I0407 08:24:09.918653 17723 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210407-082407-a665/solver.prototxt
I0407 08:24:09.918792 17723 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0407 08:24:09.918795 17723 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0407 08:24:09.918846 17723 caffe.cpp:218] Using GPUs 1
I0407 08:24:09.939227 17723 caffe.cpp:223] GPU 1: GeForce GTX TITAN X
I0407 08:24:10.134234 17723 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "step"
gamma: 0.5
momentum: 0.9
weight_decay: 0.0001
stepsize: 3366
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0407 08:24:10.135057 17723 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0407 08:24:10.135730 17723 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0407 08:24:10.135741 17723 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0407 08:24:10.135862 17723 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"
}
I0407 08:24:10.135939 17723 layer_factory.hpp:77] Creating layer train-data
I0407 08:24:10.140313 17723 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/train_db
I0407 08:24:10.140576 17723 net.cpp:84] Creating Layer train-data
I0407 08:24:10.140586 17723 net.cpp:380] train-data -> data
I0407 08:24:10.140604 17723 net.cpp:380] train-data -> label
I0407 08:24:10.140612 17723 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0407 08:24:10.145944 17723 data_layer.cpp:45] output data size: 128,3,227,227
I0407 08:24:10.275055 17723 net.cpp:122] Setting up train-data
I0407 08:24:10.275074 17723 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0407 08:24:10.275077 17723 net.cpp:129] Top shape: 128 (128)
I0407 08:24:10.275079 17723 net.cpp:137] Memory required for data: 79149056
I0407 08:24:10.275087 17723 layer_factory.hpp:77] Creating layer conv1
I0407 08:24:10.275106 17723 net.cpp:84] Creating Layer conv1
I0407 08:24:10.275111 17723 net.cpp:406] conv1 <- data
I0407 08:24:10.275121 17723 net.cpp:380] conv1 -> conv1
I0407 08:24:10.699064 17723 net.cpp:122] Setting up conv1
I0407 08:24:10.699082 17723 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 08:24:10.699084 17723 net.cpp:137] Memory required for data: 227833856
I0407 08:24:10.699103 17723 layer_factory.hpp:77] Creating layer relu1
I0407 08:24:10.699112 17723 net.cpp:84] Creating Layer relu1
I0407 08:24:10.699115 17723 net.cpp:406] relu1 <- conv1
I0407 08:24:10.699120 17723 net.cpp:367] relu1 -> conv1 (in-place)
I0407 08:24:10.699376 17723 net.cpp:122] Setting up relu1
I0407 08:24:10.699384 17723 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 08:24:10.699386 17723 net.cpp:137] Memory required for data: 376518656
I0407 08:24:10.699388 17723 layer_factory.hpp:77] Creating layer norm1
I0407 08:24:10.699396 17723 net.cpp:84] Creating Layer norm1
I0407 08:24:10.699417 17723 net.cpp:406] norm1 <- conv1
I0407 08:24:10.699422 17723 net.cpp:380] norm1 -> norm1
I0407 08:24:10.699934 17723 net.cpp:122] Setting up norm1
I0407 08:24:10.699944 17723 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 08:24:10.699945 17723 net.cpp:137] Memory required for data: 525203456
I0407 08:24:10.699949 17723 layer_factory.hpp:77] Creating layer pool1
I0407 08:24:10.699954 17723 net.cpp:84] Creating Layer pool1
I0407 08:24:10.699957 17723 net.cpp:406] pool1 <- norm1
I0407 08:24:10.699961 17723 net.cpp:380] pool1 -> pool1
I0407 08:24:10.699995 17723 net.cpp:122] Setting up pool1
I0407 08:24:10.700001 17723 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0407 08:24:10.700003 17723 net.cpp:137] Memory required for data: 561035264
I0407 08:24:10.700006 17723 layer_factory.hpp:77] Creating layer conv2
I0407 08:24:10.700014 17723 net.cpp:84] Creating Layer conv2
I0407 08:24:10.700016 17723 net.cpp:406] conv2 <- pool1
I0407 08:24:10.700021 17723 net.cpp:380] conv2 -> conv2
I0407 08:24:10.707546 17723 net.cpp:122] Setting up conv2
I0407 08:24:10.707559 17723 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 08:24:10.707562 17723 net.cpp:137] Memory required for data: 656586752
I0407 08:24:10.707571 17723 layer_factory.hpp:77] Creating layer relu2
I0407 08:24:10.707577 17723 net.cpp:84] Creating Layer relu2
I0407 08:24:10.707581 17723 net.cpp:406] relu2 <- conv2
I0407 08:24:10.707587 17723 net.cpp:367] relu2 -> conv2 (in-place)
I0407 08:24:10.708065 17723 net.cpp:122] Setting up relu2
I0407 08:24:10.708073 17723 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 08:24:10.708076 17723 net.cpp:137] Memory required for data: 752138240
I0407 08:24:10.708077 17723 layer_factory.hpp:77] Creating layer norm2
I0407 08:24:10.708086 17723 net.cpp:84] Creating Layer norm2
I0407 08:24:10.708087 17723 net.cpp:406] norm2 <- conv2
I0407 08:24:10.708091 17723 net.cpp:380] norm2 -> norm2
I0407 08:24:10.708421 17723 net.cpp:122] Setting up norm2
I0407 08:24:10.708429 17723 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 08:24:10.708431 17723 net.cpp:137] Memory required for data: 847689728
I0407 08:24:10.708434 17723 layer_factory.hpp:77] Creating layer pool2
I0407 08:24:10.708442 17723 net.cpp:84] Creating Layer pool2
I0407 08:24:10.708444 17723 net.cpp:406] pool2 <- norm2
I0407 08:24:10.708448 17723 net.cpp:380] pool2 -> pool2
I0407 08:24:10.708474 17723 net.cpp:122] Setting up pool2
I0407 08:24:10.708478 17723 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 08:24:10.708480 17723 net.cpp:137] Memory required for data: 869840896
I0407 08:24:10.708483 17723 layer_factory.hpp:77] Creating layer conv3
I0407 08:24:10.708492 17723 net.cpp:84] Creating Layer conv3
I0407 08:24:10.708494 17723 net.cpp:406] conv3 <- pool2
I0407 08:24:10.708499 17723 net.cpp:380] conv3 -> conv3
I0407 08:24:10.718997 17723 net.cpp:122] Setting up conv3
I0407 08:24:10.719015 17723 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 08:24:10.719017 17723 net.cpp:137] Memory required for data: 903067648
I0407 08:24:10.719029 17723 layer_factory.hpp:77] Creating layer relu3
I0407 08:24:10.719036 17723 net.cpp:84] Creating Layer relu3
I0407 08:24:10.719039 17723 net.cpp:406] relu3 <- conv3
I0407 08:24:10.719045 17723 net.cpp:367] relu3 -> conv3 (in-place)
I0407 08:24:10.719521 17723 net.cpp:122] Setting up relu3
I0407 08:24:10.719530 17723 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 08:24:10.719532 17723 net.cpp:137] Memory required for data: 936294400
I0407 08:24:10.719535 17723 layer_factory.hpp:77] Creating layer conv4
I0407 08:24:10.719544 17723 net.cpp:84] Creating Layer conv4
I0407 08:24:10.719547 17723 net.cpp:406] conv4 <- conv3
I0407 08:24:10.719552 17723 net.cpp:380] conv4 -> conv4
I0407 08:24:10.728870 17723 net.cpp:122] Setting up conv4
I0407 08:24:10.728894 17723 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 08:24:10.728899 17723 net.cpp:137] Memory required for data: 969521152
I0407 08:24:10.728905 17723 layer_factory.hpp:77] Creating layer relu4
I0407 08:24:10.728911 17723 net.cpp:84] Creating Layer relu4
I0407 08:24:10.728932 17723 net.cpp:406] relu4 <- conv4
I0407 08:24:10.728937 17723 net.cpp:367] relu4 -> conv4 (in-place)
I0407 08:24:10.729254 17723 net.cpp:122] Setting up relu4
I0407 08:24:10.729262 17723 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 08:24:10.729264 17723 net.cpp:137] Memory required for data: 1002747904
I0407 08:24:10.729266 17723 layer_factory.hpp:77] Creating layer conv5
I0407 08:24:10.729275 17723 net.cpp:84] Creating Layer conv5
I0407 08:24:10.729279 17723 net.cpp:406] conv5 <- conv4
I0407 08:24:10.729285 17723 net.cpp:380] conv5 -> conv5
I0407 08:24:10.736573 17723 net.cpp:122] Setting up conv5
I0407 08:24:10.736585 17723 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 08:24:10.736588 17723 net.cpp:137] Memory required for data: 1024899072
I0407 08:24:10.736598 17723 layer_factory.hpp:77] Creating layer relu5
I0407 08:24:10.736603 17723 net.cpp:84] Creating Layer relu5
I0407 08:24:10.736606 17723 net.cpp:406] relu5 <- conv5
I0407 08:24:10.736610 17723 net.cpp:367] relu5 -> conv5 (in-place)
I0407 08:24:10.737078 17723 net.cpp:122] Setting up relu5
I0407 08:24:10.737087 17723 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 08:24:10.737089 17723 net.cpp:137] Memory required for data: 1047050240
I0407 08:24:10.737092 17723 layer_factory.hpp:77] Creating layer pool5
I0407 08:24:10.737099 17723 net.cpp:84] Creating Layer pool5
I0407 08:24:10.737102 17723 net.cpp:406] pool5 <- conv5
I0407 08:24:10.737107 17723 net.cpp:380] pool5 -> pool5
I0407 08:24:10.737139 17723 net.cpp:122] Setting up pool5
I0407 08:24:10.737144 17723 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0407 08:24:10.737146 17723 net.cpp:137] Memory required for data: 1051768832
I0407 08:24:10.737149 17723 layer_factory.hpp:77] Creating layer fc6
I0407 08:24:10.737155 17723 net.cpp:84] Creating Layer fc6
I0407 08:24:10.737157 17723 net.cpp:406] fc6 <- pool5
I0407 08:24:10.737162 17723 net.cpp:380] fc6 -> fc6
I0407 08:24:11.069077 17723 net.cpp:122] Setting up fc6
I0407 08:24:11.069098 17723 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:24:11.069100 17723 net.cpp:137] Memory required for data: 1053865984
I0407 08:24:11.069108 17723 layer_factory.hpp:77] Creating layer relu6
I0407 08:24:11.069116 17723 net.cpp:84] Creating Layer relu6
I0407 08:24:11.069119 17723 net.cpp:406] relu6 <- fc6
I0407 08:24:11.069126 17723 net.cpp:367] relu6 -> fc6 (in-place)
I0407 08:24:11.069739 17723 net.cpp:122] Setting up relu6
I0407 08:24:11.069749 17723 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:24:11.069751 17723 net.cpp:137] Memory required for data: 1055963136
I0407 08:24:11.069754 17723 layer_factory.hpp:77] Creating layer drop6
I0407 08:24:11.069761 17723 net.cpp:84] Creating Layer drop6
I0407 08:24:11.069762 17723 net.cpp:406] drop6 <- fc6
I0407 08:24:11.069766 17723 net.cpp:367] drop6 -> fc6 (in-place)
I0407 08:24:11.069790 17723 net.cpp:122] Setting up drop6
I0407 08:24:11.069794 17723 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:24:11.069797 17723 net.cpp:137] Memory required for data: 1058060288
I0407 08:24:11.069798 17723 layer_factory.hpp:77] Creating layer fc7
I0407 08:24:11.069805 17723 net.cpp:84] Creating Layer fc7
I0407 08:24:11.069808 17723 net.cpp:406] fc7 <- fc6
I0407 08:24:11.069811 17723 net.cpp:380] fc7 -> fc7
I0407 08:24:11.218331 17723 net.cpp:122] Setting up fc7
I0407 08:24:11.218349 17723 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:24:11.218353 17723 net.cpp:137] Memory required for data: 1060157440
I0407 08:24:11.218361 17723 layer_factory.hpp:77] Creating layer relu7
I0407 08:24:11.218369 17723 net.cpp:84] Creating Layer relu7
I0407 08:24:11.218371 17723 net.cpp:406] relu7 <- fc7
I0407 08:24:11.218377 17723 net.cpp:367] relu7 -> fc7 (in-place)
I0407 08:24:11.218753 17723 net.cpp:122] Setting up relu7
I0407 08:24:11.218760 17723 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:24:11.218763 17723 net.cpp:137] Memory required for data: 1062254592
I0407 08:24:11.218765 17723 layer_factory.hpp:77] Creating layer drop7
I0407 08:24:11.218770 17723 net.cpp:84] Creating Layer drop7
I0407 08:24:11.218789 17723 net.cpp:406] drop7 <- fc7
I0407 08:24:11.218796 17723 net.cpp:367] drop7 -> fc7 (in-place)
I0407 08:24:11.218816 17723 net.cpp:122] Setting up drop7
I0407 08:24:11.218820 17723 net.cpp:129] Top shape: 128 4096 (524288)
I0407 08:24:11.218822 17723 net.cpp:137] Memory required for data: 1064351744
I0407 08:24:11.218824 17723 layer_factory.hpp:77] Creating layer fc8
I0407 08:24:11.218829 17723 net.cpp:84] Creating Layer fc8
I0407 08:24:11.218832 17723 net.cpp:406] fc8 <- fc7
I0407 08:24:11.218837 17723 net.cpp:380] fc8 -> fc8
I0407 08:24:11.226606 17723 net.cpp:122] Setting up fc8
I0407 08:24:11.226624 17723 net.cpp:129] Top shape: 128 196 (25088)
I0407 08:24:11.226625 17723 net.cpp:137] Memory required for data: 1064452096
I0407 08:24:11.226634 17723 layer_factory.hpp:77] Creating layer loss
I0407 08:24:11.226641 17723 net.cpp:84] Creating Layer loss
I0407 08:24:11.226644 17723 net.cpp:406] loss <- fc8
I0407 08:24:11.226649 17723 net.cpp:406] loss <- label
I0407 08:24:11.226655 17723 net.cpp:380] loss -> loss
I0407 08:24:11.226666 17723 layer_factory.hpp:77] Creating layer loss
I0407 08:24:11.227361 17723 net.cpp:122] Setting up loss
I0407 08:24:11.227370 17723 net.cpp:129] Top shape: (1)
I0407 08:24:11.227373 17723 net.cpp:132]     with loss weight 1
I0407 08:24:11.227392 17723 net.cpp:137] Memory required for data: 1064452100
I0407 08:24:11.227396 17723 net.cpp:198] loss needs backward computation.
I0407 08:24:11.227401 17723 net.cpp:198] fc8 needs backward computation.
I0407 08:24:11.227403 17723 net.cpp:198] drop7 needs backward computation.
I0407 08:24:11.227406 17723 net.cpp:198] relu7 needs backward computation.
I0407 08:24:11.227408 17723 net.cpp:198] fc7 needs backward computation.
I0407 08:24:11.227411 17723 net.cpp:198] drop6 needs backward computation.
I0407 08:24:11.227412 17723 net.cpp:198] relu6 needs backward computation.
I0407 08:24:11.227416 17723 net.cpp:198] fc6 needs backward computation.
I0407 08:24:11.227418 17723 net.cpp:198] pool5 needs backward computation.
I0407 08:24:11.227421 17723 net.cpp:198] relu5 needs backward computation.
I0407 08:24:11.227423 17723 net.cpp:198] conv5 needs backward computation.
I0407 08:24:11.227425 17723 net.cpp:198] relu4 needs backward computation.
I0407 08:24:11.227427 17723 net.cpp:198] conv4 needs backward computation.
I0407 08:24:11.227430 17723 net.cpp:198] relu3 needs backward computation.
I0407 08:24:11.227432 17723 net.cpp:198] conv3 needs backward computation.
I0407 08:24:11.227435 17723 net.cpp:198] pool2 needs backward computation.
I0407 08:24:11.227437 17723 net.cpp:198] norm2 needs backward computation.
I0407 08:24:11.227439 17723 net.cpp:198] relu2 needs backward computation.
I0407 08:24:11.227442 17723 net.cpp:198] conv2 needs backward computation.
I0407 08:24:11.227444 17723 net.cpp:198] pool1 needs backward computation.
I0407 08:24:11.227447 17723 net.cpp:198] norm1 needs backward computation.
I0407 08:24:11.227449 17723 net.cpp:198] relu1 needs backward computation.
I0407 08:24:11.227452 17723 net.cpp:198] conv1 needs backward computation.
I0407 08:24:11.227454 17723 net.cpp:200] train-data does not need backward computation.
I0407 08:24:11.227456 17723 net.cpp:242] This network produces output loss
I0407 08:24:11.227468 17723 net.cpp:255] Network initialization done.
I0407 08:24:11.227989 17723 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0407 08:24:11.228016 17723 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0407 08:24:11.228143 17723 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"
}
I0407 08:24:11.228240 17723 layer_factory.hpp:77] Creating layer val-data
I0407 08:24:11.230232 17723 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/val_db
I0407 08:24:11.230458 17723 net.cpp:84] Creating Layer val-data
I0407 08:24:11.230465 17723 net.cpp:380] val-data -> data
I0407 08:24:11.230473 17723 net.cpp:380] val-data -> label
I0407 08:24:11.230479 17723 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115716-aaf7/mean.binaryproto
I0407 08:24:11.234190 17723 data_layer.cpp:45] output data size: 32,3,227,227
I0407 08:24:11.288233 17723 net.cpp:122] Setting up val-data
I0407 08:24:11.288250 17723 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0407 08:24:11.288254 17723 net.cpp:129] Top shape: 32 (32)
I0407 08:24:11.288255 17723 net.cpp:137] Memory required for data: 19787264
I0407 08:24:11.288260 17723 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0407 08:24:11.288271 17723 net.cpp:84] Creating Layer label_val-data_1_split
I0407 08:24:11.288275 17723 net.cpp:406] label_val-data_1_split <- label
I0407 08:24:11.288280 17723 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0407 08:24:11.288287 17723 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0407 08:24:11.288331 17723 net.cpp:122] Setting up label_val-data_1_split
I0407 08:24:11.288336 17723 net.cpp:129] Top shape: 32 (32)
I0407 08:24:11.288338 17723 net.cpp:129] Top shape: 32 (32)
I0407 08:24:11.288339 17723 net.cpp:137] Memory required for data: 19787520
I0407 08:24:11.288342 17723 layer_factory.hpp:77] Creating layer conv1
I0407 08:24:11.288353 17723 net.cpp:84] Creating Layer conv1
I0407 08:24:11.288355 17723 net.cpp:406] conv1 <- data
I0407 08:24:11.288359 17723 net.cpp:380] conv1 -> conv1
I0407 08:24:11.290762 17723 net.cpp:122] Setting up conv1
I0407 08:24:11.290772 17723 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 08:24:11.290776 17723 net.cpp:137] Memory required for data: 56958720
I0407 08:24:11.290783 17723 layer_factory.hpp:77] Creating layer relu1
I0407 08:24:11.290788 17723 net.cpp:84] Creating Layer relu1
I0407 08:24:11.290791 17723 net.cpp:406] relu1 <- conv1
I0407 08:24:11.290796 17723 net.cpp:367] relu1 -> conv1 (in-place)
I0407 08:24:11.291052 17723 net.cpp:122] Setting up relu1
I0407 08:24:11.291060 17723 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 08:24:11.291062 17723 net.cpp:137] Memory required for data: 94129920
I0407 08:24:11.291065 17723 layer_factory.hpp:77] Creating layer norm1
I0407 08:24:11.291072 17723 net.cpp:84] Creating Layer norm1
I0407 08:24:11.291074 17723 net.cpp:406] norm1 <- conv1
I0407 08:24:11.291079 17723 net.cpp:380] norm1 -> norm1
I0407 08:24:11.291503 17723 net.cpp:122] Setting up norm1
I0407 08:24:11.291512 17723 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 08:24:11.291514 17723 net.cpp:137] Memory required for data: 131301120
I0407 08:24:11.291517 17723 layer_factory.hpp:77] Creating layer pool1
I0407 08:24:11.291522 17723 net.cpp:84] Creating Layer pool1
I0407 08:24:11.291525 17723 net.cpp:406] pool1 <- norm1
I0407 08:24:11.291528 17723 net.cpp:380] pool1 -> pool1
I0407 08:24:11.291553 17723 net.cpp:122] Setting up pool1
I0407 08:24:11.291558 17723 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0407 08:24:11.291559 17723 net.cpp:137] Memory required for data: 140259072
I0407 08:24:11.291561 17723 layer_factory.hpp:77] Creating layer conv2
I0407 08:24:11.291568 17723 net.cpp:84] Creating Layer conv2
I0407 08:24:11.291570 17723 net.cpp:406] conv2 <- pool1
I0407 08:24:11.291591 17723 net.cpp:380] conv2 -> conv2
I0407 08:24:11.297576 17723 net.cpp:122] Setting up conv2
I0407 08:24:11.297590 17723 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 08:24:11.297593 17723 net.cpp:137] Memory required for data: 164146944
I0407 08:24:11.297602 17723 layer_factory.hpp:77] Creating layer relu2
I0407 08:24:11.297608 17723 net.cpp:84] Creating Layer relu2
I0407 08:24:11.297612 17723 net.cpp:406] relu2 <- conv2
I0407 08:24:11.297617 17723 net.cpp:367] relu2 -> conv2 (in-place)
I0407 08:24:11.298111 17723 net.cpp:122] Setting up relu2
I0407 08:24:11.298120 17723 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 08:24:11.298122 17723 net.cpp:137] Memory required for data: 188034816
I0407 08:24:11.298125 17723 layer_factory.hpp:77] Creating layer norm2
I0407 08:24:11.298135 17723 net.cpp:84] Creating Layer norm2
I0407 08:24:11.298137 17723 net.cpp:406] norm2 <- conv2
I0407 08:24:11.298141 17723 net.cpp:380] norm2 -> norm2
I0407 08:24:11.298643 17723 net.cpp:122] Setting up norm2
I0407 08:24:11.298651 17723 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 08:24:11.298655 17723 net.cpp:137] Memory required for data: 211922688
I0407 08:24:11.298657 17723 layer_factory.hpp:77] Creating layer pool2
I0407 08:24:11.298663 17723 net.cpp:84] Creating Layer pool2
I0407 08:24:11.298666 17723 net.cpp:406] pool2 <- norm2
I0407 08:24:11.298671 17723 net.cpp:380] pool2 -> pool2
I0407 08:24:11.298697 17723 net.cpp:122] Setting up pool2
I0407 08:24:11.298702 17723 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 08:24:11.298703 17723 net.cpp:137] Memory required for data: 217460480
I0407 08:24:11.298707 17723 layer_factory.hpp:77] Creating layer conv3
I0407 08:24:11.298714 17723 net.cpp:84] Creating Layer conv3
I0407 08:24:11.298717 17723 net.cpp:406] conv3 <- pool2
I0407 08:24:11.298722 17723 net.cpp:380] conv3 -> conv3
I0407 08:24:11.308647 17723 net.cpp:122] Setting up conv3
I0407 08:24:11.308665 17723 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 08:24:11.308666 17723 net.cpp:137] Memory required for data: 225767168
I0407 08:24:11.308676 17723 layer_factory.hpp:77] Creating layer relu3
I0407 08:24:11.308682 17723 net.cpp:84] Creating Layer relu3
I0407 08:24:11.308686 17723 net.cpp:406] relu3 <- conv3
I0407 08:24:11.308691 17723 net.cpp:367] relu3 -> conv3 (in-place)
I0407 08:24:11.309175 17723 net.cpp:122] Setting up relu3
I0407 08:24:11.309183 17723 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 08:24:11.309186 17723 net.cpp:137] Memory required for data: 234073856
I0407 08:24:11.309188 17723 layer_factory.hpp:77] Creating layer conv4
I0407 08:24:11.309198 17723 net.cpp:84] Creating Layer conv4
I0407 08:24:11.309201 17723 net.cpp:406] conv4 <- conv3
I0407 08:24:11.309206 17723 net.cpp:380] conv4 -> conv4
I0407 08:24:11.318428 17723 net.cpp:122] Setting up conv4
I0407 08:24:11.318442 17723 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 08:24:11.318444 17723 net.cpp:137] Memory required for data: 242380544
I0407 08:24:11.318451 17723 layer_factory.hpp:77] Creating layer relu4
I0407 08:24:11.318459 17723 net.cpp:84] Creating Layer relu4
I0407 08:24:11.318462 17723 net.cpp:406] relu4 <- conv4
I0407 08:24:11.318466 17723 net.cpp:367] relu4 -> conv4 (in-place)
I0407 08:24:11.318779 17723 net.cpp:122] Setting up relu4
I0407 08:24:11.318786 17723 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 08:24:11.318789 17723 net.cpp:137] Memory required for data: 250687232
I0407 08:24:11.318791 17723 layer_factory.hpp:77] Creating layer conv5
I0407 08:24:11.318800 17723 net.cpp:84] Creating Layer conv5
I0407 08:24:11.318802 17723 net.cpp:406] conv5 <- conv4
I0407 08:24:11.318809 17723 net.cpp:380] conv5 -> conv5
I0407 08:24:11.326867 17723 net.cpp:122] Setting up conv5
I0407 08:24:11.326884 17723 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 08:24:11.326887 17723 net.cpp:137] Memory required for data: 256225024
I0407 08:24:11.326898 17723 layer_factory.hpp:77] Creating layer relu5
I0407 08:24:11.326905 17723 net.cpp:84] Creating Layer relu5
I0407 08:24:11.326927 17723 net.cpp:406] relu5 <- conv5
I0407 08:24:11.326932 17723 net.cpp:367] relu5 -> conv5 (in-place)
I0407 08:24:11.327430 17723 net.cpp:122] Setting up relu5
I0407 08:24:11.327437 17723 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 08:24:11.327440 17723 net.cpp:137] Memory required for data: 261762816
I0407 08:24:11.327442 17723 layer_factory.hpp:77] Creating layer pool5
I0407 08:24:11.327452 17723 net.cpp:84] Creating Layer pool5
I0407 08:24:11.327455 17723 net.cpp:406] pool5 <- conv5
I0407 08:24:11.327459 17723 net.cpp:380] pool5 -> pool5
I0407 08:24:11.327493 17723 net.cpp:122] Setting up pool5
I0407 08:24:11.327498 17723 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0407 08:24:11.327502 17723 net.cpp:137] Memory required for data: 262942464
I0407 08:24:11.327503 17723 layer_factory.hpp:77] Creating layer fc6
I0407 08:24:11.327509 17723 net.cpp:84] Creating Layer fc6
I0407 08:24:11.327512 17723 net.cpp:406] fc6 <- pool5
I0407 08:24:11.327517 17723 net.cpp:380] fc6 -> fc6
I0407 08:24:11.689973 17723 net.cpp:122] Setting up fc6
I0407 08:24:11.689996 17723 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:24:11.689997 17723 net.cpp:137] Memory required for data: 263466752
I0407 08:24:11.690006 17723 layer_factory.hpp:77] Creating layer relu6
I0407 08:24:11.690013 17723 net.cpp:84] Creating Layer relu6
I0407 08:24:11.690017 17723 net.cpp:406] relu6 <- fc6
I0407 08:24:11.690024 17723 net.cpp:367] relu6 -> fc6 (in-place)
I0407 08:24:11.690721 17723 net.cpp:122] Setting up relu6
I0407 08:24:11.690730 17723 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:24:11.690732 17723 net.cpp:137] Memory required for data: 263991040
I0407 08:24:11.690735 17723 layer_factory.hpp:77] Creating layer drop6
I0407 08:24:11.690740 17723 net.cpp:84] Creating Layer drop6
I0407 08:24:11.690743 17723 net.cpp:406] drop6 <- fc6
I0407 08:24:11.690748 17723 net.cpp:367] drop6 -> fc6 (in-place)
I0407 08:24:11.690771 17723 net.cpp:122] Setting up drop6
I0407 08:24:11.690776 17723 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:24:11.690778 17723 net.cpp:137] Memory required for data: 264515328
I0407 08:24:11.690780 17723 layer_factory.hpp:77] Creating layer fc7
I0407 08:24:11.690788 17723 net.cpp:84] Creating Layer fc7
I0407 08:24:11.690789 17723 net.cpp:406] fc7 <- fc6
I0407 08:24:11.690794 17723 net.cpp:380] fc7 -> fc7
I0407 08:24:11.847674 17723 net.cpp:122] Setting up fc7
I0407 08:24:11.847697 17723 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:24:11.847698 17723 net.cpp:137] Memory required for data: 265039616
I0407 08:24:11.847707 17723 layer_factory.hpp:77] Creating layer relu7
I0407 08:24:11.847715 17723 net.cpp:84] Creating Layer relu7
I0407 08:24:11.847719 17723 net.cpp:406] relu7 <- fc7
I0407 08:24:11.847724 17723 net.cpp:367] relu7 -> fc7 (in-place)
I0407 08:24:11.848120 17723 net.cpp:122] Setting up relu7
I0407 08:24:11.848129 17723 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:24:11.848130 17723 net.cpp:137] Memory required for data: 265563904
I0407 08:24:11.848134 17723 layer_factory.hpp:77] Creating layer drop7
I0407 08:24:11.848138 17723 net.cpp:84] Creating Layer drop7
I0407 08:24:11.848141 17723 net.cpp:406] drop7 <- fc7
I0407 08:24:11.848146 17723 net.cpp:367] drop7 -> fc7 (in-place)
I0407 08:24:11.848167 17723 net.cpp:122] Setting up drop7
I0407 08:24:11.848171 17723 net.cpp:129] Top shape: 32 4096 (131072)
I0407 08:24:11.848173 17723 net.cpp:137] Memory required for data: 266088192
I0407 08:24:11.848176 17723 layer_factory.hpp:77] Creating layer fc8
I0407 08:24:11.848182 17723 net.cpp:84] Creating Layer fc8
I0407 08:24:11.848184 17723 net.cpp:406] fc8 <- fc7
I0407 08:24:11.848189 17723 net.cpp:380] fc8 -> fc8
I0407 08:24:11.855813 17723 net.cpp:122] Setting up fc8
I0407 08:24:11.855831 17723 net.cpp:129] Top shape: 32 196 (6272)
I0407 08:24:11.855834 17723 net.cpp:137] Memory required for data: 266113280
I0407 08:24:11.855840 17723 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0407 08:24:11.855846 17723 net.cpp:84] Creating Layer fc8_fc8_0_split
I0407 08:24:11.855850 17723 net.cpp:406] fc8_fc8_0_split <- fc8
I0407 08:24:11.855877 17723 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0407 08:24:11.855885 17723 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0407 08:24:11.855919 17723 net.cpp:122] Setting up fc8_fc8_0_split
I0407 08:24:11.855923 17723 net.cpp:129] Top shape: 32 196 (6272)
I0407 08:24:11.855926 17723 net.cpp:129] Top shape: 32 196 (6272)
I0407 08:24:11.855927 17723 net.cpp:137] Memory required for data: 266163456
I0407 08:24:11.855931 17723 layer_factory.hpp:77] Creating layer accuracy
I0407 08:24:11.855935 17723 net.cpp:84] Creating Layer accuracy
I0407 08:24:11.855938 17723 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0407 08:24:11.855942 17723 net.cpp:406] accuracy <- label_val-data_1_split_0
I0407 08:24:11.855945 17723 net.cpp:380] accuracy -> accuracy
I0407 08:24:11.855952 17723 net.cpp:122] Setting up accuracy
I0407 08:24:11.855954 17723 net.cpp:129] Top shape: (1)
I0407 08:24:11.855957 17723 net.cpp:137] Memory required for data: 266163460
I0407 08:24:11.855958 17723 layer_factory.hpp:77] Creating layer loss
I0407 08:24:11.855962 17723 net.cpp:84] Creating Layer loss
I0407 08:24:11.855965 17723 net.cpp:406] loss <- fc8_fc8_0_split_1
I0407 08:24:11.855968 17723 net.cpp:406] loss <- label_val-data_1_split_1
I0407 08:24:11.855973 17723 net.cpp:380] loss -> loss
I0407 08:24:11.855978 17723 layer_factory.hpp:77] Creating layer loss
I0407 08:24:11.856649 17723 net.cpp:122] Setting up loss
I0407 08:24:11.856658 17723 net.cpp:129] Top shape: (1)
I0407 08:24:11.856660 17723 net.cpp:132]     with loss weight 1
I0407 08:24:11.856670 17723 net.cpp:137] Memory required for data: 266163464
I0407 08:24:11.856673 17723 net.cpp:198] loss needs backward computation.
I0407 08:24:11.856676 17723 net.cpp:200] accuracy does not need backward computation.
I0407 08:24:11.856679 17723 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0407 08:24:11.856681 17723 net.cpp:198] fc8 needs backward computation.
I0407 08:24:11.856684 17723 net.cpp:198] drop7 needs backward computation.
I0407 08:24:11.856686 17723 net.cpp:198] relu7 needs backward computation.
I0407 08:24:11.856688 17723 net.cpp:198] fc7 needs backward computation.
I0407 08:24:11.856690 17723 net.cpp:198] drop6 needs backward computation.
I0407 08:24:11.856693 17723 net.cpp:198] relu6 needs backward computation.
I0407 08:24:11.856695 17723 net.cpp:198] fc6 needs backward computation.
I0407 08:24:11.856698 17723 net.cpp:198] pool5 needs backward computation.
I0407 08:24:11.856700 17723 net.cpp:198] relu5 needs backward computation.
I0407 08:24:11.856703 17723 net.cpp:198] conv5 needs backward computation.
I0407 08:24:11.856704 17723 net.cpp:198] relu4 needs backward computation.
I0407 08:24:11.856707 17723 net.cpp:198] conv4 needs backward computation.
I0407 08:24:11.856709 17723 net.cpp:198] relu3 needs backward computation.
I0407 08:24:11.856711 17723 net.cpp:198] conv3 needs backward computation.
I0407 08:24:11.856714 17723 net.cpp:198] pool2 needs backward computation.
I0407 08:24:11.856716 17723 net.cpp:198] norm2 needs backward computation.
I0407 08:24:11.856719 17723 net.cpp:198] relu2 needs backward computation.
I0407 08:24:11.856721 17723 net.cpp:198] conv2 needs backward computation.
I0407 08:24:11.856724 17723 net.cpp:198] pool1 needs backward computation.
I0407 08:24:11.856726 17723 net.cpp:198] norm1 needs backward computation.
I0407 08:24:11.856729 17723 net.cpp:198] relu1 needs backward computation.
I0407 08:24:11.856730 17723 net.cpp:198] conv1 needs backward computation.
I0407 08:24:11.856734 17723 net.cpp:200] label_val-data_1_split does not need backward computation.
I0407 08:24:11.856736 17723 net.cpp:200] val-data does not need backward computation.
I0407 08:24:11.856739 17723 net.cpp:242] This network produces output accuracy
I0407 08:24:11.856741 17723 net.cpp:242] This network produces output loss
I0407 08:24:11.856756 17723 net.cpp:255] Network initialization done.
I0407 08:24:11.856820 17723 solver.cpp:56] Solver scaffolding done.
I0407 08:24:11.857239 17723 caffe.cpp:248] Starting Optimization
I0407 08:24:11.857246 17723 solver.cpp:272] Solving
I0407 08:24:11.857257 17723 solver.cpp:273] Learning Rate Policy: step
I0407 08:24:11.858923 17723 solver.cpp:330] Iteration 0, Testing net (#0)
I0407 08:24:11.858932 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:24:11.965004 17723 blocking_queue.cpp:49] Waiting for data
I0407 08:24:16.239472 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:24:16.287261 17723 solver.cpp:397]     Test net output #0: accuracy = 0.00428922
I0407 08:24:16.287310 17723 solver.cpp:397]     Test net output #1: loss = 5.28369 (* 1 = 5.28369 loss)
I0407 08:24:16.436692 17723 solver.cpp:218] Iteration 0 (0 iter/s, 4.57935s/12 iters), loss = 5.27203
I0407 08:24:16.438266 17723 solver.cpp:237]     Train net output #0: loss = 5.27203 (* 1 = 5.27203 loss)
I0407 08:24:16.438297 17723 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0407 08:24:20.569924 17723 solver.cpp:218] Iteration 12 (2.90444 iter/s, 4.13161s/12 iters), loss = 5.27739
I0407 08:24:20.569977 17723 solver.cpp:237]     Train net output #0: loss = 5.27739 (* 1 = 5.27739 loss)
I0407 08:24:20.569985 17723 sgd_solver.cpp:105] Iteration 12, lr = 0.01
I0407 08:24:25.939116 17723 solver.cpp:218] Iteration 24 (2.23502 iter/s, 5.36909s/12 iters), loss = 5.27997
I0407 08:24:25.939152 17723 solver.cpp:237]     Train net output #0: loss = 5.27997 (* 1 = 5.27997 loss)
I0407 08:24:25.939158 17723 sgd_solver.cpp:105] Iteration 24, lr = 0.01
I0407 08:24:31.323047 17723 solver.cpp:218] Iteration 36 (2.22889 iter/s, 5.38384s/12 iters), loss = 5.28048
I0407 08:24:31.323089 17723 solver.cpp:237]     Train net output #0: loss = 5.28048 (* 1 = 5.28048 loss)
I0407 08:24:31.323098 17723 sgd_solver.cpp:105] Iteration 36, lr = 0.01
I0407 08:24:36.503662 17723 solver.cpp:218] Iteration 48 (2.31637 iter/s, 5.18052s/12 iters), loss = 5.30472
I0407 08:24:36.503707 17723 solver.cpp:237]     Train net output #0: loss = 5.30472 (* 1 = 5.30472 loss)
I0407 08:24:36.503715 17723 sgd_solver.cpp:105] Iteration 48, lr = 0.01
I0407 08:24:41.875090 17723 solver.cpp:218] Iteration 60 (2.23409 iter/s, 5.37133s/12 iters), loss = 5.29112
I0407 08:24:41.875250 17723 solver.cpp:237]     Train net output #0: loss = 5.29112 (* 1 = 5.29112 loss)
I0407 08:24:41.875259 17723 sgd_solver.cpp:105] Iteration 60, lr = 0.01
I0407 08:24:47.310328 17723 solver.cpp:218] Iteration 72 (2.2079 iter/s, 5.43503s/12 iters), loss = 5.30214
I0407 08:24:47.310360 17723 solver.cpp:237]     Train net output #0: loss = 5.30214 (* 1 = 5.30214 loss)
I0407 08:24:47.310369 17723 sgd_solver.cpp:105] Iteration 72, lr = 0.01
I0407 08:24:52.677587 17723 solver.cpp:218] Iteration 84 (2.23582 iter/s, 5.36716s/12 iters), loss = 5.30355
I0407 08:24:52.677628 17723 solver.cpp:237]     Train net output #0: loss = 5.30355 (* 1 = 5.30355 loss)
I0407 08:24:52.677634 17723 sgd_solver.cpp:105] Iteration 84, lr = 0.01
I0407 08:24:57.888641 17723 solver.cpp:218] Iteration 96 (2.30284 iter/s, 5.21096s/12 iters), loss = 5.29164
I0407 08:24:57.888684 17723 solver.cpp:237]     Train net output #0: loss = 5.29164 (* 1 = 5.29164 loss)
I0407 08:24:57.888691 17723 sgd_solver.cpp:105] Iteration 96, lr = 0.01
I0407 08:24:59.706859 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:25:00.023910 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0407 08:25:03.073365 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0407 08:25:05.367254 17723 solver.cpp:330] Iteration 102, Testing net (#0)
I0407 08:25:05.367274 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:25:09.663924 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:25:09.741816 17723 solver.cpp:397]     Test net output #0: accuracy = 0.00612745
I0407 08:25:09.741852 17723 solver.cpp:397]     Test net output #1: loss = 5.29202 (* 1 = 5.29202 loss)
I0407 08:25:11.574144 17723 solver.cpp:218] Iteration 108 (0.876851 iter/s, 13.6853s/12 iters), loss = 5.28085
I0407 08:25:11.574184 17723 solver.cpp:237]     Train net output #0: loss = 5.28085 (* 1 = 5.28085 loss)
I0407 08:25:11.574191 17723 sgd_solver.cpp:105] Iteration 108, lr = 0.01
I0407 08:25:17.026501 17723 solver.cpp:218] Iteration 120 (2.20093 iter/s, 5.45225s/12 iters), loss = 5.26427
I0407 08:25:17.026636 17723 solver.cpp:237]     Train net output #0: loss = 5.26427 (* 1 = 5.26427 loss)
I0407 08:25:17.026644 17723 sgd_solver.cpp:105] Iteration 120, lr = 0.01
I0407 08:25:22.422259 17723 solver.cpp:218] Iteration 132 (2.22405 iter/s, 5.39557s/12 iters), loss = 5.26664
I0407 08:25:22.422297 17723 solver.cpp:237]     Train net output #0: loss = 5.26664 (* 1 = 5.26664 loss)
I0407 08:25:22.422303 17723 sgd_solver.cpp:105] Iteration 132, lr = 0.01
I0407 08:25:27.844897 17723 solver.cpp:218] Iteration 144 (2.21299 iter/s, 5.42253s/12 iters), loss = 5.25709
I0407 08:25:27.844942 17723 solver.cpp:237]     Train net output #0: loss = 5.25709 (* 1 = 5.25709 loss)
I0407 08:25:27.844950 17723 sgd_solver.cpp:105] Iteration 144, lr = 0.01
I0407 08:25:32.821413 17723 solver.cpp:218] Iteration 156 (2.41138 iter/s, 4.97641s/12 iters), loss = 5.25432
I0407 08:25:32.821453 17723 solver.cpp:237]     Train net output #0: loss = 5.25432 (* 1 = 5.25432 loss)
I0407 08:25:32.821460 17723 sgd_solver.cpp:105] Iteration 156, lr = 0.01
I0407 08:25:38.072118 17723 solver.cpp:218] Iteration 168 (2.28545 iter/s, 5.2506s/12 iters), loss = 5.20381
I0407 08:25:38.072175 17723 solver.cpp:237]     Train net output #0: loss = 5.20381 (* 1 = 5.20381 loss)
I0407 08:25:38.072185 17723 sgd_solver.cpp:105] Iteration 168, lr = 0.01
I0407 08:25:43.304224 17723 solver.cpp:218] Iteration 180 (2.29358 iter/s, 5.23199s/12 iters), loss = 5.24119
I0407 08:25:43.304261 17723 solver.cpp:237]     Train net output #0: loss = 5.24119 (* 1 = 5.24119 loss)
I0407 08:25:43.304268 17723 sgd_solver.cpp:105] Iteration 180, lr = 0.01
I0407 08:25:48.236456 17723 solver.cpp:218] Iteration 192 (2.43302 iter/s, 4.93213s/12 iters), loss = 5.11237
I0407 08:25:48.236572 17723 solver.cpp:237]     Train net output #0: loss = 5.11237 (* 1 = 5.11237 loss)
I0407 08:25:48.236580 17723 sgd_solver.cpp:105] Iteration 192, lr = 0.01
I0407 08:25:52.270889 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:25:52.977246 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0407 08:25:55.958739 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0407 08:25:58.261632 17723 solver.cpp:330] Iteration 204, Testing net (#0)
I0407 08:25:58.261651 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:26:02.403340 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:26:02.526929 17723 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0407 08:26:02.526962 17723 solver.cpp:397]     Test net output #1: loss = 5.18843 (* 1 = 5.18843 loss)
I0407 08:26:02.666579 17723 solver.cpp:218] Iteration 204 (0.831608 iter/s, 14.4299s/12 iters), loss = 5.16766
I0407 08:26:02.666626 17723 solver.cpp:237]     Train net output #0: loss = 5.16766 (* 1 = 5.16766 loss)
I0407 08:26:02.666633 17723 sgd_solver.cpp:105] Iteration 204, lr = 0.01
I0407 08:26:06.992321 17723 solver.cpp:218] Iteration 216 (2.77415 iter/s, 4.32565s/12 iters), loss = 5.24067
I0407 08:26:06.992363 17723 solver.cpp:237]     Train net output #0: loss = 5.24067 (* 1 = 5.24067 loss)
I0407 08:26:06.992372 17723 sgd_solver.cpp:105] Iteration 216, lr = 0.01
I0407 08:26:12.143049 17723 solver.cpp:218] Iteration 228 (2.32982 iter/s, 5.15062s/12 iters), loss = 5.21476
I0407 08:26:12.143092 17723 solver.cpp:237]     Train net output #0: loss = 5.21476 (* 1 = 5.21476 loss)
I0407 08:26:12.143100 17723 sgd_solver.cpp:105] Iteration 228, lr = 0.01
I0407 08:26:17.418216 17723 solver.cpp:218] Iteration 240 (2.27485 iter/s, 5.27507s/12 iters), loss = 5.18397
I0407 08:26:17.418262 17723 solver.cpp:237]     Train net output #0: loss = 5.18397 (* 1 = 5.18397 loss)
I0407 08:26:17.418268 17723 sgd_solver.cpp:105] Iteration 240, lr = 0.01
I0407 08:26:22.659766 17723 solver.cpp:218] Iteration 252 (2.28945 iter/s, 5.24144s/12 iters), loss = 5.2248
I0407 08:26:22.659922 17723 solver.cpp:237]     Train net output #0: loss = 5.2248 (* 1 = 5.2248 loss)
I0407 08:26:22.659934 17723 sgd_solver.cpp:105] Iteration 252, lr = 0.01
I0407 08:26:27.879107 17723 solver.cpp:218] Iteration 264 (2.29924 iter/s, 5.21912s/12 iters), loss = 5.1238
I0407 08:26:27.879164 17723 solver.cpp:237]     Train net output #0: loss = 5.1238 (* 1 = 5.1238 loss)
I0407 08:26:27.879175 17723 sgd_solver.cpp:105] Iteration 264, lr = 0.01
I0407 08:26:33.294582 17723 solver.cpp:218] Iteration 276 (2.21592 iter/s, 5.41536s/12 iters), loss = 5.08348
I0407 08:26:33.294648 17723 solver.cpp:237]     Train net output #0: loss = 5.08348 (* 1 = 5.08348 loss)
I0407 08:26:33.294665 17723 sgd_solver.cpp:105] Iteration 276, lr = 0.01
I0407 08:26:38.276167 17723 solver.cpp:218] Iteration 288 (2.40893 iter/s, 4.98147s/12 iters), loss = 5.1756
I0407 08:26:38.276208 17723 solver.cpp:237]     Train net output #0: loss = 5.1756 (* 1 = 5.1756 loss)
I0407 08:26:38.276216 17723 sgd_solver.cpp:105] Iteration 288, lr = 0.01
I0407 08:26:43.563511 17723 solver.cpp:218] Iteration 300 (2.26961 iter/s, 5.28724s/12 iters), loss = 5.2461
I0407 08:26:43.563567 17723 solver.cpp:237]     Train net output #0: loss = 5.2461 (* 1 = 5.2461 loss)
I0407 08:26:43.563580 17723 sgd_solver.cpp:105] Iteration 300, lr = 0.01
I0407 08:26:44.510615 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:26:45.663601 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0407 08:26:48.584748 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0407 08:26:50.954213 17723 solver.cpp:330] Iteration 306, Testing net (#0)
I0407 08:26:50.954236 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:26:55.065412 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:26:55.221777 17723 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0407 08:26:55.221827 17723 solver.cpp:397]     Test net output #1: loss = 5.16142 (* 1 = 5.16142 loss)
I0407 08:26:57.085628 17723 solver.cpp:218] Iteration 312 (0.887447 iter/s, 13.5219s/12 iters), loss = 5.13608
I0407 08:26:57.085667 17723 solver.cpp:237]     Train net output #0: loss = 5.13608 (* 1 = 5.13608 loss)
I0407 08:26:57.085675 17723 sgd_solver.cpp:105] Iteration 312, lr = 0.01
I0407 08:27:02.418048 17723 solver.cpp:218] Iteration 324 (2.25043 iter/s, 5.33232s/12 iters), loss = 5.21504
I0407 08:27:02.418094 17723 solver.cpp:237]     Train net output #0: loss = 5.21504 (* 1 = 5.21504 loss)
I0407 08:27:02.418102 17723 sgd_solver.cpp:105] Iteration 324, lr = 0.01
I0407 08:27:07.217141 17723 solver.cpp:218] Iteration 336 (2.50052 iter/s, 4.79899s/12 iters), loss = 5.14133
I0407 08:27:07.217190 17723 solver.cpp:237]     Train net output #0: loss = 5.14133 (* 1 = 5.14133 loss)
I0407 08:27:07.217198 17723 sgd_solver.cpp:105] Iteration 336, lr = 0.01
I0407 08:27:12.281812 17723 solver.cpp:218] Iteration 348 (2.3694 iter/s, 5.06457s/12 iters), loss = 5.09814
I0407 08:27:12.281857 17723 solver.cpp:237]     Train net output #0: loss = 5.09814 (* 1 = 5.09814 loss)
I0407 08:27:12.281863 17723 sgd_solver.cpp:105] Iteration 348, lr = 0.01
I0407 08:27:17.606396 17723 solver.cpp:218] Iteration 360 (2.25374 iter/s, 5.32449s/12 iters), loss = 5.17628
I0407 08:27:17.606436 17723 solver.cpp:237]     Train net output #0: loss = 5.17628 (* 1 = 5.17628 loss)
I0407 08:27:17.606443 17723 sgd_solver.cpp:105] Iteration 360, lr = 0.01
I0407 08:27:22.915381 17723 solver.cpp:218] Iteration 372 (2.26037 iter/s, 5.30888s/12 iters), loss = 5.13409
I0407 08:27:22.915436 17723 solver.cpp:237]     Train net output #0: loss = 5.13409 (* 1 = 5.13409 loss)
I0407 08:27:22.915446 17723 sgd_solver.cpp:105] Iteration 372, lr = 0.01
I0407 08:27:27.984854 17723 solver.cpp:218] Iteration 384 (2.36716 iter/s, 5.06936s/12 iters), loss = 5.21895
I0407 08:27:27.984964 17723 solver.cpp:237]     Train net output #0: loss = 5.21895 (* 1 = 5.21895 loss)
I0407 08:27:27.984973 17723 sgd_solver.cpp:105] Iteration 384, lr = 0.01
I0407 08:27:33.268270 17723 solver.cpp:218] Iteration 396 (2.27133 iter/s, 5.28325s/12 iters), loss = 5.14261
I0407 08:27:33.268313 17723 solver.cpp:237]     Train net output #0: loss = 5.14261 (* 1 = 5.14261 loss)
I0407 08:27:33.268321 17723 sgd_solver.cpp:105] Iteration 396, lr = 0.01
I0407 08:27:36.568516 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:27:38.007508 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0407 08:27:41.048254 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0407 08:27:43.388335 17723 solver.cpp:330] Iteration 408, Testing net (#0)
I0407 08:27:43.388358 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:27:47.602891 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:27:47.807533 17723 solver.cpp:397]     Test net output #0: accuracy = 0.0140931
I0407 08:27:47.807565 17723 solver.cpp:397]     Test net output #1: loss = 5.14544 (* 1 = 5.14544 loss)
I0407 08:27:47.941681 17723 solver.cpp:218] Iteration 408 (0.817816 iter/s, 14.6732s/12 iters), loss = 5.13915
I0407 08:27:47.941725 17723 solver.cpp:237]     Train net output #0: loss = 5.13915 (* 1 = 5.13915 loss)
I0407 08:27:47.941732 17723 sgd_solver.cpp:105] Iteration 408, lr = 0.01
I0407 08:27:52.353024 17723 solver.cpp:218] Iteration 420 (2.72032 iter/s, 4.41124s/12 iters), loss = 5.10154
I0407 08:27:52.353067 17723 solver.cpp:237]     Train net output #0: loss = 5.10154 (* 1 = 5.10154 loss)
I0407 08:27:52.353075 17723 sgd_solver.cpp:105] Iteration 420, lr = 0.01
I0407 08:27:57.460861 17723 solver.cpp:218] Iteration 432 (2.34938 iter/s, 5.10773s/12 iters), loss = 5.05281
I0407 08:27:57.460908 17723 solver.cpp:237]     Train net output #0: loss = 5.05281 (* 1 = 5.05281 loss)
I0407 08:27:57.460916 17723 sgd_solver.cpp:105] Iteration 432, lr = 0.01
I0407 08:28:02.684159 17723 solver.cpp:218] Iteration 444 (2.29744 iter/s, 5.2232s/12 iters), loss = 5.10659
I0407 08:28:02.684283 17723 solver.cpp:237]     Train net output #0: loss = 5.10659 (* 1 = 5.10659 loss)
I0407 08:28:02.684293 17723 sgd_solver.cpp:105] Iteration 444, lr = 0.01
I0407 08:28:07.770511 17723 solver.cpp:218] Iteration 456 (2.35933 iter/s, 5.08618s/12 iters), loss = 5.15221
I0407 08:28:07.770546 17723 solver.cpp:237]     Train net output #0: loss = 5.15221 (* 1 = 5.15221 loss)
I0407 08:28:07.770553 17723 sgd_solver.cpp:105] Iteration 456, lr = 0.01
I0407 08:28:12.951198 17723 solver.cpp:218] Iteration 468 (2.31634 iter/s, 5.1806s/12 iters), loss = 5.07187
I0407 08:28:12.951234 17723 solver.cpp:237]     Train net output #0: loss = 5.07187 (* 1 = 5.07187 loss)
I0407 08:28:12.951241 17723 sgd_solver.cpp:105] Iteration 468, lr = 0.01
I0407 08:28:18.006584 17723 solver.cpp:218] Iteration 480 (2.37375 iter/s, 5.05529s/12 iters), loss = 5.05976
I0407 08:28:18.006623 17723 solver.cpp:237]     Train net output #0: loss = 5.05976 (* 1 = 5.05976 loss)
I0407 08:28:18.006630 17723 sgd_solver.cpp:105] Iteration 480, lr = 0.01
I0407 08:28:23.334877 17723 solver.cpp:218] Iteration 492 (2.25217 iter/s, 5.32819s/12 iters), loss = 5.06199
I0407 08:28:23.334915 17723 solver.cpp:237]     Train net output #0: loss = 5.06199 (* 1 = 5.06199 loss)
I0407 08:28:23.334923 17723 sgd_solver.cpp:105] Iteration 492, lr = 0.01
I0407 08:28:28.561100 17723 solver.cpp:218] Iteration 504 (2.29616 iter/s, 5.22613s/12 iters), loss = 5.07016
I0407 08:28:28.561146 17723 solver.cpp:237]     Train net output #0: loss = 5.07016 (* 1 = 5.07016 loss)
I0407 08:28:28.561152 17723 sgd_solver.cpp:105] Iteration 504, lr = 0.01
I0407 08:28:28.808265 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:28:30.763113 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0407 08:28:33.780421 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0407 08:28:36.099386 17723 solver.cpp:330] Iteration 510, Testing net (#0)
I0407 08:28:36.099411 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:28:40.140475 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:28:40.377111 17723 solver.cpp:397]     Test net output #0: accuracy = 0.0189951
I0407 08:28:40.377148 17723 solver.cpp:397]     Test net output #1: loss = 5.05891 (* 1 = 5.05891 loss)
I0407 08:28:42.216809 17723 solver.cpp:218] Iteration 516 (0.878765 iter/s, 13.6555s/12 iters), loss = 5.05031
I0407 08:28:42.216850 17723 solver.cpp:237]     Train net output #0: loss = 5.05031 (* 1 = 5.05031 loss)
I0407 08:28:42.216857 17723 sgd_solver.cpp:105] Iteration 516, lr = 0.01
I0407 08:28:47.551664 17723 solver.cpp:218] Iteration 528 (2.2494 iter/s, 5.33476s/12 iters), loss = 5.13441
I0407 08:28:47.551707 17723 solver.cpp:237]     Train net output #0: loss = 5.13441 (* 1 = 5.13441 loss)
I0407 08:28:47.551713 17723 sgd_solver.cpp:105] Iteration 528, lr = 0.01
I0407 08:28:52.959352 17723 solver.cpp:218] Iteration 540 (2.21911 iter/s, 5.40758s/12 iters), loss = 5.04259
I0407 08:28:52.959394 17723 solver.cpp:237]     Train net output #0: loss = 5.04259 (* 1 = 5.04259 loss)
I0407 08:28:52.959401 17723 sgd_solver.cpp:105] Iteration 540, lr = 0.01
I0407 08:28:57.948135 17723 solver.cpp:218] Iteration 552 (2.40544 iter/s, 4.98868s/12 iters), loss = 5.10976
I0407 08:28:57.948200 17723 solver.cpp:237]     Train net output #0: loss = 5.10976 (* 1 = 5.10976 loss)
I0407 08:28:57.948215 17723 sgd_solver.cpp:105] Iteration 552, lr = 0.01
I0407 08:29:03.016604 17723 solver.cpp:218] Iteration 564 (2.36763 iter/s, 5.06835s/12 iters), loss = 5.03572
I0407 08:29:03.016650 17723 solver.cpp:237]     Train net output #0: loss = 5.03572 (* 1 = 5.03572 loss)
I0407 08:29:03.016659 17723 sgd_solver.cpp:105] Iteration 564, lr = 0.01
I0407 08:29:08.002784 17723 solver.cpp:218] Iteration 576 (2.4067 iter/s, 4.98608s/12 iters), loss = 5.01829
I0407 08:29:08.002912 17723 solver.cpp:237]     Train net output #0: loss = 5.01829 (* 1 = 5.01829 loss)
I0407 08:29:08.002923 17723 sgd_solver.cpp:105] Iteration 576, lr = 0.01
I0407 08:29:13.291340 17723 solver.cpp:218] Iteration 588 (2.26913 iter/s, 5.28837s/12 iters), loss = 4.97955
I0407 08:29:13.291395 17723 solver.cpp:237]     Train net output #0: loss = 4.97955 (* 1 = 4.97955 loss)
I0407 08:29:13.291405 17723 sgd_solver.cpp:105] Iteration 588, lr = 0.01
I0407 08:29:18.629580 17723 solver.cpp:218] Iteration 600 (2.24798 iter/s, 5.33812s/12 iters), loss = 5.03679
I0407 08:29:18.629621 17723 solver.cpp:237]     Train net output #0: loss = 5.03679 (* 1 = 5.03679 loss)
I0407 08:29:18.629628 17723 sgd_solver.cpp:105] Iteration 600, lr = 0.01
I0407 08:29:21.167765 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:29:23.545449 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0407 08:29:26.586103 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0407 08:29:28.942225 17723 solver.cpp:330] Iteration 612, Testing net (#0)
I0407 08:29:28.942245 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:29:32.954593 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:29:33.243046 17723 solver.cpp:397]     Test net output #0: accuracy = 0.028799
I0407 08:29:33.243074 17723 solver.cpp:397]     Test net output #1: loss = 4.98814 (* 1 = 4.98814 loss)
I0407 08:29:33.383533 17723 solver.cpp:218] Iteration 612 (0.813351 iter/s, 14.7538s/12 iters), loss = 4.98951
I0407 08:29:33.385114 17723 solver.cpp:237]     Train net output #0: loss = 4.98951 (* 1 = 4.98951 loss)
I0407 08:29:33.385126 17723 sgd_solver.cpp:105] Iteration 612, lr = 0.01
I0407 08:29:37.795877 17723 solver.cpp:218] Iteration 624 (2.72064 iter/s, 4.41072s/12 iters), loss = 4.98381
I0407 08:29:37.795923 17723 solver.cpp:237]     Train net output #0: loss = 4.98381 (* 1 = 4.98381 loss)
I0407 08:29:37.795933 17723 sgd_solver.cpp:105] Iteration 624, lr = 0.01
I0407 08:29:43.145399 17723 solver.cpp:218] Iteration 636 (2.24323 iter/s, 5.34942s/12 iters), loss = 4.93793
I0407 08:29:43.145524 17723 solver.cpp:237]     Train net output #0: loss = 4.93793 (* 1 = 4.93793 loss)
I0407 08:29:43.145532 17723 sgd_solver.cpp:105] Iteration 636, lr = 0.01
I0407 08:29:48.303215 17723 solver.cpp:218] Iteration 648 (2.32665 iter/s, 5.15764s/12 iters), loss = 4.9473
I0407 08:29:48.303262 17723 solver.cpp:237]     Train net output #0: loss = 4.9473 (* 1 = 4.9473 loss)
I0407 08:29:48.303272 17723 sgd_solver.cpp:105] Iteration 648, lr = 0.01
I0407 08:29:53.483829 17723 solver.cpp:218] Iteration 660 (2.31637 iter/s, 5.18051s/12 iters), loss = 4.92484
I0407 08:29:53.483871 17723 solver.cpp:237]     Train net output #0: loss = 4.92484 (* 1 = 4.92484 loss)
I0407 08:29:53.483878 17723 sgd_solver.cpp:105] Iteration 660, lr = 0.01
I0407 08:29:58.556046 17723 solver.cpp:218] Iteration 672 (2.36588 iter/s, 5.07212s/12 iters), loss = 4.98254
I0407 08:29:58.556093 17723 solver.cpp:237]     Train net output #0: loss = 4.98254 (* 1 = 4.98254 loss)
I0407 08:29:58.556100 17723 sgd_solver.cpp:105] Iteration 672, lr = 0.01
I0407 08:30:03.828969 17723 solver.cpp:218] Iteration 684 (2.27582 iter/s, 5.27281s/12 iters), loss = 4.91847
I0407 08:30:03.829021 17723 solver.cpp:237]     Train net output #0: loss = 4.91847 (* 1 = 4.91847 loss)
I0407 08:30:03.829028 17723 sgd_solver.cpp:105] Iteration 684, lr = 0.01
I0407 08:30:04.579027 17723 blocking_queue.cpp:49] Waiting for data
I0407 08:30:09.096228 17723 solver.cpp:218] Iteration 696 (2.27827 iter/s, 5.26715s/12 iters), loss = 4.84031
I0407 08:30:09.096280 17723 solver.cpp:237]     Train net output #0: loss = 4.84031 (* 1 = 4.84031 loss)
I0407 08:30:09.096289 17723 sgd_solver.cpp:105] Iteration 696, lr = 0.01
I0407 08:30:13.717962 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:30:14.133530 17723 solver.cpp:218] Iteration 708 (2.38228 iter/s, 5.0372s/12 iters), loss = 5.03562
I0407 08:30:14.133566 17723 solver.cpp:237]     Train net output #0: loss = 5.03562 (* 1 = 5.03562 loss)
I0407 08:30:14.133572 17723 sgd_solver.cpp:105] Iteration 708, lr = 0.01
I0407 08:30:16.186823 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0407 08:30:19.267858 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0407 08:30:21.596282 17723 solver.cpp:330] Iteration 714, Testing net (#0)
I0407 08:30:21.596307 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:30:25.653553 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:30:25.966414 17723 solver.cpp:397]     Test net output #0: accuracy = 0.0275735
I0407 08:30:25.966454 17723 solver.cpp:397]     Test net output #1: loss = 4.95693 (* 1 = 4.95693 loss)
I0407 08:30:27.884501 17723 solver.cpp:218] Iteration 720 (0.872676 iter/s, 13.7508s/12 iters), loss = 4.89187
I0407 08:30:27.884554 17723 solver.cpp:237]     Train net output #0: loss = 4.89187 (* 1 = 4.89187 loss)
I0407 08:30:27.884564 17723 sgd_solver.cpp:105] Iteration 720, lr = 0.01
I0407 08:30:33.190722 17723 solver.cpp:218] Iteration 732 (2.26154 iter/s, 5.30611s/12 iters), loss = 4.77996
I0407 08:30:33.190763 17723 solver.cpp:237]     Train net output #0: loss = 4.77996 (* 1 = 4.77996 loss)
I0407 08:30:33.190771 17723 sgd_solver.cpp:105] Iteration 732, lr = 0.01
I0407 08:30:38.453137 17723 solver.cpp:218] Iteration 744 (2.28037 iter/s, 5.26231s/12 iters), loss = 4.8486
I0407 08:30:38.453178 17723 solver.cpp:237]     Train net output #0: loss = 4.8486 (* 1 = 4.8486 loss)
I0407 08:30:38.453186 17723 sgd_solver.cpp:105] Iteration 744, lr = 0.01
I0407 08:30:43.906733 17723 solver.cpp:218] Iteration 756 (2.20042 iter/s, 5.45349s/12 iters), loss = 4.74988
I0407 08:30:43.906869 17723 solver.cpp:237]     Train net output #0: loss = 4.74988 (* 1 = 4.74988 loss)
I0407 08:30:43.906877 17723 sgd_solver.cpp:105] Iteration 756, lr = 0.01
I0407 08:30:49.386945 17723 solver.cpp:218] Iteration 768 (2.18977 iter/s, 5.48002s/12 iters), loss = 4.89097
I0407 08:30:49.386986 17723 solver.cpp:237]     Train net output #0: loss = 4.89097 (* 1 = 4.89097 loss)
I0407 08:30:49.386992 17723 sgd_solver.cpp:105] Iteration 768, lr = 0.01
I0407 08:30:54.441555 17723 solver.cpp:218] Iteration 780 (2.37412 iter/s, 5.05451s/12 iters), loss = 4.7461
I0407 08:30:54.441601 17723 solver.cpp:237]     Train net output #0: loss = 4.7461 (* 1 = 4.7461 loss)
I0407 08:30:54.441609 17723 sgd_solver.cpp:105] Iteration 780, lr = 0.01
I0407 08:30:59.691305 17723 solver.cpp:218] Iteration 792 (2.28587 iter/s, 5.24965s/12 iters), loss = 4.97157
I0407 08:30:59.691349 17723 solver.cpp:237]     Train net output #0: loss = 4.97157 (* 1 = 4.97157 loss)
I0407 08:30:59.691357 17723 sgd_solver.cpp:105] Iteration 792, lr = 0.01
I0407 08:31:05.030591 17723 solver.cpp:218] Iteration 804 (2.24754 iter/s, 5.33918s/12 iters), loss = 4.88612
I0407 08:31:05.030632 17723 solver.cpp:237]     Train net output #0: loss = 4.88612 (* 1 = 4.88612 loss)
I0407 08:31:05.030639 17723 sgd_solver.cpp:105] Iteration 804, lr = 0.01
I0407 08:31:06.840777 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:31:09.859714 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0407 08:31:13.789616 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0407 08:31:16.113237 17723 solver.cpp:330] Iteration 816, Testing net (#0)
I0407 08:31:16.113369 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:31:20.128140 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:31:20.474858 17723 solver.cpp:397]     Test net output #0: accuracy = 0.0410539
I0407 08:31:20.474886 17723 solver.cpp:397]     Test net output #1: loss = 4.87389 (* 1 = 4.87389 loss)
I0407 08:31:20.615257 17723 solver.cpp:218] Iteration 816 (0.769997 iter/s, 15.5845s/12 iters), loss = 4.97642
I0407 08:31:20.615307 17723 solver.cpp:237]     Train net output #0: loss = 4.97642 (* 1 = 4.97642 loss)
I0407 08:31:20.615315 17723 sgd_solver.cpp:105] Iteration 816, lr = 0.01
I0407 08:31:25.142691 17723 solver.cpp:218] Iteration 828 (2.65057 iter/s, 4.52734s/12 iters), loss = 4.7781
I0407 08:31:25.142735 17723 solver.cpp:237]     Train net output #0: loss = 4.7781 (* 1 = 4.7781 loss)
I0407 08:31:25.142741 17723 sgd_solver.cpp:105] Iteration 828, lr = 0.01
I0407 08:31:30.504657 17723 solver.cpp:218] Iteration 840 (2.23803 iter/s, 5.36186s/12 iters), loss = 4.64872
I0407 08:31:30.504696 17723 solver.cpp:237]     Train net output #0: loss = 4.64872 (* 1 = 4.64872 loss)
I0407 08:31:30.504703 17723 sgd_solver.cpp:105] Iteration 840, lr = 0.01
I0407 08:31:35.772477 17723 solver.cpp:218] Iteration 852 (2.27802 iter/s, 5.26772s/12 iters), loss = 4.6304
I0407 08:31:35.772521 17723 solver.cpp:237]     Train net output #0: loss = 4.6304 (* 1 = 4.6304 loss)
I0407 08:31:35.772529 17723 sgd_solver.cpp:105] Iteration 852, lr = 0.01
I0407 08:31:41.200486 17723 solver.cpp:218] Iteration 864 (2.2108 iter/s, 5.4279s/12 iters), loss = 4.74131
I0407 08:31:41.200531 17723 solver.cpp:237]     Train net output #0: loss = 4.74131 (* 1 = 4.74131 loss)
I0407 08:31:41.200538 17723 sgd_solver.cpp:105] Iteration 864, lr = 0.01
I0407 08:31:46.496052 17723 solver.cpp:218] Iteration 876 (2.26609 iter/s, 5.29546s/12 iters), loss = 4.70262
I0407 08:31:46.496166 17723 solver.cpp:237]     Train net output #0: loss = 4.70262 (* 1 = 4.70262 loss)
I0407 08:31:46.496176 17723 sgd_solver.cpp:105] Iteration 876, lr = 0.01
I0407 08:31:51.878010 17723 solver.cpp:218] Iteration 888 (2.22974 iter/s, 5.38178s/12 iters), loss = 4.72108
I0407 08:31:51.878053 17723 solver.cpp:237]     Train net output #0: loss = 4.72108 (* 1 = 4.72108 loss)
I0407 08:31:51.878060 17723 sgd_solver.cpp:105] Iteration 888, lr = 0.01
I0407 08:31:57.294971 17723 solver.cpp:218] Iteration 900 (2.21531 iter/s, 5.41685s/12 iters), loss = 4.6185
I0407 08:31:57.295019 17723 solver.cpp:237]     Train net output #0: loss = 4.6185 (* 1 = 4.6185 loss)
I0407 08:31:57.295030 17723 sgd_solver.cpp:105] Iteration 900, lr = 0.01
I0407 08:32:01.503384 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:32:02.766069 17723 solver.cpp:218] Iteration 912 (2.19339 iter/s, 5.47099s/12 iters), loss = 4.79812
I0407 08:32:02.766115 17723 solver.cpp:237]     Train net output #0: loss = 4.79812 (* 1 = 4.79812 loss)
I0407 08:32:02.766125 17723 sgd_solver.cpp:105] Iteration 912, lr = 0.01
I0407 08:32:04.952694 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0407 08:32:09.659520 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0407 08:32:11.972473 17723 solver.cpp:330] Iteration 918, Testing net (#0)
I0407 08:32:11.972496 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:32:15.940136 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:32:16.354485 17723 solver.cpp:397]     Test net output #0: accuracy = 0.0508578
I0407 08:32:16.354521 17723 solver.cpp:397]     Test net output #1: loss = 4.73868 (* 1 = 4.73868 loss)
I0407 08:32:18.234645 17723 solver.cpp:218] Iteration 924 (0.775775 iter/s, 15.4684s/12 iters), loss = 4.60124
I0407 08:32:18.234779 17723 solver.cpp:237]     Train net output #0: loss = 4.60124 (* 1 = 4.60124 loss)
I0407 08:32:18.234787 17723 sgd_solver.cpp:105] Iteration 924, lr = 0.01
I0407 08:32:23.464617 17723 solver.cpp:218] Iteration 936 (2.29455 iter/s, 5.22978s/12 iters), loss = 4.75197
I0407 08:32:23.464658 17723 solver.cpp:237]     Train net output #0: loss = 4.75197 (* 1 = 4.75197 loss)
I0407 08:32:23.464664 17723 sgd_solver.cpp:105] Iteration 936, lr = 0.01
I0407 08:32:28.353803 17723 solver.cpp:218] Iteration 948 (2.45444 iter/s, 4.88909s/12 iters), loss = 4.67584
I0407 08:32:28.353847 17723 solver.cpp:237]     Train net output #0: loss = 4.67584 (* 1 = 4.67584 loss)
I0407 08:32:28.353853 17723 sgd_solver.cpp:105] Iteration 948, lr = 0.01
I0407 08:32:33.704582 17723 solver.cpp:218] Iteration 960 (2.24271 iter/s, 5.35068s/12 iters), loss = 4.64987
I0407 08:32:33.704622 17723 solver.cpp:237]     Train net output #0: loss = 4.64987 (* 1 = 4.64987 loss)
I0407 08:32:33.704629 17723 sgd_solver.cpp:105] Iteration 960, lr = 0.01
I0407 08:32:38.920089 17723 solver.cpp:218] Iteration 972 (2.30087 iter/s, 5.21541s/12 iters), loss = 4.45547
I0407 08:32:38.920128 17723 solver.cpp:237]     Train net output #0: loss = 4.45547 (* 1 = 4.45547 loss)
I0407 08:32:38.920135 17723 sgd_solver.cpp:105] Iteration 972, lr = 0.01
I0407 08:32:44.165153 17723 solver.cpp:218] Iteration 984 (2.28791 iter/s, 5.24496s/12 iters), loss = 4.56012
I0407 08:32:44.165196 17723 solver.cpp:237]     Train net output #0: loss = 4.56012 (* 1 = 4.56012 loss)
I0407 08:32:44.165205 17723 sgd_solver.cpp:105] Iteration 984, lr = 0.01
I0407 08:32:49.504492 17723 solver.cpp:218] Iteration 996 (2.24751 iter/s, 5.33924s/12 iters), loss = 4.58877
I0407 08:32:49.504571 17723 solver.cpp:237]     Train net output #0: loss = 4.58877 (* 1 = 4.58877 loss)
I0407 08:32:49.504580 17723 sgd_solver.cpp:105] Iteration 996, lr = 0.01
I0407 08:32:54.566794 17723 solver.cpp:218] Iteration 1008 (2.37053 iter/s, 5.06216s/12 iters), loss = 4.62506
I0407 08:32:54.566839 17723 solver.cpp:237]     Train net output #0: loss = 4.62506 (* 1 = 4.62506 loss)
I0407 08:32:54.566846 17723 sgd_solver.cpp:105] Iteration 1008, lr = 0.01
I0407 08:32:55.558241 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:32:59.088410 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0407 08:33:03.954284 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0407 08:33:07.322785 17723 solver.cpp:330] Iteration 1020, Testing net (#0)
I0407 08:33:07.322811 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:33:11.324645 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:33:11.774003 17723 solver.cpp:397]     Test net output #0: accuracy = 0.0582108
I0407 08:33:11.774034 17723 solver.cpp:397]     Test net output #1: loss = 4.58432 (* 1 = 4.58432 loss)
I0407 08:33:11.912596 17723 solver.cpp:218] Iteration 1020 (0.691818 iter/s, 17.3456s/12 iters), loss = 4.49676
I0407 08:33:11.912659 17723 solver.cpp:237]     Train net output #0: loss = 4.49676 (* 1 = 4.49676 loss)
I0407 08:33:11.912667 17723 sgd_solver.cpp:105] Iteration 1020, lr = 0.01
I0407 08:33:16.254660 17723 solver.cpp:218] Iteration 1032 (2.76374 iter/s, 4.34195s/12 iters), loss = 4.83507
I0407 08:33:16.254704 17723 solver.cpp:237]     Train net output #0: loss = 4.83507 (* 1 = 4.83507 loss)
I0407 08:33:16.254712 17723 sgd_solver.cpp:105] Iteration 1032, lr = 0.01
I0407 08:33:21.559919 17723 solver.cpp:218] Iteration 1044 (2.26195 iter/s, 5.30516s/12 iters), loss = 4.61336
I0407 08:33:21.560052 17723 solver.cpp:237]     Train net output #0: loss = 4.61336 (* 1 = 4.61336 loss)
I0407 08:33:21.560060 17723 sgd_solver.cpp:105] Iteration 1044, lr = 0.01
I0407 08:33:26.672010 17723 solver.cpp:218] Iteration 1056 (2.34746 iter/s, 5.1119s/12 iters), loss = 4.49201
I0407 08:33:26.672060 17723 solver.cpp:237]     Train net output #0: loss = 4.49201 (* 1 = 4.49201 loss)
I0407 08:33:26.672066 17723 sgd_solver.cpp:105] Iteration 1056, lr = 0.01
I0407 08:33:31.893668 17723 solver.cpp:218] Iteration 1068 (2.29817 iter/s, 5.22155s/12 iters), loss = 4.3859
I0407 08:33:31.893708 17723 solver.cpp:237]     Train net output #0: loss = 4.3859 (* 1 = 4.3859 loss)
I0407 08:33:31.893714 17723 sgd_solver.cpp:105] Iteration 1068, lr = 0.01
I0407 08:33:37.177289 17723 solver.cpp:218] Iteration 1080 (2.27121 iter/s, 5.28352s/12 iters), loss = 4.27895
I0407 08:33:37.177332 17723 solver.cpp:237]     Train net output #0: loss = 4.27895 (* 1 = 4.27895 loss)
I0407 08:33:37.177340 17723 sgd_solver.cpp:105] Iteration 1080, lr = 0.01
I0407 08:33:42.471963 17723 solver.cpp:218] Iteration 1092 (2.26647 iter/s, 5.29458s/12 iters), loss = 4.51105
I0407 08:33:42.471999 17723 solver.cpp:237]     Train net output #0: loss = 4.51105 (* 1 = 4.51105 loss)
I0407 08:33:42.472007 17723 sgd_solver.cpp:105] Iteration 1092, lr = 0.01
I0407 08:33:47.801568 17723 solver.cpp:218] Iteration 1104 (2.25161 iter/s, 5.32951s/12 iters), loss = 4.32584
I0407 08:33:47.801613 17723 solver.cpp:237]     Train net output #0: loss = 4.32584 (* 1 = 4.32584 loss)
I0407 08:33:47.801622 17723 sgd_solver.cpp:105] Iteration 1104, lr = 0.01
I0407 08:33:51.193274 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:33:53.214184 17723 solver.cpp:218] Iteration 1116 (2.21709 iter/s, 5.41251s/12 iters), loss = 4.27827
I0407 08:33:53.214270 17723 solver.cpp:237]     Train net output #0: loss = 4.27827 (* 1 = 4.27827 loss)
I0407 08:33:53.214278 17723 sgd_solver.cpp:105] Iteration 1116, lr = 0.01
I0407 08:33:55.407362 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0407 08:33:59.839272 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0407 08:34:02.670475 17723 solver.cpp:330] Iteration 1122, Testing net (#0)
I0407 08:34:02.670496 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:34:06.547474 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:34:07.019611 17723 solver.cpp:397]     Test net output #0: accuracy = 0.0735294
I0407 08:34:07.019641 17723 solver.cpp:397]     Test net output #1: loss = 4.42815 (* 1 = 4.42815 loss)
I0407 08:34:09.013814 17723 solver.cpp:218] Iteration 1128 (0.759522 iter/s, 15.7994s/12 iters), loss = 4.27206
I0407 08:34:09.013864 17723 solver.cpp:237]     Train net output #0: loss = 4.27206 (* 1 = 4.27206 loss)
I0407 08:34:09.013871 17723 sgd_solver.cpp:105] Iteration 1128, lr = 0.01
I0407 08:34:14.457381 17723 solver.cpp:218] Iteration 1140 (2.20448 iter/s, 5.44346s/12 iters), loss = 4.36022
I0407 08:34:14.457432 17723 solver.cpp:237]     Train net output #0: loss = 4.36022 (* 1 = 4.36022 loss)
I0407 08:34:14.457439 17723 sgd_solver.cpp:105] Iteration 1140, lr = 0.01
I0407 08:34:19.843838 17723 solver.cpp:218] Iteration 1152 (2.22785 iter/s, 5.38635s/12 iters), loss = 4.38491
I0407 08:34:19.843881 17723 solver.cpp:237]     Train net output #0: loss = 4.38491 (* 1 = 4.38491 loss)
I0407 08:34:19.843889 17723 sgd_solver.cpp:105] Iteration 1152, lr = 0.01
I0407 08:34:25.003088 17723 solver.cpp:218] Iteration 1164 (2.32597 iter/s, 5.15915s/12 iters), loss = 4.39934
I0407 08:34:25.003223 17723 solver.cpp:237]     Train net output #0: loss = 4.39934 (* 1 = 4.39934 loss)
I0407 08:34:25.003232 17723 sgd_solver.cpp:105] Iteration 1164, lr = 0.01
I0407 08:34:30.362181 17723 solver.cpp:218] Iteration 1176 (2.23926 iter/s, 5.3589s/12 iters), loss = 4.24703
I0407 08:34:30.362227 17723 solver.cpp:237]     Train net output #0: loss = 4.24703 (* 1 = 4.24703 loss)
I0407 08:34:30.362236 17723 sgd_solver.cpp:105] Iteration 1176, lr = 0.01
I0407 08:34:35.720108 17723 solver.cpp:218] Iteration 1188 (2.23971 iter/s, 5.35783s/12 iters), loss = 4.30327
I0407 08:34:35.720139 17723 solver.cpp:237]     Train net output #0: loss = 4.30327 (* 1 = 4.30327 loss)
I0407 08:34:35.720145 17723 sgd_solver.cpp:105] Iteration 1188, lr = 0.01
I0407 08:34:40.965467 17723 solver.cpp:218] Iteration 1200 (2.28778 iter/s, 5.24527s/12 iters), loss = 4.16591
I0407 08:34:40.965512 17723 solver.cpp:237]     Train net output #0: loss = 4.16591 (* 1 = 4.16591 loss)
I0407 08:34:40.965520 17723 sgd_solver.cpp:105] Iteration 1200, lr = 0.01
I0407 08:34:46.085261 17723 solver.cpp:218] Iteration 1212 (2.34389 iter/s, 5.11969s/12 iters), loss = 4.03802
I0407 08:34:46.085301 17723 solver.cpp:237]     Train net output #0: loss = 4.03802 (* 1 = 4.03802 loss)
I0407 08:34:46.085309 17723 sgd_solver.cpp:105] Iteration 1212, lr = 0.01
I0407 08:34:46.342731 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:34:50.603520 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0407 08:34:55.281482 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0407 08:34:57.703897 17723 solver.cpp:330] Iteration 1224, Testing net (#0)
I0407 08:34:57.703918 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:35:01.502485 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:35:02.005105 17723 solver.cpp:397]     Test net output #0: accuracy = 0.102328
I0407 08:35:02.005134 17723 solver.cpp:397]     Test net output #1: loss = 4.22241 (* 1 = 4.22241 loss)
I0407 08:35:02.141146 17723 solver.cpp:218] Iteration 1224 (0.747398 iter/s, 16.0557s/12 iters), loss = 4.10182
I0407 08:35:02.141186 17723 solver.cpp:237]     Train net output #0: loss = 4.10182 (* 1 = 4.10182 loss)
I0407 08:35:02.141192 17723 sgd_solver.cpp:105] Iteration 1224, lr = 0.01
I0407 08:35:06.559666 17723 solver.cpp:218] Iteration 1236 (2.71589 iter/s, 4.41843s/12 iters), loss = 4.39156
I0407 08:35:06.559708 17723 solver.cpp:237]     Train net output #0: loss = 4.39156 (* 1 = 4.39156 loss)
I0407 08:35:06.559715 17723 sgd_solver.cpp:105] Iteration 1236, lr = 0.01
I0407 08:35:11.898330 17723 solver.cpp:218] Iteration 1248 (2.2478 iter/s, 5.33856s/12 iters), loss = 3.99724
I0407 08:35:11.898375 17723 solver.cpp:237]     Train net output #0: loss = 3.99724 (* 1 = 3.99724 loss)
I0407 08:35:11.898382 17723 sgd_solver.cpp:105] Iteration 1248, lr = 0.01
I0407 08:35:17.280122 17723 solver.cpp:218] Iteration 1260 (2.22978 iter/s, 5.38169s/12 iters), loss = 4.28599
I0407 08:35:17.280160 17723 solver.cpp:237]     Train net output #0: loss = 4.28599 (* 1 = 4.28599 loss)
I0407 08:35:17.280167 17723 sgd_solver.cpp:105] Iteration 1260, lr = 0.01
I0407 08:35:22.574378 17723 solver.cpp:218] Iteration 1272 (2.26665 iter/s, 5.29416s/12 iters), loss = 4.2064
I0407 08:35:22.574411 17723 solver.cpp:237]     Train net output #0: loss = 4.2064 (* 1 = 4.2064 loss)
I0407 08:35:22.574419 17723 sgd_solver.cpp:105] Iteration 1272, lr = 0.01
I0407 08:35:27.945747 17723 solver.cpp:218] Iteration 1284 (2.23411 iter/s, 5.37127s/12 iters), loss = 4.19431
I0407 08:35:27.945839 17723 solver.cpp:237]     Train net output #0: loss = 4.19431 (* 1 = 4.19431 loss)
I0407 08:35:27.945848 17723 sgd_solver.cpp:105] Iteration 1284, lr = 0.01
I0407 08:35:33.273322 17723 solver.cpp:218] Iteration 1296 (2.25249 iter/s, 5.32743s/12 iters), loss = 4.07808
I0407 08:35:33.273361 17723 solver.cpp:237]     Train net output #0: loss = 4.07808 (* 1 = 4.07808 loss)
I0407 08:35:33.273368 17723 sgd_solver.cpp:105] Iteration 1296, lr = 0.01
I0407 08:35:38.661484 17723 solver.cpp:218] Iteration 1308 (2.22715 iter/s, 5.38806s/12 iters), loss = 4.12586
I0407 08:35:38.661525 17723 solver.cpp:237]     Train net output #0: loss = 4.12586 (* 1 = 4.12586 loss)
I0407 08:35:38.661532 17723 sgd_solver.cpp:105] Iteration 1308, lr = 0.01
I0407 08:35:41.252496 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:35:43.993276 17723 solver.cpp:218] Iteration 1320 (2.25069 iter/s, 5.33169s/12 iters), loss = 4.24063
I0407 08:35:43.993317 17723 solver.cpp:237]     Train net output #0: loss = 4.24063 (* 1 = 4.24063 loss)
I0407 08:35:43.993324 17723 sgd_solver.cpp:105] Iteration 1320, lr = 0.01
I0407 08:35:46.318876 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0407 08:35:50.483672 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0407 08:35:52.803149 17723 solver.cpp:330] Iteration 1326, Testing net (#0)
I0407 08:35:52.803175 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:35:56.588863 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:35:57.150223 17723 solver.cpp:397]     Test net output #0: accuracy = 0.109681
I0407 08:35:57.150255 17723 solver.cpp:397]     Test net output #1: loss = 4.12315 (* 1 = 4.12315 loss)
I0407 08:35:59.021770 17723 solver.cpp:218] Iteration 1332 (0.798492 iter/s, 15.0283s/12 iters), loss = 3.8411
I0407 08:35:59.021912 17723 solver.cpp:237]     Train net output #0: loss = 3.8411 (* 1 = 3.8411 loss)
I0407 08:35:59.021920 17723 sgd_solver.cpp:105] Iteration 1332, lr = 0.01
I0407 08:36:04.354323 17723 solver.cpp:218] Iteration 1344 (2.25041 iter/s, 5.33235s/12 iters), loss = 4.09598
I0407 08:36:04.354370 17723 solver.cpp:237]     Train net output #0: loss = 4.09598 (* 1 = 4.09598 loss)
I0407 08:36:04.354378 17723 sgd_solver.cpp:105] Iteration 1344, lr = 0.01
I0407 08:36:09.712481 17723 solver.cpp:218] Iteration 1356 (2.23962 iter/s, 5.35806s/12 iters), loss = 3.88047
I0407 08:36:09.712523 17723 solver.cpp:237]     Train net output #0: loss = 3.88047 (* 1 = 3.88047 loss)
I0407 08:36:09.712532 17723 sgd_solver.cpp:105] Iteration 1356, lr = 0.01
I0407 08:36:14.889079 17723 solver.cpp:218] Iteration 1368 (2.31817 iter/s, 5.1765s/12 iters), loss = 3.93201
I0407 08:36:14.889118 17723 solver.cpp:237]     Train net output #0: loss = 3.93201 (* 1 = 3.93201 loss)
I0407 08:36:14.889125 17723 sgd_solver.cpp:105] Iteration 1368, lr = 0.01
I0407 08:36:16.122669 17723 blocking_queue.cpp:49] Waiting for data
I0407 08:36:20.062582 17723 solver.cpp:218] Iteration 1380 (2.31956 iter/s, 5.17341s/12 iters), loss = 3.92034
I0407 08:36:20.062625 17723 solver.cpp:237]     Train net output #0: loss = 3.92034 (* 1 = 3.92034 loss)
I0407 08:36:20.062633 17723 sgd_solver.cpp:105] Iteration 1380, lr = 0.01
I0407 08:36:25.313717 17723 solver.cpp:218] Iteration 1392 (2.28526 iter/s, 5.25103s/12 iters), loss = 3.94414
I0407 08:36:25.313760 17723 solver.cpp:237]     Train net output #0: loss = 3.94414 (* 1 = 3.94414 loss)
I0407 08:36:25.313767 17723 sgd_solver.cpp:105] Iteration 1392, lr = 0.01
I0407 08:36:30.419770 17723 solver.cpp:218] Iteration 1404 (2.3502 iter/s, 5.10596s/12 iters), loss = 3.79621
I0407 08:36:30.419885 17723 solver.cpp:237]     Train net output #0: loss = 3.79621 (* 1 = 3.79621 loss)
I0407 08:36:30.419893 17723 sgd_solver.cpp:105] Iteration 1404, lr = 0.01
I0407 08:36:35.333459 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:36:35.723642 17723 solver.cpp:218] Iteration 1416 (2.26257 iter/s, 5.3037s/12 iters), loss = 3.77206
I0407 08:36:35.723690 17723 solver.cpp:237]     Train net output #0: loss = 3.77206 (* 1 = 3.77206 loss)
I0407 08:36:35.723698 17723 sgd_solver.cpp:105] Iteration 1416, lr = 0.01
I0407 08:36:40.472396 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0407 08:36:43.969951 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0407 08:36:46.285399 17723 solver.cpp:330] Iteration 1428, Testing net (#0)
I0407 08:36:46.285419 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:36:49.959975 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:36:50.540123 17723 solver.cpp:397]     Test net output #0: accuracy = 0.128064
I0407 08:36:50.540158 17723 solver.cpp:397]     Test net output #1: loss = 3.97197 (* 1 = 3.97197 loss)
I0407 08:36:50.678001 17723 solver.cpp:218] Iteration 1428 (0.802451 iter/s, 14.9542s/12 iters), loss = 4.02428
I0407 08:36:50.678047 17723 solver.cpp:237]     Train net output #0: loss = 4.02428 (* 1 = 4.02428 loss)
I0407 08:36:50.678056 17723 sgd_solver.cpp:105] Iteration 1428, lr = 0.01
I0407 08:36:54.831110 17723 solver.cpp:218] Iteration 1440 (2.88946 iter/s, 4.15302s/12 iters), loss = 3.75233
I0407 08:36:54.831146 17723 solver.cpp:237]     Train net output #0: loss = 3.75233 (* 1 = 3.75233 loss)
I0407 08:36:54.831151 17723 sgd_solver.cpp:105] Iteration 1440, lr = 0.01
I0407 08:37:00.028870 17723 solver.cpp:218] Iteration 1452 (2.30873 iter/s, 5.19766s/12 iters), loss = 3.76738
I0407 08:37:00.028926 17723 solver.cpp:237]     Train net output #0: loss = 3.76738 (* 1 = 3.76738 loss)
I0407 08:37:00.028936 17723 sgd_solver.cpp:105] Iteration 1452, lr = 0.01
I0407 08:37:05.397434 17723 solver.cpp:218] Iteration 1464 (2.23528 iter/s, 5.36845s/12 iters), loss = 3.74539
I0407 08:37:05.397572 17723 solver.cpp:237]     Train net output #0: loss = 3.74539 (* 1 = 3.74539 loss)
I0407 08:37:05.397579 17723 sgd_solver.cpp:105] Iteration 1464, lr = 0.01
I0407 08:37:10.526257 17723 solver.cpp:218] Iteration 1476 (2.3398 iter/s, 5.12863s/12 iters), loss = 3.7997
I0407 08:37:10.526298 17723 solver.cpp:237]     Train net output #0: loss = 3.7997 (* 1 = 3.7997 loss)
I0407 08:37:10.526305 17723 sgd_solver.cpp:105] Iteration 1476, lr = 0.01
I0407 08:37:15.886348 17723 solver.cpp:218] Iteration 1488 (2.23881 iter/s, 5.35999s/12 iters), loss = 3.81453
I0407 08:37:15.886392 17723 solver.cpp:237]     Train net output #0: loss = 3.81453 (* 1 = 3.81453 loss)
I0407 08:37:15.886400 17723 sgd_solver.cpp:105] Iteration 1488, lr = 0.01
I0407 08:37:21.132552 17723 solver.cpp:218] Iteration 1500 (2.28741 iter/s, 5.2461s/12 iters), loss = 4.03712
I0407 08:37:21.132596 17723 solver.cpp:237]     Train net output #0: loss = 4.03712 (* 1 = 4.03712 loss)
I0407 08:37:21.132604 17723 sgd_solver.cpp:105] Iteration 1500, lr = 0.01
I0407 08:37:26.437347 17723 solver.cpp:218] Iteration 1512 (2.26215 iter/s, 5.3047s/12 iters), loss = 3.80538
I0407 08:37:26.437382 17723 solver.cpp:237]     Train net output #0: loss = 3.80538 (* 1 = 3.80538 loss)
I0407 08:37:26.437388 17723 sgd_solver.cpp:105] Iteration 1512, lr = 0.01
I0407 08:37:28.261370 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:37:31.597959 17723 solver.cpp:218] Iteration 1524 (2.32535 iter/s, 5.16052s/12 iters), loss = 3.86334
I0407 08:37:31.598006 17723 solver.cpp:237]     Train net output #0: loss = 3.86334 (* 1 = 3.86334 loss)
I0407 08:37:31.598016 17723 sgd_solver.cpp:105] Iteration 1524, lr = 0.01
I0407 08:37:33.732698 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0407 08:37:37.063853 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0407 08:37:39.368032 17723 solver.cpp:330] Iteration 1530, Testing net (#0)
I0407 08:37:39.368052 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:37:43.003428 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:37:43.631932 17723 solver.cpp:397]     Test net output #0: accuracy = 0.121936
I0407 08:37:43.631958 17723 solver.cpp:397]     Test net output #1: loss = 3.9337 (* 1 = 3.9337 loss)
I0407 08:37:45.587893 17723 solver.cpp:218] Iteration 1536 (0.85777 iter/s, 13.9898s/12 iters), loss = 3.34348
I0407 08:37:45.587931 17723 solver.cpp:237]     Train net output #0: loss = 3.34348 (* 1 = 3.34348 loss)
I0407 08:37:45.587939 17723 sgd_solver.cpp:105] Iteration 1536, lr = 0.01
I0407 08:37:50.766330 17723 solver.cpp:218] Iteration 1548 (2.31734 iter/s, 5.17834s/12 iters), loss = 3.6624
I0407 08:37:50.766368 17723 solver.cpp:237]     Train net output #0: loss = 3.6624 (* 1 = 3.6624 loss)
I0407 08:37:50.766376 17723 sgd_solver.cpp:105] Iteration 1548, lr = 0.01
I0407 08:37:56.124137 17723 solver.cpp:218] Iteration 1560 (2.23976 iter/s, 5.35771s/12 iters), loss = 3.66926
I0407 08:37:56.124181 17723 solver.cpp:237]     Train net output #0: loss = 3.66926 (* 1 = 3.66926 loss)
I0407 08:37:56.124187 17723 sgd_solver.cpp:105] Iteration 1560, lr = 0.01
I0407 08:38:01.462791 17723 solver.cpp:218] Iteration 1572 (2.2478 iter/s, 5.33856s/12 iters), loss = 3.62702
I0407 08:38:01.462836 17723 solver.cpp:237]     Train net output #0: loss = 3.62702 (* 1 = 3.62702 loss)
I0407 08:38:01.462842 17723 sgd_solver.cpp:105] Iteration 1572, lr = 0.01
I0407 08:38:06.388521 17723 solver.cpp:218] Iteration 1584 (2.43624 iter/s, 4.92563s/12 iters), loss = 3.7888
I0407 08:38:06.388563 17723 solver.cpp:237]     Train net output #0: loss = 3.7888 (* 1 = 3.7888 loss)
I0407 08:38:06.388571 17723 sgd_solver.cpp:105] Iteration 1584, lr = 0.01
I0407 08:38:11.401487 17723 solver.cpp:218] Iteration 1596 (2.39384 iter/s, 5.01287s/12 iters), loss = 3.93727
I0407 08:38:11.401620 17723 solver.cpp:237]     Train net output #0: loss = 3.93727 (* 1 = 3.93727 loss)
I0407 08:38:11.401628 17723 sgd_solver.cpp:105] Iteration 1596, lr = 0.01
I0407 08:38:16.797745 17723 solver.cpp:218] Iteration 1608 (2.22384 iter/s, 5.39607s/12 iters), loss = 3.45897
I0407 08:38:16.797791 17723 solver.cpp:237]     Train net output #0: loss = 3.45897 (* 1 = 3.45897 loss)
I0407 08:38:16.797798 17723 sgd_solver.cpp:105] Iteration 1608, lr = 0.01
I0407 08:38:21.018836 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:38:22.235513 17723 solver.cpp:218] Iteration 1620 (2.20683 iter/s, 5.43767s/12 iters), loss = 3.58393
I0407 08:38:22.235554 17723 solver.cpp:237]     Train net output #0: loss = 3.58393 (* 1 = 3.58393 loss)
I0407 08:38:22.235561 17723 sgd_solver.cpp:105] Iteration 1620, lr = 0.01
I0407 08:38:26.851657 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0407 08:38:29.884435 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0407 08:38:32.255113 17723 solver.cpp:330] Iteration 1632, Testing net (#0)
I0407 08:38:32.255136 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:38:35.911092 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:38:36.632715 17723 solver.cpp:397]     Test net output #0: accuracy = 0.151961
I0407 08:38:36.632755 17723 solver.cpp:397]     Test net output #1: loss = 3.86848 (* 1 = 3.86848 loss)
I0407 08:38:36.767127 17723 solver.cpp:218] Iteration 1632 (0.825795 iter/s, 14.5314s/12 iters), loss = 3.66544
I0407 08:38:36.767177 17723 solver.cpp:237]     Train net output #0: loss = 3.66544 (* 1 = 3.66544 loss)
I0407 08:38:36.767187 17723 sgd_solver.cpp:105] Iteration 1632, lr = 0.01
I0407 08:38:41.206039 17723 solver.cpp:218] Iteration 1644 (2.70343 iter/s, 4.43881s/12 iters), loss = 3.63124
I0407 08:38:41.206095 17723 solver.cpp:237]     Train net output #0: loss = 3.63124 (* 1 = 3.63124 loss)
I0407 08:38:41.206106 17723 sgd_solver.cpp:105] Iteration 1644, lr = 0.01
I0407 08:38:46.569988 17723 solver.cpp:218] Iteration 1656 (2.2372 iter/s, 5.36384s/12 iters), loss = 3.59747
I0407 08:38:46.570154 17723 solver.cpp:237]     Train net output #0: loss = 3.59747 (* 1 = 3.59747 loss)
I0407 08:38:46.570174 17723 sgd_solver.cpp:105] Iteration 1656, lr = 0.01
I0407 08:38:51.543995 17723 solver.cpp:218] Iteration 1668 (2.41264 iter/s, 4.9738s/12 iters), loss = 3.79998
I0407 08:38:51.544039 17723 solver.cpp:237]     Train net output #0: loss = 3.79998 (* 1 = 3.79998 loss)
I0407 08:38:51.544049 17723 sgd_solver.cpp:105] Iteration 1668, lr = 0.01
I0407 08:38:56.774866 17723 solver.cpp:218] Iteration 1680 (2.29412 iter/s, 5.23077s/12 iters), loss = 3.24084
I0407 08:38:56.774916 17723 solver.cpp:237]     Train net output #0: loss = 3.24084 (* 1 = 3.24084 loss)
I0407 08:38:56.774925 17723 sgd_solver.cpp:105] Iteration 1680, lr = 0.01
I0407 08:39:02.035197 17723 solver.cpp:218] Iteration 1692 (2.28127 iter/s, 5.26023s/12 iters), loss = 3.3357
I0407 08:39:02.035231 17723 solver.cpp:237]     Train net output #0: loss = 3.3357 (* 1 = 3.3357 loss)
I0407 08:39:02.035238 17723 sgd_solver.cpp:105] Iteration 1692, lr = 0.01
I0407 08:39:07.314095 17723 solver.cpp:218] Iteration 1704 (2.27324 iter/s, 5.27881s/12 iters), loss = 3.47659
I0407 08:39:07.314129 17723 solver.cpp:237]     Train net output #0: loss = 3.47659 (* 1 = 3.47659 loss)
I0407 08:39:07.314136 17723 sgd_solver.cpp:105] Iteration 1704, lr = 0.01
I0407 08:39:12.808418 17723 solver.cpp:218] Iteration 1716 (2.18411 iter/s, 5.49423s/12 iters), loss = 3.52234
I0407 08:39:12.808459 17723 solver.cpp:237]     Train net output #0: loss = 3.52234 (* 1 = 3.52234 loss)
I0407 08:39:12.808467 17723 sgd_solver.cpp:105] Iteration 1716, lr = 0.01
I0407 08:39:13.826246 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:39:18.138988 17723 solver.cpp:218] Iteration 1728 (2.25121 iter/s, 5.33047s/12 iters), loss = 3.28477
I0407 08:39:18.139122 17723 solver.cpp:237]     Train net output #0: loss = 3.28477 (* 1 = 3.28477 loss)
I0407 08:39:18.139132 17723 sgd_solver.cpp:105] Iteration 1728, lr = 0.01
I0407 08:39:20.376286 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0407 08:39:23.417090 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0407 08:39:25.744300 17723 solver.cpp:330] Iteration 1734, Testing net (#0)
I0407 08:39:25.744321 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:39:29.380932 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:39:30.083323 17723 solver.cpp:397]     Test net output #0: accuracy = 0.167279
I0407 08:39:30.083360 17723 solver.cpp:397]     Test net output #1: loss = 3.6985 (* 1 = 3.6985 loss)
I0407 08:39:31.824756 17723 solver.cpp:218] Iteration 1740 (0.87684 iter/s, 13.6855s/12 iters), loss = 3.65501
I0407 08:39:31.824801 17723 solver.cpp:237]     Train net output #0: loss = 3.65501 (* 1 = 3.65501 loss)
I0407 08:39:31.824810 17723 sgd_solver.cpp:105] Iteration 1740, lr = 0.01
I0407 08:39:36.817203 17723 solver.cpp:218] Iteration 1752 (2.40368 iter/s, 4.99234s/12 iters), loss = 3.75702
I0407 08:39:36.817255 17723 solver.cpp:237]     Train net output #0: loss = 3.75702 (* 1 = 3.75702 loss)
I0407 08:39:36.817265 17723 sgd_solver.cpp:105] Iteration 1752, lr = 0.01
I0407 08:39:42.176183 17723 solver.cpp:218] Iteration 1764 (2.23928 iter/s, 5.35887s/12 iters), loss = 3.64865
I0407 08:39:42.176226 17723 solver.cpp:237]     Train net output #0: loss = 3.64865 (* 1 = 3.64865 loss)
I0407 08:39:42.176234 17723 sgd_solver.cpp:105] Iteration 1764, lr = 0.01
I0407 08:39:47.551877 17723 solver.cpp:218] Iteration 1776 (2.23231 iter/s, 5.3756s/12 iters), loss = 3.27998
I0407 08:39:47.551920 17723 solver.cpp:237]     Train net output #0: loss = 3.27998 (* 1 = 3.27998 loss)
I0407 08:39:47.551928 17723 sgd_solver.cpp:105] Iteration 1776, lr = 0.01
I0407 08:39:53.000505 17723 solver.cpp:218] Iteration 1788 (2.20243 iter/s, 5.44852s/12 iters), loss = 3.19574
I0407 08:39:53.000612 17723 solver.cpp:237]     Train net output #0: loss = 3.19574 (* 1 = 3.19574 loss)
I0407 08:39:53.000619 17723 sgd_solver.cpp:105] Iteration 1788, lr = 0.01
I0407 08:39:58.305763 17723 solver.cpp:218] Iteration 1800 (2.26198 iter/s, 5.30509s/12 iters), loss = 3.26565
I0407 08:39:58.305809 17723 solver.cpp:237]     Train net output #0: loss = 3.26565 (* 1 = 3.26565 loss)
I0407 08:39:58.305815 17723 sgd_solver.cpp:105] Iteration 1800, lr = 0.01
I0407 08:40:03.299700 17723 solver.cpp:218] Iteration 1812 (2.40296 iter/s, 4.99384s/12 iters), loss = 3.24871
I0407 08:40:03.299738 17723 solver.cpp:237]     Train net output #0: loss = 3.24871 (* 1 = 3.24871 loss)
I0407 08:40:03.299744 17723 sgd_solver.cpp:105] Iteration 1812, lr = 0.01
I0407 08:40:06.445266 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:40:08.258623 17723 solver.cpp:218] Iteration 1824 (2.41992 iter/s, 4.95883s/12 iters), loss = 3.22432
I0407 08:40:08.258663 17723 solver.cpp:237]     Train net output #0: loss = 3.22432 (* 1 = 3.22432 loss)
I0407 08:40:08.258669 17723 sgd_solver.cpp:105] Iteration 1824, lr = 0.01
I0407 08:40:13.171088 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0407 08:40:16.165100 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0407 08:40:18.463703 17723 solver.cpp:330] Iteration 1836, Testing net (#0)
I0407 08:40:18.463724 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:40:22.095146 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:40:22.831840 17723 solver.cpp:397]     Test net output #0: accuracy = 0.182598
I0407 08:40:22.831874 17723 solver.cpp:397]     Test net output #1: loss = 3.66998 (* 1 = 3.66998 loss)
I0407 08:40:22.970252 17723 solver.cpp:218] Iteration 1836 (0.815691 iter/s, 14.7115s/12 iters), loss = 2.97092
I0407 08:40:22.970301 17723 solver.cpp:237]     Train net output #0: loss = 2.97092 (* 1 = 2.97092 loss)
I0407 08:40:22.970310 17723 sgd_solver.cpp:105] Iteration 1836, lr = 0.01
I0407 08:40:27.448606 17723 solver.cpp:218] Iteration 1848 (2.67962 iter/s, 4.47826s/12 iters), loss = 3.15149
I0407 08:40:27.448741 17723 solver.cpp:237]     Train net output #0: loss = 3.15149 (* 1 = 3.15149 loss)
I0407 08:40:27.448750 17723 sgd_solver.cpp:105] Iteration 1848, lr = 0.01
I0407 08:40:32.905237 17723 solver.cpp:218] Iteration 1860 (2.19924 iter/s, 5.45644s/12 iters), loss = 3.01751
I0407 08:40:32.905284 17723 solver.cpp:237]     Train net output #0: loss = 3.01751 (* 1 = 3.01751 loss)
I0407 08:40:32.905292 17723 sgd_solver.cpp:105] Iteration 1860, lr = 0.01
I0407 08:40:38.273058 17723 solver.cpp:218] Iteration 1872 (2.23559 iter/s, 5.36772s/12 iters), loss = 3.39048
I0407 08:40:38.273099 17723 solver.cpp:237]     Train net output #0: loss = 3.39048 (* 1 = 3.39048 loss)
I0407 08:40:38.273106 17723 sgd_solver.cpp:105] Iteration 1872, lr = 0.01
I0407 08:40:43.442463 17723 solver.cpp:218] Iteration 1884 (2.32139 iter/s, 5.16931s/12 iters), loss = 3.09343
I0407 08:40:43.442505 17723 solver.cpp:237]     Train net output #0: loss = 3.09343 (* 1 = 3.09343 loss)
I0407 08:40:43.442512 17723 sgd_solver.cpp:105] Iteration 1884, lr = 0.01
I0407 08:40:48.683889 17723 solver.cpp:218] Iteration 1896 (2.2895 iter/s, 5.24132s/12 iters), loss = 3.39125
I0407 08:40:48.683934 17723 solver.cpp:237]     Train net output #0: loss = 3.39125 (* 1 = 3.39125 loss)
I0407 08:40:48.683943 17723 sgd_solver.cpp:105] Iteration 1896, lr = 0.01
I0407 08:40:54.028069 17723 solver.cpp:218] Iteration 1908 (2.24548 iter/s, 5.34407s/12 iters), loss = 3.10517
I0407 08:40:54.028110 17723 solver.cpp:237]     Train net output #0: loss = 3.10517 (* 1 = 3.10517 loss)
I0407 08:40:54.028116 17723 sgd_solver.cpp:105] Iteration 1908, lr = 0.01
I0407 08:40:59.337733 17723 solver.cpp:218] Iteration 1920 (2.26007 iter/s, 5.30956s/12 iters), loss = 3.13277
I0407 08:40:59.337839 17723 solver.cpp:237]     Train net output #0: loss = 3.13277 (* 1 = 3.13277 loss)
I0407 08:40:59.337848 17723 sgd_solver.cpp:105] Iteration 1920, lr = 0.01
I0407 08:40:59.633671 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:41:04.538182 17723 solver.cpp:218] Iteration 1932 (2.30756 iter/s, 5.20029s/12 iters), loss = 2.98155
I0407 08:41:04.538220 17723 solver.cpp:237]     Train net output #0: loss = 2.98155 (* 1 = 2.98155 loss)
I0407 08:41:04.538228 17723 sgd_solver.cpp:105] Iteration 1932, lr = 0.01
I0407 08:41:06.512854 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0407 08:41:09.553436 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0407 08:41:11.875033 17723 solver.cpp:330] Iteration 1938, Testing net (#0)
I0407 08:41:11.875053 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:41:15.371934 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:41:16.144416 17723 solver.cpp:397]     Test net output #0: accuracy = 0.198529
I0407 08:41:16.144445 17723 solver.cpp:397]     Test net output #1: loss = 3.53994 (* 1 = 3.53994 loss)
I0407 08:41:17.954967 17723 solver.cpp:218] Iteration 1944 (0.894412 iter/s, 13.4166s/12 iters), loss = 3.14107
I0407 08:41:17.955014 17723 solver.cpp:237]     Train net output #0: loss = 3.14107 (* 1 = 3.14107 loss)
I0407 08:41:17.955022 17723 sgd_solver.cpp:105] Iteration 1944, lr = 0.01
I0407 08:41:23.222836 17723 solver.cpp:218] Iteration 1956 (2.27801 iter/s, 5.26777s/12 iters), loss = 2.90949
I0407 08:41:23.222875 17723 solver.cpp:237]     Train net output #0: loss = 2.90949 (* 1 = 2.90949 loss)
I0407 08:41:23.222882 17723 sgd_solver.cpp:105] Iteration 1956, lr = 0.01
I0407 08:41:28.406599 17723 solver.cpp:218] Iteration 1968 (2.31497 iter/s, 5.18366s/12 iters), loss = 3.30923
I0407 08:41:28.406644 17723 solver.cpp:237]     Train net output #0: loss = 3.30923 (* 1 = 3.30923 loss)
I0407 08:41:28.406652 17723 sgd_solver.cpp:105] Iteration 1968, lr = 0.01
I0407 08:41:33.511765 17723 solver.cpp:218] Iteration 1980 (2.35061 iter/s, 5.10506s/12 iters), loss = 3.2111
I0407 08:41:33.511876 17723 solver.cpp:237]     Train net output #0: loss = 3.2111 (* 1 = 3.2111 loss)
I0407 08:41:33.511885 17723 sgd_solver.cpp:105] Iteration 1980, lr = 0.01
I0407 08:41:38.775609 17723 solver.cpp:218] Iteration 1992 (2.27977 iter/s, 5.26368s/12 iters), loss = 3.06646
I0407 08:41:38.775652 17723 solver.cpp:237]     Train net output #0: loss = 3.06646 (* 1 = 3.06646 loss)
I0407 08:41:38.775660 17723 sgd_solver.cpp:105] Iteration 1992, lr = 0.01
I0407 08:41:44.103480 17723 solver.cpp:218] Iteration 2004 (2.25235 iter/s, 5.32777s/12 iters), loss = 2.98302
I0407 08:41:44.103525 17723 solver.cpp:237]     Train net output #0: loss = 2.98302 (* 1 = 2.98302 loss)
I0407 08:41:44.103534 17723 sgd_solver.cpp:105] Iteration 2004, lr = 0.01
I0407 08:41:49.327323 17723 solver.cpp:218] Iteration 2016 (2.2972 iter/s, 5.22374s/12 iters), loss = 3.44219
I0407 08:41:49.327363 17723 solver.cpp:237]     Train net output #0: loss = 3.44219 (* 1 = 3.44219 loss)
I0407 08:41:49.327370 17723 sgd_solver.cpp:105] Iteration 2016, lr = 0.01
I0407 08:41:51.830816 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:41:54.473799 17723 solver.cpp:218] Iteration 2028 (2.33174 iter/s, 5.14638s/12 iters), loss = 2.95523
I0407 08:41:54.473839 17723 solver.cpp:237]     Train net output #0: loss = 2.95523 (* 1 = 2.95523 loss)
I0407 08:41:54.473847 17723 sgd_solver.cpp:105] Iteration 2028, lr = 0.01
I0407 08:41:59.178747 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0407 08:42:02.184460 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0407 08:42:04.501832 17723 solver.cpp:330] Iteration 2040, Testing net (#0)
I0407 08:42:04.501921 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:42:08.115247 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:42:08.950178 17723 solver.cpp:397]     Test net output #0: accuracy = 0.191176
I0407 08:42:08.950217 17723 solver.cpp:397]     Test net output #1: loss = 3.54698 (* 1 = 3.54698 loss)
I0407 08:42:09.090734 17723 solver.cpp:218] Iteration 2040 (0.820975 iter/s, 14.6168s/12 iters), loss = 3.15196
I0407 08:42:09.090783 17723 solver.cpp:237]     Train net output #0: loss = 3.15196 (* 1 = 3.15196 loss)
I0407 08:42:09.090792 17723 sgd_solver.cpp:105] Iteration 2040, lr = 0.01
I0407 08:42:13.373221 17723 solver.cpp:218] Iteration 2052 (2.80218 iter/s, 4.28238s/12 iters), loss = 3.01415
I0407 08:42:13.373267 17723 solver.cpp:237]     Train net output #0: loss = 3.01415 (* 1 = 3.01415 loss)
I0407 08:42:13.373275 17723 sgd_solver.cpp:105] Iteration 2052, lr = 0.01
I0407 08:42:15.034858 17723 blocking_queue.cpp:49] Waiting for data
I0407 08:42:18.600240 17723 solver.cpp:218] Iteration 2064 (2.29581 iter/s, 5.22691s/12 iters), loss = 2.86552
I0407 08:42:18.600286 17723 solver.cpp:237]     Train net output #0: loss = 2.86552 (* 1 = 2.86552 loss)
I0407 08:42:18.600293 17723 sgd_solver.cpp:105] Iteration 2064, lr = 0.01
I0407 08:42:24.001255 17723 solver.cpp:218] Iteration 2076 (2.22185 iter/s, 5.40091s/12 iters), loss = 2.88519
I0407 08:42:24.001297 17723 solver.cpp:237]     Train net output #0: loss = 2.88519 (* 1 = 2.88519 loss)
I0407 08:42:24.001304 17723 sgd_solver.cpp:105] Iteration 2076, lr = 0.01
I0407 08:42:29.338903 17723 solver.cpp:218] Iteration 2088 (2.24822 iter/s, 5.33755s/12 iters), loss = 2.93261
I0407 08:42:29.338946 17723 solver.cpp:237]     Train net output #0: loss = 2.93261 (* 1 = 2.93261 loss)
I0407 08:42:29.338953 17723 sgd_solver.cpp:105] Iteration 2088, lr = 0.01
I0407 08:42:34.690091 17723 solver.cpp:218] Iteration 2100 (2.24253 iter/s, 5.35109s/12 iters), loss = 2.97722
I0407 08:42:34.690330 17723 solver.cpp:237]     Train net output #0: loss = 2.97722 (* 1 = 2.97722 loss)
I0407 08:42:34.690338 17723 sgd_solver.cpp:105] Iteration 2100, lr = 0.01
I0407 08:42:40.113013 17723 solver.cpp:218] Iteration 2112 (2.21295 iter/s, 5.42262s/12 iters), loss = 2.96123
I0407 08:42:40.113071 17723 solver.cpp:237]     Train net output #0: loss = 2.96123 (* 1 = 2.96123 loss)
I0407 08:42:40.113080 17723 sgd_solver.cpp:105] Iteration 2112, lr = 0.01
I0407 08:42:45.048758 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:42:45.412156 17723 solver.cpp:218] Iteration 2124 (2.26457 iter/s, 5.29902s/12 iters), loss = 3.15008
I0407 08:42:45.412211 17723 solver.cpp:237]     Train net output #0: loss = 3.15008 (* 1 = 3.15008 loss)
I0407 08:42:45.412220 17723 sgd_solver.cpp:105] Iteration 2124, lr = 0.01
I0407 08:42:50.620493 17723 solver.cpp:218] Iteration 2136 (2.30405 iter/s, 5.20823s/12 iters), loss = 3.0945
I0407 08:42:50.620538 17723 solver.cpp:237]     Train net output #0: loss = 3.0945 (* 1 = 3.0945 loss)
I0407 08:42:50.620546 17723 sgd_solver.cpp:105] Iteration 2136, lr = 0.01
I0407 08:42:52.533425 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0407 08:42:55.579455 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0407 08:42:57.952688 17723 solver.cpp:330] Iteration 2142, Testing net (#0)
I0407 08:42:57.952705 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:43:01.475342 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:43:02.337513 17723 solver.cpp:397]     Test net output #0: accuracy = 0.235907
I0407 08:43:02.337548 17723 solver.cpp:397]     Test net output #1: loss = 3.32712 (* 1 = 3.32712 loss)
I0407 08:43:04.288314 17723 solver.cpp:218] Iteration 2148 (0.877985 iter/s, 13.6677s/12 iters), loss = 2.66171
I0407 08:43:04.288358 17723 solver.cpp:237]     Train net output #0: loss = 2.66171 (* 1 = 2.66171 loss)
I0407 08:43:04.288365 17723 sgd_solver.cpp:105] Iteration 2148, lr = 0.01
I0407 08:43:09.438333 17723 solver.cpp:218] Iteration 2160 (2.33013 iter/s, 5.14992s/12 iters), loss = 2.70018
I0407 08:43:09.438464 17723 solver.cpp:237]     Train net output #0: loss = 2.70018 (* 1 = 2.70018 loss)
I0407 08:43:09.438475 17723 sgd_solver.cpp:105] Iteration 2160, lr = 0.01
I0407 08:43:14.461447 17723 solver.cpp:218] Iteration 2172 (2.38904 iter/s, 5.02293s/12 iters), loss = 2.78938
I0407 08:43:14.461493 17723 solver.cpp:237]     Train net output #0: loss = 2.78938 (* 1 = 2.78938 loss)
I0407 08:43:14.461500 17723 sgd_solver.cpp:105] Iteration 2172, lr = 0.01
I0407 08:43:19.792465 17723 solver.cpp:218] Iteration 2184 (2.25102 iter/s, 5.33091s/12 iters), loss = 2.91048
I0407 08:43:19.792510 17723 solver.cpp:237]     Train net output #0: loss = 2.91048 (* 1 = 2.91048 loss)
I0407 08:43:19.792517 17723 sgd_solver.cpp:105] Iteration 2184, lr = 0.01
I0407 08:43:25.109257 17723 solver.cpp:218] Iteration 2196 (2.25704 iter/s, 5.31669s/12 iters), loss = 3.04089
I0407 08:43:25.109303 17723 solver.cpp:237]     Train net output #0: loss = 3.04089 (* 1 = 3.04089 loss)
I0407 08:43:25.109310 17723 sgd_solver.cpp:105] Iteration 2196, lr = 0.01
I0407 08:43:30.368808 17723 solver.cpp:218] Iteration 2208 (2.28161 iter/s, 5.25945s/12 iters), loss = 2.57843
I0407 08:43:30.368849 17723 solver.cpp:237]     Train net output #0: loss = 2.57843 (* 1 = 2.57843 loss)
I0407 08:43:30.368855 17723 sgd_solver.cpp:105] Iteration 2208, lr = 0.01
I0407 08:43:35.685173 17723 solver.cpp:218] Iteration 2220 (2.25723 iter/s, 5.31626s/12 iters), loss = 2.53616
I0407 08:43:35.685231 17723 solver.cpp:237]     Train net output #0: loss = 2.53616 (* 1 = 2.53616 loss)
I0407 08:43:35.685242 17723 sgd_solver.cpp:105] Iteration 2220, lr = 0.01
I0407 08:43:37.533154 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:43:40.989470 17723 solver.cpp:218] Iteration 2232 (2.26237 iter/s, 5.30418s/12 iters), loss = 2.73044
I0407 08:43:40.990417 17723 solver.cpp:237]     Train net output #0: loss = 2.73044 (* 1 = 2.73044 loss)
I0407 08:43:40.990427 17723 sgd_solver.cpp:105] Iteration 2232, lr = 0.01
I0407 08:43:45.765579 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0407 08:43:48.770035 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0407 08:43:51.080551 17723 solver.cpp:330] Iteration 2244, Testing net (#0)
I0407 08:43:51.080571 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:43:54.630239 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:43:55.527345 17723 solver.cpp:397]     Test net output #0: accuracy = 0.235907
I0407 08:43:55.527379 17723 solver.cpp:397]     Test net output #1: loss = 3.3259 (* 1 = 3.3259 loss)
I0407 08:43:55.668399 17723 solver.cpp:218] Iteration 2244 (0.817558 iter/s, 14.6779s/12 iters), loss = 2.41541
I0407 08:43:55.669970 17723 solver.cpp:237]     Train net output #0: loss = 2.41541 (* 1 = 2.41541 loss)
I0407 08:43:55.669983 17723 sgd_solver.cpp:105] Iteration 2244, lr = 0.01
I0407 08:43:59.786105 17723 solver.cpp:218] Iteration 2256 (2.91538 iter/s, 4.1161s/12 iters), loss = 2.85119
I0407 08:43:59.786144 17723 solver.cpp:237]     Train net output #0: loss = 2.85119 (* 1 = 2.85119 loss)
I0407 08:43:59.786151 17723 sgd_solver.cpp:105] Iteration 2256, lr = 0.01
I0407 08:44:04.941855 17723 solver.cpp:218] Iteration 2268 (2.32754 iter/s, 5.15565s/12 iters), loss = 2.60854
I0407 08:44:04.941907 17723 solver.cpp:237]     Train net output #0: loss = 2.60854 (* 1 = 2.60854 loss)
I0407 08:44:04.941917 17723 sgd_solver.cpp:105] Iteration 2268, lr = 0.01
I0407 08:44:10.035516 17723 solver.cpp:218] Iteration 2280 (2.35592 iter/s, 5.09355s/12 iters), loss = 2.69688
I0407 08:44:10.035557 17723 solver.cpp:237]     Train net output #0: loss = 2.69688 (* 1 = 2.69688 loss)
I0407 08:44:10.035563 17723 sgd_solver.cpp:105] Iteration 2280, lr = 0.01
I0407 08:44:15.440341 17723 solver.cpp:218] Iteration 2292 (2.22028 iter/s, 5.40473s/12 iters), loss = 2.9273
I0407 08:44:15.440450 17723 solver.cpp:237]     Train net output #0: loss = 2.9273 (* 1 = 2.9273 loss)
I0407 08:44:15.440459 17723 sgd_solver.cpp:105] Iteration 2292, lr = 0.01
I0407 08:44:20.527715 17723 solver.cpp:218] Iteration 2304 (2.35886 iter/s, 5.08721s/12 iters), loss = 2.88004
I0407 08:44:20.527765 17723 solver.cpp:237]     Train net output #0: loss = 2.88004 (* 1 = 2.88004 loss)
I0407 08:44:20.527774 17723 sgd_solver.cpp:105] Iteration 2304, lr = 0.01
I0407 08:44:25.771044 17723 solver.cpp:218] Iteration 2316 (2.28867 iter/s, 5.24322s/12 iters), loss = 2.61382
I0407 08:44:25.771092 17723 solver.cpp:237]     Train net output #0: loss = 2.61382 (* 1 = 2.61382 loss)
I0407 08:44:25.771100 17723 sgd_solver.cpp:105] Iteration 2316, lr = 0.01
I0407 08:44:29.880079 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:44:30.991437 17723 solver.cpp:218] Iteration 2328 (2.29872 iter/s, 5.22029s/12 iters), loss = 2.6629
I0407 08:44:30.991480 17723 solver.cpp:237]     Train net output #0: loss = 2.6629 (* 1 = 2.6629 loss)
I0407 08:44:30.991488 17723 sgd_solver.cpp:105] Iteration 2328, lr = 0.01
I0407 08:44:35.827461 17723 solver.cpp:218] Iteration 2340 (2.48143 iter/s, 4.83592s/12 iters), loss = 2.55689
I0407 08:44:35.827507 17723 solver.cpp:237]     Train net output #0: loss = 2.55689 (* 1 = 2.55689 loss)
I0407 08:44:35.827515 17723 sgd_solver.cpp:105] Iteration 2340, lr = 0.01
I0407 08:44:37.967033 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0407 08:44:40.886046 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0407 08:44:43.243484 17723 solver.cpp:330] Iteration 2346, Testing net (#0)
I0407 08:44:43.243503 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:44:46.697021 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:44:47.662505 17723 solver.cpp:397]     Test net output #0: accuracy = 0.227328
I0407 08:44:47.662535 17723 solver.cpp:397]     Test net output #1: loss = 3.39603 (* 1 = 3.39603 loss)
I0407 08:44:49.455579 17723 solver.cpp:218] Iteration 2352 (0.880543 iter/s, 13.628s/12 iters), loss = 2.74007
I0407 08:44:49.455619 17723 solver.cpp:237]     Train net output #0: loss = 2.74007 (* 1 = 2.74007 loss)
I0407 08:44:49.455626 17723 sgd_solver.cpp:105] Iteration 2352, lr = 0.01
I0407 08:44:54.700973 17723 solver.cpp:218] Iteration 2364 (2.28776 iter/s, 5.2453s/12 iters), loss = 2.69422
I0407 08:44:54.701017 17723 solver.cpp:237]     Train net output #0: loss = 2.69422 (* 1 = 2.69422 loss)
I0407 08:44:54.701025 17723 sgd_solver.cpp:105] Iteration 2364, lr = 0.01
I0407 08:44:59.749199 17723 solver.cpp:218] Iteration 2376 (2.37712 iter/s, 5.04812s/12 iters), loss = 2.65707
I0407 08:44:59.749346 17723 solver.cpp:237]     Train net output #0: loss = 2.65707 (* 1 = 2.65707 loss)
I0407 08:44:59.749357 17723 sgd_solver.cpp:105] Iteration 2376, lr = 0.01
I0407 08:45:04.790129 17723 solver.cpp:218] Iteration 2388 (2.38061 iter/s, 5.04072s/12 iters), loss = 2.53539
I0407 08:45:04.790205 17723 solver.cpp:237]     Train net output #0: loss = 2.53539 (* 1 = 2.53539 loss)
I0407 08:45:04.790225 17723 sgd_solver.cpp:105] Iteration 2388, lr = 0.01
I0407 08:45:09.952536 17723 solver.cpp:218] Iteration 2400 (2.32455 iter/s, 5.16228s/12 iters), loss = 2.55545
I0407 08:45:09.952600 17723 solver.cpp:237]     Train net output #0: loss = 2.55545 (* 1 = 2.55545 loss)
I0407 08:45:09.952613 17723 sgd_solver.cpp:105] Iteration 2400, lr = 0.01
I0407 08:45:15.083227 17723 solver.cpp:218] Iteration 2412 (2.33892 iter/s, 5.13058s/12 iters), loss = 2.49658
I0407 08:45:15.083285 17723 solver.cpp:237]     Train net output #0: loss = 2.49658 (* 1 = 2.49658 loss)
I0407 08:45:15.083299 17723 sgd_solver.cpp:105] Iteration 2412, lr = 0.01
I0407 08:45:20.301991 17723 solver.cpp:218] Iteration 2424 (2.29944 iter/s, 5.21865s/12 iters), loss = 2.62531
I0407 08:45:20.302086 17723 solver.cpp:237]     Train net output #0: loss = 2.62531 (* 1 = 2.62531 loss)
I0407 08:45:20.302093 17723 sgd_solver.cpp:105] Iteration 2424, lr = 0.01
I0407 08:45:21.420270 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:45:25.716990 17723 solver.cpp:218] Iteration 2436 (2.21613 iter/s, 5.41485s/12 iters), loss = 2.66165
I0407 08:45:25.717031 17723 solver.cpp:237]     Train net output #0: loss = 2.66165 (* 1 = 2.66165 loss)
I0407 08:45:25.717039 17723 sgd_solver.cpp:105] Iteration 2436, lr = 0.01
I0407 08:45:30.322365 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0407 08:45:33.335548 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0407 08:45:35.643463 17723 solver.cpp:330] Iteration 2448, Testing net (#0)
I0407 08:45:35.643481 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:45:38.939074 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:45:39.895392 17723 solver.cpp:397]     Test net output #0: accuracy = 0.242647
I0407 08:45:39.895427 17723 solver.cpp:397]     Test net output #1: loss = 3.31635 (* 1 = 3.31635 loss)
I0407 08:45:40.035799 17723 solver.cpp:218] Iteration 2448 (0.838068 iter/s, 14.3186s/12 iters), loss = 2.53408
I0407 08:45:40.035845 17723 solver.cpp:237]     Train net output #0: loss = 2.53408 (* 1 = 2.53408 loss)
I0407 08:45:40.035852 17723 sgd_solver.cpp:105] Iteration 2448, lr = 0.01
I0407 08:45:44.404685 17723 solver.cpp:218] Iteration 2460 (2.74675 iter/s, 4.36879s/12 iters), loss = 2.54613
I0407 08:45:44.404734 17723 solver.cpp:237]     Train net output #0: loss = 2.54613 (* 1 = 2.54613 loss)
I0407 08:45:44.404743 17723 sgd_solver.cpp:105] Iteration 2460, lr = 0.01
I0407 08:45:49.596719 17723 solver.cpp:218] Iteration 2472 (2.31128 iter/s, 5.19193s/12 iters), loss = 2.73099
I0407 08:45:49.596757 17723 solver.cpp:237]     Train net output #0: loss = 2.73099 (* 1 = 2.73099 loss)
I0407 08:45:49.596765 17723 sgd_solver.cpp:105] Iteration 2472, lr = 0.01
I0407 08:45:54.938609 17723 solver.cpp:218] Iteration 2484 (2.24644 iter/s, 5.34179s/12 iters), loss = 2.46041
I0407 08:45:54.938792 17723 solver.cpp:237]     Train net output #0: loss = 2.46041 (* 1 = 2.46041 loss)
I0407 08:45:54.938803 17723 sgd_solver.cpp:105] Iteration 2484, lr = 0.01
I0407 08:46:00.263716 17723 solver.cpp:218] Iteration 2496 (2.25358 iter/s, 5.32487s/12 iters), loss = 2.39929
I0407 08:46:00.263777 17723 solver.cpp:237]     Train net output #0: loss = 2.39929 (* 1 = 2.39929 loss)
I0407 08:46:00.263787 17723 sgd_solver.cpp:105] Iteration 2496, lr = 0.01
I0407 08:46:05.556413 17723 solver.cpp:218] Iteration 2508 (2.26732 iter/s, 5.29258s/12 iters), loss = 2.63649
I0407 08:46:05.556457 17723 solver.cpp:237]     Train net output #0: loss = 2.63649 (* 1 = 2.63649 loss)
I0407 08:46:05.556464 17723 sgd_solver.cpp:105] Iteration 2508, lr = 0.01
I0407 08:46:10.933012 17723 solver.cpp:218] Iteration 2520 (2.23194 iter/s, 5.3765s/12 iters), loss = 2.51723
I0407 08:46:10.933053 17723 solver.cpp:237]     Train net output #0: loss = 2.51723 (* 1 = 2.51723 loss)
I0407 08:46:10.933061 17723 sgd_solver.cpp:105] Iteration 2520, lr = 0.01
I0407 08:46:14.223596 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:46:16.156535 17723 solver.cpp:218] Iteration 2532 (2.29734 iter/s, 5.22342s/12 iters), loss = 2.30553
I0407 08:46:16.156577 17723 solver.cpp:237]     Train net output #0: loss = 2.30553 (* 1 = 2.30553 loss)
I0407 08:46:16.156584 17723 sgd_solver.cpp:105] Iteration 2532, lr = 0.01
I0407 08:46:21.534629 17723 solver.cpp:218] Iteration 2544 (2.23131 iter/s, 5.378s/12 iters), loss = 2.01408
I0407 08:46:21.534670 17723 solver.cpp:237]     Train net output #0: loss = 2.01408 (* 1 = 2.01408 loss)
I0407 08:46:21.534677 17723 sgd_solver.cpp:105] Iteration 2544, lr = 0.01
I0407 08:46:23.718298 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0407 08:46:26.798151 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0407 08:46:29.129740 17723 solver.cpp:330] Iteration 2550, Testing net (#0)
I0407 08:46:29.129758 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:46:32.377396 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:46:33.380546 17723 solver.cpp:397]     Test net output #0: accuracy = 0.26348
I0407 08:46:33.380580 17723 solver.cpp:397]     Test net output #1: loss = 3.20129 (* 1 = 3.20129 loss)
I0407 08:46:35.304352 17723 solver.cpp:218] Iteration 2556 (0.871487 iter/s, 13.7696s/12 iters), loss = 2.52596
I0407 08:46:35.304389 17723 solver.cpp:237]     Train net output #0: loss = 2.52596 (* 1 = 2.52596 loss)
I0407 08:46:35.304396 17723 sgd_solver.cpp:105] Iteration 2556, lr = 0.01
I0407 08:46:40.551889 17723 solver.cpp:218] Iteration 2568 (2.28683 iter/s, 5.24744s/12 iters), loss = 1.76083
I0407 08:46:40.551931 17723 solver.cpp:237]     Train net output #0: loss = 1.76083 (* 1 = 1.76083 loss)
I0407 08:46:40.551939 17723 sgd_solver.cpp:105] Iteration 2568, lr = 0.01
I0407 08:46:45.735189 17723 solver.cpp:218] Iteration 2580 (2.31517 iter/s, 5.1832s/12 iters), loss = 2.49147
I0407 08:46:45.735231 17723 solver.cpp:237]     Train net output #0: loss = 2.49147 (* 1 = 2.49147 loss)
I0407 08:46:45.735239 17723 sgd_solver.cpp:105] Iteration 2580, lr = 0.01
I0407 08:46:50.951592 17723 solver.cpp:218] Iteration 2592 (2.30048 iter/s, 5.2163s/12 iters), loss = 2.53345
I0407 08:46:50.951630 17723 solver.cpp:237]     Train net output #0: loss = 2.53345 (* 1 = 2.53345 loss)
I0407 08:46:50.951637 17723 sgd_solver.cpp:105] Iteration 2592, lr = 0.01
I0407 08:46:56.339838 17723 solver.cpp:218] Iteration 2604 (2.22711 iter/s, 5.38815s/12 iters), loss = 2.53884
I0407 08:46:56.339882 17723 solver.cpp:237]     Train net output #0: loss = 2.53884 (* 1 = 2.53884 loss)
I0407 08:46:56.339890 17723 sgd_solver.cpp:105] Iteration 2604, lr = 0.01
I0407 08:47:01.706575 17723 solver.cpp:218] Iteration 2616 (2.23604 iter/s, 5.36664s/12 iters), loss = 2.38202
I0407 08:47:01.706682 17723 solver.cpp:237]     Train net output #0: loss = 2.38202 (* 1 = 2.38202 loss)
I0407 08:47:01.706691 17723 sgd_solver.cpp:105] Iteration 2616, lr = 0.01
I0407 08:47:06.626886 17723 solver.cpp:218] Iteration 2628 (2.43895 iter/s, 4.92015s/12 iters), loss = 2.3422
I0407 08:47:06.626935 17723 solver.cpp:237]     Train net output #0: loss = 2.3422 (* 1 = 2.3422 loss)
I0407 08:47:06.626943 17723 sgd_solver.cpp:105] Iteration 2628, lr = 0.01
I0407 08:47:07.095592 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:47:11.960079 17723 solver.cpp:218] Iteration 2640 (2.2501 iter/s, 5.33309s/12 iters), loss = 2.43372
I0407 08:47:11.960129 17723 solver.cpp:237]     Train net output #0: loss = 2.43372 (* 1 = 2.43372 loss)
I0407 08:47:11.960139 17723 sgd_solver.cpp:105] Iteration 2640, lr = 0.01
I0407 08:47:16.884140 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0407 08:47:19.967607 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0407 08:47:22.289803 17723 solver.cpp:330] Iteration 2652, Testing net (#0)
I0407 08:47:22.289821 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:47:25.580070 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:47:26.616115 17723 solver.cpp:397]     Test net output #0: accuracy = 0.256127
I0407 08:47:26.616148 17723 solver.cpp:397]     Test net output #1: loss = 3.20406 (* 1 = 3.20406 loss)
I0407 08:47:26.756300 17723 solver.cpp:218] Iteration 2652 (0.811027 iter/s, 14.796s/12 iters), loss = 2.40819
I0407 08:47:26.756358 17723 solver.cpp:237]     Train net output #0: loss = 2.40819 (* 1 = 2.40819 loss)
I0407 08:47:26.756366 17723 sgd_solver.cpp:105] Iteration 2652, lr = 0.01
I0407 08:47:31.152113 17723 solver.cpp:218] Iteration 2664 (2.72994 iter/s, 4.39571s/12 iters), loss = 2.21096
I0407 08:47:31.152151 17723 solver.cpp:237]     Train net output #0: loss = 2.21096 (* 1 = 2.21096 loss)
I0407 08:47:31.152158 17723 sgd_solver.cpp:105] Iteration 2664, lr = 0.01
I0407 08:47:36.417021 17723 solver.cpp:218] Iteration 2676 (2.27929 iter/s, 5.26481s/12 iters), loss = 2.39169
I0407 08:47:36.417122 17723 solver.cpp:237]     Train net output #0: loss = 2.39169 (* 1 = 2.39169 loss)
I0407 08:47:36.417130 17723 sgd_solver.cpp:105] Iteration 2676, lr = 0.01
I0407 08:47:41.853688 17723 solver.cpp:218] Iteration 2688 (2.2073 iter/s, 5.43651s/12 iters), loss = 2.18234
I0407 08:47:41.853729 17723 solver.cpp:237]     Train net output #0: loss = 2.18234 (* 1 = 2.18234 loss)
I0407 08:47:41.853736 17723 sgd_solver.cpp:105] Iteration 2688, lr = 0.01
I0407 08:47:47.018292 17723 solver.cpp:218] Iteration 2700 (2.32355 iter/s, 5.16451s/12 iters), loss = 2.46621
I0407 08:47:47.018334 17723 solver.cpp:237]     Train net output #0: loss = 2.46621 (* 1 = 2.46621 loss)
I0407 08:47:47.018342 17723 sgd_solver.cpp:105] Iteration 2700, lr = 0.01
I0407 08:47:52.428982 17723 solver.cpp:218] Iteration 2712 (2.21787 iter/s, 5.41059s/12 iters), loss = 2.42398
I0407 08:47:52.429030 17723 solver.cpp:237]     Train net output #0: loss = 2.42398 (* 1 = 2.42398 loss)
I0407 08:47:52.429039 17723 sgd_solver.cpp:105] Iteration 2712, lr = 0.01
I0407 08:47:57.459549 17723 solver.cpp:218] Iteration 2724 (2.38547 iter/s, 5.03046s/12 iters), loss = 2.59093
I0407 08:47:57.459594 17723 solver.cpp:237]     Train net output #0: loss = 2.59093 (* 1 = 2.59093 loss)
I0407 08:47:57.459600 17723 sgd_solver.cpp:105] Iteration 2724, lr = 0.01
I0407 08:48:00.106549 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:48:02.504294 17723 solver.cpp:218] Iteration 2736 (2.37876 iter/s, 5.04465s/12 iters), loss = 2.28092
I0407 08:48:02.504340 17723 solver.cpp:237]     Train net output #0: loss = 2.28092 (* 1 = 2.28092 loss)
I0407 08:48:02.504348 17723 sgd_solver.cpp:105] Iteration 2736, lr = 0.01
I0407 08:48:07.737766 17723 solver.cpp:218] Iteration 2748 (2.29298 iter/s, 5.23337s/12 iters), loss = 2.19925
I0407 08:48:07.737895 17723 solver.cpp:237]     Train net output #0: loss = 2.19925 (* 1 = 2.19925 loss)
I0407 08:48:07.737902 17723 sgd_solver.cpp:105] Iteration 2748, lr = 0.01
I0407 08:48:09.939769 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0407 08:48:13.648996 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0407 08:48:15.975252 17723 solver.cpp:330] Iteration 2754, Testing net (#0)
I0407 08:48:15.975273 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:48:18.990485 17723 blocking_queue.cpp:49] Waiting for data
I0407 08:48:19.220075 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:48:20.303061 17723 solver.cpp:397]     Test net output #0: accuracy = 0.270833
I0407 08:48:20.303094 17723 solver.cpp:397]     Test net output #1: loss = 3.24263 (* 1 = 3.24263 loss)
I0407 08:48:22.232303 17723 solver.cpp:218] Iteration 2760 (0.827913 iter/s, 14.4943s/12 iters), loss = 2.43205
I0407 08:48:22.232345 17723 solver.cpp:237]     Train net output #0: loss = 2.43205 (* 1 = 2.43205 loss)
I0407 08:48:22.232352 17723 sgd_solver.cpp:105] Iteration 2760, lr = 0.01
I0407 08:48:27.632391 17723 solver.cpp:218] Iteration 2772 (2.22223 iter/s, 5.39999s/12 iters), loss = 1.78944
I0407 08:48:27.632438 17723 solver.cpp:237]     Train net output #0: loss = 1.78944 (* 1 = 1.78944 loss)
I0407 08:48:27.632447 17723 sgd_solver.cpp:105] Iteration 2772, lr = 0.01
I0407 08:48:33.034736 17723 solver.cpp:218] Iteration 2784 (2.2213 iter/s, 5.40224s/12 iters), loss = 2.05821
I0407 08:48:33.034783 17723 solver.cpp:237]     Train net output #0: loss = 2.05821 (* 1 = 2.05821 loss)
I0407 08:48:33.034791 17723 sgd_solver.cpp:105] Iteration 2784, lr = 0.01
I0407 08:48:38.499573 17723 solver.cpp:218] Iteration 2796 (2.1959 iter/s, 5.46473s/12 iters), loss = 2.3307
I0407 08:48:38.499702 17723 solver.cpp:237]     Train net output #0: loss = 2.3307 (* 1 = 2.3307 loss)
I0407 08:48:38.499711 17723 sgd_solver.cpp:105] Iteration 2796, lr = 0.01
I0407 08:48:43.916522 17723 solver.cpp:218] Iteration 2808 (2.21535 iter/s, 5.41676s/12 iters), loss = 2.49742
I0407 08:48:43.916566 17723 solver.cpp:237]     Train net output #0: loss = 2.49742 (* 1 = 2.49742 loss)
I0407 08:48:43.916574 17723 sgd_solver.cpp:105] Iteration 2808, lr = 0.01
I0407 08:48:48.997057 17723 solver.cpp:218] Iteration 2820 (2.362 iter/s, 5.08044s/12 iters), loss = 2.14247
I0407 08:48:48.997098 17723 solver.cpp:237]     Train net output #0: loss = 2.14247 (* 1 = 2.14247 loss)
I0407 08:48:48.997105 17723 sgd_solver.cpp:105] Iteration 2820, lr = 0.01
I0407 08:48:53.918586 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:48:54.246842 17723 solver.cpp:218] Iteration 2832 (2.28585 iter/s, 5.24968s/12 iters), loss = 2.36784
I0407 08:48:54.246884 17723 solver.cpp:237]     Train net output #0: loss = 2.36784 (* 1 = 2.36784 loss)
I0407 08:48:54.246891 17723 sgd_solver.cpp:105] Iteration 2832, lr = 0.01
I0407 08:48:59.163362 17723 solver.cpp:218] Iteration 2844 (2.4408 iter/s, 4.91642s/12 iters), loss = 1.96114
I0407 08:48:59.163401 17723 solver.cpp:237]     Train net output #0: loss = 1.96114 (* 1 = 1.96114 loss)
I0407 08:48:59.163409 17723 sgd_solver.cpp:105] Iteration 2844, lr = 0.01
I0407 08:49:03.676504 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0407 08:49:08.777470 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0407 08:49:11.092075 17723 solver.cpp:330] Iteration 2856, Testing net (#0)
I0407 08:49:11.092097 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:49:14.278499 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:49:15.393504 17723 solver.cpp:397]     Test net output #0: accuracy = 0.27451
I0407 08:49:15.393543 17723 solver.cpp:397]     Test net output #1: loss = 3.20832 (* 1 = 3.20832 loss)
I0407 08:49:15.530748 17723 solver.cpp:218] Iteration 2856 (0.733173 iter/s, 16.3672s/12 iters), loss = 2.35189
I0407 08:49:15.530792 17723 solver.cpp:237]     Train net output #0: loss = 2.35189 (* 1 = 2.35189 loss)
I0407 08:49:15.530799 17723 sgd_solver.cpp:105] Iteration 2856, lr = 0.01
I0407 08:49:19.857192 17723 solver.cpp:218] Iteration 2868 (2.7737 iter/s, 4.32635s/12 iters), loss = 2.05106
I0407 08:49:19.857234 17723 solver.cpp:237]     Train net output #0: loss = 2.05106 (* 1 = 2.05106 loss)
I0407 08:49:19.857241 17723 sgd_solver.cpp:105] Iteration 2868, lr = 0.01
I0407 08:49:25.043743 17723 solver.cpp:218] Iteration 2880 (2.31372 iter/s, 5.18645s/12 iters), loss = 2.07798
I0407 08:49:25.043788 17723 solver.cpp:237]     Train net output #0: loss = 2.07798 (* 1 = 2.07798 loss)
I0407 08:49:25.043795 17723 sgd_solver.cpp:105] Iteration 2880, lr = 0.01
I0407 08:49:30.177901 17723 solver.cpp:218] Iteration 2892 (2.33733 iter/s, 5.13406s/12 iters), loss = 2.21142
I0407 08:49:30.177944 17723 solver.cpp:237]     Train net output #0: loss = 2.21142 (* 1 = 2.21142 loss)
I0407 08:49:30.177951 17723 sgd_solver.cpp:105] Iteration 2892, lr = 0.01
I0407 08:49:35.319973 17723 solver.cpp:218] Iteration 2904 (2.33373 iter/s, 5.14197s/12 iters), loss = 2.35173
I0407 08:49:35.320013 17723 solver.cpp:237]     Train net output #0: loss = 2.35173 (* 1 = 2.35173 loss)
I0407 08:49:35.320020 17723 sgd_solver.cpp:105] Iteration 2904, lr = 0.01
I0407 08:49:40.795408 17723 solver.cpp:218] Iteration 2916 (2.19165 iter/s, 5.47533s/12 iters), loss = 2.13816
I0407 08:49:40.795496 17723 solver.cpp:237]     Train net output #0: loss = 2.13816 (* 1 = 2.13816 loss)
I0407 08:49:40.795503 17723 sgd_solver.cpp:105] Iteration 2916, lr = 0.01
I0407 08:49:46.116490 17723 solver.cpp:218] Iteration 2928 (2.25524 iter/s, 5.32094s/12 iters), loss = 2.06245
I0407 08:49:46.116523 17723 solver.cpp:237]     Train net output #0: loss = 2.06245 (* 1 = 2.06245 loss)
I0407 08:49:46.116529 17723 sgd_solver.cpp:105] Iteration 2928, lr = 0.01
I0407 08:49:48.041193 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:49:51.283891 17723 solver.cpp:218] Iteration 2940 (2.32229 iter/s, 5.16732s/12 iters), loss = 2.16938
I0407 08:49:51.283931 17723 solver.cpp:237]     Train net output #0: loss = 2.16938 (* 1 = 2.16938 loss)
I0407 08:49:51.283938 17723 sgd_solver.cpp:105] Iteration 2940, lr = 0.01
I0407 08:49:56.520637 17723 solver.cpp:218] Iteration 2952 (2.29154 iter/s, 5.23664s/12 iters), loss = 2.05098
I0407 08:49:56.520689 17723 solver.cpp:237]     Train net output #0: loss = 2.05098 (* 1 = 2.05098 loss)
I0407 08:49:56.520697 17723 sgd_solver.cpp:105] Iteration 2952, lr = 0.01
I0407 08:49:58.573405 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0407 08:50:03.063293 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0407 08:50:05.719794 17723 solver.cpp:330] Iteration 2958, Testing net (#0)
I0407 08:50:05.719815 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:50:08.880159 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:50:10.115233 17723 solver.cpp:397]     Test net output #0: accuracy = 0.295956
I0407 08:50:10.115285 17723 solver.cpp:397]     Test net output #1: loss = 3.09858 (* 1 = 3.09858 loss)
I0407 08:50:12.043789 17723 solver.cpp:218] Iteration 2964 (0.773048 iter/s, 15.523s/12 iters), loss = 2.11513
I0407 08:50:12.043926 17723 solver.cpp:237]     Train net output #0: loss = 2.11513 (* 1 = 2.11513 loss)
I0407 08:50:12.043934 17723 sgd_solver.cpp:105] Iteration 2964, lr = 0.01
I0407 08:50:17.311394 17723 solver.cpp:218] Iteration 2976 (2.27816 iter/s, 5.26741s/12 iters), loss = 1.88694
I0407 08:50:17.311435 17723 solver.cpp:237]     Train net output #0: loss = 1.88694 (* 1 = 1.88694 loss)
I0407 08:50:17.311442 17723 sgd_solver.cpp:105] Iteration 2976, lr = 0.01
I0407 08:50:22.246088 17723 solver.cpp:218] Iteration 2988 (2.43181 iter/s, 4.9346s/12 iters), loss = 2.05557
I0407 08:50:22.246130 17723 solver.cpp:237]     Train net output #0: loss = 2.05557 (* 1 = 2.05557 loss)
I0407 08:50:22.246137 17723 sgd_solver.cpp:105] Iteration 2988, lr = 0.01
I0407 08:50:27.659750 17723 solver.cpp:218] Iteration 3000 (2.21665 iter/s, 5.41357s/12 iters), loss = 2.25738
I0407 08:50:27.659787 17723 solver.cpp:237]     Train net output #0: loss = 2.25738 (* 1 = 2.25738 loss)
I0407 08:50:27.659793 17723 sgd_solver.cpp:105] Iteration 3000, lr = 0.01
I0407 08:50:32.832635 17723 solver.cpp:218] Iteration 3012 (2.31983 iter/s, 5.17279s/12 iters), loss = 1.96245
I0407 08:50:32.832681 17723 solver.cpp:237]     Train net output #0: loss = 1.96245 (* 1 = 1.96245 loss)
I0407 08:50:32.832688 17723 sgd_solver.cpp:105] Iteration 3012, lr = 0.01
I0407 08:50:37.977900 17723 solver.cpp:218] Iteration 3024 (2.33229 iter/s, 5.14516s/12 iters), loss = 1.63942
I0407 08:50:37.977942 17723 solver.cpp:237]     Train net output #0: loss = 1.63942 (* 1 = 1.63942 loss)
I0407 08:50:37.977948 17723 sgd_solver.cpp:105] Iteration 3024, lr = 0.01
I0407 08:50:42.255326 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:50:43.410152 17723 solver.cpp:218] Iteration 3036 (2.20907 iter/s, 5.43215s/12 iters), loss = 1.98166
I0407 08:50:43.410193 17723 solver.cpp:237]     Train net output #0: loss = 1.98166 (* 1 = 1.98166 loss)
I0407 08:50:43.410200 17723 sgd_solver.cpp:105] Iteration 3036, lr = 0.01
I0407 08:50:48.941234 17723 solver.cpp:218] Iteration 3048 (2.1696 iter/s, 5.53098s/12 iters), loss = 1.93518
I0407 08:50:48.941273 17723 solver.cpp:237]     Train net output #0: loss = 1.93518 (* 1 = 1.93518 loss)
I0407 08:50:48.941280 17723 sgd_solver.cpp:105] Iteration 3048, lr = 0.01
I0407 08:50:53.782389 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0407 08:50:58.797488 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0407 08:51:01.140233 17723 solver.cpp:330] Iteration 3060, Testing net (#0)
I0407 08:51:01.140250 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:51:04.282519 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:51:05.477388 17723 solver.cpp:397]     Test net output #0: accuracy = 0.308824
I0407 08:51:05.477423 17723 solver.cpp:397]     Test net output #1: loss = 3.14344 (* 1 = 3.14344 loss)
I0407 08:51:05.612737 17723 solver.cpp:218] Iteration 3060 (0.719799 iter/s, 16.6713s/12 iters), loss = 2.14463
I0407 08:51:05.612792 17723 solver.cpp:237]     Train net output #0: loss = 2.14463 (* 1 = 2.14463 loss)
I0407 08:51:05.612802 17723 sgd_solver.cpp:105] Iteration 3060, lr = 0.01
I0407 08:51:10.038852 17723 solver.cpp:218] Iteration 3072 (2.71125 iter/s, 4.42601s/12 iters), loss = 2.44727
I0407 08:51:10.038890 17723 solver.cpp:237]     Train net output #0: loss = 2.44727 (* 1 = 2.44727 loss)
I0407 08:51:10.038897 17723 sgd_solver.cpp:105] Iteration 3072, lr = 0.01
I0407 08:51:15.344416 17723 solver.cpp:218] Iteration 3084 (2.26182 iter/s, 5.30547s/12 iters), loss = 1.9429
I0407 08:51:15.344532 17723 solver.cpp:237]     Train net output #0: loss = 1.9429 (* 1 = 1.9429 loss)
I0407 08:51:15.344540 17723 sgd_solver.cpp:105] Iteration 3084, lr = 0.01
I0407 08:51:20.622349 17723 solver.cpp:218] Iteration 3096 (2.27369 iter/s, 5.27776s/12 iters), loss = 1.69672
I0407 08:51:20.622396 17723 solver.cpp:237]     Train net output #0: loss = 1.69672 (* 1 = 1.69672 loss)
I0407 08:51:20.622403 17723 sgd_solver.cpp:105] Iteration 3096, lr = 0.01
I0407 08:51:25.834128 17723 solver.cpp:218] Iteration 3108 (2.30252 iter/s, 5.21168s/12 iters), loss = 1.72682
I0407 08:51:25.834165 17723 solver.cpp:237]     Train net output #0: loss = 1.72682 (* 1 = 1.72682 loss)
I0407 08:51:25.834172 17723 sgd_solver.cpp:105] Iteration 3108, lr = 0.01
I0407 08:51:31.112746 17723 solver.cpp:218] Iteration 3120 (2.27337 iter/s, 5.27851s/12 iters), loss = 1.69882
I0407 08:51:31.112808 17723 solver.cpp:237]     Train net output #0: loss = 1.69882 (* 1 = 1.69882 loss)
I0407 08:51:31.112823 17723 sgd_solver.cpp:105] Iteration 3120, lr = 0.01
I0407 08:51:36.085711 17723 solver.cpp:218] Iteration 3132 (2.4131 iter/s, 4.97285s/12 iters), loss = 1.8036
I0407 08:51:36.085755 17723 solver.cpp:237]     Train net output #0: loss = 1.8036 (* 1 = 1.8036 loss)
I0407 08:51:36.085762 17723 sgd_solver.cpp:105] Iteration 3132, lr = 0.01
I0407 08:51:37.234210 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:51:41.513224 17723 solver.cpp:218] Iteration 3144 (2.211 iter/s, 5.42741s/12 iters), loss = 2.08749
I0407 08:51:41.513264 17723 solver.cpp:237]     Train net output #0: loss = 2.08749 (* 1 = 2.08749 loss)
I0407 08:51:41.513271 17723 sgd_solver.cpp:105] Iteration 3144, lr = 0.01
I0407 08:51:46.910455 17723 solver.cpp:218] Iteration 3156 (2.2234 iter/s, 5.39713s/12 iters), loss = 1.93634
I0407 08:51:46.910568 17723 solver.cpp:237]     Train net output #0: loss = 1.93634 (* 1 = 1.93634 loss)
I0407 08:51:46.910576 17723 sgd_solver.cpp:105] Iteration 3156, lr = 0.01
I0407 08:51:49.089651 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0407 08:51:53.549649 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0407 08:51:55.858368 17723 solver.cpp:330] Iteration 3162, Testing net (#0)
I0407 08:51:55.858392 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:51:59.003214 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:52:00.250497 17723 solver.cpp:397]     Test net output #0: accuracy = 0.281863
I0407 08:52:00.250532 17723 solver.cpp:397]     Test net output #1: loss = 3.1962 (* 1 = 3.1962 loss)
I0407 08:52:02.196944 17723 solver.cpp:218] Iteration 3168 (0.785019 iter/s, 15.2862s/12 iters), loss = 1.9491
I0407 08:52:02.196990 17723 solver.cpp:237]     Train net output #0: loss = 1.9491 (* 1 = 1.9491 loss)
I0407 08:52:02.196996 17723 sgd_solver.cpp:105] Iteration 3168, lr = 0.01
I0407 08:52:07.384296 17723 solver.cpp:218] Iteration 3180 (2.31336 iter/s, 5.18725s/12 iters), loss = 2.10238
I0407 08:52:07.384336 17723 solver.cpp:237]     Train net output #0: loss = 2.10238 (* 1 = 2.10238 loss)
I0407 08:52:07.384342 17723 sgd_solver.cpp:105] Iteration 3180, lr = 0.01
I0407 08:52:12.526463 17723 solver.cpp:218] Iteration 3192 (2.33369 iter/s, 5.14207s/12 iters), loss = 1.62716
I0407 08:52:12.526505 17723 solver.cpp:237]     Train net output #0: loss = 1.62716 (* 1 = 1.62716 loss)
I0407 08:52:12.526512 17723 sgd_solver.cpp:105] Iteration 3192, lr = 0.01
I0407 08:52:17.991436 17723 solver.cpp:218] Iteration 3204 (2.19584 iter/s, 5.46487s/12 iters), loss = 1.74481
I0407 08:52:17.991556 17723 solver.cpp:237]     Train net output #0: loss = 1.74481 (* 1 = 1.74481 loss)
I0407 08:52:17.991564 17723 sgd_solver.cpp:105] Iteration 3204, lr = 0.01
I0407 08:52:23.464660 17723 solver.cpp:218] Iteration 3216 (2.19256 iter/s, 5.47305s/12 iters), loss = 2.11728
I0407 08:52:23.464700 17723 solver.cpp:237]     Train net output #0: loss = 2.11728 (* 1 = 2.11728 loss)
I0407 08:52:23.464707 17723 sgd_solver.cpp:105] Iteration 3216, lr = 0.01
I0407 08:52:28.775588 17723 solver.cpp:218] Iteration 3228 (2.25953 iter/s, 5.31083s/12 iters), loss = 1.50918
I0407 08:52:28.775632 17723 solver.cpp:237]     Train net output #0: loss = 1.50918 (* 1 = 1.50918 loss)
I0407 08:52:28.775640 17723 sgd_solver.cpp:105] Iteration 3228, lr = 0.01
I0407 08:52:31.958761 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:52:33.952282 17723 solver.cpp:218] Iteration 3240 (2.31813 iter/s, 5.17659s/12 iters), loss = 1.56622
I0407 08:52:33.952324 17723 solver.cpp:237]     Train net output #0: loss = 1.56622 (* 1 = 1.56622 loss)
I0407 08:52:33.952330 17723 sgd_solver.cpp:105] Iteration 3240, lr = 0.01
I0407 08:52:39.099306 17723 solver.cpp:218] Iteration 3252 (2.33149 iter/s, 5.14692s/12 iters), loss = 1.53779
I0407 08:52:39.099350 17723 solver.cpp:237]     Train net output #0: loss = 1.53779 (* 1 = 1.53779 loss)
I0407 08:52:39.099357 17723 sgd_solver.cpp:105] Iteration 3252, lr = 0.01
I0407 08:52:43.860399 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0407 08:52:46.931398 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0407 08:52:49.252321 17723 solver.cpp:330] Iteration 3264, Testing net (#0)
I0407 08:52:49.252449 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:52:52.307664 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:52:53.627099 17723 solver.cpp:397]     Test net output #0: accuracy = 0.295956
I0407 08:52:53.627125 17723 solver.cpp:397]     Test net output #1: loss = 3.17027 (* 1 = 3.17027 loss)
I0407 08:52:53.767813 17723 solver.cpp:218] Iteration 3264 (0.818089 iter/s, 14.6683s/12 iters), loss = 1.79241
I0407 08:52:53.767859 17723 solver.cpp:237]     Train net output #0: loss = 1.79241 (* 1 = 1.79241 loss)
I0407 08:52:53.767866 17723 sgd_solver.cpp:105] Iteration 3264, lr = 0.01
I0407 08:52:58.235622 17723 solver.cpp:218] Iteration 3276 (2.68594 iter/s, 4.46772s/12 iters), loss = 1.87342
I0407 08:52:58.235663 17723 solver.cpp:237]     Train net output #0: loss = 1.87342 (* 1 = 1.87342 loss)
I0407 08:52:58.235671 17723 sgd_solver.cpp:105] Iteration 3276, lr = 0.01
I0407 08:53:03.697962 17723 solver.cpp:218] Iteration 3288 (2.1969 iter/s, 5.46224s/12 iters), loss = 1.59953
I0407 08:53:03.698007 17723 solver.cpp:237]     Train net output #0: loss = 1.59953 (* 1 = 1.59953 loss)
I0407 08:53:03.698015 17723 sgd_solver.cpp:105] Iteration 3288, lr = 0.01
I0407 08:53:08.855751 17723 solver.cpp:218] Iteration 3300 (2.32662 iter/s, 5.15769s/12 iters), loss = 1.81293
I0407 08:53:08.855799 17723 solver.cpp:237]     Train net output #0: loss = 1.81293 (* 1 = 1.81293 loss)
I0407 08:53:08.855808 17723 sgd_solver.cpp:105] Iteration 3300, lr = 0.01
I0407 08:53:14.285604 17723 solver.cpp:218] Iteration 3312 (2.21005 iter/s, 5.42974s/12 iters), loss = 2.05625
I0407 08:53:14.285650 17723 solver.cpp:237]     Train net output #0: loss = 2.05625 (* 1 = 2.05625 loss)
I0407 08:53:14.285656 17723 sgd_solver.cpp:105] Iteration 3312, lr = 0.01
I0407 08:53:19.300022 17723 solver.cpp:218] Iteration 3324 (2.39315 iter/s, 5.01432s/12 iters), loss = 1.85163
I0407 08:53:19.300158 17723 solver.cpp:237]     Train net output #0: loss = 1.85163 (* 1 = 1.85163 loss)
I0407 08:53:19.300170 17723 sgd_solver.cpp:105] Iteration 3324, lr = 0.01
I0407 08:53:24.560346 17723 solver.cpp:218] Iteration 3336 (2.28131 iter/s, 5.26014s/12 iters), loss = 1.67871
I0407 08:53:24.560386 17723 solver.cpp:237]     Train net output #0: loss = 1.67871 (* 1 = 1.67871 loss)
I0407 08:53:24.560393 17723 sgd_solver.cpp:105] Iteration 3336, lr = 0.01
I0407 08:53:25.059458 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:53:29.980406 17723 solver.cpp:218] Iteration 3348 (2.21404 iter/s, 5.41996s/12 iters), loss = 2.02885
I0407 08:53:29.980445 17723 solver.cpp:237]     Train net output #0: loss = 2.02885 (* 1 = 2.02885 loss)
I0407 08:53:29.980453 17723 sgd_solver.cpp:105] Iteration 3348, lr = 0.01
I0407 08:53:35.315764 17723 solver.cpp:218] Iteration 3360 (2.24919 iter/s, 5.33526s/12 iters), loss = 1.6875
I0407 08:53:35.315809 17723 solver.cpp:237]     Train net output #0: loss = 1.6875 (* 1 = 1.6875 loss)
I0407 08:53:35.315817 17723 sgd_solver.cpp:105] Iteration 3360, lr = 0.01
I0407 08:53:37.588084 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0407 08:53:40.499994 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0407 08:53:42.803690 17723 solver.cpp:330] Iteration 3366, Testing net (#0)
I0407 08:53:42.803710 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:53:45.768719 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:53:47.075016 17723 solver.cpp:397]     Test net output #0: accuracy = 0.316789
I0407 08:53:47.075047 17723 solver.cpp:397]     Test net output #1: loss = 2.99511 (* 1 = 2.99511 loss)
I0407 08:53:49.060619 17723 solver.cpp:218] Iteration 3372 (0.873065 iter/s, 13.7447s/12 iters), loss = 1.85879
I0407 08:53:49.060664 17723 solver.cpp:237]     Train net output #0: loss = 1.85879 (* 1 = 1.85879 loss)
I0407 08:53:49.060672 17723 sgd_solver.cpp:105] Iteration 3372, lr = 0.005
I0407 08:53:54.143848 17723 solver.cpp:218] Iteration 3384 (2.36075 iter/s, 5.08313s/12 iters), loss = 1.69724
I0407 08:53:54.144001 17723 solver.cpp:237]     Train net output #0: loss = 1.69724 (* 1 = 1.69724 loss)
I0407 08:53:54.144011 17723 sgd_solver.cpp:105] Iteration 3384, lr = 0.005
I0407 08:53:58.910564 17723 solver.cpp:218] Iteration 3396 (2.51756 iter/s, 4.76652s/12 iters), loss = 1.7142
I0407 08:53:58.910609 17723 solver.cpp:237]     Train net output #0: loss = 1.7142 (* 1 = 1.7142 loss)
I0407 08:53:58.910616 17723 sgd_solver.cpp:105] Iteration 3396, lr = 0.005
I0407 08:54:04.010007 17723 solver.cpp:218] Iteration 3408 (2.35324 iter/s, 5.09935s/12 iters), loss = 1.41298
I0407 08:54:04.010044 17723 solver.cpp:237]     Train net output #0: loss = 1.41298 (* 1 = 1.41298 loss)
I0407 08:54:04.010052 17723 sgd_solver.cpp:105] Iteration 3408, lr = 0.005
I0407 08:54:09.111063 17723 solver.cpp:218] Iteration 3420 (2.3525 iter/s, 5.10096s/12 iters), loss = 1.18171
I0407 08:54:09.111109 17723 solver.cpp:237]     Train net output #0: loss = 1.18171 (* 1 = 1.18171 loss)
I0407 08:54:09.111115 17723 sgd_solver.cpp:105] Iteration 3420, lr = 0.005
I0407 08:54:14.393471 17723 solver.cpp:218] Iteration 3432 (2.27173 iter/s, 5.28231s/12 iters), loss = 1.34535
I0407 08:54:14.393512 17723 solver.cpp:237]     Train net output #0: loss = 1.34535 (* 1 = 1.34535 loss)
I0407 08:54:14.393517 17723 sgd_solver.cpp:105] Iteration 3432, lr = 0.005
I0407 08:54:17.260345 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:54:19.863291 17723 solver.cpp:218] Iteration 3444 (2.1939 iter/s, 5.46972s/12 iters), loss = 1.3866
I0407 08:54:19.863341 17723 solver.cpp:237]     Train net output #0: loss = 1.3866 (* 1 = 1.3866 loss)
I0407 08:54:19.863350 17723 sgd_solver.cpp:105] Iteration 3444, lr = 0.005
I0407 08:54:25.317739 17723 solver.cpp:218] Iteration 3456 (2.20008 iter/s, 5.45434s/12 iters), loss = 1.40007
I0407 08:54:25.317832 17723 solver.cpp:237]     Train net output #0: loss = 1.40007 (* 1 = 1.40007 loss)
I0407 08:54:25.317842 17723 sgd_solver.cpp:105] Iteration 3456, lr = 0.005
I0407 08:54:30.016217 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0407 08:54:33.018193 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0407 08:54:35.317196 17723 solver.cpp:330] Iteration 3468, Testing net (#0)
I0407 08:54:35.317212 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:54:35.714310 17723 blocking_queue.cpp:49] Waiting for data
I0407 08:54:38.352761 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:54:39.708324 17723 solver.cpp:397]     Test net output #0: accuracy = 0.349265
I0407 08:54:39.708359 17723 solver.cpp:397]     Test net output #1: loss = 2.92638 (* 1 = 2.92638 loss)
I0407 08:54:39.847160 17723 solver.cpp:218] Iteration 3468 (0.825923 iter/s, 14.5292s/12 iters), loss = 1.51913
I0407 08:54:39.847208 17723 solver.cpp:237]     Train net output #0: loss = 1.51913 (* 1 = 1.51913 loss)
I0407 08:54:39.847213 17723 sgd_solver.cpp:105] Iteration 3468, lr = 0.005
I0407 08:54:44.057852 17723 solver.cpp:218] Iteration 3480 (2.84995 iter/s, 4.2106s/12 iters), loss = 1.34851
I0407 08:54:44.057894 17723 solver.cpp:237]     Train net output #0: loss = 1.34851 (* 1 = 1.34851 loss)
I0407 08:54:44.057901 17723 sgd_solver.cpp:105] Iteration 3480, lr = 0.005
I0407 08:54:49.309388 17723 solver.cpp:218] Iteration 3492 (2.28509 iter/s, 5.25144s/12 iters), loss = 1.40491
I0407 08:54:49.309430 17723 solver.cpp:237]     Train net output #0: loss = 1.40491 (* 1 = 1.40491 loss)
I0407 08:54:49.309437 17723 sgd_solver.cpp:105] Iteration 3492, lr = 0.005
I0407 08:54:54.422474 17723 solver.cpp:218] Iteration 3504 (2.34697 iter/s, 5.11298s/12 iters), loss = 1.42214
I0407 08:54:54.422516 17723 solver.cpp:237]     Train net output #0: loss = 1.42214 (* 1 = 1.42214 loss)
I0407 08:54:54.422523 17723 sgd_solver.cpp:105] Iteration 3504, lr = 0.005
I0407 08:54:59.789988 17723 solver.cpp:218] Iteration 3516 (2.23572 iter/s, 5.36741s/12 iters), loss = 1.1898
I0407 08:54:59.790118 17723 solver.cpp:237]     Train net output #0: loss = 1.1898 (* 1 = 1.1898 loss)
I0407 08:54:59.790127 17723 sgd_solver.cpp:105] Iteration 3516, lr = 0.005
I0407 08:55:05.055822 17723 solver.cpp:218] Iteration 3528 (2.27892 iter/s, 5.26565s/12 iters), loss = 1.34456
I0407 08:55:05.055866 17723 solver.cpp:237]     Train net output #0: loss = 1.34456 (* 1 = 1.34456 loss)
I0407 08:55:05.055872 17723 sgd_solver.cpp:105] Iteration 3528, lr = 0.005
I0407 08:55:09.859939 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:55:10.161401 17723 solver.cpp:218] Iteration 3540 (2.35042 iter/s, 5.10548s/12 iters), loss = 0.971675
I0407 08:55:10.161442 17723 solver.cpp:237]     Train net output #0: loss = 0.971675 (* 1 = 0.971675 loss)
I0407 08:55:10.161448 17723 sgd_solver.cpp:105] Iteration 3540, lr = 0.005
I0407 08:55:15.494345 17723 solver.cpp:218] Iteration 3552 (2.2502 iter/s, 5.33285s/12 iters), loss = 0.887825
I0407 08:55:15.494383 17723 solver.cpp:237]     Train net output #0: loss = 0.887825 (* 1 = 0.887825 loss)
I0407 08:55:15.494391 17723 sgd_solver.cpp:105] Iteration 3552, lr = 0.005
I0407 08:55:20.885918 17723 solver.cpp:218] Iteration 3564 (2.22574 iter/s, 5.39147s/12 iters), loss = 0.935728
I0407 08:55:20.885978 17723 solver.cpp:237]     Train net output #0: loss = 0.935728 (* 1 = 0.935728 loss)
I0407 08:55:20.885990 17723 sgd_solver.cpp:105] Iteration 3564, lr = 0.005
I0407 08:55:23.199793 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0407 08:55:26.203869 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0407 08:55:28.505167 17723 solver.cpp:330] Iteration 3570, Testing net (#0)
I0407 08:55:28.505187 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:55:31.426573 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:55:32.820865 17723 solver.cpp:397]     Test net output #0: accuracy = 0.387255
I0407 08:55:32.820914 17723 solver.cpp:397]     Test net output #1: loss = 2.7769 (* 1 = 2.7769 loss)
I0407 08:55:34.699203 17723 solver.cpp:218] Iteration 3576 (0.86874 iter/s, 13.8131s/12 iters), loss = 1.35843
I0407 08:55:34.699256 17723 solver.cpp:237]     Train net output #0: loss = 1.35843 (* 1 = 1.35843 loss)
I0407 08:55:34.699265 17723 sgd_solver.cpp:105] Iteration 3576, lr = 0.005
I0407 08:55:40.040297 17723 solver.cpp:218] Iteration 3588 (2.24678 iter/s, 5.34099s/12 iters), loss = 1.3115
I0407 08:55:40.040340 17723 solver.cpp:237]     Train net output #0: loss = 1.3115 (* 1 = 1.3115 loss)
I0407 08:55:40.040347 17723 sgd_solver.cpp:105] Iteration 3588, lr = 0.005
I0407 08:55:45.410285 17723 solver.cpp:218] Iteration 3600 (2.23468 iter/s, 5.36988s/12 iters), loss = 1.20391
I0407 08:55:45.410336 17723 solver.cpp:237]     Train net output #0: loss = 1.20391 (* 1 = 1.20391 loss)
I0407 08:55:45.410346 17723 sgd_solver.cpp:105] Iteration 3600, lr = 0.005
I0407 08:55:50.771323 17723 solver.cpp:218] Iteration 3612 (2.23842 iter/s, 5.36093s/12 iters), loss = 1.03232
I0407 08:55:50.771361 17723 solver.cpp:237]     Train net output #0: loss = 1.03232 (* 1 = 1.03232 loss)
I0407 08:55:50.771368 17723 sgd_solver.cpp:105] Iteration 3612, lr = 0.005
I0407 08:55:56.143491 17723 solver.cpp:218] Iteration 3624 (2.23378 iter/s, 5.37207s/12 iters), loss = 1.14169
I0407 08:55:56.143546 17723 solver.cpp:237]     Train net output #0: loss = 1.14169 (* 1 = 1.14169 loss)
I0407 08:55:56.143555 17723 sgd_solver.cpp:105] Iteration 3624, lr = 0.005
I0407 08:56:01.358481 17723 solver.cpp:218] Iteration 3636 (2.30111 iter/s, 5.21488s/12 iters), loss = 0.911847
I0407 08:56:01.358527 17723 solver.cpp:237]     Train net output #0: loss = 0.911847 (* 1 = 0.911847 loss)
I0407 08:56:01.358536 17723 sgd_solver.cpp:105] Iteration 3636, lr = 0.005
I0407 08:56:03.349681 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:56:06.816291 17723 solver.cpp:218] Iteration 3648 (2.19872 iter/s, 5.45771s/12 iters), loss = 1.05539
I0407 08:56:06.816336 17723 solver.cpp:237]     Train net output #0: loss = 1.05539 (* 1 = 1.05539 loss)
I0407 08:56:06.816344 17723 sgd_solver.cpp:105] Iteration 3648, lr = 0.005
I0407 08:56:11.881395 17723 solver.cpp:218] Iteration 3660 (2.3692 iter/s, 5.065s/12 iters), loss = 0.972908
I0407 08:56:11.881444 17723 solver.cpp:237]     Train net output #0: loss = 0.972908 (* 1 = 0.972908 loss)
I0407 08:56:11.881456 17723 sgd_solver.cpp:105] Iteration 3660, lr = 0.005
I0407 08:56:16.692517 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0407 08:56:19.717088 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0407 08:56:22.019757 17723 solver.cpp:330] Iteration 3672, Testing net (#0)
I0407 08:56:22.019774 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:56:24.871659 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:56:26.330968 17723 solver.cpp:397]     Test net output #0: accuracy = 0.38174
I0407 08:56:26.330996 17723 solver.cpp:397]     Test net output #1: loss = 2.84826 (* 1 = 2.84826 loss)
I0407 08:56:26.471168 17723 solver.cpp:218] Iteration 3672 (0.822504 iter/s, 14.5896s/12 iters), loss = 0.854567
I0407 08:56:26.471238 17723 solver.cpp:237]     Train net output #0: loss = 0.854567 (* 1 = 0.854567 loss)
I0407 08:56:26.471246 17723 sgd_solver.cpp:105] Iteration 3672, lr = 0.005
I0407 08:56:30.841235 17723 solver.cpp:218] Iteration 3684 (2.74602 iter/s, 4.36995s/12 iters), loss = 0.89752
I0407 08:56:30.841290 17723 solver.cpp:237]     Train net output #0: loss = 0.89752 (* 1 = 0.89752 loss)
I0407 08:56:30.841300 17723 sgd_solver.cpp:105] Iteration 3684, lr = 0.005
I0407 08:56:35.738524 17723 solver.cpp:218] Iteration 3696 (2.45039 iter/s, 4.89718s/12 iters), loss = 1.1281
I0407 08:56:35.738629 17723 solver.cpp:237]     Train net output #0: loss = 1.1281 (* 1 = 1.1281 loss)
I0407 08:56:35.738639 17723 sgd_solver.cpp:105] Iteration 3696, lr = 0.005
I0407 08:56:41.019501 17723 solver.cpp:218] Iteration 3708 (2.27238 iter/s, 5.28082s/12 iters), loss = 1.04691
I0407 08:56:41.019546 17723 solver.cpp:237]     Train net output #0: loss = 1.04691 (* 1 = 1.04691 loss)
I0407 08:56:41.019553 17723 sgd_solver.cpp:105] Iteration 3708, lr = 0.005
I0407 08:56:46.248126 17723 solver.cpp:218] Iteration 3720 (2.2951 iter/s, 5.22853s/12 iters), loss = 0.821994
I0407 08:56:46.248160 17723 solver.cpp:237]     Train net output #0: loss = 0.821994 (* 1 = 0.821994 loss)
I0407 08:56:46.248167 17723 sgd_solver.cpp:105] Iteration 3720, lr = 0.005
I0407 08:56:51.552613 17723 solver.cpp:218] Iteration 3732 (2.26228 iter/s, 5.30439s/12 iters), loss = 0.943572
I0407 08:56:51.552671 17723 solver.cpp:237]     Train net output #0: loss = 0.943572 (* 1 = 0.943572 loss)
I0407 08:56:51.552685 17723 sgd_solver.cpp:105] Iteration 3732, lr = 0.005
I0407 08:56:55.667117 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:56:56.750506 17723 solver.cpp:218] Iteration 3744 (2.30868 iter/s, 5.19778s/12 iters), loss = 1.03797
I0407 08:56:56.750543 17723 solver.cpp:237]     Train net output #0: loss = 1.03797 (* 1 = 1.03797 loss)
I0407 08:56:56.750550 17723 sgd_solver.cpp:105] Iteration 3744, lr = 0.005
I0407 08:57:02.217519 17723 solver.cpp:218] Iteration 3756 (2.19502 iter/s, 5.46692s/12 iters), loss = 0.828175
I0407 08:57:02.217559 17723 solver.cpp:237]     Train net output #0: loss = 0.828175 (* 1 = 0.828175 loss)
I0407 08:57:02.217566 17723 sgd_solver.cpp:105] Iteration 3756, lr = 0.005
I0407 08:57:07.597725 17723 solver.cpp:218] Iteration 3768 (2.23044 iter/s, 5.38011s/12 iters), loss = 0.989436
I0407 08:57:07.597852 17723 solver.cpp:237]     Train net output #0: loss = 0.989436 (* 1 = 0.989436 loss)
I0407 08:57:07.597860 17723 sgd_solver.cpp:105] Iteration 3768, lr = 0.005
I0407 08:57:09.635589 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0407 08:57:12.654944 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0407 08:57:14.963685 17723 solver.cpp:330] Iteration 3774, Testing net (#0)
I0407 08:57:14.963706 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:57:17.893985 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:57:19.472960 17723 solver.cpp:397]     Test net output #0: accuracy = 0.376838
I0407 08:57:19.473002 17723 solver.cpp:397]     Test net output #1: loss = 2.89434 (* 1 = 2.89434 loss)
I0407 08:57:21.266072 17723 solver.cpp:218] Iteration 3780 (0.877957 iter/s, 13.6681s/12 iters), loss = 0.938009
I0407 08:57:21.266120 17723 solver.cpp:237]     Train net output #0: loss = 0.938009 (* 1 = 0.938009 loss)
I0407 08:57:21.266127 17723 sgd_solver.cpp:105] Iteration 3780, lr = 0.005
I0407 08:57:26.328308 17723 solver.cpp:218] Iteration 3792 (2.37054 iter/s, 5.06213s/12 iters), loss = 0.845021
I0407 08:57:26.328351 17723 solver.cpp:237]     Train net output #0: loss = 0.845021 (* 1 = 0.845021 loss)
I0407 08:57:26.328357 17723 sgd_solver.cpp:105] Iteration 3792, lr = 0.005
I0407 08:57:31.787833 17723 solver.cpp:218] Iteration 3804 (2.19803 iter/s, 5.45943s/12 iters), loss = 1.03655
I0407 08:57:31.787873 17723 solver.cpp:237]     Train net output #0: loss = 1.03655 (* 1 = 1.03655 loss)
I0407 08:57:31.787880 17723 sgd_solver.cpp:105] Iteration 3804, lr = 0.005
I0407 08:57:36.940649 17723 solver.cpp:218] Iteration 3816 (2.32887 iter/s, 5.15272s/12 iters), loss = 0.954289
I0407 08:57:36.940687 17723 solver.cpp:237]     Train net output #0: loss = 0.954289 (* 1 = 0.954289 loss)
I0407 08:57:36.940694 17723 sgd_solver.cpp:105] Iteration 3816, lr = 0.005
I0407 08:57:42.251655 17723 solver.cpp:218] Iteration 3828 (2.2595 iter/s, 5.31091s/12 iters), loss = 0.84998
I0407 08:57:42.251786 17723 solver.cpp:237]     Train net output #0: loss = 0.84998 (* 1 = 0.84998 loss)
I0407 08:57:42.251796 17723 sgd_solver.cpp:105] Iteration 3828, lr = 0.005
I0407 08:57:47.653993 17723 solver.cpp:218] Iteration 3840 (2.22134 iter/s, 5.40215s/12 iters), loss = 0.696847
I0407 08:57:47.654042 17723 solver.cpp:237]     Train net output #0: loss = 0.696847 (* 1 = 0.696847 loss)
I0407 08:57:47.654048 17723 sgd_solver.cpp:105] Iteration 3840, lr = 0.005
I0407 08:57:48.753749 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:57:53.009335 17723 solver.cpp:218] Iteration 3852 (2.2408 iter/s, 5.35523s/12 iters), loss = 0.672191
I0407 08:57:53.009402 17723 solver.cpp:237]     Train net output #0: loss = 0.672191 (* 1 = 0.672191 loss)
I0407 08:57:53.009416 17723 sgd_solver.cpp:105] Iteration 3852, lr = 0.005
I0407 08:57:58.185480 17723 solver.cpp:218] Iteration 3864 (2.31838 iter/s, 5.17603s/12 iters), loss = 0.586028
I0407 08:57:58.185523 17723 solver.cpp:237]     Train net output #0: loss = 0.586028 (* 1 = 0.586028 loss)
I0407 08:57:58.185529 17723 sgd_solver.cpp:105] Iteration 3864, lr = 0.005
I0407 08:58:02.704483 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0407 08:58:05.712033 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0407 08:58:08.015827 17723 solver.cpp:330] Iteration 3876, Testing net (#0)
I0407 08:58:08.015849 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:58:10.765470 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:58:12.275358 17723 solver.cpp:397]     Test net output #0: accuracy = 0.39951
I0407 08:58:12.275501 17723 solver.cpp:397]     Test net output #1: loss = 2.83881 (* 1 = 2.83881 loss)
I0407 08:58:12.416256 17723 solver.cpp:218] Iteration 3876 (0.843253 iter/s, 14.2306s/12 iters), loss = 0.859276
I0407 08:58:12.416306 17723 solver.cpp:237]     Train net output #0: loss = 0.859276 (* 1 = 0.859276 loss)
I0407 08:58:12.416313 17723 sgd_solver.cpp:105] Iteration 3876, lr = 0.005
I0407 08:58:16.581315 17723 solver.cpp:218] Iteration 3888 (2.88118 iter/s, 4.16496s/12 iters), loss = 1.0127
I0407 08:58:16.581360 17723 solver.cpp:237]     Train net output #0: loss = 1.0127 (* 1 = 1.0127 loss)
I0407 08:58:16.581368 17723 sgd_solver.cpp:105] Iteration 3888, lr = 0.005
I0407 08:58:22.080935 17723 solver.cpp:218] Iteration 3900 (2.18201 iter/s, 5.49952s/12 iters), loss = 0.561502
I0407 08:58:22.080978 17723 solver.cpp:237]     Train net output #0: loss = 0.561502 (* 1 = 0.561502 loss)
I0407 08:58:22.080986 17723 sgd_solver.cpp:105] Iteration 3900, lr = 0.005
I0407 08:58:27.288714 17723 solver.cpp:218] Iteration 3912 (2.30429 iter/s, 5.20768s/12 iters), loss = 0.502317
I0407 08:58:27.288750 17723 solver.cpp:237]     Train net output #0: loss = 0.502317 (* 1 = 0.502317 loss)
I0407 08:58:27.288756 17723 sgd_solver.cpp:105] Iteration 3912, lr = 0.005
I0407 08:58:32.473048 17723 solver.cpp:218] Iteration 3924 (2.31471 iter/s, 5.18424s/12 iters), loss = 1.13069
I0407 08:58:32.473086 17723 solver.cpp:237]     Train net output #0: loss = 1.13069 (* 1 = 1.13069 loss)
I0407 08:58:32.473093 17723 sgd_solver.cpp:105] Iteration 3924, lr = 0.005
I0407 08:58:37.974931 17723 solver.cpp:218] Iteration 3936 (2.18111 iter/s, 5.50179s/12 iters), loss = 0.608661
I0407 08:58:37.974970 17723 solver.cpp:237]     Train net output #0: loss = 0.608661 (* 1 = 0.608661 loss)
I0407 08:58:37.974977 17723 sgd_solver.cpp:105] Iteration 3936, lr = 0.005
I0407 08:58:41.667208 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:58:43.473176 17723 solver.cpp:218] Iteration 3948 (2.18255 iter/s, 5.49815s/12 iters), loss = 0.617358
I0407 08:58:43.473237 17723 solver.cpp:237]     Train net output #0: loss = 0.617358 (* 1 = 0.617358 loss)
I0407 08:58:43.473244 17723 sgd_solver.cpp:105] Iteration 3948, lr = 0.005
I0407 08:58:48.798032 17723 solver.cpp:218] Iteration 3960 (2.25363 iter/s, 5.32474s/12 iters), loss = 0.725351
I0407 08:58:48.798091 17723 solver.cpp:237]     Train net output #0: loss = 0.725351 (* 1 = 0.725351 loss)
I0407 08:58:48.798101 17723 sgd_solver.cpp:105] Iteration 3960, lr = 0.005
I0407 08:58:54.120985 17723 solver.cpp:218] Iteration 3972 (2.25443 iter/s, 5.32284s/12 iters), loss = 0.790153
I0407 08:58:54.121021 17723 solver.cpp:237]     Train net output #0: loss = 0.790153 (* 1 = 0.790153 loss)
I0407 08:58:54.121029 17723 sgd_solver.cpp:105] Iteration 3972, lr = 0.005
I0407 08:58:56.284106 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0407 08:58:59.307210 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0407 08:59:01.619652 17723 solver.cpp:330] Iteration 3978, Testing net (#0)
I0407 08:59:01.619673 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:59:04.357542 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:59:05.921720 17723 solver.cpp:397]     Test net output #0: accuracy = 0.401961
I0407 08:59:05.921751 17723 solver.cpp:397]     Test net output #1: loss = 2.82833 (* 1 = 2.82833 loss)
I0407 08:59:07.830750 17723 solver.cpp:218] Iteration 3984 (0.875299 iter/s, 13.7096s/12 iters), loss = 0.542373
I0407 08:59:07.830797 17723 solver.cpp:237]     Train net output #0: loss = 0.542373 (* 1 = 0.542373 loss)
I0407 08:59:07.830806 17723 sgd_solver.cpp:105] Iteration 3984, lr = 0.005
I0407 08:59:13.244872 17723 solver.cpp:218] Iteration 3996 (2.21647 iter/s, 5.41402s/12 iters), loss = 0.628853
I0407 08:59:13.244920 17723 solver.cpp:237]     Train net output #0: loss = 0.628853 (* 1 = 0.628853 loss)
I0407 08:59:13.244927 17723 sgd_solver.cpp:105] Iteration 3996, lr = 0.005
I0407 08:59:18.657003 17723 solver.cpp:218] Iteration 4008 (2.21728 iter/s, 5.41203s/12 iters), loss = 0.539095
I0407 08:59:18.657137 17723 solver.cpp:237]     Train net output #0: loss = 0.539095 (* 1 = 0.539095 loss)
I0407 08:59:18.657145 17723 sgd_solver.cpp:105] Iteration 4008, lr = 0.005
I0407 08:59:24.041014 17723 solver.cpp:218] Iteration 4020 (2.2289 iter/s, 5.38382s/12 iters), loss = 0.800418
I0407 08:59:24.041055 17723 solver.cpp:237]     Train net output #0: loss = 0.800418 (* 1 = 0.800418 loss)
I0407 08:59:24.041064 17723 sgd_solver.cpp:105] Iteration 4020, lr = 0.005
I0407 08:59:29.439409 17723 solver.cpp:218] Iteration 4032 (2.22293 iter/s, 5.39829s/12 iters), loss = 0.748453
I0407 08:59:29.439477 17723 solver.cpp:237]     Train net output #0: loss = 0.748453 (* 1 = 0.748453 loss)
I0407 08:59:29.439492 17723 sgd_solver.cpp:105] Iteration 4032, lr = 0.005
I0407 08:59:34.723727 17723 solver.cpp:218] Iteration 4044 (2.27092 iter/s, 5.2842s/12 iters), loss = 0.693601
I0407 08:59:34.723773 17723 solver.cpp:237]     Train net output #0: loss = 0.693601 (* 1 = 0.693601 loss)
I0407 08:59:34.723779 17723 sgd_solver.cpp:105] Iteration 4044, lr = 0.005
I0407 08:59:35.268061 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 08:59:40.097071 17723 solver.cpp:218] Iteration 4056 (2.23329 iter/s, 5.37324s/12 iters), loss = 0.581602
I0407 08:59:40.097122 17723 solver.cpp:237]     Train net output #0: loss = 0.581602 (* 1 = 0.581602 loss)
I0407 08:59:40.097133 17723 sgd_solver.cpp:105] Iteration 4056, lr = 0.005
I0407 08:59:45.397861 17723 solver.cpp:218] Iteration 4068 (2.26386 iter/s, 5.30068s/12 iters), loss = 0.76993
I0407 08:59:45.397903 17723 solver.cpp:237]     Train net output #0: loss = 0.76993 (* 1 = 0.76993 loss)
I0407 08:59:45.397910 17723 sgd_solver.cpp:105] Iteration 4068, lr = 0.005
I0407 08:59:50.318418 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0407 08:59:53.343762 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0407 08:59:55.667770 17723 solver.cpp:330] Iteration 4080, Testing net (#0)
I0407 08:59:55.667793 17723 net.cpp:676] Ignoring source layer train-data
I0407 08:59:58.450455 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:00:00.033947 17723 solver.cpp:397]     Test net output #0: accuracy = 0.400735
I0407 09:00:00.033989 17723 solver.cpp:397]     Test net output #1: loss = 2.82125 (* 1 = 2.82125 loss)
I0407 09:00:00.171819 17723 solver.cpp:218] Iteration 4080 (0.812249 iter/s, 14.7738s/12 iters), loss = 0.886368
I0407 09:00:00.171886 17723 solver.cpp:237]     Train net output #0: loss = 0.886368 (* 1 = 0.886368 loss)
I0407 09:00:00.171897 17723 sgd_solver.cpp:105] Iteration 4080, lr = 0.005
I0407 09:00:04.345717 17723 solver.cpp:218] Iteration 4092 (2.87509 iter/s, 4.17379s/12 iters), loss = 1.15315
I0407 09:00:04.345762 17723 solver.cpp:237]     Train net output #0: loss = 1.15315 (* 1 = 1.15315 loss)
I0407 09:00:04.345773 17723 sgd_solver.cpp:105] Iteration 4092, lr = 0.005
I0407 09:00:09.568050 17723 solver.cpp:218] Iteration 4104 (2.29787 iter/s, 5.22223s/12 iters), loss = 0.71006
I0407 09:00:09.568090 17723 solver.cpp:237]     Train net output #0: loss = 0.71006 (* 1 = 0.71006 loss)
I0407 09:00:09.568100 17723 sgd_solver.cpp:105] Iteration 4104, lr = 0.005
I0407 09:00:14.889394 17723 solver.cpp:218] Iteration 4116 (2.25511 iter/s, 5.32125s/12 iters), loss = 0.752767
I0407 09:00:14.889431 17723 solver.cpp:237]     Train net output #0: loss = 0.752767 (* 1 = 0.752767 loss)
I0407 09:00:14.889437 17723 sgd_solver.cpp:105] Iteration 4116, lr = 0.005
I0407 09:00:20.279289 17723 solver.cpp:218] Iteration 4128 (2.22643 iter/s, 5.3898s/12 iters), loss = 0.802451
I0407 09:00:20.279330 17723 solver.cpp:237]     Train net output #0: loss = 0.802451 (* 1 = 0.802451 loss)
I0407 09:00:20.279338 17723 sgd_solver.cpp:105] Iteration 4128, lr = 0.005
I0407 09:00:25.541172 17723 solver.cpp:218] Iteration 4140 (2.28059 iter/s, 5.26179s/12 iters), loss = 0.758621
I0407 09:00:25.541311 17723 solver.cpp:237]     Train net output #0: loss = 0.758621 (* 1 = 0.758621 loss)
I0407 09:00:25.541319 17723 sgd_solver.cpp:105] Iteration 4140, lr = 0.005
I0407 09:00:28.425740 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:00:31.074333 17723 solver.cpp:218] Iteration 4152 (2.16882 iter/s, 5.53296s/12 iters), loss = 0.636793
I0407 09:00:31.074374 17723 solver.cpp:237]     Train net output #0: loss = 0.636793 (* 1 = 0.636793 loss)
I0407 09:00:31.074381 17723 sgd_solver.cpp:105] Iteration 4152, lr = 0.005
I0407 09:00:32.940007 17723 blocking_queue.cpp:49] Waiting for data
I0407 09:00:36.422461 17723 solver.cpp:218] Iteration 4164 (2.24382 iter/s, 5.34803s/12 iters), loss = 0.582164
I0407 09:00:36.422508 17723 solver.cpp:237]     Train net output #0: loss = 0.582164 (* 1 = 0.582164 loss)
I0407 09:00:36.422516 17723 sgd_solver.cpp:105] Iteration 4164, lr = 0.005
I0407 09:00:41.759888 17723 solver.cpp:218] Iteration 4176 (2.24832 iter/s, 5.33733s/12 iters), loss = 0.54265
I0407 09:00:41.759933 17723 solver.cpp:237]     Train net output #0: loss = 0.54265 (* 1 = 0.54265 loss)
I0407 09:00:41.759940 17723 sgd_solver.cpp:105] Iteration 4176, lr = 0.005
I0407 09:00:43.853152 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0407 09:00:47.896901 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0407 09:00:50.226761 17723 solver.cpp:330] Iteration 4182, Testing net (#0)
I0407 09:00:50.226781 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:00:52.909237 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:00:54.574533 17723 solver.cpp:397]     Test net output #0: accuracy = 0.386029
I0407 09:00:54.574573 17723 solver.cpp:397]     Test net output #1: loss = 2.98107 (* 1 = 2.98107 loss)
I0407 09:00:56.436221 17723 solver.cpp:218] Iteration 4188 (0.817653 iter/s, 14.6762s/12 iters), loss = 0.469448
I0407 09:00:56.436322 17723 solver.cpp:237]     Train net output #0: loss = 0.469448 (* 1 = 0.469448 loss)
I0407 09:00:56.436331 17723 sgd_solver.cpp:105] Iteration 4188, lr = 0.005
I0407 09:01:01.771788 17723 solver.cpp:218] Iteration 4200 (2.24912 iter/s, 5.33542s/12 iters), loss = 0.626096
I0407 09:01:01.771821 17723 solver.cpp:237]     Train net output #0: loss = 0.626096 (* 1 = 0.626096 loss)
I0407 09:01:01.771826 17723 sgd_solver.cpp:105] Iteration 4200, lr = 0.005
I0407 09:01:07.107246 17723 solver.cpp:218] Iteration 4212 (2.24914 iter/s, 5.33537s/12 iters), loss = 0.548248
I0407 09:01:07.107288 17723 solver.cpp:237]     Train net output #0: loss = 0.548248 (* 1 = 0.548248 loss)
I0407 09:01:07.107296 17723 sgd_solver.cpp:105] Iteration 4212, lr = 0.005
I0407 09:01:12.322357 17723 solver.cpp:218] Iteration 4224 (2.30105 iter/s, 5.21501s/12 iters), loss = 0.544488
I0407 09:01:12.322402 17723 solver.cpp:237]     Train net output #0: loss = 0.544488 (* 1 = 0.544488 loss)
I0407 09:01:12.322408 17723 sgd_solver.cpp:105] Iteration 4224, lr = 0.005
I0407 09:01:17.585748 17723 solver.cpp:218] Iteration 4236 (2.27994 iter/s, 5.26329s/12 iters), loss = 0.606547
I0407 09:01:17.585793 17723 solver.cpp:237]     Train net output #0: loss = 0.606547 (* 1 = 0.606547 loss)
I0407 09:01:17.585801 17723 sgd_solver.cpp:105] Iteration 4236, lr = 0.005
I0407 09:01:22.665607 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:01:22.941447 17723 solver.cpp:218] Iteration 4248 (2.24065 iter/s, 5.3556s/12 iters), loss = 0.610777
I0407 09:01:22.941494 17723 solver.cpp:237]     Train net output #0: loss = 0.610777 (* 1 = 0.610777 loss)
I0407 09:01:22.941504 17723 sgd_solver.cpp:105] Iteration 4248, lr = 0.005
I0407 09:01:28.310006 17723 solver.cpp:218] Iteration 4260 (2.23528 iter/s, 5.36845s/12 iters), loss = 0.651371
I0407 09:01:28.310103 17723 solver.cpp:237]     Train net output #0: loss = 0.651371 (* 1 = 0.651371 loss)
I0407 09:01:28.310112 17723 sgd_solver.cpp:105] Iteration 4260, lr = 0.005
I0407 09:01:33.577579 17723 solver.cpp:218] Iteration 4272 (2.27815 iter/s, 5.26742s/12 iters), loss = 0.667568
I0407 09:01:33.577626 17723 solver.cpp:237]     Train net output #0: loss = 0.667568 (* 1 = 0.667568 loss)
I0407 09:01:33.577633 17723 sgd_solver.cpp:105] Iteration 4272, lr = 0.005
I0407 09:01:38.416533 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0407 09:01:44.571655 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0407 09:01:46.892302 17723 solver.cpp:330] Iteration 4284, Testing net (#0)
I0407 09:01:46.892323 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:01:49.674036 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:01:51.333230 17723 solver.cpp:397]     Test net output #0: accuracy = 0.405024
I0407 09:01:51.333268 17723 solver.cpp:397]     Test net output #1: loss = 2.84768 (* 1 = 2.84768 loss)
I0407 09:01:51.474051 17723 solver.cpp:218] Iteration 4284 (0.670531 iter/s, 17.8963s/12 iters), loss = 0.621058
I0407 09:01:51.475641 17723 solver.cpp:237]     Train net output #0: loss = 0.621058 (* 1 = 0.621058 loss)
I0407 09:01:51.475656 17723 sgd_solver.cpp:105] Iteration 4284, lr = 0.005
I0407 09:01:55.821029 17723 solver.cpp:218] Iteration 4296 (2.76157 iter/s, 4.34535s/12 iters), loss = 0.704099
I0407 09:01:55.821092 17723 solver.cpp:237]     Train net output #0: loss = 0.704099 (* 1 = 0.704099 loss)
I0407 09:01:55.821102 17723 sgd_solver.cpp:105] Iteration 4296, lr = 0.005
I0407 09:02:01.061851 17723 solver.cpp:218] Iteration 4308 (2.28977 iter/s, 5.2407s/12 iters), loss = 0.62725
I0407 09:02:01.061995 17723 solver.cpp:237]     Train net output #0: loss = 0.62725 (* 1 = 0.62725 loss)
I0407 09:02:01.062006 17723 sgd_solver.cpp:105] Iteration 4308, lr = 0.005
I0407 09:02:06.158893 17723 solver.cpp:218] Iteration 4320 (2.3544 iter/s, 5.09685s/12 iters), loss = 0.573172
I0407 09:02:06.158936 17723 solver.cpp:237]     Train net output #0: loss = 0.573172 (* 1 = 0.573172 loss)
I0407 09:02:06.158943 17723 sgd_solver.cpp:105] Iteration 4320, lr = 0.005
I0407 09:02:11.262135 17723 solver.cpp:218] Iteration 4332 (2.35149 iter/s, 5.10314s/12 iters), loss = 0.5438
I0407 09:02:11.262177 17723 solver.cpp:237]     Train net output #0: loss = 0.5438 (* 1 = 0.5438 loss)
I0407 09:02:11.262184 17723 sgd_solver.cpp:105] Iteration 4332, lr = 0.005
I0407 09:02:16.673025 17723 solver.cpp:218] Iteration 4344 (2.21779 iter/s, 5.41079s/12 iters), loss = 0.721733
I0407 09:02:16.673069 17723 solver.cpp:237]     Train net output #0: loss = 0.721733 (* 1 = 0.721733 loss)
I0407 09:02:16.673075 17723 sgd_solver.cpp:105] Iteration 4344, lr = 0.005
I0407 09:02:18.725122 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:02:22.125326 17723 solver.cpp:218] Iteration 4356 (2.20095 iter/s, 5.4522s/12 iters), loss = 0.73053
I0407 09:02:22.125368 17723 solver.cpp:237]     Train net output #0: loss = 0.73053 (* 1 = 0.73053 loss)
I0407 09:02:22.125375 17723 sgd_solver.cpp:105] Iteration 4356, lr = 0.005
I0407 09:02:27.354346 17723 solver.cpp:218] Iteration 4368 (2.29493 iter/s, 5.22892s/12 iters), loss = 0.68324
I0407 09:02:27.354388 17723 solver.cpp:237]     Train net output #0: loss = 0.68324 (* 1 = 0.68324 loss)
I0407 09:02:27.354395 17723 sgd_solver.cpp:105] Iteration 4368, lr = 0.005
I0407 09:02:32.666821 17723 solver.cpp:218] Iteration 4380 (2.25888 iter/s, 5.31237s/12 iters), loss = 0.474877
I0407 09:02:32.666930 17723 solver.cpp:237]     Train net output #0: loss = 0.474877 (* 1 = 0.474877 loss)
I0407 09:02:32.666940 17723 sgd_solver.cpp:105] Iteration 4380, lr = 0.005
I0407 09:02:34.808161 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0407 09:02:40.586119 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0407 09:02:43.050159 17723 solver.cpp:330] Iteration 4386, Testing net (#0)
I0407 09:02:43.050180 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:02:45.684432 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:02:47.378419 17723 solver.cpp:397]     Test net output #0: accuracy = 0.395833
I0407 09:02:47.378458 17723 solver.cpp:397]     Test net output #1: loss = 2.95157 (* 1 = 2.95157 loss)
I0407 09:02:49.155966 17723 solver.cpp:218] Iteration 4392 (0.727762 iter/s, 16.4889s/12 iters), loss = 0.501522
I0407 09:02:49.156013 17723 solver.cpp:237]     Train net output #0: loss = 0.501522 (* 1 = 0.501522 loss)
I0407 09:02:49.156021 17723 sgd_solver.cpp:105] Iteration 4392, lr = 0.005
I0407 09:02:54.217521 17723 solver.cpp:218] Iteration 4404 (2.37086 iter/s, 5.06145s/12 iters), loss = 0.570615
I0407 09:02:54.217586 17723 solver.cpp:237]     Train net output #0: loss = 0.570615 (* 1 = 0.570615 loss)
I0407 09:02:54.217602 17723 sgd_solver.cpp:105] Iteration 4404, lr = 0.005
I0407 09:02:59.300402 17723 solver.cpp:218] Iteration 4416 (2.36092 iter/s, 5.08277s/12 iters), loss = 0.566656
I0407 09:02:59.300446 17723 solver.cpp:237]     Train net output #0: loss = 0.566656 (* 1 = 0.566656 loss)
I0407 09:02:59.300452 17723 sgd_solver.cpp:105] Iteration 4416, lr = 0.005
I0407 09:03:04.633680 17723 solver.cpp:218] Iteration 4428 (2.25006 iter/s, 5.33318s/12 iters), loss = 0.485665
I0407 09:03:04.633813 17723 solver.cpp:237]     Train net output #0: loss = 0.485665 (* 1 = 0.485665 loss)
I0407 09:03:04.633822 17723 sgd_solver.cpp:105] Iteration 4428, lr = 0.005
I0407 09:03:09.658104 17723 solver.cpp:218] Iteration 4440 (2.38842 iter/s, 5.02423s/12 iters), loss = 0.471708
I0407 09:03:09.658144 17723 solver.cpp:237]     Train net output #0: loss = 0.471708 (* 1 = 0.471708 loss)
I0407 09:03:09.658151 17723 sgd_solver.cpp:105] Iteration 4440, lr = 0.005
I0407 09:03:13.911016 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:03:14.903470 17723 solver.cpp:218] Iteration 4452 (2.28778 iter/s, 5.24527s/12 iters), loss = 0.418967
I0407 09:03:14.903517 17723 solver.cpp:237]     Train net output #0: loss = 0.418967 (* 1 = 0.418967 loss)
I0407 09:03:14.903524 17723 sgd_solver.cpp:105] Iteration 4452, lr = 0.005
I0407 09:03:20.161060 17723 solver.cpp:218] Iteration 4464 (2.28246 iter/s, 5.25748s/12 iters), loss = 0.585909
I0407 09:03:20.161110 17723 solver.cpp:237]     Train net output #0: loss = 0.585909 (* 1 = 0.585909 loss)
I0407 09:03:20.161118 17723 sgd_solver.cpp:105] Iteration 4464, lr = 0.005
I0407 09:03:25.359829 17723 solver.cpp:218] Iteration 4476 (2.30828 iter/s, 5.19867s/12 iters), loss = 0.398539
I0407 09:03:25.359866 17723 solver.cpp:237]     Train net output #0: loss = 0.398539 (* 1 = 0.398539 loss)
I0407 09:03:25.359874 17723 sgd_solver.cpp:105] Iteration 4476, lr = 0.005
I0407 09:03:29.932260 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0407 09:03:35.136895 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0407 09:03:38.855173 17723 solver.cpp:330] Iteration 4488, Testing net (#0)
I0407 09:03:38.855197 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:03:41.483177 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:03:43.227052 17723 solver.cpp:397]     Test net output #0: accuracy = 0.402574
I0407 09:03:43.227082 17723 solver.cpp:397]     Test net output #1: loss = 2.85229 (* 1 = 2.85229 loss)
I0407 09:03:43.367568 17723 solver.cpp:218] Iteration 4488 (0.666387 iter/s, 18.0075s/12 iters), loss = 0.520058
I0407 09:03:43.367635 17723 solver.cpp:237]     Train net output #0: loss = 0.520058 (* 1 = 0.520058 loss)
I0407 09:03:43.367643 17723 sgd_solver.cpp:105] Iteration 4488, lr = 0.005
I0407 09:03:47.777120 17723 solver.cpp:218] Iteration 4500 (2.72144 iter/s, 4.40944s/12 iters), loss = 0.820858
I0407 09:03:47.777168 17723 solver.cpp:237]     Train net output #0: loss = 0.820858 (* 1 = 0.820858 loss)
I0407 09:03:47.777174 17723 sgd_solver.cpp:105] Iteration 4500, lr = 0.005
I0407 09:03:52.971431 17723 solver.cpp:218] Iteration 4512 (2.31027 iter/s, 5.1942s/12 iters), loss = 0.434792
I0407 09:03:52.971482 17723 solver.cpp:237]     Train net output #0: loss = 0.434792 (* 1 = 0.434792 loss)
I0407 09:03:52.971489 17723 sgd_solver.cpp:105] Iteration 4512, lr = 0.005
I0407 09:03:58.080567 17723 solver.cpp:218] Iteration 4524 (2.34878 iter/s, 5.10904s/12 iters), loss = 0.518991
I0407 09:03:58.080605 17723 solver.cpp:237]     Train net output #0: loss = 0.518991 (* 1 = 0.518991 loss)
I0407 09:03:58.080612 17723 sgd_solver.cpp:105] Iteration 4524, lr = 0.005
I0407 09:04:03.200544 17723 solver.cpp:218] Iteration 4536 (2.3438 iter/s, 5.11988s/12 iters), loss = 0.575568
I0407 09:04:03.200590 17723 solver.cpp:237]     Train net output #0: loss = 0.575568 (* 1 = 0.575568 loss)
I0407 09:04:03.200598 17723 sgd_solver.cpp:105] Iteration 4536, lr = 0.005
I0407 09:04:08.415647 17723 solver.cpp:218] Iteration 4548 (2.30106 iter/s, 5.215s/12 iters), loss = 0.419895
I0407 09:04:08.415813 17723 solver.cpp:237]     Train net output #0: loss = 0.419895 (* 1 = 0.419895 loss)
I0407 09:04:08.415824 17723 sgd_solver.cpp:105] Iteration 4548, lr = 0.005
I0407 09:04:09.779948 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:04:13.844573 17723 solver.cpp:218] Iteration 4560 (2.21047 iter/s, 5.42871s/12 iters), loss = 0.404962
I0407 09:04:13.844615 17723 solver.cpp:237]     Train net output #0: loss = 0.404962 (* 1 = 0.404962 loss)
I0407 09:04:13.844622 17723 sgd_solver.cpp:105] Iteration 4560, lr = 0.005
I0407 09:04:19.264792 17723 solver.cpp:218] Iteration 4572 (2.21397 iter/s, 5.42012s/12 iters), loss = 0.399626
I0407 09:04:19.264840 17723 solver.cpp:237]     Train net output #0: loss = 0.399626 (* 1 = 0.399626 loss)
I0407 09:04:19.264847 17723 sgd_solver.cpp:105] Iteration 4572, lr = 0.005
I0407 09:04:24.730738 17723 solver.cpp:218] Iteration 4584 (2.19545 iter/s, 5.46584s/12 iters), loss = 0.517604
I0407 09:04:24.730778 17723 solver.cpp:237]     Train net output #0: loss = 0.517604 (* 1 = 0.517604 loss)
I0407 09:04:24.730787 17723 sgd_solver.cpp:105] Iteration 4584, lr = 0.005
I0407 09:04:26.881525 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0407 09:04:31.851915 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0407 09:04:36.187064 17723 solver.cpp:330] Iteration 4590, Testing net (#0)
I0407 09:04:36.187085 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:04:38.767897 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:04:40.556828 17723 solver.cpp:397]     Test net output #0: accuracy = 0.416054
I0407 09:04:40.556859 17723 solver.cpp:397]     Test net output #1: loss = 2.8126 (* 1 = 2.8126 loss)
I0407 09:04:42.506345 17723 solver.cpp:218] Iteration 4596 (0.67509 iter/s, 17.7754s/12 iters), loss = 0.581726
I0407 09:04:42.506402 17723 solver.cpp:237]     Train net output #0: loss = 0.581726 (* 1 = 0.581726 loss)
I0407 09:04:42.506412 17723 sgd_solver.cpp:105] Iteration 4596, lr = 0.005
I0407 09:04:47.821568 17723 solver.cpp:218] Iteration 4608 (2.25771 iter/s, 5.31511s/12 iters), loss = 0.291869
I0407 09:04:47.821610 17723 solver.cpp:237]     Train net output #0: loss = 0.291869 (* 1 = 0.291869 loss)
I0407 09:04:47.821619 17723 sgd_solver.cpp:105] Iteration 4608, lr = 0.005
I0407 09:04:53.199059 17723 solver.cpp:218] Iteration 4620 (2.23157 iter/s, 5.37739s/12 iters), loss = 0.387093
I0407 09:04:53.199105 17723 solver.cpp:237]     Train net output #0: loss = 0.387093 (* 1 = 0.387093 loss)
I0407 09:04:53.199111 17723 sgd_solver.cpp:105] Iteration 4620, lr = 0.005
I0407 09:04:58.479909 17723 solver.cpp:218] Iteration 4632 (2.2724 iter/s, 5.28075s/12 iters), loss = 0.608453
I0407 09:04:58.480039 17723 solver.cpp:237]     Train net output #0: loss = 0.608453 (* 1 = 0.608453 loss)
I0407 09:04:58.480047 17723 sgd_solver.cpp:105] Iteration 4632, lr = 0.005
I0407 09:05:03.783507 17723 solver.cpp:218] Iteration 4644 (2.26269 iter/s, 5.30341s/12 iters), loss = 0.473116
I0407 09:05:03.783547 17723 solver.cpp:237]     Train net output #0: loss = 0.473116 (* 1 = 0.473116 loss)
I0407 09:05:03.783555 17723 sgd_solver.cpp:105] Iteration 4644, lr = 0.005
I0407 09:05:07.463328 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:05:09.152310 17723 solver.cpp:218] Iteration 4656 (2.23518 iter/s, 5.3687s/12 iters), loss = 0.503978
I0407 09:05:09.152458 17723 solver.cpp:237]     Train net output #0: loss = 0.503978 (* 1 = 0.503978 loss)
I0407 09:05:09.152469 17723 sgd_solver.cpp:105] Iteration 4656, lr = 0.005
I0407 09:05:14.474267 17723 solver.cpp:218] Iteration 4668 (2.25489 iter/s, 5.32176s/12 iters), loss = 0.503264
I0407 09:05:14.474323 17723 solver.cpp:237]     Train net output #0: loss = 0.503264 (* 1 = 0.503264 loss)
I0407 09:05:14.474334 17723 sgd_solver.cpp:105] Iteration 4668, lr = 0.005
I0407 09:05:19.728699 17723 solver.cpp:218] Iteration 4680 (2.28383 iter/s, 5.25432s/12 iters), loss = 0.447082
I0407 09:05:19.728740 17723 solver.cpp:237]     Train net output #0: loss = 0.447082 (* 1 = 0.447082 loss)
I0407 09:05:19.728747 17723 sgd_solver.cpp:105] Iteration 4680, lr = 0.005
I0407 09:05:24.374704 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0407 09:05:28.975965 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0407 09:05:32.786577 17723 solver.cpp:330] Iteration 4692, Testing net (#0)
I0407 09:05:32.786597 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:05:35.279660 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:05:37.097646 17723 solver.cpp:397]     Test net output #0: accuracy = 0.413603
I0407 09:05:37.097681 17723 solver.cpp:397]     Test net output #1: loss = 2.94645 (* 1 = 2.94645 loss)
I0407 09:05:37.227463 17723 solver.cpp:218] Iteration 4692 (0.685771 iter/s, 17.4986s/12 iters), loss = 0.603973
I0407 09:05:37.227530 17723 solver.cpp:237]     Train net output #0: loss = 0.603973 (* 1 = 0.603973 loss)
I0407 09:05:37.227542 17723 sgd_solver.cpp:105] Iteration 4692, lr = 0.005
I0407 09:05:41.478430 17723 solver.cpp:218] Iteration 4704 (2.82296 iter/s, 4.25086s/12 iters), loss = 0.522529
I0407 09:05:41.478528 17723 solver.cpp:237]     Train net output #0: loss = 0.522529 (* 1 = 0.522529 loss)
I0407 09:05:41.478536 17723 sgd_solver.cpp:105] Iteration 4704, lr = 0.005
I0407 09:05:46.783355 17723 solver.cpp:218] Iteration 4716 (2.26211 iter/s, 5.30478s/12 iters), loss = 0.458945
I0407 09:05:46.783387 17723 solver.cpp:237]     Train net output #0: loss = 0.458945 (* 1 = 0.458945 loss)
I0407 09:05:46.783393 17723 sgd_solver.cpp:105] Iteration 4716, lr = 0.005
I0407 09:05:52.060050 17723 solver.cpp:218] Iteration 4728 (2.27419 iter/s, 5.2766s/12 iters), loss = 0.586671
I0407 09:05:52.060097 17723 solver.cpp:237]     Train net output #0: loss = 0.586671 (* 1 = 0.586671 loss)
I0407 09:05:52.060104 17723 sgd_solver.cpp:105] Iteration 4728, lr = 0.005
I0407 09:05:57.131286 17723 solver.cpp:218] Iteration 4740 (2.36633 iter/s, 5.07113s/12 iters), loss = 0.574355
I0407 09:05:57.131326 17723 solver.cpp:237]     Train net output #0: loss = 0.574355 (* 1 = 0.574355 loss)
I0407 09:05:57.131335 17723 sgd_solver.cpp:105] Iteration 4740, lr = 0.005
I0407 09:06:02.501600 17723 solver.cpp:218] Iteration 4752 (2.23455 iter/s, 5.37022s/12 iters), loss = 0.328291
I0407 09:06:02.501641 17723 solver.cpp:237]     Train net output #0: loss = 0.328291 (* 1 = 0.328291 loss)
I0407 09:06:02.501648 17723 sgd_solver.cpp:105] Iteration 4752, lr = 0.005
I0407 09:06:03.073863 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:06:08.038014 17723 solver.cpp:218] Iteration 4764 (2.16751 iter/s, 5.53631s/12 iters), loss = 0.462023
I0407 09:06:08.038055 17723 solver.cpp:237]     Train net output #0: loss = 0.462023 (* 1 = 0.462023 loss)
I0407 09:06:08.038064 17723 sgd_solver.cpp:105] Iteration 4764, lr = 0.005
I0407 09:06:13.453949 17723 solver.cpp:218] Iteration 4776 (2.21572 iter/s, 5.41583s/12 iters), loss = 0.672154
I0407 09:06:13.454064 17723 solver.cpp:237]     Train net output #0: loss = 0.672154 (* 1 = 0.672154 loss)
I0407 09:06:13.454072 17723 sgd_solver.cpp:105] Iteration 4776, lr = 0.005
I0407 09:06:18.703847 17723 solver.cpp:218] Iteration 4788 (2.28583 iter/s, 5.24972s/12 iters), loss = 0.714263
I0407 09:06:18.703889 17723 solver.cpp:237]     Train net output #0: loss = 0.714263 (* 1 = 0.714263 loss)
I0407 09:06:18.703896 17723 sgd_solver.cpp:105] Iteration 4788, lr = 0.005
I0407 09:06:20.881026 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0407 09:06:25.413038 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0407 09:06:29.211277 17723 solver.cpp:330] Iteration 4794, Testing net (#0)
I0407 09:06:29.211297 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:06:31.613276 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:06:33.472012 17723 solver.cpp:397]     Test net output #0: accuracy = 0.41973
I0407 09:06:33.472038 17723 solver.cpp:397]     Test net output #1: loss = 2.877 (* 1 = 2.877 loss)
I0407 09:06:35.224951 17723 solver.cpp:218] Iteration 4800 (0.726352 iter/s, 16.5209s/12 iters), loss = 0.31703
I0407 09:06:35.225013 17723 solver.cpp:237]     Train net output #0: loss = 0.31703 (* 1 = 0.31703 loss)
I0407 09:06:35.225024 17723 sgd_solver.cpp:105] Iteration 4800, lr = 0.005
I0407 09:06:40.430024 17723 solver.cpp:218] Iteration 4812 (2.30549 iter/s, 5.20496s/12 iters), loss = 0.59332
I0407 09:06:40.430066 17723 solver.cpp:237]     Train net output #0: loss = 0.59332 (* 1 = 0.59332 loss)
I0407 09:06:40.430073 17723 sgd_solver.cpp:105] Iteration 4812, lr = 0.005
I0407 09:06:45.297859 17723 solver.cpp:218] Iteration 4824 (2.46521 iter/s, 4.86774s/12 iters), loss = 0.500066
I0407 09:06:45.297988 17723 solver.cpp:237]     Train net output #0: loss = 0.500066 (* 1 = 0.500066 loss)
I0407 09:06:45.297997 17723 sgd_solver.cpp:105] Iteration 4824, lr = 0.005
I0407 09:06:50.435536 17723 solver.cpp:218] Iteration 4836 (2.33577 iter/s, 5.1375s/12 iters), loss = 0.567851
I0407 09:06:50.435582 17723 solver.cpp:237]     Train net output #0: loss = 0.567851 (* 1 = 0.567851 loss)
I0407 09:06:50.435590 17723 sgd_solver.cpp:105] Iteration 4836, lr = 0.005
I0407 09:06:52.592437 17723 blocking_queue.cpp:49] Waiting for data
I0407 09:06:55.794108 17723 solver.cpp:218] Iteration 4848 (2.23944 iter/s, 5.35847s/12 iters), loss = 0.520056
I0407 09:06:55.794155 17723 solver.cpp:237]     Train net output #0: loss = 0.520056 (* 1 = 0.520056 loss)
I0407 09:06:55.794162 17723 sgd_solver.cpp:105] Iteration 4848, lr = 0.005
I0407 09:06:58.597368 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:07:01.273734 17723 solver.cpp:218] Iteration 4860 (2.18997 iter/s, 5.47952s/12 iters), loss = 0.488708
I0407 09:07:01.273777 17723 solver.cpp:237]     Train net output #0: loss = 0.488708 (* 1 = 0.488708 loss)
I0407 09:07:01.273784 17723 sgd_solver.cpp:105] Iteration 4860, lr = 0.005
I0407 09:07:06.635701 17723 solver.cpp:218] Iteration 4872 (2.23803 iter/s, 5.36187s/12 iters), loss = 0.693563
I0407 09:07:06.635741 17723 solver.cpp:237]     Train net output #0: loss = 0.693564 (* 1 = 0.693564 loss)
I0407 09:07:06.635748 17723 sgd_solver.cpp:105] Iteration 4872, lr = 0.005
I0407 09:07:12.150085 17723 solver.cpp:218] Iteration 4884 (2.17617 iter/s, 5.51429s/12 iters), loss = 0.590681
I0407 09:07:12.150137 17723 solver.cpp:237]     Train net output #0: loss = 0.590681 (* 1 = 0.590681 loss)
I0407 09:07:12.150147 17723 sgd_solver.cpp:105] Iteration 4884, lr = 0.005
I0407 09:07:16.913020 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0407 09:07:21.192190 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0407 09:07:24.759640 17723 solver.cpp:330] Iteration 4896, Testing net (#0)
I0407 09:07:24.759670 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:07:27.142786 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:07:29.099547 17723 solver.cpp:397]     Test net output #0: accuracy = 0.422794
I0407 09:07:29.099586 17723 solver.cpp:397]     Test net output #1: loss = 2.90385 (* 1 = 2.90385 loss)
I0407 09:07:29.238240 17723 solver.cpp:218] Iteration 4896 (0.702249 iter/s, 17.088s/12 iters), loss = 0.490099
I0407 09:07:29.238294 17723 solver.cpp:237]     Train net output #0: loss = 0.490099 (* 1 = 0.490099 loss)
I0407 09:07:29.238303 17723 sgd_solver.cpp:105] Iteration 4896, lr = 0.005
I0407 09:07:33.548956 17723 solver.cpp:218] Iteration 4908 (2.78382 iter/s, 4.31062s/12 iters), loss = 0.438471
I0407 09:07:33.548995 17723 solver.cpp:237]     Train net output #0: loss = 0.438471 (* 1 = 0.438471 loss)
I0407 09:07:33.549001 17723 sgd_solver.cpp:105] Iteration 4908, lr = 0.005
I0407 09:07:38.691434 17723 solver.cpp:218] Iteration 4920 (2.33355 iter/s, 5.14239s/12 iters), loss = 0.63931
I0407 09:07:38.691476 17723 solver.cpp:237]     Train net output #0: loss = 0.63931 (* 1 = 0.63931 loss)
I0407 09:07:38.691483 17723 sgd_solver.cpp:105] Iteration 4920, lr = 0.005
I0407 09:07:44.084962 17723 solver.cpp:218] Iteration 4932 (2.22493 iter/s, 5.39343s/12 iters), loss = 0.455848
I0407 09:07:44.085012 17723 solver.cpp:237]     Train net output #0: loss = 0.455848 (* 1 = 0.455848 loss)
I0407 09:07:44.085023 17723 sgd_solver.cpp:105] Iteration 4932, lr = 0.005
I0407 09:07:49.415005 17723 solver.cpp:218] Iteration 4944 (2.25143 iter/s, 5.32994s/12 iters), loss = 0.532654
I0407 09:07:49.415184 17723 solver.cpp:237]     Train net output #0: loss = 0.532654 (* 1 = 0.532654 loss)
I0407 09:07:49.415194 17723 sgd_solver.cpp:105] Iteration 4944, lr = 0.005
I0407 09:07:54.638856 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:07:54.890214 17723 solver.cpp:218] Iteration 4956 (2.19179 iter/s, 5.47498s/12 iters), loss = 0.345649
I0407 09:07:54.890254 17723 solver.cpp:237]     Train net output #0: loss = 0.345649 (* 1 = 0.345649 loss)
I0407 09:07:54.890261 17723 sgd_solver.cpp:105] Iteration 4956, lr = 0.005
I0407 09:07:59.878846 17723 solver.cpp:218] Iteration 4968 (2.40551 iter/s, 4.98854s/12 iters), loss = 0.541555
I0407 09:07:59.878885 17723 solver.cpp:237]     Train net output #0: loss = 0.541555 (* 1 = 0.541555 loss)
I0407 09:07:59.878892 17723 sgd_solver.cpp:105] Iteration 4968, lr = 0.005
I0407 09:08:05.108395 17723 solver.cpp:218] Iteration 4980 (2.29469 iter/s, 5.22946s/12 iters), loss = 0.435701
I0407 09:08:05.108441 17723 solver.cpp:237]     Train net output #0: loss = 0.435701 (* 1 = 0.435701 loss)
I0407 09:08:05.108450 17723 sgd_solver.cpp:105] Iteration 4980, lr = 0.005
I0407 09:08:10.468304 17723 solver.cpp:218] Iteration 4992 (2.23889 iter/s, 5.35981s/12 iters), loss = 0.504136
I0407 09:08:10.468345 17723 solver.cpp:237]     Train net output #0: loss = 0.504136 (* 1 = 0.504136 loss)
I0407 09:08:10.468353 17723 sgd_solver.cpp:105] Iteration 4992, lr = 0.005
I0407 09:08:12.739871 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0407 09:08:17.208814 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0407 09:08:19.601008 17723 solver.cpp:330] Iteration 4998, Testing net (#0)
I0407 09:08:19.601099 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:08:22.044190 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:08:23.997560 17723 solver.cpp:397]     Test net output #0: accuracy = 0.400123
I0407 09:08:23.997591 17723 solver.cpp:397]     Test net output #1: loss = 2.98855 (* 1 = 2.98855 loss)
I0407 09:08:25.859378 17723 solver.cpp:218] Iteration 5004 (0.779681 iter/s, 15.3909s/12 iters), loss = 0.586359
I0407 09:08:25.859416 17723 solver.cpp:237]     Train net output #0: loss = 0.586359 (* 1 = 0.586359 loss)
I0407 09:08:25.859424 17723 sgd_solver.cpp:105] Iteration 5004, lr = 0.005
I0407 09:08:30.900190 17723 solver.cpp:218] Iteration 5016 (2.38061 iter/s, 5.04072s/12 iters), loss = 0.296775
I0407 09:08:30.900233 17723 solver.cpp:237]     Train net output #0: loss = 0.296775 (* 1 = 0.296775 loss)
I0407 09:08:30.900241 17723 sgd_solver.cpp:105] Iteration 5016, lr = 0.005
I0407 09:08:36.274736 17723 solver.cpp:218] Iteration 5028 (2.23279 iter/s, 5.37444s/12 iters), loss = 0.489701
I0407 09:08:36.274780 17723 solver.cpp:237]     Train net output #0: loss = 0.489701 (* 1 = 0.489701 loss)
I0407 09:08:36.274786 17723 sgd_solver.cpp:105] Iteration 5028, lr = 0.005
I0407 09:08:41.767256 17723 solver.cpp:218] Iteration 5040 (2.18483 iter/s, 5.49242s/12 iters), loss = 0.442909
I0407 09:08:41.767294 17723 solver.cpp:237]     Train net output #0: loss = 0.442909 (* 1 = 0.442909 loss)
I0407 09:08:41.767302 17723 sgd_solver.cpp:105] Iteration 5040, lr = 0.005
I0407 09:08:47.064792 17723 solver.cpp:218] Iteration 5052 (2.26524 iter/s, 5.29744s/12 iters), loss = 0.505942
I0407 09:08:47.064828 17723 solver.cpp:237]     Train net output #0: loss = 0.505942 (* 1 = 0.505942 loss)
I0407 09:08:47.064836 17723 sgd_solver.cpp:105] Iteration 5052, lr = 0.005
I0407 09:08:49.123078 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:08:52.365324 17723 solver.cpp:218] Iteration 5064 (2.26396 iter/s, 5.30044s/12 iters), loss = 0.347168
I0407 09:08:52.365473 17723 solver.cpp:237]     Train net output #0: loss = 0.347168 (* 1 = 0.347168 loss)
I0407 09:08:52.365483 17723 sgd_solver.cpp:105] Iteration 5064, lr = 0.005
I0407 09:08:57.273993 17723 solver.cpp:218] Iteration 5076 (2.44476 iter/s, 4.90847s/12 iters), loss = 0.690943
I0407 09:08:57.274045 17723 solver.cpp:237]     Train net output #0: loss = 0.690943 (* 1 = 0.690943 loss)
I0407 09:08:57.274056 17723 sgd_solver.cpp:105] Iteration 5076, lr = 0.005
I0407 09:09:02.300813 17723 solver.cpp:218] Iteration 5088 (2.38725 iter/s, 5.02671s/12 iters), loss = 0.428517
I0407 09:09:02.300855 17723 solver.cpp:237]     Train net output #0: loss = 0.428517 (* 1 = 0.428517 loss)
I0407 09:09:02.300861 17723 sgd_solver.cpp:105] Iteration 5088, lr = 0.005
I0407 09:09:06.910661 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0407 09:09:11.666479 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0407 09:09:14.004793 17723 solver.cpp:330] Iteration 5100, Testing net (#0)
I0407 09:09:14.004813 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:09:16.387140 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:09:18.421223 17723 solver.cpp:397]     Test net output #0: accuracy = 0.409926
I0407 09:09:18.421273 17723 solver.cpp:397]     Test net output #1: loss = 2.87777 (* 1 = 2.87777 loss)
I0407 09:09:18.561718 17723 solver.cpp:218] Iteration 5100 (0.737974 iter/s, 16.2607s/12 iters), loss = 0.409441
I0407 09:09:18.561780 17723 solver.cpp:237]     Train net output #0: loss = 0.409441 (* 1 = 0.409441 loss)
I0407 09:09:18.561794 17723 sgd_solver.cpp:105] Iteration 5100, lr = 0.005
I0407 09:09:22.749177 17723 solver.cpp:218] Iteration 5112 (2.86577 iter/s, 4.18735s/12 iters), loss = 0.338192
I0407 09:09:22.749317 17723 solver.cpp:237]     Train net output #0: loss = 0.338192 (* 1 = 0.338192 loss)
I0407 09:09:22.749327 17723 sgd_solver.cpp:105] Iteration 5112, lr = 0.005
I0407 09:09:27.967633 17723 solver.cpp:218] Iteration 5124 (2.29961 iter/s, 5.21827s/12 iters), loss = 0.306782
I0407 09:09:27.967671 17723 solver.cpp:237]     Train net output #0: loss = 0.306782 (* 1 = 0.306782 loss)
I0407 09:09:27.967677 17723 sgd_solver.cpp:105] Iteration 5124, lr = 0.005
I0407 09:09:33.061275 17723 solver.cpp:218] Iteration 5136 (2.35592 iter/s, 5.09355s/12 iters), loss = 0.419329
I0407 09:09:33.061321 17723 solver.cpp:237]     Train net output #0: loss = 0.419329 (* 1 = 0.419329 loss)
I0407 09:09:33.061327 17723 sgd_solver.cpp:105] Iteration 5136, lr = 0.005
I0407 09:09:38.345911 17723 solver.cpp:218] Iteration 5148 (2.27078 iter/s, 5.28454s/12 iters), loss = 0.515696
I0407 09:09:38.345957 17723 solver.cpp:237]     Train net output #0: loss = 0.515697 (* 1 = 0.515697 loss)
I0407 09:09:38.345964 17723 sgd_solver.cpp:105] Iteration 5148, lr = 0.005
I0407 09:09:42.293315 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:09:43.347663 17723 solver.cpp:218] Iteration 5160 (2.39921 iter/s, 5.00165s/12 iters), loss = 0.506882
I0407 09:09:43.347710 17723 solver.cpp:237]     Train net output #0: loss = 0.506882 (* 1 = 0.506882 loss)
I0407 09:09:43.347718 17723 sgd_solver.cpp:105] Iteration 5160, lr = 0.005
I0407 09:09:48.698952 17723 solver.cpp:218] Iteration 5172 (2.24249 iter/s, 5.35119s/12 iters), loss = 0.468894
I0407 09:09:48.698998 17723 solver.cpp:237]     Train net output #0: loss = 0.468894 (* 1 = 0.468894 loss)
I0407 09:09:48.699005 17723 sgd_solver.cpp:105] Iteration 5172, lr = 0.005
I0407 09:09:53.985090 17723 solver.cpp:218] Iteration 5184 (2.27013 iter/s, 5.28603s/12 iters), loss = 0.429409
I0407 09:09:53.985257 17723 solver.cpp:237]     Train net output #0: loss = 0.429409 (* 1 = 0.429409 loss)
I0407 09:09:53.985273 17723 sgd_solver.cpp:105] Iteration 5184, lr = 0.005
I0407 09:09:59.263350 17723 solver.cpp:218] Iteration 5196 (2.27357 iter/s, 5.27805s/12 iters), loss = 0.349597
I0407 09:09:59.263393 17723 solver.cpp:237]     Train net output #0: loss = 0.349597 (* 1 = 0.349597 loss)
I0407 09:09:59.263401 17723 sgd_solver.cpp:105] Iteration 5196, lr = 0.005
I0407 09:10:01.453452 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0407 09:10:06.186498 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0407 09:10:08.551105 17723 solver.cpp:330] Iteration 5202, Testing net (#0)
I0407 09:10:08.551126 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:10:10.899850 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:10:12.947577 17723 solver.cpp:397]     Test net output #0: accuracy = 0.418505
I0407 09:10:12.947618 17723 solver.cpp:397]     Test net output #1: loss = 2.86798 (* 1 = 2.86798 loss)
I0407 09:10:14.758834 17723 solver.cpp:218] Iteration 5208 (0.774428 iter/s, 15.4953s/12 iters), loss = 0.389607
I0407 09:10:14.758877 17723 solver.cpp:237]     Train net output #0: loss = 0.389607 (* 1 = 0.389607 loss)
I0407 09:10:14.758883 17723 sgd_solver.cpp:105] Iteration 5208, lr = 0.005
I0407 09:10:19.958065 17723 solver.cpp:218] Iteration 5220 (2.30808 iter/s, 5.19913s/12 iters), loss = 0.404858
I0407 09:10:19.958109 17723 solver.cpp:237]     Train net output #0: loss = 0.404858 (* 1 = 0.404858 loss)
I0407 09:10:19.958117 17723 sgd_solver.cpp:105] Iteration 5220, lr = 0.005
I0407 09:10:25.412358 17723 solver.cpp:218] Iteration 5232 (2.20014 iter/s, 5.45419s/12 iters), loss = 0.509758
I0407 09:10:25.412454 17723 solver.cpp:237]     Train net output #0: loss = 0.509758 (* 1 = 0.509758 loss)
I0407 09:10:25.412464 17723 sgd_solver.cpp:105] Iteration 5232, lr = 0.005
I0407 09:10:30.798046 17723 solver.cpp:218] Iteration 5244 (2.22819 iter/s, 5.38554s/12 iters), loss = 0.296915
I0407 09:10:30.798086 17723 solver.cpp:237]     Train net output #0: loss = 0.296915 (* 1 = 0.296915 loss)
I0407 09:10:30.798092 17723 sgd_solver.cpp:105] Iteration 5244, lr = 0.005
I0407 09:10:36.132289 17723 solver.cpp:218] Iteration 5256 (2.24966 iter/s, 5.33415s/12 iters), loss = 0.4872
I0407 09:10:36.132330 17723 solver.cpp:237]     Train net output #0: loss = 0.4872 (* 1 = 0.4872 loss)
I0407 09:10:36.132339 17723 sgd_solver.cpp:105] Iteration 5256, lr = 0.005
I0407 09:10:37.568326 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:10:41.501083 17723 solver.cpp:218] Iteration 5268 (2.23518 iter/s, 5.36868s/12 iters), loss = 0.319737
I0407 09:10:41.501137 17723 solver.cpp:237]     Train net output #0: loss = 0.319737 (* 1 = 0.319737 loss)
I0407 09:10:41.501147 17723 sgd_solver.cpp:105] Iteration 5268, lr = 0.005
I0407 09:10:46.927017 17723 solver.cpp:218] Iteration 5280 (2.21164 iter/s, 5.42583s/12 iters), loss = 0.351197
I0407 09:10:46.927067 17723 solver.cpp:237]     Train net output #0: loss = 0.351197 (* 1 = 0.351197 loss)
I0407 09:10:46.927075 17723 sgd_solver.cpp:105] Iteration 5280, lr = 0.005
I0407 09:10:52.203101 17723 solver.cpp:218] Iteration 5292 (2.27446 iter/s, 5.27598s/12 iters), loss = 0.283926
I0407 09:10:52.203145 17723 solver.cpp:237]     Train net output #0: loss = 0.283926 (* 1 = 0.283926 loss)
I0407 09:10:52.203152 17723 sgd_solver.cpp:105] Iteration 5292, lr = 0.005
I0407 09:10:56.970032 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0407 09:11:01.530180 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0407 09:11:03.847810 17723 solver.cpp:330] Iteration 5304, Testing net (#0)
I0407 09:11:03.847827 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:11:06.112002 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:11:08.162921 17723 solver.cpp:397]     Test net output #0: accuracy = 0.413603
I0407 09:11:08.162956 17723 solver.cpp:397]     Test net output #1: loss = 2.94207 (* 1 = 2.94207 loss)
I0407 09:11:08.303411 17723 solver.cpp:218] Iteration 5304 (0.745336 iter/s, 16.1001s/12 iters), loss = 0.404529
I0407 09:11:08.303450 17723 solver.cpp:237]     Train net output #0: loss = 0.404529 (* 1 = 0.404529 loss)
I0407 09:11:08.303457 17723 sgd_solver.cpp:105] Iteration 5304, lr = 0.005
I0407 09:11:12.798732 17723 solver.cpp:218] Iteration 5316 (2.66949 iter/s, 4.49523s/12 iters), loss = 0.333329
I0407 09:11:12.798779 17723 solver.cpp:237]     Train net output #0: loss = 0.333329 (* 1 = 0.333329 loss)
I0407 09:11:12.798785 17723 sgd_solver.cpp:105] Iteration 5316, lr = 0.005
I0407 09:11:18.258607 17723 solver.cpp:218] Iteration 5328 (2.19789 iter/s, 5.45977s/12 iters), loss = 0.475054
I0407 09:11:18.258646 17723 solver.cpp:237]     Train net output #0: loss = 0.475054 (* 1 = 0.475054 loss)
I0407 09:11:18.258653 17723 sgd_solver.cpp:105] Iteration 5328, lr = 0.005
I0407 09:11:23.419214 17723 solver.cpp:218] Iteration 5340 (2.32535 iter/s, 5.16051s/12 iters), loss = 0.295724
I0407 09:11:23.419266 17723 solver.cpp:237]     Train net output #0: loss = 0.295724 (* 1 = 0.295724 loss)
I0407 09:11:23.419276 17723 sgd_solver.cpp:105] Iteration 5340, lr = 0.005
I0407 09:11:28.620898 17723 solver.cpp:218] Iteration 5352 (2.307 iter/s, 5.20156s/12 iters), loss = 0.511275
I0407 09:11:28.621006 17723 solver.cpp:237]     Train net output #0: loss = 0.511275 (* 1 = 0.511275 loss)
I0407 09:11:28.621016 17723 sgd_solver.cpp:105] Iteration 5352, lr = 0.005
I0407 09:11:32.182160 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:11:33.878643 17723 solver.cpp:218] Iteration 5364 (2.28242 iter/s, 5.25758s/12 iters), loss = 0.406875
I0407 09:11:33.878695 17723 solver.cpp:237]     Train net output #0: loss = 0.406875 (* 1 = 0.406875 loss)
I0407 09:11:33.878705 17723 sgd_solver.cpp:105] Iteration 5364, lr = 0.005
I0407 09:11:39.081925 17723 solver.cpp:218] Iteration 5376 (2.30628 iter/s, 5.20318s/12 iters), loss = 0.411872
I0407 09:11:39.081969 17723 solver.cpp:237]     Train net output #0: loss = 0.411872 (* 1 = 0.411872 loss)
I0407 09:11:39.081976 17723 sgd_solver.cpp:105] Iteration 5376, lr = 0.005
I0407 09:11:44.216269 17723 solver.cpp:218] Iteration 5388 (2.33725 iter/s, 5.13424s/12 iters), loss = 0.200998
I0407 09:11:44.216313 17723 solver.cpp:237]     Train net output #0: loss = 0.200998 (* 1 = 0.200998 loss)
I0407 09:11:44.216321 17723 sgd_solver.cpp:105] Iteration 5388, lr = 0.005
I0407 09:11:49.547359 17723 solver.cpp:218] Iteration 5400 (2.25099 iter/s, 5.33099s/12 iters), loss = 0.339643
I0407 09:11:49.547406 17723 solver.cpp:237]     Train net output #0: loss = 0.339643 (* 1 = 0.339643 loss)
I0407 09:11:49.547416 17723 sgd_solver.cpp:105] Iteration 5400, lr = 0.005
I0407 09:11:51.520428 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0407 09:11:56.274189 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0407 09:11:58.594617 17723 solver.cpp:330] Iteration 5406, Testing net (#0)
I0407 09:11:58.594637 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:12:00.866204 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:12:02.979048 17723 solver.cpp:397]     Test net output #0: accuracy = 0.397059
I0407 09:12:02.979085 17723 solver.cpp:397]     Test net output #1: loss = 3.11703 (* 1 = 3.11703 loss)
I0407 09:12:04.797865 17723 solver.cpp:218] Iteration 5412 (0.786868 iter/s, 15.2503s/12 iters), loss = 0.253224
I0407 09:12:04.797915 17723 solver.cpp:237]     Train net output #0: loss = 0.253224 (* 1 = 0.253224 loss)
I0407 09:12:04.797925 17723 sgd_solver.cpp:105] Iteration 5412, lr = 0.005
I0407 09:12:10.100078 17723 solver.cpp:218] Iteration 5424 (2.26325 iter/s, 5.30211s/12 iters), loss = 0.349961
I0407 09:12:10.100108 17723 solver.cpp:237]     Train net output #0: loss = 0.349961 (* 1 = 0.349961 loss)
I0407 09:12:10.100114 17723 sgd_solver.cpp:105] Iteration 5424, lr = 0.005
I0407 09:12:15.298318 17723 solver.cpp:218] Iteration 5436 (2.30851 iter/s, 5.19815s/12 iters), loss = 0.387611
I0407 09:12:15.298365 17723 solver.cpp:237]     Train net output #0: loss = 0.387611 (* 1 = 0.387611 loss)
I0407 09:12:15.298373 17723 sgd_solver.cpp:105] Iteration 5436, lr = 0.005
I0407 09:12:20.431505 17723 solver.cpp:218] Iteration 5448 (2.33777 iter/s, 5.13309s/12 iters), loss = 0.45505
I0407 09:12:20.431550 17723 solver.cpp:237]     Train net output #0: loss = 0.45505 (* 1 = 0.45505 loss)
I0407 09:12:20.431556 17723 sgd_solver.cpp:105] Iteration 5448, lr = 0.005
I0407 09:12:25.863926 17723 solver.cpp:218] Iteration 5460 (2.209 iter/s, 5.43233s/12 iters), loss = 0.335921
I0407 09:12:25.863957 17723 solver.cpp:237]     Train net output #0: loss = 0.335921 (* 1 = 0.335921 loss)
I0407 09:12:25.863963 17723 sgd_solver.cpp:105] Iteration 5460, lr = 0.005
I0407 09:12:26.467945 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:12:31.343238 17723 solver.cpp:218] Iteration 5472 (2.19009 iter/s, 5.47922s/12 iters), loss = 0.440531
I0407 09:12:31.343370 17723 solver.cpp:237]     Train net output #0: loss = 0.440531 (* 1 = 0.440531 loss)
I0407 09:12:31.343380 17723 sgd_solver.cpp:105] Iteration 5472, lr = 0.005
I0407 09:12:36.593665 17723 solver.cpp:218] Iteration 5484 (2.28561 iter/s, 5.25024s/12 iters), loss = 0.430535
I0407 09:12:36.593715 17723 solver.cpp:237]     Train net output #0: loss = 0.430535 (* 1 = 0.430535 loss)
I0407 09:12:36.593724 17723 sgd_solver.cpp:105] Iteration 5484, lr = 0.005
I0407 09:12:41.932251 17723 solver.cpp:218] Iteration 5496 (2.24783 iter/s, 5.33848s/12 iters), loss = 0.383366
I0407 09:12:41.932296 17723 solver.cpp:237]     Train net output #0: loss = 0.383366 (* 1 = 0.383366 loss)
I0407 09:12:41.932304 17723 sgd_solver.cpp:105] Iteration 5496, lr = 0.005
I0407 09:12:46.611061 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0407 09:12:51.664548 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0407 09:12:53.982633 17723 solver.cpp:330] Iteration 5508, Testing net (#0)
I0407 09:12:53.982653 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:12:56.212568 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:12:58.346272 17723 solver.cpp:397]     Test net output #0: accuracy = 0.410539
I0407 09:12:58.346316 17723 solver.cpp:397]     Test net output #1: loss = 2.98967 (* 1 = 2.98967 loss)
I0407 09:12:58.486809 17723 solver.cpp:218] Iteration 5508 (0.724884 iter/s, 16.5544s/12 iters), loss = 0.397238
I0407 09:12:58.488397 17723 solver.cpp:237]     Train net output #0: loss = 0.397238 (* 1 = 0.397238 loss)
I0407 09:12:58.488410 17723 sgd_solver.cpp:105] Iteration 5508, lr = 0.005
I0407 09:13:02.887137 17723 solver.cpp:218] Iteration 5520 (2.72808 iter/s, 4.3987s/12 iters), loss = 0.423102
I0407 09:13:02.887238 17723 solver.cpp:237]     Train net output #0: loss = 0.423102 (* 1 = 0.423102 loss)
I0407 09:13:02.887246 17723 sgd_solver.cpp:105] Iteration 5520, lr = 0.005
I0407 09:13:05.491374 17723 blocking_queue.cpp:49] Waiting for data
I0407 09:13:08.280582 17723 solver.cpp:218] Iteration 5532 (2.22499 iter/s, 5.39329s/12 iters), loss = 0.309459
I0407 09:13:08.280628 17723 solver.cpp:237]     Train net output #0: loss = 0.309459 (* 1 = 0.309459 loss)
I0407 09:13:08.280635 17723 sgd_solver.cpp:105] Iteration 5532, lr = 0.005
I0407 09:13:13.691628 17723 solver.cpp:218] Iteration 5544 (2.21773 iter/s, 5.41094s/12 iters), loss = 0.293354
I0407 09:13:13.691674 17723 solver.cpp:237]     Train net output #0: loss = 0.293354 (* 1 = 0.293354 loss)
I0407 09:13:13.691681 17723 sgd_solver.cpp:105] Iteration 5544, lr = 0.005
I0407 09:13:18.729940 17723 solver.cpp:218] Iteration 5556 (2.3818 iter/s, 5.03821s/12 iters), loss = 0.551574
I0407 09:13:18.729987 17723 solver.cpp:237]     Train net output #0: loss = 0.551574 (* 1 = 0.551574 loss)
I0407 09:13:18.729996 17723 sgd_solver.cpp:105] Iteration 5556, lr = 0.005
I0407 09:13:21.484719 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:13:23.922230 17723 solver.cpp:218] Iteration 5568 (2.31116 iter/s, 5.19219s/12 iters), loss = 0.333671
I0407 09:13:23.922271 17723 solver.cpp:237]     Train net output #0: loss = 0.333671 (* 1 = 0.333671 loss)
I0407 09:13:23.922278 17723 sgd_solver.cpp:105] Iteration 5568, lr = 0.005
I0407 09:13:29.022749 17723 solver.cpp:218] Iteration 5580 (2.35275 iter/s, 5.10042s/12 iters), loss = 0.390801
I0407 09:13:29.022791 17723 solver.cpp:237]     Train net output #0: loss = 0.390801 (* 1 = 0.390801 loss)
I0407 09:13:29.022799 17723 sgd_solver.cpp:105] Iteration 5580, lr = 0.005
I0407 09:13:34.331916 17723 solver.cpp:218] Iteration 5592 (2.26028 iter/s, 5.30907s/12 iters), loss = 0.382235
I0407 09:13:34.332043 17723 solver.cpp:237]     Train net output #0: loss = 0.382235 (* 1 = 0.382235 loss)
I0407 09:13:34.332052 17723 sgd_solver.cpp:105] Iteration 5592, lr = 0.005
I0407 09:13:39.556855 17723 solver.cpp:218] Iteration 5604 (2.29676 iter/s, 5.22476s/12 iters), loss = 0.540839
I0407 09:13:39.556908 17723 solver.cpp:237]     Train net output #0: loss = 0.54084 (* 1 = 0.54084 loss)
I0407 09:13:39.556916 17723 sgd_solver.cpp:105] Iteration 5604, lr = 0.005
I0407 09:13:41.581431 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0407 09:13:46.558702 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0407 09:13:48.866772 17723 solver.cpp:330] Iteration 5610, Testing net (#0)
I0407 09:13:48.866793 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:13:50.986330 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:13:53.157454 17723 solver.cpp:397]     Test net output #0: accuracy = 0.415441
I0407 09:13:53.157491 17723 solver.cpp:397]     Test net output #1: loss = 3.13394 (* 1 = 3.13394 loss)
I0407 09:13:55.036975 17723 solver.cpp:218] Iteration 5616 (0.775197 iter/s, 15.4799s/12 iters), loss = 0.342754
I0407 09:13:55.037029 17723 solver.cpp:237]     Train net output #0: loss = 0.342754 (* 1 = 0.342754 loss)
I0407 09:13:55.037037 17723 sgd_solver.cpp:105] Iteration 5616, lr = 0.005
I0407 09:14:00.306617 17723 solver.cpp:218] Iteration 5628 (2.27724 iter/s, 5.26954s/12 iters), loss = 0.523255
I0407 09:14:00.306658 17723 solver.cpp:237]     Train net output #0: loss = 0.523255 (* 1 = 0.523255 loss)
I0407 09:14:00.306663 17723 sgd_solver.cpp:105] Iteration 5628, lr = 0.005
I0407 09:14:05.641289 17723 solver.cpp:218] Iteration 5640 (2.24948 iter/s, 5.33458s/12 iters), loss = 0.347884
I0407 09:14:05.641377 17723 solver.cpp:237]     Train net output #0: loss = 0.347884 (* 1 = 0.347884 loss)
I0407 09:14:05.641386 17723 sgd_solver.cpp:105] Iteration 5640, lr = 0.005
I0407 09:14:10.853003 17723 solver.cpp:218] Iteration 5652 (2.30257 iter/s, 5.21158s/12 iters), loss = 0.405064
I0407 09:14:10.853044 17723 solver.cpp:237]     Train net output #0: loss = 0.405064 (* 1 = 0.405064 loss)
I0407 09:14:10.853050 17723 sgd_solver.cpp:105] Iteration 5652, lr = 0.005
I0407 09:14:15.784549 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:14:16.003774 17723 solver.cpp:218] Iteration 5664 (2.32979 iter/s, 5.15068s/12 iters), loss = 0.389775
I0407 09:14:16.003825 17723 solver.cpp:237]     Train net output #0: loss = 0.389776 (* 1 = 0.389776 loss)
I0407 09:14:16.003834 17723 sgd_solver.cpp:105] Iteration 5664, lr = 0.005
I0407 09:14:21.264550 17723 solver.cpp:218] Iteration 5676 (2.28108 iter/s, 5.26067s/12 iters), loss = 0.244918
I0407 09:14:21.264595 17723 solver.cpp:237]     Train net output #0: loss = 0.244918 (* 1 = 0.244918 loss)
I0407 09:14:21.264602 17723 sgd_solver.cpp:105] Iteration 5676, lr = 0.005
I0407 09:14:26.629501 17723 solver.cpp:218] Iteration 5688 (2.23678 iter/s, 5.36485s/12 iters), loss = 0.408798
I0407 09:14:26.629544 17723 solver.cpp:237]     Train net output #0: loss = 0.408798 (* 1 = 0.408798 loss)
I0407 09:14:26.629550 17723 sgd_solver.cpp:105] Iteration 5688, lr = 0.005
I0407 09:14:31.789000 17723 solver.cpp:218] Iteration 5700 (2.32585 iter/s, 5.1594s/12 iters), loss = 0.270206
I0407 09:14:31.789041 17723 solver.cpp:237]     Train net output #0: loss = 0.270206 (* 1 = 0.270206 loss)
I0407 09:14:31.789047 17723 sgd_solver.cpp:105] Iteration 5700, lr = 0.005
I0407 09:14:36.393718 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0407 09:14:41.370357 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0407 09:14:43.695647 17723 solver.cpp:330] Iteration 5712, Testing net (#0)
I0407 09:14:43.695665 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:14:45.826815 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:14:48.055858 17723 solver.cpp:397]     Test net output #0: accuracy = 0.420343
I0407 09:14:48.055887 17723 solver.cpp:397]     Test net output #1: loss = 2.94451 (* 1 = 2.94451 loss)
I0407 09:14:48.184705 17723 solver.cpp:218] Iteration 5712 (0.731907 iter/s, 16.3955s/12 iters), loss = 0.237389
I0407 09:14:48.184753 17723 solver.cpp:237]     Train net output #0: loss = 0.237389 (* 1 = 0.237389 loss)
I0407 09:14:48.184762 17723 sgd_solver.cpp:105] Iteration 5712, lr = 0.005
I0407 09:14:52.620013 17723 solver.cpp:218] Iteration 5724 (2.70562 iter/s, 4.43521s/12 iters), loss = 0.345667
I0407 09:14:52.620057 17723 solver.cpp:237]     Train net output #0: loss = 0.345667 (* 1 = 0.345667 loss)
I0407 09:14:52.620064 17723 sgd_solver.cpp:105] Iteration 5724, lr = 0.005
I0407 09:14:57.743036 17723 solver.cpp:218] Iteration 5736 (2.34241 iter/s, 5.12293s/12 iters), loss = 0.326095
I0407 09:14:57.743080 17723 solver.cpp:237]     Train net output #0: loss = 0.326095 (* 1 = 0.326095 loss)
I0407 09:14:57.743088 17723 sgd_solver.cpp:105] Iteration 5736, lr = 0.005
I0407 09:15:02.975219 17723 solver.cpp:218] Iteration 5748 (2.29354 iter/s, 5.23208s/12 iters), loss = 0.520099
I0407 09:15:02.975265 17723 solver.cpp:237]     Train net output #0: loss = 0.520099 (* 1 = 0.520099 loss)
I0407 09:15:02.975272 17723 sgd_solver.cpp:105] Iteration 5748, lr = 0.005
I0407 09:15:08.392515 17723 solver.cpp:218] Iteration 5760 (2.21517 iter/s, 5.41719s/12 iters), loss = 0.211374
I0407 09:15:08.392628 17723 solver.cpp:237]     Train net output #0: loss = 0.211374 (* 1 = 0.211374 loss)
I0407 09:15:08.392637 17723 sgd_solver.cpp:105] Iteration 5760, lr = 0.005
I0407 09:15:10.446045 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:15:13.789225 17723 solver.cpp:218] Iteration 5772 (2.22365 iter/s, 5.39654s/12 iters), loss = 0.335587
I0407 09:15:13.789268 17723 solver.cpp:237]     Train net output #0: loss = 0.335588 (* 1 = 0.335588 loss)
I0407 09:15:13.789273 17723 sgd_solver.cpp:105] Iteration 5772, lr = 0.005
I0407 09:15:18.889344 17723 solver.cpp:218] Iteration 5784 (2.35293 iter/s, 5.10002s/12 iters), loss = 0.334471
I0407 09:15:18.889384 17723 solver.cpp:237]     Train net output #0: loss = 0.334471 (* 1 = 0.334471 loss)
I0407 09:15:18.889390 17723 sgd_solver.cpp:105] Iteration 5784, lr = 0.005
I0407 09:15:24.255362 17723 solver.cpp:218] Iteration 5796 (2.23634 iter/s, 5.36592s/12 iters), loss = 0.358983
I0407 09:15:24.255406 17723 solver.cpp:237]     Train net output #0: loss = 0.358983 (* 1 = 0.358983 loss)
I0407 09:15:24.255414 17723 sgd_solver.cpp:105] Iteration 5796, lr = 0.005
I0407 09:15:29.627487 17723 solver.cpp:218] Iteration 5808 (2.23379 iter/s, 5.37203s/12 iters), loss = 0.245558
I0407 09:15:29.627529 17723 solver.cpp:237]     Train net output #0: loss = 0.245559 (* 1 = 0.245559 loss)
I0407 09:15:29.627537 17723 sgd_solver.cpp:105] Iteration 5808, lr = 0.005
I0407 09:15:31.781453 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0407 09:15:36.367934 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0407 09:15:38.702203 17723 solver.cpp:330] Iteration 5814, Testing net (#0)
I0407 09:15:38.702318 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:15:40.877828 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:15:43.238216 17723 solver.cpp:397]     Test net output #0: accuracy = 0.419118
I0407 09:15:43.238245 17723 solver.cpp:397]     Test net output #1: loss = 3.01419 (* 1 = 3.01419 loss)
I0407 09:15:45.014843 17723 solver.cpp:218] Iteration 5820 (0.77987 iter/s, 15.3872s/12 iters), loss = 0.467205
I0407 09:15:45.014899 17723 solver.cpp:237]     Train net output #0: loss = 0.467205 (* 1 = 0.467205 loss)
I0407 09:15:45.014909 17723 sgd_solver.cpp:105] Iteration 5820, lr = 0.005
I0407 09:15:50.237224 17723 solver.cpp:218] Iteration 5832 (2.29785 iter/s, 5.22227s/12 iters), loss = 0.257889
I0407 09:15:50.237265 17723 solver.cpp:237]     Train net output #0: loss = 0.257889 (* 1 = 0.257889 loss)
I0407 09:15:50.237272 17723 sgd_solver.cpp:105] Iteration 5832, lr = 0.005
I0407 09:15:55.408329 17723 solver.cpp:218] Iteration 5844 (2.32063 iter/s, 5.17101s/12 iters), loss = 0.260618
I0407 09:15:55.408376 17723 solver.cpp:237]     Train net output #0: loss = 0.260618 (* 1 = 0.260618 loss)
I0407 09:15:55.408383 17723 sgd_solver.cpp:105] Iteration 5844, lr = 0.005
I0407 09:16:00.784139 17723 solver.cpp:218] Iteration 5856 (2.23226 iter/s, 5.37571s/12 iters), loss = 0.239957
I0407 09:16:00.784188 17723 solver.cpp:237]     Train net output #0: loss = 0.239957 (* 1 = 0.239957 loss)
I0407 09:16:00.784198 17723 sgd_solver.cpp:105] Iteration 5856, lr = 0.005
I0407 09:16:04.912277 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:16:05.781855 17723 solver.cpp:218] Iteration 5868 (2.40115 iter/s, 4.99761s/12 iters), loss = 0.38392
I0407 09:16:05.781895 17723 solver.cpp:237]     Train net output #0: loss = 0.38392 (* 1 = 0.38392 loss)
I0407 09:16:05.781903 17723 sgd_solver.cpp:105] Iteration 5868, lr = 0.005
I0407 09:16:10.893364 17723 solver.cpp:218] Iteration 5880 (2.34769 iter/s, 5.11142s/12 iters), loss = 0.367686
I0407 09:16:10.893474 17723 solver.cpp:237]     Train net output #0: loss = 0.367686 (* 1 = 0.367686 loss)
I0407 09:16:10.893483 17723 sgd_solver.cpp:105] Iteration 5880, lr = 0.005
I0407 09:16:16.258760 17723 solver.cpp:218] Iteration 5892 (2.23662 iter/s, 5.36523s/12 iters), loss = 0.49764
I0407 09:16:16.258803 17723 solver.cpp:237]     Train net output #0: loss = 0.49764 (* 1 = 0.49764 loss)
I0407 09:16:16.258810 17723 sgd_solver.cpp:105] Iteration 5892, lr = 0.005
I0407 09:16:21.331074 17723 solver.cpp:218] Iteration 5904 (2.36583 iter/s, 5.07222s/12 iters), loss = 0.317316
I0407 09:16:21.331120 17723 solver.cpp:237]     Train net output #0: loss = 0.317316 (* 1 = 0.317316 loss)
I0407 09:16:21.331126 17723 sgd_solver.cpp:105] Iteration 5904, lr = 0.005
I0407 09:16:26.145505 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0407 09:16:31.121222 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0407 09:16:33.424640 17723 solver.cpp:330] Iteration 5916, Testing net (#0)
I0407 09:16:33.424659 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:16:35.569546 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:16:37.964355 17723 solver.cpp:397]     Test net output #0: accuracy = 0.41299
I0407 09:16:37.964395 17723 solver.cpp:397]     Test net output #1: loss = 3.11424 (* 1 = 3.11424 loss)
I0407 09:16:38.104704 17723 solver.cpp:218] Iteration 5916 (0.715416 iter/s, 16.7734s/12 iters), loss = 0.363039
I0407 09:16:38.104751 17723 solver.cpp:237]     Train net output #0: loss = 0.363039 (* 1 = 0.363039 loss)
I0407 09:16:38.104758 17723 sgd_solver.cpp:105] Iteration 5916, lr = 0.005
I0407 09:16:42.436401 17723 solver.cpp:218] Iteration 5928 (2.77033 iter/s, 4.33161s/12 iters), loss = 0.317299
I0407 09:16:42.436533 17723 solver.cpp:237]     Train net output #0: loss = 0.317299 (* 1 = 0.317299 loss)
I0407 09:16:42.436542 17723 sgd_solver.cpp:105] Iteration 5928, lr = 0.005
I0407 09:16:47.760562 17723 solver.cpp:218] Iteration 5940 (2.25395 iter/s, 5.32398s/12 iters), loss = 0.412606
I0407 09:16:47.760601 17723 solver.cpp:237]     Train net output #0: loss = 0.412606 (* 1 = 0.412606 loss)
I0407 09:16:47.760609 17723 sgd_solver.cpp:105] Iteration 5940, lr = 0.005
I0407 09:16:52.900280 17723 solver.cpp:218] Iteration 5952 (2.3348 iter/s, 5.13962s/12 iters), loss = 0.379558
I0407 09:16:52.900327 17723 solver.cpp:237]     Train net output #0: loss = 0.379558 (* 1 = 0.379558 loss)
I0407 09:16:52.900334 17723 sgd_solver.cpp:105] Iteration 5952, lr = 0.005
I0407 09:16:58.231801 17723 solver.cpp:218] Iteration 5964 (2.25081 iter/s, 5.33142s/12 iters), loss = 0.309804
I0407 09:16:58.231844 17723 solver.cpp:237]     Train net output #0: loss = 0.309804 (* 1 = 0.309804 loss)
I0407 09:16:58.231853 17723 sgd_solver.cpp:105] Iteration 5964, lr = 0.005
I0407 09:16:59.658674 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:17:03.678189 17723 solver.cpp:218] Iteration 5976 (2.20333 iter/s, 5.44629s/12 iters), loss = 0.352661
I0407 09:17:03.678229 17723 solver.cpp:237]     Train net output #0: loss = 0.352661 (* 1 = 0.352661 loss)
I0407 09:17:03.678236 17723 sgd_solver.cpp:105] Iteration 5976, lr = 0.005
I0407 09:17:09.112941 17723 solver.cpp:218] Iteration 5988 (2.20805 iter/s, 5.43465s/12 iters), loss = 0.188493
I0407 09:17:09.112984 17723 solver.cpp:237]     Train net output #0: loss = 0.188493 (* 1 = 0.188493 loss)
I0407 09:17:09.112991 17723 sgd_solver.cpp:105] Iteration 5988, lr = 0.005
I0407 09:17:14.330111 17723 solver.cpp:218] Iteration 6000 (2.30014 iter/s, 5.21707s/12 iters), loss = 0.275099
I0407 09:17:14.330232 17723 solver.cpp:237]     Train net output #0: loss = 0.275099 (* 1 = 0.275099 loss)
I0407 09:17:14.330242 17723 sgd_solver.cpp:105] Iteration 6000, lr = 0.005
I0407 09:17:19.650274 17723 solver.cpp:218] Iteration 6012 (2.25564 iter/s, 5.31999s/12 iters), loss = 0.326496
I0407 09:17:19.650322 17723 solver.cpp:237]     Train net output #0: loss = 0.326496 (* 1 = 0.326496 loss)
I0407 09:17:19.650329 17723 sgd_solver.cpp:105] Iteration 6012, lr = 0.005
I0407 09:17:21.843437 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0407 09:17:25.323859 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0407 09:17:27.792716 17723 solver.cpp:330] Iteration 6018, Testing net (#0)
I0407 09:17:27.792734 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:17:29.747031 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:17:32.064656 17723 solver.cpp:397]     Test net output #0: accuracy = 0.428309
I0407 09:17:32.064692 17723 solver.cpp:397]     Test net output #1: loss = 2.90885 (* 1 = 2.90885 loss)
I0407 09:17:33.899031 17723 solver.cpp:218] Iteration 6024 (0.842189 iter/s, 14.2486s/12 iters), loss = 0.47148
I0407 09:17:33.899080 17723 solver.cpp:237]     Train net output #0: loss = 0.47148 (* 1 = 0.47148 loss)
I0407 09:17:33.899089 17723 sgd_solver.cpp:105] Iteration 6024, lr = 0.005
I0407 09:17:39.106891 17723 solver.cpp:218] Iteration 6036 (2.30425 iter/s, 5.20776s/12 iters), loss = 0.197582
I0407 09:17:39.106935 17723 solver.cpp:237]     Train net output #0: loss = 0.197582 (* 1 = 0.197582 loss)
I0407 09:17:39.106942 17723 sgd_solver.cpp:105] Iteration 6036, lr = 0.005
I0407 09:17:44.407618 17723 solver.cpp:218] Iteration 6048 (2.26388 iter/s, 5.30062s/12 iters), loss = 0.374239
I0407 09:17:44.407755 17723 solver.cpp:237]     Train net output #0: loss = 0.374239 (* 1 = 0.374239 loss)
I0407 09:17:44.407766 17723 sgd_solver.cpp:105] Iteration 6048, lr = 0.005
I0407 09:17:49.345636 17723 solver.cpp:218] Iteration 6060 (2.43022 iter/s, 4.93783s/12 iters), loss = 0.305195
I0407 09:17:49.345679 17723 solver.cpp:237]     Train net output #0: loss = 0.305195 (* 1 = 0.305195 loss)
I0407 09:17:49.345685 17723 sgd_solver.cpp:105] Iteration 6060, lr = 0.005
I0407 09:17:53.042146 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:17:54.614838 17723 solver.cpp:218] Iteration 6072 (2.27743 iter/s, 5.2691s/12 iters), loss = 0.312153
I0407 09:17:54.614883 17723 solver.cpp:237]     Train net output #0: loss = 0.312153 (* 1 = 0.312153 loss)
I0407 09:17:54.614890 17723 sgd_solver.cpp:105] Iteration 6072, lr = 0.005
I0407 09:17:59.671507 17723 solver.cpp:218] Iteration 6084 (2.37315 iter/s, 5.05657s/12 iters), loss = 0.360604
I0407 09:17:59.671555 17723 solver.cpp:237]     Train net output #0: loss = 0.360605 (* 1 = 0.360605 loss)
I0407 09:17:59.671562 17723 sgd_solver.cpp:105] Iteration 6084, lr = 0.005
I0407 09:18:04.758473 17723 solver.cpp:218] Iteration 6096 (2.35902 iter/s, 5.08686s/12 iters), loss = 0.208339
I0407 09:18:04.758517 17723 solver.cpp:237]     Train net output #0: loss = 0.208339 (* 1 = 0.208339 loss)
I0407 09:18:04.758525 17723 sgd_solver.cpp:105] Iteration 6096, lr = 0.005
I0407 09:18:09.968103 17723 solver.cpp:218] Iteration 6108 (2.30347 iter/s, 5.20954s/12 iters), loss = 0.306299
I0407 09:18:09.968138 17723 solver.cpp:237]     Train net output #0: loss = 0.306299 (* 1 = 0.306299 loss)
I0407 09:18:09.968144 17723 sgd_solver.cpp:105] Iteration 6108, lr = 0.005
I0407 09:18:14.697693 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0407 09:18:19.475481 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0407 09:18:22.001130 17723 solver.cpp:330] Iteration 6120, Testing net (#0)
I0407 09:18:22.001148 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:18:24.018605 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:18:26.612298 17723 solver.cpp:397]     Test net output #0: accuracy = 0.42402
I0407 09:18:26.612331 17723 solver.cpp:397]     Test net output #1: loss = 3.10377 (* 1 = 3.10377 loss)
I0407 09:18:26.750571 17723 solver.cpp:218] Iteration 6120 (0.715039 iter/s, 16.7823s/12 iters), loss = 0.208232
I0407 09:18:26.750627 17723 solver.cpp:237]     Train net output #0: loss = 0.208232 (* 1 = 0.208232 loss)
I0407 09:18:26.750635 17723 sgd_solver.cpp:105] Iteration 6120, lr = 0.005
I0407 09:18:31.178189 17723 solver.cpp:218] Iteration 6132 (2.71032 iter/s, 4.42752s/12 iters), loss = 0.321788
I0407 09:18:31.178220 17723 solver.cpp:237]     Train net output #0: loss = 0.321788 (* 1 = 0.321788 loss)
I0407 09:18:31.178227 17723 sgd_solver.cpp:105] Iteration 6132, lr = 0.005
I0407 09:18:36.531760 17723 solver.cpp:218] Iteration 6144 (2.24153 iter/s, 5.35348s/12 iters), loss = 0.298771
I0407 09:18:36.531798 17723 solver.cpp:237]     Train net output #0: loss = 0.298771 (* 1 = 0.298771 loss)
I0407 09:18:36.531806 17723 sgd_solver.cpp:105] Iteration 6144, lr = 0.005
I0407 09:18:41.937840 17723 solver.cpp:218] Iteration 6156 (2.21976 iter/s, 5.40598s/12 iters), loss = 0.191015
I0407 09:18:41.937891 17723 solver.cpp:237]     Train net output #0: loss = 0.191015 (* 1 = 0.191015 loss)
I0407 09:18:41.937902 17723 sgd_solver.cpp:105] Iteration 6156, lr = 0.005
I0407 09:18:47.237810 17723 solver.cpp:218] Iteration 6168 (2.26421 iter/s, 5.29987s/12 iters), loss = 0.333892
I0407 09:18:47.237895 17723 solver.cpp:237]     Train net output #0: loss = 0.333892 (* 1 = 0.333892 loss)
I0407 09:18:47.237901 17723 sgd_solver.cpp:105] Iteration 6168, lr = 0.005
I0407 09:18:47.783383 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:18:52.575968 17723 solver.cpp:218] Iteration 6180 (2.24803 iter/s, 5.33802s/12 iters), loss = 0.45998
I0407 09:18:52.576004 17723 solver.cpp:237]     Train net output #0: loss = 0.45998 (* 1 = 0.45998 loss)
I0407 09:18:52.576012 17723 sgd_solver.cpp:105] Iteration 6180, lr = 0.005
I0407 09:18:57.937255 17723 solver.cpp:218] Iteration 6192 (2.23831 iter/s, 5.36119s/12 iters), loss = 0.400253
I0407 09:18:57.937304 17723 solver.cpp:237]     Train net output #0: loss = 0.400253 (* 1 = 0.400253 loss)
I0407 09:18:57.937314 17723 sgd_solver.cpp:105] Iteration 6192, lr = 0.005
I0407 09:19:03.320447 17723 solver.cpp:218] Iteration 6204 (2.2292 iter/s, 5.38309s/12 iters), loss = 0.27809
I0407 09:19:03.320483 17723 solver.cpp:237]     Train net output #0: loss = 0.27809 (* 1 = 0.27809 loss)
I0407 09:19:03.320490 17723 sgd_solver.cpp:105] Iteration 6204, lr = 0.005
I0407 09:19:08.686620 17723 solver.cpp:218] Iteration 6216 (2.23627 iter/s, 5.36607s/12 iters), loss = 0.253579
I0407 09:19:08.686673 17723 solver.cpp:237]     Train net output #0: loss = 0.253579 (* 1 = 0.253579 loss)
I0407 09:19:08.686686 17723 sgd_solver.cpp:105] Iteration 6216, lr = 0.005
I0407 09:19:10.848737 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0407 09:19:13.864594 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0407 09:19:16.588793 17723 solver.cpp:330] Iteration 6222, Testing net (#0)
I0407 09:19:16.588814 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:19:18.574918 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:19:19.905308 17723 blocking_queue.cpp:49] Waiting for data
I0407 09:19:21.133078 17723 solver.cpp:397]     Test net output #0: accuracy = 0.429534
I0407 09:19:21.133112 17723 solver.cpp:397]     Test net output #1: loss = 2.89394 (* 1 = 2.89394 loss)
I0407 09:19:23.053937 17723 solver.cpp:218] Iteration 6228 (0.835239 iter/s, 14.3671s/12 iters), loss = 0.357609
I0407 09:19:23.053978 17723 solver.cpp:237]     Train net output #0: loss = 0.357609 (* 1 = 0.357609 loss)
I0407 09:19:23.053985 17723 sgd_solver.cpp:105] Iteration 6228, lr = 0.005
I0407 09:19:28.175837 17723 solver.cpp:218] Iteration 6240 (2.34293 iter/s, 5.1218s/12 iters), loss = 0.433488
I0407 09:19:28.175880 17723 solver.cpp:237]     Train net output #0: loss = 0.433488 (* 1 = 0.433488 loss)
I0407 09:19:28.175889 17723 sgd_solver.cpp:105] Iteration 6240, lr = 0.005
I0407 09:19:33.377490 17723 solver.cpp:218] Iteration 6252 (2.307 iter/s, 5.20156s/12 iters), loss = 0.292799
I0407 09:19:33.377527 17723 solver.cpp:237]     Train net output #0: loss = 0.292799 (* 1 = 0.292799 loss)
I0407 09:19:33.377534 17723 sgd_solver.cpp:105] Iteration 6252, lr = 0.005
I0407 09:19:38.538467 17723 solver.cpp:218] Iteration 6264 (2.32518 iter/s, 5.16088s/12 iters), loss = 0.291389
I0407 09:19:38.538506 17723 solver.cpp:237]     Train net output #0: loss = 0.291389 (* 1 = 0.291389 loss)
I0407 09:19:38.538513 17723 sgd_solver.cpp:105] Iteration 6264, lr = 0.005
I0407 09:19:41.417924 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:19:43.837630 17723 solver.cpp:218] Iteration 6276 (2.26455 iter/s, 5.29907s/12 iters), loss = 0.281795
I0407 09:19:43.837671 17723 solver.cpp:237]     Train net output #0: loss = 0.281795 (* 1 = 0.281795 loss)
I0407 09:19:43.837678 17723 sgd_solver.cpp:105] Iteration 6276, lr = 0.005
I0407 09:19:48.865572 17723 solver.cpp:218] Iteration 6288 (2.38671 iter/s, 5.02784s/12 iters), loss = 0.485081
I0407 09:19:48.865684 17723 solver.cpp:237]     Train net output #0: loss = 0.485081 (* 1 = 0.485081 loss)
I0407 09:19:48.865692 17723 sgd_solver.cpp:105] Iteration 6288, lr = 0.005
I0407 09:19:54.097527 17723 solver.cpp:218] Iteration 6300 (2.29367 iter/s, 5.23179s/12 iters), loss = 0.31132
I0407 09:19:54.097575 17723 solver.cpp:237]     Train net output #0: loss = 0.311321 (* 1 = 0.311321 loss)
I0407 09:19:54.097584 17723 sgd_solver.cpp:105] Iteration 6300, lr = 0.005
I0407 09:19:59.249511 17723 solver.cpp:218] Iteration 6312 (2.32925 iter/s, 5.15188s/12 iters), loss = 0.205744
I0407 09:19:59.249552 17723 solver.cpp:237]     Train net output #0: loss = 0.205744 (* 1 = 0.205744 loss)
I0407 09:19:59.249560 17723 sgd_solver.cpp:105] Iteration 6312, lr = 0.005
I0407 09:20:04.069885 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0407 09:20:07.135699 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0407 09:20:09.988461 17723 solver.cpp:330] Iteration 6324, Testing net (#0)
I0407 09:20:09.988481 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:20:11.899159 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:20:14.522409 17723 solver.cpp:397]     Test net output #0: accuracy = 0.422794
I0407 09:20:14.522434 17723 solver.cpp:397]     Test net output #1: loss = 2.9519 (* 1 = 2.9519 loss)
I0407 09:20:14.659190 17723 solver.cpp:218] Iteration 6324 (0.77874 iter/s, 15.4095s/12 iters), loss = 0.396142
I0407 09:20:14.659225 17723 solver.cpp:237]     Train net output #0: loss = 0.396142 (* 1 = 0.396142 loss)
I0407 09:20:14.659232 17723 sgd_solver.cpp:105] Iteration 6324, lr = 0.005
I0407 09:20:18.793133 17723 solver.cpp:218] Iteration 6336 (2.90286 iter/s, 4.13386s/12 iters), loss = 0.237518
I0407 09:20:18.793175 17723 solver.cpp:237]     Train net output #0: loss = 0.237518 (* 1 = 0.237518 loss)
I0407 09:20:18.793184 17723 sgd_solver.cpp:105] Iteration 6336, lr = 0.005
I0407 09:20:24.084115 17723 solver.cpp:218] Iteration 6348 (2.26805 iter/s, 5.29088s/12 iters), loss = 0.334599
I0407 09:20:24.084255 17723 solver.cpp:237]     Train net output #0: loss = 0.3346 (* 1 = 0.3346 loss)
I0407 09:20:24.084264 17723 sgd_solver.cpp:105] Iteration 6348, lr = 0.005
I0407 09:20:29.422621 17723 solver.cpp:218] Iteration 6360 (2.2479 iter/s, 5.33831s/12 iters), loss = 0.207462
I0407 09:20:29.422665 17723 solver.cpp:237]     Train net output #0: loss = 0.207462 (* 1 = 0.207462 loss)
I0407 09:20:29.422673 17723 sgd_solver.cpp:105] Iteration 6360, lr = 0.005
I0407 09:20:34.477509 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:20:34.670894 17723 solver.cpp:218] Iteration 6372 (2.28651 iter/s, 5.24817s/12 iters), loss = 0.272583
I0407 09:20:34.670933 17723 solver.cpp:237]     Train net output #0: loss = 0.272583 (* 1 = 0.272583 loss)
I0407 09:20:34.670940 17723 sgd_solver.cpp:105] Iteration 6372, lr = 0.005
I0407 09:20:40.099916 17723 solver.cpp:218] Iteration 6384 (2.21038 iter/s, 5.42892s/12 iters), loss = 0.306488
I0407 09:20:40.099965 17723 solver.cpp:237]     Train net output #0: loss = 0.306488 (* 1 = 0.306488 loss)
I0407 09:20:40.099972 17723 sgd_solver.cpp:105] Iteration 6384, lr = 0.005
I0407 09:20:45.348507 17723 solver.cpp:218] Iteration 6396 (2.28637 iter/s, 5.24849s/12 iters), loss = 0.284852
I0407 09:20:45.348548 17723 solver.cpp:237]     Train net output #0: loss = 0.284852 (* 1 = 0.284852 loss)
I0407 09:20:45.348554 17723 sgd_solver.cpp:105] Iteration 6396, lr = 0.005
I0407 09:20:50.528477 17723 solver.cpp:218] Iteration 6408 (2.31666 iter/s, 5.17987s/12 iters), loss = 0.525476
I0407 09:20:50.528539 17723 solver.cpp:237]     Train net output #0: loss = 0.525477 (* 1 = 0.525477 loss)
I0407 09:20:50.528554 17723 sgd_solver.cpp:105] Iteration 6408, lr = 0.005
I0407 09:20:55.725690 17723 solver.cpp:218] Iteration 6420 (2.30898 iter/s, 5.1971s/12 iters), loss = 0.307899
I0407 09:20:55.725805 17723 solver.cpp:237]     Train net output #0: loss = 0.307899 (* 1 = 0.307899 loss)
I0407 09:20:55.725816 17723 sgd_solver.cpp:105] Iteration 6420, lr = 0.005
I0407 09:20:57.788995 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0407 09:21:00.964742 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0407 09:21:03.855111 17723 solver.cpp:330] Iteration 6426, Testing net (#0)
I0407 09:21:03.855130 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:21:05.633878 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:21:08.107044 17723 solver.cpp:397]     Test net output #0: accuracy = 0.431985
I0407 09:21:08.107082 17723 solver.cpp:397]     Test net output #1: loss = 3.00469 (* 1 = 3.00469 loss)
I0407 09:21:09.999143 17723 solver.cpp:218] Iteration 6432 (0.840735 iter/s, 14.2732s/12 iters), loss = 0.241522
I0407 09:21:09.999182 17723 solver.cpp:237]     Train net output #0: loss = 0.241522 (* 1 = 0.241522 loss)
I0407 09:21:09.999189 17723 sgd_solver.cpp:105] Iteration 6432, lr = 0.005
I0407 09:21:15.353475 17723 solver.cpp:218] Iteration 6444 (2.24122 iter/s, 5.35423s/12 iters), loss = 0.412065
I0407 09:21:15.353515 17723 solver.cpp:237]     Train net output #0: loss = 0.412065 (* 1 = 0.412065 loss)
I0407 09:21:15.353523 17723 sgd_solver.cpp:105] Iteration 6444, lr = 0.005
I0407 09:21:20.423384 17723 solver.cpp:218] Iteration 6456 (2.36695 iter/s, 5.06981s/12 iters), loss = 0.265322
I0407 09:21:20.423434 17723 solver.cpp:237]     Train net output #0: loss = 0.265322 (* 1 = 0.265322 loss)
I0407 09:21:20.423441 17723 sgd_solver.cpp:105] Iteration 6456, lr = 0.005
I0407 09:21:25.508301 17723 solver.cpp:218] Iteration 6468 (2.35997 iter/s, 5.08481s/12 iters), loss = 0.286483
I0407 09:21:25.508342 17723 solver.cpp:237]     Train net output #0: loss = 0.286483 (* 1 = 0.286483 loss)
I0407 09:21:25.508348 17723 sgd_solver.cpp:105] Iteration 6468, lr = 0.005
I0407 09:21:27.435180 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:21:30.646378 17723 solver.cpp:218] Iteration 6480 (2.33555 iter/s, 5.13798s/12 iters), loss = 0.175376
I0407 09:21:30.646422 17723 solver.cpp:237]     Train net output #0: loss = 0.175376 (* 1 = 0.175376 loss)
I0407 09:21:30.646430 17723 sgd_solver.cpp:105] Iteration 6480, lr = 0.005
I0407 09:21:35.851492 17723 solver.cpp:218] Iteration 6492 (2.30547 iter/s, 5.20501s/12 iters), loss = 0.363529
I0407 09:21:35.851532 17723 solver.cpp:237]     Train net output #0: loss = 0.363529 (* 1 = 0.363529 loss)
I0407 09:21:35.851538 17723 sgd_solver.cpp:105] Iteration 6492, lr = 0.005
I0407 09:21:41.054170 17723 solver.cpp:218] Iteration 6504 (2.30655 iter/s, 5.20258s/12 iters), loss = 0.115061
I0407 09:21:41.054214 17723 solver.cpp:237]     Train net output #0: loss = 0.115061 (* 1 = 0.115061 loss)
I0407 09:21:41.054220 17723 sgd_solver.cpp:105] Iteration 6504, lr = 0.005
I0407 09:21:46.350474 17723 solver.cpp:218] Iteration 6516 (2.26578 iter/s, 5.2962s/12 iters), loss = 0.197927
I0407 09:21:46.350526 17723 solver.cpp:237]     Train net output #0: loss = 0.197927 (* 1 = 0.197927 loss)
I0407 09:21:46.350534 17723 sgd_solver.cpp:105] Iteration 6516, lr = 0.005
I0407 09:21:51.172130 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0407 09:21:54.263063 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0407 09:21:56.904897 17723 solver.cpp:330] Iteration 6528, Testing net (#0)
I0407 09:21:56.904918 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:21:58.647805 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:22:01.159147 17723 solver.cpp:397]     Test net output #0: accuracy = 0.440564
I0407 09:22:01.159174 17723 solver.cpp:397]     Test net output #1: loss = 3.09967 (* 1 = 3.09967 loss)
I0407 09:22:01.299621 17723 solver.cpp:218] Iteration 6528 (0.802731 iter/s, 14.949s/12 iters), loss = 0.198191
I0407 09:22:01.299674 17723 solver.cpp:237]     Train net output #0: loss = 0.198191 (* 1 = 0.198191 loss)
I0407 09:22:01.299682 17723 sgd_solver.cpp:105] Iteration 6528, lr = 0.005
I0407 09:22:05.590034 17723 solver.cpp:218] Iteration 6540 (2.797 iter/s, 4.29031s/12 iters), loss = 0.226992
I0407 09:22:05.590080 17723 solver.cpp:237]     Train net output #0: loss = 0.226992 (* 1 = 0.226992 loss)
I0407 09:22:05.590087 17723 sgd_solver.cpp:105] Iteration 6540, lr = 0.005
I0407 09:22:10.802538 17723 solver.cpp:218] Iteration 6552 (2.3022 iter/s, 5.21241s/12 iters), loss = 0.316611
I0407 09:22:10.802580 17723 solver.cpp:237]     Train net output #0: loss = 0.316611 (* 1 = 0.316611 loss)
I0407 09:22:10.802588 17723 sgd_solver.cpp:105] Iteration 6552, lr = 0.005
I0407 09:22:16.160691 17723 solver.cpp:218] Iteration 6564 (2.23962 iter/s, 5.35805s/12 iters), loss = 0.251943
I0407 09:22:16.160734 17723 solver.cpp:237]     Train net output #0: loss = 0.251943 (* 1 = 0.251943 loss)
I0407 09:22:16.160742 17723 sgd_solver.cpp:105] Iteration 6564, lr = 0.005
I0407 09:22:20.678248 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:22:21.538858 17723 solver.cpp:218] Iteration 6576 (2.23128 iter/s, 5.37807s/12 iters), loss = 0.433876
I0407 09:22:21.538902 17723 solver.cpp:237]     Train net output #0: loss = 0.433876 (* 1 = 0.433876 loss)
I0407 09:22:21.538908 17723 sgd_solver.cpp:105] Iteration 6576, lr = 0.005
I0407 09:22:26.837884 17723 solver.cpp:218] Iteration 6588 (2.26461 iter/s, 5.29893s/12 iters), loss = 0.301823
I0407 09:22:26.837927 17723 solver.cpp:237]     Train net output #0: loss = 0.301823 (* 1 = 0.301823 loss)
I0407 09:22:26.837935 17723 sgd_solver.cpp:105] Iteration 6588, lr = 0.005
I0407 09:22:32.154449 17723 solver.cpp:218] Iteration 6600 (2.25714 iter/s, 5.31646s/12 iters), loss = 0.265304
I0407 09:22:32.154570 17723 solver.cpp:237]     Train net output #0: loss = 0.265304 (* 1 = 0.265304 loss)
I0407 09:22:32.154578 17723 sgd_solver.cpp:105] Iteration 6600, lr = 0.005
I0407 09:22:37.413899 17723 solver.cpp:218] Iteration 6612 (2.28168 iter/s, 5.25928s/12 iters), loss = 0.311041
I0407 09:22:37.413938 17723 solver.cpp:237]     Train net output #0: loss = 0.311041 (* 1 = 0.311041 loss)
I0407 09:22:37.413945 17723 sgd_solver.cpp:105] Iteration 6612, lr = 0.005
I0407 09:22:42.649277 17723 solver.cpp:218] Iteration 6624 (2.29214 iter/s, 5.23528s/12 iters), loss = 0.14351
I0407 09:22:42.649327 17723 solver.cpp:237]     Train net output #0: loss = 0.143511 (* 1 = 0.143511 loss)
I0407 09:22:42.649333 17723 sgd_solver.cpp:105] Iteration 6624, lr = 0.005
I0407 09:22:44.778488 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0407 09:22:47.790019 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0407 09:22:52.115420 17723 solver.cpp:330] Iteration 6630, Testing net (#0)
I0407 09:22:52.115442 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:22:53.840657 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:22:56.385017 17723 solver.cpp:397]     Test net output #0: accuracy = 0.426471
I0407 09:22:56.385051 17723 solver.cpp:397]     Test net output #1: loss = 3.14049 (* 1 = 3.14049 loss)
I0407 09:22:58.339797 17723 solver.cpp:218] Iteration 6636 (0.764802 iter/s, 15.6903s/12 iters), loss = 0.309342
I0407 09:22:58.339829 17723 solver.cpp:237]     Train net output #0: loss = 0.309342 (* 1 = 0.309342 loss)
I0407 09:22:58.339836 17723 sgd_solver.cpp:105] Iteration 6636, lr = 0.005
I0407 09:23:03.675227 17723 solver.cpp:218] Iteration 6648 (2.24915 iter/s, 5.33534s/12 iters), loss = 0.358012
I0407 09:23:03.675312 17723 solver.cpp:237]     Train net output #0: loss = 0.358012 (* 1 = 0.358012 loss)
I0407 09:23:03.675318 17723 sgd_solver.cpp:105] Iteration 6648, lr = 0.005
I0407 09:23:09.001051 17723 solver.cpp:218] Iteration 6660 (2.25323 iter/s, 5.32568s/12 iters), loss = 0.278763
I0407 09:23:09.001098 17723 solver.cpp:237]     Train net output #0: loss = 0.278763 (* 1 = 0.278763 loss)
I0407 09:23:09.001106 17723 sgd_solver.cpp:105] Iteration 6660, lr = 0.005
I0407 09:23:14.218868 17723 solver.cpp:218] Iteration 6672 (2.29986 iter/s, 5.21771s/12 iters), loss = 0.217365
I0407 09:23:14.218909 17723 solver.cpp:237]     Train net output #0: loss = 0.217365 (* 1 = 0.217365 loss)
I0407 09:23:14.218916 17723 sgd_solver.cpp:105] Iteration 6672, lr = 0.005
I0407 09:23:15.608348 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:23:19.607641 17723 solver.cpp:218] Iteration 6684 (2.2269 iter/s, 5.38867s/12 iters), loss = 0.275174
I0407 09:23:19.607710 17723 solver.cpp:237]     Train net output #0: loss = 0.275174 (* 1 = 0.275174 loss)
I0407 09:23:19.607724 17723 sgd_solver.cpp:105] Iteration 6684, lr = 0.005
I0407 09:23:24.888188 17723 solver.cpp:218] Iteration 6696 (2.27254 iter/s, 5.28042s/12 iters), loss = 0.360311
I0407 09:23:24.888242 17723 solver.cpp:237]     Train net output #0: loss = 0.360311 (* 1 = 0.360311 loss)
I0407 09:23:24.888250 17723 sgd_solver.cpp:105] Iteration 6696, lr = 0.005
I0407 09:23:30.089200 17723 solver.cpp:218] Iteration 6708 (2.30729 iter/s, 5.2009s/12 iters), loss = 0.180445
I0407 09:23:30.089246 17723 solver.cpp:237]     Train net output #0: loss = 0.180445 (* 1 = 0.180445 loss)
I0407 09:23:30.089252 17723 sgd_solver.cpp:105] Iteration 6708, lr = 0.005
I0407 09:23:35.491662 17723 solver.cpp:218] Iteration 6720 (2.22125 iter/s, 5.40236s/12 iters), loss = 0.280516
I0407 09:23:35.491806 17723 solver.cpp:237]     Train net output #0: loss = 0.280516 (* 1 = 0.280516 loss)
I0407 09:23:35.491816 17723 sgd_solver.cpp:105] Iteration 6720, lr = 0.005
I0407 09:23:40.334231 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0407 09:23:43.419330 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0407 09:23:48.053584 17723 solver.cpp:330] Iteration 6732, Testing net (#0)
I0407 09:23:48.053611 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:23:49.762781 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:23:52.355916 17723 solver.cpp:397]     Test net output #0: accuracy = 0.457108
I0407 09:23:52.355954 17723 solver.cpp:397]     Test net output #1: loss = 3.03881 (* 1 = 3.03881 loss)
I0407 09:23:52.496302 17723 solver.cpp:218] Iteration 6732 (0.705702 iter/s, 17.0044s/12 iters), loss = 0.109536
I0407 09:23:52.496363 17723 solver.cpp:237]     Train net output #0: loss = 0.109536 (* 1 = 0.109536 loss)
I0407 09:23:52.496376 17723 sgd_solver.cpp:105] Iteration 6732, lr = 0.0025
I0407 09:23:56.784590 17723 solver.cpp:218] Iteration 6744 (2.79839 iter/s, 4.28818s/12 iters), loss = 0.113135
I0407 09:23:56.784646 17723 solver.cpp:237]     Train net output #0: loss = 0.113135 (* 1 = 0.113135 loss)
I0407 09:23:56.784654 17723 sgd_solver.cpp:105] Iteration 6744, lr = 0.0025
I0407 09:24:01.840051 17723 solver.cpp:218] Iteration 6756 (2.37372 iter/s, 5.05535s/12 iters), loss = 0.345276
I0407 09:24:01.840097 17723 solver.cpp:237]     Train net output #0: loss = 0.345276 (* 1 = 0.345276 loss)
I0407 09:24:01.840104 17723 sgd_solver.cpp:105] Iteration 6756, lr = 0.0025
I0407 09:24:07.021613 17723 solver.cpp:218] Iteration 6768 (2.31595 iter/s, 5.18146s/12 iters), loss = 0.218324
I0407 09:24:07.021718 17723 solver.cpp:237]     Train net output #0: loss = 0.218324 (* 1 = 0.218324 loss)
I0407 09:24:07.021728 17723 sgd_solver.cpp:105] Iteration 6768, lr = 0.0025
I0407 09:24:10.748338 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:24:12.397274 17723 solver.cpp:218] Iteration 6780 (2.23235 iter/s, 5.3755s/12 iters), loss = 0.136354
I0407 09:24:12.397317 17723 solver.cpp:237]     Train net output #0: loss = 0.136354 (* 1 = 0.136354 loss)
I0407 09:24:12.397325 17723 sgd_solver.cpp:105] Iteration 6780, lr = 0.0025
I0407 09:24:17.677855 17723 solver.cpp:218] Iteration 6792 (2.27252 iter/s, 5.28048s/12 iters), loss = 0.234415
I0407 09:24:17.677906 17723 solver.cpp:237]     Train net output #0: loss = 0.234415 (* 1 = 0.234415 loss)
I0407 09:24:17.677915 17723 sgd_solver.cpp:105] Iteration 6792, lr = 0.0025
I0407 09:24:22.968816 17723 solver.cpp:218] Iteration 6804 (2.26807 iter/s, 5.29085s/12 iters), loss = 0.130439
I0407 09:24:22.968858 17723 solver.cpp:237]     Train net output #0: loss = 0.130439 (* 1 = 0.130439 loss)
I0407 09:24:22.968865 17723 sgd_solver.cpp:105] Iteration 6804, lr = 0.0025
I0407 09:24:28.203060 17723 solver.cpp:218] Iteration 6816 (2.29264 iter/s, 5.23414s/12 iters), loss = 0.138374
I0407 09:24:28.203106 17723 solver.cpp:237]     Train net output #0: loss = 0.138374 (* 1 = 0.138374 loss)
I0407 09:24:28.203114 17723 sgd_solver.cpp:105] Iteration 6816, lr = 0.0025
I0407 09:24:33.587412 17723 solver.cpp:218] Iteration 6828 (2.22872 iter/s, 5.38424s/12 iters), loss = 0.178136
I0407 09:24:33.587455 17723 solver.cpp:237]     Train net output #0: loss = 0.178136 (* 1 = 0.178136 loss)
I0407 09:24:33.587461 17723 sgd_solver.cpp:105] Iteration 6828, lr = 0.0025
I0407 09:24:35.639132 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0407 09:24:38.649065 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0407 09:24:42.476346 17723 solver.cpp:330] Iteration 6834, Testing net (#0)
I0407 09:24:42.476374 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:24:44.222311 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:24:47.008067 17723 solver.cpp:397]     Test net output #0: accuracy = 0.461397
I0407 09:24:47.008117 17723 solver.cpp:397]     Test net output #1: loss = 2.86097 (* 1 = 2.86097 loss)
I0407 09:24:49.068373 17723 solver.cpp:218] Iteration 6840 (0.775154 iter/s, 15.4808s/12 iters), loss = 0.31725
I0407 09:24:49.068428 17723 solver.cpp:237]     Train net output #0: loss = 0.31725 (* 1 = 0.31725 loss)
I0407 09:24:49.068439 17723 sgd_solver.cpp:105] Iteration 6840, lr = 0.0025
I0407 09:24:54.132351 17723 solver.cpp:218] Iteration 6852 (2.36973 iter/s, 5.06387s/12 iters), loss = 0.302486
I0407 09:24:54.132401 17723 solver.cpp:237]     Train net output #0: loss = 0.302487 (* 1 = 0.302487 loss)
I0407 09:24:54.132407 17723 sgd_solver.cpp:105] Iteration 6852, lr = 0.0025
I0407 09:24:59.290779 17723 solver.cpp:218] Iteration 6864 (2.32634 iter/s, 5.15833s/12 iters), loss = 0.209576
I0407 09:24:59.290822 17723 solver.cpp:237]     Train net output #0: loss = 0.209576 (* 1 = 0.209576 loss)
I0407 09:24:59.290829 17723 sgd_solver.cpp:105] Iteration 6864, lr = 0.0025
I0407 09:25:04.610190 17723 solver.cpp:218] Iteration 6876 (2.25593 iter/s, 5.31931s/12 iters), loss = 0.271104
I0407 09:25:04.610252 17723 solver.cpp:237]     Train net output #0: loss = 0.271104 (* 1 = 0.271104 loss)
I0407 09:25:04.610262 17723 sgd_solver.cpp:105] Iteration 6876, lr = 0.0025
I0407 09:25:05.246156 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:25:09.783083 17723 solver.cpp:218] Iteration 6888 (2.31984 iter/s, 5.17278s/12 iters), loss = 0.229179
I0407 09:25:09.783218 17723 solver.cpp:237]     Train net output #0: loss = 0.229179 (* 1 = 0.229179 loss)
I0407 09:25:09.783228 17723 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025
I0407 09:25:14.982234 17723 solver.cpp:218] Iteration 6900 (2.30815 iter/s, 5.19897s/12 iters), loss = 0.0962233
I0407 09:25:14.982278 17723 solver.cpp:237]     Train net output #0: loss = 0.0962234 (* 1 = 0.0962234 loss)
I0407 09:25:14.982285 17723 sgd_solver.cpp:105] Iteration 6900, lr = 0.0025
I0407 09:25:20.294093 17723 solver.cpp:218] Iteration 6912 (2.25914 iter/s, 5.31176s/12 iters), loss = 0.102003
I0407 09:25:20.294139 17723 solver.cpp:237]     Train net output #0: loss = 0.102004 (* 1 = 0.102004 loss)
I0407 09:25:20.294147 17723 sgd_solver.cpp:105] Iteration 6912, lr = 0.0025
I0407 09:25:25.652074 17723 solver.cpp:218] Iteration 6924 (2.23969 iter/s, 5.35788s/12 iters), loss = 0.238217
I0407 09:25:25.652113 17723 solver.cpp:237]     Train net output #0: loss = 0.238217 (* 1 = 0.238217 loss)
I0407 09:25:25.652120 17723 sgd_solver.cpp:105] Iteration 6924, lr = 0.0025
I0407 09:25:30.409894 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0407 09:25:33.488057 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0407 09:25:37.647881 17723 solver.cpp:330] Iteration 6936, Testing net (#0)
I0407 09:25:37.647904 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:25:38.272454 17723 blocking_queue.cpp:49] Waiting for data
I0407 09:25:39.366467 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:25:42.174718 17723 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0407 09:25:42.174849 17723 solver.cpp:397]     Test net output #1: loss = 2.83762 (* 1 = 2.83762 loss)
I0407 09:25:42.312412 17723 solver.cpp:218] Iteration 6936 (0.720281 iter/s, 16.6602s/12 iters), loss = 0.197332
I0407 09:25:42.312465 17723 solver.cpp:237]     Train net output #0: loss = 0.197332 (* 1 = 0.197332 loss)
I0407 09:25:42.312475 17723 sgd_solver.cpp:105] Iteration 6936, lr = 0.0025
I0407 09:25:46.631764 17723 solver.cpp:218] Iteration 6948 (2.77826 iter/s, 4.31926s/12 iters), loss = 0.0996729
I0407 09:25:46.631806 17723 solver.cpp:237]     Train net output #0: loss = 0.099673 (* 1 = 0.099673 loss)
I0407 09:25:46.631815 17723 sgd_solver.cpp:105] Iteration 6948, lr = 0.0025
I0407 09:25:52.034448 17723 solver.cpp:218] Iteration 6960 (2.22116 iter/s, 5.40259s/12 iters), loss = 0.236494
I0407 09:25:52.034494 17723 solver.cpp:237]     Train net output #0: loss = 0.236494 (* 1 = 0.236494 loss)
I0407 09:25:52.034502 17723 sgd_solver.cpp:105] Iteration 6960, lr = 0.0025
I0407 09:25:57.433776 17723 solver.cpp:218] Iteration 6972 (2.22254 iter/s, 5.39923s/12 iters), loss = 0.0884773
I0407 09:25:57.433820 17723 solver.cpp:237]     Train net output #0: loss = 0.0884775 (* 1 = 0.0884775 loss)
I0407 09:25:57.433827 17723 sgd_solver.cpp:105] Iteration 6972, lr = 0.0025
I0407 09:26:00.396134 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:26:02.752426 17723 solver.cpp:218] Iteration 6984 (2.25625 iter/s, 5.31855s/12 iters), loss = 0.129261
I0407 09:26:02.752467 17723 solver.cpp:237]     Train net output #0: loss = 0.129261 (* 1 = 0.129261 loss)
I0407 09:26:02.752475 17723 sgd_solver.cpp:105] Iteration 6984, lr = 0.0025
I0407 09:26:08.098132 17723 solver.cpp:218] Iteration 6996 (2.24483 iter/s, 5.34561s/12 iters), loss = 0.368664
I0407 09:26:08.098174 17723 solver.cpp:237]     Train net output #0: loss = 0.368664 (* 1 = 0.368664 loss)
I0407 09:26:08.098181 17723 sgd_solver.cpp:105] Iteration 6996, lr = 0.0025
I0407 09:26:13.310642 17723 solver.cpp:218] Iteration 7008 (2.3022 iter/s, 5.21241s/12 iters), loss = 0.183816
I0407 09:26:13.310766 17723 solver.cpp:237]     Train net output #0: loss = 0.183816 (* 1 = 0.183816 loss)
I0407 09:26:13.310775 17723 sgd_solver.cpp:105] Iteration 7008, lr = 0.0025
I0407 09:26:18.473307 17723 solver.cpp:218] Iteration 7020 (2.32446 iter/s, 5.16249s/12 iters), loss = 0.108543
I0407 09:26:18.473346 17723 solver.cpp:237]     Train net output #0: loss = 0.108543 (* 1 = 0.108543 loss)
I0407 09:26:18.473354 17723 sgd_solver.cpp:105] Iteration 7020, lr = 0.0025
I0407 09:26:23.422466 17723 solver.cpp:218] Iteration 7032 (2.4247 iter/s, 4.94907s/12 iters), loss = 0.180041
I0407 09:26:23.422508 17723 solver.cpp:237]     Train net output #0: loss = 0.180041 (* 1 = 0.180041 loss)
I0407 09:26:23.422514 17723 sgd_solver.cpp:105] Iteration 7032, lr = 0.0025
I0407 09:26:25.504164 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0407 09:26:28.514153 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0407 09:26:32.697281 17723 solver.cpp:330] Iteration 7038, Testing net (#0)
I0407 09:26:32.697302 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:26:34.292625 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:26:36.986835 17723 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0407 09:26:36.986876 17723 solver.cpp:397]     Test net output #1: loss = 2.89925 (* 1 = 2.89925 loss)
I0407 09:26:38.953353 17723 solver.cpp:218] Iteration 7044 (0.772662 iter/s, 15.5307s/12 iters), loss = 0.168955
I0407 09:26:38.953397 17723 solver.cpp:237]     Train net output #0: loss = 0.168955 (* 1 = 0.168955 loss)
I0407 09:26:38.953404 17723 sgd_solver.cpp:105] Iteration 7044, lr = 0.0025
I0407 09:26:44.011093 17723 solver.cpp:218] Iteration 7056 (2.37265 iter/s, 5.05764s/12 iters), loss = 0.195244
I0407 09:26:44.011193 17723 solver.cpp:237]     Train net output #0: loss = 0.195244 (* 1 = 0.195244 loss)
I0407 09:26:44.011202 17723 sgd_solver.cpp:105] Iteration 7056, lr = 0.0025
I0407 09:26:48.943266 17723 solver.cpp:218] Iteration 7068 (2.43308 iter/s, 4.93202s/12 iters), loss = 0.13238
I0407 09:26:48.943317 17723 solver.cpp:237]     Train net output #0: loss = 0.13238 (* 1 = 0.13238 loss)
I0407 09:26:48.943327 17723 sgd_solver.cpp:105] Iteration 7068, lr = 0.0025
I0407 09:26:53.920123 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:26:54.084899 17723 solver.cpp:218] Iteration 7080 (2.33394 iter/s, 5.14152s/12 iters), loss = 0.0968622
I0407 09:26:54.084944 17723 solver.cpp:237]     Train net output #0: loss = 0.0968624 (* 1 = 0.0968624 loss)
I0407 09:26:54.084951 17723 sgd_solver.cpp:105] Iteration 7080, lr = 0.0025
I0407 09:26:59.283915 17723 solver.cpp:218] Iteration 7092 (2.30817 iter/s, 5.19892s/12 iters), loss = 0.193178
I0407 09:26:59.283958 17723 solver.cpp:237]     Train net output #0: loss = 0.193178 (* 1 = 0.193178 loss)
I0407 09:26:59.283967 17723 sgd_solver.cpp:105] Iteration 7092, lr = 0.0025
I0407 09:27:04.486554 17723 solver.cpp:218] Iteration 7104 (2.30656 iter/s, 5.20254s/12 iters), loss = 0.0924165
I0407 09:27:04.486598 17723 solver.cpp:237]     Train net output #0: loss = 0.0924166 (* 1 = 0.0924166 loss)
I0407 09:27:04.486605 17723 sgd_solver.cpp:105] Iteration 7104, lr = 0.0025
I0407 09:27:09.878458 17723 solver.cpp:218] Iteration 7116 (2.2256 iter/s, 5.3918s/12 iters), loss = 0.132478
I0407 09:27:09.878502 17723 solver.cpp:237]     Train net output #0: loss = 0.132478 (* 1 = 0.132478 loss)
I0407 09:27:09.878509 17723 sgd_solver.cpp:105] Iteration 7116, lr = 0.0025
I0407 09:27:15.087738 17723 solver.cpp:218] Iteration 7128 (2.30363 iter/s, 5.20918s/12 iters), loss = 0.347352
I0407 09:27:15.087878 17723 solver.cpp:237]     Train net output #0: loss = 0.347352 (* 1 = 0.347352 loss)
I0407 09:27:15.087888 17723 sgd_solver.cpp:105] Iteration 7128, lr = 0.0025
I0407 09:27:19.730913 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0407 09:27:22.797268 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0407 09:27:27.011042 17723 solver.cpp:330] Iteration 7140, Testing net (#0)
I0407 09:27:27.011065 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:27:28.650068 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:27:31.416942 17723 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0407 09:27:31.416976 17723 solver.cpp:397]     Test net output #1: loss = 2.86754 (* 1 = 2.86754 loss)
I0407 09:27:31.552909 17723 solver.cpp:218] Iteration 7140 (0.728823 iter/s, 16.4649s/12 iters), loss = 0.125222
I0407 09:27:31.554481 17723 solver.cpp:237]     Train net output #0: loss = 0.125222 (* 1 = 0.125222 loss)
I0407 09:27:31.554494 17723 sgd_solver.cpp:105] Iteration 7140, lr = 0.0025
I0407 09:27:36.046223 17723 solver.cpp:218] Iteration 7152 (2.67159 iter/s, 4.4917s/12 iters), loss = 0.148154
I0407 09:27:36.046273 17723 solver.cpp:237]     Train net output #0: loss = 0.148154 (* 1 = 0.148154 loss)
I0407 09:27:36.046283 17723 sgd_solver.cpp:105] Iteration 7152, lr = 0.0025
I0407 09:27:41.378137 17723 solver.cpp:218] Iteration 7164 (2.25064 iter/s, 5.33181s/12 iters), loss = 0.099054
I0407 09:27:41.378186 17723 solver.cpp:237]     Train net output #0: loss = 0.0990541 (* 1 = 0.0990541 loss)
I0407 09:27:41.378196 17723 sgd_solver.cpp:105] Iteration 7164, lr = 0.0025
I0407 09:27:46.559923 17723 solver.cpp:218] Iteration 7176 (2.31585 iter/s, 5.18168s/12 iters), loss = 0.0978126
I0407 09:27:46.560034 17723 solver.cpp:237]     Train net output #0: loss = 0.0978127 (* 1 = 0.0978127 loss)
I0407 09:27:46.560045 17723 sgd_solver.cpp:105] Iteration 7176, lr = 0.0025
I0407 09:27:48.811414 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:27:51.839785 17723 solver.cpp:218] Iteration 7188 (2.27286 iter/s, 5.27969s/12 iters), loss = 0.186258
I0407 09:27:51.839834 17723 solver.cpp:237]     Train net output #0: loss = 0.186258 (* 1 = 0.186258 loss)
I0407 09:27:51.839843 17723 sgd_solver.cpp:105] Iteration 7188, lr = 0.0025
I0407 09:27:57.177480 17723 solver.cpp:218] Iteration 7200 (2.2482 iter/s, 5.33759s/12 iters), loss = 0.092208
I0407 09:27:57.177523 17723 solver.cpp:237]     Train net output #0: loss = 0.0922081 (* 1 = 0.0922081 loss)
I0407 09:27:57.177531 17723 sgd_solver.cpp:105] Iteration 7200, lr = 0.0025
I0407 09:28:02.557225 17723 solver.cpp:218] Iteration 7212 (2.23063 iter/s, 5.37965s/12 iters), loss = 0.137213
I0407 09:28:02.557265 17723 solver.cpp:237]     Train net output #0: loss = 0.137213 (* 1 = 0.137213 loss)
I0407 09:28:02.557271 17723 sgd_solver.cpp:105] Iteration 7212, lr = 0.0025
I0407 09:28:07.775455 17723 solver.cpp:218] Iteration 7224 (2.29967 iter/s, 5.21814s/12 iters), loss = 0.152205
I0407 09:28:07.775501 17723 solver.cpp:237]     Train net output #0: loss = 0.152205 (* 1 = 0.152205 loss)
I0407 09:28:07.775509 17723 sgd_solver.cpp:105] Iteration 7224, lr = 0.0025
I0407 09:28:13.094461 17723 solver.cpp:218] Iteration 7236 (2.2561 iter/s, 5.31891s/12 iters), loss = 0.212324
I0407 09:28:13.094503 17723 solver.cpp:237]     Train net output #0: loss = 0.212325 (* 1 = 0.212325 loss)
I0407 09:28:13.094511 17723 sgd_solver.cpp:105] Iteration 7236, lr = 0.0025
I0407 09:28:15.229918 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0407 09:28:18.294026 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0407 09:28:22.021073 17723 solver.cpp:330] Iteration 7242, Testing net (#0)
I0407 09:28:22.021091 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:28:23.500453 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:28:26.317742 17723 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0407 09:28:26.317777 17723 solver.cpp:397]     Test net output #1: loss = 2.86458 (* 1 = 2.86458 loss)
I0407 09:28:28.125174 17723 solver.cpp:218] Iteration 7248 (0.798374 iter/s, 15.0305s/12 iters), loss = 0.176346
I0407 09:28:28.125226 17723 solver.cpp:237]     Train net output #0: loss = 0.176346 (* 1 = 0.176346 loss)
I0407 09:28:28.125234 17723 sgd_solver.cpp:105] Iteration 7248, lr = 0.0025
I0407 09:28:33.299758 17723 solver.cpp:218] Iteration 7260 (2.31907 iter/s, 5.17448s/12 iters), loss = 0.0754882
I0407 09:28:33.299801 17723 solver.cpp:237]     Train net output #0: loss = 0.0754883 (* 1 = 0.0754883 loss)
I0407 09:28:33.299809 17723 sgd_solver.cpp:105] Iteration 7260, lr = 0.0025
I0407 09:28:38.512902 17723 solver.cpp:218] Iteration 7272 (2.30192 iter/s, 5.21304s/12 iters), loss = 0.204317
I0407 09:28:38.512939 17723 solver.cpp:237]     Train net output #0: loss = 0.204317 (* 1 = 0.204317 loss)
I0407 09:28:38.512945 17723 sgd_solver.cpp:105] Iteration 7272, lr = 0.0025
I0407 09:28:43.059015 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:28:43.880993 17723 solver.cpp:218] Iteration 7284 (2.23547 iter/s, 5.368s/12 iters), loss = 0.199815
I0407 09:28:43.881038 17723 solver.cpp:237]     Train net output #0: loss = 0.199815 (* 1 = 0.199815 loss)
I0407 09:28:43.881047 17723 sgd_solver.cpp:105] Iteration 7284, lr = 0.0025
I0407 09:28:48.883067 17723 solver.cpp:218] Iteration 7296 (2.39905 iter/s, 5.00197s/12 iters), loss = 0.101158
I0407 09:28:48.883184 17723 solver.cpp:237]     Train net output #0: loss = 0.101158 (* 1 = 0.101158 loss)
I0407 09:28:48.883193 17723 sgd_solver.cpp:105] Iteration 7296, lr = 0.0025
I0407 09:28:54.178018 17723 solver.cpp:218] Iteration 7308 (2.26638 iter/s, 5.29478s/12 iters), loss = 0.306776
I0407 09:28:54.178066 17723 solver.cpp:237]     Train net output #0: loss = 0.306776 (* 1 = 0.306776 loss)
I0407 09:28:54.178073 17723 sgd_solver.cpp:105] Iteration 7308, lr = 0.0025
I0407 09:28:59.532171 17723 solver.cpp:218] Iteration 7320 (2.24129 iter/s, 5.35405s/12 iters), loss = 0.0362766
I0407 09:28:59.532210 17723 solver.cpp:237]     Train net output #0: loss = 0.0362768 (* 1 = 0.0362768 loss)
I0407 09:28:59.532217 17723 sgd_solver.cpp:105] Iteration 7320, lr = 0.0025
I0407 09:29:04.787329 17723 solver.cpp:218] Iteration 7332 (2.28351 iter/s, 5.25506s/12 iters), loss = 0.186772
I0407 09:29:04.787379 17723 solver.cpp:237]     Train net output #0: loss = 0.186772 (* 1 = 0.186772 loss)
I0407 09:29:04.787389 17723 sgd_solver.cpp:105] Iteration 7332, lr = 0.0025
I0407 09:29:09.700500 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0407 09:29:12.744525 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0407 09:29:17.002002 17723 solver.cpp:330] Iteration 7344, Testing net (#0)
I0407 09:29:17.002024 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:29:18.460453 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:29:21.397943 17723 solver.cpp:397]     Test net output #0: accuracy = 0.477328
I0407 09:29:21.398064 17723 solver.cpp:397]     Test net output #1: loss = 2.86054 (* 1 = 2.86054 loss)
I0407 09:29:21.538306 17723 solver.cpp:218] Iteration 7344 (0.716384 iter/s, 16.7508s/12 iters), loss = 0.0800975
I0407 09:29:21.538369 17723 solver.cpp:237]     Train net output #0: loss = 0.0800976 (* 1 = 0.0800976 loss)
I0407 09:29:21.538378 17723 sgd_solver.cpp:105] Iteration 7344, lr = 0.0025
I0407 09:29:25.965656 17723 solver.cpp:218] Iteration 7356 (2.71049 iter/s, 4.42724s/12 iters), loss = 0.0755385
I0407 09:29:25.965698 17723 solver.cpp:237]     Train net output #0: loss = 0.0755386 (* 1 = 0.0755386 loss)
I0407 09:29:25.965704 17723 sgd_solver.cpp:105] Iteration 7356, lr = 0.0025
I0407 09:29:31.318883 17723 solver.cpp:218] Iteration 7368 (2.24168 iter/s, 5.35313s/12 iters), loss = 0.132703
I0407 09:29:31.318926 17723 solver.cpp:237]     Train net output #0: loss = 0.132703 (* 1 = 0.132703 loss)
I0407 09:29:31.318933 17723 sgd_solver.cpp:105] Iteration 7368, lr = 0.0025
I0407 09:29:36.310317 17723 solver.cpp:218] Iteration 7380 (2.40416 iter/s, 4.99134s/12 iters), loss = 0.0834148
I0407 09:29:36.310364 17723 solver.cpp:237]     Train net output #0: loss = 0.0834149 (* 1 = 0.0834149 loss)
I0407 09:29:36.310371 17723 sgd_solver.cpp:105] Iteration 7380, lr = 0.0025
I0407 09:29:37.794595 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:29:41.578596 17723 solver.cpp:218] Iteration 7392 (2.27783 iter/s, 5.26818s/12 iters), loss = 0.146151
I0407 09:29:41.578641 17723 solver.cpp:237]     Train net output #0: loss = 0.146151 (* 1 = 0.146151 loss)
I0407 09:29:41.578647 17723 sgd_solver.cpp:105] Iteration 7392, lr = 0.0025
I0407 09:29:46.888607 17723 solver.cpp:218] Iteration 7404 (2.25993 iter/s, 5.30991s/12 iters), loss = 0.0798383
I0407 09:29:46.888662 17723 solver.cpp:237]     Train net output #0: loss = 0.0798384 (* 1 = 0.0798384 loss)
I0407 09:29:46.888672 17723 sgd_solver.cpp:105] Iteration 7404, lr = 0.0025
I0407 09:29:51.914149 17723 solver.cpp:218] Iteration 7416 (2.38786 iter/s, 5.02543s/12 iters), loss = 0.1025
I0407 09:29:51.914291 17723 solver.cpp:237]     Train net output #0: loss = 0.1025 (* 1 = 0.1025 loss)
I0407 09:29:51.914304 17723 sgd_solver.cpp:105] Iteration 7416, lr = 0.0025
I0407 09:29:56.897863 17723 solver.cpp:218] Iteration 7428 (2.40793 iter/s, 4.98352s/12 iters), loss = 0.0428453
I0407 09:29:56.897907 17723 solver.cpp:237]     Train net output #0: loss = 0.0428454 (* 1 = 0.0428454 loss)
I0407 09:29:56.897914 17723 sgd_solver.cpp:105] Iteration 7428, lr = 0.0025
I0407 09:30:02.249238 17723 solver.cpp:218] Iteration 7440 (2.24246 iter/s, 5.35127s/12 iters), loss = 0.0984954
I0407 09:30:02.249289 17723 solver.cpp:237]     Train net output #0: loss = 0.0984955 (* 1 = 0.0984955 loss)
I0407 09:30:02.249295 17723 sgd_solver.cpp:105] Iteration 7440, lr = 0.0025
I0407 09:30:04.311679 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0407 09:30:07.329198 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0407 09:30:10.989413 17723 solver.cpp:330] Iteration 7446, Testing net (#0)
I0407 09:30:10.989431 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:30:12.546998 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:30:15.417626 17723 solver.cpp:397]     Test net output #0: accuracy = 0.472426
I0407 09:30:15.417660 17723 solver.cpp:397]     Test net output #1: loss = 2.87933 (* 1 = 2.87933 loss)
I0407 09:30:17.302850 17723 solver.cpp:218] Iteration 7452 (0.797161 iter/s, 15.0534s/12 iters), loss = 0.217558
I0407 09:30:17.302896 17723 solver.cpp:237]     Train net output #0: loss = 0.217558 (* 1 = 0.217558 loss)
I0407 09:30:17.302903 17723 sgd_solver.cpp:105] Iteration 7452, lr = 0.0025
I0407 09:30:22.464485 17723 solver.cpp:218] Iteration 7464 (2.32489 iter/s, 5.16153s/12 iters), loss = 0.148892
I0407 09:30:22.464633 17723 solver.cpp:237]     Train net output #0: loss = 0.148892 (* 1 = 0.148892 loss)
I0407 09:30:22.464641 17723 sgd_solver.cpp:105] Iteration 7464, lr = 0.0025
I0407 09:30:27.808697 17723 solver.cpp:218] Iteration 7476 (2.24551 iter/s, 5.34401s/12 iters), loss = 0.127499
I0407 09:30:27.808739 17723 solver.cpp:237]     Train net output #0: loss = 0.127499 (* 1 = 0.127499 loss)
I0407 09:30:27.808746 17723 sgd_solver.cpp:105] Iteration 7476, lr = 0.0025
I0407 09:30:31.519191 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:30:33.165760 17723 solver.cpp:218] Iteration 7488 (2.24007 iter/s, 5.35696s/12 iters), loss = 0.109675
I0407 09:30:33.165807 17723 solver.cpp:237]     Train net output #0: loss = 0.109676 (* 1 = 0.109676 loss)
I0407 09:30:33.165814 17723 sgd_solver.cpp:105] Iteration 7488, lr = 0.0025
I0407 09:30:38.433871 17723 solver.cpp:218] Iteration 7500 (2.2779 iter/s, 5.26801s/12 iters), loss = 0.175912
I0407 09:30:38.433907 17723 solver.cpp:237]     Train net output #0: loss = 0.175912 (* 1 = 0.175912 loss)
I0407 09:30:38.433913 17723 sgd_solver.cpp:105] Iteration 7500, lr = 0.0025
I0407 09:30:43.653592 17723 solver.cpp:218] Iteration 7512 (2.29901 iter/s, 5.21963s/12 iters), loss = 0.283164
I0407 09:30:43.653632 17723 solver.cpp:237]     Train net output #0: loss = 0.283164 (* 1 = 0.283164 loss)
I0407 09:30:43.653640 17723 sgd_solver.cpp:105] Iteration 7512, lr = 0.0025
I0407 09:30:49.119053 17723 solver.cpp:218] Iteration 7524 (2.19565 iter/s, 5.46536s/12 iters), loss = 0.0981661
I0407 09:30:49.119103 17723 solver.cpp:237]     Train net output #0: loss = 0.0981662 (* 1 = 0.0981662 loss)
I0407 09:30:49.119112 17723 sgd_solver.cpp:105] Iteration 7524, lr = 0.0025
I0407 09:30:54.477066 17723 solver.cpp:218] Iteration 7536 (2.23968 iter/s, 5.3579s/12 iters), loss = 0.108151
I0407 09:30:54.477154 17723 solver.cpp:237]     Train net output #0: loss = 0.108151 (* 1 = 0.108151 loss)
I0407 09:30:54.477164 17723 sgd_solver.cpp:105] Iteration 7536, lr = 0.0025
I0407 09:30:59.462772 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0407 09:31:02.414928 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0407 09:31:06.455761 17723 solver.cpp:330] Iteration 7548, Testing net (#0)
I0407 09:31:06.455790 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:31:07.909962 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:31:10.795451 17723 solver.cpp:397]     Test net output #0: accuracy = 0.487745
I0407 09:31:10.795498 17723 solver.cpp:397]     Test net output #1: loss = 2.77759 (* 1 = 2.77759 loss)
I0407 09:31:10.936105 17723 solver.cpp:218] Iteration 7548 (0.729093 iter/s, 16.4588s/12 iters), loss = 0.175459
I0407 09:31:10.936154 17723 solver.cpp:237]     Train net output #0: loss = 0.175459 (* 1 = 0.175459 loss)
I0407 09:31:10.936163 17723 sgd_solver.cpp:105] Iteration 7548, lr = 0.0025
I0407 09:31:15.288653 17723 solver.cpp:218] Iteration 7560 (2.75707 iter/s, 4.35245s/12 iters), loss = 0.0752927
I0407 09:31:15.288693 17723 solver.cpp:237]     Train net output #0: loss = 0.0752929 (* 1 = 0.0752929 loss)
I0407 09:31:15.288700 17723 sgd_solver.cpp:105] Iteration 7560, lr = 0.0025
I0407 09:31:20.636438 17723 solver.cpp:218] Iteration 7572 (2.24396 iter/s, 5.34769s/12 iters), loss = 0.0949108
I0407 09:31:20.636482 17723 solver.cpp:237]     Train net output #0: loss = 0.0949109 (* 1 = 0.0949109 loss)
I0407 09:31:20.636489 17723 sgd_solver.cpp:105] Iteration 7572, lr = 0.0025
I0407 09:31:25.987658 17723 solver.cpp:218] Iteration 7584 (2.24252 iter/s, 5.35112s/12 iters), loss = 0.103511
I0407 09:31:25.987771 17723 solver.cpp:237]     Train net output #0: loss = 0.103511 (* 1 = 0.103511 loss)
I0407 09:31:25.987785 17723 sgd_solver.cpp:105] Iteration 7584, lr = 0.0025
I0407 09:31:26.683928 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:31:31.392753 17723 solver.cpp:218] Iteration 7596 (2.2202 iter/s, 5.40493s/12 iters), loss = 0.168591
I0407 09:31:31.392802 17723 solver.cpp:237]     Train net output #0: loss = 0.168591 (* 1 = 0.168591 loss)
I0407 09:31:31.392810 17723 sgd_solver.cpp:105] Iteration 7596, lr = 0.0025
I0407 09:31:36.689040 17723 solver.cpp:218] Iteration 7608 (2.26578 iter/s, 5.29618s/12 iters), loss = 0.0402846
I0407 09:31:36.689087 17723 solver.cpp:237]     Train net output #0: loss = 0.0402847 (* 1 = 0.0402847 loss)
I0407 09:31:36.689095 17723 sgd_solver.cpp:105] Iteration 7608, lr = 0.0025
I0407 09:31:41.845986 17723 solver.cpp:218] Iteration 7620 (2.327 iter/s, 5.15685s/12 iters), loss = 0.058903
I0407 09:31:41.846021 17723 solver.cpp:237]     Train net output #0: loss = 0.0589031 (* 1 = 0.0589031 loss)
I0407 09:31:41.846029 17723 sgd_solver.cpp:105] Iteration 7620, lr = 0.0025
I0407 09:31:44.470131 17723 blocking_queue.cpp:49] Waiting for data
I0407 09:31:46.983101 17723 solver.cpp:218] Iteration 7632 (2.33598 iter/s, 5.13702s/12 iters), loss = 0.0950153
I0407 09:31:46.983141 17723 solver.cpp:237]     Train net output #0: loss = 0.0950155 (* 1 = 0.0950155 loss)
I0407 09:31:46.983148 17723 sgd_solver.cpp:105] Iteration 7632, lr = 0.0025
I0407 09:31:52.246960 17723 solver.cpp:218] Iteration 7644 (2.27974 iter/s, 5.26376s/12 iters), loss = 0.18873
I0407 09:31:52.247005 17723 solver.cpp:237]     Train net output #0: loss = 0.18873 (* 1 = 0.18873 loss)
I0407 09:31:52.247012 17723 sgd_solver.cpp:105] Iteration 7644, lr = 0.0025
I0407 09:31:54.229652 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0407 09:31:57.570150 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0407 09:32:01.333173 17723 solver.cpp:330] Iteration 7650, Testing net (#0)
I0407 09:32:01.333194 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:32:02.651099 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:32:05.631453 17723 solver.cpp:397]     Test net output #0: accuracy = 0.487132
I0407 09:32:05.631498 17723 solver.cpp:397]     Test net output #1: loss = 2.85858 (* 1 = 2.85858 loss)
I0407 09:32:07.645570 17723 solver.cpp:218] Iteration 7656 (0.7793 iter/s, 15.3984s/12 iters), loss = 0.0724095
I0407 09:32:07.645617 17723 solver.cpp:237]     Train net output #0: loss = 0.0724096 (* 1 = 0.0724096 loss)
I0407 09:32:07.645624 17723 sgd_solver.cpp:105] Iteration 7656, lr = 0.0025
I0407 09:32:12.570960 17723 solver.cpp:218] Iteration 7668 (2.4364 iter/s, 4.92529s/12 iters), loss = 0.236057
I0407 09:32:12.570999 17723 solver.cpp:237]     Train net output #0: loss = 0.236057 (* 1 = 0.236057 loss)
I0407 09:32:12.571005 17723 sgd_solver.cpp:105] Iteration 7668, lr = 0.0025
I0407 09:32:17.785727 17723 solver.cpp:218] Iteration 7680 (2.3012 iter/s, 5.21467s/12 iters), loss = 0.21472
I0407 09:32:17.785769 17723 solver.cpp:237]     Train net output #0: loss = 0.21472 (* 1 = 0.21472 loss)
I0407 09:32:17.785775 17723 sgd_solver.cpp:105] Iteration 7680, lr = 0.0025
I0407 09:32:20.602391 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:32:23.022310 17723 solver.cpp:218] Iteration 7692 (2.29162 iter/s, 5.23648s/12 iters), loss = 0.108106
I0407 09:32:23.022352 17723 solver.cpp:237]     Train net output #0: loss = 0.108106 (* 1 = 0.108106 loss)
I0407 09:32:23.022361 17723 sgd_solver.cpp:105] Iteration 7692, lr = 0.0025
I0407 09:32:28.291466 17723 solver.cpp:218] Iteration 7704 (2.27745 iter/s, 5.26906s/12 iters), loss = 0.125884
I0407 09:32:28.291569 17723 solver.cpp:237]     Train net output #0: loss = 0.125884 (* 1 = 0.125884 loss)
I0407 09:32:28.291580 17723 sgd_solver.cpp:105] Iteration 7704, lr = 0.0025
I0407 09:32:33.460232 17723 solver.cpp:218] Iteration 7716 (2.32171 iter/s, 5.16861s/12 iters), loss = 0.155225
I0407 09:32:33.460274 17723 solver.cpp:237]     Train net output #0: loss = 0.155225 (* 1 = 0.155225 loss)
I0407 09:32:33.460281 17723 sgd_solver.cpp:105] Iteration 7716, lr = 0.0025
I0407 09:32:38.652794 17723 solver.cpp:218] Iteration 7728 (2.31104 iter/s, 5.19246s/12 iters), loss = 0.143469
I0407 09:32:38.652835 17723 solver.cpp:237]     Train net output #0: loss = 0.143469 (* 1 = 0.143469 loss)
I0407 09:32:38.652843 17723 sgd_solver.cpp:105] Iteration 7728, lr = 0.0025
I0407 09:32:44.008973 17723 solver.cpp:218] Iteration 7740 (2.24045 iter/s, 5.35607s/12 iters), loss = 0.106471
I0407 09:32:44.009035 17723 solver.cpp:237]     Train net output #0: loss = 0.106472 (* 1 = 0.106472 loss)
I0407 09:32:44.009047 17723 sgd_solver.cpp:105] Iteration 7740, lr = 0.0025
I0407 09:32:48.644323 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0407 09:32:52.313305 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0407 09:32:56.258826 17723 solver.cpp:330] Iteration 7752, Testing net (#0)
I0407 09:32:56.258846 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:32:57.543368 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:33:00.508091 17723 solver.cpp:397]     Test net output #0: accuracy = 0.474265
I0407 09:33:00.508249 17723 solver.cpp:397]     Test net output #1: loss = 2.87952 (* 1 = 2.87952 loss)
I0407 09:33:00.648530 17723 solver.cpp:218] Iteration 7752 (0.721182 iter/s, 16.6394s/12 iters), loss = 0.257135
I0407 09:33:00.648581 17723 solver.cpp:237]     Train net output #0: loss = 0.257135 (* 1 = 0.257135 loss)
I0407 09:33:00.648588 17723 sgd_solver.cpp:105] Iteration 7752, lr = 0.0025
I0407 09:33:05.095850 17723 solver.cpp:218] Iteration 7764 (2.69831 iter/s, 4.44722s/12 iters), loss = 0.155556
I0407 09:33:05.095898 17723 solver.cpp:237]     Train net output #0: loss = 0.155556 (* 1 = 0.155556 loss)
I0407 09:33:05.095907 17723 sgd_solver.cpp:105] Iteration 7764, lr = 0.0025
I0407 09:33:09.926076 17723 solver.cpp:218] Iteration 7776 (2.48441 iter/s, 4.83012s/12 iters), loss = 0.0870544
I0407 09:33:09.926118 17723 solver.cpp:237]     Train net output #0: loss = 0.0870545 (* 1 = 0.0870545 loss)
I0407 09:33:09.926126 17723 sgd_solver.cpp:105] Iteration 7776, lr = 0.0025
I0407 09:33:14.941074 17723 solver.cpp:218] Iteration 7788 (2.39287 iter/s, 5.0149s/12 iters), loss = 0.129815
I0407 09:33:14.941115 17723 solver.cpp:237]     Train net output #0: loss = 0.129815 (* 1 = 0.129815 loss)
I0407 09:33:14.941123 17723 sgd_solver.cpp:105] Iteration 7788, lr = 0.0025
I0407 09:33:14.947544 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:33:20.299356 17723 solver.cpp:218] Iteration 7800 (2.23957 iter/s, 5.35818s/12 iters), loss = 0.0769342
I0407 09:33:20.299409 17723 solver.cpp:237]     Train net output #0: loss = 0.0769343 (* 1 = 0.0769343 loss)
I0407 09:33:20.299419 17723 sgd_solver.cpp:105] Iteration 7800, lr = 0.0025
I0407 09:33:25.571102 17723 solver.cpp:218] Iteration 7812 (2.27633 iter/s, 5.27164s/12 iters), loss = 0.0483944
I0407 09:33:25.571144 17723 solver.cpp:237]     Train net output #0: loss = 0.0483945 (* 1 = 0.0483945 loss)
I0407 09:33:25.571152 17723 sgd_solver.cpp:105] Iteration 7812, lr = 0.0025
I0407 09:33:30.913439 17723 solver.cpp:218] Iteration 7824 (2.24625 iter/s, 5.34223s/12 iters), loss = 0.149453
I0407 09:33:30.913563 17723 solver.cpp:237]     Train net output #0: loss = 0.149453 (* 1 = 0.149453 loss)
I0407 09:33:30.913571 17723 sgd_solver.cpp:105] Iteration 7824, lr = 0.0025
I0407 09:33:36.175472 17723 solver.cpp:218] Iteration 7836 (2.28057 iter/s, 5.26185s/12 iters), loss = 0.243926
I0407 09:33:36.175518 17723 solver.cpp:237]     Train net output #0: loss = 0.243926 (* 1 = 0.243926 loss)
I0407 09:33:36.175524 17723 sgd_solver.cpp:105] Iteration 7836, lr = 0.0025
I0407 09:33:41.360808 17723 solver.cpp:218] Iteration 7848 (2.31426 iter/s, 5.18523s/12 iters), loss = 0.124123
I0407 09:33:41.360867 17723 solver.cpp:237]     Train net output #0: loss = 0.124123 (* 1 = 0.124123 loss)
I0407 09:33:41.360878 17723 sgd_solver.cpp:105] Iteration 7848, lr = 0.0025
I0407 09:33:43.494060 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0407 09:33:47.236490 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0407 09:33:50.935535 17723 solver.cpp:330] Iteration 7854, Testing net (#0)
I0407 09:33:50.935559 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:33:52.179787 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:33:55.189224 17723 solver.cpp:397]     Test net output #0: accuracy = 0.473039
I0407 09:33:55.189254 17723 solver.cpp:397]     Test net output #1: loss = 2.88905 (* 1 = 2.88905 loss)
I0407 09:33:57.118480 17723 solver.cpp:218] Iteration 7860 (0.761543 iter/s, 15.7575s/12 iters), loss = 0.10652
I0407 09:33:57.118522 17723 solver.cpp:237]     Train net output #0: loss = 0.10652 (* 1 = 0.10652 loss)
I0407 09:33:57.118530 17723 sgd_solver.cpp:105] Iteration 7860, lr = 0.0025
I0407 09:34:02.138499 17723 solver.cpp:218] Iteration 7872 (2.39048 iter/s, 5.01992s/12 iters), loss = 0.0557668
I0407 09:34:02.138641 17723 solver.cpp:237]     Train net output #0: loss = 0.0557669 (* 1 = 0.0557669 loss)
I0407 09:34:02.138649 17723 sgd_solver.cpp:105] Iteration 7872, lr = 0.0025
I0407 09:34:07.514288 17723 solver.cpp:218] Iteration 7884 (2.23231 iter/s, 5.37559s/12 iters), loss = 0.104199
I0407 09:34:07.514325 17723 solver.cpp:237]     Train net output #0: loss = 0.104199 (* 1 = 0.104199 loss)
I0407 09:34:07.514333 17723 sgd_solver.cpp:105] Iteration 7884, lr = 0.0025
I0407 09:34:09.708031 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:34:12.804950 17723 solver.cpp:218] Iteration 7896 (2.26819 iter/s, 5.29057s/12 iters), loss = 0.157225
I0407 09:34:12.805003 17723 solver.cpp:237]     Train net output #0: loss = 0.157225 (* 1 = 0.157225 loss)
I0407 09:34:12.805011 17723 sgd_solver.cpp:105] Iteration 7896, lr = 0.0025
I0407 09:34:17.809855 17723 solver.cpp:218] Iteration 7908 (2.3977 iter/s, 5.0048s/12 iters), loss = 0.110908
I0407 09:34:17.809897 17723 solver.cpp:237]     Train net output #0: loss = 0.110908 (* 1 = 0.110908 loss)
I0407 09:34:17.809904 17723 sgd_solver.cpp:105] Iteration 7908, lr = 0.0025
I0407 09:34:23.001715 17723 solver.cpp:218] Iteration 7920 (2.31135 iter/s, 5.19177s/12 iters), loss = 0.188017
I0407 09:34:23.001755 17723 solver.cpp:237]     Train net output #0: loss = 0.188017 (* 1 = 0.188017 loss)
I0407 09:34:23.001761 17723 sgd_solver.cpp:105] Iteration 7920, lr = 0.0025
I0407 09:34:28.449951 17723 solver.cpp:218] Iteration 7932 (2.20259 iter/s, 5.44813s/12 iters), loss = 0.141701
I0407 09:34:28.449996 17723 solver.cpp:237]     Train net output #0: loss = 0.141701 (* 1 = 0.141701 loss)
I0407 09:34:28.450003 17723 sgd_solver.cpp:105] Iteration 7932, lr = 0.0025
I0407 09:34:33.683634 17723 solver.cpp:218] Iteration 7944 (2.29289 iter/s, 5.23358s/12 iters), loss = 0.166799
I0407 09:34:33.683769 17723 solver.cpp:237]     Train net output #0: loss = 0.166799 (* 1 = 0.166799 loss)
I0407 09:34:33.683779 17723 sgd_solver.cpp:105] Iteration 7944, lr = 0.0025
I0407 09:34:38.308370 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0407 09:34:42.149169 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0407 09:34:45.997539 17723 solver.cpp:330] Iteration 7956, Testing net (#0)
I0407 09:34:45.997556 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:34:47.298925 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:34:50.355309 17723 solver.cpp:397]     Test net output #0: accuracy = 0.476716
I0407 09:34:50.355337 17723 solver.cpp:397]     Test net output #1: loss = 2.95108 (* 1 = 2.95108 loss)
I0407 09:34:50.495107 17723 solver.cpp:218] Iteration 7956 (0.71381 iter/s, 16.8112s/12 iters), loss = 0.0679412
I0407 09:34:50.495179 17723 solver.cpp:237]     Train net output #0: loss = 0.0679414 (* 1 = 0.0679414 loss)
I0407 09:34:50.495188 17723 sgd_solver.cpp:105] Iteration 7956, lr = 0.0025
I0407 09:34:54.845708 17723 solver.cpp:218] Iteration 7968 (2.75832 iter/s, 4.35048s/12 iters), loss = 0.0448621
I0407 09:34:54.845757 17723 solver.cpp:237]     Train net output #0: loss = 0.0448623 (* 1 = 0.0448623 loss)
I0407 09:34:54.845764 17723 sgd_solver.cpp:105] Iteration 7968, lr = 0.0025
I0407 09:34:59.724121 17723 solver.cpp:218] Iteration 7980 (2.45987 iter/s, 4.87831s/12 iters), loss = 0.0642306
I0407 09:34:59.724169 17723 solver.cpp:237]     Train net output #0: loss = 0.0642308 (* 1 = 0.0642308 loss)
I0407 09:34:59.724177 17723 sgd_solver.cpp:105] Iteration 7980, lr = 0.0025
I0407 09:35:04.197154 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:35:04.985522 17723 solver.cpp:218] Iteration 7992 (2.28081 iter/s, 5.2613s/12 iters), loss = 0.154156
I0407 09:35:04.985564 17723 solver.cpp:237]     Train net output #0: loss = 0.154156 (* 1 = 0.154156 loss)
I0407 09:35:04.985571 17723 sgd_solver.cpp:105] Iteration 7992, lr = 0.0025
I0407 09:35:10.320103 17723 solver.cpp:218] Iteration 8004 (2.24952 iter/s, 5.33448s/12 iters), loss = 0.138573
I0407 09:35:10.320147 17723 solver.cpp:237]     Train net output #0: loss = 0.138573 (* 1 = 0.138573 loss)
I0407 09:35:10.320154 17723 sgd_solver.cpp:105] Iteration 8004, lr = 0.0025
I0407 09:35:15.773106 17723 solver.cpp:218] Iteration 8016 (2.20066 iter/s, 5.4529s/12 iters), loss = 0.0432704
I0407 09:35:15.773159 17723 solver.cpp:237]     Train net output #0: loss = 0.0432706 (* 1 = 0.0432706 loss)
I0407 09:35:15.773169 17723 sgd_solver.cpp:105] Iteration 8016, lr = 0.0025
I0407 09:35:21.030165 17723 solver.cpp:218] Iteration 8028 (2.28269 iter/s, 5.25695s/12 iters), loss = 0.131751
I0407 09:35:21.030210 17723 solver.cpp:237]     Train net output #0: loss = 0.131751 (* 1 = 0.131751 loss)
I0407 09:35:21.030215 17723 sgd_solver.cpp:105] Iteration 8028, lr = 0.0025
I0407 09:35:26.191074 17723 solver.cpp:218] Iteration 8040 (2.32522 iter/s, 5.16081s/12 iters), loss = 0.222564
I0407 09:35:26.191118 17723 solver.cpp:237]     Train net output #0: loss = 0.222564 (* 1 = 0.222564 loss)
I0407 09:35:26.191125 17723 sgd_solver.cpp:105] Iteration 8040, lr = 0.0025
I0407 09:35:31.057126 17723 solver.cpp:218] Iteration 8052 (2.46611 iter/s, 4.86596s/12 iters), loss = 0.0569647
I0407 09:35:31.057169 17723 solver.cpp:237]     Train net output #0: loss = 0.0569649 (* 1 = 0.0569649 loss)
I0407 09:35:31.057176 17723 sgd_solver.cpp:105] Iteration 8052, lr = 0.0025
I0407 09:35:33.216295 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0407 09:35:37.140748 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0407 09:35:40.813936 17723 solver.cpp:330] Iteration 8058, Testing net (#0)
I0407 09:35:40.813959 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:35:42.038832 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:35:45.188009 17723 solver.cpp:397]     Test net output #0: accuracy = 0.487132
I0407 09:35:45.188046 17723 solver.cpp:397]     Test net output #1: loss = 2.88917 (* 1 = 2.88917 loss)
I0407 09:35:47.151032 17723 solver.cpp:218] Iteration 8064 (0.745632 iter/s, 16.0937s/12 iters), loss = 0.0953866
I0407 09:35:47.151082 17723 solver.cpp:237]     Train net output #0: loss = 0.0953867 (* 1 = 0.0953867 loss)
I0407 09:35:47.151091 17723 sgd_solver.cpp:105] Iteration 8064, lr = 0.0025
I0407 09:35:52.348500 17723 solver.cpp:218] Iteration 8076 (2.30886 iter/s, 5.19736s/12 iters), loss = 0.115914
I0407 09:35:52.348547 17723 solver.cpp:237]     Train net output #0: loss = 0.115914 (* 1 = 0.115914 loss)
I0407 09:35:52.348554 17723 sgd_solver.cpp:105] Iteration 8076, lr = 0.0025
I0407 09:35:57.772011 17723 solver.cpp:218] Iteration 8088 (2.21263 iter/s, 5.42341s/12 iters), loss = 0.11987
I0407 09:35:57.772049 17723 solver.cpp:237]     Train net output #0: loss = 0.11987 (* 1 = 0.11987 loss)
I0407 09:35:57.772055 17723 sgd_solver.cpp:105] Iteration 8088, lr = 0.0025
I0407 09:35:59.241886 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:36:02.997117 17723 solver.cpp:218] Iteration 8100 (2.29665 iter/s, 5.225s/12 iters), loss = 0.119287
I0407 09:36:02.997175 17723 solver.cpp:237]     Train net output #0: loss = 0.119287 (* 1 = 0.119287 loss)
I0407 09:36:02.997185 17723 sgd_solver.cpp:105] Iteration 8100, lr = 0.0025
I0407 09:36:08.156873 17723 solver.cpp:218] Iteration 8112 (2.32574 iter/s, 5.15964s/12 iters), loss = 0.0425074
I0407 09:36:08.157037 17723 solver.cpp:237]     Train net output #0: loss = 0.0425075 (* 1 = 0.0425075 loss)
I0407 09:36:08.157049 17723 sgd_solver.cpp:105] Iteration 8112, lr = 0.0025
I0407 09:36:13.350741 17723 solver.cpp:218] Iteration 8124 (2.31051 iter/s, 5.19365s/12 iters), loss = 0.102183
I0407 09:36:13.350783 17723 solver.cpp:237]     Train net output #0: loss = 0.102184 (* 1 = 0.102184 loss)
I0407 09:36:13.350790 17723 sgd_solver.cpp:105] Iteration 8124, lr = 0.0025
I0407 09:36:18.626293 17723 solver.cpp:218] Iteration 8136 (2.27469 iter/s, 5.27545s/12 iters), loss = 0.114829
I0407 09:36:18.626336 17723 solver.cpp:237]     Train net output #0: loss = 0.114829 (* 1 = 0.114829 loss)
I0407 09:36:18.626343 17723 sgd_solver.cpp:105] Iteration 8136, lr = 0.0025
I0407 09:36:24.143242 17723 solver.cpp:218] Iteration 8148 (2.17516 iter/s, 5.51685s/12 iters), loss = 0.0730449
I0407 09:36:24.143288 17723 solver.cpp:237]     Train net output #0: loss = 0.0730451 (* 1 = 0.0730451 loss)
I0407 09:36:24.143296 17723 sgd_solver.cpp:105] Iteration 8148, lr = 0.0025
I0407 09:36:28.940026 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0407 09:36:33.898563 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0407 09:36:38.187355 17723 solver.cpp:330] Iteration 8160, Testing net (#0)
I0407 09:36:38.187464 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:36:39.416616 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:36:42.546334 17723 solver.cpp:397]     Test net output #0: accuracy = 0.491422
I0407 09:36:42.546362 17723 solver.cpp:397]     Test net output #1: loss = 2.90181 (* 1 = 2.90181 loss)
I0407 09:36:42.681655 17723 solver.cpp:218] Iteration 8160 (0.647312 iter/s, 18.5382s/12 iters), loss = 0.104059
I0407 09:36:42.681702 17723 solver.cpp:237]     Train net output #0: loss = 0.104059 (* 1 = 0.104059 loss)
I0407 09:36:42.681710 17723 sgd_solver.cpp:105] Iteration 8160, lr = 0.0025
I0407 09:36:46.960302 17723 solver.cpp:218] Iteration 8172 (2.80469 iter/s, 4.27855s/12 iters), loss = 0.0819929
I0407 09:36:46.960348 17723 solver.cpp:237]     Train net output #0: loss = 0.081993 (* 1 = 0.081993 loss)
I0407 09:36:46.960355 17723 sgd_solver.cpp:105] Iteration 8172, lr = 0.0025
I0407 09:36:52.148624 17723 solver.cpp:218] Iteration 8184 (2.31293 iter/s, 5.18822s/12 iters), loss = 0.15578
I0407 09:36:52.148679 17723 solver.cpp:237]     Train net output #0: loss = 0.15578 (* 1 = 0.15578 loss)
I0407 09:36:52.148687 17723 sgd_solver.cpp:105] Iteration 8184, lr = 0.0025
I0407 09:36:55.959144 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:36:57.503017 17723 solver.cpp:218] Iteration 8196 (2.2412 iter/s, 5.35428s/12 iters), loss = 0.118174
I0407 09:36:57.503064 17723 solver.cpp:237]     Train net output #0: loss = 0.118174 (* 1 = 0.118174 loss)
I0407 09:36:57.503072 17723 sgd_solver.cpp:105] Iteration 8196, lr = 0.0025
I0407 09:37:02.614259 17723 solver.cpp:218] Iteration 8208 (2.34781 iter/s, 5.11114s/12 iters), loss = 0.119944
I0407 09:37:02.614300 17723 solver.cpp:237]     Train net output #0: loss = 0.119944 (* 1 = 0.119944 loss)
I0407 09:37:02.614306 17723 sgd_solver.cpp:105] Iteration 8208, lr = 0.0025
I0407 09:37:07.939429 17723 solver.cpp:218] Iteration 8220 (2.25349 iter/s, 5.32507s/12 iters), loss = 0.113763
I0407 09:37:07.939477 17723 solver.cpp:237]     Train net output #0: loss = 0.113763 (* 1 = 0.113763 loss)
I0407 09:37:07.939484 17723 sgd_solver.cpp:105] Iteration 8220, lr = 0.0025
I0407 09:37:13.366928 17723 solver.cpp:218] Iteration 8232 (2.21101 iter/s, 5.42739s/12 iters), loss = 0.0734046
I0407 09:37:13.367177 17723 solver.cpp:237]     Train net output #0: loss = 0.0734048 (* 1 = 0.0734048 loss)
I0407 09:37:13.367185 17723 sgd_solver.cpp:105] Iteration 8232, lr = 0.0025
I0407 09:37:18.662575 17723 solver.cpp:218] Iteration 8244 (2.26614 iter/s, 5.29535s/12 iters), loss = 0.202632
I0407 09:37:18.662616 17723 solver.cpp:237]     Train net output #0: loss = 0.202632 (* 1 = 0.202632 loss)
I0407 09:37:18.662623 17723 sgd_solver.cpp:105] Iteration 8244, lr = 0.0025
I0407 09:37:23.803021 17723 solver.cpp:218] Iteration 8256 (2.33447 iter/s, 5.14035s/12 iters), loss = 0.068278
I0407 09:37:23.803061 17723 solver.cpp:237]     Train net output #0: loss = 0.0682781 (* 1 = 0.0682781 loss)
I0407 09:37:23.803067 17723 sgd_solver.cpp:105] Iteration 8256, lr = 0.0025
I0407 09:37:25.799464 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0407 09:37:30.566226 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0407 09:37:34.696612 17723 solver.cpp:330] Iteration 8262, Testing net (#0)
I0407 09:37:34.696632 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:37:35.818387 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:37:39.026314 17723 solver.cpp:397]     Test net output #0: accuracy = 0.47549
I0407 09:37:39.026346 17723 solver.cpp:397]     Test net output #1: loss = 2.83492 (* 1 = 2.83492 loss)
I0407 09:37:40.961351 17723 solver.cpp:218] Iteration 8268 (0.699376 iter/s, 17.1581s/12 iters), loss = 0.11857
I0407 09:37:40.961392 17723 solver.cpp:237]     Train net output #0: loss = 0.11857 (* 1 = 0.11857 loss)
I0407 09:37:40.961398 17723 sgd_solver.cpp:105] Iteration 8268, lr = 0.0025
I0407 09:37:46.091601 17723 solver.cpp:218] Iteration 8280 (2.33911 iter/s, 5.13015s/12 iters), loss = 0.0934839
I0407 09:37:46.091701 17723 solver.cpp:237]     Train net output #0: loss = 0.093484 (* 1 = 0.093484 loss)
I0407 09:37:46.091709 17723 sgd_solver.cpp:105] Iteration 8280, lr = 0.0025
I0407 09:37:51.359957 17723 solver.cpp:218] Iteration 8292 (2.27782 iter/s, 5.2682s/12 iters), loss = 0.11242
I0407 09:37:51.359997 17723 solver.cpp:237]     Train net output #0: loss = 0.11242 (* 1 = 0.11242 loss)
I0407 09:37:51.360004 17723 sgd_solver.cpp:105] Iteration 8292, lr = 0.0025
I0407 09:37:51.934556 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:37:55.915866 17723 solver.cpp:218] Iteration 8304 (2.634 iter/s, 4.55582s/12 iters), loss = 0.069613
I0407 09:37:55.915911 17723 solver.cpp:237]     Train net output #0: loss = 0.0696131 (* 1 = 0.0696131 loss)
I0407 09:37:55.915920 17723 sgd_solver.cpp:105] Iteration 8304, lr = 0.0025
I0407 09:37:58.437163 17723 blocking_queue.cpp:49] Waiting for data
I0407 09:38:00.726966 17723 solver.cpp:218] Iteration 8316 (2.49428 iter/s, 4.811s/12 iters), loss = 0.0634216
I0407 09:38:00.727011 17723 solver.cpp:237]     Train net output #0: loss = 0.0634217 (* 1 = 0.0634217 loss)
I0407 09:38:00.727020 17723 sgd_solver.cpp:105] Iteration 8316, lr = 0.0025
I0407 09:38:05.802927 17723 solver.cpp:218] Iteration 8328 (2.36413 iter/s, 5.07586s/12 iters), loss = 0.0192429
I0407 09:38:05.802973 17723 solver.cpp:237]     Train net output #0: loss = 0.019243 (* 1 = 0.019243 loss)
I0407 09:38:05.802983 17723 sgd_solver.cpp:105] Iteration 8328, lr = 0.0025
I0407 09:38:10.862761 17723 solver.cpp:218] Iteration 8340 (2.37167 iter/s, 5.05973s/12 iters), loss = 0.0610751
I0407 09:38:10.862803 17723 solver.cpp:237]     Train net output #0: loss = 0.0610752 (* 1 = 0.0610752 loss)
I0407 09:38:10.862812 17723 sgd_solver.cpp:105] Iteration 8340, lr = 0.0025
I0407 09:38:16.016948 17723 solver.cpp:218] Iteration 8352 (2.32825 iter/s, 5.15409s/12 iters), loss = 0.12777
I0407 09:38:16.016991 17723 solver.cpp:237]     Train net output #0: loss = 0.12777 (* 1 = 0.12777 loss)
I0407 09:38:16.016999 17723 sgd_solver.cpp:105] Iteration 8352, lr = 0.0025
I0407 09:38:20.719830 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0407 09:38:24.781702 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0407 09:38:28.443014 17723 solver.cpp:330] Iteration 8364, Testing net (#0)
I0407 09:38:28.443037 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:38:29.543884 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:38:32.802487 17723 solver.cpp:397]     Test net output #0: accuracy = 0.476103
I0407 09:38:32.802523 17723 solver.cpp:397]     Test net output #1: loss = 2.86672 (* 1 = 2.86672 loss)
I0407 09:38:32.941673 17723 solver.cpp:218] Iteration 8364 (0.70903 iter/s, 16.9245s/12 iters), loss = 0.103239
I0407 09:38:32.943253 17723 solver.cpp:237]     Train net output #0: loss = 0.103239 (* 1 = 0.103239 loss)
I0407 09:38:32.943267 17723 sgd_solver.cpp:105] Iteration 8364, lr = 0.0025
I0407 09:38:37.148118 17723 solver.cpp:218] Iteration 8376 (2.85386 iter/s, 4.20483s/12 iters), loss = 0.113682
I0407 09:38:37.148162 17723 solver.cpp:237]     Train net output #0: loss = 0.113682 (* 1 = 0.113682 loss)
I0407 09:38:37.148169 17723 sgd_solver.cpp:105] Iteration 8376, lr = 0.0025
I0407 09:38:42.515725 17723 solver.cpp:218] Iteration 8388 (2.23568 iter/s, 5.3675s/12 iters), loss = 0.0627889
I0407 09:38:42.515772 17723 solver.cpp:237]     Train net output #0: loss = 0.062789 (* 1 = 0.062789 loss)
I0407 09:38:42.515779 17723 sgd_solver.cpp:105] Iteration 8388, lr = 0.0025
I0407 09:38:45.327330 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:38:47.701527 17723 solver.cpp:218] Iteration 8400 (2.31406 iter/s, 5.1857s/12 iters), loss = 0.0985468
I0407 09:38:47.701565 17723 solver.cpp:237]     Train net output #0: loss = 0.0985468 (* 1 = 0.0985468 loss)
I0407 09:38:47.701571 17723 sgd_solver.cpp:105] Iteration 8400, lr = 0.0025
I0407 09:38:52.923418 17723 solver.cpp:218] Iteration 8412 (2.29806 iter/s, 5.2218s/12 iters), loss = 0.0856074
I0407 09:38:52.923517 17723 solver.cpp:237]     Train net output #0: loss = 0.0856075 (* 1 = 0.0856075 loss)
I0407 09:38:52.923525 17723 sgd_solver.cpp:105] Iteration 8412, lr = 0.0025
I0407 09:38:58.364910 17723 solver.cpp:218] Iteration 8424 (2.20534 iter/s, 5.44134s/12 iters), loss = 0.136218
I0407 09:38:58.364953 17723 solver.cpp:237]     Train net output #0: loss = 0.136218 (* 1 = 0.136218 loss)
I0407 09:38:58.364960 17723 sgd_solver.cpp:105] Iteration 8424, lr = 0.0025
I0407 09:39:03.729400 17723 solver.cpp:218] Iteration 8436 (2.23697 iter/s, 5.36439s/12 iters), loss = 0.0117578
I0407 09:39:03.729440 17723 solver.cpp:237]     Train net output #0: loss = 0.0117579 (* 1 = 0.0117579 loss)
I0407 09:39:03.729447 17723 sgd_solver.cpp:105] Iteration 8436, lr = 0.0025
I0407 09:39:08.936782 17723 solver.cpp:218] Iteration 8448 (2.30446 iter/s, 5.20728s/12 iters), loss = 0.0578684
I0407 09:39:08.936827 17723 solver.cpp:237]     Train net output #0: loss = 0.0578685 (* 1 = 0.0578685 loss)
I0407 09:39:08.936836 17723 sgd_solver.cpp:105] Iteration 8448, lr = 0.0025
I0407 09:39:13.984174 17723 solver.cpp:218] Iteration 8460 (2.37751 iter/s, 5.04729s/12 iters), loss = 0.114637
I0407 09:39:13.984221 17723 solver.cpp:237]     Train net output #0: loss = 0.114637 (* 1 = 0.114637 loss)
I0407 09:39:13.984231 17723 sgd_solver.cpp:105] Iteration 8460, lr = 0.0025
I0407 09:39:16.103969 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0407 09:39:20.392562 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0407 09:39:24.100863 17723 solver.cpp:330] Iteration 8466, Testing net (#0)
I0407 09:39:24.100932 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:39:25.136245 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:39:28.424557 17723 solver.cpp:397]     Test net output #0: accuracy = 0.491422
I0407 09:39:28.424590 17723 solver.cpp:397]     Test net output #1: loss = 2.84574 (* 1 = 2.84574 loss)
I0407 09:39:30.289940 17723 solver.cpp:218] Iteration 8472 (0.735945 iter/s, 16.3056s/12 iters), loss = 0.155235
I0407 09:39:30.289983 17723 solver.cpp:237]     Train net output #0: loss = 0.155235 (* 1 = 0.155235 loss)
I0407 09:39:30.289988 17723 sgd_solver.cpp:105] Iteration 8472, lr = 0.0025
I0407 09:39:35.644692 17723 solver.cpp:218] Iteration 8484 (2.24104 iter/s, 5.35465s/12 iters), loss = 0.0683355
I0407 09:39:35.644744 17723 solver.cpp:237]     Train net output #0: loss = 0.0683356 (* 1 = 0.0683356 loss)
I0407 09:39:35.644752 17723 sgd_solver.cpp:105] Iteration 8484, lr = 0.0025
I0407 09:39:40.954301 17723 solver.cpp:218] Iteration 8496 (2.2601 iter/s, 5.30949s/12 iters), loss = 0.0862651
I0407 09:39:40.954360 17723 solver.cpp:237]     Train net output #0: loss = 0.0862652 (* 1 = 0.0862652 loss)
I0407 09:39:40.954371 17723 sgd_solver.cpp:105] Iteration 8496, lr = 0.0025
I0407 09:39:40.989248 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:39:46.355062 17723 solver.cpp:218] Iteration 8508 (2.22196 iter/s, 5.40065s/12 iters), loss = 0.0511191
I0407 09:39:46.355113 17723 solver.cpp:237]     Train net output #0: loss = 0.0511192 (* 1 = 0.0511192 loss)
I0407 09:39:46.355124 17723 sgd_solver.cpp:105] Iteration 8508, lr = 0.0025
I0407 09:39:51.582958 17723 solver.cpp:218] Iteration 8520 (2.29542 iter/s, 5.22779s/12 iters), loss = 0.139042
I0407 09:39:51.583000 17723 solver.cpp:237]     Train net output #0: loss = 0.139042 (* 1 = 0.139042 loss)
I0407 09:39:51.583007 17723 sgd_solver.cpp:105] Iteration 8520, lr = 0.0025
I0407 09:39:56.816438 17723 solver.cpp:218] Iteration 8532 (2.29297 iter/s, 5.23338s/12 iters), loss = 0.0460321
I0407 09:39:56.816566 17723 solver.cpp:237]     Train net output #0: loss = 0.0460322 (* 1 = 0.0460322 loss)
I0407 09:39:56.816574 17723 sgd_solver.cpp:105] Iteration 8532, lr = 0.0025
I0407 09:40:02.253731 17723 solver.cpp:218] Iteration 8544 (2.20705 iter/s, 5.43711s/12 iters), loss = 0.173334
I0407 09:40:02.253770 17723 solver.cpp:237]     Train net output #0: loss = 0.173334 (* 1 = 0.173334 loss)
I0407 09:40:02.253777 17723 sgd_solver.cpp:105] Iteration 8544, lr = 0.0025
I0407 09:40:07.673326 17723 solver.cpp:218] Iteration 8556 (2.21423 iter/s, 5.4195s/12 iters), loss = 0.0369303
I0407 09:40:07.673363 17723 solver.cpp:237]     Train net output #0: loss = 0.0369304 (* 1 = 0.0369304 loss)
I0407 09:40:07.673372 17723 sgd_solver.cpp:105] Iteration 8556, lr = 0.0025
I0407 09:40:12.478533 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0407 09:40:16.693847 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0407 09:40:20.432225 17723 solver.cpp:330] Iteration 8568, Testing net (#0)
I0407 09:40:20.432246 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:40:21.417207 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:40:24.766737 17723 solver.cpp:397]     Test net output #0: accuracy = 0.489583
I0407 09:40:24.766769 17723 solver.cpp:397]     Test net output #1: loss = 2.84107 (* 1 = 2.84107 loss)
I0407 09:40:24.907522 17723 solver.cpp:218] Iteration 8568 (0.696298 iter/s, 17.234s/12 iters), loss = 0.145538
I0407 09:40:24.909121 17723 solver.cpp:237]     Train net output #0: loss = 0.145539 (* 1 = 0.145539 loss)
I0407 09:40:24.909139 17723 sgd_solver.cpp:105] Iteration 8568, lr = 0.0025
I0407 09:40:29.261171 17723 solver.cpp:218] Iteration 8580 (2.75734 iter/s, 4.35202s/12 iters), loss = 0.0584392
I0407 09:40:29.261279 17723 solver.cpp:237]     Train net output #0: loss = 0.0584393 (* 1 = 0.0584393 loss)
I0407 09:40:29.261289 17723 sgd_solver.cpp:105] Iteration 8580, lr = 0.0025
I0407 09:40:34.424834 17723 solver.cpp:218] Iteration 8592 (2.324 iter/s, 5.16351s/12 iters), loss = 0.0972383
I0407 09:40:34.424878 17723 solver.cpp:237]     Train net output #0: loss = 0.0972384 (* 1 = 0.0972384 loss)
I0407 09:40:34.424890 17723 sgd_solver.cpp:105] Iteration 8592, lr = 0.0025
I0407 09:40:36.693614 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:40:39.738409 17723 solver.cpp:218] Iteration 8604 (2.25841 iter/s, 5.31347s/12 iters), loss = 0.059836
I0407 09:40:39.738468 17723 solver.cpp:237]     Train net output #0: loss = 0.0598361 (* 1 = 0.0598361 loss)
I0407 09:40:39.738478 17723 sgd_solver.cpp:105] Iteration 8604, lr = 0.0025
I0407 09:40:45.094627 17723 solver.cpp:218] Iteration 8616 (2.24044 iter/s, 5.3561s/12 iters), loss = 0.0837077
I0407 09:40:45.094686 17723 solver.cpp:237]     Train net output #0: loss = 0.0837078 (* 1 = 0.0837078 loss)
I0407 09:40:45.094696 17723 sgd_solver.cpp:105] Iteration 8616, lr = 0.0025
I0407 09:40:50.437968 17723 solver.cpp:218] Iteration 8628 (2.24584 iter/s, 5.34322s/12 iters), loss = 0.036472
I0407 09:40:50.438024 17723 solver.cpp:237]     Train net output #0: loss = 0.0364721 (* 1 = 0.0364721 loss)
I0407 09:40:50.438035 17723 sgd_solver.cpp:105] Iteration 8628, lr = 0.0025
I0407 09:40:55.873054 17723 solver.cpp:218] Iteration 8640 (2.20792 iter/s, 5.43497s/12 iters), loss = 0.0483414
I0407 09:40:55.873111 17723 solver.cpp:237]     Train net output #0: loss = 0.0483415 (* 1 = 0.0483415 loss)
I0407 09:40:55.873121 17723 sgd_solver.cpp:105] Iteration 8640, lr = 0.0025
I0407 09:41:00.901067 17723 solver.cpp:218] Iteration 8652 (2.38668 iter/s, 5.0279s/12 iters), loss = 0.119496
I0407 09:41:00.901185 17723 solver.cpp:237]     Train net output #0: loss = 0.119497 (* 1 = 0.119497 loss)
I0407 09:41:00.901196 17723 sgd_solver.cpp:105] Iteration 8652, lr = 0.0025
I0407 09:41:06.033535 17723 solver.cpp:218] Iteration 8664 (2.33814 iter/s, 5.1323s/12 iters), loss = 0.178504
I0407 09:41:06.033588 17723 solver.cpp:237]     Train net output #0: loss = 0.178504 (* 1 = 0.178504 loss)
I0407 09:41:06.033597 17723 sgd_solver.cpp:105] Iteration 8664, lr = 0.0025
I0407 09:41:08.094249 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0407 09:41:13.061969 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0407 09:41:17.260048 17723 solver.cpp:330] Iteration 8670, Testing net (#0)
I0407 09:41:17.260068 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:41:18.205665 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:41:21.607157 17723 solver.cpp:397]     Test net output #0: accuracy = 0.489583
I0407 09:41:21.607187 17723 solver.cpp:397]     Test net output #1: loss = 2.9558 (* 1 = 2.9558 loss)
I0407 09:41:23.490203 17723 solver.cpp:218] Iteration 8676 (0.687424 iter/s, 17.4565s/12 iters), loss = 0.243724
I0407 09:41:23.490250 17723 solver.cpp:237]     Train net output #0: loss = 0.243724 (* 1 = 0.243724 loss)
I0407 09:41:23.490257 17723 sgd_solver.cpp:105] Iteration 8676, lr = 0.0025
I0407 09:41:28.878271 17723 solver.cpp:218] Iteration 8688 (2.22719 iter/s, 5.38796s/12 iters), loss = 0.0547524
I0407 09:41:28.878321 17723 solver.cpp:237]     Train net output #0: loss = 0.0547525 (* 1 = 0.0547525 loss)
I0407 09:41:28.878329 17723 sgd_solver.cpp:105] Iteration 8688, lr = 0.0025
I0407 09:41:33.319036 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:41:34.076745 17723 solver.cpp:218] Iteration 8700 (2.30842 iter/s, 5.19837s/12 iters), loss = 0.147064
I0407 09:41:34.076783 17723 solver.cpp:237]     Train net output #0: loss = 0.147064 (* 1 = 0.147064 loss)
I0407 09:41:34.076790 17723 sgd_solver.cpp:105] Iteration 8700, lr = 0.0025
I0407 09:41:39.317906 17723 solver.cpp:218] Iteration 8712 (2.28961 iter/s, 5.24107s/12 iters), loss = 0.0774947
I0407 09:41:39.317957 17723 solver.cpp:237]     Train net output #0: loss = 0.0774948 (* 1 = 0.0774948 loss)
I0407 09:41:39.317966 17723 sgd_solver.cpp:105] Iteration 8712, lr = 0.0025
I0407 09:41:44.697841 17723 solver.cpp:218] Iteration 8724 (2.23055 iter/s, 5.37983s/12 iters), loss = 0.14749
I0407 09:41:44.697878 17723 solver.cpp:237]     Train net output #0: loss = 0.14749 (* 1 = 0.14749 loss)
I0407 09:41:44.697887 17723 sgd_solver.cpp:105] Iteration 8724, lr = 0.0025
I0407 09:41:49.855398 17723 solver.cpp:218] Iteration 8736 (2.32673 iter/s, 5.15746s/12 iters), loss = 0.0796878
I0407 09:41:49.855469 17723 solver.cpp:237]     Train net output #0: loss = 0.0796879 (* 1 = 0.0796879 loss)
I0407 09:41:49.855480 17723 sgd_solver.cpp:105] Iteration 8736, lr = 0.0025
I0407 09:41:55.206494 17723 solver.cpp:218] Iteration 8748 (2.24258 iter/s, 5.35097s/12 iters), loss = 0.0479286
I0407 09:41:55.206538 17723 solver.cpp:237]     Train net output #0: loss = 0.0479287 (* 1 = 0.0479287 loss)
I0407 09:41:55.206545 17723 sgd_solver.cpp:105] Iteration 8748, lr = 0.0025
I0407 09:42:00.472391 17723 solver.cpp:218] Iteration 8760 (2.27886 iter/s, 5.26579s/12 iters), loss = 0.0745446
I0407 09:42:00.472450 17723 solver.cpp:237]     Train net output #0: loss = 0.0745447 (* 1 = 0.0745447 loss)
I0407 09:42:00.472461 17723 sgd_solver.cpp:105] Iteration 8760, lr = 0.0025
I0407 09:42:05.287096 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0407 09:42:09.944993 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0407 09:42:14.379530 17723 solver.cpp:330] Iteration 8772, Testing net (#0)
I0407 09:42:14.379550 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:42:15.341570 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:42:18.885835 17723 solver.cpp:397]     Test net output #0: accuracy = 0.484681
I0407 09:42:18.885869 17723 solver.cpp:397]     Test net output #1: loss = 2.9296 (* 1 = 2.9296 loss)
I0407 09:42:19.022109 17723 solver.cpp:218] Iteration 8772 (0.646917 iter/s, 18.5495s/12 iters), loss = 0.0760322
I0407 09:42:19.022171 17723 solver.cpp:237]     Train net output #0: loss = 0.0760323 (* 1 = 0.0760323 loss)
I0407 09:42:19.022179 17723 sgd_solver.cpp:105] Iteration 8772, lr = 0.0025
I0407 09:42:23.344997 17723 solver.cpp:218] Iteration 8784 (2.776 iter/s, 4.32277s/12 iters), loss = 0.0871405
I0407 09:42:23.345057 17723 solver.cpp:237]     Train net output #0: loss = 0.0871405 (* 1 = 0.0871405 loss)
I0407 09:42:23.345068 17723 sgd_solver.cpp:105] Iteration 8784, lr = 0.0025
I0407 09:42:28.725728 17723 solver.cpp:218] Iteration 8796 (2.23023 iter/s, 5.38061s/12 iters), loss = 0.123285
I0407 09:42:28.725782 17723 solver.cpp:237]     Train net output #0: loss = 0.123285 (* 1 = 0.123285 loss)
I0407 09:42:28.725793 17723 sgd_solver.cpp:105] Iteration 8796, lr = 0.0025
I0407 09:42:30.171921 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:42:33.976820 17723 solver.cpp:218] Iteration 8808 (2.28529 iter/s, 5.25097s/12 iters), loss = 0.101848
I0407 09:42:33.976891 17723 solver.cpp:237]     Train net output #0: loss = 0.101848 (* 1 = 0.101848 loss)
I0407 09:42:33.976902 17723 sgd_solver.cpp:105] Iteration 8808, lr = 0.0025
I0407 09:42:39.359444 17723 solver.cpp:218] Iteration 8820 (2.22944 iter/s, 5.38251s/12 iters), loss = 0.0412963
I0407 09:42:39.359561 17723 solver.cpp:237]     Train net output #0: loss = 0.0412964 (* 1 = 0.0412964 loss)
I0407 09:42:39.359571 17723 sgd_solver.cpp:105] Iteration 8820, lr = 0.0025
I0407 09:42:44.269366 17723 solver.cpp:218] Iteration 8832 (2.44411 iter/s, 4.90976s/12 iters), loss = 0.104322
I0407 09:42:44.269408 17723 solver.cpp:237]     Train net output #0: loss = 0.104322 (* 1 = 0.104322 loss)
I0407 09:42:44.269414 17723 sgd_solver.cpp:105] Iteration 8832, lr = 0.0025
I0407 09:42:49.568742 17723 solver.cpp:218] Iteration 8844 (2.26446 iter/s, 5.29927s/12 iters), loss = 0.0468659
I0407 09:42:49.568784 17723 solver.cpp:237]     Train net output #0: loss = 0.0468659 (* 1 = 0.0468659 loss)
I0407 09:42:49.568791 17723 sgd_solver.cpp:105] Iteration 8844, lr = 0.0025
I0407 09:42:54.872680 17723 solver.cpp:218] Iteration 8856 (2.26251 iter/s, 5.30384s/12 iters), loss = 0.122984
I0407 09:42:54.872727 17723 solver.cpp:237]     Train net output #0: loss = 0.122984 (* 1 = 0.122984 loss)
I0407 09:42:54.872735 17723 sgd_solver.cpp:105] Iteration 8856, lr = 0.0025
I0407 09:43:00.234710 17723 solver.cpp:218] Iteration 8868 (2.238 iter/s, 5.36193s/12 iters), loss = 0.11966
I0407 09:43:00.234758 17723 solver.cpp:237]     Train net output #0: loss = 0.11966 (* 1 = 0.11966 loss)
I0407 09:43:00.234766 17723 sgd_solver.cpp:105] Iteration 8868, lr = 0.0025
I0407 09:43:02.302850 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0407 09:43:06.658949 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0407 09:43:11.232236 17723 solver.cpp:330] Iteration 8874, Testing net (#0)
I0407 09:43:11.232372 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:43:12.195876 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:43:15.753684 17723 solver.cpp:397]     Test net output #0: accuracy = 0.479779
I0407 09:43:15.753715 17723 solver.cpp:397]     Test net output #1: loss = 2.83374 (* 1 = 2.83374 loss)
I0407 09:43:17.591938 17723 solver.cpp:218] Iteration 8880 (0.691362 iter/s, 17.357s/12 iters), loss = 0.112328
I0407 09:43:17.591981 17723 solver.cpp:237]     Train net output #0: loss = 0.112328 (* 1 = 0.112328 loss)
I0407 09:43:17.591989 17723 sgd_solver.cpp:105] Iteration 8880, lr = 0.0025
I0407 09:43:22.804446 17723 solver.cpp:218] Iteration 8892 (2.3022 iter/s, 5.21241s/12 iters), loss = 0.0885206
I0407 09:43:22.804484 17723 solver.cpp:237]     Train net output #0: loss = 0.0885206 (* 1 = 0.0885206 loss)
I0407 09:43:22.804491 17723 sgd_solver.cpp:105] Iteration 8892, lr = 0.0025
I0407 09:43:26.608438 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:43:28.239045 17723 solver.cpp:218] Iteration 8904 (2.20811 iter/s, 5.4345s/12 iters), loss = 0.0837269
I0407 09:43:28.239094 17723 solver.cpp:237]     Train net output #0: loss = 0.083727 (* 1 = 0.083727 loss)
I0407 09:43:28.239101 17723 sgd_solver.cpp:105] Iteration 8904, lr = 0.0025
I0407 09:43:33.649322 17723 solver.cpp:218] Iteration 8916 (2.21804 iter/s, 5.41017s/12 iters), loss = 0.127129
I0407 09:43:33.649364 17723 solver.cpp:237]     Train net output #0: loss = 0.127129 (* 1 = 0.127129 loss)
I0407 09:43:33.649372 17723 sgd_solver.cpp:105] Iteration 8916, lr = 0.0025
I0407 09:43:38.952389 17723 solver.cpp:218] Iteration 8928 (2.26288 iter/s, 5.30297s/12 iters), loss = 0.0512254
I0407 09:43:38.952430 17723 solver.cpp:237]     Train net output #0: loss = 0.0512254 (* 1 = 0.0512254 loss)
I0407 09:43:38.952437 17723 sgd_solver.cpp:105] Iteration 8928, lr = 0.0025
I0407 09:43:44.152242 17723 solver.cpp:218] Iteration 8940 (2.3078 iter/s, 5.19976s/12 iters), loss = 0.065827
I0407 09:43:44.152341 17723 solver.cpp:237]     Train net output #0: loss = 0.0658271 (* 1 = 0.0658271 loss)
I0407 09:43:44.152349 17723 sgd_solver.cpp:105] Iteration 8940, lr = 0.0025
I0407 09:43:49.420150 17723 solver.cpp:218] Iteration 8952 (2.27801 iter/s, 5.26775s/12 iters), loss = 0.0669075
I0407 09:43:49.420198 17723 solver.cpp:237]     Train net output #0: loss = 0.0669075 (* 1 = 0.0669075 loss)
I0407 09:43:49.420207 17723 sgd_solver.cpp:105] Iteration 8952, lr = 0.0025
I0407 09:43:54.548575 17723 solver.cpp:218] Iteration 8964 (2.33995 iter/s, 5.12832s/12 iters), loss = 0.0302166
I0407 09:43:54.548619 17723 solver.cpp:237]     Train net output #0: loss = 0.0302167 (* 1 = 0.0302167 loss)
I0407 09:43:54.548626 17723 sgd_solver.cpp:105] Iteration 8964, lr = 0.0025
I0407 09:43:59.165571 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0407 09:44:04.002527 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0407 09:44:07.717056 17723 solver.cpp:330] Iteration 8976, Testing net (#0)
I0407 09:44:07.717074 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:44:08.621968 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:44:12.079779 17723 solver.cpp:397]     Test net output #0: accuracy = 0.489583
I0407 09:44:12.079818 17723 solver.cpp:397]     Test net output #1: loss = 2.861 (* 1 = 2.861 loss)
I0407 09:44:12.219287 17723 solver.cpp:218] Iteration 8976 (0.679097 iter/s, 17.6705s/12 iters), loss = 0.0923821
I0407 09:44:12.219357 17723 solver.cpp:237]     Train net output #0: loss = 0.0923821 (* 1 = 0.0923821 loss)
I0407 09:44:12.219367 17723 sgd_solver.cpp:105] Iteration 8976, lr = 0.0025
I0407 09:44:16.650434 17723 solver.cpp:218] Iteration 8988 (2.70818 iter/s, 4.43103s/12 iters), loss = 0.0526634
I0407 09:44:16.650555 17723 solver.cpp:237]     Train net output #0: loss = 0.0526634 (* 1 = 0.0526634 loss)
I0407 09:44:16.650563 17723 sgd_solver.cpp:105] Iteration 8988, lr = 0.0025
I0407 09:44:20.036550 17723 blocking_queue.cpp:49] Waiting for data
I0407 09:44:21.742549 17723 solver.cpp:218] Iteration 9000 (2.35666 iter/s, 5.09195s/12 iters), loss = 0.0455907
I0407 09:44:21.742588 17723 solver.cpp:237]     Train net output #0: loss = 0.0455907 (* 1 = 0.0455907 loss)
I0407 09:44:21.742595 17723 sgd_solver.cpp:105] Iteration 9000, lr = 0.0025
I0407 09:44:22.441174 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:44:27.062359 17723 solver.cpp:218] Iteration 9012 (2.25576 iter/s, 5.31972s/12 iters), loss = 0.0233233
I0407 09:44:27.062403 17723 solver.cpp:237]     Train net output #0: loss = 0.0233233 (* 1 = 0.0233233 loss)
I0407 09:44:27.062412 17723 sgd_solver.cpp:105] Iteration 9012, lr = 0.0025
I0407 09:44:32.342824 17723 solver.cpp:218] Iteration 9024 (2.27257 iter/s, 5.28036s/12 iters), loss = 0.0500342
I0407 09:44:32.342882 17723 solver.cpp:237]     Train net output #0: loss = 0.0500343 (* 1 = 0.0500343 loss)
I0407 09:44:32.342892 17723 sgd_solver.cpp:105] Iteration 9024, lr = 0.0025
I0407 09:44:37.701433 17723 solver.cpp:218] Iteration 9036 (2.23943 iter/s, 5.3585s/12 iters), loss = 0.080713
I0407 09:44:37.701481 17723 solver.cpp:237]     Train net output #0: loss = 0.080713 (* 1 = 0.080713 loss)
I0407 09:44:37.701489 17723 sgd_solver.cpp:105] Iteration 9036, lr = 0.0025
I0407 09:44:42.926584 17723 solver.cpp:218] Iteration 9048 (2.29663 iter/s, 5.22505s/12 iters), loss = 0.0794305
I0407 09:44:42.926640 17723 solver.cpp:237]     Train net output #0: loss = 0.0794305 (* 1 = 0.0794305 loss)
I0407 09:44:42.926649 17723 sgd_solver.cpp:105] Iteration 9048, lr = 0.0025
I0407 09:44:47.890053 17723 solver.cpp:218] Iteration 9060 (2.41772 iter/s, 4.96335s/12 iters), loss = 0.109626
I0407 09:44:47.890172 17723 solver.cpp:237]     Train net output #0: loss = 0.109626 (* 1 = 0.109626 loss)
I0407 09:44:47.890183 17723 sgd_solver.cpp:105] Iteration 9060, lr = 0.0025
I0407 09:44:53.303161 17723 solver.cpp:218] Iteration 9072 (2.21691 iter/s, 5.41294s/12 iters), loss = 0.0853011
I0407 09:44:53.303205 17723 solver.cpp:237]     Train net output #0: loss = 0.0853011 (* 1 = 0.0853011 loss)
I0407 09:44:53.303212 17723 sgd_solver.cpp:105] Iteration 9072, lr = 0.0025
I0407 09:44:55.367444 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0407 09:44:58.426901 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0407 09:45:02.103837 17723 solver.cpp:330] Iteration 9078, Testing net (#0)
I0407 09:45:02.103857 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:45:02.908603 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:45:06.611129 17723 solver.cpp:397]     Test net output #0: accuracy = 0.491422
I0407 09:45:06.611157 17723 solver.cpp:397]     Test net output #1: loss = 2.90897 (* 1 = 2.90897 loss)
I0407 09:45:08.522401 17723 solver.cpp:218] Iteration 9084 (0.788485 iter/s, 15.2191s/12 iters), loss = 0.0343107
I0407 09:45:08.522461 17723 solver.cpp:237]     Train net output #0: loss = 0.0343107 (* 1 = 0.0343107 loss)
I0407 09:45:08.522472 17723 sgd_solver.cpp:105] Iteration 9084, lr = 0.0025
I0407 09:45:13.700839 17723 solver.cpp:218] Iteration 9096 (2.31735 iter/s, 5.17833s/12 iters), loss = 0.101523
I0407 09:45:13.700881 17723 solver.cpp:237]     Train net output #0: loss = 0.101523 (* 1 = 0.101523 loss)
I0407 09:45:13.700897 17723 sgd_solver.cpp:105] Iteration 9096, lr = 0.0025
I0407 09:45:16.577955 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:45:18.787279 17723 solver.cpp:218] Iteration 9108 (2.35926 iter/s, 5.08635s/12 iters), loss = 0.0884933
I0407 09:45:18.787380 17723 solver.cpp:237]     Train net output #0: loss = 0.0884933 (* 1 = 0.0884933 loss)
I0407 09:45:18.787389 17723 sgd_solver.cpp:105] Iteration 9108, lr = 0.0025
I0407 09:45:23.929527 17723 solver.cpp:218] Iteration 9120 (2.33368 iter/s, 5.14209s/12 iters), loss = 0.0798906
I0407 09:45:23.929575 17723 solver.cpp:237]     Train net output #0: loss = 0.0798906 (* 1 = 0.0798906 loss)
I0407 09:45:23.929584 17723 sgd_solver.cpp:105] Iteration 9120, lr = 0.0025
I0407 09:45:29.308566 17723 solver.cpp:218] Iteration 9132 (2.23093 iter/s, 5.37893s/12 iters), loss = 0.0585725
I0407 09:45:29.308632 17723 solver.cpp:237]     Train net output #0: loss = 0.0585725 (* 1 = 0.0585725 loss)
I0407 09:45:29.308643 17723 sgd_solver.cpp:105] Iteration 9132, lr = 0.0025
I0407 09:45:34.656649 17723 solver.cpp:218] Iteration 9144 (2.24384 iter/s, 5.34796s/12 iters), loss = 0.0271361
I0407 09:45:34.656690 17723 solver.cpp:237]     Train net output #0: loss = 0.0271361 (* 1 = 0.0271361 loss)
I0407 09:45:34.656697 17723 sgd_solver.cpp:105] Iteration 9144, lr = 0.0025
I0407 09:45:39.849917 17723 solver.cpp:218] Iteration 9156 (2.31073 iter/s, 5.19317s/12 iters), loss = 0.0685612
I0407 09:45:39.849963 17723 solver.cpp:237]     Train net output #0: loss = 0.0685612 (* 1 = 0.0685612 loss)
I0407 09:45:39.849970 17723 sgd_solver.cpp:105] Iteration 9156, lr = 0.0025
I0407 09:45:45.203910 17723 solver.cpp:218] Iteration 9168 (2.24136 iter/s, 5.35389s/12 iters), loss = 0.0314467
I0407 09:45:45.203959 17723 solver.cpp:237]     Train net output #0: loss = 0.0314467 (* 1 = 0.0314467 loss)
I0407 09:45:45.203967 17723 sgd_solver.cpp:105] Iteration 9168, lr = 0.0025
I0407 09:45:49.980984 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0407 09:45:53.056861 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0407 09:45:56.966351 17723 solver.cpp:330] Iteration 9180, Testing net (#0)
I0407 09:45:56.966374 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:45:57.734620 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:46:01.315217 17723 solver.cpp:397]     Test net output #0: accuracy = 0.500613
I0407 09:46:01.315254 17723 solver.cpp:397]     Test net output #1: loss = 2.87083 (* 1 = 2.87083 loss)
I0407 09:46:01.456651 17723 solver.cpp:218] Iteration 9180 (0.738346 iter/s, 16.2526s/12 iters), loss = 0.0410062
I0407 09:46:01.456709 17723 solver.cpp:237]     Train net output #0: loss = 0.0410063 (* 1 = 0.0410063 loss)
I0407 09:46:01.456719 17723 sgd_solver.cpp:105] Iteration 9180, lr = 0.0025
I0407 09:46:05.818637 17723 solver.cpp:218] Iteration 9192 (2.75111 iter/s, 4.36188s/12 iters), loss = 0.0539353
I0407 09:46:05.818681 17723 solver.cpp:237]     Train net output #0: loss = 0.0539353 (* 1 = 0.0539353 loss)
I0407 09:46:05.818687 17723 sgd_solver.cpp:105] Iteration 9192, lr = 0.0025
I0407 09:46:11.112088 17723 solver.cpp:218] Iteration 9204 (2.26699 iter/s, 5.29335s/12 iters), loss = 0.0593714
I0407 09:46:11.112128 17723 solver.cpp:237]     Train net output #0: loss = 0.0593714 (* 1 = 0.0593714 loss)
I0407 09:46:11.112134 17723 sgd_solver.cpp:105] Iteration 9204, lr = 0.0025
I0407 09:46:11.180202 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:46:16.286877 17723 solver.cpp:218] Iteration 9216 (2.31898 iter/s, 5.17469s/12 iters), loss = 0.055399
I0407 09:46:16.286928 17723 solver.cpp:237]     Train net output #0: loss = 0.055399 (* 1 = 0.055399 loss)
I0407 09:46:16.286938 17723 sgd_solver.cpp:105] Iteration 9216, lr = 0.0025
I0407 09:46:21.598384 17723 solver.cpp:218] Iteration 9228 (2.25929 iter/s, 5.3114s/12 iters), loss = 0.0815506
I0407 09:46:21.598488 17723 solver.cpp:237]     Train net output #0: loss = 0.0815506 (* 1 = 0.0815506 loss)
I0407 09:46:21.598497 17723 sgd_solver.cpp:105] Iteration 9228, lr = 0.0025
I0407 09:46:26.790244 17723 solver.cpp:218] Iteration 9240 (2.31138 iter/s, 5.1917s/12 iters), loss = 0.113091
I0407 09:46:26.790283 17723 solver.cpp:237]     Train net output #0: loss = 0.113091 (* 1 = 0.113091 loss)
I0407 09:46:26.790290 17723 sgd_solver.cpp:105] Iteration 9240, lr = 0.0025
I0407 09:46:32.045548 17723 solver.cpp:218] Iteration 9252 (2.28345 iter/s, 5.25521s/12 iters), loss = 0.0440905
I0407 09:46:32.045601 17723 solver.cpp:237]     Train net output #0: loss = 0.0440906 (* 1 = 0.0440906 loss)
I0407 09:46:32.045610 17723 sgd_solver.cpp:105] Iteration 9252, lr = 0.0025
I0407 09:46:37.435591 17723 solver.cpp:218] Iteration 9264 (2.22637 iter/s, 5.38993s/12 iters), loss = 0.0450943
I0407 09:46:37.435647 17723 solver.cpp:237]     Train net output #0: loss = 0.0450943 (* 1 = 0.0450943 loss)
I0407 09:46:37.435657 17723 sgd_solver.cpp:105] Iteration 9264, lr = 0.0025
I0407 09:46:42.498095 17723 solver.cpp:218] Iteration 9276 (2.37042 iter/s, 5.0624s/12 iters), loss = 0.0463423
I0407 09:46:42.498140 17723 solver.cpp:237]     Train net output #0: loss = 0.0463423 (* 1 = 0.0463423 loss)
I0407 09:46:42.498147 17723 sgd_solver.cpp:105] Iteration 9276, lr = 0.0025
I0407 09:46:44.709722 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0407 09:46:48.255573 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0407 09:46:52.052065 17723 solver.cpp:330] Iteration 9282, Testing net (#0)
I0407 09:46:52.052150 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:46:52.771759 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:46:56.348345 17723 solver.cpp:397]     Test net output #0: accuracy = 0.493873
I0407 09:46:56.348384 17723 solver.cpp:397]     Test net output #1: loss = 2.89948 (* 1 = 2.89948 loss)
I0407 09:46:58.355671 17723 solver.cpp:218] Iteration 9288 (0.756745 iter/s, 15.8574s/12 iters), loss = 0.0580616
I0407 09:46:58.355718 17723 solver.cpp:237]     Train net output #0: loss = 0.0580616 (* 1 = 0.0580616 loss)
I0407 09:46:58.355726 17723 sgd_solver.cpp:105] Iteration 9288, lr = 0.0025
I0407 09:47:03.578850 17723 solver.cpp:218] Iteration 9300 (2.2975 iter/s, 5.22308s/12 iters), loss = 0.0614387
I0407 09:47:03.578898 17723 solver.cpp:237]     Train net output #0: loss = 0.0614387 (* 1 = 0.0614387 loss)
I0407 09:47:03.578907 17723 sgd_solver.cpp:105] Iteration 9300, lr = 0.0025
I0407 09:47:05.930125 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:47:08.778828 17723 solver.cpp:218] Iteration 9312 (2.30775 iter/s, 5.19987s/12 iters), loss = 0.0929579
I0407 09:47:08.778893 17723 solver.cpp:237]     Train net output #0: loss = 0.092958 (* 1 = 0.092958 loss)
I0407 09:47:08.778904 17723 sgd_solver.cpp:105] Iteration 9312, lr = 0.0025
I0407 09:47:14.093672 17723 solver.cpp:218] Iteration 9324 (2.25788 iter/s, 5.31473s/12 iters), loss = 0.0902088
I0407 09:47:14.093708 17723 solver.cpp:237]     Train net output #0: loss = 0.0902089 (* 1 = 0.0902089 loss)
I0407 09:47:14.093715 17723 sgd_solver.cpp:105] Iteration 9324, lr = 0.0025
I0407 09:47:19.118669 17723 solver.cpp:218] Iteration 9336 (2.3881 iter/s, 5.02491s/12 iters), loss = 0.0722521
I0407 09:47:19.118728 17723 solver.cpp:237]     Train net output #0: loss = 0.0722521 (* 1 = 0.0722521 loss)
I0407 09:47:19.118739 17723 sgd_solver.cpp:105] Iteration 9336, lr = 0.0025
I0407 09:47:24.371039 17723 solver.cpp:218] Iteration 9348 (2.28474 iter/s, 5.25225s/12 iters), loss = 0.0663222
I0407 09:47:24.371176 17723 solver.cpp:237]     Train net output #0: loss = 0.0663222 (* 1 = 0.0663222 loss)
I0407 09:47:24.371187 17723 sgd_solver.cpp:105] Iteration 9348, lr = 0.0025
I0407 09:47:29.617692 17723 solver.cpp:218] Iteration 9360 (2.28725 iter/s, 5.24647s/12 iters), loss = 0.0783887
I0407 09:47:29.617748 17723 solver.cpp:237]     Train net output #0: loss = 0.0783888 (* 1 = 0.0783888 loss)
I0407 09:47:29.617758 17723 sgd_solver.cpp:105] Iteration 9360, lr = 0.0025
I0407 09:47:34.855077 17723 solver.cpp:218] Iteration 9372 (2.29126 iter/s, 5.23728s/12 iters), loss = 0.206869
I0407 09:47:34.855116 17723 solver.cpp:237]     Train net output #0: loss = 0.206869 (* 1 = 0.206869 loss)
I0407 09:47:34.855124 17723 sgd_solver.cpp:105] Iteration 9372, lr = 0.0025
I0407 09:47:39.377473 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0407 09:47:42.392143 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0407 09:47:46.212793 17723 solver.cpp:330] Iteration 9384, Testing net (#0)
I0407 09:47:46.212819 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:47:46.975258 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:47:50.810010 17723 solver.cpp:397]     Test net output #0: accuracy = 0.495098
I0407 09:47:50.810050 17723 solver.cpp:397]     Test net output #1: loss = 2.88723 (* 1 = 2.88723 loss)
I0407 09:47:50.950294 17723 solver.cpp:218] Iteration 9384 (0.745571 iter/s, 16.095s/12 iters), loss = 0.124721
I0407 09:47:50.950345 17723 solver.cpp:237]     Train net output #0: loss = 0.124721 (* 1 = 0.124721 loss)
I0407 09:47:50.950354 17723 sgd_solver.cpp:105] Iteration 9384, lr = 0.0025
I0407 09:47:55.432660 17723 solver.cpp:218] Iteration 9396 (2.67722 iter/s, 4.48226s/12 iters), loss = 0.0606289
I0407 09:47:55.432809 17723 solver.cpp:237]     Train net output #0: loss = 0.0606289 (* 1 = 0.0606289 loss)
I0407 09:47:55.432819 17723 sgd_solver.cpp:105] Iteration 9396, lr = 0.0025
I0407 09:48:00.007591 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:48:00.760465 17723 solver.cpp:218] Iteration 9408 (2.25242 iter/s, 5.3276s/12 iters), loss = 0.126543
I0407 09:48:00.760512 17723 solver.cpp:237]     Train net output #0: loss = 0.126543 (* 1 = 0.126543 loss)
I0407 09:48:00.760519 17723 sgd_solver.cpp:105] Iteration 9408, lr = 0.0025
I0407 09:48:05.956084 17723 solver.cpp:218] Iteration 9420 (2.30968 iter/s, 5.19552s/12 iters), loss = 0.0421727
I0407 09:48:05.956130 17723 solver.cpp:237]     Train net output #0: loss = 0.0421727 (* 1 = 0.0421727 loss)
I0407 09:48:05.956136 17723 sgd_solver.cpp:105] Iteration 9420, lr = 0.0025
I0407 09:48:11.343521 17723 solver.cpp:218] Iteration 9432 (2.22745 iter/s, 5.38734s/12 iters), loss = 0.0700876
I0407 09:48:11.343566 17723 solver.cpp:237]     Train net output #0: loss = 0.0700876 (* 1 = 0.0700876 loss)
I0407 09:48:11.343573 17723 sgd_solver.cpp:105] Iteration 9432, lr = 0.0025
I0407 09:48:16.362145 17723 solver.cpp:218] Iteration 9444 (2.39114 iter/s, 5.01852s/12 iters), loss = 0.0137207
I0407 09:48:16.362190 17723 solver.cpp:237]     Train net output #0: loss = 0.0137208 (* 1 = 0.0137208 loss)
I0407 09:48:16.362197 17723 sgd_solver.cpp:105] Iteration 9444, lr = 0.0025
I0407 09:48:21.746049 17723 solver.cpp:218] Iteration 9456 (2.22891 iter/s, 5.3838s/12 iters), loss = 0.0681731
I0407 09:48:21.746098 17723 solver.cpp:237]     Train net output #0: loss = 0.0681731 (* 1 = 0.0681731 loss)
I0407 09:48:21.746105 17723 sgd_solver.cpp:105] Iteration 9456, lr = 0.0025
I0407 09:48:27.030158 17723 solver.cpp:218] Iteration 9468 (2.271 iter/s, 5.28401s/12 iters), loss = 0.0955466
I0407 09:48:27.030263 17723 solver.cpp:237]     Train net output #0: loss = 0.0955466 (* 1 = 0.0955466 loss)
I0407 09:48:27.030272 17723 sgd_solver.cpp:105] Iteration 9468, lr = 0.0025
I0407 09:48:32.331890 17723 solver.cpp:218] Iteration 9480 (2.26348 iter/s, 5.30157s/12 iters), loss = 0.0963865
I0407 09:48:32.331945 17723 solver.cpp:237]     Train net output #0: loss = 0.0963865 (* 1 = 0.0963865 loss)
I0407 09:48:32.331955 17723 sgd_solver.cpp:105] Iteration 9480, lr = 0.0025
I0407 09:48:34.298873 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0407 09:48:37.315158 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0407 09:48:40.324957 17723 solver.cpp:330] Iteration 9486, Testing net (#0)
I0407 09:48:40.324976 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:48:41.012821 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:48:44.894554 17723 solver.cpp:397]     Test net output #0: accuracy = 0.495098
I0407 09:48:44.894584 17723 solver.cpp:397]     Test net output #1: loss = 2.87377 (* 1 = 2.87377 loss)
I0407 09:48:46.882514 17723 solver.cpp:218] Iteration 9492 (0.824717 iter/s, 14.5505s/12 iters), loss = 0.0577979
I0407 09:48:46.882556 17723 solver.cpp:237]     Train net output #0: loss = 0.0577979 (* 1 = 0.0577979 loss)
I0407 09:48:46.882563 17723 sgd_solver.cpp:105] Iteration 9492, lr = 0.0025
I0407 09:48:52.255434 17723 solver.cpp:218] Iteration 9504 (2.23346 iter/s, 5.37282s/12 iters), loss = 0.0724336
I0407 09:48:52.255477 17723 solver.cpp:237]     Train net output #0: loss = 0.0724336 (* 1 = 0.0724336 loss)
I0407 09:48:52.255484 17723 sgd_solver.cpp:105] Iteration 9504, lr = 0.0025
I0407 09:48:53.738724 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:48:57.437254 17723 solver.cpp:218] Iteration 9516 (2.31583 iter/s, 5.18173s/12 iters), loss = 0.045154
I0407 09:48:57.437369 17723 solver.cpp:237]     Train net output #0: loss = 0.045154 (* 1 = 0.045154 loss)
I0407 09:48:57.437377 17723 sgd_solver.cpp:105] Iteration 9516, lr = 0.0025
I0407 09:49:02.706758 17723 solver.cpp:218] Iteration 9528 (2.27733 iter/s, 5.26934s/12 iters), loss = 0.0407648
I0407 09:49:02.706800 17723 solver.cpp:237]     Train net output #0: loss = 0.0407648 (* 1 = 0.0407648 loss)
I0407 09:49:02.706807 17723 sgd_solver.cpp:105] Iteration 9528, lr = 0.0025
I0407 09:49:07.979194 17723 solver.cpp:218] Iteration 9540 (2.27603 iter/s, 5.27234s/12 iters), loss = 0.0285192
I0407 09:49:07.979243 17723 solver.cpp:237]     Train net output #0: loss = 0.0285192 (* 1 = 0.0285192 loss)
I0407 09:49:07.979249 17723 sgd_solver.cpp:105] Iteration 9540, lr = 0.0025
I0407 09:49:13.390933 17723 solver.cpp:218] Iteration 9552 (2.21744 iter/s, 5.41163s/12 iters), loss = 0.0553646
I0407 09:49:13.390978 17723 solver.cpp:237]     Train net output #0: loss = 0.0553647 (* 1 = 0.0553647 loss)
I0407 09:49:13.390986 17723 sgd_solver.cpp:105] Iteration 9552, lr = 0.0025
I0407 09:49:18.667313 17723 solver.cpp:218] Iteration 9564 (2.27433 iter/s, 5.27628s/12 iters), loss = 0.0638919
I0407 09:49:18.667376 17723 solver.cpp:237]     Train net output #0: loss = 0.063892 (* 1 = 0.063892 loss)
I0407 09:49:18.667387 17723 sgd_solver.cpp:105] Iteration 9564, lr = 0.0025
I0407 09:49:24.015942 17723 solver.cpp:218] Iteration 9576 (2.24362 iter/s, 5.34851s/12 iters), loss = 0.0733827
I0407 09:49:24.016000 17723 solver.cpp:237]     Train net output #0: loss = 0.0733827 (* 1 = 0.0733827 loss)
I0407 09:49:24.016011 17723 sgd_solver.cpp:105] Iteration 9576, lr = 0.0025
I0407 09:49:28.826088 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0407 09:49:31.846828 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0407 09:49:34.183467 17723 solver.cpp:330] Iteration 9588, Testing net (#0)
I0407 09:49:34.183492 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:49:34.846658 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:49:38.822677 17723 solver.cpp:397]     Test net output #0: accuracy = 0.488971
I0407 09:49:38.822706 17723 solver.cpp:397]     Test net output #1: loss = 2.91843 (* 1 = 2.91843 loss)
I0407 09:49:38.962823 17723 solver.cpp:218] Iteration 9588 (0.802853 iter/s, 14.9467s/12 iters), loss = 0.10464
I0407 09:49:38.962873 17723 solver.cpp:237]     Train net output #0: loss = 0.10464 (* 1 = 0.10464 loss)
I0407 09:49:38.962883 17723 sgd_solver.cpp:105] Iteration 9588, lr = 0.0025
I0407 09:49:43.282608 17723 solver.cpp:218] Iteration 9600 (2.77797 iter/s, 4.31969s/12 iters), loss = 0.174178
I0407 09:49:43.282644 17723 solver.cpp:237]     Train net output #0: loss = 0.174178 (* 1 = 0.174178 loss)
I0407 09:49:43.282651 17723 sgd_solver.cpp:105] Iteration 9600, lr = 0.0025
I0407 09:49:47.116397 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:49:48.666952 17723 solver.cpp:218] Iteration 9612 (2.22872 iter/s, 5.38425s/12 iters), loss = 0.0334187
I0407 09:49:48.666996 17723 solver.cpp:237]     Train net output #0: loss = 0.0334187 (* 1 = 0.0334187 loss)
I0407 09:49:48.667002 17723 sgd_solver.cpp:105] Iteration 9612, lr = 0.0025
I0407 09:49:53.968523 17723 solver.cpp:218] Iteration 9624 (2.26352 iter/s, 5.30147s/12 iters), loss = 0.0923739
I0407 09:49:53.968564 17723 solver.cpp:237]     Train net output #0: loss = 0.0923739 (* 1 = 0.0923739 loss)
I0407 09:49:53.968571 17723 sgd_solver.cpp:105] Iteration 9624, lr = 0.0025
I0407 09:49:59.240150 17723 solver.cpp:218] Iteration 9636 (2.27638 iter/s, 5.27153s/12 iters), loss = 0.0401453
I0407 09:49:59.240288 17723 solver.cpp:237]     Train net output #0: loss = 0.0401454 (* 1 = 0.0401454 loss)
I0407 09:49:59.240298 17723 sgd_solver.cpp:105] Iteration 9636, lr = 0.0025
I0407 09:50:04.505216 17723 solver.cpp:218] Iteration 9648 (2.27926 iter/s, 5.26487s/12 iters), loss = 0.0420136
I0407 09:50:04.505260 17723 solver.cpp:237]     Train net output #0: loss = 0.0420137 (* 1 = 0.0420137 loss)
I0407 09:50:04.505267 17723 sgd_solver.cpp:105] Iteration 9648, lr = 0.0025
I0407 09:50:09.699457 17723 solver.cpp:218] Iteration 9660 (2.3103 iter/s, 5.19414s/12 iters), loss = 0.0927962
I0407 09:50:09.699515 17723 solver.cpp:237]     Train net output #0: loss = 0.0927963 (* 1 = 0.0927963 loss)
I0407 09:50:09.699527 17723 sgd_solver.cpp:105] Iteration 9660, lr = 0.0025
I0407 09:50:14.735728 17723 solver.cpp:218] Iteration 9672 (2.38277 iter/s, 5.03616s/12 iters), loss = 0.0321329
I0407 09:50:14.735771 17723 solver.cpp:237]     Train net output #0: loss = 0.0321329 (* 1 = 0.0321329 loss)
I0407 09:50:14.735778 17723 sgd_solver.cpp:105] Iteration 9672, lr = 0.0025
I0407 09:50:19.878161 17723 solver.cpp:218] Iteration 9684 (2.33357 iter/s, 5.14233s/12 iters), loss = 0.0355899
I0407 09:50:19.878214 17723 solver.cpp:237]     Train net output #0: loss = 0.0355899 (* 1 = 0.0355899 loss)
I0407 09:50:19.878223 17723 sgd_solver.cpp:105] Iteration 9684, lr = 0.0025
I0407 09:50:22.083540 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0407 09:50:25.007267 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0407 09:50:27.321645 17723 solver.cpp:330] Iteration 9690, Testing net (#0)
I0407 09:50:27.321662 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:50:27.887063 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:50:30.673586 17723 blocking_queue.cpp:49] Waiting for data
I0407 09:50:31.713925 17723 solver.cpp:397]     Test net output #0: accuracy = 0.49326
I0407 09:50:31.713958 17723 solver.cpp:397]     Test net output #1: loss = 2.92592 (* 1 = 2.92592 loss)
I0407 09:50:33.662773 17723 solver.cpp:218] Iteration 9696 (0.870547 iter/s, 13.7844s/12 iters), loss = 0.0499936
I0407 09:50:33.662820 17723 solver.cpp:237]     Train net output #0: loss = 0.0499936 (* 1 = 0.0499936 loss)
I0407 09:50:33.662827 17723 sgd_solver.cpp:105] Iteration 9696, lr = 0.0025
I0407 09:50:38.859174 17723 solver.cpp:218] Iteration 9708 (2.30933 iter/s, 5.1963s/12 iters), loss = 0.0629604
I0407 09:50:38.859212 17723 solver.cpp:237]     Train net output #0: loss = 0.0629605 (* 1 = 0.0629605 loss)
I0407 09:50:38.859220 17723 sgd_solver.cpp:105] Iteration 9708, lr = 0.0025
I0407 09:50:39.604828 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:50:44.192976 17723 solver.cpp:218] Iteration 9720 (2.24984 iter/s, 5.33371s/12 iters), loss = 0.0416088
I0407 09:50:44.193029 17723 solver.cpp:237]     Train net output #0: loss = 0.0416088 (* 1 = 0.0416088 loss)
I0407 09:50:44.193039 17723 sgd_solver.cpp:105] Iteration 9720, lr = 0.0025
I0407 09:50:49.538477 17723 solver.cpp:218] Iteration 9732 (2.24492 iter/s, 5.34539s/12 iters), loss = 0.0494114
I0407 09:50:49.538524 17723 solver.cpp:237]     Train net output #0: loss = 0.0494114 (* 1 = 0.0494114 loss)
I0407 09:50:49.538532 17723 sgd_solver.cpp:105] Iteration 9732, lr = 0.0025
I0407 09:50:54.800907 17723 solver.cpp:218] Iteration 9744 (2.28036 iter/s, 5.26233s/12 iters), loss = 0.0887707
I0407 09:50:54.800949 17723 solver.cpp:237]     Train net output #0: loss = 0.0887708 (* 1 = 0.0887708 loss)
I0407 09:50:54.800957 17723 sgd_solver.cpp:105] Iteration 9744, lr = 0.0025
I0407 09:51:00.214869 17723 solver.cpp:218] Iteration 9756 (2.21653 iter/s, 5.41386s/12 iters), loss = 0.024571
I0407 09:51:00.214917 17723 solver.cpp:237]     Train net output #0: loss = 0.0245711 (* 1 = 0.0245711 loss)
I0407 09:51:00.214926 17723 sgd_solver.cpp:105] Iteration 9756, lr = 0.0025
I0407 09:51:05.596448 17723 solver.cpp:218] Iteration 9768 (2.22987 iter/s, 5.38148s/12 iters), loss = 0.0734758
I0407 09:51:05.596580 17723 solver.cpp:237]     Train net output #0: loss = 0.0734758 (* 1 = 0.0734758 loss)
I0407 09:51:05.596587 17723 sgd_solver.cpp:105] Iteration 9768, lr = 0.0025
I0407 09:51:10.801898 17723 solver.cpp:218] Iteration 9780 (2.30536 iter/s, 5.20527s/12 iters), loss = 0.0384937
I0407 09:51:10.801944 17723 solver.cpp:237]     Train net output #0: loss = 0.0384937 (* 1 = 0.0384937 loss)
I0407 09:51:10.801950 17723 sgd_solver.cpp:105] Iteration 9780, lr = 0.0025
I0407 09:51:15.516049 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0407 09:51:18.525466 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0407 09:51:20.835461 17723 solver.cpp:330] Iteration 9792, Testing net (#0)
I0407 09:51:20.835482 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:51:21.345643 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:51:25.138293 17723 solver.cpp:397]     Test net output #0: accuracy = 0.503676
I0407 09:51:25.138332 17723 solver.cpp:397]     Test net output #1: loss = 2.91938 (* 1 = 2.91938 loss)
I0407 09:51:25.278546 17723 solver.cpp:218] Iteration 9792 (0.828931 iter/s, 14.4765s/12 iters), loss = 0.0366191
I0407 09:51:25.278601 17723 solver.cpp:237]     Train net output #0: loss = 0.0366192 (* 1 = 0.0366192 loss)
I0407 09:51:25.278611 17723 sgd_solver.cpp:105] Iteration 9792, lr = 0.0025
I0407 09:51:29.532609 17723 solver.cpp:218] Iteration 9804 (2.8209 iter/s, 4.25396s/12 iters), loss = 0.13595
I0407 09:51:29.532655 17723 solver.cpp:237]     Train net output #0: loss = 0.13595 (* 1 = 0.13595 loss)
I0407 09:51:29.532662 17723 sgd_solver.cpp:105] Iteration 9804, lr = 0.0025
I0407 09:51:32.654206 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:51:34.869617 17723 solver.cpp:218] Iteration 9816 (2.24849 iter/s, 5.33691s/12 iters), loss = 0.0536344
I0407 09:51:34.869660 17723 solver.cpp:237]     Train net output #0: loss = 0.0536344 (* 1 = 0.0536344 loss)
I0407 09:51:34.869668 17723 sgd_solver.cpp:105] Iteration 9816, lr = 0.0025
I0407 09:51:40.188123 17723 solver.cpp:218] Iteration 9828 (2.25631 iter/s, 5.31841s/12 iters), loss = 0.0934296
I0407 09:51:40.190120 17723 solver.cpp:237]     Train net output #0: loss = 0.0934296 (* 1 = 0.0934296 loss)
I0407 09:51:40.190135 17723 sgd_solver.cpp:105] Iteration 9828, lr = 0.0025
I0407 09:51:45.214416 17723 solver.cpp:218] Iteration 9840 (2.38841 iter/s, 5.02425s/12 iters), loss = 0.0298396
I0407 09:51:45.214458 17723 solver.cpp:237]     Train net output #0: loss = 0.0298396 (* 1 = 0.0298396 loss)
I0407 09:51:45.214465 17723 sgd_solver.cpp:105] Iteration 9840, lr = 0.0025
I0407 09:51:50.453689 17723 solver.cpp:218] Iteration 9852 (2.29044 iter/s, 5.23917s/12 iters), loss = 0.016679
I0407 09:51:50.453737 17723 solver.cpp:237]     Train net output #0: loss = 0.016679 (* 1 = 0.016679 loss)
I0407 09:51:50.453744 17723 sgd_solver.cpp:105] Iteration 9852, lr = 0.0025
I0407 09:51:55.569012 17723 solver.cpp:218] Iteration 9864 (2.34594 iter/s, 5.11522s/12 iters), loss = 0.0793174
I0407 09:51:55.569061 17723 solver.cpp:237]     Train net output #0: loss = 0.0793175 (* 1 = 0.0793175 loss)
I0407 09:51:55.569070 17723 sgd_solver.cpp:105] Iteration 9864, lr = 0.0025
I0407 09:52:00.907660 17723 solver.cpp:218] Iteration 9876 (2.2478 iter/s, 5.33854s/12 iters), loss = 0.0554528
I0407 09:52:00.907722 17723 solver.cpp:237]     Train net output #0: loss = 0.0554528 (* 1 = 0.0554528 loss)
I0407 09:52:00.907733 17723 sgd_solver.cpp:105] Iteration 9876, lr = 0.0025
I0407 09:52:06.288239 17723 solver.cpp:218] Iteration 9888 (2.23029 iter/s, 5.38046s/12 iters), loss = 0.0603135
I0407 09:52:06.288303 17723 solver.cpp:237]     Train net output #0: loss = 0.0603136 (* 1 = 0.0603136 loss)
I0407 09:52:06.288314 17723 sgd_solver.cpp:105] Iteration 9888, lr = 0.0025
I0407 09:52:08.415422 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0407 09:52:11.459794 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0407 09:52:13.783414 17723 solver.cpp:330] Iteration 9894, Testing net (#0)
I0407 09:52:13.783433 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:52:14.266175 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:52:18.099858 17723 solver.cpp:397]     Test net output #0: accuracy = 0.492647
I0407 09:52:18.099893 17723 solver.cpp:397]     Test net output #1: loss = 2.96577 (* 1 = 2.96577 loss)
I0407 09:52:20.086962 17723 solver.cpp:218] Iteration 9900 (0.869657 iter/s, 13.7985s/12 iters), loss = 0.0961151
I0407 09:52:20.087007 17723 solver.cpp:237]     Train net output #0: loss = 0.0961152 (* 1 = 0.0961152 loss)
I0407 09:52:20.087014 17723 sgd_solver.cpp:105] Iteration 9900, lr = 0.0025
I0407 09:52:25.317467 17723 solver.cpp:218] Iteration 9912 (2.29428 iter/s, 5.2304s/12 iters), loss = 0.0405
I0407 09:52:25.317517 17723 solver.cpp:237]     Train net output #0: loss = 0.0405 (* 1 = 0.0405 loss)
I0407 09:52:25.317526 17723 sgd_solver.cpp:105] Iteration 9912, lr = 0.0025
I0407 09:52:25.342501 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:52:30.608505 17723 solver.cpp:218] Iteration 9924 (2.26803 iter/s, 5.29093s/12 iters), loss = 0.0533124
I0407 09:52:30.608548 17723 solver.cpp:237]     Train net output #0: loss = 0.0533124 (* 1 = 0.0533124 loss)
I0407 09:52:30.608556 17723 sgd_solver.cpp:105] Iteration 9924, lr = 0.0025
I0407 09:52:36.006469 17723 solver.cpp:218] Iteration 9936 (2.2231 iter/s, 5.39786s/12 iters), loss = 0.0971155
I0407 09:52:36.006520 17723 solver.cpp:237]     Train net output #0: loss = 0.0971155 (* 1 = 0.0971155 loss)
I0407 09:52:36.006529 17723 sgd_solver.cpp:105] Iteration 9936, lr = 0.0025
I0407 09:52:41.365581 17723 solver.cpp:218] Iteration 9948 (2.23922 iter/s, 5.359s/12 iters), loss = 0.00808313
I0407 09:52:41.365630 17723 solver.cpp:237]     Train net output #0: loss = 0.0080832 (* 1 = 0.0080832 loss)
I0407 09:52:41.365638 17723 sgd_solver.cpp:105] Iteration 9948, lr = 0.0025
I0407 09:52:46.646648 17723 solver.cpp:218] Iteration 9960 (2.27231 iter/s, 5.28097s/12 iters), loss = 0.072957
I0407 09:52:46.646757 17723 solver.cpp:237]     Train net output #0: loss = 0.072957 (* 1 = 0.072957 loss)
I0407 09:52:46.646765 17723 sgd_solver.cpp:105] Iteration 9960, lr = 0.0025
I0407 09:52:51.704313 17723 solver.cpp:218] Iteration 9972 (2.37271 iter/s, 5.05751s/12 iters), loss = 0.0384849
I0407 09:52:51.704349 17723 solver.cpp:237]     Train net output #0: loss = 0.0384849 (* 1 = 0.0384849 loss)
I0407 09:52:51.704355 17723 sgd_solver.cpp:105] Iteration 9972, lr = 0.0025
I0407 09:52:56.814761 17723 solver.cpp:218] Iteration 9984 (2.34817 iter/s, 5.11036s/12 iters), loss = 0.0435696
I0407 09:52:56.814805 17723 solver.cpp:237]     Train net output #0: loss = 0.0435697 (* 1 = 0.0435697 loss)
I0407 09:52:56.814812 17723 sgd_solver.cpp:105] Iteration 9984, lr = 0.0025
I0407 09:53:01.505035 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0407 09:53:04.545049 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0407 09:53:06.851832 17723 solver.cpp:330] Iteration 9996, Testing net (#0)
I0407 09:53:06.851853 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:53:07.283064 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:53:11.242287 17723 solver.cpp:397]     Test net output #0: accuracy = 0.495098
I0407 09:53:11.242323 17723 solver.cpp:397]     Test net output #1: loss = 2.90614 (* 1 = 2.90614 loss)
I0407 09:53:11.382380 17723 solver.cpp:218] Iteration 9996 (0.823754 iter/s, 14.5674s/12 iters), loss = 0.0421802
I0407 09:53:11.382441 17723 solver.cpp:237]     Train net output #0: loss = 0.0421802 (* 1 = 0.0421802 loss)
I0407 09:53:11.382452 17723 sgd_solver.cpp:105] Iteration 9996, lr = 0.0025
I0407 09:53:15.727031 17723 solver.cpp:218] Iteration 10008 (2.76209 iter/s, 4.34454s/12 iters), loss = 0.0180749
I0407 09:53:15.727074 17723 solver.cpp:237]     Train net output #0: loss = 0.018075 (* 1 = 0.018075 loss)
I0407 09:53:15.727082 17723 sgd_solver.cpp:105] Iteration 10008, lr = 0.0025
I0407 09:53:18.063906 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:53:21.164906 17723 solver.cpp:218] Iteration 10020 (2.20679 iter/s, 5.43777s/12 iters), loss = 0.0617642
I0407 09:53:21.164952 17723 solver.cpp:237]     Train net output #0: loss = 0.0617643 (* 1 = 0.0617643 loss)
I0407 09:53:21.164959 17723 sgd_solver.cpp:105] Iteration 10020, lr = 0.0025
I0407 09:53:26.437064 17723 solver.cpp:218] Iteration 10032 (2.27615 iter/s, 5.27206s/12 iters), loss = 0.0127651
I0407 09:53:26.437108 17723 solver.cpp:237]     Train net output #0: loss = 0.0127652 (* 1 = 0.0127652 loss)
I0407 09:53:26.437114 17723 sgd_solver.cpp:105] Iteration 10032, lr = 0.0025
I0407 09:53:31.643435 17723 solver.cpp:218] Iteration 10044 (2.30491 iter/s, 5.20627s/12 iters), loss = 0.0401396
I0407 09:53:31.643486 17723 solver.cpp:237]     Train net output #0: loss = 0.0401397 (* 1 = 0.0401397 loss)
I0407 09:53:31.643496 17723 sgd_solver.cpp:105] Iteration 10044, lr = 0.0025
I0407 09:53:36.989925 17723 solver.cpp:218] Iteration 10056 (2.24451 iter/s, 5.34638s/12 iters), loss = 0.0535242
I0407 09:53:36.989979 17723 solver.cpp:237]     Train net output #0: loss = 0.0535242 (* 1 = 0.0535242 loss)
I0407 09:53:36.989987 17723 sgd_solver.cpp:105] Iteration 10056, lr = 0.0025
I0407 09:53:42.385682 17723 solver.cpp:218] Iteration 10068 (2.22401 iter/s, 5.39565s/12 iters), loss = 0.103704
I0407 09:53:42.385731 17723 solver.cpp:237]     Train net output #0: loss = 0.103705 (* 1 = 0.103705 loss)
I0407 09:53:42.385738 17723 sgd_solver.cpp:105] Iteration 10068, lr = 0.0025
I0407 09:53:47.726032 17723 solver.cpp:218] Iteration 10080 (2.24709 iter/s, 5.34025s/12 iters), loss = 0.121031
I0407 09:53:47.726069 17723 solver.cpp:237]     Train net output #0: loss = 0.121031 (* 1 = 0.121031 loss)
I0407 09:53:47.726076 17723 sgd_solver.cpp:105] Iteration 10080, lr = 0.0025
I0407 09:53:52.864486 17723 solver.cpp:218] Iteration 10092 (2.33537 iter/s, 5.13836s/12 iters), loss = 0.0458972
I0407 09:53:52.864595 17723 solver.cpp:237]     Train net output #0: loss = 0.0458972 (* 1 = 0.0458972 loss)
I0407 09:53:52.864605 17723 sgd_solver.cpp:105] Iteration 10092, lr = 0.0025
I0407 09:53:55.008265 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0407 09:53:58.046511 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0407 09:54:00.348387 17723 solver.cpp:330] Iteration 10098, Testing net (#0)
I0407 09:54:00.348408 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:54:00.748504 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:54:04.733564 17723 solver.cpp:397]     Test net output #0: accuracy = 0.488358
I0407 09:54:04.733600 17723 solver.cpp:397]     Test net output #1: loss = 2.95048 (* 1 = 2.95048 loss)
I0407 09:54:06.673919 17723 solver.cpp:218] Iteration 10104 (0.868985 iter/s, 13.8092s/12 iters), loss = 0.0834106
I0407 09:54:06.673966 17723 solver.cpp:237]     Train net output #0: loss = 0.0834107 (* 1 = 0.0834107 loss)
I0407 09:54:06.673974 17723 sgd_solver.cpp:105] Iteration 10104, lr = 0.00125
I0407 09:54:11.229705 17748 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:54:11.933039 17723 solver.cpp:218] Iteration 10116 (2.2818 iter/s, 5.25902s/12 iters), loss = 0.120213
I0407 09:54:11.933087 17723 solver.cpp:237]     Train net output #0: loss = 0.120213 (* 1 = 0.120213 loss)
I0407 09:54:11.933095 17723 sgd_solver.cpp:105] Iteration 10116, lr = 0.00125
I0407 09:54:17.252871 17723 solver.cpp:218] Iteration 10128 (2.25575 iter/s, 5.31973s/12 iters), loss = 0.0222895
I0407 09:54:17.252928 17723 solver.cpp:237]     Train net output #0: loss = 0.0222896 (* 1 = 0.0222896 loss)
I0407 09:54:17.252934 17723 sgd_solver.cpp:105] Iteration 10128, lr = 0.00125
I0407 09:54:22.660627 17723 solver.cpp:218] Iteration 10140 (2.21908 iter/s, 5.40764s/12 iters), loss = 0.107818
I0407 09:54:22.660671 17723 solver.cpp:237]     Train net output #0: loss = 0.107818 (* 1 = 0.107818 loss)
I0407 09:54:22.660678 17723 sgd_solver.cpp:105] Iteration 10140, lr = 0.00125
I0407 09:54:27.931972 17723 solver.cpp:218] Iteration 10152 (2.2765 iter/s, 5.27125s/12 iters), loss = 0.0844372
I0407 09:54:27.932093 17723 solver.cpp:237]     Train net output #0: loss = 0.0844373 (* 1 = 0.0844373 loss)
I0407 09:54:27.932101 17723 sgd_solver.cpp:105] Iteration 10152, lr = 0.00125
I0407 09:54:33.121032 17723 solver.cpp:218] Iteration 10164 (2.31264 iter/s, 5.18888s/12 iters), loss = 0.0443143
I0407 09:54:33.121093 17723 solver.cpp:237]     Train net output #0: loss = 0.0443144 (* 1 = 0.0443144 loss)
I0407 09:54:33.121102 17723 sgd_solver.cpp:105] Iteration 10164, lr = 0.00125
I0407 09:54:38.157444 17723 solver.cpp:218] Iteration 10176 (2.3827 iter/s, 5.0363s/12 iters), loss = 0.0538164
I0407 09:54:38.157490 17723 solver.cpp:237]     Train net output #0: loss = 0.0538164 (* 1 = 0.0538164 loss)
I0407 09:54:38.157500 17723 sgd_solver.cpp:105] Iteration 10176, lr = 0.00125
I0407 09:54:43.437409 17723 solver.cpp:218] Iteration 10188 (2.27279 iter/s, 5.27986s/12 iters), loss = 0.108054
I0407 09:54:43.437450 17723 solver.cpp:237]     Train net output #0: loss = 0.108054 (* 1 = 0.108054 loss)
I0407 09:54:43.437458 17723 sgd_solver.cpp:105] Iteration 10188, lr = 0.00125
I0407 09:54:48.316004 17723 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0407 09:54:51.323153 17723 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0407 09:54:53.670011 17723 solver.cpp:310] Iteration 10200, loss = 0.0534418
I0407 09:54:53.670040 17723 solver.cpp:330] Iteration 10200, Testing net (#0)
I0407 09:54:53.670045 17723 net.cpp:676] Ignoring source layer train-data
I0407 09:54:54.037020 17776 data_layer.cpp:73] Restarting data prefetching from start.
I0407 09:54:58.199604 17723 solver.cpp:397]     Test net output #0: accuracy = 0.493873
I0407 09:54:58.199721 17723 solver.cpp:397]     Test net output #1: loss = 2.88337 (* 1 = 2.88337 loss)
I0407 09:54:58.199733 17723 solver.cpp:315] Optimization Done.
I0407 09:54:58.199738 17723 caffe.cpp:259] Optimization Done.