss = 4.6297 (* 1 = 4.6297 loss)
I0401 15:45:39.526818 25640 sgd_solver.cpp:105] Iteration 2688, lr = 0.001
I0401 15:45:44.699525 25640 solver.cpp:218] Iteration 2700 (2.31987 iter/s, 5.1727s/12 iters), loss = 4.40278
I0401 15:45:44.699573 25640 solver.cpp:237]     Train net output #0: loss = 4.40278 (* 1 = 4.40278 loss)
I0401 15:45:44.699581 25640 sgd_solver.cpp:105] Iteration 2700, lr = 0.001
I0401 15:45:50.023619 25640 solver.cpp:218] Iteration 2712 (2.25393 iter/s, 5.32403s/12 iters), loss = 4.60083
I0401 15:45:50.023661 25640 solver.cpp:237]     Train net output #0: loss = 4.60083 (* 1 = 4.60083 loss)
I0401 15:45:50.023667 25640 sgd_solver.cpp:105] Iteration 2712, lr = 0.001
I0401 15:45:55.399505 25640 solver.cpp:218] Iteration 2724 (2.23221 iter/s, 5.37583s/12 iters), loss = 4.67287
I0401 15:45:55.399545 25640 solver.cpp:237]     Train net output #0: loss = 4.67287 (* 1 = 4.67287 loss)
I0401 15:45:55.399551 25640 sgd_solver.cpp:105] Iteration 2724, lr = 0.001
I0401 15:45:58.186611 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:46:00.688393 25640 solver.cpp:218] Iteration 2736 (2.26893 iter/s, 5.28883s/12 iters), loss = 4.74984
I0401 15:46:00.688436 25640 solver.cpp:237]     Train net output #0: loss = 4.74984 (* 1 = 4.74984 loss)
I0401 15:46:00.688441 25640 sgd_solver.cpp:105] Iteration 2736, lr = 0.001
I0401 15:46:06.123432 25640 solver.cpp:218] Iteration 2748 (2.20792 iter/s, 5.43498s/12 iters), loss = 4.5346
I0401 15:46:06.123566 25640 solver.cpp:237]     Train net output #0: loss = 4.5346 (* 1 = 4.5346 loss)
I0401 15:46:06.123574 25640 sgd_solver.cpp:105] Iteration 2748, lr = 0.001
I0401 15:46:08.281148 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0401 15:46:12.904016 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0401 15:46:16.993625 25640 solver.cpp:330] Iteration 2754, Testing net (#0)
I0401 15:46:16.993651 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:46:20.103229 25640 blocking_queue.cpp:49] Waiting for data
I0401 15:46:20.352344 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:46:21.504642 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0618873
I0401 15:46:21.504673 25640 solver.cpp:397]     Test net output #1: loss = 4.65235 (* 1 = 4.65235 loss)
I0401 15:46:23.412811 25640 solver.cpp:218] Iteration 2760 (0.694073 iter/s, 17.2892s/12 iters), loss = 4.60638
I0401 15:46:23.412858 25640 solver.cpp:237]     Train net output #0: loss = 4.60638 (* 1 = 4.60638 loss)
I0401 15:46:23.412864 25640 sgd_solver.cpp:105] Iteration 2760, lr = 0.001
I0401 15:46:28.312286 25640 solver.cpp:218] Iteration 2772 (2.44927 iter/s, 4.89941s/12 iters), loss = 4.49408
I0401 15:46:28.312342 25640 solver.cpp:237]     Train net output #0: loss = 4.49408 (* 1 = 4.49408 loss)
I0401 15:46:28.312350 25640 sgd_solver.cpp:105] Iteration 2772, lr = 0.001
I0401 15:46:33.652773 25640 solver.cpp:218] Iteration 2784 (2.24702 iter/s, 5.34042s/12 iters), loss = 4.51777
I0401 15:46:33.652827 25640 solver.cpp:237]     Train net output #0: loss = 4.51777 (* 1 = 4.51777 loss)
I0401 15:46:33.652834 25640 sgd_solver.cpp:105] Iteration 2784, lr = 0.001
I0401 15:46:38.911590 25640 solver.cpp:218] Iteration 2796 (2.28191 iter/s, 5.25875s/12 iters), loss = 4.59206
I0401 15:46:38.911717 25640 solver.cpp:237]     Train net output #0: loss = 4.59206 (* 1 = 4.59206 loss)
I0401 15:46:38.911725 25640 sgd_solver.cpp:105] Iteration 2796, lr = 0.001
I0401 15:46:44.364617 25640 solver.cpp:218] Iteration 2808 (2.20067 iter/s, 5.45289s/12 iters), loss = 4.58779
I0401 15:46:44.364660 25640 solver.cpp:237]     Train net output #0: loss = 4.58779 (* 1 = 4.58779 loss)
I0401 15:46:44.364665 25640 sgd_solver.cpp:105] Iteration 2808, lr = 0.001
I0401 15:46:49.660917 25640 solver.cpp:218] Iteration 2820 (2.26576 iter/s, 5.29623s/12 iters), loss = 4.43453
I0401 15:46:49.660969 25640 solver.cpp:237]     Train net output #0: loss = 4.43453 (* 1 = 4.43453 loss)
I0401 15:46:49.660979 25640 sgd_solver.cpp:105] Iteration 2820, lr = 0.001
I0401 15:46:54.591853 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:46:54.921049 25640 solver.cpp:218] Iteration 2832 (2.28134 iter/s, 5.26006s/12 iters), loss = 4.55355
I0401 15:46:54.921097 25640 solver.cpp:237]     Train net output #0: loss = 4.55355 (* 1 = 4.55355 loss)
I0401 15:46:54.921103 25640 sgd_solver.cpp:105] Iteration 2832, lr = 0.001
I0401 15:47:00.000689 25640 solver.cpp:218] Iteration 2844 (2.3624 iter/s, 5.07957s/12 iters), loss = 4.47852
I0401 15:47:00.000746 25640 solver.cpp:237]     Train net output #0: loss = 4.47852 (* 1 = 4.47852 loss)
I0401 15:47:00.000756 25640 sgd_solver.cpp:105] Iteration 2844, lr = 0.001
I0401 15:47:04.743441 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0401 15:47:08.941546 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0401 15:47:11.244453 25640 solver.cpp:330] Iteration 2856, Testing net (#0)
I0401 15:47:11.244472 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:47:14.488540 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:47:15.603137 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0686275
I0401 15:47:15.603175 25640 solver.cpp:397]     Test net output #1: loss = 4.61232 (* 1 = 4.61232 loss)
I0401 15:47:15.749704 25640 solver.cpp:218] Iteration 2856 (0.761956 iter/s, 15.7489s/12 iters), loss = 4.45477
I0401 15:47:15.751296 25640 solver.cpp:237]     Train net output #0: loss = 4.45477 (* 1 = 4.45477 loss)
I0401 15:47:15.751309 25640 sgd_solver.cpp:105] Iteration 2856, lr = 0.001
I0401 15:47:20.036932 25640 solver.cpp:218] Iteration 2868 (2.80006 iter/s, 4.28563s/12 iters), loss = 4.48935
I0401 15:47:20.036981 25640 solver.cpp:237]     Train net output #0: loss = 4.48935 (* 1 = 4.48935 loss)
I0401 15:47:20.036989 25640 sgd_solver.cpp:105] Iteration 2868, lr = 0.001
I0401 15:47:25.225037 25640 solver.cpp:218] Iteration 2880 (2.31301 iter/s, 5.18804s/12 iters), loss = 4.43439
I0401 15:47:25.225101 25640 solver.cpp:237]     Train net output #0: loss = 4.43439 (* 1 = 4.43439 loss)
I0401 15:47:25.225111 25640 sgd_solver.cpp:105] Iteration 2880, lr = 0.001
I0401 15:47:30.635933 25640 solver.cpp:218] Iteration 2892 (2.21778 iter/s, 5.41082s/12 iters), loss = 4.67608
I0401 15:47:30.635974 25640 solver.cpp:237]     Train net output #0: loss = 4.67608 (* 1 = 4.67608 loss)
I0401 15:47:30.635980 25640 sgd_solver.cpp:105] Iteration 2892, lr = 0.001
I0401 15:47:35.955037 25640 solver.cpp:218] Iteration 2904 (2.25604 iter/s, 5.31905s/12 iters), loss = 4.34895
I0401 15:47:35.955081 25640 solver.cpp:237]     Train net output #0: loss = 4.34895 (* 1 = 4.34895 loss)
I0401 15:47:35.955088 25640 sgd_solver.cpp:105] Iteration 2904, lr = 0.001
I0401 15:47:41.310287 25640 solver.cpp:218] Iteration 2916 (2.24082 iter/s, 5.35518s/12 iters), loss = 4.45486
I0401 15:47:41.310397 25640 solver.cpp:237]     Train net output #0: loss = 4.45486 (* 1 = 4.45486 loss)
I0401 15:47:41.310405 25640 sgd_solver.cpp:105] Iteration 2916, lr = 0.001
I0401 15:47:46.344524 25640 solver.cpp:218] Iteration 2928 (2.38374 iter/s, 5.03411s/12 iters), loss = 4.57725
I0401 15:47:46.344581 25640 solver.cpp:237]     Train net output #0: loss = 4.57725 (* 1 = 4.57725 loss)
I0401 15:47:46.344589 25640 sgd_solver.cpp:105] Iteration 2928, lr = 0.001
I0401 15:47:48.201737 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:47:51.407522 25640 solver.cpp:218] Iteration 2940 (2.37017 iter/s, 5.06293s/12 iters), loss = 4.37614
I0401 15:47:51.407568 25640 solver.cpp:237]     Train net output #0: loss = 4.37614 (* 1 = 4.37614 loss)
I0401 15:47:51.407575 25640 sgd_solver.cpp:105] Iteration 2940, lr = 0.001
I0401 15:47:56.506170 25640 solver.cpp:218] Iteration 2952 (2.35359 iter/s, 5.09859s/12 iters), loss = 4.49898
I0401 15:47:56.506215 25640 solver.cpp:237]     Train net output #0: loss = 4.49898 (* 1 = 4.49898 loss)
I0401 15:47:56.506223 25640 sgd_solver.cpp:105] Iteration 2952, lr = 0.001
I0401 15:47:58.655465 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0401 15:48:01.662752 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0401 15:48:03.959859 25640 solver.cpp:330] Iteration 2958, Testing net (#0)
I0401 15:48:03.959879 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:48:07.140815 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:48:08.318223 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0710784
I0401 15:48:08.318260 25640 solver.cpp:397]     Test net output #1: loss = 4.59043 (* 1 = 4.59043 loss)
I0401 15:48:10.188254 25640 solver.cpp:218] Iteration 2964 (0.877063 iter/s, 13.682s/12 iters), loss = 4.44647
I0401 15:48:10.188303 25640 solver.cpp:237]     Train net output #0: loss = 4.44647 (* 1 = 4.44647 loss)
I0401 15:48:10.188308 25640 sgd_solver.cpp:105] Iteration 2964, lr = 0.001
I0401 15:48:15.528980 25640 solver.cpp:218] Iteration 2976 (2.24691 iter/s, 5.34066s/12 iters), loss = 4.34312
I0401 15:48:15.529111 25640 solver.cpp:237]     Train net output #0: loss = 4.34312 (* 1 = 4.34312 loss)
I0401 15:48:15.529119 25640 sgd_solver.cpp:105] Iteration 2976, lr = 0.001
I0401 15:48:20.864810 25640 solver.cpp:218] Iteration 2988 (2.24901 iter/s, 5.33569s/12 iters), loss = 4.65059
I0401 15:48:20.864854 25640 solver.cpp:237]     Train net output #0: loss = 4.65059 (* 1 = 4.65059 loss)
I0401 15:48:20.864861 25640 sgd_solver.cpp:105] Iteration 2988, lr = 0.001
I0401 15:48:26.145764 25640 solver.cpp:218] Iteration 3000 (2.27234 iter/s, 5.2809s/12 iters), loss = 4.5796
I0401 15:48:26.145807 25640 solver.cpp:237]     Train net output #0: loss = 4.5796 (* 1 = 4.5796 loss)
I0401 15:48:26.145812 25640 sgd_solver.cpp:105] Iteration 3000, lr = 0.001
I0401 15:48:31.165482 25640 solver.cpp:218] Iteration 3012 (2.3906 iter/s, 5.01966s/12 iters), loss = 4.5377
I0401 15:48:31.165522 25640 solver.cpp:237]     Train net output #0: loss = 4.5377 (* 1 = 4.5377 loss)
I0401 15:48:31.165527 25640 sgd_solver.cpp:105] Iteration 3012, lr = 0.001
I0401 15:48:36.469166 25640 solver.cpp:218] Iteration 3024 (2.2626 iter/s, 5.30363s/12 iters), loss = 4.32229
I0401 15:48:36.469204 25640 solver.cpp:237]     Train net output #0: loss = 4.32229 (* 1 = 4.32229 loss)
I0401 15:48:36.469209 25640 sgd_solver.cpp:105] Iteration 3024, lr = 0.001
I0401 15:48:40.578033 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:48:41.739406 25640 solver.cpp:218] Iteration 3036 (2.27696 iter/s, 5.27019s/12 iters), loss = 4.31936
I0401 15:48:41.739450 25640 solver.cpp:237]     Train net output #0: loss = 4.31936 (* 1 = 4.31936 loss)
I0401 15:48:41.739456 25640 sgd_solver.cpp:105] Iteration 3036, lr = 0.001
I0401 15:48:47.112766 25640 solver.cpp:218] Iteration 3048 (2.23326 iter/s, 5.37331s/12 iters), loss = 4.44429
I0401 15:48:47.112874 25640 solver.cpp:237]     Train net output #0: loss = 4.44429 (* 1 = 4.44429 loss)
I0401 15:48:47.112880 25640 sgd_solver.cpp:105] Iteration 3048, lr = 0.001
I0401 15:48:51.781193 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0401 15:48:54.858340 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0401 15:48:57.167632 25640 solver.cpp:330] Iteration 3060, Testing net (#0)
I0401 15:48:57.167655 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:49:00.262493 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:49:01.460944 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0716912
I0401 15:49:01.460984 25640 solver.cpp:397]     Test net output #1: loss = 4.55894 (* 1 = 4.55894 loss)
I0401 15:49:01.602357 25640 solver.cpp:218] Iteration 3060 (0.828187 iter/s, 14.4895s/12 iters), loss = 4.47119
I0401 15:49:01.602401 25640 solver.cpp:237]     Train net output #0: loss = 4.47119 (* 1 = 4.47119 loss)
I0401 15:49:01.602406 25640 sgd_solver.cpp:105] Iteration 3060, lr = 0.001
I0401 15:49:05.859172 25640 solver.cpp:218] Iteration 3072 (2.81905 iter/s, 4.25676s/12 iters), loss = 4.47241
I0401 15:49:05.859217 25640 solver.cpp:237]     Train net output #0: loss = 4.47241 (* 1 = 4.47241 loss)
I0401 15:49:05.859223 25640 sgd_solver.cpp:105] Iteration 3072, lr = 0.001
I0401 15:49:11.033841 25640 solver.cpp:218] Iteration 3084 (2.31902 iter/s, 5.1746s/12 iters), loss = 4.37571
I0401 15:49:11.033890 25640 solver.cpp:237]     Train net output #0: loss = 4.37571 (* 1 = 4.37571 loss)
I0401 15:49:11.033898 25640 sgd_solver.cpp:105] Iteration 3084, lr = 0.001
I0401 15:49:16.225430 25640 solver.cpp:218] Iteration 3096 (2.31146 iter/s, 5.19152s/12 iters), loss = 4.2455
I0401 15:49:16.225495 25640 solver.cpp:237]     Train net output #0: loss = 4.2455 (* 1 = 4.2455 loss)
I0401 15:49:16.225504 25640 sgd_solver.cpp:105] Iteration 3096, lr = 0.001
I0401 15:49:21.608922 25640 solver.cpp:218] Iteration 3108 (2.22907 iter/s, 5.38341s/12 iters), loss = 4.34011
I0401 15:49:21.609067 25640 solver.cpp:237]     Train net output #0: loss = 4.34011 (* 1 = 4.34011 loss)
I0401 15:49:21.609077 25640 sgd_solver.cpp:105] Iteration 3108, lr = 0.001
I0401 15:49:26.754045 25640 solver.cpp:218] Iteration 3120 (2.33238 iter/s, 5.14497s/12 iters), loss = 4.36698
I0401 15:49:26.754101 25640 solver.cpp:237]     Train net output #0: loss = 4.36698 (* 1 = 4.36698 loss)
I0401 15:49:26.754110 25640 sgd_solver.cpp:105] Iteration 3120, lr = 0.001
I0401 15:49:31.986538 25640 solver.cpp:218] Iteration 3132 (2.29339 iter/s, 5.23242s/12 iters), loss = 4.52437
I0401 15:49:31.986584 25640 solver.cpp:237]     Train net output #0: loss = 4.52437 (* 1 = 4.52437 loss)
I0401 15:49:31.986591 25640 sgd_solver.cpp:105] Iteration 3132, lr = 0.001
I0401 15:49:33.031034 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:49:37.226706 25640 solver.cpp:218] Iteration 3144 (2.29003 iter/s, 5.2401s/12 iters), loss = 4.45393
I0401 15:49:37.226761 25640 solver.cpp:237]     Train net output #0: loss = 4.45393 (* 1 = 4.45393 loss)
I0401 15:49:37.226770 25640 sgd_solver.cpp:105] Iteration 3144, lr = 0.001
I0401 15:49:42.427692 25640 solver.cpp:218] Iteration 3156 (2.30729 iter/s, 5.20092s/12 iters), loss = 4.55662
I0401 15:49:42.427747 25640 solver.cpp:237]     Train net output #0: loss = 4.55662 (* 1 = 4.55662 loss)
I0401 15:49:42.427753 25640 sgd_solver.cpp:105] Iteration 3156, lr = 0.001
I0401 15:49:44.454885 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0401 15:49:47.527107 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0401 15:49:49.840363 25640 solver.cpp:330] Iteration 3162, Testing net (#0)
I0401 15:49:49.840384 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:49:52.982050 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:49:54.231554 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0753676
I0401 15:49:54.231593 25640 solver.cpp:397]     Test net output #1: loss = 4.5678 (* 1 = 4.5678 loss)
I0401 15:49:56.144290 25640 solver.cpp:218] Iteration 3168 (0.874856 iter/s, 13.7165s/12 iters), loss = 4.422
I0401 15:49:56.144331 25640 solver.cpp:237]     Train net output #0: loss = 4.422 (* 1 = 4.422 loss)
I0401 15:49:56.144336 25640 sgd_solver.cpp:105] Iteration 3168, lr = 0.001
I0401 15:50:01.358505 25640 solver.cpp:218] Iteration 3180 (2.30143 iter/s, 5.21415s/12 iters), loss = 4.3382
I0401 15:50:01.358559 25640 solver.cpp:237]     Train net output #0: loss = 4.3382 (* 1 = 4.3382 loss)
I0401 15:50:01.358566 25640 sgd_solver.cpp:105] Iteration 3180, lr = 0.001
I0401 15:50:06.458303 25640 solver.cpp:218] Iteration 3192 (2.35307 iter/s, 5.09973s/12 iters), loss = 4.32961
I0401 15:50:06.458359 25640 solver.cpp:237]     Train net output #0: loss = 4.32961 (* 1 = 4.32961 loss)
I0401 15:50:06.458369 25640 sgd_solver.cpp:105] Iteration 3192, lr = 0.001
I0401 15:50:11.509354 25640 solver.cpp:218] Iteration 3204 (2.37578 iter/s, 5.05098s/12 iters), loss = 4.37367
I0401 15:50:11.509395 25640 solver.cpp:237]     Train net output #0: loss = 4.37367 (* 1 = 4.37367 loss)
I0401 15:50:11.509402 25640 sgd_solver.cpp:105] Iteration 3204, lr = 0.001
I0401 15:50:16.875497 25640 solver.cpp:218] Iteration 3216 (2.23626 iter/s, 5.36609s/12 iters), loss = 4.20848
I0401 15:50:16.875535 25640 solver.cpp:237]     Train net output #0: loss = 4.20848 (* 1 = 4.20848 loss)
I0401 15:50:16.875541 25640 sgd_solver.cpp:105] Iteration 3216, lr = 0.001
I0401 15:50:22.065497 25640 solver.cpp:218] Iteration 3228 (2.31216 iter/s, 5.18994s/12 iters), loss = 4.23321
I0401 15:50:22.065539 25640 solver.cpp:237]     Train net output #0: loss = 4.23321 (* 1 = 4.23321 loss)
I0401 15:50:22.065546 25640 sgd_solver.cpp:105] Iteration 3228, lr = 0.001
I0401 15:50:25.407778 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:50:27.347954 25640 solver.cpp:218] Iteration 3240 (2.2717 iter/s, 5.28239s/12 iters), loss = 4.23894
I0401 15:50:27.348014 25640 solver.cpp:237]     Train net output #0: loss = 4.23894 (* 1 = 4.23894 loss)
I0401 15:50:27.348026 25640 sgd_solver.cpp:105] Iteration 3240, lr = 0.001
I0401 15:50:32.697366 25640 solver.cpp:218] Iteration 3252 (2.24327 iter/s, 5.34934s/12 iters), loss = 4.23386
I0401 15:50:32.697412 25640 solver.cpp:237]     Train net output #0: loss = 4.23386 (* 1 = 4.23386 loss)
I0401 15:50:32.697419 25640 sgd_solver.cpp:105] Iteration 3252, lr = 0.001
I0401 15:50:37.388154 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0401 15:50:40.535434 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0401 15:50:42.841857 25640 solver.cpp:330] Iteration 3264, Testing net (#0)
I0401 15:50:42.841878 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:50:45.864650 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:50:47.139469 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0741422
I0401 15:50:47.139498 25640 solver.cpp:397]     Test net output #1: loss = 4.53969 (* 1 = 4.53969 loss)
I0401 15:50:47.280732 25640 solver.cpp:218] Iteration 3264 (0.822858 iter/s, 14.5833s/12 iters), loss = 4.46897
I0401 15:50:47.280778 25640 solver.cpp:237]     Train net output #0: loss = 4.46897 (* 1 = 4.46897 loss)
I0401 15:50:47.280784 25640 sgd_solver.cpp:105] Iteration 3264, lr = 0.001
I0401 15:50:51.570626 25640 solver.cpp:218] Iteration 3276 (2.79731 iter/s, 4.28983s/12 iters), loss = 4.24009
I0401 15:50:51.570672 25640 solver.cpp:237]     Train net output #0: loss = 4.24009 (* 1 = 4.24009 loss)
I0401 15:50:51.570677 25640 sgd_solver.cpp:105] Iteration 3276, lr = 0.001
I0401 15:50:56.759397 25640 solver.cpp:218] Iteration 3288 (2.31272 iter/s, 5.18871s/12 iters), loss = 4.36024
I0401 15:50:56.759524 25640 solver.cpp:237]     Train net output #0: loss = 4.36024 (* 1 = 4.36024 loss)
I0401 15:50:56.759534 25640 sgd_solver.cpp:105] Iteration 3288, lr = 0.001
I0401 15:51:02.021688 25640 solver.cpp:218] Iteration 3300 (2.28043 iter/s, 5.26216s/12 iters), loss = 4.39501
I0401 15:51:02.021734 25640 solver.cpp:237]     Train net output #0: loss = 4.39501 (* 1 = 4.39501 loss)
I0401 15:51:02.021744 25640 sgd_solver.cpp:105] Iteration 3300, lr = 0.001
I0401 15:51:07.046988 25640 solver.cpp:218] Iteration 3312 (2.38795 iter/s, 5.02524s/12 iters), loss = 4.26317
I0401 15:51:07.053206 25640 solver.cpp:237]     Train net output #0: loss = 4.26317 (* 1 = 4.26317 loss)
I0401 15:51:07.053218 25640 sgd_solver.cpp:105] Iteration 3312, lr = 0.001
I0401 15:51:12.551767 25640 solver.cpp:218] Iteration 3324 (2.18239 iter/s, 5.49855s/12 iters), loss = 4.32463
I0401 15:51:12.551808 25640 solver.cpp:237]     Train net output #0: loss = 4.32463 (* 1 = 4.32463 loss)
I0401 15:51:12.551813 25640 sgd_solver.cpp:105] Iteration 3324, lr = 0.001
I0401 15:51:17.799876 25640 solver.cpp:218] Iteration 3336 (2.28656 iter/s, 5.24805s/12 iters), loss = 4.16779
I0401 15:51:17.799921 25640 solver.cpp:237]     Train net output #0: loss = 4.16779 (* 1 = 4.16779 loss)
I0401 15:51:17.799927 25640 sgd_solver.cpp:105] Iteration 3336, lr = 0.001
I0401 15:51:18.249343 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:51:23.090728 25640 solver.cpp:218] Iteration 3348 (2.26809 iter/s, 5.29079s/12 iters), loss = 4.21716
I0401 15:51:23.090772 25640 solver.cpp:237]     Train net output #0: loss = 4.21716 (* 1 = 4.21716 loss)
I0401 15:51:23.090778 25640 sgd_solver.cpp:105] Iteration 3348, lr = 0.001
I0401 15:51:27.999768 25640 solver.cpp:218] Iteration 3360 (2.4445 iter/s, 4.90897s/12 iters), loss = 4.30423
I0401 15:51:27.999908 25640 solver.cpp:237]     Train net output #0: loss = 4.30423 (* 1 = 4.30423 loss)
I0401 15:51:27.999917 25640 sgd_solver.cpp:105] Iteration 3360, lr = 0.001
I0401 15:51:30.343727 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0401 15:51:33.527125 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0401 15:51:35.930167 25640 solver.cpp:330] Iteration 3366, Testing net (#0)
I0401 15:51:35.930187 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:51:39.023674 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:51:40.364822 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0814951
I0401 15:51:40.364859 25640 solver.cpp:397]     Test net output #1: loss = 4.43174 (* 1 = 4.43174 loss)
I0401 15:51:42.315016 25640 solver.cpp:218] Iteration 3372 (0.838276 iter/s, 14.3151s/12 iters), loss = 4.26013
I0401 15:51:42.315076 25640 solver.cpp:237]     Train net output #0: loss = 4.26013 (* 1 = 4.26013 loss)
I0401 15:51:42.315085 25640 sgd_solver.cpp:105] Iteration 3372, lr = 0.001
I0401 15:51:47.597818 25640 solver.cpp:218] Iteration 3384 (2.27155 iter/s, 5.28273s/12 iters), loss = 4.30496
I0401 15:51:47.597859 25640 solver.cpp:237]     Train net output #0: loss = 4.30496 (* 1 = 4.30496 loss)
I0401 15:51:47.597864 25640 sgd_solver.cpp:105] Iteration 3384, lr = 0.001
I0401 15:51:52.944276 25640 solver.cpp:218] Iteration 3396 (2.2445 iter/s, 5.3464s/12 iters), loss = 4.27433
I0401 15:51:52.944325 25640 solver.cpp:237]     Train net output #0: loss = 4.27433 (* 1 = 4.27433 loss)
I0401 15:51:52.944334 25640 sgd_solver.cpp:105] Iteration 3396, lr = 0.001
I0401 15:51:58.024802 25640 solver.cpp:218] Iteration 3408 (2.36199 iter/s, 5.08046s/12 iters), loss = 3.9597
I0401 15:51:58.024921 25640 solver.cpp:237]     Train net output #0: loss = 3.9597 (* 1 = 3.9597 loss)
I0401 15:51:58.024930 25640 sgd_solver.cpp:105] Iteration 3408, lr = 0.001
I0401 15:52:03.473249 25640 solver.cpp:218] Iteration 3420 (2.20252 iter/s, 5.44831s/12 iters), loss = 4.35971
I0401 15:52:03.473301 25640 solver.cpp:237]     Train net output #0: loss = 4.35971 (* 1 = 4.35971 loss)
I0401 15:52:03.473310 25640 sgd_solver.cpp:105] Iteration 3420, lr = 0.001
I0401 15:52:08.761442 25640 solver.cpp:218] Iteration 3432 (2.26924 iter/s, 5.28813s/12 iters), loss = 4.33758
I0401 15:52:08.761485 25640 solver.cpp:237]     Train net output #0: loss = 4.33758 (* 1 = 4.33758 loss)
I0401 15:52:08.761492 25640 sgd_solver.cpp:105] Iteration 3432, lr = 0.001
I0401 15:52:11.628330 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:52:14.174311 25640 solver.cpp:218] Iteration 3444 (2.21696 iter/s, 5.41281s/12 iters), loss = 4.33036
I0401 15:52:14.174362 25640 solver.cpp:237]     Train net output #0: loss = 4.33036 (* 1 = 4.33036 loss)
I0401 15:52:14.174371 25640 sgd_solver.cpp:105] Iteration 3444, lr = 0.001
I0401 15:52:19.303668 25640 solver.cpp:218] Iteration 3456 (2.3395 iter/s, 5.12929s/12 iters), loss = 4.19992
I0401 15:52:19.303705 25640 solver.cpp:237]     Train net output #0: loss = 4.19992 (* 1 = 4.19992 loss)
I0401 15:52:19.303711 25640 sgd_solver.cpp:105] Iteration 3456, lr = 0.001
I0401 15:52:24.234236 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0401 15:52:27.266973 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0401 15:52:29.571087 25640 solver.cpp:330] Iteration 3468, Testing net (#0)
I0401 15:52:29.571146 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:52:29.974045 25640 blocking_queue.cpp:49] Waiting for data
I0401 15:52:32.587553 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:52:34.053607 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0833333
I0401 15:52:34.053644 25640 solver.cpp:397]     Test net output #1: loss = 4.39775 (* 1 = 4.39775 loss)
I0401 15:52:34.195020 25640 solver.cpp:218] Iteration 3468 (0.805839 iter/s, 14.8913s/12 iters), loss = 4.2264
I0401 15:52:34.195078 25640 solver.cpp:237]     Train net output #0: loss = 4.2264 (* 1 = 4.2264 loss)
I0401 15:52:34.195088 25640 sgd_solver.cpp:105] Iteration 3468, lr = 0.001
I0401 15:52:38.367307 25640 solver.cpp:218] Iteration 3480 (2.87617 iter/s, 4.17222s/12 iters), loss = 4.22656
I0401 15:52:38.367352 25640 solver.cpp:237]     Train net output #0: loss = 4.22656 (* 1 = 4.22656 loss)
I0401 15:52:38.367358 25640 sgd_solver.cpp:105] Iteration 3480, lr = 0.001
I0401 15:52:43.748991 25640 solver.cpp:218] Iteration 3492 (2.22981 iter/s, 5.38162s/12 iters), loss = 4.17539
I0401 15:52:43.749039 25640 solver.cpp:237]     Train net output #0: loss = 4.17539 (* 1 = 4.17539 loss)
I0401 15:52:43.749047 25640 sgd_solver.cpp:105] Iteration 3492, lr = 0.001
I0401 15:52:48.846067 25640 solver.cpp:218] Iteration 3504 (2.35432 iter/s, 5.09701s/12 iters), loss = 4.17939
I0401 15:52:48.846105 25640 solver.cpp:237]     Train net output #0: loss = 4.17939 (* 1 = 4.17939 loss)
I0401 15:52:48.846112 25640 sgd_solver.cpp:105] Iteration 3504, lr = 0.001
I0401 15:52:54.203984 25640 solver.cpp:218] Iteration 3516 (2.2397 iter/s, 5.35786s/12 iters), loss = 4.00559
I0401 15:52:54.204032 25640 solver.cpp:237]     Train net output #0: loss = 4.00559 (* 1 = 4.00559 loss)
I0401 15:52:54.204039 25640 sgd_solver.cpp:105] Iteration 3516, lr = 0.001
I0401 15:52:59.584234 25640 solver.cpp:218] Iteration 3528 (2.23041 iter/s, 5.38019s/12 iters), loss = 4.07761
I0401 15:52:59.584394 25640 solver.cpp:237]     Train net output #0: loss = 4.07761 (* 1 = 4.07761 loss)
I0401 15:52:59.584403 25640 sgd_solver.cpp:105] Iteration 3528, lr = 0.001
I0401 15:53:04.724833 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:53:05.027083 25640 solver.cpp:218] Iteration 3540 (2.2048 iter/s, 5.44268s/12 iters), loss = 4.21581
I0401 15:53:05.027125 25640 solver.cpp:237]     Train net output #0: loss = 4.21581 (* 1 = 4.21581 loss)
I0401 15:53:05.027130 25640 sgd_solver.cpp:105] Iteration 3540, lr = 0.001
I0401 15:53:10.250696 25640 solver.cpp:218] Iteration 3552 (2.29729 iter/s, 5.22355s/12 iters), loss = 4.25029
I0401 15:53:10.250758 25640 solver.cpp:237]     Train net output #0: loss = 4.25029 (* 1 = 4.25029 loss)
I0401 15:53:10.250771 25640 sgd_solver.cpp:105] Iteration 3552, lr = 0.001
I0401 15:53:15.407300 25640 solver.cpp:218] Iteration 3564 (2.32715 iter/s, 5.15653s/12 iters), loss = 3.98969
I0401 15:53:15.407349 25640 solver.cpp:237]     Train net output #0: loss = 3.98969 (* 1 = 3.98969 loss)
I0401 15:53:15.407356 25640 sgd_solver.cpp:105] Iteration 3564, lr = 0.001
I0401 15:53:17.536392 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0401 15:53:20.603549 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0401 15:53:22.926187 25640 solver.cpp:330] Iteration 3570, Testing net (#0)
I0401 15:53:22.926211 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:53:26.135753 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:53:27.608490 25640 solver.cpp:397]     Test net output #0: accuracy = 0.098652
I0401 15:53:27.608522 25640 solver.cpp:397]     Test net output #1: loss = 4.35518 (* 1 = 4.35518 loss)
I0401 15:53:29.556875 25640 solver.cpp:218] Iteration 3576 (0.848086 iter/s, 14.1495s/12 iters), loss = 4.0573
I0401 15:53:29.556939 25640 solver.cpp:237]     Train net output #0: loss = 4.0573 (* 1 = 4.0573 loss)
I0401 15:53:29.556948 25640 sgd_solver.cpp:105] Iteration 3576, lr = 0.001
I0401 15:53:34.933887 25640 solver.cpp:218] Iteration 3588 (2.23175 iter/s, 5.37694s/12 iters), loss = 4.10888
I0401 15:53:34.933991 25640 solver.cpp:237]     Train net output #0: loss = 4.10888 (* 1 = 4.10888 loss)
I0401 15:53:34.933998 25640 sgd_solver.cpp:105] Iteration 3588, lr = 0.001
I0401 15:53:40.256567 25640 solver.cpp:218] Iteration 3600 (2.25455 iter/s, 5.32256s/12 iters), loss = 4.16983
I0401 15:53:40.256609 25640 solver.cpp:237]     Train net output #0: loss = 4.16983 (* 1 = 4.16983 loss)
I0401 15:53:40.256614 25640 sgd_solver.cpp:105] Iteration 3600, lr = 0.001
I0401 15:53:45.534502 25640 solver.cpp:218] Iteration 3612 (2.27364 iter/s, 5.27787s/12 iters), loss = 3.91593
I0401 15:53:45.534559 25640 solver.cpp:237]     Train net output #0: loss = 3.91593 (* 1 = 3.91593 loss)
I0401 15:53:45.534567 25640 sgd_solver.cpp:105] Iteration 3612, lr = 0.001
I0401 15:53:50.984827 25640 solver.cpp:218] Iteration 3624 (2.20173 iter/s, 5.45025s/12 iters), loss = 3.95361
I0401 15:53:50.985455 25640 solver.cpp:237]     Train net output #0: loss = 3.95361 (* 1 = 3.95361 loss)
I0401 15:53:50.985466 25640 sgd_solver.cpp:105] Iteration 3624, lr = 0.001
I0401 15:53:56.553578 25640 solver.cpp:218] Iteration 3636 (2.15491 iter/s, 5.56868s/12 iters), loss = 3.96427
I0401 15:53:56.553617 25640 solver.cpp:237]     Train net output #0: loss = 3.96427 (* 1 = 3.96427 loss)
I0401 15:53:56.553622 25640 sgd_solver.cpp:105] Iteration 3636, lr = 0.001
I0401 15:53:58.471359 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:54:01.798465 25640 solver.cpp:218] Iteration 3648 (2.28797 iter/s, 5.24483s/12 iters), loss = 3.87604
I0401 15:54:01.798512 25640 solver.cpp:237]     Train net output #0: loss = 3.87604 (* 1 = 3.87604 loss)
I0401 15:54:01.798518 25640 sgd_solver.cpp:105] Iteration 3648, lr = 0.001
I0401 15:54:06.739159 25640 solver.cpp:218] Iteration 3660 (2.42884 iter/s, 4.94063s/12 iters), loss = 4.26628
I0401 15:54:06.739279 25640 solver.cpp:237]     Train net output #0: loss = 4.26628 (* 1 = 4.26628 loss)
I0401 15:54:06.739286 25640 sgd_solver.cpp:105] Iteration 3660, lr = 0.001
I0401 15:54:11.609144 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0401 15:54:14.623294 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0401 15:54:16.966886 25640 solver.cpp:330] Iteration 3672, Testing net (#0)
I0401 15:54:16.966913 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:54:20.014686 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:54:21.530658 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0925245
I0401 15:54:21.530694 25640 solver.cpp:397]     Test net output #1: loss = 4.36117 (* 1 = 4.36117 loss)
I0401 15:54:21.668038 25640 solver.cpp:218] Iteration 3672 (0.803818 iter/s, 14.9288s/12 iters), loss = 3.95418
I0401 15:54:21.668093 25640 solver.cpp:237]     Train net output #0: loss = 3.95418 (* 1 = 3.95418 loss)
I0401 15:54:21.668102 25640 sgd_solver.cpp:105] Iteration 3672, lr = 0.001
I0401 15:54:26.412652 25640 solver.cpp:218] Iteration 3684 (2.52923 iter/s, 4.74453s/12 iters), loss = 4.14389
I0401 15:54:26.412711 25640 solver.cpp:237]     Train net output #0: loss = 4.14389 (* 1 = 4.14389 loss)
I0401 15:54:26.412725 25640 sgd_solver.cpp:105] Iteration 3684, lr = 0.001
I0401 15:54:31.480568 25640 solver.cpp:218] Iteration 3696 (2.36787 iter/s, 5.06785s/12 iters), loss = 4.06827
I0401 15:54:31.480614 25640 solver.cpp:237]     Train net output #0: loss = 4.06827 (* 1 = 4.06827 loss)
I0401 15:54:31.480620 25640 sgd_solver.cpp:105] Iteration 3696, lr = 0.001
I0401 15:54:36.965291 25640 solver.cpp:218] Iteration 3708 (2.18792 iter/s, 5.48466s/12 iters), loss = 4.14952
I0401 15:54:36.967527 25640 solver.cpp:237]     Train net output #0: loss = 4.14952 (* 1 = 4.14952 loss)
I0401 15:54:36.967540 25640 sgd_solver.cpp:105] Iteration 3708, lr = 0.001
I0401 15:54:42.449856 25640 solver.cpp:218] Iteration 3720 (2.18885 iter/s, 5.48233s/12 iters), loss = 4.03297
I0401 15:54:42.449904 25640 solver.cpp:237]     Train net output #0: loss = 4.03297 (* 1 = 4.03297 loss)
I0401 15:54:42.449910 25640 sgd_solver.cpp:105] Iteration 3720, lr = 0.001
I0401 15:54:47.855690 25640 solver.cpp:218] Iteration 3732 (2.21985 iter/s, 5.40577s/12 iters), loss = 3.90951
I0401 15:54:47.855746 25640 solver.cpp:237]     Train net output #0: loss = 3.90951 (* 1 = 3.90951 loss)
I0401 15:54:47.855753 25640 sgd_solver.cpp:105] Iteration 3732, lr = 0.001
I0401 15:54:51.905125 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:54:53.019886 25640 solver.cpp:218] Iteration 3744 (2.32373 iter/s, 5.16412s/12 iters), loss = 3.98498
I0401 15:54:53.019948 25640 solver.cpp:237]     Train net output #0: loss = 3.98498 (* 1 = 3.98498 loss)
I0401 15:54:53.019956 25640 sgd_solver.cpp:105] Iteration 3744, lr = 0.001
I0401 15:54:58.066854 25640 solver.cpp:218] Iteration 3756 (2.3777 iter/s, 5.04689s/12 iters), loss = 4.15591
I0401 15:54:58.066916 25640 solver.cpp:237]     Train net output #0: loss = 4.15591 (* 1 = 4.15591 loss)
I0401 15:54:58.066926 25640 sgd_solver.cpp:105] Iteration 3756, lr = 0.001
I0401 15:55:03.497288 25640 solver.cpp:218] Iteration 3768 (2.2098 iter/s, 5.43036s/12 iters), loss = 3.98674
I0401 15:55:03.497332 25640 solver.cpp:237]     Train net output #0: loss = 3.98674 (* 1 = 3.98674 loss)
I0401 15:55:03.497339 25640 sgd_solver.cpp:105] Iteration 3768, lr = 0.001
I0401 15:55:05.513677 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0401 15:55:08.548760 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0401 15:55:10.876498 25640 solver.cpp:330] Iteration 3774, Testing net (#0)
I0401 15:55:10.876519 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:55:14.045455 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:55:15.656327 25640 solver.cpp:397]     Test net output #0: accuracy = 0.0968137
I0401 15:55:15.656363 25640 solver.cpp:397]     Test net output #1: loss = 4.282 (* 1 = 4.282 loss)
I0401 15:55:17.588948 25640 solver.cpp:218] Iteration 3780 (0.851571 iter/s, 14.0916s/12 iters), loss = 3.98795
I0401 15:55:17.588994 25640 solver.cpp:237]     Train net output #0: loss = 3.98795 (* 1 = 3.98795 loss)
I0401 15:55:17.588999 25640 sgd_solver.cpp:105] Iteration 3780, lr = 0.001
I0401 15:55:22.795231 25640 solver.cpp:218] Iteration 3792 (2.30493 iter/s, 5.20622s/12 iters), loss = 3.98634
I0401 15:55:22.795279 25640 solver.cpp:237]     Train net output #0: loss = 3.98634 (* 1 = 3.98634 loss)
I0401 15:55:22.795285 25640 sgd_solver.cpp:105] Iteration 3792, lr = 0.001
I0401 15:55:28.114431 25640 solver.cpp:218] Iteration 3804 (2.256 iter/s, 5.31914s/12 iters), loss = 3.88275
I0401 15:55:28.114466 25640 solver.cpp:237]     Train net output #0: loss = 3.88275 (* 1 = 3.88275 loss)
I0401 15:55:28.114472 25640 sgd_solver.cpp:105] Iteration 3804, lr = 0.001
I0401 15:55:33.432534 25640 solver.cpp:218] Iteration 3816 (2.25646 iter/s, 5.31805s/12 iters), loss = 3.89045
I0401 15:55:33.432574 25640 solver.cpp:237]     Train net output #0: loss = 3.89045 (* 1 = 3.89045 loss)
I0401 15:55:33.432579 25640 sgd_solver.cpp:105] Iteration 3816, lr = 0.001
I0401 15:55:38.506557 25640 solver.cpp:218] Iteration 3828 (2.36501 iter/s, 5.07397s/12 iters), loss = 3.91292
I0401 15:55:38.506593 25640 solver.cpp:237]     Train net output #0: loss = 3.91292 (* 1 = 3.91292 loss)
I0401 15:55:38.506598 25640 sgd_solver.cpp:105] Iteration 3828, lr = 0.001
I0401 15:55:44.035184 25640 solver.cpp:218] Iteration 3840 (2.17054 iter/s, 5.52857s/12 iters), loss = 3.99245
I0401 15:55:44.035274 25640 solver.cpp:237]     Train net output #0: loss = 3.99245 (* 1 = 3.99245 loss)
I0401 15:55:44.035281 25640 sgd_solver.cpp:105] Iteration 3840, lr = 0.001
I0401 15:55:45.255086 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:55:49.342005 25640 solver.cpp:218] Iteration 3852 (2.26128 iter/s, 5.30672s/12 iters), loss = 4.0667
I0401 15:55:49.342046 25640 solver.cpp:237]     Train net output #0: loss = 4.0667 (* 1 = 4.0667 loss)
I0401 15:55:49.342051 25640 sgd_solver.cpp:105] Iteration 3852, lr = 0.001
I0401 15:55:54.832660 25640 solver.cpp:218] Iteration 3864 (2.18556 iter/s, 5.49059s/12 iters), loss = 3.99761
I0401 15:55:54.832715 25640 solver.cpp:237]     Train net output #0: loss = 3.99761 (* 1 = 3.99761 loss)
I0401 15:55:54.832722 25640 sgd_solver.cpp:105] Iteration 3864, lr = 0.001
I0401 15:55:59.684540 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0401 15:56:03.165292 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0401 15:56:06.948375 25640 solver.cpp:330] Iteration 3876, Testing net (#0)
I0401 15:56:06.948395 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:56:09.842811 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:56:11.469430 25640 solver.cpp:397]     Test net output #0: accuracy = 0.104779
I0401 15:56:11.469461 25640 solver.cpp:397]     Test net output #1: loss = 4.22755 (* 1 = 4.22755 loss)
I0401 15:56:11.605118 25640 solver.cpp:218] Iteration 3876 (0.715461 iter/s, 16.7724s/12 iters), loss = 3.90775
I0401 15:56:11.605151 25640 solver.cpp:237]     Train net output #0: loss = 3.90775 (* 1 = 3.90775 loss)
I0401 15:56:11.605157 25640 sgd_solver.cpp:105] Iteration 3876, lr = 0.001
I0401 15:56:16.051826 25640 solver.cpp:218] Iteration 3888 (2.69866 iter/s, 4.44666s/12 iters), loss = 4.07685
I0401 15:56:16.051936 25640 solver.cpp:237]     Train net output #0: loss = 4.07685 (* 1 = 4.07685 loss)
I0401 15:56:16.051942 25640 sgd_solver.cpp:105] Iteration 3888, lr = 0.001
I0401 15:56:21.489295 25640 solver.cpp:218] Iteration 3900 (2.20696 iter/s, 5.43734s/12 iters), loss = 3.60007
I0401 15:56:21.489351 25640 solver.cpp:237]     Train net output #0: loss = 3.60007 (* 1 = 3.60007 loss)
I0401 15:56:21.489359 25640 sgd_solver.cpp:105] Iteration 3900, lr = 0.001
I0401 15:56:26.886633 25640 solver.cpp:218] Iteration 3912 (2.22335 iter/s, 5.39727s/12 iters), loss = 3.69403
I0401 15:56:26.886683 25640 solver.cpp:237]     Train net output #0: loss = 3.69403 (* 1 = 3.69403 loss)
I0401 15:56:26.886690 25640 sgd_solver.cpp:105] Iteration 3912, lr = 0.001
I0401 15:56:32.200127 25640 solver.cpp:218] Iteration 3924 (2.25843 iter/s, 5.31343s/12 iters), loss = 3.72548
I0401 15:56:32.200181 25640 solver.cpp:237]     Train net output #0: loss = 3.72548 (* 1 = 3.72548 loss)
I0401 15:56:32.200189 25640 sgd_solver.cpp:105] Iteration 3924, lr = 0.001
I0401 15:56:37.663561 25640 solver.cpp:218] Iteration 3936 (2.19645 iter/s, 5.46337s/12 iters), loss = 3.79988
I0401 15:56:37.663614 25640 solver.cpp:237]     Train net output #0: loss = 3.79988 (* 1 = 3.79988 loss)
I0401 15:56:37.663621 25640 sgd_solver.cpp:105] Iteration 3936, lr = 0.001
I0401 15:56:40.972996 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:56:42.748070 25640 solver.cpp:218] Iteration 3948 (2.36014 iter/s, 5.08444s/12 iters), loss = 3.68856
I0401 15:56:42.748114 25640 solver.cpp:237]     Train net output #0: loss = 3.68856 (* 1 = 3.68856 loss)
I0401 15:56:42.748121 25640 sgd_solver.cpp:105] Iteration 3948, lr = 0.001
I0401 15:56:47.996461 25640 solver.cpp:218] Iteration 3960 (2.28644 iter/s, 5.24833s/12 iters), loss = 3.66193
I0401 15:56:47.996608 25640 solver.cpp:237]     Train net output #0: loss = 3.66193 (* 1 = 3.66193 loss)
I0401 15:56:47.996616 25640 sgd_solver.cpp:105] Iteration 3960, lr = 0.001
I0401 15:56:53.439955 25640 solver.cpp:218] Iteration 3972 (2.20453 iter/s, 5.44334s/12 iters), loss = 3.97791
I0401 15:56:53.440001 25640 solver.cpp:237]     Train net output #0: loss = 3.97791 (* 1 = 3.97791 loss)
I0401 15:56:53.440006 25640 sgd_solver.cpp:105] Iteration 3972, lr = 0.001
I0401 15:56:55.640668 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0401 15:56:58.938606 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0401 15:57:01.255693 25640 solver.cpp:330] Iteration 3978, Testing net (#0)
I0401 15:57:01.255717 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:57:04.155221 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:57:05.945943 25640 solver.cpp:397]     Test net output #0: accuracy = 0.108456
I0401 15:57:05.945976 25640 solver.cpp:397]     Test net output #1: loss = 4.26866 (* 1 = 4.26866 loss)
I0401 15:57:07.847129 25640 solver.cpp:218] Iteration 3984 (0.832922 iter/s, 14.4071s/12 iters), loss = 3.78019
I0401 15:57:07.847178 25640 solver.cpp:237]     Train net output #0: loss = 3.78019 (* 1 = 3.78019 loss)
I0401 15:57:07.847184 25640 sgd_solver.cpp:105] Iteration 3984, lr = 0.001
I0401 15:57:12.935233 25640 solver.cpp:218] Iteration 3996 (2.35847 iter/s, 5.08804s/12 iters), loss = 3.89781
I0401 15:57:12.935267 25640 solver.cpp:237]     Train net output #0: loss = 3.89781 (* 1 = 3.89781 loss)
I0401 15:57:12.935272 25640 sgd_solver.cpp:105] Iteration 3996, lr = 0.001
I0401 15:57:18.272078 25640 solver.cpp:218] Iteration 4008 (2.24854 iter/s, 5.3368s/12 iters), loss = 3.8475
I0401 15:57:18.272228 25640 solver.cpp:237]     Train net output #0: loss = 3.8475 (* 1 = 3.8475 loss)
I0401 15:57:18.272238 25640 sgd_solver.cpp:105] Iteration 4008, lr = 0.001
I0401 15:57:23.691903 25640 solver.cpp:218] Iteration 4020 (2.21416 iter/s, 5.41966s/12 iters), loss = 3.64339
I0401 15:57:23.691962 25640 solver.cpp:237]     Train net output #0: loss = 3.64339 (* 1 = 3.64339 loss)
I0401 15:57:23.691972 25640 sgd_solver.cpp:105] Iteration 4020, lr = 0.001
I0401 15:57:29.104010 25640 solver.cpp:218] Iteration 4032 (2.21728 iter/s, 5.41203s/12 iters), loss = 3.87511
I0401 15:57:29.104075 25640 solver.cpp:237]     Train net output #0: loss = 3.87511 (* 1 = 3.87511 loss)
I0401 15:57:29.104084 25640 sgd_solver.cpp:105] Iteration 4032, lr = 0.001
I0401 15:57:34.425308 25640 solver.cpp:218] Iteration 4044 (2.25512 iter/s, 5.32122s/12 iters), loss = 3.90312
I0401 15:57:34.425364 25640 solver.cpp:237]     Train net output #0: loss = 3.90312 (* 1 = 3.90312 loss)
I0401 15:57:34.425374 25640 sgd_solver.cpp:105] Iteration 4044, lr = 0.001
I0401 15:57:35.002032 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:57:39.827925 25640 solver.cpp:218] Iteration 4056 (2.22117 iter/s, 5.40255s/12 iters), loss = 3.80968
I0401 15:57:39.827967 25640 solver.cpp:237]     Train net output #0: loss = 3.80968 (* 1 = 3.80968 loss)
I0401 15:57:39.827972 25640 sgd_solver.cpp:105] Iteration 4056, lr = 0.001
I0401 15:57:45.301584 25640 solver.cpp:218] Iteration 4068 (2.19234 iter/s, 5.4736s/12 iters), loss = 3.87825
I0401 15:57:45.301625 25640 solver.cpp:237]     Train net output #0: loss = 3.87825 (* 1 = 3.87825 loss)
I0401 15:57:45.301630 25640 sgd_solver.cpp:105] Iteration 4068, lr = 0.001
I0401 15:57:50.205858 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0401 15:57:53.323155 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0401 15:57:55.681478 25640 solver.cpp:330] Iteration 4080, Testing net (#0)
I0401 15:57:55.681506 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:57:58.495224 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:58:00.141194 25640 solver.cpp:397]     Test net output #0: accuracy = 0.110907
I0401 15:58:00.141232 25640 solver.cpp:397]     Test net output #1: loss = 4.22877 (* 1 = 4.22877 loss)
I0401 15:58:00.279230 25640 solver.cpp:218] Iteration 4080 (0.801197 iter/s, 14.9776s/12 iters), loss = 3.80061
I0401 15:58:00.279286 25640 solver.cpp:237]     Train net output #0: loss = 3.80061 (* 1 = 3.80061 loss)
I0401 15:58:00.279294 25640 sgd_solver.cpp:105] Iteration 4080, lr = 0.001
I0401 15:58:04.599833 25640 solver.cpp:218] Iteration 4092 (2.77744 iter/s, 4.32053s/12 iters), loss = 3.76576
I0401 15:58:04.599880 25640 solver.cpp:237]     Train net output #0: loss = 3.76576 (* 1 = 3.76576 loss)
I0401 15:58:04.599889 25640 sgd_solver.cpp:105] Iteration 4092, lr = 0.001
I0401 15:58:09.910882 25640 solver.cpp:218] Iteration 4104 (2.25946 iter/s, 5.31099s/12 iters), loss = 3.6657
I0401 15:58:09.910928 25640 solver.cpp:237]     Train net output #0: loss = 3.6657 (* 1 = 3.6657 loss)
I0401 15:58:09.910934 25640 sgd_solver.cpp:105] Iteration 4104, lr = 0.001
I0401 15:58:15.393728 25640 solver.cpp:218] Iteration 4116 (2.18867 iter/s, 5.48279s/12 iters), loss = 3.36839
I0401 15:58:15.393769 25640 solver.cpp:237]     Train net output #0: loss = 3.36839 (* 1 = 3.36839 loss)
I0401 15:58:15.393775 25640 sgd_solver.cpp:105] Iteration 4116, lr = 0.001
I0401 15:58:20.758827 25640 solver.cpp:218] Iteration 4128 (2.2367 iter/s, 5.36504s/12 iters), loss = 3.72603
I0401 15:58:20.758991 25640 solver.cpp:237]     Train net output #0: loss = 3.72603 (* 1 = 3.72603 loss)
I0401 15:58:20.758999 25640 sgd_solver.cpp:105] Iteration 4128, lr = 0.001
I0401 15:58:25.934865 25640 solver.cpp:218] Iteration 4140 (2.31845 iter/s, 5.17586s/12 iters), loss = 3.62118
I0401 15:58:25.934931 25640 solver.cpp:237]     Train net output #0: loss = 3.62118 (* 1 = 3.62118 loss)
I0401 15:58:25.934939 25640 sgd_solver.cpp:105] Iteration 4140, lr = 0.001
I0401 15:58:28.790684 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:58:31.565886 25640 solver.cpp:218] Iteration 4152 (2.13108 iter/s, 5.63095s/12 iters), loss = 3.77672
I0401 15:58:31.565922 25640 solver.cpp:237]     Train net output #0: loss = 3.77672 (* 1 = 3.77672 loss)
I0401 15:58:31.565928 25640 sgd_solver.cpp:105] Iteration 4152, lr = 0.001
I0401 15:58:33.180434 25640 blocking_queue.cpp:49] Waiting for data
I0401 15:58:36.756098 25640 solver.cpp:218] Iteration 4164 (2.31206 iter/s, 5.19017s/12 iters), loss = 3.69142
I0401 15:58:36.756134 25640 solver.cpp:237]     Train net output #0: loss = 3.69142 (* 1 = 3.69142 loss)
I0401 15:58:36.756139 25640 sgd_solver.cpp:105] Iteration 4164, lr = 0.001
I0401 15:58:42.061574 25640 solver.cpp:218] Iteration 4176 (2.26184 iter/s, 5.30542s/12 iters), loss = 3.5784
I0401 15:58:42.061633 25640 solver.cpp:237]     Train net output #0: loss = 3.5784 (* 1 = 3.5784 loss)
I0401 15:58:42.061641 25640 sgd_solver.cpp:105] Iteration 4176, lr = 0.001
I0401 15:58:44.191366 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0401 15:58:47.256810 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0401 15:58:50.138943 25640 solver.cpp:330] Iteration 4182, Testing net (#0)
I0401 15:58:50.138963 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:58:52.856830 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:58:54.579486 25640 solver.cpp:397]     Test net output #0: accuracy = 0.116422
I0401 15:58:54.579524 25640 solver.cpp:397]     Test net output #1: loss = 4.16934 (* 1 = 4.16934 loss)
I0401 15:58:56.366950 25640 solver.cpp:218] Iteration 4188 (0.83885 iter/s, 14.3053s/12 iters), loss = 3.47143
I0401 15:58:56.367008 25640 solver.cpp:237]     Train net output #0: loss = 3.47143 (* 1 = 3.47143 loss)
I0401 15:58:56.367017 25640 sgd_solver.cpp:105] Iteration 4188, lr = 0.001
I0401 15:59:01.806190 25640 solver.cpp:218] Iteration 4200 (2.20622 iter/s, 5.43917s/12 iters), loss = 3.4941
I0401 15:59:01.806227 25640 solver.cpp:237]     Train net output #0: loss = 3.4941 (* 1 = 3.4941 loss)
I0401 15:59:01.806233 25640 sgd_solver.cpp:105] Iteration 4200, lr = 0.001
I0401 15:59:07.033865 25640 solver.cpp:218] Iteration 4212 (2.2955 iter/s, 5.22762s/12 iters), loss = 3.7109
I0401 15:59:07.033908 25640 solver.cpp:237]     Train net output #0: loss = 3.7109 (* 1 = 3.7109 loss)
I0401 15:59:07.033915 25640 sgd_solver.cpp:105] Iteration 4212, lr = 0.001
I0401 15:59:12.299962 25640 solver.cpp:218] Iteration 4224 (2.27875 iter/s, 5.26604s/12 iters), loss = 3.57193
I0401 15:59:12.299994 25640 solver.cpp:237]     Train net output #0: loss = 3.57193 (* 1 = 3.57193 loss)
I0401 15:59:12.300000 25640 sgd_solver.cpp:105] Iteration 4224, lr = 0.001
I0401 15:59:17.762138 25640 solver.cpp:218] Iteration 4236 (2.19695 iter/s, 5.46212s/12 iters), loss = 3.68488
I0401 15:59:17.762188 25640 solver.cpp:237]     Train net output #0: loss = 3.68488 (* 1 = 3.68488 loss)
I0401 15:59:17.762197 25640 sgd_solver.cpp:105] Iteration 4236, lr = 0.001
I0401 15:59:22.845825 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:59:23.122254 25640 solver.cpp:218] Iteration 4248 (2.23878 iter/s, 5.36005s/12 iters), loss = 3.78157
I0401 15:59:23.123252 25640 solver.cpp:237]     Train net output #0: loss = 3.78157 (* 1 = 3.78157 loss)
I0401 15:59:23.123260 25640 sgd_solver.cpp:105] Iteration 4248, lr = 0.001
I0401 15:59:28.446590 25640 solver.cpp:218] Iteration 4260 (2.25423 iter/s, 5.32333s/12 iters), loss = 3.72381
I0401 15:59:28.446633 25640 solver.cpp:237]     Train net output #0: loss = 3.72381 (* 1 = 3.72381 loss)
I0401 15:59:28.446640 25640 sgd_solver.cpp:105] Iteration 4260, lr = 0.001
I0401 15:59:33.797974 25640 solver.cpp:218] Iteration 4272 (2.24244 iter/s, 5.35132s/12 iters), loss = 3.5775
I0401 15:59:33.798038 25640 solver.cpp:237]     Train net output #0: loss = 3.5775 (* 1 = 3.5775 loss)
I0401 15:59:33.798046 25640 sgd_solver.cpp:105] Iteration 4272, lr = 0.001
I0401 15:59:38.757278 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0401 15:59:41.761039 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0401 15:59:45.618324 25640 solver.cpp:330] Iteration 4284, Testing net (#0)
I0401 15:59:45.618347 25640 net.cpp:676] Ignoring source layer train-data
I0401 15:59:48.339978 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:59:50.064586 25640 solver.cpp:397]     Test net output #0: accuracy = 0.116422
I0401 15:59:50.064630 25640 solver.cpp:397]     Test net output #1: loss = 4.17547 (* 1 = 4.17547 loss)
I0401 15:59:50.200117 25640 solver.cpp:218] Iteration 4284 (0.731615 iter/s, 16.4021s/12 iters), loss = 3.48801
I0401 15:59:50.200167 25640 solver.cpp:237]     Train net output #0: loss = 3.48801 (* 1 = 3.48801 loss)
I0401 15:59:50.200174 25640 sgd_solver.cpp:105] Iteration 4284, lr = 0.001
I0401 15:59:54.601120 25640 solver.cpp:218] Iteration 4296 (2.72669 iter/s, 4.40094s/12 iters), loss = 3.42948
I0401 15:59:54.612077 25640 solver.cpp:237]     Train net output #0: loss = 3.42948 (* 1 = 3.42948 loss)
I0401 15:59:54.612100 25640 sgd_solver.cpp:105] Iteration 4296, lr = 0.001
I0401 16:00:00.097486 25640 solver.cpp:218] Iteration 4308 (2.18762 iter/s, 5.48542s/12 iters), loss = 3.45847
I0401 16:00:00.097532 25640 solver.cpp:237]     Train net output #0: loss = 3.45847 (* 1 = 3.45847 loss)
I0401 16:00:00.097537 25640 sgd_solver.cpp:105] Iteration 4308, lr = 0.001
I0401 16:00:05.223111 25640 solver.cpp:218] Iteration 4320 (2.3412 iter/s, 5.12557s/12 iters), loss = 3.25716
I0401 16:00:05.223150 25640 solver.cpp:237]     Train net output #0: loss = 3.25716 (* 1 = 3.25716 loss)
I0401 16:00:05.223156 25640 sgd_solver.cpp:105] Iteration 4320, lr = 0.001
I0401 16:00:10.596426 25640 solver.cpp:218] Iteration 4332 (2.23328 iter/s, 5.37326s/12 iters), loss = 3.37264
I0401 16:00:10.596469 25640 solver.cpp:237]     Train net output #0: loss = 3.37264 (* 1 = 3.37264 loss)
I0401 16:00:10.596475 25640 sgd_solver.cpp:105] Iteration 4332, lr = 0.001
I0401 16:00:16.041455 25640 solver.cpp:218] Iteration 4344 (2.20387 iter/s, 5.44497s/12 iters), loss = 3.38844
I0401 16:00:16.041497 25640 solver.cpp:237]     Train net output #0: loss = 3.38844 (* 1 = 3.38844 loss)
I0401 16:00:16.041503 25640 sgd_solver.cpp:105] Iteration 4344, lr = 0.001
I0401 16:00:18.019423 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:00:21.300066 25640 solver.cpp:218] Iteration 4356 (2.282 iter/s, 5.25855s/12 iters), loss = 3.20953
I0401 16:00:21.300120 25640 solver.cpp:237]     Train net output #0: loss = 3.20953 (* 1 = 3.20953 loss)
I0401 16:00:21.300129 25640 sgd_solver.cpp:105] Iteration 4356, lr = 0.001
I0401 16:00:26.896721 25640 solver.cpp:218] Iteration 4368 (2.14417 iter/s, 5.59658s/12 iters), loss = 3.68463
I0401 16:00:26.896844 25640 solver.cpp:237]     Train net output #0: loss = 3.68463 (* 1 = 3.68463 loss)
I0401 16:00:26.896853 25640 sgd_solver.cpp:105] Iteration 4368, lr = 0.001
I0401 16:00:32.272264 25640 solver.cpp:218] Iteration 4380 (2.23239 iter/s, 5.37541s/12 iters), loss = 3.23071
I0401 16:00:32.272311 25640 solver.cpp:237]     Train net output #0: loss = 3.23071 (* 1 = 3.23071 loss)
I0401 16:00:32.272316 25640 sgd_solver.cpp:105] Iteration 4380, lr = 0.001
I0401 16:00:34.523555 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0401 16:00:39.092970 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0401 16:00:43.072800 25640 solver.cpp:330] Iteration 4386, Testing net (#0)
I0401 16:00:43.072821 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:00:45.768211 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:00:47.537492 25640 solver.cpp:397]     Test net output #0: accuracy = 0.121936
I0401 16:00:47.537529 25640 solver.cpp:397]     Test net output #1: loss = 4.13927 (* 1 = 4.13927 loss)
I0401 16:00:49.372995 25640 solver.cpp:218] Iteration 4392 (0.701727 iter/s, 17.1007s/12 iters), loss = 3.40393
I0401 16:00:49.373064 25640 solver.cpp:237]     Train net output #0: loss = 3.40393 (* 1 = 3.40393 loss)
I0401 16:00:49.373075 25640 sgd_solver.cpp:105] Iteration 4392, lr = 0.001
I0401 16:00:54.730257 25640 solver.cpp:218] Iteration 4404 (2.23999 iter/s, 5.35717s/12 iters), loss = 3.51309
I0401 16:00:54.730316 25640 solver.cpp:237]     Train net output #0: loss = 3.51309 (* 1 = 3.51309 loss)
I0401 16:00:54.730324 25640 sgd_solver.cpp:105] Iteration 4404, lr = 0.001
I0401 16:01:00.086766 25640 solver.cpp:218] Iteration 4416 (2.2403 iter/s, 5.35644s/12 iters), loss = 3.82554
I0401 16:01:00.086859 25640 solver.cpp:237]     Train net output #0: loss = 3.82554 (* 1 = 3.82554 loss)
I0401 16:01:00.086865 25640 sgd_solver.cpp:105] Iteration 4416, lr = 0.001
I0401 16:01:05.508914 25640 solver.cpp:218] Iteration 4428 (2.21319 iter/s, 5.42204s/12 iters), loss = 3.36663
I0401 16:01:05.508975 25640 solver.cpp:237]     Train net output #0: loss = 3.36663 (* 1 = 3.36663 loss)
I0401 16:01:05.508985 25640 sgd_solver.cpp:105] Iteration 4428, lr = 0.001
I0401 16:01:10.815240 25640 solver.cpp:218] Iteration 4440 (2.26148 iter/s, 5.30625s/12 iters), loss = 3.46022
I0401 16:01:10.815296 25640 solver.cpp:237]     Train net output #0: loss = 3.46022 (* 1 = 3.46022 loss)
I0401 16:01:10.815304 25640 sgd_solver.cpp:105] Iteration 4440, lr = 0.001
I0401 16:01:15.058847 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:01:16.191049 25640 solver.cpp:218] Iteration 4452 (2.23225 iter/s, 5.37575s/12 iters), loss = 3.34829
I0401 16:01:16.191087 25640 solver.cpp:237]     Train net output #0: loss = 3.34829 (* 1 = 3.34829 loss)
I0401 16:01:16.191092 25640 sgd_solver.cpp:105] Iteration 4452, lr = 0.001
I0401 16:01:21.771878 25640 solver.cpp:218] Iteration 4464 (2.15024 iter/s, 5.58078s/12 iters), loss = 3.41029
I0401 16:01:21.771916 25640 solver.cpp:237]     Train net output #0: loss = 3.41029 (* 1 = 3.41029 loss)
I0401 16:01:21.771922 25640 sgd_solver.cpp:105] Iteration 4464, lr = 0.001
I0401 16:01:27.098471 25640 solver.cpp:218] Iteration 4476 (2.25287 iter/s, 5.32654s/12 iters), loss = 3.28191
I0401 16:01:27.098520 25640 solver.cpp:237]     Train net output #0: loss = 3.28191 (* 1 = 3.28191 loss)
I0401 16:01:27.098528 25640 sgd_solver.cpp:105] Iteration 4476, lr = 0.001
I0401 16:01:32.092229 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0401 16:01:36.793927 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0401 16:01:39.128796 25640 solver.cpp:330] Iteration 4488, Testing net (#0)
I0401 16:01:39.128816 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:01:41.753706 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:01:43.703337 25640 solver.cpp:397]     Test net output #0: accuracy = 0.128064
I0401 16:01:43.703370 25640 solver.cpp:397]     Test net output #1: loss = 4.10844 (* 1 = 4.10844 loss)
I0401 16:01:43.844784 25640 solver.cpp:218] Iteration 4488 (0.716578 iter/s, 16.7463s/12 iters), loss = 3.39728
I0401 16:01:43.844846 25640 solver.cpp:237]     Train net output #0: loss = 3.39728 (* 1 = 3.39728 loss)
I0401 16:01:43.844852 25640 sgd_solver.cpp:105] Iteration 4488, lr = 0.001
I0401 16:01:48.344029 25640 solver.cpp:218] Iteration 4500 (2.66716 iter/s, 4.49917s/12 iters), loss = 3.55834
I0401 16:01:48.344067 25640 solver.cpp:237]     Train net output #0: loss = 3.55834 (* 1 = 3.55834 loss)
I0401 16:01:48.344072 25640 sgd_solver.cpp:105] Iteration 4500, lr = 0.001
I0401 16:01:53.756611 25640 solver.cpp:218] Iteration 4512 (2.21708 iter/s, 5.41253s/12 iters), loss = 3.5796
I0401 16:01:53.756652 25640 solver.cpp:237]     Train net output #0: loss = 3.5796 (* 1 = 3.5796 loss)
I0401 16:01:53.756659 25640 sgd_solver.cpp:105] Iteration 4512, lr = 0.001
I0401 16:01:59.102576 25640 solver.cpp:218] Iteration 4524 (2.24471 iter/s, 5.34591s/12 iters), loss = 3.16082
I0401 16:01:59.102617 25640 solver.cpp:237]     Train net output #0: loss = 3.16082 (* 1 = 3.16082 loss)
I0401 16:01:59.102623 25640 sgd_solver.cpp:105] Iteration 4524, lr = 0.001
I0401 16:02:04.545284 25640 solver.cpp:218] Iteration 4536 (2.20481 iter/s, 5.44265s/12 iters), loss = 3.42865
I0401 16:02:04.545405 25640 solver.cpp:237]     Train net output #0: loss = 3.42865 (* 1 = 3.42865 loss)
I0401 16:02:04.545413 25640 sgd_solver.cpp:105] Iteration 4536, lr = 0.001
I0401 16:02:09.649521 25640 solver.cpp:218] Iteration 4548 (2.35105 iter/s, 5.1041s/12 iters), loss = 3.26528
I0401 16:02:09.649574 25640 solver.cpp:237]     Train net output #0: loss = 3.26528 (* 1 = 3.26528 loss)
I0401 16:02:09.649582 25640 sgd_solver.cpp:105] Iteration 4548, lr = 0.001
I0401 16:02:11.140161 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:02:15.202780 25640 solver.cpp:218] Iteration 4560 (2.16092 iter/s, 5.55319s/12 iters), loss = 3.38723
I0401 16:02:15.202829 25640 solver.cpp:237]     Train net output #0: loss = 3.38723 (* 1 = 3.38723 loss)
I0401 16:02:15.202837 25640 sgd_solver.cpp:105] Iteration 4560, lr = 0.001
I0401 16:02:20.655076 25640 solver.cpp:218] Iteration 4572 (2.20093 iter/s, 5.45224s/12 iters), loss = 3.4648
I0401 16:02:20.655112 25640 solver.cpp:237]     Train net output #0: loss = 3.4648 (* 1 = 3.4648 loss)
I0401 16:02:20.655117 25640 sgd_solver.cpp:105] Iteration 4572, lr = 0.001
I0401 16:02:25.955497 25640 solver.cpp:218] Iteration 4584 (2.26399 iter/s, 5.30037s/12 iters), loss = 3.64404
I0401 16:02:25.955554 25640 solver.cpp:237]     Train net output #0: loss = 3.64404 (* 1 = 3.64404 loss)
I0401 16:02:25.955562 25640 sgd_solver.cpp:105] Iteration 4584, lr = 0.001
I0401 16:02:28.127077 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0401 16:02:31.116880 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0401 16:02:33.412113 25640 solver.cpp:330] Iteration 4590, Testing net (#0)
I0401 16:02:33.412137 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:02:36.036877 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:02:37.987581 25640 solver.cpp:397]     Test net output #0: accuracy = 0.126838
I0401 16:02:37.987618 25640 solver.cpp:397]     Test net output #1: loss = 4.14713 (* 1 = 4.14713 loss)
I0401 16:02:39.892917 25640 solver.cpp:218] Iteration 4596 (0.860996 iter/s, 13.9373s/12 iters), loss = 3.57745
I0401 16:02:39.892982 25640 solver.cpp:237]     Train net output #0: loss = 3.57745 (* 1 = 3.57745 loss)
I0401 16:02:39.892990 25640 sgd_solver.cpp:105] Iteration 4596, lr = 0.001
I0401 16:02:45.229684 25640 solver.cpp:218] Iteration 4608 (2.24858 iter/s, 5.3367s/12 iters), loss = 3.34209
I0401 16:02:45.229722 25640 solver.cpp:237]     Train net output #0: loss = 3.34209 (* 1 = 3.34209 loss)
I0401 16:02:45.229727 25640 sgd_solver.cpp:105] Iteration 4608, lr = 0.001
I0401 16:02:50.686800 25640 solver.cpp:218] Iteration 4620 (2.19899 iter/s, 5.45706s/12 iters), loss = 3.22176
I0401 16:02:50.686861 25640 solver.cpp:237]     Train net output #0: loss = 3.22176 (* 1 = 3.22176 loss)
I0401 16:02:50.686868 25640 sgd_solver.cpp:105] Iteration 4620, lr = 0.001
I0401 16:02:55.963348 25640 solver.cpp:218] Iteration 4632 (2.27425 iter/s, 5.27647s/12 iters), loss = 3.35506
I0401 16:02:55.963392 25640 solver.cpp:237]     Train net output #0: loss = 3.35506 (* 1 = 3.35506 loss)
I0401 16:02:55.963398 25640 sgd_solver.cpp:105] Iteration 4632, lr = 0.001
I0401 16:03:01.429371 25640 solver.cpp:218] Iteration 4644 (2.19541 iter/s, 5.46596s/12 iters), loss = 3.09286
I0401 16:03:01.429425 25640 solver.cpp:237]     Train net output #0: loss = 3.09286 (* 1 = 3.09286 loss)
I0401 16:03:01.429432 25640 sgd_solver.cpp:105] Iteration 4644, lr = 0.001
I0401 16:03:05.133251 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:03:06.899636 25640 solver.cpp:218] Iteration 4656 (2.1937 iter/s, 5.4702s/12 iters), loss = 3.19492
I0401 16:03:06.899767 25640 solver.cpp:237]     Train net output #0: loss = 3.19492 (* 1 = 3.19492 loss)
I0401 16:03:06.899773 25640 sgd_solver.cpp:105] Iteration 4656, lr = 0.001
I0401 16:03:12.191155 25640 solver.cpp:218] Iteration 4668 (2.26784 iter/s, 5.29138s/12 iters), loss = 2.98876
I0401 16:03:12.191197 25640 solver.cpp:237]     Train net output #0: loss = 2.98876 (* 1 = 2.98876 loss)
I0401 16:03:12.191202 25640 sgd_solver.cpp:105] Iteration 4668, lr = 0.001
I0401 16:03:17.401335 25640 solver.cpp:218] Iteration 4680 (2.30321 iter/s, 5.21012s/12 iters), loss = 3.70492
I0401 16:03:17.401382 25640 solver.cpp:237]     Train net output #0: loss = 3.70492 (* 1 = 3.70492 loss)
I0401 16:03:17.401388 25640 sgd_solver.cpp:105] Iteration 4680, lr = 0.001
I0401 16:03:21.999989 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0401 16:03:25.155650 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0401 16:03:27.448632 25640 solver.cpp:330] Iteration 4692, Testing net (#0)
I0401 16:03:27.448649 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:03:30.161490 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:03:32.025365 25640 solver.cpp:397]     Test net output #0: accuracy = 0.13848
I0401 16:03:32.025401 25640 solver.cpp:397]     Test net output #1: loss = 4.095 (* 1 = 4.095 loss)
I0401 16:03:32.160624 25640 solver.cpp:218] Iteration 4692 (0.81305 iter/s, 14.7592s/12 iters), loss = 3.17245
I0401 16:03:32.162178 25640 solver.cpp:237]     Train net output #0: loss = 3.17245 (* 1 = 3.17245 loss)
I0401 16:03:32.162189 25640 sgd_solver.cpp:105] Iteration 4692, lr = 0.001
I0401 16:03:36.150008 25640 solver.cpp:218] Iteration 4704 (3.00916 iter/s, 3.98782s/12 iters), loss = 3.61541
I0401 16:03:36.150046 25640 solver.cpp:237]     Train net output #0: loss = 3.61541 (* 1 = 3.61541 loss)
I0401 16:03:36.150051 25640 sgd_solver.cpp:105] Iteration 4704, lr = 0.001
I0401 16:03:41.539544 25640 solver.cpp:218] Iteration 4716 (2.22656 iter/s, 5.38948s/12 iters), loss = 3.29305
I0401 16:03:41.545754 25640 solver.cpp:237]     Train net output #0: loss = 3.29305 (* 1 = 3.29305 loss)
I0401 16:03:41.545776 25640 sgd_solver.cpp:105] Iteration 4716, lr = 0.001
I0401 16:03:47.066337 25640 solver.cpp:218] Iteration 4728 (2.17368 iter/s, 5.52059s/12 iters), loss = 3.10857
I0401 16:03:47.066385 25640 solver.cpp:237]     Train net output #0: loss = 3.10857 (* 1 = 3.10857 loss)
I0401 16:03:47.066393 25640 sgd_solver.cpp:105] Iteration 4728, lr = 0.001
I0401 16:03:52.321679 25640 solver.cpp:218] Iteration 4740 (2.28342 iter/s, 5.25528s/12 iters), loss = 3.18813
I0401 16:03:52.321722 25640 solver.cpp:237]     Train net output #0: loss = 3.18813 (* 1 = 3.18813 loss)
I0401 16:03:52.321728 25640 sgd_solver.cpp:105] Iteration 4740, lr = 0.001
I0401 16:03:57.811275 25640 solver.cpp:218] Iteration 4752 (2.18598 iter/s, 5.48954s/12 iters), loss = 3.3171
I0401 16:03:57.811321 25640 solver.cpp:237]     Train net output #0: loss = 3.3171 (* 1 = 3.3171 loss)
I0401 16:03:57.811326 25640 sgd_solver.cpp:105] Iteration 4752, lr = 0.001
I0401 16:03:58.362427 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:04:03.024215 25640 solver.cpp:218] Iteration 4764 (2.30199 iter/s, 5.21288s/12 iters), loss = 3.21684
I0401 16:04:03.024271 25640 solver.cpp:237]     Train net output #0: loss = 3.21684 (* 1 = 3.21684 loss)
I0401 16:04:03.024278 25640 sgd_solver.cpp:105] Iteration 4764, lr = 0.001
I0401 16:04:08.338636 25640 solver.cpp:218] Iteration 4776 (2.25803 iter/s, 5.31436s/12 iters), loss = 3.26371
I0401 16:04:08.338678 25640 solver.cpp:237]     Train net output #0: loss = 3.26371 (* 1 = 3.26371 loss)
I0401 16:04:08.338685 25640 sgd_solver.cpp:105] Iteration 4776, lr = 0.001
I0401 16:04:13.580458 25640 solver.cpp:218] Iteration 4788 (2.2893 iter/s, 5.24177s/12 iters), loss = 3.30226
I0401 16:04:13.580610 25640 solver.cpp:237]     Train net output #0: loss = 3.30226 (* 1 = 3.30226 loss)
I0401 16:04:13.580618 25640 sgd_solver.cpp:105] Iteration 4788, lr = 0.001
I0401 16:04:15.822373 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0401 16:04:19.361471 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0401 16:04:23.015935 25640 solver.cpp:330] Iteration 4794, Testing net (#0)
I0401 16:04:23.015954 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:04:25.440933 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:04:27.410192 25640 solver.cpp:397]     Test net output #0: accuracy = 0.136642
I0401 16:04:27.410230 25640 solver.cpp:397]     Test net output #1: loss = 4.1656 (* 1 = 4.1656 loss)
I0401 16:04:29.367920 25640 solver.cpp:218] Iteration 4800 (0.760105 iter/s, 15.7873s/12 iters), loss = 3.40199
I0401 16:04:29.367974 25640 solver.cpp:237]     Train net output #0: loss = 3.40199 (* 1 = 3.40199 loss)
I0401 16:04:29.367982 25640 sgd_solver.cpp:105] Iteration 4800, lr = 0.001
I0401 16:04:34.946547 25640 solver.cpp:218] Iteration 4812 (2.15109 iter/s, 5.57856s/12 iters), loss = 3.41445
I0401 16:04:34.946601 25640 solver.cpp:237]     Train net output #0: loss = 3.41445 (* 1 = 3.41445 loss)
I0401 16:04:34.946609 25640 sgd_solver.cpp:105] Iteration 4812, lr = 0.001
I0401 16:04:40.409449 25640 solver.cpp:218] Iteration 4824 (2.19666 iter/s, 5.46283s/12 iters), loss = 2.88187
I0401 16:04:40.409504 25640 solver.cpp:237]     Train net output #0: loss = 2.88187 (* 1 = 2.88187 loss)
I0401 16:04:40.409512 25640 sgd_solver.cpp:105] Iteration 4824, lr = 0.001
I0401 16:04:45.972128 25640 solver.cpp:218] Iteration 4836 (2.15726 iter/s, 5.5626s/12 iters), loss = 3.30772
I0401 16:04:45.972261 25640 solver.cpp:237]     Train net output #0: loss = 3.30772 (* 1 = 3.30772 loss)
I0401 16:04:45.972271 25640 sgd_solver.cpp:105] Iteration 4836, lr = 0.001
I0401 16:04:48.404168 25640 blocking_queue.cpp:49] Waiting for data
I0401 16:04:51.918987 25640 solver.cpp:218] Iteration 4848 (2.01792 iter/s, 5.94671s/12 iters), loss = 3.42453
I0401 16:04:51.919025 25640 solver.cpp:237]     Train net output #0: loss = 3.42453 (* 1 = 3.42453 loss)
I0401 16:04:51.919031 25640 sgd_solver.cpp:105] Iteration 4848, lr = 0.001
I0401 16:04:54.890767 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:04:57.307248 25640 solver.cpp:218] Iteration 4860 (2.22709 iter/s, 5.38821s/12 iters), loss = 3.34838
I0401 16:04:57.307291 25640 solver.cpp:237]     Train net output #0: loss = 3.34838 (* 1 = 3.34838 loss)
I0401 16:04:57.307296 25640 sgd_solver.cpp:105] Iteration 4860, lr = 0.001
I0401 16:05:02.963655 25640 solver.cpp:218] Iteration 4872 (2.12151 iter/s, 5.65635s/12 iters), loss = 3.19639
I0401 16:05:02.963706 25640 solver.cpp:237]     Train net output #0: loss = 3.19639 (* 1 = 3.19639 loss)
I0401 16:05:02.963713 25640 sgd_solver.cpp:105] Iteration 4872, lr = 0.001
I0401 16:05:08.544004 25640 solver.cpp:218] Iteration 4884 (2.15043 iter/s, 5.58028s/12 iters), loss = 3.54136
I0401 16:05:08.544059 25640 solver.cpp:237]     Train net output #0: loss = 3.54136 (* 1 = 3.54136 loss)
I0401 16:05:08.544067 25640 sgd_solver.cpp:105] Iteration 4884, lr = 0.001
I0401 16:05:13.895790 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0401 16:05:18.690817 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0401 16:05:21.116617 25640 solver.cpp:330] Iteration 4896, Testing net (#0)
I0401 16:05:21.116643 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:05:23.897980 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:05:26.028828 25640 solver.cpp:397]     Test net output #0: accuracy = 0.140319
I0401 16:05:26.028857 25640 solver.cpp:397]     Test net output #1: loss = 4.13639 (* 1 = 4.13639 loss)
I0401 16:05:26.169253 25640 solver.cpp:218] Iteration 4896 (0.680844 iter/s, 17.6252s/12 iters), loss = 3.42458
I0401 16:05:26.169296 25640 solver.cpp:237]     Train net output #0: loss = 3.42458 (* 1 = 3.42458 loss)
I0401 16:05:26.169302 25640 sgd_solver.cpp:105] Iteration 4896, lr = 0.001
I0401 16:05:31.006438 25640 solver.cpp:218] Iteration 4908 (2.48081 iter/s, 4.83713s/12 iters), loss = 3.22369
I0401 16:05:31.006480 25640 solver.cpp:237]     Train net output #0: loss = 3.22369 (* 1 = 3.22369 loss)
I0401 16:05:31.006487 25640 sgd_solver.cpp:105] Iteration 4908, lr = 0.001
I0401 16:05:36.572121 25640 solver.cpp:218] Iteration 4920 (2.15609 iter/s, 5.56563s/12 iters), loss = 3.16278
I0401 16:05:36.572180 25640 solver.cpp:237]     Train net output #0: loss = 3.16278 (* 1 = 3.16278 loss)
I0401 16:05:36.572189 25640 sgd_solver.cpp:105] Iteration 4920, lr = 0.001
I0401 16:05:42.249639 25640 solver.cpp:218] Iteration 4932 (2.11363 iter/s, 5.67745s/12 iters), loss = 3.14929
I0401 16:05:42.249689 25640 solver.cpp:237]     Train net output #0: loss = 3.14929 (* 1 = 3.14929 loss)
I0401 16:05:42.249697 25640 sgd_solver.cpp:105] Iteration 4932, lr = 0.001
I0401 16:05:47.678611 25640 solver.cpp:218] Iteration 4944 (2.21039 iter/s, 5.4289s/12 iters), loss = 2.9231
I0401 16:05:47.678668 25640 solver.cpp:237]     Train net output #0: loss = 2.9231 (* 1 = 2.9231 loss)
I0401 16:05:47.678676 25640 sgd_solver.cpp:105] Iteration 4944, lr = 0.001
I0401 16:05:53.060954 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:05:53.333144 25640 solver.cpp:218] Iteration 4956 (2.12222 iter/s, 5.65446s/12 iters), loss = 3.12951
I0401 16:05:53.333189 25640 solver.cpp:237]     Train net output #0: loss = 3.12951 (* 1 = 3.12951 loss)
I0401 16:05:53.333195 25640 sgd_solver.cpp:105] Iteration 4956, lr = 0.001
I0401 16:05:59.095575 25640 solver.cpp:218] Iteration 4968 (2.08248 iter/s, 5.76237s/12 iters), loss = 3.24019
I0401 16:05:59.095615 25640 solver.cpp:237]     Train net output #0: loss = 3.24019 (* 1 = 3.24019 loss)
I0401 16:05:59.095620 25640 sgd_solver.cpp:105] Iteration 4968, lr = 0.001
I0401 16:06:04.794828 25640 solver.cpp:218] Iteration 4980 (2.10556 iter/s, 5.6992s/12 iters), loss = 3.58418
I0401 16:06:04.794876 25640 solver.cpp:237]     Train net output #0: loss = 3.58418 (* 1 = 3.58418 loss)
I0401 16:06:04.794884 25640 sgd_solver.cpp:105] Iteration 4980, lr = 0.001
I0401 16:06:10.571646 25640 solver.cpp:218] Iteration 4992 (2.07729 iter/s, 5.77675s/12 iters), loss = 3.47092
I0401 16:06:10.571712 25640 solver.cpp:237]     Train net output #0: loss = 3.47092 (* 1 = 3.47092 loss)
I0401 16:06:10.571720 25640 sgd_solver.cpp:105] Iteration 4992, lr = 0.001
I0401 16:06:12.961834 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0401 16:06:16.114095 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0401 16:06:18.476743 25640 solver.cpp:330] Iteration 4998, Testing net (#0)
I0401 16:06:18.476768 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:06:21.226622 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:06:23.727578 25640 solver.cpp:397]     Test net output #0: accuracy = 0.132353
I0401 16:06:23.727676 25640 solver.cpp:397]     Test net output #1: loss = 4.16548 (* 1 = 4.16548 loss)
I0401 16:06:25.821056 25640 solver.cpp:218] Iteration 5004 (0.78692 iter/s, 15.2493s/12 iters), loss = 3.06989
I0401 16:06:25.821117 25640 solver.cpp:237]     Train net output #0: loss = 3.06989 (* 1 = 3.06989 loss)
I0401 16:06:25.821128 25640 sgd_solver.cpp:105] Iteration 5004, lr = 0.001
I0401 16:06:31.381497 25640 solver.cpp:218] Iteration 5016 (2.15813 iter/s, 5.56036s/12 iters), loss = 3.22263
I0401 16:06:31.381563 25640 solver.cpp:237]     Train net output #0: loss = 3.22263 (* 1 = 3.22263 loss)
I0401 16:06:31.381572 25640 sgd_solver.cpp:105] Iteration 5016, lr = 0.001
I0401 16:06:36.950399 25640 solver.cpp:218] Iteration 5028 (2.15486 iter/s, 5.56882s/12 iters), loss = 2.87904
I0401 16:06:36.950453 25640 solver.cpp:237]     Train net output #0: loss = 2.87904 (* 1 = 2.87904 loss)
I0401 16:06:36.950462 25640 sgd_solver.cpp:105] Iteration 5028, lr = 0.001
I0401 16:06:42.568318 25640 solver.cpp:218] Iteration 5040 (2.13605 iter/s, 5.61785s/12 iters), loss = 3.09953
I0401 16:06:42.568379 25640 solver.cpp:237]     Train net output #0: loss = 3.09953 (* 1 = 3.09953 loss)
I0401 16:06:42.568389 25640 sgd_solver.cpp:105] Iteration 5040, lr = 0.001
I0401 16:06:48.187422 25640 solver.cpp:218] Iteration 5052 (2.1356 iter/s, 5.61903s/12 iters), loss = 3.05248
I0401 16:06:48.187460 25640 solver.cpp:237]     Train net output #0: loss = 3.05248 (* 1 = 3.05248 loss)
I0401 16:06:48.187465 25640 sgd_solver.cpp:105] Iteration 5052, lr = 0.001
I0401 16:06:50.238896 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:06:53.693068 25640 solver.cpp:218] Iteration 5064 (2.1796 iter/s, 5.50559s/12 iters), loss = 2.67089
I0401 16:06:53.693117 25640 solver.cpp:237]     Train net output #0: loss = 2.67089 (* 1 = 2.67089 loss)
I0401 16:06:53.693126 25640 sgd_solver.cpp:105] Iteration 5064, lr = 0.001
I0401 16:06:59.141360 25640 solver.cpp:218] Iteration 5076 (2.20255 iter/s, 5.44823s/12 iters), loss = 3.0269
I0401 16:06:59.141487 25640 solver.cpp:237]     Train net output #0: loss = 3.0269 (* 1 = 3.0269 loss)
I0401 16:06:59.141494 25640 sgd_solver.cpp:105] Iteration 5076, lr = 0.001
I0401 16:07:04.638913 25640 solver.cpp:218] Iteration 5088 (2.18284 iter/s, 5.49742s/12 iters), loss = 2.69718
I0401 16:07:04.638950 25640 solver.cpp:237]     Train net output #0: loss = 2.69718 (* 1 = 2.69718 loss)
I0401 16:07:04.638955 25640 sgd_solver.cpp:105] Iteration 5088, lr = 0.001
I0401 16:07:09.671552 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0401 16:07:12.948503 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0401 16:07:15.256705 25640 solver.cpp:330] Iteration 5100, Testing net (#0)
I0401 16:07:15.256729 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:07:17.936563 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:07:20.304440 25640 solver.cpp:397]     Test net output #0: accuracy = 0.147059
I0401 16:07:20.304478 25640 solver.cpp:397]     Test net output #1: loss = 4.06611 (* 1 = 4.06611 loss)
I0401 16:07:20.444983 25640 solver.cpp:218] Iteration 5100 (0.759204 iter/s, 15.806s/12 iters), loss = 3.21817
I0401 16:07:20.446566 25640 solver.cpp:237]     Train net output #0: loss = 3.21817 (* 1 = 3.21817 loss)
I0401 16:07:20.446578 25640 sgd_solver.cpp:105] Iteration 5100, lr = 0.001
I0401 16:07:25.137332 25640 solver.cpp:218] Iteration 5112 (2.55822 iter/s, 4.69075s/12 iters), loss = 2.88866
I0401 16:07:25.137389 25640 solver.cpp:237]     Train net output #0: loss = 2.88866 (* 1 = 2.88866 loss)
I0401 16:07:25.137398 25640 sgd_solver.cpp:105] Iteration 5112, lr = 0.001
I0401 16:07:30.887205 25640 solver.cpp:218] Iteration 5124 (2.08703 iter/s, 5.7498s/12 iters), loss = 2.94414
I0401 16:07:30.887336 25640 solver.cpp:237]     Train net output #0: loss = 2.94414 (* 1 = 2.94414 loss)
I0401 16:07:30.887346 25640 sgd_solver.cpp:105] Iteration 5124, lr = 0.001
I0401 16:07:36.430781 25640 solver.cpp:218] Iteration 5136 (2.16472 iter/s, 5.54343s/12 iters), loss = 2.98956
I0401 16:07:36.430840 25640 solver.cpp:237]     Train net output #0: loss = 2.98956 (* 1 = 2.98956 loss)
I0401 16:07:36.430848 25640 sgd_solver.cpp:105] Iteration 5136, lr = 0.001
I0401 16:07:42.221541 25640 solver.cpp:218] Iteration 5148 (2.07229 iter/s, 5.79069s/12 iters), loss = 3.02437
I0401 16:07:42.221585 25640 solver.cpp:237]     Train net output #0: loss = 3.02437 (* 1 = 3.02437 loss)
I0401 16:07:42.221590 25640 sgd_solver.cpp:105] Iteration 5148, lr = 0.001
I0401 16:07:46.884040 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:07:48.064141 25640 solver.cpp:218] Iteration 5160 (2.0539 iter/s, 5.84253s/12 iters), loss = 3.07717
I0401 16:07:48.064200 25640 solver.cpp:237]     Train net output #0: loss = 3.07717 (* 1 = 3.07717 loss)
I0401 16:07:48.064209 25640 sgd_solver.cpp:105] Iteration 5160, lr = 0.001
I0401 16:07:53.340662 25640 solver.cpp:218] Iteration 5172 (2.27426 iter/s, 5.27645s/12 iters), loss = 2.88056
I0401 16:07:53.340723 25640 solver.cpp:237]     Train net output #0: loss = 2.88056 (* 1 = 2.88056 loss)
I0401 16:07:53.340731 25640 sgd_solver.cpp:105] Iteration 5172, lr = 0.001
I0401 16:07:59.311551 25640 solver.cpp:218] Iteration 5184 (2.00977 iter/s, 5.97082s/12 iters), loss = 3.48381
I0401 16:07:59.311594 25640 solver.cpp:237]     Train net output #0: loss = 3.48381 (* 1 = 3.48381 loss)
I0401 16:07:59.311600 25640 sgd_solver.cpp:105] Iteration 5184, lr = 0.001
I0401 16:08:04.940294 25640 solver.cpp:218] Iteration 5196 (2.13194 iter/s, 5.62868s/12 iters), loss = 3.47858
I0401 16:08:04.940443 25640 solver.cpp:237]     Train net output #0: loss = 3.47858 (* 1 = 3.47858 loss)
I0401 16:08:04.940454 25640 sgd_solver.cpp:105] Iteration 5196, lr = 0.001
I0401 16:08:07.303109 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0401 16:08:10.436210 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0401 16:08:12.804096 25640 solver.cpp:330] Iteration 5202, Testing net (#0)
I0401 16:08:12.804117 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:08:15.439051 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:08:17.737607 25640 solver.cpp:397]     Test net output #0: accuracy = 0.13848
I0401 16:08:17.737643 25640 solver.cpp:397]     Test net output #1: loss = 4.07691 (* 1 = 4.07691 loss)
I0401 16:08:19.676826 25640 solver.cpp:218] Iteration 5208 (0.814311 iter/s, 14.7364s/12 iters), loss = 3.13911
I0401 16:08:19.676872 25640 solver.cpp:237]     Train net output #0: loss = 3.13911 (* 1 = 3.13911 loss)
I0401 16:08:19.676877 25640 sgd_solver.cpp:105] Iteration 5208, lr = 0.001
I0401 16:08:25.396795 25640 solver.cpp:218] Iteration 5220 (2.09794 iter/s, 5.7199s/12 iters), loss = 3.26137
I0401 16:08:25.396836 25640 solver.cpp:237]     Train net output #0: loss = 3.26137 (* 1 = 3.26137 loss)
I0401 16:08:25.396842 25640 sgd_solver.cpp:105] Iteration 5220, lr = 0.001
I0401 16:08:31.232285 25640 solver.cpp:218] Iteration 5232 (2.0564 iter/s, 5.83543s/12 iters), loss = 2.7312
I0401 16:08:31.232327 25640 solver.cpp:237]     Train net output #0: loss = 2.7312 (* 1 = 2.7312 loss)
I0401 16:08:31.232333 25640 sgd_solver.cpp:105] Iteration 5232, lr = 0.001
I0401 16:08:37.208350 25640 solver.cpp:218] Iteration 5244 (2.00803 iter/s, 5.97601s/12 iters), loss = 2.85381
I0401 16:08:37.210464 25640 solver.cpp:237]     Train net output #0: loss = 2.85381 (* 1 = 2.85381 loss)
I0401 16:08:37.210476 25640 sgd_solver.cpp:105] Iteration 5244, lr = 0.001
I0401 16:08:42.993424 25640 solver.cpp:218] Iteration 5256 (2.07506 iter/s, 5.78296s/12 iters), loss = 2.93884
I0401 16:08:42.993463 25640 solver.cpp:237]     Train net output #0: loss = 2.93884 (* 1 = 2.93884 loss)
I0401 16:08:42.993468 25640 sgd_solver.cpp:105] Iteration 5256, lr = 0.001
I0401 16:08:44.300060 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:08:48.503633 25640 solver.cpp:218] Iteration 5268 (2.1778 iter/s, 5.51016s/12 iters), loss = 2.80315
I0401 16:08:48.503674 25640 solver.cpp:237]     Train net output #0: loss = 2.80315 (* 1 = 2.80315 loss)
I0401 16:08:48.503679 25640 sgd_solver.cpp:105] Iteration 5268, lr = 0.001
I0401 16:08:54.330935 25640 solver.cpp:218] Iteration 5280 (2.05929 iter/s, 5.82724s/12 iters), loss = 2.90563
I0401 16:08:54.330991 25640 solver.cpp:237]     Train net output #0: loss = 2.90563 (* 1 = 2.90563 loss)
I0401 16:08:54.330999 25640 sgd_solver.cpp:105] Iteration 5280, lr = 0.001
I0401 16:09:00.130604 25640 solver.cpp:218] Iteration 5292 (2.06911 iter/s, 5.7996s/12 iters), loss = 3.10973
I0401 16:09:00.130667 25640 solver.cpp:237]     Train net output #0: loss = 3.10973 (* 1 = 3.10973 loss)
I0401 16:09:00.130676 25640 sgd_solver.cpp:105] Iteration 5292, lr = 0.001
I0401 16:09:05.233805 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0401 16:09:08.378516 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0401 16:09:10.692994 25640 solver.cpp:330] Iteration 5304, Testing net (#0)
I0401 16:09:10.693017 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:09:13.072082 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:09:15.465430 25640 solver.cpp:397]     Test net output #0: accuracy = 0.143382
I0401 16:09:15.465471 25640 solver.cpp:397]     Test net output #1: loss = 4.04929 (* 1 = 4.04929 loss)
I0401 16:09:15.606597 25640 solver.cpp:218] Iteration 5304 (0.775398 iter/s, 15.4759s/12 iters), loss = 3.08038
I0401 16:09:15.606650 25640 solver.cpp:237]     Train net output #0: loss = 3.08038 (* 1 = 3.08038 loss)
I0401 16:09:15.606657 25640 sgd_solver.cpp:105] Iteration 5304, lr = 0.001
I0401 16:09:20.297636 25640 solver.cpp:218] Iteration 5316 (2.5581 iter/s, 4.69097s/12 iters), loss = 2.80202
I0401 16:09:20.297683 25640 solver.cpp:237]     Train net output #0: loss = 2.80202 (* 1 = 2.80202 loss)
I0401 16:09:20.297691 25640 sgd_solver.cpp:105] Iteration 5316, lr = 0.001
I0401 16:09:26.049036 25640 solver.cpp:218] Iteration 5328 (2.08647 iter/s, 5.75134s/12 iters), loss = 2.87977
I0401 16:09:26.049086 25640 solver.cpp:237]     Train net output #0: loss = 2.87977 (* 1 = 2.87977 loss)
I0401 16:09:26.049093 25640 sgd_solver.cpp:105] Iteration 5328, lr = 0.001
I0401 16:09:31.819075 25640 solver.cpp:218] Iteration 5340 (2.07973 iter/s, 5.76998s/12 iters), loss = 2.43577
I0401 16:09:31.819128 25640 solver.cpp:237]     Train net output #0: loss = 2.43577 (* 1 = 2.43577 loss)
I0401 16:09:31.819135 25640 sgd_solver.cpp:105] Iteration 5340, lr = 0.001
I0401 16:09:37.679308 25640 solver.cpp:218] Iteration 5352 (2.04772 iter/s, 5.86017s/12 iters), loss = 2.53253
I0401 16:09:37.679359 25640 solver.cpp:237]     Train net output #0: loss = 2.53253 (* 1 = 2.53253 loss)
I0401 16:09:37.679368 25640 sgd_solver.cpp:105] Iteration 5352, lr = 0.001
I0401 16:09:41.365723 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:09:43.186862 25640 solver.cpp:218] Iteration 5364 (2.17885 iter/s, 5.50749s/12 iters), loss = 2.99101
I0401 16:09:43.186925 25640 solver.cpp:237]     Train net output #0: loss = 2.99101 (* 1 = 2.99101 loss)
I0401 16:09:43.186935 25640 sgd_solver.cpp:105] Iteration 5364, lr = 0.001
I0401 16:09:48.585165 25640 solver.cpp:218] Iteration 5376 (2.22295 iter/s, 5.39823s/12 iters), loss = 2.57157
I0401 16:09:48.585214 25640 solver.cpp:237]     Train net output #0: loss = 2.57157 (* 1 = 2.57157 loss)
I0401 16:09:48.585222 25640 sgd_solver.cpp:105] Iteration 5376, lr = 0.001
I0401 16:09:54.453299 25640 solver.cpp:218] Iteration 5388 (2.04497 iter/s, 5.86807s/12 iters), loss = 2.93956
I0401 16:09:54.453339 25640 solver.cpp:237]     Train net output #0: loss = 2.93956 (* 1 = 2.93956 loss)
I0401 16:09:54.453346 25640 sgd_solver.cpp:105] Iteration 5388, lr = 0.001
I0401 16:10:00.278659 25640 solver.cpp:218] Iteration 5400 (2.05998 iter/s, 5.8253s/12 iters), loss = 2.87098
I0401 16:10:00.278709 25640 solver.cpp:237]     Train net output #0: loss = 2.87098 (* 1 = 2.87098 loss)
I0401 16:10:00.278717 25640 sgd_solver.cpp:105] Iteration 5400, lr = 0.001
I0401 16:10:02.571491 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0401 16:10:05.705974 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0401 16:10:08.011272 25640 solver.cpp:330] Iteration 5406, Testing net (#0)
I0401 16:10:08.011296 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:10:10.562916 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:10:13.016569 25640 solver.cpp:397]     Test net output #0: accuracy = 0.139706
I0401 16:10:13.016666 25640 solver.cpp:397]     Test net output #1: loss = 4.09545 (* 1 = 4.09545 loss)
I0401 16:10:15.104158 25640 solver.cpp:218] Iteration 5412 (0.80942 iter/s, 14.8254s/12 iters), loss = 2.92518
I0401 16:10:15.104218 25640 solver.cpp:237]     Train net output #0: loss = 2.92518 (* 1 = 2.92518 loss)
I0401 16:10:15.104225 25640 sgd_solver.cpp:105] Iteration 5412, lr = 0.001
I0401 16:10:20.778350 25640 solver.cpp:218] Iteration 5424 (2.11487 iter/s, 5.67412s/12 iters), loss = 2.79764
I0401 16:10:20.778396 25640 solver.cpp:237]     Train net output #0: loss = 2.79764 (* 1 = 2.79764 loss)
I0401 16:10:20.778401 25640 sgd_solver.cpp:105] Iteration 5424, lr = 0.001
I0401 16:10:26.326531 25640 solver.cpp:218] Iteration 5436 (2.1629 iter/s, 5.54812s/12 iters), loss = 2.31965
I0401 16:10:26.326587 25640 solver.cpp:237]     Train net output #0: loss = 2.31965 (* 1 = 2.31965 loss)
I0401 16:10:26.326596 25640 sgd_solver.cpp:105] Iteration 5436, lr = 0.001
I0401 16:10:31.982750 25640 solver.cpp:218] Iteration 5448 (2.12159 iter/s, 5.65615s/12 iters), loss = 2.41141
I0401 16:10:31.982793 25640 solver.cpp:237]     Train net output #0: loss = 2.41141 (* 1 = 2.41141 loss)
I0401 16:10:31.982800 25640 sgd_solver.cpp:105] Iteration 5448, lr = 0.001
I0401 16:10:37.462949 25640 solver.cpp:218] Iteration 5460 (2.18973 iter/s, 5.48014s/12 iters), loss = 2.47191
I0401 16:10:37.462994 25640 solver.cpp:237]     Train net output #0: loss = 2.47191 (* 1 = 2.47191 loss)
I0401 16:10:37.463001 25640 sgd_solver.cpp:105] Iteration 5460, lr = 0.001
I0401 16:10:38.211930 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:10:43.316293 25640 solver.cpp:218] Iteration 5472 (2.05013 iter/s, 5.85328s/12 iters), loss = 2.83163
I0401 16:10:43.316435 25640 solver.cpp:237]     Train net output #0: loss = 2.83163 (* 1 = 2.83163 loss)
I0401 16:10:43.316444 25640 sgd_solver.cpp:105] Iteration 5472, lr = 0.001
I0401 16:10:48.987308 25640 solver.cpp:218] Iteration 5484 (2.11608 iter/s, 5.67086s/12 iters), loss = 2.62988
I0401 16:10:48.987365 25640 solver.cpp:237]     Train net output #0: loss = 2.62988 (* 1 = 2.62988 loss)
I0401 16:10:48.987372 25640 sgd_solver.cpp:105] Iteration 5484, lr = 0.001
I0401 16:10:54.584210 25640 solver.cpp:218] Iteration 5496 (2.14407 iter/s, 5.59683s/12 iters), loss = 3.07879
I0401 16:10:54.584267 25640 solver.cpp:237]     Train net output #0: loss = 3.07879 (* 1 = 3.07879 loss)
I0401 16:10:54.584275 25640 sgd_solver.cpp:105] Iteration 5496, lr = 0.001
I0401 16:10:59.502593 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0401 16:11:02.589287 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0401 16:11:04.990559 25640 solver.cpp:330] Iteration 5508, Testing net (#0)
I0401 16:11:04.990577 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:11:07.452620 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:11:09.995669 25640 solver.cpp:397]     Test net output #0: accuracy = 0.144608
I0401 16:11:09.995735 25640 solver.cpp:397]     Test net output #1: loss = 4.08613 (* 1 = 4.08613 loss)
I0401 16:11:10.145810 25640 solver.cpp:218] Iteration 5508 (0.771132 iter/s, 15.5615s/12 iters), loss = 3.34587
I0401 16:11:10.145869 25640 solver.cpp:237]     Train net output #0: loss = 3.34587 (* 1 = 3.34587 loss)
I0401 16:11:10.145876 25640 sgd_solver.cpp:105] Iteration 5508, lr = 0.001
I0401 16:11:14.668244 25640 solver.cpp:218] Iteration 5520 (2.65348 iter/s, 4.52236s/12 iters), loss = 2.92215
I0401 16:11:14.668344 25640 solver.cpp:237]     Train net output #0: loss = 2.92215 (* 1 = 2.92215 loss)
I0401 16:11:14.668354 25640 sgd_solver.cpp:105] Iteration 5520, lr = 0.001
I0401 16:11:17.596879 25640 blocking_queue.cpp:49] Waiting for data
I0401 16:11:20.591157 25640 solver.cpp:218] Iteration 5532 (2.02607 iter/s, 5.9228s/12 iters), loss = 2.33181
I0401 16:11:20.591200 25640 solver.cpp:237]     Train net output #0: loss = 2.33181 (* 1 = 2.33181 loss)
I0401 16:11:20.591205 25640 sgd_solver.cpp:105] Iteration 5532, lr = 0.001
I0401 16:11:26.155676 25640 solver.cpp:218] Iteration 5544 (2.15655 iter/s, 5.56445s/12 iters), loss = 2.61668
I0401 16:11:26.155723 25640 solver.cpp:237]     Train net output #0: loss = 2.61668 (* 1 = 2.61668 loss)
I0401 16:11:26.155728 25640 sgd_solver.cpp:105] Iteration 5544, lr = 0.001
I0401 16:11:32.110461 25640 solver.cpp:218] Iteration 5556 (2.01521 iter/s, 5.95473s/12 iters), loss = 2.42536
I0401 16:11:32.110502 25640 solver.cpp:237]     Train net output #0: loss = 2.42536 (* 1 = 2.42536 loss)
I0401 16:11:32.110507 25640 sgd_solver.cpp:105] Iteration 5556, lr = 0.001
I0401 16:11:35.380407 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:11:37.942034 25640 solver.cpp:218] Iteration 5568 (2.05779 iter/s, 5.83151s/12 iters), loss = 3.04432
I0401 16:11:37.948446 25640 solver.cpp:237]     Train net output #0: loss = 3.04432 (* 1 = 3.04432 loss)
I0401 16:11:37.948465 25640 sgd_solver.cpp:105] Iteration 5568, lr = 0.001
I0401 16:11:43.712910 25640 solver.cpp:218] Iteration 5580 (2.08172 iter/s, 5.76446s/12 iters), loss = 2.72607
I0401 16:11:43.712956 25640 solver.cpp:237]     Train net output #0: loss = 2.72607 (* 1 = 2.72607 loss)
I0401 16:11:43.712962 25640 sgd_solver.cpp:105] Iteration 5580, lr = 0.001
I0401 16:11:49.588937 25640 solver.cpp:218] Iteration 5592 (2.04222 iter/s, 5.87596s/12 iters), loss = 2.85932
I0401 16:11:49.589063 25640 solver.cpp:237]     Train net output #0: loss = 2.85932 (* 1 = 2.85932 loss)
I0401 16:11:49.589071 25640 sgd_solver.cpp:105] Iteration 5592, lr = 0.001
I0401 16:11:55.547428 25640 solver.cpp:218] Iteration 5604 (2.01398 iter/s, 5.95835s/12 iters), loss = 2.93137
I0401 16:11:55.547477 25640 solver.cpp:237]     Train net output #0: loss = 2.93137 (* 1 = 2.93137 loss)
I0401 16:11:55.547484 25640 sgd_solver.cpp:105] Iteration 5604, lr = 0.001
I0401 16:11:57.809993 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0401 16:12:00.921852 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0401 16:12:03.228705 25640 solver.cpp:330] Iteration 5610, Testing net (#0)
I0401 16:12:03.228729 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:12:05.516114 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:12:07.908322 25640 solver.cpp:397]     Test net output #0: accuracy = 0.142157
I0401 16:12:07.908356 25640 solver.cpp:397]     Test net output #1: loss = 4.08759 (* 1 = 4.08759 loss)
I0401 16:12:09.862784 25640 solver.cpp:218] Iteration 5616 (0.838264 iter/s, 14.3153s/12 iters), loss = 2.75079
I0401 16:12:09.862834 25640 solver.cpp:237]     Train net output #0: loss = 2.75079 (* 1 = 2.75079 loss)
I0401 16:12:09.862841 25640 sgd_solver.cpp:105] Iteration 5616, lr = 0.001
I0401 16:12:15.295100 25640 solver.cpp:218] Iteration 5628 (2.20903 iter/s, 5.43225s/12 iters), loss = 2.51618
I0401 16:12:15.295150 25640 solver.cpp:237]     Train net output #0: loss = 2.51618 (* 1 = 2.51618 loss)
I0401 16:12:15.295157 25640 sgd_solver.cpp:105] Iteration 5628, lr = 0.001
I0401 16:12:21.021867 25640 solver.cpp:218] Iteration 5640 (2.09545 iter/s, 5.7267s/12 iters), loss = 2.24114
I0401 16:12:21.021994 25640 solver.cpp:237]     Train net output #0: loss = 2.24114 (* 1 = 2.24114 loss)
I0401 16:12:21.022003 25640 sgd_solver.cpp:105] Iteration 5640, lr = 0.001
I0401 16:12:26.598294 25640 solver.cpp:218] Iteration 5652 (2.15197 iter/s, 5.57628s/12 iters), loss = 2.37165
I0401 16:12:26.598341 25640 solver.cpp:237]     Train net output #0: loss = 2.37165 (* 1 = 2.37165 loss)
I0401 16:12:26.598349 25640 sgd_solver.cpp:105] Iteration 5652, lr = 0.001
I0401 16:12:32.354336 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:12:32.601534 25640 solver.cpp:218] Iteration 5664 (1.99894 iter/s, 6.00318s/12 iters), loss = 2.63665
I0401 16:12:32.601577 25640 solver.cpp:237]     Train net output #0: loss = 2.63665 (* 1 = 2.63665 loss)
I0401 16:12:32.601585 25640 sgd_solver.cpp:105] Iteration 5664, lr = 0.001
I0401 16:12:38.651638 25640 solver.cpp:218] Iteration 5676 (1.98346 iter/s, 6.05004s/12 iters), loss = 2.56343
I0401 16:12:38.651688 25640 solver.cpp:237]     Train net output #0: loss = 2.56343 (* 1 = 2.56343 loss)
I0401 16:12:38.651696 25640 sgd_solver.cpp:105] Iteration 5676, lr = 0.001
I0401 16:12:44.296713 25640 solver.cpp:218] Iteration 5688 (2.12577 iter/s, 5.64502s/12 iters), loss = 2.74209
I0401 16:12:44.296751 25640 solver.cpp:237]     Train net output #0: loss = 2.74209 (* 1 = 2.74209 loss)
I0401 16:12:44.296757 25640 sgd_solver.cpp:105] Iteration 5688, lr = 0.001
I0401 16:12:49.890945 25640 solver.cpp:218] Iteration 5700 (2.14509 iter/s, 5.59417s/12 iters), loss = 2.75749
I0401 16:12:49.891014 25640 solver.cpp:237]     Train net output #0: loss = 2.75749 (* 1 = 2.75749 loss)
I0401 16:12:49.891023 25640 sgd_solver.cpp:105] Iteration 5700, lr = 0.001
I0401 16:12:55.091856 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0401 16:13:00.049960 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0401 16:13:06.501190 25640 solver.cpp:330] Iteration 5712, Testing net (#0)
I0401 16:13:06.501211 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:13:08.624469 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:13:10.936280 25640 solver.cpp:397]     Test net output #0: accuracy = 0.148284
I0401 16:13:10.936381 25640 solver.cpp:397]     Test net output #1: loss = 4.10897 (* 1 = 4.10897 loss)
I0401 16:13:11.077517 25640 solver.cpp:218] Iteration 5712 (0.566399 iter/s, 21.1865s/12 iters), loss = 2.83735
I0401 16:13:11.079094 25640 solver.cpp:237]     Train net output #0: loss = 2.83735 (* 1 = 2.83735 loss)
I0401 16:13:11.079104 25640 sgd_solver.cpp:105] Iteration 5712, lr = 0.001
I0401 16:13:15.391403 25640 solver.cpp:218] Iteration 5724 (2.78274 iter/s, 4.3123s/12 iters), loss = 2.71659
I0401 16:13:15.391444 25640 solver.cpp:237]     Train net output #0: loss = 2.71659 (* 1 = 2.71659 loss)
I0401 16:13:15.391450 25640 sgd_solver.cpp:105] Iteration 5724, lr = 0.001
I0401 16:13:20.776934 25640 solver.cpp:218] Iteration 5736 (2.22822 iter/s, 5.38547s/12 iters), loss = 2.33361
I0401 16:13:20.776983 25640 solver.cpp:237]     Train net output #0: loss = 2.33361 (* 1 = 2.33361 loss)
I0401 16:13:20.776990 25640 sgd_solver.cpp:105] Iteration 5736, lr = 0.001
I0401 16:13:26.316421 25640 solver.cpp:218] Iteration 5748 (2.16629 iter/s, 5.53942s/12 iters), loss = 2.19506
I0401 16:13:26.316954 25640 solver.cpp:237]     Train net output #0: loss = 2.19506 (* 1 = 2.19506 loss)
I0401 16:13:26.316964 25640 sgd_solver.cpp:105] Iteration 5748, lr = 0.001
I0401 16:13:31.573586 25640 solver.cpp:218] Iteration 5760 (2.28284 iter/s, 5.25662s/12 iters), loss = 2.31662
I0401 16:13:31.573635 25640 solver.cpp:237]     Train net output #0: loss = 2.31662 (* 1 = 2.31662 loss)
I0401 16:13:31.573643 25640 sgd_solver.cpp:105] Iteration 5760, lr = 0.001
I0401 16:13:33.316184 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:13:36.627820 25640 solver.cpp:218] Iteration 5772 (2.37428 iter/s, 5.05416s/12 iters), loss = 2.4593
I0401 16:13:36.627882 25640 solver.cpp:237]     Train net output #0: loss = 2.4593 (* 1 = 2.4593 loss)
I0401 16:13:36.627890 25640 sgd_solver.cpp:105] Iteration 5772, lr = 0.001
I0401 16:13:42.074324 25640 solver.cpp:218] Iteration 5784 (2.20328 iter/s, 5.44643s/12 iters), loss = 2.70689
I0401 16:13:42.074373 25640 solver.cpp:237]     Train net output #0: loss = 2.70689 (* 1 = 2.70689 loss)
I0401 16:13:42.074383 25640 sgd_solver.cpp:105] Iteration 5784, lr = 0.001
I0401 16:13:47.622895 25640 solver.cpp:218] Iteration 5796 (2.16275 iter/s, 5.5485s/12 iters), loss = 2.78499
I0401 16:13:47.622939 25640 solver.cpp:237]     Train net output #0: loss = 2.78499 (* 1 = 2.78499 loss)
I0401 16:13:47.622946 25640 sgd_solver.cpp:105] Iteration 5796, lr = 0.001
I0401 16:13:53.115932 25640 solver.cpp:218] Iteration 5808 (2.18461 iter/s, 5.49297s/12 iters), loss = 2.70143
I0401 16:13:53.115975 25640 solver.cpp:237]     Train net output #0: loss = 2.70143 (* 1 = 2.70143 loss)
I0401 16:13:53.115981 25640 sgd_solver.cpp:105] Iteration 5808, lr = 0.001
I0401 16:13:55.336320 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0401 16:14:01.158985 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0401 16:14:06.816144 25640 solver.cpp:330] Iteration 5814, Testing net (#0)
I0401 16:14:06.816169 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:14:08.937849 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:14:11.384783 25640 solver.cpp:397]     Test net output #0: accuracy = 0.157475
I0401 16:14:11.384814 25640 solver.cpp:397]     Test net output #1: loss = 4.07363 (* 1 = 4.07363 loss)
I0401 16:14:13.249990 25640 solver.cpp:218] Iteration 5820 (0.596007 iter/s, 20.134s/12 iters), loss = 2.34226
I0401 16:14:13.250030 25640 solver.cpp:237]     Train net output #0: loss = 2.34226 (* 1 = 2.34226 loss)
I0401 16:14:13.250034 25640 sgd_solver.cpp:105] Iteration 5820, lr = 0.001
I0401 16:14:18.617159 25640 solver.cpp:218] Iteration 5832 (2.23584 iter/s, 5.36711s/12 iters), loss = 2.56534
I0401 16:14:18.617218 25640 solver.cpp:237]     Train net output #0: loss = 2.56534 (* 1 = 2.56534 loss)
I0401 16:14:18.617226 25640 sgd_solver.cpp:105] Iteration 5832, lr = 0.001
I0401 16:14:23.679090 25640 solver.cpp:218] Iteration 5844 (2.37067 iter/s, 5.06186s/12 iters), loss = 2.53754
I0401 16:14:23.679126 25640 solver.cpp:237]     Train net output #0: loss = 2.53754 (* 1 = 2.53754 loss)
I0401 16:14:23.679132 25640 sgd_solver.cpp:105] Iteration 5844, lr = 0.001
I0401 16:14:29.173228 25640 solver.cpp:218] Iteration 5856 (2.18417 iter/s, 5.49408s/12 iters), loss = 2.0789
I0401 16:14:29.173283 25640 solver.cpp:237]     Train net output #0: loss = 2.0789 (* 1 = 2.0789 loss)
I0401 16:14:29.173292 25640 sgd_solver.cpp:105] Iteration 5856, lr = 0.001
I0401 16:14:33.581493 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:14:34.480196 25640 solver.cpp:218] Iteration 5868 (2.26121 iter/s, 5.30689s/12 iters), loss = 2.31976
I0401 16:14:34.480252 25640 solver.cpp:237]     Train net output #0: loss = 2.31976 (* 1 = 2.31976 loss)
I0401 16:14:34.480262 25640 sgd_solver.cpp:105] Iteration 5868, lr = 0.001
I0401 16:14:39.826651 25640 solver.cpp:218] Iteration 5880 (2.24451 iter/s, 5.34639s/12 iters), loss = 2.78117
I0401 16:14:39.826696 25640 solver.cpp:237]     Train net output #0: loss = 2.78117 (* 1 = 2.78117 loss)
I0401 16:14:39.826704 25640 sgd_solver.cpp:105] Iteration 5880, lr = 0.001
I0401 16:14:45.258380 25640 solver.cpp:218] Iteration 5892 (2.20927 iter/s, 5.43167s/12 iters), loss = 2.7584
I0401 16:14:45.258424 25640 solver.cpp:237]     Train net output #0: loss = 2.7584 (* 1 = 2.7584 loss)
I0401 16:14:45.258430 25640 sgd_solver.cpp:105] Iteration 5892, lr = 0.001
I0401 16:14:50.633685 25640 solver.cpp:218] Iteration 5904 (2.23246 iter/s, 5.37523s/12 iters), loss = 2.75649
I0401 16:14:50.633744 25640 solver.cpp:237]     Train net output #0: loss = 2.75649 (* 1 = 2.75649 loss)
I0401 16:14:50.633754 25640 sgd_solver.cpp:105] Iteration 5904, lr = 0.001
I0401 16:14:55.583590 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0401 16:15:00.256866 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0401 16:15:04.486997 25640 solver.cpp:330] Iteration 5916, Testing net (#0)
I0401 16:15:04.487099 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:15:06.579493 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:15:08.875025 25640 solver.cpp:397]     Test net output #0: accuracy = 0.164216
I0401 16:15:08.875057 25640 solver.cpp:397]     Test net output #1: loss = 4.02095 (* 1 = 4.02095 loss)
I0401 16:15:09.016492 25640 solver.cpp:218] Iteration 5916 (0.652786 iter/s, 18.3827s/12 iters), loss = 2.63357
I0401 16:15:09.018059 25640 solver.cpp:237]     Train net output #0: loss = 2.63357 (* 1 = 2.63357 loss)
I0401 16:15:09.018072 25640 sgd_solver.cpp:105] Iteration 5916, lr = 0.001
I0401 16:15:13.533262 25640 solver.cpp:218] Iteration 5928 (2.6577 iter/s, 4.51519s/12 iters), loss = 2.53001
I0401 16:15:13.533320 25640 solver.cpp:237]     Train net output #0: loss = 2.53001 (* 1 = 2.53001 loss)
I0401 16:15:13.533329 25640 sgd_solver.cpp:105] Iteration 5928, lr = 0.001
I0401 16:15:19.098516 25640 solver.cpp:218] Iteration 5940 (2.15626 iter/s, 5.56518s/12 iters), loss = 2.30965
I0401 16:15:19.098559 25640 solver.cpp:237]     Train net output #0: loss = 2.30965 (* 1 = 2.30965 loss)
I0401 16:15:19.098565 25640 sgd_solver.cpp:105] Iteration 5940, lr = 0.001
I0401 16:15:24.622262 25640 solver.cpp:218] Iteration 5952 (2.17246 iter/s, 5.52368s/12 iters), loss = 2.60897
I0401 16:15:24.622306 25640 solver.cpp:237]     Train net output #0: loss = 2.60897 (* 1 = 2.60897 loss)
I0401 16:15:24.622311 25640 sgd_solver.cpp:105] Iteration 5952, lr = 0.001
I0401 16:15:30.122898 25640 solver.cpp:218] Iteration 5964 (2.18159 iter/s, 5.50057s/12 iters), loss = 2.39229
I0401 16:15:30.122941 25640 solver.cpp:237]     Train net output #0: loss = 2.39229 (* 1 = 2.39229 loss)
I0401 16:15:30.122951 25640 sgd_solver.cpp:105] Iteration 5964, lr = 0.001
I0401 16:15:31.575804 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:15:35.596753 25640 solver.cpp:218] Iteration 5976 (2.19227 iter/s, 5.47379s/12 iters), loss = 2.24526
I0401 16:15:35.596915 25640 solver.cpp:237]     Train net output #0: loss = 2.24526 (* 1 = 2.24526 loss)
I0401 16:15:35.596925 25640 sgd_solver.cpp:105] Iteration 5976, lr = 0.001
I0401 16:15:40.923777 25640 solver.cpp:218] Iteration 5988 (2.25274 iter/s, 5.32685s/12 iters), loss = 2.99923
I0401 16:15:40.923825 25640 solver.cpp:237]     Train net output #0: loss = 2.99923 (* 1 = 2.99923 loss)
I0401 16:15:40.923835 25640 sgd_solver.cpp:105] Iteration 5988, lr = 0.001
I0401 16:15:46.519212 25640 solver.cpp:218] Iteration 6000 (2.14463 iter/s, 5.59537s/12 iters), loss = 2.61579
I0401 16:15:46.519265 25640 solver.cpp:237]     Train net output #0: loss = 2.61579 (* 1 = 2.61579 loss)
I0401 16:15:46.519273 25640 sgd_solver.cpp:105] Iteration 6000, lr = 0.001
I0401 16:15:51.837990 25640 solver.cpp:218] Iteration 6012 (2.25619 iter/s, 5.31871s/12 iters), loss = 2.7513
I0401 16:15:51.838044 25640 solver.cpp:237]     Train net output #0: loss = 2.7513 (* 1 = 2.7513 loss)
I0401 16:15:51.838052 25640 sgd_solver.cpp:105] Iteration 6012, lr = 0.001
I0401 16:15:54.069327 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0401 16:15:59.653473 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0401 16:16:03.593550 25640 solver.cpp:330] Iteration 6018, Testing net (#0)
I0401 16:16:03.593573 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:16:05.665024 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:16:08.150715 25640 solver.cpp:397]     Test net output #0: accuracy = 0.172181
I0401 16:16:08.150753 25640 solver.cpp:397]     Test net output #1: loss = 3.97191 (* 1 = 3.97191 loss)
I0401 16:16:10.097820 25640 solver.cpp:218] Iteration 6024 (0.657183 iter/s, 18.2598s/12 iters), loss = 2.68692
I0401 16:16:10.097862 25640 solver.cpp:237]     Train net output #0: loss = 2.68692 (* 1 = 2.68692 loss)
I0401 16:16:10.097867 25640 sgd_solver.cpp:105] Iteration 6024, lr = 0.001
I0401 16:16:15.465776 25640 solver.cpp:218] Iteration 6036 (2.23551 iter/s, 5.3679s/12 iters), loss = 2.24486
I0401 16:16:15.465826 25640 solver.cpp:237]     Train net output #0: loss = 2.24486 (* 1 = 2.24486 loss)
I0401 16:16:15.465834 25640 sgd_solver.cpp:105] Iteration 6036, lr = 0.001
I0401 16:16:20.729249 25640 solver.cpp:218] Iteration 6048 (2.27989 iter/s, 5.2634s/12 iters), loss = 1.92181
I0401 16:16:20.729301 25640 solver.cpp:237]     Train net output #0: loss = 1.92181 (* 1 = 1.92181 loss)
I0401 16:16:20.729310 25640 sgd_solver.cpp:105] Iteration 6048, lr = 0.001
I0401 16:16:26.012523 25640 solver.cpp:218] Iteration 6060 (2.27135 iter/s, 5.28321s/12 iters), loss = 2.18697
I0401 16:16:26.012562 25640 solver.cpp:237]     Train net output #0: loss = 2.18697 (* 1 = 2.18697 loss)
I0401 16:16:26.012568 25640 sgd_solver.cpp:105] Iteration 6060, lr = 0.001
I0401 16:16:29.658604 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:16:31.341364 25640 solver.cpp:218] Iteration 6072 (2.25192 iter/s, 5.32878s/12 iters), loss = 2.05991
I0401 16:16:31.341413 25640 solver.cpp:237]     Train net output #0: loss = 2.05991 (* 1 = 2.05991 loss)
I0401 16:16:31.341419 25640 sgd_solver.cpp:105] Iteration 6072, lr = 0.001
I0401 16:16:36.562867 25640 solver.cpp:218] Iteration 6084 (2.29822 iter/s, 5.22144s/12 iters), loss = 2.3612
I0401 16:16:36.563016 25640 solver.cpp:237]     Train net output #0: loss = 2.3612 (* 1 = 2.3612 loss)
I0401 16:16:36.563026 25640 sgd_solver.cpp:105] Iteration 6084, lr = 0.001
I0401 16:16:41.774567 25640 solver.cpp:218] Iteration 6096 (2.30258 iter/s, 5.21153s/12 iters), loss = 2.26614
I0401 16:16:41.774617 25640 solver.cpp:237]     Train net output #0: loss = 2.26614 (* 1 = 2.26614 loss)
I0401 16:16:41.774626 25640 sgd_solver.cpp:105] Iteration 6096, lr = 0.001
I0401 16:16:46.714394 25640 solver.cpp:218] Iteration 6108 (2.42927 iter/s, 4.93976s/12 iters), loss = 2.49337
I0401 16:16:46.714452 25640 solver.cpp:237]     Train net output #0: loss = 2.49337 (* 1 = 2.49337 loss)
I0401 16:16:46.714459 25640 sgd_solver.cpp:105] Iteration 6108, lr = 0.001
I0401 16:16:51.620628 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0401 16:16:56.038020 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0401 16:16:59.317265 25640 solver.cpp:330] Iteration 6120, Testing net (#0)
I0401 16:16:59.317286 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:17:01.300199 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:17:03.906810 25640 solver.cpp:397]     Test net output #0: accuracy = 0.156863
I0401 16:17:03.906836 25640 solver.cpp:397]     Test net output #1: loss = 4.01713 (* 1 = 4.01713 loss)
I0401 16:17:04.047812 25640 solver.cpp:218] Iteration 6120 (0.692307 iter/s, 17.3334s/12 iters), loss = 2.37484
I0401 16:17:04.047873 25640 solver.cpp:237]     Train net output #0: loss = 2.37484 (* 1 = 2.37484 loss)
I0401 16:17:04.047880 25640 sgd_solver.cpp:105] Iteration 6120, lr = 0.001
I0401 16:17:08.472944 25640 solver.cpp:218] Iteration 6132 (2.71183 iter/s, 4.42506s/12 iters), loss = 2.32704
I0401 16:17:08.473033 25640 solver.cpp:237]     Train net output #0: loss = 2.32704 (* 1 = 2.32704 loss)
I0401 16:17:08.473040 25640 sgd_solver.cpp:105] Iteration 6132, lr = 0.001
I0401 16:17:14.006965 25640 solver.cpp:218] Iteration 6144 (2.16845 iter/s, 5.53391s/12 iters), loss = 1.87512
I0401 16:17:14.007007 25640 solver.cpp:237]     Train net output #0: loss = 1.87512 (* 1 = 1.87512 loss)
I0401 16:17:14.007012 25640 sgd_solver.cpp:105] Iteration 6144, lr = 0.001
I0401 16:17:19.477257 25640 solver.cpp:218] Iteration 6156 (2.19369 iter/s, 5.47023s/12 iters), loss = 1.78314
I0401 16:17:19.477300 25640 solver.cpp:237]     Train net output #0: loss = 1.78314 (* 1 = 1.78314 loss)
I0401 16:17:19.477306 25640 sgd_solver.cpp:105] Iteration 6156, lr = 0.001
I0401 16:17:24.834205 25640 solver.cpp:218] Iteration 6168 (2.24011 iter/s, 5.35688s/12 iters), loss = 1.7639
I0401 16:17:24.834264 25640 solver.cpp:237]     Train net output #0: loss = 1.7639 (* 1 = 1.7639 loss)
I0401 16:17:24.834273 25640 sgd_solver.cpp:105] Iteration 6168, lr = 0.001
I0401 16:17:25.458027 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:17:29.903988 25640 solver.cpp:218] Iteration 6180 (2.36728 iter/s, 5.06912s/12 iters), loss = 2.22309
I0401 16:17:29.904040 25640 solver.cpp:237]     Train net output #0: loss = 2.22309 (* 1 = 2.22309 loss)
I0401 16:17:29.904048 25640 sgd_solver.cpp:105] Iteration 6180, lr = 0.001
I0401 16:17:35.451014 25640 solver.cpp:218] Iteration 6192 (2.16335 iter/s, 5.54696s/12 iters), loss = 2.16563
I0401 16:17:35.451054 25640 solver.cpp:237]     Train net output #0: loss = 2.16563 (* 1 = 2.16563 loss)
I0401 16:17:35.451061 25640 sgd_solver.cpp:105] Iteration 6192, lr = 0.001
I0401 16:17:40.573395 25640 solver.cpp:218] Iteration 6204 (2.34269 iter/s, 5.12232s/12 iters), loss = 2.15662
I0401 16:17:40.573555 25640 solver.cpp:237]     Train net output #0: loss = 2.15662 (* 1 = 2.15662 loss)
I0401 16:17:40.573567 25640 sgd_solver.cpp:105] Iteration 6204, lr = 0.001
I0401 16:17:46.238029 25640 solver.cpp:218] Iteration 6216 (2.11847 iter/s, 5.66447s/12 iters), loss = 2.30349
I0401 16:17:46.238066 25640 solver.cpp:237]     Train net output #0: loss = 2.30349 (* 1 = 2.30349 loss)
I0401 16:17:46.238071 25640 sgd_solver.cpp:105] Iteration 6216, lr = 0.001
I0401 16:17:48.347206 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0401 16:17:52.257120 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0401 16:17:54.598083 25640 solver.cpp:330] Iteration 6222, Testing net (#0)
I0401 16:17:54.598109 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:17:56.517993 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:17:57.866700 25640 blocking_queue.cpp:49] Waiting for data
I0401 16:17:59.025455 25640 solver.cpp:397]     Test net output #0: accuracy = 0.175245
I0401 16:17:59.025493 25640 solver.cpp:397]     Test net output #1: loss = 3.96446 (* 1 = 3.96446 loss)
I0401 16:18:01.099102 25640 solver.cpp:218] Iteration 6228 (0.807482 iter/s, 14.861s/12 iters), loss = 2.02777
I0401 16:18:01.099161 25640 solver.cpp:237]     Train net output #0: loss = 2.02777 (* 1 = 2.02777 loss)
I0401 16:18:01.099171 25640 sgd_solver.cpp:105] Iteration 6228, lr = 0.001
I0401 16:18:06.349195 25640 solver.cpp:218] Iteration 6240 (2.28571 iter/s, 5.25002s/12 iters), loss = 1.8877
I0401 16:18:06.349247 25640 solver.cpp:237]     Train net output #0: loss = 1.8877 (* 1 = 1.8877 loss)
I0401 16:18:06.349256 25640 sgd_solver.cpp:105] Iteration 6240, lr = 0.001
I0401 16:18:11.582839 25640 solver.cpp:218] Iteration 6252 (2.29288 iter/s, 5.23358s/12 iters), loss = 1.72417
I0401 16:18:11.582927 25640 solver.cpp:237]     Train net output #0: loss = 1.72417 (* 1 = 1.72417 loss)
I0401 16:18:11.582934 25640 sgd_solver.cpp:105] Iteration 6252, lr = 0.001
I0401 16:18:16.947894 25640 solver.cpp:218] Iteration 6264 (2.23674 iter/s, 5.36495s/12 iters), loss = 2.02819
I0401 16:18:16.947935 25640 solver.cpp:237]     Train net output #0: loss = 2.02819 (* 1 = 2.02819 loss)
I0401 16:18:16.947940 25640 sgd_solver.cpp:105] Iteration 6264, lr = 0.001
I0401 16:18:19.767143 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:18:22.323810 25640 solver.cpp:218] Iteration 6276 (2.2322 iter/s, 5.37586s/12 iters), loss = 2.02894
I0401 16:18:22.323848 25640 solver.cpp:237]     Train net output #0: loss = 2.02894 (* 1 = 2.02894 loss)
I0401 16:18:22.323853 25640 sgd_solver.cpp:105] Iteration 6276, lr = 0.001
I0401 16:18:27.649456 25640 solver.cpp:218] Iteration 6288 (2.25327 iter/s, 5.32559s/12 iters), loss = 1.94224
I0401 16:18:27.649508 25640 solver.cpp:237]     Train net output #0: loss = 1.94224 (* 1 = 1.94224 loss)
I0401 16:18:27.649516 25640 sgd_solver.cpp:105] Iteration 6288, lr = 0.001
I0401 16:18:33.059800 25640 solver.cpp:218] Iteration 6300 (2.218 iter/s, 5.41028s/12 iters), loss = 2.422
I0401 16:18:33.059854 25640 solver.cpp:237]     Train net output #0: loss = 2.422 (* 1 = 2.422 loss)
I0401 16:18:33.059864 25640 sgd_solver.cpp:105] Iteration 6300, lr = 0.001
I0401 16:18:38.548214 25640 solver.cpp:218] Iteration 6312 (2.18645 iter/s, 5.48835s/12 iters), loss = 2.375
I0401 16:18:38.548256 25640 solver.cpp:237]     Train net output #0: loss = 2.375 (* 1 = 2.375 loss)
I0401 16:18:38.548262 25640 sgd_solver.cpp:105] Iteration 6312, lr = 0.001
I0401 16:18:43.277671 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0401 16:18:46.279354 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0401 16:18:48.595974 25640 solver.cpp:330] Iteration 6324, Testing net (#0)
I0401 16:18:48.595994 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:18:50.606649 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:18:53.122889 25640 solver.cpp:397]     Test net output #0: accuracy = 0.175858
I0401 16:18:53.122916 25640 solver.cpp:397]     Test net output #1: loss = 4.01582 (* 1 = 4.01582 loss)
I0401 16:18:53.263983 25640 solver.cpp:218] Iteration 6324 (0.815455 iter/s, 14.7157s/12 iters), loss = 2.03961
I0401 16:18:53.264029 25640 solver.cpp:237]     Train net output #0: loss = 2.03961 (* 1 = 2.03961 loss)
I0401 16:18:53.264034 25640 sgd_solver.cpp:105] Iteration 6324, lr = 0.001
I0401 16:18:57.468659 25640 solver.cpp:218] Iteration 6336 (2.85401 iter/s, 4.20462s/12 iters), loss = 2.05737
I0401 16:18:57.468708 25640 solver.cpp:237]     Train net output #0: loss = 2.05737 (* 1 = 2.05737 loss)
I0401 16:18:57.468715 25640 sgd_solver.cpp:105] Iteration 6336, lr = 0.001
I0401 16:19:02.983389 25640 solver.cpp:218] Iteration 6348 (2.17602 iter/s, 5.51466s/12 iters), loss = 1.70369
I0401 16:19:02.989624 25640 solver.cpp:237]     Train net output #0: loss = 1.70369 (* 1 = 1.70369 loss)
I0401 16:19:02.989645 25640 sgd_solver.cpp:105] Iteration 6348, lr = 0.001
I0401 16:19:08.165696 25640 solver.cpp:218] Iteration 6360 (2.31836 iter/s, 5.17608s/12 iters), loss = 1.57298
I0401 16:19:08.165735 25640 solver.cpp:237]     Train net output #0: loss = 1.57298 (* 1 = 1.57298 loss)
I0401 16:19:08.165740 25640 sgd_solver.cpp:105] Iteration 6360, lr = 0.001
I0401 16:19:13.269248 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:19:13.462265 25640 solver.cpp:218] Iteration 6372 (2.26564 iter/s, 5.29651s/12 iters), loss = 1.64827
I0401 16:19:13.462435 25640 solver.cpp:237]     Train net output #0: loss = 1.64827 (* 1 = 1.64827 loss)
I0401 16:19:13.462445 25640 sgd_solver.cpp:105] Iteration 6372, lr = 0.001
I0401 16:19:18.941304 25640 solver.cpp:218] Iteration 6384 (2.19024 iter/s, 5.47886s/12 iters), loss = 2.08338
I0401 16:19:18.941349 25640 solver.cpp:237]     Train net output #0: loss = 2.08338 (* 1 = 2.08338 loss)
I0401 16:19:18.941357 25640 sgd_solver.cpp:105] Iteration 6384, lr = 0.001
I0401 16:19:24.250186 25640 solver.cpp:218] Iteration 6396 (2.26039 iter/s, 5.30882s/12 iters), loss = 2.33558
I0401 16:19:24.250224 25640 solver.cpp:237]     Train net output #0: loss = 2.33558 (* 1 = 2.33558 loss)
I0401 16:19:24.250229 25640 sgd_solver.cpp:105] Iteration 6396, lr = 0.001
I0401 16:19:29.526042 25640 solver.cpp:218] Iteration 6408 (2.27454 iter/s, 5.2758s/12 iters), loss = 2.44826
I0401 16:19:29.526085 25640 solver.cpp:237]     Train net output #0: loss = 2.44826 (* 1 = 2.44826 loss)
I0401 16:19:29.526090 25640 sgd_solver.cpp:105] Iteration 6408, lr = 0.001
I0401 16:19:34.753829 25640 solver.cpp:218] Iteration 6420 (2.29545 iter/s, 5.22773s/12 iters), loss = 2.40862
I0401 16:19:34.753890 25640 solver.cpp:237]     Train net output #0: loss = 2.40862 (* 1 = 2.40862 loss)
I0401 16:19:34.753899 25640 sgd_solver.cpp:105] Iteration 6420, lr = 0.001
I0401 16:19:36.922192 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0401 16:19:39.996572 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0401 16:19:42.380574 25640 solver.cpp:330] Iteration 6426, Testing net (#0)
I0401 16:19:42.380592 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:19:44.266309 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:19:46.868599 25640 solver.cpp:397]     Test net output #0: accuracy = 0.17402
I0401 16:19:46.868635 25640 solver.cpp:397]     Test net output #1: loss = 4.05125 (* 1 = 4.05125 loss)
I0401 16:19:48.744642 25640 solver.cpp:218] Iteration 6432 (0.85771 iter/s, 13.9907s/12 iters), loss = 1.96036
I0401 16:19:48.744685 25640 solver.cpp:237]     Train net output #0: loss = 1.96036 (* 1 = 1.96036 loss)
I0401 16:19:48.744691 25640 sgd_solver.cpp:105] Iteration 6432, lr = 0.001
I0401 16:19:54.077476 25640 solver.cpp:218] Iteration 6444 (2.25024 iter/s, 5.33277s/12 iters), loss = 1.71704
I0401 16:19:54.077536 25640 solver.cpp:237]     Train net output #0: loss = 1.71704 (* 1 = 1.71704 loss)
I0401 16:19:54.077545 25640 sgd_solver.cpp:105] Iteration 6444, lr = 0.001
I0401 16:19:59.557544 25640 solver.cpp:218] Iteration 6456 (2.18978 iter/s, 5.47999s/12 iters), loss = 1.56268
I0401 16:19:59.557586 25640 solver.cpp:237]     Train net output #0: loss = 1.56268 (* 1 = 1.56268 loss)
I0401 16:19:59.557592 25640 sgd_solver.cpp:105] Iteration 6456, lr = 0.001
I0401 16:20:04.973228 25640 solver.cpp:218] Iteration 6468 (2.21581 iter/s, 5.41562s/12 iters), loss = 1.70203
I0401 16:20:04.973273 25640 solver.cpp:237]     Train net output #0: loss = 1.70203 (* 1 = 1.70203 loss)
I0401 16:20:04.973278 25640 sgd_solver.cpp:105] Iteration 6468, lr = 0.001
I0401 16:20:07.260350 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:20:10.597406 25640 solver.cpp:218] Iteration 6480 (2.13367 iter/s, 5.62412s/12 iters), loss = 1.78191
I0401 16:20:10.597451 25640 solver.cpp:237]     Train net output #0: loss = 1.78191 (* 1 = 1.78191 loss)
I0401 16:20:10.597457 25640 sgd_solver.cpp:105] Iteration 6480, lr = 0.001
I0401 16:20:15.905349 25640 solver.cpp:218] Iteration 6492 (2.26079 iter/s, 5.30788s/12 iters), loss = 2.18049
I0401 16:20:15.905467 25640 solver.cpp:237]     Train net output #0: loss = 2.18049 (* 1 = 2.18049 loss)
I0401 16:20:15.905473 25640 sgd_solver.cpp:105] Iteration 6492, lr = 0.001
I0401 16:20:21.632930 25640 solver.cpp:218] Iteration 6504 (2.09518 iter/s, 5.72744s/12 iters), loss = 2.01874
I0401 16:20:21.632980 25640 solver.cpp:237]     Train net output #0: loss = 2.01874 (* 1 = 2.01874 loss)
I0401 16:20:21.632988 25640 sgd_solver.cpp:105] Iteration 6504, lr = 0.001
I0401 16:20:26.854338 25640 solver.cpp:218] Iteration 6516 (2.29826 iter/s, 5.22135s/12 iters), loss = 2.17492
I0401 16:20:26.854374 25640 solver.cpp:237]     Train net output #0: loss = 2.17492 (* 1 = 2.17492 loss)
I0401 16:20:26.854379 25640 sgd_solver.cpp:105] Iteration 6516, lr = 0.001
I0401 16:20:31.669466 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0401 16:20:34.740896 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0401 16:20:37.071507 25640 solver.cpp:330] Iteration 6528, Testing net (#0)
I0401 16:20:37.071537 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:20:38.968930 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:20:41.541119 25640 solver.cpp:397]     Test net output #0: accuracy = 0.178922
I0401 16:20:41.541152 25640 solver.cpp:397]     Test net output #1: loss = 4.08176 (* 1 = 4.08176 loss)
I0401 16:20:41.682283 25640 solver.cpp:218] Iteration 6528 (0.809286 iter/s, 14.8279s/12 iters), loss = 2.25902
I0401 16:20:41.682337 25640 solver.cpp:237]     Train net output #0: loss = 2.25902 (* 1 = 2.25902 loss)
I0401 16:20:41.682343 25640 sgd_solver.cpp:105] Iteration 6528, lr = 0.001
I0401 16:20:45.941787 25640 solver.cpp:218] Iteration 6540 (2.81727 iter/s, 4.25944s/12 iters), loss = 1.67334
I0401 16:20:45.941893 25640 solver.cpp:237]     Train net output #0: loss = 1.67334 (* 1 = 1.67334 loss)
I0401 16:20:45.941900 25640 sgd_solver.cpp:105] Iteration 6540, lr = 0.001
I0401 16:20:51.211020 25640 solver.cpp:218] Iteration 6552 (2.27743 iter/s, 5.26911s/12 iters), loss = 2.04418
I0401 16:20:51.211071 25640 solver.cpp:237]     Train net output #0: loss = 2.04418 (* 1 = 2.04418 loss)
I0401 16:20:51.211076 25640 sgd_solver.cpp:105] Iteration 6552, lr = 0.001
I0401 16:20:56.385215 25640 solver.cpp:218] Iteration 6564 (2.31923 iter/s, 5.17413s/12 iters), loss = 1.61626
I0401 16:20:56.385269 25640 solver.cpp:237]     Train net output #0: loss = 1.61626 (* 1 = 1.61626 loss)
I0401 16:20:56.385277 25640 sgd_solver.cpp:105] Iteration 6564, lr = 0.001
I0401 16:21:00.834978 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:21:01.699829 25640 solver.cpp:218] Iteration 6576 (2.25796 iter/s, 5.31454s/12 iters), loss = 1.5999
I0401 16:21:01.699887 25640 solver.cpp:237]     Train net output #0: loss = 1.5999 (* 1 = 1.5999 loss)
I0401 16:21:01.699895 25640 sgd_solver.cpp:105] Iteration 6576, lr = 0.001
I0401 16:21:06.989444 25640 solver.cpp:218] Iteration 6588 (2.26862 iter/s, 5.28955s/12 iters), loss = 1.85665
I0401 16:21:06.989485 25640 solver.cpp:237]     Train net output #0: loss = 1.85665 (* 1 = 1.85665 loss)
I0401 16:21:06.989491 25640 sgd_solver.cpp:105] Iteration 6588, lr = 0.001
I0401 16:21:12.335801 25640 solver.cpp:218] Iteration 6600 (2.24454 iter/s, 5.3463s/12 iters), loss = 1.96149
I0401 16:21:12.335852 25640 solver.cpp:237]     Train net output #0: loss = 1.96149 (* 1 = 1.96149 loss)
I0401 16:21:12.335861 25640 sgd_solver.cpp:105] Iteration 6600, lr = 0.001
I0401 16:21:17.778668 25640 solver.cpp:218] Iteration 6612 (2.20475 iter/s, 5.4428s/12 iters), loss = 2.20504
I0401 16:21:17.778823 25640 solver.cpp:237]     Train net output #0: loss = 2.20504 (* 1 = 2.20504 loss)
I0401 16:21:17.778833 25640 sgd_solver.cpp:105] Iteration 6612, lr = 0.001
I0401 16:21:23.095995 25640 solver.cpp:218] Iteration 6624 (2.25684 iter/s, 5.31716s/12 iters), loss = 2.30425
I0401 16:21:23.096036 25640 solver.cpp:237]     Train net output #0: loss = 2.30425 (* 1 = 2.30425 loss)
I0401 16:21:23.096043 25640 sgd_solver.cpp:105] Iteration 6624, lr = 0.001
I0401 16:21:25.039160 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0401 16:21:28.200652 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0401 16:21:30.505033 25640 solver.cpp:330] Iteration 6630, Testing net (#0)
I0401 16:21:30.505056 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:21:32.358135 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:21:35.199167 25640 solver.cpp:397]     Test net output #0: accuracy = 0.197304
I0401 16:21:35.199198 25640 solver.cpp:397]     Test net output #1: loss = 4.08721 (* 1 = 4.08721 loss)
I0401 16:21:37.201082 25640 solver.cpp:218] Iteration 6636 (0.85076 iter/s, 14.105s/12 iters), loss = 2.22925
I0401 16:21:37.201129 25640 solver.cpp:237]     Train net output #0: loss = 2.22925 (* 1 = 2.22925 loss)
I0401 16:21:37.201135 25640 sgd_solver.cpp:105] Iteration 6636, lr = 0.001
I0401 16:21:42.697013 25640 solver.cpp:218] Iteration 6648 (2.18346 iter/s, 5.49586s/12 iters), loss = 1.91797
I0401 16:21:42.697069 25640 solver.cpp:237]     Train net output #0: loss = 1.91797 (* 1 = 1.91797 loss)
I0401 16:21:42.697077 25640 sgd_solver.cpp:105] Iteration 6648, lr = 0.001
I0401 16:21:48.143143 25640 solver.cpp:218] Iteration 6660 (2.20343 iter/s, 5.44606s/12 iters), loss = 1.78163
I0401 16:21:48.143278 25640 solver.cpp:237]     Train net output #0: loss = 1.78163 (* 1 = 1.78163 loss)
I0401 16:21:48.143287 25640 sgd_solver.cpp:105] Iteration 6660, lr = 0.001
I0401 16:21:53.675382 25640 solver.cpp:218] Iteration 6672 (2.16916 iter/s, 5.53209s/12 iters), loss = 1.69625
I0401 16:21:53.675426 25640 solver.cpp:237]     Train net output #0: loss = 1.69625 (* 1 = 1.69625 loss)
I0401 16:21:53.675431 25640 sgd_solver.cpp:105] Iteration 6672, lr = 0.001
I0401 16:21:55.145216 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:21:59.178704 25640 solver.cpp:218] Iteration 6684 (2.18052 iter/s, 5.50327s/12 iters), loss = 1.85575
I0401 16:21:59.178742 25640 solver.cpp:237]     Train net output #0: loss = 1.85575 (* 1 = 1.85575 loss)
I0401 16:21:59.178748 25640 sgd_solver.cpp:105] Iteration 6684, lr = 0.001
I0401 16:22:04.459100 25640 solver.cpp:218] Iteration 6696 (2.27258 iter/s, 5.28034s/12 iters), loss = 2.21385
I0401 16:22:04.459142 25640 solver.cpp:237]     Train net output #0: loss = 2.21385 (* 1 = 2.21385 loss)
I0401 16:22:04.459148 25640 sgd_solver.cpp:105] Iteration 6696, lr = 0.001
I0401 16:22:09.878026 25640 solver.cpp:218] Iteration 6708 (2.21449 iter/s, 5.41886s/12 iters), loss = 1.88615
I0401 16:22:09.878090 25640 solver.cpp:237]     Train net output #0: loss = 1.88615 (* 1 = 1.88615 loss)
I0401 16:22:09.878099 25640 sgd_solver.cpp:105] Iteration 6708, lr = 0.001
I0401 16:22:15.051604 25640 solver.cpp:218] Iteration 6720 (2.31951 iter/s, 5.1735s/12 iters), loss = 1.71494
I0401 16:22:15.051657 25640 solver.cpp:237]     Train net output #0: loss = 1.71494 (* 1 = 1.71494 loss)
I0401 16:22:15.051666 25640 sgd_solver.cpp:105] Iteration 6720, lr = 0.001
I0401 16:22:19.774477 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0401 16:22:22.874689 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0401 16:22:25.168733 25640 solver.cpp:330] Iteration 6732, Testing net (#0)
I0401 16:22:25.168759 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:22:27.036378 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:22:29.807380 25640 solver.cpp:397]     Test net output #0: accuracy = 0.189338
I0401 16:22:29.807416 25640 solver.cpp:397]     Test net output #1: loss = 4.11778 (* 1 = 4.11778 loss)
I0401 16:22:29.948031 25640 solver.cpp:218] Iteration 6732 (0.805566 iter/s, 14.8964s/12 iters), loss = 1.65704
I0401 16:22:29.948105 25640 solver.cpp:237]     Train net output #0: loss = 1.65704 (* 1 = 1.65704 loss)
I0401 16:22:29.948114 25640 sgd_solver.cpp:105] Iteration 6732, lr = 0.001
I0401 16:22:34.364178 25640 solver.cpp:218] Iteration 6744 (2.71736 iter/s, 4.41606s/12 iters), loss = 1.51956
I0401 16:22:34.364234 25640 solver.cpp:237]     Train net output #0: loss = 1.51956 (* 1 = 1.51956 loss)
I0401 16:22:34.364243 25640 sgd_solver.cpp:105] Iteration 6744, lr = 0.001
I0401 16:22:39.742139 25640 solver.cpp:218] Iteration 6756 (2.23136 iter/s, 5.37789s/12 iters), loss = 1.29618
I0401 16:22:39.742185 25640 solver.cpp:237]     Train net output #0: loss = 1.29618 (* 1 = 1.29618 loss)
I0401 16:22:39.742192 25640 sgd_solver.cpp:105] Iteration 6756, lr = 0.001
I0401 16:22:44.980171 25640 solver.cpp:218] Iteration 6768 (2.29096 iter/s, 5.23797s/12 iters), loss = 1.34085
I0401 16:22:44.980214 25640 solver.cpp:237]     Train net output #0: loss = 1.34085 (* 1 = 1.34085 loss)
I0401 16:22:44.980221 25640 sgd_solver.cpp:105] Iteration 6768, lr = 0.001
I0401 16:22:48.779392 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:22:50.431949 25640 solver.cpp:218] Iteration 6780 (2.20114 iter/s, 5.45172s/12 iters), loss = 1.70116
I0401 16:22:50.432042 25640 solver.cpp:237]     Train net output #0: loss = 1.70116 (* 1 = 1.70116 loss)
I0401 16:22:50.432049 25640 sgd_solver.cpp:105] Iteration 6780, lr = 0.001
I0401 16:22:55.747965 25640 solver.cpp:218] Iteration 6792 (2.25738 iter/s, 5.3159s/12 iters), loss = 1.66121
I0401 16:22:55.748023 25640 solver.cpp:237]     Train net output #0: loss = 1.66121 (* 1 = 1.66121 loss)
I0401 16:22:55.748031 25640 sgd_solver.cpp:105] Iteration 6792, lr = 0.001
I0401 16:23:01.440969 25640 solver.cpp:218] Iteration 6804 (2.10788 iter/s, 5.69293s/12 iters), loss = 1.4985
I0401 16:23:01.441027 25640 solver.cpp:237]     Train net output #0: loss = 1.4985 (* 1 = 1.4985 loss)
I0401 16:23:01.441036 25640 sgd_solver.cpp:105] Iteration 6804, lr = 0.001
I0401 16:23:06.420186 25640 solver.cpp:218] Iteration 6816 (2.41005 iter/s, 4.97915s/12 iters), loss = 1.89678
I0401 16:23:06.420226 25640 solver.cpp:237]     Train net output #0: loss = 1.89678 (* 1 = 1.89678 loss)
I0401 16:23:06.420233 25640 sgd_solver.cpp:105] Iteration 6816, lr = 0.001
I0401 16:23:11.826136 25640 solver.cpp:218] Iteration 6828 (2.2198 iter/s, 5.4059s/12 iters), loss = 1.80079
I0401 16:23:11.826179 25640 solver.cpp:237]     Train net output #0: loss = 1.80079 (* 1 = 1.80079 loss)
I0401 16:23:11.826185 25640 sgd_solver.cpp:105] Iteration 6828, lr = 0.001
I0401 16:23:13.998924 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0401 16:23:17.056973 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0401 16:23:19.404999 25640 solver.cpp:330] Iteration 6834, Testing net (#0)
I0401 16:23:19.405025 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:23:21.204937 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:23:23.885200 25640 solver.cpp:397]     Test net output #0: accuracy = 0.184436
I0401 16:23:23.885236 25640 solver.cpp:397]     Test net output #1: loss = 4.14478 (* 1 = 4.14478 loss)
I0401 16:23:25.725824 25640 solver.cpp:218] Iteration 6840 (0.863333 iter/s, 13.8996s/12 iters), loss = 1.50525
I0401 16:23:25.725880 25640 solver.cpp:237]     Train net output #0: loss = 1.50525 (* 1 = 1.50525 loss)
I0401 16:23:25.725888 25640 sgd_solver.cpp:105] Iteration 6840, lr = 0.001
I0401 16:23:30.717651 25640 solver.cpp:218] Iteration 6852 (2.40396 iter/s, 4.99176s/12 iters), loss = 1.41023
I0401 16:23:30.717707 25640 solver.cpp:237]     Train net output #0: loss = 1.41023 (* 1 = 1.41023 loss)
I0401 16:23:30.717716 25640 sgd_solver.cpp:105] Iteration 6852, lr = 0.001
I0401 16:23:36.049131 25640 solver.cpp:218] Iteration 6864 (2.25081 iter/s, 5.33141s/12 iters), loss = 1.27436
I0401 16:23:36.049180 25640 solver.cpp:237]     Train net output #0: loss = 1.27436 (* 1 = 1.27436 loss)
I0401 16:23:36.049187 25640 sgd_solver.cpp:105] Iteration 6864, lr = 0.001
I0401 16:23:41.260481 25640 solver.cpp:218] Iteration 6876 (2.3027 iter/s, 5.21128s/12 iters), loss = 1.20565
I0401 16:23:41.260545 25640 solver.cpp:237]     Train net output #0: loss = 1.20565 (* 1 = 1.20565 loss)
I0401 16:23:41.260555 25640 sgd_solver.cpp:105] Iteration 6876, lr = 0.001
I0401 16:23:41.961139 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:23:46.860129 25640 solver.cpp:218] Iteration 6888 (2.14302 iter/s, 5.59957s/12 iters), loss = 1.53278
I0401 16:23:46.860183 25640 solver.cpp:237]     Train net output #0: loss = 1.53278 (* 1 = 1.53278 loss)
I0401 16:23:46.860190 25640 sgd_solver.cpp:105] Iteration 6888, lr = 0.001
I0401 16:23:52.176614 25640 solver.cpp:218] Iteration 6900 (2.25716 iter/s, 5.31642s/12 iters), loss = 1.88892
I0401 16:23:52.176739 25640 solver.cpp:237]     Train net output #0: loss = 1.88892 (* 1 = 1.88892 loss)
I0401 16:23:52.176749 25640 sgd_solver.cpp:105] Iteration 6900, lr = 0.001
I0401 16:23:57.320858 25640 solver.cpp:218] Iteration 6912 (2.33277 iter/s, 5.1441s/12 iters), loss = 1.54285
I0401 16:23:57.320925 25640 solver.cpp:237]     Train net output #0: loss = 1.54285 (* 1 = 1.54285 loss)
I0401 16:23:57.320935 25640 sgd_solver.cpp:105] Iteration 6912, lr = 0.001
I0401 16:24:02.525911 25640 solver.cpp:218] Iteration 6924 (2.30549 iter/s, 5.20497s/12 iters), loss = 1.67141
I0401 16:24:02.525969 25640 solver.cpp:237]     Train net output #0: loss = 1.67141 (* 1 = 1.67141 loss)
I0401 16:24:02.525979 25640 sgd_solver.cpp:105] Iteration 6924, lr = 0.001
I0401 16:24:07.432476 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0401 16:24:10.338745 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0401 16:24:12.697353 25640 solver.cpp:330] Iteration 6936, Testing net (#0)
I0401 16:24:12.697376 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:24:13.320374 25640 blocking_queue.cpp:49] Waiting for data
I0401 16:24:14.433154 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:24:17.228312 25640 solver.cpp:397]     Test net output #0: accuracy = 0.195466
I0401 16:24:17.228346 25640 solver.cpp:397]     Test net output #1: loss = 4.18712 (* 1 = 4.18712 loss)
I0401 16:24:17.366295 25640 solver.cpp:218] Iteration 6936 (0.808609 iter/s, 14.8403s/12 iters), loss = 1.87282
I0401 16:24:17.366349 25640 solver.cpp:237]     Train net output #0: loss = 1.87282 (* 1 = 1.87282 loss)
I0401 16:24:17.366358 25640 sgd_solver.cpp:105] Iteration 6936, lr = 0.001
I0401 16:24:21.978981 25640 solver.cpp:218] Iteration 6948 (2.60156 iter/s, 4.61261s/12 iters), loss = 1.25718
I0401 16:24:21.979043 25640 solver.cpp:237]     Train net output #0: loss = 1.25718 (* 1 = 1.25718 loss)
I0401 16:24:21.979053 25640 sgd_solver.cpp:105] Iteration 6948, lr = 0.001
I0401 16:24:27.537783 25640 solver.cpp:218] Iteration 6960 (2.15877 iter/s, 5.55873s/12 iters), loss = 1.11682
I0401 16:24:27.537914 25640 solver.cpp:237]     Train net output #0: loss = 1.11682 (* 1 = 1.11682 loss)
I0401 16:24:27.537923 25640 sgd_solver.cpp:105] Iteration 6960, lr = 0.001
I0401 16:24:32.945212 25640 solver.cpp:218] Iteration 6972 (2.21923 iter/s, 5.40728s/12 iters), loss = 1.77471
I0401 16:24:32.945267 25640 solver.cpp:237]     Train net output #0: loss = 1.77471 (* 1 = 1.77471 loss)
I0401 16:24:32.945276 25640 sgd_solver.cpp:105] Iteration 6972, lr = 0.001
I0401 16:24:35.805254 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:24:38.418984 25640 solver.cpp:218] Iteration 6984 (2.1923 iter/s, 5.4737s/12 iters), loss = 1.77231
I0401 16:24:38.419032 25640 solver.cpp:237]     Train net output #0: loss = 1.77231 (* 1 = 1.77231 loss)
I0401 16:24:38.419039 25640 sgd_solver.cpp:105] Iteration 6984, lr = 0.001
I0401 16:24:43.864861 25640 solver.cpp:218] Iteration 6996 (2.20576 iter/s, 5.44031s/12 iters), loss = 1.48842
I0401 16:24:43.864908 25640 solver.cpp:237]     Train net output #0: loss = 1.48842 (* 1 = 1.48842 loss)
I0401 16:24:43.864914 25640 sgd_solver.cpp:105] Iteration 6996, lr = 0.001
I0401 16:24:49.292770 25640 solver.cpp:218] Iteration 7008 (2.21082 iter/s, 5.42784s/12 iters), loss = 1.66034
I0401 16:24:49.292836 25640 solver.cpp:237]     Train net output #0: loss = 1.66034 (* 1 = 1.66034 loss)
I0401 16:24:49.292845 25640 sgd_solver.cpp:105] Iteration 7008, lr = 0.001
I0401 16:24:54.743333 25640 solver.cpp:218] Iteration 7020 (2.20164 iter/s, 5.45049s/12 iters), loss = 1.59677
I0401 16:24:54.743383 25640 solver.cpp:237]     Train net output #0: loss = 1.59677 (* 1 = 1.59677 loss)
I0401 16:24:54.743392 25640 sgd_solver.cpp:105] Iteration 7020, lr = 0.001
I0401 16:25:00.095018 25640 solver.cpp:218] Iteration 7032 (2.24231 iter/s, 5.35162s/12 iters), loss = 1.51763
I0401 16:25:00.095146 25640 solver.cpp:237]     Train net output #0: loss = 1.51763 (* 1 = 1.51763 loss)
I0401 16:25:00.095153 25640 sgd_solver.cpp:105] Iteration 7032, lr = 0.001
I0401 16:25:02.245282 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0401 16:25:05.302400 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0401 16:25:07.610579 25640 solver.cpp:330] Iteration 7038, Testing net (#0)
I0401 16:25:07.610599 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:25:09.264667 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:25:12.161361 25640 solver.cpp:397]     Test net output #0: accuracy = 0.188113
I0401 16:25:12.161394 25640 solver.cpp:397]     Test net output #1: loss = 4.18047 (* 1 = 4.18047 loss)
I0401 16:25:14.173841 25640 solver.cpp:218] Iteration 7044 (0.852353 iter/s, 14.0787s/12 iters), loss = 1.50212
I0401 16:25:14.173902 25640 solver.cpp:237]     Train net output #0: loss = 1.50212 (* 1 = 1.50212 loss)
I0401 16:25:14.173910 25640 sgd_solver.cpp:105] Iteration 7044, lr = 0.001
I0401 16:25:19.436225 25640 solver.cpp:218] Iteration 7056 (2.28037 iter/s, 5.26231s/12 iters), loss = 1.59801
I0401 16:25:19.436285 25640 solver.cpp:237]     Train net output #0: loss = 1.59801 (* 1 = 1.59801 loss)
I0401 16:25:19.436293 25640 sgd_solver.cpp:105] Iteration 7056, lr = 0.001
I0401 16:25:24.802182 25640 solver.cpp:218] Iteration 7068 (2.23635 iter/s, 5.36589s/12 iters), loss = 1.30196
I0401 16:25:24.802223 25640 solver.cpp:237]     Train net output #0: loss = 1.30196 (* 1 = 1.30196 loss)
I0401 16:25:24.802228 25640 sgd_solver.cpp:105] Iteration 7068, lr = 0.001
I0401 16:25:30.017397 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:25:30.182282 25640 solver.cpp:218] Iteration 7080 (2.23047 iter/s, 5.38004s/12 iters), loss = 1.45539
I0401 16:25:30.182382 25640 solver.cpp:237]     Train net output #0: loss = 1.45539 (* 1 = 1.45539 loss)
I0401 16:25:30.182389 25640 sgd_solver.cpp:105] Iteration 7080, lr = 0.001
I0401 16:25:35.910276 25640 solver.cpp:218] Iteration 7092 (2.09502 iter/s, 5.72788s/12 iters), loss = 1.59467
I0401 16:25:35.910320 25640 solver.cpp:237]     Train net output #0: loss = 1.59467 (* 1 = 1.59467 loss)
I0401 16:25:35.910326 25640 sgd_solver.cpp:105] Iteration 7092, lr = 0.001
I0401 16:25:41.540243 25640 solver.cpp:218] Iteration 7104 (2.13147 iter/s, 5.62991s/12 iters), loss = 1.89597
I0401 16:25:41.540287 25640 solver.cpp:237]     Train net output #0: loss = 1.89597 (* 1 = 1.89597 loss)
I0401 16:25:41.540292 25640 sgd_solver.cpp:105] Iteration 7104, lr = 0.001
I0401 16:25:46.893501 25640 solver.cpp:218] Iteration 7116 (2.24165 iter/s, 5.3532s/12 iters), loss = 1.8016
I0401 16:25:46.893543 25640 solver.cpp:237]     Train net output #0: loss = 1.8016 (* 1 = 1.8016 loss)
I0401 16:25:46.893550 25640 sgd_solver.cpp:105] Iteration 7116, lr = 0.001
I0401 16:25:52.299345 25640 solver.cpp:218] Iteration 7128 (2.21985 iter/s, 5.40578s/12 iters), loss = 1.52136
I0401 16:25:52.299401 25640 solver.cpp:237]     Train net output #0: loss = 1.52136 (* 1 = 1.52136 loss)
I0401 16:25:52.299410 25640 sgd_solver.cpp:105] Iteration 7128, lr = 0.001
I0401 16:25:57.304288 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0401 16:26:00.500139 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0401 16:26:02.827401 25640 solver.cpp:330] Iteration 7140, Testing net (#0)
I0401 16:26:02.827420 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:26:04.399868 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:26:07.507525 25640 solver.cpp:397]     Test net output #0: accuracy = 0.190564
I0401 16:26:07.507553 25640 solver.cpp:397]     Test net output #1: loss = 4.09462 (* 1 = 4.09462 loss)
I0401 16:26:07.641290 25640 solver.cpp:218] Iteration 7140 (0.782173 iter/s, 15.3419s/12 iters), loss = 1.27195
I0401 16:26:07.641335 25640 solver.cpp:237]     Train net output #0: loss = 1.27195 (* 1 = 1.27195 loss)
I0401 16:26:07.641340 25640 sgd_solver.cpp:105] Iteration 7140, lr = 0.001
I0401 16:26:11.989915 25640 solver.cpp:218] Iteration 7152 (2.75953 iter/s, 4.34857s/12 iters), loss = 1.38781
I0401 16:26:11.989960 25640 solver.cpp:237]     Train net output #0: loss = 1.38781 (* 1 = 1.38781 loss)
I0401 16:26:11.989966 25640 sgd_solver.cpp:105] Iteration 7152, lr = 0.001
I0401 16:26:17.753787 25640 solver.cpp:218] Iteration 7164 (2.08196 iter/s, 5.76381s/12 iters), loss = 1.22701
I0401 16:26:17.753849 25640 solver.cpp:237]     Train net output #0: loss = 1.22701 (* 1 = 1.22701 loss)
I0401 16:26:17.753859 25640 sgd_solver.cpp:105] Iteration 7164, lr = 0.001
I0401 16:26:23.137987 25640 solver.cpp:218] Iteration 7176 (2.22878 iter/s, 5.38412s/12 iters), loss = 1.28036
I0401 16:26:23.138039 25640 solver.cpp:237]     Train net output #0: loss = 1.28036 (* 1 = 1.28036 loss)
I0401 16:26:23.138047 25640 sgd_solver.cpp:105] Iteration 7176, lr = 0.001
I0401 16:26:25.279639 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:26:28.674698 25640 solver.cpp:218] Iteration 7188 (2.16738 iter/s, 5.53664s/12 iters), loss = 1.55465
I0401 16:26:28.674743 25640 solver.cpp:237]     Train net output #0: loss = 1.55465 (* 1 = 1.55465 loss)
I0401 16:26:28.674748 25640 sgd_solver.cpp:105] Iteration 7188, lr = 0.001
I0401 16:26:33.891770 25640 solver.cpp:218] Iteration 7200 (2.30017 iter/s, 5.21701s/12 iters), loss = 1.73572
I0401 16:26:33.891901 25640 solver.cpp:237]     Train net output #0: loss = 1.73572 (* 1 = 1.73572 loss)
I0401 16:26:33.891911 25640 sgd_solver.cpp:105] Iteration 7200, lr = 0.001
I0401 16:26:39.490147 25640 solver.cpp:218] Iteration 7212 (2.14353 iter/s, 5.59823s/12 iters), loss = 1.68271
I0401 16:26:39.490195 25640 solver.cpp:237]     Train net output #0: loss = 1.68271 (* 1 = 1.68271 loss)
I0401 16:26:39.490202 25640 sgd_solver.cpp:105] Iteration 7212, lr = 0.001
I0401 16:26:44.632731 25640 solver.cpp:218] Iteration 7224 (2.33348 iter/s, 5.14253s/12 iters), loss = 1.82565
I0401 16:26:44.632766 25640 solver.cpp:237]     Train net output #0: loss = 1.82565 (* 1 = 1.82565 loss)
I0401 16:26:44.632771 25640 sgd_solver.cpp:105] Iteration 7224, lr = 0.001
I0401 16:26:50.138984 25640 solver.cpp:218] Iteration 7236 (2.17936 iter/s, 5.5062s/12 iters), loss = 1.37278
I0401 16:26:50.139031 25640 solver.cpp:237]     Train net output #0: loss = 1.37278 (* 1 = 1.37278 loss)
I0401 16:26:50.139036 25640 sgd_solver.cpp:105] Iteration 7236, lr = 0.001
I0401 16:26:52.471674 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0401 16:26:55.402781 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0401 16:26:57.714038 25640 solver.cpp:330] Iteration 7242, Testing net (#0)
I0401 16:26:57.714061 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:26:59.327020 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:27:02.348209 25640 solver.cpp:397]     Test net output #0: accuracy = 0.183824
I0401 16:27:02.348263 25640 solver.cpp:397]     Test net output #1: loss = 4.15382 (* 1 = 4.15382 loss)
I0401 16:27:04.219422 25640 solver.cpp:218] Iteration 7248 (0.85225 iter/s, 14.0804s/12 iters), loss = 1.62039
I0401 16:27:04.219564 25640 solver.cpp:237]     Train net output #0: loss = 1.62039 (* 1 = 1.62039 loss)
I0401 16:27:04.219573 25640 sgd_solver.cpp:105] Iteration 7248, lr = 0.001
I0401 16:27:09.493238 25640 solver.cpp:218] Iteration 7260 (2.27546 iter/s, 5.27366s/12 iters), loss = 1.58291
I0401 16:27:09.493294 25640 solver.cpp:237]     Train net output #0: loss = 1.58291 (* 1 = 1.58291 loss)
I0401 16:27:09.493302 25640 sgd_solver.cpp:105] Iteration 7260, lr = 0.001
I0401 16:27:14.906284 25640 solver.cpp:218] Iteration 7272 (2.2169 iter/s, 5.41297s/12 iters), loss = 1.39462
I0401 16:27:14.906334 25640 solver.cpp:237]     Train net output #0: loss = 1.39462 (* 1 = 1.39462 loss)
I0401 16:27:14.906342 25640 sgd_solver.cpp:105] Iteration 7272, lr = 0.001
I0401 16:27:19.548836 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:27:20.372668 25640 solver.cpp:218] Iteration 7284 (2.19526 iter/s, 5.46632s/12 iters), loss = 1.36851
I0401 16:27:20.372709 25640 solver.cpp:237]     Train net output #0: loss = 1.36851 (* 1 = 1.36851 loss)
I0401 16:27:20.372715 25640 sgd_solver.cpp:105] Iteration 7284, lr = 0.001
I0401 16:27:25.779647 25640 solver.cpp:218] Iteration 7296 (2.21938 iter/s, 5.40692s/12 iters), loss = 1.53514
I0401 16:27:25.779697 25640 solver.cpp:237]     Train net output #0: loss = 1.53514 (* 1 = 1.53514 loss)
I0401 16:27:25.779706 25640 sgd_solver.cpp:105] Iteration 7296, lr = 0.001
I0401 16:27:31.206828 25640 solver.cpp:218] Iteration 7308 (2.21112 iter/s, 5.42712s/12 iters), loss = 1.3269
I0401 16:27:31.206874 25640 solver.cpp:237]     Train net output #0: loss = 1.3269 (* 1 = 1.3269 loss)
I0401 16:27:31.206882 25640 sgd_solver.cpp:105] Iteration 7308, lr = 0.001
I0401 16:27:36.542309 25640 solver.cpp:218] Iteration 7320 (2.24912 iter/s, 5.33542s/12 iters), loss = 1.59705
I0401 16:27:36.542407 25640 solver.cpp:237]     Train net output #0: loss = 1.59705 (* 1 = 1.59705 loss)
I0401 16:27:36.542413 25640 sgd_solver.cpp:105] Iteration 7320, lr = 0.001
I0401 16:27:42.103402 25640 solver.cpp:218] Iteration 7332 (2.15789 iter/s, 5.56098s/12 iters), loss = 1.43161
I0401 16:27:42.103459 25640 solver.cpp:237]     Train net output #0: loss = 1.43161 (* 1 = 1.43161 loss)
I0401 16:27:42.103468 25640 sgd_solver.cpp:105] Iteration 7332, lr = 0.001
I0401 16:27:47.093742 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0401 16:27:50.119230 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0401 16:27:52.442075 25640 solver.cpp:330] Iteration 7344, Testing net (#0)
I0401 16:27:52.442093 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:27:53.988148 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:27:56.885246 25640 solver.cpp:397]     Test net output #0: accuracy = 0.197304
I0401 16:27:56.885284 25640 solver.cpp:397]     Test net output #1: loss = 4.12463 (* 1 = 4.12463 loss)
I0401 16:27:57.020159 25640 solver.cpp:218] Iteration 7344 (0.804468 iter/s, 14.9167s/12 iters), loss = 1.46139
I0401 16:27:57.023192 25640 solver.cpp:237]     Train net output #0: loss = 1.46139 (* 1 = 1.46139 loss)
I0401 16:27:57.023211 25640 sgd_solver.cpp:105] Iteration 7344, lr = 0.001
I0401 16:28:01.391238 25640 solver.cpp:218] Iteration 7356 (2.74722 iter/s, 4.36804s/12 iters), loss = 1.45598
I0401 16:28:01.391276 25640 solver.cpp:237]     Train net output #0: loss = 1.45598 (* 1 = 1.45598 loss)
I0401 16:28:01.391283 25640 sgd_solver.cpp:105] Iteration 7356, lr = 0.001
I0401 16:28:06.612809 25640 solver.cpp:218] Iteration 7368 (2.29818 iter/s, 5.22152s/12 iters), loss = 1.71091
I0401 16:28:06.612957 25640 solver.cpp:237]     Train net output #0: loss = 1.71091 (* 1 = 1.71091 loss)
I0401 16:28:06.612967 25640 sgd_solver.cpp:105] Iteration 7368, lr = 0.001
I0401 16:28:11.820317 25640 solver.cpp:218] Iteration 7380 (2.30444 iter/s, 5.20734s/12 iters), loss = 1.43414
I0401 16:28:11.820372 25640 solver.cpp:237]     Train net output #0: loss = 1.43414 (* 1 = 1.43414 loss)
I0401 16:28:11.820384 25640 sgd_solver.cpp:105] Iteration 7380, lr = 0.001
I0401 16:28:13.257783 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:28:16.967800 25640 solver.cpp:218] Iteration 7392 (2.33127 iter/s, 5.14742s/12 iters), loss = 1.56614
I0401 16:28:16.967844 25640 solver.cpp:237]     Train net output #0: loss = 1.56614 (* 1 = 1.56614 loss)
I0401 16:28:16.967849 25640 sgd_solver.cpp:105] Iteration 7392, lr = 0.001
I0401 16:28:22.334457 25640 solver.cpp:218] Iteration 7404 (2.23605 iter/s, 5.3666s/12 iters), loss = 1.45914
I0401 16:28:22.334502 25640 solver.cpp:237]     Train net output #0: loss = 1.45914 (* 1 = 1.45914 loss)
I0401 16:28:22.334508 25640 sgd_solver.cpp:105] Iteration 7404, lr = 0.001
I0401 16:28:27.459316 25640 solver.cpp:218] Iteration 7416 (2.34156 iter/s, 5.1248s/12 iters), loss = 1.52142
I0401 16:28:27.459360 25640 solver.cpp:237]     Train net output #0: loss = 1.52142 (* 1 = 1.52142 loss)
I0401 16:28:27.459367 25640 sgd_solver.cpp:105] Iteration 7416, lr = 0.001
I0401 16:28:32.620510 25640 solver.cpp:218] Iteration 7428 (2.32507 iter/s, 5.16114s/12 iters), loss = 1.42415
I0401 16:28:32.620548 25640 solver.cpp:237]     Train net output #0: loss = 1.42415 (* 1 = 1.42415 loss)
I0401 16:28:32.620553 25640 sgd_solver.cpp:105] Iteration 7428, lr = 0.001
I0401 16:28:38.061401 25640 solver.cpp:218] Iteration 7440 (2.20554 iter/s, 5.44083s/12 iters), loss = 1.36375
I0401 16:28:38.061496 25640 solver.cpp:237]     Train net output #0: loss = 1.36375 (* 1 = 1.36375 loss)
I0401 16:28:38.061502 25640 sgd_solver.cpp:105] Iteration 7440, lr = 0.001
I0401 16:28:40.125754 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0401 16:28:44.208397 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0401 16:28:46.525928 25640 solver.cpp:330] Iteration 7446, Testing net (#0)
I0401 16:28:46.525949 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:28:47.983399 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:28:50.954921 25640 solver.cpp:397]     Test net output #0: accuracy = 0.198529
I0401 16:28:50.954954 25640 solver.cpp:397]     Test net output #1: loss = 4.12991 (* 1 = 4.12991 loss)
I0401 16:28:52.789212 25640 solver.cpp:218] Iteration 7452 (0.81479 iter/s, 14.7277s/12 iters), loss = 1.3692
I0401 16:28:52.789253 25640 solver.cpp:237]     Train net output #0: loss = 1.3692 (* 1 = 1.3692 loss)
I0401 16:28:52.789258 25640 sgd_solver.cpp:105] Iteration 7452, lr = 0.001
I0401 16:28:58.148617 25640 solver.cpp:218] Iteration 7464 (2.23908 iter/s, 5.35935s/12 iters), loss = 1.36153
I0401 16:28:58.148660 25640 solver.cpp:237]     Train net output #0: loss = 1.36153 (* 1 = 1.36153 loss)
I0401 16:28:58.148666 25640 sgd_solver.cpp:105] Iteration 7464, lr = 0.001
I0401 16:29:03.416790 25640 solver.cpp:218] Iteration 7476 (2.27785 iter/s, 5.26812s/12 iters), loss = 1.14458
I0401 16:29:03.416831 25640 solver.cpp:237]     Train net output #0: loss = 1.14458 (* 1 = 1.14458 loss)
I0401 16:29:03.416836 25640 sgd_solver.cpp:105] Iteration 7476, lr = 0.001
I0401 16:29:07.105911 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:29:08.739884 25640 solver.cpp:218] Iteration 7488 (2.25435 iter/s, 5.32303s/12 iters), loss = 1.16817
I0401 16:29:08.740042 25640 solver.cpp:237]     Train net output #0: loss = 1.16817 (* 1 = 1.16817 loss)
I0401 16:29:08.740051 25640 sgd_solver.cpp:105] Iteration 7488, lr = 0.001
I0401 16:29:13.919135 25640 solver.cpp:218] Iteration 7500 (2.31701 iter/s, 5.17908s/12 iters), loss = 1.54689
I0401 16:29:13.925390 25640 solver.cpp:237]     Train net output #0: loss = 1.54689 (* 1 = 1.54689 loss)
I0401 16:29:13.925408 25640 sgd_solver.cpp:105] Iteration 7500, lr = 0.001
I0401 16:29:19.195652 25640 solver.cpp:218] Iteration 7512 (2.27693 iter/s, 5.27026s/12 iters), loss = 1.68451
I0401 16:29:19.195708 25640 solver.cpp:237]     Train net output #0: loss = 1.68451 (* 1 = 1.68451 loss)
I0401 16:29:19.195716 25640 sgd_solver.cpp:105] Iteration 7512, lr = 0.001
I0401 16:29:24.723183 25640 solver.cpp:218] Iteration 7524 (2.17098 iter/s, 5.52746s/12 iters), loss = 1.88662
I0401 16:29:24.723244 25640 solver.cpp:237]     Train net output #0: loss = 1.88662 (* 1 = 1.88662 loss)
I0401 16:29:24.723253 25640 sgd_solver.cpp:105] Iteration 7524, lr = 0.001
I0401 16:29:30.067039 25640 solver.cpp:218] Iteration 7536 (2.2456 iter/s, 5.34378s/12 iters), loss = 1.34026
I0401 16:29:30.067095 25640 solver.cpp:237]     Train net output #0: loss = 1.34026 (* 1 = 1.34026 loss)
I0401 16:29:30.067102 25640 sgd_solver.cpp:105] Iteration 7536, lr = 0.001
I0401 16:29:34.986622 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0401 16:29:39.517508 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0401 16:29:42.019904 25640 solver.cpp:330] Iteration 7548, Testing net (#0)
I0401 16:29:42.019927 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:29:43.515333 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:29:46.560178 25640 solver.cpp:397]     Test net output #0: accuracy = 0.202819
I0401 16:29:46.560211 25640 solver.cpp:397]     Test net output #1: loss = 4.09441 (* 1 = 4.09441 loss)
I0401 16:29:46.705087 25640 solver.cpp:218] Iteration 7548 (0.721241 iter/s, 16.638s/12 iters), loss = 1.15867
I0401 16:29:46.706645 25640 solver.cpp:237]     Train net output #0: loss = 1.15867 (* 1 = 1.15867 loss)
I0401 16:29:46.706661 25640 sgd_solver.cpp:105] Iteration 7548, lr = 0.001
I0401 16:29:50.855249 25640 solver.cpp:218] Iteration 7560 (2.89254 iter/s, 4.1486s/12 iters), loss = 1.49858
I0401 16:29:50.855288 25640 solver.cpp:237]     Train net output #0: loss = 1.49858 (* 1 = 1.49858 loss)
I0401 16:29:50.855294 25640 sgd_solver.cpp:105] Iteration 7560, lr = 0.001
I0401 16:29:56.230394 25640 solver.cpp:218] Iteration 7572 (2.23252 iter/s, 5.3751s/12 iters), loss = 0.947296
I0401 16:29:56.230433 25640 solver.cpp:237]     Train net output #0: loss = 0.947296 (* 1 = 0.947296 loss)
I0401 16:29:56.230438 25640 sgd_solver.cpp:105] Iteration 7572, lr = 0.001
I0401 16:30:01.723294 25640 solver.cpp:218] Iteration 7584 (2.18466 iter/s, 5.49284s/12 iters), loss = 0.985842
I0401 16:30:01.723343 25640 solver.cpp:237]     Train net output #0: loss = 0.985842 (* 1 = 0.985842 loss)
I0401 16:30:01.723351 25640 sgd_solver.cpp:105] Iteration 7584, lr = 0.001
I0401 16:30:02.406445 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:30:06.838896 25640 solver.cpp:218] Iteration 7596 (2.34579 iter/s, 5.11554s/12 iters), loss = 1.38854
I0401 16:30:06.838932 25640 solver.cpp:237]     Train net output #0: loss = 1.38854 (* 1 = 1.38854 loss)
I0401 16:30:06.838937 25640 sgd_solver.cpp:105] Iteration 7596, lr = 0.001
I0401 16:30:12.126813 25640 solver.cpp:218] Iteration 7608 (2.26935 iter/s, 5.28787s/12 iters), loss = 1.49524
I0401 16:30:12.126941 25640 solver.cpp:237]     Train net output #0: loss = 1.49524 (* 1 = 1.49524 loss)
I0401 16:30:12.126950 25640 sgd_solver.cpp:105] Iteration 7608, lr = 0.001
I0401 16:30:17.572332 25640 solver.cpp:218] Iteration 7620 (2.2037 iter/s, 5.44538s/12 iters), loss = 1.42137
I0401 16:30:17.572376 25640 solver.cpp:237]     Train net output #0: loss = 1.42137 (* 1 = 1.42137 loss)
I0401 16:30:17.572382 25640 sgd_solver.cpp:105] Iteration 7620, lr = 0.001
I0401 16:30:20.210839 25640 blocking_queue.cpp:49] Waiting for data
I0401 16:30:23.085397 25640 solver.cpp:218] Iteration 7632 (2.17667 iter/s, 5.51301s/12 iters), loss = 1.30588
I0401 16:30:23.085431 25640 solver.cpp:237]     Train net output #0: loss = 1.30588 (* 1 = 1.30588 loss)
I0401 16:30:23.085436 25640 sgd_solver.cpp:105] Iteration 7632, lr = 0.001
I0401 16:30:28.478773 25640 solver.cpp:218] Iteration 7644 (2.22497 iter/s, 5.39332s/12 iters), loss = 1.31295
I0401 16:30:28.478821 25640 solver.cpp:237]     Train net output #0: loss = 1.31295 (* 1 = 1.31295 loss)
I0401 16:30:28.478829 25640 sgd_solver.cpp:105] Iteration 7644, lr = 0.001
I0401 16:30:30.693202 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0401 16:30:35.686743 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0401 16:30:39.909243 25640 solver.cpp:330] Iteration 7650, Testing net (#0)
I0401 16:30:39.909261 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:30:41.260763 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:30:44.445317 25640 solver.cpp:397]     Test net output #0: accuracy = 0.200368
I0401 16:30:44.447295 25640 solver.cpp:397]     Test net output #1: loss = 4.04615 (* 1 = 4.04615 loss)
I0401 16:30:46.363266 25640 solver.cpp:218] Iteration 7656 (0.670974 iter/s, 17.8844s/12 iters), loss = 1.30032
I0401 16:30:46.363304 25640 solver.cpp:237]     Train net output #0: loss = 1.30032 (* 1 = 1.30032 loss)
I0401 16:30:46.363310 25640 sgd_solver.cpp:105] Iteration 7656, lr = 0.001
I0401 16:30:51.610620 25640 solver.cpp:218] Iteration 7668 (2.28689 iter/s, 5.2473s/12 iters), loss = 1.08481
I0401 16:30:51.610668 25640 solver.cpp:237]     Train net output #0: loss = 1.08481 (* 1 = 1.08481 loss)
I0401 16:30:51.610675 25640 sgd_solver.cpp:105] Iteration 7668, lr = 0.001
I0401 16:30:56.976786 25640 solver.cpp:218] Iteration 7680 (2.23626 iter/s, 5.36611s/12 iters), loss = 0.998542
I0401 16:30:56.976825 25640 solver.cpp:237]     Train net output #0: loss = 0.998542 (* 1 = 0.998542 loss)
I0401 16:30:56.976831 25640 sgd_solver.cpp:105] Iteration 7680, lr = 0.001
I0401 16:30:59.873014 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:31:02.229902 25640 solver.cpp:218] Iteration 7692 (2.28438 iter/s, 5.25306s/12 iters), loss = 1.07123
I0401 16:31:02.229961 25640 solver.cpp:237]     Train net output #0: loss = 1.07123 (* 1 = 1.07123 loss)
I0401 16:31:02.229970 25640 sgd_solver.cpp:105] Iteration 7692, lr = 0.001
I0401 16:31:07.631125 25640 solver.cpp:218] Iteration 7704 (2.22175 iter/s, 5.40115s/12 iters), loss = 1.21367
I0401 16:31:07.631173 25640 solver.cpp:237]     Train net output #0: loss = 1.21367 (* 1 = 1.21367 loss)
I0401 16:31:07.631181 25640 sgd_solver.cpp:105] Iteration 7704, lr = 0.001
I0401 16:31:12.816221 25640 solver.cpp:218] Iteration 7716 (2.31435 iter/s, 5.18504s/12 iters), loss = 1.42114
I0401 16:31:12.816262 25640 solver.cpp:237]     Train net output #0: loss = 1.42114 (* 1 = 1.42114 loss)
I0401 16:31:12.816267 25640 sgd_solver.cpp:105] Iteration 7716, lr = 0.001
I0401 16:31:18.193272 25640 solver.cpp:218] Iteration 7728 (2.23173 iter/s, 5.37699s/12 iters), loss = 1.23152
I0401 16:31:18.193392 25640 solver.cpp:237]     Train net output #0: loss = 1.23152 (* 1 = 1.23152 loss)
I0401 16:31:18.193400 25640 sgd_solver.cpp:105] Iteration 7728, lr = 0.001
I0401 16:31:23.677507 25640 solver.cpp:218] Iteration 7740 (2.18814 iter/s, 5.4841s/12 iters), loss = 1.39449
I0401 16:31:23.677548 25640 solver.cpp:237]     Train net output #0: loss = 1.39449 (* 1 = 1.39449 loss)
I0401 16:31:23.677554 25640 sgd_solver.cpp:105] Iteration 7740, lr = 0.001
I0401 16:31:28.575047 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0401 16:31:31.542222 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0401 16:31:35.669857 25640 solver.cpp:330] Iteration 7752, Testing net (#0)
I0401 16:31:35.669881 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:31:37.005419 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:31:40.128027 25640 solver.cpp:397]     Test net output #0: accuracy = 0.198529
I0401 16:31:40.128062 25640 solver.cpp:397]     Test net output #1: loss = 4.14448 (* 1 = 4.14448 loss)
I0401 16:31:40.266819 25640 solver.cpp:218] Iteration 7752 (0.723359 iter/s, 16.5893s/12 iters), loss = 1.32304
I0401 16:31:40.266860 25640 solver.cpp:237]     Train net output #0: loss = 1.32304 (* 1 = 1.32304 loss)
I0401 16:31:40.266865 25640 sgd_solver.cpp:105] Iteration 7752, lr = 0.001
I0401 16:31:44.742841 25640 solver.cpp:218] Iteration 7764 (2.68099 iter/s, 4.47596s/12 iters), loss = 1.22461
I0401 16:31:44.742902 25640 solver.cpp:237]     Train net output #0: loss = 1.22461 (* 1 = 1.22461 loss)
I0401 16:31:44.742909 25640 sgd_solver.cpp:105] Iteration 7764, lr = 0.001
I0401 16:31:49.901042 25640 solver.cpp:218] Iteration 7776 (2.32643 iter/s, 5.15813s/12 iters), loss = 1.05567
I0401 16:31:49.901156 25640 solver.cpp:237]     Train net output #0: loss = 1.05567 (* 1 = 1.05567 loss)
I0401 16:31:49.901163 25640 sgd_solver.cpp:105] Iteration 7776, lr = 0.001
I0401 16:31:55.312660 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:31:55.336621 25640 solver.cpp:218] Iteration 7788 (2.20773 iter/s, 5.43545s/12 iters), loss = 0.878717
I0401 16:31:55.336678 25640 solver.cpp:237]     Train net output #0: loss = 0.878717 (* 1 = 0.878717 loss)
I0401 16:31:55.336688 25640 sgd_solver.cpp:105] Iteration 7788, lr = 0.001
I0401 16:32:00.600814 25640 solver.cpp:218] Iteration 7800 (2.27958 iter/s, 5.26412s/12 iters), loss = 1.07959
I0401 16:32:00.600860 25640 solver.cpp:237]     Train net output #0: loss = 1.07959 (* 1 = 1.07959 loss)
I0401 16:32:00.600865 25640 sgd_solver.cpp:105] Iteration 7800, lr = 0.001
I0401 16:32:06.206205 25640 solver.cpp:218] Iteration 7812 (2.14082 iter/s, 5.60533s/12 iters), loss = 1.00875
I0401 16:32:06.206255 25640 solver.cpp:237]     Train net output #0: loss = 1.00875 (* 1 = 1.00875 loss)
I0401 16:32:06.206264 25640 sgd_solver.cpp:105] Iteration 7812, lr = 0.001
I0401 16:32:11.334762 25640 solver.cpp:218] Iteration 7824 (2.33986 iter/s, 5.1285s/12 iters), loss = 1.15696
I0401 16:32:11.334800 25640 solver.cpp:237]     Train net output #0: loss = 1.15696 (* 1 = 1.15696 loss)
I0401 16:32:11.334806 25640 sgd_solver.cpp:105] Iteration 7824, lr = 0.001
I0401 16:32:16.732460 25640 solver.cpp:218] Iteration 7836 (2.22319 iter/s, 5.39764s/12 iters), loss = 1.02042
I0401 16:32:16.732501 25640 solver.cpp:237]     Train net output #0: loss = 1.02042 (* 1 = 1.02042 loss)
I0401 16:32:16.732507 25640 sgd_solver.cpp:105] Iteration 7836, lr = 0.001
I0401 16:32:22.166941 25640 solver.cpp:218] Iteration 7848 (2.20815 iter/s, 5.43442s/12 iters), loss = 1.20558
I0401 16:32:22.167065 25640 solver.cpp:237]     Train net output #0: loss = 1.20558 (* 1 = 1.20558 loss)
I0401 16:32:22.167075 25640 sgd_solver.cpp:105] Iteration 7848, lr = 0.001
I0401 16:32:24.274148 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0401 16:32:27.357966 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0401 16:32:29.710731 25640 solver.cpp:330] Iteration 7854, Testing net (#0)
I0401 16:32:29.710752 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:32:31.196190 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:32:34.260648 25640 solver.cpp:397]     Test net output #0: accuracy = 0.212623
I0401 16:32:34.260684 25640 solver.cpp:397]     Test net output #1: loss = 4.15344 (* 1 = 4.15344 loss)
I0401 16:32:36.116243 25640 solver.cpp:218] Iteration 7860 (0.860266 iter/s, 13.9492s/12 iters), loss = 0.948581
I0401 16:32:36.116291 25640 solver.cpp:237]     Train net output #0: loss = 0.948581 (* 1 = 0.948581 loss)
I0401 16:32:36.116298 25640 sgd_solver.cpp:105] Iteration 7860, lr = 0.001
I0401 16:32:41.246595 25640 solver.cpp:218] Iteration 7872 (2.33905 iter/s, 5.13029s/12 iters), loss = 0.986352
I0401 16:32:41.246644 25640 solver.cpp:237]     Train net output #0: loss = 0.986352 (* 1 = 0.986352 loss)
I0401 16:32:41.246650 25640 sgd_solver.cpp:105] Iteration 7872, lr = 0.001
I0401 16:32:46.551043 25640 solver.cpp:218] Iteration 7884 (2.26228 iter/s, 5.30439s/12 iters), loss = 0.96727
I0401 16:32:46.551082 25640 solver.cpp:237]     Train net output #0: loss = 0.96727 (* 1 = 0.96727 loss)
I0401 16:32:46.551088 25640 sgd_solver.cpp:105] Iteration 7884, lr = 0.001
I0401 16:32:48.760346 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:32:51.872159 25640 solver.cpp:218] Iteration 7896 (2.25519 iter/s, 5.32106s/12 iters), loss = 1.06906
I0401 16:32:51.872202 25640 solver.cpp:237]     Train net output #0: loss = 1.06906 (* 1 = 1.06906 loss)
I0401 16:32:51.872208 25640 sgd_solver.cpp:105] Iteration 7896, lr = 0.001
I0401 16:32:57.223595 25640 solver.cpp:218] Iteration 7908 (2.24242 iter/s, 5.35137s/12 iters), loss = 0.822005
I0401 16:32:57.223815 25640 solver.cpp:237]     Train net output #0: loss = 0.822005 (* 1 = 0.822005 loss)
I0401 16:32:57.223826 25640 sgd_solver.cpp:105] Iteration 7908, lr = 0.001
I0401 16:33:02.320544 25640 solver.cpp:218] Iteration 7920 (2.35443 iter/s, 5.09677s/12 iters), loss = 1.11507
I0401 16:33:02.320598 25640 solver.cpp:237]     Train net output #0: loss = 1.11507 (* 1 = 1.11507 loss)
I0401 16:33:02.320606 25640 sgd_solver.cpp:105] Iteration 7920, lr = 0.001
I0401 16:33:07.546262 25640 solver.cpp:218] Iteration 7932 (2.29637 iter/s, 5.22565s/12 iters), loss = 1.18767
I0401 16:33:07.546314 25640 solver.cpp:237]     Train net output #0: loss = 1.18767 (* 1 = 1.18767 loss)
I0401 16:33:07.546321 25640 sgd_solver.cpp:105] Iteration 7932, lr = 0.001
I0401 16:33:12.929347 25640 solver.cpp:218] Iteration 7944 (2.22923 iter/s, 5.38302s/12 iters), loss = 1.1188
I0401 16:33:12.929409 25640 solver.cpp:237]     Train net output #0: loss = 1.1188 (* 1 = 1.1188 loss)
I0401 16:33:12.929417 25640 sgd_solver.cpp:105] Iteration 7944, lr = 0.001
I0401 16:33:17.603641 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0401 16:33:20.778127 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0401 16:33:23.117069 25640 solver.cpp:330] Iteration 7956, Testing net (#0)
I0401 16:33:23.117085 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:33:24.378441 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:33:27.694232 25640 solver.cpp:397]     Test net output #0: accuracy = 0.22549
I0401 16:33:27.694308 25640 solver.cpp:397]     Test net output #1: loss = 4.14564 (* 1 = 4.14564 loss)
I0401 16:33:27.832211 25640 solver.cpp:218] Iteration 7956 (0.805218 iter/s, 14.9028s/12 iters), loss = 0.973721
I0401 16:33:27.833767 25640 solver.cpp:237]     Train net output #0: loss = 0.973721 (* 1 = 0.973721 loss)
I0401 16:33:27.833779 25640 sgd_solver.cpp:105] Iteration 7956, lr = 0.001
I0401 16:33:32.324350 25640 solver.cpp:218] Iteration 7968 (2.67226 iter/s, 4.49057s/12 iters), loss = 1.07017
I0401 16:33:32.324393 25640 solver.cpp:237]     Train net output #0: loss = 1.07017 (* 1 = 1.07017 loss)
I0401 16:33:32.324398 25640 sgd_solver.cpp:105] Iteration 7968, lr = 0.001
I0401 16:33:37.479032 25640 solver.cpp:218] Iteration 7980 (2.32801 iter/s, 5.15462s/12 iters), loss = 0.760006
I0401 16:33:37.479082 25640 solver.cpp:237]     Train net output #0: loss = 0.760006 (* 1 = 0.760006 loss)
I0401 16:33:37.479090 25640 sgd_solver.cpp:105] Iteration 7980, lr = 0.001
I0401 16:33:41.887513 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:33:42.657330 25640 solver.cpp:218] Iteration 7992 (2.31739 iter/s, 5.17823s/12 iters), loss = 0.807246
I0401 16:33:42.657384 25640 solver.cpp:237]     Train net output #0: loss = 0.807246 (* 1 = 0.807246 loss)
I0401 16:33:42.657392 25640 sgd_solver.cpp:105] Iteration 7992, lr = 0.001
I0401 16:33:47.840374 25640 solver.cpp:218] Iteration 8004 (2.31527 iter/s, 5.18297s/12 iters), loss = 1.08152
I0401 16:33:47.840418 25640 solver.cpp:237]     Train net output #0: loss = 1.08152 (* 1 = 1.08152 loss)
I0401 16:33:47.840425 25640 sgd_solver.cpp:105] Iteration 8004, lr = 0.001
I0401 16:33:53.019927 25640 solver.cpp:218] Iteration 8016 (2.31683 iter/s, 5.1795s/12 iters), loss = 1.27952
I0401 16:33:53.019973 25640 solver.cpp:237]     Train net output #0: loss = 1.27952 (* 1 = 1.27952 loss)
I0401 16:33:53.019979 25640 sgd_solver.cpp:105] Iteration 8016, lr = 0.001
I0401 16:33:58.333848 25640 solver.cpp:218] Iteration 8028 (2.25825 iter/s, 5.31386s/12 iters), loss = 0.833917
I0401 16:33:58.333967 25640 solver.cpp:237]     Train net output #0: loss = 0.833917 (* 1 = 0.833917 loss)
I0401 16:33:58.333974 25640 sgd_solver.cpp:105] Iteration 8028, lr = 0.001
I0401 16:34:03.700903 25640 solver.cpp:218] Iteration 8040 (2.23592 iter/s, 5.36691s/12 iters), loss = 1.25976
I0401 16:34:03.700946 25640 solver.cpp:237]     Train net output #0: loss = 1.25976 (* 1 = 1.25976 loss)
I0401 16:34:03.700953 25640 sgd_solver.cpp:105] Iteration 8040, lr = 0.001
I0401 16:34:08.885367 25640 solver.cpp:218] Iteration 8052 (2.31463 iter/s, 5.1844s/12 iters), loss = 1.00957
I0401 16:34:08.885409 25640 solver.cpp:237]     Train net output #0: loss = 1.00957 (* 1 = 1.00957 loss)
I0401 16:34:08.885416 25640 sgd_solver.cpp:105] Iteration 8052, lr = 0.001
I0401 16:34:11.028095 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0401 16:34:14.035251 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0401 16:34:16.342603 25640 solver.cpp:330] Iteration 8058, Testing net (#0)
I0401 16:34:16.342622 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:34:17.506589 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:34:20.783401 25640 solver.cpp:397]     Test net output #0: accuracy = 0.20098
I0401 16:34:20.783447 25640 solver.cpp:397]     Test net output #1: loss = 4.08327 (* 1 = 4.08327 loss)
I0401 16:34:22.739964 25640 solver.cpp:218] Iteration 8064 (0.866142 iter/s, 13.8545s/12 iters), loss = 0.990185
I0401 16:34:22.740020 25640 solver.cpp:237]     Train net output #0: loss = 0.990185 (* 1 = 0.990185 loss)
I0401 16:34:22.740028 25640 sgd_solver.cpp:105] Iteration 8064, lr = 0.001
I0401 16:34:28.131942 25640 solver.cpp:218] Iteration 8076 (2.22556 iter/s, 5.39191s/12 iters), loss = 0.86274
I0401 16:34:28.131985 25640 solver.cpp:237]     Train net output #0: loss = 0.86274 (* 1 = 0.86274 loss)
I0401 16:34:28.131991 25640 sgd_solver.cpp:105] Iteration 8076, lr = 0.001
I0401 16:34:33.585675 25640 solver.cpp:218] Iteration 8088 (2.20035 iter/s, 5.45368s/12 iters), loss = 0.905561
I0401 16:34:33.585764 25640 solver.cpp:237]     Train net output #0: loss = 0.905561 (* 1 = 0.905561 loss)
I0401 16:34:33.585770 25640 sgd_solver.cpp:105] Iteration 8088, lr = 0.001
I0401 16:34:35.095808 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:34:38.943112 25640 solver.cpp:218] Iteration 8100 (2.23992 iter/s, 5.35734s/12 iters), loss = 0.985768
I0401 16:34:38.943152 25640 solver.cpp:237]     Train net output #0: loss = 0.985768 (* 1 = 0.985768 loss)
I0401 16:34:38.943157 25640 sgd_solver.cpp:105] Iteration 8100, lr = 0.001
I0401 16:34:44.302793 25640 solver.cpp:218] Iteration 8112 (2.23896 iter/s, 5.35962s/12 iters), loss = 1.20951
I0401 16:34:44.302841 25640 solver.cpp:237]     Train net output #0: loss = 1.20951 (* 1 = 1.20951 loss)
I0401 16:34:44.302850 25640 sgd_solver.cpp:105] Iteration 8112, lr = 0.001
I0401 16:34:49.802541 25640 solver.cpp:218] Iteration 8124 (2.18194 iter/s, 5.49968s/12 iters), loss = 0.975495
I0401 16:34:49.802599 25640 solver.cpp:237]     Train net output #0: loss = 0.975495 (* 1 = 0.975495 loss)
I0401 16:34:49.802608 25640 sgd_solver.cpp:105] Iteration 8124, lr = 0.001
I0401 16:34:55.265621 25640 solver.cpp:218] Iteration 8136 (2.19659 iter/s, 5.46301s/12 iters), loss = 1.37214
I0401 16:34:55.265667 25640 solver.cpp:237]     Train net output #0: loss = 1.37214 (* 1 = 1.37214 loss)
I0401 16:34:55.265674 25640 sgd_solver.cpp:105] Iteration 8136, lr = 0.001
I0401 16:35:00.750777 25640 solver.cpp:218] Iteration 8148 (2.18775 iter/s, 5.48509s/12 iters), loss = 1.07322
I0401 16:35:00.750818 25640 solver.cpp:237]     Train net output #0: loss = 1.07322 (* 1 = 1.07322 loss)
I0401 16:35:00.750824 25640 sgd_solver.cpp:105] Iteration 8148, lr = 0.001
I0401 16:35:05.561117 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0401 16:35:08.635481 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0401 16:35:10.963649 25640 solver.cpp:330] Iteration 8160, Testing net (#0)
I0401 16:35:10.963668 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:35:12.115288 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:35:15.448935 25640 solver.cpp:397]     Test net output #0: accuracy = 0.211397
I0401 16:35:15.448971 25640 solver.cpp:397]     Test net output #1: loss = 4.20531 (* 1 = 4.20531 loss)
I0401 16:35:15.585623 25640 solver.cpp:218] Iteration 8160 (0.808909 iter/s, 14.8348s/12 iters), loss = 1.09029
I0401 16:35:15.587633 25640 solver.cpp:237]     Train net output #0: loss = 1.09029 (* 1 = 1.09029 loss)
I0401 16:35:15.587649 25640 sgd_solver.cpp:105] Iteration 8160, lr = 0.001
I0401 16:35:20.076865 25640 solver.cpp:218] Iteration 8172 (2.67307 iter/s, 4.48922s/12 iters), loss = 0.705647
I0401 16:35:20.076920 25640 solver.cpp:237]     Train net output #0: loss = 0.705647 (* 1 = 0.705647 loss)
I0401 16:35:20.076929 25640 sgd_solver.cpp:105] Iteration 8172, lr = 0.001
I0401 16:35:25.393697 25640 solver.cpp:218] Iteration 8184 (2.25701 iter/s, 5.31676s/12 iters), loss = 0.660831
I0401 16:35:25.393739 25640 solver.cpp:237]     Train net output #0: loss = 0.660831 (* 1 = 0.660831 loss)
I0401 16:35:25.393746 25640 sgd_solver.cpp:105] Iteration 8184, lr = 0.001
I0401 16:35:28.926496 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:35:30.686615 25640 solver.cpp:218] Iteration 8196 (2.26721 iter/s, 5.29286s/12 iters), loss = 0.907074
I0401 16:35:30.686661 25640 solver.cpp:237]     Train net output #0: loss = 0.907074 (* 1 = 0.907074 loss)
I0401 16:35:30.686667 25640 sgd_solver.cpp:105] Iteration 8196, lr = 0.001
I0401 16:35:35.946301 25640 solver.cpp:218] Iteration 8208 (2.28161 iter/s, 5.25944s/12 iters), loss = 1.04513
I0401 16:35:35.946483 25640 solver.cpp:237]     Train net output #0: loss = 1.04513 (* 1 = 1.04513 loss)
I0401 16:35:35.946493 25640 sgd_solver.cpp:105] Iteration 8208, lr = 0.001
I0401 16:35:41.298641 25640 solver.cpp:218] Iteration 8220 (2.24209 iter/s, 5.35214s/12 iters), loss = 0.865565
I0401 16:35:41.298696 25640 solver.cpp:237]     Train net output #0: loss = 0.865565 (* 1 = 0.865565 loss)
I0401 16:35:41.298704 25640 sgd_solver.cpp:105] Iteration 8220, lr = 0.001
I0401 16:35:46.486701 25640 solver.cpp:218] Iteration 8232 (2.31303 iter/s, 5.18799s/12 iters), loss = 0.968477
I0401 16:35:46.486763 25640 solver.cpp:237]     Train net output #0: loss = 0.968477 (* 1 = 0.968477 loss)
I0401 16:35:46.486773 25640 sgd_solver.cpp:105] Iteration 8232, lr = 0.001
I0401 16:35:51.842100 25640 solver.cpp:218] Iteration 8244 (2.24076 iter/s, 5.35532s/12 iters), loss = 1.31533
I0401 16:35:51.842146 25640 solver.cpp:237]     Train net output #0: loss = 1.31533 (* 1 = 1.31533 loss)
I0401 16:35:51.842154 25640 sgd_solver.cpp:105] Iteration 8244, lr = 0.001
I0401 16:35:57.026679 25640 solver.cpp:218] Iteration 8256 (2.31458 iter/s, 5.18452s/12 iters), loss = 0.814093
I0401 16:35:57.026737 25640 solver.cpp:237]     Train net output #0: loss = 0.814093 (* 1 = 0.814093 loss)
I0401 16:35:57.026746 25640 sgd_solver.cpp:105] Iteration 8256, lr = 0.001
I0401 16:35:59.185964 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0401 16:36:02.186811 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0401 16:36:04.558501 25640 solver.cpp:330] Iteration 8262, Testing net (#0)
I0401 16:36:04.558521 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:36:05.723701 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:36:09.013902 25640 solver.cpp:397]     Test net output #0: accuracy = 0.219975
I0401 16:36:09.014031 25640 solver.cpp:397]     Test net output #1: loss = 4.07972 (* 1 = 4.07972 loss)
I0401 16:36:11.129412 25640 solver.cpp:218] Iteration 8268 (0.850903 iter/s, 14.1027s/12 iters), loss = 0.656452
I0401 16:36:11.129452 25640 solver.cpp:237]     Train net output #0: loss = 0.656452 (* 1 = 0.656452 loss)
I0401 16:36:11.129457 25640 sgd_solver.cpp:105] Iteration 8268, lr = 0.001
I0401 16:36:16.558246 25640 solver.cpp:218] Iteration 8280 (2.21044 iter/s, 5.42878s/12 iters), loss = 0.739148
I0401 16:36:16.558307 25640 solver.cpp:237]     Train net output #0: loss = 0.739148 (* 1 = 0.739148 loss)
I0401 16:36:16.558316 25640 sgd_solver.cpp:105] Iteration 8280, lr = 0.001
I0401 16:36:21.870723 25640 solver.cpp:218] Iteration 8292 (2.25886 iter/s, 5.3124s/12 iters), loss = 0.754452
I0401 16:36:21.870761 25640 solver.cpp:237]     Train net output #0: loss = 0.754452 (* 1 = 0.754452 loss)
I0401 16:36:21.870767 25640 sgd_solver.cpp:105] Iteration 8292, lr = 0.001
I0401 16:36:22.532341 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:36:27.129674 25640 solver.cpp:218] Iteration 8304 (2.28185 iter/s, 5.25889s/12 iters), loss = 1.14207
I0401 16:36:27.129729 25640 solver.cpp:237]     Train net output #0: loss = 1.14207 (* 1 = 1.14207 loss)
I0401 16:36:27.129737 25640 sgd_solver.cpp:105] Iteration 8304, lr = 0.001
I0401 16:36:30.017860 25640 blocking_queue.cpp:49] Waiting for data
I0401 16:36:32.355211 25640 solver.cpp:218] Iteration 8316 (2.29645 iter/s, 5.22547s/12 iters), loss = 0.870419
I0401 16:36:32.355268 25640 solver.cpp:237]     Train net output #0: loss = 0.870419 (* 1 = 0.870419 loss)
I0401 16:36:32.355276 25640 sgd_solver.cpp:105] Iteration 8316, lr = 0.001
I0401 16:36:37.512390 25640 solver.cpp:218] Iteration 8328 (2.32689 iter/s, 5.15711s/12 iters), loss = 1.04715
I0401 16:36:37.512449 25640 solver.cpp:237]     Train net output #0: loss = 1.04715 (* 1 = 1.04715 loss)
I0401 16:36:37.512459 25640 sgd_solver.cpp:105] Iteration 8328, lr = 0.001
I0401 16:36:42.965746 25640 solver.cpp:218] Iteration 8340 (2.20051 iter/s, 5.45328s/12 iters), loss = 0.866685
I0401 16:36:42.965898 25640 solver.cpp:237]     Train net output #0: loss = 0.866685 (* 1 = 0.866685 loss)
I0401 16:36:42.965909 25640 sgd_solver.cpp:105] Iteration 8340, lr = 0.001
I0401 16:36:47.993275 25640 solver.cpp:218] Iteration 8352 (2.38694 iter/s, 5.02737s/12 iters), loss = 0.668436
I0401 16:36:47.993321 25640 solver.cpp:237]     Train net output #0: loss = 0.668436 (* 1 = 0.668436 loss)
I0401 16:36:47.993327 25640 sgd_solver.cpp:105] Iteration 8352, lr = 0.001
I0401 16:36:52.793370 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0401 16:36:55.875602 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0401 16:36:58.194211 25640 solver.cpp:330] Iteration 8364, Testing net (#0)
I0401 16:36:58.194231 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:36:59.293354 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:37:02.656877 25640 solver.cpp:397]     Test net output #0: accuracy = 0.218137
I0401 16:37:02.656927 25640 solver.cpp:397]     Test net output #1: loss = 4.20743 (* 1 = 4.20743 loss)
I0401 16:37:02.798028 25640 solver.cpp:218] Iteration 8364 (0.810554 iter/s, 14.8047s/12 iters), loss = 0.883288
I0401 16:37:02.798072 25640 solver.cpp:237]     Train net output #0: loss = 0.883288 (* 1 = 0.883288 loss)
I0401 16:37:02.798079 25640 sgd_solver.cpp:105] Iteration 8364, lr = 0.001
I0401 16:37:07.062958 25640 solver.cpp:218] Iteration 8376 (2.81368 iter/s, 4.26487s/12 iters), loss = 0.737646
I0401 16:37:07.062994 25640 solver.cpp:237]     Train net output #0: loss = 0.737646 (* 1 = 0.737646 loss)
I0401 16:37:07.063000 25640 sgd_solver.cpp:105] Iteration 8376, lr = 0.001
I0401 16:37:12.269161 25640 solver.cpp:218] Iteration 8388 (2.30496 iter/s, 5.20616s/12 iters), loss = 0.671392
I0401 16:37:12.269196 25640 solver.cpp:237]     Train net output #0: loss = 0.671392 (* 1 = 0.671392 loss)
I0401 16:37:12.269202 25640 sgd_solver.cpp:105] Iteration 8388, lr = 0.001
I0401 16:37:15.166209 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:37:17.518368 25640 solver.cpp:218] Iteration 8400 (2.28608 iter/s, 5.24915s/12 iters), loss = 0.722412
I0401 16:37:17.518416 25640 solver.cpp:237]     Train net output #0: loss = 0.722412 (* 1 = 0.722412 loss)
I0401 16:37:17.518424 25640 sgd_solver.cpp:105] Iteration 8400, lr = 0.001
I0401 16:37:23.047766 25640 solver.cpp:218] Iteration 8412 (2.17024 iter/s, 5.52933s/12 iters), loss = 0.810801
I0401 16:37:23.047827 25640 solver.cpp:237]     Train net output #0: loss = 0.810801 (* 1 = 0.810801 loss)
I0401 16:37:23.047834 25640 sgd_solver.cpp:105] Iteration 8412, lr = 0.001
I0401 16:37:28.582466 25640 solver.cpp:218] Iteration 8424 (2.16817 iter/s, 5.53463s/12 iters), loss = 1.00286
I0401 16:37:28.582502 25640 solver.cpp:237]     Train net output #0: loss = 1.00286 (* 1 = 1.00286 loss)
I0401 16:37:28.582507 25640 sgd_solver.cpp:105] Iteration 8424, lr = 0.001
I0401 16:37:33.883296 25640 solver.cpp:218] Iteration 8436 (2.26382 iter/s, 5.30078s/12 iters), loss = 0.980505
I0401 16:37:33.883353 25640 solver.cpp:237]     Train net output #0: loss = 0.980505 (* 1 = 0.980505 loss)
I0401 16:37:33.883363 25640 sgd_solver.cpp:105] Iteration 8436, lr = 0.001
I0401 16:37:39.206948 25640 solver.cpp:218] Iteration 8448 (2.25412 iter/s, 5.32358s/12 iters), loss = 1.06948
I0401 16:37:39.207000 25640 solver.cpp:237]     Train net output #0: loss = 1.06948 (* 1 = 1.06948 loss)
I0401 16:37:39.207008 25640 sgd_solver.cpp:105] Iteration 8448, lr = 0.001
I0401 16:37:44.482095 25640 solver.cpp:218] Iteration 8460 (2.27485 iter/s, 5.27508s/12 iters), loss = 1.11337
I0401 16:37:44.482137 25640 solver.cpp:237]     Train net output #0: loss = 1.11337 (* 1 = 1.11337 loss)
I0401 16:37:44.482144 25640 sgd_solver.cpp:105] Iteration 8460, lr = 0.001
I0401 16:37:46.771665 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0401 16:37:49.708160 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0401 16:37:52.046317 25640 solver.cpp:330] Iteration 8466, Testing net (#0)
I0401 16:37:52.046334 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:37:53.105877 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:37:56.550531 25640 solver.cpp:397]     Test net output #0: accuracy = 0.227328
I0401 16:37:56.550559 25640 solver.cpp:397]     Test net output #1: loss = 4.12383 (* 1 = 4.12383 loss)
I0401 16:37:58.681648 25640 solver.cpp:218] Iteration 8472 (0.8451 iter/s, 14.1995s/12 iters), loss = 0.809634
I0401 16:37:58.681708 25640 solver.cpp:237]     Train net output #0: loss = 0.809634 (* 1 = 0.809634 loss)
I0401 16:37:58.681716 25640 sgd_solver.cpp:105] Iteration 8472, lr = 0.001
I0401 16:38:04.126663 25640 solver.cpp:218] Iteration 8484 (2.20388 iter/s, 5.44494s/12 iters), loss = 0.640165
I0401 16:38:04.126708 25640 solver.cpp:237]     Train net output #0: loss = 0.640165 (* 1 = 0.640165 loss)
I0401 16:38:04.126713 25640 sgd_solver.cpp:105] Iteration 8484, lr = 0.001
I0401 16:38:09.322274 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:38:09.325469 25640 solver.cpp:218] Iteration 8496 (2.30825 iter/s, 5.19875s/12 iters), loss = 0.732133
I0401 16:38:09.325500 25640 solver.cpp:237]     Train net output #0: loss = 0.732133 (* 1 = 0.732133 loss)
I0401 16:38:09.325505 25640 sgd_solver.cpp:105] Iteration 8496, lr = 0.001
I0401 16:38:14.802904 25640 solver.cpp:218] Iteration 8508 (2.19083 iter/s, 5.47738s/12 iters), loss = 0.584256
I0401 16:38:14.802953 25640 solver.cpp:237]     Train net output #0: loss = 0.584256 (* 1 = 0.584256 loss)
I0401 16:38:14.802958 25640 sgd_solver.cpp:105] Iteration 8508, lr = 0.001
I0401 16:38:20.227617 25640 solver.cpp:218] Iteration 8520 (2.21213 iter/s, 5.42465s/12 iters), loss = 0.84842
I0401 16:38:20.227754 25640 solver.cpp:237]     Train net output #0: loss = 0.84842 (* 1 = 0.84842 loss)
I0401 16:38:20.227761 25640 sgd_solver.cpp:105] Iteration 8520, lr = 0.001
I0401 16:38:25.666213 25640 solver.cpp:218] Iteration 8532 (2.20652 iter/s, 5.43844s/12 iters), loss = 1.096
I0401 16:38:25.666272 25640 solver.cpp:237]     Train net output #0: loss = 1.096 (* 1 = 1.096 loss)
I0401 16:38:25.666281 25640 sgd_solver.cpp:105] Iteration 8532, lr = 0.001
I0401 16:38:31.027577 25640 solver.cpp:218] Iteration 8544 (2.23826 iter/s, 5.3613s/12 iters), loss = 0.976412
I0401 16:38:31.027618 25640 solver.cpp:237]     Train net output #0: loss = 0.976412 (* 1 = 0.976412 loss)
I0401 16:38:31.027624 25640 sgd_solver.cpp:105] Iteration 8544, lr = 0.001
I0401 16:38:36.457206 25640 solver.cpp:218] Iteration 8556 (2.21012 iter/s, 5.42957s/12 iters), loss = 0.760783
I0401 16:38:36.457252 25640 solver.cpp:237]     Train net output #0: loss = 0.760783 (* 1 = 0.760783 loss)
I0401 16:38:36.457257 25640 sgd_solver.cpp:105] Iteration 8556, lr = 0.001
I0401 16:38:41.228374 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0401 16:38:44.245146 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0401 16:38:46.569365 25640 solver.cpp:330] Iteration 8568, Testing net (#0)
I0401 16:38:46.569391 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:38:47.611948 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:38:51.057787 25640 solver.cpp:397]     Test net output #0: accuracy = 0.227328
I0401 16:38:51.057876 25640 solver.cpp:397]     Test net output #1: loss = 4.17544 (* 1 = 4.17544 loss)
I0401 16:38:51.193938 25640 solver.cpp:218] Iteration 8568 (0.814295 iter/s, 14.7367s/12 iters), loss = 0.867352
I0401 16:38:51.195493 25640 solver.cpp:237]     Train net output #0: loss = 0.867352 (* 1 = 0.867352 loss)
I0401 16:38:51.195504 25640 sgd_solver.cpp:105] Iteration 8568, lr = 0.001
I0401 16:38:55.534370 25640 solver.cpp:218] Iteration 8580 (2.7657 iter/s, 4.33887s/12 iters), loss = 0.64114
I0401 16:38:55.534422 25640 solver.cpp:237]     Train net output #0: loss = 0.64114 (* 1 = 0.64114 loss)
I0401 16:38:55.534431 25640 sgd_solver.cpp:105] Iteration 8580, lr = 0.001
I0401 16:39:00.742616 25640 solver.cpp:218] Iteration 8592 (2.30408 iter/s, 5.20816s/12 iters), loss = 0.663932
I0401 16:39:00.742668 25640 solver.cpp:237]     Train net output #0: loss = 0.663932 (* 1 = 0.663932 loss)
I0401 16:39:00.742676 25640 sgd_solver.cpp:105] Iteration 8592, lr = 0.001
I0401 16:39:03.000275 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:39:06.083091 25640 solver.cpp:218] Iteration 8604 (2.24702 iter/s, 5.34041s/12 iters), loss = 0.823329
I0401 16:39:06.083143 25640 solver.cpp:237]     Train net output #0: loss = 0.823329 (* 1 = 0.823329 loss)
I0401 16:39:06.083151 25640 sgd_solver.cpp:105] Iteration 8604, lr = 0.001
I0401 16:39:11.586598 25640 solver.cpp:218] Iteration 8616 (2.18045 iter/s, 5.50344s/12 iters), loss = 0.890946
I0401 16:39:11.586643 25640 solver.cpp:237]     Train net output #0: loss = 0.890946 (* 1 = 0.890946 loss)
I0401 16:39:11.586650 25640 sgd_solver.cpp:105] Iteration 8616, lr = 0.001
I0401 16:39:16.701212 25640 solver.cpp:218] Iteration 8628 (2.34624 iter/s, 5.11456s/12 iters), loss = 0.666092
I0401 16:39:16.701248 25640 solver.cpp:237]     Train net output #0: loss = 0.666092 (* 1 = 0.666092 loss)
I0401 16:39:16.701254 25640 sgd_solver.cpp:105] Iteration 8628, lr = 0.001
I0401 16:39:22.182271 25640 solver.cpp:218] Iteration 8640 (2.18938 iter/s, 5.481s/12 iters), loss = 0.763877
I0401 16:39:22.182381 25640 solver.cpp:237]     Train net output #0: loss = 0.763877 (* 1 = 0.763877 loss)
I0401 16:39:22.182390 25640 sgd_solver.cpp:105] Iteration 8640, lr = 0.001
I0401 16:39:27.236423 25640 solver.cpp:218] Iteration 8652 (2.37434 iter/s, 5.05403s/12 iters), loss = 0.607744
I0401 16:39:27.236469 25640 solver.cpp:237]     Train net output #0: loss = 0.607744 (* 1 = 0.607744 loss)
I0401 16:39:27.236474 25640 sgd_solver.cpp:105] Iteration 8652, lr = 0.001
I0401 16:39:32.503863 25640 solver.cpp:218] Iteration 8664 (2.27817 iter/s, 5.26738s/12 iters), loss = 0.833202
I0401 16:39:32.503906 25640 solver.cpp:237]     Train net output #0: loss = 0.833202 (* 1 = 0.833202 loss)
I0401 16:39:32.503911 25640 sgd_solver.cpp:105] Iteration 8664, lr = 0.001
I0401 16:39:34.522184 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0401 16:39:37.533191 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0401 16:39:39.883047 25640 solver.cpp:330] Iteration 8670, Testing net (#0)
I0401 16:39:39.883066 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:39:40.874707 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:39:44.642757 25640 solver.cpp:397]     Test net output #0: accuracy = 0.218137
I0401 16:39:44.642784 25640 solver.cpp:397]     Test net output #1: loss = 4.30578 (* 1 = 4.30578 loss)
I0401 16:39:46.436568 25640 solver.cpp:218] Iteration 8676 (0.861286 iter/s, 13.9326s/12 iters), loss = 0.826774
I0401 16:39:46.436630 25640 solver.cpp:237]     Train net output #0: loss = 0.826774 (* 1 = 0.826774 loss)
I0401 16:39:46.436640 25640 sgd_solver.cpp:105] Iteration 8676, lr = 0.001
I0401 16:39:51.683199 25640 solver.cpp:218] Iteration 8688 (2.28721 iter/s, 5.24656s/12 iters), loss = 0.576486
I0401 16:39:51.683250 25640 solver.cpp:237]     Train net output #0: loss = 0.576486 (* 1 = 0.576486 loss)
I0401 16:39:51.683259 25640 sgd_solver.cpp:105] Iteration 8688, lr = 0.001
I0401 16:39:56.125144 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:39:56.888648 25640 solver.cpp:218] Iteration 8700 (2.30531 iter/s, 5.20538s/12 iters), loss = 0.85815
I0401 16:39:56.888705 25640 solver.cpp:237]     Train net output #0: loss = 0.85815 (* 1 = 0.85815 loss)
I0401 16:39:56.888713 25640 sgd_solver.cpp:105] Iteration 8700, lr = 0.001
I0401 16:40:02.136787 25640 solver.cpp:218] Iteration 8712 (2.28655 iter/s, 5.24808s/12 iters), loss = 0.710508
I0401 16:40:02.136831 25640 solver.cpp:237]     Train net output #0: loss = 0.710508 (* 1 = 0.710508 loss)
I0401 16:40:02.136839 25640 sgd_solver.cpp:105] Iteration 8712, lr = 0.001
I0401 16:40:07.371345 25640 solver.cpp:218] Iteration 8724 (2.29248 iter/s, 5.23449s/12 iters), loss = 0.538307
I0401 16:40:07.371392 25640 solver.cpp:237]     Train net output #0: loss = 0.538307 (* 1 = 0.538307 loss)
I0401 16:40:07.371397 25640 sgd_solver.cpp:105] Iteration 8724, lr = 0.001
I0401 16:40:12.958531 25640 solver.cpp:218] Iteration 8736 (2.14779 iter/s, 5.58713s/12 iters), loss = 0.714455
I0401 16:40:12.958570 25640 solver.cpp:237]     Train net output #0: loss = 0.714455 (* 1 = 0.714455 loss)
I0401 16:40:12.958575 25640 sgd_solver.cpp:105] Iteration 8736, lr = 0.001
I0401 16:40:18.417421 25640 solver.cpp:218] Iteration 8748 (2.19827 iter/s, 5.45883s/12 iters), loss = 0.855462
I0401 16:40:18.417482 25640 solver.cpp:237]     Train net output #0: loss = 0.855462 (* 1 = 0.855462 loss)
I0401 16:40:18.417491 25640 sgd_solver.cpp:105] Iteration 8748, lr = 0.001
I0401 16:40:23.769503 25640 solver.cpp:218] Iteration 8760 (2.24215 iter/s, 5.352s/12 iters), loss = 0.643005
I0401 16:40:23.769567 25640 solver.cpp:237]     Train net output #0: loss = 0.643005 (* 1 = 0.643005 loss)
I0401 16:40:23.769577 25640 sgd_solver.cpp:105] Iteration 8760, lr = 0.001
I0401 16:40:28.762399 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0401 16:40:31.799911 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0401 16:40:34.121217 25640 solver.cpp:330] Iteration 8772, Testing net (#0)
I0401 16:40:34.121244 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:40:35.199765 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:40:38.903276 25640 solver.cpp:397]     Test net output #0: accuracy = 0.22549
I0401 16:40:38.903306 25640 solver.cpp:397]     Test net output #1: loss = 4.34414 (* 1 = 4.34414 loss)
I0401 16:40:39.042574 25640 solver.cpp:218] Iteration 8772 (0.7857 iter/s, 15.273s/12 iters), loss = 0.854625
I0401 16:40:39.042616 25640 solver.cpp:237]     Train net output #0: loss = 0.854625 (* 1 = 0.854625 loss)
I0401 16:40:39.042623 25640 sgd_solver.cpp:105] Iteration 8772, lr = 0.001
I0401 16:40:43.391408 25640 solver.cpp:218] Iteration 8784 (2.7594 iter/s, 4.34878s/12 iters), loss = 0.571671
I0401 16:40:43.391449 25640 solver.cpp:237]     Train net output #0: loss = 0.571671 (* 1 = 0.571671 loss)
I0401 16:40:43.391455 25640 sgd_solver.cpp:105] Iteration 8784, lr = 0.001
I0401 16:40:48.867806 25640 solver.cpp:218] Iteration 8796 (2.19125 iter/s, 5.47633s/12 iters), loss = 0.617392
I0401 16:40:48.867874 25640 solver.cpp:237]     Train net output #0: loss = 0.617392 (* 1 = 0.617392 loss)
I0401 16:40:48.867883 25640 sgd_solver.cpp:105] Iteration 8796, lr = 0.001
I0401 16:40:50.519122 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:40:54.305717 25640 solver.cpp:218] Iteration 8808 (2.20676 iter/s, 5.43783s/12 iters), loss = 0.671383
I0401 16:40:54.305773 25640 solver.cpp:237]     Train net output #0: loss = 0.671383 (* 1 = 0.671383 loss)
I0401 16:40:54.305781 25640 sgd_solver.cpp:105] Iteration 8808, lr = 0.001
I0401 16:40:59.731832 25640 solver.cpp:218] Iteration 8820 (2.21156 iter/s, 5.42605s/12 iters), loss = 0.755247
I0401 16:40:59.731981 25640 solver.cpp:237]     Train net output #0: loss = 0.755247 (* 1 = 0.755247 loss)
I0401 16:40:59.732007 25640 sgd_solver.cpp:105] Iteration 8820, lr = 0.001
I0401 16:41:05.142230 25640 solver.cpp:218] Iteration 8832 (2.21802 iter/s, 5.41023s/12 iters), loss = 0.651265
I0401 16:41:05.142275 25640 solver.cpp:237]     Train net output #0: loss = 0.651265 (* 1 = 0.651265 loss)
I0401 16:41:05.142282 25640 sgd_solver.cpp:105] Iteration 8832, lr = 0.001
I0401 16:41:10.611353 25640 solver.cpp:218] Iteration 8844 (2.19416 iter/s, 5.46906s/12 iters), loss = 0.957573
I0401 16:41:10.611398 25640 solver.cpp:237]     Train net output #0: loss = 0.957573 (* 1 = 0.957573 loss)
I0401 16:41:10.611403 25640 sgd_solver.cpp:105] Iteration 8844, lr = 0.001
I0401 16:41:15.869333 25640 solver.cpp:218] Iteration 8856 (2.28227 iter/s, 5.25792s/12 iters), loss = 0.810808
I0401 16:41:15.869374 25640 solver.cpp:237]     Train net output #0: loss = 0.810808 (* 1 = 0.810808 loss)
I0401 16:41:15.869380 25640 sgd_solver.cpp:105] Iteration 8856, lr = 0.001
I0401 16:41:21.104722 25640 solver.cpp:218] Iteration 8868 (2.29212 iter/s, 5.23533s/12 iters), loss = 0.525309
I0401 16:41:21.104764 25640 solver.cpp:237]     Train net output #0: loss = 0.525309 (* 1 = 0.525309 loss)
I0401 16:41:21.104770 25640 sgd_solver.cpp:105] Iteration 8868, lr = 0.001
I0401 16:41:23.114462 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0401 16:41:26.212718 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0401 16:41:30.042922 25640 solver.cpp:330] Iteration 8874, Testing net (#0)
I0401 16:41:30.043059 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:41:30.953794 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:41:34.533583 25640 solver.cpp:397]     Test net output #0: accuracy = 0.234069
I0401 16:41:34.533622 25640 solver.cpp:397]     Test net output #1: loss = 4.20984 (* 1 = 4.20984 loss)
I0401 16:41:36.422088 25640 solver.cpp:218] Iteration 8880 (0.783427 iter/s, 15.3173s/12 iters), loss = 0.560427
I0401 16:41:36.422144 25640 solver.cpp:237]     Train net output #0: loss = 0.560427 (* 1 = 0.560427 loss)
I0401 16:41:36.422149 25640 sgd_solver.cpp:105] Iteration 8880, lr = 0.001
I0401 16:41:41.645766 25640 solver.cpp:218] Iteration 8892 (2.29726 iter/s, 5.22361s/12 iters), loss = 0.387886
I0401 16:41:41.645826 25640 solver.cpp:237]     Train net output #0: loss = 0.387886 (* 1 = 0.387886 loss)
I0401 16:41:41.645835 25640 sgd_solver.cpp:105] Iteration 8892, lr = 0.001
I0401 16:41:45.482240 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:41:47.073266 25640 solver.cpp:218] Iteration 8904 (2.21099 iter/s, 5.42742s/12 iters), loss = 0.857027
I0401 16:41:47.073323 25640 solver.cpp:237]     Train net output #0: loss = 0.857027 (* 1 = 0.857027 loss)
I0401 16:41:47.073331 25640 sgd_solver.cpp:105] Iteration 8904, lr = 0.001
I0401 16:41:52.258669 25640 solver.cpp:218] Iteration 8916 (2.31422 iter/s, 5.18533s/12 iters), loss = 0.879295
I0401 16:41:52.258728 25640 solver.cpp:237]     Train net output #0: loss = 0.879295 (* 1 = 0.879295 loss)
I0401 16:41:52.258738 25640 sgd_solver.cpp:105] Iteration 8916, lr = 0.001
I0401 16:41:57.712450 25640 solver.cpp:218] Iteration 8928 (2.20034 iter/s, 5.45371s/12 iters), loss = 0.426076
I0401 16:41:57.712503 25640 solver.cpp:237]     Train net output #0: loss = 0.426076 (* 1 = 0.426076 loss)
I0401 16:41:57.712512 25640 sgd_solver.cpp:105] Iteration 8928, lr = 0.001
I0401 16:42:03.020236 25640 solver.cpp:218] Iteration 8940 (2.26086 iter/s, 5.30771s/12 iters), loss = 0.573968
I0401 16:42:03.020390 25640 solver.cpp:237]     Train net output #0: loss = 0.573968 (* 1 = 0.573968 loss)
I0401 16:42:03.020399 25640 sgd_solver.cpp:105] Iteration 8940, lr = 0.001
I0401 16:42:08.338445 25640 solver.cpp:218] Iteration 8952 (2.25647 iter/s, 5.31805s/12 iters), loss = 0.604607
I0401 16:42:08.338500 25640 solver.cpp:237]     Train net output #0: loss = 0.604607 (* 1 = 0.604607 loss)
I0401 16:42:08.338510 25640 sgd_solver.cpp:105] Iteration 8952, lr = 0.001
I0401 16:42:13.729534 25640 solver.cpp:218] Iteration 8964 (2.22592 iter/s, 5.39102s/12 iters), loss = 0.587807
I0401 16:42:13.729580 25640 solver.cpp:237]     Train net output #0: loss = 0.587807 (* 1 = 0.587807 loss)
I0401 16:42:13.729586 25640 sgd_solver.cpp:105] Iteration 8964, lr = 0.001
I0401 16:42:18.385833 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0401 16:42:21.549789 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0401 16:42:25.286792 25640 solver.cpp:330] Iteration 8976, Testing net (#0)
I0401 16:42:25.286815 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:42:26.184630 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:42:29.743897 25640 solver.cpp:397]     Test net output #0: accuracy = 0.237132
I0401 16:42:29.743927 25640 solver.cpp:397]     Test net output #1: loss = 4.2852 (* 1 = 4.2852 loss)
I0401 16:42:29.884716 25640 solver.cpp:218] Iteration 8976 (0.742798 iter/s, 16.1551s/12 iters), loss = 0.610341
I0401 16:42:29.884759 25640 solver.cpp:237]     Train net output #0: loss = 0.610341 (* 1 = 0.610341 loss)
I0401 16:42:29.884764 25640 sgd_solver.cpp:105] Iteration 8976, lr = 0.001
I0401 16:42:34.137935 25640 solver.cpp:218] Iteration 8988 (2.82143 iter/s, 4.25317s/12 iters), loss = 0.57813
I0401 16:42:34.138020 25640 solver.cpp:237]     Train net output #0: loss = 0.57813 (* 1 = 0.57813 loss)
I0401 16:42:34.138025 25640 sgd_solver.cpp:105] Iteration 8988, lr = 0.001
I0401 16:42:37.678228 25640 blocking_queue.cpp:49] Waiting for data
I0401 16:42:39.268879 25640 solver.cpp:218] Iteration 9000 (2.33879 iter/s, 5.13085s/12 iters), loss = 0.550444
I0401 16:42:39.268918 25640 solver.cpp:237]     Train net output #0: loss = 0.550444 (* 1 = 0.550444 loss)
I0401 16:42:39.268924 25640 sgd_solver.cpp:105] Iteration 9000, lr = 0.001
I0401 16:42:39.924336 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:42:44.593274 25640 solver.cpp:218] Iteration 9012 (2.2538 iter/s, 5.32434s/12 iters), loss = 0.943464
I0401 16:42:44.593313 25640 solver.cpp:237]     Train net output #0: loss = 0.943464 (* 1 = 0.943464 loss)
I0401 16:42:44.593319 25640 sgd_solver.cpp:105] Iteration 9012, lr = 0.001
I0401 16:42:50.021209 25640 solver.cpp:218] Iteration 9024 (2.21081 iter/s, 5.42788s/12 iters), loss = 0.787715
I0401 16:42:50.021246 25640 solver.cpp:237]     Train net output #0: loss = 0.787715 (* 1 = 0.787715 loss)
I0401 16:42:50.021251 25640 sgd_solver.cpp:105] Iteration 9024, lr = 0.001
I0401 16:42:55.459908 25640 solver.cpp:218] Iteration 9036 (2.20643 iter/s, 5.43865s/12 iters), loss = 0.594604
I0401 16:42:55.459964 25640 solver.cpp:237]     Train net output #0: loss = 0.594604 (* 1 = 0.594604 loss)
I0401 16:42:55.459975 25640 sgd_solver.cpp:105] Iteration 9036, lr = 0.001
I0401 16:43:00.810501 25640 solver.cpp:218] Iteration 9048 (2.24277 iter/s, 5.35052s/12 iters), loss = 0.583428
I0401 16:43:00.810545 25640 solver.cpp:237]     Train net output #0: loss = 0.583428 (* 1 = 0.583428 loss)
I0401 16:43:00.810551 25640 sgd_solver.cpp:105] Iteration 9048, lr = 0.001
I0401 16:43:06.013042 25640 solver.cpp:218] Iteration 9060 (2.30659 iter/s, 5.20248s/12 iters), loss = 0.645072
I0401 16:43:06.013197 25640 solver.cpp:237]     Train net output #0: loss = 0.645072 (* 1 = 0.645072 loss)
I0401 16:43:06.013209 25640 sgd_solver.cpp:105] Iteration 9060, lr = 0.001
I0401 16:43:11.549362 25640 solver.cpp:218] Iteration 9072 (2.16757 iter/s, 5.53615s/12 iters), loss = 0.661194
I0401 16:43:11.549424 25640 solver.cpp:237]     Train net output #0: loss = 0.661194 (* 1 = 0.661194 loss)
I0401 16:43:11.549433 25640 sgd_solver.cpp:105] Iteration 9072, lr = 0.001
I0401 16:43:13.634606 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0401 16:43:18.370836 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0401 16:43:22.348776 25640 solver.cpp:330] Iteration 9078, Testing net (#0)
I0401 16:43:22.348795 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:43:23.178373 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:43:27.084743 25640 solver.cpp:397]     Test net output #0: accuracy = 0.237132
I0401 16:43:27.084775 25640 solver.cpp:397]     Test net output #1: loss = 4.28565 (* 1 = 4.28565 loss)
I0401 16:43:28.959125 25640 solver.cpp:218] Iteration 9084 (0.689272 iter/s, 17.4097s/12 iters), loss = 0.688608
I0401 16:43:28.959184 25640 solver.cpp:237]     Train net output #0: loss = 0.688608 (* 1 = 0.688608 loss)
I0401 16:43:28.959193 25640 sgd_solver.cpp:105] Iteration 9084, lr = 0.001
I0401 16:43:34.647478 25640 solver.cpp:218] Iteration 9096 (2.1096 iter/s, 5.68827s/12 iters), loss = 0.482443
I0401 16:43:34.647543 25640 solver.cpp:237]     Train net output #0: loss = 0.482443 (* 1 = 0.482443 loss)
I0401 16:43:34.647553 25640 sgd_solver.cpp:105] Iteration 9096, lr = 0.001
I0401 16:43:37.623155 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:43:39.995122 25640 solver.cpp:218] Iteration 9108 (2.24401 iter/s, 5.34757s/12 iters), loss = 0.732155
I0401 16:43:39.995177 25640 solver.cpp:237]     Train net output #0: loss = 0.732155 (* 1 = 0.732155 loss)
I0401 16:43:39.995187 25640 sgd_solver.cpp:105] Iteration 9108, lr = 0.001
I0401 16:43:45.472749 25640 solver.cpp:218] Iteration 9120 (2.19076 iter/s, 5.47755s/12 iters), loss = 0.491012
I0401 16:43:45.472810 25640 solver.cpp:237]     Train net output #0: loss = 0.491012 (* 1 = 0.491012 loss)
I0401 16:43:45.472818 25640 sgd_solver.cpp:105] Iteration 9120, lr = 0.001
I0401 16:43:51.056550 25640 solver.cpp:218] Iteration 9132 (2.1491 iter/s, 5.58373s/12 iters), loss = 0.751077
I0401 16:43:51.056607 25640 solver.cpp:237]     Train net output #0: loss = 0.751077 (* 1 = 0.751077 loss)
I0401 16:43:51.056617 25640 sgd_solver.cpp:105] Iteration 9132, lr = 0.001
I0401 16:43:56.799573 25640 solver.cpp:218] Iteration 9144 (2.08952 iter/s, 5.74295s/12 iters), loss = 0.658343
I0401 16:43:56.799624 25640 solver.cpp:237]     Train net output #0: loss = 0.658343 (* 1 = 0.658343 loss)
I0401 16:43:56.799633 25640 sgd_solver.cpp:105] Iteration 9144, lr = 0.001
I0401 16:44:02.513876 25640 solver.cpp:218] Iteration 9156 (2.10002 iter/s, 5.71424s/12 iters), loss = 0.638725
I0401 16:44:02.513921 25640 solver.cpp:237]     Train net output #0: loss = 0.638725 (* 1 = 0.638725 loss)
I0401 16:44:02.513927 25640 sgd_solver.cpp:105] Iteration 9156, lr = 0.001
I0401 16:44:07.999011 25640 solver.cpp:218] Iteration 9168 (2.18775 iter/s, 5.48508s/12 iters), loss = 0.64317
I0401 16:44:07.999161 25640 solver.cpp:237]     Train net output #0: loss = 0.64317 (* 1 = 0.64317 loss)
I0401 16:44:07.999168 25640 sgd_solver.cpp:105] Iteration 9168, lr = 0.001
I0401 16:44:13.212332 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0401 16:44:17.672767 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0401 16:44:20.265503 25640 solver.cpp:330] Iteration 9180, Testing net (#0)
I0401 16:44:20.265527 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:44:21.101667 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:44:25.074245 25640 solver.cpp:397]     Test net output #0: accuracy = 0.241422
I0401 16:44:25.074283 25640 solver.cpp:397]     Test net output #1: loss = 4.21139 (* 1 = 4.21139 loss)
I0401 16:44:25.210429 25640 solver.cpp:218] Iteration 9180 (0.697218 iter/s, 17.2113s/12 iters), loss = 0.668649
I0401 16:44:25.210481 25640 solver.cpp:237]     Train net output #0: loss = 0.668649 (* 1 = 0.668649 loss)
I0401 16:44:25.210489 25640 sgd_solver.cpp:105] Iteration 9180, lr = 0.001
I0401 16:44:29.630723 25640 solver.cpp:218] Iteration 9192 (2.71479 iter/s, 4.42023s/12 iters), loss = 0.753481
I0401 16:44:29.630766 25640 solver.cpp:237]     Train net output #0: loss = 0.753481 (* 1 = 0.753481 loss)
I0401 16:44:29.630771 25640 sgd_solver.cpp:105] Iteration 9192, lr = 0.001
I0401 16:44:35.366267 25640 solver.cpp:218] Iteration 9204 (2.09224 iter/s, 5.73549s/12 iters), loss = 0.468554
I0401 16:44:35.366320 25640 solver.cpp:237]     Train net output #0: loss = 0.468554 (* 1 = 0.468554 loss)
I0401 16:44:35.366328 25640 sgd_solver.cpp:105] Iteration 9204, lr = 0.001
I0401 16:44:35.389981 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:44:40.979915 25640 solver.cpp:218] Iteration 9216 (2.13767 iter/s, 5.61358s/12 iters), loss = 0.722109
I0401 16:44:40.980021 25640 solver.cpp:237]     Train net output #0: loss = 0.722109 (* 1 = 0.722109 loss)
I0401 16:44:40.980031 25640 sgd_solver.cpp:105] Iteration 9216, lr = 0.001
I0401 16:44:46.659981 25640 solver.cpp:218] Iteration 9228 (2.1127 iter/s, 5.67994s/12 iters), loss = 0.697975
I0401 16:44:46.660039 25640 solver.cpp:237]     Train net output #0: loss = 0.697975 (* 1 = 0.697975 loss)
I0401 16:44:46.660048 25640 sgd_solver.cpp:105] Iteration 9228, lr = 0.001
I0401 16:44:52.343176 25640 solver.cpp:218] Iteration 9240 (2.11152 iter/s, 5.68312s/12 iters), loss = 0.859577
I0401 16:44:52.343233 25640 solver.cpp:237]     Train net output #0: loss = 0.859577 (* 1 = 0.859577 loss)
I0401 16:44:52.343241 25640 sgd_solver.cpp:105] Iteration 9240, lr = 0.001
I0401 16:44:58.022809 25640 solver.cpp:218] Iteration 9252 (2.11284 iter/s, 5.67956s/12 iters), loss = 0.638853
I0401 16:44:58.022863 25640 solver.cpp:237]     Train net output #0: loss = 0.638853 (* 1 = 0.638853 loss)
I0401 16:44:58.022871 25640 sgd_solver.cpp:105] Iteration 9252, lr = 0.001
I0401 16:45:03.743592 25640 solver.cpp:218] Iteration 9264 (2.09764 iter/s, 5.72071s/12 iters), loss = 0.496411
I0401 16:45:03.743641 25640 solver.cpp:237]     Train net output #0: loss = 0.496411 (* 1 = 0.496411 loss)
I0401 16:45:03.743650 25640 sgd_solver.cpp:105] Iteration 9264, lr = 0.001
I0401 16:45:09.562420 25640 solver.cpp:218] Iteration 9276 (2.06229 iter/s, 5.81877s/12 iters), loss = 0.5177
I0401 16:45:09.562469 25640 solver.cpp:237]     Train net output #0: loss = 0.5177 (* 1 = 0.5177 loss)
I0401 16:45:09.562477 25640 sgd_solver.cpp:105] Iteration 9276, lr = 0.001
I0401 16:45:11.792251 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0401 16:45:14.968768 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0401 16:45:17.288630 25640 solver.cpp:330] Iteration 9282, Testing net (#0)
I0401 16:45:17.288653 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:45:18.039768 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:45:22.262745 25640 solver.cpp:397]     Test net output #0: accuracy = 0.240196
I0401 16:45:22.262784 25640 solver.cpp:397]     Test net output #1: loss = 4.28784 (* 1 = 4.28784 loss)
I0401 16:45:24.239149 25640 solver.cpp:218] Iteration 9288 (0.817624 iter/s, 14.6767s/12 iters), loss = 0.439225
I0401 16:45:24.239205 25640 solver.cpp:237]     Train net output #0: loss = 0.439225 (* 1 = 0.439225 loss)
I0401 16:45:24.239214 25640 sgd_solver.cpp:105] Iteration 9288, lr = 0.001
I0401 16:45:29.616878 25640 solver.cpp:218] Iteration 9300 (2.23145 iter/s, 5.37766s/12 iters), loss = 0.484691
I0401 16:45:29.616925 25640 solver.cpp:237]     Train net output #0: loss = 0.484691 (* 1 = 0.484691 loss)
I0401 16:45:29.616930 25640 sgd_solver.cpp:105] Iteration 9300, lr = 0.001
I0401 16:45:32.202473 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:45:35.402271 25640 solver.cpp:218] Iteration 9312 (2.07421 iter/s, 5.78533s/12 iters), loss = 0.461205
I0401 16:45:35.402318 25640 solver.cpp:237]     Train net output #0: loss = 0.461205 (* 1 = 0.461205 loss)
I0401 16:45:35.402326 25640 sgd_solver.cpp:105] Iteration 9312, lr = 0.001
I0401 16:45:41.182327 25640 solver.cpp:218] Iteration 9324 (2.07613 iter/s, 5.77999s/12 iters), loss = 0.796015
I0401 16:45:41.182392 25640 solver.cpp:237]     Train net output #0: loss = 0.796015 (* 1 = 0.796015 loss)
I0401 16:45:41.182401 25640 sgd_solver.cpp:105] Iteration 9324, lr = 0.001
I0401 16:45:46.902349 25640 solver.cpp:218] Iteration 9336 (2.09792 iter/s, 5.71994s/12 iters), loss = 0.625299
I0401 16:45:46.902524 25640 solver.cpp:237]     Train net output #0: loss = 0.625299 (* 1 = 0.625299 loss)
I0401 16:45:46.902534 25640 sgd_solver.cpp:105] Iteration 9336, lr = 0.001
I0401 16:45:52.526427 25640 solver.cpp:218] Iteration 9348 (2.13376 iter/s, 5.62389s/12 iters), loss = 0.480791
I0401 16:45:52.526469 25640 solver.cpp:237]     Train net output #0: loss = 0.480791 (* 1 = 0.480791 loss)
I0401 16:45:52.526474 25640 sgd_solver.cpp:105] Iteration 9348, lr = 0.001
I0401 16:45:58.215983 25640 solver.cpp:218] Iteration 9360 (2.10915 iter/s, 5.6895s/12 iters), loss = 0.790092
I0401 16:45:58.216035 25640 solver.cpp:237]     Train net output #0: loss = 0.790092 (* 1 = 0.790092 loss)
I0401 16:45:58.216044 25640 sgd_solver.cpp:105] Iteration 9360, lr = 0.001
I0401 16:46:04.062490 25640 solver.cpp:218] Iteration 9372 (2.05253 iter/s, 5.84644s/12 iters), loss = 0.624856
I0401 16:46:04.062532 25640 solver.cpp:237]     Train net output #0: loss = 0.624856 (* 1 = 0.624856 loss)
I0401 16:46:04.062538 25640 sgd_solver.cpp:105] Iteration 9372, lr = 0.001
I0401 16:46:09.018906 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0401 16:46:12.135114 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0401 16:46:14.495622 25640 solver.cpp:330] Iteration 9384, Testing net (#0)
I0401 16:46:14.495642 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:46:15.244500 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:46:19.334218 25640 solver.cpp:397]     Test net output #0: accuracy = 0.251226
I0401 16:46:19.334302 25640 solver.cpp:397]     Test net output #1: loss = 4.34225 (* 1 = 4.34225 loss)
I0401 16:46:19.465445 25640 solver.cpp:218] Iteration 9384 (0.779074 iter/s, 15.4029s/12 iters), loss = 0.475914
I0401 16:46:19.465500 25640 solver.cpp:237]     Train net output #0: loss = 0.475914 (* 1 = 0.475914 loss)
I0401 16:46:19.465510 25640 sgd_solver.cpp:105] Iteration 9384, lr = 0.001
I0401 16:46:24.173128 25640 solver.cpp:218] Iteration 9396 (2.54907 iter/s, 4.70761s/12 iters), loss = 0.464568
I0401 16:46:24.173193 25640 solver.cpp:237]     Train net output #0: loss = 0.464568 (* 1 = 0.464568 loss)
I0401 16:46:24.173202 25640 sgd_solver.cpp:105] Iteration 9396, lr = 0.001
I0401 16:46:29.019721 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:46:29.839962 25640 solver.cpp:218] Iteration 9408 (2.11762 iter/s, 5.66675s/12 iters), loss = 0.56424
I0401 16:46:29.840023 25640 solver.cpp:237]     Train net output #0: loss = 0.56424 (* 1 = 0.56424 loss)
I0401 16:46:29.840031 25640 sgd_solver.cpp:105] Iteration 9408, lr = 0.001
I0401 16:46:35.303192 25640 solver.cpp:218] Iteration 9420 (2.19653 iter/s, 5.46315s/12 iters), loss = 0.71662
I0401 16:46:35.303243 25640 solver.cpp:237]     Train net output #0: loss = 0.71662 (* 1 = 0.71662 loss)
I0401 16:46:35.303251 25640 sgd_solver.cpp:105] Iteration 9420, lr = 0.001
I0401 16:46:40.924573 25640 solver.cpp:218] Iteration 9432 (2.13473 iter/s, 5.62132s/12 iters), loss = 0.510903
I0401 16:46:40.924624 25640 solver.cpp:237]     Train net output #0: loss = 0.510903 (* 1 = 0.510903 loss)
I0401 16:46:40.924633 25640 sgd_solver.cpp:105] Iteration 9432, lr = 0.001
I0401 16:46:46.365501 25640 solver.cpp:218] Iteration 9444 (2.20553 iter/s, 5.44086s/12 iters), loss = 0.810406
I0401 16:46:46.365545 25640 solver.cpp:237]     Train net output #0: loss = 0.810406 (* 1 = 0.810406 loss)
I0401 16:46:46.365550 25640 sgd_solver.cpp:105] Iteration 9444, lr = 0.001
I0401 16:46:51.971108 25640 solver.cpp:218] Iteration 9456 (2.14074 iter/s, 5.60555s/12 iters), loss = 0.605556
I0401 16:46:51.971251 25640 solver.cpp:237]     Train net output #0: loss = 0.605556 (* 1 = 0.605556 loss)
I0401 16:46:51.971259 25640 sgd_solver.cpp:105] Iteration 9456, lr = 0.001
I0401 16:46:57.627188 25640 solver.cpp:218] Iteration 9468 (2.12167 iter/s, 5.65592s/12 iters), loss = 0.560054
I0401 16:46:57.627238 25640 solver.cpp:237]     Train net output #0: loss = 0.560054 (* 1 = 0.560054 loss)
I0401 16:46:57.627246 25640 sgd_solver.cpp:105] Iteration 9468, lr = 0.001
I0401 16:47:03.166085 25640 solver.cpp:218] Iteration 9480 (2.16652 iter/s, 5.53883s/12 iters), loss = 0.55978
I0401 16:47:03.166137 25640 solver.cpp:237]     Train net output #0: loss = 0.55978 (* 1 = 0.55978 loss)
I0401 16:47:03.166146 25640 sgd_solver.cpp:105] Iteration 9480, lr = 0.001
I0401 16:47:05.175858 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0401 16:47:08.439922 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0401 16:47:10.743432 25640 solver.cpp:330] Iteration 9486, Testing net (#0)
I0401 16:47:10.743451 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:47:11.528924 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:47:15.710577 25640 solver.cpp:397]     Test net output #0: accuracy = 0.235294
I0401 16:47:15.710610 25640 solver.cpp:397]     Test net output #1: loss = 4.35645 (* 1 = 4.35645 loss)
I0401 16:47:17.804034 25640 solver.cpp:218] Iteration 9492 (0.81979 iter/s, 14.6379s/12 iters), loss = 0.459358
I0401 16:47:17.804085 25640 solver.cpp:237]     Train net output #0: loss = 0.459358 (* 1 = 0.459358 loss)
I0401 16:47:17.804093 25640 sgd_solver.cpp:105] Iteration 9492, lr = 0.001
I0401 16:47:23.434036 25640 solver.cpp:218] Iteration 9504 (2.13146 iter/s, 5.62993s/12 iters), loss = 0.579249
I0401 16:47:23.434142 25640 solver.cpp:237]     Train net output #0: loss = 0.579249 (* 1 = 0.579249 loss)
I0401 16:47:23.434151 25640 sgd_solver.cpp:105] Iteration 9504, lr = 0.001
I0401 16:47:24.961199 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:47:28.931238 25640 solver.cpp:218] Iteration 9516 (2.18297 iter/s, 5.49709s/12 iters), loss = 0.546591
I0401 16:47:28.931277 25640 solver.cpp:237]     Train net output #0: loss = 0.546591 (* 1 = 0.546591 loss)
I0401 16:47:28.931283 25640 sgd_solver.cpp:105] Iteration 9516, lr = 0.001
I0401 16:47:34.612771 25640 solver.cpp:218] Iteration 9528 (2.11213 iter/s, 5.68147s/12 iters), loss = 0.684896
I0401 16:47:34.612828 25640 solver.cpp:237]     Train net output #0: loss = 0.684896 (* 1 = 0.684896 loss)
I0401 16:47:34.612835 25640 sgd_solver.cpp:105] Iteration 9528, lr = 0.001
I0401 16:47:40.202369 25640 solver.cpp:218] Iteration 9540 (2.14687 iter/s, 5.58952s/12 iters), loss = 0.802022
I0401 16:47:40.202425 25640 solver.cpp:237]     Train net output #0: loss = 0.802022 (* 1 = 0.802022 loss)
I0401 16:47:40.202432 25640 sgd_solver.cpp:105] Iteration 9540, lr = 0.001
I0401 16:47:45.914126 25640 solver.cpp:218] Iteration 9552 (2.10096 iter/s, 5.71168s/12 iters), loss = 0.593045
I0401 16:47:45.914182 25640 solver.cpp:237]     Train net output #0: loss = 0.593045 (* 1 = 0.593045 loss)
I0401 16:47:45.914191 25640 sgd_solver.cpp:105] Iteration 9552, lr = 0.001
I0401 16:47:51.663723 25640 solver.cpp:218] Iteration 9564 (2.08713 iter/s, 5.74952s/12 iters), loss = 0.559986
I0401 16:47:51.669960 25640 solver.cpp:237]     Train net output #0: loss = 0.559986 (* 1 = 0.559986 loss)
I0401 16:47:51.669979 25640 sgd_solver.cpp:105] Iteration 9564, lr = 0.001
I0401 16:47:56.773795 25640 solver.cpp:218] Iteration 9576 (2.35117 iter/s, 5.10384s/12 iters), loss = 0.68278
I0401 16:47:56.774405 25640 solver.cpp:237]     Train net output #0: loss = 0.68278 (* 1 = 0.68278 loss)
I0401 16:47:56.774415 25640 sgd_solver.cpp:105] Iteration 9576, lr = 0.001
I0401 16:48:01.668723 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0401 16:48:04.840210 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0401 16:48:07.151831 25640 solver.cpp:330] Iteration 9588, Testing net (#0)
I0401 16:48:07.151849 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:48:07.738572 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:48:11.635530 25640 solver.cpp:397]     Test net output #0: accuracy = 0.240809
I0401 16:48:11.635558 25640 solver.cpp:397]     Test net output #1: loss = 4.40454 (* 1 = 4.40454 loss)
I0401 16:48:11.782986 25640 solver.cpp:218] Iteration 9588 (0.799543 iter/s, 15.0086s/12 iters), loss = 0.468792
I0401 16:48:11.783043 25640 solver.cpp:237]     Train net output #0: loss = 0.468792 (* 1 = 0.468792 loss)
I0401 16:48:11.783053 25640 sgd_solver.cpp:105] Iteration 9588, lr = 0.001
I0401 16:48:16.160168 25640 solver.cpp:218] Iteration 9600 (2.74153 iter/s, 4.37711s/12 iters), loss = 0.52687
I0401 16:48:16.160214 25640 solver.cpp:237]     Train net output #0: loss = 0.52687 (* 1 = 0.52687 loss)
I0401 16:48:16.160223 25640 sgd_solver.cpp:105] Iteration 9600, lr = 0.001
I0401 16:48:20.046245 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:48:21.553985 25640 solver.cpp:218] Iteration 9612 (2.2248 iter/s, 5.39375s/12 iters), loss = 0.479976
I0401 16:48:21.554037 25640 solver.cpp:237]     Train net output #0: loss = 0.479976 (* 1 = 0.479976 loss)
I0401 16:48:21.554045 25640 sgd_solver.cpp:105] Iteration 9612, lr = 0.001
I0401 16:48:27.145493 25640 solver.cpp:218] Iteration 9624 (2.14614 iter/s, 5.59144s/12 iters), loss = 0.578327
I0401 16:48:27.145584 25640 solver.cpp:237]     Train net output #0: loss = 0.578327 (* 1 = 0.578327 loss)
I0401 16:48:27.145591 25640 sgd_solver.cpp:105] Iteration 9624, lr = 0.001
I0401 16:48:32.516286 25640 solver.cpp:218] Iteration 9636 (2.23435 iter/s, 5.37069s/12 iters), loss = 0.550971
I0401 16:48:32.516336 25640 solver.cpp:237]     Train net output #0: loss = 0.550971 (* 1 = 0.550971 loss)
I0401 16:48:32.516345 25640 sgd_solver.cpp:105] Iteration 9636, lr = 0.001
I0401 16:48:37.746192 25640 solver.cpp:218] Iteration 9648 (2.29453 iter/s, 5.22984s/12 iters), loss = 0.753606
I0401 16:48:37.746248 25640 solver.cpp:237]     Train net output #0: loss = 0.753606 (* 1 = 0.753606 loss)
I0401 16:48:37.746258 25640 sgd_solver.cpp:105] Iteration 9648, lr = 0.001
I0401 16:48:43.095880 25640 solver.cpp:218] Iteration 9660 (2.24315 iter/s, 5.34962s/12 iters), loss = 0.574838
I0401 16:48:43.095932 25640 solver.cpp:237]     Train net output #0: loss = 0.574838 (* 1 = 0.574838 loss)
I0401 16:48:43.095940 25640 sgd_solver.cpp:105] Iteration 9660, lr = 0.001
I0401 16:48:48.797767 25640 solver.cpp:218] Iteration 9672 (2.10459 iter/s, 5.70182s/12 iters), loss = 0.540522
I0401 16:48:48.797817 25640 solver.cpp:237]     Train net output #0: loss = 0.540522 (* 1 = 0.540522 loss)
I0401 16:48:48.797825 25640 sgd_solver.cpp:105] Iteration 9672, lr = 0.001
I0401 16:48:54.160578 25640 solver.cpp:218] Iteration 9684 (2.23766 iter/s, 5.36275s/12 iters), loss = 0.56457
I0401 16:48:54.160629 25640 solver.cpp:237]     Train net output #0: loss = 0.56457 (* 1 = 0.56457 loss)
I0401 16:48:54.160637 25640 sgd_solver.cpp:105] Iteration 9684, lr = 0.001
I0401 16:48:56.231333 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0401 16:48:59.233186 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0401 16:49:01.547175 25640 solver.cpp:330] Iteration 9690, Testing net (#0)
I0401 16:49:01.547200 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:49:02.195870 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:49:05.082912 25640 blocking_queue.cpp:49] Waiting for data
I0401 16:49:06.158207 25640 solver.cpp:397]     Test net output #0: accuracy = 0.239583
I0401 16:49:06.158244 25640 solver.cpp:397]     Test net output #1: loss = 4.28868 (* 1 = 4.28868 loss)
I0401 16:49:08.141978 25640 solver.cpp:218] Iteration 9696 (0.858287 iter/s, 13.9813s/12 iters), loss = 0.572394
I0401 16:49:08.142024 25640 solver.cpp:237]     Train net output #0: loss = 0.572394 (* 1 = 0.572394 loss)
I0401 16:49:08.142030 25640 sgd_solver.cpp:105] Iteration 9696, lr = 0.001
I0401 16:49:13.367259 25640 solver.cpp:218] Iteration 9708 (2.29655 iter/s, 5.22522s/12 iters), loss = 0.514604
I0401 16:49:13.367302 25640 solver.cpp:237]     Train net output #0: loss = 0.514604 (* 1 = 0.514604 loss)
I0401 16:49:13.367307 25640 sgd_solver.cpp:105] Iteration 9708, lr = 0.001
I0401 16:49:14.064626 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:49:18.807895 25640 solver.cpp:218] Iteration 9720 (2.20565 iter/s, 5.44057s/12 iters), loss = 0.452647
I0401 16:49:18.807952 25640 solver.cpp:237]     Train net output #0: loss = 0.452647 (* 1 = 0.452647 loss)
I0401 16:49:18.807960 25640 sgd_solver.cpp:105] Iteration 9720, lr = 0.001
I0401 16:49:24.057231 25640 solver.cpp:218] Iteration 9732 (2.28603 iter/s, 5.24926s/12 iters), loss = 0.618137
I0401 16:49:24.057276 25640 solver.cpp:237]     Train net output #0: loss = 0.618137 (* 1 = 0.618137 loss)
I0401 16:49:24.057281 25640 sgd_solver.cpp:105] Iteration 9732, lr = 0.001
I0401 16:49:29.315485 25640 solver.cpp:218] Iteration 9744 (2.28216 iter/s, 5.25819s/12 iters), loss = 0.456231
I0401 16:49:29.315611 25640 solver.cpp:237]     Train net output #0: loss = 0.456231 (* 1 = 0.456231 loss)
I0401 16:49:29.315620 25640 sgd_solver.cpp:105] Iteration 9744, lr = 0.001
I0401 16:49:34.893774 25640 solver.cpp:218] Iteration 9756 (2.15125 iter/s, 5.57815s/12 iters), loss = 0.309724
I0401 16:49:34.893821 25640 solver.cpp:237]     Train net output #0: loss = 0.309724 (* 1 = 0.309724 loss)
I0401 16:49:34.893827 25640 sgd_solver.cpp:105] Iteration 9756, lr = 0.001
I0401 16:49:40.284287 25640 solver.cpp:218] Iteration 9768 (2.22616 iter/s, 5.39045s/12 iters), loss = 0.756845
I0401 16:49:40.284325 25640 solver.cpp:237]     Train net output #0: loss = 0.756845 (* 1 = 0.756845 loss)
I0401 16:49:40.284330 25640 sgd_solver.cpp:105] Iteration 9768, lr = 0.001
I0401 16:49:45.445708 25640 solver.cpp:218] Iteration 9780 (2.32497 iter/s, 5.16136s/12 iters), loss = 0.325256
I0401 16:49:45.445766 25640 solver.cpp:237]     Train net output #0: loss = 0.325256 (* 1 = 0.325256 loss)
I0401 16:49:45.445775 25640 sgd_solver.cpp:105] Iteration 9780, lr = 0.001
I0401 16:49:50.462780 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0401 16:49:53.522666 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0401 16:49:55.824172 25640 solver.cpp:330] Iteration 9792, Testing net (#0)
I0401 16:49:55.824194 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:49:56.388510 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:50:00.556354 25640 solver.cpp:397]     Test net output #0: accuracy = 0.238358
I0401 16:50:00.556445 25640 solver.cpp:397]     Test net output #1: loss = 4.40231 (* 1 = 4.40231 loss)
I0401 16:50:00.697052 25640 solver.cpp:218] Iteration 9792 (0.78682 iter/s, 15.2513s/12 iters), loss = 0.479718
I0401 16:50:00.698613 25640 solver.cpp:237]     Train net output #0: loss = 0.479718 (* 1 = 0.479718 loss)
I0401 16:50:00.698626 25640 sgd_solver.cpp:105] Iteration 9792, lr = 0.001
I0401 16:50:05.156929 25640 solver.cpp:218] Iteration 9804 (2.6916 iter/s, 4.45831s/12 iters), loss = 0.567888
I0401 16:50:05.156970 25640 solver.cpp:237]     Train net output #0: loss = 0.567888 (* 1 = 0.567888 loss)
I0401 16:50:05.156976 25640 sgd_solver.cpp:105] Iteration 9804, lr = 0.001
I0401 16:50:08.362273 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:50:10.520344 25640 solver.cpp:218] Iteration 9816 (2.23741 iter/s, 5.36336s/12 iters), loss = 0.622176
I0401 16:50:10.520395 25640 solver.cpp:237]     Train net output #0: loss = 0.622176 (* 1 = 0.622176 loss)
I0401 16:50:10.520402 25640 sgd_solver.cpp:105] Iteration 9816, lr = 0.001
I0401 16:50:15.736618 25640 solver.cpp:218] Iteration 9828 (2.30053 iter/s, 5.2162s/12 iters), loss = 0.288681
I0401 16:50:15.736670 25640 solver.cpp:237]     Train net output #0: loss = 0.288681 (* 1 = 0.288681 loss)
I0401 16:50:15.736678 25640 sgd_solver.cpp:105] Iteration 9828, lr = 0.001
I0401 16:50:21.124270 25640 solver.cpp:218] Iteration 9840 (2.22734 iter/s, 5.38758s/12 iters), loss = 0.730657
I0401 16:50:21.124326 25640 solver.cpp:237]     Train net output #0: loss = 0.730657 (* 1 = 0.730657 loss)
I0401 16:50:21.124336 25640 sgd_solver.cpp:105] Iteration 9840, lr = 0.001
I0401 16:50:26.574641 25640 solver.cpp:218] Iteration 9852 (2.20172 iter/s, 5.4503s/12 iters), loss = 0.57264
I0401 16:50:26.574704 25640 solver.cpp:237]     Train net output #0: loss = 0.57264 (* 1 = 0.57264 loss)
I0401 16:50:26.574713 25640 sgd_solver.cpp:105] Iteration 9852, lr = 0.001
I0401 16:50:32.041267 25640 solver.cpp:218] Iteration 9864 (2.19517 iter/s, 5.46655s/12 iters), loss = 0.510959
I0401 16:50:32.041486 25640 solver.cpp:237]     Train net output #0: loss = 0.510959 (* 1 = 0.510959 loss)
I0401 16:50:32.041494 25640 sgd_solver.cpp:105] Iteration 9864, lr = 0.001
I0401 16:50:37.437943 25640 solver.cpp:218] Iteration 9876 (2.22369 iter/s, 5.39644s/12 iters), loss = 0.668348
I0401 16:50:37.437986 25640 solver.cpp:237]     Train net output #0: loss = 0.668348 (* 1 = 0.668348 loss)
I0401 16:50:37.437991 25640 sgd_solver.cpp:105] Iteration 9876, lr = 0.001
I0401 16:50:42.917080 25640 solver.cpp:218] Iteration 9888 (2.19015 iter/s, 5.47907s/12 iters), loss = 0.572775
I0401 16:50:42.917133 25640 solver.cpp:237]     Train net output #0: loss = 0.572775 (* 1 = 0.572775 loss)
I0401 16:50:42.917141 25640 sgd_solver.cpp:105] Iteration 9888, lr = 0.001
I0401 16:50:45.009645 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0401 16:50:49.309633 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0401 16:50:51.624917 25640 solver.cpp:330] Iteration 9894, Testing net (#0)
I0401 16:50:51.624934 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:50:52.131016 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:50:56.177469 25640 solver.cpp:397]     Test net output #0: accuracy = 0.246324
I0401 16:50:56.177496 25640 solver.cpp:397]     Test net output #1: loss = 4.38766 (* 1 = 4.38766 loss)
I0401 16:50:58.054096 25640 solver.cpp:218] Iteration 9900 (0.79277 iter/s, 15.1368s/12 iters), loss = 0.554104
I0401 16:50:58.054147 25640 solver.cpp:237]     Train net output #0: loss = 0.554104 (* 1 = 0.554104 loss)
I0401 16:50:58.054155 25640 sgd_solver.cpp:105] Iteration 9900, lr = 0.001
I0401 16:51:03.435839 25640 solver.cpp:218] Iteration 9912 (2.22979 iter/s, 5.38168s/12 iters), loss = 0.459314
I0401 16:51:03.435932 25640 solver.cpp:237]     Train net output #0: loss = 0.459314 (* 1 = 0.459314 loss)
I0401 16:51:03.435940 25640 sgd_solver.cpp:105] Iteration 9912, lr = 0.001
I0401 16:51:03.551699 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:51:08.955483 25640 solver.cpp:218] Iteration 9924 (2.1741 iter/s, 5.51953s/12 iters), loss = 0.57305
I0401 16:51:08.955539 25640 solver.cpp:237]     Train net output #0: loss = 0.57305 (* 1 = 0.57305 loss)
I0401 16:51:08.955549 25640 sgd_solver.cpp:105] Iteration 9924, lr = 0.001
I0401 16:51:14.439395 25640 solver.cpp:218] Iteration 9936 (2.18825 iter/s, 5.48384s/12 iters), loss = 0.516153
I0401 16:51:14.439451 25640 solver.cpp:237]     Train net output #0: loss = 0.516153 (* 1 = 0.516153 loss)
I0401 16:51:14.439461 25640 sgd_solver.cpp:105] Iteration 9936, lr = 0.001
I0401 16:51:19.860234 25640 solver.cpp:218] Iteration 9948 (2.21371 iter/s, 5.42077s/12 iters), loss = 0.538616
I0401 16:51:19.860277 25640 solver.cpp:237]     Train net output #0: loss = 0.538616 (* 1 = 0.538616 loss)
I0401 16:51:19.860282 25640 sgd_solver.cpp:105] Iteration 9948, lr = 0.001
I0401 16:51:25.303804 25640 solver.cpp:218] Iteration 9960 (2.20446 iter/s, 5.4435s/12 iters), loss = 0.511525
I0401 16:51:25.303864 25640 solver.cpp:237]     Train net output #0: loss = 0.511525 (* 1 = 0.511525 loss)
I0401 16:51:25.303874 25640 sgd_solver.cpp:105] Iteration 9960, lr = 0.001
I0401 16:51:30.687433 25640 solver.cpp:218] Iteration 9972 (2.22901 iter/s, 5.38356s/12 iters), loss = 0.421155
I0401 16:51:30.687479 25640 solver.cpp:237]     Train net output #0: loss = 0.421155 (* 1 = 0.421155 loss)
I0401 16:51:30.687484 25640 sgd_solver.cpp:105] Iteration 9972, lr = 0.001
I0401 16:51:35.803171 25640 solver.cpp:218] Iteration 9984 (2.34573 iter/s, 5.11568s/12 iters), loss = 0.34053
I0401 16:51:35.803306 25640 solver.cpp:237]     Train net output #0: loss = 0.34053 (* 1 = 0.34053 loss)
I0401 16:51:35.803313 25640 sgd_solver.cpp:105] Iteration 9984, lr = 0.001
I0401 16:51:41.045907 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0401 16:51:45.589239 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0401 16:51:49.460930 25640 solver.cpp:330] Iteration 9996, Testing net (#0)
I0401 16:51:49.460947 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:51:49.899971 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:51:53.841760 25640 solver.cpp:397]     Test net output #0: accuracy = 0.24326
I0401 16:51:53.841800 25640 solver.cpp:397]     Test net output #1: loss = 4.45297 (* 1 = 4.45297 loss)
I0401 16:51:53.977084 25640 solver.cpp:218] Iteration 9996 (0.660292 iter/s, 18.1738s/12 iters), loss = 0.365791
I0401 16:51:53.977129 25640 solver.cpp:237]     Train net output #0: loss = 0.365791 (* 1 = 0.365791 loss)
I0401 16:51:53.977135 25640 sgd_solver.cpp:105] Iteration 9996, lr = 0.001
I0401 16:51:58.270051 25640 solver.cpp:218] Iteration 10008 (2.79531 iter/s, 4.2929s/12 iters), loss = 0.314433
I0401 16:51:58.270107 25640 solver.cpp:237]     Train net output #0: loss = 0.314433 (* 1 = 0.314433 loss)
I0401 16:51:58.270115 25640 sgd_solver.cpp:105] Iteration 10008, lr = 0.001
I0401 16:52:00.636346 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:52:03.842149 25640 solver.cpp:218] Iteration 10020 (2.15362 iter/s, 5.57202s/12 iters), loss = 0.470421
I0401 16:52:03.842196 25640 solver.cpp:237]     Train net output #0: loss = 0.470421 (* 1 = 0.470421 loss)
I0401 16:52:03.842202 25640 sgd_solver.cpp:105] Iteration 10020, lr = 0.001
I0401 16:52:09.225006 25640 solver.cpp:218] Iteration 10032 (2.22933 iter/s, 5.38279s/12 iters), loss = 0.575396
I0401 16:52:09.225121 25640 solver.cpp:237]     Train net output #0: loss = 0.575396 (* 1 = 0.575396 loss)
I0401 16:52:09.225131 25640 sgd_solver.cpp:105] Iteration 10032, lr = 0.001
I0401 16:52:14.384672 25640 solver.cpp:218] Iteration 10044 (2.32579 iter/s, 5.15953s/12 iters), loss = 0.506069
I0401 16:52:14.384730 25640 solver.cpp:237]     Train net output #0: loss = 0.506069 (* 1 = 0.506069 loss)
I0401 16:52:14.384738 25640 sgd_solver.cpp:105] Iteration 10044, lr = 0.001
I0401 16:52:19.495573 25640 solver.cpp:218] Iteration 10056 (2.34795 iter/s, 5.11083s/12 iters), loss = 0.395621
I0401 16:52:19.495610 25640 solver.cpp:237]     Train net output #0: loss = 0.395621 (* 1 = 0.395621 loss)
I0401 16:52:19.495616 25640 sgd_solver.cpp:105] Iteration 10056, lr = 0.001
I0401 16:52:24.964576 25640 solver.cpp:218] Iteration 10068 (2.19421 iter/s, 5.46895s/12 iters), loss = 0.713859
I0401 16:52:24.964624 25640 solver.cpp:237]     Train net output #0: loss = 0.713859 (* 1 = 0.713859 loss)
I0401 16:52:24.964632 25640 sgd_solver.cpp:105] Iteration 10068, lr = 0.001
I0401 16:52:30.475507 25640 solver.cpp:218] Iteration 10080 (2.17752 iter/s, 5.51087s/12 iters), loss = 0.391107
I0401 16:52:30.475560 25640 solver.cpp:237]     Train net output #0: loss = 0.391107 (* 1 = 0.391107 loss)
I0401 16:52:30.475567 25640 sgd_solver.cpp:105] Iteration 10080, lr = 0.001
I0401 16:52:35.767900 25640 solver.cpp:218] Iteration 10092 (2.26744 iter/s, 5.29232s/12 iters), loss = 0.429544
I0401 16:52:35.767943 25640 solver.cpp:237]     Train net output #0: loss = 0.429544 (* 1 = 0.429544 loss)
I0401 16:52:35.767951 25640 sgd_solver.cpp:105] Iteration 10092, lr = 0.001
I0401 16:52:38.099635 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0401 16:52:43.139889 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0401 16:52:48.739508 25640 solver.cpp:330] Iteration 10098, Testing net (#0)
I0401 16:52:48.739528 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:52:49.186211 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:52:53.366955 25640 solver.cpp:397]     Test net output #0: accuracy = 0.234069
I0401 16:52:53.366988 25640 solver.cpp:397]     Test net output #1: loss = 4.4014 (* 1 = 4.4014 loss)
I0401 16:52:55.361299 25640 solver.cpp:218] Iteration 10104 (0.612453 iter/s, 19.5933s/12 iters), loss = 0.400732
I0401 16:52:55.361343 25640 solver.cpp:237]     Train net output #0: loss = 0.400732 (* 1 = 0.400732 loss)
I0401 16:52:55.361348 25640 sgd_solver.cpp:105] Iteration 10104, lr = 0.001
I0401 16:52:59.909042 25662 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:53:00.603829 25640 solver.cpp:218] Iteration 10116 (2.289 iter/s, 5.24247s/12 iters), loss = 0.259177
I0401 16:53:00.603888 25640 solver.cpp:237]     Train net output #0: loss = 0.259177 (* 1 = 0.259177 loss)
I0401 16:53:00.603899 25640 sgd_solver.cpp:105] Iteration 10116, lr = 0.001
I0401 16:53:05.920269 25640 solver.cpp:218] Iteration 10128 (2.25718 iter/s, 5.31636s/12 iters), loss = 0.408819
I0401 16:53:05.920320 25640 solver.cpp:237]     Train net output #0: loss = 0.408819 (* 1 = 0.408819 loss)
I0401 16:53:05.920327 25640 sgd_solver.cpp:105] Iteration 10128, lr = 0.001
I0401 16:53:11.328764 25640 solver.cpp:218] Iteration 10140 (2.21876 iter/s, 5.40843s/12 iters), loss = 0.459309
I0401 16:53:11.328814 25640 solver.cpp:237]     Train net output #0: loss = 0.459309 (* 1 = 0.459309 loss)
I0401 16:53:11.328824 25640 sgd_solver.cpp:105] Iteration 10140, lr = 0.001
I0401 16:53:16.761984 25640 solver.cpp:218] Iteration 10152 (2.20866 iter/s, 5.43315s/12 iters), loss = 0.317061
I0401 16:53:16.762111 25640 solver.cpp:237]     Train net output #0: loss = 0.317061 (* 1 = 0.317061 loss)
I0401 16:53:16.762120 25640 sgd_solver.cpp:105] Iteration 10152, lr = 0.001
I0401 16:53:21.785010 25640 solver.cpp:218] Iteration 10164 (2.38907 iter/s, 5.02288s/12 iters), loss = 0.45826
I0401 16:53:21.785076 25640 solver.cpp:237]     Train net output #0: loss = 0.45826 (* 1 = 0.45826 loss)
I0401 16:53:21.785085 25640 sgd_solver.cpp:105] Iteration 10164, lr = 0.001
I0401 16:53:27.179591 25640 solver.cpp:218] Iteration 10176 (2.22449 iter/s, 5.3945s/12 iters), loss = 0.396043
I0401 16:53:27.179646 25640 solver.cpp:237]     Train net output #0: loss = 0.396043 (* 1 = 0.396043 loss)
I0401 16:53:27.179654 25640 sgd_solver.cpp:105] Iteration 10176, lr = 0.001
I0401 16:53:32.385689 25640 solver.cpp:218] Iteration 10188 (2.30502 iter/s, 5.20603s/12 iters), loss = 0.250673
I0401 16:53:32.385735 25640 solver.cpp:237]     Train net output #0: loss = 0.250673 (* 1 = 0.250673 loss)
I0401 16:53:32.385741 25640 sgd_solver.cpp:105] Iteration 10188, lr = 0.001
I0401 16:53:37.157825 25640 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0401 16:53:43.142738 25640 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0401 16:53:48.177474 25640 solver.cpp:310] Iteration 10200, loss = 0.390056
I0401 16:53:48.177578 25640 solver.cpp:330] Iteration 10200, Testing net (#0)
I0401 16:53:48.177585 25640 net.cpp:676] Ignoring source layer train-data
I0401 16:53:48.555761 25699 data_layer.cpp:73] Restarting data prefetching from start.
I0401 16:53:52.882575 25640 solver.cpp:397]     Test net output #0: accuracy = 0.244485
I0401 16:53:52.882611 25640 solver.cpp:397]     Test net output #1: loss = 4.39685 (* 1 = 4.39685 loss)
I0401 16:53:52.882616 25640 solver.cpp:315] Optimization Done.
I0401 16:53:52.882620 25640 caffe.cpp:259] Optimization Done.