DIGITS-CNN/cars/split-investigations/70.15.15/base/caffe_output.log

I0401 13:54:14.541189 29493 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-124802-e25d/solver.prototxt
I0401 13:54:14.541429 29493 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0401 13:54:14.541435 29493 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0401 13:54:14.541514 29493 caffe.cpp:218] Using GPUs 2
I0401 13:54:14.564450 29493 caffe.cpp:223] GPU 2: GeForce GTX TITAN X
I0401 13:54:14.854508 29493 solver.cpp:44] Initializing solver from parameters:
test_iter: 76
test_interval: 89
base_lr: 0.001
display: 11
max_iter: 8900
lr_policy: "fixed"
momentum: 0.9
weight_decay: 1.0000001e-05
snapshot: 89
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 2
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0401 13:54:14.855716 29493 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0401 13:54:14.856570 29493 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0401 13:54:14.856588 29493 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0401 13:54:14.856796 29493 net.cpp:51] Initializing net from parameters:
state {
phase: TRAIN
level: 0
stage: ""
}
layer {
name: "train-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115815-ae54/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115815-ae54/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0401 13:54:14.856914 29493 layer_factory.hpp:77] Creating layer train-data
I0401 13:54:14.898180 29493 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115815-ae54/train_db
I0401 13:54:14.917387 29493 net.cpp:84] Creating Layer train-data
I0401 13:54:14.917414 29493 net.cpp:380] train-data -> data
I0401 13:54:14.917441 29493 net.cpp:380] train-data -> label
I0401 13:54:14.917456 29493 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115815-ae54/mean.binaryproto
I0401 13:54:15.074566 29493 data_layer.cpp:45] output data size: 128,3,227,227
I0401 13:54:15.226455 29493 net.cpp:122] Setting up train-data
I0401 13:54:15.226475 29493 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0401 13:54:15.226480 29493 net.cpp:129] Top shape: 128 (128)
I0401 13:54:15.226481 29493 net.cpp:137] Memory required for data: 79149056
I0401 13:54:15.226490 29493 layer_factory.hpp:77] Creating layer conv1
I0401 13:54:15.226509 29493 net.cpp:84] Creating Layer conv1
I0401 13:54:15.226513 29493 net.cpp:406] conv1 <- data
I0401 13:54:15.226524 29493 net.cpp:380] conv1 -> conv1
I0401 13:54:15.678853 29493 net.cpp:122] Setting up conv1
I0401 13:54:15.678874 29493 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 13:54:15.678879 29493 net.cpp:137] Memory required for data: 227833856
I0401 13:54:15.678896 29493 layer_factory.hpp:77] Creating layer relu1
I0401 13:54:15.678906 29493 net.cpp:84] Creating Layer relu1
I0401 13:54:15.678910 29493 net.cpp:406] relu1 <- conv1
I0401 13:54:15.678915 29493 net.cpp:367] relu1 -> conv1 (in-place)
I0401 13:54:15.679198 29493 net.cpp:122] Setting up relu1
I0401 13:54:15.679206 29493 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 13:54:15.679209 29493 net.cpp:137] Memory required for data: 376518656
I0401 13:54:15.679211 29493 layer_factory.hpp:77] Creating layer norm1
I0401 13:54:15.679219 29493 net.cpp:84] Creating Layer norm1
I0401 13:54:15.679222 29493 net.cpp:406] norm1 <- conv1
I0401 13:54:15.679251 29493 net.cpp:380] norm1 -> norm1
I0401 13:54:15.679687 29493 net.cpp:122] Setting up norm1
I0401 13:54:15.679697 29493 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0401 13:54:15.679698 29493 net.cpp:137] Memory required for data: 525203456
I0401 13:54:15.679702 29493 layer_factory.hpp:77] Creating layer pool1
I0401 13:54:15.679708 29493 net.cpp:84] Creating Layer pool1
I0401 13:54:15.679710 29493 net.cpp:406] pool1 <- norm1
I0401 13:54:15.679714 29493 net.cpp:380] pool1 -> pool1
I0401 13:54:15.679747 29493 net.cpp:122] Setting up pool1
I0401 13:54:15.679751 29493 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0401 13:54:15.679754 29493 net.cpp:137] Memory required for data: 561035264
I0401 13:54:15.679755 29493 layer_factory.hpp:77] Creating layer conv2
I0401 13:54:15.679764 29493 net.cpp:84] Creating Layer conv2
I0401 13:54:15.679767 29493 net.cpp:406] conv2 <- pool1
I0401 13:54:15.679770 29493 net.cpp:380] conv2 -> conv2
I0401 13:54:15.685607 29493 net.cpp:122] Setting up conv2
I0401 13:54:15.685622 29493 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 13:54:15.685626 29493 net.cpp:137] Memory required for data: 656586752
I0401 13:54:15.685634 29493 layer_factory.hpp:77] Creating layer relu2
I0401 13:54:15.685640 29493 net.cpp:84] Creating Layer relu2
I0401 13:54:15.685643 29493 net.cpp:406] relu2 <- conv2
I0401 13:54:15.685647 29493 net.cpp:367] relu2 -> conv2 (in-place)
I0401 13:54:15.686054 29493 net.cpp:122] Setting up relu2
I0401 13:54:15.686064 29493 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 13:54:15.686065 29493 net.cpp:137] Memory required for data: 752138240
I0401 13:54:15.686069 29493 layer_factory.hpp:77] Creating layer norm2
I0401 13:54:15.686074 29493 net.cpp:84] Creating Layer norm2
I0401 13:54:15.686076 29493 net.cpp:406] norm2 <- conv2
I0401 13:54:15.686080 29493 net.cpp:380] norm2 -> norm2
I0401 13:54:15.686347 29493 net.cpp:122] Setting up norm2
I0401 13:54:15.686355 29493 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0401 13:54:15.686357 29493 net.cpp:137] Memory required for data: 847689728
I0401 13:54:15.686359 29493 layer_factory.hpp:77] Creating layer pool2
I0401 13:54:15.686365 29493 net.cpp:84] Creating Layer pool2
I0401 13:54:15.686368 29493 net.cpp:406] pool2 <- norm2
I0401 13:54:15.686372 29493 net.cpp:380] pool2 -> pool2
I0401 13:54:15.686396 29493 net.cpp:122] Setting up pool2
I0401 13:54:15.686400 29493 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 13:54:15.686403 29493 net.cpp:137] Memory required for data: 869840896
I0401 13:54:15.686404 29493 layer_factory.hpp:77] Creating layer conv3
I0401 13:54:15.686412 29493 net.cpp:84] Creating Layer conv3
I0401 13:54:15.686414 29493 net.cpp:406] conv3 <- pool2
I0401 13:54:15.686419 29493 net.cpp:380] conv3 -> conv3
I0401 13:54:15.696312 29493 net.cpp:122] Setting up conv3
I0401 13:54:15.696332 29493 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 13:54:15.696336 29493 net.cpp:137] Memory required for data: 903067648
I0401 13:54:15.696346 29493 layer_factory.hpp:77] Creating layer relu3
I0401 13:54:15.696353 29493 net.cpp:84] Creating Layer relu3
I0401 13:54:15.696357 29493 net.cpp:406] relu3 <- conv3
I0401 13:54:15.696362 29493 net.cpp:367] relu3 -> conv3 (in-place)
I0401 13:54:15.696760 29493 net.cpp:122] Setting up relu3
I0401 13:54:15.696769 29493 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 13:54:15.696771 29493 net.cpp:137] Memory required for data: 936294400
I0401 13:54:15.696774 29493 layer_factory.hpp:77] Creating layer conv4
I0401 13:54:15.696784 29493 net.cpp:84] Creating Layer conv4
I0401 13:54:15.696786 29493 net.cpp:406] conv4 <- conv3
I0401 13:54:15.696791 29493 net.cpp:380] conv4 -> conv4
I0401 13:54:15.705865 29493 net.cpp:122] Setting up conv4
I0401 13:54:15.705884 29493 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 13:54:15.705886 29493 net.cpp:137] Memory required for data: 969521152
I0401 13:54:15.705894 29493 layer_factory.hpp:77] Creating layer relu4
I0401 13:54:15.705902 29493 net.cpp:84] Creating Layer relu4
I0401 13:54:15.705924 29493 net.cpp:406] relu4 <- conv4
I0401 13:54:15.705930 29493 net.cpp:367] relu4 -> conv4 (in-place)
I0401 13:54:15.706212 29493 net.cpp:122] Setting up relu4
I0401 13:54:15.706219 29493 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0401 13:54:15.706221 29493 net.cpp:137] Memory required for data: 1002747904
I0401 13:54:15.706224 29493 layer_factory.hpp:77] Creating layer conv5
I0401 13:54:15.706233 29493 net.cpp:84] Creating Layer conv5
I0401 13:54:15.706236 29493 net.cpp:406] conv5 <- conv4
I0401 13:54:15.706240 29493 net.cpp:380] conv5 -> conv5
I0401 13:54:15.713505 29493 net.cpp:122] Setting up conv5
I0401 13:54:15.713521 29493 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 13:54:15.713523 29493 net.cpp:137] Memory required for data: 1024899072
I0401 13:54:15.713536 29493 layer_factory.hpp:77] Creating layer relu5
I0401 13:54:15.713542 29493 net.cpp:84] Creating Layer relu5
I0401 13:54:15.713546 29493 net.cpp:406] relu5 <- conv5
I0401 13:54:15.713549 29493 net.cpp:367] relu5 -> conv5 (in-place)
I0401 13:54:15.713958 29493 net.cpp:122] Setting up relu5
I0401 13:54:15.713966 29493 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0401 13:54:15.713968 29493 net.cpp:137] Memory required for data: 1047050240
I0401 13:54:15.713970 29493 layer_factory.hpp:77] Creating layer pool5
I0401 13:54:15.713976 29493 net.cpp:84] Creating Layer pool5
I0401 13:54:15.713979 29493 net.cpp:406] pool5 <- conv5
I0401 13:54:15.713982 29493 net.cpp:380] pool5 -> pool5
I0401 13:54:15.714012 29493 net.cpp:122] Setting up pool5
I0401 13:54:15.714016 29493 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0401 13:54:15.714018 29493 net.cpp:137] Memory required for data: 1051768832
I0401 13:54:15.714020 29493 layer_factory.hpp:77] Creating layer fc6
I0401 13:54:15.714027 29493 net.cpp:84] Creating Layer fc6
I0401 13:54:15.714030 29493 net.cpp:406] fc6 <- pool5
I0401 13:54:15.714033 29493 net.cpp:380] fc6 -> fc6
I0401 13:54:16.045317 29493 net.cpp:122] Setting up fc6
I0401 13:54:16.045339 29493 net.cpp:129] Top shape: 128 4096 (524288)
I0401 13:54:16.045341 29493 net.cpp:137] Memory required for data: 1053865984
I0401 13:54:16.045351 29493 layer_factory.hpp:77] Creating layer relu6
I0401 13:54:16.045358 29493 net.cpp:84] Creating Layer relu6
I0401 13:54:16.045362 29493 net.cpp:406] relu6 <- fc6
I0401 13:54:16.045367 29493 net.cpp:367] relu6 -> fc6 (in-place)
I0401 13:54:16.045943 29493 net.cpp:122] Setting up relu6
I0401 13:54:16.045953 29493 net.cpp:129] Top shape: 128 4096 (524288)
I0401 13:54:16.045954 29493 net.cpp:137] Memory required for data: 1055963136
I0401 13:54:16.045958 29493 layer_factory.hpp:77] Creating layer drop6
I0401 13:54:16.045964 29493 net.cpp:84] Creating Layer drop6
I0401 13:54:16.045965 29493 net.cpp:406] drop6 <- fc6
I0401 13:54:16.045970 29493 net.cpp:367] drop6 -> fc6 (in-place)
I0401 13:54:16.045995 29493 net.cpp:122] Setting up drop6
I0401 13:54:16.046000 29493 net.cpp:129] Top shape: 128 4096 (524288)
I0401 13:54:16.046002 29493 net.cpp:137] Memory required for data: 1058060288
I0401 13:54:16.046005 29493 layer_factory.hpp:77] Creating layer fc7
I0401 13:54:16.046011 29493 net.cpp:84] Creating Layer fc7
I0401 13:54:16.046013 29493 net.cpp:406] fc7 <- fc6
I0401 13:54:16.046018 29493 net.cpp:380] fc7 -> fc7
I0401 13:54:16.193446 29493 net.cpp:122] Setting up fc7
I0401 13:54:16.193465 29493 net.cpp:129] Top shape: 128 4096 (524288)
I0401 13:54:16.193468 29493 net.cpp:137] Memory required for data: 1060157440
I0401 13:54:16.193475 29493 layer_factory.hpp:77] Creating layer relu7
I0401 13:54:16.193485 29493 net.cpp:84] Creating Layer relu7
I0401 13:54:16.193490 29493 net.cpp:406] relu7 <- fc7
I0401 13:54:16.193493 29493 net.cpp:367] relu7 -> fc7 (in-place)
I0401 13:54:16.193850 29493 net.cpp:122] Setting up relu7
I0401 13:54:16.193857 29493 net.cpp:129] Top shape: 128 4096 (524288)
I0401 13:54:16.193859 29493 net.cpp:137] Memory required for data: 1062254592
I0401 13:54:16.193862 29493 layer_factory.hpp:77] Creating layer drop7
I0401 13:54:16.193867 29493 net.cpp:84] Creating Layer drop7
I0401 13:54:16.193869 29493 net.cpp:406] drop7 <- fc7
I0401 13:54:16.193892 29493 net.cpp:367] drop7 -> fc7 (in-place)
I0401 13:54:16.193913 29493 net.cpp:122] Setting up drop7
I0401 13:54:16.193917 29493 net.cpp:129] Top shape: 128 4096 (524288)
I0401 13:54:16.193919 29493 net.cpp:137] Memory required for data: 1064351744
I0401 13:54:16.193922 29493 layer_factory.hpp:77] Creating layer fc8
I0401 13:54:16.193928 29493 net.cpp:84] Creating Layer fc8
I0401 13:54:16.193930 29493 net.cpp:406] fc8 <- fc7
I0401 13:54:16.193934 29493 net.cpp:380] fc8 -> fc8
I0401 13:54:16.201131 29493 net.cpp:122] Setting up fc8
I0401 13:54:16.201150 29493 net.cpp:129] Top shape: 128 196 (25088)
I0401 13:54:16.201153 29493 net.cpp:137] Memory required for data: 1064452096
I0401 13:54:16.201160 29493 layer_factory.hpp:77] Creating layer loss
I0401 13:54:16.201169 29493 net.cpp:84] Creating Layer loss
I0401 13:54:16.201171 29493 net.cpp:406] loss <- fc8
I0401 13:54:16.201176 29493 net.cpp:406] loss <- label
I0401 13:54:16.201182 29493 net.cpp:380] loss -> loss
I0401 13:54:16.201191 29493 layer_factory.hpp:77] Creating layer loss
I0401 13:54:16.201867 29493 net.cpp:122] Setting up loss
I0401 13:54:16.201876 29493 net.cpp:129] Top shape: (1)
I0401 13:54:16.201879 29493 net.cpp:132]     with loss weight 1
I0401 13:54:16.201898 29493 net.cpp:137] Memory required for data: 1064452100
I0401 13:54:16.201901 29493 net.cpp:198] loss needs backward computation.
I0401 13:54:16.201907 29493 net.cpp:198] fc8 needs backward computation.
I0401 13:54:16.201910 29493 net.cpp:198] drop7 needs backward computation.
I0401 13:54:16.201911 29493 net.cpp:198] relu7 needs backward computation.
I0401 13:54:16.201913 29493 net.cpp:198] fc7 needs backward computation.
I0401 13:54:16.201916 29493 net.cpp:198] drop6 needs backward computation.
I0401 13:54:16.201918 29493 net.cpp:198] relu6 needs backward computation.
I0401 13:54:16.201920 29493 net.cpp:198] fc6 needs backward computation.
I0401 13:54:16.201923 29493 net.cpp:198] pool5 needs backward computation.
I0401 13:54:16.201925 29493 net.cpp:198] relu5 needs backward computation.
I0401 13:54:16.201927 29493 net.cpp:198] conv5 needs backward computation.
I0401 13:54:16.201930 29493 net.cpp:198] relu4 needs backward computation.
I0401 13:54:16.201932 29493 net.cpp:198] conv4 needs backward computation.
I0401 13:54:16.201934 29493 net.cpp:198] relu3 needs backward computation.
I0401 13:54:16.201936 29493 net.cpp:198] conv3 needs backward computation.
I0401 13:54:16.201939 29493 net.cpp:198] pool2 needs backward computation.
I0401 13:54:16.201941 29493 net.cpp:198] norm2 needs backward computation.
I0401 13:54:16.201944 29493 net.cpp:198] relu2 needs backward computation.
I0401 13:54:16.201946 29493 net.cpp:198] conv2 needs backward computation.
I0401 13:54:16.201949 29493 net.cpp:198] pool1 needs backward computation.
I0401 13:54:16.201951 29493 net.cpp:198] norm1 needs backward computation.
I0401 13:54:16.201953 29493 net.cpp:198] relu1 needs backward computation.
I0401 13:54:16.201956 29493 net.cpp:198] conv1 needs backward computation.
I0401 13:54:16.201958 29493 net.cpp:200] train-data does not need backward computation.
I0401 13:54:16.201961 29493 net.cpp:242] This network produces output loss
I0401 13:54:16.201975 29493 net.cpp:255] Network initialization done.
I0401 13:54:16.202836 29493 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0401 13:54:16.202863 29493 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0401 13:54:16.202993 29493 net.cpp:51] Initializing net from parameters:
state {
phase: TEST
}
layer {
name: "val-data"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
crop_size: 227
mean_file: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115815-ae54/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115815-ae54/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0401 13:54:16.203090 29493 layer_factory.hpp:77] Creating layer val-data
I0401 13:54:16.205612 29493 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115815-ae54/val_db
I0401 13:54:16.217391 29493 net.cpp:84] Creating Layer val-data
I0401 13:54:16.217413 29493 net.cpp:380] val-data -> data
I0401 13:54:16.217425 29493 net.cpp:380] val-data -> label
I0401 13:54:16.217432 29493 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-AIN-3/digits/jobs/20210401-115815-ae54/mean.binaryproto
I0401 13:54:16.229523 29493 data_layer.cpp:45] output data size: 32,3,227,227
I0401 13:54:16.263571 29493 net.cpp:122] Setting up val-data
I0401 13:54:16.263593 29493 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0401 13:54:16.263598 29493 net.cpp:129] Top shape: 32 (32)
I0401 13:54:16.263602 29493 net.cpp:137] Memory required for data: 19787264
I0401 13:54:16.263607 29493 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0401 13:54:16.263618 29493 net.cpp:84] Creating Layer label_val-data_1_split
I0401 13:54:16.263622 29493 net.cpp:406] label_val-data_1_split <- label
I0401 13:54:16.263630 29493 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0401 13:54:16.263641 29493 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0401 13:54:16.263725 29493 net.cpp:122] Setting up label_val-data_1_split
I0401 13:54:16.263734 29493 net.cpp:129] Top shape: 32 (32)
I0401 13:54:16.263739 29493 net.cpp:129] Top shape: 32 (32)
I0401 13:54:16.263741 29493 net.cpp:137] Memory required for data: 19787520
I0401 13:54:16.263746 29493 layer_factory.hpp:77] Creating layer conv1
I0401 13:54:16.263761 29493 net.cpp:84] Creating Layer conv1
I0401 13:54:16.263764 29493 net.cpp:406] conv1 <- data
I0401 13:54:16.263770 29493 net.cpp:380] conv1 -> conv1
I0401 13:54:16.266831 29493 net.cpp:122] Setting up conv1
I0401 13:54:16.266852 29493 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 13:54:16.266856 29493 net.cpp:137] Memory required for data: 56958720
I0401 13:54:16.266870 29493 layer_factory.hpp:77] Creating layer relu1
I0401 13:54:16.266880 29493 net.cpp:84] Creating Layer relu1
I0401 13:54:16.266885 29493 net.cpp:406] relu1 <- conv1
I0401 13:54:16.266891 29493 net.cpp:367] relu1 -> conv1 (in-place)
I0401 13:54:16.267283 29493 net.cpp:122] Setting up relu1
I0401 13:54:16.267294 29493 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 13:54:16.267297 29493 net.cpp:137] Memory required for data: 94129920
I0401 13:54:16.267302 29493 layer_factory.hpp:77] Creating layer norm1
I0401 13:54:16.267311 29493 net.cpp:84] Creating Layer norm1
I0401 13:54:16.267315 29493 net.cpp:406] norm1 <- conv1
I0401 13:54:16.267321 29493 net.cpp:380] norm1 -> norm1
I0401 13:54:16.267915 29493 net.cpp:122] Setting up norm1
I0401 13:54:16.267927 29493 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0401 13:54:16.267931 29493 net.cpp:137] Memory required for data: 131301120
I0401 13:54:16.267935 29493 layer_factory.hpp:77] Creating layer pool1
I0401 13:54:16.267942 29493 net.cpp:84] Creating Layer pool1
I0401 13:54:16.267946 29493 net.cpp:406] pool1 <- norm1
I0401 13:54:16.267952 29493 net.cpp:380] pool1 -> pool1
I0401 13:54:16.267988 29493 net.cpp:122] Setting up pool1
I0401 13:54:16.267994 29493 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0401 13:54:16.267997 29493 net.cpp:137] Memory required for data: 140259072
I0401 13:54:16.268000 29493 layer_factory.hpp:77] Creating layer conv2
I0401 13:54:16.268013 29493 net.cpp:84] Creating Layer conv2
I0401 13:54:16.268015 29493 net.cpp:406] conv2 <- pool1
I0401 13:54:16.268047 29493 net.cpp:380] conv2 -> conv2
I0401 13:54:16.281944 29493 net.cpp:122] Setting up conv2
I0401 13:54:16.281968 29493 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 13:54:16.281975 29493 net.cpp:137] Memory required for data: 164146944
I0401 13:54:16.281991 29493 layer_factory.hpp:77] Creating layer relu2
I0401 13:54:16.282006 29493 net.cpp:84] Creating Layer relu2
I0401 13:54:16.282011 29493 net.cpp:406] relu2 <- conv2
I0401 13:54:16.282021 29493 net.cpp:367] relu2 -> conv2 (in-place)
I0401 13:54:16.282788 29493 net.cpp:122] Setting up relu2
I0401 13:54:16.282801 29493 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 13:54:16.282805 29493 net.cpp:137] Memory required for data: 188034816
I0401 13:54:16.282810 29493 layer_factory.hpp:77] Creating layer norm2
I0401 13:54:16.282825 29493 net.cpp:84] Creating Layer norm2
I0401 13:54:16.282830 29493 net.cpp:406] norm2 <- conv2
I0401 13:54:16.282838 29493 net.cpp:380] norm2 -> norm2
I0401 13:54:16.283612 29493 net.cpp:122] Setting up norm2
I0401 13:54:16.283625 29493 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0401 13:54:16.283630 29493 net.cpp:137] Memory required for data: 211922688
I0401 13:54:16.283634 29493 layer_factory.hpp:77] Creating layer pool2
I0401 13:54:16.283643 29493 net.cpp:84] Creating Layer pool2
I0401 13:54:16.283648 29493 net.cpp:406] pool2 <- norm2
I0401 13:54:16.283658 29493 net.cpp:380] pool2 -> pool2
I0401 13:54:16.283710 29493 net.cpp:122] Setting up pool2
I0401 13:54:16.283720 29493 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 13:54:16.283723 29493 net.cpp:137] Memory required for data: 217460480
I0401 13:54:16.283727 29493 layer_factory.hpp:77] Creating layer conv3
I0401 13:54:16.283746 29493 net.cpp:84] Creating Layer conv3
I0401 13:54:16.283751 29493 net.cpp:406] conv3 <- pool2
I0401 13:54:16.283759 29493 net.cpp:380] conv3 -> conv3
I0401 13:54:16.296639 29493 net.cpp:122] Setting up conv3
I0401 13:54:16.296663 29493 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 13:54:16.296666 29493 net.cpp:137] Memory required for data: 225767168
I0401 13:54:16.296684 29493 layer_factory.hpp:77] Creating layer relu3
I0401 13:54:16.296694 29493 net.cpp:84] Creating Layer relu3
I0401 13:54:16.296696 29493 net.cpp:406] relu3 <- conv3
I0401 13:54:16.296702 29493 net.cpp:367] relu3 -> conv3 (in-place)
I0401 13:54:16.297286 29493 net.cpp:122] Setting up relu3
I0401 13:54:16.297297 29493 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 13:54:16.297299 29493 net.cpp:137] Memory required for data: 234073856
I0401 13:54:16.297302 29493 layer_factory.hpp:77] Creating layer conv4
I0401 13:54:16.297312 29493 net.cpp:84] Creating Layer conv4
I0401 13:54:16.297315 29493 net.cpp:406] conv4 <- conv3
I0401 13:54:16.297319 29493 net.cpp:380] conv4 -> conv4
I0401 13:54:16.307723 29493 net.cpp:122] Setting up conv4
I0401 13:54:16.307745 29493 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 13:54:16.307747 29493 net.cpp:137] Memory required for data: 242380544
I0401 13:54:16.307760 29493 layer_factory.hpp:77] Creating layer relu4
I0401 13:54:16.307771 29493 net.cpp:84] Creating Layer relu4
I0401 13:54:16.307775 29493 net.cpp:406] relu4 <- conv4
I0401 13:54:16.307780 29493 net.cpp:367] relu4 -> conv4 (in-place)
I0401 13:54:16.308168 29493 net.cpp:122] Setting up relu4
I0401 13:54:16.308179 29493 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0401 13:54:16.308182 29493 net.cpp:137] Memory required for data: 250687232
I0401 13:54:16.308183 29493 layer_factory.hpp:77] Creating layer conv5
I0401 13:54:16.308193 29493 net.cpp:84] Creating Layer conv5
I0401 13:54:16.308197 29493 net.cpp:406] conv5 <- conv4
I0401 13:54:16.308202 29493 net.cpp:380] conv5 -> conv5
I0401 13:54:16.317031 29493 net.cpp:122] Setting up conv5
I0401 13:54:16.317055 29493 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 13:54:16.317059 29493 net.cpp:137] Memory required for data: 256225024
I0401 13:54:16.317071 29493 layer_factory.hpp:77] Creating layer relu5
I0401 13:54:16.317083 29493 net.cpp:84] Creating Layer relu5
I0401 13:54:16.317088 29493 net.cpp:406] relu5 <- conv5
I0401 13:54:16.317123 29493 net.cpp:367] relu5 -> conv5 (in-place)
I0401 13:54:16.317679 29493 net.cpp:122] Setting up relu5
I0401 13:54:16.317687 29493 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0401 13:54:16.317690 29493 net.cpp:137] Memory required for data: 261762816
I0401 13:54:16.317692 29493 layer_factory.hpp:77] Creating layer pool5
I0401 13:54:16.317701 29493 net.cpp:84] Creating Layer pool5
I0401 13:54:16.317704 29493 net.cpp:406] pool5 <- conv5
I0401 13:54:16.317708 29493 net.cpp:380] pool5 -> pool5
I0401 13:54:16.317746 29493 net.cpp:122] Setting up pool5
I0401 13:54:16.317754 29493 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0401 13:54:16.317757 29493 net.cpp:137] Memory required for data: 262942464
I0401 13:54:16.317759 29493 layer_factory.hpp:77] Creating layer fc6
I0401 13:54:16.317766 29493 net.cpp:84] Creating Layer fc6
I0401 13:54:16.317770 29493 net.cpp:406] fc6 <- pool5
I0401 13:54:16.317773 29493 net.cpp:380] fc6 -> fc6
I0401 13:54:16.739642 29493 net.cpp:122] Setting up fc6
I0401 13:54:16.739672 29493 net.cpp:129] Top shape: 32 4096 (131072)
I0401 13:54:16.739676 29493 net.cpp:137] Memory required for data: 263466752
I0401 13:54:16.739688 29493 layer_factory.hpp:77] Creating layer relu6
I0401 13:54:16.739702 29493 net.cpp:84] Creating Layer relu6
I0401 13:54:16.739708 29493 net.cpp:406] relu6 <- fc6
I0401 13:54:16.739717 29493 net.cpp:367] relu6 -> fc6 (in-place)
I0401 13:54:16.740661 29493 net.cpp:122] Setting up relu6
I0401 13:54:16.740674 29493 net.cpp:129] Top shape: 32 4096 (131072)
I0401 13:54:16.740677 29493 net.cpp:137] Memory required for data: 263991040
I0401 13:54:16.740681 29493 layer_factory.hpp:77] Creating layer drop6
I0401 13:54:16.740689 29493 net.cpp:84] Creating Layer drop6
I0401 13:54:16.740694 29493 net.cpp:406] drop6 <- fc6
I0401 13:54:16.740703 29493 net.cpp:367] drop6 -> fc6 (in-place)
I0401 13:54:16.740741 29493 net.cpp:122] Setting up drop6
I0401 13:54:16.740749 29493 net.cpp:129] Top shape: 32 4096 (131072)
I0401 13:54:16.740752 29493 net.cpp:137] Memory required for data: 264515328
I0401 13:54:16.740756 29493 layer_factory.hpp:77] Creating layer fc7
I0401 13:54:16.740765 29493 net.cpp:84] Creating Layer fc7
I0401 13:54:16.740769 29493 net.cpp:406] fc7 <- fc6
I0401 13:54:16.740777 29493 net.cpp:380] fc7 -> fc7
I0401 13:54:16.905133 29493 net.cpp:122] Setting up fc7
I0401 13:54:16.905161 29493 net.cpp:129] Top shape: 32 4096 (131072)
I0401 13:54:16.905164 29493 net.cpp:137] Memory required for data: 265039616
I0401 13:54:16.905174 29493 layer_factory.hpp:77] Creating layer relu7
I0401 13:54:16.905184 29493 net.cpp:84] Creating Layer relu7
I0401 13:54:16.905187 29493 net.cpp:406] relu7 <- fc7
I0401 13:54:16.905194 29493 net.cpp:367] relu7 -> fc7 (in-place)
I0401 13:54:16.905593 29493 net.cpp:122] Setting up relu7
I0401 13:54:16.905601 29493 net.cpp:129] Top shape: 32 4096 (131072)
I0401 13:54:16.905603 29493 net.cpp:137] Memory required for data: 265563904
I0401 13:54:16.905606 29493 layer_factory.hpp:77] Creating layer drop7
I0401 13:54:16.905612 29493 net.cpp:84] Creating Layer drop7
I0401 13:54:16.905616 29493 net.cpp:406] drop7 <- fc7
I0401 13:54:16.905618 29493 net.cpp:367] drop7 -> fc7 (in-place)
I0401 13:54:16.905642 29493 net.cpp:122] Setting up drop7
I0401 13:54:16.905647 29493 net.cpp:129] Top shape: 32 4096 (131072)
I0401 13:54:16.905648 29493 net.cpp:137] Memory required for data: 266088192
I0401 13:54:16.905650 29493 layer_factory.hpp:77] Creating layer fc8
I0401 13:54:16.905656 29493 net.cpp:84] Creating Layer fc8
I0401 13:54:16.905658 29493 net.cpp:406] fc8 <- fc7
I0401 13:54:16.905663 29493 net.cpp:380] fc8 -> fc8
I0401 13:54:16.913286 29493 net.cpp:122] Setting up fc8
I0401 13:54:16.913309 29493 net.cpp:129] Top shape: 32 196 (6272)
I0401 13:54:16.913312 29493 net.cpp:137] Memory required for data: 266113280
I0401 13:54:16.913321 29493 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0401 13:54:16.913329 29493 net.cpp:84] Creating Layer fc8_fc8_0_split
I0401 13:54:16.913332 29493 net.cpp:406] fc8_fc8_0_split <- fc8
I0401 13:54:16.913360 29493 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0401 13:54:16.913369 29493 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0401 13:54:16.913403 29493 net.cpp:122] Setting up fc8_fc8_0_split
I0401 13:54:16.913408 29493 net.cpp:129] Top shape: 32 196 (6272)
I0401 13:54:16.913409 29493 net.cpp:129] Top shape: 32 196 (6272)
I0401 13:54:16.913411 29493 net.cpp:137] Memory required for data: 266163456
I0401 13:54:16.913414 29493 layer_factory.hpp:77] Creating layer accuracy
I0401 13:54:16.913420 29493 net.cpp:84] Creating Layer accuracy
I0401 13:54:16.913422 29493 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0401 13:54:16.913425 29493 net.cpp:406] accuracy <- label_val-data_1_split_0
I0401 13:54:16.913429 29493 net.cpp:380] accuracy -> accuracy
I0401 13:54:16.913434 29493 net.cpp:122] Setting up accuracy
I0401 13:54:16.913437 29493 net.cpp:129] Top shape: (1)
I0401 13:54:16.913439 29493 net.cpp:137] Memory required for data: 266163460
I0401 13:54:16.913441 29493 layer_factory.hpp:77] Creating layer loss
I0401 13:54:16.913445 29493 net.cpp:84] Creating Layer loss
I0401 13:54:16.913447 29493 net.cpp:406] loss <- fc8_fc8_0_split_1
I0401 13:54:16.913450 29493 net.cpp:406] loss <- label_val-data_1_split_1
I0401 13:54:16.913453 29493 net.cpp:380] loss -> loss
I0401 13:54:16.913460 29493 layer_factory.hpp:77] Creating layer loss
I0401 13:54:16.914165 29493 net.cpp:122] Setting up loss
I0401 13:54:16.914172 29493 net.cpp:129] Top shape: (1)
I0401 13:54:16.914175 29493 net.cpp:132]     with loss weight 1
I0401 13:54:16.914184 29493 net.cpp:137] Memory required for data: 266163464
I0401 13:54:16.914187 29493 net.cpp:198] loss needs backward computation.
I0401 13:54:16.914191 29493 net.cpp:200] accuracy does not need backward computation.
I0401 13:54:16.914193 29493 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0401 13:54:16.914196 29493 net.cpp:198] fc8 needs backward computation.
I0401 13:54:16.914197 29493 net.cpp:198] drop7 needs backward computation.
I0401 13:54:16.914199 29493 net.cpp:198] relu7 needs backward computation.
I0401 13:54:16.914201 29493 net.cpp:198] fc7 needs backward computation.
I0401 13:54:16.914203 29493 net.cpp:198] drop6 needs backward computation.
I0401 13:54:16.914206 29493 net.cpp:198] relu6 needs backward computation.
I0401 13:54:16.914208 29493 net.cpp:198] fc6 needs backward computation.
I0401 13:54:16.914211 29493 net.cpp:198] pool5 needs backward computation.
I0401 13:54:16.914213 29493 net.cpp:198] relu5 needs backward computation.
I0401 13:54:16.914216 29493 net.cpp:198] conv5 needs backward computation.
I0401 13:54:16.914217 29493 net.cpp:198] relu4 needs backward computation.
I0401 13:54:16.914219 29493 net.cpp:198] conv4 needs backward computation.
I0401 13:54:16.914222 29493 net.cpp:198] relu3 needs backward computation.
I0401 13:54:16.914224 29493 net.cpp:198] conv3 needs backward computation.
I0401 13:54:16.914227 29493 net.cpp:198] pool2 needs backward computation.
I0401 13:54:16.914229 29493 net.cpp:198] norm2 needs backward computation.
I0401 13:54:16.914232 29493 net.cpp:198] relu2 needs backward computation.
I0401 13:54:16.914235 29493 net.cpp:198] conv2 needs backward computation.
I0401 13:54:16.914237 29493 net.cpp:198] pool1 needs backward computation.
I0401 13:54:16.914240 29493 net.cpp:198] norm1 needs backward computation.
I0401 13:54:16.914242 29493 net.cpp:198] relu1 needs backward computation.
I0401 13:54:16.914244 29493 net.cpp:198] conv1 needs backward computation.
I0401 13:54:16.914247 29493 net.cpp:200] label_val-data_1_split does not need backward computation.
I0401 13:54:16.914249 29493 net.cpp:200] val-data does not need backward computation.
I0401 13:54:16.914252 29493 net.cpp:242] This network produces output accuracy
I0401 13:54:16.914254 29493 net.cpp:242] This network produces output loss
I0401 13:54:16.914268 29493 net.cpp:255] Network initialization done.
I0401 13:54:16.914337 29493 solver.cpp:56] Solver scaffolding done.
I0401 13:54:16.914727 29493 caffe.cpp:248] Starting Optimization
I0401 13:54:16.914736 29493 solver.cpp:272] Solving
I0401 13:54:16.914745 29493 solver.cpp:273] Learning Rate Policy: fixed
I0401 13:54:16.916306 29493 solver.cpp:330] Iteration 0, Testing net (#0)
I0401 13:54:16.916314 29493 net.cpp:676] Ignoring source layer train-data
I0401 13:54:17.023263 29493 blocking_queue.cpp:49] Waiting for data
I0401 13:54:28.150247 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:54:28.177932 29493 solver.cpp:397]     Test net output #0: accuracy = 0.00411184
I0401 13:54:28.177964 29493 solver.cpp:397]     Test net output #1: loss = 5.28319 (* 1 = 5.28319 loss)
I0401 13:54:28.313652 29493 solver.cpp:218] Iteration 0 (0 iter/s, 11.3989s/11 iters), loss = 5.27465
I0401 13:54:28.315197 29493 solver.cpp:237]     Train net output #0: loss = 5.27465 (* 1 = 5.27465 loss)
I0401 13:54:28.315205 29493 sgd_solver.cpp:105] Iteration 0, lr = 0.001
I0401 13:54:34.384394 29493 solver.cpp:218] Iteration 11 (1.81244 iter/s, 6.06918s/11 iters), loss = 5.28566
I0401 13:54:34.384443 29493 solver.cpp:237]     Train net output #0: loss = 5.28566 (* 1 = 5.28566 loss)
I0401 13:54:34.384450 29493 sgd_solver.cpp:105] Iteration 11, lr = 0.001
I0401 13:54:39.621938 29493 solver.cpp:218] Iteration 22 (2.10025 iter/s, 5.23748s/11 iters), loss = 5.28043
I0401 13:54:39.621980 29493 solver.cpp:237]     Train net output #0: loss = 5.28043 (* 1 = 5.28043 loss)
I0401 13:54:39.621985 29493 sgd_solver.cpp:105] Iteration 22, lr = 0.001
I0401 13:54:45.550158 29493 solver.cpp:218] Iteration 33 (1.85555 iter/s, 5.92817s/11 iters), loss = 5.27303
I0401 13:54:45.550341 29493 solver.cpp:237]     Train net output #0: loss = 5.27303 (* 1 = 5.27303 loss)
I0401 13:54:45.550354 29493 sgd_solver.cpp:105] Iteration 33, lr = 0.001
I0401 13:54:50.893388 29493 solver.cpp:218] Iteration 44 (2.05875 iter/s, 5.34304s/11 iters), loss = 5.28574
I0401 13:54:50.893435 29493 solver.cpp:237]     Train net output #0: loss = 5.28574 (* 1 = 5.28574 loss)
I0401 13:54:50.893440 29493 sgd_solver.cpp:105] Iteration 44, lr = 0.001
I0401 13:54:56.124801 29493 solver.cpp:218] Iteration 55 (2.10271 iter/s, 5.23135s/11 iters), loss = 5.29192
I0401 13:54:56.124853 29493 solver.cpp:237]     Train net output #0: loss = 5.29192 (* 1 = 5.29192 loss)
I0401 13:54:56.124862 29493 sgd_solver.cpp:105] Iteration 55, lr = 0.001
I0401 13:55:01.114068 29493 solver.cpp:218] Iteration 66 (2.20476 iter/s, 4.9892s/11 iters), loss = 5.2912
I0401 13:55:01.114120 29493 solver.cpp:237]     Train net output #0: loss = 5.2912 (* 1 = 5.2912 loss)
I0401 13:55:01.114128 29493 sgd_solver.cpp:105] Iteration 66, lr = 0.001
I0401 13:55:06.177023 29493 solver.cpp:218] Iteration 77 (2.17267 iter/s, 5.06289s/11 iters), loss = 5.28059
I0401 13:55:06.177080 29493 solver.cpp:237]     Train net output #0: loss = 5.28059 (* 1 = 5.28059 loss)
I0401 13:55:06.177088 29493 sgd_solver.cpp:105] Iteration 77, lr = 0.001
I0401 13:55:12.701002 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:55:12.991748 29493 solver.cpp:218] Iteration 88 (1.61417 iter/s, 6.81465s/11 iters), loss = 5.27997
I0401 13:55:12.991812 29493 solver.cpp:237]     Train net output #0: loss = 5.27997 (* 1 = 5.27997 loss)
I0401 13:55:12.991822 29493 sgd_solver.cpp:105] Iteration 88, lr = 0.001
I0401 13:55:12.992041 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_89.caffemodel
I0401 13:55:19.552788 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_89.solverstate
I0401 13:55:23.693629 29493 solver.cpp:330] Iteration 89, Testing net (#0)
I0401 13:55:23.693650 29493 net.cpp:676] Ignoring source layer train-data
I0401 13:55:30.634311 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:55:30.673055 29493 solver.cpp:397]     Test net output #0: accuracy = 0.00740132
I0401 13:55:30.673106 29493 solver.cpp:397]     Test net output #1: loss = 5.28248 (* 1 = 5.28248 loss)
I0401 13:55:34.319875 29493 solver.cpp:218] Iteration 99 (0.515752 iter/s, 21.3281s/11 iters), loss = 5.27737
I0401 13:55:34.319922 29493 solver.cpp:237]     Train net output #0: loss = 5.27737 (* 1 = 5.27737 loss)
I0401 13:55:34.319928 29493 sgd_solver.cpp:105] Iteration 99, lr = 0.001
I0401 13:55:39.442878 29493 solver.cpp:218] Iteration 110 (2.1472 iter/s, 5.12294s/11 iters), loss = 5.27728
I0401 13:55:39.442932 29493 solver.cpp:237]     Train net output #0: loss = 5.27728 (* 1 = 5.27728 loss)
I0401 13:55:39.442940 29493 sgd_solver.cpp:105] Iteration 110, lr = 0.001
I0401 13:55:44.569736 29493 solver.cpp:218] Iteration 121 (2.14559 iter/s, 5.12679s/11 iters), loss = 5.27657
I0401 13:55:44.569799 29493 solver.cpp:237]     Train net output #0: loss = 5.27657 (* 1 = 5.27657 loss)
I0401 13:55:44.569808 29493 sgd_solver.cpp:105] Iteration 121, lr = 0.001
I0401 13:55:49.551527 29493 solver.cpp:218] Iteration 132 (2.20807 iter/s, 4.98172s/11 iters), loss = 5.27657
I0401 13:55:49.551570 29493 solver.cpp:237]     Train net output #0: loss = 5.27657 (* 1 = 5.27657 loss)
I0401 13:55:49.551575 29493 sgd_solver.cpp:105] Iteration 132, lr = 0.001
I0401 13:55:54.816951 29493 solver.cpp:218] Iteration 143 (2.08912 iter/s, 5.26537s/11 iters), loss = 5.28394
I0401 13:55:54.817103 29493 solver.cpp:237]     Train net output #0: loss = 5.28394 (* 1 = 5.28394 loss)
I0401 13:55:54.817113 29493 sgd_solver.cpp:105] Iteration 143, lr = 0.001
I0401 13:56:00.034715 29493 solver.cpp:218] Iteration 154 (2.10825 iter/s, 5.2176s/11 iters), loss = 5.29783
I0401 13:56:00.034763 29493 solver.cpp:237]     Train net output #0: loss = 5.29783 (* 1 = 5.29783 loss)
I0401 13:56:00.034772 29493 sgd_solver.cpp:105] Iteration 154, lr = 0.001
I0401 13:56:05.150604 29493 solver.cpp:218] Iteration 165 (2.15019 iter/s, 5.11583s/11 iters), loss = 5.26731
I0401 13:56:05.150657 29493 solver.cpp:237]     Train net output #0: loss = 5.26731 (* 1 = 5.26731 loss)
I0401 13:56:05.150666 29493 sgd_solver.cpp:105] Iteration 165, lr = 0.001
I0401 13:56:10.151511 29493 solver.cpp:218] Iteration 176 (2.19963 iter/s, 5.00083s/11 iters), loss = 5.27515
I0401 13:56:10.151561 29493 solver.cpp:237]     Train net output #0: loss = 5.27515 (* 1 = 5.27515 loss)
I0401 13:56:10.151567 29493 sgd_solver.cpp:105] Iteration 176, lr = 0.001
I0401 13:56:10.154556 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:56:10.531330 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_178.caffemodel
I0401 13:56:15.522550 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_178.solverstate
I0401 13:56:17.834861 29493 solver.cpp:330] Iteration 178, Testing net (#0)
I0401 13:56:17.834885 29493 net.cpp:676] Ignoring source layer train-data
I0401 13:56:25.083061 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:56:25.134814 29493 solver.cpp:397]     Test net output #0: accuracy = 0.00740132
I0401 13:56:25.134850 29493 solver.cpp:397]     Test net output #1: loss = 5.28344 (* 1 = 5.28344 loss)
I0401 13:56:28.610924 29493 solver.cpp:218] Iteration 187 (0.595904 iter/s, 18.4594s/11 iters), loss = 5.27866
I0401 13:56:28.610978 29493 solver.cpp:237]     Train net output #0: loss = 5.27866 (* 1 = 5.27866 loss)
I0401 13:56:28.610987 29493 sgd_solver.cpp:105] Iteration 187, lr = 0.001
I0401 13:56:33.606881 29493 solver.cpp:218] Iteration 198 (2.20181 iter/s, 4.99589s/11 iters), loss = 5.27568
I0401 13:56:33.606927 29493 solver.cpp:237]     Train net output #0: loss = 5.27568 (* 1 = 5.27568 loss)
I0401 13:56:33.606932 29493 sgd_solver.cpp:105] Iteration 198, lr = 0.001
I0401 13:56:38.748554 29493 solver.cpp:218] Iteration 209 (2.13941 iter/s, 5.14161s/11 iters), loss = 5.28769
I0401 13:56:38.748616 29493 solver.cpp:237]     Train net output #0: loss = 5.28769 (* 1 = 5.28769 loss)
I0401 13:56:38.748625 29493 sgd_solver.cpp:105] Iteration 209, lr = 0.001
I0401 13:56:43.525380 29493 solver.cpp:218] Iteration 220 (2.30282 iter/s, 4.77675s/11 iters), loss = 5.26438
I0401 13:56:43.525440 29493 solver.cpp:237]     Train net output #0: loss = 5.26438 (* 1 = 5.26438 loss)
I0401 13:56:43.525450 29493 sgd_solver.cpp:105] Iteration 220, lr = 0.001
I0401 13:56:48.813566 29493 solver.cpp:218] Iteration 231 (2.08014 iter/s, 5.28811s/11 iters), loss = 5.27884
I0401 13:56:48.813632 29493 solver.cpp:237]     Train net output #0: loss = 5.27884 (* 1 = 5.27884 loss)
I0401 13:56:48.813645 29493 sgd_solver.cpp:105] Iteration 231, lr = 0.001
I0401 13:56:53.910528 29493 solver.cpp:218] Iteration 242 (2.15818 iter/s, 5.09689s/11 iters), loss = 5.29172
I0401 13:56:53.910581 29493 solver.cpp:237]     Train net output #0: loss = 5.29172 (* 1 = 5.29172 loss)
I0401 13:56:53.910589 29493 sgd_solver.cpp:105] Iteration 242, lr = 0.001
I0401 13:56:59.025316 29493 solver.cpp:218] Iteration 253 (2.15065 iter/s, 5.11472s/11 iters), loss = 5.26695
I0401 13:56:59.025465 29493 solver.cpp:237]     Train net output #0: loss = 5.26695 (* 1 = 5.26695 loss)
I0401 13:56:59.025475 29493 sgd_solver.cpp:105] Iteration 253, lr = 0.001
I0401 13:57:04.397732 29493 solver.cpp:218] Iteration 264 (2.04755 iter/s, 5.37226s/11 iters), loss = 5.26746
I0401 13:57:04.397778 29493 solver.cpp:237]     Train net output #0: loss = 5.26746 (* 1 = 5.26746 loss)
I0401 13:57:04.397785 29493 sgd_solver.cpp:105] Iteration 264, lr = 0.001
I0401 13:57:04.534792 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:57:05.306509 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_267.caffemodel
I0401 13:57:08.469974 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_267.solverstate
I0401 13:57:10.815973 29493 solver.cpp:330] Iteration 267, Testing net (#0)
I0401 13:57:10.815994 29493 net.cpp:676] Ignoring source layer train-data
I0401 13:57:18.050549 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:57:18.115836 29493 solver.cpp:397]     Test net output #0: accuracy = 0.00740132
I0401 13:57:18.115866 29493 solver.cpp:397]     Test net output #1: loss = 5.28356 (* 1 = 5.28356 loss)
I0401 13:57:21.060905 29493 solver.cpp:218] Iteration 275 (0.660141 iter/s, 16.6631s/11 iters), loss = 5.27323
I0401 13:57:21.060971 29493 solver.cpp:237]     Train net output #0: loss = 5.27323 (* 1 = 5.27323 loss)
I0401 13:57:21.060979 29493 sgd_solver.cpp:105] Iteration 275, lr = 0.001
I0401 13:57:26.244942 29493 solver.cpp:218] Iteration 286 (2.12193 iter/s, 5.18396s/11 iters), loss = 5.27604
I0401 13:57:26.245002 29493 solver.cpp:237]     Train net output #0: loss = 5.27604 (* 1 = 5.27604 loss)
I0401 13:57:26.245010 29493 sgd_solver.cpp:105] Iteration 286, lr = 0.001
I0401 13:57:31.713057 29493 solver.cpp:218] Iteration 297 (2.01169 iter/s, 5.46804s/11 iters), loss = 5.27634
I0401 13:57:31.719265 29493 solver.cpp:237]     Train net output #0: loss = 5.27634 (* 1 = 5.27634 loss)
I0401 13:57:31.719285 29493 sgd_solver.cpp:105] Iteration 297, lr = 0.001
I0401 13:57:36.775300 29493 solver.cpp:218] Iteration 308 (2.17562 iter/s, 5.05604s/11 iters), loss = 5.25468
I0401 13:57:36.775357 29493 solver.cpp:237]     Train net output #0: loss = 5.25468 (* 1 = 5.25468 loss)
I0401 13:57:36.775365 29493 sgd_solver.cpp:105] Iteration 308, lr = 0.001
I0401 13:57:41.928134 29493 solver.cpp:218] Iteration 319 (2.13478 iter/s, 5.15276s/11 iters), loss = 5.27613
I0401 13:57:41.928197 29493 solver.cpp:237]     Train net output #0: loss = 5.27613 (* 1 = 5.27613 loss)
I0401 13:57:41.928206 29493 sgd_solver.cpp:105] Iteration 319, lr = 0.001
I0401 13:57:47.161924 29493 solver.cpp:218] Iteration 330 (2.10176 iter/s, 5.23371s/11 iters), loss = 5.28302
I0401 13:57:47.161978 29493 solver.cpp:237]     Train net output #0: loss = 5.28302 (* 1 = 5.28302 loss)
I0401 13:57:47.161984 29493 sgd_solver.cpp:105] Iteration 330, lr = 0.001
I0401 13:57:52.578007 29493 solver.cpp:218] Iteration 341 (2.03101 iter/s, 5.41602s/11 iters), loss = 5.28516
I0401 13:57:52.578073 29493 solver.cpp:237]     Train net output #0: loss = 5.28516 (* 1 = 5.28516 loss)
I0401 13:57:52.578091 29493 sgd_solver.cpp:105] Iteration 341, lr = 0.001
I0401 13:57:57.991822 29493 solver.cpp:218] Iteration 352 (2.03187 iter/s, 5.41374s/11 iters), loss = 5.26945
I0401 13:57:57.991878 29493 solver.cpp:237]     Train net output #0: loss = 5.26945 (* 1 = 5.26945 loss)
I0401 13:57:57.991887 29493 sgd_solver.cpp:105] Iteration 352, lr = 0.001
I0401 13:57:58.518471 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:57:59.438956 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_356.caffemodel
I0401 13:58:02.561676 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_356.solverstate
I0401 13:58:04.950974 29493 solver.cpp:330] Iteration 356, Testing net (#0)
I0401 13:58:04.950995 29493 net.cpp:676] Ignoring source layer train-data
I0401 13:58:12.372181 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:58:12.448617 29493 solver.cpp:397]     Test net output #0: accuracy = 0.00740132
I0401 13:58:12.448657 29493 solver.cpp:397]     Test net output #1: loss = 5.28337 (* 1 = 5.28337 loss)
I0401 13:58:14.880511 29493 solver.cpp:218] Iteration 363 (0.651326 iter/s, 16.8886s/11 iters), loss = 5.26193
I0401 13:58:14.880563 29493 solver.cpp:237]     Train net output #0: loss = 5.26193 (* 1 = 5.26193 loss)
I0401 13:58:14.880571 29493 sgd_solver.cpp:105] Iteration 363, lr = 0.001
I0401 13:58:22.069173 29493 solver.cpp:218] Iteration 374 (1.53118 iter/s, 7.184s/11 iters), loss = 5.26644
I0401 13:58:22.069226 29493 solver.cpp:237]     Train net output #0: loss = 5.26644 (* 1 = 5.26644 loss)
I0401 13:58:22.069234 29493 sgd_solver.cpp:105] Iteration 374, lr = 0.001
I0401 13:58:35.272256 29493 solver.cpp:218] Iteration 385 (0.833143 iter/s, 13.203s/11 iters), loss = 5.27428
I0401 13:58:35.296958 29493 solver.cpp:237]     Train net output #0: loss = 5.27428 (* 1 = 5.27428 loss)
I0401 13:58:35.296975 29493 sgd_solver.cpp:105] Iteration 385, lr = 0.001
I0401 13:58:45.938460 29493 solver.cpp:218] Iteration 396 (1.03369 iter/s, 10.6415s/11 iters), loss = 5.266
I0401 13:58:45.938525 29493 solver.cpp:237]     Train net output #0: loss = 5.266 (* 1 = 5.266 loss)
I0401 13:58:45.938534 29493 sgd_solver.cpp:105] Iteration 396, lr = 0.001
I0401 13:58:54.752938 29493 solver.cpp:218] Iteration 407 (1.25235 iter/s, 8.78347s/11 iters), loss = 5.28871
I0401 13:58:54.753002 29493 solver.cpp:237]     Train net output #0: loss = 5.28871 (* 1 = 5.28871 loss)
I0401 13:58:54.753010 29493 sgd_solver.cpp:105] Iteration 407, lr = 0.001
I0401 13:59:03.985352 29493 solver.cpp:218] Iteration 418 (1.19146 iter/s, 9.23234s/11 iters), loss = 5.28749
I0401 13:59:03.985405 29493 solver.cpp:237]     Train net output #0: loss = 5.28749 (* 1 = 5.28749 loss)
I0401 13:59:03.985412 29493 sgd_solver.cpp:105] Iteration 418, lr = 0.001
I0401 13:59:11.069700 29493 solver.cpp:218] Iteration 429 (1.55273 iter/s, 7.08428s/11 iters), loss = 5.2806
I0401 13:59:11.088979 29493 solver.cpp:237]     Train net output #0: loss = 5.2806 (* 1 = 5.2806 loss)
I0401 13:59:11.089011 29493 sgd_solver.cpp:105] Iteration 429, lr = 0.001
I0401 13:59:17.097590 29493 solver.cpp:218] Iteration 440 (1.82656 iter/s, 6.02224s/11 iters), loss = 5.28304
I0401 13:59:17.097651 29493 solver.cpp:237]     Train net output #0: loss = 5.28304 (* 1 = 5.28304 loss)
I0401 13:59:17.097657 29493 sgd_solver.cpp:105] Iteration 440, lr = 0.001
I0401 13:59:17.937772 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:59:19.303727 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_445.caffemodel
I0401 13:59:22.870019 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_445.solverstate
I0401 13:59:25.665920 29493 solver.cpp:330] Iteration 445, Testing net (#0)
I0401 13:59:25.665943 29493 net.cpp:676] Ignoring source layer train-data
I0401 13:59:36.159454 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 13:59:36.276783 29493 solver.cpp:397]     Test net output #0: accuracy = 0.00740132
I0401 13:59:36.276823 29493 solver.cpp:397]     Test net output #1: loss = 5.28203 (* 1 = 5.28203 loss)
I0401 13:59:38.728929 29493 solver.cpp:218] Iteration 451 (0.508986 iter/s, 21.6116s/11 iters), loss = 5.26343
I0401 13:59:38.728986 29493 solver.cpp:237]     Train net output #0: loss = 5.26343 (* 1 = 5.26343 loss)
I0401 13:59:38.728996 29493 sgd_solver.cpp:105] Iteration 451, lr = 0.001
I0401 13:59:44.996994 29493 solver.cpp:218] Iteration 462 (1.75495 iter/s, 6.268s/11 iters), loss = 5.25794
I0401 13:59:44.997169 29493 solver.cpp:237]     Train net output #0: loss = 5.25794 (* 1 = 5.25794 loss)
I0401 13:59:44.997180 29493 sgd_solver.cpp:105] Iteration 462, lr = 0.001
I0401 13:59:51.038316 29493 solver.cpp:218] Iteration 473 (1.82085 iter/s, 6.04114s/11 iters), loss = 5.27065
I0401 13:59:51.038378 29493 solver.cpp:237]     Train net output #0: loss = 5.27065 (* 1 = 5.27065 loss)
I0401 13:59:51.038385 29493 sgd_solver.cpp:105] Iteration 473, lr = 0.001
I0401 13:59:57.420766 29493 solver.cpp:218] Iteration 484 (1.7235 iter/s, 6.38238s/11 iters), loss = 5.25085
I0401 13:59:57.420812 29493 solver.cpp:237]     Train net output #0: loss = 5.25085 (* 1 = 5.25085 loss)
I0401 13:59:57.420819 29493 sgd_solver.cpp:105] Iteration 484, lr = 0.001
I0401 14:00:03.625536 29493 solver.cpp:218] Iteration 495 (1.77285 iter/s, 6.20471s/11 iters), loss = 5.27162
I0401 14:00:03.625597 29493 solver.cpp:237]     Train net output #0: loss = 5.27162 (* 1 = 5.27162 loss)
I0401 14:00:03.625605 29493 sgd_solver.cpp:105] Iteration 495, lr = 0.001
I0401 14:00:09.591266 29493 solver.cpp:218] Iteration 506 (1.84389 iter/s, 5.96565s/11 iters), loss = 5.26725
I0401 14:00:09.591325 29493 solver.cpp:237]     Train net output #0: loss = 5.26725 (* 1 = 5.26725 loss)
I0401 14:00:09.591332 29493 sgd_solver.cpp:105] Iteration 506, lr = 0.001
I0401 14:00:15.908990 29493 solver.cpp:218] Iteration 517 (1.74115 iter/s, 6.31765s/11 iters), loss = 5.26321
I0401 14:00:15.909101 29493 solver.cpp:237]     Train net output #0: loss = 5.26321 (* 1 = 5.26321 loss)
I0401 14:00:15.909111 29493 sgd_solver.cpp:105] Iteration 517, lr = 0.001
I0401 14:00:21.147411 29493 solver.cpp:218] Iteration 528 (2.09992 iter/s, 5.2383s/11 iters), loss = 5.26091
I0401 14:00:21.147455 29493 solver.cpp:237]     Train net output #0: loss = 5.26091 (* 1 = 5.26091 loss)
I0401 14:00:21.147461 29493 sgd_solver.cpp:105] Iteration 528, lr = 0.001
I0401 14:00:21.989079 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:00:23.385649 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_534.caffemodel
I0401 14:00:26.606349 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_534.solverstate
I0401 14:00:28.967962 29493 solver.cpp:330] Iteration 534, Testing net (#0)
I0401 14:00:28.967988 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:00:34.044005 29493 blocking_queue.cpp:49] Waiting for data
I0401 14:00:36.399022 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:00:36.511112 29493 solver.cpp:397]     Test net output #0: accuracy = 0.00740132
I0401 14:00:36.511152 29493 solver.cpp:397]     Test net output #1: loss = 5.27914 (* 1 = 5.27914 loss)
I0401 14:00:38.092607 29493 solver.cpp:218] Iteration 539 (0.649153 iter/s, 16.9452s/11 iters), loss = 5.26413
I0401 14:00:38.092653 29493 solver.cpp:237]     Train net output #0: loss = 5.26413 (* 1 = 5.26413 loss)
I0401 14:00:38.092659 29493 sgd_solver.cpp:105] Iteration 539, lr = 0.001
I0401 14:00:43.213660 29493 solver.cpp:218] Iteration 550 (2.14803 iter/s, 5.12098s/11 iters), loss = 5.2621
I0401 14:00:43.213726 29493 solver.cpp:237]     Train net output #0: loss = 5.2621 (* 1 = 5.2621 loss)
I0401 14:00:43.213735 29493 sgd_solver.cpp:105] Iteration 550, lr = 0.001
I0401 14:00:48.398425 29493 solver.cpp:218] Iteration 561 (2.12163 iter/s, 5.18468s/11 iters), loss = 5.2858
I0401 14:00:48.398548 29493 solver.cpp:237]     Train net output #0: loss = 5.2858 (* 1 = 5.2858 loss)
I0401 14:00:48.398559 29493 sgd_solver.cpp:105] Iteration 561, lr = 0.001
I0401 14:00:53.657987 29493 solver.cpp:218] Iteration 572 (2.09148 iter/s, 5.25943s/11 iters), loss = 5.24557
I0401 14:00:53.658033 29493 solver.cpp:237]     Train net output #0: loss = 5.24557 (* 1 = 5.24557 loss)
I0401 14:00:53.658039 29493 sgd_solver.cpp:105] Iteration 572, lr = 0.001
I0401 14:00:58.863283 29493 solver.cpp:218] Iteration 583 (2.11326 iter/s, 5.20523s/11 iters), loss = 5.28184
I0401 14:00:58.863329 29493 solver.cpp:237]     Train net output #0: loss = 5.28184 (* 1 = 5.28184 loss)
I0401 14:00:58.863337 29493 sgd_solver.cpp:105] Iteration 583, lr = 0.001
I0401 14:01:03.981025 29493 solver.cpp:218] Iteration 594 (2.14941 iter/s, 5.11768s/11 iters), loss = 5.25335
I0401 14:01:03.981078 29493 solver.cpp:237]     Train net output #0: loss = 5.25335 (* 1 = 5.25335 loss)
I0401 14:01:03.981088 29493 sgd_solver.cpp:105] Iteration 594, lr = 0.001
I0401 14:01:09.141712 29493 solver.cpp:218] Iteration 605 (2.13153 iter/s, 5.16062s/11 iters), loss = 5.27308
I0401 14:01:09.141773 29493 solver.cpp:237]     Train net output #0: loss = 5.27308 (* 1 = 5.27308 loss)
I0401 14:01:09.141782 29493 sgd_solver.cpp:105] Iteration 605, lr = 0.001
I0401 14:01:14.217365 29493 solver.cpp:218] Iteration 616 (2.16724 iter/s, 5.07558s/11 iters), loss = 5.26128
I0401 14:01:14.217430 29493 solver.cpp:237]     Train net output #0: loss = 5.26128 (* 1 = 5.26128 loss)
I0401 14:01:14.217439 29493 sgd_solver.cpp:105] Iteration 616, lr = 0.001
I0401 14:01:15.415171 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:01:17.089556 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_623.caffemodel
I0401 14:01:21.014860 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_623.solverstate
I0401 14:01:24.828533 29493 solver.cpp:330] Iteration 623, Testing net (#0)
I0401 14:01:24.828554 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:01:31.918190 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:01:32.073451 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0078125
I0401 14:01:32.073490 29493 solver.cpp:397]     Test net output #1: loss = 5.27134 (* 1 = 5.27134 loss)
I0401 14:01:33.158511 29493 solver.cpp:218] Iteration 627 (0.580748 iter/s, 18.9411s/11 iters), loss = 5.24347
I0401 14:01:33.158562 29493 solver.cpp:237]     Train net output #0: loss = 5.24347 (* 1 = 5.24347 loss)
I0401 14:01:33.158571 29493 sgd_solver.cpp:105] Iteration 627, lr = 0.001
I0401 14:01:38.187633 29493 solver.cpp:218] Iteration 638 (2.18729 iter/s, 5.02905s/11 iters), loss = 5.2569
I0401 14:01:38.187692 29493 solver.cpp:237]     Train net output #0: loss = 5.2569 (* 1 = 5.2569 loss)
I0401 14:01:38.187700 29493 sgd_solver.cpp:105] Iteration 638, lr = 0.001
I0401 14:01:43.498067 29493 solver.cpp:218] Iteration 649 (2.07142 iter/s, 5.31036s/11 iters), loss = 5.27329
I0401 14:01:43.498118 29493 solver.cpp:237]     Train net output #0: loss = 5.27329 (* 1 = 5.27329 loss)
I0401 14:01:43.498126 29493 sgd_solver.cpp:105] Iteration 649, lr = 0.001
I0401 14:01:48.574324 29493 solver.cpp:218] Iteration 660 (2.16698 iter/s, 5.07619s/11 iters), loss = 5.24133
I0401 14:01:48.574383 29493 solver.cpp:237]     Train net output #0: loss = 5.24133 (* 1 = 5.24133 loss)
I0401 14:01:48.574391 29493 sgd_solver.cpp:105] Iteration 660, lr = 0.001
I0401 14:01:53.771013 29493 solver.cpp:218] Iteration 671 (2.11676 iter/s, 5.19661s/11 iters), loss = 5.25025
I0401 14:01:53.771132 29493 solver.cpp:237]     Train net output #0: loss = 5.25025 (* 1 = 5.25025 loss)
I0401 14:01:53.771142 29493 sgd_solver.cpp:105] Iteration 671, lr = 0.001
I0401 14:01:58.818209 29493 solver.cpp:218] Iteration 682 (2.17948 iter/s, 5.04707s/11 iters), loss = 5.24716
I0401 14:01:58.818255 29493 solver.cpp:237]     Train net output #0: loss = 5.24716 (* 1 = 5.24716 loss)
I0401 14:01:58.818260 29493 sgd_solver.cpp:105] Iteration 682, lr = 0.001
I0401 14:02:03.873302 29493 solver.cpp:218] Iteration 693 (2.17605 iter/s, 5.05503s/11 iters), loss = 5.24808
I0401 14:02:03.873348 29493 solver.cpp:237]     Train net output #0: loss = 5.24808 (* 1 = 5.24808 loss)
I0401 14:02:03.873354 29493 sgd_solver.cpp:105] Iteration 693, lr = 0.001
I0401 14:02:09.207252 29493 solver.cpp:218] Iteration 704 (2.06229 iter/s, 5.33389s/11 iters), loss = 5.24236
I0401 14:02:09.207311 29493 solver.cpp:237]     Train net output #0: loss = 5.24236 (* 1 = 5.24236 loss)
I0401 14:02:09.207320 29493 sgd_solver.cpp:105] Iteration 704, lr = 0.001
I0401 14:02:10.635202 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:02:12.245718 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_712.caffemodel
I0401 14:02:15.521812 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_712.solverstate
I0401 14:02:17.881361 29493 solver.cpp:330] Iteration 712, Testing net (#0)
I0401 14:02:17.881386 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:02:25.229876 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:02:25.428820 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0106908
I0401 14:02:25.428859 29493 solver.cpp:397]     Test net output #1: loss = 5.24556 (* 1 = 5.24556 loss)
I0401 14:02:26.036191 29493 solver.cpp:218] Iteration 715 (0.653639 iter/s, 16.8289s/11 iters), loss = 5.24907
I0401 14:02:26.036257 29493 solver.cpp:237]     Train net output #0: loss = 5.24907 (* 1 = 5.24907 loss)
I0401 14:02:26.036263 29493 sgd_solver.cpp:105] Iteration 715, lr = 0.001
I0401 14:02:31.165740 29493 solver.cpp:218] Iteration 726 (2.14447 iter/s, 5.12948s/11 iters), loss = 5.2295
I0401 14:02:31.165776 29493 solver.cpp:237]     Train net output #0: loss = 5.2295 (* 1 = 5.2295 loss)
I0401 14:02:31.165782 29493 sgd_solver.cpp:105] Iteration 726, lr = 0.001
I0401 14:02:36.249660 29493 solver.cpp:218] Iteration 737 (2.16371 iter/s, 5.08387s/11 iters), loss = 5.23267
I0401 14:02:36.249727 29493 solver.cpp:237]     Train net output #0: loss = 5.23267 (* 1 = 5.23267 loss)
I0401 14:02:36.249735 29493 sgd_solver.cpp:105] Iteration 737, lr = 0.001
I0401 14:02:41.414707 29493 solver.cpp:218] Iteration 748 (2.12973 iter/s, 5.16497s/11 iters), loss = 5.22187
I0401 14:02:41.414750 29493 solver.cpp:237]     Train net output #0: loss = 5.22187 (* 1 = 5.22187 loss)
I0401 14:02:41.414757 29493 sgd_solver.cpp:105] Iteration 748, lr = 0.001
I0401 14:02:46.608031 29493 solver.cpp:218] Iteration 759 (2.11813 iter/s, 5.19327s/11 iters), loss = 5.20688
I0401 14:02:46.608088 29493 solver.cpp:237]     Train net output #0: loss = 5.20688 (* 1 = 5.20688 loss)
I0401 14:02:46.608095 29493 sgd_solver.cpp:105] Iteration 759, lr = 0.001
I0401 14:02:51.856406 29493 solver.cpp:218] Iteration 770 (2.09592 iter/s, 5.2483s/11 iters), loss = 5.20803
I0401 14:02:51.856478 29493 solver.cpp:237]     Train net output #0: loss = 5.20803 (* 1 = 5.20803 loss)
I0401 14:02:51.856487 29493 sgd_solver.cpp:105] Iteration 770, lr = 0.001
I0401 14:02:56.917076 29493 solver.cpp:218] Iteration 781 (2.17366 iter/s, 5.06059s/11 iters), loss = 5.16912
I0401 14:02:56.917174 29493 solver.cpp:237]     Train net output #0: loss = 5.16912 (* 1 = 5.16912 loss)
I0401 14:02:56.917181 29493 sgd_solver.cpp:105] Iteration 781, lr = 0.001
I0401 14:03:02.186816 29493 solver.cpp:218] Iteration 792 (2.08744 iter/s, 5.26962s/11 iters), loss = 5.22341
I0401 14:03:02.186872 29493 solver.cpp:237]     Train net output #0: loss = 5.22341 (* 1 = 5.22341 loss)
I0401 14:03:02.186880 29493 sgd_solver.cpp:105] Iteration 792, lr = 0.001
I0401 14:03:03.749137 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:03:05.888696 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_801.caffemodel
I0401 14:03:09.197381 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_801.solverstate
I0401 14:03:11.581944 29493 solver.cpp:330] Iteration 801, Testing net (#0)
I0401 14:03:11.581966 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:03:19.072132 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:03:19.219182 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0131579
I0401 14:03:19.219218 29493 solver.cpp:397]     Test net output #1: loss = 5.16893 (* 1 = 5.16893 loss)
I0401 14:03:19.630887 29493 solver.cpp:218] Iteration 803 (0.630589 iter/s, 17.444s/11 iters), loss = 5.21195
I0401 14:03:19.630946 29493 solver.cpp:237]     Train net output #0: loss = 5.21195 (* 1 = 5.21195 loss)
I0401 14:03:19.630955 29493 sgd_solver.cpp:105] Iteration 803, lr = 0.001
I0401 14:03:24.424520 29493 solver.cpp:218] Iteration 814 (2.29474 iter/s, 4.79356s/11 iters), loss = 5.1827
I0401 14:03:24.424566 29493 solver.cpp:237]     Train net output #0: loss = 5.1827 (* 1 = 5.1827 loss)
I0401 14:03:24.424571 29493 sgd_solver.cpp:105] Iteration 814, lr = 0.001
I0401 14:03:29.483096 29493 solver.cpp:218] Iteration 825 (2.17455 iter/s, 5.05851s/11 iters), loss = 5.19113
I0401 14:03:29.483264 29493 solver.cpp:237]     Train net output #0: loss = 5.19113 (* 1 = 5.19113 loss)
I0401 14:03:29.483273 29493 sgd_solver.cpp:105] Iteration 825, lr = 0.001
I0401 14:03:35.012120 29493 solver.cpp:218] Iteration 836 (1.98956 iter/s, 5.52885s/11 iters), loss = 5.20357
I0401 14:03:35.012159 29493 solver.cpp:237]     Train net output #0: loss = 5.20357 (* 1 = 5.20357 loss)
I0401 14:03:35.012166 29493 sgd_solver.cpp:105] Iteration 836, lr = 0.001
I0401 14:03:39.874171 29493 solver.cpp:218] Iteration 847 (2.26245 iter/s, 4.86199s/11 iters), loss = 5.193
I0401 14:03:39.874231 29493 solver.cpp:237]     Train net output #0: loss = 5.193 (* 1 = 5.193 loss)
I0401 14:03:39.874239 29493 sgd_solver.cpp:105] Iteration 847, lr = 0.001
I0401 14:03:45.066952 29493 solver.cpp:218] Iteration 858 (2.11836 iter/s, 5.19271s/11 iters), loss = 5.21515
I0401 14:03:45.067006 29493 solver.cpp:237]     Train net output #0: loss = 5.21515 (* 1 = 5.21515 loss)
I0401 14:03:45.067014 29493 sgd_solver.cpp:105] Iteration 858, lr = 0.001
I0401 14:03:50.198376 29493 solver.cpp:218] Iteration 869 (2.14368 iter/s, 5.13135s/11 iters), loss = 5.1527
I0401 14:03:50.198434 29493 solver.cpp:237]     Train net output #0: loss = 5.1527 (* 1 = 5.1527 loss)
I0401 14:03:50.198442 29493 sgd_solver.cpp:105] Iteration 869, lr = 0.001
I0401 14:03:55.503684 29493 solver.cpp:218] Iteration 880 (2.07342 iter/s, 5.30523s/11 iters), loss = 5.12557
I0401 14:03:55.503743 29493 solver.cpp:237]     Train net output #0: loss = 5.12557 (* 1 = 5.12557 loss)
I0401 14:03:55.503753 29493 sgd_solver.cpp:105] Iteration 880, lr = 0.001
I0401 14:03:57.438809 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:03:59.675694 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_890.caffemodel
I0401 14:04:02.810880 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_890.solverstate
I0401 14:04:05.113502 29493 solver.cpp:330] Iteration 890, Testing net (#0)
I0401 14:04:05.113525 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:04:12.614030 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:04:12.794224 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0139803
I0401 14:04:12.794265 29493 solver.cpp:397]     Test net output #1: loss = 5.13012 (* 1 = 5.13012 loss)
I0401 14:04:13.074476 29493 solver.cpp:218] Iteration 891 (0.626041 iter/s, 17.5707s/11 iters), loss = 5.13409
I0401 14:04:13.076045 29493 solver.cpp:237]     Train net output #0: loss = 5.13409 (* 1 = 5.13409 loss)
I0401 14:04:13.076058 29493 sgd_solver.cpp:105] Iteration 891, lr = 0.001
I0401 14:04:17.791525 29493 solver.cpp:218] Iteration 902 (2.33275 iter/s, 4.71547s/11 iters), loss = 5.21765
I0401 14:04:17.791576 29493 solver.cpp:237]     Train net output #0: loss = 5.21765 (* 1 = 5.21765 loss)
I0401 14:04:17.791584 29493 sgd_solver.cpp:105] Iteration 902, lr = 0.001
I0401 14:04:22.958690 29493 solver.cpp:218] Iteration 913 (2.12885 iter/s, 5.1671s/11 iters), loss = 5.06539
I0401 14:04:22.958736 29493 solver.cpp:237]     Train net output #0: loss = 5.06539 (* 1 = 5.06539 loss)
I0401 14:04:22.958743 29493 sgd_solver.cpp:105] Iteration 913, lr = 0.001
I0401 14:04:28.366555 29493 solver.cpp:218] Iteration 924 (2.0341 iter/s, 5.40781s/11 iters), loss = 5.13134
I0401 14:04:28.366601 29493 solver.cpp:237]     Train net output #0: loss = 5.13134 (* 1 = 5.13134 loss)
I0401 14:04:28.366609 29493 sgd_solver.cpp:105] Iteration 924, lr = 0.001
I0401 14:04:33.463148 29493 solver.cpp:218] Iteration 935 (2.15833 iter/s, 5.09654s/11 iters), loss = 5.1832
I0401 14:04:33.463284 29493 solver.cpp:237]     Train net output #0: loss = 5.1832 (* 1 = 5.1832 loss)
I0401 14:04:33.463291 29493 sgd_solver.cpp:105] Iteration 935, lr = 0.001
I0401 14:04:38.619681 29493 solver.cpp:218] Iteration 946 (2.13328 iter/s, 5.15638s/11 iters), loss = 5.17275
I0401 14:04:38.619736 29493 solver.cpp:237]     Train net output #0: loss = 5.17275 (* 1 = 5.17275 loss)
I0401 14:04:38.619745 29493 sgd_solver.cpp:105] Iteration 946, lr = 0.001
I0401 14:04:43.832588 29493 solver.cpp:218] Iteration 957 (2.11018 iter/s, 5.21284s/11 iters), loss = 5.13238
I0401 14:04:43.832643 29493 solver.cpp:237]     Train net output #0: loss = 5.13238 (* 1 = 5.13238 loss)
I0401 14:04:43.832650 29493 sgd_solver.cpp:105] Iteration 957, lr = 0.001
I0401 14:04:48.815272 29493 solver.cpp:218] Iteration 968 (2.20768 iter/s, 4.98261s/11 iters), loss = 5.11466
I0401 14:04:48.815330 29493 solver.cpp:237]     Train net output #0: loss = 5.11466 (* 1 = 5.11466 loss)
I0401 14:04:48.815337 29493 sgd_solver.cpp:105] Iteration 968, lr = 0.001
I0401 14:04:51.022660 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:04:53.532143 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_979.caffemodel
I0401 14:04:56.638864 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_979.solverstate
I0401 14:04:58.970407 29493 solver.cpp:330] Iteration 979, Testing net (#0)
I0401 14:04:58.970427 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:05:06.138614 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:05:06.325126 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0164474
I0401 14:05:06.325167 29493 solver.cpp:397]     Test net output #1: loss = 5.10578 (* 1 = 5.10578 loss)
I0401 14:05:06.457782 29493 solver.cpp:218] Iteration 979 (0.623496 iter/s, 17.6424s/11 iters), loss = 5.07596
I0401 14:05:06.463963 29493 solver.cpp:237]     Train net output #0: loss = 5.07596 (* 1 = 5.07596 loss)
I0401 14:05:06.463984 29493 sgd_solver.cpp:105] Iteration 979, lr = 0.001
I0401 14:05:10.828845 29493 solver.cpp:218] Iteration 990 (2.52011 iter/s, 4.36489s/11 iters), loss = 5.18776
I0401 14:05:10.828897 29493 solver.cpp:237]     Train net output #0: loss = 5.18776 (* 1 = 5.18776 loss)
I0401 14:05:10.828903 29493 sgd_solver.cpp:105] Iteration 990, lr = 0.001
I0401 14:05:15.991495 29493 solver.cpp:218] Iteration 1001 (2.13072 iter/s, 5.16258s/11 iters), loss = 5.12348
I0401 14:05:15.991560 29493 solver.cpp:237]     Train net output #0: loss = 5.12348 (* 1 = 5.12348 loss)
I0401 14:05:15.991571 29493 sgd_solver.cpp:105] Iteration 1001, lr = 0.001
I0401 14:05:21.370266 29493 solver.cpp:218] Iteration 1012 (2.04511 iter/s, 5.37869s/11 iters), loss = 5.11486
I0401 14:05:21.370324 29493 solver.cpp:237]     Train net output #0: loss = 5.11486 (* 1 = 5.11486 loss)
I0401 14:05:21.370337 29493 sgd_solver.cpp:105] Iteration 1012, lr = 0.001
I0401 14:05:26.518278 29493 solver.cpp:218] Iteration 1023 (2.13678 iter/s, 5.14794s/11 iters), loss = 5.11599
I0401 14:05:26.518337 29493 solver.cpp:237]     Train net output #0: loss = 5.11599 (* 1 = 5.11599 loss)
I0401 14:05:26.518345 29493 sgd_solver.cpp:105] Iteration 1023, lr = 0.001
I0401 14:05:31.682454 29493 solver.cpp:218] Iteration 1034 (2.13009 iter/s, 5.1641s/11 iters), loss = 5.06131
I0401 14:05:31.682509 29493 solver.cpp:237]     Train net output #0: loss = 5.06131 (* 1 = 5.06131 loss)
I0401 14:05:31.682518 29493 sgd_solver.cpp:105] Iteration 1034, lr = 0.001
I0401 14:05:37.050458 29493 solver.cpp:218] Iteration 1045 (2.0492 iter/s, 5.36794s/11 iters), loss = 5.17675
I0401 14:05:37.050583 29493 solver.cpp:237]     Train net output #0: loss = 5.17675 (* 1 = 5.17675 loss)
I0401 14:05:37.050593 29493 sgd_solver.cpp:105] Iteration 1045, lr = 0.001
I0401 14:05:42.639303 29493 solver.cpp:218] Iteration 1056 (1.96825 iter/s, 5.58871s/11 iters), loss = 5.10164
I0401 14:05:42.639359 29493 solver.cpp:237]     Train net output #0: loss = 5.10164 (* 1 = 5.10164 loss)
I0401 14:05:42.639369 29493 sgd_solver.cpp:105] Iteration 1056, lr = 0.001
I0401 14:05:44.881283 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:05:47.927631 29493 solver.cpp:218] Iteration 1067 (2.08008 iter/s, 5.28826s/11 iters), loss = 5.06647
I0401 14:05:47.927675 29493 solver.cpp:237]     Train net output #0: loss = 5.06647 (* 1 = 5.06647 loss)
I0401 14:05:47.927681 29493 sgd_solver.cpp:105] Iteration 1067, lr = 0.001
I0401 14:05:47.927824 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1068.caffemodel
I0401 14:05:51.019443 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1068.solverstate
I0401 14:05:53.349985 29493 solver.cpp:330] Iteration 1068, Testing net (#0)
I0401 14:05:53.350006 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:06:00.714538 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:06:00.974916 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0176809
I0401 14:06:00.974958 29493 solver.cpp:397]     Test net output #1: loss = 5.08392 (* 1 = 5.08392 loss)
I0401 14:06:04.729771 29493 solver.cpp:218] Iteration 1078 (0.65468 iter/s, 16.8021s/11 iters), loss = 5.08397
I0401 14:06:04.729826 29493 solver.cpp:237]     Train net output #0: loss = 5.08397 (* 1 = 5.08397 loss)
I0401 14:06:04.729835 29493 sgd_solver.cpp:105] Iteration 1078, lr = 0.001
I0401 14:06:09.928261 29493 solver.cpp:218] Iteration 1089 (2.11603 iter/s, 5.19842s/11 iters), loss = 5.1107
I0401 14:06:09.928419 29493 solver.cpp:237]     Train net output #0: loss = 5.1107 (* 1 = 5.1107 loss)
I0401 14:06:09.928428 29493 sgd_solver.cpp:105] Iteration 1089, lr = 0.001
I0401 14:06:10.830874 29493 blocking_queue.cpp:49] Waiting for data
I0401 14:06:15.038269 29493 solver.cpp:218] Iteration 1100 (2.15271 iter/s, 5.10984s/11 iters), loss = 5.08487
I0401 14:06:15.038309 29493 solver.cpp:237]     Train net output #0: loss = 5.08487 (* 1 = 5.08487 loss)
I0401 14:06:15.038316 29493 sgd_solver.cpp:105] Iteration 1100, lr = 0.001
I0401 14:06:20.108363 29493 solver.cpp:218] Iteration 1111 (2.16961 iter/s, 5.07004s/11 iters), loss = 5.08932
I0401 14:06:20.108415 29493 solver.cpp:237]     Train net output #0: loss = 5.08932 (* 1 = 5.08932 loss)
I0401 14:06:20.108423 29493 sgd_solver.cpp:105] Iteration 1111, lr = 0.001
I0401 14:06:25.856634 29493 solver.cpp:218] Iteration 1122 (1.91364 iter/s, 5.7482s/11 iters), loss = 5.07336
I0401 14:06:25.862861 29493 solver.cpp:237]     Train net output #0: loss = 5.07336 (* 1 = 5.07336 loss)
I0401 14:06:25.862885 29493 sgd_solver.cpp:105] Iteration 1122, lr = 0.001
I0401 14:06:31.300367 29493 solver.cpp:218] Iteration 1133 (2.02298 iter/s, 5.43751s/11 iters), loss = 5.12963
I0401 14:06:31.300410 29493 solver.cpp:237]     Train net output #0: loss = 5.12963 (* 1 = 5.12963 loss)
I0401 14:06:31.300415 29493 sgd_solver.cpp:105] Iteration 1133, lr = 0.001
I0401 14:06:36.598083 29493 solver.cpp:218] Iteration 1144 (2.07639 iter/s, 5.29766s/11 iters), loss = 5.04865
I0401 14:06:36.598138 29493 solver.cpp:237]     Train net output #0: loss = 5.04865 (* 1 = 5.04865 loss)
I0401 14:06:36.598146 29493 sgd_solver.cpp:105] Iteration 1144, lr = 0.001
I0401 14:06:39.097842 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:06:41.763458 29493 solver.cpp:218] Iteration 1155 (2.12959 iter/s, 5.16531s/11 iters), loss = 4.95354
I0401 14:06:41.763588 29493 solver.cpp:237]     Train net output #0: loss = 4.95354 (* 1 = 4.95354 loss)
I0401 14:06:41.763597 29493 sgd_solver.cpp:105] Iteration 1155, lr = 0.001
I0401 14:06:42.107369 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1157.caffemodel
I0401 14:06:45.370201 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1157.solverstate
I0401 14:06:47.728230 29493 solver.cpp:330] Iteration 1157, Testing net (#0)
I0401 14:06:47.728251 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:06:54.907295 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:06:55.118782 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0193257
I0401 14:06:55.118820 29493 solver.cpp:397]     Test net output #1: loss = 5.07109 (* 1 = 5.07109 loss)
I0401 14:06:58.592442 29493 solver.cpp:218] Iteration 1166 (0.653639 iter/s, 16.8288s/11 iters), loss = 5.02368
I0401 14:06:58.592502 29493 solver.cpp:237]     Train net output #0: loss = 5.02368 (* 1 = 5.02368 loss)
I0401 14:06:58.592511 29493 sgd_solver.cpp:105] Iteration 1166, lr = 0.001
I0401 14:07:03.727070 29493 solver.cpp:218] Iteration 1177 (2.14235 iter/s, 5.13456s/11 iters), loss = 5.12568
I0401 14:07:03.727113 29493 solver.cpp:237]     Train net output #0: loss = 5.12568 (* 1 = 5.12568 loss)
I0401 14:07:03.727119 29493 sgd_solver.cpp:105] Iteration 1177, lr = 0.001
I0401 14:07:08.689572 29493 solver.cpp:218] Iteration 1188 (2.21665 iter/s, 4.96244s/11 iters), loss = 5.10584
I0401 14:07:08.689627 29493 solver.cpp:237]     Train net output #0: loss = 5.10584 (* 1 = 5.10584 loss)
I0401 14:07:08.689635 29493 sgd_solver.cpp:105] Iteration 1188, lr = 0.001
I0401 14:07:14.252197 29493 solver.cpp:218] Iteration 1199 (1.97751 iter/s, 5.56255s/11 iters), loss = 5.07289
I0401 14:07:14.254503 29493 solver.cpp:237]     Train net output #0: loss = 5.07289 (* 1 = 5.07289 loss)
I0401 14:07:14.254518 29493 sgd_solver.cpp:105] Iteration 1199, lr = 0.001
I0401 14:07:19.943775 29493 solver.cpp:218] Iteration 1210 (1.93346 iter/s, 5.68927s/11 iters), loss = 5.14786
I0401 14:07:19.943832 29493 solver.cpp:237]     Train net output #0: loss = 5.14786 (* 1 = 5.14786 loss)
I0401 14:07:19.943840 29493 sgd_solver.cpp:105] Iteration 1210, lr = 0.001
I0401 14:07:25.559689 29493 solver.cpp:218] Iteration 1221 (1.95874 iter/s, 5.61584s/11 iters), loss = 5.11498
I0401 14:07:25.559743 29493 solver.cpp:237]     Train net output #0: loss = 5.11498 (* 1 = 5.11498 loss)
I0401 14:07:25.559751 29493 sgd_solver.cpp:105] Iteration 1221, lr = 0.001
I0401 14:07:30.869674 29493 solver.cpp:218] Iteration 1232 (2.07159 iter/s, 5.30992s/11 iters), loss = 5.11961
I0401 14:07:30.869721 29493 solver.cpp:237]     Train net output #0: loss = 5.11961 (* 1 = 5.11961 loss)
I0401 14:07:30.869727 29493 sgd_solver.cpp:105] Iteration 1232, lr = 0.001
I0401 14:07:33.640846 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:07:36.156960 29493 solver.cpp:218] Iteration 1243 (2.08049 iter/s, 5.28722s/11 iters), loss = 4.94908
I0401 14:07:36.157007 29493 solver.cpp:237]     Train net output #0: loss = 4.94908 (* 1 = 4.94908 loss)
I0401 14:07:36.157016 29493 sgd_solver.cpp:105] Iteration 1243, lr = 0.001
I0401 14:07:36.976943 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1246.caffemodel
I0401 14:07:42.163427 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1246.solverstate
I0401 14:07:44.530001 29493 solver.cpp:330] Iteration 1246, Testing net (#0)
I0401 14:07:44.530099 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:07:51.722229 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:07:51.946245 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0205592
I0401 14:07:51.946285 29493 solver.cpp:397]     Test net output #1: loss = 5.05894 (* 1 = 5.05894 loss)
I0401 14:07:54.925164 29493 solver.cpp:218] Iteration 1254 (0.586099 iter/s, 18.7682s/11 iters), loss = 5.02416
I0401 14:07:54.925225 29493 solver.cpp:237]     Train net output #0: loss = 5.02416 (* 1 = 5.02416 loss)
I0401 14:07:54.925235 29493 sgd_solver.cpp:105] Iteration 1254, lr = 0.001
I0401 14:08:00.056437 29493 solver.cpp:218] Iteration 1265 (2.14375 iter/s, 5.1312s/11 iters), loss = 5.09324
I0401 14:08:00.056489 29493 solver.cpp:237]     Train net output #0: loss = 5.09324 (* 1 = 5.09324 loss)
I0401 14:08:00.056497 29493 sgd_solver.cpp:105] Iteration 1265, lr = 0.001
I0401 14:08:05.339927 29493 solver.cpp:218] Iteration 1276 (2.08198 iter/s, 5.28342s/11 iters), loss = 5.0486
I0401 14:08:05.339970 29493 solver.cpp:237]     Train net output #0: loss = 5.0486 (* 1 = 5.0486 loss)
I0401 14:08:05.339977 29493 sgd_solver.cpp:105] Iteration 1276, lr = 0.001
I0401 14:08:10.732740 29493 solver.cpp:218] Iteration 1287 (2.03977 iter/s, 5.39276s/11 iters), loss = 5.10282
I0401 14:08:10.732780 29493 solver.cpp:237]     Train net output #0: loss = 5.10282 (* 1 = 5.10282 loss)
I0401 14:08:10.732787 29493 sgd_solver.cpp:105] Iteration 1287, lr = 0.001
I0401 14:08:15.922104 29493 solver.cpp:218] Iteration 1298 (2.11974 iter/s, 5.18931s/11 iters), loss = 5.06008
I0401 14:08:15.922273 29493 solver.cpp:237]     Train net output #0: loss = 5.06008 (* 1 = 5.06008 loss)
I0401 14:08:15.922283 29493 sgd_solver.cpp:105] Iteration 1298, lr = 0.001
I0401 14:08:21.146440 29493 solver.cpp:218] Iteration 1309 (2.1056 iter/s, 5.22415s/11 iters), loss = 5.07552
I0401 14:08:21.146493 29493 solver.cpp:237]     Train net output #0: loss = 5.07552 (* 1 = 5.07552 loss)
I0401 14:08:21.146500 29493 sgd_solver.cpp:105] Iteration 1309, lr = 0.001
I0401 14:08:26.180946 29493 solver.cpp:218] Iteration 1320 (2.18618 iter/s, 5.03161s/11 iters), loss = 4.98732
I0401 14:08:26.181006 29493 solver.cpp:237]     Train net output #0: loss = 4.98732 (* 1 = 4.98732 loss)
I0401 14:08:26.181016 29493 sgd_solver.cpp:105] Iteration 1320, lr = 0.001
I0401 14:08:29.422884 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:08:31.562290 29493 solver.cpp:218] Iteration 1331 (2.04413 iter/s, 5.38127s/11 iters), loss = 4.98849
I0401 14:08:31.562376 29493 solver.cpp:237]     Train net output #0: loss = 4.98849 (* 1 = 4.98849 loss)
I0401 14:08:31.562386 29493 sgd_solver.cpp:105] Iteration 1331, lr = 0.001
I0401 14:08:33.052434 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1335.caffemodel
I0401 14:08:36.108178 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1335.solverstate
I0401 14:08:38.450503 29493 solver.cpp:330] Iteration 1335, Testing net (#0)
I0401 14:08:38.450529 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:08:45.545019 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:08:45.783725 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0226151
I0401 14:08:45.783753 29493 solver.cpp:397]     Test net output #1: loss = 5.05105 (* 1 = 5.05105 loss)
I0401 14:08:48.044144 29493 solver.cpp:218] Iteration 1342 (0.667404 iter/s, 16.4818s/11 iters), loss = 4.95926
I0401 14:08:48.044258 29493 solver.cpp:237]     Train net output #0: loss = 4.95926 (* 1 = 4.95926 loss)
I0401 14:08:48.044267 29493 sgd_solver.cpp:105] Iteration 1342, lr = 0.001
I0401 14:08:52.825583 29493 solver.cpp:218] Iteration 1353 (2.30062 iter/s, 4.78131s/11 iters), loss = 5.06644
I0401 14:08:52.825631 29493 solver.cpp:237]     Train net output #0: loss = 5.06644 (* 1 = 5.06644 loss)
I0401 14:08:52.825639 29493 sgd_solver.cpp:105] Iteration 1353, lr = 0.001
I0401 14:08:57.834131 29493 solver.cpp:218] Iteration 1364 (2.19627 iter/s, 5.00848s/11 iters), loss = 5.05999
I0401 14:08:57.834187 29493 solver.cpp:237]     Train net output #0: loss = 5.05999 (* 1 = 5.05999 loss)
I0401 14:08:57.834194 29493 sgd_solver.cpp:105] Iteration 1364, lr = 0.001
I0401 14:09:03.224972 29493 solver.cpp:218] Iteration 1375 (2.04052 iter/s, 5.39077s/11 iters), loss = 5.01506
I0401 14:09:03.225021 29493 solver.cpp:237]     Train net output #0: loss = 5.01506 (* 1 = 5.01506 loss)
I0401 14:09:03.225028 29493 sgd_solver.cpp:105] Iteration 1375, lr = 0.001
I0401 14:09:08.485932 29493 solver.cpp:218] Iteration 1386 (2.0909 iter/s, 5.2609s/11 iters), loss = 5.04306
I0401 14:09:08.485987 29493 solver.cpp:237]     Train net output #0: loss = 5.04306 (* 1 = 5.04306 loss)
I0401 14:09:08.485996 29493 sgd_solver.cpp:105] Iteration 1386, lr = 0.001
I0401 14:09:13.665035 29493 solver.cpp:218] Iteration 1397 (2.12395 iter/s, 5.17904s/11 iters), loss = 5.07082
I0401 14:09:13.665091 29493 solver.cpp:237]     Train net output #0: loss = 5.07082 (* 1 = 5.07082 loss)
I0401 14:09:13.665099 29493 sgd_solver.cpp:105] Iteration 1397, lr = 0.001
I0401 14:09:18.983569 29493 solver.cpp:218] Iteration 1408 (2.06827 iter/s, 5.31846s/11 iters), loss = 5.00437
I0401 14:09:18.983748 29493 solver.cpp:237]     Train net output #0: loss = 5.00437 (* 1 = 5.00437 loss)
I0401 14:09:18.983757 29493 sgd_solver.cpp:105] Iteration 1408, lr = 0.001
I0401 14:09:22.319056 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:09:24.122965 29493 solver.cpp:218] Iteration 1419 (2.14041 iter/s, 5.1392s/11 iters), loss = 5.10658
I0401 14:09:24.123018 29493 solver.cpp:237]     Train net output #0: loss = 5.10658 (* 1 = 5.10658 loss)
I0401 14:09:24.123028 29493 sgd_solver.cpp:105] Iteration 1419, lr = 0.001
I0401 14:09:25.978089 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1424.caffemodel
I0401 14:09:29.208802 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1424.solverstate
I0401 14:09:31.554133 29493 solver.cpp:330] Iteration 1424, Testing net (#0)
I0401 14:09:31.554153 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:09:38.809944 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:09:39.126271 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0222039
I0401 14:09:39.126307 29493 solver.cpp:397]     Test net output #1: loss = 5.03889 (* 1 = 5.03889 loss)
I0401 14:09:40.974136 29493 solver.cpp:218] Iteration 1430 (0.652776 iter/s, 16.8511s/11 iters), loss = 5.02498
I0401 14:09:40.974181 29493 solver.cpp:237]     Train net output #0: loss = 5.02498 (* 1 = 5.02498 loss)
I0401 14:09:40.974187 29493 sgd_solver.cpp:105] Iteration 1430, lr = 0.001
I0401 14:09:46.206876 29493 solver.cpp:218] Iteration 1441 (2.10217 iter/s, 5.23268s/11 iters), loss = 5.14118
I0401 14:09:46.206929 29493 solver.cpp:237]     Train net output #0: loss = 5.14118 (* 1 = 5.14118 loss)
I0401 14:09:46.206938 29493 sgd_solver.cpp:105] Iteration 1441, lr = 0.001
I0401 14:09:51.176549 29493 solver.cpp:218] Iteration 1452 (2.21345 iter/s, 4.96961s/11 iters), loss = 4.96869
I0401 14:09:51.176667 29493 solver.cpp:237]     Train net output #0: loss = 4.96869 (* 1 = 4.96869 loss)
I0401 14:09:51.176676 29493 sgd_solver.cpp:105] Iteration 1452, lr = 0.001
I0401 14:09:56.304540 29493 solver.cpp:218] Iteration 1463 (2.14514 iter/s, 5.12786s/11 iters), loss = 4.93285
I0401 14:09:56.304594 29493 solver.cpp:237]     Train net output #0: loss = 4.93285 (* 1 = 4.93285 loss)
I0401 14:09:56.304601 29493 sgd_solver.cpp:105] Iteration 1463, lr = 0.001
I0401 14:10:01.666066 29493 solver.cpp:218] Iteration 1474 (2.05168 iter/s, 5.36145s/11 iters), loss = 5.01338
I0401 14:10:01.666115 29493 solver.cpp:237]     Train net output #0: loss = 5.01338 (* 1 = 5.01338 loss)
I0401 14:10:01.666121 29493 sgd_solver.cpp:105] Iteration 1474, lr = 0.001
I0401 14:10:06.739547 29493 solver.cpp:218] Iteration 1485 (2.16817 iter/s, 5.07341s/11 iters), loss = 5.0979
I0401 14:10:06.739594 29493 solver.cpp:237]     Train net output #0: loss = 5.0979 (* 1 = 5.0979 loss)
I0401 14:10:06.739600 29493 sgd_solver.cpp:105] Iteration 1485, lr = 0.001
I0401 14:10:12.055146 29493 solver.cpp:218] Iteration 1496 (2.0694 iter/s, 5.31554s/11 iters), loss = 4.9433
I0401 14:10:12.055183 29493 solver.cpp:237]     Train net output #0: loss = 4.9433 (* 1 = 4.9433 loss)
I0401 14:10:12.055189 29493 sgd_solver.cpp:105] Iteration 1496, lr = 0.001
I0401 14:10:15.874372 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:10:17.780084 29493 solver.cpp:218] Iteration 1507 (1.92144 iter/s, 5.72488s/11 iters), loss = 4.98209
I0401 14:10:17.780143 29493 solver.cpp:237]     Train net output #0: loss = 4.98209 (* 1 = 4.98209 loss)
I0401 14:10:17.780153 29493 sgd_solver.cpp:105] Iteration 1507, lr = 0.001
I0401 14:10:19.818974 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1513.caffemodel
I0401 14:10:22.957996 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1513.solverstate
I0401 14:10:25.282801 29493 solver.cpp:330] Iteration 1513, Testing net (#0)
I0401 14:10:25.282820 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:10:32.412784 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:10:32.671454 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0230263
I0401 14:10:32.671494 29493 solver.cpp:397]     Test net output #1: loss = 5.02739 (* 1 = 5.02739 loss)
I0401 14:10:34.358211 29493 solver.cpp:218] Iteration 1518 (0.663527 iter/s, 16.5781s/11 iters), loss = 5.00227
I0401 14:10:34.358266 29493 solver.cpp:237]     Train net output #0: loss = 5.00227 (* 1 = 5.00227 loss)
I0401 14:10:34.358274 29493 sgd_solver.cpp:105] Iteration 1518, lr = 0.001
I0401 14:10:39.471401 29493 solver.cpp:218] Iteration 1529 (2.15133 iter/s, 5.11312s/11 iters), loss = 5.04885
I0401 14:10:39.471447 29493 solver.cpp:237]     Train net output #0: loss = 5.04885 (* 1 = 5.04885 loss)
I0401 14:10:39.471453 29493 sgd_solver.cpp:105] Iteration 1529, lr = 0.001
I0401 14:10:44.493151 29493 solver.cpp:218] Iteration 1540 (2.1905 iter/s, 5.02169s/11 iters), loss = 4.98871
I0401 14:10:44.493202 29493 solver.cpp:237]     Train net output #0: loss = 4.98871 (* 1 = 4.98871 loss)
I0401 14:10:44.493211 29493 sgd_solver.cpp:105] Iteration 1540, lr = 0.001
I0401 14:10:49.789558 29493 solver.cpp:218] Iteration 1551 (2.07691 iter/s, 5.29634s/11 iters), loss = 4.96815
I0401 14:10:49.789613 29493 solver.cpp:237]     Train net output #0: loss = 4.96815 (* 1 = 4.96815 loss)
I0401 14:10:49.789621 29493 sgd_solver.cpp:105] Iteration 1551, lr = 0.001
I0401 14:10:55.048130 29493 solver.cpp:218] Iteration 1562 (2.09185 iter/s, 5.2585s/11 iters), loss = 4.99215
I0401 14:10:55.048244 29493 solver.cpp:237]     Train net output #0: loss = 4.99215 (* 1 = 4.99215 loss)
I0401 14:10:55.048254 29493 sgd_solver.cpp:105] Iteration 1562, lr = 0.001
I0401 14:11:00.017160 29493 solver.cpp:218] Iteration 1573 (2.21377 iter/s, 4.9689s/11 iters), loss = 4.95065
I0401 14:11:00.017220 29493 solver.cpp:237]     Train net output #0: loss = 4.95065 (* 1 = 4.95065 loss)
I0401 14:11:00.017227 29493 sgd_solver.cpp:105] Iteration 1573, lr = 0.001
I0401 14:11:05.253921 29493 solver.cpp:218] Iteration 1584 (2.10056 iter/s, 5.23669s/11 iters), loss = 4.97861
I0401 14:11:05.253968 29493 solver.cpp:237]     Train net output #0: loss = 4.97861 (* 1 = 4.97861 loss)
I0401 14:11:05.253976 29493 sgd_solver.cpp:105] Iteration 1584, lr = 0.001
I0401 14:11:09.206823 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:11:10.705543 29493 solver.cpp:218] Iteration 1595 (2.01777 iter/s, 5.45156s/11 iters), loss = 4.91024
I0401 14:11:10.711755 29493 solver.cpp:237]     Train net output #0: loss = 4.91024 (* 1 = 4.91024 loss)
I0401 14:11:10.711771 29493 sgd_solver.cpp:105] Iteration 1595, lr = 0.001
I0401 14:11:13.322163 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1602.caffemodel
I0401 14:11:18.000226 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1602.solverstate
I0401 14:11:20.378680 29493 solver.cpp:330] Iteration 1602, Testing net (#0)
I0401 14:11:20.378705 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:11:27.754380 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:11:28.066336 29493 solver.cpp:397]     Test net output #0: accuracy = 0.026727
I0401 14:11:28.066380 29493 solver.cpp:397]     Test net output #1: loss = 5.01485 (* 1 = 5.01485 loss)
I0401 14:11:29.178865 29493 solver.cpp:218] Iteration 1606 (0.595653 iter/s, 18.4671s/11 iters), loss = 4.91427
I0401 14:11:29.178923 29493 solver.cpp:237]     Train net output #0: loss = 4.91427 (* 1 = 4.91427 loss)
I0401 14:11:29.178931 29493 sgd_solver.cpp:105] Iteration 1606, lr = 0.001
I0401 14:11:34.111972 29493 solver.cpp:218] Iteration 1617 (2.22986 iter/s, 4.93304s/11 iters), loss = 4.94334
I0401 14:11:34.112017 29493 solver.cpp:237]     Train net output #0: loss = 4.94334 (* 1 = 4.94334 loss)
I0401 14:11:34.112023 29493 sgd_solver.cpp:105] Iteration 1617, lr = 0.001
I0401 14:11:39.290711 29493 solver.cpp:218] Iteration 1628 (2.12409 iter/s, 5.17868s/11 iters), loss = 5.02629
I0401 14:11:39.290772 29493 solver.cpp:237]     Train net output #0: loss = 5.02629 (* 1 = 5.02629 loss)
I0401 14:11:39.290781 29493 sgd_solver.cpp:105] Iteration 1628, lr = 0.001
I0401 14:11:44.416548 29493 solver.cpp:218] Iteration 1639 (2.14602 iter/s, 5.12576s/11 iters), loss = 4.94752
I0401 14:11:44.416599 29493 solver.cpp:237]     Train net output #0: loss = 4.94752 (* 1 = 4.94752 loss)
I0401 14:11:44.416605 29493 sgd_solver.cpp:105] Iteration 1639, lr = 0.001
I0401 14:11:49.800009 29493 solver.cpp:218] Iteration 1650 (2.04332 iter/s, 5.3834s/11 iters), loss = 4.9148
I0401 14:11:49.800056 29493 solver.cpp:237]     Train net output #0: loss = 4.9148 (* 1 = 4.9148 loss)
I0401 14:11:49.800062 29493 sgd_solver.cpp:105] Iteration 1650, lr = 0.001
I0401 14:11:54.935681 29493 solver.cpp:218] Iteration 1661 (2.14191 iter/s, 5.13561s/11 iters), loss = 4.96391
I0401 14:11:54.935730 29493 solver.cpp:237]     Train net output #0: loss = 4.96391 (* 1 = 4.96391 loss)
I0401 14:11:54.935739 29493 sgd_solver.cpp:105] Iteration 1661, lr = 0.001
I0401 14:11:59.690656 29493 blocking_queue.cpp:49] Waiting for data
I0401 14:12:00.170147 29493 solver.cpp:218] Iteration 1672 (2.10148 iter/s, 5.2344s/11 iters), loss = 5.01777
I0401 14:12:00.170208 29493 solver.cpp:237]     Train net output #0: loss = 5.01777 (* 1 = 5.01777 loss)
I0401 14:12:00.170219 29493 sgd_solver.cpp:105] Iteration 1672, lr = 0.001
I0401 14:12:04.397411 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:12:05.630188 29493 solver.cpp:218] Iteration 1683 (2.01467 iter/s, 5.45996s/11 iters), loss = 4.98334
I0401 14:12:05.630256 29493 solver.cpp:237]     Train net output #0: loss = 4.98334 (* 1 = 4.98334 loss)
I0401 14:12:05.630265 29493 sgd_solver.cpp:105] Iteration 1683, lr = 0.001
I0401 14:12:08.743533 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1691.caffemodel
I0401 14:12:13.355022 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1691.solverstate
I0401 14:12:17.020781 29493 solver.cpp:330] Iteration 1691, Testing net (#0)
I0401 14:12:17.020804 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:12:24.106721 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:12:24.397615 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0271382
I0401 14:12:24.397651 29493 solver.cpp:397]     Test net output #1: loss = 5.00012 (* 1 = 5.00012 loss)
I0401 14:12:25.111559 29493 solver.cpp:218] Iteration 1694 (0.564644 iter/s, 19.4813s/11 iters), loss = 4.92299
I0401 14:12:25.111606 29493 solver.cpp:237]     Train net output #0: loss = 4.92299 (* 1 = 4.92299 loss)
I0401 14:12:25.111611 29493 sgd_solver.cpp:105] Iteration 1694, lr = 0.001
I0401 14:12:30.303716 29493 solver.cpp:218] Iteration 1705 (2.11861 iter/s, 5.19209s/11 iters), loss = 4.88767
I0401 14:12:30.303840 29493 solver.cpp:237]     Train net output #0: loss = 4.88767 (* 1 = 4.88767 loss)
I0401 14:12:30.303849 29493 sgd_solver.cpp:105] Iteration 1705, lr = 0.001
I0401 14:12:35.403970 29493 solver.cpp:218] Iteration 1716 (2.15681 iter/s, 5.10012s/11 iters), loss = 4.97259
I0401 14:12:35.404021 29493 solver.cpp:237]     Train net output #0: loss = 4.97259 (* 1 = 4.97259 loss)
I0401 14:12:35.404028 29493 sgd_solver.cpp:105] Iteration 1716, lr = 0.001
I0401 14:12:40.869901 29493 solver.cpp:218] Iteration 1727 (2.01249 iter/s, 5.46586s/11 iters), loss = 4.97703
I0401 14:12:40.869963 29493 solver.cpp:237]     Train net output #0: loss = 4.97703 (* 1 = 4.97703 loss)
I0401 14:12:40.869971 29493 sgd_solver.cpp:105] Iteration 1727, lr = 0.001
I0401 14:12:45.776680 29493 solver.cpp:218] Iteration 1738 (2.24183 iter/s, 4.9067s/11 iters), loss = 4.89621
I0401 14:12:45.776731 29493 solver.cpp:237]     Train net output #0: loss = 4.89621 (* 1 = 4.89621 loss)
I0401 14:12:45.776737 29493 sgd_solver.cpp:105] Iteration 1738, lr = 0.001
I0401 14:12:51.199697 29493 solver.cpp:218] Iteration 1749 (2.02842 iter/s, 5.42295s/11 iters), loss = 5.00492
I0401 14:12:51.199750 29493 solver.cpp:237]     Train net output #0: loss = 5.00492 (* 1 = 5.00492 loss)
I0401 14:12:51.199757 29493 sgd_solver.cpp:105] Iteration 1749, lr = 0.001
I0401 14:12:56.498422 29493 solver.cpp:218] Iteration 1760 (2.076 iter/s, 5.29866s/11 iters), loss = 4.87445
I0401 14:12:56.498474 29493 solver.cpp:237]     Train net output #0: loss = 4.87445 (* 1 = 4.87445 loss)
I0401 14:12:56.498482 29493 sgd_solver.cpp:105] Iteration 1760, lr = 0.001
I0401 14:13:00.734014 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:13:01.648494 29493 solver.cpp:218] Iteration 1771 (2.13592 iter/s, 5.15s/11 iters), loss = 5.02101
I0401 14:13:01.648550 29493 solver.cpp:237]     Train net output #0: loss = 5.02101 (* 1 = 5.02101 loss)
I0401 14:13:01.648558 29493 sgd_solver.cpp:105] Iteration 1771, lr = 0.001
I0401 14:13:05.307094 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1780.caffemodel
I0401 14:13:09.577633 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1780.solverstate
I0401 14:13:15.396669 29493 solver.cpp:330] Iteration 1780, Testing net (#0)
I0401 14:13:15.396692 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:13:22.500910 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:13:22.885927 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0279605
I0401 14:13:22.885957 29493 solver.cpp:397]     Test net output #1: loss = 4.98784 (* 1 = 4.98784 loss)
I0401 14:13:23.287465 29493 solver.cpp:218] Iteration 1782 (0.508343 iter/s, 21.6389s/11 iters), loss = 4.97858
I0401 14:13:23.287506 29493 solver.cpp:237]     Train net output #0: loss = 4.97858 (* 1 = 4.97858 loss)
I0401 14:13:23.287511 29493 sgd_solver.cpp:105] Iteration 1782, lr = 0.001
I0401 14:13:28.020004 29493 solver.cpp:218] Iteration 1793 (2.32436 iter/s, 4.73248s/11 iters), loss = 4.84268
I0401 14:13:28.020054 29493 solver.cpp:237]     Train net output #0: loss = 4.84268 (* 1 = 4.84268 loss)
I0401 14:13:28.020061 29493 sgd_solver.cpp:105] Iteration 1793, lr = 0.001
I0401 14:13:33.525002 29493 solver.cpp:218] Iteration 1804 (1.99821 iter/s, 5.50493s/11 iters), loss = 5.03273
I0401 14:13:33.531200 29493 solver.cpp:237]     Train net output #0: loss = 5.03273 (* 1 = 5.03273 loss)
I0401 14:13:33.531219 29493 sgd_solver.cpp:105] Iteration 1804, lr = 0.001
I0401 14:13:39.028704 29493 solver.cpp:218] Iteration 1815 (2.00091 iter/s, 5.49751s/11 iters), loss = 4.89536
I0401 14:13:39.028761 29493 solver.cpp:237]     Train net output #0: loss = 4.89536 (* 1 = 4.89536 loss)
I0401 14:13:39.028770 29493 sgd_solver.cpp:105] Iteration 1815, lr = 0.001
I0401 14:13:44.010970 29493 solver.cpp:218] Iteration 1826 (2.20786 iter/s, 4.9822s/11 iters), loss = 4.9101
I0401 14:13:44.011006 29493 solver.cpp:237]     Train net output #0: loss = 4.9101 (* 1 = 4.9101 loss)
I0401 14:13:44.011011 29493 sgd_solver.cpp:105] Iteration 1826, lr = 0.001
I0401 14:13:49.000321 29493 solver.cpp:218] Iteration 1837 (2.20472 iter/s, 4.9893s/11 iters), loss = 4.87133
I0401 14:13:49.000378 29493 solver.cpp:237]     Train net output #0: loss = 4.87133 (* 1 = 4.87133 loss)
I0401 14:13:49.000386 29493 sgd_solver.cpp:105] Iteration 1837, lr = 0.001
I0401 14:13:54.038805 29493 solver.cpp:218] Iteration 1848 (2.18323 iter/s, 5.03841s/11 iters), loss = 4.87172
I0401 14:13:54.038862 29493 solver.cpp:237]     Train net output #0: loss = 4.87172 (* 1 = 4.87172 loss)
I0401 14:13:54.038870 29493 sgd_solver.cpp:105] Iteration 1848, lr = 0.001
I0401 14:13:58.584564 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:13:59.430629 29493 solver.cpp:218] Iteration 1859 (2.04015 iter/s, 5.39176s/11 iters), loss = 4.8984
I0401 14:13:59.430673 29493 solver.cpp:237]     Train net output #0: loss = 4.8984 (* 1 = 4.8984 loss)
I0401 14:13:59.430681 29493 sgd_solver.cpp:105] Iteration 1859, lr = 0.001
I0401 14:14:03.577131 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1869.caffemodel
I0401 14:14:06.736399 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1869.solverstate
I0401 14:14:09.103343 29493 solver.cpp:330] Iteration 1869, Testing net (#0)
I0401 14:14:09.103368 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:14:16.119930 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:14:16.467068 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0271382
I0401 14:14:16.467105 29493 solver.cpp:397]     Test net output #1: loss = 4.97132 (* 1 = 4.97132 loss)
I0401 14:14:16.755554 29493 solver.cpp:218] Iteration 1870 (0.634925 iter/s, 17.3249s/11 iters), loss = 4.93904
I0401 14:14:16.755607 29493 solver.cpp:237]     Train net output #0: loss = 4.93904 (* 1 = 4.93904 loss)
I0401 14:14:16.755615 29493 sgd_solver.cpp:105] Iteration 1870, lr = 0.001
I0401 14:14:21.352921 29493 solver.cpp:218] Iteration 1881 (2.39271 iter/s, 4.5973s/11 iters), loss = 4.87295
I0401 14:14:21.352967 29493 solver.cpp:237]     Train net output #0: loss = 4.87295 (* 1 = 4.87295 loss)
I0401 14:14:21.352973 29493 sgd_solver.cpp:105] Iteration 1881, lr = 0.001
I0401 14:14:26.245568 29493 solver.cpp:218] Iteration 1892 (2.2483 iter/s, 4.89258s/11 iters), loss = 4.8815
I0401 14:14:26.245636 29493 solver.cpp:237]     Train net output #0: loss = 4.8815 (* 1 = 4.8815 loss)
I0401 14:14:26.245646 29493 sgd_solver.cpp:105] Iteration 1892, lr = 0.001
I0401 14:14:31.642452 29493 solver.cpp:218] Iteration 1903 (2.03824 iter/s, 5.3968s/11 iters), loss = 4.89034
I0401 14:14:31.642501 29493 solver.cpp:237]     Train net output #0: loss = 4.89034 (* 1 = 4.89034 loss)
I0401 14:14:31.642508 29493 sgd_solver.cpp:105] Iteration 1903, lr = 0.001
I0401 14:14:36.723021 29493 solver.cpp:218] Iteration 1914 (2.16514 iter/s, 5.0805s/11 iters), loss = 4.94084
I0401 14:14:36.723145 29493 solver.cpp:237]     Train net output #0: loss = 4.94084 (* 1 = 4.94084 loss)
I0401 14:14:36.723153 29493 sgd_solver.cpp:105] Iteration 1914, lr = 0.001
I0401 14:14:42.119738 29493 solver.cpp:218] Iteration 1925 (2.03833 iter/s, 5.39659s/11 iters), loss = 4.80229
I0401 14:14:42.119783 29493 solver.cpp:237]     Train net output #0: loss = 4.80229 (* 1 = 4.80229 loss)
I0401 14:14:42.119788 29493 sgd_solver.cpp:105] Iteration 1925, lr = 0.001
I0401 14:14:47.216529 29493 solver.cpp:218] Iteration 1936 (2.15824 iter/s, 5.09673s/11 iters), loss = 4.86003
I0401 14:14:47.216572 29493 solver.cpp:237]     Train net output #0: loss = 4.86003 (* 1 = 4.86003 loss)
I0401 14:14:47.216578 29493 sgd_solver.cpp:105] Iteration 1936, lr = 0.001
I0401 14:14:52.060402 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:14:52.447470 29493 solver.cpp:218] Iteration 1947 (2.1029 iter/s, 5.23088s/11 iters), loss = 4.84347
I0401 14:14:52.447525 29493 solver.cpp:237]     Train net output #0: loss = 4.84347 (* 1 = 4.84347 loss)
I0401 14:14:52.447535 29493 sgd_solver.cpp:105] Iteration 1947, lr = 0.001
I0401 14:14:57.132400 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1958.caffemodel
I0401 14:15:00.267802 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1958.solverstate
I0401 14:15:02.573341 29493 solver.cpp:330] Iteration 1958, Testing net (#0)
I0401 14:15:02.573364 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:15:09.778334 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:15:10.147948 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0320724
I0401 14:15:10.147986 29493 solver.cpp:397]     Test net output #1: loss = 4.95313 (* 1 = 4.95313 loss)
I0401 14:15:10.291302 29493 solver.cpp:218] Iteration 1958 (0.616462 iter/s, 17.8438s/11 iters), loss = 4.75576
I0401 14:15:10.291358 29493 solver.cpp:237]     Train net output #0: loss = 4.75576 (* 1 = 4.75576 loss)
I0401 14:15:10.291366 29493 sgd_solver.cpp:105] Iteration 1958, lr = 0.001
I0401 14:15:14.575747 29493 solver.cpp:218] Iteration 1969 (2.56747 iter/s, 4.28438s/11 iters), loss = 4.89886
I0401 14:15:14.575803 29493 solver.cpp:237]     Train net output #0: loss = 4.89886 (* 1 = 4.89886 loss)
I0401 14:15:14.575812 29493 sgd_solver.cpp:105] Iteration 1969, lr = 0.001
I0401 14:15:19.721495 29493 solver.cpp:218] Iteration 1980 (2.13771 iter/s, 5.14568s/11 iters), loss = 4.83819
I0401 14:15:19.721550 29493 solver.cpp:237]     Train net output #0: loss = 4.83819 (* 1 = 4.83819 loss)
I0401 14:15:19.721560 29493 sgd_solver.cpp:105] Iteration 1980, lr = 0.001
I0401 14:15:24.950680 29493 solver.cpp:218] Iteration 1991 (2.1036 iter/s, 5.22912s/11 iters), loss = 4.92609
I0401 14:15:24.950728 29493 solver.cpp:237]     Train net output #0: loss = 4.92609 (* 1 = 4.92609 loss)
I0401 14:15:24.950736 29493 sgd_solver.cpp:105] Iteration 1991, lr = 0.001
I0401 14:15:30.039132 29493 solver.cpp:218] Iteration 2002 (2.16179 iter/s, 5.08839s/11 iters), loss = 4.93893
I0401 14:15:30.039187 29493 solver.cpp:237]     Train net output #0: loss = 4.93893 (* 1 = 4.93893 loss)
I0401 14:15:30.039196 29493 sgd_solver.cpp:105] Iteration 2002, lr = 0.001
I0401 14:15:35.252291 29493 solver.cpp:218] Iteration 2013 (2.11007 iter/s, 5.21309s/11 iters), loss = 4.91369
I0401 14:15:35.252346 29493 solver.cpp:237]     Train net output #0: loss = 4.91369 (* 1 = 4.91369 loss)
I0401 14:15:35.252353 29493 sgd_solver.cpp:105] Iteration 2013, lr = 0.001
I0401 14:15:40.338595 29493 solver.cpp:218] Iteration 2024 (2.1627 iter/s, 5.08624s/11 iters), loss = 4.86212
I0401 14:15:40.338735 29493 solver.cpp:237]     Train net output #0: loss = 4.86212 (* 1 = 4.86212 loss)
I0401 14:15:40.338743 29493 sgd_solver.cpp:105] Iteration 2024, lr = 0.001
I0401 14:15:45.279501 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:15:45.521512 29493 solver.cpp:218] Iteration 2035 (2.12242 iter/s, 5.18277s/11 iters), loss = 4.8433
I0401 14:15:45.527740 29493 solver.cpp:237]     Train net output #0: loss = 4.8433 (* 1 = 4.8433 loss)
I0401 14:15:45.527760 29493 sgd_solver.cpp:105] Iteration 2035, lr = 0.001
I0401 14:15:50.602115 29493 solver.cpp:218] Iteration 2046 (2.16775 iter/s, 5.07438s/11 iters), loss = 4.83564
I0401 14:15:50.602164 29493 solver.cpp:237]     Train net output #0: loss = 4.83564 (* 1 = 4.83564 loss)
I0401 14:15:50.602171 29493 sgd_solver.cpp:105] Iteration 2046, lr = 0.001
I0401 14:15:50.602351 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2047.caffemodel
I0401 14:15:53.592814 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2047.solverstate
I0401 14:15:56.285627 29493 solver.cpp:330] Iteration 2047, Testing net (#0)
I0401 14:15:56.285645 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:16:03.610795 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:16:04.012154 29493 solver.cpp:397]     Test net output #0: accuracy = 0.035773
I0401 14:16:04.012192 29493 solver.cpp:397]     Test net output #1: loss = 4.93088 (* 1 = 4.93088 loss)
I0401 14:16:07.626533 29493 solver.cpp:218] Iteration 2057 (0.646133 iter/s, 17.0244s/11 iters), loss = 4.94847
I0401 14:16:07.626588 29493 solver.cpp:237]     Train net output #0: loss = 4.94847 (* 1 = 4.94847 loss)
I0401 14:16:07.626596 29493 sgd_solver.cpp:105] Iteration 2057, lr = 0.001
I0401 14:16:12.450307 29493 solver.cpp:218] Iteration 2068 (2.28041 iter/s, 4.8237s/11 iters), loss = 4.81544
I0401 14:16:12.450449 29493 solver.cpp:237]     Train net output #0: loss = 4.81544 (* 1 = 4.81544 loss)
I0401 14:16:12.450461 29493 sgd_solver.cpp:105] Iteration 2068, lr = 0.001
I0401 14:16:17.814486 29493 solver.cpp:218] Iteration 2079 (2.0507 iter/s, 5.36403s/11 iters), loss = 4.88549
I0401 14:16:17.814543 29493 solver.cpp:237]     Train net output #0: loss = 4.88549 (* 1 = 4.88549 loss)
I0401 14:16:17.814553 29493 sgd_solver.cpp:105] Iteration 2079, lr = 0.001
I0401 14:16:22.962352 29493 solver.cpp:218] Iteration 2090 (2.13684 iter/s, 5.14779s/11 iters), loss = 4.84122
I0401 14:16:22.962421 29493 solver.cpp:237]     Train net output #0: loss = 4.84122 (* 1 = 4.84122 loss)
I0401 14:16:22.962432 29493 sgd_solver.cpp:105] Iteration 2090, lr = 0.001
I0401 14:16:28.203413 29493 solver.cpp:218] Iteration 2101 (2.09885 iter/s, 5.24098s/11 iters), loss = 4.92793
I0401 14:16:28.203467 29493 solver.cpp:237]     Train net output #0: loss = 4.92793 (* 1 = 4.92793 loss)
I0401 14:16:28.203475 29493 sgd_solver.cpp:105] Iteration 2101, lr = 0.001
I0401 14:16:33.297698 29493 solver.cpp:218] Iteration 2112 (2.15931 iter/s, 5.09422s/11 iters), loss = 4.94043
I0401 14:16:33.297751 29493 solver.cpp:237]     Train net output #0: loss = 4.94043 (* 1 = 4.94043 loss)
I0401 14:16:33.297760 29493 sgd_solver.cpp:105] Iteration 2112, lr = 0.001
I0401 14:16:38.490046 29493 solver.cpp:218] Iteration 2123 (2.11853 iter/s, 5.19228s/11 iters), loss = 4.86942
I0401 14:16:38.490089 29493 solver.cpp:237]     Train net output #0: loss = 4.86942 (* 1 = 4.86942 loss)
I0401 14:16:38.490094 29493 sgd_solver.cpp:105] Iteration 2123, lr = 0.001
I0401 14:16:38.572782 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:16:43.901041 29493 solver.cpp:218] Iteration 2134 (2.03292 iter/s, 5.41094s/11 iters), loss = 4.88313
I0401 14:16:43.901182 29493 solver.cpp:237]     Train net output #0: loss = 4.88313 (* 1 = 4.88313 loss)
I0401 14:16:43.901190 29493 sgd_solver.cpp:105] Iteration 2134, lr = 0.001
I0401 14:16:44.257184 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2136.caffemodel
I0401 14:16:47.434890 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2136.solverstate
I0401 14:16:49.795670 29493 solver.cpp:330] Iteration 2136, Testing net (#0)
I0401 14:16:49.795689 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:16:56.871450 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:16:57.310729 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0386513
I0401 14:16:57.310768 29493 solver.cpp:397]     Test net output #1: loss = 4.90931 (* 1 = 4.90931 loss)
I0401 14:17:00.626356 29493 solver.cpp:218] Iteration 2145 (0.657691 iter/s, 16.7252s/11 iters), loss = 4.95129
I0401 14:17:00.626396 29493 solver.cpp:237]     Train net output #0: loss = 4.95129 (* 1 = 4.95129 loss)
I0401 14:17:00.626402 29493 sgd_solver.cpp:105] Iteration 2145, lr = 0.001
I0401 14:17:05.859598 29493 solver.cpp:218] Iteration 2156 (2.10197 iter/s, 5.23318s/11 iters), loss = 4.88895
I0401 14:17:05.859654 29493 solver.cpp:237]     Train net output #0: loss = 4.88895 (* 1 = 4.88895 loss)
I0401 14:17:05.859663 29493 sgd_solver.cpp:105] Iteration 2156, lr = 0.001
I0401 14:17:10.894294 29493 solver.cpp:218] Iteration 2167 (2.18487 iter/s, 5.03463s/11 iters), loss = 4.82853
I0401 14:17:10.894340 29493 solver.cpp:237]     Train net output #0: loss = 4.82853 (* 1 = 4.82853 loss)
I0401 14:17:10.894346 29493 sgd_solver.cpp:105] Iteration 2167, lr = 0.001
I0401 14:17:15.891058 29493 solver.cpp:218] Iteration 2178 (2.20145 iter/s, 4.9967s/11 iters), loss = 4.84123
I0401 14:17:15.891180 29493 solver.cpp:237]     Train net output #0: loss = 4.84123 (* 1 = 4.84123 loss)
I0401 14:17:15.891186 29493 sgd_solver.cpp:105] Iteration 2178, lr = 0.001
I0401 14:17:21.187224 29493 solver.cpp:218] Iteration 2189 (2.07703 iter/s, 5.29603s/11 iters), loss = 4.8124
I0401 14:17:21.187280 29493 solver.cpp:237]     Train net output #0: loss = 4.8124 (* 1 = 4.8124 loss)
I0401 14:17:21.187289 29493 sgd_solver.cpp:105] Iteration 2189, lr = 0.001
I0401 14:17:26.243036 29493 solver.cpp:218] Iteration 2200 (2.17574 iter/s, 5.05574s/11 iters), loss = 4.80871
I0401 14:17:26.252820 29493 solver.cpp:237]     Train net output #0: loss = 4.80871 (* 1 = 4.80871 loss)
I0401 14:17:26.252841 29493 sgd_solver.cpp:105] Iteration 2200, lr = 0.001
I0401 14:17:31.453595 29493 solver.cpp:218] Iteration 2211 (2.11506 iter/s, 5.20079s/11 iters), loss = 4.79575
I0401 14:17:31.453635 29493 solver.cpp:237]     Train net output #0: loss = 4.79575 (* 1 = 4.79575 loss)
I0401 14:17:31.453640 29493 sgd_solver.cpp:105] Iteration 2211, lr = 0.001
I0401 14:17:31.690785 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:17:36.962617 29493 solver.cpp:218] Iteration 2222 (1.99674 iter/s, 5.50897s/11 iters), loss = 4.8046
I0401 14:17:36.962673 29493 solver.cpp:237]     Train net output #0: loss = 4.8046 (* 1 = 4.8046 loss)
I0401 14:17:36.962682 29493 sgd_solver.cpp:105] Iteration 2222, lr = 0.001
I0401 14:17:37.873409 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2225.caffemodel
I0401 14:17:41.069365 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2225.solverstate
I0401 14:17:43.485831 29493 solver.cpp:330] Iteration 2225, Testing net (#0)
I0401 14:17:43.485852 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:17:46.345618 29493 blocking_queue.cpp:49] Waiting for data
I0401 14:17:50.569298 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:17:51.044716 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0431743
I0401 14:17:51.044754 29493 solver.cpp:397]     Test net output #1: loss = 4.88231 (* 1 = 4.88231 loss)
I0401 14:17:54.058511 29493 solver.cpp:218] Iteration 2233 (0.643432 iter/s, 17.0958s/11 iters), loss = 4.87638
I0401 14:17:54.058565 29493 solver.cpp:237]     Train net output #0: loss = 4.87638 (* 1 = 4.87638 loss)
I0401 14:17:54.058571 29493 sgd_solver.cpp:105] Iteration 2233, lr = 0.001
I0401 14:17:59.093571 29493 solver.cpp:218] Iteration 2244 (2.18471 iter/s, 5.03499s/11 iters), loss = 4.84358
I0401 14:17:59.093619 29493 solver.cpp:237]     Train net output #0: loss = 4.84358 (* 1 = 4.84358 loss)
I0401 14:17:59.093626 29493 sgd_solver.cpp:105] Iteration 2244, lr = 0.001
I0401 14:18:04.345522 29493 solver.cpp:218] Iteration 2255 (2.09448 iter/s, 5.25189s/11 iters), loss = 5.01345
I0401 14:18:04.345579 29493 solver.cpp:237]     Train net output #0: loss = 5.01345 (* 1 = 5.01345 loss)
I0401 14:18:04.345588 29493 sgd_solver.cpp:105] Iteration 2255, lr = 0.001
I0401 14:18:09.593760 29493 solver.cpp:218] Iteration 2266 (2.09597 iter/s, 5.24817s/11 iters), loss = 4.81845
I0401 14:18:09.593808 29493 solver.cpp:237]     Train net output #0: loss = 4.81845 (* 1 = 4.81845 loss)
I0401 14:18:09.593816 29493 sgd_solver.cpp:105] Iteration 2266, lr = 0.001
I0401 14:18:14.951324 29493 solver.cpp:218] Iteration 2277 (2.05319 iter/s, 5.35751s/11 iters), loss = 4.7864
I0401 14:18:14.951366 29493 solver.cpp:237]     Train net output #0: loss = 4.7864 (* 1 = 4.7864 loss)
I0401 14:18:14.951371 29493 sgd_solver.cpp:105] Iteration 2277, lr = 0.001
I0401 14:18:19.962612 29493 solver.cpp:218] Iteration 2288 (2.19507 iter/s, 5.01123s/11 iters), loss = 4.72348
I0401 14:18:19.962735 29493 solver.cpp:237]     Train net output #0: loss = 4.72348 (* 1 = 4.72348 loss)
I0401 14:18:19.962745 29493 sgd_solver.cpp:105] Iteration 2288, lr = 0.001
I0401 14:18:25.202445 29493 solver.cpp:218] Iteration 2299 (2.09936 iter/s, 5.2397s/11 iters), loss = 4.82512
I0401 14:18:25.202488 29493 solver.cpp:237]     Train net output #0: loss = 4.82512 (* 1 = 4.82512 loss)
I0401 14:18:25.202494 29493 sgd_solver.cpp:105] Iteration 2299, lr = 0.001
I0401 14:18:25.824878 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:18:30.783035 29493 solver.cpp:218] Iteration 2310 (1.97114 iter/s, 5.58053s/11 iters), loss = 4.7283
I0401 14:18:30.783090 29493 solver.cpp:237]     Train net output #0: loss = 4.7283 (* 1 = 4.7283 loss)
I0401 14:18:30.783099 29493 sgd_solver.cpp:105] Iteration 2310, lr = 0.001
I0401 14:18:32.078541 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2314.caffemodel
I0401 14:18:35.234628 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2314.solverstate
I0401 14:18:37.622836 29493 solver.cpp:330] Iteration 2314, Testing net (#0)
I0401 14:18:37.622859 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:18:44.636149 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:18:45.050673 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0460526
I0401 14:18:45.050706 29493 solver.cpp:397]     Test net output #1: loss = 4.85618 (* 1 = 4.85618 loss)
I0401 14:18:47.578042 29493 solver.cpp:218] Iteration 2321 (0.654959 iter/s, 16.7949s/11 iters), loss = 4.92386
I0401 14:18:47.578099 29493 solver.cpp:237]     Train net output #0: loss = 4.92386 (* 1 = 4.92386 loss)
I0401 14:18:47.578107 29493 sgd_solver.cpp:105] Iteration 2321, lr = 0.001
I0401 14:18:52.435693 29493 solver.cpp:218] Iteration 2332 (2.2645 iter/s, 4.85758s/11 iters), loss = 4.80264
I0401 14:18:52.435878 29493 solver.cpp:237]     Train net output #0: loss = 4.80264 (* 1 = 4.80264 loss)
I0401 14:18:52.435887 29493 sgd_solver.cpp:105] Iteration 2332, lr = 0.001
I0401 14:18:57.832993 29493 solver.cpp:218] Iteration 2343 (2.03813 iter/s, 5.39711s/11 iters), loss = 4.98362
I0401 14:18:57.833027 29493 solver.cpp:237]     Train net output #0: loss = 4.98362 (* 1 = 4.98362 loss)
I0401 14:18:57.833034 29493 sgd_solver.cpp:105] Iteration 2343, lr = 0.001
I0401 14:19:03.186101 29493 solver.cpp:218] Iteration 2354 (2.0549 iter/s, 5.35306s/11 iters), loss = 4.65908
I0401 14:19:03.186156 29493 solver.cpp:237]     Train net output #0: loss = 4.65908 (* 1 = 4.65908 loss)
I0401 14:19:03.186165 29493 sgd_solver.cpp:105] Iteration 2354, lr = 0.001
I0401 14:19:08.293768 29493 solver.cpp:218] Iteration 2365 (2.15366 iter/s, 5.10759s/11 iters), loss = 4.6911
I0401 14:19:08.293835 29493 solver.cpp:237]     Train net output #0: loss = 4.6911 (* 1 = 4.6911 loss)
I0401 14:19:08.293845 29493 sgd_solver.cpp:105] Iteration 2365, lr = 0.001
I0401 14:19:13.491504 29493 solver.cpp:218] Iteration 2376 (2.11634 iter/s, 5.19765s/11 iters), loss = 4.65986
I0401 14:19:13.491564 29493 solver.cpp:237]     Train net output #0: loss = 4.65986 (* 1 = 4.65986 loss)
I0401 14:19:13.491572 29493 sgd_solver.cpp:105] Iteration 2376, lr = 0.001
I0401 14:19:18.614770 29493 solver.cpp:218] Iteration 2387 (2.1471 iter/s, 5.12319s/11 iters), loss = 4.73559
I0401 14:19:18.614820 29493 solver.cpp:237]     Train net output #0: loss = 4.73559 (* 1 = 4.73559 loss)
I0401 14:19:18.614825 29493 sgd_solver.cpp:105] Iteration 2387, lr = 0.001
I0401 14:19:19.301367 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:19:23.750773 29493 solver.cpp:218] Iteration 2398 (2.14177 iter/s, 5.13594s/11 iters), loss = 4.71114
I0401 14:19:23.750900 29493 solver.cpp:237]     Train net output #0: loss = 4.71114 (* 1 = 4.71114 loss)
I0401 14:19:23.750914 29493 sgd_solver.cpp:105] Iteration 2398, lr = 0.001
I0401 14:19:25.362676 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2403.caffemodel
I0401 14:19:29.560525 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2403.solverstate
I0401 14:19:33.282912 29493 solver.cpp:330] Iteration 2403, Testing net (#0)
I0401 14:19:33.282933 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:19:39.602964 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:19:40.010152 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0481086
I0401 14:19:40.010185 29493 solver.cpp:397]     Test net output #1: loss = 4.82859 (* 1 = 4.82859 loss)
I0401 14:19:41.914042 29493 solver.cpp:218] Iteration 2409 (0.605622 iter/s, 18.1631s/11 iters), loss = 4.70446
I0401 14:19:41.914086 29493 solver.cpp:237]     Train net output #0: loss = 4.70446 (* 1 = 4.70446 loss)
I0401 14:19:41.914093 29493 sgd_solver.cpp:105] Iteration 2409, lr = 0.001
I0401 14:19:46.706068 29493 solver.cpp:218] Iteration 2420 (2.29551 iter/s, 4.79197s/11 iters), loss = 4.65586
I0401 14:19:46.706110 29493 solver.cpp:237]     Train net output #0: loss = 4.65586 (* 1 = 4.65586 loss)
I0401 14:19:46.706115 29493 sgd_solver.cpp:105] Iteration 2420, lr = 0.001
I0401 14:19:51.468544 29493 solver.cpp:218] Iteration 2431 (2.30975 iter/s, 4.76242s/11 iters), loss = 4.72378
I0401 14:19:51.468585 29493 solver.cpp:237]     Train net output #0: loss = 4.72378 (* 1 = 4.72378 loss)
I0401 14:19:51.468590 29493 sgd_solver.cpp:105] Iteration 2431, lr = 0.001
I0401 14:19:56.194177 29493 solver.cpp:218] Iteration 2442 (2.32776 iter/s, 4.72558s/11 iters), loss = 4.6569
I0401 14:19:56.194268 29493 solver.cpp:237]     Train net output #0: loss = 4.6569 (* 1 = 4.6569 loss)
I0401 14:19:56.194275 29493 sgd_solver.cpp:105] Iteration 2442, lr = 0.001
I0401 14:20:00.875686 29493 solver.cpp:218] Iteration 2453 (2.34972 iter/s, 4.68141s/11 iters), loss = 4.79118
I0401 14:20:00.875728 29493 solver.cpp:237]     Train net output #0: loss = 4.79118 (* 1 = 4.79118 loss)
I0401 14:20:00.875735 29493 sgd_solver.cpp:105] Iteration 2453, lr = 0.001
I0401 14:20:06.024430 29493 solver.cpp:218] Iteration 2464 (2.13647 iter/s, 5.14869s/11 iters), loss = 4.76028
I0401 14:20:06.024468 29493 solver.cpp:237]     Train net output #0: loss = 4.76028 (* 1 = 4.76028 loss)
I0401 14:20:06.024474 29493 sgd_solver.cpp:105] Iteration 2464, lr = 0.001
I0401 14:20:11.103091 29493 solver.cpp:218] Iteration 2475 (2.16595 iter/s, 5.0786s/11 iters), loss = 4.7405
I0401 14:20:11.103147 29493 solver.cpp:237]     Train net output #0: loss = 4.7405 (* 1 = 4.7405 loss)
I0401 14:20:11.103157 29493 sgd_solver.cpp:105] Iteration 2475, lr = 0.001
I0401 14:20:12.048434 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:20:16.224591 29493 solver.cpp:218] Iteration 2486 (2.14783 iter/s, 5.12144s/11 iters), loss = 4.64904
I0401 14:20:16.224632 29493 solver.cpp:237]     Train net output #0: loss = 4.64904 (* 1 = 4.64904 loss)
I0401 14:20:16.224637 29493 sgd_solver.cpp:105] Iteration 2486, lr = 0.001
I0401 14:20:18.487136 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2492.caffemodel
I0401 14:20:21.512009 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2492.solverstate
I0401 14:20:23.860940 29493 solver.cpp:330] Iteration 2492, Testing net (#0)
I0401 14:20:23.860962 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:20:30.530557 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:20:30.952152 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0472862
I0401 14:20:30.952188 29493 solver.cpp:397]     Test net output #1: loss = 4.81012 (* 1 = 4.81012 loss)
I0401 14:20:32.434801 29493 solver.cpp:218] Iteration 2497 (0.678586 iter/s, 16.2102s/11 iters), loss = 4.63889
I0401 14:20:32.434855 29493 solver.cpp:237]     Train net output #0: loss = 4.63889 (* 1 = 4.63889 loss)
I0401 14:20:32.434861 29493 sgd_solver.cpp:105] Iteration 2497, lr = 0.001
I0401 14:20:37.280920 29493 solver.cpp:218] Iteration 2508 (2.26989 iter/s, 4.84605s/11 iters), loss = 4.77691
I0401 14:20:37.280963 29493 solver.cpp:237]     Train net output #0: loss = 4.77691 (* 1 = 4.77691 loss)
I0401 14:20:37.280969 29493 sgd_solver.cpp:105] Iteration 2508, lr = 0.001
I0401 14:20:42.286952 29493 solver.cpp:218] Iteration 2519 (2.19738 iter/s, 5.00597s/11 iters), loss = 4.68328
I0401 14:20:42.287009 29493 solver.cpp:237]     Train net output #0: loss = 4.68328 (* 1 = 4.68328 loss)
I0401 14:20:42.287017 29493 sgd_solver.cpp:105] Iteration 2519, lr = 0.001
I0401 14:20:47.228503 29493 solver.cpp:218] Iteration 2530 (2.22605 iter/s, 4.94148s/11 iters), loss = 4.69808
I0401 14:20:47.228554 29493 solver.cpp:237]     Train net output #0: loss = 4.69808 (* 1 = 4.69808 loss)
I0401 14:20:47.228561 29493 sgd_solver.cpp:105] Iteration 2530, lr = 0.001
I0401 14:20:52.115901 29493 solver.cpp:218] Iteration 2541 (2.25072 iter/s, 4.88733s/11 iters), loss = 4.7571
I0401 14:20:52.115957 29493 solver.cpp:237]     Train net output #0: loss = 4.7571 (* 1 = 4.7571 loss)
I0401 14:20:52.115964 29493 sgd_solver.cpp:105] Iteration 2541, lr = 0.001
I0401 14:20:56.992775 29493 solver.cpp:218] Iteration 2552 (2.25558 iter/s, 4.8768s/11 iters), loss = 4.77323
I0401 14:20:56.992837 29493 solver.cpp:237]     Train net output #0: loss = 4.77323 (* 1 = 4.77323 loss)
I0401 14:20:56.992846 29493 sgd_solver.cpp:105] Iteration 2552, lr = 0.001
I0401 14:21:01.966586 29493 solver.cpp:218] Iteration 2563 (2.21162 iter/s, 4.97374s/11 iters), loss = 4.58801
I0401 14:21:01.966706 29493 solver.cpp:237]     Train net output #0: loss = 4.58801 (* 1 = 4.58801 loss)
I0401 14:21:01.966714 29493 sgd_solver.cpp:105] Iteration 2563, lr = 0.001
I0401 14:21:03.087606 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:21:06.989238 29493 solver.cpp:218] Iteration 2574 (2.19013 iter/s, 5.02252s/11 iters), loss = 4.738
I0401 14:21:06.989296 29493 solver.cpp:237]     Train net output #0: loss = 4.738 (* 1 = 4.738 loss)
I0401 14:21:06.989305 29493 sgd_solver.cpp:105] Iteration 2574, lr = 0.001
I0401 14:21:09.605641 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2581.caffemodel
I0401 14:21:12.772511 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2581.solverstate
I0401 14:21:15.121265 29493 solver.cpp:330] Iteration 2581, Testing net (#0)
I0401 14:21:15.121286 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:21:21.363632 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:21:21.840279 29493 solver.cpp:397]     Test net output #0: accuracy = 0.051398
I0401 14:21:21.840318 29493 solver.cpp:397]     Test net output #1: loss = 4.78372 (* 1 = 4.78372 loss)
I0401 14:21:22.806624 29493 solver.cpp:218] Iteration 2585 (0.69544 iter/s, 15.8173s/11 iters), loss = 4.74697
I0401 14:21:22.806677 29493 solver.cpp:237]     Train net output #0: loss = 4.74697 (* 1 = 4.74697 loss)
I0401 14:21:22.806686 29493 sgd_solver.cpp:105] Iteration 2585, lr = 0.001
I0401 14:21:27.779525 29493 solver.cpp:218] Iteration 2596 (2.21202 iter/s, 4.97283s/11 iters), loss = 4.85214
I0401 14:21:27.779578 29493 solver.cpp:237]     Train net output #0: loss = 4.85214 (* 1 = 4.85214 loss)
I0401 14:21:27.779587 29493 sgd_solver.cpp:105] Iteration 2596, lr = 0.001
I0401 14:21:32.718914 29493 solver.cpp:218] Iteration 2607 (2.22702 iter/s, 4.93933s/11 iters), loss = 4.62313
I0401 14:21:32.719055 29493 solver.cpp:237]     Train net output #0: loss = 4.62313 (* 1 = 4.62313 loss)
I0401 14:21:32.719063 29493 sgd_solver.cpp:105] Iteration 2607, lr = 0.001
I0401 14:21:37.520937 29493 solver.cpp:218] Iteration 2618 (2.29077 iter/s, 4.80187s/11 iters), loss = 4.62534
I0401 14:21:37.521004 29493 solver.cpp:237]     Train net output #0: loss = 4.62534 (* 1 = 4.62534 loss)
I0401 14:21:37.521013 29493 sgd_solver.cpp:105] Iteration 2618, lr = 0.001
I0401 14:21:42.466892 29493 solver.cpp:218] Iteration 2629 (2.22408 iter/s, 4.94587s/11 iters), loss = 4.73522
I0401 14:21:42.466945 29493 solver.cpp:237]     Train net output #0: loss = 4.73522 (* 1 = 4.73522 loss)
I0401 14:21:42.466953 29493 sgd_solver.cpp:105] Iteration 2629, lr = 0.001
I0401 14:21:47.387560 29493 solver.cpp:218] Iteration 2640 (2.2355 iter/s, 4.92061s/11 iters), loss = 4.6728
I0401 14:21:47.387611 29493 solver.cpp:237]     Train net output #0: loss = 4.6728 (* 1 = 4.6728 loss)
I0401 14:21:47.387619 29493 sgd_solver.cpp:105] Iteration 2640, lr = 0.001
I0401 14:21:52.431370 29493 solver.cpp:218] Iteration 2651 (2.18092 iter/s, 5.04375s/11 iters), loss = 4.63655
I0401 14:21:52.431411 29493 solver.cpp:237]     Train net output #0: loss = 4.63655 (* 1 = 4.63655 loss)
I0401 14:21:52.431416 29493 sgd_solver.cpp:105] Iteration 2651, lr = 0.001
I0401 14:21:53.775040 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:21:57.321563 29493 solver.cpp:218] Iteration 2662 (2.24943 iter/s, 4.89013s/11 iters), loss = 4.6667
I0401 14:21:57.321612 29493 solver.cpp:237]     Train net output #0: loss = 4.6667 (* 1 = 4.6667 loss)
I0401 14:21:57.321619 29493 sgd_solver.cpp:105] Iteration 2662, lr = 0.001
I0401 14:22:00.291931 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2670.caffemodel
I0401 14:22:03.357511 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2670.solverstate
I0401 14:22:05.671345 29493 solver.cpp:330] Iteration 2670, Testing net (#0)
I0401 14:22:05.671368 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:22:12.233434 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:22:12.665937 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0522204
I0401 14:22:12.665980 29493 solver.cpp:397]     Test net output #1: loss = 4.75612 (* 1 = 4.75612 loss)
I0401 14:22:13.388067 29493 solver.cpp:218] Iteration 2673 (0.684657 iter/s, 16.0664s/11 iters), loss = 4.5561
I0401 14:22:13.388124 29493 solver.cpp:237]     Train net output #0: loss = 4.5561 (* 1 = 4.5561 loss)
I0401 14:22:13.388132 29493 sgd_solver.cpp:105] Iteration 2673, lr = 0.001
I0401 14:22:18.258055 29493 solver.cpp:218] Iteration 2684 (2.25877 iter/s, 4.86991s/11 iters), loss = 4.68272
I0401 14:22:18.258111 29493 solver.cpp:237]     Train net output #0: loss = 4.68272 (* 1 = 4.68272 loss)
I0401 14:22:18.258119 29493 sgd_solver.cpp:105] Iteration 2684, lr = 0.001
I0401 14:22:23.026476 29493 solver.cpp:218] Iteration 2695 (2.30688 iter/s, 4.76835s/11 iters), loss = 4.68785
I0401 14:22:23.026517 29493 solver.cpp:237]     Train net output #0: loss = 4.68785 (* 1 = 4.68785 loss)
I0401 14:22:23.026522 29493 sgd_solver.cpp:105] Iteration 2695, lr = 0.001
I0401 14:22:27.713351 29493 solver.cpp:218] Iteration 2706 (2.34701 iter/s, 4.68682s/11 iters), loss = 4.59884
I0401 14:22:27.713408 29493 solver.cpp:237]     Train net output #0: loss = 4.59884 (* 1 = 4.59884 loss)
I0401 14:22:27.713416 29493 sgd_solver.cpp:105] Iteration 2706, lr = 0.001
I0401 14:22:32.489799 29493 solver.cpp:218] Iteration 2717 (2.303 iter/s, 4.77638s/11 iters), loss = 4.81476
I0401 14:22:32.489840 29493 solver.cpp:237]     Train net output #0: loss = 4.81476 (* 1 = 4.81476 loss)
I0401 14:22:32.489846 29493 sgd_solver.cpp:105] Iteration 2717, lr = 0.001
I0401 14:22:37.331457 29493 solver.cpp:218] Iteration 2728 (2.27198 iter/s, 4.8416s/11 iters), loss = 4.59223
I0401 14:22:37.331593 29493 solver.cpp:237]     Train net output #0: loss = 4.59223 (* 1 = 4.59223 loss)
I0401 14:22:37.331600 29493 sgd_solver.cpp:105] Iteration 2728, lr = 0.001
I0401 14:22:42.239236 29493 solver.cpp:218] Iteration 2739 (2.24141 iter/s, 4.90763s/11 iters), loss = 4.72784
I0401 14:22:42.239284 29493 solver.cpp:237]     Train net output #0: loss = 4.72784 (* 1 = 4.72784 loss)
I0401 14:22:42.239289 29493 sgd_solver.cpp:105] Iteration 2739, lr = 0.001
I0401 14:22:43.733136 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:22:47.155304 29493 solver.cpp:218] Iteration 2750 (2.23759 iter/s, 4.916s/11 iters), loss = 4.52371
I0401 14:22:47.155350 29493 solver.cpp:237]     Train net output #0: loss = 4.52371 (* 1 = 4.52371 loss)
I0401 14:22:47.155356 29493 sgd_solver.cpp:105] Iteration 2750, lr = 0.001
I0401 14:22:50.405534 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2759.caffemodel
I0401 14:22:53.960084 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2759.solverstate
I0401 14:22:56.317306 29493 solver.cpp:330] Iteration 2759, Testing net (#0)
I0401 14:22:56.317324 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:23:02.860219 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:23:03.318526 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0497533
I0401 14:23:03.318570 29493 solver.cpp:397]     Test net output #1: loss = 4.72758 (* 1 = 4.72758 loss)
I0401 14:23:03.733009 29493 solver.cpp:218] Iteration 2761 (0.663544 iter/s, 16.5777s/11 iters), loss = 4.52829
I0401 14:23:03.734567 29493 solver.cpp:237]     Train net output #0: loss = 4.52829 (* 1 = 4.52829 loss)
I0401 14:23:03.734580 29493 sgd_solver.cpp:105] Iteration 2761, lr = 0.001
I0401 14:23:03.734860 29493 blocking_queue.cpp:49] Waiting for data
I0401 14:23:08.188864 29493 solver.cpp:218] Iteration 2772 (2.46953 iter/s, 4.45429s/11 iters), loss = 4.6039
I0401 14:23:08.188967 29493 solver.cpp:237]     Train net output #0: loss = 4.6039 (* 1 = 4.6039 loss)
I0401 14:23:08.188974 29493 sgd_solver.cpp:105] Iteration 2772, lr = 0.001
I0401 14:23:12.957388 29493 solver.cpp:218] Iteration 2783 (2.30685 iter/s, 4.7684s/11 iters), loss = 4.68256
I0401 14:23:12.957446 29493 solver.cpp:237]     Train net output #0: loss = 4.68256 (* 1 = 4.68256 loss)
I0401 14:23:12.957455 29493 sgd_solver.cpp:105] Iteration 2783, lr = 0.001
I0401 14:23:17.826558 29493 solver.cpp:218] Iteration 2794 (2.25914 iter/s, 4.86911s/11 iters), loss = 4.6142
I0401 14:23:17.826599 29493 solver.cpp:237]     Train net output #0: loss = 4.6142 (* 1 = 4.6142 loss)
I0401 14:23:17.826606 29493 sgd_solver.cpp:105] Iteration 2794, lr = 0.001
I0401 14:23:22.694897 29493 solver.cpp:218] Iteration 2805 (2.25952 iter/s, 4.86829s/11 iters), loss = 4.71859
I0401 14:23:22.694937 29493 solver.cpp:237]     Train net output #0: loss = 4.71859 (* 1 = 4.71859 loss)
I0401 14:23:22.694943 29493 sgd_solver.cpp:105] Iteration 2805, lr = 0.001
I0401 14:23:27.597177 29493 solver.cpp:218] Iteration 2816 (2.24388 iter/s, 4.90222s/11 iters), loss = 4.53858
I0401 14:23:27.597225 29493 solver.cpp:237]     Train net output #0: loss = 4.53858 (* 1 = 4.53858 loss)
I0401 14:23:27.597232 29493 sgd_solver.cpp:105] Iteration 2816, lr = 0.001
I0401 14:23:32.620527 29493 solver.cpp:218] Iteration 2827 (2.1898 iter/s, 5.02329s/11 iters), loss = 4.61639
I0401 14:23:32.620575 29493 solver.cpp:237]     Train net output #0: loss = 4.61639 (* 1 = 4.61639 loss)
I0401 14:23:32.620581 29493 sgd_solver.cpp:105] Iteration 2827, lr = 0.001
I0401 14:23:34.442366 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:23:37.607908 29493 solver.cpp:218] Iteration 2838 (2.20559 iter/s, 4.98732s/11 iters), loss = 4.44421
I0401 14:23:37.607959 29493 solver.cpp:237]     Train net output #0: loss = 4.44421 (* 1 = 4.44421 loss)
I0401 14:23:37.607967 29493 sgd_solver.cpp:105] Iteration 2838, lr = 0.001
I0401 14:23:41.624272 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2848.caffemodel
I0401 14:23:44.680505 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2848.solverstate
I0401 14:23:46.986114 29493 solver.cpp:330] Iteration 2848, Testing net (#0)
I0401 14:23:46.986135 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:23:53.413019 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:23:53.915832 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0555099
I0401 14:23:53.915872 29493 solver.cpp:397]     Test net output #1: loss = 4.69043 (* 1 = 4.69043 loss)
I0401 14:23:54.195461 29493 solver.cpp:218] Iteration 2849 (0.66315 iter/s, 16.5875s/11 iters), loss = 4.52931
I0401 14:23:54.198280 29493 solver.cpp:237]     Train net output #0: loss = 4.52931 (* 1 = 4.52931 loss)
I0401 14:23:54.198298 29493 sgd_solver.cpp:105] Iteration 2849, lr = 0.001
I0401 14:23:58.572103 29493 solver.cpp:218] Iteration 2860 (2.51496 iter/s, 4.37383s/11 iters), loss = 4.46486
I0401 14:23:58.572151 29493 solver.cpp:237]     Train net output #0: loss = 4.46486 (* 1 = 4.46486 loss)
I0401 14:23:58.572158 29493 sgd_solver.cpp:105] Iteration 2860, lr = 0.001
I0401 14:24:03.363616 29493 solver.cpp:218] Iteration 2871 (2.29575 iter/s, 4.79145s/11 iters), loss = 4.57106
I0401 14:24:03.363660 29493 solver.cpp:237]     Train net output #0: loss = 4.57106 (* 1 = 4.57106 loss)
I0401 14:24:03.363667 29493 sgd_solver.cpp:105] Iteration 2871, lr = 0.001
I0401 14:24:08.714843 29493 solver.cpp:218] Iteration 2882 (2.05563 iter/s, 5.35117s/11 iters), loss = 4.54472
I0401 14:24:08.714890 29493 solver.cpp:237]     Train net output #0: loss = 4.54472 (* 1 = 4.54472 loss)
I0401 14:24:08.714895 29493 sgd_solver.cpp:105] Iteration 2882, lr = 0.001
I0401 14:24:13.720937 29493 solver.cpp:218] Iteration 2893 (2.19735 iter/s, 5.00604s/11 iters), loss = 4.62534
I0401 14:24:13.721030 29493 solver.cpp:237]     Train net output #0: loss = 4.62534 (* 1 = 4.62534 loss)
I0401 14:24:13.721036 29493 sgd_solver.cpp:105] Iteration 2893, lr = 0.001
I0401 14:24:18.883886 29493 solver.cpp:218] Iteration 2904 (2.13061 iter/s, 5.16284s/11 iters), loss = 4.44429
I0401 14:24:18.883945 29493 solver.cpp:237]     Train net output #0: loss = 4.44429 (* 1 = 4.44429 loss)
I0401 14:24:18.883955 29493 sgd_solver.cpp:105] Iteration 2904, lr = 0.001
I0401 14:24:23.541997 29493 solver.cpp:218] Iteration 2915 (2.36151 iter/s, 4.65804s/11 iters), loss = 4.49591
I0401 14:24:23.542037 29493 solver.cpp:237]     Train net output #0: loss = 4.49591 (* 1 = 4.49591 loss)
I0401 14:24:23.542043 29493 sgd_solver.cpp:105] Iteration 2915, lr = 0.001
I0401 14:24:25.421264 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:24:28.110005 29493 solver.cpp:218] Iteration 2926 (2.40808 iter/s, 4.56795s/11 iters), loss = 4.29717
I0401 14:24:28.110044 29493 solver.cpp:237]     Train net output #0: loss = 4.29717 (* 1 = 4.29717 loss)
I0401 14:24:28.110049 29493 sgd_solver.cpp:105] Iteration 2926, lr = 0.001
I0401 14:24:32.571765 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2937.caffemodel
I0401 14:24:35.588713 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2937.solverstate
I0401 14:24:37.970321 29493 solver.cpp:330] Iteration 2937, Testing net (#0)
I0401 14:24:37.970346 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:24:44.390776 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:24:44.902860 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0534539
I0401 14:24:44.902897 29493 solver.cpp:397]     Test net output #1: loss = 4.66654 (* 1 = 4.66654 loss)
I0401 14:24:45.045631 29493 solver.cpp:218] Iteration 2937 (0.64952 iter/s, 16.9356s/11 iters), loss = 4.56155
I0401 14:24:45.047206 29493 solver.cpp:237]     Train net output #0: loss = 4.56155 (* 1 = 4.56155 loss)
I0401 14:24:45.047222 29493 sgd_solver.cpp:105] Iteration 2937, lr = 0.001
I0401 14:24:49.026922 29493 solver.cpp:218] Iteration 2948 (2.76402 iter/s, 3.97971s/11 iters), loss = 4.62026
I0401 14:24:49.026973 29493 solver.cpp:237]     Train net output #0: loss = 4.62026 (* 1 = 4.62026 loss)
I0401 14:24:49.026981 29493 sgd_solver.cpp:105] Iteration 2948, lr = 0.001
I0401 14:24:54.042970 29493 solver.cpp:218] Iteration 2959 (2.19299 iter/s, 5.01599s/11 iters), loss = 4.43087
I0401 14:24:54.043011 29493 solver.cpp:237]     Train net output #0: loss = 4.43087 (* 1 = 4.43087 loss)
I0401 14:24:54.043017 29493 sgd_solver.cpp:105] Iteration 2959, lr = 0.001
I0401 14:24:59.015972 29493 solver.cpp:218] Iteration 2970 (2.21197 iter/s, 4.97294s/11 iters), loss = 4.57723
I0401 14:24:59.016024 29493 solver.cpp:237]     Train net output #0: loss = 4.57723 (* 1 = 4.57723 loss)
I0401 14:24:59.016033 29493 sgd_solver.cpp:105] Iteration 2970, lr = 0.001
I0401 14:25:03.979605 29493 solver.cpp:218] Iteration 2981 (2.21615 iter/s, 4.96356s/11 iters), loss = 4.52613
I0401 14:25:03.979665 29493 solver.cpp:237]     Train net output #0: loss = 4.52613 (* 1 = 4.52613 loss)
I0401 14:25:03.979673 29493 sgd_solver.cpp:105] Iteration 2981, lr = 0.001
I0401 14:25:09.028640 29493 solver.cpp:218] Iteration 2992 (2.17866 iter/s, 5.04897s/11 iters), loss = 4.46867
I0401 14:25:09.028681 29493 solver.cpp:237]     Train net output #0: loss = 4.46867 (* 1 = 4.46867 loss)
I0401 14:25:09.028685 29493 sgd_solver.cpp:105] Iteration 2992, lr = 0.001
I0401 14:25:13.818017 29493 solver.cpp:218] Iteration 3003 (2.29678 iter/s, 4.78932s/11 iters), loss = 4.33114
I0401 14:25:13.818080 29493 solver.cpp:237]     Train net output #0: loss = 4.33114 (* 1 = 4.33114 loss)
I0401 14:25:13.818089 29493 sgd_solver.cpp:105] Iteration 3003, lr = 0.001
I0401 14:25:16.204856 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:25:18.906294 29493 solver.cpp:218] Iteration 3014 (2.16186 iter/s, 5.0882s/11 iters), loss = 4.28506
I0401 14:25:18.906348 29493 solver.cpp:237]     Train net output #0: loss = 4.28506 (* 1 = 4.28506 loss)
I0401 14:25:18.906359 29493 sgd_solver.cpp:105] Iteration 3014, lr = 0.001
I0401 14:25:23.807214 29493 solver.cpp:218] Iteration 3025 (2.24451 iter/s, 4.90085s/11 iters), loss = 4.42957
I0401 14:25:23.807267 29493 solver.cpp:237]     Train net output #0: loss = 4.42957 (* 1 = 4.42957 loss)
I0401 14:25:23.807276 29493 sgd_solver.cpp:105] Iteration 3025, lr = 0.001
I0401 14:25:23.807495 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3026.caffemodel
I0401 14:25:27.958319 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3026.solverstate
I0401 14:25:31.667135 29493 solver.cpp:330] Iteration 3026, Testing net (#0)
I0401 14:25:31.667155 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:25:38.139169 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:25:38.726411 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0608553
I0401 14:25:38.726441 29493 solver.cpp:397]     Test net output #1: loss = 4.6241 (* 1 = 4.6241 loss)
I0401 14:25:42.489913 29493 solver.cpp:218] Iteration 3036 (0.588782 iter/s, 18.6826s/11 iters), loss = 4.4669
I0401 14:25:42.489959 29493 solver.cpp:237]     Train net output #0: loss = 4.4669 (* 1 = 4.4669 loss)
I0401 14:25:42.489964 29493 sgd_solver.cpp:105] Iteration 3036, lr = 0.001
I0401 14:25:47.521358 29493 solver.cpp:218] Iteration 3047 (2.18628 iter/s, 5.03139s/11 iters), loss = 4.47937
I0401 14:25:47.521468 29493 solver.cpp:237]     Train net output #0: loss = 4.47937 (* 1 = 4.47937 loss)
I0401 14:25:47.521476 29493 sgd_solver.cpp:105] Iteration 3047, lr = 0.001
I0401 14:25:52.500758 29493 solver.cpp:218] Iteration 3058 (2.20915 iter/s, 4.97928s/11 iters), loss = 4.33939
I0401 14:25:52.500797 29493 solver.cpp:237]     Train net output #0: loss = 4.33939 (* 1 = 4.33939 loss)
I0401 14:25:52.500802 29493 sgd_solver.cpp:105] Iteration 3058, lr = 0.001
I0401 14:25:57.445268 29493 solver.cpp:218] Iteration 3069 (2.22471 iter/s, 4.94446s/11 iters), loss = 4.46275
I0401 14:25:57.445328 29493 solver.cpp:237]     Train net output #0: loss = 4.46275 (* 1 = 4.46275 loss)
I0401 14:25:57.445338 29493 sgd_solver.cpp:105] Iteration 3069, lr = 0.001
I0401 14:26:02.405683 29493 solver.cpp:218] Iteration 3080 (2.21759 iter/s, 4.96034s/11 iters), loss = 4.59543
I0401 14:26:02.405741 29493 solver.cpp:237]     Train net output #0: loss = 4.59543 (* 1 = 4.59543 loss)
I0401 14:26:02.405750 29493 sgd_solver.cpp:105] Iteration 3080, lr = 0.001
I0401 14:26:07.377619 29493 solver.cpp:218] Iteration 3091 (2.21245 iter/s, 4.97186s/11 iters), loss = 4.38631
I0401 14:26:07.377672 29493 solver.cpp:237]     Train net output #0: loss = 4.38631 (* 1 = 4.38631 loss)
I0401 14:26:07.377681 29493 sgd_solver.cpp:105] Iteration 3091, lr = 0.001
I0401 14:26:09.688676 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:26:12.105794 29493 solver.cpp:218] Iteration 3102 (2.32651 iter/s, 4.72811s/11 iters), loss = 4.07265
I0401 14:26:12.105855 29493 solver.cpp:237]     Train net output #0: loss = 4.07265 (* 1 = 4.07265 loss)
I0401 14:26:12.105865 29493 sgd_solver.cpp:105] Iteration 3102, lr = 0.001
I0401 14:26:16.903452 29493 solver.cpp:218] Iteration 3113 (2.29282 iter/s, 4.79758s/11 iters), loss = 4.17739
I0401 14:26:16.909665 29493 solver.cpp:237]     Train net output #0: loss = 4.17739 (* 1 = 4.17739 loss)
I0401 14:26:16.909687 29493 sgd_solver.cpp:105] Iteration 3113, lr = 0.001
I0401 14:26:17.267784 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3115.caffemodel
I0401 14:26:20.344805 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3115.solverstate
I0401 14:26:22.695217 29493 solver.cpp:330] Iteration 3115, Testing net (#0)
I0401 14:26:22.695238 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:26:28.906401 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:26:29.451705 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0649671
I0401 14:26:29.451742 29493 solver.cpp:397]     Test net output #1: loss = 4.60082 (* 1 = 4.60082 loss)
I0401 14:26:32.642163 29493 solver.cpp:218] Iteration 3124 (0.699189 iter/s, 15.7325s/11 iters), loss = 4.51141
I0401 14:26:32.642230 29493 solver.cpp:237]     Train net output #0: loss = 4.51141 (* 1 = 4.51141 loss)
I0401 14:26:32.642239 29493 sgd_solver.cpp:105] Iteration 3124, lr = 0.001
I0401 14:26:37.610535 29493 solver.cpp:218] Iteration 3135 (2.21404 iter/s, 4.9683s/11 iters), loss = 4.35269
I0401 14:26:37.610580 29493 solver.cpp:237]     Train net output #0: loss = 4.35269 (* 1 = 4.35269 loss)
I0401 14:26:37.610586 29493 sgd_solver.cpp:105] Iteration 3135, lr = 0.001
I0401 14:26:42.584379 29493 solver.cpp:218] Iteration 3146 (2.2116 iter/s, 4.97378s/11 iters), loss = 4.46574
I0401 14:26:42.584426 29493 solver.cpp:237]     Train net output #0: loss = 4.46574 (* 1 = 4.46574 loss)
I0401 14:26:42.584432 29493 sgd_solver.cpp:105] Iteration 3146, lr = 0.001
I0401 14:26:47.513921 29493 solver.cpp:218] Iteration 3157 (2.23147 iter/s, 4.92948s/11 iters), loss = 4.42297
I0401 14:26:47.513974 29493 solver.cpp:237]     Train net output #0: loss = 4.42297 (* 1 = 4.42297 loss)
I0401 14:26:47.513980 29493 sgd_solver.cpp:105] Iteration 3157, lr = 0.001
I0401 14:26:52.418807 29493 solver.cpp:218] Iteration 3168 (2.24269 iter/s, 4.90482s/11 iters), loss = 4.51379
I0401 14:26:52.418980 29493 solver.cpp:237]     Train net output #0: loss = 4.51379 (* 1 = 4.51379 loss)
I0401 14:26:52.418990 29493 sgd_solver.cpp:105] Iteration 3168, lr = 0.001
I0401 14:26:57.496645 29493 solver.cpp:218] Iteration 3179 (2.16635 iter/s, 5.07766s/11 iters), loss = 4.2449
I0401 14:26:57.496693 29493 solver.cpp:237]     Train net output #0: loss = 4.2449 (* 1 = 4.2449 loss)
I0401 14:26:57.496701 29493 sgd_solver.cpp:105] Iteration 3179, lr = 0.001
I0401 14:27:00.208307 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:27:02.427752 29493 solver.cpp:218] Iteration 3190 (2.23076 iter/s, 4.93105s/11 iters), loss = 4.13558
I0401 14:27:02.427791 29493 solver.cpp:237]     Train net output #0: loss = 4.13558 (* 1 = 4.13558 loss)
I0401 14:27:02.427798 29493 sgd_solver.cpp:105] Iteration 3190, lr = 0.001
I0401 14:27:07.337955 29493 solver.cpp:218] Iteration 3201 (2.24026 iter/s, 4.91015s/11 iters), loss = 4.27743
I0401 14:27:07.338013 29493 solver.cpp:237]     Train net output #0: loss = 4.27743 (* 1 = 4.27743 loss)
I0401 14:27:07.338023 29493 sgd_solver.cpp:105] Iteration 3201, lr = 0.001
I0401 14:27:08.172968 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3204.caffemodel
I0401 14:27:11.208657 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3204.solverstate
I0401 14:27:13.536628 29493 solver.cpp:330] Iteration 3204, Testing net (#0)
I0401 14:27:13.536651 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:27:20.000186 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:27:20.556962 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0587993
I0401 14:27:20.556988 29493 solver.cpp:397]     Test net output #1: loss = 4.57312 (* 1 = 4.57312 loss)
I0401 14:27:23.435282 29493 solver.cpp:218] Iteration 3212 (0.683346 iter/s, 16.0973s/11 iters), loss = 4.29639
I0401 14:27:23.435422 29493 solver.cpp:237]     Train net output #0: loss = 4.29639 (* 1 = 4.29639 loss)
I0401 14:27:23.435431 29493 sgd_solver.cpp:105] Iteration 3212, lr = 0.001
I0401 14:27:28.294160 29493 solver.cpp:218] Iteration 3223 (2.26397 iter/s, 4.85873s/11 iters), loss = 4.21692
I0401 14:27:28.294214 29493 solver.cpp:237]     Train net output #0: loss = 4.21692 (* 1 = 4.21692 loss)
I0401 14:27:28.294222 29493 sgd_solver.cpp:105] Iteration 3223, lr = 0.001
I0401 14:27:33.084193 29493 solver.cpp:218] Iteration 3234 (2.29647 iter/s, 4.78997s/11 iters), loss = 4.24007
I0401 14:27:33.084231 29493 solver.cpp:237]     Train net output #0: loss = 4.24007 (* 1 = 4.24007 loss)
I0401 14:27:33.084237 29493 sgd_solver.cpp:105] Iteration 3234, lr = 0.001
I0401 14:27:37.849081 29493 solver.cpp:218] Iteration 3245 (2.30858 iter/s, 4.76483s/11 iters), loss = 4.16777
I0401 14:27:37.849134 29493 solver.cpp:237]     Train net output #0: loss = 4.16777 (* 1 = 4.16777 loss)
I0401 14:27:37.849143 29493 sgd_solver.cpp:105] Iteration 3245, lr = 0.001
I0401 14:27:42.726277 29493 solver.cpp:218] Iteration 3256 (2.25543 iter/s, 4.87713s/11 iters), loss = 4.22387
I0401 14:27:42.726328 29493 solver.cpp:237]     Train net output #0: loss = 4.22387 (* 1 = 4.22387 loss)
I0401 14:27:42.726337 29493 sgd_solver.cpp:105] Iteration 3256, lr = 0.001
I0401 14:27:47.548375 29493 solver.cpp:218] Iteration 3267 (2.28119 iter/s, 4.82204s/11 iters), loss = 4.0348
I0401 14:27:47.548414 29493 solver.cpp:237]     Train net output #0: loss = 4.0348 (* 1 = 4.0348 loss)
I0401 14:27:47.548419 29493 sgd_solver.cpp:105] Iteration 3267, lr = 0.001
I0401 14:27:50.571698 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:27:52.610919 29493 solver.cpp:218] Iteration 3278 (2.17284 iter/s, 5.06249s/11 iters), loss = 3.97171
I0401 14:27:52.610963 29493 solver.cpp:237]     Train net output #0: loss = 3.97171 (* 1 = 3.97171 loss)
I0401 14:27:52.610970 29493 sgd_solver.cpp:105] Iteration 3278, lr = 0.001
I0401 14:27:57.520859 29493 solver.cpp:218] Iteration 3289 (2.24038 iter/s, 4.90988s/11 iters), loss = 4.42104
I0401 14:27:57.520989 29493 solver.cpp:237]     Train net output #0: loss = 4.42104 (* 1 = 4.42104 loss)
I0401 14:27:57.520996 29493 sgd_solver.cpp:105] Iteration 3289, lr = 0.001
I0401 14:27:58.792378 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3293.caffemodel
I0401 14:28:01.761129 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3293.solverstate
I0401 14:28:04.050949 29493 solver.cpp:330] Iteration 3293, Testing net (#0)
I0401 14:28:04.050972 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:28:10.419385 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:28:10.950184 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0666118
I0401 14:28:10.950220 29493 solver.cpp:397]     Test net output #1: loss = 4.55964 (* 1 = 4.55964 loss)
I0401 14:28:13.395349 29493 solver.cpp:218] Iteration 3300 (0.692942 iter/s, 15.8744s/11 iters), loss = 4.36165
I0401 14:28:13.395404 29493 solver.cpp:237]     Train net output #0: loss = 4.36165 (* 1 = 4.36165 loss)
I0401 14:28:13.395412 29493 sgd_solver.cpp:105] Iteration 3300, lr = 0.001
I0401 14:28:18.457165 29493 solver.cpp:218] Iteration 3311 (2.17316 iter/s, 5.06175s/11 iters), loss = 4.15578
I0401 14:28:18.457214 29493 solver.cpp:237]     Train net output #0: loss = 4.15578 (* 1 = 4.15578 loss)
I0401 14:28:18.457221 29493 sgd_solver.cpp:105] Iteration 3311, lr = 0.001
I0401 14:28:23.188958 29493 solver.cpp:218] Iteration 3322 (2.32473 iter/s, 4.73173s/11 iters), loss = 4.34471
I0401 14:28:23.188999 29493 solver.cpp:237]     Train net output #0: loss = 4.34471 (* 1 = 4.34471 loss)
I0401 14:28:23.189005 29493 sgd_solver.cpp:105] Iteration 3322, lr = 0.001
I0401 14:28:28.035410 29493 solver.cpp:218] Iteration 3333 (2.26973 iter/s, 4.8464s/11 iters), loss = 4.13186
I0401 14:28:28.035490 29493 solver.cpp:237]     Train net output #0: loss = 4.13186 (* 1 = 4.13186 loss)
I0401 14:28:28.035497 29493 sgd_solver.cpp:105] Iteration 3333, lr = 0.001
I0401 14:28:31.700439 29493 blocking_queue.cpp:49] Waiting for data
I0401 14:28:33.017483 29493 solver.cpp:218] Iteration 3344 (2.20796 iter/s, 4.98198s/11 iters), loss = 4.15724
I0401 14:28:33.017534 29493 solver.cpp:237]     Train net output #0: loss = 4.15724 (* 1 = 4.15724 loss)
I0401 14:28:33.017542 29493 sgd_solver.cpp:105] Iteration 3344, lr = 0.001
I0401 14:28:38.147974 29493 solver.cpp:218] Iteration 3355 (2.14407 iter/s, 5.13043s/11 iters), loss = 4.19819
I0401 14:28:38.148028 29493 solver.cpp:237]     Train net output #0: loss = 4.19819 (* 1 = 4.19819 loss)
I0401 14:28:38.148036 29493 sgd_solver.cpp:105] Iteration 3355, lr = 0.001
I0401 14:28:41.492697 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:28:43.191751 29493 solver.cpp:218] Iteration 3366 (2.18094 iter/s, 5.04371s/11 iters), loss = 4.21747
I0401 14:28:43.191802 29493 solver.cpp:237]     Train net output #0: loss = 4.21747 (* 1 = 4.21747 loss)
I0401 14:28:43.191808 29493 sgd_solver.cpp:105] Iteration 3366, lr = 0.001
I0401 14:28:48.061754 29493 solver.cpp:218] Iteration 3377 (2.25875 iter/s, 4.86994s/11 iters), loss = 4.29503
I0401 14:28:48.061812 29493 solver.cpp:237]     Train net output #0: loss = 4.29503 (* 1 = 4.29503 loss)
I0401 14:28:48.061820 29493 sgd_solver.cpp:105] Iteration 3377, lr = 0.001
I0401 14:28:49.785602 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3382.caffemodel
I0401 14:28:52.888749 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3382.solverstate
I0401 14:28:55.226444 29493 solver.cpp:330] Iteration 3382, Testing net (#0)
I0401 14:28:55.226464 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:29:01.767132 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:29:02.353595 29493 solver.cpp:397]     Test net output #0: accuracy = 0.067023
I0401 14:29:02.353637 29493 solver.cpp:397]     Test net output #1: loss = 4.54754 (* 1 = 4.54754 loss)
I0401 14:29:04.279675 29493 solver.cpp:218] Iteration 3388 (0.678265 iter/s, 16.2179s/11 iters), loss = 4.32098
I0401 14:29:04.279716 29493 solver.cpp:237]     Train net output #0: loss = 4.32098 (* 1 = 4.32098 loss)
I0401 14:29:04.279721 29493 sgd_solver.cpp:105] Iteration 3388, lr = 0.001
I0401 14:29:09.057003 29493 solver.cpp:218] Iteration 3399 (2.30257 iter/s, 4.77727s/11 iters), loss = 4.28863
I0401 14:29:09.057045 29493 solver.cpp:237]     Train net output #0: loss = 4.28863 (* 1 = 4.28863 loss)
I0401 14:29:09.057051 29493 sgd_solver.cpp:105] Iteration 3399, lr = 0.001
I0401 14:29:14.031194 29493 solver.cpp:218] Iteration 3410 (2.21144 iter/s, 4.97413s/11 iters), loss = 4.12374
I0401 14:29:14.031256 29493 solver.cpp:237]     Train net output #0: loss = 4.12374 (* 1 = 4.12374 loss)
I0401 14:29:14.031265 29493 sgd_solver.cpp:105] Iteration 3410, lr = 0.001
I0401 14:29:19.085717 29493 solver.cpp:218] Iteration 3421 (2.1763 iter/s, 5.05445s/11 iters), loss = 4.01082
I0401 14:29:19.085768 29493 solver.cpp:237]     Train net output #0: loss = 4.01082 (* 1 = 4.01082 loss)
I0401 14:29:19.085777 29493 sgd_solver.cpp:105] Iteration 3421, lr = 0.001
I0401 14:29:23.780694 29493 solver.cpp:218] Iteration 3432 (2.34296 iter/s, 4.69491s/11 iters), loss = 4.17336
I0401 14:29:23.780741 29493 solver.cpp:237]     Train net output #0: loss = 4.17336 (* 1 = 4.17336 loss)
I0401 14:29:23.780748 29493 sgd_solver.cpp:105] Iteration 3432, lr = 0.001
I0401 14:29:28.717756 29493 solver.cpp:218] Iteration 3443 (2.22807 iter/s, 4.937s/11 iters), loss = 4.20932
I0401 14:29:28.717811 29493 solver.cpp:237]     Train net output #0: loss = 4.20932 (* 1 = 4.20932 loss)
I0401 14:29:28.717819 29493 sgd_solver.cpp:105] Iteration 3443, lr = 0.001
I0401 14:29:31.966118 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:29:33.553968 29493 solver.cpp:218] Iteration 3454 (2.27454 iter/s, 4.83615s/11 iters), loss = 4.14033
I0401 14:29:33.554008 29493 solver.cpp:237]     Train net output #0: loss = 4.14033 (* 1 = 4.14033 loss)
I0401 14:29:33.554014 29493 sgd_solver.cpp:105] Iteration 3454, lr = 0.001
I0401 14:29:38.480523 29493 solver.cpp:218] Iteration 3465 (2.23282 iter/s, 4.9265s/11 iters), loss = 4.16981
I0401 14:29:38.480587 29493 solver.cpp:237]     Train net output #0: loss = 4.16981 (* 1 = 4.16981 loss)
I0401 14:29:38.480595 29493 sgd_solver.cpp:105] Iteration 3465, lr = 0.001
I0401 14:29:40.794934 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3471.caffemodel
I0401 14:29:45.257539 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3471.solverstate
I0401 14:29:47.611711 29493 solver.cpp:330] Iteration 3471, Testing net (#0)
I0401 14:29:47.611737 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:29:53.896203 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:29:54.491297 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0789474
I0401 14:29:54.491336 29493 solver.cpp:397]     Test net output #1: loss = 4.45444 (* 1 = 4.45444 loss)
I0401 14:29:56.016151 29493 solver.cpp:218] Iteration 3476 (0.627297 iter/s, 17.5356s/11 iters), loss = 3.96544
I0401 14:29:56.016201 29493 solver.cpp:237]     Train net output #0: loss = 3.96544 (* 1 = 3.96544 loss)
I0401 14:29:56.016208 29493 sgd_solver.cpp:105] Iteration 3476, lr = 0.001
I0401 14:30:00.939504 29493 solver.cpp:218] Iteration 3487 (2.23428 iter/s, 4.92329s/11 iters), loss = 3.99531
I0401 14:30:00.939568 29493 solver.cpp:237]     Train net output #0: loss = 3.99531 (* 1 = 3.99531 loss)
I0401 14:30:00.939576 29493 sgd_solver.cpp:105] Iteration 3487, lr = 0.001
I0401 14:30:05.880391 29493 solver.cpp:218] Iteration 3498 (2.22635 iter/s, 4.94081s/11 iters), loss = 4.02299
I0401 14:30:05.880514 29493 solver.cpp:237]     Train net output #0: loss = 4.02299 (* 1 = 4.02299 loss)
I0401 14:30:05.880522 29493 sgd_solver.cpp:105] Iteration 3498, lr = 0.001
I0401 14:30:10.711835 29493 solver.cpp:218] Iteration 3509 (2.27682 iter/s, 4.83131s/11 iters), loss = 3.9958
I0401 14:30:10.711892 29493 solver.cpp:237]     Train net output #0: loss = 3.9958 (* 1 = 3.9958 loss)
I0401 14:30:10.711900 29493 sgd_solver.cpp:105] Iteration 3509, lr = 0.001
I0401 14:30:15.845746 29493 solver.cpp:218] Iteration 3520 (2.14265 iter/s, 5.13384s/11 iters), loss = 4.18027
I0401 14:30:15.845794 29493 solver.cpp:237]     Train net output #0: loss = 4.18027 (* 1 = 4.18027 loss)
I0401 14:30:15.845803 29493 sgd_solver.cpp:105] Iteration 3520, lr = 0.001
I0401 14:30:20.858358 29493 solver.cpp:218] Iteration 3531 (2.19449 iter/s, 5.01256s/11 iters), loss = 3.92926
I0401 14:30:20.858412 29493 solver.cpp:237]     Train net output #0: loss = 3.92926 (* 1 = 3.92926 loss)
I0401 14:30:20.858419 29493 sgd_solver.cpp:105] Iteration 3531, lr = 0.001
I0401 14:30:24.320796 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:30:25.508126 29493 solver.cpp:218] Iteration 3542 (2.36575 iter/s, 4.6497s/11 iters), loss = 4.02883
I0401 14:30:25.508183 29493 solver.cpp:237]     Train net output #0: loss = 4.02883 (* 1 = 4.02883 loss)
I0401 14:30:25.508190 29493 sgd_solver.cpp:105] Iteration 3542, lr = 0.001
I0401 14:30:30.713680 29493 solver.cpp:218] Iteration 3553 (2.11315 iter/s, 5.20549s/11 iters), loss = 3.97712
I0401 14:30:30.713721 29493 solver.cpp:237]     Train net output #0: loss = 3.97712 (* 1 = 3.97712 loss)
I0401 14:30:30.713727 29493 sgd_solver.cpp:105] Iteration 3553, lr = 0.001
I0401 14:30:33.301623 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3560.caffemodel
I0401 14:30:37.991890 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3560.solverstate
I0401 14:30:41.226766 29493 solver.cpp:330] Iteration 3560, Testing net (#0)
I0401 14:30:41.226788 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:30:47.203016 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:30:47.781529 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0768914
I0401 14:30:47.781561 29493 solver.cpp:397]     Test net output #1: loss = 4.45269 (* 1 = 4.45269 loss)
I0401 14:30:48.843590 29493 solver.cpp:218] Iteration 3564 (0.606734 iter/s, 18.1299s/11 iters), loss = 4.02173
I0401 14:30:48.843647 29493 solver.cpp:237]     Train net output #0: loss = 4.02173 (* 1 = 4.02173 loss)
I0401 14:30:48.843655 29493 sgd_solver.cpp:105] Iteration 3564, lr = 0.001
I0401 14:30:53.556999 29493 solver.cpp:218] Iteration 3575 (2.3338 iter/s, 4.71334s/11 iters), loss = 4.0019
I0401 14:30:53.557058 29493 solver.cpp:237]     Train net output #0: loss = 4.0019 (* 1 = 4.0019 loss)
I0401 14:30:53.557066 29493 sgd_solver.cpp:105] Iteration 3575, lr = 0.001
I0401 14:30:58.420786 29493 solver.cpp:218] Iteration 3586 (2.26165 iter/s, 4.86371s/11 iters), loss = 3.93021
I0401 14:30:58.420845 29493 solver.cpp:237]     Train net output #0: loss = 3.93021 (* 1 = 3.93021 loss)
I0401 14:30:58.420853 29493 sgd_solver.cpp:105] Iteration 3586, lr = 0.001
I0401 14:31:03.479298 29493 solver.cpp:218] Iteration 3597 (2.17458 iter/s, 5.05844s/11 iters), loss = 4.16161
I0401 14:31:03.479342 29493 solver.cpp:237]     Train net output #0: loss = 4.16161 (* 1 = 4.16161 loss)
I0401 14:31:03.479349 29493 sgd_solver.cpp:105] Iteration 3597, lr = 0.001
I0401 14:31:08.333485 29493 solver.cpp:218] Iteration 3608 (2.26611 iter/s, 4.85413s/11 iters), loss = 4.11754
I0401 14:31:08.333591 29493 solver.cpp:237]     Train net output #0: loss = 4.11754 (* 1 = 4.11754 loss)
I0401 14:31:08.333600 29493 sgd_solver.cpp:105] Iteration 3608, lr = 0.001
I0401 14:31:13.427191 29493 solver.cpp:218] Iteration 3619 (2.15958 iter/s, 5.09358s/11 iters), loss = 3.83129
I0401 14:31:13.427254 29493 solver.cpp:237]     Train net output #0: loss = 3.83129 (* 1 = 3.83129 loss)
I0401 14:31:13.427264 29493 sgd_solver.cpp:105] Iteration 3619, lr = 0.001
I0401 14:31:17.215440 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:31:18.328747 29493 solver.cpp:218] Iteration 3630 (2.24422 iter/s, 4.90148s/11 iters), loss = 3.75988
I0401 14:31:18.328807 29493 solver.cpp:237]     Train net output #0: loss = 3.75988 (* 1 = 3.75988 loss)
I0401 14:31:18.328816 29493 sgd_solver.cpp:105] Iteration 3630, lr = 0.001
I0401 14:31:23.136629 29493 solver.cpp:218] Iteration 3641 (2.28794 iter/s, 4.80781s/11 iters), loss = 4.02905
I0401 14:31:23.136682 29493 solver.cpp:237]     Train net output #0: loss = 4.02905 (* 1 = 4.02905 loss)
I0401 14:31:23.136689 29493 sgd_solver.cpp:105] Iteration 3641, lr = 0.001
I0401 14:31:26.143859 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3649.caffemodel
I0401 14:31:31.434530 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3649.solverstate
I0401 14:31:36.135059 29493 solver.cpp:330] Iteration 3649, Testing net (#0)
I0401 14:31:36.135080 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:31:42.417403 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:31:43.066627 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0847039
I0401 14:31:43.066654 29493 solver.cpp:397]     Test net output #1: loss = 4.44775 (* 1 = 4.44775 loss)
I0401 14:31:43.623186 29493 solver.cpp:218] Iteration 3652 (0.536939 iter/s, 20.4865s/11 iters), loss = 3.88173
I0401 14:31:43.624764 29493 solver.cpp:237]     Train net output #0: loss = 3.88173 (* 1 = 3.88173 loss)
I0401 14:31:43.624781 29493 sgd_solver.cpp:105] Iteration 3652, lr = 0.001
I0401 14:31:48.294272 29493 solver.cpp:218] Iteration 3663 (2.35571 iter/s, 4.66951s/11 iters), loss = 4.02001
I0401 14:31:48.294324 29493 solver.cpp:237]     Train net output #0: loss = 4.02001 (* 1 = 4.02001 loss)
I0401 14:31:48.294332 29493 sgd_solver.cpp:105] Iteration 3663, lr = 0.001
I0401 14:31:53.259905 29493 solver.cpp:218] Iteration 3674 (2.21526 iter/s, 4.96557s/11 iters), loss = 3.78138
I0401 14:31:53.259971 29493 solver.cpp:237]     Train net output #0: loss = 3.78138 (* 1 = 3.78138 loss)
I0401 14:31:53.259979 29493 sgd_solver.cpp:105] Iteration 3674, lr = 0.001
I0401 14:31:58.156293 29493 solver.cpp:218] Iteration 3685 (2.24659 iter/s, 4.89631s/11 iters), loss = 3.93743
I0401 14:31:58.156340 29493 solver.cpp:237]     Train net output #0: loss = 3.93743 (* 1 = 3.93743 loss)
I0401 14:31:58.156348 29493 sgd_solver.cpp:105] Iteration 3685, lr = 0.001
I0401 14:32:03.194821 29493 solver.cpp:218] Iteration 3696 (2.1832 iter/s, 5.03847s/11 iters), loss = 3.93698
I0401 14:32:03.194869 29493 solver.cpp:237]     Train net output #0: loss = 3.93698 (* 1 = 3.93698 loss)
I0401 14:32:03.194873 29493 sgd_solver.cpp:105] Iteration 3696, lr = 0.001
I0401 14:32:07.798005 29493 solver.cpp:218] Iteration 3707 (2.38968 iter/s, 4.60312s/11 iters), loss = 3.83281
I0401 14:32:07.798063 29493 solver.cpp:237]     Train net output #0: loss = 3.83281 (* 1 = 3.83281 loss)
I0401 14:32:07.798072 29493 sgd_solver.cpp:105] Iteration 3707, lr = 0.001
I0401 14:32:12.129456 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:32:13.050401 29493 solver.cpp:218] Iteration 3718 (2.09431 iter/s, 5.25233s/11 iters), loss = 3.80772
I0401 14:32:13.050529 29493 solver.cpp:237]     Train net output #0: loss = 3.80772 (* 1 = 3.80772 loss)
I0401 14:32:13.050535 29493 sgd_solver.cpp:105] Iteration 3718, lr = 0.001
I0401 14:32:17.808621 29493 solver.cpp:218] Iteration 3729 (2.31186 iter/s, 4.75808s/11 iters), loss = 4.14866
I0401 14:32:17.808663 29493 solver.cpp:237]     Train net output #0: loss = 4.14866 (* 1 = 4.14866 loss)
I0401 14:32:17.808668 29493 sgd_solver.cpp:105] Iteration 3729, lr = 0.001
I0401 14:32:21.262558 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3738.caffemodel
I0401 14:32:24.376793 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3738.solverstate
I0401 14:32:26.921394 29493 solver.cpp:330] Iteration 3738, Testing net (#0)
I0401 14:32:26.921413 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:32:33.079916 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:32:33.739204 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0801809
I0401 14:32:33.739243 29493 solver.cpp:397]     Test net output #1: loss = 4.4393 (* 1 = 4.4393 loss)
I0401 14:32:34.143604 29493 solver.cpp:218] Iteration 3740 (0.673403 iter/s, 16.3349s/11 iters), loss = 3.87836
I0401 14:32:34.143651 29493 solver.cpp:237]     Train net output #0: loss = 3.87836 (* 1 = 3.87836 loss)
I0401 14:32:34.143656 29493 sgd_solver.cpp:105] Iteration 3740, lr = 0.001
I0401 14:32:38.727159 29493 solver.cpp:218] Iteration 3751 (2.39992 iter/s, 4.58349s/11 iters), loss = 4.03519
I0401 14:32:38.727203 29493 solver.cpp:237]     Train net output #0: loss = 4.03519 (* 1 = 4.03519 loss)
I0401 14:32:38.727210 29493 sgd_solver.cpp:105] Iteration 3751, lr = 0.001
I0401 14:32:43.802202 29493 solver.cpp:218] Iteration 3762 (2.1675 iter/s, 5.07498s/11 iters), loss = 3.9309
I0401 14:32:43.802381 29493 solver.cpp:237]     Train net output #0: loss = 3.9309 (* 1 = 3.9309 loss)
I0401 14:32:43.802390 29493 sgd_solver.cpp:105] Iteration 3762, lr = 0.001
I0401 14:32:48.729507 29493 solver.cpp:218] Iteration 3773 (2.23254 iter/s, 4.92711s/11 iters), loss = 4.02079
I0401 14:32:48.729552 29493 solver.cpp:237]     Train net output #0: loss = 4.02079 (* 1 = 4.02079 loss)
I0401 14:32:48.729559 29493 sgd_solver.cpp:105] Iteration 3773, lr = 0.001
I0401 14:32:53.504614 29493 solver.cpp:218] Iteration 3784 (2.30364 iter/s, 4.77504s/11 iters), loss = 3.76588
I0401 14:32:53.504668 29493 solver.cpp:237]     Train net output #0: loss = 3.76588 (* 1 = 3.76588 loss)
I0401 14:32:53.504678 29493 sgd_solver.cpp:105] Iteration 3784, lr = 0.001
I0401 14:32:58.532021 29493 solver.cpp:218] Iteration 3795 (2.18804 iter/s, 5.02734s/11 iters), loss = 3.68176
I0401 14:32:58.532074 29493 solver.cpp:237]     Train net output #0: loss = 3.68176 (* 1 = 3.68176 loss)
I0401 14:32:58.532083 29493 sgd_solver.cpp:105] Iteration 3795, lr = 0.001
I0401 14:33:02.868005 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:33:03.513415 29493 solver.cpp:218] Iteration 3806 (2.20825 iter/s, 4.98133s/11 iters), loss = 3.92551
I0401 14:33:03.513471 29493 solver.cpp:237]     Train net output #0: loss = 3.92551 (* 1 = 3.92551 loss)
I0401 14:33:03.513481 29493 sgd_solver.cpp:105] Iteration 3806, lr = 0.001
I0401 14:33:08.428261 29493 solver.cpp:218] Iteration 3817 (2.23815 iter/s, 4.91478s/11 iters), loss = 3.9882
I0401 14:33:08.428299 29493 solver.cpp:237]     Train net output #0: loss = 3.9882 (* 1 = 3.9882 loss)
I0401 14:33:08.428305 29493 sgd_solver.cpp:105] Iteration 3817, lr = 0.001
I0401 14:33:12.450282 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3827.caffemodel
I0401 14:33:15.368607 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3827.solverstate
I0401 14:33:17.682435 29493 solver.cpp:330] Iteration 3827, Testing net (#0)
I0401 14:33:17.682454 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:33:23.906704 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:33:24.539678 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0810033
I0401 14:33:24.539711 29493 solver.cpp:397]     Test net output #1: loss = 4.47233 (* 1 = 4.47233 loss)
I0401 14:33:24.804744 29493 solver.cpp:218] Iteration 3828 (0.671697 iter/s, 16.3764s/11 iters), loss = 3.97178
I0401 14:33:24.806298 29493 solver.cpp:237]     Train net output #0: loss = 3.97178 (* 1 = 3.97178 loss)
I0401 14:33:24.806311 29493 sgd_solver.cpp:105] Iteration 3828, lr = 0.001
I0401 14:33:28.964509 29493 solver.cpp:218] Iteration 3839 (2.64537 iter/s, 4.1582s/11 iters), loss = 3.72554
I0401 14:33:28.964566 29493 solver.cpp:237]     Train net output #0: loss = 3.72554 (* 1 = 3.72554 loss)
I0401 14:33:28.964574 29493 sgd_solver.cpp:105] Iteration 3839, lr = 0.001
I0401 14:33:33.796824 29493 solver.cpp:218] Iteration 3850 (2.27637 iter/s, 4.83225s/11 iters), loss = 3.76856
I0401 14:33:33.803041 29493 solver.cpp:237]     Train net output #0: loss = 3.76856 (* 1 = 3.76856 loss)
I0401 14:33:33.803061 29493 sgd_solver.cpp:105] Iteration 3850, lr = 0.001
I0401 14:33:38.630105 29493 solver.cpp:218] Iteration 3861 (2.27882 iter/s, 4.82707s/11 iters), loss = 3.80377
I0401 14:33:38.630141 29493 solver.cpp:237]     Train net output #0: loss = 3.80377 (* 1 = 3.80377 loss)
I0401 14:33:38.630147 29493 sgd_solver.cpp:105] Iteration 3861, lr = 0.001
I0401 14:33:43.375155 29493 solver.cpp:218] Iteration 3872 (2.31823 iter/s, 4.745s/11 iters), loss = 3.87783
I0401 14:33:43.375207 29493 solver.cpp:237]     Train net output #0: loss = 3.87783 (* 1 = 3.87783 loss)
I0401 14:33:43.375216 29493 sgd_solver.cpp:105] Iteration 3872, lr = 0.001
I0401 14:33:48.299510 29493 solver.cpp:218] Iteration 3883 (2.23382 iter/s, 4.92429s/11 iters), loss = 3.79634
I0401 14:33:48.299633 29493 solver.cpp:237]     Train net output #0: loss = 3.79634 (* 1 = 3.79634 loss)
I0401 14:33:48.299643 29493 sgd_solver.cpp:105] Iteration 3883, lr = 0.001
I0401 14:33:52.632336 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:33:52.977051 29493 solver.cpp:218] Iteration 3894 (2.35173 iter/s, 4.6774s/11 iters), loss = 3.88115
I0401 14:33:52.977100 29493 solver.cpp:237]     Train net output #0: loss = 3.88115 (* 1 = 3.88115 loss)
I0401 14:33:52.977108 29493 sgd_solver.cpp:105] Iteration 3894, lr = 0.001
I0401 14:33:58.010926 29493 solver.cpp:218] Iteration 3905 (2.18522 iter/s, 5.03381s/11 iters), loss = 3.73494
I0401 14:33:58.010982 29493 solver.cpp:237]     Train net output #0: loss = 3.73494 (* 1 = 3.73494 loss)
I0401 14:33:58.010990 29493 sgd_solver.cpp:105] Iteration 3905, lr = 0.001
I0401 14:34:02.420662 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3916.caffemodel
I0401 14:34:05.535517 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3916.solverstate
I0401 14:34:07.874109 29493 solver.cpp:330] Iteration 3916, Testing net (#0)
I0401 14:34:07.874132 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:34:08.446388 29493 blocking_queue.cpp:49] Waiting for data
I0401 14:34:14.020308 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:34:14.672852 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0925164
I0401 14:34:14.672894 29493 solver.cpp:397]     Test net output #1: loss = 4.38804 (* 1 = 4.38804 loss)
I0401 14:34:14.814169 29493 solver.cpp:218] Iteration 3916 (0.654638 iter/s, 16.8032s/11 iters), loss = 4.01655
I0401 14:34:14.814230 29493 solver.cpp:237]     Train net output #0: loss = 4.01655 (* 1 = 4.01655 loss)
I0401 14:34:14.814240 29493 sgd_solver.cpp:105] Iteration 3916, lr = 0.001
I0401 14:34:18.680711 29493 solver.cpp:218] Iteration 3927 (2.84498 iter/s, 3.86646s/11 iters), loss = 3.80767
I0401 14:34:18.680838 29493 solver.cpp:237]     Train net output #0: loss = 3.80767 (* 1 = 3.80767 loss)
I0401 14:34:18.680846 29493 sgd_solver.cpp:105] Iteration 3927, lr = 0.001
I0401 14:34:23.605191 29493 solver.cpp:218] Iteration 3938 (2.2338 iter/s, 4.92434s/11 iters), loss = 4.05916
I0401 14:34:23.605243 29493 solver.cpp:237]     Train net output #0: loss = 4.05916 (* 1 = 4.05916 loss)
I0401 14:34:23.605250 29493 sgd_solver.cpp:105] Iteration 3938, lr = 0.001
I0401 14:34:28.660853 29493 solver.cpp:218] Iteration 3949 (2.17581 iter/s, 5.05559s/11 iters), loss = 3.9339
I0401 14:34:28.660918 29493 solver.cpp:237]     Train net output #0: loss = 3.9339 (* 1 = 3.9339 loss)
I0401 14:34:28.660928 29493 sgd_solver.cpp:105] Iteration 3949, lr = 0.001
I0401 14:34:33.554548 29493 solver.cpp:218] Iteration 3960 (2.24783 iter/s, 4.89362s/11 iters), loss = 3.89093
I0401 14:34:33.554600 29493 solver.cpp:237]     Train net output #0: loss = 3.89093 (* 1 = 3.89093 loss)
I0401 14:34:33.554605 29493 sgd_solver.cpp:105] Iteration 3960, lr = 0.001
I0401 14:34:38.493393 29493 solver.cpp:218] Iteration 3971 (2.22727 iter/s, 4.93878s/11 iters), loss = 3.85156
I0401 14:34:38.493439 29493 solver.cpp:237]     Train net output #0: loss = 3.85156 (* 1 = 3.85156 loss)
I0401 14:34:38.493444 29493 sgd_solver.cpp:105] Iteration 3971, lr = 0.001
I0401 14:34:43.422840 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:34:43.613333 29493 solver.cpp:218] Iteration 3982 (2.14849 iter/s, 5.11988s/11 iters), loss = 3.71335
I0401 14:34:43.613376 29493 solver.cpp:237]     Train net output #0: loss = 3.71335 (* 1 = 3.71335 loss)
I0401 14:34:43.613382 29493 sgd_solver.cpp:105] Iteration 3982, lr = 0.001
I0401 14:34:48.532606 29493 solver.cpp:218] Iteration 3993 (2.23613 iter/s, 4.91921s/11 iters), loss = 3.71462
I0401 14:34:48.532683 29493 solver.cpp:237]     Train net output #0: loss = 3.71462 (* 1 = 3.71462 loss)
I0401 14:34:48.532694 29493 sgd_solver.cpp:105] Iteration 3993, lr = 0.001
I0401 14:34:53.506103 29493 solver.cpp:218] Iteration 4004 (2.21176 iter/s, 4.97341s/11 iters), loss = 4.02587
I0401 14:34:53.506247 29493 solver.cpp:237]     Train net output #0: loss = 4.02587 (* 1 = 4.02587 loss)
I0401 14:34:53.506255 29493 sgd_solver.cpp:105] Iteration 4004, lr = 0.001
I0401 14:34:53.506458 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4005.caffemodel
I0401 14:34:56.460897 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4005.solverstate
I0401 14:34:58.750322 29493 solver.cpp:330] Iteration 4005, Testing net (#0)
I0401 14:34:58.750344 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:35:05.008352 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:35:05.784708 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0921053
I0401 14:35:05.784739 29493 solver.cpp:397]     Test net output #1: loss = 4.42862 (* 1 = 4.42862 loss)
I0401 14:35:09.353420 29493 solver.cpp:218] Iteration 4015 (0.694131 iter/s, 15.8472s/11 iters), loss = 3.78496
I0401 14:35:09.353467 29493 solver.cpp:237]     Train net output #0: loss = 3.78496 (* 1 = 3.78496 loss)
I0401 14:35:09.353475 29493 sgd_solver.cpp:105] Iteration 4015, lr = 0.001
I0401 14:35:14.286729 29493 solver.cpp:218] Iteration 4026 (2.22977 iter/s, 4.93325s/11 iters), loss = 3.85729
I0401 14:35:14.286782 29493 solver.cpp:237]     Train net output #0: loss = 3.85729 (* 1 = 3.85729 loss)
I0401 14:35:14.286789 29493 sgd_solver.cpp:105] Iteration 4026, lr = 0.001
I0401 14:35:19.102613 29493 solver.cpp:218] Iteration 4037 (2.28414 iter/s, 4.81582s/11 iters), loss = 3.90285
I0401 14:35:19.102663 29493 solver.cpp:237]     Train net output #0: loss = 3.90285 (* 1 = 3.90285 loss)
I0401 14:35:19.102669 29493 sgd_solver.cpp:105] Iteration 4037, lr = 0.001
I0401 14:35:23.907423 29493 solver.cpp:218] Iteration 4048 (2.2894 iter/s, 4.80475s/11 iters), loss = 3.90421
I0401 14:35:23.907516 29493 solver.cpp:237]     Train net output #0: loss = 3.90421 (* 1 = 3.90421 loss)
I0401 14:35:23.907522 29493 sgd_solver.cpp:105] Iteration 4048, lr = 0.001
I0401 14:35:28.592989 29493 solver.cpp:218] Iteration 4059 (2.34769 iter/s, 4.68546s/11 iters), loss = 4.04012
I0401 14:35:28.593037 29493 solver.cpp:237]     Train net output #0: loss = 4.04012 (* 1 = 4.04012 loss)
I0401 14:35:28.593045 29493 sgd_solver.cpp:105] Iteration 4059, lr = 0.001
I0401 14:35:33.615792 29493 solver.cpp:218] Iteration 4070 (2.19004 iter/s, 5.02274s/11 iters), loss = 3.87589
I0401 14:35:33.622014 29493 solver.cpp:237]     Train net output #0: loss = 3.87589 (* 1 = 3.87589 loss)
I0401 14:35:33.622033 29493 sgd_solver.cpp:105] Iteration 4070, lr = 0.001
I0401 14:35:33.750795 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:35:38.284512 29493 solver.cpp:218] Iteration 4081 (2.35925 iter/s, 4.6625s/11 iters), loss = 3.43611
I0401 14:35:38.284557 29493 solver.cpp:237]     Train net output #0: loss = 3.43611 (* 1 = 3.43611 loss)
I0401 14:35:38.284562 29493 sgd_solver.cpp:105] Iteration 4081, lr = 0.001
I0401 14:35:43.230638 29493 solver.cpp:218] Iteration 4092 (2.22399 iter/s, 4.94607s/11 iters), loss = 3.72661
I0401 14:35:43.230680 29493 solver.cpp:237]     Train net output #0: loss = 3.72661 (* 1 = 3.72661 loss)
I0401 14:35:43.230687 29493 sgd_solver.cpp:105] Iteration 4092, lr = 0.001
I0401 14:35:43.680635 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4094.caffemodel
I0401 14:35:46.554383 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4094.solverstate
I0401 14:35:48.960206 29493 solver.cpp:330] Iteration 4094, Testing net (#0)
I0401 14:35:48.960230 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:35:55.087042 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:35:55.747491 29493 solver.cpp:397]     Test net output #0: accuracy = 0.0970395
I0401 14:35:55.747524 29493 solver.cpp:397]     Test net output #1: loss = 4.39445 (* 1 = 4.39445 loss)
I0401 14:35:59.060986 29493 solver.cpp:218] Iteration 4103 (0.69487 iter/s, 15.8303s/11 iters), loss = 3.744
I0401 14:35:59.061034 29493 solver.cpp:237]     Train net output #0: loss = 3.744 (* 1 = 3.744 loss)
I0401 14:35:59.061040 29493 sgd_solver.cpp:105] Iteration 4103, lr = 0.001
I0401 14:36:03.778775 29493 solver.cpp:218] Iteration 4114 (2.33163 iter/s, 4.71772s/11 iters), loss = 3.58568
I0401 14:36:03.778823 29493 solver.cpp:237]     Train net output #0: loss = 3.58568 (* 1 = 3.58568 loss)
I0401 14:36:03.778828 29493 sgd_solver.cpp:105] Iteration 4114, lr = 0.001
I0401 14:36:08.759444 29493 solver.cpp:218] Iteration 4125 (2.20856 iter/s, 4.98061s/11 iters), loss = 3.89252
I0401 14:36:08.759507 29493 solver.cpp:237]     Train net output #0: loss = 3.89252 (* 1 = 3.89252 loss)
I0401 14:36:08.759516 29493 sgd_solver.cpp:105] Iteration 4125, lr = 0.001
I0401 14:36:13.845427 29493 solver.cpp:218] Iteration 4136 (2.16284 iter/s, 5.08591s/11 iters), loss = 3.5116
I0401 14:36:13.845469 29493 solver.cpp:237]     Train net output #0: loss = 3.5116 (* 1 = 3.5116 loss)
I0401 14:36:13.845475 29493 sgd_solver.cpp:105] Iteration 4136, lr = 0.001
I0401 14:36:18.767894 29493 solver.cpp:218] Iteration 4147 (2.23468 iter/s, 4.92241s/11 iters), loss = 3.64939
I0401 14:36:18.767952 29493 solver.cpp:237]     Train net output #0: loss = 3.64939 (* 1 = 3.64939 loss)
I0401 14:36:18.767961 29493 sgd_solver.cpp:105] Iteration 4147, lr = 0.001
I0401 14:36:23.428753 29493 solver.cpp:218] Iteration 4158 (2.36012 iter/s, 4.66079s/11 iters), loss = 3.4963
I0401 14:36:23.428802 29493 solver.cpp:237]     Train net output #0: loss = 3.4963 (* 1 = 3.4963 loss)
I0401 14:36:23.428808 29493 sgd_solver.cpp:105] Iteration 4158, lr = 0.001
I0401 14:36:23.687240 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:36:28.365360 29493 solver.cpp:218] Iteration 4169 (2.22828 iter/s, 4.93655s/11 iters), loss = 3.43408
I0401 14:36:28.365458 29493 solver.cpp:237]     Train net output #0: loss = 3.43408 (* 1 = 3.43408 loss)
I0401 14:36:28.365468 29493 sgd_solver.cpp:105] Iteration 4169, lr = 0.001
I0401 14:36:33.350281 29493 solver.cpp:218] Iteration 4180 (2.20671 iter/s, 4.98481s/11 iters), loss = 3.66579
I0401 14:36:33.350335 29493 solver.cpp:237]     Train net output #0: loss = 3.66579 (* 1 = 3.66579 loss)
I0401 14:36:33.350343 29493 sgd_solver.cpp:105] Iteration 4180, lr = 0.001
I0401 14:36:34.074767 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4183.caffemodel
I0401 14:36:37.166123 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4183.solverstate
I0401 14:36:39.763310 29493 solver.cpp:330] Iteration 4183, Testing net (#0)
I0401 14:36:39.763334 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:36:46.029107 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:36:46.708016 29493 solver.cpp:397]     Test net output #0: accuracy = 0.104441
I0401 14:36:46.708045 29493 solver.cpp:397]     Test net output #1: loss = 4.32266 (* 1 = 4.32266 loss)
I0401 14:36:49.481173 29493 solver.cpp:218] Iteration 4191 (0.681924 iter/s, 16.1308s/11 iters), loss = 3.46561
I0401 14:36:49.481231 29493 solver.cpp:237]     Train net output #0: loss = 3.46561 (* 1 = 3.46561 loss)
I0401 14:36:49.481240 29493 sgd_solver.cpp:105] Iteration 4191, lr = 0.001
I0401 14:36:54.408479 29493 solver.cpp:218] Iteration 4202 (2.23249 iter/s, 4.92723s/11 iters), loss = 3.56173
I0401 14:36:54.408527 29493 solver.cpp:237]     Train net output #0: loss = 3.56173 (* 1 = 3.56173 loss)
I0401 14:36:54.408532 29493 sgd_solver.cpp:105] Iteration 4202, lr = 0.001
I0401 14:36:59.445174 29493 solver.cpp:218] Iteration 4213 (2.184 iter/s, 5.03663s/11 iters), loss = 3.71269
I0401 14:36:59.445338 29493 solver.cpp:237]     Train net output #0: loss = 3.71269 (* 1 = 3.71269 loss)
I0401 14:36:59.445346 29493 sgd_solver.cpp:105] Iteration 4213, lr = 0.001
I0401 14:37:04.342775 29493 solver.cpp:218] Iteration 4224 (2.24608 iter/s, 4.89743s/11 iters), loss = 3.53734
I0401 14:37:04.342818 29493 solver.cpp:237]     Train net output #0: loss = 3.53734 (* 1 = 3.53734 loss)
I0401 14:37:04.342823 29493 sgd_solver.cpp:105] Iteration 4224, lr = 0.001
I0401 14:37:09.325110 29493 solver.cpp:218] Iteration 4235 (2.20783 iter/s, 4.98227s/11 iters), loss = 3.26331
I0401 14:37:09.325176 29493 solver.cpp:237]     Train net output #0: loss = 3.26331 (* 1 = 3.26331 loss)
I0401 14:37:09.325184 29493 sgd_solver.cpp:105] Iteration 4235, lr = 0.001
I0401 14:37:14.421859 29493 solver.cpp:218] Iteration 4246 (2.15827 iter/s, 5.09667s/11 iters), loss = 3.32461
I0401 14:37:14.421902 29493 solver.cpp:237]     Train net output #0: loss = 3.32461 (* 1 = 3.32461 loss)
I0401 14:37:14.421908 29493 sgd_solver.cpp:105] Iteration 4246, lr = 0.001
I0401 14:37:14.991881 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:37:19.440693 29493 solver.cpp:218] Iteration 4257 (2.19177 iter/s, 5.01878s/11 iters), loss = 3.30272
I0401 14:37:19.440745 29493 solver.cpp:237]     Train net output #0: loss = 3.30272 (* 1 = 3.30272 loss)
I0401 14:37:19.440753 29493 sgd_solver.cpp:105] Iteration 4257, lr = 0.001
I0401 14:37:24.156795 29493 solver.cpp:218] Iteration 4268 (2.33247 iter/s, 4.71604s/11 iters), loss = 3.24366
I0401 14:37:24.156853 29493 solver.cpp:237]     Train net output #0: loss = 3.24366 (* 1 = 3.24366 loss)
I0401 14:37:24.156862 29493 sgd_solver.cpp:105] Iteration 4268, lr = 0.001
I0401 14:37:25.413336 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4272.caffemodel
I0401 14:37:30.511260 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4272.solverstate
I0401 14:37:32.820021 29493 solver.cpp:330] Iteration 4272, Testing net (#0)
I0401 14:37:32.820045 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:37:38.854822 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:37:39.536551 29493 solver.cpp:397]     Test net output #0: accuracy = 0.106497
I0401 14:37:39.536581 29493 solver.cpp:397]     Test net output #1: loss = 4.28383 (* 1 = 4.28383 loss)
I0401 14:37:41.865475 29493 solver.cpp:218] Iteration 4279 (0.621166 iter/s, 17.7086s/11 iters), loss = 3.36151
I0401 14:37:41.865533 29493 solver.cpp:237]     Train net output #0: loss = 3.36151 (* 1 = 3.36151 loss)
I0401 14:37:41.865541 29493 sgd_solver.cpp:105] Iteration 4279, lr = 0.001
I0401 14:37:46.612960 29493 solver.cpp:218] Iteration 4290 (2.31705 iter/s, 4.74742s/11 iters), loss = 3.62362
I0401 14:37:46.613004 29493 solver.cpp:237]     Train net output #0: loss = 3.62362 (* 1 = 3.62362 loss)
I0401 14:37:46.613010 29493 sgd_solver.cpp:105] Iteration 4290, lr = 0.001
I0401 14:37:51.253846 29493 solver.cpp:218] Iteration 4301 (2.37027 iter/s, 4.64082s/11 iters), loss = 3.26377
I0401 14:37:51.253904 29493 solver.cpp:237]     Train net output #0: loss = 3.26377 (* 1 = 3.26377 loss)
I0401 14:37:51.253913 29493 sgd_solver.cpp:105] Iteration 4301, lr = 0.001
I0401 14:37:55.861676 29493 solver.cpp:218] Iteration 4312 (2.38728 iter/s, 4.60776s/11 iters), loss = 3.4534
I0401 14:37:55.861717 29493 solver.cpp:237]     Train net output #0: loss = 3.4534 (* 1 = 3.4534 loss)
I0401 14:37:55.861723 29493 sgd_solver.cpp:105] Iteration 4312, lr = 0.001
I0401 14:38:00.772621 29493 solver.cpp:218] Iteration 4323 (2.23992 iter/s, 4.91089s/11 iters), loss = 3.2287
I0401 14:38:00.772758 29493 solver.cpp:237]     Train net output #0: loss = 3.2287 (* 1 = 3.2287 loss)
I0401 14:38:00.772765 29493 sgd_solver.cpp:105] Iteration 4323, lr = 0.001
I0401 14:38:05.698875 29493 solver.cpp:218] Iteration 4334 (2.233 iter/s, 4.92611s/11 iters), loss = 3.21521
I0401 14:38:05.698937 29493 solver.cpp:237]     Train net output #0: loss = 3.21521 (* 1 = 3.21521 loss)
I0401 14:38:05.698946 29493 sgd_solver.cpp:105] Iteration 4334, lr = 0.001
I0401 14:38:06.400003 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:38:10.540215 29493 solver.cpp:218] Iteration 4345 (2.27213 iter/s, 4.84127s/11 iters), loss = 3.31256
I0401 14:38:10.540257 29493 solver.cpp:237]     Train net output #0: loss = 3.31256 (* 1 = 3.31256 loss)
I0401 14:38:10.540263 29493 sgd_solver.cpp:105] Iteration 4345, lr = 0.001
I0401 14:38:15.495184 29493 solver.cpp:218] Iteration 4356 (2.22002 iter/s, 4.95492s/11 iters), loss = 3.1795
I0401 14:38:15.495225 29493 solver.cpp:237]     Train net output #0: loss = 3.1795 (* 1 = 3.1795 loss)
I0401 14:38:15.495232 29493 sgd_solver.cpp:105] Iteration 4356, lr = 0.001
I0401 14:38:17.259035 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4361.caffemodel
I0401 14:38:20.195798 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4361.solverstate
I0401 14:38:22.482481 29493 solver.cpp:330] Iteration 4361, Testing net (#0)
I0401 14:38:22.482502 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:38:28.306114 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:38:29.009083 29493 solver.cpp:397]     Test net output #0: accuracy = 0.111842
I0401 14:38:29.009121 29493 solver.cpp:397]     Test net output #1: loss = 4.28873 (* 1 = 4.28873 loss)
I0401 14:38:30.870502 29493 solver.cpp:218] Iteration 4367 (0.715435 iter/s, 15.3753s/11 iters), loss = 3.20312
I0401 14:38:30.870641 29493 solver.cpp:237]     Train net output #0: loss = 3.20312 (* 1 = 3.20312 loss)
I0401 14:38:30.870651 29493 sgd_solver.cpp:105] Iteration 4367, lr = 0.001
I0401 14:38:35.660609 29493 solver.cpp:218] Iteration 4378 (2.29647 iter/s, 4.78996s/11 iters), loss = 3.45562
I0401 14:38:35.660650 29493 solver.cpp:237]     Train net output #0: loss = 3.45562 (* 1 = 3.45562 loss)
I0401 14:38:35.660655 29493 sgd_solver.cpp:105] Iteration 4378, lr = 0.001
I0401 14:38:40.617796 29493 solver.cpp:218] Iteration 4389 (2.21902 iter/s, 4.95713s/11 iters), loss = 3.37075
I0401 14:38:40.617842 29493 solver.cpp:237]     Train net output #0: loss = 3.37075 (* 1 = 3.37075 loss)
I0401 14:38:40.617848 29493 sgd_solver.cpp:105] Iteration 4389, lr = 0.001
I0401 14:38:45.510921 29493 solver.cpp:218] Iteration 4400 (2.24808 iter/s, 4.89306s/11 iters), loss = 3.52614
I0401 14:38:45.510985 29493 solver.cpp:237]     Train net output #0: loss = 3.52614 (* 1 = 3.52614 loss)
I0401 14:38:45.510994 29493 sgd_solver.cpp:105] Iteration 4400, lr = 0.001
I0401 14:38:50.300074 29493 solver.cpp:218] Iteration 4411 (2.29689 iter/s, 4.78907s/11 iters), loss = 3.62811
I0401 14:38:50.300133 29493 solver.cpp:237]     Train net output #0: loss = 3.62811 (* 1 = 3.62811 loss)
I0401 14:38:50.300141 29493 sgd_solver.cpp:105] Iteration 4411, lr = 0.001
I0401 14:38:55.050213 29493 solver.cpp:218] Iteration 4422 (2.31575 iter/s, 4.75007s/11 iters), loss = 3.16026
I0401 14:38:55.050254 29493 solver.cpp:237]     Train net output #0: loss = 3.16026 (* 1 = 3.16026 loss)
I0401 14:38:55.050259 29493 sgd_solver.cpp:105] Iteration 4422, lr = 0.001
I0401 14:38:56.034996 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:38:59.882102 29493 solver.cpp:218] Iteration 4433 (2.27657 iter/s, 4.83183s/11 iters), loss = 3.50254
I0401 14:38:59.882143 29493 solver.cpp:237]     Train net output #0: loss = 3.50254 (* 1 = 3.50254 loss)
I0401 14:38:59.882148 29493 sgd_solver.cpp:105] Iteration 4433, lr = 0.001
I0401 14:39:04.792449 29493 solver.cpp:218] Iteration 4444 (2.24019 iter/s, 4.9103s/11 iters), loss = 3.08189
I0401 14:39:04.792587 29493 solver.cpp:237]     Train net output #0: loss = 3.08189 (* 1 = 3.08189 loss)
I0401 14:39:04.792596 29493 sgd_solver.cpp:105] Iteration 4444, lr = 0.001
I0401 14:39:06.890833 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4450.caffemodel
I0401 14:39:09.873894 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4450.solverstate
I0401 14:39:12.166186 29493 solver.cpp:330] Iteration 4450, Testing net (#0)
I0401 14:39:12.166205 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:39:16.860020 29493 blocking_queue.cpp:49] Waiting for data
I0401 14:39:18.010519 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:39:18.716869 29493 solver.cpp:397]     Test net output #0: accuracy = 0.117599
I0401 14:39:18.716914 29493 solver.cpp:397]     Test net output #1: loss = 4.28169 (* 1 = 4.28169 loss)
I0401 14:39:20.138520 29493 solver.cpp:218] Iteration 4455 (0.716802 iter/s, 15.3459s/11 iters), loss = 3.32497
I0401 14:39:20.138576 29493 solver.cpp:237]     Train net output #0: loss = 3.32497 (* 1 = 3.32497 loss)
I0401 14:39:20.138583 29493 sgd_solver.cpp:105] Iteration 4455, lr = 0.001
I0401 14:39:24.774200 29493 solver.cpp:218] Iteration 4466 (2.37293 iter/s, 4.63561s/11 iters), loss = 3.4154
I0401 14:39:24.774242 29493 solver.cpp:237]     Train net output #0: loss = 3.4154 (* 1 = 3.4154 loss)
I0401 14:39:24.774248 29493 sgd_solver.cpp:105] Iteration 4466, lr = 0.001
I0401 14:39:29.569483 29493 solver.cpp:218] Iteration 4477 (2.29395 iter/s, 4.79523s/11 iters), loss = 3.47987
I0401 14:39:29.569536 29493 solver.cpp:237]     Train net output #0: loss = 3.47987 (* 1 = 3.47987 loss)
I0401 14:39:29.569545 29493 sgd_solver.cpp:105] Iteration 4477, lr = 0.001
I0401 14:39:34.089241 29493 solver.cpp:218] Iteration 4488 (2.4338 iter/s, 4.51969s/11 iters), loss = 3.34793
I0401 14:39:34.089293 29493 solver.cpp:237]     Train net output #0: loss = 3.34793 (* 1 = 3.34793 loss)
I0401 14:39:34.089300 29493 sgd_solver.cpp:105] Iteration 4488, lr = 0.001
I0401 14:39:39.038681 29493 solver.cpp:218] Iteration 4499 (2.2225 iter/s, 4.94938s/11 iters), loss = 3.61176
I0401 14:39:39.038806 29493 solver.cpp:237]     Train net output #0: loss = 3.61176 (* 1 = 3.61176 loss)
I0401 14:39:39.038813 29493 sgd_solver.cpp:105] Iteration 4499, lr = 0.001
I0401 14:39:44.012642 29493 solver.cpp:218] Iteration 4510 (2.21158 iter/s, 4.97383s/11 iters), loss = 3.35592
I0401 14:39:44.012681 29493 solver.cpp:237]     Train net output #0: loss = 3.35592 (* 1 = 3.35592 loss)
I0401 14:39:44.012688 29493 sgd_solver.cpp:105] Iteration 4510, lr = 0.001
I0401 14:39:45.022213 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:39:48.582243 29493 solver.cpp:218] Iteration 4521 (2.40724 iter/s, 4.56955s/11 iters), loss = 3.51274
I0401 14:39:48.582290 29493 solver.cpp:237]     Train net output #0: loss = 3.51274 (* 1 = 3.51274 loss)
I0401 14:39:48.582298 29493 sgd_solver.cpp:105] Iteration 4521, lr = 0.001
I0401 14:39:53.500520 29493 solver.cpp:218] Iteration 4532 (2.23658 iter/s, 4.91822s/11 iters), loss = 3.1014
I0401 14:39:53.500568 29493 solver.cpp:237]     Train net output #0: loss = 3.1014 (* 1 = 3.1014 loss)
I0401 14:39:53.500576 29493 sgd_solver.cpp:105] Iteration 4532, lr = 0.001
I0401 14:39:56.041141 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4539.caffemodel
I0401 14:39:59.005484 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4539.solverstate
I0401 14:40:01.346107 29493 solver.cpp:330] Iteration 4539, Testing net (#0)
I0401 14:40:01.346128 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:40:07.145370 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:40:07.879220 29493 solver.cpp:397]     Test net output #0: accuracy = 0.11472
I0401 14:40:07.879269 29493 solver.cpp:397]     Test net output #1: loss = 4.25832 (* 1 = 4.25832 loss)
I0401 14:40:08.930918 29493 solver.cpp:218] Iteration 4543 (0.712881 iter/s, 15.4303s/11 iters), loss = 3.25884
I0401 14:40:08.930976 29493 solver.cpp:237]     Train net output #0: loss = 3.25884 (* 1 = 3.25884 loss)
I0401 14:40:08.930984 29493 sgd_solver.cpp:105] Iteration 4543, lr = 0.001
I0401 14:40:13.732836 29493 solver.cpp:218] Iteration 4554 (2.29079 iter/s, 4.80185s/11 iters), loss = 3.55017
I0401 14:40:13.733003 29493 solver.cpp:237]     Train net output #0: loss = 3.55017 (* 1 = 3.55017 loss)
I0401 14:40:13.733012 29493 sgd_solver.cpp:105] Iteration 4554, lr = 0.001
I0401 14:40:18.633587 29493 solver.cpp:218] Iteration 4565 (2.24464 iter/s, 4.90057s/11 iters), loss = 3.23756
I0401 14:40:18.633651 29493 solver.cpp:237]     Train net output #0: loss = 3.23756 (* 1 = 3.23756 loss)
I0401 14:40:18.633661 29493 sgd_solver.cpp:105] Iteration 4565, lr = 0.001
I0401 14:40:23.531731 29493 solver.cpp:218] Iteration 4576 (2.24579 iter/s, 4.89806s/11 iters), loss = 3.27933
I0401 14:40:23.531796 29493 solver.cpp:237]     Train net output #0: loss = 3.27933 (* 1 = 3.27933 loss)
I0401 14:40:23.531807 29493 sgd_solver.cpp:105] Iteration 4576, lr = 0.001
I0401 14:40:28.341409 29493 solver.cpp:218] Iteration 4587 (2.28709 iter/s, 4.80961s/11 iters), loss = 3.67753
I0401 14:40:28.341449 29493 solver.cpp:237]     Train net output #0: loss = 3.67753 (* 1 = 3.67753 loss)
I0401 14:40:28.341454 29493 sgd_solver.cpp:105] Iteration 4587, lr = 0.001
I0401 14:40:33.310694 29493 solver.cpp:218] Iteration 4598 (2.21362 iter/s, 4.96923s/11 iters), loss = 3.31547
I0401 14:40:33.310753 29493 solver.cpp:237]     Train net output #0: loss = 3.31547 (* 1 = 3.31547 loss)
I0401 14:40:33.310761 29493 sgd_solver.cpp:105] Iteration 4598, lr = 0.001
I0401 14:40:34.770727 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:40:38.223955 29493 solver.cpp:218] Iteration 4609 (2.23887 iter/s, 4.91319s/11 iters), loss = 3.12154
I0401 14:40:38.223994 29493 solver.cpp:237]     Train net output #0: loss = 3.12154 (* 1 = 3.12154 loss)
I0401 14:40:38.224000 29493 sgd_solver.cpp:105] Iteration 4609, lr = 0.001
I0401 14:40:42.810952 29493 solver.cpp:218] Iteration 4620 (2.39811 iter/s, 4.58694s/11 iters), loss = 3.0842
I0401 14:40:42.811000 29493 solver.cpp:237]     Train net output #0: loss = 3.0842 (* 1 = 3.0842 loss)
I0401 14:40:42.811008 29493 sgd_solver.cpp:105] Iteration 4620, lr = 0.001
I0401 14:40:45.714833 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4628.caffemodel
I0401 14:40:48.834184 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4628.solverstate
I0401 14:40:51.143322 29493 solver.cpp:330] Iteration 4628, Testing net (#0)
I0401 14:40:51.143345 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:40:56.893782 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:40:57.638921 29493 solver.cpp:397]     Test net output #0: accuracy = 0.122533
I0401 14:40:57.638957 29493 solver.cpp:397]     Test net output #1: loss = 4.23597 (* 1 = 4.23597 loss)
I0401 14:40:58.213393 29493 solver.cpp:218] Iteration 4631 (0.714175 iter/s, 15.4024s/11 iters), loss = 3.05179
I0401 14:40:58.213443 29493 solver.cpp:237]     Train net output #0: loss = 3.05179 (* 1 = 3.05179 loss)
I0401 14:40:58.213449 29493 sgd_solver.cpp:105] Iteration 4631, lr = 0.001
I0401 14:41:02.974800 29493 solver.cpp:218] Iteration 4642 (2.31027 iter/s, 4.76134s/11 iters), loss = 3.20695
I0401 14:41:02.974849 29493 solver.cpp:237]     Train net output #0: loss = 3.20695 (* 1 = 3.20695 loss)
I0401 14:41:02.974856 29493 sgd_solver.cpp:105] Iteration 4642, lr = 0.001
I0401 14:41:07.807818 29493 solver.cpp:218] Iteration 4653 (2.27604 iter/s, 4.83296s/11 iters), loss = 3.15282
I0401 14:41:07.807860 29493 solver.cpp:237]     Train net output #0: loss = 3.15282 (* 1 = 3.15282 loss)
I0401 14:41:07.807868 29493 sgd_solver.cpp:105] Iteration 4653, lr = 0.001
I0401 14:41:12.649174 29493 solver.cpp:218] Iteration 4664 (2.27212 iter/s, 4.8413s/11 iters), loss = 3.06826
I0401 14:41:12.649221 29493 solver.cpp:237]     Train net output #0: loss = 3.06826 (* 1 = 3.06826 loss)
I0401 14:41:12.649227 29493 sgd_solver.cpp:105] Iteration 4664, lr = 0.001
I0401 14:41:17.585716 29493 solver.cpp:218] Iteration 4675 (2.22831 iter/s, 4.93648s/11 iters), loss = 3.38534
I0401 14:41:17.585868 29493 solver.cpp:237]     Train net output #0: loss = 3.38534 (* 1 = 3.38534 loss)
I0401 14:41:17.585878 29493 sgd_solver.cpp:105] Iteration 4675, lr = 0.001
I0401 14:41:22.432562 29493 solver.cpp:218] Iteration 4686 (2.26959 iter/s, 4.84668s/11 iters), loss = 3.42169
I0401 14:41:22.432622 29493 solver.cpp:237]     Train net output #0: loss = 3.42169 (* 1 = 3.42169 loss)
I0401 14:41:22.432631 29493 sgd_solver.cpp:105] Iteration 4686, lr = 0.001
I0401 14:41:24.034224 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:41:27.333628 29493 solver.cpp:218] Iteration 4697 (2.24444 iter/s, 4.90099s/11 iters), loss = 3.18945
I0401 14:41:27.333670 29493 solver.cpp:237]     Train net output #0: loss = 3.18945 (* 1 = 3.18945 loss)
I0401 14:41:27.333676 29493 sgd_solver.cpp:105] Iteration 4697, lr = 0.001
I0401 14:41:32.223323 29493 solver.cpp:218] Iteration 4708 (2.24966 iter/s, 4.88964s/11 iters), loss = 3.15044
I0401 14:41:32.223381 29493 solver.cpp:237]     Train net output #0: loss = 3.15044 (* 1 = 3.15044 loss)
I0401 14:41:32.223389 29493 sgd_solver.cpp:105] Iteration 4708, lr = 0.001
I0401 14:41:35.694324 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4717.caffemodel
I0401 14:41:38.691119 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4717.solverstate
I0401 14:41:40.986263 29493 solver.cpp:330] Iteration 4717, Testing net (#0)
I0401 14:41:40.986284 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:41:46.999562 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:41:47.806598 29493 solver.cpp:397]     Test net output #0: accuracy = 0.124589
I0401 14:41:47.806676 29493 solver.cpp:397]     Test net output #1: loss = 4.27127 (* 1 = 4.27127 loss)
I0401 14:41:48.219687 29493 solver.cpp:218] Iteration 4719 (0.687659 iter/s, 15.9963s/11 iters), loss = 2.99779
I0401 14:41:48.219746 29493 solver.cpp:237]     Train net output #0: loss = 2.99779 (* 1 = 2.99779 loss)
I0401 14:41:48.219754 29493 sgd_solver.cpp:105] Iteration 4719, lr = 0.001
I0401 14:41:52.712939 29493 solver.cpp:218] Iteration 4730 (2.44815 iter/s, 4.49318s/11 iters), loss = 3.15504
I0401 14:41:52.712978 29493 solver.cpp:237]     Train net output #0: loss = 3.15504 (* 1 = 3.15504 loss)
I0401 14:41:52.712985 29493 sgd_solver.cpp:105] Iteration 4730, lr = 0.001
I0401 14:41:57.291539 29493 solver.cpp:218] Iteration 4741 (2.40251 iter/s, 4.57855s/11 iters), loss = 3.10415
I0401 14:41:57.291590 29493 solver.cpp:237]     Train net output #0: loss = 3.10415 (* 1 = 3.10415 loss)
I0401 14:41:57.291597 29493 sgd_solver.cpp:105] Iteration 4741, lr = 0.001
I0401 14:42:02.119475 29493 solver.cpp:218] Iteration 4752 (2.27844 iter/s, 4.82787s/11 iters), loss = 3.24482
I0401 14:42:02.119535 29493 solver.cpp:237]     Train net output #0: loss = 3.24482 (* 1 = 3.24482 loss)
I0401 14:42:02.119542 29493 sgd_solver.cpp:105] Iteration 4752, lr = 0.001
I0401 14:42:06.853637 29493 solver.cpp:218] Iteration 4763 (2.32357 iter/s, 4.73409s/11 iters), loss = 3.45351
I0401 14:42:06.853679 29493 solver.cpp:237]     Train net output #0: loss = 3.45351 (* 1 = 3.45351 loss)
I0401 14:42:06.853685 29493 sgd_solver.cpp:105] Iteration 4763, lr = 0.001
I0401 14:42:11.832554 29493 solver.cpp:218] Iteration 4774 (2.20934 iter/s, 4.97886s/11 iters), loss = 3.30863
I0401 14:42:11.832612 29493 solver.cpp:237]     Train net output #0: loss = 3.30863 (* 1 = 3.30863 loss)
I0401 14:42:11.832621 29493 sgd_solver.cpp:105] Iteration 4774, lr = 0.001
I0401 14:42:13.673736 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:42:16.559149 29493 solver.cpp:218] Iteration 4785 (2.32729 iter/s, 4.72653s/11 iters), loss = 2.91912
I0401 14:42:16.559208 29493 solver.cpp:237]     Train net output #0: loss = 2.91912 (* 1 = 2.91912 loss)
I0401 14:42:16.559218 29493 sgd_solver.cpp:105] Iteration 4785, lr = 0.001
I0401 14:42:21.431766 29493 solver.cpp:218] Iteration 4796 (2.25755 iter/s, 4.87255s/11 iters), loss = 3.32686
I0401 14:42:21.431915 29493 solver.cpp:237]     Train net output #0: loss = 3.32686 (* 1 = 3.32686 loss)
I0401 14:42:21.431926 29493 sgd_solver.cpp:105] Iteration 4796, lr = 0.001
I0401 14:42:25.370344 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4806.caffemodel
I0401 14:42:28.476200 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4806.solverstate
I0401 14:42:32.519695 29493 solver.cpp:330] Iteration 4806, Testing net (#0)
I0401 14:42:32.519717 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:42:38.524152 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:42:39.289927 29493 solver.cpp:397]     Test net output #0: accuracy = 0.129934
I0401 14:42:39.289960 29493 solver.cpp:397]     Test net output #1: loss = 4.26113 (* 1 = 4.26113 loss)
I0401 14:42:39.565682 29493 solver.cpp:218] Iteration 4807 (0.606603 iter/s, 18.1338s/11 iters), loss = 3.29375
I0401 14:42:39.567260 29493 solver.cpp:237]     Train net output #0: loss = 3.29375 (* 1 = 3.29375 loss)
I0401 14:42:39.567270 29493 sgd_solver.cpp:105] Iteration 4807, lr = 0.001
I0401 14:42:43.691152 29493 solver.cpp:218] Iteration 4818 (2.66739 iter/s, 4.12389s/11 iters), loss = 3.02001
I0401 14:42:43.691200 29493 solver.cpp:237]     Train net output #0: loss = 3.02001 (* 1 = 3.02001 loss)
I0401 14:42:43.691206 29493 sgd_solver.cpp:105] Iteration 4818, lr = 0.001
I0401 14:42:48.461398 29493 solver.cpp:218] Iteration 4829 (2.30599 iter/s, 4.77018s/11 iters), loss = 3.21389
I0401 14:42:48.461447 29493 solver.cpp:237]     Train net output #0: loss = 3.21389 (* 1 = 3.21389 loss)
I0401 14:42:48.461453 29493 sgd_solver.cpp:105] Iteration 4829, lr = 0.001
I0401 14:42:53.343900 29493 solver.cpp:218] Iteration 4840 (2.25298 iter/s, 4.88243s/11 iters), loss = 3.26295
I0401 14:42:53.344009 29493 solver.cpp:237]     Train net output #0: loss = 3.26295 (* 1 = 3.26295 loss)
I0401 14:42:53.344018 29493 sgd_solver.cpp:105] Iteration 4840, lr = 0.001
I0401 14:42:58.172560 29493 solver.cpp:218] Iteration 4851 (2.27812 iter/s, 4.82854s/11 iters), loss = 3.28106
I0401 14:42:58.172605 29493 solver.cpp:237]     Train net output #0: loss = 3.28106 (* 1 = 3.28106 loss)
I0401 14:42:58.172611 29493 sgd_solver.cpp:105] Iteration 4851, lr = 0.001
I0401 14:43:03.157801 29493 solver.cpp:218] Iteration 4862 (2.20654 iter/s, 4.98518s/11 iters), loss = 2.99354
I0401 14:43:03.157846 29493 solver.cpp:237]     Train net output #0: loss = 2.99354 (* 1 = 2.99354 loss)
I0401 14:43:03.157852 29493 sgd_solver.cpp:105] Iteration 4862, lr = 0.001
I0401 14:43:05.039475 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:43:07.905432 29493 solver.cpp:218] Iteration 4873 (2.31698 iter/s, 4.74757s/11 iters), loss = 2.73241
I0401 14:43:07.905476 29493 solver.cpp:237]     Train net output #0: loss = 2.73241 (* 1 = 2.73241 loss)
I0401 14:43:07.905483 29493 sgd_solver.cpp:105] Iteration 4873, lr = 0.001
I0401 14:43:12.703718 29493 solver.cpp:218] Iteration 4884 (2.29251 iter/s, 4.79823s/11 iters), loss = 2.95358
I0401 14:43:12.703763 29493 solver.cpp:237]     Train net output #0: loss = 2.95358 (* 1 = 2.95358 loss)
I0401 14:43:12.703770 29493 sgd_solver.cpp:105] Iteration 4884, lr = 0.001
I0401 14:43:17.011381 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4895.caffemodel
I0401 14:43:20.845005 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4895.solverstate
I0401 14:43:23.158339 29493 solver.cpp:330] Iteration 4895, Testing net (#0)
I0401 14:43:23.158360 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:43:29.003675 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:43:29.782339 29493 solver.cpp:397]     Test net output #0: accuracy = 0.129523
I0401 14:43:29.782375 29493 solver.cpp:397]     Test net output #1: loss = 4.36059 (* 1 = 4.36059 loss)
I0401 14:43:29.917541 29493 solver.cpp:218] Iteration 4895 (0.639023 iter/s, 17.2138s/11 iters), loss = 3.12024
I0401 14:43:29.917603 29493 solver.cpp:237]     Train net output #0: loss = 3.12024 (* 1 = 3.12024 loss)
I0401 14:43:29.917610 29493 sgd_solver.cpp:105] Iteration 4895, lr = 0.001
I0401 14:43:33.810200 29493 solver.cpp:218] Iteration 4906 (2.82588 iter/s, 3.8926s/11 iters), loss = 2.91952
I0401 14:43:33.810240 29493 solver.cpp:237]     Train net output #0: loss = 2.91952 (* 1 = 2.91952 loss)
I0401 14:43:33.810245 29493 sgd_solver.cpp:105] Iteration 4906, lr = 0.001
I0401 14:43:38.712035 29493 solver.cpp:218] Iteration 4917 (2.24408 iter/s, 4.90178s/11 iters), loss = 3.02275
I0401 14:43:38.712091 29493 solver.cpp:237]     Train net output #0: loss = 3.02275 (* 1 = 3.02275 loss)
I0401 14:43:38.712100 29493 sgd_solver.cpp:105] Iteration 4917, lr = 0.001
I0401 14:43:43.550598 29493 solver.cpp:218] Iteration 4928 (2.27343 iter/s, 4.8385s/11 iters), loss = 3.05763
I0401 14:43:43.550637 29493 solver.cpp:237]     Train net output #0: loss = 3.05763 (* 1 = 3.05763 loss)
I0401 14:43:43.550642 29493 sgd_solver.cpp:105] Iteration 4928, lr = 0.001
I0401 14:43:48.202504 29493 solver.cpp:218] Iteration 4939 (2.36465 iter/s, 4.65185s/11 iters), loss = 3.20536
I0401 14:43:48.202564 29493 solver.cpp:237]     Train net output #0: loss = 3.20536 (* 1 = 3.20536 loss)
I0401 14:43:48.202569 29493 sgd_solver.cpp:105] Iteration 4939, lr = 0.001
I0401 14:43:53.039006 29493 solver.cpp:218] Iteration 4950 (2.27441 iter/s, 4.83643s/11 iters), loss = 3.1333
I0401 14:43:53.039058 29493 solver.cpp:237]     Train net output #0: loss = 3.1333 (* 1 = 3.1333 loss)
I0401 14:43:53.039067 29493 sgd_solver.cpp:105] Iteration 4950, lr = 0.001
I0401 14:43:55.322211 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:43:57.800371 29493 solver.cpp:218] Iteration 4961 (2.31029 iter/s, 4.7613s/11 iters), loss = 2.64552
I0401 14:43:57.800416 29493 solver.cpp:237]     Train net output #0: loss = 2.64552 (* 1 = 2.64552 loss)
I0401 14:43:57.800422 29493 sgd_solver.cpp:105] Iteration 4961, lr = 0.001
I0401 14:44:02.683441 29493 solver.cpp:218] Iteration 4972 (2.25271 iter/s, 4.88301s/11 iters), loss = 2.97632
I0401 14:44:02.683593 29493 solver.cpp:237]     Train net output #0: loss = 2.97632 (* 1 = 2.97632 loss)
I0401 14:44:02.683604 29493 sgd_solver.cpp:105] Iteration 4972, lr = 0.001
I0401 14:44:07.545905 29493 solver.cpp:218] Iteration 4983 (2.2623 iter/s, 4.8623s/11 iters), loss = 2.88616
I0401 14:44:07.545946 29493 solver.cpp:237]     Train net output #0: loss = 2.88616 (* 1 = 2.88616 loss)
I0401 14:44:07.545953 29493 sgd_solver.cpp:105] Iteration 4983, lr = 0.001
I0401 14:44:07.546087 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4984.caffemodel
I0401 14:44:10.437438 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4984.solverstate
I0401 14:44:12.757328 29493 solver.cpp:330] Iteration 4984, Testing net (#0)
I0401 14:44:12.757354 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:44:18.696072 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:44:19.486832 29493 solver.cpp:397]     Test net output #0: accuracy = 0.130757
I0401 14:44:19.486868 29493 solver.cpp:397]     Test net output #1: loss = 4.31486 (* 1 = 4.31486 loss)
I0401 14:44:23.011307 29493 solver.cpp:218] Iteration 4994 (0.711267 iter/s, 15.4654s/11 iters), loss = 3.10669
I0401 14:44:23.011346 29493 solver.cpp:237]     Train net output #0: loss = 3.10669 (* 1 = 3.10669 loss)
I0401 14:44:23.011351 29493 sgd_solver.cpp:105] Iteration 4994, lr = 0.001
I0401 14:44:27.693948 29493 solver.cpp:218] Iteration 5005 (2.34913 iter/s, 4.68259s/11 iters), loss = 2.80995
I0401 14:44:27.693990 29493 solver.cpp:237]     Train net output #0: loss = 2.80995 (* 1 = 2.80995 loss)
I0401 14:44:27.693995 29493 sgd_solver.cpp:105] Iteration 5005, lr = 0.001
I0401 14:44:30.293938 29493 blocking_queue.cpp:49] Waiting for data
I0401 14:44:32.543608 29493 solver.cpp:218] Iteration 5016 (2.26823 iter/s, 4.84961s/11 iters), loss = 3.00586
I0401 14:44:32.543649 29493 solver.cpp:237]     Train net output #0: loss = 3.00586 (* 1 = 3.00586 loss)
I0401 14:44:32.543655 29493 sgd_solver.cpp:105] Iteration 5016, lr = 0.001
I0401 14:44:37.176687 29493 solver.cpp:218] Iteration 5027 (2.37426 iter/s, 4.63301s/11 iters), loss = 3.74655
I0401 14:44:37.176869 29493 solver.cpp:237]     Train net output #0: loss = 3.74655 (* 1 = 3.74655 loss)
I0401 14:44:37.176888 29493 sgd_solver.cpp:105] Iteration 5027, lr = 0.001
I0401 14:44:42.158542 29493 solver.cpp:218] Iteration 5038 (2.20809 iter/s, 4.98167s/11 iters), loss = 3.07696
I0401 14:44:42.158586 29493 solver.cpp:237]     Train net output #0: loss = 3.07696 (* 1 = 3.07696 loss)
I0401 14:44:42.158591 29493 sgd_solver.cpp:105] Iteration 5038, lr = 0.001
I0401 14:44:44.483044 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:44:46.714609 29493 solver.cpp:218] Iteration 5049 (2.41439 iter/s, 4.55601s/11 iters), loss = 3.04519
I0401 14:44:46.714650 29493 solver.cpp:237]     Train net output #0: loss = 3.04519 (* 1 = 3.04519 loss)
I0401 14:44:46.714656 29493 sgd_solver.cpp:105] Iteration 5049, lr = 0.001
I0401 14:44:51.394345 29493 solver.cpp:218] Iteration 5060 (2.35059 iter/s, 4.67968s/11 iters), loss = 2.98738
I0401 14:44:51.394389 29493 solver.cpp:237]     Train net output #0: loss = 2.98738 (* 1 = 2.98738 loss)
I0401 14:44:51.394395 29493 sgd_solver.cpp:105] Iteration 5060, lr = 0.001
I0401 14:44:56.008628 29493 solver.cpp:218] Iteration 5071 (2.38393 iter/s, 4.61423s/11 iters), loss = 2.8782
I0401 14:44:56.008668 29493 solver.cpp:237]     Train net output #0: loss = 2.8782 (* 1 = 2.8782 loss)
I0401 14:44:56.008674 29493 sgd_solver.cpp:105] Iteration 5071, lr = 0.001
I0401 14:44:56.384007 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5073.caffemodel
I0401 14:44:59.687166 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5073.solverstate
I0401 14:45:01.994343 29493 solver.cpp:330] Iteration 5073, Testing net (#0)
I0401 14:45:01.994362 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:45:07.700757 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:45:08.517894 29493 solver.cpp:397]     Test net output #0: accuracy = 0.127467
I0401 14:45:08.517927 29493 solver.cpp:397]     Test net output #1: loss = 4.31947 (* 1 = 4.31947 loss)
I0401 14:45:11.711769 29493 solver.cpp:218] Iteration 5082 (0.700499 iter/s, 15.7031s/11 iters), loss = 3.13349
I0401 14:45:11.711815 29493 solver.cpp:237]     Train net output #0: loss = 3.13349 (* 1 = 3.13349 loss)
I0401 14:45:11.711822 29493 sgd_solver.cpp:105] Iteration 5082, lr = 0.001
I0401 14:45:16.704535 29493 solver.cpp:218] Iteration 5093 (2.20321 iter/s, 4.99271s/11 iters), loss = 2.91875
I0401 14:45:16.704576 29493 solver.cpp:237]     Train net output #0: loss = 2.91875 (* 1 = 2.91875 loss)
I0401 14:45:16.704582 29493 sgd_solver.cpp:105] Iteration 5093, lr = 0.001
I0401 14:45:21.636653 29493 solver.cpp:218] Iteration 5104 (2.23031 iter/s, 4.93206s/11 iters), loss = 3.05809
I0401 14:45:21.636703 29493 solver.cpp:237]     Train net output #0: loss = 3.05809 (* 1 = 3.05809 loss)
I0401 14:45:21.636708 29493 sgd_solver.cpp:105] Iteration 5104, lr = 0.001
I0401 14:45:26.550318 29493 solver.cpp:218] Iteration 5115 (2.23868 iter/s, 4.9136s/11 iters), loss = 3.06638
I0401 14:45:26.550360 29493 solver.cpp:237]     Train net output #0: loss = 3.06638 (* 1 = 3.06638 loss)
I0401 14:45:26.550365 29493 sgd_solver.cpp:105] Iteration 5115, lr = 0.001
I0401 14:45:31.375053 29493 solver.cpp:218] Iteration 5126 (2.27995 iter/s, 4.82468s/11 iters), loss = 2.56672
I0401 14:45:31.375113 29493 solver.cpp:237]     Train net output #0: loss = 2.56672 (* 1 = 2.56672 loss)
I0401 14:45:31.375123 29493 sgd_solver.cpp:105] Iteration 5126, lr = 0.001
I0401 14:45:34.191522 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:45:36.288141 29493 solver.cpp:218] Iteration 5137 (2.23895 iter/s, 4.91302s/11 iters), loss = 2.50326
I0401 14:45:36.288187 29493 solver.cpp:237]     Train net output #0: loss = 2.50326 (* 1 = 2.50326 loss)
I0401 14:45:36.288192 29493 sgd_solver.cpp:105] Iteration 5137, lr = 0.001
I0401 14:45:41.128387 29493 solver.cpp:218] Iteration 5148 (2.27264 iter/s, 4.84019s/11 iters), loss = 2.8404
I0401 14:45:41.128554 29493 solver.cpp:237]     Train net output #0: loss = 2.8404 (* 1 = 2.8404 loss)
I0401 14:45:41.128564 29493 sgd_solver.cpp:105] Iteration 5148, lr = 0.001
I0401 14:45:45.766400 29493 solver.cpp:218] Iteration 5159 (2.37179 iter/s, 4.63785s/11 iters), loss = 2.72071
I0401 14:45:45.766441 29493 solver.cpp:237]     Train net output #0: loss = 2.72071 (* 1 = 2.72071 loss)
I0401 14:45:45.766446 29493 sgd_solver.cpp:105] Iteration 5159, lr = 0.001
I0401 14:45:46.537909 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5162.caffemodel
I0401 14:45:50.927165 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5162.solverstate
I0401 14:45:53.224512 29493 solver.cpp:330] Iteration 5162, Testing net (#0)
I0401 14:45:53.224531 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:45:58.953775 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:45:59.781839 29493 solver.cpp:397]     Test net output #0: accuracy = 0.121299
I0401 14:45:59.781877 29493 solver.cpp:397]     Test net output #1: loss = 4.47078 (* 1 = 4.47078 loss)
I0401 14:46:02.650296 29493 solver.cpp:218] Iteration 5170 (0.65151 iter/s, 16.8839s/11 iters), loss = 2.9785
I0401 14:46:02.650339 29493 solver.cpp:237]     Train net output #0: loss = 2.9785 (* 1 = 2.9785 loss)
I0401 14:46:02.650346 29493 sgd_solver.cpp:105] Iteration 5170, lr = 0.001
I0401 14:46:07.598805 29493 solver.cpp:218] Iteration 5181 (2.22292 iter/s, 4.94845s/11 iters), loss = 2.55449
I0401 14:46:07.598850 29493 solver.cpp:237]     Train net output #0: loss = 2.55449 (* 1 = 2.55449 loss)
I0401 14:46:07.598855 29493 sgd_solver.cpp:105] Iteration 5181, lr = 0.001
I0401 14:46:12.506767 29493 solver.cpp:218] Iteration 5192 (2.24128 iter/s, 4.9079s/11 iters), loss = 2.78509
I0401 14:46:12.506861 29493 solver.cpp:237]     Train net output #0: loss = 2.78509 (* 1 = 2.78509 loss)
I0401 14:46:12.506868 29493 sgd_solver.cpp:105] Iteration 5192, lr = 0.001
I0401 14:46:17.435698 29493 solver.cpp:218] Iteration 5203 (2.23177 iter/s, 4.92882s/11 iters), loss = 2.69579
I0401 14:46:17.435755 29493 solver.cpp:237]     Train net output #0: loss = 2.69579 (* 1 = 2.69579 loss)
I0401 14:46:17.435763 29493 sgd_solver.cpp:105] Iteration 5203, lr = 0.001
I0401 14:46:22.266773 29493 solver.cpp:218] Iteration 5214 (2.27696 iter/s, 4.83101s/11 iters), loss = 2.52165
I0401 14:46:22.266836 29493 solver.cpp:237]     Train net output #0: loss = 2.52165 (* 1 = 2.52165 loss)
I0401 14:46:22.266845 29493 sgd_solver.cpp:105] Iteration 5214, lr = 0.001
I0401 14:46:25.230695 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:46:27.168025 29493 solver.cpp:218] Iteration 5225 (2.24436 iter/s, 4.90118s/11 iters), loss = 2.37514
I0401 14:46:27.168069 29493 solver.cpp:237]     Train net output #0: loss = 2.37514 (* 1 = 2.37514 loss)
I0401 14:46:27.168076 29493 sgd_solver.cpp:105] Iteration 5225, lr = 0.001
I0401 14:46:31.824120 29493 solver.cpp:218] Iteration 5236 (2.36253 iter/s, 4.65603s/11 iters), loss = 2.67951
I0401 14:46:31.824167 29493 solver.cpp:237]     Train net output #0: loss = 2.67951 (* 1 = 2.67951 loss)
I0401 14:46:31.824175 29493 sgd_solver.cpp:105] Iteration 5236, lr = 0.001
I0401 14:46:36.546825 29493 solver.cpp:218] Iteration 5247 (2.32921 iter/s, 4.72264s/11 iters), loss = 2.45569
I0401 14:46:36.546875 29493 solver.cpp:237]     Train net output #0: loss = 2.45569 (* 1 = 2.45569 loss)
I0401 14:46:36.546882 29493 sgd_solver.cpp:105] Iteration 5247, lr = 0.001
I0401 14:46:37.707832 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5251.caffemodel
I0401 14:46:42.577047 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5251.solverstate
I0401 14:46:44.907632 29493 solver.cpp:330] Iteration 5251, Testing net (#0)
I0401 14:46:44.907656 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:46:50.847149 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:46:51.720014 29493 solver.cpp:397]     Test net output #0: accuracy = 0.124589
I0401 14:46:51.720041 29493 solver.cpp:397]     Test net output #1: loss = 4.42747 (* 1 = 4.42747 loss)
I0401 14:46:53.938992 29493 solver.cpp:218] Iteration 5258 (0.632471 iter/s, 17.3921s/11 iters), loss = 2.62039
I0401 14:46:53.939040 29493 solver.cpp:237]     Train net output #0: loss = 2.62039 (* 1 = 2.62039 loss)
I0401 14:46:53.939046 29493 sgd_solver.cpp:105] Iteration 5258, lr = 0.001
I0401 14:46:58.545576 29493 solver.cpp:218] Iteration 5269 (2.38792 iter/s, 4.60652s/11 iters), loss = 2.63371
I0401 14:46:58.545625 29493 solver.cpp:237]     Train net output #0: loss = 2.63371 (* 1 = 2.63371 loss)
I0401 14:46:58.545630 29493 sgd_solver.cpp:105] Iteration 5269, lr = 0.001
I0401 14:47:03.447314 29493 solver.cpp:218] Iteration 5280 (2.24413 iter/s, 4.90167s/11 iters), loss = 2.63834
I0401 14:47:03.447371 29493 solver.cpp:237]     Train net output #0: loss = 2.63834 (* 1 = 2.63834 loss)
I0401 14:47:03.447381 29493 sgd_solver.cpp:105] Iteration 5280, lr = 0.001
I0401 14:47:08.203534 29493 solver.cpp:218] Iteration 5291 (2.3128 iter/s, 4.75615s/11 iters), loss = 2.99348
I0401 14:47:08.203585 29493 solver.cpp:237]     Train net output #0: loss = 2.99348 (* 1 = 2.99348 loss)
I0401 14:47:08.203593 29493 sgd_solver.cpp:105] Iteration 5291, lr = 0.001
I0401 14:47:13.221015 29493 solver.cpp:218] Iteration 5302 (2.19236 iter/s, 5.01742s/11 iters), loss = 2.78187
I0401 14:47:13.221136 29493 solver.cpp:237]     Train net output #0: loss = 2.78187 (* 1 = 2.78187 loss)
I0401 14:47:13.221144 29493 sgd_solver.cpp:105] Iteration 5302, lr = 0.001
I0401 14:47:16.300498 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:47:17.807101 29493 solver.cpp:218] Iteration 5313 (2.39863 iter/s, 4.58595s/11 iters), loss = 2.36007
I0401 14:47:17.807155 29493 solver.cpp:237]     Train net output #0: loss = 2.36007 (* 1 = 2.36007 loss)
I0401 14:47:17.807164 29493 sgd_solver.cpp:105] Iteration 5313, lr = 0.001
I0401 14:47:22.610064 29493 solver.cpp:218] Iteration 5324 (2.29029 iter/s, 4.80289s/11 iters), loss = 2.51445
I0401 14:47:22.610121 29493 solver.cpp:237]     Train net output #0: loss = 2.51445 (* 1 = 2.51445 loss)
I0401 14:47:22.610128 29493 sgd_solver.cpp:105] Iteration 5324, lr = 0.001
I0401 14:47:27.475991 29493 solver.cpp:218] Iteration 5335 (2.26065 iter/s, 4.86586s/11 iters), loss = 2.64996
I0401 14:47:27.476037 29493 solver.cpp:237]     Train net output #0: loss = 2.64996 (* 1 = 2.64996 loss)
I0401 14:47:27.476044 29493 sgd_solver.cpp:105] Iteration 5335, lr = 0.001
I0401 14:47:29.100822 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5340.caffemodel
I0401 14:47:34.717978 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5340.solverstate
I0401 14:47:39.419955 29493 solver.cpp:330] Iteration 5340, Testing net (#0)
I0401 14:47:39.419973 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:47:45.287575 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:47:46.149564 29493 solver.cpp:397]     Test net output #0: accuracy = 0.124178
I0401 14:47:46.149603 29493 solver.cpp:397]     Test net output #1: loss = 4.4379 (* 1 = 4.4379 loss)
I0401 14:47:48.011013 29493 solver.cpp:218] Iteration 5346 (0.535671 iter/s, 20.535s/11 iters), loss = 2.83043
I0401 14:47:48.011059 29493 solver.cpp:237]     Train net output #0: loss = 2.83043 (* 1 = 2.83043 loss)
I0401 14:47:48.011065 29493 sgd_solver.cpp:105] Iteration 5346, lr = 0.001
I0401 14:47:52.741601 29493 solver.cpp:218] Iteration 5357 (2.32532 iter/s, 4.73053s/11 iters), loss = 2.71683
I0401 14:47:52.741662 29493 solver.cpp:237]     Train net output #0: loss = 2.71683 (* 1 = 2.71683 loss)
I0401 14:47:52.741670 29493 sgd_solver.cpp:105] Iteration 5357, lr = 0.001
I0401 14:47:57.434741 29493 solver.cpp:218] Iteration 5368 (2.34389 iter/s, 4.69306s/11 iters), loss = 2.97091
I0401 14:47:57.434789 29493 solver.cpp:237]     Train net output #0: loss = 2.97091 (* 1 = 2.97091 loss)
I0401 14:47:57.434795 29493 sgd_solver.cpp:105] Iteration 5368, lr = 0.001
I0401 14:48:02.317510 29493 solver.cpp:218] Iteration 5379 (2.25285 iter/s, 4.8827s/11 iters), loss = 2.53911
I0401 14:48:02.317553 29493 solver.cpp:237]     Train net output #0: loss = 2.53911 (* 1 = 2.53911 loss)
I0401 14:48:02.317559 29493 sgd_solver.cpp:105] Iteration 5379, lr = 0.001
I0401 14:48:07.384188 29493 solver.cpp:218] Iteration 5390 (2.17107 iter/s, 5.06662s/11 iters), loss = 2.68396
I0401 14:48:07.384239 29493 solver.cpp:237]     Train net output #0: loss = 2.68396 (* 1 = 2.68396 loss)
I0401 14:48:07.384246 29493 sgd_solver.cpp:105] Iteration 5390, lr = 0.001
I0401 14:48:10.579516 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:48:12.126225 29493 solver.cpp:218] Iteration 5401 (2.31971 iter/s, 4.74198s/11 iters), loss = 2.33088
I0401 14:48:12.126262 29493 solver.cpp:237]     Train net output #0: loss = 2.33088 (* 1 = 2.33088 loss)
I0401 14:48:12.126267 29493 sgd_solver.cpp:105] Iteration 5401, lr = 0.001
I0401 14:48:17.040737 29493 solver.cpp:218] Iteration 5412 (2.23829 iter/s, 4.91446s/11 iters), loss = 2.56557
I0401 14:48:17.040889 29493 solver.cpp:237]     Train net output #0: loss = 2.56557 (* 1 = 2.56557 loss)
I0401 14:48:17.040897 29493 sgd_solver.cpp:105] Iteration 5412, lr = 0.001
I0401 14:48:21.900049 29493 solver.cpp:218] Iteration 5423 (2.26377 iter/s, 4.85915s/11 iters), loss = 2.40739
I0401 14:48:21.900112 29493 solver.cpp:237]     Train net output #0: loss = 2.40739 (* 1 = 2.40739 loss)
I0401 14:48:21.900121 29493 sgd_solver.cpp:105] Iteration 5423, lr = 0.001
I0401 14:48:24.026811 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5429.caffemodel
I0401 14:48:28.435593 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5429.solverstate
I0401 14:48:31.509743 29493 solver.cpp:330] Iteration 5429, Testing net (#0)
I0401 14:48:31.509763 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:48:37.435905 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:48:38.298746 29493 solver.cpp:397]     Test net output #0: accuracy = 0.128289
I0401 14:48:38.298784 29493 solver.cpp:397]     Test net output #1: loss = 4.44764 (* 1 = 4.44764 loss)
I0401 14:48:39.824335 29493 solver.cpp:218] Iteration 5434 (0.613695 iter/s, 17.9242s/11 iters), loss = 2.93661
I0401 14:48:39.824385 29493 solver.cpp:237]     Train net output #0: loss = 2.93661 (* 1 = 2.93661 loss)
I0401 14:48:39.824390 29493 sgd_solver.cpp:105] Iteration 5434, lr = 0.001
I0401 14:48:44.662415 29493 solver.cpp:218] Iteration 5445 (2.27366 iter/s, 4.83802s/11 iters), loss = 2.36897
I0401 14:48:44.662461 29493 solver.cpp:237]     Train net output #0: loss = 2.36897 (* 1 = 2.36897 loss)
I0401 14:48:44.662467 29493 sgd_solver.cpp:105] Iteration 5445, lr = 0.001
I0401 14:48:49.402016 29493 solver.cpp:218] Iteration 5456 (2.3209 iter/s, 4.73954s/11 iters), loss = 3.03119
I0401 14:48:49.402144 29493 solver.cpp:237]     Train net output #0: loss = 3.03119 (* 1 = 3.03119 loss)
I0401 14:48:49.402153 29493 sgd_solver.cpp:105] Iteration 5456, lr = 0.001
I0401 14:48:54.381145 29493 solver.cpp:218] Iteration 5467 (2.20928 iter/s, 4.97899s/11 iters), loss = 2.72822
I0401 14:48:54.381206 29493 solver.cpp:237]     Train net output #0: loss = 2.72822 (* 1 = 2.72822 loss)
I0401 14:48:54.381215 29493 sgd_solver.cpp:105] Iteration 5467, lr = 0.001
I0401 14:48:59.307749 29493 solver.cpp:218] Iteration 5478 (2.23281 iter/s, 4.92654s/11 iters), loss = 2.65384
I0401 14:48:59.307791 29493 solver.cpp:237]     Train net output #0: loss = 2.65384 (* 1 = 2.65384 loss)
I0401 14:48:59.307796 29493 sgd_solver.cpp:105] Iteration 5478, lr = 0.001
I0401 14:49:02.944159 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:49:04.135943 29493 solver.cpp:218] Iteration 5489 (2.27831 iter/s, 4.82814s/11 iters), loss = 2.07241
I0401 14:49:04.135982 29493 solver.cpp:237]     Train net output #0: loss = 2.07241 (* 1 = 2.07241 loss)
I0401 14:49:04.135988 29493 sgd_solver.cpp:105] Iteration 5489, lr = 0.001
I0401 14:49:09.056178 29493 solver.cpp:218] Iteration 5500 (2.23569 iter/s, 4.92018s/11 iters), loss = 2.0144
I0401 14:49:09.056221 29493 solver.cpp:237]     Train net output #0: loss = 2.0144 (* 1 = 2.0144 loss)
I0401 14:49:09.056226 29493 sgd_solver.cpp:105] Iteration 5500, lr = 0.001
I0401 14:49:14.096448 29493 solver.cpp:218] Iteration 5511 (2.18245 iter/s, 5.04021s/11 iters), loss = 1.98851
I0401 14:49:14.096508 29493 solver.cpp:237]     Train net output #0: loss = 1.98851 (* 1 = 1.98851 loss)
I0401 14:49:14.096516 29493 sgd_solver.cpp:105] Iteration 5511, lr = 0.001
I0401 14:49:16.696302 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5518.caffemodel
I0401 14:49:21.105206 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5518.solverstate
I0401 14:49:24.564409 29493 solver.cpp:330] Iteration 5518, Testing net (#0)
I0401 14:49:24.564431 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:49:30.223989 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:49:31.099265 29493 solver.cpp:397]     Test net output #0: accuracy = 0.129934
I0401 14:49:31.099301 29493 solver.cpp:397]     Test net output #1: loss = 4.47301 (* 1 = 4.47301 loss)
I0401 14:49:32.117908 29493 solver.cpp:218] Iteration 5522 (0.610385 iter/s, 18.0214s/11 iters), loss = 2.75924
I0401 14:49:32.117945 29493 solver.cpp:237]     Train net output #0: loss = 2.75924 (* 1 = 2.75924 loss)
I0401 14:49:32.117951 29493 sgd_solver.cpp:105] Iteration 5522, lr = 0.001
I0401 14:49:36.632647 29493 solver.cpp:218] Iteration 5533 (2.43649 iter/s, 4.51469s/11 iters), loss = 2.788
I0401 14:49:36.632690 29493 solver.cpp:237]     Train net output #0: loss = 2.788 (* 1 = 2.788 loss)
I0401 14:49:36.632696 29493 sgd_solver.cpp:105] Iteration 5533, lr = 0.001
I0401 14:49:41.248847 29493 solver.cpp:218] Iteration 5544 (2.38295 iter/s, 4.61614s/11 iters), loss = 2.98053
I0401 14:49:41.248929 29493 solver.cpp:237]     Train net output #0: loss = 2.98053 (* 1 = 2.98053 loss)
I0401 14:49:41.248941 29493 sgd_solver.cpp:105] Iteration 5544, lr = 0.001
I0401 14:49:46.029572 29493 solver.cpp:218] Iteration 5555 (2.30095 iter/s, 4.78064s/11 iters), loss = 2.91037
I0401 14:49:46.029611 29493 solver.cpp:237]     Train net output #0: loss = 2.91037 (* 1 = 2.91037 loss)
I0401 14:49:46.029618 29493 sgd_solver.cpp:105] Iteration 5555, lr = 0.001
I0401 14:49:50.811658 29493 solver.cpp:218] Iteration 5566 (2.30028 iter/s, 4.78203s/11 iters), loss = 2.48557
I0401 14:49:50.811697 29493 solver.cpp:237]     Train net output #0: loss = 2.48557 (* 1 = 2.48557 loss)
I0401 14:49:50.811703 29493 sgd_solver.cpp:105] Iteration 5566, lr = 0.001
I0401 14:49:54.658291 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:49:55.701077 29493 solver.cpp:218] Iteration 5577 (2.24978 iter/s, 4.88936s/11 iters), loss = 2.11047
I0401 14:49:55.701124 29493 solver.cpp:237]     Train net output #0: loss = 2.11047 (* 1 = 2.11047 loss)
I0401 14:49:55.701133 29493 sgd_solver.cpp:105] Iteration 5577, lr = 0.001
I0401 14:50:00.523595 29493 solver.cpp:218] Iteration 5588 (2.281 iter/s, 4.82245s/11 iters), loss = 1.79473
I0401 14:50:00.523646 29493 solver.cpp:237]     Train net output #0: loss = 1.79473 (* 1 = 1.79473 loss)
I0401 14:50:00.523654 29493 sgd_solver.cpp:105] Iteration 5588, lr = 0.001
I0401 14:50:01.773969 29493 blocking_queue.cpp:49] Waiting for data
I0401 14:50:05.436530 29493 solver.cpp:218] Iteration 5599 (2.23902 iter/s, 4.91287s/11 iters), loss = 1.95701
I0401 14:50:05.436594 29493 solver.cpp:237]     Train net output #0: loss = 1.95701 (* 1 = 1.95701 loss)
I0401 14:50:05.436602 29493 sgd_solver.cpp:105] Iteration 5599, lr = 0.001
I0401 14:50:08.455901 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5607.caffemodel
I0401 14:50:12.597450 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5607.solverstate
I0401 14:50:16.396906 29493 solver.cpp:330] Iteration 5607, Testing net (#0)
I0401 14:50:16.396929 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:50:22.127243 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:50:23.034704 29493 solver.cpp:397]     Test net output #0: accuracy = 0.127467
I0401 14:50:23.034735 29493 solver.cpp:397]     Test net output #1: loss = 4.52928 (* 1 = 4.52928 loss)
I0401 14:50:23.665601 29493 solver.cpp:218] Iteration 5610 (0.603434 iter/s, 18.229s/11 iters), loss = 2.5451
I0401 14:50:23.665643 29493 solver.cpp:237]     Train net output #0: loss = 2.5451 (* 1 = 2.5451 loss)
I0401 14:50:23.665649 29493 sgd_solver.cpp:105] Iteration 5610, lr = 0.001
I0401 14:50:28.260316 29493 solver.cpp:218] Iteration 5621 (2.39408 iter/s, 4.59466s/11 iters), loss = 3.05091
I0401 14:50:28.260448 29493 solver.cpp:237]     Train net output #0: loss = 3.05091 (* 1 = 3.05091 loss)
I0401 14:50:28.260457 29493 sgd_solver.cpp:105] Iteration 5621, lr = 0.001
I0401 14:50:33.117425 29493 solver.cpp:218] Iteration 5632 (2.26479 iter/s, 4.85696s/11 iters), loss = 2.7806
I0401 14:50:33.117470 29493 solver.cpp:237]     Train net output #0: loss = 2.7806 (* 1 = 2.7806 loss)
I0401 14:50:33.117475 29493 sgd_solver.cpp:105] Iteration 5632, lr = 0.001
I0401 14:50:38.145964 29493 solver.cpp:218] Iteration 5643 (2.18754 iter/s, 5.02848s/11 iters), loss = 2.41524
I0401 14:50:38.146011 29493 solver.cpp:237]     Train net output #0: loss = 2.41524 (* 1 = 2.41524 loss)
I0401 14:50:38.146018 29493 sgd_solver.cpp:105] Iteration 5643, lr = 0.001
I0401 14:50:42.874411 29493 solver.cpp:218] Iteration 5654 (2.32637 iter/s, 4.72839s/11 iters), loss = 2.32443
I0401 14:50:42.874449 29493 solver.cpp:237]     Train net output #0: loss = 2.32443 (* 1 = 2.32443 loss)
I0401 14:50:42.874454 29493 sgd_solver.cpp:105] Iteration 5654, lr = 0.001
I0401 14:50:47.070549 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:50:47.806916 29493 solver.cpp:218] Iteration 5665 (2.23013 iter/s, 4.93245s/11 iters), loss = 1.89122
I0401 14:50:47.806958 29493 solver.cpp:237]     Train net output #0: loss = 1.89122 (* 1 = 1.89122 loss)
I0401 14:50:47.806964 29493 sgd_solver.cpp:105] Iteration 5665, lr = 0.001
I0401 14:50:52.533272 29493 solver.cpp:218] Iteration 5676 (2.3274 iter/s, 4.7263s/11 iters), loss = 2.12669
I0401 14:50:52.533316 29493 solver.cpp:237]     Train net output #0: loss = 2.12669 (* 1 = 2.12669 loss)
I0401 14:50:52.533322 29493 sgd_solver.cpp:105] Iteration 5676, lr = 0.001
I0401 14:50:57.408721 29493 solver.cpp:218] Iteration 5687 (2.25623 iter/s, 4.87539s/11 iters), loss = 2.0791
I0401 14:50:57.408787 29493 solver.cpp:237]     Train net output #0: loss = 2.0791 (* 1 = 2.0791 loss)
I0401 14:50:57.408797 29493 sgd_solver.cpp:105] Iteration 5687, lr = 0.001
I0401 14:51:00.802176 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5696.caffemodel
I0401 14:51:03.977870 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5696.solverstate
I0401 14:51:07.658581 29493 solver.cpp:330] Iteration 5696, Testing net (#0)
I0401 14:51:07.658601 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:51:13.226824 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:51:14.138638 29493 solver.cpp:397]     Test net output #0: accuracy = 0.136924
I0401 14:51:14.138669 29493 solver.cpp:397]     Test net output #1: loss = 4.57619 (* 1 = 4.57619 loss)
I0401 14:51:14.551113 29493 solver.cpp:218] Iteration 5698 (0.641686 iter/s, 17.1423s/11 iters), loss = 2.0148
I0401 14:51:14.551174 29493 solver.cpp:237]     Train net output #0: loss = 2.0148 (* 1 = 2.0148 loss)
I0401 14:51:14.551182 29493 sgd_solver.cpp:105] Iteration 5698, lr = 0.001
I0401 14:51:18.966553 29493 solver.cpp:218] Iteration 5709 (2.4913 iter/s, 4.41537s/11 iters), loss = 2.54445
I0401 14:51:18.966599 29493 solver.cpp:237]     Train net output #0: loss = 2.54445 (* 1 = 2.54445 loss)
I0401 14:51:18.966605 29493 sgd_solver.cpp:105] Iteration 5709, lr = 0.001
I0401 14:51:23.569123 29493 solver.cpp:218] Iteration 5720 (2.39 iter/s, 4.60251s/11 iters), loss = 3.2334
I0401 14:51:23.569170 29493 solver.cpp:237]     Train net output #0: loss = 3.2334 (* 1 = 3.2334 loss)
I0401 14:51:23.569176 29493 sgd_solver.cpp:105] Iteration 5720, lr = 0.001
I0401 14:51:28.305148 29493 solver.cpp:218] Iteration 5731 (2.32265 iter/s, 4.73596s/11 iters), loss = 2.17483
I0401 14:51:28.305202 29493 solver.cpp:237]     Train net output #0: loss = 2.17483 (* 1 = 2.17483 loss)
I0401 14:51:28.305210 29493 sgd_solver.cpp:105] Iteration 5731, lr = 0.001
I0401 14:51:33.216435 29493 solver.cpp:218] Iteration 5742 (2.23977 iter/s, 4.91122s/11 iters), loss = 2.10226
I0401 14:51:33.216593 29493 solver.cpp:237]     Train net output #0: loss = 2.10226 (* 1 = 2.10226 loss)
I0401 14:51:33.216601 29493 sgd_solver.cpp:105] Iteration 5742, lr = 0.001
I0401 14:51:37.730116 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:51:38.170679 29493 solver.cpp:218] Iteration 5753 (2.22039 iter/s, 4.95408s/11 iters), loss = 1.89896
I0401 14:51:38.170725 29493 solver.cpp:237]     Train net output #0: loss = 1.89896 (* 1 = 1.89896 loss)
I0401 14:51:38.170730 29493 sgd_solver.cpp:105] Iteration 5753, lr = 0.001
I0401 14:51:43.157217 29493 solver.cpp:218] Iteration 5764 (2.20597 iter/s, 4.98648s/11 iters), loss = 1.7894
I0401 14:51:43.157263 29493 solver.cpp:237]     Train net output #0: loss = 1.7894 (* 1 = 1.7894 loss)
I0401 14:51:43.157269 29493 sgd_solver.cpp:105] Iteration 5764, lr = 0.001
I0401 14:51:47.919425 29493 solver.cpp:218] Iteration 5775 (2.30988 iter/s, 4.76215s/11 iters), loss = 2.14766
I0401 14:51:47.919466 29493 solver.cpp:237]     Train net output #0: loss = 2.14766 (* 1 = 2.14766 loss)
I0401 14:51:47.919472 29493 sgd_solver.cpp:105] Iteration 5775, lr = 0.001
I0401 14:51:51.830701 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5785.caffemodel
I0401 14:51:55.246510 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5785.solverstate
I0401 14:52:01.307440 29493 solver.cpp:330] Iteration 5785, Testing net (#0)
I0401 14:52:01.307459 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:52:07.115906 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:52:08.067941 29493 solver.cpp:397]     Test net output #0: accuracy = 0.143914
I0401 14:52:08.067978 29493 solver.cpp:397]     Test net output #1: loss = 4.49354 (* 1 = 4.49354 loss)
I0401 14:52:08.349316 29493 solver.cpp:218] Iteration 5786 (0.538428 iter/s, 20.4298s/11 iters), loss = 2.23057
I0401 14:52:08.350878 29493 solver.cpp:237]     Train net output #0: loss = 2.23057 (* 1 = 2.23057 loss)
I0401 14:52:08.350888 29493 sgd_solver.cpp:105] Iteration 5786, lr = 0.001
I0401 14:52:12.701318 29493 solver.cpp:218] Iteration 5797 (2.52849 iter/s, 4.35043s/11 iters), loss = 2.56945
I0401 14:52:12.701362 29493 solver.cpp:237]     Train net output #0: loss = 2.56945 (* 1 = 2.56945 loss)
I0401 14:52:12.701368 29493 sgd_solver.cpp:105] Iteration 5797, lr = 0.001
I0401 14:52:17.614094 29493 solver.cpp:218] Iteration 5808 (2.23909 iter/s, 4.91271s/11 iters), loss = 2.71801
I0401 14:52:17.614148 29493 solver.cpp:237]     Train net output #0: loss = 2.71801 (* 1 = 2.71801 loss)
I0401 14:52:17.614156 29493 sgd_solver.cpp:105] Iteration 5808, lr = 0.001
I0401 14:52:22.299405 29493 solver.cpp:218] Iteration 5819 (2.3478 iter/s, 4.68524s/11 iters), loss = 2.82618
I0401 14:52:22.299835 29493 solver.cpp:237]     Train net output #0: loss = 2.82618 (* 1 = 2.82618 loss)
I0401 14:52:22.299844 29493 sgd_solver.cpp:105] Iteration 5819, lr = 0.001
I0401 14:52:27.179328 29493 solver.cpp:218] Iteration 5830 (2.25434 iter/s, 4.87948s/11 iters), loss = 2.3474
I0401 14:52:27.179373 29493 solver.cpp:237]     Train net output #0: loss = 2.3474 (* 1 = 2.3474 loss)
I0401 14:52:27.179379 29493 sgd_solver.cpp:105] Iteration 5830, lr = 0.001
I0401 14:52:31.753176 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:52:32.041388 29493 solver.cpp:218] Iteration 5841 (2.26244 iter/s, 4.862s/11 iters), loss = 2.17234
I0401 14:52:32.041433 29493 solver.cpp:237]     Train net output #0: loss = 2.17234 (* 1 = 2.17234 loss)
I0401 14:52:32.041440 29493 sgd_solver.cpp:105] Iteration 5841, lr = 0.001
I0401 14:52:36.819844 29493 solver.cpp:218] Iteration 5852 (2.30203 iter/s, 4.7784s/11 iters), loss = 2.18587
I0401 14:52:36.819887 29493 solver.cpp:237]     Train net output #0: loss = 2.18587 (* 1 = 2.18587 loss)
I0401 14:52:36.819893 29493 sgd_solver.cpp:105] Iteration 5852, lr = 0.001
I0401 14:52:41.590368 29493 solver.cpp:218] Iteration 5863 (2.30585 iter/s, 4.77047s/11 iters), loss = 1.99741
I0401 14:52:41.590512 29493 solver.cpp:237]     Train net output #0: loss = 1.99741 (* 1 = 1.99741 loss)
I0401 14:52:41.590521 29493 sgd_solver.cpp:105] Iteration 5863, lr = 0.001
I0401 14:52:45.834583 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5874.caffemodel
I0401 14:52:48.843223 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5874.solverstate
I0401 14:52:51.353605 29493 solver.cpp:330] Iteration 5874, Testing net (#0)
I0401 14:52:51.353627 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:52:57.005211 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:52:57.942193 29493 solver.cpp:397]     Test net output #0: accuracy = 0.154194
I0401 14:52:57.942229 29493 solver.cpp:397]     Test net output #1: loss = 4.38919 (* 1 = 4.38919 loss)
I0401 14:52:58.078389 29493 solver.cpp:218] Iteration 5874 (0.667157 iter/s, 16.4879s/11 iters), loss = 2.01246
I0401 14:52:58.078439 29493 solver.cpp:237]     Train net output #0: loss = 2.01246 (* 1 = 2.01246 loss)
I0401 14:52:58.078447 29493 sgd_solver.cpp:105] Iteration 5874, lr = 0.001
I0401 14:53:01.842365 29493 solver.cpp:218] Iteration 5885 (2.9225 iter/s, 3.76391s/11 iters), loss = 2.28488
I0401 14:53:01.842427 29493 solver.cpp:237]     Train net output #0: loss = 2.28488 (* 1 = 2.28488 loss)
I0401 14:53:01.842434 29493 sgd_solver.cpp:105] Iteration 5885, lr = 0.001
I0401 14:53:06.773658 29493 solver.cpp:218] Iteration 5896 (2.23069 iter/s, 4.93122s/11 iters), loss = 2.42968
I0401 14:53:06.773703 29493 solver.cpp:237]     Train net output #0: loss = 2.42968 (* 1 = 2.42968 loss)
I0401 14:53:06.773708 29493 sgd_solver.cpp:105] Iteration 5896, lr = 0.001
I0401 14:53:11.495371 29493 solver.cpp:218] Iteration 5907 (2.32969 iter/s, 4.72165s/11 iters), loss = 2.42884
I0401 14:53:11.495414 29493 solver.cpp:237]     Train net output #0: loss = 2.42884 (* 1 = 2.42884 loss)
I0401 14:53:11.495420 29493 sgd_solver.cpp:105] Iteration 5907, lr = 0.001
I0401 14:53:16.394845 29493 solver.cpp:218] Iteration 5918 (2.24517 iter/s, 4.89942s/11 iters), loss = 2.25084
I0401 14:53:16.394932 29493 solver.cpp:237]     Train net output #0: loss = 2.25084 (* 1 = 2.25084 loss)
I0401 14:53:16.394938 29493 sgd_solver.cpp:105] Iteration 5918, lr = 0.001
I0401 14:53:21.150434 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:53:21.155056 29493 solver.cpp:218] Iteration 5929 (2.31087 iter/s, 4.76012s/11 iters), loss = 1.67608
I0401 14:53:21.155092 29493 solver.cpp:237]     Train net output #0: loss = 1.67608 (* 1 = 1.67608 loss)
I0401 14:53:21.155098 29493 sgd_solver.cpp:105] Iteration 5929, lr = 0.001
I0401 14:53:25.862534 29493 solver.cpp:218] Iteration 5940 (2.33673 iter/s, 4.70743s/11 iters), loss = 1.84824
I0401 14:53:25.862583 29493 solver.cpp:237]     Train net output #0: loss = 1.84824 (* 1 = 1.84824 loss)
I0401 14:53:25.862589 29493 sgd_solver.cpp:105] Iteration 5940, lr = 0.001
I0401 14:53:30.685746 29493 solver.cpp:218] Iteration 5951 (2.28067 iter/s, 4.82315s/11 iters), loss = 2.04175
I0401 14:53:30.685794 29493 solver.cpp:237]     Train net output #0: loss = 2.04175 (* 1 = 2.04175 loss)
I0401 14:53:30.685801 29493 sgd_solver.cpp:105] Iteration 5951, lr = 0.001
I0401 14:53:35.502354 29493 solver.cpp:218] Iteration 5962 (2.28379 iter/s, 4.81655s/11 iters), loss = 2.16931
I0401 14:53:35.502399 29493 solver.cpp:237]     Train net output #0: loss = 2.16931 (* 1 = 2.16931 loss)
I0401 14:53:35.502405 29493 sgd_solver.cpp:105] Iteration 5962, lr = 0.001
I0401 14:53:35.502538 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5963.caffemodel
I0401 14:53:38.394030 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5963.solverstate
I0401 14:53:40.680178 29493 solver.cpp:330] Iteration 5963, Testing net (#0)
I0401 14:53:40.680197 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:53:46.388706 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:53:47.335784 29493 solver.cpp:397]     Test net output #0: accuracy = 0.151316
I0401 14:53:47.335901 29493 solver.cpp:397]     Test net output #1: loss = 4.42162 (* 1 = 4.42162 loss)
I0401 14:53:50.988009 29493 solver.cpp:218] Iteration 5973 (0.710337 iter/s, 15.4856s/11 iters), loss = 2.26272
I0401 14:53:50.988046 29493 solver.cpp:237]     Train net output #0: loss = 2.26272 (* 1 = 2.26272 loss)
I0401 14:53:50.988052 29493 sgd_solver.cpp:105] Iteration 5973, lr = 0.001
I0401 14:53:55.652434 29493 solver.cpp:218] Iteration 5984 (2.3583 iter/s, 4.66437s/11 iters), loss = 2.50181
I0401 14:53:55.652479 29493 solver.cpp:237]     Train net output #0: loss = 2.50181 (* 1 = 2.50181 loss)
I0401 14:53:55.652487 29493 sgd_solver.cpp:105] Iteration 5984, lr = 0.001
I0401 14:54:00.797456 29493 solver.cpp:218] Iteration 5995 (2.13801 iter/s, 5.14496s/11 iters), loss = 2.51763
I0401 14:54:00.797511 29493 solver.cpp:237]     Train net output #0: loss = 2.51763 (* 1 = 2.51763 loss)
I0401 14:54:00.797520 29493 sgd_solver.cpp:105] Iteration 5995, lr = 0.001
I0401 14:54:05.683071 29493 solver.cpp:218] Iteration 6006 (2.25154 iter/s, 4.88555s/11 iters), loss = 1.99177
I0401 14:54:05.683115 29493 solver.cpp:237]     Train net output #0: loss = 1.99177 (* 1 = 1.99177 loss)
I0401 14:54:05.683120 29493 sgd_solver.cpp:105] Iteration 6006, lr = 0.001
I0401 14:54:10.292399 29493 solver.cpp:218] Iteration 6017 (2.3865 iter/s, 4.60927s/11 iters), loss = 2.01829
I0401 14:54:10.292448 29493 solver.cpp:237]     Train net output #0: loss = 2.01829 (* 1 = 2.01829 loss)
I0401 14:54:10.292452 29493 sgd_solver.cpp:105] Iteration 6017, lr = 0.001
I0401 14:54:10.446015 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:54:15.255818 29493 solver.cpp:218] Iteration 6028 (2.21624 iter/s, 4.96336s/11 iters), loss = 1.63952
I0401 14:54:15.255864 29493 solver.cpp:237]     Train net output #0: loss = 1.63952 (* 1 = 1.63952 loss)
I0401 14:54:15.255870 29493 sgd_solver.cpp:105] Iteration 6028, lr = 0.001
I0401 14:54:20.238777 29493 solver.cpp:218] Iteration 6039 (2.20755 iter/s, 4.98291s/11 iters), loss = 1.83402
I0401 14:54:20.238880 29493 solver.cpp:237]     Train net output #0: loss = 1.83402 (* 1 = 1.83402 loss)
I0401 14:54:20.238886 29493 sgd_solver.cpp:105] Iteration 6039, lr = 0.001
I0401 14:54:25.033877 29493 solver.cpp:218] Iteration 6050 (2.29406 iter/s, 4.79498s/11 iters), loss = 1.99895
I0401 14:54:25.033921 29493 solver.cpp:237]     Train net output #0: loss = 1.99895 (* 1 = 1.99895 loss)
I0401 14:54:25.033926 29493 sgd_solver.cpp:105] Iteration 6050, lr = 0.001
I0401 14:54:25.406646 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6052.caffemodel
I0401 14:54:28.424588 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6052.solverstate
I0401 14:54:30.749768 29493 solver.cpp:330] Iteration 6052, Testing net (#0)
I0401 14:54:30.749786 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:54:36.406993 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:54:37.396442 29493 solver.cpp:397]     Test net output #0: accuracy = 0.149671
I0401 14:54:37.396481 29493 solver.cpp:397]     Test net output #1: loss = 4.43989 (* 1 = 4.43989 loss)
I0401 14:54:40.510325 29493 solver.cpp:218] Iteration 6061 (0.710759 iter/s, 15.4764s/11 iters), loss = 2.77198
I0401 14:54:40.510367 29493 solver.cpp:237]     Train net output #0: loss = 2.77198 (* 1 = 2.77198 loss)
I0401 14:54:40.510373 29493 sgd_solver.cpp:105] Iteration 6061, lr = 0.001
I0401 14:54:45.356992 29493 solver.cpp:218] Iteration 6072 (2.26963 iter/s, 4.84661s/11 iters), loss = 2.13104
I0401 14:54:45.357034 29493 solver.cpp:237]     Train net output #0: loss = 2.13104 (* 1 = 2.13104 loss)
I0401 14:54:45.357040 29493 sgd_solver.cpp:105] Iteration 6072, lr = 0.001
I0401 14:54:50.251042 29493 solver.cpp:218] Iteration 6083 (2.24765 iter/s, 4.89399s/11 iters), loss = 2.06016
I0401 14:54:50.251219 29493 solver.cpp:237]     Train net output #0: loss = 2.06016 (* 1 = 2.06016 loss)
I0401 14:54:50.251227 29493 sgd_solver.cpp:105] Iteration 6083, lr = 0.001
I0401 14:54:55.173221 29493 solver.cpp:218] Iteration 6094 (2.23487 iter/s, 4.922s/11 iters), loss = 1.50075
I0401 14:54:55.173262 29493 solver.cpp:237]     Train net output #0: loss = 1.50075 (* 1 = 1.50075 loss)
I0401 14:54:55.173269 29493 sgd_solver.cpp:105] Iteration 6094, lr = 0.001
I0401 14:54:59.732329 29493 solver.cpp:218] Iteration 6105 (2.41278 iter/s, 4.55905s/11 iters), loss = 1.75035
I0401 14:54:59.732380 29493 solver.cpp:237]     Train net output #0: loss = 1.75035 (* 1 = 1.75035 loss)
I0401 14:54:59.732388 29493 sgd_solver.cpp:105] Iteration 6105, lr = 0.001
I0401 14:55:00.195399 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:55:04.711817 29493 solver.cpp:218] Iteration 6116 (2.20909 iter/s, 4.97942s/11 iters), loss = 1.66974
I0401 14:55:04.711869 29493 solver.cpp:237]     Train net output #0: loss = 1.66974 (* 1 = 1.66974 loss)
I0401 14:55:04.711875 29493 sgd_solver.cpp:105] Iteration 6116, lr = 0.001
I0401 14:55:09.415464 29493 solver.cpp:218] Iteration 6127 (2.33864 iter/s, 4.70358s/11 iters), loss = 1.68064
I0401 14:55:09.415522 29493 solver.cpp:237]     Train net output #0: loss = 1.68064 (* 1 = 1.68064 loss)
I0401 14:55:09.415529 29493 sgd_solver.cpp:105] Iteration 6127, lr = 0.001
I0401 14:55:14.210026 29493 solver.cpp:218] Iteration 6138 (2.2943 iter/s, 4.79449s/11 iters), loss = 1.86455
I0401 14:55:14.210072 29493 solver.cpp:237]     Train net output #0: loss = 1.86455 (* 1 = 1.86455 loss)
I0401 14:55:14.210078 29493 sgd_solver.cpp:105] Iteration 6138, lr = 0.001
I0401 14:55:15.035080 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6141.caffemodel
I0401 14:55:18.023983 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6141.solverstate
I0401 14:55:20.352447 29493 solver.cpp:330] Iteration 6141, Testing net (#0)
I0401 14:55:20.352551 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:55:23.302839 29493 blocking_queue.cpp:49] Waiting for data
I0401 14:55:26.175616 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:55:27.176978 29493 solver.cpp:397]     Test net output #0: accuracy = 0.155428
I0401 14:55:27.177007 29493 solver.cpp:397]     Test net output #1: loss = 4.41709 (* 1 = 4.41709 loss)
I0401 14:55:29.985997 29493 solver.cpp:218] Iteration 6149 (0.697265 iter/s, 15.7759s/11 iters), loss = 2.65301
I0401 14:55:29.986042 29493 solver.cpp:237]     Train net output #0: loss = 2.65301 (* 1 = 2.65301 loss)
I0401 14:55:29.986047 29493 sgd_solver.cpp:105] Iteration 6149, lr = 0.001
I0401 14:55:34.772229 29493 solver.cpp:218] Iteration 6160 (2.29829 iter/s, 4.78617s/11 iters), loss = 2.18605
I0401 14:55:34.772270 29493 solver.cpp:237]     Train net output #0: loss = 2.18605 (* 1 = 2.18605 loss)
I0401 14:55:34.772276 29493 sgd_solver.cpp:105] Iteration 6160, lr = 0.001
I0401 14:55:39.406987 29493 solver.cpp:218] Iteration 6171 (2.3734 iter/s, 4.6347s/11 iters), loss = 1.74089
I0401 14:55:39.407033 29493 solver.cpp:237]     Train net output #0: loss = 1.74089 (* 1 = 1.74089 loss)
I0401 14:55:39.407038 29493 sgd_solver.cpp:105] Iteration 6171, lr = 0.001
I0401 14:55:44.476001 29493 solver.cpp:218] Iteration 6182 (2.17008 iter/s, 5.06895s/11 iters), loss = 1.50285
I0401 14:55:44.476063 29493 solver.cpp:237]     Train net output #0: loss = 1.50285 (* 1 = 1.50285 loss)
I0401 14:55:44.476071 29493 sgd_solver.cpp:105] Iteration 6182, lr = 0.001
I0401 14:55:49.236315 29493 solver.cpp:218] Iteration 6193 (2.31081 iter/s, 4.76024s/11 iters), loss = 1.8741
I0401 14:55:49.236361 29493 solver.cpp:237]     Train net output #0: loss = 1.8741 (* 1 = 1.8741 loss)
I0401 14:55:49.236366 29493 sgd_solver.cpp:105] Iteration 6193, lr = 0.001
I0401 14:55:49.887138 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:55:54.128022 29493 solver.cpp:218] Iteration 6204 (2.24873 iter/s, 4.89165s/11 iters), loss = 1.69908
I0401 14:55:54.128157 29493 solver.cpp:237]     Train net output #0: loss = 1.69908 (* 1 = 1.69908 loss)
I0401 14:55:54.128163 29493 sgd_solver.cpp:105] Iteration 6204, lr = 0.001
I0401 14:55:59.068076 29493 solver.cpp:218] Iteration 6215 (2.22676 iter/s, 4.93991s/11 iters), loss = 1.82629
I0401 14:55:59.068121 29493 solver.cpp:237]     Train net output #0: loss = 1.82629 (* 1 = 1.82629 loss)
I0401 14:55:59.068126 29493 sgd_solver.cpp:105] Iteration 6215, lr = 0.001
I0401 14:56:03.670500 29493 solver.cpp:218] Iteration 6226 (2.39008 iter/s, 4.60236s/11 iters), loss = 1.63246
I0401 14:56:03.670548 29493 solver.cpp:237]     Train net output #0: loss = 1.63246 (* 1 = 1.63246 loss)
I0401 14:56:03.670554 29493 sgd_solver.cpp:105] Iteration 6226, lr = 0.001
I0401 14:56:04.867238 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6230.caffemodel
I0401 14:56:07.879854 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6230.solverstate
I0401 14:56:10.186625 29493 solver.cpp:330] Iteration 6230, Testing net (#0)
I0401 14:56:10.186650 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:56:15.926687 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:56:16.922669 29493 solver.cpp:397]     Test net output #0: accuracy = 0.157895
I0401 14:56:16.922709 29493 solver.cpp:397]     Test net output #1: loss = 4.44599 (* 1 = 4.44599 loss)
I0401 14:56:19.278489 29493 solver.cpp:218] Iteration 6237 (0.70477 iter/s, 15.6079s/11 iters), loss = 2.22363
I0401 14:56:19.278543 29493 solver.cpp:237]     Train net output #0: loss = 2.22363 (* 1 = 2.22363 loss)
I0401 14:56:19.278550 29493 sgd_solver.cpp:105] Iteration 6237, lr = 0.001
I0401 14:56:23.921380 29493 solver.cpp:218] Iteration 6248 (2.36924 iter/s, 4.64283s/11 iters), loss = 1.8365
I0401 14:56:23.921419 29493 solver.cpp:237]     Train net output #0: loss = 1.8365 (* 1 = 1.8365 loss)
I0401 14:56:23.921424 29493 sgd_solver.cpp:105] Iteration 6248, lr = 0.001
I0401 14:56:28.744626 29493 solver.cpp:218] Iteration 6259 (2.28065 iter/s, 4.82319s/11 iters), loss = 1.71946
I0401 14:56:28.744735 29493 solver.cpp:237]     Train net output #0: loss = 1.71946 (* 1 = 1.71946 loss)
I0401 14:56:28.744742 29493 sgd_solver.cpp:105] Iteration 6259, lr = 0.001
I0401 14:56:33.604326 29493 solver.cpp:218] Iteration 6270 (2.26357 iter/s, 4.85958s/11 iters), loss = 1.60109
I0401 14:56:33.604387 29493 solver.cpp:237]     Train net output #0: loss = 1.60109 (* 1 = 1.60109 loss)
I0401 14:56:33.604395 29493 sgd_solver.cpp:105] Iteration 6270, lr = 0.001
I0401 14:56:38.528095 29493 solver.cpp:218] Iteration 6281 (2.23409 iter/s, 4.9237s/11 iters), loss = 1.81449
I0401 14:56:38.528139 29493 solver.cpp:237]     Train net output #0: loss = 1.81449 (* 1 = 1.81449 loss)
I0401 14:56:38.528144 29493 sgd_solver.cpp:105] Iteration 6281, lr = 0.001
I0401 14:56:39.361112 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:56:43.217787 29493 solver.cpp:218] Iteration 6292 (2.3456 iter/s, 4.68964s/11 iters), loss = 1.61266
I0401 14:56:43.217828 29493 solver.cpp:237]     Train net output #0: loss = 1.61266 (* 1 = 1.61266 loss)
I0401 14:56:43.217833 29493 sgd_solver.cpp:105] Iteration 6292, lr = 0.001
I0401 14:56:48.212214 29493 solver.cpp:218] Iteration 6303 (2.20248 iter/s, 4.99437s/11 iters), loss = 1.46308
I0401 14:56:48.212255 29493 solver.cpp:237]     Train net output #0: loss = 1.46308 (* 1 = 1.46308 loss)
I0401 14:56:48.212260 29493 sgd_solver.cpp:105] Iteration 6303, lr = 0.001
I0401 14:56:53.080083 29493 solver.cpp:218] Iteration 6314 (2.25974 iter/s, 4.86781s/11 iters), loss = 1.48261
I0401 14:56:53.080127 29493 solver.cpp:237]     Train net output #0: loss = 1.48261 (* 1 = 1.48261 loss)
I0401 14:56:53.080133 29493 sgd_solver.cpp:105] Iteration 6314, lr = 0.001
I0401 14:56:54.750465 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6319.caffemodel
I0401 14:56:57.773301 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6319.solverstate
I0401 14:57:00.073873 29493 solver.cpp:330] Iteration 6319, Testing net (#0)
I0401 14:57:00.073972 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:57:05.755237 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:57:06.816913 29493 solver.cpp:397]     Test net output #0: accuracy = 0.159128
I0401 14:57:06.816948 29493 solver.cpp:397]     Test net output #1: loss = 4.49755 (* 1 = 4.49755 loss)
I0401 14:57:08.653259 29493 solver.cpp:218] Iteration 6325 (0.706345 iter/s, 15.5731s/11 iters), loss = 2.11459
I0401 14:57:08.653311 29493 solver.cpp:237]     Train net output #0: loss = 2.11459 (* 1 = 2.11459 loss)
I0401 14:57:08.653317 29493 sgd_solver.cpp:105] Iteration 6325, lr = 0.001
I0401 14:57:13.257355 29493 solver.cpp:218] Iteration 6336 (2.38921 iter/s, 4.60402s/11 iters), loss = 1.80614
I0401 14:57:13.257405 29493 solver.cpp:237]     Train net output #0: loss = 1.80614 (* 1 = 1.80614 loss)
I0401 14:57:13.257411 29493 sgd_solver.cpp:105] Iteration 6336, lr = 0.001
I0401 14:57:17.672705 29493 solver.cpp:218] Iteration 6347 (2.49135 iter/s, 4.41528s/11 iters), loss = 1.90518
I0401 14:57:17.672752 29493 solver.cpp:237]     Train net output #0: loss = 1.90518 (* 1 = 1.90518 loss)
I0401 14:57:17.672760 29493 sgd_solver.cpp:105] Iteration 6347, lr = 0.001
I0401 14:57:22.351207 29493 solver.cpp:218] Iteration 6358 (2.35121 iter/s, 4.67844s/11 iters), loss = 1.73799
I0401 14:57:22.351249 29493 solver.cpp:237]     Train net output #0: loss = 1.73799 (* 1 = 1.73799 loss)
I0401 14:57:22.351254 29493 sgd_solver.cpp:105] Iteration 6358, lr = 0.001
I0401 14:57:27.033334 29493 solver.cpp:218] Iteration 6369 (2.34939 iter/s, 4.68207s/11 iters), loss = 1.49621
I0401 14:57:27.033375 29493 solver.cpp:237]     Train net output #0: loss = 1.49621 (* 1 = 1.49621 loss)
I0401 14:57:27.033382 29493 sgd_solver.cpp:105] Iteration 6369, lr = 0.001
I0401 14:57:28.070786 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:57:31.688196 29493 solver.cpp:218] Iteration 6380 (2.36315 iter/s, 4.65481s/11 iters), loss = 1.55277
I0401 14:57:31.688287 29493 solver.cpp:237]     Train net output #0: loss = 1.55277 (* 1 = 1.55277 loss)
I0401 14:57:31.688293 29493 sgd_solver.cpp:105] Iteration 6380, lr = 0.001
I0401 14:57:36.414777 29493 solver.cpp:218] Iteration 6391 (2.32731 iter/s, 4.72648s/11 iters), loss = 1.52014
I0401 14:57:36.414821 29493 solver.cpp:237]     Train net output #0: loss = 1.52014 (* 1 = 1.52014 loss)
I0401 14:57:36.414826 29493 sgd_solver.cpp:105] Iteration 6391, lr = 0.001
I0401 14:57:41.140309 29493 solver.cpp:218] Iteration 6402 (2.32781 iter/s, 4.72547s/11 iters), loss = 1.43504
I0401 14:57:41.140367 29493 solver.cpp:237]     Train net output #0: loss = 1.43504 (* 1 = 1.43504 loss)
I0401 14:57:41.140374 29493 sgd_solver.cpp:105] Iteration 6402, lr = 0.001
I0401 14:57:43.222889 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6408.caffemodel
I0401 14:57:46.224459 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6408.solverstate
I0401 14:57:48.524233 29493 solver.cpp:330] Iteration 6408, Testing net (#0)
I0401 14:57:48.524253 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:57:54.195751 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:57:55.318783 29493 solver.cpp:397]     Test net output #0: accuracy = 0.161595
I0401 14:57:55.318821 29493 solver.cpp:397]     Test net output #1: loss = 4.46712 (* 1 = 4.46712 loss)
I0401 14:57:56.703655 29493 solver.cpp:218] Iteration 6413 (0.706792 iter/s, 15.5633s/11 iters), loss = 1.68227
I0401 14:57:56.703701 29493 solver.cpp:237]     Train net output #0: loss = 1.68227 (* 1 = 1.68227 loss)
I0401 14:57:56.703706 29493 sgd_solver.cpp:105] Iteration 6413, lr = 0.001
I0401 14:58:01.264431 29493 solver.cpp:218] Iteration 6424 (2.4119 iter/s, 4.56072s/11 iters), loss = 1.83216
I0401 14:58:01.264483 29493 solver.cpp:237]     Train net output #0: loss = 1.83216 (* 1 = 1.83216 loss)
I0401 14:58:01.264493 29493 sgd_solver.cpp:105] Iteration 6424, lr = 0.001
I0401 14:58:06.029000 29493 solver.cpp:218] Iteration 6435 (2.30874 iter/s, 4.76451s/11 iters), loss = 1.59414
I0401 14:58:06.029120 29493 solver.cpp:237]     Train net output #0: loss = 1.59414 (* 1 = 1.59414 loss)
I0401 14:58:06.029129 29493 sgd_solver.cpp:105] Iteration 6435, lr = 0.001
I0401 14:58:10.868336 29493 solver.cpp:218] Iteration 6446 (2.2731 iter/s, 4.8392s/11 iters), loss = 1.98637
I0401 14:58:10.868386 29493 solver.cpp:237]     Train net output #0: loss = 1.98637 (* 1 = 1.98637 loss)
I0401 14:58:10.868394 29493 sgd_solver.cpp:105] Iteration 6446, lr = 0.001
I0401 14:58:15.615542 29493 solver.cpp:218] Iteration 6457 (2.31718 iter/s, 4.74715s/11 iters), loss = 1.30384
I0401 14:58:15.615586 29493 solver.cpp:237]     Train net output #0: loss = 1.30384 (* 1 = 1.30384 loss)
I0401 14:58:15.615592 29493 sgd_solver.cpp:105] Iteration 6457, lr = 0.001
I0401 14:58:16.727632 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:58:20.180763 29493 solver.cpp:218] Iteration 6468 (2.40955 iter/s, 4.56516s/11 iters), loss = 1.70712
I0401 14:58:20.180809 29493 solver.cpp:237]     Train net output #0: loss = 1.70712 (* 1 = 1.70712 loss)
I0401 14:58:20.180815 29493 sgd_solver.cpp:105] Iteration 6468, lr = 0.001
I0401 14:58:24.989559 29493 solver.cpp:218] Iteration 6479 (2.2875 iter/s, 4.80873s/11 iters), loss = 1.25725
I0401 14:58:24.989603 29493 solver.cpp:237]     Train net output #0: loss = 1.25725 (* 1 = 1.25725 loss)
I0401 14:58:24.989609 29493 sgd_solver.cpp:105] Iteration 6479, lr = 0.001
I0401 14:58:29.441764 29493 solver.cpp:218] Iteration 6490 (2.47072 iter/s, 4.45214s/11 iters), loss = 1.3868
I0401 14:58:29.441812 29493 solver.cpp:237]     Train net output #0: loss = 1.3868 (* 1 = 1.3868 loss)
I0401 14:58:29.441817 29493 sgd_solver.cpp:105] Iteration 6490, lr = 0.001
I0401 14:58:31.898391 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6497.caffemodel
I0401 14:58:34.852623 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6497.solverstate
I0401 14:58:37.147531 29493 solver.cpp:330] Iteration 6497, Testing net (#0)
I0401 14:58:37.147614 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:58:42.622655 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:58:43.687541 29493 solver.cpp:397]     Test net output #0: accuracy = 0.157895
I0401 14:58:43.687569 29493 solver.cpp:397]     Test net output #1: loss = 4.52198 (* 1 = 4.52198 loss)
I0401 14:58:44.731297 29493 solver.cpp:218] Iteration 6501 (0.719449 iter/s, 15.2895s/11 iters), loss = 1.87324
I0401 14:58:44.731338 29493 solver.cpp:237]     Train net output #0: loss = 1.87324 (* 1 = 1.87324 loss)
I0401 14:58:44.731344 29493 sgd_solver.cpp:105] Iteration 6501, lr = 0.001
I0401 14:58:49.332885 29493 solver.cpp:218] Iteration 6512 (2.39051 iter/s, 4.60153s/11 iters), loss = 1.96223
I0401 14:58:49.332932 29493 solver.cpp:237]     Train net output #0: loss = 1.96223 (* 1 = 1.96223 loss)
I0401 14:58:49.332940 29493 sgd_solver.cpp:105] Iteration 6512, lr = 0.001
I0401 14:58:54.177125 29493 solver.cpp:218] Iteration 6523 (2.27077 iter/s, 4.84418s/11 iters), loss = 1.72144
I0401 14:58:54.177173 29493 solver.cpp:237]     Train net output #0: loss = 1.72144 (* 1 = 1.72144 loss)
I0401 14:58:54.177179 29493 sgd_solver.cpp:105] Iteration 6523, lr = 0.001
I0401 14:58:58.962589 29493 solver.cpp:218] Iteration 6534 (2.29866 iter/s, 4.7854s/11 iters), loss = 1.66543
I0401 14:58:58.962644 29493 solver.cpp:237]     Train net output #0: loss = 1.66543 (* 1 = 1.66543 loss)
I0401 14:58:58.962653 29493 sgd_solver.cpp:105] Iteration 6534, lr = 0.001
I0401 14:59:03.669880 29493 solver.cpp:218] Iteration 6545 (2.33683 iter/s, 4.70723s/11 iters), loss = 1.41695
I0401 14:59:03.669922 29493 solver.cpp:237]     Train net output #0: loss = 1.41695 (* 1 = 1.41695 loss)
I0401 14:59:03.669929 29493 sgd_solver.cpp:105] Iteration 6545, lr = 0.001
I0401 14:59:05.127986 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:59:08.455957 29493 solver.cpp:218] Iteration 6556 (2.29836 iter/s, 4.78602s/11 iters), loss = 1.55122
I0401 14:59:08.456108 29493 solver.cpp:237]     Train net output #0: loss = 1.55122 (* 1 = 1.55122 loss)
I0401 14:59:08.456115 29493 sgd_solver.cpp:105] Iteration 6556, lr = 0.001
I0401 14:59:13.314174 29493 solver.cpp:218] Iteration 6567 (2.26428 iter/s, 4.85805s/11 iters), loss = 1.16081
I0401 14:59:13.314221 29493 solver.cpp:237]     Train net output #0: loss = 1.16081 (* 1 = 1.16081 loss)
I0401 14:59:13.314227 29493 sgd_solver.cpp:105] Iteration 6567, lr = 0.001
I0401 14:59:18.133975 29493 solver.cpp:218] Iteration 6578 (2.28228 iter/s, 4.81974s/11 iters), loss = 1.2213
I0401 14:59:18.134029 29493 solver.cpp:237]     Train net output #0: loss = 1.2213 (* 1 = 1.2213 loss)
I0401 14:59:18.134037 29493 sgd_solver.cpp:105] Iteration 6578, lr = 0.001
I0401 14:59:21.103073 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6586.caffemodel
I0401 14:59:24.160940 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6586.solverstate
I0401 14:59:26.455547 29493 solver.cpp:330] Iteration 6586, Testing net (#0)
I0401 14:59:26.455566 29493 net.cpp:676] Ignoring source layer train-data
I0401 14:59:31.958344 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:59:33.005349 29493 solver.cpp:397]     Test net output #0: accuracy = 0.159128
I0401 14:59:33.005391 29493 solver.cpp:397]     Test net output #1: loss = 4.52358 (* 1 = 4.52358 loss)
I0401 14:59:33.643676 29493 solver.cpp:218] Iteration 6589 (0.709237 iter/s, 15.5096s/11 iters), loss = 1.83703
I0401 14:59:33.643740 29493 solver.cpp:237]     Train net output #0: loss = 1.83703 (* 1 = 1.83703 loss)
I0401 14:59:33.643750 29493 sgd_solver.cpp:105] Iteration 6589, lr = 0.001
I0401 14:59:38.122078 29493 solver.cpp:218] Iteration 6600 (2.45628 iter/s, 4.47832s/11 iters), loss = 1.76221
I0401 14:59:38.122123 29493 solver.cpp:237]     Train net output #0: loss = 1.76221 (* 1 = 1.76221 loss)
I0401 14:59:38.122129 29493 sgd_solver.cpp:105] Iteration 6600, lr = 0.001
I0401 14:59:42.726110 29493 solver.cpp:218] Iteration 6611 (2.38924 iter/s, 4.60398s/11 iters), loss = 1.55933
I0401 14:59:42.726195 29493 solver.cpp:237]     Train net output #0: loss = 1.55933 (* 1 = 1.55933 loss)
I0401 14:59:42.726202 29493 sgd_solver.cpp:105] Iteration 6611, lr = 0.001
I0401 14:59:47.588865 29493 solver.cpp:218] Iteration 6622 (2.26214 iter/s, 4.86266s/11 iters), loss = 1.1613
I0401 14:59:47.588945 29493 solver.cpp:237]     Train net output #0: loss = 1.1613 (* 1 = 1.1613 loss)
I0401 14:59:47.588964 29493 sgd_solver.cpp:105] Iteration 6622, lr = 0.001
I0401 14:59:51.963888 29493 solver.cpp:218] Iteration 6633 (2.51431 iter/s, 4.37495s/11 iters), loss = 1.19653
I0401 14:59:51.963930 29493 solver.cpp:237]     Train net output #0: loss = 1.19653 (* 1 = 1.19653 loss)
I0401 14:59:51.963938 29493 sgd_solver.cpp:105] Iteration 6633, lr = 0.001
I0401 14:59:53.665076 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 14:59:56.641613 29493 solver.cpp:218] Iteration 6644 (2.3516 iter/s, 4.67767s/11 iters), loss = 1.18651
I0401 14:59:56.641654 29493 solver.cpp:237]     Train net output #0: loss = 1.18651 (* 1 = 1.18651 loss)
I0401 14:59:56.641661 29493 sgd_solver.cpp:105] Iteration 6644, lr = 0.001
I0401 15:00:01.399333 29493 solver.cpp:218] Iteration 6655 (2.31206 iter/s, 4.75766s/11 iters), loss = 1.13349
I0401 15:00:01.399389 29493 solver.cpp:237]     Train net output #0: loss = 1.13349 (* 1 = 1.13349 loss)
I0401 15:00:01.399397 29493 sgd_solver.cpp:105] Iteration 6655, lr = 0.001
I0401 15:00:06.329171 29493 solver.cpp:218] Iteration 6666 (2.23134 iter/s, 4.92977s/11 iters), loss = 1.39911
I0401 15:00:06.329216 29493 solver.cpp:237]     Train net output #0: loss = 1.39911 (* 1 = 1.39911 loss)
I0401 15:00:06.329221 29493 sgd_solver.cpp:105] Iteration 6666, lr = 0.001
I0401 15:00:09.590708 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6675.caffemodel
I0401 15:00:12.585726 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6675.solverstate
I0401 15:00:14.878787 29493 solver.cpp:330] Iteration 6675, Testing net (#0)
I0401 15:00:14.878888 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:00:20.365525 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:00:21.459137 29493 solver.cpp:397]     Test net output #0: accuracy = 0.151316
I0401 15:00:21.459178 29493 solver.cpp:397]     Test net output #1: loss = 4.55413 (* 1 = 4.55413 loss)
I0401 15:00:21.870277 29493 solver.cpp:218] Iteration 6677 (0.707803 iter/s, 15.5411s/11 iters), loss = 1.37736
I0401 15:00:21.870322 29493 solver.cpp:237]     Train net output #0: loss = 1.37736 (* 1 = 1.37736 loss)
I0401 15:00:21.870328 29493 sgd_solver.cpp:105] Iteration 6677, lr = 0.001
I0401 15:00:23.309620 29493 blocking_queue.cpp:49] Waiting for data
I0401 15:00:26.462165 29493 solver.cpp:218] Iteration 6688 (2.39556 iter/s, 4.59182s/11 iters), loss = 1.42898
I0401 15:00:26.462214 29493 solver.cpp:237]     Train net output #0: loss = 1.42898 (* 1 = 1.42898 loss)
I0401 15:00:26.462220 29493 sgd_solver.cpp:105] Iteration 6688, lr = 0.001
I0401 15:00:31.185708 29493 solver.cpp:218] Iteration 6699 (2.32879 iter/s, 4.72348s/11 iters), loss = 1.65125
I0401 15:00:31.185750 29493 solver.cpp:237]     Train net output #0: loss = 1.65125 (* 1 = 1.65125 loss)
I0401 15:00:31.185755 29493 sgd_solver.cpp:105] Iteration 6699, lr = 0.001
I0401 15:00:35.836972 29493 solver.cpp:218] Iteration 6710 (2.36498 iter/s, 4.6512s/11 iters), loss = 1.52593
I0401 15:00:35.837033 29493 solver.cpp:237]     Train net output #0: loss = 1.52593 (* 1 = 1.52593 loss)
I0401 15:00:35.837041 29493 sgd_solver.cpp:105] Iteration 6710, lr = 0.001
I0401 15:00:40.728129 29493 solver.cpp:218] Iteration 6721 (2.24899 iter/s, 4.89109s/11 iters), loss = 1.32115
I0401 15:00:40.728171 29493 solver.cpp:237]     Train net output #0: loss = 1.32115 (* 1 = 1.32115 loss)
I0401 15:00:40.728178 29493 sgd_solver.cpp:105] Iteration 6721, lr = 0.001
I0401 15:00:42.740007 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:00:45.603593 29493 solver.cpp:218] Iteration 6732 (2.25622 iter/s, 4.87541s/11 iters), loss = 1.41029
I0401 15:00:45.603686 29493 solver.cpp:237]     Train net output #0: loss = 1.41029 (* 1 = 1.41029 loss)
I0401 15:00:45.603693 29493 sgd_solver.cpp:105] Iteration 6732, lr = 0.001
I0401 15:00:50.331689 29493 solver.cpp:218] Iteration 6743 (2.32657 iter/s, 4.72799s/11 iters), loss = 1.414
I0401 15:00:50.331737 29493 solver.cpp:237]     Train net output #0: loss = 1.414 (* 1 = 1.414 loss)
I0401 15:00:50.331741 29493 sgd_solver.cpp:105] Iteration 6743, lr = 0.001
I0401 15:00:55.187094 29493 solver.cpp:218] Iteration 6754 (2.26555 iter/s, 4.85534s/11 iters), loss = 1.63804
I0401 15:00:55.187137 29493 solver.cpp:237]     Train net output #0: loss = 1.63804 (* 1 = 1.63804 loss)
I0401 15:00:55.187144 29493 sgd_solver.cpp:105] Iteration 6754, lr = 0.001
I0401 15:00:59.182934 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6764.caffemodel
I0401 15:01:02.243424 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6764.solverstate
I0401 15:01:04.585227 29493 solver.cpp:330] Iteration 6764, Testing net (#0)
I0401 15:01:04.585245 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:01:10.167758 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:01:11.267940 29493 solver.cpp:397]     Test net output #0: accuracy = 0.155016
I0401 15:01:11.267966 29493 solver.cpp:397]     Test net output #1: loss = 4.53403 (* 1 = 4.53403 loss)
I0401 15:01:11.546160 29493 solver.cpp:218] Iteration 6765 (0.672412 iter/s, 16.359s/11 iters), loss = 1.46381
I0401 15:01:11.547717 29493 solver.cpp:237]     Train net output #0: loss = 1.46381 (* 1 = 1.46381 loss)
I0401 15:01:11.547729 29493 sgd_solver.cpp:105] Iteration 6765, lr = 0.001
I0401 15:01:15.680604 29493 solver.cpp:218] Iteration 6776 (2.66158 iter/s, 4.13289s/11 iters), loss = 1.55959
I0401 15:01:15.680716 29493 solver.cpp:237]     Train net output #0: loss = 1.55959 (* 1 = 1.55959 loss)
I0401 15:01:15.680722 29493 sgd_solver.cpp:105] Iteration 6776, lr = 0.001
I0401 15:01:20.475870 29493 solver.cpp:218] Iteration 6787 (2.29399 iter/s, 4.79514s/11 iters), loss = 1.50986
I0401 15:01:20.475912 29493 solver.cpp:237]     Train net output #0: loss = 1.50986 (* 1 = 1.50986 loss)
I0401 15:01:20.475917 29493 sgd_solver.cpp:105] Iteration 6787, lr = 0.001
I0401 15:01:25.205507 29493 solver.cpp:218] Iteration 6798 (2.32579 iter/s, 4.72957s/11 iters), loss = 1.65347
I0401 15:01:25.205565 29493 solver.cpp:237]     Train net output #0: loss = 1.65347 (* 1 = 1.65347 loss)
I0401 15:01:25.205574 29493 sgd_solver.cpp:105] Iteration 6798, lr = 0.001
I0401 15:01:30.141279 29493 solver.cpp:218] Iteration 6809 (2.22866 iter/s, 4.9357s/11 iters), loss = 1.41397
I0401 15:01:30.141326 29493 solver.cpp:237]     Train net output #0: loss = 1.41397 (* 1 = 1.41397 loss)
I0401 15:01:30.141332 29493 sgd_solver.cpp:105] Iteration 6809, lr = 0.001
I0401 15:01:32.387214 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:01:34.885447 29493 solver.cpp:218] Iteration 6820 (2.31866 iter/s, 4.74411s/11 iters), loss = 1.36481
I0401 15:01:34.885489 29493 solver.cpp:237]     Train net output #0: loss = 1.36481 (* 1 = 1.36481 loss)
I0401 15:01:34.885497 29493 sgd_solver.cpp:105] Iteration 6820, lr = 0.001
I0401 15:01:39.827152 29493 solver.cpp:218] Iteration 6831 (2.22598 iter/s, 4.94165s/11 iters), loss = 1.38696
I0401 15:01:39.827199 29493 solver.cpp:237]     Train net output #0: loss = 1.38696 (* 1 = 1.38696 loss)
I0401 15:01:39.827208 29493 sgd_solver.cpp:105] Iteration 6831, lr = 0.001
I0401 15:01:44.536867 29493 solver.cpp:218] Iteration 6842 (2.33563 iter/s, 4.70966s/11 iters), loss = 1.49038
I0401 15:01:44.536927 29493 solver.cpp:237]     Train net output #0: loss = 1.49038 (* 1 = 1.49038 loss)
I0401 15:01:44.536934 29493 sgd_solver.cpp:105] Iteration 6842, lr = 0.001
I0401 15:01:48.702395 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6853.caffemodel
I0401 15:01:53.256645 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6853.solverstate
I0401 15:01:56.833382 29493 solver.cpp:330] Iteration 6853, Testing net (#0)
I0401 15:01:56.833406 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:02:02.245726 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:02:03.390611 29493 solver.cpp:397]     Test net output #0: accuracy = 0.155016
I0401 15:02:03.390645 29493 solver.cpp:397]     Test net output #1: loss = 4.55735 (* 1 = 4.55735 loss)
I0401 15:02:03.527997 29493 solver.cpp:218] Iteration 6853 (0.57922 iter/s, 18.9911s/11 iters), loss = 1.32614
I0401 15:02:03.529558 29493 solver.cpp:237]     Train net output #0: loss = 1.32614 (* 1 = 1.32614 loss)
I0401 15:02:03.529573 29493 sgd_solver.cpp:105] Iteration 6853, lr = 0.001
I0401 15:02:07.373090 29493 solver.cpp:218] Iteration 6864 (2.86195 iter/s, 3.84354s/11 iters), loss = 1.68979
I0401 15:02:07.373124 29493 solver.cpp:237]     Train net output #0: loss = 1.68979 (* 1 = 1.68979 loss)
I0401 15:02:07.373128 29493 sgd_solver.cpp:105] Iteration 6864, lr = 0.001
I0401 15:02:12.202180 29493 solver.cpp:218] Iteration 6875 (2.27788 iter/s, 4.82904s/11 iters), loss = 1.45565
I0401 15:02:12.202235 29493 solver.cpp:237]     Train net output #0: loss = 1.45565 (* 1 = 1.45565 loss)
I0401 15:02:12.202246 29493 sgd_solver.cpp:105] Iteration 6875, lr = 0.001
I0401 15:02:16.912209 29493 solver.cpp:218] Iteration 6886 (2.33548 iter/s, 4.70996s/11 iters), loss = 1.54087
I0401 15:02:16.912267 29493 solver.cpp:237]     Train net output #0: loss = 1.54087 (* 1 = 1.54087 loss)
I0401 15:02:16.912277 29493 sgd_solver.cpp:105] Iteration 6886, lr = 0.001
I0401 15:02:21.672968 29493 solver.cpp:218] Iteration 6897 (2.31059 iter/s, 4.76069s/11 iters), loss = 1.29043
I0401 15:02:21.673085 29493 solver.cpp:237]     Train net output #0: loss = 1.29043 (* 1 = 1.29043 loss)
I0401 15:02:21.673094 29493 sgd_solver.cpp:105] Iteration 6897, lr = 0.001
I0401 15:02:24.095999 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:02:26.600663 29493 solver.cpp:218] Iteration 6908 (2.23234 iter/s, 4.92757s/11 iters), loss = 1.34828
I0401 15:02:26.600698 29493 solver.cpp:237]     Train net output #0: loss = 1.34828 (* 1 = 1.34828 loss)
I0401 15:02:26.600703 29493 sgd_solver.cpp:105] Iteration 6908, lr = 0.001
I0401 15:02:31.597004 29493 solver.cpp:218] Iteration 6919 (2.20163 iter/s, 4.99629s/11 iters), loss = 1.01317
I0401 15:02:31.597048 29493 solver.cpp:237]     Train net output #0: loss = 1.01317 (* 1 = 1.01317 loss)
I0401 15:02:31.597052 29493 sgd_solver.cpp:105] Iteration 6919, lr = 0.001
I0401 15:02:36.452589 29493 solver.cpp:218] Iteration 6930 (2.26546 iter/s, 4.85552s/11 iters), loss = 1.16871
I0401 15:02:36.452649 29493 solver.cpp:237]     Train net output #0: loss = 1.16871 (* 1 = 1.16871 loss)
I0401 15:02:36.452658 29493 sgd_solver.cpp:105] Iteration 6930, lr = 0.001
I0401 15:02:41.260390 29493 solver.cpp:218] Iteration 6941 (2.28798 iter/s, 4.80773s/11 iters), loss = 1.39101
I0401 15:02:41.260452 29493 solver.cpp:237]     Train net output #0: loss = 1.39101 (* 1 = 1.39101 loss)
I0401 15:02:41.260462 29493 sgd_solver.cpp:105] Iteration 6941, lr = 0.001
I0401 15:02:41.260668 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6942.caffemodel
I0401 15:02:46.428568 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6942.solverstate
I0401 15:02:50.070485 29493 solver.cpp:330] Iteration 6942, Testing net (#0)
I0401 15:02:50.070509 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:02:55.780690 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:02:56.884387 29493 solver.cpp:397]     Test net output #0: accuracy = 0.160362
I0401 15:02:56.884423 29493 solver.cpp:397]     Test net output #1: loss = 4.53925 (* 1 = 4.53925 loss)
I0401 15:03:00.407687 29493 solver.cpp:218] Iteration 6952 (0.574495 iter/s, 19.1472s/11 iters), loss = 1.42879
I0401 15:03:00.407729 29493 solver.cpp:237]     Train net output #0: loss = 1.42879 (* 1 = 1.42879 loss)
I0401 15:03:00.407735 29493 sgd_solver.cpp:105] Iteration 6952, lr = 0.001
I0401 15:03:05.033576 29493 solver.cpp:218] Iteration 6963 (2.37795 iter/s, 4.62583s/11 iters), loss = 1.35581
I0401 15:03:05.033614 29493 solver.cpp:237]     Train net output #0: loss = 1.35581 (* 1 = 1.35581 loss)
I0401 15:03:05.033620 29493 sgd_solver.cpp:105] Iteration 6963, lr = 0.001
I0401 15:03:09.638131 29493 solver.cpp:218] Iteration 6974 (2.38897 iter/s, 4.6045s/11 iters), loss = 1.17375
I0401 15:03:09.638197 29493 solver.cpp:237]     Train net output #0: loss = 1.17375 (* 1 = 1.17375 loss)
I0401 15:03:09.638207 29493 sgd_solver.cpp:105] Iteration 6974, lr = 0.001
I0401 15:03:14.582473 29493 solver.cpp:218] Iteration 6985 (2.2248 iter/s, 4.94427s/11 iters), loss = 1.31629
I0401 15:03:14.582515 29493 solver.cpp:237]     Train net output #0: loss = 1.31629 (* 1 = 1.31629 loss)
I0401 15:03:14.582522 29493 sgd_solver.cpp:105] Iteration 6985, lr = 0.001
I0401 15:03:17.251904 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:03:19.370563 29493 solver.cpp:218] Iteration 6996 (2.29739 iter/s, 4.78803s/11 iters), loss = 1.2929
I0401 15:03:19.370607 29493 solver.cpp:237]     Train net output #0: loss = 1.2929 (* 1 = 1.2929 loss)
I0401 15:03:19.370615 29493 sgd_solver.cpp:105] Iteration 6996, lr = 0.001
I0401 15:03:24.113615 29493 solver.cpp:218] Iteration 7007 (2.31921 iter/s, 4.74299s/11 iters), loss = 1.31646
I0401 15:03:24.113657 29493 solver.cpp:237]     Train net output #0: loss = 1.31646 (* 1 = 1.31646 loss)
I0401 15:03:24.113663 29493 sgd_solver.cpp:105] Iteration 7007, lr = 0.001
I0401 15:03:29.084908 29493 solver.cpp:218] Iteration 7018 (2.21273 iter/s, 4.97124s/11 iters), loss = 1.30711
I0401 15:03:29.085017 29493 solver.cpp:237]     Train net output #0: loss = 1.30711 (* 1 = 1.30711 loss)
I0401 15:03:29.085024 29493 sgd_solver.cpp:105] Iteration 7018, lr = 0.001
I0401 15:03:33.875205 29493 solver.cpp:218] Iteration 7029 (2.29637 iter/s, 4.79017s/11 iters), loss = 1.00468
I0401 15:03:33.875250 29493 solver.cpp:237]     Train net output #0: loss = 1.00468 (* 1 = 1.00468 loss)
I0401 15:03:33.875257 29493 sgd_solver.cpp:105] Iteration 7029, lr = 0.001
I0401 15:03:34.226254 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7031.caffemodel
I0401 15:03:40.670882 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7031.solverstate
I0401 15:03:45.960605 29493 solver.cpp:330] Iteration 7031, Testing net (#0)
I0401 15:03:45.960629 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:03:51.526218 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:03:52.649152 29493 solver.cpp:397]     Test net output #0: accuracy = 0.150082
I0401 15:03:52.649184 29493 solver.cpp:397]     Test net output #1: loss = 4.53211 (* 1 = 4.53211 loss)
I0401 15:03:55.785122 29493 solver.cpp:218] Iteration 7040 (0.502057 iter/s, 21.9099s/11 iters), loss = 1.34637
I0401 15:03:55.785163 29493 solver.cpp:237]     Train net output #0: loss = 1.34637 (* 1 = 1.34637 loss)
I0401 15:03:55.785169 29493 sgd_solver.cpp:105] Iteration 7040, lr = 0.001
I0401 15:04:00.640604 29493 solver.cpp:218] Iteration 7051 (2.26551 iter/s, 4.85542s/11 iters), loss = 1.0535
I0401 15:04:00.640769 29493 solver.cpp:237]     Train net output #0: loss = 1.0535 (* 1 = 1.0535 loss)
I0401 15:04:00.640779 29493 sgd_solver.cpp:105] Iteration 7051, lr = 0.001
I0401 15:04:05.392084 29493 solver.cpp:218] Iteration 7062 (2.31516 iter/s, 4.7513s/11 iters), loss = 1.3382
I0401 15:04:05.392132 29493 solver.cpp:237]     Train net output #0: loss = 1.3382 (* 1 = 1.3382 loss)
I0401 15:04:05.392138 29493 sgd_solver.cpp:105] Iteration 7062, lr = 0.001
I0401 15:04:10.171041 29493 solver.cpp:218] Iteration 7073 (2.30179 iter/s, 4.7789s/11 iters), loss = 1.18386
I0401 15:04:10.171098 29493 solver.cpp:237]     Train net output #0: loss = 1.18386 (* 1 = 1.18386 loss)
I0401 15:04:10.171106 29493 sgd_solver.cpp:105] Iteration 7073, lr = 0.001
I0401 15:04:13.032059 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:04:15.155791 29493 solver.cpp:218] Iteration 7084 (2.20676 iter/s, 4.98468s/11 iters), loss = 1.07732
I0401 15:04:15.155835 29493 solver.cpp:237]     Train net output #0: loss = 1.07732 (* 1 = 1.07732 loss)
I0401 15:04:15.155840 29493 sgd_solver.cpp:105] Iteration 7084, lr = 0.001
I0401 15:04:20.048365 29493 solver.cpp:218] Iteration 7095 (2.24833 iter/s, 4.89252s/11 iters), loss = 1.07275
I0401 15:04:20.048404 29493 solver.cpp:237]     Train net output #0: loss = 1.07275 (* 1 = 1.07275 loss)
I0401 15:04:20.048409 29493 sgd_solver.cpp:105] Iteration 7095, lr = 0.001
I0401 15:04:24.997303 29493 solver.cpp:218] Iteration 7106 (2.22273 iter/s, 4.94888s/11 iters), loss = 1.27037
I0401 15:04:24.997356 29493 solver.cpp:237]     Train net output #0: loss = 1.27037 (* 1 = 1.27037 loss)
I0401 15:04:24.997365 29493 sgd_solver.cpp:105] Iteration 7106, lr = 0.001
I0401 15:04:29.764453 29493 solver.cpp:218] Iteration 7117 (2.30749 iter/s, 4.76708s/11 iters), loss = 1.36122
I0401 15:04:29.764510 29493 solver.cpp:237]     Train net output #0: loss = 1.36122 (* 1 = 1.36122 loss)
I0401 15:04:29.764519 29493 sgd_solver.cpp:105] Iteration 7117, lr = 0.001
I0401 15:04:30.570206 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7120.caffemodel
I0401 15:04:36.931936 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7120.solverstate
I0401 15:04:40.296291 29493 solver.cpp:330] Iteration 7120, Testing net (#0)
I0401 15:04:40.296312 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:04:45.817960 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:04:46.963966 29493 solver.cpp:397]     Test net output #0: accuracy = 0.158717
I0401 15:04:46.964004 29493 solver.cpp:397]     Test net output #1: loss = 4.59114 (* 1 = 4.59114 loss)
I0401 15:04:49.691694 29493 solver.cpp:218] Iteration 7128 (0.55201 iter/s, 19.9272s/11 iters), loss = 1.24729
I0401 15:04:49.691759 29493 solver.cpp:237]     Train net output #0: loss = 1.24729 (* 1 = 1.24729 loss)
I0401 15:04:49.691767 29493 sgd_solver.cpp:105] Iteration 7128, lr = 0.001
I0401 15:04:54.476869 29493 solver.cpp:218] Iteration 7139 (2.2988 iter/s, 4.7851s/11 iters), loss = 1.42069
I0401 15:04:54.476928 29493 solver.cpp:237]     Train net output #0: loss = 1.42069 (* 1 = 1.42069 loss)
I0401 15:04:54.476938 29493 sgd_solver.cpp:105] Iteration 7139, lr = 0.001
I0401 15:04:59.396663 29493 solver.cpp:218] Iteration 7150 (2.2359 iter/s, 4.91972s/11 iters), loss = 1.24882
I0401 15:04:59.396709 29493 solver.cpp:237]     Train net output #0: loss = 1.24882 (* 1 = 1.24882 loss)
I0401 15:04:59.396716 29493 sgd_solver.cpp:105] Iteration 7150, lr = 0.001
I0401 15:05:04.070986 29493 solver.cpp:218] Iteration 7161 (2.35331 iter/s, 4.67426s/11 iters), loss = 0.946424
I0401 15:05:04.071029 29493 solver.cpp:237]     Train net output #0: loss = 0.946424 (* 1 = 0.946424 loss)
I0401 15:05:04.071036 29493 sgd_solver.cpp:105] Iteration 7161, lr = 0.001
I0401 15:05:07.189905 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:05:08.885308 29493 solver.cpp:218] Iteration 7172 (2.28488 iter/s, 4.81426s/11 iters), loss = 1.21342
I0401 15:05:08.885352 29493 solver.cpp:237]     Train net output #0: loss = 1.21342 (* 1 = 1.21342 loss)
I0401 15:05:08.885360 29493 sgd_solver.cpp:105] Iteration 7172, lr = 0.001
I0401 15:05:13.639187 29493 solver.cpp:218] Iteration 7183 (2.31393 iter/s, 4.75382s/11 iters), loss = 1.27362
I0401 15:05:13.639231 29493 solver.cpp:237]     Train net output #0: loss = 1.27362 (* 1 = 1.27362 loss)
I0401 15:05:13.639237 29493 sgd_solver.cpp:105] Iteration 7183, lr = 0.001
I0401 15:05:18.499650 29493 solver.cpp:218] Iteration 7194 (2.26319 iter/s, 4.8604s/11 iters), loss = 0.852125
I0401 15:05:18.499722 29493 solver.cpp:237]     Train net output #0: loss = 0.852125 (* 1 = 0.852125 loss)
I0401 15:05:18.499733 29493 sgd_solver.cpp:105] Iteration 7194, lr = 0.001
I0401 15:05:23.326849 29493 solver.cpp:218] Iteration 7205 (2.27879 iter/s, 4.82711s/11 iters), loss = 1.11544
I0401 15:05:23.326897 29493 solver.cpp:237]     Train net output #0: loss = 1.11544 (* 1 = 1.11544 loss)
I0401 15:05:23.326903 29493 sgd_solver.cpp:105] Iteration 7205, lr = 0.001
I0401 15:05:24.624181 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7209.caffemodel
I0401 15:05:28.681480 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7209.solverstate
I0401 15:05:32.605093 29493 solver.cpp:330] Iteration 7209, Testing net (#0)
I0401 15:05:32.605119 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:05:38.127408 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:05:39.261793 29493 solver.cpp:397]     Test net output #0: accuracy = 0.154605
I0401 15:05:39.261828 29493 solver.cpp:397]     Test net output #1: loss = 4.66717 (* 1 = 4.66717 loss)
I0401 15:05:41.532719 29493 solver.cpp:218] Iteration 7216 (0.604203 iter/s, 18.2058s/11 iters), loss = 0.911081
I0401 15:05:41.532763 29493 solver.cpp:237]     Train net output #0: loss = 0.911081 (* 1 = 0.911081 loss)
I0401 15:05:41.532769 29493 sgd_solver.cpp:105] Iteration 7216, lr = 0.001
I0401 15:05:46.079756 29493 solver.cpp:218] Iteration 7227 (2.41919 iter/s, 4.54697s/11 iters), loss = 1.14215
I0401 15:05:46.079814 29493 solver.cpp:237]     Train net output #0: loss = 1.14215 (* 1 = 1.14215 loss)
I0401 15:05:46.079824 29493 sgd_solver.cpp:105] Iteration 7227, lr = 0.001
I0401 15:05:50.851359 29493 solver.cpp:218] Iteration 7238 (2.30534 iter/s, 4.77153s/11 iters), loss = 1.13065
I0401 15:05:50.851405 29493 solver.cpp:237]     Train net output #0: loss = 1.13065 (* 1 = 1.13065 loss)
I0401 15:05:50.851413 29493 sgd_solver.cpp:105] Iteration 7238, lr = 0.001
I0401 15:05:55.655156 29493 solver.cpp:218] Iteration 7249 (2.28988 iter/s, 4.80374s/11 iters), loss = 1.65345
I0401 15:05:55.655201 29493 solver.cpp:237]     Train net output #0: loss = 1.65345 (* 1 = 1.65345 loss)
I0401 15:05:55.655206 29493 sgd_solver.cpp:105] Iteration 7249, lr = 0.001
I0401 15:05:58.907097 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:06:00.540741 29493 solver.cpp:218] Iteration 7260 (2.25155 iter/s, 4.88552s/11 iters), loss = 1.25179
I0401 15:06:00.540784 29493 solver.cpp:237]     Train net output #0: loss = 1.25179 (* 1 = 1.25179 loss)
I0401 15:06:00.540791 29493 sgd_solver.cpp:105] Iteration 7260, lr = 0.001
I0401 15:06:00.901544 29493 blocking_queue.cpp:49] Waiting for data
I0401 15:06:05.533298 29493 solver.cpp:218] Iteration 7271 (2.20331 iter/s, 4.99249s/11 iters), loss = 1.11628
I0401 15:06:05.533344 29493 solver.cpp:237]     Train net output #0: loss = 1.11628 (* 1 = 1.11628 loss)
I0401 15:06:05.533350 29493 sgd_solver.cpp:105] Iteration 7271, lr = 0.001
I0401 15:06:10.431587 29493 solver.cpp:218] Iteration 7282 (2.24571 iter/s, 4.89823s/11 iters), loss = 0.837557
I0401 15:06:10.431738 29493 solver.cpp:237]     Train net output #0: loss = 0.837557 (* 1 = 0.837557 loss)
I0401 15:06:10.431746 29493 sgd_solver.cpp:105] Iteration 7282, lr = 0.001
I0401 15:06:15.369592 29493 solver.cpp:218] Iteration 7293 (2.22769 iter/s, 4.93784s/11 iters), loss = 1.14188
I0401 15:06:15.369635 29493 solver.cpp:237]     Train net output #0: loss = 1.14188 (* 1 = 1.14188 loss)
I0401 15:06:15.369642 29493 sgd_solver.cpp:105] Iteration 7293, lr = 0.001
I0401 15:06:17.224767 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7298.caffemodel
I0401 15:06:20.157191 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7298.solverstate
I0401 15:06:23.912717 29493 solver.cpp:330] Iteration 7298, Testing net (#0)
I0401 15:06:23.912736 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:06:29.587837 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:06:30.734414 29493 solver.cpp:397]     Test net output #0: accuracy = 0.153372
I0401 15:06:30.734452 29493 solver.cpp:397]     Test net output #1: loss = 4.68095 (* 1 = 4.68095 loss)
I0401 15:06:32.582883 29493 solver.cpp:218] Iteration 7304 (0.639043 iter/s, 17.2132s/11 iters), loss = 1.32375
I0401 15:06:32.582924 29493 solver.cpp:237]     Train net output #0: loss = 1.32375 (* 1 = 1.32375 loss)
I0401 15:06:32.582931 29493 sgd_solver.cpp:105] Iteration 7304, lr = 0.001
I0401 15:06:37.386950 29493 solver.cpp:218] Iteration 7315 (2.28976 iter/s, 4.80401s/11 iters), loss = 1.01693
I0401 15:06:37.387003 29493 solver.cpp:237]     Train net output #0: loss = 1.01693 (* 1 = 1.01693 loss)
I0401 15:06:37.387012 29493 sgd_solver.cpp:105] Iteration 7315, lr = 0.001
I0401 15:06:42.115111 29493 solver.cpp:218] Iteration 7326 (2.32652 iter/s, 4.72809s/11 iters), loss = 0.97481
I0401 15:06:42.115247 29493 solver.cpp:237]     Train net output #0: loss = 0.97481 (* 1 = 0.97481 loss)
I0401 15:06:42.115254 29493 sgd_solver.cpp:105] Iteration 7326, lr = 0.001
I0401 15:06:46.885993 29493 solver.cpp:218] Iteration 7337 (2.30572 iter/s, 4.77074s/11 iters), loss = 1.144
I0401 15:06:46.886039 29493 solver.cpp:237]     Train net output #0: loss = 1.144 (* 1 = 1.144 loss)
I0401 15:06:46.886044 29493 sgd_solver.cpp:105] Iteration 7337, lr = 0.001
I0401 15:06:50.549306 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:06:51.842903 29493 solver.cpp:218] Iteration 7348 (2.21915 iter/s, 4.95684s/11 iters), loss = 1.04196
I0401 15:06:51.842964 29493 solver.cpp:237]     Train net output #0: loss = 1.04196 (* 1 = 1.04196 loss)
I0401 15:06:51.842973 29493 sgd_solver.cpp:105] Iteration 7348, lr = 0.001
I0401 15:06:56.549268 29493 solver.cpp:218] Iteration 7359 (2.3373 iter/s, 4.70629s/11 iters), loss = 1.32094
I0401 15:06:56.549310 29493 solver.cpp:237]     Train net output #0: loss = 1.32094 (* 1 = 1.32094 loss)
I0401 15:06:56.549316 29493 sgd_solver.cpp:105] Iteration 7359, lr = 0.001
I0401 15:07:01.430699 29493 solver.cpp:218] Iteration 7370 (2.25347 iter/s, 4.88137s/11 iters), loss = 1.02466
I0401 15:07:01.430757 29493 solver.cpp:237]     Train net output #0: loss = 1.02466 (* 1 = 1.02466 loss)
I0401 15:07:01.430768 29493 sgd_solver.cpp:105] Iteration 7370, lr = 0.001
I0401 15:07:06.192652 29493 solver.cpp:218] Iteration 7381 (2.31001 iter/s, 4.76188s/11 iters), loss = 0.99771
I0401 15:07:06.192703 29493 solver.cpp:237]     Train net output #0: loss = 0.99771 (* 1 = 0.99771 loss)
I0401 15:07:06.192710 29493 sgd_solver.cpp:105] Iteration 7381, lr = 0.001
I0401 15:07:08.252472 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7387.caffemodel
I0401 15:07:11.292950 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7387.solverstate
I0401 15:07:13.592828 29493 solver.cpp:330] Iteration 7387, Testing net (#0)
I0401 15:07:13.592929 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:07:19.049818 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:07:20.229981 29493 solver.cpp:397]     Test net output #0: accuracy = 0.142681
I0401 15:07:20.230016 29493 solver.cpp:397]     Test net output #1: loss = 4.73656 (* 1 = 4.73656 loss)
I0401 15:07:21.690611 29493 solver.cpp:218] Iteration 7392 (0.709774 iter/s, 15.4979s/11 iters), loss = 1.21374
I0401 15:07:21.690671 29493 solver.cpp:237]     Train net output #0: loss = 1.21374 (* 1 = 1.21374 loss)
I0401 15:07:21.690680 29493 sgd_solver.cpp:105] Iteration 7392, lr = 0.001
I0401 15:07:26.060509 29493 solver.cpp:218] Iteration 7403 (2.51726 iter/s, 4.36983s/11 iters), loss = 1.26313
I0401 15:07:26.060559 29493 solver.cpp:237]     Train net output #0: loss = 1.26313 (* 1 = 1.26313 loss)
I0401 15:07:26.060565 29493 sgd_solver.cpp:105] Iteration 7403, lr = 0.001
I0401 15:07:30.877182 29493 solver.cpp:218] Iteration 7414 (2.28376 iter/s, 4.81661s/11 iters), loss = 1.06507
I0401 15:07:30.877223 29493 solver.cpp:237]     Train net output #0: loss = 1.06507 (* 1 = 1.06507 loss)
I0401 15:07:30.877228 29493 sgd_solver.cpp:105] Iteration 7414, lr = 0.001
I0401 15:07:35.837904 29493 solver.cpp:218] Iteration 7425 (2.21744 iter/s, 4.96067s/11 iters), loss = 1.12404
I0401 15:07:35.837946 29493 solver.cpp:237]     Train net output #0: loss = 1.12404 (* 1 = 1.12404 loss)
I0401 15:07:35.837951 29493 sgd_solver.cpp:105] Iteration 7425, lr = 0.001
I0401 15:07:39.643613 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:07:40.790891 29493 solver.cpp:218] Iteration 7436 (2.22091 iter/s, 4.95293s/11 iters), loss = 1.0041
I0401 15:07:40.790944 29493 solver.cpp:237]     Train net output #0: loss = 1.0041 (* 1 = 1.0041 loss)
I0401 15:07:40.790953 29493 sgd_solver.cpp:105] Iteration 7436, lr = 0.001
I0401 15:07:45.579033 29493 solver.cpp:218] Iteration 7447 (2.29737 iter/s, 4.78808s/11 iters), loss = 1.11435
I0401 15:07:45.579121 29493 solver.cpp:237]     Train net output #0: loss = 1.11435 (* 1 = 1.11435 loss)
I0401 15:07:45.579128 29493 sgd_solver.cpp:105] Iteration 7447, lr = 0.001
I0401 15:07:50.460129 29493 solver.cpp:218] Iteration 7458 (2.25364 iter/s, 4.88099s/11 iters), loss = 1.07046
I0401 15:07:50.460176 29493 solver.cpp:237]     Train net output #0: loss = 1.07046 (* 1 = 1.07046 loss)
I0401 15:07:50.460181 29493 sgd_solver.cpp:105] Iteration 7458, lr = 0.001
I0401 15:07:55.164275 29493 solver.cpp:218] Iteration 7469 (2.33839 iter/s, 4.70409s/11 iters), loss = 0.985312
I0401 15:07:55.164314 29493 solver.cpp:237]     Train net output #0: loss = 0.985312 (* 1 = 0.985312 loss)
I0401 15:07:55.164319 29493 sgd_solver.cpp:105] Iteration 7469, lr = 0.001
I0401 15:07:57.822213 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7476.caffemodel
I0401 15:08:00.992556 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7476.solverstate
I0401 15:08:03.294250 29493 solver.cpp:330] Iteration 7476, Testing net (#0)
I0401 15:08:03.294270 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:08:08.626210 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:08:09.831995 29493 solver.cpp:397]     Test net output #0: accuracy = 0.149671
I0401 15:08:09.832036 29493 solver.cpp:397]     Test net output #1: loss = 4.76835 (* 1 = 4.76835 loss)
I0401 15:08:10.780094 29493 solver.cpp:218] Iteration 7480 (0.704416 iter/s, 15.6158s/11 iters), loss = 1.23695
I0401 15:08:10.780134 29493 solver.cpp:237]     Train net output #0: loss = 1.23695 (* 1 = 1.23695 loss)
I0401 15:08:10.780139 29493 sgd_solver.cpp:105] Iteration 7480, lr = 0.001
I0401 15:08:15.491027 29493 solver.cpp:218] Iteration 7491 (2.33502 iter/s, 4.71088s/11 iters), loss = 1.17254
I0401 15:08:15.491073 29493 solver.cpp:237]     Train net output #0: loss = 1.17254 (* 1 = 1.17254 loss)
I0401 15:08:15.491081 29493 sgd_solver.cpp:105] Iteration 7491, lr = 0.001
I0401 15:08:20.231830 29493 solver.cpp:218] Iteration 7502 (2.32031 iter/s, 4.74075s/11 iters), loss = 1.16751
I0401 15:08:20.231964 29493 solver.cpp:237]     Train net output #0: loss = 1.16751 (* 1 = 1.16751 loss)
I0401 15:08:20.231971 29493 sgd_solver.cpp:105] Iteration 7502, lr = 0.001
I0401 15:08:25.289304 29493 solver.cpp:218] Iteration 7513 (2.17506 iter/s, 5.05732s/11 iters), loss = 1.09111
I0401 15:08:25.289361 29493 solver.cpp:237]     Train net output #0: loss = 1.09111 (* 1 = 1.09111 loss)
I0401 15:08:25.289369 29493 sgd_solver.cpp:105] Iteration 7513, lr = 0.001
I0401 15:08:29.219854 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:08:30.088107 29493 solver.cpp:218] Iteration 7524 (2.29227 iter/s, 4.79873s/11 iters), loss = 0.993863
I0401 15:08:30.088161 29493 solver.cpp:237]     Train net output #0: loss = 0.993863 (* 1 = 0.993863 loss)
I0401 15:08:30.088171 29493 sgd_solver.cpp:105] Iteration 7524, lr = 0.001
I0401 15:08:34.998761 29493 solver.cpp:218] Iteration 7535 (2.24006 iter/s, 4.91058s/11 iters), loss = 0.991547
I0401 15:08:34.998806 29493 solver.cpp:237]     Train net output #0: loss = 0.991547 (* 1 = 0.991547 loss)
I0401 15:08:34.998812 29493 sgd_solver.cpp:105] Iteration 7535, lr = 0.001
I0401 15:08:39.776408 29493 solver.cpp:218] Iteration 7546 (2.30242 iter/s, 4.77759s/11 iters), loss = 0.966537
I0401 15:08:39.776448 29493 solver.cpp:237]     Train net output #0: loss = 0.966537 (* 1 = 0.966537 loss)
I0401 15:08:39.776455 29493 sgd_solver.cpp:105] Iteration 7546, lr = 0.001
I0401 15:08:44.593998 29493 solver.cpp:218] Iteration 7557 (2.28332 iter/s, 4.81754s/11 iters), loss = 0.913871
I0401 15:08:44.594036 29493 solver.cpp:237]     Train net output #0: loss = 0.913871 (* 1 = 0.913871 loss)
I0401 15:08:44.594043 29493 sgd_solver.cpp:105] Iteration 7557, lr = 0.001
I0401 15:08:47.563817 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7565.caffemodel
I0401 15:08:50.556294 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7565.solverstate
I0401 15:08:52.869768 29493 solver.cpp:330] Iteration 7565, Testing net (#0)
I0401 15:08:52.869791 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:08:58.293300 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:08:59.489778 29493 solver.cpp:397]     Test net output #0: accuracy = 0.146382
I0401 15:08:59.489809 29493 solver.cpp:397]     Test net output #1: loss = 4.78658 (* 1 = 4.78658 loss)
I0401 15:09:00.043006 29493 solver.cpp:218] Iteration 7568 (0.712022 iter/s, 15.449s/11 iters), loss = 1.31515
I0401 15:09:00.044564 29493 solver.cpp:237]     Train net output #0: loss = 1.31515 (* 1 = 1.31515 loss)
I0401 15:09:00.044570 29493 sgd_solver.cpp:105] Iteration 7568, lr = 0.001
I0401 15:09:04.665269 29493 solver.cpp:218] Iteration 7579 (2.38059 iter/s, 4.6207s/11 iters), loss = 1.14355
I0401 15:09:04.665315 29493 solver.cpp:237]     Train net output #0: loss = 1.14355 (* 1 = 1.14355 loss)
I0401 15:09:04.665320 29493 sgd_solver.cpp:105] Iteration 7579, lr = 0.001
I0401 15:09:09.580832 29493 solver.cpp:218] Iteration 7590 (2.23782 iter/s, 4.9155s/11 iters), loss = 0.797267
I0401 15:09:09.580878 29493 solver.cpp:237]     Train net output #0: loss = 0.797267 (* 1 = 0.797267 loss)
I0401 15:09:09.580888 29493 sgd_solver.cpp:105] Iteration 7590, lr = 0.001
I0401 15:09:14.629652 29493 solver.cpp:218] Iteration 7601 (2.17875 iter/s, 5.04876s/11 iters), loss = 0.990472
I0401 15:09:14.629693 29493 solver.cpp:237]     Train net output #0: loss = 0.990472 (* 1 = 0.990472 loss)
I0401 15:09:14.629699 29493 sgd_solver.cpp:105] Iteration 7601, lr = 0.001
I0401 15:09:18.589259 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:09:19.277657 29493 solver.cpp:218] Iteration 7612 (2.36663 iter/s, 4.64795s/11 iters), loss = 1.27977
I0401 15:09:19.277694 29493 solver.cpp:237]     Train net output #0: loss = 1.27977 (* 1 = 1.27977 loss)
I0401 15:09:19.277700 29493 sgd_solver.cpp:105] Iteration 7612, lr = 0.001
I0401 15:09:24.114982 29493 solver.cpp:218] Iteration 7623 (2.27401 iter/s, 4.83727s/11 iters), loss = 1.30701
I0401 15:09:24.115154 29493 solver.cpp:237]     Train net output #0: loss = 1.30701 (* 1 = 1.30701 loss)
I0401 15:09:24.115164 29493 sgd_solver.cpp:105] Iteration 7623, lr = 0.001
I0401 15:09:28.749094 29493 solver.cpp:218] Iteration 7634 (2.37379 iter/s, 4.63393s/11 iters), loss = 0.821285
I0401 15:09:28.749141 29493 solver.cpp:237]     Train net output #0: loss = 0.821285 (* 1 = 0.821285 loss)
I0401 15:09:28.749148 29493 sgd_solver.cpp:105] Iteration 7634, lr = 0.001
I0401 15:09:33.593384 29493 solver.cpp:218] Iteration 7645 (2.27075 iter/s, 4.84422s/11 iters), loss = 1.085
I0401 15:09:33.593449 29493 solver.cpp:237]     Train net output #0: loss = 1.085 (* 1 = 1.085 loss)
I0401 15:09:33.593457 29493 sgd_solver.cpp:105] Iteration 7645, lr = 0.001
I0401 15:09:36.910509 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7654.caffemodel
I0401 15:09:40.048167 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7654.solverstate
I0401 15:09:42.361702 29493 solver.cpp:330] Iteration 7654, Testing net (#0)
I0401 15:09:42.361722 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:09:47.676079 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:09:48.883566 29493 solver.cpp:397]     Test net output #0: accuracy = 0.15625
I0401 15:09:48.883599 29493 solver.cpp:397]     Test net output #1: loss = 4.75713 (* 1 = 4.75713 loss)
I0401 15:09:49.281275 29493 solver.cpp:218] Iteration 7656 (0.701181 iter/s, 15.6878s/11 iters), loss = 1.18455
I0401 15:09:49.281320 29493 solver.cpp:237]     Train net output #0: loss = 1.18455 (* 1 = 1.18455 loss)
I0401 15:09:49.281327 29493 sgd_solver.cpp:105] Iteration 7656, lr = 0.001
I0401 15:09:53.657550 29493 solver.cpp:218] Iteration 7667 (2.51359 iter/s, 4.37621s/11 iters), loss = 1.36932
I0401 15:09:53.657595 29493 solver.cpp:237]     Train net output #0: loss = 1.36932 (* 1 = 1.36932 loss)
I0401 15:09:53.657601 29493 sgd_solver.cpp:105] Iteration 7667, lr = 0.001
I0401 15:09:58.438804 29493 solver.cpp:218] Iteration 7678 (2.30068 iter/s, 4.78119s/11 iters), loss = 1.14542
I0401 15:09:58.438903 29493 solver.cpp:237]     Train net output #0: loss = 1.14542 (* 1 = 1.14542 loss)
I0401 15:09:58.438910 29493 sgd_solver.cpp:105] Iteration 7678, lr = 0.001
I0401 15:10:03.489344 29493 solver.cpp:218] Iteration 7689 (2.17803 iter/s, 5.05043s/11 iters), loss = 1.17332
I0401 15:10:03.489387 29493 solver.cpp:237]     Train net output #0: loss = 1.17332 (* 1 = 1.17332 loss)
I0401 15:10:03.489394 29493 sgd_solver.cpp:105] Iteration 7689, lr = 0.001
I0401 15:10:08.137717 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:10:08.521368 29493 solver.cpp:218] Iteration 7700 (2.18603 iter/s, 5.03196s/11 iters), loss = 1.07766
I0401 15:10:08.521412 29493 solver.cpp:237]     Train net output #0: loss = 1.07766 (* 1 = 1.07766 loss)
I0401 15:10:08.521418 29493 sgd_solver.cpp:105] Iteration 7700, lr = 0.001
I0401 15:10:13.270762 29493 solver.cpp:218] Iteration 7711 (2.31612 iter/s, 4.74933s/11 iters), loss = 1.11849
I0401 15:10:13.270829 29493 solver.cpp:237]     Train net output #0: loss = 1.11849 (* 1 = 1.11849 loss)
I0401 15:10:13.270838 29493 sgd_solver.cpp:105] Iteration 7711, lr = 0.001
I0401 15:10:18.083348 29493 solver.cpp:218] Iteration 7722 (2.28571 iter/s, 4.8125s/11 iters), loss = 1.11647
I0401 15:10:18.083403 29493 solver.cpp:237]     Train net output #0: loss = 1.11647 (* 1 = 1.11647 loss)
I0401 15:10:18.083413 29493 sgd_solver.cpp:105] Iteration 7722, lr = 0.001
I0401 15:10:23.019635 29493 solver.cpp:218] Iteration 7733 (2.22843 iter/s, 4.93622s/11 iters), loss = 1.02973
I0401 15:10:23.019680 29493 solver.cpp:237]     Train net output #0: loss = 1.02973 (* 1 = 1.02973 loss)
I0401 15:10:23.019685 29493 sgd_solver.cpp:105] Iteration 7733, lr = 0.001
I0401 15:10:26.927222 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7743.caffemodel
I0401 15:10:29.954809 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7743.solverstate
I0401 15:10:32.250219 29493 solver.cpp:330] Iteration 7743, Testing net (#0)
I0401 15:10:32.250238 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:10:37.648422 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:10:38.883335 29493 solver.cpp:397]     Test net output #0: accuracy = 0.165296
I0401 15:10:38.883371 29493 solver.cpp:397]     Test net output #1: loss = 4.61396 (* 1 = 4.61396 loss)
I0401 15:10:39.161566 29493 solver.cpp:218] Iteration 7744 (0.681458 iter/s, 16.1419s/11 iters), loss = 1.06889
I0401 15:10:39.163121 29493 solver.cpp:237]     Train net output #0: loss = 1.06889 (* 1 = 1.06889 loss)
I0401 15:10:39.163133 29493 sgd_solver.cpp:105] Iteration 7744, lr = 0.001
I0401 15:10:43.461854 29493 solver.cpp:218] Iteration 7755 (2.5589 iter/s, 4.29873s/11 iters), loss = 1.0886
I0401 15:10:43.461899 29493 solver.cpp:237]     Train net output #0: loss = 1.0886 (* 1 = 1.0886 loss)
I0401 15:10:43.461905 29493 sgd_solver.cpp:105] Iteration 7755, lr = 0.001
I0401 15:10:48.096487 29493 solver.cpp:218] Iteration 7766 (2.37346 iter/s, 4.63458s/11 iters), loss = 1.19216
I0401 15:10:48.096535 29493 solver.cpp:237]     Train net output #0: loss = 1.19216 (* 1 = 1.19216 loss)
I0401 15:10:48.096541 29493 sgd_solver.cpp:105] Iteration 7766, lr = 0.001
I0401 15:10:53.022253 29493 solver.cpp:218] Iteration 7777 (2.23318 iter/s, 4.9257s/11 iters), loss = 0.999749
I0401 15:10:53.022302 29493 solver.cpp:237]     Train net output #0: loss = 0.999749 (* 1 = 0.999749 loss)
I0401 15:10:53.022310 29493 sgd_solver.cpp:105] Iteration 7777, lr = 0.001
I0401 15:10:57.578025 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:10:57.816747 29493 solver.cpp:218] Iteration 7788 (2.29433 iter/s, 4.79443s/11 iters), loss = 0.930929
I0401 15:10:57.816808 29493 solver.cpp:237]     Train net output #0: loss = 0.930929 (* 1 = 0.930929 loss)
I0401 15:10:57.816817 29493 sgd_solver.cpp:105] Iteration 7788, lr = 0.001
I0401 15:11:02.694860 29493 solver.cpp:218] Iteration 7799 (2.255 iter/s, 4.87805s/11 iters), loss = 1.04082
I0401 15:11:02.694938 29493 solver.cpp:237]     Train net output #0: loss = 1.04082 (* 1 = 1.04082 loss)
I0401 15:11:02.694944 29493 sgd_solver.cpp:105] Iteration 7799, lr = 0.001
I0401 15:11:07.369437 29493 solver.cpp:218] Iteration 7810 (2.3532 iter/s, 4.67448s/11 iters), loss = 0.92245
I0401 15:11:07.369483 29493 solver.cpp:237]     Train net output #0: loss = 0.92245 (* 1 = 0.92245 loss)
I0401 15:11:07.369489 29493 sgd_solver.cpp:105] Iteration 7810, lr = 0.001
I0401 15:11:12.077128 29493 solver.cpp:218] Iteration 7821 (2.33663 iter/s, 4.70763s/11 iters), loss = 1.16406
I0401 15:11:12.077183 29493 solver.cpp:237]     Train net output #0: loss = 1.16406 (* 1 = 1.16406 loss)
I0401 15:11:12.077190 29493 sgd_solver.cpp:105] Iteration 7821, lr = 0.001
I0401 15:11:16.202016 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7832.caffemodel
I0401 15:11:19.243100 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7832.solverstate
I0401 15:11:21.601239 29493 solver.cpp:330] Iteration 7832, Testing net (#0)
I0401 15:11:21.601266 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:11:22.523357 29493 blocking_queue.cpp:49] Waiting for data
I0401 15:11:26.963819 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:11:28.191852 29493 solver.cpp:397]     Test net output #0: accuracy = 0.162007
I0401 15:11:28.191879 29493 solver.cpp:397]     Test net output #1: loss = 4.64571 (* 1 = 4.64571 loss)
I0401 15:11:28.331419 29493 solver.cpp:218] Iteration 7832 (0.676747 iter/s, 16.2542s/11 iters), loss = 1.08635
I0401 15:11:28.332993 29493 solver.cpp:237]     Train net output #0: loss = 1.08635 (* 1 = 1.08635 loss)
I0401 15:11:28.333004 29493 sgd_solver.cpp:105] Iteration 7832, lr = 0.001
I0401 15:11:32.123946 29493 solver.cpp:218] Iteration 7843 (2.90165 iter/s, 3.79094s/11 iters), loss = 1.0973
I0401 15:11:32.123992 29493 solver.cpp:237]     Train net output #0: loss = 1.0973 (* 1 = 1.0973 loss)
I0401 15:11:32.123997 29493 sgd_solver.cpp:105] Iteration 7843, lr = 0.001
I0401 15:11:37.056944 29493 solver.cpp:218] Iteration 7854 (2.22991 iter/s, 4.93294s/11 iters), loss = 1.34637
I0401 15:11:37.057052 29493 solver.cpp:237]     Train net output #0: loss = 1.34637 (* 1 = 1.34637 loss)
I0401 15:11:37.057060 29493 sgd_solver.cpp:105] Iteration 7854, lr = 0.001
I0401 15:11:42.233814 29493 solver.cpp:218] Iteration 7865 (2.12489 iter/s, 5.17675s/11 iters), loss = 1.21366
I0401 15:11:42.233861 29493 solver.cpp:237]     Train net output #0: loss = 1.21366 (* 1 = 1.21366 loss)
I0401 15:11:42.233867 29493 sgd_solver.cpp:105] Iteration 7865, lr = 0.001
I0401 15:11:47.052301 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:11:47.058491 29493 solver.cpp:218] Iteration 7876 (2.27997 iter/s, 4.82462s/11 iters), loss = 1.00265
I0401 15:11:47.058533 29493 solver.cpp:237]     Train net output #0: loss = 1.00265 (* 1 = 1.00265 loss)
I0401 15:11:47.058538 29493 sgd_solver.cpp:105] Iteration 7876, lr = 0.001
I0401 15:11:51.779207 29493 solver.cpp:218] Iteration 7887 (2.33018 iter/s, 4.72066s/11 iters), loss = 1.19298
I0401 15:11:51.779247 29493 solver.cpp:237]     Train net output #0: loss = 1.19298 (* 1 = 1.19298 loss)
I0401 15:11:51.779253 29493 sgd_solver.cpp:105] Iteration 7887, lr = 0.001
I0401 15:11:56.798988 29493 solver.cpp:218] Iteration 7898 (2.19135 iter/s, 5.01973s/11 iters), loss = 1.01374
I0401 15:11:56.799048 29493 solver.cpp:237]     Train net output #0: loss = 1.01374 (* 1 = 1.01374 loss)
I0401 15:11:56.799057 29493 sgd_solver.cpp:105] Iteration 7898, lr = 0.001
I0401 15:12:01.654633 29493 solver.cpp:218] Iteration 7909 (2.26544 iter/s, 4.85557s/11 iters), loss = 0.912149
I0401 15:12:01.654682 29493 solver.cpp:237]     Train net output #0: loss = 0.912149 (* 1 = 0.912149 loss)
I0401 15:12:01.654690 29493 sgd_solver.cpp:105] Iteration 7909, lr = 0.001
I0401 15:12:06.547433 29493 solver.cpp:218] Iteration 7920 (2.24823 iter/s, 4.89274s/11 iters), loss = 1.1482
I0401 15:12:06.547480 29493 solver.cpp:237]     Train net output #0: loss = 1.1482 (* 1 = 1.1482 loss)
I0401 15:12:06.547485 29493 sgd_solver.cpp:105] Iteration 7920, lr = 0.001
I0401 15:12:06.547623 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7921.caffemodel
I0401 15:12:09.459456 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7921.solverstate
I0401 15:12:11.792878 29493 solver.cpp:330] Iteration 7921, Testing net (#0)
I0401 15:12:11.792920 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:12:17.035259 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:12:18.293524 29493 solver.cpp:397]     Test net output #0: accuracy = 0.157895
I0401 15:12:18.293560 29493 solver.cpp:397]     Test net output #1: loss = 4.61093 (* 1 = 4.61093 loss)
I0401 15:12:21.759069 29493 solver.cpp:218] Iteration 7931 (0.723133 iter/s, 15.2116s/11 iters), loss = 1.05938
I0401 15:12:21.759117 29493 solver.cpp:237]     Train net output #0: loss = 1.05938 (* 1 = 1.05938 loss)
I0401 15:12:21.759124 29493 sgd_solver.cpp:105] Iteration 7931, lr = 0.001
I0401 15:12:26.273877 29493 solver.cpp:218] Iteration 7942 (2.43646 iter/s, 4.51475s/11 iters), loss = 1.11226
I0401 15:12:26.273921 29493 solver.cpp:237]     Train net output #0: loss = 1.11226 (* 1 = 1.11226 loss)
I0401 15:12:26.273927 29493 sgd_solver.cpp:105] Iteration 7942, lr = 0.001
I0401 15:12:31.076943 29493 solver.cpp:218] Iteration 7953 (2.29023 iter/s, 4.80301s/11 iters), loss = 0.980547
I0401 15:12:31.076997 29493 solver.cpp:237]     Train net output #0: loss = 0.980547 (* 1 = 0.980547 loss)
I0401 15:12:31.077004 29493 sgd_solver.cpp:105] Iteration 7953, lr = 0.001
I0401 15:12:35.975265 29493 solver.cpp:218] Iteration 7964 (2.2457 iter/s, 4.89826s/11 iters), loss = 1.42642
I0401 15:12:35.975311 29493 solver.cpp:237]     Train net output #0: loss = 1.42642 (* 1 = 1.42642 loss)
I0401 15:12:35.975317 29493 sgd_solver.cpp:105] Iteration 7964, lr = 0.001
I0401 15:12:36.183544 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:12:40.643853 29493 solver.cpp:218] Iteration 7975 (2.35621 iter/s, 4.66852s/11 iters), loss = 1.2142
I0401 15:12:40.644016 29493 solver.cpp:237]     Train net output #0: loss = 1.2142 (* 1 = 1.2142 loss)
I0401 15:12:40.644026 29493 sgd_solver.cpp:105] Iteration 7975, lr = 0.001
I0401 15:12:45.612913 29493 solver.cpp:218] Iteration 7986 (2.21378 iter/s, 4.96888s/11 iters), loss = 1.30395
I0401 15:12:45.612968 29493 solver.cpp:237]     Train net output #0: loss = 1.30395 (* 1 = 1.30395 loss)
I0401 15:12:45.612977 29493 sgd_solver.cpp:105] Iteration 7986, lr = 0.001
I0401 15:12:50.468979 29493 solver.cpp:218] Iteration 7997 (2.26524 iter/s, 4.856s/11 iters), loss = 0.803369
I0401 15:12:50.469025 29493 solver.cpp:237]     Train net output #0: loss = 0.803369 (* 1 = 0.803369 loss)
I0401 15:12:50.469030 29493 sgd_solver.cpp:105] Iteration 7997, lr = 0.001
I0401 15:12:55.333170 29493 solver.cpp:218] Iteration 8008 (2.26145 iter/s, 4.86413s/11 iters), loss = 0.942561
I0401 15:12:55.333232 29493 solver.cpp:237]     Train net output #0: loss = 0.942561 (* 1 = 0.942561 loss)
I0401 15:12:55.333241 29493 sgd_solver.cpp:105] Iteration 8008, lr = 0.001
I0401 15:12:55.695223 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8010.caffemodel
I0401 15:12:58.708928 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8010.solverstate
I0401 15:13:01.035368 29493 solver.cpp:330] Iteration 8010, Testing net (#0)
I0401 15:13:01.035393 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:13:06.520906 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:13:07.804492 29493 solver.cpp:397]     Test net output #0: accuracy = 0.157484
I0401 15:13:07.804529 29493 solver.cpp:397]     Test net output #1: loss = 4.64859 (* 1 = 4.64859 loss)
I0401 15:13:10.994989 29493 solver.cpp:218] Iteration 8019 (0.702348 iter/s, 15.6618s/11 iters), loss = 0.844134
I0401 15:13:10.995093 29493 solver.cpp:237]     Train net output #0: loss = 0.844134 (* 1 = 0.844134 loss)
I0401 15:13:10.995100 29493 sgd_solver.cpp:105] Iteration 8019, lr = 0.001
I0401 15:13:15.864552 29493 solver.cpp:218] Iteration 8030 (2.25898 iter/s, 4.86944s/11 iters), loss = 0.812694
I0401 15:13:15.864598 29493 solver.cpp:237]     Train net output #0: loss = 0.812694 (* 1 = 0.812694 loss)
I0401 15:13:15.864603 29493 sgd_solver.cpp:105] Iteration 8030, lr = 0.001
I0401 15:13:20.761318 29493 solver.cpp:218] Iteration 8041 (2.24641 iter/s, 4.89671s/11 iters), loss = 0.835276
I0401 15:13:20.761361 29493 solver.cpp:237]     Train net output #0: loss = 0.835276 (* 1 = 0.835276 loss)
I0401 15:13:20.761368 29493 sgd_solver.cpp:105] Iteration 8041, lr = 0.001
I0401 15:13:25.685534 29493 solver.cpp:218] Iteration 8052 (2.23389 iter/s, 4.92416s/11 iters), loss = 0.853981
I0401 15:13:25.685578 29493 solver.cpp:237]     Train net output #0: loss = 0.853981 (* 1 = 0.853981 loss)
I0401 15:13:25.685585 29493 sgd_solver.cpp:105] Iteration 8052, lr = 0.001
I0401 15:13:26.268412 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:13:30.716975 29493 solver.cpp:218] Iteration 8063 (2.18628 iter/s, 5.03138s/11 iters), loss = 0.770552
I0401 15:13:30.717020 29493 solver.cpp:237]     Train net output #0: loss = 0.770552 (* 1 = 0.770552 loss)
I0401 15:13:30.717026 29493 sgd_solver.cpp:105] Iteration 8063, lr = 0.001
I0401 15:13:35.581321 29493 solver.cpp:218] Iteration 8074 (2.26138 iter/s, 4.86429s/11 iters), loss = 0.995604
I0401 15:13:35.581367 29493 solver.cpp:237]     Train net output #0: loss = 0.995604 (* 1 = 0.995604 loss)
I0401 15:13:35.581372 29493 sgd_solver.cpp:105] Iteration 8074, lr = 0.001
I0401 15:13:40.679567 29493 solver.cpp:218] Iteration 8085 (2.15763 iter/s, 5.09819s/11 iters), loss = 0.847723
I0401 15:13:40.679612 29493 solver.cpp:237]     Train net output #0: loss = 0.847723 (* 1 = 0.847723 loss)
I0401 15:13:40.679617 29493 sgd_solver.cpp:105] Iteration 8085, lr = 0.001
I0401 15:13:45.614398 29493 solver.cpp:218] Iteration 8096 (2.22908 iter/s, 4.93476s/11 iters), loss = 0.706707
I0401 15:13:45.614557 29493 solver.cpp:237]     Train net output #0: loss = 0.706707 (* 1 = 0.706707 loss)
I0401 15:13:45.614567 29493 sgd_solver.cpp:105] Iteration 8096, lr = 0.001
I0401 15:13:46.427088 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8099.caffemodel
I0401 15:13:49.484876 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8099.solverstate
I0401 15:13:51.812253 29493 solver.cpp:330] Iteration 8099, Testing net (#0)
I0401 15:13:51.812273 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:13:57.232003 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:13:58.568222 29493 solver.cpp:397]     Test net output #0: accuracy = 0.157895
I0401 15:13:58.568257 29493 solver.cpp:397]     Test net output #1: loss = 4.67464 (* 1 = 4.67464 loss)
I0401 15:14:01.171382 29493 solver.cpp:218] Iteration 8107 (0.707085 iter/s, 15.5568s/11 iters), loss = 1.03083
I0401 15:14:01.171443 29493 solver.cpp:237]     Train net output #0: loss = 1.03083 (* 1 = 1.03083 loss)
I0401 15:14:01.171452 29493 sgd_solver.cpp:105] Iteration 8107, lr = 0.001
I0401 15:14:05.995411 29493 solver.cpp:218] Iteration 8118 (2.28029 iter/s, 4.82395s/11 iters), loss = 0.805578
I0401 15:14:05.995462 29493 solver.cpp:237]     Train net output #0: loss = 0.805578 (* 1 = 0.805578 loss)
I0401 15:14:05.995471 29493 sgd_solver.cpp:105] Iteration 8118, lr = 0.001
I0401 15:14:10.933674 29493 solver.cpp:218] Iteration 8129 (2.22753 iter/s, 4.9382s/11 iters), loss = 0.627404
I0401 15:14:10.933732 29493 solver.cpp:237]     Train net output #0: loss = 0.627404 (* 1 = 0.627404 loss)
I0401 15:14:10.933740 29493 sgd_solver.cpp:105] Iteration 8129, lr = 0.001
I0401 15:14:15.970943 29493 solver.cpp:218] Iteration 8140 (2.18375 iter/s, 5.0372s/11 iters), loss = 0.898737
I0401 15:14:15.971035 29493 solver.cpp:237]     Train net output #0: loss = 0.898737 (* 1 = 0.898737 loss)
I0401 15:14:15.971042 29493 sgd_solver.cpp:105] Iteration 8140, lr = 0.001
I0401 15:14:16.504071 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:14:20.834597 29493 solver.cpp:218] Iteration 8151 (2.26172 iter/s, 4.86355s/11 iters), loss = 1.0142
I0401 15:14:20.834648 29493 solver.cpp:237]     Train net output #0: loss = 1.0142 (* 1 = 1.0142 loss)
I0401 15:14:20.834656 29493 sgd_solver.cpp:105] Iteration 8151, lr = 0.001
I0401 15:14:25.788477 29493 solver.cpp:218] Iteration 8162 (2.22051 iter/s, 4.95381s/11 iters), loss = 0.802196
I0401 15:14:25.788534 29493 solver.cpp:237]     Train net output #0: loss = 0.802196 (* 1 = 0.802196 loss)
I0401 15:14:25.788542 29493 sgd_solver.cpp:105] Iteration 8162, lr = 0.001
I0401 15:14:30.602010 29493 solver.cpp:218] Iteration 8173 (2.28526 iter/s, 4.81346s/11 iters), loss = 0.787356
I0401 15:14:30.602066 29493 solver.cpp:237]     Train net output #0: loss = 0.787356 (* 1 = 0.787356 loss)
I0401 15:14:30.602075 29493 sgd_solver.cpp:105] Iteration 8173, lr = 0.001
I0401 15:14:35.383170 29493 solver.cpp:218] Iteration 8184 (2.30073 iter/s, 4.78109s/11 iters), loss = 0.851869
I0401 15:14:35.383216 29493 solver.cpp:237]     Train net output #0: loss = 0.851869 (* 1 = 0.851869 loss)
I0401 15:14:35.383222 29493 sgd_solver.cpp:105] Iteration 8184, lr = 0.001
I0401 15:14:36.627151 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8188.caffemodel
I0401 15:14:39.702687 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8188.solverstate
I0401 15:14:42.002342 29493 solver.cpp:330] Iteration 8188, Testing net (#0)
I0401 15:14:42.002363 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:14:47.564412 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:14:48.863968 29493 solver.cpp:397]     Test net output #0: accuracy = 0.157484
I0401 15:14:48.864003 29493 solver.cpp:397]     Test net output #1: loss = 4.72188 (* 1 = 4.72188 loss)
I0401 15:14:51.264901 29493 solver.cpp:218] Iteration 8195 (0.692622 iter/s, 15.8817s/11 iters), loss = 0.898422
I0401 15:14:51.264950 29493 solver.cpp:237]     Train net output #0: loss = 0.898422 (* 1 = 0.898422 loss)
I0401 15:14:51.264956 29493 sgd_solver.cpp:105] Iteration 8195, lr = 0.001
I0401 15:14:56.071631 29493 solver.cpp:218] Iteration 8206 (2.28849 iter/s, 4.80666s/11 iters), loss = 0.799115
I0401 15:14:56.071678 29493 solver.cpp:237]     Train net output #0: loss = 0.799115 (* 1 = 0.799115 loss)
I0401 15:14:56.071684 29493 sgd_solver.cpp:105] Iteration 8206, lr = 0.001
I0401 15:15:00.731667 29493 solver.cpp:218] Iteration 8217 (2.36053 iter/s, 4.65997s/11 iters), loss = 0.799911
I0401 15:15:00.731714 29493 solver.cpp:237]     Train net output #0: loss = 0.799911 (* 1 = 0.799911 loss)
I0401 15:15:00.731721 29493 sgd_solver.cpp:105] Iteration 8217, lr = 0.001
I0401 15:15:05.725351 29493 solver.cpp:218] Iteration 8228 (2.20281 iter/s, 4.99362s/11 iters), loss = 0.749286
I0401 15:15:05.725399 29493 solver.cpp:237]     Train net output #0: loss = 0.749286 (* 1 = 0.749286 loss)
I0401 15:15:05.725405 29493 sgd_solver.cpp:105] Iteration 8228, lr = 0.001
I0401 15:15:06.731921 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:15:10.569404 29493 solver.cpp:218] Iteration 8239 (2.27086 iter/s, 4.84399s/11 iters), loss = 0.778025
I0401 15:15:10.569445 29493 solver.cpp:237]     Train net output #0: loss = 0.778025 (* 1 = 0.778025 loss)
I0401 15:15:10.569451 29493 sgd_solver.cpp:105] Iteration 8239, lr = 0.001
I0401 15:15:15.534364 29493 solver.cpp:218] Iteration 8250 (2.21555 iter/s, 4.96491s/11 iters), loss = 0.797947
I0401 15:15:15.534408 29493 solver.cpp:237]     Train net output #0: loss = 0.797947 (* 1 = 0.797947 loss)
I0401 15:15:15.534415 29493 sgd_solver.cpp:105] Iteration 8250, lr = 0.001
I0401 15:15:20.231398 29493 solver.cpp:218] Iteration 8261 (2.34194 iter/s, 4.69697s/11 iters), loss = 0.723074
I0401 15:15:20.231531 29493 solver.cpp:237]     Train net output #0: loss = 0.723074 (* 1 = 0.723074 loss)
I0401 15:15:20.231540 29493 sgd_solver.cpp:105] Iteration 8261, lr = 0.001
I0401 15:15:25.109789 29493 solver.cpp:218] Iteration 8272 (2.25491 iter/s, 4.87825s/11 iters), loss = 0.523084
I0401 15:15:25.109829 29493 solver.cpp:237]     Train net output #0: loss = 0.523084 (* 1 = 0.523084 loss)
I0401 15:15:25.109834 29493 sgd_solver.cpp:105] Iteration 8272, lr = 0.001
I0401 15:15:26.791033 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8277.caffemodel
I0401 15:15:29.843343 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8277.solverstate
I0401 15:15:32.143004 29493 solver.cpp:330] Iteration 8277, Testing net (#0)
I0401 15:15:32.143024 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:15:37.599329 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:15:38.935518 29493 solver.cpp:397]     Test net output #0: accuracy = 0.15625
I0401 15:15:38.935559 29493 solver.cpp:397]     Test net output #1: loss = 4.82115 (* 1 = 4.82115 loss)
I0401 15:15:40.760759 29493 solver.cpp:218] Iteration 8283 (0.702834 iter/s, 15.6509s/11 iters), loss = 0.910636
I0401 15:15:40.760818 29493 solver.cpp:237]     Train net output #0: loss = 0.910636 (* 1 = 0.910636 loss)
I0401 15:15:40.760825 29493 sgd_solver.cpp:105] Iteration 8283, lr = 0.001
I0401 15:15:45.539150 29493 solver.cpp:218] Iteration 8294 (2.30207 iter/s, 4.77831s/11 iters), loss = 0.900355
I0401 15:15:45.539211 29493 solver.cpp:237]     Train net output #0: loss = 0.900355 (* 1 = 0.900355 loss)
I0401 15:15:45.539219 29493 sgd_solver.cpp:105] Iteration 8294, lr = 0.001
I0401 15:15:50.667191 29493 solver.cpp:218] Iteration 8305 (2.1451 iter/s, 5.12797s/11 iters), loss = 0.834905
I0401 15:15:50.667351 29493 solver.cpp:237]     Train net output #0: loss = 0.834905 (* 1 = 0.834905 loss)
I0401 15:15:50.667362 29493 sgd_solver.cpp:105] Iteration 8305, lr = 0.001
I0401 15:15:55.627086 29493 solver.cpp:218] Iteration 8316 (2.21786 iter/s, 4.95973s/11 iters), loss = 1.05055
I0401 15:15:55.627131 29493 solver.cpp:237]     Train net output #0: loss = 1.05055 (* 1 = 1.05055 loss)
I0401 15:15:55.627137 29493 sgd_solver.cpp:105] Iteration 8316, lr = 0.001
I0401 15:15:56.749958 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:16:00.216610 29493 solver.cpp:218] Iteration 8327 (2.39679 iter/s, 4.58947s/11 iters), loss = 0.883696
I0401 15:16:00.216655 29493 solver.cpp:237]     Train net output #0: loss = 0.883696 (* 1 = 0.883696 loss)
I0401 15:16:00.216663 29493 sgd_solver.cpp:105] Iteration 8327, lr = 0.001
I0401 15:16:04.940256 29493 solver.cpp:218] Iteration 8338 (2.32874 iter/s, 4.72359s/11 iters), loss = 0.723892
I0401 15:16:04.940304 29493 solver.cpp:237]     Train net output #0: loss = 0.723892 (* 1 = 0.723892 loss)
I0401 15:16:04.940310 29493 sgd_solver.cpp:105] Iteration 8338, lr = 0.001
I0401 15:16:10.023757 29493 solver.cpp:218] Iteration 8349 (2.16389 iter/s, 5.08343s/11 iters), loss = 0.569952
I0401 15:16:10.023816 29493 solver.cpp:237]     Train net output #0: loss = 0.569952 (* 1 = 0.569952 loss)
I0401 15:16:10.023824 29493 sgd_solver.cpp:105] Iteration 8349, lr = 0.001
I0401 15:16:15.064855 29493 solver.cpp:218] Iteration 8360 (2.18209 iter/s, 5.04103s/11 iters), loss = 0.863019
I0401 15:16:15.064911 29493 solver.cpp:237]     Train net output #0: loss = 0.863019 (* 1 = 0.863019 loss)
I0401 15:16:15.064918 29493 sgd_solver.cpp:105] Iteration 8360, lr = 0.001
I0401 15:16:17.322340 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8366.caffemodel
I0401 15:16:20.432946 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8366.solverstate
I0401 15:16:22.758339 29493 solver.cpp:330] Iteration 8366, Testing net (#0)
I0401 15:16:22.758411 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:16:28.027606 29493 blocking_queue.cpp:49] Waiting for data
I0401 15:16:28.214115 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:16:29.645649 29493 solver.cpp:397]     Test net output #0: accuracy = 0.163651
I0401 15:16:29.645689 29493 solver.cpp:397]     Test net output #1: loss = 4.80464 (* 1 = 4.80464 loss)
I0401 15:16:31.169054 29493 solver.cpp:218] Iteration 8371 (0.683054 iter/s, 16.1041s/11 iters), loss = 0.795851
I0401 15:16:31.169119 29493 solver.cpp:237]     Train net output #0: loss = 0.795851 (* 1 = 0.795851 loss)
I0401 15:16:31.169128 29493 sgd_solver.cpp:105] Iteration 8371, lr = 0.001
I0401 15:16:36.034494 29493 solver.cpp:218] Iteration 8382 (2.26088 iter/s, 4.86536s/11 iters), loss = 0.99778
I0401 15:16:36.034548 29493 solver.cpp:237]     Train net output #0: loss = 0.99778 (* 1 = 0.99778 loss)
I0401 15:16:36.034555 29493 sgd_solver.cpp:105] Iteration 8382, lr = 0.001
I0401 15:16:41.075232 29493 solver.cpp:218] Iteration 8393 (2.18225 iter/s, 5.04068s/11 iters), loss = 0.934713
I0401 15:16:41.075273 29493 solver.cpp:237]     Train net output #0: loss = 0.934713 (* 1 = 0.934713 loss)
I0401 15:16:41.075278 29493 sgd_solver.cpp:105] Iteration 8393, lr = 0.001
I0401 15:16:46.306864 29493 solver.cpp:218] Iteration 8404 (2.10262 iter/s, 5.23157s/11 iters), loss = 0.660756
I0401 15:16:46.306926 29493 solver.cpp:237]     Train net output #0: loss = 0.660756 (* 1 = 0.660756 loss)
I0401 15:16:46.306934 29493 sgd_solver.cpp:105] Iteration 8404, lr = 0.001
I0401 15:16:47.788084 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:16:51.290218 29493 solver.cpp:218] Iteration 8415 (2.20738 iter/s, 4.98328s/11 iters), loss = 0.756694
I0401 15:16:51.290266 29493 solver.cpp:237]     Train net output #0: loss = 0.756694 (* 1 = 0.756694 loss)
I0401 15:16:51.290271 29493 sgd_solver.cpp:105] Iteration 8415, lr = 0.001
I0401 15:16:55.926358 29493 solver.cpp:218] Iteration 8426 (2.3727 iter/s, 4.63608s/11 iters), loss = 1.049
I0401 15:16:55.926501 29493 solver.cpp:237]     Train net output #0: loss = 1.049 (* 1 = 1.049 loss)
I0401 15:16:55.926512 29493 sgd_solver.cpp:105] Iteration 8426, lr = 0.001
I0401 15:17:00.690915 29493 solver.cpp:218] Iteration 8437 (2.30879 iter/s, 4.7644s/11 iters), loss = 0.753058
I0401 15:17:00.690966 29493 solver.cpp:237]     Train net output #0: loss = 0.753058 (* 1 = 0.753058 loss)
I0401 15:17:00.690974 29493 sgd_solver.cpp:105] Iteration 8437, lr = 0.001
I0401 15:17:05.648797 29493 solver.cpp:218] Iteration 8448 (2.21872 iter/s, 4.95782s/11 iters), loss = 0.772289
I0401 15:17:05.648854 29493 solver.cpp:237]     Train net output #0: loss = 0.772289 (* 1 = 0.772289 loss)
I0401 15:17:05.648861 29493 sgd_solver.cpp:105] Iteration 8448, lr = 0.001
I0401 15:17:08.108690 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8455.caffemodel
I0401 15:17:11.149981 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8455.solverstate
I0401 15:17:13.456007 29493 solver.cpp:330] Iteration 8455, Testing net (#0)
I0401 15:17:13.456030 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:17:18.885844 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:17:20.266086 29493 solver.cpp:397]     Test net output #0: accuracy = 0.154194
I0401 15:17:20.266114 29493 solver.cpp:397]     Test net output #1: loss = 4.82815 (* 1 = 4.82815 loss)
I0401 15:17:21.379833 29493 solver.cpp:218] Iteration 8459 (0.699257 iter/s, 15.731s/11 iters), loss = 0.87632
I0401 15:17:21.379897 29493 solver.cpp:237]     Train net output #0: loss = 0.87632 (* 1 = 0.87632 loss)
I0401 15:17:21.379905 29493 sgd_solver.cpp:105] Iteration 8459, lr = 0.001
I0401 15:17:26.105142 29493 solver.cpp:218] Iteration 8470 (2.32793 iter/s, 4.72523s/11 iters), loss = 0.764418
I0401 15:17:26.105247 29493 solver.cpp:237]     Train net output #0: loss = 0.764418 (* 1 = 0.764418 loss)
I0401 15:17:26.105255 29493 sgd_solver.cpp:105] Iteration 8470, lr = 0.001
I0401 15:17:30.951519 29493 solver.cpp:218] Iteration 8481 (2.26979 iter/s, 4.84626s/11 iters), loss = 0.978018
I0401 15:17:30.951567 29493 solver.cpp:237]     Train net output #0: loss = 0.978018 (* 1 = 0.978018 loss)
I0401 15:17:30.951572 29493 sgd_solver.cpp:105] Iteration 8481, lr = 0.001
I0401 15:17:35.718973 29493 solver.cpp:218] Iteration 8492 (2.30734 iter/s, 4.76739s/11 iters), loss = 0.670126
I0401 15:17:35.719025 29493 solver.cpp:237]     Train net output #0: loss = 0.670126 (* 1 = 0.670126 loss)
I0401 15:17:35.719033 29493 sgd_solver.cpp:105] Iteration 8492, lr = 0.001
I0401 15:17:37.274266 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:17:40.499274 29493 solver.cpp:218] Iteration 8503 (2.30114 iter/s, 4.78023s/11 iters), loss = 0.877957
I0401 15:17:40.499325 29493 solver.cpp:237]     Train net output #0: loss = 0.877957 (* 1 = 0.877957 loss)
I0401 15:17:40.499331 29493 sgd_solver.cpp:105] Iteration 8503, lr = 0.001
I0401 15:17:45.412353 29493 solver.cpp:218] Iteration 8514 (2.23895 iter/s, 4.91302s/11 iters), loss = 0.820004
I0401 15:17:45.412398 29493 solver.cpp:237]     Train net output #0: loss = 0.820004 (* 1 = 0.820004 loss)
I0401 15:17:45.412405 29493 sgd_solver.cpp:105] Iteration 8514, lr = 0.001
I0401 15:17:50.303261 29493 solver.cpp:218] Iteration 8525 (2.2491 iter/s, 4.89084s/11 iters), loss = 0.848308
I0401 15:17:50.303321 29493 solver.cpp:237]     Train net output #0: loss = 0.848308 (* 1 = 0.848308 loss)
I0401 15:17:50.303330 29493 sgd_solver.cpp:105] Iteration 8525, lr = 0.001
I0401 15:17:55.430866 29493 solver.cpp:218] Iteration 8536 (2.14528 iter/s, 5.12752s/11 iters), loss = 0.793486
I0401 15:17:55.430920 29493 solver.cpp:237]     Train net output #0: loss = 0.793486 (* 1 = 0.793486 loss)
I0401 15:17:55.430927 29493 sgd_solver.cpp:105] Iteration 8536, lr = 0.001
I0401 15:17:58.474809 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8544.caffemodel
I0401 15:18:01.531751 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8544.solverstate
I0401 15:18:03.921113 29493 solver.cpp:330] Iteration 8544, Testing net (#0)
I0401 15:18:03.921133 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:18:09.390046 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:18:10.875212 29493 solver.cpp:397]     Test net output #0: accuracy = 0.153783
I0401 15:18:10.875243 29493 solver.cpp:397]     Test net output #1: loss = 4.85666 (* 1 = 4.85666 loss)
I0401 15:18:11.556687 29493 solver.cpp:218] Iteration 8547 (0.682139 iter/s, 16.1258s/11 iters), loss = 0.92416
I0401 15:18:11.556746 29493 solver.cpp:237]     Train net output #0: loss = 0.92416 (* 1 = 0.92416 loss)
I0401 15:18:11.556756 29493 sgd_solver.cpp:105] Iteration 8547, lr = 0.001
I0401 15:18:16.316347 29493 solver.cpp:218] Iteration 8558 (2.31112 iter/s, 4.75959s/11 iters), loss = 0.804063
I0401 15:18:16.316395 29493 solver.cpp:237]     Train net output #0: loss = 0.804063 (* 1 = 0.804063 loss)
I0401 15:18:16.316399 29493 sgd_solver.cpp:105] Iteration 8558, lr = 0.001
I0401 15:18:21.184301 29493 solver.cpp:218] Iteration 8569 (2.2597 iter/s, 4.86789s/11 iters), loss = 1.06888
I0401 15:18:21.184345 29493 solver.cpp:237]     Train net output #0: loss = 1.06888 (* 1 = 1.06888 loss)
I0401 15:18:21.184350 29493 sgd_solver.cpp:105] Iteration 8569, lr = 0.001
I0401 15:18:26.260470 29493 solver.cpp:218] Iteration 8580 (2.16702 iter/s, 5.0761s/11 iters), loss = 0.465322
I0401 15:18:26.266729 29493 solver.cpp:237]     Train net output #0: loss = 0.465322 (* 1 = 0.465322 loss)
I0401 15:18:26.266749 29493 sgd_solver.cpp:105] Iteration 8580, lr = 0.001
I0401 15:18:28.000021 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:18:30.967108 29493 solver.cpp:218] Iteration 8591 (2.34024 iter/s, 4.70038s/11 iters), loss = 0.732039
I0401 15:18:30.967217 29493 solver.cpp:237]     Train net output #0: loss = 0.732039 (* 1 = 0.732039 loss)
I0401 15:18:30.967226 29493 sgd_solver.cpp:105] Iteration 8591, lr = 0.001
I0401 15:18:35.828218 29493 solver.cpp:218] Iteration 8602 (2.26291 iter/s, 4.86099s/11 iters), loss = 0.628913
I0401 15:18:35.828265 29493 solver.cpp:237]     Train net output #0: loss = 0.628913 (* 1 = 0.628913 loss)
I0401 15:18:35.828274 29493 sgd_solver.cpp:105] Iteration 8602, lr = 0.001
I0401 15:18:40.453676 29493 solver.cpp:218] Iteration 8613 (2.37817 iter/s, 4.6254s/11 iters), loss = 0.597237
I0401 15:18:40.453723 29493 solver.cpp:237]     Train net output #0: loss = 0.597237 (* 1 = 0.597237 loss)
I0401 15:18:40.453729 29493 sgd_solver.cpp:105] Iteration 8613, lr = 0.001
I0401 15:18:45.463572 29493 solver.cpp:218] Iteration 8624 (2.19568 iter/s, 5.00983s/11 iters), loss = 0.579757
I0401 15:18:45.463625 29493 solver.cpp:237]     Train net output #0: loss = 0.579757 (* 1 = 0.579757 loss)
I0401 15:18:45.463632 29493 sgd_solver.cpp:105] Iteration 8624, lr = 0.001
I0401 15:18:48.846057 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8633.caffemodel
I0401 15:18:51.910046 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8633.solverstate
I0401 15:18:54.264333 29493 solver.cpp:330] Iteration 8633, Testing net (#0)
I0401 15:18:54.264353 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:18:59.600977 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:19:00.985034 29493 solver.cpp:397]     Test net output #0: accuracy = 0.157484
I0401 15:19:00.985131 29493 solver.cpp:397]     Test net output #1: loss = 4.91518 (* 1 = 4.91518 loss)
I0401 15:19:01.391638 29493 solver.cpp:218] Iteration 8635 (0.690607 iter/s, 15.928s/11 iters), loss = 0.725975
I0401 15:19:01.391695 29493 solver.cpp:237]     Train net output #0: loss = 0.725975 (* 1 = 0.725975 loss)
I0401 15:19:01.391705 29493 sgd_solver.cpp:105] Iteration 8635, lr = 0.001
I0401 15:19:05.871886 29493 solver.cpp:218] Iteration 8646 (2.45526 iter/s, 4.48017s/11 iters), loss = 0.670713
I0401 15:19:05.871948 29493 solver.cpp:237]     Train net output #0: loss = 0.670713 (* 1 = 0.670713 loss)
I0401 15:19:05.871956 29493 sgd_solver.cpp:105] Iteration 8646, lr = 0.001
I0401 15:19:10.858202 29493 solver.cpp:218] Iteration 8657 (2.20607 iter/s, 4.98624s/11 iters), loss = 0.745133
I0401 15:19:10.858259 29493 solver.cpp:237]     Train net output #0: loss = 0.745133 (* 1 = 0.745133 loss)
I0401 15:19:10.858268 29493 sgd_solver.cpp:105] Iteration 8657, lr = 0.001
I0401 15:19:15.653601 29493 solver.cpp:218] Iteration 8668 (2.2939 iter/s, 4.79533s/11 iters), loss = 0.629108
I0401 15:19:15.653648 29493 solver.cpp:237]     Train net output #0: loss = 0.629108 (* 1 = 0.629108 loss)
I0401 15:19:15.653653 29493 sgd_solver.cpp:105] Iteration 8668, lr = 0.001
I0401 15:19:17.670738 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:19:20.442958 29493 solver.cpp:218] Iteration 8679 (2.29679 iter/s, 4.7893s/11 iters), loss = 0.707385
I0401 15:19:20.443001 29493 solver.cpp:237]     Train net output #0: loss = 0.707385 (* 1 = 0.707385 loss)
I0401 15:19:20.443007 29493 sgd_solver.cpp:105] Iteration 8679, lr = 0.001
I0401 15:19:25.401813 29493 solver.cpp:218] Iteration 8690 (2.21828 iter/s, 4.9588s/11 iters), loss = 0.895487
I0401 15:19:25.401856 29493 solver.cpp:237]     Train net output #0: loss = 0.895487 (* 1 = 0.895487 loss)
I0401 15:19:25.401862 29493 sgd_solver.cpp:105] Iteration 8690, lr = 0.001
I0401 15:19:30.300604 29493 solver.cpp:218] Iteration 8701 (2.24548 iter/s, 4.89873s/11 iters), loss = 0.807055
I0401 15:19:30.300663 29493 solver.cpp:237]     Train net output #0: loss = 0.807055 (* 1 = 0.807055 loss)
I0401 15:19:30.300673 29493 sgd_solver.cpp:105] Iteration 8701, lr = 0.001
I0401 15:19:35.377661 29493 solver.cpp:218] Iteration 8712 (2.16664 iter/s, 5.07699s/11 iters), loss = 0.750128
I0401 15:19:35.377784 29493 solver.cpp:237]     Train net output #0: loss = 0.750128 (* 1 = 0.750128 loss)
I0401 15:19:35.377790 29493 sgd_solver.cpp:105] Iteration 8712, lr = 0.001
I0401 15:19:39.269675 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8722.caffemodel
I0401 15:19:42.334666 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8722.solverstate
I0401 15:19:44.648350 29493 solver.cpp:330] Iteration 8722, Testing net (#0)
I0401 15:19:44.648370 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:19:50.061599 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:19:51.456511 29493 solver.cpp:397]     Test net output #0: accuracy = 0.163651
I0401 15:19:51.456542 29493 solver.cpp:397]     Test net output #1: loss = 4.96451 (* 1 = 4.96451 loss)
I0401 15:19:51.724543 29493 solver.cpp:218] Iteration 8723 (0.672917 iter/s, 16.3468s/11 iters), loss = 0.79376
I0401 15:19:51.726115 29493 solver.cpp:237]     Train net output #0: loss = 0.79376 (* 1 = 0.79376 loss)
I0401 15:19:51.726130 29493 sgd_solver.cpp:105] Iteration 8723, lr = 0.001
I0401 15:19:56.074818 29493 solver.cpp:218] Iteration 8734 (2.5295 iter/s, 4.34869s/11 iters), loss = 0.672155
I0401 15:19:56.074889 29493 solver.cpp:237]     Train net output #0: loss = 0.672155 (* 1 = 0.672155 loss)
I0401 15:19:56.074898 29493 sgd_solver.cpp:105] Iteration 8734, lr = 0.001
I0401 15:20:00.928187 29493 solver.cpp:218] Iteration 8745 (2.26651 iter/s, 4.85329s/11 iters), loss = 0.474115
I0401 15:20:00.928244 29493 solver.cpp:237]     Train net output #0: loss = 0.474115 (* 1 = 0.474115 loss)
I0401 15:20:00.928252 29493 sgd_solver.cpp:105] Iteration 8745, lr = 0.001
I0401 15:20:05.968772 29493 solver.cpp:218] Iteration 8756 (2.18232 iter/s, 5.04051s/11 iters), loss = 0.767139
I0401 15:20:05.968924 29493 solver.cpp:237]     Train net output #0: loss = 0.767139 (* 1 = 0.767139 loss)
I0401 15:20:05.968932 29493 sgd_solver.cpp:105] Iteration 8756, lr = 0.001
I0401 15:20:08.331463 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:20:10.868240 29493 solver.cpp:218] Iteration 8767 (2.24522 iter/s, 4.8993s/11 iters), loss = 0.560326
I0401 15:20:10.868285 29493 solver.cpp:237]     Train net output #0: loss = 0.560326 (* 1 = 0.560326 loss)
I0401 15:20:10.868291 29493 sgd_solver.cpp:105] Iteration 8767, lr = 0.001
I0401 15:20:15.867625 29493 solver.cpp:218] Iteration 8778 (2.2003 iter/s, 4.99932s/11 iters), loss = 0.627648
I0401 15:20:15.867684 29493 solver.cpp:237]     Train net output #0: loss = 0.627648 (* 1 = 0.627648 loss)
I0401 15:20:15.867693 29493 sgd_solver.cpp:105] Iteration 8778, lr = 0.001
I0401 15:20:20.826901 29493 solver.cpp:218] Iteration 8789 (2.2181 iter/s, 4.9592s/11 iters), loss = 0.685394
I0401 15:20:20.826963 29493 solver.cpp:237]     Train net output #0: loss = 0.685394 (* 1 = 0.685394 loss)
I0401 15:20:20.826973 29493 sgd_solver.cpp:105] Iteration 8789, lr = 0.001
I0401 15:20:25.715618 29493 solver.cpp:218] Iteration 8800 (2.25012 iter/s, 4.88864s/11 iters), loss = 0.887465
I0401 15:20:25.715680 29493 solver.cpp:237]     Train net output #0: loss = 0.887465 (* 1 = 0.887465 loss)
I0401 15:20:25.715688 29493 sgd_solver.cpp:105] Iteration 8800, lr = 0.001
I0401 15:20:30.006529 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8811.caffemodel
I0401 15:20:35.835542 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8811.solverstate
I0401 15:20:38.146741 29493 solver.cpp:330] Iteration 8811, Testing net (#0)
I0401 15:20:38.146839 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:20:43.374441 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:20:44.796473 29493 solver.cpp:397]     Test net output #0: accuracy = 0.16324
I0401 15:20:44.796502 29493 solver.cpp:397]     Test net output #1: loss = 4.88802 (* 1 = 4.88802 loss)
I0401 15:20:44.937534 29493 solver.cpp:218] Iteration 8811 (0.572265 iter/s, 19.2219s/11 iters), loss = 0.830446
I0401 15:20:44.937589 29493 solver.cpp:237]     Train net output #0: loss = 0.830446 (* 1 = 0.830446 loss)
I0401 15:20:44.937597 29493 sgd_solver.cpp:105] Iteration 8811, lr = 0.001
I0401 15:20:49.081694 29493 solver.cpp:218] Iteration 8822 (2.65439 iter/s, 4.14409s/11 iters), loss = 0.605702
I0401 15:20:49.081750 29493 solver.cpp:237]     Train net output #0: loss = 0.605702 (* 1 = 0.605702 loss)
I0401 15:20:49.081759 29493 sgd_solver.cpp:105] Iteration 8822, lr = 0.001
I0401 15:20:53.994165 29493 solver.cpp:218] Iteration 8833 (2.23923 iter/s, 4.9124s/11 iters), loss = 0.652978
I0401 15:20:53.994230 29493 solver.cpp:237]     Train net output #0: loss = 0.652978 (* 1 = 0.652978 loss)
I0401 15:20:53.994240 29493 sgd_solver.cpp:105] Iteration 8833, lr = 0.001
I0401 15:20:59.214206 29493 solver.cpp:218] Iteration 8844 (2.10729 iter/s, 5.21996s/11 iters), loss = 0.573519
I0401 15:20:59.214252 29493 solver.cpp:237]     Train net output #0: loss = 0.573519 (* 1 = 0.573519 loss)
I0401 15:20:59.214258 29493 sgd_solver.cpp:105] Iteration 8844, lr = 0.001
I0401 15:21:01.754449 29554 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:21:04.250391 29493 solver.cpp:218] Iteration 8855 (2.18422 iter/s, 5.03612s/11 iters), loss = 0.61072
I0401 15:21:04.250452 29493 solver.cpp:237]     Train net output #0: loss = 0.61072 (* 1 = 0.61072 loss)
I0401 15:21:04.250461 29493 sgd_solver.cpp:105] Iteration 8855, lr = 0.001
I0401 15:21:09.036602 29493 solver.cpp:218] Iteration 8866 (2.29831 iter/s, 4.78613s/11 iters), loss = 0.696795
I0401 15:21:09.036728 29493 solver.cpp:237]     Train net output #0: loss = 0.696795 (* 1 = 0.696795 loss)
I0401 15:21:09.036737 29493 sgd_solver.cpp:105] Iteration 8866, lr = 0.001
I0401 15:21:13.737032 29493 solver.cpp:218] Iteration 8877 (2.34028 iter/s, 4.70029s/11 iters), loss = 0.889542
I0401 15:21:13.737088 29493 solver.cpp:237]     Train net output #0: loss = 0.889542 (* 1 = 0.889542 loss)
I0401 15:21:13.737095 29493 sgd_solver.cpp:105] Iteration 8877, lr = 0.001
I0401 15:21:18.201995 29493 solver.cpp:218] Iteration 8888 (2.46366 iter/s, 4.4649s/11 iters), loss = 0.529853
I0401 15:21:18.202039 29493 solver.cpp:237]     Train net output #0: loss = 0.529853 (* 1 = 0.529853 loss)
I0401 15:21:18.202044 29493 sgd_solver.cpp:105] Iteration 8888, lr = 0.001
I0401 15:21:23.262066 29493 solver.cpp:218] Iteration 8899 (2.17391 iter/s, 5.06001s/11 iters), loss = 0.385324
I0401 15:21:23.262120 29493 solver.cpp:237]     Train net output #0: loss = 0.385324 (* 1 = 0.385324 loss)
I0401 15:21:23.262128 29493 sgd_solver.cpp:105] Iteration 8899, lr = 0.001
I0401 15:21:23.262337 29493 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8900.caffemodel
I0401 15:21:29.617086 29493 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8900.solverstate
I0401 15:21:33.218425 29493 solver.cpp:330] Iteration 8900, Testing net (#0)
I0401 15:21:33.218443 29493 net.cpp:676] Ignoring source layer train-data
I0401 15:21:38.719835 29603 data_layer.cpp:73] Restarting data prefetching from start.
I0401 15:21:40.166998 29493 solver.cpp:397]     Test net output #0: accuracy = 0.168174
I0401 15:21:40.167136 29493 solver.cpp:397]     Test net output #1: loss = 4.92353 (* 1 = 4.92353 loss)
I0401 15:21:40.167145 29493 solver.cpp:315] Optimization Done.
I0401 15:21:40.167148 29493 caffe.cpp:259] Optimization Done.