DIGITS-CNN/cars/lr-investigations/sigmoid/1e-2/50_0.1/caffe_output.log

I0407 22:23:37.799357 32630 upgrade_proto.cpp:1082] Attempting to upgrade input file specified using deprecated 'solver_type' field (enum)': /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-222336-31fc/solver.prototxt
I0407 22:23:37.799742 32630 upgrade_proto.cpp:1089] Successfully upgraded file specified using deprecated 'solver_type' field (enum) to 'type' field (string).
W0407 22:23:37.799748 32630 upgrade_proto.cpp:1091] Note that future Caffe releases will only support 'type' field (string) for a solver's type.
I0407 22:23:37.799806 32630 caffe.cpp:218] Using GPUs 0
I0407 22:23:37.848248 32630 caffe.cpp:223] GPU 0: GeForce RTX 2080
I0407 22:23:42.800009 32630 solver.cpp:44] Initializing solver from parameters:
test_iter: 51
test_interval: 102
base_lr: 0.01
display: 12
max_iter: 10200
lr_policy: "sigmoid"
gamma: -0.00098039221
momentum: 0.9
weight_decay: 0.0001
stepsize: 5100
snapshot: 102
snapshot_prefix: "snapshot"
solver_mode: GPU
device_id: 0
net: "train_val.prototxt"
train_state {
level: 0
stage: ""
}
type: "SGD"
I0407 22:23:42.801707 32630 solver.cpp:87] Creating training net from net file: train_val.prototxt
I0407 22:23:42.803831 32630 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer val-data
I0407 22:23:42.803846 32630 net.cpp:294] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0407 22:23:42.803966 32630 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-TAM-1/digits/jobs/20210407-221849-d51c/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/train_db"
batch_size: 128
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0407 22:23:42.804054 32630 layer_factory.hpp:77] Creating layer train-data
I0407 22:23:42.809190 32630 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/train_db
I0407 22:23:42.810515 32630 net.cpp:84] Creating Layer train-data
I0407 22:23:42.810525 32630 net.cpp:380] train-data -> data
I0407 22:23:42.810557 32630 net.cpp:380] train-data -> label
I0407 22:23:42.810570 32630 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/mean.binaryproto
I0407 22:23:42.837807 32630 data_layer.cpp:45] output data size: 128,3,227,227
I0407 22:23:42.965687 32630 net.cpp:122] Setting up train-data
I0407 22:23:42.965711 32630 net.cpp:129] Top shape: 128 3 227 227 (19787136)
I0407 22:23:42.965715 32630 net.cpp:129] Top shape: 128 (128)
I0407 22:23:42.965718 32630 net.cpp:137] Memory required for data: 79149056
I0407 22:23:42.965726 32630 layer_factory.hpp:77] Creating layer conv1
I0407 22:23:42.965764 32630 net.cpp:84] Creating Layer conv1
I0407 22:23:42.965770 32630 net.cpp:406] conv1 <- data
I0407 22:23:42.965781 32630 net.cpp:380] conv1 -> conv1
I0407 22:23:44.462642 32630 net.cpp:122] Setting up conv1
I0407 22:23:44.462661 32630 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 22:23:44.462664 32630 net.cpp:137] Memory required for data: 227833856
I0407 22:23:44.462683 32630 layer_factory.hpp:77] Creating layer relu1
I0407 22:23:44.462692 32630 net.cpp:84] Creating Layer relu1
I0407 22:23:44.462697 32630 net.cpp:406] relu1 <- conv1
I0407 22:23:44.462702 32630 net.cpp:367] relu1 -> conv1 (in-place)
I0407 22:23:44.463037 32630 net.cpp:122] Setting up relu1
I0407 22:23:44.463047 32630 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 22:23:44.463049 32630 net.cpp:137] Memory required for data: 376518656
I0407 22:23:44.463052 32630 layer_factory.hpp:77] Creating layer norm1
I0407 22:23:44.463060 32630 net.cpp:84] Creating Layer norm1
I0407 22:23:44.463078 32630 net.cpp:406] norm1 <- conv1
I0407 22:23:44.463084 32630 net.cpp:380] norm1 -> norm1
I0407 22:23:44.463654 32630 net.cpp:122] Setting up norm1
I0407 22:23:44.463665 32630 net.cpp:129] Top shape: 128 96 55 55 (37171200)
I0407 22:23:44.463667 32630 net.cpp:137] Memory required for data: 525203456
I0407 22:23:44.463670 32630 layer_factory.hpp:77] Creating layer pool1
I0407 22:23:44.463678 32630 net.cpp:84] Creating Layer pool1
I0407 22:23:44.463681 32630 net.cpp:406] pool1 <- norm1
I0407 22:23:44.463685 32630 net.cpp:380] pool1 -> pool1
I0407 22:23:44.463729 32630 net.cpp:122] Setting up pool1
I0407 22:23:44.463734 32630 net.cpp:129] Top shape: 128 96 27 27 (8957952)
I0407 22:23:44.463737 32630 net.cpp:137] Memory required for data: 561035264
I0407 22:23:44.463740 32630 layer_factory.hpp:77] Creating layer conv2
I0407 22:23:44.463748 32630 net.cpp:84] Creating Layer conv2
I0407 22:23:44.463752 32630 net.cpp:406] conv2 <- pool1
I0407 22:23:44.463757 32630 net.cpp:380] conv2 -> conv2
I0407 22:23:44.476099 32630 net.cpp:122] Setting up conv2
I0407 22:23:44.476110 32630 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 22:23:44.476114 32630 net.cpp:137] Memory required for data: 656586752
I0407 22:23:44.476121 32630 layer_factory.hpp:77] Creating layer relu2
I0407 22:23:44.476127 32630 net.cpp:84] Creating Layer relu2
I0407 22:23:44.476131 32630 net.cpp:406] relu2 <- conv2
I0407 22:23:44.476137 32630 net.cpp:367] relu2 -> conv2 (in-place)
I0407 22:23:44.476750 32630 net.cpp:122] Setting up relu2
I0407 22:23:44.476759 32630 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 22:23:44.476763 32630 net.cpp:137] Memory required for data: 752138240
I0407 22:23:44.476765 32630 layer_factory.hpp:77] Creating layer norm2
I0407 22:23:44.476773 32630 net.cpp:84] Creating Layer norm2
I0407 22:23:44.476776 32630 net.cpp:406] norm2 <- conv2
I0407 22:23:44.476780 32630 net.cpp:380] norm2 -> norm2
I0407 22:23:44.477172 32630 net.cpp:122] Setting up norm2
I0407 22:23:44.477183 32630 net.cpp:129] Top shape: 128 256 27 27 (23887872)
I0407 22:23:44.477186 32630 net.cpp:137] Memory required for data: 847689728
I0407 22:23:44.477190 32630 layer_factory.hpp:77] Creating layer pool2
I0407 22:23:44.477196 32630 net.cpp:84] Creating Layer pool2
I0407 22:23:44.477198 32630 net.cpp:406] pool2 <- norm2
I0407 22:23:44.477203 32630 net.cpp:380] pool2 -> pool2
I0407 22:23:44.477231 32630 net.cpp:122] Setting up pool2
I0407 22:23:44.477236 32630 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 22:23:44.477239 32630 net.cpp:137] Memory required for data: 869840896
I0407 22:23:44.477241 32630 layer_factory.hpp:77] Creating layer conv3
I0407 22:23:44.477252 32630 net.cpp:84] Creating Layer conv3
I0407 22:23:44.477254 32630 net.cpp:406] conv3 <- pool2
I0407 22:23:44.477258 32630 net.cpp:380] conv3 -> conv3
I0407 22:23:44.488667 32630 net.cpp:122] Setting up conv3
I0407 22:23:44.488678 32630 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 22:23:44.488682 32630 net.cpp:137] Memory required for data: 903067648
I0407 22:23:44.488689 32630 layer_factory.hpp:77] Creating layer relu3
I0407 22:23:44.488694 32630 net.cpp:84] Creating Layer relu3
I0407 22:23:44.488698 32630 net.cpp:406] relu3 <- conv3
I0407 22:23:44.488703 32630 net.cpp:367] relu3 -> conv3 (in-place)
I0407 22:23:44.489284 32630 net.cpp:122] Setting up relu3
I0407 22:23:44.489293 32630 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 22:23:44.489295 32630 net.cpp:137] Memory required for data: 936294400
I0407 22:23:44.489298 32630 layer_factory.hpp:77] Creating layer conv4
I0407 22:23:44.489308 32630 net.cpp:84] Creating Layer conv4
I0407 22:23:44.489311 32630 net.cpp:406] conv4 <- conv3
I0407 22:23:44.489317 32630 net.cpp:380] conv4 -> conv4
I0407 22:23:44.500768 32630 net.cpp:122] Setting up conv4
I0407 22:23:44.500782 32630 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 22:23:44.500785 32630 net.cpp:137] Memory required for data: 969521152
I0407 22:23:44.500792 32630 layer_factory.hpp:77] Creating layer relu4
I0407 22:23:44.500798 32630 net.cpp:84] Creating Layer relu4
I0407 22:23:44.500816 32630 net.cpp:406] relu4 <- conv4
I0407 22:23:44.500823 32630 net.cpp:367] relu4 -> conv4 (in-place)
I0407 22:23:44.501421 32630 net.cpp:122] Setting up relu4
I0407 22:23:44.501430 32630 net.cpp:129] Top shape: 128 384 13 13 (8306688)
I0407 22:23:44.501433 32630 net.cpp:137] Memory required for data: 1002747904
I0407 22:23:44.501436 32630 layer_factory.hpp:77] Creating layer conv5
I0407 22:23:44.501446 32630 net.cpp:84] Creating Layer conv5
I0407 22:23:44.501449 32630 net.cpp:406] conv5 <- conv4
I0407 22:23:44.501457 32630 net.cpp:380] conv5 -> conv5
I0407 22:23:44.510877 32630 net.cpp:122] Setting up conv5
I0407 22:23:44.510890 32630 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 22:23:44.510892 32630 net.cpp:137] Memory required for data: 1024899072
I0407 22:23:44.510905 32630 layer_factory.hpp:77] Creating layer relu5
I0407 22:23:44.510910 32630 net.cpp:84] Creating Layer relu5
I0407 22:23:44.510915 32630 net.cpp:406] relu5 <- conv5
I0407 22:23:44.510918 32630 net.cpp:367] relu5 -> conv5 (in-place)
I0407 22:23:44.511484 32630 net.cpp:122] Setting up relu5
I0407 22:23:44.511495 32630 net.cpp:129] Top shape: 128 256 13 13 (5537792)
I0407 22:23:44.511498 32630 net.cpp:137] Memory required for data: 1047050240
I0407 22:23:44.511502 32630 layer_factory.hpp:77] Creating layer pool5
I0407 22:23:44.511507 32630 net.cpp:84] Creating Layer pool5
I0407 22:23:44.511510 32630 net.cpp:406] pool5 <- conv5
I0407 22:23:44.511514 32630 net.cpp:380] pool5 -> pool5
I0407 22:23:44.511552 32630 net.cpp:122] Setting up pool5
I0407 22:23:44.511557 32630 net.cpp:129] Top shape: 128 256 6 6 (1179648)
I0407 22:23:44.511559 32630 net.cpp:137] Memory required for data: 1051768832
I0407 22:23:44.511562 32630 layer_factory.hpp:77] Creating layer fc6
I0407 22:23:44.511572 32630 net.cpp:84] Creating Layer fc6
I0407 22:23:44.511575 32630 net.cpp:406] fc6 <- pool5
I0407 22:23:44.511579 32630 net.cpp:380] fc6 -> fc6
I0407 22:23:44.856149 32630 net.cpp:122] Setting up fc6
I0407 22:23:44.856168 32630 net.cpp:129] Top shape: 128 4096 (524288)
I0407 22:23:44.856171 32630 net.cpp:137] Memory required for data: 1053865984
I0407 22:23:44.856180 32630 layer_factory.hpp:77] Creating layer relu6
I0407 22:23:44.856189 32630 net.cpp:84] Creating Layer relu6
I0407 22:23:44.856192 32630 net.cpp:406] relu6 <- fc6
I0407 22:23:44.856199 32630 net.cpp:367] relu6 -> fc6 (in-place)
I0407 22:23:44.856938 32630 net.cpp:122] Setting up relu6
I0407 22:23:44.856947 32630 net.cpp:129] Top shape: 128 4096 (524288)
I0407 22:23:44.856950 32630 net.cpp:137] Memory required for data: 1055963136
I0407 22:23:44.856953 32630 layer_factory.hpp:77] Creating layer drop6
I0407 22:23:44.856959 32630 net.cpp:84] Creating Layer drop6
I0407 22:23:44.856962 32630 net.cpp:406] drop6 <- fc6
I0407 22:23:44.856967 32630 net.cpp:367] drop6 -> fc6 (in-place)
I0407 22:23:44.856993 32630 net.cpp:122] Setting up drop6
I0407 22:23:44.856998 32630 net.cpp:129] Top shape: 128 4096 (524288)
I0407 22:23:44.857000 32630 net.cpp:137] Memory required for data: 1058060288
I0407 22:23:44.857003 32630 layer_factory.hpp:77] Creating layer fc7
I0407 22:23:44.857009 32630 net.cpp:84] Creating Layer fc7
I0407 22:23:44.857012 32630 net.cpp:406] fc7 <- fc6
I0407 22:23:44.857017 32630 net.cpp:380] fc7 -> fc7
I0407 22:23:45.009280 32630 net.cpp:122] Setting up fc7
I0407 22:23:45.009300 32630 net.cpp:129] Top shape: 128 4096 (524288)
I0407 22:23:45.009305 32630 net.cpp:137] Memory required for data: 1060157440
I0407 22:23:45.009312 32630 layer_factory.hpp:77] Creating layer relu7
I0407 22:23:45.009320 32630 net.cpp:84] Creating Layer relu7
I0407 22:23:45.009325 32630 net.cpp:406] relu7 <- fc7
I0407 22:23:45.009330 32630 net.cpp:367] relu7 -> fc7 (in-place)
I0407 22:23:45.009804 32630 net.cpp:122] Setting up relu7
I0407 22:23:45.009814 32630 net.cpp:129] Top shape: 128 4096 (524288)
I0407 22:23:45.009816 32630 net.cpp:137] Memory required for data: 1062254592
I0407 22:23:45.009819 32630 layer_factory.hpp:77] Creating layer drop7
I0407 22:23:45.009825 32630 net.cpp:84] Creating Layer drop7
I0407 22:23:45.009841 32630 net.cpp:406] drop7 <- fc7
I0407 22:23:45.009845 32630 net.cpp:367] drop7 -> fc7 (in-place)
I0407 22:23:45.009868 32630 net.cpp:122] Setting up drop7
I0407 22:23:45.009873 32630 net.cpp:129] Top shape: 128 4096 (524288)
I0407 22:23:45.009876 32630 net.cpp:137] Memory required for data: 1064351744
I0407 22:23:45.009878 32630 layer_factory.hpp:77] Creating layer fc8
I0407 22:23:45.009886 32630 net.cpp:84] Creating Layer fc8
I0407 22:23:45.009888 32630 net.cpp:406] fc8 <- fc7
I0407 22:23:45.009892 32630 net.cpp:380] fc8 -> fc8
I0407 22:23:45.017489 32630 net.cpp:122] Setting up fc8
I0407 22:23:45.017498 32630 net.cpp:129] Top shape: 128 196 (25088)
I0407 22:23:45.017501 32630 net.cpp:137] Memory required for data: 1064452096
I0407 22:23:45.017506 32630 layer_factory.hpp:77] Creating layer loss
I0407 22:23:45.019939 32630 net.cpp:84] Creating Layer loss
I0407 22:23:45.019955 32630 net.cpp:406] loss <- fc8
I0407 22:23:45.019966 32630 net.cpp:406] loss <- label
I0407 22:23:45.019980 32630 net.cpp:380] loss -> loss
I0407 22:23:45.019999 32630 layer_factory.hpp:77] Creating layer loss
I0407 22:23:45.021165 32630 net.cpp:122] Setting up loss
I0407 22:23:45.021174 32630 net.cpp:129] Top shape: (1)
I0407 22:23:45.021178 32630 net.cpp:132]     with loss weight 1
I0407 22:23:45.021195 32630 net.cpp:137] Memory required for data: 1064452100
I0407 22:23:45.021198 32630 net.cpp:198] loss needs backward computation.
I0407 22:23:45.021204 32630 net.cpp:198] fc8 needs backward computation.
I0407 22:23:45.021207 32630 net.cpp:198] drop7 needs backward computation.
I0407 22:23:45.021209 32630 net.cpp:198] relu7 needs backward computation.
I0407 22:23:45.021212 32630 net.cpp:198] fc7 needs backward computation.
I0407 22:23:45.021214 32630 net.cpp:198] drop6 needs backward computation.
I0407 22:23:45.021217 32630 net.cpp:198] relu6 needs backward computation.
I0407 22:23:45.021219 32630 net.cpp:198] fc6 needs backward computation.
I0407 22:23:45.021222 32630 net.cpp:198] pool5 needs backward computation.
I0407 22:23:45.021225 32630 net.cpp:198] relu5 needs backward computation.
I0407 22:23:45.021227 32630 net.cpp:198] conv5 needs backward computation.
I0407 22:23:45.021230 32630 net.cpp:198] relu4 needs backward computation.
I0407 22:23:45.021232 32630 net.cpp:198] conv4 needs backward computation.
I0407 22:23:45.021235 32630 net.cpp:198] relu3 needs backward computation.
I0407 22:23:45.021239 32630 net.cpp:198] conv3 needs backward computation.
I0407 22:23:45.021241 32630 net.cpp:198] pool2 needs backward computation.
I0407 22:23:45.021243 32630 net.cpp:198] norm2 needs backward computation.
I0407 22:23:45.021246 32630 net.cpp:198] relu2 needs backward computation.
I0407 22:23:45.021248 32630 net.cpp:198] conv2 needs backward computation.
I0407 22:23:45.021251 32630 net.cpp:198] pool1 needs backward computation.
I0407 22:23:45.021255 32630 net.cpp:198] norm1 needs backward computation.
I0407 22:23:45.021257 32630 net.cpp:198] relu1 needs backward computation.
I0407 22:23:45.021260 32630 net.cpp:198] conv1 needs backward computation.
I0407 22:23:45.021262 32630 net.cpp:200] train-data does not need backward computation.
I0407 22:23:45.021265 32630 net.cpp:242] This network produces output loss
I0407 22:23:45.021279 32630 net.cpp:255] Network initialization done.
I0407 22:23:45.022007 32630 solver.cpp:172] Creating test net (#0) specified by net file: train_val.prototxt
I0407 22:23:45.022038 32630 net.cpp:294] The NetState phase (1) differed from the phase (0) specified by a rule in layer train-data
I0407 22:23:45.022166 32630 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-TAM-1/digits/jobs/20210407-221849-d51c/mean.binaryproto"
}
data_param {
source: "/mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/val_db"
batch_size: 32
backend: LMDB
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "norm1"
type: "LRN"
bottom: "conv1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool1"
type: "Pooling"
bottom: "norm1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 2
kernel_size: 5
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "conv2"
top: "conv2"
}
layer {
name: "norm2"
type: "LRN"
bottom: "conv2"
top: "norm2"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "norm2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "relu3"
type: "ReLU"
bottom: "conv3"
top: "conv3"
}
layer {
name: "conv4"
type: "Convolution"
bottom: "conv3"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 384
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "conv4"
top: "conv4"
}
layer {
name: "conv5"
type: "Convolution"
bottom: "conv4"
top: "conv5"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 256
pad: 1
kernel_size: 3
group: 2
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "conv5"
top: "conv5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc7"
type: "InnerProduct"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 4096
weight_filler {
type: "gaussian"
std: 0.005
}
bias_filler {
type: "constant"
value: 0.1
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 196
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}
I0407 22:23:45.022261 32630 layer_factory.hpp:77] Creating layer val-data
I0407 22:23:45.026412 32630 db_lmdb.cpp:35] Opened lmdb /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/val_db
I0407 22:23:45.041004 32630 net.cpp:84] Creating Layer val-data
I0407 22:23:45.041035 32630 net.cpp:380] val-data -> data
I0407 22:23:45.041056 32630 net.cpp:380] val-data -> label
I0407 22:23:45.041072 32630 data_transformer.cpp:25] Loading mean file from: /mnt/bigdisk/DIGITS-TAM-1/digits/jobs/20210407-221849-d51c/mean.binaryproto
I0407 22:23:45.058750 32630 data_layer.cpp:45] output data size: 32,3,227,227
I0407 22:23:45.094960 32630 net.cpp:122] Setting up val-data
I0407 22:23:45.094985 32630 net.cpp:129] Top shape: 32 3 227 227 (4946784)
I0407 22:23:45.094988 32630 net.cpp:129] Top shape: 32 (32)
I0407 22:23:45.094991 32630 net.cpp:137] Memory required for data: 19787264
I0407 22:23:45.094997 32630 layer_factory.hpp:77] Creating layer label_val-data_1_split
I0407 22:23:45.095008 32630 net.cpp:84] Creating Layer label_val-data_1_split
I0407 22:23:45.095012 32630 net.cpp:406] label_val-data_1_split <- label
I0407 22:23:45.095018 32630 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_0
I0407 22:23:45.095027 32630 net.cpp:380] label_val-data_1_split -> label_val-data_1_split_1
I0407 22:23:45.095072 32630 net.cpp:122] Setting up label_val-data_1_split
I0407 22:23:45.095078 32630 net.cpp:129] Top shape: 32 (32)
I0407 22:23:45.095082 32630 net.cpp:129] Top shape: 32 (32)
I0407 22:23:45.095083 32630 net.cpp:137] Memory required for data: 19787520
I0407 22:23:45.095086 32630 layer_factory.hpp:77] Creating layer conv1
I0407 22:23:45.095096 32630 net.cpp:84] Creating Layer conv1
I0407 22:23:45.095099 32630 net.cpp:406] conv1 <- data
I0407 22:23:45.095104 32630 net.cpp:380] conv1 -> conv1
I0407 22:23:45.098523 32630 net.cpp:122] Setting up conv1
I0407 22:23:45.098534 32630 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 22:23:45.098537 32630 net.cpp:137] Memory required for data: 56958720
I0407 22:23:45.098547 32630 layer_factory.hpp:77] Creating layer relu1
I0407 22:23:45.098553 32630 net.cpp:84] Creating Layer relu1
I0407 22:23:45.098556 32630 net.cpp:406] relu1 <- conv1
I0407 22:23:45.098560 32630 net.cpp:367] relu1 -> conv1 (in-place)
I0407 22:23:45.098889 32630 net.cpp:122] Setting up relu1
I0407 22:23:45.098899 32630 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 22:23:45.098901 32630 net.cpp:137] Memory required for data: 94129920
I0407 22:23:45.098904 32630 layer_factory.hpp:77] Creating layer norm1
I0407 22:23:45.098912 32630 net.cpp:84] Creating Layer norm1
I0407 22:23:45.098915 32630 net.cpp:406] norm1 <- conv1
I0407 22:23:45.098920 32630 net.cpp:380] norm1 -> norm1
I0407 22:23:45.099491 32630 net.cpp:122] Setting up norm1
I0407 22:23:45.099501 32630 net.cpp:129] Top shape: 32 96 55 55 (9292800)
I0407 22:23:45.099504 32630 net.cpp:137] Memory required for data: 131301120
I0407 22:23:45.099507 32630 layer_factory.hpp:77] Creating layer pool1
I0407 22:23:45.099514 32630 net.cpp:84] Creating Layer pool1
I0407 22:23:45.099517 32630 net.cpp:406] pool1 <- norm1
I0407 22:23:45.099522 32630 net.cpp:380] pool1 -> pool1
I0407 22:23:45.099547 32630 net.cpp:122] Setting up pool1
I0407 22:23:45.099552 32630 net.cpp:129] Top shape: 32 96 27 27 (2239488)
I0407 22:23:45.099555 32630 net.cpp:137] Memory required for data: 140259072
I0407 22:23:45.099557 32630 layer_factory.hpp:77] Creating layer conv2
I0407 22:23:45.099565 32630 net.cpp:84] Creating Layer conv2
I0407 22:23:45.099567 32630 net.cpp:406] conv2 <- pool1
I0407 22:23:45.099591 32630 net.cpp:380] conv2 -> conv2
I0407 22:23:45.109513 32630 net.cpp:122] Setting up conv2
I0407 22:23:45.109529 32630 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 22:23:45.109532 32630 net.cpp:137] Memory required for data: 164146944
I0407 22:23:45.109542 32630 layer_factory.hpp:77] Creating layer relu2
I0407 22:23:45.109549 32630 net.cpp:84] Creating Layer relu2
I0407 22:23:45.109553 32630 net.cpp:406] relu2 <- conv2
I0407 22:23:45.109560 32630 net.cpp:367] relu2 -> conv2 (in-place)
I0407 22:23:45.110158 32630 net.cpp:122] Setting up relu2
I0407 22:23:45.110167 32630 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 22:23:45.110170 32630 net.cpp:137] Memory required for data: 188034816
I0407 22:23:45.110173 32630 layer_factory.hpp:77] Creating layer norm2
I0407 22:23:45.110184 32630 net.cpp:84] Creating Layer norm2
I0407 22:23:45.110188 32630 net.cpp:406] norm2 <- conv2
I0407 22:23:45.110193 32630 net.cpp:380] norm2 -> norm2
I0407 22:23:45.111004 32630 net.cpp:122] Setting up norm2
I0407 22:23:45.111014 32630 net.cpp:129] Top shape: 32 256 27 27 (5971968)
I0407 22:23:45.111017 32630 net.cpp:137] Memory required for data: 211922688
I0407 22:23:45.111021 32630 layer_factory.hpp:77] Creating layer pool2
I0407 22:23:45.111029 32630 net.cpp:84] Creating Layer pool2
I0407 22:23:45.111032 32630 net.cpp:406] pool2 <- norm2
I0407 22:23:45.111037 32630 net.cpp:380] pool2 -> pool2
I0407 22:23:45.111065 32630 net.cpp:122] Setting up pool2
I0407 22:23:45.111070 32630 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 22:23:45.111073 32630 net.cpp:137] Memory required for data: 217460480
I0407 22:23:45.111075 32630 layer_factory.hpp:77] Creating layer conv3
I0407 22:23:45.111084 32630 net.cpp:84] Creating Layer conv3
I0407 22:23:45.111088 32630 net.cpp:406] conv3 <- pool2
I0407 22:23:45.111093 32630 net.cpp:380] conv3 -> conv3
I0407 22:23:45.122920 32630 net.cpp:122] Setting up conv3
I0407 22:23:45.122934 32630 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 22:23:45.122937 32630 net.cpp:137] Memory required for data: 225767168
I0407 22:23:45.122947 32630 layer_factory.hpp:77] Creating layer relu3
I0407 22:23:45.122958 32630 net.cpp:84] Creating Layer relu3
I0407 22:23:45.122961 32630 net.cpp:406] relu3 <- conv3
I0407 22:23:45.122967 32630 net.cpp:367] relu3 -> conv3 (in-place)
I0407 22:23:45.123594 32630 net.cpp:122] Setting up relu3
I0407 22:23:45.123603 32630 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 22:23:45.123606 32630 net.cpp:137] Memory required for data: 234073856
I0407 22:23:45.123610 32630 layer_factory.hpp:77] Creating layer conv4
I0407 22:23:45.123620 32630 net.cpp:84] Creating Layer conv4
I0407 22:23:45.123622 32630 net.cpp:406] conv4 <- conv3
I0407 22:23:45.123628 32630 net.cpp:380] conv4 -> conv4
I0407 22:23:45.133639 32630 net.cpp:122] Setting up conv4
I0407 22:23:45.133661 32630 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 22:23:45.133663 32630 net.cpp:137] Memory required for data: 242380544
I0407 22:23:45.133668 32630 layer_factory.hpp:77] Creating layer relu4
I0407 22:23:45.133673 32630 net.cpp:84] Creating Layer relu4
I0407 22:23:45.133677 32630 net.cpp:406] relu4 <- conv4
I0407 22:23:45.133683 32630 net.cpp:367] relu4 -> conv4 (in-place)
I0407 22:23:45.134188 32630 net.cpp:122] Setting up relu4
I0407 22:23:45.134198 32630 net.cpp:129] Top shape: 32 384 13 13 (2076672)
I0407 22:23:45.134202 32630 net.cpp:137] Memory required for data: 250687232
I0407 22:23:45.134205 32630 layer_factory.hpp:77] Creating layer conv5
I0407 22:23:45.134214 32630 net.cpp:84] Creating Layer conv5
I0407 22:23:45.134218 32630 net.cpp:406] conv5 <- conv4
I0407 22:23:45.134225 32630 net.cpp:380] conv5 -> conv5
I0407 22:23:45.143997 32630 net.cpp:122] Setting up conv5
I0407 22:23:45.144009 32630 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 22:23:45.144012 32630 net.cpp:137] Memory required for data: 256225024
I0407 22:23:45.144022 32630 layer_factory.hpp:77] Creating layer relu5
I0407 22:23:45.144029 32630 net.cpp:84] Creating Layer relu5
I0407 22:23:45.144047 32630 net.cpp:406] relu5 <- conv5
I0407 22:23:45.144052 32630 net.cpp:367] relu5 -> conv5 (in-place)
I0407 22:23:45.144623 32630 net.cpp:122] Setting up relu5
I0407 22:23:45.144632 32630 net.cpp:129] Top shape: 32 256 13 13 (1384448)
I0407 22:23:45.144635 32630 net.cpp:137] Memory required for data: 261762816
I0407 22:23:45.144639 32630 layer_factory.hpp:77] Creating layer pool5
I0407 22:23:45.144649 32630 net.cpp:84] Creating Layer pool5
I0407 22:23:45.144651 32630 net.cpp:406] pool5 <- conv5
I0407 22:23:45.144656 32630 net.cpp:380] pool5 -> pool5
I0407 22:23:45.144691 32630 net.cpp:122] Setting up pool5
I0407 22:23:45.144696 32630 net.cpp:129] Top shape: 32 256 6 6 (294912)
I0407 22:23:45.144699 32630 net.cpp:137] Memory required for data: 262942464
I0407 22:23:45.144702 32630 layer_factory.hpp:77] Creating layer fc6
I0407 22:23:45.144709 32630 net.cpp:84] Creating Layer fc6
I0407 22:23:45.144712 32630 net.cpp:406] fc6 <- pool5
I0407 22:23:45.144716 32630 net.cpp:380] fc6 -> fc6
I0407 22:23:45.502180 32630 net.cpp:122] Setting up fc6
I0407 22:23:45.502200 32630 net.cpp:129] Top shape: 32 4096 (131072)
I0407 22:23:45.502203 32630 net.cpp:137] Memory required for data: 263466752
I0407 22:23:45.502211 32630 layer_factory.hpp:77] Creating layer relu6
I0407 22:23:45.502219 32630 net.cpp:84] Creating Layer relu6
I0407 22:23:45.502223 32630 net.cpp:406] relu6 <- fc6
I0407 22:23:45.502229 32630 net.cpp:367] relu6 -> fc6 (in-place)
I0407 22:23:45.503008 32630 net.cpp:122] Setting up relu6
I0407 22:23:45.503017 32630 net.cpp:129] Top shape: 32 4096 (131072)
I0407 22:23:45.503021 32630 net.cpp:137] Memory required for data: 263991040
I0407 22:23:45.503023 32630 layer_factory.hpp:77] Creating layer drop6
I0407 22:23:45.503029 32630 net.cpp:84] Creating Layer drop6
I0407 22:23:45.503032 32630 net.cpp:406] drop6 <- fc6
I0407 22:23:45.503038 32630 net.cpp:367] drop6 -> fc6 (in-place)
I0407 22:23:45.503058 32630 net.cpp:122] Setting up drop6
I0407 22:23:45.503063 32630 net.cpp:129] Top shape: 32 4096 (131072)
I0407 22:23:45.503065 32630 net.cpp:137] Memory required for data: 264515328
I0407 22:23:45.503068 32630 layer_factory.hpp:77] Creating layer fc7
I0407 22:23:45.503077 32630 net.cpp:84] Creating Layer fc7
I0407 22:23:45.503078 32630 net.cpp:406] fc7 <- fc6
I0407 22:23:45.503084 32630 net.cpp:380] fc7 -> fc7
I0407 22:23:45.656311 32630 net.cpp:122] Setting up fc7
I0407 22:23:45.656329 32630 net.cpp:129] Top shape: 32 4096 (131072)
I0407 22:23:45.656333 32630 net.cpp:137] Memory required for data: 265039616
I0407 22:23:45.656342 32630 layer_factory.hpp:77] Creating layer relu7
I0407 22:23:45.656352 32630 net.cpp:84] Creating Layer relu7
I0407 22:23:45.656355 32630 net.cpp:406] relu7 <- fc7
I0407 22:23:45.656360 32630 net.cpp:367] relu7 -> fc7 (in-place)
I0407 22:23:45.656836 32630 net.cpp:122] Setting up relu7
I0407 22:23:45.656852 32630 net.cpp:129] Top shape: 32 4096 (131072)
I0407 22:23:45.656854 32630 net.cpp:137] Memory required for data: 265563904
I0407 22:23:45.656857 32630 layer_factory.hpp:77] Creating layer drop7
I0407 22:23:45.656863 32630 net.cpp:84] Creating Layer drop7
I0407 22:23:45.656867 32630 net.cpp:406] drop7 <- fc7
I0407 22:23:45.656872 32630 net.cpp:367] drop7 -> fc7 (in-place)
I0407 22:23:45.656895 32630 net.cpp:122] Setting up drop7
I0407 22:23:45.656900 32630 net.cpp:129] Top shape: 32 4096 (131072)
I0407 22:23:45.656903 32630 net.cpp:137] Memory required for data: 266088192
I0407 22:23:45.656905 32630 layer_factory.hpp:77] Creating layer fc8
I0407 22:23:45.656913 32630 net.cpp:84] Creating Layer fc8
I0407 22:23:45.656916 32630 net.cpp:406] fc8 <- fc7
I0407 22:23:45.656920 32630 net.cpp:380] fc8 -> fc8
I0407 22:23:45.664587 32630 net.cpp:122] Setting up fc8
I0407 22:23:45.664597 32630 net.cpp:129] Top shape: 32 196 (6272)
I0407 22:23:45.664598 32630 net.cpp:137] Memory required for data: 266113280
I0407 22:23:45.664604 32630 layer_factory.hpp:77] Creating layer fc8_fc8_0_split
I0407 22:23:45.664609 32630 net.cpp:84] Creating Layer fc8_fc8_0_split
I0407 22:23:45.664613 32630 net.cpp:406] fc8_fc8_0_split <- fc8
I0407 22:23:45.664630 32630 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_0
I0407 22:23:45.664636 32630 net.cpp:380] fc8_fc8_0_split -> fc8_fc8_0_split_1
I0407 22:23:45.664664 32630 net.cpp:122] Setting up fc8_fc8_0_split
I0407 22:23:45.664669 32630 net.cpp:129] Top shape: 32 196 (6272)
I0407 22:23:45.664670 32630 net.cpp:129] Top shape: 32 196 (6272)
I0407 22:23:45.664674 32630 net.cpp:137] Memory required for data: 266163456
I0407 22:23:45.664675 32630 layer_factory.hpp:77] Creating layer accuracy
I0407 22:23:45.664680 32630 net.cpp:84] Creating Layer accuracy
I0407 22:23:45.664683 32630 net.cpp:406] accuracy <- fc8_fc8_0_split_0
I0407 22:23:45.664687 32630 net.cpp:406] accuracy <- label_val-data_1_split_0
I0407 22:23:45.664692 32630 net.cpp:380] accuracy -> accuracy
I0407 22:23:45.664698 32630 net.cpp:122] Setting up accuracy
I0407 22:23:45.664702 32630 net.cpp:129] Top shape: (1)
I0407 22:23:45.664705 32630 net.cpp:137] Memory required for data: 266163460
I0407 22:23:45.664706 32630 layer_factory.hpp:77] Creating layer loss
I0407 22:23:45.664710 32630 net.cpp:84] Creating Layer loss
I0407 22:23:45.664713 32630 net.cpp:406] loss <- fc8_fc8_0_split_1
I0407 22:23:45.664716 32630 net.cpp:406] loss <- label_val-data_1_split_1
I0407 22:23:45.664721 32630 net.cpp:380] loss -> loss
I0407 22:23:45.664726 32630 layer_factory.hpp:77] Creating layer loss
I0407 22:23:45.665416 32630 net.cpp:122] Setting up loss
I0407 22:23:45.665426 32630 net.cpp:129] Top shape: (1)
I0407 22:23:45.665427 32630 net.cpp:132]     with loss weight 1
I0407 22:23:45.665437 32630 net.cpp:137] Memory required for data: 266163464
I0407 22:23:45.665441 32630 net.cpp:198] loss needs backward computation.
I0407 22:23:45.665444 32630 net.cpp:200] accuracy does not need backward computation.
I0407 22:23:45.665448 32630 net.cpp:198] fc8_fc8_0_split needs backward computation.
I0407 22:23:45.665452 32630 net.cpp:198] fc8 needs backward computation.
I0407 22:23:45.665455 32630 net.cpp:198] drop7 needs backward computation.
I0407 22:23:45.665457 32630 net.cpp:198] relu7 needs backward computation.
I0407 22:23:45.665459 32630 net.cpp:198] fc7 needs backward computation.
I0407 22:23:45.665462 32630 net.cpp:198] drop6 needs backward computation.
I0407 22:23:45.665465 32630 net.cpp:198] relu6 needs backward computation.
I0407 22:23:45.665467 32630 net.cpp:198] fc6 needs backward computation.
I0407 22:23:45.665470 32630 net.cpp:198] pool5 needs backward computation.
I0407 22:23:45.665473 32630 net.cpp:198] relu5 needs backward computation.
I0407 22:23:45.665477 32630 net.cpp:198] conv5 needs backward computation.
I0407 22:23:45.665478 32630 net.cpp:198] relu4 needs backward computation.
I0407 22:23:45.665482 32630 net.cpp:198] conv4 needs backward computation.
I0407 22:23:45.665483 32630 net.cpp:198] relu3 needs backward computation.
I0407 22:23:45.665486 32630 net.cpp:198] conv3 needs backward computation.
I0407 22:23:45.665489 32630 net.cpp:198] pool2 needs backward computation.
I0407 22:23:45.665493 32630 net.cpp:198] norm2 needs backward computation.
I0407 22:23:45.665495 32630 net.cpp:198] relu2 needs backward computation.
I0407 22:23:45.665498 32630 net.cpp:198] conv2 needs backward computation.
I0407 22:23:45.665500 32630 net.cpp:198] pool1 needs backward computation.
I0407 22:23:45.665503 32630 net.cpp:198] norm1 needs backward computation.
I0407 22:23:45.665505 32630 net.cpp:198] relu1 needs backward computation.
I0407 22:23:45.665508 32630 net.cpp:198] conv1 needs backward computation.
I0407 22:23:45.665511 32630 net.cpp:200] label_val-data_1_split does not need backward computation.
I0407 22:23:45.665514 32630 net.cpp:200] val-data does not need backward computation.
I0407 22:23:45.665518 32630 net.cpp:242] This network produces output accuracy
I0407 22:23:45.665520 32630 net.cpp:242] This network produces output loss
I0407 22:23:45.665535 32630 net.cpp:255] Network initialization done.
I0407 22:23:45.665599 32630 solver.cpp:56] Solver scaffolding done.
I0407 22:23:45.665920 32630 caffe.cpp:248] Starting Optimization
I0407 22:23:45.665937 32630 solver.cpp:272] Solving
I0407 22:23:45.665939 32630 solver.cpp:273] Learning Rate Policy: sigmoid
I0407 22:23:45.667598 32630 solver.cpp:330] Iteration 0, Testing net (#0)
I0407 22:23:45.667608 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:23:45.757809 32630 blocking_queue.cpp:49] Waiting for data
I0407 22:23:49.918274 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:23:49.962021 32630 solver.cpp:397]     Test net output #0: accuracy = 0.00367647
I0407 22:23:49.962056 32630 solver.cpp:397]     Test net output #1: loss = 5.2771 (* 1 = 5.2771 loss)
I0407 22:23:50.063127 32630 solver.cpp:218] Iteration 0 (-1.10529e-14 iter/s, 4.39715s/12 iters), loss = 5.28929
I0407 22:23:50.064667 32630 solver.cpp:237]     Train net output #0: loss = 5.28929 (* 1 = 5.28929 loss)
I0407 22:23:50.064697 32630 sgd_solver.cpp:105] Iteration 0, lr = 0.00993307
I0407 22:23:53.704421 32630 solver.cpp:218] Iteration 12 (3.29694 iter/s, 3.63974s/12 iters), loss = 5.29262
I0407 22:23:53.704452 32630 solver.cpp:237]     Train net output #0: loss = 5.29262 (* 1 = 5.29262 loss)
I0407 22:23:53.704459 32630 sgd_solver.cpp:105] Iteration 12, lr = 0.00993228
I0407 22:23:58.475715 32630 solver.cpp:218] Iteration 24 (2.51506 iter/s, 4.77125s/12 iters), loss = 5.29405
I0407 22:23:58.475749 32630 solver.cpp:237]     Train net output #0: loss = 5.29405 (* 1 = 5.29405 loss)
I0407 22:23:58.475755 32630 sgd_solver.cpp:105] Iteration 24, lr = 0.00993149
I0407 22:24:03.235833 32630 solver.cpp:218] Iteration 36 (2.52097 iter/s, 4.76007s/12 iters), loss = 5.27586
I0407 22:24:03.235867 32630 solver.cpp:237]     Train net output #0: loss = 5.27586 (* 1 = 5.27586 loss)
I0407 22:24:03.235875 32630 sgd_solver.cpp:105] Iteration 36, lr = 0.00993068
I0407 22:24:08.111627 32630 solver.cpp:218] Iteration 48 (2.46116 iter/s, 4.87575s/12 iters), loss = 5.28053
I0407 22:24:08.111691 32630 solver.cpp:237]     Train net output #0: loss = 5.28053 (* 1 = 5.28053 loss)
I0407 22:24:08.111701 32630 sgd_solver.cpp:105] Iteration 48, lr = 0.00992987
I0407 22:24:12.871659 32630 solver.cpp:218] Iteration 60 (2.52103 iter/s, 4.75995s/12 iters), loss = 5.28088
I0407 22:24:12.871692 32630 solver.cpp:237]     Train net output #0: loss = 5.28088 (* 1 = 5.28088 loss)
I0407 22:24:12.871699 32630 sgd_solver.cpp:105] Iteration 60, lr = 0.00992905
I0407 22:24:17.660882 32630 solver.cpp:218] Iteration 72 (2.50566 iter/s, 4.78916s/12 iters), loss = 5.28857
I0407 22:24:17.660928 32630 solver.cpp:237]     Train net output #0: loss = 5.28857 (* 1 = 5.28857 loss)
I0407 22:24:17.660938 32630 sgd_solver.cpp:105] Iteration 72, lr = 0.00992821
I0407 22:24:22.607657 32630 solver.cpp:218] Iteration 84 (2.42585 iter/s, 4.94671s/12 iters), loss = 5.30566
I0407 22:24:22.607693 32630 solver.cpp:237]     Train net output #0: loss = 5.30566 (* 1 = 5.30566 loss)
I0407 22:24:22.607702 32630 sgd_solver.cpp:105] Iteration 84, lr = 0.00992737
I0407 22:24:27.471622 32630 solver.cpp:218] Iteration 96 (2.46715 iter/s, 4.8639s/12 iters), loss = 5.2803
I0407 22:24:27.471659 32630 solver.cpp:237]     Train net output #0: loss = 5.2803 (* 1 = 5.2803 loss)
I0407 22:24:27.471668 32630 sgd_solver.cpp:105] Iteration 96, lr = 0.00992651
I0407 22:24:29.112035 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:24:29.405450 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_102.caffemodel
I0407 22:24:32.552940 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_102.solverstate
I0407 22:24:34.986977 32630 solver.cpp:330] Iteration 102, Testing net (#0)
I0407 22:24:34.986995 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:24:39.743496 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:24:39.832427 32630 solver.cpp:397]     Test net output #0: accuracy = 0.00551471
I0407 22:24:39.832473 32630 solver.cpp:397]     Test net output #1: loss = 5.28642 (* 1 = 5.28642 loss)
I0407 22:24:41.597070 32630 solver.cpp:218] Iteration 108 (0.849535 iter/s, 14.1254s/12 iters), loss = 5.29324
I0407 22:24:41.597107 32630 solver.cpp:237]     Train net output #0: loss = 5.29324 (* 1 = 5.29324 loss)
I0407 22:24:41.597116 32630 sgd_solver.cpp:105] Iteration 108, lr = 0.00992565
I0407 22:24:46.509053 32630 solver.cpp:218] Iteration 120 (2.44304 iter/s, 4.91192s/12 iters), loss = 5.26269
I0407 22:24:46.509089 32630 solver.cpp:237]     Train net output #0: loss = 5.26269 (* 1 = 5.26269 loss)
I0407 22:24:46.509096 32630 sgd_solver.cpp:105] Iteration 120, lr = 0.00992478
I0407 22:24:51.324216 32630 solver.cpp:218] Iteration 132 (2.49216 iter/s, 4.8151s/12 iters), loss = 5.2905
I0407 22:24:51.324252 32630 solver.cpp:237]     Train net output #0: loss = 5.2905 (* 1 = 5.2905 loss)
I0407 22:24:51.324260 32630 sgd_solver.cpp:105] Iteration 132, lr = 0.00992389
I0407 22:24:56.164413 32630 solver.cpp:218] Iteration 144 (2.47927 iter/s, 4.84014s/12 iters), loss = 5.27839
I0407 22:24:56.164449 32630 solver.cpp:237]     Train net output #0: loss = 5.27839 (* 1 = 5.27839 loss)
I0407 22:24:56.164458 32630 sgd_solver.cpp:105] Iteration 144, lr = 0.009923
I0407 22:25:01.201314 32630 solver.cpp:218] Iteration 156 (2.38245 iter/s, 5.03684s/12 iters), loss = 5.28008
I0407 22:25:01.201359 32630 solver.cpp:237]     Train net output #0: loss = 5.28008 (* 1 = 5.28008 loss)
I0407 22:25:01.201367 32630 sgd_solver.cpp:105] Iteration 156, lr = 0.0099221
I0407 22:25:06.078928 32630 solver.cpp:218] Iteration 168 (2.46025 iter/s, 4.87755s/12 iters), loss = 5.28077
I0407 22:25:06.078964 32630 solver.cpp:237]     Train net output #0: loss = 5.28077 (* 1 = 5.28077 loss)
I0407 22:25:06.078972 32630 sgd_solver.cpp:105] Iteration 168, lr = 0.00992118
I0407 22:25:10.976097 32630 solver.cpp:218] Iteration 180 (2.45043 iter/s, 4.89711s/12 iters), loss = 5.25998
I0407 22:25:10.976233 32630 solver.cpp:237]     Train net output #0: loss = 5.25998 (* 1 = 5.25998 loss)
I0407 22:25:10.976241 32630 sgd_solver.cpp:105] Iteration 180, lr = 0.00992026
I0407 22:25:15.934857 32630 solver.cpp:218] Iteration 192 (2.42004 iter/s, 4.9586s/12 iters), loss = 5.31123
I0407 22:25:15.934898 32630 solver.cpp:237]     Train net output #0: loss = 5.31123 (* 1 = 5.31123 loss)
I0407 22:25:15.934906 32630 sgd_solver.cpp:105] Iteration 192, lr = 0.00991932
I0407 22:25:19.713043 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:25:20.369163 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_204.caffemodel
I0407 22:25:23.468806 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_204.solverstate
I0407 22:25:25.835417 32630 solver.cpp:330] Iteration 204, Testing net (#0)
I0407 22:25:25.835436 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:25:30.502734 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:25:30.638267 32630 solver.cpp:397]     Test net output #0: accuracy = 0.00796569
I0407 22:25:30.638310 32630 solver.cpp:397]     Test net output #1: loss = 5.19916 (* 1 = 5.19916 loss)
I0407 22:25:30.735139 32630 solver.cpp:218] Iteration 204 (0.8108 iter/s, 14.8002s/12 iters), loss = 5.26302
I0407 22:25:30.735184 32630 solver.cpp:237]     Train net output #0: loss = 5.26302 (* 1 = 5.26302 loss)
I0407 22:25:30.735193 32630 sgd_solver.cpp:105] Iteration 204, lr = 0.00991837
I0407 22:25:34.850286 32630 solver.cpp:218] Iteration 216 (2.91611 iter/s, 4.11508s/12 iters), loss = 5.27377
I0407 22:25:34.850329 32630 solver.cpp:237]     Train net output #0: loss = 5.27377 (* 1 = 5.27377 loss)
I0407 22:25:34.850337 32630 sgd_solver.cpp:105] Iteration 216, lr = 0.00991742
I0407 22:25:39.829335 32630 solver.cpp:218] Iteration 228 (2.41013 iter/s, 4.97898s/12 iters), loss = 5.14682
I0407 22:25:39.829372 32630 solver.cpp:237]     Train net output #0: loss = 5.14682 (* 1 = 5.14682 loss)
I0407 22:25:39.829380 32630 sgd_solver.cpp:105] Iteration 228, lr = 0.00991645
I0407 22:25:44.693403 32630 solver.cpp:218] Iteration 240 (2.46711 iter/s, 4.864s/12 iters), loss = 5.18232
I0407 22:25:44.693570 32630 solver.cpp:237]     Train net output #0: loss = 5.18232 (* 1 = 5.18232 loss)
I0407 22:25:44.693580 32630 sgd_solver.cpp:105] Iteration 240, lr = 0.00991547
I0407 22:25:49.618160 32630 solver.cpp:218] Iteration 252 (2.43676 iter/s, 4.92456s/12 iters), loss = 5.1962
I0407 22:25:49.618203 32630 solver.cpp:237]     Train net output #0: loss = 5.1962 (* 1 = 5.1962 loss)
I0407 22:25:49.618211 32630 sgd_solver.cpp:105] Iteration 252, lr = 0.00991447
I0407 22:25:54.578457 32630 solver.cpp:218] Iteration 264 (2.41924 iter/s, 4.96023s/12 iters), loss = 5.18281
I0407 22:25:54.578496 32630 solver.cpp:237]     Train net output #0: loss = 5.18281 (* 1 = 5.18281 loss)
I0407 22:25:54.578505 32630 sgd_solver.cpp:105] Iteration 264, lr = 0.00991347
I0407 22:25:59.529109 32630 solver.cpp:218] Iteration 276 (2.42395 iter/s, 4.95059s/12 iters), loss = 5.09301
I0407 22:25:59.529150 32630 solver.cpp:237]     Train net output #0: loss = 5.09301 (* 1 = 5.09301 loss)
I0407 22:25:59.529157 32630 sgd_solver.cpp:105] Iteration 276, lr = 0.00991246
I0407 22:26:04.402521 32630 solver.cpp:218] Iteration 288 (2.46237 iter/s, 4.87334s/12 iters), loss = 5.16152
I0407 22:26:04.402563 32630 solver.cpp:237]     Train net output #0: loss = 5.16152 (* 1 = 5.16152 loss)
I0407 22:26:04.402572 32630 sgd_solver.cpp:105] Iteration 288, lr = 0.00991143
I0407 22:26:09.225162 32630 solver.cpp:218] Iteration 300 (2.4883 iter/s, 4.82257s/12 iters), loss = 5.1042
I0407 22:26:09.225206 32630 solver.cpp:237]     Train net output #0: loss = 5.1042 (* 1 = 5.1042 loss)
I0407 22:26:09.225214 32630 sgd_solver.cpp:105] Iteration 300, lr = 0.00991039
I0407 22:26:10.184805 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:26:11.201387 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_306.caffemodel
I0407 22:26:14.249469 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_306.solverstate
I0407 22:26:16.610075 32630 solver.cpp:330] Iteration 306, Testing net (#0)
I0407 22:26:16.610168 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:26:21.116782 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:26:21.278002 32630 solver.cpp:397]     Test net output #0: accuracy = 0.00980392
I0407 22:26:21.278040 32630 solver.cpp:397]     Test net output #1: loss = 5.14414 (* 1 = 5.14414 loss)
I0407 22:26:23.073014 32630 solver.cpp:218] Iteration 312 (0.866566 iter/s, 13.8478s/12 iters), loss = 5.16694
I0407 22:26:23.073050 32630 solver.cpp:237]     Train net output #0: loss = 5.16694 (* 1 = 5.16694 loss)
I0407 22:26:23.073056 32630 sgd_solver.cpp:105] Iteration 312, lr = 0.00990934
I0407 22:26:28.008111 32630 solver.cpp:218] Iteration 324 (2.43159 iter/s, 4.93504s/12 iters), loss = 5.09503
I0407 22:26:28.008145 32630 solver.cpp:237]     Train net output #0: loss = 5.09503 (* 1 = 5.09503 loss)
I0407 22:26:28.008152 32630 sgd_solver.cpp:105] Iteration 324, lr = 0.00990828
I0407 22:26:32.948916 32630 solver.cpp:218] Iteration 336 (2.42878 iter/s, 4.94075s/12 iters), loss = 5.15933
I0407 22:26:32.948954 32630 solver.cpp:237]     Train net output #0: loss = 5.15933 (* 1 = 5.15933 loss)
I0407 22:26:32.948962 32630 sgd_solver.cpp:105] Iteration 336, lr = 0.0099072
I0407 22:26:37.857434 32630 solver.cpp:218] Iteration 348 (2.44476 iter/s, 4.90845s/12 iters), loss = 5.15098
I0407 22:26:37.857473 32630 solver.cpp:237]     Train net output #0: loss = 5.15098 (* 1 = 5.15098 loss)
I0407 22:26:37.857481 32630 sgd_solver.cpp:105] Iteration 348, lr = 0.00990611
I0407 22:26:42.816913 32630 solver.cpp:218] Iteration 360 (2.41964 iter/s, 4.95941s/12 iters), loss = 5.04957
I0407 22:26:42.816958 32630 solver.cpp:237]     Train net output #0: loss = 5.04957 (* 1 = 5.04957 loss)
I0407 22:26:42.816967 32630 sgd_solver.cpp:105] Iteration 360, lr = 0.00990501
I0407 22:26:47.743891 32630 solver.cpp:218] Iteration 372 (2.43561 iter/s, 4.9269s/12 iters), loss = 5.23546
I0407 22:26:47.744030 32630 solver.cpp:237]     Train net output #0: loss = 5.23546 (* 1 = 5.23546 loss)
I0407 22:26:47.744040 32630 sgd_solver.cpp:105] Iteration 372, lr = 0.0099039
I0407 22:26:52.710860 32630 solver.cpp:218] Iteration 384 (2.41604 iter/s, 4.96681s/12 iters), loss = 5.13032
I0407 22:26:52.710892 32630 solver.cpp:237]     Train net output #0: loss = 5.13032 (* 1 = 5.13032 loss)
I0407 22:26:52.710899 32630 sgd_solver.cpp:105] Iteration 384, lr = 0.00990277
I0407 22:26:57.659742 32630 solver.cpp:218] Iteration 396 (2.42482 iter/s, 4.94882s/12 iters), loss = 5.20701
I0407 22:26:57.659780 32630 solver.cpp:237]     Train net output #0: loss = 5.20701 (* 1 = 5.20701 loss)
I0407 22:26:57.659788 32630 sgd_solver.cpp:105] Iteration 396, lr = 0.00990163
I0407 22:27:00.738824 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:27:02.111680 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_408.caffemodel
I0407 22:27:05.180388 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_408.solverstate
I0407 22:27:07.537379 32630 solver.cpp:330] Iteration 408, Testing net (#0)
I0407 22:27:07.537397 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:27:12.044344 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:27:12.252913 32630 solver.cpp:397]     Test net output #0: accuracy = 0.0159314
I0407 22:27:12.252961 32630 solver.cpp:397]     Test net output #1: loss = 5.09571 (* 1 = 5.09571 loss)
I0407 22:27:12.349915 32630 solver.cpp:218] Iteration 408 (0.816878 iter/s, 14.6901s/12 iters), loss = 5.10496
I0407 22:27:12.349961 32630 solver.cpp:237]     Train net output #0: loss = 5.10496 (* 1 = 5.10496 loss)
I0407 22:27:12.349968 32630 sgd_solver.cpp:105] Iteration 408, lr = 0.00990048
I0407 22:27:16.477254 32630 solver.cpp:218] Iteration 420 (2.90749 iter/s, 4.12727s/12 iters), loss = 4.99653
I0407 22:27:16.477296 32630 solver.cpp:237]     Train net output #0: loss = 4.99653 (* 1 = 4.99653 loss)
I0407 22:27:16.477304 32630 sgd_solver.cpp:105] Iteration 420, lr = 0.00989932
I0407 22:27:21.432039 32630 solver.cpp:218] Iteration 432 (2.42194 iter/s, 4.95471s/12 iters), loss = 5.18675
I0407 22:27:21.432190 32630 solver.cpp:237]     Train net output #0: loss = 5.18675 (* 1 = 5.18675 loss)
I0407 22:27:21.432199 32630 sgd_solver.cpp:105] Iteration 432, lr = 0.00989814
I0407 22:27:26.383031 32630 solver.cpp:218] Iteration 444 (2.42384 iter/s, 4.95081s/12 iters), loss = 4.99221
I0407 22:27:26.383074 32630 solver.cpp:237]     Train net output #0: loss = 4.99221 (* 1 = 4.99221 loss)
I0407 22:27:26.383082 32630 sgd_solver.cpp:105] Iteration 444, lr = 0.00989694
I0407 22:27:31.350642 32630 solver.cpp:218] Iteration 456 (2.41568 iter/s, 4.96754s/12 iters), loss = 5.02981
I0407 22:27:31.350689 32630 solver.cpp:237]     Train net output #0: loss = 5.02981 (* 1 = 5.02981 loss)
I0407 22:27:31.350698 32630 sgd_solver.cpp:105] Iteration 456, lr = 0.00989574
I0407 22:27:36.305060 32630 solver.cpp:218] Iteration 468 (2.42212 iter/s, 4.95434s/12 iters), loss = 5.17422
I0407 22:27:36.305101 32630 solver.cpp:237]     Train net output #0: loss = 5.17422 (* 1 = 5.17422 loss)
I0407 22:27:36.305109 32630 sgd_solver.cpp:105] Iteration 468, lr = 0.00989452
I0407 22:27:41.221860 32630 solver.cpp:218] Iteration 480 (2.44065 iter/s, 4.91673s/12 iters), loss = 5.15422
I0407 22:27:41.221906 32630 solver.cpp:237]     Train net output #0: loss = 5.15422 (* 1 = 5.15422 loss)
I0407 22:27:41.221915 32630 sgd_solver.cpp:105] Iteration 480, lr = 0.00989328
I0407 22:27:46.205981 32630 solver.cpp:218] Iteration 492 (2.40769 iter/s, 4.98404s/12 iters), loss = 5.06232
I0407 22:27:46.206040 32630 solver.cpp:237]     Train net output #0: loss = 5.06232 (* 1 = 5.06232 loss)
I0407 22:27:46.206053 32630 sgd_solver.cpp:105] Iteration 492, lr = 0.00989203
I0407 22:27:51.099326 32630 solver.cpp:218] Iteration 504 (2.45235 iter/s, 4.89326s/12 iters), loss = 5.11733
I0407 22:27:51.099370 32630 solver.cpp:237]     Train net output #0: loss = 5.11733 (* 1 = 5.11733 loss)
I0407 22:27:51.099378 32630 sgd_solver.cpp:105] Iteration 504, lr = 0.00989077
I0407 22:27:51.337069 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:27:53.085551 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_510.caffemodel
I0407 22:27:57.130225 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_510.solverstate
I0407 22:28:00.425987 32630 solver.cpp:330] Iteration 510, Testing net (#0)
I0407 22:28:00.426003 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:28:04.774462 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:28:05.015400 32630 solver.cpp:397]     Test net output #0: accuracy = 0.0214461
I0407 22:28:05.015448 32630 solver.cpp:397]     Test net output #1: loss = 5.042 (* 1 = 5.042 loss)
I0407 22:28:06.795569 32630 solver.cpp:218] Iteration 516 (0.764519 iter/s, 15.6961s/12 iters), loss = 5.07318
I0407 22:28:06.795615 32630 solver.cpp:237]     Train net output #0: loss = 5.07318 (* 1 = 5.07318 loss)
I0407 22:28:06.795624 32630 sgd_solver.cpp:105] Iteration 516, lr = 0.00988949
I0407 22:28:11.773789 32630 solver.cpp:218] Iteration 528 (2.41054 iter/s, 4.97814s/12 iters), loss = 5.04808
I0407 22:28:11.773834 32630 solver.cpp:237]     Train net output #0: loss = 5.04808 (* 1 = 5.04808 loss)
I0407 22:28:11.773842 32630 sgd_solver.cpp:105] Iteration 528, lr = 0.0098882
I0407 22:28:16.690349 32630 solver.cpp:218] Iteration 540 (2.44077 iter/s, 4.91649s/12 iters), loss = 4.97116
I0407 22:28:16.690392 32630 solver.cpp:237]     Train net output #0: loss = 4.97116 (* 1 = 4.97116 loss)
I0407 22:28:16.690400 32630 sgd_solver.cpp:105] Iteration 540, lr = 0.00988689
I0407 22:28:21.667021 32630 solver.cpp:218] Iteration 552 (2.41129 iter/s, 4.97659s/12 iters), loss = 4.93715
I0407 22:28:21.667078 32630 solver.cpp:237]     Train net output #0: loss = 4.93715 (* 1 = 4.93715 loss)
I0407 22:28:21.667093 32630 sgd_solver.cpp:105] Iteration 552, lr = 0.00988556
I0407 22:28:26.640506 32630 solver.cpp:218] Iteration 564 (2.41283 iter/s, 4.97341s/12 iters), loss = 5.05124
I0407 22:28:26.640627 32630 solver.cpp:237]     Train net output #0: loss = 5.05124 (* 1 = 5.05124 loss)
I0407 22:28:26.640636 32630 sgd_solver.cpp:105] Iteration 564, lr = 0.00988423
I0407 22:28:31.573940 32630 solver.cpp:218] Iteration 576 (2.43245 iter/s, 4.93329s/12 iters), loss = 5.10147
I0407 22:28:31.573978 32630 solver.cpp:237]     Train net output #0: loss = 5.10147 (* 1 = 5.10147 loss)
I0407 22:28:31.573987 32630 sgd_solver.cpp:105] Iteration 576, lr = 0.00988287
I0407 22:28:36.548557 32630 solver.cpp:218] Iteration 588 (2.41228 iter/s, 4.97455s/12 iters), loss = 5.03908
I0407 22:28:36.548594 32630 solver.cpp:237]     Train net output #0: loss = 5.03908 (* 1 = 5.03908 loss)
I0407 22:28:36.548604 32630 sgd_solver.cpp:105] Iteration 588, lr = 0.0098815
I0407 22:28:41.515055 32630 solver.cpp:218] Iteration 600 (2.41622 iter/s, 4.96643s/12 iters), loss = 5.00796
I0407 22:28:41.515096 32630 solver.cpp:237]     Train net output #0: loss = 5.00796 (* 1 = 5.00796 loss)
I0407 22:28:41.515105 32630 sgd_solver.cpp:105] Iteration 600, lr = 0.00988012
I0407 22:28:43.864809 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:28:45.967629 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_612.caffemodel
I0407 22:28:50.011026 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_612.solverstate
I0407 22:28:52.644457 32630 solver.cpp:330] Iteration 612, Testing net (#0)
I0407 22:28:52.644476 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:28:57.123625 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:28:57.414582 32630 solver.cpp:397]     Test net output #0: accuracy = 0.0269608
I0407 22:28:57.414624 32630 solver.cpp:397]     Test net output #1: loss = 4.9833 (* 1 = 4.9833 loss)
I0407 22:28:57.509948 32630 solver.cpp:218] Iteration 612 (0.750244 iter/s, 15.9948s/12 iters), loss = 4.90642
I0407 22:28:57.509990 32630 solver.cpp:237]     Train net output #0: loss = 4.90642 (* 1 = 4.90642 loss)
I0407 22:28:57.509999 32630 sgd_solver.cpp:105] Iteration 612, lr = 0.00987872
I0407 22:29:01.647078 32630 solver.cpp:218] Iteration 624 (2.90061 iter/s, 4.13706s/12 iters), loss = 5.07496
I0407 22:29:01.647116 32630 solver.cpp:237]     Train net output #0: loss = 5.07496 (* 1 = 5.07496 loss)
I0407 22:29:01.647125 32630 sgd_solver.cpp:105] Iteration 624, lr = 0.0098773
I0407 22:29:06.573174 32630 solver.cpp:218] Iteration 636 (2.43604 iter/s, 4.92603s/12 iters), loss = 4.98338
I0407 22:29:06.573227 32630 solver.cpp:237]     Train net output #0: loss = 4.98338 (* 1 = 4.98338 loss)
I0407 22:29:06.573235 32630 sgd_solver.cpp:105] Iteration 636, lr = 0.00987586
I0407 22:29:11.527595 32630 solver.cpp:218] Iteration 648 (2.42212 iter/s, 4.95434s/12 iters), loss = 5.03096
I0407 22:29:11.527633 32630 solver.cpp:237]     Train net output #0: loss = 5.03096 (* 1 = 5.03096 loss)
I0407 22:29:11.527642 32630 sgd_solver.cpp:105] Iteration 648, lr = 0.00987441
I0407 22:29:16.507794 32630 solver.cpp:218] Iteration 660 (2.40957 iter/s, 4.98014s/12 iters), loss = 4.94279
I0407 22:29:16.507829 32630 solver.cpp:237]     Train net output #0: loss = 4.94279 (* 1 = 4.94279 loss)
I0407 22:29:16.507838 32630 sgd_solver.cpp:105] Iteration 660, lr = 0.00987295
I0407 22:29:21.453572 32630 solver.cpp:218] Iteration 672 (2.42634 iter/s, 4.94572s/12 iters), loss = 5.08332
I0407 22:29:21.453610 32630 solver.cpp:237]     Train net output #0: loss = 5.08332 (* 1 = 5.08332 loss)
I0407 22:29:21.453619 32630 sgd_solver.cpp:105] Iteration 672, lr = 0.00987146
I0407 22:29:26.403364 32630 solver.cpp:218] Iteration 684 (2.42438 iter/s, 4.94972s/12 iters), loss = 4.92193
I0407 22:29:26.403404 32630 solver.cpp:237]     Train net output #0: loss = 4.92193 (* 1 = 4.92193 loss)
I0407 22:29:26.403412 32630 sgd_solver.cpp:105] Iteration 684, lr = 0.00986996
I0407 22:29:27.179772 32630 blocking_queue.cpp:49] Waiting for data
I0407 22:29:31.385473 32630 solver.cpp:218] Iteration 696 (2.40865 iter/s, 4.98204s/12 iters), loss = 4.88782
I0407 22:29:31.385514 32630 solver.cpp:237]     Train net output #0: loss = 4.88782 (* 1 = 4.88782 loss)
I0407 22:29:31.385521 32630 sgd_solver.cpp:105] Iteration 696, lr = 0.00986844
I0407 22:29:35.925900 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:29:36.296869 32630 solver.cpp:218] Iteration 708 (2.44333 iter/s, 4.91132s/12 iters), loss = 5.00372
I0407 22:29:36.296914 32630 solver.cpp:237]     Train net output #0: loss = 5.00372 (* 1 = 5.00372 loss)
I0407 22:29:36.296922 32630 sgd_solver.cpp:105] Iteration 708, lr = 0.00986691
I0407 22:29:38.285908 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_714.caffemodel
I0407 22:29:43.232342 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_714.solverstate
I0407 22:29:45.878559 32630 solver.cpp:330] Iteration 714, Testing net (#0)
I0407 22:29:45.878576 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:29:50.332115 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:29:50.678540 32630 solver.cpp:397]     Test net output #0: accuracy = 0.0324755
I0407 22:29:50.678570 32630 solver.cpp:397]     Test net output #1: loss = 4.89545 (* 1 = 4.89545 loss)
I0407 22:29:52.485925 32630 solver.cpp:218] Iteration 720 (0.741246 iter/s, 16.189s/12 iters), loss = 4.87644
I0407 22:29:52.485963 32630 solver.cpp:237]     Train net output #0: loss = 4.87644 (* 1 = 4.87644 loss)
I0407 22:29:52.485971 32630 sgd_solver.cpp:105] Iteration 720, lr = 0.00986535
I0407 22:29:57.435667 32630 solver.cpp:218] Iteration 732 (2.4244 iter/s, 4.94968s/12 iters), loss = 4.83924
I0407 22:29:57.435793 32630 solver.cpp:237]     Train net output #0: loss = 4.83924 (* 1 = 4.83924 loss)
I0407 22:29:57.435802 32630 sgd_solver.cpp:105] Iteration 732, lr = 0.00986378
I0407 22:30:02.396407 32630 solver.cpp:218] Iteration 744 (2.41907 iter/s, 4.96059s/12 iters), loss = 4.8695
I0407 22:30:02.396446 32630 solver.cpp:237]     Train net output #0: loss = 4.8695 (* 1 = 4.8695 loss)
I0407 22:30:02.396456 32630 sgd_solver.cpp:105] Iteration 744, lr = 0.00986219
I0407 22:30:07.324587 32630 solver.cpp:218] Iteration 756 (2.43501 iter/s, 4.92812s/12 iters), loss = 4.71158
I0407 22:30:07.324626 32630 solver.cpp:237]     Train net output #0: loss = 4.71158 (* 1 = 4.71158 loss)
I0407 22:30:07.324635 32630 sgd_solver.cpp:105] Iteration 756, lr = 0.00986058
I0407 22:30:12.279808 32630 solver.cpp:218] Iteration 768 (2.42172 iter/s, 4.95516s/12 iters), loss = 4.76272
I0407 22:30:12.279844 32630 solver.cpp:237]     Train net output #0: loss = 4.76272 (* 1 = 4.76272 loss)
I0407 22:30:12.279852 32630 sgd_solver.cpp:105] Iteration 768, lr = 0.00985895
I0407 22:30:17.233062 32630 solver.cpp:218] Iteration 780 (2.42269 iter/s, 4.95318s/12 iters), loss = 4.73001
I0407 22:30:17.233108 32630 solver.cpp:237]     Train net output #0: loss = 4.73001 (* 1 = 4.73001 loss)
I0407 22:30:17.233114 32630 sgd_solver.cpp:105] Iteration 780, lr = 0.00985731
I0407 22:30:22.182235 32630 solver.cpp:218] Iteration 792 (2.42468 iter/s, 4.9491s/12 iters), loss = 4.95322
I0407 22:30:22.182274 32630 solver.cpp:237]     Train net output #0: loss = 4.95322 (* 1 = 4.95322 loss)
I0407 22:30:22.182282 32630 sgd_solver.cpp:105] Iteration 792, lr = 0.00985565
I0407 22:30:27.148242 32630 solver.cpp:218] Iteration 804 (2.41646 iter/s, 4.96595s/12 iters), loss = 4.82918
I0407 22:30:27.148274 32630 solver.cpp:237]     Train net output #0: loss = 4.82918 (* 1 = 4.82918 loss)
I0407 22:30:27.148281 32630 sgd_solver.cpp:105] Iteration 804, lr = 0.00985396
I0407 22:30:28.856561 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:30:31.587872 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_816.caffemodel
I0407 22:30:34.671656 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_816.solverstate
I0407 22:30:37.040253 32630 solver.cpp:330] Iteration 816, Testing net (#0)
I0407 22:30:37.040272 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:30:41.471899 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:30:41.861286 32630 solver.cpp:397]     Test net output #0: accuracy = 0.0367647
I0407 22:30:41.861328 32630 solver.cpp:397]     Test net output #1: loss = 4.79705 (* 1 = 4.79705 loss)
I0407 22:30:41.958242 32630 solver.cpp:218] Iteration 816 (0.810268 iter/s, 14.8099s/12 iters), loss = 4.66458
I0407 22:30:41.958287 32630 solver.cpp:237]     Train net output #0: loss = 4.66458 (* 1 = 4.66458 loss)
I0407 22:30:41.958294 32630 sgd_solver.cpp:105] Iteration 816, lr = 0.00985226
I0407 22:30:46.096824 32630 solver.cpp:218] Iteration 828 (2.89959 iter/s, 4.13852s/12 iters), loss = 4.75391
I0407 22:30:46.096861 32630 solver.cpp:237]     Train net output #0: loss = 4.75391 (* 1 = 4.75391 loss)
I0407 22:30:46.096869 32630 sgd_solver.cpp:105] Iteration 828, lr = 0.00985054
I0407 22:30:51.050282 32630 solver.cpp:218] Iteration 840 (2.42258 iter/s, 4.9534s/12 iters), loss = 4.68392
I0407 22:30:51.050318 32630 solver.cpp:237]     Train net output #0: loss = 4.68392 (* 1 = 4.68392 loss)
I0407 22:30:51.050325 32630 sgd_solver.cpp:105] Iteration 840, lr = 0.00984879
I0407 22:30:55.963047 32630 solver.cpp:218] Iteration 852 (2.44265 iter/s, 4.9127s/12 iters), loss = 4.72274
I0407 22:30:55.963083 32630 solver.cpp:237]     Train net output #0: loss = 4.72274 (* 1 = 4.72274 loss)
I0407 22:30:55.963090 32630 sgd_solver.cpp:105] Iteration 852, lr = 0.00984703
I0407 22:31:00.926002 32630 solver.cpp:218] Iteration 864 (2.41795 iter/s, 4.96289s/12 iters), loss = 4.62525
I0407 22:31:00.926124 32630 solver.cpp:237]     Train net output #0: loss = 4.62525 (* 1 = 4.62525 loss)
I0407 22:31:00.926133 32630 sgd_solver.cpp:105] Iteration 864, lr = 0.00984525
I0407 22:31:05.865512 32630 solver.cpp:218] Iteration 876 (2.42947 iter/s, 4.93936s/12 iters), loss = 4.73948
I0407 22:31:05.865558 32630 solver.cpp:237]     Train net output #0: loss = 4.73948 (* 1 = 4.73948 loss)
I0407 22:31:05.865566 32630 sgd_solver.cpp:105] Iteration 876, lr = 0.00984345
I0407 22:31:10.813848 32630 solver.cpp:218] Iteration 888 (2.42509 iter/s, 4.94827s/12 iters), loss = 4.78858
I0407 22:31:10.813879 32630 solver.cpp:237]     Train net output #0: loss = 4.78858 (* 1 = 4.78858 loss)
I0407 22:31:10.813886 32630 sgd_solver.cpp:105] Iteration 888, lr = 0.00984162
I0407 22:31:15.748224 32630 solver.cpp:218] Iteration 900 (2.43195 iter/s, 4.93432s/12 iters), loss = 4.82479
I0407 22:31:15.748260 32630 solver.cpp:237]     Train net output #0: loss = 4.82479 (* 1 = 4.82479 loss)
I0407 22:31:15.748267 32630 sgd_solver.cpp:105] Iteration 900, lr = 0.00983978
I0407 22:31:19.601855 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:31:20.686017 32630 solver.cpp:218] Iteration 912 (2.43027 iter/s, 4.93773s/12 iters), loss = 4.6406
I0407 22:31:20.686051 32630 solver.cpp:237]     Train net output #0: loss = 4.6406 (* 1 = 4.6406 loss)
I0407 22:31:20.686058 32630 sgd_solver.cpp:105] Iteration 912, lr = 0.00983792
I0407 22:31:22.685317 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_918.caffemodel
I0407 22:31:25.760833 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_918.solverstate
I0407 22:31:28.119572 32630 solver.cpp:330] Iteration 918, Testing net (#0)
I0407 22:31:28.119590 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:31:32.283607 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:31:32.688292 32630 solver.cpp:397]     Test net output #0: accuracy = 0.0435049
I0407 22:31:32.688334 32630 solver.cpp:397]     Test net output #1: loss = 4.66447 (* 1 = 4.66447 loss)
I0407 22:31:34.527607 32630 solver.cpp:218] Iteration 924 (0.866958 iter/s, 13.8415s/12 iters), loss = 4.57701
I0407 22:31:34.527648 32630 solver.cpp:237]     Train net output #0: loss = 4.57701 (* 1 = 4.57701 loss)
I0407 22:31:34.527655 32630 sgd_solver.cpp:105] Iteration 924, lr = 0.00983603
I0407 22:31:39.528481 32630 solver.cpp:218] Iteration 936 (2.39961 iter/s, 5.00081s/12 iters), loss = 4.55114
I0407 22:31:39.528512 32630 solver.cpp:237]     Train net output #0: loss = 4.55114 (* 1 = 4.55114 loss)
I0407 22:31:39.528518 32630 sgd_solver.cpp:105] Iteration 936, lr = 0.00983412
I0407 22:31:44.445807 32630 solver.cpp:218] Iteration 948 (2.44038 iter/s, 4.91727s/12 iters), loss = 4.69513
I0407 22:31:44.445842 32630 solver.cpp:237]     Train net output #0: loss = 4.69513 (* 1 = 4.69513 loss)
I0407 22:31:44.445847 32630 sgd_solver.cpp:105] Iteration 948, lr = 0.00983219
I0407 22:31:49.403265 32630 solver.cpp:218] Iteration 960 (2.42063 iter/s, 4.9574s/12 iters), loss = 4.65633
I0407 22:31:49.403297 32630 solver.cpp:237]     Train net output #0: loss = 4.65633 (* 1 = 4.65633 loss)
I0407 22:31:49.403303 32630 sgd_solver.cpp:105] Iteration 960, lr = 0.00983024
I0407 22:31:54.320956 32630 solver.cpp:218] Iteration 972 (2.4402 iter/s, 4.91763s/12 iters), loss = 4.73484
I0407 22:31:54.320992 32630 solver.cpp:237]     Train net output #0: loss = 4.73484 (* 1 = 4.73484 loss)
I0407 22:31:54.321000 32630 sgd_solver.cpp:105] Iteration 972, lr = 0.00982826
I0407 22:31:59.275014 32630 solver.cpp:218] Iteration 984 (2.42229 iter/s, 4.954s/12 iters), loss = 4.49626
I0407 22:31:59.275049 32630 solver.cpp:237]     Train net output #0: loss = 4.49626 (* 1 = 4.49626 loss)
I0407 22:31:59.275056 32630 sgd_solver.cpp:105] Iteration 984, lr = 0.00982627
I0407 22:32:04.207350 32630 solver.cpp:218] Iteration 996 (2.43296 iter/s, 4.93227s/12 iters), loss = 4.48456
I0407 22:32:04.207481 32630 solver.cpp:237]     Train net output #0: loss = 4.48456 (* 1 = 4.48456 loss)
I0407 22:32:04.207490 32630 sgd_solver.cpp:105] Iteration 996, lr = 0.00982425
I0407 22:32:09.189595 32630 solver.cpp:218] Iteration 1008 (2.40863 iter/s, 4.98209s/12 iters), loss = 4.2377
I0407 22:32:09.189636 32630 solver.cpp:237]     Train net output #0: loss = 4.2377 (* 1 = 4.2377 loss)
I0407 22:32:09.189644 32630 sgd_solver.cpp:105] Iteration 1008, lr = 0.0098222
I0407 22:32:10.172454 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:32:13.635721 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1020.caffemodel
I0407 22:32:16.717221 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1020.solverstate
I0407 22:32:19.076360 32630 solver.cpp:330] Iteration 1020, Testing net (#0)
I0407 22:32:19.076377 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:32:23.382781 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:32:23.858155 32630 solver.cpp:397]     Test net output #0: accuracy = 0.057598
I0407 22:32:23.858202 32630 solver.cpp:397]     Test net output #1: loss = 4.54605 (* 1 = 4.54605 loss)
I0407 22:32:23.955083 32630 solver.cpp:218] Iteration 1020 (0.812711 iter/s, 14.7654s/12 iters), loss = 4.60759
I0407 22:32:23.955132 32630 solver.cpp:237]     Train net output #0: loss = 4.60759 (* 1 = 4.60759 loss)
I0407 22:32:23.955140 32630 sgd_solver.cpp:105] Iteration 1020, lr = 0.00982014
I0407 22:32:28.146368 32630 solver.cpp:218] Iteration 1032 (2.86314 iter/s, 4.19121s/12 iters), loss = 4.29653
I0407 22:32:28.146418 32630 solver.cpp:237]     Train net output #0: loss = 4.29653 (* 1 = 4.29653 loss)
I0407 22:32:28.146427 32630 sgd_solver.cpp:105] Iteration 1032, lr = 0.00981805
I0407 22:32:33.126966 32630 solver.cpp:218] Iteration 1044 (2.40939 iter/s, 4.98052s/12 iters), loss = 4.42217
I0407 22:32:33.127010 32630 solver.cpp:237]     Train net output #0: loss = 4.42217 (* 1 = 4.42217 loss)
I0407 22:32:33.127018 32630 sgd_solver.cpp:105] Iteration 1044, lr = 0.00981593
I0407 22:32:38.115330 32630 solver.cpp:218] Iteration 1056 (2.40563 iter/s, 4.98829s/12 iters), loss = 4.36267
I0407 22:32:38.115473 32630 solver.cpp:237]     Train net output #0: loss = 4.36267 (* 1 = 4.36267 loss)
I0407 22:32:38.115483 32630 sgd_solver.cpp:105] Iteration 1056, lr = 0.0098138
I0407 22:32:43.101743 32630 solver.cpp:218] Iteration 1068 (2.40662 iter/s, 4.98625s/12 iters), loss = 4.23802
I0407 22:32:43.101779 32630 solver.cpp:237]     Train net output #0: loss = 4.23802 (* 1 = 4.23802 loss)
I0407 22:32:43.101788 32630 sgd_solver.cpp:105] Iteration 1068, lr = 0.00981163
I0407 22:32:48.195793 32630 solver.cpp:218] Iteration 1080 (2.35572 iter/s, 5.09398s/12 iters), loss = 4.51089
I0407 22:32:48.195839 32630 solver.cpp:237]     Train net output #0: loss = 4.51089 (* 1 = 4.51089 loss)
I0407 22:32:48.195847 32630 sgd_solver.cpp:105] Iteration 1080, lr = 0.00980945
I0407 22:32:53.301321 32630 solver.cpp:218] Iteration 1092 (2.35043 iter/s, 5.10546s/12 iters), loss = 4.2764
I0407 22:32:53.301365 32630 solver.cpp:237]     Train net output #0: loss = 4.2764 (* 1 = 4.2764 loss)
I0407 22:32:53.301373 32630 sgd_solver.cpp:105] Iteration 1092, lr = 0.00980724
I0407 22:32:58.251711 32630 solver.cpp:218] Iteration 1104 (2.42408 iter/s, 4.95032s/12 iters), loss = 4.58349
I0407 22:32:58.251747 32630 solver.cpp:237]     Train net output #0: loss = 4.58349 (* 1 = 4.58349 loss)
I0407 22:32:58.251755 32630 sgd_solver.cpp:105] Iteration 1104, lr = 0.009805
I0407 22:33:01.352502 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:33:03.179216 32630 solver.cpp:218] Iteration 1116 (2.43534 iter/s, 4.92744s/12 iters), loss = 4.30696
I0407 22:33:03.179256 32630 solver.cpp:237]     Train net output #0: loss = 4.30696 (* 1 = 4.30696 loss)
I0407 22:33:03.179263 32630 sgd_solver.cpp:105] Iteration 1116, lr = 0.00980274
I0407 22:33:05.179874 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1122.caffemodel
I0407 22:33:08.296227 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1122.solverstate
I0407 22:33:10.663409 32630 solver.cpp:330] Iteration 1122, Testing net (#0)
I0407 22:33:10.663426 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:33:14.875491 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:33:15.383787 32630 solver.cpp:397]     Test net output #0: accuracy = 0.0741422
I0407 22:33:15.383834 32630 solver.cpp:397]     Test net output #1: loss = 4.29891 (* 1 = 4.29891 loss)
I0407 22:33:17.185256 32630 solver.cpp:218] Iteration 1128 (0.856779 iter/s, 14.006s/12 iters), loss = 4.17549
I0407 22:33:17.185297 32630 solver.cpp:237]     Train net output #0: loss = 4.17549 (* 1 = 4.17549 loss)
I0407 22:33:17.185303 32630 sgd_solver.cpp:105] Iteration 1128, lr = 0.00980045
I0407 22:33:22.049448 32630 solver.cpp:218] Iteration 1140 (2.46704 iter/s, 4.86412s/12 iters), loss = 4.37727
I0407 22:33:22.049490 32630 solver.cpp:237]     Train net output #0: loss = 4.37727 (* 1 = 4.37727 loss)
I0407 22:33:22.049499 32630 sgd_solver.cpp:105] Iteration 1140, lr = 0.00979814
I0407 22:33:26.908551 32630 solver.cpp:218] Iteration 1152 (2.46963 iter/s, 4.85903s/12 iters), loss = 4.15014
I0407 22:33:26.908596 32630 solver.cpp:237]     Train net output #0: loss = 4.15014 (* 1 = 4.15014 loss)
I0407 22:33:26.908604 32630 sgd_solver.cpp:105] Iteration 1152, lr = 0.0097958
I0407 22:33:31.881536 32630 solver.cpp:218] Iteration 1164 (2.41307 iter/s, 4.97291s/12 iters), loss = 4.12757
I0407 22:33:31.881580 32630 solver.cpp:237]     Train net output #0: loss = 4.12757 (* 1 = 4.12757 loss)
I0407 22:33:31.881589 32630 sgd_solver.cpp:105] Iteration 1164, lr = 0.00979343
I0407 22:33:36.814168 32630 solver.cpp:218] Iteration 1176 (2.43281 iter/s, 4.93256s/12 iters), loss = 4.28083
I0407 22:33:36.814211 32630 solver.cpp:237]     Train net output #0: loss = 4.28083 (* 1 = 4.28083 loss)
I0407 22:33:36.814219 32630 sgd_solver.cpp:105] Iteration 1176, lr = 0.00979104
I0407 22:33:41.741133 32630 solver.cpp:218] Iteration 1188 (2.43561 iter/s, 4.92689s/12 iters), loss = 4.10831
I0407 22:33:41.741298 32630 solver.cpp:237]     Train net output #0: loss = 4.10831 (* 1 = 4.10831 loss)
I0407 22:33:41.741307 32630 sgd_solver.cpp:105] Iteration 1188, lr = 0.00978861
I0407 22:33:46.712427 32630 solver.cpp:218] Iteration 1200 (2.41395 iter/s, 4.97111s/12 iters), loss = 4.23766
I0407 22:33:46.712463 32630 solver.cpp:237]     Train net output #0: loss = 4.23766 (* 1 = 4.23766 loss)
I0407 22:33:46.712471 32630 sgd_solver.cpp:105] Iteration 1200, lr = 0.00978617
I0407 22:33:51.648252 32630 solver.cpp:218] Iteration 1212 (2.43124 iter/s, 4.93576s/12 iters), loss = 4.20473
I0407 22:33:51.648293 32630 solver.cpp:237]     Train net output #0: loss = 4.20473 (* 1 = 4.20473 loss)
I0407 22:33:51.648300 32630 sgd_solver.cpp:105] Iteration 1212, lr = 0.00978369
I0407 22:33:51.914099 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:33:56.123358 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1224.caffemodel
I0407 22:33:59.199687 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1224.solverstate
I0407 22:34:01.621654 32630 solver.cpp:330] Iteration 1224, Testing net (#0)
I0407 22:34:01.621671 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:34:05.782886 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:34:06.360237 32630 solver.cpp:397]     Test net output #0: accuracy = 0.0906863
I0407 22:34:06.360272 32630 solver.cpp:397]     Test net output #1: loss = 4.16041 (* 1 = 4.16041 loss)
I0407 22:34:06.456871 32630 solver.cpp:218] Iteration 1224 (0.810344 iter/s, 14.8085s/12 iters), loss = 4.24456
I0407 22:34:06.456914 32630 solver.cpp:237]     Train net output #0: loss = 4.24456 (* 1 = 4.24456 loss)
I0407 22:34:06.456923 32630 sgd_solver.cpp:105] Iteration 1224, lr = 0.00978119
I0407 22:34:10.575367 32630 solver.cpp:218] Iteration 1236 (2.91373 iter/s, 4.11843s/12 iters), loss = 4.09472
I0407 22:34:10.575408 32630 solver.cpp:237]     Train net output #0: loss = 4.09472 (* 1 = 4.09472 loss)
I0407 22:34:10.575417 32630 sgd_solver.cpp:105] Iteration 1236, lr = 0.00977866
I0407 22:34:15.523221 32630 solver.cpp:218] Iteration 1248 (2.42533 iter/s, 4.94779s/12 iters), loss = 3.95764
I0407 22:34:15.523345 32630 solver.cpp:237]     Train net output #0: loss = 3.95764 (* 1 = 3.95764 loss)
I0407 22:34:15.523355 32630 sgd_solver.cpp:105] Iteration 1248, lr = 0.00977609
I0407 22:34:20.460590 32630 solver.cpp:218] Iteration 1260 (2.43052 iter/s, 4.93722s/12 iters), loss = 3.8974
I0407 22:34:20.460629 32630 solver.cpp:237]     Train net output #0: loss = 3.8974 (* 1 = 3.8974 loss)
I0407 22:34:20.460637 32630 sgd_solver.cpp:105] Iteration 1260, lr = 0.0097735
I0407 22:34:25.415231 32630 solver.cpp:218] Iteration 1272 (2.422 iter/s, 4.95458s/12 iters), loss = 3.86341
I0407 22:34:25.415273 32630 solver.cpp:237]     Train net output #0: loss = 3.86341 (* 1 = 3.86341 loss)
I0407 22:34:25.415282 32630 sgd_solver.cpp:105] Iteration 1272, lr = 0.00977089
I0407 22:34:30.319756 32630 solver.cpp:218] Iteration 1284 (2.44676 iter/s, 4.90445s/12 iters), loss = 4.00851
I0407 22:34:30.319799 32630 solver.cpp:237]     Train net output #0: loss = 4.00851 (* 1 = 4.00851 loss)
I0407 22:34:30.319808 32630 sgd_solver.cpp:105] Iteration 1284, lr = 0.00976824
I0407 22:34:35.284938 32630 solver.cpp:218] Iteration 1296 (2.41686 iter/s, 4.96511s/12 iters), loss = 4.21315
I0407 22:34:35.284976 32630 solver.cpp:237]     Train net output #0: loss = 4.21315 (* 1 = 4.21315 loss)
I0407 22:34:35.284983 32630 sgd_solver.cpp:105] Iteration 1296, lr = 0.00976556
I0407 22:34:40.257035 32630 solver.cpp:218] Iteration 1308 (2.4135 iter/s, 4.97203s/12 iters), loss = 4.09765
I0407 22:34:40.257081 32630 solver.cpp:237]     Train net output #0: loss = 4.09765 (* 1 = 4.09765 loss)
I0407 22:34:40.257091 32630 sgd_solver.cpp:105] Iteration 1308, lr = 0.00976285
I0407 22:34:42.728397 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:34:45.173231 32630 solver.cpp:218] Iteration 1320 (2.44095 iter/s, 4.91612s/12 iters), loss = 3.9907
I0407 22:34:45.173274 32630 solver.cpp:237]     Train net output #0: loss = 3.9907 (* 1 = 3.9907 loss)
I0407 22:34:45.173281 32630 sgd_solver.cpp:105] Iteration 1320, lr = 0.00976011
I0407 22:34:47.180701 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1326.caffemodel
I0407 22:34:50.246990 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1326.solverstate
I0407 22:34:52.607774 32630 solver.cpp:330] Iteration 1326, Testing net (#0)
I0407 22:34:52.607792 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:34:56.769423 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:34:57.389204 32630 solver.cpp:397]     Test net output #0: accuracy = 0.109069
I0407 22:34:57.389233 32630 solver.cpp:397]     Test net output #1: loss = 4.02983 (* 1 = 4.02983 loss)
I0407 22:34:59.192781 32630 solver.cpp:218] Iteration 1332 (0.855953 iter/s, 14.0195s/12 iters), loss = 3.9059
I0407 22:34:59.192818 32630 solver.cpp:237]     Train net output #0: loss = 3.9059 (* 1 = 3.9059 loss)
I0407 22:34:59.192826 32630 sgd_solver.cpp:105] Iteration 1332, lr = 0.00975734
I0407 22:35:04.116137 32630 solver.cpp:218] Iteration 1344 (2.43739 iter/s, 4.92329s/12 iters), loss = 3.86136
I0407 22:35:04.116178 32630 solver.cpp:237]     Train net output #0: loss = 3.86136 (* 1 = 3.86136 loss)
I0407 22:35:04.116185 32630 sgd_solver.cpp:105] Iteration 1344, lr = 0.00975454
I0407 22:35:09.067528 32630 solver.cpp:218] Iteration 1356 (2.42359 iter/s, 4.95133s/12 iters), loss = 4.08586
I0407 22:35:09.067564 32630 solver.cpp:237]     Train net output #0: loss = 4.08586 (* 1 = 4.08586 loss)
I0407 22:35:09.067571 32630 sgd_solver.cpp:105] Iteration 1356, lr = 0.00975171
I0407 22:35:14.039502 32630 solver.cpp:218] Iteration 1368 (2.41356 iter/s, 4.97191s/12 iters), loss = 3.91133
I0407 22:35:14.039538 32630 solver.cpp:237]     Train net output #0: loss = 3.91133 (* 1 = 3.91133 loss)
I0407 22:35:14.039546 32630 sgd_solver.cpp:105] Iteration 1368, lr = 0.00974884
I0407 22:35:15.215027 32630 blocking_queue.cpp:49] Waiting for data
I0407 22:35:18.963997 32630 solver.cpp:218] Iteration 1380 (2.43683 iter/s, 4.92443s/12 iters), loss = 4.05052
I0407 22:35:18.964116 32630 solver.cpp:237]     Train net output #0: loss = 4.05052 (* 1 = 4.05052 loss)
I0407 22:35:18.964124 32630 sgd_solver.cpp:105] Iteration 1380, lr = 0.00974595
I0407 22:35:23.907119 32630 solver.cpp:218] Iteration 1392 (2.42768 iter/s, 4.94298s/12 iters), loss = 4.13553
I0407 22:35:23.907155 32630 solver.cpp:237]     Train net output #0: loss = 4.13553 (* 1 = 4.13553 loss)
I0407 22:35:23.907161 32630 sgd_solver.cpp:105] Iteration 1392, lr = 0.00974302
I0407 22:35:28.874513 32630 solver.cpp:218] Iteration 1404 (2.41578 iter/s, 4.96733s/12 iters), loss = 3.92709
I0407 22:35:28.874558 32630 solver.cpp:237]     Train net output #0: loss = 3.92709 (* 1 = 3.92709 loss)
I0407 22:35:28.874567 32630 sgd_solver.cpp:105] Iteration 1404, lr = 0.00974005
I0407 22:35:33.484171 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:35:33.825662 32630 solver.cpp:218] Iteration 1416 (2.42371 iter/s, 4.95108s/12 iters), loss = 3.86134
I0407 22:35:33.825700 32630 solver.cpp:237]     Train net output #0: loss = 3.86134 (* 1 = 3.86134 loss)
I0407 22:35:33.825708 32630 sgd_solver.cpp:105] Iteration 1416, lr = 0.00973706
I0407 22:35:38.286581 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1428.caffemodel
I0407 22:35:42.488749 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1428.solverstate
I0407 22:35:44.851284 32630 solver.cpp:330] Iteration 1428, Testing net (#0)
I0407 22:35:44.851301 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:35:48.952097 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:35:49.558961 32630 solver.cpp:397]     Test net output #0: accuracy = 0.112745
I0407 22:35:49.559124 32630 solver.cpp:397]     Test net output #1: loss = 3.98051 (* 1 = 3.98051 loss)
I0407 22:35:49.655807 32630 solver.cpp:218] Iteration 1428 (0.758052 iter/s, 15.83s/12 iters), loss = 3.77237
I0407 22:35:49.655845 32630 solver.cpp:237]     Train net output #0: loss = 3.77237 (* 1 = 3.77237 loss)
I0407 22:35:49.655853 32630 sgd_solver.cpp:105] Iteration 1428, lr = 0.00973403
I0407 22:35:53.719720 32630 solver.cpp:218] Iteration 1440 (2.95286 iter/s, 4.06385s/12 iters), loss = 3.74143
I0407 22:35:53.719765 32630 solver.cpp:237]     Train net output #0: loss = 3.74143 (* 1 = 3.74143 loss)
I0407 22:35:53.719774 32630 sgd_solver.cpp:105] Iteration 1440, lr = 0.00973097
I0407 22:35:58.639462 32630 solver.cpp:218] Iteration 1452 (2.43919 iter/s, 4.91967s/12 iters), loss = 4.09729
I0407 22:35:58.639504 32630 solver.cpp:237]     Train net output #0: loss = 4.09729 (* 1 = 4.09729 loss)
I0407 22:35:58.639513 32630 sgd_solver.cpp:105] Iteration 1452, lr = 0.00972787
I0407 22:36:03.604781 32630 solver.cpp:218] Iteration 1464 (2.4168 iter/s, 4.96525s/12 iters), loss = 3.58546
I0407 22:36:03.604825 32630 solver.cpp:237]     Train net output #0: loss = 3.58546 (* 1 = 3.58546 loss)
I0407 22:36:03.604832 32630 sgd_solver.cpp:105] Iteration 1464, lr = 0.00972474
I0407 22:36:08.562065 32630 solver.cpp:218] Iteration 1476 (2.42071 iter/s, 4.95721s/12 iters), loss = 3.52757
I0407 22:36:08.562101 32630 solver.cpp:237]     Train net output #0: loss = 3.52757 (* 1 = 3.52757 loss)
I0407 22:36:08.562109 32630 sgd_solver.cpp:105] Iteration 1476, lr = 0.00972157
I0407 22:36:13.489329 32630 solver.cpp:218] Iteration 1488 (2.43547 iter/s, 4.92719s/12 iters), loss = 3.88124
I0407 22:36:13.489388 32630 solver.cpp:237]     Train net output #0: loss = 3.88124 (* 1 = 3.88124 loss)
I0407 22:36:13.489399 32630 sgd_solver.cpp:105] Iteration 1488, lr = 0.00971837
I0407 22:36:18.435710 32630 solver.cpp:218] Iteration 1500 (2.42605 iter/s, 4.9463s/12 iters), loss = 3.90182
I0407 22:36:18.435745 32630 solver.cpp:237]     Train net output #0: loss = 3.90182 (* 1 = 3.90182 loss)
I0407 22:36:18.435753 32630 sgd_solver.cpp:105] Iteration 1500, lr = 0.00971513
I0407 22:36:23.356792 32630 solver.cpp:218] Iteration 1512 (2.43851 iter/s, 4.92103s/12 iters), loss = 3.69841
I0407 22:36:23.356907 32630 solver.cpp:237]     Train net output #0: loss = 3.69841 (* 1 = 3.69841 loss)
I0407 22:36:23.356916 32630 sgd_solver.cpp:105] Iteration 1512, lr = 0.00971186
I0407 22:36:25.099973 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:36:28.238459 32630 solver.cpp:218] Iteration 1524 (2.45825 iter/s, 4.88153s/12 iters), loss = 3.43463
I0407 22:36:28.238499 32630 solver.cpp:237]     Train net output #0: loss = 3.43463 (* 1 = 3.43463 loss)
I0407 22:36:28.238507 32630 sgd_solver.cpp:105] Iteration 1524, lr = 0.00970855
I0407 22:36:30.271250 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1530.caffemodel
I0407 22:36:34.070756 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1530.solverstate
I0407 22:36:37.957499 32630 solver.cpp:330] Iteration 1530, Testing net (#0)
I0407 22:36:37.957517 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:36:42.052000 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:36:42.760319 32630 solver.cpp:397]     Test net output #0: accuracy = 0.120098
I0407 22:36:42.760365 32630 solver.cpp:397]     Test net output #1: loss = 3.95919 (* 1 = 3.95919 loss)
I0407 22:36:44.580201 32630 solver.cpp:218] Iteration 1536 (0.73432 iter/s, 16.3417s/12 iters), loss = 3.54588
I0407 22:36:44.580240 32630 solver.cpp:237]     Train net output #0: loss = 3.54588 (* 1 = 3.54588 loss)
I0407 22:36:44.580246 32630 sgd_solver.cpp:105] Iteration 1536, lr = 0.0097052
I0407 22:36:49.489341 32630 solver.cpp:218] Iteration 1548 (2.44445 iter/s, 4.90907s/12 iters), loss = 3.77016
I0407 22:36:49.489384 32630 solver.cpp:237]     Train net output #0: loss = 3.77016 (* 1 = 3.77016 loss)
I0407 22:36:49.489393 32630 sgd_solver.cpp:105] Iteration 1548, lr = 0.00970181
I0407 22:36:54.435628 32630 solver.cpp:218] Iteration 1560 (2.4261 iter/s, 4.94622s/12 iters), loss = 3.5905
I0407 22:36:54.435778 32630 solver.cpp:237]     Train net output #0: loss = 3.5905 (* 1 = 3.5905 loss)
I0407 22:36:54.435788 32630 sgd_solver.cpp:105] Iteration 1560, lr = 0.00969839
I0407 22:36:59.362934 32630 solver.cpp:218] Iteration 1572 (2.43549 iter/s, 4.92714s/12 iters), loss = 3.46125
I0407 22:36:59.362969 32630 solver.cpp:237]     Train net output #0: loss = 3.46125 (* 1 = 3.46125 loss)
I0407 22:36:59.362978 32630 sgd_solver.cpp:105] Iteration 1572, lr = 0.00969493
I0407 22:37:04.336442 32630 solver.cpp:218] Iteration 1584 (2.41281 iter/s, 4.97345s/12 iters), loss = 3.70852
I0407 22:37:04.336480 32630 solver.cpp:237]     Train net output #0: loss = 3.70852 (* 1 = 3.70852 loss)
I0407 22:37:04.336488 32630 sgd_solver.cpp:105] Iteration 1584, lr = 0.00969143
I0407 22:37:09.242467 32630 solver.cpp:218] Iteration 1596 (2.446 iter/s, 4.90596s/12 iters), loss = 3.48352
I0407 22:37:09.242509 32630 solver.cpp:237]     Train net output #0: loss = 3.48352 (* 1 = 3.48352 loss)
I0407 22:37:09.242517 32630 sgd_solver.cpp:105] Iteration 1596, lr = 0.00968789
I0407 22:37:14.229894 32630 solver.cpp:218] Iteration 1608 (2.40608 iter/s, 4.98737s/12 iters), loss = 3.47608
I0407 22:37:14.229928 32630 solver.cpp:237]     Train net output #0: loss = 3.47608 (* 1 = 3.47608 loss)
I0407 22:37:14.229934 32630 sgd_solver.cpp:105] Iteration 1608, lr = 0.00968432
I0407 22:37:18.169414 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:37:19.227159 32630 solver.cpp:218] Iteration 1620 (2.40134 iter/s, 4.99721s/12 iters), loss = 3.57487
I0407 22:37:19.227195 32630 solver.cpp:237]     Train net output #0: loss = 3.57487 (* 1 = 3.57487 loss)
I0407 22:37:19.227206 32630 sgd_solver.cpp:105] Iteration 1620, lr = 0.0096807
I0407 22:37:23.701702 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1632.caffemodel
I0407 22:37:26.842382 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1632.solverstate
I0407 22:37:30.822645 32630 solver.cpp:330] Iteration 1632, Testing net (#0)
I0407 22:37:30.822664 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:37:34.821251 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:37:35.511315 32630 solver.cpp:397]     Test net output #0: accuracy = 0.150735
I0407 22:37:35.511354 32630 solver.cpp:397]     Test net output #1: loss = 3.8218 (* 1 = 3.8218 loss)
I0407 22:37:35.608111 32630 solver.cpp:218] Iteration 1632 (0.732562 iter/s, 16.3809s/12 iters), loss = 3.53794
I0407 22:37:35.608155 32630 solver.cpp:237]     Train net output #0: loss = 3.53794 (* 1 = 3.53794 loss)
I0407 22:37:35.608162 32630 sgd_solver.cpp:105] Iteration 1632, lr = 0.00967705
I0407 22:37:39.740252 32630 solver.cpp:218] Iteration 1644 (2.90411 iter/s, 4.13208s/12 iters), loss = 3.26289
I0407 22:37:39.740290 32630 solver.cpp:237]     Train net output #0: loss = 3.26289 (* 1 = 3.26289 loss)
I0407 22:37:39.740298 32630 sgd_solver.cpp:105] Iteration 1644, lr = 0.00967335
I0407 22:37:44.674691 32630 solver.cpp:218] Iteration 1656 (2.43192 iter/s, 4.93438s/12 iters), loss = 3.38381
I0407 22:37:44.674726 32630 solver.cpp:237]     Train net output #0: loss = 3.38381 (* 1 = 3.38381 loss)
I0407 22:37:44.674734 32630 sgd_solver.cpp:105] Iteration 1656, lr = 0.00966961
I0407 22:37:49.612341 32630 solver.cpp:218] Iteration 1668 (2.43034 iter/s, 4.93759s/12 iters), loss = 3.52195
I0407 22:37:49.612375 32630 solver.cpp:237]     Train net output #0: loss = 3.52195 (* 1 = 3.52195 loss)
I0407 22:37:49.612382 32630 sgd_solver.cpp:105] Iteration 1668, lr = 0.00966583
I0407 22:37:54.566047 32630 solver.cpp:218] Iteration 1680 (2.42246 iter/s, 4.95365s/12 iters), loss = 3.21694
I0407 22:37:54.566084 32630 solver.cpp:237]     Train net output #0: loss = 3.21694 (* 1 = 3.21694 loss)
I0407 22:37:54.566092 32630 sgd_solver.cpp:105] Iteration 1680, lr = 0.00966201
I0407 22:37:59.486860 32630 solver.cpp:218] Iteration 1692 (2.43865 iter/s, 4.92075s/12 iters), loss = 3.18413
I0407 22:37:59.486994 32630 solver.cpp:237]     Train net output #0: loss = 3.18413 (* 1 = 3.18413 loss)
I0407 22:37:59.487004 32630 sgd_solver.cpp:105] Iteration 1692, lr = 0.00965815
I0407 22:38:04.448376 32630 solver.cpp:218] Iteration 1704 (2.41869 iter/s, 4.96136s/12 iters), loss = 3.12174
I0407 22:38:04.448421 32630 solver.cpp:237]     Train net output #0: loss = 3.12174 (* 1 = 3.12174 loss)
I0407 22:38:04.448429 32630 sgd_solver.cpp:105] Iteration 1704, lr = 0.00965424
I0407 22:38:09.343493 32630 solver.cpp:218] Iteration 1716 (2.45146 iter/s, 4.89504s/12 iters), loss = 2.97912
I0407 22:38:09.343538 32630 solver.cpp:237]     Train net output #0: loss = 2.97912 (* 1 = 2.97912 loss)
I0407 22:38:09.343546 32630 sgd_solver.cpp:105] Iteration 1716, lr = 0.00965029
I0407 22:38:10.379976 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:38:14.324098 32630 solver.cpp:218] Iteration 1728 (2.40938 iter/s, 4.98053s/12 iters), loss = 3.51021
I0407 22:38:14.324133 32630 solver.cpp:237]     Train net output #0: loss = 3.51021 (* 1 = 3.51021 loss)
I0407 22:38:14.324141 32630 sgd_solver.cpp:105] Iteration 1728, lr = 0.0096463
I0407 22:38:16.331110 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1734.caffemodel
I0407 22:38:19.569924 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1734.solverstate
I0407 22:38:22.039463 32630 solver.cpp:330] Iteration 1734, Testing net (#0)
I0407 22:38:22.039484 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:38:26.029758 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:38:26.810990 32630 solver.cpp:397]     Test net output #0: accuracy = 0.166667
I0407 22:38:26.811034 32630 solver.cpp:397]     Test net output #1: loss = 3.73198 (* 1 = 3.73198 loss)
I0407 22:38:28.615207 32630 solver.cpp:218] Iteration 1740 (0.839688 iter/s, 14.291s/12 iters), loss = 3.19454
I0407 22:38:28.615247 32630 solver.cpp:237]     Train net output #0: loss = 3.19454 (* 1 = 3.19454 loss)
I0407 22:38:28.615255 32630 sgd_solver.cpp:105] Iteration 1740, lr = 0.00964226
I0407 22:38:33.511845 32630 solver.cpp:218] Iteration 1752 (2.45069 iter/s, 4.89657s/12 iters), loss = 3.07872
I0407 22:38:33.511960 32630 solver.cpp:237]     Train net output #0: loss = 3.07872 (* 1 = 3.07872 loss)
I0407 22:38:33.511968 32630 sgd_solver.cpp:105] Iteration 1752, lr = 0.00963818
I0407 22:38:38.464370 32630 solver.cpp:218] Iteration 1764 (2.42308 iter/s, 4.95238s/12 iters), loss = 3.08041
I0407 22:38:38.464412 32630 solver.cpp:237]     Train net output #0: loss = 3.08041 (* 1 = 3.08041 loss)
I0407 22:38:38.464421 32630 sgd_solver.cpp:105] Iteration 1764, lr = 0.00963406
I0407 22:38:43.379341 32630 solver.cpp:218] Iteration 1776 (2.44155 iter/s, 4.91491s/12 iters), loss = 2.9186
I0407 22:38:43.379384 32630 solver.cpp:237]     Train net output #0: loss = 2.9186 (* 1 = 2.9186 loss)
I0407 22:38:43.379391 32630 sgd_solver.cpp:105] Iteration 1776, lr = 0.00962989
I0407 22:38:48.349622 32630 solver.cpp:218] Iteration 1788 (2.41438 iter/s, 4.97021s/12 iters), loss = 3.25673
I0407 22:38:48.349664 32630 solver.cpp:237]     Train net output #0: loss = 3.25673 (* 1 = 3.25673 loss)
I0407 22:38:48.349673 32630 sgd_solver.cpp:105] Iteration 1788, lr = 0.00962567
I0407 22:38:53.279384 32630 solver.cpp:218] Iteration 1800 (2.43423 iter/s, 4.92969s/12 iters), loss = 2.92711
I0407 22:38:53.279422 32630 solver.cpp:237]     Train net output #0: loss = 2.92711 (* 1 = 2.92711 loss)
I0407 22:38:53.279430 32630 sgd_solver.cpp:105] Iteration 1800, lr = 0.00962141
I0407 22:38:58.213575 32630 solver.cpp:218] Iteration 1812 (2.43204 iter/s, 4.93413s/12 iters), loss = 3.67551
I0407 22:38:58.213615 32630 solver.cpp:237]     Train net output #0: loss = 3.67551 (* 1 = 3.67551 loss)
I0407 22:38:58.213624 32630 sgd_solver.cpp:105] Iteration 1812, lr = 0.0096171
I0407 22:39:01.288349 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:39:03.068991 32630 solver.cpp:218] Iteration 1824 (2.4715 iter/s, 4.85535s/12 iters), loss = 3.23985
I0407 22:39:03.069036 32630 solver.cpp:237]     Train net output #0: loss = 3.23985 (* 1 = 3.23985 loss)
I0407 22:39:03.069042 32630 sgd_solver.cpp:105] Iteration 1824, lr = 0.00961275
I0407 22:39:07.567631 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1836.caffemodel
I0407 22:39:10.734463 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1836.solverstate
I0407 22:39:14.289350 32630 solver.cpp:330] Iteration 1836, Testing net (#0)
I0407 22:39:14.289366 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:39:18.232897 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:39:19.065892 32630 solver.cpp:397]     Test net output #0: accuracy = 0.183824
I0407 22:39:19.065922 32630 solver.cpp:397]     Test net output #1: loss = 3.63909 (* 1 = 3.63909 loss)
I0407 22:39:19.161469 32630 solver.cpp:218] Iteration 1836 (0.745694 iter/s, 16.0924s/12 iters), loss = 3.10921
I0407 22:39:19.161510 32630 solver.cpp:237]     Train net output #0: loss = 3.10921 (* 1 = 3.10921 loss)
I0407 22:39:19.161518 32630 sgd_solver.cpp:105] Iteration 1836, lr = 0.00960834
I0407 22:39:23.294744 32630 solver.cpp:218] Iteration 1848 (2.90331 iter/s, 4.13321s/12 iters), loss = 3.09783
I0407 22:39:23.294780 32630 solver.cpp:237]     Train net output #0: loss = 3.09783 (* 1 = 3.09783 loss)
I0407 22:39:23.294787 32630 sgd_solver.cpp:105] Iteration 1848, lr = 0.00960389
I0407 22:39:28.135136 32630 solver.cpp:218] Iteration 1860 (2.47917 iter/s, 4.84033s/12 iters), loss = 2.82753
I0407 22:39:28.135174 32630 solver.cpp:237]     Train net output #0: loss = 2.82753 (* 1 = 2.82753 loss)
I0407 22:39:28.135182 32630 sgd_solver.cpp:105] Iteration 1860, lr = 0.00959939
I0407 22:39:33.072353 32630 solver.cpp:218] Iteration 1872 (2.43055 iter/s, 4.93715s/12 iters), loss = 3.0286
I0407 22:39:33.072394 32630 solver.cpp:237]     Train net output #0: loss = 3.0286 (* 1 = 3.0286 loss)
I0407 22:39:33.072402 32630 sgd_solver.cpp:105] Iteration 1872, lr = 0.00959484
I0407 22:39:38.033691 32630 solver.cpp:218] Iteration 1884 (2.41873 iter/s, 4.96127s/12 iters), loss = 3.25917
I0407 22:39:38.033838 32630 solver.cpp:237]     Train net output #0: loss = 3.25917 (* 1 = 3.25917 loss)
I0407 22:39:38.033847 32630 sgd_solver.cpp:105] Iteration 1884, lr = 0.00959024
I0407 22:39:42.950832 32630 solver.cpp:218] Iteration 1896 (2.44053 iter/s, 4.91697s/12 iters), loss = 3.18158
I0407 22:39:42.950875 32630 solver.cpp:237]     Train net output #0: loss = 3.18158 (* 1 = 3.18158 loss)
I0407 22:39:42.950882 32630 sgd_solver.cpp:105] Iteration 1896, lr = 0.0095856
I0407 22:39:47.932015 32630 solver.cpp:218] Iteration 1908 (2.4091 iter/s, 4.98112s/12 iters), loss = 3.14333
I0407 22:39:47.932051 32630 solver.cpp:237]     Train net output #0: loss = 3.14333 (* 1 = 3.14333 loss)
I0407 22:39:47.932058 32630 sgd_solver.cpp:105] Iteration 1908, lr = 0.0095809
I0407 22:39:52.876130 32630 solver.cpp:218] Iteration 1920 (2.42716 iter/s, 4.94406s/12 iters), loss = 3.03418
I0407 22:39:52.876165 32630 solver.cpp:237]     Train net output #0: loss = 3.03418 (* 1 = 3.03418 loss)
I0407 22:39:52.876173 32630 sgd_solver.cpp:105] Iteration 1920, lr = 0.00957615
I0407 22:39:53.171228 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:39:57.783803 32630 solver.cpp:218] Iteration 1932 (2.44518 iter/s, 4.90762s/12 iters), loss = 3.38318
I0407 22:39:57.783841 32630 solver.cpp:237]     Train net output #0: loss = 3.38318 (* 1 = 3.38318 loss)
I0407 22:39:57.783849 32630 sgd_solver.cpp:105] Iteration 1932, lr = 0.00957135
I0407 22:39:59.789041 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_1938.caffemodel
I0407 22:40:03.655289 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_1938.solverstate
I0407 22:40:06.017980 32630 solver.cpp:330] Iteration 1938, Testing net (#0)
I0407 22:40:06.017997 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:40:09.940968 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:40:10.813423 32630 solver.cpp:397]     Test net output #0: accuracy = 0.20098
I0407 22:40:10.813467 32630 solver.cpp:397]     Test net output #1: loss = 3.51773 (* 1 = 3.51773 loss)
I0407 22:40:12.622952 32630 solver.cpp:218] Iteration 1944 (0.808677 iter/s, 14.8391s/12 iters), loss = 3.07854
I0407 22:40:12.622992 32630 solver.cpp:237]     Train net output #0: loss = 3.07854 (* 1 = 3.07854 loss)
I0407 22:40:12.623000 32630 sgd_solver.cpp:105] Iteration 1944, lr = 0.00956649
I0407 22:40:17.525207 32630 solver.cpp:218] Iteration 1956 (2.44789 iter/s, 4.90219s/12 iters), loss = 2.93089
I0407 22:40:17.525244 32630 solver.cpp:237]     Train net output #0: loss = 2.93089 (* 1 = 2.93089 loss)
I0407 22:40:17.525252 32630 sgd_solver.cpp:105] Iteration 1956, lr = 0.00956159
I0407 22:40:22.498236 32630 solver.cpp:218] Iteration 1968 (2.41305 iter/s, 4.97297s/12 iters), loss = 2.7476
I0407 22:40:22.498275 32630 solver.cpp:237]     Train net output #0: loss = 2.7476 (* 1 = 2.7476 loss)
I0407 22:40:22.498281 32630 sgd_solver.cpp:105] Iteration 1968, lr = 0.00955663
I0407 22:40:27.457639 32630 solver.cpp:218] Iteration 1980 (2.41967 iter/s, 4.95934s/12 iters), loss = 2.99884
I0407 22:40:27.457674 32630 solver.cpp:237]     Train net output #0: loss = 2.99884 (* 1 = 2.99884 loss)
I0407 22:40:27.457681 32630 sgd_solver.cpp:105] Iteration 1980, lr = 0.00955162
I0407 22:40:32.442898 32630 solver.cpp:218] Iteration 1992 (2.40713 iter/s, 4.9852s/12 iters), loss = 2.95156
I0407 22:40:32.442943 32630 solver.cpp:237]     Train net output #0: loss = 2.95156 (* 1 = 2.95156 loss)
I0407 22:40:32.442951 32630 sgd_solver.cpp:105] Iteration 1992, lr = 0.00954655
I0407 22:40:37.431617 32630 solver.cpp:218] Iteration 2004 (2.40546 iter/s, 4.98865s/12 iters), loss = 2.90869
I0407 22:40:37.431659 32630 solver.cpp:237]     Train net output #0: loss = 2.90869 (* 1 = 2.90869 loss)
I0407 22:40:37.431668 32630 sgd_solver.cpp:105] Iteration 2004, lr = 0.00954143
I0407 22:40:42.388677 32630 solver.cpp:218] Iteration 2016 (2.42082 iter/s, 4.95699s/12 iters), loss = 2.88222
I0407 22:40:42.388799 32630 solver.cpp:237]     Train net output #0: loss = 2.88222 (* 1 = 2.88222 loss)
I0407 22:40:42.388808 32630 sgd_solver.cpp:105] Iteration 2016, lr = 0.00953626
I0407 22:40:44.879971 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:40:47.283229 32630 solver.cpp:218] Iteration 2028 (2.45178 iter/s, 4.89441s/12 iters), loss = 3.07218
I0407 22:40:47.283272 32630 solver.cpp:237]     Train net output #0: loss = 3.07218 (* 1 = 3.07218 loss)
I0407 22:40:47.283282 32630 sgd_solver.cpp:105] Iteration 2028, lr = 0.00953103
I0407 22:40:51.765890 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2040.caffemodel
I0407 22:40:55.503212 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2040.solverstate
I0407 22:40:58.686507 32630 solver.cpp:330] Iteration 2040, Testing net (#0)
I0407 22:40:58.686527 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:41:02.503486 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:41:03.360817 32630 solver.cpp:397]     Test net output #0: accuracy = 0.20527
I0407 22:41:03.360865 32630 solver.cpp:397]     Test net output #1: loss = 3.45559 (* 1 = 3.45559 loss)
I0407 22:41:03.457465 32630 solver.cpp:218] Iteration 2040 (0.741925 iter/s, 16.1741s/12 iters), loss = 3.06067
I0407 22:41:03.457515 32630 solver.cpp:237]     Train net output #0: loss = 3.06067 (* 1 = 3.06067 loss)
I0407 22:41:03.457523 32630 sgd_solver.cpp:105] Iteration 2040, lr = 0.00952574
I0407 22:41:07.562410 32630 solver.cpp:218] Iteration 2052 (2.92336 iter/s, 4.10487s/12 iters), loss = 2.77774
I0407 22:41:07.562459 32630 solver.cpp:237]     Train net output #0: loss = 2.77774 (* 1 = 2.77774 loss)
I0407 22:41:07.562469 32630 sgd_solver.cpp:105] Iteration 2052, lr = 0.0095204
I0407 22:41:09.142943 32630 blocking_queue.cpp:49] Waiting for data
I0407 22:41:12.457713 32630 solver.cpp:218] Iteration 2064 (2.45137 iter/s, 4.89523s/12 iters), loss = 2.99322
I0407 22:41:12.457844 32630 solver.cpp:237]     Train net output #0: loss = 2.99322 (* 1 = 2.99322 loss)
I0407 22:41:12.457855 32630 sgd_solver.cpp:105] Iteration 2064, lr = 0.009515
I0407 22:41:17.432986 32630 solver.cpp:218] Iteration 2076 (2.412 iter/s, 4.97512s/12 iters), loss = 2.94359
I0407 22:41:17.433030 32630 solver.cpp:237]     Train net output #0: loss = 2.94359 (* 1 = 2.94359 loss)
I0407 22:41:17.433038 32630 sgd_solver.cpp:105] Iteration 2076, lr = 0.00950954
I0407 22:41:22.389730 32630 solver.cpp:218] Iteration 2088 (2.42098 iter/s, 4.95668s/12 iters), loss = 2.84899
I0407 22:41:22.389765 32630 solver.cpp:237]     Train net output #0: loss = 2.84899 (* 1 = 2.84899 loss)
I0407 22:41:22.389772 32630 sgd_solver.cpp:105] Iteration 2088, lr = 0.00950402
I0407 22:41:27.291538 32630 solver.cpp:218] Iteration 2100 (2.44811 iter/s, 4.90175s/12 iters), loss = 2.84521
I0407 22:41:27.291575 32630 solver.cpp:237]     Train net output #0: loss = 2.84521 (* 1 = 2.84521 loss)
I0407 22:41:27.291584 32630 sgd_solver.cpp:105] Iteration 2100, lr = 0.00949845
I0407 22:41:32.282135 32630 solver.cpp:218] Iteration 2112 (2.40455 iter/s, 4.99053s/12 iters), loss = 3.09904
I0407 22:41:32.282176 32630 solver.cpp:237]     Train net output #0: loss = 3.09904 (* 1 = 3.09904 loss)
I0407 22:41:32.282184 32630 sgd_solver.cpp:105] Iteration 2112, lr = 0.00949281
I0407 22:41:36.926910 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:41:37.249805 32630 solver.cpp:218] Iteration 2124 (2.41565 iter/s, 4.96761s/12 iters), loss = 3.11618
I0407 22:41:37.249837 32630 solver.cpp:237]     Train net output #0: loss = 3.11618 (* 1 = 3.11618 loss)
I0407 22:41:37.249845 32630 sgd_solver.cpp:105] Iteration 2124, lr = 0.00948712
I0407 22:41:42.187449 32630 solver.cpp:218] Iteration 2136 (2.43034 iter/s, 4.93759s/12 iters), loss = 2.62174
I0407 22:41:42.187486 32630 solver.cpp:237]     Train net output #0: loss = 2.62174 (* 1 = 2.62174 loss)
I0407 22:41:42.187494 32630 sgd_solver.cpp:105] Iteration 2136, lr = 0.00948136
I0407 22:41:44.189776 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2142.caffemodel
I0407 22:41:47.316608 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2142.solverstate
I0407 22:41:51.034309 32630 solver.cpp:330] Iteration 2142, Testing net (#0)
I0407 22:41:51.034327 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:41:54.853327 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:41:55.817178 32630 solver.cpp:397]     Test net output #0: accuracy = 0.237745
I0407 22:41:55.817224 32630 solver.cpp:397]     Test net output #1: loss = 3.29893 (* 1 = 3.29893 loss)
I0407 22:41:57.613212 32630 solver.cpp:218] Iteration 2148 (0.777924 iter/s, 15.4257s/12 iters), loss = 2.75054
I0407 22:41:57.613248 32630 solver.cpp:237]     Train net output #0: loss = 2.75054 (* 1 = 2.75054 loss)
I0407 22:41:57.613255 32630 sgd_solver.cpp:105] Iteration 2148, lr = 0.00947555
I0407 22:42:02.510550 32630 solver.cpp:218] Iteration 2160 (2.45034 iter/s, 4.89728s/12 iters), loss = 2.98076
I0407 22:42:02.510591 32630 solver.cpp:237]     Train net output #0: loss = 2.98076 (* 1 = 2.98076 loss)
I0407 22:42:02.510599 32630 sgd_solver.cpp:105] Iteration 2160, lr = 0.00946967
I0407 22:42:07.465492 32630 solver.cpp:218] Iteration 2172 (2.42186 iter/s, 4.95488s/12 iters), loss = 2.4108
I0407 22:42:07.465531 32630 solver.cpp:237]     Train net output #0: loss = 2.4108 (* 1 = 2.4108 loss)
I0407 22:42:07.465540 32630 sgd_solver.cpp:105] Iteration 2172, lr = 0.00946373
I0407 22:42:12.378535 32630 solver.cpp:218] Iteration 2184 (2.44251 iter/s, 4.91297s/12 iters), loss = 2.53181
I0407 22:42:12.378579 32630 solver.cpp:237]     Train net output #0: loss = 2.53181 (* 1 = 2.53181 loss)
I0407 22:42:12.378587 32630 sgd_solver.cpp:105] Iteration 2184, lr = 0.00945773
I0407 22:42:17.287701 32630 solver.cpp:218] Iteration 2196 (2.44445 iter/s, 4.90909s/12 iters), loss = 2.80544
I0407 22:42:17.287873 32630 solver.cpp:237]     Train net output #0: loss = 2.80544 (* 1 = 2.80544 loss)
I0407 22:42:17.287883 32630 sgd_solver.cpp:105] Iteration 2196, lr = 0.00945166
I0407 22:42:22.220067 32630 solver.cpp:218] Iteration 2208 (2.43301 iter/s, 4.93217s/12 iters), loss = 2.70512
I0407 22:42:22.220108 32630 solver.cpp:237]     Train net output #0: loss = 2.70512 (* 1 = 2.70512 loss)
I0407 22:42:22.220118 32630 sgd_solver.cpp:105] Iteration 2208, lr = 0.00944554
I0407 22:42:27.190296 32630 solver.cpp:218] Iteration 2220 (2.41441 iter/s, 4.97017s/12 iters), loss = 2.39649
I0407 22:42:27.190335 32630 solver.cpp:237]     Train net output #0: loss = 2.39649 (* 1 = 2.39649 loss)
I0407 22:42:27.190343 32630 sgd_solver.cpp:105] Iteration 2220, lr = 0.00943934
I0407 22:42:28.961112 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:42:32.087404 32630 solver.cpp:218] Iteration 2232 (2.45046 iter/s, 4.89704s/12 iters), loss = 2.36818
I0407 22:42:32.087441 32630 solver.cpp:237]     Train net output #0: loss = 2.36818 (* 1 = 2.36818 loss)
I0407 22:42:32.087450 32630 sgd_solver.cpp:105] Iteration 2232, lr = 0.00943308
I0407 22:42:36.584026 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2244.caffemodel
I0407 22:42:39.668439 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2244.solverstate
I0407 22:42:44.357836 32630 solver.cpp:330] Iteration 2244, Testing net (#0)
I0407 22:42:44.357863 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:42:48.174384 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:42:49.197623 32630 solver.cpp:397]     Test net output #0: accuracy = 0.221201
I0407 22:42:49.197669 32630 solver.cpp:397]     Test net output #1: loss = 3.41852 (* 1 = 3.41852 loss)
I0407 22:42:49.294950 32630 solver.cpp:218] Iteration 2244 (0.697373 iter/s, 17.2074s/12 iters), loss = 2.79711
I0407 22:42:49.295015 32630 solver.cpp:237]     Train net output #0: loss = 2.79711 (* 1 = 2.79711 loss)
I0407 22:42:49.295027 32630 sgd_solver.cpp:105] Iteration 2244, lr = 0.00942676
I0407 22:42:53.383842 32630 solver.cpp:218] Iteration 2256 (2.93484 iter/s, 4.08881s/12 iters), loss = 2.70147
I0407 22:42:53.383885 32630 solver.cpp:237]     Train net output #0: loss = 2.70147 (* 1 = 2.70147 loss)
I0407 22:42:53.383893 32630 sgd_solver.cpp:105] Iteration 2256, lr = 0.00942037
I0407 22:42:58.312240 32630 solver.cpp:218] Iteration 2268 (2.4349 iter/s, 4.92833s/12 iters), loss = 2.55845
I0407 22:42:58.312285 32630 solver.cpp:237]     Train net output #0: loss = 2.55845 (* 1 = 2.55845 loss)
I0407 22:42:58.312294 32630 sgd_solver.cpp:105] Iteration 2268, lr = 0.00941391
I0407 22:43:03.246194 32630 solver.cpp:218] Iteration 2280 (2.43216 iter/s, 4.93388s/12 iters), loss = 2.62222
I0407 22:43:03.246237 32630 solver.cpp:237]     Train net output #0: loss = 2.62222 (* 1 = 2.62222 loss)
I0407 22:43:03.246245 32630 sgd_solver.cpp:105] Iteration 2280, lr = 0.00940739
I0407 22:43:08.200165 32630 solver.cpp:218] Iteration 2292 (2.42234 iter/s, 4.9539s/12 iters), loss = 2.56837
I0407 22:43:08.200206 32630 solver.cpp:237]     Train net output #0: loss = 2.56837 (* 1 = 2.56837 loss)
I0407 22:43:08.200215 32630 sgd_solver.cpp:105] Iteration 2292, lr = 0.00940079
I0407 22:43:13.099092 32630 solver.cpp:218] Iteration 2304 (2.44955 iter/s, 4.89886s/12 iters), loss = 2.60211
I0407 22:43:13.099129 32630 solver.cpp:237]     Train net output #0: loss = 2.60211 (* 1 = 2.60211 loss)
I0407 22:43:13.099136 32630 sgd_solver.cpp:105] Iteration 2304, lr = 0.00939413
I0407 22:43:18.061039 32630 solver.cpp:218] Iteration 2316 (2.41843 iter/s, 4.96189s/12 iters), loss = 2.47484
I0407 22:43:18.061079 32630 solver.cpp:237]     Train net output #0: loss = 2.47484 (* 1 = 2.47484 loss)
I0407 22:43:18.061087 32630 sgd_solver.cpp:105] Iteration 2316, lr = 0.0093874
I0407 22:43:21.927819 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:43:22.952311 32630 solver.cpp:218] Iteration 2328 (2.45339 iter/s, 4.8912s/12 iters), loss = 2.97736
I0407 22:43:22.952353 32630 solver.cpp:237]     Train net output #0: loss = 2.97736 (* 1 = 2.97736 loss)
I0407 22:43:22.952361 32630 sgd_solver.cpp:105] Iteration 2328, lr = 0.0093806
I0407 22:43:27.915187 32630 solver.cpp:218] Iteration 2340 (2.41799 iter/s, 4.9628s/12 iters), loss = 2.78303
I0407 22:43:27.915241 32630 solver.cpp:237]     Train net output #0: loss = 2.78303 (* 1 = 2.78303 loss)
I0407 22:43:27.915249 32630 sgd_solver.cpp:105] Iteration 2340, lr = 0.00937373
I0407 22:43:29.859577 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2346.caffemodel
I0407 22:43:32.962327 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2346.solverstate
I0407 22:43:35.336520 32630 solver.cpp:330] Iteration 2346, Testing net (#0)
I0407 22:43:35.336537 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:43:38.836552 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:43:39.800710 32630 solver.cpp:397]     Test net output #0: accuracy = 0.246324
I0407 22:43:39.800745 32630 solver.cpp:397]     Test net output #1: loss = 3.24294 (* 1 = 3.24294 loss)
I0407 22:43:41.648492 32630 solver.cpp:218] Iteration 2352 (0.873794 iter/s, 13.7332s/12 iters), loss = 2.25159
I0407 22:43:41.648540 32630 solver.cpp:237]     Train net output #0: loss = 2.25159 (* 1 = 2.25159 loss)
I0407 22:43:41.648547 32630 sgd_solver.cpp:105] Iteration 2352, lr = 0.00936679
I0407 22:43:46.588266 32630 solver.cpp:218] Iteration 2364 (2.4293 iter/s, 4.9397s/12 iters), loss = 2.64679
I0407 22:43:46.588307 32630 solver.cpp:237]     Train net output #0: loss = 2.64679 (* 1 = 2.64679 loss)
I0407 22:43:46.588315 32630 sgd_solver.cpp:105] Iteration 2364, lr = 0.00935977
I0407 22:43:51.525230 32630 solver.cpp:218] Iteration 2376 (2.43068 iter/s, 4.9369s/12 iters), loss = 2.38605
I0407 22:43:51.525274 32630 solver.cpp:237]     Train net output #0: loss = 2.38605 (* 1 = 2.38605 loss)
I0407 22:43:51.525282 32630 sgd_solver.cpp:105] Iteration 2376, lr = 0.00935269
I0407 22:43:56.478209 32630 solver.cpp:218] Iteration 2388 (2.42282 iter/s, 4.9529s/12 iters), loss = 2.166
I0407 22:43:56.478312 32630 solver.cpp:237]     Train net output #0: loss = 2.166 (* 1 = 2.166 loss)
I0407 22:43:56.478322 32630 sgd_solver.cpp:105] Iteration 2388, lr = 0.00934553
I0407 22:44:01.386307 32630 solver.cpp:218] Iteration 2400 (2.445 iter/s, 4.90798s/12 iters), loss = 2.47351
I0407 22:44:01.386340 32630 solver.cpp:237]     Train net output #0: loss = 2.47351 (* 1 = 2.47351 loss)
I0407 22:44:01.386348 32630 sgd_solver.cpp:105] Iteration 2400, lr = 0.0093383
I0407 22:44:06.337186 32630 solver.cpp:218] Iteration 2412 (2.42384 iter/s, 4.95082s/12 iters), loss = 2.49034
I0407 22:44:06.337229 32630 solver.cpp:237]     Train net output #0: loss = 2.49034 (* 1 = 2.49034 loss)
I0407 22:44:06.337237 32630 sgd_solver.cpp:105] Iteration 2412, lr = 0.00933099
I0407 22:44:11.263628 32630 solver.cpp:218] Iteration 2424 (2.43587 iter/s, 4.92637s/12 iters), loss = 2.45254
I0407 22:44:11.263665 32630 solver.cpp:237]     Train net output #0: loss = 2.45254 (* 1 = 2.45254 loss)
I0407 22:44:11.263674 32630 sgd_solver.cpp:105] Iteration 2424, lr = 0.00932361
I0407 22:44:12.304093 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:44:16.183334 32630 solver.cpp:218] Iteration 2436 (2.4392 iter/s, 4.91964s/12 iters), loss = 2.4583
I0407 22:44:16.183378 32630 solver.cpp:237]     Train net output #0: loss = 2.4583 (* 1 = 2.4583 loss)
I0407 22:44:16.183387 32630 sgd_solver.cpp:105] Iteration 2436, lr = 0.00931615
I0407 22:44:20.684319 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2448.caffemodel
I0407 22:44:23.780629 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2448.solverstate
I0407 22:44:26.142082 32630 solver.cpp:330] Iteration 2448, Testing net (#0)
I0407 22:44:26.142098 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:44:29.904667 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:44:30.997407 32630 solver.cpp:397]     Test net output #0: accuracy = 0.259191
I0407 22:44:30.997453 32630 solver.cpp:397]     Test net output #1: loss = 3.17438 (* 1 = 3.17438 loss)
I0407 22:44:31.093835 32630 solver.cpp:218] Iteration 2448 (0.804807 iter/s, 14.9104s/12 iters), loss = 2.22352
I0407 22:44:31.093880 32630 solver.cpp:237]     Train net output #0: loss = 2.22352 (* 1 = 2.22352 loss)
I0407 22:44:31.093888 32630 sgd_solver.cpp:105] Iteration 2448, lr = 0.00930862
I0407 22:44:35.149550 32630 solver.cpp:218] Iteration 2460 (2.95884 iter/s, 4.05565s/12 iters), loss = 2.4092
I0407 22:44:35.149596 32630 solver.cpp:237]     Train net output #0: loss = 2.4092 (* 1 = 2.4092 loss)
I0407 22:44:35.149605 32630 sgd_solver.cpp:105] Iteration 2460, lr = 0.00930101
I0407 22:44:40.071333 32630 solver.cpp:218] Iteration 2472 (2.43818 iter/s, 4.92171s/12 iters), loss = 2.49063
I0407 22:44:40.071378 32630 solver.cpp:237]     Train net output #0: loss = 2.49063 (* 1 = 2.49063 loss)
I0407 22:44:40.071386 32630 sgd_solver.cpp:105] Iteration 2472, lr = 0.00929332
I0407 22:44:45.027468 32630 solver.cpp:218] Iteration 2484 (2.42128 iter/s, 4.95606s/12 iters), loss = 2.47852
I0407 22:44:45.027526 32630 solver.cpp:237]     Train net output #0: loss = 2.47852 (* 1 = 2.47852 loss)
I0407 22:44:45.027539 32630 sgd_solver.cpp:105] Iteration 2484, lr = 0.00928555
I0407 22:44:49.994884 32630 solver.cpp:218] Iteration 2496 (2.41578 iter/s, 4.96734s/12 iters), loss = 2.29955
I0407 22:44:49.994920 32630 solver.cpp:237]     Train net output #0: loss = 2.29955 (* 1 = 2.29955 loss)
I0407 22:44:49.994927 32630 sgd_solver.cpp:105] Iteration 2496, lr = 0.00927771
I0407 22:44:54.903867 32630 solver.cpp:218] Iteration 2508 (2.44453 iter/s, 4.90892s/12 iters), loss = 2.21139
I0407 22:44:54.903906 32630 solver.cpp:237]     Train net output #0: loss = 2.21139 (* 1 = 2.21139 loss)
I0407 22:44:54.903915 32630 sgd_solver.cpp:105] Iteration 2508, lr = 0.00926979
I0407 22:44:59.856935 32630 solver.cpp:218] Iteration 2520 (2.42277 iter/s, 4.953s/12 iters), loss = 2.50407
I0407 22:44:59.856972 32630 solver.cpp:237]     Train net output #0: loss = 2.50407 (* 1 = 2.50407 loss)
I0407 22:44:59.856981 32630 sgd_solver.cpp:105] Iteration 2520, lr = 0.00926178
I0407 22:45:03.005616 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:45:04.756197 32630 solver.cpp:218] Iteration 2532 (2.44938 iter/s, 4.8992s/12 iters), loss = 2.01872
I0407 22:45:04.756239 32630 solver.cpp:237]     Train net output #0: loss = 2.01872 (* 1 = 2.01872 loss)
I0407 22:45:04.756247 32630 sgd_solver.cpp:105] Iteration 2532, lr = 0.0092537
I0407 22:45:09.712107 32630 solver.cpp:218] Iteration 2544 (2.42139 iter/s, 4.95584s/12 iters), loss = 2.3426
I0407 22:45:09.712152 32630 solver.cpp:237]     Train net output #0: loss = 2.3426 (* 1 = 2.3426 loss)
I0407 22:45:09.712160 32630 sgd_solver.cpp:105] Iteration 2544, lr = 0.00924553
I0407 22:45:11.711771 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2550.caffemodel
I0407 22:45:14.822945 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2550.solverstate
I0407 22:45:17.185724 32630 solver.cpp:330] Iteration 2550, Testing net (#0)
I0407 22:45:17.185742 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:45:20.835059 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:45:21.966197 32630 solver.cpp:397]     Test net output #0: accuracy = 0.291054
I0407 22:45:21.966229 32630 solver.cpp:397]     Test net output #1: loss = 3.11836 (* 1 = 3.11836 loss)
I0407 22:45:23.756964 32630 solver.cpp:218] Iteration 2556 (0.854411 iter/s, 14.0448s/12 iters), loss = 2.43726
I0407 22:45:23.757009 32630 solver.cpp:237]     Train net output #0: loss = 2.43726 (* 1 = 2.43726 loss)
I0407 22:45:23.757017 32630 sgd_solver.cpp:105] Iteration 2556, lr = 0.00923728
I0407 22:45:28.658139 32630 solver.cpp:218] Iteration 2568 (2.44843 iter/s, 4.90111s/12 iters), loss = 2.01815
I0407 22:45:28.658181 32630 solver.cpp:237]     Train net output #0: loss = 2.01815 (* 1 = 2.01815 loss)
I0407 22:45:28.658190 32630 sgd_solver.cpp:105] Iteration 2568, lr = 0.00922895
I0407 22:45:33.630492 32630 solver.cpp:218] Iteration 2580 (2.41338 iter/s, 4.97228s/12 iters), loss = 1.86068
I0407 22:45:33.630631 32630 solver.cpp:237]     Train net output #0: loss = 1.86068 (* 1 = 1.86068 loss)
I0407 22:45:33.630641 32630 sgd_solver.cpp:105] Iteration 2580, lr = 0.00922054
I0407 22:45:38.569696 32630 solver.cpp:218] Iteration 2592 (2.42962 iter/s, 4.93904s/12 iters), loss = 2.35292
I0407 22:45:38.569739 32630 solver.cpp:237]     Train net output #0: loss = 2.35292 (* 1 = 2.35292 loss)
I0407 22:45:38.569747 32630 sgd_solver.cpp:105] Iteration 2592, lr = 0.00921204
I0407 22:45:43.508360 32630 solver.cpp:218] Iteration 2604 (2.42984 iter/s, 4.93859s/12 iters), loss = 2.1932
I0407 22:45:43.508409 32630 solver.cpp:237]     Train net output #0: loss = 2.1932 (* 1 = 2.1932 loss)
I0407 22:45:43.508417 32630 sgd_solver.cpp:105] Iteration 2604, lr = 0.00920346
I0407 22:45:48.465348 32630 solver.cpp:218] Iteration 2616 (2.42086 iter/s, 4.95691s/12 iters), loss = 2.21695
I0407 22:45:48.465389 32630 solver.cpp:237]     Train net output #0: loss = 2.21695 (* 1 = 2.21695 loss)
I0407 22:45:48.465396 32630 sgd_solver.cpp:105] Iteration 2616, lr = 0.00919479
I0407 22:45:53.409449 32630 solver.cpp:218] Iteration 2628 (2.42717 iter/s, 4.94404s/12 iters), loss = 1.97336
I0407 22:45:53.409487 32630 solver.cpp:237]     Train net output #0: loss = 1.97336 (* 1 = 1.97336 loss)
I0407 22:45:53.409494 32630 sgd_solver.cpp:105] Iteration 2628, lr = 0.00918604
I0407 22:45:53.826707 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:45:58.309293 32630 solver.cpp:218] Iteration 2640 (2.44909 iter/s, 4.89978s/12 iters), loss = 2.11128
I0407 22:45:58.309332 32630 solver.cpp:237]     Train net output #0: loss = 2.11128 (* 1 = 2.11128 loss)
I0407 22:45:58.309341 32630 sgd_solver.cpp:105] Iteration 2640, lr = 0.0091772
I0407 22:46:02.807010 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2652.caffemodel
I0407 22:46:05.878386 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2652.solverstate
I0407 22:46:09.143713 32630 solver.cpp:330] Iteration 2652, Testing net (#0)
I0407 22:46:09.143730 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:46:12.776010 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:46:13.951436 32630 solver.cpp:397]     Test net output #0: accuracy = 0.301471
I0407 22:46:13.951483 32630 solver.cpp:397]     Test net output #1: loss = 3.0622 (* 1 = 3.0622 loss)
I0407 22:46:14.049971 32630 solver.cpp:218] Iteration 2652 (0.76236 iter/s, 15.7406s/12 iters), loss = 2.29148
I0407 22:46:14.050009 32630 solver.cpp:237]     Train net output #0: loss = 2.29148 (* 1 = 2.29148 loss)
I0407 22:46:14.050016 32630 sgd_solver.cpp:105] Iteration 2652, lr = 0.00916827
I0407 22:46:18.143697 32630 solver.cpp:218] Iteration 2664 (2.93136 iter/s, 4.09367s/12 iters), loss = 2.02614
I0407 22:46:18.143731 32630 solver.cpp:237]     Train net output #0: loss = 2.02614 (* 1 = 2.02614 loss)
I0407 22:46:18.143738 32630 sgd_solver.cpp:105] Iteration 2664, lr = 0.00915926
I0407 22:46:23.062305 32630 solver.cpp:218] Iteration 2676 (2.43975 iter/s, 4.91855s/12 iters), loss = 1.80384
I0407 22:46:23.062351 32630 solver.cpp:237]     Train net output #0: loss = 1.80384 (* 1 = 1.80384 loss)
I0407 22:46:23.062359 32630 sgd_solver.cpp:105] Iteration 2676, lr = 0.00915015
I0407 22:46:28.007035 32630 solver.cpp:218] Iteration 2688 (2.42686 iter/s, 4.94466s/12 iters), loss = 1.89776
I0407 22:46:28.007072 32630 solver.cpp:237]     Train net output #0: loss = 1.89776 (* 1 = 1.89776 loss)
I0407 22:46:28.007079 32630 sgd_solver.cpp:105] Iteration 2688, lr = 0.00914096
I0407 22:46:32.952062 32630 solver.cpp:218] Iteration 2700 (2.42671 iter/s, 4.94496s/12 iters), loss = 2.06142
I0407 22:46:32.952106 32630 solver.cpp:237]     Train net output #0: loss = 2.06142 (* 1 = 2.06142 loss)
I0407 22:46:32.952114 32630 sgd_solver.cpp:105] Iteration 2700, lr = 0.00913168
I0407 22:46:37.873955 32630 solver.cpp:218] Iteration 2712 (2.43812 iter/s, 4.92182s/12 iters), loss = 2.22882
I0407 22:46:37.874402 32630 solver.cpp:237]     Train net output #0: loss = 2.22882 (* 1 = 2.22882 loss)
I0407 22:46:37.874415 32630 sgd_solver.cpp:105] Iteration 2712, lr = 0.0091223
I0407 22:46:42.830372 32630 solver.cpp:218] Iteration 2724 (2.42133 iter/s, 4.95595s/12 iters), loss = 1.95962
I0407 22:46:42.830413 32630 solver.cpp:237]     Train net output #0: loss = 1.95962 (* 1 = 1.95962 loss)
I0407 22:46:42.830426 32630 sgd_solver.cpp:105] Iteration 2724, lr = 0.00911284
I0407 22:46:45.352396 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:46:47.734264 32630 solver.cpp:218] Iteration 2736 (2.44707 iter/s, 4.90383s/12 iters), loss = 1.87029
I0407 22:46:47.734302 32630 solver.cpp:237]     Train net output #0: loss = 1.87029 (* 1 = 1.87029 loss)
I0407 22:46:47.734309 32630 sgd_solver.cpp:105] Iteration 2736, lr = 0.00910328
I0407 22:46:52.672071 32630 solver.cpp:218] Iteration 2748 (2.43026 iter/s, 4.93775s/12 iters), loss = 1.99638
I0407 22:46:52.672108 32630 solver.cpp:237]     Train net output #0: loss = 1.99638 (* 1 = 1.99638 loss)
I0407 22:46:52.672116 32630 sgd_solver.cpp:105] Iteration 2748, lr = 0.00909363
I0407 22:46:54.691484 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2754.caffemodel
I0407 22:46:57.832520 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2754.solverstate
I0407 22:47:00.352543 32630 solver.cpp:330] Iteration 2754, Testing net (#0)
I0407 22:47:00.352561 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:47:03.463786 32630 blocking_queue.cpp:49] Waiting for data
I0407 22:47:03.711181 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:47:04.845276 32630 solver.cpp:397]     Test net output #0: accuracy = 0.300245
I0407 22:47:04.845304 32630 solver.cpp:397]     Test net output #1: loss = 3.11314 (* 1 = 3.11314 loss)
I0407 22:47:06.646411 32630 solver.cpp:218] Iteration 2760 (0.858722 iter/s, 13.9743s/12 iters), loss = 2.05709
I0407 22:47:06.646450 32630 solver.cpp:237]     Train net output #0: loss = 2.05709 (* 1 = 2.05709 loss)
I0407 22:47:06.646457 32630 sgd_solver.cpp:105] Iteration 2760, lr = 0.00908389
I0407 22:47:11.590440 32630 solver.cpp:218] Iteration 2772 (2.4272 iter/s, 4.94397s/12 iters), loss = 2.29343
I0407 22:47:11.590556 32630 solver.cpp:237]     Train net output #0: loss = 2.29343 (* 1 = 2.29343 loss)
I0407 22:47:11.590565 32630 sgd_solver.cpp:105] Iteration 2772, lr = 0.00907405
I0407 22:47:16.543840 32630 solver.cpp:218] Iteration 2784 (2.42265 iter/s, 4.95326s/12 iters), loss = 2.20299
I0407 22:47:16.543876 32630 solver.cpp:237]     Train net output #0: loss = 2.20299 (* 1 = 2.20299 loss)
I0407 22:47:16.543884 32630 sgd_solver.cpp:105] Iteration 2784, lr = 0.00906412
I0407 22:47:21.502215 32630 solver.cpp:218] Iteration 2796 (2.42018 iter/s, 4.95832s/12 iters), loss = 2.01832
I0407 22:47:21.502250 32630 solver.cpp:237]     Train net output #0: loss = 2.01832 (* 1 = 2.01832 loss)
I0407 22:47:21.502257 32630 sgd_solver.cpp:105] Iteration 2796, lr = 0.00905409
I0407 22:47:26.400142 32630 solver.cpp:218] Iteration 2808 (2.45005 iter/s, 4.89786s/12 iters), loss = 2.06727
I0407 22:47:26.400179 32630 solver.cpp:237]     Train net output #0: loss = 2.06727 (* 1 = 2.06727 loss)
I0407 22:47:26.400187 32630 sgd_solver.cpp:105] Iteration 2808, lr = 0.00904397
I0407 22:47:31.404006 32630 solver.cpp:218] Iteration 2820 (2.39818 iter/s, 5.0038s/12 iters), loss = 2.11019
I0407 22:47:31.404049 32630 solver.cpp:237]     Train net output #0: loss = 2.11019 (* 1 = 2.11019 loss)
I0407 22:47:31.404057 32630 sgd_solver.cpp:105] Iteration 2820, lr = 0.00903374
I0407 22:47:36.057941 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:47:36.349608 32630 solver.cpp:218] Iteration 2832 (2.42643 iter/s, 4.94553s/12 iters), loss = 1.70498
I0407 22:47:36.349648 32630 solver.cpp:237]     Train net output #0: loss = 1.70498 (* 1 = 1.70498 loss)
I0407 22:47:36.349656 32630 sgd_solver.cpp:105] Iteration 2832, lr = 0.00902343
I0407 22:47:41.270246 32630 solver.cpp:218] Iteration 2844 (2.43874 iter/s, 4.92057s/12 iters), loss = 1.96084
I0407 22:47:41.270287 32630 solver.cpp:237]     Train net output #0: loss = 1.96084 (* 1 = 1.96084 loss)
I0407 22:47:41.270294 32630 sgd_solver.cpp:105] Iteration 2844, lr = 0.00901301
I0407 22:47:45.784463 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2856.caffemodel
I0407 22:47:48.857923 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2856.solverstate
I0407 22:47:53.287953 32630 solver.cpp:330] Iteration 2856, Testing net (#0)
I0407 22:47:53.287973 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:47:56.615052 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:47:57.784018 32630 solver.cpp:397]     Test net output #0: accuracy = 0.306373
I0407 22:47:57.784049 32630 solver.cpp:397]     Test net output #1: loss = 3.03018 (* 1 = 3.03018 loss)
I0407 22:47:57.880479 32630 solver.cpp:218] Iteration 2856 (0.72245 iter/s, 16.6101s/12 iters), loss = 2.1366
I0407 22:47:57.880523 32630 solver.cpp:237]     Train net output #0: loss = 2.1366 (* 1 = 2.1366 loss)
I0407 22:47:57.880532 32630 sgd_solver.cpp:105] Iteration 2856, lr = 0.00900249
I0407 22:48:02.002707 32630 solver.cpp:218] Iteration 2868 (2.9111 iter/s, 4.12216s/12 iters), loss = 1.85432
I0407 22:48:02.002751 32630 solver.cpp:237]     Train net output #0: loss = 1.85432 (* 1 = 1.85432 loss)
I0407 22:48:02.002759 32630 sgd_solver.cpp:105] Iteration 2868, lr = 0.00899188
I0407 22:48:06.971217 32630 solver.cpp:218] Iteration 2880 (2.41525 iter/s, 4.96843s/12 iters), loss = 1.85195
I0407 22:48:06.971259 32630 solver.cpp:237]     Train net output #0: loss = 1.85195 (* 1 = 1.85195 loss)
I0407 22:48:06.971268 32630 sgd_solver.cpp:105] Iteration 2880, lr = 0.00898117
I0407 22:48:11.885145 32630 solver.cpp:218] Iteration 2892 (2.44207 iter/s, 4.91386s/12 iters), loss = 2.11268
I0407 22:48:11.885190 32630 solver.cpp:237]     Train net output #0: loss = 2.11268 (* 1 = 2.11268 loss)
I0407 22:48:11.885197 32630 sgd_solver.cpp:105] Iteration 2892, lr = 0.00897035
I0407 22:48:16.845564 32630 solver.cpp:218] Iteration 2904 (2.41918 iter/s, 4.96035s/12 iters), loss = 1.81511
I0407 22:48:16.845690 32630 solver.cpp:237]     Train net output #0: loss = 1.81511 (* 1 = 1.81511 loss)
I0407 22:48:16.845698 32630 sgd_solver.cpp:105] Iteration 2904, lr = 0.00895943
I0407 22:48:21.773164 32630 solver.cpp:218] Iteration 2916 (2.43534 iter/s, 4.92745s/12 iters), loss = 1.94715
I0407 22:48:21.773203 32630 solver.cpp:237]     Train net output #0: loss = 1.94715 (* 1 = 1.94715 loss)
I0407 22:48:21.773211 32630 sgd_solver.cpp:105] Iteration 2916, lr = 0.00894841
I0407 22:48:26.838032 32630 solver.cpp:218] Iteration 2928 (2.36929 iter/s, 5.0648s/12 iters), loss = 1.75508
I0407 22:48:26.838078 32630 solver.cpp:237]     Train net output #0: loss = 1.75508 (* 1 = 1.75508 loss)
I0407 22:48:26.838085 32630 sgd_solver.cpp:105] Iteration 2928, lr = 0.00893729
I0407 22:48:28.630952 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:48:31.727720 32630 solver.cpp:218] Iteration 2940 (2.45418 iter/s, 4.88962s/12 iters), loss = 1.64563
I0407 22:48:31.727759 32630 solver.cpp:237]     Train net output #0: loss = 1.64563 (* 1 = 1.64563 loss)
I0407 22:48:31.727767 32630 sgd_solver.cpp:105] Iteration 2940, lr = 0.00892607
I0407 22:48:36.709837 32630 solver.cpp:218] Iteration 2952 (2.40865 iter/s, 4.98205s/12 iters), loss = 2.04485
I0407 22:48:36.709882 32630 solver.cpp:237]     Train net output #0: loss = 2.04485 (* 1 = 2.04485 loss)
I0407 22:48:36.709890 32630 sgd_solver.cpp:105] Iteration 2952, lr = 0.00891474
I0407 22:48:38.705655 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_2958.caffemodel
I0407 22:48:42.878336 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_2958.solverstate
I0407 22:48:45.268970 32630 solver.cpp:330] Iteration 2958, Testing net (#0)
I0407 22:48:45.268988 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:48:48.652045 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:48:49.864370 32630 solver.cpp:397]     Test net output #0: accuracy = 0.301471
I0407 22:48:49.864418 32630 solver.cpp:397]     Test net output #1: loss = 3.09974 (* 1 = 3.09974 loss)
I0407 22:48:51.659907 32630 solver.cpp:218] Iteration 2964 (0.802676 iter/s, 14.95s/12 iters), loss = 1.75246
I0407 22:48:51.659943 32630 solver.cpp:237]     Train net output #0: loss = 1.75246 (* 1 = 1.75246 loss)
I0407 22:48:51.659951 32630 sgd_solver.cpp:105] Iteration 2964, lr = 0.0089033
I0407 22:48:56.738945 32630 solver.cpp:218] Iteration 2976 (2.36268 iter/s, 5.07897s/12 iters), loss = 1.87103
I0407 22:48:56.738986 32630 solver.cpp:237]     Train net output #0: loss = 1.87103 (* 1 = 1.87103 loss)
I0407 22:48:56.738994 32630 sgd_solver.cpp:105] Iteration 2976, lr = 0.00889176
I0407 22:49:01.705376 32630 solver.cpp:218] Iteration 2988 (2.41625 iter/s, 4.96637s/12 iters), loss = 2.03851
I0407 22:49:01.705418 32630 solver.cpp:237]     Train net output #0: loss = 2.03851 (* 1 = 2.03851 loss)
I0407 22:49:01.705426 32630 sgd_solver.cpp:105] Iteration 2988, lr = 0.00888011
I0407 22:49:06.792191 32630 solver.cpp:218] Iteration 3000 (2.35907 iter/s, 5.08675s/12 iters), loss = 1.8769
I0407 22:49:06.792227 32630 solver.cpp:237]     Train net output #0: loss = 1.8769 (* 1 = 1.8769 loss)
I0407 22:49:06.792235 32630 sgd_solver.cpp:105] Iteration 3000, lr = 0.00886836
I0407 22:49:11.793871 32630 solver.cpp:218] Iteration 3012 (2.39922 iter/s, 5.00162s/12 iters), loss = 1.76595
I0407 22:49:11.793911 32630 solver.cpp:237]     Train net output #0: loss = 1.76595 (* 1 = 1.76595 loss)
I0407 22:49:11.793920 32630 sgd_solver.cpp:105] Iteration 3012, lr = 0.0088565
I0407 22:49:16.779709 32630 solver.cpp:218] Iteration 3024 (2.40685 iter/s, 4.98577s/12 iters), loss = 2.00779
I0407 22:49:16.779759 32630 solver.cpp:237]     Train net output #0: loss = 2.00779 (* 1 = 2.00779 loss)
I0407 22:49:16.779770 32630 sgd_solver.cpp:105] Iteration 3024, lr = 0.00884453
I0407 22:49:20.696244 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:49:21.719072 32630 solver.cpp:218] Iteration 3036 (2.4295 iter/s, 4.93929s/12 iters), loss = 1.71166
I0407 22:49:21.719110 32630 solver.cpp:237]     Train net output #0: loss = 1.71166 (* 1 = 1.71166 loss)
I0407 22:49:21.719117 32630 sgd_solver.cpp:105] Iteration 3036, lr = 0.00883245
I0407 22:49:26.683254 32630 solver.cpp:218] Iteration 3048 (2.41735 iter/s, 4.96412s/12 iters), loss = 1.52375
I0407 22:49:26.683290 32630 solver.cpp:237]     Train net output #0: loss = 1.52375 (* 1 = 1.52375 loss)
I0407 22:49:26.683297 32630 sgd_solver.cpp:105] Iteration 3048, lr = 0.00882027
I0407 22:49:31.233001 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3060.caffemodel
I0407 22:49:37.465943 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3060.solverstate
I0407 22:49:40.834702 32630 solver.cpp:330] Iteration 3060, Testing net (#0)
I0407 22:49:40.834719 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:49:44.270676 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:49:45.586910 32630 solver.cpp:397]     Test net output #0: accuracy = 0.31924
I0407 22:49:45.586952 32630 solver.cpp:397]     Test net output #1: loss = 3.10223 (* 1 = 3.10223 loss)
I0407 22:49:45.683310 32630 solver.cpp:218] Iteration 3060 (0.63158 iter/s, 19s/12 iters), loss = 1.19274
I0407 22:49:45.683357 32630 solver.cpp:237]     Train net output #0: loss = 1.19274 (* 1 = 1.19274 loss)
I0407 22:49:45.683365 32630 sgd_solver.cpp:105] Iteration 3060, lr = 0.00880797
I0407 22:49:49.774181 32630 solver.cpp:218] Iteration 3072 (2.93341 iter/s, 4.0908s/12 iters), loss = 1.87007
I0407 22:49:49.774224 32630 solver.cpp:237]     Train net output #0: loss = 1.87007 (* 1 = 1.87007 loss)
I0407 22:49:49.774232 32630 sgd_solver.cpp:105] Iteration 3072, lr = 0.00879556
I0407 22:49:54.736603 32630 solver.cpp:218] Iteration 3084 (2.41821 iter/s, 4.96235s/12 iters), loss = 1.84587
I0407 22:49:54.736748 32630 solver.cpp:237]     Train net output #0: loss = 1.84587 (* 1 = 1.84587 loss)
I0407 22:49:54.736757 32630 sgd_solver.cpp:105] Iteration 3084, lr = 0.00878304
I0407 22:49:59.665477 32630 solver.cpp:218] Iteration 3096 (2.43472 iter/s, 4.9287s/12 iters), loss = 1.68176
I0407 22:49:59.665522 32630 solver.cpp:237]     Train net output #0: loss = 1.68176 (* 1 = 1.68176 loss)
I0407 22:49:59.665530 32630 sgd_solver.cpp:105] Iteration 3096, lr = 0.00877041
I0407 22:50:04.572294 32630 solver.cpp:218] Iteration 3108 (2.44561 iter/s, 4.90675s/12 iters), loss = 1.89923
I0407 22:50:04.572331 32630 solver.cpp:237]     Train net output #0: loss = 1.89923 (* 1 = 1.89923 loss)
I0407 22:50:04.572338 32630 sgd_solver.cpp:105] Iteration 3108, lr = 0.00875767
I0407 22:50:09.502185 32630 solver.cpp:218] Iteration 3120 (2.43416 iter/s, 4.92983s/12 iters), loss = 1.77277
I0407 22:50:09.502218 32630 solver.cpp:237]     Train net output #0: loss = 1.77277 (* 1 = 1.77277 loss)
I0407 22:50:09.502224 32630 sgd_solver.cpp:105] Iteration 3120, lr = 0.00874481
I0407 22:50:14.514711 32630 solver.cpp:218] Iteration 3132 (2.39403 iter/s, 5.01247s/12 iters), loss = 1.48847
I0407 22:50:14.514748 32630 solver.cpp:237]     Train net output #0: loss = 1.48847 (* 1 = 1.48847 loss)
I0407 22:50:14.514756 32630 sgd_solver.cpp:105] Iteration 3132, lr = 0.00873184
I0407 22:50:15.586302 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:50:19.405823 32630 solver.cpp:218] Iteration 3144 (2.45346 iter/s, 4.89105s/12 iters), loss = 1.63578
I0407 22:50:19.405858 32630 solver.cpp:237]     Train net output #0: loss = 1.63578 (* 1 = 1.63578 loss)
I0407 22:50:19.405865 32630 sgd_solver.cpp:105] Iteration 3144, lr = 0.00871876
I0407 22:50:24.377153 32630 solver.cpp:218] Iteration 3156 (2.41387 iter/s, 4.97127s/12 iters), loss = 1.7746
I0407 22:50:24.377192 32630 solver.cpp:237]     Train net output #0: loss = 1.7746 (* 1 = 1.7746 loss)
I0407 22:50:24.377198 32630 sgd_solver.cpp:105] Iteration 3156, lr = 0.00870556
I0407 22:50:26.361721 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3162.caffemodel
I0407 22:50:29.417098 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3162.solverstate
I0407 22:50:32.019665 32630 solver.cpp:330] Iteration 3162, Testing net (#0)
I0407 22:50:32.019685 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:50:35.407573 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:50:36.798807 32630 solver.cpp:397]     Test net output #0: accuracy = 0.3125
I0407 22:50:36.798854 32630 solver.cpp:397]     Test net output #1: loss = 2.95207 (* 1 = 2.95207 loss)
I0407 22:50:38.596510 32630 solver.cpp:218] Iteration 3168 (0.843925 iter/s, 14.2193s/12 iters), loss = 1.63294
I0407 22:50:38.596558 32630 solver.cpp:237]     Train net output #0: loss = 1.63294 (* 1 = 1.63294 loss)
I0407 22:50:38.596566 32630 sgd_solver.cpp:105] Iteration 3168, lr = 0.00869224
I0407 22:50:43.552016 32630 solver.cpp:218] Iteration 3180 (2.42158 iter/s, 4.95544s/12 iters), loss = 1.63398
I0407 22:50:43.552058 32630 solver.cpp:237]     Train net output #0: loss = 1.63398 (* 1 = 1.63398 loss)
I0407 22:50:43.552067 32630 sgd_solver.cpp:105] Iteration 3180, lr = 0.00867881
I0407 22:50:48.491398 32630 solver.cpp:218] Iteration 3192 (2.42949 iter/s, 4.93931s/12 iters), loss = 1.68775
I0407 22:50:48.491444 32630 solver.cpp:237]     Train net output #0: loss = 1.68775 (* 1 = 1.68775 loss)
I0407 22:50:48.491452 32630 sgd_solver.cpp:105] Iteration 3192, lr = 0.00866526
I0407 22:50:53.455505 32630 solver.cpp:218] Iteration 3204 (2.41739 iter/s, 4.96404s/12 iters), loss = 1.69725
I0407 22:50:53.455541 32630 solver.cpp:237]     Train net output #0: loss = 1.69725 (* 1 = 1.69725 loss)
I0407 22:50:53.455549 32630 sgd_solver.cpp:105] Iteration 3204, lr = 0.0086516
I0407 22:50:58.360855 32630 solver.cpp:218] Iteration 3216 (2.44634 iter/s, 4.90529s/12 iters), loss = 1.3881
I0407 22:50:58.361011 32630 solver.cpp:237]     Train net output #0: loss = 1.3881 (* 1 = 1.3881 loss)
I0407 22:50:58.361019 32630 sgd_solver.cpp:105] Iteration 3216, lr = 0.00863781
I0407 22:51:03.322393 32630 solver.cpp:218] Iteration 3228 (2.41869 iter/s, 4.96136s/12 iters), loss = 1.38263
I0407 22:51:03.322428 32630 solver.cpp:237]     Train net output #0: loss = 1.38263 (* 1 = 1.38263 loss)
I0407 22:51:03.322436 32630 sgd_solver.cpp:105] Iteration 3228, lr = 0.00862391
I0407 22:51:06.515534 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:51:08.246268 32630 solver.cpp:218] Iteration 3240 (2.43714 iter/s, 4.92381s/12 iters), loss = 1.56599
I0407 22:51:08.246305 32630 solver.cpp:237]     Train net output #0: loss = 1.56599 (* 1 = 1.56599 loss)
I0407 22:51:08.246313 32630 sgd_solver.cpp:105] Iteration 3240, lr = 0.00860989
I0407 22:51:13.219252 32630 solver.cpp:218] Iteration 3252 (2.41307 iter/s, 4.97292s/12 iters), loss = 1.44458
I0407 22:51:13.219297 32630 solver.cpp:237]     Train net output #0: loss = 1.44458 (* 1 = 1.44458 loss)
I0407 22:51:13.219306 32630 sgd_solver.cpp:105] Iteration 3252, lr = 0.00859575
I0407 22:51:17.693850 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3264.caffemodel
I0407 22:51:21.864061 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3264.solverstate
I0407 22:51:24.232970 32630 solver.cpp:330] Iteration 3264, Testing net (#0)
I0407 22:51:24.232988 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:51:27.525182 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:51:28.849555 32630 solver.cpp:397]     Test net output #0: accuracy = 0.320466
I0407 22:51:28.849640 32630 solver.cpp:397]     Test net output #1: loss = 3.12643 (* 1 = 3.12643 loss)
I0407 22:51:28.946223 32630 solver.cpp:218] Iteration 3264 (0.763025 iter/s, 15.7269s/12 iters), loss = 1.37661
I0407 22:51:28.946269 32630 solver.cpp:237]     Train net output #0: loss = 1.37661 (* 1 = 1.37661 loss)
I0407 22:51:28.946276 32630 sgd_solver.cpp:105] Iteration 3264, lr = 0.00858149
I0407 22:51:33.097916 32630 solver.cpp:218] Iteration 3276 (2.89044 iter/s, 4.15162s/12 iters), loss = 1.52166
I0407 22:51:33.097961 32630 solver.cpp:237]     Train net output #0: loss = 1.52166 (* 1 = 1.52166 loss)
I0407 22:51:33.097970 32630 sgd_solver.cpp:105] Iteration 3276, lr = 0.00856711
I0407 22:51:38.046408 32630 solver.cpp:218] Iteration 3288 (2.42501 iter/s, 4.94842s/12 iters), loss = 1.54008
I0407 22:51:38.046449 32630 solver.cpp:237]     Train net output #0: loss = 1.54008 (* 1 = 1.54008 loss)
I0407 22:51:38.046458 32630 sgd_solver.cpp:105] Iteration 3288, lr = 0.00855261
I0407 22:51:42.964848 32630 solver.cpp:218] Iteration 3300 (2.43983 iter/s, 4.91837s/12 iters), loss = 1.47197
I0407 22:51:42.964884 32630 solver.cpp:237]     Train net output #0: loss = 1.47197 (* 1 = 1.47197 loss)
I0407 22:51:42.964892 32630 sgd_solver.cpp:105] Iteration 3300, lr = 0.00853798
I0407 22:51:47.883301 32630 solver.cpp:218] Iteration 3312 (2.43982 iter/s, 4.91839s/12 iters), loss = 1.7227
I0407 22:51:47.883338 32630 solver.cpp:237]     Train net output #0: loss = 1.7227 (* 1 = 1.7227 loss)
I0407 22:51:47.883347 32630 sgd_solver.cpp:105] Iteration 3312, lr = 0.00852323
I0407 22:51:52.926980 32630 solver.cpp:218] Iteration 3324 (2.37925 iter/s, 5.04361s/12 iters), loss = 1.31541
I0407 22:51:52.927034 32630 solver.cpp:237]     Train net output #0: loss = 1.31541 (* 1 = 1.31541 loss)
I0407 22:51:52.927047 32630 sgd_solver.cpp:105] Iteration 3324, lr = 0.00850836
I0407 22:51:57.885764 32630 solver.cpp:218] Iteration 3336 (2.41998 iter/s, 4.95871s/12 iters), loss = 1.61057
I0407 22:51:57.885802 32630 solver.cpp:237]     Train net output #0: loss = 1.61057 (* 1 = 1.61057 loss)
I0407 22:51:57.885808 32630 sgd_solver.cpp:105] Iteration 3336, lr = 0.00849337
I0407 22:51:58.345217 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:02.898627 32630 solver.cpp:218] Iteration 3348 (2.39387 iter/s, 5.0128s/12 iters), loss = 1.76625
I0407 22:52:02.898773 32630 solver.cpp:237]     Train net output #0: loss = 1.76625 (* 1 = 1.76625 loss)
I0407 22:52:02.898782 32630 sgd_solver.cpp:105] Iteration 3348, lr = 0.00847826
I0407 22:52:07.892351 32630 solver.cpp:218] Iteration 3360 (2.4031 iter/s, 4.99356s/12 iters), loss = 1.75907
I0407 22:52:07.892395 32630 solver.cpp:237]     Train net output #0: loss = 1.75907 (* 1 = 1.75907 loss)
I0407 22:52:07.892402 32630 sgd_solver.cpp:105] Iteration 3360, lr = 0.00846301
I0407 22:52:09.947333 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3366.caffemodel
I0407 22:52:13.090550 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3366.solverstate
I0407 22:52:15.482164 32630 solver.cpp:330] Iteration 3366, Testing net (#0)
I0407 22:52:15.482183 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:52:18.860574 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:20.270895 32630 solver.cpp:397]     Test net output #0: accuracy = 0.337623
I0407 22:52:20.270937 32630 solver.cpp:397]     Test net output #1: loss = 2.96607 (* 1 = 2.96607 loss)
I0407 22:52:22.060320 32630 solver.cpp:218] Iteration 3372 (0.846986 iter/s, 14.1679s/12 iters), loss = 1.69042
I0407 22:52:22.060359 32630 solver.cpp:237]     Train net output #0: loss = 1.69042 (* 1 = 1.69042 loss)
I0407 22:52:22.060365 32630 sgd_solver.cpp:105] Iteration 3372, lr = 0.00844765
I0407 22:52:27.016407 32630 solver.cpp:218] Iteration 3384 (2.4213 iter/s, 4.95602s/12 iters), loss = 1.72411
I0407 22:52:27.016444 32630 solver.cpp:237]     Train net output #0: loss = 1.72411 (* 1 = 1.72411 loss)
I0407 22:52:27.016451 32630 sgd_solver.cpp:105] Iteration 3384, lr = 0.00843216
I0407 22:52:32.003963 32630 solver.cpp:218] Iteration 3396 (2.40602 iter/s, 4.9875s/12 iters), loss = 1.38633
I0407 22:52:32.003996 32630 solver.cpp:237]     Train net output #0: loss = 1.38633 (* 1 = 1.38633 loss)
I0407 22:52:32.004004 32630 sgd_solver.cpp:105] Iteration 3396, lr = 0.00841654
I0407 22:52:36.925855 32630 solver.cpp:218] Iteration 3408 (2.43812 iter/s, 4.92183s/12 iters), loss = 1.53347
I0407 22:52:36.925977 32630 solver.cpp:237]     Train net output #0: loss = 1.53347 (* 1 = 1.53347 loss)
I0407 22:52:36.925987 32630 sgd_solver.cpp:105] Iteration 3408, lr = 0.0084008
I0407 22:52:41.869009 32630 solver.cpp:218] Iteration 3420 (2.42767 iter/s, 4.94302s/12 iters), loss = 1.46937
I0407 22:52:41.869042 32630 solver.cpp:237]     Train net output #0: loss = 1.46937 (* 1 = 1.46937 loss)
I0407 22:52:41.869050 32630 sgd_solver.cpp:105] Iteration 3420, lr = 0.00838493
I0407 22:52:46.795030 32630 solver.cpp:218] Iteration 3432 (2.43607 iter/s, 4.92597s/12 iters), loss = 1.21461
I0407 22:52:46.795058 32630 solver.cpp:237]     Train net output #0: loss = 1.21461 (* 1 = 1.21461 loss)
I0407 22:52:46.795065 32630 sgd_solver.cpp:105] Iteration 3432, lr = 0.00836894
I0407 22:52:49.347558 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:52:51.748000 32630 solver.cpp:218] Iteration 3444 (2.42282 iter/s, 4.95291s/12 iters), loss = 1.44261
I0407 22:52:51.748037 32630 solver.cpp:237]     Train net output #0: loss = 1.44261 (* 1 = 1.44261 loss)
I0407 22:52:51.748044 32630 sgd_solver.cpp:105] Iteration 3444, lr = 0.00835281
I0407 22:52:56.629101 32630 solver.cpp:218] Iteration 3456 (2.45849 iter/s, 4.88104s/12 iters), loss = 1.60038
I0407 22:52:56.629137 32630 solver.cpp:237]     Train net output #0: loss = 1.60038 (* 1 = 1.60038 loss)
I0407 22:52:56.629144 32630 sgd_solver.cpp:105] Iteration 3456, lr = 0.00833656
I0407 22:53:01.101480 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3468.caffemodel
I0407 22:53:04.824043 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3468.solverstate
I0407 22:53:07.223172 32630 solver.cpp:330] Iteration 3468, Testing net (#0)
I0407 22:53:07.223284 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:53:07.673624 32630 blocking_queue.cpp:49] Waiting for data
I0407 22:53:10.582602 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:53:12.116539 32630 solver.cpp:397]     Test net output #0: accuracy = 0.336397
I0407 22:53:12.116570 32630 solver.cpp:397]     Test net output #1: loss = 2.9656 (* 1 = 2.9656 loss)
I0407 22:53:12.213085 32630 solver.cpp:218] Iteration 3468 (0.770025 iter/s, 15.5839s/12 iters), loss = 1.33373
I0407 22:53:12.213125 32630 solver.cpp:237]     Train net output #0: loss = 1.33373 (* 1 = 1.33373 loss)
I0407 22:53:12.213132 32630 sgd_solver.cpp:105] Iteration 3468, lr = 0.00832018
I0407 22:53:16.256486 32630 solver.cpp:218] Iteration 3480 (2.96785 iter/s, 4.04333s/12 iters), loss = 1.80184
I0407 22:53:16.256531 32630 solver.cpp:237]     Train net output #0: loss = 1.80184 (* 1 = 1.80184 loss)
I0407 22:53:16.256538 32630 sgd_solver.cpp:105] Iteration 3480, lr = 0.00830368
I0407 22:53:21.193442 32630 solver.cpp:218] Iteration 3492 (2.43068 iter/s, 4.93689s/12 iters), loss = 1.46356
I0407 22:53:21.193480 32630 solver.cpp:237]     Train net output #0: loss = 1.46356 (* 1 = 1.46356 loss)
I0407 22:53:21.193488 32630 sgd_solver.cpp:105] Iteration 3492, lr = 0.00828704
I0407 22:53:26.153349 32630 solver.cpp:218] Iteration 3504 (2.41943 iter/s, 4.95984s/12 iters), loss = 1.5128
I0407 22:53:26.153393 32630 solver.cpp:237]     Train net output #0: loss = 1.5128 (* 1 = 1.5128 loss)
I0407 22:53:26.153401 32630 sgd_solver.cpp:105] Iteration 3504, lr = 0.00827028
I0407 22:53:31.057011 32630 solver.cpp:218] Iteration 3516 (2.44718 iter/s, 4.9036s/12 iters), loss = 1.35339
I0407 22:53:31.057046 32630 solver.cpp:237]     Train net output #0: loss = 1.35339 (* 1 = 1.35339 loss)
I0407 22:53:31.057054 32630 sgd_solver.cpp:105] Iteration 3516, lr = 0.00825338
I0407 22:53:36.005028 32630 solver.cpp:218] Iteration 3528 (2.42524 iter/s, 4.94796s/12 iters), loss = 1.38388
I0407 22:53:36.005064 32630 solver.cpp:237]     Train net output #0: loss = 1.38388 (* 1 = 1.38388 loss)
I0407 22:53:36.005071 32630 sgd_solver.cpp:105] Iteration 3528, lr = 0.00823636
I0407 22:53:40.590451 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:53:40.846956 32630 solver.cpp:218] Iteration 3540 (2.47838 iter/s, 4.84187s/12 iters), loss = 1.27423
I0407 22:53:40.846995 32630 solver.cpp:237]     Train net output #0: loss = 1.27423 (* 1 = 1.27423 loss)
I0407 22:53:40.847003 32630 sgd_solver.cpp:105] Iteration 3540, lr = 0.0082192
I0407 22:53:45.767418 32630 solver.cpp:218] Iteration 3552 (2.43883 iter/s, 4.9204s/12 iters), loss = 1.60277
I0407 22:53:45.767458 32630 solver.cpp:237]     Train net output #0: loss = 1.60277 (* 1 = 1.60277 loss)
I0407 22:53:45.767465 32630 sgd_solver.cpp:105] Iteration 3552, lr = 0.00820192
I0407 22:53:50.751489 32630 solver.cpp:218] Iteration 3564 (2.4077 iter/s, 4.984s/12 iters), loss = 1.21059
I0407 22:53:50.751533 32630 solver.cpp:237]     Train net output #0: loss = 1.21059 (* 1 = 1.21059 loss)
I0407 22:53:50.751541 32630 sgd_solver.cpp:105] Iteration 3564, lr = 0.0081845
I0407 22:53:52.752192 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3570.caffemodel
I0407 22:53:55.852995 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3570.solverstate
I0407 22:53:58.222214 32630 solver.cpp:330] Iteration 3570, Testing net (#0)
I0407 22:53:58.222232 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:54:01.249871 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:54:02.691108 32630 solver.cpp:397]     Test net output #0: accuracy = 0.367647
I0407 22:54:02.691154 32630 solver.cpp:397]     Test net output #1: loss = 2.85638 (* 1 = 2.85638 loss)
I0407 22:54:04.481917 32630 solver.cpp:218] Iteration 3576 (0.873977 iter/s, 13.7303s/12 iters), loss = 1.61064
I0407 22:54:04.481957 32630 solver.cpp:237]     Train net output #0: loss = 1.61064 (* 1 = 1.61064 loss)
I0407 22:54:04.481966 32630 sgd_solver.cpp:105] Iteration 3576, lr = 0.00816695
I0407 22:54:09.448817 32630 solver.cpp:218] Iteration 3588 (2.41602 iter/s, 4.96684s/12 iters), loss = 1.33162
I0407 22:54:09.448855 32630 solver.cpp:237]     Train net output #0: loss = 1.33162 (* 1 = 1.33162 loss)
I0407 22:54:09.448863 32630 sgd_solver.cpp:105] Iteration 3588, lr = 0.00814928
I0407 22:54:14.406585 32630 solver.cpp:218] Iteration 3600 (2.42048 iter/s, 4.9577s/12 iters), loss = 1.42644
I0407 22:54:14.406720 32630 solver.cpp:237]     Train net output #0: loss = 1.42644 (* 1 = 1.42644 loss)
I0407 22:54:14.406730 32630 sgd_solver.cpp:105] Iteration 3600, lr = 0.00813147
I0407 22:54:19.347730 32630 solver.cpp:218] Iteration 3612 (2.42866 iter/s, 4.94099s/12 iters), loss = 1.31007
I0407 22:54:19.347766 32630 solver.cpp:237]     Train net output #0: loss = 1.31007 (* 1 = 1.31007 loss)
I0407 22:54:19.347774 32630 sgd_solver.cpp:105] Iteration 3612, lr = 0.00811353
I0407 22:54:24.308944 32630 solver.cpp:218] Iteration 3624 (2.4188 iter/s, 4.96115s/12 iters), loss = 1.77015
I0407 22:54:24.308987 32630 solver.cpp:237]     Train net output #0: loss = 1.77015 (* 1 = 1.77015 loss)
I0407 22:54:24.308995 32630 sgd_solver.cpp:105] Iteration 3624, lr = 0.00809545
I0407 22:54:29.242241 32630 solver.cpp:218] Iteration 3636 (2.43248 iter/s, 4.93323s/12 iters), loss = 1.26622
I0407 22:54:29.242281 32630 solver.cpp:237]     Train net output #0: loss = 1.26622 (* 1 = 1.26622 loss)
I0407 22:54:29.242290 32630 sgd_solver.cpp:105] Iteration 3636, lr = 0.00807725
I0407 22:54:31.090328 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:54:34.177558 32630 solver.cpp:218] Iteration 3648 (2.43149 iter/s, 4.93525s/12 iters), loss = 1.38292
I0407 22:54:34.177599 32630 solver.cpp:237]     Train net output #0: loss = 1.38292 (* 1 = 1.38292 loss)
I0407 22:54:34.177608 32630 sgd_solver.cpp:105] Iteration 3648, lr = 0.00805891
I0407 22:54:39.104988 32630 solver.cpp:218] Iteration 3660 (2.43538 iter/s, 4.92736s/12 iters), loss = 1.2022
I0407 22:54:39.105033 32630 solver.cpp:237]     Train net output #0: loss = 1.2022 (* 1 = 1.2022 loss)
I0407 22:54:39.105041 32630 sgd_solver.cpp:105] Iteration 3660, lr = 0.00804044
I0407 22:54:43.587764 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3672.caffemodel
I0407 22:54:46.775077 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3672.solverstate
I0407 22:54:49.153676 32630 solver.cpp:330] Iteration 3672, Testing net (#0)
I0407 22:54:49.153693 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:54:52.311308 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:54:53.919260 32630 solver.cpp:397]     Test net output #0: accuracy = 0.371936
I0407 22:54:53.919294 32630 solver.cpp:397]     Test net output #1: loss = 2.79562 (* 1 = 2.79562 loss)
I0407 22:54:54.015836 32630 solver.cpp:218] Iteration 3672 (0.804788 iter/s, 14.9108s/12 iters), loss = 1.47715
I0407 22:54:54.015882 32630 solver.cpp:237]     Train net output #0: loss = 1.47715 (* 1 = 1.47715 loss)
I0407 22:54:54.015889 32630 sgd_solver.cpp:105] Iteration 3672, lr = 0.00802184
I0407 22:54:58.140897 32630 solver.cpp:218] Iteration 3684 (2.9091 iter/s, 4.12499s/12 iters), loss = 1.24611
I0407 22:54:58.140931 32630 solver.cpp:237]     Train net output #0: loss = 1.24611 (* 1 = 1.24611 loss)
I0407 22:54:58.140938 32630 sgd_solver.cpp:105] Iteration 3684, lr = 0.0080031
I0407 22:55:03.109270 32630 solver.cpp:218] Iteration 3696 (2.41531 iter/s, 4.96832s/12 iters), loss = 1.31248
I0407 22:55:03.109309 32630 solver.cpp:237]     Train net output #0: loss = 1.31248 (* 1 = 1.31248 loss)
I0407 22:55:03.109316 32630 sgd_solver.cpp:105] Iteration 3696, lr = 0.00798424
I0407 22:55:08.047905 32630 solver.cpp:218] Iteration 3708 (2.42985 iter/s, 4.93858s/12 iters), loss = 1.28864
I0407 22:55:08.047936 32630 solver.cpp:237]     Train net output #0: loss = 1.28864 (* 1 = 1.28864 loss)
I0407 22:55:08.047945 32630 sgd_solver.cpp:105] Iteration 3708, lr = 0.00796523
I0407 22:55:13.006157 32630 solver.cpp:218] Iteration 3720 (2.42023 iter/s, 4.9582s/12 iters), loss = 1.07115
I0407 22:55:13.006207 32630 solver.cpp:237]     Train net output #0: loss = 1.07115 (* 1 = 1.07115 loss)
I0407 22:55:13.006214 32630 sgd_solver.cpp:105] Iteration 3720, lr = 0.0079461
I0407 22:55:17.969554 32630 solver.cpp:218] Iteration 3732 (2.41774 iter/s, 4.96331s/12 iters), loss = 1.0109
I0407 22:55:17.969714 32630 solver.cpp:237]     Train net output #0: loss = 1.0109 (* 1 = 1.0109 loss)
I0407 22:55:17.969724 32630 sgd_solver.cpp:105] Iteration 3732, lr = 0.00792683
I0407 22:55:21.923161 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:22.887673 32630 solver.cpp:218] Iteration 3744 (2.44005 iter/s, 4.91794s/12 iters), loss = 1.28563
I0407 22:55:22.887715 32630 solver.cpp:237]     Train net output #0: loss = 1.28563 (* 1 = 1.28563 loss)
I0407 22:55:22.887723 32630 sgd_solver.cpp:105] Iteration 3744, lr = 0.00790743
I0407 22:55:27.859378 32630 solver.cpp:218] Iteration 3756 (2.41369 iter/s, 4.97164s/12 iters), loss = 1.25515
I0407 22:55:27.859412 32630 solver.cpp:237]     Train net output #0: loss = 1.25515 (* 1 = 1.25515 loss)
I0407 22:55:27.859419 32630 sgd_solver.cpp:105] Iteration 3756, lr = 0.0078879
I0407 22:55:32.788530 32630 solver.cpp:218] Iteration 3768 (2.43452 iter/s, 4.9291s/12 iters), loss = 0.965295
I0407 22:55:32.788570 32630 solver.cpp:237]     Train net output #0: loss = 0.965295 (* 1 = 0.965295 loss)
I0407 22:55:32.788578 32630 sgd_solver.cpp:105] Iteration 3768, lr = 0.00786823
I0407 22:55:34.789212 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3774.caffemodel
I0407 22:55:37.877588 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3774.solverstate
I0407 22:55:40.282856 32630 solver.cpp:330] Iteration 3774, Testing net (#0)
I0407 22:55:40.282873 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:55:43.211943 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:55:44.731922 32630 solver.cpp:397]     Test net output #0: accuracy = 0.358456
I0407 22:55:44.731959 32630 solver.cpp:397]     Test net output #1: loss = 2.83387 (* 1 = 2.83387 loss)
I0407 22:55:46.526682 32630 solver.cpp:218] Iteration 3780 (0.873485 iter/s, 13.7381s/12 iters), loss = 1.17134
I0407 22:55:46.526722 32630 solver.cpp:237]     Train net output #0: loss = 1.17134 (* 1 = 1.17134 loss)
I0407 22:55:46.526731 32630 sgd_solver.cpp:105] Iteration 3780, lr = 0.00784843
I0407 22:55:51.459218 32630 solver.cpp:218] Iteration 3792 (2.43286 iter/s, 4.93247s/12 iters), loss = 1.3535
I0407 22:55:51.459344 32630 solver.cpp:237]     Train net output #0: loss = 1.3535 (* 1 = 1.3535 loss)
I0407 22:55:51.459353 32630 sgd_solver.cpp:105] Iteration 3792, lr = 0.0078285
I0407 22:55:56.393240 32630 solver.cpp:218] Iteration 3804 (2.43217 iter/s, 4.93387s/12 iters), loss = 1.37407
I0407 22:55:56.393281 32630 solver.cpp:237]     Train net output #0: loss = 1.37407 (* 1 = 1.37407 loss)
I0407 22:55:56.393290 32630 sgd_solver.cpp:105] Iteration 3804, lr = 0.00780843
I0407 22:56:01.311959 32630 solver.cpp:218] Iteration 3816 (2.43969 iter/s, 4.91865s/12 iters), loss = 1.17847
I0407 22:56:01.312005 32630 solver.cpp:237]     Train net output #0: loss = 1.17847 (* 1 = 1.17847 loss)
I0407 22:56:01.312013 32630 sgd_solver.cpp:105] Iteration 3816, lr = 0.00778824
I0407 22:56:06.270771 32630 solver.cpp:218] Iteration 3828 (2.41997 iter/s, 4.95874s/12 iters), loss = 1.4979
I0407 22:56:06.270810 32630 solver.cpp:237]     Train net output #0: loss = 1.4979 (* 1 = 1.4979 loss)
I0407 22:56:06.270818 32630 sgd_solver.cpp:105] Iteration 3828, lr = 0.0077679
I0407 22:56:11.203299 32630 solver.cpp:218] Iteration 3840 (2.43286 iter/s, 4.93247s/12 iters), loss = 0.926461
I0407 22:56:11.203336 32630 solver.cpp:237]     Train net output #0: loss = 0.926461 (* 1 = 0.926461 loss)
I0407 22:56:11.203343 32630 sgd_solver.cpp:105] Iteration 3840, lr = 0.00774744
I0407 22:56:12.304634 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:56:16.051720 32630 solver.cpp:218] Iteration 3852 (2.47507 iter/s, 4.84835s/12 iters), loss = 1.04951
I0407 22:56:16.051760 32630 solver.cpp:237]     Train net output #0: loss = 1.04951 (* 1 = 1.04951 loss)
I0407 22:56:16.051769 32630 sgd_solver.cpp:105] Iteration 3852, lr = 0.00772684
I0407 22:56:21.019420 32630 solver.cpp:218] Iteration 3864 (2.41563 iter/s, 4.96764s/12 iters), loss = 1.238
I0407 22:56:21.019455 32630 solver.cpp:237]     Train net output #0: loss = 1.238 (* 1 = 1.238 loss)
I0407 22:56:21.019464 32630 sgd_solver.cpp:105] Iteration 3864, lr = 0.00770611
I0407 22:56:25.462188 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3876.caffemodel
I0407 22:56:28.873371 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3876.solverstate
I0407 22:56:31.258677 32630 solver.cpp:330] Iteration 3876, Testing net (#0)
I0407 22:56:31.258694 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:56:34.252403 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:56:35.825103 32630 solver.cpp:397]     Test net output #0: accuracy = 0.379902
I0407 22:56:35.825152 32630 solver.cpp:397]     Test net output #1: loss = 2.85921 (* 1 = 2.85921 loss)
I0407 22:56:35.921710 32630 solver.cpp:218] Iteration 3876 (0.80525 iter/s, 14.9022s/12 iters), loss = 0.916137
I0407 22:56:35.921749 32630 solver.cpp:237]     Train net output #0: loss = 0.916137 (* 1 = 0.916137 loss)
I0407 22:56:35.921757 32630 sgd_solver.cpp:105] Iteration 3876, lr = 0.00768525
I0407 22:56:40.068537 32630 solver.cpp:218] Iteration 3888 (2.89382 iter/s, 4.14677s/12 iters), loss = 0.847256
I0407 22:56:40.068579 32630 solver.cpp:237]     Train net output #0: loss = 0.847256 (* 1 = 0.847256 loss)
I0407 22:56:40.068588 32630 sgd_solver.cpp:105] Iteration 3888, lr = 0.00766425
I0407 22:56:44.990269 32630 solver.cpp:218] Iteration 3900 (2.4382 iter/s, 4.92167s/12 iters), loss = 1.10191
I0407 22:56:44.990307 32630 solver.cpp:237]     Train net output #0: loss = 1.10191 (* 1 = 1.10191 loss)
I0407 22:56:44.990314 32630 sgd_solver.cpp:105] Iteration 3900, lr = 0.00764313
I0407 22:56:49.950932 32630 solver.cpp:218] Iteration 3912 (2.41906 iter/s, 4.96061s/12 iters), loss = 1.11148
I0407 22:56:49.950960 32630 solver.cpp:237]     Train net output #0: loss = 1.11148 (* 1 = 1.11148 loss)
I0407 22:56:49.950968 32630 sgd_solver.cpp:105] Iteration 3912, lr = 0.00762187
I0407 22:56:54.857874 32630 solver.cpp:218] Iteration 3924 (2.44554 iter/s, 4.90689s/12 iters), loss = 1.23274
I0407 22:56:54.857916 32630 solver.cpp:237]     Train net output #0: loss = 1.23274 (* 1 = 1.23274 loss)
I0407 22:56:54.857924 32630 sgd_solver.cpp:105] Iteration 3924, lr = 0.00760048
I0407 22:56:59.821532 32630 solver.cpp:218] Iteration 3936 (2.4176 iter/s, 4.9636s/12 iters), loss = 1.36749
I0407 22:56:59.821652 32630 solver.cpp:237]     Train net output #0: loss = 1.36749 (* 1 = 1.36749 loss)
I0407 22:56:59.821662 32630 sgd_solver.cpp:105] Iteration 3936, lr = 0.00757896
I0407 22:57:03.127794 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:04.725620 32630 solver.cpp:218] Iteration 3948 (2.44701 iter/s, 4.90395s/12 iters), loss = 1.13259
I0407 22:57:04.725653 32630 solver.cpp:237]     Train net output #0: loss = 1.13259 (* 1 = 1.13259 loss)
I0407 22:57:04.725659 32630 sgd_solver.cpp:105] Iteration 3948, lr = 0.0075573
I0407 22:57:09.689190 32630 solver.cpp:218] Iteration 3960 (2.41764 iter/s, 4.96352s/12 iters), loss = 1.0598
I0407 22:57:09.689235 32630 solver.cpp:237]     Train net output #0: loss = 1.0598 (* 1 = 1.0598 loss)
I0407 22:57:09.689242 32630 sgd_solver.cpp:105] Iteration 3960, lr = 0.00753552
I0407 22:57:14.612028 32630 solver.cpp:218] Iteration 3972 (2.43765 iter/s, 4.92277s/12 iters), loss = 1.15815
I0407 22:57:14.612066 32630 solver.cpp:237]     Train net output #0: loss = 1.15815 (* 1 = 1.15815 loss)
I0407 22:57:14.612074 32630 sgd_solver.cpp:105] Iteration 3972, lr = 0.00751361
I0407 22:57:16.622279 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_3978.caffemodel
I0407 22:57:19.737484 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_3978.solverstate
I0407 22:57:22.106856 32630 solver.cpp:330] Iteration 3978, Testing net (#0)
I0407 22:57:22.106876 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:57:25.244402 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:26.864971 32630 solver.cpp:397]     Test net output #0: accuracy = 0.366422
I0407 22:57:26.865017 32630 solver.cpp:397]     Test net output #1: loss = 2.93016 (* 1 = 2.93016 loss)
I0407 22:57:28.740375 32630 solver.cpp:218] Iteration 3984 (0.849361 iter/s, 14.1283s/12 iters), loss = 0.982663
I0407 22:57:28.740419 32630 solver.cpp:237]     Train net output #0: loss = 0.982663 (* 1 = 0.982663 loss)
I0407 22:57:28.740427 32630 sgd_solver.cpp:105] Iteration 3984, lr = 0.00749156
I0407 22:57:33.665573 32630 solver.cpp:218] Iteration 3996 (2.43648 iter/s, 4.92513s/12 iters), loss = 0.638433
I0407 22:57:33.665725 32630 solver.cpp:237]     Train net output #0: loss = 0.638433 (* 1 = 0.638433 loss)
I0407 22:57:33.665735 32630 sgd_solver.cpp:105] Iteration 3996, lr = 0.00746939
I0407 22:57:38.617841 32630 solver.cpp:218] Iteration 4008 (2.42322 iter/s, 4.9521s/12 iters), loss = 1.15734
I0407 22:57:38.617882 32630 solver.cpp:237]     Train net output #0: loss = 1.15734 (* 1 = 1.15734 loss)
I0407 22:57:38.617892 32630 sgd_solver.cpp:105] Iteration 4008, lr = 0.00744709
I0407 22:57:43.466588 32630 solver.cpp:218] Iteration 4020 (2.4749 iter/s, 4.84868s/12 iters), loss = 0.941064
I0407 22:57:43.466637 32630 solver.cpp:237]     Train net output #0: loss = 0.941064 (* 1 = 0.941064 loss)
I0407 22:57:43.466646 32630 sgd_solver.cpp:105] Iteration 4020, lr = 0.00742466
I0407 22:57:48.343425 32630 solver.cpp:218] Iteration 4032 (2.46065 iter/s, 4.87677s/12 iters), loss = 0.775982
I0407 22:57:48.343463 32630 solver.cpp:237]     Train net output #0: loss = 0.775982 (* 1 = 0.775982 loss)
I0407 22:57:48.343472 32630 sgd_solver.cpp:105] Iteration 4032, lr = 0.0074021
I0407 22:57:53.334736 32630 solver.cpp:218] Iteration 4044 (2.40421 iter/s, 4.99125s/12 iters), loss = 0.889268
I0407 22:57:53.334776 32630 solver.cpp:237]     Train net output #0: loss = 0.889268 (* 1 = 0.889268 loss)
I0407 22:57:53.334784 32630 sgd_solver.cpp:105] Iteration 4044, lr = 0.00737941
I0407 22:57:53.853026 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:57:58.354319 32630 solver.cpp:218] Iteration 4056 (2.39067 iter/s, 5.01952s/12 iters), loss = 1.22813
I0407 22:57:58.354362 32630 solver.cpp:237]     Train net output #0: loss = 1.22813 (* 1 = 1.22813 loss)
I0407 22:57:58.354370 32630 sgd_solver.cpp:105] Iteration 4056, lr = 0.0073566
I0407 22:58:03.360251 32630 solver.cpp:218] Iteration 4068 (2.39719 iter/s, 5.00587s/12 iters), loss = 1.03936
I0407 22:58:03.360285 32630 solver.cpp:237]     Train net output #0: loss = 1.03936 (* 1 = 1.03936 loss)
I0407 22:58:03.360292 32630 sgd_solver.cpp:105] Iteration 4068, lr = 0.00733365
I0407 22:58:07.839172 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4080.caffemodel
I0407 22:58:11.867249 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4080.solverstate
I0407 22:58:14.771380 32630 solver.cpp:330] Iteration 4080, Testing net (#0)
I0407 22:58:14.771399 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:58:17.734855 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:58:19.402184 32630 solver.cpp:397]     Test net output #0: accuracy = 0.384804
I0407 22:58:19.402212 32630 solver.cpp:397]     Test net output #1: loss = 2.84745 (* 1 = 2.84745 loss)
I0407 22:58:19.498530 32630 solver.cpp:218] Iteration 4080 (0.743577 iter/s, 16.1382s/12 iters), loss = 0.836888
I0407 22:58:19.498574 32630 solver.cpp:237]     Train net output #0: loss = 0.836888 (* 1 = 0.836888 loss)
I0407 22:58:19.498584 32630 sgd_solver.cpp:105] Iteration 4080, lr = 0.00731059
I0407 22:58:23.650465 32630 solver.cpp:218] Iteration 4092 (2.89027 iter/s, 4.15186s/12 iters), loss = 0.918298
I0407 22:58:23.650511 32630 solver.cpp:237]     Train net output #0: loss = 0.918298 (* 1 = 0.918298 loss)
I0407 22:58:23.650519 32630 sgd_solver.cpp:105] Iteration 4092, lr = 0.00728739
I0407 22:58:28.614652 32630 solver.cpp:218] Iteration 4104 (2.41735 iter/s, 4.96411s/12 iters), loss = 0.972846
I0407 22:58:28.614692 32630 solver.cpp:237]     Train net output #0: loss = 0.972846 (* 1 = 0.972846 loss)
I0407 22:58:28.614701 32630 sgd_solver.cpp:105] Iteration 4104, lr = 0.00726407
I0407 22:58:33.585424 32630 solver.cpp:218] Iteration 4116 (2.41414 iter/s, 4.97071s/12 iters), loss = 0.808429
I0407 22:58:33.585469 32630 solver.cpp:237]     Train net output #0: loss = 0.808429 (* 1 = 0.808429 loss)
I0407 22:58:33.585477 32630 sgd_solver.cpp:105] Iteration 4116, lr = 0.00724063
I0407 22:58:38.527962 32630 solver.cpp:218] Iteration 4128 (2.42793 iter/s, 4.94247s/12 iters), loss = 0.824188
I0407 22:58:38.528121 32630 solver.cpp:237]     Train net output #0: loss = 0.824188 (* 1 = 0.824188 loss)
I0407 22:58:38.528131 32630 sgd_solver.cpp:105] Iteration 4128, lr = 0.00721706
I0407 22:58:43.479380 32630 solver.cpp:218] Iteration 4140 (2.42363 iter/s, 4.95124s/12 iters), loss = 0.8841
I0407 22:58:43.479422 32630 solver.cpp:237]     Train net output #0: loss = 0.8841 (* 1 = 0.8841 loss)
I0407 22:58:43.479432 32630 sgd_solver.cpp:105] Iteration 4140, lr = 0.00719337
I0407 22:58:46.078459 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:58:48.459573 32630 solver.cpp:218] Iteration 4152 (2.40958 iter/s, 4.98012s/12 iters), loss = 0.977604
I0407 22:58:48.459614 32630 solver.cpp:237]     Train net output #0: loss = 0.977604 (* 1 = 0.977604 loss)
I0407 22:58:48.459622 32630 sgd_solver.cpp:105] Iteration 4152, lr = 0.00716956
I0407 22:58:50.054976 32630 blocking_queue.cpp:49] Waiting for data
I0407 22:58:53.427366 32630 solver.cpp:218] Iteration 4164 (2.41559 iter/s, 4.96772s/12 iters), loss = 0.889625
I0407 22:58:53.427409 32630 solver.cpp:237]     Train net output #0: loss = 0.889625 (* 1 = 0.889625 loss)
I0407 22:58:53.427417 32630 sgd_solver.cpp:105] Iteration 4164, lr = 0.00714562
I0407 22:58:58.400943 32630 solver.cpp:218] Iteration 4176 (2.41278 iter/s, 4.97351s/12 iters), loss = 1.02461
I0407 22:58:58.400987 32630 solver.cpp:237]     Train net output #0: loss = 1.02461 (* 1 = 1.02461 loss)
I0407 22:58:58.400996 32630 sgd_solver.cpp:105] Iteration 4176, lr = 0.00712157
I0407 22:59:00.423928 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4182.caffemodel
I0407 22:59:04.681496 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4182.solverstate
I0407 22:59:07.812940 32630 solver.cpp:330] Iteration 4182, Testing net (#0)
I0407 22:59:07.812963 32630 net.cpp:676] Ignoring source layer train-data
I0407 22:59:10.773202 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:59:12.595748 32630 solver.cpp:397]     Test net output #0: accuracy = 0.365809
I0407 22:59:12.595808 32630 solver.cpp:397]     Test net output #1: loss = 2.91308 (* 1 = 2.91308 loss)
I0407 22:59:14.388799 32630 solver.cpp:218] Iteration 4188 (0.750574 iter/s, 15.9878s/12 iters), loss = 0.87236
I0407 22:59:14.388840 32630 solver.cpp:237]     Train net output #0: loss = 0.87236 (* 1 = 0.87236 loss)
I0407 22:59:14.388849 32630 sgd_solver.cpp:105] Iteration 4188, lr = 0.00709739
I0407 22:59:19.364482 32630 solver.cpp:218] Iteration 4200 (2.41176 iter/s, 4.97562s/12 iters), loss = 0.912499
I0407 22:59:19.364531 32630 solver.cpp:237]     Train net output #0: loss = 0.912499 (* 1 = 0.912499 loss)
I0407 22:59:19.364538 32630 sgd_solver.cpp:105] Iteration 4200, lr = 0.0070731
I0407 22:59:24.332147 32630 solver.cpp:218] Iteration 4212 (2.41566 iter/s, 4.9676s/12 iters), loss = 1.08597
I0407 22:59:24.332190 32630 solver.cpp:237]     Train net output #0: loss = 1.08597 (* 1 = 1.08597 loss)
I0407 22:59:24.332199 32630 sgd_solver.cpp:105] Iteration 4212, lr = 0.00704868
I0407 22:59:29.230628 32630 solver.cpp:218] Iteration 4224 (2.44977 iter/s, 4.89841s/12 iters), loss = 0.956956
I0407 22:59:29.230675 32630 solver.cpp:237]     Train net output #0: loss = 0.956956 (* 1 = 0.956956 loss)
I0407 22:59:29.230682 32630 sgd_solver.cpp:105] Iteration 4224, lr = 0.00702415
I0407 22:59:34.187008 32630 solver.cpp:218] Iteration 4236 (2.42116 iter/s, 4.95631s/12 iters), loss = 0.763088
I0407 22:59:34.187049 32630 solver.cpp:237]     Train net output #0: loss = 0.763088 (* 1 = 0.763088 loss)
I0407 22:59:34.187057 32630 sgd_solver.cpp:105] Iteration 4236, lr = 0.0069995
I0407 22:59:38.915836 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 22:59:39.141502 32630 solver.cpp:218] Iteration 4248 (2.42207 iter/s, 4.95443s/12 iters), loss = 0.992411
I0407 22:59:39.141539 32630 solver.cpp:237]     Train net output #0: loss = 0.992411 (* 1 = 0.992411 loss)
I0407 22:59:39.141547 32630 sgd_solver.cpp:105] Iteration 4248, lr = 0.00697473
I0407 22:59:44.068506 32630 solver.cpp:218] Iteration 4260 (2.43559 iter/s, 4.92695s/12 iters), loss = 0.754796
I0407 22:59:44.068650 32630 solver.cpp:237]     Train net output #0: loss = 0.754796 (* 1 = 0.754796 loss)
I0407 22:59:44.068660 32630 sgd_solver.cpp:105] Iteration 4260, lr = 0.00694985
I0407 22:59:49.063661 32630 solver.cpp:218] Iteration 4272 (2.40241 iter/s, 4.99499s/12 iters), loss = 0.791177
I0407 22:59:49.063696 32630 solver.cpp:237]     Train net output #0: loss = 0.791177 (* 1 = 0.791177 loss)
I0407 22:59:49.063704 32630 sgd_solver.cpp:105] Iteration 4272, lr = 0.00692485
I0407 22:59:53.571666 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4284.caffemodel
I0407 22:59:56.648403 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4284.solverstate
I0407 22:59:59.224673 32630 solver.cpp:330] Iteration 4284, Testing net (#0)
I0407 22:59:59.224690 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:00:01.932096 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:00:03.699419 32630 solver.cpp:397]     Test net output #0: accuracy = 0.386029
I0407 23:00:03.699465 32630 solver.cpp:397]     Test net output #1: loss = 2.87579 (* 1 = 2.87579 loss)
I0407 23:00:03.795962 32630 solver.cpp:218] Iteration 4284 (0.814541 iter/s, 14.7322s/12 iters), loss = 0.832962
I0407 23:00:03.796023 32630 solver.cpp:237]     Train net output #0: loss = 0.832962 (* 1 = 0.832962 loss)
I0407 23:00:03.796036 32630 sgd_solver.cpp:105] Iteration 4284, lr = 0.00689974
I0407 23:00:07.876863 32630 solver.cpp:218] Iteration 4296 (2.94058 iter/s, 4.08082s/12 iters), loss = 0.40211
I0407 23:00:07.876912 32630 solver.cpp:237]     Train net output #0: loss = 0.40211 (* 1 = 0.40211 loss)
I0407 23:00:07.876921 32630 sgd_solver.cpp:105] Iteration 4296, lr = 0.00687452
I0407 23:00:12.789921 32630 solver.cpp:218] Iteration 4308 (2.44251 iter/s, 4.91298s/12 iters), loss = 0.838466
I0407 23:00:12.789964 32630 solver.cpp:237]     Train net output #0: loss = 0.838467 (* 1 = 0.838467 loss)
I0407 23:00:12.789973 32630 sgd_solver.cpp:105] Iteration 4308, lr = 0.00684919
I0407 23:00:17.732658 32630 solver.cpp:218] Iteration 4320 (2.42784 iter/s, 4.94267s/12 iters), loss = 1.0913
I0407 23:00:17.732796 32630 solver.cpp:237]     Train net output #0: loss = 1.0913 (* 1 = 1.0913 loss)
I0407 23:00:17.732806 32630 sgd_solver.cpp:105] Iteration 4320, lr = 0.00682375
I0407 23:00:22.595458 32630 solver.cpp:218] Iteration 4332 (2.46779 iter/s, 4.86264s/12 iters), loss = 0.830482
I0407 23:00:22.595494 32630 solver.cpp:237]     Train net output #0: loss = 0.830482 (* 1 = 0.830482 loss)
I0407 23:00:22.595501 32630 sgd_solver.cpp:105] Iteration 4332, lr = 0.00679819
I0407 23:00:27.519277 32630 solver.cpp:218] Iteration 4344 (2.43716 iter/s, 4.92376s/12 iters), loss = 0.979705
I0407 23:00:27.519312 32630 solver.cpp:237]     Train net output #0: loss = 0.979705 (* 1 = 0.979705 loss)
I0407 23:00:27.519320 32630 sgd_solver.cpp:105] Iteration 4344, lr = 0.00677253
I0407 23:00:29.414057 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:00:32.451340 32630 solver.cpp:218] Iteration 4356 (2.43309 iter/s, 4.93201s/12 iters), loss = 0.774675
I0407 23:00:32.451381 32630 solver.cpp:237]     Train net output #0: loss = 0.774675 (* 1 = 0.774675 loss)
I0407 23:00:32.451390 32630 sgd_solver.cpp:105] Iteration 4356, lr = 0.00674676
I0407 23:00:37.379042 32630 solver.cpp:218] Iteration 4368 (2.43525 iter/s, 4.92763s/12 iters), loss = 0.878049
I0407 23:00:37.379087 32630 solver.cpp:237]     Train net output #0: loss = 0.878049 (* 1 = 0.878049 loss)
I0407 23:00:37.379096 32630 sgd_solver.cpp:105] Iteration 4368, lr = 0.00672089
I0407 23:00:42.264570 32630 solver.cpp:218] Iteration 4380 (2.45627 iter/s, 4.88546s/12 iters), loss = 0.903578
I0407 23:00:42.264617 32630 solver.cpp:237]     Train net output #0: loss = 0.903578 (* 1 = 0.903578 loss)
I0407 23:00:42.264626 32630 sgd_solver.cpp:105] Iteration 4380, lr = 0.00669491
I0407 23:00:44.197108 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4386.caffemodel
I0407 23:00:47.250316 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4386.solverstate
I0407 23:00:49.623143 32630 solver.cpp:330] Iteration 4386, Testing net (#0)
I0407 23:00:49.623268 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:00:52.490849 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:00:54.394279 32630 solver.cpp:397]     Test net output #0: accuracy = 0.382353
I0407 23:00:54.394325 32630 solver.cpp:397]     Test net output #1: loss = 2.80704 (* 1 = 2.80704 loss)
I0407 23:00:56.197000 32630 solver.cpp:218] Iteration 4392 (0.861305 iter/s, 13.9323s/12 iters), loss = 0.748026
I0407 23:00:56.197038 32630 solver.cpp:237]     Train net output #0: loss = 0.748026 (* 1 = 0.748026 loss)
I0407 23:00:56.197047 32630 sgd_solver.cpp:105] Iteration 4392, lr = 0.00666882
I0407 23:01:01.161180 32630 solver.cpp:218] Iteration 4404 (2.41735 iter/s, 4.96412s/12 iters), loss = 0.988188
I0407 23:01:01.161221 32630 solver.cpp:237]     Train net output #0: loss = 0.988188 (* 1 = 0.988188 loss)
I0407 23:01:01.161228 32630 sgd_solver.cpp:105] Iteration 4404, lr = 0.00664264
I0407 23:01:06.087353 32630 solver.cpp:218] Iteration 4416 (2.436 iter/s, 4.92611s/12 iters), loss = 0.66077
I0407 23:01:06.087390 32630 solver.cpp:237]     Train net output #0: loss = 0.66077 (* 1 = 0.66077 loss)
I0407 23:01:06.087397 32630 sgd_solver.cpp:105] Iteration 4416, lr = 0.00661635
I0407 23:01:11.043082 32630 solver.cpp:218] Iteration 4428 (2.42147 iter/s, 4.95567s/12 iters), loss = 0.692481
I0407 23:01:11.043116 32630 solver.cpp:237]     Train net output #0: loss = 0.692481 (* 1 = 0.692481 loss)
I0407 23:01:11.043123 32630 sgd_solver.cpp:105] Iteration 4428, lr = 0.00658996
I0407 23:01:15.974242 32630 solver.cpp:218] Iteration 4440 (2.43354 iter/s, 4.9311s/12 iters), loss = 0.633179
I0407 23:01:15.974283 32630 solver.cpp:237]     Train net output #0: loss = 0.63318 (* 1 = 0.63318 loss)
I0407 23:01:15.974292 32630 sgd_solver.cpp:105] Iteration 4440, lr = 0.00656347
I0407 23:01:19.972242 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:01:20.905673 32630 solver.cpp:218] Iteration 4452 (2.4334 iter/s, 4.93137s/12 iters), loss = 0.89545
I0407 23:01:20.905709 32630 solver.cpp:237]     Train net output #0: loss = 0.89545 (* 1 = 0.89545 loss)
I0407 23:01:20.905716 32630 sgd_solver.cpp:105] Iteration 4452, lr = 0.00653689
I0407 23:01:25.823837 32630 solver.cpp:218] Iteration 4464 (2.43996 iter/s, 4.91811s/12 iters), loss = 0.691098
I0407 23:01:25.823869 32630 solver.cpp:237]     Train net output #0: loss = 0.691098 (* 1 = 0.691098 loss)
I0407 23:01:25.823875 32630 sgd_solver.cpp:105] Iteration 4464, lr = 0.00651021
I0407 23:01:30.773166 32630 solver.cpp:218] Iteration 4476 (2.4246 iter/s, 4.94927s/12 iters), loss = 1.0124
I0407 23:01:30.773200 32630 solver.cpp:237]     Train net output #0: loss = 1.0124 (* 1 = 1.0124 loss)
I0407 23:01:30.773207 32630 sgd_solver.cpp:105] Iteration 4476, lr = 0.00648343
I0407 23:01:35.238348 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4488.caffemodel
I0407 23:01:38.333978 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4488.solverstate
I0407 23:01:40.693187 32630 solver.cpp:330] Iteration 4488, Testing net (#0)
I0407 23:01:40.693207 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:01:43.369087 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:01:45.168027 32630 solver.cpp:397]     Test net output #0: accuracy = 0.385417
I0407 23:01:45.168067 32630 solver.cpp:397]     Test net output #1: loss = 2.90809 (* 1 = 2.90809 loss)
I0407 23:01:45.265321 32630 solver.cpp:218] Iteration 4488 (0.828038 iter/s, 14.4921s/12 iters), loss = 0.945162
I0407 23:01:45.265362 32630 solver.cpp:237]     Train net output #0: loss = 0.945162 (* 1 = 0.945162 loss)
I0407 23:01:45.265370 32630 sgd_solver.cpp:105] Iteration 4488, lr = 0.00645656
I0407 23:01:49.403914 32630 solver.cpp:218] Iteration 4500 (2.89958 iter/s, 4.13853s/12 iters), loss = 0.751505
I0407 23:01:49.403952 32630 solver.cpp:237]     Train net output #0: loss = 0.751505 (* 1 = 0.751505 loss)
I0407 23:01:49.403965 32630 sgd_solver.cpp:105] Iteration 4500, lr = 0.0064296
I0407 23:01:54.344540 32630 solver.cpp:218] Iteration 4512 (2.42887 iter/s, 4.94057s/12 iters), loss = 0.607413
I0407 23:01:54.344703 32630 solver.cpp:237]     Train net output #0: loss = 0.607413 (* 1 = 0.607413 loss)
I0407 23:01:54.344712 32630 sgd_solver.cpp:105] Iteration 4512, lr = 0.00640255
I0407 23:01:59.312129 32630 solver.cpp:218] Iteration 4524 (2.41575 iter/s, 4.96741s/12 iters), loss = 0.700027
I0407 23:01:59.312165 32630 solver.cpp:237]     Train net output #0: loss = 0.700028 (* 1 = 0.700028 loss)
I0407 23:01:59.312172 32630 sgd_solver.cpp:105] Iteration 4524, lr = 0.00637541
I0407 23:02:04.272354 32630 solver.cpp:218] Iteration 4536 (2.41927 iter/s, 4.96017s/12 iters), loss = 0.64298
I0407 23:02:04.272389 32630 solver.cpp:237]     Train net output #0: loss = 0.64298 (* 1 = 0.64298 loss)
I0407 23:02:04.272397 32630 sgd_solver.cpp:105] Iteration 4536, lr = 0.00634818
I0407 23:02:09.231420 32630 solver.cpp:218] Iteration 4548 (2.41984 iter/s, 4.95901s/12 iters), loss = 0.720819
I0407 23:02:09.231456 32630 solver.cpp:237]     Train net output #0: loss = 0.720819 (* 1 = 0.720819 loss)
I0407 23:02:09.231462 32630 sgd_solver.cpp:105] Iteration 4548, lr = 0.00632086
I0407 23:02:10.455515 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:02:14.130278 32630 solver.cpp:218] Iteration 4560 (2.44958 iter/s, 4.8988s/12 iters), loss = 0.706228
I0407 23:02:14.130329 32630 solver.cpp:237]     Train net output #0: loss = 0.706228 (* 1 = 0.706228 loss)
I0407 23:02:14.130337 32630 sgd_solver.cpp:105] Iteration 4560, lr = 0.00629346
I0407 23:02:19.107851 32630 solver.cpp:218] Iteration 4572 (2.41085 iter/s, 4.9775s/12 iters), loss = 0.75829
I0407 23:02:19.107890 32630 solver.cpp:237]     Train net output #0: loss = 0.75829 (* 1 = 0.75829 loss)
I0407 23:02:19.107898 32630 sgd_solver.cpp:105] Iteration 4572, lr = 0.00626597
I0407 23:02:24.030411 32630 solver.cpp:218] Iteration 4584 (2.43779 iter/s, 4.9225s/12 iters), loss = 0.563994
I0407 23:02:24.030452 32630 solver.cpp:237]     Train net output #0: loss = 0.563995 (* 1 = 0.563995 loss)
I0407 23:02:24.030459 32630 sgd_solver.cpp:105] Iteration 4584, lr = 0.00623841
I0407 23:02:26.039194 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4590.caffemodel
I0407 23:02:29.131367 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4590.solverstate
I0407 23:02:31.525600 32630 solver.cpp:330] Iteration 4590, Testing net (#0)
I0407 23:02:31.525619 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:02:34.310842 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:02:36.282436 32630 solver.cpp:397]     Test net output #0: accuracy = 0.396446
I0407 23:02:36.282483 32630 solver.cpp:397]     Test net output #1: loss = 2.95542 (* 1 = 2.95542 loss)
I0407 23:02:38.076895 32630 solver.cpp:218] Iteration 4596 (0.854311 iter/s, 14.0464s/12 iters), loss = 0.737204
I0407 23:02:38.076936 32630 solver.cpp:237]     Train net output #0: loss = 0.737204 (* 1 = 0.737204 loss)
I0407 23:02:38.076944 32630 sgd_solver.cpp:105] Iteration 4596, lr = 0.00621076
I0407 23:02:43.009312 32630 solver.cpp:218] Iteration 4608 (2.43292 iter/s, 4.93235s/12 iters), loss = 0.687537
I0407 23:02:43.009357 32630 solver.cpp:237]     Train net output #0: loss = 0.687538 (* 1 = 0.687538 loss)
I0407 23:02:43.009366 32630 sgd_solver.cpp:105] Iteration 4608, lr = 0.00618303
I0407 23:02:47.971469 32630 solver.cpp:218] Iteration 4620 (2.41833 iter/s, 4.96209s/12 iters), loss = 0.574458
I0407 23:02:47.971508 32630 solver.cpp:237]     Train net output #0: loss = 0.574458 (* 1 = 0.574458 loss)
I0407 23:02:47.971516 32630 sgd_solver.cpp:105] Iteration 4620, lr = 0.00615523
I0407 23:02:52.826398 32630 solver.cpp:218] Iteration 4632 (2.47175 iter/s, 4.85487s/12 iters), loss = 0.734354
I0407 23:02:52.826436 32630 solver.cpp:237]     Train net output #0: loss = 0.734354 (* 1 = 0.734354 loss)
I0407 23:02:52.826444 32630 sgd_solver.cpp:105] Iteration 4632, lr = 0.00612735
I0407 23:02:57.749452 32630 solver.cpp:218] Iteration 4644 (2.43754 iter/s, 4.923s/12 iters), loss = 0.617531
I0407 23:02:57.749572 32630 solver.cpp:237]     Train net output #0: loss = 0.617531 (* 1 = 0.617531 loss)
I0407 23:02:57.749580 32630 sgd_solver.cpp:105] Iteration 4644, lr = 0.0060994
I0407 23:03:01.125425 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:03:02.691268 32630 solver.cpp:218] Iteration 4656 (2.42833 iter/s, 4.94168s/12 iters), loss = 0.490399
I0407 23:03:02.691308 32630 solver.cpp:237]     Train net output #0: loss = 0.490399 (* 1 = 0.490399 loss)
I0407 23:03:02.691315 32630 sgd_solver.cpp:105] Iteration 4656, lr = 0.00607137
I0407 23:03:07.629637 32630 solver.cpp:218] Iteration 4668 (2.42998 iter/s, 4.93831s/12 iters), loss = 0.610106
I0407 23:03:07.629678 32630 solver.cpp:237]     Train net output #0: loss = 0.610107 (* 1 = 0.610107 loss)
I0407 23:03:07.629685 32630 sgd_solver.cpp:105] Iteration 4668, lr = 0.00604327
I0407 23:03:12.580178 32630 solver.cpp:218] Iteration 4680 (2.42401 iter/s, 4.95048s/12 iters), loss = 0.545254
I0407 23:03:12.580219 32630 solver.cpp:237]     Train net output #0: loss = 0.545254 (* 1 = 0.545254 loss)
I0407 23:03:12.580226 32630 sgd_solver.cpp:105] Iteration 4680, lr = 0.00601511
I0407 23:03:17.025393 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4692.caffemodel
I0407 23:03:21.709723 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4692.solverstate
I0407 23:03:24.090966 32630 solver.cpp:330] Iteration 4692, Testing net (#0)
I0407 23:03:24.090984 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:03:26.735436 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:03:28.623858 32630 solver.cpp:397]     Test net output #0: accuracy = 0.397672
I0407 23:03:28.624012 32630 solver.cpp:397]     Test net output #1: loss = 2.90221 (* 1 = 2.90221 loss)
I0407 23:03:28.718082 32630 solver.cpp:218] Iteration 4692 (0.743595 iter/s, 16.1378s/12 iters), loss = 0.924601
I0407 23:03:28.718137 32630 solver.cpp:237]     Train net output #0: loss = 0.924601 (* 1 = 0.924601 loss)
I0407 23:03:28.718145 32630 sgd_solver.cpp:105] Iteration 4692, lr = 0.00598688
I0407 23:03:32.897882 32630 solver.cpp:218] Iteration 4704 (2.871 iter/s, 4.17972s/12 iters), loss = 0.5664
I0407 23:03:32.897923 32630 solver.cpp:237]     Train net output #0: loss = 0.5664 (* 1 = 0.5664 loss)
I0407 23:03:32.897931 32630 sgd_solver.cpp:105] Iteration 4704, lr = 0.00595858
I0407 23:03:37.865976 32630 solver.cpp:218] Iteration 4716 (2.41544 iter/s, 4.96803s/12 iters), loss = 0.663533
I0407 23:03:37.866024 32630 solver.cpp:237]     Train net output #0: loss = 0.663534 (* 1 = 0.663534 loss)
I0407 23:03:37.866031 32630 sgd_solver.cpp:105] Iteration 4716, lr = 0.00593022
I0407 23:03:42.731460 32630 solver.cpp:218] Iteration 4728 (2.46639 iter/s, 4.86541s/12 iters), loss = 0.527464
I0407 23:03:42.731504 32630 solver.cpp:237]     Train net output #0: loss = 0.527465 (* 1 = 0.527465 loss)
I0407 23:03:42.731513 32630 sgd_solver.cpp:105] Iteration 4728, lr = 0.00590179
I0407 23:03:47.620899 32630 solver.cpp:218] Iteration 4740 (2.4543 iter/s, 4.88938s/12 iters), loss = 0.726684
I0407 23:03:47.620939 32630 solver.cpp:237]     Train net output #0: loss = 0.726684 (* 1 = 0.726684 loss)
I0407 23:03:47.620949 32630 sgd_solver.cpp:105] Iteration 4740, lr = 0.00587331
I0407 23:03:52.591079 32630 solver.cpp:218] Iteration 4752 (2.41443 iter/s, 4.97012s/12 iters), loss = 0.497329
I0407 23:03:52.591120 32630 solver.cpp:237]     Train net output #0: loss = 0.497329 (* 1 = 0.497329 loss)
I0407 23:03:52.591127 32630 sgd_solver.cpp:105] Iteration 4752, lr = 0.00584476
I0407 23:03:53.099107 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:03:57.500396 32630 solver.cpp:218] Iteration 4764 (2.44436 iter/s, 4.90926s/12 iters), loss = 0.567307
I0407 23:03:57.500435 32630 solver.cpp:237]     Train net output #0: loss = 0.567307 (* 1 = 0.567307 loss)
I0407 23:03:57.500443 32630 sgd_solver.cpp:105] Iteration 4764, lr = 0.00581616
I0407 23:04:02.489694 32630 solver.cpp:218] Iteration 4776 (2.40518 iter/s, 4.98923s/12 iters), loss = 0.684307
I0407 23:04:02.489873 32630 solver.cpp:237]     Train net output #0: loss = 0.684308 (* 1 = 0.684308 loss)
I0407 23:04:02.489883 32630 sgd_solver.cpp:105] Iteration 4776, lr = 0.00578751
I0407 23:04:07.425282 32630 solver.cpp:218] Iteration 4788 (2.43142 iter/s, 4.93539s/12 iters), loss = 0.469574
I0407 23:04:07.425314 32630 solver.cpp:237]     Train net output #0: loss = 0.469574 (* 1 = 0.469574 loss)
I0407 23:04:07.425321 32630 sgd_solver.cpp:105] Iteration 4788, lr = 0.0057588
I0407 23:04:09.462152 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4794.caffemodel
I0407 23:04:13.053560 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4794.solverstate
I0407 23:04:16.592828 32630 solver.cpp:330] Iteration 4794, Testing net (#0)
I0407 23:04:16.592846 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:04:19.289084 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:04:21.375295 32630 solver.cpp:397]     Test net output #0: accuracy = 0.393995
I0407 23:04:21.375344 32630 solver.cpp:397]     Test net output #1: loss = 2.91389 (* 1 = 2.91389 loss)
I0407 23:04:23.199126 32630 solver.cpp:218] Iteration 4800 (0.760757 iter/s, 15.7738s/12 iters), loss = 0.415224
I0407 23:04:23.199169 32630 solver.cpp:237]     Train net output #0: loss = 0.415224 (* 1 = 0.415224 loss)
I0407 23:04:23.199177 32630 sgd_solver.cpp:105] Iteration 4800, lr = 0.00573004
I0407 23:04:28.153806 32630 solver.cpp:218] Iteration 4812 (2.42199 iter/s, 4.95461s/12 iters), loss = 0.493974
I0407 23:04:28.153851 32630 solver.cpp:237]     Train net output #0: loss = 0.493974 (* 1 = 0.493974 loss)
I0407 23:04:28.153859 32630 sgd_solver.cpp:105] Iteration 4812, lr = 0.00570123
I0407 23:04:33.068375 32630 solver.cpp:218] Iteration 4824 (2.44175 iter/s, 4.9145s/12 iters), loss = 0.639088
I0407 23:04:33.068511 32630 solver.cpp:237]     Train net output #0: loss = 0.639088 (* 1 = 0.639088 loss)
I0407 23:04:33.068521 32630 sgd_solver.cpp:105] Iteration 4824, lr = 0.00567237
I0407 23:04:38.002735 32630 solver.cpp:218] Iteration 4836 (2.432 iter/s, 4.93421s/12 iters), loss = 0.315968
I0407 23:04:38.002774 32630 solver.cpp:237]     Train net output #0: loss = 0.315968 (* 1 = 0.315968 loss)
I0407 23:04:38.002781 32630 sgd_solver.cpp:105] Iteration 4836, lr = 0.00564347
I0407 23:04:40.008819 32630 blocking_queue.cpp:49] Waiting for data
I0407 23:04:42.918839 32630 solver.cpp:218] Iteration 4848 (2.44099 iter/s, 4.91604s/12 iters), loss = 0.553912
I0407 23:04:42.918877 32630 solver.cpp:237]     Train net output #0: loss = 0.553912 (* 1 = 0.553912 loss)
I0407 23:04:42.918885 32630 sgd_solver.cpp:105] Iteration 4848, lr = 0.00561452
I0407 23:04:45.546139 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:04:47.884836 32630 solver.cpp:218] Iteration 4860 (2.41647 iter/s, 4.96593s/12 iters), loss = 0.514736
I0407 23:04:47.884883 32630 solver.cpp:237]     Train net output #0: loss = 0.514736 (* 1 = 0.514736 loss)
I0407 23:04:47.884892 32630 sgd_solver.cpp:105] Iteration 4860, lr = 0.00558554
I0407 23:04:52.805550 32630 solver.cpp:218] Iteration 4872 (2.4387 iter/s, 4.92065s/12 iters), loss = 0.477373
I0407 23:04:52.805586 32630 solver.cpp:237]     Train net output #0: loss = 0.477373 (* 1 = 0.477373 loss)
I0407 23:04:52.805594 32630 sgd_solver.cpp:105] Iteration 4872, lr = 0.00555651
I0407 23:04:57.756021 32630 solver.cpp:218] Iteration 4884 (2.42404 iter/s, 4.95041s/12 iters), loss = 0.578615
I0407 23:04:57.756065 32630 solver.cpp:237]     Train net output #0: loss = 0.578615 (* 1 = 0.578615 loss)
I0407 23:04:57.756075 32630 sgd_solver.cpp:105] Iteration 4884, lr = 0.00552744
I0407 23:05:02.150195 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4896.caffemodel
I0407 23:05:05.196697 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4896.solverstate
I0407 23:05:07.655752 32630 solver.cpp:330] Iteration 4896, Testing net (#0)
I0407 23:05:07.655771 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:05:10.242790 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:05:12.212568 32630 solver.cpp:397]     Test net output #0: accuracy = 0.405637
I0407 23:05:12.212615 32630 solver.cpp:397]     Test net output #1: loss = 2.85025 (* 1 = 2.85025 loss)
I0407 23:05:12.308005 32630 solver.cpp:218] Iteration 4896 (0.824635 iter/s, 14.5519s/12 iters), loss = 0.704783
I0407 23:05:12.308053 32630 solver.cpp:237]     Train net output #0: loss = 0.704783 (* 1 = 0.704783 loss)
I0407 23:05:12.308060 32630 sgd_solver.cpp:105] Iteration 4896, lr = 0.00549834
I0407 23:05:16.444525 32630 solver.cpp:218] Iteration 4908 (2.90104 iter/s, 4.13645s/12 iters), loss = 0.46891
I0407 23:05:16.444564 32630 solver.cpp:237]     Train net output #0: loss = 0.46891 (* 1 = 0.46891 loss)
I0407 23:05:16.444572 32630 sgd_solver.cpp:105] Iteration 4908, lr = 0.0054692
I0407 23:05:21.365272 32630 solver.cpp:218] Iteration 4920 (2.43868 iter/s, 4.92069s/12 iters), loss = 0.576338
I0407 23:05:21.365307 32630 solver.cpp:237]     Train net output #0: loss = 0.576338 (* 1 = 0.576338 loss)
I0407 23:05:21.365315 32630 sgd_solver.cpp:105] Iteration 4920, lr = 0.00544003
I0407 23:05:26.304960 32630 solver.cpp:218] Iteration 4932 (2.42933 iter/s, 4.93963s/12 iters), loss = 0.644546
I0407 23:05:26.304997 32630 solver.cpp:237]     Train net output #0: loss = 0.644546 (* 1 = 0.644546 loss)
I0407 23:05:26.305004 32630 sgd_solver.cpp:105] Iteration 4932, lr = 0.00541084
I0407 23:05:31.246634 32630 solver.cpp:218] Iteration 4944 (2.42836 iter/s, 4.94162s/12 iters), loss = 0.546721
I0407 23:05:31.246675 32630 solver.cpp:237]     Train net output #0: loss = 0.546721 (* 1 = 0.546721 loss)
I0407 23:05:31.246682 32630 sgd_solver.cpp:105] Iteration 4944, lr = 0.00538161
I0407 23:05:35.990813 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:05:36.187580 32630 solver.cpp:218] Iteration 4956 (2.42871 iter/s, 4.94088s/12 iters), loss = 0.746736
I0407 23:05:36.187618 32630 solver.cpp:237]     Train net output #0: loss = 0.746736 (* 1 = 0.746736 loss)
I0407 23:05:36.187625 32630 sgd_solver.cpp:105] Iteration 4956, lr = 0.00535236
I0407 23:05:41.093897 32630 solver.cpp:218] Iteration 4968 (2.44586 iter/s, 4.90626s/12 iters), loss = 0.481071
I0407 23:05:41.093937 32630 solver.cpp:237]     Train net output #0: loss = 0.481071 (* 1 = 0.481071 loss)
I0407 23:05:41.093945 32630 sgd_solver.cpp:105] Iteration 4968, lr = 0.00532308
I0407 23:05:46.060242 32630 solver.cpp:218] Iteration 4980 (2.41629 iter/s, 4.96628s/12 iters), loss = 0.555358
I0407 23:05:46.060281 32630 solver.cpp:237]     Train net output #0: loss = 0.555358 (* 1 = 0.555358 loss)
I0407 23:05:46.060289 32630 sgd_solver.cpp:105] Iteration 4980, lr = 0.00529378
I0407 23:05:50.955626 32630 solver.cpp:218] Iteration 4992 (2.45132 iter/s, 4.89532s/12 iters), loss = 0.414539
I0407 23:05:50.955670 32630 solver.cpp:237]     Train net output #0: loss = 0.414539 (* 1 = 0.414539 loss)
I0407 23:05:50.955679 32630 sgd_solver.cpp:105] Iteration 4992, lr = 0.00526446
I0407 23:05:52.948011 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_4998.caffemodel
I0407 23:05:56.008431 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_4998.solverstate
I0407 23:05:58.400816 32630 solver.cpp:330] Iteration 4998, Testing net (#0)
I0407 23:05:58.400835 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:06:00.930581 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:02.924768 32630 solver.cpp:397]     Test net output #0: accuracy = 0.374387
I0407 23:06:02.924811 32630 solver.cpp:397]     Test net output #1: loss = 2.98533 (* 1 = 2.98533 loss)
I0407 23:06:04.726768 32630 solver.cpp:218] Iteration 5004 (0.871393 iter/s, 13.7711s/12 iters), loss = 0.421938
I0407 23:06:04.726807 32630 solver.cpp:237]     Train net output #0: loss = 0.421938 (* 1 = 0.421938 loss)
I0407 23:06:04.726816 32630 sgd_solver.cpp:105] Iteration 5004, lr = 0.00523512
I0407 23:06:09.652258 32630 solver.cpp:218] Iteration 5016 (2.43634 iter/s, 4.92543s/12 iters), loss = 0.384636
I0407 23:06:09.652424 32630 solver.cpp:237]     Train net output #0: loss = 0.384636 (* 1 = 0.384636 loss)
I0407 23:06:09.652433 32630 sgd_solver.cpp:105] Iteration 5016, lr = 0.00520577
I0407 23:06:14.615826 32630 solver.cpp:218] Iteration 5028 (2.4177 iter/s, 4.96339s/12 iters), loss = 0.418031
I0407 23:06:14.615861 32630 solver.cpp:237]     Train net output #0: loss = 0.418032 (* 1 = 0.418032 loss)
I0407 23:06:14.615870 32630 sgd_solver.cpp:105] Iteration 5028, lr = 0.0051764
I0407 23:06:19.487200 32630 solver.cpp:218] Iteration 5040 (2.4634 iter/s, 4.87131s/12 iters), loss = 0.401169
I0407 23:06:19.487244 32630 solver.cpp:237]     Train net output #0: loss = 0.401169 (* 1 = 0.401169 loss)
I0407 23:06:19.487252 32630 sgd_solver.cpp:105] Iteration 5040, lr = 0.00514702
I0407 23:06:24.457127 32630 solver.cpp:218] Iteration 5052 (2.41455 iter/s, 4.96986s/12 iters), loss = 0.418256
I0407 23:06:24.457165 32630 solver.cpp:237]     Train net output #0: loss = 0.418256 (* 1 = 0.418256 loss)
I0407 23:06:24.457173 32630 sgd_solver.cpp:105] Iteration 5052, lr = 0.00511763
I0407 23:06:26.349159 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:29.364220 32630 solver.cpp:218] Iteration 5064 (2.44547 iter/s, 4.90703s/12 iters), loss = 0.297284
I0407 23:06:29.364264 32630 solver.cpp:237]     Train net output #0: loss = 0.297284 (* 1 = 0.297284 loss)
I0407 23:06:29.364271 32630 sgd_solver.cpp:105] Iteration 5064, lr = 0.00508823
I0407 23:06:34.334156 32630 solver.cpp:218] Iteration 5076 (2.41455 iter/s, 4.96987s/12 iters), loss = 0.356673
I0407 23:06:34.334192 32630 solver.cpp:237]     Train net output #0: loss = 0.356673 (* 1 = 0.356673 loss)
I0407 23:06:34.334200 32630 sgd_solver.cpp:105] Iteration 5076, lr = 0.00505882
I0407 23:06:39.298523 32630 solver.cpp:218] Iteration 5088 (2.41725 iter/s, 4.96431s/12 iters), loss = 0.62362
I0407 23:06:39.298560 32630 solver.cpp:237]     Train net output #0: loss = 0.62362 (* 1 = 0.62362 loss)
I0407 23:06:39.298568 32630 sgd_solver.cpp:105] Iteration 5088, lr = 0.00502941
I0407 23:06:43.768501 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5100.caffemodel
I0407 23:06:46.826606 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5100.solverstate
I0407 23:06:49.255009 32630 solver.cpp:330] Iteration 5100, Testing net (#0)
I0407 23:06:49.255030 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:06:51.836350 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:06:54.050631 32630 solver.cpp:397]     Test net output #0: accuracy = 0.401961
I0407 23:06:54.050678 32630 solver.cpp:397]     Test net output #1: loss = 2.92173 (* 1 = 2.92173 loss)
I0407 23:06:54.147034 32630 solver.cpp:218] Iteration 5100 (0.808166 iter/s, 14.8484s/12 iters), loss = 0.407574
I0407 23:06:54.147075 32630 solver.cpp:237]     Train net output #0: loss = 0.407574 (* 1 = 0.407574 loss)
I0407 23:06:54.147083 32630 sgd_solver.cpp:105] Iteration 5100, lr = 0.005
I0407 23:06:58.306756 32630 solver.cpp:218] Iteration 5112 (2.88485 iter/s, 4.15966s/12 iters), loss = 0.758972
I0407 23:06:58.306797 32630 solver.cpp:237]     Train net output #0: loss = 0.758972 (* 1 = 0.758972 loss)
I0407 23:06:58.306805 32630 sgd_solver.cpp:105] Iteration 5112, lr = 0.00497059
I0407 23:07:03.276583 32630 solver.cpp:218] Iteration 5124 (2.4146 iter/s, 4.96976s/12 iters), loss = 0.473309
I0407 23:07:03.276630 32630 solver.cpp:237]     Train net output #0: loss = 0.473309 (* 1 = 0.473309 loss)
I0407 23:07:03.276639 32630 sgd_solver.cpp:105] Iteration 5124, lr = 0.00494118
I0407 23:07:08.185995 32630 solver.cpp:218] Iteration 5136 (2.44432 iter/s, 4.90934s/12 iters), loss = 0.389804
I0407 23:07:08.186033 32630 solver.cpp:237]     Train net output #0: loss = 0.389804 (* 1 = 0.389804 loss)
I0407 23:07:08.186041 32630 sgd_solver.cpp:105] Iteration 5136, lr = 0.00491177
I0407 23:07:13.162955 32630 solver.cpp:218] Iteration 5148 (2.41114 iter/s, 4.97689s/12 iters), loss = 0.52869
I0407 23:07:13.162998 32630 solver.cpp:237]     Train net output #0: loss = 0.52869 (* 1 = 0.52869 loss)
I0407 23:07:13.163007 32630 sgd_solver.cpp:105] Iteration 5148, lr = 0.00488237
I0407 23:07:17.151145 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:07:18.060211 32630 solver.cpp:218] Iteration 5160 (2.45038 iter/s, 4.89719s/12 iters), loss = 0.511335
I0407 23:07:18.060250 32630 solver.cpp:237]     Train net output #0: loss = 0.511335 (* 1 = 0.511335 loss)
I0407 23:07:18.060257 32630 sgd_solver.cpp:105] Iteration 5160, lr = 0.00485298
I0407 23:07:23.021261 32630 solver.cpp:218] Iteration 5172 (2.41887 iter/s, 4.96099s/12 iters), loss = 0.389516
I0407 23:07:23.021301 32630 solver.cpp:237]     Train net output #0: loss = 0.389516 (* 1 = 0.389516 loss)
I0407 23:07:23.021309 32630 sgd_solver.cpp:105] Iteration 5172, lr = 0.0048236
I0407 23:07:27.987849 32630 solver.cpp:218] Iteration 5184 (2.41618 iter/s, 4.96652s/12 iters), loss = 0.377012
I0407 23:07:27.987900 32630 solver.cpp:237]     Train net output #0: loss = 0.377012 (* 1 = 0.377012 loss)
I0407 23:07:27.987907 32630 sgd_solver.cpp:105] Iteration 5184, lr = 0.00479423
I0407 23:07:32.921205 32630 solver.cpp:218] Iteration 5196 (2.43246 iter/s, 4.93329s/12 iters), loss = 0.385583
I0407 23:07:32.921245 32630 solver.cpp:237]     Train net output #0: loss = 0.385583 (* 1 = 0.385583 loss)
I0407 23:07:32.921253 32630 sgd_solver.cpp:105] Iteration 5196, lr = 0.00476488
I0407 23:07:34.908740 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5202.caffemodel
I0407 23:07:38.017591 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5202.solverstate
I0407 23:07:40.459828 32630 solver.cpp:330] Iteration 5202, Testing net (#0)
I0407 23:07:40.459843 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:07:43.024741 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:07:45.305747 32630 solver.cpp:397]     Test net output #0: accuracy = 0.409926
I0407 23:07:45.305792 32630 solver.cpp:397]     Test net output #1: loss = 2.87912 (* 1 = 2.87912 loss)
I0407 23:07:47.107985 32630 solver.cpp:218] Iteration 5208 (0.845863 iter/s, 14.1867s/12 iters), loss = 0.367558
I0407 23:07:47.108028 32630 solver.cpp:237]     Train net output #0: loss = 0.367558 (* 1 = 0.367558 loss)
I0407 23:07:47.108036 32630 sgd_solver.cpp:105] Iteration 5208, lr = 0.00473554
I0407 23:07:52.040139 32630 solver.cpp:218] Iteration 5220 (2.43305 iter/s, 4.93208s/12 iters), loss = 0.354348
I0407 23:07:52.040285 32630 solver.cpp:237]     Train net output #0: loss = 0.354348 (* 1 = 0.354348 loss)
I0407 23:07:52.040295 32630 sgd_solver.cpp:105] Iteration 5220, lr = 0.00470622
I0407 23:07:56.992437 32630 solver.cpp:218] Iteration 5232 (2.4232 iter/s, 4.95213s/12 iters), loss = 0.334578
I0407 23:07:56.992482 32630 solver.cpp:237]     Train net output #0: loss = 0.334578 (* 1 = 0.334578 loss)
I0407 23:07:56.992491 32630 sgd_solver.cpp:105] Iteration 5232, lr = 0.00467692
I0407 23:08:01.938920 32630 solver.cpp:218] Iteration 5244 (2.426 iter/s, 4.94641s/12 iters), loss = 0.511284
I0407 23:08:01.938963 32630 solver.cpp:237]     Train net output #0: loss = 0.511284 (* 1 = 0.511284 loss)
I0407 23:08:01.938971 32630 sgd_solver.cpp:105] Iteration 5244, lr = 0.00464764
I0407 23:08:06.900256 32630 solver.cpp:218] Iteration 5256 (2.41873 iter/s, 4.96127s/12 iters), loss = 0.256644
I0407 23:08:06.900295 32630 solver.cpp:237]     Train net output #0: loss = 0.256644 (* 1 = 0.256644 loss)
I0407 23:08:06.900302 32630 sgd_solver.cpp:105] Iteration 5256, lr = 0.00461839
I0407 23:08:08.162717 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:08:11.821367 32630 solver.cpp:218] Iteration 5268 (2.43851 iter/s, 4.92104s/12 iters), loss = 0.311893
I0407 23:08:11.821409 32630 solver.cpp:237]     Train net output #0: loss = 0.311893 (* 1 = 0.311893 loss)
I0407 23:08:11.821419 32630 sgd_solver.cpp:105] Iteration 5268, lr = 0.00458916
I0407 23:08:16.789007 32630 solver.cpp:218] Iteration 5280 (2.41567 iter/s, 4.96756s/12 iters), loss = 0.320743
I0407 23:08:16.789053 32630 solver.cpp:237]     Train net output #0: loss = 0.320743 (* 1 = 0.320743 loss)
I0407 23:08:16.789062 32630 sgd_solver.cpp:105] Iteration 5280, lr = 0.00455996
I0407 23:08:21.734149 32630 solver.cpp:218] Iteration 5292 (2.42666 iter/s, 4.94507s/12 iters), loss = 0.408387
I0407 23:08:21.734196 32630 solver.cpp:237]     Train net output #0: loss = 0.408387 (* 1 = 0.408387 loss)
I0407 23:08:21.734205 32630 sgd_solver.cpp:105] Iteration 5292, lr = 0.0045308
I0407 23:08:26.179678 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5304.caffemodel
I0407 23:08:29.361143 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5304.solverstate
I0407 23:08:31.727080 32630 solver.cpp:330] Iteration 5304, Testing net (#0)
I0407 23:08:31.727099 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:08:34.119668 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:08:36.236162 32630 solver.cpp:397]     Test net output #0: accuracy = 0.401961
I0407 23:08:36.236198 32630 solver.cpp:397]     Test net output #1: loss = 2.94082 (* 1 = 2.94082 loss)
I0407 23:08:36.332711 32630 solver.cpp:218] Iteration 5304 (0.822004 iter/s, 14.5985s/12 iters), loss = 0.357037
I0407 23:08:36.332759 32630 solver.cpp:237]     Train net output #0: loss = 0.357038 (* 1 = 0.357038 loss)
I0407 23:08:36.332769 32630 sgd_solver.cpp:105] Iteration 5304, lr = 0.00450166
I0407 23:08:40.456334 32630 solver.cpp:218] Iteration 5316 (2.91011 iter/s, 4.12355s/12 iters), loss = 0.386331
I0407 23:08:40.456377 32630 solver.cpp:237]     Train net output #0: loss = 0.386331 (* 1 = 0.386331 loss)
I0407 23:08:40.456385 32630 sgd_solver.cpp:105] Iteration 5316, lr = 0.00447256
I0407 23:08:45.414089 32630 solver.cpp:218] Iteration 5328 (2.42048 iter/s, 4.95769s/12 iters), loss = 0.679533
I0407 23:08:45.414137 32630 solver.cpp:237]     Train net output #0: loss = 0.679533 (* 1 = 0.679533 loss)
I0407 23:08:45.414145 32630 sgd_solver.cpp:105] Iteration 5328, lr = 0.00444349
I0407 23:08:50.345170 32630 solver.cpp:218] Iteration 5340 (2.43358 iter/s, 4.93101s/12 iters), loss = 0.415245
I0407 23:08:50.345214 32630 solver.cpp:237]     Train net output #0: loss = 0.415245 (* 1 = 0.415245 loss)
I0407 23:08:50.345223 32630 sgd_solver.cpp:105] Iteration 5340, lr = 0.00441446
I0407 23:08:55.298256 32630 solver.cpp:218] Iteration 5352 (2.42276 iter/s, 4.95302s/12 iters), loss = 0.334418
I0407 23:08:55.298290 32630 solver.cpp:237]     Train net output #0: loss = 0.334418 (* 1 = 0.334418 loss)
I0407 23:08:55.298296 32630 sgd_solver.cpp:105] Iteration 5352, lr = 0.00438548
I0407 23:08:58.657238 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:00.194974 32630 solver.cpp:218] Iteration 5364 (2.45065 iter/s, 4.89666s/12 iters), loss = 0.199413
I0407 23:09:00.195012 32630 solver.cpp:237]     Train net output #0: loss = 0.199414 (* 1 = 0.199414 loss)
I0407 23:09:00.195020 32630 sgd_solver.cpp:105] Iteration 5364, lr = 0.00435653
I0407 23:09:05.180253 32630 solver.cpp:218] Iteration 5376 (2.40712 iter/s, 4.98521s/12 iters), loss = 0.397679
I0407 23:09:05.180294 32630 solver.cpp:237]     Train net output #0: loss = 0.397679 (* 1 = 0.397679 loss)
I0407 23:09:05.180301 32630 sgd_solver.cpp:105] Iteration 5376, lr = 0.00432763
I0407 23:09:10.159044 32630 solver.cpp:218] Iteration 5388 (2.41025 iter/s, 4.97873s/12 iters), loss = 0.251672
I0407 23:09:10.159087 32630 solver.cpp:237]     Train net output #0: loss = 0.251672 (* 1 = 0.251672 loss)
I0407 23:09:10.159096 32630 sgd_solver.cpp:105] Iteration 5388, lr = 0.00429877
I0407 23:09:15.260094 32630 solver.cpp:218] Iteration 5400 (2.35249 iter/s, 5.10099s/12 iters), loss = 0.288328
I0407 23:09:15.260151 32630 solver.cpp:237]     Train net output #0: loss = 0.288328 (* 1 = 0.288328 loss)
I0407 23:09:15.260160 32630 sgd_solver.cpp:105] Iteration 5400, lr = 0.00426996
I0407 23:09:17.322901 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5406.caffemodel
I0407 23:09:20.727919 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5406.solverstate
I0407 23:09:23.466168 32630 solver.cpp:330] Iteration 5406, Testing net (#0)
I0407 23:09:23.466185 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:09:25.728317 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:27.888648 32630 solver.cpp:397]     Test net output #0: accuracy = 0.424632
I0407 23:09:27.888695 32630 solver.cpp:397]     Test net output #1: loss = 2.93936 (* 1 = 2.93936 loss)
I0407 23:09:29.695688 32630 solver.cpp:218] Iteration 5412 (0.831284 iter/s, 14.4355s/12 iters), loss = 0.337827
I0407 23:09:29.695802 32630 solver.cpp:237]     Train net output #0: loss = 0.337827 (* 1 = 0.337827 loss)
I0407 23:09:29.695811 32630 sgd_solver.cpp:105] Iteration 5412, lr = 0.0042412
I0407 23:09:34.620031 32630 solver.cpp:218] Iteration 5424 (2.43694 iter/s, 4.92421s/12 iters), loss = 0.441344
I0407 23:09:34.620074 32630 solver.cpp:237]     Train net output #0: loss = 0.441344 (* 1 = 0.441344 loss)
I0407 23:09:34.620082 32630 sgd_solver.cpp:105] Iteration 5424, lr = 0.00421249
I0407 23:09:39.592972 32630 solver.cpp:218] Iteration 5436 (2.41309 iter/s, 4.97287s/12 iters), loss = 0.274199
I0407 23:09:39.593021 32630 solver.cpp:237]     Train net output #0: loss = 0.274199 (* 1 = 0.274199 loss)
I0407 23:09:39.593030 32630 sgd_solver.cpp:105] Iteration 5436, lr = 0.00418384
I0407 23:09:44.528867 32630 solver.cpp:218] Iteration 5448 (2.43121 iter/s, 4.93582s/12 iters), loss = 0.435709
I0407 23:09:44.528913 32630 solver.cpp:237]     Train net output #0: loss = 0.435709 (* 1 = 0.435709 loss)
I0407 23:09:44.528920 32630 sgd_solver.cpp:105] Iteration 5448, lr = 0.00415524
I0407 23:09:49.489794 32630 solver.cpp:218] Iteration 5460 (2.41894 iter/s, 4.96085s/12 iters), loss = 0.32687
I0407 23:09:49.489838 32630 solver.cpp:237]     Train net output #0: loss = 0.32687 (* 1 = 0.32687 loss)
I0407 23:09:49.489847 32630 sgd_solver.cpp:105] Iteration 5460, lr = 0.00412669
I0407 23:09:50.024614 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:09:54.384008 32630 solver.cpp:218] Iteration 5472 (2.45191 iter/s, 4.89415s/12 iters), loss = 0.374327
I0407 23:09:54.384048 32630 solver.cpp:237]     Train net output #0: loss = 0.374327 (* 1 = 0.374327 loss)
I0407 23:09:54.384057 32630 sgd_solver.cpp:105] Iteration 5472, lr = 0.00409821
I0407 23:09:59.346232 32630 solver.cpp:218] Iteration 5484 (2.4183 iter/s, 4.96216s/12 iters), loss = 0.308039
I0407 23:09:59.346276 32630 solver.cpp:237]     Train net output #0: loss = 0.308039 (* 1 = 0.308039 loss)
I0407 23:09:59.346285 32630 sgd_solver.cpp:105] Iteration 5484, lr = 0.00406978
I0407 23:10:04.242523 32630 solver.cpp:218] Iteration 5496 (2.45087 iter/s, 4.89623s/12 iters), loss = 0.372102
I0407 23:10:04.242681 32630 solver.cpp:237]     Train net output #0: loss = 0.372102 (* 1 = 0.372102 loss)
I0407 23:10:04.242691 32630 sgd_solver.cpp:105] Iteration 5496, lr = 0.00404142
I0407 23:10:08.748993 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5508.caffemodel
I0407 23:10:15.039423 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5508.solverstate
I0407 23:10:18.259003 32630 solver.cpp:330] Iteration 5508, Testing net (#0)
I0407 23:10:18.259021 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:10:20.559877 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:10:22.799249 32630 solver.cpp:397]     Test net output #0: accuracy = 0.424632
I0407 23:10:22.799295 32630 solver.cpp:397]     Test net output #1: loss = 2.82094 (* 1 = 2.82094 loss)
I0407 23:10:22.895788 32630 solver.cpp:218] Iteration 5508 (0.643326 iter/s, 18.6531s/12 iters), loss = 0.283622
I0407 23:10:22.895848 32630 solver.cpp:237]     Train net output #0: loss = 0.283622 (* 1 = 0.283622 loss)
I0407 23:10:22.895855 32630 sgd_solver.cpp:105] Iteration 5508, lr = 0.00401312
I0407 23:10:27.046664 32630 solver.cpp:218] Iteration 5520 (2.89101 iter/s, 4.1508s/12 iters), loss = 0.327509
I0407 23:10:27.046701 32630 solver.cpp:237]     Train net output #0: loss = 0.327509 (* 1 = 0.327509 loss)
I0407 23:10:27.046710 32630 sgd_solver.cpp:105] Iteration 5520, lr = 0.00398489
I0407 23:10:29.468945 32630 blocking_queue.cpp:49] Waiting for data
I0407 23:10:31.987160 32630 solver.cpp:218] Iteration 5532 (2.42893 iter/s, 4.94044s/12 iters), loss = 0.369201
I0407 23:10:31.987201 32630 solver.cpp:237]     Train net output #0: loss = 0.369201 (* 1 = 0.369201 loss)
I0407 23:10:31.987210 32630 sgd_solver.cpp:105] Iteration 5532, lr = 0.00395672
I0407 23:10:36.957388 32630 solver.cpp:218] Iteration 5544 (2.4144 iter/s, 4.97017s/12 iters), loss = 0.259542
I0407 23:10:36.957509 32630 solver.cpp:237]     Train net output #0: loss = 0.259543 (* 1 = 0.259543 loss)
I0407 23:10:36.957517 32630 sgd_solver.cpp:105] Iteration 5544, lr = 0.00392863
I0407 23:10:41.941227 32630 solver.cpp:218] Iteration 5556 (2.40785 iter/s, 4.9837s/12 iters), loss = 0.380843
I0407 23:10:41.941268 32630 solver.cpp:237]     Train net output #0: loss = 0.380843 (* 1 = 0.380843 loss)
I0407 23:10:41.941277 32630 sgd_solver.cpp:105] Iteration 5556, lr = 0.0039006
I0407 23:10:44.603715 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:10:46.876853 32630 solver.cpp:218] Iteration 5568 (2.43134 iter/s, 4.93556s/12 iters), loss = 0.154861
I0407 23:10:46.876895 32630 solver.cpp:237]     Train net output #0: loss = 0.154861 (* 1 = 0.154861 loss)
I0407 23:10:46.876904 32630 sgd_solver.cpp:105] Iteration 5568, lr = 0.00387265
I0407 23:10:51.712280 32630 solver.cpp:218] Iteration 5580 (2.48171 iter/s, 4.83537s/12 iters), loss = 0.253599
I0407 23:10:51.712318 32630 solver.cpp:237]     Train net output #0: loss = 0.253599 (* 1 = 0.253599 loss)
I0407 23:10:51.712327 32630 sgd_solver.cpp:105] Iteration 5580, lr = 0.00384477
I0407 23:10:56.668159 32630 solver.cpp:218] Iteration 5592 (2.4214 iter/s, 4.95582s/12 iters), loss = 0.297287
I0407 23:10:56.668200 32630 solver.cpp:237]     Train net output #0: loss = 0.297288 (* 1 = 0.297288 loss)
I0407 23:10:56.668207 32630 sgd_solver.cpp:105] Iteration 5592, lr = 0.00381697
I0407 23:11:01.564110 32630 solver.cpp:218] Iteration 5604 (2.45104 iter/s, 4.89589s/12 iters), loss = 0.2891
I0407 23:11:01.564147 32630 solver.cpp:237]     Train net output #0: loss = 0.2891 (* 1 = 0.2891 loss)
I0407 23:11:01.564154 32630 sgd_solver.cpp:105] Iteration 5604, lr = 0.00378924
I0407 23:11:03.612816 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5610.caffemodel
I0407 23:11:08.081938 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5610.solverstate
I0407 23:11:12.376035 32630 solver.cpp:330] Iteration 5610, Testing net (#0)
I0407 23:11:12.376053 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:11:14.718719 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:11:17.147356 32630 solver.cpp:397]     Test net output #0: accuracy = 0.427696
I0407 23:11:17.147399 32630 solver.cpp:397]     Test net output #1: loss = 2.88569 (* 1 = 2.88569 loss)
I0407 23:11:18.976583 32630 solver.cpp:218] Iteration 5616 (0.689165 iter/s, 17.4124s/12 iters), loss = 0.389081
I0407 23:11:18.976629 32630 solver.cpp:237]     Train net output #0: loss = 0.389081 (* 1 = 0.389081 loss)
I0407 23:11:18.976640 32630 sgd_solver.cpp:105] Iteration 5616, lr = 0.00376159
I0407 23:11:23.952834 32630 solver.cpp:218] Iteration 5628 (2.41149 iter/s, 4.97618s/12 iters), loss = 0.235206
I0407 23:11:23.952878 32630 solver.cpp:237]     Train net output #0: loss = 0.235206 (* 1 = 0.235206 loss)
I0407 23:11:23.952886 32630 sgd_solver.cpp:105] Iteration 5628, lr = 0.00373403
I0407 23:11:28.847556 32630 solver.cpp:218] Iteration 5640 (2.45166 iter/s, 4.89465s/12 iters), loss = 0.279091
I0407 23:11:28.847602 32630 solver.cpp:237]     Train net output #0: loss = 0.279092 (* 1 = 0.279092 loss)
I0407 23:11:28.847610 32630 sgd_solver.cpp:105] Iteration 5640, lr = 0.00370654
I0407 23:11:33.825698 32630 solver.cpp:218] Iteration 5652 (2.41057 iter/s, 4.97807s/12 iters), loss = 0.2019
I0407 23:11:33.825742 32630 solver.cpp:237]     Train net output #0: loss = 0.2019 (* 1 = 0.2019 loss)
I0407 23:11:33.825752 32630 sgd_solver.cpp:105] Iteration 5652, lr = 0.00367914
I0407 23:11:38.555131 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:11:38.723349 32630 solver.cpp:218] Iteration 5664 (2.45019 iter/s, 4.89758s/12 iters), loss = 0.178053
I0407 23:11:38.723395 32630 solver.cpp:237]     Train net output #0: loss = 0.178053 (* 1 = 0.178053 loss)
I0407 23:11:38.723404 32630 sgd_solver.cpp:105] Iteration 5664, lr = 0.00365182
I0407 23:11:43.670672 32630 solver.cpp:218] Iteration 5676 (2.42559 iter/s, 4.94725s/12 iters), loss = 0.183607
I0407 23:11:43.670717 32630 solver.cpp:237]     Train net output #0: loss = 0.183607 (* 1 = 0.183607 loss)
I0407 23:11:43.670725 32630 sgd_solver.cpp:105] Iteration 5676, lr = 0.00362459
I0407 23:11:48.641757 32630 solver.cpp:218] Iteration 5688 (2.414 iter/s, 4.97101s/12 iters), loss = 0.252277
I0407 23:11:48.641798 32630 solver.cpp:237]     Train net output #0: loss = 0.252278 (* 1 = 0.252278 loss)
I0407 23:11:48.641805 32630 sgd_solver.cpp:105] Iteration 5688, lr = 0.00359745
I0407 23:11:53.590389 32630 solver.cpp:218] Iteration 5700 (2.42494 iter/s, 4.94857s/12 iters), loss = 0.342034
I0407 23:11:53.590427 32630 solver.cpp:237]     Train net output #0: loss = 0.342034 (* 1 = 0.342034 loss)
I0407 23:11:53.590435 32630 sgd_solver.cpp:105] Iteration 5700, lr = 0.0035704
I0407 23:11:58.078619 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5712.caffemodel
I0407 23:12:01.180346 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5712.solverstate
I0407 23:12:03.543393 32630 solver.cpp:330] Iteration 5712, Testing net (#0)
I0407 23:12:03.543411 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:12:05.853240 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:12:08.322969 32630 solver.cpp:397]     Test net output #0: accuracy = 0.431373
I0407 23:12:08.323016 32630 solver.cpp:397]     Test net output #1: loss = 2.90856 (* 1 = 2.90856 loss)
I0407 23:12:08.419436 32630 solver.cpp:218] Iteration 5712 (0.809227 iter/s, 14.829s/12 iters), loss = 0.338592
I0407 23:12:08.419482 32630 solver.cpp:237]     Train net output #0: loss = 0.338592 (* 1 = 0.338592 loss)
I0407 23:12:08.419492 32630 sgd_solver.cpp:105] Iteration 5712, lr = 0.00354344
I0407 23:12:12.533506 32630 solver.cpp:218] Iteration 5724 (2.91687 iter/s, 4.114s/12 iters), loss = 0.287865
I0407 23:12:12.533668 32630 solver.cpp:237]     Train net output #0: loss = 0.287865 (* 1 = 0.287865 loss)
I0407 23:12:12.533677 32630 sgd_solver.cpp:105] Iteration 5724, lr = 0.00351657
I0407 23:12:17.540165 32630 solver.cpp:218] Iteration 5736 (2.3969 iter/s, 5.00647s/12 iters), loss = 0.239864
I0407 23:12:17.540210 32630 solver.cpp:237]     Train net output #0: loss = 0.239864 (* 1 = 0.239864 loss)
I0407 23:12:17.540218 32630 sgd_solver.cpp:105] Iteration 5736, lr = 0.00348979
I0407 23:12:22.511516 32630 solver.cpp:218] Iteration 5748 (2.41387 iter/s, 4.97128s/12 iters), loss = 0.234648
I0407 23:12:22.511562 32630 solver.cpp:237]     Train net output #0: loss = 0.234648 (* 1 = 0.234648 loss)
I0407 23:12:22.511570 32630 sgd_solver.cpp:105] Iteration 5748, lr = 0.00346311
I0407 23:12:27.677620 32630 solver.cpp:218] Iteration 5760 (2.32286 iter/s, 5.16604s/12 iters), loss = 0.201537
I0407 23:12:27.677654 32630 solver.cpp:237]     Train net output #0: loss = 0.201537 (* 1 = 0.201537 loss)
I0407 23:12:27.677661 32630 sgd_solver.cpp:105] Iteration 5760, lr = 0.00343653
I0407 23:12:29.632239 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:12:32.632493 32630 solver.cpp:218] Iteration 5772 (2.42189 iter/s, 4.95482s/12 iters), loss = 0.196661
I0407 23:12:32.632532 32630 solver.cpp:237]     Train net output #0: loss = 0.196661 (* 1 = 0.196661 loss)
I0407 23:12:32.632539 32630 sgd_solver.cpp:105] Iteration 5772, lr = 0.00341004
I0407 23:12:37.593488 32630 solver.cpp:218] Iteration 5784 (2.4189 iter/s, 4.96093s/12 iters), loss = 0.125648
I0407 23:12:37.593531 32630 solver.cpp:237]     Train net output #0: loss = 0.125648 (* 1 = 0.125648 loss)
I0407 23:12:37.593539 32630 sgd_solver.cpp:105] Iteration 5784, lr = 0.00338365
I0407 23:12:42.513221 32630 solver.cpp:218] Iteration 5796 (2.43919 iter/s, 4.91967s/12 iters), loss = 0.212764
I0407 23:12:42.513254 32630 solver.cpp:237]     Train net output #0: loss = 0.212764 (* 1 = 0.212764 loss)
I0407 23:12:42.513262 32630 sgd_solver.cpp:105] Iteration 5796, lr = 0.00335736
I0407 23:12:47.461710 32630 solver.cpp:218] Iteration 5808 (2.42501 iter/s, 4.94843s/12 iters), loss = 0.191752
I0407 23:12:47.461838 32630 solver.cpp:237]     Train net output #0: loss = 0.191752 (* 1 = 0.191752 loss)
I0407 23:12:47.461848 32630 sgd_solver.cpp:105] Iteration 5808, lr = 0.00333118
I0407 23:12:49.415690 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5814.caffemodel
I0407 23:12:52.484990 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5814.solverstate
I0407 23:12:54.842042 32630 solver.cpp:330] Iteration 5814, Testing net (#0)
I0407 23:12:54.842058 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:12:57.082741 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:12:59.603353 32630 solver.cpp:397]     Test net output #0: accuracy = 0.447304
I0407 23:12:59.603397 32630 solver.cpp:397]     Test net output #1: loss = 2.80261 (* 1 = 2.80261 loss)
I0407 23:13:01.431561 32630 solver.cpp:218] Iteration 5820 (0.859003 iter/s, 13.9697s/12 iters), loss = 0.217997
I0407 23:13:01.431607 32630 solver.cpp:237]     Train net output #0: loss = 0.217997 (* 1 = 0.217997 loss)
I0407 23:13:01.431617 32630 sgd_solver.cpp:105] Iteration 5820, lr = 0.00330509
I0407 23:13:06.459422 32630 solver.cpp:218] Iteration 5832 (2.38673 iter/s, 5.02779s/12 iters), loss = 0.291184
I0407 23:13:06.459467 32630 solver.cpp:237]     Train net output #0: loss = 0.291184 (* 1 = 0.291184 loss)
I0407 23:13:06.459475 32630 sgd_solver.cpp:105] Iteration 5832, lr = 0.00327911
I0407 23:13:11.404408 32630 solver.cpp:218] Iteration 5844 (2.42673 iter/s, 4.94492s/12 iters), loss = 0.240729
I0407 23:13:11.404446 32630 solver.cpp:237]     Train net output #0: loss = 0.240729 (* 1 = 0.240729 loss)
I0407 23:13:11.404454 32630 sgd_solver.cpp:105] Iteration 5844, lr = 0.00325324
I0407 23:13:16.381588 32630 solver.cpp:218] Iteration 5856 (2.41104 iter/s, 4.97711s/12 iters), loss = 0.09877
I0407 23:13:16.381635 32630 solver.cpp:237]     Train net output #0: loss = 0.0987701 (* 1 = 0.0987701 loss)
I0407 23:13:16.381644 32630 sgd_solver.cpp:105] Iteration 5856, lr = 0.00322747
I0407 23:13:20.538652 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:13:21.322307 32630 solver.cpp:218] Iteration 5868 (2.42883 iter/s, 4.94065s/12 iters), loss = 0.26235
I0407 23:13:21.322345 32630 solver.cpp:237]     Train net output #0: loss = 0.26235 (* 1 = 0.26235 loss)
I0407 23:13:21.322352 32630 sgd_solver.cpp:105] Iteration 5868, lr = 0.00320181
I0407 23:13:26.245432 32630 solver.cpp:218] Iteration 5880 (2.43751 iter/s, 4.92306s/12 iters), loss = 0.0904097
I0407 23:13:26.245477 32630 solver.cpp:237]     Train net output #0: loss = 0.0904098 (* 1 = 0.0904098 loss)
I0407 23:13:26.245486 32630 sgd_solver.cpp:105] Iteration 5880, lr = 0.00317625
I0407 23:13:31.183244 32630 solver.cpp:218] Iteration 5892 (2.43027 iter/s, 4.93773s/12 iters), loss = 0.182121
I0407 23:13:31.183310 32630 solver.cpp:237]     Train net output #0: loss = 0.182121 (* 1 = 0.182121 loss)
I0407 23:13:31.183332 32630 sgd_solver.cpp:105] Iteration 5892, lr = 0.00315081
I0407 23:13:36.153270 32630 solver.cpp:218] Iteration 5904 (2.41451 iter/s, 4.96994s/12 iters), loss = 0.135214
I0407 23:13:36.153311 32630 solver.cpp:237]     Train net output #0: loss = 0.135214 (* 1 = 0.135214 loss)
I0407 23:13:36.153319 32630 sgd_solver.cpp:105] Iteration 5904, lr = 0.00312548
I0407 23:13:40.574394 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_5916.caffemodel
I0407 23:13:43.665359 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_5916.solverstate
I0407 23:13:46.033555 32630 solver.cpp:330] Iteration 5916, Testing net (#0)
I0407 23:13:46.033576 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:13:48.256042 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:13:50.809868 32630 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0407 23:13:50.810034 32630 solver.cpp:397]     Test net output #1: loss = 2.74255 (* 1 = 2.74255 loss)
I0407 23:13:50.905469 32630 solver.cpp:218] Iteration 5916 (0.813443 iter/s, 14.7521s/12 iters), loss = 0.114982
I0407 23:13:50.905519 32630 solver.cpp:237]     Train net output #0: loss = 0.114982 (* 1 = 0.114982 loss)
I0407 23:13:50.905530 32630 sgd_solver.cpp:105] Iteration 5916, lr = 0.00310026
I0407 23:13:55.044503 32630 solver.cpp:218] Iteration 5928 (2.89927 iter/s, 4.13897s/12 iters), loss = 0.19918
I0407 23:13:55.044539 32630 solver.cpp:237]     Train net output #0: loss = 0.199181 (* 1 = 0.199181 loss)
I0407 23:13:55.044548 32630 sgd_solver.cpp:105] Iteration 5928, lr = 0.00307515
I0407 23:13:59.901856 32630 solver.cpp:218] Iteration 5940 (2.47051 iter/s, 4.85729s/12 iters), loss = 0.252885
I0407 23:13:59.901897 32630 solver.cpp:237]     Train net output #0: loss = 0.252885 (* 1 = 0.252885 loss)
I0407 23:13:59.901906 32630 sgd_solver.cpp:105] Iteration 5940, lr = 0.00305015
I0407 23:14:04.844769 32630 solver.cpp:218] Iteration 5952 (2.42775 iter/s, 4.94285s/12 iters), loss = 0.163826
I0407 23:14:04.844815 32630 solver.cpp:237]     Train net output #0: loss = 0.163826 (* 1 = 0.163826 loss)
I0407 23:14:04.844823 32630 sgd_solver.cpp:105] Iteration 5952, lr = 0.00302527
I0407 23:14:09.779356 32630 solver.cpp:218] Iteration 5964 (2.43185 iter/s, 4.93452s/12 iters), loss = 0.120886
I0407 23:14:09.779402 32630 solver.cpp:237]     Train net output #0: loss = 0.120886 (* 1 = 0.120886 loss)
I0407 23:14:09.779410 32630 sgd_solver.cpp:105] Iteration 5964, lr = 0.0030005
I0407 23:14:11.066004 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:14:14.673125 32630 solver.cpp:218] Iteration 5976 (2.45213 iter/s, 4.8937s/12 iters), loss = 0.136716
I0407 23:14:14.673161 32630 solver.cpp:237]     Train net output #0: loss = 0.136716 (* 1 = 0.136716 loss)
I0407 23:14:14.673171 32630 sgd_solver.cpp:105] Iteration 5976, lr = 0.00297585
I0407 23:14:19.649051 32630 solver.cpp:218] Iteration 5988 (2.41164 iter/s, 4.97587s/12 iters), loss = 0.245523
I0407 23:14:19.649091 32630 solver.cpp:237]     Train net output #0: loss = 0.245523 (* 1 = 0.245523 loss)
I0407 23:14:19.649098 32630 sgd_solver.cpp:105] Iteration 5988, lr = 0.00295132
I0407 23:14:24.606978 32630 solver.cpp:218] Iteration 6000 (2.4204 iter/s, 4.95786s/12 iters), loss = 0.131554
I0407 23:14:24.607146 32630 solver.cpp:237]     Train net output #0: loss = 0.131555 (* 1 = 0.131555 loss)
I0407 23:14:24.607156 32630 sgd_solver.cpp:105] Iteration 6000, lr = 0.0029269
I0407 23:14:29.526057 32630 solver.cpp:218] Iteration 6012 (2.43958 iter/s, 4.91888s/12 iters), loss = 0.244287
I0407 23:14:29.526096 32630 solver.cpp:237]     Train net output #0: loss = 0.244287 (* 1 = 0.244287 loss)
I0407 23:14:29.526105 32630 sgd_solver.cpp:105] Iteration 6012, lr = 0.00290261
I0407 23:14:31.528834 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6018.caffemodel
I0407 23:14:34.630041 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6018.solverstate
I0407 23:14:37.041446 32630 solver.cpp:330] Iteration 6018, Testing net (#0)
I0407 23:14:37.041465 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:14:39.238718 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:14:41.845261 32630 solver.cpp:397]     Test net output #0: accuracy = 0.466299
I0407 23:14:41.845307 32630 solver.cpp:397]     Test net output #1: loss = 2.69147 (* 1 = 2.69147 loss)
I0407 23:14:43.648082 32630 solver.cpp:218] Iteration 6024 (0.849742 iter/s, 14.1219s/12 iters), loss = 0.216977
I0407 23:14:43.648129 32630 solver.cpp:237]     Train net output #0: loss = 0.216977 (* 1 = 0.216977 loss)
I0407 23:14:43.648137 32630 sgd_solver.cpp:105] Iteration 6024, lr = 0.00287843
I0407 23:14:48.864740 32630 solver.cpp:218] Iteration 6036 (2.30035 iter/s, 5.21659s/12 iters), loss = 0.240166
I0407 23:14:48.864778 32630 solver.cpp:237]     Train net output #0: loss = 0.240166 (* 1 = 0.240166 loss)
I0407 23:14:48.864784 32630 sgd_solver.cpp:105] Iteration 6036, lr = 0.00285438
I0407 23:14:53.919997 32630 solver.cpp:218] Iteration 6048 (2.3738 iter/s, 5.05519s/12 iters), loss = 0.22263
I0407 23:14:53.920043 32630 solver.cpp:237]     Train net output #0: loss = 0.22263 (* 1 = 0.22263 loss)
I0407 23:14:53.920051 32630 sgd_solver.cpp:105] Iteration 6048, lr = 0.00283044
I0407 23:14:58.939011 32630 solver.cpp:218] Iteration 6060 (2.39094 iter/s, 5.01895s/12 iters), loss = 0.296393
I0407 23:14:58.939131 32630 solver.cpp:237]     Train net output #0: loss = 0.296393 (* 1 = 0.296393 loss)
I0407 23:14:58.939141 32630 sgd_solver.cpp:105] Iteration 6060, lr = 0.00280663
I0407 23:15:02.487731 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:15:04.009662 32630 solver.cpp:218] Iteration 6072 (2.36662 iter/s, 5.07051s/12 iters), loss = 0.22179
I0407 23:15:04.009698 32630 solver.cpp:237]     Train net output #0: loss = 0.22179 (* 1 = 0.22179 loss)
I0407 23:15:04.009706 32630 sgd_solver.cpp:105] Iteration 6072, lr = 0.00278294
I0407 23:15:09.009851 32630 solver.cpp:218] Iteration 6084 (2.39994 iter/s, 5.00013s/12 iters), loss = 0.184837
I0407 23:15:09.009888 32630 solver.cpp:237]     Train net output #0: loss = 0.184837 (* 1 = 0.184837 loss)
I0407 23:15:09.009896 32630 sgd_solver.cpp:105] Iteration 6084, lr = 0.00275937
I0407 23:15:13.971344 32630 solver.cpp:218] Iteration 6096 (2.41865 iter/s, 4.96144s/12 iters), loss = 0.099539
I0407 23:15:13.971380 32630 solver.cpp:237]     Train net output #0: loss = 0.0995391 (* 1 = 0.0995391 loss)
I0407 23:15:13.971387 32630 sgd_solver.cpp:105] Iteration 6096, lr = 0.00273593
I0407 23:15:18.866788 32630 solver.cpp:218] Iteration 6108 (2.45129 iter/s, 4.89538s/12 iters), loss = 0.193947
I0407 23:15:18.866832 32630 solver.cpp:237]     Train net output #0: loss = 0.193947 (* 1 = 0.193947 loss)
I0407 23:15:18.866840 32630 sgd_solver.cpp:105] Iteration 6108, lr = 0.00271261
I0407 23:15:23.418817 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6120.caffemodel
I0407 23:15:26.513098 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6120.solverstate
I0407 23:15:28.890282 32630 solver.cpp:330] Iteration 6120, Testing net (#0)
I0407 23:15:28.890300 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:15:31.022603 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:15:33.670274 32630 solver.cpp:397]     Test net output #0: accuracy = 0.458946
I0407 23:15:33.670310 32630 solver.cpp:397]     Test net output #1: loss = 2.76314 (* 1 = 2.76314 loss)
I0407 23:15:33.766921 32630 solver.cpp:218] Iteration 6120 (0.805367 iter/s, 14.9s/12 iters), loss = 0.134445
I0407 23:15:33.766979 32630 solver.cpp:237]     Train net output #0: loss = 0.134445 (* 1 = 0.134445 loss)
I0407 23:15:33.766990 32630 sgd_solver.cpp:105] Iteration 6120, lr = 0.00268941
I0407 23:15:37.906749 32630 solver.cpp:218] Iteration 6132 (2.89873 iter/s, 4.13975s/12 iters), loss = 0.282288
I0407 23:15:37.906795 32630 solver.cpp:237]     Train net output #0: loss = 0.282288 (* 1 = 0.282288 loss)
I0407 23:15:37.906803 32630 sgd_solver.cpp:105] Iteration 6132, lr = 0.00266635
I0407 23:15:42.861343 32630 solver.cpp:218] Iteration 6144 (2.42203 iter/s, 4.95452s/12 iters), loss = 0.112969
I0407 23:15:42.861387 32630 solver.cpp:237]     Train net output #0: loss = 0.112969 (* 1 = 0.112969 loss)
I0407 23:15:42.861394 32630 sgd_solver.cpp:105] Iteration 6144, lr = 0.0026434
I0407 23:15:47.793215 32630 solver.cpp:218] Iteration 6156 (2.43319 iter/s, 4.9318s/12 iters), loss = 0.256742
I0407 23:15:47.793260 32630 solver.cpp:237]     Train net output #0: loss = 0.256742 (* 1 = 0.256742 loss)
I0407 23:15:47.793269 32630 sgd_solver.cpp:105] Iteration 6156, lr = 0.00262059
I0407 23:15:52.759963 32630 solver.cpp:218] Iteration 6168 (2.4161 iter/s, 4.96668s/12 iters), loss = 0.168612
I0407 23:15:52.760008 32630 solver.cpp:237]     Train net output #0: loss = 0.168613 (* 1 = 0.168613 loss)
I0407 23:15:52.760016 32630 sgd_solver.cpp:105] Iteration 6168, lr = 0.0025979
I0407 23:15:53.324499 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:15:57.657058 32630 solver.cpp:218] Iteration 6180 (2.45047 iter/s, 4.89702s/12 iters), loss = 0.245709
I0407 23:15:57.657105 32630 solver.cpp:237]     Train net output #0: loss = 0.245709 (* 1 = 0.245709 loss)
I0407 23:15:57.657114 32630 sgd_solver.cpp:105] Iteration 6180, lr = 0.00257534
I0407 23:16:02.620612 32630 solver.cpp:218] Iteration 6192 (2.41766 iter/s, 4.96349s/12 iters), loss = 0.0813222
I0407 23:16:02.620754 32630 solver.cpp:237]     Train net output #0: loss = 0.0813223 (* 1 = 0.0813223 loss)
I0407 23:16:02.620762 32630 sgd_solver.cpp:105] Iteration 6192, lr = 0.00255291
I0407 23:16:07.541999 32630 solver.cpp:218] Iteration 6204 (2.43842 iter/s, 4.92122s/12 iters), loss = 0.128616
I0407 23:16:07.542047 32630 solver.cpp:237]     Train net output #0: loss = 0.128616 (* 1 = 0.128616 loss)
I0407 23:16:07.542054 32630 sgd_solver.cpp:105] Iteration 6204, lr = 0.00253061
I0407 23:16:12.518280 32630 solver.cpp:218] Iteration 6216 (2.41147 iter/s, 4.97621s/12 iters), loss = 0.0869789
I0407 23:16:12.518322 32630 solver.cpp:237]     Train net output #0: loss = 0.086979 (* 1 = 0.086979 loss)
I0407 23:16:12.518332 32630 sgd_solver.cpp:105] Iteration 6216, lr = 0.00250844
I0407 23:16:14.527710 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6222.caffemodel
I0407 23:16:17.632257 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6222.solverstate
I0407 23:16:20.011678 32630 solver.cpp:330] Iteration 6222, Testing net (#0)
I0407 23:16:20.011696 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:16:22.001348 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:16:23.305171 32630 blocking_queue.cpp:49] Waiting for data
I0407 23:16:24.505213 32630 solver.cpp:397]     Test net output #0: accuracy = 0.467524
I0407 23:16:24.505240 32630 solver.cpp:397]     Test net output #1: loss = 2.66715 (* 1 = 2.66715 loss)
I0407 23:16:26.270257 32630 solver.cpp:218] Iteration 6228 (0.872607 iter/s, 13.7519s/12 iters), loss = 0.111481
I0407 23:16:26.270303 32630 solver.cpp:237]     Train net output #0: loss = 0.111481 (* 1 = 0.111481 loss)
I0407 23:16:26.270311 32630 sgd_solver.cpp:105] Iteration 6228, lr = 0.00248639
I0407 23:16:31.231995 32630 solver.cpp:218] Iteration 6240 (2.41854 iter/s, 4.96167s/12 iters), loss = 0.287332
I0407 23:16:31.232040 32630 solver.cpp:237]     Train net output #0: loss = 0.287332 (* 1 = 0.287332 loss)
I0407 23:16:31.232049 32630 sgd_solver.cpp:105] Iteration 6240, lr = 0.00246448
I0407 23:16:36.184646 32630 solver.cpp:218] Iteration 6252 (2.42298 iter/s, 4.95258s/12 iters), loss = 0.203633
I0407 23:16:36.184808 32630 solver.cpp:237]     Train net output #0: loss = 0.203633 (* 1 = 0.203633 loss)
I0407 23:16:36.184818 32630 sgd_solver.cpp:105] Iteration 6252, lr = 0.0024427
I0407 23:16:41.116281 32630 solver.cpp:218] Iteration 6264 (2.43336 iter/s, 4.93145s/12 iters), loss = 0.195806
I0407 23:16:41.116317 32630 solver.cpp:237]     Train net output #0: loss = 0.195806 (* 1 = 0.195806 loss)
I0407 23:16:41.116325 32630 sgd_solver.cpp:105] Iteration 6264, lr = 0.00242104
I0407 23:16:43.807682 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:16:46.079286 32630 solver.cpp:218] Iteration 6276 (2.41792 iter/s, 4.96294s/12 iters), loss = 0.155129
I0407 23:16:46.079329 32630 solver.cpp:237]     Train net output #0: loss = 0.155129 (* 1 = 0.155129 loss)
I0407 23:16:46.079336 32630 sgd_solver.cpp:105] Iteration 6276, lr = 0.00239952
I0407 23:16:51.019984 32630 solver.cpp:218] Iteration 6288 (2.42884 iter/s, 4.94064s/12 iters), loss = 0.107155
I0407 23:16:51.020017 32630 solver.cpp:237]     Train net output #0: loss = 0.107155 (* 1 = 0.107155 loss)
I0407 23:16:51.020025 32630 sgd_solver.cpp:105] Iteration 6288, lr = 0.00237813
I0407 23:16:55.986208 32630 solver.cpp:218] Iteration 6300 (2.41635 iter/s, 4.96617s/12 iters), loss = 0.094955
I0407 23:16:55.986254 32630 solver.cpp:237]     Train net output #0: loss = 0.0949551 (* 1 = 0.0949551 loss)
I0407 23:16:55.986263 32630 sgd_solver.cpp:105] Iteration 6300, lr = 0.00235687
I0407 23:17:00.955581 32630 solver.cpp:218] Iteration 6312 (2.41482 iter/s, 4.96931s/12 iters), loss = 0.293861
I0407 23:17:00.955619 32630 solver.cpp:237]     Train net output #0: loss = 0.293861 (* 1 = 0.293861 loss)
I0407 23:17:00.955626 32630 sgd_solver.cpp:105] Iteration 6312, lr = 0.00233575
I0407 23:17:05.521580 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6324.caffemodel
I0407 23:17:09.856520 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6324.solverstate
I0407 23:17:12.217772 32630 solver.cpp:330] Iteration 6324, Testing net (#0)
I0407 23:17:12.217789 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:17:14.258008 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:17:16.988256 32630 solver.cpp:397]     Test net output #0: accuracy = 0.479167
I0407 23:17:16.988296 32630 solver.cpp:397]     Test net output #1: loss = 2.71418 (* 1 = 2.71418 loss)
I0407 23:17:17.084707 32630 solver.cpp:218] Iteration 6324 (0.744 iter/s, 16.129s/12 iters), loss = 0.140256
I0407 23:17:17.084751 32630 solver.cpp:237]     Train net output #0: loss = 0.140256 (* 1 = 0.140256 loss)
I0407 23:17:17.084760 32630 sgd_solver.cpp:105] Iteration 6324, lr = 0.00231475
I0407 23:17:21.256675 32630 solver.cpp:218] Iteration 6336 (2.87639 iter/s, 4.1719s/12 iters), loss = 0.180038
I0407 23:17:21.256721 32630 solver.cpp:237]     Train net output #0: loss = 0.180038 (* 1 = 0.180038 loss)
I0407 23:17:21.256731 32630 sgd_solver.cpp:105] Iteration 6336, lr = 0.00229389
I0407 23:17:26.156461 32630 solver.cpp:218] Iteration 6348 (2.44912 iter/s, 4.89971s/12 iters), loss = 0.106015
I0407 23:17:26.156507 32630 solver.cpp:237]     Train net output #0: loss = 0.106015 (* 1 = 0.106015 loss)
I0407 23:17:26.156514 32630 sgd_solver.cpp:105] Iteration 6348, lr = 0.00227316
I0407 23:17:31.140516 32630 solver.cpp:218] Iteration 6360 (2.40771 iter/s, 4.98399s/12 iters), loss = 0.126248
I0407 23:17:31.140552 32630 solver.cpp:237]     Train net output #0: loss = 0.126248 (* 1 = 0.126248 loss)
I0407 23:17:31.140559 32630 sgd_solver.cpp:105] Iteration 6360, lr = 0.00225256
I0407 23:17:35.913484 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:17:36.054109 32630 solver.cpp:218] Iteration 6372 (2.44224 iter/s, 4.91353s/12 iters), loss = 0.146312
I0407 23:17:36.054152 32630 solver.cpp:237]     Train net output #0: loss = 0.146312 (* 1 = 0.146312 loss)
I0407 23:17:36.054160 32630 sgd_solver.cpp:105] Iteration 6372, lr = 0.0022321
I0407 23:17:41.033164 32630 solver.cpp:218] Iteration 6384 (2.41013 iter/s, 4.97899s/12 iters), loss = 0.0843984
I0407 23:17:41.033289 32630 solver.cpp:237]     Train net output #0: loss = 0.0843984 (* 1 = 0.0843984 loss)
I0407 23:17:41.033298 32630 sgd_solver.cpp:105] Iteration 6384, lr = 0.00221176
I0407 23:17:45.983116 32630 solver.cpp:218] Iteration 6396 (2.42434 iter/s, 4.9498s/12 iters), loss = 0.161126
I0407 23:17:45.983163 32630 solver.cpp:237]     Train net output #0: loss = 0.161127 (* 1 = 0.161127 loss)
I0407 23:17:45.983175 32630 sgd_solver.cpp:105] Iteration 6396, lr = 0.00219157
I0407 23:17:50.895797 32630 solver.cpp:218] Iteration 6408 (2.44269 iter/s, 4.91261s/12 iters), loss = 0.184945
I0407 23:17:50.895838 32630 solver.cpp:237]     Train net output #0: loss = 0.184945 (* 1 = 0.184945 loss)
I0407 23:17:50.895846 32630 sgd_solver.cpp:105] Iteration 6408, lr = 0.0021715
I0407 23:17:55.852922 32630 solver.cpp:218] Iteration 6420 (2.42079 iter/s, 4.95706s/12 iters), loss = 0.213449
I0407 23:17:55.852963 32630 solver.cpp:237]     Train net output #0: loss = 0.213449 (* 1 = 0.213449 loss)
I0407 23:17:55.852972 32630 sgd_solver.cpp:105] Iteration 6420, lr = 0.00215157
I0407 23:17:57.845588 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6426.caffemodel
I0407 23:18:01.794996 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6426.solverstate
I0407 23:18:04.725694 32630 solver.cpp:330] Iteration 6426, Testing net (#0)
I0407 23:18:04.725713 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:18:06.754902 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:18:09.551754 32630 solver.cpp:397]     Test net output #0: accuracy = 0.487132
I0407 23:18:09.551801 32630 solver.cpp:397]     Test net output #1: loss = 2.66618 (* 1 = 2.66618 loss)
I0407 23:18:11.357193 32630 solver.cpp:218] Iteration 6432 (0.773984 iter/s, 15.5042s/12 iters), loss = 0.121844
I0407 23:18:11.357314 32630 solver.cpp:237]     Train net output #0: loss = 0.121844 (* 1 = 0.121844 loss)
I0407 23:18:11.357323 32630 sgd_solver.cpp:105] Iteration 6432, lr = 0.00213177
I0407 23:18:16.278786 32630 solver.cpp:218] Iteration 6444 (2.4383 iter/s, 4.92145s/12 iters), loss = 0.191933
I0407 23:18:16.278825 32630 solver.cpp:237]     Train net output #0: loss = 0.191933 (* 1 = 0.191933 loss)
I0407 23:18:16.278831 32630 sgd_solver.cpp:105] Iteration 6444, lr = 0.0021121
I0407 23:18:21.242471 32630 solver.cpp:218] Iteration 6456 (2.41759 iter/s, 4.96363s/12 iters), loss = 0.177719
I0407 23:18:21.242511 32630 solver.cpp:237]     Train net output #0: loss = 0.177719 (* 1 = 0.177719 loss)
I0407 23:18:21.242517 32630 sgd_solver.cpp:105] Iteration 6456, lr = 0.00209257
I0407 23:18:26.202880 32630 solver.cpp:218] Iteration 6468 (2.41919 iter/s, 4.96035s/12 iters), loss = 0.156836
I0407 23:18:26.202915 32630 solver.cpp:237]     Train net output #0: loss = 0.156836 (* 1 = 0.156836 loss)
I0407 23:18:26.202924 32630 sgd_solver.cpp:105] Iteration 6468, lr = 0.00207317
I0407 23:18:28.146759 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:18:31.097934 32630 solver.cpp:218] Iteration 6480 (2.45149 iter/s, 4.89499s/12 iters), loss = 0.225684
I0407 23:18:31.097980 32630 solver.cpp:237]     Train net output #0: loss = 0.225684 (* 1 = 0.225684 loss)
I0407 23:18:31.097990 32630 sgd_solver.cpp:105] Iteration 6480, lr = 0.0020539
I0407 23:18:36.042343 32630 solver.cpp:218] Iteration 6492 (2.42702 iter/s, 4.94434s/12 iters), loss = 0.0892352
I0407 23:18:36.042388 32630 solver.cpp:237]     Train net output #0: loss = 0.0892353 (* 1 = 0.0892353 loss)
I0407 23:18:36.042397 32630 sgd_solver.cpp:105] Iteration 6492, lr = 0.00203477
I0407 23:18:40.974571 32630 solver.cpp:218] Iteration 6504 (2.43301 iter/s, 4.93216s/12 iters), loss = 0.13083
I0407 23:18:40.974608 32630 solver.cpp:237]     Train net output #0: loss = 0.130831 (* 1 = 0.130831 loss)
I0407 23:18:40.974615 32630 sgd_solver.cpp:105] Iteration 6504, lr = 0.00201576
I0407 23:18:45.891248 32630 solver.cpp:218] Iteration 6516 (2.4407 iter/s, 4.91662s/12 iters), loss = 0.199545
I0407 23:18:45.892366 32630 solver.cpp:237]     Train net output #0: loss = 0.199545 (* 1 = 0.199545 loss)
I0407 23:18:45.892375 32630 sgd_solver.cpp:105] Iteration 6516, lr = 0.0019969
I0407 23:18:50.395193 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6528.caffemodel
I0407 23:18:56.826164 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6528.solverstate
I0407 23:19:00.391258 32630 solver.cpp:330] Iteration 6528, Testing net (#0)
I0407 23:19:00.391274 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:19:02.176669 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:19:04.762917 32630 solver.cpp:397]     Test net output #0: accuracy = 0.481618
I0407 23:19:04.762964 32630 solver.cpp:397]     Test net output #1: loss = 2.65087 (* 1 = 2.65087 loss)
I0407 23:19:04.859727 32630 solver.cpp:218] Iteration 6528 (0.632667 iter/s, 18.9673s/12 iters), loss = 0.12976
I0407 23:19:04.859769 32630 solver.cpp:237]     Train net output #0: loss = 0.12976 (* 1 = 0.12976 loss)
I0407 23:19:04.859778 32630 sgd_solver.cpp:105] Iteration 6528, lr = 0.00197816
I0407 23:19:08.865764 32630 solver.cpp:218] Iteration 6540 (2.99553 iter/s, 4.00598s/12 iters), loss = 0.106015
I0407 23:19:08.865806 32630 solver.cpp:237]     Train net output #0: loss = 0.106015 (* 1 = 0.106015 loss)
I0407 23:19:08.865813 32630 sgd_solver.cpp:105] Iteration 6540, lr = 0.00195956
I0407 23:19:13.762013 32630 solver.cpp:218] Iteration 6552 (2.45089 iter/s, 4.89618s/12 iters), loss = 0.108962
I0407 23:19:13.762053 32630 solver.cpp:237]     Train net output #0: loss = 0.108962 (* 1 = 0.108962 loss)
I0407 23:19:13.762060 32630 sgd_solver.cpp:105] Iteration 6552, lr = 0.00194109
I0407 23:19:18.674839 32630 solver.cpp:218] Iteration 6564 (2.44262 iter/s, 4.91276s/12 iters), loss = 0.0997367
I0407 23:19:18.674958 32630 solver.cpp:237]     Train net output #0: loss = 0.0997368 (* 1 = 0.0997368 loss)
I0407 23:19:18.674968 32630 sgd_solver.cpp:105] Iteration 6564, lr = 0.00192275
I0407 23:19:22.823482 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:19:23.577116 32630 solver.cpp:218] Iteration 6576 (2.44791 iter/s, 4.90213s/12 iters), loss = 0.0492391
I0407 23:19:23.577155 32630 solver.cpp:237]     Train net output #0: loss = 0.0492392 (* 1 = 0.0492392 loss)
I0407 23:19:23.577163 32630 sgd_solver.cpp:105] Iteration 6576, lr = 0.00190455
I0407 23:19:28.505815 32630 solver.cpp:218] Iteration 6588 (2.43475 iter/s, 4.92864s/12 iters), loss = 0.178427
I0407 23:19:28.505854 32630 solver.cpp:237]     Train net output #0: loss = 0.178427 (* 1 = 0.178427 loss)
I0407 23:19:28.505862 32630 sgd_solver.cpp:105] Iteration 6588, lr = 0.00188647
I0407 23:19:33.457095 32630 solver.cpp:218] Iteration 6600 (2.42365 iter/s, 4.95122s/12 iters), loss = 0.106841
I0407 23:19:33.457132 32630 solver.cpp:237]     Train net output #0: loss = 0.106841 (* 1 = 0.106841 loss)
I0407 23:19:33.457139 32630 sgd_solver.cpp:105] Iteration 6600, lr = 0.00186853
I0407 23:19:38.361138 32630 solver.cpp:218] Iteration 6612 (2.44699 iter/s, 4.90398s/12 iters), loss = 0.209935
I0407 23:19:38.361184 32630 solver.cpp:237]     Train net output #0: loss = 0.209935 (* 1 = 0.209935 loss)
I0407 23:19:38.361192 32630 sgd_solver.cpp:105] Iteration 6612, lr = 0.00185072
I0407 23:19:43.328382 32630 solver.cpp:218] Iteration 6624 (2.41586 iter/s, 4.96717s/12 iters), loss = 0.269754
I0407 23:19:43.328423 32630 solver.cpp:237]     Train net output #0: loss = 0.269754 (* 1 = 0.269754 loss)
I0407 23:19:43.328430 32630 sgd_solver.cpp:105] Iteration 6624, lr = 0.00183304
I0407 23:19:45.320191 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6630.caffemodel
I0407 23:19:48.482017 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6630.solverstate
I0407 23:19:51.527653 32630 solver.cpp:330] Iteration 6630, Testing net (#0)
I0407 23:19:51.527777 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:19:53.441804 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:19:56.295320 32630 solver.cpp:397]     Test net output #0: accuracy = 0.483456
I0407 23:19:56.295384 32630 solver.cpp:397]     Test net output #1: loss = 2.60381 (* 1 = 2.60381 loss)
I0407 23:19:58.152578 32630 solver.cpp:218] Iteration 6636 (0.809492 iter/s, 14.8241s/12 iters), loss = 0.0806369
I0407 23:19:58.152618 32630 solver.cpp:237]     Train net output #0: loss = 0.080637 (* 1 = 0.080637 loss)
I0407 23:19:58.152626 32630 sgd_solver.cpp:105] Iteration 6636, lr = 0.0018155
I0407 23:20:03.166476 32630 solver.cpp:218] Iteration 6648 (2.39338 iter/s, 5.01384s/12 iters), loss = 0.217974
I0407 23:20:03.166514 32630 solver.cpp:237]     Train net output #0: loss = 0.217974 (* 1 = 0.217974 loss)
I0407 23:20:03.166522 32630 sgd_solver.cpp:105] Iteration 6648, lr = 0.00179808
I0407 23:20:08.133195 32630 solver.cpp:218] Iteration 6660 (2.41611 iter/s, 4.96666s/12 iters), loss = 0.189752
I0407 23:20:08.133239 32630 solver.cpp:237]     Train net output #0: loss = 0.189752 (* 1 = 0.189752 loss)
I0407 23:20:08.133246 32630 sgd_solver.cpp:105] Iteration 6660, lr = 0.0017808
I0407 23:20:13.137756 32630 solver.cpp:218] Iteration 6672 (2.39785 iter/s, 5.00449s/12 iters), loss = 0.168352
I0407 23:20:13.137801 32630 solver.cpp:237]     Train net output #0: loss = 0.168353 (* 1 = 0.168353 loss)
I0407 23:20:13.137809 32630 sgd_solver.cpp:105] Iteration 6672, lr = 0.00176364
I0407 23:20:14.456140 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:20:18.086900 32630 solver.cpp:218] Iteration 6684 (2.4247 iter/s, 4.94907s/12 iters), loss = 0.293341
I0407 23:20:18.086943 32630 solver.cpp:237]     Train net output #0: loss = 0.293341 (* 1 = 0.293341 loss)
I0407 23:20:18.086952 32630 sgd_solver.cpp:105] Iteration 6684, lr = 0.00174662
I0407 23:20:23.080322 32630 solver.cpp:218] Iteration 6696 (2.40319 iter/s, 4.99336s/12 iters), loss = 0.15187
I0407 23:20:23.080435 32630 solver.cpp:237]     Train net output #0: loss = 0.15187 (* 1 = 0.15187 loss)
I0407 23:20:23.080443 32630 sgd_solver.cpp:105] Iteration 6696, lr = 0.00172972
I0407 23:20:28.030498 32630 solver.cpp:218] Iteration 6708 (2.42422 iter/s, 4.95004s/12 iters), loss = 0.119747
I0407 23:20:28.030535 32630 solver.cpp:237]     Train net output #0: loss = 0.119747 (* 1 = 0.119747 loss)
I0407 23:20:28.030542 32630 sgd_solver.cpp:105] Iteration 6708, lr = 0.00171296
I0407 23:20:32.843031 32630 solver.cpp:218] Iteration 6720 (2.49352 iter/s, 4.81247s/12 iters), loss = 0.0673343
I0407 23:20:32.843077 32630 solver.cpp:237]     Train net output #0: loss = 0.0673344 (* 1 = 0.0673344 loss)
I0407 23:20:32.843086 32630 sgd_solver.cpp:105] Iteration 6720, lr = 0.00169632
I0407 23:20:37.333999 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6732.caffemodel
I0407 23:20:41.457238 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6732.solverstate
I0407 23:20:45.119169 32630 solver.cpp:330] Iteration 6732, Testing net (#0)
I0407 23:20:45.119186 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:20:47.013442 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:20:49.934832 32630 solver.cpp:397]     Test net output #0: accuracy = 0.488971
I0407 23:20:49.934875 32630 solver.cpp:397]     Test net output #1: loss = 2.64869 (* 1 = 2.64869 loss)
I0407 23:20:50.031317 32630 solver.cpp:218] Iteration 6732 (0.698154 iter/s, 17.1882s/12 iters), loss = 0.106626
I0407 23:20:50.031363 32630 solver.cpp:237]     Train net output #0: loss = 0.106626 (* 1 = 0.106626 loss)
I0407 23:20:50.031371 32630 sgd_solver.cpp:105] Iteration 6732, lr = 0.00167982
I0407 23:20:54.150264 32630 solver.cpp:218] Iteration 6744 (2.91342 iter/s, 4.11887s/12 iters), loss = 0.122684
I0407 23:20:54.152467 32630 solver.cpp:237]     Train net output #0: loss = 0.122685 (* 1 = 0.122685 loss)
I0407 23:20:54.152479 32630 sgd_solver.cpp:105] Iteration 6744, lr = 0.00166344
I0407 23:20:59.109439 32630 solver.cpp:218] Iteration 6756 (2.42084 iter/s, 4.95695s/12 iters), loss = 0.119896
I0407 23:20:59.109490 32630 solver.cpp:237]     Train net output #0: loss = 0.119896 (* 1 = 0.119896 loss)
I0407 23:20:59.109503 32630 sgd_solver.cpp:105] Iteration 6756, lr = 0.00164719
I0407 23:21:04.032042 32630 solver.cpp:218] Iteration 6768 (2.43777 iter/s, 4.92252s/12 iters), loss = 0.168955
I0407 23:21:04.032088 32630 solver.cpp:237]     Train net output #0: loss = 0.168955 (* 1 = 0.168955 loss)
I0407 23:21:04.032097 32630 sgd_solver.cpp:105] Iteration 6768, lr = 0.00163106
I0407 23:21:07.438324 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:21:08.935534 32630 solver.cpp:218] Iteration 6780 (2.44727 iter/s, 4.90342s/12 iters), loss = 0.139274
I0407 23:21:08.935580 32630 solver.cpp:237]     Train net output #0: loss = 0.139275 (* 1 = 0.139275 loss)
I0407 23:21:08.935588 32630 sgd_solver.cpp:105] Iteration 6780, lr = 0.00161507
I0407 23:21:13.867606 32630 solver.cpp:218] Iteration 6792 (2.43309 iter/s, 4.932s/12 iters), loss = 0.0746083
I0407 23:21:13.867651 32630 solver.cpp:237]     Train net output #0: loss = 0.0746084 (* 1 = 0.0746084 loss)
I0407 23:21:13.867660 32630 sgd_solver.cpp:105] Iteration 6792, lr = 0.0015992
I0407 23:21:18.821882 32630 solver.cpp:218] Iteration 6804 (2.42219 iter/s, 4.9542s/12 iters), loss = 0.106704
I0407 23:21:18.821930 32630 solver.cpp:237]     Train net output #0: loss = 0.106704 (* 1 = 0.106704 loss)
I0407 23:21:18.821938 32630 sgd_solver.cpp:105] Iteration 6804, lr = 0.00158346
I0407 23:21:23.731523 32630 solver.cpp:218] Iteration 6816 (2.44421 iter/s, 4.90957s/12 iters), loss = 0.0913889
I0407 23:21:23.731560 32630 solver.cpp:237]     Train net output #0: loss = 0.091389 (* 1 = 0.091389 loss)
I0407 23:21:23.731568 32630 sgd_solver.cpp:105] Iteration 6816, lr = 0.00156784
I0407 23:21:28.722896 32630 solver.cpp:218] Iteration 6828 (2.40418 iter/s, 4.99131s/12 iters), loss = 0.0601485
I0407 23:21:28.723027 32630 solver.cpp:237]     Train net output #0: loss = 0.0601487 (* 1 = 0.0601487 loss)
I0407 23:21:28.723037 32630 sgd_solver.cpp:105] Iteration 6828, lr = 0.00155235
I0407 23:21:30.733860 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6834.caffemodel
I0407 23:21:33.866998 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6834.solverstate
I0407 23:21:36.689688 32630 solver.cpp:330] Iteration 6834, Testing net (#0)
I0407 23:21:36.689707 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:21:38.534741 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:21:41.298635 32630 solver.cpp:397]     Test net output #0: accuracy = 0.492647
I0407 23:21:41.298683 32630 solver.cpp:397]     Test net output #1: loss = 2.63418 (* 1 = 2.63418 loss)
I0407 23:21:43.121775 32630 solver.cpp:218] Iteration 6840 (0.833408 iter/s, 14.3987s/12 iters), loss = 0.0940911
I0407 23:21:43.121820 32630 solver.cpp:237]     Train net output #0: loss = 0.0940912 (* 1 = 0.0940912 loss)
I0407 23:21:43.121829 32630 sgd_solver.cpp:105] Iteration 6840, lr = 0.00153699
I0407 23:21:48.164372 32630 solver.cpp:218] Iteration 6852 (2.37976 iter/s, 5.04252s/12 iters), loss = 0.107204
I0407 23:21:48.164420 32630 solver.cpp:237]     Train net output #0: loss = 0.107205 (* 1 = 0.107205 loss)
I0407 23:21:48.164429 32630 sgd_solver.cpp:105] Iteration 6852, lr = 0.00152174
I0407 23:21:53.115087 32630 solver.cpp:218] Iteration 6864 (2.42393 iter/s, 4.95064s/12 iters), loss = 0.0512534
I0407 23:21:53.115128 32630 solver.cpp:237]     Train net output #0: loss = 0.0512535 (* 1 = 0.0512535 loss)
I0407 23:21:53.115135 32630 sgd_solver.cpp:105] Iteration 6864, lr = 0.00150663
I0407 23:21:58.095202 32630 solver.cpp:218] Iteration 6876 (2.40962 iter/s, 4.98005s/12 iters), loss = 0.129175
I0407 23:21:58.095249 32630 solver.cpp:237]     Train net output #0: loss = 0.129176 (* 1 = 0.129176 loss)
I0407 23:21:58.095258 32630 sgd_solver.cpp:105] Iteration 6876, lr = 0.00149164
I0407 23:21:58.717859 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:03.200423 32630 solver.cpp:218] Iteration 6888 (2.35057 iter/s, 5.10515s/12 iters), loss = 0.077988
I0407 23:22:03.200542 32630 solver.cpp:237]     Train net output #0: loss = 0.0779881 (* 1 = 0.0779881 loss)
I0407 23:22:03.200551 32630 sgd_solver.cpp:105] Iteration 6888, lr = 0.00147677
I0407 23:22:08.251842 32630 solver.cpp:218] Iteration 6900 (2.37564 iter/s, 5.05127s/12 iters), loss = 0.176305
I0407 23:22:08.251888 32630 solver.cpp:237]     Train net output #0: loss = 0.176305 (* 1 = 0.176305 loss)
I0407 23:22:08.251896 32630 sgd_solver.cpp:105] Iteration 6900, lr = 0.00146202
I0407 23:22:13.227296 32630 solver.cpp:218] Iteration 6912 (2.41187 iter/s, 4.97539s/12 iters), loss = 0.0527119
I0407 23:22:13.227340 32630 solver.cpp:237]     Train net output #0: loss = 0.052712 (* 1 = 0.052712 loss)
I0407 23:22:13.227349 32630 sgd_solver.cpp:105] Iteration 6912, lr = 0.00144739
I0407 23:22:18.202028 32630 solver.cpp:218] Iteration 6924 (2.41222 iter/s, 4.97467s/12 iters), loss = 0.274352
I0407 23:22:18.202064 32630 solver.cpp:237]     Train net output #0: loss = 0.274353 (* 1 = 0.274353 loss)
I0407 23:22:18.202072 32630 sgd_solver.cpp:105] Iteration 6924, lr = 0.00143289
I0407 23:22:22.681102 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_6936.caffemodel
I0407 23:22:25.853468 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_6936.solverstate
I0407 23:22:28.211946 32630 solver.cpp:330] Iteration 6936, Testing net (#0)
I0407 23:22:28.211961 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:22:28.825445 32630 blocking_queue.cpp:49] Waiting for data
I0407 23:22:29.943033 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:32.720432 32630 solver.cpp:397]     Test net output #0: accuracy = 0.495098
I0407 23:22:32.720477 32630 solver.cpp:397]     Test net output #1: loss = 2.65932 (* 1 = 2.65932 loss)
I0407 23:22:32.817128 32630 solver.cpp:218] Iteration 6936 (0.821073 iter/s, 14.615s/12 iters), loss = 0.140918
I0407 23:22:32.817175 32630 solver.cpp:237]     Train net output #0: loss = 0.140918 (* 1 = 0.140918 loss)
I0407 23:22:32.817184 32630 sgd_solver.cpp:105] Iteration 6936, lr = 0.00141851
I0407 23:22:36.892808 32630 solver.cpp:218] Iteration 6948 (2.94435 iter/s, 4.0756s/12 iters), loss = 0.265386
I0407 23:22:36.892938 32630 solver.cpp:237]     Train net output #0: loss = 0.265386 (* 1 = 0.265386 loss)
I0407 23:22:36.892947 32630 sgd_solver.cpp:105] Iteration 6948, lr = 0.00140425
I0407 23:22:41.815196 32630 solver.cpp:218] Iteration 6960 (2.43792 iter/s, 4.92224s/12 iters), loss = 0.0713736
I0407 23:22:41.815240 32630 solver.cpp:237]     Train net output #0: loss = 0.0713738 (* 1 = 0.0713738 loss)
I0407 23:22:41.815249 32630 sgd_solver.cpp:105] Iteration 6960, lr = 0.00139011
I0407 23:22:46.786257 32630 solver.cpp:218] Iteration 6972 (2.41401 iter/s, 4.97099s/12 iters), loss = 0.151701
I0407 23:22:46.786295 32630 solver.cpp:237]     Train net output #0: loss = 0.151701 (* 1 = 0.151701 loss)
I0407 23:22:46.786303 32630 sgd_solver.cpp:105] Iteration 6972, lr = 0.00137609
I0407 23:22:49.497942 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:22:51.756862 32630 solver.cpp:218] Iteration 6984 (2.41422 iter/s, 4.97054s/12 iters), loss = 0.126587
I0407 23:22:51.756908 32630 solver.cpp:237]     Train net output #0: loss = 0.126587 (* 1 = 0.126587 loss)
I0407 23:22:51.756916 32630 sgd_solver.cpp:105] Iteration 6984, lr = 0.00136219
I0407 23:22:56.706028 32630 solver.cpp:218] Iteration 6996 (2.42469 iter/s, 4.94909s/12 iters), loss = 0.122096
I0407 23:22:56.706080 32630 solver.cpp:237]     Train net output #0: loss = 0.122096 (* 1 = 0.122096 loss)
I0407 23:22:56.706090 32630 sgd_solver.cpp:105] Iteration 6996, lr = 0.0013484
I0407 23:23:01.656845 32630 solver.cpp:218] Iteration 7008 (2.42388 iter/s, 4.95074s/12 iters), loss = 0.0601943
I0407 23:23:01.656891 32630 solver.cpp:237]     Train net output #0: loss = 0.0601944 (* 1 = 0.0601944 loss)
I0407 23:23:01.656899 32630 sgd_solver.cpp:105] Iteration 7008, lr = 0.00133474
I0407 23:23:06.563345 32630 solver.cpp:218] Iteration 7020 (2.44577 iter/s, 4.90643s/12 iters), loss = 0.210623
I0407 23:23:06.563388 32630 solver.cpp:237]     Train net output #0: loss = 0.210623 (* 1 = 0.210623 loss)
I0407 23:23:06.563397 32630 sgd_solver.cpp:105] Iteration 7020, lr = 0.00132119
I0407 23:23:11.578918 32630 solver.cpp:218] Iteration 7032 (2.39258 iter/s, 5.0155s/12 iters), loss = 0.153301
I0407 23:23:11.579077 32630 solver.cpp:237]     Train net output #0: loss = 0.153301 (* 1 = 0.153301 loss)
I0407 23:23:11.579085 32630 sgd_solver.cpp:105] Iteration 7032, lr = 0.00130776
I0407 23:23:13.667977 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7038.caffemodel
I0407 23:23:16.732033 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7038.solverstate
I0407 23:23:19.087260 32630 solver.cpp:330] Iteration 7038, Testing net (#0)
I0407 23:23:19.087280 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:23:20.825062 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:23:23.938248 32630 solver.cpp:397]     Test net output #0: accuracy = 0.501225
I0407 23:23:23.938297 32630 solver.cpp:397]     Test net output #1: loss = 2.62896 (* 1 = 2.62896 loss)
I0407 23:23:25.741089 32630 solver.cpp:218] Iteration 7044 (0.84734 iter/s, 14.162s/12 iters), loss = 0.0395202
I0407 23:23:25.741135 32630 solver.cpp:237]     Train net output #0: loss = 0.0395203 (* 1 = 0.0395203 loss)
I0407 23:23:25.741143 32630 sgd_solver.cpp:105] Iteration 7044, lr = 0.00129444
I0407 23:23:30.647058 32630 solver.cpp:218] Iteration 7056 (2.44604 iter/s, 4.9059s/12 iters), loss = 0.0686275
I0407 23:23:30.647106 32630 solver.cpp:237]     Train net output #0: loss = 0.0686277 (* 1 = 0.0686277 loss)
I0407 23:23:30.647115 32630 sgd_solver.cpp:105] Iteration 7056, lr = 0.00128124
I0407 23:23:35.619102 32630 solver.cpp:218] Iteration 7068 (2.41353 iter/s, 4.97197s/12 iters), loss = 0.129978
I0407 23:23:35.619143 32630 solver.cpp:237]     Train net output #0: loss = 0.129979 (* 1 = 0.129979 loss)
I0407 23:23:35.619153 32630 sgd_solver.cpp:105] Iteration 7068, lr = 0.00126816
I0407 23:23:40.430033 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:23:40.540024 32630 solver.cpp:218] Iteration 7080 (2.4386 iter/s, 4.92086s/12 iters), loss = 0.199802
I0407 23:23:40.540060 32630 solver.cpp:237]     Train net output #0: loss = 0.199802 (* 1 = 0.199802 loss)
I0407 23:23:40.540068 32630 sgd_solver.cpp:105] Iteration 7080, lr = 0.00125519
I0407 23:23:45.425249 32630 solver.cpp:218] Iteration 7092 (2.45642 iter/s, 4.88516s/12 iters), loss = 0.0521977
I0407 23:23:45.425387 32630 solver.cpp:237]     Train net output #0: loss = 0.0521978 (* 1 = 0.0521978 loss)
I0407 23:23:45.425396 32630 sgd_solver.cpp:105] Iteration 7092, lr = 0.00124233
I0407 23:23:50.361363 32630 solver.cpp:218] Iteration 7104 (2.43114 iter/s, 4.93595s/12 iters), loss = 0.0623749
I0407 23:23:50.361407 32630 solver.cpp:237]     Train net output #0: loss = 0.062375 (* 1 = 0.062375 loss)
I0407 23:23:50.361414 32630 sgd_solver.cpp:105] Iteration 7104, lr = 0.00122959
I0407 23:23:55.283996 32630 solver.cpp:218] Iteration 7116 (2.43775 iter/s, 4.92256s/12 iters), loss = 0.0981473
I0407 23:23:55.284040 32630 solver.cpp:237]     Train net output #0: loss = 0.0981474 (* 1 = 0.0981474 loss)
I0407 23:23:55.284049 32630 sgd_solver.cpp:105] Iteration 7116, lr = 0.00121696
I0407 23:24:00.229327 32630 solver.cpp:218] Iteration 7128 (2.42657 iter/s, 4.94526s/12 iters), loss = 0.107294
I0407 23:24:00.229372 32630 solver.cpp:237]     Train net output #0: loss = 0.107294 (* 1 = 0.107294 loss)
I0407 23:24:00.229379 32630 sgd_solver.cpp:105] Iteration 7128, lr = 0.00120444
I0407 23:24:04.728545 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7140.caffemodel
I0407 23:24:08.711441 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7140.solverstate
I0407 23:24:11.071544 32630 solver.cpp:330] Iteration 7140, Testing net (#0)
I0407 23:24:11.071563 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:24:12.789069 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:15.873718 32630 solver.cpp:397]     Test net output #0: accuracy = 0.498162
I0407 23:24:15.873914 32630 solver.cpp:397]     Test net output #1: loss = 2.66261 (* 1 = 2.66261 loss)
I0407 23:24:15.970429 32630 solver.cpp:218] Iteration 7140 (0.76234 iter/s, 15.741s/12 iters), loss = 0.03908
I0407 23:24:15.970495 32630 solver.cpp:237]     Train net output #0: loss = 0.0390801 (* 1 = 0.0390801 loss)
I0407 23:24:15.970507 32630 sgd_solver.cpp:105] Iteration 7140, lr = 0.00119203
I0407 23:24:20.086684 32630 solver.cpp:218] Iteration 7152 (2.91533 iter/s, 4.11617s/12 iters), loss = 0.117106
I0407 23:24:20.086722 32630 solver.cpp:237]     Train net output #0: loss = 0.117106 (* 1 = 0.117106 loss)
I0407 23:24:20.086731 32630 sgd_solver.cpp:105] Iteration 7152, lr = 0.00117973
I0407 23:24:24.996002 32630 solver.cpp:218] Iteration 7164 (2.44436 iter/s, 4.90926s/12 iters), loss = 0.105561
I0407 23:24:24.996055 32630 solver.cpp:237]     Train net output #0: loss = 0.105561 (* 1 = 0.105561 loss)
I0407 23:24:24.996063 32630 sgd_solver.cpp:105] Iteration 7164, lr = 0.00116755
I0407 23:24:29.955384 32630 solver.cpp:218] Iteration 7176 (2.41969 iter/s, 4.95931s/12 iters), loss = 0.163709
I0407 23:24:29.955418 32630 solver.cpp:237]     Train net output #0: loss = 0.163709 (* 1 = 0.163709 loss)
I0407 23:24:29.955426 32630 sgd_solver.cpp:105] Iteration 7176, lr = 0.00115547
I0407 23:24:32.025667 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:24:34.865015 32630 solver.cpp:218] Iteration 7188 (2.4442 iter/s, 4.90957s/12 iters), loss = 0.0568543
I0407 23:24:34.865051 32630 solver.cpp:237]     Train net output #0: loss = 0.0568544 (* 1 = 0.0568544 loss)
I0407 23:24:34.865058 32630 sgd_solver.cpp:105] Iteration 7188, lr = 0.0011435
I0407 23:24:39.837218 32630 solver.cpp:218] Iteration 7200 (2.41345 iter/s, 4.97214s/12 iters), loss = 0.0538703
I0407 23:24:39.837263 32630 solver.cpp:237]     Train net output #0: loss = 0.0538704 (* 1 = 0.0538704 loss)
I0407 23:24:39.837272 32630 sgd_solver.cpp:105] Iteration 7200, lr = 0.00113164
I0407 23:24:44.804478 32630 solver.cpp:218] Iteration 7212 (2.41585 iter/s, 4.96719s/12 iters), loss = 0.0886614
I0407 23:24:44.804520 32630 solver.cpp:237]     Train net output #0: loss = 0.0886615 (* 1 = 0.0886615 loss)
I0407 23:24:44.804528 32630 sgd_solver.cpp:105] Iteration 7212, lr = 0.00111989
I0407 23:24:49.738994 32630 solver.cpp:218] Iteration 7224 (2.43188 iter/s, 4.93445s/12 iters), loss = 0.0960281
I0407 23:24:49.739130 32630 solver.cpp:237]     Train net output #0: loss = 0.0960282 (* 1 = 0.0960282 loss)
I0407 23:24:49.739138 32630 sgd_solver.cpp:105] Iteration 7224, lr = 0.00110824
I0407 23:24:54.711258 32630 solver.cpp:218] Iteration 7236 (2.41346 iter/s, 4.97211s/12 iters), loss = 0.0432394
I0407 23:24:54.711295 32630 solver.cpp:237]     Train net output #0: loss = 0.0432395 (* 1 = 0.0432395 loss)
I0407 23:24:54.711303 32630 sgd_solver.cpp:105] Iteration 7236, lr = 0.0010967
I0407 23:24:56.748838 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7242.caffemodel
I0407 23:24:59.826944 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7242.solverstate
I0407 23:25:02.204095 32630 solver.cpp:330] Iteration 7242, Testing net (#0)
I0407 23:25:02.204115 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:25:03.859048 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:25:06.984787 32630 solver.cpp:397]     Test net output #0: accuracy = 0.502451
I0407 23:25:06.984833 32630 solver.cpp:397]     Test net output #1: loss = 2.65509 (* 1 = 2.65509 loss)
I0407 23:25:08.805855 32630 solver.cpp:218] Iteration 7248 (0.851395 iter/s, 14.0945s/12 iters), loss = 0.0507637
I0407 23:25:08.805892 32630 solver.cpp:237]     Train net output #0: loss = 0.0507638 (* 1 = 0.0507638 loss)
I0407 23:25:08.805899 32630 sgd_solver.cpp:105] Iteration 7248, lr = 0.00108526
I0407 23:25:13.768316 32630 solver.cpp:218] Iteration 7260 (2.41818 iter/s, 4.9624s/12 iters), loss = 0.0801947
I0407 23:25:13.768350 32630 solver.cpp:237]     Train net output #0: loss = 0.0801948 (* 1 = 0.0801948 loss)
I0407 23:25:13.768357 32630 sgd_solver.cpp:105] Iteration 7260, lr = 0.00107393
I0407 23:25:18.704921 32630 solver.cpp:218] Iteration 7272 (2.43085 iter/s, 4.93655s/12 iters), loss = 0.0618976
I0407 23:25:18.704957 32630 solver.cpp:237]     Train net output #0: loss = 0.0618976 (* 1 = 0.0618976 loss)
I0407 23:25:18.704965 32630 sgd_solver.cpp:105] Iteration 7272, lr = 0.00106271
I0407 23:25:22.926466 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:25:23.652441 32630 solver.cpp:218] Iteration 7284 (2.42549 iter/s, 4.94746s/12 iters), loss = 0.0392592
I0407 23:25:23.652479 32630 solver.cpp:237]     Train net output #0: loss = 0.0392593 (* 1 = 0.0392593 loss)
I0407 23:25:23.652487 32630 sgd_solver.cpp:105] Iteration 7284, lr = 0.00105159
I0407 23:25:28.577070 32630 solver.cpp:218] Iteration 7296 (2.43676 iter/s, 4.92457s/12 iters), loss = 0.0688824
I0407 23:25:28.577109 32630 solver.cpp:237]     Train net output #0: loss = 0.0688825 (* 1 = 0.0688825 loss)
I0407 23:25:28.577116 32630 sgd_solver.cpp:105] Iteration 7296, lr = 0.00104057
I0407 23:25:33.523471 32630 solver.cpp:218] Iteration 7308 (2.42604 iter/s, 4.94634s/12 iters), loss = 0.106905
I0407 23:25:33.523516 32630 solver.cpp:237]     Train net output #0: loss = 0.106905 (* 1 = 0.106905 loss)
I0407 23:25:33.523524 32630 sgd_solver.cpp:105] Iteration 7308, lr = 0.00102965
I0407 23:25:38.333315 32630 solver.cpp:218] Iteration 7320 (2.49492 iter/s, 4.80978s/12 iters), loss = 0.0533732
I0407 23:25:38.333364 32630 solver.cpp:237]     Train net output #0: loss = 0.0533733 (* 1 = 0.0533733 loss)
I0407 23:25:38.333371 32630 sgd_solver.cpp:105] Iteration 7320, lr = 0.00101883
I0407 23:25:43.262671 32630 solver.cpp:218] Iteration 7332 (2.43443 iter/s, 4.92929s/12 iters), loss = 0.0943866
I0407 23:25:43.262708 32630 solver.cpp:237]     Train net output #0: loss = 0.0943866 (* 1 = 0.0943866 loss)
I0407 23:25:43.262717 32630 sgd_solver.cpp:105] Iteration 7332, lr = 0.00100812
I0407 23:25:47.757692 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7344.caffemodel
I0407 23:25:51.005221 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7344.solverstate
I0407 23:25:53.359584 32630 solver.cpp:330] Iteration 7344, Testing net (#0)
I0407 23:25:53.359686 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:25:54.897217 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:25:57.811322 32630 solver.cpp:397]     Test net output #0: accuracy = 0.509804
I0407 23:25:57.811358 32630 solver.cpp:397]     Test net output #1: loss = 2.66022 (* 1 = 2.66022 loss)
I0407 23:25:57.908053 32630 solver.cpp:218] Iteration 7344 (0.819376 iter/s, 14.6453s/12 iters), loss = 0.0913434
I0407 23:25:57.908128 32630 solver.cpp:237]     Train net output #0: loss = 0.0913434 (* 1 = 0.0913434 loss)
I0407 23:25:57.908143 32630 sgd_solver.cpp:105] Iteration 7344, lr = 0.000997505
I0407 23:26:02.042335 32630 solver.cpp:218] Iteration 7356 (2.90266 iter/s, 4.13413s/12 iters), loss = 0.0645521
I0407 23:26:02.042392 32630 solver.cpp:237]     Train net output #0: loss = 0.0645522 (* 1 = 0.0645522 loss)
I0407 23:26:02.042402 32630 sgd_solver.cpp:105] Iteration 7356, lr = 0.00098699
I0407 23:26:06.961817 32630 solver.cpp:218] Iteration 7368 (2.43932 iter/s, 4.9194s/12 iters), loss = 0.0214652
I0407 23:26:06.961858 32630 solver.cpp:237]     Train net output #0: loss = 0.0214653 (* 1 = 0.0214653 loss)
I0407 23:26:06.961864 32630 sgd_solver.cpp:105] Iteration 7368, lr = 0.000976573
I0407 23:26:11.925750 32630 solver.cpp:218] Iteration 7380 (2.41747 iter/s, 4.96387s/12 iters), loss = 0.0564919
I0407 23:26:11.925792 32630 solver.cpp:237]     Train net output #0: loss = 0.0564919 (* 1 = 0.0564919 loss)
I0407 23:26:11.925804 32630 sgd_solver.cpp:105] Iteration 7380, lr = 0.000966255
I0407 23:26:13.271765 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:26:16.814935 32630 solver.cpp:218] Iteration 7392 (2.45443 iter/s, 4.88912s/12 iters), loss = 0.0194148
I0407 23:26:16.814982 32630 solver.cpp:237]     Train net output #0: loss = 0.0194148 (* 1 = 0.0194148 loss)
I0407 23:26:16.814991 32630 sgd_solver.cpp:105] Iteration 7392, lr = 0.000956035
I0407 23:26:21.791299 32630 solver.cpp:218] Iteration 7404 (2.41143 iter/s, 4.97629s/12 iters), loss = 0.166782
I0407 23:26:21.791344 32630 solver.cpp:237]     Train net output #0: loss = 0.166782 (* 1 = 0.166782 loss)
I0407 23:26:21.791352 32630 sgd_solver.cpp:105] Iteration 7404, lr = 0.000945911
I0407 23:26:26.689499 32630 solver.cpp:218] Iteration 7416 (2.44992 iter/s, 4.89813s/12 iters), loss = 0.0346203
I0407 23:26:26.689690 32630 solver.cpp:237]     Train net output #0: loss = 0.0346203 (* 1 = 0.0346203 loss)
I0407 23:26:26.689702 32630 sgd_solver.cpp:105] Iteration 7416, lr = 0.000935883
I0407 23:26:31.638315 32630 solver.cpp:218] Iteration 7428 (2.42493 iter/s, 4.9486s/12 iters), loss = 0.0635721
I0407 23:26:31.638360 32630 solver.cpp:237]     Train net output #0: loss = 0.0635721 (* 1 = 0.0635721 loss)
I0407 23:26:31.638367 32630 sgd_solver.cpp:105] Iteration 7428, lr = 0.00092595
I0407 23:26:36.765297 32630 solver.cpp:218] Iteration 7440 (2.34059 iter/s, 5.12691s/12 iters), loss = 0.068862
I0407 23:26:36.765345 32630 solver.cpp:237]     Train net output #0: loss = 0.068862 (* 1 = 0.068862 loss)
I0407 23:26:36.765354 32630 sgd_solver.cpp:105] Iteration 7440, lr = 0.000916113
I0407 23:26:38.778666 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7446.caffemodel
I0407 23:26:41.893988 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7446.solverstate
I0407 23:26:44.265722 32630 solver.cpp:330] Iteration 7446, Testing net (#0)
I0407 23:26:44.265739 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:26:45.714542 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:26:48.867270 32630 solver.cpp:397]     Test net output #0: accuracy = 0.499387
I0407 23:26:48.867317 32630 solver.cpp:397]     Test net output #1: loss = 2.66041 (* 1 = 2.66041 loss)
I0407 23:26:50.674741 32630 solver.cpp:218] Iteration 7452 (0.862729 iter/s, 13.9094s/12 iters), loss = 0.130915
I0407 23:26:50.674787 32630 solver.cpp:237]     Train net output #0: loss = 0.130915 (* 1 = 0.130915 loss)
I0407 23:26:50.674794 32630 sgd_solver.cpp:105] Iteration 7452, lr = 0.000906369
I0407 23:26:55.629942 32630 solver.cpp:218] Iteration 7464 (2.42173 iter/s, 4.95513s/12 iters), loss = 0.0268599
I0407 23:26:55.629981 32630 solver.cpp:237]     Train net output #0: loss = 0.0268599 (* 1 = 0.0268599 loss)
I0407 23:26:55.629990 32630 sgd_solver.cpp:105] Iteration 7464, lr = 0.000896719
I0407 23:27:00.596865 32630 solver.cpp:218] Iteration 7476 (2.41601 iter/s, 4.96686s/12 iters), loss = 0.0738801
I0407 23:27:00.596985 32630 solver.cpp:237]     Train net output #0: loss = 0.0738801 (* 1 = 0.0738801 loss)
I0407 23:27:00.596994 32630 sgd_solver.cpp:105] Iteration 7476, lr = 0.000887162
I0407 23:27:04.061743 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:27:05.504997 32630 solver.cpp:218] Iteration 7488 (2.44499 iter/s, 4.90799s/12 iters), loss = 0.105515
I0407 23:27:05.505035 32630 solver.cpp:237]     Train net output #0: loss = 0.105515 (* 1 = 0.105515 loss)
I0407 23:27:05.505043 32630 sgd_solver.cpp:105] Iteration 7488, lr = 0.000877697
I0407 23:27:10.468173 32630 solver.cpp:218] Iteration 7500 (2.41784 iter/s, 4.96311s/12 iters), loss = 0.116839
I0407 23:27:10.468215 32630 solver.cpp:237]     Train net output #0: loss = 0.116839 (* 1 = 0.116839 loss)
I0407 23:27:10.468223 32630 sgd_solver.cpp:105] Iteration 7500, lr = 0.000868323
I0407 23:27:15.384044 32630 solver.cpp:218] Iteration 7512 (2.44111 iter/s, 4.91581s/12 iters), loss = 0.0915584
I0407 23:27:15.384089 32630 solver.cpp:237]     Train net output #0: loss = 0.0915584 (* 1 = 0.0915584 loss)
I0407 23:27:15.384097 32630 sgd_solver.cpp:105] Iteration 7512, lr = 0.000859039
I0407 23:27:20.341643 32630 solver.cpp:218] Iteration 7524 (2.42056 iter/s, 4.95753s/12 iters), loss = 0.118833
I0407 23:27:20.341684 32630 solver.cpp:237]     Train net output #0: loss = 0.118833 (* 1 = 0.118833 loss)
I0407 23:27:20.341692 32630 sgd_solver.cpp:105] Iteration 7524, lr = 0.000849846
I0407 23:27:25.268838 32630 solver.cpp:218] Iteration 7536 (2.4355 iter/s, 4.92713s/12 iters), loss = 0.0610378
I0407 23:27:25.268887 32630 solver.cpp:237]     Train net output #0: loss = 0.0610379 (* 1 = 0.0610379 loss)
I0407 23:27:25.268895 32630 sgd_solver.cpp:105] Iteration 7536, lr = 0.000840742
I0407 23:27:29.755973 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7548.caffemodel
I0407 23:27:38.568802 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7548.solverstate
I0407 23:27:42.733479 32630 solver.cpp:330] Iteration 7548, Testing net (#0)
I0407 23:27:42.733495 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:27:44.252243 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:27:47.275795 32630 solver.cpp:397]     Test net output #0: accuracy = 0.507966
I0407 23:27:47.275842 32630 solver.cpp:397]     Test net output #1: loss = 2.66672 (* 1 = 2.66672 loss)
I0407 23:27:47.372259 32630 solver.cpp:218] Iteration 7548 (0.542905 iter/s, 22.1033s/12 iters), loss = 0.0934237
I0407 23:27:47.372305 32630 solver.cpp:237]     Train net output #0: loss = 0.0934237 (* 1 = 0.0934237 loss)
I0407 23:27:47.372313 32630 sgd_solver.cpp:105] Iteration 7548, lr = 0.000831727
I0407 23:27:51.501442 32630 solver.cpp:218] Iteration 7560 (2.90619 iter/s, 4.12912s/12 iters), loss = 0.0647124
I0407 23:27:51.501478 32630 solver.cpp:237]     Train net output #0: loss = 0.0647124 (* 1 = 0.0647124 loss)
I0407 23:27:51.501485 32630 sgd_solver.cpp:105] Iteration 7560, lr = 0.0008228
I0407 23:27:56.445634 32630 solver.cpp:218] Iteration 7572 (2.42712 iter/s, 4.94413s/12 iters), loss = 0.0898006
I0407 23:27:56.445684 32630 solver.cpp:237]     Train net output #0: loss = 0.0898006 (* 1 = 0.0898006 loss)
I0407 23:27:56.445693 32630 sgd_solver.cpp:105] Iteration 7572, lr = 0.00081396
I0407 23:28:01.400068 32630 solver.cpp:218] Iteration 7584 (2.42211 iter/s, 4.95436s/12 iters), loss = 0.0574981
I0407 23:28:01.400104 32630 solver.cpp:237]     Train net output #0: loss = 0.0574981 (* 1 = 0.0574981 loss)
I0407 23:28:01.400111 32630 sgd_solver.cpp:105] Iteration 7584, lr = 0.000805206
I0407 23:28:02.022544 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:28:06.305809 32630 solver.cpp:218] Iteration 7596 (2.44614 iter/s, 4.90568s/12 iters), loss = 0.0558913
I0407 23:28:06.305847 32630 solver.cpp:237]     Train net output #0: loss = 0.0558913 (* 1 = 0.0558913 loss)
I0407 23:28:06.305855 32630 sgd_solver.cpp:105] Iteration 7596, lr = 0.000796539
I0407 23:28:11.251663 32630 solver.cpp:218] Iteration 7608 (2.4263 iter/s, 4.94579s/12 iters), loss = 0.0264154
I0407 23:28:11.251791 32630 solver.cpp:237]     Train net output #0: loss = 0.0264154 (* 1 = 0.0264154 loss)
I0407 23:28:11.251799 32630 sgd_solver.cpp:105] Iteration 7608, lr = 0.000787957
I0407 23:28:16.112398 32630 solver.cpp:218] Iteration 7620 (2.46884 iter/s, 4.86058s/12 iters), loss = 0.045664
I0407 23:28:16.112439 32630 solver.cpp:237]     Train net output #0: loss = 0.045664 (* 1 = 0.045664 loss)
I0407 23:28:16.112447 32630 sgd_solver.cpp:105] Iteration 7620, lr = 0.000779459
I0407 23:28:18.479072 32630 blocking_queue.cpp:49] Waiting for data
I0407 23:28:21.006513 32630 solver.cpp:218] Iteration 7632 (2.45195 iter/s, 4.89405s/12 iters), loss = 0.222131
I0407 23:28:21.006551 32630 solver.cpp:237]     Train net output #0: loss = 0.222131 (* 1 = 0.222131 loss)
I0407 23:28:21.006559 32630 sgd_solver.cpp:105] Iteration 7632, lr = 0.000771046
I0407 23:28:25.969905 32630 solver.cpp:218] Iteration 7644 (2.41773 iter/s, 4.96333s/12 iters), loss = 0.0483886
I0407 23:28:25.969938 32630 solver.cpp:237]     Train net output #0: loss = 0.0483886 (* 1 = 0.0483886 loss)
I0407 23:28:25.969945 32630 sgd_solver.cpp:105] Iteration 7644, lr = 0.000762716
I0407 23:28:27.967869 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7650.caffemodel
I0407 23:28:34.129714 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7650.solverstate
I0407 23:28:36.951304 32630 solver.cpp:330] Iteration 7650, Testing net (#0)
I0407 23:28:36.951324 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:28:38.444695 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:28:41.743960 32630 solver.cpp:397]     Test net output #0: accuracy = 0.514093
I0407 23:28:41.744163 32630 solver.cpp:397]     Test net output #1: loss = 2.67849 (* 1 = 2.67849 loss)
I0407 23:28:43.447240 32630 solver.cpp:218] Iteration 7656 (0.686607 iter/s, 17.4772s/12 iters), loss = 0.0436079
I0407 23:28:43.447284 32630 solver.cpp:237]     Train net output #0: loss = 0.0436079 (* 1 = 0.0436079 loss)
I0407 23:28:43.447293 32630 sgd_solver.cpp:105] Iteration 7656, lr = 0.000754468
I0407 23:28:48.396241 32630 solver.cpp:218] Iteration 7668 (2.42476 iter/s, 4.94894s/12 iters), loss = 0.0613563
I0407 23:28:48.396275 32630 solver.cpp:237]     Train net output #0: loss = 0.0613563 (* 1 = 0.0613563 loss)
I0407 23:28:48.396281 32630 sgd_solver.cpp:105] Iteration 7668, lr = 0.000746303
I0407 23:28:53.365283 32630 solver.cpp:218] Iteration 7680 (2.41498 iter/s, 4.96898s/12 iters), loss = 0.0430957
I0407 23:28:53.365324 32630 solver.cpp:237]     Train net output #0: loss = 0.0430957 (* 1 = 0.0430957 loss)
I0407 23:28:53.365330 32630 sgd_solver.cpp:105] Iteration 7680, lr = 0.000738218
I0407 23:28:56.118458 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:28:58.295619 32630 solver.cpp:218] Iteration 7692 (2.43394 iter/s, 4.93027s/12 iters), loss = 0.15368
I0407 23:28:58.295665 32630 solver.cpp:237]     Train net output #0: loss = 0.15368 (* 1 = 0.15368 loss)
I0407 23:28:58.295673 32630 sgd_solver.cpp:105] Iteration 7692, lr = 0.000730215
I0407 23:29:03.257486 32630 solver.cpp:218] Iteration 7704 (2.41848 iter/s, 4.9618s/12 iters), loss = 0.0413929
I0407 23:29:03.257531 32630 solver.cpp:237]     Train net output #0: loss = 0.0413929 (* 1 = 0.0413929 loss)
I0407 23:29:03.257540 32630 sgd_solver.cpp:105] Iteration 7704, lr = 0.000722291
I0407 23:29:08.172782 32630 solver.cpp:218] Iteration 7716 (2.44139 iter/s, 4.91523s/12 iters), loss = 0.0924805
I0407 23:29:08.172827 32630 solver.cpp:237]     Train net output #0: loss = 0.0924805 (* 1 = 0.0924805 loss)
I0407 23:29:08.172835 32630 sgd_solver.cpp:105] Iteration 7716, lr = 0.000714447
I0407 23:29:13.108151 32630 solver.cpp:218] Iteration 7728 (2.43146 iter/s, 4.9353s/12 iters), loss = 0.0950282
I0407 23:29:13.108285 32630 solver.cpp:237]     Train net output #0: loss = 0.0950282 (* 1 = 0.0950282 loss)
I0407 23:29:13.108294 32630 sgd_solver.cpp:105] Iteration 7728, lr = 0.000706682
I0407 23:29:18.041615 32630 solver.cpp:218] Iteration 7740 (2.43245 iter/s, 4.93331s/12 iters), loss = 0.123003
I0407 23:29:18.041656 32630 solver.cpp:237]     Train net output #0: loss = 0.123003 (* 1 = 0.123003 loss)
I0407 23:29:18.041664 32630 sgd_solver.cpp:105] Iteration 7740, lr = 0.000698994
I0407 23:29:22.539405 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7752.caffemodel
I0407 23:29:25.857072 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7752.solverstate
I0407 23:29:28.220628 32630 solver.cpp:330] Iteration 7752, Testing net (#0)
I0407 23:29:28.220649 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:29:29.561590 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:29:32.655750 32630 solver.cpp:397]     Test net output #0: accuracy = 0.509804
I0407 23:29:32.655797 32630 solver.cpp:397]     Test net output #1: loss = 2.66137 (* 1 = 2.66137 loss)
I0407 23:29:32.750394 32630 solver.cpp:218] Iteration 7752 (0.815844 iter/s, 14.7087s/12 iters), loss = 0.101475
I0407 23:29:32.750437 32630 solver.cpp:237]     Train net output #0: loss = 0.101475 (* 1 = 0.101475 loss)
I0407 23:29:32.750445 32630 sgd_solver.cpp:105] Iteration 7752, lr = 0.000691384
I0407 23:29:36.821720 32630 solver.cpp:218] Iteration 7764 (2.94749 iter/s, 4.07126s/12 iters), loss = 0.0834518
I0407 23:29:36.821763 32630 solver.cpp:237]     Train net output #0: loss = 0.0834518 (* 1 = 0.0834518 loss)
I0407 23:29:36.821771 32630 sgd_solver.cpp:105] Iteration 7764, lr = 0.000683851
I0407 23:29:41.782092 32630 solver.cpp:218] Iteration 7776 (2.4192 iter/s, 4.96031s/12 iters), loss = 0.0626199
I0407 23:29:41.782130 32630 solver.cpp:237]     Train net output #0: loss = 0.0626199 (* 1 = 0.0626199 loss)
I0407 23:29:41.782137 32630 sgd_solver.cpp:105] Iteration 7776, lr = 0.000676394
I0407 23:29:46.729460 32630 solver.cpp:218] Iteration 7788 (2.42556 iter/s, 4.94731s/12 iters), loss = 0.0190625
I0407 23:29:46.729614 32630 solver.cpp:237]     Train net output #0: loss = 0.0190625 (* 1 = 0.0190625 loss)
I0407 23:29:46.729622 32630 sgd_solver.cpp:105] Iteration 7788, lr = 0.000669012
I0407 23:29:46.735792 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:29:51.647275 32630 solver.cpp:218] Iteration 7800 (2.4402 iter/s, 4.91764s/12 iters), loss = 0.0842227
I0407 23:29:51.647316 32630 solver.cpp:237]     Train net output #0: loss = 0.0842226 (* 1 = 0.0842226 loss)
I0407 23:29:51.647325 32630 sgd_solver.cpp:105] Iteration 7800, lr = 0.000661705
I0407 23:29:56.615599 32630 solver.cpp:218] Iteration 7812 (2.41534 iter/s, 4.96825s/12 iters), loss = 0.0917963
I0407 23:29:56.615648 32630 solver.cpp:237]     Train net output #0: loss = 0.0917963 (* 1 = 0.0917963 loss)
I0407 23:29:56.615656 32630 sgd_solver.cpp:105] Iteration 7812, lr = 0.000654472
I0407 23:30:01.513811 32630 solver.cpp:218] Iteration 7824 (2.44991 iter/s, 4.89814s/12 iters), loss = 0.135433
I0407 23:30:01.513854 32630 solver.cpp:237]     Train net output #0: loss = 0.135433 (* 1 = 0.135433 loss)
I0407 23:30:01.513861 32630 sgd_solver.cpp:105] Iteration 7824, lr = 0.000647313
I0407 23:30:06.493211 32630 solver.cpp:218] Iteration 7836 (2.40996 iter/s, 4.97933s/12 iters), loss = 0.129445
I0407 23:30:06.493257 32630 solver.cpp:237]     Train net output #0: loss = 0.129445 (* 1 = 0.129445 loss)
I0407 23:30:06.493265 32630 sgd_solver.cpp:105] Iteration 7836, lr = 0.000640227
I0407 23:30:11.430065 32630 solver.cpp:218] Iteration 7848 (2.43073 iter/s, 4.93678s/12 iters), loss = 0.021146
I0407 23:30:11.430109 32630 solver.cpp:237]     Train net output #0: loss = 0.021146 (* 1 = 0.021146 loss)
I0407 23:30:11.430119 32630 sgd_solver.cpp:105] Iteration 7848, lr = 0.000633213
I0407 23:30:13.452039 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7854.caffemodel
I0407 23:30:17.221787 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7854.solverstate
I0407 23:30:20.643501 32630 solver.cpp:330] Iteration 7854, Testing net (#0)
I0407 23:30:20.643522 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:30:22.028160 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:30:25.431963 32630 solver.cpp:397]     Test net output #0: accuracy = 0.507966
I0407 23:30:25.431990 32630 solver.cpp:397]     Test net output #1: loss = 2.66546 (* 1 = 2.66546 loss)
I0407 23:30:27.223505 32630 solver.cpp:218] Iteration 7860 (0.759814 iter/s, 15.7933s/12 iters), loss = 0.106889
I0407 23:30:27.223551 32630 solver.cpp:237]     Train net output #0: loss = 0.106889 (* 1 = 0.106889 loss)
I0407 23:30:27.223559 32630 sgd_solver.cpp:105] Iteration 7860, lr = 0.000626271
I0407 23:30:32.092607 32630 solver.cpp:218] Iteration 7872 (2.46456 iter/s, 4.86903s/12 iters), loss = 0.127841
I0407 23:30:32.092653 32630 solver.cpp:237]     Train net output #0: loss = 0.12784 (* 1 = 0.12784 loss)
I0407 23:30:32.092661 32630 sgd_solver.cpp:105] Iteration 7872, lr = 0.0006194
I0407 23:30:37.042572 32630 solver.cpp:218] Iteration 7884 (2.42429 iter/s, 4.9499s/12 iters), loss = 0.117973
I0407 23:30:37.042618 32630 solver.cpp:237]     Train net output #0: loss = 0.117973 (* 1 = 0.117973 loss)
I0407 23:30:37.042625 32630 sgd_solver.cpp:105] Iteration 7884, lr = 0.0006126
I0407 23:30:39.158147 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:30:41.990255 32630 solver.cpp:218] Iteration 7896 (2.42541 iter/s, 4.94761s/12 iters), loss = 0.0763213
I0407 23:30:41.990299 32630 solver.cpp:237]     Train net output #0: loss = 0.0763213 (* 1 = 0.0763213 loss)
I0407 23:30:41.990307 32630 sgd_solver.cpp:105] Iteration 7896, lr = 0.000605869
I0407 23:30:46.917245 32630 solver.cpp:218] Iteration 7908 (2.4356 iter/s, 4.92692s/12 iters), loss = 0.0647647
I0407 23:30:46.917289 32630 solver.cpp:237]     Train net output #0: loss = 0.0647647 (* 1 = 0.0647647 loss)
I0407 23:30:46.917297 32630 sgd_solver.cpp:105] Iteration 7908, lr = 0.000599207
I0407 23:30:51.878913 32630 solver.cpp:218] Iteration 7920 (2.41858 iter/s, 4.96159s/12 iters), loss = 0.0866002
I0407 23:30:51.879065 32630 solver.cpp:237]     Train net output #0: loss = 0.0866002 (* 1 = 0.0866002 loss)
I0407 23:30:51.879074 32630 sgd_solver.cpp:105] Iteration 7920, lr = 0.000592615
I0407 23:30:56.799852 32630 solver.cpp:218] Iteration 7932 (2.43865 iter/s, 4.92076s/12 iters), loss = 0.0493649
I0407 23:30:56.799897 32630 solver.cpp:237]     Train net output #0: loss = 0.0493649 (* 1 = 0.0493649 loss)
I0407 23:30:56.799906 32630 sgd_solver.cpp:105] Iteration 7932, lr = 0.00058609
I0407 23:31:01.774381 32630 solver.cpp:218] Iteration 7944 (2.41232 iter/s, 4.97446s/12 iters), loss = 0.0586754
I0407 23:31:01.774420 32630 solver.cpp:237]     Train net output #0: loss = 0.0586754 (* 1 = 0.0586754 loss)
I0407 23:31:01.774428 32630 sgd_solver.cpp:105] Iteration 7944, lr = 0.000579632
I0407 23:31:06.191246 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_7956.caffemodel
I0407 23:31:09.305048 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_7956.solverstate
I0407 23:31:11.679018 32630 solver.cpp:330] Iteration 7956, Testing net (#0)
I0407 23:31:11.679035 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:31:13.042456 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:31:16.475878 32630 solver.cpp:397]     Test net output #0: accuracy = 0.506127
I0407 23:31:16.475924 32630 solver.cpp:397]     Test net output #1: loss = 2.66076 (* 1 = 2.66076 loss)
I0407 23:31:16.572438 32630 solver.cpp:218] Iteration 7956 (0.810922 iter/s, 14.798s/12 iters), loss = 0.0337216
I0407 23:31:16.572484 32630 solver.cpp:237]     Train net output #0: loss = 0.0337216 (* 1 = 0.0337216 loss)
I0407 23:31:16.572494 32630 sgd_solver.cpp:105] Iteration 7956, lr = 0.000573242
I0407 23:31:20.673632 32630 solver.cpp:218] Iteration 7968 (2.92603 iter/s, 4.10112s/12 iters), loss = 0.0637961
I0407 23:31:20.673678 32630 solver.cpp:237]     Train net output #0: loss = 0.0637961 (* 1 = 0.0637961 loss)
I0407 23:31:20.673687 32630 sgd_solver.cpp:105] Iteration 7968, lr = 0.000566917
I0407 23:31:25.632460 32630 solver.cpp:218] Iteration 7980 (2.41996 iter/s, 4.95876s/12 iters), loss = 0.0721287
I0407 23:31:25.632586 32630 solver.cpp:237]     Train net output #0: loss = 0.0721287 (* 1 = 0.0721287 loss)
I0407 23:31:25.632594 32630 sgd_solver.cpp:105] Iteration 7980, lr = 0.000560659
I0407 23:31:29.856927 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:31:30.557070 32630 solver.cpp:218] Iteration 7992 (2.43681 iter/s, 4.92446s/12 iters), loss = 0.103308
I0407 23:31:30.557113 32630 solver.cpp:237]     Train net output #0: loss = 0.103308 (* 1 = 0.103308 loss)
I0407 23:31:30.557122 32630 sgd_solver.cpp:105] Iteration 7992, lr = 0.000554465
I0407 23:31:35.500185 32630 solver.cpp:218] Iteration 8004 (2.42765 iter/s, 4.94305s/12 iters), loss = 0.0757612
I0407 23:31:35.500231 32630 solver.cpp:237]     Train net output #0: loss = 0.0757612 (* 1 = 0.0757612 loss)
I0407 23:31:35.500239 32630 sgd_solver.cpp:105] Iteration 8004, lr = 0.000548335
I0407 23:31:40.434691 32630 solver.cpp:218] Iteration 8016 (2.43189 iter/s, 4.93444s/12 iters), loss = 0.017908
I0407 23:31:40.434736 32630 solver.cpp:237]     Train net output #0: loss = 0.0179079 (* 1 = 0.0179079 loss)
I0407 23:31:40.434746 32630 sgd_solver.cpp:105] Iteration 8016, lr = 0.00054227
I0407 23:31:45.378432 32630 solver.cpp:218] Iteration 8028 (2.42735 iter/s, 4.94367s/12 iters), loss = 0.0436473
I0407 23:31:45.378476 32630 solver.cpp:237]     Train net output #0: loss = 0.0436472 (* 1 = 0.0436472 loss)
I0407 23:31:45.378484 32630 sgd_solver.cpp:105] Iteration 8028, lr = 0.000536268
I0407 23:31:50.313261 32630 solver.cpp:218] Iteration 8040 (2.43173 iter/s, 4.93476s/12 iters), loss = 0.0887869
I0407 23:31:50.313306 32630 solver.cpp:237]     Train net output #0: loss = 0.0887868 (* 1 = 0.0887868 loss)
I0407 23:31:50.313314 32630 sgd_solver.cpp:105] Iteration 8040, lr = 0.000530328
I0407 23:31:55.266261 32630 solver.cpp:218] Iteration 8052 (2.42281 iter/s, 4.95292s/12 iters), loss = 0.0877403
I0407 23:31:55.266306 32630 solver.cpp:237]     Train net output #0: loss = 0.0877403 (* 1 = 0.0877403 loss)
I0407 23:31:55.266314 32630 sgd_solver.cpp:105] Iteration 8052, lr = 0.000524451
I0407 23:31:57.274132 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8058.caffemodel
I0407 23:32:00.478018 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8058.solverstate
I0407 23:32:02.849624 32630 solver.cpp:330] Iteration 8058, Testing net (#0)
I0407 23:32:02.849643 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:32:04.182971 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:32:07.613569 32630 solver.cpp:397]     Test net output #0: accuracy = 0.503064
I0407 23:32:07.613615 32630 solver.cpp:397]     Test net output #1: loss = 2.65947 (* 1 = 2.65947 loss)
I0407 23:32:09.408682 32630 solver.cpp:218] Iteration 8064 (0.848516 iter/s, 14.1423s/12 iters), loss = 0.0988063
I0407 23:32:09.408730 32630 solver.cpp:237]     Train net output #0: loss = 0.0988062 (* 1 = 0.0988062 loss)
I0407 23:32:09.408738 32630 sgd_solver.cpp:105] Iteration 8064, lr = 0.000518635
I0407 23:32:14.507802 32630 solver.cpp:218] Iteration 8076 (2.35338 iter/s, 5.09904s/12 iters), loss = 0.0686613
I0407 23:32:14.507843 32630 solver.cpp:237]     Train net output #0: loss = 0.0686613 (* 1 = 0.0686613 loss)
I0407 23:32:14.507851 32630 sgd_solver.cpp:105] Iteration 8076, lr = 0.000512881
I0407 23:32:19.484999 32630 solver.cpp:218] Iteration 8088 (2.41103 iter/s, 4.97713s/12 iters), loss = 0.0568011
I0407 23:32:19.485040 32630 solver.cpp:237]     Train net output #0: loss = 0.0568011 (* 1 = 0.0568011 loss)
I0407 23:32:19.485049 32630 sgd_solver.cpp:105] Iteration 8088, lr = 0.000507186
I0407 23:32:20.873087 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:32:24.451143 32630 solver.cpp:218] Iteration 8100 (2.41639 iter/s, 4.96608s/12 iters), loss = 0.0408048
I0407 23:32:24.451179 32630 solver.cpp:237]     Train net output #0: loss = 0.0408047 (* 1 = 0.0408047 loss)
I0407 23:32:24.451185 32630 sgd_solver.cpp:105] Iteration 8100, lr = 0.000501552
I0407 23:32:29.437595 32630 solver.cpp:218] Iteration 8112 (2.40655 iter/s, 4.98639s/12 iters), loss = 0.0798503
I0407 23:32:29.437722 32630 solver.cpp:237]     Train net output #0: loss = 0.0798502 (* 1 = 0.0798502 loss)
I0407 23:32:29.437732 32630 sgd_solver.cpp:105] Iteration 8112, lr = 0.000495977
I0407 23:32:34.319020 32630 solver.cpp:218] Iteration 8124 (2.45837 iter/s, 4.88128s/12 iters), loss = 0.138307
I0407 23:32:34.319067 32630 solver.cpp:237]     Train net output #0: loss = 0.138307 (* 1 = 0.138307 loss)
I0407 23:32:34.319075 32630 sgd_solver.cpp:105] Iteration 8124, lr = 0.00049046
I0407 23:32:39.199438 32630 solver.cpp:218] Iteration 8136 (2.45884 iter/s, 4.88035s/12 iters), loss = 0.0620447
I0407 23:32:39.199472 32630 solver.cpp:237]     Train net output #0: loss = 0.0620446 (* 1 = 0.0620446 loss)
I0407 23:32:39.199479 32630 sgd_solver.cpp:105] Iteration 8136, lr = 0.000485002
I0407 23:32:44.179436 32630 solver.cpp:218] Iteration 8148 (2.40967 iter/s, 4.97994s/12 iters), loss = 0.0474826
I0407 23:32:44.179477 32630 solver.cpp:237]     Train net output #0: loss = 0.0474825 (* 1 = 0.0474825 loss)
I0407 23:32:44.179484 32630 sgd_solver.cpp:105] Iteration 8148, lr = 0.000479602
I0407 23:32:48.665597 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8160.caffemodel
I0407 23:32:51.762431 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8160.solverstate
I0407 23:32:54.133421 32630 solver.cpp:330] Iteration 8160, Testing net (#0)
I0407 23:32:54.133440 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:32:55.399099 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:32:58.918359 32630 solver.cpp:397]     Test net output #0: accuracy = 0.503676
I0407 23:32:58.918401 32630 solver.cpp:397]     Test net output #1: loss = 2.67266 (* 1 = 2.67266 loss)
I0407 23:32:59.014955 32630 solver.cpp:218] Iteration 8160 (0.808874 iter/s, 14.8354s/12 iters), loss = 0.0758822
I0407 23:32:59.015002 32630 solver.cpp:237]     Train net output #0: loss = 0.0758821 (* 1 = 0.0758821 loss)
I0407 23:32:59.015012 32630 sgd_solver.cpp:105] Iteration 8160, lr = 0.000474259
I0407 23:33:03.200888 32630 solver.cpp:218] Iteration 8172 (2.86679 iter/s, 4.18587s/12 iters), loss = 0.148623
I0407 23:33:03.201043 32630 solver.cpp:237]     Train net output #0: loss = 0.148623 (* 1 = 0.148623 loss)
I0407 23:33:03.201053 32630 sgd_solver.cpp:105] Iteration 8172, lr = 0.000468972
I0407 23:33:08.147173 32630 solver.cpp:218] Iteration 8184 (2.42615 iter/s, 4.94611s/12 iters), loss = 0.181369
I0407 23:33:08.147215 32630 solver.cpp:237]     Train net output #0: loss = 0.181369 (* 1 = 0.181369 loss)
I0407 23:33:08.147224 32630 sgd_solver.cpp:105] Iteration 8184, lr = 0.000463741
I0407 23:33:11.664322 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:33:13.088483 32630 solver.cpp:218] Iteration 8196 (2.42854 iter/s, 4.94124s/12 iters), loss = 0.0950348
I0407 23:33:13.088532 32630 solver.cpp:237]     Train net output #0: loss = 0.0950348 (* 1 = 0.0950348 loss)
I0407 23:33:13.088541 32630 sgd_solver.cpp:105] Iteration 8196, lr = 0.000458566
I0407 23:33:18.027793 32630 solver.cpp:218] Iteration 8208 (2.42952 iter/s, 4.93924s/12 iters), loss = 0.067754
I0407 23:33:18.027833 32630 solver.cpp:237]     Train net output #0: loss = 0.0677539 (* 1 = 0.0677539 loss)
I0407 23:33:18.027842 32630 sgd_solver.cpp:105] Iteration 8208, lr = 0.000453446
I0407 23:33:22.987546 32630 solver.cpp:218] Iteration 8220 (2.4195 iter/s, 4.95969s/12 iters), loss = 0.0528595
I0407 23:33:22.987581 32630 solver.cpp:237]     Train net output #0: loss = 0.0528595 (* 1 = 0.0528595 loss)
I0407 23:33:22.987587 32630 sgd_solver.cpp:105] Iteration 8220, lr = 0.00044838
I0407 23:33:27.895623 32630 solver.cpp:218] Iteration 8232 (2.44498 iter/s, 4.90802s/12 iters), loss = 0.0115796
I0407 23:33:27.895660 32630 solver.cpp:237]     Train net output #0: loss = 0.0115796 (* 1 = 0.0115796 loss)
I0407 23:33:27.895668 32630 sgd_solver.cpp:105] Iteration 8232, lr = 0.000443369
I0407 23:33:32.871810 32630 solver.cpp:218] Iteration 8244 (2.41151 iter/s, 4.97613s/12 iters), loss = 0.0163949
I0407 23:33:32.871846 32630 solver.cpp:237]     Train net output #0: loss = 0.0163949 (* 1 = 0.0163949 loss)
I0407 23:33:32.871853 32630 sgd_solver.cpp:105] Iteration 8244, lr = 0.000438411
I0407 23:33:37.870590 32630 solver.cpp:218] Iteration 8256 (2.40061 iter/s, 4.99872s/12 iters), loss = 0.0437822
I0407 23:33:37.871349 32630 solver.cpp:237]     Train net output #0: loss = 0.0437821 (* 1 = 0.0437821 loss)
I0407 23:33:37.871359 32630 sgd_solver.cpp:105] Iteration 8256, lr = 0.000433505
I0407 23:33:40.006248 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8262.caffemodel
I0407 23:33:43.089083 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8262.solverstate
I0407 23:33:45.450527 32630 solver.cpp:330] Iteration 8262, Testing net (#0)
I0407 23:33:45.450544 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:33:46.585908 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:33:49.876438 32630 solver.cpp:397]     Test net output #0: accuracy = 0.501225
I0407 23:33:49.876466 32630 solver.cpp:397]     Test net output #1: loss = 2.65789 (* 1 = 2.65789 loss)
I0407 23:33:51.607054 32630 solver.cpp:218] Iteration 8268 (0.873637 iter/s, 13.7357s/12 iters), loss = 0.107425
I0407 23:33:51.607090 32630 solver.cpp:237]     Train net output #0: loss = 0.107425 (* 1 = 0.107425 loss)
I0407 23:33:51.607097 32630 sgd_solver.cpp:105] Iteration 8268, lr = 0.000428653
I0407 23:33:56.700590 32630 solver.cpp:218] Iteration 8280 (2.35595 iter/s, 5.09348s/12 iters), loss = 0.0543054
I0407 23:33:56.700628 32630 solver.cpp:237]     Train net output #0: loss = 0.0543054 (* 1 = 0.0543054 loss)
I0407 23:33:56.700636 32630 sgd_solver.cpp:105] Iteration 8280, lr = 0.000423852
I0407 23:34:01.714349 32630 solver.cpp:218] Iteration 8292 (2.39344 iter/s, 5.0137s/12 iters), loss = 0.0600634
I0407 23:34:01.714390 32630 solver.cpp:237]     Train net output #0: loss = 0.0600633 (* 1 = 0.0600633 loss)
I0407 23:34:01.714397 32630 sgd_solver.cpp:105] Iteration 8292, lr = 0.000419102
I0407 23:34:02.403152 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:34:06.675969 32630 solver.cpp:218] Iteration 8304 (2.4186 iter/s, 4.96155s/12 iters), loss = 0.0373632
I0407 23:34:06.676012 32630 solver.cpp:237]     Train net output #0: loss = 0.0373632 (* 1 = 0.0373632 loss)
I0407 23:34:06.676019 32630 sgd_solver.cpp:105] Iteration 8304, lr = 0.000414404
I0407 23:34:09.506132 32630 blocking_queue.cpp:49] Waiting for data
I0407 23:34:11.636195 32630 solver.cpp:218] Iteration 8316 (2.41928 iter/s, 4.96016s/12 iters), loss = 0.0591317
I0407 23:34:11.636246 32630 solver.cpp:237]     Train net output #0: loss = 0.0591317 (* 1 = 0.0591317 loss)
I0407 23:34:11.636255 32630 sgd_solver.cpp:105] Iteration 8316, lr = 0.000409755
I0407 23:34:16.492945 32630 solver.cpp:218] Iteration 8328 (2.47082 iter/s, 4.85668s/12 iters), loss = 0.114231
I0407 23:34:16.492981 32630 solver.cpp:237]     Train net output #0: loss = 0.114231 (* 1 = 0.114231 loss)
I0407 23:34:16.492988 32630 sgd_solver.cpp:105] Iteration 8328, lr = 0.000405157
I0407 23:34:21.411953 32630 solver.cpp:218] Iteration 8340 (2.43955 iter/s, 4.91895s/12 iters), loss = 0.110084
I0407 23:34:21.411989 32630 solver.cpp:237]     Train net output #0: loss = 0.110084 (* 1 = 0.110084 loss)
I0407 23:34:21.411996 32630 sgd_solver.cpp:105] Iteration 8340, lr = 0.000400608
I0407 23:34:26.362444 32630 solver.cpp:218] Iteration 8352 (2.42403 iter/s, 4.95044s/12 iters), loss = 0.0490323
I0407 23:34:26.362478 32630 solver.cpp:237]     Train net output #0: loss = 0.0490323 (* 1 = 0.0490323 loss)
I0407 23:34:26.362484 32630 sgd_solver.cpp:105] Iteration 8352, lr = 0.000396108
I0407 23:34:30.848655 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8364.caffemodel
I0407 23:34:33.922518 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8364.solverstate
I0407 23:34:36.420946 32630 solver.cpp:330] Iteration 8364, Testing net (#0)
I0407 23:34:36.420964 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:34:37.593732 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:34:40.933341 32630 solver.cpp:397]     Test net output #0: accuracy = 0.504289
I0407 23:34:40.933521 32630 solver.cpp:397]     Test net output #1: loss = 2.67186 (* 1 = 2.67186 loss)
I0407 23:34:41.030205 32630 solver.cpp:218] Iteration 8364 (0.818125 iter/s, 14.6677s/12 iters), loss = 0.0190848
I0407 23:34:41.030249 32630 solver.cpp:237]     Train net output #0: loss = 0.0190847 (* 1 = 0.0190847 loss)
I0407 23:34:41.030257 32630 sgd_solver.cpp:105] Iteration 8364, lr = 0.000391657
I0407 23:34:45.137334 32630 solver.cpp:218] Iteration 8376 (2.9218 iter/s, 4.10706s/12 iters), loss = 0.117558
I0407 23:34:45.137370 32630 solver.cpp:237]     Train net output #0: loss = 0.117558 (* 1 = 0.117558 loss)
I0407 23:34:45.137377 32630 sgd_solver.cpp:105] Iteration 8376, lr = 0.000387254
I0407 23:34:50.089759 32630 solver.cpp:218] Iteration 8388 (2.42308 iter/s, 4.95237s/12 iters), loss = 0.0588717
I0407 23:34:50.089799 32630 solver.cpp:237]     Train net output #0: loss = 0.0588716 (* 1 = 0.0588716 loss)
I0407 23:34:50.089807 32630 sgd_solver.cpp:105] Iteration 8388, lr = 0.000382898
I0407 23:34:52.842685 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:34:54.981846 32630 solver.cpp:218] Iteration 8400 (2.45297 iter/s, 4.89202s/12 iters), loss = 0.0916427
I0407 23:34:54.981891 32630 solver.cpp:237]     Train net output #0: loss = 0.0916426 (* 1 = 0.0916426 loss)
I0407 23:34:54.981899 32630 sgd_solver.cpp:105] Iteration 8400, lr = 0.000378589
I0407 23:34:59.944725 32630 solver.cpp:218] Iteration 8412 (2.41798 iter/s, 4.96281s/12 iters), loss = 0.047099
I0407 23:34:59.944766 32630 solver.cpp:237]     Train net output #0: loss = 0.047099 (* 1 = 0.047099 loss)
I0407 23:34:59.944773 32630 sgd_solver.cpp:105] Iteration 8412, lr = 0.000374327
I0407 23:35:04.861016 32630 solver.cpp:218] Iteration 8424 (2.4409 iter/s, 4.91622s/12 iters), loss = 0.105046
I0407 23:35:04.861061 32630 solver.cpp:237]     Train net output #0: loss = 0.105046 (* 1 = 0.105046 loss)
I0407 23:35:04.861070 32630 sgd_solver.cpp:105] Iteration 8424, lr = 0.000370111
I0407 23:35:09.801426 32630 solver.cpp:218] Iteration 8436 (2.42898 iter/s, 4.94034s/12 iters), loss = 0.063297
I0407 23:35:09.801468 32630 solver.cpp:237]     Train net output #0: loss = 0.063297 (* 1 = 0.063297 loss)
I0407 23:35:09.801476 32630 sgd_solver.cpp:105] Iteration 8436, lr = 0.000365941
I0407 23:35:14.739413 32630 solver.cpp:218] Iteration 8448 (2.43017 iter/s, 4.93792s/12 iters), loss = 0.0427842
I0407 23:35:14.739538 32630 solver.cpp:237]     Train net output #0: loss = 0.0427842 (* 1 = 0.0427842 loss)
I0407 23:35:14.739547 32630 sgd_solver.cpp:105] Iteration 8448, lr = 0.000361816
I0407 23:35:19.695379 32630 solver.cpp:218] Iteration 8460 (2.42139 iter/s, 4.95582s/12 iters), loss = 0.0863513
I0407 23:35:19.695413 32630 solver.cpp:237]     Train net output #0: loss = 0.0863512 (* 1 = 0.0863512 loss)
I0407 23:35:19.695420 32630 sgd_solver.cpp:105] Iteration 8460, lr = 0.000357735
I0407 23:35:21.722411 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8466.caffemodel
I0407 23:35:24.791986 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8466.solverstate
I0407 23:35:27.211367 32630 solver.cpp:330] Iteration 8466, Testing net (#0)
I0407 23:35:27.211387 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:35:28.303035 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:35:31.946280 32630 solver.cpp:397]     Test net output #0: accuracy = 0.509804
I0407 23:35:31.946326 32630 solver.cpp:397]     Test net output #1: loss = 2.64589 (* 1 = 2.64589 loss)
I0407 23:35:33.724174 32630 solver.cpp:218] Iteration 8472 (0.855388 iter/s, 14.0287s/12 iters), loss = 0.0630996
I0407 23:35:33.724212 32630 solver.cpp:237]     Train net output #0: loss = 0.0630995 (* 1 = 0.0630995 loss)
I0407 23:35:33.724221 32630 sgd_solver.cpp:105] Iteration 8472, lr = 0.000353699
I0407 23:35:38.675314 32630 solver.cpp:218] Iteration 8484 (2.42372 iter/s, 4.95108s/12 iters), loss = 0.0670938
I0407 23:35:38.675352 32630 solver.cpp:237]     Train net output #0: loss = 0.0670938 (* 1 = 0.0670938 loss)
I0407 23:35:38.675360 32630 sgd_solver.cpp:105] Iteration 8484, lr = 0.000349707
I0407 23:35:43.601791 32630 solver.cpp:218] Iteration 8496 (2.43585 iter/s, 4.92641s/12 iters), loss = 0.103399
I0407 23:35:43.601843 32630 solver.cpp:237]     Train net output #0: loss = 0.103399 (* 1 = 0.103399 loss)
I0407 23:35:43.601857 32630 sgd_solver.cpp:105] Iteration 8496, lr = 0.000345759
I0407 23:35:43.638207 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:35:48.554540 32630 solver.cpp:218] Iteration 8508 (2.42294 iter/s, 4.95267s/12 iters), loss = 0.0481589
I0407 23:35:48.554738 32630 solver.cpp:237]     Train net output #0: loss = 0.0481589 (* 1 = 0.0481589 loss)
I0407 23:35:48.554752 32630 sgd_solver.cpp:105] Iteration 8508, lr = 0.000341853
I0407 23:35:53.517627 32630 solver.cpp:218] Iteration 8520 (2.41795 iter/s, 4.96287s/12 iters), loss = 0.0687039
I0407 23:35:53.517664 32630 solver.cpp:237]     Train net output #0: loss = 0.0687038 (* 1 = 0.0687038 loss)
I0407 23:35:53.517671 32630 sgd_solver.cpp:105] Iteration 8520, lr = 0.00033799
I0407 23:35:58.480284 32630 solver.cpp:218] Iteration 8532 (2.41809 iter/s, 4.96259s/12 iters), loss = 0.183256
I0407 23:35:58.480325 32630 solver.cpp:237]     Train net output #0: loss = 0.183256 (* 1 = 0.183256 loss)
I0407 23:35:58.480334 32630 sgd_solver.cpp:105] Iteration 8532, lr = 0.000334169
I0407 23:36:03.450582 32630 solver.cpp:218] Iteration 8544 (2.41437 iter/s, 4.97024s/12 iters), loss = 0.017986
I0407 23:36:03.450618 32630 solver.cpp:237]     Train net output #0: loss = 0.017986 (* 1 = 0.017986 loss)
I0407 23:36:03.450626 32630 sgd_solver.cpp:105] Iteration 8544, lr = 0.00033039
I0407 23:36:08.404639 32630 solver.cpp:218] Iteration 8556 (2.42229 iter/s, 4.954s/12 iters), loss = 0.0767222
I0407 23:36:08.404678 32630 solver.cpp:237]     Train net output #0: loss = 0.0767222 (* 1 = 0.0767222 loss)
I0407 23:36:08.404686 32630 sgd_solver.cpp:105] Iteration 8556, lr = 0.000326652
I0407 23:36:12.909257 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8568.caffemodel
I0407 23:36:16.025971 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8568.solverstate
I0407 23:36:18.472465 32630 solver.cpp:330] Iteration 8568, Testing net (#0)
I0407 23:36:18.472482 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:36:19.500334 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:36:22.923050 32630 solver.cpp:397]     Test net output #0: accuracy = 0.511642
I0407 23:36:22.923101 32630 solver.cpp:397]     Test net output #1: loss = 2.66731 (* 1 = 2.66731 loss)
I0407 23:36:23.019747 32630 solver.cpp:218] Iteration 8568 (0.821073 iter/s, 14.615s/12 iters), loss = 0.0561312
I0407 23:36:23.019800 32630 solver.cpp:237]     Train net output #0: loss = 0.0561311 (* 1 = 0.0561311 loss)
I0407 23:36:23.019809 32630 sgd_solver.cpp:105] Iteration 8568, lr = 0.000322955
I0407 23:36:27.208911 32630 solver.cpp:218] Iteration 8580 (2.86458 iter/s, 4.18909s/12 iters), loss = 0.0742672
I0407 23:36:27.208946 32630 solver.cpp:237]     Train net output #0: loss = 0.0742671 (* 1 = 0.0742671 loss)
I0407 23:36:27.208954 32630 sgd_solver.cpp:105] Iteration 8580, lr = 0.000319298
I0407 23:36:32.140097 32630 solver.cpp:218] Iteration 8592 (2.43352 iter/s, 4.93113s/12 iters), loss = 0.100952
I0407 23:36:32.140134 32630 solver.cpp:237]     Train net output #0: loss = 0.100952 (* 1 = 0.100952 loss)
I0407 23:36:32.140142 32630 sgd_solver.cpp:105] Iteration 8592, lr = 0.000315681
I0407 23:36:34.290133 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:36:37.099313 32630 solver.cpp:218] Iteration 8604 (2.41977 iter/s, 4.95915s/12 iters), loss = 0.0121064
I0407 23:36:37.099360 32630 solver.cpp:237]     Train net output #0: loss = 0.0121064 (* 1 = 0.0121064 loss)
I0407 23:36:37.099368 32630 sgd_solver.cpp:105] Iteration 8604, lr = 0.000312105
I0407 23:36:42.035849 32630 solver.cpp:218] Iteration 8616 (2.43089 iter/s, 4.93647s/12 iters), loss = 0.0399671
I0407 23:36:42.035893 32630 solver.cpp:237]     Train net output #0: loss = 0.0399671 (* 1 = 0.0399671 loss)
I0407 23:36:42.035902 32630 sgd_solver.cpp:105] Iteration 8616, lr = 0.000308567
I0407 23:36:46.994621 32630 solver.cpp:218] Iteration 8628 (2.41999 iter/s, 4.95871s/12 iters), loss = 0.0473402
I0407 23:36:46.994658 32630 solver.cpp:237]     Train net output #0: loss = 0.0473401 (* 1 = 0.0473401 loss)
I0407 23:36:46.994665 32630 sgd_solver.cpp:105] Iteration 8628, lr = 0.000305068
I0407 23:36:51.906780 32630 solver.cpp:218] Iteration 8640 (2.44295 iter/s, 4.9121s/12 iters), loss = 0.167398
I0407 23:36:51.906930 32630 solver.cpp:237]     Train net output #0: loss = 0.167398 (* 1 = 0.167398 loss)
I0407 23:36:51.906940 32630 sgd_solver.cpp:105] Iteration 8640, lr = 0.000301608
I0407 23:36:56.883675 32630 solver.cpp:218] Iteration 8652 (2.41122 iter/s, 4.97673s/12 iters), loss = 0.036586
I0407 23:36:56.883711 32630 solver.cpp:237]     Train net output #0: loss = 0.0365859 (* 1 = 0.0365859 loss)
I0407 23:36:56.883718 32630 sgd_solver.cpp:105] Iteration 8652, lr = 0.000298185
I0407 23:37:01.855108 32630 solver.cpp:218] Iteration 8664 (2.41382 iter/s, 4.97138s/12 iters), loss = 0.064607
I0407 23:37:01.855144 32630 solver.cpp:237]     Train net output #0: loss = 0.064607 (* 1 = 0.064607 loss)
I0407 23:37:01.855151 32630 sgd_solver.cpp:105] Iteration 8664, lr = 0.000294801
I0407 23:37:03.848764 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8670.caffemodel
I0407 23:37:06.910699 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8670.solverstate
I0407 23:37:09.269863 32630 solver.cpp:330] Iteration 8670, Testing net (#0)
I0407 23:37:09.269876 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:37:10.325544 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:37:13.951545 32630 solver.cpp:397]     Test net output #0: accuracy = 0.511642
I0407 23:37:13.951588 32630 solver.cpp:397]     Test net output #1: loss = 2.64441 (* 1 = 2.64441 loss)
I0407 23:37:15.704519 32630 solver.cpp:218] Iteration 8676 (0.866468 iter/s, 13.8493s/12 iters), loss = 0.117348
I0407 23:37:15.704561 32630 solver.cpp:237]     Train net output #0: loss = 0.117348 (* 1 = 0.117348 loss)
I0407 23:37:15.704569 32630 sgd_solver.cpp:105] Iteration 8676, lr = 0.000291453
I0407 23:37:20.651645 32630 solver.cpp:218] Iteration 8688 (2.42568 iter/s, 4.94706s/12 iters), loss = 0.0643133
I0407 23:37:20.651690 32630 solver.cpp:237]     Train net output #0: loss = 0.0643132 (* 1 = 0.0643132 loss)
I0407 23:37:20.651698 32630 sgd_solver.cpp:105] Iteration 8688, lr = 0.000288143
I0407 23:37:24.922271 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:37:25.538017 32630 solver.cpp:218] Iteration 8700 (2.45585 iter/s, 4.8863s/12 iters), loss = 0.0502378
I0407 23:37:25.538059 32630 solver.cpp:237]     Train net output #0: loss = 0.0502377 (* 1 = 0.0502377 loss)
I0407 23:37:25.538069 32630 sgd_solver.cpp:105] Iteration 8700, lr = 0.000284869
I0407 23:37:30.467945 32630 solver.cpp:218] Iteration 8712 (2.43415 iter/s, 4.92986s/12 iters), loss = 0.0622677
I0407 23:37:30.467990 32630 solver.cpp:237]     Train net output #0: loss = 0.0622677 (* 1 = 0.0622677 loss)
I0407 23:37:30.467999 32630 sgd_solver.cpp:105] Iteration 8712, lr = 0.000281631
I0407 23:37:35.433233 32630 solver.cpp:218] Iteration 8724 (2.41681 iter/s, 4.96522s/12 iters), loss = 0.0915436
I0407 23:37:35.433279 32630 solver.cpp:237]     Train net output #0: loss = 0.0915435 (* 1 = 0.0915435 loss)
I0407 23:37:35.433285 32630 sgd_solver.cpp:105] Iteration 8724, lr = 0.000278428
I0407 23:37:40.347271 32630 solver.cpp:218] Iteration 8736 (2.44202 iter/s, 4.91397s/12 iters), loss = 0.0172269
I0407 23:37:40.347321 32630 solver.cpp:237]     Train net output #0: loss = 0.0172269 (* 1 = 0.0172269 loss)
I0407 23:37:40.347329 32630 sgd_solver.cpp:105] Iteration 8736, lr = 0.000275262
I0407 23:37:45.309561 32630 solver.cpp:218] Iteration 8748 (2.41828 iter/s, 4.96221s/12 iters), loss = 0.0581039
I0407 23:37:45.309607 32630 solver.cpp:237]     Train net output #0: loss = 0.0581039 (* 1 = 0.0581039 loss)
I0407 23:37:45.309615 32630 sgd_solver.cpp:105] Iteration 8748, lr = 0.00027213
I0407 23:37:50.267830 32630 solver.cpp:218] Iteration 8760 (2.42023 iter/s, 4.9582s/12 iters), loss = 0.0943516
I0407 23:37:50.267868 32630 solver.cpp:237]     Train net output #0: loss = 0.0943516 (* 1 = 0.0943516 loss)
I0407 23:37:50.267877 32630 sgd_solver.cpp:105] Iteration 8760, lr = 0.000269033
I0407 23:37:54.741963 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8772.caffemodel
I0407 23:37:57.831542 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8772.solverstate
I0407 23:38:00.251113 32630 solver.cpp:330] Iteration 8772, Testing net (#0)
I0407 23:38:00.251130 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:38:01.200594 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:38:04.707350 32630 solver.cpp:397]     Test net output #0: accuracy = 0.510417
I0407 23:38:04.707397 32630 solver.cpp:397]     Test net output #1: loss = 2.65427 (* 1 = 2.65427 loss)
I0407 23:38:04.804049 32630 solver.cpp:218] Iteration 8772 (0.825529 iter/s, 14.5361s/12 iters), loss = 0.0676042
I0407 23:38:04.804097 32630 solver.cpp:237]     Train net output #0: loss = 0.0676041 (* 1 = 0.0676041 loss)
I0407 23:38:04.804106 32630 sgd_solver.cpp:105] Iteration 8772, lr = 0.00026597
I0407 23:38:08.932951 32630 solver.cpp:218] Iteration 8784 (2.90639 iter/s, 4.12883s/12 iters), loss = 0.131528
I0407 23:38:08.933003 32630 solver.cpp:237]     Train net output #0: loss = 0.131528 (* 1 = 0.131528 loss)
I0407 23:38:08.933017 32630 sgd_solver.cpp:105] Iteration 8784, lr = 0.000262941
I0407 23:38:13.884203 32630 solver.cpp:218] Iteration 8796 (2.42366 iter/s, 4.95118s/12 iters), loss = 0.0815396
I0407 23:38:13.884241 32630 solver.cpp:237]     Train net output #0: loss = 0.0815395 (* 1 = 0.0815395 loss)
I0407 23:38:13.884249 32630 sgd_solver.cpp:105] Iteration 8796, lr = 0.000259946
I0407 23:38:15.290150 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:38:18.778561 32630 solver.cpp:218] Iteration 8808 (2.45184 iter/s, 4.89428s/12 iters), loss = 0.0236104
I0407 23:38:18.778625 32630 solver.cpp:237]     Train net output #0: loss = 0.0236103 (* 1 = 0.0236103 loss)
I0407 23:38:18.778636 32630 sgd_solver.cpp:105] Iteration 8808, lr = 0.000256983
I0407 23:38:23.634285 32630 solver.cpp:218] Iteration 8820 (2.47136 iter/s, 4.85563s/12 iters), loss = 0.0359844
I0407 23:38:23.634336 32630 solver.cpp:237]     Train net output #0: loss = 0.0359843 (* 1 = 0.0359843 loss)
I0407 23:38:23.634348 32630 sgd_solver.cpp:105] Iteration 8820, lr = 0.000254054
I0407 23:38:28.580340 32630 solver.cpp:218] Iteration 8832 (2.42621 iter/s, 4.94598s/12 iters), loss = 0.0808497
I0407 23:38:28.580468 32630 solver.cpp:237]     Train net output #0: loss = 0.0808497 (* 1 = 0.0808497 loss)
I0407 23:38:28.580477 32630 sgd_solver.cpp:105] Iteration 8832, lr = 0.000251157
I0407 23:38:33.435498 32630 solver.cpp:218] Iteration 8844 (2.47167 iter/s, 4.85501s/12 iters), loss = 0.153475
I0407 23:38:33.435537 32630 solver.cpp:237]     Train net output #0: loss = 0.153475 (* 1 = 0.153475 loss)
I0407 23:38:33.435545 32630 sgd_solver.cpp:105] Iteration 8844, lr = 0.000248293
I0407 23:38:38.406239 32630 solver.cpp:218] Iteration 8856 (2.41416 iter/s, 4.97067s/12 iters), loss = 0.0225847
I0407 23:38:38.406284 32630 solver.cpp:237]     Train net output #0: loss = 0.0225846 (* 1 = 0.0225846 loss)
I0407 23:38:38.406292 32630 sgd_solver.cpp:105] Iteration 8856, lr = 0.00024546
I0407 23:38:43.344175 32630 solver.cpp:218] Iteration 8868 (2.4302 iter/s, 4.93786s/12 iters), loss = 0.0927806
I0407 23:38:43.344224 32630 solver.cpp:237]     Train net output #0: loss = 0.0927805 (* 1 = 0.0927805 loss)
I0407 23:38:43.344233 32630 sgd_solver.cpp:105] Iteration 8868, lr = 0.000242659
I0407 23:38:45.329372 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8874.caffemodel
I0407 23:38:48.494735 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8874.solverstate
I0407 23:38:50.854533 32630 solver.cpp:330] Iteration 8874, Testing net (#0)
I0407 23:38:50.854552 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:38:51.823354 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:38:55.709355 32630 solver.cpp:397]     Test net output #0: accuracy = 0.511029
I0407 23:38:55.709408 32630 solver.cpp:397]     Test net output #1: loss = 2.66033 (* 1 = 2.66033 loss)
I0407 23:38:57.516650 32630 solver.cpp:218] Iteration 8880 (0.846717 iter/s, 14.1724s/12 iters), loss = 0.0457909
I0407 23:38:57.516691 32630 solver.cpp:237]     Train net output #0: loss = 0.0457909 (* 1 = 0.0457909 loss)
I0407 23:38:57.516700 32630 sgd_solver.cpp:105] Iteration 8880, lr = 0.000239889
I0407 23:39:02.449744 32630 solver.cpp:218] Iteration 8892 (2.43258 iter/s, 4.93303s/12 iters), loss = 0.016709
I0407 23:39:02.449923 32630 solver.cpp:237]     Train net output #0: loss = 0.0167089 (* 1 = 0.0167089 loss)
I0407 23:39:02.449931 32630 sgd_solver.cpp:105] Iteration 8892, lr = 0.00023715
I0407 23:39:05.997406 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:39:07.378334 32630 solver.cpp:218] Iteration 8904 (2.43487 iter/s, 4.92839s/12 iters), loss = 0.0400892
I0407 23:39:07.378378 32630 solver.cpp:237]     Train net output #0: loss = 0.0400892 (* 1 = 0.0400892 loss)
I0407 23:39:07.378386 32630 sgd_solver.cpp:105] Iteration 8904, lr = 0.000234441
I0407 23:39:12.252578 32630 solver.cpp:218] Iteration 8916 (2.46196 iter/s, 4.87417s/12 iters), loss = 0.0500018
I0407 23:39:12.252621 32630 solver.cpp:237]     Train net output #0: loss = 0.0500018 (* 1 = 0.0500018 loss)
I0407 23:39:12.252629 32630 sgd_solver.cpp:105] Iteration 8916, lr = 0.000231763
I0407 23:39:17.184985 32630 solver.cpp:218] Iteration 8928 (2.43292 iter/s, 4.93234s/12 iters), loss = 0.0171391
I0407 23:39:17.185030 32630 solver.cpp:237]     Train net output #0: loss = 0.017139 (* 1 = 0.017139 loss)
I0407 23:39:17.185039 32630 sgd_solver.cpp:105] Iteration 8928, lr = 0.000229114
I0407 23:39:22.010857 32630 solver.cpp:218] Iteration 8940 (2.48663 iter/s, 4.8258s/12 iters), loss = 0.0421466
I0407 23:39:22.010900 32630 solver.cpp:237]     Train net output #0: loss = 0.0421465 (* 1 = 0.0421465 loss)
I0407 23:39:22.010910 32630 sgd_solver.cpp:105] Iteration 8940, lr = 0.000226495
I0407 23:39:26.960765 32630 solver.cpp:218] Iteration 8952 (2.42432 iter/s, 4.94984s/12 iters), loss = 0.0183309
I0407 23:39:26.960809 32630 solver.cpp:237]     Train net output #0: loss = 0.0183308 (* 1 = 0.0183308 loss)
I0407 23:39:26.960817 32630 sgd_solver.cpp:105] Iteration 8952, lr = 0.000223906
I0407 23:39:31.910868 32630 solver.cpp:218] Iteration 8964 (2.42423 iter/s, 4.95004s/12 iters), loss = 0.175569
I0407 23:39:31.910912 32630 solver.cpp:237]     Train net output #0: loss = 0.175569 (* 1 = 0.175569 loss)
I0407 23:39:31.910919 32630 sgd_solver.cpp:105] Iteration 8964, lr = 0.000221345
I0407 23:39:36.367957 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_8976.caffemodel
I0407 23:39:39.491264 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_8976.solverstate
I0407 23:39:41.863301 32630 solver.cpp:330] Iteration 8976, Testing net (#0)
I0407 23:39:41.863319 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:39:42.790280 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:39:46.543979 32630 solver.cpp:397]     Test net output #0: accuracy = 0.509191
I0407 23:39:46.544028 32630 solver.cpp:397]     Test net output #1: loss = 2.65242 (* 1 = 2.65242 loss)
I0407 23:39:46.642335 32630 solver.cpp:218] Iteration 8976 (0.814588 iter/s, 14.7314s/12 iters), loss = 0.0362251
I0407 23:39:46.642385 32630 solver.cpp:237]     Train net output #0: loss = 0.0362251 (* 1 = 0.0362251 loss)
I0407 23:39:46.642392 32630 sgd_solver.cpp:105] Iteration 8976, lr = 0.000218813
I0407 23:39:50.836831 32630 solver.cpp:218] Iteration 8988 (2.86094 iter/s, 4.19443s/12 iters), loss = 0.0659257
I0407 23:39:50.836870 32630 solver.cpp:237]     Train net output #0: loss = 0.0659257 (* 1 = 0.0659257 loss)
I0407 23:39:50.836879 32630 sgd_solver.cpp:105] Iteration 8988, lr = 0.000216309
I0407 23:39:54.066435 32630 blocking_queue.cpp:49] Waiting for data
I0407 23:39:55.779124 32630 solver.cpp:218] Iteration 9000 (2.42805 iter/s, 4.94223s/12 iters), loss = 0.0407439
I0407 23:39:55.779168 32630 solver.cpp:237]     Train net output #0: loss = 0.0407438 (* 1 = 0.0407438 loss)
I0407 23:39:55.779176 32630 sgd_solver.cpp:105] Iteration 9000, lr = 0.000213833
I0407 23:39:56.461503 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:40:00.722045 32630 solver.cpp:218] Iteration 9012 (2.42775 iter/s, 4.94285s/12 iters), loss = 0.0751957
I0407 23:40:00.722086 32630 solver.cpp:237]     Train net output #0: loss = 0.0751957 (* 1 = 0.0751957 loss)
I0407 23:40:00.722093 32630 sgd_solver.cpp:105] Iteration 9012, lr = 0.000211385
I0407 23:40:05.673146 32630 solver.cpp:218] Iteration 9024 (2.42374 iter/s, 4.95104s/12 iters), loss = 0.139546
I0407 23:40:05.673185 32630 solver.cpp:237]     Train net output #0: loss = 0.139546 (* 1 = 0.139546 loss)
I0407 23:40:05.673192 32630 sgd_solver.cpp:105] Iteration 9024, lr = 0.000208964
I0407 23:40:10.579916 32630 solver.cpp:218] Iteration 9036 (2.44563 iter/s, 4.90671s/12 iters), loss = 0.10658
I0407 23:40:10.580046 32630 solver.cpp:237]     Train net output #0: loss = 0.10658 (* 1 = 0.10658 loss)
I0407 23:40:10.580055 32630 sgd_solver.cpp:105] Iteration 9036, lr = 0.000206571
I0407 23:40:15.492542 32630 solver.cpp:218] Iteration 9048 (2.44276 iter/s, 4.91248s/12 iters), loss = 0.0112996
I0407 23:40:15.492580 32630 solver.cpp:237]     Train net output #0: loss = 0.0112995 (* 1 = 0.0112995 loss)
I0407 23:40:15.492588 32630 sgd_solver.cpp:105] Iteration 9048, lr = 0.000204204
I0407 23:40:20.439352 32630 solver.cpp:218] Iteration 9060 (2.42583 iter/s, 4.94675s/12 iters), loss = 0.037109
I0407 23:40:20.439390 32630 solver.cpp:237]     Train net output #0: loss = 0.0371089 (* 1 = 0.0371089 loss)
I0407 23:40:20.439399 32630 sgd_solver.cpp:105] Iteration 9060, lr = 0.000201864
I0407 23:40:25.364567 32630 solver.cpp:218] Iteration 9072 (2.43648 iter/s, 4.92514s/12 iters), loss = 0.0376304
I0407 23:40:25.364624 32630 solver.cpp:237]     Train net output #0: loss = 0.0376304 (* 1 = 0.0376304 loss)
I0407 23:40:25.364634 32630 sgd_solver.cpp:105] Iteration 9072, lr = 0.00019955
I0407 23:40:27.389348 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9078.caffemodel
I0407 23:40:30.446408 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9078.solverstate
I0407 23:40:32.810492 32630 solver.cpp:330] Iteration 9078, Testing net (#0)
I0407 23:40:32.810509 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:40:33.619385 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:40:37.270795 32630 solver.cpp:397]     Test net output #0: accuracy = 0.511642
I0407 23:40:37.270840 32630 solver.cpp:397]     Test net output #1: loss = 2.64884 (* 1 = 2.64884 loss)
I0407 23:40:39.073122 32630 solver.cpp:218] Iteration 9084 (0.875372 iter/s, 13.7085s/12 iters), loss = 0.115883
I0407 23:40:39.073184 32630 solver.cpp:237]     Train net output #0: loss = 0.115883 (* 1 = 0.115883 loss)
I0407 23:40:39.073194 32630 sgd_solver.cpp:105] Iteration 9084, lr = 0.000197262
I0407 23:40:44.021222 32630 solver.cpp:218] Iteration 9096 (2.42522 iter/s, 4.948s/12 iters), loss = 0.049159
I0407 23:40:44.021394 32630 solver.cpp:237]     Train net output #0: loss = 0.049159 (* 1 = 0.049159 loss)
I0407 23:40:44.021412 32630 sgd_solver.cpp:105] Iteration 9096, lr = 0.000195
I0407 23:40:46.928186 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:40:48.980216 32630 solver.cpp:218] Iteration 9108 (2.41994 iter/s, 4.9588s/12 iters), loss = 0.0456034
I0407 23:40:48.980259 32630 solver.cpp:237]     Train net output #0: loss = 0.0456034 (* 1 = 0.0456034 loss)
I0407 23:40:48.980268 32630 sgd_solver.cpp:105] Iteration 9108, lr = 0.000192763
I0407 23:40:53.844868 32630 solver.cpp:218] Iteration 9120 (2.46681 iter/s, 4.86459s/12 iters), loss = 0.0678677
I0407 23:40:53.844913 32630 solver.cpp:237]     Train net output #0: loss = 0.0678676 (* 1 = 0.0678676 loss)
I0407 23:40:53.844921 32630 sgd_solver.cpp:105] Iteration 9120, lr = 0.000190552
I0407 23:40:58.800710 32630 solver.cpp:218] Iteration 9132 (2.42142 iter/s, 4.95577s/12 iters), loss = 0.0655116
I0407 23:40:58.800756 32630 solver.cpp:237]     Train net output #0: loss = 0.0655115 (* 1 = 0.0655115 loss)
I0407 23:40:58.800765 32630 sgd_solver.cpp:105] Iteration 9132, lr = 0.000188365
I0407 23:41:03.713071 32630 solver.cpp:218] Iteration 9144 (2.44285 iter/s, 4.91229s/12 iters), loss = 0.0431078
I0407 23:41:03.713116 32630 solver.cpp:237]     Train net output #0: loss = 0.0431078 (* 1 = 0.0431078 loss)
I0407 23:41:03.713124 32630 sgd_solver.cpp:105] Iteration 9144, lr = 0.000186203
I0407 23:41:08.701304 32630 solver.cpp:218] Iteration 9156 (2.4057 iter/s, 4.98816s/12 iters), loss = 0.0945231
I0407 23:41:08.701347 32630 solver.cpp:237]     Train net output #0: loss = 0.0945231 (* 1 = 0.0945231 loss)
I0407 23:41:08.701356 32630 sgd_solver.cpp:105] Iteration 9156, lr = 0.000184065
I0407 23:41:13.626143 32630 solver.cpp:218] Iteration 9168 (2.43666 iter/s, 4.92477s/12 iters), loss = 0.0396213
I0407 23:41:13.626189 32630 solver.cpp:237]     Train net output #0: loss = 0.0396212 (* 1 = 0.0396212 loss)
I0407 23:41:13.626197 32630 sgd_solver.cpp:105] Iteration 9168, lr = 0.000181952
I0407 23:41:18.112866 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9180.caffemodel
I0407 23:41:21.219511 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9180.solverstate
I0407 23:41:24.783604 32630 solver.cpp:330] Iteration 9180, Testing net (#0)
I0407 23:41:24.783622 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:41:25.612195 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:41:29.557747 32630 solver.cpp:397]     Test net output #0: accuracy = 0.509804
I0407 23:41:29.557798 32630 solver.cpp:397]     Test net output #1: loss = 2.66577 (* 1 = 2.66577 loss)
I0407 23:41:29.654493 32630 solver.cpp:218] Iteration 9180 (0.748678 iter/s, 16.0283s/12 iters), loss = 0.0674636
I0407 23:41:29.654541 32630 solver.cpp:237]     Train net output #0: loss = 0.0674635 (* 1 = 0.0674635 loss)
I0407 23:41:29.654548 32630 sgd_solver.cpp:105] Iteration 9180, lr = 0.000179862
I0407 23:41:33.809854 32630 solver.cpp:218] Iteration 9192 (2.88788 iter/s, 4.15529s/12 iters), loss = 0.0362483
I0407 23:41:33.809891 32630 solver.cpp:237]     Train net output #0: loss = 0.0362483 (* 1 = 0.0362483 loss)
I0407 23:41:33.809900 32630 sgd_solver.cpp:105] Iteration 9192, lr = 0.000177796
I0407 23:41:38.725903 32630 solver.cpp:218] Iteration 9204 (2.44102 iter/s, 4.91598s/12 iters), loss = 0.0927871
I0407 23:41:38.725945 32630 solver.cpp:237]     Train net output #0: loss = 0.0927871 (* 1 = 0.0927871 loss)
I0407 23:41:38.725953 32630 sgd_solver.cpp:105] Iteration 9204, lr = 0.000175753
I0407 23:41:38.789403 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:41:43.590519 32630 solver.cpp:218] Iteration 9216 (2.46683 iter/s, 4.86455s/12 iters), loss = 0.0525951
I0407 23:41:43.590564 32630 solver.cpp:237]     Train net output #0: loss = 0.0525951 (* 1 = 0.0525951 loss)
I0407 23:41:43.590571 32630 sgd_solver.cpp:105] Iteration 9216, lr = 0.000173733
I0407 23:41:48.551800 32630 solver.cpp:218] Iteration 9228 (2.41876 iter/s, 4.96121s/12 iters), loss = 0.034247
I0407 23:41:48.551985 32630 solver.cpp:237]     Train net output #0: loss = 0.034247 (* 1 = 0.034247 loss)
I0407 23:41:48.551995 32630 sgd_solver.cpp:105] Iteration 9228, lr = 0.000171736
I0407 23:41:53.449784 32630 solver.cpp:218] Iteration 9240 (2.45009 iter/s, 4.89778s/12 iters), loss = 0.114687
I0407 23:41:53.449826 32630 solver.cpp:237]     Train net output #0: loss = 0.114687 (* 1 = 0.114687 loss)
I0407 23:41:53.449833 32630 sgd_solver.cpp:105] Iteration 9240, lr = 0.000169762
I0407 23:41:58.427978 32630 solver.cpp:218] Iteration 9252 (2.41055 iter/s, 4.97813s/12 iters), loss = 0.0306272
I0407 23:41:58.428021 32630 solver.cpp:237]     Train net output #0: loss = 0.0306271 (* 1 = 0.0306271 loss)
I0407 23:41:58.428030 32630 sgd_solver.cpp:105] Iteration 9252, lr = 0.000167809
I0407 23:42:03.356667 32630 solver.cpp:218] Iteration 9264 (2.43476 iter/s, 4.92862s/12 iters), loss = 0.0234608
I0407 23:42:03.356710 32630 solver.cpp:237]     Train net output #0: loss = 0.0234607 (* 1 = 0.0234607 loss)
I0407 23:42:03.356719 32630 sgd_solver.cpp:105] Iteration 9264, lr = 0.000165879
I0407 23:42:08.321187 32630 solver.cpp:218] Iteration 9276 (2.41718 iter/s, 4.96445s/12 iters), loss = 0.0848576
I0407 23:42:08.321225 32630 solver.cpp:237]     Train net output #0: loss = 0.0848575 (* 1 = 0.0848575 loss)
I0407 23:42:08.321233 32630 sgd_solver.cpp:105] Iteration 9276, lr = 0.000163971
I0407 23:42:10.302265 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9282.caffemodel
I0407 23:42:13.420120 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9282.solverstate
I0407 23:42:15.840852 32630 solver.cpp:330] Iteration 9282, Testing net (#0)
I0407 23:42:15.840868 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:42:16.569237 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:42:20.273072 32630 solver.cpp:397]     Test net output #0: accuracy = 0.509804
I0407 23:42:20.273262 32630 solver.cpp:397]     Test net output #1: loss = 2.65715 (* 1 = 2.65715 loss)
I0407 23:42:22.177382 32630 solver.cpp:218] Iteration 9288 (0.866043 iter/s, 13.8561s/12 iters), loss = 0.0970101
I0407 23:42:22.177424 32630 solver.cpp:237]     Train net output #0: loss = 0.09701 (* 1 = 0.09701 loss)
I0407 23:42:22.177433 32630 sgd_solver.cpp:105] Iteration 9288, lr = 0.000162084
I0407 23:42:27.133107 32630 solver.cpp:218] Iteration 9300 (2.42147 iter/s, 4.95566s/12 iters), loss = 0.037268
I0407 23:42:27.133147 32630 solver.cpp:237]     Train net output #0: loss = 0.0372679 (* 1 = 0.0372679 loss)
I0407 23:42:27.133157 32630 sgd_solver.cpp:105] Iteration 9300, lr = 0.000160219
I0407 23:42:29.303882 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:42:32.063252 32630 solver.cpp:218] Iteration 9312 (2.43404 iter/s, 4.93007s/12 iters), loss = 0.0684319
I0407 23:42:32.063395 32630 solver.cpp:237]     Train net output #0: loss = 0.0684318 (* 1 = 0.0684318 loss)
I0407 23:42:32.063408 32630 sgd_solver.cpp:105] Iteration 9312, lr = 0.000158375
I0407 23:42:37.026273 32630 solver.cpp:218] Iteration 9324 (2.41796 iter/s, 4.96286s/12 iters), loss = 0.0699506
I0407 23:42:37.026319 32630 solver.cpp:237]     Train net output #0: loss = 0.0699505 (* 1 = 0.0699505 loss)
I0407 23:42:37.026327 32630 sgd_solver.cpp:105] Iteration 9324, lr = 0.000156551
I0407 23:42:41.932219 32630 solver.cpp:218] Iteration 9336 (2.44605 iter/s, 4.90588s/12 iters), loss = 0.0273675
I0407 23:42:41.932263 32630 solver.cpp:237]     Train net output #0: loss = 0.0273674 (* 1 = 0.0273674 loss)
I0407 23:42:41.932271 32630 sgd_solver.cpp:105] Iteration 9336, lr = 0.000154749
I0407 23:42:46.876255 32630 solver.cpp:218] Iteration 9348 (2.4272 iter/s, 4.94396s/12 iters), loss = 0.0873491
I0407 23:42:46.876298 32630 solver.cpp:237]     Train net output #0: loss = 0.087349 (* 1 = 0.087349 loss)
I0407 23:42:46.876307 32630 sgd_solver.cpp:105] Iteration 9348, lr = 0.000152967
I0407 23:42:51.828816 32630 solver.cpp:218] Iteration 9360 (2.42302 iter/s, 4.95249s/12 iters), loss = 0.0541085
I0407 23:42:51.828941 32630 solver.cpp:237]     Train net output #0: loss = 0.0541084 (* 1 = 0.0541084 loss)
I0407 23:42:51.828950 32630 sgd_solver.cpp:105] Iteration 9360, lr = 0.000151205
I0407 23:42:56.817979 32630 solver.cpp:218] Iteration 9372 (2.40528 iter/s, 4.98902s/12 iters), loss = 0.0119304
I0407 23:42:56.818017 32630 solver.cpp:237]     Train net output #0: loss = 0.0119303 (* 1 = 0.0119303 loss)
I0407 23:42:56.818024 32630 sgd_solver.cpp:105] Iteration 9372, lr = 0.000149463
I0407 23:43:01.308861 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9384.caffemodel
I0407 23:43:04.875916 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9384.solverstate
I0407 23:43:07.260272 32630 solver.cpp:330] Iteration 9384, Testing net (#0)
I0407 23:43:07.260290 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:43:07.999428 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:43:12.033370 32630 solver.cpp:397]     Test net output #0: accuracy = 0.511029
I0407 23:43:12.033408 32630 solver.cpp:397]     Test net output #1: loss = 2.65479 (* 1 = 2.65479 loss)
I0407 23:43:12.131965 32630 solver.cpp:218] Iteration 9384 (0.783601 iter/s, 15.3139s/12 iters), loss = 0.041313
I0407 23:43:12.132006 32630 solver.cpp:237]     Train net output #0: loss = 0.041313 (* 1 = 0.041313 loss)
I0407 23:43:12.132014 32630 sgd_solver.cpp:105] Iteration 9384, lr = 0.00014774
I0407 23:43:16.284554 32630 solver.cpp:218] Iteration 9396 (2.88981 iter/s, 4.15253s/12 iters), loss = 0.0236129
I0407 23:43:16.284591 32630 solver.cpp:237]     Train net output #0: loss = 0.0236129 (* 1 = 0.0236129 loss)
I0407 23:43:16.284598 32630 sgd_solver.cpp:105] Iteration 9396, lr = 0.000146038
I0407 23:43:20.547292 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:43:21.192818 32630 solver.cpp:218] Iteration 9408 (2.44489 iter/s, 4.9082s/12 iters), loss = 0.0682674
I0407 23:43:21.192862 32630 solver.cpp:237]     Train net output #0: loss = 0.0682673 (* 1 = 0.0682673 loss)
I0407 23:43:21.192869 32630 sgd_solver.cpp:105] Iteration 9408, lr = 0.000144354
I0407 23:43:26.156523 32630 solver.cpp:218] Iteration 9420 (2.41758 iter/s, 4.96363s/12 iters), loss = 0.0330941
I0407 23:43:26.156682 32630 solver.cpp:237]     Train net output #0: loss = 0.033094 (* 1 = 0.033094 loss)
I0407 23:43:26.156692 32630 sgd_solver.cpp:105] Iteration 9420, lr = 0.00014269
I0407 23:43:31.086170 32630 solver.cpp:218] Iteration 9432 (2.43434 iter/s, 4.92947s/12 iters), loss = 0.0403497
I0407 23:43:31.086212 32630 solver.cpp:237]     Train net output #0: loss = 0.0403496 (* 1 = 0.0403496 loss)
I0407 23:43:31.086220 32630 sgd_solver.cpp:105] Iteration 9432, lr = 0.000141045
I0407 23:43:36.017868 32630 solver.cpp:218] Iteration 9444 (2.43327 iter/s, 4.93163s/12 iters), loss = 0.122136
I0407 23:43:36.017912 32630 solver.cpp:237]     Train net output #0: loss = 0.122135 (* 1 = 0.122135 loss)
I0407 23:43:36.017920 32630 sgd_solver.cpp:105] Iteration 9444, lr = 0.000139418
I0407 23:43:40.943380 32630 solver.cpp:218] Iteration 9456 (2.43633 iter/s, 4.92544s/12 iters), loss = 0.0579911
I0407 23:43:40.943423 32630 solver.cpp:237]     Train net output #0: loss = 0.057991 (* 1 = 0.057991 loss)
I0407 23:43:40.943431 32630 sgd_solver.cpp:105] Iteration 9456, lr = 0.00013781
I0407 23:43:45.892644 32630 solver.cpp:218] Iteration 9468 (2.42464 iter/s, 4.9492s/12 iters), loss = 0.0828357
I0407 23:43:45.892688 32630 solver.cpp:237]     Train net output #0: loss = 0.0828356 (* 1 = 0.0828356 loss)
I0407 23:43:45.892695 32630 sgd_solver.cpp:105] Iteration 9468, lr = 0.00013622
I0407 23:43:50.851137 32630 solver.cpp:218] Iteration 9480 (2.42012 iter/s, 4.95843s/12 iters), loss = 0.06491
I0407 23:43:50.851177 32630 solver.cpp:237]     Train net output #0: loss = 0.0649099 (* 1 = 0.0649099 loss)
I0407 23:43:50.851186 32630 sgd_solver.cpp:105] Iteration 9480, lr = 0.000134648
I0407 23:43:52.840368 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9486.caffemodel
I0407 23:43:56.386835 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9486.solverstate
I0407 23:43:58.764467 32630 solver.cpp:330] Iteration 9486, Testing net (#0)
I0407 23:43:58.764485 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:43:59.456658 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:44:03.543588 32630 solver.cpp:397]     Test net output #0: accuracy = 0.505515
I0407 23:44:03.543633 32630 solver.cpp:397]     Test net output #1: loss = 2.66122 (* 1 = 2.66122 loss)
I0407 23:44:05.353220 32630 solver.cpp:218] Iteration 9492 (0.827472 iter/s, 14.502s/12 iters), loss = 0.0771121
I0407 23:44:05.353269 32630 solver.cpp:237]     Train net output #0: loss = 0.0771121 (* 1 = 0.0771121 loss)
I0407 23:44:05.353277 32630 sgd_solver.cpp:105] Iteration 9492, lr = 0.000133094
I0407 23:44:10.281978 32630 solver.cpp:218] Iteration 9504 (2.43473 iter/s, 4.92868s/12 iters), loss = 0.0298683
I0407 23:44:10.282021 32630 solver.cpp:237]     Train net output #0: loss = 0.0298682 (* 1 = 0.0298682 loss)
I0407 23:44:10.282029 32630 sgd_solver.cpp:105] Iteration 9504, lr = 0.000131558
I0407 23:44:11.730106 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:44:15.219847 32630 solver.cpp:218] Iteration 9516 (2.43023 iter/s, 4.9378s/12 iters), loss = 0.0678127
I0407 23:44:15.219892 32630 solver.cpp:237]     Train net output #0: loss = 0.0678126 (* 1 = 0.0678126 loss)
I0407 23:44:15.219899 32630 sgd_solver.cpp:105] Iteration 9516, lr = 0.00013004
I0407 23:44:20.146921 32630 solver.cpp:218] Iteration 9528 (2.43556 iter/s, 4.927s/12 iters), loss = 0.0193209
I0407 23:44:20.146963 32630 solver.cpp:237]     Train net output #0: loss = 0.0193208 (* 1 = 0.0193208 loss)
I0407 23:44:20.146972 32630 sgd_solver.cpp:105] Iteration 9528, lr = 0.000128538
I0407 23:44:25.090209 32630 solver.cpp:218] Iteration 9540 (2.42757 iter/s, 4.94322s/12 iters), loss = 0.0894914
I0407 23:44:25.090253 32630 solver.cpp:237]     Train net output #0: loss = 0.0894913 (* 1 = 0.0894913 loss)
I0407 23:44:25.090260 32630 sgd_solver.cpp:105] Iteration 9540, lr = 0.000127054
I0407 23:44:30.000193 32630 solver.cpp:218] Iteration 9552 (2.44404 iter/s, 4.90991s/12 iters), loss = 0.0904803
I0407 23:44:30.000355 32630 solver.cpp:237]     Train net output #0: loss = 0.0904802 (* 1 = 0.0904802 loss)
I0407 23:44:30.000365 32630 sgd_solver.cpp:105] Iteration 9552, lr = 0.000125587
I0407 23:44:34.970496 32630 solver.cpp:218] Iteration 9564 (2.41443 iter/s, 4.97012s/12 iters), loss = 0.0341282
I0407 23:44:34.970533 32630 solver.cpp:237]     Train net output #0: loss = 0.0341281 (* 1 = 0.0341281 loss)
I0407 23:44:34.970541 32630 sgd_solver.cpp:105] Iteration 9564, lr = 0.000124136
I0407 23:44:39.846477 32630 solver.cpp:218] Iteration 9576 (2.46108 iter/s, 4.87592s/12 iters), loss = 0.0718796
I0407 23:44:39.846520 32630 solver.cpp:237]     Train net output #0: loss = 0.0718795 (* 1 = 0.0718795 loss)
I0407 23:44:39.846529 32630 sgd_solver.cpp:105] Iteration 9576, lr = 0.000122702
I0407 23:44:44.292634 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9588.caffemodel
I0407 23:44:48.237898 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9588.solverstate
I0407 23:44:50.605324 32630 solver.cpp:330] Iteration 9588, Testing net (#0)
I0407 23:44:50.605341 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:44:51.226402 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:44:55.275629 32630 solver.cpp:397]     Test net output #0: accuracy = 0.50674
I0407 23:44:55.275673 32630 solver.cpp:397]     Test net output #1: loss = 2.6702 (* 1 = 2.6702 loss)
I0407 23:44:55.372355 32630 solver.cpp:218] Iteration 9588 (0.772908 iter/s, 15.5258s/12 iters), loss = 0.0222479
I0407 23:44:55.372400 32630 solver.cpp:237]     Train net output #0: loss = 0.0222479 (* 1 = 0.0222479 loss)
I0407 23:44:55.372407 32630 sgd_solver.cpp:105] Iteration 9588, lr = 0.000121284
I0407 23:44:59.491202 32630 solver.cpp:218] Iteration 9600 (2.91349 iter/s, 4.11877s/12 iters), loss = 0.0306
I0407 23:44:59.491248 32630 solver.cpp:237]     Train net output #0: loss = 0.0305999 (* 1 = 0.0305999 loss)
I0407 23:44:59.491256 32630 sgd_solver.cpp:105] Iteration 9600, lr = 0.000119883
I0407 23:45:03.048686 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:45:04.506121 32630 solver.cpp:218] Iteration 9612 (2.3929 iter/s, 5.01485s/12 iters), loss = 0.0349701
I0407 23:45:04.506165 32630 solver.cpp:237]     Train net output #0: loss = 0.0349701 (* 1 = 0.0349701 loss)
I0407 23:45:04.506172 32630 sgd_solver.cpp:105] Iteration 9612, lr = 0.000118497
I0407 23:45:09.564028 32630 solver.cpp:218] Iteration 9624 (2.37256 iter/s, 5.05784s/12 iters), loss = 0.034242
I0407 23:45:09.564065 32630 solver.cpp:237]     Train net output #0: loss = 0.0342419 (* 1 = 0.0342419 loss)
I0407 23:45:09.564072 32630 sgd_solver.cpp:105] Iteration 9624, lr = 0.000117128
I0407 23:45:14.509053 32630 solver.cpp:218] Iteration 9636 (2.42671 iter/s, 4.94497s/12 iters), loss = 0.0380647
I0407 23:45:14.509088 32630 solver.cpp:237]     Train net output #0: loss = 0.0380646 (* 1 = 0.0380646 loss)
I0407 23:45:14.509095 32630 sgd_solver.cpp:105] Iteration 9636, lr = 0.000115774
I0407 23:45:19.463089 32630 solver.cpp:218] Iteration 9648 (2.4223 iter/s, 4.95398s/12 iters), loss = 0.0673966
I0407 23:45:19.463125 32630 solver.cpp:237]     Train net output #0: loss = 0.0673965 (* 1 = 0.0673965 loss)
I0407 23:45:19.463132 32630 sgd_solver.cpp:105] Iteration 9648, lr = 0.000114435
I0407 23:45:24.568270 32630 solver.cpp:218] Iteration 9660 (2.35058 iter/s, 5.10512s/12 iters), loss = 0.0176776
I0407 23:45:24.568312 32630 solver.cpp:237]     Train net output #0: loss = 0.0176775 (* 1 = 0.0176775 loss)
I0407 23:45:24.568320 32630 sgd_solver.cpp:105] Iteration 9660, lr = 0.000113112
I0407 23:45:29.540815 32630 solver.cpp:218] Iteration 9672 (2.41328 iter/s, 4.97248s/12 iters), loss = 0.0269072
I0407 23:45:29.540861 32630 solver.cpp:237]     Train net output #0: loss = 0.0269072 (* 1 = 0.0269072 loss)
I0407 23:45:29.540870 32630 sgd_solver.cpp:105] Iteration 9672, lr = 0.000111804
I0407 23:45:34.482578 32630 solver.cpp:218] Iteration 9684 (2.42832 iter/s, 4.94169s/12 iters), loss = 0.0781199
I0407 23:45:34.482714 32630 solver.cpp:237]     Train net output #0: loss = 0.0781199 (* 1 = 0.0781199 loss)
I0407 23:45:34.482722 32630 sgd_solver.cpp:105] Iteration 9684, lr = 0.00011051
I0407 23:45:36.481518 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9690.caffemodel
I0407 23:45:40.078088 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9690.solverstate
I0407 23:45:42.448740 32630 solver.cpp:330] Iteration 9690, Testing net (#0)
I0407 23:45:42.448758 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:45:43.028736 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:45:45.908221 32630 blocking_queue.cpp:49] Waiting for data
I0407 23:45:46.921663 32630 solver.cpp:397]     Test net output #0: accuracy = 0.508578
I0407 23:45:46.921715 32630 solver.cpp:397]     Test net output #1: loss = 2.66006 (* 1 = 2.66006 loss)
I0407 23:45:48.711364 32630 solver.cpp:218] Iteration 9696 (0.843371 iter/s, 14.2286s/12 iters), loss = 0.0667951
I0407 23:45:48.711416 32630 solver.cpp:237]     Train net output #0: loss = 0.066795 (* 1 = 0.066795 loss)
I0407 23:45:48.711427 32630 sgd_solver.cpp:105] Iteration 9696, lr = 0.000109232
I0407 23:45:53.814709 32630 solver.cpp:218] Iteration 9708 (2.35144 iter/s, 5.10326s/12 iters), loss = 0.121356
I0407 23:45:53.814779 32630 solver.cpp:237]     Train net output #0: loss = 0.121356 (* 1 = 0.121356 loss)
I0407 23:45:53.814795 32630 sgd_solver.cpp:105] Iteration 9708, lr = 0.000107968
I0407 23:45:54.577520 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:45:58.841002 32630 solver.cpp:218] Iteration 9720 (2.38749 iter/s, 5.02621s/12 iters), loss = 0.0249229
I0407 23:45:58.841046 32630 solver.cpp:237]     Train net output #0: loss = 0.0249229 (* 1 = 0.0249229 loss)
I0407 23:45:58.841055 32630 sgd_solver.cpp:105] Iteration 9720, lr = 0.000106719
I0407 23:46:03.885165 32630 solver.cpp:218] Iteration 9732 (2.37902 iter/s, 5.0441s/12 iters), loss = 0.112337
I0407 23:46:03.885203 32630 solver.cpp:237]     Train net output #0: loss = 0.112337 (* 1 = 0.112337 loss)
I0407 23:46:03.885210 32630 sgd_solver.cpp:105] Iteration 9732, lr = 0.000105484
I0407 23:46:08.873390 32630 solver.cpp:218] Iteration 9744 (2.40569 iter/s, 4.98817s/12 iters), loss = 0.018029
I0407 23:46:08.873534 32630 solver.cpp:237]     Train net output #0: loss = 0.0180289 (* 1 = 0.0180289 loss)
I0407 23:46:08.873544 32630 sgd_solver.cpp:105] Iteration 9744, lr = 0.000104263
I0407 23:46:13.790165 32630 solver.cpp:218] Iteration 9756 (2.44071 iter/s, 4.91661s/12 iters), loss = 0.0987479
I0407 23:46:13.790201 32630 solver.cpp:237]     Train net output #0: loss = 0.0987478 (* 1 = 0.0987478 loss)
I0407 23:46:13.790210 32630 sgd_solver.cpp:105] Iteration 9756, lr = 0.000103056
I0407 23:46:18.833611 32630 solver.cpp:218] Iteration 9768 (2.37935 iter/s, 5.04339s/12 iters), loss = 0.0368257
I0407 23:46:18.833644 32630 solver.cpp:237]     Train net output #0: loss = 0.0368256 (* 1 = 0.0368256 loss)
I0407 23:46:18.833652 32630 sgd_solver.cpp:105] Iteration 9768, lr = 0.000101863
I0407 23:46:23.896857 32630 solver.cpp:218] Iteration 9780 (2.37005 iter/s, 5.06318s/12 iters), loss = 0.0496726
I0407 23:46:23.896903 32630 solver.cpp:237]     Train net output #0: loss = 0.0496725 (* 1 = 0.0496725 loss)
I0407 23:46:23.896911 32630 sgd_solver.cpp:105] Iteration 9780, lr = 0.000100684
I0407 23:46:28.353284 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9792.caffemodel
I0407 23:46:31.544875 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9792.solverstate
I0407 23:46:33.903055 32630 solver.cpp:330] Iteration 9792, Testing net (#0)
I0407 23:46:33.903074 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:46:34.478695 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:46:38.708446 32630 solver.cpp:397]     Test net output #0: accuracy = 0.507353
I0407 23:46:38.708480 32630 solver.cpp:397]     Test net output #1: loss = 2.66044 (* 1 = 2.66044 loss)
I0407 23:46:38.804895 32630 solver.cpp:218] Iteration 9792 (0.80494 iter/s, 14.9079s/12 iters), loss = 0.0634868
I0407 23:46:38.804937 32630 solver.cpp:237]     Train net output #0: loss = 0.0634868 (* 1 = 0.0634868 loss)
I0407 23:46:38.804945 32630 sgd_solver.cpp:105] Iteration 9792, lr = 9.9518e-05
I0407 23:46:42.900038 32630 solver.cpp:218] Iteration 9804 (2.93035 iter/s, 4.09508s/12 iters), loss = 0.201427
I0407 23:46:42.900198 32630 solver.cpp:237]     Train net output #0: loss = 0.201426 (* 1 = 0.201426 loss)
I0407 23:46:42.900208 32630 sgd_solver.cpp:105] Iteration 9804, lr = 9.83655e-05
I0407 23:46:45.807844 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:46:47.792650 32630 solver.cpp:218] Iteration 9816 (2.45277 iter/s, 4.89243s/12 iters), loss = 0.0903435
I0407 23:46:47.792691 32630 solver.cpp:237]     Train net output #0: loss = 0.0903434 (* 1 = 0.0903434 loss)
I0407 23:46:47.792699 32630 sgd_solver.cpp:105] Iteration 9816, lr = 9.72262e-05
I0407 23:46:52.759701 32630 solver.cpp:218] Iteration 9828 (2.41596 iter/s, 4.96697s/12 iters), loss = 0.0674689
I0407 23:46:52.759757 32630 solver.cpp:237]     Train net output #0: loss = 0.0674689 (* 1 = 0.0674689 loss)
I0407 23:46:52.759768 32630 sgd_solver.cpp:105] Iteration 9828, lr = 9.61e-05
I0407 23:46:57.687654 32630 solver.cpp:218] Iteration 9840 (2.43513 iter/s, 4.92788s/12 iters), loss = 0.0983326
I0407 23:46:57.687697 32630 solver.cpp:237]     Train net output #0: loss = 0.0983325 (* 1 = 0.0983325 loss)
I0407 23:46:57.687705 32630 sgd_solver.cpp:105] Iteration 9840, lr = 9.49867e-05
I0407 23:47:02.624157 32630 solver.cpp:218] Iteration 9852 (2.4309 iter/s, 4.93644s/12 iters), loss = 0.0888279
I0407 23:47:02.624202 32630 solver.cpp:237]     Train net output #0: loss = 0.0888278 (* 1 = 0.0888278 loss)
I0407 23:47:02.624209 32630 sgd_solver.cpp:105] Iteration 9852, lr = 9.38862e-05
I0407 23:47:07.606860 32630 solver.cpp:218] Iteration 9864 (2.40837 iter/s, 4.98263s/12 iters), loss = 0.0662115
I0407 23:47:07.606904 32630 solver.cpp:237]     Train net output #0: loss = 0.0662115 (* 1 = 0.0662115 loss)
I0407 23:47:07.606914 32630 sgd_solver.cpp:105] Iteration 9864, lr = 9.27983e-05
I0407 23:47:12.527909 32630 solver.cpp:218] Iteration 9876 (2.43854 iter/s, 4.92098s/12 iters), loss = 0.0477574
I0407 23:47:12.527951 32630 solver.cpp:237]     Train net output #0: loss = 0.0477573 (* 1 = 0.0477573 loss)
I0407 23:47:12.527959 32630 sgd_solver.cpp:105] Iteration 9876, lr = 9.1723e-05
I0407 23:47:17.475152 32630 solver.cpp:218] Iteration 9888 (2.42563 iter/s, 4.94717s/12 iters), loss = 0.0227088
I0407 23:47:17.475288 32630 solver.cpp:237]     Train net output #0: loss = 0.0227088 (* 1 = 0.0227088 loss)
I0407 23:47:17.475298 32630 sgd_solver.cpp:105] Iteration 9888, lr = 9.06599e-05
I0407 23:47:19.480993 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9894.caffemodel
I0407 23:47:22.571905 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9894.solverstate
I0407 23:47:24.938266 32630 solver.cpp:330] Iteration 9894, Testing net (#0)
I0407 23:47:24.938283 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:47:25.467118 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:47:29.736100 32630 solver.cpp:397]     Test net output #0: accuracy = 0.50674
I0407 23:47:29.736147 32630 solver.cpp:397]     Test net output #1: loss = 2.65164 (* 1 = 2.65164 loss)
I0407 23:47:31.546203 32630 solver.cpp:218] Iteration 9900 (0.852825 iter/s, 14.0709s/12 iters), loss = 0.0101211
I0407 23:47:31.546242 32630 solver.cpp:237]     Train net output #0: loss = 0.0101211 (* 1 = 0.0101211 loss)
I0407 23:47:31.546250 32630 sgd_solver.cpp:105] Iteration 9900, lr = 8.96091e-05
I0407 23:47:36.462031 32630 solver.cpp:218] Iteration 9912 (2.44112 iter/s, 4.91577s/12 iters), loss = 0.0435068
I0407 23:47:36.462077 32630 solver.cpp:237]     Train net output #0: loss = 0.0435068 (* 1 = 0.0435068 loss)
I0407 23:47:36.462086 32630 sgd_solver.cpp:105] Iteration 9912, lr = 8.85703e-05
I0407 23:47:36.565547 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:47:41.416558 32630 solver.cpp:218] Iteration 9924 (2.42206 iter/s, 4.95446s/12 iters), loss = 0.0676373
I0407 23:47:41.416602 32630 solver.cpp:237]     Train net output #0: loss = 0.0676372 (* 1 = 0.0676372 loss)
I0407 23:47:41.416610 32630 sgd_solver.cpp:105] Iteration 9924, lr = 8.75435e-05
I0407 23:47:46.343010 32630 solver.cpp:218] Iteration 9936 (2.43586 iter/s, 4.92639s/12 iters), loss = 0.0593084
I0407 23:47:46.343051 32630 solver.cpp:237]     Train net output #0: loss = 0.0593084 (* 1 = 0.0593084 loss)
I0407 23:47:46.343060 32630 sgd_solver.cpp:105] Iteration 9936, lr = 8.65284e-05
I0407 23:47:51.233373 32630 solver.cpp:218] Iteration 9948 (2.45384 iter/s, 4.8903s/12 iters), loss = 0.0950921
I0407 23:47:51.233497 32630 solver.cpp:237]     Train net output #0: loss = 0.0950921 (* 1 = 0.0950921 loss)
I0407 23:47:51.233506 32630 sgd_solver.cpp:105] Iteration 9948, lr = 8.55251e-05
I0407 23:47:56.132727 32630 solver.cpp:218] Iteration 9960 (2.44937 iter/s, 4.89921s/12 iters), loss = 0.0389494
I0407 23:47:56.132761 32630 solver.cpp:237]     Train net output #0: loss = 0.0389494 (* 1 = 0.0389494 loss)
I0407 23:47:56.132768 32630 sgd_solver.cpp:105] Iteration 9960, lr = 8.45333e-05
I0407 23:48:01.161621 32630 solver.cpp:218] Iteration 9972 (2.38624 iter/s, 5.02883s/12 iters), loss = 0.0156351
I0407 23:48:01.161662 32630 solver.cpp:237]     Train net output #0: loss = 0.0156351 (* 1 = 0.0156351 loss)
I0407 23:48:01.161671 32630 sgd_solver.cpp:105] Iteration 9972, lr = 8.35528e-05
I0407 23:48:06.103644 32630 solver.cpp:218] Iteration 9984 (2.42819 iter/s, 4.94196s/12 iters), loss = 0.0391874
I0407 23:48:06.103682 32630 solver.cpp:237]     Train net output #0: loss = 0.0391874 (* 1 = 0.0391874 loss)
I0407 23:48:06.103690 32630 sgd_solver.cpp:105] Iteration 9984, lr = 8.25837e-05
I0407 23:48:10.538573 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_9996.caffemodel
I0407 23:48:13.601444 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_9996.solverstate
I0407 23:48:15.998070 32630 solver.cpp:330] Iteration 9996, Testing net (#0)
I0407 23:48:15.998087 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:48:16.471092 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:48:20.606441 32630 solver.cpp:397]     Test net output #0: accuracy = 0.509804
I0407 23:48:20.606472 32630 solver.cpp:397]     Test net output #1: loss = 2.66407 (* 1 = 2.66407 loss)
I0407 23:48:20.702932 32630 solver.cpp:218] Iteration 9996 (0.821963 iter/s, 14.5992s/12 iters), loss = 0.0518736
I0407 23:48:20.702976 32630 solver.cpp:237]     Train net output #0: loss = 0.0518736 (* 1 = 0.0518736 loss)
I0407 23:48:20.702984 32630 sgd_solver.cpp:105] Iteration 9996, lr = 8.16257e-05
I0407 23:48:24.908707 32630 solver.cpp:218] Iteration 10008 (2.85326 iter/s, 4.20571s/12 iters), loss = 0.0284873
I0407 23:48:24.908869 32630 solver.cpp:237]     Train net output #0: loss = 0.0284873 (* 1 = 0.0284873 loss)
I0407 23:48:24.908879 32630 sgd_solver.cpp:105] Iteration 10008, lr = 8.06787e-05
I0407 23:48:27.138221 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:48:29.906857 32630 solver.cpp:218] Iteration 10020 (2.40097 iter/s, 4.99797s/12 iters), loss = 0.0500137
I0407 23:48:29.906891 32630 solver.cpp:237]     Train net output #0: loss = 0.0500137 (* 1 = 0.0500137 loss)
I0407 23:48:29.906898 32630 sgd_solver.cpp:105] Iteration 10020, lr = 7.97426e-05
I0407 23:48:34.828866 32630 solver.cpp:218] Iteration 10032 (2.43806 iter/s, 4.92195s/12 iters), loss = 0.0227617
I0407 23:48:34.828907 32630 solver.cpp:237]     Train net output #0: loss = 0.0227616 (* 1 = 0.0227616 loss)
I0407 23:48:34.828914 32630 sgd_solver.cpp:105] Iteration 10032, lr = 7.88173e-05
I0407 23:48:39.775643 32630 solver.cpp:218] Iteration 10044 (2.42585 iter/s, 4.94672s/12 iters), loss = 0.0961335
I0407 23:48:39.775683 32630 solver.cpp:237]     Train net output #0: loss = 0.0961334 (* 1 = 0.0961334 loss)
I0407 23:48:39.775691 32630 sgd_solver.cpp:105] Iteration 10044, lr = 7.79027e-05
I0407 23:48:44.674268 32630 solver.cpp:218] Iteration 10056 (2.4497 iter/s, 4.89856s/12 iters), loss = 0.053444
I0407 23:48:44.674304 32630 solver.cpp:237]     Train net output #0: loss = 0.0534439 (* 1 = 0.0534439 loss)
I0407 23:48:44.674312 32630 sgd_solver.cpp:105] Iteration 10056, lr = 7.69986e-05
I0407 23:48:49.632948 32630 solver.cpp:218] Iteration 10068 (2.42003 iter/s, 4.95861s/12 iters), loss = 0.0545082
I0407 23:48:49.632993 32630 solver.cpp:237]     Train net output #0: loss = 0.0545081 (* 1 = 0.0545081 loss)
I0407 23:48:49.633002 32630 sgd_solver.cpp:105] Iteration 10068, lr = 7.61049e-05
I0407 23:48:54.586618 32630 solver.cpp:218] Iteration 10080 (2.42248 iter/s, 4.9536s/12 iters), loss = 0.0329978
I0407 23:48:54.586663 32630 solver.cpp:237]     Train net output #0: loss = 0.0329978 (* 1 = 0.0329978 loss)
I0407 23:48:54.586670 32630 sgd_solver.cpp:105] Iteration 10080, lr = 7.52215e-05
I0407 23:48:59.530165 32630 solver.cpp:218] Iteration 10092 (2.42744 iter/s, 4.94348s/12 iters), loss = 0.0862569
I0407 23:48:59.530292 32630 solver.cpp:237]     Train net output #0: loss = 0.0862568 (* 1 = 0.0862568 loss)
I0407 23:48:59.530301 32630 sgd_solver.cpp:105] Iteration 10092, lr = 7.43482e-05
I0407 23:49:01.534687 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10098.caffemodel
I0407 23:49:04.663394 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10098.solverstate
I0407 23:49:07.046499 32630 solver.cpp:330] Iteration 10098, Testing net (#0)
I0407 23:49:07.046520 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:49:07.454871 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:49:11.652364 32630 solver.cpp:397]     Test net output #0: accuracy = 0.511642
I0407 23:49:11.652407 32630 solver.cpp:397]     Test net output #1: loss = 2.65173 (* 1 = 2.65173 loss)
I0407 23:49:13.492377 32630 solver.cpp:218] Iteration 10104 (0.859473 iter/s, 13.962s/12 iters), loss = 0.0387208
I0407 23:49:13.492424 32630 solver.cpp:237]     Train net output #0: loss = 0.0387207 (* 1 = 0.0387207 loss)
I0407 23:49:13.492431 32630 sgd_solver.cpp:105] Iteration 10104, lr = 7.3485e-05
I0407 23:49:17.766703 32645 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:49:18.378244 32630 solver.cpp:218] Iteration 10116 (2.4561 iter/s, 4.8858s/12 iters), loss = 0.0313864
I0407 23:49:18.378283 32630 solver.cpp:237]     Train net output #0: loss = 0.0313864 (* 1 = 0.0313864 loss)
I0407 23:49:18.378290 32630 sgd_solver.cpp:105] Iteration 10116, lr = 7.26318e-05
I0407 23:49:23.297039 32630 solver.cpp:218] Iteration 10128 (2.43965 iter/s, 4.91873s/12 iters), loss = 0.0329079
I0407 23:49:23.297089 32630 solver.cpp:237]     Train net output #0: loss = 0.0329078 (* 1 = 0.0329078 loss)
I0407 23:49:23.297098 32630 sgd_solver.cpp:105] Iteration 10128, lr = 7.17885e-05
I0407 23:49:28.254313 32630 solver.cpp:218] Iteration 10140 (2.42072 iter/s, 4.9572s/12 iters), loss = 0.0264461
I0407 23:49:28.254351 32630 solver.cpp:237]     Train net output #0: loss = 0.026446 (* 1 = 0.026446 loss)
I0407 23:49:28.254359 32630 sgd_solver.cpp:105] Iteration 10140, lr = 7.09548e-05
I0407 23:49:33.101836 32630 solver.cpp:218] Iteration 10152 (2.47553 iter/s, 4.84745s/12 iters), loss = 0.117458
I0407 23:49:33.101994 32630 solver.cpp:237]     Train net output #0: loss = 0.117458 (* 1 = 0.117458 loss)
I0407 23:49:33.102003 32630 sgd_solver.cpp:105] Iteration 10152, lr = 7.01307e-05
I0407 23:49:38.026508 32630 solver.cpp:218] Iteration 10164 (2.4368 iter/s, 4.92448s/12 iters), loss = 0.0693973
I0407 23:49:38.026557 32630 solver.cpp:237]     Train net output #0: loss = 0.0693972 (* 1 = 0.0693972 loss)
I0407 23:49:38.026569 32630 sgd_solver.cpp:105] Iteration 10164, lr = 6.93162e-05
I0407 23:49:42.986879 32630 solver.cpp:218] Iteration 10176 (2.41921 iter/s, 4.9603s/12 iters), loss = 0.0370818
I0407 23:49:42.986923 32630 solver.cpp:237]     Train net output #0: loss = 0.0370818 (* 1 = 0.0370818 loss)
I0407 23:49:42.986932 32630 sgd_solver.cpp:105] Iteration 10176, lr = 6.8511e-05
I0407 23:49:47.910176 32630 solver.cpp:218] Iteration 10188 (2.43743 iter/s, 4.92323s/12 iters), loss = 0.0608822
I0407 23:49:47.910215 32630 solver.cpp:237]     Train net output #0: loss = 0.0608821 (* 1 = 0.0608821 loss)
I0407 23:49:47.910223 32630 sgd_solver.cpp:105] Iteration 10188, lr = 6.77152e-05
I0407 23:49:52.407008 32630 solver.cpp:447] Snapshotting to binary proto file snapshot_iter_10200.caffemodel
I0407 23:49:55.497314 32630 sgd_solver.cpp:273] Snapshotting solver state to binary proto file snapshot_iter_10200.solverstate
I0407 23:49:57.894382 32630 solver.cpp:310] Iteration 10200, loss = 0.0752417
I0407 23:49:57.894408 32630 solver.cpp:330] Iteration 10200, Testing net (#0)
I0407 23:49:57.894412 32630 net.cpp:676] Ignoring source layer train-data
I0407 23:49:58.251549 32652 data_layer.cpp:73] Restarting data prefetching from start.
I0407 23:50:02.543491 32630 solver.cpp:397]     Test net output #0: accuracy = 0.512868
I0407 23:50:02.543534 32630 solver.cpp:397]     Test net output #1: loss = 2.64572 (* 1 = 2.64572 loss)
I0407 23:50:02.543542 32630 solver.cpp:315] Optimization Done.
I0407 23:50:02.543548 32630 caffe.cpp:259] Optimization Done.