I0409 21:21:02.599359 25438 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210409-205048-78d5/solver.prototxt
I0409 21:21:02.599499 25438 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0409 21:21:02.599504 25438 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0409 21:21:02.599561 25438 caffe.cpp:218] Using GPUs 1
I0409 21:21:02.614440 25438 caffe.cpp:223] GPU 1: GeForce GTX 1080 Ti
I0409 21:21:02.852340 25438 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "exp"
gamma: 0.99980193
momentum: 0.9
weight_decay: 0.0001
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 1
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0409 21:21:02.853015 25438 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0409 21:21:02.853561 25438 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0409 21:21:02.853577 25438 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0409 21:21:02.853701 25438 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
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: "fc8"
type: "InnerProduct"
bottom: "fc6"
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"
}
I0409 21:21:02.853785 25438 layer_factory.hpp:77] Creating layer train-data
I0409 21:21:02.855310 25438 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/train_db
I0409 21:21:02.855517 25438 net.cpp:84] Creating Layer train-data
I0409 21:21:02.855530 25438 net.cpp:380] train-data -> data
I0409 21:21:02.855548 25438 net.cpp:380] train-data -> label
I0409 21:21:02.855558 25438 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 21:21:02.860093 25438 data_layer.cpp:45] output data size: 128,3,227,227
I0409 21:21:02.991976 25438 net.cpp:122] Setting up train-data
I0409 21:21:02.991999 25438 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0409 21:21:02.992005 25438 net.cpp:129] Top shape: 128 (128)
I0409 21:21:02.992009 25438 net.cpp:137] Memory required for data: 79149056
I0409 21:21:02.992019 25438 layer_factory.hpp:77] Creating layer conv1
I0409 21:21:02.992039 25438 net.cpp:84] Creating Layer conv1
I0409 21:21:02.992045 25438 net.cpp:406] conv1 <- data
I0409 21:21:02.992056 25438 net.cpp:380] conv1 -> conv1
I0409 21:21:03.615768 25438 net.cpp:122] Setting up conv1
I0409 21:21:03.615792 25438 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 21:21:03.615797 25438 net.cpp:137] Memory required for data: 227833856
I0409 21:21:03.615815 25438 layer_factory.hpp:77] Creating layer relu1
I0409 21:21:03.615826 25438 net.cpp:84] Creating Layer relu1
I0409 21:21:03.615831 25438 net.cpp:406] relu1 <- conv1
I0409 21:21:03.615837 25438 net.cpp:367] relu1 -> conv1 (in-place)
I0409 21:21:03.616125 25438 net.cpp:122] Setting up relu1
I0409 21:21:03.616134 25438 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 21:21:03.616138 25438 net.cpp:137] Memory required for data: 376518656
I0409 21:21:03.616143 25438 layer_factory.hpp:77] Creating layer norm1
I0409 21:21:03.616151 25438 net.cpp:84] Creating Layer norm1
I0409 21:21:03.616155 25438 net.cpp:406] norm1 <- conv1
I0409 21:21:03.616161 25438 net.cpp:380] norm1 -> norm1
I0409 21:21:03.616598 25438 net.cpp:122] Setting up norm1
I0409 21:21:03.616609 25438 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0409 21:21:03.616612 25438 net.cpp:137] Memory required for data: 525203456
I0409 21:21:03.616617 25438 layer_factory.hpp:77] Creating layer pool1
I0409 21:21:03.616626 25438 net.cpp:84] Creating Layer pool1
I0409 21:21:03.616631 25438 net.cpp:406] pool1 <- norm1
I0409 21:21:03.616636 25438 net.cpp:380] pool1 -> pool1
I0409 21:21:03.616691 25438 net.cpp:122] Setting up pool1
I0409 21:21:03.616698 25438 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0409 21:21:03.616701 25438 net.cpp:137] Memory required for data: 561035264
I0409 21:21:03.616705 25438 layer_factory.hpp:77] Creating layer conv2
I0409 21:21:03.616715 25438 net.cpp:84] Creating Layer conv2
I0409 21:21:03.616719 25438 net.cpp:406] conv2 <- pool1
I0409 21:21:03.616725 25438 net.cpp:380] conv2 -> conv2
I0409 21:21:03.623620 25438 net.cpp:122] Setting up conv2
I0409 21:21:03.623636 25438 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 21:21:03.623639 25438 net.cpp:137] Memory required for data: 656586752
I0409 21:21:03.623649 25438 layer_factory.hpp:77] Creating layer relu2
I0409 21:21:03.623657 25438 net.cpp:84] Creating Layer relu2
I0409 21:21:03.623662 25438 net.cpp:406] relu2 <- conv2
I0409 21:21:03.623669 25438 net.cpp:367] relu2 -> conv2 (in-place)
I0409 21:21:03.624125 25438 net.cpp:122] Setting up relu2
I0409 21:21:03.624136 25438 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 21:21:03.624140 25438 net.cpp:137] Memory required for data: 752138240
I0409 21:21:03.624145 25438 layer_factory.hpp:77] Creating layer norm2
I0409 21:21:03.624152 25438 net.cpp:84] Creating Layer norm2
I0409 21:21:03.624156 25438 net.cpp:406] norm2 <- conv2
I0409 21:21:03.624162 25438 net.cpp:380] norm2 -> norm2
I0409 21:21:03.624456 25438 net.cpp:122] Setting up norm2
I0409 21:21:03.624466 25438 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0409 21:21:03.624469 25438 net.cpp:137] Memory required for data: 847689728
I0409 21:21:03.624473 25438 layer_factory.hpp:77] Creating layer pool2
I0409 21:21:03.624480 25438 net.cpp:84] Creating Layer pool2
I0409 21:21:03.624485 25438 net.cpp:406] pool2 <- norm2
I0409 21:21:03.624490 25438 net.cpp:380] pool2 -> pool2
I0409 21:21:03.624516 25438 net.cpp:122] Setting up pool2
I0409 21:21:03.624522 25438 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 21:21:03.624526 25438 net.cpp:137] Memory required for data: 869840896
I0409 21:21:03.624529 25438 layer_factory.hpp:77] Creating layer conv3
I0409 21:21:03.624538 25438 net.cpp:84] Creating Layer conv3
I0409 21:21:03.624542 25438 net.cpp:406] conv3 <- pool2
I0409 21:21:03.624548 25438 net.cpp:380] conv3 -> conv3
I0409 21:21:03.641410 25438 net.cpp:122] Setting up conv3
I0409 21:21:03.641425 25438 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 21:21:03.641429 25438 net.cpp:137] Memory required for data: 903067648
I0409 21:21:03.641440 25438 layer_factory.hpp:77] Creating layer relu3
I0409 21:21:03.641449 25438 net.cpp:84] Creating Layer relu3
I0409 21:21:03.641453 25438 net.cpp:406] relu3 <- conv3
I0409 21:21:03.641460 25438 net.cpp:367] relu3 -> conv3 (in-place)
I0409 21:21:03.641896 25438 net.cpp:122] Setting up relu3
I0409 21:21:03.641907 25438 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 21:21:03.641911 25438 net.cpp:137] Memory required for data: 936294400
I0409 21:21:03.641916 25438 layer_factory.hpp:77] Creating layer conv4
I0409 21:21:03.641927 25438 net.cpp:84] Creating Layer conv4
I0409 21:21:03.641929 25438 net.cpp:406] conv4 <- conv3
I0409 21:21:03.641937 25438 net.cpp:380] conv4 -> conv4
I0409 21:21:03.654536 25438 net.cpp:122] Setting up conv4
I0409 21:21:03.654553 25438 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 21:21:03.654556 25438 net.cpp:137] Memory required for data: 969521152
I0409 21:21:03.654564 25438 layer_factory.hpp:77] Creating layer relu4
I0409 21:21:03.654575 25438 net.cpp:84] Creating Layer relu4
I0409 21:21:03.654579 25438 net.cpp:406] relu4 <- conv4
I0409 21:21:03.654587 25438 net.cpp:367] relu4 -> conv4 (in-place)
I0409 21:21:03.654923 25438 net.cpp:122] Setting up relu4
I0409 21:21:03.654932 25438 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0409 21:21:03.654935 25438 net.cpp:137] Memory required for data: 1002747904
I0409 21:21:03.654940 25438 layer_factory.hpp:77] Creating layer conv5
I0409 21:21:03.654950 25438 net.cpp:84] Creating Layer conv5
I0409 21:21:03.654954 25438 net.cpp:406] conv5 <- conv4
I0409 21:21:03.654981 25438 net.cpp:380] conv5 -> conv5
I0409 21:21:03.663396 25438 net.cpp:122] Setting up conv5
I0409 21:21:03.663410 25438 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 21:21:03.663414 25438 net.cpp:137] Memory required for data: 1024899072
I0409 21:21:03.663427 25438 layer_factory.hpp:77] Creating layer relu5
I0409 21:21:03.663435 25438 net.cpp:84] Creating Layer relu5
I0409 21:21:03.663439 25438 net.cpp:406] relu5 <- conv5
I0409 21:21:03.663445 25438 net.cpp:367] relu5 -> conv5 (in-place)
I0409 21:21:03.663924 25438 net.cpp:122] Setting up relu5
I0409 21:21:03.663935 25438 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0409 21:21:03.663939 25438 net.cpp:137] Memory required for data: 1047050240
I0409 21:21:03.663944 25438 layer_factory.hpp:77] Creating layer pool5
I0409 21:21:03.663951 25438 net.cpp:84] Creating Layer pool5
I0409 21:21:03.663955 25438 net.cpp:406] pool5 <- conv5
I0409 21:21:03.663961 25438 net.cpp:380] pool5 -> pool5
I0409 21:21:03.664000 25438 net.cpp:122] Setting up pool5
I0409 21:21:03.664005 25438 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0409 21:21:03.664008 25438 net.cpp:137] Memory required for data: 1051768832
I0409 21:21:03.664012 25438 layer_factory.hpp:77] Creating layer fc6
I0409 21:21:03.664023 25438 net.cpp:84] Creating Layer fc6
I0409 21:21:03.664026 25438 net.cpp:406] fc6 <- pool5
I0409 21:21:03.664032 25438 net.cpp:380] fc6 -> fc6
I0409 21:21:04.394352 25438 net.cpp:122] Setting up fc6
I0409 21:21:04.394374 25438 net.cpp:129] Top shape: 128 8192 (1048576)
I0409 21:21:04.394378 25438 net.cpp:137] Memory required for data: 1055963136
I0409 21:21:04.394388 25438 layer_factory.hpp:77] Creating layer relu6
I0409 21:21:04.394397 25438 net.cpp:84] Creating Layer relu6
I0409 21:21:04.394402 25438 net.cpp:406] relu6 <- fc6
I0409 21:21:04.394408 25438 net.cpp:367] relu6 -> fc6 (in-place)
I0409 21:21:04.395036 25438 net.cpp:122] Setting up relu6
I0409 21:21:04.395046 25438 net.cpp:129] Top shape: 128 8192 (1048576)
I0409 21:21:04.395049 25438 net.cpp:137] Memory required for data: 1060157440
I0409 21:21:04.395053 25438 layer_factory.hpp:77] Creating layer drop6
I0409 21:21:04.395059 25438 net.cpp:84] Creating Layer drop6
I0409 21:21:04.395063 25438 net.cpp:406] drop6 <- fc6
I0409 21:21:04.395069 25438 net.cpp:367] drop6 -> fc6 (in-place)
I0409 21:21:04.395097 25438 net.cpp:122] Setting up drop6
I0409 21:21:04.395103 25438 net.cpp:129] Top shape: 128 8192 (1048576)
I0409 21:21:04.395107 25438 net.cpp:137] Memory required for data: 1064351744
I0409 21:21:04.395109 25438 layer_factory.hpp:77] Creating layer fc8
I0409 21:21:04.395117 25438 net.cpp:84] Creating Layer fc8
I0409 21:21:04.395120 25438 net.cpp:406] fc8 <- fc6
I0409 21:21:04.395128 25438 net.cpp:380] fc8 -> fc8
I0409 21:21:04.410374 25438 net.cpp:122] Setting up fc8
I0409 21:21:04.410392 25438 net.cpp:129] Top shape: 128 196 (25088)
I0409 21:21:04.410395 25438 net.cpp:137] Memory required for data: 1064452096
I0409 21:21:04.410404 25438 layer_factory.hpp:77] Creating layer loss
I0409 21:21:04.410413 25438 net.cpp:84] Creating Layer loss
I0409 21:21:04.410418 25438 net.cpp:406] loss <- fc8
I0409 21:21:04.410423 25438 net.cpp:406] loss <- label
I0409 21:21:04.410429 25438 net.cpp:380] loss -> loss
I0409 21:21:04.410440 25438 layer_factory.hpp:77] Creating layer loss
I0409 21:21:04.411223 25438 net.cpp:122] Setting up loss
I0409 21:21:04.411233 25438 net.cpp:129] Top shape: (1)
I0409 21:21:04.411237 25438 net.cpp:132]     with loss weight 1
I0409 21:21:04.411255 25438 net.cpp:137] Memory required for data: 1064452100
I0409 21:21:04.411259 25438 net.cpp:198] loss needs backward computation.
I0409 21:21:04.411267 25438 net.cpp:198] fc8 needs backward computation.
I0409 21:21:04.411271 25438 net.cpp:198] drop6 needs backward computation.
I0409 21:21:04.411275 25438 net.cpp:198] relu6 needs backward computation.
I0409 21:21:04.411278 25438 net.cpp:198] fc6 needs backward computation.
I0409 21:21:04.411283 25438 net.cpp:198] pool5 needs backward computation.
I0409 21:21:04.411285 25438 net.cpp:198] relu5 needs backward computation.
I0409 21:21:04.411306 25438 net.cpp:198] conv5 needs backward computation.
I0409 21:21:04.411310 25438 net.cpp:198] relu4 needs backward computation.
I0409 21:21:04.411314 25438 net.cpp:198] conv4 needs backward computation.
I0409 21:21:04.411317 25438 net.cpp:198] relu3 needs backward computation.
I0409 21:21:04.411320 25438 net.cpp:198] conv3 needs backward computation.
I0409 21:21:04.411324 25438 net.cpp:198] pool2 needs backward computation.
I0409 21:21:04.411329 25438 net.cpp:198] norm2 needs backward computation.
I0409 21:21:04.411334 25438 net.cpp:198] relu2 needs backward computation.
I0409 21:21:04.411336 25438 net.cpp:198] conv2 needs backward computation.
I0409 21:21:04.411340 25438 net.cpp:198] pool1 needs backward computation.
I0409 21:21:04.411345 25438 net.cpp:198] norm1 needs backward computation.
I0409 21:21:04.411348 25438 net.cpp:198] relu1 needs backward computation.
I0409 21:21:04.411351 25438 net.cpp:198] conv1 needs backward computation.
I0409 21:21:04.411355 25438 net.cpp:200] train-data does not need backward computation.
I0409 21:21:04.411358 25438 net.cpp:242] This network produces output loss
I0409 21:21:04.411370 25438 net.cpp:255] Network initialization done.
I0409 21:21:04.440284 25438 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0409 21:21:04.440366 25438 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0409 21:21:04.440697 25438 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 8192
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: "fc8"
type: "InnerProduct"
bottom: "fc6"
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"
}
I0409 21:21:04.440924 25438 layer_factory.hpp:77] Creating layer val-data
I0409 21:21:04.866561 25438 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/val_db
I0409 21:21:04.867501 25438 net.cpp:84] Creating Layer val-data
I0409 21:21:04.867522 25438 net.cpp:380] val-data -> data
I0409 21:21:04.867537 25438 net.cpp:380] val-data -> label
I0409 21:21:04.867547 25438 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-MAN-2/digits/jobs/20210407-214532-d396/mean.binaryproto
I0409 21:21:04.984031 25438 data_layer.cpp:45] output data size: 32,3,227,227
I0409 21:21:05.061439 25438 net.cpp:122] Setting up val-data
I0409 21:21:05.061467 25438 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0409 21:21:05.061475 25438 net.cpp:129] Top shape: 32 (32)
I0409 21:21:05.061480 25438 net.cpp:137] Memory required for data: 19787264
I0409 21:21:05.061489 25438 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0409 21:21:05.061506 25438 net.cpp:84] Creating Layer label_val-data_1_split
I0409 21:21:05.061513 25438 net.cpp:406] label_val-data_1_split <- label
I0409 21:21:05.061523 25438 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0409 21:21:05.061537 25438 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0409 21:21:05.061605 25438 net.cpp:122] Setting up label_val-data_1_split
I0409 21:21:05.061614 25438 net.cpp:129] Top shape: 32 (32)
I0409 21:21:05.061620 25438 net.cpp:129] Top shape: 32 (32)
I0409 21:21:05.061625 25438 net.cpp:137] Memory required for data: 19787520
I0409 21:21:05.061630 25438 layer_factory.hpp:77] Creating layer conv1
I0409 21:21:05.061648 25438 net.cpp:84] Creating Layer conv1
I0409 21:21:05.061655 25438 net.cpp:406] conv1 <- data
I0409 21:21:05.061664 25438 net.cpp:380] conv1 -> conv1
I0409 21:21:05.064983 25438 net.cpp:122] Setting up conv1
I0409 21:21:05.065001 25438 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 21:21:05.065006 25438 net.cpp:137] Memory required for data: 56958720
I0409 21:21:05.065022 25438 layer_factory.hpp:77] Creating layer relu1
I0409 21:21:05.065032 25438 net.cpp:84] Creating Layer relu1
I0409 21:21:05.065063 25438 net.cpp:406] relu1 <- conv1
I0409 21:21:05.065073 25438 net.cpp:367] relu1 -> conv1 (in-place)
I0409 21:21:05.065785 25438 net.cpp:122] Setting up relu1
I0409 21:21:05.065800 25438 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 21:21:05.065805 25438 net.cpp:137] Memory required for data: 94129920
I0409 21:21:05.065811 25438 layer_factory.hpp:77] Creating layer norm1
I0409 21:21:05.065824 25438 net.cpp:84] Creating Layer norm1
I0409 21:21:05.065829 25438 net.cpp:406] norm1 <- conv1
I0409 21:21:05.065838 25438 net.cpp:380] norm1 -> norm1
I0409 21:21:05.066371 25438 net.cpp:122] Setting up norm1
I0409 21:21:05.066385 25438 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0409 21:21:05.066390 25438 net.cpp:137] Memory required for data: 131301120
I0409 21:21:05.066395 25438 layer_factory.hpp:77] Creating layer pool1
I0409 21:21:05.066406 25438 net.cpp:84] Creating Layer pool1
I0409 21:21:05.066411 25438 net.cpp:406] pool1 <- norm1
I0409 21:21:05.066419 25438 net.cpp:380] pool1 -> pool1
I0409 21:21:05.066465 25438 net.cpp:122] Setting up pool1
I0409 21:21:05.066473 25438 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0409 21:21:05.066478 25438 net.cpp:137] Memory required for data: 140259072
I0409 21:21:05.066483 25438 layer_factory.hpp:77] Creating layer conv2
I0409 21:21:05.066495 25438 net.cpp:84] Creating Layer conv2
I0409 21:21:05.066501 25438 net.cpp:406] conv2 <- pool1
I0409 21:21:05.066509 25438 net.cpp:380] conv2 -> conv2
I0409 21:21:05.078653 25438 net.cpp:122] Setting up conv2
I0409 21:21:05.078675 25438 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 21:21:05.078680 25438 net.cpp:137] Memory required for data: 164146944
I0409 21:21:05.078696 25438 layer_factory.hpp:77] Creating layer relu2
I0409 21:21:05.078706 25438 net.cpp:84] Creating Layer relu2
I0409 21:21:05.078711 25438 net.cpp:406] relu2 <- conv2
I0409 21:21:05.078720 25438 net.cpp:367] relu2 -> conv2 (in-place)
I0409 21:21:05.079509 25438 net.cpp:122] Setting up relu2
I0409 21:21:05.079522 25438 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 21:21:05.079527 25438 net.cpp:137] Memory required for data: 188034816
I0409 21:21:05.079533 25438 layer_factory.hpp:77] Creating layer norm2
I0409 21:21:05.079547 25438 net.cpp:84] Creating Layer norm2
I0409 21:21:05.079553 25438 net.cpp:406] norm2 <- conv2
I0409 21:21:05.079561 25438 net.cpp:380] norm2 -> norm2
I0409 21:21:05.093338 25438 net.cpp:122] Setting up norm2
I0409 21:21:05.093353 25438 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0409 21:21:05.093359 25438 net.cpp:137] Memory required for data: 211922688
I0409 21:21:05.093365 25438 layer_factory.hpp:77] Creating layer pool2
I0409 21:21:05.093375 25438 net.cpp:84] Creating Layer pool2
I0409 21:21:05.093380 25438 net.cpp:406] pool2 <- norm2
I0409 21:21:05.093389 25438 net.cpp:380] pool2 -> pool2
I0409 21:21:05.093438 25438 net.cpp:122] Setting up pool2
I0409 21:21:05.093447 25438 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 21:21:05.093451 25438 net.cpp:137] Memory required for data: 217460480
I0409 21:21:05.093456 25438 layer_factory.hpp:77] Creating layer conv3
I0409 21:21:05.093470 25438 net.cpp:84] Creating Layer conv3
I0409 21:21:05.093477 25438 net.cpp:406] conv3 <- pool2
I0409 21:21:05.093485 25438 net.cpp:380] conv3 -> conv3
I0409 21:21:05.116765 25438 net.cpp:122] Setting up conv3
I0409 21:21:05.116788 25438 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 21:21:05.116793 25438 net.cpp:137] Memory required for data: 225767168
I0409 21:21:05.116808 25438 layer_factory.hpp:77] Creating layer relu3
I0409 21:21:05.116818 25438 net.cpp:84] Creating Layer relu3
I0409 21:21:05.116824 25438 net.cpp:406] relu3 <- conv3
I0409 21:21:05.116837 25438 net.cpp:367] relu3 -> conv3 (in-place)
I0409 21:21:05.118856 25438 net.cpp:122] Setting up relu3
I0409 21:21:05.118870 25438 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 21:21:05.118875 25438 net.cpp:137] Memory required for data: 234073856
I0409 21:21:05.118880 25438 layer_factory.hpp:77] Creating layer conv4
I0409 21:21:05.118916 25438 net.cpp:84] Creating Layer conv4
I0409 21:21:05.118921 25438 net.cpp:406] conv4 <- conv3
I0409 21:21:05.118930 25438 net.cpp:380] conv4 -> conv4
I0409 21:21:05.139214 25438 net.cpp:122] Setting up conv4
I0409 21:21:05.139236 25438 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 21:21:05.139241 25438 net.cpp:137] Memory required for data: 242380544
I0409 21:21:05.139252 25438 layer_factory.hpp:77] Creating layer relu4
I0409 21:21:05.139261 25438 net.cpp:84] Creating Layer relu4
I0409 21:21:05.139266 25438 net.cpp:406] relu4 <- conv4
I0409 21:21:05.139273 25438 net.cpp:367] relu4 -> conv4 (in-place)
I0409 21:21:05.139930 25438 net.cpp:122] Setting up relu4
I0409 21:21:05.139942 25438 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0409 21:21:05.139946 25438 net.cpp:137] Memory required for data: 250687232
I0409 21:21:05.139951 25438 layer_factory.hpp:77] Creating layer conv5
I0409 21:21:05.139966 25438 net.cpp:84] Creating Layer conv5
I0409 21:21:05.139971 25438 net.cpp:406] conv5 <- conv4
I0409 21:21:05.139981 25438 net.cpp:380] conv5 -> conv5
I0409 21:21:05.151307 25438 net.cpp:122] Setting up conv5
I0409 21:21:05.151329 25438 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 21:21:05.151333 25438 net.cpp:137] Memory required for data: 256225024
I0409 21:21:05.151346 25438 layer_factory.hpp:77] Creating layer relu5
I0409 21:21:05.151355 25438 net.cpp:84] Creating Layer relu5
I0409 21:21:05.151360 25438 net.cpp:406] relu5 <- conv5
I0409 21:21:05.151368 25438 net.cpp:367] relu5 -> conv5 (in-place)
I0409 21:21:05.152001 25438 net.cpp:122] Setting up relu5
I0409 21:21:05.152014 25438 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0409 21:21:05.152017 25438 net.cpp:137] Memory required for data: 261762816
I0409 21:21:05.152022 25438 layer_factory.hpp:77] Creating layer pool5
I0409 21:21:05.152035 25438 net.cpp:84] Creating Layer pool5
I0409 21:21:05.152041 25438 net.cpp:406] pool5 <- conv5
I0409 21:21:05.152048 25438 net.cpp:380] pool5 -> pool5
I0409 21:21:05.152096 25438 net.cpp:122] Setting up pool5
I0409 21:21:05.152103 25438 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0409 21:21:05.152107 25438 net.cpp:137] Memory required for data: 262942464
I0409 21:21:05.152112 25438 layer_factory.hpp:77] Creating layer fc6
I0409 21:21:05.152122 25438 net.cpp:84] Creating Layer fc6
I0409 21:21:05.152125 25438 net.cpp:406] fc6 <- pool5
I0409 21:21:05.152132 25438 net.cpp:380] fc6 -> fc6
I0409 21:21:05.927692 25438 net.cpp:122] Setting up fc6
I0409 21:21:05.927712 25438 net.cpp:129] Top shape: 32 8192 (262144)
I0409 21:21:05.927716 25438 net.cpp:137] Memory required for data: 263991040
I0409 21:21:05.927726 25438 layer_factory.hpp:77] Creating layer relu6
I0409 21:21:05.927734 25438 net.cpp:84] Creating Layer relu6
I0409 21:21:05.927739 25438 net.cpp:406] relu6 <- fc6
I0409 21:21:05.927747 25438 net.cpp:367] relu6 -> fc6 (in-place)
I0409 21:21:05.928799 25438 net.cpp:122] Setting up relu6
I0409 21:21:05.928808 25438 net.cpp:129] Top shape: 32 8192 (262144)
I0409 21:21:05.928812 25438 net.cpp:137] Memory required for data: 265039616
I0409 21:21:05.928817 25438 layer_factory.hpp:77] Creating layer drop6
I0409 21:21:05.928822 25438 net.cpp:84] Creating Layer drop6
I0409 21:21:05.928826 25438 net.cpp:406] drop6 <- fc6
I0409 21:21:05.928833 25438 net.cpp:367] drop6 -> fc6 (in-place)
I0409 21:21:05.928858 25438 net.cpp:122] Setting up drop6
I0409 21:21:05.928862 25438 net.cpp:129] Top shape: 32 8192 (262144)
I0409 21:21:05.928866 25438 net.cpp:137] Memory required for data: 266088192
I0409 21:21:05.928869 25438 layer_factory.hpp:77] Creating layer fc8
I0409 21:21:05.928877 25438 net.cpp:84] Creating Layer fc8
I0409 21:21:05.928880 25438 net.cpp:406] fc8 <- fc6
I0409 21:21:05.928887 25438 net.cpp:380] fc8 -> fc8
I0409 21:21:05.944079 25438 net.cpp:122] Setting up fc8
I0409 21:21:05.944099 25438 net.cpp:129] Top shape: 32 196 (6272)
I0409 21:21:05.944103 25438 net.cpp:137] Memory required for data: 266113280
I0409 21:21:05.944113 25438 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0409 21:21:05.944121 25438 net.cpp:84] Creating Layer fc8_fc8_0_split
I0409 21:21:05.944144 25438 net.cpp:406] fc8_fc8_0_split <- fc8
I0409 21:21:05.944159 25438 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0409 21:21:05.944170 25438 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0409 21:21:05.944207 25438 net.cpp:122] Setting up fc8_fc8_0_split
I0409 21:21:05.944212 25438 net.cpp:129] Top shape: 32 196 (6272)
I0409 21:21:05.944216 25438 net.cpp:129] Top shape: 32 196 (6272)
I0409 21:21:05.944219 25438 net.cpp:137] Memory required for data: 266163456
I0409 21:21:05.944223 25438 layer_factory.hpp:77] Creating layer accuracy
I0409 21:21:05.944231 25438 net.cpp:84] Creating Layer accuracy
I0409 21:21:05.944234 25438 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0409 21:21:05.944238 25438 net.cpp:406] accuracy <- label_val-data_1_split_0
I0409 21:21:05.944243 25438 net.cpp:380] accuracy -> accuracy
I0409 21:21:05.944250 25438 net.cpp:122] Setting up accuracy
I0409 21:21:05.944254 25438 net.cpp:129] Top shape: (1)
I0409 21:21:05.944257 25438 net.cpp:137] Memory required for data: 266163460
I0409 21:21:05.944260 25438 layer_factory.hpp:77] Creating layer loss
I0409 21:21:05.944267 25438 net.cpp:84] Creating Layer loss
I0409 21:21:05.944270 25438 net.cpp:406] loss <- fc8_fc8_0_split_1
I0409 21:21:05.944274 25438 net.cpp:406] loss <- label_val-data_1_split_1
I0409 21:21:05.944279 25438 net.cpp:380] loss -> loss
I0409 21:21:05.944286 25438 layer_factory.hpp:77] Creating layer loss
I0409 21:21:05.945216 25438 net.cpp:122] Setting up loss
I0409 21:21:05.945225 25438 net.cpp:129] Top shape: (1)
I0409 21:21:05.945228 25438 net.cpp:132]     with loss weight 1
I0409 21:21:05.945240 25438 net.cpp:137] Memory required for data: 266163464
I0409 21:21:05.945243 25438 net.cpp:198] loss needs backward computation.
I0409 21:21:05.945247 25438 net.cpp:200] accuracy does not need backward computation.
I0409 21:21:05.945255 25438 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0409 21:21:05.945257 25438 net.cpp:198] fc8 needs backward computation.
I0409 21:21:05.945261 25438 net.cpp:198] drop6 needs backward computation.
I0409 21:21:05.945264 25438 net.cpp:198] relu6 needs backward computation.
I0409 21:21:05.945267 25438 net.cpp:198] fc6 needs backward computation.
I0409 21:21:05.945271 25438 net.cpp:198] pool5 needs backward computation.
I0409 21:21:05.945276 25438 net.cpp:198] relu5 needs backward computation.
I0409 21:21:05.945278 25438 net.cpp:198] conv5 needs backward computation.
I0409 21:21:05.945283 25438 net.cpp:198] relu4 needs backward computation.
I0409 21:21:05.945286 25438 net.cpp:198] conv4 needs backward computation.
I0409 21:21:05.945291 25438 net.cpp:198] relu3 needs backward computation.
I0409 21:21:05.945293 25438 net.cpp:198] conv3 needs backward computation.
I0409 21:21:05.945297 25438 net.cpp:198] pool2 needs backward computation.
I0409 21:21:05.945300 25438 net.cpp:198] norm2 needs backward computation.
I0409 21:21:05.945304 25438 net.cpp:198] relu2 needs backward computation.
I0409 21:21:05.945307 25438 net.cpp:198] conv2 needs backward computation.
I0409 21:21:05.945310 25438 net.cpp:198] pool1 needs backward computation.
I0409 21:21:05.945314 25438 net.cpp:198] norm1 needs backward computation.
I0409 21:21:05.945317 25438 net.cpp:198] relu1 needs backward computation.
I0409 21:21:05.945320 25438 net.cpp:198] conv1 needs backward computation.
I0409 21:21:05.945324 25438 net.cpp:200] label_val-data_1_split does not need backward computation.
I0409 21:21:05.945328 25438 net.cpp:200] val-data does not need backward computation.
I0409 21:21:05.945331 25438 net.cpp:242] This network produces output accuracy
I0409 21:21:05.945335 25438 net.cpp:242] This network produces output loss
I0409 21:21:05.945350 25438 net.cpp:255] Network initialization done.
I0409 21:21:05.945410 25438 solver.cpp:56] Solver scaffolding done.
I0409 21:21:05.945781 25438 caffe.cpp:248] Starting Optimization
I0409 21:21:05.945789 25438 solver.cpp:272] Solving
I0409 21:21:05.945793 25438 solver.cpp:273] Learning Rate Policy: exp
I0409 21:21:05.946982 25438 solver.cpp:330] Iteration 0, Testing net (#0)
I0409 21:21:05.947001 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:21:06.047677 25438 blocking_queue.cpp:49] Waiting for data
I0409 21:21:10.454826 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:21:10.503381 25438 solver.cpp:397]     Test net output #0: accuracy = 0.00857843
I0409 21:21:10.503412 25438 solver.cpp:397]     Test net output #1: loss = 5.28709 (* 1 = 5.28709 loss)
I0409 21:21:10.608121 25438 solver.cpp:218] Iteration 0 (1.14066e+37 iter/s, 4.66218s/12 iters), loss = 5.30132
I0409 21:21:10.609637 25438 solver.cpp:237]     Train net output #0: loss = 5.30132 (* 1 = 5.30132 loss)
I0409 21:21:10.609658 25438 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I0409 21:21:14.793915 25438 solver.cpp:218] Iteration 12 (2.86796 iter/s, 4.18416s/12 iters), loss = 5.31482
I0409 21:21:14.793972 25438 solver.cpp:237]     Train net output #0: loss = 5.31482 (* 1 = 5.31482 loss)
I0409 21:21:14.793982 25438 sgd_solver.cpp:105] Iteration 12, lr = 0.00997626
I0409 21:21:20.310958 25438 solver.cpp:218] Iteration 24 (2.17515 iter/s, 5.51685s/12 iters), loss = 5.3217
I0409 21:21:20.311000 25438 solver.cpp:237]     Train net output #0: loss = 5.3217 (* 1 = 5.3217 loss)
I0409 21:21:20.311009 25438 sgd_solver.cpp:105] Iteration 24, lr = 0.00995257
I0409 21:21:25.949226 25438 solver.cpp:218] Iteration 36 (2.12839 iter/s, 5.63808s/12 iters), loss = 5.30792
I0409 21:21:25.949271 25438 solver.cpp:237]     Train net output #0: loss = 5.30792 (* 1 = 5.30792 loss)
I0409 21:21:25.949281 25438 sgd_solver.cpp:105] Iteration 36, lr = 0.00992894
I0409 21:21:31.561260 25438 solver.cpp:218] Iteration 48 (2.13834 iter/s, 5.61184s/12 iters), loss = 5.30246
I0409 21:21:31.561298 25438 solver.cpp:237]     Train net output #0: loss = 5.30246 (* 1 = 5.30246 loss)
I0409 21:21:31.561306 25438 sgd_solver.cpp:105] Iteration 48, lr = 0.00990537
I0409 21:21:36.856709 25438 solver.cpp:218] Iteration 60 (2.26617 iter/s, 5.29527s/12 iters), loss = 5.30822
I0409 21:21:36.856809 25438 solver.cpp:237]     Train net output #0: loss = 5.30822 (* 1 = 5.30822 loss)
I0409 21:21:36.856822 25438 sgd_solver.cpp:105] Iteration 60, lr = 0.00988185
I0409 21:21:42.156178 25438 solver.cpp:218] Iteration 72 (2.26448 iter/s, 5.29922s/12 iters), loss = 5.31625
I0409 21:21:42.156232 25438 solver.cpp:237]     Train net output #0: loss = 5.31625 (* 1 = 5.31625 loss)
I0409 21:21:42.156244 25438 sgd_solver.cpp:105] Iteration 72, lr = 0.00985839
I0409 21:21:47.661458 25438 solver.cpp:218] Iteration 84 (2.17981 iter/s, 5.50508s/12 iters), loss = 5.29945
I0409 21:21:47.661509 25438 solver.cpp:237]     Train net output #0: loss = 5.29945 (* 1 = 5.29945 loss)
I0409 21:21:47.661520 25438 sgd_solver.cpp:105] Iteration 84, lr = 0.00983498
I0409 21:21:53.346392 25438 solver.cpp:218] Iteration 96 (2.11092 iter/s, 5.68473s/12 iters), loss = 5.3254
I0409 21:21:53.346441 25438 solver.cpp:237]     Train net output #0: loss = 5.3254 (* 1 = 5.3254 loss)
I0409 21:21:53.346452 25438 sgd_solver.cpp:105] Iteration 96, lr = 0.00981163
I0409 21:21:55.289013 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:21:55.634996 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0409 21:22:11.502102 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0409 21:22:16.832291 25438 solver.cpp:330] Iteration 102, Testing net (#0)
I0409 21:22:16.832315 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:22:21.222962 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:22:21.300354 25438 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0409 21:22:21.300393 25438 solver.cpp:397]     Test net output #1: loss = 5.29693 (* 1 = 5.29693 loss)
I0409 21:22:23.368294 25438 solver.cpp:218] Iteration 108 (0.399719 iter/s, 30.0211s/12 iters), loss = 5.33175
I0409 21:22:23.368351 25438 solver.cpp:237]     Train net output #0: loss = 5.33175 (* 1 = 5.33175 loss)
I0409 21:22:23.368364 25438 sgd_solver.cpp:105] Iteration 108, lr = 0.00978834
I0409 21:22:28.882567 25438 solver.cpp:218] Iteration 120 (2.17625 iter/s, 5.51407s/12 iters), loss = 5.29878
I0409 21:22:28.882606 25438 solver.cpp:237]     Train net output #0: loss = 5.29878 (* 1 = 5.29878 loss)
I0409 21:22:28.882616 25438 sgd_solver.cpp:105] Iteration 120, lr = 0.0097651
I0409 21:22:34.422307 25438 solver.cpp:218] Iteration 132 (2.16624 iter/s, 5.53955s/12 iters), loss = 5.24356
I0409 21:22:34.422344 25438 solver.cpp:237]     Train net output #0: loss = 5.24356 (* 1 = 5.24356 loss)
I0409 21:22:34.422354 25438 sgd_solver.cpp:105] Iteration 132, lr = 0.00974192
I0409 21:22:40.042280 25438 solver.cpp:218] Iteration 144 (2.13532 iter/s, 5.61978s/12 iters), loss = 5.30999
I0409 21:22:40.042333 25438 solver.cpp:237]     Train net output #0: loss = 5.30999 (* 1 = 5.30999 loss)
I0409 21:22:40.042346 25438 sgd_solver.cpp:105] Iteration 144, lr = 0.00971879
I0409 21:22:45.610848 25438 solver.cpp:218] Iteration 156 (2.15503 iter/s, 5.56836s/12 iters), loss = 5.24121
I0409 21:22:45.610970 25438 solver.cpp:237]     Train net output #0: loss = 5.24121 (* 1 = 5.24121 loss)
I0409 21:22:45.610980 25438 sgd_solver.cpp:105] Iteration 156, lr = 0.00969571
I0409 21:22:50.917989 25438 solver.cpp:218] Iteration 168 (2.26123 iter/s, 5.30686s/12 iters), loss = 5.18964
I0409 21:22:50.918036 25438 solver.cpp:237]     Train net output #0: loss = 5.18964 (* 1 = 5.18964 loss)
I0409 21:22:50.918047 25438 sgd_solver.cpp:105] Iteration 168, lr = 0.00967269
I0409 21:22:56.238062 25438 solver.cpp:218] Iteration 180 (2.25569 iter/s, 5.31987s/12 iters), loss = 5.15408
I0409 21:22:56.238116 25438 solver.cpp:237]     Train net output #0: loss = 5.15408 (* 1 = 5.15408 loss)
I0409 21:22:56.238126 25438 sgd_solver.cpp:105] Iteration 180, lr = 0.00964973
I0409 21:23:01.557587 25438 solver.cpp:218] Iteration 192 (2.25592 iter/s, 5.31933s/12 iters), loss = 5.26876
I0409 21:23:01.557629 25438 solver.cpp:237]     Train net output #0: loss = 5.26876 (* 1 = 5.26876 loss)
I0409 21:23:01.557639 25438 sgd_solver.cpp:105] Iteration 192, lr = 0.00962682
I0409 21:23:05.666208 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:23:06.378747 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0409 21:23:16.871102 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0409 21:23:23.119009 25438 solver.cpp:330] Iteration 204, Testing net (#0)
I0409 21:23:23.119033 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:23:27.512028 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:23:27.636289 25438 solver.cpp:397]     Test net output #0: accuracy = 0.00919118
I0409 21:23:27.636339 25438 solver.cpp:397]     Test net output #1: loss = 5.21543 (* 1 = 5.21543 loss)
I0409 21:23:27.733592 25438 solver.cpp:218] Iteration 204 (0.458448 iter/s, 26.1753s/12 iters), loss = 5.1659
I0409 21:23:27.733637 25438 solver.cpp:237]     Train net output #0: loss = 5.1659 (* 1 = 5.1659 loss)
I0409 21:23:27.733647 25438 sgd_solver.cpp:105] Iteration 204, lr = 0.00960396
I0409 21:23:32.163100 25438 solver.cpp:218] Iteration 216 (2.70921 iter/s, 4.42933s/12 iters), loss = 5.17196
I0409 21:23:32.163143 25438 solver.cpp:237]     Train net output #0: loss = 5.17196 (* 1 = 5.17196 loss)
I0409 21:23:32.163153 25438 sgd_solver.cpp:105] Iteration 216, lr = 0.00958116
I0409 21:23:37.651880 25438 solver.cpp:218] Iteration 228 (2.18636 iter/s, 5.48859s/12 iters), loss = 5.20337
I0409 21:23:37.651919 25438 solver.cpp:237]     Train net output #0: loss = 5.20337 (* 1 = 5.20337 loss)
I0409 21:23:37.651928 25438 sgd_solver.cpp:105] Iteration 228, lr = 0.00955841
I0409 21:23:42.894954 25438 solver.cpp:218] Iteration 240 (2.28882 iter/s, 5.24288s/12 iters), loss = 5.1931
I0409 21:23:42.895000 25438 solver.cpp:237]     Train net output #0: loss = 5.1931 (* 1 = 5.1931 loss)
I0409 21:23:42.895009 25438 sgd_solver.cpp:105] Iteration 240, lr = 0.00953572
I0409 21:23:48.404769 25438 solver.cpp:218] Iteration 252 (2.17801 iter/s, 5.5096s/12 iters), loss = 5.07604
I0409 21:23:48.404917 25438 solver.cpp:237]     Train net output #0: loss = 5.07604 (* 1 = 5.07604 loss)
I0409 21:23:48.404929 25438 sgd_solver.cpp:105] Iteration 252, lr = 0.00951308
I0409 21:23:53.793567 25438 solver.cpp:218] Iteration 264 (2.22697 iter/s, 5.3885s/12 iters), loss = 5.23462
I0409 21:23:53.793617 25438 solver.cpp:237]     Train net output #0: loss = 5.23462 (* 1 = 5.23462 loss)
I0409 21:23:53.793627 25438 sgd_solver.cpp:105] Iteration 264, lr = 0.00949049
I0409 21:23:59.169129 25438 solver.cpp:218] Iteration 276 (2.23241 iter/s, 5.37536s/12 iters), loss = 5.16505
I0409 21:23:59.169171 25438 solver.cpp:237]     Train net output #0: loss = 5.16505 (* 1 = 5.16505 loss)
I0409 21:23:59.169180 25438 sgd_solver.cpp:105] Iteration 276, lr = 0.00946796
I0409 21:24:04.529752 25438 solver.cpp:218] Iteration 288 (2.23863 iter/s, 5.36043s/12 iters), loss = 5.05276
I0409 21:24:04.529793 25438 solver.cpp:237]     Train net output #0: loss = 5.05276 (* 1 = 5.05276 loss)
I0409 21:24:04.529800 25438 sgd_solver.cpp:105] Iteration 288, lr = 0.00944548
I0409 21:24:09.872154 25438 solver.cpp:218] Iteration 300 (2.24626 iter/s, 5.34221s/12 iters), loss = 5.21301
I0409 21:24:09.872202 25438 solver.cpp:237]     Train net output #0: loss = 5.21301 (* 1 = 5.21301 loss)
I0409 21:24:09.872213 25438 sgd_solver.cpp:105] Iteration 300, lr = 0.00942305
I0409 21:24:10.963882 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:24:12.044052 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0409 21:24:17.609470 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0409 21:24:20.745471 25438 solver.cpp:330] Iteration 306, Testing net (#0)
I0409 21:24:20.745532 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:24:25.046919 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:24:25.206298 25438 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0409 21:24:25.206344 25438 solver.cpp:397]     Test net output #1: loss = 5.15992 (* 1 = 5.15992 loss)
I0409 21:24:27.296455 25438 solver.cpp:218] Iteration 312 (0.688714 iter/s, 17.4238s/12 iters), loss = 5.11487
I0409 21:24:27.296506 25438 solver.cpp:237]     Train net output #0: loss = 5.11487 (* 1 = 5.11487 loss)
I0409 21:24:27.296516 25438 sgd_solver.cpp:105] Iteration 312, lr = 0.00940068
I0409 21:24:32.671545 25438 solver.cpp:218] Iteration 324 (2.2326 iter/s, 5.37489s/12 iters), loss = 5.18629
I0409 21:24:32.671581 25438 solver.cpp:237]     Train net output #0: loss = 5.18629 (* 1 = 5.18629 loss)
I0409 21:24:32.671589 25438 sgd_solver.cpp:105] Iteration 324, lr = 0.00937836
I0409 21:24:38.186045 25438 solver.cpp:218] Iteration 336 (2.17616 iter/s, 5.5143s/12 iters), loss = 5.13809
I0409 21:24:38.186100 25438 solver.cpp:237]     Train net output #0: loss = 5.13809 (* 1 = 5.13809 loss)
I0409 21:24:38.186108 25438 sgd_solver.cpp:105] Iteration 336, lr = 0.0093561
I0409 21:24:43.728576 25438 solver.cpp:218] Iteration 348 (2.16516 iter/s, 5.54232s/12 iters), loss = 5.07356
I0409 21:24:43.728626 25438 solver.cpp:237]     Train net output #0: loss = 5.07356 (* 1 = 5.07356 loss)
I0409 21:24:43.728636 25438 sgd_solver.cpp:105] Iteration 348, lr = 0.00933388
I0409 21:24:49.330513 25438 solver.cpp:218] Iteration 360 (2.1422 iter/s, 5.60173s/12 iters), loss = 5.13813
I0409 21:24:49.330557 25438 solver.cpp:237]     Train net output #0: loss = 5.13813 (* 1 = 5.13813 loss)
I0409 21:24:49.330565 25438 sgd_solver.cpp:105] Iteration 360, lr = 0.00931172
I0409 21:24:54.758929 25438 solver.cpp:218] Iteration 372 (2.21067 iter/s, 5.42822s/12 iters), loss = 5.04505
I0409 21:24:54.759012 25438 solver.cpp:237]     Train net output #0: loss = 5.04505 (* 1 = 5.04505 loss)
I0409 21:24:54.759021 25438 sgd_solver.cpp:105] Iteration 372, lr = 0.00928961
I0409 21:25:00.188473 25438 solver.cpp:218] Iteration 384 (2.21023 iter/s, 5.4293s/12 iters), loss = 5.09292
I0409 21:25:00.188517 25438 solver.cpp:237]     Train net output #0: loss = 5.09292 (* 1 = 5.09292 loss)
I0409 21:25:00.188527 25438 sgd_solver.cpp:105] Iteration 384, lr = 0.00926756
I0409 21:25:05.494982 25438 solver.cpp:218] Iteration 396 (2.26146 iter/s, 5.3063s/12 iters), loss = 5.08143
I0409 21:25:05.495041 25438 solver.cpp:237]     Train net output #0: loss = 5.08143 (* 1 = 5.08143 loss)
I0409 21:25:05.495052 25438 sgd_solver.cpp:105] Iteration 396, lr = 0.00924556
I0409 21:25:08.775691 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:25:10.254925 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0409 21:25:14.446238 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0409 21:25:17.692869 25438 solver.cpp:330] Iteration 408, Testing net (#0)
I0409 21:25:17.692895 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:25:21.998315 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:25:22.212083 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0104167
I0409 21:25:22.212133 25438 solver.cpp:397]     Test net output #1: loss = 5.14303 (* 1 = 5.14303 loss)
I0409 21:25:22.309592 25438 solver.cpp:218] Iteration 408 (0.713687 iter/s, 16.8141s/12 iters), loss = 5.15115
I0409 21:25:22.309648 25438 solver.cpp:237]     Train net output #0: loss = 5.15115 (* 1 = 5.15115 loss)
I0409 21:25:22.309657 25438 sgd_solver.cpp:105] Iteration 408, lr = 0.00922361
I0409 21:25:26.849395 25438 solver.cpp:218] Iteration 420 (2.6434 iter/s, 4.53961s/12 iters), loss = 5.14226
I0409 21:25:26.849543 25438 solver.cpp:237]     Train net output #0: loss = 5.14226 (* 1 = 5.14226 loss)
I0409 21:25:26.849557 25438 sgd_solver.cpp:105] Iteration 420, lr = 0.00920171
I0409 21:25:32.485258 25438 solver.cpp:218] Iteration 432 (2.12934 iter/s, 5.63556s/12 iters), loss = 5.01161
I0409 21:25:32.485311 25438 solver.cpp:237]     Train net output #0: loss = 5.01161 (* 1 = 5.01161 loss)
I0409 21:25:32.485321 25438 sgd_solver.cpp:105] Iteration 432, lr = 0.00917986
I0409 21:25:38.100289 25438 solver.cpp:218] Iteration 444 (2.1372 iter/s, 5.61482s/12 iters), loss = 5.02846
I0409 21:25:38.100332 25438 solver.cpp:237]     Train net output #0: loss = 5.02846 (* 1 = 5.02846 loss)
I0409 21:25:38.100340 25438 sgd_solver.cpp:105] Iteration 444, lr = 0.00915807
I0409 21:25:43.477810 25438 solver.cpp:218] Iteration 456 (2.23159 iter/s, 5.37732s/12 iters), loss = 5.06705
I0409 21:25:43.477849 25438 solver.cpp:237]     Train net output #0: loss = 5.06705 (* 1 = 5.06705 loss)
I0409 21:25:43.477856 25438 sgd_solver.cpp:105] Iteration 456, lr = 0.00913632
I0409 21:25:48.936870 25438 solver.cpp:218] Iteration 468 (2.19826 iter/s, 5.45887s/12 iters), loss = 5.09115
I0409 21:25:48.936918 25438 solver.cpp:237]     Train net output #0: loss = 5.09115 (* 1 = 5.09115 loss)
I0409 21:25:48.936929 25438 sgd_solver.cpp:105] Iteration 468, lr = 0.00911463
I0409 21:25:54.406759 25438 solver.cpp:218] Iteration 480 (2.19392 iter/s, 5.46967s/12 iters), loss = 4.97195
I0409 21:25:54.406819 25438 solver.cpp:237]     Train net output #0: loss = 4.97195 (* 1 = 4.97195 loss)
I0409 21:25:54.406831 25438 sgd_solver.cpp:105] Iteration 480, lr = 0.00909299
I0409 21:25:59.931690 25438 solver.cpp:218] Iteration 492 (2.17206 iter/s, 5.52471s/12 iters), loss = 5.01975
I0409 21:25:59.931845 25438 solver.cpp:237]     Train net output #0: loss = 5.01975 (* 1 = 5.01975 loss)
I0409 21:25:59.931869 25438 sgd_solver.cpp:105] Iteration 492, lr = 0.0090714
I0409 21:26:05.145454 25438 solver.cpp:218] Iteration 504 (2.30173 iter/s, 5.21347s/12 iters), loss = 5.08486
I0409 21:26:05.145509 25438 solver.cpp:237]     Train net output #0: loss = 5.08486 (* 1 = 5.08486 loss)
I0409 21:26:05.145520 25438 sgd_solver.cpp:105] Iteration 504, lr = 0.00904986
I0409 21:26:05.397683 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:26:07.240465 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0409 21:26:13.507649 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0409 21:26:18.170904 25438 solver.cpp:330] Iteration 510, Testing net (#0)
I0409 21:26:18.170930 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:26:22.574434 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:26:22.823920 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0257353
I0409 21:26:22.823956 25438 solver.cpp:397]     Test net output #1: loss = 5.03687 (* 1 = 5.03687 loss)
I0409 21:26:24.737520 25438 solver.cpp:218] Iteration 516 (0.612511 iter/s, 19.5915s/12 iters), loss = 4.95756
I0409 21:26:24.737576 25438 solver.cpp:237]     Train net output #0: loss = 4.95756 (* 1 = 4.95756 loss)
I0409 21:26:24.737586 25438 sgd_solver.cpp:105] Iteration 516, lr = 0.00902838
I0409 21:26:29.864007 25438 solver.cpp:218] Iteration 528 (2.34088 iter/s, 5.12628s/12 iters), loss = 5.04353
I0409 21:26:29.864070 25438 solver.cpp:237]     Train net output #0: loss = 5.04353 (* 1 = 5.04353 loss)
I0409 21:26:29.864082 25438 sgd_solver.cpp:105] Iteration 528, lr = 0.00900694
I0409 21:26:35.037503 25438 solver.cpp:218] Iteration 540 (2.31961 iter/s, 5.17329s/12 iters), loss = 4.89056
I0409 21:26:35.038007 25438 solver.cpp:237]     Train net output #0: loss = 4.89056 (* 1 = 4.89056 loss)
I0409 21:26:35.038015 25438 sgd_solver.cpp:105] Iteration 540, lr = 0.00898556
I0409 21:26:40.315912 25438 solver.cpp:218] Iteration 552 (2.2737 iter/s, 5.27775s/12 iters), loss = 5.00371
I0409 21:26:40.315973 25438 solver.cpp:237]     Train net output #0: loss = 5.00371 (* 1 = 5.00371 loss)
I0409 21:26:40.315985 25438 sgd_solver.cpp:105] Iteration 552, lr = 0.00896423
I0409 21:26:45.527835 25438 solver.cpp:218] Iteration 564 (2.30251 iter/s, 5.21171s/12 iters), loss = 5.00746
I0409 21:26:45.527884 25438 solver.cpp:237]     Train net output #0: loss = 5.00746 (* 1 = 5.00746 loss)
I0409 21:26:45.527894 25438 sgd_solver.cpp:105] Iteration 564, lr = 0.00894294
I0409 21:26:50.784474 25438 solver.cpp:218] Iteration 576 (2.28291 iter/s, 5.25644s/12 iters), loss = 4.97013
I0409 21:26:50.784520 25438 solver.cpp:237]     Train net output #0: loss = 4.97013 (* 1 = 4.97013 loss)
I0409 21:26:50.784530 25438 sgd_solver.cpp:105] Iteration 576, lr = 0.00892171
I0409 21:26:56.083554 25438 solver.cpp:218] Iteration 588 (2.26463 iter/s, 5.29888s/12 iters), loss = 4.74025
I0409 21:26:56.083603 25438 solver.cpp:237]     Train net output #0: loss = 4.74025 (* 1 = 4.74025 loss)
I0409 21:26:56.083614 25438 sgd_solver.cpp:105] Iteration 588, lr = 0.00890053
I0409 21:27:01.529570 25438 solver.cpp:218] Iteration 600 (2.20353 iter/s, 5.44581s/12 iters), loss = 4.90914
I0409 21:27:01.529615 25438 solver.cpp:237]     Train net output #0: loss = 4.90914 (* 1 = 4.90914 loss)
I0409 21:27:01.529626 25438 sgd_solver.cpp:105] Iteration 600, lr = 0.0088794
I0409 21:27:04.260499 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:27:06.478857 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0409 21:27:10.604305 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0409 21:27:13.775455 25438 solver.cpp:330] Iteration 612, Testing net (#0)
I0409 21:27:13.775480 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:27:18.233448 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:27:18.529973 25438 solver.cpp:397]     Test net output #0: accuracy = 0.026348
I0409 21:27:18.530014 25438 solver.cpp:397]     Test net output #1: loss = 4.98034 (* 1 = 4.98034 loss)
I0409 21:27:18.627372 25438 solver.cpp:218] Iteration 612 (0.701866 iter/s, 17.0973s/12 iters), loss = 4.88214
I0409 21:27:18.627419 25438 solver.cpp:237]     Train net output #0: loss = 4.88214 (* 1 = 4.88214 loss)
I0409 21:27:18.627429 25438 sgd_solver.cpp:105] Iteration 612, lr = 0.00885831
I0409 21:27:22.984220 25438 solver.cpp:218] Iteration 624 (2.7544 iter/s, 4.35667s/12 iters), loss = 4.85225
I0409 21:27:22.984277 25438 solver.cpp:237]     Train net output #0: loss = 4.85225 (* 1 = 4.85225 loss)
I0409 21:27:22.984289 25438 sgd_solver.cpp:105] Iteration 624, lr = 0.00883728
I0409 21:27:28.106631 25438 solver.cpp:218] Iteration 636 (2.34274 iter/s, 5.1222s/12 iters), loss = 4.75581
I0409 21:27:28.106685 25438 solver.cpp:237]     Train net output #0: loss = 4.75581 (* 1 = 4.75581 loss)
I0409 21:27:28.106698 25438 sgd_solver.cpp:105] Iteration 636, lr = 0.0088163
I0409 21:27:33.211910 25438 solver.cpp:218] Iteration 648 (2.3506 iter/s, 5.10507s/12 iters), loss = 5.05219
I0409 21:27:33.211966 25438 solver.cpp:237]     Train net output #0: loss = 5.05219 (* 1 = 5.05219 loss)
I0409 21:27:33.211975 25438 sgd_solver.cpp:105] Iteration 648, lr = 0.00879537
I0409 21:27:38.652685 25438 solver.cpp:218] Iteration 660 (2.20566 iter/s, 5.44056s/12 iters), loss = 4.87731
I0409 21:27:38.652838 25438 solver.cpp:237]     Train net output #0: loss = 4.87731 (* 1 = 4.87731 loss)
I0409 21:27:38.652850 25438 sgd_solver.cpp:105] Iteration 660, lr = 0.00877449
I0409 21:27:44.546087 25438 solver.cpp:218] Iteration 672 (2.03629 iter/s, 5.89308s/12 iters), loss = 4.77759
I0409 21:27:44.546140 25438 solver.cpp:237]     Train net output #0: loss = 4.77759 (* 1 = 4.77759 loss)
I0409 21:27:44.546151 25438 sgd_solver.cpp:105] Iteration 672, lr = 0.00875366
I0409 21:27:49.838692 25438 solver.cpp:218] Iteration 684 (2.26741 iter/s, 5.29239s/12 iters), loss = 4.79898
I0409 21:27:49.838742 25438 solver.cpp:237]     Train net output #0: loss = 4.79898 (* 1 = 4.79898 loss)
I0409 21:27:49.838752 25438 sgd_solver.cpp:105] Iteration 684, lr = 0.00873287
I0409 21:27:50.675119 25438 blocking_queue.cpp:49] Waiting for data
I0409 21:27:55.477998 25438 solver.cpp:218] Iteration 696 (2.128 iter/s, 5.63909s/12 iters), loss = 4.76379
I0409 21:27:55.478051 25438 solver.cpp:237]     Train net output #0: loss = 4.76379 (* 1 = 4.76379 loss)
I0409 21:27:55.478063 25438 sgd_solver.cpp:105] Iteration 696, lr = 0.00871214
I0409 21:28:00.509047 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:28:00.896085 25438 solver.cpp:218] Iteration 708 (2.21489 iter/s, 5.41787s/12 iters), loss = 4.92797
I0409 21:28:00.896139 25438 solver.cpp:237]     Train net output #0: loss = 4.92797 (* 1 = 4.92797 loss)
I0409 21:28:00.896150 25438 sgd_solver.cpp:105] Iteration 708, lr = 0.00869145
I0409 21:28:03.000077 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0409 21:28:08.488966 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0409 21:28:14.766305 25438 solver.cpp:330] Iteration 714, Testing net (#0)
I0409 21:28:14.766366 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:28:19.153424 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:28:19.477541 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0318627
I0409 21:28:19.477573 25438 solver.cpp:397]     Test net output #1: loss = 4.94337 (* 1 = 4.94337 loss)
I0409 21:28:21.493147 25438 solver.cpp:218] Iteration 720 (0.582625 iter/s, 20.5964s/12 iters), loss = 4.8612
I0409 21:28:21.493196 25438 solver.cpp:237]     Train net output #0: loss = 4.8612 (* 1 = 4.8612 loss)
I0409 21:28:21.493206 25438 sgd_solver.cpp:105] Iteration 720, lr = 0.00867082
I0409 21:28:26.775928 25438 solver.cpp:218] Iteration 732 (2.27162 iter/s, 5.28257s/12 iters), loss = 4.68586
I0409 21:28:26.775969 25438 solver.cpp:237]     Train net output #0: loss = 4.68586 (* 1 = 4.68586 loss)
I0409 21:28:26.775977 25438 sgd_solver.cpp:105] Iteration 732, lr = 0.00865023
I0409 21:28:32.513736 25438 solver.cpp:218] Iteration 744 (2.09147 iter/s, 5.7376s/12 iters), loss = 4.89445
I0409 21:28:32.513784 25438 solver.cpp:237]     Train net output #0: loss = 4.89445 (* 1 = 4.89445 loss)
I0409 21:28:32.513793 25438 sgd_solver.cpp:105] Iteration 744, lr = 0.0086297
I0409 21:28:37.840704 25438 solver.cpp:218] Iteration 756 (2.25278 iter/s, 5.32676s/12 iters), loss = 4.87985
I0409 21:28:37.840749 25438 solver.cpp:237]     Train net output #0: loss = 4.87985 (* 1 = 4.87985 loss)
I0409 21:28:37.840757 25438 sgd_solver.cpp:105] Iteration 756, lr = 0.00860921
I0409 21:28:44.096455 25438 solver.cpp:218] Iteration 768 (1.91831 iter/s, 6.25552s/12 iters), loss = 4.77018
I0409 21:28:44.096503 25438 solver.cpp:237]     Train net output #0: loss = 4.77018 (* 1 = 4.77018 loss)
I0409 21:28:44.096511 25438 sgd_solver.cpp:105] Iteration 768, lr = 0.00858877
I0409 21:28:51.132305 25438 solver.cpp:218] Iteration 780 (1.70561 iter/s, 7.0356s/12 iters), loss = 4.7583
I0409 21:28:51.132421 25438 solver.cpp:237]     Train net output #0: loss = 4.7583 (* 1 = 4.7583 loss)
I0409 21:28:51.132432 25438 sgd_solver.cpp:105] Iteration 780, lr = 0.00856838
I0409 21:28:56.490491 25438 solver.cpp:218] Iteration 792 (2.23968 iter/s, 5.3579s/12 iters), loss = 4.58686
I0409 21:28:56.490557 25438 solver.cpp:237]     Train net output #0: loss = 4.58686 (* 1 = 4.58686 loss)
I0409 21:28:56.490571 25438 sgd_solver.cpp:105] Iteration 792, lr = 0.00854803
I0409 21:29:02.003921 25438 solver.cpp:218] Iteration 804 (2.1766 iter/s, 5.5132s/12 iters), loss = 4.84832
I0409 21:29:02.003980 25438 solver.cpp:237]     Train net output #0: loss = 4.84832 (* 1 = 4.84832 loss)
I0409 21:29:02.003991 25438 sgd_solver.cpp:105] Iteration 804, lr = 0.00852774
I0409 21:29:04.045622 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:29:07.227162 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0409 21:29:14.466928 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0409 21:29:26.425088 25438 solver.cpp:330] Iteration 816, Testing net (#0)
I0409 21:29:26.425163 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:29:30.909582 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:29:31.365059 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0373775
I0409 21:29:31.365097 25438 solver.cpp:397]     Test net output #1: loss = 4.90267 (* 1 = 4.90267 loss)
I0409 21:29:31.462270 25438 solver.cpp:218] Iteration 816 (0.407367 iter/s, 29.4575s/12 iters), loss = 4.77865
I0409 21:29:31.462321 25438 solver.cpp:237]     Train net output #0: loss = 4.77865 (* 1 = 4.77865 loss)
I0409 21:29:31.462332 25438 sgd_solver.cpp:105] Iteration 816, lr = 0.00850749
I0409 21:29:36.730195 25438 solver.cpp:218] Iteration 828 (2.27803 iter/s, 5.26771s/12 iters), loss = 4.97965
I0409 21:29:36.730240 25438 solver.cpp:237]     Train net output #0: loss = 4.97965 (* 1 = 4.97965 loss)
I0409 21:29:36.730248 25438 sgd_solver.cpp:105] Iteration 828, lr = 0.00848729
I0409 21:29:42.914717 25438 solver.cpp:218] Iteration 840 (1.9404 iter/s, 6.18429s/12 iters), loss = 4.6509
I0409 21:29:42.914767 25438 solver.cpp:237]     Train net output #0: loss = 4.6509 (* 1 = 4.6509 loss)
I0409 21:29:42.914777 25438 sgd_solver.cpp:105] Iteration 840, lr = 0.00846714
I0409 21:29:48.265628 25438 solver.cpp:218] Iteration 852 (2.2427 iter/s, 5.35069s/12 iters), loss = 4.45792
I0409 21:29:48.265691 25438 solver.cpp:237]     Train net output #0: loss = 4.45792 (* 1 = 4.45792 loss)
I0409 21:29:48.265703 25438 sgd_solver.cpp:105] Iteration 852, lr = 0.00844704
I0409 21:29:53.753866 25438 solver.cpp:218] Iteration 864 (2.18658 iter/s, 5.48801s/12 iters), loss = 4.59549
I0409 21:29:53.753913 25438 solver.cpp:237]     Train net output #0: loss = 4.59549 (* 1 = 4.59549 loss)
I0409 21:29:53.753922 25438 sgd_solver.cpp:105] Iteration 864, lr = 0.00842698
I0409 21:30:00.055357 25438 solver.cpp:218] Iteration 876 (1.90438 iter/s, 6.30125s/12 iters), loss = 4.57415
I0409 21:30:00.055451 25438 solver.cpp:237]     Train net output #0: loss = 4.57415 (* 1 = 4.57415 loss)
I0409 21:30:00.055461 25438 sgd_solver.cpp:105] Iteration 876, lr = 0.00840698
I0409 21:30:05.384289 25438 solver.cpp:218] Iteration 888 (2.25197 iter/s, 5.32867s/12 iters), loss = 4.70415
I0409 21:30:05.384346 25438 solver.cpp:237]     Train net output #0: loss = 4.70415 (* 1 = 4.70415 loss)
I0409 21:30:05.384356 25438 sgd_solver.cpp:105] Iteration 888, lr = 0.00838702
I0409 21:30:11.917685 25438 solver.cpp:218] Iteration 900 (1.83679 iter/s, 6.53315s/12 iters), loss = 4.69874
I0409 21:30:11.917740 25438 solver.cpp:237]     Train net output #0: loss = 4.69874 (* 1 = 4.69874 loss)
I0409 21:30:11.917750 25438 sgd_solver.cpp:105] Iteration 900, lr = 0.0083671
I0409 21:30:16.986567 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:30:18.283324 25438 solver.cpp:218] Iteration 912 (1.88519 iter/s, 6.36539s/12 iters), loss = 4.55461
I0409 21:30:18.283377 25438 solver.cpp:237]     Train net output #0: loss = 4.55461 (* 1 = 4.55461 loss)
I0409 21:30:18.283387 25438 sgd_solver.cpp:105] Iteration 912, lr = 0.00834724
I0409 21:30:20.691018 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0409 21:30:29.413188 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0409 21:30:36.666980 25438 solver.cpp:330] Iteration 918, Testing net (#0)
I0409 21:30:36.667095 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:30:41.324896 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:30:41.872709 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0422794
I0409 21:30:41.872769 25438 solver.cpp:397]     Test net output #1: loss = 4.83677 (* 1 = 4.83677 loss)
I0409 21:30:43.737044 25438 solver.cpp:218] Iteration 924 (0.471458 iter/s, 25.4529s/12 iters), loss = 4.57017
I0409 21:30:43.737102 25438 solver.cpp:237]     Train net output #0: loss = 4.57017 (* 1 = 4.57017 loss)
I0409 21:30:43.737112 25438 sgd_solver.cpp:105] Iteration 924, lr = 0.00832742
I0409 21:30:49.091044 25438 solver.cpp:218] Iteration 936 (2.24141 iter/s, 5.35378s/12 iters), loss = 4.69383
I0409 21:30:49.091104 25438 solver.cpp:237]     Train net output #0: loss = 4.69383 (* 1 = 4.69383 loss)
I0409 21:30:49.091115 25438 sgd_solver.cpp:105] Iteration 936, lr = 0.00830765
I0409 21:30:54.417449 25438 solver.cpp:218] Iteration 948 (2.25302 iter/s, 5.32618s/12 iters), loss = 4.6206
I0409 21:30:54.417507 25438 solver.cpp:237]     Train net output #0: loss = 4.6206 (* 1 = 4.6206 loss)
I0409 21:30:54.417518 25438 sgd_solver.cpp:105] Iteration 948, lr = 0.00828793
I0409 21:31:00.487421 25438 solver.cpp:218] Iteration 960 (1.97702 iter/s, 6.06973s/12 iters), loss = 4.31767
I0409 21:31:00.487476 25438 solver.cpp:237]     Train net output #0: loss = 4.31767 (* 1 = 4.31767 loss)
I0409 21:31:00.487488 25438 sgd_solver.cpp:105] Iteration 960, lr = 0.00826825
I0409 21:31:06.286761 25438 solver.cpp:218] Iteration 972 (2.06928 iter/s, 5.79911s/12 iters), loss = 4.50046
I0409 21:31:06.286821 25438 solver.cpp:237]     Train net output #0: loss = 4.50046 (* 1 = 4.50046 loss)
I0409 21:31:06.286832 25438 sgd_solver.cpp:105] Iteration 972, lr = 0.00824862
I0409 21:31:11.469420 25438 solver.cpp:218] Iteration 984 (2.31551 iter/s, 5.18244s/12 iters), loss = 4.45466
I0409 21:31:11.469555 25438 solver.cpp:237]     Train net output #0: loss = 4.45466 (* 1 = 4.45466 loss)
I0409 21:31:11.469568 25438 sgd_solver.cpp:105] Iteration 984, lr = 0.00822903
I0409 21:31:17.003293 25438 solver.cpp:218] Iteration 996 (2.16858 iter/s, 5.53357s/12 iters), loss = 4.54648
I0409 21:31:17.003340 25438 solver.cpp:237]     Train net output #0: loss = 4.54648 (* 1 = 4.54648 loss)
I0409 21:31:17.003350 25438 sgd_solver.cpp:105] Iteration 996, lr = 0.0082095
I0409 21:31:23.435176 25438 solver.cpp:218] Iteration 1008 (1.86578 iter/s, 6.43164s/12 iters), loss = 4.7
I0409 21:31:23.435230 25438 solver.cpp:237]     Train net output #0: loss = 4.7 (* 1 = 4.7 loss)
I0409 21:31:23.435243 25438 sgd_solver.cpp:105] Iteration 1008, lr = 0.00819001
I0409 21:31:24.499408 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:31:28.472738 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0409 21:31:33.501335 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0409 21:31:36.802819 25438 solver.cpp:330] Iteration 1020, Testing net (#0)
I0409 21:31:36.802840 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:31:41.874604 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:31:42.491354 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0416667
I0409 21:31:42.491405 25438 solver.cpp:397]     Test net output #1: loss = 4.90224 (* 1 = 4.90224 loss)
I0409 21:31:42.588953 25438 solver.cpp:218] Iteration 1020 (0.626528 iter/s, 19.1532s/12 iters), loss = 4.53659
I0409 21:31:42.589015 25438 solver.cpp:237]     Train net output #0: loss = 4.53659 (* 1 = 4.53659 loss)
I0409 21:31:42.589026 25438 sgd_solver.cpp:105] Iteration 1020, lr = 0.00817056
I0409 21:31:47.822168 25438 solver.cpp:218] Iteration 1032 (2.29314 iter/s, 5.23299s/12 iters), loss = 4.52143
I0409 21:31:47.822222 25438 solver.cpp:237]     Train net output #0: loss = 4.52143 (* 1 = 4.52143 loss)
I0409 21:31:47.822232 25438 sgd_solver.cpp:105] Iteration 1032, lr = 0.00815116
I0409 21:31:53.244057 25438 solver.cpp:218] Iteration 1044 (2.21334 iter/s, 5.42167s/12 iters), loss = 4.57467
I0409 21:31:53.244113 25438 solver.cpp:237]     Train net output #0: loss = 4.57467 (* 1 = 4.57467 loss)
I0409 21:31:53.244123 25438 sgd_solver.cpp:105] Iteration 1044, lr = 0.00813181
I0409 21:31:58.741063 25438 solver.cpp:218] Iteration 1056 (2.1831 iter/s, 5.49678s/12 iters), loss = 4.507
I0409 21:31:58.741112 25438 solver.cpp:237]     Train net output #0: loss = 4.507 (* 1 = 4.507 loss)
I0409 21:31:58.741123 25438 sgd_solver.cpp:105] Iteration 1056, lr = 0.0081125
I0409 21:32:04.426857 25438 solver.cpp:218] Iteration 1068 (2.11061 iter/s, 5.68557s/12 iters), loss = 4.34761
I0409 21:32:04.426901 25438 solver.cpp:237]     Train net output #0: loss = 4.34761 (* 1 = 4.34761 loss)
I0409 21:32:04.426908 25438 sgd_solver.cpp:105] Iteration 1068, lr = 0.00809324
I0409 21:32:10.600195 25438 solver.cpp:218] Iteration 1080 (1.94392 iter/s, 6.1731s/12 iters), loss = 4.38457
I0409 21:32:10.600245 25438 solver.cpp:237]     Train net output #0: loss = 4.38457 (* 1 = 4.38457 loss)
I0409 21:32:10.600255 25438 sgd_solver.cpp:105] Iteration 1080, lr = 0.00807403
I0409 21:32:16.065658 25438 solver.cpp:218] Iteration 1092 (2.19569 iter/s, 5.46524s/12 iters), loss = 4.44073
I0409 21:32:16.065757 25438 solver.cpp:237]     Train net output #0: loss = 4.44073 (* 1 = 4.44073 loss)
I0409 21:32:16.065766 25438 sgd_solver.cpp:105] Iteration 1092, lr = 0.00805486
I0409 21:32:21.725800 25438 solver.cpp:218] Iteration 1104 (2.12019 iter/s, 5.65987s/12 iters), loss = 4.51772
I0409 21:32:21.725844 25438 solver.cpp:237]     Train net output #0: loss = 4.51772 (* 1 = 4.51772 loss)
I0409 21:32:21.725852 25438 sgd_solver.cpp:105] Iteration 1104, lr = 0.00803573
I0409 21:32:25.503481 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:32:27.713588 25438 solver.cpp:218] Iteration 1116 (2.00415 iter/s, 5.98756s/12 iters), loss = 4.46136
I0409 21:32:27.713642 25438 solver.cpp:237]     Train net output #0: loss = 4.46136 (* 1 = 4.46136 loss)
I0409 21:32:27.713654 25438 sgd_solver.cpp:105] Iteration 1116, lr = 0.00801666
I0409 21:32:29.835170 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0409 21:32:34.373920 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0409 21:32:39.106603 25438 solver.cpp:330] Iteration 1122, Testing net (#0)
I0409 21:32:39.106628 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:32:43.314420 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:32:43.804561 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0465686
I0409 21:32:43.804598 25438 solver.cpp:397]     Test net output #1: loss = 4.86489 (* 1 = 4.86489 loss)
I0409 21:32:45.756865 25438 solver.cpp:218] Iteration 1128 (0.665089 iter/s, 18.0427s/12 iters), loss = 4.58645
I0409 21:32:45.756915 25438 solver.cpp:237]     Train net output #0: loss = 4.58645 (* 1 = 4.58645 loss)
I0409 21:32:45.756923 25438 sgd_solver.cpp:105] Iteration 1128, lr = 0.00799762
I0409 21:32:51.013768 25438 solver.cpp:218] Iteration 1140 (2.28281 iter/s, 5.25668s/12 iters), loss = 4.33047
I0409 21:32:51.013984 25438 solver.cpp:237]     Train net output #0: loss = 4.33047 (* 1 = 4.33047 loss)
I0409 21:32:51.014001 25438 sgd_solver.cpp:105] Iteration 1140, lr = 0.00797863
I0409 21:32:56.434160 25438 solver.cpp:218] Iteration 1152 (2.214 iter/s, 5.42004s/12 iters), loss = 4.14849
I0409 21:32:56.434212 25438 solver.cpp:237]     Train net output #0: loss = 4.14849 (* 1 = 4.14849 loss)
I0409 21:32:56.434222 25438 sgd_solver.cpp:105] Iteration 1152, lr = 0.00795969
I0409 21:33:01.772711 25438 solver.cpp:218] Iteration 1164 (2.24789 iter/s, 5.33833s/12 iters), loss = 4.21476
I0409 21:33:01.772768 25438 solver.cpp:237]     Train net output #0: loss = 4.21476 (* 1 = 4.21476 loss)
I0409 21:33:01.772779 25438 sgd_solver.cpp:105] Iteration 1164, lr = 0.00794079
I0409 21:33:06.922539 25438 solver.cpp:218] Iteration 1176 (2.33027 iter/s, 5.14962s/12 iters), loss = 4.24975
I0409 21:33:06.922585 25438 solver.cpp:237]     Train net output #0: loss = 4.24975 (* 1 = 4.24975 loss)
I0409 21:33:06.922595 25438 sgd_solver.cpp:105] Iteration 1176, lr = 0.00792194
I0409 21:33:12.595012 25438 solver.cpp:218] Iteration 1188 (2.11557 iter/s, 5.67224s/12 iters), loss = 4.3399
I0409 21:33:12.595074 25438 solver.cpp:237]     Train net output #0: loss = 4.3399 (* 1 = 4.3399 loss)
I0409 21:33:12.595088 25438 sgd_solver.cpp:105] Iteration 1188, lr = 0.00790313
I0409 21:33:17.949337 25438 solver.cpp:218] Iteration 1200 (2.24127 iter/s, 5.3541s/12 iters), loss = 4.30012
I0409 21:33:17.949371 25438 solver.cpp:237]     Train net output #0: loss = 4.30012 (* 1 = 4.30012 loss)
I0409 21:33:17.949383 25438 sgd_solver.cpp:105] Iteration 1200, lr = 0.00788437
I0409 21:33:23.212236 25438 solver.cpp:218] Iteration 1212 (2.2802 iter/s, 5.2627s/12 iters), loss = 4.51297
I0409 21:33:23.212322 25438 solver.cpp:237]     Train net output #0: loss = 4.51297 (* 1 = 4.51297 loss)
I0409 21:33:23.212332 25438 sgd_solver.cpp:105] Iteration 1212, lr = 0.00786565
I0409 21:33:23.498864 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:33:28.080314 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0409 21:33:32.243335 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0409 21:33:36.883605 25438 solver.cpp:330] Iteration 1224, Testing net (#0)
I0409 21:33:36.883626 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:33:41.352589 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:33:41.875573 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0698529
I0409 21:33:41.875612 25438 solver.cpp:397]     Test net output #1: loss = 4.67926 (* 1 = 4.67926 loss)
I0409 21:33:41.973855 25438 solver.cpp:218] Iteration 1224 (0.639625 iter/s, 18.761s/12 iters), loss = 4.35561
I0409 21:33:41.973917 25438 solver.cpp:237]     Train net output #0: loss = 4.35561 (* 1 = 4.35561 loss)
I0409 21:33:41.973927 25438 sgd_solver.cpp:105] Iteration 1224, lr = 0.00784697
I0409 21:33:46.942031 25438 solver.cpp:218] Iteration 1236 (2.41548 iter/s, 4.96796s/12 iters), loss = 4.35493
I0409 21:33:46.942090 25438 solver.cpp:237]     Train net output #0: loss = 4.35493 (* 1 = 4.35493 loss)
I0409 21:33:46.942101 25438 sgd_solver.cpp:105] Iteration 1236, lr = 0.00782834
I0409 21:33:53.083657 25438 solver.cpp:218] Iteration 1248 (1.95396 iter/s, 6.14137s/12 iters), loss = 4.19276
I0409 21:33:53.083706 25438 solver.cpp:237]     Train net output #0: loss = 4.19276 (* 1 = 4.19276 loss)
I0409 21:33:53.083715 25438 sgd_solver.cpp:105] Iteration 1248, lr = 0.00780976
I0409 21:33:58.596593 25438 solver.cpp:218] Iteration 1260 (2.17679 iter/s, 5.51271s/12 iters), loss = 4.41401
I0409 21:33:58.596712 25438 solver.cpp:237]     Train net output #0: loss = 4.41401 (* 1 = 4.41401 loss)
I0409 21:33:58.596724 25438 sgd_solver.cpp:105] Iteration 1260, lr = 0.00779122
I0409 21:34:03.781430 25438 solver.cpp:218] Iteration 1272 (2.31457 iter/s, 5.18456s/12 iters), loss = 4.14855
I0409 21:34:03.781486 25438 solver.cpp:237]     Train net output #0: loss = 4.14855 (* 1 = 4.14855 loss)
I0409 21:34:03.781497 25438 sgd_solver.cpp:105] Iteration 1272, lr = 0.00777272
I0409 21:34:08.950091 25438 solver.cpp:218] Iteration 1284 (2.32178 iter/s, 5.16844s/12 iters), loss = 4.43516
I0409 21:34:08.950158 25438 solver.cpp:237]     Train net output #0: loss = 4.43516 (* 1 = 4.43516 loss)
I0409 21:34:08.950170 25438 sgd_solver.cpp:105] Iteration 1284, lr = 0.00775426
I0409 21:34:14.051607 25438 solver.cpp:218] Iteration 1296 (2.35234 iter/s, 5.1013s/12 iters), loss = 4.17034
I0409 21:34:14.051653 25438 solver.cpp:237]     Train net output #0: loss = 4.17034 (* 1 = 4.17034 loss)
I0409 21:34:14.051663 25438 sgd_solver.cpp:105] Iteration 1296, lr = 0.00773585
I0409 21:34:19.183355 25438 solver.cpp:218] Iteration 1308 (2.33848 iter/s, 5.13154s/12 iters), loss = 4.18148
I0409 21:34:19.183414 25438 solver.cpp:237]     Train net output #0: loss = 4.18148 (* 1 = 4.18148 loss)
I0409 21:34:19.183425 25438 sgd_solver.cpp:105] Iteration 1308, lr = 0.00771749
I0409 21:34:22.028129 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:34:24.707069 25438 solver.cpp:218] Iteration 1320 (2.17254 iter/s, 5.52348s/12 iters), loss = 4.30717
I0409 21:34:24.707116 25438 solver.cpp:237]     Train net output #0: loss = 4.30717 (* 1 = 4.30717 loss)
I0409 21:34:24.707124 25438 sgd_solver.cpp:105] Iteration 1320, lr = 0.00769916
I0409 21:34:26.990522 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0409 21:34:38.500957 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0409 21:34:46.076709 25438 solver.cpp:330] Iteration 1326, Testing net (#0)
I0409 21:34:46.076737 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:34:50.346366 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:34:50.977460 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0667892
I0409 21:34:50.977492 25438 solver.cpp:397]     Test net output #1: loss = 4.72261 (* 1 = 4.72261 loss)
I0409 21:34:52.875332 25438 solver.cpp:218] Iteration 1332 (0.426024 iter/s, 28.1674s/12 iters), loss = 4.19385
I0409 21:34:52.875396 25438 solver.cpp:237]     Train net output #0: loss = 4.19385 (* 1 = 4.19385 loss)
I0409 21:34:52.875408 25438 sgd_solver.cpp:105] Iteration 1332, lr = 0.00768088
I0409 21:34:58.050122 25438 solver.cpp:218] Iteration 1344 (2.31904 iter/s, 5.17456s/12 iters), loss = 4.13223
I0409 21:34:58.050173 25438 solver.cpp:237]     Train net output #0: loss = 4.13223 (* 1 = 4.13223 loss)
I0409 21:34:58.050182 25438 sgd_solver.cpp:105] Iteration 1344, lr = 0.00766265
I0409 21:35:04.481127 25438 solver.cpp:218] Iteration 1356 (1.86603 iter/s, 6.43075s/12 iters), loss = 4.35532
I0409 21:35:04.481189 25438 solver.cpp:237]     Train net output #0: loss = 4.35532 (* 1 = 4.35532 loss)
I0409 21:35:04.481201 25438 sgd_solver.cpp:105] Iteration 1356, lr = 0.00764446
I0409 21:35:09.921419 25438 solver.cpp:218] Iteration 1368 (2.20586 iter/s, 5.44007s/12 iters), loss = 4.34686
I0409 21:35:09.921525 25438 solver.cpp:237]     Train net output #0: loss = 4.34686 (* 1 = 4.34686 loss)
I0409 21:35:09.921535 25438 sgd_solver.cpp:105] Iteration 1368, lr = 0.00762631
I0409 21:35:11.180132 25438 blocking_queue.cpp:49] Waiting for data
I0409 21:35:15.179831 25438 solver.cpp:218] Iteration 1380 (2.28218 iter/s, 5.25814s/12 iters), loss = 3.97661
I0409 21:35:15.179888 25438 solver.cpp:237]     Train net output #0: loss = 3.97661 (* 1 = 3.97661 loss)
I0409 21:35:15.179899 25438 sgd_solver.cpp:105] Iteration 1380, lr = 0.0076082
I0409 21:35:20.319726 25438 solver.cpp:218] Iteration 1392 (2.33478 iter/s, 5.13968s/12 iters), loss = 4.22347
I0409 21:35:20.319785 25438 solver.cpp:237]     Train net output #0: loss = 4.22347 (* 1 = 4.22347 loss)
I0409 21:35:20.319797 25438 sgd_solver.cpp:105] Iteration 1392, lr = 0.00759014
I0409 21:35:25.437424 25438 solver.cpp:218] Iteration 1404 (2.34491 iter/s, 5.11748s/12 iters), loss = 4.0034
I0409 21:35:25.437481 25438 solver.cpp:237]     Train net output #0: loss = 4.0034 (* 1 = 4.0034 loss)
I0409 21:35:25.437494 25438 sgd_solver.cpp:105] Iteration 1404, lr = 0.00757212
I0409 21:35:30.169521 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:35:30.535586 25438 solver.cpp:218] Iteration 1416 (2.35389 iter/s, 5.09794s/12 iters), loss = 4.15324
I0409 21:35:30.535646 25438 solver.cpp:237]     Train net output #0: loss = 4.15324 (* 1 = 4.15324 loss)
I0409 21:35:30.535657 25438 sgd_solver.cpp:105] Iteration 1416, lr = 0.00755414
I0409 21:35:35.210122 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0409 21:35:44.387104 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0409 21:35:49.171072 25438 solver.cpp:330] Iteration 1428, Testing net (#0)
I0409 21:35:49.171097 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:35:53.332638 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:35:53.940343 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0625
I0409 21:35:53.940380 25438 solver.cpp:397]     Test net output #1: loss = 4.75861 (* 1 = 4.75861 loss)
I0409 21:35:54.037569 25438 solver.cpp:218] Iteration 1428 (0.510611 iter/s, 23.5012s/12 iters), loss = 4.3259
I0409 21:35:54.037627 25438 solver.cpp:237]     Train net output #0: loss = 4.3259 (* 1 = 4.3259 loss)
I0409 21:35:54.037638 25438 sgd_solver.cpp:105] Iteration 1428, lr = 0.0075362
I0409 21:35:58.679369 25438 solver.cpp:218] Iteration 1440 (2.58532 iter/s, 4.64159s/12 iters), loss = 4.0424
I0409 21:35:58.679421 25438 solver.cpp:237]     Train net output #0: loss = 4.0424 (* 1 = 4.0424 loss)
I0409 21:35:58.679432 25438 sgd_solver.cpp:105] Iteration 1440, lr = 0.00751831
I0409 21:36:04.604923 25438 solver.cpp:218] Iteration 1452 (2.02521 iter/s, 5.92532s/12 iters), loss = 4.17061
I0409 21:36:04.604970 25438 solver.cpp:237]     Train net output #0: loss = 4.17061 (* 1 = 4.17061 loss)
I0409 21:36:04.604979 25438 sgd_solver.cpp:105] Iteration 1452, lr = 0.00750046
I0409 21:36:10.379104 25438 solver.cpp:218] Iteration 1464 (2.0783 iter/s, 5.77395s/12 iters), loss = 3.97955
I0409 21:36:10.379165 25438 solver.cpp:237]     Train net output #0: loss = 3.97955 (* 1 = 3.97955 loss)
I0409 21:36:10.379175 25438 sgd_solver.cpp:105] Iteration 1464, lr = 0.00748265
I0409 21:36:15.576537 25438 solver.cpp:218] Iteration 1476 (2.30893 iter/s, 5.19721s/12 iters), loss = 4.36886
I0409 21:36:15.576627 25438 solver.cpp:237]     Train net output #0: loss = 4.36886 (* 1 = 4.36886 loss)
I0409 21:36:15.576637 25438 sgd_solver.cpp:105] Iteration 1476, lr = 0.00746489
I0409 21:36:22.172130 25438 solver.cpp:218] Iteration 1488 (1.81948 iter/s, 6.5953s/12 iters), loss = 3.74564
I0409 21:36:22.172192 25438 solver.cpp:237]     Train net output #0: loss = 3.74564 (* 1 = 3.74564 loss)
I0409 21:36:22.172205 25438 sgd_solver.cpp:105] Iteration 1488, lr = 0.00744716
I0409 21:36:27.298462 25438 solver.cpp:218] Iteration 1500 (2.34096 iter/s, 5.12611s/12 iters), loss = 3.72868
I0409 21:36:27.298517 25438 solver.cpp:237]     Train net output #0: loss = 3.72868 (* 1 = 3.72868 loss)
I0409 21:36:27.298528 25438 sgd_solver.cpp:105] Iteration 1500, lr = 0.00742948
I0409 21:36:32.572142 25438 solver.cpp:218] Iteration 1512 (2.27555 iter/s, 5.27346s/12 iters), loss = 3.81557
I0409 21:36:32.572197 25438 solver.cpp:237]     Train net output #0: loss = 3.81557 (* 1 = 3.81557 loss)
I0409 21:36:32.572208 25438 sgd_solver.cpp:105] Iteration 1512, lr = 0.00741184
I0409 21:36:34.596055 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:36:38.266079 25438 solver.cpp:218] Iteration 1524 (2.10759 iter/s, 5.6937s/12 iters), loss = 4.10134
I0409 21:36:38.266144 25438 solver.cpp:237]     Train net output #0: loss = 4.10134 (* 1 = 4.10134 loss)
I0409 21:36:38.266155 25438 sgd_solver.cpp:105] Iteration 1524, lr = 0.00739425
I0409 21:36:40.848613 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0409 21:36:51.338024 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0409 21:36:58.927076 25438 solver.cpp:330] Iteration 1530, Testing net (#0)
I0409 21:36:58.927101 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:37:02.720448 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:37:03.362728 25438 solver.cpp:397]     Test net output #0: accuracy = 0.064951
I0409 21:37:03.362776 25438 solver.cpp:397]     Test net output #1: loss = 4.73484 (* 1 = 4.73484 loss)
I0409 21:37:05.489327 25438 solver.cpp:218] Iteration 1536 (0.440813 iter/s, 27.2224s/12 iters), loss = 4.16092
I0409 21:37:05.489382 25438 solver.cpp:237]     Train net output #0: loss = 4.16092 (* 1 = 4.16092 loss)
I0409 21:37:05.489394 25438 sgd_solver.cpp:105] Iteration 1536, lr = 0.00737669
I0409 21:37:11.124406 25438 solver.cpp:218] Iteration 1548 (2.1296 iter/s, 5.63485s/12 iters), loss = 3.74097
I0409 21:37:11.124454 25438 solver.cpp:237]     Train net output #0: loss = 3.74097 (* 1 = 3.74097 loss)
I0409 21:37:11.124464 25438 sgd_solver.cpp:105] Iteration 1548, lr = 0.00735918
I0409 21:37:16.560586 25438 solver.cpp:218] Iteration 1560 (2.20752 iter/s, 5.43596s/12 iters), loss = 3.99681
I0409 21:37:16.560645 25438 solver.cpp:237]     Train net output #0: loss = 3.99681 (* 1 = 3.99681 loss)
I0409 21:37:16.560658 25438 sgd_solver.cpp:105] Iteration 1560, lr = 0.00734171
I0409 21:37:21.901327 25438 solver.cpp:218] Iteration 1572 (2.24698 iter/s, 5.34051s/12 iters), loss = 4.09053
I0409 21:37:21.901480 25438 solver.cpp:237]     Train net output #0: loss = 4.09053 (* 1 = 4.09053 loss)
I0409 21:37:21.901497 25438 sgd_solver.cpp:105] Iteration 1572, lr = 0.00732427
I0409 21:37:27.608064 25438 solver.cpp:218] Iteration 1584 (2.1029 iter/s, 5.70641s/12 iters), loss = 3.81404
I0409 21:37:27.608119 25438 solver.cpp:237]     Train net output #0: loss = 3.81404 (* 1 = 3.81404 loss)
I0409 21:37:27.608131 25438 sgd_solver.cpp:105] Iteration 1584, lr = 0.00730688
I0409 21:37:33.214114 25438 solver.cpp:218] Iteration 1596 (2.14063 iter/s, 5.60582s/12 iters), loss = 3.83797
I0409 21:37:33.214164 25438 solver.cpp:237]     Train net output #0: loss = 3.83797 (* 1 = 3.83797 loss)
I0409 21:37:33.214174 25438 sgd_solver.cpp:105] Iteration 1596, lr = 0.00728954
I0409 21:37:38.524960 25438 solver.cpp:218] Iteration 1608 (2.25962 iter/s, 5.31063s/12 iters), loss = 3.78675
I0409 21:37:38.525012 25438 solver.cpp:237]     Train net output #0: loss = 3.78675 (* 1 = 3.78675 loss)
I0409 21:37:38.525023 25438 sgd_solver.cpp:105] Iteration 1608, lr = 0.00727223
I0409 21:37:42.577757 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:37:43.782796 25438 solver.cpp:218] Iteration 1620 (2.2824 iter/s, 5.25762s/12 iters), loss = 3.75561
I0409 21:37:43.782845 25438 solver.cpp:237]     Train net output #0: loss = 3.75561 (* 1 = 3.75561 loss)
I0409 21:37:43.782855 25438 sgd_solver.cpp:105] Iteration 1620, lr = 0.00725496
I0409 21:37:48.871822 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0409 21:37:53.275418 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0409 21:37:58.210911 25438 solver.cpp:330] Iteration 1632, Testing net (#0)
I0409 21:37:58.210932 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:38:02.140513 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:38:02.826723 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0716912
I0409 21:38:02.826771 25438 solver.cpp:397]     Test net output #1: loss = 4.59777 (* 1 = 4.59777 loss)
I0409 21:38:02.924353 25438 solver.cpp:218] Iteration 1632 (0.626928 iter/s, 19.1409s/12 iters), loss = 3.97508
I0409 21:38:02.924403 25438 solver.cpp:237]     Train net output #0: loss = 3.97508 (* 1 = 3.97508 loss)
I0409 21:38:02.924413 25438 sgd_solver.cpp:105] Iteration 1632, lr = 0.00723774
I0409 21:38:07.341168 25438 solver.cpp:218] Iteration 1644 (2.71701 iter/s, 4.41662s/12 iters), loss = 3.74477
I0409 21:38:07.341221 25438 solver.cpp:237]     Train net output #0: loss = 3.74477 (* 1 = 3.74477 loss)
I0409 21:38:07.341233 25438 sgd_solver.cpp:105] Iteration 1644, lr = 0.00722056
I0409 21:38:12.796036 25438 solver.cpp:218] Iteration 1656 (2.19996 iter/s, 5.45464s/12 iters), loss = 3.9174
I0409 21:38:12.796082 25438 solver.cpp:237]     Train net output #0: loss = 3.9174 (* 1 = 3.9174 loss)
I0409 21:38:12.796092 25438 sgd_solver.cpp:105] Iteration 1656, lr = 0.00720341
I0409 21:38:18.167691 25438 solver.cpp:218] Iteration 1668 (2.23403 iter/s, 5.37145s/12 iters), loss = 3.30018
I0409 21:38:18.167728 25438 solver.cpp:237]     Train net output #0: loss = 3.30018 (* 1 = 3.30018 loss)
I0409 21:38:18.167739 25438 sgd_solver.cpp:105] Iteration 1668, lr = 0.00718631
I0409 21:38:23.257755 25438 solver.cpp:218] Iteration 1680 (2.35763 iter/s, 5.08987s/12 iters), loss = 3.82682
I0409 21:38:23.257807 25438 solver.cpp:237]     Train net output #0: loss = 3.82682 (* 1 = 3.82682 loss)
I0409 21:38:23.257819 25438 sgd_solver.cpp:105] Iteration 1680, lr = 0.00716925
I0409 21:38:28.356699 25438 solver.cpp:218] Iteration 1692 (2.35352 iter/s, 5.09874s/12 iters), loss = 3.53575
I0409 21:38:28.356837 25438 solver.cpp:237]     Train net output #0: loss = 3.53575 (* 1 = 3.53575 loss)
I0409 21:38:28.356849 25438 sgd_solver.cpp:105] Iteration 1692, lr = 0.00715223
I0409 21:38:33.520843 25438 solver.cpp:218] Iteration 1704 (2.32385 iter/s, 5.16385s/12 iters), loss = 3.33832
I0409 21:38:33.520885 25438 solver.cpp:237]     Train net output #0: loss = 3.33832 (* 1 = 3.33832 loss)
I0409 21:38:33.520895 25438 sgd_solver.cpp:105] Iteration 1704, lr = 0.00713525
I0409 21:38:38.865541 25438 solver.cpp:218] Iteration 1716 (2.24531 iter/s, 5.34448s/12 iters), loss = 3.65259
I0409 21:38:38.865589 25438 solver.cpp:237]     Train net output #0: loss = 3.65259 (* 1 = 3.65259 loss)
I0409 21:38:38.865600 25438 sgd_solver.cpp:105] Iteration 1716, lr = 0.00711831
I0409 21:38:40.000519 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:38:44.316962 25438 solver.cpp:218] Iteration 1728 (2.20135 iter/s, 5.4512s/12 iters), loss = 3.8425
I0409 21:38:44.317016 25438 solver.cpp:237]     Train net output #0: loss = 3.8425 (* 1 = 3.8425 loss)
I0409 21:38:44.317028 25438 sgd_solver.cpp:105] Iteration 1728, lr = 0.00710141
I0409 21:38:46.532681 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0409 21:38:51.028568 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0409 21:38:54.285925 25438 solver.cpp:330] Iteration 1734, Testing net (#0)
I0409 21:38:54.285951 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:38:58.059927 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:38:58.791268 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0778186
I0409 21:38:58.791386 25438 solver.cpp:397]     Test net output #1: loss = 4.58306 (* 1 = 4.58306 loss)
I0409 21:39:00.674361 25438 solver.cpp:218] Iteration 1740 (0.733637 iter/s, 16.3569s/12 iters), loss = 3.92972
I0409 21:39:00.674417 25438 solver.cpp:237]     Train net output #0: loss = 3.92972 (* 1 = 3.92972 loss)
I0409 21:39:00.674427 25438 sgd_solver.cpp:105] Iteration 1740, lr = 0.00708455
I0409 21:39:06.067034 25438 solver.cpp:218] Iteration 1752 (2.22533 iter/s, 5.39245s/12 iters), loss = 3.6135
I0409 21:39:06.067073 25438 solver.cpp:237]     Train net output #0: loss = 3.6135 (* 1 = 3.6135 loss)
I0409 21:39:06.067082 25438 sgd_solver.cpp:105] Iteration 1752, lr = 0.00706773
I0409 21:39:11.415067 25438 solver.cpp:218] Iteration 1764 (2.2439 iter/s, 5.34782s/12 iters), loss = 3.81568
I0409 21:39:11.415117 25438 solver.cpp:237]     Train net output #0: loss = 3.81568 (* 1 = 3.81568 loss)
I0409 21:39:11.415129 25438 sgd_solver.cpp:105] Iteration 1764, lr = 0.00705094
I0409 21:39:17.016275 25438 solver.cpp:218] Iteration 1776 (2.14248 iter/s, 5.60098s/12 iters), loss = 3.49678
I0409 21:39:17.016326 25438 solver.cpp:237]     Train net output #0: loss = 3.49678 (* 1 = 3.49678 loss)
I0409 21:39:17.016336 25438 sgd_solver.cpp:105] Iteration 1776, lr = 0.0070342
I0409 21:39:22.624775 25438 solver.cpp:218] Iteration 1788 (2.1397 iter/s, 5.60827s/12 iters), loss = 3.92716
I0409 21:39:22.624825 25438 solver.cpp:237]     Train net output #0: loss = 3.92716 (* 1 = 3.92716 loss)
I0409 21:39:22.624835 25438 sgd_solver.cpp:105] Iteration 1788, lr = 0.0070175
I0409 21:39:28.276484 25438 solver.cpp:218] Iteration 1800 (2.12334 iter/s, 5.65148s/12 iters), loss = 3.50045
I0409 21:39:28.276532 25438 solver.cpp:237]     Train net output #0: loss = 3.50045 (* 1 = 3.50045 loss)
I0409 21:39:28.276542 25438 sgd_solver.cpp:105] Iteration 1800, lr = 0.00700084
I0409 21:39:33.965988 25438 solver.cpp:218] Iteration 1812 (2.10923 iter/s, 5.68928s/12 iters), loss = 3.43049
I0409 21:39:33.966106 25438 solver.cpp:237]     Train net output #0: loss = 3.43049 (* 1 = 3.43049 loss)
I0409 21:39:33.966120 25438 sgd_solver.cpp:105] Iteration 1812, lr = 0.00698422
I0409 21:39:37.276386 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:39:39.275228 25438 solver.cpp:218] Iteration 1824 (2.26033 iter/s, 5.30895s/12 iters), loss = 3.45686
I0409 21:39:39.275277 25438 solver.cpp:237]     Train net output #0: loss = 3.45686 (* 1 = 3.45686 loss)
I0409 21:39:39.275288 25438 sgd_solver.cpp:105] Iteration 1824, lr = 0.00696764
I0409 21:39:44.363726 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0409 21:39:48.621395 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0409 21:39:56.096261 25438 solver.cpp:330] Iteration 1836, Testing net (#0)
I0409 21:39:56.096285 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:39:59.804052 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:40:00.689503 25438 solver.cpp:397]     Test net output #0: accuracy = 0.091299
I0409 21:40:00.689553 25438 solver.cpp:397]     Test net output #1: loss = 4.57263 (* 1 = 4.57263 loss)
I0409 21:40:00.787006 25438 solver.cpp:218] Iteration 1836 (0.557851 iter/s, 21.5111s/12 iters), loss = 3.4396
I0409 21:40:00.787058 25438 solver.cpp:237]     Train net output #0: loss = 3.4396 (* 1 = 3.4396 loss)
I0409 21:40:00.787070 25438 sgd_solver.cpp:105] Iteration 1836, lr = 0.0069511
I0409 21:40:05.313097 25438 solver.cpp:218] Iteration 1848 (2.65141 iter/s, 4.5259s/12 iters), loss = 3.46755
I0409 21:40:05.313199 25438 solver.cpp:237]     Train net output #0: loss = 3.46755 (* 1 = 3.46755 loss)
I0409 21:40:05.313210 25438 sgd_solver.cpp:105] Iteration 1848, lr = 0.00693459
I0409 21:40:10.985057 25438 solver.cpp:218] Iteration 1860 (2.11578 iter/s, 5.67168s/12 iters), loss = 3.36113
I0409 21:40:10.985105 25438 solver.cpp:237]     Train net output #0: loss = 3.36113 (* 1 = 3.36113 loss)
I0409 21:40:10.985113 25438 sgd_solver.cpp:105] Iteration 1860, lr = 0.00691813
I0409 21:40:16.191045 25438 solver.cpp:218] Iteration 1872 (2.30513 iter/s, 5.20577s/12 iters), loss = 3.08461
I0409 21:40:16.191109 25438 solver.cpp:237]     Train net output #0: loss = 3.08461 (* 1 = 3.08461 loss)
I0409 21:40:16.191123 25438 sgd_solver.cpp:105] Iteration 1872, lr = 0.0069017
I0409 21:40:21.352604 25438 solver.cpp:218] Iteration 1884 (2.32498 iter/s, 5.16133s/12 iters), loss = 3.45954
I0409 21:40:21.352655 25438 solver.cpp:237]     Train net output #0: loss = 3.45954 (* 1 = 3.45954 loss)
I0409 21:40:21.352666 25438 sgd_solver.cpp:105] Iteration 1884, lr = 0.00688532
I0409 21:40:26.697628 25438 solver.cpp:218] Iteration 1896 (2.24517 iter/s, 5.34481s/12 iters), loss = 3.5327
I0409 21:40:26.697686 25438 solver.cpp:237]     Train net output #0: loss = 3.5327 (* 1 = 3.5327 loss)
I0409 21:40:26.697697 25438 sgd_solver.cpp:105] Iteration 1896, lr = 0.00686897
I0409 21:40:32.241972 25438 solver.cpp:218] Iteration 1908 (2.16446 iter/s, 5.5441s/12 iters), loss = 3.38149
I0409 21:40:32.242023 25438 solver.cpp:237]     Train net output #0: loss = 3.38149 (* 1 = 3.38149 loss)
I0409 21:40:32.242036 25438 sgd_solver.cpp:105] Iteration 1908, lr = 0.00685266
I0409 21:40:37.546991 25438 solver.cpp:218] Iteration 1920 (2.2621 iter/s, 5.3048s/12 iters), loss = 3.78217
I0409 21:40:37.547129 25438 solver.cpp:237]     Train net output #0: loss = 3.78217 (* 1 = 3.78217 loss)
I0409 21:40:37.547142 25438 sgd_solver.cpp:105] Iteration 1920, lr = 0.00683639
I0409 21:40:37.871233 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:40:42.875242 25438 solver.cpp:218] Iteration 1932 (2.25227 iter/s, 5.32795s/12 iters), loss = 3.56344
I0409 21:40:42.875288 25438 solver.cpp:237]     Train net output #0: loss = 3.56344 (* 1 = 3.56344 loss)
I0409 21:40:42.875298 25438 sgd_solver.cpp:105] Iteration 1932, lr = 0.00682016
I0409 21:40:45.047968 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0409 21:40:49.316601 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0409 21:40:53.056247 25438 solver.cpp:330] Iteration 1938, Testing net (#0)
I0409 21:40:53.056273 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:40:56.812233 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:40:57.615262 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0876225
I0409 21:40:57.615309 25438 solver.cpp:397]     Test net output #1: loss = 4.50478 (* 1 = 4.50478 loss)
I0409 21:40:59.609261 25438 solver.cpp:218] Iteration 1944 (0.717125 iter/s, 16.7335s/12 iters), loss = 3.78397
I0409 21:40:59.609314 25438 solver.cpp:237]     Train net output #0: loss = 3.78397 (* 1 = 3.78397 loss)
I0409 21:40:59.609326 25438 sgd_solver.cpp:105] Iteration 1944, lr = 0.00680397
I0409 21:41:04.896745 25438 solver.cpp:218] Iteration 1956 (2.2696 iter/s, 5.28727s/12 iters), loss = 3.20584
I0409 21:41:04.896793 25438 solver.cpp:237]     Train net output #0: loss = 3.20584 (* 1 = 3.20584 loss)
I0409 21:41:04.896802 25438 sgd_solver.cpp:105] Iteration 1956, lr = 0.00678782
I0409 21:41:10.102563 25438 solver.cpp:218] Iteration 1968 (2.30521 iter/s, 5.20561s/12 iters), loss = 3.10971
I0409 21:41:10.102638 25438 solver.cpp:237]     Train net output #0: loss = 3.10971 (* 1 = 3.10971 loss)
I0409 21:41:10.102648 25438 sgd_solver.cpp:105] Iteration 1968, lr = 0.0067717
I0409 21:41:15.411188 25438 solver.cpp:218] Iteration 1980 (2.26057 iter/s, 5.30839s/12 iters), loss = 3.61946
I0409 21:41:15.411227 25438 solver.cpp:237]     Train net output #0: loss = 3.61946 (* 1 = 3.61946 loss)
I0409 21:41:15.411237 25438 sgd_solver.cpp:105] Iteration 1980, lr = 0.00675562
I0409 21:41:20.700851 25438 solver.cpp:218] Iteration 1992 (2.26866 iter/s, 5.28945s/12 iters), loss = 3.20945
I0409 21:41:20.700901 25438 solver.cpp:237]     Train net output #0: loss = 3.20945 (* 1 = 3.20945 loss)
I0409 21:41:20.700911 25438 sgd_solver.cpp:105] Iteration 1992, lr = 0.00673958
I0409 21:41:26.288306 25438 solver.cpp:218] Iteration 2004 (2.14776 iter/s, 5.58723s/12 iters), loss = 3.19792
I0409 21:41:26.288432 25438 solver.cpp:237]     Train net output #0: loss = 3.19792 (* 1 = 3.19792 loss)
I0409 21:41:26.288444 25438 sgd_solver.cpp:105] Iteration 2004, lr = 0.00672358
I0409 21:41:31.762502 25438 solver.cpp:218] Iteration 2016 (2.19222 iter/s, 5.4739s/12 iters), loss = 3.31183
I0409 21:41:31.762554 25438 solver.cpp:237]     Train net output #0: loss = 3.31183 (* 1 = 3.31183 loss)
I0409 21:41:31.762567 25438 sgd_solver.cpp:105] Iteration 2016, lr = 0.00670762
I0409 21:41:34.668013 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:41:37.264369 25438 solver.cpp:218] Iteration 2028 (2.18117 iter/s, 5.50165s/12 iters), loss = 3.3131
I0409 21:41:37.264411 25438 solver.cpp:237]     Train net output #0: loss = 3.3131 (* 1 = 3.3131 loss)
I0409 21:41:37.264420 25438 sgd_solver.cpp:105] Iteration 2028, lr = 0.00669169
I0409 21:41:41.900110 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0409 21:41:49.692940 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0409 21:41:53.591804 25438 solver.cpp:330] Iteration 2040, Testing net (#0)
I0409 21:41:53.591830 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:41:57.347451 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:41:58.252581 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0992647
I0409 21:41:58.252630 25438 solver.cpp:397]     Test net output #1: loss = 4.59465 (* 1 = 4.59465 loss)
I0409 21:41:58.350028 25438 solver.cpp:218] Iteration 2040 (0.569125 iter/s, 21.085s/12 iters), loss = 3.36374
I0409 21:41:58.350078 25438 solver.cpp:237]     Train net output #0: loss = 3.36374 (* 1 = 3.36374 loss)
I0409 21:41:58.350090 25438 sgd_solver.cpp:105] Iteration 2040, lr = 0.00667581
I0409 21:42:02.872748 25438 solver.cpp:218] Iteration 2052 (2.65338 iter/s, 4.52253s/12 iters), loss = 3.08963
I0409 21:42:02.872790 25438 solver.cpp:237]     Train net output #0: loss = 3.08963 (* 1 = 3.08963 loss)
I0409 21:42:02.872799 25438 sgd_solver.cpp:105] Iteration 2052, lr = 0.00665996
I0409 21:42:04.655687 25438 blocking_queue.cpp:49] Waiting for data
I0409 21:42:08.225034 25438 solver.cpp:218] Iteration 2064 (2.24212 iter/s, 5.35207s/12 iters), loss = 3.27518
I0409 21:42:08.225085 25438 solver.cpp:237]     Train net output #0: loss = 3.27518 (* 1 = 3.27518 loss)
I0409 21:42:08.225095 25438 sgd_solver.cpp:105] Iteration 2064, lr = 0.00664414
I0409 21:42:13.846858 25438 solver.cpp:218] Iteration 2076 (2.13462 iter/s, 5.6216s/12 iters), loss = 3.30236
I0409 21:42:13.846957 25438 solver.cpp:237]     Train net output #0: loss = 3.30236 (* 1 = 3.30236 loss)
I0409 21:42:13.846967 25438 sgd_solver.cpp:105] Iteration 2076, lr = 0.00662837
I0409 21:42:19.086199 25438 solver.cpp:218] Iteration 2088 (2.29048 iter/s, 5.23908s/12 iters), loss = 3.21237
I0409 21:42:19.086246 25438 solver.cpp:237]     Train net output #0: loss = 3.21237 (* 1 = 3.21237 loss)
I0409 21:42:19.086256 25438 sgd_solver.cpp:105] Iteration 2088, lr = 0.00661263
I0409 21:42:24.631578 25438 solver.cpp:218] Iteration 2100 (2.16405 iter/s, 5.54516s/12 iters), loss = 3.12685
I0409 21:42:24.631615 25438 solver.cpp:237]     Train net output #0: loss = 3.12685 (* 1 = 3.12685 loss)
I0409 21:42:24.631624 25438 sgd_solver.cpp:105] Iteration 2100, lr = 0.00659693
I0409 21:42:30.082589 25438 solver.cpp:218] Iteration 2112 (2.20151 iter/s, 5.4508s/12 iters), loss = 3.2157
I0409 21:42:30.082633 25438 solver.cpp:237]     Train net output #0: loss = 3.2157 (* 1 = 3.2157 loss)
I0409 21:42:30.082643 25438 sgd_solver.cpp:105] Iteration 2112, lr = 0.00658127
I0409 21:42:34.882413 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:42:35.207700 25438 solver.cpp:218] Iteration 2124 (2.3415 iter/s, 5.12491s/12 iters), loss = 2.94083
I0409 21:42:35.207742 25438 solver.cpp:237]     Train net output #0: loss = 2.94083 (* 1 = 2.94083 loss)
I0409 21:42:35.207752 25438 sgd_solver.cpp:105] Iteration 2124, lr = 0.00656564
I0409 21:42:40.753985 25438 solver.cpp:218] Iteration 2136 (2.1637 iter/s, 5.54606s/12 iters), loss = 3.47946
I0409 21:42:40.754034 25438 solver.cpp:237]     Train net output #0: loss = 3.47946 (* 1 = 3.47946 loss)
I0409 21:42:40.754045 25438 sgd_solver.cpp:105] Iteration 2136, lr = 0.00655006
I0409 21:42:43.060098 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0409 21:42:49.796365 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0409 21:42:56.977121 25438 solver.cpp:330] Iteration 2142, Testing net (#0)
I0409 21:42:56.977147 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:43:00.612751 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:43:01.485591 25438 solver.cpp:397]     Test net output #0: accuracy = 0.091299
I0409 21:43:01.485637 25438 solver.cpp:397]     Test net output #1: loss = 4.70609 (* 1 = 4.70609 loss)
I0409 21:43:03.396366 25438 solver.cpp:218] Iteration 2148 (0.529996 iter/s, 22.6417s/12 iters), loss = 3.24004
I0409 21:43:03.396418 25438 solver.cpp:237]     Train net output #0: loss = 3.24004 (* 1 = 3.24004 loss)
I0409 21:43:03.396430 25438 sgd_solver.cpp:105] Iteration 2148, lr = 0.00653451
I0409 21:43:08.442445 25438 solver.cpp:218] Iteration 2160 (2.37818 iter/s, 5.04587s/12 iters), loss = 3.15266
I0409 21:43:08.442507 25438 solver.cpp:237]     Train net output #0: loss = 3.15266 (* 1 = 3.15266 loss)
I0409 21:43:08.442519 25438 sgd_solver.cpp:105] Iteration 2160, lr = 0.00651899
I0409 21:43:13.642689 25438 solver.cpp:218] Iteration 2172 (2.30768 iter/s, 5.20002s/12 iters), loss = 3.22353
I0409 21:43:13.642748 25438 solver.cpp:237]     Train net output #0: loss = 3.22353 (* 1 = 3.22353 loss)
I0409 21:43:13.642760 25438 sgd_solver.cpp:105] Iteration 2172, lr = 0.00650351
I0409 21:43:18.867638 25438 solver.cpp:218] Iteration 2184 (2.29677 iter/s, 5.22472s/12 iters), loss = 3.26811
I0409 21:43:18.867697 25438 solver.cpp:237]     Train net output #0: loss = 3.26811 (* 1 = 3.26811 loss)
I0409 21:43:18.867709 25438 sgd_solver.cpp:105] Iteration 2184, lr = 0.00648807
I0409 21:43:24.493759 25438 solver.cpp:218] Iteration 2196 (2.133 iter/s, 5.62589s/12 iters), loss = 3.09478
I0409 21:43:24.493908 25438 solver.cpp:237]     Train net output #0: loss = 3.09478 (* 1 = 3.09478 loss)
I0409 21:43:24.493922 25438 sgd_solver.cpp:105] Iteration 2196, lr = 0.00647267
I0409 21:43:29.824926 25438 solver.cpp:218] Iteration 2208 (2.25105 iter/s, 5.33085s/12 iters), loss = 3.03197
I0409 21:43:29.824976 25438 solver.cpp:237]     Train net output #0: loss = 3.03197 (* 1 = 3.03197 loss)
I0409 21:43:29.824987 25438 sgd_solver.cpp:105] Iteration 2208, lr = 0.0064573
I0409 21:43:35.113313 25438 solver.cpp:218] Iteration 2220 (2.26922 iter/s, 5.28817s/12 iters), loss = 2.92491
I0409 21:43:35.113365 25438 solver.cpp:237]     Train net output #0: loss = 2.92491 (* 1 = 2.92491 loss)
I0409 21:43:35.113376 25438 sgd_solver.cpp:105] Iteration 2220, lr = 0.00644197
I0409 21:43:37.043774 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:43:40.416721 25438 solver.cpp:218] Iteration 2232 (2.26279 iter/s, 5.30319s/12 iters), loss = 3.15877
I0409 21:43:40.416769 25438 solver.cpp:237]     Train net output #0: loss = 3.15877 (* 1 = 3.15877 loss)
I0409 21:43:40.416780 25438 sgd_solver.cpp:105] Iteration 2232, lr = 0.00642668
I0409 21:43:45.227581 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0409 21:43:49.439582 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0409 21:43:53.825495 25438 solver.cpp:330] Iteration 2244, Testing net (#0)
I0409 21:43:53.825522 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:43:57.405385 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:43:58.322981 25438 solver.cpp:397]     Test net output #0: accuracy = 0.101103
I0409 21:43:58.323086 25438 solver.cpp:397]     Test net output #1: loss = 4.75608 (* 1 = 4.75608 loss)
I0409 21:43:58.420441 25438 solver.cpp:218] Iteration 2244 (0.66655 iter/s, 18.0031s/12 iters), loss = 3.35611
I0409 21:43:58.420485 25438 solver.cpp:237]     Train net output #0: loss = 3.35611 (* 1 = 3.35611 loss)
I0409 21:43:58.420496 25438 sgd_solver.cpp:105] Iteration 2244, lr = 0.00641142
I0409 21:44:03.033354 25438 solver.cpp:218] Iteration 2256 (2.6015 iter/s, 4.61273s/12 iters), loss = 2.66792
I0409 21:44:03.033397 25438 solver.cpp:237]     Train net output #0: loss = 2.66792 (* 1 = 2.66792 loss)
I0409 21:44:03.033406 25438 sgd_solver.cpp:105] Iteration 2256, lr = 0.0063962
I0409 21:44:08.621381 25438 solver.cpp:218] Iteration 2268 (2.14753 iter/s, 5.58781s/12 iters), loss = 2.63094
I0409 21:44:08.621428 25438 solver.cpp:237]     Train net output #0: loss = 2.63094 (* 1 = 2.63094 loss)
I0409 21:44:08.621438 25438 sgd_solver.cpp:105] Iteration 2268, lr = 0.00638101
I0409 21:44:14.230087 25438 solver.cpp:218] Iteration 2280 (2.13962 iter/s, 5.60848s/12 iters), loss = 3.05451
I0409 21:44:14.230144 25438 solver.cpp:237]     Train net output #0: loss = 3.05451 (* 1 = 3.05451 loss)
I0409 21:44:14.230154 25438 sgd_solver.cpp:105] Iteration 2280, lr = 0.00636586
I0409 21:44:19.656962 25438 solver.cpp:218] Iteration 2292 (2.21131 iter/s, 5.42665s/12 iters), loss = 3.36061
I0409 21:44:19.657013 25438 solver.cpp:237]     Train net output #0: loss = 3.36061 (* 1 = 3.36061 loss)
I0409 21:44:19.657023 25438 sgd_solver.cpp:105] Iteration 2292, lr = 0.00635075
I0409 21:44:25.448755 25438 solver.cpp:218] Iteration 2304 (2.07198 iter/s, 5.79156s/12 iters), loss = 3.26071
I0409 21:44:25.448788 25438 solver.cpp:237]     Train net output #0: loss = 3.26071 (* 1 = 3.26071 loss)
I0409 21:44:25.448796 25438 sgd_solver.cpp:105] Iteration 2304, lr = 0.00633567
I0409 21:44:30.707471 25438 solver.cpp:218] Iteration 2316 (2.28201 iter/s, 5.25852s/12 iters), loss = 3.01936
I0409 21:44:30.707576 25438 solver.cpp:237]     Train net output #0: loss = 3.01936 (* 1 = 3.01936 loss)
I0409 21:44:30.707587 25438 sgd_solver.cpp:105] Iteration 2316, lr = 0.00632063
I0409 21:44:35.197418 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:44:36.391124 25438 solver.cpp:218] Iteration 2328 (2.11142 iter/s, 5.68337s/12 iters), loss = 2.8295
I0409 21:44:36.391175 25438 solver.cpp:237]     Train net output #0: loss = 2.8295 (* 1 = 2.8295 loss)
I0409 21:44:36.391187 25438 sgd_solver.cpp:105] Iteration 2328, lr = 0.00630562
I0409 21:44:41.969424 25438 solver.cpp:218] Iteration 2340 (2.15128 iter/s, 5.57808s/12 iters), loss = 3.08496
I0409 21:44:41.969460 25438 solver.cpp:237]     Train net output #0: loss = 3.08496 (* 1 = 3.08496 loss)
I0409 21:44:41.969468 25438 sgd_solver.cpp:105] Iteration 2340, lr = 0.00629065
I0409 21:44:44.241158 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0409 21:44:48.525110 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0409 21:44:56.090816 25438 solver.cpp:330] Iteration 2346, Testing net (#0)
I0409 21:44:56.090840 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:44:59.864814 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:45:00.953372 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0845588
I0409 21:45:00.953431 25438 solver.cpp:397]     Test net output #1: loss = 4.92914 (* 1 = 4.92914 loss)
I0409 21:45:02.918031 25438 solver.cpp:218] Iteration 2352 (0.572848 iter/s, 20.948s/12 iters), loss = 2.94143
I0409 21:45:02.918089 25438 solver.cpp:237]     Train net output #0: loss = 2.94143 (* 1 = 2.94143 loss)
I0409 21:45:02.918102 25438 sgd_solver.cpp:105] Iteration 2352, lr = 0.00627571
I0409 21:45:08.701275 25438 solver.cpp:218] Iteration 2364 (2.07505 iter/s, 5.783s/12 iters), loss = 3.10683
I0409 21:45:08.701330 25438 solver.cpp:237]     Train net output #0: loss = 3.10683 (* 1 = 3.10683 loss)
I0409 21:45:08.701341 25438 sgd_solver.cpp:105] Iteration 2364, lr = 0.00626081
I0409 21:45:14.375418 25438 solver.cpp:218] Iteration 2376 (2.11494 iter/s, 5.67391s/12 iters), loss = 2.66875
I0409 21:45:14.375468 25438 solver.cpp:237]     Train net output #0: loss = 2.66875 (* 1 = 2.66875 loss)
I0409 21:45:14.375478 25438 sgd_solver.cpp:105] Iteration 2376, lr = 0.00624595
I0409 21:45:19.757936 25438 solver.cpp:218] Iteration 2388 (2.22953 iter/s, 5.3823s/12 iters), loss = 3.09525
I0409 21:45:19.758006 25438 solver.cpp:237]     Train net output #0: loss = 3.09525 (* 1 = 3.09525 loss)
I0409 21:45:19.758018 25438 sgd_solver.cpp:105] Iteration 2388, lr = 0.00623112
I0409 21:45:25.124027 25438 solver.cpp:218] Iteration 2400 (2.23636 iter/s, 5.36585s/12 iters), loss = 2.75816
I0409 21:45:25.124076 25438 solver.cpp:237]     Train net output #0: loss = 2.75816 (* 1 = 2.75816 loss)
I0409 21:45:25.124086 25438 sgd_solver.cpp:105] Iteration 2400, lr = 0.00621633
I0409 21:45:30.550540 25438 solver.cpp:218] Iteration 2412 (2.21145 iter/s, 5.4263s/12 iters), loss = 2.87545
I0409 21:45:30.550583 25438 solver.cpp:237]     Train net output #0: loss = 2.87545 (* 1 = 2.87545 loss)
I0409 21:45:30.550592 25438 sgd_solver.cpp:105] Iteration 2412, lr = 0.00620157
I0409 21:45:35.860180 25438 solver.cpp:218] Iteration 2424 (2.26013 iter/s, 5.30943s/12 iters), loss = 3.01398
I0409 21:45:35.860337 25438 solver.cpp:237]     Train net output #0: loss = 3.01398 (* 1 = 3.01398 loss)
I0409 21:45:35.860352 25438 sgd_solver.cpp:105] Iteration 2424, lr = 0.00618684
I0409 21:45:36.998566 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:45:41.182524 25438 solver.cpp:218] Iteration 2436 (2.25478 iter/s, 5.32202s/12 iters), loss = 2.4877
I0409 21:45:41.182579 25438 solver.cpp:237]     Train net output #0: loss = 2.4877 (* 1 = 2.4877 loss)
I0409 21:45:41.182590 25438 sgd_solver.cpp:105] Iteration 2436, lr = 0.00617215
I0409 21:45:45.851538 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0409 21:45:52.657389 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0409 21:45:57.485541 25438 solver.cpp:330] Iteration 2448, Testing net (#0)
I0409 21:45:57.485563 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:46:01.082351 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:46:02.070400 25438 solver.cpp:397]     Test net output #0: accuracy = 0.106618
I0409 21:46:02.070458 25438 solver.cpp:397]     Test net output #1: loss = 4.76142 (* 1 = 4.76142 loss)
I0409 21:46:02.167966 25438 solver.cpp:218] Iteration 2448 (0.571843 iter/s, 20.9848s/12 iters), loss = 2.86771
I0409 21:46:02.168025 25438 solver.cpp:237]     Train net output #0: loss = 2.86771 (* 1 = 2.86771 loss)
I0409 21:46:02.168036 25438 sgd_solver.cpp:105] Iteration 2448, lr = 0.0061575
I0409 21:46:07.042764 25438 solver.cpp:218] Iteration 2460 (2.46175 iter/s, 4.87459s/12 iters), loss = 2.66293
I0409 21:46:07.042876 25438 solver.cpp:237]     Train net output #0: loss = 2.66293 (* 1 = 2.66293 loss)
I0409 21:46:07.042886 25438 sgd_solver.cpp:105] Iteration 2460, lr = 0.00614288
I0409 21:46:12.688926 25438 solver.cpp:218] Iteration 2472 (2.12545 iter/s, 5.64587s/12 iters), loss = 2.59916
I0409 21:46:12.688985 25438 solver.cpp:237]     Train net output #0: loss = 2.59916 (* 1 = 2.59916 loss)
I0409 21:46:12.688998 25438 sgd_solver.cpp:105] Iteration 2472, lr = 0.0061283
I0409 21:46:18.218353 25438 solver.cpp:218] Iteration 2484 (2.1703 iter/s, 5.52919s/12 iters), loss = 2.92294
I0409 21:46:18.218397 25438 solver.cpp:237]     Train net output #0: loss = 2.92294 (* 1 = 2.92294 loss)
I0409 21:46:18.218406 25438 sgd_solver.cpp:105] Iteration 2484, lr = 0.00611375
I0409 21:46:23.747165 25438 solver.cpp:218] Iteration 2496 (2.17054 iter/s, 5.52859s/12 iters), loss = 3.0077
I0409 21:46:23.747221 25438 solver.cpp:237]     Train net output #0: loss = 3.0077 (* 1 = 3.0077 loss)
I0409 21:46:23.747232 25438 sgd_solver.cpp:105] Iteration 2496, lr = 0.00609923
I0409 21:46:29.021507 25438 solver.cpp:218] Iteration 2508 (2.27526 iter/s, 5.27412s/12 iters), loss = 2.92512
I0409 21:46:29.021559 25438 solver.cpp:237]     Train net output #0: loss = 2.92512 (* 1 = 2.92512 loss)
I0409 21:46:29.021569 25438 sgd_solver.cpp:105] Iteration 2508, lr = 0.00608475
I0409 21:46:34.520498 25438 solver.cpp:218] Iteration 2520 (2.18231 iter/s, 5.49877s/12 iters), loss = 2.70981
I0409 21:46:34.520545 25438 solver.cpp:237]     Train net output #0: loss = 2.70981 (* 1 = 2.70981 loss)
I0409 21:46:34.520558 25438 sgd_solver.cpp:105] Iteration 2520, lr = 0.0060703
I0409 21:46:38.068229 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:46:39.911339 25438 solver.cpp:218] Iteration 2532 (2.22609 iter/s, 5.39062s/12 iters), loss = 2.63078
I0409 21:46:39.911396 25438 solver.cpp:237]     Train net output #0: loss = 2.63078 (* 1 = 2.63078 loss)
I0409 21:46:39.911407 25438 sgd_solver.cpp:105] Iteration 2532, lr = 0.00605589
I0409 21:46:45.478087 25438 solver.cpp:218] Iteration 2544 (2.15575 iter/s, 5.56652s/12 iters), loss = 2.69269
I0409 21:46:45.478137 25438 solver.cpp:237]     Train net output #0: loss = 2.69269 (* 1 = 2.69269 loss)
I0409 21:46:45.478147 25438 sgd_solver.cpp:105] Iteration 2544, lr = 0.00604151
I0409 21:46:47.681720 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0409 21:46:51.920821 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0409 21:46:56.722885 25438 solver.cpp:330] Iteration 2550, Testing net (#0)
I0409 21:46:56.722910 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:47:00.108111 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:47:01.142582 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0974265
I0409 21:47:01.142627 25438 solver.cpp:397]     Test net output #1: loss = 4.897 (* 1 = 4.897 loss)
I0409 21:47:03.049710 25438 solver.cpp:218] Iteration 2556 (0.682941 iter/s, 17.5711s/12 iters), loss = 2.9124
I0409 21:47:03.049764 25438 solver.cpp:237]     Train net output #0: loss = 2.9124 (* 1 = 2.9124 loss)
I0409 21:47:03.049775 25438 sgd_solver.cpp:105] Iteration 2556, lr = 0.00602717
I0409 21:47:08.481061 25438 solver.cpp:218] Iteration 2568 (2.20949 iter/s, 5.43113s/12 iters), loss = 2.62822
I0409 21:47:08.481155 25438 solver.cpp:237]     Train net output #0: loss = 2.62822 (* 1 = 2.62822 loss)
I0409 21:47:08.481165 25438 sgd_solver.cpp:105] Iteration 2568, lr = 0.00601286
I0409 21:47:14.190584 25438 solver.cpp:218] Iteration 2580 (2.10185 iter/s, 5.70925s/12 iters), loss = 2.55138
I0409 21:47:14.190629 25438 solver.cpp:237]     Train net output #0: loss = 2.55138 (* 1 = 2.55138 loss)
I0409 21:47:14.190639 25438 sgd_solver.cpp:105] Iteration 2580, lr = 0.00599858
I0409 21:47:19.648986 25438 solver.cpp:218] Iteration 2592 (2.19854 iter/s, 5.45818s/12 iters), loss = 3.03877
I0409 21:47:19.649045 25438 solver.cpp:237]     Train net output #0: loss = 3.03877 (* 1 = 3.03877 loss)
I0409 21:47:19.649056 25438 sgd_solver.cpp:105] Iteration 2592, lr = 0.00598434
I0409 21:47:25.133919 25438 solver.cpp:218] Iteration 2604 (2.18791 iter/s, 5.48469s/12 iters), loss = 3.04309
I0409 21:47:25.133996 25438 solver.cpp:237]     Train net output #0: loss = 3.04309 (* 1 = 3.04309 loss)
I0409 21:47:25.134011 25438 sgd_solver.cpp:105] Iteration 2604, lr = 0.00597013
I0409 21:47:30.651214 25438 solver.cpp:218] Iteration 2616 (2.17508 iter/s, 5.51705s/12 iters), loss = 3.01009
I0409 21:47:30.651268 25438 solver.cpp:237]     Train net output #0: loss = 3.01009 (* 1 = 3.01009 loss)
I0409 21:47:30.651279 25438 sgd_solver.cpp:105] Iteration 2616, lr = 0.00595596
I0409 21:47:35.957830 25438 solver.cpp:218] Iteration 2628 (2.26142 iter/s, 5.30639s/12 iters), loss = 2.53638
I0409 21:47:35.957880 25438 solver.cpp:237]     Train net output #0: loss = 2.53638 (* 1 = 2.53638 loss)
I0409 21:47:35.957891 25438 sgd_solver.cpp:105] Iteration 2628, lr = 0.00594182
I0409 21:47:36.423686 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:47:41.427201 25438 solver.cpp:218] Iteration 2640 (2.19413 iter/s, 5.46915s/12 iters), loss = 2.58555
I0409 21:47:41.427309 25438 solver.cpp:237]     Train net output #0: loss = 2.58555 (* 1 = 2.58555 loss)
I0409 21:47:41.427323 25438 sgd_solver.cpp:105] Iteration 2640, lr = 0.00592771
I0409 21:47:46.260615 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0409 21:47:50.436141 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0409 21:47:55.941278 25438 solver.cpp:330] Iteration 2652, Testing net (#0)
I0409 21:47:55.941305 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:47:59.414463 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:48:00.483757 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0906863
I0409 21:48:00.483800 25438 solver.cpp:397]     Test net output #1: loss = 5.05966 (* 1 = 5.05966 loss)
I0409 21:48:00.581259 25438 solver.cpp:218] Iteration 2652 (0.626521 iter/s, 19.1534s/12 iters), loss = 2.78373
I0409 21:48:00.581305 25438 solver.cpp:237]     Train net output #0: loss = 2.78373 (* 1 = 2.78373 loss)
I0409 21:48:00.581317 25438 sgd_solver.cpp:105] Iteration 2652, lr = 0.00591364
I0409 21:48:04.999045 25438 solver.cpp:218] Iteration 2664 (2.71641 iter/s, 4.4176s/12 iters), loss = 2.33488
I0409 21:48:04.999094 25438 solver.cpp:237]     Train net output #0: loss = 2.33488 (* 1 = 2.33488 loss)
I0409 21:48:04.999104 25438 sgd_solver.cpp:105] Iteration 2664, lr = 0.0058996
I0409 21:48:10.233225 25438 solver.cpp:218] Iteration 2676 (2.29272 iter/s, 5.23396s/12 iters), loss = 2.51875
I0409 21:48:10.233278 25438 solver.cpp:237]     Train net output #0: loss = 2.51875 (* 1 = 2.51875 loss)
I0409 21:48:10.233289 25438 sgd_solver.cpp:105] Iteration 2676, lr = 0.00588559
I0409 21:48:15.572520 25438 solver.cpp:218] Iteration 2688 (2.24758 iter/s, 5.33908s/12 iters), loss = 2.75135
I0409 21:48:15.572641 25438 solver.cpp:237]     Train net output #0: loss = 2.75135 (* 1 = 2.75135 loss)
I0409 21:48:15.572650 25438 sgd_solver.cpp:105] Iteration 2688, lr = 0.00587162
I0409 21:48:21.229110 25438 solver.cpp:218] Iteration 2700 (2.12153 iter/s, 5.65629s/12 iters), loss = 2.8303
I0409 21:48:21.229156 25438 solver.cpp:237]     Train net output #0: loss = 2.8303 (* 1 = 2.8303 loss)
I0409 21:48:21.229164 25438 sgd_solver.cpp:105] Iteration 2700, lr = 0.00585768
I0409 21:48:26.568501 25438 solver.cpp:218] Iteration 2712 (2.24754 iter/s, 5.33918s/12 iters), loss = 2.26531
I0409 21:48:26.568540 25438 solver.cpp:237]     Train net output #0: loss = 2.26531 (* 1 = 2.26531 loss)
I0409 21:48:26.568548 25438 sgd_solver.cpp:105] Iteration 2712, lr = 0.00584377
I0409 21:48:31.807132 25438 solver.cpp:218] Iteration 2724 (2.29077 iter/s, 5.23842s/12 iters), loss = 2.53341
I0409 21:48:31.807183 25438 solver.cpp:237]     Train net output #0: loss = 2.53341 (* 1 = 2.53341 loss)
I0409 21:48:31.807193 25438 sgd_solver.cpp:105] Iteration 2724, lr = 0.0058299
I0409 21:48:34.464705 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:48:37.036017 25438 solver.cpp:218] Iteration 2736 (2.29504 iter/s, 5.22867s/12 iters), loss = 2.39588
I0409 21:48:37.036072 25438 solver.cpp:237]     Train net output #0: loss = 2.39588 (* 1 = 2.39588 loss)
I0409 21:48:37.036083 25438 sgd_solver.cpp:105] Iteration 2736, lr = 0.00581605
I0409 21:48:42.643743 25438 solver.cpp:218] Iteration 2748 (2.13999 iter/s, 5.6075s/12 iters), loss = 2.40315
I0409 21:48:42.643790 25438 solver.cpp:237]     Train net output #0: loss = 2.40315 (* 1 = 2.40315 loss)
I0409 21:48:42.643801 25438 sgd_solver.cpp:105] Iteration 2748, lr = 0.00580225
I0409 21:48:44.866513 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0409 21:48:49.897049 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0409 21:48:56.121284 25438 solver.cpp:330] Iteration 2754, Testing net (#0)
I0409 21:48:56.121309 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:48:59.226919 25438 blocking_queue.cpp:49] Waiting for data
I0409 21:48:59.465932 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:49:00.593881 25438 solver.cpp:397]     Test net output #0: accuracy = 0.112745
I0409 21:49:00.593930 25438 solver.cpp:397]     Test net output #1: loss = 4.99255 (* 1 = 4.99255 loss)
I0409 21:49:02.658938 25438 solver.cpp:218] Iteration 2760 (0.599564 iter/s, 20.0146s/12 iters), loss = 2.15648
I0409 21:49:02.658985 25438 solver.cpp:237]     Train net output #0: loss = 2.15648 (* 1 = 2.15648 loss)
I0409 21:49:02.658994 25438 sgd_solver.cpp:105] Iteration 2760, lr = 0.00578847
I0409 21:49:08.095760 25438 solver.cpp:218] Iteration 2772 (2.20726 iter/s, 5.4366s/12 iters), loss = 2.16252
I0409 21:49:08.095806 25438 solver.cpp:237]     Train net output #0: loss = 2.16252 (* 1 = 2.16252 loss)
I0409 21:49:08.095815 25438 sgd_solver.cpp:105] Iteration 2772, lr = 0.00577473
I0409 21:49:13.795862 25438 solver.cpp:218] Iteration 2784 (2.10531 iter/s, 5.69988s/12 iters), loss = 2.06352
I0409 21:49:13.795902 25438 solver.cpp:237]     Train net output #0: loss = 2.06352 (* 1 = 2.06352 loss)
I0409 21:49:13.795910 25438 sgd_solver.cpp:105] Iteration 2784, lr = 0.00576102
I0409 21:49:19.132782 25438 solver.cpp:218] Iteration 2796 (2.24858 iter/s, 5.33671s/12 iters), loss = 3.26997
I0409 21:49:19.132829 25438 solver.cpp:237]     Train net output #0: loss = 3.26997 (* 1 = 3.26997 loss)
I0409 21:49:19.132838 25438 sgd_solver.cpp:105] Iteration 2796, lr = 0.00574734
I0409 21:49:24.266023 25438 solver.cpp:218] Iteration 2808 (2.3378 iter/s, 5.13302s/12 iters), loss = 2.4008
I0409 21:49:24.266160 25438 solver.cpp:237]     Train net output #0: loss = 2.4008 (* 1 = 2.4008 loss)
I0409 21:49:24.266171 25438 sgd_solver.cpp:105] Iteration 2808, lr = 0.00573369
I0409 21:49:29.768913 25438 solver.cpp:218] Iteration 2820 (2.1808 iter/s, 5.50258s/12 iters), loss = 2.65646
I0409 21:49:29.768970 25438 solver.cpp:237]     Train net output #0: loss = 2.65646 (* 1 = 2.65646 loss)
I0409 21:49:29.768981 25438 sgd_solver.cpp:105] Iteration 2820, lr = 0.00572008
I0409 21:49:35.092089 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:49:35.414315 25438 solver.cpp:218] Iteration 2832 (2.12571 iter/s, 5.64516s/12 iters), loss = 2.34781
I0409 21:49:35.414363 25438 solver.cpp:237]     Train net output #0: loss = 2.34781 (* 1 = 2.34781 loss)
I0409 21:49:35.414372 25438 sgd_solver.cpp:105] Iteration 2832, lr = 0.0057065
I0409 21:49:41.042603 25438 solver.cpp:218] Iteration 2844 (2.13217 iter/s, 5.62806s/12 iters), loss = 2.15378
I0409 21:49:41.042652 25438 solver.cpp:237]     Train net output #0: loss = 2.15378 (* 1 = 2.15378 loss)
I0409 21:49:41.042663 25438 sgd_solver.cpp:105] Iteration 2844, lr = 0.00569295
I0409 21:49:46.122257 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0409 21:49:53.368939 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0409 21:50:09.214120 25438 solver.cpp:330] Iteration 2856, Testing net (#0)
I0409 21:50:09.214179 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:50:12.697685 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:50:13.849658 25438 solver.cpp:397]     Test net output #0: accuracy = 0.0992647
I0409 21:50:13.849706 25438 solver.cpp:397]     Test net output #1: loss = 5.21711 (* 1 = 5.21711 loss)
I0409 21:50:13.946923 25438 solver.cpp:218] Iteration 2856 (0.364705 iter/s, 32.9033s/12 iters), loss = 2.49217
I0409 21:50:13.946971 25438 solver.cpp:237]     Train net output #0: loss = 2.49217 (* 1 = 2.49217 loss)
I0409 21:50:13.946979 25438 sgd_solver.cpp:105] Iteration 2856, lr = 0.00567944
I0409 21:50:18.348577 25438 solver.cpp:218] Iteration 2868 (2.72637 iter/s, 4.40146s/12 iters), loss = 2.27326
I0409 21:50:18.348616 25438 solver.cpp:237]     Train net output #0: loss = 2.27326 (* 1 = 2.27326 loss)
I0409 21:50:18.348624 25438 sgd_solver.cpp:105] Iteration 2868, lr = 0.00566595
I0409 21:50:23.945531 25438 solver.cpp:218] Iteration 2880 (2.14411 iter/s, 5.59673s/12 iters), loss = 2.34384
I0409 21:50:23.945582 25438 solver.cpp:237]     Train net output #0: loss = 2.34384 (* 1 = 2.34384 loss)
I0409 21:50:23.945593 25438 sgd_solver.cpp:105] Iteration 2880, lr = 0.0056525
I0409 21:50:29.593536 25438 solver.cpp:218] Iteration 2892 (2.12473 iter/s, 5.64777s/12 iters), loss = 2.32712
I0409 21:50:29.593591 25438 solver.cpp:237]     Train net output #0: loss = 2.32712 (* 1 = 2.32712 loss)
I0409 21:50:29.593603 25438 sgd_solver.cpp:105] Iteration 2892, lr = 0.00563908
I0409 21:50:35.270336 25438 solver.cpp:218] Iteration 2904 (2.11395 iter/s, 5.67657s/12 iters), loss = 2.53392
I0409 21:50:35.270385 25438 solver.cpp:237]     Train net output #0: loss = 2.53392 (* 1 = 2.53392 loss)
I0409 21:50:35.270395 25438 sgd_solver.cpp:105] Iteration 2904, lr = 0.00562569
I0409 21:50:40.901389 25438 solver.cpp:218] Iteration 2916 (2.13113 iter/s, 5.63082s/12 iters), loss = 2.42169
I0409 21:50:40.901512 25438 solver.cpp:237]     Train net output #0: loss = 2.42169 (* 1 = 2.42169 loss)
I0409 21:50:40.901526 25438 sgd_solver.cpp:105] Iteration 2916, lr = 0.00561233
I0409 21:50:46.560904 25438 solver.cpp:218] Iteration 2928 (2.12043 iter/s, 5.65922s/12 iters), loss = 2.2572
I0409 21:50:46.560951 25438 solver.cpp:237]     Train net output #0: loss = 2.2572 (* 1 = 2.2572 loss)
I0409 21:50:46.560962 25438 sgd_solver.cpp:105] Iteration 2928, lr = 0.00559901
I0409 21:50:48.494823 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:50:51.826202 25438 solver.cpp:218] Iteration 2940 (2.27917 iter/s, 5.26508s/12 iters), loss = 2.13941
I0409 21:50:51.826262 25438 solver.cpp:237]     Train net output #0: loss = 2.13941 (* 1 = 2.13941 loss)
I0409 21:50:51.826273 25438 sgd_solver.cpp:105] Iteration 2940, lr = 0.00558572
I0409 21:50:57.097784 25438 solver.cpp:218] Iteration 2952 (2.27645 iter/s, 5.27136s/12 iters), loss = 2.6193
I0409 21:50:57.097820 25438 solver.cpp:237]     Train net output #0: loss = 2.6193 (* 1 = 2.6193 loss)
I0409 21:50:57.097828 25438 sgd_solver.cpp:105] Iteration 2952, lr = 0.00557245
I0409 21:50:59.279726 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0409 21:51:14.167733 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0409 21:51:21.616271 25438 solver.cpp:330] Iteration 2958, Testing net (#0)
I0409 21:51:21.616298 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:51:24.902578 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:51:26.110728 25438 solver.cpp:397]     Test net output #0: accuracy = 0.102941
I0409 21:51:26.110769 25438 solver.cpp:397]     Test net output #1: loss = 5.06659 (* 1 = 5.06659 loss)
I0409 21:51:28.167414 25438 solver.cpp:218] Iteration 2964 (0.386241 iter/s, 31.0687s/12 iters), loss = 2.24736
I0409 21:51:28.167465 25438 solver.cpp:237]     Train net output #0: loss = 2.24736 (* 1 = 2.24736 loss)
I0409 21:51:28.167475 25438 sgd_solver.cpp:105] Iteration 2964, lr = 0.00555922
I0409 21:51:33.548573 25438 solver.cpp:218] Iteration 2976 (2.2301 iter/s, 5.38093s/12 iters), loss = 1.77918
I0409 21:51:33.548624 25438 solver.cpp:237]     Train net output #0: loss = 1.77918 (* 1 = 1.77918 loss)
I0409 21:51:33.548635 25438 sgd_solver.cpp:105] Iteration 2976, lr = 0.00554603
I0409 21:51:39.213214 25438 solver.cpp:218] Iteration 2988 (2.11849 iter/s, 5.66441s/12 iters), loss = 2.25183
I0409 21:51:39.213258 25438 solver.cpp:237]     Train net output #0: loss = 2.25183 (* 1 = 2.25183 loss)
I0409 21:51:39.213266 25438 sgd_solver.cpp:105] Iteration 2988, lr = 0.00553286
I0409 21:51:44.854182 25438 solver.cpp:218] Iteration 3000 (2.12738 iter/s, 5.64074s/12 iters), loss = 2.42593
I0409 21:51:44.854265 25438 solver.cpp:237]     Train net output #0: loss = 2.42593 (* 1 = 2.42593 loss)
I0409 21:51:44.854274 25438 sgd_solver.cpp:105] Iteration 3000, lr = 0.00551972
I0409 21:51:50.368777 25438 solver.cpp:218] Iteration 3012 (2.17614 iter/s, 5.51434s/12 iters), loss = 1.98513
I0409 21:51:50.368818 25438 solver.cpp:237]     Train net output #0: loss = 1.98513 (* 1 = 1.98513 loss)
I0409 21:51:50.368827 25438 sgd_solver.cpp:105] Iteration 3012, lr = 0.00550662
I0409 21:51:55.722816 25438 solver.cpp:218] Iteration 3024 (2.24139 iter/s, 5.35382s/12 iters), loss = 2.15273
I0409 21:51:55.722865 25438 solver.cpp:237]     Train net output #0: loss = 2.15273 (* 1 = 2.15273 loss)
I0409 21:51:55.722874 25438 sgd_solver.cpp:105] Iteration 3024, lr = 0.00549354
I0409 21:52:00.198101 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:52:01.299763 25438 solver.cpp:218] Iteration 3036 (2.1518 iter/s, 5.57672s/12 iters), loss = 1.94639
I0409 21:52:01.299814 25438 solver.cpp:237]     Train net output #0: loss = 1.94639 (* 1 = 1.94639 loss)
I0409 21:52:01.299824 25438 sgd_solver.cpp:105] Iteration 3036, lr = 0.0054805
I0409 21:52:06.730144 25438 solver.cpp:218] Iteration 3048 (2.20988 iter/s, 5.43016s/12 iters), loss = 2.37916
I0409 21:52:06.730192 25438 solver.cpp:237]     Train net output #0: loss = 2.37916 (* 1 = 2.37916 loss)
I0409 21:52:06.730203 25438 sgd_solver.cpp:105] Iteration 3048, lr = 0.00546749
I0409 21:52:11.527132 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0409 21:52:15.806951 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0409 21:52:19.012861 25438 solver.cpp:330] Iteration 3060, Testing net (#0)
I0409 21:52:19.012890 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:52:22.308758 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:52:23.627909 25438 solver.cpp:397]     Test net output #0: accuracy = 0.119485
I0409 21:52:23.627955 25438 solver.cpp:397]     Test net output #1: loss = 4.83569 (* 1 = 4.83569 loss)
I0409 21:52:23.725535 25438 solver.cpp:218] Iteration 3060 (0.706097 iter/s, 16.9948s/12 iters), loss = 2.15027
I0409 21:52:23.725581 25438 solver.cpp:237]     Train net output #0: loss = 2.15027 (* 1 = 2.15027 loss)
I0409 21:52:23.725592 25438 sgd_solver.cpp:105] Iteration 3060, lr = 0.00545451
I0409 21:52:28.274578 25438 solver.cpp:218] Iteration 3072 (2.63803 iter/s, 4.54885s/12 iters), loss = 2.05451
I0409 21:52:28.274627 25438 solver.cpp:237]     Train net output #0: loss = 2.05451 (* 1 = 2.05451 loss)
I0409 21:52:28.274639 25438 sgd_solver.cpp:105] Iteration 3072, lr = 0.00544156
I0409 21:52:33.749140 25438 solver.cpp:218] Iteration 3084 (2.19205 iter/s, 5.47434s/12 iters), loss = 2.1322
I0409 21:52:33.749188 25438 solver.cpp:237]     Train net output #0: loss = 2.1322 (* 1 = 2.1322 loss)
I0409 21:52:33.749200 25438 sgd_solver.cpp:105] Iteration 3084, lr = 0.00542864
I0409 21:52:39.098404 25438 solver.cpp:218] Iteration 3096 (2.24339 iter/s, 5.34905s/12 iters), loss = 2.50711
I0409 21:52:39.098457 25438 solver.cpp:237]     Train net output #0: loss = 2.50711 (* 1 = 2.50711 loss)
I0409 21:52:39.098469 25438 sgd_solver.cpp:105] Iteration 3096, lr = 0.00541575
I0409 21:52:44.278815 25438 solver.cpp:218] Iteration 3108 (2.31652 iter/s, 5.18019s/12 iters), loss = 2.37071
I0409 21:52:44.278861 25438 solver.cpp:237]     Train net output #0: loss = 2.37071 (* 1 = 2.37071 loss)
I0409 21:52:44.278870 25438 sgd_solver.cpp:105] Iteration 3108, lr = 0.00540289
I0409 21:52:49.704093 25438 solver.cpp:218] Iteration 3120 (2.21196 iter/s, 5.42506s/12 iters), loss = 2.02495
I0409 21:52:49.704200 25438 solver.cpp:237]     Train net output #0: loss = 2.02495 (* 1 = 2.02495 loss)
I0409 21:52:49.704211 25438 sgd_solver.cpp:105] Iteration 3120, lr = 0.00539006
I0409 21:52:55.163144 25438 solver.cpp:218] Iteration 3132 (2.1983 iter/s, 5.45878s/12 iters), loss = 2.02067
I0409 21:52:55.163195 25438 solver.cpp:237]     Train net output #0: loss = 2.02067 (* 1 = 2.02067 loss)
I0409 21:52:55.163206 25438 sgd_solver.cpp:105] Iteration 3132, lr = 0.00537727
I0409 21:52:56.420178 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:53:00.752987 25438 solver.cpp:218] Iteration 3144 (2.14684 iter/s, 5.58962s/12 iters), loss = 1.80724
I0409 21:53:00.753026 25438 solver.cpp:237]     Train net output #0: loss = 1.80724 (* 1 = 1.80724 loss)
I0409 21:53:00.753034 25438 sgd_solver.cpp:105] Iteration 3144, lr = 0.0053645
I0409 21:53:06.274518 25438 solver.cpp:218] Iteration 3156 (2.1734 iter/s, 5.52131s/12 iters), loss = 1.96103
I0409 21:53:06.274575 25438 solver.cpp:237]     Train net output #0: loss = 1.96103 (* 1 = 1.96103 loss)
I0409 21:53:06.274588 25438 sgd_solver.cpp:105] Iteration 3156, lr = 0.00535176
I0409 21:53:08.593266 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0409 21:53:13.172509 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0409 21:53:16.388451 25438 solver.cpp:330] Iteration 3162, Testing net (#0)
I0409 21:53:16.388478 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:53:19.678143 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:53:20.997582 25438 solver.cpp:397]     Test net output #0: accuracy = 0.115809
I0409 21:53:20.997694 25438 solver.cpp:397]     Test net output #1: loss = 4.9322 (* 1 = 4.9322 loss)
I0409 21:53:22.878911 25438 solver.cpp:218] Iteration 3168 (0.722724 iter/s, 16.6038s/12 iters), loss = 1.92872
I0409 21:53:22.878963 25438 solver.cpp:237]     Train net output #0: loss = 1.92872 (* 1 = 1.92872 loss)
I0409 21:53:22.878975 25438 sgd_solver.cpp:105] Iteration 3168, lr = 0.00533906
I0409 21:53:28.439100 25438 solver.cpp:218] Iteration 3180 (2.15829 iter/s, 5.55996s/12 iters), loss = 2.21907
I0409 21:53:28.439148 25438 solver.cpp:237]     Train net output #0: loss = 2.21907 (* 1 = 2.21907 loss)
I0409 21:53:28.439162 25438 sgd_solver.cpp:105] Iteration 3180, lr = 0.00532638
I0409 21:53:33.955368 25438 solver.cpp:218] Iteration 3192 (2.17547 iter/s, 5.51604s/12 iters), loss = 2.08119
I0409 21:53:33.955425 25438 solver.cpp:237]     Train net output #0: loss = 2.08119 (* 1 = 2.08119 loss)
I0409 21:53:33.955436 25438 sgd_solver.cpp:105] Iteration 3192, lr = 0.00531374
I0409 21:53:39.594528 25438 solver.cpp:218] Iteration 3204 (2.12806 iter/s, 5.63893s/12 iters), loss = 2.44514
I0409 21:53:39.594580 25438 solver.cpp:237]     Train net output #0: loss = 2.44514 (* 1 = 2.44514 loss)
I0409 21:53:39.594591 25438 sgd_solver.cpp:105] Iteration 3204, lr = 0.00530112
I0409 21:53:45.230291 25438 solver.cpp:218] Iteration 3216 (2.12934 iter/s, 5.63554s/12 iters), loss = 2.10209
I0409 21:53:45.230342 25438 solver.cpp:237]     Train net output #0: loss = 2.10209 (* 1 = 2.10209 loss)
I0409 21:53:45.230353 25438 sgd_solver.cpp:105] Iteration 3216, lr = 0.00528853
I0409 21:53:50.616847 25438 solver.cpp:218] Iteration 3228 (2.22786 iter/s, 5.38634s/12 iters), loss = 2.03949
I0409 21:53:50.616897 25438 solver.cpp:237]     Train net output #0: loss = 2.03949 (* 1 = 2.03949 loss)
I0409 21:53:50.616909 25438 sgd_solver.cpp:105] Iteration 3228, lr = 0.00527598
I0409 21:53:53.963709 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:53:55.874254 25438 solver.cpp:218] Iteration 3240 (2.28259 iter/s, 5.25719s/12 iters), loss = 2.13623
I0409 21:53:55.874310 25438 solver.cpp:237]     Train net output #0: loss = 2.13623 (* 1 = 2.13623 loss)
I0409 21:53:55.874320 25438 sgd_solver.cpp:105] Iteration 3240, lr = 0.00526345
I0409 21:54:01.547524 25438 solver.cpp:218] Iteration 3252 (2.11527 iter/s, 5.67304s/12 iters), loss = 1.74028
I0409 21:54:01.547577 25438 solver.cpp:237]     Train net output #0: loss = 1.74028 (* 1 = 1.74028 loss)
I0409 21:54:01.547590 25438 sgd_solver.cpp:105] Iteration 3252, lr = 0.00525095
I0409 21:54:06.486115 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0409 21:54:11.784955 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0409 21:54:15.900477 25438 solver.cpp:330] Iteration 3264, Testing net (#0)
I0409 21:54:15.900498 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:54:19.165767 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:54:20.482801 25438 solver.cpp:397]     Test net output #0: accuracy = 0.118873
I0409 21:54:20.482841 25438 solver.cpp:397]     Test net output #1: loss = 4.98211 (* 1 = 4.98211 loss)
I0409 21:54:20.579994 25438 solver.cpp:218] Iteration 3264 (0.630522 iter/s, 19.0319s/12 iters), loss = 2.0988
I0409 21:54:20.580049 25438 solver.cpp:237]     Train net output #0: loss = 2.0988 (* 1 = 2.0988 loss)
I0409 21:54:20.580060 25438 sgd_solver.cpp:105] Iteration 3264, lr = 0.00523849
I0409 21:54:25.054706 25438 solver.cpp:218] Iteration 3276 (2.68186 iter/s, 4.47451s/12 iters), loss = 1.56069
I0409 21:54:25.054780 25438 solver.cpp:237]     Train net output #0: loss = 1.56069 (* 1 = 1.56069 loss)
I0409 21:54:25.054790 25438 sgd_solver.cpp:105] Iteration 3276, lr = 0.00522605
I0409 21:54:30.722303 25438 solver.cpp:218] Iteration 3288 (2.1174 iter/s, 5.66734s/12 iters), loss = 1.98681
I0409 21:54:30.722364 25438 solver.cpp:237]     Train net output #0: loss = 1.98681 (* 1 = 1.98681 loss)
I0409 21:54:30.722379 25438 sgd_solver.cpp:105] Iteration 3288, lr = 0.00521364
I0409 21:54:36.198623 25438 solver.cpp:218] Iteration 3300 (2.19134 iter/s, 5.47609s/12 iters), loss = 2.20548
I0409 21:54:36.198664 25438 solver.cpp:237]     Train net output #0: loss = 2.20548 (* 1 = 2.20548 loss)
I0409 21:54:36.198673 25438 sgd_solver.cpp:105] Iteration 3300, lr = 0.00520126
I0409 21:54:41.579435 25438 solver.cpp:218] Iteration 3312 (2.23024 iter/s, 5.3806s/12 iters), loss = 1.98804
I0409 21:54:41.579483 25438 solver.cpp:237]     Train net output #0: loss = 1.98804 (* 1 = 1.98804 loss)
I0409 21:54:41.579493 25438 sgd_solver.cpp:105] Iteration 3312, lr = 0.00518892
I0409 21:54:47.263823 25438 solver.cpp:218] Iteration 3324 (2.11113 iter/s, 5.68417s/12 iters), loss = 2.08165
I0409 21:54:47.263864 25438 solver.cpp:237]     Train net output #0: loss = 2.08165 (* 1 = 2.08165 loss)
I0409 21:54:47.263873 25438 sgd_solver.cpp:105] Iteration 3324, lr = 0.0051766
I0409 21:54:52.866641 25438 solver.cpp:218] Iteration 3336 (2.14186 iter/s, 5.60261s/12 iters), loss = 2.14792
I0409 21:54:52.866688 25438 solver.cpp:237]     Train net output #0: loss = 2.14792 (* 1 = 2.14792 loss)
I0409 21:54:52.866698 25438 sgd_solver.cpp:105] Iteration 3336, lr = 0.00516431
I0409 21:54:53.342970 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:54:58.318339 25438 solver.cpp:218] Iteration 3348 (2.20123 iter/s, 5.45149s/12 iters), loss = 2.00553
I0409 21:54:58.318472 25438 solver.cpp:237]     Train net output #0: loss = 2.00553 (* 1 = 2.00553 loss)
I0409 21:54:58.318485 25438 sgd_solver.cpp:105] Iteration 3348, lr = 0.00515204
I0409 21:55:03.808609 25438 solver.cpp:218] Iteration 3360 (2.1858 iter/s, 5.48998s/12 iters), loss = 2.13017
I0409 21:55:03.808652 25438 solver.cpp:237]     Train net output #0: loss = 2.13017 (* 1 = 2.13017 loss)
I0409 21:55:03.808661 25438 sgd_solver.cpp:105] Iteration 3360, lr = 0.00513981
I0409 21:55:05.953526 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0409 21:55:12.017343 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0409 21:55:15.195631 25438 solver.cpp:330] Iteration 3366, Testing net (#0)
I0409 21:55:15.195652 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:55:18.400765 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:55:19.780455 25438 solver.cpp:397]     Test net output #0: accuracy = 0.113971
I0409 21:55:19.780498 25438 solver.cpp:397]     Test net output #1: loss = 4.80003 (* 1 = 4.80003 loss)
I0409 21:55:21.883484 25438 solver.cpp:218] Iteration 3372 (0.663925 iter/s, 18.0743s/12 iters), loss = 1.9034
I0409 21:55:21.883524 25438 solver.cpp:237]     Train net output #0: loss = 1.9034 (* 1 = 1.9034 loss)
I0409 21:55:21.883534 25438 sgd_solver.cpp:105] Iteration 3372, lr = 0.00512761
I0409 21:55:27.171494 25438 solver.cpp:218] Iteration 3384 (2.26937 iter/s, 5.28781s/12 iters), loss = 2.01005
I0409 21:55:27.171538 25438 solver.cpp:237]     Train net output #0: loss = 2.01005 (* 1 = 2.01005 loss)
I0409 21:55:27.171547 25438 sgd_solver.cpp:105] Iteration 3384, lr = 0.00511544
I0409 21:55:32.473446 25438 solver.cpp:218] Iteration 3396 (2.2634 iter/s, 5.30175s/12 iters), loss = 1.79467
I0409 21:55:32.473556 25438 solver.cpp:237]     Train net output #0: loss = 1.79467 (* 1 = 1.79467 loss)
I0409 21:55:32.473568 25438 sgd_solver.cpp:105] Iteration 3396, lr = 0.00510329
I0409 21:55:37.775918 25438 solver.cpp:218] Iteration 3408 (2.26321 iter/s, 5.30221s/12 iters), loss = 2.06719
I0409 21:55:37.775969 25438 solver.cpp:237]     Train net output #0: loss = 2.06719 (* 1 = 2.06719 loss)
I0409 21:55:37.775979 25438 sgd_solver.cpp:105] Iteration 3408, lr = 0.00509117
I0409 21:55:43.060776 25438 solver.cpp:218] Iteration 3420 (2.27073 iter/s, 5.28465s/12 iters), loss = 1.59785
I0409 21:55:43.060825 25438 solver.cpp:237]     Train net output #0: loss = 1.59785 (* 1 = 1.59785 loss)
I0409 21:55:43.060837 25438 sgd_solver.cpp:105] Iteration 3420, lr = 0.00507909
I0409 21:55:48.383821 25438 solver.cpp:218] Iteration 3432 (2.25444 iter/s, 5.32283s/12 iters), loss = 1.93497
I0409 21:55:48.383872 25438 solver.cpp:237]     Train net output #0: loss = 1.93497 (* 1 = 1.93497 loss)
I0409 21:55:48.383883 25438 sgd_solver.cpp:105] Iteration 3432, lr = 0.00506703
I0409 21:55:51.145099 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:55:53.658519 25438 solver.cpp:218] Iteration 3444 (2.2751 iter/s, 5.27449s/12 iters), loss = 1.83051
I0409 21:55:53.658565 25438 solver.cpp:237]     Train net output #0: loss = 1.83051 (* 1 = 1.83051 loss)
I0409 21:55:53.658576 25438 sgd_solver.cpp:105] Iteration 3444, lr = 0.005055
I0409 21:55:59.039100 25438 solver.cpp:218] Iteration 3456 (2.23033 iter/s, 5.38038s/12 iters), loss = 1.46264
I0409 21:55:59.039150 25438 solver.cpp:237]     Train net output #0: loss = 1.46264 (* 1 = 1.46264 loss)
I0409 21:55:59.039161 25438 sgd_solver.cpp:105] Iteration 3456, lr = 0.005043
I0409 21:56:03.999970 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0409 21:56:10.888296 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0409 21:56:18.663305 25438 solver.cpp:330] Iteration 3468, Testing net (#0)
I0409 21:56:18.663326 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:56:19.112865 25438 blocking_queue.cpp:49] Waiting for data
I0409 21:56:21.951764 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:56:23.437290 25438 solver.cpp:397]     Test net output #0: accuracy = 0.129289
I0409 21:56:23.437337 25438 solver.cpp:397]     Test net output #1: loss = 5.21929 (* 1 = 5.21929 loss)
I0409 21:56:23.535053 25438 solver.cpp:218] Iteration 3468 (0.489892 iter/s, 24.4952s/12 iters), loss = 1.81454
I0409 21:56:23.535122 25438 solver.cpp:237]     Train net output #0: loss = 1.81454 (* 1 = 1.81454 loss)
I0409 21:56:23.535136 25438 sgd_solver.cpp:105] Iteration 3468, lr = 0.00503102
I0409 21:56:28.185413 25438 solver.cpp:218] Iteration 3480 (2.58056 iter/s, 4.65015s/12 iters), loss = 1.41304
I0409 21:56:28.185463 25438 solver.cpp:237]     Train net output #0: loss = 1.41304 (* 1 = 1.41304 loss)
I0409 21:56:28.185477 25438 sgd_solver.cpp:105] Iteration 3480, lr = 0.00501908
I0409 21:56:33.386902 25438 solver.cpp:218] Iteration 3492 (2.30712 iter/s, 5.20128s/12 iters), loss = 1.3759
I0409 21:56:33.386952 25438 solver.cpp:237]     Train net output #0: loss = 1.3759 (* 1 = 1.3759 loss)
I0409 21:56:33.386965 25438 sgd_solver.cpp:105] Iteration 3492, lr = 0.00500716
I0409 21:56:38.762727 25438 solver.cpp:218] Iteration 3504 (2.2323 iter/s, 5.37561s/12 iters), loss = 1.79701
I0409 21:56:38.762842 25438 solver.cpp:237]     Train net output #0: loss = 1.79701 (* 1 = 1.79701 loss)
I0409 21:56:38.762856 25438 sgd_solver.cpp:105] Iteration 3504, lr = 0.00499527
I0409 21:56:43.986301 25438 solver.cpp:218] Iteration 3516 (2.2974 iter/s, 5.22329s/12 iters), loss = 1.70071
I0409 21:56:43.986356 25438 solver.cpp:237]     Train net output #0: loss = 1.70071 (* 1 = 1.70071 loss)
I0409 21:56:43.986368 25438 sgd_solver.cpp:105] Iteration 3516, lr = 0.00498341
I0409 21:56:49.384420 25438 solver.cpp:218] Iteration 3528 (2.22308 iter/s, 5.39791s/12 iters), loss = 1.57255
I0409 21:56:49.384459 25438 solver.cpp:237]     Train net output #0: loss = 1.57255 (* 1 = 1.57255 loss)
I0409 21:56:49.384469 25438 sgd_solver.cpp:105] Iteration 3528, lr = 0.00497158
I0409 21:56:54.434813 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:56:54.707873 25438 solver.cpp:218] Iteration 3540 (2.25426 iter/s, 5.32325s/12 iters), loss = 1.7754
I0409 21:56:54.707919 25438 solver.cpp:237]     Train net output #0: loss = 1.7754 (* 1 = 1.7754 loss)
I0409 21:56:54.707932 25438 sgd_solver.cpp:105] Iteration 3540, lr = 0.00495978
I0409 21:57:00.201645 25438 solver.cpp:218] Iteration 3552 (2.18438 iter/s, 5.49356s/12 iters), loss = 1.43753
I0409 21:57:00.201694 25438 solver.cpp:237]     Train net output #0: loss = 1.43753 (* 1 = 1.43753 loss)
I0409 21:57:00.201705 25438 sgd_solver.cpp:105] Iteration 3552, lr = 0.004948
I0409 21:57:05.855255 25438 solver.cpp:218] Iteration 3564 (2.12262 iter/s, 5.65339s/12 iters), loss = 1.60842
I0409 21:57:05.855305 25438 solver.cpp:237]     Train net output #0: loss = 1.60842 (* 1 = 1.60842 loss)
I0409 21:57:05.855315 25438 sgd_solver.cpp:105] Iteration 3564, lr = 0.00493626
I0409 21:57:08.092054 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0409 21:57:22.186456 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0409 21:57:26.136442 25438 solver.cpp:330] Iteration 3570, Testing net (#0)
I0409 21:57:26.136464 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:57:29.270491 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:57:30.696918 25438 solver.cpp:397]     Test net output #0: accuracy = 0.137255
I0409 21:57:30.696960 25438 solver.cpp:397]     Test net output #1: loss = 5.11582 (* 1 = 5.11582 loss)
I0409 21:57:32.595706 25438 solver.cpp:218] Iteration 3576 (0.448772 iter/s, 26.7396s/12 iters), loss = 1.60499
I0409 21:57:32.595765 25438 solver.cpp:237]     Train net output #0: loss = 1.60499 (* 1 = 1.60499 loss)
I0409 21:57:32.595777 25438 sgd_solver.cpp:105] Iteration 3576, lr = 0.00492454
I0409 21:57:37.668421 25438 solver.cpp:218] Iteration 3588 (2.3657 iter/s, 5.0725s/12 iters), loss = 1.83604
I0409 21:57:37.668478 25438 solver.cpp:237]     Train net output #0: loss = 1.83604 (* 1 = 1.83604 loss)
I0409 21:57:37.668489 25438 sgd_solver.cpp:105] Iteration 3588, lr = 0.00491284
I0409 21:57:43.101609 25438 solver.cpp:218] Iteration 3600 (2.20874 iter/s, 5.43297s/12 iters), loss = 1.58634
I0409 21:57:43.101656 25438 solver.cpp:237]     Train net output #0: loss = 1.58634 (* 1 = 1.58634 loss)
I0409 21:57:43.101665 25438 sgd_solver.cpp:105] Iteration 3600, lr = 0.00490118
I0409 21:57:48.726785 25438 solver.cpp:218] Iteration 3612 (2.13335 iter/s, 5.62496s/12 iters), loss = 1.67847
I0409 21:57:48.726835 25438 solver.cpp:237]     Train net output #0: loss = 1.67847 (* 1 = 1.67847 loss)
I0409 21:57:48.726845 25438 sgd_solver.cpp:105] Iteration 3612, lr = 0.00488954
I0409 21:57:54.365731 25438 solver.cpp:218] Iteration 3624 (2.12814 iter/s, 5.63872s/12 iters), loss = 1.30548
I0409 21:57:54.365841 25438 solver.cpp:237]     Train net output #0: loss = 1.30548 (* 1 = 1.30548 loss)
I0409 21:57:54.365854 25438 sgd_solver.cpp:105] Iteration 3624, lr = 0.00487793
I0409 21:58:00.042214 25438 solver.cpp:218] Iteration 3636 (2.11409 iter/s, 5.6762s/12 iters), loss = 1.51647
I0409 21:58:00.042258 25438 solver.cpp:237]     Train net output #0: loss = 1.51647 (* 1 = 1.51647 loss)
I0409 21:58:00.042268 25438 sgd_solver.cpp:105] Iteration 3636, lr = 0.00486635
I0409 21:58:02.153780 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:58:05.706750 25438 solver.cpp:218] Iteration 3648 (2.11853 iter/s, 5.66431s/12 iters), loss = 1.6588
I0409 21:58:05.706795 25438 solver.cpp:237]     Train net output #0: loss = 1.6588 (* 1 = 1.6588 loss)
I0409 21:58:05.706805 25438 sgd_solver.cpp:105] Iteration 3648, lr = 0.0048548
I0409 21:58:11.340337 25438 solver.cpp:218] Iteration 3660 (2.13016 iter/s, 5.63338s/12 iters), loss = 1.48798
I0409 21:58:11.340379 25438 solver.cpp:237]     Train net output #0: loss = 1.48798 (* 1 = 1.48798 loss)
I0409 21:58:11.340389 25438 sgd_solver.cpp:105] Iteration 3660, lr = 0.00484327
I0409 21:58:15.994865 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0409 21:58:31.779330 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0409 21:58:39.011653 25438 solver.cpp:330] Iteration 3672, Testing net (#0)
I0409 21:58:39.011672 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:58:42.052312 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:58:43.681471 25438 solver.cpp:397]     Test net output #0: accuracy = 0.133578
I0409 21:58:43.681520 25438 solver.cpp:397]     Test net output #1: loss = 5.10942 (* 1 = 5.10942 loss)
I0409 21:58:43.778930 25438 solver.cpp:218] Iteration 3672 (0.369941 iter/s, 32.4376s/12 iters), loss = 1.34048
I0409 21:58:43.778985 25438 solver.cpp:237]     Train net output #0: loss = 1.34048 (* 1 = 1.34048 loss)
I0409 21:58:43.778996 25438 sgd_solver.cpp:105] Iteration 3672, lr = 0.00483177
I0409 21:58:48.441829 25438 solver.cpp:218] Iteration 3684 (2.57362 iter/s, 4.6627s/12 iters), loss = 1.29954
I0409 21:58:48.441875 25438 solver.cpp:237]     Train net output #0: loss = 1.29954 (* 1 = 1.29954 loss)
I0409 21:58:48.441884 25438 sgd_solver.cpp:105] Iteration 3684, lr = 0.0048203
I0409 21:58:53.844472 25438 solver.cpp:218] Iteration 3696 (2.22122 iter/s, 5.40243s/12 iters), loss = 1.4142
I0409 21:58:53.844518 25438 solver.cpp:237]     Train net output #0: loss = 1.4142 (* 1 = 1.4142 loss)
I0409 21:58:53.844527 25438 sgd_solver.cpp:105] Iteration 3696, lr = 0.00480886
I0409 21:58:59.243836 25438 solver.cpp:218] Iteration 3708 (2.22257 iter/s, 5.39915s/12 iters), loss = 1.54651
I0409 21:58:59.243886 25438 solver.cpp:237]     Train net output #0: loss = 1.54651 (* 1 = 1.54651 loss)
I0409 21:58:59.243897 25438 sgd_solver.cpp:105] Iteration 3708, lr = 0.00479744
I0409 21:59:04.443550 25438 solver.cpp:218] Iteration 3720 (2.30791 iter/s, 5.1995s/12 iters), loss = 1.32017
I0409 21:59:04.443666 25438 solver.cpp:237]     Train net output #0: loss = 1.32017 (* 1 = 1.32017 loss)
I0409 21:59:04.443681 25438 sgd_solver.cpp:105] Iteration 3720, lr = 0.00478605
I0409 21:59:09.639204 25438 solver.cpp:218] Iteration 3732 (2.30974 iter/s, 5.19538s/12 iters), loss = 1.22011
I0409 21:59:09.639250 25438 solver.cpp:237]     Train net output #0: loss = 1.22011 (* 1 = 1.22011 loss)
I0409 21:59:09.639258 25438 sgd_solver.cpp:105] Iteration 3732, lr = 0.00477469
I0409 21:59:13.943347 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:59:15.002167 25438 solver.cpp:218] Iteration 3744 (2.23766 iter/s, 5.36275s/12 iters), loss = 1.32243
I0409 21:59:15.002219 25438 solver.cpp:237]     Train net output #0: loss = 1.32243 (* 1 = 1.32243 loss)
I0409 21:59:15.002231 25438 sgd_solver.cpp:105] Iteration 3744, lr = 0.00476335
I0409 21:59:20.590517 25438 solver.cpp:218] Iteration 3756 (2.14741 iter/s, 5.58813s/12 iters), loss = 1.13088
I0409 21:59:20.590562 25438 solver.cpp:237]     Train net output #0: loss = 1.13088 (* 1 = 1.13088 loss)
I0409 21:59:20.590571 25438 sgd_solver.cpp:105] Iteration 3756, lr = 0.00475204
I0409 21:59:25.780117 25438 solver.cpp:218] Iteration 3768 (2.31241 iter/s, 5.18939s/12 iters), loss = 1.45351
I0409 21:59:25.780167 25438 solver.cpp:237]     Train net output #0: loss = 1.45351 (* 1 = 1.45351 loss)
I0409 21:59:25.780177 25438 sgd_solver.cpp:105] Iteration 3768, lr = 0.00474076
I0409 21:59:27.910028 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0409 21:59:35.034461 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0409 21:59:38.962610 25438 solver.cpp:330] Iteration 3774, Testing net (#0)
I0409 21:59:38.962628 25438 net.cpp:676] Ignoring source layer train-data
I0409 21:59:42.060370 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 21:59:43.570828 25438 solver.cpp:397]     Test net output #0: accuracy = 0.135417
I0409 21:59:43.570875 25438 solver.cpp:397]     Test net output #1: loss = 5.10685 (* 1 = 5.10685 loss)
I0409 21:59:45.538149 25438 solver.cpp:218] Iteration 3780 (0.607367 iter/s, 19.7574s/12 iters), loss = 1.18813
I0409 21:59:45.538204 25438 solver.cpp:237]     Train net output #0: loss = 1.18813 (* 1 = 1.18813 loss)
I0409 21:59:45.538215 25438 sgd_solver.cpp:105] Iteration 3780, lr = 0.00472951
I0409 21:59:50.739840 25438 solver.cpp:218] Iteration 3792 (2.30704 iter/s, 5.20148s/12 iters), loss = 1.06173
I0409 21:59:50.739892 25438 solver.cpp:237]     Train net output #0: loss = 1.06173 (* 1 = 1.06173 loss)
I0409 21:59:50.739902 25438 sgd_solver.cpp:105] Iteration 3792, lr = 0.00471828
I0409 21:59:55.910349 25438 solver.cpp:218] Iteration 3804 (2.32095 iter/s, 5.1703s/12 iters), loss = 1.18563
I0409 21:59:55.910399 25438 solver.cpp:237]     Train net output #0: loss = 1.18563 (* 1 = 1.18563 loss)
I0409 21:59:55.910409 25438 sgd_solver.cpp:105] Iteration 3804, lr = 0.00470707
I0409 22:00:01.318686 25438 solver.cpp:218] Iteration 3816 (2.21889 iter/s, 5.40812s/12 iters), loss = 1.32563
I0409 22:00:01.318740 25438 solver.cpp:237]     Train net output #0: loss = 1.32563 (* 1 = 1.32563 loss)
I0409 22:00:01.318754 25438 sgd_solver.cpp:105] Iteration 3816, lr = 0.0046959
I0409 22:00:07.000319 25438 solver.cpp:218] Iteration 3828 (2.11215 iter/s, 5.68141s/12 iters), loss = 1.30985
I0409 22:00:07.000429 25438 solver.cpp:237]     Train net output #0: loss = 1.30985 (* 1 = 1.30985 loss)
I0409 22:00:07.000440 25438 sgd_solver.cpp:105] Iteration 3828, lr = 0.00468475
I0409 22:00:12.665311 25438 solver.cpp:218] Iteration 3840 (2.11838 iter/s, 5.66471s/12 iters), loss = 1.25992
I0409 22:00:12.665369 25438 solver.cpp:237]     Train net output #0: loss = 1.25992 (* 1 = 1.25992 loss)
I0409 22:00:12.665381 25438 sgd_solver.cpp:105] Iteration 3840, lr = 0.00467363
I0409 22:00:13.961705 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:00:18.147590 25438 solver.cpp:218] Iteration 3852 (2.18896 iter/s, 5.48206s/12 iters), loss = 1.38912
I0409 22:00:18.147645 25438 solver.cpp:237]     Train net output #0: loss = 1.38912 (* 1 = 1.38912 loss)
I0409 22:00:18.147655 25438 sgd_solver.cpp:105] Iteration 3852, lr = 0.00466253
I0409 22:00:23.490141 25438 solver.cpp:218] Iteration 3864 (2.24621 iter/s, 5.34233s/12 iters), loss = 1.4791
I0409 22:00:23.490190 25438 solver.cpp:237]     Train net output #0: loss = 1.4791 (* 1 = 1.4791 loss)
I0409 22:00:23.490200 25438 sgd_solver.cpp:105] Iteration 3864, lr = 0.00465146
I0409 22:00:28.450556 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0409 22:00:32.627388 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0409 22:00:37.391736 25438 solver.cpp:330] Iteration 3876, Testing net (#0)
I0409 22:00:37.391814 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:00:40.267026 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:00:41.825912 25438 solver.cpp:397]     Test net output #0: accuracy = 0.130515
I0409 22:00:41.825982 25438 solver.cpp:397]     Test net output #1: loss = 5.18271 (* 1 = 5.18271 loss)
I0409 22:00:41.923063 25438 solver.cpp:218] Iteration 3876 (0.65103 iter/s, 18.4323s/12 iters), loss = 1.06869
I0409 22:00:41.923117 25438 solver.cpp:237]     Train net output #0: loss = 1.06869 (* 1 = 1.06869 loss)
I0409 22:00:41.923128 25438 sgd_solver.cpp:105] Iteration 3876, lr = 0.00464042
I0409 22:00:46.616178 25438 solver.cpp:218] Iteration 3888 (2.55705 iter/s, 4.69291s/12 iters), loss = 0.952465
I0409 22:00:46.616237 25438 solver.cpp:237]     Train net output #0: loss = 0.952465 (* 1 = 0.952465 loss)
I0409 22:00:46.616250 25438 sgd_solver.cpp:105] Iteration 3888, lr = 0.0046294
I0409 22:00:51.985848 25438 solver.cpp:218] Iteration 3900 (2.23487 iter/s, 5.36945s/12 iters), loss = 1.29364
I0409 22:00:51.985894 25438 solver.cpp:237]     Train net output #0: loss = 1.29364 (* 1 = 1.29364 loss)
I0409 22:00:51.985905 25438 sgd_solver.cpp:105] Iteration 3900, lr = 0.00461841
I0409 22:00:57.489758 25438 solver.cpp:218] Iteration 3912 (2.18035 iter/s, 5.5037s/12 iters), loss = 0.981992
I0409 22:00:57.489796 25438 solver.cpp:237]     Train net output #0: loss = 0.981992 (* 1 = 0.981992 loss)
I0409 22:00:57.489804 25438 sgd_solver.cpp:105] Iteration 3912, lr = 0.00460744
I0409 22:01:02.914883 25438 solver.cpp:218] Iteration 3924 (2.21201 iter/s, 5.42492s/12 iters), loss = 1.14142
I0409 22:01:02.914933 25438 solver.cpp:237]     Train net output #0: loss = 1.14142 (* 1 = 1.14142 loss)
I0409 22:01:02.914944 25438 sgd_solver.cpp:105] Iteration 3924, lr = 0.0045965
I0409 22:01:08.583916 25438 solver.cpp:218] Iteration 3936 (2.11685 iter/s, 5.66881s/12 iters), loss = 1.41399
I0409 22:01:08.583986 25438 solver.cpp:237]     Train net output #0: loss = 1.41399 (* 1 = 1.41399 loss)
I0409 22:01:08.583995 25438 sgd_solver.cpp:105] Iteration 3936, lr = 0.00458559
I0409 22:01:12.359856 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:01:14.111485 25438 solver.cpp:218] Iteration 3948 (2.17103 iter/s, 5.52733s/12 iters), loss = 1.31291
I0409 22:01:14.111531 25438 solver.cpp:237]     Train net output #0: loss = 1.31291 (* 1 = 1.31291 loss)
I0409 22:01:14.111541 25438 sgd_solver.cpp:105] Iteration 3948, lr = 0.0045747
I0409 22:01:19.717990 25438 solver.cpp:218] Iteration 3960 (2.14046 iter/s, 5.60628s/12 iters), loss = 1.06055
I0409 22:01:19.718035 25438 solver.cpp:237]     Train net output #0: loss = 1.06055 (* 1 = 1.06055 loss)
I0409 22:01:19.718045 25438 sgd_solver.cpp:105] Iteration 3960, lr = 0.00456384
I0409 22:01:25.361332 25438 solver.cpp:218] Iteration 3972 (2.12648 iter/s, 5.64312s/12 iters), loss = 0.936889
I0409 22:01:25.361382 25438 solver.cpp:237]     Train net output #0: loss = 0.936889 (* 1 = 0.936889 loss)
I0409 22:01:25.361393 25438 sgd_solver.cpp:105] Iteration 3972, lr = 0.00455301
I0409 22:01:27.654846 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0409 22:01:31.869000 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0409 22:01:35.020704 25438 solver.cpp:330] Iteration 3978, Testing net (#0)
I0409 22:01:35.020726 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:01:37.922711 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:01:39.518823 25438 solver.cpp:397]     Test net output #0: accuracy = 0.143382
I0409 22:01:39.518996 25438 solver.cpp:397]     Test net output #1: loss = 5.31092 (* 1 = 5.31092 loss)
I0409 22:01:41.436805 25438 solver.cpp:218] Iteration 3984 (0.746503 iter/s, 16.075s/12 iters), loss = 1.0279
I0409 22:01:41.436857 25438 solver.cpp:237]     Train net output #0: loss = 1.0279 (* 1 = 1.0279 loss)
I0409 22:01:41.436869 25438 sgd_solver.cpp:105] Iteration 3984, lr = 0.0045422
I0409 22:01:46.690100 25438 solver.cpp:218] Iteration 3996 (2.28437 iter/s, 5.25308s/12 iters), loss = 1.41564
I0409 22:01:46.690150 25438 solver.cpp:237]     Train net output #0: loss = 1.41564 (* 1 = 1.41564 loss)
I0409 22:01:46.690161 25438 sgd_solver.cpp:105] Iteration 3996, lr = 0.00453141
I0409 22:01:52.383919 25438 solver.cpp:218] Iteration 4008 (2.10763 iter/s, 5.6936s/12 iters), loss = 1.30586
I0409 22:01:52.383966 25438 solver.cpp:237]     Train net output #0: loss = 1.30586 (* 1 = 1.30586 loss)
I0409 22:01:52.383978 25438 sgd_solver.cpp:105] Iteration 4008, lr = 0.00452066
I0409 22:01:57.758791 25438 solver.cpp:218] Iteration 4020 (2.2327 iter/s, 5.37466s/12 iters), loss = 1.12588
I0409 22:01:57.758849 25438 solver.cpp:237]     Train net output #0: loss = 1.12588 (* 1 = 1.12588 loss)
I0409 22:01:57.758860 25438 sgd_solver.cpp:105] Iteration 4020, lr = 0.00450992
I0409 22:02:03.426884 25438 solver.cpp:218] Iteration 4032 (2.1172 iter/s, 5.66786s/12 iters), loss = 1.3623
I0409 22:02:03.426934 25438 solver.cpp:237]     Train net output #0: loss = 1.3623 (* 1 = 1.3623 loss)
I0409 22:02:03.426944 25438 sgd_solver.cpp:105] Iteration 4032, lr = 0.00449921
I0409 22:02:09.036264 25438 solver.cpp:218] Iteration 4044 (2.13936 iter/s, 5.60916s/12 iters), loss = 1.12032
I0409 22:02:09.036314 25438 solver.cpp:237]     Train net output #0: loss = 1.12032 (* 1 = 1.12032 loss)
I0409 22:02:09.036324 25438 sgd_solver.cpp:105] Iteration 4044, lr = 0.00448853
I0409 22:02:09.619714 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:02:14.639957 25438 solver.cpp:218] Iteration 4056 (2.14153 iter/s, 5.60347s/12 iters), loss = 1.27591
I0409 22:02:14.639997 25438 solver.cpp:237]     Train net output #0: loss = 1.27591 (* 1 = 1.27591 loss)
I0409 22:02:14.640007 25438 sgd_solver.cpp:105] Iteration 4056, lr = 0.00447788
I0409 22:02:19.926926 25438 solver.cpp:218] Iteration 4068 (2.26982 iter/s, 5.28676s/12 iters), loss = 0.812204
I0409 22:02:19.926973 25438 solver.cpp:237]     Train net output #0: loss = 0.812204 (* 1 = 0.812204 loss)
I0409 22:02:19.926982 25438 sgd_solver.cpp:105] Iteration 4068, lr = 0.00446724
I0409 22:02:24.677624 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0409 22:02:30.201622 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0409 22:02:33.452188 25438 solver.cpp:330] Iteration 4080, Testing net (#0)
I0409 22:02:33.452214 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:02:36.374428 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:02:38.144371 25438 solver.cpp:397]     Test net output #0: accuracy = 0.140931
I0409 22:02:38.144407 25438 solver.cpp:397]     Test net output #1: loss = 5.42537 (* 1 = 5.42537 loss)
I0409 22:02:38.241746 25438 solver.cpp:218] Iteration 4080 (0.655228 iter/s, 18.3142s/12 iters), loss = 0.959684
I0409 22:02:38.241801 25438 solver.cpp:237]     Train net output #0: loss = 0.959684 (* 1 = 0.959684 loss)
I0409 22:02:38.241816 25438 sgd_solver.cpp:105] Iteration 4080, lr = 0.00445664
I0409 22:02:42.946499 25438 solver.cpp:218] Iteration 4092 (2.55072 iter/s, 4.70455s/12 iters), loss = 1.06657
I0409 22:02:42.946621 25438 solver.cpp:237]     Train net output #0: loss = 1.06657 (* 1 = 1.06657 loss)
I0409 22:02:42.946632 25438 sgd_solver.cpp:105] Iteration 4092, lr = 0.00444606
I0409 22:02:48.648977 25438 solver.cpp:218] Iteration 4104 (2.10446 iter/s, 5.70218s/12 iters), loss = 0.876449
I0409 22:02:48.649034 25438 solver.cpp:237]     Train net output #0: loss = 0.876449 (* 1 = 0.876449 loss)
I0409 22:02:48.649044 25438 sgd_solver.cpp:105] Iteration 4104, lr = 0.0044355
I0409 22:02:54.311475 25438 solver.cpp:218] Iteration 4116 (2.11929 iter/s, 5.66226s/12 iters), loss = 1.24057
I0409 22:02:54.311537 25438 solver.cpp:237]     Train net output #0: loss = 1.24057 (* 1 = 1.24057 loss)
I0409 22:02:54.311549 25438 sgd_solver.cpp:105] Iteration 4116, lr = 0.00442497
I0409 22:02:59.665812 25438 solver.cpp:218] Iteration 4128 (2.24127 iter/s, 5.35411s/12 iters), loss = 1.17619
I0409 22:02:59.665859 25438 solver.cpp:237]     Train net output #0: loss = 1.17619 (* 1 = 1.17619 loss)
I0409 22:02:59.665870 25438 sgd_solver.cpp:105] Iteration 4128, lr = 0.00441447
I0409 22:03:04.934474 25438 solver.cpp:218] Iteration 4140 (2.27771 iter/s, 5.26845s/12 iters), loss = 1.18017
I0409 22:03:04.934530 25438 solver.cpp:237]     Train net output #0: loss = 1.18017 (* 1 = 1.18017 loss)
I0409 22:03:04.934540 25438 sgd_solver.cpp:105] Iteration 4140, lr = 0.00440398
I0409 22:03:07.682577 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:03:10.199896 25438 solver.cpp:218] Iteration 4152 (2.27911 iter/s, 5.26521s/12 iters), loss = 1.23131
I0409 22:03:10.199932 25438 solver.cpp:237]     Train net output #0: loss = 1.23131 (* 1 = 1.23131 loss)
I0409 22:03:10.199940 25438 sgd_solver.cpp:105] Iteration 4152, lr = 0.00439353
I0409 22:03:11.903764 25438 blocking_queue.cpp:49] Waiting for data
I0409 22:03:15.648293 25438 solver.cpp:218] Iteration 4164 (2.20257 iter/s, 5.44819s/12 iters), loss = 1.05772
I0409 22:03:15.648401 25438 solver.cpp:237]     Train net output #0: loss = 1.05772 (* 1 = 1.05772 loss)
I0409 22:03:15.648412 25438 sgd_solver.cpp:105] Iteration 4164, lr = 0.0043831
I0409 22:03:20.808593 25438 solver.cpp:218] Iteration 4176 (2.32557 iter/s, 5.16003s/12 iters), loss = 0.896113
I0409 22:03:20.808645 25438 solver.cpp:237]     Train net output #0: loss = 0.896113 (* 1 = 0.896113 loss)
I0409 22:03:20.808656 25438 sgd_solver.cpp:105] Iteration 4176, lr = 0.00437269
I0409 22:03:22.930279 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0409 22:03:27.051482 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0409 22:03:32.435679 25438 solver.cpp:330] Iteration 4182, Testing net (#0)
I0409 22:03:32.435703 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:03:35.139418 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:03:36.818176 25438 solver.cpp:397]     Test net output #0: accuracy = 0.143382
I0409 22:03:36.818228 25438 solver.cpp:397]     Test net output #1: loss = 5.43886 (* 1 = 5.43886 loss)
I0409 22:03:38.879143 25438 solver.cpp:218] Iteration 4188 (0.664085 iter/s, 18.07s/12 iters), loss = 0.916622
I0409 22:03:38.879199 25438 solver.cpp:237]     Train net output #0: loss = 0.916622 (* 1 = 0.916622 loss)
I0409 22:03:38.879209 25438 sgd_solver.cpp:105] Iteration 4188, lr = 0.00436231
I0409 22:03:44.352706 25438 solver.cpp:218] Iteration 4200 (2.19245 iter/s, 5.47334s/12 iters), loss = 0.985217
I0409 22:03:44.352761 25438 solver.cpp:237]     Train net output #0: loss = 0.985217 (* 1 = 0.985217 loss)
I0409 22:03:44.352772 25438 sgd_solver.cpp:105] Iteration 4200, lr = 0.00435195
I0409 22:03:49.918602 25438 solver.cpp:218] Iteration 4212 (2.15607 iter/s, 5.56567s/12 iters), loss = 0.7519
I0409 22:03:49.918756 25438 solver.cpp:237]     Train net output #0: loss = 0.7519 (* 1 = 0.7519 loss)
I0409 22:03:49.918769 25438 sgd_solver.cpp:105] Iteration 4212, lr = 0.00434162
I0409 22:03:55.557569 25438 solver.cpp:218] Iteration 4224 (2.12817 iter/s, 5.63864s/12 iters), loss = 0.934434
I0409 22:03:55.557617 25438 solver.cpp:237]     Train net output #0: loss = 0.934434 (* 1 = 0.934434 loss)
I0409 22:03:55.557626 25438 sgd_solver.cpp:105] Iteration 4224, lr = 0.00433131
I0409 22:04:00.712242 25438 solver.cpp:218] Iteration 4236 (2.32808 iter/s, 5.15446s/12 iters), loss = 0.937814
I0409 22:04:00.712289 25438 solver.cpp:237]     Train net output #0: loss = 0.937814 (* 1 = 0.937814 loss)
I0409 22:04:00.712297 25438 sgd_solver.cpp:105] Iteration 4236, lr = 0.00432103
I0409 22:04:05.849450 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:04:06.083989 25438 solver.cpp:218] Iteration 4248 (2.234 iter/s, 5.37153s/12 iters), loss = 1.1248
I0409 22:04:06.084039 25438 solver.cpp:237]     Train net output #0: loss = 1.1248 (* 1 = 1.1248 loss)
I0409 22:04:06.084049 25438 sgd_solver.cpp:105] Iteration 4248, lr = 0.00431077
I0409 22:04:11.441278 25438 solver.cpp:218] Iteration 4260 (2.24003 iter/s, 5.35707s/12 iters), loss = 0.686256
I0409 22:04:11.441323 25438 solver.cpp:237]     Train net output #0: loss = 0.686256 (* 1 = 0.686256 loss)
I0409 22:04:11.441334 25438 sgd_solver.cpp:105] Iteration 4260, lr = 0.00430053
I0409 22:04:17.082681 25438 solver.cpp:218] Iteration 4272 (2.12721 iter/s, 5.64118s/12 iters), loss = 0.766049
I0409 22:04:17.082726 25438 solver.cpp:237]     Train net output #0: loss = 0.766049 (* 1 = 0.766049 loss)
I0409 22:04:17.082736 25438 sgd_solver.cpp:105] Iteration 4272, lr = 0.00429032
I0409 22:04:22.226686 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0409 22:04:29.640975 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0409 22:04:35.563783 25438 solver.cpp:330] Iteration 4284, Testing net (#0)
I0409 22:04:35.563809 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:04:38.367313 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:04:40.080224 25438 solver.cpp:397]     Test net output #0: accuracy = 0.147059
I0409 22:04:40.080272 25438 solver.cpp:397]     Test net output #1: loss = 5.67465 (* 1 = 5.67465 loss)
I0409 22:04:40.177809 25438 solver.cpp:218] Iteration 4284 (0.519606 iter/s, 23.0944s/12 iters), loss = 0.835888
I0409 22:04:40.177862 25438 solver.cpp:237]     Train net output #0: loss = 0.835888 (* 1 = 0.835888 loss)
I0409 22:04:40.177873 25438 sgd_solver.cpp:105] Iteration 4284, lr = 0.00428014
I0409 22:04:44.885809 25438 solver.cpp:218] Iteration 4296 (2.54896 iter/s, 4.7078s/12 iters), loss = 0.976405
I0409 22:04:44.885861 25438 solver.cpp:237]     Train net output #0: loss = 0.976405 (* 1 = 0.976405 loss)
I0409 22:04:44.885874 25438 sgd_solver.cpp:105] Iteration 4296, lr = 0.00426998
I0409 22:04:50.417039 25438 solver.cpp:218] Iteration 4308 (2.16959 iter/s, 5.531s/12 iters), loss = 1.12444
I0409 22:04:50.417093 25438 solver.cpp:237]     Train net output #0: loss = 1.12444 (* 1 = 1.12444 loss)
I0409 22:04:50.417105 25438 sgd_solver.cpp:105] Iteration 4308, lr = 0.00425984
I0409 22:04:56.061614 25438 solver.cpp:218] Iteration 4320 (2.12602 iter/s, 5.64435s/12 iters), loss = 0.807547
I0409 22:04:56.061730 25438 solver.cpp:237]     Train net output #0: loss = 0.807547 (* 1 = 0.807547 loss)
I0409 22:04:56.061743 25438 sgd_solver.cpp:105] Iteration 4320, lr = 0.00424972
I0409 22:05:01.726651 25438 solver.cpp:218] Iteration 4332 (2.11836 iter/s, 5.66475s/12 iters), loss = 0.992712
I0409 22:05:01.726698 25438 solver.cpp:237]     Train net output #0: loss = 0.992712 (* 1 = 0.992712 loss)
I0409 22:05:01.726707 25438 sgd_solver.cpp:105] Iteration 4332, lr = 0.00423964
I0409 22:05:07.399613 25438 solver.cpp:218] Iteration 4344 (2.11538 iter/s, 5.67274s/12 iters), loss = 0.850997
I0409 22:05:07.399672 25438 solver.cpp:237]     Train net output #0: loss = 0.850997 (* 1 = 0.850997 loss)
I0409 22:05:07.399683 25438 sgd_solver.cpp:105] Iteration 4344, lr = 0.00422957
I0409 22:05:09.564358 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:05:13.052767 25438 solver.cpp:218] Iteration 4356 (2.12279 iter/s, 5.65293s/12 iters), loss = 0.860095
I0409 22:05:13.052811 25438 solver.cpp:237]     Train net output #0: loss = 0.860095 (* 1 = 0.860095 loss)
I0409 22:05:13.052820 25438 sgd_solver.cpp:105] Iteration 4356, lr = 0.00421953
I0409 22:05:18.548171 25438 solver.cpp:218] Iteration 4368 (2.18373 iter/s, 5.49519s/12 iters), loss = 0.846481
I0409 22:05:18.548213 25438 solver.cpp:237]     Train net output #0: loss = 0.846481 (* 1 = 0.846481 loss)
I0409 22:05:18.548223 25438 sgd_solver.cpp:105] Iteration 4368, lr = 0.00420951
I0409 22:05:24.207347 25438 solver.cpp:218] Iteration 4380 (2.12053 iter/s, 5.65895s/12 iters), loss = 0.812539
I0409 22:05:24.207403 25438 solver.cpp:237]     Train net output #0: loss = 0.812539 (* 1 = 0.812539 loss)
I0409 22:05:24.207415 25438 sgd_solver.cpp:105] Iteration 4380, lr = 0.00419952
I0409 22:05:26.462872 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0409 22:05:37.394604 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0409 22:05:43.682942 25438 solver.cpp:330] Iteration 4386, Testing net (#0)
I0409 22:05:43.682967 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:05:46.557618 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:05:48.328861 25438 solver.cpp:397]     Test net output #0: accuracy = 0.140319
I0409 22:05:48.328903 25438 solver.cpp:397]     Test net output #1: loss = 5.86112 (* 1 = 5.86112 loss)
I0409 22:05:50.407546 25438 solver.cpp:218] Iteration 4392 (0.458026 iter/s, 26.1994s/12 iters), loss = 0.743244
I0409 22:05:50.407601 25438 solver.cpp:237]     Train net output #0: loss = 0.743244 (* 1 = 0.743244 loss)
I0409 22:05:50.407614 25438 sgd_solver.cpp:105] Iteration 4392, lr = 0.00418954
I0409 22:05:55.718472 25438 solver.cpp:218] Iteration 4404 (2.25958 iter/s, 5.31071s/12 iters), loss = 1.09139
I0409 22:05:55.718513 25438 solver.cpp:237]     Train net output #0: loss = 1.09139 (* 1 = 1.09139 loss)
I0409 22:05:55.718521 25438 sgd_solver.cpp:105] Iteration 4404, lr = 0.0041796
I0409 22:06:01.152058 25438 solver.cpp:218] Iteration 4416 (2.20857 iter/s, 5.43337s/12 iters), loss = 0.882145
I0409 22:06:01.152189 25438 solver.cpp:237]     Train net output #0: loss = 0.882145 (* 1 = 0.882145 loss)
I0409 22:06:01.152199 25438 sgd_solver.cpp:105] Iteration 4416, lr = 0.00416967
I0409 22:06:06.801412 25438 solver.cpp:218] Iteration 4428 (2.12425 iter/s, 5.64905s/12 iters), loss = 0.680793
I0409 22:06:06.801467 25438 solver.cpp:237]     Train net output #0: loss = 0.680793 (* 1 = 0.680793 loss)
I0409 22:06:06.801479 25438 sgd_solver.cpp:105] Iteration 4428, lr = 0.00415977
I0409 22:06:12.343734 25438 solver.cpp:218] Iteration 4440 (2.16525 iter/s, 5.5421s/12 iters), loss = 0.723163
I0409 22:06:12.343778 25438 solver.cpp:237]     Train net output #0: loss = 0.723163 (* 1 = 0.723163 loss)
I0409 22:06:12.343787 25438 sgd_solver.cpp:105] Iteration 4440, lr = 0.0041499
I0409 22:06:16.913166 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:06:18.000623 25438 solver.cpp:218] Iteration 4452 (2.12139 iter/s, 5.65667s/12 iters), loss = 1.02045
I0409 22:06:18.000676 25438 solver.cpp:237]     Train net output #0: loss = 1.02045 (* 1 = 1.02045 loss)
I0409 22:06:18.000686 25438 sgd_solver.cpp:105] Iteration 4452, lr = 0.00414005
I0409 22:06:23.547677 25438 solver.cpp:218] Iteration 4464 (2.1634 iter/s, 5.54683s/12 iters), loss = 0.843643
I0409 22:06:23.547725 25438 solver.cpp:237]     Train net output #0: loss = 0.843643 (* 1 = 0.843643 loss)
I0409 22:06:23.547734 25438 sgd_solver.cpp:105] Iteration 4464, lr = 0.00413022
I0409 22:06:28.885987 25438 solver.cpp:218] Iteration 4476 (2.248 iter/s, 5.33808s/12 iters), loss = 0.88154
I0409 22:06:28.886044 25438 solver.cpp:237]     Train net output #0: loss = 0.88154 (* 1 = 0.88154 loss)
I0409 22:06:28.886055 25438 sgd_solver.cpp:105] Iteration 4476, lr = 0.00412041
I0409 22:06:33.894093 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0409 22:06:38.215974 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0409 22:06:41.383862 25438 solver.cpp:330] Iteration 4488, Testing net (#0)
I0409 22:06:41.383884 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:06:44.099074 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:06:45.890102 25438 solver.cpp:397]     Test net output #0: accuracy = 0.154412
I0409 22:06:45.890152 25438 solver.cpp:397]     Test net output #1: loss = 5.89654 (* 1 = 5.89654 loss)
I0409 22:06:45.987510 25438 solver.cpp:218] Iteration 4488 (0.701714 iter/s, 17.101s/12 iters), loss = 0.936288
I0409 22:06:45.987557 25438 solver.cpp:237]     Train net output #0: loss = 0.936288 (* 1 = 0.936288 loss)
I0409 22:06:45.987566 25438 sgd_solver.cpp:105] Iteration 4488, lr = 0.00411063
I0409 22:06:50.742887 25438 solver.cpp:218] Iteration 4500 (2.52356 iter/s, 4.75518s/12 iters), loss = 0.561442
I0409 22:06:50.742936 25438 solver.cpp:237]     Train net output #0: loss = 0.561442 (* 1 = 0.561442 loss)
I0409 22:06:50.742946 25438 sgd_solver.cpp:105] Iteration 4500, lr = 0.00410087
I0409 22:06:56.334839 25438 solver.cpp:218] Iteration 4512 (2.14603 iter/s, 5.59173s/12 iters), loss = 0.704562
I0409 22:06:56.334890 25438 solver.cpp:237]     Train net output #0: loss = 0.704562 (* 1 = 0.704562 loss)
I0409 22:06:56.334903 25438 sgd_solver.cpp:105] Iteration 4512, lr = 0.00409113
I0409 22:07:01.968405 25438 solver.cpp:218] Iteration 4524 (2.13017 iter/s, 5.63335s/12 iters), loss = 0.747457
I0409 22:07:01.968444 25438 solver.cpp:237]     Train net output #0: loss = 0.747457 (* 1 = 0.747457 loss)
I0409 22:07:01.968451 25438 sgd_solver.cpp:105] Iteration 4524, lr = 0.00408142
I0409 22:07:07.620049 25438 solver.cpp:218] Iteration 4536 (2.12336 iter/s, 5.65143s/12 iters), loss = 0.68139
I0409 22:07:07.620143 25438 solver.cpp:237]     Train net output #0: loss = 0.68139 (* 1 = 0.68139 loss)
I0409 22:07:07.620153 25438 sgd_solver.cpp:105] Iteration 4536, lr = 0.00407173
I0409 22:07:13.280855 25438 solver.cpp:218] Iteration 4548 (2.11994 iter/s, 5.66054s/12 iters), loss = 0.935755
I0409 22:07:13.280901 25438 solver.cpp:237]     Train net output #0: loss = 0.935755 (* 1 = 0.935755 loss)
I0409 22:07:13.280911 25438 sgd_solver.cpp:105] Iteration 4548, lr = 0.00406206
I0409 22:07:14.610927 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:07:18.522346 25438 solver.cpp:218] Iteration 4560 (2.28952 iter/s, 5.24127s/12 iters), loss = 0.800476
I0409 22:07:18.522403 25438 solver.cpp:237]     Train net output #0: loss = 0.800476 (* 1 = 0.800476 loss)
I0409 22:07:18.522414 25438 sgd_solver.cpp:105] Iteration 4560, lr = 0.00405242
I0409 22:07:23.944955 25438 solver.cpp:218] Iteration 4572 (2.21305 iter/s, 5.42239s/12 iters), loss = 0.947623
I0409 22:07:23.944995 25438 solver.cpp:237]     Train net output #0: loss = 0.947623 (* 1 = 0.947623 loss)
I0409 22:07:23.945003 25438 sgd_solver.cpp:105] Iteration 4572, lr = 0.0040428
I0409 22:07:29.161550 25438 solver.cpp:218] Iteration 4584 (2.30044 iter/s, 5.21639s/12 iters), loss = 1.14048
I0409 22:07:29.161608 25438 solver.cpp:237]     Train net output #0: loss = 1.14048 (* 1 = 1.14048 loss)
I0409 22:07:29.161619 25438 sgd_solver.cpp:105] Iteration 4584, lr = 0.0040332
I0409 22:07:31.316746 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0409 22:07:35.529085 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0409 22:07:38.814819 25438 solver.cpp:330] Iteration 4590, Testing net (#0)
I0409 22:07:38.814934 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:07:41.422531 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:07:43.261445 25438 solver.cpp:397]     Test net output #0: accuracy = 0.148284
I0409 22:07:43.261493 25438 solver.cpp:397]     Test net output #1: loss = 6.10567 (* 1 = 6.10567 loss)
I0409 22:07:45.151155 25438 solver.cpp:218] Iteration 4596 (0.750512 iter/s, 15.9891s/12 iters), loss = 0.724634
I0409 22:07:45.151204 25438 solver.cpp:237]     Train net output #0: loss = 0.724634 (* 1 = 0.724634 loss)
I0409 22:07:45.151216 25438 sgd_solver.cpp:105] Iteration 4596, lr = 0.00402362
I0409 22:07:50.675175 25438 solver.cpp:218] Iteration 4608 (2.17242 iter/s, 5.5238s/12 iters), loss = 0.804009
I0409 22:07:50.675222 25438 solver.cpp:237]     Train net output #0: loss = 0.804009 (* 1 = 0.804009 loss)
I0409 22:07:50.675233 25438 sgd_solver.cpp:105] Iteration 4608, lr = 0.00401407
I0409 22:07:56.167991 25438 solver.cpp:218] Iteration 4620 (2.18476 iter/s, 5.4926s/12 iters), loss = 0.738099
I0409 22:07:56.168040 25438 solver.cpp:237]     Train net output #0: loss = 0.738099 (* 1 = 0.738099 loss)
I0409 22:07:56.168051 25438 sgd_solver.cpp:105] Iteration 4620, lr = 0.00400454
I0409 22:08:01.789172 25438 solver.cpp:218] Iteration 4632 (2.13487 iter/s, 5.62096s/12 iters), loss = 0.724833
I0409 22:08:01.789218 25438 solver.cpp:237]     Train net output #0: loss = 0.724833 (* 1 = 0.724833 loss)
I0409 22:08:01.789230 25438 sgd_solver.cpp:105] Iteration 4632, lr = 0.00399503
I0409 22:08:07.428808 25438 solver.cpp:218] Iteration 4644 (2.12788 iter/s, 5.63942s/12 iters), loss = 0.816332
I0409 22:08:07.428856 25438 solver.cpp:237]     Train net output #0: loss = 0.816332 (* 1 = 0.816332 loss)
I0409 22:08:07.428869 25438 sgd_solver.cpp:105] Iteration 4644, lr = 0.00398555
I0409 22:08:11.012197 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:08:12.641875 25438 solver.cpp:218] Iteration 4656 (2.302 iter/s, 5.21286s/12 iters), loss = 0.571169
I0409 22:08:12.641924 25438 solver.cpp:237]     Train net output #0: loss = 0.571169 (* 1 = 0.571169 loss)
I0409 22:08:12.641937 25438 sgd_solver.cpp:105] Iteration 4656, lr = 0.00397608
I0409 22:08:18.126636 25438 solver.cpp:218] Iteration 4668 (2.18797 iter/s, 5.48454s/12 iters), loss = 0.722522
I0409 22:08:18.126688 25438 solver.cpp:237]     Train net output #0: loss = 0.722522 (* 1 = 0.722522 loss)
I0409 22:08:18.126700 25438 sgd_solver.cpp:105] Iteration 4668, lr = 0.00396664
I0409 22:08:23.513667 25438 solver.cpp:218] Iteration 4680 (2.22766 iter/s, 5.38681s/12 iters), loss = 1.08819
I0409 22:08:23.513717 25438 solver.cpp:237]     Train net output #0: loss = 1.08819 (* 1 = 1.08819 loss)
I0409 22:08:23.513728 25438 sgd_solver.cpp:105] Iteration 4680, lr = 0.00395723
I0409 22:08:28.534970 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0409 22:08:32.714709 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0409 22:08:35.887632 25438 solver.cpp:330] Iteration 4692, Testing net (#0)
I0409 22:08:35.887655 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:08:38.578545 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:08:40.502930 25438 solver.cpp:397]     Test net output #0: accuracy = 0.158701
I0409 22:08:40.502977 25438 solver.cpp:397]     Test net output #1: loss = 6.30846 (* 1 = 6.30846 loss)
I0409 22:08:40.600332 25438 solver.cpp:218] Iteration 4692 (0.702325 iter/s, 17.0861s/12 iters), loss = 0.857873
I0409 22:08:40.600376 25438 solver.cpp:237]     Train net output #0: loss = 0.857873 (* 1 = 0.857873 loss)
I0409 22:08:40.600386 25438 sgd_solver.cpp:105] Iteration 4692, lr = 0.00394783
I0409 22:08:45.304592 25438 solver.cpp:218] Iteration 4704 (2.55099 iter/s, 4.70406s/12 iters), loss = 0.769565
I0409 22:08:45.304702 25438 solver.cpp:237]     Train net output #0: loss = 0.769565 (* 1 = 0.769565 loss)
I0409 22:08:45.304711 25438 sgd_solver.cpp:105] Iteration 4704, lr = 0.00393846
I0409 22:08:50.847919 25438 solver.cpp:218] Iteration 4716 (2.16487 iter/s, 5.54305s/12 iters), loss = 0.83104
I0409 22:08:50.847973 25438 solver.cpp:237]     Train net output #0: loss = 0.83104 (* 1 = 0.83104 loss)
I0409 22:08:50.847985 25438 sgd_solver.cpp:105] Iteration 4716, lr = 0.00392911
I0409 22:08:56.120812 25438 solver.cpp:218] Iteration 4728 (2.27589 iter/s, 5.27267s/12 iters), loss = 0.70442
I0409 22:08:56.120864 25438 solver.cpp:237]     Train net output #0: loss = 0.70442 (* 1 = 0.70442 loss)
I0409 22:08:56.120877 25438 sgd_solver.cpp:105] Iteration 4728, lr = 0.00391978
I0409 22:09:01.688872 25438 solver.cpp:218] Iteration 4740 (2.15524 iter/s, 5.56784s/12 iters), loss = 0.536215
I0409 22:09:01.688916 25438 solver.cpp:237]     Train net output #0: loss = 0.536215 (* 1 = 0.536215 loss)
I0409 22:09:01.688926 25438 sgd_solver.cpp:105] Iteration 4740, lr = 0.00391047
I0409 22:09:07.240705 25438 solver.cpp:218] Iteration 4752 (2.16153 iter/s, 5.55161s/12 iters), loss = 0.532834
I0409 22:09:07.240753 25438 solver.cpp:237]     Train net output #0: loss = 0.532834 (* 1 = 0.532834 loss)
I0409 22:09:07.240763 25438 sgd_solver.cpp:105] Iteration 4752, lr = 0.00390119
I0409 22:09:07.836421 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:09:12.682075 25438 solver.cpp:218] Iteration 4764 (2.20542 iter/s, 5.44115s/12 iters), loss = 0.682136
I0409 22:09:12.682126 25438 solver.cpp:237]     Train net output #0: loss = 0.682136 (* 1 = 0.682136 loss)
I0409 22:09:12.682137 25438 sgd_solver.cpp:105] Iteration 4764, lr = 0.00389193
I0409 22:09:18.226158 25438 solver.cpp:218] Iteration 4776 (2.16456 iter/s, 5.54386s/12 iters), loss = 0.894321
I0409 22:09:18.226267 25438 solver.cpp:237]     Train net output #0: loss = 0.894321 (* 1 = 0.894321 loss)
I0409 22:09:18.226279 25438 sgd_solver.cpp:105] Iteration 4776, lr = 0.00388269
I0409 22:09:23.549408 25438 solver.cpp:218] Iteration 4788 (2.25438 iter/s, 5.32298s/12 iters), loss = 0.804363
I0409 22:09:23.549458 25438 solver.cpp:237]     Train net output #0: loss = 0.804363 (* 1 = 0.804363 loss)
I0409 22:09:23.549469 25438 sgd_solver.cpp:105] Iteration 4788, lr = 0.00387347
I0409 22:09:25.767796 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0409 22:09:31.829301 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0409 22:09:34.993543 25438 solver.cpp:330] Iteration 4794, Testing net (#0)
I0409 22:09:34.993561 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:09:37.527276 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:09:39.445823 25438 solver.cpp:397]     Test net output #0: accuracy = 0.14277
I0409 22:09:39.445871 25438 solver.cpp:397]     Test net output #1: loss = 6.11473 (* 1 = 6.11473 loss)
I0409 22:09:41.414268 25438 solver.cpp:218] Iteration 4800 (0.671731 iter/s, 17.8643s/12 iters), loss = 0.868726
I0409 22:09:41.414320 25438 solver.cpp:237]     Train net output #0: loss = 0.868726 (* 1 = 0.868726 loss)
I0409 22:09:41.414331 25438 sgd_solver.cpp:105] Iteration 4800, lr = 0.00386427
I0409 22:09:47.053833 25438 solver.cpp:218] Iteration 4812 (2.12791 iter/s, 5.63933s/12 iters), loss = 0.787657
I0409 22:09:47.053889 25438 solver.cpp:237]     Train net output #0: loss = 0.787657 (* 1 = 0.787657 loss)
I0409 22:09:47.053900 25438 sgd_solver.cpp:105] Iteration 4812, lr = 0.0038551
I0409 22:09:52.742820 25438 solver.cpp:218] Iteration 4824 (2.10943 iter/s, 5.68875s/12 iters), loss = 0.622275
I0409 22:09:52.742900 25438 solver.cpp:237]     Train net output #0: loss = 0.622275 (* 1 = 0.622275 loss)
I0409 22:09:52.742911 25438 sgd_solver.cpp:105] Iteration 4824, lr = 0.00384594
I0409 22:09:58.394111 25438 solver.cpp:218] Iteration 4836 (2.12351 iter/s, 5.65103s/12 iters), loss = 0.627339
I0409 22:09:58.394170 25438 solver.cpp:237]     Train net output #0: loss = 0.627339 (* 1 = 0.627339 loss)
I0409 22:09:58.394183 25438 sgd_solver.cpp:105] Iteration 4836, lr = 0.00383681
I0409 22:10:00.710505 25438 blocking_queue.cpp:49] Waiting for data
I0409 22:10:03.998512 25438 solver.cpp:218] Iteration 4848 (2.14126 iter/s, 5.60417s/12 iters), loss = 0.689377
I0409 22:10:03.998558 25438 solver.cpp:237]     Train net output #0: loss = 0.689377 (* 1 = 0.689377 loss)
I0409 22:10:03.998567 25438 sgd_solver.cpp:105] Iteration 4848, lr = 0.0038277
I0409 22:10:06.905444 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:10:09.441180 25438 solver.cpp:218] Iteration 4860 (2.20489 iter/s, 5.44245s/12 iters), loss = 0.408139
I0409 22:10:09.441226 25438 solver.cpp:237]     Train net output #0: loss = 0.408139 (* 1 = 0.408139 loss)
I0409 22:10:09.441236 25438 sgd_solver.cpp:105] Iteration 4860, lr = 0.00381862
I0409 22:10:15.090802 25438 solver.cpp:218] Iteration 4872 (2.12412 iter/s, 5.6494s/12 iters), loss = 0.457946
I0409 22:10:15.090842 25438 solver.cpp:237]     Train net output #0: loss = 0.457946 (* 1 = 0.457946 loss)
I0409 22:10:15.090852 25438 sgd_solver.cpp:105] Iteration 4872, lr = 0.00380955
I0409 22:10:20.564247 25438 solver.cpp:218] Iteration 4884 (2.19249 iter/s, 5.47323s/12 iters), loss = 0.673722
I0409 22:10:20.564314 25438 solver.cpp:237]     Train net output #0: loss = 0.673722 (* 1 = 0.673722 loss)
I0409 22:10:20.564329 25438 sgd_solver.cpp:105] Iteration 4884, lr = 0.0038005
I0409 22:10:25.621727 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0409 22:10:30.734935 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0409 22:10:41.051959 25438 solver.cpp:330] Iteration 4896, Testing net (#0)
I0409 22:10:41.051987 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:10:43.623471 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:10:45.576468 25438 solver.cpp:397]     Test net output #0: accuracy = 0.152574
I0409 22:10:45.576519 25438 solver.cpp:397]     Test net output #1: loss = 6.34305 (* 1 = 6.34305 loss)
I0409 22:10:45.675359 25438 solver.cpp:218] Iteration 4896 (0.477891 iter/s, 25.1103s/12 iters), loss = 0.617193
I0409 22:10:45.675406 25438 solver.cpp:237]     Train net output #0: loss = 0.617193 (* 1 = 0.617193 loss)
I0409 22:10:45.675416 25438 sgd_solver.cpp:105] Iteration 4896, lr = 0.00379148
I0409 22:10:50.405360 25438 solver.cpp:218] Iteration 4908 (2.5371 iter/s, 4.72981s/12 iters), loss = 0.525018
I0409 22:10:50.405411 25438 solver.cpp:237]     Train net output #0: loss = 0.525018 (* 1 = 0.525018 loss)
I0409 22:10:50.405422 25438 sgd_solver.cpp:105] Iteration 4908, lr = 0.00378248
I0409 22:10:55.637894 25438 solver.cpp:218] Iteration 4920 (2.29344 iter/s, 5.23232s/12 iters), loss = 0.59624
I0409 22:10:55.638021 25438 solver.cpp:237]     Train net output #0: loss = 0.59624 (* 1 = 0.59624 loss)
I0409 22:10:55.638031 25438 sgd_solver.cpp:105] Iteration 4920, lr = 0.0037735
I0409 22:11:01.057791 25438 solver.cpp:218] Iteration 4932 (2.21419 iter/s, 5.41959s/12 iters), loss = 0.655712
I0409 22:11:01.057849 25438 solver.cpp:237]     Train net output #0: loss = 0.655712 (* 1 = 0.655712 loss)
I0409 22:11:01.057860 25438 sgd_solver.cpp:105] Iteration 4932, lr = 0.00376454
I0409 22:11:06.299791 25438 solver.cpp:218] Iteration 4944 (2.2893 iter/s, 5.24178s/12 iters), loss = 0.572317
I0409 22:11:06.299845 25438 solver.cpp:237]     Train net output #0: loss = 0.572317 (* 1 = 0.572317 loss)
I0409 22:11:06.299857 25438 sgd_solver.cpp:105] Iteration 4944, lr = 0.0037556
I0409 22:11:11.485738 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:11:11.701905 25438 solver.cpp:218] Iteration 4956 (2.22144 iter/s, 5.40189s/12 iters), loss = 0.499436
I0409 22:11:11.701951 25438 solver.cpp:237]     Train net output #0: loss = 0.499436 (* 1 = 0.499436 loss)
I0409 22:11:11.701982 25438 sgd_solver.cpp:105] Iteration 4956, lr = 0.00374669
I0409 22:11:17.359876 25438 solver.cpp:218] Iteration 4968 (2.12099 iter/s, 5.65775s/12 iters), loss = 0.463446
I0409 22:11:17.359928 25438 solver.cpp:237]     Train net output #0: loss = 0.463446 (* 1 = 0.463446 loss)
I0409 22:11:17.359941 25438 sgd_solver.cpp:105] Iteration 4968, lr = 0.00373779
I0409 22:11:22.853551 25438 solver.cpp:218] Iteration 4980 (2.18442 iter/s, 5.49345s/12 iters), loss = 0.695784
I0409 22:11:22.853602 25438 solver.cpp:237]     Train net output #0: loss = 0.695784 (* 1 = 0.695784 loss)
I0409 22:11:22.853615 25438 sgd_solver.cpp:105] Iteration 4980, lr = 0.00372892
I0409 22:11:28.377555 25438 solver.cpp:218] Iteration 4992 (2.17243 iter/s, 5.52378s/12 iters), loss = 0.648791
I0409 22:11:28.377660 25438 solver.cpp:237]     Train net output #0: loss = 0.648791 (* 1 = 0.648791 loss)
I0409 22:11:28.377671 25438 sgd_solver.cpp:105] Iteration 4992, lr = 0.00372006
I0409 22:11:30.503393 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0409 22:11:38.346184 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0409 22:11:44.605384 25438 solver.cpp:330] Iteration 4998, Testing net (#0)
I0409 22:11:44.605412 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:11:47.190137 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:11:49.369563 25438 solver.cpp:397]     Test net output #0: accuracy = 0.152574
I0409 22:11:49.369612 25438 solver.cpp:397]     Test net output #1: loss = 6.18227 (* 1 = 6.18227 loss)
I0409 22:11:51.348486 25438 solver.cpp:218] Iteration 5004 (0.522417 iter/s, 22.9702s/12 iters), loss = 0.675841
I0409 22:11:51.348526 25438 solver.cpp:237]     Train net output #0: loss = 0.675841 (* 1 = 0.675841 loss)
I0409 22:11:51.348534 25438 sgd_solver.cpp:105] Iteration 5004, lr = 0.00371123
I0409 22:11:56.569075 25438 solver.cpp:218] Iteration 5016 (2.29868 iter/s, 5.22039s/12 iters), loss = 0.752574
I0409 22:11:56.569126 25438 solver.cpp:237]     Train net output #0: loss = 0.752574 (* 1 = 0.752574 loss)
I0409 22:11:56.569136 25438 sgd_solver.cpp:105] Iteration 5016, lr = 0.00370242
I0409 22:12:01.797173 25438 solver.cpp:218] Iteration 5028 (2.29538 iter/s, 5.22788s/12 iters), loss = 0.384198
I0409 22:12:01.797252 25438 solver.cpp:237]     Train net output #0: loss = 0.384198 (* 1 = 0.384198 loss)
I0409 22:12:01.797263 25438 sgd_solver.cpp:105] Iteration 5028, lr = 0.00369363
I0409 22:12:07.017457 25438 solver.cpp:218] Iteration 5040 (2.29883 iter/s, 5.22004s/12 iters), loss = 0.68728
I0409 22:12:07.017508 25438 solver.cpp:237]     Train net output #0: loss = 0.68728 (* 1 = 0.68728 loss)
I0409 22:12:07.017519 25438 sgd_solver.cpp:105] Iteration 5040, lr = 0.00368486
I0409 22:12:12.346311 25438 solver.cpp:218] Iteration 5052 (2.25198 iter/s, 5.32864s/12 iters), loss = 0.694188
I0409 22:12:12.346356 25438 solver.cpp:237]     Train net output #0: loss = 0.694188 (* 1 = 0.694188 loss)
I0409 22:12:12.346364 25438 sgd_solver.cpp:105] Iteration 5052, lr = 0.00367611
I0409 22:12:14.389077 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:12:17.613176 25438 solver.cpp:218] Iteration 5064 (2.27849 iter/s, 5.26665s/12 iters), loss = 0.582875
I0409 22:12:17.613229 25438 solver.cpp:237]     Train net output #0: loss = 0.582875 (* 1 = 0.582875 loss)
I0409 22:12:17.613240 25438 sgd_solver.cpp:105] Iteration 5064, lr = 0.00366738
I0409 22:12:22.862618 25438 solver.cpp:218] Iteration 5076 (2.28605 iter/s, 5.24922s/12 iters), loss = 0.504741
I0409 22:12:22.862674 25438 solver.cpp:237]     Train net output #0: loss = 0.504741 (* 1 = 0.504741 loss)
I0409 22:12:22.862686 25438 sgd_solver.cpp:105] Iteration 5076, lr = 0.00365868
I0409 22:12:28.187989 25438 solver.cpp:218] Iteration 5088 (2.25346 iter/s, 5.32515s/12 iters), loss = 0.517681
I0409 22:12:28.188035 25438 solver.cpp:237]     Train net output #0: loss = 0.517681 (* 1 = 0.517681 loss)
I0409 22:12:28.188045 25438 sgd_solver.cpp:105] Iteration 5088, lr = 0.00364999
I0409 22:12:32.978494 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0409 22:12:37.199149 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0409 22:12:40.382831 25438 solver.cpp:330] Iteration 5100, Testing net (#0)
I0409 22:12:40.382849 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:12:42.898377 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:12:45.005331 25438 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0409 22:12:45.005383 25438 solver.cpp:397]     Test net output #1: loss = 6.44678 (* 1 = 6.44678 loss)
I0409 22:12:45.102671 25438 solver.cpp:218] Iteration 5100 (0.709466 iter/s, 16.9141s/12 iters), loss = 0.55861
I0409 22:12:45.102725 25438 solver.cpp:237]     Train net output #0: loss = 0.55861 (* 1 = 0.55861 loss)
I0409 22:12:45.102736 25438 sgd_solver.cpp:105] Iteration 5100, lr = 0.00364132
I0409 22:12:49.566413 25438 solver.cpp:218] Iteration 5112 (2.68845 iter/s, 4.46354s/12 iters), loss = 0.657814
I0409 22:12:49.566466 25438 solver.cpp:237]     Train net output #0: loss = 0.657814 (* 1 = 0.657814 loss)
I0409 22:12:49.566478 25438 sgd_solver.cpp:105] Iteration 5112, lr = 0.00363268
I0409 22:12:54.816009 25438 solver.cpp:218] Iteration 5124 (2.28598 iter/s, 5.24938s/12 iters), loss = 0.554309
I0409 22:12:54.816056 25438 solver.cpp:237]     Train net output #0: loss = 0.554309 (* 1 = 0.554309 loss)
I0409 22:12:54.816067 25438 sgd_solver.cpp:105] Iteration 5124, lr = 0.00362405
I0409 22:13:00.076443 25438 solver.cpp:218] Iteration 5136 (2.28127 iter/s, 5.26022s/12 iters), loss = 0.550035
I0409 22:13:00.076496 25438 solver.cpp:237]     Train net output #0: loss = 0.550035 (* 1 = 0.550035 loss)
I0409 22:13:00.076508 25438 sgd_solver.cpp:105] Iteration 5136, lr = 0.00361545
I0409 22:13:05.477375 25438 solver.cpp:218] Iteration 5148 (2.22193 iter/s, 5.40072s/12 iters), loss = 0.573739
I0409 22:13:05.477490 25438 solver.cpp:237]     Train net output #0: loss = 0.573739 (* 1 = 0.573739 loss)
I0409 22:13:05.477502 25438 sgd_solver.cpp:105] Iteration 5148, lr = 0.00360687
I0409 22:13:09.672765 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:13:10.653650 25438 solver.cpp:218] Iteration 5160 (2.31839 iter/s, 5.176s/12 iters), loss = 0.534274
I0409 22:13:10.653707 25438 solver.cpp:237]     Train net output #0: loss = 0.534274 (* 1 = 0.534274 loss)
I0409 22:13:10.653717 25438 sgd_solver.cpp:105] Iteration 5160, lr = 0.0035983
I0409 22:13:16.173732 25438 solver.cpp:218] Iteration 5172 (2.17397 iter/s, 5.51985s/12 iters), loss = 0.333038
I0409 22:13:16.173785 25438 solver.cpp:237]     Train net output #0: loss = 0.333038 (* 1 = 0.333038 loss)
I0409 22:13:16.173796 25438 sgd_solver.cpp:105] Iteration 5172, lr = 0.00358976
I0409 22:13:21.538216 25438 solver.cpp:218] Iteration 5184 (2.23703 iter/s, 5.36426s/12 iters), loss = 0.504952
I0409 22:13:21.538270 25438 solver.cpp:237]     Train net output #0: loss = 0.504952 (* 1 = 0.504952 loss)
I0409 22:13:21.538291 25438 sgd_solver.cpp:105] Iteration 5184, lr = 0.00358124
I0409 22:13:26.694835 25438 solver.cpp:218] Iteration 5196 (2.3272 iter/s, 5.1564s/12 iters), loss = 0.487617
I0409 22:13:26.694881 25438 solver.cpp:237]     Train net output #0: loss = 0.487617 (* 1 = 0.487617 loss)
I0409 22:13:26.694890 25438 sgd_solver.cpp:105] Iteration 5196, lr = 0.00357273
I0409 22:13:28.850844 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0409 22:13:32.997594 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0409 22:13:36.179816 25438 solver.cpp:330] Iteration 5202, Testing net (#0)
I0409 22:13:36.179864 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:13:38.625162 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:13:40.740360 25438 solver.cpp:397]     Test net output #0: accuracy = 0.152574
I0409 22:13:40.740399 25438 solver.cpp:397]     Test net output #1: loss = 6.51677 (* 1 = 6.51677 loss)
I0409 22:13:42.983183 25438 solver.cpp:218] Iteration 5208 (0.736747 iter/s, 16.2878s/12 iters), loss = 0.441593
I0409 22:13:42.983229 25438 solver.cpp:237]     Train net output #0: loss = 0.441593 (* 1 = 0.441593 loss)
I0409 22:13:42.983239 25438 sgd_solver.cpp:105] Iteration 5208, lr = 0.00356425
I0409 22:13:48.257283 25438 solver.cpp:218] Iteration 5220 (2.27536 iter/s, 5.27388s/12 iters), loss = 0.576967
I0409 22:13:48.257331 25438 solver.cpp:237]     Train net output #0: loss = 0.576967 (* 1 = 0.576967 loss)
I0409 22:13:48.257340 25438 sgd_solver.cpp:105] Iteration 5220, lr = 0.00355579
I0409 22:13:53.528362 25438 solver.cpp:218] Iteration 5232 (2.27666 iter/s, 5.27087s/12 iters), loss = 0.395123
I0409 22:13:53.528405 25438 solver.cpp:237]     Train net output #0: loss = 0.395123 (* 1 = 0.395123 loss)
I0409 22:13:53.528414 25438 sgd_solver.cpp:105] Iteration 5232, lr = 0.00354735
I0409 22:13:59.138844 25438 solver.cpp:218] Iteration 5244 (2.13894 iter/s, 5.61026s/12 iters), loss = 0.466001
I0409 22:13:59.138891 25438 solver.cpp:237]     Train net output #0: loss = 0.466001 (* 1 = 0.466001 loss)
I0409 22:13:59.138900 25438 sgd_solver.cpp:105] Iteration 5244, lr = 0.00353892
I0409 22:14:04.679731 25438 solver.cpp:218] Iteration 5256 (2.1658 iter/s, 5.54067s/12 iters), loss = 0.412639
I0409 22:14:04.679776 25438 solver.cpp:237]     Train net output #0: loss = 0.412639 (* 1 = 0.412639 loss)
I0409 22:14:04.679787 25438 sgd_solver.cpp:105] Iteration 5256, lr = 0.00353052
I0409 22:14:05.996742 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:14:09.873601 25438 solver.cpp:218] Iteration 5268 (2.31051 iter/s, 5.19366s/12 iters), loss = 0.424757
I0409 22:14:09.873737 25438 solver.cpp:237]     Train net output #0: loss = 0.424757 (* 1 = 0.424757 loss)
I0409 22:14:09.873749 25438 sgd_solver.cpp:105] Iteration 5268, lr = 0.00352214
I0409 22:14:15.076750 25438 solver.cpp:218] Iteration 5280 (2.30643 iter/s, 5.20285s/12 iters), loss = 0.373493
I0409 22:14:15.076809 25438 solver.cpp:237]     Train net output #0: loss = 0.373493 (* 1 = 0.373493 loss)
I0409 22:14:15.076822 25438 sgd_solver.cpp:105] Iteration 5280, lr = 0.00351378
I0409 22:14:20.359239 25438 solver.cpp:218] Iteration 5292 (2.27175 iter/s, 5.28226s/12 iters), loss = 0.565125
I0409 22:14:20.359295 25438 solver.cpp:237]     Train net output #0: loss = 0.565125 (* 1 = 0.565125 loss)
I0409 22:14:20.359308 25438 sgd_solver.cpp:105] Iteration 5292, lr = 0.00350544
I0409 22:14:25.094533 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0409 22:14:29.212164 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0409 22:14:32.354473 25438 solver.cpp:330] Iteration 5304, Testing net (#0)
I0409 22:14:32.354496 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:14:34.691202 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:14:36.811412 25438 solver.cpp:397]     Test net output #0: accuracy = 0.162377
I0409 22:14:36.811453 25438 solver.cpp:397]     Test net output #1: loss = 6.14708 (* 1 = 6.14708 loss)
I0409 22:14:36.908854 25438 solver.cpp:218] Iteration 5304 (0.725116 iter/s, 16.5491s/12 iters), loss = 0.508269
I0409 22:14:36.908896 25438 solver.cpp:237]     Train net output #0: loss = 0.508269 (* 1 = 0.508269 loss)
I0409 22:14:36.908905 25438 sgd_solver.cpp:105] Iteration 5304, lr = 0.00349711
I0409 22:14:41.371508 25438 solver.cpp:218] Iteration 5316 (2.68909 iter/s, 4.46247s/12 iters), loss = 0.498306
I0409 22:14:41.371583 25438 solver.cpp:237]     Train net output #0: loss = 0.498306 (* 1 = 0.498306 loss)
I0409 22:14:41.371593 25438 sgd_solver.cpp:105] Iteration 5316, lr = 0.00348881
I0409 22:14:46.617244 25438 solver.cpp:218] Iteration 5328 (2.28768 iter/s, 5.24549s/12 iters), loss = 0.554903
I0409 22:14:46.617293 25438 solver.cpp:237]     Train net output #0: loss = 0.554903 (* 1 = 0.554903 loss)
I0409 22:14:46.617305 25438 sgd_solver.cpp:105] Iteration 5328, lr = 0.00348053
I0409 22:14:51.886132 25438 solver.cpp:218] Iteration 5340 (2.27762 iter/s, 5.26867s/12 iters), loss = 0.418253
I0409 22:14:51.886193 25438 solver.cpp:237]     Train net output #0: loss = 0.418253 (* 1 = 0.418253 loss)
I0409 22:14:51.886205 25438 sgd_solver.cpp:105] Iteration 5340, lr = 0.00347226
I0409 22:14:57.395851 25438 solver.cpp:218] Iteration 5352 (2.17806 iter/s, 5.50949s/12 iters), loss = 0.45829
I0409 22:14:57.395895 25438 solver.cpp:237]     Train net output #0: loss = 0.45829 (* 1 = 0.45829 loss)
I0409 22:14:57.395905 25438 sgd_solver.cpp:105] Iteration 5352, lr = 0.00346402
I0409 22:15:01.245441 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:15:03.008657 25438 solver.cpp:218] Iteration 5364 (2.13806 iter/s, 5.61258s/12 iters), loss = 0.386463
I0409 22:15:03.008710 25438 solver.cpp:237]     Train net output #0: loss = 0.386463 (* 1 = 0.386463 loss)
I0409 22:15:03.008721 25438 sgd_solver.cpp:105] Iteration 5364, lr = 0.0034558
I0409 22:15:08.682644 25438 solver.cpp:218] Iteration 5376 (2.115 iter/s, 5.67376s/12 iters), loss = 0.554711
I0409 22:15:08.682689 25438 solver.cpp:237]     Train net output #0: loss = 0.554711 (* 1 = 0.554711 loss)
I0409 22:15:08.682698 25438 sgd_solver.cpp:105] Iteration 5376, lr = 0.00344759
I0409 22:15:14.353229 25438 solver.cpp:218] Iteration 5388 (2.11627 iter/s, 5.67036s/12 iters), loss = 0.405153
I0409 22:15:14.353343 25438 solver.cpp:237]     Train net output #0: loss = 0.405153 (* 1 = 0.405153 loss)
I0409 22:15:14.353353 25438 sgd_solver.cpp:105] Iteration 5388, lr = 0.00343941
I0409 22:15:19.998539 25438 solver.cpp:218] Iteration 5400 (2.12577 iter/s, 5.64502s/12 iters), loss = 0.426778
I0409 22:15:19.998587 25438 solver.cpp:237]     Train net output #0: loss = 0.426778 (* 1 = 0.426778 loss)
I0409 22:15:19.998597 25438 sgd_solver.cpp:105] Iteration 5400, lr = 0.00343124
I0409 22:15:22.317263 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0409 22:15:29.445365 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0409 22:15:35.528856 25438 solver.cpp:330] Iteration 5406, Testing net (#0)
I0409 22:15:35.528882 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:15:37.855648 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:15:40.008968 25438 solver.cpp:397]     Test net output #0: accuracy = 0.158701
I0409 22:15:40.009013 25438 solver.cpp:397]     Test net output #1: loss = 6.69067 (* 1 = 6.69067 loss)
I0409 22:15:41.857085 25438 solver.cpp:218] Iteration 5412 (0.549002 iter/s, 21.8579s/12 iters), loss = 0.430642
I0409 22:15:41.857138 25438 solver.cpp:237]     Train net output #0: loss = 0.430642 (* 1 = 0.430642 loss)
I0409 22:15:41.857148 25438 sgd_solver.cpp:105] Iteration 5412, lr = 0.00342309
I0409 22:15:47.088024 25438 solver.cpp:218] Iteration 5424 (2.29414 iter/s, 5.23072s/12 iters), loss = 0.46932
I0409 22:15:47.088107 25438 solver.cpp:237]     Train net output #0: loss = 0.46932 (* 1 = 0.46932 loss)
I0409 22:15:47.088119 25438 sgd_solver.cpp:105] Iteration 5424, lr = 0.00341497
I0409 22:15:52.373448 25438 solver.cpp:218] Iteration 5436 (2.2705 iter/s, 5.28517s/12 iters), loss = 0.427131
I0409 22:15:52.373504 25438 solver.cpp:237]     Train net output #0: loss = 0.427131 (* 1 = 0.427131 loss)
I0409 22:15:52.373515 25438 sgd_solver.cpp:105] Iteration 5436, lr = 0.00340686
I0409 22:15:58.073853 25438 solver.cpp:218] Iteration 5448 (2.1052 iter/s, 5.70017s/12 iters), loss = 0.553602
I0409 22:15:58.073911 25438 solver.cpp:237]     Train net output #0: loss = 0.553602 (* 1 = 0.553602 loss)
I0409 22:15:58.073923 25438 sgd_solver.cpp:105] Iteration 5448, lr = 0.00339877
I0409 22:16:03.522536 25438 solver.cpp:218] Iteration 5460 (2.20246 iter/s, 5.44846s/12 iters), loss = 0.394793
I0409 22:16:03.522579 25438 solver.cpp:237]     Train net output #0: loss = 0.394793 (* 1 = 0.394793 loss)
I0409 22:16:03.522588 25438 sgd_solver.cpp:105] Iteration 5460, lr = 0.0033907
I0409 22:16:04.102771 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:16:08.792502 25438 solver.cpp:218] Iteration 5472 (2.27714 iter/s, 5.26976s/12 iters), loss = 0.469856
I0409 22:16:08.792544 25438 solver.cpp:237]     Train net output #0: loss = 0.469856 (* 1 = 0.469856 loss)
I0409 22:16:08.792553 25438 sgd_solver.cpp:105] Iteration 5472, lr = 0.00338265
I0409 22:16:14.385788 25438 solver.cpp:218] Iteration 5484 (2.14551 iter/s, 5.59307s/12 iters), loss = 0.380098
I0409 22:16:14.385841 25438 solver.cpp:237]     Train net output #0: loss = 0.380098 (* 1 = 0.380098 loss)
I0409 22:16:14.385854 25438 sgd_solver.cpp:105] Iteration 5484, lr = 0.00337462
I0409 22:16:19.687794 25438 solver.cpp:218] Iteration 5496 (2.26339 iter/s, 5.30179s/12 iters), loss = 0.41705
I0409 22:16:19.687930 25438 solver.cpp:237]     Train net output #0: loss = 0.41705 (* 1 = 0.41705 loss)
I0409 22:16:19.687940 25438 sgd_solver.cpp:105] Iteration 5496, lr = 0.00336661
I0409 22:16:24.634624 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0409 22:16:31.865784 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0409 22:16:35.821103 25438 solver.cpp:330] Iteration 5508, Testing net (#0)
I0409 22:16:35.821125 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:16:38.113361 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:16:40.327795 25438 solver.cpp:397]     Test net output #0: accuracy = 0.171569
I0409 22:16:40.327826 25438 solver.cpp:397]     Test net output #1: loss = 6.12763 (* 1 = 6.12763 loss)
I0409 22:16:40.425151 25438 solver.cpp:218] Iteration 5508 (0.578686 iter/s, 20.7366s/12 iters), loss = 0.514938
I0409 22:16:40.425194 25438 solver.cpp:237]     Train net output #0: loss = 0.514938 (* 1 = 0.514938 loss)
I0409 22:16:40.425202 25438 sgd_solver.cpp:105] Iteration 5508, lr = 0.00335861
I0409 22:16:45.132314 25438 solver.cpp:218] Iteration 5520 (2.54941 iter/s, 4.70697s/12 iters), loss = 0.442371
I0409 22:16:45.132360 25438 solver.cpp:237]     Train net output #0: loss = 0.442371 (* 1 = 0.442371 loss)
I0409 22:16:45.132370 25438 sgd_solver.cpp:105] Iteration 5520, lr = 0.00335064
I0409 22:16:47.738235 25438 blocking_queue.cpp:49] Waiting for data
I0409 22:16:50.515527 25438 solver.cpp:218] Iteration 5532 (2.22924 iter/s, 5.383s/12 iters), loss = 0.381477
I0409 22:16:50.515607 25438 solver.cpp:237]     Train net output #0: loss = 0.381477 (* 1 = 0.381477 loss)
I0409 22:16:50.515616 25438 sgd_solver.cpp:105] Iteration 5532, lr = 0.00334268
I0409 22:16:56.160033 25438 solver.cpp:218] Iteration 5544 (2.12606 iter/s, 5.64425s/12 iters), loss = 0.230598
I0409 22:16:56.160075 25438 solver.cpp:237]     Train net output #0: loss = 0.230598 (* 1 = 0.230598 loss)
I0409 22:16:56.160084 25438 sgd_solver.cpp:105] Iteration 5544, lr = 0.00333475
I0409 22:17:01.657696 25438 solver.cpp:218] Iteration 5556 (2.18283 iter/s, 5.49744s/12 iters), loss = 0.404247
I0409 22:17:01.657750 25438 solver.cpp:237]     Train net output #0: loss = 0.404247 (* 1 = 0.404247 loss)
I0409 22:17:01.657763 25438 sgd_solver.cpp:105] Iteration 5556, lr = 0.00332683
I0409 22:17:04.716910 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:17:07.346884 25438 solver.cpp:218] Iteration 5568 (2.10935 iter/s, 5.68895s/12 iters), loss = 0.4516
I0409 22:17:07.346936 25438 solver.cpp:237]     Train net output #0: loss = 0.4516 (* 1 = 0.4516 loss)
I0409 22:17:07.346948 25438 sgd_solver.cpp:105] Iteration 5568, lr = 0.00331893
I0409 22:17:12.786813 25438 solver.cpp:218] Iteration 5580 (2.206 iter/s, 5.43971s/12 iters), loss = 0.383253
I0409 22:17:12.786854 25438 solver.cpp:237]     Train net output #0: loss = 0.383253 (* 1 = 0.383253 loss)
I0409 22:17:12.786864 25438 sgd_solver.cpp:105] Iteration 5580, lr = 0.00331105
I0409 22:17:18.085160 25438 solver.cpp:218] Iteration 5592 (2.26495 iter/s, 5.29814s/12 iters), loss = 0.495675
I0409 22:17:18.085208 25438 solver.cpp:237]     Train net output #0: loss = 0.495675 (* 1 = 0.495675 loss)
I0409 22:17:18.085219 25438 sgd_solver.cpp:105] Iteration 5592, lr = 0.00330319
I0409 22:17:23.290642 25438 solver.cpp:218] Iteration 5604 (2.30536 iter/s, 5.20527s/12 iters), loss = 0.430017
I0409 22:17:23.290776 25438 solver.cpp:237]     Train net output #0: loss = 0.430017 (* 1 = 0.430017 loss)
I0409 22:17:23.290786 25438 sgd_solver.cpp:105] Iteration 5604, lr = 0.00329535
I0409 22:17:25.394140 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0409 22:17:29.606564 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0409 22:17:33.457404 25438 solver.cpp:330] Iteration 5610, Testing net (#0)
I0409 22:17:33.457429 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:17:35.754351 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:17:37.994377 25438 solver.cpp:397]     Test net output #0: accuracy = 0.161152
I0409 22:17:37.994441 25438 solver.cpp:397]     Test net output #1: loss = 6.34557 (* 1 = 6.34557 loss)
I0409 22:17:40.062098 25438 solver.cpp:218] Iteration 5616 (0.715528 iter/s, 16.7708s/12 iters), loss = 0.3033
I0409 22:17:40.062147 25438 solver.cpp:237]     Train net output #0: loss = 0.3033 (* 1 = 0.3033 loss)
I0409 22:17:40.062160 25438 sgd_solver.cpp:105] Iteration 5616, lr = 0.00328752
I0409 22:17:45.365360 25438 solver.cpp:218] Iteration 5628 (2.26285 iter/s, 5.30304s/12 iters), loss = 0.417926
I0409 22:17:45.365411 25438 solver.cpp:237]     Train net output #0: loss = 0.417926 (* 1 = 0.417926 loss)
I0409 22:17:45.365422 25438 sgd_solver.cpp:105] Iteration 5628, lr = 0.00327972
I0409 22:17:50.842803 25438 solver.cpp:218] Iteration 5640 (2.19089 iter/s, 5.47722s/12 iters), loss = 0.471523
I0409 22:17:50.842857 25438 solver.cpp:237]     Train net output #0: loss = 0.471523 (* 1 = 0.471523 loss)
I0409 22:17:50.842869 25438 sgd_solver.cpp:105] Iteration 5640, lr = 0.00327193
I0409 22:17:56.116963 25438 solver.cpp:218] Iteration 5652 (2.27534 iter/s, 5.27394s/12 iters), loss = 0.361888
I0409 22:17:56.117038 25438 solver.cpp:237]     Train net output #0: loss = 0.361888 (* 1 = 0.361888 loss)
I0409 22:17:56.117049 25438 sgd_solver.cpp:105] Iteration 5652, lr = 0.00326416
I0409 22:18:01.110110 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:18:01.291450 25438 solver.cpp:218] Iteration 5664 (2.31918 iter/s, 5.17425s/12 iters), loss = 0.54419
I0409 22:18:01.291493 25438 solver.cpp:237]     Train net output #0: loss = 0.54419 (* 1 = 0.54419 loss)
I0409 22:18:01.291503 25438 sgd_solver.cpp:105] Iteration 5664, lr = 0.00325641
I0409 22:18:06.632501 25438 solver.cpp:218] Iteration 5676 (2.24684 iter/s, 5.34084s/12 iters), loss = 0.38388
I0409 22:18:06.632539 25438 solver.cpp:237]     Train net output #0: loss = 0.38388 (* 1 = 0.38388 loss)
I0409 22:18:06.632547 25438 sgd_solver.cpp:105] Iteration 5676, lr = 0.00324868
I0409 22:18:11.994132 25438 solver.cpp:218] Iteration 5688 (2.23821 iter/s, 5.36142s/12 iters), loss = 0.356649
I0409 22:18:11.994195 25438 solver.cpp:237]     Train net output #0: loss = 0.356649 (* 1 = 0.356649 loss)
I0409 22:18:11.994210 25438 sgd_solver.cpp:105] Iteration 5688, lr = 0.00324097
I0409 22:18:17.137023 25438 solver.cpp:218] Iteration 5700 (2.33342 iter/s, 5.14267s/12 iters), loss = 0.470329
I0409 22:18:17.137080 25438 solver.cpp:237]     Train net output #0: loss = 0.470329 (* 1 = 0.470329 loss)
I0409 22:18:17.137092 25438 sgd_solver.cpp:105] Iteration 5700, lr = 0.00323328
I0409 22:18:22.157994 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0409 22:18:26.329324 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0409 22:18:30.424032 25438 solver.cpp:330] Iteration 5712, Testing net (#0)
I0409 22:18:30.424052 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:18:32.651576 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:18:34.920070 25438 solver.cpp:397]     Test net output #0: accuracy = 0.178922
I0409 22:18:34.920118 25438 solver.cpp:397]     Test net output #1: loss = 6.44243 (* 1 = 6.44243 loss)
I0409 22:18:35.017531 25438 solver.cpp:218] Iteration 5712 (0.671144 iter/s, 17.8799s/12 iters), loss = 0.386134
I0409 22:18:35.017585 25438 solver.cpp:237]     Train net output #0: loss = 0.386134 (* 1 = 0.386134 loss)
I0409 22:18:35.017596 25438 sgd_solver.cpp:105] Iteration 5712, lr = 0.0032256
I0409 22:18:39.533167 25438 solver.cpp:218] Iteration 5724 (2.65755 iter/s, 4.51544s/12 iters), loss = 0.417805
I0409 22:18:39.533215 25438 solver.cpp:237]     Train net output #0: loss = 0.417805 (* 1 = 0.417805 loss)
I0409 22:18:39.533226 25438 sgd_solver.cpp:105] Iteration 5724, lr = 0.00321794
I0409 22:18:45.056346 25438 solver.cpp:218] Iteration 5736 (2.17275 iter/s, 5.52295s/12 iters), loss = 0.37672
I0409 22:18:45.056401 25438 solver.cpp:237]     Train net output #0: loss = 0.37672 (* 1 = 0.37672 loss)
I0409 22:18:45.056411 25438 sgd_solver.cpp:105] Iteration 5736, lr = 0.0032103
I0409 22:18:50.375624 25438 solver.cpp:218] Iteration 5748 (2.25604 iter/s, 5.31906s/12 iters), loss = 0.364946
I0409 22:18:50.375679 25438 solver.cpp:237]     Train net output #0: loss = 0.364946 (* 1 = 0.364946 loss)
I0409 22:18:50.375689 25438 sgd_solver.cpp:105] Iteration 5748, lr = 0.00320268
I0409 22:18:55.690011 25438 solver.cpp:218] Iteration 5760 (2.25811 iter/s, 5.31417s/12 iters), loss = 0.358996
I0409 22:18:55.690058 25438 solver.cpp:237]     Train net output #0: loss = 0.358996 (* 1 = 0.358996 loss)
I0409 22:18:55.690068 25438 sgd_solver.cpp:105] Iteration 5760, lr = 0.00319508
I0409 22:18:57.754560 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:19:00.974668 25438 solver.cpp:218] Iteration 5772 (2.27082 iter/s, 5.28444s/12 iters), loss = 0.362389
I0409 22:19:00.974715 25438 solver.cpp:237]     Train net output #0: loss = 0.362389 (* 1 = 0.362389 loss)
I0409 22:19:00.974725 25438 sgd_solver.cpp:105] Iteration 5772, lr = 0.00318749
I0409 22:19:06.681795 25438 solver.cpp:218] Iteration 5784 (2.10272 iter/s, 5.7069s/12 iters), loss = 0.311107
I0409 22:19:06.681847 25438 solver.cpp:237]     Train net output #0: loss = 0.311107 (* 1 = 0.311107 loss)
I0409 22:19:06.681859 25438 sgd_solver.cpp:105] Iteration 5784, lr = 0.00317992
I0409 22:19:12.201628 25438 solver.cpp:218] Iteration 5796 (2.17407 iter/s, 5.51961s/12 iters), loss = 0.29488
I0409 22:19:12.201678 25438 solver.cpp:237]     Train net output #0: loss = 0.29488 (* 1 = 0.29488 loss)
I0409 22:19:12.201687 25438 sgd_solver.cpp:105] Iteration 5796, lr = 0.00317237
I0409 22:19:17.377646 25438 solver.cpp:218] Iteration 5808 (2.31848 iter/s, 5.17581s/12 iters), loss = 0.498726
I0409 22:19:17.377696 25438 solver.cpp:237]     Train net output #0: loss = 0.498726 (* 1 = 0.498726 loss)
I0409 22:19:17.377705 25438 sgd_solver.cpp:105] Iteration 5808, lr = 0.00316484
I0409 22:19:19.564133 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0409 22:19:25.936758 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0409 22:19:31.838264 25438 solver.cpp:330] Iteration 5814, Testing net (#0)
I0409 22:19:31.840003 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:19:33.993266 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:19:36.378307 25438 solver.cpp:397]     Test net output #0: accuracy = 0.177696
I0409 22:19:36.378347 25438 solver.cpp:397]     Test net output #1: loss = 6.29785 (* 1 = 6.29785 loss)
I0409 22:19:38.270360 25438 solver.cpp:218] Iteration 5820 (0.574381 iter/s, 20.892s/12 iters), loss = 0.300524
I0409 22:19:38.270411 25438 solver.cpp:237]     Train net output #0: loss = 0.300524 (* 1 = 0.300524 loss)
I0409 22:19:38.270421 25438 sgd_solver.cpp:105] Iteration 5820, lr = 0.00315733
I0409 22:19:43.498261 25438 solver.cpp:218] Iteration 5832 (2.29547 iter/s, 5.22768s/12 iters), loss = 0.419161
I0409 22:19:43.498308 25438 solver.cpp:237]     Train net output #0: loss = 0.419161 (* 1 = 0.419161 loss)
I0409 22:19:43.498328 25438 sgd_solver.cpp:105] Iteration 5832, lr = 0.00314983
I0409 22:19:48.670464 25438 solver.cpp:218] Iteration 5844 (2.32019 iter/s, 5.172s/12 iters), loss = 0.257719
I0409 22:19:48.670506 25438 solver.cpp:237]     Train net output #0: loss = 0.257719 (* 1 = 0.257719 loss)
I0409 22:19:48.670516 25438 sgd_solver.cpp:105] Iteration 5844, lr = 0.00314235
I0409 22:19:53.980186 25438 solver.cpp:218] Iteration 5856 (2.26009 iter/s, 5.30952s/12 iters), loss = 0.39489
I0409 22:19:53.980224 25438 solver.cpp:237]     Train net output #0: loss = 0.39489 (* 1 = 0.39489 loss)
I0409 22:19:53.980232 25438 sgd_solver.cpp:105] Iteration 5856, lr = 0.00313489
I0409 22:19:58.731050 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:19:59.647369 25438 solver.cpp:218] Iteration 5868 (2.11754 iter/s, 5.66697s/12 iters), loss = 0.233569
I0409 22:19:59.647416 25438 solver.cpp:237]     Train net output #0: loss = 0.233569 (* 1 = 0.233569 loss)
I0409 22:19:59.647425 25438 sgd_solver.cpp:105] Iteration 5868, lr = 0.00312745
I0409 22:20:05.022236 25438 solver.cpp:218] Iteration 5880 (2.2327 iter/s, 5.37465s/12 iters), loss = 0.338815
I0409 22:20:05.022394 25438 solver.cpp:237]     Train net output #0: loss = 0.338815 (* 1 = 0.338815 loss)
I0409 22:20:05.022408 25438 sgd_solver.cpp:105] Iteration 5880, lr = 0.00312002
I0409 22:20:10.239141 25438 solver.cpp:218] Iteration 5892 (2.30035 iter/s, 5.21659s/12 iters), loss = 0.424024
I0409 22:20:10.239193 25438 solver.cpp:237]     Train net output #0: loss = 0.424024 (* 1 = 0.424024 loss)
I0409 22:20:10.239205 25438 sgd_solver.cpp:105] Iteration 5892, lr = 0.00311262
I0409 22:20:15.599529 25438 solver.cpp:218] Iteration 5904 (2.23873 iter/s, 5.36017s/12 iters), loss = 0.311847
I0409 22:20:15.599582 25438 solver.cpp:237]     Train net output #0: loss = 0.311847 (* 1 = 0.311847 loss)
I0409 22:20:15.599593 25438 sgd_solver.cpp:105] Iteration 5904, lr = 0.00310523
I0409 22:20:20.709394 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0409 22:20:24.958979 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0409 22:20:28.585229 25438 solver.cpp:330] Iteration 5916, Testing net (#0)
I0409 22:20:28.585258 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:20:30.842109 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:20:33.232908 25438 solver.cpp:397]     Test net output #0: accuracy = 0.181985
I0409 22:20:33.232954 25438 solver.cpp:397]     Test net output #1: loss = 6.15183 (* 1 = 6.15183 loss)
I0409 22:20:33.330255 25438 solver.cpp:218] Iteration 5916 (0.676813 iter/s, 17.7302s/12 iters), loss = 0.376856
I0409 22:20:33.330302 25438 solver.cpp:237]     Train net output #0: loss = 0.376856 (* 1 = 0.376856 loss)
I0409 22:20:33.330313 25438 sgd_solver.cpp:105] Iteration 5916, lr = 0.00309785
I0409 22:20:37.732777 25438 solver.cpp:218] Iteration 5928 (2.72583 iter/s, 4.40234s/12 iters), loss = 0.311153
I0409 22:20:37.732898 25438 solver.cpp:237]     Train net output #0: loss = 0.311153 (* 1 = 0.311153 loss)
I0409 22:20:37.732910 25438 sgd_solver.cpp:105] Iteration 5928, lr = 0.0030905
I0409 22:20:43.392493 25438 solver.cpp:218] Iteration 5940 (2.12036 iter/s, 5.65942s/12 iters), loss = 0.384085
I0409 22:20:43.392544 25438 solver.cpp:237]     Train net output #0: loss = 0.384085 (* 1 = 0.384085 loss)
I0409 22:20:43.392555 25438 sgd_solver.cpp:105] Iteration 5940, lr = 0.00308316
I0409 22:20:48.730219 25438 solver.cpp:218] Iteration 5952 (2.24824 iter/s, 5.33751s/12 iters), loss = 0.385801
I0409 22:20:48.730273 25438 solver.cpp:237]     Train net output #0: loss = 0.385801 (* 1 = 0.385801 loss)
I0409 22:20:48.730283 25438 sgd_solver.cpp:105] Iteration 5952, lr = 0.00307584
I0409 22:20:54.008226 25438 solver.cpp:218] Iteration 5964 (2.27368 iter/s, 5.27779s/12 iters), loss = 0.409697
I0409 22:20:54.008278 25438 solver.cpp:237]     Train net output #0: loss = 0.409697 (* 1 = 0.409697 loss)
I0409 22:20:54.008291 25438 sgd_solver.cpp:105] Iteration 5964, lr = 0.00306854
I0409 22:20:55.419227 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:20:59.326521 25438 solver.cpp:218] Iteration 5976 (2.25646 iter/s, 5.31807s/12 iters), loss = 0.223434
I0409 22:20:59.326575 25438 solver.cpp:237]     Train net output #0: loss = 0.223434 (* 1 = 0.223434 loss)
I0409 22:20:59.326586 25438 sgd_solver.cpp:105] Iteration 5976, lr = 0.00306125
I0409 22:21:04.758770 25438 solver.cpp:218] Iteration 5988 (2.20912 iter/s, 5.43202s/12 iters), loss = 0.365206
I0409 22:21:04.758829 25438 solver.cpp:237]     Train net output #0: loss = 0.365206 (* 1 = 0.365206 loss)
I0409 22:21:04.758842 25438 sgd_solver.cpp:105] Iteration 5988, lr = 0.00305398
I0409 22:21:09.990747 25438 solver.cpp:218] Iteration 6000 (2.29369 iter/s, 5.23175s/12 iters), loss = 0.449012
I0409 22:21:09.990849 25438 solver.cpp:237]     Train net output #0: loss = 0.449012 (* 1 = 0.449012 loss)
I0409 22:21:09.990857 25438 sgd_solver.cpp:105] Iteration 6000, lr = 0.00304673
I0409 22:21:15.283741 25438 solver.cpp:218] Iteration 6012 (2.26726 iter/s, 5.29273s/12 iters), loss = 0.408092
I0409 22:21:15.283787 25438 solver.cpp:237]     Train net output #0: loss = 0.408092 (* 1 = 0.408092 loss)
I0409 22:21:15.283795 25438 sgd_solver.cpp:105] Iteration 6012, lr = 0.0030395
I0409 22:21:17.437662 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0409 22:21:24.752251 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0409 22:21:30.828999 25438 solver.cpp:330] Iteration 6018, Testing net (#0)
I0409 22:21:30.829023 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:21:32.911850 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:21:35.314426 25438 solver.cpp:397]     Test net output #0: accuracy = 0.181373
I0409 22:21:35.314465 25438 solver.cpp:397]     Test net output #1: loss = 6.13242 (* 1 = 6.13242 loss)
I0409 22:21:37.270840 25438 solver.cpp:218] Iteration 6024 (0.545792 iter/s, 21.9864s/12 iters), loss = 0.209589
I0409 22:21:37.270890 25438 solver.cpp:237]     Train net output #0: loss = 0.209589 (* 1 = 0.209589 loss)
I0409 22:21:37.270900 25438 sgd_solver.cpp:105] Iteration 6024, lr = 0.00303228
I0409 22:21:42.531497 25438 solver.cpp:218] Iteration 6036 (2.28118 iter/s, 5.26044s/12 iters), loss = 0.262368
I0409 22:21:42.531829 25438 solver.cpp:237]     Train net output #0: loss = 0.262368 (* 1 = 0.262368 loss)
I0409 22:21:42.531839 25438 sgd_solver.cpp:105] Iteration 6036, lr = 0.00302508
I0409 22:21:47.857908 25438 solver.cpp:218] Iteration 6048 (2.25313 iter/s, 5.32591s/12 iters), loss = 0.343207
I0409 22:21:47.857985 25438 solver.cpp:237]     Train net output #0: loss = 0.343207 (* 1 = 0.343207 loss)
I0409 22:21:47.857997 25438 sgd_solver.cpp:105] Iteration 6048, lr = 0.0030179
I0409 22:21:53.535832 25438 solver.cpp:218] Iteration 6060 (2.11354 iter/s, 5.67768s/12 iters), loss = 0.283451
I0409 22:21:53.535878 25438 solver.cpp:237]     Train net output #0: loss = 0.283451 (* 1 = 0.283451 loss)
I0409 22:21:53.535888 25438 sgd_solver.cpp:105] Iteration 6060, lr = 0.00301074
I0409 22:21:57.256175 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:21:58.992743 25438 solver.cpp:218] Iteration 6072 (2.19914 iter/s, 5.45669s/12 iters), loss = 0.331533
I0409 22:21:58.992797 25438 solver.cpp:237]     Train net output #0: loss = 0.331533 (* 1 = 0.331533 loss)
I0409 22:21:58.992808 25438 sgd_solver.cpp:105] Iteration 6072, lr = 0.00300359
I0409 22:22:04.560899 25438 solver.cpp:218] Iteration 6084 (2.1552 iter/s, 5.56793s/12 iters), loss = 0.248123
I0409 22:22:04.560950 25438 solver.cpp:237]     Train net output #0: loss = 0.248123 (* 1 = 0.248123 loss)
I0409 22:22:04.560961 25438 sgd_solver.cpp:105] Iteration 6084, lr = 0.00299646
I0409 22:22:09.858777 25438 solver.cpp:218] Iteration 6096 (2.26515 iter/s, 5.29766s/12 iters), loss = 0.292934
I0409 22:22:09.858831 25438 solver.cpp:237]     Train net output #0: loss = 0.292934 (* 1 = 0.292934 loss)
I0409 22:22:09.858844 25438 sgd_solver.cpp:105] Iteration 6096, lr = 0.00298934
I0409 22:22:15.423864 25438 solver.cpp:218] Iteration 6108 (2.15639 iter/s, 5.56485s/12 iters), loss = 0.253578
I0409 22:22:15.423981 25438 solver.cpp:237]     Train net output #0: loss = 0.253578 (* 1 = 0.253578 loss)
I0409 22:22:15.423995 25438 sgd_solver.cpp:105] Iteration 6108, lr = 0.00298225
I0409 22:22:20.461113 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0409 22:22:27.601256 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0409 22:22:32.356007 25438 solver.cpp:330] Iteration 6120, Testing net (#0)
I0409 22:22:32.356034 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:22:34.447347 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:22:36.876313 25438 solver.cpp:397]     Test net output #0: accuracy = 0.175858
I0409 22:22:36.876361 25438 solver.cpp:397]     Test net output #1: loss = 6.48956 (* 1 = 6.48956 loss)
I0409 22:22:36.973814 25438 solver.cpp:218] Iteration 6120 (0.556865 iter/s, 21.5492s/12 iters), loss = 0.286808
I0409 22:22:36.973862 25438 solver.cpp:237]     Train net output #0: loss = 0.286808 (* 1 = 0.286808 loss)
I0409 22:22:36.973873 25438 sgd_solver.cpp:105] Iteration 6120, lr = 0.00297517
I0409 22:22:41.513525 25438 solver.cpp:218] Iteration 6132 (2.64345 iter/s, 4.53952s/12 iters), loss = 0.3725
I0409 22:22:41.513577 25438 solver.cpp:237]     Train net output #0: loss = 0.3725 (* 1 = 0.3725 loss)
I0409 22:22:41.513589 25438 sgd_solver.cpp:105] Iteration 6132, lr = 0.0029681
I0409 22:22:47.201877 25438 solver.cpp:218] Iteration 6144 (2.10966 iter/s, 5.68812s/12 iters), loss = 0.359049
I0409 22:22:47.202044 25438 solver.cpp:237]     Train net output #0: loss = 0.359048 (* 1 = 0.359048 loss)
I0409 22:22:47.202057 25438 sgd_solver.cpp:105] Iteration 6144, lr = 0.00296105
I0409 22:22:52.662593 25438 solver.cpp:218] Iteration 6156 (2.19765 iter/s, 5.46039s/12 iters), loss = 0.168864
I0409 22:22:52.662642 25438 solver.cpp:237]     Train net output #0: loss = 0.168864 (* 1 = 0.168864 loss)
I0409 22:22:52.662653 25438 sgd_solver.cpp:105] Iteration 6156, lr = 0.00295402
I0409 22:22:58.224303 25438 solver.cpp:218] Iteration 6168 (2.1577 iter/s, 5.56149s/12 iters), loss = 0.330047
I0409 22:22:58.224352 25438 solver.cpp:237]     Train net output #0: loss = 0.330047 (* 1 = 0.330047 loss)
I0409 22:22:58.224364 25438 sgd_solver.cpp:105] Iteration 6168, lr = 0.00294701
I0409 22:22:58.850723 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:23:03.631016 25438 solver.cpp:218] Iteration 6180 (2.21955 iter/s, 5.4065s/12 iters), loss = 0.285823
I0409 22:23:03.631063 25438 solver.cpp:237]     Train net output #0: loss = 0.285823 (* 1 = 0.285823 loss)
I0409 22:23:03.631072 25438 sgd_solver.cpp:105] Iteration 6180, lr = 0.00294001
I0409 22:23:09.296347 25438 solver.cpp:218] Iteration 6192 (2.11823 iter/s, 5.6651s/12 iters), loss = 0.434229
I0409 22:23:09.296396 25438 solver.cpp:237]     Train net output #0: loss = 0.434229 (* 1 = 0.434229 loss)
I0409 22:23:09.296404 25438 sgd_solver.cpp:105] Iteration 6192, lr = 0.00293303
I0409 22:23:14.942835 25438 solver.cpp:218] Iteration 6204 (2.1253 iter/s, 5.64626s/12 iters), loss = 0.218967
I0409 22:23:14.942886 25438 solver.cpp:237]     Train net output #0: loss = 0.218967 (* 1 = 0.218967 loss)
I0409 22:23:14.942898 25438 sgd_solver.cpp:105] Iteration 6204, lr = 0.00292607
I0409 22:23:20.412847 25438 solver.cpp:218] Iteration 6216 (2.19387 iter/s, 5.46979s/12 iters), loss = 0.328538
I0409 22:23:20.412943 25438 solver.cpp:237]     Train net output #0: loss = 0.328538 (* 1 = 0.328538 loss)
I0409 22:23:20.412953 25438 sgd_solver.cpp:105] Iteration 6216, lr = 0.00291912
I0409 22:23:22.528673 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0409 22:23:26.687520 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0409 22:23:30.085355 25438 solver.cpp:330] Iteration 6222, Testing net (#0)
I0409 22:23:30.085376 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:23:32.053071 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:23:33.357758 25438 blocking_queue.cpp:49] Waiting for data
I0409 22:23:34.653221 25438 solver.cpp:397]     Test net output #0: accuracy = 0.177083
I0409 22:23:34.653265 25438 solver.cpp:397]     Test net output #1: loss = 6.13339 (* 1 = 6.13339 loss)
I0409 22:23:36.744029 25438 solver.cpp:218] Iteration 6228 (0.734817 iter/s, 16.3306s/12 iters), loss = 0.183195
I0409 22:23:36.744078 25438 solver.cpp:237]     Train net output #0: loss = 0.183195 (* 1 = 0.183195 loss)
I0409 22:23:36.744089 25438 sgd_solver.cpp:105] Iteration 6228, lr = 0.00291219
I0409 22:23:42.097474 25438 solver.cpp:218] Iteration 6240 (2.24164 iter/s, 5.35323s/12 iters), loss = 0.254233
I0409 22:23:42.097530 25438 solver.cpp:237]     Train net output #0: loss = 0.254233 (* 1 = 0.254233 loss)
I0409 22:23:42.097543 25438 sgd_solver.cpp:105] Iteration 6240, lr = 0.00290528
I0409 22:23:47.323016 25438 solver.cpp:218] Iteration 6252 (2.29651 iter/s, 5.22532s/12 iters), loss = 0.198592
I0409 22:23:47.323061 25438 solver.cpp:237]     Train net output #0: loss = 0.198592 (* 1 = 0.198592 loss)
I0409 22:23:47.323072 25438 sgd_solver.cpp:105] Iteration 6252, lr = 0.00289838
I0409 22:23:52.584230 25438 solver.cpp:218] Iteration 6264 (2.28093 iter/s, 5.261s/12 iters), loss = 0.384055
I0409 22:23:52.584344 25438 solver.cpp:237]     Train net output #0: loss = 0.384055 (* 1 = 0.384055 loss)
I0409 22:23:52.584357 25438 sgd_solver.cpp:105] Iteration 6264, lr = 0.0028915
I0409 22:23:55.468925 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:23:57.896829 25438 solver.cpp:218] Iteration 6276 (2.2589 iter/s, 5.31232s/12 iters), loss = 0.323435
I0409 22:23:57.896873 25438 solver.cpp:237]     Train net output #0: loss = 0.323435 (* 1 = 0.323435 loss)
I0409 22:23:57.896883 25438 sgd_solver.cpp:105] Iteration 6276, lr = 0.00288463
I0409 22:24:03.314182 25438 solver.cpp:218] Iteration 6288 (2.21519 iter/s, 5.41714s/12 iters), loss = 0.348968
I0409 22:24:03.314234 25438 solver.cpp:237]     Train net output #0: loss = 0.348968 (* 1 = 0.348968 loss)
I0409 22:24:03.314244 25438 sgd_solver.cpp:105] Iteration 6288, lr = 0.00287779
I0409 22:24:08.583848 25438 solver.cpp:218] Iteration 6300 (2.27728 iter/s, 5.26945s/12 iters), loss = 0.190529
I0409 22:24:08.583896 25438 solver.cpp:237]     Train net output #0: loss = 0.190529 (* 1 = 0.190529 loss)
I0409 22:24:08.583907 25438 sgd_solver.cpp:105] Iteration 6300, lr = 0.00287095
I0409 22:24:14.075210 25438 solver.cpp:218] Iteration 6312 (2.18534 iter/s, 5.49114s/12 iters), loss = 0.515707
I0409 22:24:14.075263 25438 solver.cpp:237]     Train net output #0: loss = 0.515707 (* 1 = 0.515707 loss)
I0409 22:24:14.075276 25438 sgd_solver.cpp:105] Iteration 6312, lr = 0.00286414
I0409 22:24:19.173235 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0409 22:24:23.442685 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0409 22:24:26.696807 25438 solver.cpp:330] Iteration 6324, Testing net (#0)
I0409 22:24:26.696835 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:24:28.695295 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:24:31.205416 25438 solver.cpp:397]     Test net output #0: accuracy = 0.180147
I0409 22:24:31.205459 25438 solver.cpp:397]     Test net output #1: loss = 6.3945 (* 1 = 6.3945 loss)
I0409 22:24:31.302901 25438 solver.cpp:218] Iteration 6324 (0.696576 iter/s, 17.2271s/12 iters), loss = 0.333613
I0409 22:24:31.302944 25438 solver.cpp:237]     Train net output #0: loss = 0.333613 (* 1 = 0.333613 loss)
I0409 22:24:31.302953 25438 sgd_solver.cpp:105] Iteration 6324, lr = 0.00285734
I0409 22:24:35.662644 25438 solver.cpp:218] Iteration 6336 (2.75257 iter/s, 4.35956s/12 iters), loss = 0.263333
I0409 22:24:35.662690 25438 solver.cpp:237]     Train net output #0: loss = 0.263333 (* 1 = 0.263333 loss)
I0409 22:24:35.662699 25438 sgd_solver.cpp:105] Iteration 6336, lr = 0.00285055
I0409 22:24:41.163893 25438 solver.cpp:218] Iteration 6348 (2.18141 iter/s, 5.50103s/12 iters), loss = 0.174139
I0409 22:24:41.163944 25438 solver.cpp:237]     Train net output #0: loss = 0.174139 (* 1 = 0.174139 loss)
I0409 22:24:41.163955 25438 sgd_solver.cpp:105] Iteration 6348, lr = 0.00284379
I0409 22:24:46.861245 25438 solver.cpp:218] Iteration 6360 (2.10633 iter/s, 5.69712s/12 iters), loss = 0.261588
I0409 22:24:46.861296 25438 solver.cpp:237]     Train net output #0: loss = 0.261588 (* 1 = 0.261588 loss)
I0409 22:24:46.861308 25438 sgd_solver.cpp:105] Iteration 6360, lr = 0.00283703
I0409 22:24:51.861282 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:24:52.015134 25438 solver.cpp:218] Iteration 6372 (2.32843 iter/s, 5.15368s/12 iters), loss = 0.276784
I0409 22:24:52.015182 25438 solver.cpp:237]     Train net output #0: loss = 0.276784 (* 1 = 0.276784 loss)
I0409 22:24:52.015192 25438 sgd_solver.cpp:105] Iteration 6372, lr = 0.0028303
I0409 22:24:57.540772 25438 solver.cpp:218] Iteration 6384 (2.17178 iter/s, 5.52541s/12 iters), loss = 0.269649
I0409 22:24:57.540925 25438 solver.cpp:237]     Train net output #0: loss = 0.269649 (* 1 = 0.269649 loss)
I0409 22:24:57.540939 25438 sgd_solver.cpp:105] Iteration 6384, lr = 0.00282358
I0409 22:25:02.798177 25438 solver.cpp:218] Iteration 6396 (2.28263 iter/s, 5.2571s/12 iters), loss = 0.198838
I0409 22:25:02.798215 25438 solver.cpp:237]     Train net output #0: loss = 0.198838 (* 1 = 0.198838 loss)
I0409 22:25:02.798223 25438 sgd_solver.cpp:105] Iteration 6396, lr = 0.00281687
I0409 22:25:08.107312 25438 solver.cpp:218] Iteration 6408 (2.26034 iter/s, 5.30893s/12 iters), loss = 0.281359
I0409 22:25:08.107364 25438 solver.cpp:237]     Train net output #0: loss = 0.281359 (* 1 = 0.281359 loss)
I0409 22:25:08.107375 25438 sgd_solver.cpp:105] Iteration 6408, lr = 0.00281019
I0409 22:25:13.381987 25438 solver.cpp:218] Iteration 6420 (2.27511 iter/s, 5.27446s/12 iters), loss = 0.190309
I0409 22:25:13.382038 25438 solver.cpp:237]     Train net output #0: loss = 0.190309 (* 1 = 0.190309 loss)
I0409 22:25:13.382050 25438 sgd_solver.cpp:105] Iteration 6420, lr = 0.00280351
I0409 22:25:15.515019 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0409 22:25:19.798739 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0409 22:25:23.538419 25438 solver.cpp:330] Iteration 6426, Testing net (#0)
I0409 22:25:23.538447 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:25:25.509666 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:25:28.066107 25438 solver.cpp:397]     Test net output #0: accuracy = 0.189338
I0409 22:25:28.066200 25438 solver.cpp:397]     Test net output #1: loss = 6.22791 (* 1 = 6.22791 loss)
I0409 22:25:30.056815 25438 solver.cpp:218] Iteration 6432 (0.719671 iter/s, 16.6743s/12 iters), loss = 0.18352
I0409 22:25:30.056860 25438 solver.cpp:237]     Train net output #0: loss = 0.18352 (* 1 = 0.18352 loss)
I0409 22:25:30.056870 25438 sgd_solver.cpp:105] Iteration 6432, lr = 0.00279686
I0409 22:25:35.405987 25438 solver.cpp:218] Iteration 6444 (2.24343 iter/s, 5.34896s/12 iters), loss = 0.148434
I0409 22:25:35.406033 25438 solver.cpp:237]     Train net output #0: loss = 0.148434 (* 1 = 0.148434 loss)
I0409 22:25:35.406047 25438 sgd_solver.cpp:105] Iteration 6444, lr = 0.00279022
I0409 22:25:41.026271 25438 solver.cpp:218] Iteration 6456 (2.13521 iter/s, 5.62006s/12 iters), loss = 0.165177
I0409 22:25:41.026319 25438 solver.cpp:237]     Train net output #0: loss = 0.165177 (* 1 = 0.165177 loss)
I0409 22:25:41.026330 25438 sgd_solver.cpp:105] Iteration 6456, lr = 0.00278359
I0409 22:25:46.698951 25438 solver.cpp:218] Iteration 6468 (2.11549 iter/s, 5.67245s/12 iters), loss = 0.312088
I0409 22:25:46.699007 25438 solver.cpp:237]     Train net output #0: loss = 0.312088 (* 1 = 0.312088 loss)
I0409 22:25:46.699018 25438 sgd_solver.cpp:105] Iteration 6468, lr = 0.00277698
I0409 22:25:48.965564 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:25:52.382053 25438 solver.cpp:218] Iteration 6480 (2.11161 iter/s, 5.68287s/12 iters), loss = 0.133866
I0409 22:25:52.382102 25438 solver.cpp:237]     Train net output #0: loss = 0.133866 (* 1 = 0.133866 loss)
I0409 22:25:52.382112 25438 sgd_solver.cpp:105] Iteration 6480, lr = 0.00277039
I0409 22:25:57.867141 25438 solver.cpp:218] Iteration 6492 (2.18784 iter/s, 5.48487s/12 iters), loss = 0.194645
I0409 22:25:57.867182 25438 solver.cpp:237]     Train net output #0: loss = 0.194644 (* 1 = 0.194644 loss)
I0409 22:25:57.867192 25438 sgd_solver.cpp:105] Iteration 6492, lr = 0.00276381
I0409 22:26:03.403976 25438 solver.cpp:218] Iteration 6504 (2.16739 iter/s, 5.53662s/12 iters), loss = 0.149961
I0409 22:26:03.404119 25438 solver.cpp:237]     Train net output #0: loss = 0.149961 (* 1 = 0.149961 loss)
I0409 22:26:03.404129 25438 sgd_solver.cpp:105] Iteration 6504, lr = 0.00275725
I0409 22:26:08.754858 25438 solver.cpp:218] Iteration 6516 (2.24275 iter/s, 5.35057s/12 iters), loss = 0.323951
I0409 22:26:08.754909 25438 solver.cpp:237]     Train net output #0: loss = 0.323951 (* 1 = 0.323951 loss)
I0409 22:26:08.754918 25438 sgd_solver.cpp:105] Iteration 6516, lr = 0.00275071
I0409 22:26:13.896437 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0409 22:26:19.280642 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0409 22:26:27.818089 25438 solver.cpp:330] Iteration 6528, Testing net (#0)
I0409 22:26:27.818107 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:26:29.755956 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:26:32.636910 25438 solver.cpp:397]     Test net output #0: accuracy = 0.190564
I0409 22:26:32.636950 25438 solver.cpp:397]     Test net output #1: loss = 6.42012 (* 1 = 6.42012 loss)
I0409 22:26:32.734191 25438 solver.cpp:218] Iteration 6528 (0.500447 iter/s, 23.9786s/12 iters), loss = 0.130501
I0409 22:26:32.734239 25438 solver.cpp:237]     Train net output #0: loss = 0.130501 (* 1 = 0.130501 loss)
I0409 22:26:32.734248 25438 sgd_solver.cpp:105] Iteration 6528, lr = 0.00274418
I0409 22:26:37.150352 25438 solver.cpp:218] Iteration 6540 (2.71741 iter/s, 4.41597s/12 iters), loss = 0.231759
I0409 22:26:37.150471 25438 solver.cpp:237]     Train net output #0: loss = 0.231759 (* 1 = 0.231759 loss)
I0409 22:26:37.150482 25438 sgd_solver.cpp:105] Iteration 6540, lr = 0.00273766
I0409 22:26:42.334906 25438 solver.cpp:218] Iteration 6552 (2.31469 iter/s, 5.18427s/12 iters), loss = 0.156776
I0409 22:26:42.334957 25438 solver.cpp:237]     Train net output #0: loss = 0.156776 (* 1 = 0.156776 loss)
I0409 22:26:42.334969 25438 sgd_solver.cpp:105] Iteration 6552, lr = 0.00273116
I0409 22:26:47.976302 25438 solver.cpp:218] Iteration 6564 (2.12722 iter/s, 5.64117s/12 iters), loss = 0.376737
I0409 22:26:47.976348 25438 solver.cpp:237]     Train net output #0: loss = 0.376737 (* 1 = 0.376737 loss)
I0409 22:26:47.976356 25438 sgd_solver.cpp:105] Iteration 6564, lr = 0.00272468
I0409 22:26:52.774513 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:26:53.609982 25438 solver.cpp:218] Iteration 6576 (2.13013 iter/s, 5.63345s/12 iters), loss = 0.217962
I0409 22:26:53.610023 25438 solver.cpp:237]     Train net output #0: loss = 0.217962 (* 1 = 0.217962 loss)
I0409 22:26:53.610031 25438 sgd_solver.cpp:105] Iteration 6576, lr = 0.00271821
I0409 22:26:59.202793 25438 solver.cpp:218] Iteration 6588 (2.1457 iter/s, 5.59259s/12 iters), loss = 0.331932
I0409 22:26:59.202843 25438 solver.cpp:237]     Train net output #0: loss = 0.331932 (* 1 = 0.331932 loss)
I0409 22:26:59.202852 25438 sgd_solver.cpp:105] Iteration 6588, lr = 0.00271175
I0409 22:27:04.853013 25438 solver.cpp:218] Iteration 6600 (2.1239 iter/s, 5.64999s/12 iters), loss = 0.302663
I0409 22:27:04.853061 25438 solver.cpp:237]     Train net output #0: loss = 0.302663 (* 1 = 0.302663 loss)
I0409 22:27:04.853073 25438 sgd_solver.cpp:105] Iteration 6600, lr = 0.00270532
I0409 22:27:10.525849 25438 solver.cpp:218] Iteration 6612 (2.11543 iter/s, 5.67261s/12 iters), loss = 0.18965
I0409 22:27:10.525951 25438 solver.cpp:237]     Train net output #0: loss = 0.18965 (* 1 = 0.18965 loss)
I0409 22:27:10.525985 25438 sgd_solver.cpp:105] Iteration 6612, lr = 0.00269889
I0409 22:27:16.129420 25438 solver.cpp:218] Iteration 6624 (2.1416 iter/s, 5.6033s/12 iters), loss = 0.15319
I0409 22:27:16.129465 25438 solver.cpp:237]     Train net output #0: loss = 0.15319 (* 1 = 0.15319 loss)
I0409 22:27:16.129475 25438 sgd_solver.cpp:105] Iteration 6624, lr = 0.00269248
I0409 22:27:18.445648 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0409 22:27:24.467481 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0409 22:27:27.631793 25438 solver.cpp:330] Iteration 6630, Testing net (#0)
I0409 22:27:27.631814 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:27:29.539772 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:27:32.284554 25438 solver.cpp:397]     Test net output #0: accuracy = 0.193015
I0409 22:27:32.284605 25438 solver.cpp:397]     Test net output #1: loss = 6.20729 (* 1 = 6.20729 loss)
I0409 22:27:34.333801 25438 solver.cpp:218] Iteration 6636 (0.659203 iter/s, 18.2038s/12 iters), loss = 0.130989
I0409 22:27:34.333853 25438 solver.cpp:237]     Train net output #0: loss = 0.130989 (* 1 = 0.130989 loss)
I0409 22:27:34.333863 25438 sgd_solver.cpp:105] Iteration 6636, lr = 0.00268609
I0409 22:27:39.839682 25438 solver.cpp:218] Iteration 6648 (2.17958 iter/s, 5.50565s/12 iters), loss = 0.335435
I0409 22:27:39.839733 25438 solver.cpp:237]     Train net output #0: loss = 0.335435 (* 1 = 0.335435 loss)
I0409 22:27:39.839745 25438 sgd_solver.cpp:105] Iteration 6648, lr = 0.00267971
I0409 22:27:45.220029 25438 solver.cpp:218] Iteration 6660 (2.23043 iter/s, 5.38013s/12 iters), loss = 0.217086
I0409 22:27:45.220165 25438 solver.cpp:237]     Train net output #0: loss = 0.217086 (* 1 = 0.217086 loss)
I0409 22:27:45.220176 25438 sgd_solver.cpp:105] Iteration 6660, lr = 0.00267335
I0409 22:27:50.715277 25438 solver.cpp:218] Iteration 6672 (2.18383 iter/s, 5.49494s/12 iters), loss = 0.444273
I0409 22:27:50.715327 25438 solver.cpp:237]     Train net output #0: loss = 0.444273 (* 1 = 0.444273 loss)
I0409 22:27:50.715337 25438 sgd_solver.cpp:105] Iteration 6672, lr = 0.00266701
I0409 22:27:52.139307 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:27:56.141285 25438 solver.cpp:218] Iteration 6684 (2.21166 iter/s, 5.42579s/12 iters), loss = 0.370193
I0409 22:27:56.141335 25438 solver.cpp:237]     Train net output #0: loss = 0.370193 (* 1 = 0.370193 loss)
I0409 22:27:56.141346 25438 sgd_solver.cpp:105] Iteration 6684, lr = 0.00266067
I0409 22:28:01.631659 25438 solver.cpp:218] Iteration 6696 (2.18573 iter/s, 5.49015s/12 iters), loss = 0.201551
I0409 22:28:01.631707 25438 solver.cpp:237]     Train net output #0: loss = 0.201551 (* 1 = 0.201551 loss)
I0409 22:28:01.631716 25438 sgd_solver.cpp:105] Iteration 6696, lr = 0.00265436
I0409 22:28:06.978297 25438 solver.cpp:218] Iteration 6708 (2.24449 iter/s, 5.34642s/12 iters), loss = 0.339767
I0409 22:28:06.978346 25438 solver.cpp:237]     Train net output #0: loss = 0.339767 (* 1 = 0.339767 loss)
I0409 22:28:06.978355 25438 sgd_solver.cpp:105] Iteration 6708, lr = 0.00264805
I0409 22:28:12.651566 25438 solver.cpp:218] Iteration 6720 (2.11527 iter/s, 5.67304s/12 iters), loss = 0.146051
I0409 22:28:12.651621 25438 solver.cpp:237]     Train net output #0: loss = 0.146051 (* 1 = 0.146051 loss)
I0409 22:28:12.651633 25438 sgd_solver.cpp:105] Iteration 6720, lr = 0.00264177
I0409 22:28:17.422473 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0409 22:28:24.710441 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0409 22:28:34.214387 25438 solver.cpp:330] Iteration 6732, Testing net (#0)
I0409 22:28:34.214414 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:28:35.966364 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:28:38.657151 25438 solver.cpp:397]     Test net output #0: accuracy = 0.189951
I0409 22:28:38.657199 25438 solver.cpp:397]     Test net output #1: loss = 6.28147 (* 1 = 6.28147 loss)
I0409 22:28:38.754593 25438 solver.cpp:218] Iteration 6732 (0.459731 iter/s, 26.1022s/12 iters), loss = 0.168336
I0409 22:28:38.754640 25438 solver.cpp:237]     Train net output #0: loss = 0.168336 (* 1 = 0.168336 loss)
I0409 22:28:38.754652 25438 sgd_solver.cpp:105] Iteration 6732, lr = 0.0026355
I0409 22:28:43.454672 25438 solver.cpp:218] Iteration 6744 (2.55326 iter/s, 4.69988s/12 iters), loss = 0.286239
I0409 22:28:43.454725 25438 solver.cpp:237]     Train net output #0: loss = 0.286239 (* 1 = 0.286239 loss)
I0409 22:28:43.454737 25438 sgd_solver.cpp:105] Iteration 6744, lr = 0.00262924
I0409 22:28:48.695533 25438 solver.cpp:218] Iteration 6756 (2.2898 iter/s, 5.24064s/12 iters), loss = 0.186479
I0409 22:28:48.695688 25438 solver.cpp:237]     Train net output #0: loss = 0.186479 (* 1 = 0.186479 loss)
I0409 22:28:48.695701 25438 sgd_solver.cpp:105] Iteration 6756, lr = 0.002623
I0409 22:28:53.842068 25438 solver.cpp:218] Iteration 6768 (2.3318 iter/s, 5.14623s/12 iters), loss = 0.103452
I0409 22:28:53.842111 25438 solver.cpp:237]     Train net output #0: loss = 0.103452 (* 1 = 0.103452 loss)
I0409 22:28:53.842123 25438 sgd_solver.cpp:105] Iteration 6768, lr = 0.00261677
I0409 22:28:57.459007 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:28:59.017671 25438 solver.cpp:218] Iteration 6780 (2.31866 iter/s, 5.1754s/12 iters), loss = 0.17018
I0409 22:28:59.017725 25438 solver.cpp:237]     Train net output #0: loss = 0.17018 (* 1 = 0.17018 loss)
I0409 22:28:59.017737 25438 sgd_solver.cpp:105] Iteration 6780, lr = 0.00261056
I0409 22:29:04.661068 25438 solver.cpp:218] Iteration 6792 (2.12646 iter/s, 5.64317s/12 iters), loss = 0.117981
I0409 22:29:04.661121 25438 solver.cpp:237]     Train net output #0: loss = 0.11798 (* 1 = 0.11798 loss)
I0409 22:29:04.661134 25438 sgd_solver.cpp:105] Iteration 6792, lr = 0.00260436
I0409 22:29:09.914347 25438 solver.cpp:218] Iteration 6804 (2.28438 iter/s, 5.25306s/12 iters), loss = 0.182287
I0409 22:29:09.914394 25438 solver.cpp:237]     Train net output #0: loss = 0.182287 (* 1 = 0.182287 loss)
I0409 22:29:09.914403 25438 sgd_solver.cpp:105] Iteration 6804, lr = 0.00259817
I0409 22:29:15.565836 25438 solver.cpp:218] Iteration 6816 (2.12341 iter/s, 5.65128s/12 iters), loss = 0.255476
I0409 22:29:15.565881 25438 solver.cpp:237]     Train net output #0: loss = 0.255476 (* 1 = 0.255476 loss)
I0409 22:29:15.565889 25438 sgd_solver.cpp:105] Iteration 6816, lr = 0.00259201
I0409 22:29:21.144438 25438 solver.cpp:218] Iteration 6828 (2.15116 iter/s, 5.57838s/12 iters), loss = 0.1443
I0409 22:29:21.144539 25438 solver.cpp:237]     Train net output #0: loss = 0.1443 (* 1 = 0.1443 loss)
I0409 22:29:21.144549 25438 sgd_solver.cpp:105] Iteration 6828, lr = 0.00258585
I0409 22:29:23.414429 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0409 22:29:38.617122 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0409 22:29:51.764771 25438 solver.cpp:330] Iteration 6834, Testing net (#0)
I0409 22:29:51.764851 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:29:53.631686 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:29:56.332867 25438 solver.cpp:397]     Test net output #0: accuracy = 0.203431
I0409 22:29:56.332916 25438 solver.cpp:397]     Test net output #1: loss = 6.36232 (* 1 = 6.36232 loss)
I0409 22:29:58.450434 25438 solver.cpp:218] Iteration 6840 (0.321674 iter/s, 37.3048s/12 iters), loss = 0.079181
I0409 22:29:58.450496 25438 solver.cpp:237]     Train net output #0: loss = 0.079181 (* 1 = 0.079181 loss)
I0409 22:29:58.450508 25438 sgd_solver.cpp:105] Iteration 6840, lr = 0.00257971
I0409 22:30:04.163376 25438 solver.cpp:218] Iteration 6852 (2.10058 iter/s, 5.71271s/12 iters), loss = 0.170502
I0409 22:30:04.163424 25438 solver.cpp:237]     Train net output #0: loss = 0.170502 (* 1 = 0.170502 loss)
I0409 22:30:04.163434 25438 sgd_solver.cpp:105] Iteration 6852, lr = 0.00257359
I0409 22:30:09.801638 25438 solver.cpp:218] Iteration 6864 (2.1284 iter/s, 5.63804s/12 iters), loss = 0.200227
I0409 22:30:09.801690 25438 solver.cpp:237]     Train net output #0: loss = 0.200227 (* 1 = 0.200227 loss)
I0409 22:30:09.801702 25438 sgd_solver.cpp:105] Iteration 6864, lr = 0.00256748
I0409 22:30:15.250438 25438 solver.cpp:218] Iteration 6876 (2.20241 iter/s, 5.44858s/12 iters), loss = 0.348311
I0409 22:30:15.250484 25438 solver.cpp:237]     Train net output #0: loss = 0.348311 (* 1 = 0.348311 loss)
I0409 22:30:15.250494 25438 sgd_solver.cpp:105] Iteration 6876, lr = 0.00256138
I0409 22:30:15.889523 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:30:20.696728 25438 solver.cpp:218] Iteration 6888 (2.20342 iter/s, 5.44608s/12 iters), loss = 0.211733
I0409 22:30:20.696772 25438 solver.cpp:237]     Train net output #0: loss = 0.211733 (* 1 = 0.211733 loss)
I0409 22:30:20.696781 25438 sgd_solver.cpp:105] Iteration 6888, lr = 0.0025553
I0409 22:30:26.086416 25438 solver.cpp:218] Iteration 6900 (2.22656 iter/s, 5.38947s/12 iters), loss = 0.4861
I0409 22:30:26.086551 25438 solver.cpp:237]     Train net output #0: loss = 0.4861 (* 1 = 0.4861 loss)
I0409 22:30:26.086563 25438 sgd_solver.cpp:105] Iteration 6900, lr = 0.00254923
I0409 22:30:31.738548 25438 solver.cpp:218] Iteration 6912 (2.12321 iter/s, 5.65181s/12 iters), loss = 0.105526
I0409 22:30:31.738616 25438 solver.cpp:237]     Train net output #0: loss = 0.105526 (* 1 = 0.105526 loss)
I0409 22:30:31.738632 25438 sgd_solver.cpp:105] Iteration 6912, lr = 0.00254318
I0409 22:30:37.120543 25438 solver.cpp:218] Iteration 6924 (2.22975 iter/s, 5.38177s/12 iters), loss = 0.224846
I0409 22:30:37.120587 25438 solver.cpp:237]     Train net output #0: loss = 0.224846 (* 1 = 0.224846 loss)
I0409 22:30:37.120596 25438 sgd_solver.cpp:105] Iteration 6924, lr = 0.00253714
I0409 22:30:42.070760 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0409 22:30:58.884104 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0409 22:31:13.557272 25438 solver.cpp:330] Iteration 6936, Testing net (#0)
I0409 22:31:13.557301 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:31:14.204934 25438 blocking_queue.cpp:49] Waiting for data
I0409 22:31:15.287896 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:31:18.072939 25438 solver.cpp:397]     Test net output #0: accuracy = 0.204044
I0409 22:31:18.072984 25438 solver.cpp:397]     Test net output #1: loss = 6.20237 (* 1 = 6.20237 loss)
I0409 22:31:18.170480 25438 solver.cpp:218] Iteration 6936 (0.292336 iter/s, 41.0487s/12 iters), loss = 0.15287
I0409 22:31:18.170524 25438 solver.cpp:237]     Train net output #0: loss = 0.15287 (* 1 = 0.15287 loss)
I0409 22:31:18.170536 25438 sgd_solver.cpp:105] Iteration 6936, lr = 0.00253112
I0409 22:31:22.887382 25438 solver.cpp:218] Iteration 6948 (2.54415 iter/s, 4.71671s/12 iters), loss = 0.0665959
I0409 22:31:22.887426 25438 solver.cpp:237]     Train net output #0: loss = 0.0665959 (* 1 = 0.0665959 loss)
I0409 22:31:22.887436 25438 sgd_solver.cpp:105] Iteration 6948, lr = 0.00252511
I0409 22:31:28.558290 25438 solver.cpp:218] Iteration 6960 (2.11615 iter/s, 5.67068s/12 iters), loss = 0.0852389
I0409 22:31:28.558348 25438 solver.cpp:237]     Train net output #0: loss = 0.0852389 (* 1 = 0.0852389 loss)
I0409 22:31:28.558359 25438 sgd_solver.cpp:105] Iteration 6960, lr = 0.00251911
I0409 22:31:34.217844 25438 solver.cpp:218] Iteration 6972 (2.1204 iter/s, 5.65932s/12 iters), loss = 0.107049
I0409 22:31:34.217945 25438 solver.cpp:237]     Train net output #0: loss = 0.107049 (* 1 = 0.107049 loss)
I0409 22:31:34.217979 25438 sgd_solver.cpp:105] Iteration 6972, lr = 0.00251313
I0409 22:31:37.074275 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:31:39.463502 25438 solver.cpp:218] Iteration 6984 (2.28772 iter/s, 5.24539s/12 iters), loss = 0.281774
I0409 22:31:39.463553 25438 solver.cpp:237]     Train net output #0: loss = 0.281774 (* 1 = 0.281774 loss)
I0409 22:31:39.463563 25438 sgd_solver.cpp:105] Iteration 6984, lr = 0.00250717
I0409 22:31:45.121134 25438 solver.cpp:218] Iteration 6996 (2.12112 iter/s, 5.6574s/12 iters), loss = 0.323179
I0409 22:31:45.121193 25438 solver.cpp:237]     Train net output #0: loss = 0.323179 (* 1 = 0.323179 loss)
I0409 22:31:45.121204 25438 sgd_solver.cpp:105] Iteration 6996, lr = 0.00250121
I0409 22:31:50.781261 25438 solver.cpp:218] Iteration 7008 (2.12018 iter/s, 5.65989s/12 iters), loss = 0.191107
I0409 22:31:50.781307 25438 solver.cpp:237]     Train net output #0: loss = 0.191107 (* 1 = 0.191107 loss)
I0409 22:31:50.781316 25438 sgd_solver.cpp:105] Iteration 7008, lr = 0.00249528
I0409 22:31:56.095549 25438 solver.cpp:218] Iteration 7020 (2.25815 iter/s, 5.31407s/12 iters), loss = 0.25241
I0409 22:31:56.095602 25438 solver.cpp:237]     Train net output #0: loss = 0.25241 (* 1 = 0.25241 loss)
I0409 22:31:56.095613 25438 sgd_solver.cpp:105] Iteration 7020, lr = 0.00248935
I0409 22:32:01.595424 25438 solver.cpp:218] Iteration 7032 (2.18195 iter/s, 5.49966s/12 iters), loss = 0.124203
I0409 22:32:01.595468 25438 solver.cpp:237]     Train net output #0: loss = 0.124203 (* 1 = 0.124203 loss)
I0409 22:32:01.595476 25438 sgd_solver.cpp:105] Iteration 7032, lr = 0.00248344
I0409 22:32:03.858201 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0409 22:32:09.870317 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0409 22:32:16.901620 25438 solver.cpp:330] Iteration 7038, Testing net (#0)
I0409 22:32:16.901646 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:32:18.625480 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:32:21.436993 25438 solver.cpp:397]     Test net output #0: accuracy = 0.206495
I0409 22:32:21.437041 25438 solver.cpp:397]     Test net output #1: loss = 6.27308 (* 1 = 6.27308 loss)
I0409 22:32:23.504809 25438 solver.cpp:218] Iteration 7044 (0.547727 iter/s, 21.9087s/12 iters), loss = 0.168769
I0409 22:32:23.504868 25438 solver.cpp:237]     Train net output #0: loss = 0.168768 (* 1 = 0.168768 loss)
I0409 22:32:23.504878 25438 sgd_solver.cpp:105] Iteration 7044, lr = 0.00247755
I0409 22:32:28.752369 25438 solver.cpp:218] Iteration 7056 (2.28687 iter/s, 5.24734s/12 iters), loss = 0.230125
I0409 22:32:28.752419 25438 solver.cpp:237]     Train net output #0: loss = 0.230124 (* 1 = 0.230124 loss)
I0409 22:32:28.752429 25438 sgd_solver.cpp:105] Iteration 7056, lr = 0.00247166
I0409 22:32:34.243132 25438 solver.cpp:218] Iteration 7068 (2.18558 iter/s, 5.49055s/12 iters), loss = 0.33981
I0409 22:32:34.243185 25438 solver.cpp:237]     Train net output #0: loss = 0.33981 (* 1 = 0.33981 loss)
I0409 22:32:34.243196 25438 sgd_solver.cpp:105] Iteration 7068, lr = 0.0024658
I0409 22:32:39.706037 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:32:39.830102 25438 solver.cpp:218] Iteration 7080 (2.14794 iter/s, 5.58675s/12 iters), loss = 0.240752
I0409 22:32:39.830148 25438 solver.cpp:237]     Train net output #0: loss = 0.240752 (* 1 = 0.240752 loss)
I0409 22:32:39.830158 25438 sgd_solver.cpp:105] Iteration 7080, lr = 0.00245994
I0409 22:32:45.054559 25438 solver.cpp:218] Iteration 7092 (2.29698 iter/s, 5.22424s/12 iters), loss = 0.112644
I0409 22:32:45.054687 25438 solver.cpp:237]     Train net output #0: loss = 0.112644 (* 1 = 0.112644 loss)
I0409 22:32:45.054703 25438 sgd_solver.cpp:105] Iteration 7092, lr = 0.0024541
I0409 22:32:50.349027 25438 solver.cpp:218] Iteration 7104 (2.26664 iter/s, 5.29418s/12 iters), loss = 0.114935
I0409 22:32:50.349081 25438 solver.cpp:237]     Train net output #0: loss = 0.114934 (* 1 = 0.114934 loss)
I0409 22:32:50.349092 25438 sgd_solver.cpp:105] Iteration 7104, lr = 0.00244827
I0409 22:32:55.728266 25438 solver.cpp:218] Iteration 7116 (2.23089 iter/s, 5.37902s/12 iters), loss = 0.223247
I0409 22:32:55.728312 25438 solver.cpp:237]     Train net output #0: loss = 0.223247 (* 1 = 0.223247 loss)
I0409 22:32:55.728320 25438 sgd_solver.cpp:105] Iteration 7116, lr = 0.00244246
I0409 22:33:01.336939 25438 solver.cpp:218] Iteration 7128 (2.13963 iter/s, 5.60845s/12 iters), loss = 0.174635
I0409 22:33:01.336995 25438 solver.cpp:237]     Train net output #0: loss = 0.174635 (* 1 = 0.174635 loss)
I0409 22:33:01.337007 25438 sgd_solver.cpp:105] Iteration 7128, lr = 0.00243666
I0409 22:33:06.486035 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0409 22:33:10.695901 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0409 22:33:13.873587 25438 solver.cpp:330] Iteration 7140, Testing net (#0)
I0409 22:33:13.873607 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:33:15.542395 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:33:18.375651 25438 solver.cpp:397]     Test net output #0: accuracy = 0.200368
I0409 22:33:18.375720 25438 solver.cpp:397]     Test net output #1: loss = 6.21283 (* 1 = 6.21283 loss)
I0409 22:33:18.473126 25438 solver.cpp:218] Iteration 7140 (0.700295 iter/s, 17.1356s/12 iters), loss = 0.158987
I0409 22:33:18.473178 25438 solver.cpp:237]     Train net output #0: loss = 0.158987 (* 1 = 0.158987 loss)
I0409 22:33:18.473189 25438 sgd_solver.cpp:105] Iteration 7140, lr = 0.00243088
I0409 22:33:23.181406 25438 solver.cpp:218] Iteration 7152 (2.54881 iter/s, 4.70808s/12 iters), loss = 0.236502
I0409 22:33:23.181452 25438 solver.cpp:237]     Train net output #0: loss = 0.236502 (* 1 = 0.236502 loss)
I0409 22:33:23.181461 25438 sgd_solver.cpp:105] Iteration 7152, lr = 0.00242511
I0409 22:33:28.639281 25438 solver.cpp:218] Iteration 7164 (2.19875 iter/s, 5.45765s/12 iters), loss = 0.228882
I0409 22:33:28.639348 25438 solver.cpp:237]     Train net output #0: loss = 0.228882 (* 1 = 0.228882 loss)
I0409 22:33:28.639365 25438 sgd_solver.cpp:105] Iteration 7164, lr = 0.00241935
I0409 22:33:33.996711 25438 solver.cpp:218] Iteration 7176 (2.23997 iter/s, 5.3572s/12 iters), loss = 0.107977
I0409 22:33:33.996754 25438 solver.cpp:237]     Train net output #0: loss = 0.107977 (* 1 = 0.107977 loss)
I0409 22:33:33.996764 25438 sgd_solver.cpp:105] Iteration 7176, lr = 0.0024136
I0409 22:33:36.222450 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:33:39.241784 25438 solver.cpp:218] Iteration 7188 (2.28795 iter/s, 5.24487s/12 iters), loss = 0.136676
I0409 22:33:39.241824 25438 solver.cpp:237]     Train net output #0: loss = 0.136676 (* 1 = 0.136676 loss)
I0409 22:33:39.241834 25438 sgd_solver.cpp:105] Iteration 7188, lr = 0.00240787
I0409 22:33:44.879304 25438 solver.cpp:218] Iteration 7200 (2.12868 iter/s, 5.6373s/12 iters), loss = 0.115102
I0409 22:33:44.879354 25438 solver.cpp:237]     Train net output #0: loss = 0.115102 (* 1 = 0.115102 loss)
I0409 22:33:44.879366 25438 sgd_solver.cpp:105] Iteration 7200, lr = 0.00240216
I0409 22:33:50.551337 25438 solver.cpp:218] Iteration 7212 (2.11573 iter/s, 5.67181s/12 iters), loss = 0.14752
I0409 22:33:50.551432 25438 solver.cpp:237]     Train net output #0: loss = 0.14752 (* 1 = 0.14752 loss)
I0409 22:33:50.551445 25438 sgd_solver.cpp:105] Iteration 7212, lr = 0.00239645
I0409 22:33:56.065133 25438 solver.cpp:218] Iteration 7224 (2.17646 iter/s, 5.51353s/12 iters), loss = 0.177648
I0409 22:33:56.065183 25438 solver.cpp:237]     Train net output #0: loss = 0.177648 (* 1 = 0.177648 loss)
I0409 22:33:56.065193 25438 sgd_solver.cpp:105] Iteration 7224, lr = 0.00239076
I0409 22:34:01.641772 25438 solver.cpp:218] Iteration 7236 (2.15192 iter/s, 5.57641s/12 iters), loss = 0.21121
I0409 22:34:01.641821 25438 solver.cpp:237]     Train net output #0: loss = 0.21121 (* 1 = 0.21121 loss)
I0409 22:34:01.641832 25438 sgd_solver.cpp:105] Iteration 7236, lr = 0.00238509
I0409 22:34:03.661809 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0409 22:34:11.504930 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0409 22:34:14.724691 25438 solver.cpp:330] Iteration 7242, Testing net (#0)
I0409 22:34:14.724716 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:34:16.270680 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:34:19.137553 25438 solver.cpp:397]     Test net output #0: accuracy = 0.213848
I0409 22:34:19.137603 25438 solver.cpp:397]     Test net output #1: loss = 6.15601 (* 1 = 6.15601 loss)
I0409 22:34:21.098384 25438 solver.cpp:218] Iteration 7248 (0.616776 iter/s, 19.456s/12 iters), loss = 0.205246
I0409 22:34:21.100035 25438 solver.cpp:237]     Train net output #0: loss = 0.205245 (* 1 = 0.205245 loss)
I0409 22:34:21.100047 25438 sgd_solver.cpp:105] Iteration 7248, lr = 0.00237942
I0409 22:34:26.617614 25438 solver.cpp:218] Iteration 7260 (2.17493 iter/s, 5.51741s/12 iters), loss = 0.101166
I0409 22:34:26.617658 25438 solver.cpp:237]     Train net output #0: loss = 0.101166 (* 1 = 0.101166 loss)
I0409 22:34:26.617668 25438 sgd_solver.cpp:105] Iteration 7260, lr = 0.00237378
I0409 22:34:32.086850 25438 solver.cpp:218] Iteration 7272 (2.19417 iter/s, 5.46903s/12 iters), loss = 0.192154
I0409 22:34:32.086891 25438 solver.cpp:237]     Train net output #0: loss = 0.192154 (* 1 = 0.192154 loss)
I0409 22:34:32.086901 25438 sgd_solver.cpp:105] Iteration 7272, lr = 0.00236814
I0409 22:34:36.900713 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:34:37.735800 25438 solver.cpp:218] Iteration 7284 (2.12437 iter/s, 5.64873s/12 iters), loss = 0.167842
I0409 22:34:37.735844 25438 solver.cpp:237]     Train net output #0: loss = 0.167842 (* 1 = 0.167842 loss)
I0409 22:34:37.735853 25438 sgd_solver.cpp:105] Iteration 7284, lr = 0.00236252
I0409 22:34:43.414214 25438 solver.cpp:218] Iteration 7296 (2.11335 iter/s, 5.67819s/12 iters), loss = 0.204495
I0409 22:34:43.414258 25438 solver.cpp:237]     Train net output #0: loss = 0.204495 (* 1 = 0.204495 loss)
I0409 22:34:43.414270 25438 sgd_solver.cpp:105] Iteration 7296, lr = 0.00235691
I0409 22:34:49.080098 25438 solver.cpp:218] Iteration 7308 (2.11802 iter/s, 5.66566s/12 iters), loss = 0.23065
I0409 22:34:49.080157 25438 solver.cpp:237]     Train net output #0: loss = 0.23065 (* 1 = 0.23065 loss)
I0409 22:34:49.080168 25438 sgd_solver.cpp:105] Iteration 7308, lr = 0.00235131
I0409 22:34:54.660503 25438 solver.cpp:218] Iteration 7320 (2.15047 iter/s, 5.58018s/12 iters), loss = 0.169627
I0409 22:34:54.660598 25438 solver.cpp:237]     Train net output #0: loss = 0.169627 (* 1 = 0.169627 loss)
I0409 22:34:54.660606 25438 sgd_solver.cpp:105] Iteration 7320, lr = 0.00234573
I0409 22:35:00.088275 25438 solver.cpp:218] Iteration 7332 (2.21096 iter/s, 5.42752s/12 iters), loss = 0.127489
I0409 22:35:00.088315 25438 solver.cpp:237]     Train net output #0: loss = 0.127489 (* 1 = 0.127489 loss)
I0409 22:35:00.088322 25438 sgd_solver.cpp:105] Iteration 7332, lr = 0.00234016
I0409 22:35:05.106724 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0409 22:35:10.399260 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0409 22:35:16.594756 25438 solver.cpp:330] Iteration 7344, Testing net (#0)
I0409 22:35:16.594786 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:35:18.280299 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:35:21.182740 25438 solver.cpp:397]     Test net output #0: accuracy = 0.215686
I0409 22:35:21.182790 25438 solver.cpp:397]     Test net output #1: loss = 6.22414 (* 1 = 6.22414 loss)
I0409 22:35:21.280400 25438 solver.cpp:218] Iteration 7344 (0.566266 iter/s, 21.1915s/12 iters), loss = 0.190315
I0409 22:35:21.280467 25438 solver.cpp:237]     Train net output #0: loss = 0.190314 (* 1 = 0.190314 loss)
I0409 22:35:21.280481 25438 sgd_solver.cpp:105] Iteration 7344, lr = 0.0023346
I0409 22:35:26.008170 25438 solver.cpp:218] Iteration 7356 (2.53831 iter/s, 4.72756s/12 iters), loss = 0.209019
I0409 22:35:26.008271 25438 solver.cpp:237]     Train net output #0: loss = 0.209019 (* 1 = 0.209019 loss)
I0409 22:35:26.008283 25438 sgd_solver.cpp:105] Iteration 7356, lr = 0.00232906
I0409 22:35:31.678207 25438 solver.cpp:218] Iteration 7368 (2.11649 iter/s, 5.66977s/12 iters), loss = 0.115564
I0409 22:35:31.678251 25438 solver.cpp:237]     Train net output #0: loss = 0.115564 (* 1 = 0.115564 loss)
I0409 22:35:31.678261 25438 sgd_solver.cpp:105] Iteration 7368, lr = 0.00232353
I0409 22:35:37.042843 25438 solver.cpp:218] Iteration 7380 (2.23696 iter/s, 5.36442s/12 iters), loss = 0.111117
I0409 22:35:37.042894 25438 solver.cpp:237]     Train net output #0: loss = 0.111117 (* 1 = 0.111117 loss)
I0409 22:35:37.042904 25438 sgd_solver.cpp:105] Iteration 7380, lr = 0.00231802
I0409 22:35:38.560765 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:35:42.542373 25438 solver.cpp:218] Iteration 7392 (2.18209 iter/s, 5.49931s/12 iters), loss = 0.178956
I0409 22:35:42.542426 25438 solver.cpp:237]     Train net output #0: loss = 0.178956 (* 1 = 0.178956 loss)
I0409 22:35:42.542438 25438 sgd_solver.cpp:105] Iteration 7392, lr = 0.00231251
I0409 22:35:47.750707 25438 solver.cpp:218] Iteration 7404 (2.3041 iter/s, 5.20812s/12 iters), loss = 0.256089
I0409 22:35:47.750754 25438 solver.cpp:237]     Train net output #0: loss = 0.256089 (* 1 = 0.256089 loss)
I0409 22:35:47.750766 25438 sgd_solver.cpp:105] Iteration 7404, lr = 0.00230702
I0409 22:35:53.428009 25438 solver.cpp:218] Iteration 7416 (2.11376 iter/s, 5.67708s/12 iters), loss = 0.205733
I0409 22:35:53.428058 25438 solver.cpp:237]     Train net output #0: loss = 0.205733 (* 1 = 0.205733 loss)
I0409 22:35:53.428067 25438 sgd_solver.cpp:105] Iteration 7416, lr = 0.00230154
I0409 22:35:59.057873 25438 solver.cpp:218] Iteration 7428 (2.13158 iter/s, 5.62964s/12 iters), loss = 0.170361
I0409 22:35:59.058300 25438 solver.cpp:237]     Train net output #0: loss = 0.17036 (* 1 = 0.17036 loss)
I0409 22:35:59.058315 25438 sgd_solver.cpp:105] Iteration 7428, lr = 0.00229608
I0409 22:36:04.731889 25438 solver.cpp:218] Iteration 7440 (2.11513 iter/s, 5.67342s/12 iters), loss = 0.171733
I0409 22:36:04.731945 25438 solver.cpp:237]     Train net output #0: loss = 0.171733 (* 1 = 0.171733 loss)
I0409 22:36:04.731957 25438 sgd_solver.cpp:105] Iteration 7440, lr = 0.00229063
I0409 22:36:07.018083 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0409 22:36:14.231418 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0409 22:36:28.751817 25438 solver.cpp:330] Iteration 7446, Testing net (#0)
I0409 22:36:28.751842 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:36:30.308485 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:36:33.255939 25438 solver.cpp:397]     Test net output #0: accuracy = 0.21201
I0409 22:36:33.255988 25438 solver.cpp:397]     Test net output #1: loss = 6.3602 (* 1 = 6.3602 loss)
I0409 22:36:35.300658 25438 solver.cpp:218] Iteration 7452 (0.39257 iter/s, 30.5678s/12 iters), loss = 0.18192
I0409 22:36:35.300705 25438 solver.cpp:237]     Train net output #0: loss = 0.18192 (* 1 = 0.18192 loss)
I0409 22:36:35.300715 25438 sgd_solver.cpp:105] Iteration 7452, lr = 0.00228519
I0409 22:36:40.767510 25438 solver.cpp:218] Iteration 7464 (2.19513 iter/s, 5.46664s/12 iters), loss = 0.188297
I0409 22:36:40.767560 25438 solver.cpp:237]     Train net output #0: loss = 0.188296 (* 1 = 0.188296 loss)
I0409 22:36:40.767570 25438 sgd_solver.cpp:105] Iteration 7464, lr = 0.00227976
I0409 22:36:46.443003 25438 solver.cpp:218] Iteration 7476 (2.11444 iter/s, 5.67527s/12 iters), loss = 0.140285
I0409 22:36:46.443058 25438 solver.cpp:237]     Train net output #0: loss = 0.140285 (* 1 = 0.140285 loss)
I0409 22:36:46.443070 25438 sgd_solver.cpp:105] Iteration 7476, lr = 0.00227435
I0409 22:36:50.437270 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:36:52.114292 25438 solver.cpp:218] Iteration 7488 (2.11601 iter/s, 5.67106s/12 iters), loss = 0.166245
I0409 22:36:52.114346 25438 solver.cpp:237]     Train net output #0: loss = 0.166245 (* 1 = 0.166245 loss)
I0409 22:36:52.114358 25438 sgd_solver.cpp:105] Iteration 7488, lr = 0.00226895
I0409 22:36:57.791144 25438 solver.cpp:218] Iteration 7500 (2.11394 iter/s, 5.67662s/12 iters), loss = 0.183243
I0409 22:36:57.791203 25438 solver.cpp:237]     Train net output #0: loss = 0.183243 (* 1 = 0.183243 loss)
I0409 22:36:57.791214 25438 sgd_solver.cpp:105] Iteration 7500, lr = 0.00226357
I0409 22:37:03.435617 25438 solver.cpp:218] Iteration 7512 (2.12606 iter/s, 5.64425s/12 iters), loss = 0.205715
I0409 22:37:03.435726 25438 solver.cpp:237]     Train net output #0: loss = 0.205714 (* 1 = 0.205714 loss)
I0409 22:37:03.435736 25438 sgd_solver.cpp:105] Iteration 7512, lr = 0.00225819
I0409 22:37:09.085927 25438 solver.cpp:218] Iteration 7524 (2.12388 iter/s, 5.65002s/12 iters), loss = 0.186641
I0409 22:37:09.085988 25438 solver.cpp:237]     Train net output #0: loss = 0.18664 (* 1 = 0.18664 loss)
I0409 22:37:09.085996 25438 sgd_solver.cpp:105] Iteration 7524, lr = 0.00225283
I0409 22:37:14.664376 25438 solver.cpp:218] Iteration 7536 (2.15123 iter/s, 5.57821s/12 iters), loss = 0.200914
I0409 22:37:14.664430 25438 solver.cpp:237]     Train net output #0: loss = 0.200913 (* 1 = 0.200913 loss)
I0409 22:37:14.664443 25438 sgd_solver.cpp:105] Iteration 7536, lr = 0.00224748
I0409 22:37:19.711318 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0409 22:37:25.896667 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0409 22:37:35.967015 25438 solver.cpp:330] Iteration 7548, Testing net (#0)
I0409 22:37:35.967160 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:37:37.494233 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:37:40.477572 25438 solver.cpp:397]     Test net output #0: accuracy = 0.213848
I0409 22:37:40.477620 25438 solver.cpp:397]     Test net output #1: loss = 6.34294 (* 1 = 6.34294 loss)
I0409 22:37:40.574887 25438 solver.cpp:218] Iteration 7548 (0.463147 iter/s, 25.9097s/12 iters), loss = 0.132444
I0409 22:37:40.574934 25438 solver.cpp:237]     Train net output #0: loss = 0.132444 (* 1 = 0.132444 loss)
I0409 22:37:40.574949 25438 sgd_solver.cpp:105] Iteration 7548, lr = 0.00224215
I0409 22:37:45.011226 25438 solver.cpp:218] Iteration 7560 (2.70505 iter/s, 4.43615s/12 iters), loss = 0.318584
I0409 22:37:45.011277 25438 solver.cpp:237]     Train net output #0: loss = 0.318584 (* 1 = 0.318584 loss)
I0409 22:37:45.011288 25438 sgd_solver.cpp:105] Iteration 7560, lr = 0.00223682
I0409 22:37:50.247288 25438 solver.cpp:218] Iteration 7572 (2.29189 iter/s, 5.23584s/12 iters), loss = 0.120788
I0409 22:37:50.247342 25438 solver.cpp:237]     Train net output #0: loss = 0.120788 (* 1 = 0.120788 loss)
I0409 22:37:50.247354 25438 sgd_solver.cpp:105] Iteration 7572, lr = 0.00223151
I0409 22:37:55.502418 25438 solver.cpp:218] Iteration 7584 (2.28358 iter/s, 5.25491s/12 iters), loss = 0.132771
I0409 22:37:55.502467 25438 solver.cpp:237]     Train net output #0: loss = 0.13277 (* 1 = 0.13277 loss)
I0409 22:37:55.502480 25438 sgd_solver.cpp:105] Iteration 7584, lr = 0.00222621
I0409 22:37:56.164829 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:38:00.688586 25438 solver.cpp:218] Iteration 7596 (2.31394 iter/s, 5.18596s/12 iters), loss = 0.0890708
I0409 22:38:00.688642 25438 solver.cpp:237]     Train net output #0: loss = 0.0890706 (* 1 = 0.0890706 loss)
I0409 22:38:00.688654 25438 sgd_solver.cpp:105] Iteration 7596, lr = 0.00222093
I0409 22:38:05.933821 25438 solver.cpp:218] Iteration 7608 (2.28789 iter/s, 5.24502s/12 iters), loss = 0.253476
I0409 22:38:05.933872 25438 solver.cpp:237]     Train net output #0: loss = 0.253476 (* 1 = 0.253476 loss)
I0409 22:38:05.933883 25438 sgd_solver.cpp:105] Iteration 7608, lr = 0.00221565
I0409 22:38:11.076690 25438 solver.cpp:218] Iteration 7620 (2.33342 iter/s, 5.14266s/12 iters), loss = 0.0407731
I0409 22:38:11.076792 25438 solver.cpp:237]     Train net output #0: loss = 0.0407729 (* 1 = 0.0407729 loss)
I0409 22:38:11.076800 25438 sgd_solver.cpp:105] Iteration 7620, lr = 0.00221039
I0409 22:38:13.610711 25438 blocking_queue.cpp:49] Waiting for data
I0409 22:38:16.357270 25438 solver.cpp:218] Iteration 7632 (2.27259 iter/s, 5.28032s/12 iters), loss = 0.243268
I0409 22:38:16.357321 25438 solver.cpp:237]     Train net output #0: loss = 0.243268 (* 1 = 0.243268 loss)
I0409 22:38:16.357332 25438 sgd_solver.cpp:105] Iteration 7632, lr = 0.00220515
I0409 22:38:21.836402 25438 solver.cpp:218] Iteration 7644 (2.19022 iter/s, 5.47891s/12 iters), loss = 0.116183
I0409 22:38:21.836459 25438 solver.cpp:237]     Train net output #0: loss = 0.116182 (* 1 = 0.116182 loss)
I0409 22:38:21.836472 25438 sgd_solver.cpp:105] Iteration 7644, lr = 0.00219991
I0409 22:38:23.916247 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0409 22:38:29.059805 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0409 22:38:35.705189 25438 solver.cpp:330] Iteration 7650, Testing net (#0)
I0409 22:38:35.705210 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:38:37.271836 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:38:40.655397 25438 solver.cpp:397]     Test net output #0: accuracy = 0.21201
I0409 22:38:40.655438 25438 solver.cpp:397]     Test net output #1: loss = 6.27601 (* 1 = 6.27601 loss)
I0409 22:38:42.756908 25438 solver.cpp:218] Iteration 7656 (0.573618 iter/s, 20.9198s/12 iters), loss = 0.175059
I0409 22:38:42.757038 25438 solver.cpp:237]     Train net output #0: loss = 0.175058 (* 1 = 0.175058 loss)
I0409 22:38:42.757051 25438 sgd_solver.cpp:105] Iteration 7656, lr = 0.00219469
I0409 22:38:48.193151 25438 solver.cpp:218] Iteration 7668 (2.20753 iter/s, 5.43595s/12 iters), loss = 0.105203
I0409 22:38:48.193200 25438 solver.cpp:237]     Train net output #0: loss = 0.105203 (* 1 = 0.105203 loss)
I0409 22:38:48.193209 25438 sgd_solver.cpp:105] Iteration 7668, lr = 0.00218948
I0409 22:38:53.425634 25438 solver.cpp:218] Iteration 7680 (2.29346 iter/s, 5.23227s/12 iters), loss = 0.141361
I0409 22:38:53.425685 25438 solver.cpp:237]     Train net output #0: loss = 0.141361 (* 1 = 0.141361 loss)
I0409 22:38:53.425695 25438 sgd_solver.cpp:105] Iteration 7680, lr = 0.00218428
I0409 22:38:56.337798 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:38:58.628082 25438 solver.cpp:218] Iteration 7692 (2.3067 iter/s, 5.20223s/12 iters), loss = 0.225487
I0409 22:38:58.628126 25438 solver.cpp:237]     Train net output #0: loss = 0.225487 (* 1 = 0.225487 loss)
I0409 22:38:58.628135 25438 sgd_solver.cpp:105] Iteration 7692, lr = 0.00217909
I0409 22:39:03.906968 25438 solver.cpp:218] Iteration 7704 (2.2733 iter/s, 5.27868s/12 iters), loss = 0.105218
I0409 22:39:03.907014 25438 solver.cpp:237]     Train net output #0: loss = 0.105218 (* 1 = 0.105218 loss)
I0409 22:39:03.907027 25438 sgd_solver.cpp:105] Iteration 7704, lr = 0.00217392
I0409 22:39:09.094954 25438 solver.cpp:218] Iteration 7716 (2.31313 iter/s, 5.18778s/12 iters), loss = 0.111891
I0409 22:39:09.095001 25438 solver.cpp:237]     Train net output #0: loss = 0.111891 (* 1 = 0.111891 loss)
I0409 22:39:09.095012 25438 sgd_solver.cpp:105] Iteration 7716, lr = 0.00216876
I0409 22:39:14.336001 25438 solver.cpp:218] Iteration 7728 (2.28971 iter/s, 5.24084s/12 iters), loss = 0.0945186
I0409 22:39:14.336089 25438 solver.cpp:237]     Train net output #0: loss = 0.0945184 (* 1 = 0.0945184 loss)
I0409 22:39:14.336099 25438 sgd_solver.cpp:105] Iteration 7728, lr = 0.00216361
I0409 22:39:19.587736 25438 solver.cpp:218] Iteration 7740 (2.28507 iter/s, 5.25148s/12 iters), loss = 0.218651
I0409 22:39:19.587791 25438 solver.cpp:237]     Train net output #0: loss = 0.218651 (* 1 = 0.218651 loss)
I0409 22:39:19.587802 25438 sgd_solver.cpp:105] Iteration 7740, lr = 0.00215847
I0409 22:39:24.383781 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0409 22:39:31.342681 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0409 22:39:36.616240 25438 solver.cpp:330] Iteration 7752, Testing net (#0)
I0409 22:39:36.616267 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:39:37.987601 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:39:41.134928 25438 solver.cpp:397]     Test net output #0: accuracy = 0.208333
I0409 22:39:41.134976 25438 solver.cpp:397]     Test net output #1: loss = 6.31502 (* 1 = 6.31502 loss)
I0409 22:39:41.229707 25438 solver.cpp:218] Iteration 7752 (0.554496 iter/s, 21.6413s/12 iters), loss = 0.0918174
I0409 22:39:41.229776 25438 solver.cpp:237]     Train net output #0: loss = 0.0918172 (* 1 = 0.0918172 loss)
I0409 22:39:41.229790 25438 sgd_solver.cpp:105] Iteration 7752, lr = 0.00215335
I0409 22:39:45.715255 25438 solver.cpp:218] Iteration 7764 (2.67538 iter/s, 4.48534s/12 iters), loss = 0.133972
I0409 22:39:45.715382 25438 solver.cpp:237]     Train net output #0: loss = 0.133972 (* 1 = 0.133972 loss)
I0409 22:39:45.715392 25438 sgd_solver.cpp:105] Iteration 7764, lr = 0.00214823
I0409 22:39:51.423611 25438 solver.cpp:218] Iteration 7776 (2.10229 iter/s, 5.70806s/12 iters), loss = 0.0740511
I0409 22:39:51.423650 25438 solver.cpp:237]     Train net output #0: loss = 0.0740509 (* 1 = 0.0740509 loss)
I0409 22:39:51.423660 25438 sgd_solver.cpp:105] Iteration 7776, lr = 0.00214313
I0409 22:39:56.695575 25438 solver.cpp:218] Iteration 7788 (2.27628 iter/s, 5.27175s/12 iters), loss = 0.115595
I0409 22:39:56.695632 25438 solver.cpp:237]     Train net output #0: loss = 0.115595 (* 1 = 0.115595 loss)
I0409 22:39:56.695645 25438 sgd_solver.cpp:105] Iteration 7788, lr = 0.00213805
I0409 22:39:56.706696 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:40:02.227150 25438 solver.cpp:218] Iteration 7800 (2.16945 iter/s, 5.53135s/12 iters), loss = 0.259362
I0409 22:40:02.227206 25438 solver.cpp:237]     Train net output #0: loss = 0.259362 (* 1 = 0.259362 loss)
I0409 22:40:02.227217 25438 sgd_solver.cpp:105] Iteration 7800, lr = 0.00213297
I0409 22:40:07.296955 25438 solver.cpp:218] Iteration 7812 (2.36705 iter/s, 5.0696s/12 iters), loss = 0.106976
I0409 22:40:07.297001 25438 solver.cpp:237]     Train net output #0: loss = 0.106976 (* 1 = 0.106976 loss)
I0409 22:40:07.297010 25438 sgd_solver.cpp:105] Iteration 7812, lr = 0.00212791
I0409 22:40:12.906360 25438 solver.cpp:218] Iteration 7824 (2.13935 iter/s, 5.60919s/12 iters), loss = 0.0818222
I0409 22:40:12.906409 25438 solver.cpp:237]     Train net output #0: loss = 0.081822 (* 1 = 0.081822 loss)
I0409 22:40:12.906421 25438 sgd_solver.cpp:105] Iteration 7824, lr = 0.00212285
I0409 22:40:18.209553 25438 solver.cpp:218] Iteration 7836 (2.26288 iter/s, 5.30298s/12 iters), loss = 0.103774
I0409 22:40:18.209652 25438 solver.cpp:237]     Train net output #0: loss = 0.103774 (* 1 = 0.103774 loss)
I0409 22:40:18.209666 25438 sgd_solver.cpp:105] Iteration 7836, lr = 0.00211781
I0409 22:40:23.580977 25438 solver.cpp:218] Iteration 7848 (2.23415 iter/s, 5.37116s/12 iters), loss = 0.0625576
I0409 22:40:23.581017 25438 solver.cpp:237]     Train net output #0: loss = 0.0625574 (* 1 = 0.0625574 loss)
I0409 22:40:23.581027 25438 sgd_solver.cpp:105] Iteration 7848, lr = 0.00211279
I0409 22:40:25.873566 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0409 22:40:43.407550 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0409 22:40:53.308264 25438 solver.cpp:330] Iteration 7854, Testing net (#0)
I0409 22:40:53.308320 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:40:54.699791 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:40:57.814883 25438 solver.cpp:397]     Test net output #0: accuracy = 0.215074
I0409 22:40:57.814932 25438 solver.cpp:397]     Test net output #1: loss = 6.44989 (* 1 = 6.44989 loss)
I0409 22:40:59.894837 25438 solver.cpp:218] Iteration 7860 (0.330462 iter/s, 36.3128s/12 iters), loss = 0.0676167
I0409 22:40:59.894887 25438 solver.cpp:237]     Train net output #0: loss = 0.0676165 (* 1 = 0.0676165 loss)
I0409 22:40:59.894899 25438 sgd_solver.cpp:105] Iteration 7860, lr = 0.00210777
I0409 22:41:05.470062 25438 solver.cpp:218] Iteration 7872 (2.15247 iter/s, 5.575s/12 iters), loss = 0.188901
I0409 22:41:05.470116 25438 solver.cpp:237]     Train net output #0: loss = 0.188901 (* 1 = 0.188901 loss)
I0409 22:41:05.470129 25438 sgd_solver.cpp:105] Iteration 7872, lr = 0.00210277
I0409 22:41:10.969808 25438 solver.cpp:218] Iteration 7884 (2.18201 iter/s, 5.49952s/12 iters), loss = 0.0925584
I0409 22:41:10.969864 25438 solver.cpp:237]     Train net output #0: loss = 0.0925581 (* 1 = 0.0925581 loss)
I0409 22:41:10.969877 25438 sgd_solver.cpp:105] Iteration 7884, lr = 0.00209777
I0409 22:41:13.207201 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:41:16.148936 25438 solver.cpp:218] Iteration 7896 (2.31709 iter/s, 5.17891s/12 iters), loss = 0.185309
I0409 22:41:16.148993 25438 solver.cpp:237]     Train net output #0: loss = 0.185309 (* 1 = 0.185309 loss)
I0409 22:41:16.149004 25438 sgd_solver.cpp:105] Iteration 7896, lr = 0.00209279
I0409 22:41:21.306607 25438 solver.cpp:218] Iteration 7908 (2.32673 iter/s, 5.15745s/12 iters), loss = 0.137448
I0409 22:41:21.306658 25438 solver.cpp:237]     Train net output #0: loss = 0.137448 (* 1 = 0.137448 loss)
I0409 22:41:21.306668 25438 sgd_solver.cpp:105] Iteration 7908, lr = 0.00208782
I0409 22:41:26.524838 25438 solver.cpp:218] Iteration 7920 (2.29973 iter/s, 5.21801s/12 iters), loss = 0.0720571
I0409 22:41:26.524993 25438 solver.cpp:237]     Train net output #0: loss = 0.0720569 (* 1 = 0.0720569 loss)
I0409 22:41:26.525007 25438 sgd_solver.cpp:105] Iteration 7920, lr = 0.00208287
I0409 22:41:31.814270 25438 solver.cpp:218] Iteration 7932 (2.26881 iter/s, 5.28912s/12 iters), loss = 0.196652
I0409 22:41:31.814322 25438 solver.cpp:237]     Train net output #0: loss = 0.196652 (* 1 = 0.196652 loss)
I0409 22:41:31.814335 25438 sgd_solver.cpp:105] Iteration 7932, lr = 0.00207792
I0409 22:41:37.155030 25438 solver.cpp:218] Iteration 7944 (2.24696 iter/s, 5.34054s/12 iters), loss = 0.0311738
I0409 22:41:37.155072 25438 solver.cpp:237]     Train net output #0: loss = 0.0311736 (* 1 = 0.0311736 loss)
I0409 22:41:37.155082 25438 sgd_solver.cpp:105] Iteration 7944, lr = 0.00207299
I0409 22:41:41.934110 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0409 22:41:57.147174 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0409 22:42:11.054922 25438 solver.cpp:330] Iteration 7956, Testing net (#0)
I0409 22:42:11.054949 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:42:12.334048 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:42:15.536375 25438 solver.cpp:397]     Test net output #0: accuracy = 0.222426
I0409 22:42:15.536425 25438 solver.cpp:397]     Test net output #1: loss = 6.43244 (* 1 = 6.43244 loss)
I0409 22:42:15.633607 25438 solver.cpp:218] Iteration 7956 (0.311871 iter/s, 38.4774s/12 iters), loss = 0.127891
I0409 22:42:15.633656 25438 solver.cpp:237]     Train net output #0: loss = 0.127891 (* 1 = 0.127891 loss)
I0409 22:42:15.633667 25438 sgd_solver.cpp:105] Iteration 7956, lr = 0.00206807
I0409 22:42:19.986078 25438 solver.cpp:218] Iteration 7968 (2.75718 iter/s, 4.35228s/12 iters), loss = 0.0960543
I0409 22:42:19.986131 25438 solver.cpp:237]     Train net output #0: loss = 0.096054 (* 1 = 0.096054 loss)
I0409 22:42:19.986142 25438 sgd_solver.cpp:105] Iteration 7968, lr = 0.00206316
I0409 22:42:25.211230 25438 solver.cpp:218] Iteration 7980 (2.29668 iter/s, 5.22494s/12 iters), loss = 0.106919
I0409 22:42:25.211274 25438 solver.cpp:237]     Train net output #0: loss = 0.106919 (* 1 = 0.106919 loss)
I0409 22:42:25.211284 25438 sgd_solver.cpp:105] Iteration 7980, lr = 0.00205826
I0409 22:42:29.667060 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:42:30.412566 25438 solver.cpp:218] Iteration 7992 (2.3072 iter/s, 5.20112s/12 iters), loss = 0.173875
I0409 22:42:30.412623 25438 solver.cpp:237]     Train net output #0: loss = 0.173875 (* 1 = 0.173875 loss)
I0409 22:42:30.412636 25438 sgd_solver.cpp:105] Iteration 7992, lr = 0.00205337
I0409 22:42:35.682372 25438 solver.cpp:218] Iteration 8004 (2.27722 iter/s, 5.26958s/12 iters), loss = 0.15477
I0409 22:42:35.682418 25438 solver.cpp:237]     Train net output #0: loss = 0.15477 (* 1 = 0.15477 loss)
I0409 22:42:35.682427 25438 sgd_solver.cpp:105] Iteration 8004, lr = 0.0020485
I0409 22:42:40.919827 25438 solver.cpp:218] Iteration 8016 (2.29128 iter/s, 5.23724s/12 iters), loss = 0.129814
I0409 22:42:40.919878 25438 solver.cpp:237]     Train net output #0: loss = 0.129814 (* 1 = 0.129814 loss)
I0409 22:42:40.919890 25438 sgd_solver.cpp:105] Iteration 8016, lr = 0.00204363
I0409 22:42:46.151816 25438 solver.cpp:218] Iteration 8028 (2.29368 iter/s, 5.23177s/12 iters), loss = 0.212248
I0409 22:42:46.151859 25438 solver.cpp:237]     Train net output #0: loss = 0.212248 (* 1 = 0.212248 loss)
I0409 22:42:46.151867 25438 sgd_solver.cpp:105] Iteration 8028, lr = 0.00203878
I0409 22:42:51.293669 25438 solver.cpp:218] Iteration 8040 (2.33388 iter/s, 5.14165s/12 iters), loss = 0.11475
I0409 22:42:51.293718 25438 solver.cpp:237]     Train net output #0: loss = 0.11475 (* 1 = 0.11475 loss)
I0409 22:42:51.293730 25438 sgd_solver.cpp:105] Iteration 8040, lr = 0.00203394
I0409 22:42:56.507496 25438 solver.cpp:218] Iteration 8052 (2.30167 iter/s, 5.21361s/12 iters), loss = 0.0823546
I0409 22:42:56.507552 25438 solver.cpp:237]     Train net output #0: loss = 0.0823544 (* 1 = 0.0823544 loss)
I0409 22:42:56.507565 25438 sgd_solver.cpp:105] Iteration 8052, lr = 0.00202911
I0409 22:42:58.634604 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0409 22:43:08.880210 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0409 22:43:16.615036 25438 solver.cpp:330] Iteration 8058, Testing net (#0)
I0409 22:43:16.615061 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:43:17.819105 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:43:21.144783 25438 solver.cpp:397]     Test net output #0: accuracy = 0.233456
I0409 22:43:21.144826 25438 solver.cpp:397]     Test net output #1: loss = 6.3286 (* 1 = 6.3286 loss)
I0409 22:43:23.439503 25438 solver.cpp:218] Iteration 8064 (0.44558 iter/s, 26.9312s/12 iters), loss = 0.125533
I0409 22:43:23.439566 25438 solver.cpp:237]     Train net output #0: loss = 0.125533 (* 1 = 0.125533 loss)
I0409 22:43:23.439579 25438 sgd_solver.cpp:105] Iteration 8064, lr = 0.00202429
I0409 22:43:28.544916 25438 solver.cpp:218] Iteration 8076 (2.35055 iter/s, 5.10519s/12 iters), loss = 0.123551
I0409 22:43:28.544975 25438 solver.cpp:237]     Train net output #0: loss = 0.123551 (* 1 = 0.123551 loss)
I0409 22:43:28.544988 25438 sgd_solver.cpp:105] Iteration 8076, lr = 0.00201949
I0409 22:43:33.646358 25438 solver.cpp:218] Iteration 8088 (2.35238 iter/s, 5.10123s/12 iters), loss = 0.27236
I0409 22:43:33.646414 25438 solver.cpp:237]     Train net output #0: loss = 0.272359 (* 1 = 0.272359 loss)
I0409 22:43:33.646425 25438 sgd_solver.cpp:105] Iteration 8088, lr = 0.00201469
I0409 22:43:35.174747 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:43:39.187767 25438 solver.cpp:218] Iteration 8100 (2.1656 iter/s, 5.54118s/12 iters), loss = 0.183741
I0409 22:43:39.187876 25438 solver.cpp:237]     Train net output #0: loss = 0.183741 (* 1 = 0.183741 loss)
I0409 22:43:39.187886 25438 sgd_solver.cpp:105] Iteration 8100, lr = 0.00200991
I0409 22:43:44.325919 25438 solver.cpp:218] Iteration 8112 (2.33559 iter/s, 5.13788s/12 iters), loss = 0.0679317
I0409 22:43:44.325974 25438 solver.cpp:237]     Train net output #0: loss = 0.0679315 (* 1 = 0.0679315 loss)
I0409 22:43:44.325984 25438 sgd_solver.cpp:105] Iteration 8112, lr = 0.00200514
I0409 22:43:49.443173 25438 solver.cpp:218] Iteration 8124 (2.34511 iter/s, 5.11704s/12 iters), loss = 0.0965338
I0409 22:43:49.443224 25438 solver.cpp:237]     Train net output #0: loss = 0.0965336 (* 1 = 0.0965336 loss)
I0409 22:43:49.443234 25438 sgd_solver.cpp:105] Iteration 8124, lr = 0.00200038
I0409 22:43:54.943980 25438 solver.cpp:218] Iteration 8136 (2.18159 iter/s, 5.50058s/12 iters), loss = 0.105889
I0409 22:43:54.944042 25438 solver.cpp:237]     Train net output #0: loss = 0.105889 (* 1 = 0.105889 loss)
I0409 22:43:54.944054 25438 sgd_solver.cpp:105] Iteration 8136, lr = 0.00199563
I0409 22:44:00.361999 25438 solver.cpp:218] Iteration 8148 (2.21493 iter/s, 5.41779s/12 iters), loss = 0.0758498
I0409 22:44:00.362044 25438 solver.cpp:237]     Train net output #0: loss = 0.0758496 (* 1 = 0.0758496 loss)
I0409 22:44:00.362054 25438 sgd_solver.cpp:105] Iteration 8148, lr = 0.00199089
I0409 22:44:05.333045 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0409 22:44:10.125245 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0409 22:44:13.351558 25438 solver.cpp:330] Iteration 8160, Testing net (#0)
I0409 22:44:13.351583 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:44:14.625372 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:44:17.858847 25438 solver.cpp:397]     Test net output #0: accuracy = 0.220588
I0409 22:44:17.858883 25438 solver.cpp:397]     Test net output #1: loss = 6.36674 (* 1 = 6.36674 loss)
I0409 22:44:17.956151 25438 solver.cpp:218] Iteration 8160 (0.682067 iter/s, 17.5936s/12 iters), loss = 0.0519552
I0409 22:44:17.956212 25438 solver.cpp:237]     Train net output #0: loss = 0.051955 (* 1 = 0.051955 loss)
I0409 22:44:17.956223 25438 sgd_solver.cpp:105] Iteration 8160, lr = 0.00198616
I0409 22:44:22.431315 25438 solver.cpp:218] Iteration 8172 (2.68159 iter/s, 4.47496s/12 iters), loss = 0.0721413
I0409 22:44:22.431372 25438 solver.cpp:237]     Train net output #0: loss = 0.0721411 (* 1 = 0.0721411 loss)
I0409 22:44:22.431385 25438 sgd_solver.cpp:105] Iteration 8172, lr = 0.00198145
I0409 22:44:27.917887 25438 solver.cpp:218] Iteration 8184 (2.18725 iter/s, 5.48634s/12 iters), loss = 0.111565
I0409 22:44:27.917943 25438 solver.cpp:237]     Train net output #0: loss = 0.111565 (* 1 = 0.111565 loss)
I0409 22:44:27.917973 25438 sgd_solver.cpp:105] Iteration 8184, lr = 0.00197674
I0409 22:44:31.684043 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:44:33.183869 25438 solver.cpp:218] Iteration 8196 (2.27887 iter/s, 5.26576s/12 iters), loss = 0.0434356
I0409 22:44:33.183923 25438 solver.cpp:237]     Train net output #0: loss = 0.0434355 (* 1 = 0.0434355 loss)
I0409 22:44:33.183935 25438 sgd_solver.cpp:105] Iteration 8196, lr = 0.00197205
I0409 22:44:38.277673 25438 solver.cpp:218] Iteration 8208 (2.3559 iter/s, 5.09359s/12 iters), loss = 0.0838384
I0409 22:44:38.277724 25438 solver.cpp:237]     Train net output #0: loss = 0.0838382 (* 1 = 0.0838382 loss)
I0409 22:44:38.277734 25438 sgd_solver.cpp:105] Iteration 8208, lr = 0.00196737
I0409 22:44:43.336095 25438 solver.cpp:218] Iteration 8220 (2.37238 iter/s, 5.05822s/12 iters), loss = 0.12739
I0409 22:44:43.336208 25438 solver.cpp:237]     Train net output #0: loss = 0.12739 (* 1 = 0.12739 loss)
I0409 22:44:43.336218 25438 sgd_solver.cpp:105] Iteration 8220, lr = 0.0019627
I0409 22:44:48.416355 25438 solver.cpp:218] Iteration 8232 (2.36221 iter/s, 5.07999s/12 iters), loss = 0.16484
I0409 22:44:48.416402 25438 solver.cpp:237]     Train net output #0: loss = 0.16484 (* 1 = 0.16484 loss)
I0409 22:44:48.416414 25438 sgd_solver.cpp:105] Iteration 8232, lr = 0.00195804
I0409 22:44:53.528091 25438 solver.cpp:218] Iteration 8244 (2.34763 iter/s, 5.11153s/12 iters), loss = 0.120597
I0409 22:44:53.528143 25438 solver.cpp:237]     Train net output #0: loss = 0.120596 (* 1 = 0.120596 loss)
I0409 22:44:53.528156 25438 sgd_solver.cpp:105] Iteration 8244, lr = 0.00195339
I0409 22:44:58.634673 25438 solver.cpp:218] Iteration 8256 (2.35001 iter/s, 5.10637s/12 iters), loss = 0.113493
I0409 22:44:58.634719 25438 solver.cpp:237]     Train net output #0: loss = 0.113492 (* 1 = 0.113492 loss)
I0409 22:44:58.634727 25438 sgd_solver.cpp:105] Iteration 8256, lr = 0.00194875
I0409 22:45:00.688277 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0409 22:45:06.275041 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0409 22:45:09.572856 25438 solver.cpp:330] Iteration 8262, Testing net (#0)
I0409 22:45:09.572885 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:45:10.862342 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:45:14.125356 25438 solver.cpp:397]     Test net output #0: accuracy = 0.226716
I0409 22:45:14.125537 25438 solver.cpp:397]     Test net output #1: loss = 6.20406 (* 1 = 6.20406 loss)
I0409 22:45:16.154587 25438 solver.cpp:218] Iteration 8268 (0.684957 iter/s, 17.5194s/12 iters), loss = 0.221684
I0409 22:45:16.154635 25438 solver.cpp:237]     Train net output #0: loss = 0.221684 (* 1 = 0.221684 loss)
I0409 22:45:16.154645 25438 sgd_solver.cpp:105] Iteration 8268, lr = 0.00194412
I0409 22:45:21.453872 25438 solver.cpp:218] Iteration 8280 (2.26455 iter/s, 5.29907s/12 iters), loss = 0.204426
I0409 22:45:21.453918 25438 solver.cpp:237]     Train net output #0: loss = 0.204425 (* 1 = 0.204425 loss)
I0409 22:45:21.453928 25438 sgd_solver.cpp:105] Iteration 8280, lr = 0.00193951
I0409 22:45:27.135848 25438 solver.cpp:218] Iteration 8292 (2.11202 iter/s, 5.68176s/12 iters), loss = 0.0841874
I0409 22:45:27.135892 25438 solver.cpp:237]     Train net output #0: loss = 0.0841872 (* 1 = 0.0841872 loss)
I0409 22:45:27.135901 25438 sgd_solver.cpp:105] Iteration 8292, lr = 0.0019349
I0409 22:45:27.914665 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:45:32.802496 25438 solver.cpp:218] Iteration 8304 (2.11774 iter/s, 5.66642s/12 iters), loss = 0.114017
I0409 22:45:32.802556 25438 solver.cpp:237]     Train net output #0: loss = 0.114017 (* 1 = 0.114017 loss)
I0409 22:45:32.802569 25438 sgd_solver.cpp:105] Iteration 8304, lr = 0.00193031
I0409 22:45:36.058645 25438 blocking_queue.cpp:49] Waiting for data
I0409 22:45:38.268424 25438 solver.cpp:218] Iteration 8316 (2.19551 iter/s, 5.4657s/12 iters), loss = 0.075739
I0409 22:45:38.268479 25438 solver.cpp:237]     Train net output #0: loss = 0.0757388 (* 1 = 0.0757388 loss)
I0409 22:45:38.268491 25438 sgd_solver.cpp:105] Iteration 8316, lr = 0.00192573
I0409 22:45:43.593926 25438 solver.cpp:218] Iteration 8328 (2.2534 iter/s, 5.32529s/12 iters), loss = 0.168911
I0409 22:45:43.593984 25438 solver.cpp:237]     Train net output #0: loss = 0.168911 (* 1 = 0.168911 loss)
I0409 22:45:43.593995 25438 sgd_solver.cpp:105] Iteration 8328, lr = 0.00192115
I0409 22:45:48.848090 25438 solver.cpp:218] Iteration 8340 (2.284 iter/s, 5.25395s/12 iters), loss = 0.180817
I0409 22:45:48.848191 25438 solver.cpp:237]     Train net output #0: loss = 0.180817 (* 1 = 0.180817 loss)
I0409 22:45:48.848204 25438 sgd_solver.cpp:105] Iteration 8340, lr = 0.00191659
I0409 22:45:54.341922 25438 solver.cpp:218] Iteration 8352 (2.18438 iter/s, 5.49356s/12 iters), loss = 0.0898666
I0409 22:45:54.342027 25438 solver.cpp:237]     Train net output #0: loss = 0.0898665 (* 1 = 0.0898665 loss)
I0409 22:45:54.342041 25438 sgd_solver.cpp:105] Iteration 8352, lr = 0.00191204
I0409 22:45:59.098644 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0409 22:46:11.461591 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0409 22:46:18.199980 25438 solver.cpp:330] Iteration 8364, Testing net (#0)
I0409 22:46:18.199999 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:46:19.381583 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:46:22.692289 25438 solver.cpp:397]     Test net output #0: accuracy = 0.221201
I0409 22:46:22.692327 25438 solver.cpp:397]     Test net output #1: loss = 6.25572 (* 1 = 6.25572 loss)
I0409 22:46:22.789502 25438 solver.cpp:218] Iteration 8364 (0.421842 iter/s, 28.4467s/12 iters), loss = 0.0990901
I0409 22:46:22.789559 25438 solver.cpp:237]     Train net output #0: loss = 0.09909 (* 1 = 0.09909 loss)
I0409 22:46:22.789570 25438 sgd_solver.cpp:105] Iteration 8364, lr = 0.0019075
I0409 22:46:27.151419 25438 solver.cpp:218] Iteration 8376 (2.75121 iter/s, 4.36172s/12 iters), loss = 0.104729
I0409 22:46:27.151464 25438 solver.cpp:237]     Train net output #0: loss = 0.104729 (* 1 = 0.104729 loss)
I0409 22:46:27.151474 25438 sgd_solver.cpp:105] Iteration 8376, lr = 0.00190297
I0409 22:46:32.355875 25438 solver.cpp:218] Iteration 8388 (2.30581 iter/s, 5.20424s/12 iters), loss = 0.123208
I0409 22:46:32.355926 25438 solver.cpp:237]     Train net output #0: loss = 0.123208 (* 1 = 0.123208 loss)
I0409 22:46:32.355937 25438 sgd_solver.cpp:105] Iteration 8388, lr = 0.00189846
I0409 22:46:35.326521 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:46:37.733985 25438 solver.cpp:218] Iteration 8400 (2.23136 iter/s, 5.37788s/12 iters), loss = 0.0416089
I0409 22:46:37.734032 25438 solver.cpp:237]     Train net output #0: loss = 0.0416088 (* 1 = 0.0416088 loss)
I0409 22:46:37.734042 25438 sgd_solver.cpp:105] Iteration 8400, lr = 0.00189395
I0409 22:46:43.291478 25438 solver.cpp:218] Iteration 8412 (2.15933 iter/s, 5.55727s/12 iters), loss = 0.0496812
I0409 22:46:43.291529 25438 solver.cpp:237]     Train net output #0: loss = 0.0496811 (* 1 = 0.0496811 loss)
I0409 22:46:43.291540 25438 sgd_solver.cpp:105] Iteration 8412, lr = 0.00188945
I0409 22:46:48.805653 25438 solver.cpp:218] Iteration 8424 (2.1763 iter/s, 5.51395s/12 iters), loss = 0.133542
I0409 22:46:48.805713 25438 solver.cpp:237]     Train net output #0: loss = 0.133542 (* 1 = 0.133542 loss)
I0409 22:46:48.805728 25438 sgd_solver.cpp:105] Iteration 8424, lr = 0.00188497
I0409 22:46:54.098528 25438 solver.cpp:218] Iteration 8436 (2.26729 iter/s, 5.29266s/12 iters), loss = 0.0548234
I0409 22:46:54.098657 25438 solver.cpp:237]     Train net output #0: loss = 0.0548233 (* 1 = 0.0548233 loss)
I0409 22:46:54.098667 25438 sgd_solver.cpp:105] Iteration 8436, lr = 0.00188049
I0409 22:46:59.392360 25438 solver.cpp:218] Iteration 8448 (2.26691 iter/s, 5.29354s/12 iters), loss = 0.0462971
I0409 22:46:59.392407 25438 solver.cpp:237]     Train net output #0: loss = 0.046297 (* 1 = 0.046297 loss)
I0409 22:46:59.392417 25438 sgd_solver.cpp:105] Iteration 8448, lr = 0.00187603
I0409 22:47:04.706220 25438 solver.cpp:218] Iteration 8460 (2.25834 iter/s, 5.31364s/12 iters), loss = 0.141495
I0409 22:47:04.706274 25438 solver.cpp:237]     Train net output #0: loss = 0.141495 (* 1 = 0.141495 loss)
I0409 22:47:04.706284 25438 sgd_solver.cpp:105] Iteration 8460, lr = 0.00187157
I0409 22:47:06.852351 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0409 22:47:14.252565 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0409 22:47:19.091236 25438 solver.cpp:330] Iteration 8466, Testing net (#0)
I0409 22:47:19.091269 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:47:20.219660 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:47:23.569180 25438 solver.cpp:397]     Test net output #0: accuracy = 0.224265
I0409 22:47:23.569223 25438 solver.cpp:397]     Test net output #1: loss = 6.27532 (* 1 = 6.27532 loss)
I0409 22:47:25.597594 25438 solver.cpp:218] Iteration 8472 (0.574418 iter/s, 20.8907s/12 iters), loss = 0.0921474
I0409 22:47:25.597694 25438 solver.cpp:237]     Train net output #0: loss = 0.0921473 (* 1 = 0.0921473 loss)
I0409 22:47:25.597705 25438 sgd_solver.cpp:105] Iteration 8472, lr = 0.00186713
I0409 22:47:31.024171 25438 solver.cpp:218] Iteration 8484 (2.21145 iter/s, 5.42631s/12 iters), loss = 0.0567522
I0409 22:47:31.024219 25438 solver.cpp:237]     Train net output #0: loss = 0.0567521 (* 1 = 0.0567521 loss)
I0409 22:47:31.024230 25438 sgd_solver.cpp:105] Iteration 8484, lr = 0.0018627
I0409 22:47:36.525120 25438 solver.cpp:218] Iteration 8496 (2.18153 iter/s, 5.50073s/12 iters), loss = 0.104401
I0409 22:47:36.525167 25438 solver.cpp:237]     Train net output #0: loss = 0.104401 (* 1 = 0.104401 loss)
I0409 22:47:36.525179 25438 sgd_solver.cpp:105] Iteration 8496, lr = 0.00185827
I0409 22:47:36.581385 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:47:42.155660 25438 solver.cpp:218] Iteration 8508 (2.13132 iter/s, 5.63031s/12 iters), loss = 0.0832188
I0409 22:47:42.155719 25438 solver.cpp:237]     Train net output #0: loss = 0.0832187 (* 1 = 0.0832187 loss)
I0409 22:47:42.155731 25438 sgd_solver.cpp:105] Iteration 8508, lr = 0.00185386
I0409 22:47:47.406592 25438 solver.cpp:218] Iteration 8520 (2.2854 iter/s, 5.25071s/12 iters), loss = 0.130162
I0409 22:47:47.406633 25438 solver.cpp:237]     Train net output #0: loss = 0.130162 (* 1 = 0.130162 loss)
I0409 22:47:47.406641 25438 sgd_solver.cpp:105] Iteration 8520, lr = 0.00184946
I0409 22:47:53.000331 25438 solver.cpp:218] Iteration 8532 (2.14534 iter/s, 5.59352s/12 iters), loss = 0.133752
I0409 22:47:53.000378 25438 solver.cpp:237]     Train net output #0: loss = 0.133752 (* 1 = 0.133752 loss)
I0409 22:47:53.000388 25438 sgd_solver.cpp:105] Iteration 8532, lr = 0.00184507
I0409 22:47:58.580461 25438 solver.cpp:218] Iteration 8544 (2.15057 iter/s, 5.57991s/12 iters), loss = 0.126569
I0409 22:47:58.580632 25438 solver.cpp:237]     Train net output #0: loss = 0.126569 (* 1 = 0.126569 loss)
I0409 22:47:58.580646 25438 sgd_solver.cpp:105] Iteration 8544, lr = 0.00184069
I0409 22:48:04.028717 25438 solver.cpp:218] Iteration 8556 (2.20268 iter/s, 5.44792s/12 iters), loss = 0.127795
I0409 22:48:04.028764 25438 solver.cpp:237]     Train net output #0: loss = 0.127795 (* 1 = 0.127795 loss)
I0409 22:48:04.028775 25438 sgd_solver.cpp:105] Iteration 8556, lr = 0.00183632
I0409 22:48:08.996484 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0409 22:48:13.244105 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0409 22:48:22.394162 25438 solver.cpp:330] Iteration 8568, Testing net (#0)
I0409 22:48:22.394189 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:48:23.511376 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:48:26.899940 25438 solver.cpp:397]     Test net output #0: accuracy = 0.227328
I0409 22:48:26.899968 25438 solver.cpp:397]     Test net output #1: loss = 6.36358 (* 1 = 6.36358 loss)
I0409 22:48:26.997442 25438 solver.cpp:218] Iteration 8568 (0.522466 iter/s, 22.968s/12 iters), loss = 0.0928012
I0409 22:48:26.997490 25438 solver.cpp:237]     Train net output #0: loss = 0.0928011 (* 1 = 0.0928011 loss)
I0409 22:48:26.997499 25438 sgd_solver.cpp:105] Iteration 8568, lr = 0.00183196
I0409 22:48:31.659086 25438 solver.cpp:218] Iteration 8580 (2.57431 iter/s, 4.66145s/12 iters), loss = 0.143308
I0409 22:48:31.659209 25438 solver.cpp:237]     Train net output #0: loss = 0.143308 (* 1 = 0.143308 loss)
I0409 22:48:31.659224 25438 sgd_solver.cpp:105] Iteration 8580, lr = 0.00182761
I0409 22:48:37.197651 25438 solver.cpp:218] Iteration 8592 (2.16674 iter/s, 5.53827s/12 iters), loss = 0.055833
I0409 22:48:37.197705 25438 solver.cpp:237]     Train net output #0: loss = 0.0558328 (* 1 = 0.0558328 loss)
I0409 22:48:37.197716 25438 sgd_solver.cpp:105] Iteration 8592, lr = 0.00182327
I0409 22:48:39.672681 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:48:42.863088 25438 solver.cpp:218] Iteration 8604 (2.11819 iter/s, 5.66521s/12 iters), loss = 0.154052
I0409 22:48:42.863137 25438 solver.cpp:237]     Train net output #0: loss = 0.154052 (* 1 = 0.154052 loss)
I0409 22:48:42.863149 25438 sgd_solver.cpp:105] Iteration 8604, lr = 0.00181894
I0409 22:48:48.210861 25438 solver.cpp:218] Iteration 8616 (2.24402 iter/s, 5.34755s/12 iters), loss = 0.109556
I0409 22:48:48.210920 25438 solver.cpp:237]     Train net output #0: loss = 0.109556 (* 1 = 0.109556 loss)
I0409 22:48:48.210932 25438 sgd_solver.cpp:105] Iteration 8616, lr = 0.00181462
I0409 22:48:53.792466 25438 solver.cpp:218] Iteration 8628 (2.15001 iter/s, 5.58137s/12 iters), loss = 0.0698927
I0409 22:48:53.792522 25438 solver.cpp:237]     Train net output #0: loss = 0.0698926 (* 1 = 0.0698926 loss)
I0409 22:48:53.792533 25438 sgd_solver.cpp:105] Iteration 8628, lr = 0.00181031
I0409 22:48:59.398051 25438 solver.cpp:218] Iteration 8640 (2.14081 iter/s, 5.60535s/12 iters), loss = 0.0455529
I0409 22:48:59.398102 25438 solver.cpp:237]     Train net output #0: loss = 0.0455528 (* 1 = 0.0455528 loss)
I0409 22:48:59.398113 25438 sgd_solver.cpp:105] Iteration 8640, lr = 0.00180602
I0409 22:49:04.953887 25438 solver.cpp:218] Iteration 8652 (2.15998 iter/s, 5.55562s/12 iters), loss = 0.071834
I0409 22:49:04.954022 25438 solver.cpp:237]     Train net output #0: loss = 0.0718339 (* 1 = 0.0718339 loss)
I0409 22:49:04.954032 25438 sgd_solver.cpp:105] Iteration 8652, lr = 0.00180173
I0409 22:49:10.601464 25438 solver.cpp:218] Iteration 8664 (2.12492 iter/s, 5.64727s/12 iters), loss = 0.0690077
I0409 22:49:10.601501 25438 solver.cpp:237]     Train net output #0: loss = 0.0690076 (* 1 = 0.0690076 loss)
I0409 22:49:10.601511 25438 sgd_solver.cpp:105] Iteration 8664, lr = 0.00179745
I0409 22:49:12.915014 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0409 22:49:21.204030 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0409 22:49:24.400504 25438 solver.cpp:330] Iteration 8670, Testing net (#0)
I0409 22:49:24.400529 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:49:25.467135 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:49:28.930914 25438 solver.cpp:397]     Test net output #0: accuracy = 0.224877
I0409 22:49:28.930964 25438 solver.cpp:397]     Test net output #1: loss = 6.41411 (* 1 = 6.41411 loss)
I0409 22:49:30.972234 25438 solver.cpp:218] Iteration 8676 (0.589098 iter/s, 20.3701s/12 iters), loss = 0.113432
I0409 22:49:30.972287 25438 solver.cpp:237]     Train net output #0: loss = 0.113432 (* 1 = 0.113432 loss)
I0409 22:49:30.972298 25438 sgd_solver.cpp:105] Iteration 8676, lr = 0.00179318
I0409 22:49:36.521893 25438 solver.cpp:218] Iteration 8688 (2.16238 iter/s, 5.54943s/12 iters), loss = 0.0367206
I0409 22:49:36.522019 25438 solver.cpp:237]     Train net output #0: loss = 0.0367205 (* 1 = 0.0367205 loss)
I0409 22:49:36.522029 25438 sgd_solver.cpp:105] Iteration 8688, lr = 0.00178893
I0409 22:49:41.396217 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:49:42.166810 25438 solver.cpp:218] Iteration 8700 (2.12592 iter/s, 5.64461s/12 iters), loss = 0.113177
I0409 22:49:42.166855 25438 solver.cpp:237]     Train net output #0: loss = 0.113177 (* 1 = 0.113177 loss)
I0409 22:49:42.166864 25438 sgd_solver.cpp:105] Iteration 8700, lr = 0.00178468
I0409 22:49:47.825645 25438 solver.cpp:218] Iteration 8712 (2.12066 iter/s, 5.65862s/12 iters), loss = 0.131943
I0409 22:49:47.825690 25438 solver.cpp:237]     Train net output #0: loss = 0.131943 (* 1 = 0.131943 loss)
I0409 22:49:47.825700 25438 sgd_solver.cpp:105] Iteration 8712, lr = 0.00178044
I0409 22:49:53.520941 25438 solver.cpp:218] Iteration 8724 (2.10708 iter/s, 5.69508s/12 iters), loss = 0.110169
I0409 22:49:53.520987 25438 solver.cpp:237]     Train net output #0: loss = 0.110169 (* 1 = 0.110169 loss)
I0409 22:49:53.520998 25438 sgd_solver.cpp:105] Iteration 8724, lr = 0.00177621
I0409 22:49:58.948740 25438 solver.cpp:218] Iteration 8736 (2.21093 iter/s, 5.42757s/12 iters), loss = 0.074618
I0409 22:49:58.948801 25438 solver.cpp:237]     Train net output #0: loss = 0.0746179 (* 1 = 0.0746179 loss)
I0409 22:49:58.948817 25438 sgd_solver.cpp:105] Iteration 8736, lr = 0.001772
I0409 22:50:04.637933 25438 solver.cpp:218] Iteration 8748 (2.10935 iter/s, 5.68895s/12 iters), loss = 0.0585756
I0409 22:50:04.637995 25438 solver.cpp:237]     Train net output #0: loss = 0.0585755 (* 1 = 0.0585755 loss)
I0409 22:50:04.638006 25438 sgd_solver.cpp:105] Iteration 8748, lr = 0.00176779
I0409 22:50:10.265671 25438 solver.cpp:218] Iteration 8760 (2.13238 iter/s, 5.6275s/12 iters), loss = 0.0513133
I0409 22:50:10.265754 25438 solver.cpp:237]     Train net output #0: loss = 0.0513131 (* 1 = 0.0513131 loss)
I0409 22:50:10.265767 25438 sgd_solver.cpp:105] Iteration 8760, lr = 0.00176359
I0409 22:50:15.205297 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0409 22:50:22.869534 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0409 22:50:27.211813 25438 solver.cpp:330] Iteration 8772, Testing net (#0)
I0409 22:50:27.211840 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:50:28.263752 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:50:31.882225 25438 solver.cpp:397]     Test net output #0: accuracy = 0.234069
I0409 22:50:31.882269 25438 solver.cpp:397]     Test net output #1: loss = 6.34964 (* 1 = 6.34964 loss)
I0409 22:50:31.979588 25438 solver.cpp:218] Iteration 8772 (0.552659 iter/s, 21.7132s/12 iters), loss = 0.0262668
I0409 22:50:31.979656 25438 solver.cpp:237]     Train net output #0: loss = 0.0262667 (* 1 = 0.0262667 loss)
I0409 22:50:31.979671 25438 sgd_solver.cpp:105] Iteration 8772, lr = 0.00175941
I0409 22:50:36.672359 25438 solver.cpp:218] Iteration 8784 (2.55724 iter/s, 4.69256s/12 iters), loss = 0.0956768
I0409 22:50:36.672407 25438 solver.cpp:237]     Train net output #0: loss = 0.0956767 (* 1 = 0.0956767 loss)
I0409 22:50:36.672416 25438 sgd_solver.cpp:105] Iteration 8784, lr = 0.00175523
I0409 22:50:41.913084 25438 solver.cpp:218] Iteration 8796 (2.28985 iter/s, 5.24051s/12 iters), loss = 0.0724161
I0409 22:50:41.913216 25438 solver.cpp:237]     Train net output #0: loss = 0.072416 (* 1 = 0.072416 loss)
I0409 22:50:41.913226 25438 sgd_solver.cpp:105] Iteration 8796, lr = 0.00175106
I0409 22:50:43.413568 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:50:47.328764 25438 solver.cpp:218] Iteration 8808 (2.21591 iter/s, 5.41538s/12 iters), loss = 0.0980038
I0409 22:50:47.328814 25438 solver.cpp:237]     Train net output #0: loss = 0.0980037 (* 1 = 0.0980037 loss)
I0409 22:50:47.328822 25438 sgd_solver.cpp:105] Iteration 8808, lr = 0.0017469
I0409 22:50:52.827922 25438 solver.cpp:218] Iteration 8820 (2.18224 iter/s, 5.49893s/12 iters), loss = 0.100679
I0409 22:50:52.827976 25438 solver.cpp:237]     Train net output #0: loss = 0.100679 (* 1 = 0.100679 loss)
I0409 22:50:52.827987 25438 sgd_solver.cpp:105] Iteration 8820, lr = 0.00174276
I0409 22:50:58.370560 25438 solver.cpp:218] Iteration 8832 (2.16512 iter/s, 5.54241s/12 iters), loss = 0.0305298
I0409 22:50:58.370602 25438 solver.cpp:237]     Train net output #0: loss = 0.0305296 (* 1 = 0.0305296 loss)
I0409 22:50:58.370610 25438 sgd_solver.cpp:105] Iteration 8832, lr = 0.00173862
I0409 22:51:04.011343 25438 solver.cpp:218] Iteration 8844 (2.12745 iter/s, 5.64056s/12 iters), loss = 0.112113
I0409 22:51:04.011385 25438 solver.cpp:237]     Train net output #0: loss = 0.112113 (* 1 = 0.112113 loss)
I0409 22:51:04.011394 25438 sgd_solver.cpp:105] Iteration 8844, lr = 0.00173449
I0409 22:51:09.354938 25438 solver.cpp:218] Iteration 8856 (2.24577 iter/s, 5.34338s/12 iters), loss = 0.0393791
I0409 22:51:09.355000 25438 solver.cpp:237]     Train net output #0: loss = 0.039379 (* 1 = 0.039379 loss)
I0409 22:51:09.355012 25438 sgd_solver.cpp:105] Iteration 8856, lr = 0.00173037
I0409 22:51:14.554939 25438 solver.cpp:218] Iteration 8868 (2.30779 iter/s, 5.19977s/12 iters), loss = 0.0599254
I0409 22:51:14.555092 25438 solver.cpp:237]     Train net output #0: loss = 0.0599252 (* 1 = 0.0599252 loss)
I0409 22:51:14.555114 25438 sgd_solver.cpp:105] Iteration 8868, lr = 0.00172626
I0409 22:51:16.831399 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0409 22:51:23.674254 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0409 22:51:29.560573 25438 solver.cpp:330] Iteration 8874, Testing net (#0)
I0409 22:51:29.560600 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:51:30.566468 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:51:34.072605 25438 solver.cpp:397]     Test net output #0: accuracy = 0.238358
I0409 22:51:34.072644 25438 solver.cpp:397]     Test net output #1: loss = 6.34214 (* 1 = 6.34214 loss)
I0409 22:51:36.039636 25438 solver.cpp:218] Iteration 8880 (0.558557 iter/s, 21.4839s/12 iters), loss = 0.0910404
I0409 22:51:36.039680 25438 solver.cpp:237]     Train net output #0: loss = 0.0910402 (* 1 = 0.0910402 loss)
I0409 22:51:36.039688 25438 sgd_solver.cpp:105] Iteration 8880, lr = 0.00172217
I0409 22:51:41.600050 25438 solver.cpp:218] Iteration 8892 (2.1582 iter/s, 5.56019s/12 iters), loss = 0.0408867
I0409 22:51:41.600095 25438 solver.cpp:237]     Train net output #0: loss = 0.0408865 (* 1 = 0.0408865 loss)
I0409 22:51:41.600103 25438 sgd_solver.cpp:105] Iteration 8892, lr = 0.00171808
I0409 22:51:45.307569 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:51:46.791682 25438 solver.cpp:218] Iteration 8904 (2.31151 iter/s, 5.19142s/12 iters), loss = 0.0698231
I0409 22:51:46.791743 25438 solver.cpp:237]     Train net output #0: loss = 0.069823 (* 1 = 0.069823 loss)
I0409 22:51:46.791754 25438 sgd_solver.cpp:105] Iteration 8904, lr = 0.001714
I0409 22:51:52.280967 25438 solver.cpp:218] Iteration 8916 (2.18617 iter/s, 5.48906s/12 iters), loss = 0.155833
I0409 22:51:52.281016 25438 solver.cpp:237]     Train net output #0: loss = 0.155833 (* 1 = 0.155833 loss)
I0409 22:51:52.281028 25438 sgd_solver.cpp:105] Iteration 8916, lr = 0.00170993
I0409 22:51:57.662261 25438 solver.cpp:218] Iteration 8928 (2.23004 iter/s, 5.38107s/12 iters), loss = 0.0337875
I0409 22:51:57.662315 25438 solver.cpp:237]     Train net output #0: loss = 0.0337874 (* 1 = 0.0337874 loss)
I0409 22:51:57.662328 25438 sgd_solver.cpp:105] Iteration 8928, lr = 0.00170587
I0409 22:52:03.192910 25438 solver.cpp:218] Iteration 8940 (2.16982 iter/s, 5.53042s/12 iters), loss = 0.0403736
I0409 22:52:03.192968 25438 solver.cpp:237]     Train net output #0: loss = 0.0403735 (* 1 = 0.0403735 loss)
I0409 22:52:03.192979 25438 sgd_solver.cpp:105] Iteration 8940, lr = 0.00170182
I0409 22:52:08.724234 25438 solver.cpp:218] Iteration 8952 (2.16955 iter/s, 5.5311s/12 iters), loss = 0.16236
I0409 22:52:08.724282 25438 solver.cpp:237]     Train net output #0: loss = 0.16236 (* 1 = 0.16236 loss)
I0409 22:52:08.724292 25438 sgd_solver.cpp:105] Iteration 8952, lr = 0.00169778
I0409 22:52:13.998389 25438 solver.cpp:218] Iteration 8964 (2.27534 iter/s, 5.27394s/12 iters), loss = 0.150788
I0409 22:52:13.998445 25438 solver.cpp:237]     Train net output #0: loss = 0.150788 (* 1 = 0.150788 loss)
I0409 22:52:13.998456 25438 sgd_solver.cpp:105] Iteration 8964, lr = 0.00169375
I0409 22:52:18.642885 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0409 22:52:22.906672 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0409 22:52:36.023582 25438 solver.cpp:330] Iteration 8976, Testing net (#0)
I0409 22:52:36.023612 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:52:36.961007 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:52:40.640113 25438 solver.cpp:397]     Test net output #0: accuracy = 0.234681
I0409 22:52:40.640149 25438 solver.cpp:397]     Test net output #1: loss = 6.37974 (* 1 = 6.37974 loss)
I0409 22:52:40.737440 25438 solver.cpp:218] Iteration 8976 (0.448796 iter/s, 26.7382s/12 iters), loss = 0.115625
I0409 22:52:40.737493 25438 solver.cpp:237]     Train net output #0: loss = 0.115625 (* 1 = 0.115625 loss)
I0409 22:52:40.737502 25438 sgd_solver.cpp:105] Iteration 8976, lr = 0.00168973
I0409 22:52:45.514245 25438 solver.cpp:218] Iteration 8988 (2.51224 iter/s, 4.77661s/12 iters), loss = 0.0637725
I0409 22:52:45.514290 25438 solver.cpp:237]     Train net output #0: loss = 0.0637724 (* 1 = 0.0637724 loss)
I0409 22:52:45.514300 25438 sgd_solver.cpp:105] Iteration 8988, lr = 0.00168571
I0409 22:52:49.232390 25438 blocking_queue.cpp:49] Waiting for data
I0409 22:52:51.173327 25438 solver.cpp:218] Iteration 9000 (2.12057 iter/s, 5.65886s/12 iters), loss = 0.108745
I0409 22:52:51.173377 25438 solver.cpp:237]     Train net output #0: loss = 0.108745 (* 1 = 0.108745 loss)
I0409 22:52:51.173390 25438 sgd_solver.cpp:105] Iteration 9000, lr = 0.00168171
I0409 22:52:51.972287 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:52:56.519032 25438 solver.cpp:218] Iteration 9012 (2.24488 iter/s, 5.34549s/12 iters), loss = 0.129647
I0409 22:52:56.519078 25438 solver.cpp:237]     Train net output #0: loss = 0.129647 (* 1 = 0.129647 loss)
I0409 22:52:56.519088 25438 sgd_solver.cpp:105] Iteration 9012, lr = 0.00167772
I0409 22:53:02.003536 25438 solver.cpp:218] Iteration 9024 (2.18807 iter/s, 5.48428s/12 iters), loss = 0.108188
I0409 22:53:02.003592 25438 solver.cpp:237]     Train net output #0: loss = 0.108188 (* 1 = 0.108188 loss)
I0409 22:53:02.003604 25438 sgd_solver.cpp:105] Iteration 9024, lr = 0.00167374
I0409 22:53:07.519754 25438 solver.cpp:218] Iteration 9036 (2.17549 iter/s, 5.51599s/12 iters), loss = 0.0561434
I0409 22:53:07.519805 25438 solver.cpp:237]     Train net output #0: loss = 0.0561432 (* 1 = 0.0561432 loss)
I0409 22:53:07.519817 25438 sgd_solver.cpp:105] Iteration 9036, lr = 0.00166976
I0409 22:53:12.779515 25438 solver.cpp:218] Iteration 9048 (2.28157 iter/s, 5.25954s/12 iters), loss = 0.100417
I0409 22:53:12.779565 25438 solver.cpp:237]     Train net output #0: loss = 0.100417 (* 1 = 0.100417 loss)
I0409 22:53:12.779575 25438 sgd_solver.cpp:105] Iteration 9048, lr = 0.0016658
I0409 22:53:18.096581 25438 solver.cpp:218] Iteration 9060 (2.25698 iter/s, 5.31684s/12 iters), loss = 0.0932045
I0409 22:53:18.096627 25438 solver.cpp:237]     Train net output #0: loss = 0.0932043 (* 1 = 0.0932043 loss)
I0409 22:53:18.096637 25438 sgd_solver.cpp:105] Iteration 9060, lr = 0.00166184
I0409 22:53:23.272981 25438 solver.cpp:218] Iteration 9072 (2.31831 iter/s, 5.17619s/12 iters), loss = 0.173166
I0409 22:53:23.273128 25438 solver.cpp:237]     Train net output #0: loss = 0.173166 (* 1 = 0.173166 loss)
I0409 22:53:23.273138 25438 sgd_solver.cpp:105] Iteration 9072, lr = 0.0016579
I0409 22:53:25.406877 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0409 22:53:30.666678 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0409 22:53:33.838145 25438 solver.cpp:330] Iteration 9078, Testing net (#0)
I0409 22:53:33.838171 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:53:34.734383 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:53:38.440022 25438 solver.cpp:397]     Test net output #0: accuracy = 0.240809
I0409 22:53:38.440059 25438 solver.cpp:397]     Test net output #1: loss = 6.32353 (* 1 = 6.32353 loss)
I0409 22:53:40.474261 25438 solver.cpp:218] Iteration 9084 (0.697649 iter/s, 17.2006s/12 iters), loss = 0.0346934
I0409 22:53:40.474316 25438 solver.cpp:237]     Train net output #0: loss = 0.0346932 (* 1 = 0.0346932 loss)
I0409 22:53:40.474326 25438 sgd_solver.cpp:105] Iteration 9084, lr = 0.00165396
I0409 22:53:45.752036 25438 solver.cpp:218] Iteration 9096 (2.27378 iter/s, 5.27755s/12 iters), loss = 0.0204425
I0409 22:53:45.752089 25438 solver.cpp:237]     Train net output #0: loss = 0.0204424 (* 1 = 0.0204424 loss)
I0409 22:53:45.752099 25438 sgd_solver.cpp:105] Iteration 9096, lr = 0.00165003
I0409 22:53:49.108235 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:53:51.422591 25438 solver.cpp:218] Iteration 9108 (2.11628 iter/s, 5.67033s/12 iters), loss = 0.0236136
I0409 22:53:51.422636 25438 solver.cpp:237]     Train net output #0: loss = 0.0236135 (* 1 = 0.0236135 loss)
I0409 22:53:51.422644 25438 sgd_solver.cpp:105] Iteration 9108, lr = 0.00164612
I0409 22:53:56.834600 25438 solver.cpp:218] Iteration 9120 (2.21738 iter/s, 5.4118s/12 iters), loss = 0.145455
I0409 22:53:56.834693 25438 solver.cpp:237]     Train net output #0: loss = 0.145455 (* 1 = 0.145455 loss)
I0409 22:53:56.834703 25438 sgd_solver.cpp:105] Iteration 9120, lr = 0.00164221
I0409 22:54:02.197697 25438 solver.cpp:218] Iteration 9132 (2.23762 iter/s, 5.36284s/12 iters), loss = 0.0906336
I0409 22:54:02.197744 25438 solver.cpp:237]     Train net output #0: loss = 0.0906334 (* 1 = 0.0906334 loss)
I0409 22:54:02.197753 25438 sgd_solver.cpp:105] Iteration 9132, lr = 0.00163831
I0409 22:54:07.582717 25438 solver.cpp:218] Iteration 9144 (2.2285 iter/s, 5.3848s/12 iters), loss = 0.00730198
I0409 22:54:07.582775 25438 solver.cpp:237]     Train net output #0: loss = 0.0073018 (* 1 = 0.0073018 loss)
I0409 22:54:07.582787 25438 sgd_solver.cpp:105] Iteration 9144, lr = 0.00163442
I0409 22:54:13.275385 25438 solver.cpp:218] Iteration 9156 (2.10806 iter/s, 5.69243s/12 iters), loss = 0.0253707
I0409 22:54:13.275436 25438 solver.cpp:237]     Train net output #0: loss = 0.0253706 (* 1 = 0.0253706 loss)
I0409 22:54:13.275445 25438 sgd_solver.cpp:105] Iteration 9156, lr = 0.00163054
I0409 22:54:19.309839 25438 solver.cpp:218] Iteration 9168 (1.98866 iter/s, 6.03422s/12 iters), loss = 0.0546541
I0409 22:54:19.309885 25438 solver.cpp:237]     Train net output #0: loss = 0.0546539 (* 1 = 0.0546539 loss)
I0409 22:54:19.309892 25438 sgd_solver.cpp:105] Iteration 9168, lr = 0.00162667
I0409 22:54:24.080974 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0409 22:54:28.220173 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0409 22:54:31.412569 25438 solver.cpp:330] Iteration 9180, Testing net (#0)
I0409 22:54:31.412597 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:54:32.295325 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:54:36.028677 25438 solver.cpp:397]     Test net output #0: accuracy = 0.242647
I0409 22:54:36.028723 25438 solver.cpp:397]     Test net output #1: loss = 6.29303 (* 1 = 6.29303 loss)
I0409 22:54:36.126263 25438 solver.cpp:218] Iteration 9180 (0.713611 iter/s, 16.8159s/12 iters), loss = 0.060446
I0409 22:54:36.126329 25438 solver.cpp:237]     Train net output #0: loss = 0.0604459 (* 1 = 0.0604459 loss)
I0409 22:54:36.126343 25438 sgd_solver.cpp:105] Iteration 9180, lr = 0.00162281
I0409 22:54:40.510004 25438 solver.cpp:218] Iteration 9192 (2.73752 iter/s, 4.38352s/12 iters), loss = 0.0586883
I0409 22:54:40.510066 25438 solver.cpp:237]     Train net output #0: loss = 0.0586881 (* 1 = 0.0586881 loss)
I0409 22:54:40.510077 25438 sgd_solver.cpp:105] Iteration 9192, lr = 0.00161895
I0409 22:54:45.787557 25438 solver.cpp:218] Iteration 9204 (2.27388 iter/s, 5.27732s/12 iters), loss = 0.0617685
I0409 22:54:45.787616 25438 solver.cpp:237]     Train net output #0: loss = 0.0617683 (* 1 = 0.0617683 loss)
I0409 22:54:45.787627 25438 sgd_solver.cpp:105] Iteration 9204, lr = 0.00161511
I0409 22:54:45.855253 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:54:51.205003 25438 solver.cpp:218] Iteration 9216 (2.21516 iter/s, 5.41722s/12 iters), loss = 0.11609
I0409 22:54:51.205052 25438 solver.cpp:237]     Train net output #0: loss = 0.11609 (* 1 = 0.11609 loss)
I0409 22:54:51.205061 25438 sgd_solver.cpp:105] Iteration 9216, lr = 0.00161128
I0409 22:54:56.610771 25438 solver.cpp:218] Iteration 9228 (2.21995 iter/s, 5.40554s/12 iters), loss = 0.108953
I0409 22:54:56.610850 25438 solver.cpp:237]     Train net output #0: loss = 0.108953 (* 1 = 0.108953 loss)
I0409 22:54:56.610868 25438 sgd_solver.cpp:105] Iteration 9228, lr = 0.00160745
I0409 22:55:01.825912 25438 solver.cpp:218] Iteration 9240 (2.3011 iter/s, 5.2149s/12 iters), loss = 0.0383234
I0409 22:55:01.826035 25438 solver.cpp:237]     Train net output #0: loss = 0.0383232 (* 1 = 0.0383232 loss)
I0409 22:55:01.826045 25438 sgd_solver.cpp:105] Iteration 9240, lr = 0.00160363
I0409 22:55:07.114972 25438 solver.cpp:218] Iteration 9252 (2.26896 iter/s, 5.28877s/12 iters), loss = 0.11303
I0409 22:55:07.115031 25438 solver.cpp:237]     Train net output #0: loss = 0.11303 (* 1 = 0.11303 loss)
I0409 22:55:07.115042 25438 sgd_solver.cpp:105] Iteration 9252, lr = 0.00159983
I0409 22:55:12.787727 25438 solver.cpp:218] Iteration 9264 (2.11546 iter/s, 5.67252s/12 iters), loss = 0.0270212
I0409 22:55:12.787794 25438 solver.cpp:237]     Train net output #0: loss = 0.027021 (* 1 = 0.027021 loss)
I0409 22:55:12.787806 25438 sgd_solver.cpp:105] Iteration 9264, lr = 0.00159603
I0409 22:55:17.991132 25438 solver.cpp:218] Iteration 9276 (2.30629 iter/s, 5.20317s/12 iters), loss = 0.0427707
I0409 22:55:17.991199 25438 solver.cpp:237]     Train net output #0: loss = 0.0427705 (* 1 = 0.0427705 loss)
I0409 22:55:17.991214 25438 sgd_solver.cpp:105] Iteration 9276, lr = 0.00159224
I0409 22:55:20.174453 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0409 22:55:24.719730 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0409 22:55:27.894246 25438 solver.cpp:330] Iteration 9282, Testing net (#0)
I0409 22:55:27.894270 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:55:28.699761 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:55:32.536190 25438 solver.cpp:397]     Test net output #0: accuracy = 0.23223
I0409 22:55:32.536319 25438 solver.cpp:397]     Test net output #1: loss = 6.31949 (* 1 = 6.31949 loss)
I0409 22:55:34.420708 25438 solver.cpp:218] Iteration 9288 (0.730415 iter/s, 16.429s/12 iters), loss = 0.0906686
I0409 22:55:34.420758 25438 solver.cpp:237]     Train net output #0: loss = 0.0906685 (* 1 = 0.0906685 loss)
I0409 22:55:34.420766 25438 sgd_solver.cpp:105] Iteration 9288, lr = 0.00158846
I0409 22:55:39.889533 25438 solver.cpp:218] Iteration 9300 (2.19435 iter/s, 5.4686s/12 iters), loss = 0.0156541
I0409 22:55:39.889590 25438 solver.cpp:237]     Train net output #0: loss = 0.0156539 (* 1 = 0.0156539 loss)
I0409 22:55:39.889600 25438 sgd_solver.cpp:105] Iteration 9300, lr = 0.00158469
I0409 22:55:42.417068 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:55:45.534184 25438 solver.cpp:218] Iteration 9312 (2.12599 iter/s, 5.64442s/12 iters), loss = 0.0395273
I0409 22:55:45.534235 25438 solver.cpp:237]     Train net output #0: loss = 0.0395271 (* 1 = 0.0395271 loss)
I0409 22:55:45.534245 25438 sgd_solver.cpp:105] Iteration 9312, lr = 0.00158092
I0409 22:55:50.693527 25438 solver.cpp:218] Iteration 9324 (2.32598 iter/s, 5.15913s/12 iters), loss = 0.0307166
I0409 22:55:50.693580 25438 solver.cpp:237]     Train net output #0: loss = 0.0307164 (* 1 = 0.0307164 loss)
I0409 22:55:50.693591 25438 sgd_solver.cpp:105] Iteration 9324, lr = 0.00157717
I0409 22:55:55.941033 25438 solver.cpp:218] Iteration 9336 (2.28689 iter/s, 5.24729s/12 iters), loss = 0.112091
I0409 22:55:55.941077 25438 solver.cpp:237]     Train net output #0: loss = 0.112091 (* 1 = 0.112091 loss)
I0409 22:55:55.941087 25438 sgd_solver.cpp:105] Iteration 9336, lr = 0.00157343
I0409 22:56:01.211223 25438 solver.cpp:218] Iteration 9348 (2.27705 iter/s, 5.26998s/12 iters), loss = 0.0996979
I0409 22:56:01.211277 25438 solver.cpp:237]     Train net output #0: loss = 0.0996976 (* 1 = 0.0996976 loss)
I0409 22:56:01.211289 25438 sgd_solver.cpp:105] Iteration 9348, lr = 0.00156969
I0409 22:56:06.708137 25438 solver.cpp:218] Iteration 9360 (2.18314 iter/s, 5.49668s/12 iters), loss = 0.0874251
I0409 22:56:06.709592 25438 solver.cpp:237]     Train net output #0: loss = 0.0874249 (* 1 = 0.0874249 loss)
I0409 22:56:06.709606 25438 sgd_solver.cpp:105] Iteration 9360, lr = 0.00156596
I0409 22:56:11.927846 25438 solver.cpp:218] Iteration 9372 (2.29969 iter/s, 5.2181s/12 iters), loss = 0.0775752
I0409 22:56:11.927892 25438 solver.cpp:237]     Train net output #0: loss = 0.077575 (* 1 = 0.077575 loss)
I0409 22:56:11.927901 25438 sgd_solver.cpp:105] Iteration 9372, lr = 0.00156225
I0409 22:56:16.823560 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0409 22:56:29.031965 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0409 22:56:36.225988 25438 solver.cpp:330] Iteration 9384, Testing net (#0)
I0409 22:56:36.226011 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:56:37.010401 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:56:40.879509 25438 solver.cpp:397]     Test net output #0: accuracy = 0.234069
I0409 22:56:40.879554 25438 solver.cpp:397]     Test net output #1: loss = 6.32231 (* 1 = 6.32231 loss)
I0409 22:56:40.977011 25438 solver.cpp:218] Iteration 9384 (0.413105 iter/s, 29.0483s/12 iters), loss = 0.0306591
I0409 22:56:40.977062 25438 solver.cpp:237]     Train net output #0: loss = 0.0306589 (* 1 = 0.0306589 loss)
I0409 22:56:40.977072 25438 sgd_solver.cpp:105] Iteration 9384, lr = 0.00155854
I0409 22:56:45.729072 25438 solver.cpp:218] Iteration 9396 (2.52533 iter/s, 4.75186s/12 iters), loss = 0.111498
I0409 22:56:45.729131 25438 solver.cpp:237]     Train net output #0: loss = 0.111498 (* 1 = 0.111498 loss)
I0409 22:56:45.729144 25438 sgd_solver.cpp:105] Iteration 9396, lr = 0.00155484
I0409 22:56:50.466940 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:56:51.167532 25438 solver.cpp:218] Iteration 9408 (2.2066 iter/s, 5.43823s/12 iters), loss = 0.0627401
I0409 22:56:51.167582 25438 solver.cpp:237]     Train net output #0: loss = 0.0627399 (* 1 = 0.0627399 loss)
I0409 22:56:51.167590 25438 sgd_solver.cpp:105] Iteration 9408, lr = 0.00155114
I0409 22:56:56.367468 25438 solver.cpp:218] Iteration 9420 (2.30782 iter/s, 5.19972s/12 iters), loss = 0.0458819
I0409 22:56:56.367530 25438 solver.cpp:237]     Train net output #0: loss = 0.0458817 (* 1 = 0.0458817 loss)
I0409 22:56:56.367542 25438 sgd_solver.cpp:105] Iteration 9420, lr = 0.00154746
I0409 22:57:01.463658 25438 solver.cpp:218] Iteration 9432 (2.3548 iter/s, 5.09597s/12 iters), loss = 0.0579117
I0409 22:57:01.463722 25438 solver.cpp:237]     Train net output #0: loss = 0.0579115 (* 1 = 0.0579115 loss)
I0409 22:57:01.463734 25438 sgd_solver.cpp:105] Iteration 9432, lr = 0.00154379
I0409 22:57:07.413132 25438 solver.cpp:218] Iteration 9444 (2.01707 iter/s, 5.94922s/12 iters), loss = 0.0495547
I0409 22:57:07.413298 25438 solver.cpp:237]     Train net output #0: loss = 0.0495545 (* 1 = 0.0495545 loss)
I0409 22:57:07.413311 25438 sgd_solver.cpp:105] Iteration 9444, lr = 0.00154012
I0409 22:57:12.558526 25438 solver.cpp:218] Iteration 9456 (2.33233 iter/s, 5.14506s/12 iters), loss = 0.0537926
I0409 22:57:12.558588 25438 solver.cpp:237]     Train net output #0: loss = 0.0537924 (* 1 = 0.0537924 loss)
I0409 22:57:12.558599 25438 sgd_solver.cpp:105] Iteration 9456, lr = 0.00153647
I0409 22:57:17.668023 25438 solver.cpp:218] Iteration 9468 (2.34867 iter/s, 5.10928s/12 iters), loss = 0.0280838
I0409 22:57:17.668079 25438 solver.cpp:237]     Train net output #0: loss = 0.0280837 (* 1 = 0.0280837 loss)
I0409 22:57:17.668090 25438 sgd_solver.cpp:105] Iteration 9468, lr = 0.00153282
I0409 22:57:22.798063 25438 solver.cpp:218] Iteration 9480 (2.33926 iter/s, 5.12982s/12 iters), loss = 0.0632661
I0409 22:57:22.798110 25438 solver.cpp:237]     Train net output #0: loss = 0.0632659 (* 1 = 0.0632659 loss)
I0409 22:57:22.798118 25438 sgd_solver.cpp:105] Iteration 9480, lr = 0.00152918
I0409 22:57:24.920379 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0409 22:57:33.060438 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0409 22:57:38.430640 25438 solver.cpp:330] Iteration 9486, Testing net (#0)
I0409 22:57:38.430724 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:57:39.113039 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:57:42.890168 25438 solver.cpp:397]     Test net output #0: accuracy = 0.233456
I0409 22:57:42.890216 25438 solver.cpp:397]     Test net output #1: loss = 6.39522 (* 1 = 6.39522 loss)
I0409 22:57:44.842154 25438 solver.cpp:218] Iteration 9492 (0.544381 iter/s, 22.0434s/12 iters), loss = 0.0156008
I0409 22:57:44.842197 25438 solver.cpp:237]     Train net output #0: loss = 0.0156006 (* 1 = 0.0156006 loss)
I0409 22:57:44.842206 25438 sgd_solver.cpp:105] Iteration 9492, lr = 0.00152555
I0409 22:57:50.501524 25438 solver.cpp:218] Iteration 9504 (2.12046 iter/s, 5.65915s/12 iters), loss = 0.0472849
I0409 22:57:50.501571 25438 solver.cpp:237]     Train net output #0: loss = 0.0472847 (* 1 = 0.0472847 loss)
I0409 22:57:50.501579 25438 sgd_solver.cpp:105] Iteration 9504, lr = 0.00152193
I0409 22:57:52.145773 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:57:55.869390 25438 solver.cpp:218] Iteration 9516 (2.23562 iter/s, 5.36764s/12 iters), loss = 0.186363
I0409 22:57:55.869437 25438 solver.cpp:237]     Train net output #0: loss = 0.186363 (* 1 = 0.186363 loss)
I0409 22:57:55.869446 25438 sgd_solver.cpp:105] Iteration 9516, lr = 0.00151831
I0409 22:58:01.057189 25438 solver.cpp:218] Iteration 9528 (2.31321 iter/s, 5.18759s/12 iters), loss = 0.0292844
I0409 22:58:01.057224 25438 solver.cpp:237]     Train net output #0: loss = 0.0292843 (* 1 = 0.0292843 loss)
I0409 22:58:01.057231 25438 sgd_solver.cpp:105] Iteration 9528, lr = 0.00151471
I0409 22:58:06.586367 25438 solver.cpp:218] Iteration 9540 (2.17039 iter/s, 5.52896s/12 iters), loss = 0.0522704
I0409 22:58:06.586431 25438 solver.cpp:237]     Train net output #0: loss = 0.0522702 (* 1 = 0.0522702 loss)
I0409 22:58:06.586442 25438 sgd_solver.cpp:105] Iteration 9540, lr = 0.00151111
I0409 22:58:12.048655 25438 solver.cpp:218] Iteration 9552 (2.19698 iter/s, 5.46205s/12 iters), loss = 0.108676
I0409 22:58:12.048821 25438 solver.cpp:237]     Train net output #0: loss = 0.108675 (* 1 = 0.108675 loss)
I0409 22:58:12.048833 25438 sgd_solver.cpp:105] Iteration 9552, lr = 0.00150752
I0409 22:58:17.446998 25438 solver.cpp:218] Iteration 9564 (2.22304 iter/s, 5.39801s/12 iters), loss = 0.0109555
I0409 22:58:17.447041 25438 solver.cpp:237]     Train net output #0: loss = 0.0109553 (* 1 = 0.0109553 loss)
I0409 22:58:17.447049 25438 sgd_solver.cpp:105] Iteration 9564, lr = 0.00150395
I0409 22:58:22.779306 25438 solver.cpp:218] Iteration 9576 (2.25052 iter/s, 5.33209s/12 iters), loss = 0.0795065
I0409 22:58:22.779364 25438 solver.cpp:237]     Train net output #0: loss = 0.0795064 (* 1 = 0.0795064 loss)
I0409 22:58:22.779376 25438 sgd_solver.cpp:105] Iteration 9576, lr = 0.00150037
I0409 22:58:27.638265 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0409 22:58:31.847775 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0409 22:58:35.062100 25438 solver.cpp:330] Iteration 9588, Testing net (#0)
I0409 22:58:35.062125 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:58:35.679543 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:58:39.511816 25438 solver.cpp:397]     Test net output #0: accuracy = 0.232843
I0409 22:58:39.511862 25438 solver.cpp:397]     Test net output #1: loss = 6.31119 (* 1 = 6.31119 loss)
I0409 22:58:39.609347 25438 solver.cpp:218] Iteration 9588 (0.713034 iter/s, 16.8295s/12 iters), loss = 0.0421669
I0409 22:58:39.609396 25438 solver.cpp:237]     Train net output #0: loss = 0.0421667 (* 1 = 0.0421667 loss)
I0409 22:58:39.609406 25438 sgd_solver.cpp:105] Iteration 9588, lr = 0.00149681
I0409 22:58:44.069795 25438 solver.cpp:218] Iteration 9600 (2.69043 iter/s, 4.46025s/12 iters), loss = 0.0724871
I0409 22:58:44.069918 25438 solver.cpp:237]     Train net output #0: loss = 0.0724869 (* 1 = 0.0724869 loss)
I0409 22:58:44.069931 25438 sgd_solver.cpp:105] Iteration 9600, lr = 0.00149326
I0409 22:58:47.804566 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:58:49.260254 25438 solver.cpp:218] Iteration 9612 (2.31206 iter/s, 5.19017s/12 iters), loss = 0.0613742
I0409 22:58:49.260322 25438 solver.cpp:237]     Train net output #0: loss = 0.061374 (* 1 = 0.061374 loss)
I0409 22:58:49.260334 25438 sgd_solver.cpp:105] Iteration 9612, lr = 0.00148971
I0409 22:58:54.414984 25438 solver.cpp:218] Iteration 9624 (2.32806 iter/s, 5.1545s/12 iters), loss = 0.107833
I0409 22:58:54.415050 25438 solver.cpp:237]     Train net output #0: loss = 0.107833 (* 1 = 0.107833 loss)
I0409 22:58:54.415061 25438 sgd_solver.cpp:105] Iteration 9624, lr = 0.00148618
I0409 22:58:59.508848 25438 solver.cpp:218] Iteration 9636 (2.35588 iter/s, 5.09363s/12 iters), loss = 0.0286575
I0409 22:58:59.508913 25438 solver.cpp:237]     Train net output #0: loss = 0.0286573 (* 1 = 0.0286573 loss)
I0409 22:58:59.508924 25438 sgd_solver.cpp:105] Iteration 9636, lr = 0.00148265
I0409 22:59:04.599849 25438 solver.cpp:218] Iteration 9648 (2.3572 iter/s, 5.09078s/12 iters), loss = 0.0507605
I0409 22:59:04.599907 25438 solver.cpp:237]     Train net output #0: loss = 0.0507603 (* 1 = 0.0507603 loss)
I0409 22:59:04.599920 25438 sgd_solver.cpp:105] Iteration 9648, lr = 0.00147913
I0409 22:59:09.765938 25438 solver.cpp:218] Iteration 9660 (2.32294 iter/s, 5.16586s/12 iters), loss = 0.0438208
I0409 22:59:09.766038 25438 solver.cpp:237]     Train net output #0: loss = 0.0438206 (* 1 = 0.0438206 loss)
I0409 22:59:09.766052 25438 sgd_solver.cpp:105] Iteration 9660, lr = 0.00147562
I0409 22:59:15.170295 25438 solver.cpp:218] Iteration 9672 (2.22054 iter/s, 5.40409s/12 iters), loss = 0.0820774
I0409 22:59:15.170435 25438 solver.cpp:237]     Train net output #0: loss = 0.0820772 (* 1 = 0.0820772 loss)
I0409 22:59:15.170447 25438 sgd_solver.cpp:105] Iteration 9672, lr = 0.00147211
I0409 22:59:20.628196 25438 solver.cpp:218] Iteration 9684 (2.19877 iter/s, 5.45759s/12 iters), loss = 0.0989073
I0409 22:59:20.628257 25438 solver.cpp:237]     Train net output #0: loss = 0.0989071 (* 1 = 0.0989071 loss)
I0409 22:59:20.628268 25438 sgd_solver.cpp:105] Iteration 9684, lr = 0.00146862
I0409 22:59:22.983891 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0409 22:59:30.597803 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0409 22:59:33.936138 25438 solver.cpp:330] Iteration 9690, Testing net (#0)
I0409 22:59:33.936164 25438 net.cpp:676] Ignoring source layer train-data
I0409 22:59:34.613842 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:59:37.637332 25438 blocking_queue.cpp:49] Waiting for data
I0409 22:59:38.809783 25438 solver.cpp:397]     Test net output #0: accuracy = 0.232843
I0409 22:59:38.809826 25438 solver.cpp:397]     Test net output #1: loss = 6.2815 (* 1 = 6.2815 loss)
I0409 22:59:40.737174 25438 solver.cpp:218] Iteration 9696 (0.596768 iter/s, 20.1083s/12 iters), loss = 0.130422
I0409 22:59:40.737228 25438 solver.cpp:237]     Train net output #0: loss = 0.130422 (* 1 = 0.130422 loss)
I0409 22:59:40.737239 25438 sgd_solver.cpp:105] Iteration 9696, lr = 0.00146513
I0409 22:59:46.014880 25438 solver.cpp:218] Iteration 9708 (2.27381 iter/s, 5.27749s/12 iters), loss = 0.156284
I0409 22:59:46.015013 25438 solver.cpp:237]     Train net output #0: loss = 0.156284 (* 1 = 0.156284 loss)
I0409 22:59:46.015025 25438 sgd_solver.cpp:105] Iteration 9708, lr = 0.00146165
I0409 22:59:46.799846 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 22:59:51.228902 25438 solver.cpp:218] Iteration 9720 (2.30162 iter/s, 5.21372s/12 iters), loss = 0.103552
I0409 22:59:51.228963 25438 solver.cpp:237]     Train net output #0: loss = 0.103552 (* 1 = 0.103552 loss)
I0409 22:59:51.228976 25438 sgd_solver.cpp:105] Iteration 9720, lr = 0.00145818
I0409 22:59:56.540663 25438 solver.cpp:218] Iteration 9732 (2.25923 iter/s, 5.31153s/12 iters), loss = 0.121661
I0409 22:59:56.540715 25438 solver.cpp:237]     Train net output #0: loss = 0.121661 (* 1 = 0.121661 loss)
I0409 22:59:56.540726 25438 sgd_solver.cpp:105] Iteration 9732, lr = 0.00145472
I0409 23:00:02.073475 25438 solver.cpp:218] Iteration 9744 (2.16897 iter/s, 5.53258s/12 iters), loss = 0.129752
I0409 23:00:02.073527 25438 solver.cpp:237]     Train net output #0: loss = 0.129751 (* 1 = 0.129751 loss)
I0409 23:00:02.073535 25438 sgd_solver.cpp:105] Iteration 9744, lr = 0.00145127
I0409 23:00:07.273070 25438 solver.cpp:218] Iteration 9756 (2.30797 iter/s, 5.19938s/12 iters), loss = 0.138493
I0409 23:00:07.273118 25438 solver.cpp:237]     Train net output #0: loss = 0.138493 (* 1 = 0.138493 loss)
I0409 23:00:07.273126 25438 sgd_solver.cpp:105] Iteration 9756, lr = 0.00144782
I0409 23:00:12.571821 25438 solver.cpp:218] Iteration 9768 (2.26478 iter/s, 5.29853s/12 iters), loss = 0.0500117
I0409 23:00:12.571869 25438 solver.cpp:237]     Train net output #0: loss = 0.0500115 (* 1 = 0.0500115 loss)
I0409 23:00:12.571879 25438 sgd_solver.cpp:105] Iteration 9768, lr = 0.00144438
I0409 23:00:17.926810 25438 solver.cpp:218] Iteration 9780 (2.24099 iter/s, 5.35477s/12 iters), loss = 0.0743429
I0409 23:00:17.926966 25438 solver.cpp:237]     Train net output #0: loss = 0.0743428 (* 1 = 0.0743428 loss)
I0409 23:00:17.926978 25438 sgd_solver.cpp:105] Iteration 9780, lr = 0.00144095
I0409 23:00:22.894510 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0409 23:00:27.112861 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0409 23:00:30.323449 25438 solver.cpp:330] Iteration 9792, Testing net (#0)
I0409 23:00:30.323479 25438 net.cpp:676] Ignoring source layer train-data
I0409 23:00:30.911459 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:00:34.941087 25438 solver.cpp:397]     Test net output #0: accuracy = 0.239583
I0409 23:00:34.941118 25438 solver.cpp:397]     Test net output #1: loss = 6.33098 (* 1 = 6.33098 loss)
I0409 23:00:35.035338 25438 solver.cpp:218] Iteration 9792 (0.701432 iter/s, 17.1079s/12 iters), loss = 0.0342777
I0409 23:00:35.035396 25438 solver.cpp:237]     Train net output #0: loss = 0.0342776 (* 1 = 0.0342776 loss)
I0409 23:00:35.035408 25438 sgd_solver.cpp:105] Iteration 9792, lr = 0.00143753
I0409 23:00:39.467281 25438 solver.cpp:218] Iteration 9804 (2.70774 iter/s, 4.43174s/12 iters), loss = 0.171003
I0409 23:00:39.467342 25438 solver.cpp:237]     Train net output #0: loss = 0.171003 (* 1 = 0.171003 loss)
I0409 23:00:39.467352 25438 sgd_solver.cpp:105] Iteration 9804, lr = 0.00143412
I0409 23:00:42.726346 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:00:44.875026 25438 solver.cpp:218] Iteration 9816 (2.21913 iter/s, 5.40751s/12 iters), loss = 0.0784443
I0409 23:00:44.875072 25438 solver.cpp:237]     Train net output #0: loss = 0.0784441 (* 1 = 0.0784441 loss)
I0409 23:00:44.875080 25438 sgd_solver.cpp:105] Iteration 9816, lr = 0.00143072
I0409 23:00:50.149075 25438 solver.cpp:218] Iteration 9828 (2.27538 iter/s, 5.27383s/12 iters), loss = 0.16175
I0409 23:00:50.149188 25438 solver.cpp:237]     Train net output #0: loss = 0.16175 (* 1 = 0.16175 loss)
I0409 23:00:50.149197 25438 sgd_solver.cpp:105] Iteration 9828, lr = 0.00142732
I0409 23:00:55.395881 25438 solver.cpp:218] Iteration 9840 (2.28723 iter/s, 5.24653s/12 iters), loss = 0.0401648
I0409 23:00:55.395931 25438 solver.cpp:237]     Train net output #0: loss = 0.0401647 (* 1 = 0.0401647 loss)
I0409 23:00:55.395939 25438 sgd_solver.cpp:105] Iteration 9840, lr = 0.00142393
I0409 23:01:00.741436 25438 solver.cpp:218] Iteration 9852 (2.24495 iter/s, 5.34533s/12 iters), loss = 0.121254
I0409 23:01:00.741494 25438 solver.cpp:237]     Train net output #0: loss = 0.121254 (* 1 = 0.121254 loss)
I0409 23:01:00.741506 25438 sgd_solver.cpp:105] Iteration 9852, lr = 0.00142055
I0409 23:01:06.152500 25438 solver.cpp:218] Iteration 9864 (2.21777 iter/s, 5.41083s/12 iters), loss = 0.0340833
I0409 23:01:06.152551 25438 solver.cpp:237]     Train net output #0: loss = 0.0340832 (* 1 = 0.0340832 loss)
I0409 23:01:06.152560 25438 sgd_solver.cpp:105] Iteration 9864, lr = 0.00141718
I0409 23:01:11.456854 25438 solver.cpp:218] Iteration 9876 (2.26239 iter/s, 5.30413s/12 iters), loss = 0.024133
I0409 23:01:11.456912 25438 solver.cpp:237]     Train net output #0: loss = 0.0241329 (* 1 = 0.0241329 loss)
I0409 23:01:11.456924 25438 sgd_solver.cpp:105] Iteration 9876, lr = 0.00141381
I0409 23:01:16.775166 25438 solver.cpp:218] Iteration 9888 (2.25645 iter/s, 5.31808s/12 iters), loss = 0.119706
I0409 23:01:16.775219 25438 solver.cpp:237]     Train net output #0: loss = 0.119706 (* 1 = 0.119706 loss)
I0409 23:01:16.775229 25438 sgd_solver.cpp:105] Iteration 9888, lr = 0.00141045
I0409 23:01:18.919909 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0409 23:01:23.051630 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0409 23:01:26.228508 25438 solver.cpp:330] Iteration 9894, Testing net (#0)
I0409 23:01:26.228534 25438 net.cpp:676] Ignoring source layer train-data
I0409 23:01:26.800534 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:01:30.894668 25438 solver.cpp:397]     Test net output #0: accuracy = 0.240196
I0409 23:01:30.894723 25438 solver.cpp:397]     Test net output #1: loss = 6.3285 (* 1 = 6.3285 loss)
I0409 23:01:33.093510 25438 solver.cpp:218] Iteration 9900 (0.735393 iter/s, 16.3178s/12 iters), loss = 0.0577081
I0409 23:01:33.093570 25438 solver.cpp:237]     Train net output #0: loss = 0.057708 (* 1 = 0.057708 loss)
I0409 23:01:33.093581 25438 sgd_solver.cpp:105] Iteration 9900, lr = 0.00140711
I0409 23:01:38.206967 25438 solver.cpp:218] Iteration 9912 (2.34685 iter/s, 5.11323s/12 iters), loss = 0.024897
I0409 23:01:38.207034 25438 solver.cpp:237]     Train net output #0: loss = 0.0248968 (* 1 = 0.0248968 loss)
I0409 23:01:38.207046 25438 sgd_solver.cpp:105] Iteration 9912, lr = 0.00140377
I0409 23:01:38.305306 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:01:43.328321 25438 solver.cpp:218] Iteration 9924 (2.34324 iter/s, 5.12112s/12 iters), loss = 0.117188
I0409 23:01:43.328383 25438 solver.cpp:237]     Train net output #0: loss = 0.117188 (* 1 = 0.117188 loss)
I0409 23:01:43.328395 25438 sgd_solver.cpp:105] Iteration 9924, lr = 0.00140043
I0409 23:01:48.429100 25438 solver.cpp:218] Iteration 9936 (2.35268 iter/s, 5.10056s/12 iters), loss = 0.186439
I0409 23:01:48.429152 25438 solver.cpp:237]     Train net output #0: loss = 0.186439 (* 1 = 0.186439 loss)
I0409 23:01:48.429163 25438 sgd_solver.cpp:105] Iteration 9936, lr = 0.00139711
I0409 23:01:53.710686 25438 solver.cpp:218] Iteration 9948 (2.27214 iter/s, 5.28137s/12 iters), loss = 0.0251711
I0409 23:01:53.710830 25438 solver.cpp:237]     Train net output #0: loss = 0.025171 (* 1 = 0.025171 loss)
I0409 23:01:53.710842 25438 sgd_solver.cpp:105] Iteration 9948, lr = 0.00139379
I0409 23:01:58.820202 25438 solver.cpp:218] Iteration 9960 (2.3487 iter/s, 5.10921s/12 iters), loss = 0.0363389
I0409 23:01:58.820266 25438 solver.cpp:237]     Train net output #0: loss = 0.0363387 (* 1 = 0.0363387 loss)
I0409 23:01:58.820277 25438 sgd_solver.cpp:105] Iteration 9960, lr = 0.00139048
I0409 23:02:04.248384 25438 solver.cpp:218] Iteration 9972 (2.21078 iter/s, 5.42795s/12 iters), loss = 0.0547657
I0409 23:02:04.248430 25438 solver.cpp:237]     Train net output #0: loss = 0.0547655 (* 1 = 0.0547655 loss)
I0409 23:02:04.248438 25438 sgd_solver.cpp:105] Iteration 9972, lr = 0.00138718
I0409 23:02:09.469074 25438 solver.cpp:218] Iteration 9984 (2.29864 iter/s, 5.22048s/12 iters), loss = 0.129463
I0409 23:02:09.469125 25438 solver.cpp:237]     Train net output #0: loss = 0.129463 (* 1 = 0.129463 loss)
I0409 23:02:09.469135 25438 sgd_solver.cpp:105] Iteration 9984, lr = 0.00138389
I0409 23:02:14.190382 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0409 23:02:22.693006 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0409 23:02:29.430176 25438 solver.cpp:330] Iteration 9996, Testing net (#0)
I0409 23:02:29.430270 25438 net.cpp:676] Ignoring source layer train-data
I0409 23:02:29.947592 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:02:33.926415 25438 solver.cpp:397]     Test net output #0: accuracy = 0.237745
I0409 23:02:33.926465 25438 solver.cpp:397]     Test net output #1: loss = 6.42182 (* 1 = 6.42182 loss)
I0409 23:02:34.020207 25438 solver.cpp:218] Iteration 9996 (0.488791 iter/s, 24.5504s/12 iters), loss = 0.0156317
I0409 23:02:34.020253 25438 solver.cpp:237]     Train net output #0: loss = 0.0156315 (* 1 = 0.0156315 loss)
I0409 23:02:34.020264 25438 sgd_solver.cpp:105] Iteration 9996, lr = 0.0013806
I0409 23:02:38.523223 25438 solver.cpp:218] Iteration 10008 (2.66499 iter/s, 4.50283s/12 iters), loss = 0.0845787
I0409 23:02:38.523265 25438 solver.cpp:237]     Train net output #0: loss = 0.0845786 (* 1 = 0.0845786 loss)
I0409 23:02:38.523274 25438 sgd_solver.cpp:105] Iteration 10008, lr = 0.00137732
I0409 23:02:40.882215 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:02:43.808745 25438 solver.cpp:218] Iteration 10020 (2.27044 iter/s, 5.28531s/12 iters), loss = 0.0439517
I0409 23:02:43.808792 25438 solver.cpp:237]     Train net output #0: loss = 0.0439515 (* 1 = 0.0439515 loss)
I0409 23:02:43.808804 25438 sgd_solver.cpp:105] Iteration 10020, lr = 0.00137405
I0409 23:02:49.084379 25438 solver.cpp:218] Iteration 10032 (2.2747 iter/s, 5.27543s/12 iters), loss = 0.0634171
I0409 23:02:49.084419 25438 solver.cpp:237]     Train net output #0: loss = 0.0634169 (* 1 = 0.0634169 loss)
I0409 23:02:49.084429 25438 sgd_solver.cpp:105] Iteration 10032, lr = 0.00137079
I0409 23:02:54.345046 25438 solver.cpp:218] Iteration 10044 (2.28117 iter/s, 5.26046s/12 iters), loss = 0.0629923
I0409 23:02:54.345089 25438 solver.cpp:237]     Train net output #0: loss = 0.0629921 (* 1 = 0.0629921 loss)
I0409 23:02:54.345099 25438 sgd_solver.cpp:105] Iteration 10044, lr = 0.00136754
I0409 23:02:59.620061 25438 solver.cpp:218] Iteration 10056 (2.27497 iter/s, 5.2748s/12 iters), loss = 0.0448987
I0409 23:02:59.620183 25438 solver.cpp:237]     Train net output #0: loss = 0.0448985 (* 1 = 0.0448985 loss)
I0409 23:02:59.620193 25438 sgd_solver.cpp:105] Iteration 10056, lr = 0.00136429
I0409 23:03:04.932834 25438 solver.cpp:218] Iteration 10068 (2.25883 iter/s, 5.31249s/12 iters), loss = 0.0853069
I0409 23:03:04.932888 25438 solver.cpp:237]     Train net output #0: loss = 0.0853068 (* 1 = 0.0853068 loss)
I0409 23:03:04.932899 25438 sgd_solver.cpp:105] Iteration 10068, lr = 0.00136105
I0409 23:03:10.220592 25438 solver.cpp:218] Iteration 10080 (2.26949 iter/s, 5.28754s/12 iters), loss = 0.102654
I0409 23:03:10.220645 25438 solver.cpp:237]     Train net output #0: loss = 0.102654 (* 1 = 0.102654 loss)
I0409 23:03:10.220657 25438 sgd_solver.cpp:105] Iteration 10080, lr = 0.00135782
I0409 23:03:15.454274 25438 solver.cpp:218] Iteration 10092 (2.29294 iter/s, 5.23346s/12 iters), loss = 0.0858516
I0409 23:03:15.454319 25438 solver.cpp:237]     Train net output #0: loss = 0.0858514 (* 1 = 0.0858514 loss)
I0409 23:03:15.454329 25438 sgd_solver.cpp:105] Iteration 10092, lr = 0.0013546
I0409 23:03:17.689699 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0409 23:03:23.531713 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0409 23:03:32.649156 25438 solver.cpp:330] Iteration 10098, Testing net (#0)
I0409 23:03:32.649243 25438 net.cpp:676] Ignoring source layer train-data
I0409 23:03:33.135303 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:03:37.261982 25438 solver.cpp:397]     Test net output #0: accuracy = 0.240196
I0409 23:03:37.262027 25438 solver.cpp:397]     Test net output #1: loss = 6.3613 (* 1 = 6.3613 loss)
I0409 23:03:39.347980 25438 solver.cpp:218] Iteration 10104 (0.50224 iter/s, 23.893s/12 iters), loss = 0.0376586
I0409 23:03:39.348026 25438 solver.cpp:237]     Train net output #0: loss = 0.0376584 (* 1 = 0.0376584 loss)
I0409 23:03:39.348034 25438 sgd_solver.cpp:105] Iteration 10104, lr = 0.00135138
I0409 23:03:44.050670 25442 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:03:44.694152 25438 solver.cpp:218] Iteration 10116 (2.24469 iter/s, 5.34596s/12 iters), loss = 0.0522963
I0409 23:03:44.694205 25438 solver.cpp:237]     Train net output #0: loss = 0.0522961 (* 1 = 0.0522961 loss)
I0409 23:03:44.694216 25438 sgd_solver.cpp:105] Iteration 10116, lr = 0.00134817
I0409 23:03:49.913666 25438 solver.cpp:218] Iteration 10128 (2.29916 iter/s, 5.2193s/12 iters), loss = 0.0226365
I0409 23:03:49.913720 25438 solver.cpp:237]     Train net output #0: loss = 0.0226363 (* 1 = 0.0226363 loss)
I0409 23:03:49.913733 25438 sgd_solver.cpp:105] Iteration 10128, lr = 0.00134497
I0409 23:03:55.450587 25438 solver.cpp:218] Iteration 10140 (2.16736 iter/s, 5.5367s/12 iters), loss = 0.0675394
I0409 23:03:55.450631 25438 solver.cpp:237]     Train net output #0: loss = 0.0675392 (* 1 = 0.0675392 loss)
I0409 23:03:55.450640 25438 sgd_solver.cpp:105] Iteration 10140, lr = 0.00134178
I0409 23:04:00.597260 25438 solver.cpp:218] Iteration 10152 (2.3317 iter/s, 5.14647s/12 iters), loss = 0.051633
I0409 23:04:00.597306 25438 solver.cpp:237]     Train net output #0: loss = 0.0516328 (* 1 = 0.0516328 loss)
I0409 23:04:00.597316 25438 sgd_solver.cpp:105] Iteration 10152, lr = 0.00133859
I0409 23:04:05.777381 25438 solver.cpp:218] Iteration 10164 (2.31664 iter/s, 5.17991s/12 iters), loss = 0.0227742
I0409 23:04:05.777527 25438 solver.cpp:237]     Train net output #0: loss = 0.022774 (* 1 = 0.022774 loss)
I0409 23:04:05.777539 25438 sgd_solver.cpp:105] Iteration 10164, lr = 0.00133541
I0409 23:04:10.937278 25438 solver.cpp:218] Iteration 10176 (2.32577 iter/s, 5.15959s/12 iters), loss = 0.059406
I0409 23:04:10.937323 25438 solver.cpp:237]     Train net output #0: loss = 0.0594058 (* 1 = 0.0594058 loss)
I0409 23:04:10.937332 25438 sgd_solver.cpp:105] Iteration 10176, lr = 0.00133224
I0409 23:04:16.225769 25438 solver.cpp:218] Iteration 10188 (2.26917 iter/s, 5.28827s/12 iters), loss = 0.0300047
I0409 23:04:16.225821 25438 solver.cpp:237]     Train net output #0: loss = 0.0300045 (* 1 = 0.0300045 loss)
I0409 23:04:16.225829 25438 sgd_solver.cpp:105] Iteration 10188, lr = 0.00132908
I0409 23:04:21.275710 25438 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0409 23:04:28.047916 25438 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0409 23:04:41.509044 25438 solver.cpp:310] Iteration 10200, loss = 0.121603
I0409 23:04:41.509099 25438 solver.cpp:330] Iteration 10200, Testing net (#0)
I0409 23:04:41.509104 25438 net.cpp:676] Ignoring source layer train-data
I0409 23:04:41.943863 25443 data_layer.cpp:73] Restarting data prefetching from start.
I0409 23:04:46.144366 25438 solver.cpp:397]     Test net output #0: accuracy = 0.239583
I0409 23:04:46.144415 25438 solver.cpp:397]     Test net output #1: loss = 6.41704 (* 1 = 6.41704 loss)
I0409 23:04:46.144426 25438 solver.cpp:315] Optimization Done.
I0409 23:04:46.144433 25438 caffe.cpp:259] Optimization Done.